[
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbin/\nbuild/\ndevelop-eggs/\ndist/\neggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.cache\nnosetests.xml\ncoverage.xml\n\n# Translations\n*.mo\n\n# Mr Developer\n.mr.developer.cfg\n.project\n.pydevproject\n\n# Rope\n.ropeproject\n\n# Django stuff:\n*.log\n*.pot\n\n# Sphinx documentation\ndocs/_build/\n\n"
  },
  {
    "path": "LICENSE",
    "content": "GNU GENERAL PUBLIC LICENSE\n                       Version 2, June 1991\n\n Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The licenses for most software are designed to take away your\nfreedom to share and change it.  By contrast, the GNU General Public\nLicense is intended to guarantee your freedom to share and change free\nsoftware--to make sure the software is free for all its users.  This\nGeneral Public License applies to most of the Free Software\nFoundation's software and to any other program whose authors commit to\nusing it.  (Some other Free Software Foundation software is covered by\nthe GNU Lesser General Public License instead.)  You can apply it to\nyour programs, too.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthis service if you wish), that you receive source code or can get it\nif you want it, that you can change the software or use pieces of it\nin new free programs; and that you know you can do these things.\n\n  To protect your rights, we need to make restrictions that forbid\nanyone to deny you these rights or to ask you to surrender the rights.\nThese restrictions translate to certain responsibilities for you if you\ndistribute copies of the software, or if you modify it.\n\n  For example, if you distribute copies of such a program, whether\ngratis or for a fee, you must give the recipients all the rights that\nyou have.  You must make sure that they, too, receive or can get the\nsource code.  And you must show them these terms so they know their\nrights.\n\n  We protect your rights with two steps: (1) copyright the software, and\n(2) offer you this license which gives you legal permission to copy,\ndistribute and/or modify the software.\n\n  Also, for each author's protection and ours, we want to make certain\nthat everyone understands that there is no warranty for this free\nsoftware.  If the software is modified by someone else and passed on, we\nwant its recipients to know that what they have is not the original, so\nthat any problems introduced by others will not reflect on the original\nauthors' reputations.\n\n  Finally, any free program is threatened constantly by software\npatents.  We wish to avoid the danger that redistributors of a free\nprogram will individually obtain patent licenses, in effect making the\nprogram proprietary.  To prevent this, we have made it clear that any\npatent must be licensed for everyone's free use or not licensed at all.\n\n  The precise terms and conditions for copying, distribution and\nmodification follow.\n\n                    GNU GENERAL PUBLIC LICENSE\n   TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION\n\n  0. This License applies to any program or other work which contains\na notice placed by the copyright holder saying it may be distributed\nunder the terms of this General Public License.  The \"Program\", below,\nrefers to any such program or work, and a \"work based on the Program\"\nmeans either the Program or any derivative work under copyright law:\nthat is to say, a work containing the Program or a portion of it,\neither verbatim or with modifications and/or translated into another\nlanguage.  (Hereinafter, translation is included without limitation in\nthe term \"modification\".)  Each licensee is addressed as \"you\".\n\nActivities other than copying, distribution and modification are not\ncovered by this License; they are outside its scope.  The act of\nrunning the Program is not restricted, and the output from the Program\nis covered only if its contents constitute a work based on the\nProgram (independent of having been made by running the Program).\nWhether that is true depends on what the Program does.\n\n  1. You may copy and distribute verbatim copies of the Program's\nsource code as you receive it, in any medium, provided that you\nconspicuously and appropriately publish on each copy an appropriate\ncopyright notice and disclaimer of warranty; keep intact all the\nnotices that refer to this License and to the absence of any warranty;\nand give any other recipients of the Program a copy of this License\nalong with the Program.\n\nYou may charge a fee for the physical act of transferring a copy, and\nyou may at your option offer warranty protection in exchange for a fee.\n\n  2. You may modify your copy or copies of the Program or any portion\nof it, thus forming a work based on the Program, and copy and\ndistribute such modifications or work under the terms of Section 1\nabove, provided that you also meet all of these conditions:\n\n    a) You must cause the modified files to carry prominent notices\n    stating that you changed the files and the date of any change.\n\n    b) You must cause any work that you distribute or publish, that in\n    whole or in part contains or is derived from the Program or any\n    part thereof, to be licensed as a whole at no charge to all third\n    parties under the terms of this License.\n\n    c) If the modified program normally reads commands interactively\n    when run, you must cause it, when started running for such\n    interactive use in the most ordinary way, to print or display an\n    announcement including an appropriate copyright notice and a\n    notice that there is no warranty (or else, saying that you provide\n    a warranty) and that users may redistribute the program under\n    these conditions, and telling the user how to view a copy of this\n    License.  (Exception: if the Program itself is interactive but\n    does not normally print such an announcement, your work based on\n    the Program is not required to print an announcement.)\n\nThese requirements apply to the modified work as a whole.  If\nidentifiable sections of that work are not derived from the Program,\nand can be reasonably considered independent and separate works in\nthemselves, then this License, and its terms, do not apply to those\nsections when you distribute them as separate works.  But when you\ndistribute the same sections as part of a whole which is a work based\non the Program, the distribution of the whole must be on the terms of\nthis License, whose permissions for other licensees extend to the\nentire whole, and thus to each and every part regardless of who wrote it.\n\nThus, it is not the intent of this section to claim rights or contest\nyour rights to work written entirely by you; rather, the intent is to\nexercise the right to control the distribution of derivative or\ncollective works based on the Program.\n\nIn addition, mere aggregation of another work not based on the Program\nwith the Program (or with a work based on the Program) on a volume of\na storage or distribution medium does not bring the other work under\nthe scope of this License.\n\n  3. You may copy and distribute the Program (or a work based on it,\nunder Section 2) in object code or executable form under the terms of\nSections 1 and 2 above provided that you also do one of the following:\n\n    a) Accompany it with the complete corresponding machine-readable\n    source code, which must be distributed under the terms of Sections\n    1 and 2 above on a medium customarily used for software interchange; or,\n\n    b) Accompany it with a written offer, valid for at least three\n    years, to give any third party, for a charge no more than your\n    cost of physically performing source distribution, a complete\n    machine-readable copy of the corresponding source code, to be\n    distributed under the terms of Sections 1 and 2 above on a medium\n    customarily used for software interchange; or,\n\n    c) Accompany it with the information you received as to the offer\n    to distribute corresponding source code.  (This alternative is\n    allowed only for noncommercial distribution and only if you\n    received the program in object code or executable form with such\n    an offer, in accord with Subsection b above.)\n\nThe source code for a work means the preferred form of the work for\nmaking modifications to it.  For an executable work, complete source\ncode means all the source code for all modules it contains, plus any\nassociated interface definition files, plus the scripts used to\ncontrol compilation and installation of the executable.  However, as a\nspecial exception, the source code distributed need not include\nanything that is normally distributed (in either source or binary\nform) with the major components (compiler, kernel, and so on) of the\noperating system on which the executable runs, unless that component\nitself accompanies the executable.\n\nIf distribution of executable or object code is made by offering\naccess to copy from a designated place, then offering equivalent\naccess to copy the source code from the same place counts as\ndistribution of the source code, even though third parties are not\ncompelled to copy the source along with the object code.\n\n  4. You may not copy, modify, sublicense, or distribute the Program\nexcept as expressly provided under this License.  Any attempt\notherwise to copy, modify, sublicense or distribute the Program is\nvoid, and will automatically terminate your rights under this License.\nHowever, parties who have received copies, or rights, from you under\nthis License will not have their licenses terminated so long as such\nparties remain in full compliance.\n\n  5. You are not required to accept this License, since you have not\nsigned it.  However, nothing else grants you permission to modify or\ndistribute the Program or its derivative works.  These actions are\nprohibited by law if you do not accept this License.  Therefore, by\nmodifying or distributing the Program (or any work based on the\nProgram), you indicate your acceptance of this License to do so, and\nall its terms and conditions for copying, distributing or modifying\nthe Program or works based on it.\n\n  6. Each time you redistribute the Program (or any work based on the\nProgram), the recipient automatically receives a license from the\noriginal licensor to copy, distribute or modify the Program subject to\nthese terms and conditions.  You may not impose any further\nrestrictions on the recipients' exercise of the rights granted herein.\nYou are not responsible for enforcing compliance by third parties to\nthis License.\n\n  7. If, as a consequence of a court judgment or allegation of patent\ninfringement or for any other reason (not limited to patent issues),\nconditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot\ndistribute so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you\nmay not distribute the Program at all.  For example, if a patent\nlicense would not permit royalty-free redistribution of the Program by\nall those who receive copies directly or indirectly through you, then\nthe only way you could satisfy both it and this License would be to\nrefrain entirely from distribution of the Program.\n\nIf any portion of this section is held invalid or unenforceable under\nany particular circumstance, the balance of the section is intended to\napply and the section as a whole is intended to apply in other\ncircumstances.\n\nIt is not the purpose of this section to induce you to infringe any\npatents or other property right claims or to contest validity of any\nsuch claims; this section has the sole purpose of protecting the\nintegrity of the free software distribution system, which is\nimplemented by public license practices.  Many people have made\ngenerous contributions to the wide range of software distributed\nthrough that system in reliance on consistent application of that\nsystem; it is up to the author/donor to decide if he or she is willing\nto distribute software through any other system and a licensee cannot\nimpose that choice.\n\nThis section is intended to make thoroughly clear what is believed to\nbe a consequence of the rest of this License.\n\n  8. If the distribution and/or use of the Program is restricted in\ncertain countries either by patents or by copyrighted interfaces, the\noriginal copyright holder who places the Program under this License\nmay add an explicit geographical distribution limitation excluding\nthose countries, so that distribution is permitted only in or among\ncountries not thus excluded.  In such case, this License incorporates\nthe limitation as if written in the body of this License.\n\n  9. The Free Software Foundation may publish revised and/or new versions\nof the General Public License from time to time.  Such new versions will\nbe similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\nEach version is given a distinguishing version number.  If the Program\nspecifies a version number of this License which applies to it and \"any\nlater version\", you have the option of following the terms and conditions\neither of that version or of any later version published by the Free\nSoftware Foundation.  If the Program does not specify a version number of\nthis License, you may choose any version ever published by the Free Software\nFoundation.\n\n  10. If you wish to incorporate parts of the Program into other free\nprograms whose distribution conditions are different, write to the author\nto ask for permission.  For software which is copyrighted by the Free\nSoftware Foundation, write to the Free Software Foundation; we sometimes\nmake exceptions for this.  Our decision will be guided by the two goals\nof preserving the free status of all derivatives of our free software and\nof promoting the sharing and reuse of software generally.\n\n                            NO WARRANTY\n\n  11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY\nFOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN\nOTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES\nPROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED\nOR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\nMERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.  THE ENTIRE RISK AS\nTO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU.  SHOULD THE\nPROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,\nREPAIR OR CORRECTION.\n\n  12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR\nREDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,\nINCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING\nOUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED\nTO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY\nYOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER\nPROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE\nPOSSIBILITY OF SUCH DAMAGES.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nconvey the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    {description}\n    Copyright (C) {year}  {fullname}\n\n    This program is free software; you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation; either version 2 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n\n    You should have received a copy of the GNU General Public License along\n    with this program; if not, write to the Free Software Foundation, Inc.,\n    51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\nAlso add information on how to contact you by electronic and paper mail.\n\nIf the program is interactive, make it output a short notice like this\nwhen it starts in an interactive mode:\n\n    Gnomovision version 69, Copyright (C) year name of author\n    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n    This is free software, and you are welcome to redistribute it\n    under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License.  Of course, the commands you use may\nbe called something other than `show w' and `show c'; they could even be\nmouse-clicks or menu items--whatever suits your program.\n\nYou should also get your employer (if you work as a programmer) or your\nschool, if any, to sign a \"copyright disclaimer\" for the program, if\nnecessary.  Here is a sample; alter the names:\n\n  Yoyodyne, Inc., hereby disclaims all copyright interest in the program\n  `Gnomovision' (which makes passes at compilers) written by James Hacker.\n\n  {signature of Ty Coon}, 1 April 1989\n  Ty Coon, President of Vice\n\nThis General Public License does not permit incorporating your program into\nproprietary programs.  If your program is a subroutine library, you may\nconsider it more useful to permit linking proprietary applications with the\nlibrary.  If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License."
  },
  {
    "path": "README.md",
    "content": "Machine Learning Tutorial\n=========================\n\nView Notbooks here:\nhttp://nbviewer.ipython.org/github/twistedhardware/mltutorial/tree/master/notebooks/\n\nFor video tutorial check:\nhttps://www.youtube.com/user/roshanRush\n"
  },
  {
    "path": "notebooks/ARSSF/1.Introduction.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:f8d7e6885b3c2c66d6cd4b3d36c57815afb126577254e011442dd7ee1423ad81\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Automated Referee System for Saber Fencing (ARSSF)\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Final_2013_Fencing_WCH_SMS-IN_t202316.jpg/800px-Final_2013_Fencing_WCH_SMS-IN_t202316.jpg)\\n\",\n      \"\\u00a9 Marie-Lan Nguyen / Wikimedia Commons / CC-BY 3.0\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"**These are the basic rules:**\\n\",\n      \"- You score a point once your saber touches the other player upper body.\\n\",\n      \"- If both players hit with in 120ms from each other, the player with the Right of Way (RoW) gets the point. In simpler terms the attacker gets the point.\\n\",\n      \"- If both attacked at the same time and and hit each other within 120ms no point is awarded to neither one of them.\\n\",\n      \"- Players loose the RoW once they are blocked or if they miss.\\n\",\n      \"- There are other more rules...\\n\",\n      \"\\n\",\n      \"![](rules.png)\\n\",\n      \"\\n\",\n      \"References:\\n\",\n      \"\\n\",\n      \"- [Fencing on Wikipedia](http://en.wikipedia.org/wiki/Fencing)\\n\",\n      \"- [Fencing Rules on Wikipedia](http://en.wikipedia.org/wiki/Fencing_practice_and_techniques)\\n\",\n      \"- [International Fencing Federation](http://fie.org/)\\n\",\n      \"- [Sydney Sabre](http://www.sydneysabre.com/)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"The Goal\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Create a system that can detect scoring points and keep track of the results.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"What is the Approach\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"##Feature Extraction\\n\",\n      \"- Tracking Lights\\n\",\n      \"- OCR (Optical Character Recognition)\\n\",\n      \"- Tracking Players Location\\n\",\n      \"\\n\",\n      \"##Pre-Processing\\n\",\n      \"- Split the game into rounds using OCR on the score\\n\",\n      \"- Split the rounds into segments \\\"bouts\\\"\\n\",\n      \"\\n\",\n      \"##Image Processing & Computer Vision\\n\",\n      \"- OCR\\n\",\n      \" - Score\\n\",\n      \" - Player Names\\n\",\n      \"- Motion Tracking\\n\",\n      \" - Location of Left and Right Players\\n\",\n      \"- Template Matching\\n\",\n      \" - Detect Red/Green Lights\\n\",\n      \"\\n\",\n      \"##Machine Learning\\n\",\n      \"- Features:\\n\",\n      \" - Players' Motion\\n\",\n      \" - Lights\\n\",\n      \"- Result of reach segment \\\"bout\\\" is the result\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Computer Vision & Image Processing Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will be working with OpenCV and Scikit-Image. OpenCV is a computer vision library and it has capabilites to process video and extract frames. Scikit-Image is an image procesing library that might come in handy for some advanced image processing.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Features Extraction\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"import pandas as pd\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"\\n\",\n      \"import datetime\\n\",\n      \"\\n\",\n      \"import cv2\\n\",\n      \"from cv2 import cv\\n\",\n      \"\\n\",\n      \"import skimage\\n\",\n      \"import skimage.color\\n\",\n      \"import skimage.feature\\n\",\n      \"import skimage.filter\\n\",\n      \"\\n\",\n      \"from IPython.html import widgets\\n\",\n      \"from IPython.html.widgets import interact\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Open Video\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cap = cv2.VideoCapture(\\\"video/SabreFinal.mp4\\\")\\n\",\n      \"if not cap.isOpened():\\n\",\n      \"    print \\\"Unable to Open Video\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Reading Frames\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"ret, image = cap.read()\\n\",\n      \"if ret:\\n\",\n      \"    plt.imshow(image)\\n\",\n      \"else:\\n\",\n      \"    print \\\"Unable to Read Frame\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": \"iVBORw0KGgoAAAANSUhEUgAAAXMAAADdCAYAAABJ25K4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nGmCYS1ID/h9s49YUP9+vCAAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f327e56cd10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for counter in range(130):\\n\",\n      \"    ret, image = cap.read()\\n\",\n      \"if ret:\\n\",\n      \"    plt.imshow(image)\\n\",\n      \"else:\\n\",\n      \"    print \\\"Unable to Read Frame\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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jSJuHJ1j14eMZtb4lhR15Yv/PmXiXobZLOSxhgQDl05GmsQ3uGdwRhH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\\n/X9H+k4BD8tQGgAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f32114f6390>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Note:\\n\",\n      \"**OpenCV uses in BGR (Blue,Green,Red) color schema as default. But almost every other library (including Matplotlib) uses RGB as default.**\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n\",\n      \"plt.imshow(image_rgb)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 5,\n       \"text\": [\n        \"<matplotlib.image.AxesImage at 0x7f3213671c50>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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IqKY2xd8+W/+CLDXkQ5zTCmCVRDpTG2wXmBcR5nTKDQpCTPc9JY03jH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\\nAcPjuLwAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f3211514710>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Inspecting the properties of `VideoCapture` Object\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"propId Reference:\\n\",\n      \"\\n\",\n      \"http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html#videocapture-get\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"`VideoCapture.get(propId) -> retval`\\n\",\n      \"\\n\",\n      \"```\\n\",\n      \"propId\\n\",\n      \"    CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds or video capture timestamp.\\n\",\n      \"    CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.\\n\",\n      \"    CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file: 0 - start of the film, 1 - end of the film.\\n\",\n      \"    CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.\\n\",\n      \"    CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.\\n\",\n      \"    CV_CAP_PROP_FPS Frame rate.\\n\",\n      \"    CV_CAP_PROP_FOURCC 4-character code of codec.\\n\",\n      \"    CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.\\n\",\n      \"    CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .\\n\",\n      \"    CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.\\n\",\n      \"    CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).\\n\",\n      \"    CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).\\n\",\n      \"    CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).\\n\",\n      \"    CV_CAP_PROP_HUE Hue of the image (only for cameras).\\n\",\n      \"    CV_CAP_PROP_GAIN Gain of the image (only for cameras).\\n\",\n      \"    CV_CAP_PROP_EXPOSURE Exposure (only for cameras).\\n\",\n      \"    CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.\\n\",\n      \"    CV_CAP_PROP_WHITE_BALANCE Currently not supported\\n\",\n      \"    CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"propId_dict = {\\\"POS_MSEC\\\": 0,\\n\",\n      \"\\\"POS_FRAMES\\\": 1,\\n\",\n      \"\\\"POS_AVI_RATIO\\\": 2,\\n\",\n      \"\\\"FRAME_WIDTH\\\": 3,\\n\",\n      \"\\\"FRAME_HEIGHT\\\": 4,\\n\",\n      \"\\\"FPS\\\": 5,\\n\",\n      \"\\\"FOURCC\\\": 6,\\n\",\n      \"\\\"FRAME_COUNT\\\": 7,\\n\",\n      \"\\\"FORMAT\\\": 8,\\n\",\n      \"\\\"MODE\\\": 9,\\n\",\n      \"\\\"BRIGHTNESS\\\": 10,\\n\",\n      \"\\\"CONTRAST\\\": 11,\\n\",\n      \"\\\"SATURATION\\\": 12,\\n\",\n      \"\\\"HUE\\\": 13,\\n\",\n      \"\\\"GAIN\\\": 14,\\n\",\n      \"\\\"EXPOSURE\\\": 15,\\n\",\n      \"\\\"CONVERT_RGB\\\": 16,\\n\",\n      \"\\\"WHITE_BALANCE\\\": 17,\\n\",\n      \"\\\"RECTIFICATION\\\": 18,\\n\",\n      \"}\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print cap.get(1)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"131.0\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for key,value in sorted(propId_dict.iteritems(), key=lambda item: item[1], reverse=False):\\n\",\n      \"    print \\\"%s. %s: %s\\\" % (value, key, cap.get(value))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"0. POS_MSEC: 5240.0\\n\",\n        \"1. POS_FRAMES: 131.0\\n\",\n        \"2. POS_AVI_RATIO: 0.04\\n\",\n        \"3. FRAME_WIDTH: 1280.0\\n\",\n        \"4. FRAME_HEIGHT: 720.0\\n\",\n        \"5. FPS: 25.0\\n\",\n        \"6. FOURCC: 828601953.0\\n\",\n        \"7. FRAME_COUNT: 8618.0\\n\",\n        \"8. FORMAT: 0.0\\n\",\n        \"9. MODE: 0.0\\n\",\n        \"10. BRIGHTNESS: 0.0\\n\",\n        \"11. CONTRAST: 0.0\\n\",\n        \"12. SATURATION: 0.0\\n\",\n        \"13. HUE: 0.0\\n\",\n        \"14. GAIN: 0.0\\n\",\n        \"15. EXPOSURE: 0.0\\n\",\n        \"16. CONVERT_RGB: 0.0\\n\",\n        \"17. WHITE_BALANCE: 0.0\\n\",\n        \"18. RECTIFICATION: 0.0\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"FourCC\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"FourCC is a 4 digit adentifier for a codec.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_fourcc(cap):\\n\",\n      \"    int_value = cap.get(propId_dict[\\\"FOURCC\\\"])\\n\",\n      \"    format_hex = format(int(int_value), \\\"x\\\")\\n\",\n      \"    fourcc = \\\"\\\"\\n\",\n      \"    for index in range(len(format_hex))[::2]:\\n\",\n      \"        value = format_hex[index:index+2]\\n\",\n      \"        fourcc = chr(int(value, 16)) + fourcc\\n\",\n      \"    return fourcc\\n\",\n      \"get_fourcc(cap)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"'avc1'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Video Length & Current Position\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_video_length(cap, current_pos=False, in_ms=False):\\n\",\n      \"    \\\"\\\"\\\"\\n\",\n      \"    Measure the length of a VideoCapture Object\\n\",\n      \"    \\n\",\n      \"    Parameters:\\n\",\n      \"    ===========\\n\",\n      \"    cap: VideoCapture object\\n\",\n      \"    in_ms: boolean (default=False)\\n\",\n      \"      Returns the time in milliseconds if set to True.\\n\",\n      \"    current_pos: boolean (default=False)\\n\",\n      \"      Returns the time for the current position\\n\",\n      \"      \\n\",\n      \"    Return:\\n\",\n      \"    =======\\n\",\n      \"    if in_ms=False -> str\\n\",\n      \"      example for 1 second VideoCapture:\\n\",\n      \"      > get_video_length(cap, in_ms=False)\\n\",\n      \"      00:00:01.0\\n\",\n      \"    \\n\",\n      \"    if in_ms=True  -> int\\n\",\n      \"      example for 1 second VideoCapture\\n\",\n      \"      > get_video_length(cap, in_ms=True)\\n\",\n      \"      1000\\n\",\n      \"    \\\"\\\"\\\"\\n\",\n      \"    fps = cap.get(propId_dict[\\\"FPS\\\"])\\n\",\n      \"    if current_pos:\\n\",\n      \"        length_in_ms = cap.get(propId_dict[\\\"POS_MSEC\\\"])\\n\",\n      \"    else:\\n\",\n      \"        frame_count = cap.get(propId_dict[\\\"FRAME_COUNT\\\"])\\n\",\n      \"        length_in_ms = int(frame_count/fps * 1000)\\n\",\n      \"    if in_ms:\\n\",\n      \"        return length_in_ms\\n\",\n      \"    length = datetime.timedelta(milliseconds=length_in_ms)\\n\",\n      \"    return length\\n\",\n      \"\\n\",\n      \"print get_video_length(cap,current_pos=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"0:00:05.240000\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Matplotlib\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def show_image(frame=0):\\n\",\n      \"    # Set Frame\\n\",\n      \"    cap.set(propId_dict[\\\"POS_FRAMES\\\"], frame)\\n\",\n      \"    \\n\",\n      \"    dpi=72.0\\n\",\n      \"    height = cap.get(propId_dict[\\\"FRAME_HEIGHT\\\"])\\n\",\n      \"    width = cap.get(propId_dict[\\\"FRAME_WIDTH\\\"])\\n\",\n      \"\\n\",\n      \"    plt.figure(figsize=(width/dpi,height/dpi))\\n\",\n      \"    ret, image_bgr = cap.read()\\n\",\n      \"    image = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\\n\",\n      \"    plt.imshow(image)\\n\",\n      \"\\n\",\n      \"    plt.grid()\\n\",\n      \"    plt.show()\\n\",\n      \"show_image(frame=130)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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uq6wdkqCQJBaGsnD7dcLjg5OebNN9/MCt0Yy27XsdluuLi4oNt17HY7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\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2/sz9ZPcQBDWeorxboD7CUVA0C/qKB6PDn+X/WG/DkU64prSQaF1bao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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f3211501510>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"total_frames = cap.get(propId_dict[\\\"FRAME_COUNT\\\"])\\n\",\n      \"print total_frames\\n\",\n      \"interact(show_image, frame=[130,500])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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uq6wdkqCQJBaGsnD7dcLjg5OebNN9/MCt0Yy27XsdluuLi4oNt17HY7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\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2/sz9ZPcQBDWeorxboD7CUVA0C/qKB6PDn+X/WG/DkU64prSQaF1bao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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f32114bed50>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"<function __main__.show_image>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Frame Length\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fps = cap.get(propId_dict[\\\"FPS\\\"])\\n\",\n      \"print \\\"Frame Length: %s milliseconds\\\" % int(1/cap.get(propId_dict[\\\"FPS\\\"])*1000)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Frame Length: 40 milliseconds\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/ARSSF/2. OCR.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:cf7c738a6344afc75be8e6c9bbef4fbd3a120a0084e353132c9f0c56fb08892d\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"OCR for Digits\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"There are very good OCR libraries. But to get really high accuracy on this videos I'll train a specialized OCR model from scratch.\\n\",\n      \"\\n\",\n      \"**References:**\\n\",\n      \"\\n\",\n      \"http://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"import pandas as pd\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"\\n\",\n      \"import datetime\\n\",\n      \"\\n\",\n      \"import cv2\\n\",\n      \"from cv2 import cv\\n\",\n      \"\\n\",\n      \"import skimage\\n\",\n      \"import skimage.color\\n\",\n      \"import skimage.feature\\n\",\n      \"import skimage.filter\\n\",\n      \"\\n\",\n      \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n      \"from sklearn.svm import SVC\\n\",\n      \"from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\\n\",\n      \"from sklearn.grid_search import GridSearchCV\\n\",\n      \"\\n\",\n      \"from IPython.html import widgets\\n\",\n      \"from IPython.html.widgets import interact\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Open Video\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"propId_dict = {\\\"POS_MSEC\\\": 0,\\n\",\n      \"\\\"POS_FRAMES\\\": 1,\\n\",\n      \"\\\"POS_AVI_RATIO\\\": 2,\\n\",\n      \"\\\"FRAME_WIDTH\\\": 3,\\n\",\n      \"\\\"FRAME_HEIGHT\\\": 4,\\n\",\n      \"\\\"FPS\\\": 5,\\n\",\n      \"\\\"FOURCC\\\": 6,\\n\",\n      \"\\\"FRAME_COUNT\\\": 7,\\n\",\n      \"\\\"FORMAT\\\": 8,\\n\",\n      \"\\\"MODE\\\": 9,\\n\",\n      \"\\\"BRIGHTNESS\\\": 10,\\n\",\n      \"\\\"CONTRAST\\\": 11,\\n\",\n      \"\\\"SATURATION\\\": 12,\\n\",\n      \"\\\"HUE\\\": 13,\\n\",\n      \"\\\"GAIN\\\": 14,\\n\",\n      \"\\\"EXPOSURE\\\": 15,\\n\",\n      \"\\\"CONVERT_RGB\\\": 16,\\n\",\n      \"\\\"WHITE_BALANCE\\\": 17,\\n\",\n      \"\\\"RECTIFICATION\\\": 18,\\n\",\n      \"}\\n\",\n      \"\\n\",\n      \"cap = cv2.VideoCapture(\\\"video/2014 FencingWA Open 3 Sabre Final 1080.mp4\\\")\\n\",\n      \"if not cap.isOpened():\\n\",\n      \"    print \\\"Unable to Open Video\\\"\\n\",\n      \"else:\\n\",\n      \"    print \\\"FRAME_WIDTH: %s\\\" % cap.get(propId_dict[\\\"FRAME_WIDTH\\\"])\\n\",\n      \"    print \\\"FRAME_HEIGHT: %s\\\" % cap.get(propId_dict[\\\"FRAME_HEIGHT\\\"])\\n\",\n      \"    print \\\"FPS: %s\\\" % cap.get(propId_dict[\\\"FPS\\\"])\\n\",\n      \"    print \\\"FRAME_COUNT: %s\\\" % cap.get(propId_dict[\\\"FRAME_COUNT\\\"])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"FRAME_WIDTH: 1920.0\\n\",\n        \"FRAME_HEIGHT: 1080.0\\n\",\n        \"FPS: 25.0\\n\",\n        \"FRAME_COUNT: 8618.0\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Showing Frames\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Matplotlib\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def show_image(frame=0):\\n\",\n      \"    # Set Frame\\n\",\n      \"    cap.set(propId_dict[\\\"POS_FRAMES\\\"], frame)\\n\",\n      \"    \\n\",\n      \"    dpi=110.0\\n\",\n      \"    height = cap.get(propId_dict[\\\"FRAME_HEIGHT\\\"])\\n\",\n      \"    width = cap.get(propId_dict[\\\"FRAME_WIDTH\\\"])\\n\",\n      \"\\n\",\n      \"    plt.figure(figsize=(width/dpi,height/dpi))\\n\",\n      \"    ret, image_bgr = cap.read()\\n\",\n      \"    image = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\\n\",\n      \"    plt.imshow(image)\\n\",\n      \"\\n\",\n      \"    plt.grid()\\n\",\n      \"    plt.show()\\n\",\n      \"total_frames = cap.get(propId_dict[\\\"FRAME_COUNT\\\"])\\n\",\n      \"interact(show_image, frame=[130,500])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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fCvPu8z5+fnWYM0VVVRliXTahEjnQNiJhNoPp9zcnICbjyxmqZhtVqx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\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M/3iH+4hw/34axM0mgSAAMbyVXAkLD9wLxuPTrfJC9L/GwUnnlo0y4F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\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opBfMq208q1Nkl2tduEkJn1W5TpHZEzotu2LqeJ5dS+BAkztGVNe4dJ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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f99167d1ad0>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 81,\n       \"text\": [\n        \"<function __main__.show_image>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 81\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"OpenCV\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"ret, image_bgr = cap.read()\\n\",\n      \"if not ret:\\n\",\n      \"    print \\\"Unable to read frame\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cv2.imshow('Saber Fencing',image_bgr)\\n\",\n      \"cv2.waitKey(0)\\n\",\n      \"cv2.destroyAllWindows()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Normal Window\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cv2.namedWindow(\\\"Saber Fencing\\\", cv2.WINDOW_NORMAL)\\n\",\n      \"cv2.imshow(\\\"Saber Fencing\\\",image_bgr)\\n\",\n      \"cv2.waitKey(0)\\n\",\n      \"cv2.destroyAllWindows()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Full Screen\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cv2.namedWindow(\\\"Saber Fencing\\\", cv2.WND_PROP_FULLSCREEN)\\n\",\n      \"cv2.setWindowProperty(\\\"Saber Fencing\\\", cv2.WND_PROP_FULLSCREEN, cv2.cv.CV_WINDOW_FULLSCREEN)\\n\",\n      \"cv2.imshow(\\\"Saber Fencing\\\",image_bgr)\\n\",\n      \"k = cv2.waitKey(0)\\n\",\n      \"cv2.destroyAllWindows()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Building Work Interface\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"roi_list = []\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 95\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def on_mouse(event, x, y, botton, *args):\\n\",\n      \"    if event == 1 & botton == 1: #mouse down\\n\",\n      \"        roi_list.append([x,y,botton])\\n\",\n      \"    return None\\n\",\n      \"\\n\",\n      \"def show_frame(param):\\n\",\n      \"    frame = cv2.getTrackbarPos(\\\"frame\\\", \\\"Saber Fencing\\\")\\n\",\n      \"    cap.set(propId_dict[\\\"POS_FRAMES\\\"], frame)\\n\",\n      \"    ret, image_bgr = cap.read()\\n\",\n      \"    \\n\",\n      \"    color_schema = cv2.getTrackbarPos(\\\"BGR/HSV\\\", \\\"Saber Fencing\\\")\\n\",\n      \"    color_schema = {0:\\\"bgr\\\", 1:\\\"hsv\\\"}[color_schema]\\n\",\n      \"    \\n\",\n      \"    imge_or_threshold = cv2.getTrackbarPos(\\\"Image/Threshold\\\", \\\"Saber Fencing\\\")\\n\",\n      \"    imge_or_threshold = {0:\\\"image\\\",1:\\\"threshold\\\"}[imge_or_threshold]\\n\",\n      \"    \\n\",\n      \"    \\n\",\n      \"    max_b = cv2.getTrackbarPos(\\\"Max B\\\", \\\"BGR Histogram\\\")\\n\",\n      \"    min_b = cv2.getTrackbarPos(\\\"Min B\\\", \\\"BGR Histogram\\\")\\n\",\n      \"    max_g = cv2.getTrackbarPos(\\\"Max G\\\", \\\"BGR Histogram\\\")\\n\",\n      \"    min_g = cv2.getTrackbarPos(\\\"Min G\\\", \\\"BGR Histogram\\\")\\n\",\n      \"    max_r = cv2.getTrackbarPos(\\\"Max R\\\", \\\"BGR Histogram\\\")\\n\",\n      \"    min_r = cv2.getTrackbarPos(\\\"Min R\\\", \\\"BGR Histogram\\\")\\n\",\n      \"    \\n\",\n      \"    max_h = cv2.getTrackbarPos(\\\"Max H\\\", \\\"HSV Histogram\\\")\\n\",\n      \"    min_h = cv2.getTrackbarPos(\\\"Min H\\\", \\\"HSV Histogram\\\")\\n\",\n      \"    max_s = cv2.getTrackbarPos(\\\"Max S\\\", \\\"HSV Histogram\\\")\\n\",\n      \"    min_s = cv2.getTrackbarPos(\\\"Min S\\\", \\\"HSV Histogram\\\")\\n\",\n      \"    max_v = cv2.getTrackbarPos(\\\"Max V\\\", \\\"HSV Histogram\\\")\\n\",\n      \"    min_v = cv2.getTrackbarPos(\\\"Min V\\\", \\\"HSV Histogram\\\")\\n\",\n      \"    \\n\",\n      \"    if color_schema == \\\"bgr\\\":\\n\",\n      \"        display_image = image_bgr\\n\",\n      \"        if imge_or_threshold == \\\"threshold\\\":\\n\",\n      \"            display_image = cv2.inRange(display_image,\\n\",\n      \"                                        np.array([min_b, min_g, min_r]),\\n\",\n      \"                                        np.array([max_b, max_g, max_r]))\\n\",\n      \"    elif color_schema == \\\"hsv\\\":\\n\",\n      \"        display_image = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\\n\",\n      \"        if imge_or_threshold == \\\"threshold\\\":\\n\",\n      \"            display_image = cv2.inRange(display_image,\\n\",\n      \"                                        np.array([min_h, min_s, min_v]),\\n\",\n      \"                                        np.array([max_h, max_s, max_v]))\\n\",\n      \"    \\n\",\n      \"    cv2.imshow(\\\"Saber Fencing\\\",display_image)\\n\",\n      \"    \\n\",\n      \"    show_hist(image_bgr, limits=np.array([[max_b, min_b], [max_g, min_g], [max_r, min_r]]))\\n\",\n      \"    show_hist(image_bgr, color_schema=\\\"hsv\\\", limits=np.array([[max_h, min_h], [max_s, min_s], [max_v, min_v]]))\\n\",\n      \"    return image_bgr\\n\",\n      \"\\n\",\n      \"def fig2data (fig):\\n\",\n      \"    fig.canvas.draw ()\\n\",\n      \" \\n\",\n      \"    w,h = fig.canvas.get_width_height()\\n\",\n      \"    buf = numpy.ndarray(shape = (h, w,4), dtype=numpy.uint8, buffer=fig.canvas.buffer_rgba())\\n\",\n      \"    return buf\\n\",\n      \"\\n\",\n      \"def show_hist(image_bgr, color_schema=\\\"bgr\\\", limits=np.array([[0,255], [0,255], [0,255]]), color=('b','g','r')):\\n\",\n      \"    fig = plt.figure(figsize=(5,3))\\n\",\n      \"    if color_schema == \\\"bgr\\\":\\n\",\n      \"        image_hist = image_bgr\\n\",\n      \"    elif color_schema == \\\"hsv\\\":\\n\",\n      \"        image_hist = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\\n\",\n      \"        \\n\",\n      \"    for i,col in enumerate(color):\\n\",\n      \"        histr = cv2.calcHist([image_hist],[i],None,[limits[i][0]-limits[i][1]],[limits[i][1],limits[i][0]])\\n\",\n      \"        plt.plot(range(limits[i][1], limits[i][0]), histr, color = col, hold=True)\\n\",\n      \"    plt.xlim([np.min(limits, axis=0)[1], np.max(limits, axis=0)[0]])\\n\",\n      \"    data = fig2data(fig)\\n\",\n      \"    data = cv2.cvtColor(data, cv2.COLOR_RGBA2BGR)\\n\",\n      \"    cv2.imshow(color_schema.upper() + \\\" Histogram\\\",data)\\n\",\n      \"    plt.close()\\n\",\n      \"\\n\",\n      \"cv2.namedWindow(\\\"Saber Fencing\\\", cv2.WINDOW_NORMAL)\\n\",\n      \"cv2.createTrackbar(\\\"frame\\\", \\\"Saber Fencing\\\", 130, int(total_frames), show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"BGR/HSV\\\", \\\"Saber Fencing\\\", 0, 1, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Image/Threshold\\\", \\\"Saber Fencing\\\", 0, 1, show_frame)\\n\",\n      \"\\n\",\n      \"cv2.namedWindow(\\\"BGR Histogram\\\",  cv2.WINDOW_AUTOSIZE)\\n\",\n      \"cv2.namedWindow(\\\"HSV Histogram\\\",  cv2.WINDOW_AUTOSIZE)\\n\",\n      \"\\n\",\n      \"cv2.createTrackbar(\\\"Max B\\\", \\\"BGR Histogram\\\", 255, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Min B\\\", \\\"BGR Histogram\\\", 0, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Max G\\\", \\\"BGR Histogram\\\", 255, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Min G\\\", \\\"BGR Histogram\\\", 0, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Max R\\\", \\\"BGR Histogram\\\", 255, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Min R\\\", \\\"BGR Histogram\\\", 0, 255, show_frame)\\n\",\n      \"\\n\",\n      \"cv2.createTrackbar(\\\"Max H\\\", \\\"HSV Histogram\\\", 180, 180, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Min H\\\", \\\"HSV Histogram\\\", 0, 180, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Max S\\\", \\\"HSV Histogram\\\", 255, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Min S\\\", \\\"HSV Histogram\\\", 0, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Max V\\\", \\\"HSV Histogram\\\", 255, 255, show_frame)\\n\",\n      \"cv2.createTrackbar(\\\"Min V\\\", \\\"HSV Histogram\\\", 0, 255, show_frame)\\n\",\n      \"\\n\",\n      \"image_bgr = show_frame(130)\\n\",\n      \"\\n\",\n      \"cv2.moveWindow(\\\"Saber Fencing\\\", 10, 25)\\n\",\n      \"cv2.moveWindow(\\\"BGR Histogram\\\", 900, 400)\\n\",\n      \"cv2.moveWindow(\\\"HSV Histogram\\\", 900, 25)\\n\",\n      \"\\n\",\n      \"cv2.setMouseCallback(\\\"Saber Fencing\\\", on_mouse)\\n\",\n      \"\\n\",\n      \"while True:\\n\",\n      \"    k=cv2.waitKey(0)\\n\",\n      \"    if k==27:\\n\",\n      \"        image_bgr = show_frame(None)\\n\",\n      \"        roi_list = np.array(roi_list)\\n\",\n      \"        image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\\n\",\n      \"        image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\\n\",\n      \"        break\\n\",\n      \"    else:\\n\",\n      \"        print k\\n\",\n      \"    \\n\",\n      \"cv2.destroyAllWindows()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 117\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cv2.destroyAllWindows()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"OCR\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Training Set\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"I have built a training set using Gimp (a photo editing program).\\n\",\n      \"\\n\",\n      \"![](image/ocr_digits.png)\\n\",\n      \"\\n\",\n      \"I generated the original row using a text tool then replicated it several times. Then I resized each row to a different level and resized them back to their original size.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Pre-Processing the Image\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Change the image from BGR to gray scale.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"training_image = cv2.imread(\\\"image/ocr_digits.png\\\")\\n\",\n      \"print \\\"Origianl Shape: %s: \\\" % str(training_image.shape)\\n\",\n      \"training_image = cv2.cvtColor(training_image, cv2.COLOR_BGR2GRAY)\\n\",\n      \"print \\\"Final Shape: %s: \\\" % str(training_image.shape)\\n\",\n      \"plt.imshow(training_image, cmap=plt.cm.Greys)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Origianl Shape: (336, 375, 3): \\n\",\n        \"Final Shape: (336, 375): \\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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kcppSt/SWorVe3lRFGscbn5+eefS76Oe/bsscpe8e+ecQ4bNozu3bsn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\\nAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f995ce1b210>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Threshold the Image\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Fixed Threshold\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fixed_thresh = (training_image < 30) * 255\\n\",\n      \"plt.imshow(fixed_thresh, cmap=plt.cm.Greys)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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69v+0D8Me31FwD8HBGdQ0SXAbgcwFez2lVpxbm1tZW4mykfWnWsrq4m\\nnxsOh6lxC/MymUywsbGxLVK7C2ae7dFo5LX9+g/GLF/37dfTYLhixhd1RW/XcDjMtDxshtTz5O8q\\nnxyW0ky9Rd0j9X6o+453Oh0w80yWzqL9tdVqJXJpt9sz7TblUoWI/g5znK8H8PMAQiKK4uMtRPQ2\\nIvoWgNcB+AMiuhcAmPkJAHcDeALAvQA+wPrNNHE1p21N8DzocQfThlR69BuXeJxZ2f1chup5hvx6\\nXUXbv7m5mVx/WvQj33m+zfbqh2t5vvLK6yy6/s3NzcJxRPPiKvdFctGH2kXynpvxNtPw1W/gaaj+\\nhje8Idfho74iR6UXh9STMQv9Sd5qtWae9nkYDAYzT9V2u40oirxYVfoEexmLNEEQIAiCpaXO0KdM\\n9HiRPvCRq0fHtMTS5O8zmpFer5JTvV53vu955HLmTOo2w1T0EVyWl89wOEwsOBVpvkgsV99U1eWy\\n0kN1RRnZA01ULiBf+YB6vV5pbmiLMOfIXFdg9fLKzs7pA5/TE7b1umSHVPcrz3XsdBbKsqlqWLld\\noTiznrxmtr+iqLiGzC/EHyzDEknDnKP0YXGlzdXW63XnEGSqvDAMvcVp1C1YPYeSfmxsbDjNXeuM\\nx+OZso8fP+5duarRS9GI+yYq84GKXK9yBKlD1ePSb3biwWKDKM4ScM32FwTBjqUh1lenVboEV6Io\\nmlFIeroIW/SpgGVlcVS0Wi0vq7srKyvbdmesra1Zr0qn4VNOQRBgMBgk5aggOHownG63i8FgYN1v\\ndsvuCFGcQoK5Ou1rDjQr37kt6+vrWF9ft14dzsKcg2w2m4nlb0Zs9zHsHgwGuPTSS5OJfX1V2sc1\\nmTmmfJQZBMHce2djNJiWsNlffMnDJ5IeWAAwtUx068TnfGG/30+Oer3uvB9PtdOXRazT7XaTtupz\\nwWpuWK/PdQHMtLzN8l33AevK3df91PtJo9GYua/qfZt26+0ztxzl3Xe8TKpqcVZ6VX2voSw4ha88\\n6wpdSaosjypL4mAwyD0f7Ht12KRWq81V6MrCVZaEyi9uO59tZrE0y1+0e2MRurLxMUeu9xMz+6my\\n/KMoQhRFOHTo0Mye4EWoXE9hGCaWspKDmvP3uVfXFVlVLwHfnj5lMRqNsLKyss3SLHN1utfrzazO\\nFlEOvlaHfVG0/SaL5Ow6HaBbhj4s80Xl6RazzVylOYeqGAwGOzbnn0VVh+q7wuLMsjj0fWlVxrRC\\nfFuaWdjuc9WHb3ki86uOW3QfbVWw8cBR6ArXR352Xd7zdpO41rPsfcC2VDUCfDVbZeBqcewkZsfc\\nDfsgzzZcLEXd4pP76p/9+/fnOpbNrrA4s9Cf9lUKhaUoc06z1Wolc1Gj0Sj1+nXLscgQbNEeRHMO\\nrOiexclkksy/7tu3L3WOTr+3RfdFqs+rdq6srMydp7X1XHKxVLOo1+sL5xhdLWT13TSZusi9DKo6\\nx1lpX3XdFz3NJxew9/WeBzz46urlYI5vsC2LfIrb7XZp8tHvi811mWHNOp3Ots+4+kzr36/VattC\\nsOnysb3P+nV0u12rMtKY126z3qLy1+VShtz1a2APeuJtb3tbrsNHfUWOSlucyoPi9OnTyeqwGg7p\\nQ6QqPBlN0uYG5z09zdXTRZg+xcePH0+sziiKtlm6PuXjutcvDEOsrq7i+PHjAKZD3EOHDs2033Wf\\nqy4ftT9RWd2mfHzsGvDpcaZiAZjtVtiOJNTnlVxMuZt9tqxA2EUQi9PC4mSeRrHBgoCuVYtuY0ah\\nyXPYWISm5Zd2+LSEFC4Wj04eubi0v2z56HLwadHnDYBtG1VqGf0GnizOd7zjHbkOH/UVOSptcQIv\\n7DvrdDqpc2u9Xs/7/GatVnMqUwk3r5Vnu2+u0+ng5MmT2NjY2Ladpl6ve/Ut11ExAg4dOuRUjrqv\\nas5TL79eryMIAqf2K/mo2Jy637sv+fiOFAW8sM9UWYBZc8q2W4ey+o2Se5W2JFXV4rTOq+5UqWVe\\ndd2tDUBpUarVJmMXN8MoinIP3waDAYIgsN5iMplMEEXRzDmX8vIQRREuu+wyAHCux2z/wYMHvbv+\\nRVE0s6DiQz6qPHWvy5J3Wf2+TLn7yqv+sz/7s7k++/nPf965viLsKsUpCMLuwJfifOc735nrs3ff\\nffdSFWflh+qCIJy9VHWovis2wAuCcHZi63JJRHcS0Skiekw7dw0RfTXOP/THRPRa7b1biegpIjpB\\nRNcvatdCxZnRgPOI6AEiepKI7ieimvZeoQYIgiBk4RAd6dMAbjDOfRzAh5k5APBv4v9BRFcDeBeA\\nq+Pv/CYRzdWNeSzOtAYcAfAAM18B4MH4f6sGCIIgZGFrcTLzQwC2jNPfAaACXtQAfDt+fSOAzzHz\\n88x8EsDTAK6Z166Fc5zM/BARHTJOvxWA2mtzF4AhpsozaQCAk0SkGvDwonoEQRBMPM9xHgHwZSL6\\nfzE1Gv+v+PwrMKujngFw0byCbBeHzmfmU/HrUwDOt21AEVx9pIvW47qfU2HuU1T4an9a+Y1Gwykf\\nU5H6bK8jSy4mPu6D7qMNuMmn6L5bl/ab7XYtT2fZ/caGrOhIzz77LJ599tmixX0KwCoz/x4R/WMA\\ndwJ4c8Zn5277cV5VZ2YmonmVpL539OjR5LWeSyUN5WKWtp+t2+163QDf6XQSd7wwDJ3dzjqdDobD\\nYWp0Jx/tzyo/CAK0Wq1S8gSFYThTn+3WMjMC+bz6bO9Dp9NJAoqk7eO0kX+eUHs6tu1fWVnZ1m5g\\nqjiDILCWyWg0ws0334ytrS1v/aasjK5ZFueFF16ICy+8MPn/0UcfzVPcNcx8Xfz68wA+Gb/+NoBL\\ntM9djBeG8enkdH06BOAx7f8TAC6IX18I4ET8+giAI9rnvgjgp9JcqfKiBx0AwPV6fZs7WlogBBtM\\nVzfXIB/dbnemnc1mk5vNprf2Z5Wvn+v1ek7XYDIajba559lium5mHbb3IY98bNqfp8360W63C5Vv\\n9sNarZa0/cCBA8n5IAh4Mpk4t98s10e/gSeXy/e+9725jrT6UvTWJoBm/PpNAP44fn01gBGAcwBc\\nBuDPEO9xz2xbzgswG/BxALfwC8ry9iINyNtZTZ/vfr/PW1tbvLW1NfOjc1Vwikaj4eUHq9DL0n1/\\nfShoPbpPo9Hgfr+fvNfv92fKT4uCY0OWD74tZnShfr+fetjGIsiSvykfm3bPO7LqLVK+/n39+s3Y\\nDUV95LP6zebmpnPUKx1fivN973tfrsOsD8DnAPwvAH8L4FsA3gfgJwE8EuuorwAItM9/CNNFoRMA\\nfnph23I0Pq0B5wH4EoAnAdwPoFakAXlvit6B6vX6tvf1zm8bZGE0Gm2zTHwoTt2yybI4XNq/qHxf\\n4cF0suRki+/26eSRv+pXRS3CRbhel+r3ecLKufSbNFZXV73cF1+K86abbsp1+KivyJFnVf3dGW9d\\nl3aSmT8G4GOLys2DnsUvLfdNt9vNNUc2j7W1tdKTU2VFBndpf7fbTea+yvLZ19HnfncbWfLv9/ve\\nfcx951dfdoDuXq+XhPurAuI5ZIGauM4Tb9MmQs1gMMDGxkaS3Y+ZC2UMnMdwONSfnN4JggBhGOZS\\nmq4/PpUNUcnJR5zGMpPsqfsKzL/2MAy9Ks3BYJA8XOr1unPADP06fKCXl2chx3f9NlQ1WVulFWcR\\nbLL9KYIgmMnrvSyURe0rO6LCzP7pmgtH5dvW5eSKfr/U6rp5+EC/n3rZZShu/X66yEm3NM2cVbb5\\n1Hcrkle9goRhWJpFuAgVIxKwz3ljsr6+PvNDq9VqzopOb2dZEcGVRWtSNB98GmorkqmAGo1GkibX\\nx75FXU6u2S6VZQ9MHypEhF6vN/OwUfFEbXMxFcl6upNUdah+VivOnWI0Gs0MsX3ttTSVg+scmd5O\\nnxkcTYtYla/aqqzNKIoQhiE2Nzet69KVjVL8PstPq8eHrHq9HgaDQdJWc0TV6/VKcQBJs3B9yMeW\\nqirOPTNU3y2oIa9a2PExF6ZoNptoNpuJAlK5iGyHpaqdviPJM/OMQlfZP9Wc7ebmZmIF5t0kPw8V\\nUT2rfNdhu5510tdUxng8nrnuer0+IzOV5VSPap8HvQwiwvHjx7GxsYGNjY1tWVkB4MyZM5ZX4Ieq\\nznGKxblETEuz0WikDlFtUT/ayWSCVquF8XiMtbU1K88Vn6vDJmquVF27ucAVBAEajUayMKGUm631\\nPBgMZr6rhuiHDx8GML1Wl2kINQfsC9PSV2lEgNkHSavVKpzkr9fr4dChQ8k86aK1gWXO+achFudZ\\nTpqlGUVRKekWlIWlKLo6qs85DodD72ksFPN2BeiWm2v+8rT2+8pKqc9t+sq2qvpJrVZLZFSr1ZL/\\ndYu5qPsnMH1QmFZ/vV5Hu93edn6nEYvzLKZsSzOLY8eOWa3A6hZUp9PZpty3tmajdSkLqArpZG1w\\nsWhddnOkoVv6WXPUpkVuy3A4THIO6ftZq2TlVaktOrtCcU4mE5w+fXru6meVnpI6ytJUKEtzGdha\\nirq1mqet6iHQ6XS8LiLZorJwFp3/c8VHH1xW3wCQWLAm6v4rK3cn2YmtRnmoZqsMoihKjS6ks9Nz\\nMWnoKV4B9/19Op1OZ+F+x2Xv98trHYxGI+/7NXWURVbk8z6UXhUeGj7ZCc8lExmqW9Dv92cmws19\\nZ2o4WYUnYxrmPJpPayKKomSotrKysm2YrM+9FWXRPF1WPM68i0hbW1szUxVpw2T9geM6d5i2H9TH\\nYk4Zq+nD4TBRBGpu2rx2lzlftRoPTB9g8+S+00oTqO5QfWlO8abzfh7MSDN6lB89yovvIBE+ytXb\\np5eXdRQNLadHyTGDQZhRjBqNBm9tbVlfi4l5X4qyKDqU2X6b6Ejzogj5KJ/ZLdjGPPQoRUEQbHtf\\nj+JVNPpSkX7jAjwF+eh0OrkOH/UVOSptcYZhiNXV1SToQLfbTR0OVeHJaJJmXc5bECpqQahhlIoQ\\nnjU0Xeacal7Uqr9uWWVZFrar+rp8gOzVZ5/7aH3RarWSOVrlOZRG1hzlPPRR0Lx+U0ZQYhuqanFW\\nfo6z1+vNHQJmKVNbTM8JG9I8Y8pgOBzOfWj4nFMtg36/P7f9i95fxHA4nLvSX1X5qH2m8+h2u9v2\\np+albLn7pKpznMQ74K9KRGxb79raGvbt27fnJuJ9oHyyq6gM8tBqtdDr9Ur70e5m+ZTZ78uQOxGB\\nmZ00GhHxBz/4wVyf/dVf/VXn+oqw6xSnIAjVx5fivOWWW3J99o477liq4qz8UF0QhLMX26E6Ed1J\\nRKeI6DHt3FEieoaIovh4i/berUT0FBGdIKLrF7Wr0otDgiCc3TjMX34awK8D+Ix2jgEcY+aZRRMi\\nuhrAuzDNmXYRgC8R0RXMnBnhRCxOQRAqi63FycwPAdjaXiLSNPGNAD7HzM8z80lMc6ZdM69dojgF\\nQagsJayq/woRjYnoU0SkvGZeAeAZ7TPPYGp5ZrKrhuq6x0qj0fASuXuZ9SiPjVqtVsrKsSq/jAC3\\nJmr/qEt9pgdSFr6ux4f89evOi+v9LrPf+7iPZeJ5q9EnAHw0fv1vAfwagPdnfHb+6vUyd9vrHgE2\\n6B4TaR4VvtDr8eURonsS1Wo1r95OURRxGIYznjhFPZGK1ufDw8T0QMo6fOSFN+VvKx89vW7ew9UD\\nrax+b95HH3JWwJPn0Ic//OHU4xd+4Rf42muvTY60+gAcAvBYRtnJewCOADiivfdFAD81r227Yqi+\\nvr4OItoWtUdFsPa52dzFx9tEeWYQ0UxkbRXvUrXflo2NjZncOYrBYIBGo4HDhw97z1JoxhVdBrb3\\nY578lXyWHUEpL+PxOGl3Wr8nIut+r67fvI/dbhdEVCm5ZCVne+UrX5nEKs3rPUVEF2r/vh2AWnH/\\nAoCfI6JziOgyAJcD+Orcsrji+zj1fN6NRgPHjh3DaDSaifxjE+E8DRU3U+9MRSNs6wRBMNPpVRvN\\n9nc6Haso6/owJi1nj8LnPdaDRLiWb8oniyiKrN0uF8k/CIJCOXWiKEoixxf5TpH2m/1Q79udTmcm\\nuM0i77E0dLlk9ZuicjHxtY/zIx/5SK7P3nbbbTP1EdHnADQB/CiAUwBuA9AC0MDUwv4mgH/OzKfi\\nz38IwE0Avg9gjZnvm1thDnP5zrjix7RzRzGdQI3i4y3ae7cCeArACQDXZ5ngedCHcvV6PXU4gHj4\\nMplMcpUd0C/JAAAgAElEQVSZxtbWFg+Hw9Rhlu1QXR/SpQ0NV1dXneoZjUZzh4J6/b6G7cPhcCY4\\nhzpsmScfV3T5LJJ/WUFibK9rUb/X70HRfqP3i3a7PfPe5uYmHzhwwEu/gaeh+kc/+tFch4/6ihx5\\nhuqfBnCDqW8x3Q8VxMe9wLb9UDcA+E0i8jIdkBZvU1lpeeJ1ziMMQ6sUBHlJ8yle5IO/iDNnziTf\\nX1SOD/97AEkeI9+UEfdRj8yeJf9+v49+v+89p5LCx3Wl9XtfbpdmOWYcU1/9xoWq+qovXFVn5oeI\\n6FDKW3P3QwE4SURqP9TDNo1b9CN1jWqjp18FXlhV9DEvqP9gstrp0v4gCBAEQa7O7UMpmXEalxHE\\nxBY9x9K86EdFIwstQiXJU/W6Th8VDci8iDxxPPPEA10mezE6kpf9UPNY1hOv0Wig3+9jOBx6CwDR\\n6/W8lueCq4Wioj3pcqpK9JxFLDMzgJ5q2Ee9riOptPKq/MBLo6oWp63i/ASAyzCdaP0OpvuhsljK\\n6pPNULtWq6HZbCKKIu/WRx7KSBsxHo+xvr6OjY2N5PpcUauvBw8e9CYn/QesdhiYh+/dEnrZLrsZ\\nslD30zVivcqZBGyfHtHzrbfb7UL1+MrCuUyyVtXNY9lYbYBn5u+q10T0SQC/H//7bQCXaB+9OD63\\njaNHjyavW61WqXOMWaj83juBbk03Gg0vFtzKygqiKEqGY0EQOA8X0/Kr+7Bc8mSHVHnDXWTT6XRw\\n5513blP4a2truOeee2ZWlV3wmYdebS8bDoe4+eab0Wq1ZvKqA9NRRLvddqrHJ2WNrqo6VLdSnER0\\nITN/J/7X3A/1u0R0DNMheuZ+KF1xnm2sr68nW6x8Rmj/8Ic/jE6nkyhO1yG6aqfttpcszLm2NCto\\nNBol+w3TcuPkJYqiZJFGL2M0GiX7XV3KB6YWs7qfvvLQB0GQzNPqee71910xcz3pc7RFMY2fvNuI\\nFrFrFae+H4qIvoV4PxQRzeyHAgBmfoKI7gbwBKb7oT4Qb00QYvR9qYDfOThlQauAvcpSsbU6lRVV\\nxqq3rtTThv/6ftFOp+NkOas89npCP5/ll4HeT9L2car7W9S67Xa7yT5Us3+EYVi5OdBdqziZ+d0p\\np++c8/mPAfiYS6P2KrqlCRSfo1qEUgxqLuv48eMYDAapWR4X4XN1OK2di+ZK9dVdV9Isep/l+862\\nqluw9Xp9Rlaj0QiNRgNRFCGKItx4442F+pDa2N7pdLCxsTGT70nNieeNIbAMJK/6WU6n05mZB/Od\\nK8lE3ydadHVWz5lUhXz1VV8N9m2ZK6+mrJxIer+x2XmiRiZqH6s61LxqldLSVHVVfVdFR5pHlbfH\\npFmay5jYt51r0+fP8sx5qY5b1qyMSx7xshW/rtB9WOatVit5yB08eDDVglWr7ioLpi1ZVn+VHlJV\\nHarvGYuzCpZRGsu2NPciLrsO8qzeu1B2+Wn43hhvUgWPIcWe2o60LPr9/ty9jlV6MqZhWpo+LTJ9\\ncSNrVdhWPovmzMw5sKLztGr1djweo9frYXV1de7nsyyvLHSLDNi+euwL00PJB/V6fWY1/fTp09vm\\npvV6bZgXt1W/r77mbF0Qi9ORtKdglZ6MJqalWXZdPutXe/KyDtNiLrqHT5+zXFtbm4kUpbffFjPM\\nXpqPve9742vEo5eTZ2666OhFrca3Wq1tch+NRjNTM7Z5230ic5wWmHM5hw8fTjqKPkSqokdE2tzT\\nvBtcNHyd6VN8/PjxpJNHUVTq6r0rYRhidXU18d4ZDoeJT/R4PN62qmwzd6gWalSEc32hxZTPTisH\\nE32kpZwAGo0GmHnGcwgovp/zzJkX8o91u10cOnQo6RvD4TCZS57n479MqmpxLi0MkxkuKi+bm5sL\\nI2xHUZS7vDzoZfsIK5fnsKnn2LFjucr2jRm53RaznWEYzkQkbzQavLW15a2daUe32/VSvq9MAczT\\nMIdm+L4gCGYiwbvIx+w3tVptRu4+rgeewsr9xm/8Rq7DR31FjkpbnMDsvrO098pYaNGtM5s5HmVt\\nFrHybOpRw/Gsle+y5OPLB56Zsba2lgyjz5w5M2P9ubrwhWEIZs6Uj2v5uhx8zgXWajWMRqPEkUFH\\n1dfr9awt5U6ng5MnT6bOgbuUWwZVtTgrHwFeEITdh68I8J/4xCdyffaXfumXnOsrwq5ZHBIE4ezD\\ndjsSEd1JRKeI6DHt3K8S0TficJj/nYgOaO/dSkRPEdEJIrp+Ybu8XaEgCIJnHFbV0zJX3A/gNcxc\\nB/Akpml+rDJXiOIUBKGy2FqczPwQgC3j3APMrLYVPIJp2EtAy1zBzCcBqMwV2e1yvC5BEITSKHEf\\n500A/jB+XThzReVX1QVBOHspY1WdiP41gL9l5t+d87G5q9eiOAVBqCxZivPEiRM4ceKETXnvBfB/\\nA3iTdjp35grFrhiqK88P0zw/fPhw4o/sE1W+j5xAZq4b3+2fV35ZrK+vJ3W4+Ezr5WQdrrmB5snH\\nxpd/Xnk+74Pvdutk/Z5c72cZZMngqquuwtvf/vbkyFnWDQA+COBGZv5r7a0vAPg5IjqHiC7DnMwV\\nil2hOLMiU5eRZM2nD7Pp+2vi2v6VlZW55a+srHgPhOLTBz9PObqLYFEWyd8mT/wyoiHlabfLQ31e\\npPdOp5MaO2Cn2L9/f67DhKaZK/4IwN8jom8R0U0Afh3AuQAeIKKIiH4TmGauAKAyV9yLHJkrKq04\\n9ZwzwNTnWrk8qYg6URRhY2PDi+VmRjNyLU9lh1SeIKrtm5ubScSbKIqsfwSDwSC1fOX54RpFxyQt\\n940PlAeO6da2ublp/WCZJ3/l+z6ZTArLZzgcznXFM73ObNL7qnan1be6uprcB5t+o8f7zPo9DYfD\\nUkZyNtha9cz8bmZ+BTOfw8yXMPOdzHw5M1/KzEF8fED7/MeY+dXMfCUz37ewYcv079R9UPOg+wKn\\n+RTDo29tmm95GIZeyksrR/fBt6mn3W5n+uqbvs6dTsf6OnRMX2lXuWe13we6/BfJZ1n15kV9P8sX\\n3bbfRFGU+KSn/Z50H/YgCKzarreRPeiJz372s7kOH/UVOSptceqkRYFx9alVFoea22k2m4Xy8sxD\\nZVXMiu6jJ8mysQzPnDmDZrOZ5KDRMeMo2lg9JuPx2Es5afiOwqNHjE+L8qMsQSW/MvCR7TKKolQf\\neNtI81tbW4kFnpUgTx8JVQEJZOxAVgTwbrfrNNdjDpNVhkiVBdCFXq9nne4hb/l5cc3zPRqNKj2X\\nbKLH+5wnJ995wMuYU07DdQ4yKzeSimPqo//7oqpBPiptcSqllhUB3FccTjXXA/jJV61YFEFb/ch8\\nxxPd2NjwGldRxWlUcvKRW0e3aMbjMcbjMTY2NpLDZY4tS66q7LKUm5pzZmOu05a0XRdlWv7AbP/3\\nsavElTJ3LrhQWYuzqEXS6XSwublZ6DtquOzbmsqDmV/dJ2pFtt/vOz8I9HaWlStJtVe30FU4QRtU\\n31GWZ71en7mOWq2G4XDoNXyaTwu63++j0+kkuy6UHHTLv9vt5kqkV5SqZVUQi7MgRedYbOZk8uT3\\nLgNzddpX3nLz6RuGoVOcSL2dPoe1aXO69XodzWYzsdSiKHLeV6jvVxwMBkn56ryvfcBKTr7ilOqW\\nspIDEc3sEgjD0HokoTIGmJS1a8IF2+1IZTNXcRLRJUQ0IKLHiejrRLQanz+PiB4goieJ6H4iqmnf\\nKRSe6WxE30fn04pT+bEVrnNhqp0uWSbTMOfYVD5vdehzsj4sIJU2wyzf1z5gZdmqfOU+mDc3a5u/\\nXf9e2n7UKuavr+pQfZHF+TyADjO/BsDrAPwLIroKwBEADzDzFQAejP+3Cs90NmHuS202m14t3jAM\\nk6jnzWYT3W7XyUNGZc/MWt21xdxXaVpOnU5nZl+h61yb2X6zfFd8zwUqjypgaonr22BWV1cxGAys\\n7quyiJXyPHz4cDLv22g08I53vMPbrhJf7ErFyczPMvMofv0cgG9gGjXkrQDuij92F4C3xa8Lh2c6\\nmzA9NnzPs+nolmxRq033XNmpfPW+6s2y6PXyq+QpA7xwv/r9/jYLttfrJRazjTXe6/WSfhdFUZLx\\nUiXJq9pQvaqKM/fiEBEdAhBgGsfufGY+Fb91CsD58etXAHhY+9rC8ExZ6Fkc81ClPCkmeh5xYKpA\\nfc1rZqEWvlZWVpI5xbzzb2q/H5Cem10/55JffRnkWRxzWaVW12+bjdNEX/DJGo0oC13NVS7KS29i\\n69G0E+zEHs085FKcRHQugP8GYI2Zv6crNGZmIprn11lacqHRaJRrv95OY85plrEaWhaLfLP199WW\\nrp3m2LFjS7Mi1fX76H96f56Hnvp4bW2tsOLcTVR1VX2h4iSiF2OqNH+Hme+JT58ioguY+VkiuhDA\\nd+PzucMzHT16NHmthgsmymKaTCY4ffr0tvkX3TKqIqalqeY0fVjHami1b9++Pf3DsUGfMx2Px6VZ\\nwr5jAeTtz2Uu4ui/wyIWtFp4801VLc5FvqIE4DMAusb5jwO4JX59BMDt8eurAYwAnAPgMgB/hjiT\\npumDmodFeat132mfea1h6QtsYvp2+0L3OZ5Xrqq/aP7tPPnI044i7Q/DkMMw5NFolPk52/uQ1xdd\\nvW/rU+47r3redi+K4TCPdrudyN4kb7/KAzz5qv/hH/5hrsNHfUWORer89QB+HkAYh2GK4ph2twN4\\nMxE9CWAl/h9sEZ4pL7rlpnzM1TzNIg+dnSDNw0P3jDGPIvsJG40GfvEXf3GmXP37apVU1Z/leZWF\\nWpnPOszc51pHz8XW1layENFoNLZZT6r9gN3codkfsuSj8O0rb4s596hWvXXG43Gyih+GIdrtdu7y\\noyjCpz/96UT25ly18hAD/Luj2lJVX/WlaWjzSZIH8wlcq9V4NBpts4h8R9eBpaWjkxZtad5hY7Ho\\n3w+CIImCpJ/vdrve5eNqaWXdV+bZqE+25TPPRp9S8kkrv6jFpuPb4lToUYqgRbcyLUKb/qnLRcnd\\nLLfoCCUNeLI477vvvlyHj/oKtW2ZlekCKUKz2eQDBw5sUza1Wo2bzWahsvLgqjhthro2P7zNzc25\\nCrrZbM4dCtvS7Xa9KIys+wqA6/U6t9ttp3bOk4+P8pUcXKd00lhdXZ15uJj93uW+LuP35Etx3n//\\n/bmOZSvOis68zjIcDlP3lymPk7MV5amSFv2o2+2Wtk807tTOZN1X5enj6lU1Tz69Xs+5fF9ySGNe\\n+waDgdN9zQp5V8XfU1X3cVKZNz+zUiLeiXoFQVgORARmdtJoRMQPPvhgrs++6U1v2lYfEa0B+GeY\\nLnL/R2buEdF5AP4LgEsBnATwTmYuvDVnV1icgiCcndhanET09zFVmq8FUAfwj4joVchwFy+KKE5B\\nECqLw1D9SgCPMPNfM/MPAGwA+Blku4sXQhSnIAiVxUFxfh3AG+NIbi/FNJf6xch2Fy9EZQMZC4Ig\\n2C78MPMJIroDwP0A/jemjjk/MD6zyF08E1GcgiBUlizFGUXRwpCAzHwngDvjcv4dpkGHstzFi7VL\\nVtUFQfCNr1X1hx56KNdn3/jGN6atqv8YM3+XiH4cwH2YxhT+1wD+kpnvIKIjAGrMXHiBSCxOQRAq\\ni+Mezc8T0csxDcj+AWY+TUS3A7ibiN6PeDuSTcG7ZnFIZUFUuWLKzFaoIrX7Kl/51qv2+862qMpX\\nkcNV+WWh6vOVs8dstzqIKDU3jg16/3GVv56zaF78AR/t19utynPNAqpfxzL7jQ0uG+CZ+Vpmfg0z\\nN5h5EJ/7K2a+jpmvYObrbfZwqsIr73IZhuGMLy08uZ5loaIK+XCla7fb26Ik6e13rWNe+YuiD9ni\\n00fb9B1PO5Svtm35vvtPUZdaW7LaDczGJrCh7H4DTy6Xf/RHf5Tr8FFfobYtszJdIHkZjUbJDdV9\\ni3VfXl/KwQw+4arUlC9z2g90dXXVq0+8qcB0H22f6PfDh+JU7azX655a+AL6/TTL1+Xv++GyubmZ\\n+ILbXldWvzfL7/V6hcvO6jd6ua5y8aU4v/KVr+Q6RHEaqB9WWhQb1QF8BVkwn8Cu5S4qR0XBcVWc\\nadFs9Cg4LpaJiRk0w0Vx6lF5fEYXUiyKR6rk7zt6FPMLcrK9rkUPPpcH4zy56P3Gpf/7UpwPP/xw\\nrkMUp8Yixag/IW076Gg0mon241NxNpvNudFmVPvLiK7DvFhxFyVtiOqi8Hw/+EzKsLjzoPqTS/Sl\\nPIrX9voWfc/HVIwvxfnII4/kOpatOHf1qnoQBMlkvw2j0QhhGGIymaDb7SYRZ3yle10UaWZZkV3S\\nogPZULVskFVFZZ90jb4kVDfn0K5ZVV+EzQqpiqTebrfRbreTvOTLyphZZs4kPfuijwjnKi+UeuL6\\nzOOjckr5pKwdF4totVpeskiqPpiV2M9XPWmoZHBVoKph5faM4lyUjTGLtLiHu91SUHnRVVxLH+WN\\nx+PS5BJFEcIwxMrKyszhgt4flKU8Go2SsstQrEpOPhRPr9dDv99HvV7fZumvrKzgve99r3NczizK\\nTAZXFFGcwgx67hhfnV/tLwyCAJPJBL/927/tnItJKYJLL73USxvNsoFprp0f+ZEfwZkzZ5Jjc3PT\\n2z7ObrcLIkK73U7KbzabXveJAkjk7iO/OjDNKVSr1ZL2q2MwGOCyyy6zHkn4npIqE1GcQoKyCIHp\\nj7osS05PcGeDb8vVRM0Fqsjj+qGUwtrampe5VRURXx0q8rztSGUZzMuz3ul0rOWi+luaZelSbhmI\\n4hQAvGDBTSaTZG7VF2rOi5mxurrq7AGlsh6q+TTfqPnSNMtJT/vR7XadPFrS5KwPp31YXupB6HPo\\nrPcTfUW32Wziz//8zzEcDq3mhsMwnOkfuifVmTNnSpk3tUUUp7A0SxOYzpGp1XRby3OnV4d91ZtV\\nju/r8mmZ67I326ksZjU3bIPZP1qtFjqdzsz5KiCKs2SqsgqYRZmWZhadTgfNZhOTyaTQ6r2yROa9\\nr8obj8elLSSEYeh19d6kVqvhwIEDzuUMBgNsbGwUzl+fxWQywWAwQK1Wy1SMQRAAwMLQavPodDoz\\nlqyam221Wl7k4oOqKs5dvY9Tp9fr7XQTMlH7RYGpBbEMpemCOfdlDmW3traS99fW1lCr1RAEgbdF\\nER90u10cPnx47mfUPuCqoeSfd2vczTff7NVKdN0f7ZOq7uOcqziJ6BIAnwHwY5h6EvwHZj5OREcx\\nTYT0F/FHP8TM98bfuRXATZhGW15l5vtLanthS2onUJYmkD7X5sJkMgERlW4dpKXwNdux6DNpqKg/\\nWe13ub9K5vNQlqLtcFfhe3dEUWIvG28ouVSBffuqOSheZHE+D6DDzCMiOhfAnxDRA5gq0WPMPPOY\\nI6KrAbwLwNUALgLwJSK6gpnPuDRSPYHNjlml/WZp6JYm4H9OLQxDEBE2Nzcz67eRzyLrcTQazay8\\n2lianU4H6+vrGA6HmcNxX/c3yyLzvXrs6/6qhausfl9WvSaNRqPyU2A7RkHf0XsAXAfgNgD/MuX9\\nWwHcov3/RQCvS/NBzcu8KDbKJ9i3r7OvcmH4dadFmxmNRtZRaFTknyAIeDKZbCtX1VtG+D3XIBbM\\nL8hnUfttfb71SFdpUYRcy9fL8R2kZF4QDzOKV1GUL3pZcmf256v++OOP5zp81FeobQUu4hCA/wng\\n3FhxngQwBvApTMPPA8CvA/in2nc+CeBn0gRS9CboiiyKIg7DMDnvO7qNWZ9rOboCC8Nw5nCJyakH\\nYwiCIDmvRx0qQz7MfhSnik40r/1pUbHyYioYFSXK7D+ulKE450W30qN42cgnT79xkTuzP8X5xBNP\\n5DqWrThzLQ7Fw/TPA1hj5ueI6BMAPhq//W8B/BqA92d83XkCZnNzE51OB4PBYGayuNlsotfreR9O\\nbG5uOvusr62tpQ5Bz5yZnbWIO4gVaj/eeDzGaDSakU29Xke9Xi9FPqp8AE6ryJ1OBydPnsTGxgai\\nKEptv8v8o9rX2mq1MBqNZrb21Ov1pP+40mw2ve9zDYIg6ff9fn9GNrVaDc1mE0EQWM2Z5+k3rvO+\\nvqjq4tDCZG1E9GIA/wPAvcy8nvL+IQC/z8w/ESc/AjPfHr/3RQC3MfMjxnf4tttuS/5X+8gWYS5A\\nlHlzoyjKtcDgox4icl7djaJoZiElCIJSNq2XwWQy2batxnf795J8Dh486G03gC+5KI8sxUc+8hGw\\nh2RtJ06cyPXZK6+8clt9RFTDdNT7GkwNuPcBeArAfwFwKeKcQ2yRPmOu4qSpur8L06xwHe38hcz8\\nnfh1B8BrmfmfxItDvwvgGsSLQwBezUYlJFkuBWFPQ56yXD755JO5PnvFFVekKc67AGww851E9CIA\\nP4xplsv/j5k/TkS3ADjIJWS5fD2AnwfwKBGpR96HALybiBqYavFvAvjnAMDMTxDR3QCeAPB9TDPL\\niYYUBGGpENEBAG9k5vcAADN/H8BpInorADWHdheAIYDCilPyqguC4B1fFudTTz2V67OXX375TH2x\\nYfdbmBpxdQB/AqAN4BlmPhh/hgD8lfq/CHvGc0gQhL1H1uLQww8/jEceeST1vZgXATgM4JeZ+Y+J\\naB2GZcnMTERWFpxYnIIgeMeXxfn000/n+uyrX/1q0+K8AMBXmPmy+P83YLrP/JUAQmZ+loguBDBg\\n5iuLtq2a/kyCIAiwD/LBzM8C+BYRXRGfug7A4wB+H8B74nPvwdSppzAyVBcEobI47uP8FQCfJaJz\\nAPwZptuR9gO4m4jej3g7kk3BojgFQagsLoqTmccAXpvy1nXWhcaI4hQEobJU1XNIFKcgCJWlqopT\\nFocEQRAKIhanIAiVpaoWpyhOQRAqiyhOQRCEgojiFARBKIgoTkEQhIKI4hQEQShIVRWnbEcSBEEo\\niFicgiBUlqpanKI4BUGoLFVVnDJUFwRBKIhYnIIgVJZ9+6pp24niFAShsshQXRAEYY8gFqcgCJVF\\nLE5BEISC2OYcIqKXENEjRDQioieI6N/H588jogeI6Ekiup+IajbtEsUpCMKeg5n/GtNslg0A/wBA\\nGGe6PALgAWa+AsCDMFIG52Wu4rTR2kR0KxE9RUQniOh6m0YJgiAA9hYnADDz/4lfnoNpkrYtAG8F\\ncFd8/i4Ab7Np11zFWVRrE9HVAN4F4GoANwD4TSISq1YQBCtcFCcR7SOiEYBTmOZPfxzA+cx8Kv7I\\nKQDn27RroVIrqLVvBPA5Zn6emU8CeBrANTYNEwRBcLQ4z8RG38UAriWi0HifAbBNuxauqscW4yaA\\nVwH4BDM/TkRZWvsVAB7Wvv4MgItsGiYIgpDFQw89hC9/+cu5PsvMp4noDwD8QwCniOgCZn6WiC4E\\n8F2b+hcqTmY+A6BBRAcA3JemtYlonta20uiCIAhZ1uS1116La6+9Nvn/9ttvN7/3owC+z8wTIvo7\\nAN4M4CMAvgDgPQDuiP/eY9Ou3Ps4c2rtbwO4RPvaxfG5bRw9ejR53Wq10Gq1irVcEITKMBwOMRwO\\nd7oZOhcCuCseMe8D8DvM/CARRQDuJqL3AzgJ4J02hdN0mJ/x5natfR+mWvunAfwlM99BREcA1Jj5\\nSLw49LuYzmteBOBLAF7NRiVEZJ4SBGEPQURgZqfd60TE3/ve93J99mUve5lzfUVYZHEW0trM/AQR\\n3Q3gCQDfB/AB0ZCCIOw15lqcpVUqFqcg7Gl8WZzPPfdcrs+ee+65lbI4BUEQdgzxVRcEQdgjiMUp\\nCEJlEYtTEARhjyAWpyAIlUUsTkEQhD2CWJyCIFQWsTgFQRD2CGJxCoJQWcTiFARB2COIxSkIQmUR\\ni1MQBGGPIBanIAiVpaoWpyhOQRAqS1UVpwzVBUEQCiKKUxCEyuKYHvgGIjpBRE8R0S0+23XWKs6q\\n5EepQjukDdIGk6q0wxYi2g/gNwDcAOBqAO8moqt8lS+Kc4epQjukDdIGk6q0w8HivAbA08x8kpmf\\nB/CfAdzoq11nreIUBGFPcxGAb2n/PxOf84KsqguCUFkcVtVLTWq2Y8nall6pIAhLxUeyNtv6iOh1\\nAI4y8w3x/7cCOMPMd7i0KSlfsk0KgrDXIKIXAfhTAG8C8L8AfBXAu5n5Gz7Kl6G6IAh7Dmb+PhH9\\nMoD7AOwH8ClfShMQi1MQBKEwS19VL3NT6oJ6TxLRo0QUEdFX43PnEdEDRPQkEd1PRDXPdd5JRKeI\\n6DHtXGadRHRrLJcTRHR9iW04SkTPxLKIiOgtJbfhEiIaENHjRPR1IlqNzy9NFnPasGxZvISIHiGi\\nERE9QUT/Pj6/TFlktWGpstjVMPPSDkxN5qcBHALwYgAjAFctqe5vAjjPOPdxAP8qfn0LgNs91/lG\\nAAGAxxbViekm3VEsl0OxnPaV1IbbANyc8tmy2nABgEb8+lxM556uWqYs5rRhqbKIy35p/PdFAB4G\\n8IYd6BdpbVi6LHbrsWyLs9RNqTkwV/neCuCu+PVdAN7mszJmfgjAVs46bwTwOWZ+nplPYto5rymp\\nDcB2WZTZhmeZeRS/fg7ANzDdU7c0WcxpA7BEWcT1/5/45TmYGhNbWH6/SGsDsGRZ7FaWrThL3ZS6\\nAAbwJSL6GhH9P/G585n5VPz6FIDzl9COrDpfgak8FGXL5leIaExEn9KGhaW3gYgOYWoBP4IdkoXW\\nhofjU0uVBRHtI6IRptc8YObHsWRZZLQB2KF+sdtYtuLcyZWo1zNzAOAtAP4FEb1Rf5OnY5Klti9H\\nnWW15xMALgPQAPAdAL+2jDYQ0bkA/huANWb+3kwlS5JF3IbPx214DjsgC2Y+w8wNABcDuJaIQuP9\\n0mWR0oYWdqhf7EaWrTi/DeAS7f9LMPskKw1m/k789y8A/B6mQ41TRHQBABDRhQC+u4SmZNVpyubi\\n+Jx3mPm7HAPgk3hh2FVaG4joxZgqzd9h5nvi00uVhdaG/6TasBOyUDDzaQB/AOAfYof6hdaGn9xJ\\nWaSJ1ZgAAAEySURBVOw2lq04vwbgciI6RETnAHgXgC+UXSkRvZSIXha//mEA1wN4LK77PfHH3gPg\\nnvQSvJJV5xcA/BwRnUNElwG4HNNNu96Jf5iKt2Mqi9LaQEQE4FMAnmDmde2tpckiqw07IIsfVUNg\\nIvo7AN4MIMJyZZHaBqW4Y0qXxa5m2atRmA6V/xTTCeZbl1TnZZiuCo4AfF3VC+A8AF8C8CSA+wHU\\nPNf7OUy9Fv4W07nd982rE8CHYrmcAPDTJbXhJgCfAfAogDGmP9DzS27DGwCcieUfxccNy5RFRhve\\nsgOy+AkAm3E7HgXwwUV9sQRZZLVhqbLYzYdsgBcEQSiIhJUTBEEoiChOQRCEgojiFARBKIgoTkEQ\\nhIKI4hQEQSiIKE5BEISCiOIUBEEoiChOQRCEgvz/MBwVguxTR7YAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f995ce1b650>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Adaptive Threshold\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"adaptive_thresh = cv2.adaptiveThreshold(training_image,255,1,1,11,2)\\n\",\n      \"plt.imshow(adaptive_thresh, cmap=plt.cm.Greys)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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l5RofoFtjWcsufqW36T9n15nr5rJpkYH9PPwUQ/uoZtUR7nvKqci9SL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\\nJBjOQCAQMOT/A0UcbrD0I68QAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98d4013090>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Finding Digits\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Find Contours\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"contours,hierarchy = cv2.findContours(adaptive_thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)\\n\",\n      \"\\n\",\n      \"plt.imshow(adaptive_thresh, cmap=plt.cm.Greys)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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xpXXoJapowur6GN/KrMuvJtPRNdnS69zaQEH6qhyLp2WtX/5OTkqpwG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\\nAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f9954383e50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Filtering out small contours\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"boxes = []\\n\",\n      \"image_gbr = cv2.cvtColor(training_image, cv2.COLOR_GRAY2RGB)\\n\",\n      \"for cnt in contours:\\n\",\n      \"    area = cv2.contourArea(cnt)\\n\",\n      \"    if area > 100:\\n\",\n      \"        [x,y,w,h] = cv2.boundingRect(cnt)\\n\",\n      \"        if h > 29:\\n\",\n      \"            boxes.append([x,y,w,h])\\n\",\n      \"            cv2.rectangle(image_gbr, pt1=(x,y), pt2=(x+w,y+h), color=[255,0,0], thickness=2)\\n\",\n      \"print len(boxes)\\n\",\n      \"plt.imshow(image_gbr)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"60\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"<matplotlib.image.AxesImage at 0x7f98d49490d0>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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fJyH0zHlJa+KCqVCsophKG8cPToUV555RUrhdOhQweWLVtGo0aNHJb/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\\nRMQrFR0R8UpFR0S8+j9fzm+WAu/8FgAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98d48cf810>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Checking boxes - Using Nearest Interpolation\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x,y,w,h = boxes[0]\\n\",\n      \"plt.imshow(training_image[y:y+h,x:x+w], cmap=plt.cm.Greys, interpolation=\\\"nearest\\\")\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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wA8EL+fgCjrdgc1M3BrYkaUhixmR0n+A4A3zex7zsfyS3m4efPmqvu9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\\nXEP+AAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98d4049b90>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Labeling Digits\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"row = 0\\n\",\n      \"for index in range(row*10,row*10+10):\\n\",\n      \"    x,y,w,h = boxes[index]\\n\",\n      \"    plt.imshow(training_image[y:y+h,x:x+w], cmap=plt.cm.Greys, interpolation=\\\"nearest\\\")\\n\",\n      \"    plt.colorbar()\\n\",\n      \"    plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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wA8EL+fgCjrdgc1M3BrYkaUhixmR0n+A4A3zex7zsfyS3m4efPmqvu9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\\nXEP+AAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f99560c4c10>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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      \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98d474d0d0>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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lcnrgDGAavSD4X/c/ePqGKCSAFUMUGkonR5pEhFlfXySCWvSAb1vMMo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\\nNkodEQAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98dc079250>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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TNt2rSkNmvWrKSWigIa9G4WIQYDuS6Wz7NVQrSZsm6WxEq7/2Vm2ws3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\\nMub/A3Wtv9B+/+ALAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98d48da410>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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fuCtj/96U9B27Zt24I2L4TvEcper+We69atC9qampqCtpkzZwZtkyZN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\\nhMgAOZoQGSBHEyID5GhCZIAcTYgM+H9j/K69tUaDKAAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98dc02fa10>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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k2bgrFVq1ZVXb5ly5bgOgOhFlukhAZliy1SdmqxRUpILbZICanFFimh\\noiZ29DjCzKaZ2UNmtsnMNprZB9Pl481spZltNbNfqD62DFadWpTvOHCbu68zs9OBX5vZSuDdwEp3\\n/5SZ3Q58OP13itBonVmzZgXfcPLkycHYyJEja+zuwMXu9R0/fnwwFhuhFut6inWFZe16g/jIqFGj\\nRgVjsXPErHW2TjvttGAs1AUK8d/9hRdeGIxt2BAu9hqqh9ao7q4859hmdivwd4ABX3L3z5jZeOA7\\nwLnATuCd7n5gwPsVC7r7Xndflz4+BGwhmUztWuC+9GX3Ae8Y6BuLlEHWFtvMLiRJ6jcAlwB/aWav\\nIWkgV7r7bOCXVGkw61H3nxszmwHMJ6lQMMndu9NQNzApy5uLdLoch+LnA6vd/RV3PwH8CrieBjWa\\ndSV2ehj+PeBWdz9l9oF0tsR4jVeRksqR2BuBRen1qtEkc4mfQ4MazXoK3w8nSeqvufsPet/QzCa7\\n+14zmwLsq7bu+vXr+x5PmjSJSZPUsEt7dHd3s29f1a9pLjlmKe0ys0+SFOI7DKwDTvR7jZtZpkYz\\nmtiW7PUKYLO7310R+iFwE/DJ9P8fVFk9ek+4SCv1b1g2btzYkO2GEnvt2rXRijAA7n4P6ZTDZvav\\nJDW56mo0a6nVYl8OvAtYb2a9e7kcuBP4rpm9h/TKXZY3F+l0ocResGDBKQN+vvKVr1Rb99Xuvs/M\\npgN/BSwEzqOORrOWaGK7+2OEz8PfWmvjoVFFsa6ZWFdJ1sOeWJfEiBEjgrFYV1isC2nixIn17Vg/\\nWbuXIL6vWbu0YmLrxT7T2ISNsf0cO3ZsMBabmDDr76JeOfuo7zezs0i6ld/v7gfNrCGNpu48E8kh\\nT2K7++Iqy/5IHY1mLUpskRyKekupElskByW2SAkpsUVKSIktUkKDMrFDo7Ga0aWVVaybqKhVHlol\\nVvMs9ntq9e8w1hVWa1RcXoMysUXKToktUkJKbJESUmKLlJASW6SElNgiJTQoE/vIkSNVl7/00ktV\\nl0P2OltZ1yuSrN1L9cQbLbavWWX9GQ4fPhyMhSYzbJSifrfUYovkUNRKIMXcKxHJRS22SA46FBcp\\noaImtg7FRXLIU+LHzM40s/vNbIuZbTazNzaqfJYSWySHnLW7PgP8xN3nABcDXTSoEkhTD8W3bdtW\\ndfkDDzwQXCdW92rYsPDuZq2JFdOMSQCPHj0ajPX09ARjEyZMiG73vPPOC8ZiE/rF6qHFfv5Y92Ks\\niynW1fniiy8GY93d3cHYk08+GYxt2rQpGGuErFfFzWwssMjdbwJw9x7goJldC1yZvuw+4GEyJLda\\nbJH2OA/Yb2b3mtkaM/uSmY2hQZVAlNgiOeQ4FB8GLAA+7+4LSKqBnNIy5ymfpaviIjmETslWrVrF\\n6tWrY6vuBna7e+95xP0kxTj2tqISiIhksHDhwlNqqH/uc587JZ4m7i4zm+3uW0nmEt+U/mtuJRAR\\nicvZj30L8A0zGwE8A7wbGEqzK4GY2TTgq8CrSY71v+junzWzO0iKdu9PX7rc3X+WZQdEOlnOSiBP\\nkxS+76/plUCOA7e5+7q0RvavzWwlSZLf5e53xVYOVRvctWtXcJ1QvS/I3v1SpBv1Dx48GIyFRsMB\\nXHrppdHt3nTTTcHY0qVLg7Fx48YFY7EaXCdOnAjGDhw4EIxt3bo1GHv44YeDsUceeSQY27JlSzAW\\n615rhKLeeVarKN9eYG/6+JCZbQHOTsPF/IlEWqioiV13U2ZmM4D5wKp00S1m9rSZrch625tIp8t5\\n51nT1JXY6WH4/cCt7n4I+AJJB/s8YA/w6WrrHT16tO9f7K4qkWY7ceIEx44d6/tXdjWvipvZcOB7\\nwNfd/QcA7r6vIv5l4EfV1o3drijSSkOHDj2lAESjGpqOPBS3ZK9XAJvd/e6K5VMqXnYdsKE5uydS\\nbEU9FK/VYl8OvAtYb2a9l7g/AtxoZvNIro7vAN7XvF0UKa6itti1roo/RvVW/af1bPzQoUMDWi5h\\nTz31VDR+1VVXBWOxkW9Zxb7QsRFs+/aF75Dcvn17MPb4448HY+08Z+7IxBaROCW2SAkVNbGLc0uW\\niDSMWmyRHIraYiuxRXIoamLrUFykhNRid4jYqDeAUaNGBWOxSSCzTtgYW2/48OHBWGw/Y6PJYtts\\nZ3dXkUYOVlJii+SgQ3ERaRm12CI5qMUWKaGsg0DMbJSZrTazdWl5n39Ll6vEj0incvdXgKXuPo+k\\nvM9SM7uCBpX4UWKL5JBn2Ka7v5w+HEEyO+kLwLUkpX1I/39Hlv3SOXaHiHUT1RKbVCA2KWHWWmmx\\nWGxfYl1HsZ//8OHDwViz5TnHNrMhwBrgNcAX3H2TmTWkxI8SWySHnNMPnwTmpQX6fm5mS/vF3cxU\\n4kekKB599FEee+yxul7r7gfN7MfApUC3SvyItFmoxV68eDGLFy/ue37nnXf2X28C0OPuB8zsNOAq\\n4OPAD1GJH5GONQW4Lz3PHgJ8zd1/mU5B1twSPyISl/Uc2903kJTR7b/8jzSgxI+6u0RKSC12h6g1\\nIeEf/vCHYCw2gWCsS+u0004LxmJ1xvbv3x+M7d27NxiLdVvFuuXaqai3lCqxRXIoamLrUFykhNRi\\ni+SgFltEWqZW7a6mDi0T6XRFrd0VTexmDy0TkeaoeY4dGVp2Zbr8PuBhlNxNFauHBbBnz55gbPPm\\nzcHYyy+/HIyNGTMm03rPPPNMMPa73/0uGIt12am7a2BqnmOb2RAzW0cyhOwhd98ENGRomYg0Rz0t\\ndtOGlol0uo5tsXu5+0HglKFlAHmGlolIc9S6Kj6h94p3xdCytfxpaBnkGFom0umKelW81qF4U4eW\\niUhzRBO72UPLRDpdUc+xdUtph4iNpoJ4F1Os7tWOHTuCsdjorlj3W3d3dzAW65Z77rnngrFao9va\\npaiJrVtKRUpIiS2SQ56LZ2a2zMy6zGybmd3eyP1SYkuf2GF5K7VznvBWMbOhwH8Cy4C5wI1mNqdR\\n21diS5+dO3e2exeA+O2qRZOjxb4M2O7uO939OPBt4O2N2i8ltkh7nA3sqni+O13WELoqLpJDjqvi\\nTb0N29ybs33dPy5F5+65+qoG+h2vfD8zWwjc4e7L0ufLgZPu/sk8+9S3/WYltoiEmdkw4DfAW4Dn\\ngSeAG919SyO2r0NxkTZw9x4z+wDwc5J5DlY0KqlBLbZIKTX9qngzO+Ez7MtOM1tvZmvN7IkWv/c9\\nZtZtZhsqlrVl7rjAvtxhZrvTz2atmS1r0b5MM7OHzGyTmW00sw+myzWvXg5NTexmd8Jn4MASd5/v\\n7pe1+L3vJfkcKrVr7rhq++LAXelnM9/df9aifTkO3ObuFwALgX9IvyOaVy+HZrfYTe2Ez6gtd+27\\n+6Mk88VVupZkzjjS/9/Rxn2BNnw27r7X3deljw8BW0j6c9vy2ZRFsxO7qZ3wGTjwoJk9ZWbvbeN+\\n9Cra3HG3mNnTZraiHYe+ZjYDmA+spnifTUdpdmIX7crc5e4+H7iG5JBvUbt3qJcnVzHb+Xl9ATgP\\nmAfsAT7dyjc3s9OB7wG3uvtLlbECfDYdp9mJ/RwwreL5NJJWuy3cfU/6/37g+ySnCu1UmLnj3H2f\\np4Av08LPxsyGkyT119y9d5qtwnw2najZif0UMMvMZpjZCOAGkvnSWs7MRpvZq9LHY4CrgQ3xtZqu\\nMHPHpcnT6zpa9NlYck/mCmCzu99dESrMZ9OJmt6PbWbXAHfzp074f2vqG4b34zySVhqSG3O+0cp9\\nMbNvkRRZmEByzvhR4AHgu8B00rnj3P1AG/blY8ASksNwB3YA76s4x23mvlwBPAKs50+H28tJ7sRq\\n+WdTFrpBRaSENGxTpISU2CIlpMQWKSEltkgJKbFFSkiJLVJCSmyRElJii5TQ/wERfwXzPjL1bgAA\\nAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98d4016890>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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mbg1sSMKAxpzI6S/AcAR83sR87blPLQw4t48OoteCWoTzrppKBWqRaD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\\nITJGRihExsgIhcgYGaEQGfN/GKaAvjtwVy4AAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98c0c3fe10>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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iOAY2b2A+dtSrvp0bdv36B28cUXBzUvlHLllVcGNS+U4oVRgOyhFC/h\\n1r59+4Latm3bgtpLL70U1FatWhXUvMRgx44dC2p5EzLQhoYGNDQ0ZDnfXwH4EwCFRXOUWV6ILIQM\\ndMaMGZgxY0b79jPPPFPKuRYC+HsA883saIGkzPJCZMHrSXkEZtotAjAAwJLU8P/XzD6tzPJCZESZ\\n5YWIGE31EyJiYp3qJwMVAvKgNcW7+XV1dUHtsssuC2pe8i8vuVceK1KA7KtStm7dGtSWL18e1Nas\\nWRPUvIK3R48eDWq1RAYqRMTIQIWImKxhlryRgQoBeVAhokYGKkTEyECFiBjFQXNm2LBhQc0rD3/J\\nJZcEtXnz5mU6p5dQrH///kGtM1paWoKatyrFC6V4dVS8cvWbN28OakeOHAlqsSIPKkTEyIMKETHy\\noEJEjDyoEBEjDypExMRqoK5fJzmR5DKSm0huJHlnuv90kktIvkryN6oPKro73bV40nEAd5vZWpJD\\nAawmuQTAXwNYYmZfIXkPgHvTn5oxYUI4e+F1110X1K6++uqgVpiLpiN5hVI8vFDK+vXrg9qyZcuC\\n2nPPPRfUNmwIF63rjqEUj3KeQUneBeBvkFSd/5aZfY3k6QB+DGAygEYAHzGzLhefcVtlZk1mtjZ9\\nfQjAFiTJjm4C8Hj6tscBhNdeCdENyOpBSZ6PxDhnA7gIwI0kz0TisJaY2XQAzyGjAyv53wbJKQBm\\nIcmUXWdmzanUDCC8qFKIbkAZXdxzAKwws6Nm1grgeQB/jgo5sZIMNO3ePgXgLjM7WKilWcnczGRC\\nxE4ZBroRwJXpuMwQJLlwz0CFnFgpBXz7IzHO75rZ020XJDnWzJqYVNvek+XiQsRCGVn9Gkh+GUnB\\npMMA1gJo7fAeI5nJibkGyqTViwFsNrMHC6RnAXwcwJfT308XOVyIbkPIQNesWeOmdwEAM3sUaapN\\nkv+KpOZKRZxYZx70cgAfA7CeZFsrFwG4H8ATJG9HOkKV5eJCxELIQOvr61FfX9++/e1vf7vYsWPM\\nbA/JSQD+DMBcAFNRASfmGqiZvYjwc+q1WS7YGV7iLC/Bl7cqxUv+dd555wW1UaNGBbWBAwcGNY93\\n3303qHlhFADYuHFjUPNWpXh1VLzwTE8LpXiUGeN8kuRIJGHJT5vZAZIVcWKaSSQEyjNQM3vfukQz\\n24cKODEZqBCId6qfDFQIyECFiBoZqBARIwMVImJkoCUyefLkoPbBD34wqHkl6S+44IKg5oVSBg0a\\nFNQ8Dh06FNQ2bdoU1LxaKICfxGvFihVBzSs739raGtR6EzJQISJGBipExMhAhYgYGagQESMDFSJi\\nZKBCRIwMtAAvfOGtLrn88suD2ty5c4Pa2LFjM7UlK16YZd26dUFt5cqV7nlXrVoV1Hbt2hXUvFCK\\n98XMo6htkoCjcpp3TFeQgQoRMbFmlo+zVUIIAPKgQgBQF1eIqInVQNXFFQLllX4gOYLkkyS3kNxM\\n8tJKlUeRgQqBsmuzfA3AL8xsBoALATSgQpnlc+3ihj7QyJEjg8dMnz49qM2ZMyeoeatgBg8eHNSy\\n4g3veyOCXsjH++wAcOqppwa1w4cPBzUvzOIlafNqzHiad2/efvvtoOaFinbv3l10/44dO4LHdIWs\\no7gkhwO40sw+DgBm1gLgAMmbAMxP3/Y4gN8ig5HKgwpRHlMB7CX5GMlXSH6L5CmoUGZ5GagQKKuL\\n2w9APYBvmFk9kuzyJ3nKcsqjaBRXCIQfx1566SV3MTySLPK7zKxtGtiTSJK7N1Ujs7wQvZq5c+ee\\nNI30oYceOklPDfB1ktPN7FUkuXA3pT/5ZpYXordQZhz0DgDfJzkAwP8hKXDdF3lnlic5EcB3AIxB\\n0of+ppl9neR9SIqW7k3fusjMfpWlAULEQJmZ5dchKeDbkdwzyx8HcLeZrU1rhK4muQSJsT5gZg94\\nB48YUTw2e+aZZwaP8VazTJ06NagNGzbMa0omvHCBF7rw6rbMmDEjqI0ZM8Ztz7Fjx4Ka9wXLuuLD\\nW83irQLyrueFUrzVOlu2bCm6v1JhllhnEnVWPKkJQFP6+hDJLQAmpHKcn0iIDMRqoCWHWUhOATAL\\nQFuprDtIriO5OOs0JiFiocyZRLlR0iBR2r19EsBdqSd9GMA/p/K/APgqgNs7HldYvq5fv37u7BMh\\nSmHv3r3Yu3dv52/sIXRqoCT7A3gKwPfM7GkAMLM9BfojAH5a7Ng8ptiJ3s3o0aMxevTo9u2GhoaK\\nnLdbdnGZtHoxgM1m9mDB/nEFb7sVwIZ8midEdeiuXdzLAXwMwHqSa9J9nwNwG8mZSEZzdwL4VH5N\\nFCJ/YvWgnY3ivojiXvaXpZz87LPPLrq/vr4+eMw555wT1IYOHVrKZWuOF2Yp7J51ZPjw4e55q53g\\ny7uetwrGC7N4n9Fb6bJv376gVgm6pYEK0VuQgQoRMbEaqJabCREx8qBCIF4PKgMVAvEaqLq4QkRM\\nrh40VC/l+uuvDx4zadKkoFbt9Pzef9V+/cK3zgt5ZE221Rl5eADvnFn/FqeddlpQq6sLp+3xEqZV\\nglhLP6iLKwTUxRVCZEAeVAjIgwoRNVkny5McRHIFybVp2Yd/S/er9IMQtcbMjgK4ysxmIin7cBXJ\\nK1Ch0g8yUCFQ3nIzM3snfTkASTa/twDchKTkA9Lft2RpV67PoKGQiZf8a8iQIUGtpaUlqHlhj6xl\\n17OSR3iip+B9fu9v6GmVoJxnUJJ9ALwC4EwAD5vZJpIVKf2gQSIhUHbazRMAZqaFlH5N8qoOupFU\\n6QchKs3y5cvx4osvlvReMztA8ucALgbQrNIPQlSIkAedN28e5s2b1759//33dzxuFIAWM9tPcjCA\\n6wB8EcCzUOkHIWrOOACPp8+hfQB818yeS1ME5Vv6QYjeQtZnUDPbgKT8YMf9+1CB0g+9e0hRiMjJ\\n1YOG/it54ZJDhw4FNa82iTcMf+LEiaDmhVnymP7VncIsWVfzePf7j3/8Y1BramoKal5CsUoQ61Q/\\ndXGFQLwG2n3+nQvRC5EHFQLyoEKIDHRWmyXXpTRCxEKstVlcA817KY0QwqfTZ1BnKc38dP/jAH6L\\nIkYaqqfR3NxcdD8AHD58uLMmFcX775bHipXezqBBg4Kad7937doV1LZv3x7UvO9MJei2z6Ak+5Bc\\ni2TJzDIz2wSgIktphBA+pXjQ3JbSCBELsXrQksMsWZbSPP/88+2vJ0+ejClTppTZXNHb2bNnD/bs\\nybRyq1viGmi5S2nmz59fbLcQmRkzZgzGjBnTvr158+aKnLe7etBcl9IIIXw6q7Cd61IaIWKhu3rQ\\nsti5c2fR/V59Em/4vrW1NVM7vGF/b+VFb8dbeTNw4MBM59y7d29Qa2xszKRVglgNVFP9hIgYGagQ\\nKG+qH8mFJBtIbiN5TyXbVTUD9RbjVpsdO3bUugntqC3vJ+9ZQ5WEZF8A/wlgIYBzAdxGckalzl81\\nA43ppsfyRQTCz+m1IJa21CLOWYYHnQNgu5k1mtlxAD8CcHOl2qUurhDlMQHA6wXbu9J9FUELtoVA\\nWaO4uU5zZV4rPTQ/V1QLMysrRtLV72rh9UjOBXCfmS1MtxcBOGFmXy6nTe3n11IsIbJDsh+ArQCu\\nAfAmgJcB3GZmWypxfnVxhSgDM2sh+RkAv0ayXnpxpYwTkAcVImpyH8XNM4iboS2NJNeTXEPy5Spf\\n+1GSzSQ3FOyrSW6nQFvuI7krvTdrSC6sUlsmklxGchPJjSTvTPcr7xVyNtC8g7gZMAALzGyWmc2p\\n8rUfQ3IfCqlVbqdibTEAD6T3ZpaZ/apKbTkO4G4zOw/AXAB/l35HlPcK+XvQXIO4GanJrGgzW44k\\nn1MhFSmTXqG2ADW4N2bWZGZr09eHAGxBEkesyb2JjbwNNNcgbgYMwFKSq0h+sobtaCO23E53kFxH\\ncnEtupQkpwCYBWAF4rs3NSFvA41tBOpyM5sF4AYkXakra92gNiwZravl/XoYwFQAMwHsBvDVal6c\\n5FAATwG4y8wOFmoR3JuakbeBvgFgYsH2RCRetCaY2e70914AP0HSBa8lzSTHAoCX26kamNkeSwHw\\nCKp4b0j2R2Kc3zWztvQ50dybWpK3ga4C8AGSU0gOAPBRJPmMqg7JISSHpa9PAXA9gA3+UbnTltsJ\\nKKNMeiVIjaCNW1Gle8Nkjt1iAJvN7MECKZp7U0tyj4OSvAHAg3gviPtvuV4w3I6pSLwmkEzQ+H41\\n20Lyh0iSfY9C8kz1eQDPAHgCwCSkuZ3MbH8N2vIFAAuQdG8NwE4Anyp4BsyzLVcAeAHAerzXjV2E\\nZEZO1e9NbGiighARo+VmQkSMDFSIiJGBChExMlAhIkYGKkTEyECFiBgZqBARIwMVImL+H86h/a9o\\npzn4AAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f9955e9d950>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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lcoogmRAXI0ITJAjiZEBjTlOFpXV1fJ9VlXt3u/coMHDw7ask6Zlzvn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\\nZg+gsKj3aTcAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f9956191b90>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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+cmkHhxHENOkTOf+nai6GDX92pJXyQxaNwNLAf21V0THTlzQTtRhAS9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\\nKVzQTqVwQTuVwgXtVAoXtFMp/h+dQfyI41czugAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f995cdfaad0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results = []\\n\",\n      \"results.append([9,8,7,6,5,4,3,2,0,1])\\n\",\n      \"results.append([9,8,7,6,5,4,3,2,1,0])\\n\",\n      \"results.append([9,8,6,5,3,2,0,7,4,1])\\n\",\n      \"results.append([9,8,7,6,5,4,3,2,1,0])\\n\",\n      \"results.append([9,8,7,6,5,4,3,2,1,0])\\n\",\n      \"results.append([9,8,7,6,5,4,3,2,1,0])\\n\",\n      \"results = np.ravel(results)\\n\",\n      \"results\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 19,\n       \"text\": [\n        \"array([9, 8, 7, 6, 5, 4, 3, 2, 0, 1, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 9, 8, 6,\\n\",\n        \"       5, 3, 2, 0, 7, 4, 1, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 9, 8, 7, 6, 5, 4,\\n\",\n        \"       3, 2, 1, 0, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Resizing RoI / Normalize\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"roi = training_image[y:y+h,x:x+w]\\n\",\n      \"roi = roi / 255.\\n\",\n      \"roi = cv2.resize(roi,(10,14))\\n\",\n      \"plt.imshow(roi, cmap=plt.cm.Greys, interpolation=\\\"nearest\\\")\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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nbD141Q/2nAJcABwCntKvXIapaiwMiaJbshEXF7RBwJvB3493bne2Q1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\\nB6tZTThYzWri/wBekLpBASRXsQAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f99561a9190>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"features = []\\n\",\n      \"for index in range(60):\\n\",\n      \"    x,y,w,h = boxes[index]\\n\",\n      \"    roi = training_image[y:y+h,x:x+w]\\n\",\n      \"    roi = roi / 255.\\n\",\n      \"    roi = cv2.resize(roi,(10,14))\\n\",\n      \"    features.append(np.ravel(roi))\\n\",\n      \"features = np.array(features)\\n\",\n      \"features.shape\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 22,\n       \"text\": [\n        \"(60, 140)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Training Classifier\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"k-NN\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Training\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"knn_cls = KNeighborsClassifier()\\n\",\n      \"param_grid = [{\\\"n_neighbors\\\":range(1,20)}]\\n\",\n      \"knn_gs = GridSearchCV(knn_cls, param_grid=param_grid)\\n\",\n      \"knn_gs.fit(features, results)\\n\",\n      \"knn_cls = knn_gs.best_estimator_\\n\",\n      \"knn_cls\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 23,\n       \"text\": [\n        \"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\\n\",\n        \"           n_neighbors=1, p=2, weights='uniform')\"\n       ]\n      }\n     ],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Testing on training data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"knn_cls.fit(features, results)\\n\",\n      \"%timeit knn_cls.predict(features)\\n\",\n      \"knn_cls.score(features, results)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1000 loops, best of 3: 1.48 ms per loop\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 24,\n       \"text\": [\n        \"1.0\"\n       ]\n      }\n     ],\n     \"prompt_number\": 24\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"SVM\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"svc_cls = SVC()\\n\",\n      \"param_grid = [{\\\"C\\\":[1,10,100,1000],\\n\",\n      \"               \\\"gamma\\\":[1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9, 1e-10]}]\\n\",\n      \"svc_gs = GridSearchCV(svc_cls, param_grid=param_grid)\\n\",\n      \"svc_gs.fit(features, results)\\n\",\n      \"svc_cls = svc_gs.best_estimator_\\n\",\n      \"svc_cls\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 25,\n       \"text\": [\n        \"SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,\\n\",\n        \"  kernel='rbf', max_iter=-1, probability=False, random_state=None,\\n\",\n        \"  shrinking=True, tol=0.001, verbose=False)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 25\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"svc_cls.fit(features, results)\\n\",\n      \"%timeit svc_cls.predict(features)\\n\",\n      \"svc_cls.score(features, results)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1000 loops, best of 3: 662 \\u00b5s per loop\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 26,\n       \"text\": [\n        \"1.0\"\n       ]\n      }\n     ],\n     \"prompt_number\": 26\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"RandomForestClassifier\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"rf_cls = AdaBoostClassifier()\\n\",\n      \"param_grid = [{\\\"n_estimators\\\":range(1,100)}]\\n\",\n      \"rf_gs = GridSearchCV(rf_cls, param_grid=param_grid, n_jobs=-1)\\n\",\n      \"rf_gs.fit(features, results)\\n\",\n      \"rf_cls = ada_gs.best_estimator_\\n\",\n      \"rf_cls\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 176,\n       \"text\": [\n        \"AdaBoostClassifier(algorithm='SAMME.R',\\n\",\n        \"          base_estimator=DecisionTreeClassifier(compute_importances=None, criterion='gini',\\n\",\n        \"            max_depth=1, max_features=None, min_density=None,\\n\",\n        \"            min_samples_leaf=1, min_samples_split=2, random_state=None,\\n\",\n        \"            splitter='best'),\\n\",\n        \"          learning_rate=1.0, n_estimators=192, random_state=None)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 176\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"rf_cls.fit(features, results)\\n\",\n      \"%timeit rf_cls.predict(features)\\n\",\n      \"rf_cls.score(features, results)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"10 loops, best of 3: 24.5 ms per loop\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 177,\n       \"text\": [\n        \"0.80000000000000004\"\n       ]\n      }\n     ],\n     \"prompt_number\": 177\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Running Classifer on Video\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Retrieving RoI for both players' score\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"roi_list = roi_list[:,:2]\\n\",\n      \"roi_list\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 97,\n       \"text\": [\n        \"array([[ 702,  928],\\n\",\n        \"       [ 811,  968],\\n\",\n        \"       [1106,  928],\\n\",\n        \"       [1218,  968]])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 97\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"left_player_score_roi = roi_list[:2]\\n\",\n      \"right_player_score_roi = roi_list[2:]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 98\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_roi(image, roi):\\n\",\n      \"    return image[roi[0,1]:roi[1,1],roi[0,0]:roi[1,0]]\\n\",\n      \"plt.imshow(get_roi(image_rgb, left_player_score_roi))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 99,\n       \"text\": [\n        \"<matplotlib.image.AxesImage at 0x7f98c0d1f510>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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XHB6PmY5OOE5HkKFxKJM5Ndwu7LGdtjn+3AohxZ+uiTeTuIORKkNuAF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\\nAie2AAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f9955f62fd0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 99\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.imshow(get_roi(image_rgb, right_player_score_roi))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 100,\n       \"text\": [\n        \"<matplotlib.image.AxesImage at 0x7f99166adc90>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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4MS4pxLeS0mZcYyZNY3+tJ3UBcMB8C2Bg6afiHgO9OqG2obsI1ZTKnP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\\nAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f9955da9fd0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 100\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Pre-processing\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"roi = get_roi(image_hsv, left_player_score_roi)\\n\",\n      \"plt.imshow(roi[:,:,2], cmap=plt.cm.Greys)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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ALXRDqdZu/evQwPD5NMJjd1gsHFUW/xeDxoW9QwZP9Y/vlpZLGpB43c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\\n2BqGYdQBE1vDMIw68P8DzO28JGyOU+4AAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98dc067550>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 101\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"roi = get_roi(image_hsv, right_player_score_roi)\\n\",\n      \"plt.imshow(roi[:,:,2], cmap=plt.cm.Greys)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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8M8Afj1w/TNw+cBikrjanxpd39qxJdqjFJ9b5UvtlKp4NKlS3jttdew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\\nD4jYCoIg9AERW0EQhD7w3wFkP+RpUa95xAAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f995ce2b450>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 102\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"roi = get_roi(image_hsv, left_player_score_roi)\\n\",\n      \"roi = ((roi[:,:,2] > 230) * 255).astype(np.uint8)\\n\",\n      \"plt.imshow(roi, cmap=plt.cm.Greys)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\\n\",\n      \"contours,hierarchy = cv2.findContours(roi,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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jLC+iRCpA/Iwhrsq9wIItKVJMyNEHMRBdseBvt+jnIjiEhXki03QjxE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\\nYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f9916120b50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 103\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"boxes = []\\n\",\n      \"roi = get_roi(image_hsv, left_player_score_roi)\\n\",\n      \"roi = ((roi[:,:,2] < 230) * 255).astype(np.uint8)\\n\",\n      \"roi_gbr = cv2.cvtColor(roi, cv2.COLOR_GRAY2RGB)\\n\",\n      \"for cnt in contours:\\n\",\n      \"    area = cv2.contourArea(cnt)\\n\",\n      \"    if area > 100:\\n\",\n      \"        [x,y,w,h] = cv2.boundingRect(cnt)\\n\",\n      \"        if h > 29:\\n\",\n      \"            boxes.append([x,y,w,h])\\n\",\n      \"            cv2.rectangle(roi_gbr, pt1=(x,y), pt2=(x+w,y+h), color=[255,0,0], thickness=2)\\n\",\n      \"print len(boxes)\\n\",\n      \"plt.imshow(roi_gbr)\\n\",\n      \"plt.colorbar()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 41,\n       \"text\": [\n        \"<matplotlib.colorbar.Colorbar instance at 0x7f98d4790488>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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aVfqQxSJFPl5+dTVlbGVVddxdq1a1PWT15eHuXl5axbt47u7m7Onz/P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\\n0mTSAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f9955ff2110>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 41\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Prediction\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_boxes(roi):\\n\",\n      \"    boxes = []\\n\",\n      \"    \\n\",\n      \"    contours,hierarchy = cv2.findContours(roi.astype(np.uint8),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)\\n\",\n      \"    \\n\",\n      \"    for cnt in contours:\\n\",\n      \"        area = cv2.contourArea(cnt)\\n\",\n      \"        if area > 100:\\n\",\n      \"            [x,y,w,h] = cv2.boundingRect(cnt)\\n\",\n      \"            if h > 29 and w < 50:\\n\",\n      \"                boxes.append([x,y,w,h])\\n\",\n      \"    return boxes\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"def read_score(roi, image_hsv_loc=None):\\n\",\n      \"    if not image_hsv_loc is None:\\n\",\n      \"        image_hsv_final = image_hsv_loc\\n\",\n      \"    else:\\n\",\n      \"        image_hsv_final = image_hsv\\n\",\n      \"    roi = get_roi(image_hsv_final, roi)\\n\",\n      \"    roi = ((roi[:,:,2] < 230) * 255).astype(np.uint8)\\n\",\n      \"    \\n\",\n      \"    boxes = get_boxes(roi)\\n\",\n      \"    digits = []\\n\",\n      \"    roi = roi / 255.\\n\",\n      \"    for box in boxes:\\n\",\n      \"        x, y, w, h = box\\n\",\n      \"        digit_roi = cv2.resize(roi[y:y+h,x:x+w],(10,14))\\n\",\n      \"        digits.append(str(knn_cls.predict(np.ravel(digit_roi))[0]))\\n\",\n      \"    if len(boxes) > 1:\\n\",\n      \"        if boxes[0][0] > boxes[1][0]:\\n\",\n      \"            digits = digits[::-1]\\n\",\n      \"    return int(\\\"\\\".join(digits))\\n\",\n      \"        \\n\",\n      \"print \\\"Left Score: %s\\\" % read_score(left_player_score_roi)\\n\",\n      \"print \\\"Left Score: %s\\\" % read_score(right_player_score_roi)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Left Score: 14\\n\",\n        \"Left Score: 7\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 123\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Complete Video\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_image(frame=0):\\n\",\n      \"    # Set Frame\\n\",\n      \"    cap.set(propId_dict[\\\"POS_FRAMES\\\"], frame)\\n\",\n      \"    ret, image_bgr = cap.read()\\n\",\n      \"    image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\\n\",\n      \"    return image_hsv\\n\",\n      \"\\n\",\n      \"total_frames = cap.get(propId_dict[\\\"FRAME_COUNT\\\"])\\n\",\n      \"\\n\",\n      \"left_score_list = []\\n\",\n      \"right_score_list = []\\n\",\n      \"for index in range(130, int(total_frames)):\\n\",\n      \"    image_hsv = get_image(index)\\n\",\n      \"    left_score_list.append(read_score(left_player_score_roi, image_hsv))\\n\",\n      \"    right_score_list.append(read_score(right_player_score_roi, image_hsv))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"ename\": \"ValueError\",\n       \"evalue\": \"invalid literal for int() with base 10: ''\",\n       \"output_type\": \"pyerr\",\n       \"traceback\": [\n        \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\\n\\u001b[1;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n        \"\\u001b[1;32m<ipython-input-140-c79e3598ce1e>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m     13\\u001b[0m     \\u001b[0mimage_hsv\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mget_image\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mindex\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     14\\u001b[0m     \\u001b[0mleft_score_list\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mread_score\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mleft_player_score_roi\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mimage_hsv\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 15\\u001b[1;33m     \\u001b[0mright_score_list\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mread_score\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mright_player_score_roi\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mimage_hsv\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n        \"\\u001b[1;32m<ipython-input-123-8a9a5b49eb8b>\\u001b[0m in \\u001b[0;36mread_score\\u001b[1;34m(roi, image_hsv_loc)\\u001b[0m\\n\\u001b[0;32m     31\\u001b[0m         \\u001b[1;32mif\\u001b[0m \\u001b[0mboxes\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m \\u001b[1;33m>\\u001b[0m \\u001b[0mboxes\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     32\\u001b[0m             \\u001b[0mdigits\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mdigits\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m-\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 33\\u001b[1;33m     \\u001b[1;32mreturn\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\"\\\"\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mjoin\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mdigits\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m     34\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m     35\\u001b[0m \\u001b[1;32mprint\\u001b[0m \\u001b[1;34m\\\"Left Score: %s\\\"\\u001b[0m \\u001b[1;33m%\\u001b[0m \\u001b[0mread_score\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mleft_player_score_roi\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n        \"\\u001b[1;31mValueError\\u001b[0m: invalid literal for int() with base 10: ''\"\n       ]\n      }\n     ],\n     \"prompt_number\": 140\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(16,4))\\n\",\n      \"plt.plot(np.arange(130, len(left_score_list)+130), left_score_list, label=\\\"Left\\\", color=\\\"r\\\", linewidth=2, alpha=0.4)\\n\",\n      \"plt.plot(np.arange(130, len(right_score_list)+130), right_score_list, label=\\\"Right\\\", color=\\\"g\\\", linewidth=2, alpha=0.4)\\n\",\n      \"plt.ylabel(\\\"Points\\\")\\n\",\n      \"plt.xlabel(\\\"Frame\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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NFo5C5DHeZRDrMoh1mUY6Cz6K7QvuENsGBB3loOxtFHAwOeRQ2ZRznM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\\nAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98c09f2ad0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 155\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(16,4))\\n\",\n      \"fps = cap.get(propId_dict[\\\"FPS\\\"])\\n\",\n      \"plt.plot(np.arange(130, len(left_score_list)+130)/fps, left_score_list, label=\\\"Left\\\", color=\\\"r\\\", linewidth=2, alpha=0.4)\\n\",\n      \"plt.plot(np.arange(130, len(right_score_list)+130)/fps, right_score_list, label=\\\"Right\\\", color=\\\"g\\\", linewidth=2, alpha=0.4)\\n\",\n      \"plt.ylabel(\\\"Points\\\")\\n\",\n      \"plt.xlabel(\\\"Seconds\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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0nqmKlMZC8GTgf+Z0rpoYhYAHytvWUpZ95TLF9mnzfzz1fXZ+9Etild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\\nIitJkiRJ6ipOZCVJkiRJXeX/A2brUfJEiAOuAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f98c0a22990>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 157\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"game_data = pd.DataFrame(np.arange(130, len(right_score_list)+130), columns=[\\\"frame\\\"])\\n\",\n      \"game_data[\\\"left_score\\\"] = left_score_list[:-1]\\n\",\n      \"game_data[\\\"right_score\\\"] = right_score_list\\n\",\n      \"game_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>frame</th>\\n\",\n        \"      <th>left_score</th>\\n\",\n        \"      <th>right_score</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0   </th>\\n\",\n        \"      <td>  130</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1   </th>\\n\",\n        \"      <td>  131</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2   </th>\\n\",\n        \"      <td>  132</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3   </th>\\n\",\n        \"      <td>  133</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4   </th>\\n\",\n        \"      <td>  134</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5   </th>\\n\",\n        \"      <td>  135</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6   </th>\\n\",\n        \"      <td>  136</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7   </th>\\n\",\n        \"      <td>  137</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8   </th>\\n\",\n        \"      <td>  138</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9   </th>\\n\",\n        \"      <td>  139</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10  </th>\\n\",\n        \"      <td>  140</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11  </th>\\n\",\n        \"      <td>  141</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12  </th>\\n\",\n        \"      <td>  142</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13  </th>\\n\",\n        \"      <td>  143</td>\\n\",\n        \"      <td>  0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14  </th>\\n\",\n        \"      <td>  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8449</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8320</th>\\n\",\n        \"      <td> 8450</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8321</th>\\n\",\n        \"      <td> 8451</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8322</th>\\n\",\n        \"      <td> 8452</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8323</th>\\n\",\n        \"      <td> 8453</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8324</th>\\n\",\n        \"      <td> 8454</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8325</th>\\n\",\n        \"      <td> 8455</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8326</th>\\n\",\n        \"      <td> 8456</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8327</th>\\n\",\n        \"      <td> 8457</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8328</th>\\n\",\n        \"      <td> 8458</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8329</th>\\n\",\n        \"      <td> 8459</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8330</th>\\n\",\n        \"      <td> 8460</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8331</th>\\n\",\n        \"      <td> 8461</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8332</th>\\n\",\n        \"      <td> 8462</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8333</th>\\n\",\n        \"      <td> 8463</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8334</th>\\n\",\n        \"      <td> 8464</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8335</th>\\n\",\n        \"      <td> 8465</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8336</th>\\n\",\n        \"      <td> 8466</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8337</th>\\n\",\n        \"      <td> 8467</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8338</th>\\n\",\n        \"      <td> 8468</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8339</th>\\n\",\n        \"      <td> 8469</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8340</th>\\n\",\n        \"      <td> 8470</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8341</th>\\n\",\n        \"      <td> 8471</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8342</th>\\n\",\n        \"      <td> 8472</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8343</th>\\n\",\n        \"      <td> 8473</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8344</th>\\n\",\n        \"      <td> 8474</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8345</th>\\n\",\n        \"      <td> 8475</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>8346 rows \\u00d7 3 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 165,\n       \"text\": [\n        \"      frame  left_score  right_score\\n\",\n        \"0       130           0            0\\n\",\n        \"1       131           0            0\\n\",\n        \"2       132           0            0\\n\",\n        \"3       133           0            0\\n\",\n        \"4       134           0            0\\n\",\n        \"5       135           0            0\\n\",\n        \"6       136           0            0\\n\",\n        \"7       137           0            0\\n\",\n        \"8       138           0            0\\n\",\n        \"9       139           0            0\\n\",\n        \"10      140           0            0\\n\",\n        \"11      141           0            0\\n\",\n        \"12      142           0            0\\n\",\n        \"13      143           0            0\\n\",\n        \"14      144           0            0\\n\",\n        \"15      145           0            0\\n\",\n        \"16      146           0            0\\n\",\n        \"17      147           0            0\\n\",\n        \"18      148           0            0\\n\",\n        \"19      149           0            0\\n\",\n        \"20      150           0            0\\n\",\n        \"21      151           0            0\\n\",\n        \"22      152           0            0\\n\",\n        \"23      153           0            0\\n\",\n        \"24      154           0            0\\n\",\n        \"25      155           0            0\\n\",\n        \"26      156           0            0\\n\",\n        \"27      157           0            0\\n\",\n        \"28      158           0            0\\n\",\n        \"29      159           0            0\\n\",\n        \"...     ...         ...          ...\\n\",\n        \"8316   8446          15            7\\n\",\n        \"8317   8447          15            7\\n\",\n        \"8318   8448          15            7\\n\",\n        \"8319   8449          15            7\\n\",\n        \"8320   8450          15            7\\n\",\n        \"8321   8451          15            7\\n\",\n        \"8322   8452          15            7\\n\",\n        \"8323   8453          15            7\\n\",\n        \"8324   8454          15            7\\n\",\n        \"8325   8455          15            7\\n\",\n        \"8326   8456          15            7\\n\",\n        \"8327   8457          15            7\\n\",\n        \"8328   8458          15            7\\n\",\n        \"8329   8459          15            7\\n\",\n        \"8330   8460          15            7\\n\",\n        \"8331   8461          15            7\\n\",\n        \"8332   8462          15            7\\n\",\n        \"8333   8463          15            7\\n\",\n        \"8334   8464          15            7\\n\",\n        \"8335   8465          15            7\\n\",\n        \"8336   8466          15            7\\n\",\n        \"8337   8467          15            7\\n\",\n        \"8338   8468          15            7\\n\",\n        \"8339   8469          15            7\\n\",\n        \"8340   8470          15            7\\n\",\n        \"8341   8471          15            7\\n\",\n        \"8342   8472          15            7\\n\",\n        \"8343   8473          15            7\\n\",\n        \"8344   8474          15            7\\n\",\n        \"8345   8475          15            7\\n\",\n        \"\\n\",\n        \"[8346 rows x 3 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 165\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"game_data.to_csv(\\\"game_data.csv\\\", index=False)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 166\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/.ipynb_checkpoints/4 - Numpy Basics-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial Brief\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"numpy is a powerful set of tools to perform mathematical operations of on lists of numbers. It works faster than normal python lists operations and can manupilate high dimentional arrays too.\\n\",\n    \"\\n\",\n    \"Finding Help:\\n\",\n    \"\\n\",\n    \" - http://wiki.scipy.org/Tentative_NumPy_Tutorial\\n\",\n    \" - http://docs.scipy.org/doc/numpy/reference/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.\\n\",\n    \">\\n\",\n    \">  *http://www.scipy.org/*\\n\",\n    \"\\n\",\n    \"So NumPy is a part of a bigger ecosystem of libraries that build on the optimized performance of NumPy NDArray.\\n\",\n    \"\\n\",\n    \"It contain these core packages:\\n\",\n    \"\\n\",\n    \"<table>\\n\",\n    \"<tr>\\n\",\n    \"    <td style=\\\"background:Lavender;\\\"><img src=\\\"http://www.scipy.org/_static/images/numpylogo_med.png\\\"  style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td style=\\\"background:Lavender;\\\"><h4>NumPy</h4> Base N-dimensional array package </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/scipy_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/matplotlib_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\\n\",\n    \"</tr>\\n\",\n    \"<tr>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/ipython.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>IPython</h4> Enhanced Interactive Console </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/sympy_logo.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>SymPy</h4> Symbolic mathematics </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/pandas_badge2.jpg\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>Pandas</h4> Data structures & analysis </td>\\n\",\n    \"</tr>\\n\",\n    \"</table>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Importig the library\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Import numpy library as np\\n\",\n    \"This helps in writing code and it's almost a standard in scientific work\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Working with ndarray\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will generate an ndarray with np.arange method.\\n\",\n    \"\\n\",\n    \"####np.arange([start,] stop[, step,], dtype=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,  6. ,\\n\",\n       \"        6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5])\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10, 0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 4, 7])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.,  3.,  5.,  7.,  9.])\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10, 2, dtype=np.float64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Examining ndrray\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds = np.arange(1,10,2)\\n\",\n    \"ds.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(5,)\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.size\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('int32')\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.itemsize\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['\\\\x01',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x03',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x05',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x07',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\t',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00']\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x=ds.data\\n\",\n    \"list(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 3, 5, 7, 9])\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"20\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Memory Usage\\n\",\n    \"ds.size * ds.itemsize\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Why to use numpy?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will compare the time it takes to create two lists and do some basic operations on them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Generate a list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture timeit_results\\n\",\n    \"# Regular Python\\n\",\n    \"%timeit python_list_1 = range(1,1000)\\n\",\n    \"python_list_1 = range(1,1000)\\n\",\n    \"python_list_2 = range(1,1000)\\n\",\n    \"\\n\",\n    \"#Numpy\\n\",\n    \"%timeit numpy_list_1 = np.arange(1,1000)\\n\",\n    \"numpy_list_1 = np.arange(1,1000)\\n\",\n    \"numpy_list_2 = np.arange(1,1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100000 loops, best of 3: 15 us per loop\\n\",\n      \"The slowest run took 5.51 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n      \"100000 loops, best of 3: 2.25 us per loop\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print timeit_results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Function to calculate time in seconds\\n\",\n    \"def return_time(timeit_result):\\n\",\n    \"    temp_time = float(timeit_result.split(\\\" \\\")[5])\\n\",\n    \"    temp_unit = timeit_result.split(\\\" \\\")[6]\\n\",\n    \"    if temp_unit == \\\"ms\\\":\\n\",\n    \"        temp_time = temp_time * 1e-3\\n\",\n    \"    elif temp_unit == \\\"us\\\":\\n\",\n    \"        temp_time = temp_time * 1e-6\\n\",\n    \"    elif temp_unit == \\\"ns\\\":\\n\",\n    \"        temp_time = temp_time * 1e-9\\n\",\n    \"    return temp_time\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"could not convert string to float: times\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-35-1c0052a89b77>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[0mpython_time\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mreturn_time\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_results\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mstdout\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\"\\\\n\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 2\\u001b[1;33m \\u001b[0mnumpy_time\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mreturn_time\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_results\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mstdout\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\"\\\\n\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      3\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      4\\u001b[0m \\u001b[1;32mprint\\u001b[0m \\u001b[1;34m\\\"Python/NumPy: %.1f\\\"\\u001b[0m \\u001b[1;33m%\\u001b[0m \\u001b[1;33m(\\u001b[0m\\u001b[0mpython_time\\u001b[0m\\u001b[1;33m/\\u001b[0m\\u001b[0mnumpy_time\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m<ipython-input-34-640f956687ba>\\u001b[0m in \\u001b[0;36mreturn_time\\u001b[1;34m(timeit_result)\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[1;31m# Function to calculate time in seconds\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m \\u001b[1;32mdef\\u001b[0m \\u001b[0mreturn_time\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_result\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 3\\u001b[1;33m     \\u001b[0mtemp_time\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mfloat\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_result\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\" \\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m5\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      4\\u001b[0m     \\u001b[0mtemp_unit\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mtimeit_result\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\" \\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m6\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      5\\u001b[0m     \\u001b[1;32mif\\u001b[0m \\u001b[0mtemp_unit\\u001b[0m \\u001b[1;33m==\\u001b[0m \\u001b[1;34m\\\"ms\\\"\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mValueError\\u001b[0m: could not convert string to float: times\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"python_time = return_time(timeit_results.stdout.split(\\\"\\\\n\\\")[0])\\n\",\n    \"numpy_time = return_time(timeit_results.stdout.split(\\\"\\\\n\\\")[1])\\n\",\n    \"\\n\",\n    \"print \\\"Python/NumPy: %.1f\\\" % (python_time/numpy_time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Basic Operation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture timeit_python\\n\",\n    \"%%timeit\\n\",\n    \"# Regular Python\\n\",\n    \"[(x + y) for x, y in zip(python_list_1, python_list_2)]\\n\",\n    \"[(x - y) for x, y in zip(python_list_1, python_list_2)]\\n\",\n    \"[(x * y) for x, y in zip(python_list_1, python_list_2)]\\n\",\n    \"[(x / y) for x, y in zip(python_list_1, python_list_2)];\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print timeit_python\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture timeit_numpy\\n\",\n    \"%%timeit\\n\",\n    \"#Numpy\\n\",\n    \"numpy_list_1 + numpy_list_2\\n\",\n    \"numpy_list_1 - numpy_list_2\\n\",\n    \"numpy_list_1 * numpy_list_2\\n\",\n    \"numpy_list_1 / numpy_list_2;\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print timeit_numpy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"python_time = return_time(timeit_python.stdout)\\n\",\n    \"numpy_time = return_time(timeit_numpy.stdout)\\n\",\n    \"\\n\",\n    \"print \\\"Python/NumPy: %.1f\\\" % (python_time/numpy_time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Most Common Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## List Creation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"object : array_like\\n\",\n    \"    An array, any object exposing the array interface, an\\n\",\n    \"    object whose __array__ method returns an array, or any\\n\",\n    \"    (nested) sequence.\\n\",\n    \"dtype : data-type, optional\\n\",\n    \"    The desired data-type for the array.  If not given, then\\n\",\n    \"    the type will be determined as the minimum type required\\n\",\n    \"    to hold the objects in the sequence.  This argument can only\\n\",\n    \"    be used to 'upcast' the array.  For downcasting, use the\\n\",\n    \"    .astype(t) method.\\n\",\n    \"copy : bool, optional\\n\",\n    \"    If true (default), then the object is copied.  Otherwise, a copy\\n\",\n    \"    will only be made if __array__ returns a copy, if obj is a\\n\",\n    \"    nested sequence, or if a copy is needed to satisfy any of the other\\n\",\n    \"    requirements (`dtype`, `order`, etc.).\\n\",\n    \"order : {'C', 'F', 'A'}, optional\\n\",\n    \"    Specify the order of the array.  If order is 'C' (default), then the\\n\",\n    \"    array will be in C-contiguous order (last-index varies the\\n\",\n    \"    fastest).  If order is 'F', then the returned array\\n\",\n    \"    will be in Fortran-contiguous order (first-index varies the\\n\",\n    \"    fastest).  If order is 'A', then the returned array may\\n\",\n    \"    be in any order (either C-, Fortran-contiguous, or even\\n\",\n    \"    discontiguous).\\n\",\n    \"subok : bool, optional\\n\",\n    \"    If True, then sub-classes will be passed-through, otherwise\\n\",\n    \"    the returned array will be forced to be a base-class array (default).\\n\",\n    \"ndmin : int, optional\\n\",\n    \"    Specifies the minimum number of dimensions that the resulting\\n\",\n    \"    array should have.  Ones will be pre-pended to the shape as\\n\",\n    \"    needed to meet this requirement.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.array([1,2,3,4,5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Multi Dimentional Array\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.array([[1,2],[3,4],[5,6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### zeros(shape, dtype=float, order='C') and ones(shape, dtype=float, order='C')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"shape : int or sequence of ints\\n\",\n    \"    Shape of the new array, e.g., ``(2, 3)`` or ``2``.\\n\",\n    \"dtype : data-type, optional\\n\",\n    \"    The desired data-type for the array, e.g., `numpy.int8`.  Default is\\n\",\n    \"    `numpy.float64`.\\n\",\n    \"order : {'C', 'F'}, optional\\n\",\n    \"    Whether to store multidimensional data in C- or Fortran-contiguous\\n\",\n    \"    (row- or column-wise) order in memory.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.zeros((3,4))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.zeros((3,4), dtype=np.int64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.ones((3,4))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.linspace(start, stop, num=50, endpoint=True, retstep=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"start : scalar\\n\",\n    \"    The starting value of the sequence.\\n\",\n    \"stop : scalar\\n\",\n    \"    The end value of the sequence, unless `endpoint` is set to False.\\n\",\n    \"    In that case, the sequence consists of all but the last of ``num + 1``\\n\",\n    \"    evenly spaced samples, so that `stop` is excluded.  Note that the step\\n\",\n    \"    size changes when `endpoint` is False.\\n\",\n    \"num : int, optional\\n\",\n    \"    Number of samples to generate. Default is 50.\\n\",\n    \"endpoint : bool, optional\\n\",\n    \"    If True, `stop` is the last sample. Otherwise, it is not included.\\n\",\n    \"    Default is True.\\n\",\n    \"retstep : bool, optional\\n\",\n    \"    If True, return (`samples`, `step`), where `step` is the spacing\\n\",\n    \"    between samples.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.linspace(1,5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.linspace(0,2,num=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.linspace(0,2,num=4,endpoint=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### random_sample(size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"size : int or tuple of ints, optional\\n\",\n    \"    Defines the shape of the returned array of random floats. If None\\n\",\n    \"    (the default), returns a single float.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.random((2,3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.random_sample((2,3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Statistical Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set = np.random.random((2,3))\\n\",\n    \"data_set\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.max(a, axis=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"a : array_like\\n\",\n    \"    Input data.\\n\",\n    \"axis : int, optional\\n\",\n    \"    Axis along which to operate.  By default, flattened input is used.\\n\",\n    \"out : ndarray, optional\\n\",\n    \"    Alternative output array in which to place the result.  Must\\n\",\n    \"    be of the same shape and buffer length as the expected output.\\n\",\n    \"    See `doc.ufuncs` (Section \\\"Output arguments\\\") for more details.\\n\",\n    \"keepdims : bool, optional\\n\",\n    \"    If this is set to True, the axes which are reduced are left\\n\",\n    \"    in the result as dimensions with size one. With this option,\\n\",\n    \"    the result will broadcast correctly against the original `arr`.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.max(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.max(data_set, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.max(data_set, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.min(a, axis=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.min(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.mean(a, axis=None, dtype=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.mean(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.median(a, axis=None, out=None, overwrite_input=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.median(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.std(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.sum(a, axis=None, dtype=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.sum(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reshaping\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.reshape(a, newshape, order='C')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.reshape(data_set, (3,2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.reshape(data_set, (6,1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.reshape(data_set, (6))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.ravel(a, order='C')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.ravel(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Slicing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set = np.random.random((5,10))\\n\",\n    \"data_set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[1][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[1,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Slicing a range\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4,0:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[:,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Stepping\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4:1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[::]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[::2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4,::2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix Operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"# Matrix A \\n\",\n    \"A = np.array([[1,2],[3,4]])\\n\",\n    \"# Matrix B\\n\",\n    \"B = np.array([[3,4],[5,6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Addition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A+B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Subtraction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A-B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Multiplication (Element by Element)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A*B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Multiplication (Matrix Multiplication)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[13, 16],\\n\",\n       \"       [29, 36]])\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"A.dot(B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Division\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A/B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Square\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.square(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Power\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.power(A,3) #cube of matrix\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Transpose\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 3],\\n\",\n       \"       [2, 4]])\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"A.transpose()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inverse\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-2. ,  1. ],\\n\",\n       \"       [ 1.5, -0.5]])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.linalg.inv(A)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/1 - Notebooks & Cells.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:dfd361377e5ce6c5325e71d880de561f057e74ccb4d8f996a8269cc2ef04c00e\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"The basics of IPython Notebook. It is a web-based interactive development environment. We will cover the basics of managing notebooks and cell types.\\n\",\n      \"\\n\",\n      \"Finding Help:\\n\",\n      \"\\n\",\n      \"- http://ipython.org/ipython-doc/stable/index.html\\n\",\n      \"- http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Index.ipynb\\n\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Notebooks\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Notebooks are documents representing all input and output of operations. This includes code, text input and  numerical, text and rich media output. These files have **ipynb** extensions.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Cells\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Cells can contain code or documentation. We will explore working with cells.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Markdown Cells\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"This is a markdown cell. In can contain:\\n\",\n      \"\\n\",\n      \"- Markdown language\\n\",\n      \"- HTML\\n\",\n      \"- LATEX\\n\",\n      \"\\n\",\n      \"```markdown\\n\",\n      \"**Markdown** *Text*\\n\",\n      \"<strong>HTML formula</strong>\\n\",\n      \"$LATEX%\\n\",\n      \"```\\n\",\n      \"**Markdown** *Text*\\n\",\n      \"\\n\",\n      \"<strong>HTML</strong>\\n\",\n      \"\\n\",\n      \"$LATEX formula$\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Code Cells\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"May contain python code and ipython magic.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"3+5\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 1,\n       \"text\": [\n        \"8\"\n       ]\n      }\n     ],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"1+2\\n\",\n      \"3+5\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 2,\n       \"text\": [\n        \"8\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x = 1\\n\",\n      \"y = 2\\n\",\n      \"\\n\",\n      \"x + y\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 3,\n       \"text\": [\n        \"3\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"z = x + y\\n\",\n      \"z\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 7,\n       \"text\": [\n        \"3\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"IPython Magic\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%bash\\n\",\n      \"ls\\n\",\n      \"echo \\\"** Creating New File **\\\"\\n\",\n      \"touch test.txt\\n\",\n      \"ls\\n\",\n      \"echo \\\"** Deleting New File **\\\"\\n\",\n      \"rm test.txt\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Raw NBConvert\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"These cells are like markdown but they do not render their input.\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"This is a Raw NBConvert cell.\\n\",\n      \"\\n\",\n      \"It doesn't render its contents like\\n\",\n      \"**Markdown**\\n\",\n      \"<strong>HTML</strong>\\n\",\n      \"$LATEX$\\n\",\n      \"\\n\",\n      \"The problem with them is they may render come tags in IPython NBViwer.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Heading Cells\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Heading cells are great for creating bookmarks in an IPython Notebook. You can hover over headings to find their link. Usually it is capitalized slug of the title text. This helps in linking to a specific part of a notebook.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Head 1\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Head 2\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Head 3\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Head 4\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 5,\n     \"metadata\": {},\n     \"source\": [\n      \"Head 5\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 6,\n     \"metadata\": {},\n     \"source\": [\n      \"Head 6\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Linking to a heading\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Linking to a heading is like linking to an HTML Bookmark. You can do that like this:\\n\",\n      \"\\n\",\n      \"###Relative links:\\n\",\n      \"```HTML\\n\",\n      \"<a href=\\\"#Head-1\\\">Link to Head 1</a>\\n\",\n      \"```\\n\",\n      \"\\n\",\n      \"<a href=\\\"#Head-1\\\">Link to Head 1</a>\\n\",\n      \"\\n\",\n      \"###Absolute links:\\n\",\n      \"```HTML\\n\",\n      \"<a href=\\\"http://54.186.15.239:8080/notebooks/IPython%20Tutorial/1%20-%20Notebooks%20%26%20Cells.ipynb#Head-1\\\">Link to Head 1</a>\\n\",\n      \"```\\n\",\n      \"<a href=\\\"http://54.186.15.239:8080/notebooks/IPython%20Tutorial/1%20-%20Notebooks%20%26%20Cells.ipynb#Head-1\\\">Link to Head 1</a>\"\n     ]\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/2 - Markdown & LATEX.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:78f353cca5546806477ec3c905d6a8fbc320b0728d52aba857a8d6ef575084fe\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {\n      \"slideshow\": {\n       \"slide_type\": \"-\"\n      }\n     },\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {\n      \"slideshow\": {\n       \"slide_type\": \"-\"\n      }\n     },\n     \"source\": [\n      \"This covers the basics of using markdown cells. You can think of markdown cells as documention for your code with extended features.\\n\",\n      \"####Finding Help:\\n\",\n      \"\\n\",\n      \"- http://ipython.org/ipython-doc/2/interactive/tutorial.html\\n\",\n      \"- http://nbviewer.ipython.org/\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {\n      \"slideshow\": {\n       \"slide_type\": \"-\"\n      }\n     },\n     \"source\": [\n      \"#Markdown Cells\\n\",\n      \"\\n\",\n      \"This cell can contain markdown language and render it.\\n\",\n      \"\\n\",\n      \"A good reference for markdown is in this link:\\n\",\n      \"https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {\n      \"slideshow\": {\n       \"slide_type\": \"-\"\n      }\n     },\n     \"source\": [\n      \"###Most common markdown codes:\\n\",\n      \"\\n\",\n      \"#Head 1\\n\",\n      \"##Head 2\\n\",\n      \"###Head 3\\n\",\n      \"####Head 4\\n\",\n      \"\\n\",\n      \"Alt Heading 1\\n\",\n      \"============\\n\",\n      \"\\n\",\n      \"Alt Heading 2\\n\",\n      \"------------\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"#Head 1\\n\",\n      \"##Head 2\\n\",\n      \"###Head 3\\n\",\n      \"####Head 4\\n\",\n      \"\\n\",\n      \"Alt Heading 1\\n\",\n      \"============\\n\",\n      \"\\n\",\n      \"Alt Heading 2\\n\",\n      \"------------\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Font Styles:\\n\",\n      \" - *Italic*\\n\",\n      \" - _Italic_\\n\",\n      \" - **Bold**\\n\",\n      \" - __Bold__\\n\",\n      \" - ~~Throught~~\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"- *Italic*\\n\",\n      \"- _Italic_\\n\",\n      \"- **Bold**\\n\",\n      \"- __Bold__\\n\",\n      \"- ~~Throught~~\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####Lists:\\n\",\n      \"\\n\",\n      \"- Item 1\\n\",\n      \"- Item 2\\n\",\n      \"\\n\",\n      \"####Numbered List:\\n\",\n      \"\\n\",\n      \"1. Item 1\\n\",\n      \"2. Item 2\\n\",\n      \" 1. Sub Item 1\\n\",\n      \" 2. Sub Item 2\\n\",\n      \"3. Item 3\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"- Item 1\\n\",\n      \"- Item 2\\n\",\n      \"\\n\",\n      \"1. Item 1\\n\",\n      \"2. Item 2\\n\",\n      \" 1. Sub Item 1\\n\",\n      \" 2. Sub Item 2\\n\",\n      \"3. Item 3\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####Links:\\n\",\n      \"\\n\",\n      \"https://www.google.com\\n\",\n      \"\\n\",\n      \"[Google](https://www.google.com)\\n\",\n      \"\\n\",\n      \"[Google with tooltip](https://www.google.com \\\"Google's Homepage\\\")\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"https://www.google.com\\n\",\n      \"\\n\",\n      \"[Google](https://www.google.com)\\n\",\n      \"\\n\",\n      \"[Google with tooltip](https://www.google.com \\\"Google's Homepage\\\")\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####Images:\\n\",\n      \"\\n\",\n      \"![Alt Text](http://www.google.com/logos/2012/turing-doodle-static.jpg \\\"Alan Turing's 100th Birthday\\\")\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"![Alt Text](http://www.google.com/logos/2012/turing-doodle-static.jpg \\\"Alan Turing's 100th Birthday\\\")\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####Code:\\n\",\n      \"\\n\",\n      \"```python\\n\",\n      \"s = \\\"Python syntax highlighting\\\"\\n\",\n      \"print s\\n\",\n      \"```\\n\",\n      \"\\n\",\n      \"```javascript\\n\",\n      \"var s = \\\"JavaScript syntax highlighting\\\";\\n\",\n      \"alert(s);\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"<pre>\\n\",\n      \"```python\\n\",\n      \"s = \\\"Python syntax highlighting\\\"\\n\",\n      \"print s\\n\",\n      \"```\\n\",\n      \"\\n\",\n      \"```javascript\\n\",\n      \"var s = \\\"JavaScript syntax highlighting\\\";\\n\",\n      \"alert(s);\\n\",\n      \"```\\n\",\n      \"</pre>\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####Tables:\\n\",\n      \"\\n\",\n      \"| Website   | URL           | Rank |\\n\",\n      \"| ---------:|:-------------:| ----:|\\n\",\n      \"| Google    | google.com    | 1    |\\n\",\n      \"| Facebook  | facebook.com  | 2    |\\n\",\n      \"| Youtube   | youtube.com   | 3    |\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"| Website   | URL           | Rank |\\n\",\n      \"| ---------:|:-------------:| ----:|\\n\",\n      \"| Google    | google.com    | 1    |\\n\",\n      \"| Facebook  | facebook.com  | 2    |\\n\",\n      \"| Youtube   | youtube.com   | 3    |\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####Quotes\\n\",\n      \"> ##Quote from some one else\\n\",\n      \"> *with multiple lines*\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"> ##Quote from some one else\\n\",\n      \"> *with multiple lines*\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####HTML\\n\",\n      \"<hr>\\n\",\n      \"<p>Almost any <strong>HTML</strong> code will render<br />Just <i>fine</i>.</p>\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```html\\n\",\n      \"<hr>\\n\",\n      \"<p>Almost any <strong>HTML</strong> code will render<br />Just <i>fine</i>.</p>\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"####Horizontal Lines\\n\",\n      \"---\\n\",\n      \"***\\n\",\n      \"___\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"---\\n\",\n      \"***\\n\",\n      \"___\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Unicode\\n\",\n      \"\\n\",\n      \"If you can think of any symbol, it is available in unicode.\\n\",\n      \"\\n\",\n      \"- \\u00a9\\n\",\n      \"- \\u00ae\\n\",\n      \"- \\u2122\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"\\u00a9\\n\",\n      \"\\u00ae\\n\",\n      \"\\u2122\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"##Markdown cell - Latex Script:\\n\",\n      \"\\n\",\n      \"Latex is a languange to represent mathematical formula using text.\\n\",\n      \"\\n\",\n      \"For Latex documentation please check the wiki:\\n\",\n      \"\\n\",\n      \"http://en.wikibooks.org/wiki/LaTeX/Mathematics\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Inline and Block Latex\\n\",\n      \"\\n\",\n      \"Inline Formula: $x=y$\\n\",\n      \"\\n\",\n      \"Block Formula: $$x=y$$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"$x=y$\\n\",\n      \"$$x=y$$\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Greek Letters\\n\",\n      \" - Alpha: $\\\\alpha A$\\n\",\n      \" - Beta: $\\\\beta B$\\n\",\n      \" - Gamma: $\\\\gamma \\\\Gamma$\\n\",\n      \" - Pi: $\\\\pi \\\\Pi \\\\varpi$\\n\",\n      \" - Phi: $\\\\phi \\\\varphi \\\\Phi$\\n\",\n      \" - Epsilon: $\\\\epsilon \\\\varepsilon$\\n\",\n      \" - Theta: $\\\\theta \\\\Theta \\\\vartheta$\\n\",\n      \" - Rho: $\\\\rho \\\\varrho$\\n\",\n      \" - sigma: $\\\\sigma \\\\Sigma \\\\varsigma$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"Alpha: $\\\\alpha A$\\n\",\n      \"Beta: $\\\\beta B$\\n\",\n      \"Gamma: $\\\\gamma \\\\Gamma$\\n\",\n      \"Pi: $\\\\pi \\\\Pi \\\\varpi$\\n\",\n      \"Phi: $\\\\phi \\\\varphi \\\\Phi$\\n\",\n      \"Epsilon: $\\\\epsilon \\\\varepsilon$\\n\",\n      \"Theta: $\\\\theta \\\\Theta \\\\vartheta$\\n\",\n      \"Rho: $\\\\rho \\\\varrho$\\n\",\n      \"sigma: $\\\\sigma \\\\Sigma \\\\varsigma$\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Basic operations\\n\",\n      \"\\n\",\n      \" - $y = x + 5$\\n\",\n      \" - $y = x - 5$\\n\",\n      \" - $y = 5x$\\n\",\n      \" - $y = x \\\\times 5$\\n\",\n      \" - $y = \\\\frac{x}{5}$\\n\",\n      \" - $y = x \\\\div 5$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"$y = x + 5$\\n\",\n      \"$y = x - 5$\\n\",\n      \"$y = 5x$\\n\",\n      \"$y = x \\\\times 5$\\n\",\n      \"$y = \\\\frac{x}{5}$\\n\",\n      \"$y = x \\\\div 5$\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Power and Index\\n\",\n      \"\\n\",\n      \" - $y_n = x_n - x_{n-1}$\\n\",\n      \" - $y = x^2 - 2x^{x-1}$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"$y_n = x_n - x_{n-1}$\\n\",\n      \"$y = x^2 - 2x^{x-1}$\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Fraction Type\\n\",\n      \"\\n\",\n      \" - $^1/_2$\\n\",\n      \" - $\\\\frac {1}{2}$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"$^1/_2$\\n\",\n      \"$\\\\frac {1}{2}$\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Roots\\n\",\n      \"\\n\",\n      \" - $\\\\sqrt{\\\\frac{1}{3}}$\\n\",\n      \" - $\\\\sqrt[3]{2x}$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"$\\\\sqrt{\\\\frac{1}{3}}$\\n\",\n      \"$\\\\sqrt[3]{2x}$\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Sums and Integrals\\n\",\n      \"\\n\",\n      \" - Sum : $\\\\sum\\\\limits_{i=1}^{10} t_i$\\n\",\n      \" - Int : $\\\\int\\\\limits_a^b$\\n\",\n      \" - $\\\\int_0^\\\\infty \\\\sin(x^2)$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```markdown\\n\",\n      \"$\\\\sum\\\\limits_{i=1}^{10} t_i$\\n\",\n      \"$\\\\int\\\\limits_a^b$\\n\",\n      \"$\\\\int_0^\\\\infty \\\\sin(x^2)$\\n\",\n      \"```\"\n     ]\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/3 - Basic Python.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:9dc23e125c110c7d1148039a79a606e2a8497f1f9ccba0e49d300339016bc5de\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Coding basic python in IPython Notebook interactive development environment.\\n\",\n      \"####Finding Help:\\n\",\n      \"\\n\",\n      \"- http://ipython.org/ipython-doc/2/interactive/tutorial.html\\n\",\n      \"- http://nbviewer.ipython.org/\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"#Basic Help with Coding\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import sys\\n\",\n      \"import io\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"io?\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"io??\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Reference\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# IPython Quick Refence\\n\",\n      \"%quickref\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Python Refence\\n\",\n      \"help()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"Welcome to Python 2.7!  This is the online help utility.\\n\",\n        \"\\n\",\n        \"If this is your first time using Python, you should definitely check out\\n\",\n        \"the tutorial on the Internet at http://docs.python.org/2.7/tutorial/.\\n\",\n        \"\\n\",\n        \"Enter the name of any module, keyword, or topic to get help on writing\\n\",\n        \"Python programs and using Python modules.  To quit this help utility and\\n\",\n        \"return to the interpreter, just type \\\"quit\\\".\\n\",\n        \"\\n\",\n        \"To get a list of available modules, keywords, or topics, type \\\"modules\\\",\\n\",\n        \"\\\"keywords\\\", or \\\"topics\\\".  Each module also comes with a one-line summary\\n\",\n        \"of what it does; to list the modules whose summaries contain a given word\\n\",\n        \"such as \\\"spam\\\", type \\\"modules spam\\\".\\n\",\n        \"\\n\"\n       ]\n      },\n      {\n       \"name\": \"stdout\",\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"help> \\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"You are now leaving help and returning to the Python interpreter.\\n\",\n        \"If you want to ask for help on a particular object directly from the\\n\",\n        \"interpreter, you can type \\\"help(object)\\\".  Executing \\\"help('string')\\\"\\n\",\n        \"has the same effect as typing a particular string at the help> prompt.\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%pdoc io.FileIO\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%pdef io.OpenWrapper\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"No definition header found for io.OpenWrapper\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%psource io\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%pfile io\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Tab completion\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Browse an object with tab\\n\",\n      \"sys\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 10,\n       \"text\": [\n        \"<module 'sys' (built-in)>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Shift-tab to get more information about a method\\n\",\n      \"open()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"More Magic\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Good Reference:\\n\",\n      \"\\n\",\n      \"http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Cell%20Magics.ipynb\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Listing all magic\\n\",\n      \"%lsmagic\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"json\": [\n        \"{\\\"cell\\\": {\\\"prun\\\": \\\"ExecutionMagics\\\", \\\"file\\\": \\\"Other\\\", \\\"!\\\": \\\"OSMagics\\\", \\\"capture\\\": \\\"ExecutionMagics\\\", \\\"timeit\\\": \\\"ExecutionMagics\\\", \\\"script\\\": \\\"ScriptMagics\\\", \\\"pypy\\\": \\\"Other\\\", \\\"system\\\": \\\"OSMagics\\\", \\\"perl\\\": \\\"Other\\\", \\\"HTML\\\": \\\"Other\\\", \\\"bash\\\": \\\"Other\\\", \\\"python\\\": \\\"Other\\\", \\\"SVG\\\": \\\"Other\\\", \\\"javascript\\\": \\\"DisplayMagics\\\", \\\"writefile\\\": \\\"OSMagics\\\", \\\"ruby\\\": \\\"Other\\\", \\\"python3\\\": \\\"Other\\\", \\\"python2\\\": \\\"Other\\\", \\\"latex\\\": \\\"DisplayMagics\\\", \\\"sx\\\": \\\"OSMagics\\\", \\\"svg\\\": \\\"DisplayMagics\\\", \\\"html\\\": \\\"DisplayMagics\\\", \\\"sh\\\": \\\"Other\\\", \\\"time\\\": \\\"ExecutionMagics\\\", \\\"debug\\\": \\\"ExecutionMagics\\\"}, \\\"line\\\": {\\\"psource\\\": \\\"NamespaceMagics\\\", \\\"logstart\\\": \\\"LoggingMagics\\\", \\\"popd\\\": \\\"OSMagics\\\", \\\"loadpy\\\": \\\"CodeMagics\\\", \\\"install_ext\\\": \\\"ExtensionMagics\\\", \\\"colors\\\": \\\"BasicMagics\\\", \\\"who_ls\\\": \\\"NamespaceMagics\\\", \\\"lf\\\": \\\"Other\\\", \\\"install_profiles\\\": \\\"DeprecatedMagics\\\", \\\"ll\\\": \\\"Other\\\", \\\"pprint\\\": \\\"BasicMagics\\\", \\\"lk\\\": \\\"Other\\\", \\\"ls\\\": \\\"Other\\\", \\\"save\\\": \\\"CodeMagics\\\", \\\"tb\\\": \\\"ExecutionMagics\\\", \\\"lx\\\": \\\"Other\\\", \\\"pylab\\\": \\\"PylabMagics\\\", \\\"killbgscripts\\\": \\\"ScriptMagics\\\", \\\"quickref\\\": \\\"BasicMagics\\\", \\\"magic\\\": \\\"BasicMagics\\\", \\\"dhist\\\": \\\"OSMagics\\\", \\\"edit\\\": \\\"KernelMagics\\\", \\\"logstop\\\": \\\"LoggingMagics\\\", \\\"gui\\\": \\\"BasicMagics\\\", \\\"alias_magic\\\": \\\"BasicMagics\\\", \\\"debug\\\": \\\"ExecutionMagics\\\", \\\"page\\\": \\\"BasicMagics\\\", \\\"logstate\\\": \\\"LoggingMagics\\\", \\\"ed\\\": \\\"Other\\\", \\\"pushd\\\": \\\"OSMagics\\\", \\\"timeit\\\": \\\"ExecutionMagics\\\", \\\"rehashx\\\": \\\"OSMagics\\\", \\\"hist\\\": \\\"Other\\\", \\\"qtconsole\\\": \\\"KernelMagics\\\", \\\"rm\\\": \\\"Other\\\", \\\"dirs\\\": \\\"OSMagics\\\", \\\"run\\\": \\\"ExecutionMagics\\\", \\\"reset_selective\\\": \\\"NamespaceMagics\\\", \\\"rep\\\": \\\"Other\\\", \\\"pinfo2\\\": \\\"NamespaceMagics\\\", \\\"matplotlib\\\": \\\"PylabMagics\\\", \\\"unload_ext\\\": \\\"ExtensionMagics\\\", \\\"doctest_mode\\\": \\\"KernelMagics\\\", \\\"logoff\\\": \\\"LoggingMagics\\\", \\\"reload_ext\\\": \\\"ExtensionMagics\\\", \\\"pdb\\\": \\\"ExecutionMagics\\\", \\\"load\\\": \\\"CodeMagics\\\", \\\"lsmagic\\\": \\\"BasicMagics\\\", \\\"autosave\\\": \\\"KernelMagics\\\", \\\"cd\\\": \\\"OSMagics\\\", \\\"pastebin\\\": \\\"CodeMagics\\\", \\\"prun\\\": \\\"ExecutionMagics\\\", \\\"cp\\\": \\\"Other\\\", \\\"autocall\\\": \\\"AutoMagics\\\", \\\"bookmark\\\": \\\"OSMagics\\\", \\\"connect_info\\\": \\\"KernelMagics\\\", \\\"mkdir\\\": \\\"Other\\\", \\\"system\\\": \\\"OSMagics\\\", \\\"whos\\\": \\\"NamespaceMagics\\\", \\\"rmdir\\\": \\\"Other\\\", \\\"automagic\\\": \\\"AutoMagics\\\", \\\"store\\\": \\\"StoreMagics\\\", \\\"more\\\": \\\"KernelMagics\\\", \\\"pdef\\\": \\\"NamespaceMagics\\\", \\\"precision\\\": \\\"BasicMagics\\\", \\\"pinfo\\\": \\\"NamespaceMagics\\\", \\\"pwd\\\": \\\"OSMagics\\\", \\\"psearch\\\": \\\"NamespaceMagics\\\", \\\"reset\\\": \\\"NamespaceMagics\\\", \\\"recall\\\": \\\"HistoryMagics\\\", \\\"xdel\\\": \\\"NamespaceMagics\\\", \\\"xmode\\\": \\\"BasicMagics\\\", \\\"cat\\\": \\\"Other\\\", \\\"mv\\\": \\\"Other\\\", \\\"rerun\\\": \\\"HistoryMagics\\\", \\\"logon\\\": \\\"LoggingMagics\\\", \\\"history\\\": \\\"HistoryMagics\\\", \\\"pycat\\\": \\\"OSMagics\\\", \\\"unalias\\\": \\\"OSMagics\\\", \\\"install_default_config\\\": \\\"DeprecatedMagics\\\", \\\"env\\\": \\\"OSMagics\\\", \\\"load_ext\\\": \\\"ExtensionMagics\\\", \\\"config\\\": \\\"ConfigMagics\\\", \\\"profile\\\": \\\"BasicMagics\\\", \\\"pfile\\\": \\\"NamespaceMagics\\\", \\\"less\\\": \\\"KernelMagics\\\", \\\"who\\\": \\\"NamespaceMagics\\\", \\\"notebook\\\": \\\"BasicMagics\\\", \\\"man\\\": \\\"KernelMagics\\\", \\\"sx\\\": \\\"OSMagics\\\", \\\"macro\\\": \\\"ExecutionMagics\\\", \\\"clear\\\": \\\"KernelMagics\\\", \\\"alias\\\": \\\"OSMagics\\\", \\\"time\\\": \\\"ExecutionMagics\\\", \\\"sc\\\": \\\"OSMagics\\\", \\\"ldir\\\": \\\"Other\\\", \\\"pdoc\\\": \\\"NamespaceMagics\\\"}}\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"Available line magics:\\n\",\n        \"%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\\n\",\n        \"\\n\",\n        \"Available cell magics:\\n\",\n        \"%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\\n\",\n        \"\\n\",\n        \"Automagic is ON, % prefix IS NOT needed for line magics.\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Measuring Performance\\n\",\n      \"%timeit range(1000)\\n\",\n      \"%timeit xrange(1000)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"100000 loops, best of 3: 13.9 \\u00b5s per loop\\n\",\n        \"10000000 loops, best of 3: 198 ns per loop\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%timeit\\n\",\n      \"# Measuring Block Performance\\n\",\n      \"\\n\",\n      \"for x in range(1000):\\n\",\n      \"    y=x+1\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"10000 loops, best of 3: 65.1 \\u00b5s per loop\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%timeit\\n\",\n      \"# Measuring Block Performance\\n\",\n      \"\\n\",\n      \"for x in xrange(1000):\\n\",\n      \"    y=x+1\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1000 loops, best of 3: 488 \\u00b5s per loop\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Reset (Delete) all variables\\n\",\n      \"%reset\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"name\": \"stdout\",\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Once deleted, variables cannot be recovered. Proceed (y/[n])? n\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Nothing done.\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%bash\\n\",\n      \"echo $PATH\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"/usr/local/sbin:/usr/local/bin:/usr/bin:/usr/sbin:/sbin:/bin\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Output and Input\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"You have access to all code inside you code cell and the out put from them in two lists: In and Out.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"3+5\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 18,\n       \"text\": [\n        \"8\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"In[18]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 21,\n       \"text\": [\n        \"u'3+5'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"Out[18]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 22,\n       \"text\": [\n        \"8\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"_ + 3\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 23,\n       \"text\": [\n        \"11\"\n       ]\n      }\n     ],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"_ - 4\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 24,\n       \"text\": [\n        \"7\"\n       ]\n      }\n     ],\n     \"prompt_number\": 24\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/4 - Numpy Basics.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial Brief\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"numpy is a powerful set of tools to perform mathematical operations of on lists of numbers. It works faster than normal python lists operations and can manupilate high dimentional arrays too.\\n\",\n    \"\\n\",\n    \"Finding Help:\\n\",\n    \"\\n\",\n    \" - http://wiki.scipy.org/Tentative_NumPy_Tutorial\\n\",\n    \" - http://docs.scipy.org/doc/numpy/reference/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.\\n\",\n    \">\\n\",\n    \">  *http://www.scipy.org/*\\n\",\n    \"\\n\",\n    \"So NumPy is a part of a bigger ecosystem of libraries that build on the optimized performance of NumPy NDArray.\\n\",\n    \"\\n\",\n    \"It contain these core packages:\\n\",\n    \"\\n\",\n    \"<table>\\n\",\n    \"<tr>\\n\",\n    \"    <td style=\\\"background:Lavender;\\\"><img src=\\\"http://www.scipy.org/_static/images/numpylogo_med.png\\\"  style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td style=\\\"background:Lavender;\\\"><h4>NumPy</h4> Base N-dimensional array package </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/scipy_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/matplotlib_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\\n\",\n    \"</tr>\\n\",\n    \"<tr>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/ipython.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>IPython</h4> Enhanced Interactive Console </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/sympy_logo.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>SymPy</h4> Symbolic mathematics </td>\\n\",\n    \"    <td><img src=\\\"http://www.scipy.org/_static/images/pandas_badge2.jpg\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n    \"    <td><h4>Pandas</h4> Data structures & analysis </td>\\n\",\n    \"</tr>\\n\",\n    \"</table>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Importig the library\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Import numpy library as np\\n\",\n    \"This helps in writing code and it's almost a standard in scientific work\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Working with ndarray\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will generate an ndarray with np.arange method.\\n\",\n    \"\\n\",\n    \"####np.arange([start,] stop[, step,], dtype=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,  6. ,\\n\",\n       \"        6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5])\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10, 0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 4, 7])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.,  3.,  5.,  7.,  9.])\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(1,10, 2, dtype=np.float64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Examining ndrray\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds = np.arange(1,10,2)\\n\",\n    \"ds.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(5,)\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.size\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('int32')\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds.itemsize\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['\\\\x01',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x03',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x05',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x07',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\t',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00',\\n\",\n       \" '\\\\x00']\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x=ds.data\\n\",\n    \"list(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 3, 5, 7, 9])\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"ds\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"20\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Memory Usage\\n\",\n    \"ds.size * ds.itemsize\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Why to use numpy?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will compare the time it takes to create two lists and do some basic operations on them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Generate a list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture timeit_results\\n\",\n    \"# Regular Python\\n\",\n    \"%timeit python_list_1 = range(1,1000)\\n\",\n    \"python_list_1 = range(1,1000)\\n\",\n    \"python_list_2 = range(1,1000)\\n\",\n    \"\\n\",\n    \"#Numpy\\n\",\n    \"%timeit numpy_list_1 = np.arange(1,1000)\\n\",\n    \"numpy_list_1 = np.arange(1,1000)\\n\",\n    \"numpy_list_2 = np.arange(1,1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100000 loops, best of 3: 15 us per loop\\n\",\n      \"The slowest run took 5.51 times longer than the fastest. This could mean that an intermediate result is being cached.\\n\",\n      \"100000 loops, best of 3: 2.25 us per loop\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print timeit_results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Function to calculate time in seconds\\n\",\n    \"def return_time(timeit_result):\\n\",\n    \"    temp_time = float(timeit_result.split(\\\" \\\")[5])\\n\",\n    \"    temp_unit = timeit_result.split(\\\" \\\")[6]\\n\",\n    \"    if temp_unit == \\\"ms\\\":\\n\",\n    \"        temp_time = temp_time * 1e-3\\n\",\n    \"    elif temp_unit == \\\"us\\\":\\n\",\n    \"        temp_time = temp_time * 1e-6\\n\",\n    \"    elif temp_unit == \\\"ns\\\":\\n\",\n    \"        temp_time = temp_time * 1e-9\\n\",\n    \"    return temp_time\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"could not convert string to float: times\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-35-1c0052a89b77>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[0mpython_time\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mreturn_time\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_results\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mstdout\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\"\\\\n\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m0\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 2\\u001b[1;33m \\u001b[0mnumpy_time\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mreturn_time\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_results\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mstdout\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\"\\\\n\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      3\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      4\\u001b[0m \\u001b[1;32mprint\\u001b[0m \\u001b[1;34m\\\"Python/NumPy: %.1f\\\"\\u001b[0m \\u001b[1;33m%\\u001b[0m \\u001b[1;33m(\\u001b[0m\\u001b[0mpython_time\\u001b[0m\\u001b[1;33m/\\u001b[0m\\u001b[0mnumpy_time\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;32m<ipython-input-34-640f956687ba>\\u001b[0m in \\u001b[0;36mreturn_time\\u001b[1;34m(timeit_result)\\u001b[0m\\n\\u001b[0;32m      1\\u001b[0m \\u001b[1;31m# Function to calculate time in seconds\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      2\\u001b[0m \\u001b[1;32mdef\\u001b[0m \\u001b[0mreturn_time\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_result\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 3\\u001b[1;33m     \\u001b[0mtemp_time\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mfloat\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mtimeit_result\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\" \\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m5\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      4\\u001b[0m     \\u001b[0mtemp_unit\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mtimeit_result\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0msplit\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m\\\" \\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;36m6\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      5\\u001b[0m     \\u001b[1;32mif\\u001b[0m \\u001b[0mtemp_unit\\u001b[0m \\u001b[1;33m==\\u001b[0m \\u001b[1;34m\\\"ms\\\"\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mValueError\\u001b[0m: could not convert string to float: times\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"python_time = return_time(timeit_results.stdout.split(\\\"\\\\n\\\")[0])\\n\",\n    \"numpy_time = return_time(timeit_results.stdout.split(\\\"\\\\n\\\")[1])\\n\",\n    \"\\n\",\n    \"print \\\"Python/NumPy: %.1f\\\" % (python_time/numpy_time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Basic Operation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture timeit_python\\n\",\n    \"%%timeit\\n\",\n    \"# Regular Python\\n\",\n    \"[(x + y) for x, y in zip(python_list_1, python_list_2)]\\n\",\n    \"[(x - y) for x, y in zip(python_list_1, python_list_2)]\\n\",\n    \"[(x * y) for x, y in zip(python_list_1, python_list_2)]\\n\",\n    \"[(x / y) for x, y in zip(python_list_1, python_list_2)];\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print timeit_python\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%capture timeit_numpy\\n\",\n    \"%%timeit\\n\",\n    \"#Numpy\\n\",\n    \"numpy_list_1 + numpy_list_2\\n\",\n    \"numpy_list_1 - numpy_list_2\\n\",\n    \"numpy_list_1 * numpy_list_2\\n\",\n    \"numpy_list_1 / numpy_list_2;\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print timeit_numpy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"python_time = return_time(timeit_python.stdout)\\n\",\n    \"numpy_time = return_time(timeit_numpy.stdout)\\n\",\n    \"\\n\",\n    \"print \\\"Python/NumPy: %.1f\\\" % (python_time/numpy_time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Most Common Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## List Creation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"object : array_like\\n\",\n    \"    An array, any object exposing the array interface, an\\n\",\n    \"    object whose __array__ method returns an array, or any\\n\",\n    \"    (nested) sequence.\\n\",\n    \"dtype : data-type, optional\\n\",\n    \"    The desired data-type for the array.  If not given, then\\n\",\n    \"    the type will be determined as the minimum type required\\n\",\n    \"    to hold the objects in the sequence.  This argument can only\\n\",\n    \"    be used to 'upcast' the array.  For downcasting, use the\\n\",\n    \"    .astype(t) method.\\n\",\n    \"copy : bool, optional\\n\",\n    \"    If true (default), then the object is copied.  Otherwise, a copy\\n\",\n    \"    will only be made if __array__ returns a copy, if obj is a\\n\",\n    \"    nested sequence, or if a copy is needed to satisfy any of the other\\n\",\n    \"    requirements (`dtype`, `order`, etc.).\\n\",\n    \"order : {'C', 'F', 'A'}, optional\\n\",\n    \"    Specify the order of the array.  If order is 'C' (default), then the\\n\",\n    \"    array will be in C-contiguous order (last-index varies the\\n\",\n    \"    fastest).  If order is 'F', then the returned array\\n\",\n    \"    will be in Fortran-contiguous order (first-index varies the\\n\",\n    \"    fastest).  If order is 'A', then the returned array may\\n\",\n    \"    be in any order (either C-, Fortran-contiguous, or even\\n\",\n    \"    discontiguous).\\n\",\n    \"subok : bool, optional\\n\",\n    \"    If True, then sub-classes will be passed-through, otherwise\\n\",\n    \"    the returned array will be forced to be a base-class array (default).\\n\",\n    \"ndmin : int, optional\\n\",\n    \"    Specifies the minimum number of dimensions that the resulting\\n\",\n    \"    array should have.  Ones will be pre-pended to the shape as\\n\",\n    \"    needed to meet this requirement.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.array([1,2,3,4,5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Multi Dimentional Array\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.array([[1,2],[3,4],[5,6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### zeros(shape, dtype=float, order='C') and ones(shape, dtype=float, order='C')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"shape : int or sequence of ints\\n\",\n    \"    Shape of the new array, e.g., ``(2, 3)`` or ``2``.\\n\",\n    \"dtype : data-type, optional\\n\",\n    \"    The desired data-type for the array, e.g., `numpy.int8`.  Default is\\n\",\n    \"    `numpy.float64`.\\n\",\n    \"order : {'C', 'F'}, optional\\n\",\n    \"    Whether to store multidimensional data in C- or Fortran-contiguous\\n\",\n    \"    (row- or column-wise) order in memory.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.zeros((3,4))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.zeros((3,4), dtype=np.int64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.ones((3,4))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.linspace(start, stop, num=50, endpoint=True, retstep=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"start : scalar\\n\",\n    \"    The starting value of the sequence.\\n\",\n    \"stop : scalar\\n\",\n    \"    The end value of the sequence, unless `endpoint` is set to False.\\n\",\n    \"    In that case, the sequence consists of all but the last of ``num + 1``\\n\",\n    \"    evenly spaced samples, so that `stop` is excluded.  Note that the step\\n\",\n    \"    size changes when `endpoint` is False.\\n\",\n    \"num : int, optional\\n\",\n    \"    Number of samples to generate. Default is 50.\\n\",\n    \"endpoint : bool, optional\\n\",\n    \"    If True, `stop` is the last sample. Otherwise, it is not included.\\n\",\n    \"    Default is True.\\n\",\n    \"retstep : bool, optional\\n\",\n    \"    If True, return (`samples`, `step`), where `step` is the spacing\\n\",\n    \"    between samples.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.linspace(1,5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.linspace(0,2,num=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.linspace(0,2,num=4,endpoint=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### random_sample(size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"size : int or tuple of ints, optional\\n\",\n    \"    Defines the shape of the returned array of random floats. If None\\n\",\n    \"    (the default), returns a single float.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.random((2,3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.random_sample((2,3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Statistical Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set = np.random.random((2,3))\\n\",\n    \"data_set\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.max(a, axis=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"Parameters\\n\",\n    \"----------\\n\",\n    \"a : array_like\\n\",\n    \"    Input data.\\n\",\n    \"axis : int, optional\\n\",\n    \"    Axis along which to operate.  By default, flattened input is used.\\n\",\n    \"out : ndarray, optional\\n\",\n    \"    Alternative output array in which to place the result.  Must\\n\",\n    \"    be of the same shape and buffer length as the expected output.\\n\",\n    \"    See `doc.ufuncs` (Section \\\"Output arguments\\\") for more details.\\n\",\n    \"keepdims : bool, optional\\n\",\n    \"    If this is set to True, the axes which are reduced are left\\n\",\n    \"    in the result as dimensions with size one. With this option,\\n\",\n    \"    the result will broadcast correctly against the original `arr`.\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.max(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.max(data_set, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.max(data_set, axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.min(a, axis=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.min(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.mean(a, axis=None, dtype=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.mean(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.median(a, axis=None, out=None, overwrite_input=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.median(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.std(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.sum(a, axis=None, dtype=None, out=None, keepdims=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.sum(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reshaping\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.reshape(a, newshape, order='C')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.reshape(data_set, (3,2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.reshape(data_set, (6,1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.reshape(data_set, (6))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### np.ravel(a, order='C')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.ravel(data_set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Slicing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set = np.random.random((5,10))\\n\",\n    \"data_set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[1][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[1,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Slicing a range\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4,0:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[:,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Stepping\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4:1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[::]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[::2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_set[2:4,::2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix Operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"# Matrix A \\n\",\n    \"A = np.array([[1,2],[3,4]])\\n\",\n    \"# Matrix B\\n\",\n    \"B = np.array([[3,4],[5,6]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Addition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A+B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Subtraction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A-B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Multiplication (Element by Element)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A*B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Multiplication (Matrix Multiplication)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[13, 16],\\n\",\n       \"       [29, 36]])\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"A.dot(B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Division\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"A/B\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Square\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.square(A)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Power\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.power(A,3) #cube of matrix\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Transpose\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 3],\\n\",\n       \"       [2, 4]])\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"A.transpose()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inverse\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[-2. ,  1. ],\\n\",\n       \"       [ 1.5, -0.5]])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.linalg.inv(A)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/5 - Plotting Charts.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:ed0984cee88a5cab4850ff3bd2ebf60dc135f09000ed83998fa37d60393da23b\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"This cover the basics of plotting charts and controlling the style and content. We will be using matplotlib library.\\n\",\n      \"\\n\",\n      \"Finding Help:\\n\",\n      \"\\n\",\n      \" - http://matplotlib.org/\\n\",\n      \" - http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb\\n\",\n      \" - http://wiki.scipy.org/Cookbook/Matplotlib\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"<table>\\n\",\n      \"<tr>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/numpylogo_med.png\\\"  style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>NumPy</h4> Base N-dimensional array package </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/scipy_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><img src=\\\"http://www.scipy.org/_static/images/matplotlib_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\\n\",\n      \"</tr>\\n\",\n      \"<tr>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/ipython.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>IPython</h4> Enhanced Interactive Console </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/sympy_logo.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>SymPy</h4> Symbolic mathematics </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/pandas_badge2.jpg\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>Pandas</h4> Data structures & analysis </td>\\n\",\n      \"</tr>\\n\",\n      \"</table>\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"What can we do with Matplotlib?\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Plotting\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://matplotlib.org/_images/major_minor_demo1.png)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Scattering\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://matplotlib.org/_images/date_demo_rrule.png)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Bars / Histogram\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://matplotlib.org/_images/histogram_path_demo1.png)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Pie\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://matplotlib.org/_images/pie_demo_features.png)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"3D\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://matplotlib.org/_images/surface3d_demo.png)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Images / Contour\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://matplotlib.org/_images/mri_demo.png)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Importing the library\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Most of the functionality we need to plot charts is in matlotlib.pyplot. Always import it as **plt**\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import matplotlib.pyplot as plt\\n\",\n      \"import numpy as np\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Plotting\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x = [0,1,2]\\n\",\n      \"y = [0,1,4]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x, y)\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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q2urioUCtk3QyDDCDucKO1tvLy8rNra2vDrmpoaBYPBdE8LZNzmpjQ9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\\nOwA4EHEHAAci7gDgQP8P0lq/n0Xbj9cAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10cef68a10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Using NumPy to increase the the number of data points\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x_highres = np.linspace(0, 2, 20)\\n\",\n      \"y_highres = x_highres ** 2\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x, y)\\n\",\n      \"axes.plot(x_highres, y_highres)\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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oWsqqrCyJEjERQUhNmzZ6u902eeeQbHjh1Dx44d/3wgHgpJZLF0DXuG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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce545a90>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Controlling Line Style\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x_highres, y_highres, \\\"r--\\\")\\n\",\n      \"#axes.plot(x_highres, y_highres, color=\\\"red\\\", linestyle='dashed')\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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val4i4SC6qrzcL+n/8J3/621WkkDDQUUiQW/PnP0NQEGRlWJ5EAaZy7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\\nEbGh/wc0VMLvKhKj1QAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce4f5c50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x_highres, y_highres, color=\\\"red\\\", linestyle='dashed',  linewidth=3)\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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hK1tbVcddVV3HjjjcTHxzeVyc/Pp7S0lJKSEnbu3MmcOXMoLCz0aeAi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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce53ce50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x_highres, y_highres,color=\\\"red\\\", linestyle='dashed',  linewidth=3, marker='o',\\n\",\n      \"         markerfacecolor='blue', markersize=5)\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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TqbOzUUUkQ6LC+viKy5h6k+2cIqSuIVNFpGRDosd8kGqk/OxVxow3Zh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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce535390>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Line Styles & Markers\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```\\n\",\n      \"================    ===============================\\n\",\n      \"character           description\\n\",\n      \"================    ===============================\\n\",\n      \"``'-'``             solid line style\\n\",\n      \"``'--'``            dashed line style\\n\",\n      \"``'-.'``            dash-dot line style\\n\",\n      \"``':'``             dotted line style\\n\",\n      \"``'.'``             point marker\\n\",\n      \"``','``             pixel marker\\n\",\n      \"``'o'``             circle marker\\n\",\n      \"``'v'``             triangle_down marker\\n\",\n      \"``'^'``             triangle_up marker\\n\",\n      \"``'<'``             triangle_left marker\\n\",\n      \"``'>'``             triangle_right marker\\n\",\n      \"``'1'``             tri_down marker\\n\",\n      \"``'2'``             tri_up marker\\n\",\n      \"``'3'``             tri_left marker\\n\",\n      \"``'4'``             tri_right marker\\n\",\n      \"``'s'``             square marker\\n\",\n      \"``'p'``             pentagon marker\\n\",\n      \"``'*'``             star marker\\n\",\n      \"``'h'``             hexagon1 marker\\n\",\n      \"``'H'``             hexagon2 marker\\n\",\n      \"``'+'``             plus marker\\n\",\n      \"``'x'``             x marker\\n\",\n      \"``'D'``             diamond marker\\n\",\n      \"``'d'``             thin_diamond marker\\n\",\n      \"``'|'``             vline marker\\n\",\n      \"``'_'``             hline marker\\n\",\n      \"================    ===============================\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Colors\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```\\n\",\n      \"==========  ========\\n\",\n      \"character   color\\n\",\n      \"==========  ========\\n\",\n      \"'b'         blue\\n\",\n      \"'g'         green\\n\",\n      \"'r'         red\\n\",\n      \"'c'         cyan\\n\",\n      \"'m'         magenta\\n\",\n      \"'y'         yellow\\n\",\n      \"'k'         black\\n\",\n      \"'w'         white\\n\",\n      \"==========  ========\\n\",\n      \"\\n\",\n      \"In addition, you can specify colors in many weird and\\n\",\n      \"wonderful ways, including full names (``'green'``), hex\\n\",\n      \"strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or\\n\",\n      \"grayscale intensities as a string (``'0.8'``).  Of these, the\\n\",\n      \"string specifications can be used in place of a ``fmt`` group,\\n\",\n      \"but the tuple forms can be used only as ``kwargs``.\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Title and Grid\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x_highres, y_highres,color=\\\"red\\\", linestyle='dashed',  linewidth=3, marker='o',\\n\",\n      \"         markerfacecolor='blue', markersize=5)\\n\",\n      \"\\n\",\n      \"axes.set_title('$y=x^2$') ## Notice you can you LaTeX Code\\n\",\n      \"                          ## for more about LaTeX check the Tutorial about Markdown and LaTeX\\n\",\n      \"axes.grid()\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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izu5eXlpKamcsMNNzB48GBeeOGFJvu4XC569epFUlISSUlJLFq0yCfB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\\nFxEJQiruIiJB6P8D8y+aSvZCangAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce566510>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Axis Labels\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x_highres, y_highres,color=\\\"red\\\", linestyle='dashed',  linewidth=3, marker='o',\\n\",\n      \"         markerfacecolor='blue', markersize=5)\\n\",\n      \"\\n\",\n      \"axes.set_title('$y=x^2$')\\n\",\n      \"axes.grid()\\n\",\n      \"axes.set_xlabel('x')\\n\",\n      \"axes.set_ylabel('y')\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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FZRV5KIyD/+AUlJ9UUhLAx+8ANrY/JBphaG0tJShg0bxvXXX8+AAQN4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\\nYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce712e10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Controlling the Figure Size\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure(figsize=(12,8))\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"axes.plot(x_highres, y_highres,color=\\\"red\\\", linestyle='dashed',  linewidth=3, marker='o',\\n\",\n      \"         markerfacecolor='blue', markersize=5)\\n\",\n      \"\\n\",\n      \"axes.set_title('$y=x^2$')\\n\",\n      \"axes.grid()\\n\",\n      \"axes.set_xlabel('x')\\n\",\n      \"axes.set_ylabel('y')\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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ztx+u59Es+seio46EWAmWpJkiTtqrw8B+pG4lAtJcBcoKLM/lRU2Zvh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\\nGm8KAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce4e4490>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualizing Numbers as images\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"noise = np.random.random((128,128))\\n\",\n      \"noise\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"array([[ 0.31743783,  0.89968839,  0.64593546, ...,  0.92315307,\\n\",\n        \"         0.40529038,  0.57868866],\\n\",\n        \"       [ 0.03509869,  0.41812159,  0.09614452, ...,  0.35230104,\\n\",\n        \"         0.4358769 ,  0.42696774],\\n\",\n        \"       [ 0.87243532,  0.16665336,  0.30484216, ...,  0.57570625,\\n\",\n        \"         0.41494208,  0.60731167],\\n\",\n        \"       ..., \\n\",\n        \"       [ 0.8604435 ,  0.91615867,  0.44869023, ...,  0.15850609,\\n\",\n        \"         0.85534071,  0.87021376],\\n\",\n        \"       [ 0.01936096,  0.56630654,  0.49670692, ...,  0.63496359,\\n\",\n        \"         0.15847743,  0.53732171],\\n\",\n        \"       [ 0.67941664,  0.33243944,  0.41832329, ...,  0.7415799 ,\\n\",\n        \"         0.56672038,  0.93639001]])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.imshow(noise)\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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UJnGpmzfWweexPJJjGSQgFptj5cvXEMdhUyNaEH1xSMuggMyRjUFiAi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\\nQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce5663d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Color Bar\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.imshow(noise)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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vVxJHMRDNNWDMN2yoc7OD+de1V6XHFVZrl6rnqI/Z9IuTaW6IlxVBE5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\\ndxpnEnPixMl3GmcSc+LEyXea/wYnBeSBbvNIuAAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce4d91d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Color Maps\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.imshow(noise, cmap=plt.cm.gray)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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qfDbDYLTCMSiRCNRlGr1SQSCVGlyWQy9Hq9wHhkMhltbW3Y7XYymQzN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\\nvtXl38T85S9/favr/wGNa0I+WKsOFwAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce545b10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.imshow(noise, cmap=plt.cm.Paired)\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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phjwDJir0l1OsB2ysxciakfQOiwEsHJLMYorlG6/z4ZBZq5AkFbR+ok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\\nEuvSpcvHmv8HTdW41mJHLxcAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f10ce4ed310>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/6 - IPython Widgets.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:637e4665e22d75ddc62a34727f56aa8287e7dfabd88721e5f2985ec00657bd5a\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"IPython notebook provides a variaty of web widgets that can interact with python code running the the background kernel.\\n\",\n      \"\\n\",\n      \"Finding Help:\\n\",\n      \"\\n\",\n      \"- http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"<table>\\n\",\n      \"<tr>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/numpylogo_med.png\\\"  style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>NumPy</h4> Base N-dimensional array package </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/scipy_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/matplotlib_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\\n\",\n      \"</tr>\\n\",\n      \"<tr>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><img src=\\\"http://www.scipy.org/_static/images/ipython.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><h4>IPython</h4> Enhanced Interactive Console </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/sympy_logo.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>SymPy</h4> Symbolic mathematics </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/pandas_badge2.jpg\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>Pandas</h4> Data structures & analysis </td>\\n\",\n      \"</tr>\\n\",\n      \"</table>\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Why do we use widgets?\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"> ##IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS running in the browser. These widgets enable users to explore their code and data interactively.\\n\",\n      \"> *http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb*\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"\\n\",\n      \"from IPython.html import widgets\\n\",\n      \"from IPython.html.widgets import interact\\n\",\n      \"from IPython.display import display\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Build-it Widgets\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"tab1_children = [widgets.ButtonWidget(description=\\\"ButtonWidget\\\"),\\n\",\n      \"                 widgets.CheckboxWidget(description=\\\"CheckboxWidget\\\"),\\n\",\n      \"                 widgets.DropdownWidget(values=[1, 2], description=\\\"DropdownWidget\\\"),\\n\",\n      \"                 widgets.RadioButtonsWidget(values=[1, 2], description=\\\"RadioButtonsWidget\\\"),\\n\",\n      \"                 widgets.SelectWidget(values=[1, 2], description=\\\"SelectWidget\\\"),\\n\",\n      \"                 widgets.TextWidget(description=\\\"TextWidget\\\"),\\n\",\n      \"                 widgets.TextareaWidget(description=\\\"TextareaWidget\\\"),\\n\",\n      \"                 widgets.ToggleButtonWidget(description=\\\"ToggleButtonWidget\\\"),\\n\",\n      \"                 widgets.ToggleButtonsWidget(values=[\\\"Value 1\\\", \\\"Value2\\\"], description=\\\"ToggleButtonsWidget\\\"),\\n\",\n      \"                 ]\\n\",\n      \"\\n\",\n      \"tab2_children = [widgets.BoundedFloatTextWidget(description=\\\"BoundedFloatTextWidget\\\"),\\n\",\n      \"                 widgets.BoundedIntTextWidget(description=\\\"BoundedIntTextWidget\\\"),\\n\",\n      \"                 widgets.FloatSliderWidget(description=\\\"FloatSliderWidget\\\"),\\n\",\n      \"                 widgets.FloatTextWidget(description=\\\"FloatTextWidget\\\"),\\n\",\n      \"                 widgets.IntSliderWidget(description=\\\"IntSliderWidget\\\"),\\n\",\n      \"                 widgets.IntTextWidget(description=\\\"IntTextWidget\\\"),\\n\",\n      \"                 ]\\n\",\n      \"\\n\",\n      \"tab1 = widgets.ContainerWidget(children=tab1_children)\\n\",\n      \"tab2 = widgets.ContainerWidget(children=tab2_children)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"i = widgets.AccordionWidget(children=[tab1, tab2])\\n\",\n      \"\\n\",\n      \"i.set_title(0,\\\"Basic Widgets\\\")\\n\",\n      \"i.set_title(1,\\\"Numbers Input\\\")\\n\",\n      \"\\n\",\n      \"display(i)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Simple Example\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will define a function that print the factorial.\\n\",\n      \"\\n\",\n      \"$f(x) = x!$\\n\",\n      \"\\n\",\n      \"$f(x) = x \\\\times (x-1) \\\\times ... 1$\\n\",\n      \"\\n\",\n      \"$f(3) = 3! = 3 \\\\times 2 \\\\times 1 = 6$\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def factorial(x):\\n\",\n      \"    print \\\"%s!= %s\\\" % (x,np.math.factorial(x))\\n\",\n      \"\\n\",\n      \"def factorial2(x):\\n\",\n      \"    if type(x) == int:\\n\",\n      \"        if x >= 0:\\n\",\n      \"            print np.prod(np.arange(1,x+1))\\n\",\n      \"        else:\\n\",\n      \"            print \\\"ERROR: Number must be positive\\\"\\n\",\n      \"    else:\\n\",\n      \"        print \\\"ERROR: Only interger is allowed\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Now we will test it using a code cell\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"factorial(3)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"3!= 6\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Using interact function\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will link that to a slider to make the x a variable that we can control.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"i = interact(factorial, x=(0,100))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"100!= 93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Controlling a Chart\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"#This function plot x, y and adds a title\\n\",\n      \"def plt_arrays(x, y, title=\\\"\\\", color=\\\"red\\\", linestyle=\\\"dashed\\\", linewidth=2):\\n\",\n      \"    fig = plt.figure()\\n\",\n      \"    axes = fig.add_subplot(111)\\n\",\n      \"    axes.plot(x,y, color=color, linestyle=linestyle, linewidth=linewidth)\\n\",\n      \"    axes.set_title(title)\\n\",\n      \"    axes.grid()\\n\",\n      \"    plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will define a function that return the following:\\n\",\n      \"\\n\",\n      \"$f(x) = ax^3 + bx^2 + cx + d$\\n\",\n      \"\\n\",\n      \"where a,b,c and d are are constants.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def f(a, b, c, d, **kwargs):\\n\",\n      \"    x=np.linspace(-10, 10, 20)\\n\",\n      \"    y = a*(x**3) + b*(x**2) + c*x + d\\n\",\n      \"    \\n\",\n      \"    title=\\\"$f(x) = (%s)x^{3} + (%s)x^{2} + (%s)x + (%s)$\\\" % (a,b,c,d)\\n\",\n      \"    \\n\",\n      \"    plt_arrays(x,y, title=title, **kwargs)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"#Define Constants\\n\",\n      \"a=0.25\\n\",\n      \"b=2\\n\",\n      \"c=-4\\n\",\n      \"d=0\\n\",\n      \"\\n\",\n      \"f(a, b, c, d)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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urSBZYuhX374KKLWrwYFQARkfbqggtC+rr6AEREIoT6AEREJCAqAA6g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\\nBUBExKFUAEREHOr/Awq5PBBPvRmmAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f0eae068590>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"i = interact(f,\\n\",\n      \"             a=(-10.,10),\\n\",\n      \"             b=(-10.,10),\\n\",\n      \"             c=(-10.,10),\\n\",\n      \"             d=(-10.,10),\\n\",\n      \"             color = [\\\"red\\\", \\\"blue\\\", \\\"green\\\"],\\n\",\n      \"             linestyle=[\\\"solid\\\", \\\"dashed\\\"],\\n\",\n      \"             linewidth=(1,5)\\n\",\n      \"             )\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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BwcDGtra6xbt07WPm/ePFhbW2P+/PkIDQ1FTk4OQkNDER8fj7FjxyIq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\\nRK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f7b76843050>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Displaying a widget from interact function\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"i.widget\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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BwcDGtra6xbt07WPm/ePFhbW2P+/PkIDQ1FTk4OQkNDER8fj7FjxyIq\\nKgrp6ekICAhAYmKi3NE8PYWSW5GRkbIPCFEfzSe3aD65o2ruVOpI/vLly9i7dy+8vb3h5+cHoPIS\\nyQULFiAoKAg7d+6ERCLBwYOVNzV5eHggKCgIHh4eMDExwdatW2ss5VR5UfQCt7JuoY9DH6UHQAgh\\npHq8P08+pzgHR+4ewcG4gziddBoNjBvgydwnaGzamI+wCCFEp2nkSF5TpIwU7lvckZWfJWsrl5bj\\nxIMTeM/jPR4jI4QQw8DrYw2MBEYY6DyQ1U7PslFd1Ykawg2aT27RfPKH92fXjGo3itV27P4x5Jfm\\n8xANIYQYFt5r8mUVZbBba4fnRc/l1u8buQ+j243mIzRCCNFZeveoYVNjU4xwHyFbtmpohcl+k+Fq\\n7cpjVIQQYhh4PfFaZZz3OJRWlGKU5yj0dewLM2MzvkPSO3QdMrdoPrlF88kfnUjyPdv0RM82PfkO\\ngxBCDA7vNXlCCCHK07uaPCGEEM3R+SRfIa3gOwS9QNchc4vmk1s0n/zRiZr8m54VPsPvCb/jYNxB\\nNDFrgvDR4XyHRAghekmnavJPC55i3OFxOJt0FhVM5RG8qZEpsj/PhlUjKz7CJIQQnaLXNXnrxta4\\n8+SOLMEDQJm0jL7/lRBC6kinkryRwAjve7zPaj8YT8+yqQ3VPLlF88ktmk/+6FSSB4BRnuxn2ZxJ\\nOoPnhc8V9CaEEFITnarJA5WPH5aslyA199+vD2zZuCV+H/U7etj30GaIhBCic/TqefKKGAmMEOQZ\\nhN2xuzHCfQRGeY5CL0kvmBjpXKiEEKLzdO5IHgBeFb9CY9PGMDU21XJU+oueDcItmk9u0XxyR++P\\n5AGgWcNmfIdACCEGQSeP5AkhhCim19fJE0II4ZbeJPms/CxsidpCN0ZVg65D5hbNJ7doPvmjkzX5\\nKi+LXuJA3AEciDuA8ynnwYCBv70/hrkN4zs0QgjRCzpdk7+VdQu+23zlt4MAqbNSIWoq0mR4hBCi\\nkwyqJu8t9GZ91ysDBr8l/MZTRIQQol90OskLBAIEeQSx2g/EHeAhGt1GNU9u0Xxyi+aTPzqd5AFg\\nVDv2s2z+Tv0bqa9SFfQmhBDyOp2uyQMAwzDw3OqJhGcJEDYR4j2P9zDKcxS623eHkUDnf0cRQgin\\nNFKTnzRpEoRCIby8vGRty5Ytg1gshp+fH/z8/HDy5EnZupCQELi4uMDNzQ2nTp1SIXw2gUCArwK/\\nwl8f/oX02enYPHAz/Nv4U4InhBAlKJUpJ06ciIiICLk2gUCA2bNnIyYmBjExMRgwYAAAID4+HgcO\\nHEB8fDwiIiIwffp0SKVStYIc5DoIfRz6wNjIWK3XMWRU8+QWzSe3aD75o1SS9/f3h5UV++v3FP3J\\nEB4ejjFjxsDU1BQSiQTOzs6IiopSP1JCCCEqU6vmsWnTJvj4+GDy5MnIyckBAGRkZEAsFsv6iMVi\\npKenqxclqRU94Y9bNJ/covnkT52T/LRp05CcnIzY2FjY2dlhzpw51fYVCAR13U2N0nLT8KzwmUZe\\nmxBCDEGdH2tgY2Mj+/8pU6Zg8ODBAACRSITU1H8vb0xLS4NIpPju1AkTJkAikQAALC0t4evrK/uN\\nX1XDe3PZtYMrfo3/Fd//9j3uPLmDkCkhWNBjQbX968vy+vXrlZo/WlZumeaT22Waz7ovR0ZGYvfu\\n3QAgy5eqUPoSypSUFAwePBi3b98GAGRmZsLOzg4AsG7dOly/fh2//PIL4uPjMXbsWERFRSE9PR0B\\nAQFITExkHc3X5VHDP936CcFHgsHg3+38bP0QPTVapdcxRJH0pQycovnkFs0nd1TNnUol+TFjxuD8\\n+fN49uwZhEIhli9fjsjISMTGxkIgEMDBwQHbtm2DUCgEAKxZswa7du2CiYkJNmzYgP79+6sdKAA8\\nfPEQzpucWe33P7kPF2sXlV6LEEL0kUaSvCbU9UtDOm7viJuZN+XaVvZZiS96fsFVaIQQorMM6gFl\\niozyZD/m4GDcQR4i0S1VNTzCDZpPbtF88kfvkvz7nu/LLYssRAhwDEBZRRlPERFCiO7Su3INAAw/\\nMBz2Te0R5BmEbq270SMOCCH1hsHX5AkhpD4z+Jo8UYxqntyi+eQWzSd/KMkTQogBM6hyTUl5CV6V\\nvIJNE5vaOxNCiB6ql+Wa9Nx0fPnXl7Bfb49Zf87iOxxCCNEZep3ks/OzMerXUZBskGDVxVV4UvAE\\nh+IOISs/i+/QtI5qntyi+eQWzSd/9DrJN2vYDOeSz6FcWi5rK5OWYduNbTxGRQghukPva/Jf/PUF\\nVl9cLddma26LRzMfwczYTO3XJ4QQXVLvavIfd/wYxgL5rwXMys/Cb/G/8RQRIYToDr1P8uKmYoxw\\nHyHX1tehL0RNFT/D3lBRzZNbNJ/covnkT52/NESXfNr5U5x4cAIf+nyITzp/Ao+WHnyHRAghOkHv\\na/JA5ReK55XmoWmDppy8HiGE6Cp6dg0hhBiwenfilVSimie3aD65RfPJH4NP8i+LXspdR08IIfWJ\\nwZZrbmffxuaozdh7ey9+HPYj3vN4T2P7IoQQban3NfkrqVew6K9FiEyJlLX52/vjwsQLnO+LEEK0\\nrd7X5IvKi+QSPABcfHwRt7Ju8ROQllDNk1s0n9yi+eSPwSX5PpI+8GzpyWrfFLWJh2gIIYRfBleu\\nAYDvbnyHacenybU1NGmItFlpsG5srZF9EkKINtT7cg0AjPMeh2YNmsmWTYxMMNxtOArKCniMihBC\\ntM8gk7y5mTkm+02GrbktlvZaisczH+OXkb/Avpk936FpDNU8uUXzyS2aT/4YxLNrFFnSawlCAkLo\\nccOEkHrNIGvyhBBiqKgmTwghREapJD9p0iQIhUJ4eXnJ2l68eIHAwEC4urqiX79+yMnJka0LCQmB\\ni4sL3NzccOrUKe6jVtOr4ld8h8A5qnlyi+aTWzSf/FEqyU+cOBERERFybaGhoQgMDMT9+/fRt29f\\nhIaGAgDi4+Nx4MABxMfHIyIiAtOnT4dUKuU+chUxDIO/kv/C8APDYb/eHjnFObVvRAghek6pJO/v\\n7w8rKyu5tqNHjyI4OBgAEBwcjCNHjgAAwsPDMWbMGJiamkIikcDZ2RlRUVEch62aPbf2wOtbL/Td\\n0xdH7h5Bbkkufoj5gdeYuNa7d2++QzAoNJ/covnkT51r8tnZ2RAKhQAAoVCI7OxsAEBGRgbEYrGs\\nn1gsRnp6upphqudGxg3EPY2Ta9tyfQukDP9/YRBCiCZxcuJVIBBAIBDUuJ5Pn3T+hNX28OVDnHxw\\nkodoNINqntyi+eQWzSd/6nydvFAoRFZWFmxtbZGZmQkbGxsAgEgkQmpqqqxfWloaRCLFX6o9YcIE\\nSCQSAIClpSV8fX1lf9ZVfSi4WHa1dkWn0k64nn4dcPjfzpOB5T8ux7ur3+V8f3wsx8bG6lQ8+r5M\\n88ntMs1n3ZcjIyOxe/duAJDlS1UofZ18SkoKBg8ejNu3bwMA5s2bB2tra8yfPx+hoaHIyclBaGgo\\n4uPjMXbsWERFRSE9PR0BAQFITExkHc1r+zr54/ePY9C+QbLlpg2aYpLvJKztvxZGArqSlBCiH1TN\\nnUodyY8ZMwbnz5/Hs2fP0Lp1a6xYsQILFixAUFAQdu7cCYlEgoMHDwIAPDw8EBQUBA8PD5iYmGDr\\n1q28l2sAYIDLADhZOcHM2AyfdP4E473Hw6KBBd9hEUKIRtWrO17Tc9PRyqKVTvzS4VpkZKTsTz2i\\nPppPbtF8ckcjR/KGQtRU8bkBQggxVPXqSJ4QQvQdPbumjgrLCvkOgRBCOFevk3xZRRn239mPt3a+\\nhaBDQXyHo5aqS64IN2g+uUXzyZ96VZOvUlRWhP/+/V98d+M7ZOZnAgAEEODhi4dwau7Ec3SEEMKd\\nelmTr5BWwHmTM1JyUuTaZ3WdhW/6f8NLTIQQogyqySvB2MgY0ztOZ7XvitmFl0UveYiIEEI0o14m\\neQCY3H4yGpk0kmt7VfIKqy+u5iki9VDNk1s0n9yi+eRPvU3yzRs1x3jv8XJtXcVdMcJ9BE8REUII\\n9+plTb5KWm4aXDe5QmguRGjfUAR5Bhnk3bCEEMOhau6s10keAC49voSOrTqioUlDvkMhhJBa0YlX\\nFfWw72EQCZ5qntyi+eQWzSd/6n2SJ4QQQ1bvyzXVeVLwBBuubsCSXkvQwKQB3+EQQggAegql2orL\\ni7H+6nqsubgGeaV5sGpkhc/f+pzvsAghpE6oXPOac8nn0HZzWyw8uxB5pXkAgFUXVuFZ4TOeI6sd\\n1Ty5RfPJLZpP/lCSf03LJi2Rlpsm1/aq5BVWnF/BU0SEEKIeqsm/4f/++D98H/29XJuJkQnipsfB\\n1dqVp6gIIaQSXUKpphV9VsDczFyurUJagTNJZ3iKiBBC6o6S/BtszW0xv/t82XKAYwBipsZgeif2\\nA810CdU8uUXzyS2aT/7Q1TUKzO42GxceXcDMrjMxwHkAPeqAEKK3qCZPCCF6hGryhBBCZCjJ18Hz\\nwud8h8BCNU9u0Xxyi+aTP5TkVZD6KhUfHv4QjhsdkZ2fzXc4hBBSK6rJKyGvJA+hl0LxzdVvUFxe\\nDACY2mEqvhv0Hc+REULqG3qevAbMPz0fX/39lVybkcAI/3z8DzxtPHmKihBSH9GJVw34/K3P0bRB\\nU7k2KSPFvDPzeIqIjWqe3KL55BbNJ3/UTvISiQTe3t7w8/ND586dAQAvXrxAYGAgXF1d0a9fP+Tk\\n5KgdKJ9aNmmJxf6LWe0nHpygO2EJITpN7XKNg4MDbt68iebNm8va5s2bhxYtWmDevHkICwvDy5cv\\nERoaKr9jPSrXAJWPIHbb7IZHrx4BqHyezfSO07Gk1xJYN7bmOTpCSH2h9Zq8g4MDbty4AWvrfxOd\\nm5sbzp8/D6FQiKysLPTu3Rt3795VK1BdsO/2Poz9fSyGuQ1DWEAYPbCMEC17+OIh7jy5g9ySXOSV\\n5iGvJA+5Jbno2aYn+jv3Z/VnGAYf/P4BvGy80FXcFZ1EnVjPptI3Wk/yjo6OaNasGYyNjTF16lR8\\n9NFHsLKywsuXLwFUTnLz5s1ly3UNVBcwDIPrGdfRWdSZ71BYIiMj0bt3b77DMBg0n9yqbT4ZhsGd\\nJ3dwNvksziSdwc4hOyE0F7L6hVwMwaK/FrHa5741F18FfsVqT3yRCJdNLrJlI4ERvGy80KtNL2wY\\nsKFug+GZ1r8Z6vLly7Czs8PTp08RGBgINzc3VkCG8uwXgUCgkwmeEH11OOEwfk34FWeTziK74N97\\nT/5K/gtjvMaw+r95AUSV3JJche1X067KLUsZKW5l30JDk4YK+xeXF6O4vBiWDS2VHYLOUzvJ29nZ\\nAQBatmyJ4cOHIyoqSlamsbW1RWZmJmxsbBRuO2HCBEgkEgCApaUlfH19Zb/tq87G07Jyy1VtuhKP\\nvi9XtelKPPq+XNX25vo/8//EL7d/AZL/18mh8j8/hf8Eu+d2rP4WVhaVHd7onxidiEhz9utfKbii\\nsL/4hVhhPPmt8jFk3xDYv7SHRwsPjBg4Al3FXZF9JxvGRsa8zF9kZCR2794NALJ8qQq1yjWFhYWo\\nqKiAhYUFCgoK0K9fPyxduhRnzpyBtbU15s+fj9DQUOTk5Oj9idfaXHx0EZdTL2NBjwV8h0KITigu\\nL8blx5dxJukMPG08Mc57HKvPobhDCPo1iNXeumlrPJr5iFUFOHL3CIYfGM7qP9h1MI6OOcpq77C9\\nA6Izo1nt+0buw+h2o1ntX/z1BVZfXM1qt2pohcuTLsO9pTtrnbZptVyTnZ2N4cMrJ7y8vBwffPAB\\n+vXrh44dOyIoKAg7d+6ERCLBwYMH1dmNTnvw/AHmn5mPw3cPQwAB3nF+B762vlqP4/WjEqI+ms+6\\nSctNw95/9uJM0hlcenwJJRUlAIAOJR0UJvk+Dn0ggAAM5JNWam5qZT3d2kWu3b6ZPQa5DoKFmQWa\\nNmgq+291yXfboG24knoFV9Ku4GraVSTnVB7SdxN3U9j/zfJOFU8bT7i1cFO4TtfRHa9qWHpuKdZc\\nWoNyabmsra9DX5wef1rr5yEoKXGL5rNurqVdQ9edXVntpo9NkbstV2EtvOpou4FxA/Sw74EAxwAE\\nOAbAz9YPxkbGnMaXnZ+N6xnX8a7Lu6yf0QppBSzDLJFfms/a7urkq+gi7sJpLHWl9ROv9ZllQ0u5\\nBA8AZ5NLiJk/AAAWKElEQVTP4tj9YxjcdrBWY6GExC2az7rp0KoDmjVohlclr+Tay+zL8Hfq33jb\\n4W3WNqv6rIKZsRneav0WGpk20mh8QnMhBrkOUrguKz8LrSxa4f7z+3LtQZ5B1Sb4H2N/hKOVI/zb\\n+HMeK1foSF4NpRWl8NjigYcvH8q12zSxQfT/RUPUVMRTZIRoRoW0AicenMDhu4exY8gOGAnYN80P\\nPzAcR+4eYbUv7LEQa/qu0UaYanle+BxR6VG4klZZ5tk0YJPCUk1WfhacNzqjoKwAQ9sORVhAGNq2\\naKvx+OjZNVpkZmyGsIAwVnvHVh01fkTypqqz8YQbNJ/ynhU+Q9ilMDhtdMKQ/UPwQ+wPOPXwlMK+\\nfR36yv7fpokNxnqNxbxW83T+e5KrWDe2xgCXAVjRZwVOjz9dbS1+WeQyFJQVAADC74XDc6snph+f\\njicFT7QZbq2oXKOmEe4jEOAYgDNJZyCAACv7rMRC/4UKj3AI0Ucrz6/E6ourZSdRq2y5vgXvOL/D\\n6j/IdRDKpeUIcAyAZ0tPCAQCREZGQtxUrK2QNS7haQJ2RO+Qa6tgKvDtjW/xtPApDr1/iKfI2Khc\\nw4Gs/Cz039sfa/utRYBjAN/hEMKpHdE78NEfH7HaBRDg4WcP4WDlwENU/JpxcgY2Rm1ktWvjEeRU\\nruGBrbktYqbGUIInek3RVSUAMNZrrMI7QBkwlTcy1UPf9P8GPwz9Aa0sWsm1T/KdpHPfMUFJniM1\\nlWdKyktQWFao0f1TDZlb9Wk+YzJjMGz/MDhvdJZ989nrGps2xkTfibJlAQR41+VdnBh7Agv9Fyq1\\nD0ObT2MjY0zwnYAHnz7Aqj6rYGFmgcamjbGizwqF/ZNfJmPQL4NwK+uWliOlJK9xqa9S0Wt3L3z0\\nx0cGU54ihuHOkzsYeXAk2m9vj/B74cguyMahOMW15Gkdp8G6kTXmvjUXiZ8l4tjYYxjgMqDen3tq\\nbNoYi3suRuJniTj43kHYWdgp7Lf4r8U4/uA4/Lb5IfRSqFZzAdXkNej0w9MY89sYPC96DgDYMnCL\\n3lxhQAzbnD/nYN3Vdaw7TbuIuuDqFMV3fZZVlMHU2FQb4RmUGxk30On7TnJtc9+ai7CAsDrdNEk1\\neR3x38v/Rf+9/WUJHgBmRszEtbRrPEZFSCXn5s6sBA8A19Kv4WbGTYXbUIJXHcMw+PzU56z2//79\\nX0w7Pg0V0gqNx0BJXkPsLOxYP0Rl0jK8f+h9PCt8xvn+DK3myTdDn8/J7SejTbM2rPbWTVvLPfKX\\nK4Y+n9V5UvAEqbmpCtdFJEbIHQRqCiV5DRnnPQ7TOk5jtafmpmLbjW08RETqm8y8TCw9txRlFWWs\\ndWbGZljSa4lsWWQhwrfvfovEzxIx0GWgNsM0aEJzIRL+k4BPO38q125rboszH56BTRPFj2HnEtXk\\nNaikvAT+P/jjesZ1AJVX4IT0DcHct+YazBepEN3ztOApwi6HYcv1LSguL8aOwTswuf1kVr9yaTn6\\n/NgH77m/h6kdp1b7RRpEfQzDYGnkUqy8sBJWDa1wfsJ5eAm96vRaWv/6v7qqD0keAB7lPEL77e1h\\namSK/e/tR29Jb75DIgbqRdELfP3319h4baPsdnug8vG89z+5jwYmDVjbMAxDBxxatOHqBnRr3U2t\\nb5ijE686po1lGxwdfRTRU6M1muDra81TU/RxPs8mnUXIpRC5BA8Aj189xs6YnQq30VaC18f51IQZ\\nXWdUm+AZhsG6K+uQlZ/F6T4pyWtBd/vurDvjCOHaSI+R8BZ6K1y38drGevGXsz5bfXE1Zp+aDf8f\\n/PEo5xFnr0vlGp49ynmEQ/GH8Plb7MusCFFEykjBMIzCL9QIvxuOYQeGyZYbmTTCp50/xdzuc9Gi\\ncQtthklUsPHaRsyImCFbFjcVV/sETCrX6JGTD06i/fb2mHt6Lvbc2sN3OEQPnEk6g47bO2L7ze0K\\n1w9pOwQdW3VEA+MGmNFlBpJmJCEsMIwSvA7bc2uPXIIHKr9GsecPPRGTGaP261OS50GFtAJLzi3B\\nu7+8ixdFLwAAHx/7GLezb9f5NanmyS1dm89/sv/BgJ8HIPCnQMRkxWDZ+WXIK8lj9RMIBNg5ZCcS\\nP0vE+nfWw9bclodo2XRtPnWJWws3NG/UnNX+tPApPj7+sdoVD0ryPPjj/h9YeWGl3M1SReVFGHlw\\nJF4Vv6phS1LfFJQWYGL4RPh+54uIxAhZ+5OCJ/j6768VbuMt9DaoZ7cbus6izjg/4TzszOWfe2Pf\\nzB6/vv+r2ifHqSbPA4ZhMP7wePx8+2fWuhHuIzh5Y4lhYBgGXXZ0kd1r8brGpo3x4NMHdFLfQDx8\\n8RABPwUgJScFwiZCXJx4ES7WLqx+VJPXAwKBANsGbYNnS/nnThsLjNG9dXeeoiK6SCAQ4Ot+io/Y\\nPVp64GXRSy1HRDTFqbkTLk28hO6tu+PU+FMKE3xdUJLnSROzJvgt6DdYmFkAAOzM7XAu+Bxmd5td\\np6N4qnlyS9vzyTAM7j+/r3BdzzY9MaTtENmyg6UD9o/cj2tTruncF1RUhz6fyhE1FeHixIvVXgpb\\nl+oHJXketW3RFruG7kJvSW9ET42Gfxt/vkMiPLj0+BK67eyGTt93qvbhdaF9Q2HTxAbr+69Hwn8S\\nMKrdqHr/LHdDVd1BHsMwWHBmgeqvRzV5/kkZabU/sHTbueG69+weFpxdgCN3j8jaZnSZgfXvrFfY\\nv6S8ROGjCUj9sObiGiz+azGwTLUjejoU0AE1JfhJRydh7d9r6Reigdl2Yxs8t3rKJXgA2Hp9Kx6+\\neKhwG0rw9dfmqM2VCb4OKMnrsFUXVmF37G58fvpzTDs+DeXS8mr7Us2TW5qezx72PSBlpKz2MmkZ\\nFv21SKP75gN9PuuOYRjEZsUCAKZ2mKry9pTkddTP//yMJZH/Pu97281tGPTLIOSW5PIYFeGKp40n\\nxnmPY7V72Xhhku8kHiIiukogEGD74O34afhP2PruVtW3p5q87rmVdQudd3RGaUUpa52XjRcixkXQ\\ntdF6oKisCFuub8Ewt2Fwbu7MWp/8MhltN7dFmbQMIgsRVr29CuO9xyt8Jg0hVXTmOvmIiAi4ubnB\\nxcUFYWFhmtqNQWpn0w4fd/hY4TpTY1M0bdBUyxERVZRVlOG7G9/BeZMz5p6ei6WRSxX2c7BywOdv\\nfY41b6/B/U/vY4LvBErwhHMaOZKvqKhA27ZtcebMGYhEInTq1An79u2Du7v7vzumI/labby2EbP+\\nnCWr3YqbinFtyjWFR/GRkZHo3bu3liM0XHWZzwppBfbf2Y8lkUuQ9DJJ1i6AALEfx1Z77XN9QJ9P\\n7ujEkXxUVBScnZ0hkUhgamqK0aNHIzw8XBO7MmifdfkM4aPD0cS0CczNzHFszDEq0+iwzPxMTDo6\\nSS7BAwADBl+e+5KnqEh9p5Ekn56ejtatW8uWxWIx0tPTNbErgzfIdRAuTryI34J+g4+tj8I+RWVF\\n6NGzh5YjM2x1OeoUNxUr/PJ2ADh2/xgr+dcndBTPH40kebp5h1t+dn7o59RP4TopI8UHv3+AwfsG\\n05U3WlRSXqKwfZH/IjQxbSLX9p7He7gz7Q4crRy1ERohckw08aIikQipqamy5dTUVIjF7EefTpgw\\nARKJBABgaWkJX19f2W/8qutqabnm5eOlx3H47mHgCuB30w+RSyPRullrnYlPX5fXr1/P+jwyDAOB\\ngwBrr6zFkztPEBYYpnD7WV1nYdWeVegk6oRv//MtOrTqgMjISGQjW2fGpwvzqUvx6fJyZGQkdu/e\\nDQCyfKkKjZx4LS8vR9u2bXH27Fm0atUKnTt3phOvGrDtxjZ8fPx/V+EkA3CofNDZsbHH0N6uPa+x\\n6bvI104UllaUYv+d/Vh3dZ3sphQAOD/hPHq26cna9lXxK8RmxaKXpJe2wtV5r88nUY+quVNj18mf\\nPHkSM2fOREVFBSZPnoyFCxfK75iSvFrSctPgtNFJ4bX0jU0b41zwuWq/FZ6oJmBPAM4mn2W1d2/d\\nHRcnXqTyJNEqnUnyte6Ykrzajt47ijG/jUFhWaFce/fW3XHmwzNoaNKQp8gMy47oHfjoj48Urjs+\\n9jgGugzUckSkPtOJSyiJdgxpOwQXJlyo/Nqw5Mo2JysnHBl9hBK8ihiGQUpOimy5qiYKAB94fYCW\\njVuytrFvZo+yijItRKf/Xp9Pol2U5PVch1YdcG3KNTg2d4RVQyuc+OAEWjRuwer3ougF9t3ehwpp\\nBQ9R6q7i8mLsiN4Br2+90GVHFxSXF7P6NDJtJHdpZBdRF+wfuR8PP3uIoW5DtRkuISqjco2ByCvJ\\nQ+KLRPjZ+SlcvzxyOZadXwbn5s5Y0H0BxvuMh5mxmZaj1B3Z+dnYen0rvr3xLZ4WPpW17xqyCxP9\\nJirsP/PPmfis82fo1rqbNkMlRA7V5AlLXkke2qxvg5fF/34fqLipGHPfmosp7aegsWljHqPjxzt7\\n38GfD/9ktXvZeOHWx7foZCrRWVSTr6dqqnluu7lNLsEDlVfnzIiYgbgncRqOTDd90vkThe23n9zG\\n2eSzVEPmGM0nfyjJG7iS8hKsvbJW4bp+Tv3QSdRJyxFpXm5JLg7FHcKHhz9E+23tFR71DHQZCFdr\\nV1a7v70/Gpk00kaYhGgFlWvqgYuPLmL1xdWs8kRkcKTCG3ay87MhZaSws7DTVohqYxgGW65vQfi9\\ncJxPOY8y6b9XvcRMjYGvrS9rm63Xt+I/J/4DEyMTjPIchVldZ6FDqw7aDJsQlVG5hrD4t/FHxLgI\\nXP/oOoa5DQNQeS29ors1AWDF+RVw2OCA6ceny11WqMsEAgF+vv0zziSdkUvwAPDHvT8UbhPsE4wv\\ne36JlBkp2DtiLyV4YpDoSN5AqHLbeNyTOJRUlCh89EFmXiYcNjigpOLfB3B5C70xynMUFvnz+92j\\nuSW5+DPxT7i1cIOX0Iu1fvWF1fji3Bes9k6tOiHqoyiV9kW34XOL5pM7quZOjTygjOg2TxvPatd9\\nc+UbuQQPAP9k/4MerRU/yvjS40s4+eAk3Fu6w72FO9xauKGJWROFfVX1vPA55p6ei4y8DGTmZyLh\\naQLKpGWY0WUG1r+zntV/cNvBCpN8/NN4vCp+hWYNm3ESFyH6hI7kiczzwudos74NCsoKWOs2Ddik\\n8IqUJeeWYOWFlXJtbZq1wbzu8zC903S59uLyYmy/uR2ZeZnIyM+o/G9eBkorSnH/0/us184tyUWz\\nUHZidrRyROKniazLHBmGgWSDBI9fPYbIQoRBroMw2HUw3nZ4G41M6WQqMQx0JE/qzNjIGJ91+Qxb\\nrm9hPZveo6WHwm0SniWw2h69eqSwr5HACDMiZihcV1pRyro5y8LMAk1Mm7B+6SS9TMLdZ3fh3tJd\\nrl0gEODbd7+Frbkt/Gz96Fp3QkAnXg0GF9chWza0xJq+a/Bo5iOs6rMKIguRbJ17C3eF2yQ8ZSf5\\n6vqbGZspfAYMAGTlZ7HaBAJBtVf4/HFf8cnUgS4D0d6uvdoJnq7r5hbNJ38oyRMWy4aWWNxzMVJn\\npSJ9djrOfngWtua2rH7l0nLcf84uswBgHWVXqS5pZ+ZlKmx/8zttW1m0wtQOU9HDnr7ukBBlUE2e\\n1FlxeTF+/udnJDxLQPzTeCQ8S0BKTgqsGlrh+bznCo+mB/w8ABGJEaz234N+x3D34az20w9Po6i8\\nCHbmdmhl0QqtLFpRGYbUa/TsGsKrwrJCpOWmKbybFAD23NqDpJdJaGXRSpa47SzsYNPEBiZGdIqI\\nkNpQkq+n6DpkbtF8covmkzt0xyshhBAZOpInhBA9QkfyhBBCZCjJGwi6DplbNJ/covnkDyV5Qggx\\nYFSTJ4QQPUI1eUIIITKU5A0E1Ty5RfPJLZpP/lCSJ4QQA0Y1eUII0SNUkyeEECJT5yS/bNkyiMVi\\n+Pn5wc/PDydPnpStCwkJgYuLC9zc3HDq1ClOAiU1o5ont2g+uUXzyZ86J3mBQIDZs2cjJiYGMTEx\\nGDBgAAAgPj4eBw4cQHx8PCIiIjB9+nRIpVLOAiaKxcbG8h2CQaH55BbNJ3/UKtcoqguFh4djzJgx\\nMDU1hUQigbOzM6KiotTZDVFCTk4O3yEYFJpPbtF88ketJL9p0yb4+Phg8uTJsjcxIyMDYrFY1kcs\\nFiM9PV29KAkhhNRJjUk+MDAQXl5erH9Hjx7FtGnTkJycjNjYWNjZ2WHOnDnVvg59k4/mpaSk8B2C\\nQaH55BbNJ48YDiQnJzPt2rVjGIZhQkJCmJCQENm6/v37M1evXmVt4+TkxACgf/SP/tE/+qfCPycn\\nJ5Xyc52/by0zMxN2dpVfynz48GF4eXkBAIYMGYKxY8di9uzZSE9Px4MHD9C5c2fW9omJiXXdNSGE\\nECXVOcnPnz8fsbGxEAgEcHBwwLZt2wAAHh4eCAoKgoeHB0xMTLB161Yq1xBCCE94u+OVEEKI5mn9\\njtdDhw7B09MTxsbGiI6OlltHN1Gp580b1CIiIvgOSe9ERETAzc0NLi4uCAsL4zscvSeRSODt7Q0/\\nPz+FZVtSs0mTJkEoFMrK4QDw4sULBAYGwtXVFf369av98lR1TrjWRUJCAnPv3j2md+/ezM2bN2Xt\\ncXFxjI+PD1NaWsokJyczTk5OTEVFhbbD02vLli1j1q5dy3cYequ8vJxxcnJikpOTmdLSUsbHx4eJ\\nj4/nOyy9JpFImOfPn/Mdht66cOECEx0dLbuwhWEYZu7cuUxYWBjDMAwTGhrKzJ8/v8bX0PqRvJub\\nG1xdXVntdBMVNxiqvtVZVFQUnJ2dIZFIYGpqitGjRyM8PJzvsPQefSbrzt/fH1ZWVnJtR48eRXBw\\nMAAgODgYR44cqfE1dOYBZXQTFTcU3aBGlJOeno7WrVvLlukzqD6BQICAgAB07NgR33//Pd/hGITs\\n7GwIhUIAgFAoRHZ2do3963x1TU0CAwORlZXFal+zZg0GDx6s9OvQVTls1c3t6tWrMW3aNCxZsgQA\\n8OWXX2LOnDnYuXOntkPUW/R5497ly5dhZ2eHp0+fIjAwEG5ubvD39+c7LIMhEAhq/dxqJMmfPn1a\\n5W1EIhFSU1Nly2lpaRCJRFyGZRCUndspU6ao9AuVsD+Dqampcn9dEtVV3UvTsmVLDB8+HFFRUZTk\\n1SQUCpGVlQVbW1tkZmbCxsamxv68lmter9UNGTIE+/fvR2lpKZKTk6u9iYpULzMzU/b/r9+gRpTT\\nsWNHPHjwACkpKSgtLcWBAwcwZMgQvsPSW4WFhcjLywMAFBQU4NSpU/SZ5MCQIUPw448/AgB+/PFH\\nDBs2rOYNNHdeWLHff/+dEYvFTMOGDRmhUMi88847snWrV69mnJycmLZt2zIRERHaDk3vjR8/nvHy\\n8mK8vb2ZoUOHMllZWXyHpHdOnDjBuLq6Mk5OTsyaNWv4DkevJSUlMT4+PoyPjw/j6elJ81kHo0eP\\nZuzs7BhTU1NGLBYzu3btYp4/f8707duXcXFxYQIDA5mXL1/W+Bp0MxQhhBgwnbm6hhBCCPcoyRNC\\niAGjJE8IIQaMkjwhhBgwSvKEEGLAKMkTQogBoyRPCCEGjJI8IYQYsP8HMhhen0Sb4bYAAAAASUVO\\nRK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f7b76831390>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/7 - Pandas.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:70ccda0f1b2566f84d3e72be919a30bf72241f1bdc3a696822a3894649cb1f78\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Pandas is powerful and easy-to-use library for data analysis. Is has two main object to represents data: Series and DataFrame.\\n\",\n      \"\\n\",\n      \"Finding Help:\\n\",\n      \"\\n\",\n      \"- http://pandas.pydata.org/pandas-docs/stable/10min.html\\n\",\n      \"- http://pandas.pydata.org/pandas-docs/stable/tutorials.html\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"<table>\\n\",\n      \"<tr>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/numpylogo_med.png\\\"  style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>NumPy</h4> Base N-dimensional array package </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/scipy_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/matplotlib_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\\n\",\n      \"</tr>\\n\",\n      \"<tr>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/ipython.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>IPython</h4> Enhanced Interactive Console </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/sympy_logo.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>SymPy</h4> Symbolic mathematics </td>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><img src=\\\"http://www.scipy.org/_static/images/pandas_badge2.jpg\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><h4>Pandas</h4> Data structures & analysis </td>\\n\",\n      \"</tr>\\n\",\n      \"</table>\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"import pandas as pd\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Working with Series\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Series is an array like object.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"pd.Series(self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x = pd.Series([1,2,3,4,5])\\n\",\n      \"x\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 2,\n       \"text\": [\n        \"0    1\\n\",\n        \"1    2\\n\",\n        \"2    3\\n\",\n        \"3    4\\n\",\n        \"4    5\\n\",\n        \"dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Notice that generated an index for your item\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Basic Operation\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x + 100\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 3,\n       \"text\": [\n        \"0    101\\n\",\n        \"1    102\\n\",\n        \"2    103\\n\",\n        \"3    104\\n\",\n        \"4    105\\n\",\n        \"dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"(x ** 2) + 100\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"0    101\\n\",\n        \"1    104\\n\",\n        \"2    109\\n\",\n        \"3    116\\n\",\n        \"4    125\\n\",\n        \"dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x > 2\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 5,\n       \"text\": [\n        \"0    False\\n\",\n        \"1    False\\n\",\n        \"2     True\\n\",\n        \"3     True\\n\",\n        \"4     True\\n\",\n        \"dtype: bool\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`any()` and `all()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"larger_than_2 = x > 2\\n\",\n      \"larger_than_2\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"0    False\\n\",\n        \"1    False\\n\",\n        \"2     True\\n\",\n        \"3     True\\n\",\n        \"4     True\\n\",\n        \"dtype: bool\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"larger_than_2.any()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 7,\n       \"text\": [\n        \"True\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"larger_than_2.all()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 8,\n       \"text\": [\n        \"False\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`apply()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def f(x):\\n\",\n      \"    if x % 2 == 0:\\n\",\n      \"        return x * 2\\n\",\n      \"    else:\\n\",\n      \"        return x * 3\\n\",\n      \"\\n\",\n      \"x.apply(f)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"0     3\\n\",\n        \"1     4\\n\",\n        \"2     9\\n\",\n        \"3     8\\n\",\n        \"4    15\\n\",\n        \"dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"**Avoid looping over your data**\\n\",\n      \"\\n\",\n      \"This is a `%%timeit` results from `apply()` and a for loop.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%timeit\\n\",\n      \"\\n\",\n      \"ds = pd.Series(range(10000))\\n\",\n      \"\\n\",\n      \"for counter in range(len(ds)):\\n\",\n      \"    ds[counter] = f(ds[counter])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1 loops, best of 3: 241 ms per loop\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%timeit\\n\",\n      \"\\n\",\n      \"ds = pd.Series(range(10000))\\n\",\n      \"\\n\",\n      \"ds = ds.apply(f)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"10 loops, best of 3: 40 ms per loop\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`astype()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x.astype(np.float64)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"0    1\\n\",\n        \"1    2\\n\",\n        \"2    3\\n\",\n        \"3    4\\n\",\n        \"4    5\\n\",\n        \"dtype: float64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`copy()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y = x\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y[0]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 14,\n       \"text\": [\n        \"1\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y[0] = 100\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 16,\n       \"text\": [\n        \"0    100\\n\",\n        \"1      2\\n\",\n        \"2      3\\n\",\n        \"3      4\\n\",\n        \"4      5\\n\",\n        \"dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 17,\n       \"text\": [\n        \"0    100\\n\",\n        \"1      2\\n\",\n        \"2      3\\n\",\n        \"3      4\\n\",\n        \"4      5\\n\",\n        \"dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"**Avoid using copy (is you can) to save memory**\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y = x.copy()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x[0]=1\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 20,\n       \"text\": [\n        \"0    1\\n\",\n        \"1    2\\n\",\n        \"2    3\\n\",\n        \"3    4\\n\",\n        \"4    5\\n\",\n        \"dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n  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3.000000\\n\",\n        \"75%      4.000000\\n\",\n        \"max      5.000000\\n\",\n        \"dtype: float64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"DataFrame\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"pd.DataFrame(self, data=None, index=None, columns=None, dtype=None, copy=False)\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"data = [1,2,3,4,5,6,7,8,9]\\n\",\n      \"df = pd.DataFrame(data, columns=[\\\"x\\\"])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": 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class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>x</th>\\n\",\n        \"      <th>x_plus_2</th>\\n\",\n        \"      <th>x_square</th>\\n\",\n        \"      <th>x_factorial</th>\\n\",\n        \"      <th>is_even</th>\\n\",\n        \"      <th>odd_even</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0</th>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>  3</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>      1</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1</th>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"      <td>      2</td>\\n\",\n        \"   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     \"6  7         9        49         5040        1      odd\\n\",\n        \"7  8        10        64        40320        0     even\\n\",\n        \"8  9        11        81       362880        1      odd\\n\",\n        \"\\n\",\n        \"[9 rows x 6 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 30\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"`drop()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df = df.drop(\\\"is_even\\\", 1)\\n\",\n      \"df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      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  \"      <td> 7</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"      <td> 49</td>\\n\",\n        \"      <td>   5040</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7</th>\\n\",\n        \"      <td> 8</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"      <td> 64</td>\\n\",\n        \"      <td>  40320</td>\\n\",\n        \"      <td> even</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8</th>\\n\",\n        \"      <td> 9</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"      <td> 81</td>\\n\",\n        \"      <td> 362880</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>9 rows \\u00d7 5 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 31,\n       \"text\": [\n        \"   x  x_plus_2  x_square  x_factorial odd_even\\n\",\n        \"0  1         3         1            1      odd\\n\",\n        \"1  2         4         4            2     even\\n\",\n        \"2  3         5         9            6      odd\\n\",\n        \"3  4         6        16           24     even\\n\",\n        \"4  5         7        25          120      odd\\n\",\n        \"5  6         8        36          720     even\\n\",\n        \"6  7         9        49         5040      odd\\n\",\n        \"7  8        10        64        40320     even\\n\",\n        \"8  9        11        81       362880      odd\\n\",\n        \"\\n\",\n        \"[9 rows x 5 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 31\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Multi Column Select\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df[[\\\"x\\\", \\\"odd_even\\\"]]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>x</th>\\n\",\n        \"      <th>odd_even</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0</th>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1</th>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td> even</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2</th>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3</th>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"      <td> even</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4</th>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5</th>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"      <td> even</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6</th>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7</th>\\n\",\n        \"      <td> 8</td>\\n\",\n        \"      <td> even</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8</th>\\n\",\n        \"      <td> 9</td>\\n\",\n        \"      <td>  odd</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>9 rows \\u00d7 2 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 32,\n       \"text\": [\n        \"   x odd_even\\n\",\n        \"0  1      odd\\n\",\n        \"1  2     even\\n\",\n        \"2  3      odd\\n\",\n        \"3  4     even\\n\",\n        \"4  5      odd\\n\",\n        \"5  6     even\\n\",\n        \"6  7      odd\\n\",\n        \"7  8     even\\n\",\n        \"8  9      odd\\n\",\n        \"\\n\",\n        \"[9 rows x 2 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 32\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Controlling display options\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"pd.options.display.max_columns= 60\\n\",\n      \"pd.options.display.max_rows= 6\\n\",\n      \"pd.options.display.notebook_repr_html = False\\n\",\n      \"df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 33,\n       \"text\": [\n        \"   x  x_plus_2  x_square  x_factorial odd_even\\n\",\n        \"0  1         3         1            1      odd\\n\",\n        \"1  2         4         4            2     even\\n\",\n        \"2  3         5         9            6      odd\\n\",\n        \"3  4         6        16           24     even\\n\",\n        \"4  5         7        25          120      odd\\n\",\n        \"5  6         8        36          720     even\\n\",\n        \"  ..       ...       ...          ...      ...\\n\",\n        \"\\n\",\n        \"[9 rows x 5 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 33\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Filtering\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df[df[\\\"odd_even\\\"] == \\\"odd\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 34,\n       \"text\": [\n        \"   x  x_plus_2  x_square  x_factorial odd_even\\n\",\n        \"0  1         3         1            1      odd\\n\",\n        \"2  3         5         9            6      odd\\n\",\n        \"4  5         7        25          120      odd\\n\",\n        \"6  7         9        49         5040      odd\\n\",\n        \"8  9        11        81       362880      odd\\n\",\n        \"\\n\",\n        \"[5 rows x 5 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 34\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df[df.odd_even == \\\"even\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 35,\n       \"text\": [\n        \"   x  x_plus_2  x_square  x_factorial odd_even\\n\",\n        \"1  2         4         4            2     even\\n\",\n        \"3  4         6        16           24     even\\n\",\n        \"5  6         8        36          720     even\\n\",\n        \"7  8        10        64        40320     even\\n\",\n        \"\\n\",\n        \"[4 rows x 5 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 35\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Chaining Filters\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"`|` OR\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df[(df.odd_even == \\\"even\\\") | (df.x_square < 20)]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 36,\n       \"text\": [\n        \"   x  x_plus_2  x_square  x_factorial odd_even\\n\",\n        \"0  1         3         1            1      odd\\n\",\n        \"1  2         4         4            2     even\\n\",\n        \"2  3         5         9            6      odd\\n\",\n        \"3  4         6        16           24     even\\n\",\n        \"5  6         8        36          720     even\\n\",\n        \"7  8        10        64        40320     even\\n\",\n        \"\\n\",\n        \"[6 rows x 5 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 36\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"`&` AND\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df[(df.odd_even == \\\"even\\\") & (df.x_square < 20)]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 37,\n       \"text\": [\n        \"   x  x_plus_2  x_square  x_factorial odd_even\\n\",\n        \"1  2         4         4            2     even\\n\",\n        \"3  4         6        16           24     even\\n\",\n        \"\\n\",\n        \"[2 rows x 5 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 37\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Furter Chaining\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df[(df.odd_even == \\\"even\\\") & (df.x_square < 20)][\\\"x_plus_2\\\"][:1]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 38,\n       \"text\": [\n        \"1    4\\n\",\n        \"Name: x_plus_2, dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 38\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`scatter_matrix()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"pd.scatter_matrix(df, diagonal=\\\"kde\\\", figsize=(10,10));\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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jYPmTEDXnsN1q2DP+20JWVw8iRcdRWsXg1XXOH91ytvv4SlbFQv4irV\\nirhD17R5kdMJr79ufahh86zYWHjuOXjrLdNJREREzFLT5gHz5kFWFtx3n+kkgemPHRJ27zadRERE\\nxBw1bR4weLC1NpsuT/COKlXg2Wd1J6mIiJRvuqatjJYvh0cftdZl85MdtALS8eNw9dXWOniXXea9\\n19F1J+IO1Yu4SrUi7tA1bV4yeDC8+qoaNm+rWtVqjj/+2HQSERERMzTSVgZr11obwu/cCRUrevzw\\n8id790LTprBrF0RHe+c19NewuEP1Iq5SrYg7NNLmBR99BC++qIbNV2rXhltvhS++MJ1ERETE9zTS\\nVkqHD1tbLe3caV0oL76xZo11l+7OnVChguePr7+GxR2qF3GVakXcoZE2D/v8c3joITVsvnbDDdYi\\nuxMmmE4iIiLiW37ZtPXr14+EhAT69u171tcPHTpE27ZtadWqFQsWLABg3759tG3blltuuYUJPvpN\\nnpNjNW19+vjk5eRPXnoJPvjAWtRYRESkvPC7pi01NZXMzEyWLl1KXl4eKSkpxY8NGTKEwYMHM3fu\\nXAYNGgTAO++8w9tvv82iRYv44osvKCws9HrGceMgLs6aHhXfu/12OHECfv7ZdBIRERHf8VrTtnnz\\n5nO+tnjx4gt+3+rVq+nYsSMA7du3Jzk5ufixjRs3Eh8fT0REBJGRkZw6dYrdu3fTuHFjgoKCqFGj\\nBtu3b/fYezgfp9O6AeFPg4DiQ8HB1mK7n31mOkngy8/PZ/nyFaxZs0bX49hUTk4OS5cuY926dfo3\\n9ANHjhxh4cJFHDx40HQUsSGvNW0PPPAA77zzDk6nk6ysLF588UVee+21C35fWloakZGRAERHR5OW\\nllb82JmjaH88Vr9+fRYvXkxWVharVq0iPT3d82/mDMnJ1pZVHTp49WXkAnr1gqlTrUV3xXtmzVrA\\nZ5/t4oMPfiY1NdV0HCmFiRNn8/nnB3nvvWVs2bLFdJxyrbCwkCFDvmHkyFzefvtbcnJyTEcSm/Fa\\n07Z69Wr2799PfHw8LVq04JJLLmHlypUX/L7o6GgyMjIASE9PJyYm5r9hz9gnKiMjg9jYWP7+978z\\nfPhwHnjgAa655hpq1Khx3uMmJSUVf7gy4leS4cPhqae0ZZVp1apBp07w1VemkwS2rKwcgoKicToj\\nyM7WLxg7ysrKISQkBqezkpoEw4qKisjKKiA8vDo5OUU+uZxHAovX1vEPCQmhUqVKZGdnk5OTQ716\\n9c5qukoSHx/PsGHD6Nq1KwsWLKBXr17FjzVu3JhVq1bRqFEjMjIyqFy5MpUrV2by5MlkZ2fTs2dP\\n6tSpc97jJiUllfk9nTxpbVz+7rtlPpR4wHPPwSOPwN/+pibaW+6+uz0wn/Dw6tx4YwvTcaQUHnjg\\ndipVWkjVqrW5/vrrTccp1ypUqMBLL3Vh6dK13Hjj3URERJiOJDbjtXXarr/+ejp16sTAgQM5duwY\\nTz/9NBUrVnTpDs++ffuSmppKXFwc//rXv+jTpw8ff/wxBw8epGfPnmRnZ/PGG2/Qvn17Zs2axXvv\\nvUdwcDBDhw4lLi7u3DfpofVxPvkEVq60bkQQ85xOaNYMBg2ybk7wBK2lJO5QvYirVCvijpLqxWtN\\n288//0zz5s3P+tqoUaPo2bOnN17uL3nih8XphEaNrMatTRsPBZMyGzECZs+GSZM8czydWMUdqhdx\\nlWpF3OHzps2feOKHZeVKeOwx2LoVHA7P5JKyy8iwtrfatg2qVy/78XRiFXeoXsRVqhVxh3ZEKKM/\\nbkBQw+ZfoqKsGxK+/dZ0EhEREe/SSJsLMjLg8sth+3bPjOaIZy1ZAs8/Dxs2lL2p1l/D4g7Vi7hK\\ntSLu0EhbGUycaF3HpobNPyUkWFuLnbF5hoiISMBR0+aCUaPAwP0T4iKHw7recORI00lERES8R9Oj\\nF7B7N7RoAQcPQmioh4OJx+zfD02awIEDUKlS6Y+jKQxxh+pFXKVaEXdoerSUvv0WHnxQDZu/u+wy\\na822yZNNJxEREfEONW1/wem0pkYffdR0EnFFz566i1RERAKXmra/kJwMISHWCI74v3vusdbTO3rU\\ndBIRERHPU9P2F775xhpl09ps9lC5Mtx5J7iwU5qIiIjtqGkrQW6utdRHt26mk4g7HnkExowxnUJE\\nRMTz1LSVYM4caNjQusBd7KNjR2sR5F27TCcxr7CwkJEjv+dvf3uf1NS1puNIGRw4cIABAz5m6NAR\\nZGRkmI4jZTBz5nz69n2PWbPmm44iNqSmrQTffQcPPWQ6hbirQgXo2hXGjTOdxLw9e/awZMkJCgs7\\n8+23C0zHkTKYPXsFhw83Z9266qxdqwbcrjIzM5kw4ScqVHiU77//iczMTNORxGbUtJ1HVhbMmgX3\\n3286iZRGt27WFGl5XxKpevXqxMScIi1tDg0basjYzq6+uhYFBT9RqdJ2Lr30UtNxpJTCwsKoWzeK\\no0enULduFGFhYaYjic1ocd3zmDABRoyAuXO9GEq8pqgI6tWDKVOsBXfdEWgLYGZkZPD7779Tp04d\\ngoODTccJOL6qF6fTyf79+wkLC+Oiiy7y+uuJ5/1RKzk5ORw4cIBatWqpaZMS2Wpx3X79+pGQkEDf\\nvn3P+vqhQ4do27YtrVq1YsECa7pn+/bt3HzzzbRu3ZqBAwd65PXHj9fUqJ0FBcHDD2uKFCAqKoor\\nrrhCDZvNORwOLr/8cjVsASAsLIwrr7xSDZuUit81bampqWRmZrJ06VLy8vJIOWMX8CFDhjB48GDm\\nzp3LoEGDAPjss894++23Wb58OatWrSrzRboZGTBvHtx3X5kOI4Y98IA1YhpAg2YiIlLO+V3Ttnr1\\najp27AhA+/btSU5OLn5s48aNxMfHExERQWRkJKdOnaJatWqkpaVRWFgIQMWKFcv0+tOmQUICxMaW\\n6TBiWJMm1ojbL7+YTiIiIuIZfte0paWlERkZCUB0dDRpaWnFj/3RmP3xWHp6Or1796ZPnz5cc801\\ntGzZssxN2/jx1l6jYm8OB3TpooV2RUQkcISYDvBn0dHRxVOc6enpxMTEFD8WFPTfHjMjI4Po6Gj6\\n9OnDhAkTaNq0Kffffz979+6ldu3a5xw3KSmp+PPExEQSExPPec7Jk7B0qRZnDRRdu1rXJr71lna1\\nEBER+/O7pi0+Pp5hw4bRtWtXFixYQK9evYofa9y4MatWraJRo0ZkZGQQGRlJRkYGsbGxOBwOoqOj\\nOX369HmPe2bTVpKpU6FdO4iK8tS7EZOaNoXCQli3zv27SEVERPyN302PxsXFERYWRkJCAiEhITRr\\n1ow+ffoA0L9/fwYMGECHDh0YMGAAAK+99hrdu3cnISGBihUrct1115X6tSdN0tpsgURTpCIiEki0\\nTtv/OX0aLr0U9u6FM2ZkxeZ+/tlabHfrVtemSANtnTbxLtWLuEq1Iu6w1TptJsyeDfHxatgCTbNm\\nkJcHGzaYTiIiIlI2atr+z6RJ0Lmz6RTiaX9MkU6caDqJiIhI2Wh6FMjNhYsvhl9/hRo1fBhMfGLV\\nKujdGzZvvvBzNYUh7lC9iKtUK+IOTY/+hQULoFEjNWyBqkULSEuDbdtMJxERESk9NW1oajTQBQVB\\np07Wki52UlBQwI4dO85aYFoCx8mTJ9mxY8dZi4ZL4MvOzmbbtm1kZ2ebjiI2VO6nRwsKoGZN+Okn\\nqFPHt7nEd378EQYNguXL//p5/jSFMXLk9yxZcpKoqHTefPOpsxaaFv9Q2no5ceIEAwd+QUZGFO3b\\nV6dnT601FOgcDgcFBQW8/vqn7N0bQd26WQwc+PxZi8aL/EHToyVYsQIuu0wNW6Br0wY2boQjR0wn\\ncd2vvx4iKqotGRnRHDt2zHQc8aDff/+djIxYoqLasGXLQdNxxEdycnLYt+801avfxZ49GeTk5JiO\\nJDZT7ps2TY2WDxUrQseOMGOG6SSu69atHZUqzeSWW2KpW7eu6TjiQVdccQW33BJFePhsunVrZzqO\\n+EhERAQPPngjTucYHn64JeHh4aYjic2U6+lRpxNq14Y5c6BBAwPBxKfGjoVx42D69JKf40/To+L/\\nVC/iKtWKuEPTo+exdi2EhalhKy/uuAOWLIHMTNNJRERE3Feum7bp0+Huu02nEF+JiYEbb4S5c00n\\nERERcZ+aNjVt5co998CUKaZTiIiIuK/cXtN26BA0bGjdTVihgqFg4nP79kHTpnD4MISEnPu4rjsR\\nd6hexFWqFXGHrmn7k5kz4dZb1bCVN5dfbt18cqH12kRERPxNuW3aNDVaft1zj/12RxAREfHLpq1f\\nv34kJCTQt2/fs75+6NAh2rZtS6tWrViwYAEA77zzDm3atKFNmzZUrlzZpS1/srNh8WK4/XZvpBd/\\nd/fd1kiriIiInfhd05aamkpmZiZLly4lLy+PlJSU4seGDBnC4MGDmTt3LoMGDQLg1VdfZdGiRUyc\\nOJHmzZu7tNXPwoXWdU2xsV57G+LHmjSxlv3QBvIiImInfte0rV69mo4dOwLQvn17kpOTix/buHEj\\n8fHxREREEBkZyalTp4ofmzp1Kvfcc49Lr6Gp0fLN4YC77rLX7ggiIiJ+17SlpaURGRkJQHR09FnT\\nnYWFhcWf//mxKVOmcN99913w+E6n9cv6rrs8GFpsR02biIjYjd81bdHR0WRkZACQnp5+1nRnUNB/\\n42ZkZBD7f/Obp06d4tixY9SuXfuCx//lFwgPh/r1PRxcbKVdO0hJARcugRQREfELfte0xcfHF99k\\nsGDBAuLj44sfa9y4MatWrSIzM5OMjAwqV64MwOzZs7njjjv+8rhJSUkkJSXxt78l0aTJYq/lF3sI\\nD4eEBGvfWRERETvwu6YtLi6OsLAwEhISCAkJoVmzZvTp0weA/v37M2DAADp06MCAAQOKv2fKlCl0\\n7tz5L4/7R9N2+nQSzz2X6M23IDbhzSnS/Px8Jk+ezejRk4pHjqX8yMrK4rvvpvL999PJyckxHUf8\\nyIYNGxk27Ds2bdpsOorYULnaEUG7IMiZ9u+HuDirHoKDra95atXy5ORkPvlkB8HBVejYMZcePf76\\njwqxp5LqZcqU2UyYkI3Tmc9jj1WnY8e2BtKJP3E4HGRlZfHiix8SHHw7RUWz+fe/X6JixYqmo4kf\\n0o4IWGtz3XabGjaxXHYZ1KoFq1Z5/tiVKlUiKOgUhYUnqFy5kudfQPxa5cqVcDrTcDgyiIjQv79Y\\ngoODCQ8PJjPzNyIiQs66TlvEFeVqpG3ZMmtEpWVL04nEX0ybBlddBQ0aWP/tqZE2p9PJunXryM7O\\nplmzZlTQXwoBqaR6KSwsZM2aNQQFBdG0aVP9cpbiWjl8+DBbt27lmmuuoUaNGqZjiZ8q6dxSrpo2\\nkQtRrYg7VC/iKtWKuEPToyIiIiI2pqZNRERExAbUtImIiIjYgJo2ERERERtQ0yYiIiJiA2raRERE\\nRGxATZuIiIiIDahpExEREbEBNW0iIiIiNqCmTURERMQG1LSJiIiI2ICaNpHzcDqdZGZmaq9AcUle\\nXh55eXmmY4gN6NwiZRFiOoCIv3E6nQwbNpbk5D20bl2PJ554CIfDYTqW+Kndu3czdOh3BAU56N//\\nYWrXrm06kvgpnVukrPxypK1fv34kJCTQt2/fs75+6NAh2rZtS6tWrViwYAEARUVFvPzyy3To0IEH\\nH3zQRFwJMKdPnyY5eR+XX/4Ky5fvIisry3Qk8WM//7yR3NybycpqyZo1G03HET+mc4uUld81bamp\\nqWRmZrJ06VLy8vJISUkpfmzIkCEMHjyYuXPnMmjQIAAmTpzItddey7x58xg/fryp2BJAKleuTIsW\\ntdi3733i4+sQHh5uOpL4sRtuuJbQ0GWEha0kLu5a03HEj+ncImXlcPrZxPp//vMfqlevTpcuXZg0\\naRIHDx7kxRdfBKBt27YsXLgQgE6dOjFmzBief/55qlevTmpqKt26deOJJ54455gOh0PXD4hL/qgV\\np9PJ6dOnqVy5sqYvpER/1EtOTg4Oh4OKFSuajiR+SucWcUdJfYvfXdOWlpZGvXr1AIiOjmbTpk3F\\njxUWFvLbb79x5513sn79ek6cOMHhw4dZsWIFR48eJTs7m06dOnHRRReZii8BwuFwEBkZaTqG2ERY\\nWJjpCGITOrdIWfhd0xYdHU1GRgYA6enpxMTEFD8WFBRElSpVWLhwIVdeeSUxMTHExsYyZswYhg0b\\nRlhYGDuJWDBKAAAgAElEQVR27Dinabvlllv0F424RLUi7lC9iKtUK+KO6Ojo837d75q2+Ph4hg0b\\nRteuXVmwYAG9evUqfqxx48b88ssvNGrUiMLCQiIjI2nZsiUHDhwAYOvWrdSpU+ecYy5ZskTTo3KO\\n/Px81q1bR1RUFFdffTWgqXRxj+pFzkfnFnHHwYMH2b9/Pw0bNqRy5coAJTb4fncjQlxcHGFhYSQk\\nJBASEkKzZs3o06cPAP3792fAgAF06NCh+Lb6xx9/nHHjxjFr1iyaNGlCzZo1TcYXG5k8+Uc++mgT\\nb701i23btpmOIyIBQucWcdXJkyd5881v+fTT/fz732Mv+Hy/G2kD+Oijj876748//hiASy+9tHip\\njzZt2gDW3Tg//PADvXr14oUXXijxmElJScWfJyYmkpiY6NnQYjvHjmUQEnI5BQUUT8mLiJSVzi3i\\nqqysLHJyKhIRcRW//z7/gs/3y6bNVX8eav6roeczmzYRgK5dOwJzqVatCtdff73pOCISIHRuEVfV\\nrFmT7t3j2LhxPXfdde8Fn+93S35cSEFBAbfddhupqanccMMNDB48mPfff5/ly5dTt25d+vfvT6dO\\nnc76Hl1LIK5SrYg7VC/iKtWKuKOkerFd01Ya+mERV6lWxB2qF3GVakXcUVK9+N2NCCIiIiJyLjVt\\nIiIiIjagpk1ERETEBtS0iYiIiNiAmjYRERERG1DTJiIiImIDatpEREREbEBNm4iIiIgNqGkTERER\\nsQHbNW2//fYbTZs2pVKlShQVFQHw7rvvcvPNN9O9e3cKCgoMJxQRERHxPNs1bVWqVGHhwoXcdNNN\\nABw9epTFixezbNkyGjduzJQpUwwnFBEREfE82zVtFStWJCYmBgCn00lKSgqJiYkAtG/fnuTkZIPp\\nxFcKCgoYNeoH3njjc3bt2mU6joj4sdTUtQwc+BlTp86x7f6fBQUFjB49iTfe+JydO3eajiOG2K5p\\n+7P09HSioqIAiIqKIi0tzXAi8YWtW7cyf34ahw7dxLffzjUdR0T82PDhM0lPv43Jkzdz5MgR03FK\\nZfv27cybd4JDh+IZNWqO6ThiSIjpAGXhcDiIjo7mwIEDAGRkZBSPwv1ZUlJS8eeJiYnFo3NiT1Wr\\nVqVSpeNkZa2hTp3qpuOIiB+rV+8iNm5cTpUqBURGRpqOUypVqlShUqUTZGWlUK/eRabjiCEOp03H\\nitu0acP8+fM5fvw4vXv3ZsaMGQwdOpR69erRpUuXs57rcDhsOyQuJTt8+DDHjx+nfv36hIR45u8P\\n1Yq4Q/ViDzk5OWzfvp1atWoRGxtrJIMnauXIkSMcO3bMo+c88U8l1YvtmraCggJuu+02UlNTueGG\\nGxg8eDCLFy9m+vTp1K5dm6+//vqcYtaJVVxVnmuloAD27IGDB+H33+HYMcjMBKcTioogJARiYqyP\\natWgXj2oWROCbH+RRemV53oR96hWxB0B07SVhn5YxFXlpVby82HtWlixAlauhA0bYPduuOQSuPxy\\nqymrVg3CwyE42GrM8vMhPR3S0uDIEdi1y/rvK6+E5s2hRQto2RIaNgSHw/Q79I3yUi9SdqoVcYea\\ntsB/m+IBgVwrWVkwaxZMmWL976WXQqtW1keTJlbzVamSe8fMzIRff4Wff4bVq2HJEqu5u+MO6NwZ\\n2re3mr5AFcj1Ip6lWhF3qGkL/LcpHhCItbJuHQwfDt99B82awX33QadO1tSmpzmdsHUrzJwJ48db\\nU609esCzz0Lt2p5/PdMCsV7EO1Qr4g41bYH/NsUDAqlWVqyAN9+ETZvgySehd2+oVcu3GTZvhi+/\\nhK+/tkbfXn3Vmj4NFIFUL+JdqhVxh5q2wH+b4gGBUCu//gr9+lkjXn//Ozz6KISGms2Ulgaffw4f\\nfQR33WU1k5dcYjaTJwRCvYhvqFbEHSXVSzm+70sksGRmwssvw803Q8eOVtP25JPmGzaw7jh97TWr\\noYyNtUbbPvwQCgtNJxMRsQ+NtImcwa61snq1de1Y8+bwwQdQo4bpRH9t+3Z4/HHrGriRI+Gqq0wn\\nKh271ov4nmpF3KGRNpEA5HTC229bNxa89RaMGeP/DRtYTdrixdCli3X36sSJphOJiPg/jbSJnMFO\\ntXL6NPTqBfv2waRJ1hIedpSaCvffb30MGWIt4msXdqoXMUu1Iu4I6JG2goICHnroIdq2bcurr75q\\nOo6I1/32G7RuDZGR1tpodm3YAJo2hTVrrKVJ7r8fsrNNJxIR8U8B0bRNnjyZuLg4Fi5cSHZ2NuvX\\nrzcdScRrdu+2bja4/35rOY2wMNOJyq5KFWttt8hIa0HeEydMJxIR8T8B0bTt3r2bRo0aAdCkSRNW\\nrlxpOJFcSGFhIQcOHCBbwypu2brVatj69YN//jOwtosKDYVRo+Cmm6BdOzh50nQi8YWioiIOHjxI\\nZmam6Sg+lZWVxYEDBygqKjIdRWwkIJq2+vXrs2TJEgAWLlxIWlqa4URyIV99NYEBAybz+uufq3Fz\\n0d690KGDtcbZ88+bTuMdQUHw3nvQti3ceitkZJhOJN42fvx0Bgz4gYED/0NGOfkHz8zM5P/9v/8w\\nYMAkRo+eZDqO2EhANG1333032dnZtG/fnrCwMC6++OJznpOUlFT8sXjxYt+HlLOkpOzioose4rff\\nQjh+/LjpOH7v8GFr2vDll62bDwKZw2E1bs2bw5136hq3QLdmzS5iY+/j2LFojhw5YjqOT/z+++8c\\nPRpOtWpdWbNml+k4YiMBd/fo008/TVJSEpecsdy67trxP4sWLWf8+KU0a1aPXr26Euwnu4r7Y61k\\nZkJCAtxzDwwcaDqN7xQVQffu1v+OHWuNwvkbf6wXu1m9OoVvvplHgwY1eeaZR6hQoYLpSF5xZq0U\\nFBTwxRfjWbduL926taV165sMpxN/E9DbWB06dIhu3boRFBTEo48+Ss+ePc96XCdWcZW/1UpRETz4\\nIISHW/t3BtI1bK7IybGmStu3hzfeMJ3mXP5WL+K/VCvijoBu2i5EPyziKn+rlddfhzlzYOHCwLhL\\ntDR+/926OWHwYHjoIdNpzuZv9SL+S7Ui7lDTFvhvUzzAn2pl9mx46in4+Wc4z2Wa5cratdZNGMuX\\nQ/36ptP8lz/Vi/g31Yq4I6AX1xUJNIcOQe/e1rVc5b1hA2jSxBpp69oVsrJMpxERMUMjbSJn8Ida\\nKSy0RpXatLHWYhOL02ndmBAeDiNGmE5j8Yd6EXtQrYg7NNImYhPvvms1KP/4h+kk/sXhgM8/h/nz\\nYdYs02lERHxPI20iZzBdK1u2WMt7pKRA7drGYvi1RYugRw/YsAFiY81mMV0vYh+qFXGHbkQI/Lcp\\nHmCyVgoLrS2quneH554zEsE2XnwR0tOtba9M0rlFXKVaEXdoelTEz33yCVSoAM88YzqJ/xsyBFas\\ngB9/NJ1ERMR3NNImcgZTtbJ/P8TFQXIyXHWVz1/elmbNgj59YONGc2vY6dwirlKtiDs00ibix155\\nxdoEXg2b6+64Axo1gqFDTScREfGNgBhpy83N5YEHHiAjI4Po6Gi+//57QkNDix/XXzjiKhO1smQJ\\n9Oxp3YQQHu7Tl7a9vXuhaVNrAeJ69Xz/+jq3iKtUK+KOgB5p+/HHH2nevDmLFi2iRYsW/KgLXcQm\\nCgqsKb733lPDVhq1a8NLL8HLL5tOIiLifT5v2tavX89NN91ErVq1eOqppzh58mTxYy1atCjVMatV\\nq0ZaWhoAaWlpVKtWzSNZ5fzy8/OZNWs+M2bMJTc313QcWxsxAqpUgS5dTCexr379rCVSVq40nSSw\\nFRYWsmDBEiZPnkVmZqbpOLZ14sQJJkyYzsqVqzXyJm7zedP27LPPkpSUxIYNG7j66qtp1aoVO3bs\\nAKxmoDTi4+NJTU2lYcOGrFmzhvj4eE9Glj9Ztmwl3357mLFjTzBv3hLTcWwrMxPefBPef99aOFZK\\np1Il6//HV16xFiUW7/jll18YOXIHP/yQx6RJc0zHsa2RI6cwfXpFhg37iV27dpmOIzbj86bt1KlT\\n3HbbbcTGxvLyyy/z6aefctttt7Fq1apSH3P06NHceeedbNy4kTvuuINvv/32nOckJSUVfyxevLgM\\n70CCgoKAAqDg/z6X0vjXv6x12Zo2NZ3E/rp3h1OnYOpU00kCl/WzXojTmU+FCsGm49hWcHAQTmce\\nUKjzp7gtxNcv6HA4SE9PJzo6GoA2bdowadIkOnfufNZUqTsyMjKI/b+l0atWrUpGRsY5z0lKSip1\\nZjlb69bWSGZRURE339zScBp7OnECPvhAU3qeEhxs3UXaty/cdReE+PzMFviaNGnC88/ncepUJgkJ\\n+rkvrd697+Pqq1dTs2YidevWNR1HbMbnd4+OGTOGevXqnTOFuW/fPt58801GlGIn6JMnT/Lggw+S\\nn59PaGgo48ePJyYmpvhx3bUjrvJVrbz6Kpw8CcOHe/2lyg2nE9q0gd69rbtxfUHnFnGVakXcYbtt\\nrF588UU++eQTjxxLPyziKl/UypEj0KABrF8PtWp59aXKnUWL4OmnYfNm34y26dwirlKtiDtst+TH\\n8uXLTUcQ8YoPPoCHH1bD5g2JiXDxxfDdd6aTiIh4nt82bSKB6MQJa5mP/v1NJwlMDgf8v/8HgwZB\\nYaHpNCIinqWmTcSHPvkE7r3XWhRWvKNtW6hWDcaPN51ERMSz1LSJ+MipU/Dvf8Nrr5lOEtgcDvjf\\n/4V33tG6bSISWPyiaSssLDxnmY7/+Z//MZRGxDuGDYN27eDqq00nCXy33gpFRTB/vukkIiKeY6xp\\ne/jhh8nIyCAzM5NGjRrRoEEDhg4dWvz4Y489ZiqaiMfl58NHH1lLfYj3ORzWnqTvvWc6iYiI5xhr\\n2jZv3kxUVBRTpkzh9ttvZ8+ePYwePdpUHBGv+uEHuOoqiIsznaT8ePhh2LjRWlpFRCQQGGvaCgoK\\nyM/PZ8qUKdx9991UqFABhzZglAD10UfWav3iOxUrwgsvWHu7iogEAmNN29NPP02dOnU4ffo0CQkJ\\n7Nmzp3hrK3fNmTOHNm3a0KZNG2rWrMm0adM8nFak9JKT4fffre2VxLeefhqmT4eDB00nEREpO7/Z\\nEcHpdFJYWEhIGZcxv+mmm1i4cCHh4eHFX9NK1OIqb9TKgw9Cy5age2vMeP55awmQ11/3/LF1bhFX\\nqVbEHSXVi7FtlV9//fWzQv0xNTpw4MBSH3PXrl3UqFHjrIZNzq+oqIigIL+4eTig7dsH8+ZZC+qK\\nGc89Bx06WMuAVKhgOo0ZRUVFOBwOXYLiJ3T+ldIyVjURERFERERQuXJlgoODmTVrFnv27CnTMSdN\\nmkTnzp09EzBAFRUVMWzYWHr3foOpU+eYjhPwPv8cevSAqCjTScqv666zllmZMsV0EjM2bdrMc8+9\\nzT//+THp6emm45R7338/nd693+Drrydo5E3cZqxpe/nll3nppZd46aWX+N///V+WLFnCzp07y3TM\\nGTNm0KlTJw8lDEzHjh1j5crDXHppfyZPTqaoqMh0pICVlwcjR8Izz5hOIs89B599ZjqFGXPm/ExQ\\n0L3s23cZW7ZsMR2nXMvNzWXmzLXUqvUaixbtUhMtbjM2PfpnmZmZHCzD1cKHDx8mNDSU2NjY8z6e\\nlJRU/HliYiKJiYmlfi07i42N5corK7Jjx+e0bHm1hui9aPp0qF8fGjQwnUTuvde6pnDzZrj2WtNp\\nfKtFi/ps2DCTqCgndeu2Mh2nXAsNDeWGGy5jzZrPuPbaGCIjI01HEpsxdiNCo0aNij8vKiri6NGj\\nDBw4kBdffLFUxxs+fDgFBQU899xz5zymC0DPlp+fz8mTJ6lWrZqatj/xZK107AiPPgrdunnkcFJG\\nAwfCyZPW/q+eYpdzy4kTJ6hYsSIRERGmo5Rbf9RKYWEhx48fp0qVKmW+8U4CV0nnFmNN25nXr4WE\\nhFCjRg0qeOkqYbucWMU8T9XKrl1w442wfz+EhXkgmJTZgQPQuLH1b+Kp3kXnFnGVakXcUVK9+HyY\\n5cSJE5w4cYKoqKjij/DwcE6dOsWJEyd8HUfEK0aMsG5AUMPmP2rVglatYOJE00lERErH5yNtderU\\nKfG2c4fDwa5duzz+mvoLR1zliVrJy4PLL4fFi+GaazyTSzxj0iT4+GPr38YTdG4RV6lWxB1+Nz3q\\nS/phEVd5olZ++MFqDJYs8VAo8Zi8PGvEbeVKuPLKsh9P5xZxlWpF3OF3i+s6nU4mTZrE8uXLCQoK\\nonXr1tx3332m4oh4zNdfw+OPm04h5xMaCt27W/9GgwaZTiMi4h5jI23PPvssO3fu5OGHH8bpdDJ+\\n/HiuuOIKPvPCYkr6C0dcVdZaOXrUWsj1wAGoXNmDwcRjNmyA22+HvXshOLhsx9K5RVylWhF3+N1I\\n26JFi9i8eXPxkhOPPfYY15a3BZQk4IwdC/fco4bNnzVqBJdcAvPnw623mk4jIuI6Y4t0XXnllezb\\nt6/4v/ft28eVnrjIRMSgUaOgZ0/TKeRCeve2dqsQEbETY9OjCQkJ/Pzzz7Ro0QKHw8FPP/1E8+bN\\niYqKwuFwMG3aNI+9loalxVVlqZUNG+DOO2HPHtCaxf7t5EmoU8f6typhExWX6NwirlKtiDv8bnr0\\njTfeOOdrf4QsaUkQEX82apR1kbsaNv8XGwvt21tLgOimERGxC79d8iM+Pp7k5GSXnz9q1ChGjRpF\\nUVER3377LTVr1ix+TH/hiKtKWysFBdbabAsXam02u/jhB/j0U+vfrLR0bhFXqVbEHX430nYhOTk5\\nLj/34MGDLF26lPnz53sxkUjJ5s+Hyy5Tw2Ynd94JTz4JBw/CpZeaTiMicmEBMZEzZ84cCgsLad++\\nPX369KGoqMh0JK8pKChg7dq1Z+3dKuaNHauN4e0mLAzuvRe++850kgsrKipi/fr1bN++3XQUKaO0\\ntDRSUlJIS0szHUVsKCCatiNHjpCfn8/8+fMJDw9n6tSppiN5zYQJM3nvvbW88cZENW5+IicHpk+H\\nBx4wnUTc1a2b1XD7ux9/XMjQoasZNGgWmzZtMh1HSqmgoIC33vqSTz7ZzVtvfUlBQYHpSGIzxpq2\\nzZs3n/O1xaXcEDAmJoaEhAQA2rZty5YtW855TlJSUvFHaV/HH/z220lCQ68iP7+a/lLzE7NnQ9Om\\ncPHFppOIuxIT4bff4NdfTSf5a0eOnCQ4uB75+Rdz8uRJ03GklPLz8zl2LJeoqCYcP55Lfn6+6Uhi\\nM8auaXvggQfo0aMH/fv3Jzs7m1dffZWff/6ZVatWAdaNBa5q2bIlI0aMAOCXX36hXr165zwnKSnJ\\nI7lNe+ihjnz33Vxq1oylYcOGpuMI1vTagw+aTiGlERwMDz0EY8bAm2+aTlOyTp3acurUTCIjK9Gs\\nWTPTcaSUKlWqxFNPdWDRoiW0adOBSpUqmY4kNmPs7tHMzExeffVVUlJSOH36NI888givvfZa8Q4J\\n7nrllVdISUmhevXqjB07lpCQ//ajumtHXOVurZw+bV3EvnMnVKvmxWDiNWvWWFPbO3aAu6sN6dwi\\nrlKtiDv87u7RkJAQKlWqRHZ2Njk5OdSrV6/UDRvAu+++68F0Iq6ZMQNatlTDZmdNm1pr661ZAxrE\\nEhF/ZuyathYtWhAWFkZKSgrLli1j7NixdO3a1VQckVL57jtrek3sy+GArl1h4kTTSURE/pqx6dGf\\nf/6Z5s2bn/W1UaNG0dMLGzdqWFpc5U6tpKdbC+ru2wfR0V4OJl6Vmmo1bu5OkercIq5SrYg7SqoX\\nv90RwZP0wyKucqdWvvkGJk+GKVO8HEq8zumEK6+0Rtvi4lz/Pp1bxFWqFXFHSfUSEOu0iZgwfrzu\\nGg0UDgd06QITJphOIiJSMo20iZzB1VpJT7e2rTp4ECIjfRBMvC4lBR5+GLZtc32KVOcWcZVqRdyh\\nkTYRD5o501qYVQ1b4LjhBigogPXrTScRETk/NW0ipTBpEtx3n+kU4kl/TJHqLlIR8VeaHhU5gyu1\\nkpUFl1yiBXUD0U8/Qc+esGWLa1OkOreIq1Qr4g5Nj4p4yNy51iKsatgCT/PmkJ0NGzeaTiIicq6A\\naNr27NlDjRo1aNOmDbfddpvpOBLgJk+Gzp1NpxBvcDisf9vJk00nERE5V0A0bQAdO3Zk0aJF/Pjj\\nj6ajSADLz7e2rrr3XtNJxFvuvRemTjWdQkTkXAHTtC1atIiEhAQ++ugj01FKVFBQwNixUxg69EsO\\nHDhgOo6UwuLFcNVV1ibxEphatYK9e62dLrylqKiIiRNnMmTIF+zatct7LyR+Z8WK1bz55nBWrvzJ\\ndBSxoYBo2mrWrMn27dtZtGgR8+fPZ8OGDaYjndemTZuYPfsk27Y1ZOzYOabjSClMmqSp0UAXEgJ3\\n3gnTpnnvNXbu3Mm0afvZs6cZX301y3svJH4lKyuLL79cwO+/t+OLL+aRnZ1tOpLYTIjpAJ4QGhpa\\n/Pldd93Fxo0badSo0VnPSUpKKv48MTGRxMREH6X7rypVqhAaeozc3M3UqlXV568vZVNUZG1ZtXSp\\n6STibffeC59+Ci+84J3jR0dHU6lSBpmZ64mLq+KdFxG/ExoaSrVqFTlyJIVLLql01u8uEVcExJIf\\np0+fpnLlygD06NGDPn36nLUZvT/dar1//35OnDjBddddR0hIQPTMAeWvamXlSnjmGS2+Wh5kZlrL\\nuuzbBzExJT+vLOeWQ4cOcfToUa699lr98i4H/qiVtLQ0du3axRVXXEF0dLTpWOKnAnrJj2XLltGs\\nWTNatWpFrVq1zmrY/M1ll13G9ddfr4bNhiZP1g0I5UVEBNxyC8zy4sxlzZo1adKkiRq2ciYmJoam\\nTZuqYZNSCYiRtgvxp5E28W9/VSvXXAPffmut0SaB78svYc4c+P77kp+jc4u4SrUi7iipXtS0iZyh\\npFrZvt0aeTlwAIICYnxaLuTIEahf3/rfihXP/xydW8RVqhVxR0BPj4p424wZcNddatjKkxo1oGFD\\nWLTIdBIREYt+BYm4YPp0uPtu0ynE1+65x7pjWETEH2h6VOQM56uVtDS4/HI4fBjCww0FEyO2bYPE\\nxJKnxXVuEVepVsQdmh4VKaUff7SuZ1PDVv5cfTVER0NqqukkIiJq2kQuaPp063o2KZ/uusu6plFE\\nxDQ1bSJ/oaDAGmlT01Z+qWkTEX+hpk3kL6xYAXXqaIP48qxlS9i1Cw4dMp1ERMq7gGnaPvzwQ26+\\n+WbTMSTAzJihu0bLuwoV4NZbvbs7goiIKwKiacvNzWXdunU4HA7TUSTAaKkPAasGpk83nUJEyruA\\naNq+/PJLHn30Ua/fTl1UVMThw4fJy8vz6uuIf9i+HU6dgqZNTScR0267zVpkNzvb9e85efIk6enp\\n3gsltlRQUMDhw4cpKCgwHUVsyPa7lufn57NkyRKee+45r7/WiBHfsXLlUerUCWbAgKe10XOAmz4d\\n7rwTNIArVapAkyaweDHcfvuFn79x4yY++mgmDoeT/v07c9VVV3k9o/i/oqIi3n9/JJs3Z9GoUWX+\\n9rfeBGmbFXGD7atl9OjRPPLIIxd8XlJSUvHH4sWL3X4dp9PJ6tXbqVnzcfbsKeDEiROlSCt2oqlR\\nOZM7d5Fu3LiDoqKW5OU149dfd3o3mNhGZmYmmzcfo1atp9iw4Qg5OTmmI4nN2H6kbdu2baxdu5bP\\nP/+cTZs28emnn/L888+f87ykpKQyvY7D4aBLl1ZMnPgBLVtexUUXXVSm44l/y86GX3+Fdu1MJxF/\\ncddd0K2ba89t3foGVq8eS0hIEM2bd/duMLGNypUrc+ut1zJ//rvccUcclSpVMh1JbCagtrFKSEhg\\n6dKl53zdk9uHOJ1O3fAQwM6slYICCLH9nzXiKU4nFBaeXRN/dW754+s6XwicXSv6PSIXUtK5JaCa\\ntpJozzdxlWpF3KF6EVepVsQd2ntURERExMbUtImIiIjYgJo2ERERERtQ0yYiIiJiA2raRERERGxA\\nTZuIiIiIDahpExEREbEBNW0iIiIiNqCmTURERMQGAqJp27RpE61atSIhIYFnn33WdBwRERERjwuI\\npq1+/fqsWLGCpUuXkpubyy+//GI6koiIiIhHBcR22CFn7OCcnZ1NTEzMOc/Jz89n8eLlOBwObrml\\nFRUqVPBlRBEJUDq3iDt2797NL79s5oYbGlK7dm3TccRmAmKkDWDatGk0atSIsLAw6tate87jCxYs\\n5auvDjJy5H6WLFlhIKGIBCKdW8RVubm5DB06nunTIxk6dBx5eXmmI4nNBEzT1qlTJzZs2EBkZCTz\\n5s075/HRo79hw4bJbNgwmbVrNX0qIp7hdDpxOIIAB06n03Qc8WNOp7O4XoqKVCvivoCYHs3LyyM0\\nNBSAqKio8/718tVXX7Bw4TIA2rVL8Gk+EQlc7dol4HBY55bExNaG04g/CwsL45VXHmDNms00b/5Q\\n8e8tEVc5nAHwp+G0adP44IMPcDqd1K1bl5EjRxIU9N9BRIdDfwGLa1Qr4g7Vi7hKtSLuKKleAmKk\\nrVOnTnTq1KnEx2+55RYcDocPE4ldqVbEHaoXcZVqRdwRHR193q/bbqQtNzeXBx54gIyMDKKjo/n+\\n++/517/+xbRp06hduzZff/31WXeTgv7CEdepVsQdqhdxlWpF3FFSvdjuRoQff/yR5s2bs2jRIlq0\\naMG4ceNYvHgxy5Yto3HjxkyZMsV0RLGJHTt28NJL7zFkyHBOnz5tOo74scLCQr744jteeGEIK1as\\nNh1HRAKEu+cW2zVt1apVIy0tDYCTJ0+yb98+2rRpA0D79u1JTk42GU9sZPr0FWRmtmfTplg2bdpk\\nOo74sQMHDrBs2XEqVuzJuHGLTccRkQDh7rnFdk1bfHw8qampNGzYkDVr1nDllVcSGRkJWHeO/tHQ\\niVxIo0Z1yMlZTETELmrVqmU6jvixatWqcdFFORw7NpkmTbQgqoh4hrvnFtvdiDB69GjuvPNOXnrp\\nJY1eGpMAACAASURBVN5//33y8/PJyMgAICMj47y7IQAkJSUVf56YmEhiYqIP0oo/a9cugQYNriQ8\\nPJzY2FjTccSPRUREkJT0DMePH+fSSy81HUdEAoS75xbbNW0ZGRnFv2CrVq3Knj17+Omnn3jllVeY\\nP38+8fHx5/2+M5s2EbAu9NQvYHFVREQEERERpmOISIBx59xiu7tHT548yYMPPkh+fj6hoaGMHz+e\\n4cOHM336dN09KmWmWhF3qF7EVaoVcUdJ9WK7pq009MMirlKtiDu8WS8FBTBvHsydC5s3w9GjEBoK\\ntWtD06bQqRNce61XXlq8QOcWcYeatsB/m+IBqhVxhzfqpaAA/vMfePddqFUL7roL4uLgoosgLw92\\n7YLVq2HyZLj4Ynj1VejcGYJsd1tZ+aJzi7hDTVvgv03xANWKuMPT9bJhA3TvDtWrw9Ch1ohaSYqK\\nYNo0GDwYKlSwGr3rr/dYFPEwnVvEHQGzuK6ISCD67jto2xZeesmaFv2rhg2skbV777VG3Xr1gg4d\\n4J13rGZORAKTRtpEzqBaEXd4ql4+/9waMZs5Exo3Lt0x9u+HBx+E2FgYOxZK2LpQDNG5RdyhkTYR\\nET80bJg1QrZkSekbNoDLLrOOUbs2JCTAoUOeyygi/kFNm4iIIbNmQVISzJ8P9eqV/XgVKsCnn8LD\\nD0PLlrBjR9mPKSL+w3aL64qIBILNm+Gxx2DqVLjiCs8d1+GA116DqlWhXTtYtMgzDaGImKemTUTE\\nx7KzrevP3n4bStjEpcyefNJaPqRt2/9Om4qIvalpExHxsZdeguuug969vfs6zz4LubnQsSOsXGmN\\nvomIfdmuaZszZw5DhgwBYOvWrfznP/9h27ZtTJs2rcRtrERE/MW8eda1bOvWWVOZ3ta3Lxw8aC0P\\nMm8ehIV5/zVFxDtsveTHTTfdxIQJE3jmmWeYOXMmQ4cOpV69enTp0uWs5+lWa3GVakXc4W69ZGVB\\no0bwySdwxx1eDPYnRUXw0EPW2m5jx2r3BBN0bhF3BNySH7t27aJGjRps3LiRxMREANq3b09ycrLZ\\nYCIiJRg0CJo1823DBlaTNmqUtZbbG2/49rVFxHNsO484adIkOnfuTFpaGlFRUQBERUWRlpZmOJmI\\nyLm2boURI2D9ejOvHxYGP/wAzZv/d8N5EbEX2zZtM2bMYPLkySQnJ3PgwAEAMjIyiImJOe/zk5KS\\nij9PTEwsHp0TEfGF116DV16BSy4xl+Hii2HCBKthW7YM6tc3l0VE3GfLpu3w4cOEhoYSGxtLs2bN\\n+Oyzz3jllVeYP38+8SXcP39m0yYi4kvLl0NqqnU9mWk33WRN0953n7VvaWSk6UQi4ipbXtM2bdo0\\n7r33XgAuuugiEhISuPnmm1m/fn3x10VE/IHTaY2wDRoElSqZTmN56ilo1QqeeMLKJyL2YOu7R12l\\nu3bEVaoVcYcr9TJ1KgwcCL/84l93bWZnw403wosvWgvxinfp3CLuKKle1LSJnEG1Iu64UL04ndaF\\n///4B3Tu7MNgLtqyBW6+2dox4brrTKcJbDq3iDsCbskPERF/N2eOtSOBv1610aABDB0KDzxgrSEn\\nIv5NI20iZ1CtiDv+ql6cTmjd2pp+fOghHwdzg9MJ3btDeLi1JIl4h84t4g6NtImI+NDixXDsGHTt\\najrJX3M44PPPrbzjx5tOIyJ/RU2b2FJRURGzZy9g+PDvOHz4sOk44ucOHTrEsGHjmDNnkc9GO957\\nD/r3h+Bgn7xcmURGwrhx1qjg/v2m08j5OJ1OfvxxIcOHf8dvv/1mOo4YoqZNbGnbtm2MG7ed5ORL\\n+frr6abjiJ8bOXIaq1dfzpgxW9ixY4fXX2/rVkhJgW7dvP5SHtOsGfzP//x/9u49Lqo6/+P4awBR\\nbsOl1BJNc8suBq2XaJFLiNi6lZeubkWWbpdVs4ubZr+2lmzL1G5rWdvN0tS2bDfS2qxUUCzEvKRA\\nF3WzVDS1ZBzAEQTO74/JScvLDDJzZob38/HgIR04Z97HPowfvuec7xduvNG5Vqn4l40bNzJ37tes\\nWNGRV1/Ve15LpaZNAlJkZCShoTXU1e0gLi7K7Dji5+Lioqit3U5Y2D4iIyO9/nrTpsFttzmXjgok\\nEyZAXR08/bTZSeSXIiMjCQvbR23tDuLivF/D4p/0IIIErA0bNrB792569uxJRDPNWqpaCU779u1j\\n7dq1tG/fnjPOOKPZjnukeqmshK5d4YsvzF2yqqk2b4aUFFi8GJKTzU4TPJrjvWXjxo3s2rWLHj16\\n+OSXDzFPUM3TNmvWLGbNmkVjYyOzZ89mzpw5zJ8/n86dO/Paa68RFnb46lz6h1jcpVoRTxypXqZO\\ndS4K//rrJoVqBq+9Bk88AZ99Fnijhf5K7y3iiaB5erSiooJly5axaNEilixZQlhYGIWFhRQVFZGc\\nnEx+fr7ZEUWkhWpogOnTnfeGBbIbb3QuJv9//2d2EhE5VMA1bR9++CENDQ3k5ORwxx13sGrVKrKy\\nsgDIycmhuLjY3IAi0mJ9/DGcfLLzpv5AZrHACy/AW285L5OKiH8IuKZt586dHDhwgEWLFhEZGcne\\nvXuxWq0AWK1WbDabyQlFpKV68UXnYuzB4KSTYMYMuOkm2LPH7DQiAgHYtMXFxZGZmQlAdnY2mzdv\\nxm63A2C324mLizMznoi0UDt2QEEBXHut2Umaz8UXw5VXwp//7Fw5QUTMFXBNW58+fVi/fj0Aa9eu\\npVOnTixduhSARYsWkZqaesT98vLyXB+FhYW+iisiLcRrr8FVVzknqg0mkyY5n4QN5AcrRIJFQD49\\nOm7cOFatWkXbtm2ZM2cOTz31FAsWLNDTo3LCVCviiYP10tgIZ57pXFUgJcXsVM1v3TrIyYGSEud0\\nJuI5vbeIJ4Jqyg9P6YdF3KVaEU8crJdFi+Cee2DtWudN/MHoySfh7bdh2TL4xe/F4ga9t4gngmbK\\nDxERf1NRAXffHbwNG8Bdd0FUFDz6qNlJRFoujbSJHEK1Ip5oafVSUQE9e8K778Lvfmd2msDS0mpF\\nToxG2kRE5IQkJsLzz0NuLlRVmZ1GpOXRSJvIIVQr4omWWi9/+pNzCpAZM8xOEjhaaq1I02ikTURE\\nmsU//gFFRfDvf5udRKRl0UibyCFUK+KJllwvJSUwaBCsXg0dO5qdxv+15FoRz2mkTUREms2FF8Lt\\ntzuXuWpsNDuNSMugpk1ERJrkvvvA4YCnnjI7iUjLYErTVlRUxKuvvgrA7t272bx5sxkxRETkBISF\\nwezZMHkyfPaZ2WlEgp/Pm7a8vDymTJnCpEmTAKirqyM3N9ft/b/99lvat29P3759GTBgAABTp04l\\nIyOD3Nxc6uvrvZJbRER+7fTT4bnnYOhQsNnMTiMS3HzetL3zzju8++67REVFAZCYmEiVhxP+XHzx\\nxRQUFLBw4UJ27dpFYWEhRUVFJCcnk5+f743YIiJyFFddBZdcAjff7JwKRES8w+dNW+vWrQkJ+fll\\na2pqPD5GQUEBmZmZPP3006xevZqsrCwAcnJyKC4ubq6oIiLipscfh82bYfp0s5OIBC+fL/t79dVX\\nc9ttt2Gz2XjxxReZMWMGN998s9v7d+jQgY0bNxIeHs7gwYOpqqqiXbt2AFitVmwanxcR8bk2beCt\\ntyA11bnEVe/eZicSCT4+bdoMw2Do0KF89dVXxMTEsGHDBh5++GH69+/v9jHCw8Ndn1922WVYrVYq\\nKioAsNvtxMXFHXG/vLw81+dZWVmu0TkxR3n5F+Tnf0LPnl0ZMCAbSzCvtC0npLa2lrlz57NnTzW5\\nuZfQvn17syPJUfzmN86RtqFDYc0aiI01O5F/MgyD9977mNLS77jiikzOPvsssyNJgPDp5LqGYZCU\\nlERZWVmTj1FdXU10dDQAN9xwA2PGjGHixIm89957TJkyha5du3LVVVcdto8mNfQ/Y8Y8hmEMobp6\\nIY8+eg0dOnQwOxKgWvFHK1as4JlnviE8PJGUlApGjrzO7EguqpcjGz0adu6EefNAv485HVorW7Zs\\n4cEH3yUyMps2bT7gySfvMTmd+Bu/mFzXYrHQq1cvVq5c2eRjFBUV0bt3b9LS0ujYsSMpKSlkZmaS\\nkZHB+vXrGTJkSDMmFm/p1Okk7PY1REfXERMTY3Yc8WMJCQmEh3/PgQMbSEw8yew44oYnnoDvvnP+\\nKb9mtVqJjHRQVbWWTp1U0+I+ny9jddZZZ7Fp0yY6d+7seoLUYrGwfv16r72mfhv2P/v27ePrr7+m\\nY8eOtG3b1uw4LqoV/7R582aqq6s599xzCQ0NNTuOi+rl6LZsca6a8PrrkJNjdhrz/bJWdu7cyfbt\\n2zn77LOJiIgwMZn4o6O9t/i8afv222+PuL1Lly5ee029sYq7VCviCdXLsRUUwLXXwooV4MW3+ICg\\nWhFP+E3TdtCuXbvYv3+/679PO+00r72WfljEXaoV8YTq5fiefhpmzoRPPoHISLPTmEe1Ip7wi3va\\nAObPn8+ZZ57J6aefzkUXXUSXLl34wx/+4OsYIiLiA3feCd27w623auJdkRPl86btr3/9K8XFxXTr\\n1o3NmzezePFiLrzwQl/HEBERH7BY4MUXobzcOeomIk3n86atVatWnHzyyTQ2NtLQ0EDfvn1ZtWqV\\nr2OIiIiPREZCfj5MnQrvv292GpHA5fMVEeLj46mqqiIjI4Prr7+edu3aueZdExGR4NS5M/znPzBo\\nECxaBMnJZicSCTw+fxChurqaiIgIGhsbmTNnDna7neuvv56TTvLeXDW6AVTcpVoRT6hePPfmmzB+\\nvPOJ0lNPNTuN76hWxBN+9/SoL+mHRdylWhFPqF6a5uGHYf58WLq05TxRqloRT/hN0xYdHe1aZ7Ku\\nro4DBw4QHR2N3W732mvqh0XcpVoRT6hemsYwYNgwcDici8yH+Pzuat9TrYgn/GbKj+rqaqqqqqiq\\nqsLhcPCf//yHUaNGeXSMp556ioyMDACmTp1KRkYGubm51NfXeyOyiIg0I4sFXn4Zdu2CsWM1FYiI\\nu0z9/SYkJIQhQ4awcOFCt/epra1l3bp1WCwWdu/eTWFhIUVFRSQnJ5Ofn+/FtCIi0lxat4Z334XF\\ni2HKFLPTiAQGnz89+u9//9v1eWNjI6tXr/Zo3bVXXnmFG2+8kQcffJBVq1aRlZUFQE5ODnPmzOGq\\nq65q7sgiIuIF8fGwcCGkpUG7djB8uNmJRPybz5u2BQsWuO5pCwsLo0uXLrz77rtu7XvgwAGWLl3q\\nupxqs9mwWq0AWK1WbDabd0KLiIhXJCY6G7esLGjbFi67zOxEIv7L503ba6+91uR9X3/9da677jrX\\nf8fGxrJt2zYA7HY7cXFxR903Ly/P9XlWVpZrhE5ERMx19tnOS6WXXeb8s08fsxOJ+CefPz06ZsyY\\nw56K+OXn06ZNO+q+EyZM4PPPP8disVBSUsJdd93FypUree+995gyZQpdu3Y94uVRPbUj7lKtiCdU\\nL81r4UK48Ub48EP47W/NTtO8VCviCb95enT//v2sWbOGbt26ceaZZ7J27Vrq6uro3bs3vXr1Oua+\\njz32GAsXLuSDDz7gvPPO48EHHyQzM5OMjAzWr1/PkCFDfHQWIiLS3AYMgOnT4Q9/gC++MDuNiP/x\\n+UjbhRdeyPLly2nVqhXgvE8tPT2dkpISr72mfsMRd6lWxBOqF++YPRsmTICCAjjzTLPTNA/VinjC\\nb0babDbbYRPpVlVV6QGCAFRZWcmePXvMjiEBoL6+noqKCg4cOGB2FAkQubnwt79BTg58+63ZaZqX\\nYRh8//331NTUmB1FApDPH0SYMGECPXv2pG/fvhiGwdKlSw97SED834YNG5g6NZ+GBrj77ktISjrP\\n7EjipwzD4OmnX6O0dB9nndWKe++9ldDQULNjSQC45Rbnign9+sGyZc6nTIPB/Pkf8Z//fEF8fD1/\\n+9vNxMfHmx1JAojPm7bhw4czYMAASkpKsFgsTJ48mVNOOcXXMeQEfP31N9TV9SIkJJzy8v+paZOj\\nqq2tpbR0B4mJd/P1189SXV1NbGys2bEkQNxxx8+NW0FBcCwwv2rVJuLihrBnzzIqKirUtIlHfH55\\n9JNPPiEmJoYhQ4Zgt9uZMmUK3333na9jyAlISenBKaes46STSsjI6G12HPFjbdq0YfDgXuzc+SS/\\n//3ZrnkVRdx1773Oy6V9+8L27WanOXGXX55Gbe1czjuvjjPOOMPsOBJgfP4gQlJSEuvWraO0tJSb\\nbrqJm2++mbfeeoulS5d67TV1A6i4S7UinlC9+M6kSfDqq7BkCXTsaHYaz6lWxBN+8yBCWFgYISEh\\n5OfnM3r0aEaPHk1VVZWvY4iISAC57z64+Wbnyglbt5qdRsQcPr+nLSYmhkcffZTZs2dTVFREQ0OD\\nnioTEZHjGj8ewsKcjduSJdC5s9mJRHzL5yNtb775Jm3atGHGjBmccsopVFRUMG7cOF/HEBGRADR2\\nLIwZ42zcgm06EJHj8fk9bceTmppKcXHxUb9eXl7Orbc6pw3o3r07zz//PFOnTmX+/Pl07tyZ1157\\njbCwwwcQdS+BuEu1Ip5QvZjnmWfgiSecI25du5qd5vhUK+IJv7mn7Xj2799/zK+fddZZfPLJJyxb\\ntoza2lo+++wzCgsLKSoqIjk5mfz8fB8lFRERs4wZ47xcmpUFmzaZnUbEN/yuaTueQ0fRHA4Hn332\\nGVlZWQDk5OQcc5RORESCx6hRcP/9zulANmwwO42I9wVc0wYwf/58kpKSaN26NfHx8a65n6xWq5bE\\nEhFpQW67DfLyIDsbvvrK7DQi3uXzpu2LL7741bbCwkKPjjFo0CBKS0uxWq1ERUW51jK12+3ExcUd\\ncZ+8vDzXh6evJyIi/utPf4JHHnE2buXlZqcR8R6fT/lxzTXXcMMNNzB+/HgcDgf33nsvn332GStW\\nrABg1qxZx9y/rq6O8PBw4OeRtaVLlzJu3DgWLVpEamrqEffT+qYiIsHrxhshNNS5yPxHH0FSktmJ\\nRJqfz0faSkpK2Lp1K6mpqaSkpHDqqafy6aefur6edJyftIULF5KVlcVFF13Etm3buP7668nMzCQj\\nI4P169czZMgQb5+CiIj4odxceOopuPhiWLfO7DQizc/nU37U1tby17/+lY8++oiamhr+/ve/88c/\\n/tGrr6lHrcVdqhXxhOrFP739Ntx+O/z3v9Czp9lpnFQr4gm/mfIjJSWFNm3asGrVKoqKipg7dy5X\\nX321r2OIiEiQuuoqeP55+MMfYNUqs9OINB+fj7R99tlnXHDBBYdtmzVrFsOGDfPaa+o3HHGXakU8\\noXrxb/PnO9crXbAALrzQ3CyqFfHE0erF71ZE8Ab9sIi7VCviCdWL/3v/fRg+HPLzoU8f83KoVsQT\\nfnN5VMxlGAZz5rzD6NGP8fHHS82OI35u69atjB//JBMnPkdlZaXZcUQ8duml8PrrMGQIFBWZnQbe\\nf38Ro0c/xrx576mJE4+paWthdu/ezUcffUN09K288UYhjY2NZkcSP/bRR8X8+GMqX3/dic8/1+N4\\nEph+/3uYOxeuuALMnKaztraWefOKiY6+jfffL3fNMSriLjVtLUxcXBydOoXx/ff/Ijn5NEJCVAJy\\ndOee24XGxmIiI7/ktNM6mR1HpMlycuDNN+Hqq6GgwJwM4eHhnHNOe77//g1OPz2KqKgoc4JIwNI9\\nbS2Qw+Fg165dJCYmHraWq6hWjmTHjh2Eh4dz0kknmR3F76heAk9hIVxzDbz7LhxlLnavOFgrdXV1\\n7Nixg1NOOYXWrVv7LoAEFD2IEPynKc1AtSKeUL0Epg8+gJtugoULoUcP37ymakU8oQcRREREcM7f\\n9txzcMkl8OWXZqcRcV/ANW0lJSWkpaWRkZHB2LFjAZg6dSoZGRnk5uZSX19vckIREfF3V14Jkyc7\\nl7z65huz04i4J+Cati5dulBQUEBRURG7du1i2bJlFBYWUlRURHJyMvn5+WZHFBGRADBsGNx3n/Mh\\nhW3bzE4jcnwB17S1b9+e8PBwAFq1akV5eTlZWVkA5OTkUFxcbGI6EREJJKNGwZ//7JwWRFMRir8L\\nuKbtoPXr17N7927i4uKwWq0AWK1WbDabyclERCSQjB/vbNoGD4b9+81OI3J0ATnfw549exgzZgzz\\n5s1j1apVbPtpXNtutxMXF3fEffLy8lyfZ2VluUbnREREHn8crrsOcnOd87mFhpqdSOTXAm7Kj/r6\\negYNGsRDDz3EBRdcwK5duxgxYgTvvfceU6ZMoWvXrlx11VWH7aNHrcVdqhXxhOoluNTWOp8s7d4d\\npk0Di6X5jq1aEU8EzZQfB0fXxo8fT9++ffnmm2/IzMwkIyOD9evXM2TIELMjiohIAGrdGt55B5Yu\\nhalTzU4j8msBN9LWFPoNR9ylWhFPqF6CU0UFpKXBpElw7bXNc0zVinjiaPUSkPe0tXSGYWBpznF7\\nCWqqFxHPJCbCggXQrx907QoXXti8x9fPpDRVwF0ebelKS8v4858fYeLE6VRXV5sdR/xYXV0dTz01\\ng1tueZiiIk2FI+KJpCSYMQOuuAK2bm2eYxqGwb/+9S5/+tNE/vWvdzXyJh5T0xZg3ntvBa1aXc2m\\nTSfz9ddfmx1H/NiWLVv4/PN64uJu5Z13PjU7jkjAuewyGDsWBg2CmpoTP15NTQ0ffFDOqaeO44MP\\nyti3b9+JH1RaFDVtASYlpRvV1QuIjf2O0047zew44sdOPfVU2revZvfuN7jwwjPNjiMSkMaOhZ49\\n4YYboLHxxI4VGRnJeee1Zdu2FznvvHZEREQ0T0hpMfQgQgDavXs3kZGRREVFmR0l6ARbrezfv5+9\\ne/fSrl073UPjBcFWL3JkdXXQvz+kp8MjjzTtGAdrpb6+nh9++IGTTz6ZsDDdVi5HdrT3FjVtIodQ\\nrYgnVC8txw8/QO/e8OSTzvvcPKVaEU+oaQv+05RmoFoRT6heWpbVq52T7y5bBmef7dm+qhXxRNBM\\nrisiImKGXr3gscfg8suhqsrsNNISBVzTtmPHDnr27ElERASNP90VOnXqVDIyMsjNzaW+vt7khCIi\\nEqxGjIDMTBg+HDRwJr4WcE1bQkICS5Ys4Xe/+x0Au3btorCwkKKiIpKTk8nPzzc5oYiIBLNp05xz\\nt2mpK/G1gGvaWrduTVxcHOCcqHDVqlVkZWUBkJOTQ3GxJhEVERHvad0a3n4bnnoKCgrMTiMtScA1\\nbb+0d+9erFYrAFarFZvNZnIiEREJdp06waxZkJsLu3aZnUZaioBu2iwWC7GxsdjtdgDsdrtrFM5f\\nVVRUUFZWpnvv5Ljq6upYv349O3fuNDuKiBxB//5w002eTbxrt9tZt26d698tEU8EdNNmGAa9e/dm\\n6dKlACxatIjU1NQjfm9eXp7ro7Cw0Icpf7Zt2zb+9re5TJ5cwttvv29KBgkcr7wyj6lT15KXN5Mf\\nf/zR7DgicgQPPeRc4sqd+9vq6+t59NGXeeKJch577BUaGhq8H1CCSsBNx1xfX8+AAQNYt24dAwYM\\n4JFHHiEzM5OMjAw6d+7M2LFjj7hfXl6eb4MewY8//khtbVvatDmXLVtKzY4jfu67734gJqYfNTVF\\n2Gw2TjrpJLMjicgvhIXBG284J97NyIA+fY7+vbW1tezcuZ/4+FR27JhLXV2dlrISj2hyXR86cOAA\\nb7wxn4qKPVx//e+1dqgf8pdaAfj66w289VYBZ5+dyJVXXkJISEAPjAclf6oXMdeCBXD77bB2LSQk\\n/PrrB2tl2bJPKShYT79+vyU9/Xe+DyoBQSsiBP9pSjNQrYgnVC9yqLvvhm++gfx8+OVSv6oV8YRW\\nRBAREfGiyZOhogKef97sJBKsNNImcgjVinhC9SK/tGEDpKVBYSF07/7zdtWKeEIjbSIiIl7WrRtM\\nmgTXXQe1tWankWCjps1D+/fv129L4paGhgbq6urMjiEiPvanP8FZZ8GSJb/+mmEY+ndEmkyXRz0w\\nb957vPfeWn7720TuuONGQkNDmyGd+JPmqpVdu3bx2GMzqak5wJ13Xs65557TDOnE3+iSlxxNYyMc\\n+sC3xWKhsbGRl1/+F0VFm7joom6MGHENll8+sSCCLo+eMMMw+OCD1XTs+Bc+/9zO7t27zY4kfuzL\\nL79k9+7uwB8oKlpndhwR8bEjzdBTXV1NUdFmOne+l2XLNrFv3z7fB5OApqbNTRaLhb59k9i2bRrn\\nnBOhiU7lmLp160ZcXCkNDQtJTe1+/B1EJOhFR0dzwQWJfPfdk1x44WlERkaaHUkCjC6PesAwDOx2\\nO9HR0bo0GqSa83JXbW0t9fX1REVFNcvxxP/o8qi462CtNDY2UlVVhdVq1aVROaqgvzx69913k5mZ\\nyV133XXU76mrqzuhG8MPLlCvhi34GYZBTU3NCf2D3Lp1azVsInIYi8VCWFjArSApfiIomrY1a9ZQ\\nU1PDsmXLqKurY9WqVb/6ns2bN3PnnU9w991P8t1335mQUgKFYRi88MJcRo16mpdf/pdGUkSkWei9\\nRU5UUDRtJSUlXHzxxQDk5ORQXFz8q+/57LMyamsz2LevD6tXl/k6ogSQ6upqiou3cNpp41i+/Bvd\\nLCwizULvLXKigqJps9lsxMTEABAbG4vNZvvV9/TqdS7h4UW0afMpPXqc6+uIEkCio6NJSenIli1P\\nkJraRTcLi0iz0HuLnKiguLAeGxuL3W4HYO/evcTFxf3qe15//XVOPrkegO+++47TTz/dpxklcFgs\\nFkaNymXYsGqio6N1s7CINAu9t8iJCoqnR9euXcsLL7zAP//5T0aPHs3w4cPp3bu36+t6wkvcpVoR\\nT6hexF2qFfHE0eolKEbaevToQZs2bcjMzKRHjx6HNWwAF110kX6jEbeoVsQTqhdxl2pFPBEbG3vE\\n7UEx0nY8+g1HjuTAgQOsW7cOq9VKt27dANWKHF1FRQVbt27lvPPOIzo6GlC9yJHpvUVOVFCP4MJ0\\n+wAAIABJREFUtIk0xTvvLGT+/D2EhVXyf/+H681V5JcqKyt5+OHZ1NR0pXv39UyYcKvZkcSP6b1F\\nvCUonh4VaYoffrATFnYa9fXxrgdZRI5k37597N/fmqioM9m9W7Uix6b3FvEWrzZt5eXlpKWlkZmZ\\nyciRIwHnddq+ffuSnZ3tmppjzpw5pKWlMXDgQKqqqgBYsmQJffr0ITs7m4qKCgDKyspIT08nPT2d\\n0tJSALZv3052djZpaWksXrzYm6cjQebqqy8mJWU7gwYlcP7555sdR/xYhw4dyM3twdlnr2fkyCFm\\nxxE/p/cW8Rav3tNWX1/vWq5jxIgRjBkzhjvuuIOioiLX9xw4cIB+/fpRWFjI22+/zZYtW7jnnnvI\\nzs5mwYIFlJeXM2vWLJ599lmuuOIKnnnmmZ8emx5Ffn4+d9xxB9deey3JyclcdtllFBQU/PokdS+B\\nuEm1Ip5QvYi7VCviCVPWHj10fTWHw0FsbCxffvklmZmZ3HfffQBs3LiRpKQkQkJCXKsZOBwOIiIi\\niIqKIiUlhfLycsB5X0liYiIdOnRwjdKVlZWRmppKVFQUMTExrpE6ERERkWDi9Xva5s+fT1JSEm3a\\ntKFr165s2rSJZcuWUVlZyYIFC9i7dy9WqxUAq9WKzWbDZrO5tgE0NDQA0NjY6Np2sAM9+DU4+moI\\nIiIiIoHO603boEGDKC0tJSYmho8//ti1WsGQIUMoKys7bDUDu91OXFzcYdsAQkNDAQ6b4yYkJOSw\\nPw/uHx8ff8QceXl5ro/CwsJmPUcRERERb/PqlB91dXWEh4cDzlE0h8NBY2MjISEhLF++nPPPP59u\\n3bpRVlZGY2MjixYtIjU1lcjISBwOBzU1NZSXl9O9e3cAEhISqKiowGKxuCaeS05OZsWKFSQlJWG3\\n213zJ/1SXl6eN09VRERExKu82rQtXLiQJ598EsMwOP3007n88su54IILiI6OpmvXrjz88MNYLBZu\\nueUWMjIySEhIYO7cuQDcf//99O/fn4iICGbOnAnAQw89xNChQ7FYLEyfPh2A8ePHM2zYMBwOBxMn\\nTvTm6YiIiIiYRisiiBxCtSKeUL2Iu1Qr4gmtiCAiImKyAwfgv/+F1avBZoO2beHCC6FvX2jVyux0\\n4u+0IoKIiIiPhITAq6+CYUDXrlBTAw884Pz8pZfgkEkSRH5Fl0dFDqFaEU+oXsRdx6uVlSvhrrsg\\nKgpmz4b27X0YTvyOKZPrioiIyPGlpMCyZc4/09Jg82azE4k/0j1tIiIifiAsDB55BBIT4aKL4JNP\\noFMns1OJP1HTJiIi4kdGjQKHA/7wB1i+HH6ak15E97SJHEq1Ip5QvYi7PK0Vw4DRo2HHDvjPf+CQ\\nBYGkBdA9bSIiIgHCYoGnnoItW+DZZ81OI/5CI20ih1CtiCdUL+KuptbK//7nnMetqAjOOccLwcQv\\naaRNREQkwPzmN5CXB7fcojncxMtNW3l5OWlpaWRmZjJy5EgApk6dSkZGBrm5udTX1wMwZ84c0tLS\\nGDhwIFVVVQAsWbKEPn36kJ2dTUVFBQBlZWWkp6eTnp5OaWkpANu3byc7O5u0tDQWL17szdMRERHx\\nuVGjnPe4Pf+82UnEdIYXHThwwPX58OHDjZUrVxqXXHKJYRiGMXnyZGPevHlGXV2dkZGRYTQ0NBhv\\nvvmmMXXqVMMwDKNv375GdXW1UVJSYowePdowDMO4/PLLjW3bthkVFRXG4MGDDcMwjDFjxhiffvqp\\nUV1dbWRlZR0xh5dPU4KIakU8oXoRd51orZSXG8bJJxvGDz80UyDxa0erF6+OtIWF/TyjiMPh4LPP\\nPiMrKwuAnJwciouL2bRpE0lJSYSEhLi2ORwOIiIiiIqKIiUlhfLycgAqKytJTEykQ4cO2Gw2wDn6\\nlpqaSlRUFDExMa6ROhERkWBx7rkwdChMnGh2EjGT1+9pmz9/PklJSbRu3Zr4+HisVisAVqsVm82G\\nzWY75jaAhoYGABoPuaBv/HSD3sGvAcTGxrqaORERkWDyt7/B3Lnw9ddmJxGzeL1pGzRoEKWlpVit\\nVqKiorDb7QDY7Xbi4uKIjY095jaA0NBQwPk0hSt4SMhhfx7cPz4+/og58vLyXB+FhYXNeo4iIiLe\\n1rYt3Huv80NaJq+uiFBXV0d4eDjw8yja0qVLGTduHIsWLSI1NZVu3bpRVlZGY2Oja1tkZCQOh4Oa\\nmhrKy8vp3r07AAkJCVRUVGCxWIiNjQUgOTmZFStWkJSUhN1uJzo6+ohZ8vLyvHmqIiIiXjd6tHP+\\ntjVroGdPs9OIr3m1aVu4cCFPPvkkhmFw+umn89BDD/H999+TkZFB586dGTt2LGFhYdxyyy1kZGSQ\\nkJDA3LlzAbj//vvp378/ERERzJw5E4CHHnqIoUOHYrFYmD59OgDjx49n2LBhOBwOJupiv4iIBLGI\\nCJgwwTkNyPz5ZqcRX9PkuiKHUK2IJ1Qv4q7mrJX9++GMMyA/H3r3bpZDip/R5LoiIiJBoE0b52jb\\nQw+ZnUR8TSNtIodQrYgnVC/iruaulf37oUsXWLwYfrrtW4KIRtpERESCRJs2cPvt8MQTZicRX9JI\\nm8ghVCviCdWLuMsbtfLjj3DmmVBWBh06NOuhxWQaaRMREQkiJ50E118PzzxjdhLxFY20iRxCtSKe\\nUL2Iu7xVK998AykpsHkzxMQ0++HFJBppExERCTJdu0LfvvDaa2YnEV9Q0yYiIhLAxoyB554DDfoG\\nPzVtIiIiASwjA8LCYMkSs5OIt6lpExERCWAWi3NN0p9Wd5Qg5tWmraSkhLS0NDIyMhg7diwAsbGx\\n9O3bl+zsbGw2GwBz5swhLS2NgQMHUlVVBcCSJUvo06cP2dnZVFRUAFBWVkZ6ejrp6emUlpYCsH37\\ndrKzs0lLS2Px4sXePB0RERG/lJsLS5fC1q1mJxFv8urTozt37iQ+Pp7w8HByc3OZMGECI0eOpKio\\nyPU9Bw4coF+/fhQWFvL222+zZcsW7rnnHrKzs1mwYAHl5eXMmjWLZ599liuuuIJnnnkGi8XCqFGj\\nyM/P54477uDaa68lOTmZyy67jIKCgl+fpJ7wEjepVsQTqhdxly9q5Y47wGqFv//dqy8jPmDK06Pt\\n27cnPDwcgFatWhEaGsqXX35JZmYm9913HwAbN24kKSmJkJAQcnJyKC4uxuFwEBERQVRUFCkpKZSX\\nlwNQWVlJYmIiHTp0cI3SlZWVkZqaSlRUFDExMa6ROhERkZZk1Ch4+WWorTU7iXiLT+5pW79+Pbt3\\n7+acc85h06ZNLFu2jMrKShYsWMDevXuxWq0AWK1WbDYbNpvNtQ2goaEBgMbGRte2gx3owa+B89Lr\\nwWZORESkJTn7bOc6pO+8Y3YS8ZYwb7/Anj17GDNmDPPmzQMgLi4OgCFDhrB27VoGDx6M3W4HwG63\\nExcXR2xsrGsbQGhoKOAcLjwoJCTksD8P7h8fH3/EHHl5ea7Ps7KyyMrKOvGTExER8SO33uocbfvj\\nH81OIt5wzKZtz549x9w5ISHhmF+vr68nNzeXxx9/nHbt2rFv3z5at25NaGgoy5cv5/zzz6dbt26U\\nlZXR2NjIokWLSE1NJTIyEofDQU1NDeXl5XTv3t31ehUVFVgsFmJjYwFITk5mxYoVJCUlYbfbiY6O\\nPmKWQ5s2ERGRYDRkiHMh+c2b4fTTzU4jze2YDyJ06dLlsNGtX9q8efMxD/7GG29w5513upquSZMm\\nMXr0aKKjo+natSszZszAYrEwe/Zsnn/+eRISEpg7dy4xMTEsXryYBx54gIiICGbOnEnHjh0pLS1l\\n5MiRWCwWpk+fTnJyMhUVFQwbNgyHw8HEiRPJycn59UnqZmFxk2pFPKF6EXf5slbuvhuio+Hhh33y\\ncuIFR6sXrT0qcgjVinhC9SLu8mWtlJXBgAHw7bfOSXcl8BytXtz+31lZWcnGjRvZv3+/a1tmZmbz\\npBMREZFmcd550LEjfPghXHqp2WmkObk10vbSSy8xbdo0tm7dSo8ePVixYgWpqaksCZA1M/TbsLhL\\ntSKeUL2Iu3xdKy+/DO+9B/n5PntJaUYnNE/bP/7xD1auXEmXLl0oKChg7dq1rgcBRERExL8MHQqF\\nhbBjh9lJpDm51bS1adOGiIgIAPbv38/ZZ5/N119/7dVgIiIi0jQxMXDVVTBzptlJpDm51bR16tSJ\\nyspKhgwZQv/+/Rk0aBBdunTxcjQRERFpqptvdl4m1RX84OHx06OFhYXY7XYGDBjgWqLK3+m+E3GX\\nakU8oXoRd5lRK4YBSUkwfTpcdJFPX1pOUJOm/LDb7Vit1qNOsnu8yXX9hd5YxV2qFfGE6kXcZVat\\nPPkkrFuny6SBpklN26WXXsr7779/xEl2LRYL33zzTfMn9QK9sYq7VCviCdWLuMusWtm1C7p1g+++\\nAz0/GDiaPLmuYRhs3bqV0047zWvhvE1vrOIu1Yp4QvUi7jKzVq68En7/e+e6pBIYTmjKj0suuaTZ\\nA4mIiIj3jRgBr7xidgppDsdt2iwWC7169WLlypW+yCMiIiLN6Pe/h23bnMtbSWBza6Tt4AoIXbt2\\nJSkpiaSkJJKTk4+7X0lJCWlpaWRkZDB27FgApk6dSkZGBrm5udTX1wMwZ84c0tLSGDhwIFVVVQAs\\nWbKEPn36kJ2dTUVFBQBlZWWkp6eTnp5OaWkpANu3byc7O5u0tDQWL17s+d+AiIhIEAsLgxtvhBkz\\nzE4iJ8qtKT++/fZb5zf/9DDCwV2ON1fbzp07iY+PJzw8nNzcXG699VYmT57M+++/z5QpU+jatSuD\\nBw+mX79+FBYW8vbbb7NlyxbuuecesrOzWbBgAeXl5cyaNYtnn32WK664gmeeeQaLxcKoUaPIz8/n\\njjvu4NprryU5OZnLLruMgoKCX5+k7jsRN6lWxBOqF3GX2bWyaRP06eMccQuQ2bpatBO6p61Lly7Y\\nbDbmz5/PggUL2Lt3r1uT67Zv3941l1urVq0oLy8nKysLgJycHIqLi9m0aRNJSUmEhIS4tjkcDiIi\\nIoiKiiIlJYXy8nLAuWh9YmIiHTp0wGazAc7Rt9TUVKKiooiJiXGN1ImIiIjTGWfAOefAggVmJ5ET\\n4fbao7m5uezevZudO3eSm5vLtGnT3H6R9evXs3v3buLi4rBarQBYrVZsNhs2m+2Y2wAaGhoAaGxs\\ndG072IEe/BpAbGysq5kTERGRn/3pT7pEGujC3Pmml19+mZKSEqKiogCYMGECv/vd77jjjjuOu++e\\nPXsYM2YM8+bNY9WqVWzbtg1wTtwbFxdHbGwsdrv9qNsAQkNDAQ6bKy4kJOSwPw/uHx8ff8QceXl5\\nrs+zsrJcI34iIiItwZVXwl13QUUFJCaanUaawq2mDQ5vjg79/Fjq6+vJzc3l8ccfp127dvTu3Zvn\\nnnuOcePGsWjRIlJTU+nWrRtlZWU0Nja6tkVGRuJwOKipqaG8vJzu3bsDzhUYKioqsFgsxP40S2By\\ncjIrVqwgKSkJu91OdHT0EbMc2rSJiIi0NFFRcPXVztUR/u//zE4jTeFW0zZ8+HAuvPBCrrjiCgzD\\nID8/nxEjRhx3v4Oja+PHjwdg0qRJZGZmkpGRQefOnRk7dixhYWHccsstZGRkkJCQwNy5cwG4//77\\n6d+/PxEREcz8af2Nhx56iKFDh2KxWJg+fToA48ePZ9iwYTgcDiZOnNikvwQREZGWYMQIuP56mDAB\\n3Bx/ET/i9oLxq1evZvny5VgsFjIyMujRo4e3szUbs5/akcChWhFPqF7EXf5SK1pEPjA0eRkrgBtu\\nuIHXX3/9uNv8lb/8sIj/U62IJ1Qv4i5/qpUnn4TPP4dZs8xOIkdzQlN+lP1iGuX6+npWr17dPMlE\\nRETEZ264AebPh717zU4injpm0/boo48SExNDaWkpMTExro927doxaNAgX2UUERGRZtK2LfTrB//6\\nl9lJxFNuXR697777mDRpki/yeIU/DUuLf1OtiCdUL+Iuf6uV//4XHnoISkrMTiJHckKXRy+44ILD\\nJq212Wzk5+c3XzoRERHxmYsv1iLygcitkbbzzz+fdevWHbbtt7/9LZ9//rnXgjUnf/sNR/yXakU8\\noXoRd/ljrdx/PzgczgcTxL+c0EjbkXY8dPkoERERCSzDh8Ps2VBXZ3YScZdbTVuvXr0YO3Ys//vf\\n/9i0aRN33303vXr18nY2ERER8ZIzzoBzz9Ui8oHErabtmWeeoVWrVgwdOpQ//vGPtGnTxrUigYiI\\niASmESPglVfMTiHucntFhEDmj/cSiH9SrYgnVC/iLn+tlX37oGNHWL/e+af4hxO6p23Xrl3cc889\\nXHLJJfTt25e+ffuSnZ193P127NhBz549iYiIoLGxEYDY2FjX/gefSJ0zZw5paWkMHDiQqqoqAJYs\\nWUKfPn3Izs6moqICcE7ym56eTnp6OqWlpQBs376d7Oxs0tLSWLx4sTunIyIiIkBk5M+LyEsAMNyQ\\nk5NjvPTSS8ZZZ51lFBYWGjfddJMxbty44+63f/9+o7Ky0sjKyjIaGhoMwzCM9PT0w76nrq7OyMjI\\nMBoaGow333zTmDp1qmEYhtG3b1+jurraKCkpMUaPHm0YhmFcfvnlxrZt24yKigpj8ODBhmEYxpgx\\nY4xPP/3UqK6uNrKyso6Yw83TFFGtiEdUL+Iuf66VkhLD6NrVMH76Z1r8wNHqxa2Rth9//JGbb76Z\\n8PBwLrroIl599VWWLFly3P1at25NXFzcYdu+/PJLMjMzue+++wDYuHEjSUlJhISEkJOTQ3FxMQ6H\\ng4iICKKiokhJSaG8vByAyspKEhMT6dChg2uUrqysjNTUVKKiooiJiXGN1ImIiMjxXXABRETAsmVm\\nJ5HjcatpCw8PB+CUU07hvffeY82aNVRWVjbpBTdt2sSyZcuorKxkwYIF7N27F6vVCoDVasVms2Gz\\n2Vzb4OfpRQ5eYoWfpyE5dOqR2NjYwyYBFhERkWOzWOBPf4IZM8xOIscT5s43/fWvf8Vms/HEE08w\\nZswY7HY7Tz31VJNe8ODI25AhQ1i7di2DBw/GbrcDYLfbiYuLIzY21rUNIDQ0FHDemHdQSEjIYX8e\\n3D8+Pv6Ir5uXl+f6PCsri6ysrCblFxERCTa5uc5lrfbuhdhYs9PI0Ryzabv33nuZPHky+/btIy4u\\njri4OAoLC5v0QoZhsG/fPlq3bk1oaCjLly/n/PPPp1u3bpSVldHY2MiiRYtITU0lMjISh8NBTU0N\\n5eXldO/eHYCEhAQqKiqwWCzE/lRVycnJrFixgqSkJOx2O9HR0Ud8/UObNhEREflZ27aQk+NcRP62\\n28xOI0dzzCk/zjvvPEpLS+nZsydr1671+OD19fUMGDCANWvW0KtXLx555BFGjhxJdHQ0Xbt2ZcaM\\nGVgsFmbPns3zzz9PQkICc+fOJSYmhsWLF/PAAw8QERHBzJkz6dixI6WlpYwcORKLxcL06dNJTk6m\\noqKCYcOG4XA4mDhxIjk5Ob8+ST991Fr8j2pFPKF6EXcFQq3897+QlwcrV5qdRI5WL8ds2saNG8dL\\nL71EdXU1ERERvzrgoZcw/Vkg/LCIf1CtiCdUL+KuQKiVhgbo3Bk++ACSksxO07I1qWk7aNCgQcyf\\nP98rwXwhEH5YxD+oVsQTqhdxV6DUyv33OyfcbeJt69JMTqhpO57U1FSKi4tP9DBeEyg/LGI+1Yp4\\nQvUi7gqUWvnmG0hJgS1bnBPvijlOaEWE49m/f39zHEZERERM1LUr9OkDc+aYnUSOpFmaNhEREQkO\\nY8bAtGkQAAODLY6aNhEREXHJyXE+lLB0qdlJ5Jfcatq++OKLX21r6nxtIiIi4r8sFrj9dudom/gX\\nt5q2a665hsmTJ7smyB0zZgwTJkxwfX3WrFleCygiIiK+NWyYc6Ttu+/MTiKHcqtpKykpYevWraSm\\nppKSksKpp57Kp59+6vp6kiZ0ERERCRrR0XDjjfDcc2YnkUO51bSFhYURERGBw+Fg//79dO3a9bA1\\nP0VERCS4jB4Nr7zinLdN/INbnVdKSgpt2rRh1apVFBUVMXfuXK6++mpvZxMRERGT/OY3kJEBM2aY\\nnUQOcqtpe/nll3n44Ydp1aoVp556KvPnz2fgwIHH3W/Hjh307NmTiIgIGhsbAZg6dSoZGRnk5uZS\\nX18PwJw5c0hLS2PgwIFUVVUBsGTJEvr06UN2djYVFRUAlJWVkZ6eTnp6OqWlpQBs376d7Oxs0tLS\\nWLx4sed/AyIiInJE994LTzwBP/1zLWYzvGj//v1GZWWlkZWVZTQ0NBg7d+40LrnkEsMwDGPy5MnG\\nvHnzjLq6OiMjI8NoaGgw3nzzTWPq1KmGYRhG3759jerqaqOkpMQYPXq0YRiGcfnllxvbtm0zKioq\\njMGDBxuGYRhjxowxPv30U6O6utrIyso6Yg4vn6YEEdWKeEL1Iu4K5FrJzDSMOXPMTtGyHK1evHpj\\nWuvWrYmLizvYHLJq1SqysrIAyMnJobi4mE2bNpGUlERISIhrm8PhICIigqioKFJSUigvLwegsrKS\\nxMREOnTogM1mA5yjb6mpqURFRRETE+MaqRMREZETd++9MGWKJtv1Bz59mmDv3r1YrVYArFYrNpsN\\nm812zG0ADQ0NAK5LrIBrTa6DXwOIjY11NXMiIiJy4v7wB2hshA8/NDuJhPnqhSwWC7GxsWzbtg0A\\nu91OXFwcsbGx2O32o24DCA0NdR3joINPrx76FKvdbic+Pv6Ir5+Xl+f6PCsryzXiJyIiIkdnscD4\\n8TB5MgwYYHaals1nTZthGPTu3ZvnnnuOcePGsWjRIlJTU+nWrRtlZWU0Nja6tkVGRuJwOKipqaG8\\nvJzu3bsDkJCQQEVFhasBBEhOTmbFihUkJSVht9uJjo4+4usf2rSJiIiI+4YOhQcegOXLIT3d7DQt\\nl8UwvHeVur6+ngEDBrBmzRp69erFI488QmFhIQsWLKBz58689tprhIWFMXv2bJ5//nkSEhKYO3cu\\nMTExLF68mAceeICIiAhmzpxJx44dKS0tZeTIkVgsFqZPn05ycjIVFRUMGzYMh8PBxIkTycnJ+fVJ\\nWix48TQliKhWxBOqF3FXMNTKjBkwZw5oogbvO1q9eLVp8xfB8MMivqFaEU+oXsRdwVArBw7AOefA\\nyy+D7jDyrqPVi5Y1EBERkeNq1QoefND5EeD9Z8BS0yYiIiJuue462LVLl0jNoqZNRERE3BIWBn/7\\nG/z1rxptM4OaNhEREXHb0KHO+9veesvsJC2PHkQQOYRqRTyhehF3BVutFBbCTTfBV19BmzZmpwk+\\nehBBREREmkVWFvToAf/4h9lJWhaNtIkcQrUinlC9iLuCsVY2boTUVPjiC2jXzuw0wUXztAX/aUoz\\nUK2IJ1Qv4q5grZW77wa7HV55xewkwUVNW/CfpjQD1Yp4QvUi7grWWrHb4dxzYe5cyMw0O03w0D1t\\nIiIi0qysVpg2DW67DWprzU4T/NS0iYiISJNdfjmceSZMmWJ2kuDn86bt22+/pX379vTt25cBAwYA\\nMHXqVDIyMsjNzaW+vh6AOXPmkJaWxsCBA6mqqgJgyZIl9OnTh+zsbCoqKgAoKysjPT2d9PR0SktL\\nfX06IiIiLZrFAs8+63ySVP8Me5cpI20XX3wxBQUFLFy4kF27dlFYWEhRURHJycnk5+dz4MABXnjh\\nBYqKirjhhht44YUXAPj73//Oxx9/zGOPPcakSZMAePDBB3nzzTd56623eOCBB8w4HRERkRbttNOc\\nI23XX6/LpN5kStNWUFBAZmYmTz/9NKtXryYrKwuAnJwciouL2bRpE0lJSYSEhLi2ORwOIiIiiIqK\\nIiUlhfLycgAqKytJTEykQ4cO2Gw2M05HRESkxRs+HM44w7nElXhHmK9fsEOHDmzcuJHw8HAGDx5M\\nVVUV7X6a4MVqtWKz2bDZbFit1qNuA2hoaACgsbHRtS0Yn8wREREJBBYLvPginH8+DBgA/fqZnSj4\\n+LxpCw8Pd31+2WWXYbVaXfen2e124uLiiI2NxW63H3UbQGhoKOB8LPagkJCjDxzm5eW5Ps/KynKN\\n7omIiEjzOPlkeP1152XSkhLnZVNpPj5v2qqrq4mOjgbgk08+YcyYMcydO5dx48axaNEiUlNT6dat\\nG2VlZTQ2Nrq2RUZG4nA4qKmpoby8nO7duwOQkJBARUUFFovlsJG4Xzq0aZPgsHPnTn788Ue6detG\\nWJjPS1kCSH19PRs2bODkk092jexLy7F//342bdpEYmIi8fHxZscJetnZ8Je/wJVXQlGR1iZtTj7/\\nl66oqIgHHniA1q1bk5mZSUpKCpmZmWRkZNC5c2fGjh1LWFgYt9xyCxkZGSQkJDB37lwA7r//fvr3\\n709ERAQzZ84E4KGHHmLo0KFYLBamT5/u69MRk+zcuZMHH5yJw5FA//5fccMNV5gdSfzY66+/Q0GB\\nncjIH3n44eG0bdvW7EjiQ9OmvU5ZWTgJCR/wyCOjiIqKMjtS0PvLX2DVKuf8ba+95rx0KidOKyJI\\nQCovL2fy5BVERvamQ4diHnzwz81yXNVKcHrwwens3p1JTc0K/u//Mjj77LOb5biql8Dw5z8/QuvW\\n12Gz5TN58rWccsopPs/QEmulpgb69oWLL4a//93sNIFFKyJIUDnrrLPo1y+WU0/9lOuv7292HPFz\\nN9xwMe3aFdG//0mceeaZZscRH7vllkuwWv/L5ZefQ/v27c2O02JERcH778O8efDMM2an8R+GAQ4H\\n7NgBX30Fa9fCTxNiHJdG2kQOoVoRT6hexF0tuVa+/RYyMpxTgdx2m9lpfKeyEj7/3NmUbdgAmzc7\\n/y62bHE2bnFxEBsLERHwm9/Av//9875HqxfdvS0iIiJe06ULFBRATo5zhOmuu8xO1PwMA77+GgoL\\nYelSKC6GH390Tn/SowckJcGgQXD66c4napt6W6VG2kQOoVoRT6hexF2qFecIU79+cPUptgbNAAAg\\nAElEQVTVznvcjjFLV0CorIQPPoAFC5xNaevWznv4srKgTx/nRMNNPcej1YuaNpFDqFbEE6oXcZdq\\nxWn3brjqKoiPd87nFhNjdiLPfPstzJ8P774Ln33mbNAGDoT+/Z0jis1FTVvwn6Y0A9WKeEL1Iu5S\\nrfysrg7GjHFeSnz9dUhJMTvR0RkGrFnjbNLefRe2b4fLLoPBg52Nmrdmj1HTFvynKc1AtSKeUL2I\\nu1QrvzZvHtx+O/z5zzBhgvOGfH9QW+u8L+3dd52jahERziZt0CDnZc+fFmTyKjVtwX+a0gxUK+IJ\\n1Yu4S7VyZBUVcOedsHo1TJnivHRqxkS8e/bAf//rbNI++gjOOcfZqA0eDGef7ftMatqC/zSlGahW\\nxBOqF3GXauXYCgqcqygcOAD33gvXXAOHLFXe7BoanNNxLF7sbNbWrHEuvzVoEFx6KZg9nZ+atuA/\\nTWkGqhXxhOpF3KVaOT7DcI5yTZ4MZWXOxu2Pf4QLL4RWrU7s2LW1sG4drFzpbBALC6FdO+fTrL//\\nvXM6En+5PAstoGm7++67Wb16NT179uTpp58+7Gv6YfE/1dXVfPHFF3Tq1IlTTz3V7DguqhX/tGHD\\nBqqrq0lOTiYszH+ml1S9nDiHw0FZWRmnnHIKnTp1MjuO16hWPLN5M8yZ45xw9ptvnJPz9uwJ550H\\nZ57pbLjatv15NM4wnMtm7dkDP/zg3GfTJti40dmsffGFc78LLoDMTGezlpho7jkeS1A3bWvWrOGf\\n//wnL774IqNGjWLEiBH07t3b9XX9sPifRx/9J19+mUBMzGYee2wkVqvV7EiAasUfffXVV0yatJCG\\nhliuuKI9V1xxidmRXFQvJ27atJmsXBlOZORWHnlkOG3btjU7kleoVppu925YtszZfJWVwf/+59y2\\nezc0Nv58v1nr1pCQ4Pw4/XRnk3bGGc5Gr0cPiIw09zw8EdQrIpSUlHDxxRcDkJOTQ3Fx8WFNG8CB\\nAwcoLFyOxWLhoovSaNWEsdYdO3awYsVazj33N5x11lke728YBitXrmLXrh+56KI+TWpUqqqqKCj4\\nhJNPjic1NQVLE+6OLCsrZ/36jaSn9+S0007zeP+9e/cyY8Z/qK9vZMSIIZx00kkeH2Pnzr1ER6ex\\nb18FNTU1ftO0HbR582bWrv2CXr3Oo3Pnzh7v39jYSFHRp9TUOOjbN52IJoy7//jjjyxfvpLOnTvw\\n29+e7/H+AJ9/vo5vvtlGZuaFnHzyyR7vv2/fPj78sJDIyDb065fZpFGuZcs+5cMP15CVlUT//hd5\\nvP/evXuprz+ZsLAO/PDDTo/397YjvbfU1dVRWLicsLBQMjPTjvj3tmbN52zduoOMjAtJSEg45ms4\\nHA4WLy4iOjqCjIw+hHrw+FpVVRUffbSU+HgrWVnphDRxts99+/bx2mv/obKyhhEjBjXbCPnOnXuJ\\njEyjtnYPVVVVQdu0SdO1bQtXXun8OJRhOO9NO9jbnOgl1EAQ4PMRO9lsNmJ+mqEvNjYWm832q+9Z\\nvHgZr75awaRJH7F06Scev4ZhGDz++GzefTeSsWOfOOJrHM+GDRt49tkSpk1bw+zZCzzeH2DOnAW8\\n9dYB8vLe4Msvv/R4/7179/L00+8xe/Y2Hn98bpN+81u+vITVq9vy8cd7WLLkU4/3Bxg9+nLOOGM1\\nSUkhnHLKKU06BkBhYWGT9z3aMWpra5ky5U1efHETU6a8QV1dncfHXL16NS+++BVPPvkJ8+d/3KRc\\nL7zwNv/+dwgTJrxARUWFx/tv376df/xjEf/855c899xbTcqwYMEi3n57P1OmfMTKlZ95vH9tbS2v\\nvrqEffsG88QTc6iqqvL4GD169ODSS6OJi1vMFVf093j/Q51ovRxp/6eemsarr1YwY8ZW13vLxx8v\\nZebM73n55W8pKvr1z8iWLVv4xz8KePttCy+99O/j5po//2PeeMPOSy9tYM2aNW5nA3jrrf/yzjsG\\nM2Z8xfr16z06t0OtXbuWTz5pzf/+150FC5Y26RhHcsstgzjnnDKuuaYb3333ncf7N0eG5j7G8fZv\\njozNfSx/O447x7JYICzM2awdq2ELxHM7mqBo2mJjY7Hb7YCzKYmLi/vV97z++kxKS9+htPQdPv98\\nbZNexzDAYglhx45vmri/AVjYubMMOLFh8p07NzZpv5CQEEJDoaJiJa1aNW2ymcTE9oSGbmLnziV0\\n7Ni0R2y6devGX/5yE1VVlU0aLTzIG2/QhmFgGAY7d66jsbFp/59+/n/9RZNzGYaBxRLC99//r8mX\\nVQwDdu5c1+QMrVqFYhi17NpVTliY5/XSqlUrOnWysmdPAdXV39GmTRuPjxEeHs511w0hPr5Vk0YL\\nD+WNf4zXrVuHxRIC/Hw5w1lDFo72FnuwPpz7ebfRCAsLxTDqgPpjjtAdL0O7du1o3XoL9fXrOe20\\ndk06xpF06dKFu+++kUsv7c/SpUduBj2hpi04jtOcx/K345zIsYLi8mhqaiovvPACV199NYsXL2b4\\n8OG/+p5XX32ZJUuKmD17JnfeebvHr2GxWPjLX66juHgtkZFnHbExPJ6zzjqLkSP38o//b+/Oo6I6\\nzz+AfxVlQJxhaUR/4DIIxqPACEiQRRAQAi5M0GKiAi6nLlWrralBTZRqxCh1aZCjjSepMUjqUXLE\\nJWlxAwQNIgxEFrXRAhqRBBdGHBwZYN7fH5YbENQ7wxUHeD7n9Jzh9r7PPHfuHOfNe9/nfRO+R1SU\\nXOf2ABAZGQYbmwsQif4Po0aN0rm9WCxGTEwE1q8vxgcfROvVYXJxGYONG83xt7+Vw9PzLZ3bGzoT\\nExN88MG7WLNmHWJiZsJYj7rzsWPHYsGCJ/j88wzI5fqNDi1eHIGsrFyIRFIMHjxY5/Y2NjZYuTIY\\nn3yiwNKl7+qVw5QpQZBIvsc//2ndZsoBH71798bq1b9DRUUFkpOv6jUtwdANHz4MPj5P/+PF3388\\nACA4eAKMjLJhZGSE8eO92rQZNmwYli/3w61bVZgwYToSExNf+B5yeTDMzLLRv/9AuLm56ZTfjBmT\\nMWhQDiwshsLJyUmnti2NGDECH3/8Hh4/fowRI0boHYcQor9u0WlzdXWFiYkJ/Pz84Orq2u6Pi7Gx\\nMUJDJ+LixWy9fzhsbW0REWGLkpJ8vdr36tUL3t7jcOrUCL3ncInFYsjloSgouKj3CJW9vT3efNOh\\nQ48lpVIpLC0tOzRKZsjs7e3h6DgSdnZ2erU3MjJCQIAfzp1L12s+GwC88cYbmD59CoqKdH8s2WzM\\nGBlGjXpT7xEqkUiEoKAAnD9/Tu+5UGZmZnB0dIRIJNKrvaEzMjJCaOjEVsdEIhEmTQp6YTt3dzfw\\n7Qebmppi6tS39cqvX79+CAmZ+PITedDnPx4IIcLpFtWjL+Pv7y/IsDvp/szNzfHw4cPXnQbpIuj7\\nQvii7wrRxYQJE9p9hNojOm2EEEIIIV1dtyhEIIQQQgjp7qjTRgghhBDSBfS4TtulS5d0Or+kpATX\\nrl1rdezixYtCptSpiouLcfDgQeTl6T+5XR/Hjx/H48ePO/U9uzK6T4QQQp7Vbee0abXaNscYYwgJ\\nCcGZM2d4xXj//fdRXV2Nvn374u7du9i3bx+sra0REBCAjIwM3rkUFhbCwsICdnZ2OH36NDQaDSZN\\nmqR3Nd7u3buxbNky3ueHhoYiLS0Nn376Kc6cOYOpU6fiwoULGDx4MLZs2cIrxp07d2BjYwOtVotj\\nx47h6tWrGD58OCIiInitkm9jY4OhQ4di4MCBmD59OuRyOSwtLXlfAwBoNBqkpaXhjTfegJeXF5KT\\nk1FbW4vIyEjeS7AUFRXh+++/h1KpxKBBgxASEsKt7C70fQJ0u1d0n1p70b16mcbGRhw9ehQ5OTlQ\\nKpWwsLCAl5cXwsPDO7x36Y4dO2BsbNwqLt/lUF5lXsRw5efnt7nn+iyhI7RHjx5BqVTC0tIS/fv3\\n7zZxDDEnoeJ0206bqakpPD092xy/fPkyHjx4wCuGr68vsrOzATz9AVm+fDm2b9+OmJgY3p22JUuW\\noL6+Hmq1GiYmJhCLxZBIJLh9+zb279/PK4dn9yArLS2Fk5MTsrKyeOXQ3Mn08/NDRkYGt8Cmj48P\\nLlzgtztEYGAg0tPTsWLFCvTr1w+BgYEoLCyEQqHA4cMvX22/OYeysjIcOXIE3377LYyNjREeHo6l\\nS5fyyiE8PBweHh5QKpVQKBSYPHkyfvOb3+DgwYM4efLkS9uvXr0aarUaLi4uSE9Ph4mJCYyMjODj\\n44OcnJwO3Seg4/eK7tOvXnSv5syZ89L2UVFRkMlkCAoKgkQiQW1tLc6cOYOioiIkJyfzyqGsrKzV\\nvezVqxc2bdqEtLQ07N69m6sGPHv2LPr06YOEhIROyUuIjl9HY1AO/GP86U9/gkajQVBQkF7fmWbF\\nxcVYv349lEolGGPo3bs3JBIJNm3aBJlMxjsOAJw9exZxcXEQi8Xc4vS1tbX46KOPEBT04qVqDDmO\\nIeYk5LUBAFg35erqympqatocnzhxIu8Y3t7erL6+nvv7/v37bNKkSWzAgAG8Y/j6+nKvnZycuNd+\\nfn682u/cuZPNnTuXpaenc8dCQ0N5vz9jjFlbW7OoqChma2vLHj9+zB0fO3Ys7xjNn9uzn5+/vz+v\\n9u2dV1VVxfbu3cs7h5YxHB0ddc4hICCg1d/N1xIYGNjh+8RYx+8V3adfvehe8TF+/Hidjrenf//+\\nbN68ea3+N3DgQGZjY9Pm3Jbfn1edV2RkJIuPj2cKhYJdv36dKRQKFh8fzyIjIzstBuXAP8bzvht8\\nvzPNfHx8WGVlZatjlZWVOn13mnl7ezOVStXqmEqlYl5eXl06jiHmJOS1McZYtx2P/+6779pd1DQt\\nLY13jJ07d6KmpgYDBz5d7dzKygrHjx9HSkoK7xhNTU3c682bN3Ov+S5Ku3LlStTX1+Mf//gHPvvs\\nM8yePVvnLY1yc3O5182jNyqVCps2beIdY86cOViwYAGGDBmCqKgo+Pn5oaioiPcQ/5o1a9ocGzRo\\nEBYtWsQ7B4lEgri4OCiVSlhbW2PHjh2wtLTkvViytbU14uPj4ezsjHPnzmH06NEAnj5K7+h9Ajp+\\nr+g+/epF94oPuVyOKVOmwN/fnxvROnfuHMLCwnjn4OTkhPj4eFhb/7pl08qVK3H8+HF88803EIvF\\nqK2txdmzZ3nvUiBEXjdv3mwzKufm5gZfX99Oi0E58I8xduxYLFq0CG+//bZe35mWnv33hP1vyz1d\\niUQiFBUVwcvr1906iouLdV4I3NDiGGJOQl4b0I0fjxqK0tJSjBw5stVQe/OcH7lct62sGhoacODA\\nAfz444/YunWr0Km+VGVlJU6ePIlffvkFFhYW8Pb2xpgxYzrt/evr61vNlTpw4ABqa2sRFRXFa95V\\nY2MjUlNTUV5ejpEjRyIsLAy9e/fGnTt3UFNTI9h9Al7vverq9wl48b2ysbHhFaO6uhr5+fl4+PAh\\nzM3N4e7ujoqKCnh4ePBq39DQ0G5HMykpCXV1dVAqlTA3N4eXlxdcXV15xRQir23btiEzM7NNx8/P\\nzw8xMTGdEoNyaBsjICCA65Q9G6OgoAC5ubl6f2eAp0VxsbGxqKmpgVarRa9evWBlZYWNGzfC2dlZ\\np1h37tzB1q1bUVJSgqamJvTu3RsymQwxMTGwtbXtsnEMMSchrw2gThshpBtqHpFr/uetea5haGgo\\nTp8+rVOMlnSN8SryAoCsrCyUlpbCwsKC6/iVlZW1O4/3efLy8pCXlwdLS0uYmZlBqVQiMjLyhZvK\\nt3T37l3k5+dzHZH8/HzExsbyfn/gaQdWoVAgPz8f9vb2cHBw4N15vXPnDvr06cN1gMvLyzFkyBDM\\nnDmT96jusWPH4ObmhpKSEu463N3dW42uvoxGo8HBgwdx69YtODg4oKGhARUVFVixYoVee1QT8iLU\\naSOEdDtCFCIJEeNVxBSiqr1lgZRIJIJEIun0AqmOVkt3tOgGEKZaurnwpqamBgqFAlOmTNG58IaP\\nyspKbNmyBaWlpWhqaoKRkREcHR2xZs0anfeEba+owdzcHB9//LFORQ2GFscQcxLy2gB030IEQkjP\\nJUQhkhAxXkXMlhPPL1++zPz8/NilS5d0KvQwhAKp5nx9fX1ZY2Mjd9zb25tX+44W3bQ897///S/b\\ntm0bmzBhAgsODma7d+/WOQZj+hfe8BEQEMByc3NbHcvNzeVdnNOSUEUNhhbHEHMS8toY68aFCISQ\\nnkuIQiQhYryKmFqtFhqNBsbGxpDJZEhNTUVUVBRKS0t5xzCEAqkrV64gOjoaZWVl0Gg03OdSX1/P\\nq31Hi25aGj58OFatWoVVq1bh559/xvHjx3m3FaLwho8nT57A0dGx1TFHR0eo1Wq94j17v5ieRQ2G\\nFscQcxLy2ujxKCGEdCG5ubmQSqVcVTvwtHAjJSUFs2bN4hXDEAqkKioquNc2NjYwNjaGSqVCdnY2\\nJk2axCtGR4tuTp48iZCQEN7nt0eIwhs+0tPTERcXB1NTU65oQq1W46OPPsLEiRN1iiVUUYOhxTHE\\nnIS8NoA6bYQQQkiXoVaruaKJfv36ve50SCejx6M9TF5eHhYsWIBLly6hsbER48aNw+HDh7l1sAgh\\nhBieR48eYe/evW12Xli8eDHEYrFOsYQqajC0OIaYk5DXBoAKEXqidevWsVWrVrFly5axrVu3vu50\\nCCGEvMTUqVPZoUOH2P3791lDQwO7f/8+O3ToEJs6darOsYQqajC0OIaYk5DXxhhj+u+ETbqs2NhY\\nnDp1Cvn5+bwXkCSEEPL6PHjwABEREbCyskKfPn1gZWWFiIgIvZafEaqowdDiGGJOQheQ0OPRHuje\\nvXuoq6tDU1MT1Go1zYsgbUilUhQUFMDKyqrDsb7++mv89a9/BWMMYrEYf//73/Vbn4h0e6zFosOk\\ntaVLl8Lf3x/Ozs5cIUJJSQmWLFmic6y4uDiEhYW1KWrQZcs8Q4xjiDkJeW0AFSL0SHK5HLNnz0ZZ\\nWRmqqqqQmJj4ulMiBsbOzg4KhUKQTltOTg5Gjx4Nc3NzpKWlYcOGDbh48aIAWZKO4Du/taqqCu+9\\n9x4ePXqExsZGfPbZZ/Dx8cGXX36JrVu3wsLCAjKZDCYmJkhMTMS8efMQFhaG3/72twCA/v37Q6VS\\nQaVSITw8HDU1NWhoaEBcXBzkcjkqKioQEhICT09PKBQK/Otf/8KhQ4eQkpKC+vp6TJs2DRs2bHgN\\nn5DhaWhowI0bN7hChDfffLNVBbCuhCpqMLQ4hpiTUHFopK2HSUpKgkgkwsyZM6HVauHt7c3tvUe6\\nB74/xpmZmYiNjYVEIsGNGzcQEBCAPXv2tBrlqKioQFhYGIqLiwEA27dvR11dHf7yl79g165d2Lt3\\nL/r06YPRo0fj4MGD7ebTcqPkcePG4fbt26/gqomu3nrrLcjlcqxbtw5qtRrR0dHtFiQdPHgQoaGh\\n+PDDD6HVavH48WNUVVVhw4YNKCgogEQiQUBAALcB+rOjZM1/m5qaIjU1FWKxGPfu3YOXlxe3vMiN\\nGzdw4MABeHh44NSpU7hx4wYuXboErVaLd955B9nZ2Tpt4t4dNTY24tixY20KEcLDw3XuuAlV1GBo\\ncQwxJyGvDQAVIhDSHfEpNsnIyGAmJiasvLycNTU1seDgYPbNN98wxhiTSqXs/v37rLy8vNWK+du3\\nb2cbN25kjDFmY2PDNBoNY4yxhw8f8spr27ZtbOHChR25NCIgjUbDZDIZGzduHNNqte2ek5WVxRwc\\nHNiGDRvYDz/8wBhjLDU1lc2dO5c7Z9euXewPf/gDY4yxefPmcd8jxhjr378/917Lli1jMpmMubi4\\nsH79+rFffvmFlZeXMzs7O+78P//5z0wqlTIXFxfm4uLCRowYwfbt2yf0pXc5kZGRLD4+nikUCnb9\\n+nWmUChYfHw8i4yM1DmWUEUNhhbHEHMS8toYo0IEQrolvsUmHh4ekEql6N27N2bNmoXz58+/NDb7\\n34wKmUyG2bNn4+uvv+a1yXhGRgb27duH+Ph4/hdCXqnm+a0qleq5E6N9fX2RnZ0NW1tbzJs3DwcO\\nHGiz72jL13369IFWqwXw6+4NwNO5jffu3UNBQQEKCwthbW2NJ0+eAADMzMxavefatWtRWFiIwsJC\\n/Pjjj5g/f76g190V3bx5EzExMXBzc4ODgwPc3NwQExODmzdv6hxLqKIGQ4tjiDkJeW0APR4lpFvi\\nW2zS8lEW+99mxi21/AEG0OqH/bvvvkNWVhZOnDiBzZs3o7i4+Lmdt6KiIixcuBBpaWmCrhJPOmbx\\n4sWIi4tDWVkZVq9e3e781lu3bsHW1hYLFixAfX09CgsLERMTgz/+8Y948OABxGIxUlJS4OrqCuBp\\nEYtCocCMGTNw/PhxNDQ0AABqa2thbW0NIyMjZGRkPLezERISgvXr1yMyMhJmZmaorKyEsbExBgwY\\n8Oo+iC5ALpdjypQp8Pf35ya0nzt3DmFhYTrHEqqowdDiGGJOQl4bQIUIhHRLfIpNMjMzMXnyZFy5\\ncgVDhw7FpEmT8Pvf/x7Tpk3jChHEYjFsbGzwn//8B2ZmZpgwYQImT56M9evX4+bNm5BKpWhoaIBU\\nKsXVq1chkUjavM+tW7cQGBiI5ORkeHp6dsblEx6SkpJw4sQJpKSkcPNbt27d2mZ+a1JSErZt24a+\\nfftCLBYjKSkJw4YNw/79+7FlyxZYWFjAxcUFxsbGSExMRHV1Nd555x2o1WqEhoZiz549qK2txf37\\n9xEWFgaVSgV3d3fk5ubi3//+N7RaLeRyOYqKirj33LVrF7744gsAgFgsRnJyMuzs7Drz4zFI1dXV\\nUCgUyM/Ph729PRwcHODh4aFXLKGKGhoaGnD9+nU8fPiww3GEKrIwtJyEygegThsh3Q7fH+Nz584h\\nNjYWYrEYN27cQGBgIPbs2QPg6Qba+fn5sLKyQmJiIhISEmBrawt7e3tIpVJ8+OGHCAgIwMOHD8EY\\nQ3R09HMfwy5cuBBHjhzB0KFDAQB9+/bFpUuXXulnQDrXV199hfz8fKpEf4VCQ0ORlpaGTz/9FGfO\\nnMHUqVNx4cIFDB48GFu2bNEpVmNjI44ePdrhoobmtgBw4sQJlJSUwMHBARERETot2yJUPoaYk1D5\\nNKNOGyE9VGZmJnbs2IETJ0687lRIF/fVV19BoVBg165drzuVbisgIAAZGRnw8/NDRkYGNxXBx8cH\\nFy5c0ClWVFQUZDIZgoKCuEd2Z86cQVFREZKTk3nHCQwMRHp6OtauXYuamhqEh4fj/PnzqKysxJdf\\nftnp+RhiTkLl04zmtBHSQ/Xq1YsWMSWc4uJizJkzp9UxExMT5OTkvLTt3LlzMXfu3FeVGgFw5coV\\nREdHo6ysDBqNBqampgCA+vp6nWPdvHmzTcfDzc1N52VVmsd8Lly4gKysLABPRwQnTJjwWvIxxJyE\\nyqcZddoI6eZe9GOs7z8c7dm/fz8SEhJaHRs/fjw9MusinJ2dUVhY+LrTIM+Rm5vLvW4eZVOpVHqt\\nrC9UUUNBQQF8fX1x9epV7jFgU1MTVCrVa8nHEHNqLx+tVqtzPs3o8SghhBDSw1RXVyM/P5+bHO/u\\n7o6Kigq9CxuaPX78GCUlJTrHycrKQmlpKSwsLLh8ysrK9CpeKikpgZGREUaNGsXlVFRUpHOsvLw8\\n5OXlwdLSEmZmZlAqlYiMjOS1xFFLhYWFMDc3x/Dhw3H69Gmo1WrY2tpi7NixOsUBqNNGCCGE9CjN\\ny/iwFnu9MsYQGhqK06dP6xynJX3ivP/++6iurkbfvn1x9+5d7Nu3D9bW1tw8Pl0IFWvJkiWor6+H\\nWq2GSCSCRCKBRCLB7du3sX//fr3imJiYQCwW6xWnGT0eJYQQQnoQMzOzdkedLl++/Fri5OXlITs7\\nG8DTNR1nzJiB7du36xRD6FilpaXcHDRnZ2duKz9dp5QIFacZddoIIYSQHmTUqFFITU3llqJoFhQU\\n9FriNO+cYWxsDJlMhtTUVERFRaG0tFSnOELGampq4l5v3ryZe61r8ZZQcbh29HiUEEII6Tmqqqpg\\nZWUFkUjU6nhjY6NOa5AJFSc3NxdSqRQDBw5sFSMlJQWzZs3iHUfIWKWlpRg5cmSr69BoNEhLS4Nc\\nLu/0OM2o00YIIYQQ0gXQhvGEEEIIIV0AddoIIYQQQroA6rQRQgghhHQB1GkjhBBCCOkCqNNGCCGE\\nkA754IMP4OTkhNWrV+vc9pNPPtHrPadMmYLa2toXniOVSvHgwQO94hsiqh4lhBBCSIdYWFigpqZG\\nr/XHxGIxHj16xPv8ljs5vIydnR0UCgWsrKx0zssQ0UgbIYQQQtrIy8vDmDFjUF9fj7q6Ojg5OeHK\\nlSttzpPL5VCpVHBzc8Phw4fx7bffwtPTE25ubggODkZ1dTWApxvcz58/HzKZDGPGjMGRI0ewdu1a\\nqNVquLq6Ijo6GgCwc+dOODs7w9nZGQkJCQCAiooKjBw5EnPnzoWzszN++umnVqNo06ZNg7u7O5yc\\nnPD555930ifU+WikjRBCCCHtWr9+PZ48eQK1Wo0hQ4Y89/Fny9EypVLJ7ZLwxRdf4Nq1a9i+fTtW\\nr16NhoYG7Ny5s9V5LdsqFArMnz8fubm50Gq1GDduHJKTk2FhYQF7e3vk5ORwm9G3HEWrqamBpaUl\\n1Go1PDw8kJWVBUtLy2430kbbWBFCCCGkXbGxsXB3d4epqSkSExN5tfnpp5/w7rvv4ueff4ZGo8Hw\\n4cMBAGfPnsWhQ4e4857d/goAzp8/j+nTp8PU1BQAMH36dGRnZ0Mul2PYsGFch+1ZCQkJOHr0KPf+\\n169ff+65XRk9HiWEEEJIu+7du4e6ujqoVCqo1WpebZYvX44VK1agqKgIe/fubdXuZQ/3evXq1eoc\\nxhg3d83MzKzdNpmZmTh79iwuXryIH374Aa6urnjy5AmvXLsa6rQRQgghpF2LFytSTZQAAAFESURB\\nVC9GXFwcZs+ezbsytLa2FjY2NgCA/fv3c8eDg4Oxe/du7m+lUgkA6Nu3LxobGwEAvr6+OHr0KNRq\\nNerq6nD06FH4+vq+sLNXW1sLS0tLmJiY4Nq1a7h48aKul9llUKeNEEIIIW0kJSVBJBJh5syZWLNm\\nDfLy8pCZmdnuuS0rOTds2IAZM2bA3d0dAwYM4P6/devWoaamBs7OznBxceFiLVq0CDKZDNHR0XB1\\ndcW8efPg4eEBT09PLFy4EGPGjGnzHi3/Dg0NRWNjI0aPHo21a9fCy8tL4E/CcFAhAiGEEEJIF0Aj\\nbYQQQgghXQBVjxJCCCHkpYqLizFnzpxWx0xMTJCTk/OaMup56PEoIYQQQkgXQI9HCSGEEEK6AOq0\\nEUIIIYR0AdRpI4QQQgjpAqjTRgghhBDSBVCnjRBCCCGkC/h/vzgGVH4ohUgAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ffb8c0bea10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 39\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"df.describe()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 40,\n       \"text\": [\n        \"              x  x_plus_2   x_square    x_factorial\\n\",\n        \"count  9.000000  9.000000   9.000000       9.000000\\n\",\n        \"mean   5.000000  7.000000  31.666667   45457.000000\\n\",\n        \"std    2.738613  2.738613  28.080242  119758.341137\\n\",\n        \"min    1.000000  3.000000   1.000000       1.000000\\n\",\n        \"25%    3.000000  5.000000   9.000000       6.000000\\n\",\n        \"50%    5.000000  7.000000  25.000000     120.000000\\n\",\n        \"            ...       ...        ...            ...\\n\",\n        \"\\n\",\n        \"[8 rows x 4 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 40\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Reading Data from CSV/TSV Files\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"url = \\\"http://www.google.com/finance/historical?q=TADAWUL:TASI&output=csv\\\"\\n\",\n      \"stocks_data = pd.read_csv(url)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 41\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"stocks_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 42,\n       \"text\": [\n        \"       \\ufeffDate      Open      High       Low     Close     Volume\\n\",\n        \"0  11-Aug-14  10579.12  10603.30  10547.21  10596.55  197234714\\n\",\n        \"1  10-Aug-14  10552.48  10614.11  10551.77  10579.12  199773735\\n\",\n        \"2   7-Aug-14  10478.34  10585.38  10478.34  10552.48  202329194\\n\",\n        \"3   6-Aug-14  10450.52  10494.12  10398.25  10478.34  192868941\\n\",\n        \"4   5-Aug-14  10405.81  10501.38  10405.81  10450.52  287651475\\n\",\n        \"5   4-Aug-14  10302.88  10409.47  10290.95  10405.81  223099538\\n\",\n        \"         ...       ...       ...       ...       ...        ...\\n\",\n        \"\\n\",\n        \"[241 rows x 6 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 42\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"stocks_data[\\\"change_amount\\\"] = stocks_data[\\\"Close\\\"] - stocks_data[\\\"Open\\\"]\\n\",\n      \"stocks_data[\\\"change_percentage\\\"] = stocks_data[\\\"change_amount\\\"] / stocks_data[\\\"Close\\\"]\\n\",\n      \"stocks_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 43,\n       \"text\": [\n        \"       \\ufeffDate      Open      High       Low     Close     Volume  \\\\\\n\",\n        \"0  11-Aug-14  10579.12  10603.30  10547.21  10596.55  197234714   \\n\",\n        \"1  10-Aug-14  10552.48  10614.11  10551.77  10579.12  199773735   \\n\",\n        \"2   7-Aug-14  10478.34  10585.38  10478.34  10552.48  202329194   \\n\",\n        \"3   6-Aug-14  10450.52  10494.12  10398.25  10478.34  192868941   \\n\",\n        \"4   5-Aug-14  10405.81  10501.38  10405.81  10450.52  287651475   \\n\",\n        \"5   4-Aug-14  10302.88  10409.47  10290.95  10405.81  223099538   \\n\",\n        \"         ...       ...       ...       ...       ...        ...   \\n\",\n        \"\\n\",\n        \"   change_amount  change_percentage  \\n\",\n        \"0          17.43           0.001645  \\n\",\n        \"1          26.64           0.002518  \\n\",\n        \"2          74.14           0.007026  \\n\",\n        \"3          27.82           0.002655  \\n\",\n        \"4          44.71           0.004278  \\n\",\n        \"5         102.93           0.009892  \\n\",\n        \"             ...                ...  \\n\",\n        \"\\n\",\n        \"[241 rows x 8 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 43\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/8 - SymPy.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:a8af92a8c641e7042d9850c27a420d6398556a582dddc3d5298f6d06777a7900\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"SymPy is symbolic mathematics library written completely in Python and doesn't require any dependencies.\\n\",\n      \"\\n\",\n      \"Finding Help:\\n\",\n      \"\\n\",\n      \"- http://docs.sympy.org/latest/index.html\\n\",\n      \"- http://sympy.org/en/features.html\\n\",\n      \"- http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-5-Sympy.ipynb\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"<table>\\n\",\n      \"<tr>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/numpylogo_med.png\\\"  style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>NumPy</h4> Base N-dimensional array package </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/scipy_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/matplotlib_med.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\\n\",\n      \"</tr>\\n\",\n      \"<tr>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/ipython.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>IPython</h4> Enhanced Interactive Console </td>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><img src=\\\"http://www.scipy.org/_static/images/sympy_logo.png\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td style=\\\"background:Lavender;\\\"><h4>SymPy</h4> Symbolic mathematics </td>\\n\",\n      \"    <td><img src=\\\"http://www.scipy.org/_static/images/pandas_badge2.jpg\\\" style=\\\"width:50px;height:50px;\\\" /></td>\\n\",\n      \"    <td><h4>Pandas</h4> Data structures & analysis </td>\\n\",\n      \"</tr>\\n\",\n      \"</table>\\n\",\n      \"\\n\",\n      \"> ###SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.\\n\",\n      \"> **http://sympy.org/**\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import SymPy\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sympy import *\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"3 + math.sqrt(3)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 2,\n       \"text\": [\n        \"4.732050807568877\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": 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  \"    ___\\n\",\n        \"3\\u22c5\\u2572\\u2571 3 \"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = sqrt(8)\\n\",\n      \"expr\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$2 \\\\sqrt{2}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"    ___\\n\",\n        \"2\\u22c5\\u2572\\u2571 2 \"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`symbols()` & `Symbol()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x, y = symbols(\\\"x y\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     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3\\n\",\n        \"(x + y) \"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Assumptions for symbols\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"a = Symbol(\\\"a\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"a.is_imaginary\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"b = Symbol(\\\"b\\\", integer=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"b.is_imaginary\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 13,\n       \"text\": [\n        \"False\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"c = Symbol(\\\"c\\\", positive=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"c.is_positive\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 15,\n       \"text\": [\n        \"True\"\n       ]\n      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 ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"I ** 2\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$-1$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 18,\n       \"text\": [\n        \"-1\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`Rational()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"Rational(1,3)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{1}{3}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 19,\n       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     \"$$0.833333333333333$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 21,\n       \"text\": [\n        \"0.833333333333333\"\n       ]\n      }\n     ],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"N(pi, 100)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117068$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 22,\n       \"text\": [\n        \"3.1415926535897932384626433832795028841971693993751058209749445923078164062862\\n\",\n        \"08998628034825342117068\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n  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   \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 26,\n       \"text\": [\n        \"   2\\n\",\n        \"\\u03c0\\u22c5x \"\n       ]\n      }\n     ],\n     \"prompt_number\": 26\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr.subs(x, 3)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$9 \\\\pi$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 27,\n       \"text\": [\n        \"9\\u22c5\\u03c0\"\n       ]\n      }\n     ],\n     \"prompt_number\": 27\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"N(_)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$28.2743338823081$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 28,\n       \"text\": [\n        \"28.2743338823081\"\n       ]\n      }\n     ],\n     \"prompt_number\": 28\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`factor()` and `expand()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = (x + y) ** 2\\n\",\n      \"expr\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\left(x + y\\\\right)^{2}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 29,\n       \"text\": [\n        \"       2\\n\",\n        \"(x + y) \"\n       ]\n      }\n     ],\n     \"prompt_number\": 29\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expand(expr)\"\n     ],\n     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 \"prompt_number\": 36\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`apart()` and `together()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = 1/(x**2 + 2*x)\\n\",\n      \"expr\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{1}{x^{2} + 2 x}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 37,\n       \"text\": [\n        \"   1    \\n\",\n        \"\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\n\",\n        \" 2      \\n\",\n        \"x  + 2\\u22c5x\"\n       ]\n      }\n     ],\n     \"prompt_number\": 37\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"apart(expr)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$- \\\\frac{1}{2 x + 4} + \\\\frac{1}{2 x}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 38,\n       \"text\": [\n        \"      1        1 \\n\",\n        \"- \\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500 + \\u2500\\u2500\\u2500\\n\",\n        \"  2\\u22c5(x + 2)   2\\u22c5x\"\n       ]\n      }\n     ],\n     \"prompt_number\": 38\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"together(_)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{1}{x \\\\left(x + 2\\\\right)}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 39,\n       \"text\": [\n        \"    1    \\n\",\n        \"\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\n\",\n        \"x\\u22c5(x + 2)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 39\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculus\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"diff(sin(x), x)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\cos{\\\\left (x \\\\right )}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 40,\n       \"text\": [\n        \"cos(x)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 40\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"diff(log(x**2 + 1) + 2*x, x)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{2 x}{x^{2} + 1} + 2$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 41,\n       \"text\": [\n        \" 2\\u22c5x      \\n\",\n        \"\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500 + 2\\n\",\n        \" 2        \\n\",\n        \"x  + 1    \"\n       ]\n      }\n     ],\n     \"prompt_number\": 41\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"integrate(cos(x), x)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\sin{\\\\left (x \\\\right )}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 42,\n       \"text\": [\n        \"sin(x)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 42\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"Integral(sin(x), (x,0,pi))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\int_{0}^{\\\\pi} \\\\sin{\\\\left (x \\\\right )}\\\\, dx$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 43,\n       \"text\": [\n        \"\\u03c0          \\n\",\n        \"\\u2320          \\n\",\n        \"\\u23ae sin(x) dx\\n\",\n        \"\\u2321          \\n\",\n        \"0          \"\n       ]\n      }\n     ],\n     \"prompt_number\": 43\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"N(_)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$2.0$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 44,\n       \"text\": [\n        \"2.00000000000000\"\n       ]\n      }\n     ],\n     \"prompt_number\": 44\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`Sum()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = Sum(1/(x**2 + 2*x), (x, 1, 10))\\n\",\n      \"expr\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\sum_{x=1}^{10} \\\\frac{1}{x^{2} + 2 x}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 45,\n       \"text\": [\n        \"  10          \\n\",\n        \" ____         \\n\",\n        \" \\u2572            \\n\",\n        \"  \\u2572      1    \\n\",\n        \"   \\u2572  \\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\n\",\n        \"   \\u2571   2      \\n\",\n        \"  \\u2571   x  + 2\\u22c5x\\n\",\n        \" \\u2571            \\n\",\n        \" \\u203e\\u203e\\u203e\\u203e         \\n\",\n        \"x = 1         \"\n       ]\n      }\n     ],\n     \"prompt_number\": 45\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr.doit()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{175}{264}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 46,\n       \"text\": [\n        \"175\\n\",\n        \"\\u2500\\u2500\\u2500\\n\",\n        \"264\"\n       ]\n      }\n     ],\n     \"prompt_number\": 46\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`Product()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = Product(1/(x**2 + 2*x), (x, 1, 10))\\n\",\n      \"expr\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\prod_{x=1}^{10} \\\\frac{1}{x^{2} + 2 x}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 47,\n       \"text\": [\n        \"    10             \\n\",\n        \"\\u252c\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u252c         \\n\",\n        \"\\u2502        \\u2502    1    \\n\",\n        \"\\u2502        \\u2502 \\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\u2500\\n\",\n        \"\\u2502        \\u2502  2      \\n\",\n        \"\\u2502        \\u2502 x  + 2\\u22c5x\\n\",\n        \"\\u2502        \\u2502         \\n\",\n        \"  x = 1            \"\n       ]\n      }\n     ],\n     \"prompt_number\": 47\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr.doit()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{1}{869100503040000}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 48,\n       \"text\": [\n        \"1/869100503040000\"\n       ]\n      }\n     ],\n     \"prompt_number\": 48\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"`Solve()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = 2*x + 1\\n\",\n      \"solve(expr)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\begin{bmatrix}- \\\\frac{1}{2}\\\\end{bmatrix}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 49,\n       \"text\": [\n        \"[-1/2]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 49\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = x**2 - 1\\n\",\n      \"solve(expr)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\begin{bmatrix}-1, & 1\\\\end{bmatrix}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 50,\n       \"text\": [\n        \"[-1, 1]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 50\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr_1 = 2*x + y + 3\\n\",\n      \"expr_2 = 2*y - x\\n\",\n      \"solve([expr_1, expr_2],(x,y))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\begin{Bmatrix}x : - \\\\frac{6}{5}, & y : - \\\\frac{3}{5}\\\\end{Bmatrix}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 51,\n       \"text\": [\n        \"{x: -6/5, y: -3/5}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 51\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Units\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sympy.physics import units as u\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 52\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"5. * u.milligram\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$5.0 \\\\cdot 10^{-6} kg$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 53,\n       \"text\": [\n        \"5.0e-6\\u22c5kg\"\n       ]\n      }\n     ],\n     \"prompt_number\": 53\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"1./2 * u.inch\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$0.0127 m$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 54,\n       \"text\": [\n        \"0.0127\\u22c5m\"\n       ]\n      }\n     ],\n     \"prompt_number\": 54\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"1. * u.nano\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$1.0 \\\\cdot 10^{-9}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 55,\n       \"text\": [\n        \"1.00000000000000e-9\"\n       ]\n      }\n     ],\n     \"prompt_number\": 55\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"u.watt\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{kg m^{2}}{s^{3}}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 56,\n       \"text\": [\n        \"    2\\n\",\n        \"kg\\u22c5m \\n\",\n        \"\\u2500\\u2500\\u2500\\u2500\\u2500\\n\",\n        \"   3 \\n\",\n        \"  s  \"\n       ]\n      }\n     ],\n     \"prompt_number\": 56\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"u.ohm\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$\\\\frac{kg m^{2}}{A^{2} s^{3}}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 57,\n       \"text\": [\n        \"    2\\n\",\n        \"kg\\u22c5m \\n\",\n        \"\\u2500\\u2500\\u2500\\u2500\\u2500\\n\",\n        \" 2  3\\n\",\n        \"A \\u22c5s \"\n       ]\n      }\n     ],\n     \"prompt_number\": 57\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Converting from Kilometers/hours to Miles/hours\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"kmph = u.km / u.hour\\n\",\n      \"mph = u.mile / u.hour\\n\",\n      \"\\n\",\n      \"N(mph / kmph)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$1.609344$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 58,\n       \"text\": [\n        \"1.60934400000000\"\n       ]\n      }\n     ],\n     \"prompt_number\": 58\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"80 * N(mph / kmph)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$128.74752$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 69,\n       \"text\": [\n        \"128.747520000000\"\n       ]\n      }\n     ],\n     \"prompt_number\": 69\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Working with NumPy / Pandas and Matplotlib\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def sympy_expr(x_val):\\n\",\n      \"    expr = x**2 + sqrt(3)*x - Rational(1,3)\\n\",\n      \"    return expr.subs(x, x_val)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 59\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sympy_expr(3)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"latex\": [\n        \"$$3 \\\\sqrt{3} + \\\\frac{26}{3}$$\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 60,\n       \"text\": [\n        \"    ___   26\\n\",\n        \"3\\u22c5\\u2572\\u2571 3  + \\u2500\\u2500\\n\",\n        \"          3 \"\n       ]\n      }\n     ],\n     \"prompt_number\": 60\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"import pandas as pd\\n\",\n      \"import matplotlib.pyplot as plt\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 61\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"list1 = np.arange(1,1000)\\n\",\n      \"list2 = pd.Series(list1)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 62\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%timeit [sympy_expr(item) for item in list1]\\n\",\n      \"%timeit [sympy_expr(item) for item in list2]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1 loops, best of 3: 103 ms per loop\\n\",\n        \"10 loops, best of 3: 105 ms per loop\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 63\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%timeit np.vectorize(sympy_expr)(list1)\\n\",\n      \"%timeit list2.apply(sympy_expr)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"10 loops, best of 3: 776 ms per loop\\n\",\n        \"1 loops, best of 3: 1.09 s per loop\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 64\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"expr = x**2 + sqrt(3)*x - Rational(1,3)\\n\",\n      \"\\n\",\n      \"lf = lambdify(x, expr)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 65\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%timeit lf(list1)\\n\",\n      \"%timeit lf(list2)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1000 loops, best of 3: 242 \\u00b5s per loop\\n\",\n        \"100 loops, best of 3: 4.8 ms per loop\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 66\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"x_vals = np.linspace(-5.,5.)\\n\",\n      \"y_vals = lf(x_vals)\\n\",\n      \"\\n\",\n      \"axes.grid()\\n\",\n      \"axes.plot(x_vals, y_vals)\\n\",\n      \"\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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ylTnPl5wZafDlLAhRB+V1OjFqp6/HFo2VJ3NOYwqoVyfM+JZvJnSYig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\\nuJQUcCGEcCkp4EII4VJSwIUQwqX+H67cpRQGSCAQAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7fc397382a90>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 67\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/BLEGH.md",
    "content": "\n# Tutorial Brief\n\nIPython notebook provides a variaty of web widgets that can interact with python code running the the background kernel.\n\nFinding Help:\n\n- http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb\n\n<table>\n<tr>\n    <td><img src=\"http://www.scipy.org/_static/images/numpylogo_med.png\"  style=\"width:50px;height:50px;\" /></td>\n    <td><h4>NumPy</h4> Base N-dimensional array package </td>\n    <td><img src=\"http://www.scipy.org/_static/images/scipy_med.png\" style=\"width:50px;height:50px;\" /></td>\n    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\n    <td><img src=\"http://www.scipy.org/_static/images/matplotlib_med.png\" style=\"width:50px;height:50px;\" /></td>\n    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\n</tr>\n<tr>\n    <td style=\"background:Lavender;\"><img src=\"http://www.scipy.org/_static/images/ipython.png\" style=\"width:50px;height:50px;\" /></td>\n    <td style=\"background:Lavender;\"><h4>IPython</h4> Enhanced Interactive Console </td>\n    <td><img src=\"http://www.scipy.org/_static/images/sympy_logo.png\" style=\"width:50px;height:50px;\" /></td>\n    <td><h4>SymPy</h4> Symbolic mathematics </td>\n    <td><img src=\"http://www.scipy.org/_static/images/pandas_badge2.jpg\" style=\"width:50px;height:50px;\" /></td>\n    <td><h4>Pandas</h4> Data structures & analysis </td>\n</tr>\n</table>\n\n# Why do we use widgets?\n\n> ##IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS running in the browser. These widgets enable users to explore their code and data interactively.\n> *http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb*\n\n# Import libraries\n\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\n```\n\n\n```python\nfrom __future__ import print_function\nfrom ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets\nfrom IPython.display import display\n```\n\n## Build-it Widgets\n\n\n```python\n# had to rename the modules from the widget library e.g. widgets.ButtonWidget is now widgegts.Button\n# also had to conda install ipython-notebook to get the widgets package (for those of you who still get errors and aren't sure you've installed it.)\n```\n\n\n```python\n    tab1_children = [widgets.Button(description=\"ButtonWidget\"),\n                 widgets.Checkbox(description=\"CheckboxWidget\"),\n                 widgets.Dropdown (values=[1, 2], description=\"DropdownWidget\"),\n                 widgets.RadioButtons(values=[1, 2], description=\"RadioButtonsWidget\"),\n                 widgets.Select(values=[1, 2], description=\"SelectWidget\"),\n                 widgets.Text(description=\"TextWidget\"),\n                 widgets.Textarea(description=\"TextareaWidget\"),\n                 widgets.ToggleButton(description=\"ToggleButtonWidget\"),\n                 widgets.ToggleButtons (values=[\"Value 1\", \"Value2\"], description=\"ToggleButtonsWidget\"),\n                 ]\n\n    tab2_children = [widgets.BoundedFloatText(description=\"BoundedFloatTextWidget\"),\n                 widgets.BoundedIntText(description=\"BoundedIntTextWidget\"),\n                 widgets.FloatSlider(description=\"FloatSliderWidget\"),\n                 widgets.FloatText(description=\"FloatTextWidget\"),\n                 widgets.IntSlider(description=\"IntSliderWidget\"),\n                 widgets.IntText(description=\"IntTextWidget\"),\n                 ]\n\n    tab1 = widgets.Box(children=tab1_children)\n    tab2 = widgets.Box(children=tab2_children)\n\n\n    i = widgets.Accordion (children=[tab1, tab2])\n\n    i.set_title(0,\"Basic Widgets\")\n    i.set_title(1,\"Numbers Input\")\n\ndisplay(i)\n```\n\n# Simple Example\n\nWe will define a function that print the factorial.\n\n$f(x) = x!$\n\n$f(x) = x \\times (x-1) \\times ... 1$\n\n$f(3) = 3! = 3 \\times 2 \\times 1 = 6$\n\n\n```python\n#new version of Python changes Print from statement to function. () needed now.\ndef factorial(x):\n    print (\"%s! = %s\" % (x,np.math.factorial(x)));\n\ndef factorial2(x):\n    if type(x) == int:\n        if x >= 0:\n            print (np.prod(np.arange(1,x+1)))\n        else:\n            print (\"ERROR: Number must be positive\")\n    else:\n        print (\"ERROR: Only interger is allowed\")\n```\n\nNow we will test it using a code cell\n\n\n```python\nfactorial(3)\n```\n\n    3! = 6\n\n\n\n```python\nfactorial2(3)\n```\n\n    6\n\n\n# Using interact function\n\nWe will link that to a slider to make the x a variable that we can control.\n\n\n```python\ni = interact(factorial, x=(0,100))\n```\n\n    50! = 30414093201713378043612608166064768844377641568960512000000000000\n\n\n## Controlling a Chart\n\n\n```python\n#This function plot x, y and adds a title\ndef plt_arrays(x, y, title=\"\", color=\"red\", linestyle=\"dashed\", linewidth=2):\n    fig = plt.figure()\n    axes = fig.add_subplot(111)\n    axes.plot(x,y, color=color, linestyle=linestyle, linewidth=linewidth)\n    axes.set_title(title)\n    axes.grid()\n    plt.show()\n```\n\nWe will define a function that return the following:\n\n$f(x) = ax^3 + bx^2 + cx + d$\n\nwhere a,b,c and d are are constants.\n\n\n```python\ndef f(a, b, c, d, **kwargs):\n    x=np.linspace(-10, 10, 20)\n    y = a*(x**3) + b*(x**2) + c*x + d \n    \n    title=\"$f(x) = (%s)x^{3} + (%s)x^{2} + (%s)x + (%s)$\" % (a,b,c,d)\n    \n    plt_arrays(x,y, title=title, **kwargs)\n```\n\n\n```python\n#Define Constants\na=0.25\nb=2\nc=-4\nd=0\n\n\nf(a, b, c, d,)\n```\n\n\n![png](output_24_0.png)\n\n\n\n```python\ni = interact(f,\n             a=(-10.,10),\n             b=(-10.,10),\n             c=(-10.,10),\n             d=(-10.,10),\n             color = [\"red\", \"blue\", \"green\"],\n             linestyle=[\"solid\", \"dashed\"],\n             linewidth=(1,5)\n             )\n```\n\n\n![png](output_25_0.png)\n\n\n## Displaying a widget from interact function\n\n\n```python\ni.widget\n```\n\n\n![png](output_27_0.png)\n\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/Indian General Elections 2014.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:aedd7fdf9fbe39328e0b0c1093fb2872e62db2ec9bb7a30d8a84c29e3152db3d\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"This is an a review of how to use IPython Notebooks.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Data Sources\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"- http://en.wikipedia.org/wiki/List_of_constituencies_of_the_Lok_Sabha\\n\",\n      \"- http://eci.nic.in/eci_main1/GE2014/ge.html\\n\",\n      \"- http://eci.nic.in/eci/eci.html\\n\",\n      \"- http://eci.nic.in/eci_main1/current/ImpIns04042014.pdf\\n\",\n      \"- http://eci.nic.in/eci_main1/GE2014/PC_WISE_TURNOUT.htm\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Loading the data using Pandas\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pandas as pd\\n\",\n      \"#This line is to make float division the default. For int division use //\\n\",\n      \"from __future__ import division\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"path = \\\"data/2014_indian_election_turnout.csv\\\"\\n\",\n      \"csv_data = pd.read_csv(path)\\n\",\n      \"csv_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"      <th>PC Name</th>\\n\",\n        \"      <th>Male Electors</th>\\n\",\n        \"      <th>Male Voters</th>\\n\",\n        \"      <th>Female Electors</th>\\n\",\n        \"      <th>Female Voters</th>\\n\",\n        \"      <th>Total Electors</th>\\n\",\n        \"      <th>Total Voters</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td> 1 - Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td>  142782</td>\\n\",\n        \"      <td> 101178</td>\\n\",\n        \"      <td>  126578</td>\\n\",\n        \"      <td>  89150</td>\\n\",\n        \"      <td>  269360</td>\\n\",\n        \"      <td>  190328</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 1 - Adilabad </td>\\n\",\n        \"      <td>  687389</td>\\n\",\n        \"      <td> 519364</td>\\n\",\n        \"      <td>  698844</td>\\n\",\n        \"      <td> 526475</td>\\n\",\n        \"      <td> 1386233</td>\\n\",\n        \"      <td> 1045839</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               2 - Peddapalle </td>\\n\",\n        \"      <td>  725767</td>\\n\",\n        \"      <td> 520598</td>\\n\",\n        \"      <td>  699594</td>\\n\",\n        \"      <td> 501586</td>\\n\",\n        \"      <td> 1425361</td>\\n\",\n        \"      <td> 1022184</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               3 - Karimnagar </td>\\n\",\n        \"      <td>  777458</td>\\n\",\n        \"      <td> 552489</td>\\n\",\n        \"      <td>  773376</td>\\n\",\n        \"      <td> 573202</td>\\n\",\n        \"      <td> 1550834</td>\\n\",\n        \"      <td> 1125691</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 4 - Nizamabad</td>\\n\",\n        \"      <td>  724504</td>\\n\",\n        \"      <td> 469712</td>\\n\",\n        \"      <td>  771689</td>\\n\",\n        \"      <td> 564212</td>\\n\",\n        \"      <td> 1496193</td>\\n\",\n        \"      <td> 1033924</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 5 - Zahirabad</td>\\n\",\n        \"      <td>  717811</td>\\n\",\n        \"      <td> 546746</td>\\n\",\n        \"      <td>  727435</td>\\n\",\n        \"      <td> 548060</td>\\n\",\n        \"      <td> 1445246</td>\\n\",\n        \"      <td> 1094806</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                     6 - Medak</td>\\n\",\n        \"      <td>  775903</td>\\n\",\n        \"      <td> 606863</td>\\n\",\n        \"      <td>  760812</td>\\n\",\n        \"      <td> 584233</td>\\n\",\n        \"      <td> 1536715</td>\\n\",\n        \"      <td> 1191096</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                7 - Malkajgiri</td>\\n\",\n        \"      <td> 1723413</td>\\n\",\n        \"      <td> 878093</td>\\n\",\n        \"      <td> 1459912</td>\\n\",\n        \"      <td> 742304</td>\\n\",\n        \"      <td> 3183325</td>\\n\",\n        \"      <td> 1620397</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               8 - Secundrabad</td>\\n\",\n        \"      <td> 1012378</td>\\n\",\n        \"      <td> 545749</td>\\n\",\n        \"      <td>  881269</td>\\n\",\n        \"      <td> 458020</td>\\n\",\n        \"      <td> 1893647</td>\\n\",\n        \"      <td> 1003769</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 9 - Hyderabad</td>\\n\",\n        \"      <td>  961290</td>\\n\",\n        \"      <td> 526510</td>\\n\",\n        \"      <td>  862374</td>\\n\",\n        \"      <td> 444911</td>\\n\",\n        \"      <td> 1823664</td>\\n\",\n        \"      <td>  971421</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                10 - CHELVELLA</td>\\n\",\n        \"      <td> 1153049</td>\\n\",\n        \"      <td> 696749</td>\\n\",\n        \"      <td> 1032130</td>\\n\",\n        \"      <td> 619113</td>\\n\",\n        \"      <td> 2185179</td>\\n\",\n        \"      <td> 1315862</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              11 - Mahbubnagar</td>\\n\",\n        \"      <td>  709711</td>\\n\",\n        \"      <td> 511764</td>\\n\",\n        \"      <td>  708961</td>\\n\",\n        \"      <td> 503036</td>\\n\",\n        \"      <td> 1418672</td>\\n\",\n        \"      <td> 1014800</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             12 - Nagarkurnool</td>\\n\",\n        \"      <td>  745038</td>\\n\",\n        \"      <td> 570342</td>\\n\",\n        \"      <td>  732300</td>\\n\",\n        \"      <td> 538626</td>\\n\",\n        \"      <td> 1477338</td>\\n\",\n        \"      <td> 1108968</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 13 - Nalgonda</td>\\n\",\n        \"      <td>  747281</td>\\n\",\n        \"      <td> 602193</td>\\n\",\n        \"      <td>  748299</td>\\n\",\n        \"      <td> 587206</td>\\n\",\n        \"      <td> 1495580</td>\\n\",\n        \"      <td> 1189399</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 14 - Bhongir </td>\\n\",\n        \"      <td>  756963</td>\\n\",\n        \"      <td> 622333</td>\\n\",\n        \"      <td>  735288</td>\\n\",\n        \"      <td> 589610</td>\\n\",\n        \"      <td> 1492251</td>\\n\",\n        \"      <td> 1211943</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 15 - Warangal</td>\\n\",\n        \"      <td>  771756</td>\\n\",\n        \"      <td> 592484</td>\\n\",\n        \"      <td>  766025</td>\\n\",\n        \"      <td> 582147</td>\\n\",\n        \"      <td> 1537781</td>\\n\",\n        \"      <td> 1174631</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>            16 - Mahabubabad  </td>\\n\",\n        \"      <td>  688398</td>\\n\",\n        \"      <td> 562073</td>\\n\",\n        \"      <td>  698945</td>\\n\",\n        \"      <td> 562297</td>\\n\",\n        \"      <td> 1387343</td>\\n\",\n        \"      <td> 1124370</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 17 - Khammam </td>\\n\",\n        \"      <td>  712329</td>\\n\",\n        \"      <td> 588728</td>\\n\",\n        \"      <td>  727960</td>\\n\",\n        \"      <td> 594169</td>\\n\",\n        \"      <td> 1440289</td>\\n\",\n        \"      <td> 1182897</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   18 - Aruku </td>\\n\",\n        \"      <td>  622416</td>\\n\",\n        \"      <td> 451350</td>\\n\",\n        \"      <td>  650308</td>\\n\",\n        \"      <td> 458264</td>\\n\",\n        \"      <td> 1272724</td>\\n\",\n        \"      <td>  909614</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               19 - Srikakulam</td>\\n\",\n        \"      <td>  706828</td>\\n\",\n        \"      <td> 505010</td>\\n\",\n        \"      <td>  707161</td>\\n\",\n        \"      <td> 546436</td>\\n\",\n        \"      <td> 1413989</td>\\n\",\n        \"      <td> 1051446</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             20 - Vizianagaram</td>\\n\",\n        \"      <td>  700837</td>\\n\",\n        \"      <td> 555005</td>\\n\",\n        \"      <td>  703290</td>\\n\",\n        \"      <td> 565311</td>\\n\",\n        \"      <td> 1404127</td>\\n\",\n        \"      <td> 1120316</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>            21 - Visakhapatnam</td>\\n\",\n        \"      <td>  875187</td>\\n\",\n        \"      <td> 585270</td>\\n\",\n        \"      <td>  847850</td>\\n\",\n        \"      <td> 578288</td>\\n\",\n        \"      <td> 1723037</td>\\n\",\n        \"      <td> 1163558</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               22 - Anakapalli</td>\\n\",\n        \"      <td>  689132</td>\\n\",\n        \"      <td> 563818</td>\\n\",\n        \"      <td>  712342</td>\\n\",\n        \"      <td> 584254</td>\\n\",\n        \"      <td> 1401474</td>\\n\",\n        \"      <td> 1148072</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 23 - Kakinada</td>\\n\",\n        \"      <td>  709101</td>\\n\",\n        \"      <td> 558689</td>\\n\",\n        \"      <td>  709189</td>\\n\",\n        \"      <td> 541310</td>\\n\",\n        \"      <td> 1418290</td>\\n\",\n        \"      <td> 1099999</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              24 - Amalapuram </td>\\n\",\n        \"      <td>  682607</td>\\n\",\n        \"      <td> 568534</td>\\n\",\n        \"      <td>  675259</td>\\n\",\n        \"      <td> 552393</td>\\n\",\n        \"      <td> 1357866</td>\\n\",\n        \"      <td> 1120927</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              25 - Rajahmundry</td>\\n\",\n        \"      <td>  701707</td>\\n\",\n        \"      <td> 577999</td>\\n\",\n        \"      <td>  719581</td>\\n\",\n        \"      <td> 576382</td>\\n\",\n        \"      <td> 1421288</td>\\n\",\n        \"      <td> 1154381</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               26 - Narsapuram</td>\\n\",\n        \"      <td>  652668</td>\\n\",\n        \"      <td> 539357</td>\\n\",\n        \"      <td>  672475</td>\\n\",\n        \"      <td> 549594</td>\\n\",\n        \"      <td> 1325143</td>\\n\",\n        \"      <td> 1088951</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   27 - Eluru </td>\\n\",\n        \"      <td>  706916</td>\\n\",\n        \"      <td> 597603</td>\\n\",\n        \"      <td>  720844</td>\\n\",\n        \"      <td> 604093</td>\\n\",\n        \"      <td> 1427760</td>\\n\",\n        \"      <td> 1201696</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>           28 - Machilipatnam </td>\\n\",\n        \"      <td>  675774</td>\\n\",\n        \"      <td> 567908</td>\\n\",\n        \"      <td>  693537</td>\\n\",\n        \"      <td> 573157</td>\\n\",\n        \"      <td> 1369311</td>\\n\",\n        \"      <td> 1141065</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               29 - Vijayawada</td>\\n\",\n        \"      <td>  781156</td>\\n\",\n        \"      <td> 602198</td>\\n\",\n        \"      <td>  783357</td>\\n\",\n        \"      <td> 592877</td>\\n\",\n        \"      <td> 1564513</td>\\n\",\n        \"      <td> 1195075</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   30 - Guntur</td>\\n\",\n        \"      <td>  773027</td>\\n\",\n        \"      <td> 617483</td>\\n\",\n        \"      <td>  798989</td>\\n\",\n        \"      <td> 627443</td>\\n\",\n        \"      <td> 1572016</td>\\n\",\n        \"      <td> 1244926</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             31 - Narasaraopet</td>\\n\",\n        \"      <td>  748465</td>\\n\",\n        \"      <td> 638080</td>\\n\",\n        \"      <td>  766396</td>\\n\",\n        \"      <td> 643978</td>\\n\",\n        \"      <td> 1514861</td>\\n\",\n        \"      <td> 1282058</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 32 - Bapatla </td>\\n\",\n        \"      <td>  686482</td>\\n\",\n        \"      <td> 587223</td>\\n\",\n        \"      <td>  706483</td>\\n\",\n        \"      <td> 597411</td>\\n\",\n        \"      <td> 1392965</td>\\n\",\n        \"      <td> 1184634</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  33 - Ongole </td>\\n\",\n        \"      <td>  736245</td>\\n\",\n        \"      <td> 603025</td>\\n\",\n        \"      <td>  734238</td>\\n\",\n        \"      <td> 605200</td>\\n\",\n        \"      <td> 1470483</td>\\n\",\n        \"      <td> 1208225</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  34 - Nandyal</td>\\n\",\n        \"      <td>  783266</td>\\n\",\n        \"      <td> 603562</td>\\n\",\n        \"      <td>  793862</td>\\n\",\n        \"      <td> 601394</td>\\n\",\n        \"      <td> 1577128</td>\\n\",\n        \"      <td> 1204956</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  35 - Kurnool</td>\\n\",\n        \"      <td>  738791</td>\\n\",\n        \"      <td> 544802</td>\\n\",\n        \"      <td>  743016</td>\\n\",\n        \"      <td> 520930</td>\\n\",\n        \"      <td> 1481807</td>\\n\",\n        \"      <td> 1065732</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                36 - Anantapur</td>\\n\",\n        \"      <td>  775509</td>\\n\",\n        \"      <td> 612890</td>\\n\",\n        \"      <td>  761403</td>\\n\",\n        \"      <td> 592164</td>\\n\",\n        \"      <td> 1536912</td>\\n\",\n        \"      <td> 1205054</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 37 - Hindupur</td>\\n\",\n        \"      <td>  734638</td>\\n\",\n        \"      <td> 601932</td>\\n\",\n        \"      <td>  711865</td>\\n\",\n        \"      <td> 575325</td>\\n\",\n        \"      <td> 1446503</td>\\n\",\n        \"      <td> 1177257</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   38 - Kadapa</td>\\n\",\n        \"      <td>  765036</td>\\n\",\n        \"      <td> 588805</td>\\n\",\n        \"      <td>  785543</td>\\n\",\n        \"      <td> 611857</td>\\n\",\n        \"      <td> 1550579</td>\\n\",\n        \"      <td> 1200662</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  39 - Nellore</td>\\n\",\n        \"      <td>  796583</td>\\n\",\n        \"      <td> 593468</td>\\n\",\n        \"      <td>  809557</td>\\n\",\n        \"      <td> 594180</td>\\n\",\n        \"      <td> 1606140</td>\\n\",\n        \"      <td> 1187648</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                40 - Tirupati </td>\\n\",\n        \"      <td>  778778</td>\\n\",\n        \"      <td> 604834</td>\\n\",\n        \"      <td>  795766</td>\\n\",\n        \"      <td> 608230</td>\\n\",\n        \"      <td> 1574544</td>\\n\",\n        \"      <td> 1213064</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 41 - Rajampet</td>\\n\",\n        \"      <td>  735347</td>\\n\",\n        \"      <td> 573019</td>\\n\",\n        \"      <td>  752151</td>\\n\",\n        \"      <td> 585298</td>\\n\",\n        \"      <td> 1487498</td>\\n\",\n        \"      <td> 1158317</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                42 - Chittoor </td>\\n\",\n        \"      <td>  723996</td>\\n\",\n        \"      <td> 598259</td>\\n\",\n        \"      <td>  728145</td>\\n\",\n        \"      <td> 600656</td>\\n\",\n        \"      <td> 1452141</td>\\n\",\n        \"      <td> 1198915</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>            1 - ARUNACHAL WEST</td>\\n\",\n        \"      <td>  219334</td>\\n\",\n        \"      <td> 159978</td>\\n\",\n        \"      <td>  227181</td>\\n\",\n        \"      <td> 175687</td>\\n\",\n        \"      <td>  446515</td>\\n\",\n        \"      <td>  335665</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>            2 - ARUNACHAL EAST</td>\\n\",\n        \"      <td>  160293</td>\\n\",\n        \"      <td> 129313</td>\\n\",\n        \"      <td>  152579</td>\\n\",\n        \"      <td> 131978</td>\\n\",\n        \"      <td>  312872</td>\\n\",\n        \"      <td>  261291</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                1 - Karimganj </td>\\n\",\n        \"      <td>  615198</td>\\n\",\n        \"      <td> 482484</td>\\n\",\n        \"      <td>  550799</td>\\n\",\n        \"      <td> 404436</td>\\n\",\n        \"      <td> 1165997</td>\\n\",\n        \"      <td>  886920</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   2 - Silchar</td>\\n\",\n        \"      <td>  554540</td>\\n\",\n        \"      <td> 428949</td>\\n\",\n        \"      <td>  505635</td>\\n\",\n        \"      <td> 370881</td>\\n\",\n        \"      <td> 1060175</td>\\n\",\n        \"      <td>  799830</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>       3 - Autonomous District</td>\\n\",\n        \"      <td>  358880</td>\\n\",\n        \"      <td> 279409</td>\\n\",\n        \"      <td>  343010</td>\\n\",\n        \"      <td> 263871</td>\\n\",\n        \"      <td>  701890</td>\\n\",\n        \"      <td>  543280</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>48</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                    4 - Dhubri</td>\\n\",\n        \"      <td>  798124</td>\\n\",\n        \"      <td> 709191</td>\\n\",\n        \"      <td>  754430</td>\\n\",\n        \"      <td> 660433</td>\\n\",\n        \"      <td> 1552554</td>\\n\",\n        \"      <td> 1369624</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 5 - Kokrajhar</td>\\n\",\n        \"      <td>  776071</td>\\n\",\n        \"      <td> 636657</td>\\n\",\n        \"      <td>  729405</td>\\n\",\n        \"      <td> 587212</td>\\n\",\n        \"      <td> 1505476</td>\\n\",\n        \"      <td> 1223869</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   6 - Barpeta</td>\\n\",\n        \"      <td>  755559</td>\\n\",\n        \"      <td> 634405</td>\\n\",\n        \"      <td>  674616</td>\\n\",\n        \"      <td> 571458</td>\\n\",\n        \"      <td> 1430175</td>\\n\",\n        \"      <td> 1205863</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>51</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   7 - Gauhati</td>\\n\",\n        \"      <td>  988067</td>\\n\",\n        \"      <td> 785494</td>\\n\",\n        \"      <td>  934203</td>\\n\",\n        \"      <td> 726235</td>\\n\",\n        \"      <td> 1922270</td>\\n\",\n        \"      <td> 1511729</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 8 - Mangaldoi</td>\\n\",\n        \"      <td>  791539</td>\\n\",\n        \"      <td> 651272</td>\\n\",\n        \"      <td>  724137</td>\\n\",\n        \"      <td> 581965</td>\\n\",\n        \"      <td> 1515676</td>\\n\",\n        \"      <td> 1233237</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                    9 - Tezpur</td>\\n\",\n        \"      <td>  654866</td>\\n\",\n        \"      <td> 505643</td>\\n\",\n        \"      <td>  604702</td>\\n\",\n        \"      <td> 475045</td>\\n\",\n        \"      <td> 1259568</td>\\n\",\n        \"      <td>  980688</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                  10 - Nowgong</td>\\n\",\n        \"      <td>  792424</td>\\n\",\n        \"      <td> 639392</td>\\n\",\n        \"      <td>  731457</td>\\n\",\n        \"      <td> 590682</td>\\n\",\n        \"      <td> 1523881</td>\\n\",\n        \"      <td> 1230074</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>55</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 11 - Kaliabor</td>\\n\",\n        \"      <td>  752785</td>\\n\",\n        \"      <td> 605292</td>\\n\",\n        \"      <td>  705080</td>\\n\",\n        \"      <td> 561198</td>\\n\",\n        \"      <td> 1457865</td>\\n\",\n        \"      <td> 1166490</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   12 - Jorhat</td>\\n\",\n        \"      <td>  634236</td>\\n\",\n        \"      <td> 483829</td>\\n\",\n        \"      <td>  600212</td>\\n\",\n        \"      <td> 447507</td>\\n\",\n        \"      <td> 1234448</td>\\n\",\n        \"      <td>  931336</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>57</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                13 - Dibrugarh</td>\\n\",\n        \"      <td>  579657</td>\\n\",\n        \"      <td> 462412</td>\\n\",\n        \"      <td>  544648</td>\\n\",\n        \"      <td> 428556</td>\\n\",\n        \"      <td> 1124305</td>\\n\",\n        \"      <td>  890968</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>58</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                14 - Lakhimpur</td>\\n\",\n        \"      <td>  735263</td>\\n\",\n        \"      <td> 572334</td>\\n\",\n        \"      <td>  695731</td>\\n\",\n        \"      <td> 539641</td>\\n\",\n        \"      <td> 1430994</td>\\n\",\n        \"      <td> 1111975</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>59</th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"      <td>             1 - Valmiki Nagar</td>\\n\",\n        \"      <td>  786297</td>\\n\",\n        \"      <td> 484621</td>\\n\",\n        \"      <td>  670279</td>\\n\",\n        \"      <td> 415493</td>\\n\",\n        \"      <td> 1456576</td>\\n\",\n        \"      <td>  900114</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>543 rows \\u00d7 8 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 2,\n       \"text\": [\n        \"                        State                        PC Name  Male Electors  \\\\\\n\",\n        \"0   Andaman & Nicobar Islands  1 - Andaman & Nicobar Islands         142782   \\n\",\n        \"1              Andhra Pradesh                  1 - Adilabad          687389   \\n\",\n        \"2              Andhra Pradesh                2 - Peddapalle          725767   \\n\",\n        \"3              Andhra Pradesh                3 - Karimnagar          777458   \\n\",\n        \"4              Andhra Pradesh                  4 - Nizamabad         724504   \\n\",\n        \"5              Andhra Pradesh                  5 - Zahirabad         717811   \\n\",\n        \"6              Andhra Pradesh                      6 - Medak         775903   \\n\",\n        \"7              Andhra Pradesh                 7 - Malkajgiri        1723413   \\n\",\n        \"8              Andhra Pradesh                8 - Secundrabad        1012378   \\n\",\n        \"9              Andhra Pradesh                  9 - Hyderabad         961290   \\n\",\n        \"10             Andhra Pradesh                 10 - CHELVELLA        1153049   \\n\",\n        \"11             Andhra Pradesh               11 - Mahbubnagar         709711   \\n\",\n        \"12             Andhra Pradesh              12 - Nagarkurnool         745038   \\n\",\n        \"13             Andhra Pradesh                  13 - Nalgonda         747281   \\n\",\n        \"14             Andhra Pradesh                  14 - Bhongir          756963   \\n\",\n        \"15             Andhra Pradesh                  15 - Warangal         771756   \\n\",\n        \"16             Andhra Pradesh             16 - Mahabubabad           688398   \\n\",\n        \"17             Andhra Pradesh                  17 - Khammam          712329   \\n\",\n        \"18             Andhra Pradesh                    18 - Aruku          622416   \\n\",\n        \"19             Andhra Pradesh                19 - Srikakulam         706828   \\n\",\n        \"20             Andhra Pradesh              20 - Vizianagaram         700837   \\n\",\n        \"21             Andhra Pradesh             21 - Visakhapatnam         875187   \\n\",\n        \"22             Andhra Pradesh                22 - Anakapalli         689132   \\n\",\n        \"23             Andhra Pradesh                  23 - Kakinada         709101   \\n\",\n        \"24             Andhra Pradesh               24 - Amalapuram          682607   \\n\",\n        \"25             Andhra Pradesh               25 - Rajahmundry         701707   \\n\",\n        \"26             Andhra Pradesh                26 - Narsapuram         652668   \\n\",\n        \"27             Andhra Pradesh                    27 - Eluru          706916   \\n\",\n        \"28             Andhra Pradesh            28 - Machilipatnam          675774   \\n\",\n        \"29             Andhra Pradesh                29 - Vijayawada         781156   \\n\",\n        \"30             Andhra Pradesh                    30 - Guntur         773027   \\n\",\n        \"31             Andhra Pradesh              31 - Narasaraopet         748465   \\n\",\n        \"32             Andhra Pradesh                  32 - Bapatla          686482   \\n\",\n        \"33             Andhra Pradesh                   33 - Ongole          736245   \\n\",\n        \"34             Andhra Pradesh                   34 - Nandyal         783266   \\n\",\n        \"35             Andhra Pradesh                   35 - Kurnool         738791   \\n\",\n        \"36             Andhra Pradesh                 36 - Anantapur         775509   \\n\",\n        \"37             Andhra Pradesh                  37 - Hindupur         734638   \\n\",\n        \"38             Andhra Pradesh                    38 - Kadapa         765036   \\n\",\n        \"39             Andhra Pradesh                   39 - Nellore         796583   \\n\",\n        \"40             Andhra Pradesh                 40 - Tirupati          778778   \\n\",\n        \"41             Andhra Pradesh                  41 - Rajampet         735347   \\n\",\n        \"42             Andhra Pradesh                 42 - Chittoor          723996   \\n\",\n        \"43          Arunachal Pradesh             1 - ARUNACHAL WEST         219334   \\n\",\n        \"44          Arunachal Pradesh             2 - ARUNACHAL EAST         160293   \\n\",\n        \"45                      Assam                 1 - Karimganj          615198   \\n\",\n        \"46                      Assam                    2 - Silchar         554540   \\n\",\n        \"47                      Assam        3 - Autonomous District         358880   \\n\",\n        \"48                      Assam                     4 - Dhubri         798124   \\n\",\n        \"49                      Assam                  5 - Kokrajhar         776071   \\n\",\n        \"50                      Assam                    6 - Barpeta         755559   \\n\",\n        \"51                      Assam                    7 - Gauhati         988067   \\n\",\n        \"52                      Assam                  8 - Mangaldoi         791539   \\n\",\n        \"53                      Assam                     9 - Tezpur         654866   \\n\",\n        \"54                      Assam                   10 - Nowgong         792424   \\n\",\n        \"55                      Assam                  11 - Kaliabor         752785   \\n\",\n        \"56                      Assam                    12 - Jorhat         634236   \\n\",\n        \"57                      Assam                 13 - Dibrugarh         579657   \\n\",\n        \"58                      Assam                 14 - Lakhimpur         735263   \\n\",\n        \"59                      Bihar              1 - Valmiki Nagar         786297   \\n\",\n        \"                          ...                            ...            ...   \\n\",\n        \"\\n\",\n        \"    Male Voters  Female Electors  Female Voters  Total Electors  Total Voters  \\n\",\n        \"0        101178           126578          89150          269360        190328  \\n\",\n        \"1        519364           698844         526475         1386233       1045839  \\n\",\n        \"2        520598           699594         501586         1425361       1022184  \\n\",\n        \"3        552489           773376         573202         1550834       1125691  \\n\",\n        \"4        469712           771689         564212         1496193       1033924  \\n\",\n        \"5        546746           727435         548060         1445246       1094806  \\n\",\n        \"6        606863           760812         584233         1536715       1191096  \\n\",\n        \"7        878093          1459912         742304         3183325       1620397  \\n\",\n        \"8        545749           881269         458020         1893647       1003769  \\n\",\n        \"9        526510           862374         444911         1823664        971421  \\n\",\n        \"10       696749          1032130         619113         2185179       1315862  \\n\",\n        \"11       511764           708961         503036         1418672       1014800  \\n\",\n        \"12       570342           732300         538626         1477338       1108968  \\n\",\n        \"13       602193           748299         587206         1495580       1189399  \\n\",\n        \"14       622333           735288         589610         1492251       1211943  \\n\",\n        \"15       592484           766025         582147         1537781       1174631  \\n\",\n        \"16       562073           698945         562297         1387343       1124370  \\n\",\n        \"17       588728           727960         594169         1440289       1182897  \\n\",\n        \"18       451350           650308         458264         1272724        909614  \\n\",\n        \"19       505010           707161         546436         1413989       1051446  \\n\",\n        \"20       555005           703290         565311         1404127       1120316  \\n\",\n        \"21       585270           847850         578288         1723037       1163558  \\n\",\n        \"22       563818           712342         584254         1401474       1148072  \\n\",\n        \"23       558689           709189         541310         1418290       1099999  \\n\",\n        \"24       568534           675259         552393         1357866       1120927  \\n\",\n        \"25       577999           719581         576382         1421288       1154381  \\n\",\n        \"26       539357           672475         549594         1325143       1088951  \\n\",\n        \"27       597603           720844         604093         1427760       1201696  \\n\",\n        \"28       567908           693537         573157         1369311       1141065  \\n\",\n        \"29       602198           783357         592877         1564513       1195075  \\n\",\n        \"30       617483           798989         627443         1572016       1244926  \\n\",\n        \"31       638080           766396         643978         1514861       1282058  \\n\",\n        \"32       587223           706483         597411         1392965       1184634  \\n\",\n        \"33       603025           734238         605200         1470483       1208225  \\n\",\n        \"34       603562           793862         601394         1577128       1204956  \\n\",\n        \"35       544802           743016         520930         1481807       1065732  \\n\",\n        \"36       612890           761403         592164         1536912       1205054  \\n\",\n        \"37       601932           711865         575325         1446503       1177257  \\n\",\n        \"38       588805           785543         611857         1550579       1200662  \\n\",\n        \"39       593468           809557         594180         1606140       1187648  \\n\",\n        \"40       604834           795766         608230         1574544       1213064  \\n\",\n        \"41       573019           752151         585298         1487498       1158317  \\n\",\n        \"42       598259           728145         600656         1452141       1198915  \\n\",\n        \"43       159978           227181         175687          446515        335665  \\n\",\n        \"44       129313           152579         131978          312872        261291  \\n\",\n        \"45       482484           550799         404436         1165997        886920  \\n\",\n        \"46       428949           505635         370881         1060175        799830  \\n\",\n        \"47       279409           343010         263871          701890        543280  \\n\",\n        \"48       709191           754430         660433         1552554       1369624  \\n\",\n        \"49       636657           729405         587212         1505476       1223869  \\n\",\n        \"50       634405           674616         571458         1430175       1205863  \\n\",\n        \"51       785494           934203         726235         1922270       1511729  \\n\",\n        \"52       651272           724137         581965         1515676       1233237  \\n\",\n        \"53       505643           604702         475045         1259568        980688  \\n\",\n        \"54       639392           731457         590682         1523881       1230074  \\n\",\n        \"55       605292           705080         561198         1457865       1166490  \\n\",\n        \"56       483829           600212         447507         1234448        931336  \\n\",\n        \"57       462412           544648         428556         1124305        890968  \\n\",\n        \"58       572334           695731         539641         1430994       1111975  \\n\",\n        \"59       484621           670279         415493         1456576        900114  \\n\",\n        \"            ...              ...            ...             ...           ...  \\n\",\n        \"\\n\",\n        \"[543 rows x 8 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Understanding the data\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"- **State**: State Name.\\n\",\n      \"- **PC Name**:  Parliamentary Constituencies Names.\\n\",\n      \"- **Male Electors**: The number of elegeble male voters.\\n\",\n      \"- **Male Voters**: The number of male voters turnout.\\n\",\n      \"- **Female Electors**: The number of elegeble female voters.\\n\",\n      \"- **Female Voters**: The number of female voters turnout.\\n\",\n      \"- **Total Electors**: The number of elegeble voters.\\n\",\n      \"- **Total Voters**: The number of voters turnout.\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"![](http://upload.wikimedia.org/wikipedia/commons/3/3a/Lok_Sabha_constituencies_of_India.png)\\n\",\n      \"![](http://upload.wikimedia.org/wikipedia/commons/c/c1/State_and_union_territories_map.png)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculating Turnout\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data[\\\"Male Turnout\\\"] = csv_data[\\\"Male Voters\\\"] / csv_data[\\\"Male Electors\\\"]\\n\",\n      \"csv_data[\\\"Female Turnout\\\"] = csv_data[\\\"Female Voters\\\"] / csv_data[\\\"Female Electors\\\"]\\n\",\n      \"csv_data[\\\"Total Turnout\\\"] = csv_data[\\\"Total Voters\\\"] / csv_data[\\\"Total Electors\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Statistical Analysis of Turnout\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data.describe()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Male Electors</th>\\n\",\n        \"      <th>Male Voters</th>\\n\",\n        \"      <th>Female Electors</th>\\n\",\n        \"      <th>Female Voters</th>\\n\",\n        \"      <th>Total Electors</th>\\n\",\n        \"      <th>Total Voters</th>\\n\",\n        \"      <th>Male Turnout</th>\\n\",\n        \"      <th>Female Turnout</th>\\n\",\n        \"      <th>Total Turnout</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>count</th>\\n\",\n        \"      <td>     543.000000</td>\\n\",\n        \"      <td>    543.000000</td>\\n\",\n        \"      <td>     543.000000</td>\\n\",\n        \"      <td>    543.000000</td>\\n\",\n        \"      <td>     543.000000</td>\\n\",\n        \"      <td>     543.000000</td>\\n\",\n        \"      <td> 543.000000</td>\\n\",\n        \"      <td> 543.000000</td>\\n\",\n        \"      <td> 543.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>mean</th>\\n\",\n        \"      <td>  804883.127072</td>\\n\",\n        \"      <td> 540030.900552</td>\\n\",\n        \"      <td>  731215.360958</td>\\n\",\n        \"      <td> 479861.918969</td>\\n\",\n        \"      <td> 1536098.488029</td>\\n\",\n        \"      <td> 1019892.819521</td>\\n\",\n        \"      <td>   0.680646</td>\\n\",\n        \"      <td>   0.663664</td>\\n\",\n        \"      <td>   0.672688</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>std</th>\\n\",\n        \"      <td>  165950.096614</td>\\n\",\n        \"      <td> 105316.219120</td>\\n\",\n        \"      <td>  127159.414206</td>\\n\",\n        \"      <td>  96137.678162</td>\\n\",\n        \"      <td>  288064.231735</td>\\n\",\n        \"      <td>  192716.272135</td>\\n\",\n        \"      <td>   0.100641</td>\\n\",\n        \"      <td>   0.111115</td>\\n\",\n        \"      <td>   0.102917</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>min</th>\\n\",\n        \"      <td>   25433.000000</td>\\n\",\n        \"      <td>  21585.000000</td>\\n\",\n        \"      <td>   24489.000000</td>\\n\",\n        \"      <td>  21654.000000</td>\\n\",\n        \"      <td>   49922.000000</td>\\n\",\n        \"      <td>   43239.000000</td>\\n\",\n        \"      <td>   0.283097</td>\\n\",\n        \"      <td>   0.232644</td>\\n\",\n        \"      <td>   0.259047</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25%</th>\\n\",\n        \"      <td>  728644.000000</td>\\n\",\n        \"      <td> 491359.000000</td>\\n\",\n        \"      <td>  691996.500000</td>\\n\",\n        \"      <td> 430761.500000</td>\\n\",\n        \"      <td> 1425052.000000</td>\\n\",\n        \"      <td>  940426.500000</td>\\n\",\n        \"      <td>   0.608490</td>\\n\",\n        \"      <td>   0.578545</td>\\n\",\n        \"      <td>   0.592252</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50%</th>\\n\",\n        \"      <td>  814968.000000</td>\\n\",\n        \"      <td> 549484.000000</td>\\n\",\n        \"      <td>  740045.000000</td>\\n\",\n        \"      <td> 484001.000000</td>\\n\",\n        \"      <td> 1555779.000000</td>\\n\",\n        \"      <td> 1033783.000000</td>\\n\",\n        \"      <td>   0.680857</td>\\n\",\n        \"      <td>   0.656567</td>\\n\",\n        \"      <td>   0.667207</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>75%</th>\\n\",\n        \"      <td>  899919.500000</td>\\n\",\n        \"      <td> 597803.000000</td>\\n\",\n        \"      <td>  791426.500000</td>\\n\",\n        \"      <td> 540898.000000</td>\\n\",\n        \"      <td> 1692364.500000</td>\\n\",\n        \"      <td> 1129424.500000</td>\\n\",\n        \"      <td>   0.763453</td>\\n\",\n        \"      <td>   0.756988</td>\\n\",\n        \"      <td>   0.755237</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>max</th>\\n\",\n        \"      <td> 1723413.000000</td>\\n\",\n        \"      <td> 878093.000000</td>\\n\",\n        \"      <td> 1459912.000000</td>\\n\",\n        \"      <td> 742304.000000</td>\\n\",\n        \"      <td> 3183325.000000</td>\\n\",\n        \"      <td> 1620397.000000</td>\\n\",\n        \"      <td>   0.888572</td>\\n\",\n        \"      <td>   0.892712</td>\\n\",\n        \"      <td>   0.882175</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>8 rows \\u00d7 9 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"        Male Electors    Male Voters  Female Electors  Female Voters  \\\\\\n\",\n        \"count      543.000000     543.000000       543.000000     543.000000   \\n\",\n        \"mean    804883.127072  540030.900552    731215.360958  479861.918969   \\n\",\n        \"std     165950.096614  105316.219120    127159.414206   96137.678162   \\n\",\n        \"min      25433.000000   21585.000000     24489.000000   21654.000000   \\n\",\n        \"25%     728644.000000  491359.000000    691996.500000  430761.500000   \\n\",\n        \"50%     814968.000000  549484.000000    740045.000000  484001.000000   \\n\",\n        \"75%     899919.500000  597803.000000    791426.500000  540898.000000   \\n\",\n        \"max    1723413.000000  878093.000000   1459912.000000  742304.000000   \\n\",\n        \"\\n\",\n        \"       Total Electors    Total Voters  Male Turnout  Female Turnout  \\\\\\n\",\n        \"count      543.000000      543.000000    543.000000      543.000000   \\n\",\n        \"mean   1536098.488029  1019892.819521      0.680646        0.663664   \\n\",\n        \"std     288064.231735   192716.272135      0.100641        0.111115   \\n\",\n        \"min      49922.000000    43239.000000      0.283097        0.232644   \\n\",\n        \"25%    1425052.000000   940426.500000      0.608490        0.578545   \\n\",\n        \"50%    1555779.000000  1033783.000000      0.680857        0.656567   \\n\",\n        \"75%    1692364.500000  1129424.500000      0.763453        0.756988   \\n\",\n        \"max    3183325.000000  1620397.000000      0.888572        0.892712   \\n\",\n        \"\\n\",\n        \"       Total Turnout  \\n\",\n        \"count     543.000000  \\n\",\n        \"mean        0.672688  \\n\",\n        \"std         0.102917  \\n\",\n        \"min         0.259047  \\n\",\n        \"25%         0.592252  \\n\",\n        \"50%         0.667207  \\n\",\n        \"75%         0.755237  \\n\",\n        \"max         0.882175  \\n\",\n        \"\\n\",\n        \"[8 rows x 9 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Quick Facts\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Total Electors\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print '{0:,}'.format(csv_data[\\\"Total Electors\\\"].sum())\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"834,101,479\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Gender Ration (Men to Women)\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print '%.2f%%' % (csv_data[\\\"Female Electors\\\"].sum() / csv_data[\\\"Male Electors\\\"].sum()*100)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"90.85%\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Total Voters\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print '{0:,}'.format(csv_data[\\\"Total Voters\\\"].sum())\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"553,801,801\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Total Turnout\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print '%.2f%%' % (csv_data[\\\"Total Voters\\\"].sum() / csv_data[\\\"Total Electors\\\"].sum() * 100)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"66.40%\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Analyzing States\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"states = pd.DataFrame(csv_data[\\\"State\\\"].unique(), columns=[\\\"State\\\"])\\n\",\n      \"print \\\"The number of states: %s\\\" % len(states)\\n\",\n      \"states\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"The number of states: 35\\n\"\n       ]\n      },\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>                Chandigarh</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>               Chattisgarh</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>      Dadra &amp; Nagar Haveli</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>               Daman &amp; Diu</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>                       Goa</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>                   Gujarat</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>                   Haryana</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>          Himachal Pradesh</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>           Jammu &amp; Kashmir</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>                 Jharkhand</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>                 Karnataka</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>                    Kerala</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>               Lakshadweep</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>            Madhya Pradesh</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>               Maharashtra</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>                   Manipur</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>                 Meghalaya</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td>                   Mizoram</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>                  Nagaland</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>              NCT OF Delhi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>                    Orissa</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>                Puducherry</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td>                    Punjab</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>                 Rajasthan</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>                    Sikkim</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>                Tamil Nadu</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>                   Tripura</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>             Uttar Pradesh</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>               Uttarakhand</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>               West Bengal</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>35 rows \\u00d7 1 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"                        State\\n\",\n        \"0   Andaman & Nicobar Islands\\n\",\n        \"1              Andhra Pradesh\\n\",\n        \"2           Arunachal Pradesh\\n\",\n        \"3                       Assam\\n\",\n        \"4                       Bihar\\n\",\n        \"5                  Chandigarh\\n\",\n        \"6                 Chattisgarh\\n\",\n        \"7        Dadra & Nagar Haveli\\n\",\n        \"8                 Daman & Diu\\n\",\n        \"9                         Goa\\n\",\n        \"10                    Gujarat\\n\",\n        \"11                    Haryana\\n\",\n        \"12           Himachal Pradesh\\n\",\n        \"13            Jammu & Kashmir\\n\",\n        \"14                  Jharkhand\\n\",\n        \"15                  Karnataka\\n\",\n        \"16                     Kerala\\n\",\n        \"17                Lakshadweep\\n\",\n        \"18             Madhya Pradesh\\n\",\n        \"19                Maharashtra\\n\",\n        \"20                    Manipur\\n\",\n        \"21                  Meghalaya\\n\",\n        \"22                    Mizoram\\n\",\n        \"23                   Nagaland\\n\",\n        \"24               NCT OF Delhi\\n\",\n        \"25                     Orissa\\n\",\n        \"26                 Puducherry\\n\",\n        \"27                     Punjab\\n\",\n        \"28                  Rajasthan\\n\",\n        \"29                     Sikkim\\n\",\n        \"30                 Tamil Nadu\\n\",\n        \"31                    Tripura\\n\",\n        \"32              Uttar Pradesh\\n\",\n        \"33                Uttarakhand\\n\",\n        \"34                West Bengal\\n\",\n        \"\\n\",\n        \"[35 rows x 1 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Calculating constituencies\\n\",\n      \"seats = csv_data[\\\"State\\\"].value_counts()\\n\",\n      \"states[\\\"Seats\\\"] = seats.sort_index().values\\n\",\n      \"states.sort(\\\"Seats\\\", ascending=False)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>             Uttar Pradesh</td>\\n\",\n        \"      <td> 80</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>               Maharashtra</td>\\n\",\n        \"      <td> 48</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>               West Bengal</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"      <td> 40</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>                Tamil Nadu</td>\\n\",\n        \"      <td> 39</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>            Madhya Pradesh</td>\\n\",\n        \"      <td> 29</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>                 Karnataka</td>\\n\",\n        \"      <td> 28</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>                   Gujarat</td>\\n\",\n        \"      <td> 26</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>                 Rajasthan</td>\\n\",\n        \"      <td> 25</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>                    Orissa</td>\\n\",\n        \"      <td> 21</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>                    Kerala</td>\\n\",\n        \"      <td> 20</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>                 Jharkhand</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td>                    Punjab</td>\\n\",\n        \"      <td> 13</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>               Chattisgarh</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>                   Haryana</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>                  Nagaland</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>           Jammu &amp; Kashmir</td>\\n\",\n        \"      <td>  6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>               Uttarakhand</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>          Himachal Pradesh</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>                       Goa</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>                 Meghalaya</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>                   Manipur</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>                   Tripura</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>                Chandigarh</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>               Lakshadweep</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>      Dadra &amp; Nagar Haveli</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>               Daman &amp; Diu</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td>                   Mizoram</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>              NCT OF Delhi</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>                Puducherry</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>                    Sikkim</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>35 rows \\u00d7 2 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 10,\n       \"text\": [\n        \"                        State  Seats\\n\",\n        \"32              Uttar Pradesh     80\\n\",\n        \"19                Maharashtra     48\\n\",\n        \"34                West Bengal     42\\n\",\n        \"1              Andhra Pradesh     42\\n\",\n        \"4                       Bihar     40\\n\",\n        \"30                 Tamil Nadu     39\\n\",\n        \"18             Madhya Pradesh     29\\n\",\n        \"15                  Karnataka     28\\n\",\n        \"10                    Gujarat     26\\n\",\n        \"28                  Rajasthan     25\\n\",\n        \"25                     Orissa     21\\n\",\n        \"16                     Kerala     20\\n\",\n        \"3                       Assam     14\\n\",\n        \"14                  Jharkhand     14\\n\",\n        \"27                     Punjab     13\\n\",\n        \"6                 Chattisgarh     11\\n\",\n        \"11                    Haryana     10\\n\",\n        \"23                   Nagaland      7\\n\",\n        \"13            Jammu & Kashmir      6\\n\",\n        \"33                Uttarakhand      5\\n\",\n        \"12           Himachal Pradesh      4\\n\",\n        \"9                         Goa      2\\n\",\n        \"21                  Meghalaya      2\\n\",\n        \"20                    Manipur      2\\n\",\n        \"2           Arunachal Pradesh      2\\n\",\n        \"31                    Tripura      2\\n\",\n        \"5                  Chandigarh      1\\n\",\n        \"17                Lakshadweep      1\\n\",\n        \"7        Dadra & Nagar Haveli      1\\n\",\n        \"8                 Daman & Diu      1\\n\",\n        \"22                    Mizoram      1\\n\",\n        \"24               NCT OF Delhi      1\\n\",\n        \"26                 Puducherry      1\\n\",\n        \"29                     Sikkim      1\\n\",\n        \"0   Andaman & Nicobar Islands      1\\n\",\n        \"\\n\",\n        \"[35 rows x 2 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculating Total States Electors and Voters\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"total_electors = pd.pivot_table(csv_data, values=\\\"Total Electors\\\", rows=[\\\"State\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"total_votes = pd.pivot_table(csv_data, values=\\\"Total Voters\\\", rows=[\\\"State\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"states[\\\"Total Electors\\\"] = total_electors.sort_index().values\\n\",\n      \"states[\\\"Total Voters\\\"] = total_votes.sort_index().values\\n\",\n      \"states.sort(\\\"Total Electors\\\", ascending=False)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"      <th>Total Electors</th>\\n\",\n        \"      <th>Total Voters</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>             Uttar Pradesh</td>\\n\",\n        \"      <td> 80</td>\\n\",\n        \"      <td> 138965820</td>\\n\",\n        \"      <td> 81092302</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>               Maharashtra</td>\\n\",\n        \"      <td> 48</td>\\n\",\n        \"      <td>  80717283</td>\\n\",\n        \"      <td> 48718844</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"      <td>  64938750</td>\\n\",\n        \"      <td> 48358545</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"      <td> 40</td>\\n\",\n        \"      <td>  63761796</td>\\n\",\n        \"      <td> 35885366</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>               West Bengal</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"      <td>  62833128</td>\\n\",\n        \"      <td> 51622555</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>                Tamil Nadu</td>\\n\",\n        \"      <td> 39</td>\\n\",\n        \"      <td>  55114505</td>\\n\",\n        \"      <td> 40620440</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>            Madhya Pradesh</td>\\n\",\n        \"      <td> 29</td>\\n\",\n        \"      <td>  48118040</td>\\n\",\n        \"      <td> 29639796</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>                 Karnataka</td>\\n\",\n        \"      <td> 28</td>\\n\",\n        \"      <td>  46212109</td>\\n\",\n        \"      <td> 31038888</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>                 Rajasthan</td>\\n\",\n        \"      <td> 25</td>\\n\",\n        \"      <td>  42969447</td>\\n\",\n        \"      <td> 27110044</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>                   Gujarat</td>\\n\",\n        \"      <td> 26</td>\\n\",\n        \"      <td>  40603104</td>\\n\",\n        \"      <td> 25824003</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>                    Orissa</td>\\n\",\n        \"      <td> 21</td>\\n\",\n        \"      <td>  29196041</td>\\n\",\n        \"      <td> 21532275</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>                    Kerala</td>\\n\",\n        \"      <td> 20</td>\\n\",\n        \"      <td>  24326649</td>\\n\",\n        \"      <td> 17975893</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>                 Jharkhand</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"      <td>  20326743</td>\\n\",\n        \"      <td> 12982940</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td>                    Punjab</td>\\n\",\n        \"      <td> 13</td>\\n\",\n        \"      <td>  19608008</td>\\n\",\n        \"      <td> 13845132</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"      <td>  18885274</td>\\n\",\n        \"      <td> 15085883</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>               Chattisgarh</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"      <td>  17623049</td>\\n\",\n        \"      <td> 12255579</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>                   Haryana</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"      <td>  16097749</td>\\n\",\n        \"      <td> 11495150</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>                  Nagaland</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"      <td>  12711236</td>\\n\",\n        \"      <td>  8271766</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>           Jammu &amp; Kashmir</td>\\n\",\n        \"      <td>  6</td>\\n\",\n        \"      <td>   7202163</td>\\n\",\n        \"      <td>  3566863</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>               Uttarakhand</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"      <td>   7129939</td>\\n\",\n        \"      <td>  4391890</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>          Himachal Pradesh</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"      <td>   4810071</td>\\n\",\n        \"      <td>  3098501</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>                   Tripura</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   2388819</td>\\n\",\n        \"      <td>  2023859</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>                   Manipur</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1774325</td>\\n\",\n        \"      <td>  1412637</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>                 Meghalaya</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1567241</td>\\n\",\n        \"      <td>  1078058</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>              NCT OF Delhi</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>   1182948</td>\\n\",\n        \"      <td>  1038910</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>                       Goa</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1060777</td>\\n\",\n        \"      <td>   817000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>                Puducherry</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    901357</td>\\n\",\n        \"      <td>   740017</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>    759387</td>\\n\",\n        \"      <td>   596956</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td>                   Mizoram</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    702170</td>\\n\",\n        \"      <td>   433201</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>                Chandigarh</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    615214</td>\\n\",\n        \"      <td>   453455</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>                    Sikkim</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    370611</td>\\n\",\n        \"      <td>   308967</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    269360</td>\\n\",\n        \"      <td>   190328</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>      Dadra &amp; Nagar Haveli</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    196617</td>\\n\",\n        \"      <td>   165286</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>               Daman &amp; Diu</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    111827</td>\\n\",\n        \"      <td>    87233</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>               Lakshadweep</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>     49922</td>\\n\",\n        \"      <td>    43239</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>35 rows \\u00d7 4 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"                        State  Seats  Total Electors  Total Voters\\n\",\n        \"32              Uttar Pradesh     80       138965820      81092302\\n\",\n        \"19                Maharashtra     48        80717283      48718844\\n\",\n        \"1              Andhra Pradesh     42        64938750      48358545\\n\",\n        \"4                       Bihar     40        63761796      35885366\\n\",\n        \"34                West Bengal     42        62833128      51622555\\n\",\n        \"30                 Tamil Nadu     39        55114505      40620440\\n\",\n        \"18             Madhya Pradesh     29        48118040      29639796\\n\",\n        \"15                  Karnataka     28        46212109      31038888\\n\",\n        \"28                  Rajasthan     25        42969447      27110044\\n\",\n        \"10                    Gujarat     26        40603104      25824003\\n\",\n        \"25                     Orissa     21        29196041      21532275\\n\",\n        \"16                     Kerala     20        24326649      17975893\\n\",\n        \"14                  Jharkhand     14        20326743      12982940\\n\",\n        \"27                     Punjab     13        19608008      13845132\\n\",\n        \"3                       Assam     14        18885274      15085883\\n\",\n        \"6                 Chattisgarh     11        17623049      12255579\\n\",\n        \"11                    Haryana     10        16097749      11495150\\n\",\n        \"23                   Nagaland      7        12711236       8271766\\n\",\n        \"13            Jammu & Kashmir      6         7202163       3566863\\n\",\n        \"33                Uttarakhand      5         7129939       4391890\\n\",\n        \"12           Himachal Pradesh      4         4810071       3098501\\n\",\n        \"31                    Tripura      2         2388819       2023859\\n\",\n        \"20                    Manipur      2         1774325       1412637\\n\",\n        \"21                  Meghalaya      2         1567241       1078058\\n\",\n        \"24               NCT OF Delhi      1         1182948       1038910\\n\",\n        \"9                         Goa      2         1060777        817000\\n\",\n        \"26                 Puducherry      1          901357        740017\\n\",\n        \"2           Arunachal Pradesh      2          759387        596956\\n\",\n        \"22                    Mizoram      1          702170        433201\\n\",\n        \"5                  Chandigarh      1          615214        453455\\n\",\n        \"29                     Sikkim      1          370611        308967\\n\",\n        \"0   Andaman & Nicobar Islands      1          269360        190328\\n\",\n        \"7        Dadra & Nagar Haveli      1          196617        165286\\n\",\n        \"8                 Daman & Diu      1          111827         87233\\n\",\n        \"17                Lakshadweep      1           49922         43239\\n\",\n        \"\\n\",\n        \"[35 rows x 4 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculating State Turnout\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"states[\\\"Turnout\\\"] = states[\\\"Total Voters\\\"] / states[\\\"Total Electors\\\"]\\n\",\n      \"states.sort(\\\"Turnout\\\", ascending=False)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"      <th>Total Electors</th>\\n\",\n        \"      <th>Total Voters</th>\\n\",\n        \"      <th>Turnout</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>              NCT OF Delhi</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>   1182948</td>\\n\",\n        \"      <td>  1038910</td>\\n\",\n        \"      <td> 0.878238</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>               Lakshadweep</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>     49922</td>\\n\",\n        \"      <td>    43239</td>\\n\",\n        \"      <td> 0.866131</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>                   Tripura</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   2388819</td>\\n\",\n        \"      <td>  2023859</td>\\n\",\n        \"      <td> 0.847222</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>      Dadra &amp; Nagar Haveli</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    196617</td>\\n\",\n        \"      <td>   165286</td>\\n\",\n        \"      <td> 0.840650</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>                    Sikkim</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    370611</td>\\n\",\n        \"      <td>   308967</td>\\n\",\n        \"      <td> 0.833669</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>               West Bengal</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"      <td>  62833128</td>\\n\",\n        \"      <td> 51622555</td>\\n\",\n        \"      <td> 0.821582</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>                Puducherry</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    901357</td>\\n\",\n        \"      <td>   740017</td>\\n\",\n        \"      <td> 0.821003</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"      <td>  18885274</td>\\n\",\n        \"      <td> 15085883</td>\\n\",\n        \"      <td> 0.798817</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>                   Manipur</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1774325</td>\\n\",\n        \"      <td>  1412637</td>\\n\",\n        \"      <td> 0.796155</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>    759387</td>\\n\",\n        \"      <td>   596956</td>\\n\",\n        \"      <td> 0.786102</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>               Daman &amp; Diu</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    111827</td>\\n\",\n        \"      <td>    87233</td>\\n\",\n        \"      <td> 0.780071</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>                       Goa</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1060777</td>\\n\",\n        \"      <td>   817000</td>\\n\",\n        \"      <td> 0.770190</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"      <td>  64938750</td>\\n\",\n        \"      <td> 48358545</td>\\n\",\n        \"      <td> 0.744679</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>                    Kerala</td>\\n\",\n        \"      <td> 20</td>\\n\",\n        \"      <td>  24326649</td>\\n\",\n        \"      <td> 17975893</td>\\n\",\n        \"      <td> 0.738938</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>                    Orissa</td>\\n\",\n        \"      <td> 21</td>\\n\",\n        \"      <td>  29196041</td>\\n\",\n        \"      <td> 21532275</td>\\n\",\n        \"      <td> 0.737507</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>                Chandigarh</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    615214</td>\\n\",\n        \"      <td>   453455</td>\\n\",\n        \"      <td> 0.737069</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>                Tamil Nadu</td>\\n\",\n        \"      <td> 39</td>\\n\",\n        \"      <td>  55114505</td>\\n\",\n        \"      <td> 40620440</td>\\n\",\n        \"      <td> 0.737019</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>                   Haryana</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"      <td>  16097749</td>\\n\",\n        \"      <td> 11495150</td>\\n\",\n        \"      <td> 0.714084</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    269360</td>\\n\",\n        \"      <td>   190328</td>\\n\",\n        \"      <td> 0.706593</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td>                    Punjab</td>\\n\",\n        \"      <td> 13</td>\\n\",\n        \"      <td>  19608008</td>\\n\",\n        \"      <td> 13845132</td>\\n\",\n        \"      <td> 0.706096</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>               Chattisgarh</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"      <td>  17623049</td>\\n\",\n        \"      <td> 12255579</td>\\n\",\n        \"      <td> 0.695429</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>                 Meghalaya</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1567241</td>\\n\",\n        \"      <td>  1078058</td>\\n\",\n        \"      <td> 0.687870</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>                 Karnataka</td>\\n\",\n        \"      <td> 28</td>\\n\",\n        \"      <td>  46212109</td>\\n\",\n        \"      <td> 31038888</td>\\n\",\n        \"      <td> 0.671661</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>                  Nagaland</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"      <td>  12711236</td>\\n\",\n        \"      <td>  8271766</td>\\n\",\n        \"      <td> 0.650744</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>          Himachal Pradesh</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"      <td>   4810071</td>\\n\",\n        \"      <td>  3098501</td>\\n\",\n        \"      <td> 0.644169</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>                 Jharkhand</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"      <td>  20326743</td>\\n\",\n        \"      <td> 12982940</td>\\n\",\n        \"      <td> 0.638712</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>                   Gujarat</td>\\n\",\n        \"      <td> 26</td>\\n\",\n        \"      <td>  40603104</td>\\n\",\n        \"      <td> 25824003</td>\\n\",\n        \"      <td> 0.636011</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>                 Rajasthan</td>\\n\",\n        \"      <td> 25</td>\\n\",\n        \"      <td>  42969447</td>\\n\",\n        \"      <td> 27110044</td>\\n\",\n        \"      <td> 0.630914</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td>                   Mizoram</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    702170</td>\\n\",\n        \"      <td>   433201</td>\\n\",\n        \"      <td> 0.616946</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>            Madhya Pradesh</td>\\n\",\n        \"      <td> 29</td>\\n\",\n        \"      <td>  48118040</td>\\n\",\n        \"      <td> 29639796</td>\\n\",\n        \"      <td> 0.615981</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>               Uttarakhand</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"      <td>   7129939</td>\\n\",\n        \"      <td>  4391890</td>\\n\",\n        \"      <td> 0.615979</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>               Maharashtra</td>\\n\",\n        \"      <td> 48</td>\\n\",\n        \"      <td>  80717283</td>\\n\",\n        \"      <td> 48718844</td>\\n\",\n        \"      <td> 0.603574</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>             Uttar Pradesh</td>\\n\",\n        \"      <td> 80</td>\\n\",\n        \"      <td> 138965820</td>\\n\",\n        \"      <td> 81092302</td>\\n\",\n        \"      <td> 0.583541</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"      <td> 40</td>\\n\",\n        \"      <td>  63761796</td>\\n\",\n        \"      <td> 35885366</td>\\n\",\n        \"      <td> 0.562804</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>           Jammu &amp; Kashmir</td>\\n\",\n        \"      <td>  6</td>\\n\",\n        \"      <td>   7202163</td>\\n\",\n        \"      <td>  3566863</td>\\n\",\n        \"      <td> 0.495249</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>35 rows \\u00d7 5 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"                        State  Seats  Total Electors  Total Voters   Turnout\\n\",\n        \"24               NCT OF Delhi      1         1182948       1038910  0.878238\\n\",\n        \"17                Lakshadweep      1           49922         43239  0.866131\\n\",\n        \"31                    Tripura      2         2388819       2023859  0.847222\\n\",\n        \"7        Dadra & Nagar Haveli      1          196617        165286  0.840650\\n\",\n        \"29                     Sikkim      1          370611        308967  0.833669\\n\",\n        \"34                West Bengal     42        62833128      51622555  0.821582\\n\",\n        \"26                 Puducherry      1          901357        740017  0.821003\\n\",\n        \"3                       Assam     14        18885274      15085883  0.798817\\n\",\n        \"20                    Manipur      2         1774325       1412637  0.796155\\n\",\n        \"2           Arunachal Pradesh      2          759387        596956  0.786102\\n\",\n        \"8                 Daman & Diu      1          111827         87233  0.780071\\n\",\n        \"9                         Goa      2         1060777        817000  0.770190\\n\",\n        \"1              Andhra Pradesh     42        64938750      48358545  0.744679\\n\",\n        \"16                     Kerala     20        24326649      17975893  0.738938\\n\",\n        \"25                     Orissa     21        29196041      21532275  0.737507\\n\",\n        \"5                  Chandigarh      1          615214        453455  0.737069\\n\",\n        \"30                 Tamil Nadu     39        55114505      40620440  0.737019\\n\",\n        \"11                    Haryana     10        16097749      11495150  0.714084\\n\",\n        \"0   Andaman & Nicobar Islands      1          269360        190328  0.706593\\n\",\n        \"27                     Punjab     13        19608008      13845132  0.706096\\n\",\n        \"6                 Chattisgarh     11        17623049      12255579  0.695429\\n\",\n        \"21                  Meghalaya      2         1567241       1078058  0.687870\\n\",\n        \"15                  Karnataka     28        46212109      31038888  0.671661\\n\",\n        \"23                   Nagaland      7        12711236       8271766  0.650744\\n\",\n        \"12           Himachal Pradesh      4         4810071       3098501  0.644169\\n\",\n        \"14                  Jharkhand     14        20326743      12982940  0.638712\\n\",\n        \"10                    Gujarat     26        40603104      25824003  0.636011\\n\",\n        \"28                  Rajasthan     25        42969447      27110044  0.630914\\n\",\n        \"22                    Mizoram      1          702170        433201  0.616946\\n\",\n        \"18             Madhya Pradesh     29        48118040      29639796  0.615981\\n\",\n        \"33                Uttarakhand      5         7129939       4391890  0.615979\\n\",\n        \"19                Maharashtra     48        80717283      48718844  0.603574\\n\",\n        \"32              Uttar Pradesh     80       138965820      81092302  0.583541\\n\",\n        \"4                       Bihar     40        63761796      35885366  0.562804\\n\",\n        \"13            Jammu & Kashmir      6         7202163       3566863  0.495249\\n\",\n        \"\\n\",\n        \"[35 rows x 5 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculating Electors Per Seat\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"states[\\\"Electors Per Seat\\\"] = states[\\\"Total Electors\\\"] / states[\\\"Seats\\\"]\\n\",\n      \"states.sort(\\\"Electors Per Seat\\\", ascending=False)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"      <th>Total Electors</th>\\n\",\n        \"      <th>Total Voters</th>\\n\",\n        \"      <th>Turnout</th>\\n\",\n        \"      <th>Electors Per Seat</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>                  Nagaland</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"      <td>  12711236</td>\\n\",\n        \"      <td>  8271766</td>\\n\",\n        \"      <td> 0.650744</td>\\n\",\n        \"      <td> 1815890.857143</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>             Uttar Pradesh</td>\\n\",\n        \"      <td> 80</td>\\n\",\n        \"      <td> 138965820</td>\\n\",\n        \"      <td> 81092302</td>\\n\",\n        \"      <td> 0.583541</td>\\n\",\n        \"      <td> 1737072.750000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>                 Rajasthan</td>\\n\",\n        \"      <td> 25</td>\\n\",\n        \"      <td>  42969447</td>\\n\",\n        \"      <td> 27110044</td>\\n\",\n        \"      <td> 0.630914</td>\\n\",\n        \"      <td> 1718777.880000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>               Maharashtra</td>\\n\",\n        \"      <td> 48</td>\\n\",\n        \"      <td>  80717283</td>\\n\",\n        \"      <td> 48718844</td>\\n\",\n        \"      <td> 0.603574</td>\\n\",\n        \"      <td> 1681610.062500</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>            Madhya Pradesh</td>\\n\",\n        \"      <td> 29</td>\\n\",\n        \"      <td>  48118040</td>\\n\",\n        \"      <td> 29639796</td>\\n\",\n        \"      <td> 0.615981</td>\\n\",\n        \"      <td> 1659242.758621</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>                 Karnataka</td>\\n\",\n        \"      <td> 28</td>\\n\",\n        \"      <td>  46212109</td>\\n\",\n        \"      <td> 31038888</td>\\n\",\n        \"      <td> 0.671661</td>\\n\",\n        \"      <td> 1650432.464286</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>                   Haryana</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"      <td>  16097749</td>\\n\",\n        \"      <td> 11495150</td>\\n\",\n        \"      <td> 0.714084</td>\\n\",\n        \"      <td> 1609774.900000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>               Chattisgarh</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"      <td>  17623049</td>\\n\",\n        \"      <td> 12255579</td>\\n\",\n        \"      <td> 0.695429</td>\\n\",\n        \"      <td> 1602095.363636</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"      <td> 40</td>\\n\",\n        \"      <td>  63761796</td>\\n\",\n        \"      <td> 35885366</td>\\n\",\n        \"      <td> 0.562804</td>\\n\",\n        \"      <td> 1594044.900000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>                   Gujarat</td>\\n\",\n        \"      <td> 26</td>\\n\",\n        \"      <td>  40603104</td>\\n\",\n        \"      <td> 25824003</td>\\n\",\n        \"      <td> 0.636011</td>\\n\",\n        \"      <td> 1561657.846154</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"      <td>  64938750</td>\\n\",\n        \"      <td> 48358545</td>\\n\",\n        \"      <td> 0.744679</td>\\n\",\n        \"      <td> 1546160.714286</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td>                    Punjab</td>\\n\",\n        \"      <td> 13</td>\\n\",\n        \"      <td>  19608008</td>\\n\",\n        \"      <td> 13845132</td>\\n\",\n        \"      <td> 0.706096</td>\\n\",\n        \"      <td> 1508308.307692</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>               West Bengal</td>\\n\",\n        \"      <td> 42</td>\\n\",\n        \"      <td>  62833128</td>\\n\",\n        \"      <td> 51622555</td>\\n\",\n        \"      <td> 0.821582</td>\\n\",\n        \"      <td> 1496026.857143</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>                 Jharkhand</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"      <td>  20326743</td>\\n\",\n        \"      <td> 12982940</td>\\n\",\n        \"      <td> 0.638712</td>\\n\",\n        \"      <td> 1451910.214286</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>               Uttarakhand</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"      <td>   7129939</td>\\n\",\n        \"      <td>  4391890</td>\\n\",\n        \"      <td> 0.615979</td>\\n\",\n        \"      <td> 1425987.800000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>                Tamil Nadu</td>\\n\",\n        \"      <td> 39</td>\\n\",\n        \"      <td>  55114505</td>\\n\",\n        \"      <td> 40620440</td>\\n\",\n        \"      <td> 0.737019</td>\\n\",\n        \"      <td> 1413192.435897</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>                    Orissa</td>\\n\",\n        \"      <td> 21</td>\\n\",\n        \"      <td>  29196041</td>\\n\",\n        \"      <td> 21532275</td>\\n\",\n        \"      <td> 0.737507</td>\\n\",\n        \"      <td> 1390287.666667</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"      <td>  18885274</td>\\n\",\n        \"      <td> 15085883</td>\\n\",\n        \"      <td> 0.798817</td>\\n\",\n        \"      <td> 1348948.142857</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>                    Kerala</td>\\n\",\n        \"      <td> 20</td>\\n\",\n        \"      <td>  24326649</td>\\n\",\n        \"      <td> 17975893</td>\\n\",\n        \"      <td> 0.738938</td>\\n\",\n        \"      <td> 1216332.450000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>          Himachal Pradesh</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"      <td>   4810071</td>\\n\",\n        \"      <td>  3098501</td>\\n\",\n        \"      <td> 0.644169</td>\\n\",\n        \"      <td> 1202517.750000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>           Jammu &amp; Kashmir</td>\\n\",\n        \"      <td>  6</td>\\n\",\n        \"      <td>   7202163</td>\\n\",\n        \"      <td>  3566863</td>\\n\",\n        \"      <td> 0.495249</td>\\n\",\n        \"      <td> 1200360.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>                   Tripura</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   2388819</td>\\n\",\n        \"      <td>  2023859</td>\\n\",\n        \"      <td> 0.847222</td>\\n\",\n        \"      <td> 1194409.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>              NCT OF Delhi</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>   1182948</td>\\n\",\n        \"      <td>  1038910</td>\\n\",\n        \"      <td> 0.878238</td>\\n\",\n        \"      <td> 1182948.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>                Puducherry</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    901357</td>\\n\",\n        \"      <td>   740017</td>\\n\",\n        \"      <td> 0.821003</td>\\n\",\n        \"      <td>  901357.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>                   Manipur</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1774325</td>\\n\",\n        \"      <td>  1412637</td>\\n\",\n        \"      <td> 0.796155</td>\\n\",\n        \"      <td>  887162.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>                 Meghalaya</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1567241</td>\\n\",\n        \"      <td>  1078058</td>\\n\",\n        \"      <td> 0.687870</td>\\n\",\n        \"      <td>  783620.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td>                   Mizoram</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    702170</td>\\n\",\n        \"      <td>   433201</td>\\n\",\n        \"      <td> 0.616946</td>\\n\",\n        \"      <td>  702170.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>                Chandigarh</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    615214</td>\\n\",\n        \"      <td>   453455</td>\\n\",\n        \"      <td> 0.737069</td>\\n\",\n        \"      <td>  615214.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>                       Goa</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>   1060777</td>\\n\",\n        \"      <td>   817000</td>\\n\",\n        \"      <td> 0.770190</td>\\n\",\n        \"      <td>  530388.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>  2</td>\\n\",\n        \"      <td>    759387</td>\\n\",\n        \"      <td>   596956</td>\\n\",\n        \"      <td> 0.786102</td>\\n\",\n        \"      <td>  379693.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>                    Sikkim</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    370611</td>\\n\",\n        \"      <td>   308967</td>\\n\",\n        \"      <td> 0.833669</td>\\n\",\n        \"      <td>  370611.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    269360</td>\\n\",\n        \"      <td>   190328</td>\\n\",\n        \"      <td> 0.706593</td>\\n\",\n        \"      <td>  269360.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>      Dadra &amp; Nagar Haveli</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    196617</td>\\n\",\n        \"      <td>   165286</td>\\n\",\n        \"      <td> 0.840650</td>\\n\",\n        \"      <td>  196617.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>               Daman &amp; Diu</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>    111827</td>\\n\",\n        \"      <td>    87233</td>\\n\",\n        \"      <td> 0.780071</td>\\n\",\n        \"      <td>  111827.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>               Lakshadweep</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td>     49922</td>\\n\",\n        \"      <td>    43239</td>\\n\",\n        \"      <td> 0.866131</td>\\n\",\n        \"      <td>   49922.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>35 rows \\u00d7 6 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 13,\n       \"text\": [\n        \"                        State  Seats  Total Electors  Total Voters   Turnout  \\\\\\n\",\n        \"23                   Nagaland      7        12711236       8271766  0.650744   \\n\",\n        \"32              Uttar Pradesh     80       138965820      81092302  0.583541   \\n\",\n        \"28                  Rajasthan     25        42969447      27110044  0.630914   \\n\",\n        \"19                Maharashtra     48        80717283      48718844  0.603574   \\n\",\n        \"18             Madhya Pradesh     29        48118040      29639796  0.615981   \\n\",\n        \"15                  Karnataka     28        46212109      31038888  0.671661   \\n\",\n        \"11                    Haryana     10        16097749      11495150  0.714084   \\n\",\n        \"6                 Chattisgarh     11        17623049      12255579  0.695429   \\n\",\n        \"4                       Bihar     40        63761796      35885366  0.562804   \\n\",\n        \"10                    Gujarat     26        40603104      25824003  0.636011   \\n\",\n        \"1              Andhra Pradesh     42        64938750      48358545  0.744679   \\n\",\n        \"27                     Punjab     13        19608008      13845132  0.706096   \\n\",\n        \"34                West Bengal     42        62833128      51622555  0.821582   \\n\",\n        \"14                  Jharkhand     14        20326743      12982940  0.638712   \\n\",\n        \"33                Uttarakhand      5         7129939       4391890  0.615979   \\n\",\n        \"30                 Tamil Nadu     39        55114505      40620440  0.737019   \\n\",\n        \"25                     Orissa     21        29196041      21532275  0.737507   \\n\",\n        \"3                       Assam     14        18885274      15085883  0.798817   \\n\",\n        \"16                     Kerala     20        24326649      17975893  0.738938   \\n\",\n        \"12           Himachal Pradesh      4         4810071       3098501  0.644169   \\n\",\n        \"13            Jammu & Kashmir      6         7202163       3566863  0.495249   \\n\",\n        \"31                    Tripura      2         2388819       2023859  0.847222   \\n\",\n        \"24               NCT OF Delhi      1         1182948       1038910  0.878238   \\n\",\n        \"26                 Puducherry      1          901357        740017  0.821003   \\n\",\n        \"20                    Manipur      2         1774325       1412637  0.796155   \\n\",\n        \"21                  Meghalaya      2         1567241       1078058  0.687870   \\n\",\n        \"22                    Mizoram      1          702170        433201  0.616946   \\n\",\n        \"5                  Chandigarh      1          615214        453455  0.737069   \\n\",\n        \"9                         Goa      2         1060777        817000  0.770190   \\n\",\n        \"2           Arunachal Pradesh      2          759387        596956  0.786102   \\n\",\n        \"29                     Sikkim      1          370611        308967  0.833669   \\n\",\n        \"0   Andaman & Nicobar Islands      1          269360        190328  0.706593   \\n\",\n        \"7        Dadra & Nagar Haveli      1          196617        165286  0.840650   \\n\",\n        \"8                 Daman & Diu      1          111827         87233  0.780071   \\n\",\n        \"17                Lakshadweep      1           49922         43239  0.866131   \\n\",\n        \"\\n\",\n        \"    Electors Per Seat  \\n\",\n        \"23     1815890.857143  \\n\",\n        \"32     1737072.750000  \\n\",\n        \"28     1718777.880000  \\n\",\n        \"19     1681610.062500  \\n\",\n        \"18     1659242.758621  \\n\",\n        \"15     1650432.464286  \\n\",\n        \"11     1609774.900000  \\n\",\n        \"6      1602095.363636  \\n\",\n        \"4      1594044.900000  \\n\",\n        \"10     1561657.846154  \\n\",\n        \"1      1546160.714286  \\n\",\n        \"27     1508308.307692  \\n\",\n        \"34     1496026.857143  \\n\",\n        \"14     1451910.214286  \\n\",\n        \"33     1425987.800000  \\n\",\n        \"30     1413192.435897  \\n\",\n        \"25     1390287.666667  \\n\",\n        \"3      1348948.142857  \\n\",\n        \"16     1216332.450000  \\n\",\n        \"12     1202517.750000  \\n\",\n        \"13     1200360.500000  \\n\",\n        \"31     1194409.500000  \\n\",\n        \"24     1182948.000000  \\n\",\n        \"26      901357.000000  \\n\",\n        \"20      887162.500000  \\n\",\n        \"21      783620.500000  \\n\",\n        \"22      702170.000000  \\n\",\n        \"5       615214.000000  \\n\",\n        \"9       530388.500000  \\n\",\n        \"2       379693.500000  \\n\",\n        \"29      370611.000000  \\n\",\n        \"0       269360.000000  \\n\",\n        \"7       196617.000000  \\n\",\n        \"8       111827.000000  \\n\",\n        \"17       49922.000000  \\n\",\n        \"\\n\",\n        \"[35 rows x 6 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Reading the Results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"path = \\\"data/2014_indian_elections_results.csv\\\"\\n\",\n      \"results_data = pd.read_csv(path, sep=\\\"\\\\t\\\")\\n\",\n      \"results_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Name of State/ UT</th>\\n\",\n        \"      <th>Parliamentary Constituency</th>\\n\",\n        \"      <th>Candidate Name</th>\\n\",\n        \"      <th>Total Votes Polled</th>\\n\",\n        \"      <th>Winner or Not?</th>\\n\",\n        \"      <th>Party Abbreviation</th>\\n\",\n        \"      <th>Party Name</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>                 GODAM NAGESH</td>\\n\",\n        \"      <td> 430847</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>    TRS</td>\\n\",\n        \"      <td>             Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>             NETHAWATH RAMDAS</td>\\n\",\n        \"      <td>  41032</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>                RAMESH RATHOD</td>\\n\",\n        \"      <td> 184198</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    TDP</td>\\n\",\n        \"      <td>                          Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>              RATHOD SADASHIV</td>\\n\",\n        \"      <td>  94420</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    BSP</td>\\n\",\n        \"      <td>                   Bahujan Samaj Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>             MOSALI CHINNAIAH</td>\\n\",\n        \"      <td>   8859</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>                       NARESH</td>\\n\",\n        \"      <td> 259557</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    INC</td>\\n\",\n        \"      <td>              Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>                PAWAR KRISHNA</td>\\n\",\n        \"      <td>   5055</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>                BANKA SAHADEV</td>\\n\",\n        \"      <td>   4787</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>   Adilabad</td>\\n\",\n        \"      <td>            None of the Above</td>\\n\",\n        \"      <td>  17084</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   NOTA</td>\\n\",\n        \"      <td>                     None of the Above</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>                  BALKA SUMAN</td>\\n\",\n        \"      <td> 565496</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>    TRS</td>\\n\",\n        \"      <td>             Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>            None of the Above</td>\\n\",\n        \"      <td>   5361</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   NOTA</td>\\n\",\n        \"      <td>                     None of the Above</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>                G. VIVEKANAND</td>\\n\",\n        \"      <td> 274338</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    INC</td>\\n\",\n        \"      <td>              Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>              KALVALA SHANKAR</td>\\n\",\n        \"      <td>   9290</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   PPOI</td>\\n\",\n        \"      <td>                Pyramid Party of India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>              MOUTAM RAVINDER</td>\\n\",\n        \"      <td>   2674</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>            VELTHURU MALLAIAH</td>\\n\",\n        \"      <td>  45977</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>  RP(K)</td>\\n\",\n        \"      <td>           Republican Paksha (Khoripa)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>       DR.JANAPATI SARAT BABU</td>\\n\",\n        \"      <td>  63334</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    TDP</td>\\n\",\n        \"      <td>                          Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>             KAMILLA JAYA RAO</td>\\n\",\n        \"      <td>   2742</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>         THALLAPALLI SRINIVAS</td>\\n\",\n        \"      <td>   3699</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>          VENKATA SWAMY INJAM</td>\\n\",\n        \"      <td>   3866</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   MaSP</td>\\n\",\n        \"      <td>              Mahajana Socialist Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>                 GORRE RAMESH</td>\\n\",\n        \"      <td>   2361</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>             BUPELLI NARAYANA</td>\\n\",\n        \"      <td>   7258</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>       BOTHA VENKATA MALLAIAH</td>\\n\",\n        \"      <td>   2245</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   BCUF</td>\\n\",\n        \"      <td>                    B. C. United Front</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>               JINNA RAMADEVI</td>\\n\",\n        \"      <td>   9199</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>                    J.V. RAJU</td>\\n\",\n        \"      <td>   3020</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    NIP</td>\\n\",\n        \"      <td>                       New India Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>          GADDALA VINAY KUMAR</td>\\n\",\n        \"      <td>   3266</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>              KALVALA SANJEEV</td>\\n\",\n        \"      <td>   8609</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    RPI</td>\\n\",\n        \"      <td>             Republican Party of India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Peddapalle</td>\\n\",\n        \"      <td>          TAGARAM SHANKAR LAL</td>\\n\",\n        \"      <td>   9449</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    BSP</td>\\n\",\n        \"      <td>                   Bahujan Samaj Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>       VINOD KUMAR BOINAPALLY</td>\\n\",\n        \"      <td> 505783</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>    TRS</td>\\n\",\n        \"      <td>             Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>   SRINIVAS REDDY LINGAMPALLY</td>\\n\",\n        \"      <td>   3865</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>            RANAVENI LAKSHMAN</td>\\n\",\n        \"      <td>   2062</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    NIP</td>\\n\",\n        \"      <td>                       New India Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>          MALLESH YADAV BARLA</td>\\n\",\n        \"      <td>   2760</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>         REDDI MALLA SRINIVAS</td>\\n\",\n        \"      <td>   8040</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td> RPI(A)</td>\\n\",\n        \"      <td>         Republican Party of India (A)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>                  BURLA. RAVI</td>\\n\",\n        \"      <td>   4769</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    NCP</td>\\n\",\n        \"      <td>            Nationalist Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>             KANDEM PRABHAKAR</td>\\n\",\n        \"      <td>   4731</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>                    B. LAXMAN</td>\\n\",\n        \"      <td>  11333</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    BSP</td>\\n\",\n        \"      <td>                   Bahujan Samaj Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>                SHAIK MAHMOOD</td>\\n\",\n        \"      <td>  39325</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   WPOI</td>\\n\",\n        \"      <td>                Welfare Party Of India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>             PONNAM PRABHAKAR</td>\\n\",\n        \"      <td> 300706</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    INC</td>\\n\",\n        \"      <td>              Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td> CHENNAMANENI VIDYA SAGAR RAO</td>\\n\",\n        \"      <td> 214828</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    BJP</td>\\n\",\n        \"      <td>                Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>           MEESALA RAJI REDDY</td>\\n\",\n        \"      <td>   3080</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>  YSRCP</td>\\n\",\n        \"      <td> Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>     MOHAMMED AMJAD MOHIUDDIN</td>\\n\",\n        \"      <td>   6720</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>              UPPULETI.LAXMAN</td>\\n\",\n        \"      <td>   2448</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>           L. PRABHAKAR REDDY</td>\\n\",\n        \"      <td>   3944</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   PPOI</td>\\n\",\n        \"      <td>                Pyramid Party of India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>              PULIKOTA RAMESH</td>\\n\",\n        \"      <td>   1777</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>      DR.DHARMAIAH VENDAVETLA</td>\\n\",\n        \"      <td>   3773</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   BMUP</td>\\n\",\n        \"      <td>                   Bahujan Mukti Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td> Karimnagar</td>\\n\",\n        \"      <td>            None of the Above</td>\\n\",\n        \"      <td>   5747</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   NOTA</td>\\n\",\n        \"      <td>                     None of the Above</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>          KALVAKUNTLA KAVITHA</td>\\n\",\n        \"      <td> 439307</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>    TRS</td>\\n\",\n        \"      <td>             Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>               BANJA VEERAPPA</td>\\n\",\n        \"      <td>   3651</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   PPOI</td>\\n\",\n        \"      <td>                Pyramid Party of India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>  CHIRULINGADRULA VENKATESHAM</td>\\n\",\n        \"      <td>   3091</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>48</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>             KANDEM PRABHAKAR</td>\\n\",\n        \"      <td>   3990</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>          MALIK MOHTASIM KHAN</td>\\n\",\n        \"      <td>  43814</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   WPOI</td>\\n\",\n        \"      <td>                Welfare Party Of India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>                S. CHANDRAIAH</td>\\n\",\n        \"      <td>   2817</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>51</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>                     BAGWAN B</td>\\n\",\n        \"      <td>   8129</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>                   ABDUL GANI</td>\\n\",\n        \"      <td>   4791</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   BCUF</td>\\n\",\n        \"      <td>                    B. C. United Front</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>       ENDALA LAKSHMINARAYANA</td>\\n\",\n        \"      <td> 225333</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    BJP</td>\\n\",\n        \"      <td>                Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>     ABDUL KAREEM KHAN (BABU)</td>\\n\",\n        \"      <td>   1601</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>55</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>                TALARI RAMULU</td>\\n\",\n        \"      <td>   7421</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    BSP</td>\\n\",\n        \"      <td>                   Bahujan Samaj Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>                SAGAR SUDDALA</td>\\n\",\n        \"      <td>   1978</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   BMUP</td>\\n\",\n        \"      <td>                   Bahujan Mukti Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>57</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>            MADHU YASKHI GOUD</td>\\n\",\n        \"      <td> 272123</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    INC</td>\\n\",\n        \"      <td>              Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>58</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>             RAPELLY SRINIVAS</td>\\n\",\n        \"      <td>   2621</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>   AAAP</td>\\n\",\n        \"      <td>                       Aam Aadmi Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>59</th>\\n\",\n        \"      <td> Andhra Pradesh</td>\\n\",\n        \"      <td>  Nizamabad</td>\\n\",\n        \"      <td>     KOTAPATI NARSIMHAM NAIDU</td>\\n\",\n        \"      <td>   2462</td>\\n\",\n        \"      <td>  no</td>\\n\",\n        \"      <td>    IND</td>\\n\",\n        \"      <td>                           Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>8794 rows \\u00d7 7 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 14,\n       \"text\": [\n        \"   Name of State/ UT Parliamentary Constituency                Candidate Name  \\\\\\n\",\n        \"0     Andhra Pradesh                   Adilabad                  GODAM NAGESH   \\n\",\n        \"1     Andhra Pradesh                   Adilabad              NETHAWATH RAMDAS   \\n\",\n        \"2     Andhra Pradesh                   Adilabad                 RAMESH RATHOD   \\n\",\n        \"3     Andhra Pradesh                   Adilabad               RATHOD SADASHIV   \\n\",\n        \"4     Andhra Pradesh                   Adilabad              MOSALI CHINNAIAH   \\n\",\n        \"5     Andhra Pradesh                   Adilabad                        NARESH   \\n\",\n        \"6     Andhra Pradesh                   Adilabad                 PAWAR KRISHNA   \\n\",\n        \"7     Andhra Pradesh                   Adilabad                 BANKA SAHADEV   \\n\",\n        \"8     Andhra Pradesh                   Adilabad             None of the Above   \\n\",\n        \"9     Andhra Pradesh                 Peddapalle                   BALKA SUMAN   \\n\",\n        \"10    Andhra Pradesh                 Peddapalle             None of the Above   \\n\",\n        \"11    Andhra Pradesh                 Peddapalle                 G. VIVEKANAND   \\n\",\n        \"12    Andhra Pradesh                 Peddapalle               KALVALA SHANKAR   \\n\",\n        \"13    Andhra Pradesh                 Peddapalle               MOUTAM RAVINDER   \\n\",\n        \"14    Andhra Pradesh                 Peddapalle             VELTHURU MALLAIAH   \\n\",\n        \"15    Andhra Pradesh                 Peddapalle        DR.JANAPATI SARAT BABU   \\n\",\n        \"16    Andhra Pradesh                 Peddapalle              KAMILLA JAYA RAO   \\n\",\n        \"17    Andhra Pradesh                 Peddapalle          THALLAPALLI SRINIVAS   \\n\",\n        \"18    Andhra Pradesh                 Peddapalle           VENKATA SWAMY INJAM   \\n\",\n        \"19    Andhra Pradesh                 Peddapalle                  GORRE RAMESH   \\n\",\n        \"20    Andhra Pradesh                 Peddapalle              BUPELLI NARAYANA   \\n\",\n        \"21    Andhra Pradesh                 Peddapalle        BOTHA VENKATA MALLAIAH   \\n\",\n        \"22    Andhra Pradesh                 Peddapalle                JINNA RAMADEVI   \\n\",\n        \"23    Andhra Pradesh                 Peddapalle                     J.V. RAJU   \\n\",\n        \"24    Andhra Pradesh                 Peddapalle           GADDALA VINAY KUMAR   \\n\",\n        \"25    Andhra Pradesh                 Peddapalle               KALVALA SANJEEV   \\n\",\n        \"26    Andhra Pradesh                 Peddapalle           TAGARAM SHANKAR LAL   \\n\",\n        \"27    Andhra Pradesh                 Karimnagar        VINOD KUMAR BOINAPALLY   \\n\",\n        \"28    Andhra Pradesh                 Karimnagar    SRINIVAS REDDY LINGAMPALLY   \\n\",\n        \"29    Andhra Pradesh                 Karimnagar             RANAVENI LAKSHMAN   \\n\",\n        \"30    Andhra Pradesh                 Karimnagar           MALLESH YADAV BARLA   \\n\",\n        \"31    Andhra Pradesh                 Karimnagar          REDDI MALLA SRINIVAS   \\n\",\n        \"32    Andhra Pradesh                 Karimnagar                   BURLA. RAVI   \\n\",\n        \"33    Andhra Pradesh                 Karimnagar              KANDEM PRABHAKAR   \\n\",\n        \"34    Andhra Pradesh                 Karimnagar                     B. LAXMAN   \\n\",\n        \"35    Andhra Pradesh                 Karimnagar                 SHAIK MAHMOOD   \\n\",\n        \"36    Andhra Pradesh                 Karimnagar              PONNAM PRABHAKAR   \\n\",\n        \"37    Andhra Pradesh                 Karimnagar  CHENNAMANENI VIDYA SAGAR RAO   \\n\",\n        \"38    Andhra Pradesh                 Karimnagar            MEESALA RAJI REDDY   \\n\",\n        \"39    Andhra Pradesh                 Karimnagar      MOHAMMED AMJAD MOHIUDDIN   \\n\",\n        \"40    Andhra Pradesh                 Karimnagar               UPPULETI.LAXMAN   \\n\",\n        \"41    Andhra Pradesh                 Karimnagar            L. PRABHAKAR REDDY   \\n\",\n        \"42    Andhra Pradesh                 Karimnagar               PULIKOTA RAMESH   \\n\",\n        \"43    Andhra Pradesh                 Karimnagar       DR.DHARMAIAH VENDAVETLA   \\n\",\n        \"44    Andhra Pradesh                 Karimnagar             None of the Above   \\n\",\n        \"45    Andhra Pradesh                  Nizamabad           KALVAKUNTLA KAVITHA   \\n\",\n        \"46    Andhra Pradesh                  Nizamabad                BANJA VEERAPPA   \\n\",\n        \"47    Andhra Pradesh                  Nizamabad   CHIRULINGADRULA VENKATESHAM   \\n\",\n        \"48    Andhra Pradesh                  Nizamabad              KANDEM PRABHAKAR   \\n\",\n        \"49    Andhra Pradesh                  Nizamabad           MALIK MOHTASIM KHAN   \\n\",\n        \"50    Andhra Pradesh                  Nizamabad                 S. CHANDRAIAH   \\n\",\n        \"51    Andhra Pradesh                  Nizamabad                      BAGWAN B   \\n\",\n        \"52    Andhra Pradesh                  Nizamabad                    ABDUL GANI   \\n\",\n        \"53    Andhra Pradesh                  Nizamabad        ENDALA LAKSHMINARAYANA   \\n\",\n        \"54    Andhra Pradesh                  Nizamabad      ABDUL KAREEM KHAN (BABU)   \\n\",\n        \"55    Andhra Pradesh                  Nizamabad                 TALARI RAMULU   \\n\",\n        \"56    Andhra Pradesh                  Nizamabad                 SAGAR SUDDALA   \\n\",\n        \"57    Andhra Pradesh                  Nizamabad             MADHU YASKHI GOUD   \\n\",\n        \"58    Andhra Pradesh                  Nizamabad              RAPELLY SRINIVAS   \\n\",\n        \"59    Andhra Pradesh                  Nizamabad      KOTAPATI NARSIMHAM NAIDU   \\n\",\n        \"                 ...                        ...                           ...   \\n\",\n        \"\\n\",\n        \"    Total Votes Polled Winner or Not? Party Abbreviation  \\\\\\n\",\n        \"0               430847            yes                TRS   \\n\",\n        \"1                41032             no                IND   \\n\",\n        \"2               184198             no                TDP   \\n\",\n        \"3                94420             no                BSP   \\n\",\n        \"4                 8859             no                IND   \\n\",\n        \"5               259557             no                INC   \\n\",\n        \"6                 5055             no                IND   \\n\",\n        \"7                 4787             no                IND   \\n\",\n        \"8                17084             no               NOTA   \\n\",\n        \"9               565496            yes                TRS   \\n\",\n        \"10                5361             no               NOTA   \\n\",\n        \"11              274338             no                INC   \\n\",\n        \"12                9290             no               PPOI   \\n\",\n        \"13                2674             no                IND   \\n\",\n        \"14               45977             no              RP(K)   \\n\",\n        \"15               63334             no                TDP   \\n\",\n        \"16                2742             no                IND   \\n\",\n        \"17                3699             no                IND   \\n\",\n        \"18                3866             no               MaSP   \\n\",\n        \"19                2361             no                IND   \\n\",\n        \"20                7258             no                IND   \\n\",\n        \"21                2245             no               BCUF   \\n\",\n        \"22                9199             no                IND   \\n\",\n        \"23                3020             no                NIP   \\n\",\n        \"24                3266             no                IND   \\n\",\n        \"25                8609             no                RPI   \\n\",\n        \"26                9449             no                BSP   \\n\",\n        \"27              505783            yes                TRS   \\n\",\n        \"28                3865             no                IND   \\n\",\n        \"29                2062             no                NIP   \\n\",\n        \"30                2760             no                IND   \\n\",\n        \"31                8040             no             RPI(A)   \\n\",\n        \"32                4769             no                NCP   \\n\",\n        \"33                4731             no                IND   \\n\",\n        \"34               11333             no                BSP   \\n\",\n        \"35               39325             no               WPOI   \\n\",\n        \"36              300706             no                INC   \\n\",\n        \"37              214828             no                BJP   \\n\",\n        \"38                3080             no              YSRCP   \\n\",\n        \"39                6720             no                IND   \\n\",\n        \"40                2448             no                IND   \\n\",\n        \"41                3944             no               PPOI   \\n\",\n        \"42                1777             no                IND   \\n\",\n        \"43                3773             no               BMUP   \\n\",\n        \"44                5747             no               NOTA   \\n\",\n        \"45              439307            yes                TRS   \\n\",\n        \"46                3651             no               PPOI   \\n\",\n        \"47                3091             no                IND   \\n\",\n        \"48                3990             no                IND   \\n\",\n        \"49               43814             no               WPOI   \\n\",\n        \"50                2817             no                IND   \\n\",\n        \"51                8129             no                IND   \\n\",\n        \"52                4791             no               BCUF   \\n\",\n        \"53              225333             no                BJP   \\n\",\n        \"54                1601             no                IND   \\n\",\n        \"55                7421             no                BSP   \\n\",\n        \"56                1978             no               BMUP   \\n\",\n        \"57              272123             no                INC   \\n\",\n        \"58                2621             no               AAAP   \\n\",\n        \"59                2462             no                IND   \\n\",\n        \"                   ...            ...                ...   \\n\",\n        \"\\n\",\n        \"                               Party Name  \\n\",\n        \"0               Telangana Rashtra Samithi  \\n\",\n        \"1                             Independent  \\n\",\n        \"2                            Telugu Desam  \\n\",\n        \"3                     Bahujan Samaj Party  \\n\",\n        \"4                             Independent  \\n\",\n        \"5                Indian National Congress  \\n\",\n        \"6                             Independent  \\n\",\n        \"7                             Independent  \\n\",\n        \"8                       None of the Above  \\n\",\n        \"9               Telangana Rashtra Samithi  \\n\",\n        \"10                      None of the Above  \\n\",\n        \"11               Indian National Congress  \\n\",\n        \"12                 Pyramid Party of India  \\n\",\n        \"13                            Independent  \\n\",\n        \"14            Republican Paksha (Khoripa)  \\n\",\n        \"15                           Telugu Desam  \\n\",\n        \"16                            Independent  \\n\",\n        \"17                            Independent  \\n\",\n        \"18               Mahajana Socialist Party  \\n\",\n        \"19                            Independent  \\n\",\n        \"20                            Independent  \\n\",\n        \"21                     B. C. United Front  \\n\",\n        \"22                            Independent  \\n\",\n        \"23                        New India Party  \\n\",\n        \"24                            Independent  \\n\",\n        \"25              Republican Party of India  \\n\",\n        \"26                    Bahujan Samaj Party  \\n\",\n        \"27              Telangana Rashtra Samithi  \\n\",\n        \"28                            Independent  \\n\",\n        \"29                        New India Party  \\n\",\n        \"30                            Independent  \\n\",\n        \"31          Republican Party of India (A)  \\n\",\n        \"32             Nationalist Congress Party  \\n\",\n        \"33                            Independent  \\n\",\n        \"34                    Bahujan Samaj Party  \\n\",\n        \"35                 Welfare Party Of India  \\n\",\n        \"36               Indian National Congress  \\n\",\n        \"37                 Bharatiya Janata Party  \\n\",\n        \"38  Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"39                            Independent  \\n\",\n        \"40                            Independent  \\n\",\n        \"41                 Pyramid Party of India  \\n\",\n        \"42                            Independent  \\n\",\n        \"43                    Bahujan Mukti Party  \\n\",\n        \"44                      None of the Above  \\n\",\n        \"45              Telangana Rashtra Samithi  \\n\",\n        \"46                 Pyramid Party of India  \\n\",\n        \"47                            Independent  \\n\",\n        \"48                            Independent  \\n\",\n        \"49                 Welfare Party Of India  \\n\",\n        \"50                            Independent  \\n\",\n        \"51                            Independent  \\n\",\n        \"52                     B. C. United Front  \\n\",\n        \"53                 Bharatiya Janata Party  \\n\",\n        \"54                            Independent  \\n\",\n        \"55                    Bahujan Samaj Party  \\n\",\n        \"56                    Bahujan Mukti Party  \\n\",\n        \"57               Indian National Congress  \\n\",\n        \"58                        Aam Aadmi Party  \\n\",\n        \"59                            Independent  \\n\",\n        \"                                      ...  \\n\",\n        \"\\n\",\n        \"[8794 rows x 7 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"Analyzing The results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"winners = results_data[results_data[\\\"Winner or Not?\\\"] == \\\"yes\\\"].sort(\\\"Name of State/ UT\\\")\\n\",\n      \"winners\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Name of State/ UT</th>\\n\",\n        \"      <th>Parliamentary Constituency</th>\\n\",\n        \"      <th>Candidate Name</th>\\n\",\n        \"      <th>Total Votes Polled</th>\\n\",\n        \"      <th>Winner or Not?</th>\\n\",\n        \"      <th>Party Abbreviation</th>\\n\",\n        \"      <th>Party Name</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8548</th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td>                     BISHNU PADA RAY</td>\\n\",\n        \"      <td>  90969</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0   </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Adilabad</td>\\n\",\n        \"      <td>                        GODAM NAGESH</td>\\n\",\n        \"      <td> 430847</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9   </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Peddapalle</td>\\n\",\n        \"      <td>                         BALKA SUMAN</td>\\n\",\n        \"      <td> 565496</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27  </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Karimnagar</td>\\n\",\n        \"      <td>              VINOD KUMAR BOINAPALLY</td>\\n\",\n        \"      <td> 505783</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45  </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 Nizamabad</td>\\n\",\n        \"      <td>                 KALVAKUNTLA KAVITHA</td>\\n\",\n        \"      <td> 439307</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>62  </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 Zahirabad</td>\\n\",\n        \"      <td>                          B.B. PATIL</td>\\n\",\n        \"      <td> 508661</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>73  </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                     Medak</td>\\n\",\n        \"      <td>       KALVAKUNTLA CHANDRASEKHAR RAO</td>\\n\",\n        \"      <td> 657492</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>87  </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Malkajgiri</td>\\n\",\n        \"      <td>                      CH.MALLA REDDY</td>\\n\",\n        \"      <td> 523336</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>118 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               Secundrabad</td>\\n\",\n        \"      <td>                  BANDARU DATTATREYA</td>\\n\",\n        \"      <td> 438271</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>149 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 Hyderabad</td>\\n\",\n        \"      <td>                    ASADUDDIN OWAISI</td>\\n\",\n        \"      <td> 513868</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> AIMIM</td>\\n\",\n        \"      <td> All India Majlis-E-Ittehadul Muslimeen</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>166 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 CHELVELLA</td>\\n\",\n        \"      <td>             KONDA VISHWESHWAR REDDY</td>\\n\",\n        \"      <td> 435077</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>182 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               Mahbubnagar</td>\\n\",\n        \"      <td>                  AP JITHENDER REDDY</td>\\n\",\n        \"      <td> 334228</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>192 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              Nagarkurnool</td>\\n\",\n        \"      <td>                      YELLAIAH NANDI</td>\\n\",\n        \"      <td> 420075</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>199 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Nalgonda</td>\\n\",\n        \"      <td>               GUTHA SUKHENDER REDDY</td>\\n\",\n        \"      <td> 472093</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>209 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   Bhongir</td>\\n\",\n        \"      <td>             DR. BOORA NARSAIAH GOUD</td>\\n\",\n        \"      <td> 448245</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>223 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Warangal</td>\\n\",\n        \"      <td>                     KADIYAM SRIHARI</td>\\n\",\n        \"      <td> 661639</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>236 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               Mahabubabad</td>\\n\",\n        \"      <td>         PROF. AZMEERA SEETARAM NAIK</td>\\n\",\n        \"      <td> 320569</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>254 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   Khammam</td>\\n\",\n        \"      <td>          PONGULETI  SRINIVASA REDDY</td>\\n\",\n        \"      <td> 421957</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>282 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                     Aruku</td>\\n\",\n        \"      <td>                   KOTHAPALLI GEETHA</td>\\n\",\n        \"      <td> 413191</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>294 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Srikakulam</td>\\n\",\n        \"      <td>            RAMMOHAN NAIDU KINJARAPU</td>\\n\",\n        \"      <td> 556163</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>305 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              Vizianagaram</td>\\n\",\n        \"      <td>       ASHOK GAJAPATHI RAJU PUSAPATI</td>\\n\",\n        \"      <td> 536549</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>315 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             Visakhapatnam</td>\\n\",\n        \"      <td>               KAMBHAMPATI HARI BABU</td>\\n\",\n        \"      <td> 566832</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>338 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Anakapalli</td>\\n\",\n        \"      <td> MUTTAMSETTI SRINIVASA RAO (AVANTHI)</td>\\n\",\n        \"      <td> 568463</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>347 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Kakinada</td>\\n\",\n        \"      <td>                    THOTA NARASIMHAM</td>\\n\",\n        \"      <td> 514402</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>370 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Amalapuram</td>\\n\",\n        \"      <td>            DR PANDULA RAVINDRA BABU</td>\\n\",\n        \"      <td> 594547</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>385 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               Rajahmundry</td>\\n\",\n        \"      <td>                MURALI MOHAN MAGANTI</td>\\n\",\n        \"      <td> 630573</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>400 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Narsapuram</td>\\n\",\n        \"      <td>                 GOKARAJU GANGA RAJU</td>\\n\",\n        \"      <td> 540306</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>415 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                     Eluru</td>\\n\",\n        \"      <td>     MAGANTI VENKATESWARA RAO (BABU)</td>\\n\",\n        \"      <td> 623471</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>431 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             Machilipatnam</td>\\n\",\n        \"      <td>              KONAKALLA NARAYANA RAO</td>\\n\",\n        \"      <td> 587280</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>443 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                Vijayawada</td>\\n\",\n        \"      <td>                   KESINENI SRINIVAS</td>\\n\",\n        \"      <td> 592696</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>466 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                    Guntur</td>\\n\",\n        \"      <td>                       JAYADEV GALLA</td>\\n\",\n        \"      <td> 618417</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>479 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              Narasaraopet</td>\\n\",\n        \"      <td>              SAMBASIVA RAO RAYAPATI</td>\\n\",\n        \"      <td> 632464</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>491 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   Bapatla</td>\\n\",\n        \"      <td>                     MALYADRI SRIRAM</td>\\n\",\n        \"      <td> 578145</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>506 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                    Ongole</td>\\n\",\n        \"      <td>                     Y.V.SUBBA REDDY</td>\\n\",\n        \"      <td> 589960</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>522 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   Nandyal</td>\\n\",\n        \"      <td>                         S.P.Y REDDY</td>\\n\",\n        \"      <td> 622411</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>537 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   Kurnool</td>\\n\",\n        \"      <td>                        BUTTA RENUKA</td>\\n\",\n        \"      <td> 472782</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>550 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 Anantapur</td>\\n\",\n        \"      <td>                  J.C. DIVAKAR REDDI</td>\\n\",\n        \"      <td> 606509</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>564 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Hindupur</td>\\n\",\n        \"      <td>                   KRISTAPPA NIMMALA</td>\\n\",\n        \"      <td> 604291</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>577 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                    Kadapa</td>\\n\",\n        \"      <td>                  Y.S. AVINASH REDDY</td>\\n\",\n        \"      <td> 671983</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>592 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   Nellore</td>\\n\",\n        \"      <td>            MEKAPATI RAJAMOHAN REDDY</td>\\n\",\n        \"      <td> 576396</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>607 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Tirupati</td>\\n\",\n        \"      <td>          VARAPRASAD RAO VELAGAPALLI</td>\\n\",\n        \"      <td> 580376</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>622 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Rajampet</td>\\n\",\n        \"      <td>                    P.V.MIDHUN REDDY</td>\\n\",\n        \"      <td> 601752</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>632 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  Chittoor</td>\\n\",\n        \"      <td>                NARAMALLI SIVAPRASAD</td>\\n\",\n        \"      <td> 594862</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>640 </th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>            ARUNACHAL WEST</td>\\n\",\n        \"      <td>                        KIREN RIJIJU</td>\\n\",\n        \"      <td> 169367</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>649 </th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>            ARUNACHAL EAST</td>\\n\",\n        \"      <td>                        NINONG ERING</td>\\n\",\n        \"      <td> 118455</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>653 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 Karimganj</td>\\n\",\n        \"      <td>                   RADHESHYAM BISWAS</td>\\n\",\n        \"      <td> 362866</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> AIUDF</td>\\n\",\n        \"      <td>      All India United Democratic Front</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>669 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   Silchar</td>\\n\",\n        \"      <td>                        SUSHMITA DEV</td>\\n\",\n        \"      <td> 336451</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>687 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>       Autonomous District</td>\\n\",\n        \"      <td>                   BIREN SINGH ENGTI</td>\\n\",\n        \"      <td> 213152</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>693 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                    Dhubri</td>\\n\",\n        \"      <td>                     BADRUDDIN AJMAL</td>\\n\",\n        \"      <td> 592569</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> AIUDF</td>\\n\",\n        \"      <td>      All India United Democratic Front</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>709 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 Kokrajhar</td>\\n\",\n        \"      <td>           NABA KUMAR SARANIA (HIRA)</td>\\n\",\n        \"      <td> 634428</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   IND</td>\\n\",\n        \"      <td>                            Independent</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>716 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   Barpeta</td>\\n\",\n        \"      <td>                   SIRAJ UDDIN AJMAL</td>\\n\",\n        \"      <td> 394702</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td> AIUDF</td>\\n\",\n        \"      <td>      All India United Democratic Front</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>732 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   Gauhati</td>\\n\",\n        \"      <td>                  BIJOYA CHAKRAVARTY</td>\\n\",\n        \"      <td> 764985</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>751 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 Mangaldoi</td>\\n\",\n        \"      <td>                          RAMEN DEKA</td>\\n\",\n        \"      <td> 486357</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>764 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                    Tezpur</td>\\n\",\n        \"      <td>                   RAM PRASAD SARMAH</td>\\n\",\n        \"      <td> 446511</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>774 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   Nowgong</td>\\n\",\n        \"      <td>                        RAJEN GOHAIN</td>\\n\",\n        \"      <td> 494146</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>783 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                  Kaliabor</td>\\n\",\n        \"      <td>                        GOURAV GOGOI</td>\\n\",\n        \"      <td> 443315</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>797 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                    Jorhat</td>\\n\",\n        \"      <td>                KAMAKHYA PRASAD TASA</td>\\n\",\n        \"      <td> 456420</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>808 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 Dibrugarh</td>\\n\",\n        \"      <td>                       RAMESWAR TELI</td>\\n\",\n        \"      <td> 494364</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>815 </th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 Lakhimpur</td>\\n\",\n        \"      <td>                  SARBANANDA SONOWAL</td>\\n\",\n        \"      <td> 612543</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>829 </th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"      <td>             Valmiki Nagar</td>\\n\",\n        \"      <td>                SATISH CHANDRA DUBEY</td>\\n\",\n        \"      <td> 364011</td>\\n\",\n        \"      <td> yes</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>543 rows \\u00d7 7 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 15,\n       \"text\": [\n        \"              Name of State/ UT Parliamentary Constituency  \\\\\\n\",\n        \"8548  Andaman & Nicobar Islands  Andaman & Nicobar Islands   \\n\",\n        \"0                Andhra Pradesh                   Adilabad   \\n\",\n        \"9                Andhra Pradesh                 Peddapalle   \\n\",\n        \"27               Andhra Pradesh                 Karimnagar   \\n\",\n        \"45               Andhra Pradesh                  Nizamabad   \\n\",\n        \"62               Andhra Pradesh                  Zahirabad   \\n\",\n        \"73               Andhra Pradesh                      Medak   \\n\",\n        \"87               Andhra Pradesh                 Malkajgiri   \\n\",\n        \"118              Andhra Pradesh                Secundrabad   \\n\",\n        \"149              Andhra Pradesh                  Hyderabad   \\n\",\n        \"166              Andhra Pradesh                  CHELVELLA   \\n\",\n        \"182              Andhra Pradesh                Mahbubnagar   \\n\",\n        \"192              Andhra Pradesh               Nagarkurnool   \\n\",\n        \"199              Andhra Pradesh                   Nalgonda   \\n\",\n        \"209              Andhra Pradesh                    Bhongir   \\n\",\n        \"223              Andhra Pradesh                   Warangal   \\n\",\n        \"236              Andhra Pradesh                Mahabubabad   \\n\",\n        \"254              Andhra Pradesh                    Khammam   \\n\",\n        \"282              Andhra Pradesh                      Aruku   \\n\",\n        \"294              Andhra Pradesh                 Srikakulam   \\n\",\n        \"305              Andhra Pradesh               Vizianagaram   \\n\",\n        \"315              Andhra Pradesh              Visakhapatnam   \\n\",\n        \"338              Andhra Pradesh                 Anakapalli   \\n\",\n        \"347              Andhra Pradesh                   Kakinada   \\n\",\n        \"370              Andhra Pradesh                 Amalapuram   \\n\",\n        \"385              Andhra Pradesh                Rajahmundry   \\n\",\n        \"400              Andhra Pradesh                 Narsapuram   \\n\",\n        \"415              Andhra Pradesh                      Eluru   \\n\",\n        \"431              Andhra Pradesh              Machilipatnam   \\n\",\n        \"443              Andhra Pradesh                 Vijayawada   \\n\",\n        \"466              Andhra Pradesh                     Guntur   \\n\",\n        \"479              Andhra Pradesh               Narasaraopet   \\n\",\n        \"491              Andhra Pradesh                    Bapatla   \\n\",\n        \"506              Andhra Pradesh                     Ongole   \\n\",\n        \"522              Andhra Pradesh                    Nandyal   \\n\",\n        \"537              Andhra Pradesh                    Kurnool   \\n\",\n        \"550              Andhra Pradesh                  Anantapur   \\n\",\n        \"564              Andhra Pradesh                   Hindupur   \\n\",\n        \"577              Andhra Pradesh                     Kadapa   \\n\",\n        \"592              Andhra Pradesh                    Nellore   \\n\",\n        \"607              Andhra Pradesh                   Tirupati   \\n\",\n        \"622              Andhra Pradesh                   Rajampet   \\n\",\n        \"632              Andhra Pradesh                   Chittoor   \\n\",\n        \"640           Arunachal Pradesh             ARUNACHAL WEST   \\n\",\n        \"649           Arunachal Pradesh             ARUNACHAL EAST   \\n\",\n        \"653                       Assam                  Karimganj   \\n\",\n        \"669                       Assam                    Silchar   \\n\",\n        \"687                       Assam        Autonomous District   \\n\",\n        \"693                       Assam                     Dhubri   \\n\",\n        \"709                       Assam                  Kokrajhar   \\n\",\n        \"716                       Assam                    Barpeta   \\n\",\n        \"732                       Assam                    Gauhati   \\n\",\n        \"751                       Assam                  Mangaldoi   \\n\",\n        \"764                       Assam                     Tezpur   \\n\",\n        \"774                       Assam                    Nowgong   \\n\",\n        \"783                       Assam                   Kaliabor   \\n\",\n        \"797                       Assam                     Jorhat   \\n\",\n        \"808                       Assam                  Dibrugarh   \\n\",\n        \"815                       Assam                  Lakhimpur   \\n\",\n        \"829                       Bihar              Valmiki Nagar   \\n\",\n        \"                            ...                        ...   \\n\",\n        \"\\n\",\n        \"                           Candidate Name  Total Votes Polled Winner or Not?  \\\\\\n\",\n        \"8548                      BISHNU PADA RAY               90969            yes   \\n\",\n        \"0                            GODAM NAGESH              430847            yes   \\n\",\n        \"9                             BALKA SUMAN              565496            yes   \\n\",\n        \"27                 VINOD KUMAR BOINAPALLY              505783            yes   \\n\",\n        \"45                    KALVAKUNTLA KAVITHA              439307            yes   \\n\",\n        \"62                             B.B. PATIL              508661            yes   \\n\",\n        \"73          KALVAKUNTLA CHANDRASEKHAR RAO              657492            yes   \\n\",\n        \"87                         CH.MALLA REDDY              523336            yes   \\n\",\n        \"118                    BANDARU DATTATREYA              438271            yes   \\n\",\n        \"149                      ASADUDDIN OWAISI              513868            yes   \\n\",\n        \"166               KONDA VISHWESHWAR REDDY              435077            yes   \\n\",\n        \"182                    AP JITHENDER REDDY              334228            yes   \\n\",\n        \"192                        YELLAIAH NANDI              420075            yes   \\n\",\n        \"199                 GUTHA SUKHENDER REDDY              472093            yes   \\n\",\n        \"209               DR. BOORA NARSAIAH GOUD              448245            yes   \\n\",\n        \"223                       KADIYAM SRIHARI              661639            yes   \\n\",\n        \"236           PROF. AZMEERA SEETARAM NAIK              320569            yes   \\n\",\n        \"254            PONGULETI  SRINIVASA REDDY              421957            yes   \\n\",\n        \"282                     KOTHAPALLI GEETHA              413191            yes   \\n\",\n        \"294              RAMMOHAN NAIDU KINJARAPU              556163            yes   \\n\",\n        \"305         ASHOK GAJAPATHI RAJU PUSAPATI              536549            yes   \\n\",\n        \"315                 KAMBHAMPATI HARI BABU              566832            yes   \\n\",\n        \"338   MUTTAMSETTI SRINIVASA RAO (AVANTHI)              568463            yes   \\n\",\n        \"347                      THOTA NARASIMHAM              514402            yes   \\n\",\n        \"370              DR PANDULA RAVINDRA BABU              594547            yes   \\n\",\n        \"385                  MURALI MOHAN MAGANTI              630573            yes   \\n\",\n        \"400                   GOKARAJU GANGA RAJU              540306            yes   \\n\",\n        \"415       MAGANTI VENKATESWARA RAO (BABU)              623471            yes   \\n\",\n        \"431                KONAKALLA NARAYANA RAO              587280            yes   \\n\",\n        \"443                     KESINENI SRINIVAS              592696            yes   \\n\",\n        \"466                         JAYADEV GALLA              618417            yes   \\n\",\n        \"479                SAMBASIVA RAO RAYAPATI              632464            yes   \\n\",\n        \"491                       MALYADRI SRIRAM              578145            yes   \\n\",\n        \"506                       Y.V.SUBBA REDDY              589960            yes   \\n\",\n        \"522                           S.P.Y REDDY              622411            yes   \\n\",\n        \"537                          BUTTA RENUKA              472782            yes   \\n\",\n        \"550                    J.C. DIVAKAR REDDI              606509            yes   \\n\",\n        \"564                     KRISTAPPA NIMMALA              604291            yes   \\n\",\n        \"577                    Y.S. AVINASH REDDY              671983            yes   \\n\",\n        \"592              MEKAPATI RAJAMOHAN REDDY              576396            yes   \\n\",\n        \"607            VARAPRASAD RAO VELAGAPALLI              580376            yes   \\n\",\n        \"622                      P.V.MIDHUN REDDY              601752            yes   \\n\",\n        \"632                  NARAMALLI SIVAPRASAD              594862            yes   \\n\",\n        \"640                          KIREN RIJIJU              169367            yes   \\n\",\n        \"649                          NINONG ERING              118455            yes   \\n\",\n        \"653                     RADHESHYAM BISWAS              362866            yes   \\n\",\n        \"669                          SUSHMITA DEV              336451            yes   \\n\",\n        \"687                     BIREN SINGH ENGTI              213152            yes   \\n\",\n        \"693                       BADRUDDIN AJMAL              592569            yes   \\n\",\n        \"709             NABA KUMAR SARANIA (HIRA)              634428            yes   \\n\",\n        \"716                     SIRAJ UDDIN AJMAL              394702            yes   \\n\",\n        \"732                    BIJOYA CHAKRAVARTY              764985            yes   \\n\",\n        \"751                            RAMEN DEKA              486357            yes   \\n\",\n        \"764                     RAM PRASAD SARMAH              446511            yes   \\n\",\n        \"774                          RAJEN GOHAIN              494146            yes   \\n\",\n        \"783                          GOURAV GOGOI              443315            yes   \\n\",\n        \"797                  KAMAKHYA PRASAD TASA              456420            yes   \\n\",\n        \"808                         RAMESWAR TELI              494364            yes   \\n\",\n        \"815                    SARBANANDA SONOWAL              612543            yes   \\n\",\n        \"829                  SATISH CHANDRA DUBEY              364011            yes   \\n\",\n        \"                                      ...                 ...            ...   \\n\",\n        \"\\n\",\n        \"     Party Abbreviation                              Party Name  \\n\",\n        \"8548                BJP                  Bharatiya Janata Party  \\n\",\n        \"0                   TRS               Telangana Rashtra Samithi  \\n\",\n        \"9                   TRS               Telangana Rashtra Samithi  \\n\",\n        \"27                  TRS               Telangana Rashtra Samithi  \\n\",\n        \"45                  TRS               Telangana Rashtra Samithi  \\n\",\n        \"62                  TRS               Telangana Rashtra Samithi  \\n\",\n        \"73                  TRS               Telangana Rashtra Samithi  \\n\",\n        \"87                  TDP                            Telugu Desam  \\n\",\n        \"118                 BJP                  Bharatiya Janata Party  \\n\",\n        \"149               AIMIM  All India Majlis-E-Ittehadul Muslimeen  \\n\",\n        \"166                 TRS               Telangana Rashtra Samithi  \\n\",\n        \"182                 TRS               Telangana Rashtra Samithi  \\n\",\n        \"192                 INC                Indian National Congress  \\n\",\n        \"199                 INC                Indian National Congress  \\n\",\n        \"209                 TRS               Telangana Rashtra Samithi  \\n\",\n        \"223                 TRS               Telangana Rashtra Samithi  \\n\",\n        \"236                 TRS               Telangana Rashtra Samithi  \\n\",\n        \"254               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"282               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"294                 TDP                            Telugu Desam  \\n\",\n        \"305                 TDP                            Telugu Desam  \\n\",\n        \"315                 BJP                  Bharatiya Janata Party  \\n\",\n        \"338                 TDP                            Telugu Desam  \\n\",\n        \"347                 TDP                            Telugu Desam  \\n\",\n        \"370                 TDP                            Telugu Desam  \\n\",\n        \"385                 TDP                            Telugu Desam  \\n\",\n        \"400                 BJP                  Bharatiya Janata Party  \\n\",\n        \"415                 TDP                            Telugu Desam  \\n\",\n        \"431                 TDP                            Telugu Desam  \\n\",\n        \"443                 TDP                            Telugu Desam  \\n\",\n        \"466                 TDP                            Telugu Desam  \\n\",\n        \"479                 TDP                            Telugu Desam  \\n\",\n        \"491                 TDP                            Telugu Desam  \\n\",\n        \"506               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"522               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"537               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"550                 TDP                            Telugu Desam  \\n\",\n        \"564                 TDP                            Telugu Desam  \\n\",\n        \"577               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"592               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"607               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"622               YSRCP   Yuvajana Sramika Rythu Congress Party  \\n\",\n        \"632                 TDP                            Telugu Desam  \\n\",\n        \"640                 BJP                  Bharatiya Janata Party  \\n\",\n        \"649                 INC                Indian National Congress  \\n\",\n        \"653               AIUDF       All India United Democratic Front  \\n\",\n        \"669                 INC                Indian National Congress  \\n\",\n        \"687                 INC                Indian National Congress  \\n\",\n        \"693               AIUDF       All India United Democratic Front  \\n\",\n        \"709                 IND                             Independent  \\n\",\n        \"716               AIUDF       All India United Democratic Front  \\n\",\n        \"732                 BJP                  Bharatiya Janata Party  \\n\",\n        \"751                 BJP                  Bharatiya Janata Party  \\n\",\n        \"764                 BJP                  Bharatiya Janata Party  \\n\",\n        \"774                 BJP                  Bharatiya Janata Party  \\n\",\n        \"783                 INC                Indian National Congress  \\n\",\n        \"797                 BJP                  Bharatiya Janata Party  \\n\",\n        \"808                 BJP                  Bharatiya Janata Party  \\n\",\n        \"815                 BJP                  Bharatiya Janata Party  \\n\",\n        \"829                 BJP                  Bharatiya Janata Party  \\n\",\n        \"                    ...                                     ...  \\n\",\n        \"\\n\",\n        \"[543 rows x 7 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Loading the results to the origianl Data Frame\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data[\\\"Candidate Name\\\"] = winners[\\\"Candidate Name\\\"].values\\n\",\n      \"csv_data[\\\"Party Abbreviation\\\"] = winners[\\\"Party Abbreviation\\\"].values\\n\",\n      \"csv_data[\\\"Party Name\\\"] = winners[\\\"Party Name\\\"].values\\n\",\n      \"csv_data[\\\"Total Votes Polled\\\"] = winners[\\\"Total Votes Polled\\\"].values\\n\",\n      \"csv_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"      <th>PC Name</th>\\n\",\n        \"      <th>Male Electors</th>\\n\",\n        \"      <th>Male Voters</th>\\n\",\n        \"      <th>Female Electors</th>\\n\",\n        \"      <th>Female Voters</th>\\n\",\n        \"      <th>Total Electors</th>\\n\",\n        \"      <th>Total Voters</th>\\n\",\n        \"      <th>Male Turnout</th>\\n\",\n        \"      <th>Female Turnout</th>\\n\",\n        \"      <th>Total Turnout</th>\\n\",\n        \"      <th>Candidate Name</th>\\n\",\n        \"      <th>Party Abbreviation</th>\\n\",\n        \"      <th>Party Name</th>\\n\",\n        \"      <th>Total Votes Polled</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td> 1 - Andaman &amp; Nicobar Islands</td>\\n\",\n        \"      <td>  142782</td>\\n\",\n        \"      <td> 101178</td>\\n\",\n        \"      <td>  126578</td>\\n\",\n        \"      <td>  89150</td>\\n\",\n        \"      <td>  269360</td>\\n\",\n        \"      <td>  190328</td>\\n\",\n        \"      <td> 0.708619</td>\\n\",\n        \"      <td> 0.704309</td>\\n\",\n        \"      <td> 0.706593</td>\\n\",\n        \"      <td>                     BISHNU PADA RAY</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td>  90969</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 1 - Adilabad </td>\\n\",\n        \"      <td>  687389</td>\\n\",\n        \"      <td> 519364</td>\\n\",\n        \"      <td>  698844</td>\\n\",\n        \"      <td> 526475</td>\\n\",\n        \"      <td> 1386233</td>\\n\",\n        \"      <td> 1045839</td>\\n\",\n        \"      <td> 0.755561</td>\\n\",\n        \"      <td> 0.753351</td>\\n\",\n        \"      <td> 0.754447</td>\\n\",\n        \"      <td>                        GODAM NAGESH</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 430847</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               2 - Peddapalle </td>\\n\",\n        \"      <td>  725767</td>\\n\",\n        \"      <td> 520598</td>\\n\",\n        \"      <td>  699594</td>\\n\",\n        \"      <td> 501586</td>\\n\",\n        \"      <td> 1425361</td>\\n\",\n        \"      <td> 1022184</td>\\n\",\n        \"      <td> 0.717307</td>\\n\",\n        \"      <td> 0.716967</td>\\n\",\n        \"      <td> 0.717140</td>\\n\",\n        \"      <td>                         BALKA SUMAN</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 565496</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               3 - Karimnagar </td>\\n\",\n        \"      <td>  777458</td>\\n\",\n        \"      <td> 552489</td>\\n\",\n        \"      <td>  773376</td>\\n\",\n        \"      <td> 573202</td>\\n\",\n        \"      <td> 1550834</td>\\n\",\n        \"      <td> 1125691</td>\\n\",\n        \"      <td> 0.710635</td>\\n\",\n        \"      <td> 0.741169</td>\\n\",\n        \"      <td> 0.725862</td>\\n\",\n        \"      <td>              VINOD KUMAR BOINAPALLY</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 505783</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 4 - Nizamabad</td>\\n\",\n        \"      <td>  724504</td>\\n\",\n        \"      <td> 469712</td>\\n\",\n        \"      <td>  771689</td>\\n\",\n        \"      <td> 564212</td>\\n\",\n        \"      <td> 1496193</td>\\n\",\n        \"      <td> 1033924</td>\\n\",\n        \"      <td> 0.648322</td>\\n\",\n        \"      <td> 0.731139</td>\\n\",\n        \"      <td> 0.691037</td>\\n\",\n        \"      <td>                 KALVAKUNTLA KAVITHA</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 439307</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 5 - Zahirabad</td>\\n\",\n        \"      <td>  717811</td>\\n\",\n        \"      <td> 546746</td>\\n\",\n        \"      <td>  727435</td>\\n\",\n        \"      <td> 548060</td>\\n\",\n        \"      <td> 1445246</td>\\n\",\n        \"      <td> 1094806</td>\\n\",\n        \"      <td> 0.761685</td>\\n\",\n        \"      <td> 0.753414</td>\\n\",\n        \"      <td> 0.757522</td>\\n\",\n        \"      <td>                          B.B. PATIL</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 508661</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                     6 - Medak</td>\\n\",\n        \"      <td>  775903</td>\\n\",\n        \"      <td> 606863</td>\\n\",\n        \"      <td>  760812</td>\\n\",\n        \"      <td> 584233</td>\\n\",\n        \"      <td> 1536715</td>\\n\",\n        \"      <td> 1191096</td>\\n\",\n        \"      <td> 0.782138</td>\\n\",\n        \"      <td> 0.767907</td>\\n\",\n        \"      <td> 0.775092</td>\\n\",\n        \"      <td>       KALVAKUNTLA CHANDRASEKHAR RAO</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 657492</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                7 - Malkajgiri</td>\\n\",\n        \"      <td> 1723413</td>\\n\",\n        \"      <td> 878093</td>\\n\",\n        \"      <td> 1459912</td>\\n\",\n        \"      <td> 742304</td>\\n\",\n        \"      <td> 3183325</td>\\n\",\n        \"      <td> 1620397</td>\\n\",\n        \"      <td> 0.509508</td>\\n\",\n        \"      <td> 0.508458</td>\\n\",\n        \"      <td> 0.509027</td>\\n\",\n        \"      <td>                      CH.MALLA REDDY</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 523336</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               8 - Secundrabad</td>\\n\",\n        \"      <td> 1012378</td>\\n\",\n        \"      <td> 545749</td>\\n\",\n        \"      <td>  881269</td>\\n\",\n        \"      <td> 458020</td>\\n\",\n        \"      <td> 1893647</td>\\n\",\n        \"      <td> 1003769</td>\\n\",\n        \"      <td> 0.539076</td>\\n\",\n        \"      <td> 0.519728</td>\\n\",\n        \"      <td> 0.530072</td>\\n\",\n        \"      <td>                  BANDARU DATTATREYA</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 438271</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 9 - Hyderabad</td>\\n\",\n        \"      <td>  961290</td>\\n\",\n        \"      <td> 526510</td>\\n\",\n        \"      <td>  862374</td>\\n\",\n        \"      <td> 444911</td>\\n\",\n        \"      <td> 1823664</td>\\n\",\n        \"      <td>  971421</td>\\n\",\n        \"      <td> 0.547712</td>\\n\",\n        \"      <td> 0.515914</td>\\n\",\n        \"      <td> 0.532675</td>\\n\",\n        \"      <td>                    ASADUDDIN OWAISI</td>\\n\",\n        \"      <td> AIMIM</td>\\n\",\n        \"      <td> All India Majlis-E-Ittehadul Muslimeen</td>\\n\",\n        \"      <td> 513868</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                10 - CHELVELLA</td>\\n\",\n        \"      <td> 1153049</td>\\n\",\n        \"      <td> 696749</td>\\n\",\n        \"      <td> 1032130</td>\\n\",\n        \"      <td> 619113</td>\\n\",\n        \"      <td> 2185179</td>\\n\",\n        \"      <td> 1315862</td>\\n\",\n        \"      <td> 0.604267</td>\\n\",\n        \"      <td> 0.599840</td>\\n\",\n        \"      <td> 0.602176</td>\\n\",\n        \"      <td>             KONDA VISHWESHWAR REDDY</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 435077</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              11 - Mahbubnagar</td>\\n\",\n        \"      <td>  709711</td>\\n\",\n        \"      <td> 511764</td>\\n\",\n        \"      <td>  708961</td>\\n\",\n        \"      <td> 503036</td>\\n\",\n        \"      <td> 1418672</td>\\n\",\n        \"      <td> 1014800</td>\\n\",\n        \"      <td> 0.721088</td>\\n\",\n        \"      <td> 0.709540</td>\\n\",\n        \"      <td> 0.715317</td>\\n\",\n        \"      <td>                  AP JITHENDER REDDY</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 334228</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             12 - Nagarkurnool</td>\\n\",\n        \"      <td>  745038</td>\\n\",\n        \"      <td> 570342</td>\\n\",\n        \"      <td>  732300</td>\\n\",\n        \"      <td> 538626</td>\\n\",\n        \"      <td> 1477338</td>\\n\",\n        \"      <td> 1108968</td>\\n\",\n        \"      <td> 0.765521</td>\\n\",\n        \"      <td> 0.735526</td>\\n\",\n        \"      <td> 0.750653</td>\\n\",\n        \"      <td>                      YELLAIAH NANDI</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"      <td> 420075</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 13 - Nalgonda</td>\\n\",\n        \"      <td>  747281</td>\\n\",\n        \"      <td> 602193</td>\\n\",\n        \"      <td>  748299</td>\\n\",\n        \"      <td> 587206</td>\\n\",\n        \"      <td> 1495580</td>\\n\",\n        \"      <td> 1189399</td>\\n\",\n        \"      <td> 0.805845</td>\\n\",\n        \"      <td> 0.784721</td>\\n\",\n        \"      <td> 0.795276</td>\\n\",\n        \"      <td>               GUTHA SUKHENDER REDDY</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"      <td> 472093</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 14 - Bhongir </td>\\n\",\n        \"      <td>  756963</td>\\n\",\n        \"      <td> 622333</td>\\n\",\n        \"      <td>  735288</td>\\n\",\n        \"      <td> 589610</td>\\n\",\n        \"      <td> 1492251</td>\\n\",\n        \"      <td> 1211943</td>\\n\",\n        \"      <td> 0.822145</td>\\n\",\n        \"      <td> 0.801876</td>\\n\",\n        \"      <td> 0.812158</td>\\n\",\n        \"      <td>             DR. BOORA NARSAIAH GOUD</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 448245</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 15 - Warangal</td>\\n\",\n        \"      <td>  771756</td>\\n\",\n        \"      <td> 592484</td>\\n\",\n        \"      <td>  766025</td>\\n\",\n        \"      <td> 582147</td>\\n\",\n        \"      <td> 1537781</td>\\n\",\n        \"      <td> 1174631</td>\\n\",\n        \"      <td> 0.767709</td>\\n\",\n        \"      <td> 0.759958</td>\\n\",\n        \"      <td> 0.763848</td>\\n\",\n        \"      <td>                     KADIYAM SRIHARI</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 661639</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>            16 - Mahabubabad  </td>\\n\",\n        \"      <td>  688398</td>\\n\",\n        \"      <td> 562073</td>\\n\",\n        \"      <td>  698945</td>\\n\",\n        \"      <td> 562297</td>\\n\",\n        \"      <td> 1387343</td>\\n\",\n        \"      <td> 1124370</td>\\n\",\n        \"      <td> 0.816494</td>\\n\",\n        \"      <td> 0.804494</td>\\n\",\n        \"      <td> 0.810448</td>\\n\",\n        \"      <td>         PROF. AZMEERA SEETARAM NAIK</td>\\n\",\n        \"      <td>   TRS</td>\\n\",\n        \"      <td>              Telangana Rashtra Samithi</td>\\n\",\n        \"      <td> 320569</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 17 - Khammam </td>\\n\",\n        \"      <td>  712329</td>\\n\",\n        \"      <td> 588728</td>\\n\",\n        \"      <td>  727960</td>\\n\",\n        \"      <td> 594169</td>\\n\",\n        \"      <td> 1440289</td>\\n\",\n        \"      <td> 1182897</td>\\n\",\n        \"      <td> 0.826483</td>\\n\",\n        \"      <td> 0.816211</td>\\n\",\n        \"      <td> 0.821291</td>\\n\",\n        \"      <td>          PONGULETI  SRINIVASA REDDY</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 421957</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   18 - Aruku </td>\\n\",\n        \"      <td>  622416</td>\\n\",\n        \"      <td> 451350</td>\\n\",\n        \"      <td>  650308</td>\\n\",\n        \"      <td> 458264</td>\\n\",\n        \"      <td> 1272724</td>\\n\",\n        \"      <td>  909614</td>\\n\",\n        \"      <td> 0.725158</td>\\n\",\n        \"      <td> 0.704688</td>\\n\",\n        \"      <td> 0.714699</td>\\n\",\n        \"      <td>                   KOTHAPALLI GEETHA</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 413191</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               19 - Srikakulam</td>\\n\",\n        \"      <td>  706828</td>\\n\",\n        \"      <td> 505010</td>\\n\",\n        \"      <td>  707161</td>\\n\",\n        \"      <td> 546436</td>\\n\",\n        \"      <td> 1413989</td>\\n\",\n        \"      <td> 1051446</td>\\n\",\n        \"      <td> 0.714474</td>\\n\",\n        \"      <td> 0.772718</td>\\n\",\n        \"      <td> 0.743603</td>\\n\",\n        \"      <td>            RAMMOHAN NAIDU KINJARAPU</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 556163</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             20 - Vizianagaram</td>\\n\",\n        \"      <td>  700837</td>\\n\",\n        \"      <td> 555005</td>\\n\",\n        \"      <td>  703290</td>\\n\",\n        \"      <td> 565311</td>\\n\",\n        \"      <td> 1404127</td>\\n\",\n        \"      <td> 1120316</td>\\n\",\n        \"      <td> 0.791917</td>\\n\",\n        \"      <td> 0.803809</td>\\n\",\n        \"      <td> 0.797874</td>\\n\",\n        \"      <td>       ASHOK GAJAPATHI RAJU PUSAPATI</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 536549</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>            21 - Visakhapatnam</td>\\n\",\n        \"      <td>  875187</td>\\n\",\n        \"      <td> 585270</td>\\n\",\n        \"      <td>  847850</td>\\n\",\n        \"      <td> 578288</td>\\n\",\n        \"      <td> 1723037</td>\\n\",\n        \"      <td> 1163558</td>\\n\",\n        \"      <td> 0.668737</td>\\n\",\n        \"      <td> 0.682064</td>\\n\",\n        \"      <td> 0.675295</td>\\n\",\n        \"      <td>               KAMBHAMPATI HARI BABU</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 566832</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               22 - Anakapalli</td>\\n\",\n        \"      <td>  689132</td>\\n\",\n        \"      <td> 563818</td>\\n\",\n        \"      <td>  712342</td>\\n\",\n        \"      <td> 584254</td>\\n\",\n        \"      <td> 1401474</td>\\n\",\n        \"      <td> 1148072</td>\\n\",\n        \"      <td> 0.818157</td>\\n\",\n        \"      <td> 0.820187</td>\\n\",\n        \"      <td> 0.819189</td>\\n\",\n        \"      <td> MUTTAMSETTI SRINIVASA RAO (AVANTHI)</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 568463</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 23 - Kakinada</td>\\n\",\n        \"      <td>  709101</td>\\n\",\n        \"      <td> 558689</td>\\n\",\n        \"      <td>  709189</td>\\n\",\n        \"      <td> 541310</td>\\n\",\n        \"      <td> 1418290</td>\\n\",\n        \"      <td> 1099999</td>\\n\",\n        \"      <td> 0.787884</td>\\n\",\n        \"      <td> 0.763280</td>\\n\",\n        \"      <td> 0.775581</td>\\n\",\n        \"      <td>                    THOTA NARASIMHAM</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 514402</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              24 - Amalapuram </td>\\n\",\n        \"      <td>  682607</td>\\n\",\n        \"      <td> 568534</td>\\n\",\n        \"      <td>  675259</td>\\n\",\n        \"      <td> 552393</td>\\n\",\n        \"      <td> 1357866</td>\\n\",\n        \"      <td> 1120927</td>\\n\",\n        \"      <td> 0.832886</td>\\n\",\n        \"      <td> 0.818046</td>\\n\",\n        \"      <td> 0.825506</td>\\n\",\n        \"      <td>            DR PANDULA RAVINDRA BABU</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 594547</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>              25 - Rajahmundry</td>\\n\",\n        \"      <td>  701707</td>\\n\",\n        \"      <td> 577999</td>\\n\",\n        \"      <td>  719581</td>\\n\",\n        \"      <td> 576382</td>\\n\",\n        \"      <td> 1421288</td>\\n\",\n        \"      <td> 1154381</td>\\n\",\n        \"      <td> 0.823704</td>\\n\",\n        \"      <td> 0.800997</td>\\n\",\n        \"      <td> 0.812208</td>\\n\",\n        \"      <td>                MURALI MOHAN MAGANTI</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 630573</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               26 - Narsapuram</td>\\n\",\n        \"      <td>  652668</td>\\n\",\n        \"      <td> 539357</td>\\n\",\n        \"      <td>  672475</td>\\n\",\n        \"      <td> 549594</td>\\n\",\n        \"      <td> 1325143</td>\\n\",\n        \"      <td> 1088951</td>\\n\",\n        \"      <td> 0.826388</td>\\n\",\n        \"      <td> 0.817271</td>\\n\",\n        \"      <td> 0.821761</td>\\n\",\n        \"      <td>                 GOKARAJU GANGA RAJU</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 540306</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   27 - Eluru </td>\\n\",\n        \"      <td>  706916</td>\\n\",\n        \"      <td> 597603</td>\\n\",\n        \"      <td>  720844</td>\\n\",\n        \"      <td> 604093</td>\\n\",\n        \"      <td> 1427760</td>\\n\",\n        \"      <td> 1201696</td>\\n\",\n        \"      <td> 0.845366</td>\\n\",\n        \"      <td> 0.838036</td>\\n\",\n        \"      <td> 0.841665</td>\\n\",\n        \"      <td>     MAGANTI VENKATESWARA RAO (BABU)</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 623471</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>           28 - Machilipatnam </td>\\n\",\n        \"      <td>  675774</td>\\n\",\n        \"      <td> 567908</td>\\n\",\n        \"      <td>  693537</td>\\n\",\n        \"      <td> 573157</td>\\n\",\n        \"      <td> 1369311</td>\\n\",\n        \"      <td> 1141065</td>\\n\",\n        \"      <td> 0.840382</td>\\n\",\n        \"      <td> 0.826426</td>\\n\",\n        \"      <td> 0.833313</td>\\n\",\n        \"      <td>              KONAKALLA NARAYANA RAO</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 587280</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>               29 - Vijayawada</td>\\n\",\n        \"      <td>  781156</td>\\n\",\n        \"      <td> 602198</td>\\n\",\n        \"      <td>  783357</td>\\n\",\n        \"      <td> 592877</td>\\n\",\n        \"      <td> 1564513</td>\\n\",\n        \"      <td> 1195075</td>\\n\",\n        \"      <td> 0.770906</td>\\n\",\n        \"      <td> 0.756841</td>\\n\",\n        \"      <td> 0.763864</td>\\n\",\n        \"      <td>                   KESINENI SRINIVAS</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 592696</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   30 - Guntur</td>\\n\",\n        \"      <td>  773027</td>\\n\",\n        \"      <td> 617483</td>\\n\",\n        \"      <td>  798989</td>\\n\",\n        \"      <td> 627443</td>\\n\",\n        \"      <td> 1572016</td>\\n\",\n        \"      <td> 1244926</td>\\n\",\n        \"      <td> 0.798786</td>\\n\",\n        \"      <td> 0.785296</td>\\n\",\n        \"      <td> 0.791930</td>\\n\",\n        \"      <td>                       JAYADEV GALLA</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 618417</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>             31 - Narasaraopet</td>\\n\",\n        \"      <td>  748465</td>\\n\",\n        \"      <td> 638080</td>\\n\",\n        \"      <td>  766396</td>\\n\",\n        \"      <td> 643978</td>\\n\",\n        \"      <td> 1514861</td>\\n\",\n        \"      <td> 1282058</td>\\n\",\n        \"      <td> 0.852518</td>\\n\",\n        \"      <td> 0.840268</td>\\n\",\n        \"      <td> 0.846321</td>\\n\",\n        \"      <td>              SAMBASIVA RAO RAYAPATI</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 632464</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 32 - Bapatla </td>\\n\",\n        \"      <td>  686482</td>\\n\",\n        \"      <td> 587223</td>\\n\",\n        \"      <td>  706483</td>\\n\",\n        \"      <td> 597411</td>\\n\",\n        \"      <td> 1392965</td>\\n\",\n        \"      <td> 1184634</td>\\n\",\n        \"      <td> 0.855409</td>\\n\",\n        \"      <td> 0.845613</td>\\n\",\n        \"      <td> 0.850441</td>\\n\",\n        \"      <td>                     MALYADRI SRIRAM</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 578145</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  33 - Ongole </td>\\n\",\n        \"      <td>  736245</td>\\n\",\n        \"      <td> 603025</td>\\n\",\n        \"      <td>  734238</td>\\n\",\n        \"      <td> 605200</td>\\n\",\n        \"      <td> 1470483</td>\\n\",\n        \"      <td> 1208225</td>\\n\",\n        \"      <td> 0.819055</td>\\n\",\n        \"      <td> 0.824256</td>\\n\",\n        \"      <td> 0.821652</td>\\n\",\n        \"      <td>                     Y.V.SUBBA REDDY</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 589960</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  34 - Nandyal</td>\\n\",\n        \"      <td>  783266</td>\\n\",\n        \"      <td> 603562</td>\\n\",\n        \"      <td>  793862</td>\\n\",\n        \"      <td> 601394</td>\\n\",\n        \"      <td> 1577128</td>\\n\",\n        \"      <td> 1204956</td>\\n\",\n        \"      <td> 0.770571</td>\\n\",\n        \"      <td> 0.757555</td>\\n\",\n        \"      <td> 0.764019</td>\\n\",\n        \"      <td>                         S.P.Y REDDY</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 622411</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  35 - Kurnool</td>\\n\",\n        \"      <td>  738791</td>\\n\",\n        \"      <td> 544802</td>\\n\",\n        \"      <td>  743016</td>\\n\",\n        \"      <td> 520930</td>\\n\",\n        \"      <td> 1481807</td>\\n\",\n        \"      <td> 1065732</td>\\n\",\n        \"      <td> 0.737424</td>\\n\",\n        \"      <td> 0.701102</td>\\n\",\n        \"      <td> 0.719211</td>\\n\",\n        \"      <td>                        BUTTA RENUKA</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 472782</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                36 - Anantapur</td>\\n\",\n        \"      <td>  775509</td>\\n\",\n        \"      <td> 612890</td>\\n\",\n        \"      <td>  761403</td>\\n\",\n        \"      <td> 592164</td>\\n\",\n        \"      <td> 1536912</td>\\n\",\n        \"      <td> 1205054</td>\\n\",\n        \"      <td> 0.790307</td>\\n\",\n        \"      <td> 0.777727</td>\\n\",\n        \"      <td> 0.784075</td>\\n\",\n        \"      <td>                  J.C. DIVAKAR REDDI</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 606509</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 37 - Hindupur</td>\\n\",\n        \"      <td>  734638</td>\\n\",\n        \"      <td> 601932</td>\\n\",\n        \"      <td>  711865</td>\\n\",\n        \"      <td> 575325</td>\\n\",\n        \"      <td> 1446503</td>\\n\",\n        \"      <td> 1177257</td>\\n\",\n        \"      <td> 0.819359</td>\\n\",\n        \"      <td> 0.808194</td>\\n\",\n        \"      <td> 0.813864</td>\\n\",\n        \"      <td>                   KRISTAPPA NIMMALA</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 604291</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                   38 - Kadapa</td>\\n\",\n        \"      <td>  765036</td>\\n\",\n        \"      <td> 588805</td>\\n\",\n        \"      <td>  785543</td>\\n\",\n        \"      <td> 611857</td>\\n\",\n        \"      <td> 1550579</td>\\n\",\n        \"      <td> 1200662</td>\\n\",\n        \"      <td> 0.769644</td>\\n\",\n        \"      <td> 0.778897</td>\\n\",\n        \"      <td> 0.774331</td>\\n\",\n        \"      <td>                  Y.S. AVINASH REDDY</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 671983</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                  39 - Nellore</td>\\n\",\n        \"      <td>  796583</td>\\n\",\n        \"      <td> 593468</td>\\n\",\n        \"      <td>  809557</td>\\n\",\n        \"      <td> 594180</td>\\n\",\n        \"      <td> 1606140</td>\\n\",\n        \"      <td> 1187648</td>\\n\",\n        \"      <td> 0.745017</td>\\n\",\n        \"      <td> 0.733957</td>\\n\",\n        \"      <td> 0.739442</td>\\n\",\n        \"      <td>            MEKAPATI RAJAMOHAN REDDY</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 576396</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                40 - Tirupati </td>\\n\",\n        \"      <td>  778778</td>\\n\",\n        \"      <td> 604834</td>\\n\",\n        \"      <td>  795766</td>\\n\",\n        \"      <td> 608230</td>\\n\",\n        \"      <td> 1574544</td>\\n\",\n        \"      <td> 1213064</td>\\n\",\n        \"      <td> 0.776645</td>\\n\",\n        \"      <td> 0.764333</td>\\n\",\n        \"      <td> 0.770422</td>\\n\",\n        \"      <td>          VARAPRASAD RAO VELAGAPALLI</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 580376</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                 41 - Rajampet</td>\\n\",\n        \"      <td>  735347</td>\\n\",\n        \"      <td> 573019</td>\\n\",\n        \"      <td>  752151</td>\\n\",\n        \"      <td> 585298</td>\\n\",\n        \"      <td> 1487498</td>\\n\",\n        \"      <td> 1158317</td>\\n\",\n        \"      <td> 0.779250</td>\\n\",\n        \"      <td> 0.778166</td>\\n\",\n        \"      <td> 0.778702</td>\\n\",\n        \"      <td>                    P.V.MIDHUN REDDY</td>\\n\",\n        \"      <td> YSRCP</td>\\n\",\n        \"      <td>  Yuvajana Sramika Rythu Congress Party</td>\\n\",\n        \"      <td> 601752</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td>            Andhra Pradesh</td>\\n\",\n        \"      <td>                42 - Chittoor </td>\\n\",\n        \"      <td>  723996</td>\\n\",\n        \"      <td> 598259</td>\\n\",\n        \"      <td>  728145</td>\\n\",\n        \"      <td> 600656</td>\\n\",\n        \"      <td> 1452141</td>\\n\",\n        \"      <td> 1198915</td>\\n\",\n        \"      <td> 0.826329</td>\\n\",\n        \"      <td> 0.824913</td>\\n\",\n        \"      <td> 0.825619</td>\\n\",\n        \"      <td>                NARAMALLI SIVAPRASAD</td>\\n\",\n        \"      <td>   TDP</td>\\n\",\n        \"      <td>                           Telugu Desam</td>\\n\",\n        \"      <td> 594862</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>            1 - ARUNACHAL WEST</td>\\n\",\n        \"      <td>  219334</td>\\n\",\n        \"      <td> 159978</td>\\n\",\n        \"      <td>  227181</td>\\n\",\n        \"      <td> 175687</td>\\n\",\n        \"      <td>  446515</td>\\n\",\n        \"      <td>  335665</td>\\n\",\n        \"      <td> 0.729381</td>\\n\",\n        \"      <td> 0.773335</td>\\n\",\n        \"      <td> 0.751744</td>\\n\",\n        \"      <td>                        KIREN RIJIJU</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 169367</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td>         Arunachal Pradesh</td>\\n\",\n        \"      <td>            2 - ARUNACHAL EAST</td>\\n\",\n        \"      <td>  160293</td>\\n\",\n        \"      <td> 129313</td>\\n\",\n        \"      <td>  152579</td>\\n\",\n        \"      <td> 131978</td>\\n\",\n        \"      <td>  312872</td>\\n\",\n        \"      <td>  261291</td>\\n\",\n        \"      <td> 0.806729</td>\\n\",\n        \"      <td> 0.864981</td>\\n\",\n        \"      <td> 0.835137</td>\\n\",\n        \"      <td>                        NINONG ERING</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"      <td> 118455</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                1 - Karimganj </td>\\n\",\n        \"      <td>  615198</td>\\n\",\n        \"      <td> 482484</td>\\n\",\n        \"      <td>  550799</td>\\n\",\n        \"      <td> 404436</td>\\n\",\n        \"      <td> 1165997</td>\\n\",\n        \"      <td>  886920</td>\\n\",\n        \"      <td> 0.784274</td>\\n\",\n        \"      <td> 0.734271</td>\\n\",\n        \"      <td> 0.760654</td>\\n\",\n        \"      <td>                   RADHESHYAM BISWAS</td>\\n\",\n        \"      <td> AIUDF</td>\\n\",\n        \"      <td>      All India United Democratic Front</td>\\n\",\n        \"      <td> 362866</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   2 - Silchar</td>\\n\",\n        \"      <td>  554540</td>\\n\",\n        \"      <td> 428949</td>\\n\",\n        \"      <td>  505635</td>\\n\",\n        \"      <td> 370881</td>\\n\",\n        \"      <td> 1060175</td>\\n\",\n        \"      <td>  799830</td>\\n\",\n        \"      <td> 0.773522</td>\\n\",\n        \"      <td> 0.733496</td>\\n\",\n        \"      <td> 0.754432</td>\\n\",\n        \"      <td>                        SUSHMITA DEV</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"      <td> 336451</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>       3 - Autonomous District</td>\\n\",\n        \"      <td>  358880</td>\\n\",\n        \"      <td> 279409</td>\\n\",\n        \"      <td>  343010</td>\\n\",\n        \"      <td> 263871</td>\\n\",\n        \"      <td>  701890</td>\\n\",\n        \"      <td>  543280</td>\\n\",\n        \"      <td> 0.778558</td>\\n\",\n        \"      <td> 0.769281</td>\\n\",\n        \"      <td> 0.774024</td>\\n\",\n        \"      <td>                   BIREN SINGH ENGTI</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"      <td> 213152</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>48</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                    4 - Dhubri</td>\\n\",\n        \"      <td>  798124</td>\\n\",\n        \"      <td> 709191</td>\\n\",\n        \"      <td>  754430</td>\\n\",\n        \"      <td> 660433</td>\\n\",\n        \"      <td> 1552554</td>\\n\",\n        \"      <td> 1369624</td>\\n\",\n        \"      <td> 0.888572</td>\\n\",\n        \"      <td> 0.875407</td>\\n\",\n        \"      <td> 0.882175</td>\\n\",\n        \"      <td>                     BADRUDDIN AJMAL</td>\\n\",\n        \"      <td> AIUDF</td>\\n\",\n        \"      <td>      All India United Democratic Front</td>\\n\",\n        \"      <td> 592569</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 5 - Kokrajhar</td>\\n\",\n        \"      <td>  776071</td>\\n\",\n        \"      <td> 636657</td>\\n\",\n        \"      <td>  729405</td>\\n\",\n        \"      <td> 587212</td>\\n\",\n        \"      <td> 1505476</td>\\n\",\n        \"      <td> 1223869</td>\\n\",\n        \"      <td> 0.820359</td>\\n\",\n        \"      <td> 0.805056</td>\\n\",\n        \"      <td> 0.812945</td>\\n\",\n        \"      <td>           NABA KUMAR SARANIA (HIRA)</td>\\n\",\n        \"      <td>   IND</td>\\n\",\n        \"      <td>                            Independent</td>\\n\",\n        \"      <td> 634428</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   6 - Barpeta</td>\\n\",\n        \"      <td>  755559</td>\\n\",\n        \"      <td> 634405</td>\\n\",\n        \"      <td>  674616</td>\\n\",\n        \"      <td> 571458</td>\\n\",\n        \"      <td> 1430175</td>\\n\",\n        \"      <td> 1205863</td>\\n\",\n        \"      <td> 0.839650</td>\\n\",\n        \"      <td> 0.847086</td>\\n\",\n        \"      <td> 0.843158</td>\\n\",\n        \"      <td>                   SIRAJ UDDIN AJMAL</td>\\n\",\n        \"      <td> AIUDF</td>\\n\",\n        \"      <td>      All India United Democratic Front</td>\\n\",\n        \"      <td> 394702</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>51</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   7 - Gauhati</td>\\n\",\n        \"      <td>  988067</td>\\n\",\n        \"      <td> 785494</td>\\n\",\n        \"      <td>  934203</td>\\n\",\n        \"      <td> 726235</td>\\n\",\n        \"      <td> 1922270</td>\\n\",\n        \"      <td> 1511729</td>\\n\",\n        \"      <td> 0.794981</td>\\n\",\n        \"      <td> 0.777385</td>\\n\",\n        \"      <td> 0.786429</td>\\n\",\n        \"      <td>                  BIJOYA CHAKRAVARTY</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 764985</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 8 - Mangaldoi</td>\\n\",\n        \"      <td>  791539</td>\\n\",\n        \"      <td> 651272</td>\\n\",\n        \"      <td>  724137</td>\\n\",\n        \"      <td> 581965</td>\\n\",\n        \"      <td> 1515676</td>\\n\",\n        \"      <td> 1233237</td>\\n\",\n        \"      <td> 0.822792</td>\\n\",\n        \"      <td> 0.803667</td>\\n\",\n        \"      <td> 0.813655</td>\\n\",\n        \"      <td>                          RAMEN DEKA</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 486357</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                    9 - Tezpur</td>\\n\",\n        \"      <td>  654866</td>\\n\",\n        \"      <td> 505643</td>\\n\",\n        \"      <td>  604702</td>\\n\",\n        \"      <td> 475045</td>\\n\",\n        \"      <td> 1259568</td>\\n\",\n        \"      <td>  980688</td>\\n\",\n        \"      <td> 0.772132</td>\\n\",\n        \"      <td> 0.785585</td>\\n\",\n        \"      <td> 0.778591</td>\\n\",\n        \"      <td>                   RAM PRASAD SARMAH</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 446511</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                  10 - Nowgong</td>\\n\",\n        \"      <td>  792424</td>\\n\",\n        \"      <td> 639392</td>\\n\",\n        \"      <td>  731457</td>\\n\",\n        \"      <td> 590682</td>\\n\",\n        \"      <td> 1523881</td>\\n\",\n        \"      <td> 1230074</td>\\n\",\n        \"      <td> 0.806881</td>\\n\",\n        \"      <td> 0.807542</td>\\n\",\n        \"      <td> 0.807198</td>\\n\",\n        \"      <td>                        RAJEN GOHAIN</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 494146</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>55</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                 11 - Kaliabor</td>\\n\",\n        \"      <td>  752785</td>\\n\",\n        \"      <td> 605292</td>\\n\",\n        \"      <td>  705080</td>\\n\",\n        \"      <td> 561198</td>\\n\",\n        \"      <td> 1457865</td>\\n\",\n        \"      <td> 1166490</td>\\n\",\n        \"      <td> 0.804070</td>\\n\",\n        \"      <td> 0.795935</td>\\n\",\n        \"      <td> 0.800136</td>\\n\",\n        \"      <td>                        GOURAV GOGOI</td>\\n\",\n        \"      <td>   INC</td>\\n\",\n        \"      <td>               Indian National Congress</td>\\n\",\n        \"      <td> 443315</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                   12 - Jorhat</td>\\n\",\n        \"      <td>  634236</td>\\n\",\n        \"      <td> 483829</td>\\n\",\n        \"      <td>  600212</td>\\n\",\n        \"      <td> 447507</td>\\n\",\n        \"      <td> 1234448</td>\\n\",\n        \"      <td>  931336</td>\\n\",\n        \"      <td> 0.762853</td>\\n\",\n        \"      <td> 0.745582</td>\\n\",\n        \"      <td> 0.754455</td>\\n\",\n        \"      <td>                KAMAKHYA PRASAD TASA</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 456420</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>57</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                13 - Dibrugarh</td>\\n\",\n        \"      <td>  579657</td>\\n\",\n        \"      <td> 462412</td>\\n\",\n        \"      <td>  544648</td>\\n\",\n        \"      <td> 428556</td>\\n\",\n        \"      <td> 1124305</td>\\n\",\n        \"      <td>  890968</td>\\n\",\n        \"      <td> 0.797734</td>\\n\",\n        \"      <td> 0.786849</td>\\n\",\n        \"      <td> 0.792461</td>\\n\",\n        \"      <td>                       RAMESWAR TELI</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 494364</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>58</th>\\n\",\n        \"      <td>                     Assam</td>\\n\",\n        \"      <td>                14 - Lakhimpur</td>\\n\",\n        \"      <td>  735263</td>\\n\",\n        \"      <td> 572334</td>\\n\",\n        \"      <td>  695731</td>\\n\",\n        \"      <td> 539641</td>\\n\",\n        \"      <td> 1430994</td>\\n\",\n        \"      <td> 1111975</td>\\n\",\n        \"      <td> 0.778407</td>\\n\",\n        \"      <td> 0.775646</td>\\n\",\n        \"      <td> 0.777065</td>\\n\",\n        \"      <td>                  SARBANANDA SONOWAL</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 612543</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>59</th>\\n\",\n        \"      <td>                     Bihar</td>\\n\",\n        \"      <td>             1 - Valmiki Nagar</td>\\n\",\n        \"      <td>  786297</td>\\n\",\n        \"      <td> 484621</td>\\n\",\n        \"      <td>  670279</td>\\n\",\n        \"      <td> 415493</td>\\n\",\n        \"      <td> 1456576</td>\\n\",\n        \"      <td>  900114</td>\\n\",\n        \"      <td> 0.616333</td>\\n\",\n        \"      <td> 0.619881</td>\\n\",\n        \"      <td> 0.617966</td>\\n\",\n        \"      <td>                SATISH CHANDRA DUBEY</td>\\n\",\n        \"      <td>   BJP</td>\\n\",\n        \"      <td>                 Bharatiya Janata Party</td>\\n\",\n        \"      <td> 364011</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>543 rows \\u00d7 15 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 16,\n       \"text\": [\n        \"                        State                        PC Name  Male Electors  \\\\\\n\",\n        \"0   Andaman & Nicobar Islands  1 - Andaman & Nicobar Islands         142782   \\n\",\n        \"1              Andhra Pradesh                  1 - Adilabad          687389   \\n\",\n        \"2              Andhra Pradesh                2 - Peddapalle          725767   \\n\",\n        \"3              Andhra Pradesh                3 - Karimnagar          777458   \\n\",\n        \"4              Andhra Pradesh                  4 - Nizamabad         724504   \\n\",\n        \"5              Andhra Pradesh                  5 - Zahirabad         717811   \\n\",\n        \"6              Andhra Pradesh                      6 - Medak         775903   \\n\",\n        \"7              Andhra Pradesh                 7 - Malkajgiri        1723413   \\n\",\n        \"8              Andhra Pradesh                8 - Secundrabad        1012378   \\n\",\n        \"9              Andhra Pradesh                  9 - Hyderabad         961290   \\n\",\n        \"10             Andhra Pradesh                 10 - CHELVELLA        1153049   \\n\",\n        \"11             Andhra Pradesh               11 - Mahbubnagar         709711   \\n\",\n        \"12             Andhra Pradesh              12 - Nagarkurnool         745038   \\n\",\n        \"13             Andhra Pradesh                  13 - Nalgonda         747281   \\n\",\n        \"14             Andhra Pradesh                  14 - Bhongir          756963   \\n\",\n        \"15             Andhra Pradesh                  15 - Warangal         771756   \\n\",\n        \"16             Andhra Pradesh             16 - Mahabubabad           688398   \\n\",\n        \"17             Andhra Pradesh                  17 - Khammam          712329   \\n\",\n        \"18             Andhra Pradesh                    18 - Aruku          622416   \\n\",\n        \"19             Andhra Pradesh                19 - Srikakulam         706828   \\n\",\n        \"20             Andhra Pradesh              20 - Vizianagaram         700837   \\n\",\n        \"21             Andhra Pradesh             21 - Visakhapatnam         875187   \\n\",\n        \"22             Andhra Pradesh                22 - Anakapalli         689132   \\n\",\n        \"23             Andhra Pradesh                  23 - Kakinada         709101   \\n\",\n        \"24             Andhra Pradesh               24 - Amalapuram          682607   \\n\",\n        \"25             Andhra Pradesh               25 - Rajahmundry         701707   \\n\",\n        \"26             Andhra Pradesh                26 - Narsapuram         652668   \\n\",\n        \"27             Andhra Pradesh                    27 - Eluru          706916   \\n\",\n        \"28             Andhra Pradesh            28 - Machilipatnam          675774   \\n\",\n        \"29             Andhra Pradesh                29 - Vijayawada         781156   \\n\",\n        \"30             Andhra Pradesh                    30 - Guntur         773027   \\n\",\n        \"31             Andhra Pradesh              31 - Narasaraopet         748465   \\n\",\n        \"32             Andhra Pradesh                  32 - Bapatla          686482   \\n\",\n        \"33             Andhra Pradesh                   33 - Ongole          736245   \\n\",\n        \"34             Andhra Pradesh                   34 - Nandyal         783266   \\n\",\n        \"35             Andhra Pradesh                   35 - Kurnool         738791   \\n\",\n        \"36             Andhra Pradesh                 36 - Anantapur         775509   \\n\",\n        \"37             Andhra Pradesh                  37 - Hindupur         734638   \\n\",\n        \"38             Andhra Pradesh                    38 - Kadapa         765036   \\n\",\n        \"39             Andhra Pradesh                   39 - Nellore         796583   \\n\",\n        \"40             Andhra Pradesh                 40 - Tirupati          778778   \\n\",\n        \"41             Andhra Pradesh                  41 - Rajampet         735347   \\n\",\n        \"42             Andhra Pradesh                 42 - Chittoor          723996   \\n\",\n        \"43          Arunachal Pradesh             1 - ARUNACHAL WEST         219334   \\n\",\n        \"44          Arunachal Pradesh             2 - ARUNACHAL EAST         160293   \\n\",\n        \"45                      Assam                 1 - Karimganj          615198   \\n\",\n        \"46                      Assam                    2 - Silchar         554540   \\n\",\n        \"47                      Assam        3 - Autonomous District         358880   \\n\",\n        \"48                      Assam                     4 - Dhubri         798124   \\n\",\n        \"49                      Assam                  5 - Kokrajhar         776071   \\n\",\n        \"50                      Assam                    6 - Barpeta         755559   \\n\",\n        \"51                      Assam                    7 - Gauhati         988067   \\n\",\n        \"52                      Assam                  8 - Mangaldoi         791539   \\n\",\n        \"53                      Assam                     9 - Tezpur         654866   \\n\",\n        \"54                      Assam                   10 - Nowgong         792424   \\n\",\n        \"55                      Assam                  11 - Kaliabor         752785   \\n\",\n        \"56                      Assam                    12 - Jorhat         634236   \\n\",\n        \"57                      Assam                 13 - Dibrugarh         579657   \\n\",\n        \"58                      Assam                 14 - Lakhimpur         735263   \\n\",\n        \"59                      Bihar              1 - Valmiki Nagar         786297   \\n\",\n        \"                          ...                            ...            ...   \\n\",\n        \"\\n\",\n        \"    Male Voters  Female Electors  Female Voters  Total Electors  Total Voters  \\\\\\n\",\n        \"0        101178           126578          89150          269360        190328   \\n\",\n        \"1        519364           698844         526475         1386233       1045839   \\n\",\n        \"2        520598           699594         501586         1425361       1022184   \\n\",\n        \"3        552489           773376         573202         1550834       1125691   \\n\",\n        \"4        469712           771689         564212         1496193       1033924   \\n\",\n        \"5        546746           727435         548060         1445246       1094806   \\n\",\n        \"6        606863           760812         584233         1536715       1191096   \\n\",\n        \"7        878093          1459912         742304         3183325       1620397   \\n\",\n        \"8        545749           881269         458020         1893647       1003769   \\n\",\n        \"9        526510           862374         444911         1823664        971421   \\n\",\n        \"10       696749          1032130         619113         2185179       1315862   \\n\",\n        \"11       511764           708961         503036         1418672       1014800   \\n\",\n        \"12       570342           732300         538626         1477338       1108968   \\n\",\n        \"13       602193           748299         587206         1495580       1189399   \\n\",\n        \"14       622333           735288         589610         1492251       1211943   \\n\",\n        \"15       592484           766025         582147         1537781       1174631   \\n\",\n        \"16       562073           698945         562297         1387343       1124370   \\n\",\n        \"17       588728           727960         594169         1440289       1182897   \\n\",\n        \"18       451350           650308         458264         1272724        909614   \\n\",\n        \"19       505010           707161         546436         1413989       1051446   \\n\",\n        \"20       555005           703290         565311         1404127       1120316   \\n\",\n        \"21       585270           847850         578288         1723037       1163558   \\n\",\n        \"22       563818           712342         584254         1401474       1148072   \\n\",\n        \"23       558689           709189         541310         1418290       1099999   \\n\",\n        \"24       568534           675259         552393         1357866       1120927   \\n\",\n        \"25       577999           719581         576382         1421288       1154381   \\n\",\n        \"26       539357           672475         549594         1325143       1088951   \\n\",\n        \"27       597603           720844         604093         1427760       1201696   \\n\",\n        \"28       567908           693537         573157         1369311       1141065   \\n\",\n        \"29       602198           783357         592877         1564513       1195075   \\n\",\n        \"30       617483           798989         627443         1572016       1244926   \\n\",\n        \"31       638080           766396         643978         1514861       1282058   \\n\",\n        \"32       587223           706483         597411         1392965       1184634   \\n\",\n        \"33       603025           734238         605200         1470483       1208225   \\n\",\n        \"34       603562           793862         601394         1577128       1204956   \\n\",\n        \"35       544802           743016         520930         1481807       1065732   \\n\",\n        \"36       612890           761403         592164         1536912       1205054   \\n\",\n        \"37       601932           711865         575325         1446503       1177257   \\n\",\n        \"38       588805           785543         611857         1550579       1200662   \\n\",\n        \"39       593468           809557         594180         1606140       1187648   \\n\",\n        \"40       604834           795766         608230         1574544       1213064   \\n\",\n        \"41       573019           752151         585298         1487498       1158317   \\n\",\n        \"42       598259           728145         600656         1452141       1198915   \\n\",\n        \"43       159978           227181         175687          446515        335665   \\n\",\n        \"44       129313           152579         131978          312872        261291   \\n\",\n        \"45       482484           550799         404436         1165997        886920   \\n\",\n        \"46       428949           505635         370881         1060175        799830   \\n\",\n        \"47       279409           343010         263871          701890        543280   \\n\",\n        \"48       709191           754430         660433         1552554       1369624   \\n\",\n        \"49       636657           729405         587212         1505476       1223869   \\n\",\n        \"50       634405           674616         571458         1430175       1205863   \\n\",\n        \"51       785494           934203         726235         1922270       1511729   \\n\",\n        \"52       651272           724137         581965         1515676       1233237   \\n\",\n        \"53       505643           604702         475045         1259568        980688   \\n\",\n        \"54       639392           731457         590682         1523881       1230074   \\n\",\n        \"55       605292           705080         561198         1457865       1166490   \\n\",\n        \"56       483829           600212         447507         1234448        931336   \\n\",\n        \"57       462412           544648         428556         1124305        890968   \\n\",\n        \"58       572334           695731         539641         1430994       1111975   \\n\",\n        \"59       484621           670279         415493         1456576        900114   \\n\",\n        \"            ...              ...            ...             ...           ...   \\n\",\n        \"\\n\",\n        \"    Male Turnout  Female Turnout  Total Turnout  \\\\\\n\",\n        \"0       0.708619        0.704309       0.706593   \\n\",\n        \"1       0.755561        0.753351       0.754447   \\n\",\n        \"2       0.717307        0.716967       0.717140   \\n\",\n        \"3       0.710635        0.741169       0.725862   \\n\",\n        \"4       0.648322        0.731139       0.691037   \\n\",\n        \"5       0.761685        0.753414       0.757522   \\n\",\n        \"6       0.782138        0.767907       0.775092   \\n\",\n        \"7       0.509508        0.508458       0.509027   \\n\",\n        \"8       0.539076        0.519728       0.530072   \\n\",\n        \"9       0.547712        0.515914       0.532675   \\n\",\n        \"10      0.604267        0.599840       0.602176   \\n\",\n        \"11      0.721088        0.709540       0.715317   \\n\",\n        \"12      0.765521        0.735526       0.750653   \\n\",\n        \"13      0.805845        0.784721       0.795276   \\n\",\n        \"14      0.822145        0.801876       0.812158   \\n\",\n        \"15      0.767709        0.759958       0.763848   \\n\",\n        \"16      0.816494        0.804494       0.810448   \\n\",\n        \"17      0.826483        0.816211       0.821291   \\n\",\n        \"18      0.725158        0.704688       0.714699   \\n\",\n        \"19      0.714474        0.772718       0.743603   \\n\",\n        \"20      0.791917        0.803809       0.797874   \\n\",\n        \"21      0.668737        0.682064       0.675295   \\n\",\n        \"22      0.818157        0.820187       0.819189   \\n\",\n        \"23      0.787884        0.763280       0.775581   \\n\",\n        \"24      0.832886        0.818046       0.825506   \\n\",\n        \"25      0.823704        0.800997       0.812208   \\n\",\n        \"26      0.826388        0.817271       0.821761   \\n\",\n        \"27      0.845366        0.838036       0.841665   \\n\",\n        \"28      0.840382        0.826426       0.833313   \\n\",\n        \"29      0.770906        0.756841       0.763864   \\n\",\n        \"30      0.798786        0.785296       0.791930   \\n\",\n        \"31      0.852518        0.840268       0.846321   \\n\",\n        \"32      0.855409        0.845613       0.850441   \\n\",\n        \"33      0.819055        0.824256       0.821652   \\n\",\n        \"34      0.770571        0.757555       0.764019   \\n\",\n        \"35      0.737424        0.701102       0.719211   \\n\",\n        \"36      0.790307        0.777727       0.784075   \\n\",\n        \"37      0.819359        0.808194       0.813864   \\n\",\n        \"38      0.769644        0.778897       0.774331   \\n\",\n        \"39      0.745017        0.733957       0.739442   \\n\",\n        \"40      0.776645        0.764333       0.770422   \\n\",\n        \"41      0.779250        0.778166       0.778702   \\n\",\n        \"42      0.826329        0.824913       0.825619   \\n\",\n        \"43      0.729381        0.773335       0.751744   \\n\",\n        \"44      0.806729        0.864981       0.835137   \\n\",\n        \"45      0.784274        0.734271       0.760654   \\n\",\n        \"46      0.773522        0.733496       0.754432   \\n\",\n        \"47      0.778558        0.769281       0.774024   \\n\",\n        \"48      0.888572        0.875407       0.882175   \\n\",\n        \"49      0.820359        0.805056       0.812945   \\n\",\n        \"50      0.839650        0.847086       0.843158   \\n\",\n        \"51      0.794981        0.777385       0.786429   \\n\",\n        \"52      0.822792        0.803667       0.813655   \\n\",\n        \"53      0.772132        0.785585       0.778591   \\n\",\n        \"54      0.806881        0.807542       0.807198   \\n\",\n        \"55      0.804070        0.795935       0.800136   \\n\",\n        \"56      0.762853        0.745582       0.754455   \\n\",\n        \"57      0.797734        0.786849       0.792461   \\n\",\n        \"58      0.778407        0.775646       0.777065   \\n\",\n        \"59      0.616333        0.619881       0.617966   \\n\",\n        \"             ...             ...            ...   \\n\",\n        \"\\n\",\n        \"                         Candidate Name Party Abbreviation  \\\\\\n\",\n        \"0                       BISHNU PADA RAY                BJP   \\n\",\n        \"1                          GODAM NAGESH                TRS   \\n\",\n        \"2                           BALKA SUMAN                TRS   \\n\",\n        \"3                VINOD KUMAR BOINAPALLY                TRS   \\n\",\n        \"4                   KALVAKUNTLA KAVITHA                TRS   \\n\",\n        \"5                            B.B. PATIL                TRS   \\n\",\n        \"6         KALVAKUNTLA CHANDRASEKHAR RAO                TRS   \\n\",\n        \"7                        CH.MALLA REDDY                TDP   \\n\",\n        \"8                    BANDARU DATTATREYA                BJP   \\n\",\n        \"9                      ASADUDDIN OWAISI              AIMIM   \\n\",\n        \"10              KONDA VISHWESHWAR REDDY                TRS   \\n\",\n        \"11                   AP JITHENDER REDDY                TRS   \\n\",\n        \"12                       YELLAIAH NANDI                INC   \\n\",\n        \"13                GUTHA SUKHENDER REDDY                INC   \\n\",\n        \"14              DR. BOORA NARSAIAH GOUD                TRS   \\n\",\n        \"15                      KADIYAM SRIHARI                TRS   \\n\",\n        \"16          PROF. AZMEERA SEETARAM NAIK                TRS   \\n\",\n        \"17           PONGULETI  SRINIVASA REDDY              YSRCP   \\n\",\n        \"18                    KOTHAPALLI GEETHA              YSRCP   \\n\",\n        \"19             RAMMOHAN NAIDU KINJARAPU                TDP   \\n\",\n        \"20        ASHOK GAJAPATHI RAJU PUSAPATI                TDP   \\n\",\n        \"21                KAMBHAMPATI HARI BABU                BJP   \\n\",\n        \"22  MUTTAMSETTI SRINIVASA RAO (AVANTHI)                TDP   \\n\",\n        \"23                     THOTA NARASIMHAM                TDP   \\n\",\n        \"24             DR PANDULA RAVINDRA BABU                TDP   \\n\",\n        \"25                 MURALI MOHAN MAGANTI                TDP   \\n\",\n        \"26                  GOKARAJU GANGA RAJU                BJP   \\n\",\n        \"27      MAGANTI VENKATESWARA RAO (BABU)                TDP   \\n\",\n        \"28               KONAKALLA NARAYANA RAO                TDP   \\n\",\n        \"29                    KESINENI SRINIVAS                TDP   \\n\",\n        \"30                        JAYADEV GALLA                TDP   \\n\",\n        \"31               SAMBASIVA RAO RAYAPATI                TDP   \\n\",\n        \"32                      MALYADRI SRIRAM                TDP   \\n\",\n        \"33                      Y.V.SUBBA REDDY              YSRCP   \\n\",\n        \"34                          S.P.Y REDDY              YSRCP   \\n\",\n        \"35                         BUTTA RENUKA              YSRCP   \\n\",\n        \"36                   J.C. DIVAKAR REDDI                TDP   \\n\",\n        \"37                    KRISTAPPA NIMMALA                TDP   \\n\",\n        \"38                   Y.S. AVINASH REDDY              YSRCP   \\n\",\n        \"39             MEKAPATI RAJAMOHAN REDDY              YSRCP   \\n\",\n        \"40           VARAPRASAD RAO VELAGAPALLI              YSRCP   \\n\",\n        \"41                     P.V.MIDHUN REDDY              YSRCP   \\n\",\n        \"42                 NARAMALLI SIVAPRASAD                TDP   \\n\",\n        \"43                         KIREN RIJIJU                BJP   \\n\",\n        \"44                         NINONG ERING                INC   \\n\",\n        \"45                    RADHESHYAM BISWAS              AIUDF   \\n\",\n        \"46                         SUSHMITA DEV                INC   \\n\",\n        \"47                    BIREN SINGH ENGTI                INC   \\n\",\n        \"48                      BADRUDDIN AJMAL              AIUDF   \\n\",\n        \"49            NABA KUMAR SARANIA (HIRA)                IND   \\n\",\n        \"50                    SIRAJ UDDIN AJMAL              AIUDF   \\n\",\n        \"51                   BIJOYA CHAKRAVARTY                BJP   \\n\",\n        \"52                           RAMEN DEKA                BJP   \\n\",\n        \"53                    RAM PRASAD SARMAH                BJP   \\n\",\n        \"54                         RAJEN GOHAIN                BJP   \\n\",\n        \"55                         GOURAV GOGOI                INC   \\n\",\n        \"56                 KAMAKHYA PRASAD TASA                BJP   \\n\",\n        \"57                        RAMESWAR TELI                BJP   \\n\",\n        \"58                   SARBANANDA SONOWAL                BJP   \\n\",\n        \"59                 SATISH CHANDRA DUBEY                BJP   \\n\",\n        \"                                    ...                ...   \\n\",\n        \"\\n\",\n        \"                                Party Name  Total Votes Polled  \\n\",\n        \"0                   Bharatiya Janata Party               90969  \\n\",\n        \"1                Telangana Rashtra Samithi              430847  \\n\",\n        \"2                Telangana Rashtra Samithi              565496  \\n\",\n        \"3                Telangana Rashtra Samithi              505783  \\n\",\n        \"4                Telangana Rashtra Samithi              439307  \\n\",\n        \"5                Telangana Rashtra Samithi              508661  \\n\",\n        \"6                Telangana Rashtra Samithi              657492  \\n\",\n        \"7                             Telugu Desam              523336  \\n\",\n        \"8                   Bharatiya Janata Party              438271  \\n\",\n        \"9   All India Majlis-E-Ittehadul Muslimeen              513868  \\n\",\n        \"10               Telangana Rashtra Samithi              435077  \\n\",\n        \"11               Telangana Rashtra Samithi              334228  \\n\",\n        \"12                Indian National Congress              420075  \\n\",\n        \"13                Indian National Congress              472093  \\n\",\n        \"14               Telangana Rashtra Samithi              448245  \\n\",\n        \"15               Telangana Rashtra Samithi              661639  \\n\",\n        \"16               Telangana Rashtra Samithi              320569  \\n\",\n        \"17   Yuvajana Sramika Rythu Congress Party              421957  \\n\",\n        \"18   Yuvajana Sramika Rythu Congress Party              413191  \\n\",\n        \"19                            Telugu Desam              556163  \\n\",\n        \"20                            Telugu Desam              536549  \\n\",\n        \"21                  Bharatiya Janata Party              566832  \\n\",\n        \"22                            Telugu Desam              568463  \\n\",\n        \"23                            Telugu Desam              514402  \\n\",\n        \"24                            Telugu Desam              594547  \\n\",\n        \"25                            Telugu Desam              630573  \\n\",\n        \"26                  Bharatiya Janata Party              540306  \\n\",\n        \"27                            Telugu Desam              623471  \\n\",\n        \"28                            Telugu Desam              587280  \\n\",\n        \"29                            Telugu Desam              592696  \\n\",\n        \"30                            Telugu Desam              618417  \\n\",\n        \"31                            Telugu Desam              632464  \\n\",\n        \"32                            Telugu Desam              578145  \\n\",\n        \"33   Yuvajana Sramika Rythu Congress Party              589960  \\n\",\n        \"34   Yuvajana Sramika Rythu Congress Party              622411  \\n\",\n        \"35   Yuvajana Sramika Rythu Congress Party              472782  \\n\",\n        \"36                            Telugu Desam              606509  \\n\",\n        \"37                            Telugu Desam              604291  \\n\",\n        \"38   Yuvajana Sramika Rythu Congress Party              671983  \\n\",\n        \"39   Yuvajana Sramika Rythu Congress Party              576396  \\n\",\n        \"40   Yuvajana Sramika Rythu Congress Party              580376  \\n\",\n        \"41   Yuvajana Sramika Rythu Congress Party              601752  \\n\",\n        \"42                            Telugu Desam              594862  \\n\",\n        \"43                  Bharatiya Janata Party              169367  \\n\",\n        \"44                Indian National Congress              118455  \\n\",\n        \"45       All India United Democratic Front              362866  \\n\",\n        \"46                Indian National Congress              336451  \\n\",\n        \"47                Indian National Congress              213152  \\n\",\n        \"48       All India United Democratic Front              592569  \\n\",\n        \"49                             Independent              634428  \\n\",\n        \"50       All India United Democratic Front              394702  \\n\",\n        \"51                  Bharatiya Janata Party              764985  \\n\",\n        \"52                  Bharatiya Janata Party              486357  \\n\",\n        \"53                  Bharatiya Janata Party              446511  \\n\",\n        \"54                  Bharatiya Janata Party              494146  \\n\",\n        \"55                Indian National Congress              443315  \\n\",\n        \"56                  Bharatiya Janata Party              456420  \\n\",\n        \"57                  Bharatiya Janata Party              494364  \\n\",\n        \"58                  Bharatiya Janata Party              612543  \\n\",\n        \"59                  Bharatiya Janata Party              364011  \\n\",\n        \"                                       ...                 ...  \\n\",\n        \"\\n\",\n        \"[543 rows x 15 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculaing the winning margin\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data[\\\"Winner Percentage\\\"] = csv_data[\\\"Total Votes Polled\\\"] / csv_data[\\\"Total Voters\\\"]\\n\",\n      \"csv_data.sort(\\\"Winner Percentage\\\", ascending=False)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State</th>\\n\",\n        \"      <th>PC Name</th>\\n\",\n        \"      <th>Male Electors</th>\\n\",\n        \"      <th>Male Voters</th>\\n\",\n        \"      <th>Female Electors</th>\\n\",\n        \"      <th>Female Voters</th>\\n\",\n        \"      <th>Total Electors</th>\\n\",\n        \"      <th>Total Voters</th>\\n\",\n        \"      <th>Male Turnout</th>\\n\",\n        \"      <th>Female Turnout</th>\\n\",\n        \"      <th>Total Turnout</th>\\n\",\n        \"      <th>Candidate Name</th>\\n\",\n        \"      <th>Party Abbreviation</th>\\n\",\n        \"      <th>Party Name</th>\\n\",\n        \"      <th>Total Votes Polled</th>\\n\",\n        \"      <th>Winner Percentage</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>138</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                         24 - Surat</td>\\n\",\n        \"      <td>  803829</td>\\n\",\n        \"      <td> 539368</td>\\n\",\n        \"      <td>  680239</td>\\n\",\n        \"      <td> 408554</td>\\n\",\n        \"      <td> 1484068</td>\\n\",\n        \"      <td>  947922</td>\\n\",\n        \"      <td> 0.670998</td>\\n\",\n        \"      <td> 0.600604</td>\\n\",\n        \"      <td> 0.638732</td>\\n\",\n        \"      <td>                           DARSHANA VIKRAM JARDOSH</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 718412</td>\\n\",\n        \"      <td> 0.757881</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>134</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                      20 - Vadodara</td>\\n\",\n        \"      <td>  849077</td>\\n\",\n        \"      <td> 625045</td>\\n\",\n        \"      <td>  789244</td>\\n\",\n        \"      <td> 536532</td>\\n\",\n        \"      <td> 1638321</td>\\n\",\n        \"      <td> 1161577</td>\\n\",\n        \"      <td> 0.736146</td>\\n\",\n        \"      <td> 0.679805</td>\\n\",\n        \"      <td> 0.709005</td>\\n\",\n        \"      <td>                                     NARENDRA MODI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 845464</td>\\n\",\n        \"      <td> 0.727859</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>139</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                       25 - Navsari</td>\\n\",\n        \"      <td>  972090</td>\\n\",\n        \"      <td> 649206</td>\\n\",\n        \"      <td>  792532</td>\\n\",\n        \"      <td> 511541</td>\\n\",\n        \"      <td> 1764622</td>\\n\",\n        \"      <td> 1160747</td>\\n\",\n        \"      <td> 0.667846</td>\\n\",\n        \"      <td> 0.645452</td>\\n\",\n        \"      <td> 0.657788</td>\\n\",\n        \"      <td>                                       C. R. PATIL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 820831</td>\\n\",\n        \"      <td> 0.707158</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>278</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                  26 - Mumbai North</td>\\n\",\n        \"      <td>  972645</td>\\n\",\n        \"      <td> 527803</td>\\n\",\n        \"      <td>  811225</td>\\n\",\n        \"      <td> 418759</td>\\n\",\n        \"      <td> 1783870</td>\\n\",\n        \"      <td>  946562</td>\\n\",\n        \"      <td> 0.542647</td>\\n\",\n        \"      <td> 0.516206</td>\\n\",\n        \"      <td> 0.530623</td>\\n\",\n        \"      <td>                             GOPAL CHINAYYA SHETTY</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 664004</td>\\n\",\n        \"      <td> 0.701490</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>120</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                    6 - Gandhinagar</td>\\n\",\n        \"      <td>  900744</td>\\n\",\n        \"      <td> 620889</td>\\n\",\n        \"      <td>  833228</td>\\n\",\n        \"      <td> 514606</td>\\n\",\n        \"      <td> 1733972</td>\\n\",\n        \"      <td> 1135495</td>\\n\",\n        \"      <td> 0.689307</td>\\n\",\n        \"      <td> 0.617605</td>\\n\",\n        \"      <td> 0.654852</td>\\n\",\n        \"      <td>                                        L.K.ADVANI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 773539</td>\\n\",\n        \"      <td> 0.681235</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>355</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                         7 - Jaipur</td>\\n\",\n        \"      <td> 1047468</td>\\n\",\n        \"      <td> 716874</td>\\n\",\n        \"      <td>  910350</td>\\n\",\n        \"      <td> 579932</td>\\n\",\n        \"      <td> 1957818</td>\\n\",\n        \"      <td> 1296806</td>\\n\",\n        \"      <td> 0.684387</td>\\n\",\n        \"      <td> 0.637043</td>\\n\",\n        \"      <td> 0.662373</td>\\n\",\n        \"      <td>                                  RAMCHARAN BOHARA</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 863358</td>\\n\",\n        \"      <td> 0.665757</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>241</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                       18 - VIDISHA</td>\\n\",\n        \"      <td>  872410</td>\\n\",\n        \"      <td> 621326</td>\\n\",\n        \"      <td>  761960</td>\\n\",\n        \"      <td> 452147</td>\\n\",\n        \"      <td> 1634370</td>\\n\",\n        \"      <td> 1073473</td>\\n\",\n        \"      <td> 0.712195</td>\\n\",\n        \"      <td> 0.593400</td>\\n\",\n        \"      <td> 0.656811</td>\\n\",\n        \"      <td>                                     SUSHMA SWARAJ</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 714348</td>\\n\",\n        \"      <td> 0.665455</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>364</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                       16 - Jodhpur</td>\\n\",\n        \"      <td>  910466</td>\\n\",\n        \"      <td> 600132</td>\\n\",\n        \"      <td>  816897</td>\\n\",\n        \"      <td> 478466</td>\\n\",\n        \"      <td> 1727363</td>\\n\",\n        \"      <td> 1078598</td>\\n\",\n        \"      <td> 0.659148</td>\\n\",\n        \"      <td> 0.585712</td>\\n\",\n        \"      <td> 0.624419</td>\\n\",\n        \"      <td>                           GAJENDRASINGH SHEKHAWAT</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 713515</td>\\n\",\n        \"      <td> 0.661521</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>370</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                     22 - Rajsamand</td>\\n\",\n        \"      <td>  879526</td>\\n\",\n        \"      <td> 508738</td>\\n\",\n        \"      <td>  819875</td>\\n\",\n        \"      <td> 473381</td>\\n\",\n        \"      <td> 1699401</td>\\n\",\n        \"      <td>  982119</td>\\n\",\n        \"      <td> 0.578423</td>\\n\",\n        \"      <td> 0.577382</td>\\n\",\n        \"      <td> 0.577921</td>\\n\",\n        \"      <td>                              HARIOM SINGH RATHORE</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 644794</td>\\n\",\n        \"      <td> 0.656533</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>415</th>\\n\",\n        \"      <td>          Tripura</td>\\n\",\n        \"      <td>                   2 - Tripura East</td>\\n\",\n        \"      <td>  581599</td>\\n\",\n        \"      <td> 489156</td>\\n\",\n        \"      <td>  558670</td>\\n\",\n        \"      <td> 461954</td>\\n\",\n        \"      <td> 1140269</td>\\n\",\n        \"      <td>  951110</td>\\n\",\n        \"      <td> 0.841054</td>\\n\",\n        \"      <td> 0.826882</td>\\n\",\n        \"      <td> 0.834110</td>\\n\",\n        \"      <td>                                JITENDRA CHOUDHURY</td>\\n\",\n        \"      <td> CPM</td>\\n\",\n        \"      <td> Communist Party of India  (Marxist)</td>\\n\",\n        \"      <td> 623771</td>\\n\",\n        \"      <td> 0.655835</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>255</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                        3 - Jalgaon</td>\\n\",\n        \"      <td>  909027</td>\\n\",\n        \"      <td> 594892</td>\\n\",\n        \"      <td>  798906</td>\\n\",\n        \"      <td> 395440</td>\\n\",\n        \"      <td> 1707933</td>\\n\",\n        \"      <td>  990332</td>\\n\",\n        \"      <td> 0.654427</td>\\n\",\n        \"      <td> 0.494977</td>\\n\",\n        \"      <td> 0.579842</td>\\n\",\n        \"      <td>                                   A.T. NANA PATIL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 647773</td>\\n\",\n        \"      <td> 0.654097</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>363</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                          15 - Pali</td>\\n\",\n        \"      <td>  994082</td>\\n\",\n        \"      <td> 581099</td>\\n\",\n        \"      <td>  898948</td>\\n\",\n        \"      <td> 514488</td>\\n\",\n        \"      <td> 1893030</td>\\n\",\n        \"      <td> 1095587</td>\\n\",\n        \"      <td> 0.584558</td>\\n\",\n        \"      <td> 0.572322</td>\\n\",\n        \"      <td> 0.578748</td>\\n\",\n        \"      <td>                                     P P CHOUDHARY</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 711772</td>\\n\",\n        \"      <td> 0.649672</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>310</th>\\n\",\n        \"      <td>     NCT OF Delhi</td>\\n\",\n        \"      <td> 4 - NEW DELHI                     </td>\\n\",\n        \"      <td>  830322</td>\\n\",\n        \"      <td> 546295</td>\\n\",\n        \"      <td>  659825</td>\\n\",\n        \"      <td> 423517</td>\\n\",\n        \"      <td> 1490147</td>\\n\",\n        \"      <td>  969812</td>\\n\",\n        \"      <td> 0.657932</td>\\n\",\n        \"      <td> 0.641863</td>\\n\",\n        \"      <td> 0.650816</td>\\n\",\n        \"      <td>                                          UDIT RAJ</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 629860</td>\\n\",\n        \"      <td> 0.649466</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>249</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                        26 - INDORE</td>\\n\",\n        \"      <td> 1106461</td>\\n\",\n        \"      <td> 736759</td>\\n\",\n        \"      <td> 1008842</td>\\n\",\n        \"      <td> 580058</td>\\n\",\n        \"      <td> 2115303</td>\\n\",\n        \"      <td> 1316817</td>\\n\",\n        \"      <td> 0.665870</td>\\n\",\n        \"      <td> 0.574974</td>\\n\",\n        \"      <td> 0.622519</td>\\n\",\n        \"      <td>                             SUMITRA MAHAJAN (TAI)</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 854972</td>\\n\",\n        \"      <td> 0.649272</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>240</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                   17 - HOSHANGABAD</td>\\n\",\n        \"      <td>  835492</td>\\n\",\n        \"      <td> 593957</td>\\n\",\n        \"      <td>  732635</td>\\n\",\n        \"      <td> 437218</td>\\n\",\n        \"      <td> 1568127</td>\\n\",\n        \"      <td> 1031175</td>\\n\",\n        \"      <td> 0.710907</td>\\n\",\n        \"      <td> 0.596775</td>\\n\",\n        \"      <td> 0.657584</td>\\n\",\n        \"      <td>                                 UDAY PRATAP SINGH</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 669128</td>\\n\",\n        \"      <td> 0.648899</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>313</th>\\n\",\n        \"      <td>     NCT OF Delhi</td>\\n\",\n        \"      <td> 7 - SOUTH DELHI                   </td>\\n\",\n        \"      <td> 1005289</td>\\n\",\n        \"      <td> 638406</td>\\n\",\n        \"      <td>  747452</td>\\n\",\n        \"      <td> 464004</td>\\n\",\n        \"      <td> 1752741</td>\\n\",\n        \"      <td> 1102410</td>\\n\",\n        \"      <td> 0.635047</td>\\n\",\n        \"      <td> 0.620781</td>\\n\",\n        \"      <td> 0.628963</td>\\n\",\n        \"      <td>                                       NEIPHIU RIO</td>\\n\",\n        \"      <td> NPF</td>\\n\",\n        \"      <td>                  Naga Peoples Front</td>\\n\",\n        \"      <td> 713372</td>\\n\",\n        \"      <td> 0.647102</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>121</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                 7 - Ahmedabad East</td>\\n\",\n        \"      <td>  852765</td>\\n\",\n        \"      <td> 560074</td>\\n\",\n        \"      <td>  749067</td>\\n\",\n        \"      <td> 425451</td>\\n\",\n        \"      <td> 1601832</td>\\n\",\n        \"      <td>  985525</td>\\n\",\n        \"      <td> 0.656774</td>\\n\",\n        \"      <td> 0.567975</td>\\n\",\n        \"      <td> 0.615249</td>\\n\",\n        \"      <td>                                      PARESH RAWAL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 633582</td>\\n\",\n        \"      <td> 0.642888</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>122</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                 8 - Ahmedabad West</td>\\n\",\n        \"      <td>  800933</td>\\n\",\n        \"      <td> 534008</td>\\n\",\n        \"      <td>  733467</td>\\n\",\n        \"      <td> 430601</td>\\n\",\n        \"      <td> 1534400</td>\\n\",\n        \"      <td>  964609</td>\\n\",\n        \"      <td> 0.666732</td>\\n\",\n        \"      <td> 0.587076</td>\\n\",\n        \"      <td> 0.628656</td>\\n\",\n        \"      <td>                               DR. KIRIT P SOLANKI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 617104</td>\\n\",\n        \"      <td> 0.639745</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>451</th>\\n\",\n        \"      <td>    Uttar Pradesh</td>\\n\",\n        \"      <td>                    36 - Rae Bareli</td>\\n\",\n        \"      <td>  857875</td>\\n\",\n        \"      <td> 437735</td>\\n\",\n        \"      <td>  737079</td>\\n\",\n        \"      <td> 387401</td>\\n\",\n        \"      <td> 1594954</td>\\n\",\n        \"      <td>  825136</td>\\n\",\n        \"      <td> 0.510255</td>\\n\",\n        \"      <td> 0.525590</td>\\n\",\n        \"      <td> 0.517342</td>\\n\",\n        \"      <td>                                      SONIA GANDHI</td>\\n\",\n        \"      <td> INC</td>\\n\",\n        \"      <td>            Indian National Congress</td>\\n\",\n        \"      <td> 526434</td>\\n\",\n        \"      <td> 0.637997</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>242</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                        19 - BHOPAL</td>\\n\",\n        \"      <td> 1039004</td>\\n\",\n        \"      <td> 639683</td>\\n\",\n        \"      <td>  917932</td>\\n\",\n        \"      <td> 490499</td>\\n\",\n        \"      <td> 1956936</td>\\n\",\n        \"      <td> 1130182</td>\\n\",\n        \"      <td> 0.615669</td>\\n\",\n        \"      <td> 0.534352</td>\\n\",\n        \"      <td> 0.577526</td>\\n\",\n        \"      <td>                                       ALOK SANJAR</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 714178</td>\\n\",\n        \"      <td> 0.631914</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>245</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                        22 - UJJAIN</td>\\n\",\n        \"      <td>  790889</td>\\n\",\n        \"      <td> 571525</td>\\n\",\n        \"      <td>  734592</td>\\n\",\n        \"      <td> 444880</td>\\n\",\n        \"      <td> 1525481</td>\\n\",\n        \"      <td> 1016405</td>\\n\",\n        \"      <td> 0.722636</td>\\n\",\n        \"      <td> 0.605615</td>\\n\",\n        \"      <td> 0.666285</td>\\n\",\n        \"      <td>                          PROF. CHINTAMANI MALVIYA</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 641101</td>\\n\",\n        \"      <td> 0.630753</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>350</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                        2 - Bikaner</td>\\n\",\n        \"      <td>  847064</td>\\n\",\n        \"      <td> 526983</td>\\n\",\n        \"      <td>  744004</td>\\n\",\n        \"      <td> 402768</td>\\n\",\n        \"      <td> 1591068</td>\\n\",\n        \"      <td>  929751</td>\\n\",\n        \"      <td> 0.622129</td>\\n\",\n        \"      <td> 0.541352</td>\\n\",\n        \"      <td> 0.584357</td>\\n\",\n        \"      <td>                                 ARJUN RAM MEGHWAL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 584932</td>\\n\",\n        \"      <td> 0.629128</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>125</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                     11 - Porbandar</td>\\n\",\n        \"      <td>  807383</td>\\n\",\n        \"      <td> 471495</td>\\n\",\n        \"      <td>  731840</td>\\n\",\n        \"      <td> 337938</td>\\n\",\n        \"      <td> 1539223</td>\\n\",\n        \"      <td>  809433</td>\\n\",\n        \"      <td> 0.583979</td>\\n\",\n        \"      <td> 0.461765</td>\\n\",\n        \"      <td> 0.525871</td>\\n\",\n        \"      <td>                  RADADIYA VITHALBHAI  HANSRAJBHAI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 508437</td>\\n\",\n        \"      <td> 0.628140</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>414</th>\\n\",\n        \"      <td>          Tripura</td>\\n\",\n        \"      <td>                   1 - Tripura West</td>\\n\",\n        \"      <td>  635976</td>\\n\",\n        \"      <td> 546566</td>\\n\",\n        \"      <td>  612574</td>\\n\",\n        \"      <td> 526183</td>\\n\",\n        \"      <td> 1248550</td>\\n\",\n        \"      <td> 1072749</td>\\n\",\n        \"      <td> 0.859413</td>\\n\",\n        \"      <td> 0.858971</td>\\n\",\n        \"      <td> 0.859196</td>\\n\",\n        \"      <td>                               SANKAR PRASAD DATTA</td>\\n\",\n        \"      <td> CPM</td>\\n\",\n        \"      <td> Communist Party of India  (Marxist)</td>\\n\",\n        \"      <td> 671665</td>\\n\",\n        \"      <td> 0.626116</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>354</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                   6 - Jaipur Rural</td>\\n\",\n        \"      <td>  906275</td>\\n\",\n        \"      <td> 554084</td>\\n\",\n        \"      <td>  793187</td>\\n\",\n        \"      <td> 459607</td>\\n\",\n        \"      <td> 1699462</td>\\n\",\n        \"      <td> 1013691</td>\\n\",\n        \"      <td> 0.611386</td>\\n\",\n        \"      <td> 0.579443</td>\\n\",\n        \"      <td> 0.596478</td>\\n\",\n        \"      <td>                        RAJYAVARDHAN SINGH RATHORE</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 632930</td>\\n\",\n        \"      <td> 0.624382</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>252</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                         29 - BETUL</td>\\n\",\n        \"      <td>  836835</td>\\n\",\n        \"      <td> 574150</td>\\n\",\n        \"      <td>  770987</td>\\n\",\n        \"      <td> 473569</td>\\n\",\n        \"      <td> 1607822</td>\\n\",\n        \"      <td> 1047719</td>\\n\",\n        \"      <td> 0.686097</td>\\n\",\n        \"      <td> 0.614237</td>\\n\",\n        \"      <td> 0.651639</td>\\n\",\n        \"      <td>                                      JYOTI DHURVE</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 643651</td>\\n\",\n        \"      <td> 0.614336</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>280</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>             28 - Mumbai North East</td>\\n\",\n        \"      <td>  923016</td>\\n\",\n        \"      <td> 485775</td>\\n\",\n        \"      <td>  745341</td>\\n\",\n        \"      <td> 375986</td>\\n\",\n        \"      <td> 1668357</td>\\n\",\n        \"      <td>  861761</td>\\n\",\n        \"      <td> 0.526291</td>\\n\",\n        \"      <td> 0.504448</td>\\n\",\n        \"      <td> 0.516533</td>\\n\",\n        \"      <td>                                    KIRIT  SOMAIYA</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 525285</td>\\n\",\n        \"      <td> 0.609548</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>307</th>\\n\",\n        \"      <td>     NCT OF Delhi</td>\\n\",\n        \"      <td> 1 - CHANDNI CHOWK                 </td>\\n\",\n        \"      <td>  791317</td>\\n\",\n        \"      <td> 550825</td>\\n\",\n        \"      <td>  655911</td>\\n\",\n        \"      <td> 431038</td>\\n\",\n        \"      <td> 1447228</td>\\n\",\n        \"      <td>  981863</td>\\n\",\n        \"      <td> 0.696086</td>\\n\",\n        \"      <td> 0.657159</td>\\n\",\n        \"      <td> 0.678444</td>\\n\",\n        \"      <td>                                      MANOJ TIWARI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 596125</td>\\n\",\n        \"      <td> 0.607137</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>356</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                          8 - Alwar</td>\\n\",\n        \"      <td>  871339</td>\\n\",\n        \"      <td> 587242</td>\\n\",\n        \"      <td>  756728</td>\\n\",\n        \"      <td> 475063</td>\\n\",\n        \"      <td> 1628067</td>\\n\",\n        \"      <td> 1062305</td>\\n\",\n        \"      <td> 0.673954</td>\\n\",\n        \"      <td> 0.627786</td>\\n\",\n        \"      <td> 0.652495</td>\\n\",\n        \"      <td>                                        CHAND NATH</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 642278</td>\\n\",\n        \"      <td> 0.604608</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>332</th>\\n\",\n        \"      <td>           Orissa</td>\\n\",\n        \"      <td>                          19 - Aska</td>\\n\",\n        \"      <td>  750999</td>\\n\",\n        \"      <td> 449485</td>\\n\",\n        \"      <td>  657781</td>\\n\",\n        \"      <td> 446796</td>\\n\",\n        \"      <td> 1408780</td>\\n\",\n        \"      <td>  896281</td>\\n\",\n        \"      <td> 0.598516</td>\\n\",\n        \"      <td> 0.679247</td>\\n\",\n        \"      <td> 0.636211</td>\\n\",\n        \"      <td>                                LADU KISHORE SWAIN</td>\\n\",\n        \"      <td> BJD</td>\\n\",\n        \"      <td>                     Biju Janata Dal</td>\\n\",\n        \"      <td> 541473</td>\\n\",\n        \"      <td> 0.604133</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>357</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                      9 - BHARATPUR</td>\\n\",\n        \"      <td>  911069</td>\\n\",\n        \"      <td> 548105</td>\\n\",\n        \"      <td>  775828</td>\\n\",\n        \"      <td> 414327</td>\\n\",\n        \"      <td> 1686897</td>\\n\",\n        \"      <td>  962432</td>\\n\",\n        \"      <td> 0.601606</td>\\n\",\n        \"      <td> 0.534045</td>\\n\",\n        \"      <td> 0.570534</td>\\n\",\n        \"      <td>                                     BAHADUR SINGH</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 579825</td>\\n\",\n        \"      <td> 0.602458</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>246</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                      23 - MANDSOUR</td>\\n\",\n        \"      <td>  838076</td>\\n\",\n        \"      <td> 640484</td>\\n\",\n        \"      <td>  788495</td>\\n\",\n        \"      <td> 520865</td>\\n\",\n        \"      <td> 1626571</td>\\n\",\n        \"      <td> 1161349</td>\\n\",\n        \"      <td> 0.764231</td>\\n\",\n        \"      <td> 0.660581</td>\\n\",\n        \"      <td> 0.713986</td>\\n\",\n        \"      <td>                                      SUDHIR GUPTA</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 698335</td>\\n\",\n        \"      <td> 0.601314</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>256</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                          4 - Raver</td>\\n\",\n        \"      <td>  842682</td>\\n\",\n        \"      <td> 554159</td>\\n\",\n        \"      <td>  750688</td>\\n\",\n        \"      <td> 455054</td>\\n\",\n        \"      <td> 1593370</td>\\n\",\n        \"      <td> 1009213</td>\\n\",\n        \"      <td> 0.657613</td>\\n\",\n        \"      <td> 0.606183</td>\\n\",\n        \"      <td> 0.633383</td>\\n\",\n        \"      <td>                             KHADASE RAKSHA NIKHIL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 605452</td>\\n\",\n        \"      <td> 0.599925</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>369</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                   21 - Chittorgarh</td>\\n\",\n        \"      <td>  928572</td>\\n\",\n        \"      <td> 632047</td>\\n\",\n        \"      <td>  889575</td>\\n\",\n        \"      <td> 540582</td>\\n\",\n        \"      <td> 1818147</td>\\n\",\n        \"      <td> 1172629</td>\\n\",\n        \"      <td> 0.680666</td>\\n\",\n        \"      <td> 0.607686</td>\\n\",\n        \"      <td> 0.644958</td>\\n\",\n        \"      <td>                             CHANDRA PRAKASH JOSHI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 703236</td>\\n\",\n        \"      <td> 0.599709</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>129</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                     15 - Bhavnagar</td>\\n\",\n        \"      <td>  834571</td>\\n\",\n        \"      <td> 519525</td>\\n\",\n        \"      <td>  759960</td>\\n\",\n        \"      <td> 397877</td>\\n\",\n        \"      <td> 1594531</td>\\n\",\n        \"      <td>  917402</td>\\n\",\n        \"      <td> 0.622505</td>\\n\",\n        \"      <td> 0.523550</td>\\n\",\n        \"      <td> 0.575343</td>\\n\",\n        \"      <td>                   DR. BHARATIBEN DHIRUBHAI SHIYAL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 549529</td>\\n\",\n        \"      <td> 0.599006</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>429</th>\\n\",\n        \"      <td>    Uttar Pradesh</td>\\n\",\n        \"      <td>                   14 - Bulandshahr</td>\\n\",\n        \"      <td>  930831</td>\\n\",\n        \"      <td> 549484</td>\\n\",\n        \"      <td>  805616</td>\\n\",\n        \"      <td> 460226</td>\\n\",\n        \"      <td> 1736447</td>\\n\",\n        \"      <td> 1009710</td>\\n\",\n        \"      <td> 0.590316</td>\\n\",\n        \"      <td> 0.571272</td>\\n\",\n        \"      <td> 0.581480</td>\\n\",\n        \"      <td>                                       BHOLA SINGH</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 604449</td>\\n\",\n        \"      <td> 0.598636</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>436</th>\\n\",\n        \"      <td>    Uttar Pradesh</td>\\n\",\n        \"      <td>                      21 - Mainpuri</td>\\n\",\n        \"      <td>  900568</td>\\n\",\n        \"      <td> 580173</td>\\n\",\n        \"      <td>  752497</td>\\n\",\n        \"      <td> 419092</td>\\n\",\n        \"      <td> 1653065</td>\\n\",\n        \"      <td>  999265</td>\\n\",\n        \"      <td> 0.644230</td>\\n\",\n        \"      <td> 0.556935</td>\\n\",\n        \"      <td> 0.604492</td>\\n\",\n        \"      <td>                               MULAYAM SINGH YADAV</td>\\n\",\n        \"      <td>  SP</td>\\n\",\n        \"      <td>                     Samajwadi Party</td>\\n\",\n        \"      <td> 595918</td>\\n\",\n        \"      <td> 0.596356</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>115</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                        1 - Kachchh</td>\\n\",\n        \"      <td>  806343</td>\\n\",\n        \"      <td> 520905</td>\\n\",\n        \"      <td>  727439</td>\\n\",\n        \"      <td> 425335</td>\\n\",\n        \"      <td> 1533782</td>\\n\",\n        \"      <td>  946240</td>\\n\",\n        \"      <td> 0.646009</td>\\n\",\n        \"      <td> 0.584702</td>\\n\",\n        \"      <td> 0.616933</td>\\n\",\n        \"      <td>                           CHAVDA VINOD LAKHAMASHI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 562855</td>\\n\",\n        \"      <td> 0.594833</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>497</th>\\n\",\n        \"      <td>      Uttarakhand</td>\\n\",\n        \"      <td>                        2 - Garhwal</td>\\n\",\n        \"      <td>  652891</td>\\n\",\n        \"      <td> 323143</td>\\n\",\n        \"      <td>  616192</td>\\n\",\n        \"      <td> 358881</td>\\n\",\n        \"      <td> 1269083</td>\\n\",\n        \"      <td>  682024</td>\\n\",\n        \"      <td> 0.494942</td>\\n\",\n        \"      <td> 0.582417</td>\\n\",\n        \"      <td> 0.537415</td>\\n\",\n        \"      <td> (MAJ GEN (RETD.) ) BHUWAN CHANDRA KHANDURI (AVSM)</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 405690</td>\\n\",\n        \"      <td> 0.594832</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>131</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                         17 - Kheda</td>\\n\",\n        \"      <td>  833232</td>\\n\",\n        \"      <td> 542955</td>\\n\",\n        \"      <td>  766244</td>\\n\",\n        \"      <td> 412951</td>\\n\",\n        \"      <td> 1599476</td>\\n\",\n        \"      <td>  955906</td>\\n\",\n        \"      <td> 0.651625</td>\\n\",\n        \"      <td> 0.538929</td>\\n\",\n        \"      <td> 0.597637</td>\\n\",\n        \"      <td>    CHAUHAN DEVUSINH JESINGBHAI (CHAUHAN DEVUSINH)</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 568235</td>\\n\",\n        \"      <td> 0.594447</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>288</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                        36 - Shirur</td>\\n\",\n        \"      <td>  973236</td>\\n\",\n        \"      <td> 613828</td>\\n\",\n        \"      <td>  850876</td>\\n\",\n        \"      <td> 475678</td>\\n\",\n        \"      <td> 1824112</td>\\n\",\n        \"      <td> 1089506</td>\\n\",\n        \"      <td> 0.630708</td>\\n\",\n        \"      <td> 0.559045</td>\\n\",\n        \"      <td> 0.597280</td>\\n\",\n        \"      <td>                        ADHALRAO SHIVAJI DATTATREY</td>\\n\",\n        \"      <td> SHS</td>\\n\",\n        \"      <td>                            Shivsena</td>\\n\",\n        \"      <td> 643415</td>\\n\",\n        \"      <td> 0.590557</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>243</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                       20 - RAJGARH</td>\\n\",\n        \"      <td>  827001</td>\\n\",\n        \"      <td> 598652</td>\\n\",\n        \"      <td>  751747</td>\\n\",\n        \"      <td> 412072</td>\\n\",\n        \"      <td> 1578748</td>\\n\",\n        \"      <td> 1010724</td>\\n\",\n        \"      <td> 0.723883</td>\\n\",\n        \"      <td> 0.548153</td>\\n\",\n        \"      <td> 0.640206</td>\\n\",\n        \"      <td>                                      RODMAL NAGAR</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 596727</td>\\n\",\n        \"      <td> 0.590396</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>418</th>\\n\",\n        \"      <td>    Uttar Pradesh</td>\\n\",\n        \"      <td>                  3 - Muzaffarnagar</td>\\n\",\n        \"      <td>  875186</td>\\n\",\n        \"      <td> 620606</td>\\n\",\n        \"      <td>  713297</td>\\n\",\n        \"      <td> 486828</td>\\n\",\n        \"      <td> 1588483</td>\\n\",\n        \"      <td> 1107434</td>\\n\",\n        \"      <td> 0.709113</td>\\n\",\n        \"      <td> 0.682504</td>\\n\",\n        \"      <td> 0.697165</td>\\n\",\n        \"      <td>                        (DR.) SANJEEV KUMAR BALYAN</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 653391</td>\\n\",\n        \"      <td> 0.590004</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>373</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                25 - JHALAWAR-BARAN</td>\\n\",\n        \"      <td>  868977</td>\\n\",\n        \"      <td> 635659</td>\\n\",\n        \"      <td>  800865</td>\\n\",\n        \"      <td> 510705</td>\\n\",\n        \"      <td> 1669842</td>\\n\",\n        \"      <td> 1146364</td>\\n\",\n        \"      <td> 0.731503</td>\\n\",\n        \"      <td> 0.637692</td>\\n\",\n        \"      <td> 0.686510</td>\\n\",\n        \"      <td>                                    DUSHYANT SINGH</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 676102</td>\\n\",\n        \"      <td> 0.589780</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>124</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                        10 - Rajkot</td>\\n\",\n        \"      <td>  864760</td>\\n\",\n        \"      <td> 593333</td>\\n\",\n        \"      <td>  790957</td>\\n\",\n        \"      <td> 463736</td>\\n\",\n        \"      <td> 1655717</td>\\n\",\n        \"      <td> 1057069</td>\\n\",\n        \"      <td> 0.686124</td>\\n\",\n        \"      <td> 0.586297</td>\\n\",\n        \"      <td> 0.638436</td>\\n\",\n        \"      <td>                  KUNDARIYA MOHANBHAI KALYANJIBHAI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 621524</td>\\n\",\n        \"      <td> 0.587969</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>113</th>\\n\",\n        \"      <td>              Goa</td>\\n\",\n        \"      <td>                      1 - North Goa</td>\\n\",\n        \"      <td>  255870</td>\\n\",\n        \"      <td> 200184</td>\\n\",\n        \"      <td>  259571</td>\\n\",\n        \"      <td> 206447</td>\\n\",\n        \"      <td>  515441</td>\\n\",\n        \"      <td>  406631</td>\\n\",\n        \"      <td> 0.782366</td>\\n\",\n        \"      <td> 0.795339</td>\\n\",\n        \"      <td> 0.788899</td>\\n\",\n        \"      <td>                                SHRIPAD YESSO NAIK</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 237903</td>\\n\",\n        \"      <td> 0.585059</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>296</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                        44 - Sangli</td>\\n\",\n        \"      <td>  861582</td>\\n\",\n        \"      <td> 558707</td>\\n\",\n        \"      <td>  787525</td>\\n\",\n        \"      <td> 487952</td>\\n\",\n        \"      <td> 1649107</td>\\n\",\n        \"      <td> 1046659</td>\\n\",\n        \"      <td> 0.648466</td>\\n\",\n        \"      <td> 0.619602</td>\\n\",\n        \"      <td> 0.634682</td>\\n\",\n        \"      <td>                                  SANJAYKAKA PATIL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 611563</td>\\n\",\n        \"      <td> 0.584300</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>293</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                        41 - Latur </td>\\n\",\n        \"      <td>  897919</td>\\n\",\n        \"      <td> 578053</td>\\n\",\n        \"      <td>  784688</td>\\n\",\n        \"      <td> 479103</td>\\n\",\n        \"      <td> 1682607</td>\\n\",\n        \"      <td> 1057156</td>\\n\",\n        \"      <td> 0.643770</td>\\n\",\n        \"      <td> 0.610565</td>\\n\",\n        \"      <td> 0.628285</td>\\n\",\n        \"      <td>                         DR. SUNIL BALIRAM GAIKWAD</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 616509</td>\\n\",\n        \"      <td> 0.583177</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>244</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                         21 - DEWAS</td>\\n\",\n        \"      <td>  843555</td>\\n\",\n        \"      <td> 654121</td>\\n\",\n        \"      <td>  773660</td>\\n\",\n        \"      <td> 489847</td>\\n\",\n        \"      <td> 1617215</td>\\n\",\n        \"      <td> 1143968</td>\\n\",\n        \"      <td> 0.775434</td>\\n\",\n        \"      <td> 0.633155</td>\\n\",\n        \"      <td> 0.707369</td>\\n\",\n        \"      <td>                                    MANOHAR UNTWAL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 665646</td>\\n\",\n        \"      <td> 0.581875</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>499</th>\\n\",\n        \"      <td>      Uttarakhand</td>\\n\",\n        \"      <td>      4 - Nainital-udhamsingh Nagar</td>\\n\",\n        \"      <td>  857781</td>\\n\",\n        \"      <td> 590700</td>\\n\",\n        \"      <td>  753029</td>\\n\",\n        \"      <td> 510735</td>\\n\",\n        \"      <td> 1610810</td>\\n\",\n        \"      <td> 1101435</td>\\n\",\n        \"      <td> 0.688637</td>\\n\",\n        \"      <td> 0.678241</td>\\n\",\n        \"      <td> 0.683777</td>\\n\",\n        \"      <td>                             BHAGAT SINGH KOSHYARI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 636769</td>\\n\",\n        \"      <td> 0.578127</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>118</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                       4 - Mahesana</td>\\n\",\n        \"      <td>  777821</td>\\n\",\n        \"      <td> 544710</td>\\n\",\n        \"      <td>  720398</td>\\n\",\n        \"      <td> 459548</td>\\n\",\n        \"      <td> 1498219</td>\\n\",\n        \"      <td> 1004258</td>\\n\",\n        \"      <td> 0.700303</td>\\n\",\n        \"      <td> 0.637908</td>\\n\",\n        \"      <td> 0.670301</td>\\n\",\n        \"      <td>                        PATEL JAYSHREEBEN KANUBHAI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 580250</td>\\n\",\n        \"      <td> 0.577790</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>150</th>\\n\",\n        \"      <td>          Haryana</td>\\n\",\n        \"      <td>                     10 - Faridabad</td>\\n\",\n        \"      <td>  969407</td>\\n\",\n        \"      <td> 651411</td>\\n\",\n        \"      <td>  770945</td>\\n\",\n        \"      <td> 479315</td>\\n\",\n        \"      <td> 1740352</td>\\n\",\n        \"      <td> 1130726</td>\\n\",\n        \"      <td> 0.671969</td>\\n\",\n        \"      <td> 0.621724</td>\\n\",\n        \"      <td> 0.649711</td>\\n\",\n        \"      <td>                                       KRISHAN PAL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 652516</td>\\n\",\n        \"      <td> 0.577077</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>496</th>\\n\",\n        \"      <td>      Uttarakhand</td>\\n\",\n        \"      <td>                  1 - Tehri Garhwal</td>\\n\",\n        \"      <td>  712039</td>\\n\",\n        \"      <td> 403088</td>\\n\",\n        \"      <td>  640806</td>\\n\",\n        \"      <td> 373126</td>\\n\",\n        \"      <td> 1352845</td>\\n\",\n        \"      <td>  776214</td>\\n\",\n        \"      <td> 0.566104</td>\\n\",\n        \"      <td> 0.582276</td>\\n\",\n        \"      <td> 0.573764</td>\\n\",\n        \"      <td>                             MALA RAJYA LAXMI SHAH</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 446733</td>\\n\",\n        \"      <td> 0.575528</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>286</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                          34 - Pune</td>\\n\",\n        \"      <td>  949567</td>\\n\",\n        \"      <td> 533686</td>\\n\",\n        \"      <td>  886269</td>\\n\",\n        \"      <td> 459588</td>\\n\",\n        \"      <td> 1835836</td>\\n\",\n        \"      <td>  993274</td>\\n\",\n        \"      <td> 0.562031</td>\\n\",\n        \"      <td> 0.518565</td>\\n\",\n        \"      <td> 0.541047</td>\\n\",\n        \"      <td>                                      ANIL SHIROLE</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 569825</td>\\n\",\n        \"      <td> 0.573684</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>116</th>\\n\",\n        \"      <td>          Gujarat</td>\\n\",\n        \"      <td>                    2 - Banaskantha</td>\\n\",\n        \"      <td>  796124</td>\\n\",\n        \"      <td> 513893</td>\\n\",\n        \"      <td>  719587</td>\\n\",\n        \"      <td> 372741</td>\\n\",\n        \"      <td> 1515711</td>\\n\",\n        \"      <td>  886634</td>\\n\",\n        \"      <td> 0.645494</td>\\n\",\n        \"      <td> 0.517993</td>\\n\",\n        \"      <td> 0.584962</td>\\n\",\n        \"      <td>                     CHAUDHARY HARIBHAI PARTHIBHAI</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 507856</td>\\n\",\n        \"      <td> 0.572791</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>290</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                        38 - Shirdi</td>\\n\",\n        \"      <td>  765921</td>\\n\",\n        \"      <td> 520229</td>\\n\",\n        \"      <td>  693791</td>\\n\",\n        \"      <td> 412416</td>\\n\",\n        \"      <td> 1459712</td>\\n\",\n        \"      <td>  932645</td>\\n\",\n        \"      <td> 0.679220</td>\\n\",\n        \"      <td> 0.594438</td>\\n\",\n        \"      <td> 0.638924</td>\\n\",\n        \"      <td>                           LOKHANDE SADASHIV KISAN</td>\\n\",\n        \"      <td> SHS</td>\\n\",\n        \"      <td>                            Shivsena</td>\\n\",\n        \"      <td> 532936</td>\\n\",\n        \"      <td> 0.571424</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>371</th>\\n\",\n        \"      <td>        Rajasthan</td>\\n\",\n        \"      <td>                      23 - Bhilwara</td>\\n\",\n        \"      <td>  904030</td>\\n\",\n        \"      <td> 581524</td>\\n\",\n        \"      <td>  850847</td>\\n\",\n        \"      <td> 522566</td>\\n\",\n        \"      <td> 1754877</td>\\n\",\n        \"      <td> 1104090</td>\\n\",\n        \"      <td> 0.643257</td>\\n\",\n        \"      <td> 0.614172</td>\\n\",\n        \"      <td> 0.629155</td>\\n\",\n        \"      <td>                                   SUBHASH BAHERIA</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 630317</td>\\n\",\n        \"      <td> 0.570893</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>151</th>\\n\",\n        \"      <td> Himachal Pradesh</td>\\n\",\n        \"      <td>                         1 - Kangra</td>\\n\",\n        \"      <td>  645888</td>\\n\",\n        \"      <td> 392927</td>\\n\",\n        \"      <td>  612713</td>\\n\",\n        \"      <td> 406518</td>\\n\",\n        \"      <td> 1258601</td>\\n\",\n        \"      <td>  799445</td>\\n\",\n        \"      <td> 0.608352</td>\\n\",\n        \"      <td> 0.663472</td>\\n\",\n        \"      <td> 0.635185</td>\\n\",\n        \"      <td>                                      SHANTA KUMAR</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 456163</td>\\n\",\n        \"      <td> 0.570600</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>251</th>\\n\",\n        \"      <td>   Madhya Pradesh</td>\\n\",\n        \"      <td>                       28 - KHANDWA</td>\\n\",\n        \"      <td>  912747</td>\\n\",\n        \"      <td> 677574</td>\\n\",\n        \"      <td>  846663</td>\\n\",\n        \"      <td> 579753</td>\\n\",\n        \"      <td> 1759410</td>\\n\",\n        \"      <td> 1257327</td>\\n\",\n        \"      <td> 0.742346</td>\\n\",\n        \"      <td> 0.684751</td>\\n\",\n        \"      <td> 0.714630</td>\\n\",\n        \"      <td>            NANDKUMAR SINGH CHOUHAN (NANDU BHAIYA)</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 717357</td>\\n\",\n        \"      <td> 0.570541</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>289</th>\\n\",\n        \"      <td>      Maharashtra</td>\\n\",\n        \"      <td>                   37 - Ahmadnagar </td>\\n\",\n        \"      <td>  898819</td>\\n\",\n        \"      <td> 595960</td>\\n\",\n        \"      <td>  800589</td>\\n\",\n        \"      <td> 466358</td>\\n\",\n        \"      <td> 1699408</td>\\n\",\n        \"      <td> 1062318</td>\\n\",\n        \"      <td> 0.663048</td>\\n\",\n        \"      <td> 0.582519</td>\\n\",\n        \"      <td> 0.625111</td>\\n\",\n        \"      <td>                      GANDHI DILIPKUMAR MANSUKHLAL</td>\\n\",\n        \"      <td> BJP</td>\\n\",\n        \"      <td>              Bharatiya Janata Party</td>\\n\",\n        \"      <td> 605185</td>\\n\",\n        \"      <td> 0.569683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>543 rows \\u00d7 16 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 17,\n       \"text\": [\n        \"                State                             PC Name  Male Electors  \\\\\\n\",\n        \"138           Gujarat                          24 - Surat         803829   \\n\",\n        \"134           Gujarat                       20 - Vadodara         849077   \\n\",\n        \"139           Gujarat                        25 - Navsari         972090   \\n\",\n        \"278       Maharashtra                   26 - Mumbai North         972645   \\n\",\n        \"120           Gujarat                     6 - Gandhinagar         900744   \\n\",\n        \"355         Rajasthan                          7 - Jaipur        1047468   \\n\",\n        \"241    Madhya Pradesh                        18 - VIDISHA         872410   \\n\",\n        \"364         Rajasthan                        16 - Jodhpur         910466   \\n\",\n        \"370         Rajasthan                      22 - Rajsamand         879526   \\n\",\n        \"415           Tripura                    2 - Tripura East         581599   \\n\",\n        \"255       Maharashtra                         3 - Jalgaon         909027   \\n\",\n        \"363         Rajasthan                           15 - Pali         994082   \\n\",\n        \"310      NCT OF Delhi  4 - NEW DELHI                              830322   \\n\",\n        \"249    Madhya Pradesh                         26 - INDORE        1106461   \\n\",\n        \"240    Madhya Pradesh                    17 - HOSHANGABAD         835492   \\n\",\n        \"313      NCT OF Delhi  7 - SOUTH DELHI                           1005289   \\n\",\n        \"121           Gujarat                  7 - Ahmedabad East         852765   \\n\",\n        \"122           Gujarat                  8 - Ahmedabad West         800933   \\n\",\n        \"451     Uttar Pradesh                     36 - Rae Bareli         857875   \\n\",\n        \"242    Madhya Pradesh                         19 - BHOPAL        1039004   \\n\",\n        \"245    Madhya Pradesh                         22 - UJJAIN         790889   \\n\",\n        \"350         Rajasthan                         2 - Bikaner         847064   \\n\",\n        \"125           Gujarat                      11 - Porbandar         807383   \\n\",\n        \"414           Tripura                    1 - Tripura West         635976   \\n\",\n        \"354         Rajasthan                    6 - Jaipur Rural         906275   \\n\",\n        \"252    Madhya Pradesh                          29 - BETUL         836835   \\n\",\n        \"280       Maharashtra              28 - Mumbai North East         923016   \\n\",\n        \"307      NCT OF Delhi  1 - CHANDNI CHOWK                          791317   \\n\",\n        \"356         Rajasthan                           8 - Alwar         871339   \\n\",\n        \"332            Orissa                           19 - Aska         750999   \\n\",\n        \"357         Rajasthan                       9 - BHARATPUR         911069   \\n\",\n        \"246    Madhya Pradesh                       23 - MANDSOUR         838076   \\n\",\n        \"256       Maharashtra                           4 - Raver         842682   \\n\",\n        \"369         Rajasthan                    21 - Chittorgarh         928572   \\n\",\n        \"129           Gujarat                      15 - Bhavnagar         834571   \\n\",\n        \"429     Uttar Pradesh                    14 - Bulandshahr         930831   \\n\",\n        \"436     Uttar Pradesh                       21 - Mainpuri         900568   \\n\",\n        \"115           Gujarat                         1 - Kachchh         806343   \\n\",\n        \"497       Uttarakhand                         2 - Garhwal         652891   \\n\",\n        \"131           Gujarat                          17 - Kheda         833232   \\n\",\n        \"288       Maharashtra                         36 - Shirur         973236   \\n\",\n        \"243    Madhya Pradesh                        20 - RAJGARH         827001   \\n\",\n        \"418     Uttar Pradesh                   3 - Muzaffarnagar         875186   \\n\",\n        \"373         Rajasthan                 25 - JHALAWAR-BARAN         868977   \\n\",\n        \"124           Gujarat                         10 - Rajkot         864760   \\n\",\n        \"113               Goa                       1 - North Goa         255870   \\n\",\n        \"296       Maharashtra                         44 - Sangli         861582   \\n\",\n        \"293       Maharashtra                         41 - Latur          897919   \\n\",\n        \"244    Madhya Pradesh                          21 - DEWAS         843555   \\n\",\n        \"499       Uttarakhand       4 - Nainital-udhamsingh Nagar         857781   \\n\",\n        \"118           Gujarat                        4 - Mahesana         777821   \\n\",\n        \"150           Haryana                      10 - Faridabad         969407   \\n\",\n        \"496       Uttarakhand                   1 - Tehri Garhwal         712039   \\n\",\n        \"286       Maharashtra                           34 - Pune         949567   \\n\",\n        \"116           Gujarat                     2 - Banaskantha         796124   \\n\",\n        \"290       Maharashtra                         38 - Shirdi         765921   \\n\",\n        \"371         Rajasthan                       23 - Bhilwara         904030   \\n\",\n        \"151  Himachal Pradesh                          1 - Kangra         645888   \\n\",\n        \"251    Madhya Pradesh                        28 - KHANDWA         912747   \\n\",\n        \"289       Maharashtra                    37 - Ahmadnagar          898819   \\n\",\n        \"                  ...                                 ...            ...   \\n\",\n        \"\\n\",\n        \"     Male Voters  Female Electors  Female Voters  Total Electors  \\\\\\n\",\n        \"138       539368           680239         408554         1484068   \\n\",\n        \"134       625045           789244         536532         1638321   \\n\",\n        \"139       649206           792532         511541         1764622   \\n\",\n        \"278       527803           811225         418759         1783870   \\n\",\n        \"120       620889           833228         514606         1733972   \\n\",\n        \"355       716874           910350         579932         1957818   \\n\",\n        \"241       621326           761960         452147         1634370   \\n\",\n        \"364       600132           816897         478466         1727363   \\n\",\n        \"370       508738           819875         473381         1699401   \\n\",\n        \"415       489156           558670         461954         1140269   \\n\",\n        \"255       594892           798906         395440         1707933   \\n\",\n        \"363       581099           898948         514488         1893030   \\n\",\n        \"310       546295           659825         423517         1490147   \\n\",\n        \"249       736759          1008842         580058         2115303   \\n\",\n        \"240       593957           732635         437218         1568127   \\n\",\n        \"313       638406           747452         464004         1752741   \\n\",\n        \"121       560074           749067         425451         1601832   \\n\",\n        \"122       534008           733467         430601         1534400   \\n\",\n        \"451       437735           737079         387401         1594954   \\n\",\n        \"242       639683           917932         490499         1956936   \\n\",\n        \"245       571525           734592         444880         1525481   \\n\",\n        \"350       526983           744004         402768         1591068   \\n\",\n        \"125       471495           731840         337938         1539223   \\n\",\n        \"414       546566           612574         526183         1248550   \\n\",\n        \"354       554084           793187         459607         1699462   \\n\",\n        \"252       574150           770987         473569         1607822   \\n\",\n        \"280       485775           745341         375986         1668357   \\n\",\n        \"307       550825           655911         431038         1447228   \\n\",\n        \"356       587242           756728         475063         1628067   \\n\",\n        \"332       449485           657781         446796         1408780   \\n\",\n        \"357       548105           775828         414327         1686897   \\n\",\n        \"246       640484           788495         520865         1626571   \\n\",\n        \"256       554159           750688         455054         1593370   \\n\",\n        \"369       632047           889575         540582         1818147   \\n\",\n        \"129       519525           759960         397877         1594531   \\n\",\n        \"429       549484           805616         460226         1736447   \\n\",\n        \"436       580173           752497         419092         1653065   \\n\",\n        \"115       520905           727439         425335         1533782   \\n\",\n        \"497       323143           616192         358881         1269083   \\n\",\n        \"131       542955           766244         412951         1599476   \\n\",\n        \"288       613828           850876         475678         1824112   \\n\",\n        \"243       598652           751747         412072         1578748   \\n\",\n        \"418       620606           713297         486828         1588483   \\n\",\n        \"373       635659           800865         510705         1669842   \\n\",\n        \"124       593333           790957         463736         1655717   \\n\",\n        \"113       200184           259571         206447          515441   \\n\",\n        \"296       558707           787525         487952         1649107   \\n\",\n        \"293       578053           784688         479103         1682607   \\n\",\n        \"244       654121           773660         489847         1617215   \\n\",\n        \"499       590700           753029         510735         1610810   \\n\",\n        \"118       544710           720398         459548         1498219   \\n\",\n        \"150       651411           770945         479315         1740352   \\n\",\n        \"496       403088           640806         373126         1352845   \\n\",\n        \"286       533686           886269         459588         1835836   \\n\",\n        \"116       513893           719587         372741         1515711   \\n\",\n        \"290       520229           693791         412416         1459712   \\n\",\n        \"371       581524           850847         522566         1754877   \\n\",\n        \"151       392927           612713         406518         1258601   \\n\",\n        \"251       677574           846663         579753         1759410   \\n\",\n        \"289       595960           800589         466358         1699408   \\n\",\n        \"             ...              ...            ...             ...   \\n\",\n        \"\\n\",\n        \"     Total Voters  Male Turnout  Female Turnout  Total Turnout  \\\\\\n\",\n        \"138        947922      0.670998        0.600604       0.638732   \\n\",\n        \"134       1161577      0.736146        0.679805       0.709005   \\n\",\n        \"139       1160747      0.667846        0.645452       0.657788   \\n\",\n        \"278        946562      0.542647        0.516206       0.530623   \\n\",\n        \"120       1135495      0.689307        0.617605       0.654852   \\n\",\n        \"355       1296806      0.684387        0.637043       0.662373   \\n\",\n        \"241       1073473      0.712195        0.593400       0.656811   \\n\",\n        \"364       1078598      0.659148        0.585712       0.624419   \\n\",\n        \"370        982119      0.578423        0.577382       0.577921   \\n\",\n        \"415        951110      0.841054        0.826882       0.834110   \\n\",\n        \"255        990332      0.654427        0.494977       0.579842   \\n\",\n        \"363       1095587      0.584558        0.572322       0.578748   \\n\",\n        \"310        969812      0.657932        0.641863       0.650816   \\n\",\n        \"249       1316817      0.665870        0.574974       0.622519   \\n\",\n        \"240       1031175      0.710907        0.596775       0.657584   \\n\",\n        \"313       1102410      0.635047        0.620781       0.628963   \\n\",\n        \"121        985525      0.656774        0.567975       0.615249   \\n\",\n        \"122        964609      0.666732        0.587076       0.628656   \\n\",\n        \"451        825136      0.510255        0.525590       0.517342   \\n\",\n        \"242       1130182      0.615669        0.534352       0.577526   \\n\",\n        \"245       1016405      0.722636        0.605615       0.666285   \\n\",\n        \"350        929751      0.622129        0.541352       0.584357   \\n\",\n        \"125        809433      0.583979        0.461765       0.525871   \\n\",\n        \"414       1072749      0.859413        0.858971       0.859196   \\n\",\n        \"354       1013691      0.611386        0.579443       0.596478   \\n\",\n        \"252       1047719      0.686097        0.614237       0.651639   \\n\",\n        \"280        861761      0.526291        0.504448       0.516533   \\n\",\n        \"307        981863      0.696086        0.657159       0.678444   \\n\",\n        \"356       1062305      0.673954        0.627786       0.652495   \\n\",\n        \"332        896281      0.598516        0.679247       0.636211   \\n\",\n        \"357        962432      0.601606        0.534045       0.570534   \\n\",\n        \"246       1161349      0.764231        0.660581       0.713986   \\n\",\n        \"256       1009213      0.657613        0.606183       0.633383   \\n\",\n        \"369       1172629      0.680666        0.607686       0.644958   \\n\",\n        \"129        917402      0.622505        0.523550       0.575343   \\n\",\n        \"429       1009710      0.590316        0.571272       0.581480   \\n\",\n        \"436        999265      0.644230        0.556935       0.604492   \\n\",\n        \"115        946240      0.646009        0.584702       0.616933   \\n\",\n        \"497        682024      0.494942        0.582417       0.537415   \\n\",\n        \"131        955906      0.651625        0.538929       0.597637   \\n\",\n        \"288       1089506      0.630708        0.559045       0.597280   \\n\",\n        \"243       1010724      0.723883        0.548153       0.640206   \\n\",\n        \"418       1107434      0.709113        0.682504       0.697165   \\n\",\n        \"373       1146364      0.731503        0.637692       0.686510   \\n\",\n        \"124       1057069      0.686124        0.586297       0.638436   \\n\",\n        \"113        406631      0.782366        0.795339       0.788899   \\n\",\n        \"296       1046659      0.648466        0.619602       0.634682   \\n\",\n        \"293       1057156      0.643770        0.610565       0.628285   \\n\",\n        \"244       1143968      0.775434        0.633155       0.707369   \\n\",\n        \"499       1101435      0.688637        0.678241       0.683777   \\n\",\n        \"118       1004258      0.700303        0.637908       0.670301   \\n\",\n        \"150       1130726      0.671969        0.621724       0.649711   \\n\",\n        \"496        776214      0.566104        0.582276       0.573764   \\n\",\n        \"286        993274      0.562031        0.518565       0.541047   \\n\",\n        \"116        886634      0.645494        0.517993       0.584962   \\n\",\n        \"290        932645      0.679220        0.594438       0.638924   \\n\",\n        \"371       1104090      0.643257        0.614172       0.629155   \\n\",\n        \"151        799445      0.608352        0.663472       0.635185   \\n\",\n        \"251       1257327      0.742346        0.684751       0.714630   \\n\",\n        \"289       1062318      0.663048        0.582519       0.625111   \\n\",\n        \"              ...           ...             ...            ...   \\n\",\n        \"\\n\",\n        \"                                        Candidate Name Party Abbreviation  \\\\\\n\",\n        \"138                            DARSHANA VIKRAM JARDOSH                BJP   \\n\",\n        \"134                                      NARENDRA MODI                BJP   \\n\",\n        \"139                                        C. R. PATIL                BJP   \\n\",\n        \"278                              GOPAL CHINAYYA SHETTY                BJP   \\n\",\n        \"120                                         L.K.ADVANI                BJP   \\n\",\n        \"355                                   RAMCHARAN BOHARA                BJP   \\n\",\n        \"241                                      SUSHMA SWARAJ                BJP   \\n\",\n        \"364                            GAJENDRASINGH SHEKHAWAT                BJP   \\n\",\n        \"370                               HARIOM SINGH RATHORE                BJP   \\n\",\n        \"415                                 JITENDRA CHOUDHURY                CPM   \\n\",\n        \"255                                    A.T. NANA PATIL                BJP   \\n\",\n        \"363                                      P P CHOUDHARY                BJP   \\n\",\n        \"310                                           UDIT RAJ                BJP   \\n\",\n        \"249                              SUMITRA MAHAJAN (TAI)                BJP   \\n\",\n        \"240                                  UDAY PRATAP SINGH                BJP   \\n\",\n        \"313                                        NEIPHIU RIO                NPF   \\n\",\n        \"121                                       PARESH RAWAL                BJP   \\n\",\n        \"122                                DR. KIRIT P SOLANKI                BJP   \\n\",\n        \"451                                       SONIA GANDHI                INC   \\n\",\n        \"242                                        ALOK SANJAR                BJP   \\n\",\n        \"245                           PROF. CHINTAMANI MALVIYA                BJP   \\n\",\n        \"350                                  ARJUN RAM MEGHWAL                BJP   \\n\",\n        \"125                   RADADIYA VITHALBHAI  HANSRAJBHAI                BJP   \\n\",\n        \"414                                SANKAR PRASAD DATTA                CPM   \\n\",\n        \"354                         RAJYAVARDHAN SINGH RATHORE                BJP   \\n\",\n        \"252                                       JYOTI DHURVE                BJP   \\n\",\n        \"280                                     KIRIT  SOMAIYA                BJP   \\n\",\n        \"307                                       MANOJ TIWARI                BJP   \\n\",\n        \"356                                         CHAND NATH                BJP   \\n\",\n        \"332                                 LADU KISHORE SWAIN                BJD   \\n\",\n        \"357                                      BAHADUR SINGH                BJP   \\n\",\n        \"246                                       SUDHIR GUPTA                BJP   \\n\",\n        \"256                              KHADASE RAKSHA NIKHIL                BJP   \\n\",\n        \"369                              CHANDRA PRAKASH JOSHI                BJP   \\n\",\n        \"129                    DR. BHARATIBEN DHIRUBHAI SHIYAL                BJP   \\n\",\n        \"429                                        BHOLA SINGH                BJP   \\n\",\n        \"436                                MULAYAM SINGH YADAV                 SP   \\n\",\n        \"115                            CHAVDA VINOD LAKHAMASHI                BJP   \\n\",\n        \"497  (MAJ GEN (RETD.) ) BHUWAN CHANDRA KHANDURI (AVSM)                BJP   \\n\",\n        \"131     CHAUHAN DEVUSINH JESINGBHAI (CHAUHAN DEVUSINH)                BJP   \\n\",\n        \"288                         ADHALRAO SHIVAJI DATTATREY                SHS   \\n\",\n        \"243                                       RODMAL NAGAR                BJP   \\n\",\n        \"418                         (DR.) SANJEEV KUMAR BALYAN                BJP   \\n\",\n        \"373                                     DUSHYANT SINGH                BJP   \\n\",\n        \"124                   KUNDARIYA MOHANBHAI KALYANJIBHAI                BJP   \\n\",\n        \"113                                 SHRIPAD YESSO NAIK                BJP   \\n\",\n        \"296                                   SANJAYKAKA PATIL                BJP   \\n\",\n        \"293                          DR. SUNIL BALIRAM GAIKWAD                BJP   \\n\",\n        \"244                                     MANOHAR UNTWAL                BJP   \\n\",\n        \"499                              BHAGAT SINGH KOSHYARI                BJP   \\n\",\n        \"118                         PATEL JAYSHREEBEN KANUBHAI                BJP   \\n\",\n        \"150                                        KRISHAN PAL                BJP   \\n\",\n        \"496                              MALA RAJYA LAXMI SHAH                BJP   \\n\",\n        \"286                                       ANIL SHIROLE                BJP   \\n\",\n        \"116                      CHAUDHARY HARIBHAI PARTHIBHAI                BJP   \\n\",\n        \"290                            LOKHANDE SADASHIV KISAN                SHS   \\n\",\n        \"371                                    SUBHASH BAHERIA                BJP   \\n\",\n        \"151                                       SHANTA KUMAR                BJP   \\n\",\n        \"251             NANDKUMAR SINGH CHOUHAN (NANDU BHAIYA)                BJP   \\n\",\n        \"289                       GANDHI DILIPKUMAR MANSUKHLAL                BJP   \\n\",\n        \"                                                   ...                ...   \\n\",\n        \"\\n\",\n        \"                              Party Name  Total Votes Polled  \\\\\\n\",\n        \"138               Bharatiya Janata Party              718412   \\n\",\n        \"134               Bharatiya Janata Party              845464   \\n\",\n        \"139               Bharatiya Janata Party              820831   \\n\",\n        \"278               Bharatiya Janata Party              664004   \\n\",\n        \"120               Bharatiya Janata Party              773539   \\n\",\n        \"355               Bharatiya Janata Party              863358   \\n\",\n        \"241               Bharatiya Janata Party              714348   \\n\",\n        \"364               Bharatiya Janata Party              713515   \\n\",\n        \"370               Bharatiya Janata Party              644794   \\n\",\n        \"415  Communist Party of India  (Marxist)              623771   \\n\",\n        \"255               Bharatiya Janata Party              647773   \\n\",\n        \"363               Bharatiya Janata Party              711772   \\n\",\n        \"310               Bharatiya Janata Party              629860   \\n\",\n        \"249               Bharatiya Janata Party              854972   \\n\",\n        \"240               Bharatiya Janata Party              669128   \\n\",\n        \"313                   Naga Peoples Front              713372   \\n\",\n        \"121               Bharatiya Janata Party              633582   \\n\",\n        \"122               Bharatiya Janata Party              617104   \\n\",\n        \"451             Indian National Congress              526434   \\n\",\n        \"242               Bharatiya Janata Party              714178   \\n\",\n        \"245               Bharatiya Janata Party              641101   \\n\",\n        \"350               Bharatiya Janata Party              584932   \\n\",\n        \"125               Bharatiya Janata Party              508437   \\n\",\n        \"414  Communist Party of India  (Marxist)              671665   \\n\",\n        \"354               Bharatiya Janata Party              632930   \\n\",\n        \"252               Bharatiya Janata Party              643651   \\n\",\n        \"280               Bharatiya Janata Party              525285   \\n\",\n        \"307               Bharatiya Janata Party              596125   \\n\",\n        \"356               Bharatiya Janata Party              642278   \\n\",\n        \"332                      Biju Janata Dal              541473   \\n\",\n        \"357               Bharatiya Janata Party              579825   \\n\",\n        \"246               Bharatiya Janata Party              698335   \\n\",\n        \"256               Bharatiya Janata Party              605452   \\n\",\n        \"369               Bharatiya Janata Party              703236   \\n\",\n        \"129               Bharatiya Janata Party              549529   \\n\",\n        \"429               Bharatiya Janata Party              604449   \\n\",\n        \"436                      Samajwadi Party              595918   \\n\",\n        \"115               Bharatiya Janata Party              562855   \\n\",\n        \"497               Bharatiya Janata Party              405690   \\n\",\n        \"131               Bharatiya Janata Party              568235   \\n\",\n        \"288                             Shivsena              643415   \\n\",\n        \"243               Bharatiya Janata Party              596727   \\n\",\n        \"418               Bharatiya Janata Party              653391   \\n\",\n        \"373               Bharatiya Janata Party              676102   \\n\",\n        \"124               Bharatiya Janata Party              621524   \\n\",\n        \"113               Bharatiya Janata Party              237903   \\n\",\n        \"296               Bharatiya Janata Party              611563   \\n\",\n        \"293               Bharatiya Janata Party              616509   \\n\",\n        \"244               Bharatiya Janata Party              665646   \\n\",\n        \"499               Bharatiya Janata Party              636769   \\n\",\n        \"118               Bharatiya Janata Party              580250   \\n\",\n        \"150               Bharatiya Janata Party              652516   \\n\",\n        \"496               Bharatiya Janata Party              446733   \\n\",\n        \"286               Bharatiya Janata Party              569825   \\n\",\n        \"116               Bharatiya Janata Party              507856   \\n\",\n        \"290                             Shivsena              532936   \\n\",\n        \"371               Bharatiya Janata Party              630317   \\n\",\n        \"151               Bharatiya Janata Party              456163   \\n\",\n        \"251               Bharatiya Janata Party              717357   \\n\",\n        \"289               Bharatiya Janata Party              605185   \\n\",\n        \"                                     ...                 ...   \\n\",\n        \"\\n\",\n        \"     Winner Percentage  \\n\",\n        \"138           0.757881  \\n\",\n        \"134           0.727859  \\n\",\n        \"139           0.707158  \\n\",\n        \"278           0.701490  \\n\",\n        \"120           0.681235  \\n\",\n        \"355           0.665757  \\n\",\n        \"241           0.665455  \\n\",\n        \"364           0.661521  \\n\",\n        \"370           0.656533  \\n\",\n        \"415           0.655835  \\n\",\n        \"255           0.654097  \\n\",\n        \"363           0.649672  \\n\",\n        \"310           0.649466  \\n\",\n        \"249           0.649272  \\n\",\n        \"240           0.648899  \\n\",\n        \"313           0.647102  \\n\",\n        \"121           0.642888  \\n\",\n        \"122           0.639745  \\n\",\n        \"451           0.637997  \\n\",\n        \"242           0.631914  \\n\",\n        \"245           0.630753  \\n\",\n        \"350           0.629128  \\n\",\n        \"125           0.628140  \\n\",\n        \"414           0.626116  \\n\",\n        \"354           0.624382  \\n\",\n        \"252           0.614336  \\n\",\n        \"280           0.609548  \\n\",\n        \"307           0.607137  \\n\",\n        \"356           0.604608  \\n\",\n        \"332           0.604133  \\n\",\n        \"357           0.602458  \\n\",\n        \"246           0.601314  \\n\",\n        \"256           0.599925  \\n\",\n        \"369           0.599709  \\n\",\n        \"129           0.599006  \\n\",\n        \"429           0.598636  \\n\",\n        \"436           0.596356  \\n\",\n        \"115           0.594833  \\n\",\n        \"497           0.594832  \\n\",\n        \"131           0.594447  \\n\",\n        \"288           0.590557  \\n\",\n        \"243           0.590396  \\n\",\n        \"418           0.590004  \\n\",\n        \"373           0.589780  \\n\",\n        \"124           0.587969  \\n\",\n        \"113           0.585059  \\n\",\n        \"296           0.584300  \\n\",\n        \"293           0.583177  \\n\",\n        \"244           0.581875  \\n\",\n        \"499           0.578127  \\n\",\n        \"118           0.577790  \\n\",\n        \"150           0.577077  \\n\",\n        \"496           0.575528  \\n\",\n        \"286           0.573684  \\n\",\n        \"116           0.572791  \\n\",\n        \"290           0.571424  \\n\",\n        \"371           0.570893  \\n\",\n        \"151           0.570600  \\n\",\n        \"251           0.570541  \\n\",\n        \"289           0.569683  \\n\",\n        \"                   ...  \\n\",\n        \"\\n\",\n        \"[543 rows x 16 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Analyzing Winners By Party\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results = pd.DataFrame(csv_data[\\\"Party Abbreviation\\\"].value_counts(), columns=[\\\"Seats\\\"])\\n\",\n      \"results[\\\"Seats Percentage\\\"] = results[\\\"Seats\\\"] / len(winners)\\n\",\n      \"results\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"      <th>Seats Percentage</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BJP</th>\\n\",\n        \"      <td> 282</td>\\n\",\n        \"      <td> 0.519337</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INC</th>\\n\",\n        \"      <td>  44</td>\\n\",\n        \"      <td> 0.081031</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ADMK</th>\\n\",\n        \"      <td>  37</td>\\n\",\n        \"      <td> 0.068140</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AITC</th>\\n\",\n        \"      <td>  34</td>\\n\",\n        \"      <td> 0.062615</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BJD</th>\\n\",\n        \"      <td>  20</td>\\n\",\n        \"      <td> 0.036832</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SHS</th>\\n\",\n        \"      <td>  18</td>\\n\",\n        \"      <td> 0.033149</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TDP</th>\\n\",\n        \"      <td>  16</td>\\n\",\n        \"      <td> 0.029466</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TRS</th>\\n\",\n        \"      <td>  11</td>\\n\",\n        \"      <td> 0.020258</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CPM</th>\\n\",\n        \"      <td>   9</td>\\n\",\n        \"      <td> 0.016575</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>YSRCP</th>\\n\",\n        \"      <td>   9</td>\\n\",\n        \"      <td> 0.016575</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>LJP</th>\\n\",\n        \"      <td>   6</td>\\n\",\n        \"      <td> 0.011050</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NCP</th>\\n\",\n        \"      <td>   6</td>\\n\",\n        \"      <td> 0.011050</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SP</th>\\n\",\n        \"      <td>   5</td>\\n\",\n        \"      <td> 0.009208</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AAAP</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RJD</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SAD</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AIUDF</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IND</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BLSP</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JKPDP</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INLD</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JMM</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IUML</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JD(U)</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AD</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JD(S)</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RSP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>KEC(M)</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CPI</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SDF</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AINRC</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SWP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NPEP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AIMIM</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NPF</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>PMK</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>36 rows \\u00d7 2 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 18,\n       \"text\": [\n        \"        Seats  Seats Percentage\\n\",\n        \"BJP       282          0.519337\\n\",\n        \"INC        44          0.081031\\n\",\n        \"ADMK       37          0.068140\\n\",\n        \"AITC       34          0.062615\\n\",\n        \"BJD        20          0.036832\\n\",\n        \"SHS        18          0.033149\\n\",\n        \"TDP        16          0.029466\\n\",\n        \"TRS        11          0.020258\\n\",\n        \"CPM         9          0.016575\\n\",\n        \"YSRCP       9          0.016575\\n\",\n        \"LJP         6          0.011050\\n\",\n        \"NCP         6          0.011050\\n\",\n        \"SP          5          0.009208\\n\",\n        \"AAAP        4          0.007366\\n\",\n        \"RJD         4          0.007366\\n\",\n        \"SAD         4          0.007366\\n\",\n        \"AIUDF       3          0.005525\\n\",\n        \"IND         3          0.005525\\n\",\n        \"BLSP        3          0.005525\\n\",\n        \"JKPDP       3          0.005525\\n\",\n        \"INLD        2          0.003683\\n\",\n        \"JMM         2          0.003683\\n\",\n        \"IUML        2          0.003683\\n\",\n        \"JD(U)       2          0.003683\\n\",\n        \"AD          2          0.003683\\n\",\n        \"JD(S)       2          0.003683\\n\",\n        \"RSP         1          0.001842\\n\",\n        \"KEC(M)      1          0.001842\\n\",\n        \"CPI         1          0.001842\\n\",\n        \"SDF         1          0.001842\\n\",\n        \"AINRC       1          0.001842\\n\",\n        \"SWP         1          0.001842\\n\",\n        \"NPEP        1          0.001842\\n\",\n        \"AIMIM       1          0.001842\\n\",\n        \"NPF         1          0.001842\\n\",\n        \"PMK         1          0.001842\\n\",\n        \"\\n\",\n        \"[36 rows x 2 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"all_votes = pd.pivot_table(results_data, values=\\\"Total Votes Polled\\\", rows=\\\"Party Abbreviation\\\", aggfunc=\\\"sum\\\")\\n\",\n      \"all_votes_average = pd.pivot_table(results_data, values=\\\"Total Votes Polled\\\", rows=\\\"Party Abbreviation\\\", aggfunc=\\\"mean\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"get_total_votes = lambda x: all_votes[x]\\n\",\n      \"get_average_votes = lambda x: all_votes_average[x]\\n\",\n      \"\\n\",\n      \"results[\\\"Total Votes\\\"] = pd.Series(results.index.values).apply(get_total_votes).values\\n\",\n      \"results[\\\"Average Votes\\\"] = pd.Series(results.index.values).apply(get_average_votes).values\\n\",\n      \"results\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"      <th>Seats Percentage</th>\\n\",\n        \"      <th>Total Votes</th>\\n\",\n        \"      <th>Average Votes</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BJP</th>\\n\",\n        \"      <td> 282</td>\\n\",\n        \"      <td> 0.519337</td>\\n\",\n        \"      <td> 171657549</td>\\n\",\n        \"      <td> 401069.039720</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INC</th>\\n\",\n        \"      <td>  44</td>\\n\",\n        \"      <td> 0.081031</td>\\n\",\n        \"      <td> 106938242</td>\\n\",\n        \"      <td> 230470.349138</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ADMK</th>\\n\",\n        \"      <td>  37</td>\\n\",\n        \"      <td> 0.068140</td>\\n\",\n        \"      <td>  18115825</td>\\n\",\n        \"      <td> 452895.625000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AITC</th>\\n\",\n        \"      <td>  34</td>\\n\",\n        \"      <td> 0.062615</td>\\n\",\n        \"      <td>  21259681</td>\\n\",\n        \"      <td> 162287.641221</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BJD</th>\\n\",\n        \"      <td>  20</td>\\n\",\n        \"      <td> 0.036832</td>\\n\",\n        \"      <td>   9491497</td>\\n\",\n        \"      <td> 451976.047619</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SHS</th>\\n\",\n        \"      <td>  18</td>\\n\",\n        \"      <td> 0.033149</td>\\n\",\n        \"      <td>  10262982</td>\\n\",\n        \"      <td> 176947.965517</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TDP</th>\\n\",\n        \"      <td>  16</td>\\n\",\n        \"      <td> 0.029466</td>\\n\",\n        \"      <td>  14094545</td>\\n\",\n        \"      <td> 469818.166667</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TRS</th>\\n\",\n        \"      <td>  11</td>\\n\",\n        \"      <td> 0.020258</td>\\n\",\n        \"      <td>   6736490</td>\\n\",\n        \"      <td> 396264.117647</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CPM</th>\\n\",\n        \"      <td>   9</td>\\n\",\n        \"      <td> 0.016575</td>\\n\",\n        \"      <td>  17986773</td>\\n\",\n        \"      <td> 193406.161290</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>YSRCP</th>\\n\",\n        \"      <td>   9</td>\\n\",\n        \"      <td> 0.016575</td>\\n\",\n        \"      <td>  13991280</td>\\n\",\n        \"      <td> 368191.578947</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>LJP</th>\\n\",\n        \"      <td>   6</td>\\n\",\n        \"      <td> 0.011050</td>\\n\",\n        \"      <td>   2295929</td>\\n\",\n        \"      <td> 327989.857143</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NCP</th>\\n\",\n        \"      <td>   6</td>\\n\",\n        \"      <td> 0.011050</td>\\n\",\n        \"      <td>   8635554</td>\\n\",\n        \"      <td> 239876.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SP</th>\\n\",\n        \"      <td>   5</td>\\n\",\n        \"      <td> 0.009208</td>\\n\",\n        \"      <td>  18672916</td>\\n\",\n        \"      <td>  94786.375635</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AAAP</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"      <td>  11325635</td>\\n\",\n        \"      <td>  26216.747685</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RJD</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"      <td>   7442313</td>\\n\",\n        \"      <td> 248077.100000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SAD</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"      <td>   3636148</td>\\n\",\n        \"      <td> 363614.800000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AIUDF</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>   2333040</td>\\n\",\n        \"      <td> 129613.333333</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IND</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>  16743719</td>\\n\",\n        \"      <td>   5175.801855</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BLSP</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>   1078473</td>\\n\",\n        \"      <td> 269618.250000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JKPDP</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>    732644</td>\\n\",\n        \"      <td> 146528.800000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INLD</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   2799899</td>\\n\",\n        \"      <td> 279989.900000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JMM</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   1637990</td>\\n\",\n        \"      <td>  77999.523810</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IUML</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   1100096</td>\\n\",\n        \"      <td>  44003.840000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JD(U)</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   5992196</td>\\n\",\n        \"      <td>  64432.215054</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AD</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>    821820</td>\\n\",\n        \"      <td> 117402.857143</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JD(S)</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   3731481</td>\\n\",\n        \"      <td> 109749.441176</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RSP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   1666380</td>\\n\",\n        \"      <td> 277730.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>KEC(M)</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    424194</td>\\n\",\n        \"      <td> 424194.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CPI</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   4327298</td>\\n\",\n        \"      <td>  64586.537313</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SDF</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    163698</td>\\n\",\n        \"      <td> 163698.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AINRC</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    255826</td>\\n\",\n        \"      <td> 255826.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SWP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   1105073</td>\\n\",\n        \"      <td> 552536.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NPEP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    576444</td>\\n\",\n        \"      <td>  82349.142857</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AIMIM</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    685729</td>\\n\",\n        \"      <td> 137145.800000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NPF</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    994505</td>\\n\",\n        \"      <td> 497252.500000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>PMK</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   1827566</td>\\n\",\n        \"      <td> 203062.888889</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>36 rows \\u00d7 4 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 20,\n       \"text\": [\n        \"        Seats  Seats Percentage  Total Votes  Average Votes\\n\",\n        \"BJP       282          0.519337    171657549  401069.039720\\n\",\n        \"INC        44          0.081031    106938242  230470.349138\\n\",\n        \"ADMK       37          0.068140     18115825  452895.625000\\n\",\n        \"AITC       34          0.062615     21259681  162287.641221\\n\",\n        \"BJD        20          0.036832      9491497  451976.047619\\n\",\n        \"SHS        18          0.033149     10262982  176947.965517\\n\",\n        \"TDP        16          0.029466     14094545  469818.166667\\n\",\n        \"TRS        11          0.020258      6736490  396264.117647\\n\",\n        \"CPM         9          0.016575     17986773  193406.161290\\n\",\n        \"YSRCP       9          0.016575     13991280  368191.578947\\n\",\n        \"LJP         6          0.011050      2295929  327989.857143\\n\",\n        \"NCP         6          0.011050      8635554  239876.500000\\n\",\n        \"SP          5          0.009208     18672916   94786.375635\\n\",\n        \"AAAP        4          0.007366     11325635   26216.747685\\n\",\n        \"RJD         4          0.007366      7442313  248077.100000\\n\",\n        \"SAD         4          0.007366      3636148  363614.800000\\n\",\n        \"AIUDF       3          0.005525      2333040  129613.333333\\n\",\n        \"IND         3          0.005525     16743719    5175.801855\\n\",\n        \"BLSP        3          0.005525      1078473  269618.250000\\n\",\n        \"JKPDP       3          0.005525       732644  146528.800000\\n\",\n        \"INLD        2          0.003683      2799899  279989.900000\\n\",\n        \"JMM         2          0.003683      1637990   77999.523810\\n\",\n        \"IUML        2          0.003683      1100096   44003.840000\\n\",\n        \"JD(U)       2          0.003683      5992196   64432.215054\\n\",\n        \"AD          2          0.003683       821820  117402.857143\\n\",\n        \"JD(S)       2          0.003683      3731481  109749.441176\\n\",\n        \"RSP         1          0.001842      1666380  277730.000000\\n\",\n        \"KEC(M)      1          0.001842       424194  424194.000000\\n\",\n        \"CPI         1          0.001842      4327298   64586.537313\\n\",\n        \"SDF         1          0.001842       163698  163698.000000\\n\",\n        \"AINRC       1          0.001842       255826  255826.000000\\n\",\n        \"SWP         1          0.001842      1105073  552536.500000\\n\",\n        \"NPEP        1          0.001842       576444   82349.142857\\n\",\n        \"AIMIM       1          0.001842       685729  137145.800000\\n\",\n        \"NPF         1          0.001842       994505  497252.500000\\n\",\n        \"PMK         1          0.001842      1827566  203062.888889\\n\",\n        \"\\n\",\n        \"[36 rows x 4 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculating votes that resulted in winning seats\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"all_winning_votes = pd.pivot_table(csv_data, values=\\\"Total Votes Polled\\\", rows=\\\"Party Abbreviation\\\", aggfunc=\\\"sum\\\")\\n\",\n      \"all_winning_votes_average = pd.pivot_table(csv_data, values=\\\"Total Votes Polled\\\", rows=\\\"Party Abbreviation\\\", aggfunc=\\\"mean\\\")\\n\",\n      \"all_winning_votes_percentages = pd.pivot_table(csv_data, values=\\\"Winner Percentage\\\", rows=\\\"Party Abbreviation\\\", aggfunc=\\\"mean\\\")\\n\",\n      \"average_winning_elecors = pd.pivot_table(csv_data, values=\\\"Total Electors\\\", rows=\\\"Party Abbreviation\\\", aggfunc=\\\"mean\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"get_total_winning_votes = lambda x: all_winning_votes[x]\\n\",\n      \"get_average_winning_votes = lambda x: all_winning_votes_average[x]\\n\",\n      \"get_average_winning_percentages = lambda x: all_winning_votes_percentages[x]\\n\",\n      \"get_average_winning_elecors = lambda x: average_winning_elecors[x]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"results[\\\"Total Winning Votes\\\"] = pd.Series(results.index.values).apply(get_total_winning_votes).values\\n\",\n      \"results[\\\"Average Winning Votes\\\"] = pd.Series(results.index.values).apply(get_average_winning_votes).values\\n\",\n      \"results[\\\"Average Winning Percentage\\\"] = pd.Series(results.index.values).apply(get_average_winning_percentages).values\\n\",\n      \"results[\\\"Average Winning Electors\\\"] = pd.Series(results.index.values).apply(get_average_winning_elecors).values\\n\",\n      \"\\n\",\n      \"results[\\\"Loosing Votes\\\"] = results[\\\"Total Votes\\\"] - results[\\\"Total Winning Votes\\\"]\\n\",\n      \"results[\\\"Winning Votes Ratio\\\"] = results[\\\"Total Winning Votes\\\"] / results[\\\"Total Votes\\\"]\\n\",\n      \"\\n\",\n      \"results\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"      <th>Seats Percentage</th>\\n\",\n        \"      <th>Total Votes</th>\\n\",\n        \"      <th>Average Votes</th>\\n\",\n        \"      <th>Total Winning Votes</th>\\n\",\n        \"      <th>Average Winning Votes</th>\\n\",\n        \"      <th>Average Winning Percentage</th>\\n\",\n        \"      <th>Average Winning Electors</th>\\n\",\n        \"      <th>Loosing Votes</th>\\n\",\n        \"      <th>Winning Votes Ratio</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BJP</th>\\n\",\n        \"      <td> 282</td>\\n\",\n        \"      <td> 0.519337</td>\\n\",\n        \"      <td> 171657549</td>\\n\",\n        \"      <td> 401069.039720</td>\\n\",\n        \"      <td> 139553310</td>\\n\",\n        \"      <td> 494869</td>\\n\",\n        \"      <td> 0.493188</td>\\n\",\n        \"      <td> 1606886</td>\\n\",\n        \"      <td> 32104239</td>\\n\",\n        \"      <td> 0.812975</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INC</th>\\n\",\n        \"      <td>  44</td>\\n\",\n        \"      <td> 0.081031</td>\\n\",\n        \"      <td> 106938242</td>\\n\",\n        \"      <td> 230470.349138</td>\\n\",\n        \"      <td>  18219598</td>\\n\",\n        \"      <td> 414081</td>\\n\",\n        \"      <td> 0.428075</td>\\n\",\n        \"      <td> 1384055</td>\\n\",\n        \"      <td> 88718644</td>\\n\",\n        \"      <td> 0.170375</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ADMK</th>\\n\",\n        \"      <td>  37</td>\\n\",\n        \"      <td> 0.068140</td>\\n\",\n        \"      <td>  18115825</td>\\n\",\n        \"      <td> 452895.625000</td>\\n\",\n        \"      <td>  17415881</td>\\n\",\n        \"      <td> 470699</td>\\n\",\n        \"      <td> 0.451634</td>\\n\",\n        \"      <td> 1413200</td>\\n\",\n        \"      <td>   699944</td>\\n\",\n        \"      <td> 0.961363</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AITC</th>\\n\",\n        \"      <td>  34</td>\\n\",\n        \"      <td> 0.062615</td>\\n\",\n        \"      <td>  21259681</td>\\n\",\n        \"      <td> 162287.641221</td>\\n\",\n        \"      <td>  18366652</td>\\n\",\n        \"      <td> 540195</td>\\n\",\n        \"      <td> 0.431486</td>\\n\",\n        \"      <td> 1512020</td>\\n\",\n        \"      <td>  2893029</td>\\n\",\n        \"      <td> 0.863919</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BJD</th>\\n\",\n        \"      <td>  20</td>\\n\",\n        \"      <td> 0.036832</td>\\n\",\n        \"      <td>   9491497</td>\\n\",\n        \"      <td> 451976.047619</td>\\n\",\n        \"      <td>   9169818</td>\\n\",\n        \"      <td> 458490</td>\\n\",\n        \"      <td> 0.449398</td>\\n\",\n        \"      <td> 1389275</td>\\n\",\n        \"      <td>   321679</td>\\n\",\n        \"      <td> 0.966109</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SHS</th>\\n\",\n        \"      <td>  18</td>\\n\",\n        \"      <td> 0.033149</td>\\n\",\n        \"      <td>  10262982</td>\\n\",\n        \"      <td> 176947.965517</td>\\n\",\n        \"      <td>   9010919</td>\\n\",\n        \"      <td> 500606</td>\\n\",\n        \"      <td> 0.510340</td>\\n\",\n        \"      <td> 1676883</td>\\n\",\n        \"      <td>  1252063</td>\\n\",\n        \"      <td> 0.878002</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TDP</th>\\n\",\n        \"      <td>  16</td>\\n\",\n        \"      <td> 0.029466</td>\\n\",\n        \"      <td>  14094545</td>\\n\",\n        \"      <td> 469818.166667</td>\\n\",\n        \"      <td>   9362168</td>\\n\",\n        \"      <td> 585135</td>\\n\",\n        \"      <td> 0.493163</td>\\n\",\n        \"      <td> 1554833</td>\\n\",\n        \"      <td>  4732377</td>\\n\",\n        \"      <td> 0.664241</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TRS</th>\\n\",\n        \"      <td>  11</td>\\n\",\n        \"      <td> 0.020258</td>\\n\",\n        \"      <td>   6736490</td>\\n\",\n        \"      <td> 396264.117647</td>\\n\",\n        \"      <td>   5307344</td>\\n\",\n        \"      <td> 482485</td>\\n\",\n        \"      <td> 0.430386</td>\\n\",\n        \"      <td> 1532891</td>\\n\",\n        \"      <td>  1429146</td>\\n\",\n        \"      <td> 0.787850</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CPM</th>\\n\",\n        \"      <td>   9</td>\\n\",\n        \"      <td> 0.016575</td>\\n\",\n        \"      <td>  17986773</td>\\n\",\n        \"      <td> 193406.161290</td>\\n\",\n        \"      <td>   4069667</td>\\n\",\n        \"      <td> 452185</td>\\n\",\n        \"      <td> 0.455675</td>\\n\",\n        \"      <td> 1264327</td>\\n\",\n        \"      <td> 13917106</td>\\n\",\n        \"      <td> 0.226259</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>YSRCP</th>\\n\",\n        \"      <td>   9</td>\\n\",\n        \"      <td> 0.016575</td>\\n\",\n        \"      <td>  13991280</td>\\n\",\n        \"      <td> 368191.578947</td>\\n\",\n        \"      <td>   4950808</td>\\n\",\n        \"      <td> 550089</td>\\n\",\n        \"      <td> 0.478040</td>\\n\",\n        \"      <td> 1495688</td>\\n\",\n        \"      <td>  9040472</td>\\n\",\n        \"      <td> 0.353850</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>LJP</th>\\n\",\n        \"      <td>   6</td>\\n\",\n        \"      <td> 0.011050</td>\\n\",\n        \"      <td>   2295929</td>\\n\",\n        \"      <td> 327989.857143</td>\\n\",\n        \"      <td>   1983574</td>\\n\",\n        \"      <td> 330595</td>\\n\",\n        \"      <td> 0.375127</td>\\n\",\n        \"      <td> 1579884</td>\\n\",\n        \"      <td>   312355</td>\\n\",\n        \"      <td> 0.863953</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NCP</th>\\n\",\n        \"      <td>   6</td>\\n\",\n        \"      <td> 0.011050</td>\\n\",\n        \"      <td>   8635554</td>\\n\",\n        \"      <td> 239876.500000</td>\\n\",\n        \"      <td>   2594704</td>\\n\",\n        \"      <td> 432450</td>\\n\",\n        \"      <td> 0.483694</td>\\n\",\n        \"      <td> 1419258</td>\\n\",\n        \"      <td>  6040850</td>\\n\",\n        \"      <td> 0.300468</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SP</th>\\n\",\n        \"      <td>   5</td>\\n\",\n        \"      <td> 0.009208</td>\\n\",\n        \"      <td>  18672916</td>\\n\",\n        \"      <td>  94786.375635</td>\\n\",\n        \"      <td>   2458349</td>\\n\",\n        \"      <td> 491669</td>\\n\",\n        \"      <td> 0.471728</td>\\n\",\n        \"      <td> 1714211</td>\\n\",\n        \"      <td> 16214567</td>\\n\",\n        \"      <td> 0.131653</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AAAP</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"      <td>  11325635</td>\\n\",\n        \"      <td>  26216.747685</td>\\n\",\n        \"      <td>   1716952</td>\\n\",\n        \"      <td> 429238</td>\\n\",\n        \"      <td> 0.401053</td>\\n\",\n        \"      <td> 1464262</td>\\n\",\n        \"      <td>  9608683</td>\\n\",\n        \"      <td> 0.151599</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RJD</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"      <td>   7442313</td>\\n\",\n        \"      <td> 248077.100000</td>\\n\",\n        \"      <td>   1429688</td>\\n\",\n        \"      <td> 357422</td>\\n\",\n        \"      <td> 0.367278</td>\\n\",\n        \"      <td> 1636930</td>\\n\",\n        \"      <td>  6012625</td>\\n\",\n        \"      <td> 0.192103</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SAD</th>\\n\",\n        \"      <td>   4</td>\\n\",\n        \"      <td> 0.007366</td>\\n\",\n        \"      <td>   3636148</td>\\n\",\n        \"      <td> 363614.800000</td>\\n\",\n        \"      <td>   1817385</td>\\n\",\n        \"      <td> 454346</td>\\n\",\n        \"      <td> 0.411916</td>\\n\",\n        \"      <td> 1543844</td>\\n\",\n        \"      <td>  1818763</td>\\n\",\n        \"      <td> 0.499811</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AIUDF</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>   2333040</td>\\n\",\n        \"      <td> 129613.333333</td>\\n\",\n        \"      <td>   1350137</td>\\n\",\n        \"      <td> 450045</td>\\n\",\n        \"      <td> 0.389700</td>\\n\",\n        \"      <td> 1382908</td>\\n\",\n        \"      <td>   982903</td>\\n\",\n        \"      <td> 0.578703</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IND</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>  16743719</td>\\n\",\n        \"      <td>   5175.801855</td>\\n\",\n        \"      <td>   1374887</td>\\n\",\n        \"      <td> 458295</td>\\n\",\n        \"      <td> 0.463283</td>\\n\",\n        \"      <td> 1271567</td>\\n\",\n        \"      <td> 15368832</td>\\n\",\n        \"      <td> 0.082114</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BLSP</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>   1078473</td>\\n\",\n        \"      <td> 269618.250000</td>\\n\",\n        \"      <td>   1072804</td>\\n\",\n        \"      <td> 357601</td>\\n\",\n        \"      <td> 0.427755</td>\\n\",\n        \"      <td> 1522716</td>\\n\",\n        \"      <td>     5669</td>\\n\",\n        \"      <td> 0.994743</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JKPDP</th>\\n\",\n        \"      <td>   3</td>\\n\",\n        \"      <td> 0.005525</td>\\n\",\n        \"      <td>    732644</td>\\n\",\n        \"      <td> 146528.800000</td>\\n\",\n        \"      <td>    533629</td>\\n\",\n        \"      <td> 177876</td>\\n\",\n        \"      <td> 0.472012</td>\\n\",\n        \"      <td> 1232333</td>\\n\",\n        \"      <td>   199015</td>\\n\",\n        \"      <td> 0.728361</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INLD</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   2799899</td>\\n\",\n        \"      <td> 279989.900000</td>\\n\",\n        \"      <td>   1000848</td>\\n\",\n        \"      <td> 500424</td>\\n\",\n        \"      <td> 0.411830</td>\\n\",\n        \"      <td> 1589081</td>\\n\",\n        \"      <td>  1799051</td>\\n\",\n        \"      <td> 0.357459</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JMM</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   1637990</td>\\n\",\n        \"      <td>  77999.523810</td>\\n\",\n        \"      <td>    715322</td>\\n\",\n        \"      <td> 357661</td>\\n\",\n        \"      <td> 0.385344</td>\\n\",\n        \"      <td> 1300163</td>\\n\",\n        \"      <td>   922668</td>\\n\",\n        \"      <td> 0.436707</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IUML</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   1100096</td>\\n\",\n        \"      <td>  44003.840000</td>\\n\",\n        \"      <td>    816226</td>\\n\",\n        \"      <td> 408113</td>\\n\",\n        \"      <td> 0.473571</td>\\n\",\n        \"      <td> 1189616</td>\\n\",\n        \"      <td>   283870</td>\\n\",\n        \"      <td> 0.741959</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JD(U)</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   5992196</td>\\n\",\n        \"      <td>  64432.215054</td>\\n\",\n        \"      <td>    740808</td>\\n\",\n        \"      <td> 370404</td>\\n\",\n        \"      <td> 0.380420</td>\\n\",\n        \"      <td> 1767467</td>\\n\",\n        \"      <td>  5251388</td>\\n\",\n        \"      <td> 0.123629</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AD</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>    821820</td>\\n\",\n        \"      <td> 117402.857143</td>\\n\",\n        \"      <td>    812325</td>\\n\",\n        \"      <td> 406162</td>\\n\",\n        \"      <td> 0.426682</td>\\n\",\n        \"      <td> 1718643</td>\\n\",\n        \"      <td>     9495</td>\\n\",\n        \"      <td> 0.988446</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>JD(S)</th>\\n\",\n        \"      <td>   2</td>\\n\",\n        \"      <td> 0.003683</td>\\n\",\n        \"      <td>   3731481</td>\\n\",\n        \"      <td> 109749.441176</td>\\n\",\n        \"      <td>   1034211</td>\\n\",\n        \"      <td> 517105</td>\\n\",\n        \"      <td> 0.442053</td>\\n\",\n        \"      <td> 1615299</td>\\n\",\n        \"      <td>  2697270</td>\\n\",\n        \"      <td> 0.277158</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RSP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   1666380</td>\\n\",\n        \"      <td> 277730.000000</td>\\n\",\n        \"      <td>    408528</td>\\n\",\n        \"      <td> 408528</td>\\n\",\n        \"      <td> 0.464735</td>\\n\",\n        \"      <td> 1219415</td>\\n\",\n        \"      <td>  1257852</td>\\n\",\n        \"      <td> 0.245159</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>KEC(M)</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    424194</td>\\n\",\n        \"      <td> 424194.000000</td>\\n\",\n        \"      <td>    424194</td>\\n\",\n        \"      <td> 424194</td>\\n\",\n        \"      <td> 0.510072</td>\\n\",\n        \"      <td> 1161463</td>\\n\",\n        \"      <td>        0</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CPI</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   4327298</td>\\n\",\n        \"      <td>  64586.537313</td>\\n\",\n        \"      <td>    389209</td>\\n\",\n        \"      <td> 389209</td>\\n\",\n        \"      <td> 0.422821</td>\\n\",\n        \"      <td> 1275288</td>\\n\",\n        \"      <td>  3938089</td>\\n\",\n        \"      <td> 0.089943</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SDF</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    163698</td>\\n\",\n        \"      <td> 163698.000000</td>\\n\",\n        \"      <td>    163698</td>\\n\",\n        \"      <td> 163698</td>\\n\",\n        \"      <td> 0.529824</td>\\n\",\n        \"      <td>  370611</td>\\n\",\n        \"      <td>        0</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AINRC</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    255826</td>\\n\",\n        \"      <td> 255826.000000</td>\\n\",\n        \"      <td>    255826</td>\\n\",\n        \"      <td> 255826</td>\\n\",\n        \"      <td> 0.345703</td>\\n\",\n        \"      <td>  901357</td>\\n\",\n        \"      <td>        0</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SWP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   1105073</td>\\n\",\n        \"      <td> 552536.500000</td>\\n\",\n        \"      <td>    640428</td>\\n\",\n        \"      <td> 640428</td>\\n\",\n        \"      <td> 0.538686</td>\\n\",\n        \"      <td> 1630598</td>\\n\",\n        \"      <td>   464645</td>\\n\",\n        \"      <td> 0.579535</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NPEP</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    576444</td>\\n\",\n        \"      <td>  82349.142857</td>\\n\",\n        \"      <td>    239301</td>\\n\",\n        \"      <td> 239301</td>\\n\",\n        \"      <td> 0.522410</td>\\n\",\n        \"      <td>  586501</td>\\n\",\n        \"      <td>   337143</td>\\n\",\n        \"      <td> 0.415133</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AIMIM</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    685729</td>\\n\",\n        \"      <td> 137145.800000</td>\\n\",\n        \"      <td>    513868</td>\\n\",\n        \"      <td> 513868</td>\\n\",\n        \"      <td> 0.528986</td>\\n\",\n        \"      <td> 1823664</td>\\n\",\n        \"      <td>   171861</td>\\n\",\n        \"      <td> 0.749375</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NPF</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>    994505</td>\\n\",\n        \"      <td> 497252.500000</td>\\n\",\n        \"      <td>    713372</td>\\n\",\n        \"      <td> 713372</td>\\n\",\n        \"      <td> 0.647102</td>\\n\",\n        \"      <td> 1752741</td>\\n\",\n        \"      <td>   281133</td>\\n\",\n        \"      <td> 0.717314</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>PMK</th>\\n\",\n        \"      <td>   1</td>\\n\",\n        \"      <td> 0.001842</td>\\n\",\n        \"      <td>   1827566</td>\\n\",\n        \"      <td> 203062.888889</td>\\n\",\n        \"      <td>    468194</td>\\n\",\n        \"      <td> 468194</td>\\n\",\n        \"      <td> 0.425111</td>\\n\",\n        \"      <td> 1358273</td>\\n\",\n        \"      <td>  1359372</td>\\n\",\n        \"      <td> 0.256184</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>36 rows \\u00d7 10 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 22,\n       \"text\": [\n        \"        Seats  Seats Percentage  Total Votes  Average Votes  \\\\\\n\",\n        \"BJP       282          0.519337    171657549  401069.039720   \\n\",\n        \"INC        44          0.081031    106938242  230470.349138   \\n\",\n        \"ADMK       37          0.068140     18115825  452895.625000   \\n\",\n        \"AITC       34          0.062615     21259681  162287.641221   \\n\",\n        \"BJD        20          0.036832      9491497  451976.047619   \\n\",\n        \"SHS        18          0.033149     10262982  176947.965517   \\n\",\n        \"TDP        16          0.029466     14094545  469818.166667   \\n\",\n        \"TRS        11          0.020258      6736490  396264.117647   \\n\",\n        \"CPM         9          0.016575     17986773  193406.161290   \\n\",\n        \"YSRCP       9          0.016575     13991280  368191.578947   \\n\",\n        \"LJP         6          0.011050      2295929  327989.857143   \\n\",\n        \"NCP         6          0.011050      8635554  239876.500000   \\n\",\n        \"SP          5          0.009208     18672916   94786.375635   \\n\",\n        \"AAAP        4          0.007366     11325635   26216.747685   \\n\",\n        \"RJD         4          0.007366      7442313  248077.100000   \\n\",\n        \"SAD         4          0.007366      3636148  363614.800000   \\n\",\n        \"AIUDF       3          0.005525      2333040  129613.333333   \\n\",\n        \"IND         3          0.005525     16743719    5175.801855   \\n\",\n        \"BLSP        3          0.005525      1078473  269618.250000   \\n\",\n        \"JKPDP       3          0.005525       732644  146528.800000   \\n\",\n        \"INLD        2          0.003683      2799899  279989.900000   \\n\",\n        \"JMM         2          0.003683      1637990   77999.523810   \\n\",\n        \"IUML        2          0.003683      1100096   44003.840000   \\n\",\n        \"JD(U)       2          0.003683      5992196   64432.215054   \\n\",\n        \"AD          2          0.003683       821820  117402.857143   \\n\",\n        \"JD(S)       2          0.003683      3731481  109749.441176   \\n\",\n        \"RSP         1          0.001842      1666380  277730.000000   \\n\",\n        \"KEC(M)      1          0.001842       424194  424194.000000   \\n\",\n        \"CPI         1          0.001842      4327298   64586.537313   \\n\",\n        \"SDF         1          0.001842       163698  163698.000000   \\n\",\n        \"AINRC       1          0.001842       255826  255826.000000   \\n\",\n        \"SWP         1          0.001842      1105073  552536.500000   \\n\",\n        \"NPEP        1          0.001842       576444   82349.142857   \\n\",\n        \"AIMIM       1          0.001842       685729  137145.800000   \\n\",\n        \"NPF         1          0.001842       994505  497252.500000   \\n\",\n        \"PMK         1          0.001842      1827566  203062.888889   \\n\",\n        \"\\n\",\n        \"        Total Winning Votes  Average Winning Votes  \\\\\\n\",\n        \"BJP               139553310                 494869   \\n\",\n        \"INC                18219598                 414081   \\n\",\n        \"ADMK               17415881                 470699   \\n\",\n        \"AITC               18366652                 540195   \\n\",\n        \"BJD                 9169818                 458490   \\n\",\n        \"SHS                 9010919                 500606   \\n\",\n        \"TDP                 9362168                 585135   \\n\",\n        \"TRS                 5307344                 482485   \\n\",\n        \"CPM                 4069667                 452185   \\n\",\n        \"YSRCP               4950808                 550089   \\n\",\n        \"LJP                 1983574                 330595   \\n\",\n        \"NCP                 2594704                 432450   \\n\",\n        \"SP                  2458349                 491669   \\n\",\n        \"AAAP                1716952                 429238   \\n\",\n        \"RJD                 1429688                 357422   \\n\",\n        \"SAD                 1817385                 454346   \\n\",\n        \"AIUDF               1350137                 450045   \\n\",\n        \"IND                 1374887                 458295   \\n\",\n        \"BLSP                1072804                 357601   \\n\",\n        \"JKPDP                533629                 177876   \\n\",\n        \"INLD                1000848                 500424   \\n\",\n        \"JMM                  715322                 357661   \\n\",\n        \"IUML                 816226                 408113   \\n\",\n        \"JD(U)                740808                 370404   \\n\",\n        \"AD                   812325                 406162   \\n\",\n        \"JD(S)               1034211                 517105   \\n\",\n        \"RSP                  408528                 408528   \\n\",\n        \"KEC(M)               424194                 424194   \\n\",\n        \"CPI                  389209                 389209   \\n\",\n        \"SDF                  163698                 163698   \\n\",\n        \"AINRC                255826                 255826   \\n\",\n        \"SWP                  640428                 640428   \\n\",\n        \"NPEP                 239301                 239301   \\n\",\n        \"AIMIM                513868                 513868   \\n\",\n        \"NPF                  713372                 713372   \\n\",\n        \"PMK                  468194                 468194   \\n\",\n        \"\\n\",\n        \"        Average Winning Percentage  Average Winning Electors  Loosing Votes  \\\\\\n\",\n        \"BJP                       0.493188                   1606886       32104239   \\n\",\n        \"INC                       0.428075                   1384055       88718644   \\n\",\n        \"ADMK                      0.451634                   1413200         699944   \\n\",\n        \"AITC                      0.431486                   1512020        2893029   \\n\",\n        \"BJD                       0.449398                   1389275         321679   \\n\",\n        \"SHS                       0.510340                   1676883        1252063   \\n\",\n        \"TDP                       0.493163                   1554833        4732377   \\n\",\n        \"TRS                       0.430386                   1532891        1429146   \\n\",\n        \"CPM                       0.455675                   1264327       13917106   \\n\",\n        \"YSRCP                     0.478040                   1495688        9040472   \\n\",\n        \"LJP                       0.375127                   1579884         312355   \\n\",\n        \"NCP                       0.483694                   1419258        6040850   \\n\",\n        \"SP                        0.471728                   1714211       16214567   \\n\",\n        \"AAAP                      0.401053                   1464262        9608683   \\n\",\n        \"RJD                       0.367278                   1636930        6012625   \\n\",\n        \"SAD                       0.411916                   1543844        1818763   \\n\",\n        \"AIUDF                     0.389700                   1382908         982903   \\n\",\n        \"IND                       0.463283                   1271567       15368832   \\n\",\n        \"BLSP                      0.427755                   1522716           5669   \\n\",\n        \"JKPDP                     0.472012                   1232333         199015   \\n\",\n        \"INLD                      0.411830                   1589081        1799051   \\n\",\n        \"JMM                       0.385344                   1300163         922668   \\n\",\n        \"IUML                      0.473571                   1189616         283870   \\n\",\n        \"JD(U)                     0.380420                   1767467        5251388   \\n\",\n        \"AD                        0.426682                   1718643           9495   \\n\",\n        \"JD(S)                     0.442053                   1615299        2697270   \\n\",\n        \"RSP                       0.464735                   1219415        1257852   \\n\",\n        \"KEC(M)                    0.510072                   1161463              0   \\n\",\n        \"CPI                       0.422821                   1275288        3938089   \\n\",\n        \"SDF                       0.529824                    370611              0   \\n\",\n        \"AINRC                     0.345703                    901357              0   \\n\",\n        \"SWP                       0.538686                   1630598         464645   \\n\",\n        \"NPEP                      0.522410                    586501         337143   \\n\",\n        \"AIMIM                     0.528986                   1823664         171861   \\n\",\n        \"NPF                       0.647102                   1752741         281133   \\n\",\n        \"PMK                       0.425111                   1358273        1359372   \\n\",\n        \"\\n\",\n        \"        Winning Votes Ratio  \\n\",\n        \"BJP                0.812975  \\n\",\n        \"INC                0.170375  \\n\",\n        \"ADMK               0.961363  \\n\",\n        \"AITC               0.863919  \\n\",\n        \"BJD                0.966109  \\n\",\n        \"SHS                0.878002  \\n\",\n        \"TDP                0.664241  \\n\",\n        \"TRS                0.787850  \\n\",\n        \"CPM                0.226259  \\n\",\n        \"YSRCP              0.353850  \\n\",\n        \"LJP                0.863953  \\n\",\n        \"NCP                0.300468  \\n\",\n        \"SP                 0.131653  \\n\",\n        \"AAAP               0.151599  \\n\",\n        \"RJD                0.192103  \\n\",\n        \"SAD                0.499811  \\n\",\n        \"AIUDF              0.578703  \\n\",\n        \"IND                0.082114  \\n\",\n        \"BLSP               0.994743  \\n\",\n        \"JKPDP              0.728361  \\n\",\n        \"INLD               0.357459  \\n\",\n        \"JMM                0.436707  \\n\",\n        \"IUML               0.741959  \\n\",\n        \"JD(U)              0.123629  \\n\",\n        \"AD                 0.988446  \\n\",\n        \"JD(S)              0.277158  \\n\",\n        \"RSP                0.245159  \\n\",\n        \"KEC(M)             1.000000  \\n\",\n        \"CPI                0.089943  \\n\",\n        \"SDF                1.000000  \\n\",\n        \"AINRC              1.000000  \\n\",\n        \"SWP                0.579535  \\n\",\n        \"NPEP               0.415133  \\n\",\n        \"AIMIM              0.749375  \\n\",\n        \"NPF                0.717314  \\n\",\n        \"PMK                0.256184  \\n\",\n        \"\\n\",\n        \"[36 rows x 10 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Statistical Analysis of Results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results.describe()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Seats</th>\\n\",\n        \"      <th>Seats Percentage</th>\\n\",\n        \"      <th>Total Votes</th>\\n\",\n        \"      <th>Average Votes</th>\\n\",\n        \"      <th>Total Winning Votes</th>\\n\",\n        \"      <th>Average Winning Votes</th>\\n\",\n        \"      <th>Average Winning Percentage</th>\\n\",\n        \"      <th>Average Winning Electors</th>\\n\",\n        \"      <th>Loosing Votes</th>\\n\",\n        \"      <th>Winning Votes Ratio</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>count</th>\\n\",\n        \"      <td>  36.000000</td>\\n\",\n        \"      <td> 36.000000</td>\\n\",\n        \"      <td> 3.600000e+01</td>\\n\",\n        \"      <td>     36.000000</td>\\n\",\n        \"      <td> 3.600000e+01</td>\\n\",\n        \"      <td>     36.000000</td>\\n\",\n        \"      <td> 36.000000</td>\\n\",\n        \"      <td>      36.000000</td>\\n\",\n        \"      <td>       36.000000</td>\\n\",\n        \"      <td> 36.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>mean</th>\\n\",\n        \"      <td>  15.083333</td>\\n\",\n        \"      <td>  0.027778</td>\\n\",\n        \"      <td> 1.365393e+07</td>\\n\",\n        \"      <td> 236299.539018</td>\\n\",\n        \"      <td> 7.252629e+06</td>\\n\",\n        \"      <td> 435134.944444</td>\\n\",\n        \"      <td>  0.453897</td>\\n\",\n        \"      <td> 1412726.416667</td>\\n\",\n        \"      <td>  6401299.944444</td>\\n\",\n        \"      <td>  0.560735</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>std</th>\\n\",\n        \"      <td>  46.987156</td>\\n\",\n        \"      <td>  0.086533</td>\\n\",\n        \"      <td> 3.248419e+07</td>\\n\",\n        \"      <td> 150275.821702</td>\\n\",\n        \"      <td> 2.324585e+07</td>\\n\",\n        \"      <td> 113343.272362</td>\\n\",\n        \"      <td>  0.059478</td>\\n\",\n        \"      <td>  305902.010647</td>\\n\",\n        \"      <td> 15550652.495029</td>\\n\",\n        \"      <td>  0.323618</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>min</th>\\n\",\n        \"      <td>   1.000000</td>\\n\",\n        \"      <td>  0.001842</td>\\n\",\n        \"      <td> 1.636980e+05</td>\\n\",\n        \"      <td>   5175.801855</td>\\n\",\n        \"      <td> 1.636980e+05</td>\\n\",\n        \"      <td> 163698.000000</td>\\n\",\n        \"      <td>  0.345703</td>\\n\",\n        \"      <td>  370611.000000</td>\\n\",\n        \"      <td>        0.000000</td>\\n\",\n        \"      <td>  0.082114</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25%</th>\\n\",\n        \"      <td>   1.000000</td>\\n\",\n        \"      <td>  0.001842</td>\\n\",\n        \"      <td> 1.094690e+06</td>\\n\",\n        \"      <td> 115489.503151</td>\\n\",\n        \"      <td> 6.137282e+05</td>\\n\",\n        \"      <td> 384507.750000</td>\\n\",\n        \"      <td>  0.420095</td>\\n\",\n        \"      <td> 1274357.750000</td>\\n\",\n        \"      <td>   305233.750000</td>\\n\",\n        \"      <td>  0.253428</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50%</th>\\n\",\n        \"      <td>   3.000000</td>\\n\",\n        \"      <td>  0.005525</td>\\n\",\n        \"      <td> 3.683814e+06</td>\\n\",\n        \"      <td> 216766.619013</td>\\n\",\n        \"      <td> 1.211470e+06</td>\\n\",\n        \"      <td> 451115.000000</td>\\n\",\n        \"      <td>  0.450516</td>\\n\",\n        \"      <td> 1479975.000000</td>\\n\",\n        \"      <td>  1308612.000000</td>\\n\",\n        \"      <td>  0.579119</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>75%</th>\\n\",\n        \"      <td>   9.000000</td>\\n\",\n        \"      <td>  0.016575</td>\\n\",\n        \"      <td> 1.199205e+07</td>\\n\",\n        \"      <td> 364758.994737</td>\\n\",\n        \"      <td> 4.289952e+06</td>\\n\",\n        \"      <td> 496257.750000</td>\\n\",\n        \"      <td>  0.486061</td>\\n\",\n        \"      <td> 1608989.250000</td>\\n\",\n        \"      <td>  5441697.250000</td>\\n\",\n        \"      <td>  0.863928</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>max</th>\\n\",\n        \"      <td> 282.000000</td>\\n\",\n        \"      <td>  0.519337</td>\\n\",\n        \"      <td> 1.716575e+08</td>\\n\",\n        \"      <td> 552536.500000</td>\\n\",\n        \"      <td> 1.395533e+08</td>\\n\",\n        \"      <td> 713372.000000</td>\\n\",\n        \"      <td>  0.647102</td>\\n\",\n        \"      <td> 1823664.000000</td>\\n\",\n        \"      <td> 88718644.000000</td>\\n\",\n        \"      <td>  1.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>8 rows \\u00d7 10 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 23,\n       \"text\": [\n        \"            Seats  Seats Percentage   Total Votes  Average Votes  \\\\\\n\",\n        \"count   36.000000         36.000000  3.600000e+01      36.000000   \\n\",\n        \"mean    15.083333          0.027778  1.365393e+07  236299.539018   \\n\",\n        \"std     46.987156          0.086533  3.248419e+07  150275.821702   \\n\",\n        \"min      1.000000          0.001842  1.636980e+05    5175.801855   \\n\",\n        \"25%      1.000000          0.001842  1.094690e+06  115489.503151   \\n\",\n        \"50%      3.000000          0.005525  3.683814e+06  216766.619013   \\n\",\n        \"75%      9.000000          0.016575  1.199205e+07  364758.994737   \\n\",\n        \"max    282.000000          0.519337  1.716575e+08  552536.500000   \\n\",\n        \"\\n\",\n        \"       Total Winning Votes  Average Winning Votes  Average Winning Percentage  \\\\\\n\",\n        \"count         3.600000e+01              36.000000                   36.000000   \\n\",\n        \"mean          7.252629e+06          435134.944444                    0.453897   \\n\",\n        \"std           2.324585e+07          113343.272362                    0.059478   \\n\",\n        \"min           1.636980e+05          163698.000000                    0.345703   \\n\",\n        \"25%           6.137282e+05          384507.750000                    0.420095   \\n\",\n        \"50%           1.211470e+06          451115.000000                    0.450516   \\n\",\n        \"75%           4.289952e+06          496257.750000                    0.486061   \\n\",\n        \"max           1.395533e+08          713372.000000                    0.647102   \\n\",\n        \"\\n\",\n        \"       Average Winning Electors    Loosing Votes  Winning Votes Ratio  \\n\",\n        \"count                 36.000000        36.000000            36.000000  \\n\",\n        \"mean             1412726.416667   6401299.944444             0.560735  \\n\",\n        \"std               305902.010647  15550652.495029             0.323618  \\n\",\n        \"min               370611.000000         0.000000             0.082114  \\n\",\n        \"25%              1274357.750000    305233.750000             0.253428  \\n\",\n        \"50%              1479975.000000   1308612.000000             0.579119  \\n\",\n        \"75%              1608989.250000   5441697.250000             0.863928  \\n\",\n        \"max              1823664.000000  88718644.000000             1.000000  \\n\",\n        \"\\n\",\n        \"[8 rows x 10 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualizing the results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import matplotlib.pyplot as plt\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 24\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(18,8))\\n\",\n      \"cmap = plt.cm.hsv\\n\",\n      \"colors = cmap(np.linspace(0., 1., len(results[\\\"Seats\\\"])))\\n\",\n      \"plt.pie(results[\\\"Seats\\\"], labels=results.index, explode=np.array(range(len(results)))/10, autopct='%1.1f%%',\\n\",\n      \"        colors = colors)\\n\",\n      \"\\n\",\n      \"plt.axis(\\\"equal\\\")\\n\",\n      \"\\n\",\n      \"plt.title(\\\"Indian general election 2014 results of seats per party\\\")\\n\",\n      \"\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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+45Dhw4QGpqKh07drygye9iY2M5fPgwLpfL99iuXbt8+42Li+O+++4j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\\nrkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f509003d710>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 25\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(18,8))\\n\",\n      \"\\n\",\n      \"plt.pie(results.sort(\\\"Total Votes\\\", ascending=False)[\\\"Total Votes\\\"], labels=results.sort(\\\"Total Votes\\\", ascending=False).index,\\n\",\n      \"        explode=np.array(range(len(results)))**1.5/40, autopct='%1.1f%%', colors = colors)\\n\",\n      \"\\n\",\n      \"plt.axis(\\\"equal\\\")\\n\",\n      \"\\n\",\n      \"plt.title(\\\"Indian general election 2014 results of votes per party\\\")\\n\",\n      \"\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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CRq1aoFQJ8+fdi8eTO7d+/mzz//ZOTIkUDGB+OkpCTXdi4usq61vwva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\\n/g/InUU7Ua2X0gAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f5090039890>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 26\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(18,10))\\n\",\n      \"for counter in range(len(results)):\\n\",\n      \"    x = results[\\\"Total Votes\\\"][counter]\\n\",\n      \"    y = results[\\\"Seats\\\"][counter]\\n\",\n      \"    label = \\\"%s\\\\n%s\\\" % (results.index[counter], y)\\n\",\n      \"    plt.scatter(x,y, c = colors[counter],\\n\",\n      \"                s=y/x * 1e9, label =results.index[counter])\\n\",\n      \"    plt.annotate(label, \\n\",\n      \"        xy = (x, y), xytext = (-20, 20),\\n\",\n      \"        textcoords = 'offset points', ha = 'right', va = 'bottom',\\n\",\n      \"        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\\n\",\n      \"        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))\\n\",\n      \"\\n\",\n      \"plt.xscale(\\\"log\\\")\\n\",\n      \"plt.yscale(\\\"log\\\")\\n\",\n      \"plt.xlim(results[\\\"Total Votes\\\"].min()*0.95, results[\\\"Total Votes\\\"].max()*1.05)\\n\",\n      \"plt.ylim(0)\\n\",\n      \"\\n\",\n      \"plt.xlabel(\\\"Total Votes (log)\\\")\\n\",\n      \"plt.ylabel(\\\"Seats (log)\\\")\\n\",\n      \"\\n\",\n      \"plt.title(\\\"Indian General Elections 2014 Seats vs Total Votes on a log scale\\\")\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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To6WsHBwRo0aNAFnZdjx44pPz/f7nfsXAyHDh0q8hlFR0cXO5az79S+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\\nEEIIIYQQQgghhNH9PwqtklthNqK7AAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f509024a690>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 27\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print results\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"        Seats  Seats Percentage  Total Votes  Average Votes  \\\\\\n\",\n        \"BJP       282          0.519337    171657549  401069.039720   \\n\",\n        \"INC        44          0.081031    106938242  230470.349138   \\n\",\n        \"ADMK       37          0.068140     18115825  452895.625000   \\n\",\n        \"AITC       34          0.062615     21259681  162287.641221   \\n\",\n        \"BJD        20          0.036832      9491497  451976.047619   \\n\",\n        \"SHS        18          0.033149     10262982  176947.965517   \\n\",\n        \"TDP        16          0.029466     14094545  469818.166667   \\n\",\n        \"TRS        11          0.020258      6736490  396264.117647   \\n\",\n        \"CPM         9          0.016575     17986773  193406.161290   \\n\",\n        \"YSRCP       9          0.016575     13991280  368191.578947   \\n\",\n        \"LJP         6          0.011050      2295929  327989.857143   \\n\",\n        \"NCP         6          0.011050      8635554  239876.500000   \\n\",\n        \"SP          5          0.009208     18672916   94786.375635   \\n\",\n        \"AAAP        4          0.007366     11325635   26216.747685   \\n\",\n        \"RJD         4          0.007366      7442313  248077.100000   \\n\",\n        \"SAD         4          0.007366      3636148  363614.800000   \\n\",\n        \"AIUDF       3          0.005525      2333040  129613.333333   \\n\",\n        \"IND         3          0.005525     16743719    5175.801855   \\n\",\n        \"BLSP        3          0.005525      1078473  269618.250000   \\n\",\n        \"JKPDP       3          0.005525       732644  146528.800000   \\n\",\n        \"INLD        2          0.003683      2799899  279989.900000   \\n\",\n        \"JMM         2          0.003683      1637990   77999.523810   \\n\",\n        \"IUML        2          0.003683      1100096   44003.840000   \\n\",\n        \"JD(U)       2          0.003683      5992196   64432.215054   \\n\",\n        \"AD          2          0.003683       821820  117402.857143   \\n\",\n        \"JD(S)       2          0.003683      3731481  109749.441176   \\n\",\n        \"RSP         1          0.001842      1666380  277730.000000   \\n\",\n        \"KEC(M)      1          0.001842       424194  424194.000000   \\n\",\n        \"CPI         1          0.001842      4327298   64586.537313   \\n\",\n        \"SDF         1          0.001842       163698  163698.000000   \\n\",\n        \"AINRC       1          0.001842       255826  255826.000000   \\n\",\n        \"SWP         1          0.001842      1105073  552536.500000   \\n\",\n        \"NPEP        1          0.001842       576444   82349.142857   \\n\",\n        \"AIMIM       1          0.001842       685729  137145.800000   \\n\",\n        \"NPF         1          0.001842       994505  497252.500000   \\n\",\n        \"PMK         1          0.001842      1827566  203062.888889   \\n\",\n        \"\\n\",\n        \"        Total Winning Votes  Average Winning Votes  \\\\\\n\",\n        \"BJP               139553310                 494869   \\n\",\n        \"INC                18219598                 414081   \\n\",\n        \"ADMK               17415881                 470699   \\n\",\n        \"AITC               18366652                 540195   \\n\",\n        \"BJD                 9169818                 458490   \\n\",\n        \"SHS                 9010919                 500606   \\n\",\n        \"TDP                 9362168                 585135   \\n\",\n        \"TRS                 5307344                 482485   \\n\",\n        \"CPM                 4069667                 452185   \\n\",\n        \"YSRCP               4950808                 550089   \\n\",\n        \"LJP                 1983574                 330595   \\n\",\n        \"NCP                 2594704                 432450   \\n\",\n        \"SP                  2458349                 491669   \\n\",\n        \"AAAP                1716952                 429238   \\n\",\n        \"RJD                 1429688                 357422   \\n\",\n        \"SAD                 1817385                 454346   \\n\",\n        \"AIUDF               1350137                 450045   \\n\",\n        \"IND                 1374887                 458295   \\n\",\n        \"BLSP                1072804                 357601   \\n\",\n        \"JKPDP                533629                 177876   \\n\",\n        \"INLD                1000848                 500424   \\n\",\n        \"JMM                  715322                 357661   \\n\",\n        \"IUML                 816226                 408113   \\n\",\n        \"JD(U)                740808                 370404   \\n\",\n        \"AD                   812325                 406162   \\n\",\n        \"JD(S)               1034211                 517105   \\n\",\n        \"RSP                  408528                 408528   \\n\",\n        \"KEC(M)               424194                 424194   \\n\",\n        \"CPI                  389209                 389209   \\n\",\n        \"SDF                  163698                 163698   \\n\",\n        \"AINRC                255826                 255826   \\n\",\n        \"SWP                  640428                 640428   \\n\",\n        \"NPEP                 239301                 239301   \\n\",\n        \"AIMIM                513868                 513868   \\n\",\n        \"NPF                  713372                 713372   \\n\",\n        \"PMK                  468194                 468194   \\n\",\n        \"\\n\",\n        \"        Average Winning Percentage  Average Winning Electors  Loosing Votes  \\\\\\n\",\n        \"BJP                       0.493188                   1606886       32104239   \\n\",\n        \"INC                       0.428075                   1384055       88718644   \\n\",\n        \"ADMK                      0.451634                   1413200         699944   \\n\",\n        \"AITC                      0.431486                   1512020        2893029   \\n\",\n        \"BJD                       0.449398                   1389275         321679   \\n\",\n        \"SHS                       0.510340                   1676883        1252063   \\n\",\n        \"TDP                       0.493163                   1554833        4732377   \\n\",\n        \"TRS                       0.430386                   1532891        1429146   \\n\",\n        \"CPM                       0.455675                   1264327       13917106   \\n\",\n        \"YSRCP                     0.478040                   1495688        9040472   \\n\",\n        \"LJP                       0.375127                   1579884         312355   \\n\",\n        \"NCP                       0.483694                   1419258        6040850   \\n\",\n        \"SP                        0.471728                   1714211       16214567   \\n\",\n        \"AAAP                      0.401053                   1464262        9608683   \\n\",\n        \"RJD                       0.367278                   1636930        6012625   \\n\",\n        \"SAD                       0.411916                   1543844        1818763   \\n\",\n        \"AIUDF                     0.389700                   1382908         982903   \\n\",\n        \"IND                       0.463283                   1271567       15368832   \\n\",\n        \"BLSP                      0.427755                   1522716           5669   \\n\",\n        \"JKPDP                     0.472012                   1232333         199015   \\n\",\n        \"INLD                      0.411830                   1589081        1799051   \\n\",\n        \"JMM                       0.385344                   1300163         922668   \\n\",\n        \"IUML                      0.473571                   1189616         283870   \\n\",\n        \"JD(U)                     0.380420                   1767467        5251388   \\n\",\n        \"AD                        0.426682                   1718643           9495   \\n\",\n        \"JD(S)                     0.442053                   1615299        2697270   \\n\",\n        \"RSP                       0.464735                   1219415        1257852   \\n\",\n        \"KEC(M)                    0.510072                   1161463              0   \\n\",\n        \"CPI                       0.422821                   1275288        3938089   \\n\",\n        \"SDF                       0.529824                    370611              0   \\n\",\n        \"AINRC                     0.345703                    901357              0   \\n\",\n        \"SWP                       0.538686                   1630598         464645   \\n\",\n        \"NPEP                      0.522410                    586501         337143   \\n\",\n        \"AIMIM                     0.528986                   1823664         171861   \\n\",\n        \"NPF                       0.647102                   1752741         281133   \\n\",\n        \"PMK                       0.425111                   1358273        1359372   \\n\",\n        \"\\n\",\n        \"        Winning Votes Ratio  \\n\",\n        \"BJP                0.812975  \\n\",\n        \"INC                0.170375  \\n\",\n        \"ADMK               0.961363  \\n\",\n        \"AITC               0.863919  \\n\",\n        \"BJD                0.966109  \\n\",\n        \"SHS                0.878002  \\n\",\n        \"TDP                0.664241  \\n\",\n        \"TRS                0.787850  \\n\",\n        \"CPM                0.226259  \\n\",\n        \"YSRCP              0.353850  \\n\",\n        \"LJP                0.863953  \\n\",\n        \"NCP                0.300468  \\n\",\n        \"SP                 0.131653  \\n\",\n        \"AAAP               0.151599  \\n\",\n        \"RJD                0.192103  \\n\",\n        \"SAD                0.499811  \\n\",\n        \"AIUDF              0.578703  \\n\",\n        \"IND                0.082114  \\n\",\n        \"BLSP               0.994743  \\n\",\n        \"JKPDP              0.728361  \\n\",\n        \"INLD               0.357459  \\n\",\n        \"JMM                0.436707  \\n\",\n        \"IUML               0.741959  \\n\",\n        \"JD(U)              0.123629  \\n\",\n        \"AD                 0.988446  \\n\",\n        \"JD(S)              0.277158  \\n\",\n        \"RSP                0.245159  \\n\",\n        \"KEC(M)             1.000000  \\n\",\n        \"CPI                0.089943  \\n\",\n        \"SDF                1.000000  \\n\",\n        \"AINRC              1.000000  \\n\",\n        \"SWP                0.579535  \\n\",\n        \"NPEP               0.415133  \\n\",\n        \"AIMIM              0.749375  \\n\",\n        \"NPF                0.717314  \\n\",\n        \"PMK                0.256184  \\n\",\n        \"\\n\",\n        \"[36 rows x 10 columns]\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 28\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 28\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/data/2014_indian_election_turnout.csv",
    "content": "State,PC Name,Male Electors,Male Voters,Female Electors,Female Voters,Total Electors,Total Voters\nAndaman & Nicobar Islands,1 - Andaman & Nicobar Islands,142782,101178,126578,89150,269360,190328\nAndhra Pradesh,1 - Adilabad ,687389,519364,698844,526475,1386233,1045839\nAndhra Pradesh,2 - Peddapalle ,725767,520598,699594,501586,1425361,1022184\nAndhra Pradesh,3 - Karimnagar ,777458,552489,773376,573202,1550834,1125691\nAndhra Pradesh,4 - Nizamabad,724504,469712,771689,564212,1496193,1033924\nAndhra Pradesh,5 - Zahirabad,717811,546746,727435,548060,1445246,1094806\nAndhra Pradesh,6 - Medak,775903,606863,760812,584233,1536715,1191096\nAndhra Pradesh,7 - Malkajgiri,1723413,878093,1459912,742304,3183325,1620397\nAndhra Pradesh,8 - Secundrabad,1012378,545749,881269,458020,1893647,1003769\nAndhra Pradesh,9 - Hyderabad,961290,526510,862374,444911,1823664,971421\nAndhra Pradesh,10 - CHELVELLA,1153049,696749,1032130,619113,2185179,1315862\nAndhra Pradesh,11 - Mahbubnagar,709711,511764,708961,503036,1418672,1014800\nAndhra Pradesh,12 - Nagarkurnool,745038,570342,732300,538626,1477338,1108968\nAndhra Pradesh,13 - Nalgonda,747281,602193,748299,587206,1495580,1189399\nAndhra Pradesh,14 - Bhongir ,756963,622333,735288,589610,1492251,1211943\nAndhra Pradesh,15 - Warangal,771756,592484,766025,582147,1537781,1174631\nAndhra Pradesh,16 - Mahabubabad  ,688398,562073,698945,562297,1387343,1124370\nAndhra Pradesh,17 - Khammam ,712329,588728,727960,594169,1440289,1182897\nAndhra Pradesh,18 - Aruku ,622416,451350,650308,458264,1272724,909614\nAndhra Pradesh,19 - Srikakulam,706828,505010,707161,546436,1413989,1051446\nAndhra Pradesh,20 - Vizianagaram,700837,555005,703290,565311,1404127,1120316\nAndhra Pradesh,21 - Visakhapatnam,875187,585270,847850,578288,1723037,1163558\nAndhra Pradesh,22 - Anakapalli,689132,563818,712342,584254,1401474,1148072\nAndhra Pradesh,23 - Kakinada,709101,558689,709189,541310,1418290,1099999\nAndhra Pradesh,24 - Amalapuram ,682607,568534,675259,552393,1357866,1120927\nAndhra Pradesh,25 - Rajahmundry,701707,577999,719581,576382,1421288,1154381\nAndhra Pradesh,26 - Narsapuram,652668,539357,672475,549594,1325143,1088951\nAndhra Pradesh,27 - Eluru ,706916,597603,720844,604093,1427760,1201696\nAndhra Pradesh,28 - Machilipatnam ,675774,567908,693537,573157,1369311,1141065\nAndhra Pradesh,29 - Vijayawada,781156,602198,783357,592877,1564513,1195075\nAndhra Pradesh,30 - Guntur,773027,617483,798989,627443,1572016,1244926\nAndhra Pradesh,31 - Narasaraopet,748465,638080,766396,643978,1514861,1282058\nAndhra Pradesh,32 - Bapatla ,686482,587223,706483,597411,1392965,1184634\nAndhra Pradesh,33 - Ongole ,736245,603025,734238,605200,1470483,1208225\nAndhra Pradesh,34 - Nandyal,783266,603562,793862,601394,1577128,1204956\nAndhra Pradesh,35 - Kurnool,738791,544802,743016,520930,1481807,1065732\nAndhra Pradesh,36 - Anantapur,775509,612890,761403,592164,1536912,1205054\nAndhra Pradesh,37 - Hindupur,734638,601932,711865,575325,1446503,1177257\nAndhra Pradesh,38 - Kadapa,765036,588805,785543,611857,1550579,1200662\nAndhra Pradesh,39 - Nellore,796583,593468,809557,594180,1606140,1187648\nAndhra Pradesh,40 - Tirupati ,778778,604834,795766,608230,1574544,1213064\nAndhra Pradesh,41 - Rajampet,735347,573019,752151,585298,1487498,1158317\nAndhra Pradesh,42 - Chittoor ,723996,598259,728145,600656,1452141,1198915\nArunachal Pradesh,1 - ARUNACHAL WEST,219334,159978,227181,175687,446515,335665\nArunachal Pradesh,2 - ARUNACHAL EAST,160293,129313,152579,131978,312872,261291\nAssam,1 - Karimganj ,615198,482484,550799,404436,1165997,886920\nAssam,2 - Silchar,554540,428949,505635,370881,1060175,799830\nAssam,3 - Autonomous District,358880,279409,343010,263871,701890,543280\nAssam,4 - Dhubri,798124,709191,754430,660433,1552554,1369624\nAssam,5 - Kokrajhar,776071,636657,729405,587212,1505476,1223869\nAssam,6 - Barpeta,755559,634405,674616,571458,1430175,1205863\nAssam,7 - Gauhati,988067,785494,934203,726235,1922270,1511729\nAssam,8 - Mangaldoi,791539,651272,724137,581965,1515676,1233237\nAssam,9 - Tezpur,654866,505643,604702,475045,1259568,980688\nAssam,10 - Nowgong,792424,639392,731457,590682,1523881,1230074\nAssam,11 - Kaliabor,752785,605292,705080,561198,1457865,1166490\nAssam,12 - Jorhat,634236,483829,600212,447507,1234448,931336\nAssam,13 - Dibrugarh,579657,462412,544648,428556,1124305,890968\nAssam,14 - Lakhimpur,735263,572334,695731,539641,1430994,1111975\nBihar,1 - Valmiki Nagar,786297,484621,670279,415493,1456576,900114\nBihar,2 - Paschim Champaran,769352,469114,643721,385686,1413073,854800\nBihar,3 - Purvi Champaran,788561,442103,664402,380568,1452963,822671\nBihar,4 - Sheohar,794679,433416,695045,409478,1489724,842894\nBihar,5 - Sitamarhi,832370,449303,742544,451285,1574914,900588\nBihar,6 - Madhubani,877284,413878,750685,446575,1627969,860453\nBihar,7 - Jhanjharpur,884884,452082,765869,489167,1650753,941249\nBihar,8 - Supaul,796979,468183,728613,502345,1525592,970528\nBihar,9 - Araria,842884,492492,744464,483319,1587348,975811\nBihar,10 - Kishanganj,762877,471209,676113,457281,1438990,928490\nBihar,11 - Katihar,769044,495564,677434,482266,1446478,977830\nBihar,12 - Purnia,833610,511512,749357,506220,1582967,1017732\nBihar,13 - Madhepura,899271,508914,826307,525885,1725578,1034799\nBihar,14 - Darbhanga,797470,407047,697976,421295,1495446,828342\nBihar,15 - Muzaffarpur,830943,490954,720420,457933,1551363,948887\nBihar,16 - Vaishali,854781,482598,732421,443339,1587202,925937\nBihar,17 - Gopalganj (SC),861019,446731,791748,456852,1652767,903583\nBihar,18 - Siwan,841735,443867,722125,440154,1563860,884021\nBihar,19 - Maharajganj,882264,420364,762260,426290,1644524,846654\nBihar,20 - Saran,833361,450964,705383,412290,1538744,863254\nBihar,21 - Hajipur (SC),895047,478252,754500,426501,1649547,904753\nBihar,22 - Ujiarpur,768123,433926,660322,424994,1428445,858920\nBihar,23 - Samastipur (SC),805601,430736,697404,432463,1503005,863199\nBihar,24 - Begusarai,949825,546041,828934,531814,1778759,1077855\nBihar,25 - Khagaria,793168,445965,698900,450266,1492068,896231\nBihar,26 - Bhagalpur,895185,522657,790154,451359,1685339,974016\nBihar,27 - Banka,825010,461098,724446,438255,1549456,899353\nBihar,28 - Munger,921436,506184,775110,408166,1696546,914350\nBihar,29 - Nalanda,1036575,503097,915392,418664,1951967,921761\nBihar,30 - Patna Sahib,1052278,511447,893971,370815,1946249,882262\nBihar,31 - Pataliputra,934086,553915,801988,424734,1736074,978649\nBihar,32 - Arrah,1010040,516366,822292,376847,1832332,893213\nBihar,33 - Buxar,878312,493930,762255,394274,1640567,888204\nBihar,34 - Sasaram (SC),859397,474117,748350,372671,1607747,846788\nBihar,35 - Karakat,845707,453576,724282,336351,1569989,789927\nBihar,36 - Jahanabad,751585,443737,671661,367779,1423246,811516\nBihar,37 - Aurangabad,825574,430800,710579,355474,1536153,786274\nBihar,38 - Gaya (SC),790868,444510,709883,364868,1500751,809378\nBihar,39 - Nawada,887050,478217,788739,406224,1675789,884441\nBihar,40 - Jamui (SC),828406,415743,722530,359896,1550936,775639\nChandigarh,1 - CHANDIGARH,333621,244956,281593,208499,615214,453455\nChattisgarh,1 - SARGUJA,771303,610877,751769,576444,1523072,1187321\nChattisgarh,2 - RAIGARH,807353,630570,798471,615616,1605824,1246186\nChattisgarh,3 - JANJGIR-CHAMPA,890439,558677,847093,514670,1737532,1073347\nChattisgarh,4 - KORBA,725821,546046,693789,506674,1419610,1052720\nChattisgarh,5 - BILASPUR,889222,573253,838103,517204,1727325,1090457\nChattisgarh,6 - RAJNANDGAON,794427,601874,793668,576422,1588095,1178296\nChattisgarh,7 - DURG,945656,660504,910125,597838,1855781,1258342\nChattisgarh,8 - RAIPUR,979133,659070,925097,591775,1904230,1250845\nChattisgarh,9 - MAHASAMUND,757276,576917,758471,554292,1515747,1131209\nChattisgarh,10 - BASTAR,632814,387112,665245,382801,1298059,769913\nChattisgarh,11 - KANKER,722339,515793,725435,501150,1447774,1016943\nDadra & Nagar Haveli,1 - Dadar & Nagar Haveli,106215,87800,90402,77486,196617,165286\nDaman & Diu,1 - Daman & diu,57011,42378,54816,44855,111827,87233\nGoa,1 - North Goa,255870,200184,259571,206447,515441,406631\nGoa,2 - South Goa,272438,195582,272898,214787,545336,410369\nGujarat,1 - Kachchh,806343,520905,727439,425335,1533782,946240\nGujarat,2 - Banaskantha,796124,513893,719587,372741,1515711,886634\nGujarat,3 - Patan,846195,529513,782446,426286,1628641,955799\nGujarat,4 - Mahesana,777821,544710,720398,459548,1498219,1004258\nGujarat,5 - Sabarkantha,833521,587807,782319,506195,1615840,1094002\nGujarat,6 - Gandhinagar,900744,620889,833228,514606,1733972,1135495\nGujarat,7 - Ahmedabad East,852765,560074,749067,425451,1601832,985525\nGujarat,8 - Ahmedabad West,800933,534008,733467,430601,1534400,964609\nGujarat,9 - Surendranagar,877745,542339,778912,402338,1656657,944677\nGujarat,10 - Rajkot,864760,593333,790957,463736,1655717,1057069\nGujarat,11 - Porbandar,807383,471495,731840,337938,1539223,809433\nGujarat,12 - Jamnagar,771004,486177,699948,366466,1470952,852643\nGujarat,13 - Junagadh,772017,516158,713526,425220,1485543,941378\nGujarat,14 - Amreli,777662,457547,708624,351269,1486286,808816\nGujarat,15 - Bhavnagar,834571,519525,759960,397877,1594531,917402\nGujarat,16 - Anand,781120,541176,715739,429718,1496859,970894\nGujarat,17 - Kheda,833232,542955,766244,412951,1599476,955906\nGujarat,18 - Panchmahal,820230,516017,756437,417444,1576667,933461\nGujarat,19 - Dahod,712184,469459,699581,430922,1411765,900381\nGujarat,20 - Vadodara,849077,625045,789244,536532,1638321,1161577\nGujarat,21 - Chhota Udaipur,798160,593192,738145,507350,1536305,1100542\nGujarat,22 - Bharuch,734861,564668,682687,495543,1417548,1060211\nGujarat,23 - Bardoli,829648,637345,784458,568834,1614106,1206179\nGujarat,24 - Surat,803829,539368,680239,408554,1484068,947922\nGujarat,25 - Navsari,972090,649206,792532,511541,1764622,1160747\nGujarat,26 - Valsad,775073,582311,736988,539892,1512061,1122203\nHaryana,1 - Ambala,909708,667400,782519,551595,1692227,1218995\nHaryana,2 - Kurukshetra,807700,618607,690759,517285,1498459,1135892\nHaryana,3 - Sirsa,886303,692891,774254,586214,1660557,1279105\nHaryana,4 - Hisar,825688,637718,691918,518196,1517606,1155914\nHaryana,5 - Karnal,913321,666808,771000,526692,1684321,1193500\nHaryana,6 - Sonipat,777824,551168,639364,434469,1417188,985637\nHaryana,7 - Rohtak,850125,578351,717379,465980,1567504,1044331\nHaryana,8 - Bhiwani-Mahendragarh,792166,559833,681746,470598,1473912,1030431\nHaryana,9 - Gurgaon,984370,727397,861253,593222,1845623,1320619\nHaryana,10 - Faridabad,969407,651411,770945,479315,1740352,1130726\nHimachal Pradesh,1 - Kangra,645888,392927,612713,406518,1258601,799445\nHimachal Pradesh,2 - Mandi,587833,377569,562575,348525,1150408,726094\nHimachal Pradesh,3 - Hamirpur,633572,395804,614127,439401,1247699,835205\nHimachal Pradesh,4 - Shimla,607137,403332,546226,334425,1153363,737757\nJammu & Kashmir,1 - Baramulla,624008,256249,566737,209743,1190745,465992\nJammu & Kashmir,2 - Srinagar,630716,178554,574517,133658,1205233,312212\nJammu & Kashmir,3 - Anantnag,684820,211582,616203,163697,1301023,375279\nJammu & Kashmir,4 - Ladakh,86259,58035,80504,59994,166763,118029\nJammu & Kashmir,5 - Udhampur,798910,557757,691334,484001,1490244,1041758\nJammu & Kashmir,6 - Jammu,977308,665407,870847,588186,1848155,1253593\nJharkhand,1 - Rajmahal,691254,491007,661918,460556,1353172,951563\nJharkhand,2 - Dumka,646885,466521,600270,436539,1247155,903060\nJharkhand,3 - Godda,825706,550327,764922,499115,1590628,1049442\nJharkhand,4 - Chatra,696514,383079,616031,329892,1312545,712971\nJharkhand,5 - Kodarma,870016,535513,769613,489393,1639629,1024906\nJharkhand,6 - Giridih,811986,531267,703151,438730,1515137,969997\nJharkhand,7 - Dhanbad,1032005,642644,857989,501258,1889994,1143902\nJharkhand,8 - Ranchi,868783,562894,779681,486889,1648464,1049783\nJharkhand,9 - Jamshedpur,811632,548176,770033,500964,1581665,1049140\nJharkhand,10 - Singhbhum,583444,403528,569126,391758,1152570,795286\nJharkhand,11 - Khunti,565544,373120,546308,363835,1111852,736955\nJharkhand,12 - Lohardaga,578883,333938,540261,317522,1119144,651460\nJharkhand,13 - Palamau,890010,523454,755947,453869,1645957,977323\nJharkhand,14 - Hazaribagh,812487,515008,706344,452144,1518831,967152\nKarnataka,1 - Chikkodi,746113,564257,696183,506846,1442296,1071103\nKarnataka,2 - Belgaum,807598,570120,773419,508427,1581017,1078547\nKarnataka,3 - Bagalkot,793338,561781,775282,517529,1568620,1079310\nKarnataka,4 - Bijapur,847815,521565,774820,445192,1622635,966757\nKarnataka,5 - Gulbarga,878311,523062,843679,474576,1721990,997638\nKarnataka,6 - Raichur,835969,508764,825637,460125,1661606,968889\nKarnataka,7 - Bidar,839154,506363,761808,453021,1600962,959384\nKarnataka,8 - Koppal,770316,526001,764789,480507,1535105,1006508\nKarnataka,9 - Bellary,750789,542128,737156,503644,1487945,1045772\nKarnataka,10 - Haveri,807076,595257,751673,520711,1558749,1115968\nKarnataka,11 - Dharwad,809486,562991,769538,478235,1579024,1041226\nKarnataka,12 - Uttara Kannada,742865,521541,707734,479497,1450599,1001038\nKarnataka,13 - Davanagere,772126,581292,750586,533576,1522712,1114868\nKarnataka,14 - Shimoga,778767,572407,783474,556601,1562241,1129008\nKarnataka,15 - Udupi Chikmagalur,679286,511470,708009,522638,1387295,1034108\nKarnataka,16 - Hassan,789668,586090,771668,561082,1561336,1147172\nKarnataka,17 - Dakshina Kannada,774500,595647,790781,611827,1565281,1207474\nKarnataka,18 - Chitradurga,844864,573250,816408,523249,1661272,1096499\nKarnataka,19 - Tumkur,764561,565612,753957,535872,1518518,1101484\nKarnataka,20 - Mandya,839052,604906,830210,587732,1669262,1192638\nKarnataka,21 - Mysore,867893,598003,855241,561591,1723134,1159594\nKarnataka,22 - Chamarajanagar,789383,585209,766396,547820,1555779,1133029\nKarnataka,23 - Bangalore Rural,1135845,758619,1054552,696625,2190397,1455244\nKarnataka,24 - Bangalore North,1260356,721638,1141116,635080,2401472,1356718\nKarnataka,25 - Bangalore central,1010586,574475,921077,500114,1931663,1074589\nKarnataka,26 - Bangalore South,1051316,597291,948566,516435,1999882,1113726\nKarnataka,27 - Chikkballapur,843740,653492,814602,609782,1658342,1263274\nKarnataka,28 - Kolar,755450,582601,737525,544722,1492975,1127323\nKerala,1 - Kasaragod,595047,459537,648683,514678,1243730,974215\nKerala,2 - Kannur,548454,439557,621812,507560,1170266,947117\nKerala,3 - Vadakara,561168,437556,621336,521786,1182504,959342\nKerala,4 - Wayanad,614822,454300,634598,460706,1249420,915006\nKerala,5 - Kozhikode,571709,457548,610775,485461,1182484,943009\nKerala,6 - Malappuram,598207,415980,600237,437487,1198444,853467\nKerala,7 - Ponnani,574106,405116,606683,466476,1180789,871592\nKerala,8 - Palakkad,587379,444510,621379,465812,1208758,910322\nKerala,9 - Alathur ,591950,452858,624401,474370,1216351,927228\nKerala,10 - Thrissur,606518,434202,668770,486303,1275288,920505\nKerala,11 - Chalakudy,565081,436712,585410,447321,1150491,884033\nKerala,12 - Ernakulam,565809,428565,590658,422269,1156467,850834\nKerala,13 - Idukki,578849,425049,579886,394717,1158735,819766\nKerala,14 - Kottayam,573571,426879,587892,404757,1161463,831636\nKerala,15 - Alappuzha,611877,477885,659447,519579,1271324,997464\nKerala,16 - Mavelikkara ,592702,416969,659966,472091,1252668,889060\nKerala,17 - Pathanamthitta,631495,425771,692411,443681,1323906,869452\nKerala,18 - Kollam,575296,408322,644119,470734,1219415,879056\nKerala,19 - Attingal,575780,396246,675618,463104,1251398,859350\nKerala,20 - Thiruvananthapuram,614438,434623,658310,438816,1272748,873439\nLakshadweep,1 - Lakshadweep,25433,21585,24489,21654,49922,43239\nMadhya Pradesh,1 - MORENA,938466,538708,764026,315571,1702492,854279\nMadhya Pradesh,2 - BHIND,890851,469603,707318,259481,1598169,729084\nMadhya Pradesh,3 - GWALIOR,1024155,595934,852848,394978,1877003,990912\nMadhya Pradesh,4 - GUNA,857328,581485,748291,395144,1605619,976629\nMadhya Pradesh,5 - SAGAR,815357,536674,704827,355255,1520184,891929\nMadhya Pradesh,6 - TIKAMGARH,820908,466689,708095,299632,1529003,766321\nMadhya Pradesh,7 - DAMOH,882506,557919,768600,354161,1651106,912080\nMadhya Pradesh,8 - KHAJURAHO,907312,517341,795482,357136,1702794,874477\nMadhya Pradesh,9 - SATNA,772026,499928,686058,413516,1458084,913444\nMadhya Pradesh,10 - REWA,821800,457663,722919,372339,1544719,830002\nMadhya Pradesh,11 - SIDHI,915700,548026,820350,441370,1736050,989396\nMadhya Pradesh,12 - SHAHDOL,806945,527939,754376,440579,1561321,968518\nMadhya Pradesh,13 - JABALPUR,897949,558617,813734,443567,1711683,1002184\nMadhya Pradesh,14 - MANDLA,925971,641915,898453,576603,1824424,1218518\nMadhya Pradesh,15 - BALAGHAT,822667,571601,807102,541763,1629769,1113364\nMadhya Pradesh,16 - CHHINDWARA,721482,584759,679795,522739,1401277,1107498\nMadhya Pradesh,17 - HOSHANGABAD,835492,593957,732635,437218,1568127,1031175\nMadhya Pradesh,18 - VIDISHA,872410,621326,761960,452147,1634370,1073473\nMadhya Pradesh,19 - BHOPAL,1039004,639683,917932,490499,1956936,1130182\nMadhya Pradesh,20 - RAJGARH,827001,598652,751747,412072,1578748,1010724\nMadhya Pradesh,21 - DEWAS,843555,654121,773660,489847,1617215,1143968\nMadhya Pradesh,22 - UJJAIN,790889,571525,734592,444880,1525481,1016405\nMadhya Pradesh,23 - MANDSOUR,838076,640484,788495,520865,1626571,1161349\nMadhya Pradesh,24 - RATLAM,860947,570248,841701,512433,1702648,1082681\nMadhya Pradesh,25 - DHAR,858093,586865,810348,489951,1668441,1076816\nMadhya Pradesh,26 - INDORE,1106461,736759,1008842,580058,2115303,1316817\nMadhya Pradesh,27 - KHARGONE,866897,614411,836374,538114,1703271,1152525\nMadhya Pradesh,28 - KHANDWA,912747,677574,846663,579753,1759410,1257327\nMadhya Pradesh,29 - BETUL,836835,574150,770987,473569,1607822,1047719\nMaharashtra,1 - Nandurbar ,852379,584580,820336,532096,1672715,1116676\nMaharashtra,2 - Dhule,878610,543252,795859,439831,1674469,983083\nMaharashtra,3 - Jalgaon,909027,594892,798906,395440,1707933,990332\nMaharashtra,4 - Raver,842682,554159,750688,455054,1593370,1009213\nMaharashtra,5 - Buldhana,845547,535993,750687,442633,1596234,978626\nMaharashtra,6 - Akola,869922,544432,776541,434059,1646463,978491\nMaharashtra,7 - Amravati ,848050,556250,763315,447811,1611365,1004061\nMaharashtra,8 - Wardha,817514,554658,747039,458787,1564553,1013445\nMaharashtra,9 - Ramtek ,887157,576939,790088,473377,1677245,1050316\nMaharashtra,10 - Nagpur ,979995,579529,920792,505529,1900787,1085058\nMaharashtra,11 - Bhandara - gondiya,841442,615957,814842,581239,1656284,1197196\nMaharashtra,12 - Gadchiroli-Chimur,753927,537086,715840,490043,1469767,1027129\nMaharashtra,13 - Chandrapur,921099,602923,832591,506820,1753690,1109743\nMaharashtra,14 - Yavatmal-Washim,916049,585785,827449,447164,1743498,1032949\nMaharashtra,15 - Hingoli ,839540,576774,746654,474390,1586194,1051164\nMaharashtra,16 - Nanded,878553,549945,808504,463405,1687057,1013350\nMaharashtra,17 - Parbhani,945899,637950,857893,524283,1803792,1162233\nMaharashtra,18 - Jalna,866726,596359,745328,469900,1612054,1066259\nMaharashtra,19 - Aurangabad,846023,554669,743370,428264,1589393,982933\nMaharashtra,20 - Dindori ,808018,546989,722121,423193,1530139,970182\nMaharashtra,21 - Nashik,850457,524572,742780,412833,1593237,937405\nMaharashtra,22 - Palghar ,830432,532639,747645,459979,1578077,992618\nMaharashtra,23 - Bhiwandi,945405,503035,751184,372570,1696589,875605\nMaharashtra,24 - Kalyan,1045495,470781,876551,353415,1922046,824196\nMaharashtra,25 - Thane,1142158,591189,931284,463000,2073442,1054189\nMaharashtra,26 - Mumbai North,972645,527803,811225,418759,1783870,946562\nMaharashtra,27 - Mumbai North West,986908,503191,788520,392326,1775428,895517\nMaharashtra,28 - Mumbai North East,923016,485775,745341,375986,1668357,861761\nMaharashtra,29 - Mumbai North central,967474,472265,771420,373027,1738894,845292\nMaharashtra,30 - Mumbai South central,793801,424521,654084,344539,1447885,769060\nMaharashtra,31 - Mumbai   South,828964,437435,656882,343280,1485846,780715\nMaharashtra,32 - Raigad,752491,485586,780290,502180,1532781,987766\nMaharashtra,33 - Maval,1035960,647176,917781,527159,1953741,1174335\nMaharashtra,34 - Pune,949567,533686,886269,459588,1835836,993274\nMaharashtra,35 - Baramati,960387,602604,853156,463952,1813543,1066556\nMaharashtra,36 - Shirur,973236,613828,850876,475678,1824112,1089506\nMaharashtra,37 - Ahmadnagar ,898819,595960,800589,466358,1699408,1062318\nMaharashtra,38 - Shirdi,765921,520229,693791,412416,1459712,932645\nMaharashtra,39 - Beed,963462,681038,829188,551164,1792650,1232202\nMaharashtra,40 - Osmanabad,932838,612411,793955,505740,1726793,1118151\nMaharashtra,41 - Latur ,897919,578053,784688,479103,1682607,1057156\nMaharashtra,42 - Solapur ,893736,527346,809019,423855,1702755,951201\nMaharashtra,43 - Madha,912736,595626,814572,483993,1727308,1079619\nMaharashtra,44 - Sangli,861582,558707,787525,487952,1649107,1046659\nMaharashtra,45 - Satara,884020,519692,835978,456989,1719998,976681\nMaharashtra,46 - Ratnagiri - sindhudurg,665668,444364,701693,451892,1367361,896256\nMaharashtra,47 - Kolhapur,909326,664298,848974,595991,1758300,1260289\nMaharashtra,48 - Hatkanangle,850574,631647,780024,557224,1630598,1188871\nManipur,1 - Inner manipur,417122,306721,438237,334150,855359,640871\nManipur,2 - Outer manipur,454309,378706,464657,393060,918966,771766\nMeghalaya,1 - Shillong,480409,292944,500331,327043,980740,619987\nMeghalaya,2 - Tura ,297230,231830,289271,226241,586501,458071\nMizoram,1 - MIZORAM,346219,216167,355951,217034,702170,433201\nNagaland,1 - Nagaland,600490,529325,582458,509585,1182948,1038910\nNCT OF Delhi,1 - CHANDNI CHOWK                 ,791317,550825,655911,431038,1447228,981863\nNCT OF Delhi,2 - NORTH EAST DELHI              ,1079406,741003,878301,576335,1957707,1317338\nNCT OF Delhi,3 - EAST DELHI                    ,1023325,676648,806253,519688,1829578,1196336\nNCT OF Delhi,4 - NEW DELHI                     ,830322,546295,659825,423517,1490147,969812\nNCT OF Delhi,5 - NORTH WEST DELHI              ,1212553,756939,981872,599097,2194425,1356036\nNCT OF Delhi,6 - WEST DELHI                    ,1108886,743406,930524,604565,2039410,1347971\nNCT OF Delhi,7 - SOUTH DELHI                   ,1005289,638406,747452,464004,1752741,1102410\nOrissa,1 - Bargarh,751140,595911,679577,527390,1430717,1123301\nOrissa,2 - Sundargarh ,718689,515665,691843,495046,1410532,1010711\nOrissa,3 - Sambalpur,669036,505640,628062,478669,1297098,984309\nOrissa,4 - Keonjhar ,691369,550822,655314,533047,1346683,1083869\nOrissa,5 - Mayurbhanj ,670100,523329,657455,530070,1327555,1053399\nOrissa,6 - Balasore,709342,531966,656876,517484,1366218,1049450\nOrissa,7 - Bhadrak ,774566,537966,694932,543373,1469498,1081339\nOrissa,8 - Jajpur ,696385,511297,607348,469138,1303733,980435\nOrissa,9 - Dhenkanal,722328,551897,641139,490204,1363467,1042101\nOrissa,10 - Bolangir,814968,605802,748057,564603,1563025,1170405\nOrissa,11 - Kalahandi,750694,568938,723441,548598,1474135,1117536\nOrissa,12 - Nabarangpur ,645875,512721,651335,509451,1297210,1022172\nOrissa,13 - Kandhamal,582340,417777,561262,421638,1143602,839415\nOrissa,14 - Cuttack,731918,522770,639699,455834,1371617,978604\nOrissa,15 - Kendrapara ,828491,580768,726953,560231,1555444,1140999\nOrissa,16 - Jagatsinghpur ,797923,588605,701750,543327,1499673,1131932\nOrissa,17 - Puri,742939,540898,661642,498491,1404581,1039389\nOrissa,18 - Bhubaneswar,835850,483992,691918,407830,1527768,891822\nOrissa,19 - Aska,750999,449485,657781,446796,1408780,896281\nOrissa,20 - Berhampur,680089,451051,654179,454311,1334268,905362\nOrissa,21 - Koraput ,629268,485223,671169,504221,1300437,989444\nPuducherry,1 - Puducherry,432048,351360,469309,388657,901357,740017\nPunjab,1 - Gurdaspur,784477,532819,715860,509880,1500337,1042699\nPunjab,2 - Amritsar,779164,537641,698098,469555,1477262,1007196\nPunjab,3 - Khadoor Sahib,817062,536595,746194,503923,1563256,1040518\nPunjab,4 - Jalandhar,808530,532246,742967,508516,1551497,1040762\nPunjab,5 - Hoshiarpur,762065,473343,723221,487954,1485286,961297\nPunjab,6 - Anandpur Sahib,817186,554256,747535,532307,1564721,1086563\nPunjab,7 - Ludhiana,835632,600960,725569,499497,1561201,1100457\nPunjab,8 - Fatehgarh Sahib,740390,551251,656567,479703,1396957,1030954\nPunjab,9 - Faridkot,768206,548771,686869,483336,1455075,1032107\nPunjab,10 - Firozpur,808857,590511,713254,514901,1522111,1105412\nPunjab,11 - Bathinda,813119,627099,712170,549668,1525289,1176767\nPunjab,12 - Sangrur,758197,583487,666546,515980,1424743,1099467\nPunjab,13 - Patiala,834231,593646,746042,527287,1580273,1120933\nRajasthan,1 - Ganganagar,909749,681063,808672,575743,1718421,1256806\nRajasthan,2 - Bikaner,847064,526983,744004,402768,1591068,929751\nRajasthan,3 - Churu,928040,600164,825785,530940,1753825,1131104\nRajasthan,4 - Jhunjhunu,893354,524368,803434,482097,1696788,1006465\nRajasthan,5 - Sikar,944231,555682,826193,510068,1770424,1065750\nRajasthan,6 - Jaipur Rural,906275,554084,793187,459607,1699462,1013691\nRajasthan,7 - Jaipur,1047468,716874,910350,579932,1957818,1296806\nRajasthan,8 - Alwar,871339,587242,756728,475063,1628067,1062305\nRajasthan,9 - BHARATPUR,911069,548105,775828,414327,1686897,962432\nRajasthan,10 - KARAULI-DHOLPUR,845665,491661,703997,354280,1549662,845941\nRajasthan,11 - Dausa,814648,519866,709447,410626,1524095,930492\nRajasthan,12 - TONK-SAWAI MADHOPUR,910730,583685,800045,459355,1710775,1043040\nRajasthan,13 - Ajmer,869354,614697,813938,541617,1683292,1156314\nRajasthan,14 - Nagaur,886731,532331,791931,471688,1678662,1004019\nRajasthan,15 - Pali,994082,581099,898948,514488,1893030,1095587\nRajasthan,16 - Jodhpur,910466,600132,816897,478466,1727363,1078598\nRajasthan,17 - Barmer,895593,655642,781989,563477,1677582,1219119\nRajasthan,18 - Jalore,964047,581038,860921,506234,1824968,1087272\nRajasthan,19 - Udaipur,930007,601374,887933,591828,1817940,1193202\nRajasthan,20 - Banswara,863920,579152,828582,592036,1692502,1171188\nRajasthan,21 - Chittorgarh,928572,632047,889575,540582,1818147,1172629\nRajasthan,22 - Rajsamand,879526,508738,819875,473381,1699401,982119\nRajasthan,23 - Bhilwara,904030,581524,850847,522566,1754877,1104090\nRajasthan,24 - Kota,913386,634766,831153,520194,1744539,1154960\nRajasthan,25 - JHALAWAR-BARAN,868977,635659,800865,510705,1669842,1146364\nSikkim,1 - Sikkim,190886,158222,179725,150745,370611,308967\nTamil Nadu,1 - Thiruvallur ,852794,645535,850039,608905,1702833,1254440\nTamil Nadu,2 - Chennai North,707696,464795,714690,445319,1422386,910114\nTamil Nadu,3 - Chennai South,901207,552474,894573,528689,1795780,1081163\nTamil Nadu,4 - Chennai central,665278,422278,662749,392616,1328027,814894\nTamil Nadu,5 - Sriperumbudur,982862,666226,963641,620421,1946503,1286647\nTamil Nadu,6 - Kancheepuram ,736993,575483,743130,552916,1480123,1128399\nTamil Nadu,7 - Arakkonam,692883,543546,708662,545506,1401545,1089052\nTamil Nadu,8 - Vellore,652064,483973,660187,490797,1312251,974770\nTamil Nadu,9 - Krishnagiri,705468,545948,674489,522443,1379957,1068391\nTamil Nadu,10 - Dharmapuri,696496,567633,661777,533712,1358273,1101345\nTamil Nadu,11 - Tiruvannamalai,675321,529027,677645,538420,1352966,1067447\nTamil Nadu,12 - Arani,683921,548023,685747,548023,1369668,1096046\nTamil Nadu,13 - Viluppuram,694932,537023,692075,530618,1387007,1067641\nTamil Nadu,14 - Kallakurichi,707219,542991,705280,564250,1412499,1107241\nTamil Nadu,15 - Salem,759981,593783,738369,556513,1498350,1150296\nTamil Nadu,16 - Namakkal,661113,526519,668439,532236,1329552,1058755\nTamil Nadu,17 - Erode,656659,511553,664736,497029,1321395,1008582\nTamil Nadu,18 - Tiruppur,697730,543998,677859,506192,1375589,1050190\nTamil Nadu,19 - Nilgiris ,629731,473163,639432,459913,1269163,933076\nTamil Nadu,20 - Coimbatore,869034,608666,851177,567954,1720211,1176620\nTamil Nadu,21 - Pollachi,686943,514739,694562,497928,1381505,1012667\nTamil Nadu,22 - Dindigul,696160,538540,704371,544824,1400531,1083364\nTamil Nadu,23 - Karur,641693,511860,656629,534674,1298322,1046534\nTamil Nadu,24 - Tiruchirappalli,689228,492050,697912,496200,1387140,988250\nTamil Nadu,25 - Perambalur,634285,500157,651149,530669,1285434,1030826\nTamil Nadu,26 - Cuddalore ,625652,486263,622256,498275,1247908,984538\nTamil Nadu,27 - Chidambaram ,686864,531368,679325,557055,1366189,1088423\nTamil Nadu,28 - Mayiladuthurai,679940,503949,670378,521589,1350318,1025538\nTamil Nadu,29 - Nagapattinam ,606339,454047,604287,488155,1210626,942202\nTamil Nadu,30 - Thanjavur,662576,482240,677474,530018,1340050,1012258\nTamil Nadu,31 - Sivaganga,704489,475004,707884,552032,1412373,1027036\nTamil Nadu,32 - Madurai,717175,486336,724259,492010,1441434,978346\nTamil Nadu,33 - Theni ,720709,529187,720593,546396,1441302,1075583\nTamil Nadu,34 - Virudhunagar,669589,497074,680906,513856,1350495,1010930\nTamil Nadu,35 - Ramanathapuram,731467,465888,724424,535160,1455891,1001048\nTamil Nadu,36 - Thoothukkudi,651366,449405,659231,467373,1310597,916778\nTamil Nadu,37 - Tenkasi ,688958,490748,693123,527190,1382081,1017938\nTamil Nadu,38 - Tirunelveli,705592,469328,714843,493002,1420435,962330\nTamil Nadu,39 - Kanniyakumari,743378,489129,724418,501613,1467796,990742\nTripura,1 - Tripura West,635976,546566,612574,526183,1248550,1072749\nTripura,2 - Tripura East,581599,489156,558670,461954,1140269,951110\nUttar Pradesh,1 - Saharanpur,873318,653286,735515,541022,1608833,1194308\nUttar Pradesh,2 - Kairana,840623,618538,691132,500786,1531755,1119324\nUttar Pradesh,3 - Muzaffarnagar,875186,620606,713297,486828,1588483,1107434\nUttar Pradesh,4 - Bijnor,848606,577146,713459,483200,1562065,1060346\nUttar Pradesh,5 - Nagina,795554,484006,697857,458190,1493411,942196\nUttar Pradesh,6 - Moradabad,961962,627713,810084,500252,1772046,1127965\nUttar Pradesh,7 - Rampur,872084,526970,744900,429419,1616984,956389\nUttar Pradesh,8 - Sambhal,931603,600271,761642,456731,1693245,1057002\nUttar Pradesh,9 - Amroha,829446,589901,714796,505959,1544242,1095860\nUttar Pradesh,10 - Meerut,971956,632847,792432,480537,1764388,1113384\nUttar Pradesh,11 - Baghpat,850681,582774,654494,421489,1505175,1004263\nUttar Pradesh,12 - Ghaziabad,1333051,782170,1024495,560150,2357546,1342320\nUttar Pradesh,13 - Gautam Buddha Nagar,1105855,690761,880262,508477,1986117,1199238\nUttar Pradesh,14 - Bulandshahr,930831,549484,805616,460226,1736447,1009710\nUttar Pradesh,15 - Aligarh,965995,596785,827131,467572,1793126,1064357\nUttar Pradesh,16 - Hathras,969908,596931,789019,452342,1758927,1049273\nUttar Pradesh,17 - Mathura,931615,619091,750645,457777,1682260,1076868\nUttar Pradesh,18 - Agra,1003777,622009,810962,448391,1814739,1070400\nUttar Pradesh,19 - Fatehpur Sikri,877271,565971,703311,401997,1580582,967968\nUttar Pradesh,20 - Firozabad,902514,643433,734224,461173,1636738,1104606\nUttar Pradesh,21 - Mainpuri,900568,580173,752497,419092,1653065,999265\nUttar Pradesh,22 - Etah,856974,520292,720483,405989,1577457,926281\nUttar Pradesh,23 - Badaun,978040,595491,791105,432103,1769145,1027594\nUttar Pradesh,24 - Aonla,917060,572792,736517,422849,1653577,995641\nUttar Pradesh,25 - Bareilly,911410,575414,752671,442477,1664081,1017891\nUttar Pradesh,26 - Pilibhit,903420,586205,767731,464342,1671151,1050547\nUttar Pradesh,27 - Shahjahanpur,1100020,664459,879274,465382,1979294,1129841\nUttar Pradesh,28 - Kheri,904863,606301,774603,471567,1679466,1077868\nUttar Pradesh,29 - Dhaurahra,846798,605309,711241,454962,1558039,1060271\nUttar Pradesh,30 - Sitapur,837981,582088,712282,444884,1550263,1026972\nUttar Pradesh,31 - Hardoi,939831,565543,774556,407351,1714387,972894\nUttar Pradesh,32 - Misrikh,948290,590503,777295,407811,1725585,998314\nUttar Pradesh,33 - Unnao,1194396,674580,969996,527023,2164392,1201603\nUttar Pradesh,34 - Mohanlalganj,995313,638688,842881,477900,1838194,1116588\nUttar Pradesh,35 - Lucknow,1052171,582227,897785,451556,1949956,1033783\nUttar Pradesh,36 - Rae Bareli,857875,437735,737079,387401,1594954,825136\nUttar Pradesh,37 - Amethi,890595,461365,779248,413260,1669843,874625\nUttar Pradesh,38 - Sultanpur,910349,499378,793349,465603,1703698,964981\nUttar Pradesh,39 - Pratapgarh,923668,457693,792957,436763,1716625,894456\nUttar Pradesh,40 - Farrukhabad,891934,560566,721847,410111,1613781,970677\nUttar Pradesh,41 - Etawah,938271,530688,768966,408916,1707237,939604\nUttar Pradesh,42 - Kannauj,1000007,629781,808882,484679,1808889,1114460\nUttar Pradesh,43 - Kanpur,889345,485765,721903,349314,1611248,835079\nUttar Pradesh,44 - Akbarpur,977662,559901,790961,411474,1768623,971375\nUttar Pradesh,45 - Jalaun,1045684,579206,857648,529960,1903332,1109166\nUttar Pradesh,46 - Jhansi,1034085,734103,897930,586701,1932015,1320804\nUttar Pradesh,47 - Hamirpur,949328,548768,788665,426472,1737993,975240\nUttar Pradesh,48 - Banda,884813,474053,717104,384273,1601917,858326\nUttar Pradesh,49 - Fatehpur,985922,575050,818855,481638,1804777,1056688\nUttar Pradesh,50 - Kaushambi,955324,491057,783670,418981,1738994,910038\nUttar Pradesh,51 - Phulpur,1063897,541728,849377,418613,1913274,960341\nUttar Pradesh,52 - Allahabad,917403,507043,749166,384539,1666569,891582\nUttar Pradesh,53 - Barabanki,925946,611206,795336,456962,1721282,1068168\nUttar Pradesh,54 - Faizabad,936036,558096,802665,464620,1738701,1022716\nUttar Pradesh,55 - Ambedkar Nagar,923553,541200,795221,493204,1718774,1034404\nUttar Pradesh,56 - Bahraich,882776,522627,755869,411636,1638645,934263\nUttar Pradesh,57 - Kaiserganj,928269,527337,783698,416020,1711967,943357\nUttar Pradesh,58 - Shrawasti,976415,559177,811665,420461,1788080,979638\nUttar Pradesh,59 - Gonda,935168,488241,775659,385491,1710827,873732\nUttar Pradesh,60 - Domariyaganj,961957,490141,799458,444886,1761415,935027\nUttar Pradesh,61 - Basti,1022508,549746,825105,498788,1847613,1048534\nUttar Pradesh,62 - Sant Kabir Nagar,1045430,520999,858897,490650,1904327,1011649\nUttar Pradesh,63 - Maharajganj,946234,547576,796897,508502,1743131,1056078\nUttar Pradesh,64 - Gorakhpur,1055476,569717,849022,470482,1904498,1040199\nUttar Pradesh,65 - Kushi Nagar,930637,505827,750355,444618,1680992,950445\nUttar Pradesh,66 - Deoria,997314,508610,809612,462947,1806926,971557\nUttar Pradesh,67 - Bansgaon,979644,459744,780466,418133,1760110,877877\nUttar Pradesh,68 - Lalganj,906751,456696,754732,441959,1661483,898655\nUttar Pradesh,69 - Azamgarh,941548,504787,761674,455431,1703222,960218\nUttar Pradesh,70 - Ghosi,1040034,547483,851079,492173,1891113,1039656\nUttar Pradesh,71 - Salempur,904632,450367,757105,404382,1661737,854749\nUttar Pradesh,72 - Ballia,973384,573053,794887,369158,1768271,942211\nUttar Pradesh,73 - Jaunpur,1002938,527542,845904,479601,1848842,1007143\nUttar Pradesh,74 - Machhlishahr,1026789,513121,865180,484781,1891969,997902\nUttar Pradesh,75 - Ghazipur,983352,529153,818167,457520,1801519,986673\nUttar Pradesh,76 - Chandauli,927629,546179,741511,434393,1669140,980572\nUttar Pradesh,77 - Varanasi,986224,594293,781262,436392,1767486,1030685\nUttar Pradesh,78 - Bhadohi,1016000,527290,818598,454262,1834598,981552\nUttar Pradesh,79 - Mirzapur,938504,557621,782157,450006,1720661,1007627\nUttar Pradesh,80 - Robertsganj,901163,497152,738398,388721,1639561,885873\nUttarakhand,1 - Tehri Garhwal,712039,403088,640806,373126,1352845,776214\nUttarakhand,2 - Garhwal,652891,323143,616192,358881,1269083,682024\nUttarakhand,3 - Almora,640059,312965,614269,343560,1254328,656525\nUttarakhand,4 - Nainital-udhamsingh Nagar,857781,590700,753029,510735,1610810,1101435\nUttarakhand,5 - Hardwar,888328,638871,754545,536821,1642873,1175692\nWest Bengal,1 - Cooch behar,848649,688855,764768,643554,1613417,1332409\nWest Bengal,2 - Alipurduars,755765,619981,715146,603585,1470911,1223566\nWest Bengal,3 - Jalpaiguri,795704,676450,735765,625667,1531469,1302117\nWest Bengal,4 - Darjeeling,737184,587678,699942,554331,1437126,1142009\nWest Bengal,5 - Raiganj,724014,564181,663512,544012,1387526,1108193\nWest Bengal,6 - Balurghat,653848,549173,600649,513880,1254497,1063053\nWest Bengal,7 - Maldaha Uttar,740749,579515,684679,583470,1425428,1162985\nWest Bengal,8 - Maldaha Dakshin,692386,546640,654757,545767,1347143,1092407\nWest Bengal,9 - Jangipur,714890,559542,676766,559542,1391656,1119084\nWest Bengal,10 - Baharampur,752943,579487,700840,575100,1453783,1154587\nWest Bengal,11 - Murshidabad,782286,652896,729812,634867,1512098,1287763\nWest Bengal,12 - Krishnanagar,769981,639431,706802,608483,1476783,1247914\nWest Bengal,13 - Ranaghat,831318,693242,771531,659954,1602849,1353196\nWest Bengal,14 - Bangaon,796650,661484,744063,622204,1540713,1283688\nWest Bengal,15 - Barrackpore,682366,567820,604856,483310,1287222,1051130\nWest Bengal,16 - Dum dum,715569,591353,690412,541291,1405981,1132644\nWest Bengal,17 - Barasat,774305,665637,738487,603694,1512792,1269331\nWest Bengal,18 - Basirhat,777768,677519,712828,596252,1490596,1273771\nWest Bengal,19 - Joynagar,757902,631335,700822,554717,1458724,1186052\nWest Bengal,20 - Mathurapur,772279,659848,716505,605683,1488784,1265531\nWest Bengal,21 - Diamond harbour,816259,683496,739655,577800,1555914,1261296\nWest Bengal,22 - Jadavpur,811441,666972,784305,605390,1595746,1272362\nWest Bengal,23 - Kolkata Dakshin,890251,627205,795045,540774,1685296,1167979\nWest Bengal,24 - Kolkata Uttar,797437,535236,636548,420542,1433985,955778\nWest Bengal,25 - Howrah,802653,621783,702446,503616,1505099,1125399\nWest Bengal,26 - Uluberia,761951,628832,686681,557195,1448632,1186027\nWest Bengal,27 - Srerampur,847931,681607,776107,608826,1624038,1290433\nWest Bengal,28 - Hooghly,836584,697366,793458,651504,1630042,1348870\nWest Bengal,29 - Arambagh,833629,707525,766664,654409,1600293,1361934\nWest Bengal,30 - Tamluk,796779,685563,730494,652121,1527273,1337684\nWest Bengal,31 - Kanthi,777345,664251,713064,626560,1490409,1290811\nWest Bengal,32 - Ghatal,835803,691145,774686,675564,1610489,1366709\nWest Bengal,33 - Jhargram,753840,646539,721272,611074,1475112,1257613\nWest Bengal,34 - Medinipur,770363,647026,729310,613006,1499673,1260032\nWest Bengal,35 - Purulia,764758,627033,707175,577642,1471933,1204675\nWest Bengal,36 - Bankura,775893,645606,727919,590713,1503812,1236319\nWest Bengal,37 - Bishnupur,759317,667837,707604,604233,1466921,1272070\nWest Bengal,38 - Bardhaman Purba,795545,683049,736699,637873,1532244,1320922\nWest Bengal,39 - Burdwan - durgapur,821377,700899,762121,630343,1583498,1331242\nWest Bengal,40 - Asansol,791896,633230,677788,509165,1469684,1142395\nWest Bengal,41 - Bolpur,798384,681996,740045,622760,1538429,1304756\nWest Bengal,42 - Birbhum,773457,670443,721651,605376,1495108,1275819\n"
  },
  {
    "path": "notebooks/IPython-Tutorial/data/2014_indian_elections_results.csv",
    "content": "Name of State/ UT\tParliamentary Constituency\tCandidate Name\tTotal Votes Polled\tWinner or Not?\tParty Abbreviation\tParty Name\nAndhra Pradesh\tAdilabad\tGODAM NAGESH\t430847\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tAdilabad\tNETHAWATH RAMDAS\t41032\tno\tIND\tIndependent\nAndhra Pradesh\tAdilabad\tRAMESH RATHOD\t184198\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tAdilabad\tRATHOD SADASHIV\t94420\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tAdilabad\tMOSALI CHINNAIAH\t8859\tno\tIND\tIndependent\nAndhra Pradesh\tAdilabad\tNARESH\t259557\tno\tINC\tIndian National Congress\nAndhra Pradesh\tAdilabad\tPAWAR KRISHNA\t5055\tno\tIND\tIndependent\nAndhra Pradesh\tAdilabad\tBANKA SAHADEV\t4787\tno\tIND\tIndependent\nAndhra Pradesh\tAdilabad\tNone of the Above\t17084\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tPeddapalle\tBALKA SUMAN\t565496\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tPeddapalle\tNone of the Above\t5361\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tPeddapalle\tG. VIVEKANAND\t274338\tno\tINC\tIndian National Congress\nAndhra Pradesh\tPeddapalle\tKALVALA SHANKAR\t9290\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tPeddapalle\tMOUTAM RAVINDER\t2674\tno\tIND\tIndependent\nAndhra Pradesh\tPeddapalle\tVELTHURU MALLAIAH\t45977\tno\tRP(K)\tRepublican Paksha (Khoripa)\nAndhra Pradesh\tPeddapalle\tDR.JANAPATI SARAT BABU\t63334\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tPeddapalle\tKAMILLA JAYA RAO\t2742\tno\tIND\tIndependent\nAndhra Pradesh\tPeddapalle\tTHALLAPALLI SRINIVAS\t3699\tno\tIND\tIndependent\nAndhra Pradesh\tPeddapalle\tVENKATA SWAMY INJAM\t3866\tno\tMaSP\tMahajana Socialist Party\nAndhra Pradesh\tPeddapalle\tGORRE RAMESH\t2361\tno\tIND\tIndependent\nAndhra Pradesh\tPeddapalle\tBUPELLI NARAYANA\t7258\tno\tIND\tIndependent\nAndhra Pradesh\tPeddapalle\tBOTHA VENKATA MALLAIAH\t2245\tno\tBCUF\tB. C. United Front\nAndhra Pradesh\tPeddapalle\tJINNA RAMADEVI\t9199\tno\tIND\tIndependent\nAndhra Pradesh\tPeddapalle\tJ.V. RAJU\t3020\tno\tNIP\tNew India Party\nAndhra Pradesh\tPeddapalle\tGADDALA VINAY KUMAR\t3266\tno\tIND\tIndependent\nAndhra Pradesh\tPeddapalle\tKALVALA SANJEEV\t8609\tno\tRPI\tRepublican Party of India\nAndhra Pradesh\tPeddapalle\tTAGARAM SHANKAR LAL\t9449\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tKarimnagar\tVINOD KUMAR BOINAPALLY\t505783\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tKarimnagar\tSRINIVAS REDDY LINGAMPALLY\t3865\tno\tIND\tIndependent\nAndhra Pradesh\tKarimnagar\tRANAVENI LAKSHMAN\t2062\tno\tNIP\tNew India Party\nAndhra Pradesh\tKarimnagar\tMALLESH YADAV BARLA\t2760\tno\tIND\tIndependent\nAndhra Pradesh\tKarimnagar\tREDDI MALLA SRINIVAS\t8040\tno\tRPI(A)\tRepublican Party of India (A)\nAndhra Pradesh\tKarimnagar\tBURLA. RAVI\t4769\tno\tNCP\tNationalist Congress Party\nAndhra Pradesh\tKarimnagar\tKANDEM PRABHAKAR\t4731\tno\tIND\tIndependent\nAndhra Pradesh\tKarimnagar\tB. LAXMAN\t11333\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tKarimnagar\tSHAIK MAHMOOD\t39325\tno\tWPOI\tWelfare Party Of India\nAndhra Pradesh\tKarimnagar\tPONNAM PRABHAKAR\t300706\tno\tINC\tIndian National Congress\nAndhra Pradesh\tKarimnagar\tCHENNAMANENI VIDYA SAGAR RAO\t214828\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tKarimnagar\tMEESALA RAJI REDDY\t3080\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tKarimnagar\tMOHAMMED AMJAD MOHIUDDIN\t6720\tno\tIND\tIndependent\nAndhra Pradesh\tKarimnagar\tUPPULETI.LAXMAN\t2448\tno\tIND\tIndependent\nAndhra Pradesh\tKarimnagar\tL. PRABHAKAR REDDY\t3944\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tKarimnagar\tPULIKOTA RAMESH\t1777\tno\tIND\tIndependent\nAndhra Pradesh\tKarimnagar\tDR.DHARMAIAH VENDAVETLA\t3773\tno\tBMUP\tBahujan Mukti Party\nAndhra Pradesh\tKarimnagar\tNone of the Above\t5747\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNizamabad\tKALVAKUNTLA KAVITHA\t439307\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tNizamabad\tBANJA VEERAPPA\t3651\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tNizamabad\tCHIRULINGADRULA VENKATESHAM\t3091\tno\tIND\tIndependent\nAndhra Pradesh\tNizamabad\tKANDEM PRABHAKAR\t3990\tno\tIND\tIndependent\nAndhra Pradesh\tNizamabad\tMALIK MOHTASIM KHAN\t43814\tno\tWPOI\tWelfare Party Of India\nAndhra Pradesh\tNizamabad\tS. CHANDRAIAH\t2817\tno\tIND\tIndependent\nAndhra Pradesh\tNizamabad\tBAGWAN B\t8129\tno\tIND\tIndependent\nAndhra Pradesh\tNizamabad\tABDUL GANI\t4791\tno\tBCUF\tB. C. United Front\nAndhra Pradesh\tNizamabad\tENDALA LAKSHMINARAYANA\t225333\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tNizamabad\tABDUL KAREEM KHAN (BABU)\t1601\tno\tIND\tIndependent\nAndhra Pradesh\tNizamabad\tTALARI RAMULU\t7421\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tNizamabad\tSAGAR SUDDALA\t1978\tno\tBMUP\tBahujan Mukti Party\nAndhra Pradesh\tNizamabad\tMADHU YASKHI GOUD\t272123\tno\tINC\tIndian National Congress\nAndhra Pradesh\tNizamabad\tRAPELLY SRINIVAS\t2621\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tNizamabad\tKOTAPATI NARSIMHAM NAIDU\t2462\tno\tIND\tIndependent\nAndhra Pradesh\tNizamabad\tNone of the Above\t7266\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNizamabad\tSINGAREDDY RAVINDRA REDDY\t3529\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tZahirabad\tB.B. PATIL\t508661\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tZahirabad\tMARRI DURGESH\t18027\tno\tRPI(A)\tRepublican Party of India (A)\nAndhra Pradesh\tZahirabad\tSURESH KUMAR SHETKAR\t364030\tno\tINC\tIndian National Congress\nAndhra Pradesh\tZahirabad\tTANDALE AMBAJI RAO\t2323\tno\tIND\tIndependent\nAndhra Pradesh\tZahirabad\tSYED FEROZUDDIN\t8180\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tZahirabad\tDR. SUNEEL KUMAR CHOWDHARY\t3285\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tZahirabad\tRAMA RAO\t4426\tno\tIND\tIndependent\nAndhra Pradesh\tZahirabad\tK.MADAN MOHAN RAO\t157497\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tZahirabad\tBENJAMIN RAJU\t4804\tno\tIND\tIndependent\nAndhra Pradesh\tZahirabad\tMAHMOOD MOHIUDDIN\t12383\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tZahirabad\tNone of the Above\t11190\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMedak\tKALVAKUNTLA CHANDRASEKHAR RAO\t657492\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tMedak\tIQBAL ALI ZAFAR\t5010\tno\tMBT\tMajlis Bachao Tahreek\nAndhra Pradesh\tMedak\tNone of the Above\t10781\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMedak\tBOMMA BALESHAM\t3297\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tMedak\tDR.P. SHRAVAN KUMAR REDDY\t260463\tno\tINC\tIndian National Congress\nAndhra Pradesh\tMedak\tMEKA. SUBBA RAO\t4441\tno\tIND\tIndependent\nAndhra Pradesh\tMedak\tBALARAJU MITTAPALLY\t2973\tno\tIND\tIndependent\nAndhra Pradesh\tMedak\tGURJADA BHEERAIAH YADAV\t2053\tno\tIND\tIndependent\nAndhra Pradesh\tMedak\tPRABHUGOUD, PULLAIAHGARI\t11752\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tMedak\tKOMPELLI PRABHUDAS\t7599\tno\tRPI(A)\tRepublican Party of India (A)\nAndhra Pradesh\tMedak\tCHAGANLA NARENDRA NATH\t181804\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tMedak\tGAJABINKAR BANSILAL\t3281\tno\tIND\tIndependent\nAndhra Pradesh\tMedak\tY. SHANKAR GOUD\t6643\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tMedak\tKUNDETI. RAVI.\t33507\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tCH.MALLA REDDY\t523336\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tMalkajgiri\tV.DINESH REDDY\t115170\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tMalkajgiri\tSATHYANARAYANA YAADAV PEDDIBOYINA\t597\tno\tBCBDP\tB.C.Bharata Desam Party\nAndhra Pradesh\tMalkajgiri\tB.VEERA HANUMANTHA REDDY\t775\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tHANUMANTH RAO MYNAMPALLY\t494965\tno\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tMalkajgiri\tSARVEY SATHYANARAYANA\t233711\tno\tINC\tIndian National Congress\nAndhra Pradesh\tMalkajgiri\tPOLISETTY KRISHNA RAO\t496\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tMURARI.T.N\t1390\tno\tSHS\tShivsena\nAndhra Pradesh\tMalkajgiri\tM.NAGESWARA RAO\t1301\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tBANDI SUDHAKAR\t4094\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tMalkajgiri\tDR.N.V.SUDHA KIRAN\t8291\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tMalkajgiri\tJAJULA BHASKAR\t8003\tno\tSHP\tShramajeevi Party\nAndhra Pradesh\tMalkajgiri\tKANTE SAYANNA\t2262\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tPADURI SRINIVAS REDDY\t803\tno\tTLP\tTelangana Loksatta Party\nAndhra Pradesh\tMalkajgiri\tPEESARI SATISH REDDY\t1325\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tRAHUL PANDIT\t899\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tANJANEYULU.E\t2677\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tDR.JAYAPRAKASH  NARAYAN .N\t158243\tno\tLSP\tLok Satta Party\nAndhra Pradesh\tMalkajgiri\tPOTTURI TULASI DAS\t411\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tKARUNAKAR.R\t6133\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tMalkajgiri\tVATTIKUTI RAMARAO GOUD\t1032\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tD.BIKSHAPATHI\t5717\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tSUTHARAPU PADMAIAH\t538\tno\tAPRSSP\tAndhra Pradesh Rashtra Samaikya Samithi Party\nAndhra Pradesh\tMalkajgiri\tM.T.VARUGHESE\t1026\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tGAJABINKAR BANSILAL\t1416\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tNone of the Above\t8716\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMalkajgiri\tSURAKANTI VENKAT REDDY\t2049\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tMalkajgiri\tDASARI BHARATH REDDY\t1696\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tDIVAKAR DHARANIKOTA SUDHAKAR\t18543\tno\tAIMIM\tAll India Majlis-E-Ittehadul Muslimeen\nAndhra Pradesh\tMalkajgiri\tPROF. K.NAGESHWAR\t13236\tno\tIND\tIndependent\nAndhra Pradesh\tMalkajgiri\tKOYALKAR BHOJ RAJ\t1546\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tBANDARU DATTATREYA\t438271\tyes\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tSecundrabad\tSYED MADANI QUADRI\t376\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tDR.N.S.UDAY KUMAR\t928\tno\tpjdl\tPrem Janata Dal\nAndhra Pradesh\tSecundrabad\tKHAJA KHALID ANWAR\t2863\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tSecundrabad\tK. SRINIVASULU\t3691\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tSecundrabad\tAVULA RANGANAYAKULU\t1144\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tSONU SARSAL VALMIKI\t2311\tno\tTECPI\tTelangana Communist Party of India\nAndhra Pradesh\tSecundrabad\tS. LAKSHMAN RAO\t886\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tSYED SAJID ALI\t45187\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tSecundrabad\tMOHAMMED RIZWAN HUSSAIN\t370\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tK.SAI KRISHNA MOHAN RAO\t2603\tno\tICSP\tIndian Christian Secular Party\nAndhra Pradesh\tSecundrabad\tNARLA MOHAN RAO\t145120\tno\tAIMIM\tAll India Majlis-E-Ittehadul Muslimeen\nAndhra Pradesh\tSecundrabad\tDR.MALATHI LATHA EPPA\t873\tno\tJAP\tJantantra Party\nAndhra Pradesh\tSecundrabad\tNone of the Above\t6572\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tSecundrabad\tCH. MURAHARI\t554\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAndhra Pradesh\tSecundrabad\tSHAKTHI SATYAVATHI\t638\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tSecundrabad\tKARANKOTE NARENDER\t474\tno\tAIFB\tAll India Forward Bloc\nAndhra Pradesh\tSecundrabad\tB.DEEPAK KUMAR\t1813\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nAndhra Pradesh\tSecundrabad\tMOHD ABUBAKAR PARWAIZ SIDDIQUI\t322\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tABDUS SATTAR MUJAHED\t809\tno\tABML(S)\tAkhil Bharatiya Muslim League (Secular)\nAndhra Pradesh\tSecundrabad\tMICHAEL DANIEL OVELZ\t291\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tKUMAR CHOWDARY YADAV.G\t547\tno\tsps\tSamaikyandhra Parirakshana Samithi\nAndhra Pradesh\tSecundrabad\tANANTH RAO GUNDEPALLY\t779\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tCHAYA RATAN MUSINIPALLY\t11184\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tSecundrabad\tJ.RAMULU\t2058\tno\tRSP\tRevolutionary Socialist Party\nAndhra Pradesh\tSecundrabad\tRAGHU DESHABOINA\t1496\tno\tBMUP\tBahujan Mukti Party\nAndhra Pradesh\tSecundrabad\tT.BHEEMSEN\t143847\tno\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tSecundrabad\tM.ANJAN KUMAR YADAV\t183536\tno\tINC\tIndian National Congress\nAndhra Pradesh\tSecundrabad\tKLHAJA SIRAJUDDIN AZAZ\t959\tno\tmmsp\tMajlis Markaz-e-Siyasee Party\nAndhra Pradesh\tSecundrabad\tB.SUBRAMANYAM\t2481\tno\tIND\tIndependent\nAndhra Pradesh\tSecundrabad\tMOHD IMTIAZ HUSSAIN\t786\tno\tIUML\tIndian Union Muslim League\nAndhra Pradesh\tHyderabad\tASADUDDIN OWAISI\t513868\tyes\tAIMIM\tAll India Majlis-E-Ittehadul Muslimeen\nAndhra Pradesh\tHyderabad\tNone of the Above\t5013\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tHyderabad\tLUBNA SARWATH\t6118\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tHyderabad\tN.MAHENDER SINGH\t485\tno\tNBNP\tNava Bharat National Party\nAndhra Pradesh\tHyderabad\tM A HABEEB\t5992\tno\tIND\tIndependent\nAndhra Pradesh\tHyderabad\tBODDU SAINATH REDDY\t10743\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tHyderabad\tS KRISHNA REDDY\t49310\tno\tINC\tIndian National Congress\nAndhra Pradesh\tHyderabad\tSYED MUSTAFA MAHMOOD\t2102\tno\tMBT\tMajlis Bachao Tahreek\nAndhra Pradesh\tHyderabad\tVENU GOPAL.P\t3100\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tHyderabad\tRASHID SHAREEF\t37195\tno\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tHyderabad\tS.KHAJA MOINUDDIN\t1593\tno\tAIACP\tAll India Azaad Congress Party\nAndhra Pradesh\tHyderabad\tSHAIK MOIN\t626\tno\tIND\tIndependent\nAndhra Pradesh\tHyderabad\tDR.BHAGAVANTH RAO\t311414\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tHyderabad\tMD OSMAN\t728\tno\tIND\tIndependent\nAndhra Pradesh\tHyderabad\tJAYA VINDHYALA\t598\tno\tSP(I)\tSocialist Party (India)\nAndhra Pradesh\tHyderabad\tSYED ABDUL GAFFER\t740\tno\tIND\tIndependent\nAndhra Pradesh\tHyderabad\tBINGHI RAJASHEKAR\t21796\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tCHELVELLA\tKONDA VISHWESHWAR REDDY\t435077\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tCHELVELLA\tCHAN PASHA\t56835\tno\tABML(S)\tAkhil Bharatiya Muslim League (Secular)\nAndhra Pradesh\tCHELVELLA\tG.MALLESHAM GOUD\t24660\tno\tIND\tIndependent\nAndhra Pradesh\tCHELVELLA\tKUMMARI GIRI\t5789\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tCHELVELLA\tREGATTE VENKAT REDDY\t7462\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tCHELVELLA\tSYED HASHMATHULLAH QUADRI\t1411\tno\tIUML\tIndian Union Muslim League\nAndhra Pradesh\tCHELVELLA\tMD YUNUS MIYA\t2904\tno\tMCPI\tMarxist Communist Party of India (United)\nAndhra Pradesh\tCHELVELLA\tSANIKE RAM GOPAL YADAV\t2058\tno\tIND\tIndependent\nAndhra Pradesh\tCHELVELLA\tSUTHARAPU PADMAIAH\t2214\tno\tAPRSSP\tAndhra Pradesh Rashtra Samaikya Samithi Party\nAndhra Pradesh\tCHELVELLA\tDR.GOLLA RAMAIAH\t1765\tno\tTLP\tTelangana Loksatta Party\nAndhra Pradesh\tCHELVELLA\tTULLA VEERENDER\t353203\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tCHELVELLA\tPATLOLLA KARTIK REDDY\t362054\tno\tINC\tIndian National Congress\nAndhra Pradesh\tCHELVELLA\tSAMALA RAVINDAR\t7732\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tCHELVELLA\tKONDA RAGHAVA REDDY\t40135\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tCHELVELLA\tDR. P. SADHU SATYANADHAN\t2545\tno\tICSP\tIndian Christian Secular Party\nAndhra Pradesh\tCHELVELLA\tNone of the Above\t10018\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMahbubnagar\tAP JITHENDER REDDY\t334228\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tMahbubnagar\tNone of the Above\t9037\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMahbubnagar\tS. SHIVA SHANKAR\t3252\tno\tIND\tIndependent\nAndhra Pradesh\tMahbubnagar\tDR. CHANDRA SEKHAR. A\t4033\tno\tIND\tIndependent\nAndhra Pradesh\tMahbubnagar\tKADIRE KRISHNA\t15779\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tMahbubnagar\tDR. JANARDHAN REDDY NAGAM\t272791\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tMahbubnagar\tH.A. RAHMAN\t9105\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tMahbubnagar\tIBRAHIM SYED\t4549\tno\tIND\tIndependent\nAndhra Pradesh\tMahbubnagar\tJAIPAL REDDY SUDINI\t331638\tno\tINC\tIndian National Congress\nAndhra Pradesh\tMahbubnagar\tS.R.PREMAIAH\t30388\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tNagarkurnool\tYELLAIAH NANDI\t420075\tyes\tINC\tIndian National Congress\nAndhra Pradesh\tNagarkurnool\tBAHADHUR SRINIVAS\t12089\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tNagarkurnool\tBUDDULA SRINIVAS\t54680\tno\tIND\tIndependent\nAndhra Pradesh\tNagarkurnool\tBAKKANI NARSIMLU\t183352\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tNagarkurnool\tMAREDU GOPAL\t22985\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tNagarkurnool\tDR. MANDA JAGANNATH\t403399\tno\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tNagarkurnool\tNone of the Above\t12388\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNalgonda\tGUTHA SUKHENDER REDDY\t472093\tyes\tINC\tIndian National Congress\nAndhra Pradesh\tNalgonda\tMANUPURI DAYA SAGAR\t4495\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tNalgonda\tMARRI NEHEMIAH\t56259\tno\tIND\tIndependent\nAndhra Pradesh\tNalgonda\tNANDYALA NARSIMHA REDDY\t54423\tno\tCPM\tCommunist Party of India  (Marxist)\nAndhra Pradesh\tNalgonda\tDR. PALLA RAJESHWAR REDDY\t260677\tno\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tNalgonda\tKUNDARAPU RAMESH\t11517\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tNalgonda\tGUNNAM NAGI REDDY\t39385\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tNalgonda\tTERA CHINNAPA REDDY\t278937\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tNalgonda\tTEJAVATH BELLAIAH NAIK\t2308\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tNalgonda\tNone of the Above\t9305\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tBhongir\tDR. BOORA NARSAIAH GOUD\t448245\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tBhongir\tNone of the Above\t10921\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tBhongir\tCHERUPALLY SEETHARAMULU\t54040\tno\tCPM\tCommunist Party of India  (Marxist)\nAndhra Pradesh\tBhongir\tNAVULLA MALLA REDDY\t26805\tno\tIND\tIndependent\nAndhra Pradesh\tBhongir\tVARIKUPPALA KRISHNA VADDERA\t35011\tno\tIND\tIndependent\nAndhra Pradesh\tBhongir\tGUDURU JANARDHAN REDDY\t2341\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tBhongir\tINDRASENA REDDY NALLU\t183217\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tBhongir\tGUAHATI MOTHILAL\t2600\tno\tAIMIM\tAll India Majlis-E-Ittehadul Muslimeen\nAndhra Pradesh\tBhongir\tRAVULAPALLY KONDAL RAO\t7722\tno\tIND\tIndependent\nAndhra Pradesh\tBhongir\tNAGULA SRINIVAS YADAV\t1859\tno\tMaSP\tMahajana Socialist Party\nAndhra Pradesh\tBhongir\tBOILLA NARENDER\t6850\tno\tRPI\tRepublican Party of India\nAndhra Pradesh\tBhongir\tKAPILAVAI DILEEP KUMAR\t5552\tno\tRLD\tRashtriya Lok Dal\nAndhra Pradesh\tBhongir\tARVAPALLY AMBATI\t9029\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tBhongir\tKOMATIREDDY RAJGOPAL REDDY\t417751\tno\tINC\tIndian National Congress\nAndhra Pradesh\tWarangal\tKADIYAM SRIHARI\t661639\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tWarangal\tRAJAIAH SIRICILLA\t269065\tno\tINC\tIndian National Congress\nAndhra Pradesh\tWarangal\tVENU ESAMPELLY\t1541\tno\tAIFB\tAll India Forward Bloc\nAndhra Pradesh\tWarangal\tVENKANNA NEERUDU\t2098\tno\tBMUP\tBahujan Mukti Party\nAndhra Pradesh\tWarangal\tJANNU MAHENDAR\t3211\tno\tIND\tIndependent\nAndhra Pradesh\tWarangal\tKANAKAM RAMESH\t2013\tno\tIND\tIndependent\nAndhra Pradesh\tWarangal\tVENKANNA KANNAM\t2258\tno\tMCPI\tMarxist Communist Party of India (United)\nAndhra Pradesh\tWarangal\tPARAMESHWAR RAMAGALLA\t187139\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tWarangal\tCHILUVERU PRATHAP\t9178\tno\tIND\tIndependent\nAndhra Pradesh\tWarangal\tGANNARAPU SURENDER\t9536\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tWarangal\tTHUPPARI NARASIMHA SWAMY\t7350\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tWarangal\tCHINTHA SWAMY\t5569\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tWarangal\tNone of the Above\t14034\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMahabubabad\tPROF. AZMEERA SEETARAM NAIK\t320569\tyes\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tMahabubabad\tNone of the Above\t6705\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMahabubabad\tPADIGA YARRAIAH\t3492\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tLAXMAN BUKYA\t14026\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tLAXMINARAYANA BORRA\t5888\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tKAVITHA RANI LUNAVATH\t6627\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tMahabubabad\tPONAKA SAMMAKKA\t3475\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tBODA RAASI\t6224\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tMahabubabad\tPAYAM CHANDHAR RAO (CHINNA CHANDHRANNA)\t94014\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tTELLAM VENKATA RAO\t128465\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tMahabubabad\tBANOTH MOHANLAL\t215904\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tMahabubabad\tBALUNAYAK BHUKYA\t3094\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tDATLA NAGESHWAR RAO\t6854\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tMETLA PAPAIAH\t3922\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tGUGULOTU JAGAN\t3671\tno\tMCPI\tMarxist Communist Party of India (United)\nAndhra Pradesh\tMahabubabad\tEESAM SUDHAKAR\t2199\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tV. GOURI NAGAMANI\t13664\tno\tIND\tIndependent\nAndhra Pradesh\tMahabubabad\tP. BALRAM\t285577\tno\tINC\tIndian National Congress\nAndhra Pradesh\tKhammam\tPONGULETI  SRINIVASA REDDY\t421957\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tKhammam\tLINGALA RAVI KUMAR\t9454\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tNAGIREDDY RAJI REDDY\t4480\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tBANOTH LAXMA NAIK\t1338\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tBUSIREDDY SHANKAR REDDY\t963\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tMEDARAMETLA  RAMA RAO\t4135\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tKhammam\tBANOTH KISHAN NAYAK\t1611\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tRAMESH KOMMU\t1267\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tKAKI BHASKAR\t5354\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tNAMA NAGESWARA RAO\t409983\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tKhammam\tKANKANALA NARAYANA\t187653\tno\tCPI\tCommunist Party of India\nAndhra Pradesh\tKhammam\tSANAPA POMMAIAH\t886\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tBUDAN BAIG SHAIK\t89063\tno\tTRS\tTelangana Rashtra Samithi\nAndhra Pradesh\tKhammam\tGUGULOTHU SARVAN\t1488\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tGUGULOTHU RANJITH KUMAR\t1432\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tMUDIGE NAGESWARA RAO\t1194\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tMARIKANTI VASUDASU\t877\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tMOKALLA RAMESH\t5386\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tKALTHI VENKATESWARLU\t1059\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tJERRIPOTHULA SRINIVAS\t1116\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tNAGARJUNA CHERUKURU\t4999\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tKhammam\tKALTHI RAMULAMMA\t980\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tBACHALA LAXMAIAH\t6494\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tGOKINEPALLI VENKATESWARA RAO\t8053\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tDODDA RAM BABU\t4757\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tKhammam\tKANAKAM TIRUMALA RAO\t916\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tRAMU KANDULA\t1019\tno\tIND\tIndependent\nAndhra Pradesh\tKhammam\tNone of the Above\t4983\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tAruku\tKOTHAPALLI GEETHA\t413191\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tAruku\tNone of the Above\t16532\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tAruku\tGUMMIDI SANDHYARANI\t321793\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tAruku\tMIDIYAM BABURAO\t38898\tno\tCPM\tCommunist Party of India  (Marxist)\nAndhra Pradesh\tAruku\tIILLA RAMI REDDY\t5692\tno\tIND\tIndependent\nAndhra Pradesh\tAruku\tKANGALA BALUDORA\t23215\tno\tIND\tIndependent\nAndhra Pradesh\tAruku\tKISHORE CHANDRA DEO\t52884\tno\tINC\tIndian National Congress\nAndhra Pradesh\tAruku\tVANUGU SANKARARAO\t4614\tno\tIND\tIndependent\nAndhra Pradesh\tAruku\tBURJABARIKI DHANARAJU\t8569\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tAruku\tSALLANGI RATNAM\t7688\tno\tIND\tIndependent\nAndhra Pradesh\tAruku\tBIDDIKA RAMAIAH\t7587\tno\tIND\tIndependent\nAndhra Pradesh\tAruku\tCHETTI SANKARARAO\t8951\tno\tIND\tIndependent\nAndhra Pradesh\tSrikakulam\tRAMMOHAN NAIDU KINJARAPU\t556163\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tSrikakulam\tKADIYAM JAYALAKSHMI\t5021\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tSrikakulam\tJAIDEV INJARAPU\t2557\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tSrikakulam\tKILLI KRUPARANI\t24163\tno\tINC\tIndian National Congress\nAndhra Pradesh\tSrikakulam\tBODDEPALLI RAJARAO\t8047\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tSrikakulam\tKINJARAPU TEJESWARA RAO\t2074\tno\tIND\tIndependent\nAndhra Pradesh\tSrikakulam\tTOTA TEJESWARA RAO\t2144\tno\tIND\tIndependent\nAndhra Pradesh\tSrikakulam\tVASUDEVA RAO BODDU\t5131\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAndhra Pradesh\tSrikakulam\tREDDY SHANTHI\t428591\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tSrikakulam\tPAIDI RAJA RAO\t11422\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tSrikakulam\tNone of the Above\t6133\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tVizianagaram\tASHOK GAJAPATHI RAJU PUSAPATI\t536549\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tVizianagaram\tNone of the Above\t6528\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tVizianagaram\tJHANSI LAKSHMI BOTCHA\t122487\tno\tINC\tIndian National Congress\nAndhra Pradesh\tVizianagaram\tHARIKISHAN KUPPILI\t3603\tno\tIND\tIndependent\nAndhra Pradesh\tVizianagaram\tYELLA RAO SIYYADULA\t5125\tno\tIND\tIndependent\nAndhra Pradesh\tVizianagaram\tVENKATA SWETHA CHALAPATHI KUMARA KRISHNA RANGARAO RAVU\t429638\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tVizianagaram\tPRASADA RAO NANDA\t6401\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tVizianagaram\tTADIVAKA RAMESH NAIDU\t3388\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tVizianagaram\tBONU KRISHNA\t4092\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tVizianagaram\tNARU SIMHADRI NAIDU\t2505\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tVisakhapatnam\tKAMBHAMPATI HARI BABU\t566832\tyes\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tVisakhapatnam\tCHALAPATHIRAO RONANKI\t1594\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tDR|| BHAVANI SANKAR SASTRY.A\t1910\tno\tICSP\tIndian Christian Secular Party\nAndhra Pradesh\tVisakhapatnam\tSREENADH REDDY\t1379\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tNAGENDRA SAIRAM\t2377\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tKURADA SATYANARAYANA PATNAIK\t1927\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tSABBAM HARI\t6644\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tVisakhapatnam\tKUTIKUPPALA SURYA RAO\t1889\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tIMANDI VENKATA KURMARAO\t14947\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tVisakhapatnam\tJ. PADMAJA\t1540\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tSEEMAKURTHI SRINUVASU (BHAGHAVAN SRINU)\t1759\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tBOLISETTI SATYANARAYANA (SATYA)\t50632\tno\tINC\tIndian National Congress\nAndhra Pradesh\tVisakhapatnam\tDURGAPRASAD GUNTU\t2611\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tKUNA RAMAM\t6175\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tVisakhapatnam\tUMAMAHESWARARAO ARETI\t1751\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tJT RAMARAO\t4323\tno\tsps\tSamaikyandhra Parirakshana Samithi\nAndhra Pradesh\tVisakhapatnam\tY.S VIJAYAMMA\t476344\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tVisakhapatnam\tTANUKU DIVAKAR\t5418\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tGEDELA KRISHNARAO\t1118\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tCHANDRASEKHAR BUDDA\t1348\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tDADI JYOTHI BHAVANI\t2717\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tVisakhapatnam\tBRAHMA SWAROOP KASIBHATTA\t994\tno\tIND\tIndependent\nAndhra Pradesh\tVisakhapatnam\tNone of the Above\t7329\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tAnakapalli\tMUTTAMSETTI SRINIVASA RAO (AVANTHI)\t568463\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tAnakapalli\tNone of the Above\t8604\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tAnakapalli\tVIJAYA LAKSHMI THOTA\t17770\tno\tINC\tIndian National Congress\nAndhra Pradesh\tAnakapalli\tGUDIVADA AMARNADH\t520531\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tAnakapalli\tSARISA LEELA PRASANNA KUMARI\t2875\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tAnakapalli\tVADLAMURI KRISHNA SWAROOP\t5506\tno\tDABAP\tDalita Bahujana Party\nAndhra Pradesh\tAnakapalli\tAPPALARAJU SADARAM\t3228\tno\tIND\tIndependent\nAndhra Pradesh\tAnakapalli\tKARANAM TIRUPATHI RAO\t14834\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tAnakapalli\tKONDARAJU JAMPANA\t6261\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tKakinada\tTHOTA NARASIMHAM\t514402\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tKakinada\tPEPAKAYALA SUBHA RAJENDRA\t1161\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tKOLA VENKATA PRASAD VARMA\t3473\tno\tRJD\tRashtriya Janata Dal\nAndhra Pradesh\tKakinada\tDONAM NEELAKANTAM\t1697\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tMOTHA SARADA\t6836\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nAndhra Pradesh\tKakinada\tROKKAM DAYAKAR\t1837\tno\tRP(K)\tRepublican Paksha (Khoripa)\nAndhra Pradesh\tKakinada\tTUMMALAPALLI SATYA RAMAKRISHNA\t6442\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tKakinada\tMUTHABATHULA RATNA KUMAR\t2511\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tKakinada\tTEKUMUDI VENKATA RAO\t3826\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tKakinada\tVINJAMALLA CHITTI BABU\t1400\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tGUTTULA ANNAPURNA SESHAGIRI RAO (BATHPARTY-SESHU)\t1417\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tMERLA BHAVANI SANKARA PRASAD\t910\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tDONKA LEELAVATHI\t3756\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tCHALAMALASETTY SUNIL\t510971\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tKakinada\tCHAGANTI SURYANARAYANA MURTY\t1236\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tARJUNARAO YEGUPATI\t1495\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAndhra Pradesh\tKakinada\tPRABHAKARA RAO MADIKI\t496\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tJALLURI VENKATESWARLU (J.V)\t6636\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tKARRA VENKATA APPALA NARASIMHA RAO\t1907\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tSRINIVAS DANGETI\t2356\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tKakinada\tKUNCHE RAMADAS\t1122\tno\tIND\tIndependent\nAndhra Pradesh\tKakinada\tMALLIPUDI MANGAPATI PALLAM RAJU\t19754\tno\tINC\tIndian National Congress\nAndhra Pradesh\tKakinada\tNone of the Above\t4358\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tAmalapuram\tDR PANDULA RAVINDRA BABU\t594547\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tAmalapuram\tNone of the Above\t6141\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tAmalapuram\tAITHABATHULA JOGESWARA VENKATA BUTCHI MAHESWARA RAO\t12182\tno\tINC\tIndian National Congress\nAndhra Pradesh\tAmalapuram\tG.V.HARSHA KUMAR\t9931\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tAmalapuram\tPINIPE VISWARUPU\t473971\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tAmalapuram\tKOMMU SATYAVATHI\t625\tno\tIND\tIndependent\nAndhra Pradesh\tAmalapuram\tYALANGI RAMESH\t1171\tno\tIND\tIndependent\nAndhra Pradesh\tAmalapuram\tUNDRU GANI RAJU\t862\tno\tIND\tIndependent\nAndhra Pradesh\tAmalapuram\tVADDI WINSTON CHURCHILL\t1684\tno\tIND\tIndependent\nAndhra Pradesh\tAmalapuram\tKUNCHE PEDASATYANARAYANA\t4560\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tAmalapuram\tKONKI VENKATA RAO\t3233\tno\tRP(K)\tRepublican Paksha (Khoripa)\nAndhra Pradesh\tAmalapuram\tKASI CHANDRAMOULI\t1110\tno\tIND\tIndependent\nAndhra Pradesh\tAmalapuram\tGEDDAM SAMPADA RAO\t7219\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tAmalapuram\tBHASKARA RAO BUNGA\t824\tno\tIND\tIndependent\nAndhra Pradesh\tAmalapuram\tVIJAYA CHAKRAVARTHY PANTHAGADA\t2867\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nAndhra Pradesh\tRajahmundry\tMURALI MOHAN MAGANTI\t630573\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tRajahmundry\tANNAMDEVULA DHARMA RAO\t590\tno\tIND\tIndependent\nAndhra Pradesh\tRajahmundry\tPOLU PEDA VENGALA REDDY\t765\tno\tIND\tIndependent\nAndhra Pradesh\tRajahmundry\tMUTYALA RAMBABU\t1282\tno\tIND\tIndependent\nAndhra Pradesh\tRajahmundry\tCHEEKATLA VENKATESWARA RAO\t1356\tno\tIND\tIndependent\nAndhra Pradesh\tRajahmundry\tMULLAPUDI SATYANARAYANA\t11718\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tRajahmundry\tKOLLABATHULA RICHMOND CAREY\t5481\tno\tGaAP\tGareeb Aadmi Party\nAndhra Pradesh\tRajahmundry\tMARRE BABJI\t6079\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tRajahmundry\tPALLA VENKATA NAIDU(PRADEEP)\t989\tno\tIND\tIndependent\nAndhra Pradesh\tRajahmundry\tILLA GOVINDA RAJAN\t1390\tno\tIND\tIndependent\nAndhra Pradesh\tRajahmundry\tSANGISETTI SRINIVASA RAO\t1329\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tRajahmundry\tBODDU VENKATARAMANA CHOWDARY\t463139\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tRajahmundry\tDURGESH KANDULA\t21243\tno\tINC\tIndian National Congress\nAndhra Pradesh\tRajahmundry\tMEDA SRINIVAS\t991\tno\tRPC(S)\tRashtriya Praja Congress  (Secular)\nAndhra Pradesh\tRajahmundry\tNone of the Above\t7456\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNarsapuram\tGOKARAJU GANGA RAJU\t540306\tyes\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tNarsapuram\tNone of the Above\t8004\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNarsapuram\tGITADAS DAS\t23270\tno\tpjdl\tPrem Janata Dal\nAndhra Pradesh\tNarsapuram\tVANKA RAVINDRANATH\t454955\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tNarsapuram\tMEDAPATI  VARAHALA REDDY\t1250\tno\tIND\tIndependent\nAndhra Pradesh\tNarsapuram\tVOLETI VENKATA LAKSHMANA RAO\t1298\tno\tIND\tIndependent\nAndhra Pradesh\tNarsapuram\tVANGALAPUDI YASHYAA\t1281\tno\tIND\tIndependent\nAndhra Pradesh\tNarsapuram\tBADAMPUDI SIVA\t1605\tno\tIND\tIndependent\nAndhra Pradesh\tNarsapuram\tNALLI RAJESH\t6708\tno\tRP(K)\tRepublican Paksha (Khoripa)\nAndhra Pradesh\tNarsapuram\tADDANKI (SEKHAR BABU) DORABABU\t1344\tno\tIND\tIndependent\nAndhra Pradesh\tNarsapuram\tCHINTAPALLI KANAKA RAO\t8489\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tNarsapuram\tSAROJA  POLASAPALLI\t6840\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tNarsapuram\tSHIVAJI GOTTUMUKKALA\t2286\tno\tIND\tIndependent\nAndhra Pradesh\tNarsapuram\tS.V.P.K.H.G.KRISHNAM RAJU\t4232\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tNarsapuram\tKANUMURU BAPI RAJU\t27083\tno\tINC\tIndian National Congress\nAndhra Pradesh\tEluru\tMAGANTI VENKATESWARA RAO (BABU)\t623471\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tEluru\tJULURI BHASKARA VEERA VENKATA ANIL KUMAR GUPTA\t751\tno\tIND\tIndependent\nAndhra Pradesh\tEluru\tMENDEM SANTOSH KUMAR ( PEDABABULU)\t3423\tno\tILP(AP)\tIndian Labour Party (Ambedkar Phule)\nAndhra Pradesh\tEluru\tK.V.V.SATYANARAYANA\t2873\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tEluru\tASHOK YANTRAPATI\t1212\tno\tMaSP\tMahajana Socialist Party\nAndhra Pradesh\tEluru\tGOVADA VIJAYA KUMARI\t4483\tno\tpjdl\tPrem Janata Dal\nAndhra Pradesh\tEluru\tMUSUNURI NAGESWARA RAO\t11770\tno\tINC\tIndian National Congress\nAndhra Pradesh\tEluru\tU VENKATESWARARAO (U.V)\t3971\tno\tIND\tIndependent\nAndhra Pradesh\tEluru\tTHOTA CHANDRA SEKHAR\t521545\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tEluru\tSAVANAPUDI NAGARAJU\t2318\tno\tMCPI\tMarxist Communist Party of India (United)\nAndhra Pradesh\tEluru\tK.VIJAYA LAKSHMI PANDIT\t1320\tno\tIND\tIndependent\nAndhra Pradesh\tEluru\tGANGADHARA RAO NANNAPANENI\t1302\tno\tIND\tIndependent\nAndhra Pradesh\tEluru\tNETALA RAMESH BABU\t11670\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tEluru\tC.V.SARADA DEVI\t2720\tno\tIND\tIndependent\nAndhra Pradesh\tEluru\tU .V. RAMA RAO\t1323\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tEluru\tNone of the Above\t7544\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMachilipatnam\tKONAKALLA NARAYANA RAO\t587280\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tMachilipatnam\tNone of the Above\t8171\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tMachilipatnam\tKOLUSU PARTHA SARATHY\t506223\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tMachilipatnam\tYUNDURI SUBRAMANYASWARA RAO (MANI)\t1597\tno\tIND\tIndependent\nAndhra Pradesh\tMachilipatnam\tJAMPANA SRINIVASA GOUD\t931\tno\tIND\tIndependent\nAndhra Pradesh\tMachilipatnam\tPRASADA RAO PEGGEM\t4085\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tMachilipatnam\tSISTLA RAMESH\t14111\tno\tINC\tIndian National Congress\nAndhra Pradesh\tMachilipatnam\tKAMMILI SRINIVAS\t7692\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tMachilipatnam\tMOHAMMAD ISHAQ\t1770\tno\tIND\tIndependent\nAndhra Pradesh\tMachilipatnam\tPARASA SRINIVASA RAO\t3399\tno\tIND\tIndependent\nAndhra Pradesh\tMachilipatnam\tYARLAGADDA RAMA MOHAN RAO\t1405\tno\tIND\tIndependent\nAndhra Pradesh\tMachilipatnam\tVADAPALLI RAGHUNADH\t4401\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tVijayawada\tKESINENI SRINIVAS\t592696\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tVijayawada\tRAJA KUMARI GUNDAPANENI\t2570\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tVijayawada\tM.PHANI PRAKASH\t2809\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tKAMBALA. SUBRAHMANYAM (SASTRY)\t7538\tno\tWPOI\tWelfare Party Of India\nAndhra Pradesh\tVijayawada\tCHIGURUPATI RAMALINGESWARA RAO\t3579\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tVijayawada\tTATINENI SRINIVASA RAO\t2456\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tHAR MOHINDER SINGH SAHANI\t3088\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tVijayawada\tISHWARYA LAKSHMI CHINNAM\t5296\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tVijayawada\tSUNKARA KRISHNA MURTHY\t803\tno\tARPS\tAndhra Rastra Praja Samithi\nAndhra Pradesh\tVijayawada\tV.R.K.MURTHY.\t748\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tYARAM KATAM RAJU\t1402\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tP.V. KRISHNAIAH\t423\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tKONERU RAMACHANDRA PRASAD\t1096\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tCHATRAGADDA PRATAP\t950\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tAHMED ANSARI SYED\t504\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tALTAF ALI MOHAMED\t1167\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tKONERU RAJENDRA PRASAD\t1014\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tDASARI VENKATA NAGESH\t1873\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tSURAPANENI SARATBABU\t1521\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tAVINASH DEVINENI\t39751\tno\tINC\tIndian National Congress\nAndhra Pradesh\tVijayawada\tKONERU RAJENDRA PRASAD\t517834\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tVijayawada\tBOLISETTY HARIBABU\t667\tno\tIND\tIndependent\nAndhra Pradesh\tVijayawada\tNone of the Above\t5290\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tGuntur\tJAYADEV GALLA\t618417\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tGuntur\tNone of the Above\t7596\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tGuntur\tABDUL WAHEED SHAIK\t46818\tno\tINC\tIndian National Congress\nAndhra Pradesh\tGuntur\tEMANI CHANDRA SEKHAR RAO\t1455\tno\tNPT\tNavodyam Party\nAndhra Pradesh\tGuntur\tMALLELA VENKATA RAO\t5608\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tGuntur\tARUDALA VENKATESWARA RAO\t908\tno\tIND\tIndependent\nAndhra Pradesh\tGuntur\tMALLELA BABU RAO\t4223\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tGuntur\tUDAIA KUMAR MUSUKU\t2852\tno\tICSP\tIndian Christian Secular Party\nAndhra Pradesh\tGuntur\tBALASHOWRY VALLABHANENI\t549306\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tGuntur\tKATA VEERA VARA PRASAD\t2344\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tGuntur\tBITRA VENKATA SIVANNARAYANA\t1169\tno\tIND\tIndependent\nAndhra Pradesh\tGuntur\tDR. KOUTHARAPU RAVINDRA\t2594\tno\tGRIP\tGreat India Party\nAndhra Pradesh\tGuntur\tABBURI KOTESWARA RAO\t1636\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tNarasaraopet\tSAMBASIVA RAO RAYAPATI\t632464\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tNarasaraopet\tRAVU SUBRAHMANYAM\t1025\tno\tIND\tIndependent\nAndhra Pradesh\tNarasaraopet\tAYODHYA RAMIREDDY ALLA\t597184\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tNarasaraopet\tJANGALA SINGARAIAH YADAV\t973\tno\tIND\tIndependent\nAndhra Pradesh\tNarasaraopet\tMEKALA HANUMANTHA RAO YADAV\t4911\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tNarasaraopet\tYELURI SRILATHA\t5142\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tNarasaraopet\tROSAIAH SIVAPARAPU\t1877\tno\tMCPI\tMarxist Communist Party of India (United)\nAndhra Pradesh\tNarasaraopet\tKANUMURI KOTESWARA RAO\t1404\tno\tIND\tIndependent\nAndhra Pradesh\tNarasaraopet\tPARUVURI RAMESH\t1118\tno\tIND\tIndependent\nAndhra Pradesh\tNarasaraopet\tAKKI REDDY BHIMANADULA\t7032\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tNarasaraopet\tKONDAPALLI VENKATESWARLU\t22943\tno\tINC\tIndian National Congress\nAndhra Pradesh\tNarasaraopet\tNone of the Above\t5985\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tBapatla\tMALYADRI SRIRAM\t578145\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tBapatla\tNone of the Above\t6146\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tBapatla\tGADDE THYAGARAJU\t749\tno\tIND\tIndependent\nAndhra Pradesh\tBapatla\tRATNABABU MAMIDI\t1206\tno\tIND\tIndependent\nAndhra Pradesh\tBapatla\tR.D.WILSON\t3657\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tBapatla\tBABURAO GOLLA\t823\tno\tIND\tIndependent\nAndhra Pradesh\tBapatla\tGOGULAMUDI NARASIMHA RAO\t720\tno\tIND\tIndependent\nAndhra Pradesh\tBapatla\tBANDELA NAGESWARA RAO\t668\tno\tIND\tIndependent\nAndhra Pradesh\tBapatla\tDWARAPALLI PHILIP\t8113\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tBapatla\tYALLAMATI RAMESH\t8414\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tBapatla\tVARIKUTI AMRUTHAPANI\t545391\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tBapatla\tPANABAKA LAKSHMI\t23072\tno\tINC\tIndian National Congress\nAndhra Pradesh\tBapatla\tNAKKALA VEERANJANEYULU\t615\tno\tIND\tIndependent\nAndhra Pradesh\tBapatla\tPERLI PRASANNA DEEVENA KUMAR (PRASANNA)\t2967\tno\tILP(AP)\tIndian Labour Party (Ambedkar Phule)\nAndhra Pradesh\tBapatla\tEDA CHENNAIAH\t3948\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tOngole\tY.V.SUBBA REDDY\t589960\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tOngole\tKRISHNA RAO VEMULA\t5863\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tOngole\tBELLAMKONDA VENKATRAO\t1001\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tDARISI PAVAN KUMAR\t13357\tno\tINC\tIndian National Congress\nAndhra Pradesh\tOngole\tMAGUNTA SREENIVASULU REDDY\t574302\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tOngole\tDAMA MOHAN RAO\t492\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tKAVURI VENUBABU NAIDU\t3207\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tS.S.N.RAJA YADAV\t4299\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tOngole\tPIDAPARTHI MADHUSUDHANA REDDY\t624\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tCHAPALA VENKATA RAO\t880\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tB. BALAJEE RAO\t657\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tRADHAKRISHNA SOMISETTY\t1936\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tPAMMI VENKA REDDY\t1387\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tOngole\tGOLLAPALLI KISHORE KUMAR REDDY\t3724\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tOngole\tKIRAN DASARANNA MATTEGUNTA\t755\tno\tIND\tIndependent\nAndhra Pradesh\tOngole\tNone of the Above\t5781\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNandyal\tS.P.Y REDDY\t622411\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tNandyal\tNone of the Above\t7115\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNandyal\tP.NARENDRA DEV\t936\tno\tIND\tIndependent\nAndhra Pradesh\tNandyal\tNOSSAM MALLIKARJUNA REDDY\t7189\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tNandyal\tM RAMANA REDDY\t5400\tno\tIND\tIndependent\nAndhra Pradesh\tNandyal\tVANGALA SREENIVASA REDDY\t2437\tno\tIND\tIndependent\nAndhra Pradesh\tNandyal\tOBULESU THUDUM\t11784\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tNandyal\tK.P.KAMBAGIRI SWAMI\t2708\tno\tIND\tIndependent\nAndhra Pradesh\tNandyal\tI.V PAKKIRA REDDY\t1036\tno\tIND\tIndependent\nAndhra Pradesh\tNandyal\tULLAGI DAVID JAYA KUMAR\t2499\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tNandyal\tP.V.N. REDDY\t5598\tno\tAIMIM\tAll India Majlis-E-Ittehadul Muslimeen\nAndhra Pradesh\tNandyal\tB.Y.RAMAIAH\t16378\tno\tINC\tIndian National Congress\nAndhra Pradesh\tNandyal\tPIDISELA VIJAY KUMAR\t1325\tno\tIND\tIndependent\nAndhra Pradesh\tNandyal\tGADDALA BALASWAMY\t1495\tno\tIND\tIndependent\nAndhra Pradesh\tNandyal\tN.MD.FAROOK\t516645\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tKurnool\tBUTTA RENUKA\t472782\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tKurnool\tR.J.MALLIKAL\t5746\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tKurnool\tVISSA KIRAN KUMAR\t6522\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tKurnool\tB.T. NAIDU\t428651\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tKurnool\tKOTLA JAYASURYAPRAKASH REDDY\t116603\tno\tINC\tIndian National Congress\nAndhra Pradesh\tKurnool\tNAGELLA ELIA\t1990\tno\tWPOI\tWelfare Party Of India\nAndhra Pradesh\tKurnool\tG.JAKEER HUSSAIN\t3600\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tKurnool\tKONDIPARTHI .VENKAT  PRASAD\t1614\tno\tRPI\tRepublican Party of India\nAndhra Pradesh\tKurnool\tD YUGANDHAR\t2820\tno\tIND\tIndependent\nAndhra Pradesh\tKurnool\tKOTHURU.SATYANARAYANA GUPTA\t2021\tno\tIND\tIndependent\nAndhra Pradesh\tKurnool\tD CHAND BASHA\t2605\tno\tIND\tIndependent\nAndhra Pradesh\tKurnool\tNone of the Above\t9385\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tKurnool\tNOOR AHMED MADANI\t11393\tno\tSDP\tSocialistic Democratic Party\nAndhra Pradesh\tAnantapur\tJ.C. DIVAKAR REDDI\t606509\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tAnantapur\tNone of the Above\t8798\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tAnantapur\tKASANI NAGARAJU\t6201\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tAnantapur\tSHAIK. MOHAMMAD SHAFI.\t3018\tno\tIND\tIndependent\nAndhra Pradesh\tAnantapur\tT.KULDEEP CHOWDARY\t6517\tno\tSTR\tSamaikya Telugu Rajyam\nAndhra Pradesh\tAnantapur\tANIL CHOWDARY.  P\t16659\tno\tINC\tIndian National Congress\nAndhra Pradesh\tAnantapur\tPAVAN KUMAR\t2070\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tAnantapur\tB. SRINIVASULU\t1589\tno\tIND\tIndependent\nAndhra Pradesh\tAnantapur\tG. NAGARAJU\t1086\tno\tIND\tIndependent\nAndhra Pradesh\tAnantapur\tBOYA ANIL KUMAR\t2779\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tAnantapur\tANANTHA VENKATARAMIREDDY\t545240\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tAnantapur\tD. SOMANATH\t2263\tno\tIND\tIndependent\nAndhra Pradesh\tAnantapur\tBUZIREDDY VENUGOPAL REDDY\t1381\tno\tICSP\tIndian Christian Secular Party\nAndhra Pradesh\tAnantapur\tG. LALITHA\t944\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAndhra Pradesh\tHindupur\tKRISTAPPA NIMMALA\t604291\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tHindupur\tD. SOMANATH\t2032\tno\tIND\tIndependent\nAndhra Pradesh\tHindupur\tASADULLAH K.M\t1099\tno\tIND\tIndependent\nAndhra Pradesh\tHindupur\tMADDIPATLA SUDHAKAR NAIDU\t2332\tno\tIND\tIndependent\nAndhra Pradesh\tHindupur\tDUDDUKUNTA SREEDHAR REDDY\t506966\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tHindupur\tG.C VENKATARAMUDU\t36452\tno\tINC\tIndian National Congress\nAndhra Pradesh\tHindupur\tK.PEDDA REDDI\t1152\tno\tIND\tIndependent\nAndhra Pradesh\tHindupur\tANAND\t3101\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tHindupur\tAPPIREDDYGARI KONDA REDDY\t1257\tno\tIND\tIndependent\nAndhra Pradesh\tHindupur\tCHINTA SARATH KUMAR REDDY\t1275\tno\tIND\tIndependent\nAndhra Pradesh\tHindupur\tK.MOHAMMAD SHAMEER BASHA\t4590\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tHindupur\tAMUDALA SUBRAMANYAM\t4521\tno\tRPS\tRayalaseema Parirakshana Samithi\nAndhra Pradesh\tHindupur\tNone of the Above\t8189\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tKadapa\tY.S. AVINASH REDDY\t671983\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tKadapa\tNone of the Above\t6058\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tKadapa\tDR. SHAIK GHOUSE PEER\t3219\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tKadapa\tYELLIPALEM RAMESH.REDDY\t3809\tno\tJD(U)\tJanata Dal  (United)\nAndhra Pradesh\tKadapa\tPOREDDY PRABHAKARA REDDY\t1217\tno\tIND\tIndependent\nAndhra Pradesh\tKadapa\tG.V.A.K. REDDY\t888\tno\tJD(S)\tJanata Dal  (Secular)\nAndhra Pradesh\tKadapa\tRACHINENI. VENUGOPAL\t1034\tno\tRPC(S)\tRashtriya Praja Congress  (Secular)\nAndhra Pradesh\tKadapa\tMALIKIREDDY. HANUMANTHA REDDY\t5515\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tKadapa\tVARADI SOBHARANI\t2240\tno\tDABAP\tDalita Bahujana Party\nAndhra Pradesh\tKadapa\tB. PULLAIAH\t2565\tno\tANC\tAmbedkar National Congress\nAndhra Pradesh\tKadapa\tPUTHA. LAKSHMI REDDY\t933\tno\tAIFB\tAll India Forward Bloc\nAndhra Pradesh\tKadapa\tSAJID HUSSAIN\t3401\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tKadapa\tGAJJALA.RAMA SUBBA REDDY\t1821\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tKadapa\tAJAYA KUMAR VEENA\t14319\tno\tINC\tIndian National Congress\nAndhra Pradesh\tKadapa\tSRINIVASA REDDY REDDEPPAGARI\t481660\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tNellore\tMEKAPATI RAJAMOHAN REDDY\t576396\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tNellore\tPATTAPU RAVI\t3299\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tNellore\tADALA PRABHAKARA REDDY\t562918\tno\tTDP\tTelugu Desam\nAndhra Pradesh\tNellore\tKOLATI GOPINATH\t999\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tNellore\tMEDA MALLA REDDY\t820\tno\tIND\tIndependent\nAndhra Pradesh\tNellore\tCHEMIKALA TIRUPATI\t1424\tno\tARPS\tAndhra Rastra Praja Samithi\nAndhra Pradesh\tNellore\tMALLI VENKATESWARLU\t686\tno\tIND\tIndependent\nAndhra Pradesh\tNellore\tPANDHITI SUBBAIAH\t736\tno\tIND\tIndependent\nAndhra Pradesh\tNellore\tNAVEEN SUKPALLI\t626\tno\tIND\tIndependent\nAndhra Pradesh\tNellore\tSYED HANEEF\t5578\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tNellore\tMALYADRI RAVULAKOLLU\t875\tno\tIND\tIndependent\nAndhra Pradesh\tNellore\tANANDA RAO SOMPALLI\t760\tno\tIND\tIndependent\nAndhra Pradesh\tNellore\tNARAYANA REDDY VAKATI\t22870\tno\tINC\tIndian National Congress\nAndhra Pradesh\tNellore\tNone of the Above\t5549\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tNellore\tCHANDRA SEKHAR YADAV\t4112\tno\tRDHP\tRajyadhikara Party\nAndhra Pradesh\tTirupati\tVARAPRASAD RAO VELAGAPALLI\t580376\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tTirupati\tNone of the Above\t8690\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tTirupati\tDR. U RAMACHANDRA, M.B.B.S\t6036\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tTirupati\tK.S. MUNIRATHNAM\t1696\tno\tIND\tIndependent\nAndhra Pradesh\tTirupati\tKALAVAPALLI SAHADEVAIAH\t2510\tno\tIND\tIndependent\nAndhra Pradesh\tTirupati\tKARUMANCHI JAYARAM\t542951\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tTirupati\tTHYDULA SUBBARAMAIAH\t1403\tno\tIND\tIndependent\nAndhra Pradesh\tTirupati\tNUNE VENGAIAH\t1829\tno\tIND\tIndependent\nAndhra Pradesh\tTirupati\tNEERUGUTTA NAGESWARA\t7280\tno\tAAAP\tAam Aadmi Party\nAndhra Pradesh\tTirupati\tSONGA RATNAMMA\t1645\tno\tIND\tIndependent\nAndhra Pradesh\tTirupati\tM SOLMON\t9816\tno\tARPS\tAndhra Rastra Praja Samithi\nAndhra Pradesh\tTirupati\tKATTAMANCHI PRABHAKAR\t1455\tno\tIND\tIndependent\nAndhra Pradesh\tTirupati\tDEGALA SURYA NARAYANA\t2876\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tTirupati\tCHINTA MOHAN\t33333\tno\tINC\tIndian National Congress\nAndhra Pradesh\tTirupati\tKOTHAPALLI SUBRAMANYAM\t11168\tno\tCPM\tCommunist Party of India  (Marxist)\nAndhra Pradesh\tRajampet\tP.V.MIDHUN REDDY\t601752\tyes\tYSRCP\tYuvajana Sramika Rythu Congress Party\nAndhra Pradesh\tRajampet\tG.MUJEEB HUSSAIN\t59777\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tRajampet\tANNAYYAGARI SAI PRATHAP\t29332\tno\tINC\tIndian National Congress\nAndhra Pradesh\tRajampet\tS NARENDRA BABU\t15086\tno\tMaSP\tMahajana Socialist Party\nAndhra Pradesh\tRajampet\tN.  DEVA\t3896\tno\tHJP\tHindustan Janta Party\nAndhra Pradesh\tRajampet\tV PATTABHI\t3549\tno\tIND\tIndependent\nAndhra Pradesh\tRajampet\tVENKATA SUBBAIAH NALLUGARI\t8189\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tRajampet\tSHAIK JEELANI SAHEB\t2630\tno\tIND\tIndependent\nAndhra Pradesh\tRajampet\tD.  PURANDESWARI\t426990\tno\tBJP\tBharatiya Janata Party\nAndhra Pradesh\tRajampet\tNone of the Above\t7116\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tChittoor\tNARAMALLI SIVAPRASAD\t594862\tyes\tTDP\tTelugu Desam\nAndhra Pradesh\tChittoor\tNone of the Above\t6996\tno\tNOTA\tNone of the Above\nAndhra Pradesh\tChittoor\tJITHENDRA KUMAR.DHANDU\t15897\tno\tBSP\tBahujan Samaj Party\nAndhra Pradesh\tChittoor\tUDAYA C.S\t3803\tno\tPPOI\tPyramid Party of India\nAndhra Pradesh\tChittoor\tDR.B.RAJAGOPAL\t16572\tno\tINC\tIndian National Congress\nAndhra Pradesh\tChittoor\tS. ANJAPPA\t4321\tno\tIND\tIndependent\nAndhra Pradesh\tChittoor\tP. PUSHPA RAJ\t5740\tno\tJaSPa\tJai Samaikyandhra Party\nAndhra Pradesh\tChittoor\tG.SAMANYAKIRAN\t550724\tno\tYSRCP\tYuvajana Sramika Rythu Congress Party\nArunachal Pradesh\tARUNACHAL WEST\tKIREN RIJIJU\t169367\tyes\tBJP\tBharatiya Janata Party\nArunachal Pradesh\tARUNACHAL WEST\tNone of the Above\t1816\tno\tNOTA\tNone of the Above\nArunachal Pradesh\tARUNACHAL WEST\tTAKAM SANJOY\t127629\tno\tINC\tIndian National Congress\nArunachal Pradesh\tARUNACHAL WEST\tJALLEY SONAM\t14664\tno\tPPA\tPeople's Party of Arunachal\nArunachal Pradesh\tARUNACHAL WEST\tGICHO KABAK\t6065\tno\tNCP\tNationalist Congress Party\nArunachal Pradesh\tARUNACHAL WEST\tGUMJUM HAIDER\t9135\tno\tAITC\tAll India Trinamool Congress\nArunachal Pradesh\tARUNACHAL WEST\tHABUNG PAYENG\t3647\tno\tAAAP\tAam Aadmi Party\nArunachal Pradesh\tARUNACHAL WEST\tTABA TAKU\t980\tno\tLB\tLok Bharati\nArunachal Pradesh\tARUNACHAL WEST\tSUBU KECHI\t2362\tno\tIND\tIndependent\nArunachal Pradesh\tARUNACHAL EAST\tNINONG ERING\t118455\tyes\tINC\tIndian National Congress\nArunachal Pradesh\tARUNACHAL EAST\tTAPIR GAO\t105977\tno\tBJP\tBharatiya Janata Party\nArunachal Pradesh\tARUNACHAL EAST\tWANGMAN LOWANGCHA\t32354\tno\tPPA\tPeople's Party of Arunachal\nArunachal Pradesh\tARUNACHAL EAST\tNone of the Above\t4505\tno\tNOTA\tNone of the Above\nAssam\tKarimganj\tRADHESHYAM BISWAS\t362866\tyes\tAIUDF\tAll India United Democratic Front\nAssam\tKarimganj\tKRISHNA DAS\t260772\tno\tBJP\tBharatiya Janata Party\nAssam\tKarimganj\tPROBASH CH. SARKAR\t3114\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAssam\tKarimganj\tRINKU MALAKAR\t2275\tno\tSP\tSamajwadi Party\nAssam\tKarimganj\tSABUJ DAS\t1974\tno\tIND\tIndependent\nAssam\tKarimganj\tSAJALENDU MALAKAR\t2465\tno\tIND\tIndependent\nAssam\tKarimganj\tPINTU MALAKAR\t1664\tno\tIND\tIndependent\nAssam\tKarimganj\tLALIT MOHON SUKLABAIDYA\t226562\tno\tINC\tIndian National Congress\nAssam\tKarimganj\tSUJIT MALLICK\t3437\tno\tIND\tIndependent\nAssam\tKarimganj\tTARUN KUMAR DAS\t3658\tno\tAAAP\tAam Aadmi Party\nAssam\tKarimganj\tSANJIT KUMAR DAS\t1880\tno\tIND\tIndependent\nAssam\tKarimganj\tGOUTAM MALAKAR\t2447\tno\tIND\tIndependent\nAssam\tKarimganj\tMAHANANDA DAS\t1592\tno\tIND\tIndependent\nAssam\tKarimganj\tRABINDRA NATH CHOUDHURY\t5556\tno\tAGP\tAsom Gana Parishad\nAssam\tKarimganj\tPRADIP SUKLABAIDYA\t2392\tno\tIND\tIndependent\nAssam\tKarimganj\tNone of the Above\t4266\tno\tNOTA\tNone of the Above\nAssam\tSilchar\tSUSHMITA DEV\t336451\tyes\tINC\tIndian National Congress\nAssam\tSilchar\tNone of the Above\t4310\tno\tNOTA\tNone of the Above\nAssam\tSilchar\tJAYANTA MALLICK\t1605\tno\tIND\tIndependent\nAssam\tSilchar\tNIRUPAM MANDAL\t2462\tno\tIND\tIndependent\nAssam\tSilchar\tKUTUB AHMED MAZUMDER\t85530\tno\tAIUDF\tAll India United Democratic Front\nAssam\tSilchar\tKABINDRA PURKAYASTHA\t301210\tno\tBJP\tBharatiya Janata Party\nAssam\tSilchar\tSUKAMAL DAS\t6221\tno\tIND\tIndependent\nAssam\tSilchar\tBIJOY KRISHNA NATH\t7679\tno\tAGP\tAsom Gana Parishad\nAssam\tSilchar\tMADHU SUDHAN DAS\t3151\tno\tIND\tIndependent\nAssam\tSilchar\tABDUL MANNAN BARBHUIYA\t3118\tno\tAAAP\tAam Aadmi Party\nAssam\tSilchar\tRASHID AHMED LASKAR\t3347\tno\tSP\tSamajwadi Party\nAssam\tSilchar\tKAMAL DAS\t2170\tno\tIND\tIndependent\nAssam\tSilchar\tNILU MANDOL\t1922\tno\tSHS\tShivsena\nAssam\tSilchar\tSHUVADIP DATTA\t8577\tno\tIND\tIndependent\nAssam\tSilchar\tWAZID REJA OSMANI\t8282\tno\tAITC\tAll India Trinamool Congress\nAssam\tSilchar\tUPENDRA CHANDRA DAS\t2740\tno\tIND\tIndependent\nAssam\tSilchar\tREJAMOND ALI BARBHUIYA\t12460\tno\tCPM\tCommunist Party of India  (Marxist)\nAssam\tSilchar\tARUNANGSHU BHATTACHARJEE\t8595\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAssam\tAutonomous District\tBIREN SINGH ENGTI\t213152\tyes\tINC\tIndian National Congress\nAssam\tAutonomous District\tCHOMANG KRO\t108299\tno\tIND\tIndependent\nAssam\tAutonomous District\tPRATIMA ENGHEEPI\t11762\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAssam\tAutonomous District\tJOY RAM ENGLENG\t189057\tno\tBJP\tBharatiya Janata Party\nAssam\tAutonomous District\tASHWENG J. RENGMA\t9263\tno\tJMM\tJharkhand Mukti Morcha\nAssam\tAutonomous District\tNone of the Above\t11747\tno\tNOTA\tNone of the Above\nAssam\tDhubri\tBADRUDDIN AJMAL\t592569\tyes\tAIUDF\tAll India United Democratic Front\nAssam\tDhubri\tSURAT JAMAN MONDAL\t5935\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAssam\tDhubri\tAZMAL HUSSAIN\t4468\tno\tAAAP\tAam Aadmi Party\nAssam\tDhubri\tANOWAR HUSSAIN\t9897\tno\tAGP\tAsom Gana Parishad\nAssam\tDhubri\tDR. DEBAMOY SANYAL\t298985\tno\tBJP\tBharatiya Janata Party\nAssam\tDhubri\tWAZED ALI CHOUDHURY\t362839\tno\tINC\tIndian National Congress\nAssam\tDhubri\tNone of the Above\t5811\tno\tNOTA\tNone of the Above\nAssam\tDhubri\tALI HUSSAIN\t23816\tno\tIND\tIndependent\nAssam\tDhubri\tMOHAR UDDIN MONDAL\t40208\tno\tIND\tIndependent\nAssam\tDhubri\tMOHENDRA CHANDRA ROY\t4607\tno\tIND\tIndependent\nAssam\tDhubri\tYOUSUB ALI AHMED\t3277\tno\tSP\tSamajwadi Party\nAssam\tDhubri\tKASIR UDDIN SK.\t1455\tno\tIUML\tIndian Union Muslim League\nAssam\tDhubri\tZESMINA KHATUN\t7874\tno\tAITC\tAll India Trinamool Congress\nAssam\tDhubri\tKASHEM ALI AKONDA\t2785\tno\tIND\tIndependent\nAssam\tDhubri\tRAM EKBAL SHAHANI\t2375\tno\tIND\tIndependent\nAssam\tDhubri\tMONIRUL HUSSAIN\t2723\tno\tIND\tIndependent\nAssam\tKokrajhar\tNABA KUMAR SARANIA (HIRA)\t634428\tyes\tIND\tIndependent\nAssam\tKokrajhar\tNone of the Above\t18183\tno\tNOTA\tNone of the Above\nAssam\tKokrajhar\tSANSUMA KHUNGGUR BWISWMUTHIARY\t11239\tno\tIND\tIndependent\nAssam\tKokrajhar\tSABDA RAM RABHA\t20074\tno\tIND\tIndependent\nAssam\tKokrajhar\tCHANDAN BRAHMA\t243759\tno\tBOPF\tBodoland Peoples Front\nAssam\tKokrajhar\tURKHAO GWRA BRAHMA\t278649\tno\tIND\tIndependent\nAssam\tKokrajhar\tRANJIT SHEKHAR MOOSHAHARY\t17537\tno\tAITC\tAll India Trinamool Congress\nAssam\tBarpeta\tSIRAJ UDDIN AJMAL\t394702\tyes\tAIUDF\tAll India United Democratic Front\nAssam\tBarpeta\tKHURSHIDA ANOWARA BEGUM\t5710\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAssam\tBarpeta\tABUL AWAL\t3099\tno\tSP\tSamajwadi Party\nAssam\tBarpeta\tUDDHAB BARMAN\t27575\tno\tCPM\tCommunist Party of India  (Marxist)\nAssam\tBarpeta\tDR. PERVEZ ALI AHMED\t10944\tno\tAITC\tAll India Trinamool Congress\nAssam\tBarpeta\tNone of the Above\t4785\tno\tNOTA\tNone of the Above\nAssam\tBarpeta\tPHANI BHUSAN CHOUDHURY\t73733\tno\tAGP\tAsom Gana Parishad\nAssam\tBarpeta\tSAMSUL HOQUE\t24444\tno\tIND\tIndependent\nAssam\tBarpeta\tKAMAL UDDIN\t3433\tno\tIND\tIndependent\nAssam\tBarpeta\tCHANDRA MOHAN PATOWARY\t352361\tno\tBJP\tBharatiya Janata Party\nAssam\tBarpeta\tBHADRESWAR BARMAN\t1695\tno\tIND\tIndependent\nAssam\tBarpeta\tISMAIL HUSSAIN\t277802\tno\tINC\tIndian National Congress\nAssam\tBarpeta\tGAUTAM KUMAR SARMA\t1859\tno\tIND\tIndependent\nAssam\tBarpeta\tENTAZ ALI\t1937\tno\tLB\tLok Bharati\nAssam\tBarpeta\tBARUN KARMAKAR\t1649\tno\tIND\tIndependent\nAssam\tBarpeta\tDILIR KHAN\t20135\tno\tIND\tIndependent\nAssam\tGauhati\tBIJOYA CHAKRAVARTY\t764985\tyes\tBJP\tBharatiya Janata Party\nAssam\tGauhati\tANIL BHAGAWATI\t5782\tno\tIND\tIndependent\nAssam\tGauhati\tDR. FARUK AAHAMMED BHUYAN\t3540\tno\tIND\tIndependent\nAssam\tGauhati\tGOPI BARUAH\t2411\tno\tLB\tLok Bharati\nAssam\tGauhati\tPRANJAL BORDOLOY\t4809\tno\tAAAP\tAam Aadmi Party\nAssam\tGauhati\tKHARGESWAR DAS\t2532\tno\tIND\tIndependent\nAssam\tGauhati\tNone of the Above\t6720\tno\tNOTA\tNone of the Above\nAssam\tGauhati\tGOPI NATH DAS\t137254\tno\tAIUDF\tAll India United Democratic Front\nAssam\tGauhati\tMD. ZAKIR HUSSAIN\t2275\tno\tIND\tIndependent\nAssam\tGauhati\tDHIRAJ MEDHI\t2845\tno\tIND\tIndependent\nAssam\tGauhati\tPADMESHWAR PHUKAN\t2629\tno\tIND\tIndependent\nAssam\tGauhati\tAJAD ALI\t2385\tno\tIND\tIndependent\nAssam\tGauhati\tBIRENDRA PRASAD BAISHYA\t86546\tno\tAGP\tAsom Gana Parishad\nAssam\tGauhati\tMANASH BORAH\t449201\tno\tINC\tIndian National Congress\nAssam\tGauhati\tPRADIP KALITA\t7345\tno\tIND\tIndependent\nAssam\tGauhati\tKAZI NEKIB AHMED\t6636\tno\trdsc\tRegional Democratic Secular Congress\nAssam\tGauhati\tBANDANA BARMAN BARUAH\t3240\tno\tSP\tSamajwadi Party\nAssam\tGauhati\tBIJU PHUKAN\t8162\tno\tAITC\tAll India Trinamool Congress\nAssam\tGauhati\tBENEDICT ALOK ARENG\t12432\tno\tIND\tIndependent\nAssam\tMangaldoi\tRAMEN DEKA\t486357\tyes\tBJP\tBharatiya Janata Party\nAssam\tMangaldoi\tAROON BAROOA\t4164\tno\trdsc\tRegional Democratic Secular Congress\nAssam\tMangaldoi\tSAHNUR ALI\t5531\tno\tIND\tIndependent\nAssam\tMangaldoi\tSAHADEV DAS\t86347\tno\tBOPF\tBodoland Peoples Front\nAssam\tMangaldoi\tRATUL KUMAR CHOUDHURY\t3658\tno\tSP\tSamajwadi Party\nAssam\tMangaldoi\tNAMRATA SARMA\t3424\tno\tAAAP\tAam Aadmi Party\nAssam\tMangaldoi\tDEBA KUMAR BARMAN\t8341\tno\tAITC\tAll India Trinamool Congress\nAssam\tMangaldoi\tPARESH BAISHYA\t74710\tno\tAIUDF\tAll India United Democratic Front\nAssam\tMangaldoi\tSHACHINDRA NATH BRAHMA\t12443\tno\tIND\tIndependent\nAssam\tMangaldoi\tSWARNALATA CHALIHA\t7600\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAssam\tMangaldoi\tKIRIP CHALIHA\t463473\tno\tINC\tIndian National Congress\nAssam\tMangaldoi\tMADHAB RAJBANGSHI\t66467\tno\tAGP\tAsom Gana Parishad\nAssam\tMangaldoi\tNone of the Above\t10722\tno\tNOTA\tNone of the Above\nAssam\tTezpur\tRAM PRASAD SARMAH\t446511\tyes\tBJP\tBharatiya Janata Party\nAssam\tTezpur\tNone of the Above\t16667\tno\tNOTA\tNone of the Above\nAssam\tTezpur\tELIAS KUJUR\t5207\tno\tIND\tIndependent\nAssam\tTezpur\tMONI KUMAR SUBBA\t62730\tno\tIND\tIndependent\nAssam\tTezpur\tRAJEN SAIKIA\t4246\tno\tAIFB\tAll India Forward Bloc\nAssam\tTezpur\tGOPI CHAND SHAHABADI\t8712\tno\tAITC\tAll India Trinamool Congress\nAssam\tTezpur\tJOSEPH TOPPO\t40489\tno\tAGP\tAsom Gana Parishad\nAssam\tTezpur\tKHEMRAJ CHETRY\t24910\tno\tCPM\tCommunist Party of India  (Marxist)\nAssam\tTezpur\tLAKSHIKANTA KURMI\t10725\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAssam\tTezpur\tBHUPEN KUMAR BORAH\t360491\tno\tINC\tIndian National Congress\nAssam\tNowgong\tRAJEN GOHAIN\t494146\tyes\tBJP\tBharatiya Janata Party\nAssam\tNowgong\tDR. ADITYA LANGTHASA\t314012\tno\tAIUDF\tAll India United Democratic Front\nAssam\tNowgong\tJONJONALI BARUAH\t350587\tno\tINC\tIndian National Congress\nAssam\tNowgong\tMRIDULA BARKAKOTY\t35142\tno\tAGP\tAsom Gana Parishad\nAssam\tNowgong\tSELIMA SULTANA\t6462\tno\tIND\tIndependent\nAssam\tNowgong\tDIPAK KUMAR BORAH\t6835\tno\tAITC\tAll India Trinamool Congress\nAssam\tNowgong\tRAFIQUL ISLAM\t2783\tno\tAIFB\tAll India Forward Bloc\nAssam\tNowgong\tNone of the Above\t8782\tno\tNOTA\tNone of the Above\nAssam\tNowgong\tFARUK HAZARIKA\t11325\tno\tIND\tIndependent\nAssam\tKaliabor\tGOURAV GOGOI\t443315\tyes\tINC\tIndian National Congress\nAssam\tKaliabor\tBINOD GOGOI\t9583\tno\tIND\tIndependent\nAssam\tKaliabor\tJITEN GOGOI\t8271\tno\tIND\tIndependent\nAssam\tKaliabor\tARUP KUMAR MAHANTA\t5433\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAssam\tKaliabor\tMRINAL KUMAR SAIKIA\t349441\tno\tBJP\tBharatiya Janata Party\nAssam\tKaliabor\tJINTI GOGOI\t2584\tno\tAAAP\tAam Aadmi Party\nAssam\tKaliabor\tSAHABA AHMED\t2411\tno\tIND\tIndependent\nAssam\tKaliabor\tMITHUAL KUMAR\t2966\tno\tAIFB\tAll India Forward Bloc\nAssam\tKaliabor\tMONTU SAIKIA\t9133\tno\tIND\tIndependent\nAssam\tKaliabor\tSAMSUL ALAM\t6538\tno\tAITC\tAll India Trinamool Congress\nAssam\tKaliabor\tRAHMAT ULLAH\t4985\tno\tIND\tIndependent\nAssam\tKaliabor\tBIJOY KUMAR TIRU\t231295\tno\tAIUDF\tAll India United Democratic Front\nAssam\tKaliabor\tDR. ARUN KUMAR SARMA\t78132\tno\tAGP\tAsom Gana Parishad\nAssam\tKaliabor\tNone of the Above\t12403\tno\tNOTA\tNone of the Above\nAssam\tJorhat\tKAMAKHYA PRASAD TASA\t456420\tyes\tBJP\tBharatiya Janata Party\nAssam\tJorhat\tNone of the Above\t14648\tno\tNOTA\tNone of the Above\nAssam\tJorhat\tNASIR AHMED\t7331\tno\tAIUDF\tAll India United Democratic Front\nAssam\tJorhat\tRAJ KUMAR DOWARAH\t4737\tno\tIND\tIndependent\nAssam\tJorhat\tGUNIN BASUMATARI\t5754\tno\tSP\tSamajwadi Party\nAssam\tJorhat\tDRUPAD BORGOHAIN\t28930\tno\tCPI\tCommunist Party of India\nAssam\tJorhat\tPRODIP HAZARIKA\t46626\tno\tAGP\tAsom Gana Parishad\nAssam\tJorhat\tBIJOY KRISHNA HANDIQUE\t354000\tno\tINC\tIndian National Congress\nAssam\tJorhat\tMANOROM GOGOI\t3659\tno\tAAAP\tAam Aadmi Party\nAssam\tJorhat\tHOREN BORGOHAIN\t3472\tno\tAIFB\tAll India Forward Bloc\nAssam\tJorhat\tRIBULAYA GOGOI\t5759\tno\tAITC\tAll India Trinamool Congress\nAssam\tDibrugarh\tRAMESWAR TELI\t494364\tyes\tBJP\tBharatiya Janata Party\nAssam\tDibrugarh\tCHENI RAM MORAN\t7112\tno\tAIFB\tAll India Forward Bloc\nAssam\tDibrugarh\tNAVAJYOTI KALITA\t8582\tno\tAITC\tAll India Trinamool Congress\nAssam\tDibrugarh\tANUP PHUKON\t45710\tno\tAGP\tAsom Gana Parishad\nAssam\tDibrugarh\tSUBHAS SEN\t9374\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAssam\tDibrugarh\tPABAN SINGH GHATOWAR\t309017\tno\tINC\tIndian National Congress\nAssam\tDibrugarh\tNone of the Above\t16809\tno\tNOTA\tNone of the Above\nAssam\tLakhimpur\tSARBANANDA SONOWAL\t612543\tyes\tBJP\tBharatiya Janata Party\nAssam\tLakhimpur\tNone of the Above\t11204\tno\tNOTA\tNone of the Above\nAssam\tLakhimpur\tKHAIRUL ISLAM\t4119\tno\tIND\tIndependent\nAssam\tLakhimpur\tHARI PRASAD DIHINGIA\t81753\tno\tAGP\tAsom Gana Parishad\nAssam\tLakhimpur\tDR. HIRAMONI DEKA SONOWAL\t2893\tno\tAAAP\tAam Aadmi Party\nAssam\tLakhimpur\tKESHAB GOGOI\t2752\tno\tIND\tIndependent\nAssam\tLakhimpur\tHRISHIKESH BARUAH\t6013\tno\tIND\tIndependent\nAssam\tLakhimpur\tDHEBOR GOHAIN BORUAH\t9095\tno\tAITC\tAll India Trinamool Congress\nAssam\tLakhimpur\tRANEE NARAH\t320405\tno\tINC\tIndian National Congress\nAssam\tLakhimpur\tTANISH ORANG\t4721\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAssam\tLakhimpur\tRANJIT SINGH GORH\t6335\tno\tIND\tIndependent\nAssam\tLakhimpur\tDAVID HORO\t5903\tno\tIND\tIndependent\nAssam\tLakhimpur\tHEM KANTA MIRI\t6896\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nAssam\tLakhimpur\tMAMUN IMDADUL HAQUE CHAWDHURY\t37343\tno\tAIUDF\tAll India United Democratic Front\nBihar\tValmiki Nagar\tSATISH CHANDRA DUBEY\t364011\tyes\tBJP\tBharatiya Janata Party\nBihar\tValmiki Nagar\tNone of the Above\t15515\tno\tNOTA\tNone of the Above\nBihar\tValmiki Nagar\tPROF. AMARESH PRASAD\t27603\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tValmiki Nagar\tNANDLAL RAM\t10781\tno\tIND\tIndependent\nBihar\tValmiki Nagar\tDHARMESH PRASAD VARMA\t6008\tno\tAAAP\tAam Aadmi Party\nBihar\tValmiki Nagar\tAMAR SAHANI\t35888\tno\tBED\tBharatiya Ekta Dal\nBihar\tValmiki Nagar\tMANOHAR MANOJ\t11509\tno\tIND\tIndependent\nBihar\tValmiki Nagar\tDILIP VARMA\t58817\tno\tIND\tIndependent\nBihar\tValmiki Nagar\tVIRENDRA PRASAD GUPTA\t12581\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tValmiki Nagar\tDACTAR MAHTO\t5519\tno\tJPS\tJanvadi Party(Socialist)\nBihar\tValmiki Nagar\tSHAILESH KUMAR DIWAKAR\t11668\tno\tBSP\tBahujan Samaj Party\nBihar\tValmiki Nagar\tDRAVYAN URAON\t8013\tno\tIND\tIndependent\nBihar\tValmiki Nagar\tOM SHANTI BABA\t4371\tno\tIND\tIndependent\nBihar\tValmiki Nagar\tBAIDYA NATH PRASAD MAHATO\t81612\tno\tJD(U)\tJanata Dal  (United)\nBihar\tValmiki Nagar\tPURNMASI RAM\t246218\tno\tINC\tIndian National Congress\nBihar\tPaschim Champaran\tDR. SANJAY JAISWAL\t371232\tyes\tBJP\tBharatiya Janata Party\nBihar\tPaschim Champaran\tANAND PRASAD\t5818\tno\tIND\tIndependent\nBihar\tPaschim Champaran\tPRABHU RAJ NARAIN RAO\t17157\tno\tCPM\tCommunist Party of India  (Marxist)\nBihar\tPaschim Champaran\tANAND KAUSHAL SINGH\t4297\tno\tAAAP\tAam Aadmi Party\nBihar\tPaschim Champaran\tPRAKASH JHA\t260978\tno\tJD(U)\tJanata Dal  (United)\nBihar\tPaschim Champaran\tSUNIL KUMAR RAO\t4940\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tPaschim Champaran\tRAGHUNATH JHA\t121800\tno\tRJD\tRashtriya Janata Dal\nBihar\tPaschim Champaran\tSYED SHAMIM AKHTAR\t12415\tno\tBSP\tBahujan Samaj Party\nBihar\tPaschim Champaran\tRATAN KUMAR SARKAR\t6474\tno\tIND\tIndependent\nBihar\tPaschim Champaran\tMOHAN KUMAR\t11260\tno\tIND\tIndependent\nBihar\tPaschim Champaran\tSYED IRSHAD AKHTAR\t9123\tno\tIND\tIndependent\nBihar\tPaschim Champaran\tNAFISH AHAMAD\t10502\tno\tIND\tIndependent\nBihar\tPaschim Champaran\tNone of the Above\t18804\tno\tNOTA\tNone of the Above\nBihar\tPurvi Champaran\tRADHA MOHAN SINGH\t400452\tyes\tBJP\tBharatiya Janata Party\nBihar\tPurvi Champaran\tAMIT KUMAR CHOUBEY\t7512\tno\tAAAP\tAam Aadmi Party\nBihar\tPurvi Champaran\tSHAMBHU DAS\t11371\tno\tIND\tIndependent\nBihar\tPurvi Champaran\tNone of the Above\t13261\tno\tNOTA\tNone of the Above\nBihar\tPurvi Champaran\tSURESH PASWAN\t10356\tno\tBSP\tBahujan Samaj Party\nBihar\tPurvi Champaran\tPRAMOD KUMAR PANDEY\t10505\tno\tIND\tIndependent\nBihar\tPurvi Champaran\tRAGHUNATH GUPTA\t2459\tno\tSP\tSamajwadi Party\nBihar\tPurvi Champaran\tANIL KUMAR SINGH\t2819\tno\tJMBP\tJai Maha Bharath Party\nBihar\tPurvi Champaran\tSHATRUGHAN TIWARI\t4893\tno\tBJKVP\tBajjikanchal Vikas Party\nBihar\tPurvi Champaran\tMOQIM ANSARI\t2811\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nBihar\tPurvi Champaran\tPARAS NATH PANDEY\t3027\tno\tJPS\tJanvadi Party(Socialist)\nBihar\tPurvi Champaran\tBINOD KUMAR SRIVASTAVA\t208289\tno\tRJD\tRashtriya Janata Dal\nBihar\tPurvi Champaran\tAVANEESH KUMAR SINGH\t128604\tno\tJD(U)\tJanata Dal  (United)\nBihar\tPurvi Champaran\tDILIP SINGH\t10759\tno\tIND\tIndependent\nBihar\tPurvi Champaran\tMD. NASIRUDDIN\t2607\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tPurvi Champaran\tPAWAN KUMAR\t2946\tno\tKS\tKalinga Sena\nBihar\tSheohar\tRAMA DEVI\t372506\tyes\tBJP\tBharatiya Janata Party\nBihar\tSheohar\tMD NASRUDDIN ANSARI\t3072\tno\tNaLP\tNational Lokmat Party\nBihar\tSheohar\tNone of the Above\t11670\tno\tNOTA\tNone of the Above\nBihar\tSheohar\tSHAHID ALI KHAN\t79108\tno\tJD(U)\tJanata Dal  (United)\nBihar\tSheohar\tMD ANWARUL HAQUE\t236267\tno\tRJD\tRashtriya Janata Dal\nBihar\tSheohar\tSHATRUGHNA SAHU\t8256\tno\tAAAP\tAam Aadmi Party\nBihar\tSheohar\tSANJAY PRASAD\t8245\tno\tIND\tIndependent\nBihar\tSheohar\tANGESH KUMAR\t26446\tno\tBSP\tBahujan Samaj Party\nBihar\tSheohar\tLAXMAN PASWAN\t18681\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tSheohar\tLOVELY ANAND\t46008\tno\tSP\tSamajwadi Party\nBihar\tSheohar\tRAMASHISH MAHTO\t5941\tno\tIND\tIndependent\nBihar\tSheohar\tJAGRANATH SAHANI\t4399\tno\tSKLP\tSarvajan Kalyan Loktantrik Party\nBihar\tSheohar\tJATA SHANKAR AATREY\t4641\tno\travp\tRajnaitik Vikalp Party\nBihar\tSheohar\tRAM LOBHIT PASWAN\t2648\tno\tBMUP\tBahujan Mukti Party\nBihar\tSheohar\tRAMASHANKAR SINGH\t2845\tno\tAIFB\tAll India Forward Bloc\nBihar\tSheohar\tSHIVANANDAN PRASAD\t12161\tno\tIND\tIndependent\nBihar\tSitamarhi\tRAM KUMAR SHARMA\t411265\tyes\tBLSP\tRashtriya Lok Samta Party\nBihar\tSitamarhi\tNone of the Above\t5949\tno\tNOTA\tNone of the Above\nBihar\tSitamarhi\tJAY KUMAR CHAUDHARY\t3880\tno\tLD\tLok Dal\nBihar\tSitamarhi\tCHANDRIKA PRASAD\t8846\tno\tIND\tIndependent\nBihar\tSitamarhi\tSYED ABU DOJANA\t4692\tno\tSP\tSamajwadi Party\nBihar\tSitamarhi\tRAJEEV RANJAN\t11250\tno\tAITC\tAll India Trinamool Congress\nBihar\tSitamarhi\tAJAY KUMAR\t5454\tno\tIND\tIndependent\nBihar\tSitamarhi\tSATYENDRA BHANDARI\t7853\tno\tBMUP\tBahujan Mukti Party\nBihar\tSitamarhi\tSONE LAL SAH\t6115\tno\tIND\tIndependent\nBihar\tSitamarhi\tSURENDRA KUMAR\t4501\tno\tBJKVP\tBajjikanchal Vikas Party\nBihar\tSitamarhi\tSITARAM YADAV\t263300\tno\tRJD\tRashtriya Janata Dal\nBihar\tSitamarhi\tMAHESH KUMAR\t8521\tno\tBSP\tBahujan Samaj Party\nBihar\tSitamarhi\tMAHESH NANDAN SINGH\t20613\tno\tIND\tIndependent\nBihar\tSitamarhi\tARJUN RAI\t97188\tno\tJD(U)\tJanata Dal  (United)\nBihar\tSitamarhi\tMOHAMMAD FAIYAZ AHMAD\t7586\tno\tIND\tIndependent\nBihar\tSitamarhi\tNARENDRA MISHRA\t6078\tno\tIND\tIndependent\nBihar\tSitamarhi\tKISHORI DAS\t18043\tno\tAAAP\tAam Aadmi Party\nBihar\tSitamarhi\tMAHENDRA PRASAD\t3695\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tSitamarhi\tVINOD SAH\t2996\tno\tIND\tIndependent\nBihar\tSitamarhi\tBHARAT BHUSHAN SAHNI\t2763\tno\tSKLP\tSarvajan Kalyan Loktantrik Party\nBihar\tMadhubani\tHUKM DEO NARAYAN YADAV\t358040\tyes\tBJP\tBharatiya Janata Party\nBihar\tMadhubani\tDHRUB NARAYAN KARN\t6750\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tMadhubani\tHARI NARAYAN YADAV\t10115\tno\tBSP\tBahujan Samaj Party\nBihar\tMadhubani\tPROF. GHULAM GHOUS\t56392\tno\tJD(U)\tJanata Dal  (United)\nBihar\tMadhubani\tABDUL BARI SIDDIQUI\t337505\tno\tRJD\tRashtriya Janata Dal\nBihar\tMadhubani\tNEERAJ PATHAK\t9718\tno\tAAAP\tAam Aadmi Party\nBihar\tMadhubani\tKUMARI RITA\t30942\tno\tSHS\tShivsena\nBihar\tMadhubani\tANIRUD SAHU\t9474\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tMadhubani\tFAIZ AHMAD\t8464\tno\tJPJD\tJai Prakash Janata Dal\nBihar\tMadhubani\tNone of the Above\t18937\tno\tNOTA\tNone of the Above\nBihar\tMadhubani\tVIJAY KUMAR\t7453\tno\tIND\tIndependent\nBihar\tMadhubani\tRATNESHWAR JHA\t6663\tno\tAKBMP\tAkhil Bhartiya Mithila Party\nBihar\tJhanjharpur\tBIRENDRA KUMAR CHAUDHARY\t335481\tyes\tBJP\tBharatiya Janata Party\nBihar\tJhanjharpur\tHAIDAR\t5653\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nBihar\tJhanjharpur\tBARUN KUMAR JHA\t13833\tno\tIND\tIndependent\nBihar\tJhanjharpur\tJANAK ROY\t8689\tno\tIND\tIndependent\nBihar\tJhanjharpur\tBIPIN KUMAR JHA\t7358\tno\tBVM\tBharat Vikas Morcha\nBihar\tJhanjharpur\tRAM CHANDRA SAFI\t8083\tno\tIND\tIndependent\nBihar\tJhanjharpur\tPAWAN KUMAR\t17790\tno\tIND\tIndependent\nBihar\tJhanjharpur\tVIGHNESH BHAGAT\t3727\tno\tBMUP\tBahujan Mukti Party\nBihar\tJhanjharpur\tTILIYA DEVI\t9441\tno\tAAAP\tAam Aadmi Party\nBihar\tJhanjharpur\tMD. AKHTAR ALI\t9388\tno\tJPJD\tJai Prakash Janata Dal\nBihar\tJhanjharpur\tNone of the Above\t8043\tno\tNOTA\tNone of the Above\nBihar\tJhanjharpur\tCHHEDI RAM\t7632\tno\tIND\tIndependent\nBihar\tJhanjharpur\tDR. BINAY KUMAR JHA\t3956\tno\tIND\tIndependent\nBihar\tJhanjharpur\tCHANDRA MOHAN JHA\t9823\tno\tIND\tIndependent\nBihar\tJhanjharpur\tIBRAHIM ABBASI\t4811\tno\tIND\tIndependent\nBihar\tJhanjharpur\tMANGANI LAL MANDAL\t280073\tno\tRJD\tRashtriya Janata Dal\nBihar\tJhanjharpur\tMANILAL SAHU\t14190\tno\tBSP\tBahujan Samaj Party\nBihar\tJhanjharpur\tMD. FAIYAZ KHAN\t5439\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tJhanjharpur\tDEVENDRA PRASAD YADAV\t183591\tno\tJD(U)\tJanata Dal  (United)\nBihar\tJhanjharpur\tAWDHESH KUMAR SHINGH\t4248\tno\tIND\tIndependent\nBihar\tSupaul\tRANJEET RANJAN\t332927\tyes\tINC\tIndian National Congress\nBihar\tSupaul\tDILESHWAR KAMAIT\t273255\tno\tJD(U)\tJanata Dal  (United)\nBihar\tSupaul\tKAMESHWAR CHAUPAL\t249693\tno\tBJP\tBharatiya Janata Party\nBihar\tSupaul\tJAY NARAYAN YADAV\t11566\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tSupaul\tAMAN KUMAR SAMAJSEVI\t21233\tno\tBSP\tBahujan Samaj Party\nBihar\tSupaul\tPARMESHWAR JHA\t9604\tno\tAAAP\tAam Aadmi Party\nBihar\tSupaul\tSHAKALDEV MANDAL\t5964\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tSupaul\tRAMDEV SHARMA\t10687\tno\tIND\tIndependent\nBihar\tSupaul\tBINOD KUMAR SAHU\t8908\tno\tIND\tIndependent\nBihar\tSupaul\tDEV NARAYAN YADAV\t4825\tno\tBMUP\tBahujan Mukti Party\nBihar\tSupaul\tMOHAMMAD AMANULLAH\t5116\tno\tIUML\tIndian Union Muslim League\nBihar\tSupaul\tSURESH KUMAR AZAD\t14754\tno\tJHP\tJai Hind Party\nBihar\tSupaul\tNone of the Above\t21996\tno\tNOTA\tNone of the Above\nBihar\tAraria\tTASLEEM UDDIN\t407978\tyes\tRJD\tRashtriya Janata Dal\nBihar\tAraria\tABDUL RAHMAN\t17724\tno\tBSP\tBahujan Samaj Party\nBihar\tAraria\tRAJESH KUMAR\t4646\tno\tBVM\tBharat Vikas Morcha\nBihar\tAraria\tRAMANAND RISHIDEO\t3411\tno\tLD\tLok Dal\nBihar\tAraria\tMD. ASLAM BEG\t5149\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tAraria\tNone of the Above\t16608\tno\tNOTA\tNone of the Above\nBihar\tAraria\tBIDAYANAND PASWAN\t3350\tno\tBMUP\tBahujan Mukti Party\nBihar\tAraria\tCHANDRA BHUSHAN\t5685\tno\tAAAP\tAam Aadmi Party\nBihar\tAraria\tVIJAY KUMAR MANDAL\t221769\tno\tJD(U)\tJanata Dal  (United)\nBihar\tAraria\tPRADEEP KUMAR SINGH\t261474\tno\tBJP\tBharatiya Janata Party\nBihar\tAraria\tPANKAJ KISHOR MANDAL\t10704\tno\tIND\tIndependent\nBihar\tAraria\tALAMDAR HUSSAIN\t5645\tno\tIND\tIndependent\nBihar\tAraria\tSANJAY KUMAR RISHIDEV\t5292\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tAraria\tSAR WAT JAHRE ANSARI\t6376\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tKishanganj\tMOHAMMAD ASRARUL HAQUE\t493461\tyes\tINC\tIndian National Congress\nBihar\tKishanganj\tALIMUDDIN ANSARI\t15010\tno\tAAAP\tAam Aadmi Party\nBihar\tKishanganj\tMD. GULAB SIDDIKI\t5559\tno\tLKJP\tLoktantrik Janata Party (Secular)\nBihar\tKishanganj\tDR DILIP  KUMAR JAISWAL\t298849\tno\tBJP\tBharatiya Janata Party\nBihar\tKishanganj\tMOHAMMAD WASIQUR RAHMAN\t5422\tno\tIUML\tIndian Union Muslim League\nBihar\tKishanganj\tAKHTARUL IMAN\t55822\tno\tJD(U)\tJanata Dal  (United)\nBihar\tKishanganj\tCHHOTE LAL MAHTO\t11392\tno\tIND\tIndependent\nBihar\tKishanganj\tMD.NAUSHAD\t14903\tno\tBSP\tBahujan Samaj Party\nBihar\tKishanganj\tCHITRANJAN SINGH\t5152\tno\tIND\tIndependent\nBihar\tKishanganj\tMANUWER ALAM\t5714\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tKishanganj\tNone of the Above\t17206\tno\tNOTA\tNone of the Above\nBihar\tKatihar\tTARIQ ANWAR\t431292\tyes\tNCP\tNationalist Congress Party\nBihar\tKatihar\tNone of the Above\t3287\tno\tNOTA\tNone of the Above\nBihar\tKatihar\tSURESH RAY\t4450\tno\tRVNP\tRashtravadi Janata Party\nBihar\tKatihar\tNALINI MANDAL\t7969\tno\tIND\tIndependent\nBihar\tKatihar\tBALESHWAR MARANDI\t33593\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tKatihar\tSATYANARAYAN BHAGAT\t2550\tno\tPSS\tProutist Sarva Samaj\nBihar\tKatihar\tMD. TARIQ ANWAR\t4514\tno\tIND\tIndependent\nBihar\tKatihar\tMD. IQBAL AHMAD\t1553\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tKatihar\tKANHAI MANDAL\t10516\tno\tIND\tIndependent\nBihar\tKatihar\tMAHBUB ALAM\t9461\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tKatihar\tSHIVENDRA KUMAR\t2685\tno\tBSP\tBahujan Samaj Party\nBihar\tKatihar\tRAJ KUMAR MANDAL\t2141\tno\tIND\tIndependent\nBihar\tKatihar\tSANJAY SINGH\t1675\tno\tIND\tIndependent\nBihar\tKatihar\tASHOK KUMAR\t9000\tno\tIND\tIndependent\nBihar\tKatihar\tDR. RAM PRAKASH MAHTO\t100765\tno\tJD(U)\tJanata Dal  (United)\nBihar\tKatihar\tFULMANI HEMBRAM\t3096\tno\tBVM\tBharat Vikas Morcha\nBihar\tKatihar\tBIBHAKER JHA @ VICTOR JHA\t4323\tno\tAAAP\tAam Aadmi Party\nBihar\tKatihar\tMAHAMMAD HAMID MOBARAK\t8392\tno\tAITC\tAll India Trinamool Congress\nBihar\tKatihar\tMAHESH MANDAL\t1980\tno\tBMUP\tBahujan Mukti Party\nBihar\tKatihar\tABDUR RAHMAN\t2489\tno\tBMF\tBharatiya Momin Front\nBihar\tKatihar\tASHOK KU BHAGAT\t15547\tno\tIND\tIndependent\nBihar\tKatihar\tNIKHIL KUMAR CHOUDHARY\t316552\tno\tBJP\tBharatiya Janata Party\nBihar\tPurnia\tSANTOSH KUMAR\t418826\tyes\tJD(U)\tJanata Dal  (United)\nBihar\tPurnia\tMD SHAHID AKHTAR\t5137\tno\tBMUP\tBahujan Mukti Party\nBihar\tPurnia\tPANKAJ KUMAR SINGH\t7912\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tPurnia\tNAVLESH PATHAK\t18776\tno\tIND\tIndependent\nBihar\tPurnia\tMANSUR ALAM\t3370\tno\tSP\tSamajwadi Party\nBihar\tPurnia\tUDAY SINGH ALIAS PAPPU SINGH\t302157\tno\tBJP\tBharatiya Janata Party\nBihar\tPurnia\tAMARNATH TIWARI\t124344\tno\tINC\tIndian National Congress\nBihar\tPurnia\tFAGU MARANDI\t3440\tno\tJDP\tJharkhand Disom Party\nBihar\tPurnia\tPRAMOD NARAYAN PODDAR\t4664\tno\tBJJD\tBhartiya Jantantrik Janata Dal\nBihar\tPurnia\tRAJ KUMAR ORAON\t11196\tno\tBSP\tBahujan Samaj Party\nBihar\tPurnia\tANIL KUMAR MAHTO\t9162\tno\tJPS\tJanvadi Party(Socialist)\nBihar\tPurnia\tMD AKHTAR ALI\t5849\tno\tIND\tIndependent\nBihar\tPurnia\tSYED SHAH EQBAL ALAM\t6324\tno\tBEMP\tBhartiya Ekta Manch Party\nBihar\tPurnia\tSANJAY SINGH\t9196\tno\tIND\tIndependent\nBihar\tPurnia\tMD SHAMSHER ALAM\t50446\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tPurnia\tKUNJ BIHARI PASWAN\t8321\tno\tIND\tIndependent\nBihar\tPurnia\tSUDEEP ROY\t16630\tno\tAAAP\tAam Aadmi Party\nBihar\tPurnia\tNone of the Above\t11982\tno\tNOTA\tNone of the Above\nBihar\tMadhepura\tRAJESH RANJAN @ PAPPU YADAV\t368937\tyes\tRJD\tRashtriya Janata Dal\nBihar\tMadhepura\tNone of the Above\t21924\tno\tNOTA\tNone of the Above\nBihar\tMadhepura\tMINA DEVI\t9039\tno\tIND\tIndependent\nBihar\tMadhepura\tSAJAN KUMAR JHA\t4396\tno\tJMBP\tJai Maha Bharath Party\nBihar\tMadhepura\tPRASANNA KUMAR\t5370\tno\tIND\tIndependent\nBihar\tMadhepura\tMOHAMMAD ARSHAD HUSSAIN\t9585\tno\tPECP\tPeace Party\nBihar\tMadhepura\tBHIKHA PASWAN\t6649\tno\tBVM\tBharat Vikas Morcha\nBihar\tMadhepura\tRAJO SAH\t12247\tno\tIND\tIndependent\nBihar\tMadhepura\tVIJAY KUMAR SINGH\t252534\tno\tBJP\tBharatiya Janata Party\nBihar\tMadhepura\tANWAR ALAM\t9538\tno\tAAAP\tAam Aadmi Party\nBihar\tMadhepura\tCHANDRASHEKHAR YADAV\t3768\tno\tBMUP\tBahujan Mukti Party\nBihar\tMadhepura\tSHARAD YADAV\t312728\tno\tJD(U)\tJanata Dal  (United)\nBihar\tMadhepura\tGULJAR KUMAR\t18084\tno\tBSP\tBahujan Samaj Party\nBihar\tDarbhanga\tKIRTI AZAD\t314949\tyes\tBJP\tBharatiya Janata Party\nBihar\tDarbhanga\tMD. ALI ASHRAF FATMI\t279906\tno\tRJD\tRashtriya Janata Dal\nBihar\tDarbhanga\tBRAHMADEO PRASAD KARYEE\t2929\tno\tBJKVP\tBajjikanchal Vikas Party\nBihar\tDarbhanga\tKRISHNA KUMAR PASWAN\t10080\tno\tIND\tIndependent\nBihar\tDarbhanga\tSANJAY KUMAR JHA\t104494\tno\tJD(U)\tJanata Dal  (United)\nBihar\tDarbhanga\tCOLONEL LAKSHMESHWAR MISHRA\t9111\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tDarbhanga\tDR. PRABHAT RANJAN DAS\t7550\tno\tAAAP\tAam Aadmi Party\nBihar\tDarbhanga\tKEDAR KU. KESHAV\t25277\tno\tSHS\tShivsena\nBihar\tDarbhanga\tSADRE ALAM KHAN\t4558\tno\tAKBMP\tAkhil Bhartiya Mithila Party\nBihar\tDarbhanga\tFAISAL AHMAD\t2900\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tDarbhanga\tDURGA NAND MAHAVIR NAYAK\t8835\tno\tBSP\tBahujan Samaj Party\nBihar\tDarbhanga\tPRAVIN KUMAR JHA\t16125\tno\tIND\tIndependent\nBihar\tDarbhanga\tPRADEEP PASWAN\t5963\tno\tIND\tIndependent\nBihar\tDarbhanga\tJAINATH\t2956\tno\tBMUP\tBahujan Mukti Party\nBihar\tDarbhanga\tHADAY NA. YADAV\t11606\tno\tCPM\tCommunist Party of India  (Marxist)\nBihar\tDarbhanga\tNone of the Above\t21103\tno\tNOTA\tNone of the Above\nBihar\tMuzaffarpur\tAJAY NISHAD\t469295\tyes\tBJP\tBharatiya Janata Party\nBihar\tMuzaffarpur\tDEVENDRA RAKESH\t2299\tno\tBJKVP\tBajjikanchal Vikas Party\nBihar\tMuzaffarpur\tNone of the Above\t9690\tno\tNOTA\tNone of the Above\nBihar\tMuzaffarpur\tSHATRUDHAN SAHNI\t8223\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tMuzaffarpur\tAKSHAY VERMA\t7569\tno\tSKLP\tSarvajan Kalyan Loktantrik Party\nBihar\tMuzaffarpur\tKAMLESH CHOUDHARY\t3147\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tMOHMMAD TAIYAB\t2269\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tAJITANSH GAUR\t2755\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tPANKAJ KUMAR SRIVASTAVA\t3365\tno\tJPJD\tJai Prakash Janata Dal\nBihar\tMuzaffarpur\tRAVI SHANKAR PASWAN\t3334\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tNAVAL KISHORE JHA\t2364\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tMuzaffarpur\tSURESH KUMAR\t5211\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tPRABHAT SUMAN\t6945\tno\tAAAP\tAam Aadmi Party\nBihar\tMuzaffarpur\tNILU SINGH\t2271\tno\tPBI\tProutist Bloc, India\nBihar\tMuzaffarpur\tASHOK KUMAR SINGH\t3734\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nBihar\tMuzaffarpur\tAKHILESH PD SINGH\t246873\tno\tINC\tIndian National Congress\nBihar\tMuzaffarpur\tVIJENDRA CHAUDHARY\t85140\tno\tJD(U)\tJanata Dal  (United)\nBihar\tMuzaffarpur\tMOHAMMAD ANWARUL HAQUE\t2354\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tASHOK KUMAR JHA\t19945\tno\tSHS\tShivsena\nBihar\tMuzaffarpur\tDEEPAK KUMAR\t1809\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tSHRAVAN KUMAR JHA\t3746\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tMADHUR SHEEL\t4309\tno\tJMBP\tJai Maha Bharath Party\nBihar\tMuzaffarpur\tRUPESH KUMAR KUAR\t2370\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tNAGESHWAR PRASAD SINGH\t9202\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tRAGHVENDRA PRATAP SINGH\t13283\tno\travp\tRajnaitik Vikalp Party\nBihar\tMuzaffarpur\tMAHESH PASWAN\t1749\tno\tIND\tIndependent\nBihar\tMuzaffarpur\tKUNAL PRIYADARSHI\t2471\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nBihar\tMuzaffarpur\tRAJESH KUMAR\t6118\tno\tAIFB\tAll India Forward Bloc\nBihar\tMuzaffarpur\tRAJEEV RANJAN\t9069\tno\tBSP\tBahujan Samaj Party\nBihar\tMuzaffarpur\tMAHESH RAI\t7978\tno\tBMUP\tBahujan Mukti Party\nBihar\tVaishali\tRAMA KISHORE SINGH\t305450\tyes\tLJP\tLok Jan Shakti Party\nBihar\tVaishali\tJITENDRA PRASAD\t12369\tno\tIND\tIndependent\nBihar\tVaishali\tRAAJ MANGAL PRASAD\t7768\tno\tAAAP\tAam Aadmi Party\nBihar\tVaishali\tSHANKAR MAHTO\t23677\tno\tBSP\tBahujan Samaj Party\nBihar\tVaishali\tPARMESHWAR RAM\t4000\tno\tBMUP\tBahujan Mukti Party\nBihar\tVaishali\tDR. MOHAMMAD NABI HASSAN\t22455\tno\tIND\tIndependent\nBihar\tVaishali\tMOHAMMAD UMAR ANSARI\t8254\tno\tIND\tIndependent\nBihar\tVaishali\tRAGHUVANSH PRASAD SINGH\t206183\tno\tRJD\tRashtriya Janata Dal\nBihar\tVaishali\tNone of the Above\t6060\tno\tNOTA\tNone of the Above\nBihar\tVaishali\tBALENDRA SINGH\t15178\tno\tIND\tIndependent\nBihar\tVaishali\tVINOD PANDIT\t6430\tno\tLPSP\tLokpriya Samaj Party\nBihar\tVaishali\tJAYNARAYAN SAH\t3191\tno\tBJKVP\tBajjikanchal Vikas Party\nBihar\tVaishali\tLALJI KUMAR RAKESH\t2667\tno\tIND\tIndependent\nBihar\tVaishali\tMD.HASIB\t11075\tno\tRSMD\tRashtriya Samanta Dal\nBihar\tVaishali\tRAMESHWAR SAH\t5483\tno\tIND\tIndependent\nBihar\tVaishali\tRAM PUKAR RAI\t7182\tno\tIND\tIndependent\nBihar\tVaishali\tRANJEET KUMAR JHA\t11540\tno\tIND\tIndependent\nBihar\tVaishali\tANU SHUKLA\t104229\tno\tIND\tIndependent\nBihar\tVaishali\tINDRADEO ROY\t5218\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nBihar\tVaishali\tMUKESH RAM\t5497\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nBihar\tVaishali\tVIJAY KUMAR SAHNI\t145182\tno\tJD(U)\tJanata Dal  (United)\nBihar\tVaishali\tSANDHYA DEVI\t2983\tno\tIND\tIndependent\nBihar\tVaishali\tMD. NAUSHAD\t3866\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nBihar\tGopalganj (SC)\tJANAK RAM\t478773\tyes\tBJP\tBharatiya Janata Party\nBihar\tGopalganj (SC)\tRAMASHANKAR RAM\t7147\tno\tRJJM\tRashtriya Jan-Jagram Morcha\nBihar\tGopalganj (SC)\tJALESHWAR NATH RAM\t6264\tno\tBMUP\tBahujan Mukti Party\nBihar\tGopalganj (SC)\tSURENDRA PASWAN\t4717\tno\tRBCP\tRashtriya Bahujan Congress Party\nBihar\tGopalganj (SC)\tJITENDRA MANJHI\t12423\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tGopalganj (SC)\tMUDRIKA BAITHA\t8491\tno\tIND\tIndependent\nBihar\tGopalganj (SC)\tSATEYNDRA BAITHA\t9509\tno\tIND\tIndependent\nBihar\tGopalganj (SC)\tDINANATH MANJHI\t13983\tno\tIND\tIndependent\nBihar\tGopalganj (SC)\tNone of the Above\t17841\tno\tNOTA\tNone of the Above\nBihar\tGopalganj (SC)\tSUNITA KUMAR\t4586\tno\tSP\tSamajwadi Party\nBihar\tGopalganj (SC)\tCHANDRADIP RAM\t16751\tno\tBSP\tBahujan Samaj Party\nBihar\tGopalganj (SC)\tDR. JYOTI BHARTI\t191837\tno\tINC\tIndian National Congress\nBihar\tGopalganj (SC)\tSURENDRA RAM\t16680\tno\tBED\tBharatiya Ekta Dal\nBihar\tGopalganj (SC)\tANIL KUMAR\t100419\tno\tJD(U)\tJanata Dal  (United)\nBihar\tGopalganj (SC)\tRAM KUMAR MANJHI\t14162\tno\tIND\tIndependent\nBihar\tSiwan\tOM PRAKASH YADAV\t372670\tyes\tBJP\tBharatiya Janata Party\nBihar\tSiwan\tMANOJ KUMAR SINGH\t79239\tno\tJD(U)\tJanata Dal  (United)\nBihar\tSiwan\tGANESH RAM\t14584\tno\tBSP\tBahujan Samaj Party\nBihar\tSiwan\tKHALIL MIYA\t9580\tno\tGGP\tGondvana Gantantra Party\nBihar\tSiwan\tPARMESHWAR  MANJHI\t5683\tno\tBMUP\tBahujan Mukti Party\nBihar\tSiwan\tRAM BINAY RAY\t11757\tno\tBED\tBharatiya Ekta Dal\nBihar\tSiwan\tRANJAN KUMAR\t8851\tno\tBVM\tBharat Vikas Morcha\nBihar\tSiwan\tNone of the Above\t21409\tno\tNOTA\tNone of the Above\nBihar\tSiwan\tSATYA PRAKASH PANDEY\t10542\tno\tAAAP\tAam Aadmi Party\nBihar\tSiwan\tHENA SHAHAB\t258823\tno\tRJD\tRashtriya Janata Dal\nBihar\tSiwan\tPARAMANAND GOND\t4016\tno\tSWSP\tSwatantra Samaj Party\nBihar\tSiwan\tAMAR NATH YADAV\t81006\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tSiwan\tNARAD PNDIT\t3250\tno\tJPJD\tJai Prakash Janata Dal\nBihar\tSiwan\tNAGENDRA TIWARI\t2611\tno\tLKJP\tLoktantrik Janata Party (Secular)\nBihar\tMaharajganj\t\"JANARDAN SINGH \"\"SIGRIWAL\"\"\"\t320753\tyes\tBJP\tBharatiya Janata Party\nBihar\tMaharajganj\tNone of the Above\t23404\tno\tNOTA\tNone of the Above\nBihar\tMaharajganj\tUMASHANKAR TIWARI\t11599\tno\tIND\tIndependent\nBihar\tMaharajganj\tBYAS MANJHI\t4796\tno\tBMUP\tBahujan Mukti Party\nBihar\tMaharajganj\tNAGENDRA PRASAD\t11189\tno\tBSP\tBahujan Samaj Party\nBihar\tMaharajganj\tBRINDA PATHAK\t17186\tno\tIND\tIndependent\nBihar\tMaharajganj\tRINA RANI\t6430\tno\tAAAP\tAam Aadmi Party\nBihar\tMaharajganj\tDR. GOPAL PRASAD\t10602\tno\tIND\tIndependent\nBihar\tMaharajganj\tSHIV NATH MAHTO\t8874\tno\tIND\tIndependent\nBihar\tMaharajganj\tMANORANJAN SINGH\t149483\tno\tJD(U)\tJanata Dal  (United)\nBihar\tMaharajganj\tPRABHU NATH SINGH\t282338\tno\tRJD\tRashtriya Janata Dal\nBihar\tSaran\tRAJIV PRATAP RUDY\t355120\tyes\tBJP\tBharatiya Janata Party\nBihar\tSaran\tSALEEM PERWEZ\t107008\tno\tJD(U)\tJanata Dal  (United)\nBihar\tSaran\tPARMATMA SINGH\t5099\tno\tAAAP\tAam Aadmi Party\nBihar\tSaran\tLALU RAI\t9957\tno\tIND\tIndependent\nBihar\tSaran\tBAL MUKUND CHAUHAN\t15500\tno\tBSP\tBahujan Samaj Party\nBihar\tSaran\tRANJAN KUMAR\t3217\tno\tJMBP\tJai Maha Bharath Party\nBihar\tSaran\tLALAN PRASAD\t14687\tno\tIND\tIndependent\nBihar\tSaran\tNIRAJ RAM\t2818\tno\tBMUP\tBahujan Mukti Party\nBihar\tSaran\tRAJESH KUMAR\t9748\tno\tBED\tBharatiya Ekta Dal\nBihar\tSaran\tNone of the Above\t19163\tno\tNOTA\tNone of the Above\nBihar\tSaran\tRABRI DEVI\t314172\tno\tRJD\tRashtriya Janata Dal\nBihar\tSaran\tCHANDAN KUMAR ALIAS CHANDAN GUPTA\t6765\tno\tBJPARTY\tBihar Janta Party\nBihar\tHajipur (SC)\tRAMVILAS PASWAN\t455652\tyes\tLJP\tLok Jan Shakti Party\nBihar\tHajipur (SC)\tDINA NATH RAM\t21045\tno\tIND\tIndependent\nBihar\tHajipur (SC)\tSANJEEV PRASAD TONI\t230152\tno\tINC\tIndian National Congress\nBihar\tHajipur (SC)\tNone of the Above\t15094\tno\tNOTA\tNone of the Above\nBihar\tHajipur (SC)\tSURESH PASWAN\t7544\tno\tIND\tIndependent\nBihar\tHajipur (SC)\tSATYA NARAYAN PASWAN\t3216\tno\tAIFB\tAll India Forward Bloc\nBihar\tHajipur (SC)\tVIPHIYA DEVI\t24399\tno\tIND\tIndependent\nBihar\tHajipur (SC)\tRAJ KR. PASWAN\t6344\tno\tAAAP\tAam Aadmi Party\nBihar\tHajipur (SC)\tUPENDER RAM\t8896\tno\tIND\tIndependent\nBihar\tHajipur (SC)\tVIRCHANDRA PASWAN\t4185\tno\tSP\tSamajwadi Party\nBihar\tHajipur (SC)\tUJJAWAL KANT HUNKAR\t8957\tno\tBMF\tBharatiya Momin Front\nBihar\tHajipur (SC)\tRINA RAGNI\t3882\tno\tBMUP\tBahujan Mukti Party\nBihar\tHajipur (SC)\tDHANESHWAR RAM\t12688\tno\tBSP\tBahujan Samaj Party\nBihar\tHajipur (SC)\tRAM SUNDAR DAS\t95790\tno\tJD(U)\tJanata Dal  (United)\nBihar\tHajipur (SC)\tRANDHIR KUMAR RAM\t3711\tno\tBJKVP\tBajjikanchal Vikas Party\nBihar\tHajipur (SC)\tVIDHAN CHANDRA RANA\t3198\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tUjiarpur\tNITYANAND RAI\t317352\tyes\tBJP\tBharatiya Janata Party\nBihar\tUjiarpur\tPHOOL BABU SINGH\t6967\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tUjiarpur\tASHWAMEDH DEVI\t119669\tno\tJD(U)\tJanata Dal  (United)\nBihar\tUjiarpur\tMADHURI\t7661\tno\tBMUP\tBahujan Mukti Party\nBihar\tUjiarpur\tVINDESHWARI SAHNI\t7969\tno\tPSS\tProutist Sarva Samaj\nBihar\tUjiarpur\tSURESH CHAUDHARI\t8917\tno\tAAAP\tAam Aadmi Party\nBihar\tUjiarpur\tALOK KUMAR MEHTA\t256883\tno\tRJD\tRashtriya Janata Dal\nBihar\tUjiarpur\tMD. TAHIR ARAMAN\t2536\tno\tNADP\tNaya Daur Party\nBihar\tUjiarpur\tPRITHIVINATH PRASAD\t2482\tno\tMHD\tMahamukti Dal\nBihar\tUjiarpur\tSHIVANI UMANI\t12520\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tUjiarpur\tDHARMENDRA SAHANI\t15198\tno\tBSP\tBahujan Samaj Party\nBihar\tUjiarpur\tRAM DEO VERMA\t53044\tno\tCPM\tCommunist Party of India  (Marxist)\nBihar\tUjiarpur\tPREM LATA DEVI\t3152\tno\tJPJD\tJai Prakash Janata Dal\nBihar\tUjiarpur\tMITHILESH KUMAR JHA\t7810\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tUjiarpur\tARJUN KUMAR SAH\t4201\tno\tIND\tIndependent\nBihar\tUjiarpur\tAMRENDRA KUMAR YADAV\t5631\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nBihar\tUjiarpur\tMINTU PASWAN\t3098\tno\tIND\tIndependent\nBihar\tUjiarpur\tKISHOR KUMAR\t5669\tno\tBLSP\tRashtriya Lok Samta Party\nBihar\tUjiarpur\tJITENDRA KUMAR RAI\t11990\tno\tSHS\tShivsena\nBihar\tUjiarpur\tNone of the Above\t6171\tno\tNOTA\tNone of the Above\nBihar\tSamastipur (SC)\tRAM CHANDRA PASWAN\t270401\tyes\tLJP\tLok Jan Shakti Party\nBihar\tSamastipur (SC)\tNone of the Above\t29211\tno\tNOTA\tNone of the Above\nBihar\tSamastipur (SC)\tAMIT KUMAR\t7423\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tSamastipur (SC)\tSHATRUDHAN KUMAR PASWAN\t12322\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nBihar\tSamastipur (SC)\tASHOK PASWAN\t6603\tno\tBMUP\tBahujan Mukti Party\nBihar\tSamastipur (SC)\tMITHILESH KUMAR RAJAK\t13504\tno\tIND\tIndependent\nBihar\tSamastipur (SC)\tGANESH KUMAR\t9954\tno\tAAAP\tAam Aadmi Party\nBihar\tSamastipur (SC)\tBALESHWAR PASWAN\t15837\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tSamastipur (SC)\tVIJAY KUMAR RAM\t11813\tno\travp\tRajnaitik Vikalp Party\nBihar\tSamastipur (SC)\tLAL BAHADUR SADA\t9665\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tSamastipur (SC)\tDR. ASHOK KUMAR\t263529\tno\tINC\tIndian National Congress\nBihar\tSamastipur (SC)\tMAHESHWAR HAZARI\t200124\tno\tJD(U)\tJanata Dal  (United)\nBihar\tSamastipur (SC)\tRAM CHANDRA RAM\t12813\tno\tBSP\tBahujan Samaj Party\nBihar\tBegusarai\tBHOLA SINGH\t428227\tyes\tBJP\tBharatiya Janata Party\nBihar\tBegusarai\tRAM PADARATH YADAV\t9014\tno\tBSP\tBahujan Samaj Party\nBihar\tBegusarai\tRAJENDRA YADAV\t4605\tno\tIND\tIndependent\nBihar\tBegusarai\tMOHAMMAD JAVED\t3405\tno\tIND\tIndependent\nBihar\tBegusarai\tRAJENDRA PRASAD SINGH\t192639\tno\tCPI\tCommunist Party of India\nBihar\tBegusarai\tVAIJ NATH PASWAN\t12143\tno\tIND\tIndependent\nBihar\tBegusarai\tMD. TANVEER HASSAN\t369892\tno\tRJD\tRashtriya Janata Dal\nBihar\tBegusarai\tPANKAJ PASWAN\t3323\tno\tIND\tIndependent\nBihar\tBegusarai\tNone of the Above\t26622\tno\tNOTA\tNone of the Above\nBihar\tBegusarai\tSUNIL KUMAR\t8639\tno\tAAAP\tAam Aadmi Party\nBihar\tBegusarai\tSUSHIL KUMAR\t11144\tno\tIND\tIndependent\nBihar\tBegusarai\tPINKI DEVI\t4858\tno\tBMUP\tBahujan Mukti Party\nBihar\tBegusarai\tHARI KANT PRASAD\t3344\tno\tBBC\tBharatiya Bahujan Congress\nBihar\tKhagaria\tCHOUDHARY MAHBOOB ALI KAISER\t313806\tyes\tLJP\tLok Jan Shakti Party\nBihar\tKhagaria\tUMESH CHANDRA BHARTI\t14926\tno\tIND\tIndependent\nBihar\tKhagaria\tMAKKHAN SAH\t6651\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tKhagaria\tTEJ BAHADUR SINGH\t4053\tno\tPSS\tProutist Sarva Samaj\nBihar\tKhagaria\tGANESH MUKHIYA NISHAD\t4522\tno\tSKLP\tSarvajan Kalyan Loktantrik Party\nBihar\tKhagaria\tJAGDISH CHANDRA VASU\t24490\tno\tCPM\tCommunist Party of India  (Marxist)\nBihar\tKhagaria\tRAJEEV RANJAN\t5722\tno\tLD\tLok Dal\nBihar\tKhagaria\tNone of the Above\t23868\tno\tNOTA\tNone of the Above\nBihar\tKhagaria\tRAJESH KUMAR\t6480\tno\tBSP\tBahujan Samaj Party\nBihar\tKhagaria\tKRISHNA KUMARI YADAV\t237803\tno\tRJD\tRashtriya Janata Dal\nBihar\tKhagaria\tBRAHAMDEO MUKHIA\t5215\tno\tRND\tRashtriya Naujawan Dal\nBihar\tKhagaria\tDINESH CHANDRA YADAV\t220316\tno\tJD(U)\tJanata Dal  (United)\nBihar\tKhagaria\tDR. SWAMI VIVEKANAND\t14491\tno\tAAAP\tAam Aadmi Party\nBihar\tKhagaria\tSATISH PRASAD SINGH\t13888\tno\tIND\tIndependent\nBihar\tBhagalpur\tSHAILESH KUMAR URPH BULO MANDAL\t367623\tyes\tRJD\tRashtriya Janata Dal\nBihar\tBhagalpur\tNAUSHABA KHANAM\t11265\tno\tBSP\tBahujan Samaj Party\nBihar\tBhagalpur\tNISHI KANT MANDAL\t3147\tno\tBMUP\tBahujan Mukti Party\nBihar\tBhagalpur\tABU QAISER\t132256\tno\tJD(U)\tJanata Dal  (United)\nBihar\tBhagalpur\tSYED SHAHNAWAZ HUSSAIN\t358138\tno\tBJP\tBharatiya Janata Party\nBihar\tBhagalpur\tRINKI KUMARI\t5778\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tBhagalpur\tPROF. YOGENDRA MAHTO\t5830\tno\tAAAP\tAam Aadmi Party\nBihar\tBhagalpur\tINDRADEO KUMAR SINGH\t12710\tno\tIND\tIndependent\nBihar\tBhagalpur\tGAURAV KUMAR GHOSH\t3251\tno\tNTP\tNational Tiger Party\nBihar\tBhagalpur\tRAMJEE MANDAL\t18937\tno\tIND\tIndependent\nBihar\tBhagalpur\tPUROSOTTAM CHOUBEY\t2416\tno\tSP\tSamajwadi Party\nBihar\tBhagalpur\tKUMKUM SINGH\t8025\tno\tBVM\tBharat Vikas Morcha\nBihar\tBhagalpur\tSUMAN KUMAR\t3444\tno\tIND\tIndependent\nBihar\tBhagalpur\tANAND KUMAR JAIN\t4734\tno\tIND\tIndependent\nBihar\tBhagalpur\tRANJAN KUMAR\t5165\tno\tIND\tIndependent\nBihar\tBhagalpur\tSIKANDAR TANTI\t7122\tno\tIND\tIndependent\nBihar\tBhagalpur\tVIREN DAS\t3005\tno\tSaBP\tSankhyanupati Bhagidari Party\nBihar\tBhagalpur\tMAHADEO KUMAR\t9295\tno\tIND\tIndependent\nBihar\tBhagalpur\tNone of the Above\t11875\tno\tNOTA\tNone of the Above\nBihar\tBanka\tJAI PRAKASH NARAYAN YADAV\t285150\tyes\tRJD\tRashtriya Janata Dal\nBihar\tBanka\tAKHIL BHARTIYA SHARMA\t4721\tno\tIND\tIndependent\nBihar\tBanka\tALIMUDDIN ANSARI\t12871\tno\tIND\tIndependent\nBihar\tBanka\tMITHLESH KUMAR SINGH\t2977\tno\tNLP\tNational Loktantrik Party\nBihar\tBanka\tNone of the Above\t9753\tno\tNOTA\tNone of the Above\nBihar\tBanka\tPUTUL KUMARI\t275006\tno\tBJP\tBharatiya Janata Party\nBihar\tBanka\tSANJAY KUMAR -I\t220708\tno\tCPI\tCommunist Party of India\nBihar\tBanka\tVIJAY BHARTI\t4317\tno\travp\tRajnaitik Vikalp Party\nBihar\tBanka\tSANJAY KUMAR-II\t10558\tno\tBSP\tBahujan Samaj Party\nBihar\tBanka\tNIRAJ KUMAR\t6058\tno\tAAAP\tAam Aadmi Party\nBihar\tBanka\tSUBODH NARAYAN JHA\t4412\tno\tAITC\tAll India Trinamool Congress\nBihar\tBanka\tNARESH KUMAR PRIYADARSHI\t30814\tno\tIND\tIndependent\nBihar\tBanka\tMUKESH KUMAR SINHA\t2405\tno\tBEMP\tBhartiya Ekta Manch Party\nBihar\tBanka\tDEEPAK KUMAR\t2481\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nBihar\tBanka\tUMAKANT YADAV\t2266\tno\tBMUP\tBahujan Mukti Party\nBihar\tBanka\tMAHBOOB ALAM ANSARI\t2280\tno\tBMF\tBharatiya Momin Front\nBihar\tBanka\tSARGUN SAH\t11056\tno\tIND\tIndependent\nBihar\tBanka\tINDRARAJ SINGH SAINI\t11520\tno\tIND\tIndependent\nBihar\tMunger\tVEENA DEVI\t352911\tyes\tLJP\tLok Jan Shakti Party\nBihar\tMunger\tNone of the Above\t15420\tno\tNOTA\tNone of the Above\nBihar\tMunger\tASHOK KUMAR SINGH\t50469\tno\tSHS\tShivsena\nBihar\tMunger\tSURYODAY PASWAN\t5113\tno\tSaBP\tSankhyanupati Bhagidari Party\nBihar\tMunger\tPRAGATI MEHTA\t182971\tno\tRJD\tRashtriya Janata Dal\nBihar\tMunger\tSANDEEP\t7481\tno\tAAAP\tAam Aadmi Party\nBihar\tMunger\tSHARWAN KUMAR ANAND\t8112\tno\tIND\tIndependent\nBihar\tMunger\tMUZAFFAR FAKHRUDDIN\t10264\tno\tIND\tIndependent\nBihar\tMunger\tRAJIV RANJAN SINGH ALIAS LALAN SINGH\t243827\tno\tJD(U)\tJanata Dal  (United)\nBihar\tMunger\tKAMLESHWARI MANDAL\t11225\tno\tBSP\tBahujan Samaj Party\nBihar\tMunger\tUCHIT KUMAR\t11122\tno\tIND\tIndependent\nBihar\tMunger\tRAM BADAN RAI\t6769\tno\tSP\tSamajwadi Party\nBihar\tMunger\tSHANKAR PRASAD\t3956\tno\tBMUP\tBahujan Mukti Party\nBihar\tMunger\tPRAMOD KUMAR\t4710\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nBihar\tNalanda\tKAUSHLENDRA KUMAR\t321982\tyes\tJD(U)\tJanata Dal  (United)\nBihar\tNalanda\tYOGENDRA PRASAD\t4600\tno\tBIP\tBharatiya Inqalab Party\nBihar\tNalanda\tSATYA NAND SHARMA\t312355\tno\tLJP\tLok Jan Shakti Party\nBihar\tNalanda\tRAKESH RANJAN SINHA\t13381\tno\tRPI(A)\tRepublican Party of India (A)\nBihar\tNalanda\tNAYEB ALI\t4613\tno\tAITC\tAll India Trinamool Congress\nBihar\tNalanda\tLATA SINHA\t2237\tno\tIND\tIndependent\nBihar\tNalanda\tNIRAJ SHARMA\t2342\tno\travp\tRajnaitik Vikalp Party\nBihar\tNalanda\tSHASHI YADAV\t19477\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tNalanda\tPRAMOD KUMAR NIRALA\t2779\tno\tSaBP\tSankhyanupati Bhagidari Party\nBihar\tNalanda\tVISHWAJIT KUMAR\t2604\tno\tIND\tIndependent\nBihar\tNalanda\tNARESH PRASAD SINGH\t3620\tno\tIND\tIndependent\nBihar\tNalanda\tSHAILESH KUMAR\t15370\tno\tAPCP\tAti Picchara party\nBihar\tNalanda\tPARNAV PRAKASH\t18224\tno\tAAAP\tAam Aadmi Party\nBihar\tNalanda\tSANJAY KUMAR\t23595\tno\tBSP\tBahujan Samaj Party\nBihar\tNalanda\tASHISH RANJAN SINHA\t127270\tno\tINC\tIndian National Congress\nBihar\tNalanda\tBHOLA SAO\t2213\tno\tKVD\tKrantikari Vikas Dal\nBihar\tNalanda\tMUNNA PRASAD\t6048\tno\tIND\tIndependent\nBihar\tNalanda\tMAHESH PASWAN\t4236\tno\tBBMP\tBharat Bhrashtachar Mitao Party\nBihar\tNalanda\tAWADH KUMAR\t6346\tno\tIND\tIndependent\nBihar\tNalanda\tLALLU RAM\t6788\tno\tBMUP\tBahujan Mukti Party\nBihar\tNalanda\tDHIRENDRA KUMAR\t8757\tno\tJPS\tJanvadi Party(Socialist)\nBihar\tNalanda\tKAUSHAL KUMAR KAUSHALENDRA SINHA ALIAS BHARAT MANAS\t7472\tno\tIND\tIndependent\nBihar\tNalanda\tNone of the Above\t5452\tno\tNOTA\tNone of the Above\nBihar\tPatna Sahib\tSHATRUGHANA SINHA\t485905\tyes\tBJP\tBharatiya Janata Party\nBihar\tPatna Sahib\tNone of the Above\t7727\tno\tNOTA\tNone of the Above\nBihar\tPatna Sahib\tKUMAR RAJIV\t2914\tno\tIND\tIndependent\nBihar\tPatna Sahib\tGAJENDRA KUMAR\t2166\tno\tSaBP\tSankhyanupati Bhagidari Party\nBihar\tPatna Sahib\tSHREEKANT LABH\t3178\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tPatna Sahib\tDR.GOPAL PRASAD SINHA\t91024\tno\tJD(U)\tJanata Dal  (United)\nBihar\tPatna Sahib\tPARWEZ AHMAD\t1856\tno\tLKJP\tLoktantrik Janata Party (Secular)\nBihar\tPatna Sahib\tSHYAM NANDAN PRASAD\t1834\tno\tIND\tIndependent\nBihar\tPatna Sahib\tGANESH SAW\t9691\tno\tBSP\tBahujan Samaj Party\nBihar\tPatna Sahib\tLAL BAHADUR SINGH PATEL\t2502\tno\tKVD\tKrantikari Vikas Dal\nBihar\tPatna Sahib\tJAUHAR AZAD\t1834\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tPatna Sahib\tNAND KISHORE SINGH\t6057\tno\tIND\tIndependent\nBihar\tPatna Sahib\tKUNAL SINGH\t220100\tno\tINC\tIndian National Congress\nBihar\tPatna Sahib\tVIJAY KUMAR SAHNI\t1243\tno\tIND\tIndependent\nBihar\tPatna Sahib\tDR. RAKESH DUTTA MISHRA\t2687\tno\tBJKD\tBharatiya Jan Kranti Dal (Democratic)\nBihar\tPatna Sahib\tGANGA VISHNU PRASAD\t8834\tno\tIND\tIndependent\nBihar\tPatna Sahib\tUMESH KUMAR\t10836\tno\tSP\tSamajwadi Party\nBihar\tPatna Sahib\tANIL KUMAR\t2478\tno\tIND\tIndependent\nBihar\tPatna Sahib\tPARVEEN AMANULLAH\t16381\tno\tAAAP\tAam Aadmi Party\nBihar\tPatna Sahib\tCHANDRA BHUSHAN PRASAD CHANDRAVANSHI\t3015\tno\tBMUP\tBahujan Mukti Party\nBihar\tPataliputra\tRAM KRIPAL YADAV\t383262\tyes\tBJP\tBharatiya Janata Party\nBihar\tPataliputra\tPANCHAM LAL\t2520\tno\tIND\tIndependent\nBihar\tPataliputra\tDURGESH NANDAN SINGH ALIAS DURGESH NANDAN\t16645\tno\tIND\tIndependent\nBihar\tPataliputra\tRAMESHWAR PRASAD\t51623\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tPataliputra\tABINASH KUMAR\t5854\tno\tBJKD\tBharatiya Jan Kranti Dal (Democratic)\nBihar\tPataliputra\tDEO KUMAR VERMA\t2484\tno\tRPI(A)\tRepublican Party of India (A)\nBihar\tPataliputra\tNIRAJ KUMAR\t8084\tno\tIND\tIndependent\nBihar\tPataliputra\tMISHA BHARTI\t342940\tno\tRJD\tRashtriya Janata Dal\nBihar\tPataliputra\tMINENDRA KUMAR SINGH\t1697\tno\tIND\tIndependent\nBihar\tPataliputra\tJAI PRAKASH YADAV\t4804\tno\tIND\tIndependent\nBihar\tPataliputra\tRAJ KUMAR RAM\t17188\tno\tBSP\tBahujan Samaj Party\nBihar\tPataliputra\tRANJAN PRASAD YADAV\t97228\tno\tJD(U)\tJanata Dal  (United)\nBihar\tPataliputra\tKUMAR INDRA BHUSHAN\t5412\tno\tIND\tIndependent\nBihar\tPataliputra\tSUNIL KUMAR SINGH\t4555\tno\tIND\tIndependent\nBihar\tPataliputra\tMUKUL KUMAR SHARMA\t3015\tno\tSaBP\tSankhyanupati Bhagidari Party\nBihar\tPataliputra\tNAGENDRA KUMAR\t4302\tno\tBVM\tBharat Vikas Morcha\nBihar\tPataliputra\tKUNDAN PRASAD SINGH\t5637\tno\tAAAP\tAam Aadmi Party\nBihar\tPataliputra\tSUNIL KUMAR\t6269\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nBihar\tPataliputra\tCHANDRA DEV PRASAD\t2268\tno\tBMUP\tBahujan Mukti Party\nBihar\tPataliputra\tSHATRUDHAN SAW\t8184\tno\tBED\tBharatiya Ekta Dal\nBihar\tPataliputra\tNone of the Above\t4678\tno\tNOTA\tNone of the Above\nBihar\tArrah\tRAJ KUMAR SINGH\t391074\tyes\tBJP\tBharatiya Janata Party\nBihar\tArrah\tSRIBHAGWAN SINGH KUSHWAHA\t255204\tno\tRJD\tRashtriya Janata Dal\nBihar\tArrah\tSHANKAR DAYAL PASWAN\t5610\tno\tIND\tIndependent\nBihar\tArrah\tASHOK RAM\t7305\tno\tIND\tIndependent\nBihar\tArrah\tMEENA SINGH\t75962\tno\tJD(U)\tJanata Dal  (United)\nBihar\tArrah\tGOPAL SINGH\t5879\tno\tIND\tIndependent\nBihar\tArrah\tBAIJNATH YADAV\t6886\tno\tBMUP\tBahujan Mukti Party\nBihar\tArrah\tDR. SURENDRA KUMAR PRASAD\t7518\tno\tAAAP\tAam Aadmi Party\nBihar\tArrah\tRAJU YADAV\t98805\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tArrah\tBHARAT BHUSHAN PANDEY\t10950\tno\tABJS\tAkhil Bharatiya Jan Sangh\nBihar\tArrah\tSHAMBHU PRASAD SHARMA\t5680\tno\tHVD\tHindustan Vikas Dal\nBihar\tArrah\tRAMESH CHANDRA BADAL\t7637\tno\tBSP\tBahujan Samaj Party\nBihar\tArrah\tNone of the Above\t14703\tno\tNOTA\tNone of the Above\nBihar\tBuxar\tASHWINI KUMAR CHOUBEY\t319012\tyes\tBJP\tBharatiya Janata Party\nBihar\tBuxar\tNone of the Above\t9179\tno\tNOTA\tNone of the Above\nBihar\tBuxar\tSWETA PATHAK\t7879\tno\tAAAP\tAam Aadmi Party\nBihar\tBuxar\tSHYAM LAL SINGH KUSHWAHA\t117012\tno\tJD(U)\tJanata Dal  (United)\nBihar\tBuxar\tPRADEEP KUMAR PRASAD\t6901\tno\tPMSP\tPragatisheel Manav Samaj Party\nBihar\tBuxar\tJAGADANAND SINGH\t186674\tno\tRJD\tRashtriya Janata Dal\nBihar\tBuxar\tINDU SINGH\t13051\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tBuxar\tLALLAN RUPNARAIN PATHAK\t5294\tno\tIND\tIndependent\nBihar\tBuxar\tMOKARRAM HUSSAIN\t7514\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nBihar\tBuxar\tDADAN YADAV\t184788\tno\tBSP\tBahujan Samaj Party\nBihar\tBuxar\tSHEO GOPAL CHOUDHARY\t12422\tno\tIND\tIndependent\nBihar\tBuxar\tTAFIR HUSSIAN\t2241\tno\tHVD\tHindustan Vikas Dal\nBihar\tBuxar\tPARSHURAM PRASAD\t2066\tno\tBMUP\tBahujan Mukti Party\nBihar\tBuxar\tNARENDRA PRATAP SINGH\t6130\tno\tIND\tIndependent\nBihar\tBuxar\tRAJENDRA KUMAR SINGH CHOUHAN\t2740\tno\tBVM\tBharat Vikas Morcha\nBihar\tBuxar\tMONIBUR RAHMAN\t1864\tno\tJDR\tJanta Dal Rashtravadi\nBihar\tBuxar\tSHIO KUMAR SHARMA\t3437\tno\tJPS\tJanvadi Party(Socialist)\nBihar\tSasaram (SC)\tCHHEDI PASWAN\t366087\tyes\tBJP\tBharatiya Janata Party\nBihar\tSasaram (SC)\tNone of the Above\t13379\tno\tNOTA\tNone of the Above\nBihar\tSasaram (SC)\tBALESHWAR BHARTI\t31528\tno\tBSP\tBahujan Samaj Party\nBihar\tSasaram (SC)\tRAJNARAIN RAO\t4395\tno\tIND\tIndependent\nBihar\tSasaram (SC)\tNAND LAL RAM\t2808\tno\tIND\tIndependent\nBihar\tSasaram (SC)\tGEETA ARYA\t11005\tno\tAAAP\tAam Aadmi Party\nBihar\tSasaram (SC)\tMEIRA KUMAR\t302760\tno\tINC\tIndian National Congress\nBihar\tSasaram (SC)\tRAVIKANT CHAUDHARY\t3980\tno\tIND\tIndependent\nBihar\tSasaram (SC)\tTETRI DEVI\t7486\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tSasaram (SC)\tSAROJ RAM\t3906\tno\tBMUP\tBahujan Mukti Party\nBihar\tSasaram (SC)\tKARRA PARASU RAMAIAH\t93310\tno\tJD(U)\tJanata Dal  (United)\nBihar\tSasaram (SC)\tSURENDRA RAM\t6144\tno\tIND\tIndependent\nBihar\tKarakat\tUPENDRA KUSHWAHA\t338892\tyes\tBLSP\tRashtriya Lok Samta Party\nBihar\tKarakat\tKANTI SINGH\t233651\tno\tRJD\tRashtriya Janata Dal\nBihar\tKarakat\tMAHABALI SINGH\t76709\tno\tJD(U)\tJanata Dal  (United)\nBihar\tKarakat\tSANJAY KEWAT\t45503\tno\tBSP\tBahujan Samaj Party\nBihar\tKarakat\tGHULAM KUNDANAM\t6938\tno\tAAAP\tAam Aadmi Party\nBihar\tKarakat\tDINESH KUMAR\t2359\tno\tBMUP\tBahujan Mukti Party\nBihar\tKarakat\tBHAIRAW DAYAL SINGH\t6974\tno\tIND\tIndependent\nBihar\tKarakat\tRAM DAYAL SINGH\t5459\tno\tIND\tIndependent\nBihar\tKarakat\tSATYA NARAYAN SINGH\t9919\tno\tIND\tIndependent\nBihar\tKarakat\tMD. DILBAS ANSARI\t2813\tno\tIND\tIndependent\nBihar\tKarakat\tIMRAN ALI\t2048\tno\tIND\tIndependent\nBihar\tKarakat\tVEENA BHARTI\t4002\tno\tJPS\tJanvadi Party(Socialist)\nBihar\tKarakat\tPRADEEP KUMAR JOSHI\t6994\tno\tRSWD\tRashtra Sewa Dal\nBihar\tKarakat\tRAJANI DUBEY\t4795\tno\tABJS\tAkhil Bharatiya Jan Sangh\nBihar\tKarakat\tRAJA RAM SINGH\t32686\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tKarakat\tNone of the Above\t10185\tno\tNOTA\tNone of the Above\nBihar\tJahanabad\tDR. ARUN KUMAR\t322647\tyes\tBLSP\tRashtriya Lok Samta Party\nBihar\tJahanabad\tRAVINDRA SINGH\t10580\tno\tBSP\tBahujan Samaj Party\nBihar\tJahanabad\tANIL KUMAR SHARMA\t100851\tno\tJD(U)\tJanata Dal  (United)\nBihar\tJahanabad\tRAMADHAR SINGH\t34365\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tJahanabad\tSURENDRA PRASAD YADAV\t280307\tno\tRJD\tRashtriya Janata Dal\nBihar\tJahanabad\tRAHUL RANJAN\t3521\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tJahanabad\tNone of the Above\t10352\tno\tNOTA\tNone of the Above\nBihar\tJahanabad\tMANJAY KUMAR\t8874\tno\tIND\tIndependent\nBihar\tJahanabad\tNITYANAND SINGH\t10625\tno\tIND\tIndependent\nBihar\tJahanabad\tARJUN THAKUR\t7757\tno\tIND\tIndependent\nBihar\tJahanabad\tSUDAY YADAV\t4202\tno\tIND\tIndependent\nBihar\tJahanabad\tCHANDRA BHUSHAN SHARMA\t3711\tno\tAAAP\tAam Aadmi Party\nBihar\tJahanabad\tMD. ISTIYAK\t6041\tno\tAITC\tAll India Trinamool Congress\nBihar\tJahanabad\tPARSHURAM VIND\t2721\tno\tBMUP\tBahujan Mukti Party\nBihar\tJahanabad\tMANJU DEVI\t2826\tno\tKVD\tKrantikari Vikas Dal\nBihar\tJahanabad\tNARENDRA KUMAR\t2136\tno\tSHS\tShivsena\nBihar\tAurangabad\tSUSHIL KUMAR SINGH\t307941\tyes\tBJP\tBharatiya Janata Party\nBihar\tAurangabad\tDURGA THAKUR\t3696\tno\tIND\tIndependent\nBihar\tAurangabad\tSOMARU PASWAN\t8678\tno\tIND\tIndependent\nBihar\tAurangabad\tBIJEET KUMAR\t6121\tno\tIND\tIndependent\nBihar\tAurangabad\tRAMJI SINGH\t7195\tno\tIND\tIndependent\nBihar\tAurangabad\tUPENDRA NATH\t9255\tno\tBMUP\tBahujan Mukti Party\nBihar\tAurangabad\tRAKESH SHARMA\t5726\tno\tIND\tIndependent\nBihar\tAurangabad\tBAGI KUMAR VERMA\t136137\tno\tJD(U)\tJanata Dal  (United)\nBihar\tAurangabad\tUSHA SHARAN\t4608\tno\tSSD\tShoshit Samaj Dal\nBihar\tAurangabad\tSANTOSH KUMAR\t27833\tno\tBSP\tBahujan Samaj Party\nBihar\tAurangabad\tRAGINI LATA SINGH\t6703\tno\tAAAP\tAam Aadmi Party\nBihar\tAurangabad\tRITA DEVI\t3333\tno\tSP\tSamajwadi Party\nBihar\tAurangabad\tNIKHIL KUMAR\t241594\tno\tINC\tIndian National Congress\nBihar\tAurangabad\tNone of the Above\t17454\tno\tNOTA\tNone of the Above\nBihar\tGaya (SC)\tHARI MANJHI\t326230\tyes\tBJP\tBharatiya Janata Party\nBihar\tGaya (SC)\tJITAN RAM MANJHI\t131828\tno\tJD(U)\tJanata Dal  (United)\nBihar\tGaya (SC)\tDR. DEV KUMAR CHOUDHARY\t19651\tno\tIND\tIndependent\nBihar\tGaya (SC)\tNIRANJAN KUMAR\t7624\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tGaya (SC)\tRAMJI MANJHI\t210726\tno\tRJD\tRashtriya Janata Dal\nBihar\tGaya (SC)\tSHIV SHANKAR KUMAR\t5924\tno\tBBMP\tBharat Bhrashtachar Mitao Party\nBihar\tGaya (SC)\tBHUNESWAR DAS\t7549\tno\tRAHM\tRashtriya Ahinsa Manch\nBihar\tGaya (SC)\tKAPIL CHOUDHARY\t9004\tno\tAITC\tAll India Trinamool Congress\nBihar\tGaya (SC)\tMAMTA KUMARI\t6065\tno\tSP\tSamajwadi Party\nBihar\tGaya (SC)\tSHIV KUMAR KANT\t16028\tno\tBSP\tBahujan Samaj Party\nBihar\tGaya (SC)\tDHANANJAY KISHORE\t7312\tno\tAAAP\tAam Aadmi Party\nBihar\tGaya (SC)\tINDRA DEO CHOUDHARY\t5544\tno\tBMUP\tBahujan Mukti Party\nBihar\tGaya (SC)\tASHOK KUMAR\t36863\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tGaya (SC)\tNone of the Above\t19030\tno\tNOTA\tNone of the Above\nBihar\tNawada\tGIRIRAJ SINGH\t390248\tyes\tBJP\tBharatiya Janata Party\nBihar\tNawada\tVIJAY RAM\t3291\tno\tMOSP\tMoolniwasi Samaj Party\nBihar\tNawada\tMANISH KUMAR\t6443\tno\tIND\tIndependent\nBihar\tNawada\tSURENDRA RAJBANSHI\t3612\tno\tSP\tSamajwadi Party\nBihar\tNawada\tUMAKANT RAHI\t2169\tno\tSSD\tShoshit Samaj Dal\nBihar\tNawada\tKAUSHAL YADAV\t168217\tno\tJD(U)\tJanata Dal  (United)\nBihar\tNawada\tRAJ BALLABH PRASAD\t250091\tno\tRJD\tRashtriya Janata Dal\nBihar\tNawada\tPRAMENDRA KUMAR YADAV\t1650\tno\tBED\tBharatiya Ekta Dal\nBihar\tNawada\tSATISH KUMAR\t6736\tno\tIND\tIndependent\nBihar\tNawada\tSOHAIL ANWER\t7997\tno\tBSP\tBahujan Samaj Party\nBihar\tNawada\tASHOK KUMAR\t2596\tno\travp\tRajnaitik Vikalp Party\nBihar\tNawada\tRAJENDRA SINGH\t4057\tno\tAAAP\tAam Aadmi Party\nBihar\tNawada\tADITYA NARAYAN\t3743\tno\tIND\tIndependent\nBihar\tNawada\tSAFDAR ISMAIL\t12072\tno\tIND\tIndependent\nBihar\tNawada\tMOHAMMAD JAHID HUSAIN\t3138\tno\tAITC\tAll India Trinamool Congress\nBihar\tNawada\tAZAD GEETA PRASAD SHARMA\t3568\tno\tIND\tIndependent\nBihar\tNawada\tSHULOCHANA DEVI\t7324\tno\tIND\tIndependent\nBihar\tNawada\tNone of the Above\t7489\tno\tNOTA\tNone of the Above\nBihar\tJamui (SC)\tCHIRAG KUMAR PASWAN\t285354\tyes\tLJP\tLok Jan Shakti Party\nBihar\tJamui (SC)\tUDAY NARAIN CHOUDHARY\t198599\tno\tJD(U)\tJanata Dal  (United)\nBihar\tJamui (SC)\tSUDHANSU SHEKHAR BHASKAR\t199407\tno\tRJD\tRashtriya Janata Dal\nBihar\tJamui (SC)\tBRAHMDEV ANAND PASWAN\t15008\tno\tBSP\tBahujan Samaj Party\nBihar\tJamui (SC)\tJAY PRAKASH DAS\t6253\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nBihar\tJamui (SC)\tVISHUN CHOUDHARY\t7147\tno\tSHS\tShivsena\nBihar\tJamui (SC)\tVINOD KUMAR DAS\t7159\tno\tAAAP\tAam Aadmi Party\nBihar\tJamui (SC)\tDILIP KUMAR\t7635\tno\tIND\tIndependent\nBihar\tJamui (SC)\tUPENDRA RAVIDAS\t10748\tno\tJMM\tJharkhand Mukti Morcha\nBihar\tJamui (SC)\tSUBHASH PASWAN\t10452\tno\tIND\tIndependent\nBihar\tJamui (SC)\tMANOJ KUMAR MANJHI\t8360\tno\tIND\tIndependent\nBihar\tJamui (SC)\tNone of the Above\t19517\tno\tNOTA\tNone of the Above\nGoa\tNorth Goa\tSHRIPAD YESSO NAIK\t237903\tyes\tBJP\tBharatiya Janata Party\nGoa\tNorth Goa\tADV. SUHAS NAIK\t5640\tno\tCPI\tCommunist Party of India\nGoa\tNorth Goa\tDAYANAND G. NARVEKAR\t4128\tno\tIND\tIndependent\nGoa\tNorth Goa\tMITTU MUJAVAR\t1813\tno\tIND\tIndependent\nGoa\tNorth Goa\tRAVI NAIK\t132304\tno\tINC\tIndian National Congress\nGoa\tNorth Goa\tNone of the Above\t5770\tno\tNOTA\tNone of the Above\nGoa\tNorth Goa\tRAJU DSOUZA\t3216\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nGoa\tNorth Goa\tDR.DATTARAM DESAI\t15857\tno\tAAAP\tAam Aadmi Party\nGoa\tSouth Goa\tADV. NARENDRA KESHAV SAWAIKAR\t198776\tyes\tBJP\tBharatiya Janata Party\nGoa\tSouth Goa\tADV.JAWAHAR LEONARDO DIAS\t530\tno\tGOA\tGoemcarancho Otrec Astro\nGoa\tSouth Goa\tSAVIO OLIVER RODRIGUES\t783\tno\tGSRP\tGoa Su-Raj Party\nGoa\tSouth Goa\tSHALOM MATHEW FRANCISCO SARDINHA\t2586\tno\tIND\tIndependent\nGoa\tSouth Goa\tBALKRISHNA SALGAONKAR\t683\tno\tIND\tIndependent\nGoa\tSouth Goa\tCLIFFTON DE SOUZA\t666\tno\tBMUP\tBahujan Mukti Party\nGoa\tSouth Goa\tNone of the Above\t4333\tno\tNOTA\tNone of the Above\nGoa\tSouth Goa\tGOVIND SHEPU GAUDE\t7152\tno\tIND\tIndependent\nGoa\tSouth Goa\tSWATI S. KERKAR\t11246\tno\tAAAP\tAam Aadmi Party\nGoa\tSouth Goa\tCHURCHILL ALEMAO\t11941\tno\tAITC\tAll India Trinamool Congress\nGoa\tSouth Goa\tALEIXO REGINALDO LOURENCO\t166446\tno\tINC\tIndian National Congress\nGoa\tSouth Goa\tADV. RAJU MANGESHKAR\t4437\tno\tCPI\tCommunist Party of India\nGoa\tSouth Goa\tVENUS HABIB\t790\tno\tIND\tIndependent\nGujarat\tKachchh\tCHAVDA VINOD LAKHAMASHI\t562855\tyes\tBJP\tBharatiya Janata Party\nGujarat\tKachchh\tHIRJI PUNJABHAI SIJU\t21106\tno\tBMUP\tBahujan Mukti Party\nGujarat\tKachchh\tDR.DINESH PARMAR\t308373\tno\tINC\tIndian National Congress\nGujarat\tKachchh\tNone of the Above\t16879\tno\tNOTA\tNone of the Above\nGujarat\tKachchh\tDANICHA GOVINDBHAI PUNAMCHAND\t15797\tno\tAAAP\tAam Aadmi Party\nGujarat\tKachchh\tKAMALBHAI MATANG\t21230\tno\tBSP\tBahujan Samaj Party\nGujarat\tBanaskantha\tCHAUDHARY HARIBHAI PARTHIBHAI\t507856\tyes\tBJP\tBharatiya Janata Party\nGujarat\tBanaskantha\tMAHENDRABHAI KESARABHAI BUMBADIA\t3302\tno\tIND\tIndependent\nGujarat\tBanaskantha\tSHRIMALI ASHOKBHAI BALCHNDBHAI\t2535\tno\tIND\tIndependent\nGujarat\tBanaskantha\tSANJAYKUMAR SOMNATHBHAI RAVAL\t6571\tno\tAAAP\tAam Aadmi Party\nGujarat\tBanaskantha\tBABAJI THAKOR\t10897\tno\tIND\tIndependent\nGujarat\tBanaskantha\tNone of the Above\t17397\tno\tNOTA\tNone of the Above\nGujarat\tBanaskantha\tTHAKOR BHUPATAJI RAVAJI\t1770\tno\tIND\tIndependent\nGujarat\tBanaskantha\tDABHI NAVAJIBHAI MADHABHAI\t2118\tno\tIND\tIndependent\nGujarat\tBanaskantha\tMADHU NIRUPABEN NATVARLAL\t2064\tno\tIND\tIndependent\nGujarat\tBanaskantha\tCHOUDHARY ADAMBHAI NASIRBHAI\t2007\tno\tSP\tSamajwadi Party\nGujarat\tBanaskantha\tGAMAR VADHABHAI RADHABHAI\t825\tno\tIND\tIndependent\nGujarat\tBanaskantha\tSOLANKI SAYBABHAI NANABHAI\t7207\tno\tIND\tIndependent\nGujarat\tBanaskantha\tSOLANKI DINESHKUMAR ALJIBHAI\t5388\tno\tIND\tIndependent\nGujarat\tBanaskantha\tPATEL JOITABHAI KASNABHAI\t305522\tno\tINC\tIndian National Congress\nGujarat\tBanaskantha\tMAHANT PARSOTAMGIRI TURANTGIRI\t11175\tno\tBSP\tBahujan Samaj Party\nGujarat\tPatan\tLILADHARBHAI KHODAJI VAGHELA\t518538\tyes\tBJP\tBharatiya Janata Party\nGujarat\tPatan\tRATHOD BHAVSINHBHAI DAHYABHAI\t379819\tno\tINC\tIndian National Congress\nGujarat\tPatan\tVAGHDA JIVABHAI DEVABHAI\t7492\tno\tIND\tIndependent\nGujarat\tPatan\tPARMAR MAGANBHAI AMARABHAI\t9900\tno\tBSP\tBahujan Samaj Party\nGujarat\tPatan\tJAGRALA  IMRAN MAHEMUD\t1978\tno\tIND\tIndependent\nGujarat\tPatan\tDESAI ISHWARBHAI MAHADEVBHAI\t1752\tno\tIND\tIndependent\nGujarat\tPatan\tNone of the Above\t12061\tno\tNOTA\tNone of the Above\nGujarat\tPatan\tPARMAR BHAICHANDBHAI SOMABHAI\t910\tno\tRPIE\tRepublican Party of India Ektavadi\nGujarat\tPatan\tCHHAGANBHAI NARANBHAI PRAJAPATI\t2539\tno\tIND\tIndependent\nGujarat\tPatan\tBHORANIYA SOYABBHAI HASAMBHAI\t2550\tno\tIND\tIndependent\nGujarat\tPatan\tMOLAPIYA ABDULKUDUS ABDULMAJID\t5629\tno\tIND\tIndependent\nGujarat\tPatan\tBABUBHAI KARSHANBHAI RABARI\t1843\tno\tSP\tSamajwadi Party\nGujarat\tPatan\tIMRANKHAN MAHMADKHAN NAGORI\t1840\tno\tIND\tIndependent\nGujarat\tPatan\tGOVINDJI OKHAJI THAKOR\t6188\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tPatan\tJAKASIJI JOGAJI THAKOR\t2760\tno\tBMUP\tBahujan Mukti Party\nGujarat\tMahesana\tPATEL JAYSHREEBEN KANUBHAI\t580250\tyes\tBJP\tBharatiya Janata Party\nGujarat\tMahesana\tPATEL DILIPBHAI SOMABHAI\t1032\tno\tHJP\tHindustan Janta Party\nGujarat\tMahesana\tTHAKOR SAMARATJI BALVANTSINH\t2427\tno\tIND\tIndependent\nGujarat\tMahesana\tPRAJAPATI VITTHALBHAI VIRDAS\t5982\tno\tIND\tIndependent\nGujarat\tMahesana\tTHAKOR KEVALJI NATHAJI\t9766\tno\tBSP\tBahujan Samaj Party\nGujarat\tMahesana\tNone of the Above\t11615\tno\tNOTA\tNone of the Above\nGujarat\tMahesana\tGIRISHJI JENAJI DABHI\t4825\tno\tBNJD\tBharatiya National Janta Dal\nGujarat\tMahesana\tTHAKOR MANSANGJI PUNJAJI\t1251\tno\tBMUP\tBahujan Mukti Party\nGujarat\tMahesana\tTHAKOR BABUJI DHANAJI\t1804\tno\tIND\tIndependent\nGujarat\tMahesana\tTHAKOR BALDEVJI MAGANJI\t1653\tno\tIND\tIndependent\nGujarat\tMahesana\tZALA INDRAJITSINH MAHENDRASINH\t1340\tno\tSP\tSamajwadi Party\nGujarat\tMahesana\tMAHMAD AZAM HAIDERKHAN PATHAN\t6139\tno\tIND\tIndependent\nGujarat\tMahesana\tSMT VANDANABEN DINESHKUMAR PATEL\t3731\tno\tAAAP\tAam Aadmi Party\nGujarat\tMahesana\tCHAUDHARY DINESHBHAI SALUBHAI\t1084\tno\tVHS\tVishva Hindustani Sangathan\nGujarat\tMahesana\tPATEL JIVABHAI AMBALAL\t371359\tno\tINC\tIndian National Congress\nGujarat\tSabarkantha\tRATHOD DIPSINH SHANKARSINH\t552205\tyes\tBJP\tBharatiya Janata Party\nGujarat\tSabarkantha\tBALUSINGH SOMANSINGH NARVE\t3527\tno\tIND\tIndependent\nGujarat\tSabarkantha\tPATEL PURUSOTTAMBHAI AMBALAL\t1876\tno\tIND\tIndependent\nGujarat\tSabarkantha\tPATEL BHOGILAL HARIBHAI\t9046\tno\tBNJD\tBharatiya National Janta Dal\nGujarat\tSabarkantha\tSHEKH ARIF KHAN\t2314\tno\tSP\tSamajwadi Party\nGujarat\tSabarkantha\tNone of the Above\t22334\tno\tNOTA\tNone of the Above\nGujarat\tSabarkantha\tVELANI SHANTILAL KARAMSHI\t6816\tno\tIND\tIndependent\nGujarat\tSabarkantha\tPATEL ISHWARBHAI SAKRAJI\t1674\tno\tIND\tIndependent\nGujarat\tSabarkantha\tSOLANKI CHHAGANBHAI KEVALABHAI\t9795\tno\tIND\tIndependent\nGujarat\tSabarkantha\tSHANKERSINH VAGHELA BAPU\t467750\tno\tINC\tIndian National Congress\nGujarat\tSabarkantha\tBHAMBHI CHANDUBHAI MULCHANDBHAI\t16665\tno\tBSP\tBahujan Samaj Party\nGujarat\tGandhinagar\tL.K.ADVANI\t773539\tyes\tBJP\tBharatiya Janata Party\nGujarat\tGandhinagar\tNIRANJAN GHOSH\t6068\tno\tBSP\tBahujan Samaj Party\nGujarat\tGandhinagar\tPAGI GANPATBHAI MAVJIBHAI\t924\tno\tIND\tIndependent\nGujarat\tGandhinagar\tTHAKOR CHANDRASINH JAVANSINH\t668\tno\tIND\tIndependent\nGujarat\tGandhinagar\tTHAKOR RAVAJI JUHAJI\t809\tno\tVHS\tVishva Hindustani Sangathan\nGujarat\tGandhinagar\tBRAHMBHATT SANJAYBHAI AMARKUMAR\t1474\tno\tIND\tIndependent\nGujarat\tGandhinagar\tPATEL PIYUSHKUMAR DASHRATHLAL\t653\tno\tNYP\tNational Youth Party\nGujarat\tGandhinagar\tMANOJ J. NAYAK\t683\tno\tLoRP\tLoktantrik Rashrtavadi Party\nGujarat\tGandhinagar\tRITURAJ MEHTA\t19966\tno\tAAAP\tAam Aadmi Party\nGujarat\tGandhinagar\tRAHUL CHIMANBHAI MEHTA\t9767\tno\tIND\tIndependent\nGujarat\tGandhinagar\tKIRITBHAI ISHVARBHAI PATEL\t290418\tno\tINC\tIndian National Congress\nGujarat\tGandhinagar\tDIPIKABEN SUTARIYA\t684\tno\tIND\tIndependent\nGujarat\tGandhinagar\tDESAI KHODABHAI LALJIBHAI\t793\tno\tIND\tIndependent\nGujarat\tGandhinagar\tM. K. SHAH\t3767\tno\tIND\tIndependent\nGujarat\tGandhinagar\tAJMALBHAI RAMABHAI VANIYA\t1639\tno\tBMUP\tBahujan Mukti Party\nGujarat\tGandhinagar\tVAGHELA KISHORSINH MAHOBATSINH\t6705\tno\tIND\tIndependent\nGujarat\tGandhinagar\tBIRJUSINH MOTISINH KSHATRIYA\t984\tno\tIND\tIndependent\nGujarat\tGandhinagar\tNone of the Above\t12777\tno\tNOTA\tNone of the Above\nGujarat\tGandhinagar\tRAJ PRAJAPATI\t3177\tno\tIND\tIndependent\nGujarat\tAhmedabad East\tPARESH RAWAL\t633582\tyes\tBJP\tBharatiya Janata Party\nGujarat\tAhmedabad East\tROSHAN PRIYAVADAN SHAH\t4046\tno\tIND\tIndependent\nGujarat\tAhmedabad East\tPATEL HIMMATSINGH PRAHLADSINGH\t306949\tno\tINC\tIndian National Congress\nGujarat\tAhmedabad East\tROHIT RAJUBHAI VIRJIBHAI ALIAS MANOJBHAI SONTARIYA\t6023\tno\tBSP\tBahujan Samaj Party\nGujarat\tAhmedabad East\tNone of the Above\t14358\tno\tNOTA\tNone of the Above\nGujarat\tAhmedabad East\tVIJAYKUMAR M. VADHEL\t704\tno\tHND\tHindusthan Nirman Dal\nGujarat\tAhmedabad East\tNARANBHAI T. SENGAL (DR.N.T.SENGAL)\t782\tno\tBSDL\tBahujan Suraksha Dal\nGujarat\tAhmedabad East\tBUDDHPRIYA JASVANT SOMABHAI\t698\tno\tPAP\tPrajatantra Aadhar Party\nGujarat\tAhmedabad East\tATIKBHAI MEV\t1094\tno\tIND\tIndependent\nGujarat\tAhmedabad East\tDASHRATHBHAI M. DEVDA\t2299\tno\tIND\tIndependent\nGujarat\tAhmedabad East\tADITYA RAVAL\t987\tno\tVHS\tVishva Hindustani Sangathan\nGujarat\tAhmedabad East\tDINESH VAGHELA\t11349\tno\tAAAP\tAam Aadmi Party\nGujarat\tAhmedabad East\tKHALIFA SAMSUDDIN NASIRUDDIN (JUGNU)\t895\tno\tABCD(A)\tAkhil Bharatiya Congress Dal (Ambedkar)\nGujarat\tAhmedabad East\tANILKUMAR SHARMA\t1201\tno\tIND\tIndependent\nGujarat\tAhmedabad East\tDUTT AAKASH - ADVOCATE\t558\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tAhmedabad West\tDR. KIRIT P SOLANKI\t617104\tyes\tBJP\tBharatiya Janata Party\nGujarat\tAhmedabad West\tISHWARBAHI DHANABHAI MAKWANA\t296793\tno\tINC\tIndian National Congress\nGujarat\tAhmedabad West\tJ J MEVADA\t17332\tno\tAAAP\tAam Aadmi Party\nGujarat\tAhmedabad West\tCHAVDA MANSUKHBHAI NAGARBHAI\t6205\tno\tBSP\tBahujan Samaj Party\nGujarat\tAhmedabad West\tDR J G PARMAR\t2564\tno\tBMUP\tBahujan Mukti Party\nGujarat\tAhmedabad West\tSOLANKI RAMESHBHAI DANABHAI\t1256\tno\tIND\tIndependent\nGujarat\tAhmedabad West\tAMRUT SONARA\t941\tno\tBSDL\tBahujan Suraksha Dal\nGujarat\tAhmedabad West\tSOLANKI VITTHALBHAI MAGANBHAI\t2837\tno\tIND\tIndependent\nGujarat\tAhmedabad West\tPARMAR HARJIVANBHAI KALABHAI\t807\tno\tIND\tIndependent\nGujarat\tAhmedabad West\tAAYAR MULJIBHAI KHANABHAI\t808\tno\tIND\tIndependent\nGujarat\tAhmedabad West\tNone of the Above\t16571\tno\tNOTA\tNone of the Above\nGujarat\tAhmedabad West\tNARENDRA SANKHALIYA\t1391\tno\tLSWP\tLoktantrik Samajwadi Party\nGujarat\tSurendranagar\tFATEPARA DEVAJIBHAI GOVINDBHAI\t529003\tyes\tBJP\tBharatiya Janata Party\nGujarat\tSurendranagar\tBAR AJAMALBHAI KARMANBHAI\t2251\tno\tBMUP\tBahujan Mukti Party\nGujarat\tSurendranagar\tPARMAR VASHARAMBHAI BAVALBHAI\t1751\tno\tIND\tIndependent\nGujarat\tSurendranagar\tMANSINH SHIVUBHA ZALA\t2154\tno\tIND\tIndependent\nGujarat\tSurendranagar\tVADALIYA KALUBHAI MALUBHAI\t3109\tno\tIND\tIndependent\nGujarat\tSurendranagar\tMAJETHIYA SAMARATBHAI JERAMBHAI\t1341\tno\tHJP\tHindustan Janta Party\nGujarat\tSurendranagar\tMAKWANA VASHARAMBHAI KARSHANBHAI\t1413\tno\tIND\tIndependent\nGujarat\tSurendranagar\tSANKALIYA GANGARAMBHAI TAPUBHAI\t11208\tno\tIND\tIndependent\nGujarat\tSurendranagar\tVORA BHAVANBHAI DEVAJIBHAI\t4821\tno\tIND\tIndependent\nGujarat\tSurendranagar\tVAGHELA PRAKASHBHAI BACHUBHAI\t6433\tno\tIND\tIndependent\nGujarat\tSurendranagar\tCHAVDA PALABHAI NANJIBHAI\t1227\tno\tIND\tIndependent\nGujarat\tSurendranagar\tSAPARA VIPULBHAI RAMESHBHAI\t14524\tno\tIND\tIndependent\nGujarat\tSurendranagar\tNone of the Above\t11024\tno\tNOTA\tNone of the Above\nGujarat\tSurendranagar\tKOLI PATEL SOMABHAI GANDALAL\t326096\tno\tINC\tIndian National Congress\nGujarat\tSurendranagar\tPARMAR PRABHUBHAI GOKALBHAI\t2968\tno\tIND\tIndependent\nGujarat\tSurendranagar\tJETHABHAI MANJIBHAI PATEL\t13375\tno\tAAAP\tAam Aadmi Party\nGujarat\tSurendranagar\tPARMAR MINAXIBEN VIJAYBHAI\t10753\tno\tBSP\tBahujan Samaj Party\nGujarat\tSurendranagar\tMAKWANA UKABHAI AMRABHAI\t1226\tno\tIND\tIndependent\nGujarat\tRajkot\tKUNDARIYA MOHANBHAI KALYANJIBHAI\t621524\tyes\tBJP\tBharatiya Janata Party\nGujarat\tRajkot\tPRAVINCHANDRA CHUNILAL PAREKH\t1562\tno\tIND\tIndependent\nGujarat\tRajkot\tANIRUDDHSINH JUVANSINH ZALA\t1636\tno\tIND\tIndependent\nGujarat\tRajkot\tKUNVARJIBHAI MOHANBHAI BAVALIYA\t375096\tno\tINC\tIndian National Congress\nGujarat\tRajkot\tVINODBHAI PRAGJIBHAI NAGANI\t5752\tno\tIND\tIndependent\nGujarat\tRajkot\tNone of the Above\t18249\tno\tNOTA\tNone of the Above\nGujarat\tRajkot\tROHITBHAI MOHANBHAI SAGATHIYA\t1344\tno\tBMUP\tBahujan Mukti Party\nGujarat\tRajkot\tRAJESHBHAI LAVJIBHAI PIPALVA\t808\tno\tLoRP\tLoktantrik Rashrtavadi Party\nGujarat\tRajkot\tPOPATBHAI DEVAJIBHAI CHAVDA (BHUVA)\t1408\tno\tIND\tIndependent\nGujarat\tRajkot\tJITENDRA BHURABHAI CHAUHAN\t990\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nGujarat\tRajkot\tBASIRBHAI SULEMANBHAI SAMA\t2076\tno\tIND\tIndependent\nGujarat\tRajkot\tANKUR AMRUTKUMAR DHAMELIYA(PATEL)\t10785\tno\tAAAP\tAam Aadmi Party\nGujarat\tRajkot\tPATHAN BISMILLAKHAN ABDULKHAN (BABAKHAN PATHAN)\t1080\tno\tIND\tIndependent\nGujarat\tRajkot\tNARENDRABHAI MAHAJAN\t1161\tno\tIND\tIndependent\nGujarat\tRajkot\tDENGADA PRAVINBHAI MEGHAJIBHAI\t945\tno\tRPI(A)\tRepublican Party of India (A)\nGujarat\tRajkot\tJIVANBHAI DEVAJIBHAI PARMAR\t12653\tno\tBSP\tBahujan Samaj Party\nGujarat\tPorbandar\tRADADIYA VITHALBHAI  HANSRAJBHAI\t508437\tyes\tBJP\tBharatiya Janata Party\nGujarat\tPorbandar\tSADIYA VRAJALAL PABABHAI\t12180\tno\tBSP\tBahujan Samaj Party\nGujarat\tPorbandar\tJADEJA KANDHALBHAI SARMANBHAI\t240466\tno\tNCP\tNationalist Congress Party\nGujarat\tPorbandar\tNone of the Above\t16443\tno\tNOTA\tNone of the Above\nGujarat\tPorbandar\tPATEL BUTANI RAJESHBHAI MAGANBHAI\t2868\tno\tIND\tIndependent\nGujarat\tPorbandar\tKHUNTI BHARATBHAI MALDEBHAI\t2205\tno\tIND\tIndependent\nGujarat\tPorbandar\tIRFANSHAH HABIBSHAH SUHARAVARDI\t3825\tno\tSP\tSamajwadi Party\nGujarat\tPorbandar\tKHOKHANI LALITABEN MANSUKHBHAI\t1569\tno\tIND\tIndependent\nGujarat\tPorbandar\tJOSHI HARISH LILADHAR\t1024\tno\tIND\tIndependent\nGujarat\tPorbandar\tTUKADIA G.R.\t1704\tno\tIND\tIndependent\nGujarat\tPorbandar\tRATHOD CHANDULAL MOHANLAL\t3009\tno\tIND\tIndependent\nGujarat\tPorbandar\tAGHERA DHIRAJLAL BHURABHAI\t1671\tno\tIND\tIndependent\nGujarat\tPorbandar\tMANSUKH SUNDARAJI DHOKAI\t7939\tno\tAAAP\tAam Aadmi Party\nGujarat\tPorbandar\tVAKIL VINZUDA RANJITBHAI NARANBHAI\t4475\tno\tIND\tIndependent\nGujarat\tPorbandar\tUNADAKAT PRAKASH VALLABHADAS\t1618\tno\tIND\tIndependent\nGujarat\tJamnagar\tPOONAMBEN HEMATBHAI MAADAM\t484412\tyes\tBJP\tBharatiya Janata Party\nGujarat\tJamnagar\tSAIYAD ABUBAKAR IBRAHIM\t940\tno\tSP\tSamajwadi Party\nGujarat\tJamnagar\tVANIYA GANGAJIBHAI\t795\tno\tIND\tIndependent\nGujarat\tJamnagar\tSAMA YUSUF\t8234\tno\tBSP\tBahujan Samaj Party\nGujarat\tJamnagar\tKASAMBHAI\t1254\tno\tRKEP\tRashtriya Komi Ekta Party\nGujarat\tJamnagar\tPANDYA CHIRAGBHAI HARIOMBHAI\t1541\tno\tIND\tIndependent\nGujarat\tJamnagar\tDALIT JITESH BABUBHAI RATHORE\t706\tno\tIND\tIndependent\nGujarat\tJamnagar\tMEMAN RAFIK ABUBAKAR POPATPUTRA\t5811\tno\tIND\tIndependent\nGujarat\tJamnagar\tVAGHER ALI ISHAK PALANI\t3946\tno\tIND\tIndependent\nGujarat\tJamnagar\tAHIR VIKRAMBHAI ARJANBHAI MADAM\t309123\tno\tINC\tIndian National Congress\nGujarat\tJamnagar\tDALIT ASHOK NATHABHAI CHAVDA\t744\tno\tIND\tIndependent\nGujarat\tJamnagar\tJHALA RAJENDRASINH\t4911\tno\tAAAP\tAam Aadmi Party\nGujarat\tJamnagar\tDHARAVIYA VALLABHBHAI\t1043\tno\tIND\tIndependent\nGujarat\tJamnagar\tSACHADA HABIB ISHABHAI\t819\tno\tIND\tIndependent\nGujarat\tJamnagar\tSODHA SALIMBHAI NURMAMADBHAI\t885\tno\tIND\tIndependent\nGujarat\tJamnagar\tSUTHAR HANSABEN HARSUKHBHAI GORECHA\t592\tno\tIND\tIndependent\nGujarat\tJamnagar\tDHANJIBHAI LALJIBHAI RANEVADIA\t777\tno\tIND\tIndependent\nGujarat\tJamnagar\tVAGHER JAVIDBHAI OSMANBHAI NOLE\t1247\tno\tIND\tIndependent\nGujarat\tJamnagar\tCHANDRAVIJAYSINH TAKHUBHA RANA\t876\tno\tIND\tIndependent\nGujarat\tJamnagar\tSUMARA AMANDBHAI NOORMAMADBHAI SUMARA\t760\tno\tIND\tIndependent\nGujarat\tJamnagar\tSANDHI MAMADBHAI HAJIBHAI SAFIA\t1377\tno\tIND\tIndependent\nGujarat\tJamnagar\tMAMAD HAJI BOLIM\t8596\tno\tIND\tIndependent\nGujarat\tJamnagar\tNone of the Above\t6588\tno\tNOTA\tNone of the Above\nGujarat\tJamnagar\tNARIYA PRAVINBHAI VALLABHBHAI\t1811\tno\tIND\tIndependent\nGujarat\tJamnagar\tPADHIYAR LALJIBHAI KARABHAI\t1188\tno\tIND\tIndependent\nGujarat\tJamnagar\tBATHWAR NANJIBHAI\t3667\tno\tIND\tIndependent\nGujarat\tJunagadh\tCHUDASAMA RAJESHBHAI NARANBHAI\t513179\tyes\tBJP\tBharatiya Janata Party\nGujarat\tJunagadh\tNone of the Above\t17022\tno\tNOTA\tNone of the Above\nGujarat\tJunagadh\tSAIYED ALTAF HUSAIN ABDULLAH MIYAN\t2717\tno\tIND\tIndependent\nGujarat\tJunagadh\tKADRI IBRAHIM SAIYED HUSEN\t2409\tno\tSP\tSamajwadi Party\nGujarat\tJunagadh\tHARILAL RANCHHODBHAI CHAUHAN\t7574\tno\tIND\tIndependent\nGujarat\tJunagadh\tPUNJABHAI BHIMABHAI VANSH\t377347\tno\tINC\tIndian National Congress\nGujarat\tJunagadh\tSOLANKI HARIBHAI BOGHABHAI\t2565\tno\tBMUP\tBahujan Mukti Party\nGujarat\tJunagadh\tGADHIYA SOYEB HUSHENBHAI\t1891\tno\tIND\tIndependent\nGujarat\tJunagadh\tATUL GOVINDBHAI SHEKHADA\t16674\tno\tAAAP\tAam Aadmi Party\nGujarat\tAmreli\tKACHHADIYA NARANBHAI BHIKHABHAI\t436715\tyes\tBJP\tBharatiya Janata Party\nGujarat\tAmreli\tSANGANI VIJAYBHAI DINESHBHAI\t1938\tno\tYuS\tYuva Sarkar\nGujarat\tAmreli\tCHAVADA MANUBHAI PARSHOTTMBHAI\t13803\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tAmreli\tSUKHADIYA NATHALAL VALLABHBHAI\t15520\tno\tAAAP\tAam Aadmi Party\nGujarat\tAmreli\tMEHULBHAI HIMATBHAI SUKHADIA\t3439\tno\tIND\tIndependent\nGujarat\tAmreli\tDAFADA RAMJIBHAI NANJIBHAI\t7822\tno\tBSP\tBahujan Samaj Party\nGujarat\tAmreli\tDHIMECHA NARESHBHAI NANJIBHAI\t2062\tno\tIND\tIndependent\nGujarat\tAmreli\tJADAV MUSTAKBHAI\t1553\tno\tIND\tIndependent\nGujarat\tAmreli\tRAMANI SURESHBHAI DHIRUBHAI\t10114\tno\tIND\tIndependent\nGujarat\tAmreli\tRATHOD JIVANBHAI R.\t1809\tno\tRPI(A)\tRepublican Party of India (A)\nGujarat\tAmreli\tNATHABHAI DAYABHAI TOTA\t4032\tno\tIND\tIndependent\nGujarat\tAmreli\tVALODARA VRAJLAL JIVABHAI\t8167\tno\tIND\tIndependent\nGujarat\tAmreli\tUSMANBHAI P. MAGHARA\t2216\tno\tIND\tIndependent\nGujarat\tAmreli\tTHUMMAR VIRJIBHAI KESHAVBHAI (VIRJIBHAI THUMMAR)\t280483\tno\tINC\tIndian National Congress\nGujarat\tAmreli\tNone of the Above\t19143\tno\tNOTA\tNone of the Above\nGujarat\tBhavnagar\tDR. BHARATIBEN DHIRUBHAI SHIYAL\t549529\tyes\tBJP\tBharatiya Janata Party\nGujarat\tBhavnagar\tNone of the Above\t9590\tno\tNOTA\tNone of the Above\nGujarat\tBhavnagar\tVAGHELA NARENDRABHAI SHAVJIBHAI\t9113\tno\tIND\tIndependent\nGujarat\tBhavnagar\tBHAVES GHANSHYAMBHAI RAJYAGURU\t5912\tno\tIND\tIndependent\nGujarat\tBhavnagar\tRATHOD PRAVINSINH CHANDRASINH\t5530\tno\tIND\tIndependent\nGujarat\tBhavnagar\tMARU MANHAR VALAJIBHAI\t2257\tno\tIND\tIndependent\nGujarat\tBhavnagar\tKHADRANI ASIMBHAI PIRBHAI\t1359\tno\tIND\tIndependent\nGujarat\tBhavnagar\tRASIDKHAN HASANKHAN PATHAN\t2707\tno\tIND\tIndependent\nGujarat\tBhavnagar\tKAGADA RAMESHBHAI PUNABHAI\t3542\tno\tIND\tIndependent\nGujarat\tBhavnagar\tGOHIL PRAVINSINH DHIRUBHA\t2276\tno\tIND\tIndependent\nGujarat\tBhavnagar\tGOHEL BHARATBHAI BHIMABHAI\t1734\tno\tIND\tIndependent\nGujarat\tBhavnagar\tJAGADISHBHAI AMARABHAI VEGAD\t1525\tno\tIND\tIndependent\nGujarat\tBhavnagar\tMEHTA YASHVANTRAY ODHAVJIBHAI\t1852\tno\tIND\tIndependent\nGujarat\tBhavnagar\tVEGAD NATHABHAI (VEGADBHAI PRAGNACHAKSHU CANDIDATE)\t7550\tno\tIND\tIndependent\nGujarat\tBhavnagar\tGITA CHETAN PAUNDA (ADVOCATE GITABA JADEJA)\t9345\tno\tBSP\tBahujan Samaj Party\nGujarat\tBhavnagar\tDR. KANUBHAI V. KALSARIA\t49540\tno\tAAAP\tAam Aadmi Party\nGujarat\tBhavnagar\tRATHOD PRAVINBHAI JINABHAI\t254041\tno\tINC\tIndian National Congress\nGujarat\tAnand\tDILIP PATEL\t490829\tyes\tBJP\tBharatiya Janata Party\nGujarat\tAnand\tSOLANKI BHARATBHAI MADHAVSINH\t427403\tno\tINC\tIndian National Congress\nGujarat\tAnand\tPURSOTTAMBHAI ALIAS KANUBHAI MATHURBHAI CHAUHAN\t6480\tno\tBSP\tBahujan Samaj Party\nGujarat\tAnand\tMAHIDA MAHENDRASINH SAHEBSINH\t1790\tno\tSP\tSamajwadi Party\nGujarat\tAnand\tPATEL JAYESHBHAI ARVINDBHAI\t1459\tno\tIND\tIndependent\nGujarat\tAnand\tPADHIYAR VIKRAMSINH (VAKIL)\t876\tno\tRSPS\tRashtriya Samaj Paksha\nGujarat\tAnand\tRABARI HITESHKUMAR LALJIBHAI\t2975\tno\tIND\tIndependent\nGujarat\tAnand\tPATEL DILIPBHAI MANILAL\t1466\tno\tIND\tIndependent\nGujarat\tAnand\tHITENDRASINH MOHANSINH PARMAR\t1260\tno\tBNJD\tBharatiya National Janta Dal\nGujarat\tAnand\tRAVJIBHAI S PARMAR\t2442\tno\tAAAP\tAam Aadmi Party\nGujarat\tAnand\tPATEL NAINESHKUMAR UMEDBHAI\t1063\tno\tIND\tIndependent\nGujarat\tAnand\tGIRISHBHAI DAS (ADVOCATE)\t4003\tno\tBMUP\tBahujan Mukti Party\nGujarat\tAnand\tPARMAR AMARSINH DAHYABHAI\t1265\tno\tIND\tIndependent\nGujarat\tAnand\tVAHORA FIROJBHAI WALIMAHAMADBHAI (KASORWALA)\t6689\tno\tIND\tIndependent\nGujarat\tAnand\tVAGHELA BHARAT P\t4022\tno\tIND\tIndependent\nGujarat\tAnand\tNone of the Above\t16872\tno\tNOTA\tNone of the Above\nGujarat\tKheda\tCHAUHAN DEVUSINH JESINGBHAI (CHAUHAN DEVUSINH)\t568235\tyes\tBJP\tBharatiya Janata Party\nGujarat\tKheda\tRANVEER PRANAYRAJ GOVINDBHAI\t944\tno\tBMUP\tBahujan Mukti Party\nGujarat\tKheda\tPARIKH VIRAL HASMUKHBHAI\t846\tno\tIND\tIndependent\nGujarat\tKheda\tMALEK YAKUBMIYA NABIMIYA\t955\tno\tIND\tIndependent\nGujarat\tKheda\tRATANSINH UDESINH CHAUHAN\t3495\tno\tIND\tIndependent\nGujarat\tKheda\tROSHAN PRIYAVADAN SHAH\t7442\tno\tIND\tIndependent\nGujarat\tKheda\tPANDAV BHAILALBHAI KALUBHAI\t5791\tno\tBSP\tBahujan Samaj Party\nGujarat\tKheda\tCHAUHAN DEVUSING MOTISHING\t1860\tno\tIND\tIndependent\nGujarat\tKheda\tBADHIWALA LABHUBHAI JIVRAJBHAI\t3742\tno\tAAAP\tAam Aadmi Party\nGujarat\tKheda\tABDUL RAZAKKHAN PATHAN\t1024\tno\tADPT\tApna Desh Party\nGujarat\tKheda\tMALEK SADIK HUSHEN MAHAMMD HUSHEN\t1444\tno\tIND\tIndependent\nGujarat\tKheda\tNone of the Above\t20333\tno\tNOTA\tNone of the Above\nGujarat\tKheda\tMALEK SABIRHUSEN ISMAELBHAI\t2720\tno\tIND\tIndependent\nGujarat\tKheda\tPATHAN AMANULLAKHA SITABKHA\t803\tno\tIND\tIndependent\nGujarat\tKheda\tKHRISTI ADWARD KHUSHALBHAI\t938\tno\tIND\tIndependent\nGujarat\tKheda\tDINSHA PATEL\t335334\tno\tINC\tIndian National Congress\nGujarat\tPanchmahal\tCHAUHAN PRABHATSINH PRATAPSINH\t508274\tyes\tBJP\tBharatiya Janata Party\nGujarat\tPanchmahal\tNone of the Above\t25981\tno\tNOTA\tNone of the Above\nGujarat\tPanchmahal\tVANKAR MANILAL BHANABHAI\t11375\tno\tIND\tIndependent\nGujarat\tPanchmahal\tRAMSINH PARMAR\t337678\tno\tINC\tIndian National Congress\nGujarat\tPanchmahal\tPIYUSHKUMAR DILIPBHAI PARMAR\t6935\tno\tAAAP\tAam Aadmi Party\nGujarat\tPanchmahal\tPATEL PANKAJBHAI RAVJIBHAI\t5426\tno\tIND\tIndependent\nGujarat\tPanchmahal\tMANSURI MUKHATYAR MOHAMMAD (PANTER M. LALA)\t10857\tno\tIND\tIndependent\nGujarat\tPanchmahal\tGORA SHOEB MOHMADHANIF\t1497\tno\tIND\tIndependent\nGujarat\tPanchmahal\tGIRI RAMCHANDRA VAIJNATH\t15956\tno\tBSP\tBahujan Samaj Party\nGujarat\tPanchmahal\tSHAIKH KALIM ABDULLATIF\t1993\tno\tSP\tSamajwadi Party\nGujarat\tPanchmahal\tSHAIKH MAJITMIYA JIVAMIYA\t1337\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tPanchmahal\tCHAVADA HARISHCHANDRASINH PRABHATSINH\t3751\tno\tIND\tIndependent\nGujarat\tPanchmahal\tS. N. CHAVADA (CHAVADA VAKIL)\t2401\tno\tIND\tIndependent\nGujarat\tDahod\tJASVANTSINH SUMANBHAI BHABHOR\t511111\tyes\tBJP\tBharatiya Janata Party\nGujarat\tDahod\tBHABHOR MAVJIBHAI TITUBHAI\t4167\tno\tSP\tSamajwadi Party\nGujarat\tDahod\tKUMARI INDUBEN NATHUBHAI SANGADA\t6838\tno\tIND\tIndependent\nGujarat\tDahod\tBAMANIYA JYOTISHKUMAR BAPUBHAI\t11233\tno\tBSP\tBahujan Samaj Party\nGujarat\tDahod\tMEDA JAGDISHBHAI MANILAL\t5078\tno\tBNJD\tBharatiya National Janta Dal\nGujarat\tDahod\tBHURA NAVALABHAI MANABHAI\t11244\tno\tIND\tIndependent\nGujarat\tDahod\tK.C.MUNIA ADVOCATE\t4849\tno\tAAAP\tAam Aadmi Party\nGujarat\tDahod\tNone of the Above\t32305\tno\tNOTA\tNone of the Above\nGujarat\tDahod\tTAVIYAD DR. PRABHABEN KISHORSINH\t280757\tno\tINC\tIndian National Congress\nGujarat\tDahod\tRAMSINGBHAI NANJIBHAI KALARA\t3841\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tDahod\tKATARA SINGAJIBHAI JALJIBHAI\t28958\tno\tCPM\tCommunist Party of India  (Marxist)\nGujarat\tVadodara\tNARENDRA MODI\t845464\tyes\tBJP\tBharatiya Janata Party\nGujarat\tVadodara\tMISTRI MADHUSUDAN DEVRAM\t275336\tno\tINC\tIndian National Congress\nGujarat\tVadodara\tROHIT MADHUSUDAN MOHANBHAI\t5782\tno\tBSP\tBahujan Samaj Party\nGujarat\tVadodara\tSUNIL DIGAMBAR KULKARNI\t10101\tno\tAAAP\tAam Aadmi Party\nGujarat\tVadodara\tJADAV AMBALAL KANABHAI\t1382\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tVadodara\tPATHAN MAHEMUDKHAN RAZAKKHAN\t1109\tno\tADPT\tApna Desh Party\nGujarat\tVadodara\tPATHAN SAHEBKHAN ASIFKHAN\t2101\tno\tSP\tSamajwadi Party\nGujarat\tVadodara\tNone of the Above\t18053\tno\tNOTA\tNone of the Above\nGujarat\tVadodara\tTAPAN DASGUPTA\t2249\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nGujarat\tChhota Udaipur\tRAMSINH RATHWA\t607916\tyes\tBJP\tBharatiya Janata Party\nGujarat\tChhota Udaipur\tNone of the Above\t28815\tno\tNOTA\tNone of the Above\nGujarat\tChhota Udaipur\tNARANBHAI JEMALABHAI RATHVA\t428187\tno\tINC\tIndian National Congress\nGujarat\tChhota Udaipur\tPROF. ARJUNBHAI VERSINGBHAI RATHVA\t23116\tno\tAAAP\tAam Aadmi Party\nGujarat\tChhota Udaipur\tVASAVA PRAFULBHAI DEVJIBHAI\t12508\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tBharuch\tVASAVA MANSUKHBHAI DHANJIBHAI\t548902\tyes\tBJP\tBharatiya Janata Party\nGujarat\tBharuch\tANILKUMAR CHHITUBHAI BHAGAT\t49289\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tBharuch\tJAYENDRASINH RANA\t4818\tno\tAAAP\tAam Aadmi Party\nGujarat\tBharuch\tSAIYAD MOHSIN BAPU NANUMIYAWALA\t1406\tno\tBMUP\tBahujan Mukti Party\nGujarat\tBharuch\tBHURA SHABBIRBHAI VALIBHAI\t2694\tno\tIND\tIndependent\nGujarat\tBharuch\tSHAILESHKUMAR MAGANBHAI PARMAR\t5125\tno\tIND\tIndependent\nGujarat\tBharuch\tVIRSANGBHAI PARBATBHAI GOHIL\t3353\tno\tIND\tIndependent\nGujarat\tBharuch\tRAFIKBHAI SULEMAN SAPA\t2644\tno\tIND\tIndependent\nGujarat\tBharuch\tSUKHRAMSINGH\t7275\tno\tBSP\tBahujan Samaj Party\nGujarat\tBharuch\tNone of the Above\t23615\tno\tNOTA\tNone of the Above\nGujarat\tBharuch\tNITIN ISHWARLAL VAKIL (ADVOCATE)\t2661\tno\tIND\tIndependent\nGujarat\tBharuch\tSINDHI MAYYUDEEN UMARBHAI\t9862\tno\tIND\tIndependent\nGujarat\tBharuch\tSAYYED ASIF ZAFAR ALI\t1083\tno\tADPT\tApna Desh Party\nGujarat\tBharuch\tANANDKUMAR SARVARSINH VASAVA\t1855\tno\tIND\tIndependent\nGujarat\tBharuch\tPATEL JAYESHBHAI AMBALALBHAI (JAYESH KAKA)\t395629\tno\tINC\tIndian National Congress\nGujarat\tBardoli\tVASAVA PARBHUBHAI NAGARBHAI\t622769\tyes\tBJP\tBharatiya Janata Party\nGujarat\tBardoli\tGAMIT SURENDRABHAI SIMABHAI\t5351\tno\tIND\tIndependent\nGujarat\tBardoli\tCHAUDHARI CHANDUBHAI MACHALABHAI\t10842\tno\tAAAP\tAam Aadmi Party\nGujarat\tBardoli\tGAMIT MOVALIYABHAI NOPARIYABHAI\t11625\tno\tBSP\tBahujan Samaj Party\nGujarat\tBardoli\tRATHOD RAMESHBHAI BHIKHABHAI\t8607\tno\tIND\tIndependent\nGujarat\tBardoli\tBHAILALBHAI CHHANABHAI RATHOD\t5334\tno\tADSP\tAadivasi Sena Party\nGujarat\tBardoli\tNone of the Above\t19991\tno\tNOTA\tNone of the Above\nGujarat\tBardoli\tCHAUDHARI RENIYABHAI SHANKARBHAI\t2184\tno\tHND\tHindusthan Nirman Dal\nGujarat\tBardoli\tCHAUDHARI REVABEN SHANKARBHAI\t13270\tno\tCPI\tCommunist Party of India\nGujarat\tBardoli\tCHAUDHARI TUSHARBHAI AMARSINHBHAI\t498885\tno\tINC\tIndian National Congress\nGujarat\tBardoli\tJAGATSINH LALJIBHAI VASAVA\t7321\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tSurat\tDARSHANA VIKRAM JARDOSH\t718412\tyes\tBJP\tBharatiya Janata Party\nGujarat\tSurat\tMUKESHBHAI LAVJIBHAI AMBALIYA\t2695\tno\tIND\tIndependent\nGujarat\tSurat\tOMPRAKASH SHRIVASTAV\t6346\tno\tBSP\tBahujan Samaj Party\nGujarat\tSurat\tKIRITBHAI HARJIBHAI VASANI\t2640\tno\tYuS\tYuva Sarkar\nGujarat\tSurat\tMOHANBHAI B. PATEL\t18877\tno\tAAAP\tAam Aadmi Party\nGujarat\tSurat\tDESAI NAISHADHBHAI BHUPATBHAI\t185222\tno\tINC\tIndian National Congress\nGujarat\tSurat\tVASAVA KISHORBHAI CHHOTUBHAI\t1187\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tSurat\tNone of the Above\t10936\tno\tNOTA\tNone of the Above\nGujarat\tSurat\tMAVJIBHAI LAXMANBHAI SANDIS\t1607\tno\tIND\tIndependent\nGujarat\tNavsari\tC. R. PATIL\t820831\tyes\tBJP\tBharatiya Janata Party\nGujarat\tNavsari\tSONAL KELLOGG\t1089\tno\tVoP\tVoters Party\nGujarat\tNavsari\tLATABEN ASHOKKUMAR DWIVEDI\t7560\tno\tIND\tIndependent\nGujarat\tNavsari\tRAMJAN MANSURI(JOURNALIST)\t1787\tno\tIND\tIndependent\nGujarat\tNavsari\tPERCY MUNSHI\t2235\tno\tIND\tIndependent\nGujarat\tNavsari\tASLAM MISTRY\t3853\tno\tBBC\tBharatiya Bahujan Congress\nGujarat\tNavsari\tRAVSHAHEB BHIMRAV PATIL (BANDHU)\t2888\tno\tIND\tIndependent\nGujarat\tNavsari\tPYARELAL BHARTI\t834\tno\tHND\tHindusthan Nirman Dal\nGujarat\tNavsari\tROHIT GANDHI\t4267\tno\tIND\tIndependent\nGujarat\tNavsari\tMAKSUD MIRZA\t262715\tno\tINC\tIndian National Congress\nGujarat\tNavsari\tVIMAL PATEL (ENDHAL)\t2739\tno\tIND\tIndependent\nGujarat\tNavsari\tNone of the Above\t9322\tno\tNOTA\tNone of the Above\nGujarat\tNavsari\tKESHAVJI L. SARADVA\t1059\tno\tIND\tIndependent\nGujarat\tNavsari\tWARDE RAJUBHAI BHIMRAO\t2156\tno\tBMUP\tBahujan Mukti Party\nGujarat\tNavsari\tPATEL BHUPENDRAKUMAR DHIRUBHAI\t1264\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tNavsari\tCHAUHAN KESAVBHAI MALABHAI (MASTER)\t11240\tno\tBSP\tBahujan Samaj Party\nGujarat\tNavsari\tARUN S. PATHAK(JOURNALIST)\t1030\tno\tIND\tIndependent\nGujarat\tNavsari\tMEHUL PATEL\t14299\tno\tAAAP\tAam Aadmi Party\nGujarat\tNavsari\tSAIYED MEHMUD AHMED\t6069\tno\tIND\tIndependent\nGujarat\tNavsari\tHASAN SHAIKH\t3510\tno\tIND\tIndependent\nGujarat\tValsad\tDR. K.C.PATEL\t617772\tyes\tBJP\tBharatiya Janata Party\nGujarat\tValsad\tPATEL SHAILESHBHAI GANDABHAI\t2982\tno\tJD(U)\tJanata Dal  (United)\nGujarat\tValsad\tPATEL GOVINDBHAI RANCHHODBHAI\t8047\tno\tAAAP\tAam Aadmi Party\nGujarat\tValsad\tDR.PANKAJKUMAR PARBHUBHAI PATEL\t6028\tno\tADSP\tAadivasi Sena Party\nGujarat\tValsad\tTALAVIYA BABUBHAI CHHAGANBHAI\t2967\tno\tBMUP\tBahujan Mukti Party\nGujarat\tValsad\tPATEL BUDHABHAI RANCHHODBHAI\t12757\tno\tIND\tIndependent\nGujarat\tValsad\tKISHANBHAI  VESTABHAI PATEL\t409768\tno\tINC\tIndian National Congress\nGujarat\tValsad\tGAURANGBHAI RAMESHBHAI PATEL\t11372\tno\tHJP\tHindustan Janta Party\nGujarat\tValsad\tVADIA LAXMANBHAI CHHAGANBHAI\t9702\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nGujarat\tValsad\tTHAKRIYA RATILAL VAJIRBHAI\t14202\tno\tBSP\tBahujan Samaj Party\nGujarat\tValsad\tNone of the Above\t26606\tno\tNOTA\tNone of the Above\nHaryana\tAmbala\tRATTAN LAL KATARIA\t612121\tyes\tBJP\tBharatiya Janata Party\nHaryana\tAmbala\tNone of the Above\t7816\tno\tNOTA\tNone of the Above\nHaryana\tAmbala\tRAJ KUMAR BALMIKI\t272047\tno\tINC\tIndian National Congress\nHaryana\tAmbala\tRAMESH CHAND\t2715\tno\tIND\tIndependent\nHaryana\tAmbala\tRAM JAWARI\t1290\tno\tRAKP\tRashtriya Karmyog Party\nHaryana\tAmbala\tISHWAR SINGH\t1446\tno\tIND\tIndependent\nHaryana\tAmbala\tBHURA RAM\t3388\tno\tIND\tIndependent\nHaryana\tAmbala\tRAJENDER KUMAR BHATAULI\t3432\tno\tBSCP\tBhartiya Shakti Chetna Party\nHaryana\tAmbala\tSURINDER PAL SINGH\t63626\tno\tAAAP\tAam Aadmi Party\nHaryana\tAmbala\tARUN KUMAR\t11123\tno\tCPI\tCommunist Party of India\nHaryana\tAmbala\tPHOOL CHAND RATTUWALA\t3095\tno\tIND\tIndependent\nHaryana\tAmbala\tDR. KUSUM SHERWAL\t129571\tno\tINLD\tIndian National Lok Dal\nHaryana\tAmbala\tDR. KAPOOR SINGH\t102627\tno\tBSP\tBahujan Samaj Party\nHaryana\tAmbala\tGURNAM SINGH\t933\tno\tIND\tIndependent\nHaryana\tAmbala\tBHUPINDER KUMAR\t3765\tno\tIND\tIndependent\nHaryana\tKurukshetra\tRAJ KUMAR SAINI\t418112\tyes\tBJP\tBharatiya Janata Party\nHaryana\tKurukshetra\tBALBIR SINGH SAINI\t288376\tno\tINLD\tIndian National Lok Dal\nHaryana\tKurukshetra\tCHUNI MANOJ KUMAR\t2770\tno\tIND\tIndependent\nHaryana\tKurukshetra\tCHATTAR SINGH\t68926\tno\tBSP\tBahujan Samaj Party\nHaryana\tKurukshetra\tRANBIR SINGH SHARMA\t2765\tno\tIND\tIndependent\nHaryana\tKurukshetra\tVISHNU BHAGWAN AGGARWAL\t2083\tno\tIND\tIndependent\nHaryana\tKurukshetra\tBALWINDER KAUR\t32554\tno\tAAAP\tAam Aadmi Party\nHaryana\tKurukshetra\tJASVINDER JASHT BHUKHRI\t3887\tno\tIND\tIndependent\nHaryana\tKurukshetra\tNAVEEN JINDAL\t287722\tno\tINC\tIndian National Congress\nHaryana\tKurukshetra\tMASTER DHARAMPAL DHIMAN BRAHAMAN\t1994\tno\tIND\tIndependent\nHaryana\tKurukshetra\tRAM KISHAN\t1635\tno\tLPR\tLok Parivartan Party(DC)\nHaryana\tKurukshetra\tRAMESHWAR KUMAR SAINI\t2344\tno\tBSCP\tBhartiya Shakti Chetna Party\nHaryana\tKurukshetra\tCA. SATISH SINGAL\t760\tno\tIND\tIndependent\nHaryana\tKurukshetra\tKANTA ALLARIA\t1534\tno\tIBSPK\tIndian Bahujan Sandesh Party (Kanshiram)\nHaryana\tKurukshetra\tONKAR SINGH\t1483\tno\tNAJC\tNational Janhit Congress (AB)\nHaryana\tKurukshetra\tRAJ KUMAR SAINI\t6061\tno\tIND\tIndependent\nHaryana\tKurukshetra\tKULDEEP SINGH MALLHI\t1665\tno\tRPI\tRepublican Party of India\nHaryana\tKurukshetra\tGURMEET SINGH\t1092\tno\tLD\tLok Dal\nHaryana\tKurukshetra\tDAVINDER KUMAR\t2041\tno\tIND\tIndependent\nHaryana\tKurukshetra\tANIL KUMAR\t1593\tno\tIND\tIndependent\nHaryana\tKurukshetra\tASHWANI MALHOTRA\t3251\tno\tAITC\tAll India Trinamool Congress\nHaryana\tKurukshetra\tKIRAN PAL\t762\tno\tBIP\tBharatiya Inqalab Party\nHaryana\tKurukshetra\tNone of the Above\t2482\tno\tNOTA\tNone of the Above\nHaryana\tSirsa\tCHARANJEET SINGH RORI\t506370\tyes\tINLD\tIndian National Lok Dal\nHaryana\tSirsa\tNone of the Above\t4033\tno\tNOTA\tNone of the Above\nHaryana\tSirsa\tSANJIV BHOJRAJ\t2350\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nHaryana\tSirsa\tBALWANT SINGH\t2201\tno\tBhSMP\tBharatiya Sant Mat Party\nHaryana\tSirsa\tMANJEET SINGH\t1334\tno\tIND\tIndependent\nHaryana\tSirsa\tRAJ KUMAR NAGAR\t4208\tno\tSP\tSamajwadi Party\nHaryana\tSirsa\tHANSRAJ HANSA\t2901\tno\tJM\tJan Morcha\nHaryana\tSirsa\tRANVIR KHOBRA\t7308\tno\tIND\tIndependent\nHaryana\tSirsa\tJAGDISH\t2145\tno\tIND\tIndependent\nHaryana\tSirsa\tFAKIR CHAND\t1197\tno\tIND\tIndependent\nHaryana\tSirsa\tJAIPAL BHATTI\t1365\tno\tIND\tIndependent\nHaryana\tSirsa\tASHOK TANWAR\t390634\tno\tINC\tIndian National Congress\nHaryana\tSirsa\tMANGERAM\t20750\tno\tBSP\tBahujan Samaj Party\nHaryana\tSirsa\tSURENDER KUMAR (URF) MENPAL PHOOLA\t3432\tno\tIND\tIndependent\nHaryana\tSirsa\tCOMRADE RAM KUMAR\t11194\tno\tCPM\tCommunist Party of India  (Marxist)\nHaryana\tSirsa\tDR. SUSHIL INDORA\t241067\tno\tHJCBL\tHaryana Janhit Congress (BL)\nHaryana\tSirsa\tRAJBIR PALNA\t5636\tno\tIND\tIndependent\nHaryana\tSirsa\tPOONAM CHAND RATTI\t66844\tno\tAAAP\tAam Aadmi Party\nHaryana\tSirsa\tMEWA SINGH\t4136\tno\tIND\tIndependent\nHaryana\tHisar\tDUSHYANT CHAUTALA\t494478\tyes\tINLD\tIndian National Lok Dal\nHaryana\tHisar\tNARESH BATRA\t587\tno\tIND\tIndependent\nHaryana\tHisar\tSATBIR\t363\tno\tIND\tIndependent\nHaryana\tHisar\tVIKAS\t684\tno\tIND\tIndependent\nHaryana\tHisar\tKULDEEP BISHNOI\t462631\tno\tHJCBL\tHaryana Janhit Congress (BL)\nHaryana\tHisar\tRAM PHAL\t463\tno\tIND\tIndependent\nHaryana\tHisar\tHANSRAJ\t245\tno\tIND\tIndependent\nHaryana\tHisar\tKULDEEP S/O MADAN LAL\t959\tno\tIND\tIndependent\nHaryana\tHisar\tKULDEEP S/O SAJJAN\t395\tno\tIND\tIndependent\nHaryana\tHisar\tSUSHIL KAUSHIK\t700\tno\tIND\tIndependent\nHaryana\tHisar\tVISHNU KUMAR\t1048\tno\tIND\tIndependent\nHaryana\tHisar\tPHUL SINGH\t6533\tno\tCPM\tCommunist Party of India  (Marxist)\nHaryana\tHisar\tDR. RAJESH MAHENDIA\t1723\tno\tIND\tIndependent\nHaryana\tHisar\tSUSHIL NIDHI\t273\tno\tIND\tIndependent\nHaryana\tHisar\tOM PARKASH\t1471\tno\tBhSMP\tBharatiya Sant Mat Party\nHaryana\tHisar\tSUNDER\t134\tno\tIND\tIndependent\nHaryana\tHisar\tSHIV KUMAR\t447\tno\tIND\tIndependent\nHaryana\tHisar\tSITA RAM\t128\tno\tIND\tIndependent\nHaryana\tHisar\tUMED SINGH\t342\tno\tIND\tIndependent\nHaryana\tHisar\tARUN KUMAR\t307\tno\tIND\tIndependent\nHaryana\tHisar\tBALWAN SINGH\t213\tno\tIND\tIndependent\nHaryana\tHisar\tRAKESH KUMAR (RINKU BHAI)\t176\tno\tIND\tIndependent\nHaryana\tHisar\tDR. YUDHBIR SINGH KHYALIA\t28490\tno\tAAAP\tAam Aadmi Party\nHaryana\tHisar\tMAMTA RANI\t1195\tno\tRPP(LB)\tRashtrawadi Parivartan Party (L.B,)\nHaryana\tHisar\tSHRAVAN KUMAR ASIJA\t371\tno\tIND\tIndependent\nHaryana\tHisar\tKULDEEP S/O ISHWAR SINGH\t1242\tno\tIND\tIndependent\nHaryana\tHisar\tMANGE RAM\t30446\tno\tBSP\tBahujan Samaj Party\nHaryana\tHisar\tKULDEEP S/O NARAIN SINGH\t5251\tno\tIND\tIndependent\nHaryana\tHisar\tATAM PARKASH\t300\tno\tIND\tIndependent\nHaryana\tHisar\tKULDEEP S/O PARDUMN\t2587\tno\tIND\tIndependent\nHaryana\tHisar\tRAJ SINGH\t600\tno\tIND\tIndependent\nHaryana\tHisar\tSAMPAT SINGH\t102509\tno\tINC\tIndian National Congress\nHaryana\tHisar\tNARENDER\t408\tno\tIND\tIndependent\nHaryana\tHisar\tVIJAY KUMAR SHARMA\t1788\tno\tIND\tIndependent\nHaryana\tHisar\tKARTAR SINGH\t1143\tno\tIND\tIndependent\nHaryana\tHisar\tYUDHVIR\t197\tno\tIND\tIndependent\nHaryana\tHisar\tKULDEEP SINGH S/O SHISHPAL\t705\tno\tBMUP\tBahujan Mukti Party\nHaryana\tHisar\tBALJEET\t208\tno\tIND\tIndependent\nHaryana\tHisar\tKULDEEP S/O KARAN SINGH\t1886\tno\tIND\tIndependent\nHaryana\tHisar\tNARINDER KUMAR\t388\tno\tIND\tIndependent\nHaryana\tHisar\tRAJ KUMAR\t255\tno\tIND\tIndependent\nHaryana\tHisar\tNone of the Above\t1645\tno\tNOTA\tNone of the Above\nHaryana\tKarnal\tASHWINI KUMAR\t594817\tyes\tBJP\tBharatiya Janata Party\nHaryana\tKarnal\tNone of the Above\t2929\tno\tNOTA\tNone of the Above\nHaryana\tKarnal\tSAVITRI\t1468\tno\tIND\tIndependent\nHaryana\tKarnal\tRAJESH KUMAR\t1937\tno\tIND\tIndependent\nHaryana\tKarnal\tARVIND TIWARI\t1260\tno\tIND\tIndependent\nHaryana\tKarnal\tASHISH\t1314\tno\tAIFB\tAll India Forward Bloc\nHaryana\tKarnal\tMAHMOOD HASSAN\t2723\tno\tIND\tIndependent\nHaryana\tKarnal\tDAVINDER SHARMA\t806\tno\tRAKP\tRashtriya Karmyog Party\nHaryana\tKarnal\tSHASHI SAINI\t1544\tno\tIND\tIndependent\nHaryana\tKarnal\tJAGDISH\t3442\tno\tIND\tIndependent\nHaryana\tKarnal\tRAMPHAL SHARMA\t940\tno\tIND\tIndependent\nHaryana\tKarnal\tPALE RAM KASHYAP\t2654\tno\tRJP(E)\tRashtriya Janshakti Party(Eklavya)\nHaryana\tKarnal\tPURAN SINGH PANWAR\t865\tno\tRVNP\tRashtravadi Janata Party\nHaryana\tKarnal\tPARAMJIT SINGH\t32060\tno\tAAAP\tAam Aadmi Party\nHaryana\tKarnal\tJAGBIR SINGH RANA\t2775\tno\tIND\tIndependent\nHaryana\tKarnal\tDEVENDER JAGLAN\t759\tno\tRPI(A)\tRepublican Party of India (A)\nHaryana\tKarnal\tKHATAB SINGH MOR\t885\tno\tBSCP\tBhartiya Shakti Chetna Party\nHaryana\tKarnal\tJASWINDER SINGH SANDHU\t187902\tno\tINLD\tIndian National Lok Dal\nHaryana\tKarnal\tKISMAT SHARMA\t1757\tno\tIND\tIndependent\nHaryana\tKarnal\tMAM CHAND\t10945\tno\tCPI\tCommunist Party of India\nHaryana\tKarnal\tMAHINDER PAL\t1276\tno\tIND\tIndependent\nHaryana\tKarnal\tMARATHA VIRENDER VERMA\t102628\tno\tBSP\tBahujan Samaj Party\nHaryana\tKarnal\tARVIND KUMAR SHARMA\t234670\tno\tINC\tIndian National Congress\nHaryana\tKarnal\tDULI CHAND\t1144\tno\tLPR\tLok Parivartan Party(DC)\nHaryana\tSonipat\tRAMESH CHANDER\t347203\tyes\tBJP\tBharatiya Janata Party\nHaryana\tSonipat\tSANT DHARAMVIR CHOTIWALA\t943\tno\tIND\tIndependent\nHaryana\tSonipat\tDALBIR SINGH CHAHAL\t671\tno\tRVNP\tRashtravadi Janata Party\nHaryana\tSonipat\tHARI PARKASH COMRADE\t1757\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nHaryana\tSonipat\tSUKHBIR SINGH\t5533\tno\tIND\tIndependent\nHaryana\tSonipat\tPADAM SINGH\t264404\tno\tINLD\tIndian National Lok Dal\nHaryana\tSonipat\tJAGBIR SINGH MALIK\t269789\tno\tINC\tIndian National Congress\nHaryana\tSonipat\tJAI SINGH\t48597\tno\tAAAP\tAam Aadmi Party\nHaryana\tSonipat\tSUMAN SINGH\t24103\tno\tBSP\tBahujan Samaj Party\nHaryana\tSonipat\tBHUPESHWAR DAYAL\t2525\tno\tIND\tIndependent\nHaryana\tSonipat\tBIJENDER KUMAR\t965\tno\tIND\tIndependent\nHaryana\tSonipat\tANUP SING DAHIYA\t259\tno\tRJAVP\tRashtriya Jatigat Aarakshan Virodhi Party\nHaryana\tSonipat\tASHOK KUMAR VASHISHTH\t492\tno\tRPI(A)\tRepublican Party of India (A)\nHaryana\tSonipat\tANIL KUMAR\t468\tno\tRBHP\tRashtriya Bahujan Hitay Party\nHaryana\tSonipat\tSANJAY\t3950\tno\tIND\tIndependent\nHaryana\tSonipat\tPADAM SINGH\t1375\tno\tIND\tIndependent\nHaryana\tSonipat\tJAGBIR\t357\tno\tIND\tIndependent\nHaryana\tSonipat\tSITENDER SINGH\t899\tno\tIND\tIndependent\nHaryana\tSonipat\tSATYA NARAYAN\t550\tno\tIND\tIndependent\nHaryana\tSonipat\tGULAB SINGH\t916\tno\tPHRC\tPoorvanchal Rashtriya Congress\nHaryana\tSonipat\tRAMESH\t4405\tno\tIND\tIndependent\nHaryana\tSonipat\tJASWANT\t656\tno\tIND\tIndependent\nHaryana\tSonipat\tMANOJ KUMAR\t2417\tno\tIND\tIndependent\nHaryana\tSonipat\tNone of the Above\t2403\tno\tNOTA\tNone of the Above\nHaryana\tRohtak\tDEEPENDER SINGH HOODA\t490063\tyes\tINC\tIndian National Congress\nHaryana\tRohtak\tNone of the Above\t4932\tno\tNOTA\tNone of the Above\nHaryana\tRohtak\tANIL\t829\tno\tRBHP\tRashtriya Bahujan Hitay Party\nHaryana\tRohtak\tMANOJ KUMAR\t18690\tno\tBSP\tBahujan Samaj Party\nHaryana\tRohtak\tOM PARKASH DHANKAR\t319436\tno\tBJP\tBharatiya Janata Party\nHaryana\tRohtak\tJAI SINGH\t614\tno\tIND\tIndependent\nHaryana\tRohtak\tRAVINDER KUMAR\t418\tno\tIBSPK\tIndian Bahujan Sandesh Party (Kanshiram)\nHaryana\tRohtak\tSUNIL KUMAR\t947\tno\tAITC\tAll India Trinamool Congress\nHaryana\tRohtak\tSATPAL\t2449\tno\tIND\tIndependent\nHaryana\tRohtak\tSAJJAN SINGH\t2032\tno\tIND\tIndependent\nHaryana\tRohtak\tVISHNU DUTT\t3151\tno\tIND\tIndependent\nHaryana\tRohtak\tNAVEEN JAIHIND\t46759\tno\tAAAP\tAam Aadmi Party\nHaryana\tRohtak\tKARAN SINGH\t394\tno\tIND\tIndependent\nHaryana\tRohtak\tSHAMSHER SINGH KHARKARA\t151120\tno\tINLD\tIndian National Lok Dal\nHaryana\tRohtak\tJAI KARAN MANDAUTHI\t2497\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nHaryana\tBhiwani-Mahendragarh\tDHARAMBIR S/O BHALE RAM\t404542\tyes\tBJP\tBharatiya Janata Party\nHaryana\tBhiwani-Mahendragarh\tBAHADUR SINGH\t275148\tno\tINLD\tIndian National Lok Dal\nHaryana\tBhiwani-Mahendragarh\tPURAN MAL SHARMA\t3195\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tSHRUTI CHOUDHRY\t268115\tno\tINC\tIndian National Congress\nHaryana\tBhiwani-Mahendragarh\tOMBIR\t1617\tno\tLPR\tLok Parivartan Party(DC)\nHaryana\tBhiwani-Mahendragarh\tVED PAL TANWAR\t27834\tno\tBSP\tBahujan Samaj Party\nHaryana\tBhiwani-Mahendragarh\tLALIT AGGARWAL\t22200\tno\tAAAP\tAam Aadmi Party\nHaryana\tBhiwani-Mahendragarh\tLAL CHAND\t6265\tno\tRBC\tRepublican Backward Congress\nHaryana\tBhiwani-Mahendragarh\tSAMSER\t391\tno\tNLP\tNational Loktantrik Party\nHaryana\tBhiwani-Mahendragarh\tPARDEEP SINGH TANWAR\t1334\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tNARESH KUMAR\t1493\tno\tSP\tSamajwadi Party\nHaryana\tBhiwani-Mahendragarh\tDHARM BIR S/O HARI SINGH\t995\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tRAKESH KUMAR\t293\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tCHITTER SINGH BALI\t1060\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tSUDHIR CHANDWAS\t523\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tNARENDER PICHOPA-KHURD\t3151\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tDHARAMBIR S/O BANWARI\t708\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tVEDPARKASH BHARTIYA\t1020\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tSATPAL S/O SUBE\t826\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tMASTER SHER SINGH\t3441\tno\tCPM\tCommunist Party of India  (Marxist)\nHaryana\tBhiwani-Mahendragarh\tSHER SINGH\t361\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tRAJIV\t603\tno\tBMUP\tBahujan Mukti Party\nHaryana\tBhiwani-Mahendragarh\tSATPAL S/O LOKRAM\t297\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tSUKHBIR SINGH\t275\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tMAHAVIR SINGH\t393\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tDHARAMBIR SINGH\t2357\tno\tIND\tIndependent\nHaryana\tBhiwani-Mahendragarh\tNone of the Above\t1994\tno\tNOTA\tNone of the Above\nHaryana\tGurgaon\tINDERJIT SINGH RAO\t644780\tyes\tBJP\tBharatiya Janata Party\nHaryana\tGurgaon\tNone of the Above\t2657\tno\tNOTA\tNone of the Above\nHaryana\tGurgaon\tAKBAR KASMI\t1429\tno\tRUC\tRashtriya Ulama Council\nHaryana\tGurgaon\tMANOJ YADAV\t3043\tno\tIND\tIndependent\nHaryana\tGurgaon\tHEMANT KUMAR SAINI\t1153\tno\tBMUP\tBahujan Mukti Party\nHaryana\tGurgaon\tASHOK BHARDWAJ\t713\tno\tNAJC\tNational Janhit Congress (AB)\nHaryana\tGurgaon\tAHSHAN ALI\t455\tno\tIND\tIndependent\nHaryana\tGurgaon\tMAHENDER JAT\t1021\tno\tIND\tIndependent\nHaryana\tGurgaon\tRAJEEV YADAV\t493\tno\tIND\tIndependent\nHaryana\tGurgaon\tUSHA RANI VERMA\t489\tno\tIND\tIndependent\nHaryana\tGurgaon\tRAJESH\t566\tno\tIND\tIndependent\nHaryana\tGurgaon\tOMBIR SHARMA\t566\tno\tIND\tIndependent\nHaryana\tGurgaon\tSURINDER KUMAR KHULLAR\t686\tno\tIND\tIndependent\nHaryana\tGurgaon\tKUSHESHWAR BHAGAT\t7821\tno\tIND\tIndependent\nHaryana\tGurgaon\tUMED SINGH\t927\tno\tRMOP\tRASHTRIYA MORCHA PARTY\nHaryana\tGurgaon\tDHARAMPAL\t65009\tno\tBSP\tBahujan Samaj Party\nHaryana\tGurgaon\tYOGENDRA YADAV\t79452\tno\tAAAP\tAam Aadmi Party\nHaryana\tGurgaon\tRAO DHARAM PAL\t133713\tno\tINC\tIndian National Congress\nHaryana\tGurgaon\tABDUL LATIF\t699\tno\tIND\tIndependent\nHaryana\tGurgaon\tZAKIR HUSSAIN\t370058\tno\tINLD\tIndian National Lok Dal\nHaryana\tGurgaon\tKARAN SINGH JOURNALIST\t938\tno\tIND\tIndependent\nHaryana\tGurgaon\tFAUJI JAI KAWAR TYAGI DIXIT\t3147\tno\tIND\tIndependent\nHaryana\tGurgaon\tMUL CHAND\t804\tno\tIND\tIndependent\nHaryana\tFaridabad\tKRISHAN PAL\t652516\tyes\tBJP\tBharatiya Janata Party\nHaryana\tFaridabad\tDR. KSHETRA PAL SINGH\t1943\tno\tIND\tIndependent\nHaryana\tFaridabad\tPT. RAJENDER SHARMA\t66000\tno\tBSP\tBahujan Samaj Party\nHaryana\tFaridabad\tHAMID KHAN\t2762\tno\thkd\tHindustan Kranti Dal\nHaryana\tFaridabad\tDEEPAK GAUR\t479\tno\tAVIRP\tAARAKSHAN VIRODHI PARTY\nHaryana\tFaridabad\tSANJAY MAURYA\t1098\tno\tIND\tIndependent\nHaryana\tFaridabad\tNANAK CHAND\t2551\tno\tIND\tIndependent\nHaryana\tFaridabad\tSUKHVEER\t1082\tno\tRBCP\tRashtriya Bahujan Congress Party\nHaryana\tFaridabad\tPURSHOTAM DAGAR\t67437\tno\tAAAP\tAam Aadmi Party\nHaryana\tFaridabad\tADVOCATE KAMAL CHAND ARYA\t967\tno\tBMUP\tBahujan Mukti Party\nHaryana\tFaridabad\tRAVINDER BHATI\t1689\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nHaryana\tFaridabad\tMOHD. MUKIM\t1735\tno\tNLP\tNational Loktantrik Party\nHaryana\tFaridabad\tAVTAR SINGH BHADANA\t185643\tno\tINC\tIndian National Congress\nHaryana\tFaridabad\tMANDHEER SINGH MAAN\t644\tno\tIND\tIndependent\nHaryana\tFaridabad\tSHUSHILA\t525\tno\tIND\tIndependent\nHaryana\tFaridabad\tVIJAY RAJ\t407\tno\tIND\tIndependent\nHaryana\tFaridabad\tKUSUM KUMARI\t620\tno\tAIPF\tAll India Peoples' Front (Radical)\nHaryana\tFaridabad\tMUKESH KUMAR SINGH\t442\tno\tIND\tIndependent\nHaryana\tFaridabad\tCHAUDHARY DAYA CHAND\t711\tno\tBSCP\tBhartiya Shakti Chetna Party\nHaryana\tFaridabad\tLALIT MITTAL\t420\tno\tIND\tIndependent\nHaryana\tFaridabad\tR K ANAND\t132472\tno\tINLD\tIndian National Lok Dal\nHaryana\tFaridabad\tNIRMALA\t894\tno\tJD(U)\tJanata Dal  (United)\nHaryana\tFaridabad\tKHUSH DIL SEHGAL (KAPOOR)\t685\tno\tABHKP\tAkhil Bharatiya Hind Kranti Party\nHaryana\tFaridabad\tAHERWAN DHARMENDER RAWAT\t664\tno\tIND\tIndependent\nHaryana\tFaridabad\tLAXMAN\t657\tno\tIND\tIndependent\nHaryana\tFaridabad\tRAJENDER\t409\tno\tIND\tIndependent\nHaryana\tFaridabad\tKHEME THAKUR\t1940\tno\tSP\tSamajwadi Party\nHaryana\tFaridabad\tNone of the Above\t3334\tno\tNOTA\tNone of the Above\nHimachal Pradesh\tKangra\tSHANTA KUMAR\t456163\tyes\tBJP\tBharatiya Janata Party\nHimachal Pradesh\tKangra\tPREM CHAND VISHVAKARMA\t4106\tno\tIND\tIndependent\nHimachal Pradesh\tKangra\tDHANI RAM\t1085\tno\tIND\tIndependent\nHimachal Pradesh\tKangra\tCHANDER KUMAR\t286091\tno\tINC\tIndian National Congress\nHimachal Pradesh\tKangra\tBALDEV RAJ\t997\tno\tHiSP\tHimachal Swabhiman Party\nHimachal Pradesh\tKangra\tDR. RAJAN SUSHANT\t24430\tno\tAAAP\tAam Aadmi Party\nHimachal Pradesh\tKangra\tDR. ATUL THAKUR BHARMORI\t1133\tno\tIND\tIndependent\nHimachal Pradesh\tKangra\tBHUPINDER MEHRA\t5585\tno\tIND\tIndependent\nHimachal Pradesh\tKangra\tRAMAN KUMAR\t637\tno\tBMUP\tBahujan Mukti Party\nHimachal Pradesh\tKangra\tLAL HUSSAIN\t5949\tno\tBSP\tBahujan Samaj Party\nHimachal Pradesh\tKangra\tPARVESH YADAV\t808\tno\tSP\tSamajwadi Party\nHimachal Pradesh\tKangra\tARTI SONI\t3757\tno\tSHS\tShivsena\nHimachal Pradesh\tKangra\tNone of the Above\t8704\tno\tNOTA\tNone of the Above\nHimachal Pradesh\tMandi\tRAM SWAROOP SHARMA\t362824\tyes\tBJP\tBharatiya Janata Party\nHimachal Pradesh\tMandi\tNone of the Above\t6191\tno\tNOTA\tNone of the Above\nHimachal Pradesh\tMandi\tPUNI CHAND\t923\tno\tSP\tSamajwadi Party\nHimachal Pradesh\tMandi\tBHAG CHAND RANA\t1868\tno\tIND\tIndependent\nHimachal Pradesh\tMandi\tSUBHASH MOHAN SNEHI\t1928\tno\tIND\tIndependent\nHimachal Pradesh\tMandi\tJAI CHAND THAKUR\t9359\tno\tAAAP\tAam Aadmi Party\nHimachal Pradesh\tMandi\tLALA RAM\t5167\tno\tBSP\tBahujan Samaj Party\nHimachal Pradesh\tMandi\tDEVENDER DEV\t901\tno\tBMUP\tBahujan Mukti Party\nHimachal Pradesh\tMandi\tPRATIBHA SINGH\t322968\tno\tINC\tIndian National Congress\nHimachal Pradesh\tMandi\tKUSHAL BHARDWAJ\t13965\tno\tCPM\tCommunist Party of India  (Marxist)\nHimachal Pradesh\tHamirpur\tANURAG SINGH THAKUR\t448035\tyes\tBJP\tBharatiya Janata Party\nHimachal Pradesh\tHamirpur\tRAKESH CHOUDHARY\t5827\tno\tBSP\tBahujan Samaj Party\nHimachal Pradesh\tHamirpur\tAMIN CHAND\t647\tno\tIND\tIndependent\nHimachal Pradesh\tHamirpur\tASHISH KUMAR\t603\tno\tIND\tIndependent\nHimachal Pradesh\tHamirpur\tKAMAL KANTA BATRA\t15329\tno\tAAAP\tAam Aadmi Party\nHimachal Pradesh\tHamirpur\tSHIV DUTT VASISHT\t952\tno\tSHS\tShivsena\nHimachal Pradesh\tHamirpur\tRAJINDER SINGH RANA\t349632\tno\tINC\tIndian National Congress\nHimachal Pradesh\tHamirpur\tDEV RAJ BHARDWAJ\t1692\tno\tIND\tIndependent\nHimachal Pradesh\tHamirpur\tURMILA SHARMA\t2328\tno\tSP\tSamajwadi Party\nHimachal Pradesh\tHamirpur\tRAJINDER SINGH RANA\t3687\tno\tIND\tIndependent\nHimachal Pradesh\tHamirpur\tNone of the Above\t6473\tno\tNOTA\tNone of the Above\nHimachal Pradesh\tShimla\tVIRENDER KASHYAP\t385973\tyes\tBJP\tBharatiya Janata Party\nHimachal Pradesh\tShimla\tNone of the Above\t7788\tno\tNOTA\tNone of the Above\nHimachal Pradesh\tShimla\tSUBHASH CHANDER\t14233\tno\tAAAP\tAam Aadmi Party\nHimachal Pradesh\tShimla\tVIRENDER KUMAR KASHYAP\t6173\tno\tIND\tIndependent\nHimachal Pradesh\tShimla\tSHURVEER SINGH\t4385\tno\tSP\tSamajwadi Party\nHimachal Pradesh\tShimla\tJAGAT RAM\t11434\tno\tCPM\tCommunist Party of India  (Marxist)\nHimachal Pradesh\tShimla\tMOHAN LAL BRAKTA\t301786\tno\tINC\tIndian National Congress\nHimachal Pradesh\tShimla\tGURNAM SINGH KOLI\t5985\tno\tBSP\tBahujan Samaj Party\nJammu & Kashmir\tBaramulla\tMUZAFFAR HUSSAIN BAIG\t175277\tyes\tJKPDP\tJammu & Kashmir Peoples Democratic Party\nJammu & Kashmir\tBaramulla\tSALAMUDDIN BAJAD\t71154\tno\tJPC\tJammu & Kashmir People Conference\nJammu & Kashmir\tBaramulla\tTANVEER HUSSAIN\t5788\tno\tIND\tIndependent\nJammu & Kashmir\tBaramulla\tSYED MOHD RAFIQ SHAH\t4141\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nJammu & Kashmir\tBaramulla\tGHULAM MOHAMMAD MIR\t6558\tno\tBJP\tBharatiya Janata Party\nJammu & Kashmir\tBaramulla\tSHARIEF UD-DIN SHARIQ\t146058\tno\tJKN\tJammu & Kashmir National Conference\nJammu & Kashmir\tBaramulla\tMOHD SHAFI BHAT\t3143\tno\tBSP\tBahujan Samaj Party\nJammu & Kashmir\tBaramulla\tAB. HAMID MALIK\t3655\tno\tIND\tIndependent\nJammu & Kashmir\tBaramulla\tBASHIR AHMAD JAGAL\t2694\tno\tPRISM\tPrism\nJammu & Kashmir\tBaramulla\tASHIQ HUSSAIN GANIE\t1943\tno\tIND\tIndependent\nJammu & Kashmir\tBaramulla\tER. ABDUL RASHID SHEIKH\t22090\tno\tIND\tIndependent\nJammu & Kashmir\tBaramulla\tNISAR AHMAD AHANGER\t2050\tno\tIND\tIndependent\nJammu & Kashmir\tBaramulla\tABDUL HUSSAIN\t7301\tno\tIND\tIndependent\nJammu & Kashmir\tBaramulla\tAYAZ AHMAD SOFI\t5566\tno\tGaAP\tGareeb Aadmi Party\nJammu & Kashmir\tBaramulla\tMOHD ABDULLAH CHATWAL\t4006\tno\tIND\tIndependent\nJammu & Kashmir\tBaramulla\tNone of the Above\t4568\tno\tNOTA\tNone of the Above\nJammu & Kashmir\tSrinagar\tTARIQ HAMEED KARRA\t157923\tyes\tJKPDP\tJammu & Kashmir Peoples Democratic Party\nJammu & Kashmir\tSrinagar\tNone of the Above\t4979\tno\tNOTA\tNone of the Above\nJammu & Kashmir\tSrinagar\tAGA SYED MOHSIN\t16050\tno\tIND\tIndependent\nJammu & Kashmir\tSrinagar\tMOHAMMAD SHAFI GUROO\t1157\tno\tIND\tIndependent\nJammu & Kashmir\tSrinagar\tFAYAZ AHMAD BHAT\t4467\tno\tBJP\tBharatiya Janata Party\nJammu & Kashmir\tSrinagar\tCHETAN SHARMA\t608\tno\tIND\tIndependent\nJammu & Kashmir\tSrinagar\tMUSHTAQ SHAMIM\t521\tno\tABML(S)\tAkhil Bharatiya Muslim League (Secular)\nJammu & Kashmir\tSrinagar\tFAROOQ ABDULLAH\t115643\tno\tJKN\tJammu & Kashmir National Conference\nJammu & Kashmir\tSrinagar\tMIRZA SAJAD HUSSAIN BEIGH\t2802\tno\tIND\tIndependent\nJammu & Kashmir\tSrinagar\tDR RAJA MUZAFFAR BHAT\t3271\tno\tAAAP\tAam Aadmi Party\nJammu & Kashmir\tSrinagar\tBASHIR MOHD RESHI\t1215\tno\tIND\tIndependent\nJammu & Kashmir\tSrinagar\tMOHAMMAD MAQBOOL MALIK\t583\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nJammu & Kashmir\tSrinagar\tRIYAZ AHMAD WANI\t461\tno\tIND\tIndependent\nJammu & Kashmir\tSrinagar\tABDUL RASHID TANTRAY\t1668\tno\tIND\tIndependent\nJammu & Kashmir\tSrinagar\tRABIA ALTAF\t864\tno\tIND\tIndependent\nJammu & Kashmir\tAnantnag\tMEHBOOBA MUFTI\t200429\tyes\tJKPDP\tJammu & Kashmir Peoples Democratic Party\nJammu & Kashmir\tAnantnag\tMOHAMMAD YAQOOB RATHER\t1568\tno\tAKAKRP\tAll Jammu and Kashmir Republican Party\nJammu & Kashmir\tAnantnag\tSYED ABID AHMAD SHAH\t4345\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nJammu & Kashmir\tAnantnag\tASIF JEELANI\t803\tno\tAIFB\tAll India Forward Bloc\nJammu & Kashmir\tAnantnag\tMOHAMMAD SHARIF\t3312\tno\tBSP\tBahujan Samaj Party\nJammu & Kashmir\tAnantnag\tMOHD YOUSIF  GANIE\t3286\tno\tASP\tAmbedkar Samaj Party\nJammu & Kashmir\tAnantnag\tABDUL AHAD MIR\t3439\tno\tIND\tIndependent\nJammu & Kashmir\tAnantnag\tNone of the Above\t5936\tno\tNOTA\tNone of the Above\nJammu & Kashmir\tAnantnag\tTANVEER HUSSAIN KHAN\t7340\tno\tIND\tIndependent\nJammu & Kashmir\tAnantnag\tGH. NABI SHAH\t1837\tno\tSP\tSamajwadi Party\nJammu & Kashmir\tAnantnag\tMUSHTAQ AHMAD MALIK\t4720\tno\tBJP\tBharatiya Janata Party\nJammu & Kashmir\tAnantnag\tDR. TANVIR MAQBOOL DAR\t3252\tno\tAAAP\tAam Aadmi Party\nJammu & Kashmir\tAnantnag\tMIRZA MEHBOOB BEG\t135012\tno\tJKN\tJammu & Kashmir National Conference\nJammu & Kashmir\tLadakh\tTHUPSTAN CHHEWANG\t31111\tyes\tBJP\tBharatiya Janata Party\nJammu & Kashmir\tLadakh\tNone of the Above\t1207\tno\tNOTA\tNone of the Above\nJammu & Kashmir\tLadakh\tTSERING SAMPHEL\t26402\tno\tINC\tIndian National Congress\nJammu & Kashmir\tLadakh\tSYED MOHD KAZIM\t28234\tno\tIND\tIndependent\nJammu & Kashmir\tLadakh\tGHULAM RAZA\t31075\tno\tIND\tIndependent\nJammu & Kashmir\tUdhampur\tDR. JITENDRA SINGH\t487369\tyes\tBJP\tBharatiya Janata Party\nJammu & Kashmir\tUdhampur\tNone of the Above\t10478\tno\tNOTA\tNone of the Above\nJammu & Kashmir\tUdhampur\tNAZAKAT HUSSAIN\t9217\tno\tIND\tIndependent\nJammu & Kashmir\tUdhampur\tGIRDHARI LAL\t1971\tno\tBHBP\tBharatiya Bahujan Party\nJammu & Kashmir\tUdhampur\tJAGDISH KUMAR\t1809\tno\tJMBP\tJai Maha Bharath Party\nJammu & Kashmir\tUdhampur\tSMT. AMRIT BARSHA\t1842\tno\tSP\tSamajwadi Party\nJammu & Kashmir\tUdhampur\tDHARAM PAL BALGOTRA\t16437\tno\tBSP\tBahujan Samaj Party\nJammu & Kashmir\tUdhampur\tANIL KUMAR GUPTA\t5755\tno\tIND\tIndependent\nJammu & Kashmir\tUdhampur\tPROF. BHIM SINGH\t25312\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nJammu & Kashmir\tUdhampur\tSHAM LAL\t2187\tno\tAJKMP\tAll J & K Kisan Majdoor Party\nJammu & Kashmir\tUdhampur\tBANSI LAL\t7339\tno\tIND\tIndependent\nJammu & Kashmir\tUdhampur\tMOHD ARSHAD MALIK\t30461\tno\tJKPDP\tJammu & Kashmir Peoples Democratic Party\nJammu & Kashmir\tUdhampur\tGHULAM NABI AZAD\t426393\tno\tINC\tIndian National Congress\nJammu & Kashmir\tUdhampur\tANIL KHAJURIA\t15188\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tJUGAL KISHORE\t619995\tyes\tBJP\tBharatiya Janata Party\nJammu & Kashmir\tJammu\tASHOK KUMAR SHARMA\t2582\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tSATISH POONCHI\t1981\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tMOHD. ABBAS KHAN\t2469\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tPERSEEN SINGH\t7173\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tMOHD. IMTIAZ\t7423\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tGURSAGAR SINGH\t6042\tno\tSDP\tSocialistic Democratic Party\nJammu & Kashmir\tJammu\tMEHAR MANAV BHAGAT\t2013\tno\tBHBP\tBharatiya Bahujan Party\nJammu & Kashmir\tJammu\tABDUL MAJID CHOUHAN\t4177\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tHARI CHAND JALMERIA\t9071\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nJammu & Kashmir\tJammu\tVIJAY KUMAR\t5125\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tASHOK KUMAR\t31199\tno\tBSP\tBahujan Samaj Party\nJammu & Kashmir\tJammu\tLABHA RAM\t5046\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tSUBASH CHANDER\t2205\tno\tAJKMP\tAll J & K Kisan Majdoor Party\nJammu & Kashmir\tJammu\tYASHPAL SHARMA\t168554\tno\tJKPDP\tJammu & Kashmir Peoples Democratic Party\nJammu & Kashmir\tJammu\tNIRMAL SINGH\t5286\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tMADAN LAL SHARMA\t362715\tno\tINC\tIndian National Congress\nJammu & Kashmir\tJammu\tBAHADUR SINGH CHOWDHARY\t3164\tno\tSP\tSamajwadi Party\nJammu & Kashmir\tJammu\tRAJ KUMAR\t2991\tno\tIND\tIndependent\nJammu & Kashmir\tJammu\tNone of the Above\t4382\tno\tNOTA\tNone of the Above\nKarnataka\tChikkodi\tPRAKASH BABANNA HUKKERI\t474373\tyes\tINC\tIndian National Congress\nKarnataka\tChikkodi\tPRATAPARAO  PATIL\t42738\tno\tNCP\tNationalist Congress Party\nKarnataka\tChikkodi\tSHRINIK ANNASAHEB JANGATE\t3622\tno\tIND\tIndependent\nKarnataka\tChikkodi\tKATTI RAMESH VISHWANATH\t471370\tno\tBJP\tBharatiya Janata Party\nKarnataka\tChikkodi\tKADAPURE MACHINDRA DAVALU\t14493\tno\tBSP\tBahujan Samaj Party\nKarnataka\tChikkodi\tMOHAN GURAPPA MOTANNAVAR\t3458\tno\tIND\tIndependent\nKarnataka\tChikkodi\tSHRIMANT BALASAHEB PATIL\t39992\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tChikkodi\tASHFAQ AHMED MADAKI\t3529\tno\tAAAP\tAam Aadmi Party\nKarnataka\tChikkodi\tMAGADUM GOUSMOHIUDDIN ISMAIL MAGDUM\t2624\tno\tIND\tIndependent\nKarnataka\tChikkodi\tJITENDRA SUBHASH NERLE\t1414\tno\tIND\tIndependent\nKarnataka\tChikkodi\tAPPASAHEB SRIPATHI KURANE\t1457\tno\tSJPA\tSarva Janata Party\nKarnataka\tChikkodi\tBASAPPA KALLAPPA HANCHANAL\t1744\tno\tIND\tIndependent\nKarnataka\tChikkodi\tNone of the Above\t10289\tno\tNOTA\tNone of the Above\nKarnataka\tBelgaum\tANGADI SURESH CHANNABASAPPA\t554417\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBelgaum\tDAYANAND GURUPUTRAYYA CHIKKAMATH\t1261\tno\tSP\tSamajwadi Party\nKarnataka\tBelgaum\tRAMAPPA (RAMCHANDRA) MAREPPA CHALAWADI\t3504\tno\tIND\tIndependent\nKarnataka\tBelgaum\tRAMESH G KADALASKAR\t3535\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBelgaum\tANTAKKANAVAR PARAPPA SHANKAREPPA\t773\tno\tIND\tIndependent\nKarnataka\tBelgaum\tMOHAN YALLAPPA MORE\t1457\tno\tIND\tIndependent\nKarnataka\tBelgaum\tLAXMI R. HEBBALKAR\t478557\tno\tINC\tIndian National Congress\nKarnataka\tBelgaum\tS. P. RATHOD\t1753\tno\tIND\tIndependent\nKarnataka\tBelgaum\tKHANAGOUDAR S B\t920\tno\tIND\tIndependent\nKarnataka\tBelgaum\tSANTOSH M. SHET @ RAIKAR\t3245\tno\tIND\tIndependent\nKarnataka\tBelgaum\tMAJUKAR HANMANT KRISHANA\t1187\tno\tIND\tIndependent\nKarnataka\tBelgaum\tBAGWAN NASIR PAPULSAB\t5477\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBelgaum\tANGADI MUTTAPPA CHANNASANGAPPA\t8524\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBelgaum\tSANTOSH MANOHAR BAVADEKAR\t1656\tno\tIND\tIndependent\nKarnataka\tBelgaum\tDR. GAONKAR RAJMAHENDRA SAKHARAM\t781\tno\tIND\tIndependent\nKarnataka\tBelgaum\tNone of the Above\t11500\tno\tNOTA\tNone of the Above\nKarnataka\tBagalkot\tGADDIGOUDAR PARVTAGOUDA CHANDANAGOUDA\t571548\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBagalkot\tSHANKAR BIDARI\t10959\tno\tIND\tIndependent\nKarnataka\tBagalkot\tPARASHURAM MANASING RATHOD\t3897\tno\tIND\tIndependent\nKarnataka\tBagalkot\tY.C KAMBLE\t7370\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBagalkot\tNAGAPPA PAKKIRAPPA SHIRAGUPPI\t1194\tno\tIND\tIndependent\nKarnataka\tBagalkot\tMUTTAPPA MUDAKAPPA HIREKUMBI\t2037\tno\tIND\tIndependent\nKarnataka\tBagalkot\tVASANAGOUDA NINGANAGOUDA BHANDI\t4325\tno\tIND\tIndependent\nKarnataka\tBagalkot\tNone of the Above\t10764\tno\tNOTA\tNone of the Above\nKarnataka\tBagalkot\tAJAY KUMAR SARNAIK\t454988\tno\tINC\tIndian National Congress\nKarnataka\tBagalkot\tKANABUR SADASHIV SIDAMALLAPPA\t1180\tno\tJD(U)\tJanata Dal  (United)\nKarnataka\tBagalkot\tHUNASHYAL RAVI\t7237\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBagalkot\tMAHESH BASAPPA NANDIHAL\t813\tno\tKAP\tKarunaadu Party\nKarnataka\tBagalkot\tKAZI KUTUBUDDIN\t2004\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBagalkot\tAJJODI BHEEMAPPA DHOOLAPPA\t994\tno\tIND\tIndependent\nKarnataka\tBijapur\tRAMESH JIGAJINAGI\t471757\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBijapur\tBHAGWAN DHARMANNA KAMBALE\t1340\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tBijapur\tSUDHAKAR KANAMADI\t2818\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBijapur\tHANMANT MOTHER-BASALINGAVVA HOLER\t2775\tno\tIND\tIndependent\nKarnataka\tBijapur\tRAJU SHRIMANT PADAGANUR\t1422\tno\tIND\tIndependent\nKarnataka\tBijapur\tK SHIVRAM\t57551\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBijapur\tRUDRESH DAYAPPA DODAMANI\t2259\tno\tIND\tIndependent\nKarnataka\tBijapur\tSHRIKANT DEVENDRAPPA ARAKERI\t1930\tno\tKAP\tKarunaadu Party\nKarnataka\tBijapur\tSATISH ASANGIHAL\t2194\tno\tIND\tIndependent\nKarnataka\tBijapur\tSHRIDHAR NARAYANKAR\t4717\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBijapur\tRAVI (RAJESH) Y VALLYAPUR\t1283\tno\tIND\tIndependent\nKarnataka\tBijapur\tSIDDAPPA B GANJI\t2691\tno\tIND\tIndependent\nKarnataka\tBijapur\tHUNNUR MARAGANNA MALAPPA\t3795\tno\tIND\tIndependent\nKarnataka\tBijapur\tPRAKASH RATHOD\t401938\tno\tINC\tIndian National Congress\nKarnataka\tBijapur\tNone of the Above\t8287\tno\tNOTA\tNone of the Above\nKarnataka\tGulbarga\tMALLIKARJUN KHARGE\t507193\tyes\tINC\tIndian National Congress\nKarnataka\tGulbarga\tRAMU\t4085\tno\tIND\tIndependent\nKarnataka\tGulbarga\tSHANKAR JADHAV\t2877\tno\tBHPP\tBharatiya Peoples Party\nKarnataka\tGulbarga\tB.T.LALITHA NAIK\t9074\tno\tAAAP\tAam Aadmi Party\nKarnataka\tGulbarga\tS.M. SHARMA\t4943\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKarnataka\tGulbarga\tD.G. SAGAR\t15690\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tGulbarga\tREVUNAIK BELAMAGI\t432460\tno\tBJP\tBharatiya Janata Party\nKarnataka\tGulbarga\tDANNI MAHADEV B.\t11428\tno\tBSP\tBahujan Samaj Party\nKarnataka\tGulbarga\tNone of the Above\t9888\tno\tNOTA\tNone of the Above\nKarnataka\tRaichur\tB.V.NAYAK\t443659\tyes\tINC\tIndian National Congress\nKarnataka\tRaichur\tD.B.NAYAK\t21706\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tRaichur\tMARUTI\t3618\tno\tNDEP\tNational Development Party\nKarnataka\tRaichur\tRAJA THIMAPPA NAYAK\t12254\tno\tBSP\tBahujan Samaj Party\nKarnataka\tRaichur\tBHIMARAYA JARADABANDI\t4234\tno\tAAAP\tAam Aadmi Party\nKarnataka\tRaichur\tM NAGARAJ\t6938\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKarnataka\tRaichur\tCHINNAYYA NAYAK\t6540\tno\tIND\tIndependent\nKarnataka\tRaichur\tK SOMASHEKAR YADGIRI\t6788\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKarnataka\tRaichur\tARAKERA SHIVANAGOUDA NAYAK\t442160\tno\tBJP\tBharatiya Janata Party\nKarnataka\tRaichur\tRANGANAYAK\t4935\tno\tPPOI\tPyramid Party of India\nKarnataka\tRaichur\tBHAGAVANTAPPA\t2881\tno\tLSP\tLok Satta Party\nKarnataka\tRaichur\tNone of the Above\t13176\tno\tNOTA\tNone of the Above\nKarnataka\tBidar\tBHAGWANTH KHUBA\t459290\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBidar\tGOVIND\t1427\tno\tSP\tSamajwadi Party\nKarnataka\tBidar\tMOHAMMED RAFIQ\t2095\tno\tIND\tIndependent\nKarnataka\tBidar\tHATTARKI YASHWANTHRAO SHIVARAYA\t1414\tno\tBHPP\tBharatiya Peoples Party\nKarnataka\tBidar\tRAMANNA\t1378\tno\tHJP\tHindustan Janta Party\nKarnataka\tBidar\tSHRAVANA S.BHANDE MUNGANAL\t2862\tno\tRSPS\tRashtriya Samaj Paksha\nKarnataka\tBidar\tSIDDARAMAPPA NAGAPPA KARABASTI CHINCHANSOOR\t1742\tno\tIND\tIndependent\nKarnataka\tBidar\tNAGANATH PATIL\t2204\tno\tIND\tIndependent\nKarnataka\tBidar\tNone of the Above\t2817\tno\tNOTA\tNone of the Above\nKarnataka\tBidar\tBABU PASHA\t2869\tno\tIND\tIndependent\nKarnataka\tBidar\tN. DHARAM SINGH\t367068\tno\tINC\tIndian National Congress\nKarnataka\tBidar\tISMAIL ZABI ULLAH\t5818\tno\tIND\tIndependent\nKarnataka\tBidar\tBALBHEEM VEERAPPA UNNE\t7118\tno\tIND\tIndependent\nKarnataka\tBidar\tSYED WAHEED\t1302\tno\tBMUP\tBahujan Mukti Party\nKarnataka\tBidar\tABDUL HAMEED FARAAN\t3747\tno\tWPOI\tWelfare Party Of India\nKarnataka\tBidar\tPRABHU\t5097\tno\tIND\tIndependent\nKarnataka\tBidar\tMIRZA SHAFEE BAIG\t1462\tno\tIND\tIndependent\nKarnataka\tBidar\tSUBHASH BHIMSHA SINDANKERA\t1406\tno\tIND\tIndependent\nKarnataka\tBidar\tKALLALING HUGAR NELOGI\t2307\tno\tIND\tIndependent\nKarnataka\tBidar\tSHAMALA.M.UDANOOR\t1259\tno\tIND\tIndependent\nKarnataka\tBidar\tMACHENDAR\t4379\tno\tIND\tIndependent\nKarnataka\tBidar\tBANDEPPA KHASHEMPUR\t58728\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBidar\tRAMESH GAYKAWAD\t1976\tno\tANC\tAmbedkar National Congress\nKarnataka\tBidar\tSHANKAR BHAYYA\t15079\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBidar\tCHANDRAKANT\t4540\tno\tAAAP\tAam Aadmi Party\nKarnataka\tKoppal\tKARADI SANGANNA AMARAPPA\t486383\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tKoppal\tCOM I:- D H PUJAR\t2636\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKarnataka\tKoppal\tNAGAPPA. G.  KARATAGI\t2894\tno\tIND\tIndependent\nKarnataka\tKoppal\tTHIMMAPPA UPPAR\t2459\tno\tBDBRAJP\tBharatiya Dr. B.R.Ambedkar Janta Party\nKarnataka\tKoppal\tSYED  ARIF\t9529\tno\tBSP\tBahujan Samaj Party\nKarnataka\tKoppal\tBASAVARAJ HITNAL\t453969\tno\tINC\tIndian National Congress\nKarnataka\tKoppal\tBHARADWHAJA\t2089\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nKarnataka\tKoppal\tSHIVAKUMAR NAVALISIDDAPPA TONTAPUR\t3425\tno\tAAAP\tAam Aadmi Party\nKarnataka\tKoppal\tRAMESH KOTI\t1867\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tKoppal\tK. M. RANGANATHREDDY\t2305\tno\tSP\tSamajwadi Party\nKarnataka\tKoppal\tANOJIRAO G\t2012\tno\tIND\tIndependent\nKarnataka\tKoppal\tNAZEER HUSSAIN\t2129\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nKarnataka\tKoppal\tB.MANOHARA HANUMANTAPPA\t4433\tno\tIND\tIndependent\nKarnataka\tKoppal\tGOVINDHAREDDY PACHARALLI\t2839\tno\tIND\tIndependent\nKarnataka\tKoppal\tV. GOVINDA\t6300\tno\tIND\tIndependent\nKarnataka\tKoppal\tSURESH\t8292\tno\tIND\tIndependent\nKarnataka\tKoppal\tNone of the Above\t12947\tno\tNOTA\tNone of the Above\nKarnataka\tBellary\tB. SREERAMULU\t534406\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBellary\tG. NATARAJA\t1997\tno\tKAP\tKarunaadu Party\nKarnataka\tBellary\tNone of the Above\t11320\tno\tNOTA\tNone of the Above\nKarnataka\tBellary\tMALAGI SHIVAKUMAR GIRIYAPPA\t6038\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBellary\tN.Y. HANUMANTAPPA\t449262\tno\tINC\tIndian National Congress\nKarnataka\tBellary\tB. RAMUDU (ADDIGERI RAMANNA)\t4557\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBellary\tCOMRADE S. BHEEMAPPA\t2042\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKarnataka\tBellary\tR. RAVINAYAKA\t12613\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBellary\tK. RAGHAVENDRA\t3501\tno\tPPOI\tPyramid Party of India\nKarnataka\tBellary\tDODDAMANI PRASAD\t2456\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nKarnataka\tBellary\tN.B. KRISHNAPPA NAYAKA\t3888\tno\tIND\tIndependent\nKarnataka\tBellary\tN.R. MALLAYYA SWAMY\t5206\tno\tIND\tIndependent\nKarnataka\tBellary\tA. DEVDAS\t8486\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKarnataka\tHaveri\tUDASI SHIVAKUMAR CHANNABASAPPA\t566790\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tHaveri\tHANUMANTAPPA  D. KABBAR\t5997\tno\tIND\tIndependent\nKarnataka\tHaveri\tSHIVAKUMAR  TALAWAR\t3780\tno\tIND\tIndependent\nKarnataka\tHaveri\tB.M.JAYADEV MATHAD\t2296\tno\tIND\tIndependent\nKarnataka\tHaveri\tRAVI  MENASINAKAI\t9814\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tHaveri\tKABBINAD VERABHADRAPPA VEERAPPA\t1224\tno\tIND\tIndependent\nKarnataka\tHaveri\tCHANDRASHEKHARAYYA GURULINGAYYA KULKARNI\t1314\tno\tIND\tIndependent\nKarnataka\tHaveri\tMEHABOOBKHAN  PATHAN\t2329\tno\tIND\tIndependent\nKarnataka\tHaveri\tKUBER VASANNA\t883\tno\tIND\tIndependent\nKarnataka\tHaveri\tH M SIRQUAZI\t1602\tno\tAAAP\tAam Aadmi Party\nKarnataka\tHaveri\tBASAVANTEPPA HONNAPPA HULLATTI\t974\tno\tSJPA\tSarva Janata Party\nKarnataka\tHaveri\tBIDARAGADDI  V.B\t3528\tno\tIND\tIndependent\nKarnataka\tHaveri\tSURESH M.MUNDARGI\t771\tno\tRCMP\tRani Chennamma Party\nKarnataka\tHaveri\tSHIDDAPPA KALLAPPA POOJAR\t15656\tno\tIND\tIndependent\nKarnataka\tHaveri\tSALEEM  AHMED\t479219\tno\tINC\tIndian National Congress\nKarnataka\tHaveri\tHALAPPA  TIMMENAHALLI\t6229\tno\tBSP\tBahujan Samaj Party\nKarnataka\tHaveri\tGIRISHGOUDA C.BISTANAGOUDAR\t946\tno\tIND\tIndependent\nKarnataka\tHaveri\tD PRASAD\t1490\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tHaveri\tRUDRESH RAMANNA HADAGALI\t7320\tno\tIND\tIndependent\nKarnataka\tHaveri\tNone of the Above\t3806\tno\tNOTA\tNone of the Above\nKarnataka\tDharwad\tPRALHAD JOSHI\t545395\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tDharwad\tMARUTI RAMAPPA HANASI\t1587\tno\tIND\tIndependent\nKarnataka\tDharwad\tSHAKEEL ABDULSATTAR DODWAD\t1368\tno\tIND\tIndependent\nKarnataka\tDharwad\tBANKAPUR. HANUMANTHAPPA MALLAPPA\t8836\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tDharwad\tVIKAS B. SOPPIN\t2174\tno\tIND\tIndependent\nKarnataka\tDharwad\tPRAMOD HANUMANTHARAO MUTHALIK\t5465\tno\tIND\tIndependent\nKarnataka\tDharwad\tIRAPPA BHARAMAPPA MADAR\t6858\tno\tBSP\tBahujan Samaj Party\nKarnataka\tDharwad\tIMAMSAB MEHBOOBSAB TORGAL.\t995\tno\tIND\tIndependent\nKarnataka\tDharwad\tVINAY KULKARNI\t431738\tno\tINC\tIndian National Congress\nKarnataka\tDharwad\tIRSHAD BEGAM ADHONI.\t1053\tno\tIND\tIndependent\nKarnataka\tDharwad\tGURUSHANTAPPA H. CHALAVADI.\t4128\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tDharwad\tSANJAY KOTHARI\t2359\tno\tIND\tIndependent\nKarnataka\tDharwad\tSHREEKANT KABADI\t3018\tno\tIND\tIndependent\nKarnataka\tDharwad\tDOKKA ANANDARAJ\t985\tno\tIND\tIndependent\nKarnataka\tDharwad\tGANGADHAR S. BADIGER\t1785\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKarnataka\tDharwad\tSHAILENDRA PATIL\t6196\tno\tIND\tIndependent\nKarnataka\tDharwad\tHEMANT KUMAR\t4349\tno\tAAAP\tAam Aadmi Party\nKarnataka\tDharwad\tNone of the Above\t12937\tno\tNOTA\tNone of the Above\nKarnataka\tUttara Kannada\tANANTKUMAR HEGDE\t546939\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tUttara Kannada\tBASAVARAJ DUNDAPPA HADAPAD\t2032\tno\tRCMP\tRani Chennamma Party\nKarnataka\tUttara Kannada\tNAIK SANTHOSH\t7404\tno\tBSP\tBahujan Samaj Party\nKarnataka\tUttara Kannada\tELIYAS KATI\t6281\tno\tSJPA\tSarva Janata Party\nKarnataka\tUttara Kannada\tRAGHAVENDRA K. THANE\t6215\tno\tAAAP\tAam Aadmi Party\nKarnataka\tUttara Kannada\tSADANAND DESHBHANDARI ANNAPPA\t3565\tno\tIND\tIndependent\nKarnataka\tUttara Kannada\tASHOK SAHADEVAPPA CHALAVADI\t4246\tno\tIND\tIndependent\nKarnataka\tUttara Kannada\tOLWIN MARIAN LOPIS\t1840\tno\tBMUP\tBahujan Mukti Party\nKarnataka\tUttara Kannada\tPRASHANT R DESHPANDE\t406239\tno\tINC\tIndian National Congress\nKarnataka\tUttara Kannada\tNone of the Above\t16277\tno\tNOTA\tNone of the Above\nKarnataka\tDavanagere\tG M SIDDESHWARA\t518894\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tDavanagere\tK SYED ZABIULLA\t1582\tno\tIND\tIndependent\nKarnataka\tDavanagere\tH.K. KENCHVEERAPPA\t3972\tno\tVJCP\tVichara Jagruthi Congress Paksha\nKarnataka\tDavanagere\tA.K.PARASHURAMA KOTEMALLUR\t5581\tno\tBSP\tBahujan Samaj Party\nKarnataka\tDavanagere\tSIDDESH\t1216\tno\tIND\tIndependent\nKarnataka\tDavanagere\tPARASURAM KURUBAR\t1332\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nKarnataka\tDavanagere\tG.M.SIDDESHI\t2732\tno\tIND\tIndependent\nKarnataka\tDavanagere\tS.S. SUBHAN KHAN\t3312\tno\tIND\tIndependent\nKarnataka\tDavanagere\tNone of the Above\t4536\tno\tNOTA\tNone of the Above\nKarnataka\tDavanagere\tALUR M.G. SWAMY\t1122\tno\tIND\tIndependent\nKarnataka\tDavanagere\tCOM. H.K. RAMACHANDRAPPA\t8064\tno\tCPI\tCommunist Party of India\nKarnataka\tDavanagere\tMAHIMA J PATEL\t46911\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tDavanagere\tSRI NAGARAJAPPA\t1226\tno\tIND\tIndependent\nKarnataka\tDavanagere\tK.J. BASAVARAJ\t2582\tno\tAAAP\tAam Aadmi Party\nKarnataka\tDavanagere\tA.T. DADAKHALANDAR\t1331\tno\tIND\tIndependent\nKarnataka\tDavanagere\tG.C.PATEL\t1145\tno\tSJPA\tSarva Janata Party\nKarnataka\tDavanagere\tDR. SRIDHARA UDUPA\t946\tno\tIND\tIndependent\nKarnataka\tDavanagere\tB. GNANA PRAKASH\t7097\tno\tIND\tIndependent\nKarnataka\tDavanagere\tS.S. MALLIKARJUN\t501287\tno\tINC\tIndian National Congress\nKarnataka\tShimoga\tB. S. YEDDYURAPPA\t606216\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tShimoga\tANIL.M.R\t1517\tno\tIND\tIndependent\nKarnataka\tShimoga\tK.G. SREEDHAR\t7542\tno\tAAAP\tAam Aadmi Party\nKarnataka\tShimoga\tMANJUNATH BHANDARY\t242911\tno\tINC\tIndian National Congress\nKarnataka\tShimoga\tGEETHA  SHIVARAJKUMAR\t240636\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tShimoga\tB. DHARMAPPA\t4537\tno\tJD(U)\tJanata Dal  (United)\nKarnataka\tShimoga\tNone of the Above\t7077\tno\tNOTA\tNone of the Above\nKarnataka\tShimoga\tKUNAJE  MANJUNATHA  GOWDA\t5053\tno\tBSP\tBahujan Samaj Party\nKarnataka\tShimoga\tFAYAZ AHAMED   ALIAS   MOHAMAD FAYAZ AHAMED\t1339\tno\tSJPA\tSarva Janata Party\nKarnataka\tShimoga\tMAINUDDIN M.S.\t1295\tno\tIND\tIndependent\nKarnataka\tShimoga\tH.  SURESHA POOJARI\t2709\tno\tIND\tIndependent\nKarnataka\tShimoga\tSHRILATHA SHETTY\t2073\tno\tIND\tIndependent\nKarnataka\tShimoga\tB.K. SHASHI KUMAR\t3869\tno\tKAP\tKarunaadu Party\nKarnataka\tShimoga\tS. SATISH SALIAN\t2234\tno\tIND\tIndependent\nKarnataka\tUdupi Chikmagalur\tSHOBHA KARANDLAJE\t581168\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tUdupi Chikmagalur\tSRINIVASA POOJARY\t1899\tno\tIND\tIndependent\nKarnataka\tUdupi Chikmagalur\tMANJUNATHA G\t1089\tno\tIND\tIndependent\nKarnataka\tUdupi Chikmagalur\tSUDHEER KANCHAN\t1689\tno\tIND\tIndependent\nKarnataka\tUdupi Chikmagalur\tM D MAINUDDIN KHAN ALIAS ASFIYA\t1214\tno\tIND\tIndependent\nKarnataka\tUdupi Chikmagalur\tK JAYAPRAKASH HEGDE\t399525\tno\tINC\tIndian National Congress\nKarnataka\tUdupi Chikmagalur\tNone of the Above\t7828\tno\tNOTA\tNone of the Above\nKarnataka\tUdupi Chikmagalur\tGURUDEVA S  H\t6049\tno\tAAAP\tAam Aadmi Party\nKarnataka\tUdupi Chikmagalur\tCOMRADE C J JAGANNATH\t1612\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKarnataka\tUdupi Chikmagalur\tZAKIR HUSSAIN\t7449\tno\tBSP\tBahujan Samaj Party\nKarnataka\tUdupi Chikmagalur\tS VIJAYA KUMAR\t9691\tno\tCPI\tCommunist Party of India\nKarnataka\tUdupi Chikmagalur\tV. DHANANJAYA KUMAR\t14895\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tHassan\tH.D. DEVEGOWDA\t509841\tyes\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tHassan\tDR. BANUPRAKASH. K\t1456\tno\tIND\tIndependent\nKarnataka\tHassan\tM.MAHESH (HARSHA)\t3062\tno\tIND\tIndependent\nKarnataka\tHassan\tC.H. VIJAYASHANKAR\t165688\tno\tBJP\tBharatiya Janata Party\nKarnataka\tHassan\tSANTHOSH MOHAN\t8018\tno\tAAAP\tAam Aadmi Party\nKarnataka\tHassan\tT.D. JAGADEESHA\t2083\tno\tIND\tIndependent\nKarnataka\tHassan\tJAVARAIAH\t1581\tno\tIND\tIndependent\nKarnataka\tHassan\tS.N. MALLAPPA\t2388\tno\tIND\tIndependent\nKarnataka\tHassan\tS.B. RAJEGOWDA\t3841\tno\tIND\tIndependent\nKarnataka\tHassan\tMANJUNATHA\t3536\tno\tBDBRAJP\tBharatiya Dr. B.R.Ambedkar Janta Party\nKarnataka\tHassan\tH.D. REVANNA\t6798\tno\tKAP\tKarunaadu Party\nKarnataka\tHassan\tA.P. AHAMAD\t18905\tno\tBSP\tBahujan Samaj Party\nKarnataka\tHassan\tNone of the Above\t7334\tno\tNOTA\tNone of the Above\nKarnataka\tHassan\tMANJU. A.\t409379\tno\tINC\tIndian National Congress\nKarnataka\tHassan\tRUDREGOWDA\t3262\tno\tJVBP\tJai Vijaya Bharathi Party\nKarnataka\tDakshina Kannada\tNALIN KUMAR KATEEL\t642739\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tDakshina Kannada\tSUBRAMANYA GOWDA K.\t2450\tno\tIND\tIndependent\nKarnataka\tDakshina Kannada\tK.N. SOMASHEKAR\t3421\tno\tIND\tIndependent\nKarnataka\tDakshina Kannada\tADVOCATE ANANDA GATTY\t1116\tno\tIND\tIndependent\nKarnataka\tDakshina Kannada\tDEEPAK RAJESH COELHO\t905\tno\tIND\tIndependent\nKarnataka\tDakshina Kannada\tNone of the Above\t7109\tno\tNOTA\tNone of the Above\nKarnataka\tDakshina Kannada\tSUPREETH KUMAR POOJARY\t657\tno\tHJP\tHindustan Janta Party\nKarnataka\tDakshina Kannada\tMAXIM PINTO\t1072\tno\tIND\tIndependent\nKarnataka\tDakshina Kannada\tK. KUSHALA BELLARE\t801\tno\tIND\tIndependent\nKarnataka\tDakshina Kannada\tJANARDHANA POOJARY\t499030\tno\tINC\tIndian National Congress\nKarnataka\tDakshina Kannada\tMOOSE KUNHI ALIAS ABDUL KAREEM\t4471\tno\tBSP\tBahujan Samaj Party\nKarnataka\tDakshina Kannada\tHANEEF KHAN KODAJE\t27254\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKarnataka\tDakshina Kannada\tSUDATTA JAIN SHIRTHADY\t1613\tno\tIND\tIndependent\nKarnataka\tDakshina Kannada\tK YADAVA SHETTY\t9394\tno\tCPM\tCommunist Party of India  (Marxist)\nKarnataka\tDakshina Kannada\tM. R. VASUDEVA\t5442\tno\tAAAP\tAam Aadmi Party\nKarnataka\tChitradurga\tB.N.CHANDRAPPA\t467511\tyes\tINC\tIndian National Congress\nKarnataka\tChitradurga\tT.SOMESH SHILPI\t4511\tno\tIND\tIndependent\nKarnataka\tChitradurga\tGULLIHATTY.D.SHEKHAR\t202108\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tChitradurga\tJANARDHANA SWAMY\t366220\tno\tBJP\tBharatiya Janata Party\nKarnataka\tChitradurga\tT.D.RAJAGIRI\t9441\tno\tBSP\tBahujan Samaj Party\nKarnataka\tChitradurga\tT.DAYANANDA\t1818\tno\tIND\tIndependent\nKarnataka\tChitradurga\tNone of the Above\t8895\tno\tNOTA\tNone of the Above\nKarnataka\tChitradurga\tMOHANA DASARI\t14226\tno\tAAAP\tAam Aadmi Party\nKarnataka\tChitradurga\tS. MEETYA NAIK\t3558\tno\tSP\tSamajwadi Party\nKarnataka\tChitradurga\tM.KUMBAIAH\t3970\tno\tJD(U)\tJanata Dal  (United)\nKarnataka\tChitradurga\tH.SIDDAGANGAPPA\t2951\tno\tANC\tAmbedkar National Congress\nKarnataka\tChitradurga\tBASAVANAGIDEVA SHARANARU\t2171\tno\tIND\tIndependent\nKarnataka\tChitradurga\tT.BHAVANA\t3601\tno\tIND\tIndependent\nKarnataka\tChitradurga\tN.S.MANJUNATHA NERENAHAL\t2702\tno\tIND\tIndependent\nKarnataka\tChitradurga\tGANESHA\t2816\tno\tIND\tIndependent\nKarnataka\tTumkur\tMUDDAHANUMEGOWDA.S.P.\t429868\tyes\tINC\tIndian National Congress\nKarnataka\tTumkur\tNone of the Above\t12934\tno\tNOTA\tNone of the Above\nKarnataka\tTumkur\tL.P.YADUNANDANA\t1508\tno\tIND\tIndependent\nKarnataka\tTumkur\tS.B.HANUMANTHARAYAPPA\t6301\tno\tIND\tIndependent\nKarnataka\tTumkur\tSHIVAKUMAR\t1674\tno\tIND\tIndependent\nKarnataka\tTumkur\tGOVINDARJU.G\t1063\tno\tIND\tIndependent\nKarnataka\tTumkur\tA.S.D'SILVA\t8322\tno\tAAAP\tAam Aadmi Party\nKarnataka\tTumkur\tCHINNAPPA.V\t10448\tno\tCPI\tCommunist Party of India\nKarnataka\tTumkur\tBRAHMANANDA REDDY\t1672\tno\tIND\tIndependent\nKarnataka\tTumkur\tG.NAGENDRA\t1094\tno\tIND\tIndependent\nKarnataka\tTumkur\tANWAR\t1245\tno\tIND\tIndependent\nKarnataka\tTumkur\tNILUFAR JAMANI AHMED\t4732\tno\tBSP\tBahujan Samaj Party\nKarnataka\tTumkur\tK.V.SRINIVAS KALKERE\t2195\tno\tIND\tIndependent\nKarnataka\tTumkur\tH.L.MOHAN KUMAR\t1528\tno\tHJP\tHindustan Janta Party\nKarnataka\tTumkur\tG.S.BASAVARAJ\t355827\tno\tBJP\tBharatiya Janata Party\nKarnataka\tTumkur\tA.KRISHNAPPA\t258683\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tTumkur\tB.VENKATESHA\t2390\tno\tKAP\tKarunaadu Party\nKarnataka\tMandya\tC.S.PUTTARAJU\t524370\tyes\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tMandya\tKRISHNAMURTHY KOMMERAHALLI\t1643\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tMandya\tARUNAKUMARA\t1179\tno\tIND\tIndependent\nKarnataka\tMandya\tASHRAF\t2163\tno\tIND\tIndependent\nKarnataka\tMandya\tN.C.PUTTARAJU\t4021\tno\tIND\tIndependent\nKarnataka\tMandya\tJAYACHANDRA\t1119\tno\tBMUP\tBahujan Mukti Party\nKarnataka\tMandya\tM.KRISHNAMURTHY\t22391\tno\tBSP\tBahujan Samaj Party\nKarnataka\tMandya\tPROF. B.SHIVALINGAIAH\t86993\tno\tBJP\tBharatiya Janata Party\nKarnataka\tMandya\tK.UDAYAKUMARA\t3070\tno\tIND\tIndependent\nKarnataka\tMandya\tDR. C.S.HANUMANTHAPPA\t4098\tno\tAAAP\tAam Aadmi Party\nKarnataka\tMandya\tH.K.KRISHNA\t8604\tno\tKAP\tKarunaadu Party\nKarnataka\tMandya\tBHANUPRAKASH.K.L\t4354\tno\tIND\tIndependent\nKarnataka\tMandya\tK.SHIVANANDA\t1165\tno\tHJP\tHindustan Janta Party\nKarnataka\tMandya\tK.MAHADEVAPPA\t1423\tno\tBDBRAJP\tBharatiya Dr. B.R.Ambedkar Janta Party\nKarnataka\tMandya\tARAVIND PREMANAND\t1172\tno\tIND\tIndependent\nKarnataka\tMandya\tRAMYA\t518852\tno\tINC\tIndian National Congress\nKarnataka\tMandya\tNone of the Above\t6021\tno\tNOTA\tNone of the Above\nKarnataka\tMysore\tPRATHAP SIMHA\t503908\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tMysore\tPADMAMMA M V\t5650\tno\tAAAP\tAam Aadmi Party\nKarnataka\tMysore\tC MOHAN KUMAR\t13637\tno\tBSP\tBahujan Samaj Party\nKarnataka\tMysore\tCHANDRASHEKARAIAH\t138587\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tMysore\tD ESWAR THOREMAVU\t1246\tno\tIND\tIndependent\nKarnataka\tMysore\tD S NIRVANAPPA\t978\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKarnataka\tMysore\tNone of the Above\t8924\tno\tNOTA\tNone of the Above\nKarnataka\tMysore\tBOJANNA N SOMAIAH\t2032\tno\tIND\tIndependent\nKarnataka\tMysore\tHUNSUR K CHANDRASHEKAR\t1342\tno\tDPPS\tDemocratic Prajakranthi Party Secularist\nKarnataka\tMysore\tTHIMME GOWDA\t1516\tno\tSJPA\tSarva Janata Party\nKarnataka\tMysore\tSAMPATHU N\t2078\tno\tIND\tIndependent\nKarnataka\tMysore\tRATHI POOVAIAH\t3726\tno\tKAP\tKarunaadu Party\nKarnataka\tMysore\tBANNUR KUMAR\t1215\tno\tIND\tIndependent\nKarnataka\tMysore\tPRAVEEN M S\t1439\tno\tIND\tIndependent\nKarnataka\tMysore\tADAGOORU H VISHWANATH\t472300\tno\tINC\tIndian National Congress\nKarnataka\tMysore\tS P MAHADEVAPPA\t1016\tno\tJD(U)\tJanata Dal  (United)\nKarnataka\tChamarajanagar\tR. DHRUVANARAYANA\t567782\tyes\tINC\tIndian National Congress\nKarnataka\tChamarajanagar\tM SHIVANNA (KOTE)\t58760\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tChamarajanagar\tPRADEEP KUMAR M\t2492\tno\tKAP\tKarunaadu Party\nKarnataka\tChamarajanagar\tKATHAVARAY\t1949\tno\tasdp\tDr. Ambedkar Samajvadi Democratic Party\nKarnataka\tChamarajanagar\tSAMPATH KUMAR\t3357\tno\tAAAP\tAam Aadmi Party\nKarnataka\tChamarajanagar\tS. GOVINDA NAIK\t5398\tno\tIND\tIndependent\nKarnataka\tChamarajanagar\tNIRMALA KUMARI\t6604\tno\tIND\tIndependent\nKarnataka\tChamarajanagar\tCHAUDAHALLY JAVARAIAH\t2008\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nKarnataka\tChamarajanagar\tB GURUSWAMY\t2043\tno\tSP\tSamajwadi Party\nKarnataka\tChamarajanagar\tSHIVAMALLU\t34846\tno\tBSP\tBahujan Samaj Party\nKarnataka\tChamarajanagar\tNone of the Above\t12697\tno\tNOTA\tNone of the Above\nKarnataka\tChamarajanagar\tKANDEGALA SRINIVASAMURTHY\t1825\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKarnataka\tChamarajanagar\tBANNUR SURENDRA\t2762\tno\tSJPA\tSarva Janata Party\nKarnataka\tChamarajanagar\tA. R. KRISHNA MURTHY\t426600\tno\tBJP\tBharatiya Janata Party\nKarnataka\tChamarajanagar\tANANDA G\t3906\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tD K SURESH\t652723\tyes\tINC\tIndian National Congress\nKarnataka\tBangalore Rural\tS.SIDDARAMAIAH (HEGGADE)\t2142\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tSHANKARAPPA\t1008\tno\tJVBP\tJai Vijaya Bharathi Party\nKarnataka\tBangalore Rural\tBASAVEGOWDA\t6154\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tBangalore Rural\tC THOPAIAH\t11594\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBangalore Rural\tSURESH\t2782\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tM.P.MUNAWER SHARIFF\t2577\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tKENCHAIAH\t859\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tJ.T.PRAKASH\t1503\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tR. PRABHAKARA REDDY\t317870\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBangalore Rural\tRAVI KRISHNA REDDY\t17195\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBangalore Rural\tK A MOHAN\t1345\tno\tKDC\tKamarajar Deseeya Congress\nKarnataka\tBangalore Rural\tJ NATARAJU\t958\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tMALLESHA\t5420\tno\tIND\tIndependent\nKarnataka\tBangalore Rural\tMUNIRAJU GOWDA . P\t421243\tno\tBJP\tBharatiya Janata Party\nKarnataka\tBangalore Rural\tNone of the Above\t9871\tno\tNOTA\tNone of the Above\nKarnataka\tBangalore North\tD.V SADANANDA GOWDA\t718326\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBangalore North\tNone of the Above\t11996\tno\tNOTA\tNone of the Above\nKarnataka\tBangalore North\tMU.VENKATESH SIMHA BHOVI\t1033\tno\tIND\tIndependent\nKarnataka\tBangalore North\tDR.MEERLAYAQ HUSSAIN\t612\tno\tIND\tIndependent\nKarnataka\tBangalore North\tMANOHAR H.A.\t919\tno\tIND\tIndependent\nKarnataka\tBangalore North\tSUNIL.V.J\t1561\tno\tIND\tIndependent\nKarnataka\tBangalore North\tABDUL AZEEM\t92681\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBangalore North\tBABU  MATHEW\t28107\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBangalore North\tMUTHURAJU. M\t829\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tBangalore North\tC.NARAYANA SWAMY\t488562\tno\tINC\tIndian National Congress\nKarnataka\tBangalore North\tT.S.CHAITHANYA (S.J.R)\t911\tno\tIND\tIndependent\nKarnataka\tBangalore North\tS. RAMU\t1131\tno\tIND\tIndependent\nKarnataka\tBangalore North\tMOHAMMED QUTBUDDIN\t680\tno\tIND\tIndependent\nKarnataka\tBangalore North\tSUN STAR D.JAYARAM\t3784\tno\tGaAP\tGareeb Aadmi Party\nKarnataka\tBangalore North\tV VELU\t5586\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBangalore central\tP.C. MOHAN\t557130\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBangalore central\tNone of the Above\t8449\tno\tNOTA\tNone of the Above\nKarnataka\tBangalore central\tNANDINI ALVA\t20387\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBangalore central\tG. NARAYANASWAMY (KAPALI)\t1228\tno\tIND\tIndependent\nKarnataka\tBangalore central\tBHOVI RAMA C.B.K\t942\tno\tIND\tIndependent\nKarnataka\tBangalore central\tB.K. NARAYANA-SWAMY\t700\tno\tIND\tIndependent\nKarnataka\tBangalore central\tMODI SAIFULLA\t3204\tno\tIND\tIndependent\nKarnataka\tBangalore central\tSURAIYA ZAITHOON\t519\tno\tIND\tIndependent\nKarnataka\tBangalore central\tISMAIL KHAN SAIF ALI\t2276\tno\tAITC\tAll India Trinamool Congress\nKarnataka\tBangalore central\tRAMANUJ SINGH\t1836\tno\tIND\tIndependent\nKarnataka\tBangalore central\tAMBROSE D'MELLO\t366\tno\tIND\tIndependent\nKarnataka\tBangalore central\tKADUGUDI SONNAPPA\t543\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tBangalore central\tH.L. MANJUNATHA\t707\tno\tKCVP\tKannada Chalavali Vatal Paksha\nKarnataka\tBangalore central\tE.SALAM\t365\tno\tIND\tIndependent\nKarnataka\tBangalore central\tB.V.VENKATESHAPPA\t668\tno\tIND\tIndependent\nKarnataka\tBangalore central\tK.MURTHY\t1507\tno\tIND\tIndependent\nKarnataka\tBangalore central\tPRASHANTH.S.M\t903\tno\tIND\tIndependent\nKarnataka\tBangalore central\tSHAFI AHMED\t549\tno\tIND\tIndependent\nKarnataka\tBangalore central\tDHANANJAYA. H.H\t422\tno\tIND\tIndependent\nKarnataka\tBangalore central\tDR. MEER LAYAQ HUSSAIN\t1878\tno\tIND\tIndependent\nKarnataka\tBangalore central\tV. BALAKRISHNAN\t39869\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBangalore central\tR. MOHAN RAJU\t5806\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBangalore central\tSEETHA RAMAN.E\t2543\tno\tIND\tIndependent\nKarnataka\tBangalore central\tSYED ASIF BUKHAARI\t411\tno\tIND\tIndependent\nKarnataka\tBangalore central\tRIZWAN ARSHAD\t419630\tno\tINC\tIndian National Congress\nKarnataka\tBangalore central\tBUDAYYA .B. PUJERI\t553\tno\tKAP\tKarunaadu Party\nKarnataka\tBangalore central\tZAHEEDA  SHEREEN\t1198\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKarnataka\tBangalore South\tANANTH KUMAR\t633816\tyes\tBJP\tBharatiya Janata Party\nKarnataka\tBangalore South\tM. UMADEVI\t918\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKarnataka\tBangalore South\tPRABHU\t2325\tno\tIND\tIndependent\nKarnataka\tBangalore South\tG. VENKATESH BHOVI\t736\tno\tIND\tIndependent\nKarnataka\tBangalore South\tNANDAN NILEKANI\t405241\tno\tINC\tIndian National Congress\nKarnataka\tBangalore South\tDR. PRADEEP GADUGESH\t549\tno\tIND\tIndependent\nKarnataka\tBangalore South\tDR. KODUR VENKATESH\t502\tno\tIND\tIndependent\nKarnataka\tBangalore South\tYOGESH\t2061\tno\tIND\tIndependent\nKarnataka\tBangalore South\tSHAMBULINGEGOWDA (GANDIVADI)\t581\tno\tIND\tIndependent\nKarnataka\tBangalore South\tNINA P NAYAK\t21403\tno\tAAAP\tAam Aadmi Party\nKarnataka\tBangalore South\tGOVINDAIAH B.R.\t672\tno\tIND\tIndependent\nKarnataka\tBangalore South\tT. E. MOSES\t604\tno\tIND\tIndependent\nKarnataka\tBangalore South\tT.K. DASAR\t929\tno\tIND\tIndependent\nKarnataka\tBangalore South\tRUTH MANORAMA\t25677\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tBangalore South\tSYED MEHABOOB\t365\tno\tJD(U)\tJanata Dal  (United)\nKarnataka\tBangalore South\tN. HANUMEGOWDA\t414\tno\tIND\tIndependent\nKarnataka\tBangalore South\tRAVIKUMARA T\t781\tno\tSP\tSamajwadi Party\nKarnataka\tBangalore South\tGAYATHRI\t484\tno\tPPOI\tPyramid Party of India\nKarnataka\tBangalore South\tMAHADEVA SWAMY. B. M\t428\tno\tBDBRAJP\tBharatiya Dr. B.R.Ambedkar Janta Party\nKarnataka\tBangalore South\tKHAN ABDUL R\t2747\tno\tBSP\tBahujan Samaj Party\nKarnataka\tBangalore South\tSYED RAFIQ AHMED\t246\tno\tIND\tIndependent\nKarnataka\tBangalore South\tPRAMOD  MUTALIK\t4247\tno\tIND\tIndependent\nKarnataka\tBangalore South\tG.R. SHIVASHANKAR\t586\tno\tAIFB\tAll India Forward Bloc\nKarnataka\tBangalore South\tNone of the Above\t7414\tno\tNOTA\tNone of the Above\nKarnataka\tChikkballapur\tM VEERAPPA MOILY\t424800\tyes\tINC\tIndian National Congress\nKarnataka\tChikkballapur\tNone of the Above\t7682\tno\tNOTA\tNone of the Above\nKarnataka\tChikkballapur\tJAYAPRAKASHA YADAV\t1586\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tG C VENKATARAMANAPPA\t892\tno\tRPI(A)\tRepublican Party of India (A)\nKarnataka\tChikkballapur\tYAKUB SHARIFF\t931\tno\tBMUP\tBahujan Mukti Party\nKarnataka\tChikkballapur\tVENKATESH D T\t3410\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tB N BACHE GOWDA\t415280\tno\tBJP\tBharatiya Janata Party\nKarnataka\tChikkballapur\tH D KUMARA SWAMY\t346339\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tChikkballapur\tH N RAMAKRISHNA REDDY\t686\tno\tPPOI\tPyramid Party of India\nKarnataka\tChikkballapur\tK S SWAMY\t4689\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tD N NARASIMHAMURTHY\t1897\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tB N GOPALAGOWDA\t1465\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tNANJAPPA\t1215\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tCHITRA PRASAD\t6279\tno\tBSP\tBahujan Samaj Party\nKarnataka\tChikkballapur\tG N RAVI\t7127\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tT V MANJUNATHA GOWDA\t3895\tno\tIND\tIndependent\nKarnataka\tChikkballapur\tREDDY G K C\t799\tno\tJD(U)\tJanata Dal  (United)\nKarnataka\tChikkballapur\tG V SREERAMA REDDY\t26071\tno\tCPM\tCommunist Party of India  (Marxist)\nKarnataka\tChikkballapur\tROBIN MATHEWS NAREKATTU\t1404\tno\tSP\tSamajwadi Party\nKarnataka\tChikkballapur\tK ARKESH\t6827\tno\tAAAP\tAam Aadmi Party\nKarnataka\tKolar\tK.H.MUNIYAPPA\t418926\tyes\tINC\tIndian National Congress\nKarnataka\tKolar\tK.NARENDRABABU\t1089\tno\tIND\tIndependent\nKarnataka\tKolar\tDR.C.MOHAN\t2903\tno\tIND\tIndependent\nKarnataka\tKolar\tBEEDA SRINIVAS N\t1404\tno\tIND\tIndependent\nKarnataka\tKolar\tA.PRASAD\t1275\tno\tIND\tIndependent\nKarnataka\tKolar\tKOTIGANAHALLI RAMAIAH\t17563\tno\tAAAP\tAam Aadmi Party\nKarnataka\tKolar\tV.MUNISWAMY\t4803\tno\tSP\tSamajwadi Party\nKarnataka\tKolar\tKOLAR KESAVA\t371076\tno\tJD(S)\tJanata Dal  (Secular)\nKarnataka\tKolar\tM.S.NARAYANASWAMY\t1291\tno\tIND\tIndependent\nKarnataka\tKolar\tEM.NARAYANASWAMY\t267322\tno\tBJP\tBharatiya Janata Party\nKarnataka\tKolar\tN.VENKATESH\t3602\tno\tIND\tIndependent\nKarnataka\tKolar\tH.N.NARAYANASWAMY\t1583\tno\tIND\tIndependent\nKarnataka\tKolar\tVINAYAKA PRASAD.H.N\t2074\tno\tIND\tIndependent\nKarnataka\tKolar\tNARAYANASWAMY\t1517\tno\tIND\tIndependent\nKarnataka\tKolar\tG.N.VENKATESH\t6233\tno\tIND\tIndependent\nKarnataka\tKolar\tH.V.ANJAPPA\t920\tno\tIND\tIndependent\nKarnataka\tKolar\tR.RAVINDRA KUMAR\t1144\tno\tBDBRAJP\tBharatiya Dr. B.R.Ambedkar Janta Party\nKarnataka\tKolar\tB.V.VENKATAGIRIYAPPA\t6716\tno\tIND\tIndependent\nKarnataka\tKolar\tV.SRINIVASA\t1424\tno\tIND\tIndependent\nKarnataka\tKolar\tD.M.AMBAREESHA\t4934\tno\tBSP\tBahujan Samaj Party\nKarnataka\tKolar\tM.MUNIYAPPA\t2667\tno\tIND\tIndependent\nKarnataka\tKolar\tNone of the Above\t5098\tno\tNOTA\tNone of the Above\nKarnataka\tKolar\tK.H.MADHUSOODAN\t1759\tno\tIND\tIndependent\nKerala\tKasaragod\tP KARUNAKARAN\t384964\tyes\tCPM\tCommunist Party of India  (Marxist)\nKerala\tKasaragod\tNone of the Above\t6103\tno\tNOTA\tNone of the Above\nKerala\tKasaragod\tABBAS MOTHALAPPARA\t632\tno\tAITC\tAll India Trinamool Congress\nKerala\tKasaragod\tK SURENDRAN\t172826\tno\tBJP\tBharatiya Janata Party\nKerala\tKasaragod\tADV. T SIDDIQUE\t378043\tno\tINC\tIndian National Congress\nKerala\tKasaragod\tABOOBACKER SIDDIQ\t880\tno\tIND\tIndependent\nKerala\tKasaragod\tGOTHRA MOOPAN NELLIKADAN KANNAN\t2655\tno\tIND\tIndependent\nKerala\tKasaragod\tAMBALATHARA KUNHIKRISHNAN\t4996\tno\tAAAP\tAam Aadmi Party\nKerala\tKasaragod\tADV. BASHEER ALADY\t3104\tno\tBSP\tBahujan Samaj Party\nKerala\tKasaragod\tP K RAMAN\t1222\tno\tIND\tIndependent\nKerala\tKasaragod\tKARUNAKARAN PAINGAPPADAN\t1002\tno\tIND\tIndependent\nKerala\tKasaragod\tKARUNAKARAN KALIPPURAYIL\t824\tno\tIND\tIndependent\nKerala\tKasaragod\tK K ASHOKAN\t3057\tno\tIND\tIndependent\nKerala\tKasaragod\tMANOHARAN K\t4194\tno\tIND\tIndependent\nKerala\tKasaragod\tABDUL SALAM N U\t9713\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tKannur\tP K SREEMATHI TEACHER\t427622\tyes\tCPM\tCommunist Party of India  (Marxist)\nKerala\tKannur\tP P MOHANAN\t2371\tno\tIND\tIndependent\nKerala\tKannur\tSASIDHARAN K V\t6106\tno\tAAAP\tAam Aadmi Party\nKerala\tKannur\tSREEMATHI PUTHALATH\t1500\tno\tIND\tIndependent\nKerala\tKannur\tK SUDHAKARAN (SREESHYLAM)\t2745\tno\tIND\tIndependent\nKerala\tKannur\tN ABDULLA DAVUD\t2109\tno\tBSP\tBahujan Samaj Party\nKerala\tKannur\tK SUDHAKARAN (KOLLON HOUSE)\t4240\tno\tIND\tIndependent\nKerala\tKannur\tP P ABOOBACKAR\t1536\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tKannur\tK SUDHAKARAN\t421056\tno\tINC\tIndian National Congress\nKerala\tKannur\tK K ABDUL JABBAR\t19170\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tKannur\tP C MOHANAN MASTER\t51636\tno\tBJP\tBharatiya Janata Party\nKerala\tKannur\tNone of the Above\t7026\tno\tNOTA\tNone of the Above\nKerala\tVadakara\tMULLAPPALLY RAMACHANDRAN\t416479\tyes\tINC\tIndian National Congress\nKerala\tVadakara\tNone of the Above\t6107\tno\tNOTA\tNone of the Above\nKerala\tVadakara\tADV.A.N.SHAMSEER\t413173\tno\tCPM\tCommunist Party of India  (Marxist)\nKerala\tVadakara\tP.SHARAFUDEEN\t1679\tno\tIND\tIndependent\nKerala\tVadakara\tP.ABDUL HAMEED  MASTER\t15058\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tVadakara\tV.K.SAJEEVAN\t76313\tno\tBJP\tBharatiya Janata Party\nKerala\tVadakara\tSASEENDRAN\t2150\tno\tBSP\tBahujan Samaj Party\nKerala\tVadakara\tADV.P.KUMARANKUTTY\t17229\tno\tIND\tIndependent\nKerala\tVadakara\tATTUVEPPIL KUNHIKKANNAN\t731\tno\tIND\tIndependent\nKerala\tVadakara\tA.P.SHAMSEER\t3485\tno\tIND\tIndependent\nKerala\tVadakara\tA.M.SMITHA\t693\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tVadakara\tALI AKBAR\t6245\tno\tAAAP\tAam Aadmi Party\nKerala\tWayanad\tM I SHANAVAS\t377035\tyes\tINC\tIndian National Congress\nKerala\tWayanad\tADV. P P A SAGEER\t10684\tno\tAAAP\tAam Aadmi Party\nKerala\tWayanad\tSATHYAN PUTHANVEETTIL\t2855\tno\tIND\tIndependent\nKerala\tWayanad\tSATHYAN THAZHEMANGAD\t5476\tno\tIND\tIndependent\nKerala\tWayanad\tABRAHAM BENHUR\t1308\tno\tIND\tIndependent\nKerala\tWayanad\tCLEATUS\t1517\tno\tIND\tIndependent\nKerala\tWayanad\tSATHYAN MOKERI\t356165\tno\tCPI\tCommunist Party of India\nKerala\tWayanad\tP R RASMILNATH\t80752\tno\tBJP\tBharatiya Janata Party\nKerala\tWayanad\tJALEEL NEELAMBRA\t14327\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tWayanad\tSAM P MATHEW\t1222\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tWayanad\tSINOJ A C\t1104\tno\tIND\tIndependent\nKerala\tWayanad\tRAMLA MAMPAD\t12645\tno\tWPOI\tWelfare Party Of India\nKerala\tWayanad\tVAPPAN\t1317\tno\tBSP\tBahujan Samaj Party\nKerala\tWayanad\tP V ANVAR\t37123\tno\tIND\tIndependent\nKerala\tWayanad\tSATHEESH CHANDRAN\t741\tno\tAITC\tAll India Trinamool Congress\nKerala\tWayanad\tNone of the Above\t10735\tno\tNOTA\tNone of the Above\nKerala\tKozhikode\tM .K RAGHAVAN\t397615\tyes\tINC\tIndian National Congress\nKerala\tKozhikode\tNone of the Above\t6381\tno\tNOTA\tNone of the Above\nKerala\tKozhikode\tADV.N.P.PRATHAP KUMAR\t6993\tno\tIND\tIndependent\nKerala\tKozhikode\tMUSTHAFA KOMMERI\t10596\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tKozhikode\tA.VIJAYARAGHAVAN\t380732\tno\tCPM\tCommunist Party of India  (Marxist)\nKerala\tKozhikode\tC K.PADMANABHAN\t115760\tno\tBJP\tBharatiya Janata Party\nKerala\tKozhikode\tTHRISSUR NAZEER\t665\tno\tIND\tIndependent\nKerala\tKozhikode\tVELAYUDHAN K.P.\t1909\tno\tBSP\tBahujan Samaj Party\nKerala\tKozhikode\tMUHAMMED RIYAS\t473\tno\tIND\tIndependent\nKerala\tKozhikode\tK P RATHEESH\t13934\tno\tAAAP\tAam Aadmi Party\nKerala\tKozhikode\tV M RAGHAVAN\t964\tno\tIND\tIndependent\nKerala\tKozhikode\tVIJAYARAGHAVAN.K.\t1991\tno\tIND\tIndependent\nKerala\tKozhikode\tM RAGHAVAN\t2331\tno\tIND\tIndependent\nKerala\tKozhikode\tM.VIJAYARAGHAVAN\t2665\tno\tIND\tIndependent\nKerala\tMalappuram\tE. AHAMED\t437723\tyes\tIUML\tIndian Union Muslim League\nKerala\tMalappuram\tSREEDHARAN\t1330\tno\tIND\tIndependent\nKerala\tMalappuram\tANWAR SHAKEEL OMAR\t1215\tno\tIND\tIndependent\nKerala\tMalappuram\tADV. N.SREEPRAKASH\t64705\tno\tBJP\tBharatiya Janata Party\nKerala\tMalappuram\tP.K.SAINABA\t242984\tno\tCPM\tCommunist Party of India  (Marxist)\nKerala\tMalappuram\tGOPINATHAN.N\t2491\tno\tIND\tIndependent\nKerala\tMalappuram\tPROF. P.ISMAIL\t29216\tno\tWPOI\tWelfare Party Of India\nKerala\tMalappuram\tDR. M.V. IBRAHIM\t1376\tno\tIND\tIndependent\nKerala\tMalappuram\tILYAS\t2745\tno\tBSP\tBahujan Samaj Party\nKerala\tMalappuram\tNASARUDHEEN\t47853\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tMalappuram\tNone of the Above\t21829\tno\tNOTA\tNone of the Above\nKerala\tPonnani\tE. T. MOHAMMED BASHEER\t378503\tyes\tIUML\tIndian Union Muslim League\nKerala\tPonnani\tNone of the Above\t7494\tno\tNOTA\tNone of the Above\nKerala\tPonnani\tV.T.IKRAMUL HAQUE\t26640\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tPonnani\tV.ABDURAHMAN\t353093\tno\tIND\tIndependent\nKerala\tPonnani\tK.NARAYANAN MASTER\t75212\tno\tBJP\tBharatiya Janata Party\nKerala\tPonnani\tABULLAIS.T.P\t11034\tno\tIND\tIndependent\nKerala\tPonnani\tT.AYYAPPAN\t2153\tno\tBSP\tBahujan Samaj Party\nKerala\tPonnani\tBINDU M.K.\t2261\tno\tIND\tIndependent\nKerala\tPonnani\tABDURAHIMAN VARIKKOTTIL\t2434\tno\tIND\tIndependent\nKerala\tPonnani\tABDURAHIMAN VAYARAKATH\t1220\tno\tIND\tIndependent\nKerala\tPonnani\tABDURAHMAN VADAKKATHINAKATH\t2044\tno\tIND\tIndependent\nKerala\tPonnani\tSHYLOCK P.V.\t9504\tno\tAAAP\tAam Aadmi Party\nKerala\tPalakkad\tM B RAJESH\t412897\tyes\tCPM\tCommunist Party of India  (Marxist)\nKerala\tPalakkad\tSOBHASURENDRAN\t136541\tno\tBJP\tBharatiya Janata Party\nKerala\tPalakkad\tVEERENDRAKUMAR O P\t3946\tno\tIND\tIndependent\nKerala\tPalakkad\tB PADMANABHAN\t4933\tno\tAAAP\tAam Aadmi Party\nKerala\tPalakkad\tPRADEEP\t770\tno\tIND\tIndependent\nKerala\tPalakkad\tABOOBAKKER SIDHIKH\t828\tno\tAITC\tAll India Trinamool Congress\nKerala\tPalakkad\tC.K. RAMAKRISHNAN\t1092\tno\tIND\tIndependent\nKerala\tPalakkad\tM P VEERENDRAKUMAR\t307597\tno\tSJD\tSocialist Janata (Democratic)\nKerala\tPalakkad\tABHIMOD\t1231\tno\tIND\tIndependent\nKerala\tPalakkad\tSREEJITH\t1416\tno\tIND\tIndependent\nKerala\tPalakkad\tHARI ARUMBIL\t2009\tno\tBSP\tBahujan Samaj Party\nKerala\tPalakkad\tTHENNILAPURAM RADHAKRISHNAN\t8667\tno\tWPOI\tWelfare Party Of India\nKerala\tPalakkad\tV S  SHANAVAS\t1946\tno\tIND\tIndependent\nKerala\tPalakkad\tE S KHAJA HUSSAIN\t12504\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tPalakkad\tRAJESH S\t2654\tno\tSHS\tShivsena\nKerala\tPalakkad\tNone of the Above\t11291\tno\tNOTA\tNone of the Above\nKerala\tAlathur\tP.K.BIJU\t411808\tyes\tCPM\tCommunist Party of India  (Marxist)\nKerala\tAlathur\tNone of the Above\t21417\tno\tNOTA\tNone of the Above\nKerala\tAlathur\tPREMAKUMARI\t4436\tno\tBSP\tBahujan Samaj Party\nKerala\tAlathur\tKRISHNAN ERANHIKKAL\t7820\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tAlathur\tSHEEBA\t374496\tno\tINC\tIndian National Congress\nKerala\tAlathur\tSHAJU MON VATTEKKAD\t87803\tno\tBJP\tBharatiya Janata Party\nKerala\tAlathur\tALBIN M U\t5260\tno\tIND\tIndependent\nKerala\tAlathur\tK S VELAYUDHAN\t4392\tno\tIND\tIndependent\nKerala\tAlathur\tKRISHNANKUTTY V\t932\tno\tIND\tIndependent\nKerala\tAlathur\tK BIJU\t2911\tno\tIND\tIndependent\nKerala\tAlathur\tA BIJU\t1692\tno\tIND\tIndependent\nKerala\tAlathur\tR BIJU\t2159\tno\tIND\tIndependent\nKerala\tAlathur\tVIJAYAN AMBALAKKAD\t2102\tno\tIND\tIndependent\nKerala\tThrissur\tC. N. JAYADEVAN\t389209\tyes\tCPI\tCommunist Party of India\nKerala\tThrissur\tJIJU CHEENIKKAL\t450\tno\tIND\tIndependent\nKerala\tThrissur\tMANIYANGATTIL SUKUMARAN\t949\tno\tIND\tIndependent\nKerala\tThrissur\tSUFEERA K. P.\t6894\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tThrissur\tSARAH JOSEPH\t44638\tno\tAAAP\tAam Aadmi Party\nKerala\tThrissur\tADV. A. JAYARAM\t2110\tno\tBSP\tBahujan Samaj Party\nKerala\tThrissur\tHAJI MOIDEEN SHAH\t659\tno\tJD(U)\tJanata Dal  (United)\nKerala\tThrissur\tJASMINSHA\t3959\tno\tIND\tIndependent\nKerala\tThrissur\tM. NARAYANANKUTTY (M. N. KUTTY)\t616\tno\tIND\tIndependent\nKerala\tThrissur\tK. P. DHANAPALAN\t350982\tno\tINC\tIndian National Congress\nKerala\tThrissur\tT. L. SANTHOSH\t6044\tno\tIND\tIndependent\nKerala\tThrissur\tSOMAN PILLAI\t546\tno\tigp\tIndian Gandhiyan Party\nKerala\tThrissur\tK.SIVARAMAN\t718\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tThrissur\tK.P. SREESAN\t102681\tno\tBJP\tBharatiya Janata Party\nKerala\tThrissur\tNone of the Above\t10050\tno\tNOTA\tNone of the Above\nKerala\tChalakudy\tINNOCENT\t358440\tyes\tIND\tIndependent\nKerala\tChalakudy\tNone of the Above\t10552\tno\tNOTA\tNone of the Above\nKerala\tChalakudy\tK M NOORDEEN\t35189\tno\tAAAP\tAam Aadmi Party\nKerala\tChalakudy\tK. AMBUJAKSHAN\t12942\tno\tWPOI\tWelfare Party Of India\nKerala\tChalakudy\tADV. B. GOPALAKRISHNAN\t92848\tno\tBJP\tBharatiya Janata Party\nKerala\tChalakudy\tP C CHACKO\t344556\tno\tINC\tIndian National Congress\nKerala\tChalakudy\tJAYAN KONIKARA\t932\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tChalakudy\tM G PURUSHOTHAMAN\t1942\tno\tBSP\tBahujan Samaj Party\nKerala\tChalakudy\tSHAFEER MUHAMMED\t14386\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tChalakudy\tEDAPPALLY BASHEER\t1346\tno\tIND\tIndependent\nKerala\tChalakudy\tANNAMMA JOY\t862\tno\tAITC\tAll India Trinamool Congress\nKerala\tChalakudy\tSAJI THURUTHIKUNNEL\t1114\tno\tSHS\tShivsena\nKerala\tChalakudy\tVINCENT\t4596\tno\tIND\tIndependent\nKerala\tChalakudy\tJAISON PANIKULANGARA\t2052\tno\tIND\tIndependent\nKerala\tChalakudy\tT D BABURAJAN\t900\tno\tIND\tIndependent\nKerala\tChalakudy\tABDUL KAREEM\t1376\tno\tRJD\tRashtriya Janata Dal\nKerala\tErnakulam\tPROF. K.V. THOMAS\t353841\tyes\tINC\tIndian National Congress\nKerala\tErnakulam\tANIL KUMAR\t1180\tno\tIND\tIndependent\nKerala\tErnakulam\tK C KARTHIKEYAN\t2685\tno\tBSP\tBahujan Samaj Party\nKerala\tErnakulam\tDENSIL MENDEZ\t2630\tno\tJD(U)\tJanata Dal  (United)\nKerala\tErnakulam\tKISHORE\t5011\tno\tIND\tIndependent\nKerala\tErnakulam\tADV. RADHAKRISHNAN P T\t2104\tno\tIND\tIndependent\nKerala\tErnakulam\tRAJANEESH BABU\t8246\tno\tIND\tIndependent\nKerala\tErnakulam\tJUNO JOHN BABY\t1077\tno\tIND\tIndependent\nKerala\tErnakulam\tK.V. BHASKARAN\t22733\tno\tIND\tIndependent\nKerala\tErnakulam\tANITHA PRATAP\t51517\tno\tAAAP\tAam Aadmi Party\nKerala\tErnakulam\tZULFIKAL ALI\t14825\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tErnakulam\tP.D. CHANDRABHANU\t6156\tno\tSRP\tSocialist Republican Party\nKerala\tErnakulam\tA.N. RADHAKRISHNAN\t99003\tno\tBJP\tBharatiya Janata Party\nKerala\tErnakulam\tCHRISTY FERNANDEZ\t266794\tno\tIND\tIndependent\nKerala\tErnakulam\tN J PIOUS\t2347\tno\tIND\tIndependent\nKerala\tErnakulam\tKRISHNANKUTTY M.K.\t950\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tErnakulam\tNone of the Above\t9735\tno\tNOTA\tNone of the Above\nKerala\tIdukki\tADV.JOICE GEORGE\t382019\tyes\tIND\tIndependent\nKerala\tIdukki\tNone of the Above\t12338\tno\tNOTA\tNone of the Above\nKerala\tIdukki\tP.C.JOLLY\t550\tno\tIND\tIndependent\nKerala\tIdukki\tADV.SABU VARGHESE\t50438\tno\tBJP\tBharatiya Janata Party\nKerala\tIdukki\tADV.DEAN KURIAKOSE\t331477\tno\tINC\tIndian National Congress\nKerala\tIdukki\tADV.CHITTOOR RAJAMANNAR\t415\tno\tIND\tIndependent\nKerala\tIdukki\tMUHAMMED SHARAFUDHEEN\t10401\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tIdukki\tSILVI SUNIL\t11215\tno\tAAAP\tAam Aadmi Party\nKerala\tIdukki\tAPPANCHIRA PONNAPPAN\t2477\tno\tBSP\tBahujan Samaj Party\nKerala\tIdukki\tT.K.TOMY\t3971\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tIdukki\tSOMINI PRABHAKARAN\t1925\tno\tIND\tIndependent\nKerala\tIdukki\tJAMES JOSEPH\t1158\tno\tIND\tIndependent\nKerala\tIdukki\tSHOBY JOSEPH\t1179\tno\tIND\tIndependent\nKerala\tIdukki\tANISH MARIYIL\t629\tno\tIND\tIndependent\nKerala\tIdukki\tJOYCE GEORGE\t6024\tno\tIND\tIndependent\nKerala\tIdukki\tJOICE GEORGE\t3057\tno\tIND\tIndependent\nKerala\tIdukki\tBIJU JOSEPH\t493\tno\tIND\tIndependent\nKerala\tKottayam\tJOSE K. MANI\t424194\tyes\tKEC(M)\tKerala Congress  (M)\nKerala\tKottayam\tPRAVEEN K. MOHAN\t925\tno\tIND\tIndependent\nKerala\tKottayam\tRATHEESH PERUMAL\t2157\tno\tIND\tIndependent\nKerala\tKottayam\tJAMES JOSEPH\t1831\tno\tIND\tIndependent\nKerala\tKottayam\tSASIKKUTTAN VAKATHANAM\t1048\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nKerala\tKottayam\tN.K.BIJU\t2879\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKerala\tKottayam\tADV.ANIL AIKKARA\t26381\tno\tAAAP\tAam Aadmi Party\nKerala\tKottayam\tADV.NOBLE MATHEW\t44357\tno\tIND\tIndependent\nKerala\tKottayam\tADV.MATHEW T. THOMAS\t303595\tno\tJD(S)\tJanata Dal  (Secular)\nKerala\tKottayam\tROY ARACKAL\t3513\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tKottayam\tSREENI K. JACOB\t6732\tno\tBSP\tBahujan Samaj Party\nKerala\tKottayam\tNone of the Above\t14024\tno\tNOTA\tNone of the Above\nKerala\tAlappuzha\tK C VENUGOPAL\t462525\tyes\tINC\tIndian National Congress\nKerala\tAlappuzha\tNone of the Above\t11338\tno\tNOTA\tNone of the Above\nKerala\tAlappuzha\tC B CHANDRABABU\t443118\tno\tCPM\tCommunist Party of India  (Marxist)\nKerala\tAlappuzha\tP V NATESAN\t3385\tno\tBSP\tBahujan Samaj Party\nKerala\tAlappuzha\tADV.M.A.BINDU\t5921\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKerala\tAlappuzha\tJAYACHANDRAN SARASAMMA\t481\tno\tIND\tIndependent\nKerala\tAlappuzha\tM M PAULOSE\t402\tno\tIND\tIndependent\nKerala\tAlappuzha\tPROF.A.V.THAMARAKSHAN\t43051\tno\tRSPK(B)\tRevolutionary Socialist Party of Kerala  (Bolshevik) RSP.B\nKerala\tAlappuzha\tVENUGOPAL P C\t3149\tno\tIND\tIndependent\nKerala\tAlappuzha\tD MOHANAN\t9414\tno\tAAAP\tAam Aadmi Party\nKerala\tAlappuzha\tCHANDRABABU G\t1615\tno\tIND\tIndependent\nKerala\tAlappuzha\tT S BALAKRISHNAN\t1363\tno\tIND\tIndependent\nKerala\tAlappuzha\tS.B.BASHEER\t709\tno\tIND\tIndependent\nKerala\tAlappuzha\tTHULASEEDHARAN PALLICKAL\t10993\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tMavelikkara\tKODIKUNNIL SURESH\t402432\tyes\tINC\tIndian National Congress\nKerala\tMavelikkara\tPALLICKAL SURENDRAN\t1486\tno\tIND\tIndependent\nKerala\tMavelikkara\tPIRAVATHOOR SREEDHARAN\t1207\tno\tIND\tIndependent\nKerala\tMavelikkara\tJYOTHISH PERUMPULICKAL\t8946\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tMavelikkara\tADV.P.K.JAYAKRISHNAN\t3603\tno\tBSP\tBahujan Samaj Party\nKerala\tMavelikkara\tADV.P.SUDHEER\t79743\tno\tBJP\tBharatiya Janata Party\nKerala\tMavelikkara\tN SADANANDAN\t7753\tno\tAAAP\tAam Aadmi Party\nKerala\tMavelikkara\tCHENGARA SURENDRAN\t369695\tno\tCPI\tCommunist Party of India\nKerala\tMavelikkara\tSASIKALA K S\t4736\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKerala\tMavelikkara\tNone of the Above\t9459\tno\tNOTA\tNone of the Above\nKerala\tPathanamthitta\tANTO ANTONY\t358842\tyes\tINC\tIndian National Congress\nKerala\tPathanamthitta\tNone of the Above\t16538\tno\tNOTA\tNone of the Above\nKerala\tPathanamthitta\tS RADHAMANI\t2901\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKerala\tPathanamthitta\tSABUKUTTY JOY (SABU PUTHUCHIRA)\t682\tno\tSLAP\tSocial Action Party\nKerala\tPathanamthitta\tADV. PEELIPOSE THOMAS\t302651\tno\tIND\tIndependent\nKerala\tPathanamthitta\tMANSOOR THENGANA\t11353\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tPathanamthitta\tALEYAMMA VARGHESE\t1236\tno\tAITC\tAll India Trinamool Congress\nKerala\tPathanamthitta\tMATHEW PAREY\t384\tno\tIND\tIndependent\nKerala\tPathanamthitta\tAJITH KUMAR K. K\t748\tno\tIND\tIndependent\nKerala\tPathanamthitta\tPUSHPANGATHAN\t953\tno\tIND\tIndependent\nKerala\tPathanamthitta\tM T REMESH\t138954\tno\tBJP\tBharatiya Janata Party\nKerala\tPathanamthitta\tSELEENA PRAKKANAM\t10384\tno\tBSP\tBahujan Samaj Party\nKerala\tPathanamthitta\tPEELIPOSE\t16493\tno\tIND\tIndependent\nKerala\tPathanamthitta\tSANTHOSH KUMAR\t4847\tno\tIND\tIndependent\nKerala\tPathanamthitta\tLALITHA MALAYALAPPUZHA\t460\tno\tIND\tIndependent\nKerala\tPathanamthitta\tSHILVIN KOTTACKAKATHU\t1005\tno\tIND\tIndependent\nKerala\tPathanamthitta\tS RADHAKRISHNAN\t1021\tno\tIND\tIndependent\nKerala\tKollam\tN.K.PREMACHANDRAN\t408528\tyes\tRSP\tRevolutionary Socialist Party\nKerala\tKollam\tC. INDULAL EX-SERVICE MAN\t1709\tno\tIND\tIndependent\nKerala\tKollam\tM. BABY\t3364\tno\tIND\tIndependent\nKerala\tKollam\tV.S. PREMACHANDRAN\t3333\tno\tIND\tIndependent\nKerala\tKollam\tR. PREMACHANDRAN\t2808\tno\tIND\tIndependent\nKerala\tKollam\tADV.S.PRAHLADAN\t4266\tno\tBSP\tBahujan Samaj Party\nKerala\tKollam\tA.K.SALAHUDEEN\t12812\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tKollam\tP.M. VELAYUDHAN\t58671\tno\tBJP\tBharatiya Janata Party\nKerala\tKollam\tA. JOSEKUTTY\t1702\tno\tIND\tIndependent\nKerala\tKollam\tK. BHASKARAN\t3108\tno\tIND\tIndependent\nKerala\tKollam\tM.A. BABY\t370879\tno\tCPM\tCommunist Party of India  (Marxist)\nKerala\tKollam\tNone of the Above\t7876\tno\tNOTA\tNone of the Above\nKerala\tAttingal\tDR.A .SAMPATH\t392478\tyes\tCPM\tCommunist Party of India  (Marxist)\nKerala\tAttingal\tNone of the Above\t6924\tno\tNOTA\tNone of the Above\nKerala\tAttingal\tGIRIJA KUMARI.S\t90528\tno\tBJP\tBharatiya Janata Party\nKerala\tAttingal\tSUNILKRISHNAN\t3850\tno\tIND\tIndependent\nKerala\tAttingal\tSRI.M.K.MANOJ KUMAR\t11225\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tAttingal\tSMT. PRIYA SUNIL\t4862\tno\tWPOI\tWelfare Party Of India\nKerala\tAttingal\tANIL KUMAR N S\t8586\tno\tBSP\tBahujan Samaj Party\nKerala\tAttingal\tDAS K VARKALA\t2375\tno\tIND\tIndependent\nKerala\tAttingal\tADV.BINDHU KRISHNA\t323100\tno\tINC\tIndian National Congress\nKerala\tAttingal\tSRI. VIVEKANANDAN\t615\tno\tIND\tIndependent\nKerala\tAttingal\tSRI.IRINCHAYAM SURESH\t1052\tno\tIND\tIndependent\nKerala\tAttingal\tSRI .ADV.M. R.SARIN\t576\tno\tJD(U)\tJanata Dal  (United)\nKerala\tAttingal\tSRI.SURESHKUMAR THONNAKKAL\t647\tno\tIND\tIndependent\nKerala\tAttingal\tSRI. VAKKOM G AJITH\t5511\tno\tSHS\tShivsena\nKerala\tAttingal\tSRI. HARIHARAN K S\t4064\tno\tIND\tIndependent\nKerala\tAttingal\tSRI. ADV. CHIRAYINKEEZHU GOPINATHAN\t736\tno\tRPI(A)\tRepublican Party of India (A)\nKerala\tAttingal\tSRI.SAMBATH ANILKUMAR\t2221\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tDR. SHASHI THAROOR\t297806\tyes\tINC\tIndian National Congress\nKerala\tThiruvananthapuram\tSRI. JAIN WILSON\t2105\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI. ADV. PERURKADA HARIKUMAR\t902\tno\tSHS\tShivsena\nKerala\tThiruvananthapuram\tSMT. SYAMALAKUMARI\t1851\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI. THOMAS JOSEPH\t261\tno\tRPI(A)\tRepublican Party of India (A)\nKerala\tThiruvananthapuram\tSRI.BENET BABU BENJAMIN\t4231\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSMT. R CHANDRIKA KATTAKADA\t292\tno\tRPI\tRepublican Party of India\nKerala\tThiruvananthapuram\tSRI.ANIL KUMAR Y\t371\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI. AJIT JOY\t14153\tno\tAAAP\tAam Aadmi Party\nKerala\tThiruvananthapuram\tSRI.GEORGE MANKIDIYIL\t843\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI. J SUDHAKARAN\t3260\tno\tBSP\tBahujan Samaj Party\nKerala\tThiruvananthapuram\tSRI. V S JAYAKUMAR\t445\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI. O V SREEDATH\t356\tno\tSRP\tSocialist Republican Party\nKerala\tThiruvananthapuram\tSRI. DOCTOR BENNET ABRAHAM\t248941\tno\tCPI\tCommunist Party of India\nKerala\tThiruvananthapuram\tSRI. O M JYOTHEENDRA NATH\t2445\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI.KUNNIL SHAJAHAN\t4818\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nKerala\tThiruvananthapuram\tSRI. KUNNUKUZHI S MANI\t1717\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI. O RAJAGOPAL\t282336\tno\tBJP\tBharatiya Janata Party\nKerala\tThiruvananthapuram\tFR.STEPHEN\t1271\tno\tIND\tIndependent\nKerala\tThiruvananthapuram\tSRI.SHAJAR KHAN\t1691\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nKerala\tThiruvananthapuram\tNone of the Above\t3344\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tMORENA\tANOOP MISHRA\t375567\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tMORENA\tNone of the Above\t4792\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tMORENA\tRAM NARESH\t3546\tno\tIND\tIndependent\nMadhya Pradesh\tMORENA\tANOOP NAGAR\t852\tno\tIND\tIndependent\nMadhya Pradesh\tMORENA\tMANESH KUMAR RAWAT\t975\tno\tJMBP\tJai Maha Bharath Party\nMadhya Pradesh\tMORENA\tKULDEEP SINGH\t1058\tno\tIND\tIndependent\nMadhya Pradesh\tMORENA\tVISNU SHARMA VIRTHARE\t2114\tno\tAITC\tAll India Trinamool Congress\nMadhya Pradesh\tMORENA\tAKASH GUPTA\t809\tno\tIND\tIndependent\nMadhya Pradesh\tMORENA\tSHAKTI SINGH TOMAR\t1145\tno\tRAHM\tRashtriya Ahinsa Manch\nMadhya Pradesh\tMORENA\tBRINDAWAN SINGH SIKARWAR\t242586\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tMORENA\tDR GOVIND SINGH\t184253\tno\tINC\tIndian National Congress\nMadhya Pradesh\tMORENA\tSIYARAM SINGH CHOUDRI\t6471\tno\tIND\tIndependent\nMadhya Pradesh\tMORENA\tRAJESH KUSHWAH\t5554\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tMORENA\tDEVENDRA KUMAR AGRVAL\t13806\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tMORENA\tJAYANT SINGH TOMAR\t1429\tno\tLSWP\tLoktantrik Samajwadi Party\nMadhya Pradesh\tMORENA\tSATANDRA SINGH (TONY TOMAR)\t4347\tno\tIND\tIndependent\nMadhya Pradesh\tMORENA\tARJUN SINGH (PAPPU KUSHWAH)\t3006\tno\tRSMD\tRashtriya Samanta Dal\nMadhya Pradesh\tMORENA\tCHHAVENDRA\t1969\tno\tIND\tIndependent\nMadhya Pradesh\tBHIND\tDR. BHAGIRATH PRASAD\t404474\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tBHIND\tVIJAYSINGH KORI\t2699\tno\tAIFB\tAll India Forward Bloc\nMadhya Pradesh\tBHIND\tKRISHNA DEVI\t7730\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tBHIND\tHAKIM SINGH\t2899\tno\tIND\tIndependent\nMadhya Pradesh\tBHIND\tIMARTI DEVI\t244513\tno\tINC\tIndian National Congress\nMadhya Pradesh\tBHIND\tPHOOLSINGH BARAIYA\t20403\tno\tBA S D\tBahujan Sangharshh Dal\nMadhya Pradesh\tBHIND\tMANISH KATROLIYA\t33803\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tBHIND\tB.S. THANESWARIYA\t1793\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tBHIND\tANITA HITENDRA CHAUDHARY\t5198\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tBHIND\tNone of the Above\t5572\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tGWALIOR\tNARENDRA SINGH TOMAR\t442796\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tGWALIOR\tNEELAM AGRWAL\t11510\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tGWALIOR\tASIF KHAN\t4969\tno\tIND\tIndependent\nMadhya Pradesh\tGWALIOR\tK. P. KAMTE\t1607\tno\tBRMD\tBharatiya Rashtriya Mazdoor Dal\nMadhya Pradesh\tGWALIOR\tPRAJESH CHOUDHARY\t1981\tno\tIND\tIndependent\nMadhya Pradesh\tGWALIOR\tBALWANT SINGH KUSHWAH ADVOCATE\t5327\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tGWALIOR\tNARAYAN SINGH KUSHWAH\t3963\tno\tIND\tIndependent\nMadhya Pradesh\tGWALIOR\tALOK SHARMA\t68196\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tGWALIOR\tHIRADAY NARAYAN SINGH TOMAR (GUDDU)\t2244\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nMadhya Pradesh\tGWALIOR\tMADAN MOHAN URF MOHAN SINGH TOMAR\t1477\tno\tbjyp\tBhartiya Jan Yug Party\nMadhya Pradesh\tGWALIOR\tAKHILESH YADAV\t10297\tno\tCPM\tCommunist Party of India  (Marxist)\nMadhya Pradesh\tGWALIOR\tASHOK SINGH\t413097\tno\tINC\tIndian National Congress\nMadhya Pradesh\tGWALIOR\tC.L. KARODIYA (PRIJAPATI)\t4719\tno\tJND\tJan-Nyay Dal\nMadhya Pradesh\tGWALIOR\tRAM PATHWAR KORI (RAMSEBAK)\t1687\tno\tIND\tIndependent\nMadhya Pradesh\tGWALIOR\tVIKAS SHARMA\t1408\tno\tBhNaP\tBharatiya Navyuvak Party\nMadhya Pradesh\tGWALIOR\tKISHAN LAL KUSHWAH\t1412\tno\tRSMD\tRashtriya Samanta Dal\nMadhya Pradesh\tGWALIOR\tSHIV SINGH KUSHAWAH\t2101\tno\tMAHP\tMahanwadi Party\nMadhya Pradesh\tGWALIOR\tDEEPAK KUMAR BANSAL RANGWALE (PRERAK)\t2122\tno\tIND\tIndependent\nMadhya Pradesh\tGWALIOR\tCOMRADE SUNIL GOPAL\t2054\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nMadhya Pradesh\tGWALIOR\tGEETA RAJPUT\t1067\tno\tALHP\tAl-Hind Party\nMadhya Pradesh\tGWALIOR\tCAPTAN BABU LAL GOSWAMI\t2659\tno\tIND\tIndependent\nMadhya Pradesh\tGWALIOR\tNone of the Above\t4219\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tGUNA\tJYOTIRADITYA M SCINDIA\t517036\tyes\tINC\tIndian National Congress\nMadhya Pradesh\tGUNA\tNone of the Above\t12481\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tGUNA\tSHAILENDRA SINGH KUSHWAH\t4073\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tGUNA\tLAKHAN SINGH BAGHEL\t27412\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tGUNA\tCHANDRAPAL SINGH TOMAR\t2197\tno\tIND\tIndependent\nMadhya Pradesh\tGUNA\tNAND KISHORE TYAGI\t2072\tno\tIND\tIndependent\nMadhya Pradesh\tGUNA\tANWAR KHAN\t1321\tno\tIND\tIndependent\nMadhya Pradesh\tGUNA\tLALLI RAM RAI\t5575\tno\tIND\tIndependent\nMadhya Pradesh\tGUNA\tGIRRAJ YADAV\t3294\tno\tIND\tIndependent\nMadhya Pradesh\tGUNA\tMAHENDRA KUMAR\t2845\tno\tIND\tIndependent\nMadhya Pradesh\tGUNA\tJAIBHANSINGH PAWAIYA\t396244\tno\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tGUNA\tNARAYANRAO GUPCHUP\t2079\tno\tIND\tIndependent\nMadhya Pradesh\tSAGAR\tLAXMI NARAYAN YADAV\t482580\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tSAGAR\tMAHENDRA KUMAR CHOUDHARY\t1632\tno\tIND\tIndependent\nMadhya Pradesh\tSAGAR\tKASHIRAM PATEL\t1128\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tSAGAR\tM. HANIF QURASHI\t3856\tno\tIND\tIndependent\nMadhya Pradesh\tSAGAR\tSHITIN KESHRWANI\t2020\tno\tIND\tIndependent\nMadhya Pradesh\tSAGAR\tGOVIND SINGH RAJPUT\t361843\tno\tINC\tIndian National Congress\nMadhya Pradesh\tSAGAR\tSAROJ KATIYAR KURMI\t19917\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tSAGAR\tATUL MISHRA\t6328\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tSAGAR\tVINAY KUMAR\t1565\tno\tBSCP\tBhartiya Shakti Chetna Party\nMadhya Pradesh\tSAGAR\tBHARAT SEN ADVOCATE\t1556\tno\tIND\tIndependent\nMadhya Pradesh\tSAGAR\tNone of the Above\t9504\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tTIKAMGARH\tDR. VIRENDRA KUMAR\t422979\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tTIKAMGARH\tAHIRWAR DR. KAMLESH VERMA\t214248\tno\tINC\tIndian National Congress\nMadhya Pradesh\tTIKAMGARH\tKHANGAR NEERAJ\t15194\tno\tCPI\tCommunist Party of India\nMadhya Pradesh\tTIKAMGARH\tAHIRWAR SEWAK RAM\t23975\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tTIKAMGARH\tRAMESH CHAND VERMA\t4992\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tTIKAMGARH\tAHIRWAR RAMCHARAN\t3094\tno\tPHRC\tPoorvanchal Rashtriya Congress\nMadhya Pradesh\tTIKAMGARH\tGIRDHARI LAL KORI\t5219\tno\tIND\tIndependent\nMadhya Pradesh\tTIKAMGARH\tCHATURBHUJ SNEHI\t4035\tno\tIND\tIndependent\nMadhya Pradesh\tTIKAMGARH\tDR. AMBESH KUMARI AHIRWAR\t47497\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tTIKAMGARH\tLAKHAN LAL CHADHAR\t4548\tno\tIND\tIndependent\nMadhya Pradesh\tTIKAMGARH\tGANESH KUMHAR\t2230\tno\tIND\tIndependent\nMadhya Pradesh\tTIKAMGARH\tSHEEL CHANDRA AHIRWAR\t3855\tno\tIND\tIndependent\nMadhya Pradesh\tTIKAMGARH\tBHAGWAN DAS BANSHKAR\t4400\tno\tIND\tIndependent\nMadhya Pradesh\tTIKAMGARH\tNone of the Above\t10055\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tDAMOH\tPRAHALAD SINGH PATEL\t513079\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tDAMOH\tNone of the Above\t12600\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tDAMOH\tIRFAN KHAN\t2580\tno\tIND\tIndependent\nMadhya Pradesh\tDAMOH\tPRAHLAD SINGH PATEL\t9266\tno\tIND\tIndependent\nMadhya Pradesh\tDAMOH\tPRAHLAD SINGH LODI\t9343\tno\tIND\tIndependent\nMadhya Pradesh\tDAMOH\tDIVAN UPENDR PRATAP SINGH (RAJA SAB)\t16350\tno\tBSCP\tBhartiya Shakti Chetna Party\nMadhya Pradesh\tDAMOH\tANANT LAL BASOR\t2836\tno\tIND\tIndependent\nMadhya Pradesh\tDAMOH\tSANTOSH BHARTI\t11298\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tDAMOH\tDR.GULAB KHAN MASTER\t3429\tno\tIND\tIndependent\nMadhya Pradesh\tDAMOH\tCHOUDHARY MAHENDRA PRATAP SINGH\t299780\tno\tINC\tIndian National Congress\nMadhya Pradesh\tDAMOH\tDEVENDRA CHAOURASIA\t31519\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tKHAJURAHO\tNAGENDRA SINGH\t474966\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tKHAJURAHO\tSMT. DURGA SHARMA / ALIAS MADHU KAPIL\t1936\tno\tBC\tBundelkhand Congress\nMadhya Pradesh\tKHAJURAHO\tSANJEEV KUMAR MISHRA\t3187\tno\tAIFB\tAll India Forward Bloc\nMadhya Pradesh\tKHAJURAHO\tSHRIDHAR KHARE\t10997\tno\tIND\tIndependent\nMadhya Pradesh\tKHAJURAHO\tAMIT BHATNAGAR\t8930\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tKHAJURAHO\tAVNISH TIWARI\t2625\tno\tSHS\tShivsena\nMadhya Pradesh\tKHAJURAHO\tAYENDRA  KUMAR SHARMA\t4084\tno\tIND\tIndependent\nMadhya Pradesh\tKHAJURAHO\tANWAR KHAN\t3505\tno\tIND\tIndependent\nMadhya Pradesh\tKHAJURAHO\tKRISHAN SHARAN BHAIYA RAJA BUNDELA\t1679\tno\tBBPSP\tBrihattar Bharat Prajatantra Sewa Party\nMadhya Pradesh\tKHAJURAHO\tRAJA PATERIA\t227476\tno\tINC\tIndian National Congress\nMadhya Pradesh\tKHAJURAHO\tMAHESH CHANDRA\t3012\tno\tRSMD\tRashtriya Samanta Dal\nMadhya Pradesh\tKHAJURAHO\tSIDDHARTH SUKHLAL KUSHWAHA\t40069\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tKHAJURAHO\tRAM NATH LODHEE\t13826\tno\tIND\tIndependent\nMadhya Pradesh\tKHAJURAHO\tJAGDEESH KUMAR PATEL\t2549\tno\tAD\tApna Dal\nMadhya Pradesh\tKHAJURAHO\tRAM LAKHAN SINGH\t60368\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tKHAJURAHO\tIMRAN\t1484\tno\tPECP\tPeace Party\nMadhya Pradesh\tKHAJURAHO\tJAYANT PRATAP SINGH / VIJAY SINGH\t5906\tno\tIND\tIndependent\nMadhya Pradesh\tKHAJURAHO\tNone of the Above\t7878\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tSATNA\tGANESH SINGH\t375288\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tSATNA\tNone of the Above\t13036\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tSATNA\tSHYAM LAL KUSHWAHA\t2155\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tSATNA\tAJAY PRATAP SINGH\t2021\tno\tIND\tIndependent\nMadhya Pradesh\tSATNA\tSHRI RAM SHUKLA RAJJAN\t7986\tno\tIND\tIndependent\nMadhya Pradesh\tSATNA\tMAHGOO SINGH MUTHEL\t950\tno\tDaVP\tDalit Vikas Party(Bharat)\nMadhya Pradesh\tSATNA\tRAJ KUMAR GOTAM\t1202\tno\tBSCP\tBhartiya Shakti Chetna Party\nMadhya Pradesh\tSATNA\tVINOD KUMAR RAVI\t924\tno\tBSDS\tBhartiya Shramik Dal Socialist\nMadhya Pradesh\tSATNA\tDHARMENDRA SINGH TIWARI\t124602\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tSATNA\tDADHIBAL PRASAD PATEL\t1503\tno\tAD\tApna Dal\nMadhya Pradesh\tSATNA\tAJAY  SINGH RAHUL BHAIYYA\t366600\tno\tINC\tIndian National Congress\nMadhya Pradesh\tSATNA\tASHOK KUMAR SAKET\t1694\tno\tIND\tIndependent\nMadhya Pradesh\tSATNA\tKESHAV PRASAD TRIPATHI AD. K.P.\t2253\tno\tIND\tIndependent\nMadhya Pradesh\tSATNA\tBIRENDRA BHAI PATEL\t5305\tno\tIND\tIndependent\nMadhya Pradesh\tSATNA\tJAGDISH SINGH\t7925\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tREWA\tJANARDAN MISHRA\t383320\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tREWA\tPURNANAND TIWARI\t5835\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tREWA\tPRABHUNATH PRASAD SAKET\t4389\tno\tIND\tIndependent\nMadhya Pradesh\tREWA\tDEORAJ SINGH PATEL\t175567\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tREWA\tSUNDERLAL TIWARI\t214594\tno\tINC\tIndian National Congress\nMadhya Pradesh\tREWA\tPRAMOD TIWARI\t4794\tno\tBSCP\tBhartiya Shakti Chetna Party\nMadhya Pradesh\tREWA\tR.D. VISHWAKARMA\t3164\tno\tAD\tApna Dal\nMadhya Pradesh\tREWA\tBRAMHADATTA MISHRA URF CHHOTE\t4718\tno\tIND\tIndependent\nMadhya Pradesh\tREWA\tJANARDAN MISHRA\t3608\tno\tIND\tIndependent\nMadhya Pradesh\tREWA\tABDUL WAFATI ANSARI URF YUVRAJ TAILORS\t7527\tno\tSAP\tSamata Party\nMadhya Pradesh\tREWA\tVISHWA NATH DWIVEDI\t5057\tno\tIND\tIndependent\nMadhya Pradesh\tREWA\tGIRISH KUMAR GAUTAM\t3260\tno\tIND\tIndependent\nMadhya Pradesh\tREWA\tRAMKISHAN NIRAT (SAKET)\t2055\tno\tRPI(A)\tRepublican Party of India (A)\nMadhya Pradesh\tREWA\tMANGLESHWAR BHAIYA\t1456\tno\tNADP\tNaya Daur Party\nMadhya Pradesh\tREWA\tNone of the Above\t10658\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tSIDHI\tRITI PATHAK\t475678\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tSIDHI\tSMT.  ASHA SINGH\t10037\tno\tRsAD\tRashtriya Apna Dal\nMadhya Pradesh\tSIDHI\tMOHD. JAHANGEER ANSARI\t3763\tno\tIND\tIndependent\nMadhya Pradesh\tSIDHI\t\"SUNIL SONI \"\"KHADDI-42\"\"\"\t6627\tno\tBA S D\tBahujan Sangharshh Dal\nMadhya Pradesh\tSIDHI\tPUSPENDRA SINGH\t23298\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tSIDHI\tBHAGAWATI CHARAN PANDEY(NEELU)\t10725\tno\tIND\tIndependent\nMadhya Pradesh\tSIDHI\tLAL BAHADUR SINGH\t1979\tno\tSamSP\tSamata Samadhan Party\nMadhya Pradesh\tSIDHI\tURMILA\t4348\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nMadhya Pradesh\tSIDHI\tBRAHMANAND PRATAP SINGH\t5472\tno\tIND\tIndependent\nMadhya Pradesh\tSIDHI\tRAMAKANT PANDEY MALAIHNA\t7579\tno\tIND\tIndependent\nMadhya Pradesh\tSIDHI\tPANKAJ SINGH PATEL\t9691\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tSIDHI\tRAMA SHANKAR SHAHWAL\t39387\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tSIDHI\tSANAT KUMAR TRIPATHI\t5830\tno\tBSCP\tBhartiya Shakti Chetna Party\nMadhya Pradesh\tSIDHI\tINDRAJEET KUMAR\t367632\tno\tINC\tIndian National Congress\nMadhya Pradesh\tSIDHI\tNone of the Above\t17350\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tSHAHDOL\tDALPAT SINGH PARASTE\t525419\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tSHAHDOL\tNone of the Above\t21376\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tSHAHDOL\tPHOOL SINGH PARASTE\t19409\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tSHAHDOL\tER. VIJAY KUMAR KOL\t20312\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tSHAHDOL\tRAMRATAN SINGH PAWLEY\t20834\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tSHAHDOL\tPARMESHWAR SINGH\t27619\tno\tCPI\tCommunist Party of India\nMadhya Pradesh\tSHAHDOL\tMAHAVEER PARSHAD MANJHI\t7507\tno\tIND\tIndependent\nMadhya Pradesh\tSHAHDOL\tRAM GOPAL SINGH\t5320\tno\tBSCP\tBhartiya Shakti Chetna Party\nMadhya Pradesh\tSHAHDOL\tRAJES NANDINI SINGH\t284118\tno\tINC\tIndian National Congress\nMadhya Pradesh\tSHAHDOL\tSAHDEV SINGH PARASTE\t5089\tno\tNPEP\tNational Peoples Party\nMadhya Pradesh\tSHAHDOL\tJOGENDRA SINGH\t7151\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tSHAHDOL\tRAM SINGH\t3849\tno\tCSM\tChhattisgarh Swabhiman Manch\nMadhya Pradesh\tSHAHDOL\tYOGESH KUMAR SINGH AD.\t5580\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tSHAHDOL\tLALA KOL\t8288\tno\tIND\tIndependent\nMadhya Pradesh\tSHAHDOL\tRAJU BAIGA\t6647\tno\tIND\tIndependent\nMadhya Pradesh\tJABALPUR\tRAKESH SINGH\t564609\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tJABALPUR\t\"ER. PRAVIN GAJBHIYE \"\"DADA\"\"\"\t1412\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tJABALPUR\tDR. DHAI AKSHAR (RAKESH SONKAR)\t2007\tno\tIND\tIndependent\nMadhya Pradesh\tJABALPUR\tVINAY KUMAR JAIN\t6400\tno\tIND\tIndependent\nMadhya Pradesh\tJABALPUR\tLAL DASH\t2234\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tJABALPUR\tVIKRANT\t2797\tno\tIND\tIndependent\nMadhya Pradesh\tJABALPUR\tVIRENDRA KUMAR SHARMA\t7164\tno\tIND\tIndependent\nMadhya Pradesh\tJABALPUR\tMANAK LAL GONTIYA\t4261\tno\tAPoI\tAmbedkarite Party of India\nMadhya Pradesh\tJABALPUR\tDIGVIJAY SINGH (SENIOR JOURNLIST)\t1852\tno\tBSCP\tBhartiya Shakti Chetna Party\nMadhya Pradesh\tJABALPUR\tNAVEEN JAIN (YOGACHARYA)\t2990\tno\tIND\tIndependent\nMadhya Pradesh\tJABALPUR\tMALKHAN PATEL\t1562\tno\tIND\tIndependent\nMadhya Pradesh\tJABALPUR\tVIVEK KRISHNA TANKHA\t355970\tno\tINC\tIndian National Congress\nMadhya Pradesh\tJABALPUR\tAFTAB ALAM\t16008\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tJABALPUR\tCAPTAIN HANFEE, VEER CHAKRA\t15977\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tJABALPUR\tMOHD. ADIL\t9053\tno\tIUML\tIndian Union Muslim League\nMadhya Pradesh\tJABALPUR\tNone of the Above\t7888\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tMANDLA\tFAGGAN SINGH KULASTE\t585720\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tMANDLA\tKHUMAN SINGH ARMO\t11548\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tMANDLA\tANUJ GANGA SINGH PATTA\t56572\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tMANDLA\tSHAMBHU SINGH MARAVI\t21266\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tMANDLA\tOMKAR SINGH MARKAM\t475251\tno\tINC\tIndian National Congress\nMadhya Pradesh\tMANDLA\tBAISHAKHOO MARAVI\t4956\tno\tBHSSP\tBhartiya Satya Sangharsh Party\nMadhya Pradesh\tMANDLA\tINDER SINGH UIKEY\t11930\tno\tAPoI\tAmbedkarite Party of India\nMadhya Pradesh\tMANDLA\tPHOOL SINGH DHURWEY\t4980\tno\tCSM\tChhattisgarh Swabhiman Manch\nMadhya Pradesh\tMANDLA\tGHANSHYAM KUDAPE\t11265\tno\tIND\tIndependent\nMadhya Pradesh\tMANDLA\tMAHATALAL BARKADE\t6724\tno\tNPEP\tNational Peoples Party\nMadhya Pradesh\tMANDLA\tNone of the Above\t28306\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tBALAGHAT\tBODHSINGH BHAGAT\t480594\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tBALAGHAT\tNone of the Above\t6922\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tBALAGHAT\tGAUTAM RAMTEKE\t3250\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nMadhya Pradesh\tBALAGHAT\tDR. ASRA ANJUM FAROOQUI\t3243\tno\tRPI(A)\tRepublican Party of India (A)\nMadhya Pradesh\tBALAGHAT\tSHANKAR KANSRE LODHI (SAMAJ SEWAK)\t6374\tno\tIND\tIndependent\nMadhya Pradesh\tBALAGHAT\tASIF KHAN\t2652\tno\tMNDP\tMinorities Democratic Party\nMadhya Pradesh\tBALAGHAT\tLAXMINARAYAN DEHARIYA\t4427\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tBALAGHAT\tVIVEK MISHRA (LALU BHAIYA)\t4397\tno\tJMBP\tJai Maha Bharath Party\nMadhya Pradesh\tBALAGHAT\tSANDEEP SONI\t10444\tno\tIND\tIndependent\nMadhya Pradesh\tBALAGHAT\tNANDLAL UIKEY\t3008\tno\tCSM\tChhattisgarh Swabhiman Manch\nMadhya Pradesh\tBALAGHAT\tJABALPUR HIGH COURT ADVOCATE AND WRITER SUKHARAM ASTKAR LANJI\t11767\tno\tIND\tIndependent\nMadhya Pradesh\tBALAGHAT\tANUBHA MUNJARE\t99392\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tBALAGHAT\tHINA LIKHIRAM KAWRE\t384553\tno\tINC\tIndian National Congress\nMadhya Pradesh\tBALAGHAT\tASHOK KUMAR MASIH\t20297\tno\tCPI\tCommunist Party of India\nMadhya Pradesh\tBALAGHAT\tU. K. CHOUDHARY (MUNNA BHAIYA)\t9898\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tBALAGHAT\tYOGESH NANAJI SAMRITE\t46345\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tBALAGHAT\tSMT. HIRASAN UIKEY\t15801\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tCHHINDWARA\tKAMAL NATH\t559755\tyes\tINC\tIndian National Congress\nMadhya Pradesh\tCHHINDWARA\tDHANIRAM YADUVANSHI\t6601\tno\tIND\tIndependent\nMadhya Pradesh\tCHHINDWARA\tPARDESHI HARTAPSHAH TIRGAM\t25628\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tCHHINDWARA\tCHOUDHARY CHANDRABHAN KUBER SINGH\t443218\tno\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tCHHINDWARA\tER. KRIPA SHANKAR YADAV\t2140\tno\tIND\tIndependent\nMadhya Pradesh\tCHHINDWARA\tNITINKUMAR SAHU\t12241\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tCHHINDWARA\tSUBHASH SHUKLA\t2035\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tCHHINDWARA\tMAHESH MADHAVLAL DUBEY\t5696\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tCHHINDWARA\tARVIND DONGRE\t2484\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tCHHINDWARA\tSITARAM SAREYAM\t12762\tno\tIND\tIndependent\nMadhya Pradesh\tCHHINDWARA\tJALASRAM NAGWANSHI\t3117\tno\tIND\tIndependent\nMadhya Pradesh\tCHHINDWARA\tRAMESH PATIL\t2872\tno\tAPoI\tAmbedkarite Party of India\nMadhya Pradesh\tCHHINDWARA\tDWARKA PRASAD PAHADE\t3450\tno\tIND\tIndependent\nMadhya Pradesh\tCHHINDWARA\tNone of the Above\t25499\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tHOSHANGABAD\tUDAY PRATAP SINGH\t669128\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tHOSHANGABAD\tNone of the Above\t18741\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tHOSHANGABAD\tJAY PRAKASH PATEL\t5225\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tHOSHANGABAD\t\"DEVENDRA PATEL \"\"GUDDU BHAIYA\"\"\"\t279168\tno\tINC\tIndian National Congress\nMadhya Pradesh\tHOSHANGABAD\tUDAY PRATAP  SINGH\t6378\tno\tIND\tIndependent\nMadhya Pradesh\tHOSHANGABAD\tDevendra Patel urf Gudda Bhaiyya\t13412\tno\tIND\tIndependent\nMadhya Pradesh\tHOSHANGABAD\tMAYA VISHWAKARMA\t17837\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tHOSHANGABAD\tPRADEEP AHIRWAR\t14154\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tHOSHANGABAD\tMUKESH BABU AHIRWAR\t7132\tno\tIND\tIndependent\nMadhya Pradesh\tVIDISHA\tSUSHMA SWARAJ\t714348\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tVIDISHA\tENGINEEAR BHAGWAT SINGH RAJPUT\t5204\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tVIDISHA\tAMAR SINGH PATEL\t9699\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tVIDISHA\tNARMADA PRASHAD AHIRWAR\t1270\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tVIDISHA\tMANOJ KUMAR YADAV\t3293\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tVIDISHA\tSHOIAB KHAN\t2200\tno\tPRSP\tPrajatantrik Samadhan Party\nMadhya Pradesh\tVIDISHA\tLAKSHMAN SINGH\t303650\tno\tINC\tIndian National Congress\nMadhya Pradesh\tVIDISHA\tNone of the Above\t10618\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tVIDISHA\tBABULAL\t1088\tno\tSamSP\tSamata Samadhan Party\nMadhya Pradesh\tVIDISHA\tKAMLESH SALLAM\t10824\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tVIDISHA\tRAMDAYAL KORI\t4159\tno\tIND\tIndependent\nMadhya Pradesh\tVIDISHA\tCHANDRESH SINGH\t5359\tno\tIND\tIndependent\nMadhya Pradesh\tVIDISHA\tGANESH RAJAK\t1761\tno\tSAP\tSamata Party\nMadhya Pradesh\tBHOPAL\tALOK SANJAR\t714178\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tBHOPAL\tNone of the Above\t5181\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tBHOPAL\tGAUTAM SHINDE\t614\tno\tAD\tApna Dal\nMadhya Pradesh\tBHOPAL\tMEHBOOB KHAN\t569\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tMAHANT GOPAL DAS BABA SHAHAB\t570\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tRAJARAM MEHAR\t796\tno\tPSS\tProutist Sarva Samaj\nMadhya Pradesh\tBHOPAL\tMD. ATEEK\t625\tno\tPRSP\tPrajatantrik Samadhan Party\nMadhya Pradesh\tBHOPAL\tSATISH BABU NAIDU\t832\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tASHOK PAWAR\t551\tno\tSamSP\tSamata Samadhan Party\nMadhya Pradesh\tBHOPAL\tABDUL TAHIR (BABLU - ADVOCATE)\t1878\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tGUNDESH KHAMBRA\t681\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tRAJESH KEER\t1348\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tBHOPAL\tBRAJENDRA GAPPU CHATURVEDI\t684\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tCOLONEL KESARI SINGH\t1840\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tPEEYUSH SONI\t421\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tDANIEL JOHN\t465\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tBALBHADRA MISHRA\t523\tno\tbjyp\tBhartiya Jan Yug Party\nMadhya Pradesh\tBHOPAL\tBADSHAH KHAN\t622\tno\tNADP\tNaya Daur Party\nMadhya Pradesh\tBHOPAL\tM C MITTAL JAI GURUDEO TATDHARI\t620\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tPRABHAWATI SHAKYA KHOT\t425\tno\tIND\tIndependent\nMadhya Pradesh\tBHOPAL\tMASTER SAJID SIDDIQUI\t5557\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nMadhya Pradesh\tBHOPAL\tSHAILENDRA KUMAR SHAILI\t7643\tno\tCPI\tCommunist Party of India\nMadhya Pradesh\tBHOPAL\tJ C BARAI\t628\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nMadhya Pradesh\tBHOPAL\tRACHNA DHINGRA\t21298\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tBHOPAL\tP C SHARMA (PRAKASH MANGILAL SHARMA )\t343482\tno\tINC\tIndian National Congress\nMadhya Pradesh\tBHOPAL\tVIJAY KUMAR\t1494\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nMadhya Pradesh\tBHOPAL\tSHEEBA MALIK\t5742\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tBHOPAL\tSUNIL BORSE\t10152\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tBHOPAL\tMUKESH KUMAR BANSAL\t763\tno\tSaVP\tSamta Vikas Party\nMadhya Pradesh\tRAJGARH\tRODMAL NAGAR\t596727\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tRAJGARH\tDR.SHIVNARAYAN VERMA\t13864\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tRAJGARH\tROD JI\t8818\tno\tIND\tIndependent\nMadhya Pradesh\tRAJGARH\tIMAMUDDIN KHAN\t4334\tno\tIND\tIndependent\nMadhya Pradesh\tRAJGARH\tDR.PRASHANT TRIPATHI\t8699\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tRAJGARH\tAMLABE NARAYAN SINGH\t367990\tno\tINC\tIndian National Congress\nMadhya Pradesh\tRAJGARH\tNone of the Above\t10292\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tDEWAS\tMANOHAR UNTWAL\t665646\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tDEWAS\tSAJJAN SINGH VERMA\t405333\tno\tINC\tIndian National Congress\nMadhya Pradesh\tDEWAS\tHEMRAJ SINGH BAMNIYA\t4657\tno\tBA S D\tBahujan Sangharshh Dal\nMadhya Pradesh\tDEWAS\tSUJEET SANGTE\t4216\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tDEWAS\tSOUDAN SINGH DALMIYA\t1509\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tDEWAS\tKAMAL SINGH CHOUHAN\t16376\tno\tPRSP\tPrajatantrik Samadhan Party\nMadhya Pradesh\tDEWAS\tRADHESHYAM MALVIYA\t4862\tno\tIND\tIndependent\nMadhya Pradesh\tDEWAS\tGOKUL PRASAD DONGARE\t17238\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tDEWAS\tNone of the Above\t10253\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tDEWAS\tSOBHAL SINGH SISODIYA\t1002\tno\tSamSP\tSamata Samadhan Party\nMadhya Pradesh\tDEWAS\tSAJAN SINGH\t10113\tno\tIND\tIndependent\nMadhya Pradesh\tDEWAS\tMANOHAR AHIRIYA\t2763\tno\tIND\tIndependent\nMadhya Pradesh\tUJJAIN\tPROF. CHINTAMANI MALVIYA\t641101\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tUJJAIN\tRADHESHAYAM PANAVAR\t1257\tno\tAIFB\tAll India Forward Bloc\nMadhya Pradesh\tUJJAIN\tSURENDRA MEHAR\t3636\tno\tIND\tIndependent\nMadhya Pradesh\tUJJAIN\tNone of the Above\t12287\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tUJJAIN\tER. BRAJMOHAN MALVIYA\t848\tno\tPRSP\tPrajatantrik Samadhan Party\nMadhya Pradesh\tUJJAIN\tNANDLAL CHAUHAN\t927\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tUJJAIN\tANEETA HINDOLIYA\t5917\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tUJJAIN\tRAMPRASAD JATWA\t9969\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tUJJAIN\tLALCHAND GOMAY BERWA\t2015\tno\tIND\tIndependent\nMadhya Pradesh\tUJJAIN\tASHOK\t1669\tno\tIND\tIndependent\nMadhya Pradesh\tUJJAIN\tBHAWARLAL TUFAN\t902\tno\tJMBP\tJai Maha Bharath Party\nMadhya Pradesh\tUJJAIN\tPREMCHAND GUDDU\t331438\tno\tINC\tIndian National Congress\nMadhya Pradesh\tUJJAIN\tANIL PALE\t4439\tno\tBA S D\tBahujan Sangharshh Dal\nMadhya Pradesh\tMANDSOUR\tSUDHIR GUPTA\t698335\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tMANDSOUR\tMEENAKSHI NATARAJAN\t394686\tno\tINC\tIndian National Congress\nMadhya Pradesh\tMANDSOUR\tBANSHI LAL PATIDAR\t3501\tno\tBA S D\tBahujan Sangharshh Dal\nMadhya Pradesh\tMANDSOUR\tBANO BI\t3441\tno\tBMSM\tBharatiya Minorities Suraksha Mahasangh\nMadhya Pradesh\tMANDSOUR\tPARAS SAKLECHA DADA\t10183\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tMANDSOUR\tRAJU MALVIYA\t1513\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tMANDSOUR\tKRANTIPARIVARTAN\t8104\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tMANDSOUR\tMINAKSHI CHOUHAN\t3151\tno\tIND\tIndependent\nMadhya Pradesh\tMANDSOUR\tPROF. PURUSHOTTAM MEGHWAL\t2626\tno\tIND\tIndependent\nMadhya Pradesh\tMANDSOUR\tSUDHIR GAUTAM\t7615\tno\tIND\tIndependent\nMadhya Pradesh\tMANDSOUR\tSHAIKH AZIZUDDIN QURAISHI\t7992\tno\tAIFB\tAll India Forward Bloc\nMadhya Pradesh\tMANDSOUR\tMINAKSHI PANDEY\t8333\tno\tIND\tIndependent\nMadhya Pradesh\tMANDSOUR\tISMAILE MEW\t1616\tno\tIND\tIndependent\nMadhya Pradesh\tMANDSOUR\tNILESH TIWARI\t1685\tno\tIND\tIndependent\nMadhya Pradesh\tMANDSOUR\tNone of the Above\t8568\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tRATLAM\tDILEEPSINGH BHURIA\t545970\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tRATLAM\tNone of the Above\t30364\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tRATLAM\tILIYAS BHAI MACHAR\t3581\tno\tMNDP\tMinorities Democratic Party\nMadhya Pradesh\tRATLAM\tBHADIYA DAWAR\t6638\tno\tIND\tIndependent\nMadhya Pradesh\tRATLAM\tMEENA ATUL DAVID\t10980\tno\tIND\tIndependent\nMadhya Pradesh\tRATLAM\tKANTILAL BHURIA\t437523\tno\tINC\tIndian National Congress\nMadhya Pradesh\tRATLAM\tBABUSINGH\t18485\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tRATLAM\tBHERUSINGH DAMOR\t13116\tno\tJD(U)\tJanata Dal  (United)\nMadhya Pradesh\tRATLAM\tCOMRADE LATA BHABOR\t6078\tno\tCPM\tCommunist Party of India  (Marxist)\nMadhya Pradesh\tRATLAM\tENARI MACHAR\t6085\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tRATLAM\tKAILASH VASUNIYA\t3861\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tDHAR\tSAVITRI THAKUR\t558387\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tDHAR\tDR. HEMLATA JAMUNA DEVI\t10683\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tDHAR\tUMANG SINGHAR\t454059\tno\tINC\tIndian National Congress\nMadhya Pradesh\tDHAR\tRAMA SINGH PATEL\t8303\tno\tIND\tIndependent\nMadhya Pradesh\tDHAR\tAJAY RAWAT\t14666\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tDHAR\tBHERULAL IMLIYAR\t3800\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tDHAR\tKARAN BURMAN GAREEB\t11481\tno\tSamSP\tSamata Samadhan Party\nMadhya Pradesh\tDHAR\tNone of the Above\t15437\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tINDORE\tSUMITRA MAHAJAN (TAI)\t854972\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tINDORE\tJAMEELA BI\t981\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tMOMD.TANVEER\t1576\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tNone of the Above\t5944\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tINDORE\tDHARAMDAS AHIRWAR\t7422\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tINDORE\tPARMANAND TOLANI\t1768\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tSANTOSH KUMAR DEEN BANDHU\t517\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tRAMCHANDR PARMAR\t477\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tINDORE\tANIL TRIVEDI\t35169\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tINDORE\tSYED NIZAM AHMED\t740\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tMOHAMMAD IMTIYAZ KHAN\t3862\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tSATYANARAYAN PATEL\t388071\tno\tINC\tIndian National Congress\nMadhya Pradesh\tINDORE\tMAMTA BHATI\t579\tno\tRAHM\tRashtriya Ahinsa Manch\nMadhya Pradesh\tINDORE\tHARISH SAHU\t400\tno\tSamSP\tSamata Samadhan Party\nMadhya Pradesh\tINDORE\tPRAVEEN KUMAR AJMERA JAIN\t1867\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tSHAKTI SUDHA SHARMA\t740\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tDR. NEHA SHARMA\t1619\tno\tSP\tSamajwadi Party\nMadhya Pradesh\tINDORE\tGHANSHYAM CHANDEL\t598\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tRAVI SIRWAIYA\t1356\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tP.T. JAGDISH CHANDRA BHATT\t1352\tno\tJMBP\tJai Maha Bharath Party\nMadhya Pradesh\tINDORE\tRAMCHANDRA VASAINI (RAMA SETH)\t3725\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tRAKESH KUMAR PATEL\t2528\tno\tIND\tIndependent\nMadhya Pradesh\tINDORE\tAJEET KUMAR JAIN PATWA (DHARTI PAKAD)\t554\tno\tIND\tIndependent\nMadhya Pradesh\tKHARGONE\tSUBHASH PATEL\t649354\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tKHARGONE\tRAMESH PATEL\t391475\tno\tINC\tIndian National Congress\nMadhya Pradesh\tKHARGONE\tNAHARSINGH RICHHUJI BARDE\t8266\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tKHARGONE\tKAILASH AWASYA\t31222\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tKHARGONE\tNone of the Above\t22141\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tKHARGONE\tGANGARAM PRATAP GAWLE\t8544\tno\tAMS\tAadijan Mukti Sena\nMadhya Pradesh\tKHARGONE\tKAILASH PATEL\t7040\tno\tIND\tIndependent\nMadhya Pradesh\tKHARGONE\tCOMRADE JYOTI GORE\t25930\tno\tCPI\tCommunist Party of India\nMadhya Pradesh\tKHARGONE\tGAFFAR NOOR MO.\t8553\tno\tIND\tIndependent\nMadhya Pradesh\tKHANDWA\tNANDKUMAR SINGH CHOUHAN (NANDU BHAIYA)\t717357\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tKHANDWA\tNANDLAL SINGH CHOUHAN (NANDU BHAIYA)\t5307\tno\tIND\tIndependent\nMadhya Pradesh\tKHANDWA\tNANDLAL SINGH CHOUHAN\t6226\tno\tIND\tIndependent\nMadhya Pradesh\tKHANDWA\tSHARIF RAJGEER\t2255\tno\tABVCP\tAkhil Bhartiya Vikas Congress Party\nMadhya Pradesh\tKHANDWA\tZIAUDDIN INAMDAR\t1075\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tKHANDWA\tBABA ABDUL HAMID\t12036\tno\tIND\tIndependent\nMadhya Pradesh\tKHANDWA\tNone of the Above\t17149\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tKHANDWA\tKASHINATH UPADHYAY\t2429\tno\tIND\tIndependent\nMadhya Pradesh\tKHANDWA\tALOK AGARWAL\t16799\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tKHANDWA\tSANJAY S/O LAXMAN SOLANKI\t13691\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tKHANDWA\tISHAMUDDIN (LEDAR) SAYYADH\t1134\tno\tSAP\tSamata Party\nMadhya Pradesh\tKHANDWA\tBHAIYALAL UIKEY\t1208\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tKHANDWA\tVINOD KUMAR BHAGWAN BIRADE\t1783\tno\tRPI(A)\tRepublican Party of India (A)\nMadhya Pradesh\tKHANDWA\tDR. RAMESHSINGH - SOLANKI\t1235\tno\tPRSP\tPrajatantrik Samadhan Party\nMadhya Pradesh\tKHANDWA\tARUN SUBHASH CHANDRA YADAV\t457643\tno\tINC\tIndian National Congress\nMadhya Pradesh\tBETUL\tJYOTI DHURVE\t643651\tyes\tBJP\tBharatiya Janata Party\nMadhya Pradesh\tBETUL\tKALLUSHING KUMRE\t4546\tno\tGGP\tGondvana Gantantra Party\nMadhya Pradesh\tBETUL\tFAGRAM\t4668\tno\tSWJP\tSamajwadi Jan Parishad\nMadhya Pradesh\tBETUL\tRAJESH SARIYAM\t20627\tno\tAAAP\tAam Aadmi Party\nMadhya Pradesh\tBETUL\tNone of the Above\t26726\tno\tNOTA\tNone of the Above\nMadhya Pradesh\tBETUL\tSOHAN LAL UIKEY\t16461\tno\tBSP\tBahujan Samaj Party\nMadhya Pradesh\tBETUL\tBHUPENDRA SINGH UIKEY\t12933\tno\tIND\tIndependent\nMadhya Pradesh\tBETUL\tGABBAR SINH WARKADE\t3070\tno\tBMUP\tBahujan Mukti Party\nMadhya Pradesh\tBETUL\t\"AJAY SHAH \"\"MAKRAI\"\"\"\t315037\tno\tINC\tIndian National Congress\nMaharashtra\tNandurbar\tDR.GAVIT HEENA VAIJAYKUMAR\t579486\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tNandurbar\tGAVIT MANIKRAO HODLYA\t472581\tno\tINC\tIndian National Congress\nMaharashtra\tNandurbar\tVASAVE AMIT SHEKLAL\t12133\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tNandurbar\tARJUNSING DIVANSING VASAVE\t4712\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tNandurbar\tRANJIT JUGLA PADVI\t3498\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tNandurbar\tVIRENDRA RAVAJI VALVI\t3511\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tNandurbar\tMAHESH JAYSING PAWARA\t4389\tno\tIND\tIndependent\nMaharashtra\tNandurbar\tADV.SOBJI DEVALYA GAVIT\t9184\tno\tIND\tIndependent\nMaharashtra\tNandurbar\tC.S.VALVI\t6004\tno\tIND\tIndependent\nMaharashtra\tNandurbar\tNone of the Above\t21178\tno\tNOTA\tNone of the Above\nMaharashtra\tDhule\tDR. BHAMRE SUBHASH RAMRAO\t529450\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tDhule\tNone of the Above\t2503\tno\tNOTA\tNone of the Above\nMaharashtra\tDhule\tNIHAL AH.AB.RAHEMAN DANEWALA\t1148\tno\tSP\tSamajwadi Party\nMaharashtra\tDhule\tMO. ISMAIL JUMMAN\t740\tno\tIND\tIndependent\nMaharashtra\tDhule\tABDUL HAMID SHAIKH HABIB\t914\tno\tIND\tIndependent\nMaharashtra\tDhule\tSHAIKH MUKHATAR AH. MO. QASIM\t765\tno\tWPOI\tWelfare Party Of India\nMaharashtra\tDhule\tJAGDANE RAMESH SOMA\t828\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tDhule\tMAHEMOOD BHAIJAAN\t792\tno\tAwVP\tAwami Vikas Party\nMaharashtra\tDhule\tSAMBHAJI DEVIDAS PATIL\t1149\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tDhule\tSAYYAD MOHAMMAD QAYAM\t1600\tno\tIND\tIndependent\nMaharashtra\tDhule\tAMRISHBHAI RASIKLAL PATEL\t398727\tno\tINC\tIndian National Congress\nMaharashtra\tDhule\tRAMESH RAGHO MORE\t8057\tno\tIND\tIndependent\nMaharashtra\tDhule\tJAFAR BAPUJI PATHAN\t1355\tno\tIND\tIndependent\nMaharashtra\tDhule\tPINJARI JAYNUDDIN HUSEN\t1545\tno\tIND\tIndependent\nMaharashtra\tDhule\tISHI YOGESH YASHWANT\t9897\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tDhule\tDR BHUPESH PRAKASH PATIL\t6117\tno\tIND\tIndependent\nMaharashtra\tDhule\tANSARI NIHAL AH. MOHD. HAROON\t9339\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tDhule\tDEORE MAHENDRA TRYAMBAK\t2091\tno\tIND\tIndependent\nMaharashtra\tDhule\tSAYYAD SALIM SAYYAD ALIM\t5278\tno\tIND\tIndependent\nMaharashtra\tDhule\tGAZI ATEZAD MUBEEN KHAN\t788\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tA.T. NANA PATIL\t647773\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tJalgaon\tDR.MUBIN A. KHALIL A.\t1676\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tPATIL SANDIP YUVRAJ\t2879\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tDR. SANGRAM GOKULSINGH  PATIL\t7390\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tJalgaon\tMORE ISHWAR DAYARAM (EX-SERVICEMAN)\t2718\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tJalgaon\tBAGUL  V. T.(JIBHAU)\t10838\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tJalgaon\tADV. ASHOK DAGADU SHINDE\t1708\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tJalgaon\tLALIT GAURISHANKAR SHARMA\t8140\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tNANASO M.P. PAGARE\t1917\tno\tRVNP\tRashtravadi Janata Party\nMaharashtra\tJalgaon\tANNASAHEB DR.SATISH BHASKARRAO PATIL\t264248\tno\tNCP\tNationalist Congress Party\nMaharashtra\tJalgaon\tMAMASHRI SURESH PATIL (PATRAKAR)\t958\tno\tHJP\tHindustan Janta Party\nMaharashtra\tJalgaon\tRAGIB AHMED ABDUL NABI BAHADUR\t2675\tno\tSP\tSamajwadi Party\nMaharashtra\tJalgaon\tABRAR BEG SARVAR BEG MIRZA\t3212\tno\tRPI\tRepublican Party of India\nMaharashtra\tJalgaon\tASHA SAHEBRAO PATIL\t1579\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tJalgaon\tASHOK TARACHAND PATIL\t5801\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tIQBAL RAHEMAN DESHMUKH\t2246\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tSATISH BHASKAR PATIL (NANDGAON BK)\t6157\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tSATISH BHASKAR PATIL (VARKHEDI)\t1537\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tSATISH BHASKAR PATIL (MUDI PRA DANGRI)\t3998\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tVIJAY BHIMRAO NIKAM\t9614\tno\tIND\tIndependent\nMaharashtra\tJalgaon\tNone of the Above\t3268\tno\tNOTA\tNone of the Above\nMaharashtra\tRaver\tKHADASE RAKSHA NIKHIL\t605452\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tRaver\tNone of the Above\t4212\tno\tNOTA\tNone of the Above\nMaharashtra\tRaver\tZAGADYA PURMYA PAWARA\t3707\tno\tIND\tIndependent\nMaharashtra\tRaver\tMANISHDADA SATISH JAIN\t14599\tno\tIND\tIndependent\nMaharashtra\tRaver\tSURESH HIRAMAN INGALE\t1258\tno\tIND\tIndependent\nMaharashtra\tRaver\tDNYANESHWAR AMALE-PATIL\t1220\tno\tAIFB\tAll India Forward Bloc\nMaharashtra\tRaver\tKHATIK MOHAMMAD HAROON\t4376\tno\tSP\tSamajwadi Party\nMaharashtra\tRaver\tSONAWANE VANDANA SHIVAJI\t978\tno\tIND\tIndependent\nMaharashtra\tRaver\tSHAIKH SHAHERA BI. JAINODDIN SHAIKH\t1375\tno\tIND\tIndependent\nMaharashtra\tRaver\tD.D.WANI (PHOTOGRAPHER) (DNYANESHWAR DIVAKAR WANI)\t3349\tno\tIND\tIndependent\nMaharashtra\tRaver\tASHOK TRYAMBAK INGALE\t1131\tno\tIND\tIndependent\nMaharashtra\tRaver\tJ.G.SURWADE\t1634\tno\tIND\tIndependent\nMaharashtra\tRaver\tWAMANRAO MADHAV SASANE\t1757\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tRaver\tMANYAR SHE. SAJID SHE. KADAR\t1606\tno\tMNDP\tMinorities Democratic Party\nMaharashtra\tRaver\tSHAIKH SHAKIR\t4009\tno\tIND\tIndependent\nMaharashtra\tRaver\tMANISHDADA JAIN\t287384\tno\tNCP\tNationalist Congress Party\nMaharashtra\tRaver\tGOVINDA NARAYAN GUNJAL\t2844\tno\tIND\tIndependent\nMaharashtra\tRaver\tDR. ULHAS VASUDEO PATIL\t21332\tno\tIND\tIndependent\nMaharashtra\tRaver\tRAJEEV SATYENDRA SHARMA\t3756\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tRaver\tMOHAN SHAMRAO CHAVHAN (MANOJSIR VARDIKAR)\t8797\tno\tIND\tIndependent\nMaharashtra\tRaver\tBHANDE DASHRATH MOTIRAM\t29752\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tRaver\tKADRI SANIYA SAIYED ALI\t2658\tno\tIND\tIndependent\nMaharashtra\tRaver\tGAIKWAD BHAGWAN MADHAV\t1206\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tRaver\tSHAIKH MO. ASGAR SAKHAWAT\t821\tno\tRPI\tRepublican Party of India\nMaharashtra\tBuldhana\tJADHAV PRATAPRAO GANPATRAO\t509145\tyes\tSHS\tShivsena\nMaharashtra\tBuldhana\tNone of the Above\t10546\tno\tNOTA\tNone of the Above\nMaharashtra\tBuldhana\tABDUL  HAFEEZ ABDUL AZIZ\t33783\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tBuldhana\tINGLE KRUSHANARAO GANPATRAO\t349566\tno\tNCP\tNationalist Congress Party\nMaharashtra\tBuldhana\tPARMESHWAR HIRAMAN GAWAI\t6983\tno\tARP\tAmbedkarist Republican Party\nMaharashtra\tBuldhana\tNAMDEO PUNDLIK DONGARDIVE\t6959\tno\tIND\tIndependent\nMaharashtra\tBuldhana\tBALASAHEB SHANKAR DARADE\t29793\tno\tIND\tIndependent\nMaharashtra\tBuldhana\tSANJAY MAHADEV WANKHADE\t4520\tno\tIND\tIndependent\nMaharashtra\tBuldhana\tSAMBARE DINKAR TUKARAM\t2006\tno\tIND\tIndependent\nMaharashtra\tBuldhana\tSANDESH ASHOK AMBEDKAR\t7927\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tBuldhana\tCOMMANDER (RTD) ASHOK  RAUT\t2077\tno\tIND\tIndependent\nMaharashtra\tBuldhana\tKRUSHNARAO INGLE\t1787\tno\tIND\tIndependent\nMaharashtra\tBuldhana\tCHINCHOLE GANGARAM KALUJI\t1917\tno\tIND\tIndependent\nMaharashtra\tBuldhana\tSMT. PRABHATAI MADHUKAR PARLEWAR\t1725\tno\tNRSN\tNarayani Sena\nMaharashtra\tBuldhana\tADV. RAVINDRA DNYANDEO BHOJANE\t2448\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tBuldhana\tRAVINDRASING RAMSING PAWAR\t1265\tno\tLB\tLok Bharati\nMaharashtra\tBuldhana\tVASANTRAO SUKHDEV DANDAGE\t2750\tno\tSP\tSamajwadi Party\nMaharashtra\tBuldhana\tSUDHIR BABAN SURVE\t3429\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tAkola\tDHOTRE SANJAY SHAMRAO\t456472\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tAkola\tNone of the Above\t6206\tno\tNOTA\tNone of the Above\nMaharashtra\tAkola\tAJAY PANJABRAO HINGANKAR\t8076\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tAkola\tPATEL HIDAYAT ULLA BARKAT ULLA\t253356\tno\tINC\tIndian National Congress\nMaharashtra\tAkola\tBHAI B.C. KAMBLE\t7858\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tAkola\tAMBEDKAR PRAKASH YASHWANT\t238776\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tAkola\tSANDIP KISANRAO WANKHEDE\t3756\tno\tIND\tIndependent\nMaharashtra\tAkola\tSHAIKH HAMEED IMAM\t3991\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tAmravati\tADSUL ANANDRAO VITHOBA\t467212\tyes\tSHS\tShivsena\nMaharashtra\tAmravati\tGUNWANT SUDAMRAO DEVPARE\t98200\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tAmravati\tJYOTI GAJANAN MAKODE\t3724\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tAmravati\tBHAVNA BHAVESH WASNIK\t6424\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tAmravati\tRAMTEKE RAMESH DEVIDASRAO\t1442\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nMaharashtra\tAmravati\tDR.RAJENDRA RAMKRISHNA GAWAI\t54278\tno\tRPI\tRepublican Party of India\nMaharashtra\tAmravati\tDONGRE PRIYA ANIL\t1734\tno\tIND\tIndependent\nMaharashtra\tAmravati\tADV. BANDYA SANE ALIAS BANDU SAMPATRAO SANE\t3732\tno\tIND\tIndependent\nMaharashtra\tAmravati\tKIRANTAI DADARAO ALIAS KOKUBHAU KOKATE\t970\tno\tIND\tIndependent\nMaharashtra\tAmravati\tJYOTI SANJAY DEWKAR\t1312\tno\tIND\tIndependent\nMaharashtra\tAmravati\tMANOHAR BALIRAM SONONE\t2489\tno\tIND\tIndependent\nMaharashtra\tAmravati\tSUNIL DAVID AMRAVATI\t1463\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tAmravati\tNAVNEET RAVI RANA\t329280\tno\tNCP\tNationalist Congress Party\nMaharashtra\tAmravati\tATHAWALEY SANJAY HIRAMANJI\t1649\tno\tIND\tIndependent\nMaharashtra\tAmravati\tASHATAI ABHYANKAR AMRAVATI\t809\tno\tIND\tIndependent\nMaharashtra\tAmravati\tVISHWANATH GOTUJI JAMNEKAR\t2011\tno\tIND\tIndependent\nMaharashtra\tAmravati\tSIRSAT HARIDAS ANKUSH\t5268\tno\tIND\tIndependent\nMaharashtra\tAmravati\tRAJU MAHADEORAO SONONE\t10052\tno\tIND\tIndependent\nMaharashtra\tAmravati\tRAJU S. MANKAR\t7873\tno\tIND\tIndependent\nMaharashtra\tAmravati\tNone of the Above\t4139\tno\tNOTA\tNone of the Above\nMaharashtra\tWardha\tRAMDAS CHANDRABHANJI TADAS\t537518\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tWardha\tDESHMUKH JAIDEEP RAJENDRA\t1431\tno\tIND\tIndependent\nMaharashtra\tWardha\tSHAMBHARKAR RAJENDRA GULABRAO\t4436\tno\tIND\tIndependent\nMaharashtra\tWardha\tMANISH VINAYAKRAO TELRANDHE\t5127\tno\tIND\tIndependent\nMaharashtra\tWardha\tDR. ANKUSH VIJAYRAO NAWALE\t1858\tno\tSP\tSamajwadi Party\nMaharashtra\tWardha\tTADAS VILAS KRUSHNAJI\t2124\tno\tIND\tIndependent\nMaharashtra\tWardha\tNARENDRA CHINDUJI MESHRAM\t2627\tno\tGGP\tGondvana Gantantra Party\nMaharashtra\tWardha\tARVIND SHAMRAO LILLORE\t614\tno\tIND\tIndependent\nMaharashtra\tWardha\tNone of the Above\t3343\tno\tNOTA\tNone of the Above\nMaharashtra\tWardha\tBHASKAR MAROTRAO NEWARE\t3156\tno\tIND\tIndependent\nMaharashtra\tWardha\tCHETAN BHIMRAJ PENDAM\t90866\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tWardha\tJAGDISH BHAGWAN KADU\t853\tno\tIND\tIndependent\nMaharashtra\tWardha\tDEEPAKSINGH TRYAMBAKRAO DESHMUKH\t1122\tno\tIND\tIndependent\nMaharashtra\tWardha\tUBALE SHRIKRUSHNA CHAMPATRAO\t6405\tno\tARP\tAmbedkarist Republican Party\nMaharashtra\tWardha\tRAUT JAGANATH NILKANTH\t2638\tno\tIND\tIndependent\nMaharashtra\tWardha\tKISHOR RAMBHAU KINKAR\t2518\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tWardha\tRAVI ALIAS SHAILESH GANESHRAO YELNE\t3881\tno\tIND\tIndependent\nMaharashtra\tWardha\tSANJAYBHAU NARAYANRAO CHIDAM\t770\tno\tIND\tIndependent\nMaharashtra\tWardha\tMEGHE SAGAR DATTATRAYA\t321735\tno\tINC\tIndian National Congress\nMaharashtra\tWardha\tMOHD. ALIM PATEL MOHD. WAHID\t15738\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tWardha\tSHAMBABA ALIAS SHAMSUNDAR PANJABRAO NICHIT\t3440\tno\tIND\tIndependent\nMaharashtra\tWardha\tSACHIN PANDURANGJI RAUT ALIAS GURUBHAU\t1245\tno\tIND\tIndependent\nMaharashtra\tRamtek\tKRUPAL BALAJI TUMANE\t519892\tyes\tSHS\tShivsena\nMaharashtra\tRamtek\tRAHULKANT ALIAS RAMESH SINHA\t1542\tno\tIND\tIndependent\nMaharashtra\tRamtek\tNone of the Above\t4816\tno\tNOTA\tNone of the Above\nMaharashtra\tRamtek\tRAHUL SHESHRAOJI MESHRAM\t1189\tno\tIND\tIndependent\nMaharashtra\tRamtek\tKIRAN PREM KUMAR RODGE (PATANKAR)\t95051\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tRamtek\tPANKAJ SEWAKRAM MASURKAR\t1453\tno\tHJP\tHindustan Janta Party\nMaharashtra\tRamtek\tKHOBRAGADE CHANDRABHAN JAIRAM\t2925\tno\tIND\tIndependent\nMaharashtra\tRamtek\tGOUTAM SHRIRAM WASNIK\t6353\tno\tIND\tIndependent\nMaharashtra\tRamtek\tTUMANE GOPAL AJABRAO\t6304\tno\tIND\tIndependent\nMaharashtra\tRamtek\tDONGRE ASHOK NAMAJI\t3999\tno\tIND\tIndependent\nMaharashtra\tRamtek\tGURUDAS UDDHAV BAWANE\t3419\tno\tIND\tIndependent\nMaharashtra\tRamtek\tNARESH MAHADEO PATIL\t2841\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tRamtek\tCOM. BANDU RAMCHANDRA MESHRAM\t1602\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nMaharashtra\tRamtek\tPROF. DR. NATTHU MADHAO LOKHANDE\t4756\tno\tIND\tIndependent\nMaharashtra\tRamtek\tNILESH MAHADEO DHOKE\t2537\tno\tIND\tIndependent\nMaharashtra\tRamtek\tLOKHANDE GANESH HAIBATRAO\t1735\tno\tIND\tIndependent\nMaharashtra\tRamtek\tGANESH BAPURAO PATIL\t6222\tno\tGGP\tGondvana Gantantra Party\nMaharashtra\tRamtek\tCHAWARE MAYA NAMDEO\t2682\tno\tSP\tSamajwadi Party\nMaharashtra\tRamtek\tMUKUL WASNIK\t344101\tno\tINC\tIndian National Congress\nMaharashtra\tRamtek\tPRATAP GOSWAMI\t25889\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tRamtek\tDHIMATI VIDYA KISHOR BHIMTE\t3634\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tRamtek\tDR. SANDEEP NATTHUJI NANDESHWAR\t2640\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tRamtek\tDR. SUNILBHAU VISHWANATHJI NARNAVRE\t2310\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nMaharashtra\tRamtek\tSANDESH BHIVRAM BHALEKAR\t2424\tno\tAIFB\tAll India Forward Bloc\nMaharashtra\tNagpur\tGADKARI NITIN JAIRAM\t587767\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tNagpur\tADV. SURESH  SHINDE\t677\tno\tIND\tIndependent\nMaharashtra\tNagpur\tDR.  MOHAN RAMRAO GAIKWAD\t96433\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tNagpur\tSUNIL DATTU PENDOR\t836\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tNagpur\tRAJESH SURESH SADHANKAR\t537\tno\tIND\tIndependent\nMaharashtra\tNagpur\tKANPHADE RAJENDRAKUMAR  EX. ADDITIONAL COLLECTOR\t2269\tno\tIND\tIndependent\nMaharashtra\tNagpur\tBABURAO DAMADUJI MESHRAM\t539\tno\tIND\tIndependent\nMaharashtra\tNagpur\tDILIP NARAYAN BHORKAR\t415\tno\tIND\tIndependent\nMaharashtra\tNagpur\tDR. DHARMENDRA  MANDLIK (PARATE)\t426\tno\tHJP\tHindustan Janta Party\nMaharashtra\tNagpur\tPHUSEY GHANSHYAM RAMAJI\t655\tno\tRPI\tRepublican Party of India\nMaharashtra\tNagpur\tDHIRAJ JAIDEV GAJBHIYE\t390\tno\tIND\tIndependent\nMaharashtra\tNagpur\tBASHIR KHAN\t466\tno\tMNDP\tMinorities Democratic Party\nMaharashtra\tNagpur\tBABULAL SUNDARLAL BANJARE\t419\tno\tIND\tIndependent\nMaharashtra\tNagpur\tAJAZ KHAN\t542\tno\tIND\tIndependent\nMaharashtra\tNagpur\tSHAHID SHARIF\t452\tno\tJD(U)\tJanata Dal  (United)\nMaharashtra\tNagpur\tPANKAJ PRAFULKUMAR BHANSALI\t439\tno\tIND\tIndependent\nMaharashtra\tNagpur\tKAVITA VINODKUMAR TIBDIWAL\t2408\tno\tIND\tIndependent\nMaharashtra\tNagpur\tDR. PRADEEP RAMBHAUJI  NAGRALE\t1366\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tNagpur\tCHANDA  SUDHIR MANWATKAR\t1880\tno\tIND\tIndependent\nMaharashtra\tNagpur\tRAVINDRA MANIK BORKAR\t760\tno\tIND\tIndependent\nMaharashtra\tNagpur\tMOHAN MAROTRAO KAREMORE\t665\tno\tIND\tIndependent\nMaharashtra\tNagpur\tGAUS MOHAMMED SHEIKH AYYUB SHEIKH\t784\tno\tIND\tIndependent\nMaharashtra\tNagpur\tSOMKUVAR VIJAY SITARAM\t1077\tno\tIND\tIndependent\nMaharashtra\tNagpur\tSHRIDHAR NARAYAN SALVE\t1187\tno\tIND\tIndependent\nMaharashtra\tNagpur\tSENAPATI UTTAM SAPAN\t2017\tno\tIND\tIndependent\nMaharashtra\tNagpur\tJIVAN RAMTEKE\t807\tno\tIND\tIndependent\nMaharashtra\tNagpur\tANJALI ANISH DAMANIA\t69081\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tNagpur\tNone of the Above\t3460\tno\tNOTA\tNone of the Above\nMaharashtra\tNagpur\tSHAKIL WASI AHAMAD\t542\tno\tIND\tIndependent\nMaharashtra\tNagpur\tANITA NAGDIGAMBAR TEKAM\t660\tno\tIND\tIndependent\nMaharashtra\tNagpur\tATEEQUE AHMAD MALIK\t373\tno\tIND\tIndependent\nMaharashtra\tNagpur\tCHIMOTE RAMMURTI KESHVRAO\t528\tno\tGGP\tGondvana Gantantra Party\nMaharashtra\tNagpur\tVILAS MUTTEMWAR\t302939\tno\tINC\tIndian National Congress\nMaharashtra\tNagpur\tANSARI JAVED AHEMAD\t1262\tno\tDESEP\tDemocratic Secular Party\nMaharashtra\tBhandara - gondiya\tNANABHAU FALGUNRAO PATOLE\t606129\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tBhandara - gondiya\tNone of the Above\t4032\tno\tNOTA\tNone of the Above\nMaharashtra\tBhandara - gondiya\tENG. SANJAY RAGHUNATH NASARE\t50958\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tBhandara - gondiya\tARUN NAGORAO GAJBHIYE\t1876\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tISHWAR LAHANU NANDAGAWALI\t5941\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tCHOLE OMPRAKASH BAKARAM\t4417\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tDHANANJAY SHAMLALJI RAJABHOJ\t6128\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tADV.DADASAHEB ALIAS DHANU BHIKAJI WALTHARE\t10133\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tJITENDRA AADKUJI RAUT\t1077\tno\tABMP\tAkhil Bharatiya Manavata Paksha\nMaharashtra\tBhandara - gondiya\tDR.KOHAPARE HARSHAWARDHAN MUNESHWAR\t1544\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tBhandara - gondiya\tPATEL PRAFUL MANOHARBHAI\t456875\tno\tNCP\tNationalist Congress Party\nMaharashtra\tBhandara - gondiya\tSAYYAD AFJALALI ALIAS CHHANUBHAI\t1578\tno\tMNDP\tMinorities Democratic Party\nMaharashtra\tBhandara - gondiya\tTHAKARE RAMESHWAR (RAMESHWAR) SUNDARLAL\t1260\tno\tSP\tSamajwadi Party\nMaharashtra\tBhandara - gondiya\tPRASHANT SHYAMSUNDAR MISHRA\t8647\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tBhandara - gondiya\tRAMAN MOTIRAM BANSOD\t1429\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tBhandara - gondiya\tRANJEET AMARDAS SUKHDEVE\t987\tno\tASP\tAmbedkar Samaj Party\nMaharashtra\tBhandara - gondiya\tDAHARWAL GANESHDEO RAGHUJI\t1542\tno\tGGP\tGondvana Gantantra Party\nMaharashtra\tBhandara - gondiya\tPATLE ANJALI AGASTIKUMAR\t1955\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tBhandara - gondiya\tBANTE JITENDRA RAMAJI\t1729\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tMIRZA VAHED BEG AHMADBEG\t899\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tMORESHWAR RAMAJI MESHRAM\t10127\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tMATE MANOHAR ALIAS MAMA\t1380\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tVARSHA GOVIND TIDKE\t594\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tWADHAVE DAMODHAR NATTHU\t577\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tSUSHMA VITTHAL NAGPURE\t1477\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tVISHAL ARVIND BHOYAR\t4169\tno\tIND\tIndependent\nMaharashtra\tBhandara - gondiya\tZANZAD SHALIKRAM SHANKARRAO\t9736\tno\tIND\tIndependent\nMaharashtra\tGadchiroli-Chimur\tASHOK MAHADEORAO NETE\t535982\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tGadchiroli-Chimur\tNone of the Above\t24488\tno\tNOTA\tNone of the Above\nMaharashtra\tGadchiroli-Chimur\tDR. NAMDEO DALLUJI USENDI\t299112\tno\tINC\tIndian National Congress\nMaharashtra\tGadchiroli-Chimur\tSATISH GOKULDAS PENDAM\t8156\tno\tAITC\tAll India Trinamool Congress\nMaharashtra\tGadchiroli-Chimur\tDR. GAJBE RAMESHKUMAR BABURAOJI\t45458\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tGadchiroli-Chimur\tRAMRAO GOVINDA NANNAWARE\t66906\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tGadchiroli-Chimur\tADV. PRABHAKAR MAHAGUJI DADMAL\t3422\tno\tRPI\tRepublican Party of India\nMaharashtra\tGadchiroli-Chimur\tDIWAKAR PENDAM (EX. SERVICEMAN)\t3730\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tGadchiroli-Chimur\tVINOD ANKUSH NANNAWARE\t4287\tno\tSP\tSamajwadi Party\nMaharashtra\tGadchiroli-Chimur\tDANDEKAR BABURAO LAXMAN\t6470\tno\tIND\tIndependent\nMaharashtra\tGadchiroli-Chimur\tNAMDEO ANANDRAO KANNAKE\t22512\tno\tCPI\tCommunist Party of India\nMaharashtra\tGadchiroli-Chimur\tDEORAO MONBA NANNAWARE\t6606\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tChandrapur\tAHIR HANSRAJ GANGARAM\t508049\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tChandrapur\tNone of the Above\t8257\tno\tNOTA\tNone of the Above\nMaharashtra\tChandrapur\tKUMBHARE HANSRAJ GULAB\t49229\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tChandrapur\tASHOK VITHOBA KHANDALE\t8302\tno\tRPI\tRepublican Party of India\nMaharashtra\tChandrapur\tDEOTALE SANJAY WAMANRAO\t271780\tno\tINC\tIndian National Congress\nMaharashtra\tChandrapur\tNITIN VASANT POHANE\t3772\tno\tLB\tLok Bharati\nMaharashtra\tChandrapur\tNANDKISHOR GANGARAM RANGARI\t4850\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tChandrapur\tPANKAJKUMAR  SHARMA\t3216\tno\tAITC\tAll India Trinamool Congress\nMaharashtra\tChandrapur\tFIRAJ  PATHAN\t4341\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tChandrapur\tSIDDHARTH  RAUT\t2188\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tChandrapur\tQURESHI M. EKHALAK M. YUSUF\t4176\tno\tSP(I)\tSocialist Party (India)\nMaharashtra\tChandrapur\tGHAYWAN ROSHAN JAMBUWANTRAO\t2736\tno\tRP(K)\tRepublican Paksha (Khoripa)\nMaharashtra\tChandrapur\tCHATAP WAMANRAO SADASHIV\t204413\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tChandrapur\tVINOD DINANATH MESHRAM\t8936\tno\tIND\tIndependent\nMaharashtra\tChandrapur\tSANJAY NILKANTH GAWANDE\t2167\tno\tIND\tIndependent\nMaharashtra\tChandrapur\tATUL ASHOK MUNGINWAR\t2693\tno\tIND\tIndependent\nMaharashtra\tChandrapur\tKARTIK GAJANAN KODAPE\t3879\tno\tIND\tIndependent\nMaharashtra\tChandrapur\tNAMDEO  SHEDMAKE\t5829\tno\tIND\tIndependent\nMaharashtra\tChandrapur\tPRAMOD MANGARUJI SORTE\t10930\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tGAWALI BHAVANA PUNDLIKRAO\t477905\tyes\tSHS\tShivsena\nMaharashtra\tYavatmal-Washim\tNone of the Above\t5583\tno\tNOTA\tNone of the Above\nMaharashtra\tYavatmal-Washim\tRATHOD NARESH FULSING\t6423\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tYavatmal-Washim\tRATHOD MOHAN MALLU\t22143\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tYavatmal-Washim\tGAIKAWAD RAJENDRA VITTHAL\t7482\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tGURNULE RAMESH VITHOBA\t2484\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tSAWAKE RAMKRUSHNA PUNDLIKRAO\t2193\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nMaharashtra\tYavatmal-Washim\tSUDHAKAR NAMDEORAO GHAYAWAN\t3602\tno\tRPI(KH)\tRepublican Party of India (Khobragade)\nMaharashtra\tYavatmal-Washim\tULHAS VASRAMJI JADHAO\t7166\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tNARESH MAHADEORAO GUGHANE\t3422\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tCHAVAN VINOD NARAYAN\t2177\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tPATIL UPENDRA BABARAO\t895\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tPRAKASH RAGHUNATH RAUT\t2514\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tPRASHANT PANDHARINATH SURVE\t1558\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tKAMBLE UTTAM BHAGAJI\t1402\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tYavatmal-Washim\tRAGHUNATH SHANKARRAO SHELKE\t1911\tno\tARP\tAmbedkarist Republican Party\nMaharashtra\tYavatmal-Washim\tRATHOD INDUWAR KASHIRAM\t4627\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tYavatmal-Washim\tAHMAD PARVEZ IQBAL AHMAD NUR MOHAMMAD\t3262\tno\tSP\tSamajwadi Party\nMaharashtra\tYavatmal-Washim\tDHOTE JAMBUWANTRAO BAPURAO\t4708\tno\tAIFB\tAll India Forward Bloc\nMaharashtra\tYavatmal-Washim\tABRAR AHMAD MAHMUD KHAN\t5672\tno\tWPOI\tWelfare Party Of India\nMaharashtra\tYavatmal-Washim\tRAJU OR BHARATRAJE UDDHAORAO PATIL\t26194\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tYavatmal-Washim\tRATHOD BALIRAM PARASHRAM\t48981\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tYavatmal-Washim\tADV.SHIVAJIRAO SHIVARAMJI MOGHE\t384089\tno\tINC\tIndian National Congress\nMaharashtra\tYavatmal-Washim\tSURESH RAJARAM MUKHMALE\t2130\tno\tMVA\tMaharashtra Vikas Aghadi\nMaharashtra\tYavatmal-Washim\tMESHRAM DNYANESHWAR TUKARAM\t1036\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tSHYAMKUMAR BAPURAO LOKHANDE\t1218\tno\tIND\tIndependent\nMaharashtra\tYavatmal-Washim\tSHAIKH JABBAR SHAIKH USUF\t2172\tno\tIND\tIndependent\nMaharashtra\tHingoli\tRAJEEV SHANKARRAO SATAV\t467397\tyes\tINC\tIndian National Congress\nMaharashtra\tHingoli\tJADHAV CHUNNILAL MOHAN\t25145\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tHingoli\tD.B.NAIK\t14986\tno\tCPM\tCommunist Party of India  (Marxist)\nMaharashtra\tHingoli\tWANKHEDE SUBHASH BAPURAO\t465765\tno\tSHS\tShivsena\nMaharashtra\tHingoli\tUTTAMRAO PANDURANG RATHOD\t9770\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tHingoli\tDESAI PANDURANG DEVRAO\t1675\tno\tJD(S)\tJanata Dal  (Secular)\nMaharashtra\tHingoli\tRAMRAO HARISING RATHOD\t9577\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tHingoli\tPRINCIPAL DR.VITHAL NAMDEORAO KADAM\t3729\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tHingoli\tSHAIKH NAYEEM SHAIKH LAL\t2514\tno\tSP\tSamajwadi Party\nMaharashtra\tHingoli\tSANTOSH NAMDEORAO SAGNE\t1526\tno\tAwVP\tAwami Vikas Party\nMaharashtra\tHingoli\tUTTAM RATHOD\t2147\tno\tIND\tIndependent\nMaharashtra\tHingoli\tDESHMUKH BALASAHEB DESAI\t2154\tno\tIND\tIndependent\nMaharashtra\tHingoli\tDHABE UTTAM MAROTI\t7878\tno\tIND\tIndependent\nMaharashtra\tHingoli\tDEVJI GANGARAM ASOLE\t3165\tno\tIND\tIndependent\nMaharashtra\tHingoli\tNAIKWADE DIGAMBAR GUNAJI\t2049\tno\tIND\tIndependent\nMaharashtra\tHingoli\tBHAROSE ANANT RAMRAO\t2506\tno\tIND\tIndependent\nMaharashtra\tHingoli\tWANKHEDE SUBHASH\t6157\tno\tIND\tIndependent\nMaharashtra\tHingoli\tSATAV ATMARAM RAGHOJI\t5000\tno\tIND\tIndependent\nMaharashtra\tHingoli\tSATAV RAJU SHANKAR\t3522\tno\tIND\tIndependent\nMaharashtra\tHingoli\tSURYAWANSHI ASHOK BHAGWANRAO\t1292\tno\tIND\tIndependent\nMaharashtra\tHingoli\tSURAJ VISHWANATH KONDARWAD\t1322\tno\tIND\tIndependent\nMaharashtra\tHingoli\tWANKHEDE SUBHASH NAGORAO\t6284\tno\tJD(U)\tJanata Dal  (United)\nMaharashtra\tHingoli\tGHUNNAR PRAKASH VITTHALRAO\t2497\tno\tIND\tIndependent\nMaharashtra\tHingoli\tNone of the Above\t3107\tno\tNOTA\tNone of the Above\nMaharashtra\tNanded\tASHOK SHANKARRAO CHAVAN\t493075\tyes\tINC\tIndian National Congress\nMaharashtra\tNanded\tPATHAN ZAFAR ALIKHAN MEHMOOD ALIKHAN\t1993\tno\tIND\tIndependent\nMaharashtra\tNanded\tDR. HANSRAJ DADARAOJI VAIDYA\t22809\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tNanded\tALTAF AHMED IQBAL AHMED\t1547\tno\tIUML\tIndian Union Muslim League\nMaharashtra\tNanded\tVENUGOPAL SHASTRI TUMULURI S/O RAMKRISHNA\t1040\tno\tIND\tIndependent\nMaharashtra\tNanded\tSHINDE BALAJI VISHWANATHRAO\t1724\tno\tSP\tSamajwadi Party\nMaharashtra\tNanded\tGHUNNAR PRAKASH VITHALRAO\t1753\tno\tIND\tIndependent\nMaharashtra\tNanded\tRAMCHANDRA GANGARAM  BHARANDE\t3067\tno\tIND\tIndependent\nMaharashtra\tNanded\tHANMANTE VIJAY CHANDRAO\t1479\tno\tIND\tIndependent\nMaharashtra\tNanded\tRAMCHANDRA GYANOBA SAWANT\t4206\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tNanded\tSAYYED TANVEER SAYYADHAMAJA\t1288\tno\tMBT\tMajlis Bachao Tahreek\nMaharashtra\tNanded\tSHAIKH AFZALUDDIN AZEEMUDDIN\t1065\tno\tLB\tLok Bharati\nMaharashtra\tNanded\tNone of the Above\t3155\tno\tNOTA\tNone of the Above\nMaharashtra\tNanded\tRAJRATNA AMBEDKAR\t28447\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tNanded\tMOHD. MAROOF AB. RAUF\t4771\tno\tIND\tIndependent\nMaharashtra\tNanded\tD. B. PATIL\t411620\tno\tBJP\tBharatiya Janata Party\nMaharashtra\tNanded\tSONTAKKE LAXMAN KISHAN\t1667\tno\tIND\tIndependent\nMaharashtra\tNanded\tADV. DEELIP PRABHAKARRAO KULKARNI (PHULWALKAR)\t1285\tno\tIND\tIndependent\nMaharashtra\tNanded\tBOCHARE SHANKAR MAHADJI\t5694\tno\tIND\tIndependent\nMaharashtra\tNanded\tGRANTHI NARENDRASINGH ANMOLSINGH\t3397\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tNanded\tDATTARAM BALAJI SURWANSHI\t2882\tno\tIND\tIndependent\nMaharashtra\tNanded\tFEROZ KHAN GAZI\t8088\tno\tIND\tIndependent\nMaharashtra\tNanded\tPANJABRAO SHRIHARI WADJE PATIL\t5825\tno\tIND\tIndependent\nMaharashtra\tNanded\tADV. DHONDOPANT NAGORAO HANUMANTE\t1473\tno\tIND\tIndependent\nMaharashtra\tParbhani\tJADHAV SANJAY (BANDU) HARIBHAU\t578455\tyes\tSHS\tShivsena\nMaharashtra\tParbhani\tRAMRAO DHANSING RATHOD SIR NIRANKARI\t3120\tno\tIND\tIndependent\nMaharashtra\tParbhani\tSK. SALEEM\t2947\tno\tMBT\tMajlis Bachao Tahreek\nMaharashtra\tParbhani\tCOMRED BHISE GANPAT DEVRAO\t7251\tno\tIND\tIndependent\nMaharashtra\tParbhani\tBHAMBALE VIJAY MANIKRAO\t451300\tno\tNCP\tNationalist Congress Party\nMaharashtra\tParbhani\tSALMA SHRINIWAS KULKARNI\t4449\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tParbhani\tBABAN (ANNA) MULEY\t5836\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tParbhani\tGULMIR KHAN\t33716\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tParbhani\tCOMRADE RAJAN KSHIRSAGAR\t12404\tno\tCPI\tCommunist Party of India\nMaharashtra\tParbhani\tNone of the Above\t17502\tno\tNOTA\tNone of the Above\nMaharashtra\tParbhani\tKAJALE PRADIP MAHADEV\t4781\tno\tIND\tIndependent\nMaharashtra\tParbhani\tNISAR SUBHAN KHAN PATHAN\t12341\tno\tIND\tIndependent\nMaharashtra\tParbhani\tPRAMOD MAROTI PANDITKAR\t4103\tno\tIND\tIndependent\nMaharashtra\tParbhani\tADV. AJAY SITARAM KARANDE PATIL\t5507\tno\tSP\tSamajwadi Party\nMaharashtra\tParbhani\tMD. ILIYAS MD. AMIR MANIYAR\t8006\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tParbhani\tASHOK BABARAO (AMBHORE) DUDHGAONKAR\t1869\tno\tANC\tAmbedkar National Congress\nMaharashtra\tParbhani\tSAYED ABDUL RAHIM\t2492\tno\tWPOI\tWelfare Party Of India\nMaharashtra\tParbhani\tUDHAV RANGRAO PAWAR (PATIL)\t6154\tno\tIND\tIndependent\nMaharashtra\tJalna\tDANVE RAOSAHEB DADARAO\t591428\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tJalna\tUNGE RAMBHAU KUNDLIK\t2240\tno\tIND\tIndependent\nMaharashtra\tJalna\tSANJAY NIVRUTTI HIVRALE\t1821\tno\tANC\tAmbedkar National Congress\nMaharashtra\tJalna\tRAMBHAU HARIBHAU UGLE\t3283\tno\tRSSDP\tRashtriya Sant Sandesh Party\nMaharashtra\tJalna\tDR. WANKHEDE SHARADCHANDRA GANAPATRAO\t23719\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tJalna\tRATHOD RAMESH LALSING\t2462\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tJalna\tM. JAVED A. WAHAB\t7704\tno\tIND\tIndependent\nMaharashtra\tJalna\tKARANDE GANESH NAVNATHRAO\t2874\tno\tIND\tIndependent\nMaharashtra\tJalna\tAGRAWAL KUNJBIHARI JUGALKISHOR\t3469\tno\tSP\tSamajwadi Party\nMaharashtra\tJalna\tSAYYAD HUSAIN AHMAD\t1655\tno\tIND\tIndependent\nMaharashtra\tJalna\tBABASAHEB PATIL SHINDE\t6547\tno\tIND\tIndependent\nMaharashtra\tJalna\tKAKARWAL PRATAPSING MAHAJAN\t4237\tno\tIND\tIndependent\nMaharashtra\tJalna\tAUTADE VILAS KESHAVRAO\t384630\tno\tINC\tIndian National Congress\nMaharashtra\tJalna\tSAPKAL LILABAI DHARMA\t1110\tno\tIND\tIndependent\nMaharashtra\tJalna\tVITTHAL SAHEBRAO SHELKE\t4587\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tJalna\tASHOK VITTHAL SONWANE\t1894\tno\tIND\tIndependent\nMaharashtra\tJalna\tMOHAMMAD ASAD SHAIKH AHEMAD\t1342\tno\tMNDP\tMinorities Democratic Party\nMaharashtra\tJalna\tAD. MAHESH VITTHALRAO KHARAT\t1967\tno\tIND\tIndependent\nMaharashtra\tJalna\tNADE DNYANESHWAR DAGDUJI\t6953\tno\tIND\tIndependent\nMaharashtra\tJalna\tRAMBHAU VITTHAL CHINCHORE\t1120\tno\tIND\tIndependent\nMaharashtra\tJalna\tMIRZA AFSAR BEG\t2467\tno\tWPOI\tWelfare Party Of India\nMaharashtra\tJalna\tDILIP DATTA MHASKE\t4622\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tJalna\tNone of the Above\t4128\tno\tNOTA\tNone of the Above\nMaharashtra\tAurangabad\tCHANDRAKANT BHAURAO KHAIRE\t520902\tyes\tSHS\tShivsena\nMaharashtra\tAurangabad\tBANSWAL MANNALAL PREMCHAND\t893\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tAurangabad\tGAYAKE SADASHIV AMBADAS\t2646\tno\tSP\tSamajwadi Party\nMaharashtra\tAurangabad\tJAGDIP VISHWANATH SHINDE\t1530\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tAHAMAD AZIZ AHAMAD MATEEN\t1572\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tSHAIK SAYYAD SHAIK MAHMOOD\t1581\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tSUBHASH KALYANRAO LOMTE\t11974\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tAurangabad\tNone of the Above\t6372\tno\tNOTA\tNone of the Above\nMaharashtra\tAurangabad\tJEVRIKAR INDRAKUMAR DNYANOBA\t37419\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tAurangabad\tKAILAS CHANDRABHAN THENGDE\t2123\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tTRIBHUVAN MADHUKAR PADMAKAR\t6135\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tDR. FEROZ KHAN MURTUZA KHAN\t978\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tPATIL NITIN SURESH\t358902\tno\tINC\tIndian National Congress\nMaharashtra\tAurangabad\tBALASAHEB ASARAM SARATE\t880\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tRAJU BABURAO DOLE\t1238\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tNADEEM RANA\t2922\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tABDUL KHUDUS ABDUL SAMAD SHAIKH\t3775\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tAurangabad\tBALASAHEB VITTHAL AWARE\t614\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tDANDGE NANASAHEB DAMODHAR\t5901\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tBANSODE UDHAV GOVARDHAN\t1795\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tBHANUDAS RAMDAS SARODE\t934\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tSAYYAD SHAFIYODDIN VAHIYODDIN\t2885\tno\tWPOI\tWelfare Party Of India\nMaharashtra\tAurangabad\tJAWAHARLAL LAXMAN BHAGURE\t878\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tNANDARKAR VISHAL UDDHAV\t1306\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tANKUSH MUNJAJI TUPSAMUNDRE\t2019\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tPHULARE SURESH ASARAM\t2520\tno\tIND\tIndependent\nMaharashtra\tAurangabad\tJADHAV PUSHPA SHANTILAL\t1038\tno\tANC\tAmbedkar National Congress\nMaharashtra\tAurangabad\tMOHMMAD KISMATWALA KASIM KISMATWALA\t1201\tno\tGaAP\tGareeb Aadmi Party\nMaharashtra\tDindori\tCHAVAN HARISHCHANDRA DEORAM\t542784\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tDindori\tDR. BHARATI PRAVIN PAWAR\t295165\tno\tNCP\tNationalist Congress Party\nMaharashtra\tDindori\tCOM. HEMANT MOTIRAM WAGHERE\t72599\tno\tCPM\tCommunist Party of India  (Marxist)\nMaharashtra\tDindori\tPROF. DNYANESHWAR DAMU MALI\t4067\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tDindori\tMALI SHARAD SAHEBRAO\t17724\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tDindori\tPRABHAT CHINDHU SONAWANE\t5578\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tDindori\tNone of the Above\t10897\tno\tNOTA\tNone of the Above\nMaharashtra\tDindori\tBHARAT KISAN MATE\t2024\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tDindori\tKAILAS SAKHARAM MORE\t3900\tno\tIND\tIndependent\nMaharashtra\tDindori\tAJITDADA BHIKA PAWAR\t5177\tno\tIND\tIndependent\nMaharashtra\tDindori\tGAIKWAD ABHIJEET KALYANRAO\t10267\tno\tIND\tIndependent\nMaharashtra\tNashik\tGODSE HEMANT TUKARAM\t494735\tyes\tSHS\tShivsena\nMaharashtra\tNashik\tPATHAN AYUB MADDEKHAN\t697\tno\tIUML\tIndian Union Muslim League\nMaharashtra\tNashik\tFASALE BABAN NAMDEO\t3792\tno\tIND\tIndependent\nMaharashtra\tNashik\tNone of the Above\t8916\tno\tNOTA\tNone of the Above\nMaharashtra\tNashik\tPRAKASH GIRDHAR KANOJE\t1121\tno\tIND\tIndependent\nMaharashtra\tNashik\tDR. PRADEEP PAWAR\t63050\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tNashik\tVISHNU RAMDAS JADHAV\t1666\tno\tIND\tIndependent\nMaharashtra\tNashik\tPATHAN SAKHAWATKHA YASINKHA\t845\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tNashik\tMAKSUD ILIYAS KHAN\t1253\tno\tIND\tIndependent\nMaharashtra\tNashik\tPRAMOD GYANIRAM NATHEKAR\t1580\tno\tIND\tIndependent\nMaharashtra\tNashik\tCHHAGAN BHUJBAL\t307399\tno\tNCP\tNationalist Congress Party\nMaharashtra\tNashik\tVIJAY BALIRAM PANDHARE\t9672\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tNashik\tADV. TANAJI SUKDEO JAIBHAVE\t17154\tno\tCPM\tCommunist Party of India  (Marxist)\nMaharashtra\tNashik\tDINKAR DHARMA PATIL\t20896\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tNashik\tAVHAD MAHESH ZUNJAR\t1204\tno\tAIFB\tAll India Forward Bloc\nMaharashtra\tNashik\tSARKATE DEVIDAS PIRAJI\t3425\tno\tIND\tIndependent\nMaharashtra\tPalghar\tADV. CHINTAMAN NAVASHA WANGA\t533201\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tPalghar\tBALIRAM SUKUR JADHAV\t293681\tno\tBVA\tBahujan Vikas Aaghadi\nMaharashtra\tPalghar\tKHARAPADE LADAKYA RUPA\t76890\tno\tCPM\tCommunist Party of India  (Marxist)\nMaharashtra\tPalghar\tDILIP ATMARAM DUMADA\t10742\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tPalghar\tHARIBHAU SOMA VARTHA\t12974\tno\tIND\tIndependent\nMaharashtra\tPalghar\tGAVARI SHYAM ANANT\t8176\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tPalghar\tPANDURANG JETHYA PARDHI\t16182\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tPalghar\tMOHAN BARKU GUHE\t6691\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tPalghar\tKASHINATH LAXMAN KOKERA\t4327\tno\tIND\tIndependent\nMaharashtra\tPalghar\tSACHIN DAMODAR SHINGDA\t7957\tno\tIND\tIndependent\nMaharashtra\tPalghar\tNone of the Above\t21797\tno\tNOTA\tNone of the Above\nMaharashtra\tBhiwandi\tKAPIL MORESHWAR PATIL\t411070\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tBhiwandi\tNone of the Above\t9311\tno\tNOTA\tNone of the Above\nMaharashtra\tBhiwandi\tENGINEER NAVID BETAB\t2435\tno\tIND\tIndependent\nMaharashtra\tBhiwandi\tYOGESH MOTIRAM KATHORE\t3058\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tBhiwandi\tADV. DR. ANITA BALASAHEB KOLEKAR\t1376\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tBhiwandi\tHARISHCHANDRA DATTU PATIL\t4058\tno\tIND\tIndependent\nMaharashtra\tBhiwandi\tSHAH ABRAR AHMED NAZEER\t2120\tno\tPECP\tPeace Party\nMaharashtra\tBhiwandi\tSURESH KALURAM JADHAV\t5941\tno\tIND\tIndependent\nMaharashtra\tBhiwandi\tANSARI JAHID MUKHTAR\t2126\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tBhiwandi\tANSARI JALAL ENGINEER\t11055\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tBhiwandi\tANSARI MUMTAZ ABDUL SATAR\t14068\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tBhiwandi\tSURESH (BALYA MAMA) GOPINATH MHATRE\t93647\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tBhiwandi\tPATIL VISHWANATH RAMCHANDRA\t301620\tno\tINC\tIndian National Congress\nMaharashtra\tBhiwandi\tMADHUKAR VITTHAL PATIL\t13720\tno\tCPI\tCommunist Party of India\nMaharashtra\tKalyan\tDR.SHRIKANT EKNATH SHINDE\t440892\tyes\tSHS\tShivsena\nMaharashtra\tKalyan\tSHASHIKANT DADA RASAL\t2096\tno\tIND\tIndependent\nMaharashtra\tKalyan\tSHIVA KRISHNAMURTHY IYAR\t788\tno\tIND\tIndependent\nMaharashtra\tKalyan\tPRAMOD (RAJU) RATAN PATIL\t122349\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tKalyan\tSULOCHANA DHARMENDRA SONKAMBLE\t1229\tno\tRBS\tRepublican Bahujan Sena\nMaharashtra\tKalyan\tKIRATKAR DAYANAND TULSHIRAM\t19643\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tKalyan\tSUDHAKAR NARAYAN SHINDE\t726\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tKalyan\tANAND PRAKASH PARANJPE\t190143\tno\tNCP\tNationalist Congress Party\nMaharashtra\tKalyan\tMOHAMMED AHMED MUKHTAR KHAN ALIAS AHMED NETA\t2467\tno\tIND\tIndependent\nMaharashtra\tKalyan\tNARESH ARVIND THAKUR\t20347\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tKalyan\tBELAMKAR MILIND CHANDRAKANT\t540\tno\tLB\tLok Bharati\nMaharashtra\tKalyan\tTELGOTE PRAKASH SHAMRAO\t1600\tno\tIND\tIndependent\nMaharashtra\tKalyan\tMOHAMMAD HAMEED SAYYED\t3047\tno\tSP\tSamajwadi Party\nMaharashtra\tKalyan\tBHOSALE SHAMU RAMU\t1869\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tKalyan\tDILIP PRABHAKAR ALONI (JOSHI)\t1222\tno\tIND\tIndependent\nMaharashtra\tKalyan\tMOTE CHANDRAKANT RAMBHAJI\t1279\tno\tIND\tIndependent\nMaharashtra\tKalyan\tANIL MORE\t3531\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tKalyan\tASMITA PUSHKAR PURANIK\t1243\tno\tIND\tIndependent\nMaharashtra\tKalyan\tNone of the Above\t9185\tno\tNOTA\tNone of the Above\nMaharashtra\tThane\tVICHARE RAJAN BABURAO\t595364\tyes\tSHS\tShivsena\nMaharashtra\tThane\tNone of the Above\t13174\tno\tNOTA\tNone of the Above\nMaharashtra\tThane\tSANJAY DILIP KUMAR DAS\t918\tno\tIND\tIndependent\nMaharashtra\tThane\tRAJU SHRAVAN CHAVAN\t1681\tno\tRBS\tRepublican Bahujan Sena\nMaharashtra\tThane\tNAJAKATALI KABIR ANSARI\t1549\tno\tIND\tIndependent\nMaharashtra\tThane\tSOHANLAL PUROHIT\t2479\tno\tIND\tIndependent\nMaharashtra\tThane\tTRILOKI GHURAHU YADAV\t1066\tno\tIND\tIndependent\nMaharashtra\tThane\tSHAFIK AHAMAD SADAT KHAN\t1865\tno\tIND\tIndependent\nMaharashtra\tThane\tNARENDRA PRABHASHANKAR PANDEY\t2141\tno\tIND\tIndependent\nMaharashtra\tThane\tKIRTAWADE VIDHYADHAR BHIMRAO\t10982\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tThane\tSALMAN AJIMULLAH HASMI\t1402\tno\tRAPa\tRashtriya Aam Party\nMaharashtra\tThane\tQAMRUZZAMAN KUTUBALI MINAI\t1196\tno\tPECP\tPeace Party\nMaharashtra\tThane\tJAIN SURENDRAKUMAR K.\t641\tno\tABJS\tAkhil Bharatiya Jan Sangh\nMaharashtra\tThane\tABHAY SUBRAO KULKARNI\t1891\tno\tIND\tIndependent\nMaharashtra\tThane\tRAMCHANDRA BHAGOJI GAWADE\t493\tno\tIND\tIndependent\nMaharashtra\tThane\tRATHOD BABUSINGH VASRAM\t636\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tThane\tVISHAL DINKAR SALVE\t705\tno\tIND\tIndependent\nMaharashtra\tThane\tNITIN SHANKAR DESHPANDE\t2175\tno\tDaP\tDharmarajya Paksha\nMaharashtra\tThane\tBHANUDAS DHOTARE\t488\tno\tIND\tIndependent\nMaharashtra\tThane\tSANJEEV VISHNU SANE\t41535\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tThane\tSANJEEV GANESH NAIK\t314065\tno\tNCP\tNationalist Congress Party\nMaharashtra\tThane\tABHIJIT RAMESH PANSE\t48863\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tThane\tGOPINATH SAKHARAM MHASKE\t2412\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tThane\tSHRINATH MITHANTHAYA\t891\tno\tHJP\tHindustan Janta Party\nMaharashtra\tThane\tRAJENDRA GAJBHIYE\t905\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tThane\tASHOK SHANKAR JADHAV\t4022\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tThane\tVINOD DEEPCHAND GANGWAL\t650\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tGOPAL CHINAYYA SHETTY\t664004\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tMumbai North\tSINGH ASHOK CHANDRAPAL\t5438\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMumbai North\tSANJAY BRIJKISHORLAL NIRUPAM\t217422\tno\tINC\tIndian National Congress\nMaharashtra\tMumbai North\tGONDANE DIVAKAR LAXMAN\t1539\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tMumbai North\tTHORAT SUNIL UTTAMRAO\t653\tno\tAIFB\tAll India Forward Bloc\nMaharashtra\tMumbai North\tHAJI AKHTAR PAPERWALA\t626\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tYADAV KAMLESH SHOBHNATH\t5506\tno\tSP\tSamajwadi Party\nMaharashtra\tMumbai North\tSATISH PARASMAL JAIN\t32364\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMumbai North\tMOHAMMAD AKHTAR MEHBOOB SHAIKH\t462\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tMumbai North\tPRABHAKARAN G. TITUS\t331\tno\tPPIS\tPeople's Party of India(secular)\nMaharashtra\tMumbai North\tSAKHARAM PANDURANG DUNGHAV\t699\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tMumbai North\tARJUN BALU ARDE\t1650\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tMumbai North\tPRASHANT NARSINH KHARATMAL\t1612\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tNITIN RAVINDRA RAJVARDHAN\t796\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMumbai North\tDINESH SHAH\t394\tno\tSVPP\tSardar Vallabhbhai Patel Party\nMaharashtra\tMumbai North\tKAPIL KANTILAL SONI\t2078\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tNone of the Above\t8758\tno\tNOTA\tNone of the Above\nMaharashtra\tMumbai North\tSHAIKH FATEH MOHAMMAD\t768\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tSUBODH G. RANJAN\t278\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tSATYAVIJAY VITHOBA CHAVAN\t327\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tARJUN CHAUDHARY\t556\tno\tIND\tIndependent\nMaharashtra\tMumbai North\tMILIND SHANKAR REPE\t301\tno\tIND\tIndependent\nMaharashtra\tMumbai North West\tGAJANAN CHANDRAKANT KIRTIKAR\t464820\tyes\tSHS\tShivsena\nMaharashtra\tMumbai North West\tMOHAN  GALVANKAR\t694\tno\tlpi\tThe Lok Party of India\nMaharashtra\tMumbai North West\tPRAVIN  CHANDRAKANT  KAURPURIYA\t888\tno\tHSCP\tHindustan Swaraj Congress Party\nMaharashtra\tMumbai North West\tBHOLE PUSHPA MILIND\t10275\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMumbai North West\tMANGESH MADHUKAR HUMNE\t1082\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMumbai North West\tVIJAYANAND  SHANKARRAO  SHINDE\t514\tno\tHND\tHindusthan Nirman Dal\nMaharashtra\tMumbai North West\tNITIN H.  KHARE\t1078\tno\tIND\tIndependent\nMaharashtra\tMumbai North West\tPRATIK  PRABHAKAR  TORASKAR\t515\tno\tIND\tIndependent\nMaharashtra\tMumbai North West\tRINA  MAHENDRAKUMAR  ZAVERI\t1530\tno\tIND\tIndependent\nMaharashtra\tMumbai North West\tSONKAMBLE  GAJANAN  TUKARAM\t1366\tno\tIND\tIndependent\nMaharashtra\tMumbai North West\tNone of the Above\t11009\tno\tNOTA\tNone of the Above\nMaharashtra\tMumbai North West\tMAYANK RAMESH GANDHI\t51860\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMumbai North West\tRAKHI SAWANT\t2006\tno\tRAPa\tRashtriya Aam Party\nMaharashtra\tMumbai North West\tKAMAT GURUDAS VASANT\t281792\tno\tINC\tIndian National Congress\nMaharashtra\tMumbai North West\tMAHESH WAMAN MANJREKAR\t66088\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tMumbai North East\tKIRIT  SOMAIYA\t525285\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tMumbai North East\tNone of the Above\t7114\tno\tNOTA\tNone of the Above\nMaharashtra\tMumbai North East\tDEEPAK DIGAMBAR SHINDE\t1359\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tKHAN SHAMIM BANU\t902\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tTUPSOUNDARYA SUNITA MOHAN\t881\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tFIROZ AHMED SIDDIQUI\t2243\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tBANSODE DILIP BAPU\t2784\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tSABIR MEHANDI HASAN\t626\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tSANJAY SAOJI DESHPANDE\t2752\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tMATCHINDRA  HANUMANTRAO CHATE\t17427\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMumbai North East\tSANJAY DINA PATIL\t208163\tno\tNCP\tNationalist Congress Party\nMaharashtra\tMumbai North East\tPROF. AVINASH DOLAS\t8833\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tMumbai North East\tTUKARAM BABURAO MANE\t1838\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMumbai North East\tPRAKASH SADASHIV PANCHARAS\t1325\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tMumbai North East\tPRASHANT DNYANESHWAR GANGAWANE\t1308\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tMumbai North East\tMEDHA PATKAR\t76451\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMumbai North East\tSAYED GULAMHUSAIN  AMIRHAMZA\t1012\tno\tRPI\tRepublican Party of India\nMaharashtra\tMumbai North East\tHAYAT ABDULLAH SHAIKH\t621\tno\tIUML\tIndian Union Muslim League\nMaharashtra\tMumbai North East\tADV. AMARSEN BABURAO BABAR\t468\tno\tIND\tIndependent\nMaharashtra\tMumbai North East\tANANDA   SAKHARAM  BHALERAO\t369\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tPOONAM MAHAJAN ALIAS POONAM VAJENDLA RAO\t478535\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tMumbai North central\tNone of the Above\t6937\tno\tNOTA\tNone of the Above\nMaharashtra\tMumbai North central\tNIYAZ AHMED ALLAUDDIN SHAIKH\t512\tno\tAwVP\tAwami Vikas Party\nMaharashtra\tMumbai North central\tPHIROZE PALKHIVALA\t34824\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMumbai North central\tBIRJE HEMANT ANANT\t693\tno\tAIFB\tAll India Forward Bloc\nMaharashtra\tMumbai North central\tMEHER A. MALLICK\t588\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMumbai North central\tDINESH PHULSING RATHOD\t1261\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tFRANCIS LAWRENCE FERNANDES\t653\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tMINAKSHI MUKUND JADHAV\t1022\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tCHAWAN SURYAKANT GANGARAM\t787\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tMOHAMMED RAFIK HUSSAIN SHAIKH\t308\tno\tNLP\tNational Loktantrik Party\nMaharashtra\tMumbai North central\tUPESH M. BARIA\t334\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tGUPTA KANHAIYALAL RAJARAM\t858\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tSANJU VERMA\t626\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tSHITAL KUMAR JAIN\t1380\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tSURESH GIGA DABHI\t872\tno\tIND\tIndependent\nMaharashtra\tMumbai North central\tABU FARHAN AZMI\t9873\tno\tSP\tSamajwadi Party\nMaharashtra\tMumbai North central\tAZIMUDDIN KHAN\t2099\tno\tlpi\tThe Lok Party of India\nMaharashtra\tMumbai North central\tASHOK KISAN LOKHANDE\t581\tno\tRPI\tRepublican Party of India\nMaharashtra\tMumbai North central\tUMESH SONBA AVHAD\t657\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tMumbai North central\tSHINDE ANAND VYANKATRAO\t10128\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMumbai North central\tDUTT PRIYA SUNIL\t291764\tno\tINC\tIndian National Congress\nMaharashtra\tMumbai South central\tRAHUL RAMESH SHEWALE\t381275\tyes\tSHS\tShivsena\nMaharashtra\tMumbai South central\tADV.AIYYAR GANESH\t14762\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMumbai South central\tGANESH BHIMRAO BUDHE\t514\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tDILIP GAIKWAD\t1338\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tCLIFFORD MARTIS\t800\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tMOIN SOHAIL SAYYED\t1212\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tEKNATH M. GAIKWAD\t242933\tno\tINC\tIndian National Congress\nMaharashtra\tMumbai South central\tSANGHAPAL HIRACHAND GADEKAR\t919\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tMumbai South central\tSUNDAR BALAKRISHNAN\t27715\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMumbai South central\tSHINDE SARJERAO MAHADEV\t418\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tHUSSAIN AKBAR SAYYED\t1430\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMumbai South central\tADITYA RAJAN SHIRODKAR\t73113\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tMumbai South central\tMAHENDRA TULSHIRAM BHINGARDIVE\t919\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tPAL JITENDRA KUMAR\t453\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tNOOR MOHAMMAD SAHEB RAJA KHAN\t1588\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tABBAS HUSSAIN SHAIKH\t5575\tno\tSP\tSamajwadi Party\nMaharashtra\tMumbai South central\tKHAN SIRAJ AHMED\t545\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tNAMDEV SAHEBRAO SABLE\t1184\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tMR. NADAR SELVAKUMAR PERIMBRAJ\t686\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tASLAM HANIF KHOT\t1018\tno\tPECP\tPeace Party\nMaharashtra\tMumbai South central\tADV.VAISHALI SURENDRA BORDE\t1083\tno\tIND\tIndependent\nMaharashtra\tMumbai South central\tNone of the Above\t9580\tno\tNOTA\tNone of the Above\nMaharashtra\tMumbai   South\tARVIND SAWANT\t374780\tyes\tSHS\tShivsena\nMaharashtra\tMumbai   South\tNone of the Above\t9573\tno\tNOTA\tNone of the Above\nMaharashtra\tMumbai   South\tGAURAV SHARMA\t561\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tSHAHZAD SHAMSHER KHAN PATHAN\t920\tno\tAwVP\tAwami Vikas Party\nMaharashtra\tMumbai   South\tSHEHBAJ RATHOD\t415\tno\tJMBP\tJai Maha Bharath Party\nMaharashtra\tMumbai   South\tJAHEDA MOHAMMED HANIF SHAIKH\t703\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tSACHIN PAWAR\t1266\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMumbai   South\tBAIG MIRZA KALIM\t677\tno\tlpi\tThe Lok Party of India\nMaharashtra\tMumbai   South\tFARNANDES SANTAN\t1078\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tBASIT FAITH\t765\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tKATARIA JITENDRA KAMABHAI\t595\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tRAJENDRA DAULAT DESAI\t1059\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tPRAKASH REDDY\t6903\tno\tCPI\tCommunist Party of India\nMaharashtra\tMumbai   South\tMEERA SANYAL\t40388\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMumbai   South\tSHANKAR  SONAWANE\t459\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tRATAN DEVRAM BODKE\t1325\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tDR. OMRAJ L. RATHOD\t675\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tBALA NANDGAONKAR\t84864\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tMumbai   South\tABDUL LATIF ABDUL KADAR SHAIKH\t400\tno\tIND\tIndependent\nMaharashtra\tMumbai   South\tABDUS SALAM KHAN  QASMI\t6677\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMumbai   South\tDEORA MILIND MURLI\t246632\tno\tINC\tIndian National Congress\nMaharashtra\tRaigad\tANANT GEETE\t396178\tyes\tSHS\tShivsena\nMaharashtra\tRaigad\tYASHWANT JAYRAM GAIKWAD\t10510\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tRaigad\tAZIZ ABDUL KADIR MUKADAM\t2763\tno\tSP\tSamajwadi Party\nMaharashtra\tRaigad\tRAMESHBHAI KADAM\t129730\tno\tPWPI\tPeasants And Workers Party of India\nMaharashtra\tRaigad\tTATKARE SUNIL DATTATREY\t394068\tno\tNCP\tNationalist Congress Party\nMaharashtra\tRaigad\tSANDEEP PANDURANG PARTE\t4287\tno\tHJP\tHindustan Janta Party\nMaharashtra\tRaigad\tDR. APARANTI SANJAY YASHWANT\t6759\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tRaigad\tDR. GHONE ADESH YASHAWANT\t3308\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tRaigad\tSUNIL TATKARE\t9849\tno\tIND\tIndependent\nMaharashtra\tRaigad\tMUZZAFAR JAINUDDIN CHAUDHARI  ALIAS MODI\t9952\tno\tIND\tIndependent\nMaharashtra\tRaigad\tNone of the Above\t20362\tno\tNOTA\tNone of the Above\nMaharashtra\tMaval\tAPPA ALIAS SHRIRANG CHANDU BARNE\t512226\tyes\tSHS\tShivsena\nMaharashtra\tMaval\tNone of the Above\t11186\tno\tNOTA\tNone of the Above\nMaharashtra\tMaval\tJAGTAP LAXMAN MURLIDHAR\t1600\tno\tIND\tIndependent\nMaharashtra\tMaval\tJAGTAP LAXMAN SITARAM\t8765\tno\tIND\tIndependent\nMaharashtra\tMaval\tSIMA DHARMANNA MANIKADI\t1765\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tMaval\tADV.VAISHALI SURENDRA BORDE\t2536\tno\tIND\tIndependent\nMaharashtra\tMaval\tSIR MANUEL SABINO D'SOUZA\t4311\tno\tIND\tIndependent\nMaharashtra\tMaval\tSUBHASH GOPALRAO BODHE\t3429\tno\tIND\tIndependent\nMaharashtra\tMaval\tNANAWARE PRAMOD MAHADEO\t4276\tno\tIND\tIndependent\nMaharashtra\tMaval\tBHAPKAR MARUTI SAHEBRAO\t30566\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMaval\tH.B.P.ADV.R.K.PATIL\t2336\tno\tIND\tIndependent\nMaharashtra\tMaval\tRAJENDRA MARUTI KATE\t2016\tno\tIND\tIndependent\nMaharashtra\tMaval\tBARNE SHRIRANG CHIMAJI\t11259\tno\tJD(U)\tJanata Dal  (United)\nMaharashtra\tMaval\tNARWEKAR RAHUL SURESH\t182293\tno\tNCP\tNationalist Congress Party\nMaharashtra\tMaval\tABHIJIT ANIL APTE\t1085\tno\tIND\tIndependent\nMaharashtra\tMaval\tBHIMAPUTRA TEXAS GAIKWAD\t25982\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMaval\tJAGTAP LAXMANBHAU PANDURANG\t354829\tno\tPWPI\tPeasants And Workers Party of India\nMaharashtra\tMaval\tGHARAT SANTOSH ROHIDAS\t8940\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMaval\tNURJAHAN YASIN SHAIKH\t2024\tno\tIND\tIndependent\nMaharashtra\tMaval\tBHIMRAO ANNA KADALE\t2911\tno\tIND\tIndependent\nMaharashtra\tPune\tANIL SHIROLE\t569825\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tPune\tDR.VISHWAJEET PATANGRAO KADAM\t254056\tno\tINC\tIndian National Congress\nMaharashtra\tPune\tCOLONEL JAYANTRAO CHITALE\t1884\tno\tIND\tIndependent\nMaharashtra\tPune\tNone of the Above\t6438\tno\tNOTA\tNone of the Above\nMaharashtra\tPune\tNANA KSHIRSAGAR\t1797\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tPune\tSANJAY LAXMAN PADWAL\t477\tno\tbns\tBhartiya Navjawan Sena (Paksha)\nMaharashtra\tPune\tDAMBALE RAHUL DHARMA\t2259\tno\tIND\tIndependent\nMaharashtra\tPune\tSHASHIKANT BHIKAJI OWHAL\t359\tno\tIND\tIndependent\nMaharashtra\tPune\tAZAM VAKIL MANIYAR\t353\tno\tIND\tIndependent\nMaharashtra\tPune\tSUSHAMA PANDURANG GAIKWAD\t590\tno\tIND\tIndependent\nMaharashtra\tPune\tSAIYYAD AFSER  IBRAHIM\t1131\tno\tSP\tSamajwadi Party\nMaharashtra\tPune\tNILESH DHANAVE\t840\tno\tIND\tIndependent\nMaharashtra\tPune\tDINESH PANDURANG PAWAR\t692\tno\tIND\tIndependent\nMaharashtra\tPune\tMANCHAK TRIMBAKRAO KARALE\t1028\tno\tIND\tIndependent\nMaharashtra\tPune\tUMESH BHUJANG MASANKHAMB\t494\tno\tIND\tIndependent\nMaharashtra\tPune\tBANSODE CHAYYA TUKARAM\t512\tno\tIND\tIndependent\nMaharashtra\tPune\tDEVKULE BIBHISHAN JAGANNATH\t1230\tno\tIND\tIndependent\nMaharashtra\tPune\tSHIVAJI  PANDURANG ADHAV\t868\tno\tIND\tIndependent\nMaharashtra\tPune\tDEEPAK NATHARAM PAIGUDE\t93502\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tPune\tKHIRID SIMKUMAR BHAGWAN\t380\tno\tIND\tIndependent\nMaharashtra\tPune\tYADAV GANGADHAR NATHU\t455\tno\tIND\tIndependent\nMaharashtra\tPune\tCOL SURESH PATIL (RTD)\t1324\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tPune\tIMTIYAZ PEERZADE\t14727\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tPune\tWARE SUBHASH SHANKARRAO\t28657\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tPune\tASHAPAK SHAIKH\t585\tno\tIND\tIndependent\nMaharashtra\tPune\tSANTOSH PAWAR\t565\tno\tIND\tIndependent\nMaharashtra\tPune\tVIJAY LAXMAN SARODE\t303\tno\tIND\tIndependent\nMaharashtra\tPune\tARUN BHATIA\t7222\tno\tPG\tPeoples Guardian\nMaharashtra\tPune\tRUPALI TAMBOLI\t378\tno\tIND\tIndependent\nMaharashtra\tPune\tAJAY VASANT PAITHANKAR\t343\tno\tIND\tIndependent\nMaharashtra\tBaramati\tSUPRIYA SULE\t521562\tyes\tNCP\tNationalist Congress Party\nMaharashtra\tBaramati\tNone of the Above\t14216\tno\tNOTA\tNone of the Above\nMaharashtra\tBaramati\tMAHADIK PRALHAD DAGADU\t2891\tno\tIND\tIndependent\nMaharashtra\tBaramati\tRAVINDRA TAKALE\t7845\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tBaramati\tTATYASAHEB SITARAM TELE\t8811\tno\tJD(S)\tJanata Dal  (Secular)\nMaharashtra\tBaramati\tCHAUDHARI KALURAM VINAYAK\t24908\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tBaramati\tKOKARE SHIVAJI JAYSING\t4739\tno\tIND\tIndependent\nMaharashtra\tBaramati\tSURESHDADA BABURAO VEER\t3345\tno\tIND\tIndependent\nMaharashtra\tBaramati\tSURESH KHOPADE\t26396\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tBaramati\tMAHADEV JAGANNATH JANKAR\t451843\tno\tRSPS\tRashtriya Samaj Paksha\nMaharashtra\tShirur\tADHALRAO SHIVAJI DATTATREY\t643415\tyes\tSHS\tShivsena\nMaharashtra\tShirur\tWAGHMARE SARJERAO BHIKA\t19783\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tShirur\tNIKAM SOPANRAO PANDHARINATH\t16663\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tShirur\tGEETARAM RAMCHANDRA KADAM\t1906\tno\tSP(I)\tSocialist Party (India)\nMaharashtra\tShirur\tUMESH ARJUN KANCHAN\t1137\tno\tIND\tIndependent\nMaharashtra\tShirur\tASHOK SHIRPATI KHANDEBHARAD\t36448\tno\tMNS\tMaharashtra Navnirman sena\nMaharashtra\tShirur\tNone of the Above\t11995\tno\tNOTA\tNone of the Above\nMaharashtra\tShirur\tNIKAM DEVDATTA JAYWANT\t341601\tno\tNCP\tNationalist Congress Party\nMaharashtra\tShirur\tBAJARE SITARAM NAMDEO (BABA)\t1933\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tShirur\tVIKAS SUDAM KUSEKAR\t1174\tno\tAIFB\tAll India Forward Bloc\nMaharashtra\tShirur\tKATORE MAHADEV NIWRITTI\t1792\tno\tIND\tIndependent\nMaharashtra\tShirur\tDAMBALE RAM  DHARMA\t2433\tno\tIND\tIndependent\nMaharashtra\tShirur\tNILESH YASHWANT MHASKULE\t2118\tno\tIND\tIndependent\nMaharashtra\tShirur\tMILINDRAJE DIGAMBAR BHOSALE\t3258\tno\tIND\tIndependent\nMaharashtra\tShirur\tSONALI CHANDRASHEKHAR THORAT\t3850\tno\tIND\tIndependent\nMaharashtra\tAhmadnagar\tGANDHI DILIPKUMAR MANSUKHLAL\t605185\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tAhmadnagar\tNone of the Above\t7473\tno\tNOTA\tNone of the Above\nMaharashtra\tAhmadnagar\tKAKADE KISAN YASHWANT\t8386\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tAhmadnagar\tDEEPALI SAYYED\t7120\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tAhmadnagar\tPETRAS KISAN GAWARE\t2525\tno\tIND\tIndependent\nMaharashtra\tAhmadnagar\tDESHMUKH VIKAS NANASAHEB\t2202\tno\tIND\tIndependent\nMaharashtra\tAhmadnagar\tH.B.P. AJAY SAHEBRAO BARASKAR MAHARAJ\t6003\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tAhmadnagar\tKOLSEPATIL BABAN GANGADHAR\t12683\tno\tIND\tIndependent\nMaharashtra\tAhmadnagar\tPOPAT MANIK FULE\t1061\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tAhmadnagar\tLAXMAN BHANUDAS SONALE\t3497\tno\tIND\tIndependent\nMaharashtra\tAhmadnagar\tDAMALE SHIVAJIRAO WAMAN\t1385\tno\tbns\tBhartiya Navjawan Sena (Paksha)\nMaharashtra\tAhmadnagar\tANIL JAYSING GHANVAT\t3086\tno\tIND\tIndependent\nMaharashtra\tAhmadnagar\tSHRIDHAR JAKHUJI DAREKAR\t5649\tno\tIND\tIndependent\nMaharashtra\tAhmadnagar\tRAJEEV APPASAHEB RAJALE\t396063\tno\tNCP\tNationalist Congress Party\nMaharashtra\tShirdi\tLOKHANDE SADASHIV KISAN\t532936\tyes\tSHS\tShivsena\nMaharashtra\tShirdi\tBHAUSAHEB RAJARAM WAKCHAURE\t333014\tno\tINC\tIndian National Congress\nMaharashtra\tShirdi\tSANTOSH MADHUKAR ROHAM\t9296\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tShirdi\tSHENDE RAVINDRA VITTHAL\t4728\tno\tIND\tIndependent\nMaharashtra\tShirdi\tADV. MAHENDRA DADASAHEB SHINDE\t10381\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tShirdi\tNITIN NAVNATH UDMALE\t11580\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tShirdi\tBALASAHEB DASHRATH BAGUL\t4319\tno\tIND\tIndependent\nMaharashtra\tShirdi\tWAGH GANGADHAR RADHAJI\t2874\tno\tIND\tIndependent\nMaharashtra\tShirdi\tRAGHUNATH RAMJI MAKASARE\t3193\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tShirdi\tUDDHAVRAO BALWANT GAIKWAD\t1499\tno\tIND\tIndependent\nMaharashtra\tShirdi\tGHOLAP SANDEEP BHASKAR\t2229\tno\tIND\tIndependent\nMaharashtra\tShirdi\tSARODE POPAT RAMBHAU\t1429\tno\tLB\tLok Bharati\nMaharashtra\tShirdi\tPAWAR VIJAY BANDERAO\t2334\tno\tlpi\tThe Lok Party of India\nMaharashtra\tShirdi\tNone of the Above\t9879\tno\tNOTA\tNone of the Above\nMaharashtra\tShirdi\tSAYAJI SHANKAR KHARAT\t2954\tno\tIND\tIndependent\nMaharashtra\tBeed\tMUNDE GOPINATHRAO PANDURANG\t635995\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tBeed\tDHAS SURESH RAMCHANDRA\t499541\tno\tNCP\tNationalist Congress Party\nMaharashtra\tBeed\tTEJAS ANKUSH GHUMARE\t5357\tno\tIND\tIndependent\nMaharashtra\tBeed\tARJUN HANMANTA NIMBALKAR\t3758\tno\tIND\tIndependent\nMaharashtra\tBeed\tSAYYED MUBIN RAHIMTULLA\t1122\tno\tIND\tIndependent\nMaharashtra\tBeed\tDR. OWHAL JITENDRA MAHADEV\t1075\tno\tIND\tIndependent\nMaharashtra\tBeed\tSHRIKANT VISHNU GADALE\t931\tno\tIND\tIndependent\nMaharashtra\tBeed\tROTHOD DIGAMBAR RAMRAO\t14166\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tBeed\tAGHAV JAYPRAKASH SITARAM\t3247\tno\tIND\tIndependent\nMaharashtra\tBeed\tMANIKRAO MADHAVRAO BAVNE\t1584\tno\tSP\tSamajwadi Party\nMaharashtra\tBeed\tSAYYED MINHAJ ALI SAYYAD WAJED ALI (PENDKHAJURWALE)\t3617\tno\tIND\tIndependent\nMaharashtra\tBeed\tRAHUL PRABHAKAR KAMBLE\t1733\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tBeed\tKALE KRISHNA BHANUDAS\t3163\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tBeed\tBHAGWAT HARIHAR TAWARE\t2698\tno\tIND\tIndependent\nMaharashtra\tBeed\tDONGARE MILIND KASHINATH\t1930\tno\tPRCP\tPrabuddha Republican Party\nMaharashtra\tBeed\tSHAIKH SHAFIQ SHABBIR\t846\tno\tIND\tIndependent\nMaharashtra\tBeed\tSAYYED SALIM FATTU\t452\tno\tIND\tIndependent\nMaharashtra\tBeed\tGANGADHAR SITARAM KALKUTE\t848\tno\tIND\tIndependent\nMaharashtra\tBeed\tNADU MADHAV\t4283\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tBeed\tJETHLIA SANTOSHBAPU  MITTHULAL\t1270\tno\tIND\tIndependent\nMaharashtra\tBeed\tINAMDAR JABBERMIYA DAGERASA\t3040\tno\tIND\tIndependent\nMaharashtra\tBeed\tHOKE (PATIL)RAJEDRA ACHYUTRAO\t1264\tno\tIND\tIndependent\nMaharashtra\tBeed\tSOLANKE PARMESHWAR KALYANRAO\t520\tno\tIND\tIndependent\nMaharashtra\tBeed\tSOLANKE PRKASH BHAGWANRAO\t8634\tno\tIND\tIndependent\nMaharashtra\tBeed\tSHAIKH IPTEKHAR ZAKEEBABA\t1628\tno\tWPOI\tWelfare Party Of India\nMaharashtra\tBeed\tTATE ASHOK SANTRAM\t1162\tno\tARP\tAmbedkarist Republican Party\nMaharashtra\tBeed\tKALE BAJIRAO SHANKAR\t1018\tno\tIND\tIndependent\nMaharashtra\tBeed\tAVINASH NAMDEV WANKHEDE\t2797\tno\tIND\tIndependent\nMaharashtra\tBeed\tSHINGARE GOVIND BHARAT\t944\tno\tSVRP\tShivrajya Party\nMaharashtra\tBeed\tTHORAT DHANRAJ VITTHAL\t1065\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tBeed\tASHOK DHONDIBA SONWANE\t5383\tno\tIND\tIndependent\nMaharashtra\tBeed\tSAYYED NAVIDUJJAMMA\t3732\tno\tIND\tIndependent\nMaharashtra\tBeed\tPRASHANT VISHNU SASANE\t1036\tno\tIND\tIndependent\nMaharashtra\tBeed\tSAKSAMUDRE BHAGWAN KACHRUBA\t1533\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tBeed\tASHOK BHAGOJI THORAT\t3264\tno\tIND\tIndependent\nMaharashtra\tBeed\tSUMITRABAI DASHRATH PAWAR\t314\tno\tIND\tIndependent\nMaharashtra\tBeed\tSHAIKH CHAND SHIAKH MIYA\t1734\tno\tIND\tIndependent\nMaharashtra\tBeed\tSADULLA ASDULLA INAMDAR\t2482\tno\tIND\tIndependent\nMaharashtra\tBeed\tVEER SHESHERAO CHOKHOBA\t713\tno\tIND\tIndependent\nMaharashtra\tBeed\tNone of the Above\t2323\tno\tNOTA\tNone of the Above\nMaharashtra\tOsmanabad\tGAIKWAD RAVINDRA VISHWANATH\t607699\tyes\tSHS\tShivsena\nMaharashtra\tOsmanabad\tNone of the Above\t4613\tno\tNOTA\tNone of the Above\nMaharashtra\tOsmanabad\tTARKASE PUSHPA MURLIDHAR\t4126\tno\tHND\tHindusthan Nirman Dal\nMaharashtra\tOsmanabad\tSHINDE RAJENDRA BHAIRAVNATH\t1313\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tOsmanabad\tPADMSINH VIJAYSINH MUNDHE\t3361\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tSAYYAD MOHAMD SAYYAD IBRAHIM\t1528\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tOsmanabad\tALTAF HUSAIN YENEGURE\t1640\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tADV. SHAILENDRA RAMESHWAR YAVALKAR\t2257\tno\tSP\tSamajwadi Party\nMaharashtra\tOsmanabad\tTUPSUNDARE BALAJI BAPURAO\t7038\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tKSHIRSAGAR VIJAY MARUTI\t1482\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tSHAIKH MUBARAK MAINODDIN\t1418\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tKAKASAHEB BABURAO RATHOD\t7245\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tTUKARAM DASRAO GANGAWANE\t10210\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tGAIKWAD RAVINDRA VISHWANATH\t7169\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tSIDDIK IBRAHIM BOUDIWALI\t1429\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tDHALE PADMASHEEL RAMCHANDRA\t28322\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tOsmanabad\tPATIL MANOHAR ANANDRAO\t2251\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tPATIL PADMASINHA BAJIRAO\t373374\tno\tNCP\tNationalist Congress Party\nMaharashtra\tOsmanabad\tDR. RAMESH SUBRAV BANSODE\t1701\tno\tRPI\tRepublican Party of India\nMaharashtra\tOsmanabad\tKSHIRSAGAR SHIVAJI JAGANNATH\t1978\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tVIKRAM ASHOK SAWALE\t4240\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tOsmanabad\tSUSHILKUMAR VINAYAK PADULE\t1963\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tROKDE MILIND SOMNATH\t3725\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tOsmanabad\tGAIKWAD UMAJI PANDURANG\t2355\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tBHOSALE REVAN VISHWANATH\t1288\tno\tJD(S)\tJanata Dal  (Secular)\nMaharashtra\tOsmanabad\tNAVNATH DASHRATH UPLEKAR\t4092\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tSMT. JADHAV UJVALA EKNATH\t3466\tno\tIND\tIndependent\nMaharashtra\tOsmanabad\tROHAN SUBHASH DESHMUKH\t26868\tno\tIND\tIndependent\nMaharashtra\tLatur\tDR. SUNIL BALIRAM GAIKWAD\t616509\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tLatur\tDIPRATNA PRABHAKARRAO NILANGEKAR\t9829\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tLatur\tBANSODE DATTATRAY GUNDERAO\t363114\tno\tINC\tIndian National Congress\nMaharashtra\tLatur\tKAMBLE DEEPAK ARVIND\t20029\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tLatur\tGAJANAN PANDURANG MANE\t3401\tno\tIND\tIndependent\nMaharashtra\tLatur\tBHARAT DAMODAR LONDHE\t2035\tno\tIND\tIndependent\nMaharashtra\tLatur\tJARIPATKE DNYANOBA SOPANRAO\t2052\tno\tIND\tIndependent\nMaharashtra\tLatur\tPADMAKAR KONDIBA OVHAL\t1941\tno\tIND\tIndependent\nMaharashtra\tLatur\tKAMBLE MILIND HARIBHAU\t942\tno\tIND\tIndependent\nMaharashtra\tLatur\tSHIVKHANDESHWAR NARAYAN DAHGE\t1337\tno\tBJSP\tBharatiya Jawala Shakti Paksha\nMaharashtra\tLatur\tJOGDAND FAKIRA VAIJNATH\t1306\tno\tIND\tIndependent\nMaharashtra\tLatur\tSURYAWANSI SIDHARTH TUKARAM\t2830\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tLatur\tBHARAT MAROTI KADAM\t2724\tno\tAPoI\tAmbedkarite Party of India\nMaharashtra\tLatur\tGAJENDRA LIMBAJI AVGHADE\t1287\tno\tIND\tIndependent\nMaharashtra\tLatur\tRAJKUMAR BHIMRAO CHANDANSHIVE\t1765\tno\tIND\tIndependent\nMaharashtra\tLatur\tBALAJI DNYANOBA KAMBLE\t1840\tno\tSP\tSamajwadi Party\nMaharashtra\tLatur\tSHINDE SUDHIR SHRIPATRAO\t8678\tno\tIND\tIndependent\nMaharashtra\tLatur\tUDARE MINAKSHI MAHADEV\t1541\tno\tIND\tIndependent\nMaharashtra\tLatur\tNone of the Above\t13996\tno\tNOTA\tNone of the Above\nMaharashtra\tSolapur\tSHARAD BANSODE\t517879\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tSolapur\tWAGHMARE PUNDALIK ONKARI\t1399\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tSolapur\tNone of the Above\t13778\tno\tNOTA\tNone of the Above\nMaharashtra\tSolapur\tSUNITA VIJAYKUMAR UGHADE\t2706\tno\tIND\tIndependent\nMaharashtra\tSolapur\tSHINDE RAJKUMAR LAXMANRAO\t1286\tno\tIND\tIndependent\nMaharashtra\tSolapur\tSONKAMBLE GAUTAM VIJAYKUMAR\t2458\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tSolapur\tRAJENDRA DNYANDEO RANDIVE\t1460\tno\tIND\tIndependent\nMaharashtra\tSolapur\tPAWAR YUVRAJ SIDRAM\t1243\tno\tIND\tIndependent\nMaharashtra\tSolapur\tSIDRAM KRISHNA DHOBALE\t1683\tno\tIND\tIndependent\nMaharashtra\tSolapur\tKEWALE CHAYA NAGNATH\t1654\tno\tIND\tIndependent\nMaharashtra\tSolapur\tSONAWANE SHIVAJI CHANDRAKANT\t5487\tno\tIND\tIndependent\nMaharashtra\tSolapur\tDATTATRAYA VITTHAL THORAT\t824\tno\tIND\tIndependent\nMaharashtra\tSolapur\tBHISE KRISHNA NAGNATH\t1504\tno\tIND\tIndependent\nMaharashtra\tSolapur\tSHINDE SUSHILKUMAR SAMBHAJIRAO\t368205\tno\tINC\tIndian National Congress\nMaharashtra\tSolapur\tADV. SANJEEV SIDRAM SADAFULE\t19041\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tSolapur\tVIKRAM UTTAM KASABE\t1333\tno\tIND\tIndependent\nMaharashtra\tSolapur\tLALIT BABAR\t9261\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMadha\tMOHITE PATIL VIJAYSINH SHANKARRAO\t489989\tyes\tNCP\tNationalist Congress Party\nMaharashtra\tMadha\tSUNITA MOHAN TUPSOUNDRYA\t2522\tno\tIND\tIndependent\nMaharashtra\tMadha\tADV. SAVITA BALKRISHNA SHINDE\t7160\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tMadha\tBANSODE KUNDAN FULCHAND\t15790\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tMadha\tSADABHAU RAMCHANDRA KHOT\t464645\tno\tSWP\tSwabhimani Paksha\nMaharashtra\tMadha\tWALKE VIKRAM VASUDEO\t1969\tno\tIND\tIndependent\nMaharashtra\tMadha\tJAGTAP NITIN MARUTI\t2189\tno\tIND\tIndependent\nMaharashtra\tMadha\tNAVANATH BHIMRAO PATIL\t8853\tno\tHiPPa\tHindusthan Praja Paksha\nMaharashtra\tMadha\tBALIRAM SUKHDEV MORE\t4139\tno\tIND\tIndependent\nMaharashtra\tMadha\tADV.SUBHASH BALASAHEB PATIL (ANNA)\t3293\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tMadha\tPRAFULL SHIVRAO KADAM\t2301\tno\tIND\tIndependent\nMaharashtra\tMadha\tDR.PRAMOD RAMCHANDRA GAWADE\t1926\tno\tIND\tIndependent\nMaharashtra\tMadha\tNAGAMANI KISAN JAKKAN\t8829\tno\tIND\tIndependent\nMaharashtra\tMadha\tSURESH SHAMRAO GHADGE\t1989\tno\tAITC\tAll India Trinamool Congress\nMaharashtra\tMadha\tBHAJANLAL YASHVANT NIMGAONKAR\t4625\tno\tIND\tIndependent\nMaharashtra\tMadha\tJADHAV SUNIL GUNDA\t7115\tno\tIND\tIndependent\nMaharashtra\tMadha\tROHIT RAMRUSHNA MORE\t6886\tno\tIND\tIndependent\nMaharashtra\tMadha\tDAGADU MANIK PAWAR\t2117\tno\tIND\tIndependent\nMaharashtra\tMadha\tSATHI BASHIR AHMED BASHAMIYAN SHAIKH\t3367\tno\tIND\tIndependent\nMaharashtra\tMadha\tPOL SUDHIR ARJUN\t2814\tno\tIND\tIndependent\nMaharashtra\tMadha\tDR.BHISE INDRAKUMAR DEVRAO\t3094\tno\tIND\tIndependent\nMaharashtra\tMadha\tSHAIKH SHAHAJAN PAIGAMBAR\t2336\tno\tIND\tIndependent\nMaharashtra\tMadha\tDEVKATE SOMANATH ALIAS SOMESHWAR CHANDRAKANT\t2275\tno\tIND\tIndependent\nMaharashtra\tMadha\tMOHITE-PATIL PRATAPSINH SHANKARRAO\t25187\tno\tIND\tIndependent\nMaharashtra\tMadha\tNone of the Above\t4209\tno\tNOTA\tNone of the Above\nMaharashtra\tSangli\tSANJAYKAKA PATIL\t611563\tyes\tBJP\tBharatiya Janata Party\nMaharashtra\tSangli\tSUREKHA ALIAS SURAIYYA SHAHIN SHAIKH\t849\tno\tMVA\tMaharashtra Vikas Aghadi\nMaharashtra\tSangli\tTANAJIRAO JAGANNATH  JADHAV(CHINCHANIKAR)\t1774\tno\tIND\tIndependent\nMaharashtra\tSangli\tASHOK DNANU MANE(BHAU)\t1494\tno\tIND\tIndependent\nMaharashtra\tSangli\tCHOPADE SHRIPRASAD JANARDAN\t4309\tno\tIND\tIndependent\nMaharashtra\tSangli\tDHATTURE HAFIZBHAI HUSAIN\t997\tno\tIND\tIndependent\nMaharashtra\tSangli\tMADHUKAR BABURAO KAMBALE\t1844\tno\tIND\tIndependent\nMaharashtra\tSangli\tSHARMILA BABA WAKSHE\t2481\tno\tIND\tIndependent\nMaharashtra\tSangli\tNone of the Above\t6110\tno\tNOTA\tNone of the Above\nMaharashtra\tSangli\tSHRIRANG BABURAO PAKHARE\t1661\tno\tIND\tIndependent\nMaharashtra\tSangli\tSURESH PARTH MANE (NANA)\t8353\tno\tIND\tIndependent\nMaharashtra\tSangli\tSAMINA ABDULMAJID KHAN\t4699\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tSangli\tBANDGAR NANASO BALASO\t11378\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tSangli\tADV. K. D. SHINDE\t2460\tno\tJD(S)\tJanata Dal  (Secular)\nMaharashtra\tSangli\tSIDDHA SHIVAPPA BHOSALE\t4309\tno\tIND\tIndependent\nMaharashtra\tSangli\tPATIL PRATIK PRAKASHBAPU\t372271\tno\tINC\tIndian National Congress\nMaharashtra\tSangli\tPOURNIMA VILAS KORPALE\t1702\tno\tIND\tIndependent\nMaharashtra\tSangli\tNITIN GANPATRAO SAVGAVE\t8405\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tSatara\tSHRIMANT CHH.  UDAYANRAJE PRATAPSINHA BHONSALE\t522531\tyes\tNCP\tNationalist Congress Party\nMaharashtra\tSatara\tBICHUKALE ABHIJIT VAMANRAO\t3644\tno\tIND\tIndependent\nMaharashtra\tSatara\tCHANDRAKANT TATU KHANDAIT\t12270\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tSatara\tSANDIP AMRUTRAO MOZAR\t18215\tno\tIND\tIndependent\nMaharashtra\tSatara\tKADAM SAGAR UTTAMRAO\t10047\tno\tIND\tIndependent\nMaharashtra\tSatara\tRAJENDRA MADHUKAR CHORAGE\t82489\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tSatara\tPRASHANT VASANT CHAVAN\t14523\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tSatara\tALTAF ABDULGANI SHIKALGAR\t13760\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tSatara\tASHOK WAMAN GAIKWAD\t71808\tno\tRPI(A)\tRepublican Party of India (A)\nMaharashtra\tSatara\tGAWDE SUKHDEV BHANUDAS\t2435\tno\tIND\tIndependent\nMaharashtra\tSatara\tPURUSHOTTAM JADHAV\t155937\tno\tIND\tIndependent\nMaharashtra\tSatara\tNone of the Above\t10589\tno\tNOTA\tNone of the Above\nMaharashtra\tSatara\tADV. VARSHA  MADGULKAR\t9405\tno\tIND\tIndependent\nMaharashtra\tSatara\tPANDURANG RAMCHANDRA SHINDE\t11304\tno\tIND\tIndependent\nMaharashtra\tSatara\tDR. PRAKASH SHAMRAO PAWAR\t2164\tno\tIND\tIndependent\nMaharashtra\tSatara\tSUBHASH NIVRUTTI SHILEWANT\t15073\tno\tIND\tIndependent\nMaharashtra\tSatara\tUMESH MUKUND WAGHMARE\t4711\tno\tIND\tIndependent\nMaharashtra\tSatara\tSUHAS VISHWASRAO DESHMUKH\t12122\tno\tIND\tIndependent\nMaharashtra\tSatara\tDR. VIJAYSINHA  DILIPRAO PATIL\t3654\tno\tIND\tIndependent\nMaharashtra\tRatnagiri - sindhudurg\tVINAYAK BHAURAO RAUT\t493088\tyes\tSHS\tShivsena\nMaharashtra\tRatnagiri - sindhudurg\tADV. DIPAK DATTARAM NEVGI\t2904\tno\tSP(I)\tSocialist Party (India)\nMaharashtra\tRatnagiri - sindhudurg\tAYARE RAJENDRA LAHU\t13088\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tRatnagiri - sindhudurg\tVINOD HANUMANTRAO SAWANT\t1395\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nMaharashtra\tRatnagiri - sindhudurg\tAJINKYA DHONDU GAWADE\t2332\tno\tIND\tIndependent\nMaharashtra\tRatnagiri - sindhudurg\tARUN HARISHCHANDRA MANJREKAR\t5999\tno\tIND\tIndependent\nMaharashtra\tRatnagiri - sindhudurg\tSUNIL ALIAS YASHWANT VASANT PEDNEKAR\t7328\tno\tIND\tIndependent\nMaharashtra\tRatnagiri - sindhudurg\tNILESH NARAYAN RANE\t343037\tno\tINC\tIndian National Congress\nMaharashtra\tRatnagiri - sindhudurg\tABHIJIT SHRIRAM HEGSHETYE\t12700\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tRatnagiri - sindhudurg\tBIRJE YASHWANT ANANT\t1992\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tRatnagiri - sindhudurg\tNone of the Above\t12393\tno\tNOTA\tNone of the Above\nMaharashtra\tKolhapur\tDHANANJAY BHIMRAO MAHADIK\t607665\tyes\tNCP\tNationalist Congress Party\nMaharashtra\tKolhapur\tNone of the Above\t7015\tno\tNOTA\tNone of the Above\nMaharashtra\tKolhapur\tNARAYAN GUNDU POWAR\t5495\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tKolhapur\tKURANE AJAY PRAKASH\t9291\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tKolhapur\tKAMBLE SUKUMAR DATTU\t915\tno\tIND\tIndependent\nMaharashtra\tKolhapur\tPATIL BAJRANG KRISHNA\t5676\tno\tIND\tIndependent\nMaharashtra\tKolhapur\tKAMBLE DAYANAND VASANT\t8635\tno\tBMUP\tBahujan Mukti Party\nMaharashtra\tKolhapur\tJEEVAN ALIAS C.A.PATIL\t898\tno\tIND\tIndependent\nMaharashtra\tKolhapur\tMHAMANE ANIL SUBHASH\t736\tno\tIND\tIndependent\nMaharashtra\tKolhapur\tATUL PRASANNAKUMAR DIGHE\t7067\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tKolhapur\tYASHWANT BALKRUSHNA JOSHI\t1415\tno\tIND\tIndependent\nMaharashtra\tKolhapur\tSAMPATRAO SHAMRAO PAWAR PATIL\t13162\tno\tPWPI\tPeasants And Workers Party of India\nMaharashtra\tKolhapur\tSANJAY SADASHIV MANDALIK\t574406\tno\tSHS\tShivsena\nMaharashtra\tKolhapur\tPANDURANG TUKARAM CHOUGALE\t836\tno\tIND\tIndependent\nMaharashtra\tKolhapur\tSANDEEP GUNDOPANT SANKPAL\t10963\tno\tIND\tIndependent\nMaharashtra\tKolhapur\tSHEREGAR CHETAN VENKATESH\t6114\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tRAJU SHETTY\t640428\tyes\tSWP\tSwabhimani Paksha\nMaharashtra\tHatkanangle\tSUSHANT SANTOSH DIGE\t2999\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tAWADE KALLAPPA BABURAO\t462618\tno\tINC\tIndian National Congress\nMaharashtra\tHatkanangle\tKAMBLE SUKUMAR DATTU\t2342\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tRAGHUNATHDADA PATIL\t9015\tno\tAAAP\tAam Aadmi Party\nMaharashtra\tHatkanangle\tYUVRAJ SHIVAJI KAMBLE\t6585\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tTHORAT ANANDRAO TUKARAM\t2637\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tPATIL S R TATYA\t3318\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tSURESHDADA PATIL\t25648\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tDIGAMBAR ALIAS GABBAR SUDAM SAKAT\t5186\tno\tBBM\tBharipa Bahujan Mahasangh\nMaharashtra\tHatkanangle\tKAMBLE CHANDRAKANT TUKARAM\t11499\tno\tBSP\tBahujan Samaj Party\nMaharashtra\tHatkanangle\tMANE ARVIND BHIVA\t2361\tno\tIND\tIndependent\nMaharashtra\tHatkanangle\tSHAIKH SHABBIR NABI\t4176\tno\tMPS(T)\tMaharashtra Parivartan Sena (T)\nMaharashtra\tHatkanangle\tNone of the Above\t10059\tno\tNOTA\tNone of the Above\nManipur\tInner manipur\tDR. THOKCHOM MEINYA\t292102\tyes\tINC\tIndian National Congress\nManipur\tInner manipur\tDR. IBOMCHA SINGH\t3275\tno\tAAAP\tAam Aadmi Party\nManipur\tInner manipur\tOINAM JOTIN SINGH\t1403\tno\tJMBP\tJai Maha Bharath Party\nManipur\tInner manipur\tSARANGTHEM MANAOBI\t39903\tno\tAITC\tAll India Trinamool Congress\nManipur\tInner manipur\tDR. GURUMAYUM TONSANA SHARMA\t2005\tno\tMDPF\tManipur Democratic Peoples's Front\nManipur\tInner manipur\tR.K. RANJAN SINGH\t92443\tno\tBJP\tBharatiya Janata Party\nManipur\tInner manipur\tINDIRA OINAM\t7014\tno\tIND\tIndependent\nManipur\tInner manipur\tMOIRANGTHEM NARA\t197428\tno\tCPI\tCommunist Party of India\nManipur\tInner manipur\tNone of the Above\t5298\tno\tNOTA\tNone of the Above\nManipur\tOuter manipur\tTHANGSO BAITE\t296770\tyes\tINC\tIndian National Congress\nManipur\tOuter manipur\tNone of the Above\t2206\tno\tNOTA\tNone of the Above\nManipur\tOuter manipur\tLAMLALMOI @ MOMOI GANGTE\t603\tno\tIND\tIndependent\nManipur\tOuter manipur\tM. KHAMCHINPAU @ K. ZOU\t4331\tno\tAAAP\tAam Aadmi Party\nManipur\tOuter manipur\tAMARSON SANKHIL\t401\tno\tIND\tIndependent\nManipur\tOuter manipur\tSOSO LORHO\t281133\tno\tNPF\tNaga Peoples Front\nManipur\tOuter manipur\tLAMMINLIEN @ LIEN GANGTE\t1085\tno\tJD(U)\tJanata Dal  (United)\nManipur\tOuter manipur\tMANI CHARENAMEI\t34995\tno\tIND\tIndependent\nManipur\tOuter manipur\tKIM GANGTE\t12752\tno\tAITC\tAll India Trinamool Congress\nManipur\tOuter manipur\tCHUNGKHOKAI DOUNGEL\t61662\tno\tNCP\tNationalist Congress Party\nManipur\tOuter manipur\tGANGMUMEI KAMEI\t75828\tno\tBJP\tBharatiya Janata Party\nMeghalaya\tShillong\tVINCENT H. PALA\t209340\tyes\tINC\tIndian National Congress\nMeghalaya\tShillong\tARMERINGTON KHARSHIING\t8815\tno\tAAAP\tAam Aadmi Party\nMeghalaya\tShillong\tPAUL LYNGDOH\t106817\tno\tUDP\tUnited Democratic Party\nMeghalaya\tShillong\tRICHARD D. SHABONG\t7418\tno\tCPI\tCommunist Party of India\nMeghalaya\tShillong\tSHIBUN LYNGDOH\t95979\tno\tBJP\tBharatiya Janata Party\nMeghalaya\tShillong\tDENIS SIANGSHAI\t5092\tno\tIND\tIndependent\nMeghalaya\tShillong\tIVORYNA SHYLLA\t6605\tno\tIND\tIndependent\nMeghalaya\tShillong\tPRECHARD B. M. BASAIAWMOIT\t168961\tno\tIND\tIndependent\nMeghalaya\tShillong\tNone of the Above\t10960\tno\tNOTA\tNone of the Above\nMeghalaya\tTura\tPURNO AGITOK SANGMA\t239301\tyes\tNPEP\tNational Peoples Party\nMeghalaya\tTura\tNone of the Above\t19185\tno\tNOTA\tNone of the Above\nMeghalaya\tTura\tDARYL WILLIAM CH MOMIN\t199585\tno\tINC\tIndian National Congress\nMizoram\tMIZORAM\tC. L. RUALA\t210485\tyes\tINC\tIndian National Congress\nMizoram\tMIZORAM\tM.LALMANZUALA\t11890\tno\tAAAP\tAam Aadmi Party\nMizoram\tMIZORAM\tNone of the Above\t6495\tno\tNOTA\tNone of the Above\nMizoram\tMIZORAM\tROBERT ROMAWIA ROYTE\t204331\tno\tIND\tIndependent\nNagaland\tNagaland\tNEIPHIU RIO\t713372\tyes\tNPF\tNaga Peoples Front\nNagaland\tNagaland\tAKHEI ACHUMI\t9695\tno\tSP(I)\tSocialist Party (India)\nNagaland\tNagaland\tNone of the Above\t2696\tno\tNOTA\tNone of the Above\nNagaland\tNagaland\tK. V. PUSA\t313147\tno\tINC\tIndian National Congress\nOdisha\tBargarh\tPRABHAS KUMAR SINGH\t383230\tyes\tBJD\tBiju Janata Dal\nOdisha\tBargarh\tLINGARAJ PRADHAN\t15672\tno\tAAAP\tAam Aadmi Party\nOdisha\tBargarh\tSUBASH CHOUHAN\t372052\tno\tBJP\tBharatiya Janata Party\nOdisha\tBargarh\tASHOK BISI\t21100\tno\tCPI\tCommunist Party of India\nOdisha\tBargarh\tKULAMANI URMA\t26216\tno\tPCHVP\tPaschimanchal Vikas Party\nOdisha\tBargarh\tASHOK MITTAL\t7912\tno\tAITC\tAll India Trinamool Congress\nOdisha\tBargarh\tGAYAN CHAND\t8009\tno\tBSP\tBahujan Samaj Party\nOdisha\tBargarh\tSANJAY BHOI\t274610\tno\tINC\tIndian National Congress\nOdisha\tBargarh\tNone of the Above\t14500\tno\tNOTA\tNone of the Above\nOdisha\tSundargarh\tJUAL @ JUEL ORAM\t340508\tyes\tBJP\tBharatiya Janata Party\nOdisha\tSundargarh\tNone of the Above\t15835\tno\tNOTA\tNone of the Above\nOdisha\tSundargarh\tDILIP KUMAR TIRKEY\t321679\tno\tBJD\tBiju Janata Dal\nOdisha\tSundargarh\tSAGAR SINGH MANAKI\t8448\tno\tKOKD\tKosal Kranti Dal\nOdisha\tSundargarh\tJUSTIN LUGUN\t9472\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nOdisha\tSundargarh\tMANSID EKKA\t7282\tno\tIND\tIndependent\nOdisha\tSundargarh\tBASIL EKKA\t7429\tno\tAAAP\tAam Aadmi Party\nOdisha\tSundargarh\tLAWRENCIA LAKRA\t9001\tno\tIND\tIndependent\nOdisha\tSundargarh\tLILY PREMA KUJUR\t3903\tno\tBMUP\tBahujan Mukti Party\nOdisha\tSundargarh\tSATISH KUMAR MINZ\t3951\tno\tIND\tIndependent\nOdisha\tSundargarh\tHEMANAND BISWAL\t269335\tno\tINC\tIndian National Congress\nOdisha\tSundargarh\tDAS CHARAN MAJHI\t4408\tno\tJDP\tJharkhand Disom Party\nOdisha\tSundargarh\tBAGI LAKRA\t9460\tno\tBSP\tBahujan Samaj Party\nOdisha\tSambalpur\tNAGENDRA KUMAR PRADHAN\t358618\tyes\tBJD\tBiju Janata Dal\nOdisha\tSambalpur\tSHANKARLAL AGRAWALA\t3110\tno\tPBI\tProutist Bloc, India\nOdisha\tSambalpur\tTARANI BHOI\t2559\tno\tPCHVP\tPaschimanchal Vikas Party\nOdisha\tSambalpur\tNATHU RAM\t12211\tno\tBSP\tBahujan Samaj Party\nOdisha\tSambalpur\tMD. ALI HUSSAIN\t4763\tno\tIND\tIndependent\nOdisha\tSambalpur\tASHOK PANDA\t4018\tno\tIND\tIndependent\nOdisha\tSambalpur\tSURESH PUJARI\t328042\tno\tBJP\tBharatiya Janata Party\nOdisha\tSambalpur\tAMARNATH PRADHAN\t242131\tno\tINC\tIndian National Congress\nOdisha\tSambalpur\tANANDA KUMAR SAMAL\t7029\tno\tAAAP\tAam Aadmi Party\nOdisha\tSambalpur\tDUSMANTA KAR\t4012\tno\tIND\tIndependent\nOdisha\tSambalpur\tPRASADI PRADHAN\t4502\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nOdisha\tSambalpur\tNone of the Above\t13314\tno\tNOTA\tNone of the Above\nOdisha\tKeonjhar\tSAKUNTALA LAGURI\t434471\tyes\tBJD\tBiju Janata Dal\nOdisha\tKeonjhar\tSUDARSHAN LOHAR\t16299\tno\tRAIM\tRashtriya Indepndent Morcha\nOdisha\tKeonjhar\tSUJIT KUMAR LUGUN\t16420\tno\tIND\tIndependent\nOdisha\tKeonjhar\tBISHNUPRIYA NAIK\t12236\tno\tojm\tOdisha Jan Morcha\nOdisha\tKeonjhar\tJAGAT MOHAN NAIK\t8694\tno\tBSP\tBahujan Samaj Party\nOdisha\tKeonjhar\tBASUDEB NAIK\t101999\tno\tIND\tIndependent\nOdisha\tKeonjhar\tANANTA NAYAK\t277154\tno\tBJP\tBharatiya Janata Party\nOdisha\tKeonjhar\tMADHABA SARDAR\t190531\tno\tINC\tIndian National Congress\nOdisha\tKeonjhar\tNone of the Above\t26065\tno\tNOTA\tNone of the Above\nOdisha\tMayurbhanj\tRAMA CHANDRA HANSDAH\t393779\tyes\tBJD\tBiju Janata Dal\nOdisha\tMayurbhanj\tNone of the Above\t23517\tno\tNOTA\tNone of the Above\nOdisha\tMayurbhanj\tDEVI PRASANNA BESRA\t8133\tno\tIND\tIndependent\nOdisha\tMayurbhanj\tNEPOLE RAGHU MURMU\t270913\tno\tBJP\tBharatiya Janata Party\nOdisha\tMayurbhanj\tANTA ALOK BASKEY\t7052\tno\tAJSUP\tAJSU Party\nOdisha\tMayurbhanj\tDASHMAT SOREN\t12041\tno\tBSP\tBahujan Samaj Party\nOdisha\tMayurbhanj\tDEBASHIS MARNDI\t172984\tno\tJMM\tJharkhand Mukti Morcha\nOdisha\tMayurbhanj\tCHHABI MAHALI\t8183\tno\tAAAP\tAam Aadmi Party\nOdisha\tMayurbhanj\tPRABHUDAN MARANDI\t14632\tno\tAOP\tAama Odisha Party\nOdisha\tMayurbhanj\tSHYAM SUNDAR HANSDAH\t142165\tno\tINC\tIndian National Congress\nOdisha\tBalasore\tRABINDRA KUMAR JENA\t433768\tyes\tBJD\tBiju Janata Dal\nOdisha\tBalasore\tBICHITRA NANDA DAS\t3240\tno\tBSP\tBahujan Samaj Party\nOdisha\tBalasore\tSHRIKANTA KUMAR JENA\t277517\tno\tINC\tIndian National Congress\nOdisha\tBalasore\tPRATAP CHANDRA SARANGI\t291943\tno\tBJP\tBharatiya Janata Party\nOdisha\tBalasore\tKARTIKESWAR PATRA\t9856\tno\tAITC\tAll India Trinamool Congress\nOdisha\tBalasore\tBISWAJIT RAY\t1823\tno\tIND\tIndependent\nOdisha\tBalasore\tJAGANNATH DAS\t1856\tno\tIND\tIndependent\nOdisha\tBalasore\tNone of the Above\t7716\tno\tNOTA\tNone of the Above\nOdisha\tBalasore\tPRASANTA KUMAR MISHRA\t14538\tno\tCPI\tCommunist Party of India\nOdisha\tBalasore\tMAMATA KUNDU\t2664\tno\tAAAP\tAam Aadmi Party\nOdisha\tBalasore\tSAROJ KUMAR PANDA\t4529\tno\tIND\tIndependent\nOdisha\tBhadrak\tARJUN CHARAN SETHI\t502338\tyes\tBJD\tBiju Janata Dal\nOdisha\tBhadrak\tNone of the Above\t6750\tno\tNOTA\tNone of the Above\nOdisha\tBhadrak\tSUSANTA KUMAR JENA\t2639\tno\tIND\tIndependent\nOdisha\tBhadrak\tARJUN CHARAN MALLIK\t7607\tno\tBSP\tBahujan Samaj Party\nOdisha\tBhadrak\tSARAT DAS\t216617\tno\tBJP\tBharatiya Janata Party\nOdisha\tBhadrak\tNARAYAN CHANDRA JENA\t5961\tno\tAAAP\tAam Aadmi Party\nOdisha\tBhadrak\tSANGRAM KESHARI JENA\t322979\tno\tINC\tIndian National Congress\nOdisha\tBhadrak\tRAMESH JENA\t11930\tno\tCPI\tCommunist Party of India\nOdisha\tBhadrak\tNETRANANDA MALLICK\t4518\tno\tAOP\tAama Odisha Party\nOdisha\tJajpur\tRITA TARAI\t541349\tyes\tBJD\tBiju Janata Dal\nOdisha\tJajpur\tRANJIT MALIK\t3409\tno\tBMUP\tBahujan Mukti Party\nOdisha\tJajpur\tNABIN KUMAR DALAI\t29142\tno\tIND\tIndependent\nOdisha\tJajpur\tGAYADHAR MALLICK\t2753\tno\tskd\tSamata Kranti Dal\nOdisha\tJajpur\tDEBENDRA KUMAR MALLIK\t6926\tno\tBSP\tBahujan Samaj Party\nOdisha\tJajpur\tAMIYA KANTA MALLICK\t150789\tno\tBJP\tBharatiya Janata Party\nOdisha\tJajpur\tASOK DAS\t221078\tno\tINC\tIndian National Congress\nOdisha\tJajpur\tNone of the Above\t8655\tno\tNOTA\tNone of the Above\nOdisha\tJajpur\tKALANDI MALIK\t8269\tno\tAAAP\tAam Aadmi Party\nOdisha\tJajpur\tSUBAS CHANDRA MALLICK\t8065\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nOdisha\tDhenkanal\tTATHAGATA SATPATHY\t453277\tyes\tBJD\tBiju Janata Dal\nOdisha\tDhenkanal\tJIMUTA PRASAD MISHRA\t5120\tno\tAAAP\tAam Aadmi Party\nOdisha\tDhenkanal\tPRIYABRATA GARNAIK\t2039\tno\tskd\tSamata Kranti Dal\nOdisha\tDhenkanal\tKALAKAR NAIK\t8712\tno\tAPoI\tAmbedkarite Party of India\nOdisha\tDhenkanal\tPREMANANDA SAHOO\t6574\tno\tBSP\tBahujan Samaj Party\nOdisha\tDhenkanal\tNIRMAL SAMAL\t7505\tno\tIND\tIndependent\nOdisha\tDhenkanal\tPRADYUMNA KUMAR NAIK\t2298\tno\tBMUP\tBahujan Mukti Party\nOdisha\tDhenkanal\tSUDHIR KUMAR SAMAL\t213794\tno\tINC\tIndian National Congress\nOdisha\tDhenkanal\tSUPARNO SATPATHY\t5447\tno\tAOP\tAama Odisha Party\nOdisha\tDhenkanal\tER. GURU PRASAD PRADHAN\t3918\tno\tIND\tIndependent\nOdisha\tDhenkanal\tRUDRA NARAYAN PANY\t315937\tno\tBJP\tBharatiya Janata Party\nOdisha\tDhenkanal\tMANASI SWAIN\t4275\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nOdisha\tDhenkanal\tNone of the Above\t13205\tno\tNOTA\tNone of the Above\nOdisha\tBolangir\tKALIKESH NARAYAN SINGH DEO\t453519\tyes\tBJD\tBiju Janata Dal\nOdisha\tBolangir\tNone of the Above\t21043\tno\tNOTA\tNone of the Above\nOdisha\tBolangir\tACHUTANANDA NAG\t14626\tno\tAPoI\tAmbedkarite Party of India\nOdisha\tBolangir\tNABA KRUSHNA DANTA\t9915\tno\tIND\tIndependent\nOdisha\tBolangir\tSHANKAR KUMAR PATRA\t8438\tno\tAOP\tAama Odisha Party\nOdisha\tBolangir\tSARASWATI NANDA\t24515\tno\tAAAP\tAam Aadmi Party\nOdisha\tBolangir\tSARAT PATTNAYAK\t277616\tno\tINC\tIndian National Congress\nOdisha\tBolangir\tRANA NAG\t11513\tno\tBSP\tBahujan Samaj Party\nOdisha\tBolangir\tSANGEETA KUMARI SINGH DEO\t349220\tno\tBJP\tBharatiya Janata Party\nOdisha\tKalahandi\tARKA KESHARI DEO\t370871\tyes\tBJD\tBiju Janata Dal\nOdisha\tKalahandi\tBIGHNA RAJ PANDA\t8600\tno\tSAMO\tSamruddha Odisha\nOdisha\tKalahandi\tPARAMESWAR KAND\t4435\tno\tskd\tSamata Kranti Dal\nOdisha\tKalahandi\tSATYA NARAYAN MAHAR\t6360\tno\tAAAP\tAam Aadmi Party\nOdisha\tKalahandi\tSUNIL CHANDRA NAYAK\t7329\tno\tojm\tOdisha Jan Morcha\nOdisha\tKalahandi\tPRADIPTA KUMAR NAIK\t314524\tno\tBJP\tBharatiya Janata Party\nOdisha\tKalahandi\tBHAKTA CHARAN DAS\t307967\tno\tINC\tIndian National Congress\nOdisha\tKalahandi\tBAIKUNTHA DHANGADA MAJHI\t5007\tno\tKS\tKalinga Sena\nOdisha\tKalahandi\tSIBA HATI\t18869\tno\tSP\tSamajwadi Party\nOdisha\tKalahandi\tSAHADEV JAL\t5858\tno\tBMUP\tBahujan Mukti Party\nOdisha\tKalahandi\tSAROJ KUMAR NAIK\t14115\tno\tBSP\tBahujan Samaj Party\nOdisha\tKalahandi\tRAJENDRA KUMAR MUND\t10839\tno\tIND\tIndependent\nOdisha\tKalahandi\tDAITARY PRADHAN\t8698\tno\tAOP\tAama Odisha Party\nOdisha\tKalahandi\tBALARAM HOTA\t4885\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nOdisha\tKalahandi\tCHHABILAL NIAL\t8299\tno\tAPoI\tAmbedkarite Party of India\nOdisha\tKalahandi\tNone of the Above\t20880\tno\tNOTA\tNone of the Above\nOdisha\tNabarangpur\tBALABHADRA MAJHI\t373887\tyes\tBJD\tBiju Janata Dal\nOdisha\tNabarangpur\tGOPAL PUJARI\t18675\tno\tAOP\tAama Odisha Party\nOdisha\tNabarangpur\tARJUNA BHATARA\t24832\tno\tojm\tOdisha Jan Morcha\nOdisha\tNabarangpur\tPRADEEP KUMAR MAJHI\t371845\tno\tINC\tIndian National Congress\nOdisha\tNabarangpur\tCHANDRADHWAJA MAJHI\t18388\tno\tIND\tIndependent\nOdisha\tNabarangpur\tPARSURAM MAJHI\t138430\tno\tBJP\tBharatiya Janata Party\nOdisha\tNabarangpur\tGOPINATH JANI\t31707\tno\tBSP\tBahujan Samaj Party\nOdisha\tNabarangpur\tNone of the Above\t44408\tno\tNOTA\tNone of the Above\nOdisha\tKandhamal\tHEMENDRA CHANDRA SINGH\t421458\tyes\tBJD\tBiju Janata Dal\nOdisha\tKandhamal\tNARENDRA MOHANTY\t9522\tno\tAAAP\tAam Aadmi Party\nOdisha\tKandhamal\tHARIHAR KARAN\t240441\tno\tINC\tIndian National Congress\nOdisha\tKandhamal\tSUKANTA KUMAR PANIGRAHI\t108744\tno\tBJP\tBharatiya Janata Party\nOdisha\tKandhamal\tLAMBODAR KANHAR\t20472\tno\tIND\tIndependent\nOdisha\tKandhamal\tRAM NAYAK\t11080\tno\tBSP\tBahujan Samaj Party\nOdisha\tKandhamal\tBILASINI NAYAK\t7741\tno\tAOP\tAama Odisha Party\nOdisha\tKandhamal\tBASANTA KUMAR PRADHAN\t5798\tno\tIND\tIndependent\nOdisha\tKandhamal\tNone of the Above\t14159\tno\tNOTA\tNone of the Above\nOdisha\tCuttack\tBHARTRUHARI MAHATAB\t526085\tyes\tBJD\tBiju Janata Dal\nOdisha\tCuttack\tSANJAY KUMAR SAHOO\t5733\tno\tIND\tIndependent\nOdisha\tCuttack\tPRAMOD KUMAR MALLICK\t3639\tno\tBSP\tBahujan Samaj Party\nOdisha\tCuttack\tJIBESH KISHORE SUNDARAY\t2082\tno\tAITC\tAll India Trinamool Congress\nOdisha\tCuttack\tMAHAMEGHABAHAN AIRA KHARABELA SWAIN\t58246\tno\tAOP\tAama Odisha Party\nOdisha\tCuttack\tBIKASH DAS\t4531\tno\tAAAP\tAam Aadmi Party\nOdisha\tCuttack\tSAMIR DEY\t146093\tno\tBJP\tBharatiya Janata Party\nOdisha\tCuttack\tSABYASACHI MAHAPATRA\t1812\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nOdisha\tCuttack\tAPARAJITA  MOHANTY\t219323\tno\tINC\tIndian National Congress\nOdisha\tCuttack\tBINAPANI DAS\t2171\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nOdisha\tCuttack\tNone of the Above\t8889\tno\tNOTA\tNone of the Above\nOdisha\tKendrapara\tBAIJAYANT PANDA\t601574\tyes\tBJD\tBiju Janata Dal\nOdisha\tKendrapara\tSUKUMAR MALLICK\t2221\tno\tIND\tIndependent\nOdisha\tKendrapara\tSK. FARUK\t6199\tno\tBSP\tBahujan Samaj Party\nOdisha\tKendrapara\tDHARANIDHAR NAYAK\t392466\tno\tINC\tIndian National Congress\nOdisha\tKendrapara\tMD.SAMIM AKTAR\t6029\tno\tAAAP\tAam Aadmi Party\nOdisha\tKendrapara\tBISHNU PRASAD DAS\t118707\tno\tBJP\tBharatiya Janata Party\nOdisha\tKendrapara\tPRAMOD SAHU\t6193\tno\tIND\tIndependent\nOdisha\tKendrapara\tNone of the Above\t7610\tno\tNOTA\tNone of the Above\nOdisha\tJagatsinghpur\tKULAMANI SAMAL\t624492\tyes\tBJD\tBiju Janata Dal\nOdisha\tJagatsinghpur\tNone of the Above\t7624\tno\tNOTA\tNone of the Above\nOdisha\tJagatsinghpur\tPRADIP KUMAR MALLICK\t6895\tno\tBSP\tBahujan Samaj Party\nOdisha\tJagatsinghpur\tBIBHU PRASAD TARAI\t348098\tno\tINC\tIndian National Congress\nOdisha\tJagatsinghpur\tBAIDHAR MALLICK\t117448\tno\tBJP\tBharatiya Janata Party\nOdisha\tJagatsinghpur\tAJAY KUMAR BEHERA\t18099\tno\tCPI\tCommunist Party of India\nOdisha\tJagatsinghpur\tMANAS KUMAR BEHERA\t3347\tno\tIND\tIndependent\nOdisha\tJagatsinghpur\tANUPAMA SETHY\t5929\tno\tAAAP\tAam Aadmi Party\nOdisha\tPuri\tPINAKI MISRA\t523161\tyes\tBJD\tBiju Janata Dal\nOdisha\tPuri\tASHOK PRADHAN\t6969\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nOdisha\tPuri\tNIRAD BARAN KHUNTIA\t4262\tno\tAAAP\tAam Aadmi Party\nOdisha\tPuri\tBHASKAR CHANDRA MOHANTY\t7094\tno\tBSP\tBahujan Samaj Party\nOdisha\tPuri\tSUCHARITA MOHANTY\t259800\tno\tINC\tIndian National Congress\nOdisha\tPuri\tSABYASACHI MOHAPATRA\t13190\tno\tKS\tKalinga Sena\nOdisha\tPuri\tASHOK SAHU\t215763\tno\tBJP\tBharatiya Janata Party\nOdisha\tPuri\tNone of the Above\t9150\tno\tNOTA\tNone of the Above\nOdisha\tBhubaneswar\tPRASANNA KUMAR PATASANI\t439252\tyes\tBJD\tBiju Janata Dal\nOdisha\tBhubaneswar\tNone of the Above\t8140\tno\tNOTA\tNone of the Above\nOdisha\tBhubaneswar\tBIJAY MOHANTY\t145783\tno\tINC\tIndian National Congress\nOdisha\tBhubaneswar\tMANAS RANJAN MISHRA\t3783\tno\tIND\tIndependent\nOdisha\tBhubaneswar\tPRITHIVIRAJ HARICHANDAN\t249775\tno\tBJP\tBharatiya Janata Party\nOdisha\tBhubaneswar\tKULAMANI NAYAK\t1128\tno\tPBI\tProutist Bloc, India\nOdisha\tBhubaneswar\tRABINDRA NATH BEHERA\t1745\tno\tSP\tSamajwadi Party\nOdisha\tBhubaneswar\tBISMAYA MAHAPATRA\t7112\tno\tAAAP\tAam Aadmi Party\nOdisha\tBhubaneswar\tMADHU SUDAN YADAV\t3457\tno\tBSP\tBahujan Samaj Party\nOdisha\tBhubaneswar\tSUNJOY HANS\t29505\tno\tAOP\tAama Odisha Party\nOdisha\tBhubaneswar\tPRANA KRUSHNA DALABEHERA\t1194\tno\tSAMO\tSamruddha Odisha\nOdisha\tBhubaneswar\tPRAMILA BEHERA\t948\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nOdisha\tAska\tLADU KISHORE SWAIN\t541473\tyes\tBJD\tBiju Janata Dal\nOdisha\tAska\tSRILOKANATH RATHA\t229476\tno\tINC\tIndian National Congress\nOdisha\tAska\tKALI CHARANA NAYAK\t5192\tno\tIND\tIndependent\nOdisha\tAska\tBIJAYA KUMAR MAHAPATRO\t9127\tno\tBSP\tBahujan Samaj Party\nOdisha\tAska\tBANSIDHAR TRIPATHY\t7553\tno\tAITC\tAll India Trinamool Congress\nOdisha\tAska\tMAHESH CHANDRA MOHANTY\t67361\tno\tBJP\tBharatiya Janata Party\nOdisha\tAska\tPRABHAT KUMAR MAHANTY\t11063\tno\tAAAP\tAam Aadmi Party\nOdisha\tAska\tK. SHYAMBABU SUBUDHI\t5293\tno\tIND\tIndependent\nOdisha\tAska\tSANKAR SAHU\t4361\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nOdisha\tAska\tNone of the Above\t15382\tno\tNOTA\tNone of the Above\nOdisha\tBerhampur\tSIDHANT MOHAPATRA\t398107\tyes\tBJD\tBiju Janata Dal\nOdisha\tBerhampur\tDHANADA KANTA MISHRA\t8248\tno\tAAAP\tAam Aadmi Party\nOdisha\tBerhampur\tK. SHYAMBABU SUBUDHI\t3383\tno\tIND\tIndependent\nOdisha\tBerhampur\tALI KISHOR PATNAIK\t35968\tno\tCPM\tCommunist Party of India  (Marxist)\nOdisha\tBerhampur\tSRIHARI PATANAIK\t4081\tno\tIND\tIndependent\nOdisha\tBerhampur\tA. RAGHUNATH  VARMA\t3719\tno\tIND\tIndependent\nOdisha\tBerhampur\tRAMA CHANDRA PANDA\t158811\tno\tBJP\tBharatiya Janata Party\nOdisha\tBerhampur\tMANOJ CHOUDHURY\t3314\tno\tAITC\tAll India Trinamool Congress\nOdisha\tBerhampur\tCHANDRA SEKHAR SAHU\t270387\tno\tINC\tIndian National Congress\nOdisha\tBerhampur\tBIDHAN CHANDRA PANDA\t2773\tno\tskd\tSamata Kranti Dal\nOdisha\tBerhampur\tASWINI KUMAR PADHY\t3865\tno\tBSP\tBahujan Samaj Party\nOdisha\tBerhampur\tNone of the Above\t12706\tno\tNOTA\tNone of the Above\nOdisha\tKoraput\tJHINA HIKAKA\t395109\tyes\tBJD\tBiju Janata Dal\nOdisha\tKoraput\tPRAKASH HIKAKA\t9609\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nOdisha\tKoraput\tGIRIDHAR GAMANG\t375781\tno\tINC\tIndian National Congress\nOdisha\tKoraput\tSIBASANKAR ULAKA\t89788\tno\tBJP\tBharatiya Janata Party\nOdisha\tKoraput\tLINGARAJ GOMANGO\t13861\tno\tIND\tIndependent\nOdisha\tKoraput\tMEGHANADA SABAR\t13343\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nOdisha\tKoraput\tKAILASH SABARA\t8910\tno\tskd\tSamata Kranti Dal\nOdisha\tKoraput\tPRASKA RENGA\t34957\tno\tBSP\tBahujan Samaj Party\nOdisha\tKoraput\tBANAMALI MAJHI\t14854\tno\tAPoI\tAmbedkarite Party of India\nOdisha\tKoraput\tNone of the Above\t33232\tno\tNOTA\tNone of the Above\nPunjab\tGurdaspur\tVINOD KHANNA\t482255\tyes\tBJP\tBharatiya Janata Party\nPunjab\tGurdaspur\tNone of the Above\t4625\tno\tNOTA\tNone of the Above\nPunjab\tGurdaspur\tROHIT MAINGI\t4057\tno\tIND\tIndependent\nPunjab\tGurdaspur\tSIKANDER SINGH\t2124\tno\tIND\tIndependent\nPunjab\tGurdaspur\tGURPREET SINGH KHANNA\t1785\tno\tBGTD\tBharatiya Gaon Taj Dal\nPunjab\tGurdaspur\tMUKESH\t1649\tno\tIND\tIndependent\nPunjab\tGurdaspur\tSANTOSH KUMARI\t3645\tno\tMEDP\tMegh Desham Party\nPunjab\tGurdaspur\tAMIT AGGARWAL\t1195\tno\tIND\tIndependent\nPunjab\tGurdaspur\tVARINDER SINGH\t11839\tno\tCPI\tCommunist Party of India\nPunjab\tGurdaspur\tSUCHA SINGH CHHOTEPUR\t173376\tno\tAAAP\tAam Aadmi Party\nPunjab\tGurdaspur\tGURMEET SINGH BAKHATPURA\t2875\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nPunjab\tGurdaspur\tSUKHWINDER SINGH\t5621\tno\tBSP\tBahujan Samaj Party\nPunjab\tGurdaspur\tPETER MASIH\t1463\tno\tSSPD\tSARVAJAN SAMAJ PARTY (D)\nPunjab\tGurdaspur\tPARTAP SINGH BAJWA\t346190\tno\tINC\tIndian National Congress\nPunjab\tAmritsar\tCAPTAIN AMARINDER SINGH\t482876\tyes\tINC\tIndian National Congress\nPunjab\tAmritsar\tBHAGWANT SINGH\t2303\tno\tIND\tIndependent\nPunjab\tAmritsar\tPARDEEP SINGH WALIA\t5870\tno\tBSP\tBahujan Samaj Party\nPunjab\tAmritsar\tDR. DALJIT SINGH\t82633\tno\tAAAP\tAam Aadmi Party\nPunjab\tAmritsar\tSHAM LAL GANDHIWADI\t2691\tno\tIND\tIndependent\nPunjab\tAmritsar\tARUN JAITLEY\t380106\tno\tBJP\tBharatiya Janata Party\nPunjab\tAmritsar\tYUSAF MOHAMMAD\t922\tno\tSP\tSamajwadi Party\nPunjab\tAmritsar\tREHMAT MASIH\t1214\tno\tIND\tIndependent\nPunjab\tAmritsar\tRATTAN SINGH RANDHAWA\t4350\tno\tIND\tIndependent\nPunjab\tAmritsar\tBAL KRISHAN SHARMA\t2878\tno\tIND\tIndependent\nPunjab\tAmritsar\tBUTA SINGH\t941\tno\tBSP(A)\tBahujan Samaj Party  (Ambedkar)\nPunjab\tAmritsar\tKANWALJIT SINGH SAHOTA\t2123\tno\tIND\tIndependent\nPunjab\tAmritsar\tBALBIR SINGH\t827\tno\tBGTD\tBharatiya Gaon Taj Dal\nPunjab\tAmritsar\tAMARJIT SINGH ASAL\t12902\tno\tCPI\tCommunist Party of India\nPunjab\tAmritsar\tGAGANDEEP KUMAR\t3294\tno\tIND\tIndependent\nPunjab\tAmritsar\tSURINDER KUMAR KHOSLA\t2396\tno\tIND\tIndependent\nPunjab\tAmritsar\tMOHINDER SINGH\t1140\tno\tIND\tIndependent\nPunjab\tAmritsar\tKRISHAN SHARMA\t503\tno\tNBDP\tNav Bharat Democratic Party\nPunjab\tAmritsar\tDR. INDERPAL\t3083\tno\tIND\tIndependent\nPunjab\tAmritsar\tAMARINDER SINGH\t1151\tno\tIND\tIndependent\nPunjab\tAmritsar\tGURDIAL SINGH GILL\t604\tno\tDBSP\tDemocratic Bharatiya Samaj Party\nPunjab\tAmritsar\tARUN KUMAR JOSHI\t9023\tno\tIND\tIndependent\nPunjab\tAmritsar\tSURINDER SINGH\t833\tno\tDCP\tDemocratic Congress Party\nPunjab\tAmritsar\tNone of the Above\t2533\tno\tNOTA\tNone of the Above\nPunjab\tKhadoor Sahib\tRANJIT SINGH BRAHMPURA\t467332\tyes\tSAD\tShiromani Akali Dal\nPunjab\tKhadoor Sahib\tNone of the Above\t5624\tno\tNOTA\tNone of the Above\nPunjab\tKhadoor Sahib\tGURPAL SINGH\t1182\tno\taimpr\tAll India Mazdoor Party (Rangreta)\nPunjab\tKhadoor Sahib\tKAWALJIT SINGH\t962\tno\tIND\tIndependent\nPunjab\tKhadoor Sahib\tBALDEEP SINGH\t144521\tno\tAAAP\tAam Aadmi Party\nPunjab\tKhadoor Sahib\tHARMINDER SINGH GILL\t366763\tno\tINC\tIndian National Congress\nPunjab\tKhadoor Sahib\tSUKHWANT SINGH\t1053\tno\tIND\tIndependent\nPunjab\tKhadoor Sahib\tSTEPHEN MASIH\t4329\tno\tSSPD\tSARVAJAN SAMAJ PARTY (D)\nPunjab\tKhadoor Sahib\tSUKHDEV SINGH CHOHAN\t801\tno\tIND\tIndependent\nPunjab\tKhadoor Sahib\tBHUPINDER SINGH\t3282\tno\tIND\tIndependent\nPunjab\tKhadoor Sahib\tBALWANT SINGH SULTANPURI\t2930\tno\tBSP(A)\tBahujan Samaj Party  (Ambedkar)\nPunjab\tKhadoor Sahib\tBALWINDER SINGH\t3804\tno\tIND\tIndependent\nPunjab\tKhadoor Sahib\tGURNAM SINGH\t9307\tno\tIND\tIndependent\nPunjab\tKhadoor Sahib\tSIMRANJIT SINGH MANN\t13990\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tKhadoor Sahib\tMAHINDER SINGH HAMIRA\t1720\tno\tIND\tIndependent\nPunjab\tKhadoor Sahib\tSUCHA SINGH MANN\t8491\tno\tBSP\tBahujan Samaj Party\nPunjab\tKhadoor Sahib\tSUKHCHAIN CHAND\t1313\tno\tDBSP\tDemocratic Bharatiya Samaj Party\nPunjab\tKhadoor Sahib\tJASBIR SINGH\t3114\tno\tIND\tIndependent\nPunjab\tJalandhar\tSANTOKH SINGH CHAUDHARY\t380479\tyes\tINC\tIndian National Congress\nPunjab\tJalandhar\tJAGAN NATH BAJWA\t1083\tno\tIND\tIndependent\nPunjab\tJalandhar\tPAWAN KUMAR TINU\t309498\tno\tSAD\tShiromani Akali Dal\nPunjab\tJalandhar\tMANJIT KAUR W/O SUKHWINDER SINGH\t1044\tno\tIND\tIndependent\nPunjab\tJalandhar\tVIJAY HANS\t1192\tno\tDBSP\tDemocratic Bharatiya Samaj Party\nPunjab\tJalandhar\tMANJEET KAUR W/O SUKHDEV SINGH\t1952\tno\tIND\tIndependent\nPunjab\tJalandhar\tLOVE KISHOR\t3265\tno\tIND\tIndependent\nPunjab\tJalandhar\tTARSEM PETER\t6249\tno\tIND\tIndependent\nPunjab\tJalandhar\tSUKHWINDER SINGH KOTLI\t46914\tno\tBSP\tBahujan Samaj Party\nPunjab\tJalandhar\tSUBHASH GORIA\t3497\tno\tSHS\tShivsena\nPunjab\tJalandhar\tPARAMJIT KUMAR\t717\tno\tBMUP\tBahujan Mukti Party\nPunjab\tJalandhar\tDARSHAN NAHAR\t10074\tno\tIND\tIndependent\nPunjab\tJalandhar\tVIJAY KUMAR\t1528\tno\tIND\tIndependent\nPunjab\tJalandhar\tJYOTI MANN\t254121\tno\tAAAP\tAam Aadmi Party\nPunjab\tJalandhar\tTARA SINGH GILL\t2268\tno\tBSP(A)\tBahujan Samaj Party  (Ambedkar)\nPunjab\tJalandhar\tKAMAL THAPAR\t933\tno\tIND\tIndependent\nPunjab\tJalandhar\tNIRMAL SINGH BOLINA\t2256\tno\tIND\tIndependent\nPunjab\tJalandhar\tKULDIP KUMAR\t953\tno\tIND\tIndependent\nPunjab\tJalandhar\tDHARMINDER\t2459\tno\tIND\tIndependent\nPunjab\tJalandhar\tVIVEK\t741\tno\tIND\tIndependent\nPunjab\tJalandhar\tAMIT JASSI\t779\tno\tIND\tIndependent\nPunjab\tJalandhar\tK.K. SABHARWAL\t1454\tno\tIND\tIndependent\nPunjab\tJalandhar\tAJAY KUMAR BHAGAT\t692\tno\tIND\tIndependent\nPunjab\tJalandhar\tJARNAIL SINGH\t1178\tno\tBhJaSP\tBharti Jan Suraksha Party\nPunjab\tJalandhar\tNone of the Above\t5436\tno\tNOTA\tNone of the Above\nPunjab\tHoshiarpur\tVIJAY SAMPLA\t346643\tyes\tBJP\tBharatiya Janata Party\nPunjab\tHoshiarpur\tNone of the Above\t5976\tno\tNOTA\tNone of the Above\nPunjab\tHoshiarpur\tDEEPAK KUMAR\t1446\tno\tBMUP\tBahujan Mukti Party\nPunjab\tHoshiarpur\tJASWINDER SINGH\t1230\tno\tSHS\tShivsena\nPunjab\tHoshiarpur\tRAVI DUTT\t2564\tno\tIND\tIndependent\nPunjab\tHoshiarpur\tBISHAN DASS\t1853\tno\tIND\tIndependent\nPunjab\tHoshiarpur\tRAM KISHAN\t1689\tno\tIND\tIndependent\nPunjab\tHoshiarpur\tVIJAY KUMAR\t889\tno\tSSPD\tSARVAJAN SAMAJ PARTY (D)\nPunjab\tHoshiarpur\tPAWAN KUMAR\t3216\tno\tIND\tIndependent\nPunjab\tHoshiarpur\tMOHINDER SINGH KAYPEE\t333061\tno\tINC\tIndian National Congress\nPunjab\tHoshiarpur\tBHAGWAN SINGH CHAUHAN\t40497\tno\tBSP\tBahujan Samaj Party\nPunjab\tHoshiarpur\tANU KUMAR\t911\tno\tIND\tIndependent\nPunjab\tHoshiarpur\tDAVINDER SINGH\t1488\tno\tIND\tIndependent\nPunjab\tHoshiarpur\tANOOP SINGH\t2158\tno\tBhJaSP\tBharti Jan Suraksha Party\nPunjab\tHoshiarpur\tYAMINI GOMAR\t213388\tno\tAAAP\tAam Aadmi Party\nPunjab\tHoshiarpur\tSHAMSHER SINGH\t2527\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tHoshiarpur\tLAKHVIR SINGH\t1062\tno\tDBSP\tDemocratic Bharatiya Samaj Party\nPunjab\tHoshiarpur\tOM PARKASH JAKHU\t699\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tPREM SINGH CHANDUMAJRA\t347394\tyes\tSAD\tShiromani Akali Dal\nPunjab\tAnandpur Sahib\tDAVINDER KUMAR VERMA\t1995\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tHIMMAT SINGH SHERGILL\t306008\tno\tAAAP\tAam Aadmi Party\nPunjab\tAnandpur Sahib\tK.S.MAKHAN\t69124\tno\tBSP\tBahujan Samaj Party\nPunjab\tAnandpur Sahib\tBALBIR SINGH JADLA\t10483\tno\tCPM\tCommunist Party of India  (Marxist)\nPunjab\tAnandpur Sahib\tFAQIR CHAND KARIHA\t3869\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tJASWANT SINGH MANN\t1706\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tAnandpur Sahib\tAMBIKA SONI\t323697\tno\tINC\tIndian National Congress\nPunjab\tAnandpur Sahib\tDALJIT SINGH\t2713\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tHARCHARAN SINGH\t1423\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tBALJIT KAUR\t4250\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tGURSHARN SINGH PANESAR\t1943\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tNAVNEET CHAUTALA\t1307\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tGURMEET SINGH\t1010\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tGURNAM SINGH\t722\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tRESHAM KAHLON\t837\tno\tBA S D\tBahujan Sangharshh Dal\nPunjab\tAnandpur Sahib\tMANDEEP SINGH BITTU\t1427\tno\tIND\tIndependent\nPunjab\tAnandpur Sahib\tRAMESH RANI PARJAPATI\t718\tno\tBMUP\tBahujan Mukti Party\nPunjab\tAnandpur Sahib\tNone of the Above\t5937\tno\tNOTA\tNone of the Above\nPunjab\tLudhiana\tRAVNEET SINGH BITTU\t300459\tyes\tINC\tIndian National Congress\nPunjab\tLudhiana\tNone of the Above\t3220\tno\tNOTA\tNone of the Above\nPunjab\tLudhiana\tMANPREET SINGH AYALI\t256590\tno\tSAD\tShiromani Akali Dal\nPunjab\tLudhiana\tSIMARJEET SINGH BAINS\t210917\tno\tIND\tIndependent\nPunjab\tLudhiana\tNAVPREET SINGH BEDI\t3144\tno\tIND\tIndependent\nPunjab\tLudhiana\tHARVINDER SINGH PHOOLKA\t280750\tno\tAAAP\tAam Aadmi Party\nPunjab\tLudhiana\tAKSHAYBAR NATH MISHRA\t2925\tno\tJD(U)\tJanata Dal  (United)\nPunjab\tLudhiana\tMOHAMMAD MAKSOOD ANSARI\t1111\tno\tBMUP\tBahujan Mukti Party\nPunjab\tLudhiana\tKISHAN KUMAR SHARMA\t1483\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nPunjab\tLudhiana\tDARSHAN SINGH\t1028\tno\tSHS\tShivsena\nPunjab\tLudhiana\tHARI RAM SAHNI\t1168\tno\tAIFB\tAll India Forward Bloc\nPunjab\tLudhiana\tTARSEM SINGH BANGA\t1934\tno\tIND\tIndependent\nPunjab\tLudhiana\tBITTU\t1286\tno\tRPI(A)\tRepublican Party of India (A)\nPunjab\tLudhiana\tRAJIV KUMAR KALRA\t4653\tno\tIND\tIndependent\nPunjab\tLudhiana\tGURDEEP SINGH KAHLON\t4509\tno\tIND\tIndependent\nPunjab\tLudhiana\tJASWINDER SINGH BAINS\t3238\tno\tIND\tIndependent\nPunjab\tLudhiana\tSIMERJIT SINGH\t3980\tno\tIND\tIndependent\nPunjab\tLudhiana\tRAJ KUMAR ATWAL\t1089\tno\tIND\tIndependent\nPunjab\tLudhiana\tMOHD. NASEEM ANSARI\t1984\tno\taicp\tNew All India Congress Party\nPunjab\tLudhiana\tSUKHWINDER SINGH SEKHON\t4167\tno\tCPM\tCommunist Party of India  (Marxist)\nPunjab\tLudhiana\tKULWINDER PAL SINGH\t986\tno\tIND\tIndependent\nPunjab\tLudhiana\tNAVJOT SINGH MANDAIR\t8317\tno\tBSP\tBahujan Samaj Party\nPunjab\tLudhiana\tJAI PARKASH JAIN\t1519\tno\tIND\tIndependent\nPunjab\tFatehgarh Sahib\tHARINDER SINGH KHALSA\t367293\tyes\tAAAP\tAam Aadmi Party\nPunjab\tFatehgarh Sahib\tPARAMJIT SINGH\t1266\tno\tIND\tIndependent\nPunjab\tFatehgarh Sahib\tSARABJEET SINGH\t12683\tno\tBSP\tBahujan Samaj Party\nPunjab\tFatehgarh Sahib\tGURDEEP SINGH\t1930\tno\tIVD\tInqalab Vikas Dal\nPunjab\tFatehgarh Sahib\tAVTAR SINGH\t848\tno\tIND\tIndependent\nPunjab\tFatehgarh Sahib\tRANJIT SINGH\t1679\tno\tIND\tIndependent\nPunjab\tFatehgarh Sahib\tLACHHMAN SINGH S/O S.GULZAR SINGH\t2141\tno\tIND\tIndependent\nPunjab\tFatehgarh Sahib\tNIRMAL SINGH\t2149\tno\tNCP\tNationalist Congress Party\nPunjab\tFatehgarh Sahib\tDHARAM SINGH\t3173\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tFatehgarh Sahib\tSADHU SINGH\t313149\tno\tINC\tIndian National Congress\nPunjab\tFatehgarh Sahib\tKULWANT SINGH\t312815\tno\tSAD\tShiromani Akali Dal\nPunjab\tFatehgarh Sahib\tLAKHVIR SINGH\t1733\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nPunjab\tFatehgarh Sahib\tMANPREET SINGH\t886\tno\tIND\tIndependent\nPunjab\tFatehgarh Sahib\tLACHHMAN SINGH S/O S.SAROOP SINGH\t2852\tno\tIND\tIndependent\nPunjab\tFatehgarh Sahib\tHARPREET SINGH\t2352\tno\tBMUP\tBahujan Mukti Party\nPunjab\tFatehgarh Sahib\tNone of the Above\t4005\tno\tNOTA\tNone of the Above\nPunjab\tFaridkot\tPROF. SADHU SINGH\t450751\tyes\tAAAP\tAam Aadmi Party\nPunjab\tFaridkot\tNone of the Above\t3816\tno\tNOTA\tNone of the Above\nPunjab\tFaridkot\tPARAMJEET KAUR W/O GURPREET SINGH\t1623\tno\tIND\tIndependent\nPunjab\tFaridkot\tGOBIND SINGH\t865\tno\tRPI(A)\tRepublican Party of India (A)\nPunjab\tFaridkot\tJASVEER SINGH\t1693\tno\tIND\tIndependent\nPunjab\tFaridkot\tJOGINDER SINGH\t251222\tno\tINC\tIndian National Congress\nPunjab\tFaridkot\tREENA KUMARI\t2473\tno\tIND\tIndependent\nPunjab\tFaridkot\tPARAMJIT KAUR GULSHAN\t278235\tno\tSAD\tShiromani Akali Dal\nPunjab\tFaridkot\tDARSHAN SINGH\t1605\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tFaridkot\tJOGINDER SINGH S/O KESAR SINGH\t2667\tno\tIND\tIndependent\nPunjab\tFaridkot\tSEEMA RANI\t1726\tno\tIND\tIndependent\nPunjab\tFaridkot\tSATPAL SINGH\t3680\tno\tIKL\tIndian Krantikari Lehar\nPunjab\tFaridkot\tPARAMJIT KAUR W/O AMRIK SINGH\t1159\tno\tBMUP\tBahujan Mukti Party\nPunjab\tFaridkot\tMOTI LAL\t1512\tno\tBhSMP\tBharatiya Sant Mat Party\nPunjab\tFaridkot\tMANGAT RAM MANGA\t2213\tno\tSHS\tShivsena\nPunjab\tFaridkot\tJARNAIL SINGH\t1358\tno\tNBDP\tNav Bharat Democratic Party\nPunjab\tFaridkot\tSANT RAM\t8282\tno\tBSP\tBahujan Samaj Party\nPunjab\tFaridkot\tGURCHARAN SINGH\t1531\tno\tRPI\tRepublican Party of India\nPunjab\tFaridkot\tKASHMIR SINGH\t14573\tno\tCPI\tCommunist Party of India\nPunjab\tFaridkot\tBADAL SINGH\t1123\tno\tIND\tIndependent\nPunjab\tFirozpur\tSHER SINGH GHUBAYA\t487932\tyes\tSAD\tShiromani Akali Dal\nPunjab\tFirozpur\tRAJINDER BHATHEJA\t1546\tno\tIND\tIndependent\nPunjab\tFirozpur\tSUNIL JAKHAR\t456512\tno\tINC\tIndian National Congress\nPunjab\tFirozpur\tSWARAN SINGH\t766\tno\tIND\tIndependent\nPunjab\tFirozpur\tBALJINDER SINGH\t681\tno\tBGTD\tBharatiya Gaon Taj Dal\nPunjab\tFirozpur\tMANOJ KUMAR MONGA\t1896\tno\tIND\tIndependent\nPunjab\tFirozpur\tRAJ GILL\t3311\tno\tIND\tIndependent\nPunjab\tFirozpur\tCHANAN SINGH WATTU\t1237\tno\tIND\tIndependent\nPunjab\tFirozpur\tSATNAM PAUL KAMBOJ\t113412\tno\tAAAP\tAam Aadmi Party\nPunjab\tFirozpur\tPARAMJEET SINGH DESAL\t976\tno\tIND\tIndependent\nPunjab\tFirozpur\tDHIAN SINGH MAND\t3655\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tFirozpur\tGURJANT SINGH\t1088\tno\tIND\tIndependent\nPunjab\tFirozpur\tPANJAB SINGH KATWAL\t1759\tno\tIND\tIndependent\nPunjab\tFirozpur\tSAMPURAN SINGH\t682\tno\tIND\tIndependent\nPunjab\tFirozpur\tRAM KUMAR PARJAPAT\t22274\tno\tBSP\tBahujan Samaj Party\nPunjab\tFirozpur\tNone of the Above\t7685\tno\tNOTA\tNone of the Above\nPunjab\tBathinda\tHARSIMRAT KAUR BADAL\t514727\tyes\tSAD\tShiromani Akali Dal\nPunjab\tBathinda\tBHUPESH KUMAR\t1487\tno\tIND\tIndependent\nPunjab\tBathinda\tSHAMINDER SINGH\t4610\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nPunjab\tBathinda\tNAVNEET\t1893\tno\tSHS\tShivsena\nPunjab\tBathinda\tGURDEEP SINGH\t1404\tno\tIKL\tIndian Krantikari Lehar\nPunjab\tBathinda\tKARTAR SINGH\t916\tno\tIND\tIndependent\nPunjab\tBathinda\tGEETA RANI\t4380\tno\tABSR\tAkhil Bharatiya Shivsena Rashtrawadi\nPunjab\tBathinda\tGURCHARAN SINGH\t820\tno\tIND\tIndependent\nPunjab\tBathinda\tRAJINDER SINGH\t1960\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tBathinda\tJAGDISH RAI SHARMA\t1248\tno\tNBDP\tNav Bharat Democratic Party\nPunjab\tBathinda\tSUSHIL KUMAR JINDAL\t1330\tno\tIND\tIndependent\nPunjab\tBathinda\tVIJAY KUMAR\t2258\tno\tIND\tIndependent\nPunjab\tBathinda\tMAKHAN LAL\t2587\tno\tBSP(A)\tBahujan Samaj Party  (Ambedkar)\nPunjab\tBathinda\tSURESH KUMAR GOYAL\t944\tno\tJanSP\tJanral Samaj Party\nPunjab\tBathinda\tDYAL CHAND\t955\tno\tIND\tIndependent\nPunjab\tBathinda\tJASPAL SINGH\t703\tno\tIND\tIndependent\nPunjab\tBathinda\tGURMEET SINGH RANGHRETA\t1386\tno\tPLP\tPunjab Labour Party\nPunjab\tBathinda\tSANJEEV KUMAR THAPAR\t2833\tno\tIND\tIndependent\nPunjab\tBathinda\tJAGDEEP SINGH GEHRI\t1665\tno\tIND\tIndependent\nPunjab\tBathinda\tSATISH ARORA\t5936\tno\tIND\tIndependent\nPunjab\tBathinda\tSUKHWINDER SINGH\t1086\tno\tBMUP\tBahujan Mukti Party\nPunjab\tBathinda\tBHAGWANT SINGH SAMAON\t5984\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nPunjab\tBathinda\tJASRAJ SINGH LONGIA\t87901\tno\tAAAP\tAam Aadmi Party\nPunjab\tBathinda\tASHISH\t6626\tno\tIND\tIndependent\nPunjab\tBathinda\tJAGDEEP SINGH\t668\tno\tIND\tIndependent\nPunjab\tBathinda\tNone of the Above\t4701\tno\tNOTA\tNone of the Above\nPunjab\tBathinda\tMANPREET SINGH BADAL S/O GURDEV SINGH\t4618\tno\tIND\tIndependent\nPunjab\tBathinda\tMANPREET SINGH BADAL S/O GURDAS SINGH\t495332\tno\tINC\tIndian National Congress\nPunjab\tBathinda\tSWARAN SINGH\t2077\tno\tIND\tIndependent\nPunjab\tBathinda\tKULDEEP SINGH\t13732\tno\tBSP\tBahujan Samaj Party\nPunjab\tSangrur\tBHAGWANT MANN\t533237\tyes\tAAAP\tAam Aadmi Party\nPunjab\tSangrur\tVIJAY INDER SINGLA\t181410\tno\tINC\tIndian National Congress\nPunjab\tSangrur\tGURPREET SINGH\t2746\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nPunjab\tSangrur\tKARNAIL SINGH\t4127\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tSangrur\tPARVEEN KAUR\t798\tno\tIND\tIndependent\nPunjab\tSangrur\tKARAM SINGH\t1599\tno\tIND\tIndependent\nPunjab\tSangrur\tDHARAM SINGH\t3292\tno\tIND\tIndependent\nPunjab\tSangrur\tSUKHDEV SINGH DHINDSA\t321516\tno\tSAD\tShiromani Akali Dal\nPunjab\tSangrur\tMANOJ KUMAR\t1854\tno\tIND\tIndependent\nPunjab\tSangrur\tNone of the Above\t2188\tno\tNOTA\tNone of the Above\nPunjab\tSangrur\tGURDEEP SINGH\t1069\tno\tDBSP\tDemocratic Bharatiya Samaj Party\nPunjab\tSangrur\tBHAGWANT SINGH\t2334\tno\tIND\tIndependent\nPunjab\tSangrur\tSUKHDEV SINGH\t1233\tno\tIND\tIndependent\nPunjab\tSangrur\tSUKHDEV RAM SHARMA\t6934\tno\tCPI\tCommunist Party of India\nPunjab\tSangrur\tJIT SINGH\t5879\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nPunjab\tSangrur\tJASWANT SINGH\t1556\tno\tBMUP\tBahujan Mukti Party\nPunjab\tSangrur\tJOTI DEVAJI ANSUIA\t6831\tno\tIND\tIndependent\nPunjab\tSangrur\tBIJENDER\t5395\tno\tIND\tIndependent\nPunjab\tSangrur\tMAHMOOD AHMED THIND\t2261\tno\tIUML\tIndian Union Muslim League\nPunjab\tSangrur\tJOGINDER SINGH AULAKH\t3315\tno\tCPM\tCommunist Party of India  (Marxist)\nPunjab\tSangrur\tMADAN BHATTI\t8408\tno\tBSP\tBahujan Samaj Party\nPunjab\tSangrur\tHARINDER KUMAR\t1485\tno\tIND\tIndependent\nPunjab\tPatiala\tDR. DHARAM VIRA GANDHI\t365671\tyes\tAAAP\tAam Aadmi Party\nPunjab\tPatiala\tPRENEET KAUR\t344729\tno\tINC\tIndian National Congress\nPunjab\tPatiala\tMOHINDER PAL SINGH\t2773\tno\tSAD(M)\tShiromani Akali Dal (Amritsar)(Simranjit Singh Mann)\nPunjab\tPatiala\tDEEPINDER SINGH DHILLON\t340109\tno\tSAD\tShiromani Akali Dal\nPunjab\tPatiala\tRANJIT KAUR\t2178\tno\tBGTD\tBharatiya Gaon Taj Dal\nPunjab\tPatiala\tRAMISHER SINGH BILLA\t2625\tno\tIND\tIndependent\nPunjab\tPatiala\tPARVEEN KUMAR\t4089\tno\tIND\tIndependent\nPunjab\tPatiala\tGURPREET SINGH\t2378\tno\tBSP(A)\tBahujan Samaj Party  (Ambedkar)\nPunjab\tPatiala\tNIRMAL SINGH DHALIWAL\t8537\tno\tCPI\tCommunist Party of India\nPunjab\tPatiala\tPARVESH BHATEJA\t4915\tno\tIND\tIndependent\nPunjab\tPatiala\tGURBHEJ SINGH\t2217\tno\tIND\tIndependent\nPunjab\tPatiala\tDHARAM PAL\t1271\tno\tIND\tIndependent\nPunjab\tPatiala\tRANDHIR SINGH\t1234\tno\tBMUP\tBahujan Mukti Party\nPunjab\tPatiala\tLAKHWINDER SINGH MALHI\t809\tno\tIND\tIndependent\nPunjab\tPatiala\tHARISH KUMAR\t2325\tno\tSHS\tShivsena\nPunjab\tPatiala\tNone of the Above\t3008\tno\tNOTA\tNone of the Above\nPunjab\tPatiala\tRAJINDER SINGH PAWAR\t1625\tno\tIND\tIndependent\nPunjab\tPatiala\tAMRIK SINGH\t936\tno\taicp\tNew All India Congress Party\nPunjab\tPatiala\tBIBI KAMALDEEP KAUR RAJOANA\t15313\tno\tIND\tIndependent\nPunjab\tPatiala\tRAM SINGH DHIMAN\t13014\tno\tBSP\tBahujan Samaj Party\nPunjab\tPatiala\tRAVINDER SINGH\t1177\tno\tBhSMP\tBharatiya Sant Mat Party\nRajasthan\tGanganagar\tNIHALCHAND\t658130\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tGanganagar\tADVOCATE KARTAR SINGH BAINS\t5025\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tGanganagar\tSHIMLA DEVI NAYAK (ADVOCATE)\t106585\tno\tnuzp\tNational Unionist Zamindara Party\nRajasthan\tGanganagar\tOM PARKASH\t3750\tno\tIND\tIndependent\nRajasthan\tGanganagar\tAMAR SINGH\t6686\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tGanganagar\tHETRAM\t2943\tno\tRJVP\tRajasthan Vikas Party\nRajasthan\tGanganagar\tDR. BALKRISHAN BAWARI\t40016\tno\tAAAP\tAam Aadmi Party\nRajasthan\tGanganagar\tPALARAM\t14944\tno\tCPM\tCommunist Party of India  (Marxist)\nRajasthan\tGanganagar\tSUNIL BHADRAWAL\t3729\tno\tLD\tLok Dal\nRajasthan\tGanganagar\tMASTER BHANWARLAL MEGHWAL\t366389\tno\tINC\tIndian National Congress\nRajasthan\tGanganagar\tJASVINDER SINGH\t3395\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tGanganagar\tBALRAJ BAWARI\t8788\tno\tIND\tIndependent\nRajasthan\tGanganagar\tBALWANT SINGH HARIYALA\t5364\tno\tBhSMP\tBharatiya Sant Mat Party\nRajasthan\tGanganagar\tDR. PALUSKAR K. T.\t5232\tno\tPRCP\tPrabuddha Republican Party\nRajasthan\tGanganagar\tTITAR SINGH\t3924\tno\tIND\tIndependent\nRajasthan\tGanganagar\tDULICHAND MEGHWAL\t11981\tno\tIND\tIndependent\nRajasthan\tGanganagar\tKUMBHA RAM\t3007\tno\tIND\tIndependent\nRajasthan\tGanganagar\tNone of the Above\t6918\tno\tNOTA\tNone of the Above\nRajasthan\tBikaner\tARJUN RAM MEGHWAL\t584932\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tBikaner\tNone of the Above\t13492\tno\tNOTA\tNone of the Above\nRajasthan\tBikaner\tMANGILAL NAYAK\t16839\tno\tnuzp\tNational Unionist Zamindara Party\nRajasthan\tBikaner\tJAGDISH KUMAR NAYAK\t5164\tno\tIND\tIndependent\nRajasthan\tBikaner\tDR. GAURI SHANKER DAABI\t14148\tno\tAAAP\tAam Aadmi Party\nRajasthan\tBikaner\tGOPAL BALMIKI\t4398\tno\tIND\tIndependent\nRajasthan\tBikaner\tRAM RATAN MEGHWAL\t2538\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tBikaner\tER. SHANKAR PANNU\t276853\tno\tINC\tIndian National Congress\nRajasthan\tBikaner\tBHANWAR LAL\t11387\tno\tBSP\tBahujan Samaj Party\nRajasthan\tChuru\tRAHUL KASWAN\t595756\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tChuru\tINDRASINGH JAT\t10778\tno\tCPM\tCommunist Party of India  (Marxist)\nRajasthan\tChuru\tABHINESH MAHARSHI\t301017\tno\tBSP\tBahujan Samaj Party\nRajasthan\tChuru\tSYOPAT RAM\t2330\tno\tBhSMP\tBharatiya Sant Mat Party\nRajasthan\tChuru\tTORURAM MEGHWAL\t2054\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tChuru\tMOHAMMAD ALI KHAN\t2313\tno\tBA S D\tBahujan Sangharshh Dal\nRajasthan\tChuru\tAJAY KUMAR\t2938\tno\tIND\tIndependent\nRajasthan\tChuru\tRAJPAL\t3727\tno\tIND\tIndependent\nRajasthan\tChuru\tSALIM GUJAR\t4018\tno\tIND\tIndependent\nRajasthan\tChuru\tGORDHAN SARSWAT\t3991\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tChuru\tPRATAP SINGH\t176912\tno\tINC\tIndian National Congress\nRajasthan\tChuru\tKRISHAN KUMAR SARAN\t13977\tno\tAAAP\tAam Aadmi Party\nRajasthan\tChuru\tNone of the Above\t11293\tno\tNOTA\tNone of the Above\nRajasthan\tJhunjhunu\tSANTOSH AHLAWAT\t488182\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tJhunjhunu\tNone of the Above\t8038\tno\tNOTA\tNone of the Above\nRajasthan\tJhunjhunu\tURMILA\t5581\tno\tMEDP\tMegh Desham Party\nRajasthan\tJhunjhunu\tRAJ BALA OLA\t254347\tno\tINC\tIndian National Congress\nRajasthan\tJhunjhunu\tDR. RAJKUMAR SHARMA\t206288\tno\tIND\tIndependent\nRajasthan\tJhunjhunu\tOM PRAKASH JHARODA\t2506\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nRajasthan\tJhunjhunu\tGENERAL R S KADYAN\t10386\tno\tAAAP\tAam Aadmi Party\nRajasthan\tJhunjhunu\tRAJBALA\t11740\tno\tIND\tIndependent\nRajasthan\tJhunjhunu\tBHAN SINGH\t1885\tno\tBhSMP\tBharatiya Sant Mat Party\nRajasthan\tJhunjhunu\tRAHISA BANO\t10423\tno\tBSP\tBahujan Samaj Party\nRajasthan\tJhunjhunu\tBHUDAR MAL\t2979\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tJhunjhunu\tPRAVEEN KUMAR\t1178\tno\tIND\tIndependent\nRajasthan\tJhunjhunu\tMAHABEER PRASAD KUMAWAT\t2932\tno\tIND\tIndependent\nRajasthan\tSikar\tSUMEDHANAND SARASWATI\t499428\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tSikar\tVIMAL INDORIA\t968\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tSikar\tAMARA RAM\t53134\tno\tCPM\tCommunist Party of India  (Marxist)\nRajasthan\tSikar\tMAJOR SURENDRA KUMAR PUNIA\t15666\tno\tAAAP\tAam Aadmi Party\nRajasthan\tSikar\tAJAYPAL\t7036\tno\tIND\tIndependent\nRajasthan\tSikar\tSWAMI BHAGIRATH SINGH KHARRANTE BHADHADAR\t5387\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nRajasthan\tSikar\tDULI CHAND KUMAWAT\t4375\tno\tIND\tIndependent\nRajasthan\tSikar\tPRATAP SINGH\t10666\tno\tIND\tIndependent\nRajasthan\tSikar\tKANHAIYA LAL BALAI\t2169\tno\tIND\tIndependent\nRajasthan\tSikar\tSUBHASH MAHARIA\t188841\tno\tIND\tIndependent\nRajasthan\tSikar\tGULAM NABI AZAD\t4112\tno\tBSP\tBahujan Samaj Party\nRajasthan\tSikar\tRAMESH SHARMA\t1635\tno\tbhp\tBhartiya Party\nRajasthan\tSikar\tSURENDRA KUMAR PHULWARIYA\t3633\tno\tIND\tIndependent\nRajasthan\tSikar\tPRATAP SINGH JAT\t260232\tno\tINC\tIndian National Congress\nRajasthan\tSikar\tSATISH KUMAR SHARMA\t1058\tno\tJGP\tJago Party\nRajasthan\tSikar\tNone of the Above\t7410\tno\tNOTA\tNone of the Above\nRajasthan\tJaipur Rural\tRAJYAVARDHAN SINGH RATHORE\t632930\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tJaipur Rural\tNone of the Above\t11533\tno\tNOTA\tNone of the Above\nRajasthan\tJaipur Rural\tJAGDISH PRASAD\t1378\tno\tLD\tLok Dal\nRajasthan\tJaipur Rural\tSHAYAM SINGH RATHORE\t3722\tno\tLS\tLok Shakti\nRajasthan\tJaipur Rural\tAJAY KUMAR SHARMA\t5774\tno\tJGP\tJago Party\nRajasthan\tJaipur Rural\tKAMAL  KUMAR  GAUR\t9480\tno\tIND\tIndependent\nRajasthan\tJaipur Rural\tNARAYAN LAL PARIHAR\t1327\tno\tBNNP\tBharat Nav Nirman Party\nRajasthan\tJaipur Rural\tDR. OM SINGH MEENA\t4520\tno\tIND\tIndependent\nRajasthan\tJaipur Rural\tNAVIN PILANIA\t31617\tno\tNPEP\tNational Peoples Party\nRajasthan\tJaipur Rural\tTEK CHAND SONWAL\t3047\tno\tAPoI\tAmbedkarite Party of India\nRajasthan\tJaipur Rural\tDHIRAJ GURJAR\t1412\tno\tSP\tSamajwadi Party\nRajasthan\tJaipur Rural\tDR. C.P. JOSHI\t300034\tno\tINC\tIndian National Congress\nRajasthan\tJaipur Rural\tANIL KUMAR\t6917\tno\tAAAP\tAam Aadmi Party\nRajasthan\tJaipur\tRAMCHARAN BOHARA\t863358\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tJaipur\tHAFIZ KHAN WARASI\t5020\tno\tIND\tIndependent\nRajasthan\tJaipur\tABDUL AZIZ BHARTIYA\t1103\tno\tRUC\tRashtriya Ulama Council\nRajasthan\tJaipur\tIFTEKAR QURISHI\t621\tno\tAIFB\tAll India Forward Bloc\nRajasthan\tJaipur\tKAILASH CHAND SAINI\t566\tno\tHJP\tHindustan Janta Party\nRajasthan\tJaipur\tRAM SINGH RAJ\t2072\tno\tIND\tIndependent\nRajasthan\tJaipur\tMUNNA DASS DABODIA\t4856\tno\tIND\tIndependent\nRajasthan\tJaipur\tNEERU AGARWAL\t1566\tno\tnuzp\tNational Unionist Zamindara Party\nRajasthan\tJaipur\tPAWAN SINGH SHEKHAWAT\t1987\tno\tIND\tIndependent\nRajasthan\tJaipur\tANIL KUMAR SHARMA\t4599\tno\tJGP\tJago Party\nRajasthan\tJaipur\tDR. VIRENDRA SINGH\t55118\tno\tAAAP\tAam Aadmi Party\nRajasthan\tJaipur\tDR. MAHESH JOSHI\t324013\tno\tINC\tIndian National Congress\nRajasthan\tJaipur\tRAJU LAL BAIRWA\t1899\tno\tAPoI\tAmbedkarite Party of India\nRajasthan\tJaipur\tFAROOQ SHAH\t5193\tno\tBSP\tBahujan Samaj Party\nRajasthan\tJaipur\tDR. TANMAY\t893\tno\tIPGP\tIndian Peoples Green Party\nRajasthan\tJaipur\tD.K. (DEV KRISHAN) CHHANGANI\t15597\tno\tCPI\tCommunist Party of India\nRajasthan\tJaipur\tNone of the Above\t8345\tno\tNOTA\tNone of the Above\nRajasthan\tAlwar\tCHAND NATH\t642278\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tAlwar\tNone of the Above\t2512\tno\tNOTA\tNone of the Above\nRajasthan\tAlwar\tHARIPRASAD\t3580\tno\tIND\tIndependent\nRajasthan\tAlwar\tJAGDISH PRASAD BAIRWA\t3390\tno\tIND\tIndependent\nRajasthan\tAlwar\tCHAUDHARY NASRUDEEN TANWAR\t662\tno\tSP(I)\tSocialist Party (India)\nRajasthan\tAlwar\tDR. DASHRATH KUMAR HINUNIA\t3612\tno\tAPoI\tAmbedkarite Party of India\nRajasthan\tAlwar\tRAMESH PANVAL\t1227\tno\tIND\tIndependent\nRajasthan\tAlwar\tKISHAN LAL\t2572\tno\tIND\tIndependent\nRajasthan\tAlwar\tHARI SINGH SHARMA\t3672\tno\tIND\tIndependent\nRajasthan\tAlwar\tKHOOBRAJ NEERAJ\t702\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tAlwar\tNISHAR AHMED\t441\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tAlwar\tAZAD HUSAIN\t19483\tno\tBSP\tBahujan Samaj Party\nRajasthan\tAlwar\tARUN KUMAR ROOPAK\t3412\tno\tNADP\tNaya Daur Party\nRajasthan\tAlwar\tMANGURAM SAIN\t566\tno\tABCD(A)\tAkhil Bharatiya Congress Dal (Ambedkar)\nRajasthan\tAlwar\tKHAIM CHAND\t1187\tno\tBYS\tBharatiya Yuva Shakti\nRajasthan\tAlwar\tDR VIRENDRA VIDROHI\t8517\tno\tAAAP\tAam Aadmi Party\nRajasthan\tAlwar\tVIRJANAND\t1845\tno\tSP\tSamajwadi Party\nRajasthan\tAlwar\tMURSHID AHMED\t2160\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tAlwar\tBHANWAR JITENDRA SINGH\t358383\tno\tINC\tIndian National Congress\nRajasthan\tAlwar\tSURESH SHARMA\t2104\tno\tJGP\tJago Party\nRajasthan\tBHARATPUR\tBAHADUR SINGH\t579825\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tBHARATPUR\tMAHENDAR KUMAR JATAV\t22090\tno\tBSP\tBahujan Samaj Party\nRajasthan\tBHARATPUR\tAMAR CHAND POHIYA\t3969\tno\tRJVP\tRajasthan Vikas Party\nRajasthan\tBHARATPUR\tGOPAL PAHARIA\t7013\tno\tAAAP\tAam Aadmi Party\nRajasthan\tBHARATPUR\tLAKHAN SINGH\t960\tno\tIPGP\tIndian Peoples Green Party\nRajasthan\tBHARATPUR\tMAMRAJ\t1787\tno\tIND\tIndependent\nRajasthan\tBHARATPUR\tVIMLA UMAR\t1792\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tBHARATPUR\tSHEETAL JATAV\t2071\tno\tIND\tIndependent\nRajasthan\tBHARATPUR\tPREM CHAND DHAMANI\t835\tno\tIND\tIndependent\nRajasthan\tBHARATPUR\tDR.SURESH JATAV\t334357\tno\tINC\tIndian National Congress\nRajasthan\tBHARATPUR\tGHISU LAL\t611\tno\tIND\tIndependent\nRajasthan\tBHARATPUR\tMADHURI PUSHKAR\t1187\tno\tIND\tIndependent\nRajasthan\tBHARATPUR\tNone of the Above\t5935\tno\tNOTA\tNone of the Above\nRajasthan\tKARAULI-DHOLPUR\tMANOJ RAJORIA\t402407\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tKARAULI-DHOLPUR\tNone of the Above\t5934\tno\tNOTA\tNone of the Above\nRajasthan\tKARAULI-DHOLPUR\tKISHOR KUMAR\t3376\tno\tIND\tIndependent\nRajasthan\tKARAULI-DHOLPUR\tKANCHAN BAI\t3014\tno\tIND\tIndependent\nRajasthan\tKARAULI-DHOLPUR\tASHOK KUMAR\t1385\tno\tIND\tIndependent\nRajasthan\tKARAULI-DHOLPUR\tRAM SINGH\t9458\tno\tNPEP\tNational Peoples Party\nRajasthan\tKARAULI-DHOLPUR\tGHANSHYAM\t3726\tno\tAAAP\tAam Aadmi Party\nRajasthan\tKARAULI-DHOLPUR\tG.R.BARUA\t25150\tno\tBSP\tBahujan Samaj Party\nRajasthan\tKARAULI-DHOLPUR\tLAXMIKANT\t4767\tno\tIND\tIndependent\nRajasthan\tKARAULI-DHOLPUR\tDHANRAJ BAIRWA\t745\tno\tSP(I)\tSocialist Party (India)\nRajasthan\tKARAULI-DHOLPUR\tRAMESH\t1629\tno\tBYS\tBharatiya Yuva Shakti\nRajasthan\tKARAULI-DHOLPUR\tLAKKHIRAM\t375191\tno\tINC\tIndian National Congress\nRajasthan\tKARAULI-DHOLPUR\tSHRELAL KHARE\t922\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tKARAULI-DHOLPUR\tKAMAL SINGH\t7286\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tKARAULI-DHOLPUR\tPHOOLSINGH\t951\tno\tJGP\tJago Party\nRajasthan\tDausa\tHARISH CHANDRA MEENA\t315059\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tDausa\tBIMLA DEVI MEENA\t20037\tno\tIND\tIndependent\nRajasthan\tDausa\tRUKMANI MEENA JHAKHIWAL\t917\tno\tSP(I)\tSocialist Party (India)\nRajasthan\tDausa\tNAMONARAIN MEENA\t181272\tno\tINC\tIndian National Congress\nRajasthan\tDausa\tDR. KIRODI LAL\t269655\tno\tNPEP\tNational Peoples Party\nRajasthan\tDausa\tDR. NASEEM GURJAR\t5792\tno\tIND\tIndependent\nRajasthan\tDausa\tSURAJ BHAN DHANKA\t2357\tno\tSP\tSamajwadi Party\nRajasthan\tDausa\tDHARMI GHUNAWAT\t1635\tno\tIPGP\tIndian Peoples Green Party\nRajasthan\tDausa\tSHIV PAL GURJAR\t82549\tno\tIND\tIndependent\nRajasthan\tDausa\tCHANDU LAL MEENA (GEROTA)\t15303\tno\tBSP\tBahujan Samaj Party\nRajasthan\tDausa\tANJU DEVI DHANKA\t16935\tno\tIND\tIndependent\nRajasthan\tDausa\tANJALI BHADANA\t3072\tno\tIND\tIndependent\nRajasthan\tDausa\tDR. SANJEET KUMAR DHANKA\t3202\tno\tAAAP\tAam Aadmi Party\nRajasthan\tDausa\tKHEMRAJ MEENA\t4297\tno\tBYS\tBharatiya Yuva Shakti\nRajasthan\tDausa\tNone of the Above\t8410\tno\tNOTA\tNone of the Above\nRajasthan\tTONK-SAWAI MADHOPUR\tSUKHBIR SINGH JAUNAPURIA\t548179\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tTONK-SAWAI MADHOPUR\tNone of the Above\t3664\tno\tNOTA\tNone of the Above\nRajasthan\tTONK-SAWAI MADHOPUR\tRAM HANS\t867\tno\tNADP\tNaya Daur Party\nRajasthan\tTONK-SAWAI MADHOPUR\tKORI LAL\t1924\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tJAGMOHAN MEENA\t14600\tno\tNPEP\tNational Peoples Party\nRajasthan\tTONK-SAWAI MADHOPUR\tVARIS ALI\t2361\tno\tAAP\tawami aamjan party\nRajasthan\tTONK-SAWAI MADHOPUR\tCHANDAN LAL MEENA\t6522\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tRAMESH CHAND\t751\tno\tLD\tLok Dal\nRajasthan\tTONK-SAWAI MADHOPUR\tSAMPATTI BAI ALIAS SUNITA VERMA\t1611\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tGOVIND PRASAD\t3125\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tRAM PARKASH\t6177\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tDINESH SAINI\t2205\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tTONK-SAWAI MADHOPUR\tMAKKHAN\t10515\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tSIYA RAM\t1104\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tHARI RAM\t1045\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tMUKESH\t2665\tno\tAAAP\tAam Aadmi Party\nRajasthan\tTONK-SAWAI MADHOPUR\tBAJARANG LAL\t699\tno\tRAHM\tRashtriya Ahinsa Manch\nRajasthan\tTONK-SAWAI MADHOPUR\tSURESH\t10392\tno\tBSP\tBahujan Samaj Party\nRajasthan\tTONK-SAWAI MADHOPUR\tBHAR SINGH\t701\tno\tSP(I)\tSocialist Party (India)\nRajasthan\tTONK-SAWAI MADHOPUR\tDINESH\t2147\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tTONK-SAWAI MADHOPUR\tKAMAL KUMAR DHOBI\t1723\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tBABULAL JANGID\t7195\tno\tIND\tIndependent\nRajasthan\tTONK-SAWAI MADHOPUR\tMOHAMMED AZHARUDDIN\t412868\tno\tINC\tIndian National Congress\nRajasthan\tAjmer\tSANWAR LAL JAT\t637874\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tAjmer\tJAGDISH SINGH RAWAT\t1374\tno\tIND\tIndependent\nRajasthan\tAjmer\tSANWAR LAL\t12149\tno\tNCP\tNationalist Congress Party\nRajasthan\tAjmer\tMUKUL MISHRA\t1869\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nRajasthan\tAjmer\tBHANWAR LAL SONI\t4970\tno\tIND\tIndependent\nRajasthan\tAjmer\tANITA JAIN\t910\tno\tIND\tIndependent\nRajasthan\tAjmer\tRAMLAL\t1040\tno\tAKBAP\tAkhil Bhartiya Aamjan Party\nRajasthan\tAjmer\tSURENDRA KUMAR JAIN\t5184\tno\tIND\tIndependent\nRajasthan\tAjmer\tKRISHNA KUMAR DADHICH\t1015\tno\tIND\tIndependent\nRajasthan\tAjmer\tSACHIN PILOT\t465891\tno\tINC\tIndian National Congress\nRajasthan\tAjmer\tJAGDISH\t7974\tno\tBSP\tBahujan Samaj Party\nRajasthan\tAjmer\tNARAIN DAS SINDHI\t3518\tno\tIND\tIndependent\nRajasthan\tAjmer\tNone of the Above\t12546\tno\tNOTA\tNone of the Above\nRajasthan\tNagaur\tC R CHOUDHARY\t414791\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tNagaur\tNone of the Above\t12185\tno\tNOTA\tNone of the Above\nRajasthan\tNagaur\tHARIRAM\t8658\tno\tIND\tIndependent\nRajasthan\tNagaur\tAYUB KHAN\t7300\tno\tSP\tSamajwadi Party\nRajasthan\tNagaur\tHANUMAN BENIWAL\t159980\tno\tIND\tIndependent\nRajasthan\tNagaur\tABDUL AZIZ\t11239\tno\tBSP\tBahujan Samaj Party\nRajasthan\tNagaur\tSUNIL\t5251\tno\tIND\tIndependent\nRajasthan\tNagaur\tCHHOTURAM\t15234\tno\tIND\tIndependent\nRajasthan\tNagaur\tHANUMANRAM\t3976\tno\tAAAP\tAam Aadmi Party\nRajasthan\tNagaur\tBHANWAR SINGH RATHORE\t5776\tno\tAITC\tAll India Trinamool Congress\nRajasthan\tNagaur\tMULARAM\t4332\tno\tIND\tIndependent\nRajasthan\tNagaur\tDR JYOTI MIRDHA\t339573\tno\tINC\tIndian National Congress\nRajasthan\tNagaur\tNEMICHAND\t4372\tno\tAVIRP\tAARAKSHAN VIRODHI PARTY\nRajasthan\tNagaur\tHARIRAM MEHRADA\t11352\tno\tBA S D\tBahujan Sangharshh Dal\nRajasthan\tPali\tP P CHOUDHARY\t711772\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tPali\tDEV KISHAN DEHIYA\t1264\tno\tMEDP\tMegh Desham Party\nRajasthan\tPali\tUDARAM\t2058\tno\tIND\tIndependent\nRajasthan\tPali\tKANHAIYALAL PAREEK\t1890\tno\tIND\tIndependent\nRajasthan\tPali\tMAHESH BHANDARI\t5031\tno\tAAAP\tAam Aadmi Party\nRajasthan\tPali\tSHOBHARAM MALI\t2825\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tPali\tSHER SINGH SAFA\t5198\tno\tIND\tIndependent\nRajasthan\tPali\tDR. RAMLAL MOHBARSHA\t5794\tno\tIND\tIndependent\nRajasthan\tPali\tBHOJRAJ\t16469\tno\tBSP\tBahujan Samaj Party\nRajasthan\tPali\tMUNNI DEVI GODARA\t312733\tno\tINC\tIndian National Congress\nRajasthan\tPali\tOM PRAKASH MALI\t3306\tno\tIND\tIndependent\nRajasthan\tPali\tDINESH MEV\t2358\tno\tIND\tIndependent\nRajasthan\tPali\tJAGDISH\t7186\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tPali\tNone of the Above\t17703\tno\tNOTA\tNone of the Above\nRajasthan\tJodhpur\tGAJENDRASINGH SHEKHAWAT\t713515\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tJodhpur\tNone of the Above\t15085\tno\tNOTA\tNone of the Above\nRajasthan\tJodhpur\tSOHAN LAL GOYAL\t3764\tno\tIND\tIndependent\nRajasthan\tJodhpur\tGOPARAM MEGHWAL\t13511\tno\tBSP\tBahujan Samaj Party\nRajasthan\tJodhpur\tVIGYAN MODI\t8552\tno\tAAAP\tAam Aadmi Party\nRajasthan\tJodhpur\tCHANDRESH KUMARI\t303464\tno\tINC\tIndian National Congress\nRajasthan\tJodhpur\tJETHARAM MEGHWAL\t831\tno\tIND\tIndependent\nRajasthan\tJodhpur\tRAVI GARG\t1206\tno\tBNNP\tBharat Nav Nirman Party\nRajasthan\tJodhpur\tRAM DAYAL\t2321\tno\tIND\tIndependent\nRajasthan\tJodhpur\tDHANRAJ GEHLOT\t7104\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tJodhpur\tMEHMOODA BEGAM ABBASI\t2631\tno\tIND\tIndependent\nRajasthan\tJodhpur\tRUGHA RAM CHOUDHARY ADVOCATE\t4855\tno\tIND\tIndependent\nRajasthan\tJodhpur\tDUNGAR MAL PUNIYA\t1041\tno\tIND\tIndependent\nRajasthan\tJodhpur\tJAYDA\t718\tno\tIND\tIndependent\nRajasthan\tBarmer\tCOL. SONA RAM\t488747\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tBarmer\tJASWANT SINGH\t401286\tno\tIND\tIndependent\nRajasthan\tBarmer\tMASTER BHAIRARAM MEGHWAL\t13924\tno\tIND\tIndependent\nRajasthan\tBarmer\tHARISH CHAUDHARY\t220881\tno\tINC\tIndian National Congress\nRajasthan\tBarmer\tPOPATRAM\t7557\tno\tIND\tIndependent\nRajasthan\tBarmer\tMANGILAL GAUR\t14906\tno\tAAAP\tAam Aadmi Party\nRajasthan\tBarmer\tKHET SINGH RAJPUROHIT\t12366\tno\tIND\tIndependent\nRajasthan\tBarmer\tRAMA RAM\t17563\tno\tIND\tIndependent\nRajasthan\tBarmer\tMAHANT ARJUNPURI\t6380\tno\tIND\tIndependent\nRajasthan\tBarmer\tBABULAL\t6737\tno\tIND\tIndependent\nRajasthan\tBarmer\tMALAM SINGH\t12883\tno\tBSP\tBahujan Samaj Party\nRajasthan\tBarmer\tNone of the Above\t15889\tno\tNOTA\tNone of the Above\nRajasthan\tJalore\tDEVJI PATEL\t580508\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tJalore\tNone of the Above\t11183\tno\tNOTA\tNone of the Above\nRajasthan\tJalore\tMANARA RAM\t6146\tno\tIND\tIndependent\nRajasthan\tJalore\tS. BUTA SINGH\t175344\tno\tIND\tIndependent\nRajasthan\tJalore\tSHANKAR LAL DARJI\t16399\tno\tIND\tIndependent\nRajasthan\tJalore\tMOOLA RAM\t10316\tno\tIND\tIndependent\nRajasthan\tJalore\tOTARAM\t29998\tno\tBSP\tBahujan Samaj Party\nRajasthan\tJalore\tCHHAGANARAM / BHALARAM\t3960\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tJalore\tRAN SINGH RAJPUROHIT\t2731\tno\tJGP\tJago Party\nRajasthan\tJalore\tMAHENDRA K. CHOUDHARY\t13008\tno\tIND\tIndependent\nRajasthan\tJalore\tNARINGA RAM YADAV\t3200\tno\tIND\tIndependent\nRajasthan\tJalore\tANJANA UDAI LAL\t199363\tno\tINC\tIndian National Congress\nRajasthan\tJalore\tCHHAGANARAM / PRHALAD\t15811\tno\tHJP\tHindustan Janta Party\nRajasthan\tJalore\tSUKH DEV\t2017\tno\tAAP\tawami aamjan party\nRajasthan\tJalore\tBHANWAR LAL WAGHELA\t4817\tno\tIND\tIndependent\nRajasthan\tJalore\tNARAYAN LAL\t9875\tno\tIND\tIndependent\nRajasthan\tJalore\tTARUN KUMAR\t2596\tno\tIND\tIndependent\nRajasthan\tUdaipur\tARJUNLAL MEENA\t660373\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tUdaipur\tLAXMILAL GAMETI\t6680\tno\tSP(I)\tSocialist Party (India)\nRajasthan\tUdaipur\tDR. VELARAM MEENA\t11449\tno\tAAAP\tAam Aadmi Party\nRajasthan\tUdaipur\tPRABHU LAL MEENA\t4529\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tUdaipur\tLAXMAN BHIL\t20690\tno\tBSP\tBahujan Samaj Party\nRajasthan\tUdaipur\tMEGHRAJ TAWAR\t33743\tno\tCPI\tCommunist Party of India\nRajasthan\tUdaipur\tGOTAM LAL MEENA\t5442\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nRajasthan\tUdaipur\tRAGHUVIR SINGH\t423611\tno\tINC\tIndian National Congress\nRajasthan\tUdaipur\tNone of the Above\t26685\tno\tNOTA\tNone of the Above\nRajasthan\tBanswara\tMANSHANKAR NINAMA\t577433\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tBanswara\tNone of the Above\t34404\tno\tNOTA\tNone of the Above\nRajasthan\tBanswara\tBHANJI BHAI\t21760\tno\tJD(U)\tJanata Dal  (United)\nRajasthan\tBanswara\tRESHAM MALVIYA\t485517\tno\tINC\tIndian National Congress\nRajasthan\tBanswara\tVIJAYPAL\t27886\tno\tBSP\tBahujan Samaj Party\nRajasthan\tBanswara\tSOMA LAL KOTED\t10541\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tBanswara\tHARISH RAWAT\t13647\tno\tAAAP\tAam Aadmi Party\nRajasthan\tChittorgarh\tCHANDRA PRAKASH JOSHI\t703236\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tChittorgarh\tMANGI LAL SOLANKI\t1291\tno\tBMUP\tBahujan Mukti Party\nRajasthan\tChittorgarh\tSHANTI LAL TRIVEDI\t1564\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nRajasthan\tChittorgarh\tGULAB CHAND SAHLOT\t1692\tno\tIND\tIndependent\nRajasthan\tChittorgarh\tDR. NARENDRA KUMAR GUPTA\t5939\tno\tAAAP\tAam Aadmi Party\nRajasthan\tChittorgarh\tSANJAY KUMAR\t4467\tno\tBSP\tBahujan Samaj Party\nRajasthan\tChittorgarh\tBABU MEGHWAL\t3069\tno\tIND\tIndependent\nRajasthan\tChittorgarh\tRADHA DEVI\t21593\tno\tCPI\tCommunist Party of India\nRajasthan\tChittorgarh\tGIRIJA VYAS\t386379\tno\tINC\tIndian National Congress\nRajasthan\tChittorgarh\tYASHWANT PURI\t12675\tno\tIND\tIndependent\nRajasthan\tChittorgarh\tMOHAMMAD IMTIYAZ\t4971\tno\tIND\tIndependent\nRajasthan\tChittorgarh\tBHANWAR LAL\t3263\tno\tIND\tIndependent\nRajasthan\tChittorgarh\tVIJAYA RAY\t1240\tno\tRJVP\tRajasthan Vikas Party\nRajasthan\tChittorgarh\tPRABHU LAL GUJJAR\t1216\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tChittorgarh\tNone of the Above\t20034\tno\tNOTA\tNone of the Above\nRajasthan\tRajsamand\tHARIOM SINGH RATHORE\t644794\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tRajsamand\tNone of the Above\t17182\tno\tNOTA\tNone of the Above\nRajasthan\tRajsamand\tIDRISH MOHAMMAD\t10407\tno\tJGP\tJago Party\nRajasthan\tRajsamand\tKHIMARAM SALVI\t2559\tno\tIND\tIndependent\nRajasthan\tRajsamand\tBHANWAR LAL MALI\t6418\tno\tIND\tIndependent\nRajasthan\tRajsamand\tSANJAY CHOUDHARY\t12479\tno\tIND\tIndependent\nRajasthan\tRajsamand\tLAXMAN\t1392\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tRajsamand\tPRADEEP SINGH BHATNAGAR\t4897\tno\tAAAP\tAam Aadmi Party\nRajasthan\tRajsamand\tSURYA BHAWANI SINGH CHAWARA\t10160\tno\tIND\tIndependent\nRajasthan\tRajsamand\tGULAM FARID\t2977\tno\tSP\tSamajwadi Party\nRajasthan\tRajsamand\tGOPAL SINGH SHEKHAWAT\t249089\tno\tINC\tIndian National Congress\nRajasthan\tRajsamand\tNEERU RAM JAT\t14579\tno\tBSP\tBahujan Samaj Party\nRajasthan\tRajsamand\tHEERA KATHAT\t5186\tno\tIND\tIndependent\nRajasthan\tBhilwara\tSUBHASH BAHERIA\t630317\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tBhilwara\tSURENDAR SINGH RANAWAT\t6737\tno\tIND\tIndependent\nRajasthan\tBhilwara\tASLAM SHEKH ADVOCATE\t1794\tno\tIND\tIndependent\nRajasthan\tBhilwara\tSUNIL AGIWAL\t5909\tno\tAAAP\tAam Aadmi Party\nRajasthan\tBhilwara\tASHOK CHANDNA\t384053\tno\tINC\tIndian National Congress\nRajasthan\tBhilwara\tSHYAM LAL SALVI\t5374\tno\tIND\tIndependent\nRajasthan\tBhilwara\tRADHY SHYAM VAISHNAV\t5684\tno\tBA S D\tBahujan Sangharshh Dal\nRajasthan\tBhilwara\tRATAN LAL DHOBI\t7110\tno\tIND\tIndependent\nRajasthan\tBhilwara\tGOVIND KUMAR BAIRWA\t15088\tno\tBSP\tBahujan Samaj Party\nRajasthan\tBhilwara\tCHANDRA PRAKASH GOCHER\t10344\tno\tBYS\tBharatiya Yuva Shakti\nRajasthan\tBhilwara\tSATYA PRAKASH SHARMA\t11982\tno\tIND\tIndependent\nRajasthan\tBhilwara\tNone of the Above\t19698\tno\tNOTA\tNone of the Above\nRajasthan\tKota\tOM BIRLA\t644822\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tKota\tNone of the Above\t12760\tno\tNOTA\tNone of the Above\nRajasthan\tKota\tRAJESH KUMAR\t1168\tno\tJMBP\tJai Maha Bharath Party\nRajasthan\tKota\tAJMAT ALI\t7559\tno\tBYS\tBharatiya Yuva Shakti\nRajasthan\tKota\tMAHESH KUMAR RANIWAL\t1010\tno\tAVIRP\tAARAKSHAN VIRODHI PARTY\nRajasthan\tKota\tMOHAN LAL MAHAWAR\t4822\tno\tIND\tIndependent\nRajasthan\tKota\tASHOK KUMAR JAIN\t16981\tno\tAAAP\tAam Aadmi Party\nRajasthan\tKota\tSHYAM SUNDER SHARMA\t5091\tno\tIND\tIndependent\nRajasthan\tKota\tIJYARAJ SINGH\t444040\tno\tINC\tIndian National Congress\nRajasthan\tKota\tASHOK KUMAR\t10862\tno\tBSP\tBahujan Samaj Party\nRajasthan\tKota\tTEJPAL\t1825\tno\tIND\tIndependent\nRajasthan\tKota\tMAHENDRA KUMAR\t2307\tno\tIND\tIndependent\nRajasthan\tKota\tASFAK ALI\t1713\tno\tIND\tIndependent\nRajasthan\tJHALAWAR-BARAN\tDUSHYANT SINGH\t676102\tyes\tBJP\tBharatiya Janata Party\nRajasthan\tJHALAWAR-BARAN\tJAVED KHAN\t13617\tno\tBYS\tBharatiya Yuva Shakti\nRajasthan\tJHALAWAR-BARAN\tCHANDRA SINGH\t23587\tno\tBSP\tBahujan Samaj Party\nRajasthan\tJHALAWAR-BARAN\tSULEMAN PUTA\t11602\tno\tIND\tIndependent\nRajasthan\tJHALAWAR-BARAN\tPRAMOD BHAYA\t394556\tno\tINC\tIndian National Congress\nRajasthan\tJHALAWAR-BARAN\tBALDAR\t7836\tno\tIND\tIndependent\nRajasthan\tJHALAWAR-BARAN\tNone of the Above\t19064\tno\tNOTA\tNone of the Above\nSikkim\tSikkim\tPREM DAS RAI\t163698\tyes\tSDF\tSikkim Democratic Front\nSikkim\tSikkim\tNone of the Above\t4332\tno\tNOTA\tNone of the Above\nSikkim\tSikkim\tNAR BAHADUR KHATIWADA\t7279\tno\tBJP\tBharatiya Janata Party\nSikkim\tSikkim\tTEK NATH DHAKAL\t121956\tno\tSKM\tSikkim Krantikari Morcha\nSikkim\tSikkim\tAKAR DHOJ LIMBU\t7189\tno\tINC\tIndian National Congress\nSikkim\tSikkim\tNAKUL DAS RAI\t1972\tno\tAITC\tAll India Trinamool Congress\nSikkim\tSikkim\tKAUSHAL RAI\t2541\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tThiruvallur\tVENUGOPAL.P.(DR)\t628499\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tThiruvallur\tNone of the Above\t23598\tno\tNOTA\tNone of the Above\nTamil Nadu\tThiruvallur\tBALAMURUGAN.B\t8260\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tThiruvallur\tSRINIVASAN.D\t2642\tno\tSP\tSamajwadi Party\nTamil Nadu\tThiruvallur\tCHANDRASEKARAN.M\t1597\tno\tIND\tIndependent\nTamil Nadu\tThiruvallur\tSATHYAMURTHY.C\t11137\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tThiruvallur\tYUVARAJ.V\t204734\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tThiruvallur\tKANNAN A.S\t13794\tno\tCPI\tCommunist Party of India\nTamil Nadu\tThiruvallur\tJAYAKUMAR.M\t43960\tno\tINC\tIndian National Congress\nTamil Nadu\tThiruvallur\tRAVIKUMAR.D\t305069\tno\tVCK\tViduthalai Chiruthaigal Katchi\nTamil Nadu\tThiruvallur\tVETRISELVAM.K\t2441\tno\tIND\tIndependent\nTamil Nadu\tThiruvallur\tVENUGOPAL.P\t3085\tno\tIND\tIndependent\nTamil Nadu\tThiruvallur\tRAVIKUMAR.M\t2966\tno\tIND\tIndependent\nTamil Nadu\tThiruvallur\tSURESH KUMAR.S\t1062\tno\tIND\tIndependent\nTamil Nadu\tThiruvallur\tDOSS.R\t1596\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tVENKATESH BABU .T.G\t406704\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tChennai North\tSOUNDARAPANDIAN .M\t86989\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tChennai North\tBIJU CHACKO\t24190\tno\tINC\tIndian National Congress\nTamil Nadu\tChennai North\tJANARTHANAN .J\t4960\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tChennai North\tVIJAYA KUMAR .K\t1145\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tChennai North\tSRINIVASON .S\t6819\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tChennai North\tANBAZHAGAN .G\t208\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tGIRIRAJ. D\t414\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tKESAVAN. L\t1198\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tKOLANGI. M\t1494\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tCHARAVANAN. M.P.\t475\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tSIVASHANMUGA PERUMAL. S\t712\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tSEETHA MALLAR. A\t469\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tSRINIVASAN. J\t964\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tSUNDAR. G\t870\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tSENTHIL KUMAR. K\t472\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tDEEPAK. C\t475\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tDURAI BABU. R\t297\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tDEVARAJ. G\t386\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tSIVAKUMAR . V\t502\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nTamil Nadu\tChennai North\tNIJAM MOHAIDEEN .M\t14585\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nTamil Nadu\tChennai North\tBALAJI. E\t311\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tPRABHAKARAN. K\t457\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tPONRAJ. P\t208\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tPAULRAJ .T\t387\tno\tMMKA\tMakkal Manadu Katchi\nTamil Nadu\tChennai North\tBABU MAILAN .A\t569\tno\tLSP\tLok Satta Party\nTamil Nadu\tChennai North\tPAUL KANAGARAJ. R.C\t1589\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tRAMASAMY. M.D.\t234\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tRAJ. N\t216\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tRAJA. B\t337\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tMAHENDRAN. K.P.P.\t343\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tRAMESH KANNAN .G\t257\tno\tRPI\tRepublican Party of India\nTamil Nadu\tChennai North\tMARIMUTHU. P\t777\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tMARIRAJ. T.R\t559\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tMOHAMED MAJASUDDIN\t302\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tYOGARAJ. S\t206\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tRAVIKUMAR. T\t211\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tRAVI PARIYANAR. D\t600\tno\tIND\tIndependent\nTamil Nadu\tChennai North\tNone of the Above\t17472\tno\tNOTA\tNone of the Above\nTamil Nadu\tChennai North\tVASUKI .U\t23751\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tChennai North\tGIRIRAJAN .R\t307000\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tChennai South\tDR. J. JAYAVARDHAN\t438404\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tChennai South\tGANESAN  S\t1730\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nTamil Nadu\tChennai South\tK.R.RAMASWAMY@TRAFFIC RAMASWAMY\t1670\tno\tIND\tIndependent\nTamil Nadu\tChennai South\t(KUPPAL)G.DEVADOSS\t514\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tRAJINI NIVETHA\t142\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. BALU\t264\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tR. RAVICHANDRAN\t216\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tV. BALAJI\t1540\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tChennai South\tS.S.RADHAKRISHNAN\t376\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tTHANGARAJ\t189\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. DEVADOSS\t235\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. PRAGASAM\t648\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tT.K.S.ELANGOVAN\t301779\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tChennai South\tS. SEETHALAKSHMI\t1303\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tChennai South\tJAHIR HUSSAIN. M\t17312\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tChennai South\tS. PARTHIBAN\t175\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tLOGANATHAN\t117\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tA. IRUDAYADASS\t963\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tJ.S. MAHALINGAM\t269\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tM. ARUMUGAM\t219\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tK. MANI\t275\tno\tJD(U)\tJanata Dal  (United)\nTamil Nadu\tChennai South\tKANTHAVEL  D\t878\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. BALAMURALI\t420\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. RAJESHWARI\t155\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. VEERAMANI\t105\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS.V. RAMANI\t24420\tno\tINC\tIndian National Congress\nTamil Nadu\tChennai South\tB. KUMAR SRI SRI\t236\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tBHAVANI JANAKIRAM\t247\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tCOLONEL KADAR BASHA\t434\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tA. JAIGANESH\t4501\tno\tLSP\tLok Satta Party\nTamil Nadu\tChennai South\tLA.GANESAN\t256786\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tChennai South\tT. DHANASEKAR\t332\tno\tNOC\tNational Organisation Congress\nTamil Nadu\tChennai South\tT.K. RAMAJAYAM\t1194\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tR. KANDASAMY@BABU\t374\tno\tAPM\tAmbedkar People's Movement\nTamil Nadu\tChennai South\tM. JAYARAMAN\t194\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tJOSEPH GUNASEKAR\t245\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tT. PONNUSAMY\t539\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tA. MAGESH\t121\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tPON. SIVA KUMAR\t274\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. CHARUMATHI\t261\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tS. SESU RAJ\t201\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tK. SHANKAR\t504\tno\tIND\tIndependent\nTamil Nadu\tChennai South\tNone of the Above\t20402\tno\tNOTA\tNone of the Above\nTamil Nadu\tChennai central\tS.R. VIJAYAKUMAR\t333296\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tChennai central\tDAYANIDHI MARAN\t287455\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tChennai central\tS.D. KRISHNAKUMAR\t1528\tno\tRPI(A)\tRepublican Party of India (A)\nTamil Nadu\tChennai central\tC.D. MEYYAPPAN\t25981\tno\tINC\tIndian National Congress\nTamil Nadu\tChennai central\tJ. CONSTANDINE RAVINDRAN\t114798\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tChennai central\tMURALIKRISHNAN (A) SAMARAN\t2531\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tChennai central\tAUDITOR GOPINARAYANAN\t805\tno\tMMKA\tMakkal Manadu Katchi\nTamil Nadu\tChennai central\tB. AMBETH VENKATESH\t390\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tD. RAMESH\t259\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tV. CHANDRAMOHAN\t324\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tD. BABU\t261\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tJ. PRABHAKAR\t19553\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tChennai central\tP. AHILA\t579\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tM. PRABAKARAN\t336\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tP. MANIMARAN\t391\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tM. MURUGAN\t697\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tT. RAVIKUMAR\t973\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tK.RAVEENDRAN\t369\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tARUNDHATHI JAIGANESH (A) S.JAIGANESH\t390\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tM. MOHAMED RASIK\t2019\tno\tIND\tIndependent\nTamil Nadu\tChennai central\tNone of the Above\t21959\tno\tNOTA\tNone of the Above\nTamil Nadu\tSriperumbudur\tRAMACHANDRAN, K.N. THIRU\t545820\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tSriperumbudur\tNone of the Above\t27676\tno\tNOTA\tNone of the Above\nTamil Nadu\tSriperumbudur\tPALANI, A. THIRU\t781\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tBHARATHIDASAN R. THIRU.\t811\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tMADHAVARAJ, K.V. THIRU.\t1165\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tPUGAZHENDI, A. THIRU\t1726\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tRAMESH, THIRU\t2782\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tRAMACHANDIRAN, K. THIRU\t791\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tRAJAGOPAL MALLAR, P. THIRU\t2622\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tSRIDHAR,M.THIRU\t477\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tSRIDHAR, C. THIRU\t497\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tSriperumbudur\tAYODHI, L.( DR).\t699\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tSAMPATH. K. THIRU\t1301\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tSHANMUGAM, K. THIRU\t291\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tSHANMUGAM, G. THIRU\t372\tno\tIND\tIndependent\nTamil Nadu\tSriperumbudur\tJAGATHRAKSHAKAN, S. THIRU\t443174\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tSriperumbudur\tARUL ANBARASU\t39015\tno\tINC\tIndian National Congress\nTamil Nadu\tSriperumbudur\tMOHAMED ABBAS, S. THIRU\t4977\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tSriperumbudur\tPULAVAR. E.SU. MANI. THIRU\t870\tno\tNOC\tNational Organisation Congress\nTamil Nadu\tSriperumbudur\tBHARATHI, K. THIRU\t4743\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nTamil Nadu\tSriperumbudur\tMASILAMANI, R. (DR).\t187094\tno\tMDMK\tMarumalarchi Dravida Munnetra Kazhagam\nTamil Nadu\tSriperumbudur\tVASIGARAN, S.A.N. THIRU\t18963\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tKancheepuram\tMARAGATHAM  K\t499395\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tKancheepuram\tSATHIYARAJ  J.S\t6807\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tKancheepuram\tSELVAM  G\t352529\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tKancheepuram\tSATHIYANATHAN, N.\t1089\tno\tIND\tIndependent\nTamil Nadu\tKancheepuram\tPARGUNAN  A.\t1368\tno\tIND\tIndependent\nTamil Nadu\tKancheepuram\tBALAMURUGAN, R\t2895\tno\tIND\tIndependent\nTamil Nadu\tKancheepuram\tSRINIVASAN C.\t1090\tno\tIND\tIndependent\nTamil Nadu\tKancheepuram\tMARAGATHAM M\t3618\tno\tIND\tIndependent\nTamil Nadu\tKancheepuram\tVISWANATHAN  P\t33313\tno\tINC\tIndian National Congress\nTamil Nadu\tKancheepuram\tULAGANATHAN  .A\t1479\tno\tIND\tIndependent\nTamil Nadu\tKancheepuram\tSATHYA  C.E.\t207080\tno\tMDMK\tMarumalarchi Dravida Munnetra Kazhagam\nTamil Nadu\tKancheepuram\tNone of the Above\t17736\tno\tNOTA\tNone of the Above\nTamil Nadu\tArakkonam\tHARI, G.\t493534\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tArakkonam\tNone of the Above\t10370\tno\tNOTA\tNone of the Above\nTamil Nadu\tArakkonam\tPANCHU.UDAYAKUMAR\t6025\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tGAJENDRAN, S.\t6570\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tELANGO, D.\t517\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tELANGO, N.\t918\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tELANGOVAN, A.\t804\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tELANGOVAN, D.\t1556\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tSADHU MUTHU KRISTNAN ERAJANDRAN, T.M.S.\t484\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tSHETTU, S.\t570\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tGOPINATHAN, G.\t1955\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tHARI, S.\t1458\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tARUN, N.\t610\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tRAVI, V.\t2198\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tHARI, S.\t599\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tHARIKRISHNAN, P.\t541\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tDASS, D.\t7354\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tArakkonam\tVELU, R.\t233762\tno\tPMK\tPattali Makkal Katchi\nTamil Nadu\tArakkonam\tRAJESH, R\t56337\tno\tINC\tIndian National Congress\nTamil Nadu\tArakkonam\tRAJESH, S.\t4021\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tArakkonam\tELANGO, N.R\t252768\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tArakkonam\tVELU, D.\t920\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tVELU, V.\t783\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tSRIDHAR, R.S\t723\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tRAVI, N.D.\t471\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tVELU, R.\t747\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tVADIVEL, A.\t1023\tno\tIND\tIndependent\nTamil Nadu\tArakkonam\tVISWANATHAN, P.\t1434\tno\tIND\tIndependent\nTamil Nadu\tVellore\tSENGUTTUVAN, B.\t383719\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tVellore\tIMDAD SHARIFF, A.\t3742\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tVellore\tTHIRUPATHY, T.\t1293\tno\tIND\tIndependent\nTamil Nadu\tVellore\tABDUL RAHMAN, M.\t205896\tno\tIUML\tIndian Union Muslim League\nTamil Nadu\tVellore\tGANGADARAN, K.\t991\tno\tIND\tIndependent\nTamil Nadu\tVellore\tSHANMUGAM, A.C.\t324326\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tVellore\tGOPU, C.\t6056\tno\tIND\tIndependent\nTamil Nadu\tVellore\tLALITHA, K.\t561\tno\tIND\tIndependent\nTamil Nadu\tVellore\tSUGUMAR, K.\t828\tno\tIND\tIndependent\nTamil Nadu\tVellore\tSHAREEF BASHA, H.\t496\tno\tSAP\tSamata Party\nTamil Nadu\tVellore\tMANIMARAN, M.\t430\tno\tIND\tIndependent\nTamil Nadu\tVellore\tDHANDAPANI, M.\t558\tno\tIND\tIndependent\nTamil Nadu\tVellore\tGANGADHARAN, K.\t430\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tVellore\tGOVINDAN, C.\t4169\tno\tIND\tIndependent\nTamil Nadu\tVellore\tVENKATESAN, R.\t764\tno\tIND\tIndependent\nTamil Nadu\tVellore\tSIVAPRAKASAM, R.S.\t3603\tno\tIND\tIndependent\nTamil Nadu\tVellore\tJAYAPRAKASH\t2791\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tVellore\tESWARAN, R.\t790\tno\tIND\tIndependent\nTamil Nadu\tVellore\tKESAVAN, M.\t1708\tno\tIND\tIndependent\nTamil Nadu\tVellore\tVIJAY ELANCHEZIAN, J.\t21650\tno\tINC\tIndian National Congress\nTamil Nadu\tVellore\tNANDHA KUMAR, V.\t745\tno\tSP\tSamajwadi Party\nTamil Nadu\tVellore\tABDUL RAHMAN, N.\t581\tno\tIND\tIndependent\nTamil Nadu\tVellore\tABDUL RAHMAN, B.\t1052\tno\tIND\tIndependent\nTamil Nadu\tVellore\tGEORGE, V.A.\t491\tno\tBDBRAJP\tBharatiya Dr. B.R.Ambedkar Janta Party\nTamil Nadu\tVellore\tNone of the Above\t7100\tno\tNOTA\tNone of the Above\nTamil Nadu\tKrishnagiri\tASHOK KUMAR.K\t480491\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tKrishnagiri\tNone of the Above\t16020\tno\tNOTA\tNone of the Above\nTamil Nadu\tKrishnagiri\tCHINNA PILLAPPA.P\t273900\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tKrishnagiri\tVIRGINIYA BENATIN FRANCILES\t2530\tno\tIND\tIndependent\nTamil Nadu\tKrishnagiri\tMANI.G.K\t224963\tno\tPMK\tPattali Makkal Katchi\nTamil Nadu\tKrishnagiri\tMUNIRAJ.K\t2135\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tKrishnagiri\tCHANBASHA.M\t8618\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tKrishnagiri\tVINOTHKUMAR.P\t2261\tno\tIND\tIndependent\nTamil Nadu\tKrishnagiri\tGOWDA.NSM\t3033\tno\tJD(S)\tJanata Dal  (Secular)\nTamil Nadu\tKrishnagiri\tSARAVANAN.V\t2737\tno\tIND\tIndependent\nTamil Nadu\tKrishnagiri\tCHELLAKUMAR. DR. A\t38885\tno\tINC\tIndian National Congress\nTamil Nadu\tKrishnagiri\tSOUNDARAJAN.M\t1744\tno\tIND\tIndependent\nTamil Nadu\tKrishnagiri\tASHOK KUMAR.A\t3875\tno\tIND\tIndependent\nTamil Nadu\tKrishnagiri\tGANDHI.TVS\t1519\tno\tIND\tIndependent\nTamil Nadu\tKrishnagiri\tSUNDARAM.S\t3665\tno\tUCPI\tUnited Communist Party of India\nTamil Nadu\tKrishnagiri\tPATTABIRAMA.C\t2015\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tANBUMANI RAMADOSS\t468194\tyes\tPMK\tPattali Makkal Katchi\nTamil Nadu\tDharmapuri\tSAVITHIRI.K\t3164\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tDharmapuri\tANANDKUMAR.K\t1978\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tRAMA SUGANTHAN\t15455\tno\tINC\tIndian National Congress\nTamil Nadu\tDharmapuri\tANBUMANI.G\t1370\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tARIVUDAINAMBI.M\t1403\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tSAMBATH.M\t1310\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tRAJINIKANTH.S\t8180\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tDharmapuri\tANGURAJ.P\t3905\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tNATARAJAN.R\t2199\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tTHAMARAISELVAN.R\t180297\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tDharmapuri\tKUMARAN.V\t2413\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tNone of the Above\t12693\tno\tNOTA\tNone of the Above\nTamil Nadu\tDharmapuri\tPADMARAJAN.K.DR\t4934\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tARIVAZHAGAN.P\t2802\tno\tIND\tIndependent\nTamil Nadu\tDharmapuri\tMOHAN.P.S\t391048\tno\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tTiruvannamalai\tVANAROJA R\t500751\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tTiruvannamalai\tNone of the Above\t9595\tno\tNOTA\tNone of the Above\nTamil Nadu\tTiruvannamalai\tMANIKKAVEL M R\t1555\tno\tJD(S)\tJanata Dal  (Secular)\nTamil Nadu\tTiruvannamalai\tARULJOTHI A\t904\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tPRABAKARAN L\t679\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tARUMUGAM R\t973\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tLOGANATHAN P\t1729\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tANNADURAI C N\t332145\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tTiruvannamalai\tVAITHIYANATHAN S\t931\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tMURUGESAN K\t10708\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tVELMURUGAN A\t724\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tSUBRAMANIYAN A\t17854\tno\tINC\tIndian National Congress\nTamil Nadu\tTiruvannamalai\tARUNACHALAM P\t736\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tRAMESH P\t2445\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tEDIROLI MANIAN G\t157954\tno\tPMK\tPattali Makkal Katchi\nTamil Nadu\tTiruvannamalai\tANBALAGAN D\t1686\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tKOTHANDAPANI B\t4688\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tMURUGAN P\t991\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tBASKARAN  M R\t4328\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tTiruvannamalai\tSENTHIL S\t3867\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tKAMARAJ M\t1369\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tANNADURAI B\t1693\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tANNADURAI A\t865\tno\tIND\tIndependent\nTamil Nadu\tTiruvannamalai\tARUMUGAM C\t6278\tno\tSP\tSamajwadi Party\nTamil Nadu\tTiruvannamalai\tRAVINDRAN T\t1999\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tArani\tV.ELUMALAI\t502721\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tArani\tA.GANESAN\t5573\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tArani\tR.SIVANANDAM\t258877\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tArani\tM.K.VISHNU PRASAD\t27717\tno\tINC\tIndian National Congress\nTamil Nadu\tArani\tV.ANNA VENKATESH\t2372\tno\tADSMK\tAnaithindia Dravidar Samudaya Munnetra Kazhagam\nTamil Nadu\tArani\tA.K.MOORTHY\t253332\tno\tPMK\tPattali Makkal Katchi\nTamil Nadu\tArani\tP.LOGANATHAN\t4331\tno\tIND\tIndependent\nTamil Nadu\tArani\tS.ELUMALAI\t513\tno\tIND\tIndependent\nTamil Nadu\tArani\tK.ELUMALAI\t1243\tno\tIND\tIndependent\nTamil Nadu\tArani\tR.SARASWATHI\t668\tno\tIND\tIndependent\nTamil Nadu\tArani\tH.SENTHILKUMAR\t798\tno\tIND\tIndependent\nTamil Nadu\tArani\tS.SENTHILNATHAN\t1746\tno\tIND\tIndependent\nTamil Nadu\tArani\tM.DURAIRAJ\t910\tno\tIND\tIndependent\nTamil Nadu\tArani\tA.MAHADEVAN\t1309\tno\tIND\tIndependent\nTamil Nadu\tArani\tV.MOORTHY\t8059\tno\tIND\tIndependent\nTamil Nadu\tArani\tS.RAMAMOORTHY\t1760\tno\tIND\tIndependent\nTamil Nadu\tArani\tK.MOORTHY\t10263\tno\tIND\tIndependent\nTamil Nadu\tArani\tS.MANIKANDAN\t1952\tno\tIND\tIndependent\nTamil Nadu\tArani\tM.MUNUSAMY\t2598\tno\tIND\tIndependent\nTamil Nadu\tArani\tNone of the Above\t9304\tno\tNOTA\tNone of the Above\nTamil Nadu\tViluppuram\tRAJENDRAN S\t482704\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tViluppuram\tNone of the Above\t11440\tno\tNOTA\tNone of the Above\nTamil Nadu\tViluppuram\tVENKATESAN S\t2798\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tKADRVEL M\t3430\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tUMASANKAR K\t209663\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tViluppuram\tMUTHAIYAN N\t4780\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tUMASANKAR M\t1212\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tMUTHIYAN\t1774\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tDEVI S\t3443\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tRAJAMANICKAM K\t1197\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tMUTHAIYAN K DR\t289337\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tViluppuram\tANANDAN G\t17408\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tViluppuram\tARASAN K\t811\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tSRINIVASAN K\t2294\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tKARUNSIRUTHAI KALIYAMURTHI G\t2683\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tViluppuram\tDESINGU K\t1607\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tELUMALAI D\t2337\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tRANI K\t21461\tno\tINC\tIndian National Congress\nTamil Nadu\tViluppuram\tRAMESH A\t2663\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tARUMUGAM R\t693\tno\tIND\tIndependent\nTamil Nadu\tViluppuram\tVENKATESAN M\t3906\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nTamil Nadu\tKallakurichi\tKAMARAJ. K\t533383\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tKallakurichi\tGOVINDASAMY.A\t1015\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tESWARAN. V. P\t164183\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tKallakurichi\tBOJAN. G\t2255\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tLAKSHMANAN. S\t3261\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tVELLIYAN. K\t946\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tVARADAN. M\t5054\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tSEKAR. K\t962\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tPERUMAL. A. K.\t3512\tno\tJMM(U)\tJharkhand Mukti Morcha  (Ulgulan)\nTamil Nadu\tKallakurichi\tSHAJAHAN. A\t1499\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tSAKTHIVEL.S\t5929\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tKallakurichi\tVETRIVEL. K\t2029\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tMOHAMMED YASIN. K\t2910\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tKallakurichi\tMANIMARAN. R\t309876\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tKallakurichi\tRAMACHANDIRAN. T\t1013\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tMADAIYAN. S\t7314\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tMANNAN. M. P\t6186\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tNARAYANSAMY. P\t1684\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tPANDIAN. R\t1221\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tKAMARAJ. R\t1334\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tDEVADASS. R\t39677\tno\tINC\tIndian National Congress\nTamil Nadu\tKallakurichi\tVENNILA.  K\t1097\tno\tIND\tIndependent\nTamil Nadu\tKallakurichi\tNone of the Above\t10901\tno\tNOTA\tNone of the Above\nTamil Nadu\tSalem\tPANNERSELVAM.V\t556546\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tSalem\tNone of the Above\t20601\tno\tNOTA\tNone of the Above\nTamil Nadu\tSalem\tSHANMUGAM G\t1623\tno\tMMKA\tMakkal Manadu Katchi\nTamil Nadu\tSalem\tVENKATESAN R\t623\tno\tRPI(A)\tRepublican Party of India (A)\nTamil Nadu\tSalem\tRAJA A\t2203\tno\tIND\tIndependent\nTamil Nadu\tSalem\tTHIAGARAJAN K\t777\tno\tIND\tIndependent\nTamil Nadu\tSalem\tRAJAKANNU A\t1151\tno\tIND\tIndependent\nTamil Nadu\tSalem\tSENTHIL KUMAR M\t2903\tno\tIND\tIndependent\nTamil Nadu\tSalem\tVINAYAGAMOORTHI T\t1619\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tSalem\tMASS GANESH A\t474\tno\tTTNC\tThrinamool Tamil Nadu Congress\nTamil Nadu\tSalem\tPRAKASAM R\t484\tno\tIND\tIndependent\nTamil Nadu\tSalem\tKUPPAYEE R\t1701\tno\tIND\tIndependent\nTamil Nadu\tSalem\tARUMUGAM M\t731\tno\tIND\tIndependent\nTamil Nadu\tSalem\tUMARANI. S\t288936\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tSalem\tGOVINDARAJU K\t3261\tno\tIND\tIndependent\nTamil Nadu\tSalem\tSAKTHIVEL S\t2581\tno\tIND\tIndependent\nTamil Nadu\tSalem\tNANDAKUMAR G\t357\tno\tIND\tIndependent\nTamil Nadu\tSalem\tKANDASAMY R\t2550\tno\tIND\tIndependent\nTamil Nadu\tSalem\tABDUL WAHID I\t526\tno\tIND\tIndependent\nTamil Nadu\tSalem\tSATHEESH KUMAR E\t5198\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tSalem\tSUDHISH L K\t201265\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tSalem\tUMARANI S\t1232\tno\tIND\tIndependent\nTamil Nadu\tSalem\tCHINNUSAMY N\t5374\tno\tIND\tIndependent\nTamil Nadu\tSalem\tRANGARAJAN MOHAN KUMARAMANGALAM\t46477\tno\tINC\tIndian National Congress\nTamil Nadu\tSalem\tPANNEERSELVAM N\t574\tno\tIND\tIndependent\nTamil Nadu\tSalem\tM AHAMED SHAHJAHAN\t529\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tSUNDARAM P.R\t563272\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tNamakkal\tGNANAPANDITHAN.P\t700\tno\tRPI(A)\tRepublican Party of India (A)\nTamil Nadu\tNamakkal\tPANNEERSELVAM.S\t823\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tDHARMALINGAM.M\t473\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tMURALI.V\t2487\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tSIVAJI.S.M\t4808\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tKALAIVANAN.M\t1711\tno\tJD(S)\tJanata Dal  (Secular)\nTamil Nadu\tNamakkal\tNATARAJAN.M\t1182\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tRAMAJEYAM.G\t915\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tSAKTHIVEL.S.P\t3920\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tVELUSAMY.S\t4827\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tNamakkal\tSUBRAMANI.S\t3070\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tSELVARAJ.S\t835\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tSELVAM.G\t3362\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tVEL S.K\t146882\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tNamakkal\tSUBRAMANIYAN G.R\t19800\tno\tINC\tIndian National Congress\nTamil Nadu\tNamakkal\tPERUMAL.A\t858\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tKRISHNAN.M\t2079\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tRAMASAMY.N\t1874\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tVADIVEL.R\t737\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tNamakkal\tGANDHISELVAN.S\t268898\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tNamakkal\tRAMASAMY.P\t717\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tSELVARAJ.R\t1788\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tNamakkal\tPONMUDI.P\t787\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tCHELLAKUMARASAMY.T.S(DR)\t4348\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tNamakkal\tGUNASEKARAN.S\t1600\tno\tIND\tIndependent\nTamil Nadu\tNamakkal\tNone of the Above\t16002\tno\tNOTA\tNone of the Above\nTamil Nadu\tErode\tSELVAKUMARA CHINNAYAN S\t466995\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tErode\tMAYILSAMY K\t1592\tno\tIND\tIndependent\nTamil Nadu\tErode\tSETHUPATHY\t5917\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tErode\tKUMARASAMY K.K\t4654\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tErode\tGANESHAMURTHI.A\t255432\tno\tMDMK\tMarumalarchi Dravida Munnetra Kazhagam\nTamil Nadu\tErode\tVIJAYAKUMAR S\t7478\tno\tIND\tIndependent\nTamil Nadu\tErode\tGOPI P\t26726\tno\tINC\tIndian National Congress\nTamil Nadu\tErode\tPALANIAPPAN JOSIYAR C.M\t519\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tErode\tGUNASEKARAN T.K\t1298\tno\tUCPI\tUnited Communist Party of India\nTamil Nadu\tErode\tTHANGARAJ V\t818\tno\tIND\tIndependent\nTamil Nadu\tErode\tDURAISAMY.A\t610\tno\tIND\tIndependent\nTamil Nadu\tErode\tRAJAGOPAL V\t1969\tno\tIND\tIndependent\nTamil Nadu\tErode\tPAVITHRAVALLI H\t217260\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tErode\tNone of the Above\t16268\tno\tNOTA\tNone of the Above\nTamil Nadu\tErode\tMURUGESAN_S\t1046\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tV.SATHYABAMA\t442778\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tTiruppur\tA.K.SHEIKDAVOOD\t788\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tK.SUBBARAYAN\t33331\tno\tCPI\tCommunist Party of India\nTamil Nadu\tTiruppur\tE.V.K.S. ELANGOVAN\t47554\tno\tINC\tIndian National Congress\nTamil Nadu\tTiruppur\tR.CHAKRAVARTHI RAJA GOPALA KRISHNAN\t3087\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tTiruppur\tM.PALANISAMY\t4074\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tR.GOPALSAMY\t2167\tno\tLSP\tLok Satta Party\nTamil Nadu\tTiruppur\tP.SELVABOOBATHI\t4264\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tN.SIVAKUMAR\t949\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tTiruppur\tM.RAJENDRAN\t1991\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tN.DINESHKUMAR\t263463\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tTiruppur\tK.SENTHILKUMAR\t1926\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tR.NANDHAKUMAR\t900\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tA.PONNUSAMY\t1193\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tP.BALASUBRAMANIYAN\t916\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tM.SENTHILNATHAN\t205411\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tTiruppur\tPASTOR A.JAMES\t1153\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tR.R.MURUGAN\t6422\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tR.SATHYABAMA\t1097\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tR.SATHYABHAMA\t1193\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tJ.LAXMI\t908\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tTiruppur\tN.GURUSAMY\t4665\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tTiruppur\tS.K.DINESHKUMAR\t1820\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tR.MURUGESAN\t2010\tno\tMMKA\tMakkal Manadu Katchi\nTamil Nadu\tTiruppur\tA.MOGHEETH KHAN\t1038\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tT.S.SENTHILKUMAR\t1151\tno\tIND\tIndependent\nTamil Nadu\tTiruppur\tNone of the Above\t13941\tno\tNOTA\tNone of the Above\nTamil Nadu\tNilgiris\tGOPALAKRISHNAN, C.\t463700\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tNilgiris\tNone of the Above\t46559\tno\tNOTA\tNone of the Above\nTamil Nadu\tNilgiris\tRAJA, A.\t358760\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tNilgiris\tDR.BALAN, P.P.\t2704\tno\tIND\tIndependent\nTamil Nadu\tNilgiris\tPONNUSAMY, P.\t2733\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tNilgiris\tGUNASEKARAN, K.\t1650\tno\tRPI(A)\tRepublican Party of India (A)\nTamil Nadu\tNilgiris\tSUBRAMANIYAM, P.\t1711\tno\tIND\tIndependent\nTamil Nadu\tNilgiris\tESWARAN, T.\t1655\tno\tMMKA\tMakkal Manadu Katchi\nTamil Nadu\tNilgiris\tRANI, M.T.\t12525\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tNilgiris\tGANDHI, P.\t37702\tno\tINC\tIndian National Congress\nTamil Nadu\tNilgiris\tKALA, M.\t3377\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tCoimbatore\tNAGARAJAN, P.\t431717\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tCoimbatore\tGANESHKUMAR, K.\t217083\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tCoimbatore\tTAMILNADU SELVAM, ERA.\t1959\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tCoimbatore\tNATARAJAN, P.R.\t34197\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tCoimbatore\tPRABHU, R.\t56962\tno\tINC\tIndian National Congress\nTamil Nadu\tCoimbatore\tRADHAKRISHNAN, C.P.\t389701\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tCoimbatore\tDORAISAMY, A.\t550\tno\tMMKA\tMakkal Manadu Katchi\nTamil Nadu\tCoimbatore\tJAGADEESWARAN, S.\t596\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tCoimbatore\tABDUL SALAM, A.\t766\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tKALAPIRAR KUPPUSAMY, C.\t1051\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tCoimbatore\tCHANDRAN, D.\t1360\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nTamil Nadu\tCoimbatore\tPON CHANDRAN.\t6680\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tCoimbatore\tALPHONESRAJ, M.\t1306\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tANBUSELVAN.\t799\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tRAJAGOPAL, M.\t1232\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tKANAGASABAPATHY, G.\t2329\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tKITTUSAMY, P.\t2122\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tSUBASH, K.R.\t1756\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tTAMILSELVAN, K.\t640\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tRANJITHKUMAR, M.\t1100\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tRADHAKRISHNAN, R.\t1672\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tNAGARAJ, S.\t1699\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tMOHANRAJ, K.\t607\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tJERLLD AMALAJOTHI, I.\t858\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tSRIDHARAN, S.\t450\tno\tIND\tIndependent\nTamil Nadu\tCoimbatore\tNone of the Above\t17428\tno\tNOTA\tNone of the Above\nTamil Nadu\tPollachi\tMAHENDRAN.C\t417092\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tPollachi\tNone of the Above\t12947\tno\tNOTA\tNone of the Above\nTamil Nadu\tPollachi\tMARY STELLA. M\t4942\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tPALANISAMY. N\t2192\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tRAMASAMY. K\t2071\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tMAHENDRAN. M\t1171\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tPRABHU.  J\t1599\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tRAJENDRAN. M\t1004\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tKATHIRESAN. L\t1523\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tEASWARAN . R\t891\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tSELVARAJ\t30014\tno\tINC\tIndian National Congress\nTamil Nadu\tPollachi\tSATHISH KUMAR .L\t895\tno\tSP\tSamajwadi Party\nTamil Nadu\tPollachi\tPALANISAMY. V\t2472\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tKATHIRAVAN. K\t626\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tKARTHIKEYAN. PON\t494\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tDEIVASIGAMANI.K\t834\tno\tIND\tIndependent\nTamil Nadu\tPollachi\tMANOHARAN.M\t3953\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tPollachi\tPONGALUR PALANISAMY.N\t251829\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tPollachi\tESWARAN.E.R.\t276118\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tDindigul\tUDHAYA KUMAR .M\t510462\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tDindigul\tNone of the Above\t10591\tno\tNOTA\tNone of the Above\nTamil Nadu\tDindigul\tKRISHNAMOORTHY A\t93794\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tDindigul\tMANIVANNAN N\t5859\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tRAJENDHRAN. R\t7580\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tPAULDURAI  V K\t2604\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tCHANDRAMOHAN P\t1261\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tKUPPURAJ C\t627\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tGANDHIRAJAN S\t382617\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tDindigul\tNAGARAJAN V\t1202\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tELANCHEZHIAN.S\t3395\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tDindigul\tTHANGAPANDIAN.R\t786\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tDHANDAPANI.K\t863\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tBAGATSINGH PALANICHAMY M\t1903\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tDindigul\tCHITTHAN N.S.V\t35632\tno\tINC\tIndian National Congress\nTamil Nadu\tDindigul\tVENKATESAN G\t3588\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tSUDHARSHNAR.K\t592\tno\tIND\tIndependent\nTamil Nadu\tDindigul\tPANDI.N\t19455\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tDindigul\tBALASUBRAMANI.R\t553\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tKarur\tTHAMBIDURAI,M.\t540722\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tKarur\tKRISHNAN, N.S.\t76560\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tKarur\tBALASUBRAMANIAN,  G.\t4486\tno\tIND\tIndependent\nTamil Nadu\tKarur\tRAVICHANDRAN, V.\t563\tno\tUMK\tUlzaipali Makkal Katchy\nTamil Nadu\tKarur\tGOWRIMANI, S.\t1433\tno\tIND\tIndependent\nTamil Nadu\tKarur\tKANNAN, P.\t1436\tno\tIND\tIndependent\nTamil Nadu\tKarur\tJOTHIMANI, S.\t30459\tno\tINC\tIndian National Congress\nTamil Nadu\tKarur\tCHINNASAMY, M.\t345475\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tKarur\tMAHENDRAN, T.\t1024\tno\tIND\tIndependent\nTamil Nadu\tKarur\tMURUGESAN, M.\t848\tno\tSP\tSamajwadi Party\nTamil Nadu\tKarur\tNone of the Above\t13763\tno\tNOTA\tNone of the Above\nTamil Nadu\tKarur\tVIJAY, J.\t847\tno\tIND\tIndependent\nTamil Nadu\tKarur\tRAVI, S.P.\t732\tno\tIND\tIndependent\nTamil Nadu\tKarur\tVIGNESHWARAN, M.\t981\tno\tIND\tIndependent\nTamil Nadu\tKarur\tTHAMILALAGAN, A.\t1387\tno\tIND\tIndependent\nTamil Nadu\tKarur\tSELVARAJ, S.\t3552\tno\tIND\tIndependent\nTamil Nadu\tKarur\tMARUTHAIVEERAN, V.\t5694\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tKarur\tMANICKAVASAGAM, A.\t831\tno\tIND\tIndependent\nTamil Nadu\tKarur\tVALAIYAPATHI, R.\t2440\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tKarur\tSARASWATHY, K.\t813\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tKarur\tCHINNASAMY, M.\t3719\tno\tIND\tIndependent\nTamil Nadu\tKarur\tKANNAN, S.\t668\tno\tIND\tIndependent\nTamil Nadu\tKarur\tMANEESHANKAR, M.G.\t1276\tno\tRSPS\tRashtriya Samaj Paksha\nTamil Nadu\tKarur\tPRAKASH, N.\t4280\tno\tIND\tIndependent\nTamil Nadu\tKarur\tMURUGAAN, N.\t437\tno\tIND\tIndependent\nTamil Nadu\tKarur\tCHINNASAMY, V.\t2108\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tKUMAR.P\t458478\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tTiruchirappalli\tSRIDEVI.S\t2348\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tVEERAMALAI.A\t589\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tTHADDAUS FERNANDEZ,\t751\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tASRAF ALI.M\t734\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tVIJAYKUMAR.AMG\t94785\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tTiruchirappalli\tRAVI.P\t4885\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tTiruchirappalli\tARUNACHALAM.A\t3239\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tESWARAN.J\t2484\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tBALA KRISHNAN.K\t485\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tSUNDARRAJ.S\t784\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tRAGAVAN. R\t1283\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tPALANIVEL.P\t687\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tNone of the Above\t22848\tno\tNOTA\tNone of the Above\nTamil Nadu\tTiruchirappalli\tANWAR DEEN.S\t731\tno\tSAP\tSamata Party\nTamil Nadu\tTiruchirappalli\tSARUBALA  R THONDAIMAN\t51537\tno\tINC\tIndian National Congress\nTamil Nadu\tTiruchirappalli\tKUMAR.P\t4077\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tDINESHKUMAR.G\t1119\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tVIJAYAKUMAR.P\t680\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tJOTHI.P\t1003\tno\tSP\tSamajwadi Party\nTamil Nadu\tTiruchirappalli\tANBALAGAN.M\t636\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tLARANCE.Y\t1198\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tANBHALAGAN.MU\t308002\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tTiruchirappalli\tMOHANDASS.M\t355\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tNATARAJAN.S\t1710\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tTiruchirappalli\tBADHUSHA.M\t669\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tSRIDHAR.S\t17039\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tTiruchirappalli\tSHADHICK BATCHA.A\t3083\tno\tIND\tIndependent\nTamil Nadu\tTiruchirappalli\tKARUNAKARAN.M\t606\tno\tLSP\tLok Satta Party\nTamil Nadu\tTiruchirappalli\tASAITHAMBI.PALA\t1425\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nTamil Nadu\tPerambalur\tMARUTHARAJAA, R.P.\t462693\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tPerambalur\tRAMAR YADAV,  K.\t6324\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tPerambalur\tABDUL RAHMAN,  R.\t1018\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tRENGARAJU, M.\t633\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tVELAYUTHAM, C.\t2995\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tPARIVENDAR  PACHAMUTHU, T.R.\t238887\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tPerambalur\tSELVARAJ, V.M.\t2924\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tPerambalur\tSEEMANUR  PRABU, S.\t249645\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tPerambalur\tNone of the Above\t11605\tno\tNOTA\tNone of the Above\nTamil Nadu\tPerambalur\tSIVAPERUMAL,  K.\t986\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tANBIL  THANGAMANI, K.\t1636\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tTHOZHAR  TAMILSELVAN,  P.\t1509\tno\tUCPI\tUnited Communist Party of India\nTamil Nadu\tPerambalur\tRAJASEKARAN,  M.,\t31998\tno\tINC\tIndian National Congress\nTamil Nadu\tPerambalur\tSUBRAMANI, D.\t1201\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tCHINNA RAJENDRAN, C.\t877\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tVISWANATHAN, A.\t2369\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tVADIVELU, P.\t1821\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tRAJASEKAR, T.\t1490\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tPRABU,  S.\t2211\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tPACHAMUTHU, N.\t2762\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tMARUTHARAJ, P.\t3940\tno\tIND\tIndependent\nTamil Nadu\tPerambalur\tMANI, P.\t1302\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tARUNMOZHITHEVAN.A\t481429\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tCuddalore\tSHRUTI MISHRA\t944\tno\tUPI\tUnion Party of India\nTamil Nadu\tCuddalore\tALAGIRI.S\t26650\tno\tINC\tIndian National Congress\nTamil Nadu\tCuddalore\tSENGUTTUVAN.R\t862\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tSENTHILMURUGAN.S\t2642\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tCuddalore\tTHIRUNAVUKKARASU\t1782\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tNANDAGOPALAKRISHNAN.K.\t278304\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tCuddalore\tRAJKUMAR.V\t7678\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tSYED MOHIDEEN.A.G.\t3473\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tCuddalore\tJAISHANKAR.P.\t6250\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tRADHAKRISHNAN.K.R\t1370\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tANANDHARAJAN.S\t1309\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tNone of the Above\t10338\tno\tNOTA\tNone of the Above\nTamil Nadu\tCuddalore\tBASKAR.A\t872\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tJAYASANKAR.C.R\t147606\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tCuddalore\tSIVAGNANASAMBANDAN.T\t1257\tno\tIND\tIndependent\nTamil Nadu\tCuddalore\tBALASUBRAMANIAN.K\t11122\tno\tCPI\tCommunist Party of India\nTamil Nadu\tCuddalore\tGIRIJA.S\t650\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tCHANDRAKASI, M\t429536\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tChidambaram\tPARVATHI, S\t1543\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tSENTHAMIZHSELVI, K\t1469\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tVALLAL PERUMAN, P\t28988\tno\tINC\tIndian National Congress\nTamil Nadu\tChidambaram\tTHIRUMAAVALAVAN, THOL\t301041\tno\tVCK\tViduthalai Chiruthaigal Katchi\nTamil Nadu\tChidambaram\tNone of the Above\t12138\tno\tNOTA\tNone of the Above\nTamil Nadu\tChidambaram\tTHIRUSANGU, A\t3370\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tEZHUMALAI, V\t6824\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nTamil Nadu\tChidambaram\tSUDHAMANIRATHINEM\t279016\tno\tPMK\tPattali Makkal Katchi\nTamil Nadu\tChidambaram\tCHANDRAGASAN, N\t1182\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tPAZHANIVEL, P\t1772\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tANANDAN ALIAS KAVIGNAR ANANTHAMAALAI, S\t8875\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tRAJENDRAN, K\t5179\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tChidambaram\tRAJKUMAR, S\t4896\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tSANMUGAM, A\t1071\tno\tIND\tIndependent\nTamil Nadu\tChidambaram\tPUSHPARAJ, S\t1523\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tBHARATHI MOHAN R.K\t513729\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tMayiladuthurai\tNone of the Above\t13181\tno\tNOTA\tNone of the Above\nTamil Nadu\tMayiladuthurai\tMUNUSAMY.V\t1994\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tGUNASEKARAN.N\t4149\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tMANI SHANKAR AIYAR\t58465\tno\tINC\tIndian National Congress\nTamil Nadu\tMayiladuthurai\tTIMOTHY.T\t2327\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tPANDIYAN .R\t11613\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tRASAVELU.K\t3654\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tMayiladuthurai\tARUMUGAM. C\t2028\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tAGORAM. K\t144085\tno\tPMK\tPattali Makkal Katchi\nTamil Nadu\tMayiladuthurai\tIRULAPPAN.V\t2643\tno\tSP\tSamajwadi Party\nTamil Nadu\tMayiladuthurai\tTHIRUNAVUKKARASU. R\t4714\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tSHAMSUDEEN. M\t2303\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tNAGARAJAN. M\t1066\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tFATHIMA ALIAS BHARATHI. R\t7582\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tRAGURAMAN. A\t7214\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tREVATHI.A\t6286\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tJARINA BEGAM. N\t1056\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tJEYAPRAKASH. B\t770\tno\tIND\tIndependent\nTamil Nadu\tMayiladuthurai\tHYDER ALI.S\t236679\tno\tMAMAK\tManithaneya Makkal Katchi\nTamil Nadu\tNagapattinam\tGOPAL. DR. K\t434174\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tNagapattinam\tSENTHILPANDIAN. THALAI. T.A.P\t23967\tno\tINC\tIndian National Congress\nTamil Nadu\tNagapattinam\tPALANISAMY. G\t90313\tno\tCPI\tCommunist Party of India\nTamil Nadu\tNagapattinam\tVIJAYAN. A.K.S\t328095\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tNagapattinam\tVADIVEL RAVANAN\t43506\tno\tPMK\tPattali Makkal Katchi\nTamil Nadu\tNagapattinam\tMOHAN. G\t1754\tno\tIND\tIndependent\nTamil Nadu\tNagapattinam\tMURUGAIYAN. K\t1033\tno\tIND\tIndependent\nTamil Nadu\tNagapattinam\tDEVAKI. G\t1847\tno\tIND\tIndependent\nTamil Nadu\tNagapattinam\tTHANGASAMY. V\t1851\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tNagapattinam\tNone of the Above\t15662\tno\tNOTA\tNone of the Above\nTamil Nadu\tThanjavur\tPARASURAMAN.K\t510307\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tThanjavur\tNone of the Above\t12218\tno\tNOTA\tNone of the Above\nTamil Nadu\tThanjavur\tARANGARAJAN.M\t2253\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tThanjavur\tRAJKUMAR.R\t1409\tno\tIND\tIndependent\nTamil Nadu\tThanjavur\tGUNASEKARAN.T\t894\tno\tIND\tIndependent\nTamil Nadu\tThanjavur\tPANASAI ARANGAN.N\t1830\tno\tIND\tIndependent\nTamil Nadu\tThanjavur\tMURUGANANTHAM.M\t58521\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tThanjavur\tTAMILSELVI.S\t23215\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tThanjavur\tSIVAKUMAR.V\t1511\tno\tIND\tIndependent\nTamil Nadu\tThanjavur\tKRISHNASAMY VANDAYAR.T\t30232\tno\tINC\tIndian National Congress\nTamil Nadu\tThanjavur\tBAALU.T.R\t366188\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tThanjavur\tMUMMOORTHI.S@S.M.MOORTHY\t2511\tno\tNMK\tNamadhu Makkal Katchi\nTamil Nadu\tThanjavur\tMADHAVAN.T\t1169\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tSENTHILNATHAN PR\t475993\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tSivaganga\tPALANI S\t1699\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tTHAMILARIMA S\t2131\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tSivaganga\tNATCHIAPPAN SP\t794\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tLENIN M\t741\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tNELSON A\t2987\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tRAJA A\t1337\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tDHURAI RAAJ SUBHA\t246608\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tSivaganga\tRADHAKRISHNAN A\t641\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tSELVARAJ A\t2050\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tKARTI P CHIDAMBARAM\t104678\tno\tINC\tIndian National Congress\nTamil Nadu\tSivaganga\tMURUGAN P\t736\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tNone of the Above\t8042\tno\tNOTA\tNone of the Above\nTamil Nadu\tSivaganga\tMEGARAJ MALLAR\t565\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tGUNASEKARAN P\t1269\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tKRISHNAN S\t20473\tno\tCPI\tCommunist Party of India\nTamil Nadu\tSivaganga\tARIMAZHAM THIYAGI SUBRAMANIAN MUTHURAJA M\t3021\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tVELLADHURAI A\t3023\tno\tETMK\tEzhuchi Tamilargal Munnetra Kazhagam\nTamil Nadu\tSivaganga\tBALAKRISHNAN P\t949\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tBALU S\t2196\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tSivaganga\tRAJA H\t133763\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tSivaganga\tPERIYAKARUPPAN S\t837\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tRAKKAMUTHU V\t3056\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tSivaganga\tSIVAGNANAM AL\t1007\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tKARTHIKARAJAN T\t873\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tRAJASEKAR.S @ S.R.THEVAR\t4029\tno\tDFBL\tDesiya Forward Bloc\nTamil Nadu\tSivaganga\tSARAVANAN M\t2100\tno\tIND\tIndependent\nTamil Nadu\tSivaganga\tKALAIMANI K\t1438\tno\tMMKA\tMakkal Manadu Katchi\nTamil Nadu\tMadurai\tR.GOPALAKRISHNAN\t454167\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tMadurai\tBHARATHI KANNAMMA\t1232\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tK.K.RAMESH\t432\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tB.VIKRAMAN\t30108\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tMadurai\tG.MARIA JOSEPH XAVIOUR\t568\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tS.MANICKAVASAGAM\t341\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tP.RAMASUBRAMANIAN\t565\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tM.GOPALAKRISHNAN\t2770\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tM.KAMACIS\t5423\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tMadurai\tM.MOHAMMED ALI\t904\tno\tSAP\tSamata Party\nTamil Nadu\tMadurai\tV.ANDICHAMY\t4461\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tP.CHANDRABOSE\t728\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tT.N.BHARATH NACHIAPPAN\t32143\tno\tINC\tIndian National Congress\nTamil Nadu\tMadurai\tN ALAGU MUTHU VELAYUDAM\t487\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tM VENKATESAN\t394\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tD SIVAMUTHU KUMAR\t147300\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tMadurai\tA THAVAMANI\t1255\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tMadurai\tV VELUSAMY\t256731\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tMadurai\tNone of the Above\t19866\tno\tNOTA\tNone of the Above\nTamil Nadu\tMadurai\tM.MEENAKSHISUNDARAM\t429\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tP.JEYAKUMAR\t985\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tS.ARIVUDAINAMBI\t441\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tV.KARUPPAIAH\t2039\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tT.RAMASAMY\t432\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tJ.MANIKANDESWARAN\t1576\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tS.GANESAN\t363\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tV.G.RAMDOSS\t2745\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tA.KANNAN\t420\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tG.KANNAN\t555\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tP.GOPALAKRISHNAN\t6337\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tK.THANGAPANDI\t666\tno\tIND\tIndependent\nTamil Nadu\tMadurai\tK. RAJA\t1483\tno\tDFBL\tDesiya Forward Bloc\nTamil Nadu\tTheni\tPARTHIPAN, R.\t571254\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tTheni\tNone of the Above\t10312\tno\tNOTA\tNone of the Above\nTamil Nadu\tTheni\tKANNAN, S.\t1273\tno\tIND\tIndependent\nTamil Nadu\tTheni\tINNASIMUTHU, M.S.\t991\tno\tIND\tIndependent\nTamil Nadu\tTheni\tINIYAVAN, R.\t739\tno\tIND\tIndependent\nTamil Nadu\tTheni\tKARUPPIAH, R.\t679\tno\tIND\tIndependent\nTamil Nadu\tTheni\tMAMALLAN THA. KANAGAMUTHU\t2681\tno\tIND\tIndependent\nTamil Nadu\tTheni\tSENTHIL RAJAN MALLAR, K.\t2908\tno\tIND\tIndependent\nTamil Nadu\tTheni\tMANI, S.V.\t1349\tno\tIND\tIndependent\nTamil Nadu\tTheni\tRABEEK, K.\t767\tno\tIND\tIndependent\nTamil Nadu\tTheni\tRAJA, V.\t695\tno\tIND\tIndependent\nTamil Nadu\tTheni\tMARIAPPAN, S.K.\t3521\tno\tIND\tIndependent\nTamil Nadu\tTheni\tMUTHURAMALINGAM, M.\t859\tno\tIND\tIndependent\nTamil Nadu\tTheni\tMOHAMED MIAKHAN, P.\t1954\tno\tIND\tIndependent\nTamil Nadu\tTheni\tRAMPRAKASH, J.\t3213\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tTheni\tASATH, M.M.\t1082\tno\tIND\tIndependent\nTamil Nadu\tTheni\tRAMAYA, R.\t742\tno\tIND\tIndependent\nTamil Nadu\tTheni\tKRISHNAMOORTHY, K.\t946\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tTheni\tMEENAKSHI SUNDARAM, G.K.\t1107\tno\tSP\tSamajwadi Party\nTamil Nadu\tTheni\tVOLTAIRE, M.J.\t696\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nTamil Nadu\tTheni\tAARON RASHID, J.M.\t71432\tno\tINC\tIndian National Congress\nTamil Nadu\tTheni\tTHANGADURAI, V.\t5299\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tTheni\tPON. MUTHURAMALINGAM\t256722\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tTheni\tALAGUSUNDARAM, K.\t134362\tno\tMDMK\tMarumalarchi Dravida Munnetra Kazhagam\nTamil Nadu\tVirudhunagar\tRADHAKRISHNAN T\t406694\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tVirudhunagar\tNone of the Above\t12225\tno\tNOTA\tNone of the Above\nTamil Nadu\tVirudhunagar\tSAMUELRAJ K\t20157\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tVirudhunagar\tMANICKAM TAGORE B\t38482\tno\tINC\tIndian National Congress\nTamil Nadu\tVirudhunagar\tKARTHIKAISAMY P\t2249\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tVirudhunagar\tSANTHANA KRISHNAN G\t3411\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tVirudhunagar\tPRADEEPKUMAR D\t681\tno\tLSP\tLok Satta Party\nTamil Nadu\tVirudhunagar\tMAHALINGAM T\t1017\tno\tDFBL\tDesiya Forward Bloc\nTamil Nadu\tVirudhunagar\tMOHANRAJ J\t1517\tno\tJJ\tJebamani Janata\nTamil Nadu\tVirudhunagar\tVAIKO\t261143\tno\tMDMK\tMarumalarchi Dravida Munnetra Kazhagam\nTamil Nadu\tVirudhunagar\tKANNAN G\t1133\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tSANKAR A\t532\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tSATHESH M\t903\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tSURESH GANDHI C\t711\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tSOLAIRAJ K\t2721\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tSOUNDRAPANDIYAN K\t3470\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tDHANUSHKODI M\t2522\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tPALANICHAMY MALLAR P\t700\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tRETHINAVELU S\t241505\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tVirudhunagar\tKAMALAVELSELVAN\t1685\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tVirudhunagar\tPANDIARAJAN R\t922\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tPALPANDI S DR\t2342\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tMANMATHAN M\t714\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tMADHAVAN P\t642\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tMARIMUTHU C\t629\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tRADHAKRISHNAN PV\t1068\tno\tIND\tIndependent\nTamil Nadu\tVirudhunagar\tLAKSHMI KANDHAN S\t1155\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tANWHAR RAAJHAA.A\t405945\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tRamanathapuram\tNone of the Above\t6279\tno\tNOTA\tNone of the Above\nTamil Nadu\tRamanathapuram\tKUPPURAMU .D\t171082\tno\tBJP\tBharatiya Janata Party\nTamil Nadu\tRamanathapuram\tPERIYASAMY .P\t985\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tTHIRUNNAVUKKARASAR .SU\t62160\tno\tINC\tIndian National Congress\nTamil Nadu\tRamanathapuram\tVELCHSAMY .R\t577\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tKASIRAJAN .N\t1415\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tAZHAGU MEENA .A\t835\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tMURUGESWARI\t814\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tAMIRTHALINGAM .U\t941\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tSATHIAH .G\t1129\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tTHENNARASU .P\t1321\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tKANNATHASAN .R\t999\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tMOOKAIYA .S\t423\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tRAVI .K\t478\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tBALAKRISHNAN .S.S.\t1171\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tVETRIVEL .S\t914\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tGOPAL .R\t3969\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tSASI KUMAR .S\t1220\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tTHIAGARAJAN .R\t1037\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tAYYAPPAN .S\t785\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tNOOR JIYAVUDEEN.M.I\t12541\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nTamil Nadu\tRamanathapuram\tPALPANDI .C\t1355\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tGOKILA .K\t3471\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tSUBBIAH .M\t645\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tMANIVASAGAM\t2684\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tARASAKUMAR B.T.\t10945\tno\tDFBL\tDesiya Forward Bloc\nTamil Nadu\tRamanathapuram\tSIVAGURUNATHAN .K\t2123\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tRamanathapuram\tANNAMALAI .S\t3125\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tALLAPICHAI .E\t747\tno\tIND\tIndependent\nTamil Nadu\tRamanathapuram\tUMA MAGESWARI R.T.\t12312\tno\tCPI\tCommunist Party of India\nTamil Nadu\tRamanathapuram\tMOHAMED JALEEL .S\t286621\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tThoothukkudi\tJEYASINGH THIYAGARAJ NATTERJEE.J\t366052\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tThoothukkudi\tJOEL. S\t182191\tno\tMDMK\tMarumalarchi Dravida Munnetra Kazhagam\nTamil Nadu\tThoothukkudi\tMOHANRAJ. A\t14993\tno\tCPI\tCommunist Party of India\nTamil Nadu\tThoothukkudi\tANANDHARAJ, M.\t739\tno\tIND\tIndependent\nTamil Nadu\tThoothukkudi\tALWARSAMY KARTHIKEYAN.L\t1727\tno\tIND\tIndependent\nTamil Nadu\tThoothukkudi\tRAMKUMAR. V\t784\tno\tIND\tIndependent\nTamil Nadu\tThoothukkudi\tPANNEERSELVAM.C\t1129\tno\tIND\tIndependent\nTamil Nadu\tThoothukkudi\tSANTHADEVI. S\t765\tno\tIND\tIndependent\nTamil Nadu\tThoothukkudi\tSAMUEL. A.S.\t1157\tno\tIND\tIndependent\nTamil Nadu\tThoothukkudi\tVINSTON ANTO. S\t983\tno\tIND\tIndependent\nTamil Nadu\tThoothukkudi\tJEGAN. P\t242050\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tThoothukkudi\tSHANMUGAM, A.P.C.V.\t63080\tno\tINC\tIndian National Congress\nTamil Nadu\tThoothukkudi\tAYYADURAI. S\t3205\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tThoothukkudi\tNone of the Above\t11447\tno\tNOTA\tNone of the Above\nTamil Nadu\tThoothukkudi\tPUSHPARAYAN.M.\t26476\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tTenkasi\tVASANTHI.M\t424586\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tTenkasi\tNone of the Above\t14492\tno\tNOTA\tNone of the Above\nTamil Nadu\tTenkasi\tJANAGAR RAJA\t1777\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tKANNAN\t2917\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tTenkasi\tLINGAM.P\t23528\tno\tCPI\tCommunist Party of India\nTamil Nadu\tTenkasi\tGUNASEKARAN.K.\t8022\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tTenkasi\tDR.KRISHNASAMY.K.\t262812\tno\tPT\tPuthiya Tamilagam\nTamil Nadu\tTenkasi\tDR.JAYAKUMAR.K.\t58963\tno\tINC\tIndian National Congress\nTamil Nadu\tTenkasi\tMANMATHAN,M.\t3331\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tMAGESH.S.\t2163\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tMUNEESWARAN\t2513\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tRADHAKRISHNAN.S.\t3904\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tVELLADURAI.P.\t1216\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tKRISHNASAMY.A.\t12115\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tGANESAN.S.\t1809\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tALAGUMALAI.V.\t1215\tno\tIND\tIndependent\nTamil Nadu\tTenkasi\tMEENASHISUNDARAVADIVEL.T.\t888\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tTenkasi\tDR.SADHAN THIRUMALAIKUMAR.T.\t190233\tno\tMDMK\tMarumalarchi Dravida Munnetra Kazhagam\nTamil Nadu\tTenkasi\tPOONGANI.J.\t1454\tno\tSAP\tSamata Party\nTamil Nadu\tTirunelveli\tPRABAKARAN.K.R.P\t398139\tyes\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tTirunelveli\tDHANARAJ.R.\t3062\tno\tICSP\tIndian Christian Secular Party\nTamil Nadu\tTirunelveli\tDHEVANATHAN YADAV.T.\t19381\tno\tTNMC\tTamil Nadu Makkal Congress\nTamil Nadu\tTirunelveli\tMOHAMED MUBARAK.V.M.S\t14877\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nTamil Nadu\tTirunelveli\tSESURAJ.M.P\t18353\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tTirunelveli\tSIVANANENTHAPERUMAL.S.\t127370\tno\tDMDK\tDesiya Murpokku Dravida Kazhagam\nTamil Nadu\tTirunelveli\tDEVADASA SUNDARAM\t272040\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tTirunelveli\tTHEVENDRAN.T.\t4692\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tTirunelveli\tRAMASUBBU.S.S\t62863\tno\tINC\tIndian National Congress\nTamil Nadu\tTirunelveli\tELIZABETH.D\t651\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tDR.GANESAN.M\t1262\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tSURESH KUMAR.S\t1281\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tSELVAGANAPATHY.G\t3233\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tDEVADOSS.A.R\t3394\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tPATHMANABAN.K\t1823\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tMURUGAN.S\t1159\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tMOHAN.T\t2518\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tRAJAMARAVAN @ MANICKAVASAGAM.S\t1391\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tRAJI YADAV\t1579\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tRAJESHKANNAN\t947\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tLAKSHMANAN @ LAKKAN.A\t1850\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tVENKATESH.A\t1052\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tSARAVANAN.P\t740\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tSUBRAMANIAN.K\t1196\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tSUBRAMANIAN @ S.S.S.MANI.S\t1751\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tVEILUMUTHUKUMAR.P\t1387\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tGERMANUS.S\t1446\tno\tIND\tIndependent\nTamil Nadu\tTirunelveli\tNone of the Above\t12893\tno\tNOTA\tNone of the Above\nTamil Nadu\tKanniyakumari\tRADHAKRISHNAN P.\t372906\tyes\tBJP\tBharatiya Janata Party\nTamil Nadu\tKanniyakumari\tNone of the Above\t4150\tno\tNOTA\tNone of the Above\nTamil Nadu\tKanniyakumari\tWILSON I.\t421\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tJOSE BILBIN J.\t504\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tKAMARAJ K.\t955\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tKAMARAJ G.\t591\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tSARASWATHI S.\t536\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tSEETHARAMADHAS P.\t930\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tNALLA THAMPY C.\t2391\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tNAGOOR MEERAN PEER MOHAMEAD U.\t3851\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tPADMANABHAN S.\t1246\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tBALA SUBRAMANIAN T.\t1737\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tMANIKANDAN I.\t1557\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tRAJESH KUMAR K.R.\t1437\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tVASANTHA KUMAR L.\t1474\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tVASANTHEESVARAN E.\t791\tno\tIND\tIndependent\nTamil Nadu\tKanniyakumari\tRAAJARATHINUM F.M.\t117933\tno\tDMK\tDravida Munnetra Kazhagam\nTamil Nadu\tKanniyakumari\tBELLARMIN A.V.\t35284\tno\tCPM\tCommunist Party of India  (Marxist)\nTamil Nadu\tKanniyakumari\tVASANTHA KUMAR H.\t244244\tno\tINC\tIndian National Congress\nTamil Nadu\tKanniyakumari\tJAWAHAR J.\t3236\tno\tBSP\tBahujan Samaj Party\nTamil Nadu\tKanniyakumari\tJOHN THANKAM D.\t176239\tno\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nTamil Nadu\tKanniyakumari\tANTHONY MUTHU\t1647\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nTamil Nadu\tKanniyakumari\tUDAYAKUMAR S.P.\t15314\tno\tAAAP\tAam Aadmi Party\nTamil Nadu\tKanniyakumari\tPAULRAJ C.M.\t436\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nTamil Nadu\tKanniyakumari\tSHALIN RICHARD J.\t398\tno\tIVP\tIndians Victory Party\nTamil Nadu\tKanniyakumari\tRADHAKRISHNAN T.\t534\tno\tIND\tIndependent\nTripura\tTripura West\tSANKAR PRASAD DATTA\t671665\tyes\tCPM\tCommunist Party of India  (Marxist)\nTripura\tTripura West\tSALIL SAHA\t4981\tno\tAAAP\tAam Aadmi Party\nTripura\tTripura West\tPJECH DEBBARMA\t2061\tno\tJMBP\tJai Maha Bharath Party\nTripura\tTripura West\tPARTHA KARMAKAR\t4226\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nTripura\tTripura West\tBINAY DEBBARMA\t11040\tno\tIPFT\tIndigenousn People's Front Of Tripura\nTripura\tTripura West\tSUDHINDRA CHANDRA DASGUPTA\t54706\tno\tBJP\tBharatiya Janata Party\nTripura\tTripura West\tARUN KUMAR BHOWMIK\t8202\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nTripura\tTripura West\tKAMAL KANTI DEBNATH\t3747\tno\tIND\tIndependent\nTripura\tTripura West\tRATAN CHAKRABORTI\t117727\tno\tAITC\tAll India Trinamool Congress\nTripura\tTripura West\tSUBAL BHOWMIK\t4690\tno\tTPGC\tTripura Pragatishil Gramin Congress\nTripura\tTripura West\tRAKHAL RAJ DATTA\t2879\tno\tAMB\tAmra Bangalee\nTripura\tTripura West\tJOGANATI DATTA\t5947\tno\tIND\tIndependent\nTripura\tTripura West\tARUNODAY SAHA\t168179\tno\tINC\tIndian National Congress\nTripura\tTripura West\tNone of the Above\t12699\tno\tNOTA\tNone of the Above\nTripura\tTripura East\tJITENDRA CHOUDHURY\t623771\tyes\tCPM\tCommunist Party of India  (Marxist)\nTripura\tTripura East\tNARENDRA CHANDRA DEB BARMA\t10286\tno\tIPFT\tIndigenousn People's Front Of Tripura\nTripura\tTripura East\tPATAL KANYA JAMATIA\t3004\tno\tIND\tIndependent\nTripura\tTripura East\tBIKASH DEBBARMA\t3116\tno\tJMBP\tJai Maha Bharath Party\nTripura\tTripura East\tPRABIDDHA REANG\t5066\tno\tIND\tIndependent\nTripura\tTripura East\tSACHITRA DEBBARMA\t139413\tno\tINC\tIndian National Congress\nTripura\tTripura East\tFALGUNI TRIPURA\t4444\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nTripura\tTripura East\tBHRIGURAM REANG\t77028\tno\tAITC\tAll India Trinamool Congress\nTripura\tTripura East\tSUBARNA MALA DEBBARMA\t4152\tno\tAMB\tAmra Bangalee\nTripura\tTripura East\tBASU MOG\t4262\tno\tTPGC\tTripura Pragatishil Gramin Congress\nTripura\tTripura East\tNone of the Above\t11084\tno\tNOTA\tNone of the Above\nTripura\tTripura East\tKARNA BIJOY JAMATIA\t4871\tno\tAAAP\tAam Aadmi Party\nTripura\tTripura East\tPARIKSHIT DEBBARMA\t60613\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tSaharanpur\tRAGHAV LAKHANPAL\t472999\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tSaharanpur\tNone of the Above\t6267\tno\tNOTA\tNone of the Above\nUttar Pradesh\tSaharanpur\tJAGDISH SINGH RANA\t235033\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tSaharanpur\tIMRAN MASOOD\t407909\tno\tINC\tIndian National Congress\nUttar Pradesh\tSaharanpur\tRAGHUVEER SINGH BODH\t1066\tno\tIND\tIndependent\nUttar Pradesh\tSaharanpur\tSHAHANAVAJ\t553\tno\tRJ\tRashtriya Janmorcha\nUttar Pradesh\tSaharanpur\tSHAZAN MASOOD URF SHADAN MASOOD\t52765\tno\tSP\tSamajwadi Party\nUttar Pradesh\tSaharanpur\tYOGESH KUMAR\t3065\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tSaharanpur\tCP VERMA\t2114\tno\tIND\tIndependent\nUttar Pradesh\tSaharanpur\tSESHRAJ\t4057\tno\tIND\tIndependent\nUttar Pradesh\tSaharanpur\tRATAN SINGH\t1366\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tSaharanpur\tHANSRAJ SAINI\t4068\tno\tIND\tIndependent\nUttar Pradesh\tSaharanpur\tRAJENDER KUMAR\t466\tno\tARVP\tAdarsh Rashtriya Vikas Party\nUttar Pradesh\tSaharanpur\tMOHD. FIROZ AFTAB\t692\tno\tIND\tIndependent\nUttar Pradesh\tSaharanpur\tSHIV KUMAR\t1888\tno\tASP\tAmbedkar Samaj Party\nUttar Pradesh\tKairana\tHUKUM SINGH\t565909\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tKairana\tDIVAKAR KASHYAP\t6562\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tKairana\tRAMKARAN KASHYAP\t1378\tno\tBaVaP\tBhartiya Vanchitsamaj Party\nUttar Pradesh\tKairana\tPARDIP\t1050\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tKairana\tSUNITA\t946\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tKairana\tKARTAR SINGH BHADANA\t42706\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tKairana\tMOHANLAL\t1741\tno\tIND\tIndependent\nUttar Pradesh\tKairana\tNAHID HASAN\t329081\tno\tSP\tSamajwadi Party\nUttar Pradesh\tKairana\tKANWAR HASAN\t160414\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tKairana\tSUNIL KUMAR KASANA\t2663\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tKairana\tKRISHANPAL\t2971\tno\tIND\tIndependent\nUttar Pradesh\tKairana\tNone of the Above\t3903\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMuzaffarnagar\t(DR.) SANJEEV KUMAR BALYAN\t653391\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMuzaffarnagar\tNone of the Above\t4739\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMuzaffarnagar\tMOHAMMED YAMIN\t1931\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tMuzaffarnagar\tMANGE RAM KASHYAP\t362\tno\tMKUP\tMajdoor Kisan Union Party\nUttar Pradesh\tMuzaffarnagar\tPANKAJ AGARWAL\t12937\tno\tINC\tIndian National Congress\nUttar Pradesh\tMuzaffarnagar\tKADIR RANA\t252241\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMuzaffarnagar\tGUFRAN AHMAD\t1682\tno\tIND\tIndependent\nUttar Pradesh\tMuzaffarnagar\tSHANDAR GUFRAN\t1864\tno\tIND\tIndependent\nUttar Pradesh\tMuzaffarnagar\tRAKESH\t3482\tno\tIND\tIndependent\nUttar Pradesh\tMuzaffarnagar\tSHABNAM\t487\tno\tIND\tIndependent\nUttar Pradesh\tMuzaffarnagar\tCHATAR SINGH KASHYAP\t350\tno\tVAJP\tVanchit Jamat Party\nUttar Pradesh\tMuzaffarnagar\tPHOOL SINGH PAL\t3431\tno\tIND\tIndependent\nUttar Pradesh\tMuzaffarnagar\tMO. YAMIN\t543\tno\tAIRSP\tAll India Ravidas Samata Party\nUttar Pradesh\tMuzaffarnagar\tANKUR\t723\tno\tIND\tIndependent\nUttar Pradesh\tMuzaffarnagar\tANIL KUMAR\t2238\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMuzaffarnagar\tRAJESH DEVI ALIAS RADHIKA BALIYAN\t4129\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tMuzaffarnagar\tVIRENDER SINGH\t160810\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMuzaffarnagar\tNASEEM AHMAD ANSARI\t750\tno\tIND\tIndependent\nUttar Pradesh\tMuzaffarnagar\tLALIT CHAUHAN\t499\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tMuzaffarnagar\tJAGBIR SINGH\t845\tno\tIND\tIndependent\nUttar Pradesh\tBijnor\tKUNWAR BHARTENDRA\t486913\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBijnor\tMOHAMAD ADEEB\t1756\tno\tRWSP\tRashtrawadi Samaj Party\nUttar Pradesh\tBijnor\tKAMAL\t2545\tno\tIND\tIndependent\nUttar Pradesh\tBijnor\tMOHAMMAD MOOSA\t15801\tno\tPECP\tPeace Party\nUttar Pradesh\tBijnor\tGAURAV\t2483\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBijnor\tJAYA PRADA NAHATA\t24348\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tBijnor\tSHAHNAWAZ RANA\t281139\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBijnor\tGAJRESH KUMAR\t3317\tno\tIND\tIndependent\nUttar Pradesh\tBijnor\tUMESH ALIES PANI\t2967\tno\tIND\tIndependent\nUttar Pradesh\tBijnor\tMALOOK NAGAR\t230124\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBijnor\tUDAY ALIES UDAYVEER\t2456\tno\tBMF\tBharatiya Momin Front\nUttar Pradesh\tBijnor\tPARVEZ AQIL\t722\tno\tAIMF\tAll India Minorities Front\nUttar Pradesh\tBijnor\tNone of the Above\t5775\tno\tNOTA\tNone of the Above\nUttar Pradesh\tNagina\tYASHWANT SINGH\t367825\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tNagina\tSHARISHTHA KUMAR SINGH\t861\tno\tJMBP\tJai Maha Bharath Party\nUttar Pradesh\tNagina\tSHAMSHER\t3577\tno\tIND\tIndependent\nUttar Pradesh\tNagina\tOMPRAKASH RAVI\t2967\tno\tSHS\tShivsena\nUttar Pradesh\tNagina\tNone of the Above\t6470\tno\tNOTA\tNone of the Above\nUttar Pradesh\tNagina\tBHAGWAN DASS RATHOR\t4581\tno\tMD\tMahan Dal\nUttar Pradesh\tNagina\tVINOD KUMAR\t852\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tNagina\tMANJU DEVI\t1027\tno\tLD\tLok Dal\nUttar Pradesh\tNagina\tTEJ SINGH\t1845\tno\tASP\tAmbedkar Samaj Party\nUttar Pradesh\tNagina\tYASHVIR SINGH\t275435\tno\tSP\tSamajwadi Party\nUttar Pradesh\tNagina\tSARIKA\t3695\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tNagina\tGIRISH CHANDRA\t245685\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tNagina\tSHEESHRAM SINGH RAVI\t21334\tno\tPECP\tPeace Party\nUttar Pradesh\tNagina\tSUNIL\t4328\tno\tIND\tIndependent\nUttar Pradesh\tNagina\tASARPAT SINGH\t1714\tno\tIND\tIndependent\nUttar Pradesh\tMoradabad\tKUNWER SARVESH KUMAR\t485224\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMoradabad\tROOPA CHANDRA SINGH\t1282\tno\tIND\tIndependent\nUttar Pradesh\tMoradabad\tVIMAL KUMAR\t4469\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tMoradabad\tBEGUM NOOR BANO URF MEHTAB\t19732\tno\tINC\tIndian National Congress\nUttar Pradesh\tMoradabad\tRAJ KUMAR GUPTA\t1278\tno\tIND\tIndependent\nUttar Pradesh\tMoradabad\tSATISH CHANDRA SHARMA\t2267\tno\tNAP\tNaitik Party\nUttar Pradesh\tMoradabad\tKAMAR ALAM\t1059\tno\tIBSP\tIndian Bahujan Samajwadi Party\nUttar Pradesh\tMoradabad\tSAURABH PRATAP SINGH\t5647\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tMoradabad\tAMIT KUMAR\t3047\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMoradabad\tJAIPAL SINGH SAINI\t1334\tno\tSHS\tShivsena\nUttar Pradesh\tMoradabad\tTEAJ SINGH SAINI\t1710\tno\tRSMD\tRashtriya Samanta Dal\nUttar Pradesh\tMoradabad\tSANDEEP KUMAR\t2971\tno\tSSKP\tSarva Samaj Kalyan Party\nUttar Pradesh\tMoradabad\tNAFISUDDIN\t3180\tno\tCPM\tCommunist Party of India  (Marxist)\nUttar Pradesh\tMoradabad\tHAJI MOHD YAQOOB\t160945\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMoradabad\tMOHD ZAFAR IQBAL\t956\tno\tABML(S)\tAkhil Bharatiya Muslim League (Secular)\nUttar Pradesh\tMoradabad\tDR S T HASAN\t397720\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMoradabad\tHARIOM\t4097\tno\tNADP\tNaya Daur Party\nUttar Pradesh\tMoradabad\tENGINEER MOHAMMAD IRFAN\t25840\tno\tPECP\tPeace Party\nUttar Pradesh\tMoradabad\tNone of the Above\t5207\tno\tNOTA\tNone of the Above\nUttar Pradesh\tRampur\tDR. NEPAL SINGH\t358616\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tRampur\tNone of the Above\t6905\tno\tNOTA\tNone of the Above\nUttar Pradesh\tRampur\tNAVED MIAN\t2448\tno\tIND\tIndependent\nUttar Pradesh\tRampur\tJANNAT NISHA\t4473\tno\tAIMF\tAll India Minorities Front\nUttar Pradesh\tRampur\tJAGDISH SARAN SAGAR\t1834\tno\tIND\tIndependent\nUttar Pradesh\tRampur\tVIJENDRA SINGH\t2079\tno\tRAC\tRashtriya Congress (Babu Jagjivanram)\nUttar Pradesh\tRampur\tTEJPAL SINGH\t2063\tno\tIND\tIndependent\nUttar Pradesh\tRampur\tNAWAB KAZIM ALI KHAN\t156466\tno\tINC\tIndian National Congress\nUttar Pradesh\tRampur\tSYED SALMAN ALI\t1996\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tRampur\tAKBAR MASOOD\t817\tno\tIND\tIndependent\nUttar Pradesh\tRampur\tAKBAR HUSAIN\t81006\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tRampur\tNASEER AHMAD KHAN\t335181\tno\tSP\tSamajwadi Party\nUttar Pradesh\tRampur\tJAGMOHAN\t2505\tno\tIND\tIndependent\nUttar Pradesh\tSambhal\tSATYAPAL SINGH\t360242\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tSambhal\tDR SHAFIQ- UR RAHMAN BARQ\t355068\tno\tSP\tSamajwadi Party\nUttar Pradesh\tSambhal\tACHARYA PRAMOD KRISHNAM\t16034\tno\tINC\tIndian National Congress\nUttar Pradesh\tSambhal\tDHARAM YADAV URF D.P.YADAV\t36134\tno\tRPD\tRashtriya Parivartan Dal\nUttar Pradesh\tSambhal\tDR. RASHID ALI\t6376\tno\tPECP\tPeace Party\nUttar Pradesh\tSambhal\tAQEEL-UR -REHMAN KHAN\t252640\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tSambhal\tRASHID\t2791\tno\tIND\tIndependent\nUttar Pradesh\tSambhal\tAQEEL AHMAD\t2005\tno\tIND\tIndependent\nUttar Pradesh\tSambhal\tFIROZ HAIDER\t3648\tno\tIND\tIndependent\nUttar Pradesh\tSambhal\tNANAD RAM KHADAGVANSHI\t2322\tno\tIND\tIndependent\nUttar Pradesh\tSambhal\tMOHD ASLAM URF PASHA\t3833\tno\tRPI(A)\tRepublican Party of India (A)\nUttar Pradesh\tSambhal\tNUTAN VIJAY ADVOCATE\t6686\tno\tIND\tIndependent\nUttar Pradesh\tSambhal\tREKHA DEVI YADAV\t1565\tno\tSSAD\tSanyukt Samajwadi Dal\nUttar Pradesh\tSambhal\tNone of the Above\t7658\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAmroha\tKANWAR SINGH TANWAR\t528880\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAmroha\tNone of the Above\t7779\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAmroha\tKHAJAN SINGH\t2448\tno\tRAC\tRashtriya Congress (Babu Jagjivanram)\nUttar Pradesh\tAmroha\tSARVESH KUMAR SAINI\t980\tno\tSHS\tShivsena\nUttar Pradesh\tAmroha\tSURENDRA SINGH\t2425\tno\tIND\tIndependent\nUttar Pradesh\tAmroha\tRAKESH\t749\tno\tPECP\tPeace Party\nUttar Pradesh\tAmroha\tHUSNEIN RAZAA\t858\tno\taicp\tNew All India Congress Party\nUttar Pradesh\tAmroha\tMAHESH\t713\tno\tRPI(A)\tRepublican Party of India (A)\nUttar Pradesh\tAmroha\tRAKESH TIKAIT\t9539\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tAmroha\tRAJPAL\t478\tno\tARVP\tAdarsh Rashtriya Vikas Party\nUttar Pradesh\tAmroha\tFARHAT HASAN\t162983\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAmroha\tNARESH CHAND\t1431\tno\tIND\tIndependent\nUttar Pradesh\tAmroha\tNARESH KUMAR\t2493\tno\tIND\tIndependent\nUttar Pradesh\tAmroha\tSYED KALBE RUSHAID RIZVI\t2601\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAmroha\tBRAJLAL\t837\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAmroha\tHUMERA AKHTAR\t370666\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMeerut\tRAJENDRA AGARWAL\t532981\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMeerut\tMOHD.SHAHID AKHLAK\t300655\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMeerut\tSUNIL DUTT\t1734\tno\tIND\tIndependent\nUttar Pradesh\tMeerut\tMUKESH KUMAR\t1542\tno\tIND\tIndependent\nUttar Pradesh\tMeerut\tDR. (EX. MAJ) HIMANSHU SINGH\t11793\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tMeerut\tAPHAJAL\t382\tno\tIND\tIndependent\nUttar Pradesh\tMeerut\tMOHD. SAJID SAIFI\t1063\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMeerut\tMOHD.USMAN GHAZI\t1451\tno\tRsAD\tRashtriya Apna Dal\nUttar Pradesh\tMeerut\tATUL KHODAWAL\t818\tno\tIND\tIndependent\nUttar Pradesh\tMeerut\tANIL\t348\tno\tIND\tIndependent\nUttar Pradesh\tMeerut\tIQRA CHOUDHARY\t734\tno\tIND\tIndependent\nUttar Pradesh\tMeerut\tNAGMA\t42911\tno\tINC\tIndian National Congress\nUttar Pradesh\tMeerut\tSHAHID MANZOOR\t211759\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMeerut\tNone of the Above\t5213\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBaghpat\tDR. SATYA PAL SINGH\t423475\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBaghpat\tGHULAM MOHAMMED\t213609\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBaghpat\tSOMENDAR DHAKA\t5828\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBaghpat\tPRASHANT CHAUDHRI\t141743\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBaghpat\tKRISHAN PAL\t601\tno\tNAP\tNaitik Party\nUttar Pradesh\tBaghpat\tAJAY PAL\t440\tno\tIND\tIndependent\nUttar Pradesh\tBaghpat\tANIL KUMAR\t3025\tno\tPECP\tPeace Party\nUttar Pradesh\tBaghpat\tAJIT SINGH\t199516\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tBaghpat\tARVIND\t992\tno\tIND\tIndependent\nUttar Pradesh\tBaghpat\tSUBHASH CHAND KASHYAP\t1967\tno\tIND\tIndependent\nUttar Pradesh\tBaghpat\tJAIVINDRA KUMAR\t3915\tno\tIND\tIndependent\nUttar Pradesh\tBaghpat\tNISHANT\t2708\tno\tIND\tIndependent\nUttar Pradesh\tBaghpat\tRUKSANA\t2092\tno\tIND\tIndependent\nUttar Pradesh\tBaghpat\tYUNUSH\t441\tno\tNNP\tNavbharat Nirman Party\nUttar Pradesh\tBaghpat\tNone of the Above\t3911\tno\tNOTA\tNone of the Above\nUttar Pradesh\tGhaziabad\tVIJAY KUMAR SINGH\t758482\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tGhaziabad\tSURESH PAL\t1591\tno\tBSKP\tBharatiya Sarvodaya Kranti Party\nUttar Pradesh\tGhaziabad\tSHAZIA ILMI MALIK\t89147\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGhaziabad\tMUKUL\t173085\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tGhaziabad\tSUDHAN KUMAR\t106984\tno\tSP\tSamajwadi Party\nUttar Pradesh\tGhaziabad\tKESHAV CHOUDHARY\t627\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tSABIR\t3409\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tSANJEEV KUMAR\t4639\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tSALEEM MALIK\t1486\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tNARESH GAUTAM\t1548\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tGhaziabad\tNITIN\t624\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tRINKU KUMAR TYAGI\t1185\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tRAJ BABBAR\t191222\tno\tINC\tIndian National Congress\nUttar Pradesh\tGhaziabad\tVINOD KUMAR TYAGI\t992\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tRAMPHAL SHAKYA\t1094\tno\tIND\tIndependent\nUttar Pradesh\tGhaziabad\tNone of the Above\t6205\tno\tNOTA\tNone of the Above\nUttar Pradesh\tGautam Buddha Nagar\tDR.MAHESH  SHARMA\t599702\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tGautam Buddha Nagar\tRAVINDER SHARMA\t503\tno\tRTKP\tRashtriya Kranti Party\nUttar Pradesh\tGautam Buddha Nagar\tKADIR KHAN JAISWAL\t2684\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tGRISA KUMAR\t3496\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tRAKESH\t1225\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tMAHESH\t794\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tSUNIL GAUTAM\t623\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tKADEEM\t4198\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tMOHD. SABIR ANSARI\t4506\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tGautam Buddha Nagar\tSAFRUDDIN\t491\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tSAVITRI\t996\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tLT COL SP SINGH\t1183\tno\tNBDP\tNav Bharat Democratic Party\nUttar Pradesh\tGautam Buddha Nagar\tSEEMA\t977\tno\tRND\tRashtriya Naujawan Dal\nUttar Pradesh\tGautam Buddha Nagar\tUDAYVEER\t1526\tno\tRtrJP\tRashtriya Janta Party\nUttar Pradesh\tGautam Buddha Nagar\tGAJENDRA\t649\tno\tIOP\tIndian Oceanic Party\nUttar Pradesh\tGautam Buddha Nagar\tRAM VILAS\t656\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tGautam Buddha Nagar\tSONIA SHARMA\t2530\tno\tABSC\tAkhil Bharatiya Samajwadi Congress\nUttar Pradesh\tGautam Buddha Nagar\tRAMESH CHAND TOMAR\t12696\tno\tINC\tIndian National Congress\nUttar Pradesh\tGautam Buddha Nagar\tKISHAN PAL SINGH\t32358\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGautam Buddha Nagar\tSATISH KUMAR\t198237\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tGautam Buddha Nagar\tSUMITI\t978\tno\tIND\tIndependent\nUttar Pradesh\tGautam Buddha Nagar\tDHARMENDER\t4130\tno\tDND\tDharam Nirpeksh Dal\nUttar Pradesh\tGautam Buddha Nagar\tNARENDRA BHATI\t319490\tno\tSP\tSamajwadi Party\nUttar Pradesh\tGautam Buddha Nagar\tRUVEZ ALAM RAHI\t773\tno\tNLP\tNational Loktantrik Party\nUttar Pradesh\tGautam Buddha Nagar\tNone of the Above\t3837\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBulandshahr\tBHOLA SINGH\t604449\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBulandshahr\tNone of the Above\t6915\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBulandshahr\tVIJAI PAL\t4117\tno\tRtJP\tRashtriya Janpriya Party\nUttar Pradesh\tBulandshahr\tPYARE LAL\t4448\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tBulandshahr\tHARVIR\t5690\tno\tIND\tIndependent\nUttar Pradesh\tBulandshahr\tPRADEEP KUMAR JATAV\t182476\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBulandshahr\tDR. RAHUL DIPANKAR\t9727\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBulandshahr\tANJU URF MUSKAN\t59116\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tBulandshahr\tKAMLESH\t128737\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBulandshahr\tSURESH\t2580\tno\tJMBP\tJai Maha Bharath Party\nUttar Pradesh\tBulandshahr\tRAGHURAJ PRATAP SINGH\t1455\tno\tRTKP\tRashtriya Kranti Party\nUttar Pradesh\tAligarh\tSATISH KUMAR\t514622\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAligarh\tVINOD KUMAR BAGHEL\t1723\tno\tRSPS\tRashtriya Samaj Paksha\nUttar Pradesh\tAligarh\tSANJAY KUMAR\t1842\tno\tIND\tIndependent\nUttar Pradesh\tAligarh\tNATTHI LAL SHARMA\t4659\tno\tIND\tIndependent\nUttar Pradesh\tAligarh\tBADSHAH\t978\tno\tPECP\tPeace Party\nUttar Pradesh\tAligarh\tRAKESH CHANDRA RAWAT\t665\tno\tRSD\tRashtriya Sawarn Dal\nUttar Pradesh\tAligarh\tDEVANAND\t785\tno\tLD\tLok Dal\nUttar Pradesh\tAligarh\tPRADEEP KUMAR\t626\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAligarh\tDURGESH CHANDRA GAUTAM\t3076\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tAligarh\tMOHD. SABIR RAHI\t8978\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAligarh\tAMITA SINGH JAT\t3376\tno\tSSKP\tSarva Samaj Kalyan Party\nUttar Pradesh\tAligarh\tZAFAR ALAM\t226284\tno\tSP\tSamajwadi Party\nUttar Pradesh\tAligarh\tDR. ARVIND KUMAR SINGH\t227886\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAligarh\tBIJENDRA SINGH\t62674\tno\tINC\tIndian National Congress\nUttar Pradesh\tAligarh\tNone of the Above\t6183\tno\tNOTA\tNone of the Above\nUttar Pradesh\tHathras\tRAJESH KUMAR DIWAKER\t544277\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tHathras\tNone of the Above\t5669\tno\tNOTA\tNone of the Above\nUttar Pradesh\tHathras\tDALVIR SINGH\t1599\tno\tIND\tIndependent\nUttar Pradesh\tHathras\tNIRANJAN SINGH\t2847\tno\tIND\tIndependent\nUttar Pradesh\tHathras\tRAJA RAM\t2169\tno\tIND\tIndependent\nUttar Pradesh\tHathras\tNIRANJAN SINGH DHANGAR\t86109\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tHathras\tMANOJ KUMAR SONI\t217891\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tHathras\tOMPRKASH\t2778\tno\tIND\tIndependent\nUttar Pradesh\tHathras\tSUNHARI LAL\t5043\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tHathras\tRAMJI LAL SUMAN\t180891\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMathura\tHEMA MALINI\t574633\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMathura\tMAHESH PRATAP SHARMA\t2396\tno\tRVP\tRashtriya Vikas Party\nUttar Pradesh\tMathura\tMAHARAM SINGH\t655\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMathura\tPT. UDYAN SHARMA 'MUNNA'\t1066\tno\tPECP\tPeace Party\nUttar Pradesh\tMathura\tRAM BABU SAINI\t2082\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tBHUPENDRA KUMAR  DHANGAR\t1054\tno\tRSOSP\tRashtriya Shoshit Samaj Party\nUttar Pradesh\tMathura\tPT. YOGESH KUMAR DWIVEDI\t173572\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMathura\tCHANDAN SINGH\t36673\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMathura\tANUJ KUMAR GARG\t7858\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tMathura\tTHAKUR HEMENDRA VEER SINGH\t1858\tno\tSSKP\tSarva Samaj Kalyan Party\nUttar Pradesh\tMathura\tJAYANT CHAUDHARY\t243890\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tMathura\tMOHAN SHYAM\t731\tno\tGaAP\tGareeb Aadmi Party\nUttar Pradesh\tMathura\tFAKKAR BABA 'RAMAYANI'\t2160\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tOM PRAKASH\t2847\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tHEMA MALINI\t10158\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tPARSOTTAM\t1338\tno\tRMGP\tRashtriya mahan Gantantra Party\nUttar Pradesh\tMathura\tBHUDEV\t2715\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tRADHACHARAN\t4795\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tPRAMOD KRISHNA\t1759\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tSATISH CHANDRA SHARMA\t2675\tno\tIND\tIndependent\nUttar Pradesh\tMathura\tNone of the Above\t1953\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAgra\tDR. RAM SHANKAR KATHERIA\t583716\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAgra\tNone of the Above\t5191\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAgra\tANIL\t3646\tno\tBKPP\tBharatiya Kisan Parivartan Party\nUttar Pradesh\tAgra\tOM PRAKASH\t4243\tno\tIND\tIndependent\nUttar Pradesh\tAgra\tAMAR SINGH\t2453\tno\tIND\tIndependent\nUttar Pradesh\tAgra\tRAMESH\t602\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tAgra\tRAVINDRA SINGH\t7804\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAgra\tANITA\t765\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nUttar Pradesh\tAgra\tVINOD KUMAR SINGH\t2584\tno\tIND\tIndependent\nUttar Pradesh\tAgra\tNARAYAN SINGH SUMAN\t283453\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAgra\tDINESH KUMAR GAUTAM\t3657\tno\tIND\tIndependent\nUttar Pradesh\tAgra\tASHOK KUMAR\t1416\tno\tIND\tIndependent\nUttar Pradesh\tAgra\tCHANDRA PAL\t557\tno\tA S P\tAdarsh Samaj Party\nUttar Pradesh\tAgra\tPREM SINGH\t771\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAgra\tMAHARAJ SINGH DHANGAR\t134708\tno\tSP\tSamajwadi Party\nUttar Pradesh\tAgra\tUPENDRA SINGH\t34834\tno\tINC\tIndian National Congress\nUttar Pradesh\tFatehpur Sikri\tBABULAL\t426589\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tFatehpur Sikri\tRANI PAKSHALIKA SINGH\t213397\tno\tSP\tSamajwadi Party\nUttar Pradesh\tFatehpur Sikri\tPITAM SINGH\t691\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tFatehpur Sikri\tVIJAY SINGH KUSHWAHA\t5223\tno\tSSAD\tSanyukt Samajwadi Dal\nUttar Pradesh\tFatehpur Sikri\tMANIK\t643\tno\tLD\tLok Dal\nUttar Pradesh\tFatehpur Sikri\tPANKAJ KUMAR SHARMA\t2052\tno\tRSMD\tRashtriya Samanta Dal\nUttar Pradesh\tFatehpur Sikri\tSEEMA UPADHYAY\t253483\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tFatehpur Sikri\tPEETAM SINGH\t872\tno\tRTKP\tRashtriya Kranti Party\nUttar Pradesh\tFatehpur Sikri\tAMAR SINGH\t24185\tno\tRLD\tRashtriya Lok Dal\nUttar Pradesh\tFatehpur Sikri\tKHEMRAJ SINGH\t951\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tAMBEDKARI HASNURAM AMBEDKARI\t3512\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tMEGH SINGH\t1529\tno\tPECP\tPeace Party\nUttar Pradesh\tFatehpur Sikri\tRAM KUMAR DIXIT\t1657\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tSANTOSH KUMAR\t3184\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tMAHAVIR SINGH SOLANKI\t583\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tASHOK KUMAR\t2553\tno\tGaAP\tGareeb Aadmi Party\nUttar Pradesh\tFatehpur Sikri\tROHAN SINGH\t1063\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tFatehpur Sikri\tDIWAN SINGH\t3101\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tRAKESH SINGH DHANKAR\t650\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tANIL KUMAR\t3809\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tLAXMI\t3117\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tFatehpur Sikri\tBRAJENDR SINGH\t2725\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tRAM BRAJ YADAV\t2057\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tSEEMA DEVI\t1571\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tSATISH KUMAR\t2235\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tMUNNA LAL\t497\tno\tRMGP\tRashtriya mahan Gantantra Party\nUttar Pradesh\tFatehpur Sikri\tSHOBHNA BAUDDHA\t1006\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tNATHOLI\t2356\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur Sikri\tNone of the Above\t2677\tno\tNOTA\tNone of the Above\nUttar Pradesh\tFirozabad\tAKSHAY YADAV\t534583\tyes\tSP\tSamajwadi Party\nUttar Pradesh\tFirozabad\tNone of the Above\t4654\tno\tNOTA\tNone of the Above\nUttar Pradesh\tFirozabad\tRAJVEER\t2425\tno\tIND\tIndependent\nUttar Pradesh\tFirozabad\tMIRJA MUKARRAM BAIG\t1470\tno\tIND\tIndependent\nUttar Pradesh\tFirozabad\tSHAMSHER KHA\t3526\tno\tPECP\tPeace Party\nUttar Pradesh\tFirozabad\tRAKESH YADAV\t2808\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tFirozabad\tKHAJAN SINGH\t753\tno\tLD\tLok Dal\nUttar Pradesh\tFirozabad\tLAKSHMI  NARAYAN\t939\tno\tRJPK\tRashtriya Janwadi Party (Krantikari)\nUttar Pradesh\tFirozabad\tLAXMI\t620\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tFirozabad\tPROF. S.P. SINGH BAGHEL\t420524\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tFirozabad\tATUL CHATURVEDI\t7447\tno\tINC\tIndian National Congress\nUttar Pradesh\tFirozabad\tJAY PRAKASH\t1248\tno\tSHS\tShivsena\nUttar Pradesh\tFirozabad\tTH. VISHWADEEP SINGH\t118909\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tFirozabad\tRAHAT AFROZ\t498\tno\tIUML\tIndian Union Muslim League\nUttar Pradesh\tFirozabad\tRAJENDRA PRASAD\t506\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tFirozabad\tUPENDRA SINGH\t3696\tno\tBKPP\tBharatiya Kisan Parivartan Party\nUttar Pradesh\tMainpuri\tMULAYAM SINGH YADAV\t595918\tyes\tSP\tSamajwadi Party\nUttar Pradesh\tMainpuri\tABU NASIM PATHAN\t2095\tno\tPECP\tPeace Party\nUttar Pradesh\tMainpuri\tMUKESH KUMAR\t916\tno\tIND\tIndependent\nUttar Pradesh\tMainpuri\tRAJESHWARI\t1746\tno\tIND\tIndependent\nUttar Pradesh\tMainpuri\tSATENDRA VEER SINGH CHAUHAN\t872\tno\tAIFB\tAll India Forward Bloc\nUttar Pradesh\tMainpuri\tSHATRUGHAN SINGH CHAUHAN\t231252\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMainpuri\tBABA HARDEV SINGH\t5588\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tMainpuri\tSHISHUPAL SINGH\t561\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tMainpuri\tALOK NANDAN\t5645\tno\tIND\tIndependent\nUttar Pradesh\tMainpuri\tDR. SANGHMITRA MAURYA\t142833\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMainpuri\tMUKESH KUMAR SHAKYA\t2542\tno\tIND\tIndependent\nUttar Pradesh\tMainpuri\tHAKIM SINGH\t2279\tno\tIND\tIndependent\nUttar Pradesh\tMainpuri\tASHOK KUMAR\t695\tno\tIND\tIndependent\nUttar Pradesh\tMainpuri\tNone of the Above\t6323\tno\tNOTA\tNone of the Above\nUttar Pradesh\tEtah\tRAJVEER SINGH (RAJU BHAIYA)\t474978\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tEtah\tNone of the Above\t6201\tno\tNOTA\tNone of the Above\nUttar Pradesh\tEtah\tRAJVEER SINGH\t5848\tno\tIND\tIndependent\nUttar Pradesh\tEtah\tKU. DEVENDRA SINGH YADAV\t273977\tno\tSP\tSamajwadi Party\nUttar Pradesh\tEtah\tRAJENDRA\t1021\tno\tBRAVP\tBraj Vikas Party\nUttar Pradesh\tEtah\tRAKESH GANDHI\t7123\tno\tIND\tIndependent\nUttar Pradesh\tEtah\tDANVEER\t1333\tno\tIND\tIndependent\nUttar Pradesh\tEtah\tNOOR MOHAMMAD KHAN\t137127\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tEtah\tTHAKUR HEMENDRA VEER SINGH\t2034\tno\tSSKP\tSarva Samaj Kalyan Party\nUttar Pradesh\tEtah\tVINAY\t2008\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tEtah\tTH. JOGINDRA SINGH BHADORIYA\t12445\tno\tMD\tMahan Dal\nUttar Pradesh\tEtah\tHANS  RAJ SINGH\t2186\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBadaun\tDHARMENDRA YADAV\t498378\tyes\tSP\tSamajwadi Party\nUttar Pradesh\tBadaun\tAJEET PAL SINGH\t3421\tno\tPECP\tPeace Party\nUttar Pradesh\tBadaun\tPAGLANAND\t5748\tno\tMD\tMahan Dal\nUttar Pradesh\tBadaun\tMAHENDRA PAL\t860\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tBadaun\tREENA\t2065\tno\tRSMD\tRashtriya Samanta Dal\nUttar Pradesh\tBadaun\tSANTOSH KUMAR GUPTA (SATYMARGI)\t1448\tno\tRPI(A)\tRepublican Party of India (A)\nUttar Pradesh\tBadaun\tVAGISH PATHAK\t332031\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBadaun\tMUNSHI LAL\t5651\tno\tIND\tIndependent\nUttar Pradesh\tBadaun\tATUL KUMAR\t4195\tno\tIND\tIndependent\nUttar Pradesh\tBadaun\tHAR BHAGWAN\t4083\tno\tIND\tIndependent\nUttar Pradesh\tBadaun\tMADAN LAL\t791\tno\tNP\tNational Party\nUttar Pradesh\tBadaun\tVIRENDRA PAL GUPTA\t810\tno\tRVNP\tRashtravadi Janata Party\nUttar Pradesh\tBadaun\tANIL KUMAR SHARMA\t1993\tno\tLPI(V)\tLabour Party of India (V.V. Prasad)\nUttar Pradesh\tBadaun\tAKMAL KHAN URF CHAMAN\t156973\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBadaun\tHEMA MEHRA\t2861\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBadaun\tNone of the Above\t6286\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAonla\tDHARMENDRA KUMAR\t409907\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAonla\tMOHD. ZARRAR KHAN\t1440\tno\tBNIP\tBhartiya Naujawan Inklav Party\nUttar Pradesh\tAonla\tPRAMOD KUMAR YADAV\t1189\tno\tBKrD\tBhartiya Krishak Dal\nUttar Pradesh\tAonla\tNARESH SINGH SOLANKI\t2304\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAonla\tKUNWAR SARVRAJ SINGH\t271478\tno\tSP\tSamajwadi Party\nUttar Pradesh\tAonla\tJITENDRA SINGH YADAV\t855\tno\tNAP\tNaitik Party\nUttar Pradesh\tAonla\tARSHADURRAZA KHAN\t3019\tno\tIND\tIndependent\nUttar Pradesh\tAonla\tYOGENDRA KUMAR VERMA\t4332\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nUttar Pradesh\tAonla\tSALEEM IQBAL SHERVANI\t93861\tno\tINC\tIndian National Congress\nUttar Pradesh\tAonla\tSUNITA SHAKYA\t190200\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAonla\tNone of the Above\t10496\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAonla\tOM VEER\t1152\tno\tBRVP\tBhartiya Vikas Party\nUttar Pradesh\tAonla\tCAPTAIN P.C. SHARMA\t2711\tno\tJKM\tJawan Kisan Morcha\nUttar Pradesh\tAonla\tIRSHAD ANSARI ADVOCATE\t2697\tno\tPECP\tPeace Party\nUttar Pradesh\tBareilly\tSANTOSH KUMAR GANGWAR\t518258\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBareilly\tNETRAM\t2038\tno\tIND\tIndependent\nUttar Pradesh\tBareilly\tLOTAN SINGH PATEL\t1402\tno\tIND\tIndependent\nUttar Pradesh\tBareilly\tKAMAL KISHORE ENGINEER\t1806\tno\tIND\tIndependent\nUttar Pradesh\tBareilly\tJAVED KHAN\t964\tno\tIND\tIndependent\nUttar Pradesh\tBareilly\tMANOJ VIKAT\t1567\tno\tIND\tIndependent\nUttar Pradesh\tBareilly\tPRAVEEN SINGH ARON\t84213\tno\tINC\tIndian National Congress\nUttar Pradesh\tBareilly\tSUNIL KUMAR\t3703\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBareilly\tMASSARAT WARSI (PAPPU BHAI)\t7639\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tBareilly\tASLAM ADVOCATE\t3338\tno\tPECP\tPeace Party\nUttar Pradesh\tBareilly\tAYESHA ISLAM\t277573\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBareilly\tSAYAD RASHID ALI\t1885\tno\tIND\tIndependent\nUttar Pradesh\tBareilly\tLAEEK AHMAD\t719\tno\tNAP\tNaitik Party\nUttar Pradesh\tBareilly\tUMESH GAUTAM\t106049\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBareilly\tNone of the Above\t6737\tno\tNOTA\tNone of the Above\nUttar Pradesh\tPilibhit\tMANEKA SANJAY GANDHI\t546934\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tPilibhit\tNone of the Above\t11521\tno\tNOTA\tNone of the Above\nUttar Pradesh\tPilibhit\tRAJEEV AGRAWAL\t5547\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tPilibhit\tSANJAY KAPOOR\t29169\tno\tINC\tIndian National Congress\nUttar Pradesh\tPilibhit\tMUNESH SINGH\t6151\tno\tIND\tIndependent\nUttar Pradesh\tPilibhit\tARAMAN KHAN\t2170\tno\tIND\tIndependent\nUttar Pradesh\tPilibhit\tRAVINDRA KUMAR DEVNATH\t3145\tno\tIND\tIndependent\nUttar Pradesh\tPilibhit\tANIS AHMAD KHAN ALIAS PHOOL BABU\t196294\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tPilibhit\tSANJAY KUMAR\t1260\tno\tNAP\tNaitik Party\nUttar Pradesh\tPilibhit\tNILIMA SHARMA\t2670\tno\tPECP\tPeace Party\nUttar Pradesh\tPilibhit\tRAM AUTAR\t3187\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tPilibhit\tBUDHSEN VERMA\t239882\tno\tSP\tSamajwadi Party\nUttar Pradesh\tPilibhit\tMOHD NAEEM ANSARI\t2617\tno\tIND\tIndependent\nUttar Pradesh\tShahjahanpur\tKRISHNA RAJ\t525132\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tShahjahanpur\tPYARE ALIAS RAJKUMAR GAUTAM\t1378\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nUttar Pradesh\tShahjahanpur\tRAKESH\t2522\tno\tIND\tIndependent\nUttar Pradesh\tShahjahanpur\tROSHAN LAL ALIAS ROSHAN KANAUJIYA\t7871\tno\tIND\tIndependent\nUttar Pradesh\tShahjahanpur\tMEVARAM\t1105\tno\tNAP\tNaitik Party\nUttar Pradesh\tShahjahanpur\tNone of the Above\t9964\tno\tNOTA\tNone of the Above\nUttar Pradesh\tShahjahanpur\tSMT REETA SINGH ALIAS REETA BHARTI\t3610\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tShahjahanpur\tRAMLAL VERMA\t1613\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tShahjahanpur\tKISHAN LAL\t2790\tno\tLD\tLok Dal\nUttar Pradesh\tShahjahanpur\tMITHILESH KUMAR\t242913\tno\tSP\tSamajwadi Party\nUttar Pradesh\tShahjahanpur\tRAMMURTI\t1384\tno\tBKrD\tBhartiya Krishak Dal\nUttar Pradesh\tShahjahanpur\tSUNITA SUJATA NAGPAL\t12945\tno\tPECP\tPeace Party\nUttar Pradesh\tShahjahanpur\tUMED SINGH KASHYAP\t289603\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tShahjahanpur\tCHETRAM\t27011\tno\tINC\tIndian National Congress\nUttar Pradesh\tKheri\tAJAY KUMAR\t398578\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tKheri\tRAVI PRAKASH VERMA\t160112\tno\tSP\tSamajwadi Party\nUttar Pradesh\tKheri\tVIPNESH\t16401\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tKheri\tARVIND GIRI\t288304\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tKheri\tRAMGOPAL MISHRA\t2608\tno\tIND\tIndependent\nUttar Pradesh\tKheri\tZAFAR ALI NAQVI\t183940\tno\tINC\tIndian National Congress\nUttar Pradesh\tKheri\tNone of the Above\t12031\tno\tNOTA\tNone of the Above\nUttar Pradesh\tKheri\tKRISHNA ADHIKARI\t3230\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tKheri\tASHRAF ALI\t3124\tno\tPECP\tPeace Party\nUttar Pradesh\tKheri\tRAMESHWARI\t3514\tno\tIND\tIndependent\nUttar Pradesh\tKheri\tASHOK\t3165\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tKheri\tDINESH KUMAR\t2861\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tDhaurahra\tREKHA\t360357\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tDhaurahra\tANAND KUMAR\t9967\tno\tNCP\tNationalist Congress Party\nUttar Pradesh\tDhaurahra\tKIRTI SINGH\t2788\tno\tSHS\tShivsena\nUttar Pradesh\tDhaurahra\tAJAY VERMA\t6144\tno\tBJKD\tBharatiya Jan Kranti Dal (Democratic)\nUttar Pradesh\tDhaurahra\tANAND BHADAURIYA\t234032\tno\tSP\tSamajwadi Party\nUttar Pradesh\tDhaurahra\tSWAMI DAYAL\t3491\tno\tIND\tIndependent\nUttar Pradesh\tDhaurahra\tDAUD AHMAD\t234682\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tDhaurahra\tLEKHRAJ\t12776\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tDhaurahra\tKUWAR JITIN PRASAD\t170994\tno\tINC\tIndian National Congress\nUttar Pradesh\tDhaurahra\tNone of the Above\t8138\tno\tNOTA\tNone of the Above\nUttar Pradesh\tDhaurahra\tRAM BALI\t2144\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nUttar Pradesh\tDhaurahra\tSUKHVINDRA SINGH\t4021\tno\tNBDP\tNav Bharat Democratic Party\nUttar Pradesh\tDhaurahra\tRAJENDRA KUMAR SINGH\t1730\tno\tPECP\tPeace Party\nUttar Pradesh\tDhaurahra\tVIVEK MISHRA\t3745\tno\tNAP\tNaitik Party\nUttar Pradesh\tDhaurahra\tMALA DEVI\t1929\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nUttar Pradesh\tDhaurahra\tVIJAY KUMAR TIWARI\t3333\tno\tIND\tIndependent\nUttar Pradesh\tSitapur\tRAJESH VERMA\t417546\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tSitapur\tTURAB ALI\t6646\tno\tIND\tIndependent\nUttar Pradesh\tSitapur\tJAGMOHAN SINGH VERMA\t2550\tno\tAIFB\tAll India Forward Bloc\nUttar Pradesh\tSitapur\tSANJAY TIWARI\t2373\tno\tNAP\tNaitik Party\nUttar Pradesh\tSitapur\tRAM AVTAR NISHAD\t2504\tno\tjhspt\tJai Hind Samaj Party\nUttar Pradesh\tSitapur\tSANT KUMAR\t1544\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nUttar Pradesh\tSitapur\tSHEFALI MISRA\t2319\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tSitapur\tNone of the Above\t12682\tno\tNOTA\tNone of the Above\nUttar Pradesh\tSitapur\tPUSHPA DEVI\t3575\tno\tIND\tIndependent\nUttar Pradesh\tSitapur\tERAJ BEG\t2195\tno\tNEP\tNagrik Ekta Party\nUttar Pradesh\tSitapur\tJAGDISH NARAYAN SHUKLA\t8089\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tSitapur\tBHARAT TRIPATHI\t156170\tno\tSP\tSamajwadi Party\nUttar Pradesh\tSitapur\tR. K. MISRA\t2205\tno\tNBDP\tNav Bharat Democratic Party\nUttar Pradesh\tSitapur\tCHAUDHARY LAL BAGHEL\t1992\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tSitapur\tVAISHALI ALI\t29104\tno\tINC\tIndian National Congress\nUttar Pradesh\tSitapur\tKAISER JAHAN\t366519\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tSitapur\tSUCHITA KUMAR\t8959\tno\tIND\tIndependent\nUttar Pradesh\tHardoi\tANSHUL VERMA\t360501\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tHardoi\tUSHA VERMA\t276543\tno\tSP\tSamajwadi Party\nUttar Pradesh\tHardoi\tSHIVE PRASAD VERMA\t279158\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tHardoi\tSHIVEKUMAR\t4737\tno\tIND\tIndependent\nUttar Pradesh\tHardoi\tSUNIL BHARTI\t4045\tno\tIND\tIndependent\nUttar Pradesh\tHardoi\tSANJAY KUMAR\t830\tno\tBKrD\tBhartiya Krishak Dal\nUttar Pradesh\tHardoi\tSARVESH KUMAR\t23298\tno\tINC\tIndian National Congress\nUttar Pradesh\tHardoi\tRAMPAL\t1326\tno\tIND\tIndependent\nUttar Pradesh\tHardoi\tRAMDAYAL\t1576\tno\tPECP\tPeace Party\nUttar Pradesh\tHardoi\tSAROJ KUMAR\t3287\tno\tIND\tIndependent\nUttar Pradesh\tHardoi\tSARVESH\t4130\tno\tIND\tIndependent\nUttar Pradesh\tHardoi\tCHANDRAPAL\t2003\tno\tBRPP\tBharatiya Republican Paksha\nUttar Pradesh\tHardoi\tHIRA LAL VERMA\t1670\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tHardoi\tRAMLAKHAN GAUTAM\t834\tno\tISSP\tIndian Savarn Samaj Party\nUttar Pradesh\tHardoi\tSANTRAM\t1296\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tHardoi\tNone of the Above\t7660\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMisrikh\tANJU BALA\t412575\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMisrikh\tNone of the Above\t9633\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMisrikh\tSWADESH KUMAR\t2970\tno\tIND\tIndependent\nUttar Pradesh\tMisrikh\tSHABNUM\t1785\tno\tIND\tIndependent\nUttar Pradesh\tMisrikh\tMANSHARAM GAUTAM\t704\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nUttar Pradesh\tMisrikh\tRAJESH KUMAR S/O BABOORAM\t951\tno\tIND\tIndependent\nUttar Pradesh\tMisrikh\tRAJESH KUMAR S/O PARMESHWAR DEEN\t1158\tno\tBRPP\tBharatiya Republican Paksha\nUttar Pradesh\tMisrikh\tKANEDI\t2773\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMisrikh\tOM PRAKASH\t33075\tno\tINC\tIndian National Congress\nUttar Pradesh\tMisrikh\tHANS MUKHI KATHERIA\t8635\tno\tIND\tIndependent\nUttar Pradesh\tMisrikh\tRAMPAL\t2234\tno\tIND\tIndependent\nUttar Pradesh\tMisrikh\tJAI PRAKASH\t194759\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMisrikh\tHARINAM\t1003\tno\tSP(I)\tSocialist Party (India)\nUttar Pradesh\tMisrikh\tASHOK KUMAR RAWAT\t325212\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMisrikh\tNAGPAL\t847\tno\tBKrD\tBhartiya Krishak Dal\nUttar Pradesh\tUnnao\tSWAMI SACHCHIDANAND HARI SAKSHI\t518834\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tUnnao\tBRAJESH PATHAK\t200176\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tUnnao\tARUN SHANKAR SHUKLA\t208661\tno\tSP\tSamajwadi Party\nUttar Pradesh\tUnnao\tANNU TANDON\t197098\tno\tINC\tIndian National Congress\nUttar Pradesh\tUnnao\tARVIND KUMAR\t4214\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tUnnao\tROOP NARAYAN SINGH\t1818\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tUnnao\tANIL KUMAR MISHRA\t1881\tno\tSP(I)\tSocialist Party (India)\nUttar Pradesh\tUnnao\tSHALU DHARMENDRA\t2440\tno\tSARP\tSamtawadi Republican Party\nUttar Pradesh\tUnnao\tANIL KUMAR CHAUDHARY\t9618\tno\tIND\tIndependent\nUttar Pradesh\tUnnao\tSUKHDEV\t4401\tno\tMwSP\tManavtawadi Samaj Party\nUttar Pradesh\tUnnao\tSUMIT TEWARI\t3398\tno\tNBDP\tNav Bharat Democratic Party\nUttar Pradesh\tUnnao\tRAKESH KUMAR\t2771\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tUnnao\tANIL KUMAR\t3214\tno\tIND\tIndependent\nUttar Pradesh\tUnnao\tRAJNIKANT\t3987\tno\tIND\tIndependent\nUttar Pradesh\tUnnao\tMOHAMMAD AMIR KHAN\t4148\tno\tIND\tIndependent\nUttar Pradesh\tUnnao\tRAMESHWAR\t9277\tno\tIND\tIndependent\nUttar Pradesh\tUnnao\tCHETRAM PASI\t2592\tno\tRPI(A)\tRepublican Party of India (A)\nUttar Pradesh\tUnnao\tAKHILESH KUMAR\t5128\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nUttar Pradesh\tUnnao\tSHIVKANT\t3978\tno\tIND\tIndependent\nUttar Pradesh\tUnnao\tSHIVNATH\t3472\tno\tIND\tIndependent\nUttar Pradesh\tUnnao\tGIRJA SHANKAR @ RAJU\t5871\tno\tSWPI\tSwarajya Party Of India\nUttar Pradesh\tUnnao\tNone of the Above\t4626\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMohanlalganj\tKAUSHAL KISHORE\t455274\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMohanlalganj\tOM PRAKASH\t1427\tno\tPECP\tPeace Party\nUttar Pradesh\tMohanlalganj\tREENA CHAUDHARY\t6304\tno\tIND\tIndependent\nUttar Pradesh\tMohanlalganj\tSUNIL GAUTAM\t10031\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tMohanlalganj\tMAHENDRA KUMAR RAWAT\t1617\tno\tASaP\tAwami Samta Party\nUttar Pradesh\tMohanlalganj\tSANTOSH KUMAR DHARKAR\t4280\tno\tINL\tIndian National League\nUttar Pradesh\tMohanlalganj\tMEERA DEVI\t2425\tno\tMaSPa\tMahila Swabhiman Party\nUttar Pradesh\tMohanlalganj\tSHATROHANLAL RAWAT\t4214\tno\tIND\tIndependent\nUttar Pradesh\tMohanlalganj\tNone of the Above\t4708\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMohanlalganj\tGAUTAM BHARTI\t2225\tno\tRRP\tRashtriya Rashtrawadi Party\nUttar Pradesh\tMohanlalganj\tBRIJLAL\t1632\tno\tSARP\tSamtawadi Republican Party\nUttar Pradesh\tMohanlalganj\tGOPAL KANAUJIYA\t1826\tno\tRPI(A)\tRepublican Party of India (A)\nUttar Pradesh\tMohanlalganj\tNARENDRA GAUTAM\t52598\tno\tINC\tIndian National Congress\nUttar Pradesh\tMohanlalganj\tMAHESH KUMAR\t7412\tno\tIND\tIndependent\nUttar Pradesh\tMohanlalganj\tAMIT RAWAT\t5115\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tMohanlalganj\tMEENA RAWAT\t1782\tno\tNAP\tNaitik Party\nUttar Pradesh\tMohanlalganj\tSUSHILA SAROJ\t242366\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMohanlalganj\tJAGDISH PRASAD GAUTAM\t1494\tno\tMwSP\tManavtawadi Samaj Party\nUttar Pradesh\tMohanlalganj\tR.K CHAUDHARY\t309858\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tLucknow\tRAJ NATH SINGH\t561106\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tLucknow\tSHISHU PAL YADAV\t851\tno\tRVLP\tRASHTRIYA VIKLANG PARTY\nUttar Pradesh\tLucknow\tMOHD. SARWAR MALIK\t1486\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tLucknow\tRAMPHAL RAWAT\t502\tno\tBHNKP\tBharatiya Nav Kranti Party\nUttar Pradesh\tLucknow\tVIJAY BAHADUR SINGH\t575\tno\tMwSP\tManavtawadi Samaj Party\nUttar Pradesh\tLucknow\tVINAY PRAKASH MISHRA\t441\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tMANOJ SINGH\t572\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tGHANSHYAM SINGH\t282\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tNAKUL DUBEY\t64449\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tLucknow\tASOK PANDE\t930\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tTARA PATKAR\t743\tno\tSWPI\tSwarajya Party Of India\nUttar Pradesh\tLucknow\tRAJ KUMAR SINGH\t523\tno\tNYP\tNational Youth Party\nUttar Pradesh\tLucknow\tRADHA KRISHNAN\t542\tno\tUPRP\tUttar Pradesh Republican Party\nUttar Pradesh\tLucknow\tPADAM CHANDRA GUPTA\t661\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tPROF. RITA BAHUGUNA JOSHI\t288357\tno\tINC\tIndian National Congress\nUttar Pradesh\tLucknow\tNAND KUMAR LODHI RAJPOOT\t445\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nUttar Pradesh\tLucknow\tHARI RAM ALIAS HARIBHAI PATEL\t694\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tLucknow\tNone of the Above\t4596\tno\tNOTA\tNone of the Above\nUttar Pradesh\tLucknow\tSUBHASH CHANDRA LEHRI\t719\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tSHAILENDRA SINGH YADAV\t607\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tSANJEEV MEHROTRA ALIAS PIKKU BHAI\t1174\tno\tBKP\tBahujan Kranti Party (Marxwad-Ambedkarwad)\nUttar Pradesh\tLucknow\tRAJEEV RANJAN TIWARI ALIAS RAJ BEHARI\t391\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tABHISHEK MISRA\t56771\tno\tSP\tSamajwadi Party\nUttar Pradesh\tLucknow\tSUMAN VERMA\t1901\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tLucknow\tSUNIL SINGH\t849\tno\tLD\tLok Dal\nUttar Pradesh\tLucknow\tYOGESH CHANDRA BHATT\t337\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tADARSH SHASTRI\t389\tno\tIND\tIndependent\nUttar Pradesh\tLucknow\tRABI DUBEY\t361\tno\tNEP\tNagrik Ekta Party\nUttar Pradesh\tLucknow\tSYED JAVED AHMED JAFRI\t41429\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tLucknow\tSYED ALI HUSAIN\t1100\tno\tIND\tIndependent\nUttar Pradesh\tRae Bareli\tSONIA GANDHI\t526434\tyes\tINC\tIndian National Congress\nUttar Pradesh\tRae Bareli\tSHAKIL AHAMAD\t7914\tno\tIND\tIndependent\nUttar Pradesh\tRae Bareli\tMANGAL SAIN\t4418\tno\tIND\tIndependent\nUttar Pradesh\tRae Bareli\tMOHAMMAD ASHRAF\t1586\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nUttar Pradesh\tRae Bareli\tJAWAHAR LAL\t1463\tno\tNJC\tNehru Janhit Congress\nUttar Pradesh\tRae Bareli\tARCHANA SRIVASTAVA\t10383\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tRae Bareli\tRATIRAM\t1661\tno\tSARP\tSamtawadi Republican Party\nUttar Pradesh\tRae Bareli\tSAHAR NAQVI\t2108\tno\tLD\tLok Dal\nUttar Pradesh\tRae Bareli\tPRAMOD KUREEL\t6688\tno\tIND\tIndependent\nUttar Pradesh\tRae Bareli\tAJAY AGRAWAL\t173721\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tRae Bareli\tKAILASH KUMAR\t2645\tno\tHKRD\tHindustan Krantikari Dal\nUttar Pradesh\tRae Bareli\tKIRAN CHAUDHARI\t5205\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tRae Bareli\tNone of the Above\t5409\tno\tNOTA\tNone of the Above\nUttar Pradesh\tRae Bareli\tPRAVESH SINGH\t63633\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tRae Bareli\tSHIV SEWAK\t1493\tno\tRAHM\tRashtriya Ahinsa Manch\nUttar Pradesh\tRae Bareli\tSURESH CHANDRA\t3330\tno\tMwSP\tManavtawadi Samaj Party\nUttar Pradesh\tRae Bareli\tSAHADAT ALI KHAN\t3538\tno\tIND\tIndependent\nUttar Pradesh\tRae Bareli\tANJU SINGH\t3507\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tAmethi\tRAHUL GANDHI\t408651\tyes\tINC\tIndian National Congress\nUttar Pradesh\tAmethi\tNone of the Above\t1784\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAmethi\tMITHILESH KUMARI\t2726\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tGANGESH KUMAR GUPTA\t1307\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tSWAMINATH\t5537\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tCHHAYA SINGH\t4109\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tAmethi\tRAMSIDDH YADAV\t2189\tno\tMwSP\tManavtawadi Samaj Party\nUttar Pradesh\tAmethi\tDR. KUMAR VISHVAS\t25527\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAmethi\tDHARMENDRA PRATAP SINGH\t57716\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAmethi\tSONAM KINNAR\t1252\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tZAHIDA BEGAM\t1387\tno\tPECP\tPeace Party\nUttar Pradesh\tAmethi\tGOPAL SWAROOP GANDHI\t5467\tno\tKMBS\tKisan Majdoor Berojgar Sangh\nUttar Pradesh\tAmethi\tSMRITI ZUBIN IRANI\t300748\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAmethi\tBHAGWAN DEEN\t1299\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tRAVI KUMAR\t1064\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tAmethi\tRAM SAJIWAN\t2384\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tUMAKANT\t3305\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tNARESH SINGH BHADAURIYA\t1255\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tRADHE MOHAN\t1753\tno\tssrd\tSanatan Sanskriti Raksha Dal\nUttar Pradesh\tAmethi\tSANJAY KUMAR\t1015\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tRAM ASREY\t1572\tno\tRaMSP\tRashtriya Manav samman Party\nUttar Pradesh\tAmethi\tGIRIJA BAKS\t2454\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tSHIV KUMAR\t2289\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tJAGAT SINGH CHAUDHARY\t1043\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tBABU LAL\t1617\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAmethi\tJITENDRA SINGH\t2201\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tLAL BAHADUR\t2845\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tC L MAURYA\t1871\tno\tPGSP\tPragatisheel Samaj Party\nUttar Pradesh\tAmethi\tRAMESH CHANDRA\t6422\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tMOHD HASAN LAHRI\t4457\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tAmethi\tVASDEO\t2546\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nUttar Pradesh\tAmethi\tRAM NEWAJ\t1140\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tAmethi\tDWARIKA PRASAD\t2345\tno\tIND\tIndependent\nUttar Pradesh\tAmethi\tVEDPAL SHASATRI\t6464\tno\tVIP\tVANCHITSAMAJ INSAAF PARTY\nUttar Pradesh\tAmethi\tTULSIRAM\t4884\tno\tIND\tIndependent\nUttar Pradesh\tSultanpur\tFEROZE VARUN GANDHI\t410348\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tSultanpur\tVARUN GANDHI\t14021\tno\tIND\tIndependent\nUttar Pradesh\tSultanpur\tRAM MILAN\t1887\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tSultanpur\tMOHD SHAMIM\t1637\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tSultanpur\tPAWAN PANDEY\t231446\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tSultanpur\tSHAKEEL AHMED\t228144\tno\tSP\tSamajwadi Party\nUttar Pradesh\tSultanpur\tBRIJESH KUMAR\t5752\tno\tIND\tIndependent\nUttar Pradesh\tSultanpur\tSARTAJ KHAN\t2363\tno\tADPT\tApna Desh Party\nUttar Pradesh\tSultanpur\tAMEETA SINGH\t41983\tno\tINC\tIndian National Congress\nUttar Pradesh\tSultanpur\tGIRISH LAL\t3804\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tSultanpur\tDR. NAFEES AHMAD\t3381\tno\tRaIP\tRastriya Insaaf Party\nUttar Pradesh\tSultanpur\tSHAILENDRA PRATAP SINGH\t5835\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tSultanpur\tSHIV SHANKAR\t1601\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tSultanpur\tNone of the Above\t5412\tno\tNOTA\tNone of the Above\nUttar Pradesh\tSultanpur\tVIJAY\t4630\tno\tIND\tIndependent\nUttar Pradesh\tSultanpur\tPITAMBAR NISHAD\t2737\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tPratapgarh\tKUWAR HARIVANSH SINGH\t375789\tyes\tAD\tApna Dal\nUttar Pradesh\tPratapgarh\tNone of the Above\t9098\tno\tNOTA\tNone of the Above\nUttar Pradesh\tPratapgarh\tASIF NIZAMUDDIN SIDDIQUE\t207567\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tPratapgarh\tRAJENDRA SHARMA\t1571\tno\tRshJP\tRashtriya Janshanti Party\nUttar Pradesh\tPratapgarh\tSHESHNATH\t2458\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nUttar Pradesh\tPratapgarh\tSANTOSH KUMAR SINGH\t6309\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tPratapgarh\tKIRAN MISHRA\t3002\tno\tIND\tIndependent\nUttar Pradesh\tPratapgarh\tFOOLCHANDRA\t1859\tno\tSHS\tShivsena\nUttar Pradesh\tPratapgarh\tRAJKUMARI RATNA SINGH\t138620\tno\tINC\tIndian National Congress\nUttar Pradesh\tPratapgarh\tBAL KRISHNA\t2160\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tPratapgarh\tRAM SEWAK\t4207\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tPratapgarh\tAJMAL KHAN\t3342\tno\tIND\tIndependent\nUttar Pradesh\tPratapgarh\tNIRMALA DEVI\t2766\tno\tBaVaP\tBhartiya Vanchitsamaj Party\nUttar Pradesh\tPratapgarh\tHARIVANSH\t5618\tno\tIND\tIndependent\nUttar Pradesh\tPratapgarh\tPRAMOD KUMAR SINGH PATEL\t120107\tno\tSP\tSamajwadi Party\nUttar Pradesh\tPratapgarh\t\"ASHOK SHUKLA \"\" SENANI\"\"\"\t9983\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tFarrukhabad\tMUKESH RAJPUT\t406195\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tFarrukhabad\tNone of the Above\t1986\tno\tNOTA\tNone of the Above\nUttar Pradesh\tFarrukhabad\tRAMNIWAS KATHERIA\t941\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tFarrukhabad\tRAM KISHORE DIWEDI\t4974\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tKISHAN PAL SINGH\t2933\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tANSHU MAHAN\t2066\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tANOKHEY LAL\t1202\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tMUKESH RAJPUT\t2420\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tSACHIN SINGH YADAV\t58703\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tVIMLA DEVI\t2140\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tANURAG RAJPUT\t1605\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tMALIKHAN SINGH ADVOCATE\t4659\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tTARIK PARVEZ\t1857\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tFarrukhabad\tSURESH SARASWAT ALAIS ILLAYCHIWALA\t1013\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tRAMESHWAR SINGH YADAV\t255693\tno\tSP\tSamajwadi Party\nUttar Pradesh\tFarrukhabad\tMO. TAMAJID\t2655\tno\tPECP\tPeace Party\nUttar Pradesh\tFarrukhabad\tANJU\t2254\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tFarrukhabad\tSALMAN KHURSHID\t95543\tno\tINC\tIndian National Congress\nUttar Pradesh\tFarrukhabad\tVRAHMA SARAN JATAV\t1809\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tJAIVEER SINGH\t114521\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tFarrukhabad\tKRISHAN KUMAR DIXIT ALIAS K. K. DIXIT\t2107\tno\tRVLP\tRASHTRIYA VIKLANG PARTY\nUttar Pradesh\tFarrukhabad\tIMAMUDDIN HUSAIN HUSAIN\t1537\tno\tIND\tIndependent\nUttar Pradesh\tFarrukhabad\tDAYA SHANKAR\t1864\tno\tARVP\tAdarsh Rashtriya Vikas Party\nUttar Pradesh\tEtawah\tASHOK KUMAR DOHAREY\t439646\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tEtawah\tPREMDAS KATERIYA\t266700\tno\tSP\tSamajwadi Party\nUttar Pradesh\tEtawah\tDR MUKESH SINGH DOHARE\t3178\tno\tPECP\tPeace Party\nUttar Pradesh\tEtawah\tHAR BILASH DOHERY\t2677\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tEtawah\tUJIYARE LAL DOHARE\t5923\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tEtawah\tSIYA RAM PASWAN\t1788\tno\tALHP\tAl-Hind Party\nUttar Pradesh\tEtawah\tAJAY PAL SINGH JATAV\t192804\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tEtawah\tHANS MUKHI KORI\t13397\tno\tINC\tIndian National Congress\nUttar Pradesh\tEtawah\tVINEET KUMAR GAUTAM\t4954\tno\tASP\tAmbedkar Samaj Party\nUttar Pradesh\tEtawah\tNone of the Above\t6165\tno\tNOTA\tNone of the Above\nUttar Pradesh\tEtawah\tPREM CHANDRA\t2372\tno\tKMBS\tKisan Majdoor Berojgar Sangh\nUttar Pradesh\tKannauj\tDIMPLE YADAV\t489164\tyes\tSP\tSamajwadi Party\nUttar Pradesh\tKannauj\tSUBODH KUMAR\t927\tno\tPECP\tPeace Party\nUttar Pradesh\tKannauj\tPRADEEP KUMAR\t964\tno\tIND\tIndependent\nUttar Pradesh\tKannauj\tIMRAN BIN ZAFAR\t4826\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tKannauj\tGANGA CHARAN\t1282\tno\tRTKP\tRashtriya Kranti Party\nUttar Pradesh\tKannauj\tNone of the Above\t7380\tno\tNOTA\tNone of the Above\nUttar Pradesh\tKannauj\tMUHAR SINGH AMVADI\t2891\tno\tIND\tIndependent\nUttar Pradesh\tKannauj\tNIRMAL TIWARI\t127785\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tKannauj\tSUBRAT PATHAK\t469257\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tKannauj\tPUSHPA DEVI\t806\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tKannauj\tSURENDRA NATH\t888\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tKannauj\tSANJAY SHAKYA\t2567\tno\tIND\tIndependent\nUttar Pradesh\tKannauj\tRAKESH KUMAR TIWARI ADVOCATE\t5723\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tDR.MURLI MANOHAR JOSHI\t474712\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tKanpur\tMUZAFFAR HUSAIN KHAN\t1069\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tMAHMOOD HUSAIN RAHMANI\t11925\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tKanpur\tANIL AWASTHI\t1701\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tKanpur\tSALEEM AHMAD\t53218\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tKanpur\tASHOK PASWAN ADVOCATE\t1398\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tSRIPRAKASH JAISWAL\t251766\tno\tINC\tIndian National Congress\nUttar Pradesh\tKanpur\tSURENDRA MOHAN AGARWAL\t25723\tno\tSP\tSamajwadi Party\nUttar Pradesh\tKanpur\tKUMARI SARASWATI\t475\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tPREM CHANDRA PANDEY\t2350\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tHIMMAT LAL\t683\tno\tABRS\tAkhil Bharatiya Rajarya Sabha\nUttar Pradesh\tKanpur\tBETALAL DIWAKAR\t665\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tVINAYAK RAO TOPE\t723\tno\tSWPI\tSwarajya Party Of India\nUttar Pradesh\tKanpur\tNone of the Above\t2278\tno\tNOTA\tNone of the Above\nUttar Pradesh\tKanpur\tKISHOR BAJPAI\t3005\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tVISHWANATH KATARIA\t929\tno\tIND\tIndependent\nUttar Pradesh\tKanpur\tANAND MEHROTRA\t361\tno\tNEP\tNagrik Ekta Party\nUttar Pradesh\tKanpur\tDR.NIKHIL GUPTA\t580\tno\tNBDP\tNav Bharat Democratic Party\nUttar Pradesh\tKanpur\tDEVENDRA KUMAR SHARMA\t296\tno\tABAS\tAkhil Bharatiya Ashok Sena\nUttar Pradesh\tKanpur\tRAMESH KUMAR GUPTA\t433\tno\tPSJP\tParivartan Samaj Party\nUttar Pradesh\tKanpur\tMOHD.NASIR KHAN\t364\tno\tPECP\tPeace Party\nUttar Pradesh\tKanpur\tMOHAMMAD.ATEEQ\t425\tno\tRWSP\tRashtrawadi Samaj Party\nUttar Pradesh\tAkbarpur\tDEVENDRA SINGH @ BHOLE SINGH\t481584\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAkbarpur\tASHOK YADAV\t1853\tno\tIND\tIndependent\nUttar Pradesh\tAkbarpur\tJASWANT SINGH\t1037\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAkbarpur\tNone of the Above\t3948\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAkbarpur\tBHARTI DEVI PASI ADVOCATE\t5432\tno\tIND\tIndependent\nUttar Pradesh\tAkbarpur\tGOPI RAMAN\t523\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tAkbarpur\tRAJARAM PAL\t96827\tno\tINC\tIndian National Congress\nUttar Pradesh\tAkbarpur\tDR. OMKAR NATH KATIYAR\t597\tno\tALHP\tAl-Hind Party\nUttar Pradesh\tAkbarpur\tVIJAY KUMAR SHARMA\t4285\tno\tIND\tIndependent\nUttar Pradesh\tAkbarpur\tNEERAJ KUMAR ADVOCATE\t2259\tno\tIND\tIndependent\nUttar Pradesh\tAkbarpur\tMUNNI DEVI\t5331\tno\tIND\tIndependent\nUttar Pradesh\tAkbarpur\tDR. BAJARANG PRATAP SONKAR\t799\tno\tSARP\tSamtawadi Republican Party\nUttar Pradesh\tAkbarpur\tRAMENDRA SINGH TOMAR\t2754\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tAkbarpur\tRAM SEVAK\t3403\tno\tIND\tIndependent\nUttar Pradesh\tAkbarpur\tARVIND KUMAR\t7914\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAkbarpur\tAMAR SINGH\t1890\tno\tJPS\tJanvadi Party(Socialist)\nUttar Pradesh\tAkbarpur\tANIL SHUKLA WARSI\t202587\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAkbarpur\tLAL SINGH TOMAR\t147002\tno\tSP\tSamajwadi Party\nUttar Pradesh\tAkbarpur\tRAMA KANT VERMA\t1350\tno\tPECP\tPeace Party\nUttar Pradesh\tJalaun\tBHANU PRATAP SINGH VERMA\t548631\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tJalaun\tNone of the Above\t10291\tno\tNOTA\tNone of the Above\nUttar Pradesh\tJalaun\tGHANSHAYAM ANURAGI\t180921\tno\tSP\tSamajwadi Party\nUttar Pradesh\tJalaun\tMANOJ KUMAR\t4561\tno\tBRPP\tBharatiya Republican Paksha\nUttar Pradesh\tJalaun\tGANGASINGH\t2525\tno\tALHP\tAl-Hind Party\nUttar Pradesh\tJalaun\tRAMNARAYAN\t4944\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tJalaun\tBRIJLAL KHABRI\t261429\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tJalaun\tRAM SINGH\t4761\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tJalaun\tDALCHANDRA ANURAGI\t3337\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tJalaun\tRAJKUMAR\t2816\tno\tPECP\tPeace Party\nUttar Pradesh\tJalaun\tVIJAY CHAUDHARI\t82903\tno\tINC\tIndian National Congress\nUttar Pradesh\tJalaun\tJAYRAM\t2047\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tJhansi\tUMA BHARATI\t575889\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tJhansi\tRACHNA SHARMA\t4522\tno\tIND\tIndependent\nUttar Pradesh\tJhansi\tMAULANA MOHAMMAD\t1819\tno\tPECP\tPeace Party\nUttar Pradesh\tJhansi\tARCHANA\t5536\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tJhansi\tDR. CHANDRAPAL SINGH YADAV\t385422\tno\tSP\tSamajwadi Party\nUttar Pradesh\tJhansi\tPRADEEP JAIN 'ADITYA'\t84089\tno\tINC\tIndian National Congress\nUttar Pradesh\tJhansi\tNone of the Above\t13959\tno\tNOTA\tNone of the Above\nUttar Pradesh\tJhansi\tRAM KUMAR 'ANK SHASTRI'\t11443\tno\tIND\tIndependent\nUttar Pradesh\tJhansi\tCHINTAMANI\t2029\tno\tARWP\tAkhil Rashtrawadi Party\nUttar Pradesh\tJhansi\tNARESH KUMAR\t4215\tno\tRrSP\tRashtriya Sarvajan Party\nUttar Pradesh\tJhansi\tKULDEEP KUMAR\t2036\tno\tIND\tIndependent\nUttar Pradesh\tJhansi\tDILSHAD AHMAD\t2727\tno\tIND\tIndependent\nUttar Pradesh\tJhansi\tRAJENDRA KUMAR\t8600\tno\tIND\tIndependent\nUttar Pradesh\tJhansi\tSUNIL MASSEY\t2121\tno\tSHS\tShivsena\nUttar Pradesh\tJhansi\tTULA RAM\t2605\tno\tIND\tIndependent\nUttar Pradesh\tJhansi\tANURADHA SHARMA\t213792\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tHamirpur\tKUNWAR PUSHPENDRA SINGH CHANDEL\t453884\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tHamirpur\tKIRAN\t4079\tno\tIND\tIndependent\nUttar Pradesh\tHamirpur\tVIMLESH\t23137\tno\tIND\tIndependent\nUttar Pradesh\tHamirpur\tATUL KUMAR PRAJAPATI\t9663\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tHamirpur\tAVINASH KUMAR MISHRA\t6297\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tHamirpur\t\"PRITAM SINGH LODHI \"\"KISAAN\"\"\"\t78229\tno\tINC\tIndian National Congress\nUttar Pradesh\tHamirpur\tRAKESH KUMAR GOSWAMI\t176356\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tHamirpur\tNone of the Above\t10449\tno\tNOTA\tNone of the Above\nUttar Pradesh\tHamirpur\tRAMSAJIVAN\t2955\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tHamirpur\tKAILASH PRASAD NISHAD\t1756\tno\tEKSP\tEklavya Samaj Party\nUttar Pradesh\tHamirpur\tBISHAMBHAR PRASAD NISHAD\t187096\tno\tSP\tSamajwadi Party\nUttar Pradesh\tHamirpur\tRAMESH PRAJAPATI\t2981\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tHamirpur\tVIVEK KUMAR\t5749\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nUttar Pradesh\tHamirpur\tPRAVESH KUMAR VYAS\t4314\tno\tIND\tIndependent\nUttar Pradesh\tHamirpur\tANIL KUMAR\t4860\tno\tASP\tAmbedkar Samaj Party\nUttar Pradesh\tHamirpur\tSHRIPAL\t3435\tno\tBC\tBundelkhand Congress\nUttar Pradesh\tBanda\tBHAIRON PRASAD MISHRA\t342066\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBanda\tSUSHEELA SINGH\t2931\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tBanda\tBAL KUMAR PATEL ALIAS RAJKUMAR\t189730\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBanda\tR. K. SINGH PATEL\t226278\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBanda\tNone of the Above\t13824\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBanda\tMO. SAGEER\t3201\tno\tPECP\tPeace Party\nUttar Pradesh\tBanda\tNARENDRA KUMAR SHRIVASTAVA\t5115\tno\tIND\tIndependent\nUttar Pradesh\tBanda\tPRABHAKAR PRASAD\t2605\tno\tBKSL\tBharatiya Kisan Sena Loktantrik\nUttar Pradesh\tBanda\tAMAR SINGH RATHAUR\t6439\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBanda\tVIJAY PRAKASH\t7140\tno\tIND\tIndependent\nUttar Pradesh\tBanda\tPAPPU YADAV\t2436\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tBanda\tVISHVAMBHAR NATH\t4755\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tBanda\tVIVEK KUMAR SINGH\t36650\tno\tINC\tIndian National Congress\nUttar Pradesh\tBanda\tRAMCHANDRA SARAS\t15156\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tFatehpur\tNIRANJAN JYOTI\t485994\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tFatehpur\tASHOK MISHRA\t3029\tno\tSARP\tSamtawadi Republican Party\nUttar Pradesh\tFatehpur\tRUKMINI DEVI NISHAD\t1345\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tFatehpur\tMOHAMMAD JAVED\t1657\tno\tNBDP\tNav Bharat Democratic Party\nUttar Pradesh\tFatehpur\tRANJANA KAKKAR\t5448\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur\tCHANDRABHAN SINGH\t2498\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur\tARVIND KUMAR\t1967\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur\tNone of the Above\t11607\tno\tNOTA\tNone of the Above\nUttar Pradesh\tFatehpur\tRAKESH SACHAN\t179724\tno\tSP\tSamajwadi Party\nUttar Pradesh\tFatehpur\tSRIDEVI\t5838\tno\tIND\tIndependent\nUttar Pradesh\tFatehpur\tAFZAL SIDDIQUI\t298788\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tFatehpur\tRAMKRISHNA TRIPATHI\t7578\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tFatehpur\tRAMLOCHAN SEN\t2173\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tFatehpur\tZAINUL ABDIN\t2454\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tFatehpur\tUSHA MAURYA\t46588\tno\tINC\tIndian National Congress\nUttar Pradesh\tKaushambi\tVINOD KUMAR SONKAR\t331593\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tKaushambi\tBEENA RANI\t4957\tno\tIND\tIndependent\nUttar Pradesh\tKaushambi\tRAJENDRA SONKAR\t6386\tno\tLD\tLok Dal\nUttar Pradesh\tKaushambi\tCHHEDDOO\t3340\tno\tIND\tIndependent\nUttar Pradesh\tKaushambi\tMALTI DEVI\t6548\tno\tRSMD\tRashtriya Samanta Dal\nUttar Pradesh\tKaushambi\tSURESH PASI\t201196\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tKaushambi\tSWARNALATA SUMAN\t3692\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tKaushambi\tSHRIPAL\t7341\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tKaushambi\tMOHAN LAL\t9180\tno\tIND\tIndependent\nUttar Pradesh\tKaushambi\tSHAILENDRA KUMAR\t288746\tno\tSP\tSamajwadi Party\nUttar Pradesh\tKaushambi\tMAHENDRA KUMAR\t31904\tno\tINC\tIndian National Congress\nUttar Pradesh\tKaushambi\tNone of the Above\t15155\tno\tNOTA\tNone of the Above\nUttar Pradesh\tPhulpur\tKESHAV PRASAD MAURYA\t503564\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tPhulpur\tNone of the Above\t8424\tno\tNOTA\tNone of the Above\nUttar Pradesh\tPhulpur\tNATHU RAM BOUDHA\t1306\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tPhulpur\tDHARAM RAJ SINGH PATEL\t195256\tno\tSP\tSamajwadi Party\nUttar Pradesh\tPhulpur\tMOHD. KAIF\t58127\tno\tINC\tIndian National Congress\nUttar Pradesh\tPhulpur\tDAKKHINI PRASAD KUSHWAHA\t1111\tno\tRGD\tRashtriya Garib Dal\nUttar Pradesh\tPhulpur\tMILAN MUKERJEE\t5652\tno\tIND\tIndependent\nUttar Pradesh\tPhulpur\tGANESHJI TRIPATHI\t1521\tno\tRaBaP\tRashtriya Bandhutwa Party\nUttar Pradesh\tPhulpur\tSHIMALA SHRI\t7384\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tPhulpur\tPARVEZ ASHFAQUE SIDDIQUI\t1302\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tPhulpur\tSURENDRA KUMAR MISHRA\t1585\tno\tLD\tLok Dal\nUttar Pradesh\tPhulpur\tGYANENDRA KUMAR SRIVASTAVA (GYANI BHAI)\t2706\tno\tNCP\tNationalist Congress Party\nUttar Pradesh\tPhulpur\tZIAOUL HAQUE\t3392\tno\tIND\tIndependent\nUttar Pradesh\tPhulpur\tKAPIL MUNI KARWARIYA\t163710\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tPhulpur\tDR. NEERAJ\t2187\tno\tIND\tIndependent\nUttar Pradesh\tPhulpur\tMITHILESH KUMAR\t3114\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tSHYAMA CHARAN GUPTA\t313772\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAllahabad\tSHYAM SUNDAR DAS\t2115\tno\tssrd\tSanatan Sanskriti Raksha Dal\nUttar Pradesh\tAllahabad\tSHYAM CHARAN\t2593\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tRAJENDRA PRASAD\t1730\tno\tkajp\tKalyankari Jantantrik Party\nUttar Pradesh\tAllahabad\tCHANDRA PRAKASH TIWARI ALIAS C. P. TIWARI (ADVOCATE)\t1221\tno\tBKSL\tBharatiya Kisan Sena Loktantrik\nUttar Pradesh\tAllahabad\tMOHD. AMEEN AZHAR ANSARI\t4426\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tAllahabad\tSANJAY ROBINSON\t3315\tno\tNAP\tNaitik Party\nUttar Pradesh\tAllahabad\tSHAILENDRA KUMAR PRAJAPATI\t1212\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tADARSH SHASTRI\t6439\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAllahabad\tBRIJESH KUMAR\t1438\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tNISTHA DEV MAURYA\t2181\tno\tPGSP\tPragatisheel Samaj Party\nUttar Pradesh\tAllahabad\tDRAUPADI DEVI\t8321\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tGAYTRI PRASAD\t1293\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tAllahabad\tMAHENDRA KUMAR\t4190\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAllahabad\tSHIV PRASAD AGRAHARI\t4323\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tKUNWAR REWATI RAMAN SINGH ALIAS MANI\t251763\tno\tSP\tSamajwadi Party\nUttar Pradesh\tAllahabad\tNAND GOPAL GUPTA NANDI\t102453\tno\tINC\tIndian National Congress\nUttar Pradesh\tAllahabad\tHIRA LAL\t1711\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tAJEET KUMAR\t3388\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tKESHRI DEVI\t162073\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAllahabad\tWAHID ALI ALIAS WAJID ALI\t2332\tno\tRUC\tRashtriya Ulama Council\nUttar Pradesh\tAllahabad\tHANSRAJ\t1928\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tPARMANAND\t3744\tno\tIND\tIndependent\nUttar Pradesh\tAllahabad\tNone of the Above\t3621\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBarabanki\tPRIYANKA SINGH RAWAT\t454214\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBarabanki\tNone of the Above\t8726\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBarabanki\tABHILAKH RAWAT\t3038\tno\tIND\tIndependent\nUttar Pradesh\tBarabanki\tBABU LAL\t2035\tno\tMwSP\tManavtawadi Samaj Party\nUttar Pradesh\tBarabanki\tNITIN SINGH\t1315\tno\tNAP\tNaitik Party\nUttar Pradesh\tBarabanki\tMEDI LAL\t2171\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tBarabanki\tDINESH CHANDRA GAUTAM\t3126\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBarabanki\tRAJRANI RAWAT\t159284\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBarabanki\tMAHESH\t3836\tno\tIND\tIndependent\nUttar Pradesh\tBarabanki\tKHANJAN LAL\t7136\tno\tIND\tIndependent\nUttar Pradesh\tBarabanki\tP. L. PUNIA\t242336\tno\tINC\tIndian National Congress\nUttar Pradesh\tBarabanki\tKISHAN LAL\t3261\tno\tSHS\tShivsena\nUttar Pradesh\tBarabanki\tRAJESH KUMAR VIDYARTHI\t7980\tno\tPECP\tPeace Party\nUttar Pradesh\tBarabanki\tP. C. KUREEL\t2560\tno\tINL\tIndian National League\nUttar Pradesh\tBarabanki\tKAMLA PRASAD RAWAT\t167150\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tFaizabad\tLALLU SINGH\t491761\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tFaizabad\tJITENDRA KUMAR SINGH (BABLU BHAIYA)\t141827\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tFaizabad\tMITRASEN YADAV\t208986\tno\tSP\tSamajwadi Party\nUttar Pradesh\tFaizabad\tNone of the Above\t11537\tno\tNOTA\tNone of the Above\nUttar Pradesh\tFaizabad\tMAKASUD ALI\t5963\tno\tPECP\tPeace Party\nUttar Pradesh\tFaizabad\tJAI RAM SINGH JAI\t5702\tno\tLPSP\tLokpriya Samaj Party\nUttar Pradesh\tFaizabad\tNIRMAL KHATRI\t129917\tno\tINC\tIndian National Congress\nUttar Pradesh\tFaizabad\tMAYURI TIWARI\t4795\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tFaizabad\tRAM JANAM\t6308\tno\tMADP\tMoulik Adhikar Party\nUttar Pradesh\tFaizabad\tRADHIKA PRASAD\t9055\tno\tISSP\tIndian Savarn Samaj Party\nUttar Pradesh\tFaizabad\tNASREEN BANO\t2678\tno\tRUC\tRashtriya Ulama Council\nUttar Pradesh\tFaizabad\tSHAILENDRA KUMAR SHUKLA\t4187\tno\tNAP\tNaitik Party\nUttar Pradesh\tAmbedkar Nagar\tHARI OM PANDEY\t432104\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAmbedkar Nagar\tRAM BAHAL VERMA\t1137\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tAmbedkar Nagar\tASHOK SINGH\t22775\tno\tINC\tIndian National Congress\nUttar Pradesh\tAmbedkar Nagar\tTIGER RAMNIHOR PATEL\t7040\tno\tIND\tIndependent\nUttar Pradesh\tAmbedkar Nagar\tJ.S. KASHYAP(JAHAR SINGH KASHYAP)\t6896\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAmbedkar Nagar\tSURESH KUMAR BAUDDH\t7694\tno\tMADP\tMoulik Adhikar Party\nUttar Pradesh\tAmbedkar Nagar\tSREERAM\t4138\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tAmbedkar Nagar\tBRIJESH KUMAR\t3025\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tAmbedkar Nagar\tVINOD KUMAR TIWARI\t4707\tno\tIND\tIndependent\nUttar Pradesh\tAmbedkar Nagar\tRAM MURTI VERMA\t234467\tno\tSP\tSamajwadi Party\nUttar Pradesh\tAmbedkar Nagar\tGOPAL NISHAD\t5792\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tAmbedkar Nagar\tSURENDRA KUMAR\t2590\tno\tARVP\tAdarsh Rashtriya Vikas Party\nUttar Pradesh\tAmbedkar Nagar\tBALMUKUND DHURIYA\t1942\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tAmbedkar Nagar\tNone of the Above\t7422\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAmbedkar Nagar\tRAKESH PANDEY\t292675\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBahraich\tSADHVI SAVITRI BAI FOOLE\t432392\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBahraich\tNone of the Above\t13498\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBahraich\tDR.VIJAY KUMAR\t96904\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBahraich\tCOMANDO KAMAL KISHOR\t24421\tno\tINC\tIndian National Congress\nUttar Pradesh\tBahraich\tSHABBIR AHMAD\t336747\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBahraich\tHIRA LAL RAO\t4026\tno\tANC\tAmbedkar National Congress\nUttar Pradesh\tBahraich\tRAM NARESH\t5826\tno\tIND\tIndependent\nUttar Pradesh\tBahraich\tRAMASHANKER GOND\t5735\tno\tjhspt\tJai Hind Samaj Party\nUttar Pradesh\tBahraich\tMADHAV\t2676\tno\tLD\tLok Dal\nUttar Pradesh\tBahraich\tJAGAT RAM\t8319\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBahraich\tVINOD KUMAR\t3719\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tKaiserganj\tBRIJ BHUSAN SHARAN SINGH\t381500\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tKaiserganj\tCHAMAN LAL KASAUNDHAN\t1822\tno\tLD\tLok Dal\nUttar Pradesh\tKaiserganj\tPRAHLAD  NATH MISHRA\t3867\tno\tIND\tIndependent\nUttar Pradesh\tKaiserganj\tNIRHU\t1466\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nUttar Pradesh\tKaiserganj\tNISHANT KANCHAN\t3246\tno\tIND\tIndependent\nUttar Pradesh\tKaiserganj\tVINOD KUMAR ALIAS  PANDIT SINGH\t303282\tno\tSP\tSamajwadi Party\nUttar Pradesh\tKaiserganj\tMUKESH SRIVASTAV ALIAS GYANENDRA PRATAP\t57401\tno\tINC\tIndian National Congress\nUttar Pradesh\tKaiserganj\tMUNNI\t5245\tno\tIND\tIndependent\nUttar Pradesh\tKaiserganj\tNIHALUDDIN AHAMAD\t2119\tno\tAIPF\tAll India Peoples' Front (Radical)\nUttar Pradesh\tKaiserganj\tKRISHNA KUMAR OJHA\t146726\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tKaiserganj\tJAMEER AHMAD\t3905\tno\tIND\tIndependent\nUttar Pradesh\tKaiserganj\tRAMPHER  ALIAS  CHUNTI\t11366\tno\tIND\tIndependent\nUttar Pradesh\tKaiserganj\tRIZWAN AHMAD\t3710\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tKaiserganj\tGOVIND PRASAD\t5849\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tKaiserganj\tNone of the Above\t11853\tno\tNOTA\tNone of the Above\nUttar Pradesh\tShrawasti\tDADDAN MISHRA\t345964\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tShrawasti\tNone of the Above\t14587\tno\tNOTA\tNone of the Above\nUttar Pradesh\tShrawasti\tARJUN YADAV\t3730\tno\tIND\tIndependent\nUttar Pradesh\tShrawasti\tRAM ADHAR\t6278\tno\tIND\tIndependent\nUttar Pradesh\tShrawasti\tATIQ AHMAD\t260051\tno\tSP\tSamajwadi Party\nUttar Pradesh\tShrawasti\tVINAY KUMAR PANDEY 'VINNU'\t20006\tno\tINC\tIndian National Congress\nUttar Pradesh\tShrawasti\tRAM SUDHI\t2961\tno\tJSEP\tJan Shakti Ekta Party\nUttar Pradesh\tShrawasti\tRATNESH\t2578\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tShrawasti\tLAL JI VERMA\t194890\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tShrawasti\tRIZWAN ZAHEER\t101817\tno\tPECP\tPeace Party\nUttar Pradesh\tShrawasti\tPRAKSH CHANDRA MISHRA\t4489\tno\tJSK\tJan Sevak Party\nUttar Pradesh\tShrawasti\tSAXEN\t3188\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tShrawasti\tTEJ BAHADUR\t3474\tno\tIND\tIndependent\nUttar Pradesh\tShrawasti\tVIJAY KUMAR\t5542\tno\tIND\tIndependent\nUttar Pradesh\tShrawasti\tARUN KUMAR\t10083\tno\tIND\tIndependent\nUttar Pradesh\tGonda\tKIRTI VARDHAN SINGH\t359643\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tGonda\tTILAK RAM\t5374\tno\tIND\tIndependent\nUttar Pradesh\tGonda\tMAHESH KUMAR\t2555\tno\tJPS\tJanvadi Party(Socialist)\nUttar Pradesh\tGonda\tAJAY  KUMAR  PRABHAKAR\t3200\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tGonda\tRAJESH  KUMAR  MAURYA\t2199\tno\tSP(I)\tSocialist Party (India)\nUttar Pradesh\tGonda\tSATYA PRAKASH\t4748\tno\tSHS\tShivsena\nUttar Pradesh\tGonda\tMASOOD ALAM KHAN\t42744\tno\tPECP\tPeace Party\nUttar Pradesh\tGonda\tAKBAR AHMAD DUMPY\t116178\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tGonda\tNANDITA SHUKLA\t199227\tno\tSP\tSamajwadi Party\nUttar Pradesh\tGonda\tOM PRAKASH\t18450\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tGonda\tPREM KUMAR\t1900\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tGonda\tMASHOOQ AHMAD\t3358\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGonda\tRAJENDRA PRASAD\t1519\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tGonda\tBENI PRASAD VERMA\t102254\tno\tINC\tIndian National Congress\nUttar Pradesh\tGonda\tBRIJNANDAN TIWARI\t2395\tno\tIND\tIndependent\nUttar Pradesh\tGonda\tNone of the Above\t7988\tno\tNOTA\tNone of the Above\nUttar Pradesh\tDomariyaganj\tJAGDAMBIKA PAL\t298845\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tDomariyaganj\tNone of the Above\t6775\tno\tNOTA\tNone of the Above\nUttar Pradesh\tDomariyaganj\tRAM UGRAH\t7762\tno\tIND\tIndependent\nUttar Pradesh\tDomariyaganj\tBADRE ALAM\t3174\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tDomariyaganj\tKALI RAM\t4939\tno\tIND\tIndependent\nUttar Pradesh\tDomariyaganj\tRAVI KUMAR\t2535\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tDomariyaganj\tPINGLE PRASAD\t5521\tno\tS(J)\tSwaraj (J)\nUttar Pradesh\tDomariyaganj\tMANOJ KUMAR\t2709\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tDomariyaganj\tVASUNDHARA\t88117\tno\tINC\tIndian National Congress\nUttar Pradesh\tDomariyaganj\tHAMIDULLAH CHAUDHARY\t7159\tno\tNCP\tNationalist Congress Party\nUttar Pradesh\tDomariyaganj\tMUHAMMAD MUQEEM\t195257\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tDomariyaganj\tSANTRAM\t3238\tno\tBRABSVP\tBharatiya Rashtriya Bahujan Samaj Vikas Party\nUttar Pradesh\tDomariyaganj\tMUKESH NARAYAN SHUKLA URF GYANESH NARAYAN SHUKLA\t10952\tno\tIND\tIndependent\nUttar Pradesh\tDomariyaganj\tARJUN SINGH\t9529\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tDomariyaganj\tDR. MOHD. AYUB\t99242\tno\tPECP\tPeace Party\nUttar Pradesh\tDomariyaganj\tDR. SATYENDRA  NATH PANDEY\t9372\tno\tAAMJP\tAam Janata Party\nUttar Pradesh\tDomariyaganj\tMATA PRASAD PANDEY\t174778\tno\tSP\tSamajwadi Party\nUttar Pradesh\tDomariyaganj\tDIWAKAR PRASAD\t5123\tno\tIND\tIndependent\nUttar Pradesh\tBasti\tHARISH CHANDRA  ALIAS  HARISH DWIVEDI\t357680\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBasti\tSITA RAM\t2240\tno\tJPS\tJanvadi Party(Socialist)\nUttar Pradesh\tBasti\tRAM SHANKAR GUPTA\t4521\tno\tIND\tIndependent\nUttar Pradesh\tBasti\tAMBIKA SINGH\t27673\tno\tINC\tIndian National Congress\nUttar Pradesh\tBasti\tBHAGWANDAS\t3532\tno\tBSP(K)\tBahujan Sangharsh Party (Kanshiram)\nUttar Pradesh\tBasti\tRAM PRASAD CHAUDHARY\t283747\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBasti\tPURUSHOTTAM PANDEY\t8172\tno\tPECP\tPeace Party\nUttar Pradesh\tBasti\tARUN KUMAR\t3587\tno\tIND\tIndependent\nUttar Pradesh\tBasti\tRAM KARAN ALIAS R.K. GAUTAM\t2803\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tBasti\tKRISHNA  KUMAR UPADHYAY ADVOCATE\t4823\tno\tIND\tIndependent\nUttar Pradesh\tBasti\tSHIVPOOJAN RAJBHAR\t7063\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tBasti\tANAND RAJPAL\t8407\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBasti\t\"BRIJ KISHOR SINGH \"\"DIMPAL\"\"\"\t324118\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBasti\tNone of the Above\t10168\tno\tNOTA\tNone of the Above\nUttar Pradesh\tSant Kabir Nagar\tSHARAD TRIPATHI\t348892\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tSant Kabir Nagar\tNone of the Above\t4747\tno\tNOTA\tNone of the Above\nUttar Pradesh\tSant Kabir Nagar\tSANT SIROMANI BABOOLAL YADAV MAHARAJ JI\t3900\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tSant Kabir Nagar\tAMIT\t2796\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tBHISM SHANKAR ALIAS KUSHAL TIWARI\t250914\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tSant Kabir Nagar\tSHAYAM LAL\t1477\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tSHABBEER HUSEN\t8993\tno\tjhspt\tJai Hind Samaj Party\nUttar Pradesh\tSant Kabir Nagar\tROHIT KUMAR PANDEY\t22029\tno\tINC\tIndian National Congress\nUttar Pradesh\tSant Kabir Nagar\tRAJARAM\t69193\tno\tPECP\tPeace Party\nUttar Pradesh\tSant Kabir Nagar\tRAJMANI\t5036\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tSant Kabir Nagar\tJYOTI SINGH\t1884\tno\tBSP(K)\tBahujan Sangharsh Party (Kanshiram)\nUttar Pradesh\tSant Kabir Nagar\tAFTAB ALAM KHAN\t7458\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tSant Kabir Nagar\tJANTRI LAL CHAUHAN\t2439\tno\tJPS\tJanvadi Party(Socialist)\nUttar Pradesh\tSant Kabir Nagar\tLOTAN URF LAUTAN PRASAD\t3618\tno\tSSD\tShoshit Samaj Dal\nUttar Pradesh\tSant Kabir Nagar\tKAILASH NATH BELDAR\t3181\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tBHAL CHANDRA YADAV\t240169\tno\tSP\tSamajwadi Party\nUttar Pradesh\tSant Kabir Nagar\tKRIPANARAYAN RAI\t1215\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tJAIYANTRI PRASAD\t1335\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tMOHAMMAD ALI\t4082\tno\tPPI\tParcham Party of India\nUttar Pradesh\tSant Kabir Nagar\tMOHAMMAD IRFAN\t2164\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tAPARNA\t11046\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tRAVIKANT\t1721\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tSant Kabir Nagar\tSURENDRA KUMAR CHAUDHARY\t5866\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tRAJENDRA KUMAR\t2278\tno\tRJPK\tRashtriya Janwadi Party (Krantikari)\nUttar Pradesh\tSant Kabir Nagar\tSUBODH CHANDRA\t3061\tno\tIND\tIndependent\nUttar Pradesh\tSant Kabir Nagar\tPREM PRAKASH\t2155\tno\tIPP\tIndian Peace Party\nUttar Pradesh\tMaharajganj\tPANKAJ\t471542\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMaharajganj\tARAT KHARVAR\t3752\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMaharajganj\tMANGAL LAL\t1491\tno\tLD\tLok Dal\nUttar Pradesh\tMaharajganj\tRAKESH KUMAR GIRI\t2545\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tSHREE AKHILESH KUMAR\t5422\tno\tADUP\tApna Dal United Party\nUttar Pradesh\tMaharajganj\tAKHILESH\t213974\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMaharajganj\tKAVILAS\t5195\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tMaharajganj\tMUMMATAJ\t3582\tno\tPECP\tPeace Party\nUttar Pradesh\tMaharajganj\tKASHI NATH SHUKLA\t231084\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMaharajganj\tANIL KUMAR SHAHANI\t15517\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tHARSH VARDHAN\t57193\tno\tINC\tIndian National Congress\nUttar Pradesh\tMaharajganj\tNone of the Above\t4545\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMaharajganj\tAJAYDHAR\t4079\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tTEJ PRATAP\t6131\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tSHIVCHARAN\t3455\tno\tjhspt\tJai Hind Samaj Party\nUttar Pradesh\tMaharajganj\tRAKESH KUMAR GUPTA\t1828\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tRADHEYSHYAM\t1961\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tMaharajganj\tVIMLESH TRIPATHI\t2339\tno\tNAP\tNaitik Party\nUttar Pradesh\tMaharajganj\tDAYARAM NISHAD\t4210\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tDINANATH\t2149\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tDINESH KUMAR\t1671\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tRAMSAMUJH\t5001\tno\tS(J)\tSwaraj (J)\nUttar Pradesh\tMaharajganj\tGOVIND DAS\t4216\tno\tIND\tIndependent\nUttar Pradesh\tMaharajganj\tPASHUPATI NATH GUPTA\t3196\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGorakhpur\tADITYANATH\t539127\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tGorakhpur\tACHHE LAL GUPTA\t5339\tno\tIND\tIndependent\nUttar Pradesh\tGorakhpur\tASTBHUJA PRASAD TRIPATHI\t45719\tno\tINC\tIndian National Congress\nUttar Pradesh\tGorakhpur\tFAIZ AHAMAD FAIZI\t5046\tno\tPECP\tPeace Party\nUttar Pradesh\tGorakhpur\tINDRA DEO YADAVA\t2398\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tGorakhpur\tRAMKISHUN\t4387\tno\tIND\tIndependent\nUttar Pradesh\tGorakhpur\tMOHD. WASEEM KHAN\t2016\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tGorakhpur\tSUBASH KUMAR MAURYA\t2123\tno\tRJPK\tRashtriya Janwadi Party (Krantikari)\nUttar Pradesh\tGorakhpur\tRAJIT MISHRA\t1171\tno\tNAP\tNaitik Party\nUttar Pradesh\tGorakhpur\tASHOK\t4194\tno\tIND\tIndependent\nUttar Pradesh\tGorakhpur\tNone of the Above\t8153\tno\tNOTA\tNone of the Above\nUttar Pradesh\tGorakhpur\tRAJMATI NISHAD\t226344\tno\tSP\tSamajwadi Party\nUttar Pradesh\tGorakhpur\tRADHE MOHAN MISRA\t11873\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGorakhpur\tRAM BHUAL NISHAD\t176412\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tGorakhpur\tMUNNA\t4024\tno\tSWPI\tSwarajya Party Of India\nUttar Pradesh\tGorakhpur\tRAJESH\t1873\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tKushi Nagar\tRAJESH PANDEY URF GUDDU\t370051\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tKushi Nagar\tMARKANDE\t4270\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tKushi Nagar\tAKHAND\t3802\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tKushi Nagar\tRADHE SHYAM SINGH\t111256\tno\tSP\tSamajwadi Party\nUttar Pradesh\tKushi Nagar\tKASHEEM ALI\t9024\tno\tPECP\tPeace Party\nUttar Pradesh\tKushi Nagar\tAMEERUDDIN\t3843\tno\tADUP\tApna Dal United Party\nUttar Pradesh\tKushi Nagar\tNone of the Above\t10102\tno\tNOTA\tNone of the Above\nUttar Pradesh\tKushi Nagar\tJAHID\t2677\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tKushi Nagar\tRAMDHANEE\t5485\tno\tDNP\tDeshbhakt Nirman Party\nUttar Pradesh\tKushi Nagar\tDR. SANGAM MISHRA\t132881\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tKushi Nagar\tRAMPRATAP\t4805\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tKushi Nagar\tBACHCHOOLAAL\t2145\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tKushi Nagar\tARVIND\t3630\tno\tS(J)\tSwaraj (J)\nUttar Pradesh\tKushi Nagar\tKUNWAR RATANJIT PRATAP NARAIN SINGH\t284511\tno\tINC\tIndian National Congress\nUttar Pradesh\tKushi Nagar\tGOBARDHAN PRASAD\t1963\tno\tSP(I)\tSocialist Party (India)\nUttar Pradesh\tDeoria\tKALRAJ MISHRA\t496500\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tDeoria\tNIYAJ AHMAD\t231114\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tDeoria\tSHYAM SUNDAR\t5130\tno\tPECP\tPeace Party\nUttar Pradesh\tDeoria\tUMESH\t4343\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tDeoria\tBALESHWAR YADAV\t150852\tno\tSP\tSamajwadi Party\nUttar Pradesh\tDeoria\tRAJU\t5406\tno\tMOP\tModerate Party\nUttar Pradesh\tDeoria\tRAM PRAVESH\t6408\tno\tBED\tBharatiya Ekta Dal\nUttar Pradesh\tDeoria\tSABHA KUNWAR\t37752\tno\tINC\tIndian National Congress\nUttar Pradesh\tDeoria\tAWADH KISHOR NIGAM\t1153\tno\tARVP\tAdarsh Rashtriya Vikas Party\nUttar Pradesh\tDeoria\tRAM PREET\t1906\tno\tMBCOI\tMost Backward Classes Of India\nUttar Pradesh\tDeoria\tINTZARUL\t1833\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tDeoria\tNone of the Above\t12405\tno\tNOTA\tNone of the Above\nUttar Pradesh\tDeoria\tVED PRAKASH MALL\t1916\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tDeoria\tHEMANT KISHOR SRIVASTAVA\t5763\tno\tIND\tIndependent\nUttar Pradesh\tDeoria\tBARKHA KINNAR\t4124\tno\tIND\tIndependent\nUttar Pradesh\tDeoria\tAROON KUMAR TRIPATHI\t4952\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBansgaon\tKAMLESH PASWAN\t417959\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBansgaon\tKUNJAWATI\t4346\tno\tIND\tIndependent\nUttar Pradesh\tBansgaon\tNone of the Above\t13495\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBansgaon\tSADAL PRASAD\t228443\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBansgaon\tCHANDRIKA PRASAD BHARATI\t1327\tno\tRJPK\tRashtriya Janwadi Party (Krantikari)\nUttar Pradesh\tBansgaon\tARJUN PRASAD RAW\t2896\tno\tBaSaP\tBhartiya  Sarvjan Party\nUttar Pradesh\tBansgaon\tRAMPRIT JAKHMEE\t3080\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tBansgaon\tSANJAI KUMAR\t50675\tno\tINC\tIndian National Congress\nUttar Pradesh\tBansgaon\tLALCHAND PRASAD JATAV\t9769\tno\tASP\tAmbedkar Samaj Party\nUttar Pradesh\tBansgaon\tRAMNARESH\t2963\tno\tISSP\tIndian Savarn Samaj Party\nUttar Pradesh\tBansgaon\tGORAKH PRASAD PASWAN\t133675\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBansgaon\tSHEO SHANKAR\t4013\tno\tIND\tIndependent\nUttar Pradesh\tBansgaon\tRAMNIVAS  PASWAN\t5236\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tLalganj\tNEELAM SONKAR\t324016\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tLalganj\tJIYA LAL\t5668\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tLalganj\tLAL BAHADUR\t1911\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tLalganj\tBECHAI SAROJ\t260930\tno\tSP\tSamajwadi Party\nUttar Pradesh\tLalganj\tHARIPRASAD SONKAR\t10523\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tLalganj\tBALIHARI BABU\t21832\tno\tINC\tIndian National Congress\nUttar Pradesh\tLalganj\tSUKHANAYAN\t1398\tno\tJSEP\tJan Shakti Ekta Party\nUttar Pradesh\tLalganj\tDINESH KUMAR\t1733\tno\tAa S P\tAsankhya Samaj Party\nUttar Pradesh\tLalganj\tACHCHHELAL\t4536\tno\tIND\tIndependent\nUttar Pradesh\tLalganj\tAVADHESH\t2129\tno\tIND\tIndependent\nUttar Pradesh\tLalganj\tPRADEEP KUMAR\t14368\tno\tRUC\tRashtriya Ulama Council\nUttar Pradesh\tLalganj\tNone of the Above\t7713\tno\tNOTA\tNone of the Above\nUttar Pradesh\tLalganj\tSHRIPATI\t4620\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tLalganj\tJANGBAHADUR\t3307\tno\tIND\tIndependent\nUttar Pradesh\tLalganj\tDR. BALIRAM\t233971\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAzamgarh\tMULAYAM SINGH YADAV\t340306\tyes\tSP\tSamajwadi Party\nUttar Pradesh\tAzamgarh\tSHAMSHAD AHMAD\t4875\tno\tIND\tIndependent\nUttar Pradesh\tAzamgarh\tSHAH ALAM ALIAS GUDDU JAMALI\t266528\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tAzamgarh\tNone of the Above\t5660\tno\tNOTA\tNone of the Above\nUttar Pradesh\tAzamgarh\tM. AMIR RASHADI\t13271\tno\tRUC\tRashtriya Ulama Council\nUttar Pradesh\tAzamgarh\tPRAMOD SINGH\t3752\tno\tIND\tIndependent\nUttar Pradesh\tAzamgarh\tRAJDHARI RAJBHAR\t1695\tno\tMOSP\tMoolniwasi Samaj Party\nUttar Pradesh\tAzamgarh\tMADAN\t5207\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tAzamgarh\tSANJAY KUMAR CHATURVEDI\t4051\tno\tISSP\tIndian Savarn Samaj Party\nUttar Pradesh\tAzamgarh\tDURG VIJAY YADAV\t1566\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tAzamgarh\tARVIND KUMAR JAISWAL\t17950\tno\tINC\tIndian National Congress\nUttar Pradesh\tAzamgarh\tKIRAN SHARAMA\t1298\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tAzamgarh\tSANJAY CHAUHAN\t3517\tno\tIND\tIndependent\nUttar Pradesh\tAzamgarh\tSANTOSH KUMAR GAUTAM\t3866\tno\tMwSP\tManavtawadi Samaj Party\nUttar Pradesh\tAzamgarh\tRAM SINGH CHAUHAN\t2507\tno\tIND\tIndependent\nUttar Pradesh\tAzamgarh\tPRAKASH CHANDRA\t2103\tno\tssrd\tSanatan Sanskriti Raksha Dal\nUttar Pradesh\tAzamgarh\tRAMAKANT YADAV\t277102\tno\tBJP\tBharatiya Janata Party\nUttar Pradesh\tAzamgarh\tSUGRIV CHAUHAN\t1882\tno\tIND\tIndependent\nUttar Pradesh\tAzamgarh\tRAJESH YADAV\t3082\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGhosi\tHARINARAYAN RAJBHAR\t379797\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tGhosi\tHARINATH\t1786\tno\tNAP\tNaitik Party\nUttar Pradesh\tGhosi\tRASHTRA KUNWAR SINGH\t19315\tno\tINC\tIndian National Congress\nUttar Pradesh\tGhosi\tHARISH CHANDRA\t4130\tno\tRJPK\tRashtriya Janwadi Party (Krantikari)\nUttar Pradesh\tGhosi\tVISHUN DAYAL\t2721\tno\tIND\tIndependent\nUttar Pradesh\tGhosi\tRAJKUMAR CHAUHAN\t2246\tno\tJPS\tJanvadi Party(Socialist)\nUttar Pradesh\tGhosi\tNone of the Above\t4457\tno\tNOTA\tNone of the Above\nUttar Pradesh\tGhosi\tBADRI NATH YADAV\t7650\tno\tRaCP\tRashtriya Congress(J) Party\nUttar Pradesh\tGhosi\tHEMANT KUMAR\t2949\tno\tBaSaP\tBhartiya  Sarvjan Party\nUttar Pradesh\tGhosi\tATUL KUMAR SINGH ANJAAN\t18162\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tGhosi\tVIJAY KUMAR UPADHYAY\t3826\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nUttar Pradesh\tGhosi\tDOODHNATH CHAUHAN\t8163\tno\tIND\tIndependent\nUttar Pradesh\tGhosi\tMOKHTAR ANSARI\t166443\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tGhosi\tMANISH KUMAR RAI\t4188\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGhosi\tDARA SINGH CHAUHAN\t233782\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tGhosi\tRAJEEV KUMAR RAI\t165887\tno\tSP\tSamajwadi Party\nUttar Pradesh\tGhosi\tSURYA KUMAR\t4030\tno\tIND\tIndependent\nUttar Pradesh\tGhosi\tRAVI SHANKAR BHARAT\t2543\tno\tJaRaP\tJan Raajya Party\nUttar Pradesh\tGhosi\tDR. SHAMEEM ABIDI\t7581\tno\tPECP\tPeace Party\nUttar Pradesh\tSalempur\tRAVINDRA KUSHAWAHA\t392213\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tSalempur\tSUSHANT RAJ PATHAK\t3631\tno\tIND\tIndependent\nUttar Pradesh\tSalempur\tOM PRAKASH RAJBHAR\t66084\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tSalempur\tHARIBANSH SAHAI KUSHWAHA\t159688\tno\tSP\tSamajwadi Party\nUttar Pradesh\tSalempur\tRAM KRIPAL\t4377\tno\tIND\tIndependent\nUttar Pradesh\tSalempur\tDR.BHOLA PANDEY\t41890\tno\tINC\tIndian National Congress\nUttar Pradesh\tSalempur\t\"RAVI SHANKER SINGH\"\"PAPPU\"\"\"\t159871\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tSalempur\tSHRIRAM CHAUDHARY\t3572\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tSalempur\tDR SANJAY KR. SINGH\t1675\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tSalempur\tONKAR SINGH\t4947\tno\tSaSaP\tSamajwadi Samaj Party\nUttar Pradesh\tSalempur\tSIYARAM DAS\t2709\tno\tIND\tIndependent\nUttar Pradesh\tSalempur\tNone of the Above\t8322\tno\tNOTA\tNone of the Above\nUttar Pradesh\tSalempur\tFIROZ AKHTAR\t1507\tno\tRUC\tRashtriya Ulama Council\nUttar Pradesh\tSalempur\tCHANDRA PRATAP SINGH\t4263\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBallia\tBHARAT SINGH\t359758\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBallia\tSUCHIT GOND\t2506\tno\tGGP\tGondvana Gantantra Party\nUttar Pradesh\tBallia\tCOL(RETD) BHARAT SINGH SHAURYA CHAKRA\t3297\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tBallia\tMANNU YADAV\t980\tno\tNAP\tNaitik Party\nUttar Pradesh\tBallia\tAVADHESH VERMA\t2529\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tBallia\tNEERAJ SHEKHAR\t220324\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBallia\tVIRENDRA KUMAR PATHAK\t141684\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBallia\tSUDHA RAI\t13501\tno\tINC\tIndian National Congress\nUttar Pradesh\tBallia\tRAMROOP\t6435\tno\tjhspt\tJai Hind Samaj Party\nUttar Pradesh\tBallia\tNone of the Above\t6670\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBallia\tRAMJI\t1096\tno\tBSD\tBharatiya Samaj Dal\nUttar Pradesh\tBallia\tMANISH\t6166\tno\tIND\tIndependent\nUttar Pradesh\tBallia\tSHESHNATH\t3674\tno\tIND\tIndependent\nUttar Pradesh\tBallia\tMANOJ\t6833\tno\tIND\tIndependent\nUttar Pradesh\tBallia\tAFZAL ANSARI\t163943\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tBallia\tBHOLA NATH\t2815\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tKRISHNA PRATAP 'K.P.'\t367149\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tJaunpur\tSUBHASH PANDEY\t220839\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tJaunpur\tRAVISHANKAR MAURYA\t2608\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nUttar Pradesh\tJaunpur\tDR. K. P. YADAV\t43471\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tJaunpur\tGULAB CHAND DUBEY\t1751\tno\tSHS\tShivsena\nUttar Pradesh\tJaunpur\tJOGENDRA PRASAD\t7281\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tPRAMOD KUMAR ALIAS PRAMOD BHAI PATEL\t7206\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tJaunpur\tDR. SANJIVAN BIND\t1863\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tVIMAL KUMAR YADAV\t2109\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tYOGISH CHANDRA DUBEY\t2773\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tRAJESH PRAJAPATI\t2329\tno\tSaDa\tSarvshreshth Dal\nUttar Pradesh\tJaunpur\tANIL KUMAR SINGH\t2694\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tNone of the Above\t2595\tno\tNOTA\tNone of the Above\nUttar Pradesh\tJaunpur\tARVIND\t2204\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tJaunpur\tRAVI KISHAN\t42759\tno\tINC\tIndian National Congress\nUttar Pradesh\tJaunpur\tDHANANJAY SINGH\t64137\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tPREMCHAND BIND\t6814\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tJaunpur\tANUPATI RAM YADAV\t4026\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tJaunpur\tRAVIKANT YADAV\t20832\tno\tIND\tIndependent\nUttar Pradesh\tJaunpur\tSARFARAJ\t2064\tno\tPECP\tPeace Party\nUttar Pradesh\tJaunpur\tPARASNATH YADAV\t180003\tno\tSP\tSamajwadi Party\nUttar Pradesh\tJaunpur\tSHAHABUDDIN\t19636\tno\tRUC\tRashtriya Ulama Council\nUttar Pradesh\tMachhlishahr\tRAM CHARITRA NISHAD\t438210\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tMachhlishahr\tSHRAVAN KUMAR PASWAN\t7833\tno\tIND\tIndependent\nUttar Pradesh\tMachhlishahr\tLALJI\t1543\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tMachhlishahr\tSUBAS CHANDRA\t18777\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tMachhlishahr\tHARINATH\t5279\tno\tIND\tIndependent\nUttar Pradesh\tMachhlishahr\tSURESH GAUTAM\t1467\tno\tARVP\tAdarsh Rashtriya Vikas Party\nUttar Pradesh\tMachhlishahr\tSIKANDAR\t2114\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMachhlishahr\tHARI\t2669\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tMachhlishahr\tTUFANI NISHAD\t36275\tno\tINC\tIndian National Congress\nUttar Pradesh\tMachhlishahr\tNone of the Above\t8448\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMachhlishahr\tOMPRAKASH ALIAS AMBEDKAR\t2708\tno\tIND\tIndependent\nUttar Pradesh\tMachhlishahr\tRAJESH\t1552\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tMachhlishahr\tRAJESH SONKAR\t4362\tno\tIND\tIndependent\nUttar Pradesh\tMachhlishahr\tTUFANI\t191387\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMachhlishahr\tBHOLANATH ALIAS B.P. SAROJ\t266055\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMachhlishahr\tRAJ ALIAS VIRENDRA KUMAR SONKAR\t9223\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGhazipur\tMANOJ SINHA\t306929\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tGhazipur\tSATYA DEV YADAV\t6381\tno\tNAP\tNaitik Party\nUttar Pradesh\tGhazipur\tBRIJ BHUSHAN\t6925\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tGhazipur\tRAJENDRA NATH RAM\t3468\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tGhazipur\tKAILASH NATH SINGH YADAV\t241645\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tGhazipur\tKUBER\t4889\tno\tIND\tIndependent\nUttar Pradesh\tGhazipur\tARUN KUMAR SINGH\t34093\tno\tIND\tIndependent\nUttar Pradesh\tGhazipur\tDEENDAYAL\t2122\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tGhazipur\tLAKSHAMAN\t1588\tno\tIND\tIndependent\nUttar Pradesh\tGhazipur\tSMT SHIVKANYA KUSHWAHA\t274477\tno\tSP\tSamajwadi Party\nUttar Pradesh\tGhazipur\tISHWARI\t6512\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tGhazipur\tBHARAT\t2484\tno\tJRPa\tJanta Raj Party\nUttar Pradesh\tGhazipur\tDHARM YADAV ALIAS D.P.YADAV\t59510\tno\tRPD\tRashtriya Parivartan Dal\nUttar Pradesh\tGhazipur\tISHWAR DEV\t4019\tno\tARVP\tAdarsh Rashtriya Vikas Party\nUttar Pradesh\tGhazipur\tGUPTESHWAR\t1111\tno\tLD\tLok Dal\nUttar Pradesh\tGhazipur\tNone of the Above\t5611\tno\tNOTA\tNone of the Above\nUttar Pradesh\tGhazipur\tOMKAR CHAUHAN\t2416\tno\tJPS\tJanvadi Party(Socialist)\nUttar Pradesh\tGhazipur\tPAPPU\t3585\tno\tEKSP\tEklavya Samaj Party\nUttar Pradesh\tGhazipur\tMO. MAKSUD KHAN\t18908\tno\tINC\tIndian National Congress\nUttar Pradesh\tChandauli\tDR MAHENDRA NATH PANDEY\t414135\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tChandauli\tSANJAY\t2148\tno\tIND\tIndependent\nUttar Pradesh\tChandauli\tRAMASHANKER SINGH MAURYA\t1423\tno\tBSKD\tBhartiya Samajik Kranti Dal\nUttar Pradesh\tChandauli\tRAJA JAISWAL\t6170\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tChandauli\tCHANCHAL\t1362\tno\tJRPa\tJanta Raj Party\nUttar Pradesh\tChandauli\tGULAVAS\t1482\tno\tIND\tIndependent\nUttar Pradesh\tChandauli\tRAJENDRA\t7767\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tChandauli\tIRSHAD\t15598\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tChandauli\tSHASHI SINHA\t2988\tno\tESKP\tEx-Sainik Kissan Party\nUttar Pradesh\tChandauli\tBINDU\t2769\tno\tIND\tIndependent\nUttar Pradesh\tChandauli\tDR. HARIDWAR SINGH YADAV\t8756\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tChandauli\tANIL KUMAR MAURYA\t257379\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tChandauli\tNone of the Above\t3186\tno\tNOTA\tNone of the Above\nUttar Pradesh\tChandauli\tJITENDRA\t2502\tno\tGGP\tGondvana Gantantra Party\nUttar Pradesh\tChandauli\tSANJEEV SINGH\t4993\tno\tSBSP\tSuheldev Bhartiya Samaj Party\nUttar Pradesh\tChandauli\tTARUN PATEL URF TARUNENDRA CHAND PATEL\t27194\tno\tINC\tIndian National Congress\nUttar Pradesh\tChandauli\tRAM DULAR\t2386\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tChandauli\tVINAI KUMAR SINGH\t3882\tno\tINL\tIndian National League\nUttar Pradesh\tChandauli\tRAJNARAYAN\t4221\tno\tIND\tIndependent\nUttar Pradesh\tChandauli\tHEERALAL\t4768\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tChandauli\tRAMKISHUN\t204145\tno\tSP\tSamajwadi Party\nUttar Pradesh\tChandauli\tMRITUNJAYA PANDEY\t1318\tno\tJaRaPa\tJan Raksha Party\nUttar Pradesh\tVaranasi\tNARENDRA MODI\t581022\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tVaranasi\tVIJAY PRAKASH JAISAWAL\t60579\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tVaranasi\tMANOJ KUMAR CHAUBEY\t1074\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tDEVI PRASAD NAND\t1740\tno\tMADP\tMoulik Adhikar Party\nUttar Pradesh\tVaranasi\tHIRALAL YADAV\t2457\tno\tCPM\tCommunist Party of India  (Marxist)\nUttar Pradesh\tVaranasi\tA.K. AGGARWAL\t2037\tno\tAGRJP\tAgar Jan Party\nUttar Pradesh\tVaranasi\tKAILASH CHAURASIYA\t45291\tno\tSP\tSamajwadi Party\nUttar Pradesh\tVaranasi\tSATYAPRAKASH SRIVASTAVA\t599\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tLALLAN\t997\tno\tMaJP\tManavadhikar Janshakti Party\nUttar Pradesh\tVaranasi\tNARENDRA NATH DUBEY ADIG\t2277\tno\tJSEP\tJan Shakti Ekta Party\nUttar Pradesh\tVaranasi\tPRABHAT KUMAR\t1089\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tBASEER KINNAR\t2850\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tINDIRA TIWARI\t2674\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tVaranasi\tRAM LAKHAN GUPTA\t1376\tno\tJAP\tJantantra Party\nUttar Pradesh\tVaranasi\tAJAY RAI\t75614\tno\tINC\tIndian National Congress\nUttar Pradesh\tVaranasi\tSHIVHARI AGARWAL\t2000\tno\tBhNP\tBharat Nirman Party\nUttar Pradesh\tVaranasi\tRAMPYARE SINGH\t423\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tPRAMOD KUMAR\t1807\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tJOHNSON THOMAS\t1524\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tBACHCHELAL\t1172\tno\tLPSP\tLokpriya Samaj Party\nUttar Pradesh\tVaranasi\tIFTEKHAR KURAISHI\t2323\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tSHIV KUMAR SHAH\t312\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tNone of the Above\t2051\tno\tNOTA\tNone of the Above\nUttar Pradesh\tVaranasi\tNARENDRA BAHADUR SINGH\t1306\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tAHMED SOHEL SIDDIQUI\t937\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tHARI LAL\t838\tno\tRaIP\tRastriya Insaaf Party\nUttar Pradesh\tVaranasi\tMAHENDRA MISHRA\t573\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tRAJESH BHARTI SURYA\t4327\tno\tRaAD\tRashtriya Ambedkar Dal\nUttar Pradesh\tVaranasi\tSARVESH KUMAR GUPTA\t3023\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tSATISH SHANKAR JAYASWAL\t459\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tRAJENDRA PRASAD (GARIB DAS)\t1367\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tGHANSHYAM\t1151\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tPRAKASH PRASAD\t1361\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tABHIMANYU SINGH PATEL\t1644\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tVaranasi\tARUN\t3634\tno\tBSCP\tBhartiya Shakti Chetna Party\nUttar Pradesh\tVaranasi\tBACHCHAN PRASAD YADAV\t1192\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tHEMANT KUMAR YADAV\t1202\tno\tINL\tIndian National League\nUttar Pradesh\tVaranasi\tRAJIV KUMAR MISHRA\t677\tno\tIND\tIndependent\nUttar Pradesh\tVaranasi\tSANTOSH KUMAR\t1434\tno\tRVMP\tRashtriya Vikas Manch Party\nUttar Pradesh\tVaranasi\tOM GURU CHARAN DAS ALIAS VIMAL KUMAR SINGH\t1144\tno\tssrd\tSanatan Sanskriti Raksha Dal\nUttar Pradesh\tVaranasi\tARVIND KEJRIWAL\t209238\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tVaranasi\tUSMAN\t878\tno\tRsAD\tRashtriya Apna Dal\nUttar Pradesh\tVaranasi\tRAVINDRA KUMAR\t1012\tno\tIND\tIndependent\nUttar Pradesh\tBhadohi\tVIRENDRA SINGH\t403544\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tBhadohi\tNone of the Above\t8966\tno\tNOTA\tNone of the Above\nUttar Pradesh\tBhadohi\tRAKESH DHAR TRIPATHI\t245505\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tBhadohi\tJEEVAN KUMAR MALLAH\t4164\tno\tIND\tIndependent\nUttar Pradesh\tBhadohi\tSEEMA MISHRA\t238615\tno\tSP\tSamajwadi Party\nUttar Pradesh\tBhadohi\tDR. AKHILESH KUMAR DWIVEDI\t7730\tno\tNCP\tNationalist Congress Party\nUttar Pradesh\tBhadohi\tSARTAJ IMAM\t22574\tno\tINC\tIndian National Congress\nUttar Pradesh\tBhadohi\tTEJ BAHADUR YADAV ADVOCATE\t26995\tno\tJD(U)\tJanata Dal  (United)\nUttar Pradesh\tBhadohi\tAJAY KUMAR MAURYA\t5807\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tBhadohi\tJAGJEEVAN RAM\t1963\tno\tIND\tIndependent\nUttar Pradesh\tBhadohi\tCHANDRASHEKHAR\t2191\tno\tIND\tIndependent\nUttar Pradesh\tBhadohi\tRAJESH KUMAR ALIAS KHANNA MAURYA\t1919\tno\tAMP\tAdarsh Manavtawadi Party\nUttar Pradesh\tBhadohi\tSANTRAJ SINGH\t4823\tno\tIND\tIndependent\nUttar Pradesh\tBhadohi\tRAM SAGAR BIND\t2443\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tBhadohi\tRAM SAKHA TRIPATHI\t4313\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tANUPRIYA SINGH PATEL\t436536\tyes\tAD\tApna Dal\nUttar Pradesh\tMirzapur\tDR. KARNAIL SINGH\t3627\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tBHAGWAN DAS\t3324\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tMirzapur\tSURENDRA SINGH PATEL\t108859\tno\tSP\tSamajwadi Party\nUttar Pradesh\tMirzapur\tLALITESH PATI TRIPATHI\t152666\tno\tINC\tIndian National Congress\nUttar Pradesh\tMirzapur\tJEERA BHARTI\t4148\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttar Pradesh\tMirzapur\tVINEET SAHAI\t4548\tno\tAITC\tAll India Trinamool Congress\nUttar Pradesh\tMirzapur\tNone of the Above\t4539\tno\tNOTA\tNone of the Above\nUttar Pradesh\tMirzapur\tGIRJA SHANKAR URF CHUNMUN\t2363\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tBRIJESH KUMAR RAI\t4318\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tMirzapur\tRADHESHYAM INSAN\t2138\tno\tBRPI\tBhartiya Republican Party (Insan)\nUttar Pradesh\tMirzapur\tAJAD\t6762\tno\tGGP\tGondvana Gantantra Party\nUttar Pradesh\tMirzapur\tRAJA RAM\t2010\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tSANTOSH KUMAR\t6811\tno\tSPP\tSamyak Parivartan Party\nUttar Pradesh\tMirzapur\tSARVESH DUBEY\t4304\tno\tRPI(A)\tRepublican Party of India (A)\nUttar Pradesh\tMirzapur\tDEVI\t4639\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tRAM MURAT\t7947\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tSHIVRAJ SAHNI\t4405\tno\tPMSP\tPragatisheel Manav Samaj Party\nUttar Pradesh\tMirzapur\tHINCHH LAL\t2685\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tSUKCHARA NAND URF AALOO BABA\t4787\tno\tSHS\tShivsena\nUttar Pradesh\tMirzapur\tSAMUDRA BIND\t217457\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tMirzapur\tJITTOO RAM RATNA\t4209\tno\tLD\tLok Dal\nUttar Pradesh\tMirzapur\tASHOK KUMAR\t11245\tno\tIND\tIndependent\nUttar Pradesh\tMirzapur\tADESH TYAGI\t3300\tno\tIND\tIndependent\nUttar Pradesh\tRobertsganj\tCHHOTELAL\t378211\tyes\tBJP\tBharatiya Janata Party\nUttar Pradesh\tRobertsganj\tRAMBRIKSHA\t9408\tno\tIND\tIndependent\nUttar Pradesh\tRobertsganj\tASHOK KUMAR KANNAOUJIYA\t24363\tno\tCPI\tCommunist Party of India\nUttar Pradesh\tRobertsganj\tLAWRENCE ANTHONY\t7703\tno\tIND\tIndependent\nUttar Pradesh\tRobertsganj\tGUN DEVI ( GYANI DEVI)\t5307\tno\tQED\tQaumi Ekta Dal\nUttar Pradesh\tRobertsganj\tMANGOI\t2966\tno\tBMUP\tBahujan Mukti Party\nUttar Pradesh\tRobertsganj\tRAJKUMAR\t2982\tno\tshsap\tShoshit Sandesh Party\nUttar Pradesh\tRobertsganj\tNone of the Above\t18489\tno\tNOTA\tNone of the Above\nUttar Pradesh\tRobertsganj\tSHARADA PRASAD\t187725\tno\tBSP\tBahujan Samaj Party\nUttar Pradesh\tRobertsganj\tARUN SINGH(CHERO)\t5613\tno\tIND\tIndependent\nUttar Pradesh\tRobertsganj\tS. R. DARAPURI\t4891\tno\tAIPF\tAll India Peoples' Front (Radical)\nUttar Pradesh\tRobertsganj\tBHAGWATI PRASAD CHAUDHARY\t86235\tno\tINC\tIndian National Congress\nUttar Pradesh\tRobertsganj\tRAVI SHANKAR KANNAUJIA\t9913\tno\tAAAP\tAam Aadmi Party\nUttar Pradesh\tRobertsganj\tPAKAUDI LAL KOL\t135966\tno\tSP\tSamajwadi Party\nUttar Pradesh\tRobertsganj\tSHANKAR KOL\t6101\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nWest Bengal\tCooch behar\tRENUKA SINHA\t526499\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tCooch behar\tNone of the Above\t11409\tno\tNOTA\tNone of the Above\nWest Bengal\tCooch behar\tDALENDRA NATH RAY\t4169\tno\tAMB\tAmra Bangalee\nWest Bengal\tCooch behar\tKAMAL KRISHNA BAIRAGI\t7125\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tCooch behar\tGIRINDRA NATH BARMAN\t15683\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tCooch behar\tHAREKRISHNA SARKAR\t5796\tno\tRPI(A)\tRepublican Party of India (A)\nWest Bengal\tCooch behar\tPIJUSH BARMAN\t5137\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tCooch behar\tKESHAB CHANDRA RAY\t74540\tno\tINC\tIndian National Congress\nWest Bengal\tCooch behar\tHITENDRA DAS\t5059\tno\tIND\tIndependent\nWest Bengal\tCooch behar\tHEM CHANDRA BARMAN\t217653\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tCooch behar\tDIPAK KUMAR ROY\t439392\tno\tAIFB\tAll India Forward Bloc\nWest Bengal\tCooch behar\tNRIPEN KARJEE\t6742\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tCooch behar\tBANGSHI BADAN BARMAN\t13205\tno\tIND\tIndependent\nWest Bengal\tAlipurduars\tDASRATH TIRKEY\t362453\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tAlipurduars\tISHABELA KUJUR\t5961\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tAlipurduars\tBILKAN BARA\t5340\tno\tSWJP\tSamajwadi Jan Parishad\nWest Bengal\tAlipurduars\tPAUL DEXION KHARIYA\t10450\tno\tIND\tIndependent\nWest Bengal\tAlipurduars\tMANOHAR TIRKEY\t341056\tno\tRSP\tRevolutionary Socialist Party\nWest Bengal\tAlipurduars\tBIRENDRA BARA (ORAON)\t335857\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tAlipurduars\tSUBASH CHIK BARAIK\t7131\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tAlipurduars\tJOSEPH MUNDA\t116718\tno\tINC\tIndian National Congress\nWest Bengal\tAlipurduars\tELIASH NARJINARY\t11039\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tAlipurduars\tDR. KAPIN CH. BORO\t7676\tno\tIND\tIndependent\nWest Bengal\tAlipurduars\tNone of the Above\t19885\tno\tNOTA\tNone of the Above\nWest Bengal\tJalpaiguri\tBIJOY CHANDRA BARMAN\t494773\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tJalpaiguri\tNone of the Above\t16541\tno\tNOTA\tNone of the Above\nWest Bengal\tJalpaiguri\tHAREKRISHNA SARKAR\t7636\tno\tRPI(A)\tRepublican Party of India (A)\nWest Bengal\tJalpaiguri\tSUBHAS BISWAS\t8545\tno\tIND\tIndependent\nWest Bengal\tJalpaiguri\tHARIPADA BARMAN\t6608\tno\tRJNP\tRashtriya Janasachetan Party (R.J.P.)\nWest Bengal\tJalpaiguri\tDILIP SARKAR\t4900\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tJalpaiguri\tSUKHBILAS BARMA\t87588\tno\tINC\tIndian National Congress\nWest Bengal\tJalpaiguri\tDHIRENDRA NATH ROY\t2835\tno\tAMB\tAmra Bangalee\nWest Bengal\tJalpaiguri\tSATYALAL SARKAR\t221593\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tJalpaiguri\tKSHITISH CHANDRA MANDAL\t12147\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tJalpaiguri\tMAHENDRA KUMAR ROY\t425167\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tJalpaiguri\tDHRITIMAN ROY\t4501\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tJalpaiguri\tHARIBHAKTA SARDAR\t9283\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tDarjeeling\tS.S.AHLUWALIA\t488257\tyes\tBJP\tBharatiya Janata Party\nWest Bengal\tDarjeeling\tKAKALI MAJUMDAR (ROY)\t6040\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tDarjeeling\tSAMAN PATHAK (SURAJ)\t167186\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tDarjeeling\tSUJAY GHATAK\t90076\tno\tINC\tIndian National Congress\nWest Bengal\tDarjeeling\tGOUTAM BHATTACHARYA\t3993\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tDarjeeling\tBHAI CHUNG BHUTIA\t291018\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tDarjeeling\tMAHENDRA P. LAMA\t55767\tno\tIND\tIndependent\nWest Bengal\tDarjeeling\tNIMA LAMA\t2906\tno\tgrac\tGorkha Rashtriya Congress\nWest Bengal\tDarjeeling\tARUN KUMAR AGARWAL\t5449\tno\tIND\tIndependent\nWest Bengal\tDarjeeling\tLALIT SINGHA\t2100\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tDarjeeling\tNIRANJAN SAHA\t2029\tno\tAMB\tAmra Bangalee\nWest Bengal\tDarjeeling\tRABINDRA ROY BASUNIA\t6100\tno\tIND\tIndependent\nWest Bengal\tDarjeeling\tSUNIL PANDIT\t3043\tno\tRJNP\tRashtriya Janasachetan Party (R.J.P.)\nWest Bengal\tDarjeeling\tNone of the Above\t18045\tno\tNOTA\tNone of the Above\nWest Bengal\tRaiganj\tMD. SALIM\t317515\tyes\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tRaiganj\tZAMEERUL HASAN\t3471\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tRaiganj\tNIMU BHOWMICK\t203131\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tRaiganj\tPASARUL ALAM\t3614\tno\tAAAP\tAam Aadmi Party\nWest Bengal\tRaiganj\tPABITRA RANJAN DASMUNSHI (SATYA)\t192698\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tRaiganj\tDEEPA DASMUNSI\t315881\tno\tINC\tIndian National Congress\nWest Bengal\tRaiganj\tDULAL RAJBANSHI\t8592\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tRaiganj\tSWAPAN KUMAR DAS\t5629\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tRaiganj\tBABLU SOREN\t5544\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tRaiganj\tNANDAKISHOR SINGHA\t3338\tno\tAMB\tAmra Bangalee\nWest Bengal\tRaiganj\tSUDIIP RANJAN SEN\t32303\tno\tSP\tSamajwadi Party\nWest Bengal\tRaiganj\tNone of the Above\t10929\tno\tNOTA\tNone of the Above\nWest Bengal\tRaiganj\tSUBRATA ADHIKARY\t5548\tno\tIND\tIndependent\nWest Bengal\tBalurghat\tARPITA GHOSH\t409641\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBalurghat\tNone of the Above\t11691\tno\tNOTA\tNone of the Above\nWest Bengal\tBalurghat\tSIBEN SARKAR\t5647\tno\tIND\tIndependent\nWest Bengal\tBalurghat\tISLAM NURAL\t2644\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBalurghat\tABDUS SADEK SARKAR\t10547\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tBalurghat\tBIMALENDU SARKAR (BIMAL)\t302677\tno\tRSP\tRevolutionary Socialist Party\nWest Bengal\tBalurghat\tOM PRAKAS MISHRA\t80715\tno\tINC\tIndian National Congress\nWest Bengal\tBalurghat\tBISWAPRIYA ROY CHOUDHURY\t223014\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBalurghat\tSASTI CHANDRA SARKAR\t3969\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tBalurghat\tMANAS CHAKRABORTY (GANESH)\t1466\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nWest Bengal\tBalurghat\tSOM HEMRAM\t4774\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tBalurghat\tRANENDRANATH MALI\t1687\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tBalurghat\tRAJEN HANSDA\t4581\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tMaldaha Uttar\tMAUSAM NOOR\t388609\tyes\tINC\tIndian National Congress\nWest Bengal\tMaldaha Uttar\tNURUL ISLAM MAJIDI\t7128\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tMaldaha Uttar\tBISHNUPADA BARMAN\t4225\tno\tAMB\tAmra Bangalee\nWest Bengal\tMaldaha Uttar\tMILAN DAS\t12163\tno\tSP\tSamajwadi Party\nWest Bengal\tMaldaha Uttar\tABDUL KHALEQUE\t4650\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tMaldaha Uttar\tSUBHASH KRISHNA GOSWAMI\t179000\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tMaldaha Uttar\tKHAGEN MURMU\t322904\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tMaldaha Uttar\tGAUTAM SARKAR\t8988\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tMaldaha Uttar\tIMANUYEL HEMRAM (BAIDYA)\t6805\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tMaldaha Uttar\tMINARA KHATUN\t3348\tno\tHKRD\tHindustan Krantikari Dal\nWest Bengal\tMaldaha Uttar\tCHITTA RANJAN KIRTTANIA\t6229\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tMaldaha Uttar\tSOUMITRA RAY\t197313\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tMaldaha Uttar\tMONATAN HEMBRAM\t4696\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tMaldaha Uttar\tNone of the Above\t10481\tno\tNOTA\tNone of the Above\nWest Bengal\tMaldaha Uttar\tSUREN MURMU\t6446\tno\tIND\tIndependent\nWest Bengal\tMaldaha Dakshin\tABU HASEM KHAN CHOWDHURY\t380291\tyes\tINC\tIndian National Congress\nWest Bengal\tMaldaha Dakshin\tNone of the Above\t8392\tno\tNOTA\tNone of the Above\nWest Bengal\tMaldaha Dakshin\tNARESH RISHI\t6616\tno\tIND\tIndependent\nWest Bengal\tMaldaha Dakshin\tMD EZARUDDIN\t3377\tno\tJeSM\tJamat-E-Seratul Mustakim\nWest Bengal\tMaldaha Dakshin\tDIPAK SINGH\t1449\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tMaldaha Dakshin\tMD NAJRUL ISLAM\t21207\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tMaldaha Dakshin\tNIKHIL CHANDRA MANDAL\t6138\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tMaldaha Dakshin\tNASMUL HOQUE\t2466\tno\tPDS\tParty for Democratic Socialism\nWest Bengal\tMaldaha Dakshin\tSADHAN CHATTERJEE\t10536\tno\tIND\tIndependent\nWest Bengal\tMaldaha Dakshin\tMD MOAZZEM HOSSAIN\t192632\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tMaldaha Dakshin\tABUL HASNAT KHAN\t209480\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tMaldaha Dakshin\tBISNU PADA ROY\t216180\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tMaldaha Dakshin\tMURSED SEKH\t2720\tno\tRPI(A)\tRepublican Party of India (A)\nWest Bengal\tMaldaha Dakshin\tMD FARUQUE HOSSAIN (SAHITYARATNA)\t3282\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tMaldaha Dakshin\tFIROZ AKHTAR\t1342\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tMaldaha Dakshin\tMANJUR ALAHI MUNSHI\t11426\tno\tIND\tIndependent\nWest Bengal\tMaldaha Dakshin\tBIMAL MARDI\t1921\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tMaldaha Dakshin\tIMTIAZ AHMED MOLLAH\t12952\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nWest Bengal\tJangipur\tABHIJIT MUKHERJEE\t378201\tyes\tINC\tIndian National Congress\nWest Bengal\tJangipur\tMD. SAHABUDDIN\t17257\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nWest Bengal\tJangipur\tSANJIT SINGH\t5507\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tJangipur\tSK NURUL ISLAM\t207455\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tJangipur\tMD. GINNATULLA SK\t2318\tno\tJeSM\tJamat-E-Seratul Mustakim\nWest Bengal\tJangipur\tABDUS SAYEED\t7926\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tJangipur\tSAMRAT GHOSH\t96751\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tJangipur\tDHANANJAY BANERJEE\t3019\tno\tAMB\tAmra Bangalee\nWest Bengal\tJangipur\tMONIRUL ISLAM\t9476\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tJangipur\tMUZAFFAR HOSSAIN\t370040\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tJangipur\tNone of the Above\t11079\tno\tNOTA\tNone of the Above\nWest Bengal\tJangipur\tABHIJIT SARKAR\t10055\tno\tIND\tIndependent\nWest Bengal\tBaharampur\tADHIR RANJAN CHOWDHURY\t583549\tyes\tINC\tIndian National Congress\nWest Bengal\tBaharampur\tNone of the Above\t11192\tno\tNOTA\tNone of the Above\nWest Bengal\tBaharampur\tABDUS SUKUR\t4322\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tBaharampur\tINDRANIL SEN\t226982\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tBaharampur\tDEBES ADHIKARI\t81656\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBaharampur\tBADRUL SEKH\t11642\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nWest Bengal\tBaharampur\tPRAMOTHES MUKHERJEE\t225699\tno\tRSP\tRevolutionary Socialist Party\nWest Bengal\tBaharampur\tKUSHADHWAJ BALA\t4676\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBaharampur\tMD. EZARUDDIN\t4869\tno\tJeSM\tJamat-E-Seratul Mustakim\nWest Bengal\tMurshidabad\tBADARUDDOZA KHAN\t426947\tyes\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tMurshidabad\tABDUL BARI\t5383\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tMurshidabad\tNone of the Above\t10156\tno\tNOTA\tNone of the Above\nWest Bengal\tMurshidabad\tASIM KUMAR DAS\t4539\tno\tIND\tIndependent\nWest Bengal\tMurshidabad\tSWAPAN KUMAR MANDAL\t4260\tno\tAMB\tAmra Bangalee\nWest Bengal\tMurshidabad\tMD. NAJMUL HOQUE\t11333\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tMurshidabad\tMD. KHODABOX SEKH\t6445\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tMurshidabad\tABDUL MANNAN HOSSAIN\t408494\tno\tINC\tIndian National Congress\nWest Bengal\tMurshidabad\tKAMARUJJAMAN KHANDEKAR(BAKUL)\t8378\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tMurshidabad\tSUJIT KUMAR GHOSH\t101069\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tMurshidabad\tALI MOHAMMAD\t289027\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tMurshidabad\tJITENDRA NATH HALDER\t5722\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tMurshidabad\tMASUDUL ISLAM\t6010\tno\tSDPI\tSOCIAL DEMOCRATIC PARTY OF INDIA\nWest Bengal\tKrishnanagar\tTAPAS PAUL\t438789\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tKrishnanagar\tSUBIMAL SENGUPTA\t7777\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nWest Bengal\tKrishnanagar\tHARASHIT BISWAS\t6168\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tKrishnanagar\tKAMAL DATTA\t7214\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tKrishnanagar\tSATYA BRATA MOOKHERJEE\t329387\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tKrishnanagar\tAHMED RAZIA\t74789\tno\tINC\tIndian National Congress\nWest Bengal\tKrishnanagar\tMOLLA JAFORULLA\t4865\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tKrishnanagar\tJHA SHANTANU\t367534\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tKrishnanagar\tAHAMED HOSSAIN\t3749\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tKrishnanagar\tNone of the Above\t7642\tno\tNOTA\tNone of the Above\nWest Bengal\tRanaghat\tTAPAS MANDAL\t590451\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tRanaghat\tNone of the Above\t14626\tno\tNOTA\tNone of the Above\nWest Bengal\tRanaghat\tSUBHAS CHANDRA SARKAR\t8658\tno\tIND\tIndependent\nWest Bengal\tRanaghat\tPRATAP KANTI RAY\t92218\tno\tINC\tIndian National Congress\nWest Bengal\tRanaghat\tSUPRAVAT BISWAS\t233670\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tRanaghat\tPARESH HALDER\t7952\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tRanaghat\tUTPALA BISWAS\t9116\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tRanaghat\tCHAITANYA BARAI\t3829\tno\tNSBP\tNirjatita Samaj Biplabi Party\nWest Bengal\tRanaghat\tNADIYAR CHAND MONDAL\t3992\tno\tRPI(A)\tRepublican Party of India (A)\nWest Bengal\tRanaghat\tARCHANA BISWAS\t388684\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBangaon\tKAPIL KRISHNA THAKUR\t551213\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBangaon\tPINAKI RANJAN BHARATI\t1071\tno\tTRMRPPI\tThe Religion of Man Revolving Political Party of India\nWest Bengal\tBangaon\tILA MONDAL\t43866\tno\tINC\tIndian National Congress\nWest Bengal\tBangaon\tDEBESH DAS\t404612\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBangaon\tPRANITA MANDAL\t8738\tno\tIND\tIndependent\nWest Bengal\tBangaon\tCHANDAN MALLICK\t9207\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBangaon\tSWAPAN MONDAL\t3589\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBangaon\tTARAPADA BISWAS\t2848\tno\tAMB\tAmra Bangalee\nWest Bengal\tBangaon\tK.D. BISWAS\t244783\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBangaon\tSHYAM PRASAD MONDAL\t2624\tno\tPDS\tParty for Democratic Socialism\nWest Bengal\tBangaon\tSARAT CHANDRA MANDAL\t1172\tno\tRAHM\tRashtriya Ahinsa Manch\nWest Bengal\tBangaon\tNone of the Above\t9965\tno\tNOTA\tNone of the Above\nWest Bengal\tBarrackpore\tDINESH TRIVEDI\t479206\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBarrackpore\tNone of the Above\t10979\tno\tNOTA\tNone of the Above\nWest Bengal\tBarrackpore\tMIHIR BISWAS\t1787\tno\tAAAP\tAam Aadmi Party\nWest Bengal\tBarrackpore\tRUMESH KUMAR HANDA\t230401\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBarrackpore\tDINA SHANKAR SINGH\t5036\tno\tIND\tIndependent\nWest Bengal\tBarrackpore\tSHARMISTHA CHOUDHURY\t2036\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nWest Bengal\tBarrackpore\tOM PRAKASH SHAW\t3555\tno\tIND\tIndependent\nWest Bengal\tBarrackpore\tSAMRAT TOPADAR\t30491\tno\tINC\tIndian National Congress\nWest Bengal\tBarrackpore\tPRADIP CHAUDHURI\t2200\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBarrackpore\tGIRISH CHANDRA SINGH\t3132\tno\tIND\tIndependent\nWest Bengal\tBarrackpore\tSUBHASHINI ALI\t272433\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBarrackpore\tTAPAS SARKAR\t5458\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBarrackpore\tOMPRAKASH RAJBHAR\t4416\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nWest Bengal\tDum dum\tSAUGATA ROY\t483244\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tDum dum\tASIM KUMAR DASGUPTA\t328310\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tDum dum\tSATYABRATA BANDYOPADHYAY\t1618\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tDum dum\tASHOK KUNDU\t4823\tno\tPDS\tParty for Democratic Socialism\nWest Bengal\tDum dum\tDHANANJAY (SHAKTI) MOITRA\t34116\tno\tINC\tIndian National Congress\nWest Bengal\tDum dum\tSIKHA SEN ROY\t1544\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nWest Bengal\tDum dum\tNARENDRA NATH GHOSH (NAREN GHOSH)\t4077\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tDum dum\tTAPAN SIKDAR\t254819\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tDum dum\tSUKHA RANJAN BANIK\t3256\tno\tIND\tIndependent\nWest Bengal\tDum dum\tNone of the Above\t16837\tno\tNOTA\tNone of the Above\nWest Bengal\tBarasat\tDR. KAKALI GHOSHDOSTIDAR\t525387\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBarasat\tNone of the Above\t12700\tno\tNOTA\tNone of the Above\nWest Bengal\tBarasat\tBIKASH HAZRA\t1478\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tBarasat\tMORTTAJA HOSEN\t6911\tno\tIND\tIndependent\nWest Bengal\tBarasat\tSUKLA BHUIMALI\t1332\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nWest Bengal\tBarasat\tKALYAN BASAK\t4122\tno\tIND\tIndependent\nWest Bengal\tBarasat\tSHRI KAUSHIK DAS GUPTA\t6375\tno\tIND\tIndependent\nWest Bengal\tBarasat\tRIJU GHOSAL\t40660\tno\tINC\tIndian National Congress\nWest Bengal\tBarasat\tPARESH CHANDRA GHOSH\t2439\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBarasat\tMD. RAFIKUL ISLAM\t6895\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tBarasat\tP.C. SORCAR JUNIOR\t296608\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBarasat\tDR. MORTOZA HOSSAIN\t352246\tno\tAIFB\tAll India Forward Bloc\nWest Bengal\tBarasat\tSUKUMAR BALA\t12178\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBasirhat\tIDRIS ALI\t492326\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBasirhat\tNURUL HUDA\t382667\tno\tCPI\tCommunist Party of India\nWest Bengal\tBasirhat\tSAMIK BHATTACHARYA\t233887\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBasirhat\tAJAY KUMAR BAIN\t6532\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBasirhat\tCHOWDHURY SIDDIQULLAH\t25178\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tBasirhat\tMD. HAFIZ\t5976\tno\tIND\tIndependent\nWest Bengal\tBasirhat\tGOPAL DAS\t7016\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBasirhat\tABDUR RAHIM QUAZI\t102130\tno\tINC\tIndian National Congress\nWest Bengal\tBasirhat\tRANJIT GAYEN\t8088\tno\tIND\tIndependent\nWest Bengal\tBasirhat\tNone of the Above\t9971\tno\tNOTA\tNone of the Above\nWest Bengal\tJoynagar\tPRATIMA MONDAL\t494746\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tJoynagar\tNone of the Above\t5823\tno\tNOTA\tNone of the Above\nWest Bengal\tJoynagar\tSUBHAS NASKAR\t5161\tno\tIND\tIndependent\nWest Bengal\tJoynagar\tTARANGA MONDAL\t13333\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tJoynagar\tDR. RABIN NASKAR\t2657\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tJoynagar\tMANIKLAL SARDAR\t5908\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tJoynagar\tSUBHAS NASKAR\t386362\tno\tRSP\tRevolutionary Socialist Party\nWest Bengal\tJoynagar\tARNAB ROY\t38493\tno\tINC\tIndian National Congress\nWest Bengal\tJoynagar\tANANYA SARKAR\t2909\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tJoynagar\tKRISHNAPADA MAJUMDER\t113206\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tJoynagar\tDR. TARUN MANDAL\t117454\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tMathurapur\tCHOUDHURY MOHAN JATUA\t627761\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tMathurapur\tPURNA CHANDRA NAIYA\t10191\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tMathurapur\tSHRI MANORANJAN HALDER\t47277\tno\tINC\tIndian National Congress\nWest Bengal\tMathurapur\tTAPAN NASKAR\t65965\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tMathurapur\tRINKU NASKAR\t489325\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tMathurapur\tRABINDRANATH MISTRI\t3212\tno\tPDS\tParty for Democratic Socialism\nWest Bengal\tMathurapur\tMANTU RAM HALDER\t4674\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tMathurapur\tSOUMEN SARADAR\t4364\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tMathurapur\tABANINDRA NATH BAIDYA\t2088\tno\tIND\tIndependent\nWest Bengal\tMathurapur\tNANDADULAL MANDAL\t1357\tno\tRJNP\tRashtriya Janasachetan Party (R.J.P.)\nWest Bengal\tMathurapur\tNone of the Above\t9317\tno\tNOTA\tNone of the Above\nWest Bengal\tDiamond harbour\tABHISHEK BANERJEE\t508481\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tDiamond harbour\tNone of the Above\t10657\tno\tNOTA\tNone of the Above\nWest Bengal\tDiamond harbour\tJAYNAL ABEDIN MONDAL\t1856\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tDiamond harbour\tHABIBUR RAHAMAN MOLLA\t1260\tno\tRJNP\tRashtriya Janasachetan Party (R.J.P.)\nWest Bengal\tDiamond harbour\tALOK SEN\t977\tno\tIND\tIndependent\nWest Bengal\tDiamond harbour\tSOUGATA GHOSH\t6634\tno\tIND\tIndependent\nWest Bengal\tDiamond harbour\tNAZMA YASMEEN\t3272\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tDiamond harbour\tABHIJIT DAS (BOBBY)\t200858\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tDiamond harbour\tMD. QAMRUZZAMAN QAMAR\t63047\tno\tINC\tIndian National Congress\nWest Bengal\tDiamond harbour\tBECHU MONDAL\t3607\tno\tIND\tIndependent\nWest Bengal\tDiamond harbour\tBIJAN MAJUMDER\t1348\tno\tIND\tIndependent\nWest Bengal\tDiamond harbour\tSAILEN SADHUKHAN\t6633\tno\tIND\tIndependent\nWest Bengal\tDiamond harbour\tDR. ABUL HASNAT\t437183\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tDiamond harbour\tSUDIP KUMAR SAHA\t8740\tno\tIND\tIndependent\nWest Bengal\tDiamond harbour\tAJAY GHOSH\t2529\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tDiamond harbour\tDILIP KUMAR MANDAL\t2892\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tDiamond harbour\tSAMIR PUTATUNDA\t1322\tno\tPDS\tParty for Democratic Socialism\nWest Bengal\tJadavpur\tSUGATA BOSE\t584244\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tJadavpur\tDR. ASOK KUMAR SAMANTA\t11317\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tJadavpur\tSANDHYA MANDAL\t3945\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tJadavpur\tSUJAN CHAKRABORTY\t459041\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tJadavpur\tSAMIR AICH\t26344\tno\tINC\tIndian National Congress\nWest Bengal\tJadavpur\tDR. SARUP PRASAD GHOSH\t155511\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tJadavpur\tMANGAL KUMAR SARDAR\t3406\tno\tIND\tIndependent\nWest Bengal\tJadavpur\tKARTIK KAYAL\t2837\tno\tIND\tIndependent\nWest Bengal\tJadavpur\tPINTU SANPUI\t1340\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tJadavpur\tSHAMALI DAS\t2885\tno\tIND\tIndependent\nWest Bengal\tJadavpur\tHASIBUL ISLAM MIR\t1464\tno\tRJNP\tRashtriya Janasachetan Party (R.J.P.)\nWest Bengal\tJadavpur\tSUSANTA KUMAR NASKAR\t3067\tno\tIND\tIndependent\nWest Bengal\tJadavpur\tASHOK KUMAR SHAW\t1294\tno\tIND\tIndependent\nWest Bengal\tJadavpur\tNone of the Above\t15667\tno\tNOTA\tNone of the Above\nWest Bengal\tKolkata Dakshin\tSUBRATA BAKSHI\t431715\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tKolkata Dakshin\tNone of the Above\t19504\tno\tNOTA\tNone of the Above\nWest Bengal\tKolkata Dakshin\tSYED MD WASIM RAZA\t1195\tno\tAIMF\tAll India Minorities Front\nWest Bengal\tKolkata Dakshin\tKARAN SINGH\t1103\tno\tIJP\tIndian Justice Party\nWest Bengal\tKolkata Dakshin\tHITANGSHU BANERJEE\t2921\tno\tAMB\tAmra Bangalee\nWest Bengal\tKolkata Dakshin\tMALA ROY\t113453\tno\tINC\tIndian National Congress\nWest Bengal\tKolkata Dakshin\tK. P. MD. SHARIF\t3776\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tKolkata Dakshin\tTATHAGATA ROY\t295376\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tKolkata Dakshin\tMD. HESHAMUDDIN\t1554\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tKolkata Dakshin\tBADRI MONDAL\t1844\tno\tIND\tIndependent\nWest Bengal\tKolkata Dakshin\tSHAMALI DAS\t3356\tno\tIND\tIndependent\nWest Bengal\tKolkata Dakshin\tNANDINI MUKHERJEE\t278414\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tKolkata Dakshin\tASHOKE BISWAS\t4335\tno\tIND\tIndependent\nWest Bengal\tKolkata Dakshin\tOMPRAKASH PRAJAPATI\t4564\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tKolkata Dakshin\tRAVEENDRAN. T.P (RAVI PALOOR)\t875\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nWest Bengal\tKolkata Dakshin\tZUBAIR RABBANI\t3994\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tKolkata Uttar\tSUDIP BANDYOPADHYAY\t343687\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tKolkata Uttar\tPHUL CHAND RAM\t2127\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tKolkata Uttar\tSOMENDRANATH MITRA\t130783\tno\tINC\tIndian National Congress\nWest Bengal\tKolkata Uttar\tRAHUL (BISWAJIT) SINHA\t247461\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tKolkata Uttar\tSANJOY SAHA\t4808\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tMD. IBRAHIM KHAN\t749\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tKolkata Uttar\tKALI PADA JANA\t1860\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tKAUSHIK BISWAS\t1810\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tDR. DEVENDRA PANDEY\t4168\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tBAGCHI RUPA\t196053\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tKolkata Uttar\tDEBAJYOTI SENGUPTA\t1435\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tALOK CHATURVEDI\t4399\tno\tAAAP\tAam Aadmi Party\nWest Bengal\tKolkata Uttar\tSUNIRMAL BASU\t518\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tSAUMEN MITRA\t3752\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tRATHINDRA NATH ROY\t1149\tno\tIND\tIndependent\nWest Bengal\tKolkata Uttar\tMITUL KUMAR SINGH\t548\tno\tRAHM\tRashtriya Ahinsa Manch\nWest Bengal\tKolkata Uttar\tS.M. HABIBULLAH\t660\tno\tGaAP\tGareeb Aadmi Party\nWest Bengal\tKolkata Uttar\tTARUN MUKHERJEE\t708\tno\tRJNP\tRashtriya Janasachetan Party (R.J.P.)\nWest Bengal\tKolkata Uttar\tNone of the Above\t9103\tno\tNOTA\tNone of the Above\nWest Bengal\tHowrah\tPRASUN BANERJEE\t488461\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tHowrah\tNone of the Above\t9929\tno\tNOTA\tNone of the Above\nWest Bengal\tHowrah\tSONIA DAS\t4441\tno\tIND\tIndependent\nWest Bengal\tHowrah\tDINESH KUMAR SHARMA\t2343\tno\tIND\tIndependent\nWest Bengal\tHowrah\tSAJAL KUMAR DAS\t1572\tno\tIND\tIndependent\nWest Bengal\tHowrah\tSUKUMAR BARAL\t635\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tHowrah\tMOHAMMAD SIRAJUDDIN SEKH\t1079\tno\tGaAP\tGareeb Aadmi Party\nWest Bengal\tHowrah\tSANJIB SARKAR\t2105\tno\tIND\tIndependent\nWest Bengal\tHowrah\tSURAJ NARAYAN SINGH\t2186\tno\tAAAP\tAam Aadmi Party\nWest Bengal\tHowrah\tMRITYUNJAY SARKAR\t2034\tno\tIND\tIndependent\nWest Bengal\tHowrah\tMANOJ KUMAR PANDEY\t63254\tno\tINC\tIndian National Congress\nWest Bengal\tHowrah\tSHIB CHANDRA RAM\t3241\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tHowrah\tSOUMITRA SENGUPTA\t1814\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tHowrah\tGEORGE BAKER\t248120\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tHowrah\tGOBARDDHAN MANNA\t2680\tno\tIUC\tIndian Unity Centre\nWest Bengal\tHowrah\tSRIDIP BHATTACHARYA\t291505\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tUluberia\tSULTAN AHMED\t570785\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tUluberia\tSABIR UDDIN MOLLA\t369563\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tUluberia\tABUL QASEM\t6607\tno\tIUC\tIndian Unity Centre\nWest Bengal\tUluberia\tRANJIT KISHORE MOHANTY\t137137\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tUluberia\tMINATI SARKAR\t2902\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tUluberia\tHASAN NAWAJ\t5709\tno\tAIUDF\tAll India United Democratic Front\nWest Bengal\tUluberia\tREKHA DAS\t3918\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tUluberia\tASHIS KUMAR DAS\t2437\tno\tIND\tIndependent\nWest Bengal\tUluberia\tSUJATA DUTTA\t1400\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tUluberia\tRAMESH DHARA\t2529\tno\tIND\tIndependent\nWest Bengal\tUluberia\tSUSANTA KUMAR DALUI\t2990\tno\tIND\tIndependent\nWest Bengal\tUluberia\tASIT MITRA\t67826\tno\tINC\tIndian National Congress\nWest Bengal\tUluberia\tDILIP KUMAR HAIT\t3947\tno\tIND\tIndependent\nWest Bengal\tUluberia\tNone of the Above\t8277\tno\tNOTA\tNone of the Above\nWest Bengal\tSrerampur\tKALYAN BANERJEE\t514933\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tSrerampur\tNone of the Above\t15374\tno\tNOTA\tNone of the Above\nWest Bengal\tSrerampur\tRAJU DEY SARKAR\t4277\tno\tIND\tIndependent\nWest Bengal\tSrerampur\tKAILASH NASKAR\t2674\tno\tIND\tIndependent\nWest Bengal\tSrerampur\tMANASA SEN\t3406\tno\tWPOI\tWelfare Party Of India\nWest Bengal\tSrerampur\tDR. SOMNATH GHOSH\t6613\tno\tIND\tIndependent\nWest Bengal\tSrerampur\tBAPPI LAHIRI\t287712\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tSrerampur\tNASIRUDDIN MIR\t4240\tno\tIUC\tIndian Unity Centre\nWest Bengal\tSrerampur\tTIRTHANKAR RAY\t362407\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tSrerampur\tMD. SHANAWAZ\t2698\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tSrerampur\tABDUL MANNAN\t86099\tno\tINC\tIndian National Congress\nWest Bengal\tHooghly\tDR. RATNA DE (NAG)\t614312\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tHooghly\tSAJAL ADHIKARI\t9152\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nWest Bengal\tHooghly\tPABAN MAZUMDER\t7682\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tHooghly\tVIJAY KUMAR MAHATO\t5519\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tHooghly\tSHUKCHAND MURMU\t3197\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tHooghly\tPRITAM GHOSH\t42226\tno\tINC\tIndian National Congress\nWest Bengal\tHooghly\tSANGHAMITRA MUKHERJEE\t3766\tno\tRJNP\tRashtriya Janasachetan Party (R.J.P.)\nWest Bengal\tHooghly\tPRADIP SAHA\t425228\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tHooghly\tDR. CHANDAN MITRA\t221271\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tHooghly\tNone of the Above\t16517\tno\tNOTA\tNone of the Above\nWest Bengal\tArambagh\tAPARUPA PODDAR (AFRIN ALI)\t748764\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tArambagh\tNone of the Above\t17837\tno\tNOTA\tNone of the Above\nWest Bengal\tArambagh\tGANESH BAG\t7062\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tArambagh\tSAKTIMOHAN MALIK\t401919\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tArambagh\tSAMBHUNATH MALIK\t27872\tno\tINC\tIndian National Congress\nWest Bengal\tArambagh\tMADHUSUDAN BAG\t158480\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tTamluk\tADHIKARI SUVENDU\t716928\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tTamluk\tNone of the Above\t11643\tno\tNOTA\tNone of the Above\nWest Bengal\tTamluk\tKAMAL BAG\t3110\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tTamluk\tMANIK CHANDRA MONDAL\t1500\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tTamluk\tKALISANKAR JANA\t2658\tno\tIND\tIndependent\nWest Bengal\tTamluk\tANWAR ALI SK\t29645\tno\tINC\tIndian National Congress\nWest Bengal\tTamluk\tABDUR REZAK SK\t1682\tno\tIND\tIndependent\nWest Bengal\tTamluk\tBADSHA ALAM\t86265\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tTamluk\tBIBEKANANDA RAY\t10197\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tTamluk\tRAJYASHREE CHAUDHURI\t3609\tno\tIND\tIndependent\nWest Bengal\tTamluk\tSEKH IBRAHIM ALI\t470447\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tKanthi\tADHIKARI SISIR KUMAR\t676749\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tKanthi\tSINHA TAPAS\t447259\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tKanthi\tMANAS PRADHAN\t7335\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tKanthi\tANSAR ALI SK\t8298\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tKanthi\tKAMALENDU PAHARI\t111082\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tKanthi\tKUNAL BANERJEE\t27230\tno\tINC\tIndian National Congress\nWest Bengal\tKanthi\tSK GOLAM NABI AJAD\t3260\tno\tIND\tIndependent\nWest Bengal\tKanthi\tNone of the Above\t9598\tno\tNOTA\tNone of the Above\nWest Bengal\tGhatal\tADHIKARI DEEPAK (DEV)\t685696\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tGhatal\tGOBARDHAN GHOSH\t1983\tno\tRaJSP\tRashtriya Janadhikar Suraksha Party\nWest Bengal\tGhatal\tGOPAL MONDAL\t4498\tno\tIND\tIndependent\nWest Bengal\tGhatal\tNone of the Above\t14418\tno\tNOTA\tNone of the Above\nWest Bengal\tGhatal\tMANAS RANJAN BHUNIA\t122928\tno\tINC\tIndian National Congress\nWest Bengal\tGhatal\tGOPAL MURMU\t2154\tno\tJHAP\tJharkhand Anushilan Party\nWest Bengal\tGhatal\tSANTOSH RANA\t424805\tno\tCPI\tCommunist Party of India\nWest Bengal\tGhatal\tANJAN JANA\t8080\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tGhatal\tMD. ALAM\t94842\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tGhatal\tGOUTAM KOURI\t5547\tno\tAMB\tAmra Bangalee\nWest Bengal\tGhatal\tGANGARAM SASMAL\t1758\tno\tPDS\tParty for Democratic Socialism\nWest Bengal\tJhargram\tUMA SAREN\t674504\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tJhargram\tBUDDHADEB MANDI\t15114\tno\tJMM\tJharkhand Mukti Morcha\nWest Bengal\tJhargram\tMILAN MANDI\t6013\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tJhargram\tHIRANMOY MAJHI\t8616\tno\tIND\tIndependent\nWest Bengal\tJhargram\tGOMASTA PRASAD SOREN\t8364\tno\tAKBJHP\tAkhil Bhartiya Jharkhand Party\nWest Bengal\tJhargram\tRAJIB MUDI\t5058\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tJhargram\tDR. PULIN BIHARI BASKE\t326621\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tJhargram\tCHUNIBALA HANSDA\t7608\tno\tJKP(N)\tJharkhand Party  (Naren)\nWest Bengal\tJhargram\tSUBODH KUMAR MANDI\t7453\tno\tIND\tIndependent\nWest Bengal\tJhargram\tANITA HANSDA\t40513\tno\tINC\tIndian National Congress\nWest Bengal\tJhargram\tRAMPADA HANSDA\t8478\tno\tAMB\tAmra Bangalee\nWest Bengal\tJhargram\tBIKASH MUDI\t122459\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tJhargram\tMURARI MOHAN BASKE\t3877\tno\tJHAP\tJharkhand Anushilan Party\nWest Bengal\tJhargram\tNone of the Above\t22935\tno\tNOTA\tNone of the Above\nWest Bengal\tMedinipur\tSANDHYA ROY\t579860\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tMedinipur\tNone of the Above\t13626\tno\tNOTA\tNone of the Above\nWest Bengal\tMedinipur\tDR. BIMAL KUMAR RAJ\t48883\tno\tINC\tIndian National Congress\nWest Bengal\tMedinipur\tPRABHAKAR TEWARI\t180112\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tMedinipur\tTUSHAR JANA\t9783\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tMedinipur\tPRABODH PANDA\t395194\tno\tCPI\tCommunist Party of India\nWest Bengal\tMedinipur\tKAVITA RANI\t8639\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tMedinipur\tRAMKRISNA SARKAR\t4655\tno\tIND\tIndependent\nWest Bengal\tMedinipur\tSOUMEN SAMANTA\t6062\tno\tPDS\tParty for Democratic Socialism\nWest Bengal\tMedinipur\tASHOK BERA\t7883\tno\tAMB\tAmra Bangalee\nWest Bengal\tMedinipur\tMUKIM KHAN\t5335\tno\tJMM\tJharkhand Mukti Morcha\nWest Bengal\tPurulia\tDR. MRIGANKA MAHATO\t468277\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tPurulia\tMONBODH MAHATO\t6774\tno\tIND\tIndependent\nWest Bengal\tPurulia\tPURNA CHANDRA TUDU\t4073\tno\tIND\tIndependent\nWest Bengal\tPurulia\tTAPAN KUMAR MAHATO\t7386\tno\tJMM\tJharkhand Mukti Morcha\nWest Bengal\tPurulia\tDHIREN RAJAK\t5584\tno\tIND\tIndependent\nWest Bengal\tPurulia\tNEPAL MAHATA\t257923\tno\tINC\tIndian National Congress\nWest Bengal\tPurulia\tMIHIR KUMAR RAJWAR\t9850\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tPurulia\tAJIT PRASAD MAHATA\t9012\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nWest Bengal\tPurulia\tBAIDYANATH HANSDA\t7168\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tPurulia\tSUBARNA  KUMAR\t11266\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tPurulia\tNARAHARI  MAHATO\t314400\tno\tAIFB\tAll India Forward Bloc\nWest Bengal\tPurulia\tBIKASH BANERJEE\t86236\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tPurulia\tNone of the Above\t16726\tno\tNOTA\tNone of the Above\nWest Bengal\tBankura\tSREEMATI  DEV VARMA (MOON MOON SEN)\t483455\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBankura\tNone of the Above\t23682\tno\tNOTA\tNone of the Above\nWest Bengal\tBankura\tSUDHIR KUMAR MURMU\t7579\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nWest Bengal\tBankura\tSUBHAS KUMAR SARKAR\t251183\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBankura\tNILMADHAB GUPTA\t22021\tno\tINC\tIndian National Congress\nWest Bengal\tBankura\tHULU KHETRAPAL\t7564\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tBankura\tSHIB SANKAR GHOSH\t3105\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nWest Bengal\tBankura\tPANMANI BESRA\t5909\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tBankura\tSHYAMAL SIKDAR\t9045\tno\tIND\tIndependent\nWest Bengal\tBankura\tBINOY CHOWDHURY\t7388\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBankura\tPARESH MARANDI\t10229\tno\tJMM\tJharkhand Mukti Morcha\nWest Bengal\tBankura\tBANERJEE PRABIR\t6209\tno\tJHAP\tJharkhand Anushilan Party\nWest Bengal\tBankura\tGOUR CHANDRA HEMBRAM\t4876\tno\tIND\tIndependent\nWest Bengal\tBankura\tKABITA SINGHABABU\t9125\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBankura\tACHARIA BASUDEB\t384949\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBishnupur\tKHAN SAUMITRA\t578870\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBishnupur\tSUSMITA BAURI\t429185\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBishnupur\tJOYDEB BAURI\t7816\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tBishnupur\tJAGADANANDA ROY\t10127\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBishnupur\tTARANI ROY\t6854\tno\tIND\tIndependent\nWest Bengal\tBishnupur\tJAYANTA MONDAL\t179530\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBishnupur\tNARAYAN CHANDRA KHAN\t27054\tno\tINC\tIndian National Congress\nWest Bengal\tBishnupur\tSADANANDA MANDAL\t4886\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBishnupur\tDINESH LOHAR\t6820\tno\tIND\tIndependent\nWest Bengal\tBishnupur\tNone of the Above\t20928\tno\tNOTA\tNone of the Above\nWest Bengal\tBardhaman Purba\tSUNIL KUMAR MONDAL\t574660\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBardhaman Purba\tNone of the Above\t13951\tno\tNOTA\tNone of the Above\nWest Bengal\tBardhaman Purba\tPROSANTA BAG\t3410\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tBardhaman Purba\tSWAPAN MALIK\t9666\tno\tJMM\tJharkhand Mukti Morcha\nWest Bengal\tBardhaman Purba\tCHANDANA MAJHI\t68884\tno\tINC\tIndian National Congress\nWest Bengal\tBardhaman Purba\tISWAR CHANDRA DAS\t460181\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBardhaman Purba\tSANTOSH ROY\t170828\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBardhaman Purba\tMUKUL BISWAS\t5442\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBardhaman Purba\tKALICHARAN SARDAR\t7981\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBardhaman Purba\tPEJUSH KUMAR SAHANA\t5919\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nWest Bengal\tBurdwan - durgapur\tDR. MAMTAZ SANGHAMITA\t554521\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBurdwan - durgapur\tMD. HARUN\t11862\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBurdwan - durgapur\tSUNIL KUMAR PURKAIT\t7574\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBurdwan - durgapur\tDEBASREE CHAUDHURI\t237205\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBurdwan - durgapur\tSK. SAIDUL HAQUE\t447190\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBurdwan - durgapur\tNone of the Above\t16886\tno\tNOTA\tNone of the Above\nWest Bengal\tBurdwan - durgapur\tAGASTY PRADIP\t44355\tno\tINC\tIndian National Congress\nWest Bengal\tBurdwan - durgapur\tDR. DHANAPATI DAS\t6665\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tBurdwan - durgapur\tSARADA MONI SAMANTA\t4984\tno\tIND\tIndependent\nWest Bengal\tAsansol\tBABUL SUPRIYA BARAL (BABUL SUPRIYO)\t419983\tyes\tBJP\tBharatiya Janata Party\nWest Bengal\tAsansol\tNone of the Above\t11479\tno\tNOTA\tNone of the Above\nWest Bengal\tAsansol\tATUL CHANDRA  BOURI\t4256\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tAsansol\tBURO MURMU\t2434\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tAsansol\tINDRANI MISHRA\t48502\tno\tINC\tIndian National Congress\nWest Bengal\tAsansol\tJARASANDHA SINHA\t4663\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tAsansol\tDOLA SEN\t349503\tno\tAITC\tAll India Trinamool Congress\nWest Bengal\tAsansol\tBANSA GOPAL CHOUDHURY\t255829\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tAsansol\tMD. REYAZUDDIN\t4947\tno\tIUML\tIndian Union Muslim League\nWest Bengal\tAsansol\tKANAI BANERJEE\t5728\tno\tJMM\tJharkhand Mukti Morcha\nWest Bengal\tAsansol\tANANTA LAL GUPTA\t3115\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tAsansol\tSUJIT KAR\t5016\tno\tIND\tIndependent\nWest Bengal\tAsansol\tMD. MUSTAQIM\t2450\tno\tGaAP\tGareeb Aadmi Party\nWest Bengal\tAsansol\tJYOTIRMOY MAITI\t10227\tno\tIND\tIndependent\nWest Bengal\tAsansol\tMANASH SARKAR\t14263\tno\tIND\tIndependent\nWest Bengal\tBolpur\tANUPAM HAZRA\t630693\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBolpur\tNone of the Above\t17322\tno\tNOTA\tNone of the Above\nWest Bengal\tBolpur\tTAPAN KUMAR SAHA\t46953\tno\tINC\tIndian National Congress\nWest Bengal\tBolpur\tSAMIRAN DAS\t5952\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBolpur\tKAMINI MOHAN SARKAR\t197474\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBolpur\tSANJIB MALIK\t6371\tno\tJDP\tJharkhand Disom Party\nWest Bengal\tBolpur\tBIJOY DOLUI\t5410\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBolpur\tDOME RAMCHANDRA\t394581\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBirbhum\tSATABDI ROY\t460568\tyes\tAITC\tAll India Trinamool Congress\nWest Bengal\tBirbhum\tDR. ELAHI KAMRE MAHAMMAD\t393305\tno\tCPM\tCommunist Party of India  (Marxist)\nWest Bengal\tBirbhum\tPRASANTA ROY\t7194\tno\tSP\tSamajwadi Party\nWest Bengal\tBirbhum\tAYESHA KHATUN\t6593\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nWest Bengal\tBirbhum\tNURUL ISLAM\t2205\tno\tJD(U)\tJanata Dal  (United)\nWest Bengal\tBirbhum\tSAJAL DAS\t2982\tno\tBMUP\tBahujan Mukti Party\nWest Bengal\tBirbhum\tSHIBRATAN SHARMA\t7989\tno\tJMM\tJharkhand Mukti Morcha\nWest Bengal\tBirbhum\tSUTTAM DAS\t5448\tno\tIND\tIndependent\nWest Bengal\tBirbhum\tPRABIR MUKHOPADHYAY\t7141\tno\tBSP\tBahujan Samaj Party\nWest Bengal\tBirbhum\tJOY BANERJEE\t235753\tno\tBJP\tBharatiya Janata Party\nWest Bengal\tBirbhum\tSYED SIRAJ JIMMI\t132084\tno\tINC\tIndian National Congress\nWest Bengal\tBirbhum\tNone of the Above\t14557\tno\tNOTA\tNone of the Above\nChhattisgarh\tSARGUJA\tKAMALBHAN SINGH MARABI\t585336\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tSARGUJA\tNone of the Above\t31104\tno\tNOTA\tNone of the Above\nChhattisgarh\tSARGUJA\tRAMASAPNA SINGH\t11691\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tSARGUJA\tDUBRAJ SINGH MARKAM\t7577\tno\tIND\tIndependent\nChhattisgarh\tSARGUJA\tSHIVKUMAR SINGH SHYAM\t7374\tno\tRGOP\tRashtriya Gondvana Party\nChhattisgarh\tSARGUJA\tBAHADUR RAM\t10861\tno\tIND\tIndependent\nChhattisgarh\tSARGUJA\tRAJNATH\t15515\tno\tIND\tIndependent\nChhattisgarh\tSARGUJA\tKUMAIT RAM SANDIL\t6146\tno\tIND\tIndependent\nChhattisgarh\tSARGUJA\tCOMRADE SURENDRA LAL SINGH\t14849\tno\tCPM\tCommunist Party of India  (Marxist)\nChhattisgarh\tSARGUJA\tADVOCATE KUNJ BIHARI SINGH PAIKRA\t4998\tno\tBMUP\tBahujan Mukti Party\nChhattisgarh\tSARGUJA\tDHARMJEET SINGH MARKAM\t21633\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tSARGUJA\tTULESHWAR SINGH\t11384\tno\tAITC\tAll India Trinamool Congress\nChhattisgarh\tSARGUJA\tBALDEO PRASAD SINGH MARAVI\t14702\tno\tGGP\tGondvana Gantantra Party\nChhattisgarh\tSARGUJA\tRAMNATH CHEARWA\t6051\tno\tSSD\tShoshit Samaj Dal\nChhattisgarh\tSARGUJA\tRAM DEV RAM\t438100\tno\tINC\tIndian National Congress\nChhattisgarh\tRAIGARH\tVISHNU DEO SAI\t662478\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tRAIGARH\tPAULUS KUJUR\t5094\tno\tBMUP\tBahujan Mukti Party\nChhattisgarh\tRAIGARH\tPRAKASH KUMAR URANW\t12860\tno\tIND\tIndependent\nChhattisgarh\tRAIGARH\tKRIPARAM SIDAR\t11227\tno\tGGP\tGondvana Gantantra Party\nChhattisgarh\tRAIGARH\tTARA SINGH RATHIYA\t12133\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tRAIGARH\tARTI SINGH\t445728\tno\tINC\tIndian National Congress\nChhattisgarh\tRAIGARH\tKRIPA SHANKAR BHAGAT\t27152\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tRAIGARH\tJOSEPH BARA\t5168\tno\tIND\tIndependent\nChhattisgarh\tRAIGARH\tRAM NARAYAN AYAM\t8747\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tRAIGARH\tDHANANJAY RATHIYA\t8046\tno\tIND\tIndependent\nChhattisgarh\tRAIGARH\tBAJARANG SIDAR\t11098\tno\tIND\tIndependent\nChhattisgarh\tRAIGARH\tAMRIT TIRKEY\t7975\tno\tIND\tIndependent\nChhattisgarh\tRAIGARH\tNone of the Above\t28480\tno\tNOTA\tNone of the Above\nChhattisgarh\tJANJGIR-CHAMPA\tKAMLA PATLE\t518909\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tJANJGIR-CHAMPA\tPREMSHANKAR MAHILANGE\t5309\tno\tIND\tIndependent\nChhattisgarh\tJANJGIR-CHAMPA\tDEEPAK NARANG\t3977\tno\tIND\tIndependent\nChhattisgarh\tJANJGIR-CHAMPA\tKAUSHAL PRASAD NAVARANG\t2724\tno\tIND\tIndependent\nChhattisgarh\tJANJGIR-CHAMPA\tLAKHAN LAL CHAUHAN\t2859\tno\tBMUP\tBahujan Mukti Party\nChhattisgarh\tJANJGIR-CHAMPA\tNOHAR SINGH SONWANI\t2852\tno\tSP\tSamajwadi Party\nChhattisgarh\tJANJGIR-CHAMPA\tCHAITRAM SURYAVANSHI ADHIWAKTA\t3208\tno\tIND\tIndependent\nChhattisgarh\tJANJGIR-CHAMPA\tSATYENDRA BHANDARI\t6275\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tJANJGIR-CHAMPA\tNone of the Above\t18438\tno\tNOTA\tNone of the Above\nChhattisgarh\tJANJGIR-CHAMPA\tJEEVAN LAL BHARATI\t2962\tno\tIND\tIndependent\nChhattisgarh\tJANJGIR-CHAMPA\tLAKHAN LAL LAHRE\t7535\tno\tIND\tIndependent\nChhattisgarh\tJANJGIR-CHAMPA\tPREM CHAND JAYASI\t343948\tno\tINC\tIndian National Congress\nChhattisgarh\tJANJGIR-CHAMPA\tDADOO RAM MANHAR\t16862\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tJANJGIR-CHAMPA\tDUJ RAM BOUDDH\t125617\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tJANJGIR-CHAMPA\tVRINDA CHAUHAN\t8982\tno\tIND\tIndependent\nChhattisgarh\tJANJGIR-CHAMPA\tJAGDISH PRASAD NIRALA\t2890\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tDR. BANSHILAL MAHTO\t439002\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tKORBA\tMOHAN LAL BARGAHI\t2679\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tRAJESH KUMAR MAHANT\t2333\tno\tJD(U)\tJanata Dal  (United)\nChhattisgarh\tKORBA\tRAJENDRA SINGH KANWAR\t2700\tno\tCSM\tChhattisgarh Swabhiman Manch\nChhattisgarh\tKORBA\tCHARAN DAS MAHANT\t434737\tno\tINC\tIndian National Congress\nChhattisgarh\tKORBA\tFATTE SINGH SHYAM\t2671\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tLAXMAN LAL KAIWART\t4265\tno\tchp\tChhattisgarhiya Party\nChhattisgarh\tKORBA\tPRATAP BHANU\t9495\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tKAMAL DEO\t1998\tno\tBSCP\tBhartiya Shakti Chetna Party\nChhattisgarh\tKORBA\tHIRASINGH MARKAM\t52753\tno\tGGP\tGondvana Gantantra Party\nChhattisgarh\tKORBA\tPHULESHWAR PRASAD SURAJIHA\t4111\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tDR.URMILA SINGH MARKO\t2768\tno\tRGOP\tRashtriya Gondvana Party\nChhattisgarh\tKORBA\tDHANSINGH DHURVE\t14198\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tKORBA\tRAJESH KUMAR PANDEY\t3676\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tCHANDRA BHUSHAN PRATAP SINGH KANWAR\t5238\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tKORBA\tSHAMBHU PRASAD SHARMA ADVOCATE\t1808\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tKEWAL GOSWAMI\t2478\tno\tSP\tSamajwadi Party\nChhattisgarh\tKORBA\tAMAR NATH PANDEY\t14594\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tKORBA\tCHARAN BHAYA\t6919\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tJEEVAN LAL RAOUTEL\t18459\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tS.R. ANCHAL\t5657\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tJATA SHANKAR PANDAY\t6461\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tHARBHAJAN SINGH SHYAM\t2728\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tSEKH RAUF\t2422\tno\tIND\tIndependent\nChhattisgarh\tKORBA\tNone of the Above\t8570\tno\tNOTA\tNone of the Above\nChhattisgarh\tBILASPUR\tLAKHAN LAL SAHU\t561387\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tBILASPUR\tDINESH KUMAR SINGH KHUSRO NIKHIL\t12024\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tRAMESHWAR KEWAT\t1661\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tDAYADAS LAHRE\t2988\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tGANESH YADAV\t4279\tno\tGGP\tGondvana Gantantra Party\nChhattisgarh\tBILASPUR\tMANIK DAHARIYA\t2059\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tAJAY PALI\t2237\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tLAKHANLAL SAHU\t910\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tLAKHAN LAL SAHU\t910\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tLAKHAN LAL SAHU\t1458\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tDHANIRAM YADAV\t1706\tno\tSP\tSamajwadi Party\nChhattisgarh\tBILASPUR\tNone of the Above\t7566\tno\tNOTA\tNone of the Above\nChhattisgarh\tBILASPUR\tSALIKRAM JOGI\t1836\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tRAM SINGH PANDRAM (PENDRO)\t4034\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tSARITA VIJAY SONI\t2244\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tDHANIRAM KHARE\t1287\tno\tRP(K)\tRepublican Paksha (Khoripa)\nChhattisgarh\tBILASPUR\tDHARAM BHARGAV\t26340\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tBILASPUR\tRAMJI SAHU\t1776\tno\tCSM\tChhattisgarh Swabhiman Manch\nChhattisgarh\tBILASPUR\tKARUNA SHUKLA\t384951\tno\tINC\tIndian National Congress\nChhattisgarh\tBILASPUR\tDILIP KOSHLE\t6911\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tJAITRAM KHANDE\t2369\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tLAKHANLAL SAHU\t855\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tBALDAU PRASAD SAHU\t13339\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tENG. RAMFAL MANDREY\t4294\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tBILASPUR\tPRAKASH SURYA\t14918\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tDAOSINGH MARAVI\t5385\tno\tIND\tIndependent\nChhattisgarh\tBILASPUR\tANAND MISHRA\t20733\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tRAJNANDGAON\tABHISHEK SINGH\t643473\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tRAJNANDGAON\tNone of the Above\t32384\tno\tNOTA\tNone of the Above\nChhattisgarh\tRAJNANDGAON\tBHASKAR DWIVEDI\t10733\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tRAJNANDGAON\tKHEMRAJ LODHI\t5546\tno\tIND\tIndependent\nChhattisgarh\tRAJNANDGAON\tBHAGAT SONI\t3636\tno\tCSM\tChhattisgarh Swabhiman Manch\nChhattisgarh\tRAJNANDGAON\tSANTOSH CHANDRAKAR\t6007\tno\tGGP\tGondvana Gantantra Party\nChhattisgarh\tRAJNANDGAON\tMURAD BHAI\t5255\tno\tIND\tIndependent\nChhattisgarh\tRAJNANDGAON\tWAMANRAO BAGDE\t10891\tno\tIND\tIndependent\nChhattisgarh\tRAJNANDGAON\tSAHUKAR BHASKAR\t11478\tno\tIND\tIndependent\nChhattisgarh\tRAJNANDGAON\tLODHI SEEMA KAUSHIK\t3228\tno\tSP\tSamajwadi Party\nChhattisgarh\tRAJNANDGAON\tRAMESH YADAV\t5941\tno\tIND\tIndependent\nChhattisgarh\tRAJNANDGAON\tANAND SAHU\t20458\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tRAJNANDGAON\tKAMLESHWAR VERMA\t407562\tno\tINC\tIndian National Congress\nChhattisgarh\tRAJNANDGAON\tNARENDRA BANSOD ADVOCATE\t11704\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tDURG\tTAMRADHWAJ SAHU\t570687\tyes\tINC\tIndian National Congress\nChhattisgarh\tDURG\tADHIWAKTA NARENDRA GUPTA\t1413\tno\tbhmm\tBhrashtachar Mukti Morcha\nChhattisgarh\tDURG\tJINENDRA GHRITLAHARE\t4537\tno\tIND\tIndependent\nChhattisgarh\tDURG\tVISHWA RATNA SINHA\t17454\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tDURG\tDIWAKAR BAIRAGI\t1150\tno\tIND\tIndependent\nChhattisgarh\tDURG\tSAROJ PANDEY\t553839\tno\tBJP\tBharatiya Janata Party\nChhattisgarh\tDURG\tMANNU LAL PARGANIHA\t2159\tno\tCSM\tChhattisgarh Swabhiman Manch\nChhattisgarh\tDURG\tENGG. CHANDRAPRAKASH MANDLE\t2301\tno\tIND\tIndependent\nChhattisgarh\tDURG\tJAI PRAKASH NAIR\t2560\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nChhattisgarh\tDURG\tMUNNA CHANDRAKAR\t15600\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tDURG\tARUN JOSHI\t16529\tno\tIND\tIndependent\nChhattisgarh\tDURG\tAATMA RAM SAHU\t10371\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nChhattisgarh\tDURG\tSHAILENDRA BANJARE (SHAKTIPURTA)\t2903\tno\tSSBD\tShakti Sena (Bharat Desh)\nChhattisgarh\tDURG\tSUBODH KUMAR GUPTA\t4711\tno\tRPI(A)\tRepublican Party of India (A)\nChhattisgarh\tDURG\tRADHA BAI YADAV\t1011\tno\tIND\tIndependent\nChhattisgarh\tDURG\tNone of the Above\t11907\tno\tNOTA\tNone of the Above\nChhattisgarh\tDURG\tBHARAT BHUSHAN PANDEY\t1427\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nChhattisgarh\tDURG\tKSHITIJ KUMAR SHAMBHARKAR\t8120\tno\tRP(K)\tRepublican Paksha (Khoripa)\nChhattisgarh\tDURG\tA.K.MOHAN\t2286\tno\tAITC\tAll India Trinamool Congress\nChhattisgarh\tDURG\tSHITKARAN MAHILVAR\t1225\tno\tIND\tIndependent\nChhattisgarh\tDURG\tBALDAU PRASAD SAHU\t859\tno\tIND\tIndependent\nChhattisgarh\tDURG\tALFRED VIKAS LOUISE\t7098\tno\tIND\tIndependent\nChhattisgarh\tDURG\tSAHETRU SINGH\t2736\tno\tIND\tIndependent\nChhattisgarh\tDURG\tHIRALAL ANNANT\t2901\tno\tIND\tIndependent\nChhattisgarh\tDURG\tSHRIKANT SHAH\t5340\tno\tJD(U)\tJanata Dal  (United)\nChhattisgarh\tDURG\tSARJU PRASAD SHUKLA\t2114\tno\tIND\tIndependent\nChhattisgarh\tDURG\tSHEEMA RAHANGDALE\t5104\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tRAIPUR\tRAMESH BAIS\t654922\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tRAIPUR\tSHAMEEM KHAN\t6750\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tIMRRAN PASHA\t937\tno\tAKAAD\tAazadi Ka Antim Aandolan Dal\nChhattisgarh\tRAIPUR\tKRANTI CHANDRAKAR\t2521\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tLALIT KUMAR PATEL\t2184\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tTARZAN JANGDE\t962\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tKRISHNANANDAN SINGH\t1527\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tUMESH KUMAR VERMA\t3527\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tRAMGOPAL VERMA\t2619\tno\tAITC\tAll India Trinamool Congress\nChhattisgarh\tRAIPUR\tANIL KUMAR UMRE\t2700\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tRAIPUR\tHORILAL YADAV\t662\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tNone of the Above\t5796\tno\tNOTA\tNone of the Above\nChhattisgarh\tRAIPUR\tKAMALUDDIN\t4316\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tBASMATI KSHATRIYA\t817\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tPRAVIN JAIN\t729\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tSHANKAR LAL VARANDANI\t2726\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tRIJHENDRA DAHARIYA\t3398\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tBISAHAT KURRE\t1365\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nChhattisgarh\tRAIPUR\tMEERA SHARMA\t1405\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tAJAY VERMA\t4963\tno\tCSM\tChhattisgarh Swabhiman Manch\nChhattisgarh\tRAIPUR\tGURUJI VIRENDRA KUMAR DAHARIYA\t13147\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tRAIPUR\tSATYA NARAYAN SHARMA (SATTU BHAIYA)\t483276\tno\tINC\tIndian National Congress\nChhattisgarh\tRAIPUR\tRAMKRISHNA VERMA\t1596\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tMAHESH HARPAL\t1562\tno\tSSBD\tShakti Sena (Bharat Desh)\nChhattisgarh\tRAIPUR\tNAARAD PRASAD NISHAD\t2215\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tABDUL SAFIQ KHAN\t6250\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tNARESH TANDI\t1329\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tYASMIN BEG\t1086\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tMOINUDIN TANVAR(MONU)\t922\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tTEEKAM KUMAR TONDARE\t741\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tSATANAMI SANTOSH DAHARIYA\t1657\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tOMPRAKASH MESHRAM\t1026\tno\tRP(K)\tRepublican Paksha (Khoripa)\nChhattisgarh\tRAIPUR\tNAVEEN GUPTA\t1507\tno\tSP\tSamajwadi Party\nChhattisgarh\tRAIPUR\tSAIYYAD JAVED ALI\t724\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tSANDEEP TIWARI\t15139\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tRAIPUR\tOM PRAKASH DANDE\t12699\tno\tIND\tIndependent\nChhattisgarh\tRAIPUR\tMADAN LAL SAHU\t1143\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nChhattisgarh\tMAHASAMUND\tCHANDU LAL SAHU (CHANDU BHAIYA)\t503514\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tMAHASAMUND\tNone of the Above\t9955\tno\tNOTA\tNone of the Above\nChhattisgarh\tMAHASAMUND\tCHANDU LAL SAHU\t10797\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tCHANDOO LAL SAHU\t4718\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tCHANDURAM SAHU\t2167\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tCHANDU RAM SAHU\t3732\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tCHANDU RAM SAHU\t1628\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tDR. RUPANAND SOI\t1338\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tPREETI DHRUW\t3090\tno\tGGP\tGondvana Gantantra Party\nChhattisgarh\tMAHASAMUND\tCHANDU LAL SAHU\t12308\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tMOTILAL SAHU\t753\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tCHANDU LAL SAHU\t2268\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tMOTI LAL SAHU\t904\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tMOHAN LAL PATEL\t1395\tno\tBSCP\tBhartiya Shakti Chetna Party\nChhattisgarh\tMAHASAMUND\tCHANDU LAL SAHU\t7091\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tDEV PRASAD KELKAR\t1053\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tSUKHNANDAN DESHKAR\t3670\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tCHANDU LAL SAHU\t5497\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tGANGADHAR PATEL\t1279\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tVIJAY KUMAR PATEL\t1523\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tCHANDU LAL SAHU\t20255\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tAJIT JOGI\t502297\tno\tINC\tIndian National Congress\nChhattisgarh\tMAHASAMUND\tKANHAIYA LAL SAHU\t10600\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tMAHASAMUND\tCHAMPA LAL PATEL\t2409\tno\tIND\tIndependent\nChhattisgarh\tMAHASAMUND\tPATEL SHRI DHAR CHANDRAKAR\t1665\tno\tAD\tApna Dal\nChhattisgarh\tMAHASAMUND\tLAKSHMAN MASTURIYA\t5524\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tMAHASAMUND\tABHA PANDEY (GUDIA)\t5868\tno\tCSM\tChhattisgarh Swabhiman Manch\nChhattisgarh\tMAHASAMUND\tHEMANT PRADHAN\t3911\tno\tIND\tIndependent\nChhattisgarh\tBASTAR\tDINESH KASHYAP\t385829\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tBASTAR\tBIMALA SORI\t33883\tno\tCPI\tCommunist Party of India\nChhattisgarh\tBASTAR\tCOM. DEVCHAND DURUV\t6180\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nChhattisgarh\tBASTAR\tSONI SORI\t16903\tno\tAAAP\tAam Aadmi Party\nChhattisgarh\tBASTAR\tDEEPAK KARMA (BUNTY)\t261470\tno\tINC\tIndian National Congress\nChhattisgarh\tBASTAR\tARJUN SINGH THAKUR\t8966\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tBASTAR\tAD. MANBODH BAGHEL\t9774\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tBASTAR\tSHANKAR THAKUR\t8136\tno\tSP\tSamajwadi Party\nChhattisgarh\tBASTAR\tNone of the Above\t38772\tno\tNOTA\tNone of the Above\nChhattisgarh\tKANKER\tVIKRAM DEV USENDI\t465215\tyes\tBJP\tBharatiya Janata Party\nChhattisgarh\tKANKER\tNone of the Above\t31917\tno\tNOTA\tNone of the Above\nChhattisgarh\tKANKER\tFOOLODEVI NETAM\t430057\tno\tINC\tIndian National Congress\nChhattisgarh\tKANKER\tRAMESH KUMAR GAWDE\t23482\tno\tCPI\tCommunist Party of India\nChhattisgarh\tKANKER\tRAMSAY KORRAM\t9391\tno\tBSP\tBahujan Samaj Party\nChhattisgarh\tKANKER\tJAMUNA MANDAVI\t8972\tno\tCSM\tChhattisgarh Swabhiman Manch\nChhattisgarh\tKANKER\tMALIK RAM THAKUR\t8963\tno\tAPoI\tAmbedkarite Party of India\nChhattisgarh\tKANKER\tHEMLAL MARKAM\t7586\tno\tGGP\tGondvana Gantantra Party\nChhattisgarh\tKANKER\tAMAL SAY SORI\t6618\tno\tIND\tIndependent\nChhattisgarh\tKANKER\tMAHENDRA GAWADE\t14136\tno\tIND\tIndependent\nChhattisgarh\tKANKER\tSANTOSH KUMAR DHRUW\t10606\tno\tAAAP\tAam Aadmi Party\nJharkhand\tRajmahal\tVIJAY KUMAR HANSDAK\t379507\tyes\tJMM\tJharkhand Mukti Morcha\nJharkhand\tRajmahal\tDR. ANIL MURMU\t97374\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tRajmahal\tSUNIRAM HEMBROM\t6460\tno\tIND\tIndependent\nJharkhand\tRajmahal\tDR. SURAE SOREN\t7906\tno\tIND\tIndependent\nJharkhand\tRajmahal\tHEMLAL MURMU\t338170\tno\tBJP\tBharatiya Janata Party\nJharkhand\tRajmahal\tARJUN PRASAD SINGH\t6761\tno\tAJSUP\tAJSU Party\nJharkhand\tRajmahal\tKRISHNA SINGH\t16354\tno\tIUML\tIndian Union Muslim League\nJharkhand\tRajmahal\tJYOTIN SOREN\t58034\tno\tCPM\tCommunist Party of India  (Marxist)\nJharkhand\tRajmahal\tNone of the Above\t19875\tno\tNOTA\tNone of the Above\nJharkhand\tRajmahal\tBARNARD HEMBROM\t8686\tno\tRKSP\tRashtriya Krantikari Samajwadi Party\nJharkhand\tRajmahal\tTALA HANSDA\t7661\tno\tIND\tIndependent\nJharkhand\tRajmahal\tARUN MARANDI\t4775\tno\tBSP\tBahujan Samaj Party\nJharkhand\tDumka\tSHIBU SOREN\t335815\tyes\tJMM\tJharkhand Mukti Morcha\nJharkhand\tDumka\tSTEPHAN BESRA\t4310\tno\tJDP\tJharkhand Disom Party\nJharkhand\tDumka\tPULIS HEMRAM\t10283\tno\tIND\tIndependent\nJharkhand\tDumka\tUMANATH KOLE\t3733\tno\tIND\tIndependent\nJharkhand\tDumka\tNone of the Above\t18325\tno\tNOTA\tNone of the Above\nJharkhand\tDumka\tCHHAYA KOLE\t26442\tno\tCPI\tCommunist Party of India\nJharkhand\tDumka\tBAGHRAI SOREN\t9436\tno\tIND\tIndependent\nJharkhand\tDumka\tSOKOL HANSDA\t9059\tno\tBSP\tBahujan Samaj Party\nJharkhand\tDumka\tSUNIL SOREN\t296785\tno\tBJP\tBharatiya Janata Party\nJharkhand\tDumka\tBABU LAL MARANDI\t158122\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tDumka\tBITIYA MANJHI\t5813\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tDumka\tSAHADEO SOREN\t3739\tno\tAAAP\tAam Aadmi Party\nJharkhand\tDumka\tSHRILAL KISKU\t10347\tno\tIND\tIndependent\nJharkhand\tDumka\tBABULAL SOREN\t6284\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tDumka\tJOSHIL MURMU\t4567\tno\tIND\tIndependent\nJharkhand\tGodda\tNISHIKANT DUBEY\t380500\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tGodda\tMANRAJ\t20871\tno\tBSP\tBahujan Samaj Party\nJharkhand\tGodda\tRAJVARDHAN AZAD\t14034\tno\tJD(U)\tJanata Dal  (United)\nJharkhand\tGodda\tNIRANJAN PRASAD YADAV\t9059\tno\tIND\tIndependent\nJharkhand\tGodda\tSUBODH PRASAD\t10998\tno\tAJSUP\tAJSU Party\nJharkhand\tGodda\tPRADIP YADAV\t193506\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tGodda\tMAHENDRA MURMU\t3904\tno\tJVD\tJharkhand Vikas Dal\nJharkhand\tGodda\tSUNIL KUMAR GUPTA\t22349\tno\tIND\tIndependent\nJharkhand\tGodda\tDAMODAR SINGH\t28246\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tGodda\tGITA MANDAL\t4344\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tGodda\tFURKAN ANSARI\t319818\tno\tINC\tIndian National Congress\nJharkhand\tGodda\tMD. JENUL\t4466\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tGodda\tMD. NOOR HASSAN\t5132\tno\tSAP\tSamata Party\nJharkhand\tGodda\tNAVIN CHANDRA JHA\t7278\tno\tIND\tIndependent\nJharkhand\tGodda\tMANOJ KUMAR RAY\t7447\tno\tIND\tIndependent\nJharkhand\tGodda\tJAI SHANKAR JHA\t5080\tno\tAAAP\tAam Aadmi Party\nJharkhand\tGodda\tNone of the Above\t12410\tno\tNOTA\tNone of the Above\nJharkhand\tChatra\tSUNIL KUMAR SINGH\t295862\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tChatra\tNone of the Above\t5791\tno\tNOTA\tNone of the Above\nJharkhand\tChatra\tBANWARI SAW\t21261\tno\tCPI\tCommunist Party of India\nJharkhand\tChatra\tHAJI JAINUL ABEDIN\t14929\tno\tBSP\tBahujan Samaj Party\nJharkhand\tChatra\tRAMNARESH SINGH\t8341\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tChatra\tCHANDRADEO THAKUR\t4725\tno\tSP(I)\tSocialist Party (India)\nJharkhand\tChatra\tMAHESH SINGH YADAV\t5783\tno\tJD(U)\tJanata Dal  (United)\nJharkhand\tChatra\tDHIRAJ PRASAD SAHU\t117836\tno\tINC\tIndian National Congress\nJharkhand\tChatra\tBALWANT KUMAR\t6845\tno\tJKP\tJharkhand Party\nJharkhand\tChatra\tNILAM DEVI\t104176\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tChatra\tKESHWAR YADAV\t29754\tno\tSP\tSamajwadi Party\nJharkhand\tChatra\tSAGAR RAM\t5743\tno\tABHM\tAkhil Bharat Hindu Mahasabha\nJharkhand\tChatra\tSUDHANSHU SUMAN\t5843\tno\tIND\tIndependent\nJharkhand\tChatra\tNAGMANI\t35674\tno\tAJSUP\tAJSU Party\nJharkhand\tChatra\tROBIN SINGH\t2016\tno\tIND\tIndependent\nJharkhand\tChatra\tNAUSHAD ALAM\t7841\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tChatra\tMISTAR ALAM ASRAFEE\t4647\tno\tRJM\tRashtriya Jankranti Morcha\nJharkhand\tChatra\tSIMA CHANDRAVANSI\t17980\tno\tAAAP\tAam Aadmi Party\nJharkhand\tChatra\tRAJ KUMAR PAHAN\t10771\tno\tRADP\tRashtriya Deshaj Party\nJharkhand\tChatra\tASHISH BHUSHAN PATHAK\t2944\tno\tIND\tIndependent\nJharkhand\tChatra\tANIL KUMAR ORAON\t4209\tno\tIND\tIndependent\nJharkhand\tKodarma\tRAVINDRA KR. RAY\t365410\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tKodarma\tYOGENDRA RAM\t4516\tno\tABJS\tAkhil Bharatiya Jan Sangh\nJharkhand\tKodarma\tDR. SURAJ MANDAL\t6966\tno\tJVD\tJharkhand Vikas Dal\nJharkhand\tKodarma\tSHIVANATH SAV\t11117\tno\tIND\tIndependent\nJharkhand\tKodarma\tKRISHNA SINGH\t18727\tno\tJD(U)\tJanata Dal  (United)\nJharkhand\tKodarma\tJAIDEO CHAUDHARY\t7838\tno\tSP\tSamajwadi Party\nJharkhand\tKodarma\tMD. JALIL ANSARI\t4245\tno\tNLP\tNational Loktantrik Party\nJharkhand\tKodarma\tMANOJ KUMAR\t2873\tno\tIND\tIndependent\nJharkhand\tKodarma\tVIJAY KUMAR CHOURASIYA\t11880\tno\tAAAP\tAam Aadmi Party\nJharkhand\tKodarma\tMAHENDRA RAVIDAS\t6141\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tKodarma\tKANCHAN KUMARI\t20669\tno\tIND\tIndependent\nJharkhand\tKodarma\tPRANAV KUMAR VERMA\t160638\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tKodarma\tRAGHUNANDAN PRASAD VISHWAKARMA\t6992\tno\tAIFB\tAll India Forward Bloc\nJharkhand\tKodarma\tPHALJIT MAHTO\t5996\tno\tIND\tIndependent\nJharkhand\tKodarma\tRAMESHWAR PRASAD YADAV\t6334\tno\tIND\tIndependent\nJharkhand\tKodarma\tNAJRUL HASSAN HASHMI\t25522\tno\tAJSUP\tAJSU Party\nJharkhand\tKodarma\tTILAKDHARI PD SINGH\t60330\tno\tINC\tIndian National Congress\nJharkhand\tKodarma\tBISHESHWAR PRASAD\t12459\tno\tBSP\tBahujan Samaj Party\nJharkhand\tKodarma\tRAJKUMAR YADAV\t266756\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tKodarma\tMANJU KUMARI\t12785\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tKodarma\tNone of the Above\t6712\tno\tNOTA\tNone of the Above\nJharkhand\tGiridih\tRAVINDRA KUMAR PANDEY\t391913\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tGiridih\tSHIVA MAHTO\t3660\tno\tIND\tIndependent\nJharkhand\tGiridih\tSOHRAB SHAH\t5072\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tGiridih\tKARTIK MAHTO\t6341\tno\tBSP\tBahujan Samaj Party\nJharkhand\tGiridih\tDILIP KR RAJAK\t6406\tno\tIND\tIndependent\nJharkhand\tGiridih\tSAJJADUR RAB HASAMI\t3512\tno\tIND\tIndependent\nJharkhand\tGiridih\tGURJEET SINGH\t7071\tno\tAAAP\tAam Aadmi Party\nJharkhand\tGiridih\tNone of the Above\t4879\tno\tNOTA\tNone of the Above\nJharkhand\tGiridih\tJAGARNATH MAHTO\t351600\tno\tJMM\tJharkhand Mukti Morcha\nJharkhand\tGiridih\tSABA AHMAD\t57380\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tGiridih\tJALESHWAR MAHATO\t40010\tno\tJD(U)\tJanata Dal  (United)\nJharkhand\tGiridih\tSANJEEV KUMAR SINHA\t2988\tno\tAIFB\tAll India Forward Bloc\nJharkhand\tGiridih\tMRINAL KANTI DEV\t1997\tno\tSLP(L)\tSocialist Party (Lohia)\nJharkhand\tGiridih\tASHUTOSH VERMA\t11355\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tGiridih\tBIDESHI MAHTO\t4325\tno\tJDP\tJharkhand Disom Party\nJharkhand\tGiridih\tJEETENDRA KUMAR\t2825\tno\tSP\tSamajwadi Party\nJharkhand\tGiridih\tSANJEEV KUMAR MAHATO\t13132\tno\tIND\tIndependent\nJharkhand\tGiridih\tUMESH CHANDRA MEHTA\t55531\tno\tAJSUP\tAJSU Party\nJharkhand\tDhanbad\tPASHUPATI NATH SINGH\t543491\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tDhanbad\tRANBIR SINGH\t1516\tno\tIND\tIndependent\nJharkhand\tDhanbad\tDR. KRISHNA CHANDRA SINGHRAJ\t13200\tno\tIND\tIndependent\nJharkhand\tDhanbad\tCHANDRA SHEKHAR DUBEY\t29937\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tDhanbad\tPARSHURAM PASWAN\t1359\tno\tIND\tIndependent\nJharkhand\tDhanbad\tANAND MAHATO\t110185\tno\tMCO\tMarxist Co-Ordination\nJharkhand\tDhanbad\tHEMLATA S. MOHAN\t21277\tno\tAJSUP\tAJSU Party\nJharkhand\tDhanbad\tSAMARESH SINGH\t90926\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tDhanbad\tKISHOR KUMAR MURMU\t4531\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tDhanbad\tAJAY KUMAR DUBEY\t250537\tno\tINC\tIndian National Congress\nJharkhand\tDhanbad\tAKLU RAM MAHTO\t4169\tno\tSP\tSamajwadi Party\nJharkhand\tDhanbad\tGANPAT MAHTO\t5441\tno\tIND\tIndependent\nJharkhand\tDhanbad\tRAMESH BHARATI\t2052\tno\tIND\tIndependent\nJharkhand\tDhanbad\tJAYANT PANDEY\t2518\tno\tAIFB\tAll India Forward Bloc\nJharkhand\tDhanbad\tHIRALAL SANKHWAR\t8580\tno\tJKP\tJharkhand Party\nJharkhand\tDhanbad\tPAWAN MAHATO\t811\tno\tIND\tIndependent\nJharkhand\tDhanbad\tRAMESH KUMAR RAY\t965\tno\tIND\tIndependent\nJharkhand\tDhanbad\tSUMAN BANERJEE\t2104\tno\tIND\tIndependent\nJharkhand\tDhanbad\tVIJAY KANT MISHRA\t1537\tno\tIND\tIndependent\nJharkhand\tDhanbad\tMANILAL MAHTO\t1888\tno\tIND\tIndependent\nJharkhand\tDhanbad\tTARCH MURMU\t1190\tno\tIND\tIndependent\nJharkhand\tDhanbad\tBINOD SINGH\t1457\tno\tIND\tIndependent\nJharkhand\tDhanbad\tMANTOSH KUMAR MANDAL\t3413\tno\tAMB\tAmra Bangalee\nJharkhand\tDhanbad\tAHMAD HUSSAIN ANSARI\t3120\tno\tJDP\tJharkhand Disom Party\nJharkhand\tDhanbad\tRAMA KANT VERMA\t8948\tno\tAAAP\tAam Aadmi Party\nJharkhand\tDhanbad\tNone of the Above\t7675\tno\tNOTA\tNone of the Above\nJharkhand\tDhanbad\tGAUTAM KUMAR MAHATO\t3000\tno\tIND\tIndependent\nJharkhand\tDhanbad\tMD. ZUBAIR\t3094\tno\tAIMF\tAll India Minorities Front\nJharkhand\tDhanbad\tMUNSHI HEMBRAM\t1486\tno\tIND\tIndependent\nJharkhand\tDhanbad\tRITU RANI SINGH\t3814\tno\tIND\tIndependent\nJharkhand\tDhanbad\tPREM PRAKASH PASWAN\t1226\tno\tIND\tIndependent\nJharkhand\tDhanbad\tMADAN MOHAN RAM\t8455\tno\tBSP\tBahujan Samaj Party\nJharkhand\tRanchi\tRAM TAHAL CHOUDHARY\t448729\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tRanchi\tSAJIYA HAIDAR\t2195\tno\tIND\tIndependent\nJharkhand\tRanchi\tMAN SINGH MARDI\t4240\tno\tJKP(N)\tJharkhand Party  (Naren)\nJharkhand\tRanchi\tTHADDEUS LAKRA\t1504\tno\tIND\tIndependent\nJharkhand\tRanchi\tVIKAS CHANDRA SHARMA\t14898\tno\tCPIM\tCommunist Party of India (Marxist-Leninist) Red Star\nJharkhand\tRanchi\tAMIT KUMAR SINGH\t1987\tno\tIND\tIndependent\nJharkhand\tRanchi\tBALRAM KUMAR BEDIA\t2889\tno\tIND\tIndependent\nJharkhand\tRanchi\tRANJIT MAHTO\t3823\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tRanchi\tSURESH TOPPO\t4085\tno\tJDP\tJharkhand Disom Party\nJharkhand\tRanchi\tNone of the Above\t6900\tno\tNOTA\tNone of the Above\nJharkhand\tRanchi\tAMANULLAH\t8497\tno\tAAAP\tAam Aadmi Party\nJharkhand\tRanchi\tLAL JOTINDRA DEV\t3411\tno\tAKBJHP\tAkhil Bhartiya Jharkhand Party\nJharkhand\tRanchi\tBISHESHWAR MAHTO\t2130\tno\tSAP\tSamata Party\nJharkhand\tRanchi\t\"YUGESHWAR MARAR \"\"DEEN\"\"\"\t4409\tno\tMMM\tManav Mukti Morcha\nJharkhand\tRanchi\tBIRENDRA KUMAR JAISWAL\t1366\tno\tIND\tIndependent\nJharkhand\tRanchi\tDURGA MUNDA\t2502\tno\tBSP\tBahujan Samaj Party\nJharkhand\tRanchi\tBANDHU TIRKEY\t46126\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tRanchi\tBAHADUR  URAON\t3877\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tRanchi\tRAMPODO MAHTO\t1296\tno\tIND\tIndependent\nJharkhand\tRanchi\tABUL HASAN\t1219\tno\tIND\tIndependent\nJharkhand\tRanchi\tRAM LAL MAHTO\t4190\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nJharkhand\tRanchi\tKAFILUR RAHMAN\t4696\tno\tIND\tIndependent\nJharkhand\tRanchi\tRAJENDRA SINGH MUNDA\t10178\tno\tCPM\tCommunist Party of India  (Marxist)\nJharkhand\tRanchi\tARSAD AYUB\t2083\tno\tIND\tIndependent\nJharkhand\tRanchi\tCHINTAMANI MAHTO\t1605\tno\tIND\tIndependent\nJharkhand\tRanchi\tSUBODH KANT SAHAY\t249426\tno\tINC\tIndian National Congress\nJharkhand\tRanchi\tANJANI PANDEY\t1250\tno\tIND\tIndependent\nJharkhand\tRanchi\tAMITABH CHOUDHARY\t67712\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tRanchi\tSUDESH KUMAR MAHTO\t142560\tno\tAJSUP\tAJSU Party\nJharkhand\tJamshedpur\tBIDYUT BARAN MAHATO\t464153\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tJamshedpur\tAJAY KUMAR\t364277\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tJamshedpur\tSEKH AKHIRUDDIN\t4822\tno\tJKP(N)\tJharkhand Party  (Naren)\nJharkhand\tJamshedpur\tUPENDRA KARMAKAR\t4429\tno\tIND\tIndependent\nJharkhand\tJamshedpur\tANGAD MAHATO\t12632\tno\tAMB\tAmra Bangalee\nJharkhand\tJamshedpur\tMOBIN KHAN\t5406\tno\tIND\tIndependent\nJharkhand\tJamshedpur\tHARI ORAWN\t4485\tno\tIND\tIndependent\nJharkhand\tJamshedpur\tOM PRAKASH ANAND\t5655\tno\tIND\tIndependent\nJharkhand\tJamshedpur\tNIROOP MAHANTY\t138109\tno\tJMM\tJharkhand Mukti Morcha\nJharkhand\tJamshedpur\tNone of the Above\t15629\tno\tNOTA\tNone of the Above\nJharkhand\tJamshedpur\tD.G.RAJA\t3414\tno\tSP\tSamajwadi Party\nJharkhand\tJamshedpur\tDAMAN CHANDRA BHAKAT\t4606\tno\tBSP\tBahujan Samaj Party\nJharkhand\tJamshedpur\tKUMAR CHANDRA MARDI\t7145\tno\tAAAP\tAam Aadmi Party\nJharkhand\tJamshedpur\tJOSAI MARDI\t5378\tno\tRADP\tRashtriya Deshaj Party\nJharkhand\tJamshedpur\tSITA RAM TUDU\t3700\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nJharkhand\tJamshedpur\tDINESH MAHATO\t5300\tno\tIND\tIndependent\nJharkhand\tSinghbhum\tLAXMAN GILUWA\t303131\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tSinghbhum\tRAJENDRA PURTY\t11507\tno\tJKP\tJharkhand Party\nJharkhand\tSinghbhum\tSNEHLATA JOJO\t12877\tno\tIND\tIndependent\nJharkhand\tSinghbhum\tNAVEEN MURMU\t8607\tno\tRADP\tRashtriya Deshaj Party\nJharkhand\tSinghbhum\tRUPSINGH HEMBROM\t8895\tno\tIND\tIndependent\nJharkhand\tSinghbhum\tNone of the Above\t27037\tno\tNOTA\tNone of the Above\nJharkhand\tSinghbhum\tBUDHRAM LAGURI\t8346\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tSinghbhum\tMANOHAR SINKU\t9303\tno\tBSP\tBahujan Samaj Party\nJharkhand\tSinghbhum\tSALKHAN MURMU\t25547\tno\tJDP\tJharkhand Disom Party\nJharkhand\tSinghbhum\tCHITRASEN SINKU\t111796\tno\tINC\tIndian National Congress\nJharkhand\tSinghbhum\tDASHRATH GAGRAI\t35681\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tSinghbhum\tJOHN MIRAN MUNDA\t16952\tno\tAIFB\tAll India Forward Bloc\nJharkhand\tSinghbhum\tGEETA KORA\t215607\tno\tJBSP\tJai Bharat Samanta Party\nJharkhand\tKhunti\tKARIA MUNDA\t269185\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tKhunti\tBUDAY MUNDA\t4841\tno\tAKBJHP\tAkhil Bhartiya Jharkhand Party\nJharkhand\tKhunti\tKALI CHARAN MUNDA\t147017\tno\tINC\tIndian National Congress\nJharkhand\tKhunti\tANOSH EKKA\t176937\tno\tJKP\tJharkhand Party\nJharkhand\tKhunti\tSUBODH PURTY\t6407\tno\tBSP\tBahujan Samaj Party\nJharkhand\tKhunti\tNEIL TIRKEY\t27158\tno\tAJSUP\tAJSU Party\nJharkhand\tKhunti\tJITENDRA MANKI  ALIAS JITENDRA PRASAD MANKI\t6596\tno\tIND\tIndependent\nJharkhand\tKhunti\tBASANT KUMAR LONGA\t24514\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tKhunti\tJAITUN TUTI\t11364\tno\tIND\tIndependent\nJharkhand\tKhunti\tDAYAMANI BARLA\t11822\tno\tAAAP\tAam Aadmi Party\nJharkhand\tKhunti\tASRITA TUTI\t4349\tno\tIND\tIndependent\nJharkhand\tKhunti\tNone of the Above\t23816\tno\tNOTA\tNone of the Above\nJharkhand\tKhunti\tKALYAN NAG\t4958\tno\tIND\tIndependent\nJharkhand\tKhunti\tNITIMA BODRA BARI\t4425\tno\tSP\tSamajwadi Party\nJharkhand\tKhunti\tMAHADEV RAVINATH PAHAN\t13566\tno\tRADP\tRashtriya Deshaj Party\nJharkhand\tLohardaga\tSUDARSHAN BHAGAT\t226666\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tLohardaga\tNone of the Above\t16764\tno\tNOTA\tNone of the Above\nJharkhand\tLohardaga\tRAMESHWAR ORAON\t220177\tno\tINC\tIndian National Congress\nJharkhand\tLohardaga\tMAHENDRA  ORAON\t6436\tno\tIND\tIndependent\nJharkhand\tLohardaga\tRANJIT ORAON\t6037\tno\tIND\tIndependent\nJharkhand\tLohardaga\tJAYRAM INDWAR\t11794\tno\tBSP\tBahujan Samaj Party\nJharkhand\tLohardaga\tNAWAL KISHOR SINGH\t5870\tno\tIND\tIndependent\nJharkhand\tLohardaga\tLAL SAY KUMAR BHAGAT\t13252\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tLohardaga\tCHAMRA LINDA\t118355\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tLohardaga\tBIRENDRA BHAGAT\t26109\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tPalamau\tVISHNU DAYAL RAM\t476513\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tPalamau\tSHUSHMA MEHTA\t8386\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tPalamau\tLALDEO RAM\t4432\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tPalamau\tRAMPATI RANJAN\t20481\tno\tBSP\tBahujan Samaj Party\nJharkhand\tPalamau\tGHURAN RAM\t156832\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tPalamau\tKAMESHWAR BAITHA\t37043\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tPalamau\tSHYAM LAL RAM\t9592\tno\tIND\tIndependent\nJharkhand\tPalamau\tASHOK  RAM\t5648\tno\tIND\tIndependent\nJharkhand\tPalamau\tJORAWAR RAM\t7538\tno\tJD(U)\tJanata Dal  (United)\nJharkhand\tPalamau\tMANOJ KUMAR\t212571\tno\tRJD\tRashtriya Janata Dal\nJharkhand\tPalamau\tRAM BADAN RAM\t4976\tno\tSP\tSamajwadi Party\nJharkhand\tPalamau\tVINOD RAM\t5879\tno\tIND\tIndependent\nJharkhand\tPalamau\tRAVINDRA KUMAR RAVI\t9145\tno\tIND\tIndependent\nJharkhand\tPalamau\tNone of the Above\t18287\tno\tNOTA\tNone of the Above\nJharkhand\tHazaribagh\tJAYANT SINHA\t406931\tyes\tBJP\tBharatiya Janata Party\nJharkhand\tHazaribagh\tNone of the Above\t6827\tno\tNOTA\tNone of the Above\nJharkhand\tHazaribagh\tBHUVNESHWAR PRASAD MEHTA\t30326\tno\tCPI\tCommunist Party of India\nJharkhand\tHazaribagh\tARUN KUMAR MISHRA\t30408\tno\tJVM\tJharkhand Vikas Morcha (Prajatantrik)\nJharkhand\tHazaribagh\tYASHPAL YADAV\t3540\tno\tIND\tIndependent\nJharkhand\tHazaribagh\tSAURABH NARAIN SINGH\t247803\tno\tINC\tIndian National Congress\nJharkhand\tHazaribagh\tTETRI @ SHEELA DEVI\t8159\tno\tBSP\tBahujan Samaj Party\nJharkhand\tHazaribagh\tMO. SHANUL HAQUE\t3189\tno\tIUML\tIndian Union Muslim League\nJharkhand\tHazaribagh\tMITHILESH KUMAR DANGI\t7140\tno\tAAAP\tAam Aadmi Party\nJharkhand\tHazaribagh\tRAMAVTAR MAHTO\t3365\tno\tIND\tIndependent\nJharkhand\tHazaribagh\tPRAMOD KUMAR MAHTHA\t3076\tno\tpms\tPragatisheel Magahi Samaj\nJharkhand\tHazaribagh\tRAMESHWAR RAM KUSHWAHA\t4423\tno\tAIFB\tAll India Forward Bloc\nJharkhand\tHazaribagh\tJAVED ISLAM\t8453\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nJharkhand\tHazaribagh\tLOKNATH MAHTO\t156186\tno\tAJSUP\tAJSU Party\nJharkhand\tHazaribagh\tMAHENDRA KISHOR MEHTA\t3752\tno\tIND\tIndependent\nJharkhand\tHazaribagh\tDR. DHIRENDRA KUMAR RAJ\t4481\tno\tIND\tIndependent\nJharkhand\tHazaribagh\tABDUL RAHIM\t2941\tno\tBMUP\tBahujan Mukti Party\nJharkhand\tHazaribagh\tAJIT KUMAR SINGH\t8360\tno\tAITC\tAll India Trinamool Congress\nJharkhand\tHazaribagh\tPRAYAG PRASAD\t8085\tno\tIND\tIndependent\nJharkhand\tHazaribagh\tBADRI GOPE\t14555\tno\tIND\tIndependent\nJharkhand\tHazaribagh\tBALDEO GANJHU\t5152\tno\tIND\tIndependent\nUttarakhand\tTehri Garhwal\tMALA RAJYA LAXMI SHAH\t446733\tyes\tBJP\tBharatiya Janata Party\nUttarakhand\tTehri Garhwal\tNone of the Above\t10762\tno\tNOTA\tNone of the Above\nUttarakhand\tTehri Garhwal\tJAI PRAKASH UPADHYAY\t1619\tno\tIND\tIndependent\nUttarakhand\tTehri Garhwal\tPREM SINGH YADAV\t2007\tno\tIND\tIndependent\nUttarakhand\tTehri Garhwal\tGAURAV PUNDIR\t2302\tno\tIND\tIndependent\nUttarakhand\tTehri Garhwal\tJAUHARI LAL SUMAN(MUNI)\t877\tno\tBMNSP\tBharatiya Mool Niwasi Samaj Party\nUttarakhand\tTehri Garhwal\tHAJI MOHD. YOUSUF\t900\tno\tPECP\tPeace Party\nUttarakhand\tTehri Garhwal\tSOHAN PAL SINGH\t6672\tno\tIND\tIndependent\nUttarakhand\tTehri Garhwal\tRAJU MAURYA\t1935\tno\tIND\tIndependent\nUttarakhand\tTehri Garhwal\tSHISHPAL SINGH\t11538\tno\tBSP\tBahujan Samaj Party\nUttarakhand\tTehri Garhwal\tANOOP NAUTIYAL\t24043\tno\tAAAP\tAam Aadmi Party\nUttarakhand\tTehri Garhwal\tNAVNEET SINGH GOSAIN\t1629\tno\traup\tRashtriya Uttarakhand Party\nUttarakhand\tTehri Garhwal\tSAKET BAHUGUNA\t254230\tno\tINC\tIndian National Congress\nUttarakhand\tTehri Garhwal\tSHIV PRASAD DEOLI\t6577\tno\tCPM\tCommunist Party of India  (Marxist)\nUttarakhand\tTehri Garhwal\tBARHM DEV JHA\t1554\tno\tIND\tIndependent\nUttarakhand\tTehri Garhwal\tJAPINDER SINGH\t1958\tno\tRLD\tRashtriya Lok Dal\nUttarakhand\tTehri Garhwal\tSATENDRA SHARMA\t878\tno\tNADP\tNaya Daur Party\nUttarakhand\tGarhwal\t(MAJ GEN (RETD.) ) BHUWAN CHANDRA KHANDURI (AVSM)\t405690\tyes\tBJP\tBharatiya Janata Party\nUttarakhand\tGarhwal\tINDRESH MAIKHURI\t7470\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttarakhand\tGarhwal\t(DR) RAKESH SINGH\t6727\tno\tAAAP\tAam Aadmi Party\nUttarakhand\tGarhwal\tNARENDRA SINGH NEGI\t6525\tno\tCPI\tCommunist Party of India\nUttarakhand\tGarhwal\tRENU AGARWAL\t3051\tno\tIND\tIndependent\nUttarakhand\tGarhwal\tDHEER SINGH BISHT\t9250\tno\tBSP\tBahujan Samaj Party\nUttarakhand\tGarhwal\tABDUL SALAM\t4510\tno\tIND\tIndependent\nUttarakhand\tGarhwal\tBHUPAL SINGH GUSAIN (UKD)\t6699\tno\tIND\tIndependent\nUttarakhand\tGarhwal\tANUP NEGI\t2279\tno\tIND\tIndependent\nUttarakhand\tGarhwal\t(DR) HARAK SINGH RAWAT\t221164\tno\tINC\tIndian National Congress\nUttarakhand\tGarhwal\tNone of the Above\t8659\tno\tNOTA\tNone of the Above\nUttarakhand\tAlmora\tAJAY TAMTA\t348186\tyes\tBJP\tBharatiya Janata Party\nUttarakhand\tAlmora\tNone of the Above\t15245\tno\tNOTA\tNone of the Above\nUttarakhand\tAlmora\tBANSI RAM\t3857\tno\tIND\tIndependent\nUttarakhand\tAlmora\tAMAR PRAKASH\t2045\tno\tUPP\tUTTARAKHAND PARIVARTAN PARTY\nUttarakhand\tAlmora\tSAJJAN LAL TAMTA\t8387\tno\tIND\tIndependent\nUttarakhand\tAlmora\tVIJAY KUMAR\t2392\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttarakhand\tAlmora\tHARISH CHANDRA ARYA\t7916\tno\tAAAP\tAam Aadmi Party\nUttarakhand\tAlmora\tBAHADUR RAM DHAUNI\t14150\tno\tBSP\tBahujan Samaj Party\nUttarakhand\tAlmora\tBHAGAWATI PRASAD TRIKOTI\t1851\tno\tSP\tSamajwadi Party\nUttarakhand\tAlmora\tPRADEEP TAMTA\t252496\tno\tINC\tIndian National Congress\nUttarakhand\tNainital-udhamsingh Nagar\tBHAGAT SINGH KOSHYARI\t636769\tyes\tBJP\tBharatiya Janata Party\nUttarakhand\tNainital-udhamsingh Nagar\tLAIK AHMED\t59245\tno\tBSP\tBahujan Samaj Party\nUttarakhand\tNainital-udhamsingh Nagar\tKHASHTI SUYAL\t802\tno\tBCP\tBhartiya Chaitanya Party\nUttarakhand\tNainital-udhamsingh Nagar\tASHARAM KASHYAP\t1037\tno\tRMGP\tRashtriya mahan Gantantra Party\nUttarakhand\tNainital-udhamsingh Nagar\tBHUWAN CHANDRA JOSHI\t3180\tno\tIND\tIndependent\nUttarakhand\tNainital-udhamsingh Nagar\tSHEESHPALARYA\t2706\tno\tANC\tAmbedkar National Congress\nUttarakhand\tNainital-udhamsingh Nagar\tBALLI SINGH CHEEMA\t13472\tno\tAAAP\tAam Aadmi Party\nUttarakhand\tNainital-udhamsingh Nagar\tD.S.RAWAL\t3392\tno\tBKLJP\tBharat Ki Lok Jimmedar Party\nUttarakhand\tNainital-udhamsingh Nagar\tKAILASH PANDEY\t1530\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nUttarakhand\tNainital-udhamsingh Nagar\tKAILASH CHANDRA\t1635\tno\tIND\tIndependent\nUttarakhand\tNainital-udhamsingh Nagar\tAVTAR SINGH\t9825\tno\tSP\tSamajwadi Party\nUttarakhand\tNainital-udhamsingh Nagar\tK.C.SINGH BABA\t352052\tno\tINC\tIndian National Congress\nUttarakhand\tNainital-udhamsingh Nagar\tRAM SINGH\t1064\tno\tPECP\tPeace Party\nUttarakhand\tNainital-udhamsingh Nagar\tSHAKIL AHMED\t2529\tno\tIND\tIndependent\nUttarakhand\tNainital-udhamsingh Nagar\tBHAGWAN SWAROOP\t1869\tno\tIND\tIndependent\nUttarakhand\tNainital-udhamsingh Nagar\tNone of the Above\t10328\tno\tNOTA\tNone of the Above\nUttarakhand\tHardwar\tRAMESH POKHRIYAL NISHANK\t592320\tyes\tBJP\tBharatiya Janata Party\nUttarakhand\tHardwar\tNone of the Above\t3049\tno\tNOTA\tNone of the Above\nUttarakhand\tHardwar\tRAZIA BEG\t3741\tno\tIND\tIndependent\nUttarakhand\tHardwar\tPRAMOD RANA\t765\tno\tIND\tIndependent\nUttarakhand\tHardwar\tANAND KUMAR VERMA\t710\tno\tHKRD\tHindustan Krantikari Dal\nUttarakhand\tHardwar\tSANJAY GOYAL\t915\tno\tNADP\tNaya Daur Party\nUttarakhand\tHardwar\tVISHAL CHAUDHARY\t1043\tno\tSHS\tShivsena\nUttarakhand\tHardwar\tD.D.SHARMA\t2067\tno\tIND\tIndependent\nUttarakhand\tHardwar\tV. K. KAMPANI\t637\tno\tPHRC\tPoorvanchal Rashtriya Congress\nUttarakhand\tHardwar\tABDUL ALEEM\t1682\tno\tIND\tIndependent\nUttarakhand\tHardwar\tISHWAR CHAND\t3740\tno\tIND\tIndependent\nUttarakhand\tHardwar\tRAMNARESH YADAV\t1117\tno\tkajp\tKalyankari Jantantrik Party\nUttarakhand\tHardwar\tPERVEZ AQIL\t454\tno\tAIMF\tAll India Minorities Front\nUttarakhand\tHardwar\tSUNIL\t820\tno\tIND\tIndependent\nUttarakhand\tHardwar\tMUKESH SAINI\t664\tno\tBMUP\tBahujan Mukti Party\nUttarakhand\tHardwar\tRAKESH\t1015\tno\tIND\tIndependent\nUttarakhand\tHardwar\tBRIGADIER GOVIND PRASAD BARTHWAL VSM\t1888\tno\tIND\tIndependent\nUttarakhand\tHardwar\tASRAF\t2535\tno\tIND\tIndependent\nUttarakhand\tHardwar\tANITA SAINI\t5765\tno\tSP\tSamajwadi Party\nUttarakhand\tHardwar\tJAI SINGH\t2769\tno\tIND\tIndependent\nUttarakhand\tHardwar\tMOHAR SINGH KATARIA\t1051\tno\tIND\tIndependent\nUttarakhand\tHardwar\tJAVED\t614\tno\tABML(S)\tAkhil Bharatiya Muslim League (Secular)\nUttarakhand\tHardwar\tKANCHAN CHOUDHRY\t18170\tno\tAAAP\tAam Aadmi Party\nUttarakhand\tHardwar\tRENUKA RAWAT\t414498\tno\tINC\tIndian National Congress\nUttarakhand\tHardwar\tHAJI MOHD ISLAM\t113663\tno\tBSP\tBahujan Samaj Party\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tBISHNU PADA RAY\t90969\tyes\tBJP\tBharatiya Janata Party\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tNone of the Above\t1564\tno\tNOTA\tNone of the Above\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tPRAKASH MINJ\t1587\tno\tIND\tIndependent\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tANITA MONDAL\t2283\tno\tAITC\tAll India Trinamool Congress\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tSARAVANAN\t847\tno\tIND\tIndependent\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tN K P NAIR\t436\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tV V KHALID\t429\tno\tIND\tIndependent\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tGOUR CHANDRA MAJUMDER\t1139\tno\tBSP\tBahujan Samaj Party\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tR.S.UMA BHARATTHY\t1151\tno\tNCP\tNationalist Congress Party\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tKULDEEP RAI SHARMA\t83157\tno\tINC\tIndian National Congress\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tSANJAY MESHACK\t3737\tno\tAAAP\tAam Aadmi Party\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tRAGHUBIR SINGH\t379\tno\tSP\tSamajwadi Party\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tA PANDIAN\t225\tno\tAIFB\tAll India Forward Bloc\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tN KANNAN\t314\tno\tIND\tIndependent\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tC G SAJI KUMAR\t334\tno\tIND\tIndependent\nAndaman & Nicobar Islands\tAndaman & Nicobar Islands\tK.G.DAS\t1777\tno\tCPM\tCommunist Party of India  (Marxist)\nChandigarh\tCHANDIGARH\tKHER KIRRON ANUPAM\t191362\tyes\tBJP\tBharatiya Janata Party\nChandigarh\tCHANDIGARH\tRAMESH CHANDER\t743\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tREENA SHARMA\t2643\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tSURENDER KUMAR\t550\tno\tBMUP\tBahujan Mukti Party\nChandigarh\tCHANDIGARH\tPAWAN KUMAR SHARMA\t997\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tSHAMSHER SINGH\t2054\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tSURINDER BHARDWAJ\t434\tno\tJD(U)\tJanata Dal  (United)\nChandigarh\tCHANDIGARH\tJASMER SINGH\t389\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tDEEPAK SHUKLA\t613\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tPAWAN KUMAR BANSAL\t121720\tno\tINC\tIndian National Congress\nChandigarh\tCHANDIGARH\tJANNAT JAHAN-UL-HAQ (NAINA)\t15934\tno\tBSP\tBahujan Samaj Party\nChandigarh\tCHANDIGARH\tDHARMENDER SINGH\t566\tno\tHEP\tHindustan Ekta Party\nChandigarh\tCHANDIGARH\tAJAY SINGLA\t660\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tKHEM LAL BANSAL\t640\tno\tIVD\tInqalab Vikas Dal\nChandigarh\tCHANDIGARH\tAMARJIT KAUR\t397\tno\tIND\tIndependent\nChandigarh\tCHANDIGARH\tGULKIRAT KAUR PANAG\t108679\tno\tAAAP\tAam Aadmi Party\nChandigarh\tCHANDIGARH\tKAWALJIT SINGH\t1968\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nChandigarh\tCHANDIGARH\tNone of the Above\t3106\tno\tNOTA\tNone of the Above\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tPATEL NATUBHAI GOMANBHAI\t80790\tyes\tBJP\tBharatiya Janata Party\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tKAMUBEN KHALPABHAI PATEL\t418\tno\tIND\tIndependent\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tPRAKASH VANSHA KHANVELKAR\t1214\tno\tIND\tIndependent\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tBHURKUD DILIPBHAI NAVJIBHAI\t1154\tno\tIND\tIndependent\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tKHULAT JANIYABHAI CHANDUBHAI\t1611\tno\tNCP\tNationalist Congress Party\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tDELKAR MOHANBHAI SANJIBHAI\t74576\tno\tINC\tIndian National Congress\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tPATEL HEMANTBHAI KESHAVBHAI\t982\tno\tBSP\tBahujan Samaj Party\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tKISHAN NATHU GHUTIYA\t378\tno\tSHS\tShivsena\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tPATEL CHHOTUBHAI BABUBHAI\t621\tno\tAAAP\tAam Aadmi Party\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tVIJAYBHAI NATHUBHAI BHOYA\t232\tno\tJD(U)\tJanata Dal  (United)\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tUMEDBHAI LALLUBHAI PATEL\t348\tno\tBMUP\tBahujan Mukti Party\nDadra & Nagar Haveli\tDadar & Nagar Haveli\tNone of the Above\t2962\tno\tNOTA\tNone of the Above\nDaman & Diu\tDaman & diu\tPATEL LALUBHAI BABUBHAI\t46960\tyes\tBJP\tBharatiya Janata Party\nDaman & Diu\tDaman & diu\tNone of the Above\t1316\tno\tNOTA\tNone of the Above\nDaman & Diu\tDaman & diu\tBHAVESH PATEL\t490\tno\tBSP\tBahujan Samaj Party\nDaman & Diu\tDaman & diu\tKESSUR GOAN\t729\tno\tAAAP\tAam Aadmi Party\nDaman & Diu\tDaman & diu\tKETAN DAHYABHAI PATEL\t37738\tno\tINC\tIndian National Congress\nNCT OF Delhi\tCHANDNI CHOWK\tDR. HARSH VARDHAN\t437938\tyes\tBJP\tBharatiya Janata Party\nNCT OF Delhi\tCHANDNI CHOWK\tMD. AFAQ\t251\tno\tJMBP\tJai Maha Bharath Party\nNCT OF Delhi\tCHANDNI CHOWK\tABDUL AMIR AMIRO\t1765\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tDILDAR HUSSAIN BEG\t28605\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tVIJAY SHANKER CHAUBEY\t1829\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tGAGAN RASTOGI\t1424\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tSURENDER SINGH\t789\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tBIR SINGH SONI\t881\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tMUSARRAT JAHAN\t592\tno\tRJ\tRashtriya Janmorcha\nNCT OF Delhi\tCHANDNI CHOWK\tMOHD. MURSALEEM\t538\tno\tPECP\tPeace Party\nNCT OF Delhi\tCHANDNI CHOWK\tASHUTOSH\t301618\tno\tAAAP\tAam Aadmi Party\nNCT OF Delhi\tCHANDNI CHOWK\tHARIOM SHARMA\t3531\tno\tAITC\tAll India Trinamool Congress\nNCT OF Delhi\tCHANDNI CHOWK\tNARENDRA KUMAR PANDEY\t7746\tno\tBSP\tBahujan Samaj Party\nNCT OF Delhi\tCHANDNI CHOWK\tBALRAM BARI\t602\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tJAGMOHAN SINGH BAKSHI\t1553\tno\tATBP\tAtulya Bharat Party\nNCT OF Delhi\tCHANDNI CHOWK\tDR. TARUN KUMAR\t263\tno\tVSP\tVishva SHakti Party\nNCT OF Delhi\tCHANDNI CHOWK\tASHUTOSH\t4504\tno\tIND\tIndependent\nNCT OF Delhi\tCHANDNI CHOWK\tDHAN RAJ CHAUHAN\t308\tno\tNADP\tNaya Daur Party\nNCT OF Delhi\tCHANDNI CHOWK\tALTAF HUSAIN\t272\tno\tbjdi\tBhartiya Janta Dal (Integrated)\nNCT OF Delhi\tCHANDNI CHOWK\tAJAY KUMAR KHEMKA\t1643\tno\tkajp\tKalyankari Jantantrik Party\nNCT OF Delhi\tCHANDNI CHOWK\tKAPIL SIBAL\t176206\tno\tINC\tIndian National Congress\nNCT OF Delhi\tCHANDNI CHOWK\tTARIQ MIRZA\t253\tno\tRPI(A)\tRepublican Party of India (A)\nNCT OF Delhi\tCHANDNI CHOWK\tJAGJEET SINGH\t290\tno\tBGTD\tBharatiya Gaon Taj Dal\nNCT OF Delhi\tCHANDNI CHOWK\tRASHMI KASHYAP\t538\tno\tBPC\tBhartiya Pragatisheel Congress\nNCT OF Delhi\tCHANDNI CHOWK\tSANJAY GANDHI\t3390\tno\tBRAVP\tBraj Vikas Party\nNCT OF Delhi\tCHANDNI CHOWK\tNone of the Above\t4534\tno\tNOTA\tNone of the Above\nNCT OF Delhi\tNORTH EAST DELHI\tMANOJ TIWARI\t596125\tyes\tBJP\tBharatiya Janata Party\nNCT OF Delhi\tNORTH EAST DELHI\tANAND KUMAR\t452041\tno\tAAAP\tAam Aadmi Party\nNCT OF Delhi\tNORTH EAST DELHI\tABDUL SAMI SALMANI\t28135\tno\tBSP\tBahujan Samaj Party\nNCT OF Delhi\tNORTH EAST DELHI\tA.K. AGGARWAL\t3191\tno\tAGRJP\tAgar Jan Party\nNCT OF Delhi\tNORTH EAST DELHI\tPROF. NARENDERA SHARMA\t508\tno\tSUCI\tSOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)\nNCT OF Delhi\tNORTH EAST DELHI\tDINESH PAL SINGH\t1263\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tDAN BAHADUR YADAV\t1921\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tJAI PARKASH AGARWAL\t214792\tno\tINC\tIndian National Congress\nNCT OF Delhi\tNORTH EAST DELHI\tDHARAMVEER SINGH\t601\tno\tREP\tRashtriya Ekta Party\nNCT OF Delhi\tNORTH EAST DELHI\tMUKESH KUMAR ARORA\t1669\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tALI HUSAN\t1695\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tMOHD. HASNAIN\t879\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tVINAY\t818\tno\tBSKP\tBharatiya Sarvodaya Kranti Party\nNCT OF Delhi\tNORTH EAST DELHI\tHARSH KUMAR\t663\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tRAJAN LAL\t504\tno\tBRPP\tBharatiya Republican Paksha\nNCT OF Delhi\tNORTH EAST DELHI\tRAMESH SINGH SIRAL\t465\tno\tNADP\tNaya Daur Party\nNCT OF Delhi\tNORTH EAST DELHI\tOM DUTT SHARMA\t658\tno\tSHS\tShivsena\nNCT OF Delhi\tNORTH EAST DELHI\tMOHD. ARIF SIDDIQUE\t784\tno\tAITC\tAll India Trinamool Congress\nNCT OF Delhi\tNORTH EAST DELHI\tPRADESH KUMAR\t489\tno\tANC\tAmbedkar National Congress\nNCT OF Delhi\tNORTH EAST DELHI\tH.K. MAHENDRU\t2419\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tROSHAN ALI\t745\tno\tNLP\tNational Loktantrik Party\nNCT OF Delhi\tNORTH EAST DELHI\tAMIT KUMAR SHARMA\t1892\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH EAST DELHI\tMOHD. HASNAN KHAN\t1257\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nNCT OF Delhi\tNORTH EAST DELHI\tNone of the Above\t3824\tno\tNOTA\tNone of the Above\nNCT OF Delhi\tEAST DELHI\tMAHEISH GIRRI\t572202\tyes\tBJP\tBharatiya Janata Party\nNCT OF Delhi\tEAST DELHI\tNone of the Above\t4975\tno\tNOTA\tNone of the Above\nNCT OF Delhi\tEAST DELHI\tRAJESH\t422\tno\tANC\tAmbedkar National Congress\nNCT OF Delhi\tEAST DELHI\tMOHAMMAD SHAKEEL SAIFI\t18575\tno\tBSP\tBahujan Samaj Party\nNCT OF Delhi\tEAST DELHI\tDEEPAK KUMAR\t674\tno\tIND\tIndependent\nNCT OF Delhi\tEAST DELHI\tDR. NABHIT KAPUR\t210\tno\tNADP\tNaya Daur Party\nNCT OF Delhi\tEAST DELHI\tJAGANNATH TIWARI\t1153\tno\tIND\tIndependent\nNCT OF Delhi\tEAST DELHI\tVIRENDRA MAYER\t395\tno\tSJP(R)\tSamajwadi Janata Party (Rashtriya)\nNCT OF Delhi\tEAST DELHI\tMOHD NASEER\t339\tno\tPECP\tPeace Party\nNCT OF Delhi\tEAST DELHI\tARUN THAKUR\t1708\tno\tSPP\tSamyak Parivartan Party\nNCT OF Delhi\tEAST DELHI\tRAM BRIKSH MALL\t527\tno\tHND\tHindusthan Nirman Dal\nNCT OF Delhi\tEAST DELHI\tPRAVEEN KUMAR\t325\tno\tBJM\tBhartiya Jan Manch\nNCT OF Delhi\tEAST DELHI\tSHAKEEL AHMED\t1515\tno\tJMBP\tJai Maha Bharath Party\nNCT OF Delhi\tEAST DELHI\tMOHD SHAHID SIDDIQUI\t3894\tno\tAITC\tAll India Trinamool Congress\nNCT OF Delhi\tEAST DELHI\tSANDEEP DIKSHIT\t203240\tno\tINC\tIndian National Congress\nNCT OF Delhi\tEAST DELHI\tOMPAL SINGH\t349\tno\tkajp\tKalyankari Jantantrik Party\nNCT OF Delhi\tEAST DELHI\tRAJMOHAN GANDHI\t381739\tno\tAAAP\tAam Aadmi Party\nNCT OF Delhi\tEAST DELHI\tPREM SINGH\t761\tno\tSP(I)\tSocialist Party (India)\nNCT OF Delhi\tEAST DELHI\tMOHAMMED NAEEM\t466\tno\tIND\tIndependent\nNCT OF Delhi\tEAST DELHI\tMANJU CHHIBBER\t402\tno\tRPI(A)\tRepublican Party of India (A)\nNCT OF Delhi\tEAST DELHI\tPADAM CHAND\t404\tno\tIND\tIndependent\nNCT OF Delhi\tEAST DELHI\tKUMAR VIVEK GAUTAM\t978\tno\tIND\tIndependent\nNCT OF Delhi\tEAST DELHI\tMANJEET SINGH\t415\tno\tIND\tIndependent\nNCT OF Delhi\tEAST DELHI\tJAI RAM LAL\t668\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nNCT OF Delhi\tNEW DELHI\tMEENAKASHI LEKHI\t453350\tyes\tBJP\tBharatiya Janata Party\nNCT OF Delhi\tNEW DELHI\tASHISH KHETAN\t290642\tno\tAAAP\tAam Aadmi Party\nNCT OF Delhi\tNEW DELHI\tSWADESH OHRI\t582\tno\tATBP\tAtulya Bharat Party\nNCT OF Delhi\tNEW DELHI\tNAVEEN CHANDRA\t199\tno\tNADP\tNaya Daur Party\nNCT OF Delhi\tNEW DELHI\tVISHAL KHOSLA\t298\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tAJAY MAKAN\t182893\tno\tINC\tIndian National Congress\nNCT OF Delhi\tNEW DELHI\tLUKMAN KHAN\t203\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tSOLOMON GEORGE\t6088\tno\tBSP\tBahujan Samaj Party\nNCT OF Delhi\tNEW DELHI\tNIKHIL SABLANIA\t444\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tK P SANKARAN MENON\t499\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tDAL CHAND\t662\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tSUJEET JHA\t388\tno\tbjdi\tBhartiya Janta Dal (Integrated)\nNCT OF Delhi\tNEW DELHI\tANJUMAN AGNIHOTRI\t887\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tGHANSHYAM DASS\t862\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tPADMAJA KANDUKURI\t213\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tBISWAJIT RANJIT CHATTERJEE\t909\tno\tAITC\tAll India Trinamool Congress\nNCT OF Delhi\tNEW DELHI\tPRADEEP VARMA\t161\tno\tBVLP\tBharat Vishal Party\nNCT OF Delhi\tNEW DELHI\tLAKSHMI NARAYAN\t366\tno\tBHBP\tBharatiya Bahujan Party\nNCT OF Delhi\tNEW DELHI\tDEVI SINGH\t217\tno\tRaJPa\tRashtriya Jankranti Party\nNCT OF Delhi\tNEW DELHI\tHARKRISHAN DAS NIJHAWAN\t429\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tDHEERAJ PURI\t347\tno\tSHS\tShivsena\nNCT OF Delhi\tNEW DELHI\tMEENA SINGLA\t205\tno\tPHRC\tPoorvanchal Rashtriya Congress\nNCT OF Delhi\tNEW DELHI\tSUNITA CHAUDHARY\t528\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nNCT OF Delhi\tNEW DELHI\tNARENDRA SINGH RAWAT\t687\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tRAMANUJ PATEL\t158\tno\tSPP\tSamyak Parivartan Party\nNCT OF Delhi\tNEW DELHI\tRATHEESH\t532\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tRUBINA KHAN\t1260\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tVED PRAKASH\t186\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tRAVI KUMAR GUPTA\t20028\tno\tIND\tIndependent\nNCT OF Delhi\tNEW DELHI\tNone of the Above\t5589\tno\tNOTA\tNone of the Above\nNCT OF Delhi\tNORTH WEST DELHI\tUDIT RAJ\t629860\tyes\tBJP\tBharatiya Janata Party\nNCT OF Delhi\tNORTH WEST DELHI\tVIJAY KUMAR\t662\tno\tRPI(A)\tRepublican Party of India (A)\nNCT OF Delhi\tNORTH WEST DELHI\tSUNIL CHHIKARA\t1584\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH WEST DELHI\tRAJESH KUMAR\t545\tno\tRBHP\tRashtriya Bahujan Hitay Party\nNCT OF Delhi\tNORTH WEST DELHI\tRAM KARAN SAURAN\t1621\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH WEST DELHI\tSHAILENDER KUMAR\t3446\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH WEST DELHI\tDHARAMRAJ\t1680\tno\tBPC\tBhartiya Pragatisheel Congress\nNCT OF Delhi\tNORTH WEST DELHI\tNone of the Above\t8826\tno\tNOTA\tNone of the Above\nNCT OF Delhi\tNORTH WEST DELHI\tRAKHI BIRLA\t523058\tno\tAAAP\tAam Aadmi Party\nNCT OF Delhi\tNORTH WEST DELHI\tBASANT PANWAR\t21485\tno\tBSP\tBahujan Samaj Party\nNCT OF Delhi\tNORTH WEST DELHI\tKAMINI KAUR\t666\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH WEST DELHI\tKRISHNA\t157468\tno\tINC\tIndian National Congress\nNCT OF Delhi\tNORTH WEST DELHI\tBHUP SINGH\t1107\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH WEST DELHI\tJODHRAJ PAHARIA\t2279\tno\tIND\tIndependent\nNCT OF Delhi\tNORTH WEST DELHI\tINDER SINGH\t1749\tno\tAa S P\tAsankhya Samaj Party\nNCT OF Delhi\tWEST DELHI\tPARVESH SAHIB SINGH VERMA\t651395\tyes\tBJP\tBharatiya Janata Party\nNCT OF Delhi\tWEST DELHI\tJARNAIL SINGH\t84722\tno\tIND\tIndependent\nNCT OF Delhi\tWEST DELHI\tJARNAIL SINGH\t5960\tno\tIND\tIndependent\nNCT OF Delhi\tWEST DELHI\tMAHABAL MISHRA\t193266\tno\tINC\tIndian National Congress\nNCT OF Delhi\tWEST DELHI\tRAKESH KUMAR\t458\tno\tbjdi\tBhartiya Janta Dal (Integrated)\nNCT OF Delhi\tWEST DELHI\tBABU SINGH DUKHIYA\t1654\tno\tIND\tIndependent\nNCT OF Delhi\tWEST DELHI\tSUNIL SOURABH\t3624\tno\tIND\tIndependent\nNCT OF Delhi\tWEST DELHI\tISTAK KHAN\t988\tno\tIND\tIndependent\nNCT OF Delhi\tWEST DELHI\tDEEPAK KUMAR\t415\tno\tRJAP\tRashtriya Janadhikar Party\nNCT OF Delhi\tWEST DELHI\tHAR GOBIND ARORA\t703\tno\tSHS\tShivsena\nNCT OF Delhi\tWEST DELHI\tVIRENDER MOHAN VATS\t3078\tno\tIND\tIndependent\nNCT OF Delhi\tWEST DELHI\tRAJ PAL SINGH\t8707\tno\tBSP\tBahujan Samaj Party\nNCT OF Delhi\tWEST DELHI\tCHARAN JEET SINGH KHALSA\t451\tno\tAIFB\tAll India Forward Bloc\nNCT OF Delhi\tWEST DELHI\tKARAM CHAND LATHWAL\t353\tno\tBPC\tBhartiya Pragatisheel Congress\nNCT OF Delhi\tWEST DELHI\tJARNAIL SINGH\t382809\tno\tAAAP\tAam Aadmi Party\nNCT OF Delhi\tWEST DELHI\tDINESH KUMAR KUSHWAHA\t651\tno\tABSP\tAkhand Bharat Samaj Party\nNCT OF Delhi\tWEST DELHI\tUDAY KUMAR SINGH KUSHWAHA\t805\tno\tBMUP\tBahujan Mukti Party\nNCT OF Delhi\tWEST DELHI\tNone of the Above\t7932\tno\tNOTA\tNone of the Above\nNCT OF Delhi\tSOUTH DELHI\tRAMESH BIDHURI\t497980\tyes\tBJP\tBharatiya Janata Party\nNCT OF Delhi\tSOUTH DELHI\tNone of the Above\t4010\tno\tNOTA\tNone of the Above\nNCT OF Delhi\tSOUTH DELHI\tSUMANT KUMAR\t1640\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tSANJAY KUMAR RAI\t10873\tno\tBSP\tBahujan Samaj Party\nNCT OF Delhi\tSOUTH DELHI\tTEJ PAL SINGH\t1215\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tNARESH KUMAR CHANDALIYA\t913\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tMOHAN KANUGA\t561\tno\tJKNPP\tJammu & Kashmir National Panthers Party\nNCT OF Delhi\tSOUTH DELHI\tCHO. OMBIR SINGH NAUGHWAR\t303\tno\tBHBP\tBharatiya Bahujan Party\nNCT OF Delhi\tSOUTH DELHI\tSREERUPA MITRA CHAUDHURY\t803\tno\tAITC\tAll India Trinamool Congress\nNCT OF Delhi\tSOUTH DELHI\tRAKESH KUMAR\t1071\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tRAMESH KUMAR\t125213\tno\tINC\tIndian National Congress\nNCT OF Delhi\tSOUTH DELHI\tHAFIZ MUSTAQ\t357\tno\tJSMP\tJan Samanta Party\nNCT OF Delhi\tSOUTH DELHI\tCOL DEVINDER SEHRAWAT\t390980\tno\tAAAP\tAam Aadmi Party\nNCT OF Delhi\tSOUTH DELHI\tD. K. CHOPRA\t412\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tSHAHID\t2841\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tRUBY YADAV\t56749\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tSHRICHAND TANWAR\t4475\tno\tCPI\tCommunist Party of India\nNCT OF Delhi\tSOUTH DELHI\tDR. DUSMANTA KUMAR GIRI\t560\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tSUNIL KUMAR\t1140\tno\tIND\tIndependent\nNCT OF Delhi\tSOUTH DELHI\tKIRAN PAL SINGH\t314\tno\tIND\tIndependent\nLakshadweep\tLakshadweep\tMOHAMMED FAIZAL P.P.\t21665\tyes\tNCP\tNationalist Congress Party\nLakshadweep\tLakshadweep\tM.P. SAYED MOHAMMED KOYA\t187\tno\tBJP\tBharatiya Janata Party\nLakshadweep\tLakshadweep\tKOMALAM KOYA\t488\tno\tSP\tSamajwadi Party\nLakshadweep\tLakshadweep\tDR.ABDUL MUNEER\t465\tno\tCPM\tCommunist Party of India  (Marxist)\nLakshadweep\tLakshadweep\tNAJMUDHEEN C.T\t181\tno\tCPI\tCommunist Party of India\nLakshadweep\tLakshadweep\tHAMDULLAH SAYEED\t20130\tno\tINC\tIndian National Congress\nLakshadweep\tLakshadweep\tNone of the Above\t123\tno\tNOTA\tNone of the Above\nPuducherry\tPuducherry\tR. RADHAKRISHNAN\t255826\tyes\tAINRC\tAll India N.R. Congress\nPuducherry\tPuducherry\tG. PALANI\t438\tno\tCPI(ML)(L)\tCommunist Party of India  (Marxist-Leninist)  (Liberation)\nPuducherry\tPuducherry\tM.V. OMALINGAM\t132657\tno\tADMK\tAll India Anna Dravida Munnetra Kazhagam\nPuducherry\tPuducherry\tPROFESSOR M. RAMADASS\t2494\tno\tIND\tIndependent\nPuducherry\tPuducherry\tS. KRISHNAMOORTHY\t2060\tno\tBSP\tBahujan Samaj Party\nPuducherry\tPuducherry\tG. SHANMUGAM\t1726\tno\tIND\tIndependent\nPuducherry\tPuducherry\tDR. V. RANGARAJAN\t8307\tno\tAAAP\tAam Aadmi Party\nPuducherry\tPuducherry\tS. EZHILARASAN\t1777\tno\tIND\tIndependent\nPuducherry\tPuducherry\tM. CHITTIBABU\t1340\tno\tIND\tIndependent\nPuducherry\tPuducherry\tI. SENTHIL KUMAR\t477\tno\tIND\tIndependent\nPuducherry\tPuducherry\tSANJIVGANTHI .S\t5896\tno\tIND\tIndependent\nPuducherry\tPuducherry\tK. MANJINI\t767\tno\tIND\tIndependent\nPuducherry\tPuducherry\tK. RAMADASS\t1236\tno\tIND\tIndependent\nPuducherry\tPuducherry\tR. VISWANATHAN\t12709\tno\tCPI\tCommunist Party of India\nPuducherry\tPuducherry\tRAMAMURTHI\t1442\tno\tIND\tIndependent\nPuducherry\tPuducherry\tP. BAKKIARAJ\t610\tno\tIND\tIndependent\nPuducherry\tPuducherry\tP. DHANARASU @ MATHIMAHARAJA\t198\tno\tIND\tIndependent\nPuducherry\tPuducherry\tNone of the Above\t22268\tno\tNOTA\tNone of the Above\nPuducherry\tPuducherry\tP. ASARAVEL\t3472\tno\tIND\tIndependent\nPuducherry\tPuducherry\tR.K.R. ANANTHARAMAN\t22754\tno\tPMK\tPattali Makkal Katchi\nPuducherry\tPuducherry\tS. SUDARSANAN\t1270\tno\tIND\tIndependent\nPuducherry\tPuducherry\tV. NARAYANASAMY\t194972\tno\tINC\tIndian National Congress\nPuducherry\tPuducherry\tA.M.H. NAZEEM\t60580\tno\tDMK\tDravida Munnetra Kazhagam\nPuducherry\tPuducherry\tMARIE UTHRIANATHAN\t366\tno\tSAP\tSamata Party\nPuducherry\tPuducherry\tS. CHITRAKALA\t309\tno\tJD(U)\tJanata Dal  (United)\nPuducherry\tPuducherry\tKALIAYAMURTHY\t747\tno\tIND\tIndependent\nPuducherry\tPuducherry\tV. VIJAYA\t746\tno\tIND\tIndependent\nPuducherry\tPuducherry\tR. VALAVAN\t497\tno\tIND\tIndependent\nPuducherry\tPuducherry\tJ. DHANDAPANI\t1144\tno\tIND\tIndependent\nPuducherry\tPuducherry\tPUVALA NAGESWARA RAO\t465\tno\tIND\tIndependent\nPuducherry\tPuducherry\tG. SUTHA\t467\tno\tIND\tIndependent\n"
  },
  {
    "path": "notebooks/Lesson 1 - Understanding the tools.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:228eb1cfce02c0cea520b712be7113ec81098576ee07fcaf0a133e421f622343\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Lesson Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"In this you will learn all about the following:\\n\",\n      \"\\n\",\n      \"- Platform: AWS\\n\",\n      \"- Programming language: Python\\n\",\n      \"- IDE: IPython Notebooks\\n\",\n      \"- Libraries: Numpy, Pandas\\n\",\n      \"- Administration and Monitoring with Ajenti\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Amazon Web Services\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"AWS is a cloud computing service offering a range of managed and unmanaged services. We will be working with EC2 which is commonly used for performing \\\"Machine Learning\\\" processing in production environment.\\n\",\n      \"\\n\",\n      \"AWS EC2 offers a FREE Trier which is what we will be working with. So if you don't have an AWS account yet, please create one.\\n\",\n      \"\\n\",\n      \"https://aws.amazon.com\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Alternative - Local Setup\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Ubuntu (VM)\\n\",\n      \"Install:\\n\",\n      \"\\n\",\n      \"- python-dev\\n\",\n      \"- python-pip\\n\",\n      \"- python-ipython\\n\",\n      \"- python-numpy\\n\",\n      \"- python-scipy\\n\",\n      \"\\n\",\n      \"Start Server:\\n\",\n      \"\\n\",\n      \"ipython notebook\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Python\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will be coding most of our work in python, so it important to be familiar with the language. One good I found was \\\"Learn Python the Hard Way\\\" which is available for free online.\\n\",\n      \"\\n\",\n      \"http://learnpythonthehardway.org/book/\\n\",\n      \"\\n\",\n      \"I'll go over some basics that will get you started.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x = 10\\n\",\n      \"y = 5\\n\",\n      \"z = x + y\\n\",\n      \"print z\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"word_1 = \\\"Hello\\\"\\n\",\n      \"word_2 = \\\"Python\\\"\\n\",\n      \"sentence = word_1 + \\\" \\\" + word_2\\n\",\n      \"print sentence\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Let's do some math\\n\",\n      \"print x + y\\n\",\n      \"print x - y\\n\",\n      \"print x * y\\n\",\n      \"print x / y\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Formating String\\n\",\n      \"template = \\\"Hello %s\\\"\\n\",\n      \"print template % \\\"Python\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Using string formating to improve math results\\n\",\n      \"print \\\"Add Result: %s\\\" % (x + y)\\n\",\n      \"print \\\"Subtract Result: %s\\\" % (x - y)\\n\",\n      \"print \\\"Multiply Result: %s\\\" % (x * y)\\n\",\n      \"print \\\"Divide Result: %s\\\" % (x / y)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Passing Multiple Parameters to a string\\n\",\n      \"template = \\\"%s %s %s = %s\\\"\\n\",\n      \"print template % (x, \\\"+\\\", y, x+y)\\n\",\n      \"print template % (x, \\\"-\\\", y, x-y)\\n\",\n      \"print template % (x, \\\"*\\\", y, x*y)\\n\",\n      \"print template % (x, \\\"/\\\", y, x/y)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# List, Tupal and Dictionary\\n\",\n      \"numbers_list = [1,2,3,4,5]\\n\",\n      \"numbers_tupal = (1,2,3,4,5)\\n\",\n      \"numbers_dict = {1:1, 2:2, 3:3, 4:4, 5:5}\\n\",\n      \"\\n\",\n      \"# List\\n\",\n      \"print \\\"Full List: %s\\\" % numbers_list\\n\",\n      \"print \\\"List Length: %s\\\" % len(numbers_list)\\n\",\n      \"print \\\"First Item: %s\\\" % numbers_list[0]\\n\",\n      \"print \\\"Last Item: %s\\\" % numbers_list[-1]\\n\",\n      \"print \\\"\\\"\\n\",\n      \"\\n\",\n      \"# Tupal\\n\",\n      \"print \\\"Full Tupal: %s\\\" % str(numbers_tupal)\\n\",\n      \"print \\\"Tupal Length: %s\\\" % len(numbers_tupal)\\n\",\n      \"print \\\"First Item: %s\\\" % numbers_tupal[0]\\n\",\n      \"print \\\"Last Item: %s\\\" % numbers_tupal[-1]\\n\",\n      \"print \\\"\\\"\\n\",\n      \"\\n\",\n      \"# Dictionary\\n\",\n      \"print \\\"Full Dictionary: %s\\\" % str(numbers_dict)\\n\",\n      \"print \\\"Dictionary Length: %s\\\" % len(numbers_dict)\\n\",\n      \"print \\\"First Item: %s\\\" % numbers_dict.values()[0]\\n\",\n      \"print \\\"Last Item: %s\\\" % numbers_dict.values()[-1]\\n\",\n      \"print \\\"\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"IPython Notebooks\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"IPython Notebook will be our Intergrated Development Environment (IDE). It is web-based and support interactive development. This comes in handy once we are working with data and we would develop out logic based on the data and results.\\n\",\n      \"\\n\",\n      \"In can write code and execute it on the spot and have others access your work to truely work as a team.\\n\",\n      \"\\n\",\n      \"It also support inline charts and LATEX to visualize your work and results.\\n\",\n      \"\\n\",\n      \"Let's test LATEX first to see can it can make our functions more readable\\n\",\n      \"\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"Text code for function\\n\",\n      \"f(x) = (x ** 2 + 2 * x) / (1-epsilon)\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"LATEX code for a function\\n\",\n      \"\\n\",\n      \"### $f(x) = \\\\frac{(x ^ 2 + 2x)}{(1-\\\\epsilon)}$\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Charts\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"A good resource for finding chart examples is:\\n\",\n      \"\\n\",\n      \"http://matplotlib.org/gallery.html\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import matplotlib.pyplot as plt\\n\",\n      \"X = [1,2,3,4,5]\\n\",\n      \"Y = [3,2,5,0,1]\\n\",\n      \"plt.plot(X, Y)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.scatter(X, Y)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.scatter(X, Y, c=Y)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.scatter( X, Y, c=Y, marker=\\\"*\\\", s=400)\\n\",\n      \"# Complete list of markers\\n\",\n      \"# http://matplotlib.org/api/markers_api.html\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"A Teaser\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from mpl_toolkits.mplot3d import axes3d\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"from matplotlib import cm\\n\",\n      \"\\n\",\n      \"fig = plt.figure(figsize=(10,10))\\n\",\n      \"ax = fig.gca(projection='3d')\\n\",\n      \"X, Y, Z = axes3d.get_test_data(0.05)\\n\",\n      \"ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.3)\\n\",\n      \"cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)\\n\",\n      \"cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)\\n\",\n      \"cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)\\n\",\n      \"\\n\",\n      \"ax.set_xlabel('X')\\n\",\n      \"ax.set_xlim(-40, 40)\\n\",\n      \"ax.set_ylabel('Y')\\n\",\n      \"ax.set_ylim(-40, 40)\\n\",\n      \"ax.set_zlabel('Z')\\n\",\n      \"ax.set_zlim(-100, 100)\\n\",\n      \"\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Numpy and Pandas\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"**Numpy**: A powerful library for dealing with large amount of numbers. It performs operation much faster than pure python.\\n\",\n      \"\\n\",\n      \"**Pandas**: Introduces DataSeries and DataFrame which are based on Numpy arrays. These objects provide many out-of-the-box tools to analize and perform complex operations on your data.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"import pandas as pd\\n\",\n      \"array = np.arange(100)\\n\",\n      \"array = array.reshape((10,10))\\n\",\n      \"df = pd.DataFrame(array)\\n\",\n      \"print df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Performs Operations on Complete List\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print df[1] - df[0]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print df[1] + df[0]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print df[1] * df[0] - df[3]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Read Data From CSV\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We can read CSV file located on our system or on the internet.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Upload A File to the System with Ajenti\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"You can open Ajenti by opening this link:\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.display import HTML\\n\",\n      \"\\n\",\n      \"input_form = \\\"\\\"\\\"\\n\",\n      \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n      \"<p>User: root<br> Password: admin</p>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"javascript = \\\"\\\"\\\"\\n\",\n      \"<script type=\\\"text/Javascript\\\">\\n\",\n      \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n      \"</script>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"HTML(input_form + javascript)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"\\n\",\n        \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n        \"<p>User: root<br> Password: admin</p>\\n\",\n        \"\\n\",\n        \"<script type=\\\"text/Javascript\\\">\\n\",\n        \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n        \"</script>\\n\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 1,\n       \"text\": [\n        \"<IPython.core.display.HTML at 0x7fc6e58037d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Read File from Local System\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data = pd.read_csv(\\\"data/yourfile.csv\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### End of Lesson 1\\n\",\n      \"For questions please leave them on:\\n\",\n      \"\\n\",\n      \"- <a href=\\\"https://www.youtube.com/user/roshanRush\\\">YouTube</a>\\n\",\n      \"- <a href=\\\"https://twitter.com/twistedhardware\\\">Twitter</a>\\n\",\n      \"- <a href=\\\"https://plus.google.com/+Tcsaroshan/posts\\\">Google+</a>\\n\",\n      \"\\n\",\n      \"<a href=\\\"/notebooks/Lesson 1 - Intoroduction to Machine Learning.ipynb\\\">Next Lesson - Intoroduction to Machine Learning</a>\\n\",\n      \"\\n\",\n      \"In the next lesson:\\n\",\n      \"- kNN Classification\"\n     ]\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/Lesson 2 - Intoduction to ML.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:45b4298c39bf443ad1aa93c9ae7e76d74675d96d0e15fcd1ea6a1b1fbba72d97\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Lesson Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"In this you will learn all about the following:\\n\",\n      \"\\n\",\n      \"- Library: scikit-learn\\n\",\n      \"- How to choose: Classification, Regression, Clustering\\n\",\n      \"- Data Sets\\n\",\n      \"- Classification Example\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.display import HTML\\n\",\n      \"\\n\",\n      \"input_form = \\\"\\\"\\\"\\n\",\n      \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n      \"<p>User: root<br> Password: admin</p>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"javascript = \\\"\\\"\\\"\\n\",\n      \"<script type=\\\"text/Javascript\\\">\\n\",\n      \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n      \"</script>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"HTML(input_form + javascript)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"\\n\",\n        \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n        \"<p>User: root<br> Password: admin</p>\\n\",\n        \"\\n\",\n        \"<script type=\\\"text/Javascript\\\">\\n\",\n        \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n        \"</script>\\n\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 1,\n       \"text\": [\n        \"<IPython.core.display.HTML at 0x7fd7d320a7d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Scikit-Learn\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"http://scikit-learn.org\\n\",\n      \"\\n\",\n      \"scikit-learn. Machine Learning in Python. Simple and efficient tools for data mining and data analysis.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Algorithms Types\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"There are three types of algorithms that we use according to the type of the problem.\\n\",\n      \"\\n\",\n      \"## Clustering\\n\",\n      \"<img src=\\\"http://scikit-learn.org/stable/_images/plot_dbscan_11.png\\\" />\\n\",\n      \"clustering is used to group data into groups according to their similarities.\\n\",\n      \"\\n\",\n      \"## Classification\\n\",\n      \"<img src=\\\"http://scikit-learn.org/stable/_images/plot_weighted_samples_11.png\\\" />\\n\",\n      \"Classification is used to classify data into pre-defined groups.\\n\",\n      \"\\n\",\n      \"## Regression\\n\",\n      \"<img src=\\\"http://scikit-learn.org/stable/_images/plot_gp_regression_11.png\\\" />\\n\",\n      \"Regression is used to find the next value in a series.\\n\",\n      \"\\n\",\n      \"<img src=\\\"http://scikit-learn.org/stable/_static/ml_map.png\\\" />\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Data Sets\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"You need data to work with machine learning. This is some data sets to get you started.\\n\",\n      \"\\n\",\n      \"http://archive.ics.uci.edu/ml/index.html\\n\",\n      \"\\n\",\n      \"http://scikit-learn.org/stable/datasets/\\n\",\n      \"\\n\",\n      \"Inside these data sets you will find records of data. These records can have number, boolean, string, category, image ... etc.\\n\",\n      \"\\n\",\n      \"### Let's Examine some data\\n\",\n      \"\\n\",\n      \"This is a sample of a data set that is called Adults:\\n\",\n      \"\\n\",\n      \"http://archive.ics.uci.edu/ml/datasets/Adult\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Data Description\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"- **age**: continuous.\\n\",\n      \"- **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\\n\",\n      \"- **fnlwgt**: continuous.\\n\",\n      \"- **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\\n\",\n      \"- **education-num**: continuous.\\n\",\n      \"- **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\\n\",\n      \"- **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\\n\",\n      \"- **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\\n\",\n      \"- **race**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\\n\",\n      \"- **sex**: Female, Male.\\n\",\n      \"- **capital-gain**: continuous.\\n\",\n      \"- **capital-loss**: continuous.\\n\",\n      \"- **hours-per-week**: continuous.\\n\",\n      \"- **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Data Sample\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K\\n\",\n      \"38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K\\n\",\n      \"53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K\\n\",\n      \"28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, <=50K\\n\",\n      \"37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, <=50K\\n\",\n      \"49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jamaica, <=50K\\n\",\n      \"52, Self-emp-not-inc, 209642, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K\\n\",\n      \"31, Private, 45781, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 14084, 0, 50, United-States, >50K\\n\",\n      \"42, Private, 159449, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 5178, 0, 40, United-States, >50K\\n\",\n      \"37, Private, 280464, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 80, United-States, >50K\\n\",\n      \"30, State-gov, 141297, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, India, >50K\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Classification Example\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will be using the dataset above to make some prediction. Let's start by classification model to predict if someone makes more or less than 50K Annual.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pandas as pd\\n\",\n      \"import numpy as np\\n\",\n      \"import matplotlib.pyplot as plt\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Load Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"url = \\\"http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\\\"\\n\",\n      \"path = \\\"data/adult.data\\\"\\n\",\n      \"columns = [\\n\",\n      \"    \\\"age\\\",\\n\",\n      \"    \\\"workclass\\\",\\n\",\n      \"    \\\"fnlwgt\\\",\\n\",\n      \"    \\\"education\\\",\\n\",\n      \"    \\\"education-num\\\",\\n\",\n      \"    \\\"marital-status\\\",\\n\",\n      \"    \\\"occupation\\\",\\n\",\n      \"    \\\"relationship\\\",\\n\",\n      \"    \\\"race\\\",\\n\",\n      \"    \\\"sex\\\",\\n\",\n      \"    \\\"capital-gain\\\",\\n\",\n      \"    \\\"capital-loss\\\",\\n\",\n      \"    \\\"hours-per-week\\\",\\n\",\n      \"    \\\"native-country\\\",\\n\",\n      \"    \\\"income\\\"\\n\",\n      \"    ]\\n\",\n      \"csv_data = pd.read_csv(url, sep=\\\", \\\", names=columns, nrows=20000).dropna()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Statistical Analysis of Numerical Features\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data.describe()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Visual Inspection of Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from pandas.tools.plotting import scatter_matrix\\n\",\n      \"scatter_matrix(csv_data, alpha=0.05, figsize=(18, 18), diagonal='kde')\\n\",\n      \"pass\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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MHgY7W6Y5pdS5zUl5WRMkUI7Zz+pp1OQJJk657Kk6FYT2S5rDRAHXlS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\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nnK5T2vlcZrtPP28e6zG+fBlmUSi0VxnCpBEDbHCYKAvr4cEJBIJPeY\\nT7FY5L77/o3+/gp/+ZfnkB7HA7TCMBw2n0KhMOL14ni9lWu6TqVbS0ahB9tIu1JuSrsaKTdrtXpX\\nqPl8/XaQp56qP9Pi4YchHoc//mM4/fRdD2ys1cB1wbLgj/6o3sOHyGTQvlPalXJT2pnyU9qVChmj\\n0EYr7Uq5Ke1qaG5u21bvkcN1IZut9wqSydQfbnnyyXDmmbBo0dTGK28v2ndKu1JuSjtTfkq7Gldu\\nhvvR5s2bw3e/+91hPB4Pfd8PwzAMr7vuuvCUU04JzzvvvLBWq4VhGIY/+MEPwpNOOil8//vfH+bz\\n+TAMw/CRRx4Jly1bFp522mnhm2++GYZhGK5duzY8+eSTw5NPPjl87rnnwjAMw97e3vC0004LTzrp\\npPDhhx8eMY5TTz01BPTSq+1eyk292vWl3NSrnV/KT73a9aXc1KudX8pPvdr1lc1m91pb2K8tMqrV\\nKpVKhT/90z/lkUceoa+vjwsvvJCHHnqI6667joULF3L22WdzxhlnsHr1au677z42bdrEF77wBU4/\\n/XQefPBB1q9fzx133MHNN9/MOeecw0033YRhGFxyySXcf//9fPazn+WjH/0oS5cu5f3vfz+//OUv\\n94hD1UdpV8pNaVfKTWlnyk9pV8pNaWfKT2lXbddrSSwWaz50KgxD1qxZw4oVKwBYuXIld911F4sX\\nL2bJkiWYpsnKlSu56KKLqFQqJBIJUqkUJ5xwAv/zf/5PAAYGBpg7dy6w6+nk69atY9myZQB0dXVR\\nKBTo6upqOeYgCPD9eg8jjX8bDynavZ9i3/cJgqD5AJkgCHAch2h014Mqi8UiQPNBMo7jUCwW6erq\\nwnEcEonEHg95aSxbOp3GdV1s226uk8a/jb6RK5VKM5Zyucy0adMoFotks1mAYfFv2bKl+V4+n2f+\\n/Pm4rkskEqFcLhONRtm+fTs9PT34vk80GmXHjh0ATJ8+Hdd1SaVSuK5LNBod9q9t282nwjceSDl0\\nHfq+3+x3ufG3aZrNhG2sr8b6H6nP54bd1/tQtVoN0zSbD/QcT3/Uu48zlRrL7zgOvu+TStW7iwp3\\n9hriui5Qz6vG+q8/sbm+HMViEcdxiMVizfdnz55NKpXCsiyq1SrRaBTLsqhUKsTjcWzbxrKs5ueV\\nSgXXdYnH483hLMsimUwShiGWZY36UNWh63Ks70lkqoVT3If7pk2beOGFFzjxxBOb++vRrFu3DoBj\\njjlmzOEmskzj2dc2/J//838A+MhHPjLmcJVKhQ0bNjB37lx6esbuju6ee+5h48aN/O3f/u2YwwG8\\n8sorZDIZZs6cuddhRdrJSSedxEsvvdQ8lxKR+nl8sVgknU63fO5dLpex7dEflFnvmSSku7t7xGPi\\nbbfdBsCFF1444viNc+Ghx+eh1xTPP/88Tz75JO9973tHPDYVi0X6+/uZMWNG8+H5Q+PI5/M8+eST\\nHHnkkRwySj/jmzZtIp1Oj3o87evrIx6Pk06nmz3ATOSc23VdPM8jmUyOe5ypNqXdrw4ODpLJZADI\\nZDLkcjlyudyY7wHNC7Kh3ac1LoAbnwFks1lyuVzLhYwwDMnlyoShjePkiMe7sG2XbDZFtVqlUPCB\\nkO5uME2TwUGHIDBJJDxSqQRbtvRTLNpEInnmz59FoVDg+ecHAXjHO+oXpU888TKum2Vw8BnmzTuG\\nVKqfY445rBlDLpfj8cc34/vQ01Nl+vT5bNnyOvH4bPr61jFjxiJ8v5dFi+ZRKuXZurVKPl9m69Zt\\nmOYMXPdJDjnkncTjb7B48dGYZpXu7hSvvPIKjz7az8DAViqVCpnMHObPf40lS45l8+aXSCRm8+yz\\nD2PbR1Io/JZ3v/vd9PVtYsOGMmEYY8GCFznkkCOB15k5cx653Kt0d8+lWHyDRGI6rpvHsuLYtkUq\\nZRKPp6jV8sRiqea6rFYHiUZTeF4B04wShj5hCLYdIZMJiEajDA6WCQKbSMQlk0nt8R35vk8u5xCG\\nJqmU3+xBBqBUqlCpgGkGdHcnCIKg2edzV5c/4pPcKxWHUqneF3V3d3zKixmDg2W2bSvwyivbsO0E\\ns2aZzJo1k1KpQn9/nkIh5NVX32TbtgoDA0VsO8Cy4hQKRfr7S2zdOoDr+jsvTEySyRhHHDGNU045\\nBtv2iUSymGYVwwipVCKk0yGLFs2muztJImHy8stbef75LZRKHqmUSVdXjB07HBKJOHPnxpg+fRap\\nVISZM5PE48PX59D1n8nEKBSq+P6u7UOknRQKZWo1q7mP398+//k7qFTewX333cw///MVow63bt06\\nbrzxWYIgzvnn7+DUU08dddhy2cFxDAyjSnd3ctRixtBjXTw+9vZ57bXXctNNYNse/f3f5pJLLhl1\\n2O997wG2bJlLIrGOSy/9IInEyNO95557+Ku/+hW12gIee+wT/Ou/3jnqNH/zm2d47LEqkchrfPSj\\nS1XMkAPGSSedxOOPzwfO1q/gIkO88MImSqXEHtdA49Xfn2P79gDb9jj00O49ihmbNm3i17/OUSoV\\nOOWU+Rx88LRhx/kbbriB664bxPdtNm68kmuuuWbY+JVKhbVrt+D7UebNqzB37sF4nte8pqhWt/LR\\nj/4TnreUn//877n77uE9ZgH84hdP0deXJR5/gfe85zgiEZtEwiabTWCaJl/60j/S23skicRd/OM/\\nfnrYdS/Ar361hpdesojHN3HOOe/ao2eV1157kxdeqGJZ2zjhhDl4XoT69U4wrp6rHMfhjTfygM3M\\nmR7d3Zm9jtMOpqz/I8MwyGaz5PP1/nbz+Tzd3d17fQ9oXlwO/dWocYI29EQtn88zbZTH4q9atar5\\nWr169YjDhGFIGBqYpo3rBpim3ex7OAhCDMMCzJ3D7Rq20W9xteph23E8zxzy63kUiDarXq5rE4lk\\n6e+vEYmkqVSCYcWYerUvhmEkKBZDIEIu5xGJpCkUfCKRNK5rEIb1+fm+DVhUKhaGkSCXq1+sDg5W\\nMU2bMKyvs1yujG1n8LwojhPHsrLk8/U+kQsFl0gkzcBAgGV1UyhY1Go+1SrUakkMI00+72Pbacrl\\n+jKWy/7OfwMMI0q9oYCN55n4PhiGieeFw9al4/gYhoXngWFY+D47+4uu9zNeX89gmja+P/IBv9HH\\ntGlazfW+67OwucyNFgyGYWEYew47fByLMDSHFcqmQmP5PQ88L4ppJiiXvZ3vGVSrBp4XwXUNXDdK\\nEKQolUyq1QiOE8V1I9Rq3fh+lnI5i+t24fs9VCr1cSoVgzCMUamYVKsBEG9+X0FgUKt51Gr1eYRh\\nnGrVolYD308RBDEqFZ8gqG8DI/ULP3T9B0FAEAzfPkTaSWP/1NjH70+Dg4NUKhESiUX094897x07\\ndhAEBxGJzGLz5tyYw3pesHN/N/ZFU2P7rO+Px97vbdpUwLbn4fsHDzsuj2RwMCAWO4RabeyTqFwu\\nR602E8NYwJYtpTGH7e8vkkh0U6ul9zp/kXaydetWYCEwDzh4iqMRaR+OE4x4DTT+8T0sa9f11u7y\\n+QqWlSYM0ziOu8dxPpfL4ftzMc3DWL/+93uMX4+pfh7uukN/TK9fU6xbtx7H6cG2F/LGG9U9xndd\\nl1wuIBabTj4Pvm/svM7YdWzu7w9JJBbieSMX/CuVGrHYTDwv2myNPVSp5BCNpvD92M7W+eaEzrnr\\n5wE2hhHd63lAO5myFhlhGHLcccfx7W9/m8svv5yHH36YZcuWccQRR7Bu3TqCIGi+l0wmqVQqlEol\\n1q9fz+LFiwHo6emht7e3WRQBWLp0KU888QRLliwhn8+P2hfwqlWr9hqjaZp0dUWo1WrMmdNNrVYj\\nFothGAbxeIwgcDBNo9lsJ5228Dyv+cv03Lk99PcXSaeT2LZNT08P8+Ztb8ZuWRZLl/awdWsfZ5/9\\nborFQWbOnDGsFcCMGTP4gz8oUq16zJ69AM+rcdJJCxgcHOSMM46mWBxk3ryZJBIWXV0zyGTyQDeH\\nHGJSLjssW/ZOCoU+liw5nEikRiwWxTAMli49ilLpOZYunY3jVCkWB1m6dDG2XePEExcyODjIn/3Z\\ncWzatIXjjz+cRCLOYYfN59BDt1EsVjn66MUEQZkjj1yI45R517vm4ThljjlmNq7rMWtWz84iBMRi\\nUSAknU7j+7vW5Zw5WXzfJ51O4XkBpllfb0EQEI/X+37OZGJUqzXi8TgjiUQipNP1HV8iMfxkOZmM\\n4Tgutm01b5VIJp3m9EdSn0a9L+qpvgWisfymGZBMetRqPrNmzcayInR1GcyYYZHPV1iw4FC2bBlg\\ncLBAJtNDrebjeWlKpQzbtvXtvGUptrOAZPKOd8xn/vyZRCIm5bJHIjGNSCTK4GCBbHY6mUyCaNQi\\nFktw2GEeyaRLqeSQzSaJxxP09w8SidjMmtWDaVpEo/Ye6x6Gr/9oNEo6HQ7bPkTaSSYTx3F27eP3\\np2w2y+c+9y5+8YvH+OAHV4w57KmnnsrmzT9lYKCPD3zgrDGHTafjOE79dsGxWpdZlkUmE6FW80bd\\nNzZcddVneOONv2PatCh/8zffHHPYc889nv/3/57n2GP/YNTWGACf+tSneOSRi3njjQ3ccMMXxpzm\\nSSctBZ6jpyfNInWNIweQV155Zee+ZS6f+MSZUx2OSNs46qiD2LJlz2ug8Zo1q5v+/jzRaHTEY9iR\\nRy6kUnmJaNRm0aLZxGLDb6G85pprWL/+LxgY8Lnppq/uMX46nWbevAqVSoW5c+utAKPRKKlU/Zri\\nj//4TD7zmZf59a//hU996kN7jB+NRjnjjIW8/PIWFi06kunTo5imSSRiNZf3s589lQcf/DWnn754\\nj9YYACeccCRr125k5syRby1ZtOgQ4E1iMZvZs2dTqTgEgb/XY3pDMplk5kwXz3MPmNYYsJ+7X/U8\\njz/5kz/hqaee4thjj+VrX/saq1ev5sEHH2T+/Pl8//vfx7ZtfvCDH/Cd73yHnp4e7r77brq6unjk\\nkUe48sorSSQS3H777RxyyCGsXbuWT3/60xiGwS233MLSpUvp7e3l/PPPp1Kp8Hd/93esXLlyz4VW\\nkz5pU8pNaVfKTWlnyk9pV8pNaWfKT2lX48nN/VrIaBfaaKVdKTelXSk3pZ0pP6VdKTelnSk/pV2N\\nJzen7BkZIiIiIiIiIiITpUKGiIiIiIiIiBwwVMgQERERERERkQOGChkiIiIiIiIicsBQIUNERERE\\nREREDhgqZIiIiIiIiIjIAUOFDBERERERERE5YKiQISIiIiIiIiIHDBUyREREpGN8/vPwy19OdRQi\\nIiKyL6mQISIiIh3DsuA//3OqoxAREZF9SYUMERER6RjTp0MuN9VRiIiIyL6kQoaIiIh0jGwWBgen\\nOgoRERHZl1TIEBERkY6RyaiQISIi0ulUyBAREZGOoRYZIiIinU+FDBEREekYKmSIiIh0PhUyRERE\\npGMkk1CpTHUUIiIisi+pkCEiIiIdIxYDx5nqKERERGRfUiFDREREOkY8DtXqVEchIiIi+5IKGSIi\\nItIxYjEVMkRERDqdChkiIiLSMVTIEBER6XwqZIiIiEjHUCFDRESk86mQISIiIh1DhQwREZHOp0KG\\niIiIdIxYDFwXwnCqIxEREZF9RYUMERER6RimCZYFtdpURyIiIiL7ypQXMqrVKmeffTannXYaH/zg\\nB3Fdl+uvv57ly5fz8Y9/HM/zALjrrrs4+eSTOeussygUCgA8+uijnHTSSZx++un09vYCsG7dOk45\\n5RROOeUU1q5dO2XLJSIiIlNDt5eIiIh0tikvZPzsZz/j+OOP55e//CUnnHACP/zhD1m9ejWPPfYY\\nS5cu5f7776dWq3Hrrbfy2GOP8YlPfIJbb70VgK9+9av84he/4Nprr+XrX/86AFdddRX33HMPP/rR\\nj7jyyiunctFERERkCqiQISIi0tmmvJAxY8YMcrkcAAMDA2zatInTTjsNgJUrV/L444+zYcMGlixZ\\ngmmazfcqlQqJRIJUKsUJJ5zA+vXrm9OYO3cuc+bMaU5XRERE3j5iMXCcqY5CRERE9pUpL2QsW7aM\\np556imOOOYbf/va3HH744XR1dQGQyWTI5XLkcjkymcyo7wH4vg9AEATN90I96UtERORtJx5XiwwR\\nEZFOZk91AHfeeSfve9/7uOyyy7jhhhuo1Wrk83kA8vk83d3dZLPZMd8DsCwLAMMwmu+Z5uh1mlWr\\nVjX/v2LFClasWDGJSyUiIiJTRbeWiIiIdLYpL2Tk83mmTZsGwPTp03nttdf4zW9+w+WXX87DDz/M\\nsmXLOOKII1i3bh1BEDTfSyaTVCoVSqUS69evZ/HixQD09PTQ29uLYRjDWmzsbmghQ0RERDqHChki\\nIiKdbcoLGR//+Mc599xzufPOO4lGo9xzzz380z/9E8uXL2f+/Plceuml2LbNRRddxPLly+np6eHu\\nu+8G4IorruDMM88kkUhw++23A3D11Vdz7rnnYhgGt9xyy1QumoiIiEwBFTJEREQ6mxG+DR8kYRiG\\nnp8hbUm5Ke1KuSntbPf8fM974Jpr4NRTpzAoEbTvlPam/JR2NZ7cnPKHfYqIiIhMpmhULTJEREQ6\\nmQoZIiIi0lEiEfC8qY5CRERE9hUVMkRERKSjRCJQq011FCIiIrKvqJAhIiIiHUWFDBERkc6mQoaI\\niIh0FBUyREREOpsKGSIiItJRVMgQERHpbCpkiIiISEexbT3sU0REpJOpkCEiIiIdRS0yREREOpsK\\nGSIiItJRVMgQERHpbCpkiIiISEdRIUNERKSzqZAhIiIiHUWFDBERkc6mQoaIiIh0FBUyREREOpsK\\nGSIiItJR1GuJiIhIZ1MhQ0RERDqKWmSIiIh0NhUyREREpKOokCEiItLZVMgQERGRjqJChoiISGez\\nWx3R9322bt2KN+Qm1Hnz5k1KUCIiIiKtUiFDRESks7VUyLjpppu4+uqrmTVrFpZlNd9fu3btpAUm\\nIiIi0goVMkRERDpbS4WMb37zm7z00ktMnz59suMREREReUvUa4mIiEhna+kZGfPmzSOTyUx2LCIi\\nIiJvmVpkiIiIdLaWWmQcdthhnHbaabzvfe8jGo0CYBgGl1566aQGJyIiIjJRKmSIiIh0tpYKGfPm\\nzWPevHm4rovruoRhiGEYkx2biIiIyISpkCEiItLZWipkrFq1apLDEBEREZkcKmSIiIh0tgkVMj73\\nuc9x4403ctZZZ+3xmWEYPPDAA5MWmIiIiEgrVMgQERHpbBMqZJx//vkAXHbZZZMaxB133MEdd9xB\\nEAT84Ac/4K677uKBBx5g/vz5fP/738e2be666y6+/e1v09PTw913301XVxePPvooX/7yl4nH49x5\\n553MnTuXdevWcfHFFwPwne98hyVLlkxqrCIiItLe1GuJiIhIZzPCMAynMoDe3l6+8pWv8N3vfheA\\nbdu2ceGFF/LQQw9x3XXXsXDhQs4++2zOOOMMVq9ezX333cemTZv4whe+wOmnn86DDz7I+vXrueOO\\nO7j55ps555xzuOmmmzAMg0suuYT7779/j3kahsEUL7bIiJSb0q6Um9LOds/PRx6Br30NHn10CoMS\\nQftOaW/KT2lX48nNlrpfffnll/nQhz7E0UcfzWGHHcZhhx3GwoULWwry5z//Ob7vs3LlSj772c+y\\nZs0aVqxYAcDKlSt5/PHH2bBhA0uWLME0zeZ7lUqFRCJBKpXihBNOYP369QAMDAwwd+5c5syZQy6X\\naykmEREROXDp1hIREZHO1lIh48ILL+Tiiy8mEomwevVqLrjgAs4777yWAti6dSu1Wo2HH36YZDLJ\\n4OAgmUwGgEwmQy6XI5fLjfkegO/7AARB0HxPFUYREZG3HxUyREREOltLvZZUKhVWrlxJGIbMnz+f\\nVatW8Yd/+Idcc801E55Wd3c373nPewA4/fTTWbNmDZFIBIB8Pk93dzfZbJZ8Pj/qewCWZQEM6wbW\\nNEev0wzteWXFihXNViAiIiJyYFMhQ0REpLO1VMiIx+P4vs/hhx/OzTffzJw5cyiVSi0FcNJJJ/HP\\n//zPADz99NMceuih3HPPPVx++eU8/PDDLFu2jCOOOIJ169YRBEHzvWQySaVSoVQqsX79ehYvXgxA\\nT08Pvb29GIYxrMXG7tSFrIiISGdSIUNERKSztVTI+OY3v0m5XOZb3/oWV155Jfl8nttvv72lAN75\\nzneSSCQ47bTTmDlzJpdeeim///3vWb58OfPnz+fSSy/Ftm0uuugili9f3uy1BOCKK67gzDPPJJFI\\nNOd/9dVXc+6552IYBrfccktLMYmIiMiBKxJRryUiIiKdbMp7LZkKekKvtCvlprQr5aa0s93z8+WX\\n4X3vg9/9bgqDEkH7Tmlvyk9pV+PJzZZaZJx11lnDJt64jeP444/nU5/6FPF4vJXJioiIiLxlurVE\\nRESks7XUa8lhhx1GOp3mL//yL7nooovo6uqiq6uLl19+mYsuumiyYxQREREZNxUyREREOltLLTJ+\\n/etfs2bNmubfH/jABzjuuONYs2ZN86GbIiIiIlNBhQwREZHO1lKLjFKpxOuvv978+/XXX2/2WhKN\\nRicnMhEREZEWqJAhIiLS2VpqkXHDDTewfPlyFi5cCMDGjRv59re/TalU4oILLpjUAEVEREQmQr2W\\niIiIdLaWey1xHIcXX3wRwzA44ogjSCQSkx3bPqMn9Eq7Um5Ku1JuSjvbPT8rFZg2DRxnCoMSQftO\\naW/KT2lX48nNlm4tAYjH47zrXe/illtuOaCKGCIiItLZdGuJiIhIZ2u5kNHw5JNPTkYcIiIiIpPC\\nsiAI6i8RERHpPG+5kDFr1qzJiENERERkUhiGWmWIiIh0spafkXEg0/1g0q6Um9KulJvSzkbKz1QK\\ntm6FdHqKghJB+05pb8pPaVfjyc0J9Vpy1llnjTmzBx54YCKTExEREdkn1HOJiIhI55pQIeOyyy7b\\nV3GIiIiITBrb1q0lIiIinWpChYwVK1bsozBEREREJo+ekSEiItK5JlTIaHj55Zf50pe+xPr163F2\\ndtJuGAYbN26c1OBEREREWqFChoiISOdqqdeSCy+8kIsvvphIFyxW7gAAIABJREFUJMLq1au54IIL\\nOO+88yY7NhEREZGWqJAhIiLSuVoqZFQqFVauXEkYhsyfP59Vq1bx0EMPTXZsIiIiIi1RIUNERKRz\\ntXRrSTwex/d9Dj/8cG6++WbmzJlDqVSa7NhEREREWqJeS0RERDpXS4WMG2+8kXK5zLe+9S2uvPJK\\n8vk8t99++2THJiIiItIS9VoiIiLSuVq6teTVV1+lq6uLQw89lO9///v8+Mc/ZtOmTZMdm4iIiEhL\\ndGuJiIhI52qpkPH1r399XO+JiIiITIVoVIUMERGRTjWhW0v+7d/+jX/913+lt7eXz372s4RhCECh\\nUCASieyTAEVEREQmKhIB153qKERERGRfmFAhY86cORx77LH89Kc/5dhjj20WMjKZDN/4xjf2SYAi\\nIiIiE6UWGSIiIp3LCBvViAmo1WoHdAsMwzBoYbFF9jnlprQr5aa0s5Hy8/3vh099Cs46a4qCEkH7\\nTmlvyk9pV+PJzQm1yPjwhz/Mvffeyx/+4R+OOLPnnntuYhGKiIiI7ANqkSEiItK5JvSwzxtvvBGA\\nBx98cI/XAw888JYC+cY3vsHy5csBuP7661m+fDkf//jH8XZ2An/XXXdx8sknc9ZZZ1EoFAB49NFH\\nOemkkzj99NPp7e0FYN26dZxyyimccsoprF279i3F1CrXdRkcLFHTGZS0mUZuurpxXN4Cx6lqHydt\\nT8/IkHZTLlcoFMr4vj/VoYi0vTAMKRbLFApltRqREU2okDFnzhwAFixYQDwe59lnn2Xt2rXE43EW\\nLFjQchDVapVnn30WwzDYvn07q1ev5rHHHmPp0qXcf//91Go1br31Vh577DE+8YlPcOuttwLw1a9+\\nlV/84hdce+21zV5TrrrqKu655x5+9KMfceWVV7YcU6vCMKRQcIEEhUJ1v89fZCzFYiM3XR0UpCVB\\nEFAqeUCCYlH7OGlfapEh7cTzPCoV8Lwo5bL2nSJ747ou1aqF61pUq6pKy55a6n71u9/9LieccAI/\\n/vGPue+++/ijP/ojvve977UcxPe+9z0uuOACwjBkzZo1rFixAoCVK1fy+OOPs2HDBpYsWYJpms33\\nKpUKiUSCVCrFCSecwPr16wEYGBhg7ty5zJkzh1wu13JMrTIMA9s28LwqkUhLq1dkn9mVmwaGYUx1\\nOHIAMk0T20b7OGl7apEh7cQ0TUwzIAhcIhFrqsMRaXuWZQEe4GFZOt+QPU3oGRkN1113HU8//TTT\\np08HYMeOHSxbtow///M/n/C0arUav/rVr7jkkksAyOVyZDIZoN4bSi6X2+t7QLOZXhAEzfem6hfn\\nTCaJ7/vYdkurV2Sf6eqq52b94CDSGu3j5ECgFhnSTkzTJJtNEIahjsEi42DbNt3d9R/dtM3ISFo6\\nC50xYwbpdLr5dzqdZsaMGS0FcOedd/Kxj32s+Xc2m+XNN98EIJ/P093dTTabJZ/Pj/oe7Erwob8y\\nm+bo1btVq1Y1/79ixYpmK5DJUG+VoRN8aT/KTZkMyiM5EKhFhrSbsc5LRWRPKmDIWFo6E120aBEn\\nnngiZ599NgA//elPWbp0KTfccAOGYXDppZeOe1ovv/wyzzzzDP/4j//I+vXrWbNmDb/5zW+4/PLL\\nefjhh1m2bBlHHHEE69atIwiC5nvJZJJKpUKpVGL9+vUsXrwYgJ6eHnp7ezEMY1iLjd0NLWSIiIhI\\nZ1GLDBERkc7VciFj0aJFzdYPZ599NoZhUCwWJzyta6+9tvn/97znPVx11VVcd911LF++nPnz53Pp\\npZdi2zYXXXQRy5cvp6enh7vvvhuAK664gjPPPJNEIsHtt98OwNVXX825556LYRjccsstrSyeiIiI\\nHODUIkNERKRzGeHbsOsCwzDUY4O0JeWmtCvlprSzkfLzyivrxYyrrpqioETQvlPam/JT2tV4crOl\\nFhnbtm3juuuu4/nnn6dSqTRn9uijj7YyOREREZFJFY1CVb1cioiIdKSWnjp03nnncdRRR7Fx40ZW\\nrVrFggULOO644yY7NhEREZGWRCJ6RoaIiEinaqmQsWPHDv7iL/6CaDTKqaeeym233abWGCIiItI2\\nolE9I0NERKRTtVTIiEajABx88MH8y7/8C0899RQDAwOTGtj+4nkeYRjieR5BEOD7Pr7vEwQBnudN\\ndXgHlMa6lKnVyOeh34Xv+3vN56E5H4YhjuMQBME+jVWkXYy03exPvu9TLBbxfX9cw45nuLc7tciQ\\ndlMul+nr65vqMET2qnFdNB5v9Zppso+/B+IxcqrPQQ5ULT0j48tf/jK5XI4bbriBz3zmM+Tzeb7x\\njW9Mdmz7XLFYplo1cN1BbDtOENQwDAsICcMAy4qSTvvE47GpDrXtNdalbVfJZJLNHm1k/8vny9Rq\\nJtFolUwmhe/75HIOYWiQyQTNQuRQYRgyOFgmCCxiMZfBwTLlskk8nueQQ2bq+5SOVyiUcV2TSKRK\\nNpva7/N/4YVNFApRuru3c/TRh406nOd5DA5WAYOuLnvE7Vnq1CJD2km5XObnP3+eajXKMcfs4Jhj\\njpzqkERGVK1WKRR8TDMkm41jWdaowwZBQC5XJghMUimPRCI+4flN5vF36DEyk4kQiUTe0vT2l2Kx\\nguvWr6Om4hzkQNVSi4wf/ehHhGHIkiVLWL16NQ8//DA/+clPJju2fa5WqxcrqtUAw7DxPAhDkyAw\\n8TwwzQi+r1+kx6OxLuvrUNXEqRKGIb4Pth3F8+rfQ72ibmEYNrXayBXqIAgIAhPTjFCr+VSrAbad\\nwnVRqwx5W/C8sLnd7O99mO/7OA4kkz2USmNvb0EQEIb17VnHp7GpRYa0k3K5jO8niUZnMTBQmepw\\nREblefXroiAw93oO2Dh/rF8DtHZMmszjbz1eG7AOqGOk5+26jtJ59/i11CLjueeeY9q0ac2/e3p6\\nePrppyctqP2lqytOuVxl1qz6r9bJZIzG9mMYFr5fa6my+HbUWJfJZATTbKk+JpPAMAzS6QiOUyWV\\nqrckikQixOMeQTB6pdyyLFIpi1rNJZFIEImY5PMFMpnUmJV4kU7R1RWjUqmSTEb3ewsky7JYuDDL\\n1q3bWbBgxpjDRiIREgmHMPSIxXR8GotaZEg7mTFjBkcdtYOBgS0sXTp6qyuRqZZIxAgCB9s299qi\\nwbZtUikPz3NJJltrwT6Zx9/6Oa8DcEAdI9PpxjrQddREtFTICMOQ/v5+enp6AOjv7z/g7kWC+saX\\nybS0CmQ3WpftIxqN7tHcPJVK7HW8eDxGfOc+P522SafVtE3ePiKRqW2C2tPT0zymjsUwjHFtz6IW\\nGdJ+dDuJHAhM06SrKznu4d/qj76Tefw9UI+RU30OcqBq6crzsssuY9myZfzZn/0ZYRhy7733csUV\\nV0x2bCIiIiItUYsMERGRztVSIeP888/n2GOP5dFHH8UwDH7yk5/wjne8Y7JjExEREWmJWmSIiIh0\\nrpbvBVi8eDGLFy+ezFhEREREJoVaZIiIiHQuPU1EREREOo5aZIiIiHQuFTJERESk46hFhoiISOdS\\nIWMSuK6L4ziTNj3P8wiCAM/z8H0f3/fxPA/P8/Y6n8bwjfHHM/3d4w/DEM/zJtSX89BxHMcZNu9G\\n/NKaIAhwHIdyuYzrugRBgOu6zXXayItCodBc943v2Pd9arUajuM0v4fG99rIFRGZer7vUywWx7VN\\nTuSY09gXTKZisUixWBzXvHO53Lj2/67rjmuajemOZ5nUIkPazYYNG/j3f//3qQ5DpK20ct0xUY1r\\nntGM97g21NDrm3K5TF9f36jDNpax8RopvvEeL2UX9Zf5FjmOwxtv5AGT2bM90un0W5pepeJQLofU\\nahUsK0oQ1De6MDQYGMhh22my2TKzZu3ZTV+tVmNw0CUMQ4LAw7KipFLesG6RGtP3PAfTjOB5NXK5\\nCqYZZfZsl0wmQz5fplYziUarZDLj64KzVKrgOAaFwlaq1SiRSMj8+T0YhsHgoEMQGKTTPvF4a31M\\nv135vs/27Xl6ewepVGpMn56kuztGsRgSi1kkkwaOE/Laa1soFCAeD5k9O0tXV5po1CAMDbZty2EY\\nUQyjwsyZBxGNhiSTUfL5GhCSzcawbe0KRKbSCy9solCI0t29naOPPmzU4SZyzKnvl8EwfKZNS2EY\\nxluOc8uWLTz3XB7wOOGEOXR3d4867HPPvcLWrTbd3b0sWzb6M7Vc1+W553qp1SzmzSsyd+7Bow7r\\nOFWKRR/TDMlm41iWNeqwapEh7WTDhg188pM/pFSaxn/8x7N89auXT3VIIm2hUCjjuiaRSJVsdnzX\\nHRPhui6Fgsdo57y5XI7f/GYzYLN0aYaDDx79GNTgeR6Dg1XAIB6v8uija6lWM8yfv4Xjjz9mj+Hz\\n+TKVSkClUiGTSdPVFRCNRpufj/d4KcOpRcZbVP9lKIphxHHdt15F87wA04xQq0EYmvi+ge8bgEmt\\nZmJZSRxn5PkEQYBh2ASBgeexs1AR7DF9w7Bx3ZAwNKnVQsLQxjDiOI63s2IYYttRPG/8ldFaLcC2\\no5RKHpaVIAjs5q9mQWBimhF8f3J/FXw7CIKAWg2CIILvx/E8k2LRxTBiBIGF43gEgUmlYhOGSRzH\\noloNCYL6d+t54DgGppmgUgmxrAieF+J5PmARhtak/1orIhPj+z6OA8lkD6XS2NvjRI45jeNJGJqT\\ntp0Xiw5hmMT3U3ttFZLPV0mnD6JQMMb8lane0iyKbWcplcauPPh+fZmCYO/LpBYZ0k42btyI6x5M\\nMvlONm7MT3U4Im1j6HXHvmiV4fv1a5/RznnrrZZThGGSYnF8rR2DICAMLQzDplAoUq0miUZnMTBQ\\n2WPYxrWVadrUaiaGYe9xTTT0eDmZrfw7nRHuy3Y8bcowjEnbUIIgoK8vB0BPT+Yt/7Lt+z7lchXT\\nhN1DrDcn9pgxIzOsitcQhiHlcj35DQN8PySZjA37xaoxfcsymtN3HIdq1WfWrG5s26ZWq1Gp1Egk\\nIkQikXHF7XkelYqLYQTk8w7xuE1PT/2XukrFwfMCUqk4pqna2VhGys1yuUJ/f55q1aWrK0U6naBU\\ncrBti1QqTqXiMjCQo1BwSCajdHfX8zASsQjDenO3atUnlYpiWVHicZtIJNLMlWQyPim/1Epnm8z9\\npuypv7+frVuLzJmTJZvNjjrcRI45jf1yJGJNWms4z/N46aXXADjyyAVjzr+vr49Nm3YwZ052r79w\\n9fZuoVRyOfTQmSQSiVGHC4Jg5/7PHNbacKT8/N3v4L3vrf8rMlWG5uaXv3w9Gzfm+eu/Pov/z96b\\nRtl11Qe+vzOfc+e6NUpVKo2WJ1m2AROCB4xtxixYPMhqh27gBR687rz3yOrQNOStkKz4rbwmBD50\\nBwghvbofIQwdoIMNtAONsWUbPMq2JGuypJJU83Tr1h3PPLwPt6pU5ap7q2SXVCX7/L5ouPvsu8++\\ne/jv//4Pb37zmze4ZTExm2NvfyXnjoshDENM00YQhBVl3ovZ1+ZZfOYyDI1Tp84xPV3l2mv76Ojo\\nWFbedV0syyOKAmRZJpFYeiYqlUqcPTu5pv3y9cJaxmasyHgdEIbhksnSsNxo9IEgCGs6xM4/82oO\\nvFHU0LTGyozmrPfYDIJgQZG1+O8v5+W/zcX83usxNmI2P6+3dTNmfXBdd0XF+6utU5KkJevZSuPz\\n/Hl429tgcHBdvz4m5qJYPDY9z8PzPBKJxAa3KiamQby3L2el88pqZ5hLecaZrxt4XcnbaxmbsWP8\\na5xKpY7nCei6QDJpYNsO9bpPGHoIgowoCqv6GTd8yzwEISKXS7yiSRpF0VzsDUgmpSU3aZca3/ep\\nVm1EUSCdNhBFkWrVxPNCUil13YXsy00jSJ6Looik0xeEo1rNxHHA9y1M00eWZdraDFKppQJUEASU\\nyw2tciaj4Xk+9XqAokAmk2i5YK7H2IiJibk0NFsbXg1BEFCpWAhCY31oNeenpoqUyyGGEdLX17Uu\\n318sFjlzpowohuzfv63l+q2qsWtJzOahVqvxgx88Tr0uc+edPdxwww0b3aSYmMtGGIaUyyYAmYzR\\n8tyxkax0XplvexgKpNPKsn1nPc44jfOZh65LJJMXrBIbMrqFZbmIoohhKGSzrWXz1xPxqeM1zHx8\\nBVnWcd1G0FDXDZAkfS6OgkQUyatGyfe8AFHUCMPVy7Zqi+8LKIqB41zeTBmu6xNFGr4vL0QLdl0B\\nSUpgWVe+lGtZHpKUwHWX+qG7bogs65TLLoKQxPfVFfu+ESlaJooUPM/HcQIUxcD3hVV90OfHxlrG\\nUUxMzOXFNBtrg+OwbvPTdT2iSCMIVLxVtATVqoth5LEs1i0Se7FoIcudBEESy1rui7wYRYmDfcZs\\nHorFIqVSB7q+j1Onmmc3iIl5LeJ5HmGoEoYqnrd5M3MsPq/Mn50aWf4kRFHDcZa3fT3OOPW6i6qm\\nsO1wiewdBAFRpOC6IlGk4PtCLG8vIlZkvIYRRZFEQiIMTZLJhvYwkVCJIotsVkXTQhTFX9UfTddV\\nRNFB08JXHANEkiR0XVjSlsuFqsqAgyh6yLKMJEkoSojvmxjG+vviXW50Xcb3TRQlXKLhTiYVwtAk\\nn9eRJBtFsUmnl/vJK4qCovjIsoemqSSTKmFoouvCqhrz9RgbMTExl4b5tUFV18/cVVHm11N31Tnf\\n3Z3C86Zob1fXbX3o7c2jqtN0dLirZgmLLTJiNhN9fX3s2lUhCA5xyy27N7o5MTGXFVmWEQQXQXDn\\n9pHNyeLzSiLROK/IsoyuR4CNYSw/w6zHGSeRUHCcGpomLNmvFUVBVQMyGQFV9TEMIZa3FxHHyIh5\\nXTAfD2S1/9toXunYXO1dNuO7xlxZxOvmlcmlmPubcT1ZOVAytLfDKoYbMTGXlJePzZfHLYuJ2Ugu\\n596+OM5DzHI24966kaxlbMYracyGcrkWz5UWhitpsVg1au8q73IlvWtMzOuVS7EeXoq5f6WsJ7FF\\nRsxm5EqZPzEx681mDlS5OKDmRrFZ+2YzE9umxGwY84FtFEUgnY4D16zEfAAh34/IZLRLkpYqJiZm\\n46nVTGw7JJGQSCSapx+NWTuSBEEAYQjxBXjMRrN4P0+nr/xA4zExrxXmg1hHEWQyeuy6cQWx4Vv7\\n008/za233srtt9/OZz7zGQC+/OUvc/vtt/ORj3xkIUDYd7/7XW699Vbe9773Ua1WAXj44Yd561vf\\nyl133cXo6CgAR48e5bbbbuO2227jxRdf3JiXilkTjuMjy40glasFlXy90ghOKiFJBrYdXy3GxLwW\\nCcMQx4nQtBS2HQfxWi8EATQNHGejWxITs3Q/XylgYExMzMbg+z5hqALapg5EGrOcDVdk7Nixg0ce\\neYTHH3+cqakpHnvsMQ4cOMDjjz/O/v37uf/++/E8j29+85s8/vjjfPSjH+Wb3/wmAH/xF3/BL3/5\\nS/7yL/+SL37xiwD82Z/9Gf/4j//ID37wA/70T/90I18tZhUMQyEITHSdTZuGaaORZRlFCQhDa8UA\\nQzExMVc+oiii6yKeVyORiK2u1hPDiGNkxGwOFu/nuh7P85iYzYKiKEiSiyA4qGo8N68kNtx2pru7\\ne+HviqJw7Ngx7rzzTgDuuecevvvd73L99ddzww03IIoi99xzD5/61KewLAvDMEgmk7z5zW/m85//\\nPACzs7P09vYCUCqVLvv7xKwdVVXJ51WCIKBUqnHBs0QAQsJQwPNs6nWfdFpGknRqtRKHDo2QTIZY\\nlogsi9x00y5UVUeSIoJAWPhT1+UluZyjKKJaNYkiSKX0JcoT13Wp1100rWHWHYYh1WpD+k2njXUP\\nzOX7PhMTRcIQtm7NNzVjEwQBSYKZmRph6NHR0bbw/Ph4kTCMyGQMajWLUqnK7GyVdDpJW1sKQRAo\\nlarIskJXV4pTpybRNIn9+3czM1NidLREOq2wZUvXurj2WJaNbfskk7HJbEzMxVIqlZmaqtPXl0HX\\nO5uW8zyPxx8/hO/Db/3WXrLZ7GVsJdi2zQsvDCBJsH//bnRdb1q2WCwyODhLV1eS3t6epuWCIOD8\\n+VEqFY+rrupumY1kYmKChx8+QT6vc9ddb1x1rTEMsO3V3ysm5lIzOTnJxz72fzM4KHDffe/k937v\\n9za6STExqzIvO4dhRDptrHjxWKuZeF5IOn1p3DI8z6NWc1BViWRyueul7/tUqzaKIpJKJZZ97rou\\nExMlRBG2bu1YJtOXSiW+971f4vsiH/vY3eTz+VXb9PIzxcxMmXrdpbs7QyKxvA3zMnIioaBpyzMI\\nrnW/XAthGDI2VsD3Q7Zuzb+mZfINt8iY58iRI0xPT5PL5chkMgBkMhlKpRKlUqnl/wELOXUXuyhs\\ndNCWmLVh2y5hqFGvh5hmhOOIVKs+UaQxMlJDUboYHCwjSUleeGGYINjFCy/UGB5WKZc7GR6eJAgU\\nZmddRDFBoVBHkpLU6/6SMeB5Hp4nEwQqjrPUTaNebzxrWRFBEOB5HkGg4PsKrrv+Lh2maWKaOrad\\noFKptSw7NVVDljuZnfXx5iLXNfzpDSxLZWbGolAIKRRgZibN5KTAxITLxITNzIyCZbVx+PAArttD\\nsZhkbGySsTGLMOxidDTANKOFel8pYRhimsFcv7uvqq6YmNcbQRAwOmqh6zsYHKy0LDs+Ps7MTDu1\\n2jaGhycuUwsvMDIyQb3ezexslkKh0LLs2bOz6PoORkctXLf5umBZFlNTElHUzdhYsWWdR4+OEEXX\\nMTaWZmJi9ffX9dgiI2ZzcODAAY4fvxn4ff7mb3610c2JiVkTnufhuhJhqGHby9dx3/dxHAFBMDDN\\nSyP/1WoOopjAtqOF895iLMtFEAxsW1gISbCYSsXEdVOYpo5pmss+HxgYYHx8F6XSDTz55AtratPi\\nM0WtVqdcDpGkDqamlsv0URRRr/tzMvLK8vZa98u1YJom9bqK72colVqfMa50NtwiAxpaqE9/+tP8\\n8Ic/5ODBg4yMjABQqVTI5XJks1kqlUrT/4MLrgmLb5Vb3aL/+Z//+cLf77zzzgUrkJjLj6rK2LaL\\nokRAhCgGaJpAFLlksxL1eoH2dh3XNdm1K8fRo+fo7HRRVQtZDshmOxEEn0RCxPMs0mkV1zXRNHHJ\\neJAkCUGwAAFFWaqd1HWZet1CURrloijC9+tEUYQsN78dfKXouo4gFBFFkUQi07JsJqMyMzODYbCg\\n6TYMDUEoEYYumqYQhgG27aGqNVKpFJoWIcsyvu8gCEW2b+/k/PlJVDWko2Mnvj/L5OQ02WyEokSv\\n2rVHFEUUBVzXJJGI3YRiYubxPA9RFFvOMUmSSKehXB6iu7u5hQNANptFlk8CEu3tvevc2tXp6Mgx\\nODiKLAfkcrtals3nNUZHz5DPyy1vhFRVRddtPM+lo6O1hcmOHTmGhs6g6y75/I5V2xu7lsRsFq65\\n5hoM458xzUHe+97dG92cmJgVCYKAMAwXgstLkoQoukRRsOI6LkkSkuQQBDa6fmmOlYtl9JXOdpom\\nU63aSFKEKC63dkgkVMrlGpIUoqrLrS22bt2KojwGaFx99f4V2+D7jcvRxf0yf6bQdQ1Ns3GcIu3t\\ny/tIEAQ0TcRxTHR95bNpe7vO5OQQ2Syv2oJCVVVkuUYYui0tHF8LCNEGmy34vs/73/9+7rvvPm65\\n5Rampqb4xCc+wc9+9jP+6q/+il27dvGBD3yAu+++m0ceeYQf/ehHDA0N8dnPfpa77rqLn/70pxw7\\ndoxvf/vbfO1rX+ODH/wgX/3qVxEEgT/4gz/ggQceWPadlzNncszaCMNwQekwn0c5iiJEUcT3fWRZ\\nnguI5zI9XUdVJXK5hnuILMsLZefzszfL0z6fXmmlzxY/43kepVJDI5rNKpfELCsMQ8IwXGKG12xs\\n+r4/t2heUMy4rkupZCMIMokEaJpKFEXYtoNlCQhCSCajzikZFBzHWeiv+ecbG5S4bhljmvV7zJVP\\nvG5ePJZlU69HCEK4sF61wnXdNa01jZsgb0Xz1cuBbdvIsryqCXGlUqdWCzAMkVwu2XKdCYKAIFhZ\\nUH45tVoNVV3qwtZsfL7pTfCNb8Att6xabUzMJWF+bLquy8GDp5iYmOXOO28gn89tdNNiYpasnUEQ\\nUC7bRJGErkcLbhytZOe1fL4erCZfzp8jmu0zvu8jimLTOizLwrKsFd1KGi7wNlEkkkwKC27ri997\\nJZn+Yt9hrTLAWlhLezY7a5E7N/zt5q0wPve5zwHwxS9+kTvuuIPbb7+d7du385nPfAZZlvnUpz7F\\n7bffTj6f53vf+x4Af/Inf8I73vEODMPg7//+7wG47777uPfeexEEga9//esb9l4xF8fiiT2/CM3/\\nOT8JGwtFRCqVJgz9JYL0fNn5epotFK0WucXPNBbExqHjUh3eWi2oL2elhajxvIwoykCwUMZxPCRJ\\nJor8JYqLl/vkXQrlTKzEiIm5QBhGiKJEGK4tP/1a56SiKBuairlVXIzFBEGEYSQIw9WDVDRu9dZm\\nzXUxN0y6HsfIiNkchGFIe3sPXV078P3YBTNm8xFFEWEoIAgiYXjBRaOV7LyWz9eD1eTL1T5f7UBv\\nGAaGsXLq84bCQpjbzy+4tix+77XI9Kt9vp5y+cWcMa5kNtwiYyOIbxavXMIwxLIcZFlcMVjOejFv\\n2QCg69olX6Dnudix6TgOvh9iGNrCgnW5+ijm9UW8bl48r/e52PCd9lBV+ZIrXpqNz3e8A/79v4d3\\nvvOSfn1MTFMWj81KpYJt++TzmSv6pjTmtcPL186V5MqYuF82givCIiMm5mJouEms/ebu5azV/E0Q\\nhCUZTzYrmqahKBfccuZN61aK6ryY+aC467UYXw6zwpiYKw1RFFedi4uZz8b1WkGW5bnMSxsXNyeO\\nkRGzmUgkEqiqHysxYjYtmqZxqfTu6y17rifzQUKbzc1L2S+XgteLXB6vpDFXFMVikTNnyihKyPXX\\nb7soM6yG759FFAlkMuqGmmavF67rUq16CEKErkuYZohqCQwGAAAgAElEQVQsQzbbPJ2q7/tUKg1r\\nk0xGe9UCVRiGlMsmYSiQTl+aeCIxMa91Tpw4R7Uq0d4Ou3f3b3Rz1oV63cK2GwGFM5nkhrQhdi2J\\n2SzYts1TT53CdWWuuy5DX1/fRjcpJuaysd6y53riui7DwyXCEHp7UxsWf2q9eC2ed5qxeUbR6wDT\\nNPn1r48hSRE33LALRdF59tmDHDs2y5ve1EEQZEmnI6pVgXRaRNOSQMjg4DiuG2LbZapVmb1700CG\\nkZGjPPjgMHv2hOzd+wZ0PaKnpxtJUpmdLVAqhfj+NMPDAb29DqOjGjt2qGQyvYRhlQcfPEQYWrS3\\n5wgChbe97SpSqS5U1WZmJiSKZnjxxQr5fEA63UkQ2Ni2iyBoiKJFtaqyf38eSWpDVWucP++we3eW\\nvXv3Ylk1Hn74MIIQcPXV29H1BAMDZ5mcdNm3L49hdKAoLoWCRz6vs317LxBRqdSJIgHHsajVfK66\\nqnuJP3Sh0EhFattlLMu6aEVGFCkIgojnBZt+YkdRxNjYFA899CyDg5P09XWxe3cnpZLH7GyB8fES\\nR44cY2iojq471GqQSiXo788yPl5Blhv+fBMTk0SRQK1WJ5HIcd11W+jr287WrVvZv38rPT15zp2b\\nJgjcOWsXlWzWQFFkyuUqZ86MMTo6gaYl6OvLIssJZFmgvT1HOq2TTCYJQ5koEhgZMentbaejo23F\\nd2oEIvUwDAVdv4JU2zExr4DFue2TSaOpcjEIAr7wha9x/LjE7/xOiq985c+a1jk5OckHP/jHuK7K\\nf/kv/yf7968cYR2gUChw9OgYnZ0Jrr9+T8u2PvroQUZHq9x22276+5srUv74j/+YL33pKWCGf/7n\\nL/Pud7+7adkPf/hf8dOfevT3Fzl37vGWN0N33PGvGB6WuO++e/jYxz7WtNzQ0BDf+c5j5PMyH/vY\\n+1cVOGOLjJjNgm3bvP3t7wL2AL+OXfViNiW1monnhaTT+ropGyzLplyuAxqqquL7AY7j4boB6bSO\\n7wcryoZhGFKrWUQRpNNG0z3Etm3Gx0sYhkxXV37ZM+VymaefPoUsR1x//R5EEYIgpFZzSKU0jh8/\\nwt13fx5I8sUvvpdPf/rTuG5AKqXhOA6nTg3zn/7T/8fEhMznPnc3b33rrViWRxj6KIpKOm0gSTcD\\nGd7znjT3338/9bqLrssL1t3f+tYPeeqpcd7+9q28+93vIZVaGgD8D//wD/mHfxjhhhtEHnvsR8ve\\nsVAo8OSTZ+jpSXHLLftW6GOLgYEJVFVi27ZOwlBGFCVc10dRFMIwpFq1EARIpZb3ZRAEDAyM4roB\\nu3f3XFbr0LX+zivx2rY3uQT4vk+5XMeyVr/iiaIIz/MWTKmGhsapVDqZnm7jySdf5JlnjvPwwwUM\\n4+185zsvoGlX8eijg3jeFo4eneX48TJPP32O48fhzBmdhx4apVTazk9+coxk8jq++c1DKMrv8tOf\\nWrz4osRzz3kcOTLL8eM1nn22ztRUnvvvPw/cyd/93RF8/6386EdnGBlJ8f3vP8PY2Bt58cUunnjC\\nZ2JiP/ff/zyu28P/+B/HcJx+vvvdwyjK3Rw4UOTcuTwHD1qcOJHkxIkEv/jFGNPTvfzsZ0fRtKv4\\np396CUW5maefLjA7a3Hw4EmmprYxNNTO0aOjDA15PPNMBUV5Mw8+eBpd38FTTw0B2zlzxqZcdpmd\\ntSmXJWZn4dy5OqK4leHh4pI+3bo1jyRN09UVXHRKIUVRUNUASWqkLN3suK7L6dPTnDqVYGBgC8eP\\nRzzyyAjDwwZPPGFy5IjBwYM6o6M385vfSAwOXsXhwx386lc1Tp3axTPPqDzySMDx490cPryTgYF9\\nnD+/n6eeknjySZeBgYizZ+u88MI0586JnD4tc/iwydCQwIkTFU6f9njhhVlOnlQ5dEjj0KEUDz44\\nzrlzCV56SWJkROHMGRfbTlCr2UxPV1HVTqambKwVTg6NPNreQh7tIAgoFsuMjk6vaT7FxGwW6nWL\\natVcMZ/9YizLBfSmue3nKRaLPPFEkXJ5H//9v59oWee3v/1thoffyszMe/iP//HvW5Y9cWIU1+3l\\n7FmLWq15LvliscixY3Vcdy+//vWZlnV+6UtfBz4CfJyPfvSjLcv+9Kch8P8wNLSH+++/v2m5b3/7\\n2xw/fiNB8L/yta/9qmWdDz74HMXiTRw71sbJkydbloVYkRGzeWhra6Mxdz4F7NjYxsTErEAjrpGA\\nIBhz+9erJwxDTDNA07KYZpkoshBFAdsGQTCo150lsuFiBV8jQ5eM7yu4rtf0O4rFGq6bpFiMqFar\\nC884TuMdhocnKBTaOXVKYnx8ltnZkJkZD8tKUC6LfPaznwXeC7ybL3zhr7HtCFFMYJouExNVnn76\\nOE8/3UWlcjvf+tZjmGZAECiMjdUplXz+3b/7LHAr8CX++Z8dajUXSUpimsHcIb3Gr341jqL8L/z4\\nx0MEgbrsfb71rXFU9es891wXBw4cWOg7z2v0yfPPn0OSrmVgIKRQKCzrg4mJIq7bycyMiuM4aFqI\\nJLnoeuPC13U9gkDB9xU8b3lflstlZmZULCvPxERx2eeXksW/8/xvtlZii4w1EgQBnudjmg6CkKBe\\nd4giE1mWF6wCPM8jCEI0TUUQBGo1C8cREUWHtrYk2WyCev0s5XIJ23bJZLKY5lnOnPk5fX0CIyMD\\npFIB5fIorluem0Q+k5NH8LwkklRhdHSAjg6YnT3LTTclOHXqYXK5SQqFYSSpTH//tYRhDd+foloV\\n6OioMzn5LO3tPkNDh5DlMrY9Q0+Pzvnzz9He7iLLFkHwItu3ZxgcPEcY1jh9eoieHpGZmSfo7fXw\\n/VESiRpB4BKGAprmMDMzSC7nMDIyQC7ncurUCyQS07huRC6XRZaHEASfZDKN75skk1VGRg6ye7dG\\ntTpKf38ayxqloyNE08S59GQWYSiSyUQ4zgTd3UuVFalUihtvfGU5kQVBIJ2+cszFZFmmvV1HUSYw\\nzUkqFYNcTmd29gyiWEeWLXK5MYaGRjGMkHr9MG1tKRIJj5mZl/D9WRzHI4oSKMoIYCHLeVRVJpvN\\nE4bTpNMZFCViZqZEGJokEgbF4jkymTxhaCHLdXy/jOOcBTrJ50NkeRJdF0kkDBIJCVG06exM4nk+\\n9XoJQYio1UJk2Vti9dJYOD08z0LTRDzPZ2bGR5JSzM6alzWoakzMK8XzPGZmTMKwoZxr5TKhaTKu\\n6yBJEZLU3AIpm80SRVUqlRm6u82W39/f349tHyCKklxzTU/LsoYhMTg4RFub29ICTdM0oqjKyMhp\\n3vCG1nNw795eTp16CIDrr7++ZVkYAr4FHONd7/qbpqX6+/sRhAepVMa4557WSubt2xM8+eTzqGqd\\nnp4bVvn+2LUkZrPxa2AUOL/B7YiJWU4je5RDENjo+vocEUVRRJbBNGtIkk4UKURRhCSFBIFNMikj\\nSQGOY6Gq4hI5UJIkBKHhjiLLzfdQSQoplYooSoCi9BAEjYsDRdHm/oSpqWGCoIIs59C0AE0TqdXq\\nqKpMZ2cnMAII9PUZyHKE51nIsk+9XsHzBCzrJK5r8a53aShKI724ZdlIkswtt9wC/C3wXeAImiZh\\n2xaKMh8rK0kiUWVg4Ofs2FEjilxkealFeU+PzdmzXyaff4k77rgDgErFwvclFMWlvz/PoUNnyGRC\\nstnssj7IZg2mp6dRlJBEIr/MYl2WJcCZ66/lfZlKpVCUYcJw5RS0l5JGPK3GRj3/m6352UvRoNci\\nlYpFGKrYtjsnnFpEURJB8MlmBURRpFJxARnft0ilEgRBiCSphGFAFEXk83ne/naNQqHAwYMTRJHA\\njTfuY9eu61DVGv39fQRBF4qiUyplmZ72yOV8dP1GQEfTeujru5rt22+muzvHrbd+noGBEaanr6dS\\nacd1a/T0qCQSCW64YRuynCKKdhKGGpbVS7Uqk0xuo729B0XZg647dHR0IIoixWKRalUGdPr6RAyj\\ni2z2X7B9e44oehvVakAUhYBAtVrl1KkpIMHWrdeybVsfe/bcgyxnCcOdZLMJ2tv3sm/fdlRVpV5v\\nmHBde20niqLT1qajqiKG0Y9t28iyjCg2Fq+urtxcwMougiB4XcdbkCSJa6/dwcc/nub8+SKOI9DW\\nliAILAwjh2UVqddv4+c/P4llaXjeGDt3XkU2GzI8XGZw0GZycoJMpotduzz27t1LIiGRyQhkMtvQ\\n9TrXXLON8XGHHTt8EgkXx1GYna2gqjl0PSCVkqlWQ8rlGYpFjy1b2ti1K00ulyUMQxRFWRJMyLIs\\nTFNcFmnY8zwqlQAwMIyQZDKB53moaojr1tE0JVZixFwReJ7HxESNKNIQRbelIkNVVdra5DWlpvvQ\\nh+7izBmDm29+V8tyt912G/fd10e5XOH977+qZdn+/q1omoumtc4lr2kad999E5Yl0t3des395Cf/\\nL77whQE0Tef3f7+5WwnAv/23H+fZZ+Haa69u+f433XQTX/7y/0GhYPGe9+xuWecb33gL7e1VVFUi\\nl8u1LAuxRUbM5uEjH/kI3/nOJNBOw70kJmZzIQgC2Wxi3YNEZrNJVNWiXhcW/d/S7zGMYFlgaFmW\\nyeUan7dqTzKZYseOFIIQoqoqhiEtqbuzs5u77soiSQJ9fTqZTGYu1WyIKIp88IMf5Mknz+K6Pp/8\\n5P9OJpPAsmxMU8PzVK6//ho+/GGfVCrPnXe2kckkEcWIej1LFMG+ffu46aY3Uq+neNe7vkAyaaDr\\nF94nDEPuvff9DA3V2LXrLbS1LXef+M//+f/lqafGeNObPr4QuD8IIiRJJgh8rrlmN/39ZtP06/l8\\nnptuSjVNZb5aX6qqys0379iQs5ckSbS1vbJxFysyLpJEQieT0XAcAdMUgOY+jum0gW27qKqKKIqo\\nqkpnZ0Rb21ZkWaJadbDtPtLpDLLsk89ryLKB74ek0x20tUVUKgaSVEIUNXbuzJLJJOnsbFsQSq+7\\nbie1WhcDAwVEMUN3d9ecBtMmigSiqBtR1JCkPBCSSOhz8SGkJf5PqVSKWq2Gabr09e2lXndoa0tj\\nGAZRFGEYDqLYOJx2dibIZJL4vkA+r6JpGu3tW6jVbBKJDgRBmks32BDwZdkhCELa2zvw/RBdVxcm\\nma4vzQwiCMLCIN7ISPebBVmW2bo1TzKp4Dgemqagqm3Ytocs9zA5aXPPPQlGRkbo7d1BFIns2tVD\\nsTjO4OAs5XI7uq6ybdtWJClJKiXR25tBUUQymW1EUUR7uzJ3syxRqTh0deWpVIp0d7ehqgqmaSPL\\nOTyvMZaTyeaBRBOJBKLY0PiutNAKwrxWuPF5X1+eINj88UpiYuYRRZFs1iAMJVKp1deotWzKsizz\\n4Q+/hXPnitxwQ+sAgF1dXbz1rQ5RtIVt21azyNDp7tYRxbClIkGWZXbu7MS2XVKp1lZrd93123z4\\nwyAIInfddVfLsu98501s2VLhqqu0lulnc7kcd965i3rdZvfu1u+fz2cAUFV5TQHZYkVGzGbhc5/7\\nHN/5zh8DJX73d9+x0c2JiVmRtSjeXwkNeb8hH6qquux7msn8a9lDEwkdQXCQZWXhfLS47lwus/D3\\neWuGxeeND33oQ5w8+QC+b3Pvve9c9FlEOq3T1pbmve/diaIo3HjjXqBxAdDWJhBFoGn9/Jt/cwdn\\nzzq8/e1bl72PIAhs2ZIlk+kgmw1WfKetW3O87W1tJJP+wuVuJqPhuh6aps+9Z+s9bzUFxGp92UwJ\\ncjl4peNOiF6H0YbWkpf25QRBgO83UmbN/8iu6yIIwsIhzPf9BU3Waj/GfOwM13WxbZtsNrtsALpu\\nw0/IsiyCIGhq6tNwyXARRXHhvebrmm+Toijrql2db/9a3vXV4Ps+nuejqsqaJ1cQBLiud1HPbBYu\\ndmzato1pNlycoihClmVkWUZRFGzbXvDLt20HSRIxDANN0xb6xXGcOcsIdS4A06UZL3BhPL+erWyu\\nZF7JuvlaxTRNfN8nlUqt2zyZ3wsSicSqAdZqtRpRFJFOp1uWm98bJEla1wjxx44dQxAErrvuupbl\\nXNelXC6TSqUueeCwZuPzr/4Kpqfhy1++pF8fE9OUxWPzgQceYHx8gnvv/RdzMTNiYjaWeG9vMDEx\\nAUBPz4ULAtd1F6wjGn0kLIQPmP8cGnLt9PQ05XKFrVu3rKhwWG2P930f0zTRdT2Wk+dYy9iMFRkx\\nm5YoipidbUQ5FgSHtra1xcaYnW2YfUMjNsmV5LKw3mNzcR+KokMud6EPbduhVouAhjVGvHDGtCJe\\nNzcHnudRLvuAQColXPbsP47jUK1uvnWj2fj86lfhpZfga1/bgEbFxHBhbNq2zfBwjShSyeV8urou\\nrx96TMxKxHv76vi+T6nkIggSuh6STC5VzIdhyOyshSAoKIp/RcXj28ysZWzGriWXEN/3sSwXRREJ\\ngghRFBbS8DQrq+vLfZ8syyYMG88HQUQU+VSrDum0Bkgr1m/bzlyMDgHPCxGEkErFJpGQmZ2tYxgq\\nPT2dL6sfggAMo+H6EQQBluUiSRCGtKzfMNRXfOPn+z6FQnnu+yPCMCKTMYgigbGxCQqFkC1bJARB\\nQJYbbZTlRjrDKIowTRvfD5FlgSBo3JbatodhhEBz//W1Uq9b+H64LFXSq2G+3VEUrZqWsVaz51x1\\nFEzTAULCMKJcrhGGEZomU687aJqMrmuUy1VqNQvbdkgkNARBRtPSaFrI2bPDRFHIjh1b59yMPEZH\\np0gmRbZt66JWc3AcB1GUSaV0crn0Qts8z6NatQlDH11XiSIBWRabjumYmJi1c/bsOY4fH+fmm7fT\\n29vbtJzv+zz55PPYdsg99+xrqciYX8MVRWzp2gHz1lnhwvrfjEKhwJe+9APSaY3PfOZe2tvbm5ad\\nmpri+PFRduzIs2PH9pbfPzNTwnF8OjuzLV3NfN+nWKwgyyL5fBwjI+bKQRRF3ve+jzM9Df/hP3yA\\nf/2v/7eNblJMzLpw7two9brLjh2dF51REBrWCqbpYRjyqnvVSoRhiGU1LI9X2hPDMKRetxFFYc4N\\nZanMXSqV+MpXvkW5bPOnf/oJurq6ltUxOTmFaQbs2dOxoMiYPz/pusqjjx5gYMDkPe/Zx7591170\\nO3iet5Cy9XJfUFzJxOlXLxFBEFCt2vi+ytRUDdMUMM2oaQq+SsUmCDQqFWeJ9qkxucE0YWbGxLZF\\nTp2axnUznD49vaT+er2R7tX3fWq1AMsSmZio4jgSp09PYtspnn32HFNTCc6da6Tja0ycCNOE6elG\\n/bVaI3JsrWbjeQpTU3VMk2X127bE5GQV31epVl95WPhyuUa1qjEyYjI56VKpKExMVHBdmYkJh0xm\\nC8PDVQTBoFAw8X0Fy2KhLZYFvq9QKJhzgU39dct/7XkelgVhqM0pEdaHhomZiONILVMNWZY7904w\\nM1PFsgQmJhxGR6uMjPgMDvocPTrJ8LDASy9VOHeuzMCAy/HjDocOWRw+bDM8bFOreYyMFDl1KuLk\\nSZHTpwuEIThOFc/TMM0cJ0+OUKmoDA05VCoixaK3YDYHUKs52LbI7CxMTpoLY2J+TM8HToqJibk4\\nXNflwIFharWreeSRUy3Ljo6OMjqapFRqZ2BguGXZ+TW8Vgtapopt7Ff+XNnWa/mPf/w/GRq6lsOH\\nt/Kb3/ymZdmnnjpLsbiV55+fXrKWvBzbtikUAiwryfR0uWWdxWKFUkllejrENFtneIFG1pJYkRGz\\nGfjiF7/ISy/to1j8OF/4wg82ujkxMetCrVZjehpct5Ph4VeWtrNadREEg1rNe0XWIfW6jePI1GrB\\niucsx3HxPBnLElZMPfrzn/+cxx7r49ixffzX//rDZZ9blkWpBI6TpFisAo19u14H2xY5c2aAhx6q\\nMDV1HT/5ycGLbj9AterMpaP1Y1n6IogtMi4BlUodzxPwPBtFkWmcp0MEIWp68y7Ljckly0uDnTT8\\nr725+AeNepJJCdc1MQyRIJh/plH/vL+2KEYEQYBtm7iuRBR5+L5NOq0QhiaKEiwKttO44TfNOpYV\\n0d4uAUlkWcSyPBRFmGs/CwFoGvX7KIpAGPpzZV4ZqioRRTYNJWyAKDoEgUOpVMaypjh6tMTOnTK+\\nbxMEFocPT5DJyNx44x4kSZorH2AYEkHgoihQrVbRtJBSSUJVpWVmYC/H8zxqtUaaxCBoWBqkUsai\\n+kMMY/2mSxRFjI6OE4Zw9dXNA/YJQsiZMwPUahYdHSkMI00Y+ti2jedZhKFAEDg4zhSi2PgNbLuE\\n63pEkYUsh9RqIYVCgKK4BIGHIOjoeg7LsgEVz5siCGwMI8J1q6hqI41qGMqE4QXzOFWVME0bUfTR\\nNBHLqhNFIalUFlEUKZVMokggnVY2jbl5TMx6E0URtZo1F5R5fRSmqqoyMzPIc89NcOONrYW4ZDJJ\\ntXqCIFBIJltbOcyv4aIYtozlIYoilmXiOCYdHa1vgvr62piaOowsuxjGW1qWte1Zzpyx6eqqIYo3\\nNS3XiOHhEwQmhtE68K+qygiCiyD4a+r7dBpqtVWLxcRcchoxMR4EZkgkpje6OTExF4Xv+1SrNooi\\nLgkKraoqnlekXK5y7bXL04JCw3LBtn2SSXVF+VBVRRzHRlWXnoHCMKRWs4iiRgKFZvuY77ucPTtJ\\nMimSz29b9rkkiUSRP3dOWr5vJBIJ6vUXiSKVvr4tKzwvUa3OUKsJbN3asEK8cD4T6OrqxHEmKRQO\\ns23bxSkhwjCkWrWoVMp4Xo10WkEUX701+euFWJGxRnzfx3E8VFVuavbqui6u6+M4IZqWIghcJMkl\\nk9Epl6tz2TpWHpyGoRKGFqqqUK9biCIUi42bKVVVgAjft5mcnGLnzq1zOXe7KRYrJJMaghAgyxei\\nzVpWlWrVRNcTCIJINttFOq2yY8deCoUiiUTbwmKSywlzQTVTeJ6AKDbqSCYNVNVDFHPUauaS+l23\\nSqVi09/fMyeENhd+S6UKruuTz2eQZXlZX6ZSKTo7Q2RZR9c1fN9nZkbBdSM0rZ19+3YRhpPkchqF\\nQkQm04/rVjFNk2w2S1tbci6FUpIgCBCEDIKgUyrNADqW5aJprYXeWs1BFBPMzs6STKYIw4algaIo\\nC/WvZ+BQ13VR1RwgYllO00B4rhsASSSpDcuy6OjQiSKXTCaDrhewbRNZ7mJwcIgoEimXTfbu7UcU\\nJWZnJ5FlmUpFYGJimlwuxY4dCbZuzZPNZpiZqQIG+byOLCvoegemOUN/fwbHgUwmz+xsGUEQ8TyX\\nQqFCW1uStrYkrutTr3skk3ksy0KSJKJIRhTlueCh69ZVMTGbCt/3cV0RUVSxLJd0en0svzQtR3d3\\nGlmutyybTqdpb1cAmba25m4dAIoiUSrNkk43d1+DhiBlmha1WkAyKdAqq+nOnXvYvv0YEHDtta3N\\nZ9PpNtraPNrbsy1vmBRFYfv2dnzfX5bJ6uVkMimCoIyiGGtSmGazUG5t5BETc1nYv38/8FNgiquv\\nvnmjmxMTc1HU6w2LAcdx0fULMrUoilx11U6CAJLJ5YqGxv4SoChJ6vX6iut2Op0gkViefrVhea0Q\\nReC6XlOXC8vyyeW6cF0T13WX7SOKoqBp3pyL+tI92/M8br75jfT0/BJRVLj55vet+B2OE+I4DlHU\\nged5uK6P61aRZRnDMPid3/ktCgWXN7xhC7ZtU6mYpFL6QuDP+Wc0TVnSBsdx8X2FUsnHceqI4vyZ\\nJnaaWAuxImONVCo2gqDPZRgR5g5u0UIgkiAIqFQ8okgCHFy3AoQ4jsrY2DSCkKZS8TAMc2GCzQt2\\njdt1KJc9wKZSqeM4JqaZxrZrKIqPphkMDk6jKHmCYJy2toZSZHo6JIoK9PR0oCgClco4juMwMhLh\\nugqiOEY6naOvL8fs7CyZTBulkkcYqrjuFLIso+s61WoVywoolWxSqcbh1DAMTLNxy27bIoIQAnUc\\nx+H06TqOI+H7Y/T3d6EoCsVikVQqtZC5pREHwmR0tGE5EEVlOjpyVCo2nieiKA7JZIjvB7iujOeB\\nLPsIgoDj+Pi+RCIR4rrTdHY2Mmq0tRmUy0UMI8QwGj5sgtD4PeYzbsgyuK5DKqUShg6KsnoqV12X\\nqdctEgkRQfARxQhJUpfUv57ouo4kFRFFkUQi07Scooi4bpFKxURREkRRAlEMmZmpMTZWplSqUKtN\\nMTY2Q6WikU4LiKJCsVgjDJPUamNMTdUZHCxhGBq7d7djGElOnz6F58nU6z7ZrEJ7e5bp6WnCsKFk\\nMQyJmZkpRFFhaqrGyMgkgtDNuXPnueaabRhGFs8r4Dh1kkkZQRDQtBDXNdH1i9Mkz8+D+UV78b/n\\nzeGvtOwzMRd4+e+7mQmC5YLUy5EkiTCsY9sBnZ2ts4YAC1lDVqt3drbEwYMDpNOtU6oODQ3x6KOD\\niKLMVVel6OlZ7ss7z8REiYkJD8PwuPrq5pHQG2v6NMViSBg6bNnS0bTOgweP8OtfV/F9n+eff56+\\nvubpUicmZnj88UFuu61/1aDLURThOM6qigzLciiXG8pSw/BWTd2cycSKjJjNwYMPPgiUgRwvvHBk\\no5sTE3NRaJpMpVJHUUQk6cI6LUkSqtqIUaeqy/c5URRRFHBdk0Si+T640v7b+LeDIIAgyAvnrpeT\\nSqlUKhV0nSVKgiBopDq1bYdy2SeKwjlL4kYcj5mZGVxX4MEHH+bnPw+RZY877jjAtdfuXchUCTA7\\nO8vRo+PYtkRv7xi5XB7T9Bkf98nlEvh+lampcV54YYCbb34P4+MlgiBNqVRm585GFsBy2WZgYJir\\nruonn78QR0RRZATBYWxskvFxn56eMjt2rCwHzL+PIAgrylZrlTdeS8RZS9ZIuVzH8yR8v46iGPi+\\nTa1mI0kQReD7IUEQIUlJZNkik2ljZGSYUkmmUhnGNFNomseOHVkURaVatXAcKJWmqNd1RkaOUa93\\nMD5+kNnZPmR5lO7u3YiiTaEwRakkY9sTKMpVTMcQdP4AACAASURBVE8/jCxfR1vbODt3vg1Nm2L3\\n7hupVMY4eHAIx4nYvl1H1/sJgglyuasZHX0SSbqaavUQmnYdgjCGLHfiutacBrCdXG6abPZGOjtn\\n6eraS7l8luFhkUSiTDrdh6r6bNnSiW27HDlymlotTV9fna1b91Eun0MUtyPLBfbtu5EoMhGEhvvB\\nc8+NEIYpbrgB+vp2USxOMz0tkMn4bNnSj2mWsCwZQfBJp1UkSSUIGhYSvb1JstkUluURBArgkUzK\\nS9LgQuOwVCpZgIIse6RSDRO0+bRJa8lcMq8BvZhnXg1hGBKG4ZJFd/HYrNVqPPTQc/zsZ88xPT3N\\nli2ddHR00NXVBlR55pkRjh0bx7IKlMsVXDdBOp0gl6sxMuJRLkf4/rwEHwIektSwykgm+wiCEsnk\\nVtrbVW67bTfT0wWmp126uhL09nazfXueclnFdV18f5rZWYNqtcquXW389m/vI59Po+sGYeiiKAbl\\n8gxhKNPRkSKbTeE4LqIoLByeGmmsIjSt8e963ZpLaywAAplMw9WpUKgSBBG6LlKtNnwd29sTcRTo\\nDeaVrJu+78/F/YFsVmtpFdUoW0PX1VVzpTduaQI0TV03BUmtZuI4oCgRmUxzZZxt2/ziFwexLJHb\\nbutveZBvBLssoWkBb3zj7pYWBIJwA9CLpg1i2yealvvqV7/KH/7hA4DAvfd28N/+2/eblv2Hf/gR\\nP/zhSfbsUfiLv/h0034tlUp85jN/x9RUjrvvFvijP/pU0zqvu+46TpzYCYR84AM6P/7xj5uW3b79\\nDoaGdpHPn2F8/OGm72+aJj//+XPUagq33NLBtdfuaVrnE088yfe/P4BhOHz+8x9YCDbabHwODMA9\\n98C5c02rjIm5pMyPzYZM8WZgN/D9OFNEzKZgrXu7bTtUKi6KIpDLJRfiHmmaRhRFRFHUcj9uZWUw\\nOjrJzIxHW5vCtm3dSz6Looh63cRxBGQ5IpNJLJPPLctmcrKCpol0d+fn3CVtTDNEliOKxSkeeOA4\\npVKRq6/eyr59fYiiz4EDUwhClW984885erQHUOnufoqHHnqciQmT9naNRCLBqVOn+OQn/xbXzfPp\\nT/fxR3/0GapVl2eeeZ4okuntVbn11k8Du9my5RQPPvh9ZmdVoMKePdvwPI8/+ZOvUyj0cOONdb7y\\nlc8v65tPfOLzPPFEyN69Fg888NVlyoh63WR21kbTRHK5JJWKiyBAJtOQrQqFAi++OIUkRbzlLVe9\\nIhfvxXL64j6el88SCX3Vy4b1ZC1jc/NfkW0SMpkE2ayEpqlIksbY2AwDAw4HD07y5JODHD5colCY\\nRRBCPE9AUQzGx0tUKnD+fAnXVRgbK3PkSJ1nnpniueemGByUefjhIer1XTz00HngDTz+eIFK5QZe\\neAEOHSry5JNDPPOMwcBAnuPHa9j2Tg4dEqnVfpeHHy5RKl3Nr389w+HDPr/85QDHj+c5e3YbzzxT\\npFzu4eTJIopyE088MUsU3c7Bgw6WdRXPPWdz8mSKkyd1Tp0SKBR28dRTM7juLn71q7PY9tV873sH\\nGR7ey09+MsLx4wpPP23x9NMTHDvmcP68j+t28+yzM1jWHh57bATDuJ6zZx1cV2dszGRsTOD8eQ/H\\n0UgkOjh0aJqzZ2V+8YtjWFYvTz99Hs/LMDZmEwQ69XrIzIxAtapRLrsYRpZq1WR2ttIyUNzLg0w6\\njkupVMW2bUzTxvM8wjBcfTIsxBcRL7kSI4oiLMvBcZoHNioUSjz/fJHnn9d48cV2Dh/2OXPGYXjY\\n4OjRKqdPu4yOdnLuXIaJiR6KxZ0MDwscOZJjZqYP398CXA30A9cD1xMEexkY0BkY6OKll5IUClmG\\nhw2ef77Co4/6nDjRzoED0/zmNxaPPjrFxIRFqZRlclLFdRV8v51SqYuzZycQRRlZ1jHNAN+Hs2cr\\nVCpJxsaKWJZNvQ6Vio/v+3OLoE+tFmHbDo7j4jgS9XqE44SAgu8Hc6kdoVaTKRQqRJFGFGk4TvNA\\nhZeDKGq4GsWC58URBAFRJCMICp63cqDjeSYmikxOwvBwpWlQ5Pk6SyWbarURhXy9cN0QRTHwPFr+\\nzlNTUwwNSZTLXZw8OdayzrGxEkHQTqmkUK1Wm5b71a9+BWwDPoLjtHYX+cY3vgHcBtzDP/7jYy3L\\n/tM/HWRi4mYOHHA5f/5803KFQoFqNU8u90ZOnmzt2nLixAngTuAt3H///S3LDg3lgH9JsbiTxx9/\\nvGm5SqVCoZBAEPoYGiq0rPPQoUHgBorFrZxbg3Yidi2J2VzcBtwNtLa8ionZbLhugKom8H2BarVK\\ntRpSrYYt5fPFtFJyDA/PYppJRkZml7khCoKAbfs4ToRpeiu6KZbLdXw/SbUqLLTHcQJkWcf3BQYG\\nRikU8jz3nM/IiMZzz41z5MgwrruFUinD0aMvAL8FvIXJyUlOnqwxMZHgyJECIyMODz/8NNXqfqLo\\nXfzsZyfIZhPU65McPQovvhjx13/9t8BNwIcYH2/DdTXSaR1VzVCteoyM1Dl7FjTtHRw+vFzGEUWR\\nxx4bwbLu4uDBYMW9bXq6zMSEy/BwCdNspHqNInnBcnlyskQQdGGaKSqVypp+k8V4nkel4s8FMF2a\\n3GBsrEihIDM62lo+syybUqm+YkDV1XilCQNi15I1IggCiqKQTgvUajaGISGKAr5v4/sCqtpwbZAk\\nBcMICYI6qZRCudxIE5pIiASBiCA0fLUSiSqJhMfevVl8f4A3vKEDyzrJdddlcZzDdHbOkMvtolyu\\noqolEgmDTEams7NCb29AvX6A3l6Q5Sk6OkBVfbq60pTLI0iSwvbtBqlUnd27dXz/Bd7xjh5mZx/m\\npptUNO0M118PrjuFKLoIgocgnGb79m5keZjrr+/ANM+wZ08OzztPTw+kUibpdEQyKaPr0NMjoSg2\\n+XySIDjNnXf2Y9sH+e3f7qStzSKRUBkbc0ilJPbsEfC8GbLZTiAgn1cJw1m2b88jCBV6ejRM00LX\\nI9LpCFG0aW/PIMseo6OzGMZWFKXI1VcnqNcdhoYsJClg27Y8kiRRLpuEoYBhNIKQDg3VcBwD0xwh\\nn99CGNrkckkURSKbNTaFyVUjG4ow57biruj3l8ul6O6WyGYLgEdHh8o110AqVWTbtj4mJ6coFIbx\\nvBqyDGFokUhYBEGWWm2MIDBp6CqrQDvgAjqS5KGqw2QydfL5El1dAl1dOapVmXK5jqI4GIaEYWh0\\ndVWo1UIymRyJhMj4+Ai9vZ1s2dJJIiEiii6dnQlM06a9vWFel0wqi26gLrxPvW5i2wKCIJLJpIgi\\nF1UVkaQQSfLQNGPOQsUkikLS6RS+39A4p9MXn85rPalUTDxPRFWdlrf1MUtRVRVVtYiiCE1rHXC3\\ncdsTzf3+zRUJURRx/vwYtZrIzp3GulnqpFIq9Xp9Yfw2oxGX5yy2PUFvb+vDSH9/J9XqJOl047lm\\nNFK9lYCX5v5szv79+zlx4hyQ4uablwclW0xbm8bw8BSplNsyJV5/fz/790ecO/cC73rX3pZ1JpP/\\nP3tvHmXZVZ15/u583zzEizkycohQpjKVEpqFBgYh2hhTBrqtooC2EbapKozNqsLG7eqi7cYLl3HR\\ndhu3FyVMlzFQDcYMNoOhAYMEQvOUKJWZyikih8gYX8Sb7313vv3HuRE5xstIShi1Hd9auTLWe/ud\\ne+65556z9z57fzuHZf0ICLj66qt7yg4PN5if/zKyPM3LXvaydeX6+vro6ztCvd5g+/beBKYvf/lu\\nTp48yMBAxLZtt/SUBeHIaLVE5ORP2D+9iU1sAE8CZ4CFn3ZHNrGJK0I6rdPpdHHdLr6v43k22Wya\\nOI5pNu2EI+PHKx2aSsnUag0KhfUOEmMcx0fXw0t+n0rpWJaDYcRr6YaZjJ6kjCvk8ykajSk0rUoQ\\nVKhU+ti2bYiVlRn6+1dtgn2AiGIoFCSiqEMqpVEoyNx++4189rOfJQxXeO1rrzrn4FNBlnXuueeV\\n/M3ffAKQyeXa5HIGsqxSKOikUjKQ4vrrTY4d+wJ33jlAs+mRy0XnRU0MD2eZnp6iUAgoXoKoynUd\\nWi0FRekm6Sh+YpuKCInR0Qr1+jymKZHP99YNLoXVcb2U/iVut3ea8CoXiqqm6HRsSqXeaZ/nYjV6\\nF85GmGy435upJT8efN9nbq5GGAbUahZRFCcVQXTGxvJksxmq1TqLizaa5qPrJooCtu2gKDLlsiBz\\nMU2Ter1OqVRiaWmJVCrNwkKNKAqYnm4iSRCGTWRZ5YYbrqJer2HbMVNTs2zfPkSzaVEoGIRhGk2L\\nURSRdlEsqtRqda6+ehe27VAo5FhcXCSdzrG0ZKMoEZKkoOs6hiG8a8VigWazyeDgIPV6nVwux4ED\\nBxgeHsbzvCSdI43n+UhSiOMEDA4WCQKfvr6+80h0PM9naclClmWKRdGnWq1Fu+1QqWRxnC6FQgFF\\nUfC8AMdRiOOIfF5G00Q+WRAEPP/8LIrSj2VNUSoVCUOXbHYrYdhly5ZUQmYZIssqmiZSSvbtmyaK\\n+lhaOs7k5C4cp0F/fwlNU8nlpB8r3OrFhu/7NJsekiSRy6lrfTp3bsZxzNJSjR/96DCNhs3k5Ai7\\ndm1FkiRarRbHj8/ywgsnmJ1dol7vUqnoDA1VqNebLC11iSKD5eV5wjCmWm3T7aoMD6e57rpx8vk0\\nfX15BgaKWJbP8HAJ2+7iOM2kKoJKuVxkx44Kti0RhsIBlc1mabfb6LpOPn8+t4fgevHJZDJIkqjC\\ns8ovEseiD5qWRpI8yuXseeUgV51LYXi2TKSm9TYo/7EQxzG1moWipIii7nm5jf+c8GKsm71Qr7ep\\n1x00TWJkpLSuw7Hb7fLMM/NoWpFMpsHevTt6tvtic3REUcTc3DKO47NlS6Vnzfs4jtfeg15cDmEY\\n8rrX3cdjj83z5jdfw2c/+3+tK3vo0CHe8pb/A993+cQnfoNXveqOdWWnpk7xzW8+ytatFd7whtf0\\ndOLOzJyhWq2xe/dV65IPAzz55DO86U3/DsMw+Na3PtbTmfHJT36JBx98nuuvH+d97/vlHozzAYuL\\nHXw/pFw2ezoLwzCkXq8n+9xZZa/X/EynoVqFzKYPchM/BazOzfvvv5/3vOd3gTJwbDPCbxMvCVzJ\\n3h5FEfV6F1k28LwWpVI24X/wURQdWXbJ56/8cKFeb9PtRpimRLl8MW9cu23jujKu2yGTMclmzYu4\\nMJrNFoahk7nEQj87u8CBAw1c12HbNpnJSZG+eOZMDdNUOXhwHz/3cx8AQj7+8ffx9rf/ApZlUSgU\\ncF2PWq3Do48+SRzH3HPPnYyMDBKGIU8//Ty+73PTTXv5yEc+xnPPHeR3fufXuf76awnDEMMwkGUZ\\n3/dZWlqhXu9gGDkqlXLC9Xc2TePb3/4B3/rW41x33Q7e+c57L9J/5+eXqddjFCVgYqL/ksa+KHgg\\n9dxrgXUdBb4vosQvtJOCIMC2BcdjLxuq0ejgeeLQ+3LVIs+F67pJdTGJdPrsuGxkbm5GZFwBzuVQ\\n0DSNLVv68TwPx4nodl2efvo4rmsyOOjT17eFXC4CDOI4YmmpSRDYHDw4QxCEjI4OoCgmkmQhyxX6\\n+ztMTk7QaFRZWbHQNI922yKKXCTJwHUVnnrqOIYxQDbrcPvtdzM9fQDPK1KtNnBdn1RKYWJiBMdx\\neeSRk9i2SbN5gOHhnTz77JM0GnnC8EdYVpZs1uWqq64ijjvJ4qNz8uRxIM/i4mEgh67X0fVRlpc7\\ngIEk+ShKB4ipVPKk0zozMwvU6zKjoxZDQyMEQZtWK0TToFgUeWy1WpsgEDnyum4CAb6v0Wi06HbB\\nMCQKhTyKopBOp9aM4DiGnTsrNBoW3a5KGA7R6cyQyaxQKqXX8rQMQ5SWXVW+x8ZKNBou/f0jGIZP\\nuZxFVUGWQzRNyHS7Dp4XkslcmefvxYKohiInZXUvfX1JkjBNlXY7YmWlTV+fxZkzNTIZjcXFNidO\\nVHnooYMcP+7i+1WKxQF27QqIYxXXDVlYOE6tZhNFKVqtebLZYXTdxDRlwlBiZqbF448foVDYRaVS\\n45ZbttNsFgkCjfHxNIaRYmbGwrLa9PdniCKDhYU6i4sN0mmT7dtVDEOn3XbWytWemzsnSnL51Otd\\nJAnSaRXfd5GkKKnaoq+VKjYMUR5rNZ/xUjmQPy0IZ5OO47hkMld+0vBPFS82q7aqSgmnityzXcMw\\nSKUCms1Ftmwp9WzT931qNRtZliiXMz0N+UajxcqKTalkUi6vX7YjCALm55v4PpTLVk9HhiRJ5xGG\\nrYcoinj44aO47na+8Y1He8rOz8/TbIYEgcKxY4d7OjJ0XaFU6iOfL/YcU9d1OXBgCcfJAie48cY9\\n68o+9tijVKtDGEbE1NRUT0eGriuo6jCKcvn7P3HiDJYlcc015Z6OjG63y/R0FdOUyefzG5qDq+kl\\nm46MTfw08cADDwCDwHbg2E+5N5vYxPqIIpG6KcvSeQapLMvkchqu65HL5df2NtP08bwu6fSPx59g\\nGCqW1cYwLj4ochwHVZVQlIgwNIhjE8tyKRTO7iuNRpuFBR9Vddi2TcX3Q+IYMhkzOVDNIcunUBSX\\nVOoqOp0uEBNFoq0vfOGLQBqQ+dznPs1b3/ov0bQ8INFsOtRqPt/+9gFcV6VczqHrNyNJMpVKBUVR\\nsSyLAweaLC7u4YEHnmRycoJ63SeX6zI42Jcc/mXxPDBNCVl2LrrXWq2D645gWZdO1RkYKJFK2ei6\\nekmdwvM8ZmdraJrMyEjlor3RcRxmZ0XKyeho/pJcF+sduKiqetHh5YsJTdPQNFFm93LRuxdi05Fx\\nGYiSqh627dJqBeh6CKjoukwUxbRaDV54waZabTA/36JS2coPfvBD7rjjKp555hle/vJX8tRTTyJJ\\nA0xPP0+9PojnuTz88CNUKlcRRae59tp7mJ4+Qhxn+eEPf0C9vpXp6cdJpbbjujWKxTz5/DBHj55g\\n796dzMw8iSQVefrpF4jjGzl27HmuuupWPG8FxxHeq2efbZBKbWVubh+veMUo3/veQXK52zlw4ACT\\nk6+h253HNG00TWVhYQ5FyWBZK2zZUuH48ae5/vq7OXz4OcbHr+HUqYM0GhpR1OK663ZSKvXz7LNP\\nI8sFut0G4+O38sQTP2LvXpNGYw7DGCKKuvT320RRyMoKyLJJs1lldHQLp08vMzS0jX37nsQwhlEU\\nm7vuKqPrOrVaKznB1NE0E10XxD9B4LOyUqNQ0Ni+ffg8g+TcetYAlUqRQsFHVdVLKrlhGGJZEYpi\\nXLQY/mNiIykuU1OneOqpFaanG5w547J3r0sqFXLyZMj0dJ3p6QynT+eYn58mk9nC8ePHGB4usrDg\\n47oylkVSEhhkucPcnM2xY1VyuVF0XefkyRXK5SlGRiIWFrqoaj9x7HPkSEg2a5BKFYgil8VFl76+\\nLrlchuVlBVWN8f0TFIt9gE4QhPT3B+Tz2TWi1DAM8f0QSdLxPBfDkHBdj3o9xnG6lMsxvh+jaWna\\n7TqaJqOqOYLAfdHL3f73QqRI/PQjeV4qEA4oEQ6aTl/ZprMeggAymSxx7Pd8/nEck81mMAxQ1d7P\\nxLa7tFoQRQGZjNuTRPTYsTnabZOlpQa33ba+gdxutzl8uE4cm6TTS5TL5XXbrNVE1RBdjxgfL607\\nhw4fPozrFoBbaDaP9Lyn73//+5w5I967b3/7Ud71rl9ZV/bLX36IZ54xyGROce21O9aIMS+E7/sc\\nOzZHs5lGkhRuvHH963/8439NGP4LbNvh/vvv5w1veMO6sn/zN49Sq93C6dP7+cVfrFGpXLoaiud5\\nyHKRvr48tt07tebQoZP86EcB0GZwsMjg4GBPeYC+PlhehpGRy4puYhM/MXzpS18C3glMAg//dDuz\\niU30QLfr4vsacRyi6+dXh1rVh1YJ62VZpNCf6yiO45h22yYMY/L5FLbtYFkupVLmks7/hYUWkpRn\\ncbHF9u0pgiBAlmU8z+PMmQ6W5ZDJgKoqa1VSziUYrVZXOHSoja579PcbxHEmqYDokUqZnDkzy3PP\\ndajXG/T1bWVmZoVUSiWODXzf59Of/gLwK4DKQw/9KY1GlzDUiKKIWq3Jt771JE884aHr43zhCz9k\\nfHw3tm0zPd2kv78PxznMY4+1gQn+/u8f401vegNhWMK2G/T1FZBlmWPHluh0JOLYYu/eCYIgOE8n\\nePDBF2i3b+eRR57ibW+rXbRfK4pCsbh+pbRGo0MY5nFdF8dxLtJ3HMcjjsVnlypRe+Ezu1IdPIoi\\nwlDCMNJ4nn3FBwdRFBPHvTnKLoVNR8YlsPowXVekjziOTrdbY2LiGp555lGiqILjLDI6Ok6j0eDk\\nySqSpCFJTTzvOLLc4eDBI4ThPMePn6TRWCEIJLrdLmE4j203mJtrUavVcN0DNBqDZDJH0LStHDp0\\nGsPYSr3usLi4iKYFhOE84GKaTc6cOUQctzhzxqFeb5PNNjFNn2r1NFDn1CkPXTcwjA6mWafT8Th2\\nrM7S0hK2vYjrWtTri6iqjec5OI5Ho+GgqjqybOH7dUwzol6v0WwucPRojqmpw2ja9fh+wNBQFcfx\\nabV0hoa2MTX1TeL4MK5bpdtNc/r0CsVilm63Trl8Nb7vcvToQRQlQ1+fh+el8Lwas7MSntfBdR1U\\n1cayLCzLYmEhJAxj4niBVKqAJDmAQV9fnr4+MM0+6nULSYqRpFVDIwZk0mnhuLjUy+c4znkli4Rn\\n18M0XzrG8oVwHJeVFYd9+w4zM9NhZqaD44T4foNsdhjXXUCWTxFFLcDB91dotepE0QqO00+zWUsI\\nd2QgJIo6zM8HyHKJVGoeXfeRpBz1+gKTk3202x5BcBLPc1CUHSwt1VHVBpomMTmZx3Egk7EIgjaW\\npRAEKqYpMTNzmlQqg+tqFApiTHVdw/dlJMkDpGQBNeh0XCQpT6NRR5JiLKtDHNdIpfL4vkQct8lk\\njBfFiREEAe22g6JI5HIvnQiP/78jiiJ8HzQtjeNYXKbAyIYRxyGtlksqBbK8fvpOEAScOrWM6+qA\\nzcDA+o6EKIqYmppG0xTGxtavhAFCCbAsA8vq9JQLw5CjR4/huiYjI705MjqdLpalYVkuIyPBuo4M\\ny7KAeeAFoN6zzXq9DrSANJZV7Sk7NXWGM2fGMYwanU5nXUdGGIYsLTVotyPq9d5KRKFgAqcB95Ih\\nvOdCUTTiWHBD9YpKSaVS+P5RlpfPMD7eO1Wo1Wpw+nQXVe1umFBsdBTm5uC66zYkvolN/ASxAmQQ\\nZVg3sYmXJjRNwXF8JClGli8+pQ+CgJUVsVf29WVRVTXhORA6l2mqeJ6SRCt0WVy0kKQMJ04cY3S0\\nn9HRygV6Xki7XSeTERXtHEfoj4riE8c69XoTWc5jmhGGEeI4MisrVUwzRSqlUKu1OHWqhaZ1ufPO\\nCEkKiGNx2BGGIc8/f4SnnmrjeQ327j3I+PguJEljdnaGXM4gji3EvnruwUwMSHheRLHYh+M0CcMV\\nNE1EEKpqllarTblcJJ1OY1mncV2NsTELw1BpNtsYRrRWsWhqapZOp4imVbn22p2E4fnElqWSzuzs\\nUfr7vZ7pnetB12UajUVkOUJVt1z0fTabptOpAZBOX6w3+b6/9swcx7ui1BAQ0TrptILj2GQyV3bw\\n5/s+YSgqpXieyDDYKDYdGRdgNadZlFqNOXWqgWkO02pVkaSjWFYLXR+lXnfQ9RaWZbO01CCOddLp\\nAMdx6HZjJCmi27VoNn2q1Sadjo6mGbz2teO023m++EXwfY0gMCmVhjl+/ElUtY7rhgwPz6EoOktL\\nEq5rE4YpGo0ukmRRKjksLp6k2Rxnbm42URSryHI/vl/n9GkVTctzzTVlBgaGOHBgiVari6pCKiUx\\nOFhgYMAgkymzY0eFdrvNvn1HCIIVJiZiJMmlry8L2HieT7stY9sh7fZxdN0mk6lQKhmUSjGStMjI\\nSBHfl5BlmJ+fRtdjXFc4V44ff4Eo8snnyxhGDklaQVV1ms0Yw9AwDJM4dgmCLs8/v0QQdNH1DLKs\\n0e22MU2VMLTZuXMrR48ewDTzhOEcxeIYjtOhVBKnpvPzc4BGPq+RyRRwnDaynMI0YwYGSrTbHebn\\nHYLAJZ9PYZop0mkFVVUuMiyiSKQJyfJZo9c0jbV0F98PMQztHzFaQMKyajiOhutaPProQcBiaMgn\\nm3W44YaX4ThTRJFLtXqITKaA78tJNISDIPpUgSJgAjXa7UW6XY9sNiYMLYrFAu12C8fp0OkYSJKL\\nZR1DVSMqlTL9/SWOHz9EuZzB8/pwXQ/f14ljlampE+h6Bs9TqNe7KEoO2w5Jp30kKY1te/T16ei6\\nhsh9U5FljzDUkGWDIAgBD98PyGYz5PMXP5MfF47jEwQqKysdwjCgVFqfbPFcRFFEuy0IKnO5lwY5\\n7EsJsiyTSsk4jiDGfLGgKCqlUpo49tatFQ+rqR1VlpeVy4Y6LixUOXiwga7Dnj0DPQ1vw5A4eXKG\\nsbHe3CydToduVyEIMokDYn3kcpnEiWr05MgQPB55oIIIb10fp06dQihbBkeOnO4p2+lUOXasQblc\\nvez1azWLdtuk1bJ7tjk2NsITTxwFAsbHb+spe9ddE3zve0d52cv6yeXWP0lyHIc4zpLLib1uYGD9\\nNovFAv39HrKsb7gM3MgIzM5uSHQTm/gJo8YqoeAmNvFSha7rFIvKeXwLq/qxcHK4tFoiKiKd7pLL\\n5XBdH0kyCYKQOBZcDnEcomkKcRywuDhDvS4OEtPpJpXKWWPa81bJviXimLU0/nQ6TTbboFj0keUA\\nVZWRJAVJ0uh0OkBEHIccO3aMxx5bJp12uPfea+nryyZ9UGg2uzQaDs1mE1UNGBvLMT6e5ciR0+j6\\nAL6/WqHDYbWYp7DfAsJQJY49ZDkgk2kjSbNMTOxG101sO6DdXmBlRWZyMo2uq3hegK57pFIazaaF\\n5wW0Wj6m6Se6ZYvRUTDN6KL9q1JJIctVrQa0NwAAIABJREFUKhXlkntbFEV0Ohaapl7S0aFpBoOD\\nJWRZuqQOo6oqY2NnN9dOx8b3I7JZoZ+oqkoYNvB9ER3740DXNRRF7qlvXAqqqiJJ3eQ+riw96Z+1\\nI2PVaSEennjx6nWLOBah754XkMlEdDoL+H6I72dQFJVOZ5p02qNeX6BWW8BxDNrtNisrVfr6xpmb\\nW+D662+hXveo17usrHQwzWEcR8bzdDKZQXbsWML3Y7rdCpq2hGmqqKoIx8/nh2i3lzDNEo7TZGnJ\\nxDBMHKdBOp2j0wmYmCgxO+szOnoNJ0+eZNeuIu12DdMMKBRMosghk5EZGuonnR7EtkcZHOxnaWmE\\n7dvHkOUWfX05fL+DaW4H0iwvT7F9+xCt1jI33DDJ4cPPk0qlWFoyGB+fII4tdL1EX984fX0tUimT\\nubllFKVCtXqCG24YY2qqTiaTp9FYZGSkH01Tse0FDENDlnVsu0u93kZRBrEsn9HRIktLTWzbwPNA\\n15fQtBSKYpJKFeh0qpw+fZhms0kYDrK0NMPMTIt83gAcut0up06JEDhZrjIwsA3XXWZ0dCftdptU\\nSk9eVoUgkAjDiDgWpU1Bo68vTSZz1nCwbYdOJ8J1uxhGKklP8dA0jVbLJYpUPK9LsfiTJ3xUFBnL\\nauD7MZ1OQBjWyOd3YlnQ35+m1fKpVmcJAh3bTpNOCyeGovgEQYwsF4iiInASmEYYPyqWNUCpVKTT\\ncZFllTgu4DgSsjxGo9FITkd9xsb6MQyV5eVFstkh4niQw4dX0PU+FKXBxMQ2dF2cMsuyx9BQJjFu\\n0+TzJrWaRalUwPNEVRkI6evLYhhGQtjXwfPaaFoWSYrQNA9JepGO9yHpez0pv6VsiKsAVj3DwvFy\\npZ7hfy5Ip1MbjsTYKNlmNmviOF4yn9aXtW2bRx45SrudI5WSedWrblpXdmWlxuKigyR5NJtNhofX\\nZ/KuVi26XY1azSEMw3XnSjabRdchCDqYZg+LG5Gfa1kupqn13Njn5+dZZQQ/+/+l0Wg0AMENMjvb\\nOyKj0YgpFCYTrqIaI+vkVgiSXR/bbuO6vZ/TqVMLiBKS3cSpsj50vcj11++kWFzCdddP7ZFlmeXl\\nGt2uxNDQpdNPVpHLZRkbU5Bld8P8RqsRGZvYxE8fW5N/m/vKJl7aWD3EieOYTsem0eiQShVxnADD\\nkJDlIIm2MxISyYgo6iYVLjTy+RSu62FZIZZlJ1HXdVzXZdu2bYRhmFSqUzlxYpHZ2YCREYWrr96K\\n43iJcSuhqmlSKR/P89C0NIahEYYeuu7heRqqGjMzU8fzhul25zh8+ATbt2ukUiaFQkwYQqUywPi4\\nTzod09/fjyxLqGpMu10ll1vd80LATe49Q6GgY9ttoiiNqsakUhNkMqNUq7UkMjEgjgdR1RGmp59D\\nVYfJZHbjeR0cJyKbrdBorBDHCr7voSgS6bSJJPnounqRs2F2NmRo6DW0WvtoNBoXpa3Ozy+zuBii\\naT5XXz18kU7R7dpMT1fRNKhUthMEAXF8toqLsG3F/amqgutKKEoqcU5pBEFAp+Ph+xH5fG9Sz0tB\\nVK/pEscammZvuMKf67p0uz6OYyU215XZAf+sHRntto3nKbhuDV1P02438TwDz3Op1erIcpZGo0uh\\nME69vkg2m+PYMZtCYQ9TU0+Tze7CsroEQZNsNk2rJaHrMUNDWUZHNRoNiWYzptVqEUU2cSzYag1D\\nZ2CgjCTl8f2YsbHd2PbxRDEDz9OJIp3x8TK5XJc4riPLAeVyjmxWYny8QLns0tcnY9tVDCNA01oU\\niwFhGOF5VVKpLaTTIwwNNSgWFSyrH993GB7OMjCgYpppoigmm82ybdsSjuOSTpdwHItKRcP369x4\\n4xbabYVyeZxGI8A0JbZsKaLrbYLAJAhK5PMyui6jaXk8z0XXY4rFDFGkUautIMsyg4NpUqkCmuaS\\nz5fpdBZRVYVcLk02q6DrFXzfp9Np0+nk0PUMY2NdikWPajVgfr5NqzWPokgsLh5GUXYQRXO8+tUv\\nA1I8//x+fF9n69YU4+Ml6vU5oijE911sW8J1IY5tDEOiXDaIIp+VFQVVTVGvW5jm2VQGz/NoNmPC\\nUDi4hPdXvCatloXn6eTz4T+KI6PV6nDiRA3HMUmnZSQpjaZ5jIyECT/FBO32GWq1iDhOE8d1dD0k\\nnR4hk3GJ4xadTha4CnEKJIiM0ukYXe8k3vIikhSQShlUKhq2bZFKpcnlDFIplziWKRS2JpEzcyhK\\nSF/fGLa9jK7HGIZMpZLF9x103cA0dRRFcBikUibdrk8qpeM4YnxlWV77199fxLYdHEecsrtuBt/3\\nKRalDRsovaBpGoODeWw7RFE2XrVCXFuQDl2pZ3gT52O1pFYcQz6v9zTmfd9nZaVNNqv3JNt0HIfl\\n5YAgKHDmzMW11s+FYL4OURSpJyknwIkTsywvj9Nuz102DalSKeH7xkX8PBfCdQPS6SJRFOD7/rr3\\nL6pvKIh3tLfyINrQAJNKpff1+/tTHDxYJZ93LlnObRWe59FqRQRBkWbzTM82JyYGePrpOuCuMb+v\\n366DbXcwjGbPyCaR0yuI12y7d0RIf3+RLVskNC294fDb0VHYt29DopvYxE8YPqIUeng5wU1s4h8d\\nYRhetFY7jsPcXBfL8kmlqgnZv7BzADRNZXa2ju9LVCo6juNTq3lksxKplEkcq8mencJ1LXK5PoIg\\noNl0iCIF0/TZv3+KxcUhTp8+yZ13Xksul0aWZWzbpl7vMjW1RDZbwvM6DA+XKRSEkRwEKrLsMzEx\\nxMGDLSCiUOhnaalLqaSSTsdEUcTQUJk9eywyGYN0Os2ZMy7Vqo6IFF7V50uIyGXI5SSazTaFgpqU\\nfTWT9G2ZgYE8O3cOcerUEqWSjO+32LJliHzeol4/yfCwST5v0m4HlMsqqioqKtq2TbutI0ldms2A\\nXC46Ty8pFmPm5o4xMGBdslx6p9MlivJYlksQBBfpE5blkk4PEYY2nU6HODYBmWw2wjQNPM9jNYg0\\nmw1R1SgplKCuPedqNUCSdLLZ1nmHvBuHhCQJDsmNQESZBNh2wPx8QLGYQtc7FIsbJxb9J+vIeN/7\\n3sczzzzDjTfeyEc/+tFLyjSbbebmOkhSF9PMEUUOrqvQ7dpJro6SeIo6bNtWJpdrMTExQBCYDA+X\\nCQIHXc8yPCxICnfvlpBllYmJ1zI8PEKhcAOuO4SiXMXAwCSKYpHNOhgG3HDDLiCDbWcYGMjj+1ej\\nqiMsLExQKmn09W1nbMxAknYwNyfhOBGFwgjpdIVM5lXk81tQlDtoNivE8S3ccMMePG8bCwsykiRT\\nKgXEscWNN+4kn8/huuN0OkO4LkxODhCGDoqSplTK8Za39ANQrXbxPAVdzzI0NEq3W0SWVXx/O6Cj\\nqjKVSiopldclimJuv30XQRCTTt+O58ns3XsLihLgODvodExsu4uixORyZUyzQ6EgkU5vp9tVcJwy\\num6QyxXI5w06HY3jx0MkSaVYzFMqpZifD8lktnP06FNMTu5gZcVmfLwf1/VZWVkmCAI8L4WuDxBF\\nZ3CcRXbtGqFcNmk2NZaWmkSRzdDQEIoiY5pqUtq1TqvVJZVy8DwoFjXK5WISXmVjGBp9fSKtQNME\\n4U8mkyKb1VGUjeVlXw6eJ5iJ1/N6xnHM6OgWrrmmRbW6zNVXv5xiMY+iGDiORbUak04Lr7FpBmQy\\nPqXSIGEoWKbn5iJmZgJarZgwlOnry6DrGoODMtdcM4Sq6qysyAwNZdm6NcXgYJk9e/JIkoplOQwO\\n9ielWC2CIGTbtqswjJBSKYeiFBkYGKbdbqAoGqoqHFnCKRSv3dfqven6xfcaRRHZbJ5USsLzQhRF\\nJY6jF7UkXSplYhjRGgHpRqAoCqWS2CQ3eTX++yAi3dSkekfY05GxsNACiqystMhmvXXfi1wux1VX\\nlWi3Q3buHO95fUmCIGglubK9t7tyOUer1aRUSvWcg5qmUSjouK50WY4IVZVpNNpomoSirJ/aJBwC\\nEbAEBD3bvOuuu3jwwUVA4qabbu0pe+utN1IoFFDVpZ5Gfz6fZ+/eYRynj4mJ3uP02tf+HI888hSK\\nkubOO+/sKTsy0k8QpOjrG+3pyFAUhcHBAmGYokcGCgClUpFrrzUTwreNnRjt3Quf+tSGRDexiZ8w\\nltiMxtjESxGCmyJGVeM1RwGslja1aDRsTDMCQjIZFdMUFUFc16Xd9pFlk3a7S6fjAQXq9Sblcg7f\\n7yJJHVxXplKR6esTFQrDUEKWFeI4otl0qdcjwjDCtsH3m+TzmSQaxMJ1PbJZ/7w1P5dLJZG2aXbv\\nvoqlpZMYRplSKU2r5RIEHRSljKJoZDJ5isUyuZyI+FTViHxeIwyhXE6xe/duXnihBqSQpAEGB8sM\\nDAg9IJ9PEwQe11yzC0WpMDQEfX1FTFMjlysQBBGm6bJr1w5se4hduyzK5Ty5nI+qioqMYRiyc6ew\\niyB7SWP/nnvuZPduiUpl9yUP3kZHKywstDDN9CUPZiqVApZVQ9OkhLMDJEle02eEPiscqIqiJdH7\\nZ6vPicoqSqKbXzlHx2qFP5GCv7Hfi2dBEpUdEMcumrYZkcGzzz6LZVk89NBDvOc97+Hpp5/m5ptv\\nvkjuyJHTLC8XmJnZDwygqm22bh1F03SiaAFFMZFlBc+DdDrDNddsZXJyiJmZRW666SZcV4RVeZ4I\\nQV9eHsDzdLZuNRgerjAwcDNHjpzhVa+6k243wjCKlMtC8Q7DFmEI+fwwnY6D4wxRrUoMD+cZGDAx\\nzQGuvXYyMWaXk7x0DV3PUKstEUUSd9xxDZ6n0te3nYGBPmy7w/PPrxCGETt2FMjlijSbDep1i0Ih\\nRX+/giyPMjRUxrY7BIEoeVgsCgM+jhvYdoRhaDhOKylxagA5ul0PiPG8kDiWGB7OEAQhvl8mjnUs\\nq46uG4CfhH6liKI2mYxCsZglikKKxcGE3TiD5/l0uzphqNPtdoA0uVya3bsbxHGMaeaJY4PRUR3H\\nOcPYWIFiMcvu3TsYGGhQKJSZnJyk0Whw5oyFLMts3z7O7t1b0bSAbFbH91PEsUEUQSoVrYV3B0FA\\nf38ZUDhzZoFCocTKyhKplEkYhqRSJpIUrTkxYLXklI7nhT92ealz4Xke1ao4eaxU4ksuSuVykZe/\\nfBxZtuh0himX02iaiablOHDgJLLskkrtIJPxWVqaZXh4AsgjyzaKkiKOc6jqIvV6jVKpxNatBXI5\\nhdHRSbZt62d4uAREnD7dolgsEMcWAwOjLC7alMsV+vt1xseL2HaWhYUMjqNSKOjs3DmCoij4fkgu\\nV6TV6hKGLv39KWRZQtPUi4yWSxkciqKQySi4bkipVCIMxYK6kdw6EQLnoqrCadPL4fDjlAjddGC8\\nOBDOLJH3aBi935t0WqVeb2EYUU+nQz6f5y1vuYmVFZdrr+1dhqLRaBPH/UiSS6vV6ilbLOYYGDAp\\nFHrzXoi5EREE0lrazHoQ61yI78eEYbjuXBT8ERHQumT5uXNx77338qlPfRjPg/vue1tP2de97jaK\\nxSOMjl5NqbR+qdpsNst9993O3NwSe/b0KFkClMt59uzZSRz761YhWcW2bUOYpkap5PZ8p3K5HDfd\\nNESn4zAy0jtdJ4oiPC9CUUTI7Ebe1RtvhOefB9eFywTmbGITPzG8+c1v5itfmWM1fH0Tm3gpwXVD\\nVDVNEHTXDNwwDAmCkFLJxPNc8vkBWq0OjUZAJtOlUBBlzQ8deoH5+S4/8zM70fUMnY5FJiMnjn+J\\nvr4+DEOm0+mSz4s9RxCHB6RSJr7folZ7gXTaw3FsJClNux0gSS6SZJDL5THNmFIpvbaPyrK8plu6\\nbkAqVUZVHQxDQtdzRBEEQUQ6rRLHDvl8GlmWSKVSXHWVzrZteWRZHHj96q/+Ku9//zcAmbe97Y3n\\nHX4VCmmuumqU179+O52Ozi23jAKQyWQYH1eJohjbthgdLdJsaoyMlJFl+Ty9XlEUdu0aYGWlQ7E4\\nRDodJ/bVWVxzzRiGscjISN8ldaBsNsO2bcZaVPOFyGQy7Nx59ntJchN7SlxH13UKBXFP59o2qzBN\\nk+3bi4Rh9GNGY6yWUb0yfox8Pk0mY9DfL5xnl4uevRBS/GIef75EcP/999Pf38+9997L3/7t3zI7\\nO8t73/vete8lSTqHTO5/BP4OkfP7FLAT4bGaBm4EHj1H5sL/7wJ+hCBoG0OU07or+f9C2TuBR4C9\\nQAdYBq6/QPbNwFeAlwOPJ21NIQhorgYeO0fmwt/cBTwLDCBCk48DtyW/WZV9I/C1c/p4B1BFMNDf\\nnPTvwn6f2/7zwKqS3QJets69rvZlOJGTknG9lOzq/68GDiJCq7dcMJZvAL6RyH4l6fcxoD/pyxKw\\nCzgMvAL4PqZ5N1H0BLJ8C45zCNgGLJHLpUinxykWF4nj7dj2U3Q6L6NUmmJw8JXk80sMD1+LorQw\\njAwg47odul2T8fGAOB5jzx4VSRpleBg0rUwUtTl+vEUqJXH33S8jk8ldVLrI8zw6HQ/DUMhkUjz0\\n0EO8613/lSDw+eQn/y2vfvWrgbNzM4oifuM3/j333//niNQQA5hL7nm1xrSGMH60ZE4VEIzoMWJO\\nNpPvVhdEGaFAmcl3uUS2m8wZN5FPAXbyrx+wEKfEheR5OpwlEO0iTpeURCZMrqOfc63Vz0uIsNoY\\nUMjnB5iYKOK6ImSuWCxw9dWD7NixjW43oN2uIUl59uwZIo59jh5tUKnouG6M44jT+HRaOMLy+QLj\\n4yUymQJR5NHpuDSbXQYHS1x//QTZbIZms0212kaWI8rlYrKpyfi+z/x8jUbDZnAwn6R9icW+Wl1h\\ndrZJuZxibGxwbdEPw5B2u5t4oFPnbQbdrsPiYhNdVxgcLF0xWeil2u52HbrdgHRawzSNNUJS4KLr\\nX4jVuWeaSs9SqecSPwVBeN714OzcBHj723+NZ56ReOtb+/n93//9ddu87777+MxnTgEh73jHDj79\\n6U+vKyvG/OeBp4nj3oQGkjSIWCe/1jN6QrR5F2J+/mADsmI9urzcXsT8f3iD17cYHJxjYWHhknK1\\n2mqptY1e/1WId2oj138j8ARBMNtzLkrSGHDrBq9/JWP6JuCrRFG0rtMhlUrhOLsR1Rw2OqYNbrst\\nw+OPP772ea/f3XMPvPOd8Eu/tK7IJjbxE8H5OucNrDfPPc/j6FHBSrtz5yiyLDM3VyOKIsbGKnQ6\\nNisrNqWSeVH6Xa1W46/+6h84eXKJ17/+eu6+++a1KCzHcZifb6BpMktLi5w6ZXPddRVGR0fXPh8Z\\nqRBFEc8+e4RGw6K/v0B/f4mRkQqyLLOwsEy3GzA4mGd+fplTpxZ46KEnePzxGW68cYgDBw7w9a/P\\nUSp12LOnwCOPHEHoix2EzjCG0Ge3Ai733nsH/f0F7r//h8nn48B+hI4g9PGhoevZtesu3vGOm7j9\\n9lt49NFn+MxnHmB2toFpduh2i+zcWeTXfu31fO5zP+L06ePMzEwxP59l69aQD3/4t7n77luZmTnB\\nf/tvT5BKubzhDa9hYmKQoaF+LsRzzx3iueeW2LOnzM03ny1xdG5ZylzOfFFSXy/EuXt6JmOwtNTA\\ndQOGh4sbJjW+HDqdDouLHXI5/bxqX6vz87vf/S5f+MIRnnjiH+h0YiwrZnHxGLAdOIrY8zSghaIM\\nJxUVNQxDwXXnOL+s8CuBh7jttvvwPNi379vABOfbWRYwzp49MYcOPcaqjbJjx88hSXkyGZ/9+7+N\\nmBsHEPPiBFu33kIUeczMPIOwQw4CswjboAvsQ+yRTeAoQ0M3sbDwQ+AmhF56AKGnrqZezwILCJtJ\\nQdh9Q0l/TyDsJAkoI+yTZxA2xwsIXcBIPhtB7KFfQejg1wFPctdd/xMnTrSYnf0KwhZ8HLiJW24Z\\nIgwNOp0KP//zRf7kTz68NgZTU1N89avP0W4vsHXrBCsrc/zWb/0yQu85yfvf/4fcc88WRkdHOXas\\nwY4dOW644VpW98Y77yywb5+MbWf4oz+6h1//9ffwne98i7e+9c+JopiPfvQ+XvWquwCJ2dkVyuUs\\nCwtTvOlNHwCyvO51/XzsYx+jVCqRzer84R/+IZ/85Azp9CL/+l+/k7vu2s727duRJIVms0kcy2Sz\\nJp/73Dc4ccLhZ392K7feeiuyrJJKaZimTqvVRVEkMhmT+fkV2m1xmLt//0G+/vWH+exnHwNCvvjF\\nf8e999573tzshX+SERmNRoMdO0QJt0KhwMGDB9eRfCPwYURlh/sQRvRdiMm/gHA0jAL/HuF4+Fvg\\nNcBvIoyyPuBahGFWQrycb0/a/ARi8n8UYXj+C4RhvwfxQhwA7kYYiquyf4QwFP8X4POJ/HVJ+yNJ\\nX94DNIBPITaI/xvx4r0GYawPJf36LsIBUEn60wH+Evifk3sdQbzAy0n7dyA2119BLC7/D8KJ8ZvJ\\nWL0yab8f8fKfTO5JB/4MsSj9GWKReDNi4dmFcJJkgWsQRu37kr78LfA/IGqqB8DrgacT2bFkPH85\\nubdPIRT8vzjnXh9JrqElY/mzyT1/DHgbpvkXNBr/Cnh/8gxuAyza7eM4zu0sLj7OxMRvcObMB9m2\\n7Y85ceLfsnv3b/Pkk/87t946Qbt9nExmEMfp0mjETE6+lm9+8y95xzvexxe/+J9597v/JQ8++BVe\\n+cqrOXjwBFG0jXY75vTpRXbv7r+odJFleShKGsdxMM2Qr33ta9j264E8H//436w5Mlbh+z733/9X\\nCMfMHckzPYZYSNsIB4bC2VJRbYSCcADhcBgFTiFecS35vQMsJt83krkiIRxBZiLrJ7KLiA1hN2KR\\nDxBOslryWw2xoDuJXDr520r6SCLXj3ifmkn/asm1ZFqtPM89t0wmI+G64zSb4Psd6nUFXS+zvCwx\\nPLybZ56Zo9t1yGRu5fHHHyaf35lsnPXklLmfcjlHvd5i794h5udFapjnGYDJykob0zRoNj3CMEej\\n0SGdljBNH9M0sG2PZhOiqI96vUupJMIXRQqVDQyxvNygv99dUww9zyeKDEAQBp/rQW42baIoj217\\ndLvuZXkULsRq23EcreVB2naArmexrA6GoeP7PkGgrclf6Nk/F52Oh6pm6HZFWOilnB6Co0SQanU6\\nFlEko2kZbNu6qO39+/fz+OMlBgZ+k89//vfo4cfgM5/5DPA5IM1nPvPmno4MsfZ+BPhEz81LGAP/\\nEfglLl/CcAtizZwHfnAZ2Tch1srMBjbPNyEUlstd/wbgQ8ATLC7+h3WlhBPjrcAHEXvB5fBGVh0p\\nvfGqpM3Po6rqZcb0fcC/YWNlIX8Z8X5fbkx/FvhzRKSYvO71HccB/hViDrz3kjJncTPwvwLP8cQT\\n/3EDfRX40IfgjW+E6Wl473vhXB61ZhNyOfgxgrc2sYkrxD2Iff34Rd/Uak0sq7D2dzpt4HmC26rV\\n6lCvOxjGANXqEsXi+Wv51NQUs7MDdLs72L+/yu7dNbZvFyfHrZZNHBdZXq5x6FCb0dFb2L//WXK5\\nEnFcxLIcHMfBtm0ajSyWlcb3u6TT+lrp+nZbRtPKzMzM0WxqnDmj8cADK5jm29m//zm+8Y1p4APU\\n69/jkUeeROiH2xEHYBqwA3Gw9ibgMN/73kGEnp1BrH33AJ9FODX+BPgLFhaeY/fuX+HLX/48Q0N7\\nePTRFWZmrqPR0Gm3H6NQuJtjx1r8l//yNWT5F5ieTrG4KAGv4OTJKb7znRPs2jXJN76xD1m+hwMH\\nHuWOO2RmZlrrODKWKJfvYP/+p7j66s4aT4FIYRZlKV3X/4k4Mjzv7J7earXpdFR0PU+t1mJk5MVx\\nZFSrHQxjgHp9mXL5YvLzr33tBJa1k9OnF2m1LKKogND7r0r+xQj9zSUMnwLeAfTjur+KWLdfi9AB\\ntwH/GfhdnnhCQ+wTb0EcWK4edv4eYs3/WQ4d+jnEnvYxIGZ6+uuk03+AprnA/wv8GvB9xP7095w6\\n9WcIO2kQsb9/BDgC/A5iju1D2Dk+8CEWFm5FHE6/B6GDfiLp6zzCtjgK/Kekj0WEI+NtCP33aYSO\\nO414b69B2GcvQzgyfjn5za8i5vx9CDuuCLwL+AIPP+wCP0TYO7+IsMVewVNPfZlCYS8jIz/DV77y\\n2aQvHwZKTExM8JGPTHPgwONoWszhwwXEfv6fgL/jj//4jwiC/8CNN5oMDFzP8eNHEDr9B4HDPPLI\\nbyTjuYs/+ZM/5F3v+nV+7/fux/f/DdDhc5/7CpOTdxKGHp1OhThWefe73w18ANjCt7/9AZaXIZdT\\nsCyPT396Gl3/ACdO3M+pUyaSNEuptI1UKqZalZPCEIvs3x8zPv4LfPWrf8fu3beSy+k4TkQUOcSx\\nge9HtNttbFun0fCxLIv9+0Mef3w6Gf9R3v/+P1pzZGwE/yQdGYVCYS2MuNlsXpLk7IMf/CAiOuEk\\nwvgbBs4gXoIawjB0gQcRxuLqJHwQ+K8I71sJ8dJKiNPtZYTB9xjCgH4Y+N+AhxAOhecRC7eFeMGy\\nwDfPkf0w8ABC+X4IsRjUEd6+40k/M4gX+j3APyAcDd9GbBT7kzZLCAfCc0lfPpTIvp+zkRKHkvv1\\nESfvM8k9fQn4DvDu5DcjiEiHNsK41ZI+zCI8mg8Cv5uMZQnx0vcn7S8hoghSiBf+ecQL/A/JWH4X\\nYXA/l4z1PGJKDibX/GrS3ur4/DZiQcwk97qS9Gs1uuD7a/doWb8LHMQwPovrHk6eSxcI0LSnKJVO\\nEEVfQtOmaTY/TC43w/LyXzA5uUSlskI220SWbVw3IpvtEgSPcsMNCrOzf8/NN5dYWnqK3bt1ZLnG\\n9u0ZTp48g2GI3LY4di9KpTBNFdsWZXBlWebuu+/mS1/6O4JA5ed//hVcCFVVue66Xezf/1TyiZ6M\\nfzm5XxmxqejJ/Ogkc+RMMt59yfivGqEZhEOtjSjz2E7GTE5+ayRjFCffdxDzdIazjpNMMoZdxJyf\\nTv5ejQqJkjZkxGa3uph7SXuz5/zltY57AAAgAElEQVRWRVWLDA2piBKz82SzKSqVPGNjBnHsoGnL\\n6HrIli05fD+gWv0RO3ZEhOFpVFVn164smYzCykqVbFYQ2aqqxeBggGU5NJs2uVyZQmEIRVFIpxW6\\n3Q6plIuihKiqGBvDUEmlQlqtFdLp9NrmrigKhYLGwsIixaJ63jMV1XichLH7/CiHTMZIyjTLGMaV\\nh+ed27aiiLQZw5BxXQvDkBMWb1GqSnA/9FZyTFPw/mja+mk2iqKgqqIUciYjchw9z8Y0Lz7Bv+66\\n6xge/lPm5/9PXv7y3p7yO+64g0cf/SIAExMTl7nzhxHO3JO88pWvvIzsI4j5dDlDegZxOrKRUO6n\\nEWvwdzfA0/IgYo19/jJy+xCO2JmeUuK09jqEEnKxgXMxvsNqmbje+AHCkXtyA7LPIsb/gQ3I/v0G\\nZECMz+8CB3qOqWmaOM53EPvE5Vg5nwY+g1jrNo477oAnnoA/+AOYnIR3vANuvRX+8i/h0Udh61b4\\n/Ofh+uuvqNlNbOIK8SBi7l4cnZXNplCUxeTvQXRdR5JqRBGk00WCIKLZXKZQUC9ay0dGRkilHqDT\\n6TI5OUm5nD2nXZN2u0WhIDM6qrOysp/Jycza57oeoevZJFR/DsfxKZd1dN1Z+9wwWrhug4GBAr7f\\nIJdz2bVL5fDhrzM+rrNjh8709CdR1TZjYw4nTz6O0FnnEHrpPELHdIFlJie3MjKS4qtfPYjQbarJ\\n99PAnyLWApV2+wvcdVeJwUGDPXty7Nv3BK5rYZpVouhBSiWN173uGr7//UPk8/tZXDwMNMnnPW67\\n7TYGB0vcdNM43/zmYwwNLaBp7nljcy62bs0yNfUUIyPyeWSLiqKgKC5xHL5o5eEvhKoqSJJDHIuq\\nYJrWwPe9yxI6XwkKBZOVlWUymUtzRt1++wBf//pxisUDpFImrjtHrXYM8eymEIdgRcTctRF7xSo5\\n88OIZ7gvkf894Afs3fs6PO9nOHr0AcRceAphK3UQa/kKwinwJPBbwBOMjv4CijKNqtZpNmPgrxF7\\nmAIcoL//l7CsF7DtVR10P8KR8decfa++jjhcs/n/2LvzGMnO8tD/37MvtfY6q8dje2wzjD3sJtgZ\\nM/F1uIkSR4qUcIEYRVkgQYmSyBFSEkQgi4SBm0SgKMJRFhGCkz9yiS+BXLYYmyH4BzZewGMb2zMe\\n2zOedu+1nv2c3x+nq6Z7uru6e6aX6pnnIyEP3VV1TlW/dd73fc7zPq9lPUoQnCGf3zTIv38PzHtc\\np2j4v5OPiyHvD58lv4GozJ27R94///fceZ//nKfJA/f3kwd+Pgs8y2te83omJn6Wqamvko+rHwPa\\n7NsXkmXP0mz6vP71GidO/Dv52PmHPP3003zxi8cZHR2jUCgwMjJJ3p//NfAiQ0PXcvhwlauvdjlz\\n5mnyVZ5j5PPTTt/4JeD/46d/ej+6nvKzP/smjh//v0CLt7/9FqrVCEVRSZJJHMfhXe96F3/1V/8K\\njHLNNSrFYoyixNi2wVve4vLgg5+hUnmakZHbuO66Ko6ToOsqxWKErsPQ0Ah797Z59dUv8Za3lLGs\\ndK7mhY5tm0RRiKJkuK6LYUzjugGua7Jzp8ehQ6OcOPF/gSrvfOfiOVEvl+TSkscee4x77rmHz3zm\\nM/zWb/0Wv/Irv7KgRsb8u20LU/4W6/xuuf9e6GO3++sbhkEURcs+9qabbpq7y0a3Cv1zzz2HruvE\\nccy+fft46aWXeN3rXscTTzzBRz7yEYDucz7+8Y9zxx138B//8R/85m/+Jp/5zGf44Ac/yCc/+Uk+\\n8pGPcPTo0W5HXiwW+f73v8/73/9+/vVf/5V3vetdfO1rX+Md73gH3/jGN7j99tt56qmngM5adLji\\niiv4wQ9+wOHDh/nOd77DzTffzPPPP8+BAwcYGxtb0ImlaUq9Xmfv3r1MTk4yPDxMs5lH6/MMC5sg\\nyCdKnU5uqb/H/KI60NlykQVbQs5vm1mW8W//9m+8853vXPJvu1WKxSKVSoV2u81b3/pWdF0nCAKu\\nv/56yuUyg4ODFItFXNft/rtarXa3XW21WlQqFQzDYHh4mFar1a2UXSwWu3duVVUlDEMcJ5/MN5vN\\nufoqYXf9YZLkhYs6W0x1ts3sbOuVT9DPddhJknTXPs7/G6Vp2n38+X+7zhbNS31fYOm/dacewoXW\\n2ljqtc9vP72Of77zn7uax53/nPOzFDrfn5X84z/+IwC/8iu/suJjV5NGuNbHdj6f1T52I15z3759\\nK25VupHH3w6fab68xF/1a77tbW/jO9/5zpqPA3DmDHzqU/CjH8E73wm/8Avw7/8Ov/M78Hd/l2du\\nLOX55+FDH4IHH4Qf/3H4xCdgLvlTiGWdP+aE5b87nT6tswzs/O2re20j7nneXJ0vZ9Eysvmv0263\\nu1shL/X6cRx3xzLz+4D5xw7DkDRNmZycZO/evQB861vf4jWveQ3Dw8M89NBDfOUrX+FnfuZn2L9/\\nP9/5znd461vfyve+9z2Ghoa49dZbCcOQb3/72+i6ztvf/nY+/OEP86d/+qf82I/9GH/zN3/D4cOH\\nOX36NPv27eue6+zsLGEY4rouzWYT27YZHh7uLtuL45jTp09z6NAhisVidyzRaDTQdX3FIsGdsd35\\n1tLfXqj5x+iMSdY7+2Op9jO/fY6PjzM6Osqzzz5LpZJnBz333HPs378fgCeffJKf+qmf4uTJk3z7\\n23lG4O23387u3bs5ePAgjz/+OAC//du/zV/8xV90X/PAgXwb8E984hMA/OIv/iL79+/nH/7hH3jP\\ne94D5H3A5OQkAFNTUwwNDZFlGR/60IcA+Nu//VuyLOPJJ59k586dANx777184AMfIIqibvHtJEn4\\n3d/9XSBf3vrmN7+Z06dPc8UVVwD5DZYPfvCDPPDAA3zqU58C4Pjx4xw6dAiAhx9+mDe/+c0cPXq0\\nW5bgU5/6FMeOHQPgX/7lX7jhhhsAuPHGGwH40Y9+xHXXXYeiKN05z0c+8hE+/OEP02g0GB8fZ+fO\\nnezatav7uR06dIhqtcqTTz7JwYMHARZkTs7OzmLbdndsCvDzP//z/Od//ieTk5NzmZz5977zff71\\nX/91AP7u7/6OZ555BoDrrruu+z1++umnAbj22mu787EOXdf5z//8TwDe8Y53oOv6gnFgp97k7Oxs\\nt22cfx1TFIWJiQlGRka6v+88f6n2Dfk1ptlscvp0HoB57Wtfu+D1VurXL8lABsDv/d7v8eijj/KG\\nN7yh21A71jLgEWIzSdsU/Urapuhn69E+v/vdPKjxutfB7bfnQYq9e6Hdhi98Af7pn+Cuu+CXfgn+\\n5V/gf/9v+P3fhzvvhN2782KirRbMzUWxLHCc/L9SP/jSFYbwwgtw//15G9qxAz7+8XO/l2un6GfS\\nPkW/uqwDGb3Il1b0K2mbol9J2xT9bL3aZ7sN/+f/wEMPwYsv5tkblgVHj8Lv/m4esOg4cQI+8hH4\\n2tdgcjJ/XJ62DVmWBzY8D6IoD2icX8x9fnBjuX+vt434Cl/ur+l5sGdP3kZuvhne8AZ4y1vOPUau\\nnaKfSfsU/eqyLfa5kre//e0bmh4mxIWStin6lbRN0c82un1+73v5UpJefD//31La7aV/Li4NL74I\\nn/1s/r/zybVT9DNpn6JfdZaw9CIZGUL0EWmbol9J2xT9TNqn6FfSNkU/k/Yp+pVkZAixAXw/oN2O\\nsG0N13VWfsIS8gKibbIMyuXFhbku5LmdfcjTNFvzawohttbk5DS1WsjQkEu1Wt7q09m2fN/nzJlZ\\nLEtl9+7hVRW5FWIzhSFs0OYXQmwbjUabKEoplSyM89fdbTIZP29f0sMLsUbtdoRhFPG8c1V31yqK\\nIpLEJMsswjC6oOemqUkUnas4nFcb1y/oNYUQWyeOY2ZmYhxnJ1NTsgbhYkxPN9G0Qdpts7sLlhD9\\n4utfhzvu2OqzEGJrxXFMGCpomku7HW716RBFkYyftykJZAixRratEYYtLEu54Lt9+XaeIZDvsXwh\\nz1WUEF0/FzXOtw6Nuvs2CyG2B13XKZVUWq1xKhW5VXsxymWbKJrGtkNs297q0xFigRtugCee2Oqz\\nEGJraZqGrqckiYfjbG02BnTG1REXMiYXW0tqZAhxAebvrXyhltqXfLVtc7k9zTdjr3NxeZLr5saL\\n4xhdl0HUhZjfPtfj+izEepnfNqMIXDff0UaaqOgHW9m3Z1nWN+NVGT/3n9W0TbmMCnEB1mOQrCjK\\nBV8wl3vuxbymEGJrSRBjfUgQQ/Qrw8i36K3VtvpMhNh6/TRelfHz9iS9vRBCCCGEEJtgZAQmJrb6\\nLIQQYvuTQIYQQgghhBCboFKRjAwhhFgPEsgQQgghhBBiExQK0Gpt9VkIIcT2J4EMIYQQQgghNkGh\\nAG3ZZVkIIS6aBDKEEEIIIYTYBK4rGRlCCLEeJJAhhBBCCCHEJpClJUIIsT76OpDxV3/1Vxw5cgSA\\nT37ykxw5coQ777yTOI4B+PznP88tt9zCHXfcQaPRAOD+++/n5ptv5rbbbuPMmTNbdu5CCCGEEELM\\nJ0tLhBBiffRtICMIAp544gkURWFiYoIHHniAY8eOcfjwYe677z6iKOKee+7h2LFjvPe97+Wee+4B\\n4M///M/5+te/zt13383HPvaxLX4XQgghhBBC5GRpiRBCrI++DWT8/d//Pb/8y79MlmU88sgjHD16\\nFIDbb7+dhx56iOeff54bb7wRVVW7P/M8D8dxKBQK3HTTTRw/fnxr34QQQgghhBBzZGmJEEKsj74M\\nZERRxIMPPshP/MRPADA7O0u5XAagXC4zOzu74s8AkiTZ/JMXQgghhBBiCZYFQbDVZyGEENufvtUn\\nsJTPfe5zvOc97+n+/0qlwunTpwGo1+tUq1UqlQr1en3ZnwFomrbsMT760Y92/3306NFuxocQQggh\\nhBAbwbZhYmKrz0IIIba/vgxkPPvsszz++ON85jOf4fjx4zzyyCN873vf44Mf/CDf+MY3eNvb3sZ1\\n113Hk08+SZqm3Z+5rovnebRaLY4fP86hQ4eWPcb8QIYQQgghhBAbTTIyhBBiffRlIOPuu+/u/vvW\\nW2/lj//4j/nEJz7BkSNHuPLKK7nrrrvQdZ33ve99HDlyhMHBQe69914APvShD/GTP/mTOI7DZz/7\\n2a16C0IIIYQQQiwggQwhhFgfSpZl2VafxGZTFIXL8G2LbUDapuhX0jZFP5P2KfrV+W3zc5+Dr34V\\n/vmft/CkhJgj107Rr1bTNvuy2KcQQgghhBCXGsnIEEKI9SGBDCGEEEIIITaBbYPvb/VZCCHE9ieB\\njD6TZRlBEBDH8bKPSZKEIAhke1mxrDAMCcNwyd9FUUQQBJJKKMQ8aZpu6XW1c11P03RLjn+pSNOU\\ner1Ou93e6lMRYkmSkSFE/+k1bgYZO/crCWT0mVbLo9mEWm35AXWt5tFsKtTr3iafndgOwjCkXk+o\\n15NFF+U4jqnVIhqNDN+XkZQQHfV6m2ZToVbzNn2gkmXZvOu6TMAvxuTkLGNjcOZMG19ue4s+JIEM\\nIfpLFEXdcXOwxJczjmPq9Xzs3G5Lv9JPJJAhxGVIUZStPgUh+kq/3GTpl/MQQmwMCWQI0V/O3bzo\\nPTaWsXP/6cvtVy9nhYKDrodomommaUs+plJxiKIYw3A2+ey2vyRJyLIMXd/+TT9NU5IkwTCMBT83\\nTZNyOez+ez5d16lUMtI0xTStTTtXIfpduXzuurrZgxVFUbrXddN0N/XY21EcxyiKsmQfOTxcRddn\\nMQwL27a34OyE6E0CGUIslGUZcRyj6/qWBAs64+Ysy7CsxWNjXdcplztjZ+lX+sn2n81dYhRFwbZ7\\nTzA1TVs2yCGWly+rCMgyhXI5XTTJ307SNKVW80gSFceJKRQWBrV6vbfzAx9CiK2/rm718bcL3w9o\\nNhMUJaNatRd9ZkEQkmUOUZQHeuUzFf1GAhlCLFSvt4ljDV0PqVQKW3IOK80JZOzcn2RpibhspGlK\\nlmmoqk6SbO+CelmWkaYKqqqTppKLLoS4PCRJiqLoZJm6ZGHUNM26v5eibKIfya4lQiyUJBmaZhDH\\nmVy3xZpIRoa4bJimSaHgk6bJtk851jSNQkEjiiJcV5aICCEuD45jAQGqqix5h2z+7y+FJYTi0iMZ\\nGUIsVCpZ+H5AoWBJHQqxJtLLi8uK42zvAMZ8tm2xzeMxQgixJqqqLlpKt5bfC7HVJJAhxEKGYcjS\\nDXFBZGmJEEIIIYQQm0ACGUIIsT4kkCGEEEIIIcQmsCypkSGEEOtBAhlCCCGEEEJsAtOEOAapaSiE\\nEBdHAhlCCCGEEEJsAkUBXYco2uozEUKI7U0CGUIIIYQQQmwSw5BAhhBCXCwJZAghhBBCCLFJTFMC\\nGUIIcbEkkCGEEEIIIcQmMQwIw60+CyGE2N4kkCGEEEIIIcQmkYwMIYS4eBLIEEIIIYQQYpNIRoYQ\\nQlw8CWQIIYQQQgixSSQjQwghLp4EMoQQQgghhNgkkpEhhBAXTwIZQgghhBBCbBLJyBBCiIsngQwh\\nhBBCCCE2iWRkCCHExZNAhhBCCCGEEJvEMCQjQwghLpYEMoQQQgghhNgkpikZGUIIcbEkkCGEEEII\\nIcQmkYwMIYS4eBLIEEIIIYQQYpNIsU8hhLh4EsgQQgghhBBik0ixTyGEuHh9Gcg4fvw4t9xyC7fe\\neisf+MAHAPjkJz/JkSNHuPPOO4njGIDPf/7z3HLLLdxxxx00Gg0A7r//fm6++WZuu+02zpw5s2Xv\\nQQghhBBCiPNJRoYQQly8vgxkXH/99fz3f/833/rWtwiCgIcffpgHHniAY8eOcfjwYe677z6iKOKe\\ne+7h2LFjvPe97+Wee+4B4M///M/5+te/zt13383HPvaxLX4nQgghhBBCnCMZGUIIcfH6MpCh63r3\\n357n8fDDD3P06FEAbr/9dh566CGef/55brzxRlRV7f7M8zwcx6FQKHDTTTdx/PjxLXoHWyeKIkLp\\nHS9pcRwThiFZlm31qQghtoBc55eXZRlhGBLJ7W7RxyQjQwgxX5qmBEFAkiRbfSrbSl8GMgC++MUv\\ncuONN2JZFgMDA5TLZQDK5TKzs7PMzs72/Blw2TWGMAyp1WLq9UQGuZeoJEmo1QIajYx229/q0xFC\\nbLIoiqjVIur1hCAItvp0+o7vB9TrKbVaeNmNAcT2IRkZQoj5Gg2PZhNqNU9uVK6BvvJDtsbP/dzP\\n8XM/93P8zu/8DoVCgdOnTwNQr9epVqtUKhXq9fqyPwPQNG3Z1//oRz/a/ffRo0e7GR+XBkW+BJew\\nLANVVUhT+RsLcbnJr+0K+XV+q8+m/6RphqKoZJn0g6J/SUaGEGK+LOv0XVt9JttLXwYywjDENE3g\\nXLbFgw8+yAc/+EG+8Y1v8La3vY3rrruOJ598kjRNuz9zXRfP82i1Whw/fpxDhw4te4z5gYxLhWma\\nlEr5HTrLsrb4bMRG0DSNSsUkjhMsy97q0xFCbDLTNCmXQ9I0w7LMrT6dvuO6NpoWoqr6gmWqQvQT\\nycgQQsxXKjmEYYRh2CiKstWns230ZS//la98hb/8y78kyzKuuuoq/uRP/oSxsTGOHDnClVdeyV13\\n3YWu67zvfe/jyJEjDA4Ocu+99wLwoQ99iJ/8yZ/EcRw++9nPbvE72XwSwLj0GYaBYRhbfRpCiC3S\\nCfSLxRRFwbalHxT9zTAkI0MIcY6maTjO8isJxNKU7DLMvVQUSTkV/UnapuhX0jZFP5P2KfrVUm3z\\nwx/Ogxl//MdbdFJCzJFrp+hXq2mbm1bsM0mSBfUrhBBCCCGEuNxIRoYQQly8DQ1kvPvd76Zer9Nq\\ntbjxxhs5ePAgn/jEJzbykNuO5/nMzDTXtMuI7wfMzDTx/bVXrI/jmJmZJvV6k1qtRb3eWjHale+U\\n0aJWa879r0Wapms+trhwYRgyM9PE85bfqSSKIl55ZZLTpycIwxDfD3juuZd5+ukXabVam3i2Qly6\\nsiyj2WwzM9MkjuOtPp1Nl6Zpt+9YqR9YzXWr85pjY5O88MIYvt/7sUmSMDPTpNFoL9l3jY2Nce+9\\n3+S//ush2b1L9C0p9inEORczr1kPcRxz+vQ4L700vmS/vh79fme+t9W7ja22X94uNjSQ8dRTT1Eu\\nl7nvvvv46Z/+aU6dOsXnPve5jTzktpKmKa1Wgqq6tFqrG3BlWUarFaFpBVqttfeC7XaIojjU6xFB\\noBJFOtEKvanvh6SpRbOZ4HkQxwZBIAPEzdRshmhagXY7WXbyUK97+L5FFLnMzraZnW3TaFiEYZXx\\n8cYmn7EQl6Y4jgkCFbDxvMvvOhgEIUlirqrvWM11Czpbh6tkWZXp6WbP18w/c5swVJccUD7++Atk\\n2Ws5ccJmbGxsVe9JiM0mxT6FOGf+vGYrlrn4vk+7bROGRer1xX3Qxfb7WZbRbscXPHdbT63W6vrl\\n7WJDAxlxHBNFEffddx933HEHhmFIJdZ5VFWdSy/0sKzVFXhRFAXTVOees/Y/n2lqJImPZSloWgJE\\nPbepBTAMjTQNME3Q9QxFiTCMvqwTe8myLI0wbGMYebtZiuuagEeWtXBdk0LBRNPaJMkMpZIUvxNi\\nPWiahqrGpGmAZV1+18H82h+iKPGKu4Ks5roFoOs6phmSJHXK5d67MZlm3h+parxk33XgwAhB8DzF\\n4iyDg4Orek9CbDbJyBDiHMvK5zWmqW7JPNE0TTTNR1GauO7iPuhi+/35czfT3LSqDkvq9Mu63rtf\\n3i42dBT2G7/xG+zfv5/Dhw9z6623curUKSqVykYectupVAqkabqmxlQquWt+TodtW5imgaK4c3sW\\nKyteNEzTZGBA7z4uy7JLovFvJ4WCg+P0/ps7js3+/SZZlnUH+DfcYJNlmWxDKMQ6UVWVarVw2V4H\\ndV2nWlVX1Xes5rrVec0rrxwlTdMVr1Xz+6Oljn/gwAF27tyJaZqyu4voW5KRIcQ5xaKL617YvGY9\\nmKbJVVcNL9sHrUe/fzFzt/Xkug62vfXnsV42ddeSLMtIkmTLJ1VSoVf0K2mbol9J2xT9TNqn6FdL\\ntc1/+Af49rfz/wqxleTaKfrVlu9a8qlPfYp6vU6WZfzar/0ab3zjG/mv//qvjTykEEIIIYQQfUsy\\nMoQQ4uJtaCDj7//+7ymXy3zta19jenqaz33uc/zBH/zBRh5SCCGEEEKIviU1MoQQ4uJtaCCjkw7y\\n5S9/mfe+973ccMMNG3k4IYQQQggh+ppkZAghxMXb0GIVb3rTm3jHO97ByZMn+djHPka9Xt92xUU8\\nzycMEwoFa8tre2yVfJtYH1VVcF173SoKx3FMqxVgmhqO07tS/eXM9wOCIMZ1TQzD6PnYTntV1Yw0\\nVS7rdivEdhIEAb6/8vc834LbI8ugULB79qmX4zXW8zxOnZrAtjWuumrPVp+OEEvKd6zb6rMQ4tKz\\nlj6yI01TxsenSVMYHa3KuHkb2fClJXfffTePPPIIhUKBKIr4h21U2ShJElqthCyzaTT8BT/fisI4\\nnWKpnXPYLJ4XEAQangdhGJKm6YJzWa3zz7/VCsgym3Y77f48TdNLYl/j9ZIHkWLS1GJmZvHe1p3H\\nhGFIFEU0mzFxbPDqqy3S1KLZDLrtNcsyoiiSz1eIZXietyXHzbKMZjMmDDUajaDnY6Moot2GIFAJ\\ngt63dJe6xq6HjbhOr6VP6fRBS3n55QlOnGhz6pRPrVZbz1MUYt2YpmRkCLERwjAkCDSiSF+xj+xo\\nt9vMzuo0myb1+tJj7aXM7wvjOKbZXP1zl9KZY21XFzI3vFgbGnLSNI2XX36Zz3/+8wAcPXqUO+64\\nYyMPua5UVUXXIY4DHCffztLzfNrtFE3LqFTcTdvvOMsyarU2SaIQxx667mBZ+ZZFveRf6BhdV4jj\\nDMvS17wlXZYlzMw0SdOQOHbRNJ0si1FVk2JRx7atVZ1/vd4mjhXS1EdVbeLYR9MUNC3fBihJEmo1\\nnyyDcnnl7IPLgaqqZFnEyZMT6LqCZelUKqXu79M05aWXJpmZaaMoEYVCEdPU8TyfqakZSiWFJFFQ\\n1YQwjKjVYopFnR07Kt0tWnvJsox2Ow/irWc2jhD95sSJl5iaglIp4eDBqzb12Iqi8Oqr4zSbOkND\\nGYODxWUfm19Lm6QpuO7yjwMwTY1WK0DX12+r2DAMGRuroyiwc2el53U6iiJ8P1qx30nTtNu/rdSn\\nhGFIoxGhKBnVqrvoff3wh4/yz//8KqVSi4MH3yNbvou+JBkZQmyMfGwbkGWg66uf74yPnyVJFEZH\\nd67q8fPnLK6r8t3vPsPsrMbBgy7XX39gzec9Pj5NrZbiOCl7946u+fn9oDPPs22FQsHZlGNuaEbG\\nH/zBH/DpT3+aQ4cO8drXvpZPf/rT/OEf/uFGHnJdKYpCpeJSrZrdP0gYJmiaNTch37yoWZqmxLGC\\nqpq02zGG4RAEvY+fpimNRkSSWJw9WyNJLJrNaM3ZJIqiUa2WcF0XMEhTjTAETbOIotXfQYtjBV23\\naTQiDMNB1y2qVZNqtYCiKHOZAzpgEMebG9HrZ45j4rpFyuUdeN7CkU+eOp7i+y71uolh2DiOwu7d\\nuygWHRRFQ9dtwjCbC8CVCILVZ/SEYYjvq/i+Sii3j8QlbGYmolTaQ622vtkLq5HvXe8yPDwC9B54\\n5f1SiWq1hKL07sIdx2ZgwFrXoHuz2cb3LdptgyDonT2SZ4RZNBq9+500TUkSbVV9ShQlqKpFlulL\\n/p1eeKHJ3r0/hqpezcsvv7y6NyXEJpNin0JsDF3XGRhwqFbtNd0QHR4eZXR0hNVO7ebPWWq1OpOT\\nGoaxj5MnZy7ovBuNEMuq4nn52H67yed5zM05Nm8MtaGBjC9/+ct87Wtf41d/9Vf5tV/7Nb7yla/w\\npS99aSMPue4URVmwVqpQsAAfx1FWdUd7vWiahuMoKErAyEiRJGlRKq084NU0iOMQx9GJ4xBNY80D\\nWts2Mc2EatXEdRUcJ6NSMR0dCaAAACAASURBVFHVAMdZXbRT0zRcVyXLPHbsyM+/UDDQdb17PoZh\\nYJoJuh5hWWvLGpkvn9x72/JCsBTHsRkcVNG0OgMDhQW/0zQN245Q1RlGRlQcJ2VgoIKuRxSLKtVq\\ngSzzKJdNhoZsDKNBtaqv+uKeZ4TEcxk426u+jRBrcfXVA/j+KfbvL2/qtR3y79nAgE67fZbh4d53\\nMQzDwLYzLCtd1XVS07R1zaRyXRtd97GsEMvqnY2n6ypxHKLrvfsdXddxHFbVp9h23vdYVrrkOub/\\n+T/fgGl+l4MHa7zhDW9Y3ZsSYpNJsU9xOYmiiFbL27SbBKqqrqkftywL182zIVx3dfWk5s9ZRkaG\\nueoqjSw7wWtfu7qMjvPt2FEkTacZGjK3ZY0OVVVxXY0s8ygWV87UXy9KtoHFHg4fPsw3v/lNhoaG\\nAJiamuInfuIn+MEPfrBRh1wVRVG2pMbFVuisV9I0rfvfS315QF5LwiZNfQYH82yPMAxRFKXvl6us\\ntW3OzDTJMos09alU1hZ9juZuB630nE7Hs9mTO9FfLqfr5mbLsoyJiTpZZqDrMUND5a0+pZ5We02Y\\n3/9sdL/TaZ/NZpvJyTa6rrJzZ3lbDgjFpWWpa+djj8Gv/mr+XyG20kb37WmaMjvrAXkQulrtvSRy\\nq/i+T5ZlOM6FLYno1MuQPmf9rKZtbuin/Yd/+Ie88Y1v5OjRowA8+OCD3H333Rt5yG2n2WwThimF\\ngrHi3a3zhWFIsxmiqilpqqJpCuVyXjOj0WgTxxmlktWdqK71y7Xc6/d7IERVFeI4QVXzL4HvBzSb\\nKZBRqaw8ce9nnVojSZJRLttomsL0dJMkCSiVzO5ac4By2Vkw0Zj/XNtWmZjIdzjZscOmXC4td0gU\\nRVn2NYUQFy/LMsbHp2m1LAYGegcy0jSlXm/P1RLq/X3sXMNNU12xntJqJUlCve6hKFAuL65RMV8Q\\nhLTbEZal9Vwvm2XZkn3WhXj++ef5f//vNAMDMXfeeRvFYn8OmsXlTTIyxOUinzNkZFmKqvbf/KHV\\n8qjX20xPtzFNmz17srml9Oekacorr0wSBCl79lSx7cVZG+22TximFIvmmmsRrre8tIBHmmaUSvYl\\nHVzZ0Hf27ne/m7e//e08/PDDKIrCxz/+cXbuvLCUm0tJftfIIwxjarU2qlogilrs2NE7kDE2Nonn\\nxezYUcZ1XV5+eYzTpyOyrMnevXuJY58nnnhuruGW0TSHgYGMUqmC59U4e9Zn164Ce/bsnBuM5pNT\\nRYEsU7qDYs/z8byYMAxxnCrT05MkiYaqguMYS35Bp6enOXlyhtFRm337zm151xmgJsm5L9PsbJ2p\\nqTYDAzaDg9U1f35pmvYcPJdKDnEco+vuvOr2q794ZlnWN8Ea3/fn6puEVKsVbFslinQ0TScIIlzX\\notEIKRYHOHt2miCI8H2FiYlpzp49y969w7zmNfuYnW0zNTUDuAwNlZmamqDRgP3799JqhZTL5yYo\\naRpTr/tzdzQHOHHiJZ59tsYVVwxw3XU7KBTOXeA7E6WVJipCiOVlWUYc5/WIwrB33Yl2u80Pf3iG\\nNM248cYdDA4OLPvYycka4+MRlpVy3XXWugQhX3jhFJ/97HfQdZP3v/9Wdu3atexjH330B/zwh22u\\nucbmttvesux1u9Vq8YUv/DczMzE/9VPXcvDgdT3PoVcf8Nd//U/ce28T121y6NAAR44cWf2bE2KT\\nSI0Msd1EUUSj4WOaWs/AuO8HtNsRjqPjOPZcXSeHJEkwjAsPqF/M2DxJEhoND1VVKJXO3ZBN0xTf\\nTwkCeOyx0xSLLoXCjkWBjHa7zQsvNEhTE0WZ5Oqr93aDBVmW4bom99//GKdPRxw5spMbb3zNonN4\\n/vlTvPhinWuuGWT//r2Lfj82Nsbx4+NceWWZAwf2L/p9u93m1VfrOI7Ozp3Di34fhiFnzkxjGCrD\\nw2XiWEdVNYIg6gYyOtkNS32O8+dr2+mm5YYsev/+97/Po48+yqOPPsrY2Bh79+5lz549vPLKKzz6\\n6KMbcchNk2UZQRAsWX8hiqLu73w/WHYtWBzHhKEK2HPb40GSLK4uEwQBYRjOVWlvUKuBpg0zOZlv\\n7zMx0URVB5idjfD9kKmpaVqtQRqNQV5+eZI4Npme9tD1As88M0WhsJ8zZzzCMKTd9piaihkba9Jo\\nxICN74dzDTkgjnXCMCaKWmhagqJopGm2bIrPyZMzFAr7GRsLFxSFzN+rBtgEQTR33m0sa5SpKX/N\\nBVPb7TYnTozz0kvj+L6/6Fi+n08CTNOk2WzywguTjI/XcN2MUkld8U5fFEVMT7eo1Vp9sQXSzEyL\\nJCkxPa0yMxNw9myDKGqRpj6mqZNlGdPTMzzxxNOcOTNLo2EwNjbDqVMznDrl8tRTLR555DkefXSM\\nH/7QY2LC58UXX0XXhzEMi6mp01Qq+QXb90PAZmoqwPdtPM9mZmaG8fEUVd3J2bPN7jauQRDMrXkM\\n0fUCnifb5gpxoVRVxTAy2u0pLKv34MH3faanY2o1hUaj1fOxtVqbMNSp15fusy7E97//JCdOjPDU\\nU+qKy0Qff3yMNL2GH/xgmna7vezjZmZmOH3aRtMO88QTL/Z8zVarxVNPvczzz59Z8j195SuP43mH\\nmJoa4f7771/dmxJik0lGhthuZmebzM6mjI+3u0uTz5dn/vrEsU6jEXTnDJqmYZrmsoGIJEl6zpt8\\nP2B6uk293rvPW04QRKSpRRTpC85dVVUsS6Fen+TMmVc5ceIs9XpjmXMIabf97jnGcUwc66SpxcTE\\nJD/6UZMkuZJHHjm16LlxHPPccw3K5ddy4sT0kq//xBOvAHt56qlpfN9f9PvJySaaNkytxpK/n51t\\nkmVVWi2TOI5R1Yg09bGsfN6TJAkzM625ecXizzmKou58LZ8PbA8bkpHx+7//+z2jZt/85jc34rCb\\nIs+kyLf2GRhQ54ohZsRxTK0WkKYqvl+jUKiiqh6VyuLUW03TUNWANFXYtasMKBQK59Jf0zQlCEJa\\nLYiiAMjQNANFaRMEKSMjeUrTFVcMcvr0LPv2ldm5s0SlovDMM2NAxr59AzhOQrFYIgia7Nrl0mi8\\nRLWaX0w6eyurqoamxWRZ0L3IBEFAqxVTKMDQUJFKxaZWC9C05YtEjo7avPLKi1QqyoKMDU3T0LSA\\nNE0wzfzLVSrp1GrjlEoqnudjGMtvzZcHQmIsy0DTNGZn2xjGIPX6LNDCdV3K5RBdzwfrYBAEHpVK\\ngdlZH8MYJAjqqKqyqqU7QRDPbQ0bkyTJlhe4LBYtZmamSdNZ2u0SO3aMYNsZrmsQhjFxHGJZQ5RK\\nLuCTZR579xY5efJ5xscjms2USmWY2Vkbw0gxzZADB0aZnW1hGBm7du2kE5syTZ0gCCgUVJpND01T\\nKJUqlMt1gmCKPXsKWJZJq+URBBppGmJZGVHUxjTZ8s9KbH95JtWlmwK5nDxtdZrJSRNVneLaa/ct\\n+9j8Gt0mTXU0bfklYZBfP2o1D9teW+GzXoaHS6Tpy6iqz8DAlT0fu3dvmZdfPsXoqLroDtd8lUqF\\n3btDGo0fcfjwnmUfBzA+XsP3KzSbbUZH25TLC5fh7N6tcebMj4BXqFR6n58QW0UyMsR2k6YZK93/\\n7swhfD/FtqNVZ1Dk2cAmiuItuf2450UYRoEo8rp1l9bCMDTabR9FAUVZuCykWHSxLIiiEr6v43mL\\ngyW6rlOtmoQh3SX8ee0nD1Aol0sMDqZMTz/PwYOLl4bqus6OHSbj4z/iiiuW6wsDTp06SbXaXnJO\\nVCyanD79CoWCimkOLvF7m1qtjmmm2PYwxaK+IIslimKyLH/dOI4XfYa6rqNpbbIs2fKlMWuxISPG\\nBx54YCNeti/l+wh7xHHCzEwTRbFRlIBCATzPJ00VDAPK5XO7TahqZzeJDFUtLEiT9f08iBDHAarq\\nzDXC/Dn79u1A17XuQH/Pnp2MjISYZl4bQVEK7No13D1Gmqa02wFBkLJ//x50Xes2TtM0UJQmhgHD\\nw4Pour7kRLRTIHNwMC/WttxFaWhoEMPwKRQWNv7Oe02ShFdemSYIwLJiKpUCntfklVcCNK3Jvn2D\\niyYwWZZRq/koikUQeAwMFBkcLHLmzDSuG2PbS6dUd05xeLjI2bOTFAr6kuvZlmLbBmHoYxgKur55\\nVXeXk2UKhqFRKg0SBD5h2KBSKfPSS9Moio3rJjhOQhz7VKtFHEen0QgpFErs2hWjKCGWVeL6611K\\nJY0DB3aiqjbFYkCSgG0XgDyTwjAMqlWNgQF3rj0pqKrK6153DVEUYRgGrZbH9LRHEOTR3ErFolq1\\nttVFT/Sn6elZpqZCLAv27h2+rAJjaZpy6tQ0rdZO4vjVVT1nNWuNLcukUlGxrLXvVrWca665mhtu\\nqKGqNlde2TtQ8I53/Bjj4zMMDVV6/j3L5TK/9Eu3EgTBosDE+apVl8nJWRyHJa/rr3/9IR57LMa2\\nTQ4dOrS6NyXEJpOMDLHdDA6W0DQP03R6ZjcXiy62raPra691tFw3VSiYNJstLOvCg/KKotBq5bWY\\nXDeauwGYU1WVsbEzZJkBLN8Hzd/yXNO07k6CaZpy000HmZryufrqypLPvfbaKxgd9RgYWDqQcfDg\\nNezereK6S2dPGobJjh3DaNrS2fGu63LVVSaqqnb72/n9vmHoqGpe32qpJT4L56bbZ/y1abe+3v/+\\n9/O3f/u3m3W4DVMsOoRhiKZZqKpKGIZzDV/DNA1c10HXdQqFDDAxDJcwbC9a0zs/KDD/50EQo2k2\\noGLbKYaR1x3IsmzJjILOBLLzGucHA6IILKuI77cYHDxXwyBNMwYGhsiydMHzO8fJjxV0o3krNeog\\nSCgUqkTR0u81zzJRsawqU1Mvcc01u5iYmMZ1qyRJvOySFVXNLxCaln9Wtm1zzTV5nZXOshLTNMmy\\njHLZmluDl79P13W55pq1rcfTdX3JaPBWSNOUKALbLjI7O8PIyAiFQv63UhSTJNFQlIQrrhjtPsfz\\nfMbHI/buvRZVPUOh4LJv3yA7dpjs3r2DIAjxPA3D0CkUQkwTTPPcZKDzd5vfUSjKuSybIEipVAaY\\nnh7DccpomjkXpRfi4tRqPpY1TBDMEsfxZRUcUxSFXbsGiSKXUmlkxceOjg6jaeaCQdVSDMNgaMgh\\nTdfv1m8cw5ve9FaSJFg2vbjDti327VtdXaxz/U5v1WqFw4edueU4iwfKxeIebr31atJ07LJqQ2J7\\nMQzJyBDbi2EYDA+vHJwolx2iKO6OxVdjpeeYpsng4IVfz6MoQVEswjDAdS2CIKJYPJetoCgK1177\\nGjTNZqnVLWmaUiyWURSTOD4XgZxfa8NxCuzbN4qiLM7oyJdkp5TLgwRBi0Jh0UPYvXuQarWNbZeX\\nnHOpqoKmKahqsuyNiV4ZrXngpff8ptcN6361aSGXhx9+eLMOtS6yLFtyzX8nQ6Ez0TMMA8OIMc2E\\ngQEbXY8pl10MQ6dUskmSFo6jLnjN5WoJJEmC65pkmYdlZRQKBUwzr357/gCvs74pSZLu654fDFBV\\nFcdRSZIWxeLCC0CekeGj6xGapnXPSVEUyuXO+7BX3aCLRZMkaeG6WvcLmK8fi+eOl98ZTJJJrrxy\\ngDhusW/fEMViwI4dS0d38wJBLqWSQqFgLapNYpomhmEwOTnL2bPTpGmKZVnbKpLYS2dPZttO2bVL\\nw3GibkX/wUGDgYGY4eEKcRzPBT0isiylUEgYHIy59toKV19dYHTUYefOkbltoTQUxScIGvh+RpLk\\nmRed9pSmKWEYLrtOsVAwSJI2w8NlbBt0PcI0t+8uMKJ/jIwUabfPUCikl90EVNd13vSmKxgcnOb1\\nr++d5VAsFhkeTnGcOoODS9/5OfdYG8NIKJWMFe9idbZKXcnOncO0288Shi8yMLB8odGNoij5MsHl\\n7gj+r//145jmMa69doI3vvGNm3x2QqyOaUpGhrh0zJ8zaZqGba+tuPSFPGctLMtAVQPKZQVFCSkU\\n9O5N1izLGB0dRddPkKZPs2/fzkX9oW3blEoZmtZgaOjcks7O+zYMg5ERG9tusWPH4n5ZURQKBZ0g\\nqOO6S/dduq5TLpeXHf+4rk25rFKp2Fsyz+nMN/vNpmVkjI6OrvygPpHvedwmyxRKpYW7dHieT70e\\nYlkqlUphbuJfmFsGkW+JNzPTAAxcN49+hWHIzIxHmkYoijZ3Rz0iTWFgII+8NZttggAMI1sxYtZo\\ntAlDSFMfVbVJ0wBFMVDVPO12fgN3XYellibnARWVOE6Ynm6iqjrlch4YsG0Lw9DXdEFRVRVdV9G0\\nzhIZnzNn6gDs2VPGtm1GRxev6ar0HoejqiqmaVKrtUgSgyzzGRwsdAMsvu8zM5MCFp53FscpMDhY\\nXPVSkn7nODZhmKBpI2RZiGEYKIrCwEB+IR0be5WXX25SLObLZyYmGiRJMheIGqZQUNmzZ5hWy2N8\\nvEW93mJ4uIyiKNh2Gc9rkqZtpqZaZFkEKExM+JTLJldfPYphGN1AiWma2LaFqio0GhGKklGtOpdM\\n4EhsrSxTGRnZiaLEK+5MtBadwVU/t9Msy3j00ed49VWdJHmO225bXJG8I45jJid9siwvCN1rz3vD\\nMBgYWDnQ2NmaOYqgUNBwnOWvn6dPn+HkyQBVzajVahSWurU071w9L8Sylq+DtN6++93HePnlAlNT\\nk5w+fZqDBw9uynGFWAvJyBDbWZZltNt5wUnHsajXPeKYubHo1i/LPp+maVSrC+dW+UYKEaqaMTU1\\nzgsvaKSpyunTZ7CswoL+sLPteRDkz7Nte660QN4Xl0oGoGKa7pKbN0CeFaKqBnG88g2DpczPjt5s\\n7baH52XoekalsnyfvxU2bWT31a9+dbMOddGSJCFJVLJMJ4oWNrharUWzqTA1le/+0Rkkd56jaRZn\\nz9ZotVSmp/PdRfICkhZBAEmi4vsJp0+3mJ7WmJqaJUkSwjBfRhJF57bHSZJkUfZGnp6UoKom9XqI\\nohi02wlgkGV6zztq86Np+RfJII4VokhBUUzCMM92aDTazM4GNBrLV5k/X6PhkyQWjUY0V5vDJ0lc\\nksResLPIWuVbI/lz7z3/Incm1kmSzC17SYjjFrOzCb5f5OzZ2Qs+3mbK28zKF7S8HWTdorIdrVab\\n48df5exZjaefHmN8vM7ERESzaXDqlIfnqTQaMb7v43kRaWqTJC5paqBpGWHYxHV1JidrTE0pnD3r\\nMzXlkWUVwtAgDEPiOGZ2NqBeT/G8/O8QhgmqahHHyrrthCBEZxnbegb8oyhiZsZjdtZb1Xdtq4Rh\\nyHPPNWm1Rnj22VrPx87MzPDSSzGnTyeMjU2sy/E7y9hU1ezWv1nOmTMTzMyUmZpymZjoffxOv9Bs\\nRpt2J+fLX36MqanreeGFgW2XCSouH4aRL9PqwxucQizSGXN3hGGI76v4vorneXNBOZ0wXH0/u9os\\nwPOPvZzO3GA558+pOnOzJNE4efIkzzzj88wzEU888SPiWEHX7e77abfb1GoGaTrE+Hit+3pZps/N\\nnyLiWMEwnCX70CzLiKIMw3AIw8XnuNr5wFYJggRdt4kien7GW2FDMjLuuOOOZX+nKApf/OIXN+Kw\\n60ZRFHy/RRSB6y6MPOm6hqqm3QmeYcRUKja6rmPb0dyOGS6KkmKaOnEc4zh5RkGxqKIoGWEYY5o6\\naRrRaHgoio2mJSRJay51P5nb8zhGVaFSsbvZEXkqVMTsrAcETE/PdFP8dV0lTRXCMFwUtWs22/h+\\nnvFRKOQZF7oeYBjq3EWijW0XybJsLqhSIAxbq963WddVgiBE1zvLU4o0m9OoKth2dW4rIHVuecPK\\nza6zBOeVV6bxPJVCIWVwsESaQq0WkSQtFEVD01R27cozNM6eTQmCJgMDq2vW63nXd606u9xkGVQq\\n5rJp0p7nE8cqSdJEVVWmpjxKpYhCwSWKUsrlMi+8cJo0baMo+Y4P4+MNwOLpp5+iVDJJ092Mjro4\\njoKiRLiuxcBAtft3ME0dCDFNhaEhk0ZjlkrFxTCMuQuW0k3BA3Ack2ZzliDI0PWUgQFj262pE5un\\n0zmvlOGV1x+K0PWVl4d1lq2tlHkVxwmKYpJl6YqVzjuDqrwSee/23Lk+rdcOK6Zp0mi8wokTL/Pm\\nN/deVxyGIc8++yPCUOV1r7t+xdfuXHt7faaqqhIEHp7X6u6KtZxrrrmC733vMTQtYc+e1/Z8rKbl\\n/ZFhrK7Y6Gr7m14KhYyxsa9i2zV27frxi3otITaKooCu51kZl9lKOrHNnMs8UCiV8uy6vD/JA9S6\\nruP7dXw/ZefOwqr60izLmJ5uEMdQLpvLZgF2xspAN2t8Kb4f0GwmaFpGpbI4UziKImq1cMGcynFM\\nWq0Aw1AolUrU6w+RZSqjo2/BthXC0KNQyLNLHMehXJ7C815leDivY2UYBqbpkaYxruugKAG+36Rc\\ndhZ9BvnSEgPPW7zUv3NuipIX0O/HnduKRYvZ2TqFQv8t39+w7Ve3syzLcJwihYJOli2841ytFtH1\\ngCiy0TSHNE26jbVQcCgUwHVNPC8iy1RqtYgo8tA0a66R2mhakWLRIwwjoqiCrltkWcrAQBHP85md\\nDQmCNqZZJE1ZMADPsgxNMxkZqTI1Nc7g4BBx7FMq2URRxEsv1UhTuOKK4oLt7vLghEujMTM3Ac2X\\noXR2BgFt3vvofNlWP0EtFh0c59yXVtd19u0b7V4AkwSSxMcwXGw7olBYfrDeWdoTxzA2NoNp7qRe\\nz5eN+H4bx6ni+yGallcQ1jQVy7KoVkM8L+6Z9tRZD9ds+sQxczuabH4aXD6501EUSJKU5QpAh2GC\\naTqEYZ7GF8cavt/EcWwKBYvdu11mZx2mpopMT7e55poSWWbg+wZp2mJyMuGll17m+uuL3HTTAfbu\\nXVxIcGSkShBMAgVUFXbvrhLHPqdP11GUlJERF01Tu5PGvAOwsSyLJAnnirFuzLpGsb2tNmAH+WR6\\nNd/FNE158cVJ0tRkYKDN8PDiJWsdlmUShh6qqqxYeKzRaBNFKroe9LyGdK5paapQLCbrcv3Igw1F\\n9uy5knb7hZ6PjaKIQmEnpZKO5/XOTW+1PFqtBF3Plywudz1P0xTbducGbYv3p59vYGCA//E/Xo+m\\nKT23VAUoldxVbZXXWdqSJFAsGheVPttup1SrrwXOUq/XL/h1hNhonS1YJZAh+lmeyaChKCpxnGKa\\nnV3u8gltXlst39Y0DONuX2oYwYIdG+eLoojpaX9uK9Q2jmMv2VecGysrPcfKnWUbabr00tT8pobR\\nze7QtHwHyErl3DTYdYdQFIs4TrvzuQ5N09ixY5g4zrp9vqIo3Z1P8sBFSj6XSvF9jzBUFownbNta\\ncryQ16rTu++3HwMZQRChqhZhmOI4F3/DYT1tyKd19OjRjXjZTdGpwu44EMfhojt++V7COmnq0Gr5\\ncwPkhd+sTmOdmWmiqiatVgvX1bt38fKJoIPjOPh+QBD43ahfFKVomolhpOh6iGHoGMa5hp9/cUw8\\nz2PnzjJR5FMoaGiaRqvVIstsFAXCMMZ1z6UrFQoGvu9hWSq6bncnoFmWkWUaoBJFCaa5/Jetl07w\\n4nydWhygEAQZlmUQx0HP1+o8R9N0XNdEVQN0Pd8BJo4TNC2gXNaYnfWJY4VqtTKX4mVQKpXx/RZL\\nLRvvTEDyvZQVHKdIEHhbEsjI6034c7vELD/BKhQsWi1/bv1dRLud4ThOt7jQnj2jRFHM9PQpDEOn\\n3Z5lZGSEej0kjiN03SRNTQxjAM8LqFRKi45hGAaVSok0NZierlEu20xPz6KqFSAhSfJiq1EUzdVC\\n0efOK8C2NQliiGWtNmAHndTLvPhwrzaVp4/qaJpLu917oqqq6qrXc8ZxHiSOY69nZkA+aEsBddm1\\nsBdidLTAq6/GjI72Lhw0MjLCVVdNkCQKe/b03hGk0WjTahkoSkC57Cw7QNI0DddVCcNzfdFydF1n\\nx44BFGXlLdqW6xfOF8fxXBDJJAiCiwpklMsDFAoWuu4yNDR0wa8jxEbrbMHao8yMEFvOMAwcxydN\\nF2ZBzr/Balk6WaaiqhBFeV8aRcv3pfmNC50kyTAMvVsn0DRZsC1qvuGBN/fv5bMFXdei3Q7QdXXJ\\nPseyTOLYn9t6dOkdIK+4YhhVtTHNpYIN+VjGNHWSZPGS6nx5Zr4cxfc90jRD0xzi2O9+BmmaZ/Pr\\nur6g78zPzZurgbF+9f3yDQCydamrkc9NHZLEX5fMyfW0oWGfZ599lj/6oz/i+PHj3ToHiqJw8uTJ\\njTzsBQvDkHo9ryNRqeg9swZUVV3wZVtKqWTTbgcMDbmkaYKuL94u7vygQaGQfxltW1821aqzk8n5\\nisUi1WpeH6Jcrs5lW3hkmTG3BKZAkthzr3/uy27bMWka9yzwdqEMw8B1Y5IkpVAoEMchrrvyQLlQ\\nyFPHr7xyhDDM15XFcZ4lUijkAaBiMX+dzqTethWCYHHaVkdnPZthmMRxgyRpUyptTVGiPM1s5a2p\\n5keMTdPAcRZfqHfuHGTfviaaVsFx2miaRqVSYHh4B61WwOTkLAMDGkNDy0+QSiULz4sYHjZJU4/d\\nu6vMznpomorrurTbPp6XL02pVpVFkWwhlrLagB1As+kRhvmuOr2KyJqmydCQju83GRxcfr/3tcq/\\nAwGua/bspBVFIYpC4lihVFqfa6au69x007WMjUXs27er52PL5TK33no9SZKyY0fvXUMMQ8c0FVRV\\nX3Hg4Tj2kgHg89m2RbmcoqrrV3hM13VMMyRJvIvuh37hF26h3X6GgYESN9xww7qcnxAboZORIUQ/\\nW2m8mo8HLaIo7Y7LV+pLdV1neNgljlNc12J21ltySfv8rIdeNE3r+biV5mwHDhzg1ltniOOY17/+\\n0KLfG4ZBoZAQx9GSNz81TcOyIAzbFIv5+84/g3OZ7bVamzQ10HVvwQ0WRVEoFld+j2uRZ8PmBfyL\\nxeCib9h25rO2rffdI4TsJwAAIABJREFU0hIl28AKXLfccgt/8id/wl133cV//Md/8I//+I8kScKf\\n/dmfbdQhVyUvKLf4bQdBQKORoShQKqnbfgvALMuYmWkDeVZDtVokTVM8L58Mn7+l63aSpinNZh6l\\nLRZXt3NGvqQkj5QWi3ZfZhIs1zZXUqvVmZ5uU6lYZJmFouhYVrKo8/H9gCzLU+PWElHNUwXzpVbV\\n6sZtkSX614W2zdXKdyYyybKQgYH+3Q0nr48Uoij51sirCUiuhu8HeF5EobB0oPpCRFFEqxViGOqK\\n5xmGIVGUYNvmtvx+d9rnSy+9wvHjM5hmxi23HLhkdrAS29dy1869e+Ghh+CKK7bgpISYs9F9+2oE\\nQUC7HeE4xpZkSWdZxsTEDGmaMTJSXfc+MJ+PtQALRQlW3J3yYuV1N2IURcV1sw25Ub0ZVtM2N3Sk\\n6Hket99+O1mWceWVV/LRj36UL3/5yys+77vf/S633HILR44c4a677gLgk5/8JEeOHOHOO+/s7pTw\\n+c9/nltuuYU77riDRqMBwP3338/NN9/MbbfdxpkzZ9Z0vpZlUSwqFAoXdqcpDEM8z1+2omvnizIx\\nMUOj0WB6enbRY/NAg08QBDSbLTzPW/Q6URRRrzeX3DHC9wN8P1+60VmGYhgBpVI+iG21fIJAp9FI\\nCIKgZ5Xczrn4vk+93qLV8ub+fzBv95MYz/PXvdqu5/nUaq1ld8VQVZVyuUC5XFj1hKcT2a1UCttq\\noJ7vVBN0PwvfD+a2QvIJw5BarY7n5ctPbNtG1yMmJ8eYnp6hXm/QbntzaW8RzWZKu61020iSJN2/\\nca2W/43DMKTVas8dI39uoWDjOCml0tq25RVitUqlvM5OuWz2bRAD8jtJxaKKaUa47voNDiYmJnj8\\n8adXrOuQZRnf//5jPPTQYwRB72V6+TrmwopBjLy4dEQQ6DSbvWtkZFk2tySy97GhM5hqda83myFJ\\nIr70pS/y/e9/py/XGgvRIRkZQuTyjAYdXd+a8WUYhnzvez/koYeO02g0l32M5/nLTqzz7VybS84B\\n800Q7LkxjtOdX0XzLgCNRoNnn32Jycnpi34/hmFQKuVBjH7cDnc9behosbPP7oEDB/jrv/5rvvCF\\nL9BqtVZ83v79+/nmN7/JsWPHGB8f51vf+hYPPPAAx44d4/Dhw9x3331EUcQ999zDsWPH/n/23jxI\\nsqu+8/3cPe/Nm0tl7Vsvqla3pJZarV2AgBESeAGM7Yc9ZnGM/Rz4vfGzeZgxXsJhj8YmwjZhBzYx\\nYQJswEw8MxiDbcBINjtIWCtae9+quvbKrNzz7tv742aV1Kqq7JbULTVQ34iKqKg6ee7Je88953d+\\ny/fLL/7iL/Kxj30MgA9+8IN87Wtf40//9E/5kz/5kxcx5hfODwGpIdhqBTiOiGVtbgimzguRpSWf\\n48crrK6KrK6eKxVqWS6OI7KwUGNuzmZu7lxnRpIkzM/XWFmJWVionvPZ9ACa0OmkB99U59nH9yUc\\nJzUkRVEgSSKiyKfZ9Ls8E5s7C9ptB9sWKZc7+L5MqxXQaPjd/v11olDHkWi1NjpcXizCMMSyYuJY\\nw7ZfvHTrDwvq9Razs21mZ2tYlkWnk1CpuDQaAcvLTWZm2jz5ZJMnn6wwO9ukXG6RJH3MzYWcPdui\\nWl1TxxGAmCRJ08IBOp30+a2stAlDhU4npFZzqVQ85ufbzM11qFY7XQLczA98ltI2Ll+IooiuZ3oS\\ngl4OSOWlIzwvVWq6GPB9ny9/+RBHjvTzL//SWzL08OHD3Htvi298w+f733/6olw/lbGGOI6QpN6Z\\nWp6X7gHtdnxeae122wN0LKu3LN7FxJ/92ad48MEr+d//2+bb3/72y3LNbWzjxUBVU46MbWzjRx2t\\nlovrSrRavR3plwrf//73+eIXm3zlKxYPPvjYhv+HYUirFWLbAra9cYxRFDE3V2d5OWJ5eXNHhCyn\\nlAEpp6GLbYu0Wv56IPjEiTKOM8iZM41zHBwvFpqmoeuZy4rP4lLgkoYr/uqv/grbtvnIRz7CH/zB\\nH9Bqtfj0pz993s8NDw+v/64oCocPH14nEL377rv5+7//e/bv3891112HKIrcfffdvOc978FxHHRd\\nJ5vNcuutt/I7v/M7L2i86cE/naCu6+J5EUNDxXOiOlEUrRPK6HqGOI5ZXW10mXodPE9gcjIHGARB\\ngG37aFqqjCFJErZdIwh8oiik3e505XpCCgUNQVCIoqBLjpkgCDJJkmyStZEgSQpxbG8Yf7ttIQgC\\nnucTRQKKoqLreYIgdTRkszqqGuB5Cr6vdCNxFrKsIIop4Z2iCARBQhgGpOQ2EnHsI4opIy8k3cyP\\noMvMG5DJhLRaYldOLEGWN6YypxkEUZeIbeupJ4oikhQTxz6ZzOUbmX250Om4WJaEIHioaozniTiO\\nh65ryHLC6mqN5eUWmibSblcplRQ8r9qV+lWpVOrs2zdIGHqcPr2IpolMTe1AURQ6HZskCVEUkTB0\\nsW0LUZRJmZcDPE9EUQSKRf2cTIwwDLFtH1WVyGS0boQ2xDDUddnWNTJcw/jhX0i38fLiQteSS4Eo\\niqhUmgQBTEyYFy3acfLkUxw+fJxXv7r3mieKIq7rIIoR0LuuNnUKe6iq1DO1VBRFOp0m1arF1FRv\\nAlHHsXnooRNIErz+9df1dG4KQkS1WiOXkxDFl4fRcH7+NE88cQZBqKHrt7ws19zGNl4Mth0Z29hG\\nClFMCcFfbDKm7/uUy00yGZmBgY3cUeezSV3X5eGHv0UQwB133EC93qTT8RkczJHJpO0tyyaKBDRt\\n414axzErK6s0mxK7d2uMjQ30HK8g0A0qPitNnsnA8vIihUK4nU34AnBJ79T09DS33HILuVyOv/u7\\nvwPgc5/7HLfffvsFff7pp5+mUqlQLBbXU43z+TyNRoNGo0E+n9/yb8ALLnfwPB/XFbFtm0bDRdcL\\nLC5WKBYLGIaKLMs0Gh06HQFJChkdVbBtm9lZhyCImJ+fIZPpZ35+Gl3vI0k6CMIA+XzIFVdM4Hk2\\nCwuLeJ6PqgokSYvZ2Q6KMkI2a3HDDTd0I+4NwtDh6NFjiGLM7KxJkkjIcoBliQwOiti2hKYFPPzw\\ncYaGRESxQBw7rKzYWJbL4uICrpvniitistkR9uzJIoolGo05vvnNGQYGEnbv3kMQODSbEZ4H118/\\nxtjYTs6cOYnrKhSLMQMD/UDA9PQqsgz9/XniWMDzIjxPQpYdMpkSKyunWF4W6e9POHDgBgQhQBDq\\niGIqoWrbPiMjfeh6H+VyFVlW6eszKBbz+L7P8nIDSRLIZjMIgkguly4cP8plDEmSsLBQ5p//+ZtU\\nqz6DgwrF4himCTt27OTMmVnOnJnm8OFFFhfLhGFCEGgIgk2hkCWXk8lk8rRaPseOHaHZtBkc3EVf\\nX4Zrrx3iqqt2UCz2E8cJe/YMousqMzMtKpUKY2M5cjkTxxFpNKBcXkJVNXRdAURkWaJUGiYIUmUd\\nywqRZZ1Wy0IUHcrlBrKcRVFEWi2HfF6/aFwC2/jRRupMjpGkDJblXjTS2ZWVMvPzbYaGDCYntybc\\nDIIAx0mAlJi5WHzp1w6CgC9/+QlWV3dSrc5yzz2/sWXbUqnE0aOfwnXhHe/4P3v2e+TICR56aImh\\nIY23vvVVW66njuPwyCMLeF4W2z7Nq199cMs+P/nJ/8Vv/dY3EcWAj3/8/+BXfuVXtmz7a7/2//BP\\n/9Rm716bp5/+6pYOzVarxZ/+6d8yMxPw3ve+vqeN0Ol0eOyxM+TzGgcOTG0w+L7ylf8P+HGSZIbf\\n/M3f5JFHHtmyr21s45WEqsIFVGhtYxs/9FBViXbbWi+DX8MaH57vBwiCiK4rGMZGW3J5ucbqqoQo\\nWhiGRhgmJElasiqKIisrZR54YBpRTPixHzuAaZ7LUfHZz36WZ545Csh86lMnueGGOzHNAU6dWmJk\\npEi1WueP//jjLC4K/Oqv3sJP/MSPYRgqJ07MEUWwY0eJP/iDj3H2rMIv/uIIv/d7/y+W5Z8j3LC8\\nXGZpqUU+r1AsljAMUFVt/Xz79a//O5/97EluvjnPjTf+9w37pWVZrKy00XWZ0dGNjhLf91lcrKGq\\nMiMjpRdcovtK8Qg6jott+yRJhKKo68/sQnFJw92blXZcaLlHrVbjN37jN/jkJz9JoVBYrxtutVoU\\ni8Xz/g3o+RDuueee9Z+19FNJEkmSEFkGVU3w/Ta+D3Gs0emku43vBwSBgOuGXenSpJtqbNNqJbhu\\nluPHGxSL13HoUBlNG2Fx0SUIJBYX63Q6RZpNk1otRtPGmJ5uEcd9tNsOUeTTaHTIZEaZnm4Sx1ew\\nsmJw6lRCpVLgwQdrwF4eeWSZ0dGDfOMb0yjKjXznO3XqdZPpaYdKRWdlRePIkQTfn+Khh8qMjLyG\\n+++fI5vdzT//8zFE8U4ef1xmdjZhdlbiyJGAVmuSw4fPEIYwO1tHVac4fLiCqg5z6NASjrOT6WmJ\\npaWYZlNnZqZFkvSzsNBE1wd56KF5stmbOHbMwrJcarUWjpNhZcVlcRHieJKZmTJxnJYxKMoQlYpN\\nHMc0Gh1836ReT2g2faJIJQjCH2knBqSHi+PHy8zPD2HbV/LUU3UcZ5z5+ZhqtcPKisTZs1mmpwdY\\nXd3N8nKBxcV+ZmcHOHNmnOnpEnNzJk891WZx8QZqtQOcOmUyN2dw+nSeo0clTpzwaLfzrKxIzM42\\nWVxMqFZLLC2p2HaGMMzQbgvUagYrKwLLyyGdjoHnidh2B0miK8kKYeghCBGdTkIc57HttF5Q1ws4\\nTvSKk0lt44cDoigiijFhmGYaXCzMzbXJZneztNSb80dRFPJ5BcPgvApMF4pWq0WjkUPX38ziYu/3\\n5MEHH6TdfjWy/Ga++tUHerY9fHgJVT3A3FxMo9HYsl3KleOTJBnq9d6pvR//+D8CP00c383nPve5\\nnm0//3kbVf04R45cwb//+79v2e7IkSM8/fQ48JN8/vMP9+zz9OllkmQHq6uZLb7TKPB/AW/h0Ud7\\nl+lsYxuvJLYzMraxjRSOE6HrfRtsxSAICEOFRiMmDBUcJ9lif066XHAhQRAQBDJhqKyXfy4uVrCs\\nYZrNAuVyecOn77//fuBngHfx3e8+ThSFtFoVNC1LvR7z2GNPcvr0NcBb+PKXDxOGErVajU4nh+f1\\nc999X+XMmRzZ7C/whS+cptPxkaQsth0Rx3G39KSNKI4xPW0hCBnSoOCzjvjPfnaG/v7f58EH5U3V\\nPatVC0nqp90W1pVAn4tGo0McF2m35U3/fz4EQZqFHUUqrvvyLExrpbpxrK4/4xdasntJMjLuu+8+\\n7r33XhYWFnjve9+7Pinb7fYF1T+HYci73/1u/vzP/5yhoSFuvvlm/vqv/5oPfOADfP3rX+dVr3oV\\ne/fu5dChQ8RxvP43wzBwHAfLsjh8+DD792+U0FnDPffcs+FviqLQ1ycCGQYHc92HGhNFaYlDHMcU\\niyZJ4qKqqeqDYRjs2BHi+xrZbEIYRuTz/ayuHuKGG0aJ4wV27cqhKFAqmeTzq5hm3NUiXuLWWyex\\n7Qr79u1G1xPGx0vMzy/T36/ieWUKBZswTF/cK64QsO1TTE31Ua1Oc801RcrlJ5icTJCkGqUSKEqb\\nTCZkaioiCI5y662DnD37KFNTRZaWjrF/f4HDh++nv7+JINTRdZGRkQ6SNMe1146SJC2uuWaYhYXj\\nXHVVH7XaAuPjWU6cOIWu+10vncP4eIY4LrN37yBxXOXmmydZXn6CffsMhocNwlCi3bbJZMA0XeJ4\\nhampfjIZn/HxApa1Si6XTr9sVqPZ7KCqAaoqEUUukqQRx/FlTfp3qZHKTGoYxjKuG3HgwABJMssV\\nV+js2JHDshoUiy1KpWU0rUMcB4Shg223yWQ6DAzkKBRcBKGF71cQRYdSSWH37jHGxmzGx3MUChLZ\\nbItcTkPXDVqtVXy/Q6FQpFBIUBSRSsXBcTpomoamSUCT/v7+rhSu0SUDTZ+XJOUIghayHFAoKOi6\\n3pVUlF5UecmP+hz4UcOFPG9BECgUDKIouqh8Gv39GpXKLIVC70wwRVGYnOwjDEP0C9ErvQCMjo5y\\n660qTz/9ed70pl092+7ZswdB+BfabZ/9+1/Xs+2+fUN873tPMjIinZOt+HwYhsF11w2zsFDj4MHd\\nPft85zvfyD33fB4Q+Jmf+ZmebXfudDh9+r9TKp3kx3/8x7dsNzExQS73dVqtZX7hF3pff3jY5NCh\\nJygWNUzzxk1aLAF/A8zxmte8pmdf29jGK4lUrvGVHsU2tvHKYC0YLIoimibiOBaZjHiOrZjuxR66\\nnpAkPrK8ufzn8HAJWbZQFA3DMGg2U+EFRUlLGsfHBzly5CSGkTAwcHD92mGYBk1/6qd+imPHvgLI\\nvP3t/4nJySKiGJMkKrbtsG/fPrLZT2JZp7nzzikkKcQw8ojiPFEEt956E6Oj3+b06b/j539+AE2T\\naLdbGMaz483lZObnpykUBMBDVc8tUbnxRpPvfOcjXHWVw549ezZ8x2JRZ2GhjGFIm5Z0GoZKvd5A\\nkhJU9YWrosiyjCBYRFGwYWyXCqIooijgOD6aliAIAYrywgJEl8SRMTY2xk033cQXv/hFbrrppnVH\\nRj6f58Mf/vB5P/+P//iPPPbYY/z2b/82kGZxvO51r+O1r30tO3fu5P3vfz+yLPOe97yH1772tZRK\\nJT7zmc8A8Pu///u88Y1vRNf1C+LjeD6ea8DKskwmkzoRXNenVnNIEh9JEonjiHrdQRQhm1UwTY2h\\noTyu6+I4eU6dmqdUKlKthmiaSJJ45HIZ8nmJVsvi0UencV2DnTttNG2YKFpFEEo4TplqNaa/X+T6\\n66eQpAEee+wEjUaV4eE8nuchiiEzMzPkciJRVGVkRCeOLRQlZnp6mSAIcV2bKFKxrIDV1UVWVxuE\\noYWieOzcadLpwAMPnEBVQ9761gMYRo7HHpul06kiy7Po+h5OnjyFbfejaTUkaYB8HlotjSSRaTRq\\neJ7K1JRGoWCwb99Odu+OKBYNarW0NOaZZ5YQBLj77msRRYnFxVWWluaZnMxjmgaSFDM3t4phKOza\\nlda0tVoucZzQbNqIokI2K//QM+5uhTSF3kcUfTIZEMX09zjOMDe3zNzcEo1Gk3a7QqMRIQgt+voy\\nVCpQqSxTLh+h3c6QJG0yGRHDUBkbm+LgwSJXXjlAJqPTagnEcYTvt7Ftmfn5ZRoNiyRxGBnpY37+\\nBPffX6HZrHHttaMMDAwyNlag0RCpVBQEYQnbDmk22wwPl5icHGZoqEh/f9QlMwqRZR9d35h/nxLS\\ngqZtrjXe6di4boKmXZiO+DZ+sGFZNqurHQxDYWCguKXjK0kSarUWrhtRKhmbppm+GIyM9CMIq5vW\\n1z4XcRxTq7UJgoTBwYsnY72ycpKFhWWWlq7u2a7ZbPLoow/geRonT47yxje+ccu2fX0FhoaqDA+b\\nPR1EURRx8uQMi4suo6MaY2PDW7adnZ0FGkBzXS1sKxw4cAUzM3WmprbuD9Jymbe+9TaqVY+DBw/0\\nbPv000/zuc89Qz7vcdNNVzAyshmnxwxQRxQvQt3PNrZxibCdkbGNH1UkSUKrZROGkM3KXXvX3XCI\\nlWWZvj6JYjHd57fax1RVpVCIkGWpayN0ukoh2rrzQ5YjRFEgiiIaDYtqtc7ZszUKBb27l80BCrOz\\nDWZny9RqLoODGSYnxxgb62N8XGdhoU2h0Idl2SSJCgQIgoAsG9Tri9i2TBTlsCyHet0nDFOFRYDv\\nfvcRnnyyxm239XPjjfs2BExGRwfJZo8xOjq06Xf0/YBms40gpPdoTQ1s7YwkyzKO08QwtC3v0xrn\\n4uUUIMzns5hmvC61+kLHdkkcGddffz3XX38973rXu15UxOwd73gH73jHO8752+23377u2FjDu9/9\\nbt797nef87e77rqLu+6664UPegukE1TG81xUNUu12qGvb4Bms4FhyDiOx/KyhSgqBEEbWTb48pe/\\nQxzvYWnp37jzzp/n5Mmj3HqryfLyAgsLUC7XmJ83yOWu4J/+6YvcfvvNfPObX+bnfu5X+epXv8fu\\n3Xdy5MgzJEmTdrvGE0/ESJLJ448f5ZZb3sgDD3yJ6677aT7zmY9x9dX/mW996+scPHgHi4tH6HQG\\nsayQZnOJq646yJe+9Hl+8iffxr/+61/xEz/xE9x//6c5ePBN/Md/fJ8guBWwePTRWfbuvZlTpwL2\\n7r2V73znGd72thu5994HuPHGN/H973+WvXuv5PTpZSqVOrqeY36+wvj4TZTLT/HmN9/Ek08+xNVX\\n38bDDz9OsbiTY8eOYdvjiGLCQw8dZXx8J888U6avbx8zM0/y2tfeztmzp8jlJoEGBw4Y3YOLSpLE\\n+L6LaWbwfY/MD6b88UuG47jMzVmcPVui3e7gOLMcPDhJubyK63rMzAScPBmyuppjeRlkOQeUkeVh\\nLKuA6z4NDAMJ0AayWJZBu+1w8mQHTWuSz48wPBzieT6yLLCwoLO0JLK6KhNFx5if95iby+K6KseP\\ntwmCETRNx/drDA7u4ejROdptkXo9ZN8+t9ungSiKeF6M68qEoU8m457jjEhlqmJAAPxNnVWeF6Oq\\nWXzfIkmSbcLQH3LMzVWwrDzQpFDIbkkiGQQBtVqALOeo1drndWTEcbpBnm/+HDu2SBgOsLq6wg03\\n6FtmZXieR6MBkpShXu8wMtLbkXEh13/mmWd4/HEV+HX+7d8+1bO/j3zkIzQadwN5PvrRz/Jrv/Zr\\nW7Z95JET3bKzGQ4caFPcgtCjXq/zxBMWirKX++8/wg03XLdln5/85BeADwIt/vIv/5IPfOADW7b9\\n9reXyeXey/Hjn+app57i+uuv37SdbduI4jhTU+OcPTvNjh07tuzza187jiS9jkrlLIcOHdrEkTEM\\n/Apwmvvv/+st+9nGNl5pbDsytvGjiiiKCEMRSVLxPJdKxUKWS6yu1igUcufslxeyf9u2i+fJuG6I\\nILhAliQRsW2XTCbD8eNzVCqDOE6D8fFpRkZ2cvp0mTNnIJOp8tGPfhT4YyDHI4+8j+lpmyQZxLYX\\n6esb4hvf+BaPP15CFPfzmc88wOTk9ahqgzNnEvL5HA899CVOnpxAku7ms5/9BP/tv/nkckM4zmqX\\ndNvi619fIp9/M/fe+3ne8haXfF4954x8773H0bR38cAD/8Tc3ByTk5PnfMczZ1aw7UHq9Qr9/VUE\\nIYcgpEFOVVWZnV2iXDYJQ5tSqcbAwLk8GqnySur8WHPwPP//SaIgyzK+H7ysCnJrzosXY+dfEpfM\\nz/3czwFw4403ct11153zc+BA72jL5YpsViGKLPr7DZLEoa9PQ1FCRNFDkjIEQYLjJIhilnLZJo51\\nfD+k0ykjij6SJJEkIUtLZRwHcrk54vhJrrwyjyBUGRnRqFaPMzKSsLR0DFle7TpHIqKoRhA00DSP\\nxcWz6LpPp7OIqsasrJwlDG0kycU0VRxnAUVpsHt3gCwf5sYbi9j2E1x3XYalpfvp7/fpdGYYH8+Q\\nzR4nlzvN2NggpZLONdcIBMH9vOENE1Srj3PbbSU6nYeYnJTR9TZDQ6k6iap6DA/LKMoK4+M5arV5\\nBgYMwrBJNisiCBGjo4OYZg1dr6IoJq4roqoecVyhWNRotRxc16HV6uC6HoIgoCgKihKiaTG5nEyS\\nOBhGephZ4yFxHIdarUOzaf3Qcy7kciYDA6BpcwhCi+HhIkFQxjRdxsbyFAoihlEjn2+QyUyTybQp\\nFmNM8xia9gyFQgicJI2e1oAlfP8krrvK8nKTRqOBJKU/k5M6IyMxQ0N1stkqptkhjkOuvnocw1ig\\nr+8ssuzRbs8hSTV27uwjSWoMDGiYZoJhuIiiRxD4XWZokWxWJkksTFPh+WtTulglpOo8m3//tXfO\\nMORtJ8aPAFRVQRBSBZ1ezzuVMBNIEotstrcccBAE1OsO9bp1XvJnRRGJY5/zUfPIsoyqRoBDNtvb\\nieH7PvW6Q6Nh9ZQfTQ2GDuAAvWVKb7rpJuAx4DGuuaZ3poOuC5TLZwjDTk+jxDAMkqTG/PzTFIu9\\nzYL9+3cDDwGPc911Wzs8AK66KkMQfIVSqdXTOVEoFNi50yeKjnL11RM9+7zrrn0kyf1MTMxx7bXX\\nbtJiBSgDlZ79bGMbrzS2HRnb+GFBHMeEYXjB7WVZRtOSdTvfNBWiqE02q7xoey/dYxOy2SyG4ZLJ\\n2OTzaYnF2Fgful4nk2kxNDSBIESoaoxpypim0s2sPAw8A4Cq+jjOAsUidDoNSqUdhOEijnOWHTvy\\nZDIRuq7TbJZZXV1g374pVHUJ37+f0VGB4WGTICjT3692743MyIhPtfptduzIIggqYXiuTbJ/f4F2\\n+3tMTIiUSqUN3y+fV6nV5pAkF8MwWOMFWUOShMzPL9FoVDYNxKTXU0gSeVN7SFEUNC0GXDKZ3rbV\\n5QQhuQSnwcXFRcbGxpiZmdn0/7t27brYl3xBWEtfuRhIkoR6vUUUpWQunhdx+vRZVlZcBgdlxsZ2\\no2kwOJilXq9z9KhHGIbs2KExPDxKtTpPp+NRKhUwjD7m5hYAHddt09+fQ5JEqtUqQRCTJDLZbBHb\\nrmCaBU6dOkMQFDGMgD17CsiyyPJympYzOZnBMAx0XcH3febm6iRJkaWlafr7xwnDDrouous6fX0K\\noigzMjKAKIosLtYIQ5V2exXDMJGkhCCwEYQE11UAAVUNABFV1chmi4ShhWmmE7/ZtMhkVGRZxPd9\\nWq2EOBbI50FRJGw7IooytFplVDWPosTs3DnQsy59eXmVZlPE85pMTEyQJBH5vLSpcZ4y4IZkMtJL\\nVspIJW1TnpJcTrvkHsrnz03LspmeXmV+fgXft5icTFVHkiTk0KHT2DYkiUSttkCtZjMxMc7kZIEj\\nR04iin0cP/40liVQLrfo799DsRiTJCLF4iCK0uHVr96HpmUZHS0wOlqk0ehQrzuUy3WmpnbQaNTI\\n51UWFjpYloxjt6Y2AAAgAElEQVQkqQwNCVx1VarqkEanLeLYJ44l+vpGCAKLUimLIKQpfFEUIcsy\\nnY5zzn0MgoAkSXrKN27j8sHFXDc3g+/7NBpW16jpLdUZhmG3/rW30eM4Lo4jkSQxuZzQc675vk+n\\n08E0zfPOybXrn69dunaoRFFAoSBvuX6022327fvPtFolJiddjh79/JZ9zs3N8Vu/9Wk8z+eDH3wH\\n1167dSnKyZOzrKzEKIrPDTfs2nK8juNw//2nCMMs/f0+t9121ZZ93nffN/jDP/xXBCHmf/7Pd3Hr\\nrbdu2fbLX/4GTzzRYscOkXe/+80XTVKuVquhquo5zPNr81MQssBtgMXv/u4bLphgfBvbuFTYau18\\nxzvgrW+Fd77zFRjUNrbRxXPn54uxeVPSfoc4FslmhZ5S3736CMNwSw6M8yFJEoIg6JaQbNxn4jhm\\naalCHEdkszkMI83+Pn16mWxW5bvf/Tq/9Ev/Csj89m9fzX/9r/83QSBRLIpEUUKl4nDvvQ+j6wZ3\\n3LGT/fuvwLIsZmddBEGmr8/nU5/6OrOzPnfdNc473/nm9WunqisyZ88uEAQOpplhfHxogzrHo48+\\nxaFDK0xM5Ljrrts23Id6vY1lxUDEyEh+/ZmtPaNyucbcnEsUBVx77WDX2XHuPeh0HAQhVYi8nMpL\\ntsKF2J2XxJHxXCwvL/Pwww8jiiK33HLLFvWsLy9eqEG+ltZ+oentjuPiuiGqKuB5MbquoOsZfN/n\\n9OkFkiRhZKSEJClks1o3jcfHsnwgpNFwyWQkDMNYj1YnSZp+67oRhYKGICgEgc38fIuBAZ0dO8YJ\\nw5Dl5RpxDGNjpfWXOUkSLMuiUumgaancl67L6LqOIEAuZ5zzvVzXw3GCrhJFKouUzerrL8FzJY3W\\nvms2q25qJD9fzictOfA37b8XpqeXSZIirltlYCCLpink88amz6Na7aCqJp7Xob8/+5Ki+UEQ0GpF\\nSJKCJHmXnKvh+XMzjmPabQdRFFAUEccJyWRkZFmiWvWwLI8g6DAy0odt+4RhzNBQHs/zqVYtfL9D\\nsxnSaFSp110yGQVRVLpEgUOMjY1g2wmmaaLrMYoiYVk+ohgTx+K6dFS93mRurkKSwMTEAP39G1PU\\n1+awrisbSkVe7vu4jYuPS+3IuBSIoohOJ9WON039vGvBxS5hCsOQTsdDlgWy2a2vnyQJf/mXn+Lh\\nh6v89E9P8Qu/8LNb9lkul/na1+YAidtvLzI1tWvLtrZts7LSIptVGRraGOF5LqanF6jXHaamBikU\\nClu2a7VafOlLDyJJ8LM/+596coR0Oh0qlQ6FQoZS6dLyVazNz/e//3f5m785i2HUuO++P+HGGzcj\\nBN3GNl4+bLV2/pf/AnfeCb/0Sy//mLaxjTU8d36GYUijESDLKqLokc+f31ZLP+MjigqKElwy++5S\\nlhhXKhXe976/Jook/sf/+HkmJibx/QjT1Lp8Hg7Ly2VAZteuQUzTJI5jlpdrhGHM2FiJ48dnqFRs\\nrr127JyyjjU7JAh8RFHGNLVNz0tLS1WCQEMUPcbG+jY4GsIwpN12tzwzeZ7H4mIDRREZHS39UCg/\\nXojdeUk4Mtbwt3/7t/zRH/0Rd955JwC//uu/zh/+4R/21J2/HLBGQhNFCRCRJBKiGBHH0nqUv9Fo\\n8Oij06iqwE037UFVnz3IZzIaqqoQBCFh6K6nD0mSRF9fyhyv6xpRFCFJElEUrX9Wlg0ymWcj1pL0\\nLDttPm+s11s7joOuFxgfV9F1pftZmbGx9OVZi4Y7jofnxWSzCrt3p06kKIoQxWfTt58fXRRFAUkS\\nu4de8ZyXKYpSbeY4jkmSBF3P0Iu4XxCeJWpst218P8Y0Ffr6NmfU3YqIZnS0SK3WYWioQDbb2zmh\\n6xKO09nAfvx8XMiiKMsysuwThuF508gvFXI5Hdt2abU8cjkNWZao19ssLjbIZhVGR4uEYYIsy1Qq\\ni5w4sYBpSoyMDDEyMsLgYIDr9lEqFfA8jxMnFhgeFhkYGKRYNNF1H9+3CUOJViv1FptmljAMEcVU\\nraevr0A+byIIQk+ypc084Wvz/JW+j9v40YMkSRQKvbM71uA4Lq2WSzarYppbG2JxHDM/v0oQxIyO\\n5jdEPZ4LWZbJ5YTz1vgKgsCVV+6m3VbOm7GYy+VQlBq+H1Mq7ezZNlXV2pr467kolQpkMjqZ85AS\\nZbNZDhzYSSajnjdap2ka/f2pQ/R8WJMyf6nG1xvecAf/8i/HGB4eZWpq6iX1tY1tXEpsq5Zs43KD\\nJEkoikcYPlvafT7IsoxpRgRB8JIkyT3P29Ix7roerZaLrsvkchv39CAIaLc9JEnANDO0Wg4A+XzK\\ndxWGIaurLSRJpL8/v74nRlG0bteaZkoTkM0aZLM62eyzZ5KBgQKKImDbLrqur6ueDAykmRGyLHPN\\nNVOb7mHP2iG9bZFiMUur5WIYm2dLpMSnW6uRaJrG7t29y01/GHFJHRkf+tCHeOKJJ+jv7wegWq3y\\nqle96rJxZKSawwGQpvaqqorrul0NYhXPC0iSiEzGwLY79PX1EwQukiRw6tQcq6tZHKeFKJ5maGiU\\n4WG1KwHrEwQi1WoFVR2g07EwjAy1Wp3HHy/jeR62XcEwSuzZk2VsbCeNRpUw1Gm1lonjPI5TQVUL\\nKIrE/v3D6LpOtdoiCBKWlpYIghyt1jx9fRNoWsTVV6cvf7PpEccxcRwiimlZSS7Xh21bZDIatu2w\\numqjKDA8nEoInjlTIQgSdu8uks1mabd9JElnZaWOrhtIUkKhYBAEAXGskiQJ5XIDTTPIZqUtVUXW\\n7q2iKERRhO+DomSxbWvTxSqKIppNlyRhAwlOJpNhbOzC0tUMQ0fXk3McNa2WiyBAoZBmcbTbNkGQ\\nkMttnkmyhlTqMfuKkE0GQUCz6WHbFpblIstF5uZmGBoaoNXyMIwBZmdPc/z4PLqu4zguTz65RCZT\\nACyuuMLBMBJOnVrF89LUN8vyaTZVJicLaJqBZVl0Oh6djke7bVEsjjI0ZCMIMa1WgGlmKBZ1slkV\\ny4pJkpDBwQKSJOF5HkEQoOvPbhStlnfO80uVR0BVk03v44WWCGzj8sJzS4bOp4bRaqVZXIXC1gSa\\nlxKWZQHpAbwXyuUWSZLDsto9sydc16XRCBFFlVqt09OR4boelhUhigmFgr7lvep0Onz0o1+jWp3g\\n8OF/4B/+YessgpMnT/KFLzxJEAjs2mVyxx1bS4ymZXbx+hq+1XcKgoCjRxcJAomRkQ5XXrk1n8X3\\nvvcffOxjZ5CkiPe972DPjId220MUddptl1Jp63c8jWzVCUMYGHhpSjRvf/vv4Xm3Mj29zIc//OFN\\npda3sY3LAdscGdu43PBibd5MRutJzO95Hr7v4/spZ+DziacPHz7J2bMdJiYMDhzYt+HzKyt1arWU\\nQ2/v3jST/bnlKK2WxenTi2iayp49o3heGjzw/QBdl2g0WszOOghCjK7LmKZ5zv78iU98gs99rkIU\\nKQwOfpw/+7M/6mYS+11CzYBvf/sUYWiwf3+T8fExGg2HmZkymqazb18foBBFAoYhblpeE0URQRCi\\nqsqmtkAUxUiSShz/YGW+vtK4pI6MgYGBc2pYTdPcwKL6SqLddghDhbNnz+J5eVZXTxAEOkkSEkUt\\ndH0Ay1qhr28ntj3D+LhCkjRQlClcN2RlpUqStEmSvQhChtXVNoWChuO4FAologiSJEKShG4WhY1l\\niVSrDZrNhJGRfk6cmGfHjn00Gj6Dg6OcPHmasbFdLC9X1ktIXNdFURTqdZ8k0VhcbLFr1xTHjh3p\\nvsBt9u5dI26RSZKIIIjIZjOEoUsQWGSzqVOg0bAIgiyua9PX53fr0gVEMUOt1sY0TRRFIAj8rmdR\\nI4q89cOmKNoEQYgsZxBFlSDwN128Um6MCEgoFFJnRrppPzuW5yOKIpJEBgTCMHpJfBTPXYB9PwS0\\n9UVPFEWCQESWNRzHuSCOhlfikB2GEe22R7kcY9stJClAFDU8DxRFoFyeYWUlpNnUCMMVfN9FkorM\\nzs5RLBqsrDiIosPKioDvy6ysNIkimSBQcN1FhoZMRkf7mZ7u4PsqjUYHSYpJkhqKUsL3s9TrVcCg\\n02kCWcJQRFU7mGYqQxWGMvl8k+Hhvm6WTkrMufb8fD9GUQyCwNmwMaaOK48kkdB19yXzmWzj5UGS\\nJDSbTpfd2umZ8ZA6qtRzDIqXE81mi+XliCSJ2LFDOE/2hIjnBcjy+d/1Wq2F7wvk87me7YIgQhAU\\noihcz4TbDK7rcvToKmF4Pa3Wwz37fOqppzhypB/Q+drXHujpyPD9CEnKrK/hWzmSgiBgZcUiivoR\\nhHpPR8aZM4sIwgSe51KtVnuOVVFSniRZ7r2GpipGaUZiypPy4tcCz2sA1wFZ7rvvvm1HxjYuW2w7\\nMrZxueJi2rxxHDM3V6de91lYqDE8PMi+ffE6oWWSJDz++DyyfA2rq0fYv3/Phr1qbm6ZcjmHLDfY\\nsyfNLk8z59NzyVNPHeOppzRkeZnBQQPblkkSYZ3sM81ml9Yz/+Dc/blSqdBuZxFFjSNHngaeS46Z\\n0OnU8bwMqlqg0agxNARRBI6jk8kUaLUsstk+JEnD990NmerP2k0qjmNvmlmxZi+HoUMcxz8QHBaX\\nAy6pI2Nqaorbb7+dt73tbQB88Ytf5MCBA/zFX/wFgiDw/ve//1Je/oKxplu7liokSTKqqlMo5EiS\\nFn19fURRhb6+Ap2Og2XZFIsFXvOaQUQxolQSkeV0gvp+gKYJJInD4KDB4uIyAwNmNx3JYMcOj6Gh\\nAWZmFgjDGSYm8qysLDA+nsV1a+zd28+hQ4911SAsNE1GUUZwHA9IU/OvuKJIp3OGq68exPcdDEPp\\nGuASqrrmyYuwrDp9fQZRBLKcLgqGoVCpVMhkBBqNlBxHli3C0EYQdFZWakhS3JWZNeh0aui6jOsK\\niKKw/vJZlkMYeuh6mlkRhiGO46MoIkkCvu9RqVhAgmn2oygKuZxBkiTdOi8bRREJgpSXYa2cRtNS\\nPg1N09f7XfNgvthorqrKuK6LJKWGcqqQ4hEENrnchRNNro1F09SXZYHRNBWosrq6DKTKIGEos7BQ\\nZ/fuYYaHiySJw7FjZwmCDoLgUSwGTE2NIQg6QRCRy+Uolw+ztFRjaKgfRYEoWuXqq69kYCAHeMRx\\ni2KxH9PMUCj47NgxTLudllepath1oEi021VUNdsl04M4ThAEkVR9hK7qjN3Vtk6fn2mq2LZDNrtR\\neSSte1srVemtKLGNywtJAoIgnjdysOb8BFDVi+eocl2PIIgwDK3nuhBFMbbtIooJSdL7+kNDBSzL\\nwzAKPY04URQplcyuU6/3+mEYGrbtdTluequG6Hqd6elvsmtX73chlWT7V+JY5core/M/yDKcPn2G\\n/n4TSRrdsp2qqoyOZrHtkJGR3lwar3vdrTzwwOcwzZgbb/zVnm1VVcKy2hhG72wYRVGQZQ/XdRga\\neqlcGhLwbaDCL//yL7/EvraxjUuHbUfGNi5XJEmC5/mIYm+S7AvtSxDE7o8EiOeobQiCwOholqWl\\nZYaHN8/cNM0svq8jy8Fz+k3tkCQBUZS6mSQpiXyxmCO1L9O9vFQq4vs1ZDmzHmDPZBQcp42iSLzp\\nTW/iE5/4X8SxxE/91JsIgoAoipHlEN/3EQSNIDhOtRpyxx1vIJuVAZXJyRhZ7jAyMsK3vvUgJ09W\\neNvbbts0wFOv11letpmYyG3qyEgSj0OHppmcLNLfP7nh/+dDHMc4TmpvvBjC1V5YO+NpmnzRCfp9\\n38fzQnR98/L08+GSOzKmpqbWjcK3ve1tCIJAp9O5lJe9YORyOr4fcO21O2k0muzbdyWdTmp0Z7Nj\\nuK6Ppl2J47hcc801uG5AoVAkDGVKpT50PTXATNMkiiLK5TZBEGOaGoVClqefXsF1Bzh5coWrriog\\nijpjYyFhqGOaeZJExPc7SNIArtti164hHn+8TjZ7Fa1WhV27hpBlmXY7zYZIeQYUoMDISD/l8gK2\\nLSBJEZ4n4/sJhYLSjX4KZDIilUqLYrFEu+3Q12ciSQpDQyVqtVWmpy2SJKZU0lBVnXrdQdc1jh07\\nwsTENSwunuHgwatptRpEEYhigiyn2sLPj5632y6CoFMul4ljhXa7iSDoSJJ8jiRTWtaRphufPTuL\\n68oYRsLevWNIknRObXpawtLEdSWyWZvh4d4G9laQZZlS6dxFI5+/sLr5NcRx3NVfVvH93lHoi4VU\\nwlTFdWWCQMR1m2QyRQQhYHbWZmSkn/FxCd8vcviwRBhK5PMSAwMSvq+TJDGdTodMZhzTHKPZbLFn\\nT46REQ3PS4hjG0UZZu9eE9MM0bQRBEFF16G/X8W2XYJgiEqljmnqyLKOaRrIcnqAGx838f0AwzDX\\n33FFkfA8HcsCWQ7O4Y55PlL+gJgoinsSBm7j8kKaeprB98N1h+NWEEWxZ03n83EhUYgoinjmmRna\\nbdizJ8eOHWNbtlUUGXARBOG8jlBFUSgWz58FJooikhQTxxGa1nsdSJnUY2S5N1m04zjE8RDDwzfR\\nbD7Ss89iscitt15LGIoMDg72bHv2bBnfH2ZhoUap5KBvQWgkyzLXXTeJ6/qb1h+fO9aA173urQhC\\ngGVZ66Wjm2FmZoVOR6dSKbN//+SWzzZJ1giHU4P0pUAUDeJ4HJA4derUS+prG9u4lFDVlHx9G9u4\\n3OC6HrYt4DgWmmZTKBgv6AD73L1ckiTGxnKUSj6jo6na4POz81/96mup19v09W2e5XjNNTupVGqY\\n5uhzeAN1PC9AVTPcdtu1lEqnyeUGGR8f7wbUeA7/oMzExNA5ffp+iCybRFFIGIbs3r2TOBaQ5Uy3\\npERFln1aLZ/V1YAzZ3JMTk5x5MhZXv/6m8lkNPr7U97DpaUlvvWtBrp+A1/4wkO8733nOiKiKOLU\\nqVXieJRTp5bYuXOj3fLMMwvE8QTHjy8yMjL8gh0GjuPhOCJJEq2f1S4W2m0XyOD7Hn19L05ZZjOk\\nggYBkpRZP6e+UFxSR8blntKZyvRIyLJMoZBHluV1ojNN09YzNEwz6h7IfBzHo932yGQECoW+dTLE\\nMAyxbRfPE1grxbbtDvPzDrJcQxQnCQIP35eJIgFVjZFlHUFwCAIHXU8nhSBE1Os1FMVCEBIkKY14\\nRlFAtdoijjOYZkA+30e77SJJgzhOlSjyUVVpncRTEOKu4RxSrzcwzZQXwnEsOh2BVqtNo6ERxwFD\\nQ0WyWb1rTPtks6mDxTAkoihAUdL+0jqxrSZvTL3e6Dqpil2C0Wh9TM+FLAsEQYBtu6jqBPV6mXK5\\nia5rFArGevskSbDtEFE0sO3WRX/+LwTpASQhSWJezioTTVMpFDLYdoSqysSxR5L4+H5ItdoglwOQ\\nEUWIIgtJ6kNVS/h+Ov8UJYvnLeK6MZomEscunqdRLOZotTpUqwK6rjA+Ptyt96sgij579oyhKAKK\\nopHNZlAUGcvqEIY+spwuNLqubzgYWZZDvR6haWyogdwM29KrP5hISXAv7vaR8jlEyDJbKhJBWoax\\nuhqRyQyzvFzu6ciIohjTLAHRORGgreD7/nnnZBzH3dI6iTDs3afj+IiiThAEhGG4pWFhmiaC0MSy\\nypRKvR39iqLgeRauG2OavZ+BpsnU6+3uftO77Wbv82bIZlWiqIEgxKhqb2Ix1w0BBd+Pzuuk6nRc\\nwlBC15OXVGYWxzaQAyq4rvui+9nGNi41VBUc55UexTa2sTnCMOwq0fXRbvv092+9N67xZqmqSqdj\\n026n5PRrwUld19E0jULhWUfF2hlLFEUURVnPzN5sHHEck8/n0bRn9zFJkjCM1PEtyzJXXbV3PWCh\\naep5eT5EUeiWnCSsrKxQqylAhkplBUGAOI4QRQFNE0kSH+jQbNbOIa9e+w65XJrBv7p6iomJjXut\\nLMvouozjpCqPm0GSIsrlFUzTeVGOgjiOqNc7iGJMsVjCcVziOEHXL4zwuxcURcR1AyTp4vIFpkEm\\nCEMfTXtxY7ykjoxyucyHPvQhjhw5gtNdrQVB4Jvf/OalvOwFo1yu0WzG1GpzNJsanldDFHVUVWVs\\nTEfXTarVGq6rYJo+pjnM6uoi7bZMPi9gmiphGPD975+g0wno68uh6yWiqIFl+SiKzORkjiBImJk5\\njSgmxHFfN+0pQFUFVFWjWl2hv3+QIAiYmBgljmuY5gR9falEjyRJOI5DNltA03KIYpNcTqBY1Fla\\nqpPPQy6XOgymp1N513zewHECNE1HkjLU66vouoJlOaiqSS5n0m63MQwJ05TRtJhCQaHZrHHLLXuJ\\noghdn8K2PfL5DJ7nI8vShqjmWqmI74ckidCdlBGFQoZsVkHT1A3R9lzOIAxDrrxyjOXlNqoqomk5\\n4jjt61n1FJH+/pTE0jQvfQZEL6RRaL1LcPjycTloWoadOxUWF1c5dWqV1dWTXHHFAFNTB/G8kHK5\\nzfR0HUnyGB0N0HWL+XmbZtNGVSMGBiYplUKq1SojIxOMjMgcPrzAzMxZdu/WGRu7hvn5RfJ5Ed93\\nqFRS4ttWy2LPnl0MDsL4eB7f94miHEEQE0Vp6vvzF8l001AYGDAJAmvLCHgURdi2t15S9ELQ6aRq\\nQmuyxdv44YHrhiSJgu8HPfkcVFWl3V7i1Kl57rhjaycGgGkaeF4HSZLOm/Vz+vRZ5ucdhoZUrr76\\nip5tFxYW8DyRUqm3nLimybTbDrIsIMtbX9/zPLJZidXVk+RyvVNCy+UyDz98ijhWOXNmkNdsTZHB\\n4GCRRmOBUsk87/ti2w6el8rN9YrkZDI6Tz31PSRJ5C1v2dOzz507B2g2XXK5/p7XT5LUeSGKMpJ0\\nfodTLxSLGo3GUWCZoaFrX1Jf29jGpYSmQbP5So9iG9vYCEkSiWMHSQqIY59Mpjep96FDs3geTE5m\\nqddtPM+kXi9z9dU7EQSBZtPCcUKCICCb1cnlVCzLJ4ogm5U5duwstZpMPl/mppuuXD8s27bN8eMr\\n1GotRkYGkeWYgYE8hYJxzp6ytLTE4cM1dD3m4MErqFQskgTGx4uoaurUsCxnvewiLTGX6XTKGIZK\\nGIbMzDwNSLjuzeRy6jqZvWFkyOU0stmDhGHEnj3jQOpgWVioEkUJo6MF3v72G1lebnDLLTefc3/W\\nSj4OHNiB57kUi5vvm1NTk+RybQqF4gbHw4U4JURRoljMEcdRN1gsIIoSoui/5FKTbFZH00IkSbvo\\njoxCwVgnbX0xuKSF/u9617u46qqrOHPmDPfccw+7du3i5ptvPv8HXya02z66XmJx0UUQhqhWZVw3\\nS6ejcuZMg4UFn1On6sAQR4+uouslZmbKWJbB9HSTZjPgzJklzpzRqdWGWVgoo6oGKysuUKJWs6jV\\nGszMzLKwUODIEY+lpWVqtRbz8w5LSyLPPLOCqu7k0KFZ5ufbTE/PsbLiMj9fpt2OaLeD9bTbUkkC\\nWgwPF1DVlI+j3Y5YXm5x8uQKDz98mGPHfI4d83nyyTnqdYWFhWUsy6HValOruVSrLVoth1qtieum\\nKT2Vik257HDqVB3X7WdhoUaxWOxG0wxmZ1c5dKjC4cMr6xGuVKrQolZr02pFVKstkkRDkhT6+7MU\\nCgaGUewS14Tn3Pd0AVHo6yuwb98I+/btQBR9FGVjxLJYzDE8nF8n7HklsSaF+3IR8ERR+vxrNZHD\\nh20eeKDFiRPjPPpog3q9hij6NBodLCvDqVNV5uZUTp/2mZ52OXtW4tixNo8+eoLHHvOZndWZmYk4\\nfHgR3x8iSXZSrSa02y5xrNNqZTh0aI5Tp05x7NhZyuWYdttGlnV8P8SyXCqVFp4Htu0TBAGWleB5\\nUpe/JX2uhiEDqfNrq8Xu/2fvzaMjOeu730/tVb2rtc6m8dgz9ngZGxtjY8PYDjYETsgCLyTX3MA9\\nEODekASMCYEEeEN8EpJAMG9OEmJOYgiEJTiA3zeAk1xs1jfxAngDwg128DKLpNHWe+1V94/qbkkj\\nqdWakdStmedzjo6k7lqeqnrqWX7P7/f7Nhouvq9Rq4XL6kYngiDAcSSiyMC2RWDxmUfI/HwF1+28\\nGuG6LrncHi655CocZ2355JGRPIODuTVDS55+uoxpnsvRo07Hepl4ruXR9VGmpzt7T+i6TrGYJp/v\\nLBkdhiHHjkEU/QxPPdXZ++z+++9nZmaIUmmMr33t2x23ffbZaer1AkePNvA6BOMnUt0xspyiXu/8\\nbt1552f4538ucc89s2suSqTTFrmcRTrdeRClqiquW2VmZmrNZKtBEHD8+AwzM3Mrfl8qlYArgIu4\\n6667Oh5LIOglIkeGoF/x/RDTzFIoDJHNqmSzqyfKtm0b19UxjDHm5ho0Gj6OE9Jo+O3kmkEAsqzj\\neTKg4boeYSijKCauGzA352Kao1Qq7hLvyXq9judlqNeT8PdKJSCODer1pTFZzz47Q71eYGICZmZm\\n8DwT30+10wXUanUmJhyOHq235zETE7OUSjpTUx5f+9rXgHHgAF/84pepVl1qtZipqTls2yWVSjXV\\nRnSiKGges8bERMj0tMz09CyTk1VmZmSeeOJpSqUK5XIdz/Oo121mZjyOHy/TaCwsBp6M49jU63F7\\n4b9FcgxwXQXXXb3BUBSJ6elJqtW5phJgS8Hy9A0PrXnbZsx/Wsc+VQPJps7IZmdneeMb34iu61x/\\n/fV84hOf6BtvDIDR0Qy+f4LLLtuBph3nootS7NwZMjhYY2SkgGUpjIyohOEkl146iu+f4NxzR0il\\nfEZGLFRVZmAgTyZTxjBmufDCUQYGAs45ZwDXrSDLMDo6jqpauG4dcEml0liWQRxLTe1lCc+bQVUV\\nJCnDsWMVNG2QRkNtejko7Zd6bGyI884ba2fer9d9TLPA9HSNajVFraZg22Vk2QMiPC9oaiOnkGUd\\nw0jybKTTWeJYRlFShKFCoxHg+zJh6BOGDprWctVKPqtUqrhumkpFptFoNMNoYnxfa+o2JzkzUqmQ\\nHTuKDCLjE0sAACAASURBVA6ajIzk0PVwRePEYpJ4c4WBgQy53PLB/smx7YnSRZ16/cz3x0zChCJc\\nt4Gup9H1eeL4WXbvHiSfN9i/fyejozkGBiCXM8nnc9TrdVQ1olarI8vpZuhTQBDE6HrIjh0j5PM+\\npvkMF188xv79FhdcYOF5dQYHx8hmhxkaGiWfN0inQdMiXFeh0UhWtCUpwjDUdtk8z6Fet9vPw7JM\\nisVMR+uvqsrEcYAsx+tqFJO6EhJFLrq+9TKegs1FllWGhgYwzVTHMBDLshgcjAiCY+zatXb4UhJq\\nt3YHuWdPlnr9vxgb6xw2Y1kW+XyIppUZHOysWtI6/1pYlsX4eI7BQZv9+zsre1144YVks9NY1hQX\\nX3x+x23jOGrKZnf2ckjCLGOCwMEwOr9b8/NzOM4Y1WqaubmVjQktqlUXSUpRqwUdy5BI85no+gil\\nUqPjMefmKjQaKWZnk9W65XjAHFDpqFIjEPQaYcgQ9CumqSPLLoaxdg6zTCbD8DBE0XHGxwcZGEhj\\nWSEDA8mYXpZlUikFRfHJ5yUUxSeVsjBNiGObdNrgwIEhFOUY+/cPLRnzZ7NZUqkaIyMhu3alGRoy\\niWN/2RhwdLSAJM0yMBAyODiIrjtoWqMd2hKGEaA0k4MmIftRFOJ5IY7js2PHDsAHAsbHh4ljmSCI\\nsW0Z25aafU2adHq4ucjbSlIdoqoecRwxMxPi+8nCwYkTHq4rU636uK6H40RNpb8sExMrL1bUagGW\\nNYRtS0uMHcl4OySKAhRl9TFzkqB/hCjK4vs+hYJJobDcK/5MY1N9s1shAmNjY3zlK19h586dzM/P\\nb+Yp10Umk2lnrz13kSdxHMeUStVm/PO5NBo+o6NFTNNk9+5BZmYqpFJjpNMWkpRlcDCN7wcMDw8h\\nSVIz269LOr2XUsnh0kvPRdMUJCkLJBYtWXYJQ5dDhy5pujxlqNerHDw4RqXSYHQ0Q6Ggo+u0DQFJ\\nnHXYVs0455xRJicb5POjSJLLwECRYtFoTv4NXDdE14eR5Zh8XqdSOcHgoEU2C+ecM8z0dAMwGRiw\\nUFW4/PJxoggGBpIEci1XovPP38lTT5XRNJlMJrPopYoZGDCQZZedO5NEnIqykBMjn19qwDi5/KdC\\no+E2V+R9dH1jk9n0G5IkUSxmOHhwjGIxzXOeM4jn1RgZGWLv3p2oqsr55+9kdLRAsejh+7B374VI\\nksbUVJIPRVFk9u3LUa02GB0tcODAvmbC1RKjo8MUixlMU2N6ep5SSSOVijHNQXbvTrN//zlEUUS5\\n7KKqMcWihaZBNpsYSAoFk3K5jiznm4lxu3MNsywTTQua78H6DBmFQrodUyk4s0inE0+bVErt6D2h\\nKApXXHGAIAg2tIM+cOAcxsfdNY9pGAYXXLCTMIzXDAPpFtM0ueWWG7nvvv/gla98VcdtDx8+zBvf\\nOEul4nDzzb/Ycds9e0aQ5XnS6VzH3B+SJJHLpTqG9LR49at/nh/+8BtYlswLOsW1sFR+tdM7q6oq\\nihLg+w1Sqc45SnRdJQwbqOrKeT9e8YpXcvfdPwLq3HHHHR2PJRD0EmHIEPQriqKsK6n9vn272n/H\\nsUyhIKNpUduQb1nmsgWuZAE0UUcZHR1k586RZf2PaZpcfPHe5nHj9hzr5O127RpDVQ1MUyOdTreN\\n2K1+J5NJUa3OoqpyOxfi6OggilJH03Te+ta38vjjf0S9XuUDH7iNdFoBPFRVRZLCZv6oCWq1OcbG\\nEgWwVCrFwYOJkSSVMtm5c5JSqcbYWB5dj4njAFWVMYwUQeAyNGQShgs5EU9meDjD/LxNOp1aMrdR\\nVZVCIbnuTmNs01QplxsoSoSqpk5Z6XG7samGjPe+972USiU+/OEP81u/9VtUKhU+8pGPbOYpNwRJ\\nkhgYyBEEAU89NUcc5zhxosL4uImmaezYsZClPZHoMZFlCdf1ME0Dx3FxnIChoQLFYoTnWfznf06Q\\ny2kUi1mSpJFpFEXj6NHj1GoR+/YNsnt3Bt9P8fTTU+Tzqab8a0ytZuP7ARMTc3iews6dFqOjQ4yM\\nFMnlUijKKHNzFSQpZmamQhRJ7N+fR1VVPM+nXneZna0TxyNMT0+TzfqYptaMH3eYnW2gKAGaZmFZ\\naRoNmyBIpPuCAAzD4tChTHPVLqkyqgqu65FOd469bpU/CEKOH5/FtmXSaR/TTDMwkKJQyK3r2ei6\\nguu6yHKMopzZVkagGTemsWfPAK7r02gUGBhIZGwnJ+eo1epMT8/geRk0zUJRLHbsyGMYFer1NK7r\\nMTU1Rxga1GrwzDM/wXVlxsb2Ui57eF6ZbDaLLKfYtavI/v070HUN0zSJooh63UVRItJpmZmZCoqi\\nYJoauVwG23ZpNBwajRqmqVIoDFKpJHGJ2ay15sTlVOh2dV2w/UhUbLqrF4qyPF/P6eL7PrYdEMd0\\n9ChKkhDbOE6AZW1M0lPP83jkkSkajX08/vjTvOxlq29bLpd54IFn8TyFiYkpxsdXl2mLorid02et\\nxGfT0/PU6x6jo7mOngwXXHCQ//bfJpHliPPOO6/jdbXyIa31rFRVZXy8SBAE7UHmahQKOUzTWTXh\\nbLKSlQNUJiYmOh5LIOglQrVEcCaSzaYIw7CrPrqljhLHYdNbY/k+3SxceV6AZeWBsJnLbmnf4Loe\\nvq8SBElf7/shQRAxPJxG0zQmJ5/F80YBmcnJaQ4dugTTNAjDkFqtxhNPTPHTn85gGDmKxXIzd6FP\\nFAVomo4kSRiGhu/Ps3PnIHv2FNsGF0mSGBpSGBrauyQPICzNTZXNWpRKM6TTy+dF3dzLpO8soWmn\\nJmO6XdnUZc277rqLOI45dOgQ3/zmN7n33nu5++67N/OUG07y/kSoaqdbFTd/WoPcEEVJU6t5mKbJ\\n5GQJRdnFiRMKtVpAHJv4vofruhw75qJpe3jqqVlUVWV6uopl7aRalXEcB9/38TwFx5GYnvaAAnNz\\nC3HZppnIIOp6nrk5m/l5k3o9T6lURZZlbDtC13P4fkgr3ETTss1yGLgu1GoyQZBmaqqKJFlMT9dR\\nlDTT03UkycJx4qb0a/JiBEGA7ytoWnbNXAVJIlAZx5GYmfFQ1UF++tNZNG2E6elGV0oCizEMg2LR\\nYmAgfVasytdqiUqIbUdUqzFRlKdc9pmfr1Ovy5RKOnNzCo2Gwfy8TxwrnDjhoChD1OsKR49WiONd\\nHD+uUCpZPP20TBDs5fjxBradHLte94HkGSeZo5OJhOf5BIGG5ymUyx6um6HRsKhWAxwnUeiRpBSq\\namJZ2WZ9VQlDvWMcn0DQj9RqHoqSptEI266nK+E4DpWKShQtbYtPB9d1OXrUI5W6lCeeqHbc9qGH\\nHqJSuYQguIb77vvfHbdtNDw0LUsQSKvG5UJiSCmXIxRliBMnOl/T8eOT5PPPIZs9xNTUVMdtgeaK\\n1trGx8WqYWthmuaqA7VvfetJ4Gbgedxzzz1dHU8g6AXCI0NwJiJJUtftfksRsDWHOh2Sfntlg73n\\n+YShhufJzZwecnMulqhpPfzww9RqF+F5V/Cd7zzY3k9RFGZmbKIox9RUBOSoVOo0GiFRpFMqRYSh\\nzuzsHDMzMoODz+WnP51rz5laZWkZ3k3TbM9dFuemqtVcpqZqZLPjzM8H68of16JUqqEoI9i2eVYp\\ndm2qyebxxx9nYGCg/X+xWOSRRx7ZzFNuKKqqsmdPAc/zSKWKy76P4xhN08jnk79bVjZNA9+3m5I9\\nMUNDSfZe0/SxrByqGrflBQcHdWq1Y+zcaTbVRkxOnJjHMKS2a5Ek2ahqzM6dBq5bZmQkv2R1TdMU\\nHMclldKoVhtIkksmM9xMoAK+73DRRYn8q6btJgw9cjkTzwvJ5bKUSjXi2GFwME8QOGQyGr5vk81q\\nBIGDpi21iCaroS5h6GCaaseVvsQamYQm7Nhh4Lrz7Ns3gG3PkU6fmhbx2WDAaGEYCo5jY5oKURRS\\nr1fIZnVMU6PRqKPrNmNjFoODIYahMDqax/McHKfByIhKKlVgaspF0wKyWfB9DcOoMj5usWfPEKCj\\naWozTEpaYilO8ra4KEqMZSnNGMEYXU+kimXZQZZ9DCORjdJ1A9d1m/lfFo7TmhQKTwpBP6PrMq5r\\no2md66qu62hahSDwyOU2JgdDNpvl+utH+clPvseVV3b2crjsssvI579EFBlcffUVHbctFjOUSg6G\\n0XmFRlUT5SrbnmN4uHNox8jIIJb1FLpuMDzcWd0F1vYE2WhuvvkQd9xxF+l0mV/5lfdu2XkFgvVi\\nGMKQITi7MU0DWfaQJOW0QsVTKRNN85vKV8u9FzKZFI5TQ5aTkJB63SOKQlQ12fbqq69m9+7/ieva\\nXH/9DUv2TaeTnIXnnqtSLDrs2TOCJIHjeGhaSBx75HJZRkcnmZ9/igsvHFx2/pVo5abyfZt0WiWb\\n1SmX57CsU/NaTqV0yuUKqhqh670XSNgqNtWQEccxc3NzFIuJEWBubq7jqlA/ouv6irHFQRBQqThI\\nEuTzqSWT61wuTRiGeJ7P3FwdSVIYHy9gGGo75qxWc/D9iIMH9zQlTENmZ+vIckQ+byDLcTPpWUtn\\nWeKcc3YgyzKO4zE7W8eyFNLpRC52YEClWEwxNpZkCXacgLm5OrmcQSaj4LoyQSATxyFxHCFJSayx\\nosiMjOTbSTWTGOl0M6Qh1dRYXurO35LLSbxPHGq1Oum0uqI7tizLDAwkeQ0GB9NEUdQMefE6xmwL\\nEtJpC9MMm/GKcdtdz/M80mmjGcenIEkwMNDKX1IgDMN2nXQch6NHB3n66XnS6TSapjfDclTyeR3P\\nC6nVfNLppZ1IEpcnU6/bBIFGJpNIURUK6ebER8H3k7CTbDbdDMlSl+SwSJKzJolA83nrrInZE2w/\\nMpkUlrW2O6yqquzdO9JuyzaKQ4fOJwwnueSSfR23O//88/nAB36FSqXC8553ccdtUymrnZOokzFB\\nlmV27x7q6pqCIGBqahbDUPC83R23tW2HWs3HspR20rXN5lWvehXf/e4XGB8/l+c85zlbck6B4FQQ\\nHhkCARsyFzh5Ie5kNE1jdDTfVFz0MAwJSYo5cqSEYchks1kuvXSYarXB3r1Lc3KMjQ1RLHpccMEI\\nruvSaISoKhSLFoqSaY95L754L7btd5UEvFXmXC7V3j9Jln/qMqSZTIZ9+8x155/b7mzqlb7jHe/g\\nmmuu4X3vex/vfe97ueaaa3jnO9+5mafcMlzXR5JMokhb0QVIURQcJ0DT0pTLHoaRJY51oiiR3pmd\\ndSiXE4lLXddxnBDDyFAqJds6DoShiudJ+H6iMOL7ySDbdSMMI4PjnJzVVmrLgwaBgqJYOI6PLMvM\\nzlap1yWOHy83P4+JY735kySQqVYblEo2c3MlymWnqbm88gC49ZnrxhhGhkbDX/VetbIWL86xIYwY\\n3dOaWC1213OcoFlPJCTJJAxVZmYSuadEEtdpN46KojA/HwMjPP20g22buG6Gej1ierrM1FQd0NuZ\\nmBcjyzKeFzdlWHU0LY3nJdvZdoBl5fB9aYnXxeIGNAiCdj3z/fW7ygkEW0m3hrbFbdlG0Gg0OHYs\\n5JJLXsoTT5TW3Nb301jWHiqVzmEgrutSKtlUq52VQKD7a3rmmeN43rmUSjvWzEHR6ndmZ+vrDiM8\\nVf7xHx+iUPg/OHHiAh544IEtOadAcCoIQ4ZAsHXIstyeP7luTLlso2lFHEfnhz/8IRMTe7DtQzz6\\n6I+pVOrMzdVxm0lsFuZWMYpiEQRK29AhyzJhGBLHGplMccWx9GqcPGY+3XFF4i199hgxYJMNGa97\\n3ev40pe+xMjICGNjY9x999287nWv28xTbhmGoQEOqprIyjmOuyyuOpXSCII6AwMG4KJpCwloJCnJ\\nv7GQ0VfF82oUiyZRZJPJKKhqgGlCEjbsYBiJzq5lKXhejVRq5QqvqiquW2ZmZqItUbRwHp0oskmn\\nk+Mrio+qqk0PEglFsSiVXHQ9MZR0ihWXZRnTlPG8Gum0MExsJZalEYYNcjkNVQ2asrkZGo0I31cA\\nA89LjEuapjE4KOP7U+zcmSGKSuh6CV0PMIwcsiw3Q5+SuhLHMbbt4DVHWKmUShQ5ZLMxkuQ26z6k\\n0zqeV8M0V7f+JrrTHrLsoWlnT/IhwZmN53nYttOxfVwPqVSKvXsNpqYe5MCBzpKyqqoSBHV8v4qu\\nd3bFte3Es87zOufIWA+7d48Rhv+Fpj3D2NhYx21b/c5WhpZccskw1eq3SKefZP/+/Vt2XoFgvQhD\\nhkCwtbTmWqmUwsBAmjCcI5XyOOecc5Ckn+J5/8mePTvwPAlVTWHbwUn7J3OoxfM5SBZBDAPCsEEq\\ndeaqKfYjUrxRI7FtREu+ZyPwfZ9yOQAkMhkJ01xbSSOOYyqVRlO+z9hwCVHHcThypEYc6xQKASMj\\nRWzbodEIMM0kHGUlqtUGnhehKCFhqGBZCqnUytsKNodTrZsLoU4xcdxyWVsI5YjjmGq1wdxcDdPM\\noGmQzxuUSnVKJYd8PodpxmSzKep1G8dJwpAKhbMr+7FgdTay3dzOBEFAqZTE9JpmtGp7ulkkoWJV\\nQGLHjhTp9OohG57nUa166LpMNrsxoR31eoPjx5Owxz17sh0TdHbT72wUrfr5ve89xgMPlNH1gF/+\\n5SsoFDobhgSCzWa1tvOBB+Btb4MHH1xhJ4FgixB9O0xOTvKv//pTQOa663YyNDSE50VksyunFxBs\\nDd3UTTFD2RBaN7m7Vackx0T3+szrRZblphvvgiuvZZmYZueka4sHuludoE1weqiqSrG4enKfxLCR\\n5LHwPAVJCpBlmWIxiySpgIwsJ/VFllsNx9ndsQkEq7OQu2irURSFQiENSGuoaSXusIODGzsIU9Vk\\nJauldNQJyzKxttgWvmPHKM997iiSZGMYZ75Et2D7IjwyBIL+wLIszj9/N6CQyegbZvgXbD7CkHGa\\nrKRa0mt0XWfv3hxBEJBKLbyM6zFMbKURI45jXNdDljsn61kvURThuh6qenrZkLcbvu8TBCGQ5MhY\\nPJjPZCx830eWjXY4SD5vEEURmpasrJqmgaIk2Z+FN4ZAsBRVVcnn47Zq1VbT6z7HMAxct4osSz25\\n/rX6i5GRQWz7SDN5q/AoFPQvQrVEIOgdiTCCi6LI5PN5zjvPJQhChoeHe100wToQs5QNoB8nyZ3c\\nffsNx3FpNCTiOCSf9zfsftbrDr6vEkUeAwPyWaGYEUURlYqH60Y4jteU+V1QiFkps/PJxoq1sj8L\\nBGc7vW7ze3l+x3HxPI1Einnr1acaDacZ+hZQKCxPUDo/XyWKhiiXPQoFZ1v1hYKzC+GRIRD0jsV9\\nSSoVIMtZdF3Ccdyu0gQI+oOzK7WpoC9ZiIHa2HAWSWqFyGyth0mvSa43+REIBIKNJGlX4p6FH7ZC\\n31Y7tarKxHESWnm2ZW8XbC+EIUMg6C2tuUfSV7TmIT0ulGBdCI8MQc8xTQNZTpLnbWQoQzptoetL\\nwyjOdGRZJpdLQkXiOJGL6vXqsUAgOHMwDANJ8pCk3oSWWJaJoqzeXxQKOXS9gaqawrNM0NcIQ4ZA\\n0DtSKRNNWwijVhS/r9IECLpDGDK2iCiKkCSp4wpWGIZnRfjDSmxGw3G2hki0JhjrWS1NksNy1hh8\\nBIJ+xPO2PlTjVOh1GTudP5EFN0VbJuh7dB1ct9elEAjOTk6eI6w1RxP0J8KQsQUkEng+khRTKKRW\\nHGDVag1cFzQtJpfbPEUTwZlNo2Fj2zGqGjfzY6zdKIdhSLnsEMeQy+nCg0Mg6AFHj57AtmWyWRgb\\nG+p1cbYttVqNiYkGqhqxZ8+QSFgs6FuER4ZA0B94nseRIyWiCHbtyiwRShD0N2LJYgPwfR+vQ2/k\\nugGybBBFCmEYrriN4wREkYzjhF3rOSeqHC5BEJxSuU8H3/dxXfes157uBUnWfhff95d957ohqmri\\n+6z4bFrPLQzDdt0Jw5A4VgEN23Y61mWBQLDxRFFEuewRBCrlstPr4qxJrVajVqv1uhgrMjdX48SJ\\nKhMTVdGWCfoaoVoiEPQHnucxOVnmxIkqjiNeyu2EMGScJr7vUy77VCoRjrOyj2AqZSDLLqYZr7o6\\nFEUB5XKDIFg+OV2NWs2mVoNKxW2HBmwFyQq+R62WZP0VbC2NhkOtBuWyt8wwlskYRFGDdFpZ5vkT\\nBAGVik+1GnPixDz1ukSl4qKqKroeEscNHEeiUomwbfFcBYKtxHUbHD8+SxD097tXqVQ4dszj2DGP\\nRqPR6+IsY3Z2nieeKPPss3PCkCHoazSN5qJDr0siEJzdNBoNfvrTMk88MUulUup1cQTroG8NGRMT\\nE1xxxRVYltWepH/oQx/i8OHD/Oqv/mrbC+Ezn/kML3jBC/j5n/95qtUqAF//+te59tpredGLXsSx\\nY8e2oLSd3fcVRSGfT5PJrO7qr2k6xWJ2XZI/cXx2qXEIlrKg9rKApmkMDGSwrNUlB5P9lg6estkU\\n+XwaSerbJkEgOKPJ53Ps3buDVCrT66J0QdJObKUBvVt0XWPHjhGKxYFeF0Ug6IgkLRgzBAJB74ii\\niKGhAUZHRxFZF7YXUtynsQGu62LbNq94xSu47777mJmZ4fWvfz1f/epX+eAHP8i5557LL/7iL3Lj\\njTfyzW9+ky984Qs8++yz/PZv/zYvetGL+PKXv8yPfvQjPvWpT/GXf/mXS4690gTwdPA8jziOMYxT\\n1x0OwxDP89E0teuY3iiK8DwfVd1YtY9u8H2fMIwwDF0YUzaQbupmElrioSjrVyRpPTdNU1esb626\\nrOviuQqWstHtpmApjuPQaDhkMqmeJ9Nci0qlgizLZDL9Y3Rp1c8gCDh6dJJMxmRoSOQaEfSeTm1n\\nJgMTE5DNbnGhBIImom9PmJycJAgCdu/e3euiCJp0Uzf7dvnVMAwKhQKQTNy+973vccMNNwBw0003\\ncf/99/Pkk09y6NAhZFluf2bbNpZlkU6nueqqq/jRj3606WXVdX2ZEWO1XBiroSgKlmWiqmozZ8Ha\\njUqSnd3oSTIzTdMwTUNMdreAOI6XrHxKkoRpGh2NGFEUrbha2npui+vbYlp1WTxXQT8Tx/G629h+\\nxzRNisXChhsxuu1P1kMul+srI8ZiVFVl9+4xisVir4siEKyJaYLT39FkAsFZwdDQEGNjY70uhmCd\\nbBv/mXK5TC6XA5JBVKlUolQqdfwMVjcovP/972//fcMNN7SNJBtBpVLH9yUMAzKZ9WW+dRyXWi1A\\nVSGf7051QnDmkiQBbBBFEpmM2pXXj1AhEZzJxHFMpdLA9yGdVjqGUZ3t2LZDoxGtS8VouyNUSwTb\\nCcsC2+51KQSCsxuhWrJ92RY9vCRJ5PN5jh49CiRurYVCgXw+T6VSWfUzSDwdVmKxIWMjieMY3wdN\\ns/C8+rr3d90AVTUJgkRZQgzCzm7CMCQMFRRFw/M8uoleaqmQSJJMEITCkCE4o4iiCN+XUFUDz3Ow\\nrF6XqH/xvBBFSfqTKIpW7Q/PJCoVB0XJ4XkNPM8TfaigrxGGDIGg93ieRxiagIzjeMKQsY3o29CS\\nxcRxzJVXXsm3vvUtAO69916uueYazj//fH74wx8SRVH7s1QqhW3b1Ot1HnroIS6++OItLaskSaTT\\nKmFYJ5NZv4twOm0ADpYliQGYoBkKEiPLLpbVXX3SNA1dD1EUD8Po71h7gWC9JGFREnFsk0qJ+t2J\\nVEonjm1MUzorjBgAQ0M5VLVCPh+Jwaig7xGGDIGg96RSKTIZD9NskMv1Z9ikYGX6dqYcBAEvfelL\\neeyxx3jpS1/KH/3RH3Hddddx+PBh9u7dy6233oqqqrzpTW/i8OHDFItFPvvZzwLwnve8hxe/+MVY\\nlsUnP/nJLS+7aRprqo/YtkMQRKiqRBDE7d+GoVIopDesLJ7n4boBlqWv2zASBAG27aFpyrrUVAQb\\nS6fwpNbzNU2t7XkhSRLZ7PJ9wjCk0XC7ep627RCGEbqu4roBQeCjaRqplLlM1lUg2GrSaYv0xjWT\\n24ooipiZSeThhoYKHd/HRMWoO4+syclpKhWbnTuLfZv/ohuCIOCZZ44zMGAxNiaSfQr6G2HIEAi2\\nDt/3cRwfw1CX5aNSVRlVlZFlGcdx8f2QVMpoLwK05m3ptBgH9xN9q1qymfQ6Q6/v+5TLASDTaFTJ\\n5QaYm5umWBwmDG2KxfSGxDJHUcT8vI2imMSxzcDA+gan5XKdKDKIIo9CwThrVvR6yXrqZhzHzM83\\nkGWTKLIpFjs/30qlQRjqaz7PVv2UJAXXraCqaebn6xSLGVKpmHRa+PKfjfS63RQk1Go1jh0LkCSZ\\noaGIYrFw2se0bZsf/GAaVS2i69Nccsm+DSjp1tKqn9/97g85cmSIKJrjhhtGhHKJoOd0ajt/5mfg\\nfe+DF71oiwslEDQ5m/r2ubkasmwtm2st7lcHBnxk2UKWdWTZJZ9PEwQBpZKPLKsYRiDGwVvEtlYt\\nOZORZRlZjojjAE2TCAIP01QIAg9VlTYsIZskSSgKBIGHpq3/UauqTBj6yHLUU+vjWuoEJ6t6nC0s\\nfr6qurzORFHUbgDiOEaWY6LIR5KijnVMlmUkKamfhqESxyGqGgIhqiqaDIFgs+hGiSXxrHOIYwdd\\n3xinSl3X0TQf1y2TTm/vcJ1s1sK2jyPLdZEfSND3CI8MgWDrUFUJ17Xbc63WOFlVVWTZQ5I8TFNH\\nkiKiyG+PeSVJQpaTzxRFjIP7ib4NLTmTURSFQsEiiiJk2WomYRsiDMMN9XpIkqSmTvm46bSFYQTI\\ncu/kONdSgImiiFKpQRxLZLPahksX9ju53MrP1/d9KhUPSYJczqBedwkCCV0PSadTHQ1TSf00m417\\niiAIKBaTOiDytggEm0O3aleappHPG4Rh1JWKUTcoisLFF+/B87xtHVYCsHv3KPPzLrmcSfpsjT8S\\nbBuEIUMg2DqSqUwEyLhuohIpyzH5fIq9ewtEUYRpmkRRRBRF7THv4nmbGAf3F+Jp9IjEK0Nu/w1s\\nGzOTKAAAIABJREFUystxupPPXr6w3SjAtBQ6ZFnF83zOMjvGqs/X90MkSSeOI3zfJwgkFMUgipyu\\nvGsWG0ZEoy0QbC7rUbsKwxBNS6HrG6tKpOv6GWEIrlQcRkfPxfeFaomg/xGGDIFg6/D9GMvK4vt1\\nXDdZqA1DnzAMl/R/i+donT4T9B7xRAR9iyRJpFJKRwUYVVUxjGhdqh5nA4ahoSgeuh5imiamKVQe\\nBIJ+pZu2roVQJepMsZhpq5aYptnr4ggEHRGGDIFg68hkdMKwTjqtkUoZyLKLacYiDHEbI5YqBH2N\\nZZlYHXLqSJLU0Q37bEVRFPL5Bbfqs1nlQSDYDqzV1rVYTZVIkGCaJuPjwoAh2B5YFjhOr0shEJwd\\nnOx5mM+LafB2R3hkCAQCgUAgEAgEW4zwyBAIBIJTRxgyzlCCIMDzvFPaN4oiPM87bSUQ3/fxff+0\\njiFIYuI9z+soQdTNNp3wPI8gCE61iAKB4DQJgoBarbYt3sN+b9vn5uao1Wq9LoZAsCbCkCEQ9B+n\\nMqY+3XF4HMd4nteVeplgAeFTs40Jw5ByuYxlWaiq2k78mHzuEscKhlFDkiCV6qxUsZjZ2QqNRkg6\\nrTA0VDilsnmeR6USAjH5fBLXHYZhM1Gd1jMVlO1GHMeUyzZxrKFpDXK5JD4kiiKCIGgnspubq1Mq\\n1SkWTQYHBwCaST6TbU6O/0uSC/qoqoptO5TLiRRwsZhakujT932hViLY1myHOhxFEU88cZS5OZ8d\\nO9Kce+7OjtsHQUAc9yau1/M8jhyZQ1Fkdu0a6LvY4mPHJvnGN/4Twwh5+cuvweomXkcg6BGWBXNz\\nvS6FQHDms9JYYPFnk5OTeJ7H7t27KZcdokjBNO2uwtejKGrvYxg2mYzVHmN3O/eqVhv4vook2QwM\\npMU8qUv6d2QnWJP/+q9jzM7qRNEE+/fvRVUl8vnkuzhOXoCnnprGMAZIp2fYvXukq+MePz6P4+Qw\\nzflTNmQkFkkJkIjjmDiOqVQcwrD7hkGwcB8lSSaKFqy81apNEKioqk0mY/LMMxPMzFgcPz7PNdck\\nxo6ZmQbVakAupzE0lFoy4Wg1mLLcoNGwaTTMZuO5MOi3bYd6PUaSIgoFaUOlgQWCrcB1XarViMSg\\n2r8JvYIg4Pvffwrf38Px4090NGT4vk+57AMSuVy85UojMzOz/OQnNhCSTksMDw9v6fnX4p57vsl3\\nvpMFZrj00l1ccMEFvS6SQLAqwiNDINh8VhoLLB7jVipH+NrXpgGN66+3GRrajSwrRFH33hFxTHuf\\nxWPsQqE7o0QYxkiSzCk6dJy1CEPGNsbzQlTVol5vVXy5vUqXy0W4rodlWSiKie93n03KNDVUVUFV\\nT33Qr+s66bSLJEnout7UZE5ecuE21T2yLJPN6vh+iGEsGBnCMG7eSx9FUdA0mWzWQFUTr5ekMZQA\\nlTiWlrm6tfaP4wBd15EkCVlWlxgroihuNsrxKbvKCQS9JIpaA4Oor+uwLMsUChmCQCO9RlbexcbN\\nMDy98L9TQZIUMpmkTe9H42YqlWFwME0Y2n1ZPoFgMcKQIRBsPiuNBRaPcV3XRZJSKIqK44Tkcsm4\\n2zS78+iTZXnJPuVyo3ns7kMwczkL1/XRNEN4Y6wDYcjYxpx33hiTk3McOLADw1CRZam94tjKzKso\\nMrVag0Kh2PVx9+4dolJpkMsNnXLZJEnCshYyxycTcg3fD4Qk3jrRNG3ZSnIuZ+K6PoaR3MsLLtjD\\n1NQcudwAlmURxzEDAxGWlcjSnrxqm8tZOI7XNGIYzWPlljSelmUgyx6yrPS1W75AsBqmaQAukiRv\\nuefCelBVlec/fx9TUyV27drfcVtd18lkXOI4bl7f1jIyMkgUTaMoCsVi9/3KVvGyl12LZT3CyMgY\\n+/d3vpcCQa8RhgyBYPNZaSxgWQaS5KEoCkNDB3Hd/8D3PS688OCK4+61WLzPwhi7e6OEoiikUsL4\\nvl6kuJ+XqTYJSVq+Qi0Q9AOibgr6FVE3Bf2MqJ+CfqVT3fziF+Gzn01+CwS9QLSdgn6lm7opVEsE\\nAoFAIBAIBIItJp0GIbAjEAgEp4YwZAgEAoFAIBAIBFtMLgfVaq9LIRAIBNsTYcgQCAQCgUAgEAi2\\nmFwOKpVel0IgEAi2JyKDn2BbEQQB9bqLJLXkXSGTsbrWad6O1Os2QRCRThunlHTT931qNRfTVJck\\nYBUIBOsnjmPqdZswjMlkzA1TxrBtB8cJSKeXJ+cVdI/neUxOltB1mZGR4hndNwi2P7kclMu9LoVA\\nsJQwDKnVHBRFIp221qWi4Tgutu1jWVpPElILzi5EDy/YVti2RxQZVCoBtg1BoOH73csbbTeCIMBx\\nIIoMbNs7pWPUai6ynKLRiIT0rUBwmgRBgOvKp/VOnkwcxzQaIYqSplbbmGOerczMVAiCHOWyjON0\\nLzsuEPSCfF54ZAj6D8fxCEMdx5EJgqDr/RJDv4+ipKnXfZFEVLDpCEOGYFuh6wpR5GIYoCgR4G/Y\\nimg/IssyshwShi6admrXaZoqQeCgqrFYnRQIThNFUZCkgChy0fWNaXskSULTwPdtdF28o6dDLmcS\\nhhV03ROeLYK+J5NJkn1GUa9LIhAsoGkKcewhy8G6xtiSJKHrcrsvW48nh0BwKgj51bOYlpV1PeEK\\np7LPRhNF0ZJneCZNzleqm3EcE8drGyGCIECSpBU7nTAM28dZrza2QACi3VxMt+/keoiiCM/zMIzu\\ndef7nTiOCYJkILzZ7fTi+lmpVNB1HdMUoXSC3rNW25nNwvHjyW+BYKtZrX62xtqn0h+FYbjtFhnX\\nmt+EYUgURWIMvYV0M+4UOTLOUjzPo1IJgJhCoTvDhO/7VCo+cQy5XNSz1a7WgPhMGeyvRTcdieu6\\nVKshyfNcOZdGpeISRTLpdChyZQgEp8GpDu46UanYhKGC5zXI5dIbeuxeUa028H0FWXYpFNJb0mbP\\nzZWYno5QVYc9e2ThlSHoe1oJP4UhQ9BPnI7xebsZMdaa34RhSKnkEMdiDN1vnDlL2YJ1EYYRkqQA\\nClGXPo3JKqSMJClEkViZ7SeiKG4+T3lF62Ucx0SRJJ6dQNCHxHFMGMbIskoYnjnvZ+ua4njrjM5B\\nECFJOlGkdt23CQS9RCiXCAS9ZfH8JgyX9xvJuFrMf/oR4ZFxlmKaBlHkIMtS1ytWmqaRTrtEUYRh\\nCGtkP7H4ea7k9qaqKplMSBiGWJbIIi0Q9BOSJJHLGbiuf0aFQ2SzJo7jYxj6lnnQFYs5oIKuq2fU\\nvRScuQjlEoGgtyye36zUb6iqSjotxtD9iDBkbCOiKFGdUFWVIAhQVbU9OOyUH2ElJCmRVFoPkiQJ\\nd6o+YKVnvdrzXFxnOslgtXJo9DL3iUBwNqNpWtext71+X7vtbxRFwTDiLXUzVlWVVErfdq7NgrMX\\n4ZEhEPSWpD+TkaRoVaO7pqkiaX4fImYt24QoiiiVGkSRgueVMYwMmuaRy6VxHJdaLUSSYgoFUwzg\\nzmBs26HRiJGkiHy+87OO47gdd9+qKysRBAHlskscSz3NfSIQCNYmCAJKJRfozfu6NL/Syvl4WrRy\\nZCiKR6GwNXk/jh2b5NixEFn2uPjiMSxrfQZ7gWCrER4ZAkFvcRyHZ56pADK7dgVkMpkl34dhSLmc\\n5MhIpQKxqNtHCLPSNqEVvyXLKq4boigaQZDEaSX5LlTiWBYxwWc4SS6M5Fmvlck3ibsHRdE6xt1H\\nUUQcK834fFF/BIJ+JmnjlVVjeTeb9eRXCsMYRdGIIrZM8cbzQiQpQxTphGG4JecUCE6H4WGYnu51\\nKQSCs5dEsURHknQ8L1j2/UIODVXkyOgzhEfGNkFRFNJpBd/32bEjj++7pNNJqEASr+Wumh9BcOaw\\n+Fmv5VYuyzLZrIbruh2tx7quk047RFEoYsoFgj5nrVjezaaVj0eS1u5vslkT23ZJpbQty5Gxa9cw\\nMI2uK8tW1QSCfmR0FKamel0KgeDsJZVKMTxcIQgCCoXCsu+TkMWAKApEjow+Q4q3apmkj+hGl1Yg\\n6AWibgr6FVE3Bf2MqJ+CfmWtuvnRj8Ljj8Mdd2xhoQSCJqLtFPQr3dRNEVoiEAgEAoFAIBD0gNFR\\nmJzsdSkEAoFg+3HGGjLe/va3c91113HLLbf0uigCgUAgEAgEAsEyRGiJQCAQnBpnpCHj4Ycfpl6v\\n8+1vfxvP8/je977X6yIJBAKBQCAQCARL2LsXnnmm16UQCASC7ccZmezzwQcf5CUveQkAN910E/ff\\nfz9XXnnlsu22KvmY4PSRZZkoUgGPVCrF6OhoW8u5WCwyOTnJnj17OHIk4PLLx/jJTyq8+MWHeOih\\np7jyyr089thj5PN5Dhw4QLlc5tJLL+Xxx49w2WXj3HffY1x99XlUqzIXX7wb3/cZHR3FcRwACoUC\\n09PTjI+PMzVV4fLLD+I4IcPDBWzbxjRNjh49iq7r7N+/nziO0XV9Sf2KogjP81FVpZ2k8+1vfzsA\\nH/nIR1a85qeffpp9+/Zt4l3dfExzgPHxHeRyeQzDwPdD6nWbXbtG2bv3HPbu3cnsbIVGo8quXbsY\\nHx9ncLBIEPjIskIQxIyNjaJpEseOTbFr1xi27eK6LmNjoyiKgud5qKpCsTjQTj7ouh627aBpKgMD\\nBVKpFACNho0sSxiG0ZatjKKIRsPGdV3S6RSmaeJ5HtCSsK1iGDq6bqBpalvytrXNyfKXYRhSrzdQ\\nVQXLsk6pnXFdF0mS2scOwxDfD7o6/8msVPdWwvd9oiiR84yiCN8P0HVtRc30f/iHf+Db3/4Bb37z\\nq3nOc57T8fyGYbSvqROL79NaMZGnsu1mHHOtbUdGRphuyhGcKdfU6/OPj49z5MiRVbdb+r4VgblV\\nj/Wud72bP/3TP2n///znv5wDBwbYu3cvX//6Dzh8+CDvfvfvkcmkeOCBB3jHO36fhx76env7I0eO\\nsHv3bgAajQZBEFCr1firv/ordu3axVve8pZl73IURfz4xz8mCAIuu+wyAL74xS8yMTHBa17zGorF\\n4qrlnZiYoNFoMD4+vmai1W7fe8HGMjg4yNzcHHfeeSdveMMbutpn1y4olaBahWx2kwsoEKzC5OQk\\n3/rWQzzxxOPYto3neRw5cgTPy1KrJZa2er1OOp3mhS98Gb//+7cC8KlPfYHXve5VAFxzzTVEUcSD\\nDz7KwYP7eMtb3kKlUuG9733vknP9j/9xJ1/60idxXZe3ve1tvOY1r2l/9573vIcwDMnn8xw6dBUv\\nf/mNS/a99dbbUJQqDzzwAI8+OsUv//Jh7rzzzvb38/NlBgbyALz2tW9lZERjz56LuOWWX1v12m+9\\n9TZuv/2/A/CmN/0Of/M3H+x4r6677jrOO+9KPvGJ2wH4gz+4vX0/AG6++WY+97l7uOKK83jta1/L\\n0aNH+fCHP9z+fteuXXzuc1/i0Ucf5NixGi9/+WEOHz7c/t62bR5//HHCMCSVylOvl/jrv/5rPv3p\\nTwPwla/cx+WXHySVSjE5OcvQUJ53vetdfPzjHweSvvGVr3wl5XKZP/uzP+OSSy5B0zR+6Zd+iTiO\\n+fzn76Jer+F5Hp7nYRgGY2NjjI6OUqvV+NjHPsYv/MIvoOsGuq7x6KOPcscdn+ecc/LceOONHDx4\\nsDnPkXHdZK6k6zqPPvooTz55nMOHn8vY2BggtfugxX1ho9Foj2Hn5uaoVh3e+c5bUFWVe+65p+O9\\nP5kzMtnnH//xH3PFFVfwsz/7s9x33338+7//O+973/va37eSh0jSDcD/DfwF8ErgK8BzABeYAC4G\\nvgX8BvAB4PeADwOvB/4n8FzgGcAATOC/gBcDnwHeDXwUeEvz903A/cD5gN/c7yrgm80ytLb9APAa\\n4BvAAcABbGAn8F3gl4G/B94G/Bnw2819XgH8CBhuludh4PrmOd+46PgfAX6++fm5QA1oABcC/y/w\\n1uY1vmPR8f8WeD7wGLCXxJHnyeY1fRx47wrX+hCwp3kfh5rlvxt4D/BB4HeAvwJuBu4CDjePmQNG\\ngH8Dfg74xEnH/1vghua9aF3rceCy5jNZfN//svlcvwxcSzKgnQUuBb5BKvWLNBpfIZP5NWq1v+CS\\nS97FkSOfYv/+l1Gr/RjTHEZVIYrmGRu7lhMn/pkrr3w95fJXeMUr3sSRI/dzwQVX8cQTj2LbwygK\\n3HBDjvPOO0A6zRKlkEqlThBogE+hYPGOd7yDv/mbMWQ5xate9Ui78WnVzaRhSQMXAM9rXuuRZj2r\\nA0qzHmWBAAiBPHAUyACDwEzz8xjY0dyvCqSBeWAUiJr3RCEZ9DvNezrbfM5jzW0jFiYF4aLz0Tx+\\nS+bQbx7LbO43ApRJ6tmO5m+luc9As4wWUMA0FUZGXEZHC1jWMPX6NPn8OIODLr7vYhjn4Dj/H7q+\\nE9AZHHQoFLJ4nkyhkGNoKGb//nFmZ+exbYlSqYFlmVx4YZEDB0YIAp9KRcZxGuzePUCxmBgzqtUG\\nzz5bol6Xyedhz54cjYZEFEVMTExg2zlcd4YDB/ZgWRIDAxlc16VWi4njmFxOWWJMOHFinlJJAgJ2\\n706TSlmsB9d1qVZjICafV9E0jfn5GlGkI8seAwMZPM+jUknu+cnnP5lyuVX3PIrF9IqGlTAMKZUc\\nQEXXA3w/Jo51FMWjUEiUH1p1c3Jykquv/kPgRorFr/LII3+76rkNw8DzbiN55u/sOEGVpEMkbew/\\nAf+w6rZJ+f8vkvfiTuL44Q7HlEjagRrw7jXOf5ik3fhH4O41zv924Dzgj4njox2OuRv4XZK+4SNr\\nHPMVwKuBjxLH31njmv6E5D3/zTWu6QrgncC/Ap/s4p7+LPChPr+nrfMfBz7QPuZCvz4KvIukP/lr\\nkj55mKT9OU7S3x0EpoAvNs/5GuAp4Dsk/fKTJM/j3/nMZ97MJZeMc/PNH+A//iND0rf9AnAMuIMj\\nR44wMjLCM8+UiGOTX//1t/PkkzcSBP/BRz/6PF7wgp9l8bv82GM/4N57HVQVbropxbFjx/jd3/0J\\nmraLa699kttvf+eK1z0zM8O99x5HUTJceKHPJZdcsOo9guV9zkoGScHGcu2113L//c8jGect1ONu\\nEtZddhl84hNwxRWbX06BYDGt+vnOd/41jzyS4v77H6TRkJAkq1mHdwPPkowNDZKx5FPA1YBOMlf4\\nNeAi4HZgH/Aq4F8oFi9gbu5LwAuAQ8C/k8w3PknS31xEMkZXSOZWX0FRxojjPKlUlVrtUyRzpIeB\\nnyGZ5/wD8P+QzC+uA/43Sbt8K/BTkrnFbSRzuU+RzBn+Hng/Sb/1FeDyRfsfJZnL/DZJv/p+kv7w\\nHJJ5DyTj2PHmtf0TcEnz2CfvcxXJPKXQvL4Hm/vfS9IXXd/8+3LgHxkc/BkGBq4in/8m3//+k+17\\nAHfzB3/wIN///vc577wix49X+fznPwr8KvA94GF+//ffzcUXH8D3x9D1GV796uezuG9MxlM72bXr\\n73nssft4/etv5stffgHg8+IXf5d3vOMDVCoOlUrEzp15fv3XX8Izz/yfJPOw23nggQcYHx8mlZJ4\\n4Qt/k7m5l1CpfINf+ZVreP7zh7nhhqtQVYUTJ6poWlIvbrvtf6Eoz6VQeITf+73XYlkGhiFjGBK2\\nLQMRhhFy/LjLzEwVCPnOd37IJz5xBz/5yfOAPNdeezf/9m//tqRuduKM7Nny+TyVSgWAcrm8opTO\\n+9//fuBp4AskFRSSSdip0M1+K+nZd6Nx39rGJHnRN5LKCp+dvHo1RzKJXWu/VtkW3wtnleO3JlL2\\nWgWkuyrqkUyMW1RXOX5MMulfzPIyBoGDJK38bBYvgrVWxrsZH672MkZRY409W0aC1k/Ewv2LWDBU\\ntLaNT9rv5GPBwjNa/FzDRdu0zhEv+j9etF206HNI7mm06IdF2ywu0+LvVytTDJx8r2JkmaZ2d4wk\\ngSQlvwEkCWRZIo6j9v9xnOzT+t0+UhyvOJFPNMLjZZ8tnGPlfVZiIzy9NsNZbCOPqWnrbSu7aes2\\nk5XarI3g2CYddyNZ3SPh1LetnUpB1mCz6kjA0rasRYPl7dFyDONkQ2TrRVrt/Y9P+n/l44bhwjuk\\naSFB4NJ0HOtIGJ7ch62MUCLYXhw6BN//fq9L0RtcF779bfjXf4WmA5ugRyinPM2on/T/yQ2fS3dj\\n/pMrgLfKdq3x78m0PpOa51yNkJX390/aBtbX3y2eU6w2OVh+3qSpXv3mK0pEYnjZKOI15y6yvHL/\\nZZrDHffTtIV+tTUe7m5cLHMq44Az0iPjkUce4WMf+xh33HEHv/Ebv8HrX//6JaElCys3IrRkuyDL\\nMqqq4nmdQ0uCIODSSy/lySef5NChQzz11FNcfvnlPPxwssJ44MABAC6//HIee+wxxsfHeeyxx7jq\\nqquYn59n9+7OoSWVSoWDBw8ShiGFwkJoyYkTJ9rHj6Ko7U7fInHV91GUzqEliwefR48eZc+ePZt1\\nS7eE4eFhduzYQTabxTQTD5Vqtcro6CjnnHMOu3btolQq4TgOw8PD7Nu3j4GBAcIwbN+L0dFRAKam\\npti5cye2bRMEAYODgyiKgu/7aJpGoVBYEnbhOA6qqlIoLISW2LaNJC0NLYnjmEajgeu6pFLLQ0uq\\n1Wp7e1VdO7QjCVVpIMvyKYeWnHzsMAwJgqCr859Mq+4t3nclgiAgDMN2aEkQBGjaQmjJ4rr5L//y\\nL3znO9/h1a8WoSUitGRrz79aaMlK/XqxWGRubnXjzLvf/W7+5E8WQkte/vKXMzAwwL59+3jsscd4\\n4QtfyJvf/GZSqRQ//vGPueWWW/j61xdCSyYmJprus+A4Dp7nLQstOfk9bYWWKIrCwYMHgc0LLTm5\\nzxFsPiuFlnRjVPr4x+FrX4PPfW4rStkf+D783d/BbbfB2Bjkcokx5/nPh7e+FV760u4WigSnx2Jv\\ny4ceeojHH18ILZmbm0OWZaamptpjqiAIeNnLXsattyahFF/4whd41asWQkviOOaRRx7h/PPP501v\\netOKoSV33nknn/zkJwmCgN/8zd9cNbTkqquu4sYbl4aW3HbbbcRxzL333svU1BSHDy8NLSmXy+Tz\\nicfwW9/6VjRN46KLLuLXfm310JLbbruN//7fk9CS3/md3+GDH1w7tOTKK6/k9tuT0JLbb7+9fT8g\\nCS255557OO+8lUNLDhw4wKc//WkeeeQR5ubmOHx49dCSfD5PqbQ0tOS+++7j4MEktGR2dpZ8fnlo\\nyRve8AaeeeYZ7rnnHmRZXhJactddd1GrLYSWpNNphoaG2L9/PxMTE+3QEsMw0LQktOTzn/88+Xye\\nm266iQsuuADDMJAkCc/z2mHJjz76KMePH+e5z01CSyRJavdBq4WW1Go1KpUKt9yyPLSkm7bzjDRk\\nANxyyy08/PDDXH755fz5n//5ku/ESoWgXxF1U9CviLop6GdE/RT0K93UzakpuPBC+MEPkpwZ240g\\ngKefhiefTH6eeipJYOp5oKpgGGCayY9hJEaMr34VDhyAP/xDuPrq5DiOA5//PHzkI8m+t94KN98M\\n6XRPL++MRrSdgn7ljDNkTExM8HM/93P8+Mc/pl6vI8syH/rQh/inf/on9u7dy9/93d+hqiqf+cxn\\n+OhHP0qxWOSzn/0s2ZOyJ4mXVtCviLop6FdE3RT0M6J+CvqVbuvmn/4p/MVfwEteAplM4o3Qcu9u\\n7R7Hp//3RhzLdWFmZuFnfh527oT9+5Ofc89N1FhMMzFauG5ipGj9Brj++tVzgsQxfOMb8OEPJ793\\n7QKrGeUVhgs/QZAc33GSn1b5dD3x8Mhmk5/W35lMYlhR1SSMovXTus+LQ1ZbvzfbeXuzj3/yc3/X\\nu2BwcPH5Rdsp6E+6qpvxNsJxnHh+fj6+4YYb4jAM46mpqf+fvTcPkiw5Czx/735xR151dHVVdfUl\\ntVpodCIaJNRCvSBgkBBgjXHYMjCImWVA7JhmezDMEDBrjA0IMM2CqTFYQIPQglYc2pGENIyE1OLU\\n2Yda3VV9d3UdWXlExvHinf7c948XEZ1ZmRGZVZWVR5X/zNKqMsPDw+P59z739/l3qO/6ru9SSin1\\na7/2a+ojH/mIStNUvfGNb1R5nqsPf/jD6r3vfe+6ft70pjetDt7XP/pnz/xo2dQ/e/VHy6b+2cs/\\nWj71z1790bKpf/byj5ZP/bNXfxqNxqa2gX0VOOl53ijeWinFl7/8Ze6++26gKLP6oQ99iDvvvJNv\\n+IZvwDRN7rnnHt75zneu6+eBBx7Q1kfNnkRbxjV7FS2bmr2Mlk/NXkXLpmYvo+VTs1fZSo65fWXI\\nuJhOp0O9XgegXq/Tbrdpt9vr/rYRRdWSgrvvvntkEBkmpLs4WePFKKVIkhTTfDHRSbVaxTRNgiCg\\n0wmo1ytkWU6p5GEY5qCygtqw/zzPSdMMyzKRsqiWkCQJpmlSrRblD5eWlmi3A5rNKu12wOxsEymh\\nXPZHiRQvZjiWarVEEEQ0GtVRf+MYjsU0Dfr9CM9zsCwb0zRGCcs6nQ5RlDI3N41lWUgpSZIU27bI\\nc4llmaNkZMX1UZimQZ5LPM9dUwpu9bXcLHHhpbQdjsmyzFESuM3es5NsRdayLKPd7pLnglLJx3U9\\n+v3+KPFjnueEYUQQBDSbTWZnpymVSqRpyrlz53juubNYFlSrNY4fP0qlUkZKhee5GIZBFMUIIfB9\\njyiKieME3/col0trkskVyTMTDAMcx8XzXLIsQ0qFbVtkmcB1nYnJJDWa7eL06dMsLLR56UtvnqjP\\n1KCU8Vbu/SAImJ9fYna2uWGlq9UMddrwPtoOHnroIR566Ene8IZXceutt05s22q1yPOcubnJ2cOF\\nEHS7Ab7vjhLejiMMQ+I4pV6vbpoc8syZM9i2PUp0OY4oimi1Oltad5aWlgiCmBtvPDTx87Ms4+mn\\nT1MuOxw7dmzd64899hj33vuz3H57g7/8y78kyzJ6vT7lso/neWvWjzRNUUptut6vZrgGrV5l5T6c\\nAAAgAElEQVTjNJqt8sM//MP88z8/z0c/ej+veMUrdns4e47h/tNxbJ2kVqPRbMq+1RKGYdBoNDhz\\npqjT3e12aTaba0qvDv+2EasNGUOSJKHXKzxaDCOduPGN44R+H8IwoNcT2LbHwYMBtVqFkycvoNQs\\nDz/8OLfe+jLieIGDB2fJ8wzTLLK3Xtx/txuhlEevt0K12qDdbpEkLpZlcPRoiJSSr351AaXqPP30\\nP3H77XfxpS89xGte81oMo8uJE+uVfp7nnDy5SJ43OH36IW666ZWcP3+BV72qNPGBs9OJAI9z587g\\n+3NE0TIHDsxgWQaNhoEQglOnOihVIormOXHiCN1uiJQeQdCmXK5hGBmNhjH4bgKlDJIkpFyukWUR\\n9fqLmZuG11KpnGbTmLg5DMOYODZRStBsmhMXun4/JstswrCH4/hYlkmjke2JzWeapvR6wzKnydiN\\n9LlzLc6eTel2Y5rNiFrNYGkpJ44FWRYiJbzwQpt+3+bIkT6vfKXkppumWV6O+au/OsXp04KFhQu8\\n5CVHed3rJLfeeoCZmVmEiDFNg8XFmCwzgAU6HUWvl9Fsxhw5AjMzJpZlIYRgaSmk08nJsoSZmSqV\\nSoIQNmCSJG1KpQZxHDE1NflhRaO5UlqtFp/+9AvY9mGWlr7GW99619i2SZISBAqQm977X/7yMwhx\\nA88/f5o3vWn8w3xhHBAYhkWex1QqF5fnvHSCIOD97/8HlPpmvvCFT3L//T87tm2n0+HUqQiwyPNF\\nDh0ab8xYWGgThj5KBZw44U78TmfPBhhGmTBsceONB8b2+dxzZ3j88RxIeNWr5icaM5544hxpOsu5\\ncxd49avHrzvtdpuvfrWFUmX6/ee4887xhpyvf/0ZnnzSIc/blMtlZmdn17x+993/G4uL9/L1r5/j\\nvvvu46d/+t1kWZWVlQ4HD1ZJEhulBOWyIAwNNtPBF/PiGpQyNWVq461my7zrXe/iT//0EPAtvOEN\\n76Tb/cJuD2nPMdwLR1HM1FRFVxfUaDQT2beFjZRSvPa1r+WBBx4A4NOf/jR33XUXt99+O48++ihS\\nytHftkqhMDd2r4rjhOXlHlFUZCmSUtJuB/R6AVLmXFyPXsp8UI9ZYpoGhmFMTOhTJHNSa/4t+iz6\\nXb0Bdd2ilnzh1CA3KU8lsSywbQPDmFyfNwhCWq2AXq/H8nKXIOjTanUJwwilFFJKut2QdjtAymzN\\ne4fXbjj+oQfEkLXfbe2FyPOcdjug2+3RbgesrATk+fixFl4tm5fPXf15k+Z2d5n8PdI05ty5BZaX\\nV5BSjLwoer0+WZYCijxPB2WUEpRSmKY5+M5iIJuCMIxZXm6zsLDM8nIXIbLR50s5NKgUMtDtFh4f\\nq8dlmgZSSpSS68Y8vM4azU4wNAALkbI1m+Qw3HIyllXo1a0wlHnT3L5N9sLCMk888TTd7sLEdoXR\\nYYGzZ+fJ83TTfqXcbI0oMM1h28mNi6WoKNW72YlpYfTpkiTRJn3a5PlQX03Gtot5GvfRWRYCS8DZ\\nQUnhlJWVLv1+MYYX19chl6e79POV5lIpPKj6QJ8s25quuRYIw4hWK9i0BDcUB4qt1ot7bY1Go5nE\\nvqpaIoTgrW99K1/96ld5zWtew6/+6q/yuc99jo997GNrqpb8yZ/8Cffff/9lVS25uOb7kOXlANet\\nkqYB09MVwjAenKgrfL9w8x+6zkZR4epfqVQQQuB5HnmeY1nW6AHQtm2klKON4Oqa78O2cRxjmubI\\nJbjdbhMEAdVqdRRKMKzdOy60ZDiWUqlEFEVUq1VKpfUniHmes7KSYNsei4sXmJqaodVawvfLWJZi\\nerpEnuckiYNhmOR5B1BMT78YWjIc/3AzPPxuWZaNHrDzPMd117pj9/sRQaBIkhjHcfA8n1Ipp1Ra\\n/52UUmRZhmFM9twYtk3TdDSmrbxnJ9lI1i6WzRdeWGBpSZKmPW6+uUmtVuPs2WVM00OpiGazwpkz\\ni+S5ie+b3HTTDZRKJcIwZGVlhfPnz9PvxwjhUavN4nkOs7N1KhWTRqNCmqZkWYbneQRBwMJCSLlc\\noV63aDRevG+yLBuN17ZtXNdFCIFSauS14TjOpg9Bmv3LXoqjnZ+fp9Vqceutt24aMjIMLdns3o/j\\nmKWlJZrN5qZhEEOd5jjOtpwYhmHI7/7uX9PvOxw4YPBv/s3bxrYNgoBTp1ZQyuDmm6tMT48Pg5FS\\nEgTBxDViSBzHpGk6CpGcxJkzZ3Ach4MHD05sd/78MmGY43kmN9wwPbHfpaUlhBDMzs5uGlpy/vx5\\nHMfh8OHDo78P5fPXf/3/5gMfeADPgz//81+mXp8hSQxMU3LgQG3NWjBuvZ/E6nVFu75rtsJQNk+f\\nPs3b3vazJEnOT/7kO3j3u//1bg/tqiOlZGUlwnHKCNFnenqzELMuSrkolTI7u7ku0lw5e2lt12hW\\nc82VX90uLuem7fcj4ljieQbVankQd5tiGIpGozxR2cZxQhDkmKai0Sg2k51OjJQG1aqF7289Pvdq\\noZSi2w0RApRKMQwXKRNM08O2oV4vI6Wk04lQChoNf9s2ccNrKaUADAzD3Nb+9xMXy2a322NhIcRx\\nDG68cQbLsuj1QtJU4fsmlUppnWxeLG+tVofFxYQ0jWg0ylSrFWo1d53cFfMbopRBrebuKaOPZvfR\\nm52ry1//9T8yP29w4oTNm9/8urHt0jTlhRdaSAlHjzY3NVBsN0VYXGHEbDa9TcL7Ct3kuga12uQc\\nHVfKUD7/+Z+/yMc+Nk+1mvHOd76JSqVKGOY4DmtCGjWanWIom2EY8vu//7f0+xbf/u0Hee1rx9Qe\\nvcbodvtkGZRKJuXy5FC8ndQZmgK9tmv2KtqQMYbLvWkvdrsd9hHHCVIqymV/w9O5fj8iSWykFDQa\\nxaav0xGYpo3niW2Jsd4MIQRxnOF59sQH1OF3vPjfIcPvvN1xixfPx/UaF7mRbOZ5PgoXGXLxvKz+\\nfShvWZbgeZI4FrhuDSljpqeLjfw4w9t2zm/h7ZHj+66OI78GuBy9qZQijgt3Yt/3rtv7ejOUUly4\\n0KbbzWg2XQ4cmJxstAgHG38fX02iKCYMC1mo180tJV3eiXEO5fPcuQUefXSZctnkta89ju/7OzYG\\njWYjhrIZxzEPPniafl9x++0Njh2bnCz3WuJS7kF9v+4s2pBR8NBDkOfwmtfs9kg0Q7Yim9ffkfcV\\ncLFiNYyiYkm/z6AqSbJhOESp5CFljG2/mOW8UskRItuw/dWg14sBnySJmZ62xz5QDL/jxf8OuVoP\\nIvoBZzwbGQEunpfVvw/lLY5jlGoCAZaVUK16m24Otmse8jyn18swDBchYhoNfRJ6PZJl2UA/GhhG\\nuie8z/YqSqmBd8XmeSJ2c5Pv+4V+AbbktbUbYy2Xy9j2izkI9EORZq9gGBa+b28pb821xKXcg/p+\\n1ewG/+pfwcMPg7bp7C+0IeMKKRRuhlIGlrXx5TRNc52L3E4ZMIZYlkGWietu8bweWS1vaSpwXZt6\\nfWezfxceJAqlcixLG6muVwoDhkQpME293IzDMAx838VxbFx3c0PGbmIYxo54EV4uvu8yN2djmkI/\\nEGn2FKZpMjdXQwibclnLpkazl3jssd0egeZy0KEl24AQxcnPleR0kFIiRJE09GpsvpRSCCGwLEtv\\n7vYw2ymblzPnwwSe25EfQ0o5qmygPW72P5crm9uhH68HhBAkSYLv+zoU6zJYLZ/tdhvXdUeJsjWa\\n3WS1bIZhSJqmNJuTw8c0mp1Ch5YUXhieB1kGUQQ7nHpKM4atyKZ+ot0GbNu+4k16rxfR6yk6nXCb\\nRrWWYZZ2bcS4frjUORdC0OmkdDpilNfgSjBNc9uqOmj2L9uhH691lFL0ejFxbBMEuuzglRBFMUL4\\nRJGaWMZbo9lp8jwnitRAPvV9rtHsFdptKJXg2DE4f363R6O5FPRT7R5BSoVhmEi5PvGlRrMTKKVQ\\nqqgao2VQo9lZpGSwBuh770qQsij1rZQ+ZdTsLYZrbJFIXcumRrNXmJ+HQ4dgagpWVnZ7NJpLQR+T\\n7RHq9RJJkuG66yufFIuf0t4U1zlSykHOgavj4eA4DtWqHCQd1EkZNZqdwjAMGg2fNBV43ub5J/Sa\\nMJ5SyQNibNvSnkCaPYVt29RqOUKIHc+TptFoxtNuw/R0EVKiDRn7C73K7xEsy6JcXh8Xnec5nU6E\\nUgb1urstuQs0+48sy+h2UwxD0WiUrloMvTZgaDS7w1ZDcIo1IUYp9JqwAUWpcYVpZjqcUrOnkFIS\\nhhlSGth2hufp9Vaj2Qt0u1CvQ6WiDRn7Db3C73HyPEdKG8NwybJrM95XKUWSJKRputtD2bOkqcAw\\nXKS0N4z7TtOUJLnyvBYazX4gyzLiOEFKudtD2XHyPEcpe1Da+NpcE66EOM5IU4hjqXNkaPYUUkri\\nWJKmkCRi8zdoNJodYWjI0KEl+w9tyNjjOI6D50ksK8Xzrs2TtzhO6PUU3a4gy7LdHs6exPfdgQzI\\ndSewaZrS7eYEgdLGDM01j5SSbjclDA36/esvYZ7jOLhuPtAH7m4PZ8+hlCSKtGFcszdJ05QoSlDq\\n+jPCajR7FW3I2L/o0JLLJM9zut0IyzKo1cqXlLdAKUUQRAghMU1FnhtUqy6u++KmNIoinnjiHI5j\\ncPjwLGASBCFSGliWQggD2y7+9TyLSqW0Yf+1mo9t2yRJQr+fUSrZa2Izi2z5IXmuqNevXsjCdiOl\\npNeLAKjVStes+7BSioWFFsvLPaRU+L5NljWp1fw1Bo0oioljgeP4a9xV+/0+Dz/8LKZpcOuth7Es\\nl2rV29AdPQhCskyOfX2r4+31wkHCPRCCdbKt0WwH253Isd3usrwcMjXlMz09vjTiRvp1O3j00VM8\\n+WSbO+6Y5aUvvWXi56/+2WmCIODBB5/Bti1e9apb8CfUqRu37lxNTp06xR/+4Zc4cMDj3e/+fhqN\\nBv1+RJrmVKselmURBNFIn0aRwHFMqlVdqlVzdQmCgPe97//hwoWMf/tvX8+3fusbdntI1wVXsl/X\\nXB9oQ8b+5dp8+tsB4jhFKY8ssy/Zi0AIQZqaSOnSasXYdoV+f+3p0eJihzSdpdXyWFrqI4RNp5Nh\\nGCWWlkJsu8LiYh/LKhPHa8vM5XlOkhgYRokoKvrt9zNsu0IY5mvcsbMsI00twCcMY4TYeXfHUsmn\\nXjdpNJwtP0CnaYYQDnnuXNNeHGma0ukoVlYsFhcNlpZM0tRcIy+O42DbMDVVR4i1C/TZs0tE0QHa\\n7TpnzqxgGCXCcP1JpRBiJDMbvb5Vsiwjy2yEcGi3kw1lW6O5EkzTpNHwqFahWt08MeZWWV4O8bwD\\nLC+nE0NWhvob/JF+vVKyLOPxx3tUq6/gsccWJ7YVQpBlNlJ6pOnO6775+SWS5Aa63QZLS0sT245b\\nd64mn/3s17Htb+HcucOcPHmSPM8HOTPK9PvpmuvXbvcxzTJJYuzK2qe5vnjqqad49tk5PO87+dSn\\nHt7t4Vw3xHEK+KSppe9zzYYMDRnNZpH4U7N/0IaMy8R1bSDFMLJLPpGzLAvTFEBGrWaTZSG+v7aP\\nRqOEYbRw3T7lso1pCsplkzyPqdddsiykXncRIsK21RpPCtM0sW1Jnsd4XtGv51lkWYjrGmu8F2zb\\nxrIEUsZEUUqnk9HvR5d9XS4X1720pHW2bQEpSqX7xovkcnAcB8uKybIuQnRw3RDTzPG8F7+zYRhU\\nqx6GIdb8HWBmpoZhLOC6baanS2tkYjWWZa2TmcvBsiwMI8M0BZWKtaFsazRXim3beJ63rSdrtZpL\\nFC1RqZgTPbyG+lvK5IruldU4jkOlknL69INMTU3WZ8N7DFIcZ+fvrdnZJoZxAdtu0WyO91yBF9cd\\nx2HHvOZe/eoj9Hpfwbaf4vjx44P1UCFEhOdZa65fteoP1lB5Ta8jmr3BoUOHaDTO025/nte//uhu\\nD+e6wXVtlEqwLKHvc82GrE722e/v9mg0l4KhrsNC64axPfXlx5XDLLwcBJ7njDVyrC6fJ6XccJM3\\n9LIwTXNd24v/ndQ/FKd4UZTg++sNBkophBB0uxmm6WKaCY1G5bKuyU4ynMNryU1wI9kMw4ggUEgp\\nmZpyxmbil1KSphl5LimVvFGbLMswDAPLsiaWbLxYZqQsYs0ty7ykaiar52WcfGr2H9ulN/cyQogt\\nGaajKEaInEple8LalFKsrITEscT3DaanqxPbh2FhbC6V1pfr3gmGp5pbuVY7pQOG8tlu9/ja156n\\n0SjzspcdG41x9Ti0jtLsJEPZFELw9a8/x8pKn3/xL04wNVXf7aFdN1zt8vX7methbd+Mn/xJeP3r\\nC4+MP/sz+Iu/2O0RaWBrsqmPSq+AcQaEbjfBNH3SNGZqauMN6WqFunpztVrJrrYcX9z24n8n9Q/Q\\n68WAT7cbMz1tr3nNMIzBiaAky1JKpf1REux6WZB83yPPY0zTmliuLc9zgkBimhaQjPKmrDZcTbpm\\nF8tMFCUkiYVSOZaVbdljZnUf+gFBs5/YyoO5EIIwVBiGQxQla/ITXS6FV5WDZWWUy5PzyaRpkei0\\nuF+THcs9sZpL8ULcaR0Qxwm12hxKZWsMU6vHoXWUZjcoDIA+9XqNONaJuXcSfZ9rJqE9MvYv2pCx\\nzRiGgWlCngscZ+sP2lEUE4YCz7s6SccsyyBNBZO86nzfY0LeNs0uYZomtdpamRgmr1KqCEMq3N1N\\nDEMiJVjWlS/apmkgZY5h6BNLjWZI8RBc3GemuX3G1DQVCKEQIp9oNCwMGBKlwLL0En4xhqGI4wTH\\nya8bY7dmf2AYBkJkZJnENLcvv49Go7kyhoaMclkbMvYbehd0FWg0yuR5jm1v3bMhigSuWyVJ+lQq\\nats3YLVaGSEEluWu61sIgVLqsitVaK4eSimyLMOyrDUeOkIIpCzmMk0zSqXi9UbD27a5LJV8bDvD\\nNE0dV6rRDLAsi2bTR8r1pZAvFyklSaJw3SpRFFCa8IxTeM8VyTN1NaD1lMsVDh+2ME19CqvZW5im\\nyYEDDaSEclmfGmk0e4WhIcPztCFjv6ENGVcB05ycLG4jSiWbMAzwfeuqnCINw0cuRghBu50AJtWq\\nvKRcCJqrTxjGRJGBaWY0my/G4xd5MkIAXPfFp57tKgU5RBu3NJr1XGxYvFJM08T3TeI4oFyefA9n\\nWUYQSMDAtlNtzLgIpXLSNMeytv9AQKO5EgqPDIEQJkrlm79Bo9HsCL0eVKvgutqQsd/QhoyriBCC\\nfr/IbL+ZgaBU8vH9yRuvVqtNGApmZ6v4m8SAFMnoJOWyN3HDXSRRMTEMc5RQJU1ToiijVHLWbJKV\\nUoRhjJQK02RwqjC5/9XEcUKSCCoVb9sfuK9VhMjp93Mgo14vEnhGUUya5tRq/sTruPp6K6Xo9SLC\\nsE8UCWZmakxPT644UHz+1mVYo7mY1TqjUvG37YQ8imKyTFIuu9eMLun1OjzzzBK3335oYt6LQk8b\\nGIZJnu9MSdOdoNVqE8eC2dn6FRlnFheX+PSnH2NqyuTYsf9lG0eo0Vw5X/nKoywuRnz7t7+Mer22\\n28PRaDRAFBVhJdojY/+h/S4vQko5NkOqlBtvGpVSG77W68XkuUu/L0avSynH9pNlGTAMG5CjsSil\\niOOYpaWMOC6zsNBd18fwPUqpQTI4RZY5hGGy5rMv/o6O41CtmpTLCs9zyfOcXi8FSvR6KXEcj/pO\\n05Q4NokiWFlJEMIlDMcnrFr9OXlePJAr5RMEydhroLkYSRxHmGZR/zyO49F17PWKygVDWcmyjDzP\\nByc+gl4vJUlM2u0+vV5CGJqcOtVhednn3LmQNE1Hn7JahqWUoznv9xOU8un389HfNJqtMtQZWWaT\\nptmm7Sfp3yFDXZJlNv3+5gnzttLn6rZbbTes3LEdFNUMlllYqPLww+cmtnVdF9cVSNnbknExTdNR\\nBazt4lKu6VaI45jlZUkYllla6l5RX3//94/x4IM9Hnigw1NPPbWl92znXGo04zhz5gwPPLDAww/3\\n+fu/f2y3h7OjbNfeYbXu2W49pLl+iSIolXSyz/3ItXGUtU0UJUiLjXG9vtZrIAhC4ljhecaaxIt5\\nntPpRChlUK+vLW0qRMrKSoznSaanyxP7f/rp0ywu5kCPmZlDQEazWQcK5a+UQbfbQoiQqSnJykoV\\nz4NqtUwQBJw/HyJlytRUbVCuVWIYiiiKSRKFlAmm6SFlgmV5WFaRy8MwDHzfG1RbCckyyPMEsHjh\\nhdOcPw+VSspLXnICw1AYRuF94TiQ5ym+v7EtbPV3bTT8QZ4FhRAJeR6zsmLguqxLYql5kSiK+NKX\\nnuCJJzocOuRz7Ng0ee7hupJGo45tWxhGlzRVLC526PczDCOnVCrhurC01GF+PuXQIY/bb7+B5eU2\\ny8vLnD/f4rbbZjDNAwBr5t7zFO12xPx8j6mpEjMzZfI8wTRzer0YKQ2qVXti9RSNZkjhrZUMEtBO\\nPmWPosJI5zhQr5fHeqcZhkEQBKSpyezs5NCnLMvodlMMQ42S4o5jqONdF+r18eWnhRC88MISQpgc\\nPlymWp1cKnUr2LbNH/3RRzhzZpZXvjLgLW95xdi2rVaLj3/8EcDm7ruPcezYsbFtz56d54UXIqpV\\nuOOOY9sSDjPU7Uopms3J13Sr2LaNZQmyLNy0astm/O3f/hUf/rCP45zj+79/lpe+9KUT2585s0C/\\nD1NTNgcOTF/RZ2s0k3jmmWf44z/+f0nTI6Rpwo/92Hfv9pB2hG63T5pCqWReUZWn1fvKUskkDIt9\\nbrNZ1vlwNFeENmTsX/SdvwohcpRyAGfdCVaSSFy3TJqutQDnefEew3ARYu17bNtlZqZOuVwe1BDP\\ngaL/i9uurGQ0GsdYWEgxzRpxbJBlIISJECaG4TI9PcXx4zM4ThnHKZEkhZEjCFIsq44QDmmqMAyP\\nSsWlXrexLA/XLRMEKa5bpt/PMQyXPDfXfMfihNHAcUp4nkej4dDtSprNm2m3LdJUYZo+tZrDzEyZ\\nubkGzaY71gU6ywRKOShlI4TAMAwajTLNpottezhOad211Kyl348IwzLV6k1IWabdznGcOWy7hONA\\nszlNEKRkmUEcG6RpiU5HIoRHv28ghE21egSoYlkm1arPHXfcwbFjhzhy5OCaU43h3AdBQr+vMM0p\\nksTCsmyaTZdazSfPLSzLJ011bK9ma9i2TbPpMzXlb5pvJY4FjlNCCGPi6Z1SikqlyvR0Hcua3KcQ\\nhb5Tar1Ov5g0LXR8ljFRL6VpSpb5WFadbjee2OdWCYIAw7iRV7/6BwnDye7mrVYLIQ6h1E0sLLQn\\ntl1ZiahWjxCG1hoPrCshywTgUqxj2+PJYNs2x45Nc/x4dWDAv3wefzxjdvYdeN7refTRRye2lVIS\\nRVCpzA48ETWaq8f58+fxvG9idvZ7OXny+vACKrxFwXXLJMmV7R1W7yujqDicU8redo8zzfXH0JDh\\neZDnkG3uQKrZI2hDxipc18G2M2w7W7fprlQc8rxPpeKsOSl0HAfHEVhWiute/B4X00ypVm1M08R1\\nHSwrw7KydW2PH68Tx89x552zmGaH2VmHSsWkXDaoVExcN2d2tozvw9xcZTQWgOnpKobRptEwqFYt\\nbDvD9z1c16VctsjzPgcOVMnzPrOzJQwjwfPUGo8Qy7LwfQMpw1EOi5e97BBJ8gQ33eRRrzt4nsTz\\nvFGpz0mx6Z7nDq6jGF1LwzCwbXvstdSspdmsc+yYQa12nltu8bj55ga2vciBAz6zs3XyvM/0dIVy\\n2WBmxqLRiDl2zKdSSTl40OXEiSa12hIHD1rUahUOHKhRKkUcPVrM53D+Vs/99HSV6WmbUqlNo1Hk\\nQLFtG8dx8H2FUhGlkk4uqNk6W02MWa16SBni+8bE9pZlUS6bWFa26el9oXPTNXpoHEO9VC5PTrjs\\n+z71usAw2kxPX7k3BkC1WuUtb2kQRX/Gvfe+ZGLbY8eOMTu7QL3+DC996c0T2x49Ok2WvcDcnEVp\\nUimUS8Dz3C1f00vBtu1Ncz9thZ/5mbfgOP+Nl73sEe69996JbU3TZGbGJcsWOHhwe+ZSoxnHt33b\\nt/Hylz+K532Qf/fv3rLbw9kRTNMc7UOr1SvbO6zeV9ZqFQwjxnXlNZMnSbM7SAlxDL4PhlF4ZYTh\\nbo9Ks1UMdR0eiRuGoT0B9gBF9v0E17WuyN3wWuJi2RxeI8cxqVZ1GI5m99B68+oSRTFxLCiXHR26\\ndRkM5bPIDxTjOIUbuzaWa3aboWwqpej3I7JMbpqsW6PZKa73tT0MYXq6MGYAHD4MX/kK3HDD7o5L\\nszXZ3NceGUmS8Pa3v503v/nNfO/3fi9pmvLe976XN77xjfzoj/7odZHASwixIwroaiR6DMMU0ywT\\nRUq7Bo4hDFMMo0SSGFuW58uZK53IU6PZPZRSBEFGlln0epsnMNWMJ4pS4hiCIL8u9gCa/UOe5wRB\\nTpIYRJEOZdJo9gLDsJIhOk/G/mJfGzI+9alP8brXvY7PfvazfOM3fiN/+qd/yuc+9zn+7u/+jle8\\n4hV89KMf3e0hXlX6/YhOJ6PTCa+qMSNJEjqdlHY72laDg+taCBHhOEonahqD59nkeYxtyy2558dx\\nQrt9aXO1U3Kk0Wg2xjAMFhaWePbZJdrtzm4PZ18TRSHPP7/M2bOLWp9p9hRSSs6dW+S555aIIu27\\nrtHsBbQhY3+zr/3aZmdnabeLZGcrKyvkec6b3/xmAO655x4+9KEP8QM/8AOX1XccJ0ipKJW8UZb8\\nxcUOMzM1LMvBcaxRrft2uzsogSno9RJMU7Cw0OPIkWls28d1TbrdwtV1mMgsjhPyXGEYOd1uTLNZ\\nJssUrmuystLH902efvoMruvwilfcgVKKMOzT7cY0GqVBpRRJnvtYlsC2DWzbYnl5BYCpqTpKGdi2\\niRCSOA6Zn19hZqaKabqUy+4o2/78/CJ5nuO6FkGQUq26a/41TUmW+RiGolZzsSyLJNKFD+IAACAA\\nSURBVEkQQlIqeZhmkTg0jtPR5xmGYmWlKKN35MghYJgkLx+18TyHqSl3dD2GFVQmUSRnS7Btc537\\ndZZlpKnA991tyaS/F8hzwUMPPUyr1eL48Ru59dbjTE01kVLS6/VptVZYXu5i2zkrKyGdTsChQ0cG\\n1RqWcF2bPDdwXZuDB6dZWGhhmha33XacAwdmyXPJ2bPnWVkRxHFAlkUcPXqIW2+9CcuyWF5u0evF\\nlMsutm1TKnm02z0sy2J6ugFAGMZIKalUSluKmR/Kwep5KkoMb00GNJogCAjDlOnp+ra5Zw91/PR0\\nlUajsS19bhUpJX/5l/8fDzzwAt/zPS/j537uX09s/5/+0/sA+Nmf/TGmpqbGtkvTlHY7WKPvxzE/\\nP8/ycsDx44cmtu12u/zxH/8FAD/zMz8+sc/nnnuOBx98mjvuOLJp9ZAvfvGLnD3b481vfg3NZnNs\\nuzRNmZ9v4boWhw7NrXv9V37l/+T97/97qtUlLlx4nDzPabW6eJ5Do1EjTcWa9WO4bniesyVZStOU\\ns2cXaTRKTE/rKieatUzSTVJK7rrrW0jTOj/+42/mD//wd3dplHsLKSVxnGKaxT42STJc176sHDzD\\nvejlvl9z/RFFUF4VuV0ua0PGfmJfGzLuuusufvEXf5GXv/zlHDhwgHe+8510u8XDc71eHxk5LpUi\\nL4HENC2UiqlUSpw8eQHTPMgLLzzLHXfchmFkTE1ZRFHEhQuCNM04f36Rev0Q//RPX+WWW+7i4Ye/\\nzN13fyunTz9FuXyIJOlw/HiRJHNlpY9tl3j22ReYmzvByZOneMlLXsETT3ydqambeeihf6DbPYxS\\nfdL0MW666QTPPLOM583yxS8+xm23vZLTp7/G8eO3kWUB1WqDTmeJ+XmJYRh0u4vMzR0kDHvUak3+\\n8R+fpFp9KY888givfOUr6HRijh61SZKEZ59NEUKysnKaw4dfwhe+8CC33/5avvCFL3PLLa9icfFZ\\npqen8TyLLMswDINeL8cwbKSMqdXKdLsR4LO8vEK5XOfChXlWVoqqF6VSi2azSa+XYRguCwstms0Z\\nkiRmaqpCHCeEoYFSEstan2h1NVGUkCQWUSQGBpxChKWUgzKLLkLENBrjyyfuF6SUfOELJ/n85yOe\\nfTbn1lsX+NZvdXj9633CMGNhIeFLX5onilyeeeYJoEkcJ8zNPYkQKWF4gPPnn6ZanaFadfH9s+R5\\nMY+t1jle+1oTy/J44okerVbGyZPPMDV1O2fOLNBo1Gk0qjz9dECSWPR65zhx4ihCnCcMi/KuSnVx\\nHJduNyfLJEIkTE9bE71rCgNMtm6eLkUGNNc3xYNkiGVVSdMWN954YFv6ffLJCyh1kKWlC7z61dUd\\nNYYuLi7yB3/wBI7zdu6//88mGjL+5E/+hE9+8gZs2wP+G+95z/8+tu38fJs0rbKyEnDihDsyvl9M\\nHMc88kgL0zxAt/s8d91159g+P/KRv+bTn74BIVKOHfvvvO1tbxvb9q/+6kGUei0nT36Ff//vbxqb\\nzPO5557jU58K8P2DwFd4xzvGJ0I8e3aR5eUSUkaUSp11Rqf3v/8vgF8iCE5yzz338MEP/hWtloll\\nJczOZlSrU6P1w7Isut0E0/RJ05ipqc0Tfj733DwrK1UWFwNe9rLNDUSa64c4jjl7NsYwfIRoc8MN\\ns2tef8Mb3kCa/jDwUv7oj35ZGzIGDPd1UgqUirDtKnEcMz1tX3KOm+FeNEkSpqYu/f2a64+LPTJK\\npRfzZWj2PvvakPHBD36Q7/7u7+bd7343v/mbvzk4hS4MGd1ud+Kpzi//8i+P/n/33Xdz9913j343\\nTRPDkEgJplkoQd836fcjbNvAMMA0C3dg0zQxzcIDwfMUSgnKZUiS7iBTc4rnKfI8GYQHGJgmmKZC\\nqRzPkwgR4fs2QqSUSiZSJtTrJu12hOtm1OsHBpZqiZQp5bJBlkXUai7lso0QDqAGfRcxwbZtDx4K\\ni1P9SsUiTQNKJQOlBKZZZHouSqOmKGXgOCZCRNRqzqB/ByEiKhWLSsXFdc3BtTEwDAUU3wfAsozB\\nSZcBSFzXxDCyQZvS6D1S5riuiZQ5lsXg78UDLMhNFx3TNJAyxzDUmrar+3eca2PhMgyDctlGqQjH\\nibEsC8cpZM5xLCDH8wRJoiiXi2SBvi+o10sIIQnDcFBRJ8Y0c8plSRgmWJaN6yocpzA6eJ5JtepS\\nrVpI2cG2LVzXHsi2IM8FxfNPjudZxHGGYSgsyx3MvwRyTHPzKjRr5WDt/G1VBjTXN0XFJBCiOHXb\\nLhyn0PGuy654dJXLKXHcotmcnNm/VqsBZxGizM03z05s67omUZRi22wavue6iijqU6lMvqZzc2Wy\\nbB6ASuXGiW3rdZMLF5YolSaHePi+j23HRNEKtdpkjyzXtciyaFApbKNr1QZawDKHDh3Ctg2USjBN\\nheuW1qwfxRoOUuaDtWtzCt2bYpr5WMOQ5vqk0E2SPC+8fi7m4MGDwDKFfC7s9PD2LKv3daZZ7A8v\\nN9p4uBe1LB1WptkaFxsyfF8bMvYT+7pqyW//9m9TqVT4iZ/4CT7wgQ9w+vRpvvjFL/Lxj3+cX//1\\nX+fmm2/eMLRkK1lQh0k0hyfDaZoSBAGVSmXwgGeONrtxXLjWG4ZBmhYJnBYXF7nxxhsRQuD7Pv1+\\nH8uyRu6seZ4jZWFMCIKAWq1GkiQ4jkMcx7iuy7lz5/A8j0OHDo2ysadpsXmLoohyuTzqY7gpi+OY\\nPM+pVqujMQ37a7UKz4gkSXBdd1SOLwiCQWiJu6b/Uqk0+l2pYoEZnqgNxz+8PkopsizDtoua3qZp\\nsrKygm3bI4PS8D3DUBTbtkeb66Gnx1Zce7MsW3P9h0gpEaIoCbhfH4Yvlk0hBKdPnyYMQ5rNJjMz\\nM5RKJZRSJElCmqb0ej1c16XX66GUGp1QLi8v47ru6No6jkOWZbiuy8zMzEh+4jgmHmjtbrfL1NQU\\nzWYTIcQghEiM5qlSqRBFEaZpjuQnTVOUUriuu6VcJ6tlf/U8XYoMaHaevZTZPE3TkW7drvw6eZ7T\\n6XSoVqu78oD65S9/mY9//OP81E/9FDdski79M5/5DEEQ8Pa3v31iu+H9bdv2pt+p0+kQhiGzs7Ob\\nekR95jOfoVKp8E3f9E0T28VxzMmTJ7npppsmHixAEdrSbrc3DUEZjtWyrDXeEKvl8yUveQnNZpMv\\nfOELCCGI43iks4b6bLh+XM660el01qyhGs2QoW4qr/JVXy2b3/Ed38GpU6d47rnndmmEe5Ph+m9Z\\n1mgveTm6ffVeVOde2xp7aW3fDT73OfilX4IHHih+//7vhx/6IbjMzASabWQrsrmvDRkrKyv84A/+\\n4Ojh7MMf/jC/93u/x8c+9jGOHz/OBz7wgQ0fiq72TbuyEiClg22LXQ1xUEqxstJHShvXzanXK/T7\\nEXFsYBg5zWbpqin6NE3pdgvvkEbD0aECW+RqyGaWZSwu9un1cmo1i9nZ8qYPNf1+RBQZmGaO75uD\\nmtqSZtPThobrlOt9s3O1efDBp0nTOpVKwMtffmJHP1tKSacTkecWvi/3ZannoXw+9dRzPPlkjmUl\\nfPM3H9OhH5pdZyibQRDwj/94mjz3uO02i1tvvWm3h6bRXPdr+yc/Cf/1v8KnPlX8/iM/At/5nfCj\\nP7q749JsTTb39RPJ1NQUf/M3f7Pmb/fddx/33XffLo2oMB4UISkWaRrR7Ya4rjU2gWEcJ6RpPkqm\\nOIkoihFCUi57W3J9LuqWG1iWTZ4XRgUpFWChlBx5R1wNis8xR+PQbB9ZlhFFGZ5nrUt4uhHDRJq9\\nnsRx7C3NhxAS03SRMkeIfPB/1nmLFG7rJqXSxrHvmuubIklxjFKKSqW0bz2lrjZ5niMEOE6JLOvu\\n+OevXrc2q3YkpSQM40Hom7/jcyqlpN+Px+qdXi+i01EYRn9UfjXPc8Iw0bpKs6sIIVhYaJNlZebm\\n9vX2W6O5ZtgoR0YU7d54NJeG9rvaZgzDoF73cN0MACk9+n2BlHJNu2GoSPGaR683OSCr2IhJhHAJ\\ngq0Fb5mmSb3uDvJsFHdppeLjuhnVqnVVT9Y9z6VSKcoY6Tji7SUIEqT0CIL1crWa4WumaeK6NtPT\\nLp63tfmoVn08T1CvO9RqFUolSbVqrvGs6fcThHAJQ7mtZXk11w5pmhLHJklikSTpbg9n2yge/Mff\\ne5eKZVncfvssU1Mdbrvt4Lb1eymfX6s5eJ6gWp38oJ8kKWlqE0UGWZZt2vd2XicoEgNmmUO/rzb8\\n/EajRrmcMTfnj3RdGE5+j0azE9i2Tb3u4fsS19XVufYi262vNHsfnSNjf6NNwlcBxylCKYo8Buko\\nqeWQwo03JM9BygwoEi5OYpiANM9TfH/rieiGYxmSphlpyiCBo3vVTtMMw9AnX1cJxzFJkiKB37j5\\ni6KYMMwJwz6+X8ayFI1GBc9TW5pzy7KoVF7U7BvNpW2bxHGGaV49zx7N/qbwHEtQCizr2jBoDvW3\\nlAa1mrNthtpGo7HjZV9X47ouW/kqtm0hZTpIej15C1GEqElcF+r17QmztCwTKTNMU22od1zXHKxt\\n+eh1xylKOpqmumZKc2v2H7ZtU6t5OI5Ns7n/wreuda6GvtLsfbRHxv5GGzIuEaUUaZpiWRZhGA5O\\nu4vdnxACIcQoaWa5XMb3CyNEmqaYpkmv1yPLMpQqAyalkkOl4mCaJkmSYJomURThuu6ofOz09DRK\\nKWxb0u/3yPMai4uLlMvlUcLEYUK31QmmNiJJBEIYZJkAOqPknGmaMj09jWVZg1PUeGToKLJIy0G1\\njMIokmUZUsqJxpAgCACuKEa5cLkuErGZpjm6/qvHcj3Q6/WA4mHD8xwMIyVJEpaXI6IoIs9zPM/D\\ndV0qlQoLCy1Ms8zycki1qkiSLocOGXS7CVHUJ89zlFLMzc0RRRFZllGtVnEcZ5SwNooi+v0+nufh\\ned4oYeCLybhM6nUT03TXzMnw/doTR1Mk+x1WNpr8ACmlJMsyLGtzb7GlpSU6nQ7Hjh3bVA9sVR6H\\nn2/b9sSx5nlOkijAII6zif1eir46deoUjz76KHfdddemyT6/9KUvAfC6171uYjshBGEY4rru2NKn\\nq9vmeT7SteNwHIde7wwAMzOTc3n0+wlpapBlkmp1ck6mMAwRQlCtVie28zyXPA/HztNTTz3Lr/zK\\nf+GGGw7y+tf/X+R5jmFArWaNvtuVrE3X6xqkuXJs2+b++3+Ns2fP8Vu/9WscO3Zkt4e062zn/XSl\\nfSVJjutWyLLwqoZea/YWYag9MvYz2pBxiQRBRJpatFoXyPMSQiTUag6O47K83MFxaqysPM3MzHEc\\nZ4kTJw4NEmyatFoXuHCheICs1TpUq9OUSsUDYpEg1GVh4SymOcW5c6fodstImXHHHSn1epNnnjmP\\n40xz5syD3HjjnSTJM7z85S+h1VpEiBKGkXL8uLnJhlUSBClh2Ma2mwTBCr1eRqlU59ixRY4cOcTZ\\nsy2yrEocn+fIkRuI4y6OU8I0c5rNojpKp5NiGDa+H685uR/S7XY5f76ITz5yJLjsDWOnE6GUi2WF\\nNJtVwjAmjk0go9G4PipcdLs9zp3LiKKIZtOjUvFotbosLmYsLbU4f75DuTyFEB1uv/1mPG8Rx6nQ\\n7c4TRT1WVhS27XHq1AKue4B+fwnDcGk0Zjhy5FmSxENKj2ZzicOHmySJxfLyEufO9VlczPC8nIMH\\nq8zNTTE1lVOvl1bNfzHHxZyk+H5GFJkoJWk2Db3R12z5BDwIokEp6YRm0xy7iQzDkM997nmUmuLc\\nua/zxje+cmyfcZwQBGpL8lh4WbgYRsTUVGWi51IcJwhRhOpNYqv6qtfr8au/+tckyev4n//zQ/zu\\n7/4fY/v82te+xkc+0sMwPDqdv+eee94wtu3CQpsgcDGMLidO2GM/v/AySQAb142p1cYbxJ9++mm+\\n9KUEAN8/z+HDh8e27XS6dDouvh9z8OB4b5M0TTl7NkQpl2azzYED02PbxnFCFBXlpy1LrPtO9977\\n8ywuvgN4lltv/RV+/ud/ESldTDNlasojDEPOnk0xDJNDh7rU6/Wxn7URwzlVKmVqan31LI1mHO97\\n3/v4yEdmgdfzXd/1cywv/8NuD2nXieOEfh8go9m8sj3di/o2pdm89HuzUnHo9/uUy7riyfXERh4Z\\nA1u3Zh9w7T8F7gCXGp5hGAaVSpmpqRq2PTkeb6iIhyfoV0pxQuoCKUmytbFe7scaRvFAq7lyhjI2\\nTtaKPxdzpRS4rkezaeB5JnmekWUmShmDNgrDYNB248nNc7Vm3rcSNrq6vU7qqNlLbKc8GoYxeNC3\\nMM29r98KT7attd36dbIBsWm+CcdxqderbPY8calx6ZPWpSLcxQPWllO9uL1SW9NrGs12MT8/DxTr\\nsGHoB+WL2a7E8JfbzdD7VHN9sVGOjMXF3RuP5tLY1+VXL5crKTUkpSRNMwyDUSiI67oopUZhEK7r\\nEscp5bI/em11aAlArVZbE5qR5zlZJrCsIrTEtm1arRa2bY9CS6IoIo4zqtUSQRBRq5WxLBvT3Hpo\\nyeqxRFE0CnvJ83xNaEkYxvi+CxiDmGSJYRgjN+qNQkuEEFiWNfp9u0JLskzgutdHaMlGsjks26aU\\nwvM8TNMkz3P6/Qgpc6IoQogc33exbYd6vUYYhqM66v1+RJalVKsV+v0Qx7GRUo5CS8IwIk0z6vW1\\noSX9frjGLd22bTzvxdCS4fwDa+YkSZI1sqK5NrjaJdqGutW2txZasrTU4dixw1sIp9uaPA51jeNM\\nDi2BrYXWwaWHlnzxi4/ylrdsLbQkSRLe8Ibx3hhQ6OQgCPH9rYWWCJGPdO0knnjiCRzH4cSJyaEl\\nl/L5Ww0tGXdNh/L5+OOP8653vYtjx47xB3/wBxvOaxAESCkv2Rtj0udrNONYrTt/7Md+jNOnT/M7\\nv/M73Hnnnbs8st1neD9tx55B35uXx/VefvU//keYmoKf//ni9/vvh0ceKf7V7C5bkU1tyNBsC0EQ\\nkiQGti1pNHSSpMtlO2SzSFilsG1Fo1HW3hGabUHrTQ0URpwitAwaDW/PhPcN5bMIJ8oHCY4n5+XQ\\naHaCoWwWIVwReW5QrVr4vj791+w+1/va/q53wS23wM/9XPH7H/0RPPAAfOADuzosDVuTzb2xA9mD\\ndLtdhJA0m/U1GyEpJVFU1KOXsqgAsdlilKYpi4stLAuGVSqnpuoYholpGgghsW0TISSGoVha6lKp\\nOJTLlTX9B0FAGKaUyy5hmFKpeICJ41jkucQ0DaRUg89MyHM1qCwhRu/xfZs0lXiehW07E8ef5zlx\\nnOI41jpLeZqmCCHxfXdw6h/T7eb4PtRqhY9Wu90FoFwuoZQafVffd9eceA43n8MHbqUUvu9t+AAu\\npSRJUkzT2PcugFFUZBMa910BgqBPq9VGymIuHcdjerpOmqa0Wj1su7im5bKHYZh0uwFLSy0Mo0q9\\n7lMq2TiOw+OPP8Ejj5xkerrBnXfejut69PsRvu9RrRZJY4eVSdI0JctySiVv3UOAlJJ2u4tpGjQa\\n9YGnULKhjFwpcZxMlAXNtcVQ37iuvaUEnuNkdL/yiU98gs9+9uv8y3/5jdx9990T237wg39BFMX8\\n0A+9jVqttjMDHBAEfT7/+a9i2y5vfes3TmybpintdkC16m/qObO4uEgU5Rw6NL1pAtU4TsauAR/5\\nyF/wnvfcz4kTJf7H//gEQoirIivj9JMQgnY7wPftK/JG1OxPLt4brUZKyfd+7/fwxBMu//k//zA/\\n/uP/6y6NcmfIsow0Fev2fJfDpewHLmUt0Wg2ypGhk33uH3bFkBHH8To301arxfT0+ARfO0kYhly4\\nIDAMFyHWJh7r92OyzKHV6uJ5JUzTwLKyicry7NlFlpdLXLjwAr5fwXV95uYWmZ09SBj2qFYbLC62\\naDRmOHXqSQzjAGna4sSJwmJvmumgnxDTLPPkk89z+PAtnD17juPHj7K83KFcrpJlMVCEHays9HGc\\nCp3OeWZmjvLYY09x7NjtPPHE08zNHSVNu0xPV0f9b7RxDIKYPC/CZKamXkx+JISg2xWYpk2eF4nh\\nOp0+YVgmTUOOHIFuN2BxkUE1jIxKpUoUBVSrDYSI13htxHFCGBokSTxwL/SAZMOSn1GUkCQWUooN\\nE73tF9I0HSS4AsNINzQmpWnKmTNdnn8+JooiDCPn6NHD9PsLxLHByorBwsIZZmcPkedLeJ7B8nLO\\n+fMB5bLipptSZmbqRFGLT3ziOR55xKVSWeb55x/l1ltvIgig0ciYnk44fHgOyyqq8fR6ArCRcn3S\\nv06nx9KSiVICxwkRAvLcJYpSpqasbUt8l2UZQSAHccQby4Lm2mKtvrHGPnTmeU63m2Ga7oYyuh9p\\nt9v81m89RKn0XZw69dGJhoxPfvKTfPSj4DgHgf/OT/3Uj+zYOAEeeeQUDz9cIc8zjh17jFe+cnyy\\n1bNnWyjVpNNpc8st/tg5DYKAp56KcJwqeb7IiRPjqzmEYTxaA0xz/dp7332/z/z8T/Pcc4/ynve8\\nh//wH34Rw3C2VVbSNCUIikODi/X30lKbIPDJ85jjx+1NQ2o01w5CiNH6OdwbreYXfuEX+Pzn7wJe\\nzn33vf+aNmRIKen1UsAlTSOmpi7fqHep+4GtriUaDWycI0OXX90/7Mrd/X3f931rkoSdP3+ee+65\\nZzeGsiFFuVFBnqfYtnnRawZS5liWASgMQ21qHXZdiyyLcByFaeYYRlG6UimJZYGU+cDDI8f3LaTM\\nsO3Cw6L4jKIEqm2DlALXNREixfPMwVgYJNUsxmOaYJoKkJhm8R7ft1a9R2CaCssqxjBu/JZVfNeL\\nXzYMA8NQg/JUxYvlske57OD7xaay+D4Cw8gxzWJ8tl30N3zP6v6UKsZaJKGU69qsvv7FmDe/7nuZ\\nYuxy4nc1DAPbNjEMgWUpCpuNwHVtTFMhZYrrmuS5wHXVSDY9D6pVm0rFw7IMXNfB9xVKdbDtmHLZ\\nwrZNLCujyPxfjKWY12Jux42rkJlsMK/mSEaKudvehIrD67Of51mzdYbyu5ksFfqQDXXJfqZcTomi\\nM1Srk5flwrMhJss6HD2688b/RmNo/E43fUh3nGKt2szebFkWlpWT5ymuO9kYOpQTUBs+oNRqAjgN\\nXODw4cODtWp7ZWWon4oxrO23yGEkME1dvvF6o5CL8etnUeHnPHCWSkXs9PB2lNXXothjXGlfW98P\\nDHXEft8nanaGjQwZ2iNj/7Arx9nveMc7uPfee/nzP/9zXnjhBd72trfxG7/xG7sxlA3xfZ8jRwR5\\nnlMqleh0OpTLZbIsw3FsoEiMGEXR6AQ6yzKEEKRpilKK+fl5jh49ihCCublp8nyeUukI3W4XkNRq\\nHt3ueW688UaiKGJqqsz58y9w/Pgczz//PHNzcxhGCDgkiU+e5zhOzMrKaebmZpmff4xbbrmFxcVn\\nOXToEHHcwXEcooEZ8ejRBmmacsMNh7lw4QK33XaIkye/xokTJxCiKIe6sHB2sBGt/v/svXmUHVd9\\n7/upuU6duWd1qwcNtmbJNlg2AdsYGzOFgIHkmSxyCWEIgfuA5CZeL4ELrJvkBkzg5RGmkAUkJoZr\\nIDFxgMVgJxgb8IgtI8mSNUs9D2esU/Pw/qjuI2voo5Ytuy35fNbq1dM+u3ZV7b1r12//ft8fvu9j\\nGAaWZZHJZHBdF03T8LwK6XSa0dHRpuhjEATz4SQmHR0r8X2fvr4O6vU66XQHjuOgqir9/SGCoDYX\\ndsViEhKhqirVapVUKjVfp0YUWYhisqsVRRGyLM9f7xN321IpnTi2kCSZOI4JguNeGQvXX9cX3/lb\\nIAzD5nGW40GnKAr5/PGfFyszMJBjYuJJJiYOsmbNGsLQo1TS6OrqIpuVqdcNJiYOUanYpFIp1q7t\\nxXVTxLHL6OghyuXDrFixgvXrJRQluZcbNhSJ4yo9PZl5I1sD1xWYmUlEY2HhZSmL53nzYVZBs52Z\\nTOI5Y9s2mqYhy05TNDbJiJK4fJ98Xc/mmifZdRI38qfrGrrQPySpvSNzPpBO64iifUYBTVEUyWQU\\nfN8nlTo19fPJWJaFKJ4pLXXiKXj48GH6+vooFAoty1arVSzLapl6dAHf9+eNfou/oBcKBS69NOY/\\n/uOjfPjDn25Z3zXXXMMdd3yI2dlZXvOaf2lZNglzqKDr+hnDHJY6Pi+55BI+8Ymb6OrqYv36z7Ws\\nM5NReeyxn7F58+aWYzCVStHRETI3d4SBgSta1plK6Rw5sod8Po8knarH9OST9zbb/4EPxPMehDWi\\nSGluoPi+jyzLTTHuSqUCQLFYbHnsBSzLQtOSPnXy/NTT00EmYyHLRlvw+AWGJEnIcoBl1ejqOnVu\\n+OM//mP+5E+Svnn48IWlSRBFUXPtB4nXUi6n47ouiqI0x9yJmYQSEfskHCegWCwiCEKznoW13dms\\nB5INthjDiNC0VNuQ0eaMnC60pO2Rcf6wLIaMd7/73biuyxve8AaOHDnCl770JV760pcuR1NOSxiG\\nuK5AEEjs2rUPQejE9w/Q27uaOJ6hu7uXmZk5bFshihoUCklO+qNHpxGEHHfe+T00bStR9C1e9arX\\nUC7vIZdbQaVygHJZxHUd6vUKqdQAvb2H2LjxxfzsZ/8JrOLgwdu56KKXUa0+zMjIKgQhIJWSAIWf\\n//xxdH0VTz75NXp7b2Bq6gts3fpb1Ov/wbZtl2OaY5RKKooicdFFEn19q9ix4wEkaZi77/4Suv5S\\nbPs2Xvva11IuP87UlE4ce6TTPvn8SixrlN7eDZTLD7Fy5Tampx+gp2czR478FN/vJYrm6OoqkEpl\\nKJenMIwBhoZ2sG7dBhynhq7nOHZsGt9PEYaJK2EUhdRqNqKoo2kzZLMd7Ny5jyjqIJstsXnzqmZo\\nie8nL8SJ1kMNRUmh6wHp9PEZxrad+bIWopio0OfzMZIkceTILEGgk81a9Pd3TpEdKgAAIABJREFU\\nLXp/E8EthziWTqn/uWQpD+Rdu/bzV391H6OjaQzjX1m9eoRMppM1azKsXp1n9+4qDzywk+lpyOV0\\nLrooYmhoCw8//CuOHpUIgjIrVqRwXYlyOSadLtPRcYyhoRF6e0UURcVx0ojiLLKcYWrKpFjMsXp1\\nmosvHiQMPQ4fNqlUGug66HoKWRbmDV9Z0umAzs5OLMsjm9WIoohCIU9vr0+hkD3hXM72mj/TsKFa\\nzcL3JRTFawvQngdYloPjSLiuS7G4+Mt0FEWYpk8cS4Dbsi+ZpsnEhAdEDA7S0phx660/ZnKyj2z2\\nCf74j29ctFy1WuUnP9lPEKTYvLnG5s3rFi1r2w6NRowgRBQKektjxl/+5c+Ad3HddTcTxw8vWu6j\\nH/0oX/mKThiO8IEPfIDPfvazi5bdu/cwY2MKqjrFlVeuWfT8oyiiVnMIQwld98lkFg/BeOtb38rt\\nt68BIAz/iC+2kHb/yEe+zOHDG8jlbudLX/qjRbOETE5O8q1vPYnvZ6nX7+P66xfPxnLffQ/xwAMR\\nijLGW98q093dfcL/k37zCeAJBEFgZqbEwYMNfN+jq0ufz/Ilousiw8MFZmbm2LXLJIoCLr88orOz\\nc9FjAxw6NMbkJCiKx9atpw+BOZMeSJsLE8dxePjhUTxPp9E4zNq1Iyf8P+mbbwc2XHACi+PjszQa\\nKqqaaKN5no4gzFIsdlGrlclkMqRSHrnc8WdxqVRlfNxl586D5PNd9PWV6ezM4DgpVLXGqlV9zbJL\\nXQ8cb4fD8HDPuT3JNhckbY+M85vndJvy05/+NJ/+9Kf5zGc+g+u6HDt2jG3btnH//ffzmc985rls\\nSkuS1JQicQy+LyLLKUwzRFF0Go2QOBbw/RhBkIhjkTBM8tF7nkAYCliWQCazgnrdQ9MyuG6MJKk4\\nTkgQqHieRKMRo2k5Go0YRdExTR/D6MayBOLYwLZlPE/A95NjeV6E68rEcYa5OYFMZoBKJULTiti2\\nTBzLuK6A78tEkYHrhgSBgG0rGEYHpVJMNjtIoyGiaTnm5nwEwcB1NXxfRhCSNolijlLJQdfzVKs+\\nipKmXg9R1TSum7Q9igQaDRFRzFIu20iSjO9HSJKM44QIgkIYCsSxQBRBFEmIooLrhoiihGWFaFoG\\nx4kIw3B+F1+cLy8AIkGQXN8F8dLj9yZGFCWiCOI4KRtF0fyXiKLoBEHU8v7GcUwcC6et//lEEmNq\\nY9sGgtCLbas4jk4U5fB9CdMMcBwB2zbw/TxBkKFelwhDDdOUiOM8vp/DslL4foooyhJFKTxPIwjS\\nOI6MacaIYoZGQ8bzZMLQIAx1HEcgDCVcNySKVKJIJwhkfF8ijlVcV0IUdVxXJI5loigZC1EkI4oy\\nvh+ecC7Lcc3DMEaSZMIwvqAWjBcqYZiM7ThuvcB/al8Kw9Zj3fOC+f4pEwStXbkbjZBMZgWOc9wz\\n6XQku/opNC2LbfuLloPj89WZzulXv/oV0AGsB1p7TlQqFcJwEFFczWOPjbcsW6u5iGIa2xZann+S\\nUQFEUSIMW4+V/furwEqgv+nJsBilUkwuN0IQtDZcOo5DEOjIcpF63W1Z1jRdUqkCvp+Z93A8mQFg\\naP47BEEIqICG78eEoQAoRJE4n/I3IIpSCEIKy2p9bADfD5HlDL4vNVNVt2kDST/2PAVFyVOrLdaX\\nTuyfFwq+H6EoOp4X4XkLP4ckazTxtHOL6waAhO9LCEIKz4vxvHB+HZesgZ5uOxbGd5s2Z6LtkXF+\\n85ymX/34xz9+ilvZU3//2Mc+9py0YymWcMdxCcMI225Qr7sUCga+H5NKKfOeAAKm6SDLIpqWuNJZ\\nVoNazaFen+PAgRnWr+9H13OkUhK2HSLLAtVqHQDXdTFNj3XrViKKKo5jcujQNN3dBjMzFr29GWQ5\\n2T3LZNKEYcjU1DiHDs1RKIhMTbmsWlWkWg3p7U2jqlkUJclaATA01A+IuG6D8fEKYPHrX4+yaVM/\\nuVw3ui4wMVECoKurg3rdpqMjRalk09OTxTR98nmdatUhm1U5dGiaQsEgnU52msLQpVbzufjiQURR\\nRlVlgiDRuajXbTRNmhftBNf18LyATEYnjgWCwKNUMunoyJDP55uZL5K4xuTBI4oCYRifojS/kDXm\\nqfGnCyrWC1ldCoXMGV16n69ZD07um7Zt881v/hsPPbSXbdvWkM/niSLYtGktnZ1FDh8+xp49Bxkb\\nmyCfz/OiF20gDGXm5qZ55JFdyLLE+vWrqdctJidnyGYzrFjRTSql09fXgyxLVCo18vkMnhdSr9dR\\n1RQrVnTS0VFEUWQmJqawbQfDSFIZimKirRJFAvl8et44Ec5nEEi8Y3K5zCk7KM/1NQ+CANf128rl\\n54hnewexVZakk1noS2dSw1/IsgPQ0dE6XOTw4cM8+ugBNmwYYP369S3L7tlzgHrdZtOm1S133xfm\\nK0kSz5jd6k1vehP//u+HeOc7L+fLX/5yy7JXX/0WAG699W8ZGRlZtJxpmhw5MklnZ4a+vr5Fy8HS\\nr+nevXt54xvfA8ATT9zTss7777+f73//IV7+8o1cd911Lcvee++DzM7WufbaF7UM7anVavziF4/T\\n0ZFh+/bjQqML/XPt2rUcONAAJonjeP7ZOQtANpvsBluWi6YpFAo5wjDk4MGjyLLE0NDAGQWLPc9j\\nbGyGTEY9xRukTZvR0VGqVYc1a1Y2PaCeOncma94+Vq82OHDgwDK29NziOA61mkUmk5yzaTrNNV+i\\nUSOdMrf4vk+5XMc0q9h2yOBgL6qqNOt5Op5NT21H2zNqaVxo3kFny7Zt8M//DAu61U8+Ca97Hezb\\nt7ztarO0vvmcGjJOptFokE4/9y7f5+OgjeOYWi3JEpFOy0vOP16rNfB9iCIXUdRQVeGcqvwv1K/r\\n4jMO0QjDkGrVBgRyOe28zUjyTDi5b/q+P6/8HRPHPuWyj6rCwEAnkiTNx3+7QEw+n2r+rVp1EASa\\nf2vT5plyPs6b5xNPPnmUSiWku1ttmbXjfGJ6ukS1GpBKwcqVz66b90L/PHr0KA8+OIOm+Vx33db2\\ny0ybZWehb1qWxU9/ugvLEtm+vZuhoaHlblqbNi/4Z/tFF8H3vgfr5qNEjx2Dl7wERkeXt11tltY3\\nl2Ur+he/+AUbN25s7nrt2LGD973vfcvRlPOGKIrwfQFZTs274y31M6AoBrWah6IYeF58TtztfN+f\\nF2mKUZQklAUSg4vruidkpVkqSZiJAqj4/oWt6L1UkhANlThWqFZtZLmA40hNN/HkOiX/D8PkHriu\\nh+vGuG5Mo9F4QT+g2iw/nuctyQU/iiJc1z1jCMiFSBiGVCoh2ewQc3MXTnBuve6RSnVh2zxn9/Xw\\n4TkEYQWmmaNarS7pM0vto23aPBMsy5rXMRvk8OG55W7O84bEe9Jtr1XaLAttjYzzm2UxZHzoQx/i\\nhz/8IV1diSDjtm3buOee1i6qL3QkSULXIYosDGNpSuiiKJJKiQRBg56eNEHQwDCeeQaHxHPCw7JE\\nwCcIGmQyC+E1DqYJ1arXfLFeKrIsoygBouiiqu1QAABNUxAEF0UJ6OnJE8cl8vnjQqGqqiCKyf9l\\nWZ4P0wloNFympmZxXQXLas/IbZaHJOtNSK0W4rqt9QcajWTuqNXcF1xssyRJDAykaDQOs3Ll6QUx\\nz0e6uw1cd5rOTvU587AbHu7CcfaTTlfPmH0GEqN8tRosqY+2afNMyGQy5PM2pvkkw8OLC5K/kEjW\\nky6mCY1GW5igzXPP6TQy2oaM84dl890/2aXu+RBGsCCuKAiQyaTOeRx/EARMTia6FH19HSecs2ma\\nzMyYFIsGhcLpF7KLKcmHYYhpOkiSQDp9Yropw0hhGMyntzr3O06plH5CSEkQBJTLHooSUyy2Tnd4\\ncvtFMQl7Od/SZS30G1EUyGTObbqvMAzns+gkmT5GRnpPqF+SJAqFzPx1N3EclyCI59MDm1Sr1il6\\nA47jMDFRRVXBMNIoikgmYxDHMaVSlXrdpasrQybTzvTR5lxx5jGxoJm0lE25RsPG80IyGe2C0T8p\\nl6tMTJTp6TlzNqPJyRJBENHf3/G8eHYuRqKp89wec25ujieeOEyhIPDyl29aUoreZE5dWt9r0+bp\\nIssye/fuZ3oaNmzIMDw8uNxNel7RHn9tlgPHOdUjw7aT/nievY68IFmWFdDQ0BA///nPgeQF+7Of\\n/SwbNmxYjqacgOf5BIECxPi+j6Zp86rmHrquN3NbB0Gy+z03N4emaRiGMZ+7WmyWKZVK9PX1UalU\\nyGQy+L5Po9Fgbi7ZaYyicVRVpaenB8/zmJoyqVQiLKuGKCbp2xqNBoqioKrqfCaVmFqtRrFYxDRN\\nCoUCk5OTaFoKz1OACN+vEMcxhmEQBAHpdHreUOBhmj6eF+L7HqqqUqlU0HUdXdcJgoBUKoVt2xiG\\ngeclZWq1GplMBkEQEIRE+T4MQ7JZmSAIUVWFSqVCoVCYdx0WSKd1ICQIguZLdxzHTa2GBQE2SZLm\\nc4Z7eJ5EHEdomn/Ci/dC2efzYt11PcJQJQgifN8/o1Dh2WCaHo1GzORkhd7eGEUR0TSNRqPRDN+p\\nVCrMzlao10MMo4N8XiEMG/i+Q70e4DgBMzMysixTrVaxrABVXYFlmfT0hMRxyMqVEbIsc+jQLLpe\\nJI5NZFkijpOxEIYhuq43z2+hzyzcQ+CE3xfcRCVJumBeNNucPaqqkst5xHE8Lwa7OJlMCsuy0TS1\\npRE5DENsO0aWU1iWQz7fun+FYYggCEsyTM/OzpLP58/YZyuVCo7jnFFAc6n4vs+Pf7ybJ544TBD8\\nRsvYecuyuOeeHczMTPO2t73mjCKmC8+kM51/cl1tMpnWWVMA7rjjDgYGBti+fXvLclNTJrt2HWJg\\noPu0AsBPZXZ2FsuylqQbYJomsiyfNp3svfc+wV//9d8DBq94xSYuu+wyPC/xEDzZqLGQ8UrTgnmv\\nx6XpaVSrVRRFedr6GwvP87Z+0YWHaZp4nkdHR8cp/9u/fz9/9mefAxoMDPwFl112yakVvAAxDJFG\\nw0TTkpTtS52znkqSeSlCkqSn9fk2L0ziODFkPPVRIssgihAE0F6+Pv9ZFrHPmZkZPvjBD3LXXXcR\\nxzE33HADn/3sZ8+Yv/1csZh4yHHhRMjlkswKR4/O4roitj1HOt2N686iKB0cOrSLXbt8JMln+/Yh\\n0uksx46NI0kdHDz4GIZxEY6zj5GRSwiCaTo7+6lWJxgb87Esm9nZaTStn4svDti69XLuvvs/qVR6\\nCMP9vPjFV1OtHsJ1sxiGyNBQjlQqy09/+hBB0EM6Pcrq1Zezf/8jSNIIojjJhg0bqdfnOHy4AUA2\\n69HVtYoVK6C/f5AdOx6nXM5imgfo7V3PxMQeDh6MEcWQiy826OwcRlVrGMYKRLFMR8cgR4/uw/OK\\n6LrFxo2rcV2b8XGTKBJYsUIjmy3y61/vIQjyZDJ1LrroYsLQQZY1wtBDEOT56xwjSQrZrDxvHGng\\n+yKKEpHLpWk0GoyPW4hixOBg8QRDQLXaIAhENC1e1CNlufF9n1rNmxfX1J/R4vTkvjkzM8f3v/8r\\n9u+vs3KlwfXXb0VRYh58cJzDhyc5evQwBw64HDs2iqKkGRyU2bp1HUEQMjZWo1YrYVkhtg22PYnv\\n95BK1di6dSPFooCipBEElRe9qJ++vgy/+MU4vu+zdWs3PT3dHDs2w7FjFXwf0umQQqGL7m6DDRt6\\nUdUsshyRz6cxTQvHAUWJCQKPY8dqlEoN+vuLjIwUT/vC0eb84tkWBGs07HkDRUw+v7hn1oLwcdIn\\nJVKpxftWEtriI4qcUUD43nsfY2pKJZdrcMMNly9arlKp8LWv3Ydtp7jqqixXXdX6ZX6ppNOvwLKu\\nprv7Lqan71u03Be/+EX+/M93E0UyN9/cyUc+8pFFy87Olpibi9C0iKGhrkUX9mEYsnPnUSxLoq9P\\naik2+ulPf5q/+ZspRDHgk5/cwjve8Y5Fy37wg3/Jrl0r6ek5wje+8fEW7ZzlU5/6Pp5X4Prrs7zu\\nda9YtOzo6Ci7d1tIks8VVww3DS8L/TPpN38EHOOaa+p873s/YN++acJQZOVKg76+ruY5T05WGB2t\\nEAQBK1YUGRwsntGItXPnXnbudNE0m1e9astZGzMSMeaFdYbaNvReQJimyb/928M4jsFll+m8+MVb\\ngeN984orruDBB7uAYeCLL3hNCNdNst/t2nUAy1Lp7ZXp68vRaMhoWsjQUPeSPFzjOKZSaRCGIvV6\\nCc/TSaUihoaeXYHhC4UXstin50E6DSfL+mWzMDYGuQsn0vO85Hkr9tnd3c03vvENpqenmZmZ4bbb\\nbnvOjBitkGWZQiFFoZBCluV5ASLQtBzlsoum5ZiZ8ZAkg8lJF+jDtguUSiZBIGJZMrJcYGoqIp+/\\niCNHLDKZfqanfWxbxrJEcrkuFCVFrZYhCHoYHW2gaTksS2HNmq00GiphmGJqyieO85hmikrFYnbW\\npFw20PUh9u41kaRODh+uk8+vpVxWUBQd34+J4wK+n6VSkZHlbiYmqvg+VCoRfX0jmKaMJOWZnLSQ\\n5T7CsJtqVSQMs0xPB+h6JzMzyblOT1uIYoFSKcJ1Q1w3xLYlfF+hXnfx/YhaDTKZAaamXBRFR5Y1\\n8nmNTEZHkjSiSCQIQBCUeeFK8P0YWdbm2xsDIsVijtxJM0YcxwQByLKG5z1/Y+YVRaFYTFEsGud8\\nhy15iGfp6rqYOM7jOFAum1iWTLWqMTUl4brd2HY/MILj5Gg0DGo1nSDow3Uz2HYftr2CUqmA543g\\nur14noTjZPC8NLq+kqkpj337RikUhkmnewEN15WxbZVaLYXv55idlfH9HLatUak0kGWtmevd96Pm\\n77btE8cGQZDG9yVcty2i1+bMLPShMBRaamQIgoCuy+i60Ex9vRhhGCGKClEknVF3Y3bWJZcboVaL\\nWooVT05OUqvlkeVhxsbqrU9qiRw8eBDfX0sqdQP1euvF97FjxxCEjej6dh58cH/LsrWag2lGVCpW\\nS7FNz/NwXQnD6KbRaD1ef/7zJ1DVy4BLOXjwYMuyExMaw8M3YNs9jLaQgJ+dnaXR6COV2syBA9Mt\\n6yyXbRoNmUolxDltIPMW4MXAeu655x4syyUMdSQph20fv69JatwIMAjDNL4PQXBmXady2cYw+gnD\\nLJZlnbH8ySRphpOvtqj1hYVpmoRhnnR6mNnZxin/f/TRR4GNwKuBzc91855TfN/HspyWWmmJF2tM\\nvS4hyx3U6zHVqoWm5fA8Yck6a4lnlYgkqVSrHqlUAdd97gSG25y/nOyNsUBbJ+P8YVk8Mvbu3cv7\\n3vc+Jicn2bVrF48//jh33nlny52lc8nZWB9LpQqm6WEYIpYVNb9HkcMjjxzAMGQ2b16LJCnU6xVK\\nJReoMTnpMzycwXU1VDUiCAzARxCSiXnnzkO4rsAVVwzS1dWHZZV44olpcrkQ1y2STofEMSiKQC7X\\ngSgqjI3tY2rKob/fwPcLpFImpVJAb6/ByMgIURSwd+8hfB86OtL4vkh3dw5FyeG6s5TLLtmsRL0e\\noqoBu3ePI8shF1+8CkGQyec1bDumqyuF54mYZpkjR1w6O0XWrRvB9z0OHpwlDAVWry6g6wazs7NM\\nT5uMjHRgGDk0LUkNG0URjYbTjC+LY0indURRxPM8bNsnlToeNtNoJBoZhqGfYIF3XRfHCTCMF8bO\\n1cl9MwxDdu16kn37Jujv72DduhFUVea++3YzO2thmpMcPjzN5OQEgqBy8cWrWbWqD1EMGR0tE0Uu\\npVKNyUmTMLSwLI/OzjQjI5tIpXRSqWQSLxb76Orq5PDhA6xYUWTNmpXEMczNlZmermDbHl1dGaJI\\noqsrx6pV/Xhe1LzfyaLFQ9NkBAEmJ8uYpklHR4GenmLbhfoC4NnetQmCRKRWVVt7WYRhSLnsIIoq\\niuK3TCfdam45mYmJCX7961HWrOlgzZo1Ldt6xx13Y5oer3zlpeckvCQMQ171qt/mgQdkbryxg1tv\\n/dKiZUdHR3nrW/+CWs3nH/7hg1x55ZWLlt2//yhjYxGplMcll4y0DHsbG5ukXLYZGeluGV7y4IMP\\n8t73fhZdD/j2tz/NwMDi3ht333033/rWr9i+fZB3vvOmRcsBfPWrdzA52eBtb7u6ZXjJwYNH2bFj\\nBlmOeeUrNze9vRb6Zz7fS622EdjHsWP309fXx+joNL4fsnJl1wnhJdVqndnZCmEY0dVVoFDIntEd\\nvVqt8sADT9Lfn2Pz5nUty54O13WZmGgQx9Dbm8Iwnlna8jbPLx5++HGOHatwzTWbm+ElC31zdHSU\\nwcErgbUoyg48r7y8jX2WiOOYctkCVATBpVg8/XwShiGW5TI9PcPcnEVfX57u7iIzM3VyOY1CIb/k\\nY9q2g+eFCEJIpeKQy+mL6s21OZEXskfG1BRs2QLTJ9nPh4bg3ntheHh52tUmYSl9c1kMGVdffTWf\\n+tSneO9738ujjz5KHMds3ryZXbt2PSfHf64GreO4NBo+UeQhCAaCEFIoJIuuSsUhjiXSaU5YtCfu\\n1QKiGFIopIiiiErFAwQyGRFd1yiXTaIo0UGQJBVNE08bdpE8TBrEsYKiBORyx8UbwzCkVkuETXM5\\nA1EUqdctfD8ik1FRVXU+XEAkilxEUQAi4lhBEERyOekZa0F4nodpeiiK2PJl5IXEUvpm4kZpEYYS\\nmhaRzSYaLaOjM8zOeqTTAqtW9TTvTxAEVCruvCBshChCqeTjujGFgoBhqMzNNVDVHJLUoFjMk81q\\nqKpKHMfU6xZBEJPNXjjCim3OnufLYieZE22iSELXo+dtyNnZEIYh99//BI1Gho4Ohxe/eP2iZYMg\\nYHR0ljiGgYGOlvPw0aPjTExE6HrApk0rz4nW0MKzAyCfN85JHPrZzDOlUoWZmQgIGB7OnWLIuOOO\\n73PHHRaZTJmPfOQ36e/vf8btO9s2tiLJkuIDwjl5jrZ5/rPQN8fGxvjYx75LvV7gt387y1ve8lvL\\n3bRnhVZrzzbPP54vz/bl4MgRuOoqOHr0xL9ffDH8x3/AurO3Vbc5hyylby6LgqJlWVxxxRXN3wVB\\nuCBfkGzbR1HSeJ5AJpOESCzckHw+8VpYWMQkYRQBuq7i+3U0TWuK1BUKyQulKIr4vk8+b8zreUiA\\nim17pFLhKbvegiCQz6cIwxBFOXGx73k+cawRhlFTpNF1QVEMLMtGVVXS6RSq6uM4EmGoE4YB6XSE\\noshLul8Lbn2LLZ5t20eSDFzXwTBObX+b0yMIArmcPi+CquL7/rwrvIKmyYhiSKVSp6cnCdeSJIlU\\nClwXJMkgjh3Wri3Ofy5GkjKAgiQ5RFEGWU43+0AYhvi+iCxrWJZ9RmHFNm2ebURRJJ/X5+e11gKi\\n5wuCINDf30m5XKen51SRwKfiOA6mKSIIMqZp0dGx+ItwOp2ht9dHloVzlk3J83x8/7gQ8JlEXJdC\\nEAT4voQkKTiO2/L50tFRQFVNZNk4rfZOV9cAw8MHyGSWFl+/VM7VXKgoCvn88Z/bvHAIw5De3m46\\nOkQyme7lbs6zRqu1Z5s2zydOzliyQCqVZC5p8/xnWQwZ3d3d7N9/PLb3O9/5DitWrFiOpjyrpFIK\\njUYDTRObGR+SnRjI55UTdmLqdQvPkyiVJojjDFE0R7GYQ5YlCgUNSZKoVGziWELXg/mUpw1KpRqK\\nEtLVdXqLtyRJpzUQKIqMbTtIEshysqumqjG+b5FOJ4srQRBQVRVBEKjVHGQZdH1pO3CJyF4w7/ER\\nn3bBpuvJQlxVl5ZRoM1xFu5rvW7hugKVSoU4lrHtOWo1Bd/PoKo1CoUcjYaN60pMTMwQhgaZTExP\\nT4FUKoXnedTrNuAjills20SWTbJZrXkcWXYJAotstr1z2Ob5wWLz2vmKKIrs2XOUej2HaY6ycuXi\\nOhlRFDE9PUMQQG9v7xlqjvC8GEEIz9lLfRxHVKsmogj5/Llx3ZZlGVn25jNtndkw0ir0ZWzsEHv2\\nWKTT5jmNkT+Xc2HbgPHC5ciRcWq1PFu2HANestzNeda40OboNhcmtn16jQxdb2tknC8siyHjc5/7\\nHO95z3vYs2cP/f39rFq1ittuu+1p1XXrrbdy6623EkUR//Iv/8Jtt93GnXfeyfDwMP/0T/+0rGk7\\ndV1D09TmAjIRmxOf8vNxgiBGkuR53QENz7OIYwFIROoS9xoBQZCIosQYoigqnZ0qcew9Ra19aciy\\nTLGYPuEzuVz6tPUoikJHh3xW9UdRjCAku3aLuQVpmtY0lLR5eiTXWSaOZTo6Cuh6jG0rCILSFMoK\\nw+ReqKqOrncgCHYzTZmqqnR0KJimiO8rZLMZ8nm1OW6SnZXT94s2bdqcG5KU3im6uvqIomMty4qi\\nyMDAAHGcpIhrhSwrdHYaxLF/zlyHRVGiWMw123IuOJfzjKbl2Lx5NYIwt2SxwKXQngvbPFOiKOLi\\ni7cSxx1kMnPL3Zw2bV7wtBL7bHtknB8sy1v+mjVruPvuu2k0GoRheEq2iqUyNjbGz372M+666y4A\\npqen+elPf8q9997LLbfcwne/+13e8pa3LKkuz/OQJKm5g7OwQFMUZX6RKVOpVCgUChw+fJiRkZGm\\nYrrnJSrvuVyOgwcPsnr1anbv3s3GjRvZsWMHl1xyCXv27KGzsxPLSkaG44iMj4+zfft27rvvPq68\\n8kq+//0fcsMN1/GDH/yI66+/loMHDyKKArVasvu0cuVKfvSjH/Pa176axx57jC1btvCd7/wbN974\\nBsbGxprthSR85+DBg1x//fU8+OCDbN++nR/+8Ie8+tWvZv/+/axdu5bZ2VkgUdoGWLVqFXv37mX9\\n+vU8/vjjbN26tfl9oeyChb2jo6N5rqOjo6xcubL5/ejRo+RyOTwvuT5BkCMMQ1KpFEePHmVoaAjL\\nsjAMA9u2MQyjmSVgYbGtqiqO46DrevP7yffq5EW0ICTZDkRRbH5fYKFXJN0IAAAgAElEQVTepaby\\nemrZBWPM6ep9JiylTbZts3v3bvbu3cvll1+ObdtkMpl5le6IMAyZmJii0ahz5MgxBgcHkCSZ8fFJ\\nVq0awvd9Dh06RCqVxvMcggA2bVpHKnURtm1j20kISXJNFXK5DI1GjGEYzfOWJGle4d8mm80ShiGa\\npuG6LqqqNrU0Fq7LU8OlFq7Xwv8W6lw456ee++mubRAEzc8/9d5GUdQ0tpzLe9Jm6Sz1up/N2FuY\\na5fC+Pj4kvQPFl5ml7I7uDCnnYlSqUSpVGLt2rVnLLswX7U6vizLXHppP93dHZTLrQUAM5kMv/d7\\nVxJF8OCDvzhDWZ1vfvN2fud33rKk87/rrru4/vrrW5bRNJUbbng5AD/72T1nrPP9738/n//8589Y\\nbufOnezcuZObbmotCgrwxBNPALBhw4ZT/nfttS/iTW8qAvDRj8bNEBjTNMnlcvi+j23b6LqOYRg4\\njsPo6Ch9fX2oamLEXWyuX1gf3Hfffbz85S9ftH2e57XUvjh5TJwpDHOxz7V5frKwFjqZkZERPvax\\nVQBnHOdPl+XsI0+dvxee3QttcV0XXddPaN/CJl0Yhhw6dIgVK1aQyWTYv38/q1evborDL1VHZmFt\\nkIRp66f8D56+8bU99i5MFgstaXtknD8smyHjyiuv5KqrruKqq65i06ZNT6ueH/3oR4RhyPXXX8/G\\njRt59atf3VxcXH/99dx2221LMmSUSiX2769SLo+xe3cNQfDo7JTJZPoYHFTJ5/v45S9/yeiowu7d\\nP2FiYhBFOcoVV7wEy5pk794SnidjmocRhHUcO/ZfGMblTE//AMO4niD4AF1dr6Bef5wgMAhDH8sq\\no6qbqdXeia5fS7X6f6Fpb8B1/5B0+kYajb8BugAPkIACsIdU6qXY9gcYHPxtjh37TeC1wMfp6roG\\n2z6IohiEoY5pTqCqG4jjD5LPv4qZmTcjSa9Dkj7MpZe+Gdd9nHK5iOfNMT1dIQy76O8fpbPzFUxP\\n/xDDeAVzc79HZ+frCIL/gaJsQBSn0fUccWyQzVaR5Q2k0ztJpa7ENP+LTOZaGo1fIMvbcd0ncJwi\\nqhqybl2BXG6QffsewHUvYvXqKV7+8jfheWOo6gBBMI0kFfG8MrYtAArd3SKpVC++P42i9DAwoLJp\\n09rmvZLlkBUrOlAUGYgRRXleVDXxUBEEFU0TyGQSI0mt5iIIkM+nWi7oEwFSH0GIyeeT2M5azSYM\\nadZrGK2zKiyFMAypVm3iGPJ5/bQL2KmpKV7/+g/w0EMTgAIs9AkHSAERYAExyVBOA8cADegGJuf/\\nXgQEYGq+zFPbvnAtIiBDKhWRShlomkoqFRHHBUTRwTQtgsBAUar09Y2Qy3nEcQfZLAwNFclkCmSz\\nOqtW9bNxYz+Dg73MzFSp1z06OtIMD3ehqirVqoXrBkRRiKapzfuRCOMGyHIiPgswNjbD1FSDbFah\\nszPR8Ygil5kZE9sOGRrKk06nsO0IRaEtKPYcUqs18H1IpcSWWReW0s8XKJUqzM15pFLQ39+16IKz\\nXq/z4Q9/lenpFK97XSe/93tvXrRO0zR54okpBAE2beo/IWPFyXzgA3/No48qbNpU40tf+qtFy+3f\\nv593v/vLhGGWP/iDQX7/93+/xTkl85WmwYYNAy0X5N3dNwBvp1jcTBwvnqo0WUjfCCTGmVaeFpdd\\n9moef3yIP/iDf2THji+c9sUfEgPO9u0fxDQHednLvsJdd31z0TqT+3Jjsy2tjp+09e184QuXEce/\\nWrTczp072bLl/wG6+MY3vsGdd965aNl3vetdfOUrHjDDv/7ru3nTm950wv+LxSLwduAYgiDw0EN7\\n+K//ephdu+pkMg6ZjMaePRWGhrp5/evX8nd/930eftikUGjw53/+FjZtupihoR4kSZ3PGmYgCAKj\\no9Ps3z/L3//9LfzgBzr5/P/H/ff/v4yMjJxw/IV7bhiwYcPQKc8b3/ep173mM8bzPO6//0nCUGLT\\nps5Fs+BEUUS1mnhqZrMvjCxe5xu1Wo0/+7N/YHo6zTvesZLf+q0TxTyT8fAKYJBisXjOBRaXs49M\\nT5eoVgPSaRFRhPHxOooSkctlefLJI0SRwfCwRnd3H4IAqipgmj7Hjo3zxS9+i8ce81m1Kiad9tm9\\nO8fgYIP/+T/fThDo9PSojIwsnh0JknM/enSWPXsO43nQ15dm+/bk3cKyLCYmTEQxYnCw66w9tdtj\\n78JlsdCStkfG+cOybGPu2rWL97znPczNzfGnf/qnrFmzhje+8Y1nXc/U1BS+73PXXXdhGAbVarXp\\n3ZHL5ahUKot+9uMf/3jz6/vf/wma1sfBgzWCoJ9Go4+JiYgoGmDfviqa1sGOHTVGRl7Lo49a5PM3\\ncuhQhlIpx8REilptDY3GxRw8mENVX8P4eAd9fR9gbm4FPT1/ysTEChxnG7Oza3GcrdTrF1GtXkKj\\ncSmuu5F8/qPAOtLp/wVsw/PeBlwMXAOsATYB24GNxPEfAENcdNEngA2kUv8b2MTs7DYajU247hos\\nawVxvBXXvRbPW0VHx18Al5NO/xWet5ZabQ2HDhWJokuoVouE4eUIwusYH1+Brr+ZqakVXHTR/6ZW\\nW4Uk/Q7HjnUTx79BqbSWSmWAINjCvn1FFOVlPPqozMDA2/j1rw0GBt7Gzp0qkrSdvXuz2PZmXHcz\\nhw5Bvb6CffuyFIs38tBDPr29L2LXriqGsZZ9+1yCoJfZ2RSlUgbfX8HRozai2MOuXVWgk2PHkns5\\nO2uiaX00Ggq2HRIEEp4HUSTTaEQoikG16qKqaRwnmt+NCxEEjThWzuhqnKTv0ogimTAM578kBEGl\\nXg9QFAPHeeZx18kOnIogaPj+6es7cuQIe/eKwMuA3wSuBH6DJP/85fNfl83//RrgNcAW4Or58peR\\n9JtrgeuAS+e/rgUuma/nyvmfXwZcgW2vp9HYTKm0mrm5VVQqm6lUhqjVLsayrqBcHqFcvojR0Ty2\\nvY65uRGOHTOYmupidjaPbXcyO+tjWQGNhkwQFHAcBctyCcOQIBCJYwXXFYljtbkT6bqJa30QiM3r\\nXq+HKEo3liVTqTioappy2cayVCSpk1rNw7J8FMXA908N12rz7BBFEb7P/FhoPZ6W0s8XqFYdNK0D\\n2xZbahuMj48zNtZFd/cbeOihxV/4AUolE+giDHPYZ1iV7N5tMzDwbnbtyrXcLd2xYweWtQV4JT/6\\n0f5Fy8Hx+cpxUi2Pn7zgbAY+RjKuz8R1JC/rrdm500AU/wzf/w1+/OMfL1runnvuwTTXo+sf4JFH\\nlhKO8fb5NpyJ1wJ/C2zl/e9//6KlbrnlFuAq4N38+Mf1ljV+97sTzeP/4Ac/OE2JbcDvAC8HoFyG\\n8XGNINjMsWM5Dh0K8bxt2PYI9933BJOTBoLw21QqKzlwwGF6OqRUshEEFd8Xmju8ScrfHu65J0TT\\n3kKj8SK++93vnnL0hXter6unveeeFyAIGmGYPGNM08Tz8ojiAFNT5qLnHYYhUSQjCBque+60P9qc\\nO3bv3s309Eqy2Vfxk58cXaTUVcBNJM/dc0sQBESRgiBoeN5z20fqdY9Uqot6PaBc9pDlLmo1EdMM\\nKZdVMplVHD1aba7FLMsjDBVGR8s8+WQOSXoNMzM9/OpXZXp738uRI1keemgfudwwc3PuCc93z/Oa\\nXrwLOI6D68rMzMhI0jCVitD0mjZNB0HI4Xl604P6bEjWJPKyXNc2zy6tPDLahozzg2XxyJDlJOvF\\nQnhAd3f3EkTLTqVQKHD11VcD8IpXvIKHH364aSmt1WoUCoVFP/vxj3+8+bNpmuzbN87llw+zY8dB\\nRFGmoyOLKB5h48ZefH+WG24Y5JFHbuemm1bzy1/+My95icvq1Q6OA4oyiutK9PSYmOY3ueYaj+np\\nv+LKKy1mZj7Mtm0mmvZfdHZOUatNEoY+tl0mjsfJ5w9jmh+kq+sIlvVecrldiOIteN4+HKdEFM0i\\nigGGMQjsAz7BmjVlDhz4fXp7DzE19R5E8Qm6ukRghlQqj+va2LZLGB4hm52iXv8zCoXHsKz30Ne3\\nj97eYQYGqtTrj5DJ1Bkff4I43s3q1RZR9E9ceaXD/v3vZOvWaRzny2zZMoMs/4zu7hKa1kkYjjE8\\nLALf4zd/02B6+ku8+c0Zpqe/xOtfn2N29i4uvzzEth/CMAw2buwhlZrmpS+Fqalb+d3fHWF29gGu\\nv36ESmUPV1zRietOkU4LOI5PEEzR318kio7R2xuzd+8+Vq9Odi76+zvYt2+c7m6JfF5FkmJAJI59\\nCgWVIGjQ3Z3G903S6UTXQ9MUXNdGloUzKmjruoLvOyiKgCwnonOq6hMEAR0dOkHQIJN55qKXSf+3\\nieMYVT39LvH69et5zWu6uf32OwEVKAE9QDDveRIBiVAnhCSeFnOAQeKxUZr/nEoy1Gsk3hoGiYcH\\nJN4cycumpqXJ52UMw0DT0mQyImE4hapG1Ot1Go0nUVWX3t6Yri6Iol3k8waDg52kUiUKhTSdnSVW\\nrRohn9fwfYdarUI2myGbNZAkCU3zEITE80KWfRQlOXfDSNL9aprY3C3p6tIYH5+iUDDo7MzieXV6\\ne7MIQg3bnqOzs5NUSqPRaJBKnRpq1ObZQRRFUikRx2k0hYEXYyn9fIHu7gxTU7Pk82pLz4V169ax\\nZctPOHjwG7zznZedoc48c3PjKIpIJtN6R+/Vrx7khz/8NK98Zdf8rv7pueqqq/ja1/6GcvlRfvd3\\nX9GyzoX5qlhUWmYpCMOFrE0mcOrL8VORZZkg+DbJmG/NO97RzVe+8hcUi+N88IP3L1ru7W9/Ox/7\\n2A3MzHyYt7518XM/zt8CSxHz+0/gPcAdfP7zi+8+33zzzXzrW/83rns373vfpS1rfNe7tvDJT/4t\\num7yP/7Hl09TYgeJ91oDgJUrFTZtEnj88V8xMiKQzxfYtetRVqzo48YbX8LY2Lcplb5Kb6/MmjVX\\nUCgIyLJAGFoYhto8xxUrMpjmBDfemOfb3/48K1e6fOhDpxpSFu55Z6dyWg8gTVPwPAdVTZ4xhUKB\\nQmEKy6oyPDy06Hkn6yaPMDzVbb7N84Mrr7ySNWseYHz8O7zxjS9epNSdwN3AY+f8+IqioCgWYeij\\n663n23NNZ6fB3Nw03d1J3xwbm6W7WyKXUxkeFqjX97FpUx+C4CJJAul0ilrNYfPmQV72sl/w05/+\\nH7Zs6aOnZwM/+tHfcvXVBtdeu4Va7QCDg4Xm8911Xer1ZJOqUDgeTq3rOtmsxerVUK0eZXi42Bwn\\nhUKGRqNEOi2i62cfyi7LMqqajD1Na4+9C4lWHhnt0JLzAyFehuTBhmGwZcsW/uRP/oTrrruOrq6u\\np1XPjh07+Md//Ec+97nP8clPfpL+/n5uv/12vve973HLLbewevXq04aWLOYOG0URpmkjCALptN7y\\nxSjZSXEQRYFMJnVC3JzjuNi2j6oKeF6MogiEIc3JG6DRsAnDmExGR5IkPM+j0fBIpRR0PUnNWq/b\\np62/UqlRLifZPnS9CPgUi6kT2tto2ARBRDqtIcsypVKJI0fK9PSkGRjow/M8nnxyDEUR6evrAERE\\nMSYMBdLpxHXO930aDQ9VPdV9/OT6FyOO41POdamEYcijjx4mjruQ5VkuvXTNkj97vnJy3wyCgImJ\\nEo2GTW9vkXw+c8J9TtwpJ9m7d5T+/hX09xcQhJAnn5zk0KFJOjtTyHKK7m6dlStXkMsZhGHI4cMT\\nzM5W6ezMMTzct6SFcRRFVCo2phkQxy4DAx3LKqbb5rnlQs81f/Ic3Krcvn2T+L7A8HCOYjH/jI8d\\nRRHf+9597N9vcemlOa699jcWLWvbNl//+g+xrID/9t+uo6OjdbrWpR5/bKyE70vk8wKdnYtvAiw8\\nO1RVYs2agedNZoKF/vn1r3+DT3ziHvJ5jx//+O9bZjg5mUbDxnFEIGxmC2vT5pmy0DdN0+TGG29m\\nairm/e9/MX/4h+9c7qadM5Y6fz5THMfFNGMgJpeTlqyf0WZxLvRneyu++lW491742tdO/Pv73w8b\\nNsB//+/L0642CUvpm8uyhfnNb36Tq666ii984QvcdNNNfPSjH20Kdp4N27ZtI5VKce211/LII49w\\n0003cfXVV3PVVVfx+OOPn3W4iuf5BIGC50kniE+eLhzBdX3CUMV1xRNc3JIX9wBJSjM1VUcUDWZm\\nLIJAwXEEgiAgCAIsK8b3ZSzLBcA0PSQpTaMREMcxrusRhiqeJ2HbNp7nNUXLpqcbiGInphliGBH5\\nvHbCy20QBNg2RJGGbSdudAcPltH1EcbGkrpmZko0GnlmZxUmJqrYNpTLLqBjWclnGg0P0LHt49cg\\niiJc18W2k/Yv1L8YQRDguiJhqOI4Z+fSJ0kS69f30t9vs379mUX9LkSq1QbVKpTLKlNTVrOvJW7G\\niafE0aMWgjDE1FQVQfAJApG5OQ9VHeTAgTpBkGZyUqJW83Ecj7m5OrOzElNTBlNTUCo1ltQWURTn\\nvVxCstkijYb7bJ56mzbPKSfPwYsRBAFxrKJpuXMSZrZQ59hYQE/Pizh0aPHwAkj0JPbt6+LIkRX8\\n8pePnpPjx3GSIjuVUhGE1i/vMzMlarUMc3Ma1Wr1nBwfaIoXL4WF+e903Hrrz3Gc13D48Fpuv/32\\ns2qDYeik05DLKW0jRpunxWJrRoC7776bvXuHcN1X8+///vhz3LJnl6XOn2fD6a6lrmtks0LbiNHm\\nnLBYaElbI+P8YVm2U9/whjfwhje8gT179vCDH/yAv/u7v+OWW25pxrOdDZ/61KdO+P3mm2/m5ptv\\nflrtkmWJOE5EISVJI45jqlWLIBBIpY57UwAoioRte0gSSNJx92pBEFAU8H2bdFohCBwyGZk49hDF\\npN7E88MkDEV6e5OdcFUVcV0bRUnqSNri4fsec3MOUSSSTsekUjkajSrlsk9nJ6cVnRRFEUkKCcMI\\nw0hucbGoMDc3RjabZJVIvDxKiKJPEKhUqyaZDIShi2EkCzhNk7AsF1lOMlIEQUC16hIEAZVKDUHQ\\n6elpbXmXJAlBcInj8GkJJGUymbPaUbvQMAyNSmWUyUkPQdARhA48z6NW84njENf1CAKbUmmafF6j\\nWg1Q1ZBCQebAgSmyWQlBaBAELqaZIpeT5r1o5nCcGRwnjyC0Drd5Kqqqks0GhKGPqrYX+m0uHE6e\\ngxcvp5JKQRS5ZLNLHzutj61SKPjMzOxh1arW86Sqqhw58kt8X0bX152T4yfhQvL8s6718TVNxjSn\\nEUVQlMFzcvzE28sijkXSaanljq7ruphmiCjG5POpUzwnBwY8Hnjg5yjKOOvWXXNW7RAE4VndTW5z\\nYXOmftzd3Y3r/h9Ms8S2bRfWRsBS58+lEscxlUqDKBIxDPGEta6mtcdom3NDW+zz/GdZDBlvfvOb\\neeyxx1izZg1XX301X//619m+fftyNAVY8DLwkKQk9nvhb47j4vsxiqJj2+a8gUEkDCNEUSCOXUQx\\nSdnqeT6iKBBFMbIsYNs2xWJ2XlVeZWJiGsPQ8LwkZCOZ6CN8P8S2HSRJIAgs0ukk84Isy+i6TxQl\\noQWOE9PZqVIsyoRhRBjaSFKaUqmCrqsYRrKgrlRqQPIC7PtB03gwONiHqs5RLHZQKlWQZZGhofR8\\nVgEfz3MIQ4lGo0wYKhw9Okl3dw5N01AUGcdxCQKfSsUhDENEUULXVQQhuV5hGOK6PpKUXANJElFV\\ndX4XXyEIEqu6bTtomtpMb+e6HrIsNdtZqdSIoohCIfesaB64rksUxei6dt6k0VKUmGxWpV63mZ6e\\nIwwD6nWo101SKRXDkBHFGkHQQa3mYhgRw8N54tilXheJ4zJRBEePHuPwYZP+/i50XWH16hz/P3tv\\nHiVHed77f2rp6qreu2fXSCONRqMdJAFiX8yOwTsOxsQ2N07i63NC4l9i+8S5to8Th1yuEyf5/ULs\\nG0Mc2zEJjo2DAdvEhrCIRUhCG0JoNKPRSCNp9um9u/aq3x/VM0hI3RK2QGDme84caXqefuutqnd5\\n3mf5PtFonImJHLFYhHg8VgsP1QmHlWPSiY6eI8lkUDY3GBMOnuej6wbJZAxN0/B9H8MwEUXhN0bh\\nCOa4g6LIc+k0rxOGESjs4bDScM7NRIrFYhrJZON0Ddu2cRx3di2ph9c3Fj3Gx8eZN6+poZQsy3R2\\nprBtZ3a9Ptn1Z9bDRnKxGDzzzHOcddZlDdtMp9MIgo0oGkQijfO9y+UyfX37aW1N0dVVn39BEAQq\\nlQKTk3l6exc1bFOSZKrVKXzfJxxuXH42MLpWicXUhulrQdlECUkKYdvWCRXLGdi2i2G4CIJHLOYe\\n9/5bWpZQKv2QaBSam5uPGyuWZeG6HuFwQDbsut7r2g+OXgtPl0d4pk9vp31pDscjGBsuvg+y7B1n\\nyJg3bx7F4m4MYweeVz997O2IWCyCprmvO5LptWv0zPwCH9cVkSQFyzJP6DWfwxx+XRhGfUNGqTHv\\n9BzeIjgjGvkXvvAF1q1b95Y5EOi6iWlK6HplNrrC80zCYQ1BqM7m6BiGRKmUIxpNks1O4XkRHEcn\\nkXBQVQ3LMlDVCMPD4yQSbVSrWbq72xkaOsLEhIJp5unq8pFlmYmJIoKgYVk6HR2dHDp0iGi0nXI5\\nT3d3K6ZpYRgSU1NVDh6sIAgK1WoW142yd+9hksllDA7u4aKLLsH3y3R3KxiGwfh4wLCuaVVSqQyC\\nYBKNahw5ksV1k2zffoC2tvkUi1OoaoxKRWdyskIopLF//yjt7Ut55ZUtLFhwLnv37uPSS9eRzxcJ\\nhyOUyzrT0xV8X0LTbGRZYyY7qVjU8f0wlUqBSCSO7zukUiK+71Mqufi+gGGUiEYTWJZOMhmlUjGw\\nLBmwSKVEdF1nfNwh4Osok0q9flKmRgjK3nk144v5a5dQfaNhWRa5nMX4uM3BgzqSVEIUo5imRbGo\\nA2F8P0uhYDIyIhAKZZmczLNsWS+2XWb79jHyeY18/jCSpFCtuoTDGolEhc7OJlRVQJZLZDLteN40\\ny5dL5HIGpZKEoujMmyfOHv4qFQPblvF9h3gcslmDalWmWp0miPzUKBYLLF4cwrYdqlUB3/cQRftt\\nX6rM932KRQMIYxgGmcw7N0Lo9SIoZ+wBAoJgNTQm9PcfQdcz+H6WtWu1uofEwPhqIQhBxFujqIig\\nrC+AhyBYDQ+eL764H8eZx+joMFdcEau7PzmOQ7nsIQghqlXjmEi916JaNTAMcXY9rNdmoVDg7/9+\\nE4LwLv72b3/OlVdeUrfNjRs3sm+fj+fF2bhxBxddVL/KyRNPbGNycj6CcIgPfCBWl0+jWq3y/POj\\ntcoZu7nuuvptvvRSHy++6OP7Ft3dr7B2bf3qC8G+kyCXy9PT01rX6CTLMppm4zgmkUjjdTk46NhI\\nkn/CQ//Xv34v8EkqlYN88Ytf5J57/hVBULAsnWg0TLHoIIoyllXBtiUEQcLzGr/Ho3H0WphKnQrh\\naWM4jkOx6LzufszhrQdBmEkf9ohGj5/rd9xxB4ZxEbCUH//47je/g28wfpW5YBjmMfpCUM1Ewfct\\nNE3AcUyi0d8Mh8gc3nrQ9fqpJRMTb35/5vD6cUYsCevXv6okfepTn+Kee07EPP7mwTB0BgeLuG4Z\\nQQgjij7RaBhFcahWJ7DtMJs2PcOuXSIdHZMsWrQewxjDddP4vklXVwZRDLFlyzZKJY3mZp1odBnD\\nw0+yb5+CKA4jCKuwrGFEUasd/IoIQieiuA9BWIIg9OH7a0gkBoFufH+cfD6Grk/iumFUtYmxsc2I\\nYg/x+CDp9LWY5nPce+8zpNNZLr74KkqlEbZuHceyHFpbBaLRLgRhP6bZgSAcRlGWc+jQ0+zZE0fT\\nxrj++htQVZ+9e8fxfY0FCxw6O3MUi32MjoaZnNzAj370AtHoGC0tZ2Ga0wwNTeG6Ct3dCtHoItav\\nj9PRsYShoT6GhhyamnwuvfQSbLvEzp15wCMajSEIYfbufZlSKczq1VHmzeuhUBjj4EGbRALOOWcZ\\n5XKJvr5RXFdAFNN4nkg8HiYUCtU2OxvHMSiVArLJ4DAj0NXVhCiGEEUPzxOJxU5c9cDzPIrFMp4H\\nqtrYk3oqCAwjJrIsEI9HXpcnLehLFd+HREI7oQLgeR579uzhb/7mfnbvnsJ1J8lkmhHFMtPTErbt\\noSg2+XwOkAAPEEmlolSrQZRQUJ1kimCqh2pyYdLpKMkkRKPzSCajdHTE6OpKYlkCIJBIJFizppPW\\n1ib27TvMvn1HqFRc5s1LsX79EnI5neHhEpJkEYvFsO0w8+eH0fUylmUjCCqqKuE4MeJxbVY5d123\\nZoSZyaEV6t7/rwNdN6hWHTRNOo6o9vVCEAQEIXgfc2nzrw9Bel4J3xeIRhsbgCzLYHj4MJGICdSv\\nMCIIArt2DZDLuaxZkyYeX9zg+h75fAVRhHi8sWF0cnKUgYEp5s/XkeWz68o5jsOGDS9SKsFVVy0m\\nGp1fV7ZUKrJrV5Zo1Oe88xpHLzz55L8AQ8B9wF/VlfvBD37Atm1ZIAkc4k/+5Pfrym7Z8hwPPlhl\\n3rwit9xSv02ABx98hJGRJO99r9bQkPHxj9/CoUMLAdiwAZ555pm6stdf/1EGBppJp8eYmnq6rlw2\\nm+X3fu//MDUl8ZnPnMfNN99cV/bIkcPcf/9Omprgd3/3phOkHg4CLwPD/Od/PsfnP38nBw5M1dJG\\nbSxLIZWKsXRpC4lEmqeeepHvfe8xotEwn//8B1mzZsUJ14zx8WkOHpxk8+bNPPXUOF1dIb7yld85\\nLnro9e4LgaG0jOOIdHS8tY3rc2gMy7J4+ukXKRTgqqsWkUwem/r1s5/9DDgPKAAHz0QX3xRMTWXJ\\n5SyamlQymRTbt/czPa0zf36U1tb2Wb3OcRyy2TL//u8/Z9OmcS65pJlrrrmY0VGXtrYQZ53VgyiK\\ntX2kguv6JBJqrXKTQ7EYRDNHIsrsnJuezrJ/f4729girVjVecxi9GOcAACAASURBVF+LwPBto6rS\\nnEHxHQLDgBMFgM6llrx9cMbrFW7ZsuVMdwHDcInFkrhuCEWJEw6nkCQRcJieFlGUTp57LsuKFbey\\nY4dOOLyIkRERVe1AFFswzRCVisfoaJpE4gp27y6xbNl5PPlkjqam/8nWrREikV7Gx5OUyyspFBZx\\n8GALsnw+27YJpNMfZetWm7PP/l2ef97H867lhRccpqaWMzXVg643Ua22Mzq6gETi4+zdG+Hss29i\\nxw6Jjo5Ps3dvkrGxGH19OsXiciqVXgYHY8A5PPecRUvL7/HsswWWL/8oW7aIhEI3MzW1gokJmJwU\\nMYwuotHzmJ726e5ehSQ1sX79Cvr7XTKZD/HKKwrFYgv79nno+goE4Wz27nWIRNawceMEmtbFpk1j\\ndHZeVavhHRzSXXce5XIz5bKF5wlMTam0t1/I5s2TRKPtDA4WaWrqpVCQqVY9LEtEVWMkkxkMQ0CS\\nIrPEo7puI8tRxscrCEKKiQmbclnDMOJMTZXxvBCFQiATkJQeD0EQiEQ04vHobErMrwNdt5GkCLYt\\nnjJJ3Qxs2655HcI1g8OJ+zswME6ptBDPO5ticSnl8hrGx+djGGupVleQz/cAZwHXABcBl5PPL8Cy\\nVgNXEtSqXwFcTFC/fi2wHtc9j2x2IY7Ty+RkBMNYQn9/M4VCD5OTURxnEfv2SezZk+XgwQjj4/MZ\\nG2sml2tmcNCgUonXqsl0IUkpWls70HWZ6Wkf04whimE8T0KWoxiGN0vMZ5o2vh/GMIINxPfDmOaJ\\n7//XQbXqoCgxqlX3tBCPJZORGsHY6eFEeCchHA6jaWGg8YEuHo/T3Z2ho6Nxake1WqVSCdPc3Mv4\\nePUkVxeJx6NEItpJD5TRaJrzzltJOj0Px6lP4pnL5bCsFpLJFRw4MNWwzWLRJB5vwXWj6A20os98\\n5jPABcAfAZc3bPPFF18kmO/vZseO/oayfX0eixe/D9PsYWhoqK7cyMgIrttFb+9V7NvXmMD00KFD\\nwO8Av8Ozzz7bUHZgQCOTuYdcroef/KR+Wdknn3yS0dE1qOrH+fGPG5el3LnzCPH4eorFLg4fPnwC\\niXXA9UAQ1eI4EcrlCKVSksOHI7huC7YdxfM0XNegv/8AlnU5k5PnsXlz/wnXjODAZeI4zWzalEdV\\nr2NysvOEz1TXbURRw7JOfV+IRiMkk5GTEq3O4a2NQqFAqdREKrWWgYHpOlJXAzcB7W9iz948eJ7H\\n9LRFKJRmfLxIuVxmfNwnHO5h374SoqhhGMGebxg22WyZl14ySac/yI4dMv39h5k3bwm6LszqDUF6\\nmIwgqLP6gmEEDhPbliiVqoiihm1L9PdPkEis5MgR63Xz7lWrNooSO0ZnmcNvNubIPt/+eNMNGZ7n\\n8fzzz8/+3tbW9mZ34TgoikC5nEdVBSIRm3DYRBAEPE/E8wqMjw+yenWUffv+g5UrFarVAyxerKCq\\nOdrbLTo6VNrbNVpbSxQKz3LhhRlyuVdYvz7KgQPfZsUKA1E8yLJlLun0AZqbx1m6tITnvcjixQaD\\ng99nyRKbwcH7WLtWZ2TkZ3R0lIjHt9HRcZjFi6u0tk6wbFkWXf8h69b5DA7+khUrREZG7mXRomla\\nWqp0dwPsRhAGWLq0jCzvYu1aiQMHvsu73pXiwIEHufRSH897mLa2/XR3q3R2yrS2HkKSdnPhhW3I\\n8ijnnz8fx5ng8ss7KRQeYeFCHUmaIJMxyWT6iUb7WbpUoljcRm+vwqFDA6xdm2Z8/DmWLg28/Z2d\\naURxHE3L0tYWoblZZcECn2JxG+ed10apNEZPTxrbHmbePBFFgUhEQpIsPK9CLCbjONVZ4jlVDUKB\\nW1ujuG6e5uYQmqYTChVJpTQEwSYWk7DtSl2yOlmWCYchFPJQlF8/GElVgz7Ksve6IwpkWUYULcAk\\nFDpxX0KhEOecs4iWloOI4nYSiQNo2kt0do4Sje5EVV8imdxHKLQfeBrYCGwhkzmAorxS+2wrsI+g\\nZv0LwDYUZSfR6B4ymSOo6jBdXTna2ibo7p4mkxlh0SILWR5AlktEoy6x2DiStB9NmyASGWfBgjCt\\nrR6trWXa2w26ukJoWglBcMnn87juFIlEQDDrugbhsDAbUh48dxNF8VEUv/b/0x8YpqoSpllGVcXT\\nknMuiq9yvszh1BFUYDKpVg18v7FiGJBNWkiS1zAdSVVVWls9TPMAXV31y4RCMN5CIY9wmJOmMvb0\\nZHDdSRYujDSUbW5uJpMp4zj7WbKk8f6VSIQpFscQhDJagyTv733ve8CzwN3Akw3bvP7664FfAo+x\\ncuWihrIXX9xBpfIYCxZkmT+/fuTIkiVL6OkxsKznueKK+lwaQC0C4afAd1i6dGlD2a6uLNnsH5NI\\n7OaDH/xgXbkrr7yS5uZdFIv3c911jctsr13bieO8RFvbJO3tJzoMbgceATYDkEi4tLToJJMVOjp0\\nEokC6bRJIiHR2pph8eI2TPNZZHkjS5e2nHDNkGWZVCqE709ywQVxbPu/aW+fYsmS4z2+qirjujqh\\n0KntC5Ik1dL8PMLht0a67Rx+NWQyGdrbq1jWHlaubK0jtQl4CBh7E3v25iHYIw36+wepVk1s26O5\\n2aVaHaS7W8N1dVQ1WN9930UQQvT2CuTzP2HVKoc1axZTLh+itTU8uw4H+pKN5xmz+kI4LON5BpLk\\nEI2quK6OLLssXpxhamoP7e3SKZWVPxqaJmOa5WN0ljn8ZmOO7PPtD8E/A8WD165dy44djb0ubyRe\\nW5e2VKpimhLgkkjINV4HD9+HfD6LLAckRkE1ExnXVRDFIJ8vEtHQNLVGVFehVDLJZEK10DePatVD\\nFH08TyAW0zCMPACqmiKbLTAykkcUk8TjOr29bRw4MEZfX5VUSmL16qZZRa1cLiPLIUZHxzFNCdfV\\nMM086XSCREJD0wQqlQo7duQRRZHFixU0TSUSCTM2Nk1XVwee5xGJRBgfHyceT3DkSBFFUWhvj2Db\\nNrIc5HwnkzF8P+DyKJfLVKsWk5MesiySTjsoioLjKJimU8tfTCBJNpFIiHA4qPYykysKwSbked4s\\n0ZqiKLO/O46DLMuzpWWLxcASHgrZSJJMNKrOKoMz7c58d8bbFRCq+rPv9VRCeU8Xodqv09bMGDz6\\n+68dm77vs3//CLt2TSPLEdrbbVpb0wwN5dB1HVn2aG5ewPDwPlpbm9D1POl0EssyyWZNDh3KEYmo\\nGIaDrnvMm5eiszOJpqkUCjaxWAxNM2huThKJhGsKg8jw8CSOE8f3y7S2aoyNGZimh6bZdHe3z75T\\nAMOw0PUqk5M+shymqcklk0kdc0/1ntnpfBcner5zxHmnD79KrXnDMBgamgYEFi5MN0zzKRQqOE4I\\nsEmnj69G8VrMrB0nw+sZB6faJgRewpPxv5RKFSYmDMJhkfb2eN22dV3nc5/7Lg8/PMTHPraEu+76\\nVN02N2/ezPe+l2fbthf5X/9rPe9977V1ZScnc+zbN0Fra5JFi1pOerAul8snrRI1MjLOn/7pN7j1\\n1tu44IJmmpubG1w/z/bt/Zx9di9tbamTvodsNluXx+NoGIZx3CFlZnxeffUneOKJnxOJ3Ma3v30x\\nt956K67rUq3qCII4S9g7E7I+MjLNxIRDtZpn7douotH6UVcza161Wm34nH6VtWduvfrNwYyeM4OZ\\nsfmjH/2Ij3/8MUzzAS699DqeeeYHZ7CXbwx832dqqkQ+7yKKApmMTDodO0bXmxnnlYpe4xDyEEV9\\ndu677olJQ187R+rpEq9nHT/ZNd4J+FX29t8U/PZvw7vfDR/72LGfP/IIfOtb8NOfnpl+zSHAqYzN\\nM2L+v+aaa3jggQe4+eab3xILRjSqIoomkiTNLn6RiInjuEQiKp4nU6noNDW1YVllNM3D81xcN0K5\\n7CGKZk0pAkUJUSpZtTZtMhkVRZGwbRfPc7DtOAChkENHR4JMJkQup9PR0VYr6xemqUlAURySyeTs\\nYV1RFEolB0VJoaomruuQTreh6xbxeKhWclVixQoHXTeJRtO4LgwOTpFMdjA6mmfhwsBD0NzcjGla\\nNSU8GCCqqlIoWHhemHLZmCVXUhQFVVXx/QKyLJDJtABgmgGzvCwrGIZFLHb8wWPm96PDa2c+e+2/\\ngiCgKArRqIFluViWjO8rVKuvljicGSsz3zl6o5v526mMp9M55n6dtk61r52dTfi+g+v6tLU1o6oq\\nIyMFotF5hEJV4nGL5cvnk816FIsath3B8wxcVyUWS6AoAu3tcTRNJRwW6ehIEovFMAwdy/IADd/X\\nyOertLQE47OjI0WxqKOqESIRjbY2n8nJMul0C5WKRTqt1CodVDFNGYiSyZQQRYdUKtlw8Tn6b2/k\\n/H8rrC2nEzMGvLcTbNvGsiR8X8A0rYaGjFhMRdctFCV0Svd5qs/i9YyD1/N8T1VWliVO1gVZllm8\\nOMW55zbR2dn4IL9y5Urmz/9P0ukOzjlndUPZctlA0zLoulX3cHA0TuWeQiGZj3zkA0iScUpEvkuW\\nLDnld5BKNY6webUPobr309s7nyeeuIF02pyNmPA8j2o1KGsuiu4xB6pUSiOfz5FINJ00FWTm+Zys\\nWs2vsvb8pq1X72TUIxXu6OggHhcxzffQ0nJyg93rxZuxr54MM3NKEMrIskQsFhgcZ3Tro/sWpBzO\\nVCyp7/w4+vOj7/G1TqAZ/DqFBObm4TsL9VJLVHUuIuPtgjNiyPinf/on/u7v/q4WUhkscoIgUCwW\\nz0R3aiVCtZr3cALP82qEQgqhkIgoykhSCNe18X0X25YxTZNSyUAQXFxXxfdh585+qlUFxzlEKNRF\\nc7PDWWetpFwuoOsC2ewEzz9/EFEUuOCCBTQ1taFpPplMhkOHxiiVRAShxLx587GsPDt2DAMevu/i\\neSL79x/AtlMsWODS3b2Mw4cHKBYVIIskpREEi+XLF5BIqLzyyn4sS0IUS5imRCxmksvFKZdzjI/b\\nuG4JiCCKIMsOoVCY4eExXFdFUSrEYs1Uq9OYpoqqOrS3t+L7MD1dIqhA4AEyU1NZbFumVCoRiSQB\\nG0EIYdsGhYKF69qUSkHVlWjUJxRK0NKikkolasRKDrIMiURAihYcthUKBR3Pc5Dlt9fB7XTDMEzG\\nx4tkszbFYoFs1qapScZ1bUZGxrCsSUBlZGSUfD7wOiaTcYrFAgcPjlAsQjQaEKLOn99BuVwkGk2y\\nbFkzvb2dDAyMc/DgKKYJsizS0hIjkYizaFErmYzCpk0TGIbFypXtiKJMuVyhpUXDtm3Gxwtks0VM\\nEzKZKAsWpGcVuKCyhI7vQzKpHqNYBJE3JqIYcE+83Q7nZwLlchXT9FFV8W1FQhas8SKiKBIONz70\\nSpJELHbyewvIEas4DkSj8nElDn9VBN5Bj3BYIBar75W3LIs9e45g29Dbm2lYKtYwgqpQkuTR3KzV\\nVbBDoRB33/1dxseX8corz3LHHR+u2+Zzzz3Hd76zGccJsWqVykc/+tG6so5jMj5eQtNMRLF+Goxt\\n2zzzzA6yWZHVq1MsX14/vWNgYC/f/vYmJMln/vzrGt7/xMQkR454pNM+bW31jRSe53H48BS2DW1t\\nkYbRDoVCgYGBLJLksWrVguMOjd/5zj8C7+HIkX384z/+I9/97ncplSocOpTF81xSKYVy2SWVirBg\\nQROGYVGpWIyNZSmXE7S1mbS3148ysSyLUsk+Zt+awxxOBTt27GBqqg+Yx4MPfgv4f09b24323Dcb\\npmkjCIH+3KgfM7p3uVxmaKiIJEE6reE44gnXYcdxKBQMBAGSydNPEj6Hdx7mUkve/jgjJ4hyuYzn\\neTV27xKlUumMGTGORrVq4LoxbFujWnUQRRVVVUilQnR0NJNISITDCrIckAolkzEikQi+H0LXXUwz\\nQio1n/Fxga6u1UxP23iexORkBUFIMDqqI0nzEYQFjI5WEYQE09MGoVCE0dEKqVQ3nhejtVUgHBaR\\n5XZKJZlyOYSuh5mclGhq6uXw4QrRaCv79uVIp5eyf79BpRKlWFTJZivkclV8P0ImMx9VjbFoUQZZ\\nDsgXDxzIAynyeQFFiaJpcXTdwzR9BEGlubmFfN4jGm3l4MESqtpBLgeOI2Ka4LoSnheQc/q+zOSk\\njijGGRurEApFKJVsQKFYtPC8KLouUSzKuG6asTGdcDhDLhcQ9JmmU3uWr5I6QbC5JZMayWToLV8i\\n9Y2GaTpkswbFIkxMePh+mvHxKqlUC21tLZimiue1UigkCYXm09bWg6YpRCJd2HYborgYXe/CMNrJ\\n5wWmp2NYVhd795bYv3+KYjGFZc2jWAwDHUxPh6lWU1SrMgcPTuB5LVhWO5OTVWKxBJqmoKoKluVg\\n2zKimEJVo0Qixyocruvi+yEEIYxtH0seaFnB3PK8UENSxTkECHgmPBQlimGcHvLSNwuRSITu7hQL\\nFsSOGyOvhe/7WJZ1Uq94sHcIyLKGaZ6+8WOaLooSxTT9hs9Y13UMQ0MUWxgfLzRs07J8otEkoVCk\\n4X09//zzZLPdzJ9/B5OTjZ/TwMAAgnARmnYdzz+/u6GsLGt0djaTTKYbktdVq1Wy2TCJxAr27882\\nbLO//wiRyFpkeQn79+9vKFsue7S3L8TzFCzrxCTMEKSK6LqMJKVqpY7rY3KyjCi2YFknJlC1rEXA\\nu4Hz2LJlS23++GQy7UhSuJaamcI0FapVHV13icXSGAbE4+0UCk7DZ2UYDpKk4jivn+R5Du9s3H33\\n3cA5wA1A72lt++g917LO3L7q+z7VqoPvy+j6qRF5F4sGgpDAshRKJX12HX7tPLRtB0EIA8qc7jCH\\n04I5ss+3P86IIcPzPL7//e/z1a9+FYDh4WE2b958JrpyDGKxCIpSJhq1SCbDgDHLGyAIAqFQiGg0\\njOtWSaUUwmGfaFRC0wQSCYVUyqJUOsjatRkKhV10dyfJ52fIJ3MsW9ZEc/Mkzc3jLFvWgiDkmTcv\\ngeNUWLKkiXy+n0xGwPMiRCIxRHGCtjaR5maBTMZj8WKFYrGP1atbqVRGOPfceRSLr9DTEwYmkeUc\\ngiAiihLJpI0kTdHb24ai+EQiMrlcmWhUIJ8fJR6HWMwmGnVqip5DIuEDRXp6UlQqI6xc2YxlHaG9\\nPbjHaFQkHPaRZYd4XEYQLKJRyOUmSSZDOE6FVCoIFUyng8iOdFqkrU0gHM6xcGEc256ipSXwtkUi\\nCq5bRVWPrz8uio0t+e8UCIJLoVClVCqiaSUqlVGamsLE45BM2qxalSIanWTpUocFCyosWuRwzjmt\\ntLXZdHXptLQcpLMzSzo9xvz5Pk1NVfL5bSSTPuGwRjw+QVtbgRUrRFpaCnR1uTQ3l8hkYOnSBajq\\nJInEGAsXZlAUj2g0SL9SVYVIxCMc1mtlXMVjwsxlWUaWHQTBRFGO9cQHnnmDUMg5pdD0dzqCajsy\\nllUiEpHfdh7gmfS0k6Fc1imVfAoFo+FBMojyANetEomcOIT7V0EkEqo9Y6nhM47FYiQSOoIwzrx5\\njcPDI5EQ5XIe1602HOvnn38+CxYc4siROzn77Mbr3mWXXUY6vRFRfIT3ve+KhrKqKmAYZRTFbbie\\nJpNJOjs9KpVdrF7dmMB0zZoluO4OotEhVq9unNqycGEGwximoyNcN9w+6Kda24uyZDKNOTra25MI\\nwjiJhH7CyI2eHgf4IfAkn/3sZxEEgUQijCAUaW0N09QURlULaFqQ6uR5LoZRprlZwrImaWlRG0aJ\\naVoIz9NRFH/OIzyH14XPfvazwAbgB3R1nd6D+NF77smi395oOI5FLlfGcU7NkJHJxBCEPJGIRSYT\\nx7JKaJp43DxUlBCCYCJJJ+cnmsMcTgVzERlvf5wRss9Pf/rTiKLIE088QV9fH9lsluuuu65WVu6N\\nx2vz9y3LYmwsTy43yQ9/+DS27XDOOd04jsLwcB/9/S7J5EG2bVPIZMbIZlO47j42b9bx/Ul6ehYT\\njbYyNPQyjpOiUtkKrEeSnsR1Lydgoz8fGCGZXAhAobALWE6wqV0NPA5cBTwBXAH0E1jsB4AScC5B\\n5Ym1BNUoZmSP/s7LQAvQBBwAlhwl+wzwHgJG/HOBA2haB7peIKhpvpxo9BU07RLmz9/L4cPLSCS2\\nsH//SkKh7cRia/C8USKRVmKxMLZdwXXTCMIBDGMZnZ19ZLOr0LR9yPJSIpEiqprBti0EwcCyUqxY\\nIVGpdLJsWYWRkWYymSyjoyFSqTLNzcuJx316eroJhYLUFF336e5O4XkxZLnInj1lFi2KsGRJL4FO\\nLtZ4A8BxQFEC62oqpQAhwKFYNFEUEU3TZvPUfT/wqlqWR2trAlVVsW2bSsUiHJbelCgQ13UZHDyC\\n67r09HTOKvlHj03TNPmjP/pf3HPPz4A4sAfoBopAmIDfZKz2N6/2IwImkAL0mkys9p1q7f8iINV+\\nLCAJTNXaCQMOEOSAK0oUVYVisVL7nkd7ezvz5sWIxVoQRZNkMkUqFSEej+O6Hk1NGTo6kpimi21L\\nrF7dyaWXrqNaNXjllSF03WHFivmkUkmmp4vYto3ve0SjMTo60oiiyMGDYziOx6JFbSc9BHueR6mk\\nI4oC0aiKrps4joemhWple8Ua4alZ8xB5NaNk4wPLHI7F0WMzm81TLFq0tjaOtKg3zk+E7dtfYsOG\\nEXp6Itx446UN381nP/vn9Pcb/OEfXsV1111XV66/v58//MN7iMdDfOtbn6OpqX5p169//et8//tD\\nXHFFM//wD39RVw7g/PNvZWzM5Wtf+1DD1I4tW7Zy992P09YW5q677qhrTHCcGaNesJ432pYDI8s6\\nwAZebih7xRVXsmGDCLyM6442fKaCMA9YwWWX2WzYsOEk119NsMZuP4W+nvyeAtkFQA8339zMAw88\\nUFfur/7qr/jSlx4jFMqxZcu/smbNmtlrvUrUdwWwjzvv/DN8X+TAgRH6+kZxHJ1wOOCBWrp0MZ//\\n/If54z++iw0bxoFx7r77c5x33vkMD+/n5ZdLrF3bxFVXXU4kEmJ4eIR//ddf8O///h0GB0NAEd/f\\neVz/DMNgYqKIqsq0th5v6PJ9n3JZx/d9YjEN13XZsWMvuZzOBRcsbZiqM4e3Pm677XMcOmTyuc9d\\nw/vf/37g1bG5b98+envfxYxudrrVb9/3qVR0XNcnHj85YfKvC8dxqFRMZFmkVCqwa9cRWluj2LbL\\nwECeTEbg7LN7ee65TQwNGVx//VIWLuxBkgSiUY1KpcKzz+7iq1/9/3jllSkuvnghN964jnvvfZk1\\na9L86Z/+Dslkmra2FKFQ6JjrqapCuWwgSQKqqjA5mce2HaanJ9mx4wiLFqW48MK1aJrK1NQ0W7fu\\np6lJY+3a5XNOstfgnUz2uW4dfPvbcM45x34+NARXXgkHDpyRbs2hhlMZm2dEi9+0aRPf/OY3Z8vR\\nZTIZbPvULLdvBKamiphmlJ//fAf79y/jpZdaefTRIV55BR56qIKi3Ma9946jKH/Iww8XyOdvZePG\\nEK57G573AQYGmtm9u4NCYSWVyv8kUDL/HtddD/wNcCPwXuBiCoWzKBRW1D77bQLjwt3ApcA3CBSw\\nPyBQ/s4HLgN+H3g/Qe3x/we4siZ7KfAPwEdq8lcRhNReTWCsuKn28w3gAuArtc9vAS5G1y8ALgTe\\nB7yPSmUpudz/YMeOJgThz9m/fxHwGWz7OnK5NRQK6xgdXcvAwLkcONDK2NgHGB5uRZL+jO3buxDF\\nL9LX18qRI1ezebNGX18vfX097NiRYWTkUn7ykzKe92HuuecIinIb9913kFzuvTz2mENfXwubN2u8\\n8MI0e/bIPPPMNLq+hkceGUCWe/iP/3iJUOgCnnlmnKkpj0OHqkxPe2SzHqOjVQwjwr59eUQxw/79\\n0wiCxsGD07hukrExg0rFo1x2KZddKhWPsTEDz0sxMRGkNJXLJoKgUam4b0r98Gw2Sy6nUakkmZw8\\ncSj3wMAB7r13B3AdcCvBWPkAcC3BuPgowbt/d+3/twK3EbzfDxO81xmZm4FP1P52W+077621dwvB\\nmLkG+CBB2OuVwGVY1rsoFtcQjJsPAlcwNnYW27e3smtXFzt3drB1axcbN8q88ILEjh3tbNsW4+mn\\ni2zcCEND7WzdWmFoaILBwRxDQxrj4828/PIEk5NVpqZgdFRkeNijXFbI5SrkciWy2TClUpzx8fxJ\\nFzHTtHBdBcuS0HUdXQfPC5PNlvG8MLpOrQSoh21L5HIBz41lnbk15+0Mx3GYnrYQxQyjo41TAk9l\\nnM9g69ZRQqFV9Pc7TE9P15XbvHkzTz3VhGl+nLvvfqJhm9/+9gNMTl7D0NB5PPjggw1l7713P7L8\\nJ/z0pznGx8fryt15553s2bOOSuXT/PVf/6xhmz/4wTMYxrXs3t3K008/XVcuMGLcCvwlwbw8GT4G\\n/PFJpTZs8IAvAB/h9ttvryv3B3/wB7Xrf5VnnjkVT+cf1/pwMnyQYP+5ndbWeuUogygTuAP4Mj/+\\nceN0lbvuegj4Mrb9ce66664TSCwH/gS4jS996Q6efjrEc8/59PWtZvfu5WzerDI4eB47d3bzzW8+\\nwoYNeYI99sP87d/+ByMjKg8/PIrjXMIvf5llaqrM1FSRZ5/tp6+vg8HBeK2vH+QTn/jEcVfPZss4\\nToJCITBqvBaWZWFZEo4TwjQtpqamGBwUMYxudu8+1PDe5/DWxkMPPcTOnYuoVm/lW9966ri/9/b2\\nEuzJf0IwTk8vbNvGMERcNyBhf6NRqZj4voqu+2zffgjHWczevQb9/Tksq5XhYYGXXx7l2WcNPO9y\\nfvjDnbW+idi2zcGDYzz11ACbN7dQrf4RzzwT5847f4Hv386TT8bYuPEIphklny8DoOsBIb1hBLxR\\nrqtgmiK5XJFKRSGbFXnssf1UKsvYtctmYiJwlPT1jWAYCxkclMjn82/4c5nD2wdzqSVvf5wRs6Si\\nKMfklk5OTp5Rz6go+kxPT9LZGWfbtr0kkyatrRKaZjB/MbhErwAAIABJREFUfomJiUfp6ppmauqH\\nNDdP43nPEQ4PYZpPARVCIZdQSMC29yGKz+F5LwH3APuA7wG7Cbzfh4H5gAFMEHjJj5a9B9gFbAT6\\ngDxQJojIOAgMEURwbK3JPgt8u/YduyZTIvCwj9auN/CavmwHzgZeqcmWAZfAI38YTXuKcnkAXf8u\\nsBP4OfAisAiYrv0kgH1Eo7vJ5w9TqdwH7KJU+ldgFE3bj++PIIoJolEPSRrFtnfS1ZWnVHqC7m69\\n9kyrOM4m2tqqqOoYsmwTjaaQ5RLt7Ra6PkBvr0apNMySJTFyuZ1kMg6WZeB5FURRRpZFJAlct0xT\\nk4xp5kmlwjiOQTIZxrZLxGICr41CjEYFbLtIU1PgIVYUCcMwCIXeHNZqTdOQpEk8D2KxE4ent7c3\\nsXRpiL17dwDjBFE6RYJ3oBC83zxBFEWIILoiSNsIIir02u8qwXt2at8L0n9AIHj3UYLIjhTBGHKY\\nib4IvmvWfqZr7ajEYjKRiIvvV4nFDFIpE1W1gSLxeIbmZhnbNpAkj6amFJlMFFnW0bRxLAuam5NE\\noyGKxQqqaiPLLqKoo6pxQiEJWS7UShafvGSjLEv4voUgBGuLaZq4rkcspmAYJqLoEwppyLKB47io\\nqo/v28jy6SGJfKdBlmXCYTDNPMlk49SOUxnnM+jslNm+vZ/m5grxeLyuXE9PD8nkg0xP/4Jrrkk0\\nbPOcc3p4/PEtKIrLqlXXN5RduFBn7977aW/P0dZWP73i4osvBv4v1eoIq1Y1bJL169vp799MLFZk\\n2bLL68oFkQRnAQ8AWxo3CjWZUxm/u4BHgd3cfvuf1ZW69dZb+eY3v1z77ZVTaHcDwZpwMmwk2H8O\\nMDExUVfqkksu4dlnNwEjBOtafVx6aYZf/OJh4BA33viBE0j0EexbowA0NU1TrRYplwtAlmjUxHUN\\n4vF2LrtsHQ899BhTU48B46xfvwBFKbFggU2ptIWWFoNUKoaqCixc2EwsthEYBn4J9HP++R857uqR\\niEKlUkaWHWT5+PEpSRKCYOL7QdWvZDJJODxMtaozf35Lw3ufw1sbvb29hMO/wDCqnH12PcPdIMFe\\n3Xfary9JEqIYkNLL8hufeqEoEtWqiSR5dHTEGBw8SCrlEIlEOXBginTaYd68JJmMTrG4hZUrU/i+\\nhSiCJIVoaorR1ZWhqekAk5P/TXPzIZYvjzE09AgtLVN0d78b36+gqsFJMxSSsCwLUfQIh0OUyxai\\n6BONqhSLZWTZZOHCCIcODdDUJJBKRZFlmdbWCGNjh0kkPFR1wRv+XObw9sFcasnbH2ckteS+++7j\\nhz/8IVu3buX222/ngQce4M477+SWW255U64/E6riui6WZVOp6Bw+nEdVZSIRH1mWqVQqjIxM0t7e\\nxMDAEGvWrOLBB3/KDTdczdNPP09HRzuPPvoisuyxcmUbmpbEdXUee2wDt9zyW/zLv3yPT37yE3zj\\nG9/ghhuu5oUXtnHNNZczPBx4MFtaQjz66GOce+4lbN68ld///f/BP//zN/na1/6KP/uzL/P+99/E\\nnj39SBJMTExQLNpcd91lPPLIf/EXf/Elvvzlr/KVr3yFv/iL/80ll1zEc89t5eqrL6evbxBZljj3\\n3F4ef/xZrrjifH7845/y13/9l3zjG/dy3XXv4kc/eoh3vesScjkXUXQYHx+jXLa5/fYPsmPHLj75\\nyU/wgx/8B+eddwlf//r/5dJLL8A0p1mwoJORkQKWBatWzePll/eycuVZbNmym9tuex8vvPAsF154\\nPhs3bmL9+nMZGQkU17POWsH27TtYsWI5u3btYf36dTz++NNcccXF9Pfvo7e3h4mJLKLoYRgyjgPL\\nl3fgui7t7W1YlkUsFmNychLfF8lmTRRFoqMjQSgUolqtUq0aNDdn8Dxv1lAmCAKlUhlVDR+XT1mt\\nVimXqzQ3Z2bDDF3XRRTFN42DYIb87kT15iHwruze3cff/M3Xefnll7noogsJh1XC4RCm6TA0tJ/p\\n6Wk2bz6A40wQGLNEZgwR4XAS0zyxF7ypaSWplIKmicyb10Z7eyeKolKtioiiiyS5RKMp0ukYiYSG\\n4zjk8zqaFmHBgmbWrVuD4zgUi2XGxsbJZFrIZDpwHIOurmYkScZxAv6VpqYM8Xi8RgJWJZvNkUwm\\nicWi2LZNtRrsFqoaxvOCeRkOK7XnEJRMPFk+7EwUTVAGOSAJkyTpmHc68/lry/j+Kgh4ZQLjSTj8\\nzjCIHD02LctC1w1isehJeQJONM5PhImJHH19h2htTdDbu6Buu67r8m//9mM2bdrFHXd8ghUr6pPm\\nWZbFT3/6SxQF3vOe9zS8/vDwGI888l/ccMM1LF7cWXcdKBQK3Hffk+zZM8Add/wWy5cvqtum4zj8\\n8pdPsGTJIpYuXVpXzjTN2RQqUWzHdUfrymaz2dkUme997z4+8Ynfriu7bdtOPvCBT/Ke91zO3Xd/\\nveEz/e53f8BPfvJT7rnnH+joqH+g3rDhGa644vLZfjd6r7t27eLss8/mrLPO4qWXXqorB3D//fdz\\n333/zsMP/6ThmPI8jy996cu85z031YxKAY5NLQnwz//8z9x4442Mj0/S3z9AU1MaVVXJZnMsXtzN\\nsmVL2bu3n7/8y//NDTdcww033IiqKmiaxoEDB2lvbyMWiyFJgaF7fHyCUqnEPffcwy23/BaXXnrp\\nCftXKpUJh+tzwxy9XgFUKhVKpTJtba1vOw6cORyLI0eO8PLLu7n22muOKS/v+z6PPvooN95446zs\\nG6F+v3ZsvdGY2WM9z2N6OksymSAcDnPkyAi27dLcHKSL5nI52tvbGR2dIBQSaWtrq1UrGmF09AhP\\nPvk0v/VbN9PZ2cnDD/+MVauWs3z5cjzPO2b/n7keQKVSJRSSCYfDs+SfnucxMTFJMpkgFoshCMLs\\nZ5GIRiLR2Pj9TsQ7ObWkvR22b4eOjmM/tyyIRuEMJgvMgVMbm2fEkAGwZ88e/vu//xuAq6++mhUr\\nVrxp1555MPl8EHq+f/8guZyKJNmcf34HhmHw+OMj2HaIbHYfq1ZdxOOPP8iCBdcyMPBfXHjhtQwN\\nbcPzkphmmUgkgmFYbNo0SEvLOezZcz8XXvj7bN58D+ef/we89NJ9LFlyJdXqQbq7O3Bdj8OHc2ha\\nJ/v3/5Keng9z4MCPufbaT/P8899n5cr3cOjQi6xa1cv4+GFeeSWoWFCt7uL889/Hli0PcM45t7Nt\\n23e47LLb2bLlB7S1XcL09D6SyRS2DbncOO3t6xgYeJR1627npZf+hZtuuoMNG/6F3t73MTa2naVL\\nF1Iu5zh40CKZnE9b2wGuuOK9jIxsZ/Xqi/nZz36E75+NYezh+usvY3T0IH19JrbtI4o5enrOYdeu\\np1i27Cqmpzdx/fXvY3BwO5lML5OTgyhKinA4zOLFIdLpJrZt24+idPDYY4/Q3X0t4+PPcNNNN3Hk\\nyB5EsY1sdpRSySUWS7F8uciKFcsJhRzi8Vdz8EdHJzlyxEGSHFasCJTtgwfLCIJCKuUck5NcKlWx\\nbRnPs0mn1Vnl2LIshobySFKESMRg3rz6pfbebBw9affvP8Qvf7mPJ58M+CJisSKtrfMwTZ1stsTI\\niMDw8C7K5QyVygiVSgLfnwY6SCTyVCpFXDdOEHmh82qEhU4iMQ9ZNohEYqRSCmed1YUklfH9ONPT\\nk3heGkkySKdtkslufL9EPC7i+zGWLetkyZIQ4XCY7dvHGB31aWoyWbgwRU/PAhIJv1b5xyASkUmn\\no6RSCrIsMz2dJ5sV8DyHBQs0TNMjn7dxHAiHHWxbxPNkEgkfTQvhOKHj3t9bAUH5ZR/wSSSkkx7S\\nfxMwMzZ93yebrSAIYQTBJJ1uTM54qti+fYB8PoYsF7jggsV1n+nevXv54hefxPNWsGzZLu666466\\nbb744kts3y7i+zY33NBEV1dXXdnHH3+RcrmNcHiMa69dVzePempqin/7t124rsJ554W5/PLz6ra5\\ne/c+Dh9W8bwqF13USip14hKkX/va1/jCF54mSAm7F9/fWLfN7u5uDhz4GEFk3Nfx/fppMB/5yBcZ\\nHLwQQdjB/fd/lCVLlpxQrlqt8p//uRPHydDZmePaay+s22Y6vZJ8/lNAmQsv/BkbN9bv680334nj\\nXI1tb+D++z9dl/+hXC6za1cWUdRobbXo7u6s2+bERJZ8Xsb3LRYujB1Twj0wZCSAPwf2Avdw+PA0\\nL700wtSURyjkEg47RKPtNDU5tLVpfP/7LyKKPdj2Vm699UMIQpFUKoIgaPi+SSYT8AVlsxVKJYdC\\noUwkEiWVCpHJHF8CslLRMU0J33dIJpWT5uMHRmILQZBQVe9tVV55DsfCdV3yeRNBkFEUZ7Z86LFG\\ntk8By4A/x/fPfLW+04Vcrozvh/F9k0hEor9/GsNQSSQMenvbkWWZkZFxhocFfN9mxYooluUxOemx\\nc+cgiUQriYSFprn4/jwEIcu6dV1150+1aqDrAr7vkkyGZo0dM/0Ai3Q6KI+s6wbVqoDveyST8hxR\\n6GvwTjZkJJNw8CC8dmsOIubANGGOUuXM4VTG5hl7PdVqddZrfqISam8GRFHAdT0kSSQaDSPLAQt5\\n4G3VqVRMNM3HdQ0kCRynRCjkIUkCmiYhyxGq1SC8VpYVFMXFMIrEYjKybKGqIWy7gKLYSJKIKHpE\\no2HAw7bLiKKDJIEkmciyT6EwjaLYGEaJUAgMw6ZcLlIs6kAJRSmRyxURRR3bLhIOO+TzU4BVO7SX\\naxVPIui6i+NYuK5BuTyBpvm1/sP09DSeV6JcLmBZDo5TpVot4Xk2pVIByzLJ5bK1UH2oVk327h2m\\nXJ7CdUP4fohw2MY0DVRVJhz2iMVUPM8CXCwrCOcHAUFwMAyPXK5EECngoKpCrS8+4NZSA2xCIYjF\\nQrU2/RqR57HeqXA4RDwepFHMeAHAw/c9ZPlYD4QoCjUP/LHpIqIoIorgus5x33krQZIkHMekXJ6k\\nWHTR9SKCICFJQXqNadpYlo7rlvH9KoIQwvctoIwg2IRCPq4bkFtChVfJQG1EsUoQ3iojSS5go2ki\\ngiBRLkOlYqCqAqoqIopBG5GIiucJiKJLKKQgigKS5OF5AUlqMqkRDiuEQg6O4wPB+xOEV72kohgo\\nE4Lg195DYAw4+sf3XURRQhBmQu7fnHSf14OgPx7gv+X69mYgeDfBWni6EI2GsSwZSWoc4RJE91TR\\n9RyC0Di9IRKR8X2TIFqpMWRZwnF0VLXxpun7PvF4CNeNkUw23kLDYRnPcwHnFAjmJglSxRpfP6jU\\nEaQEtrQ0lpUkEVl2OBU7myh6eJ59Uk9uS4tDPj8BVIhGow1lPa/MyMgg0WhjfhRRFNH1MrZtk8k0\\nNljKsojvz5Abnwil2k+x1jaAw+joFJ5XRlUhHneIRpNIUoRw2MUwqkgS5HJ5IhETWY7jOB5HT+3g\\n/z6e55DPl5AkhXS6fsTF0eveyREYCF+7383h7YhjI/+OR5FgfP5mlQ+1LItSyUTTPKLROKIYzJWj\\nH4PvexSLZQTBQxBiyLKIIHi4rkU+XyAWCyHLAX/VyQJK6ukHoijU5u6xa2Mg+87cr+dQH4Zx4tQS\\nQXg1vaRBpusc3gI4IxEZX/3qV/nRj37Ehz70IXzf56GHHuLD/z977x0l11Xl/35urFu3cnV1de6W\\nutWSLFmWZCUcMRjwwM8zHubxwMPPiyF4gMfwIw/p4QGGNGNgADND8IwXyeYBHowBDzY2Nk44gW05\\nKIeWWuqcKtfN9/1xq1otq7taNnKC/q7Vq7ur9j333HNP2Gefvb/7da/jiiuuWPziU4C5buZ1d7RS\\nqYSqqkSjUWzbZt++YQzDpLU1geta6LrO2NgYLS0t5HI5RFGmWAw2aopi4vs+hmEwOTlJa2sr4+Pj\\ndHd3s2fPHlasWMHU1BTpdBD6YNs209MmhUIBXdeZmcmRyTTh+x6xWATHKaHrUXI5i2q1wv790ziO\\nSTweJh5P0NERAwwcR6VUAsOoks/naWpKkEoFqexUVWViYgJVjVEuGyQSkdrmNszRo5PIsoAoxnFd\\nm6mpHIIgsGpVD5qmIssSsViCXG6GatWgVCoD2Zr3SZlQSKOtLYHrOui6Tj6fJ5vNzqaozeVy6LqO\\n7/vYto3rhhEEEVk2a7GbMocPH2bZsmUIgoCqqpRKJWRZxrZtbNumvb191qVw7sLjeR6VSqWWAjSY\\nfQzDwHGcE1Lx1e8/XyrX+jW6rr+gMlfMtT66rsvOnQd44IEnmZqy8H2fZDJMS0sLvl/FsgwOHx5j\\namoKy9KYmhpF0xyamxVWrFiG4zjcdtt9HDiQo1JxaWpyyGTaWbbsdFy3SCoVIhYLs2pVD729vciy\\nTKlUwjAUCoU8sZhIU1PQZ8PhMOFwmEqlgqZppNOB50upVGJ6eppkMkk8HkcURTRNwzRNPM9DluXZ\\ncJ+6kbBSqSCKQSYZz/OwrODZ6qzknucRCoUQRXHB9/dCQJ2g+M/ldOepfdN1g7F8qsaP67rk8/nZ\\nvrYQLMvi5z+/i0rFYcuWlaxZ09ew3N27dxMKhVi+fHlDuaGhCfJ5A11X6OlpWVDhra8PlmXT19d+\\nnMfYU+E4DqOjo4TD4YYZU3K5HKnUKqCMprVRre5bUHZ0dJSNG19BpVLh4YeDsJWFMD09zXXXXcf6\\n9es5//yFOTpc12VgYJRSqUpHR4bm5vk9RwDuvff3vP71H0TTNG644Uo2bNiwoOw11/yCwcESTU0S\\nb3/7JQuGWti2zfBwHtf1Sac1ksnGmuPk5CSqqh7nJr5QaMmb3/xmBgeHGRgokMsFhweJhExnZ5Jl\\ny1oYGwvm0HA4STSaRhAcmptjuK6LJEmzHheu69a8JwJPTlH0aWoKnzA3NVp3FkJ93vtz8Oz6U0f9\\nXc7VXep986tf/Srvf/8xkt4/hVNw27aRJInp6RKCoOG6Bs3NMUzTpFqtEolEZvt1uVxldLSIIEBn\\nZxJVVcnlckxMlCmXDTKZCC0tTUxPTxONRhc1lFqWNat31lHX6+eO3YVklxDgz9Ujw/MCbwvXhfmW\\n++Zm2LEDGvBUL+FZxgvWI+Paa6/l8ccfn1VqPvaxj7F+/frnzJBRhyiKsxNsfWNWRyikIcthbNsj\\nEskQCvmsWbOGcrlKIqHjeSbRaECI6PsSoigRj8uoqsrg4Diq2sno6EEymU1UKmW2bdtGqVRiz548\\nhmEgigLNzRkOHNhHONzL5OQAPT1nIElFens3MjU1yvBwFd+XWb8+hiRplMslBCFBc7NKf/8yjh4d\\nZWjIpFQaJxbLYJol4vEWFCVEPC7S0bESz6siSSFmZqbwvDi53BCtrSuxrByyHMY0qwiCgiSFKRbz\\nQCeJhE00GiIabQIUKpU8o6NVBEGls7O9pkAGrv65XJlQKI2iuMTjEapVg3A4gyB4pFJBarnBwWk8\\nT6S1NUwsFmVwcJxYrI9y2aK7O5ghnmqEaPTOniq7kHJcN5LMh8VSer4QIEkSzc1p2traGR0dIZer\\nIMsi6bTJqlXLcRyT5uYuJieLDA4O0dnZxYoVbZx+eguyHOaJJ4bYuDGLrh8hl7M488xuVqzQGBx0\\nGRmZpliUCIUSqGoP7e0dgE5zs40gUOOV8PA8FVl2SSQiNbdZA8tyyOVsNE0lk8nQ2tp6Qt3rG9Fq\\n1aBcDn4nk0E6uLnKSd3wUcdTFf8XsmL/56wQPVVJPFVlPnUeng+yLLNt23psG5qaGo9jy7LIZLoB\\nH8dp7BUhSQKyrKMojU5TA4TDYRRFx/fdhnK27aBpTbVTR3fBNksmk2zbdjEjI+vo7d3esMzR0VFe\\n+tJ/wPM0Dh8+0NCQMTIyzYoVr8G2yw2fXxAEZFlG15OLnoR6XpXzz//fgHscaff8zxVidDRGNFpp\\nOOfKskwyGcZxfHS98ZjP5QrMzIiAMWu0nwtF6cC23wDs4sCBA0iSRHt7FsPwcJxpXNdBklQkSUYQ\\nBDo6Oujo6KBcrmIYHqGQOm//rn+WSgkUi1aNaPrE99lo3Wn0/Ev400CjdzkxMUGQMWw18MPnqkrP\\nGiqVKpUKiKKFpkmYpo2uB+NK07QTxryqyiSTOqJ4rJ1isRijowVMU8UwXKpVE0mKY5ouut7Yg2K+\\ncTZXr19Mdgl/3jAMCIXmN2LAEuHniwXPy+rZ0dFBtVo97kS9s7PzOa+HYRhMT5eIRlUkScE0DW68\\n8U5c12FiYopSCfr6dIrFCGvWJPE8nUJhhJtu2o+iTLJ79xDJpIjvK+RyHu3tMrmcTih0lN27JVKp\\nMY4cydDfX8G2kxSLRxgfb8LzHMrl/UQiy8lmy0xPt5NMHmJ4uI2OjiNMTnYgCEcIh/uJRODQoYcw\\nzQSCUCIUWsW6dUXGx1P09Rns3q3T21thaipFtTpCJtODKEpUqxNIUoLW1jy7dkl0dZUYGEjR3DyO\\nYSxHVWfwfZ1YDAYGRpGkZvr7K5RKXXR1zbB/v8SmTTqjo3FWrxaxrBShkIPvC0xNVTnrrLWUyz7F\\n4jCPP25y5pkauVyYtjaPPXtKpFIh+vt7SSRC7NlzhGpVZMuWNlw3jig65PM62azH+PgUui5RLltI\\nkkelYlIqmbS3pymVPJYvb8L3FeJxDd8XCYXkWTLPwcFRgFrWEujoaEZV1VoKMhtNU056s+k4DtWq\\nhSQxJzd50D91XTvl7oiBB4+J5/kLlu/7Pvfd93ve8IYP4DhVQCOZzCCKDuGwxPj4DLZtEbiiDwJp\\nAi6MEEE2kjDBEC8CJr/8pcExd+wugkw6CcBG0wREMYqu2wiCQiKhsHr1GlpbWzGMKkNDR6lWTTKZ\\nJpYtW0FfXydtbRlaWmLYtsuRI2P4vkUoFGPFiizFokc0KlMoWHieQiSi0d2dJBwOk8sVME2TcDhC\\nLBYmGo0QiYRnLa/1dlEUCdN00DQF1/WwbRddD2FZdsN2g2ADa5oO4fDiMerPBqpVA8/zCYdDLyiP\\nn+carusyOjqBZbl0d7c2NH6Mjo5y222Psm5dW8NTflEUueuue9i5c4Q3v/mVJJMLk7cVCkWuueYW\\nkskQb3nLXzWs61133cO11z7MJZes4m1vu6zhmP/MZ/6NmRmTj3/8MrZs2bSgXKlU5rHHjhIKCZx1\\n1poF5XK5HA8++F3gLAYHfwd8d0HZzZs31wwIHVx//VDD04rvfOcHfOtbj3D66fCb3/x/CxqMfd/n\\nb//2b3joIYUrrriQT31q4UOFl770pcByYIQf/9hoeP8PfvCDHD4cR9MO8qY3HW1oSLniiis5cmSK\\nT3/67ZxxxhkLlrlnzz7+/d9vJpmU+dd/fc8JGxTbHgLuAwaZmOjiF7+4lUOHRhkbG0UQoriuyaFD\\nQ2zcuIa3vvUS7r77Hq688ke0tET4u7/7W1asaKepKUW5bBOPq0Qi8dl5ZMeOvdxxx33cf/8AZ57Z\\nzoc+9I7Z+xqGOTtHNernJzP3L+HFi9HRCapVi9bW9AmeZYEX36PANEEmsuMReCwGKXt1va4fn9q+\\ncirXRtf1EcWAx0oQ/Jr3WRA+XSqZhEIBH8W+fQcYGyuxbl034+MFRBHWru3HcRwGBo7y/e//gh07\\nxjj33D5Wr27nxz9+hC1bWvi7v3s9kqTMjql6+wRhwy6jozPEYhrpdJLp6QKOYxMKqeTzZVzXoa0t\\nc9KHZHBMD6zrmc8nlnSIZx8LhZXUsWTIeHHgeQktueSSS/j973/Pq171KgBuu+02tm7dSmdnJ4Ig\\ncNVVVz2r969vmAYGRvH9JPn8KB0dLfzmN7/lgQcSDA4+zuhogXR6Lbnc7bz85X/P3r03cMklf8+X\\nv/xpotG/5oEHvomivIJKZTcgIUkZXPdJUqn/i5mZa0inr2B6+hNo2vsxjK8hSW/AdW8AthHESB4E\\nzgJuQ1XfjWV9GbgCuJIgz/gvgH6C9KlnEGxKbwMuBX5CLPYZisX/F13/BJXKl4CXE6RLbUcQIvj+\\nI8ArgNvQ9fdSqXyKSOQKyuWvAK+vldVOwJ1gAmuBu0km30wu93VaWj7J2Nhn2LDh0+zc+W1WrryA\\nXG4HjpMhHE4gSbvYsuVSbr/9atas+T9s3/55/uIvPs2dd36ZlpZLyOXuZePGM/F9g/HxSZqaVmHb\\nD/Pa176L3/3uOrZu/UsOHbqXV77yLxkY2E4s1sPw8EGKxRCqKmPbE6xbdzaGsZOzzz6XmZkhent7\\n8DyTZFJjenqagwchn5/BcUyamjrIZqssX97B9HQJUQzjulXS6chJLf75fBnPCzE2NoIgJLCsMolE\\niEgkQjR66jNTWJZFoRCc/EYiQcYOON6NanR0jL/+6yt48EEF6Ki93xiBoaJIkDbVr/0drX1eJw+T\\nCVKvztT+jtd+mwSpdpMEhgy3VoZKQArqAC3ACLLcRDJpY9sxCoUSvi+jKALLl0fo74/W0mDKFItl\\nhoc9SqUcy5b1YFlDrF27ibGxQ2QyWarVEh0dUTo70ygKzMz4FAom6bRKOh2mt7eZZFIhHNZm2yVo\\nozK6nqzxySiIoorjFBGEwOih6z7h8PGrUL3tZmYqiKKG71dPGRnlycK2bfJ5B1GUCIXcPxnyvmfi\\nfjo9Pc2ePRaiKNHTA62tC2fDuPrqX1AqbcQwnuBd7zp7QWLMPXv28MlPPkQ4fCax2G+46qr3Lljm\\nD394I7ffHsNxLN76Vr22CZ8fL3nJh1CUSymXr+e22z68YCjI9773PT772QqS1M6ZZ97ND3/45QXL\\nfPDBx9m/PwbkuOiiLjKZ+YmFgznq1cBbga/h+/csWGYg+08Ec8E/Nnwn0egrKJffDtzLt761lne8\\n4x3zygVko8PA/wI+j+/fucj9rySYdz7T8P6CcA7wfuBHXH/9pbzuda+bV+7666/nQx8aQNM209Fx\\nPXfc8c0Fy3z3uz/D73+/Ac8b5ROfyHLJJZfM1utYaMnXCMg+v8E73/kzduw4SrEIilKmUJgANtPS\\nMsQ73nEaX/ziTzl8+EIMYzevepXKK15xAa2tCh0QNuwuAAAgAElEQVQdKyiXR9mwYQWSZCOKLjfc\\nsINvf/t2CoVe4vEZPve507jwwgvnkDwqKIrdMNxoobl/CS9+1ElrZTlBIpFn5cqAXPj4vnk5sB74\\nHL5/fHYi0zQplYK/6/vvYtE/ZX3F9/1Tuja6rku1GngnHTgwwtCQiCg6JJMWqVQ3xeI0miZw6637\\nSSb7mJ6+j87OjXieyPr1Aqoa4t57j/Bv/3Y7ltVHJnMY3z+Epl1OpfI/fOlLr2Plyv7ZMWUYJuVy\\n3UA+gmVlcN08LS0iphknny/iuhXyeVAUjfZ2if7+7EkbbGZmSgjC09Mdnw04jkM+byMIco2c+NnV\\nIf5cQ0uGhmDLFhgenv/7DRvgO9+BjRuf23ot4RhesKElr33ta3nta18LBJW84IIL5o1vfbYRCsmU\\nSgbBHtWjrS2B540TifiEw2UEYQpdNxgb248oTrNr1xOoaoFi8SDh8CSFwi6CnPc+rmsC43jeQWAC\\n234YGEEQBoAxXHc/waZyN8HmcZKA3G0Y294FDAB/AIaAqdp3GSAPPE6wuRwDRoAjFIv3AANUqw/V\\nrhkBxgEB3xdq9xoDDlGpPAiMU6n8ARgmEpmgXJ4i2PjWSdgOAAdxnO3AUSYm7gAmmJ5+GEWZBKYJ\\nhXK4roPnGWSzNpXKYRSlxPj4H1DVHPn8HjRtmlLpIKqaIxw2cN0qipLDssZJpwVKpSOkUgKFwiSh\\nkEUuNwGUcZw8sRiUyyUgTCol4Dg5YjGByckpZNnG8xwcx2RmxqNatal7Eti2jyBUCYcDC7osC9i2\\njSwLJ92fJCm4JhSSajGfHooiAi6ieOpDCIJ6ufi+gCjOPww1LURra5jA28IGhgn6hA9UCN6fBBgE\\n7ztMQAIIgVGC2vfV2ud1b4wcQR/0OGbckAmIQK3a/+OIoo8geIhiBd+fBjwcJ0S1GsHzWpDlVsJh\\nFdOsAgaynAdypNMeUERRihQK1PK8R5Bll3BYoVKxUBSzljYtIAWVpGNp6oK6CSiKiOvas+R+rmvX\\njFxBu0nSsXbzfZ9CoYLrBoSxAZmrjaI894qIIAi1sBwfWX7hZFp5PiBJEoJQxfMkVLWx0pxMKoyM\\nHELXzYZhCLFYDFWdpFDYzcqVjWOoW1vjGMYYgmCQSi3sOQGQTpscPfoEzc1mQz6L7u5uVPUWHMei\\no6NxKEwkEiIUqiJJbkPvsGDtWwdsB/Y2LDPATgJj5GIoAI8AO3nJS962oNRFF13ERz/6CeA+dL20\\noNwx7OeY0bQRJgnWr5EFjRgQhNZo2jiGsZPlyxu/02XLsjz++AS+P0Nn55kLSO0hWNNAEGaACUzT\\nwPc9RLFAqfQkghC8k1RKZv/+XQjCCKlUL7ruEI+H8bwKqlqfnwQqFYtyeRpZzlMqHSCR8Ojp6and\\nQ0AUfVzXWZSw82Tm/iW8OKGqKoriYtslIpGFTvQHCQ4hRk/4Jjh5t2t/B/OFINj4Pqesr5zKtVGS\\nJKLRYJNtmmWGhkrEYtDd3YLnGYRCPqqqUK2OkM/7tLb6+H65RqTYjCAI2HYJVS1gmiNkMiaOozA+\\n/iTJpEE2m8bzjo2pgCzcQRC8mi5RRFVdNE3DMExEMTCqqKqJ57nIcvxpeTPU9cCnozs+G6jrEL6/\\n+HyyhGeOJY+MPw08b+lX63j44YfZtKmxgnmqMZfs0zCMWRcyQRDYt28ftm0zMlJgZmaGTKYTy3I5\\ncmSEdLqN6ekRSiWDQqHIoUM5BEFEFIuYpksqlcIwRFavTjI5eYD169dx5523cuaZ2xgaMhgfH+OR\\nRwwEQaCtbZhwWKarax0jI4Hrf6mUp69vNU888TAtLc1MToKmyUxODtZSWbZTKJTQNJ1UKsvk5BHW\\nrg3CKcplmWJxBtdtQtN0wuHDRKMKY2MSxaJGoXCUlSvXEIt5OM4UTU1N7NiRR5I0mpoMDMOgVGrC\\nNDXGxvbT0rKOeHyCM87IsmHDagqFI8RiMXI5AxDo6+tgYmKcfD7E0aN5WlrCZDJRPA/GxoJnXLcu\\nQSgUwjQVKpUqK1a0MT09iSynqFQkoEw6nSQcVlEUG03Tam6PJul0mpmZGVQ1judJiKJNMhlkifH9\\nMK5r47oFFEVB0zRc1511IfT9Y/HwJ7sY1a+RJAnLsmoZNYIF8NkKTagTzc4t/6nWxwMHjvDBD/4z\\n9923k1WrOshkOvG8MK47zfj4JFNTIUZGhqhWx4EgEbaiVNE0tZYeNEOgOFkEhgoJEFGUMKGQRihk\\n0NbWSTqt0tKikEwGaVPD4SwrVqxDlvO4boSBgQEOHaoQjbazbJnOypUpzjijn5aWGKWSgWWBbZdJ\\nJsO0trYyNTWF4/jkcjKC4JPJSLS2ZhAEgVLtyCkUCs2SgM5tg3q7BFlbgvfo+z6uG2w8XNfF9/0T\\nrsnnbSRJRRRNolFtlozy+VBI5qvjix3P9NRmZmYGgFQqtajs7t27aW1tXdAbo47h4WH27dvX0MMC\\nghR9v/3tfeh6lHPO2dDQXXjHjn3cccddnH/+eZxxxsqGZJ8//OF/MzWV5+///k0NT99932dkZIRQ\\nKNTQOPLZz36WK674BsEYfqRhO7/uda/jpz+dAFx6e30OHPjdgrJXXPF9fvazw2QyFt///t8vmH62\\nWq3yuc/9J3/4wxN84QsfY+PG3gXL/MQnvsjnPvc5wOXrX/8C7373wulvX/3qd3H33Q/R19fDvfd+\\n5zhyzqfi5ptvJpfLcemlly46Zm+55RZSqRTbtm2b/ezYYUgbsI7AiPIoTzyxn/37h5meLmNZVaLR\\nFgYHD7JixXI2b17JzMw4P//5LSSTCf76ry+kqSmNqqpYlkUoFLi0i6LI2FieiQmD/fsPcOjQIdat\\nO43zzz991ujmed7sHLUY5pv7l/CngWq1imVZx6UaPv6gbjNBGOjjJ3hkwIl941T3lXo/PdVr4759\\nw3helEplkrVrO2fDx33fZ8eOoziOSiolkUgE921ubqZcNjh6NMfQ0CQDA49zwQXbEEWNBx74Az09\\nXWzdGoSYzR1T9fYQRZFyuYyqqoRCIQwjCHOr63Ce56Hr+tNqt7l64PMdzvFc6hB/rh4ZO3bA618f\\n/J4PL3sZfOITcOGFz229lnAML1iPjLm4/PLLefTRR5+Xe4uiiK4fr4SuXr2aYrHIxMQw8XgWz5si\\nk2ljaOgww8M2ilLljDPWUiqNkM12UCxOU6m0oWkx4vEcut5Bf79GS8ureeyx7Vx44YepVg9y0UXL\\nqFZn6OzcS6mUp1Raia4vp6npANu2nYPnTSMILZTLh1i9eiOl0hCVShjfN0ilNqNpcfbv34NlNaPr\\nRxCEHhKJTrq7N5LPD1IqqbhuHtN0sW0XUWwhHO5Bku5A0/rp6mqnu3sdUCQS6eLo0b0YxgyKInLm\\nmc3E483cdttdFIsJwmGPcFigry/CpZdezPj4MKJ4Nq5bYM0aDUUJMTNTpqmpm2LxCVauXElbm8/q\\n1f0YRonh4TKK4tLa2o4gCEiSjefB9HSFSKQP2x4nk9GIRNKoqo6qikSjJ55s6rpOpVLFMFx0XUNR\\nFCIRgUKhiu/btXhnAUmSjotFfSbM1HOvea6IQE9mgVJVkVWrzsMw1tPRIdLa6mNZCUTRZONGncce\\n283KlWcwNXWUqakCuu6zbdtpmKbM44/vZGrKYHo6jiRlMYwy6XSMeNwgHF6GLPusWpWluTnNGWc0\\ncdppq9m9+yii2IQkjdPf30MoJDM1VSEWi5JK5TAMk5YWmXS6h1LJZ/XqBC0tzVSrDuGwTDisMTNT\\nIpHowHUrhELB6VJ7ewpFUSgWKwhCDEEI3CXnUxbmtkv9nQQnnoHsfPHnkiShqha2XSUSCR1niHo+\\ncKqJMF+sCLIWhQABy7IWjTtevXr1SZXb3t5Oe3v7onKFQg5F6cJxvFmldyEYhseqVa+gXC7hed6C\\n7zCXy+E43cRiUQ4eHGD9+rUNyjRR1TS+7zYk+3z729/OFVd8kyCc8EjDZwrIdatAmJUryw1lU6kq\\nHR1RWlqKDYlUFUXh5S8/l40bz0dVG6eFdJwpQqG/QZK8RRWM887bSjr9ErLZfMN51XVdtm49H9+X\\nqFSMhq7U1arB5s0XAO4CBKajBKGWwXMsW9aGqspMTlr4voEsy6xatYl4PEIioeL7MV760ouJRARa\\nW1tmdYK5a0rdkOr7LsuXZ1m7dhnxePi4/vR05pwlA8afLhbLuhSEfCaZzyMDTuwbp7qvPFtrY3Nz\\nlKmpCp2dCcplC8/TMAyHRCJCV1eaYtEiEpGQpCiBF7OLpik0NYURhDjLll1EPK5hWRW2bDmPcNib\\n19gytz1ic/Jizp1fnim/xQspq8mSDvHso1qFRlHjSx4ZLw4sraZz4DgO4+M5yuUSoVCIcDiM75tk\\nMglCoTCOE0ZVNVIpnXC4CUmCYlHkwIEcnucQjeo0N8cYGhph9+4KU1MDqGoc1x2tZduQuOCCTYyO\\njnDLLYcxjCKl0hEkqQldLyLLMWRZZMWKFkZGKgwPmyhKmNZWHUkKUyhkqFR0stlltLauwDSHcZzg\\nBKi7uwVFaSKTiWMYBtu3TyJJCp7nY9sWmhYik4lSKJTYv/8oxWKRvr5lCIJDNKqgqjKbNm0iHO5i\\n1y6fSOQ0WluLZDIx8nmFyckcrlskm1UIhyV8X0JRVCKRCMlkEl03EUWBaDRCNiti21UOHhzB8xzS\\naQXTBMtya0SkUxSLM/T1xWltjSAIAUeFJAmzpI916HqYubYmWZZJp6OYplnzOPjThue5VCoBeemR\\nI1OMj1ts2LCOSsXFcawawdYYihJiw4Zu4nGdzs4OBgdLRCJhUqkmRkfDWFYT1WqJ3t44lpXH8yQU\\nxcM0QddFEokIw8Pj7N17mETCobXVI5WKUKlYTE5OMDWVo7m5idNPX47jFACNcrlKpVKhXA7Sp8bj\\nqVp6wjKqqiOKHolEBF0/RrrqeT5B+NOpbSdBEBqejC/h+UGw0RUI3vmpe+nVqoFluUQioYaKvucJ\\nCIKEKC7uKjw1Nc3AgEJ7u9FQThAEikUT14V8vnHMuuf5CIKI75/M86eBJIIwP49GHYFnxwggk0o1\\nTikriiEMI8ciyUUAOHx4mMFBm7a2xsTbxaKLaZYBG8/zGsqm00l6ejLEYicSG86F7/uMj89gmj49\\nPfGGhoy5bbowDMBl48aNtXU5iWWV8DyZtrYkruuh6xqRiMbg4AiHD0/R1qbgeR0LlqjrYSKRJK5b\\nRlUVZPmZbQh936dUCrTjSER73k9+l/DcYePGjTz6qEDgIXnq4fs+5XIVz/OJRuc/KDAME8NwiETU\\nP2rT7jgO5bKJqkqEwxrJZJxkMo7v+0xNFSmXDQTBJBYLE4vptZTqPpYVhIr6vo8oikiSSC6X5+jR\\nITo6UvT1tWFZLuPjJRIJ7ThjxVy4rkupZCDL4hJp7hKeESoVaJThd8mQ8eLA876CfvKTn/yjy/jK\\nV77CeeedB8AXv/hFzjvvPC677LJZF7STRalUoVLR8P00HR3Q0eHQ09OOpikkEnE6OjRUVUPTEpRK\\noCgJPC9EKBRHFKNMTQkYRoLt23Po+unMzEBPj45l+XheF0eO+IyNlVCUNMuWKbS0VCiV2pHll3Df\\nfcNEIu3MzLiz1nxVzVKpyExOipRKcYaHPaLRXvbsmUZRWtm3L08k0ka5rNTImxQ8TyEWy9DdrZFO\\nF7GsZpLJbfzud0PIch8PPTSK62YxTZ1QyCWRCOF5MSQpRVtbiO5um2w2Q1tbshYuIKOqMVpaFHp6\\ngoUqFovS1xehpcWgq6uFaFSjWg1SdR4+PEW5rHL0aAnDUMjn4fBhA9NMY9sW6bTM3r1lZHktDzww\\ngmVJTE1VsSwJwxBrrN6N4bouqqoSjQpEo08/1d2LCa4r093dTGtrBMsKE4+/jP37c+h6CssKkc87\\nVKs9FIsyR4+qeN5aBgaOMjHhAxsZGZFpaurB8xyam1sRRYG2trVEIh2oagJFaWJ8XGVgAA4dchGE\\nNkwTfD9GsRiEDR05IuK6fbguyHKF1av78LwSnZ1tDA3lKZUUqtUw+XyJctkkFkti2wau6yEIYYrF\\nY0pbLBZG1z3icfVZV+A9z1t0o7WEZxeqqhKLiUSjwikjzA0UWBfHUSiXzYay8XiUjg6Njg7tBO+7\\np8K2RTKZFJ4nNTQ6BIbjGF1dEbLZxkYHXdfQdZ94XG5ocBkdHQVWAVvx/cbGiXK5TEDOvIFDh4Ya\\nyu7aNUYisYWZmRRDQwvLTk5OcviwgKKsYfv2ow3LPHhwCEXZRji8lcnJyYay2Wyazk7o7k41HIuO\\n4+C6wVpTqTTe5C3epi0ERNrrZr09A16dEJYlMzNjUSjIVCqBQWx62iQcbmN62sM057+3IAgkEhrh\\nsIuiSHheCMPgaesYEJB9WpaEbcsL3m8Jf5rYuXMnsAk4HzgxbfkfiyBjm4jjqBjGiX3L8zzKZae2\\nLjeeOxdDuWzi+xrlsndcGmZBEAiHJSQJNC2KYRiUyx6+r+G6wcYxFhNRFAXDsMjnHQYHPUyznYkJ\\nC0FwyOfzJBKtjI0t7HFmGBauq2IYwjMah0tYQqUCjdSCJUPGiwPPi0fGvffey4YNG4hGoxSLRT7w\\ngQ/w3ve+d5Y46+nANE0ee+wxBEFgYmKCO++8k3vuuYcrr7ySG2+8sSHB2FOhqjK+X0IQAvfdUChE\\noVDBcQTS6SilkkA2G0VVXVpaNAzDR5YjGIaF5wmYZpVcbpLm5iql0kHWrs3S2RnD89IcOXIUx5kC\\ngpjBdDqG68ax7VHGxx8lkTA5cOAg8XiFVEqloyNBqWSgqiLptIym2SxfrqGqRVpaRAYHDxGPSyhK\\niZ6eMLGYAgQbRPBpbo6jKHH27h3AtvezalWaXG6Q3t5YjYAzcAWUJPC8Coah0NvbTCbTSrVaolwW\\niMezCIJLMhlGlsN4nonrelSrJh0dKXRdZ3h4B4VCAUXJMzmZp1CYoVy2MM0cyaREIgGeB56Xo709\\nSTweprtbZ2pqP9msjCh6aJoIBKRGktT4hCDwxHAQRb+Wj/x5t8U9q0gkwvT2NmFZAolElXz+IC0t\\nKtmsiiAU0XUf1x1EVQ3icZdCYZDuboVQyKFQmCCZdIjHPRRFIxwO09MTJRyOUKk4OI6LbVuoahVB\\n0HBdEU0zyGSypNMgSQbZrM7ExDSjo+M0NUXJZpPEYmG6uppQFJFQSMd1DUBE0yKIojibLs6yfKrV\\nEpHIsWlGFMUTMo08Gwg4M8xaGzY+tV/Cs4tTnfFHEATK5TK2LdLU1Pi9hkIqLS0JBGFxV910OsTg\\nYJlUqrHRIRwO092dxvMCt+jF6noy/T1IN7qXY5mqFkYymUSWD+K6Clu2LGsou25dlrGxw2SzNm1t\\nbQvKJRIJdD2YX9avX5jHAuD889fx4IOHkSSbzs71DWUVxcWyBHzfbhhaIssy4TB4nkMk0ri/LN6m\\nYwThOcP09/cDgedDOFxGlkFVwfNMJEkkFNJIp0NMT+dJpdRa6siF61h3dS+XLUTRQxSfft8OCHBN\\nfB9k+U/XCL+EE9Hb28uuXUcJvNTmDy35YxBwO9j4vossn6hLiaKILINtGzW965lDVSUqFRNZ9k/Q\\nwwKPZgAfRVGRZQvXNdF1+bjMK4oioaqQStnMzMyQSMiIoowoiszMjNPRsfBxuaJIVKsWknSMGHUJ\\nS3g6OBmPDKOxg+YSXgB4Xsg+161bx+OPP87jjz/Om9/8Zi6//HJ+8pOfcNdddz3tsr7xjW9w2mmn\\n8U//9E98/OMf58knn+Qf//EfeeSRR7juuuv48pdPTI1XJw/53ve+x9VX38jy5RGuu+5WVq1Ks2fP\\nnlPxiEs4aYQIsmR0sXp1hEqlwuAg9PbKjIzAxo2tHDnicP75/ezcOclZZ61kdNRn1aoMiUTglhuL\\nxdi7d4yLLnoJ09MGPT3N7N07xLJlzTiOTCIRYvfuQTIZnba2NopFi5Ure3AcH9OsMDw8RVdXlmLR\\nIJHQMU2XaFSb3fg0Nx+fMtJ1XSzLRlEab3jmwvOC0z5ZlmbdOX/0ox9RLsPb3nbprNxcYptcLndS\\nBIkvLEQIh6PE40mi0SSdnU20trbheWVM00eSwqRSKsuW9ZNOJ8lmU0xP56hULFpbMzQ3Z0ilkgiC\\nSCikUiiUiUZ1kskYhUKZSESfzSAQCiloWghFUQiHw6iqgm07mKaBYViIYrSmuJnoenhBzx3P86hU\\nqjVysPBx79QwDGZm8kSj+oIurk/FM+kfzwSmGRhqTpWhwLZtHMclFJrfW2Zu39y+fTvbt+/j3HM3\\nsmLFioblvvKVr2R8HB577LaGcq95zWu4+eabARp6RNSJ6gJI+P7Cp3FveMMb+MlPfrJomcBxrsme\\n5zV0VZ77XaNy/+M//mOWDLORXKVSITJHo2oku3PnTtauXXtSsnPrWSexmw/Ht2njMqenczQ1BfNS\\nsVicJVle7P6maTb0nuvo6GB4eJpf/vJ6Lr744gXlbr75Zl7zmtfT3Z3m8OHDx93rqZnPtm17GcXi\\nFDt3Pn5COaLYwlVXfZJPfvILTE0FvCQXX/xG3va215NKpfnd77azbdsaLqwxveVyOX75y1/xzW9+\\nl/vv3862bb3ccsutJJPHG36q1Sp33HEf2WyczZs3Y5oWgnD8OK17p9TH2ejoKKWSwbJlnUtG1xc5\\nrrjiCnbsGOGjH307W7duBY71zeuuu47LLrtsVna+cVYoBNmA6sS4phkYvU4m9erOnTsxTZ/Vq3sX\\n5OmoVquUShUSiRiyLFMolJBlcXYcj46OUihUSKeTaJo6+/nc9aFYLDI9nWdqapIDBwZZs6YPSZK4\\n4YZb6Opq5ZxztvGlL32F++7bzktfuo2uri6SyTTRqMz1199OU5PGe9/7TlpaWvj3f/8un//8P+O6\\nZTKZDG9845u46qofs3Jllo997H0kEk2cddZmmpszmKbFAw88Qi43STQaYWIiR2dnK2eeuZ49ewZw\\nnAqyHCYS0YjHY8TjcaLRCI7jUCpVAA/fF4hG9XnnIsuyKJUq6Lr2nHGlLQTLsnBdD00LnbKwmfn0\\nUPjzJfv8wQ/g17+Ga6+d//v3vAf6+uC9C2d4X8KzjJPpm8+LISOIE3yUT3/603R0dHD55Zdz5pln\\n8sgjjzytcmzb5rLLLuPHP/4x5513Hu9617soFAq84x3vYP/+/XzhC1/gmmuuOeG6esM0Nf0Fpvlu\\nyuVPA38L3Ejg8jdAkAKyH3gQeAvwDeBDwD8DbwVuAFYQpMHUgBhBmrmLgP8GPg58qXbNlcCrCFLr\\n9ROkvTwIbARuAj4IfAV4f+33JcBtwBaCVJlloBl4rFbO9cB7a3V6D/CfwEuBR4EOIAXcBVwI/A54\\n95y6fBq4DLgDWE2QQm+cIK/53cDba7L/BHyi9hxX18rfThDHrQOHgJcB3wM+DHy99hzfqMk+XHvW\\nwwTEVr21dvnInLb8IvAPwH8AFxCcoglAJ3AP8DfAj2vXfAlF+RC2/W0UZQuCsI9kchWS5OI4oyQS\\nZ+E4d3PRRf8P999/NVu3/gO7d1/HhRe+gQcfvAFYj+MU6ex06OhYS3PzBJs3n82vf/0bWlrOY8+e\\nmzj33EsYGnqEvr61VCozpNMxYrEkK1ZIxxkzZmZK+H5ggEmlTi7XeKFQxrYVwCaVCnPTTTdx9dVV\\nQOeyy8pcemlgzJg7aDdtuoBHHnFq/cQkSKGaJOhzBkGcbYqgPwlAlCAdcIggW0muJhOuXZevXReq\\nfZ4gSPWWq13r1T5vJkgBXAH6CFL0egQZT6yafF+tPGqflwjGQJ4gtVyEoN9qtXt6BC7fE0CEZDJE\\nR0dbLa1rCNuO0tTk0dUVp7k5SmtrG8XiCIrSiq7bhMM+sVgbnpdHFCUEIYwsmzQ3J0ilIrS3JwiF\\nPCDE0aMzRKNRFKVCNBpGkvSaB09o3o1csVhhYsLAdUVSKZGmphiCIOC6Lrt3D1EuR1CUMmvWtJyU\\n0eCZ9I+nC8uyKBY9fB9isT8+bMN1XWZmDERRQVGceflG6n2zVCrx7nf/AEV5CZHI3Xz1qwuv8kGG\\njS1AlFe84kZuu21hY4YgXAC8A/g1r371OL/61a8WkBOA/w28Avgavr8wWXQg++8E/fOji2z6zyGY\\nV68DfrGgbFDmx4F24Av4/sKhGILQCXwMGKat7TsML5CwPijzXOprje//YZFn+hDBmP3UIs+0sfZM\\ntwPXLvJMlxGsGV/G959Y5P7/QjA//PMi999G8E7/G7h5QdmzzjqLBx44i2BeOfk2Peecu7j33ntn\\n6xUYMiSCtWUPwdr1Ko6t1RLBXJoh8Ny4H1hOsG4fAnaydeu5+H4ZXX8NkvQwP/jB35HNZvnxj+/k\\nC1+4jx07HgS2AeO89a06X/7yJ44zZvzwh79ix45mYJK3vnUViUQ74BOPS/NunnK5HPffP44o6nR2\\nGqxd29gwuIQXLn72s5/xznc+iiStYdmyG7nvvh8BTzWyvZUgNOzDJxhhS6USQ0PBZx0dMqqqUigE\\n6cgXm+cHBwe55ZYpBEFh61aR9evXnCDjeR4HDowjCHEkqUAioTE5KeL7Fl1dOpZlcf/945TLLppm\\n0d/fQVdXwG9RXx9ct8iBAwWOHi1x66334XnLicdn2LXrQSYnz8ZxdtHZOc1NN5UIxvMjdHWtJZuN\\ncPDgQ5RKZyKKVV71KodVq2J84xt7qVQMAj3nXgLd+H3A/6BpAm95y3tYtWqSN7zhAh57bB8///kI\\nhw8XKBaPEA530dMT4vTTXXx/JXv2HKapSSMSUenpSbJx43I6O3VyuSqVisrg4CCdnV2EQlWWLz8x\\ntGdgYBTXjSMIBZYvzz5vHr8B15iFIEhomteQM+jpIJ8v47oqYJFMHuNQ+XM1ZHz72/Dww3D11fN/\\n/5GPQCoFH/3oc1uvJRzDyfTN52WUxmIxPv/5z3Pttddy8cUX47ruSXEjPBU/+MEPeOMb3zj7fyKR\\nmLVmFwqFhin8PvWpT2EYA5jm1QRp2oqATyscYwsAACAASURBVLCxh4Dx3CVQfkaBGYLN3QTHiJoU\\ngo2bVbvOJdiwmbVrcgQby1zt83JNrkDAOl//bKL2WaX2eX1xs2r1KtXKFAmU12rtXlWCTWal9n+9\\nXqXas9Q3vwcJNrpHa3IFgs1lgWAjW3/WUq08v1amUvu/Xv/6ZrdYu3exdn2eYHM8VLunVfu+UCur\\nOuez6dozV+b8X08NKsxpH6fW5k6t3nk8bwwoIooxBMFFkoKTK9+v4nkOmuYCJqpqYVkGqmqjqnLN\\ntdEGqjXFwSEUUpBlGVX1cZzAPVIQbFRVINh31t/BiZkGglzmT497IVBgfOp72kgkgm2bQGXB2H1N\\nk2vtW/+pv3ej9lOd095GrU2d2k+9jSs1GacmY865rsyx/lYv0619V7+fXfvcrn3uzpG3aj/mHLl6\\nP5xbP+cpcg5g4rplRNFBFF0EwcbzbMBFEFw8z0YUqX3nERxS1sOPQJJc6q9FEKgRh9UnvODdxGI6\\nsZg+2/aN4c++n7mGB0kScd2gLieL4PJj7/rZQvCsx59AP1MEeeuPteNiUFUP286hKI1d44M5uACU\\njktHOD8KBPNB49jt4Hllgv57Mu7ZM7VyTwYVgvVgMdSNhI05Ko59X6KlpWUR2Wit3JMJNwjadHG4\\nHFsjFkN93jgZN+08QV0XQ5hgHmh8uhm0TX0uO5l7Uyt3PtTXqPpaXi/X5thaWeX4+apA8OwegmCT\\nTut4Xh5J8mY9JEQRfN8l6HsmkMf3vXnnBscJ5rv6XLD4OHXwPJdQaMkb48UO3y9hWXlCoYU2nxWC\\n/rnQouJRX8PmlHqS87yN7zfWpUUxCOGSJLFWX6+21tbr4+B5DqoqHve5IBzzVAvKsAiHJWzbqYWZ\\nCnheAd+3URSfY4ceLoJgIIoesuwAVQShiiz7tXI1jo2/un4wCXiIoovrWiiKWFuXfCTJxfdNJMnH\\n84LvIhEVQfARRauWQrVCqWRSLldm6x6kZm3sbSZJIp73QuHb8E96PT5ZBO3g1f5eIkctl5fIPv8U\\n8Lx4ZIyOjnLdddexdetWzjvvPAYHB7nzzjt505ve9LTK+ehHP8r27dsRBIEHH3yQ973vfTz00EPc\\ndNNNXHnllfT29s7LkVG38Nxxxx389Kc/RRRFbr31Vnp7e7nlllueRg1kAiXlJCjh/yiECTb5J6OM\\nvjDQ3NzMxMRE7b/AAtzXt4oDB/aQTCYpFAqsXr2aqakpOjo6mZ7OEY1GCYVkSqUSmzdvZu/evZx+\\n+ukcPHiQDRs2UCgUWLlyJaZp0tvbS7lcJhaL4XkexWKRTZs2USqV6OnpYefOnfT29uI4Dslkkl27\\ndqHrOi0tLRiGwYoVK2qkaxajo6N0dXVRLpdrBgYbWZZRFAXXdU9IW+h53qzMyabI8n0f27aRJGn2\\nmttvv51yucxf/dVfzcrNtT5WKhWy2R7K5fk2VjLHTheff8iyPNu+0WiUdDpNLBajra2NRCJBtRqE\\nbmhaELLT3t5HIhEjk4ljWSbVapVMJkM6nZ51qVVVlUqlgqZpRKNRqtUqoVDgZmmaJrIsz5YXCgUh\\nJo7jYNs2vu+j64ERw7btWojJ/JsE3/cxTRPXDYh2557CWJZFuVxG07RFUuodwzPpH88EdePvqUoX\\n5zgOnuehKMq8Ss7cvrl//34OHTrE6aefXksHujDe8pa3MDMzw4033thQ7sorr+QjH/kIK1asYN++\\nxjwR9fq97GUv44477lhQbm4qz8VCG042tMS27ePKabSE/su/fJGPfezDpNMZRkeHF3xXlmUdd9ra\\nqMz77ruPc845B4Cf/vRX/M3fvHpB2acT2nGy4TJDQ6N0dgZ8G+Pj4yeE3j2TMm3bZuvWcxgeHuFr\\nX/s3Lr30/15QFqC7u5tYLMaOHTuOu9dTQ0te//rXUyqV5vXu6e3t5/vf/w7ve9/7+MMfAg+Yq666\\nhs7OFlauXMYTTzzBihUr2Lx5MxCclj/wwAPcdNNN3HLLLWzevJlvfetbJ4TWVKtVnnzySVKp1Ow6\\ns1hax8nJSRzHIZPJLIWWvMjxne98h4MHD/LOd76Tjo4gC069b15zzTVcfvnls7LzjYlKJdDz6gcc\\n9fXsZEjNBwYGME1z3jTWjuPMehoahoGu68hyoG+Jojh7v3pf1DQNVVVnP7csC8dxCIfDlMtlqtUq\\n1WqVXbt20dPTQzQa5Ve/+hXLli3j9NNP57/+67/Yvn07W7ZsmR2vra2t/OQnPyEajXLZZZfR09PD\\nDTfcwPvf/35GRkbYvHkzb3vb2/jSl75Ef38/73nPe4jH46xbt45YLIZpmuzZs4disYiqqkxPT5PN\\nZlm5ciWjo6N4nocoilQqFtFoM6oq0taWnG1XURRxXXf22edro7rO8XyTyC+2Hj8TzKeHwp+vR8Zn\\nPxsYKj73ufm//9d/hakpuPLK57ZeSziGF2RoieM4vPKVr+S3v/3tKS33/PPP5+677+bKK6/kl7/8\\nJT09PXz3u9+dd7J6asNUqwaVil8jufRrC4eFJIXYsWMvnpfBMPbT1raWBx+8k2JxORMTD2Dbbfh+\\nkWXL0shyE7ncAKHQGlz3YXp6zsc0d3D66S9lYmI3qtqF48zQ2pphbGyI669/HMdpIR7fxaZNl7Bn\\nzy1ksy9jYOB/SKVezuTk7zDNTmAKXQ8RDjdTLu+hqek8VPVJzj77f7Fr153o+kamph4hkVhJqTSC\\nokTwPIGdO/cRCnUDj7Nx48Xcc8/1JBKvJJ//LRs2/CUHDvyG0dFWfN9k82ad1tZV7N27nUhkHVNT\\n99LX90oGB++kre1c9u37LZK0AtMcI5tNoetpYJx0up/9+39PNvsSBgZuZfnyC4F9XHTRy5iYOMrk\\npIRpmjQ1ecRizTzxxJOEQv3AftatO5d8/iCdnSvZt+8P7N+vEgrZvPrV/WSzLezcOYjrxmhtNenu\\n7kVRXOLxyOy7EgSPRCI45cvnDVwXHMdAUXQ0zScafXGm4pzbN8fGJvj2t3/OD34wQC5XJByusnLl\\nSlauFBkbcxgbA9sewbIEDhyYqZGgiui6iSBEKRanCU5XPQJvog6CU6Ax4vEeQiGV5cvjnH12O7qe\\nZmTERtMMens76e1tJpUKoao6pmlRKuUZGrKYnCzT3x+noyNGtapgGFWi0Sjt7UG2AVXVqFZLJBJp\\nslmFZDJOoVBieLhMoVChqSmMLEsUChVEUSabjRKPa5RKgTEwHlcW3Zj7vk8uV8Z1pRf1u36x4Zko\\nO5VKhSNHAkW5rU1ryKcwPj7NzAyoqkVPz8JuvZVKhV//+glMM0Zfn8eWLac3KHOcRx6ZRpJctm7t\\nbOgV8s1v/oShoVay2THe856FN9KFQoE77tiL62qccUaE/v6Fs4wMDAyyf7+JJFls27bsOB6Mp0IQ\\nzgb+ArgO31+Yq2nbtm089NBpgE5b240LhqsAXHLJP/Dww/1o2i7uvffTCxqdDMPg61+/mUolwxln\\nWLz2tRc2KPMSfvGL04ASH/hAaF4eqjo2bfpbxsYuIBb7A7t2/eeCcqZpcv/9+3Eclb6+EMuXdy8o\\naxgmpZKLKPokEse4jI43ZPwfYD9wMwcPDjE8bDM5OY4khWqnxT4tLc0kkwIjIzPcfvtRFEXg5S9v\\n4bTT+hvOKdPTOSYmgtPlnp70kuFhCYvi+L75aoJQp6+f0s1jneDa9wXicfm4jXh9zAiCTzKpPW0D\\nu+d55PNVXFckEjlGtlsfC4ri0N19bCzMzBQZGJjB8wRaW0Pouo7nSUiShesqQBDqKcsyExMTPPjg\\nMBMTBv39cTZtWnZShwaNninQE10UBeLxBkfuSwD+fA0ZH/84RKPB7/lw1VWwbx98/evPbb2WcAwv\\nyNASWQ4YiXO5k3FLPXncfffdAHz4wx/mnnvu4dprrz1pBcOyHCoVuzZRy4CKroeJxyOUyw75vEu5\\n7GAYFlNTFQQhTrmskExmUZQUU1MihYLB6KiIqmYZH7cIhaJMTztAhLExE9MMMTXlcPDgEHv2HGRy\\nMo5tt3H06P/P3ptH2XWW556/Pe+zz3yqTs1VUknWbA2WPGAGYwOBsEzn5joJcy5kgtw0pHt1shKy\\nusO9dPqum4Qk0KRXh/QNBNtAEzBughNMwIAnRsmyZFmyBpemKtV45mHPQ/+xT5U1nSObK1s21LNW\\nrVPDW9/e+9vf+H7v+zx1ajWBs2dtdH2QpSUJUSxSLkukUqN4XpFSKaDVipiZsTFNldlZi2QyT7UK\\nihJfb36+zblzFWo1gaUli3pdIQwHaTR0RkY2UCqF+H6RuTmHZlNkaqpNvS5Rr2uUSk1MM2B62qNa\\nlZmddclmx1lcdImiPOWyj6IM4fspWi0R0xSYnbVoNEQWFz3S6fUsLNio6gDVKnieQrMZ0WhIeF6C\\nIPCwLItqFYIgxdmzDaLIYGnJQpKSNBoS+fwwmjZIu21SrTZotyVkOUOlYiNJCr4fN2TfDzBND8uK\\nTyliiU0RiBm0LcvHti8MrVzWVreslw/9cBRFWJZNu2117aS27bOwYNNspmi1ktRqakeGt41lKQhC\\nkWZTYWlJxLYTwDBhmKDVStFuG8S8GEViLgwFGCDmOAloNDSaTYNq1ePMmSaVSoQkpWm3IxYXG9Rq\\nDmfOLGKaEUEQYJohliXhOAlsW6VeD7AskVpNwHHAsnw8TyIMJRwHRFHHceIQTc8LCEMZSBCrpMmk\\nUmlyuQyCoHSk2yRAel5yqfF7jxVuPG9VXvXljPj0L8Q0Azyvd+Rao2FSrdpUq2ZPOT3P8wiCBIYx\\nQLXaO+7TtuOx4mKJwMuhXhfJZCao10XsHlTlrutSqbhUKg7N5pWi5CSy2SKpVK7nZPwXf/EXxOkX\\ng0BfzxLjSLcBoI+5uaWetvPzDpq2Dt/v70g/Xh61Wo1aLSIM85w713tu3rdvmjjKLsPRo0d72tZq\\nMopyPa2WxtzcXFc7z/MwjByZTJErZZnGss4yYSj2GC/WArEKWqnUYHa2ztKSSRgqOE6IaTokk1lM\\n06PVshGEPjStQBRd2TFqmi6OE9Fue6uyj6v4KbCWmJfl6sqvhmFIFEmIokwQXNgvlvtMFPXqM73L\\nDgIBUZTx/ef+37Z9JEnH8+Ixs9WyOpGuAWGodFT9PKJIRJIUHMfvcNg8N9e3Wi6+L2KaOtWqg+s+\\nP0niXs+USOjk84lVJ8YqeqLdXpVf/VnANTlKSCaTbN++nTe/+c0rYWuCIPCpT33qWtwOAJ7nk0oZ\\nSJKLLMuk0xnabRNFActyWFxskM2KFApZLOtZtm3rI5GwUFWVdDqP74u86lUy5fIzTExsxvcrbNw4\\niKaVedWrJqjXqyQSNtVqnqGhrezc+TBhWCWb3YggLLJrVz+qephf+7Vt1OuHue22PczOVtE0hdnZ\\nCWo1h2QyhyCc5tZbJ5Gked74xutYXDzB5s1J6nUZVc2RzbZJpVLkcgquO8Vtt+1Bko7xznfewFNP\\nPcntt4+Sz8/zxjdu4OjRJZJJjVtvHSeRCKlUDBznDHfcMUYqdZRf+qVtLC2d4s47r0dVl0gkcnhe\\nGs9zkaS1WFaTzZvX0m7/gPe//1UsLJzkDW+YYGDAZWRklKNHl2i1WhjGILKsMzmpEoZzvP7161DV\\nRV796klUtcKb37yFqalZVFVg7dpNhGHEpk0etl1h7dq1iKJNKvVcjnUURSubAkVRSCYDPM9H1zWC\\nIFrJh1wOx7NtB8uKv5ck95qHDEK8cG+3QRBEJMm9LCN5JmOwZ88afvjDBxFFk74+l6GhKXbtuplG\\nY4Hp6RkGB2WqVchma8zPHyCR6COfzxMEi9Tr88zOmsSEoE1iAtgU0E8qdY6BgTR9fYNs3jzG6GiS\\ner2M5+VJJNKYZovJyWGCoEkqZTA+nkfTygwNuWzZMkixWODs2SUGB5NIEoyNpXEcG0GAwcEcUWST\\nz8cqH7lciiCok8lAJpM6z8MqoKoSmqYSRfHGUVWvzBQeK9UoOI7bU4bx4jDzVVxdPJ/6VRQFVY3T\\nNDStd7+LopAgiE/Le5GsZbNZrr8+yezsKfbs2XjF+4yZ9sUrOjJuv32Sffue5JZbRnqS6sUSnD6u\\nGxFFvdvr6GgRWEJVpZ7RKO9617v4yEc+BzwMTPUs8/Wvfz2nTv0I0Ljttu5pJQDvec9r+fu//w4b\\nNhgrCgqXQ6FQYOvWFKdPP8Mtt1waln4+3v72t/HJT8ZkoK997Wt72v76r9/Affd9hte+tp+BgYGu\\ndqlUitHRFs1mjbVrR3qWmUhohKF1CfP+hXgEmGH37t2EoYggSAwOquRyLVw3RSKRxvcXmZwcJp9P\\nEASHkWWdXbt29Lx2fH0VUTTRdWk1GmMVl0W3sXHXrl0cOPA4MMvVll9VVZVk0iYMg0sUNxIJjSiy\\nkSQRRVFe8NwoyzKpVNBxOD43Nvb3Z1haqpNMarguCIKC49jkcgnC0MV1HQYH+wABz3NJp7M4joco\\nPpdqNTpapFaro2kO4+N9PaPWLn4mcFbKuviZrhVR5ypeOTDN3o6MVApaz4eKahXXFNdkFr7rrru4\\n6667LvjdtdxwtFoNpqZqKIrH4GAecJmZqeB5EbbtoKoS09NnOHUqgW0/yfDwq/H9FoXCOKrqoygq\\n6bRBsZhl3bp+HnvscSqVBCMjTcbGhlhcPMnCgoNttyiVPAQBXvOaV5HNjnDs2EEkqY9mU0XX+wnD\\nGdau3Yjv10ilBjHNKo5jI4oqqiqjaRlmZ08gCKMkElXGxrYhSS6ZzADttofnBQSBzGtfewOFwhiq\\n2iaV6iORkNmwoYhtzzAwsJaZmePI8jCC4DI0NIiu5+jvn8Pz+hkby3HTTTcwN3eGkREDRTG5/vrr\\ncByL+fkWjhNQq1WQpCz9/R7Z7BC2XUfTijhOGUVJ4PsWw8ODNBoqe/dOEYYphoYUJiYmyOdd1q1b\\nT6k0T6MRoOsRe/bsIIoC6nULQZBYu3YUwzBotUwcJ8J1Y84BWZYwDBFRjDdHYRjiugGeFyHLIrqu\\nEgRtKhUTTYtltqIopFJpIYoRmczLQ840JleMVSdE8fLdUNc11q0bZceOrWQyDYaHZbZtG2F0dAhZ\\nTrN+/Rr27p3CNOv09w+TSGh4noTnge8nyOc3oChWJ1rDQ1VdGo148zU8rDM+nkcQ0jQaNcbHiwwP\\nF6nXFarVNum0RhAEOI7D3FyZcrlNu91E11Mkk1UsKyQIJBRFJAwj9u+fot0O6OtTGR8fRlUFTp+u\\nkEiIjI0VGRgoXPYZl/FCWblVVe3pkIpVPVxkWSCTMVYdGlcZrZaJbYckk3JPZ5IkSQwM5IgikOXe\\n4cxhGEfGRZHbcxHqeR6zsxUqlZBSqdQzXUSW434WE9L1TlkqlSo0Ggazs7We7SXmXXFwXQ1B6H0i\\nr6oqk5OjPW0g5nyICZOvPD7F6SEWkGJ8vHeUWblcI5UqEoYNTNPs6kyRZZlDh04wP6+xZs0UN920\\nvWuZmuai6wNIknfFOt21aweieD0TE71PWeMTXxFJ0nHd3s5m27aZn2+iaSIjI90kjkeBFvv3f5dc\\nziBWfcgwMpLh1Kkq5XKbfN5AURTm50vMzooMD/O8TqtlWURVFSRp1VG6igsRRRGNhonvR6TTl85R\\nBw4cIFYGGntRrt9tLI7n8hBRDPG8Fq7LFcfui6HrGpdTJNX1BIIgIElRR+I5VlcZHh64yC7+vLi/\\nCoJAsThAKhWniDzfPiWK4sq6wfM8Gg1ndb5fxQvClcg+Vx0ZrwxcE0fG+9///mtx2Uvgui5nz57l\\n9OkFjh8vE4Y+X/vaQcIwwvdFyuUmxaLB7OxZ5udVCoUkp09XaLefRRBsms0j6LrE/PwUmpZgYGCY\\nIBCYmVlEEBpUKg2y2UeoVJpMTbk4jokk+RiGweHDFpa1j4mJ6wjDRU6cWCCK9tJqLTI+HlKpzJLL\\nTeA4TZaWniEMwTCKSNI05fI8jcYUjcYZbr5ZxjBkTp78OrqexHXXI8sOP/7xD4kin2JxDE0zUBQR\\nxzmBqsocOrQPQZCQpDW4rsXTT09hmm1SqSF836dUMvnBD76HqqaQJI1EQmXfvqdQVYVWKySKQk6f\\nfpZ222JgoMjAgI0sm2Qy8wSBhGlqWJaJ57nUai1aLZ8gqDMykiGZFHEcl8OHf4JlSTSbBmFoYpoO\\niqLQbjdQFB3bXsSyTDKZAWzbJ5PRMQyZVCpFvb5AKpUiDFU8z6PV8gjDONrCdVsIgo4kRbiui6JI\\n+H5IMqlfoDTSarVwXZdsNntJvqjrxhup5aiPIAhWSK6CILgs+dL5qjvPh5hLURQUJY5ZU9VuqiXx\\niYNpLlKvT2PbFqZ5gnZ7mnw+z9TUHFNTDqdOHeHcuWeJSWcN4jSNBM8pwySImcCbxBEZk8hyiUrF\\nJZVaT7ms4jgmnueQSEjYtkSpZDM7exjDGKJcdrBthXK5ThRVOXnyNIODOUZGhtB1h0JhmKeeOks2\\nO8L09ALNpoWua2Szg7TbIblci0QisbKAiaJoJXRf13V838dxHBKJBJIkrfwsy/LKyfgy8dXF9RqG\\nYWfxdCF5leP4SFICz3MJguAVd3J6tYk8ryZicl2HKJIJQ7vnYlhRFObmYr6H/v7uXBYArhtSLs+Q\\nz6fxfb9rHzJNk4cfPkKl4uD7a1m/fn3XMlXVQBRNoujKbeDBB/dy+rTHmTNJ7rrrDT2jMo4dO8jC\\nQo077/xAzzIB9u7du0L82A2f//zngcPESiSLPcuL7+uHgIbrvqGn7cGDVRqNJKYpUiqVukZFzMzM\\ncPBgmVZLI51u8973di9Tlvuw7UcA0PXdPa//3e9+lx/84BgTE8O8/e1v6KrQZNs2Bw8eo1azuOWW\\njWzc2D16pdGwOXlyHk2T6e9PXea93gA8RCwBDZmMRDIZ4Loex48f4+zZedLpCXxfZ3FxiSefPEm9\\n3k+5XGbHjirDw4MrJMEXjyu2bVOrNWk2WxiGRhRd6qANwxDTNFfIiJ8Puo1vq3hlIQgC5uYquK7P\\n6GiBvr7Lvc85YiW5q49u84br+oiijue5OI5LMpnDNJsvyJFxOViWhywb+L5DKhWnlyiKQhiGOI5D\\nFEXoun6BY7rZbBIEAel0GkmScF2fIBBZWDhFMjmM68osLCyQzWbJZrOXdWpf/Jy27REEEr4fy8bG\\njhXpkrXdKlZxPq4UkZFOQ7P50t3PKn46XJPV/eTkpeRogiBw8uSLM7h3wze/+SNKpQEefPBBSqUt\\nnDr1FRYXb8KyThFrzG8Bfogk/RpB8GkMYzOm+RQzMzdQr3+X4eFbaTbP0GoZQAJB+BdyuV+gWn2A\\nTObdNBr/yunTOzh48D5E8c1Y1gEMY5AgCGi15tG0mxHF/4c9ez7It771CMnkr1Kvf4c1a4ZoNPYx\\nMFDFNGepVLKIYoZm81uMj/87pqd/yMjIRmq1x5ie3saxY59H138Bz3uGTOYMgpCgUmmgKHtotT7H\\njh0fZGrqH9iy5Xc4ePAfSSTeiu8/ychICc9zOp7sIaLoMTZseCvHj/8za9f+B+bmvsDrX/9+pqf/\\nhcHB3XjeEhBhWQFPPXUSWd6O532FV7/6fUxPf4NXveo/cOTI1xgcvIVK5QCeN4ZpzlMu1zGMDczP\\nP4MgjPHII/9CPv8LHD36JcbH34hlHeHGG2+l3S5TqwUEgUitdpZs9gYajS+wffudNJuPctNNb+bs\\n2e+SSGwgik7x6ldHKIrC7GyZINCYmztDOr2OZvNpJiY2IYp1FGWyw64dIsuxc8J1XY4eXcLzEgwN\\nzV9wYhrzVoDrthEEiSiKKJebKEoSWa6RzxdRFPOC3EvHcWg2Q3w/5u1QFJVUyrlsusgyTNNkbi6W\\nnRXF1mVPSuv1Jl/84vf42tfm8LzlCbnGv/zLaXK5Z6nXfYKgRJxbf0Pn78s/K8R57AvEjox+npPD\\ndahU8kCKpSWVU6dq7N//NLouIooilqWjqhbJ5ACKcpy+vnHCsE29XsE0dUTRIZdLkU7Psm5dCkE4\\nRhAUaLcPk8328fTTLUZGXPr6bPr6XIrFLI7jkMvFpzGNRpulJZcwjCgWXep1F8dRSKVqDA5mKZfb\\n1GoBuu4wOBif5NdqDiCRTF64cW42LXxfRhAs8vnkioNJ1xWaTQtVFV5xi5nl9gQR2ezL05lRKtWw\\nLI1cLmBgoLvM9dGjR7n//kWiSMH3D7Br166utt/85uMcOTJCIvE0e/aso7+//7J2c3NzfPGL+zDN\\njZw8+U+8851v7lrmsWOHefTRBqLosmlTrLLSDV/60sNUq79IIvEQf//3f9jV7tvf/jb/8A+niaJJ\\n6vX/jQceuKer7b/+63d49FEdQTjMhz+cWFExuBi//uu/DuwB3gnc3bU8gI997GPAB4AMX/nKXwGf\\n7mp77NjjHD16K6o61ZP3Y3FxkUcffQbX3c7i4reA/6mr7X/5L38E/D7Q5sMf/jAf+tCHutr+7d/+\\nAPglnnjicU6ePNm1/mdmZrj77oO47hCW9RgbN67tWubhw0f43vdcZLnBunWZTjTL+XgS+M/AUeAA\\njz02i6KEPPXUGY4cadBqtRkZqfC6140wPDzM00/PcfjwaYaGRGZn12IYaWRZxHUlBMEmn49PeH3f\\nZ3q6wb59pzh3LqBYlBgezl0i814q1ahWRUTRZM0a8YrOCd/3u45vq3hlodVq8ZOfnMV1U2zbVufW\\nWy833u0ANhO30asHz/Oo1z3i6KPwAkespsUpH5omIEkqrtsimfzvd5oZhkqzaaKqIq2WTbkcIIoe\\nqZRJswlRJFIs+uTzsRJZrdbg5MkGjgNr1gQMDeVxXYfvfGc/c3M6J09OkcuFlEp9qOo53vKWrRSL\\nF0apxSSfF8+PIfW63ZFL1gD9kjXBKlZxMUxzNSLjZwHXxJGxd+/ele9t2+a+++6jXC6/5PcRExWl\\nkGWdNWvGOXvWQJIMRHGYMKwTbwB1VHUEy0oxMLCT06eHSSQGabf7SSaHsKwlBEEnigyiSCOTGaNa\\nHSKb3YppfodUaoAoytPfn6ZWy5DL09/MQgAAIABJREFU9eG6AZZVR9clHCdPoTCBLGfJ57dRrw+Q\\nSBSxrD6Gh9dRrXrYdgFFyeJ5WdLpMXR9mKGhbbRa3ySfHyMMtU7kQYr+/ixR5FCpaCSTBSwrTT4/\\nShhqJJOD+L5AMtlHvZ6hWBzCNOs0GhaaVsT3dQYHJzlxIkUqNUgYJhgcHGVxMUU63U+1apNKgSDY\\nRJFKImHg+yIDAxM8+6yCoqRRlAwjI6M4zhS+n0WWLSTJYHh4HYbRplAo4DgqipIBDNasWcfc3DyJ\\nRLLjPPARRQnX1UgkkszNxaeqtg2SpGPbIZLkEQQxL4am6QiChCBEeF4cmgwihUKOdnt54S6SyyUR\\nRQFRFPF9H98HRVHxvAtHqTCMEAQREBAEkSDwiaVvhQ7/hsjFnH3xzwJRFH8f2/Rm2Y1DmEWgO/lW\\nLAEpo+sFPE/s3EeEICQIghBZXiYw1Im7skgsx2oQn0YKgE0csl4gjtQIiCMzVGISUK3zrAk0TSeK\\nAhKJNGGoEAQGuVyObLaIYRRIJJI0Gil8v4GuS6TTBVIpjShSyOWu66QaFJFlA8OwGBsbRFXbnYWE\\ncF6Ey/KzR4RhRBQJCIJEEPgd8tYIUZSIovC8ehRWUoku974urm5FUSgUXn4OgOeD5fYUf//yZBI3\\njCSJRApJavS0syyLet1EEBRMs7dDyfNkisVRPK832WSpVMIw+kgk1qMoZ3vattsBhpFBFJ0rEjOK\\nYppMZj2CsJezZ89eZoMco9lsIst9CEI/zWbvPOx63UGWM/i+hnVF1rBlMt7uDtDnUAQyV7QyjAFG\\nR3cTRW5Psk2AdHoUVb0JSepN4BljkngcuRJUYD1xBEl3mKaJphXJ5UZptXpL73oeDA8XCQK1SwpS\\nBkizXD/NpocomliWjW1LSJJBJtNPJpNCFFUKhSGuv94gmXSQJJ0oWiYSVDpjenTe2CMCMrlcimRS\\nvOwmKQxDJEklivwXQKx4+fFtFa8sCIKAYaRJp/PESmGXQ4a4/6ZfjDtAEOJ0z/MhyzKFQvcop58W\\n58+zpVK9Q+QJYRggijEh7/nkoHH7ljoysPH8LooimqaTTmcJwwbtto0o6vh+85LngOX+KF4QYSuK\\nEoVCmigKCEMXSVpeL61iFd1xJbLPdHrVkfFKwEsuv9oNu3fvZv/+/S/JtZbJBiuVCseOzZDNyvzo\\nR08xMdHPPfc8AEBfn8jBg7OsWWPwzDMl3vSmnRw+3GLHjixzcxabNw9RLnv09SkcPHic6ek2e/YM\\n02yKZLMShw6ZvOENa9D1LLmcztNPz7NmTQ7Pi4giaDRqHD++xG23bcF1VRxngUcffZZt2/JYVoKB\\ngURHwjLJ008/zcxMi92713HmTJXx8SwHDy5w002TVCoBhYLGkSMn2LRphGx2GIByeZpjx+a59dat\\nPPtsiw0b0hw7VmfdOoMDB06xZk2OsbGNBIHP1NQpFhYsdu1ah+NIZDI+J07U2LNnFNApFtMsLTXJ\\nZjWiSMW2bX7yk8PMzzvcfPMwkpRhcjLH3JzJxESWWs0mmZSoVOKFeyolMTNTZsuWLYBKozHP9HSV\\nDRv6abV8BgezCIKKJIUsLrZotWIZUMsKyeVymKbCwIBCImEgigELCw6plMz69TEpXKlk4nkRimIT\\nBBGFQgbPi0inEwiCiCgKnc2xsHJKUalUsCyXYrFwQQhinPbgIEnPLQZ838N1fZLJBEEQoWnKBaf8\\nURRhmtaKoyQMI3Rdu+JJQK0WbwLjBbV4QduEeNI/fvwEn/jEP3LkyAksyyWR0Ni0aYL+/kHOnSuz\\nsHCGo0fPMj09A9SBCFXNAzau6xGnlJw/Eg8AGfr6FKLIQpZTbN26g5GRIoODKQQhlsz1vABRVNi0\\naYxcro9UKoHr2pw4cRZFkSkU8iQSKsPDoxiGTK3WYnCwj3bbQxBCxseH8TzI51OdENLnTibDMKTV\\nanfaRhLXdTFNm1TKQFXVzs8WiqJgGHH+reu6BEF4Sb3Gub8eqtotV/6VhyiKcJyYU6BXVM9LjfPb\\nZsxQb5PJGD1PnBuNBg88cABRFHn9669jZKQ7U3+pVOLhh3/E5OQwe/bs6XkvX/jCF3jiiWf5wAfe\\nwebN3ckpq9UaDz20j1RK4Y1vvLXnvX72s/fw3/7b9/nlX97MH/3R/9y1/3qex0c+8p945plFPvGJ\\nj7BpU/eUEcuyeOihxxgaynPTTTd1tXMcp5OGMAbM9HRgVSoV+vpiZZPXve52Hn20u5T5/v37+au/\\n+iy33rqJD3/4w13tAD7xiU/w+ONH+JM/+SA33nhjV7u/+Iu/5CMf+eOV++5Vpx//+Mf567/+Ju9+\\n907+5m/+puf1H3roIWZmqvzqr761JzGq4zjs3fsUhUKSrVu3rvz+QonLGPfccy9bt96EomjMzc1S\\nLlcJgjYbNqxj06brCMOApaUyZ87MUyik2LRpI4YRh8I7jnsJoWir1aJWa1AqVSkU0pd1dvm+T6PR\\nQlXlns9xPrqNb6t45eHs2bM0Ghbr1o1fQGYfRREPP/wwd9xxx4rt1V5+O47zvNcfVxtBENBstpEk\\nkURCxzQtwjAinU6urJfCMKRSqREEPoVCfoWkc35+gZMnp1mzZphMJsPx49MUixnGxkYuiahcXqOd\\nv547f90mSdLP3JrgxcbPq/zqDTfAZz4Du7tkSJ47BzfdBD0UzlfxIuP5tM1r4sh44oknVgbZMAzZ\\nt28ff/d3f8fBgwdfkutfXDFxqJqPJEVks8bKpvI54iaBMFz2ElvIcszILAgyQeBRr9sIgsToaArD\\nMJiZWcQ0RTIZGBrqx7Yd2u2AIHARxfM91gph6CCKOvV6mTBM4Dg1NC2HLAesWVPshLPW8P2QMLSR\\n5RSK4nSUUiwkKYGiRGQyBkEQ0Gg4RBFks9oFg3hMmknHNtkhR3LxPJdGw+pI2ZkoSoa+PplCoXu4\\nuOu6HDo0je/LrFljMDRUvGKdR1FEvR7XZRDEdbh8L8uIF4Dx/WcyKoqiUK+38TwBXYdUylipS0EI\\nyeUMwjDk7NkyQSAyNKSTTqcvedZeaDTi8jWNK8rudcNyXQoCZLMvXKP9fFyu0zYabSwrxLZtkkmD\\nZFKkUjGZn2+iKCBJMDVVptFok0hIDAzkmZwscvr0OZ58skQYmuzcOUmz2WRuziaZVEmnQ1S1iKa1\\nGBqaQBQd8vk8iiJgGAqlUhvT9MnlVPr7s9i2w/x8E8uyMQydfN4gkzFW+oeuCy+YsHMVryz8NIsd\\n27aZno4ddsvjYzdYlo1phshydNUI20zT5Ny5FqII4+O5npvu+fkSjQYYRsjYWHeFjTAMqdVMokgk\\nlZJ6cmnEaUI+ohiRyxldIghivPGNv8fc3Fq2bj3Lfff9X13tlpaWePDBY/i+wJveNN41cuSFIAxD\\nZmZKOI54xfG/VCqxf/8iohhxyy0TpNPdT5ZfyFj834vl9nnHHW/h4Yc3A9Pcd997uOOOX8T3Q6LI\\nQ5Z1DEO8IH0jCALqdfuCeWcVq7iaWG6bX/7yl3nHO+4B1rN581M880x3J+QqVvFS4efVkbFxIzzw\\nAGzadPm/NxowNhZ/ruLa4Pm0zWvirvyDP/iDlUWqLMusXbuWL3/5yy/5fSyfnHiej2mKCEJAvV5C\\nlmVKpRK1mk06naHRcEmlNIrFIZaW5llaapBIiBSLI0BAFEVIkky16lOp1DFND88TsSyBc+fmSacN\\nIOikTcQL+WbzHGEokskYlMtzLC01qNclgqDBmjUyui4xO7tAGHqUyz6WFbC0NIOiZEgmA8bGRFIp\\nmf5+Ddd1efzxvei6QDo9jCAIOI6IKMqk0waO41GrtajVPJJJgZmZWXRdRhDSBIGP6zoEgYhpNvE8\\nE0hSqdTo788RhqAoEu22japKK8SXw8N9eF6EKMYedlWVcV0fVZUplWqkUvpKjnur1cL3Q3RdxfcD\\n6vUIy3KQJA/Pc9F1FUmSEUUBWQ4IggBJijfFmYzRCdWNnQOqqtBum8iyhOd5eJ5HOp3upCa4nfBh\\nH1VN43ntFanWRqOFLIsXnJBFUdSJ5DBw3fbK7xzHRRSFlU2P68Z8DpqmXnZz5fsBEJ8sxPd+dTgZ\\n4vtuMj+/QLvdptVqI0kSa9euIQwFRDHkySf3cuLESRYWbDRtAFGUSCY1du0axDQdfF/j1KlTRFGV\\ncrlFqxWSTsf8EanUCMPDKrmcgmX5zM2VaDQaXHfdEOl0FllWmZ+v4Lom7bZNrQZhKCHLIu22S7NZ\\nx3UhnS6wtFShVquRShmXVZFwHAeIB6VWy8Qw9OdNhLcMz/NwnJjIS9e1Vxz3xc8SfN/H8/yOekP3\\n96DrOpOT8vMiMmy3HZrNAFkOSaV6OwSnpqaYm6uwY8cmMpnuKRYxcXPcn6+EdttkerrC0FCa0dFi\\nV0eKKIrMzp6m2XTZvXtbzzJN02FmpoIsR6TTY10dGY1Ggx/+8Gks62mq1d71JEkS3/ve12m327zl\\nLX/a07ZWq7Fv39OMjfX3jFzxfZ/jx09Rrfps2jTY05HhOD7f+MY3MYwEu3e/o+f16/U2jYaHYUik\\nUomrIol4uTH6fDz88GPAccDhmWdu5C1veQuiGOfOL5/SWpa9Em0RBAFRJAMCvh9c0ZGxfPrbW/71\\nxcGV5qJVvLzRbreB/cDTHD36s33M6/s+zWYbTVNWHNiu63bWS6Ao8kr/CcNwZY2WSCRwXQ9FufoR\\nFUEQk/5ead5axc8HrkT2mUzG6SdxyvhLd1+reGG4Jo6Mhx9++Fpc9hLMzlZw3RSLi+dQlByVyiy2\\nnaVSOcORI0vo+gi12qNs2PA6guAQr3tdH488coAo2ka1eoTt2yGRiAdiUdQQBIuBgTWcPPkMg4Ob\\nmZ8/zPr1e6jXTzIyMoYkRfT1NbFtmyNHHMJQYX7+CSYmdvHwww9RKNxCvX4CTRuh0ThHsTiKbbep\\nVpt4Hhw7Nk0utwXfP4YgjJFOlxgbK/L97z/BT36SpFY7zfXXm+TzfciyRy43gm1PMT5+HT/+8TMY\\nxgaeffb7jI3dTKt1lnXrBpGkWIFAEBI8++wp+vu38OMf/4gbbvgF9u8/yO7de5idPd2RiJ1DklQM\\nI41hzJPJ9HP2bJVcbpS5uWcZHr6OY8cOIYojSFKFm2+OWdvPnbOJIhlJKlMoFJmdPYsg9NFonGNi\\nYgOWVaZYLOA4Ns1mgKJoCEKLXC6zwj69jFKpRq0m4zhNCoV4cyRJLoIg4XkRQSASRQFB0CaZjNVF\\nqtU6S0sAPuPj5gXhnsmkgmW1SaXiBbFp2ti2SBT55HLxorvZ9AGJILAvG3WgaSqua3X0zK9eVEK7\\nbXL48BKHDi1y6tQSlYpPJqNy000i+Tw880yVf/qnk8zPCzSbTbJZB1G0GRrawIkTJ1i7tp/9+48T\\nBBJ7957FskJcV0BVA6LIRxRttm7NsrAQoaopZmbOIcsFpqZ8du50EASJajVElpudvPGAYjF2IFQq\\nLpaVxrYrGIaF4/jYtkEqVWX7dpVE4rl6iKVQQ0CgXJ5HkopUKjXWrRt43hubONrIpV53kCSJdDp8\\nUXJ+V3FlxNFVNoKg4TgWuVzv9/B8F6PNZotKRUZRHIaHu2+kl5aW+Nd/PY0sj7Gw8AS/8it3dLVt\\nNFpMT7uIYsjgoNnTmfL97x+l2VzDqVNTbNgw3tV2ZmaGb32rhiRlqdf38ou/+NquZU5Pz3PqlEAU\\n2YyNtS8hhlzGwYMHsawIeDfz85/pWh7AJz/5Sf75n/sIwzX8yZ/8Offc88mutg888EPm5sb5yU9O\\n8nu/N9T1+rZts3fvAp43jGkeYceO7ukyn/nM/8u//VsKUbTZsOGf+Y3f+I2utufOlWg2syhKibGx\\ny5O3vlBYloNlCStj9KXtSwb+R+AEf/qnH+Otb30H6XQeRYnI5yNE0cP3FaLIJZ+PnRGqanUcBFce\\nv5fnCPDI5cSXbEMUH7z4CIJEGDoYxiop6CsNv/mbvwn8JjGR/Meu8d28uFhcrNFu60CLtWvjdXKj\\n4dNs2qiqjK6H5PMSoiiurOuiyCaTsdG0LJZlX3WyznrdAjRs2yKfX10//LzjSmSfkhTLBl/JbhXX\\nFtfEkVGr1fjYxz7Go48+CsDtt9/ORz/60cue5L7YCEMfSRJJpRI4joZtxyEssgxB4JJICKRSEhCH\\n0qfTOrYtYRhJCoU8sTymTRQFiGJ8Ip9M6iSTOrquAAGSJHb4EyCTSaKqEqJYJ4pA0yTAJ5XSKBQy\\nCEIGw9AIAr3zvyH9/WlEUWdpqUw+n8ZxEqRS+op8J8Q8DqoKyaROOp3Atj3CMAACwtDHMLSOhGnc\\nG1VVIJPREYSYWTqKNFIpA01TkGWZIPBXyMdim+ciDWJCSJ102qDdbgIhsiwBIaIY1yn4Fy0ww45H\\nM87f1DQDyxKpVBqEoUWxuOzxDDvldN/gxiRPcS60IAikUgayLFOrWURRfFp1fhizKIoIQngBOdQy\\nYm107Tzb5Tzr+Ofnwpq6hzaJokg2++KMcpIkEEVxvcbvMiQM4+iWVEpHln1iXgyFdDpDMql3wvKr\\naJrG6GiBSiWgUrHQdRVVFVHVZRLRJOl0GlGMn1NVFVQ1ZgBXFIlEIollOfi+jaKoZLNJxsdz9PUl\\nWFxsYFlhJ81Eo1w2sawAQYguWXg893PUqd/l53n+WC5j+V2sesevLZb78tVcZGqaRjqtIkm9y9Q0\\nDdt28f0Ww8NXIvAUSac14oi43uXquoznKSjKldn8ZTnC9+NxuxcMQ0XXBcDraRejCOSISaa7o1Ao\\nIMstwKFYvPIm2vc9ZPnKJJLZrIHn6aRSvUkICwWFZFLG8+Se0TBAh3QvnhuvFuL5qNcY0OT8OgzD\\ngHq9iaZJ5HLZFTLm88f4OGryheKlD8W+8rOv4uWMRCKBZenExNsv/Xr3pUTcVp+b688ffy9uw7Gi\\nXGx78Rrsat9TTBB+9ctexSsPVyL7hOeUS1YdGS9fXBOOjLvuuovt27fzvve9jyiKuPfee3nqqae4\\n//77X5LrL29Oy+Uy+/btY9euXXzqU5/ihhtu4Gtf+xqDg7GO/MLCAjfffDOHDh3igx/8IF/4whd4\\n5zvfyec//3luvvlm5ubmKBQK6HqBRqPFnj1bmJ2d4cYbb2Tv3r3cdNNNPP7442zatAnHcRgaGqLZ\\nESVWFIWFhQUmJyeZm5tjaGiIAwcOsHPnTp555hkmJyeZmZkhk8mgqirNZhNN0zh27Bh33nkn9Xqd\\noaEhjh07Ri6X45FHHiGXy7Fhw4YLrlUsFpmZmSGbzXLixAk2b97M2bNnGRwcpFqtkkzGfBmtVoti\\nscj09DRbtmzhzJkzbNy4kWq1Si6XY35+fuWU3bIsxsbGaLVaGIbByZMnGR4eZmlpibGxMRqNBqlU\\nCtu20TRtRfLUMAyq1SpDQ0OcO3eORCLNwkKdVMogmRRIJBJUq1Xa7TbXXXfdyj0ts/3Pz8+Ty+WQ\\n5TjksFKpADA0NEQYhh1Z2xZ9fX34vo+qqlQqFQzDoFKpoKoqfX19K/lWvu+jKAqWZZFIJFaI4oIg\\nuCAvK1ZI8Ugmk0RR9KISSF2cDzY7O8uXv/xljh07xuTkJKqqUigU0DSNSqXC7Owsx44dY2FhAV3X\\nGRwcxDRtWi2LwcFBarUlyuUa7XYbx7FYu3YNmqbRbrdJJpNs2LCBgYEBdF3HMAwWFhYYHBxkeHiY\\nbDaL67pIUpxSZBgGQ0NDpFIpWq0WrVYLXddJpVJ4nke73SaTyZBOp1EUZcXRtPz+BUFAURSq1SqJ\\nRKJzEqoSRdEFbP2yLHdJ4fFXtOkN4zm+Ad/3V64VhmHP97Nse6VT1Djc/MV91680nN82l9+Frl+Z\\nE6Zer+N5Xlc51WWEYcjRo0fp7+9nYKA7R0UQBDz44Pc4c2aaX/u1f8fAQKFnuY899hjpdLqn9CvA\\n4cOHueeee3jnO9/JDTfc0NP27rvv5vDhw/zZn/1ZT44M3/d58MEHGR8fv+L1L17oPx/bK9nZts1f\\n//Vfs2vXLu68886etg899BCHDx/md3/3d3s+E8Dg4CD9/f0cPny4p92RI8+yf/9T7Ny5nW3b1vd0\\nUJdKJUqlUs8UGIifeXp6Gk3TGBwcXPn9cvt8//t/i7vv/iwAx48fxzCSlMsO5XKJgQFj5X90XSfZ\\nWZ26rosoiiiKgm3bSJK0Mk6c/16W5xnLslBVtWt6nO/7iKJ4VVJpzofnLUt8K6upJS9TxKpo/gVt\\nY7ltmqa50ube/vb38E//9PlrdZsvCpYPWmRZxrZtFhYWSKfTFArxGF0qlWi32/T396/M/xDXWavV\\nQlVVNE3DNM2OAszleYWe7zx+MZZToxVFuep985WMn0eODN8HTYs/ew2l69fDv/0bXNc9SHEVLyJe\\ntmSfO3fuvITY83K/e7GwXDF/+Id/zsGD4zz00F8CbwX2AcOACdSAjcBDwG8D/zfwe8A96Po7sO2H\\ngJ0oShXDcEkkNpDJnGB8/E5mZv6ZwcFf5vjxuxkYeBft9nfZvfuNmOazVCqxMsTwcIu+vl1I0hTr\\n19/O3r33k0zexqlTXyebvZ2FhUdIp7cShtXOyXuWmZlD5PO3MjJyjN/6rd/la1/7EtPT13Hq1NdJ\\np28mDBdZt26A4eFRCgWXfH6Mw4efxDC2cvToA/T1vRHL+gk7d/4C09NP0GrlaberRFFEIjFEGB5j\\nfPw2lpZ+xOTkbTQah9iy5dVUKgeBCWy7AsjougFUGRjYyI9+9BCieD2Li48zOXkHun6OO++8jcXF\\naQ4etDDNNmNjMrncCE8//SS6vol6fT9jY6/izJkfMzi4g2ZziomJzVQqs5w6VUNRDNLpJuPjN6Bp\\nc0xO7uS73/069fomEokZfvu37+TcubM89NASnmfzmtf0MTq6nlOnTiJJg0jSPGvWbGFq6hCmWaTR\\nOE0uN0oiIbJxY4FkMk21WkOWU8zPn0GWB1CUOmvXTiJJAblcEsuymJ5u4fs+YeiiKElU1SWTKaDr\\n0U9NDPp82ybERIW/8zv/B1/84jyxbOoSijKA5y1LA0OsSGIBfcRSqw6xUkm+8327YxsQy71ViKVZ\\nl2Vl5c6X1+EpKSCKbVKpBIVChmxWxPPixUU2K7Jt2ySFgkalYhNFEalUgsHBVCe9JiCTSZDPG4yM\\nZMnlMshyLHcbBCKplIrv2zSbArValb6+AqkUqKpOq+V0Fhgq+bxONnsp2WNMsmgRhgLJZEzYF6et\\n+IRhQBSFndQn4QIyv2XEpL7x6XAvUlbf96nVHARBIJNRVsn/Olhum88R94poWtTzNHtubo5HHplF\\nlhVe85oiw8PDXW2/+c3HOXxYIZ2u8L73vaHrZvrMmTP8x//4jzjOGnbvLvPxj/9h1zIfeeQR7rmn\\nhKJYfOhDu7j++uu72q5deyel0m1ksz/h3LmvdrW79957+dCHHsfzitx++zTf+MbdXW0/85kvcf/9\\nDslknf/8n990gcrGxRCE24nnoc8TRYd62AnA+4ilWj/Rc5L/9//+w+zfvwldP8Kjj/6nCzb+56NU\\nKnHnnf87rdb13HjjSe6++8+7ljkwMMDS0nuBgN27H+eJJ57oavue93yUY8fWMDJygq9/vXuZpVKJ\\nv/zLh7AsgzvuULjrrrd2tT148Ag//nELXXd529u2rmySLlQt+UNgCvj/+PSnv86zz1Y4erSKovhc\\nf/0YGzeOsWXLGBMTWURRpFz2UJSIRCKi2ZRptxsMDfWRSDwXbReGIfW6RanUwLYjkkmZiYnCJc7O\\nmLQ26hBSXx1ekFW8MuD7PmfPVggCmb4+cYVr5sK2+XZgLfCXP1Obx+X+EYYiguBw4sQCZ8449PcL\\n3HrrdViWxde//gzNJuzYobF9+2by+fhwKCZPljAMAc8LmJoqdQjctUuIl5fn/CiKyOW01cOGq4Cf\\nR0dGpRI7KarV3na7dsHnPhd/ruKlx/Npm9dkhk0kEjz22GMrPz/++OM92exfLJw65VMo3AwMAjuJ\\nN4PDwFDnc1vn80biiecWYADb3rVi53kTeF4/qjrK0lKSMJykXNYpFG6h0chQKNxKq5VBkoaoVESC\\nYIQoGqFcllGUccrlgHx+HbOzEf39O1hcDBDF9ZTLORRlAMvK02ymgPhT07YxPe1hGAXOnXPIZjdS\\nqymo6kZMM4vjSHheklotArKUywKFwkbm5kJSqY0sLIDrZlhYiHDdDI6TwDQ1JClPuSyh6yMsLMT3\\nVCq5GMYACwsiYZii3RZwHBXPMyiXA1otkdlZkb6+LczMtJDlARYWXBwHSiUL388QBAa2LWJZEuWy\\nRCo1zvy8Qza7hkolJJMZw3WThKHWKb9AGOZYXBSAAouLbQyjwPS0T1/fFkxTw3FiEqkoygODnbxH\\nlXZbQNf7WFz00LQUpZKHLPfTbsuIYgLfT2BZHiDieSKgUan4GEaBRsNHkhTCMO40scdfJ4pkfB8k\\nKYFth8iyiuddOUz7aqDdbjM7axNLMq4FBvC8EWIJ1SFgtPM1Qux0GyXe3KzpfI11/jbc+bqu8/No\\n5+8jwHjnbwMEQR7PG8VxRnHdIqaZpdVKYJp9eN4ArZZCrQblcoDvpwmCFLadwLJkHEfB8xI4jkoQ\\n6DiOgOcJhKGI5wFoRJGAZfmduhQAFdcNCQKBMJQJAgmQCYLLnzSHYUgUiYiisqJNHwSxLn0QCAQB\\nCIJ8gW79+QiCOKUmVujp/g6Xte7jcl+ad/1KQkxqC7Ksdq3rZXieB6SJohT1utnTtlRqk0qN02pp\\nNHrQhMd/GyWbvYGlJbdnmbOzNQxjHM8bolwu97St19No2mtpNDKcOXOmq92JEyfwvHXI8q3MzvYW\\nmZ+erpBMbsbzij2fKT6lzQE3Aet7lhljG3H/7o2ZGZdUaie+P8ZTTz3V1e7o0aPY9gSp1Hamp+2e\\nZS4tVTr3uJHp6emetqWSz+DgrbTbGao9VoylUgnLSiEI40xP915Z1mommjaCbSdptS5X/wPEHARx\\n/dTrEmFoIEkjuG6eVkvDtiWLd9Q+AAAgAElEQVR8X8K2fRwnHpOCICYxFgQd1wUQ8f3nxqI4GkPA\\n80SCQMXzYiftxfD95XFG6DnOrOJnDzEJsogg6Nh2t7S3QWAT0N2p+krEcv8QBBnbjgnqZbmAbcs4\\njoNlWURRGkXpo9WKiCKxM6dHK/O654W4boDvy0hSCtsOLrnO8pwP0mr/WsVPjWYTrpAZCcSpJZ1A\\n+lW8THFNIjIOHDjA+973Pmq1GhDn/H7uc59j586dL8n1lz08jz/+OF/60g/R9QXuvvsQoniOcjnO\\n0fb9KpK0Hl0/gyBMMDQ0y8zMGBs31llc7COTaZPLjSHLJuPj64GI4eEUjYbGunUCJ09GDA1ZHD7s\\nsmNHAsNYRy4ncPp0vJjeuHGYej1k+/ZBKpUITTM5cqTCwEDIoUM1BgYkwjCBIAgUiwaOA/X6EnNz\\nHu94x6vo6xtlaWmahx8+TiYTMj1tMjqaYHIyjn+amOin3Q5RFJtnn62RTLocP95mfFxG0/rIZhXO\\nni11FCAiGg2XoaEcjUbIddflmJ21GR9PYtsKqZRMtdrCMNTOphTSaY12O8DzKpw4USOZjFhc1Fmz\\nRuXGG69HVUUOHDiOIAhMTBTx/ZgbZXGxzehoglotYnjYYH7eIpfTkCQVQfA5fjxm8l6/fphq1ea6\\n64r4vky7XeaJJ86yc+cIW7dux/ddHn44Pgm85ZZtRJGE57WpVByGhtKEoYwgeJw8uUQmoyDLGrIM\\nw8ODSFI8sdq2jyj6NBoufX1JVDWBqsZyimEYUirF7VMUwXVjuVffjzCMF+8U4GLv4ze+8S3++I//\\nT44dm2XjxgFyuQKOYwISjmMjSSKLiwvMzjaJI4mWoy884qiLmGTzuRx9pfMVABL5fB+FQpZ0Ok2x\\nmKDVioCQgYFBCoU+stkEqhoRRQrptMaaNZtIpXQsq46mqSQSOvl8BkmScF2PREInkdDp78+hqkpH\\nbjjC83x0XUMUoVptE/O/KGQyBlEUn7LEzNACyaTelWjRsmx8PySZ1BFFsROua3fqDoIgfj+Xi7YI\\nw5B22+4wo3cnyju/TMPQV0O4Ozi/bbqui+P4JBJqz77geR4HDhwjiiJuuGFzz+iWmZkZfvzjE4yP\\n57n55t7HH3/3d/dy5MgCH/jAW9i+fXtXu8XFRe6777sAvO99/8NKWPfl8Ed/9L/yla8c4c1vHuXT\\nn/7bnu/9ttt+lZkZl89+9n/h9ttv7/lMn/jEfUxOZvnQh7qTYkZR1Dm53woc6XkC8ZrXvIYf/CB2\\ntNxyywQ/+tEPutp++9vf5r/+1/u5/voBPvWp3uSCf/qnH+fgwQU+8IHbedvb3tbV7v777+dXfuXD\\ngMBXv/op7rrrrq623/nOd7j33u/zpjet473vfW9XO9/3uffeB5ifN3nXu17H2rXdJWVbrRaPPfYU\\nxWKKG2/csfL7C0+9dwBP8cAD32F8fIBSqcH+/c+gaSIbN65naKifYrGfYjFHFEVUqy10XSGR0CiX\\nm8gyJBIGmiZfMBbF8t8WpumQTGqXVXcJggDTdK44zqziZxOVSg3b9unvz6y0neW2WSqVKBaLwHbg\\n0M/cKbhtO3hegKbJVCp1Tp6cY3g4x+Rk3J/37XuKcrnFjh3Xkc9nV/jJlv/PMOK1V7lcw7Y9BgZy\\nlxxyrs7PVx8/jxEZhw7Bu98df/bCW98Kv//78ecqXnq8bFNLbNvmq1/9KlNTU9RqNbLZLIIg8NGP\\nfvQluf5yxcQhoD623WR21sT3W5w71yaKXAxDR1UzrF2boFAosrAwT7OpIIr/P3tvHiTHdd95fvKs\\nrKqso7urb3SjuwHiIgCKB3iIp0hapmRZt+T1aB2W7B07VpZjx3ZYofDOzkheH2MrpLDHZ2issT22\\n5YNaS0PbI9HWTfEST4AgcQONvrvrrsrK+9g/sqoJEKhC8wRF9jeioxCoX756+fK9l+/93u/3/daB\\nNLousmPHJIIgUK+bhGG8AQQJQfCJIhnLamJZAum0zNjY8/nhsaymie9HGEYT1xVIpwUSiTSeZ9Js\\nhihKiK7rBIHPwkKZKBLZtWsYXdc5c2aRUslmeFhjcnIcz/NoNBxkWSCTiZ0fnfKz2QSKotBotPA8\\nCd83kSSFIHBpNl0kSWBiooAsy22biHgDLGPbLUwzQpYDstkcohgTwp0bKmsYJo4TUq1WCMMsUdSg\\nvz+PJEEUCYiiQDabRJKkC8rXNOmiKiDnYmWlRLPpMzCgXXTReG5bdu61VKpQrbpkMjIjI68MUz5A\\ns2niuiGplPyqLVBfOGht22F5uUqxWMX3Q5LJBIODeTIZFcPwKBYrtFounudSLNZwXYG+vthZkUgo\\nzM3N8oMflDCMCrt2baVQSJJKJRkYyHHddTOYps/8fLnNKSIiSQrJpEo+n0ZRNBIJFV2PTy7r9Rat\\nlo/vewwM6GSzSXQ9hWXZrKzUEUWB4eHceeSpm3jj4KUsdmK1GYsoglwu+bqWvHv22RMsLLgMDkpc\\nc013noYgCDhyJFYB2rGj0JOk+ty5OSbh7b7ovuWW/51Tp/LcfLPPl7/8p13tDh8+zMc+9heEocR/\\n+k9v5T3vec/GbvAS6MzP6bTScwzXajWeeGIOSYLrrps5T9L6pSIIAup1mzAU23xJ3efXF77vOu+j\\nTv+87robeeKJLUCJ++//Nd7+9rfTalm0Wm6bqylJNqtthqRv4jVDp2/ef//93HPPbwMDXHPNPE88\\n8YPLXbXXDHHqiUkUsTn+Xmd4MzoyHnwQPvnJ+LMXPvQh+PCH489NvPZ43aaWvOc97+G+++5D0zTG\\nx8fRdb3nSdmrBcvyUZQ08/N1qlWRw4dXse0CpjmEZYXo+hC1WoCipGk2Q/r7CywttUgkxqnVJFqt\\nFrZtE4YKQSBjmiGSlKRWc1FVnbU1k2RyiGYzxDTNdtihh2VZ2HaE40SUSg7J5BArKwaqqrO8bCAI\\nGarVgFrNYmWlimWlEMVhFheL1Ot11tZMcrltrK7GXmnH8RFFrR0qa6+XHwQSth2fxGcyKbJZCVVV\\nCEOZatVscxXEjqVYX1tAkpLU6w6qqrO6aqKqAzQaIb4vEgQyQfB8qF8YhjhOLN2Zy+XYskVlaCiP\\noqRptQKCQCYM1fU6eZ6ALKeo1WxUNQ4bfGEH9TyvHY7e0SEPSadHqFYvHu4cE2tJiKKG48ShnNWq\\nSzo9Qr3uv2Khh517VZQ0ltVbKeGVhGV5uK6MaWZpNhNYloZti5RKLSwLKhWBVktjft7h9OkI1x3l\\n9OkW1WqSuTmTkyctYIpWa5BKJcHcXIBljeF5BU6dWqFS8TCMNOVyyNqaQL2eoNEQcRwJVYVcTkUU\\nRXxfQlFSRFGEqqYQhCSOE5N0Nps2gpAhCJKYZu9Q/028uRBzzKhAAtfdiHLH5UOrFTE8PIXjnD/P\\nvRCGYVCtivh+ltXVes8ybdtrz8PRRdMQOnjyySc5cWKAkZH/i4cf7p2C8/jjjxME+xGE23jsseO9\\nb2qDCIKAVsvDtsNLjuFSqUapJLG0xHpU5cuFJEnkchq53KWdxOe+7y72nJ54IgTeD9zE5z//eaIo\\nwrYDRFGj0YglqD3vtZvDN7GJDj73uc8BNwLv48kn31wbx/hdoCAIGo7z+n4XbOKNj0ZjY6klmcxm\\nasnrHZfFJbq4uMj9999/OX76PKRSCq1WC9+vc+SIRxS1SKfPksmkGB3tQxTrbNmSxfdb5PMK1WqZ\\nsbEUrrtIOh3hukp7geQiyxKKIhGGFv39Gq5rMDqawTDWUFUf0xSp15sIggxE7XxpjUwmwrLWGB3N\\n4LoGfX0qa2tFXLeGZcUnfYpSxfMMbFthZSVCURzq9VOMjcXOH01TcF2bKPJptRSCIKReryOK2vrJ\\nWkcxQhAsarUWptmkWIyQZaGdPyyRSIDnWQwMJHFdg4mJDI1GmYEBBUUJkaQIWT5XqlRE00Rs2yCT\\niX9LUTyaTQtdl4ginyBwaLWkdhqATRCEFAopXNcgmTyfEd7zPOp1DxDIZMI2waRMvb7CwMDFF7ex\\ngolLEDzPEj4woFEur9DXp75iRGvxvQo4Tot0+rUjf0ynVVIpE11vkkpFaJqApiUIAgHDcFAUA9f1\\ncd0mktTAMBqMjuZYXZ3FthXAwjAO4fstmk2T/v5+fP8sophnYGCa1dUa5XIJz3PI5XQEIV7g+75G\\nGMbqMLF6h0u12kBRFILAQhSVdqqISCajYVkNFEVA19/YknKbeHGI2eHjjbmq9o6+utzIZCQWFs4y\\nOCj3jBxRVZVabQ3blhgf766uArFUdb3uIMth1zkM4MorrySXW+DEid9n587evBv79u1DFP8Hvi9z\\n8813976pDSKKIorFGpYlMzws0t/fXYLVdU2ee24W8Lnppt5SrS8GkiRtKGKn876TZeG891EHAwOr\\nlMtfBtZ45zs/jCAIJJMStZpBGDo0myK53MuPItnEJl4s3vGOd/Bv//YV4CT5/PLlrs5rinitZhEE\\nHonEZsrVJi4vNurIyOXgFfLXb+JVwmVxZLz1rW/l0KFD7N+//9LGryIkSUSWBRKJJNPTg9h2in37\\nsvT1xSkMYQj9/c/nOQ4OJoB++vrS2LaDaQoIQoQsi0iStJ633+FfyGb7GB1VaDQMqlUbxzERRYUo\\nglQqRSqlk0ikSaeT1Ot15ueLpFIi27aNUamoBEESQYiYmBhFlmXOnKkhCDIjIyOMjRXw/TjcX1Ul\\n+vt1XNel0QiBCF1Pk0zGqSr1eotUSm3npwsoikQikWJyMoMggGU5CIK5Xn/XdbEsj2Qyy+Cgus4t\\nIIoXhkWn08nz9JUVRaG/X1m/RhBCgkBCEMT2PffmJgABQRDpBGoMDfUzNBQreMzNrZHNauTzz88+\\ngiCss8p30N+fp7+3IiOWZeO6Aen0xvkudD3FKxBF/aKgqioTE0NMTMQbJtO0cN0AzwvRdY1EIqDR\\ncBFFla1bJwiCBsPDA4yOuiws2KhqxLXX7qNcDvA8k1RKZO/eEXbunKBeD0gmTaamxoCwHXYtk80m\\nyGTSbW33OOdc1zWq1RaimGgrmjy/gUkmNbZufWkLkyiKaLXi1INO/9vEGweiKNLXd/k2jefOXZfK\\npx4ZGWZ4OEEUOedwLVwIQRDYsmWEKFJQlN4bb0mSGRhIEIaXjgB429tuYW2tj337rJ52Y2NjfOhD\\ndxAEItu27ehpa9s2a2sNdF29aGreuUgm43eSKPZ2pASBQC6XRRAuz6mqLMv093fvU/v2vZXvfncr\\nkGZmZgaAVCqJLEuoavyuiFNZ4nfnJo/FJl4rxP1xKzDEvn1v7Hed53mYpoumySQS8aHHC9dqm9jE\\n5cJGHRn9/ZdWNtnE5cVr6sjokLIFQcCf//mfMz09vS6xJwhCT1b1VwOG4XD69BLZbB/f/vbXmJgY\\n4V/+pYbjeOzefQW1msmNN05TLK5w8803873vfZ/bbruFL3zhC7ztbW8jndZRFJl6XcKyGoyN5ZAk\\nEceBSsWgvz+NJJkEARhGE1GMaDZbyLJMOq1RrdaYnp4iCAJOnCjhullarRq6XmdkJEOz2UJVYweE\\n53kUChKl0jJDQ9Pt9JGAatVC02QMo9nOVTaRpKhNyNgAElgWhKFDOh0vzgUhdnS0WkVAIAzHqNc9\\ngiAmhwwCGUFI0Gw65HIitu3SbAZIkkAYGiiKcp5GumVZJJNJKpUK/f392LaN5/lYlkgUyaiqgyhK\\naJpOEARtYkgXVVVxXRdZltc/U6mAIPCQZW3dxjAMikWjrbDSQtdTyLK8njYSqygEiKKIbdvn5Wxb\\nloWqqusnfWEY4nkerVaIJCVotRySyRBVVdfL6GxiOhuZjuTk5dhk+77PmTNnWFhYYGxsjCBIkEpl\\nsKwyrutQqwUsLjYwzSJRJCNJGSoVkyBYYXU1xPclTp9eIIpCTp1aYHz8LZimgeNUsG2BVisgl8tz\\n4sQxhoZmWF1tkM/LZLNNhoaS7bQmkURCQxRlHMdG15V2qpCHqsaOro4uuyRJ61rynTbsPPNO+3WY\\nygVBwPM8DCNuf8dxe24qOs8nDENEUTzv+YRh+LL5F86t8yZYf24vF24sAdGVwPVcvHC8doNhGKys\\nrLD9EuLujuOyuFghmUwyOir1rEM2m+Sppw7ylrfs79kHJEnCcarMz6+wffvNPX8/ldKoVBbJZHRk\\nubsyVxiGJBJZvvrV32Pbtl/oWWa1WuVTn/otQOXmmz/Hjh3dVU7W1hq0WglM00bX3a73L8syiYTN\\n008/zjvf+faev5/J6Cwufp0oEsjlLq1Jd/LkyUs+J4j7WxAEG+onvu8jiuJF52TH0Yii3wFgefk2\\nwjDE9/32YUM8/ztOhCSlMU0HSXLXndmv1By/OZds4mKIU6H+GgDL+neXtzKvMppNB0hQr5sMDqrr\\nawHP85DlOOqt2/h4Oe+ezbG3iY2g0YjTRi6F/n547rlXvz6beOl4Tck+Z2dne34/NTX1mtSjQx7y\\nW7/1Zxw6lOCBB/6U5eV9RNHjxNKUIrAK7CCff4axsZ+gVvsKIyMf4skn/xD4cVT1KT7ykZ8klXIo\\nFj1kOc9VV0lMTl7F8eNPIEnb8f3jXHnlW1lYeJpiMU+t9hyOk8d1HYrFRXK57XzgAwXuvPMe7rvv\\nX1lezhEEJ7niimuIogq53DCOY2JZFqCxvDyHIAwyPGxwxRVv4eTJpyiV+imXn8GyBgmCEtPTk6hq\\nAlW1SadHgBK53AyJRJ2xsWmKxXnqdY25uWd59lmXKDKZmkqTyYwhyzVSqS0UCg5bt+5EFE3y+QGW\\nl+c4cqSF4zTI5zOoaoprrhmiUChw5MgZmk2JkycPY9sFwnCOyck9+H6VXG4AQRDxfZtEQmdoSCGb\\nHaDRqOD7Gq5bR5YzmGaJ1dUIsBgd7SeRSFKrVYEklcoCsjzM2toxEomt5HIOd999LWEY0mg4uK7L\\n2bNrhKFEvb6GKBbYulVk587trKwUmZ01SCZh795JgiBgfr5CEHTIVXWq1RKQQpIscrkBwtBBEBRE\\nEfL5eOMRk7kKZDLKhhbZr0TfhHhT90d/9A/87u/eT6UikE5X2bt3F9msQrPpUS6bVCp1Wi2fKAqQ\\nJHDdANeV2mXUiCUdO7KPCWKi1RSZjIAoJkgk0ohiDVkeQZaLJBIjBEEDQdCRZZ90OkOhoHPVVUNM\\nTc3geT75vEa5XCcIFKLIZnh4mPHxLFNTYwwPx/woMalXSBB4hKFEJpNgbKyPMAypVEwMwyGb1TCM\\nJqWSTyYjsX37SFdVi5iYN8TzLCQpgarG5IkAjYaJ50E6/dJPV2Nteg9BiMjnU2/6yJCY+FEgkYgj\\nkeClEYIZhsHRo0UAdu0a7EkMubi4wuKiSyoVsHv3ZNeFrGEYfOYzf8XycpK7707z0Y92Z+F68MEH\\n+cIXjqOqAZ/85O1cccUVXW1/7dc+z3PP9bFlS5E//MNPdrV7/PHHef/7/wTX7ePnfz7NZz7TXQ3k\\noYce4t57F0ilTH7pl95FodCdfFgQ9gE/AvwtUdQ97Dx2Fn6YeDz/Zc9ncvjwMQ4fNsnnfe666y1d\\nx1epVOLDH/4NyuUt3HVXi89//j93LfMTn/gEf/RHLqLo8Qd/cICPf/zjXW0///n/waFDCuPjVX7z\\nN7vbua7Ls8/O43ki09N6W9nh4jAMg+VlE1EM2bq1sO6EOF+15KeBk8CDHD58BsuKyOVUVFXCdUXA\\nJZPJ4vsOipLAth10PUU2q/ZU1tkIYmUTH1GMLiDH3sSbE+f3zVuI5Yt7j90fdiwvl1haMpHlkO3b\\nhxFFOHjwLGtrFmNjaWZmRujry1zgcIgJ5EFRIrLZFxfBYdsOhuEjyzEx/aYzY2N4M5J9/uf/HCvd\\nffrTve3+9m/hvvviz0289njdkX1OTU31/HutcfRoA7iCpaUkUXQAmCKWbdsCDAK3UasNomlvZ2mp\\nH0m6HZgAbsV1r+DsWZvlZY1GI0MYjvPcc2WKRZGTJ1tks1OcOdNAEIY4dKiCIIyxtCTTaOSp1TSq\\n1Ryi+Ba+973T+L5AGGpMTU1RrysEQT9nzxpUKj7FosvqqkC1qnDqlIUsT/Pss2sEQZqDB9fw/Qwn\\nT9Zx3S2UShmWlgJqNZXjx02KRYmFhRaalmVxsUat5rK01CSTGWB11aLZ7KdSyVGvS6jqEIuLAYnE\\nBOWyw8BAFlCQpASViocoZmi1QppNAcdJUyxWaTSaNBoRmcw4x47V0LQZzpxx8P0czWYKWU6hKAlW\\nV1sUiw7z8xUMw2VxsYZti8zPVzBNOHJkhTNn6pw8WcMwfBxHYnm5Sa0WceRIiWZTZnXVZmCggCwn\\nOXlylpWVVYrFGktLaywvt5ifN5iba5HJTLK8HOfkr601EcU+6vWQWq2GYRgEQQpRjFNqEokQzxMQ\\nhBTlsoUsa7RaAYKgtlVnLEzTpNl0aDQcLOuVJbKMJSydrt83myYHDy5Rr08TBNfSaAyytpZmdTXH\\n0lKGpaUMa2sFWq1BTHOSZnMYx8kQRduAK4ARYDvxomkHsAsoAAM0myr1+hiNxjgrK0kcZ5LV1TSW\\nNUa12o9hjNFojFAqTVAq9XP8eIO5uTr1eoJyWWRlRaRW0zl1ymNursXqqoXnyViWg+tGNJsBpilg\\nGBFhmMZ1xfZpTEAQSARBEteNMIyAbLYfRemdH2/bPrKsYZohoqjieQKu62KaFo4TtSN4uhM0XvpZ\\nBIhigijqTfT4ZkAURXgeKEoS1704Wa7v+1iWfUky3ThyTMWyNOr13ikLS0t1PC9FpeKtR3FcDCsr\\nK8zOplDVa3n66dWeZcYS09vwvHGWlpZ62h46VMP393PyJG0Oo4vj4YcfxrJ2E0W38q1vrfQs89FH\\nz9BqjbG0lGZubq6r3W//9m8TS6++nXjMdkfc5vuAm3raAVhWfLLpefQkGz169CgrKwVU9U4efrjS\\ns8xHHz0D7CAMt/HUU0/1tH388RXCcCfHjzusrHRvK9d1sW0NWR6kWu2dWtNsOnheAssSu/STEeLN\\n4vUALCzUsW2JWs2jVnNwHBHT9EmlQNM0JCnRdm4oeJ7floLsnjYTk4d2t/G8eC4JAukVI5t+KQjD\\nENt2ej73TVwOXAfcTNxP38gQcByHMBRxHL+9ntIIwwEMQ8BxwouOD8vy8H0By/IvuoGJogjHcS46\\n9l03QJY1gkB807/HN9EbzebGU0sqvV+Jm7jMeFMfFYyOuszOfoNMZgX4GvAM8AhwEDiMKP4Tw8MV\\narX/xrZtVVZX/x5YBO4FnmXHDoWJCY/BwSaCEGvUnz17hmzWZnn5OXbuzFMsnuDqq0cQxZNccYWA\\nri+Ry1WZnq4hCN/jjjv20mxaDA0laDQWmZlJUa+fpr9fIIpaqKqLphnIcpnxcZ96/Um2bctTLK6Q\\nSnmcPTtHoZBAFI8wMlIllzMIw1UkyaRaXUUUHcrlIr5vsbRkEoY+nldi27YBFGWWvr4yV16ZZWSk\\nyZ49eUzzGLt3DyMIDkNDOlFkMTSk4boVdF0inXYQhBKqmsbzVPr7BWx7lttvH6fZfJIDBwroepVd\\nu5KMjmokEiaVis38fIXV1SKlkk29XqVYrGPbLWq1JtXqGidPrrK4WEEQamiaheuGLC9XMIwSs7NL\\nCIJFo1FiYeEsp04pPPTQPM89t8aZMw1On56jWrUpFAIc5zh7944BMDio02otoSgWvq/h+wkSiRaS\\nVEcQFFxXwfedNqFpkjA0KRSSyLKHINiYpkCt5rGyUqNcdmi1eqsJvBjEfCYBzWbU1ZmRy+m85S1D\\n9PUdBf4VXS8yMtJkenoFTVsiCM4QnzoeB54C1oCzwJPtvyXgW8DT7b79XWAeKAEGgvAMkvQU/f01\\nXPcZhoYsUqljDA4uMTx8mrGx04yOPoumnUHTBFw3wvMWUNU6Y2MhQXAKQajSbBq4boNUyiWfz+K6\\nFomEiCxbjI2lSKVM8nmxnZKkkkqBrjvoukQyKWOadZJJqefJpa4n2s9HQxRdEomIVsvHcWR83yaK\\nLFKplx4tk0yqiKJDIhG+6WXhBEEglZIIgha6fmGbRlFEvW5jmiKNRu8xkUwmEYQm0Ljk6Vo6LVEu\\nLxNFds9T8cnJSUZHK9Tr/8b114/1LHPv3u2E4TNo2pme0RgAQ0MeZ8/+C5q21jPy6sYbb0TTfkAY\\n3sddd/V2OoyNZWk0DiGKcz2jMe6++27i8fnXwEbiWL8D/P0lrYrFErOzTc6eLfc81bjmmmsoFBao\\nVL7IXXf13mDdccce4jnlKa677rqetjMzMnNzD5DJGPT3IC7SdZ2hoQBVLTI21pvgKJVS8LwmgmB3\\nGasrwP9HPPeBabqsra0gSRGaFlAqFXFdiVbLJZNREUWXfF5Elj2CIKTVgkbD67oRMk2bVgvqdfei\\nNsmkiiA4JJNc1rnEMCxMU6Bedy6rQ2UTL8R3gK8Q99M3Lur1OpVKxOJiBUkKyeVyjI5G9PdXGBtT\\nyWQunkIYhgGGYeP7F3cUOo5LsxnRaAQXOBNTKZUostC0yzv2NvH6x4vhyNh0ZLy+8aYe6bt376VQ\\nGGNiwqJQuIPl5ceR5TFEUWFoqM74+DVo2jx79tzIt7/9vxgdvZ2HH04xNnYXnneaj3zkNiQppNkM\\n8H2BM2dmyeWmsawEe/bcRK12km3b9lCrjZBOD2AYJVqtWLUklQrJZgusrJzm7Nk18vksO3fupFzu\\no69vHMMooWlZPM/CMCwkSUOWp8nnRyiX5xkY2EqjMcOePTuwrCz79+8AXARBxfM8jh6dR5bzpNMZ\\ndu+e5vRpm1yuH9e12blzitHRFFu37kUQYO/eHJlMpn0SJqOqAZnM8/ncUTTINdcM4nkW+byGoij4\\nfuwNHxsbIZ1OUiz2s3VrRCIRMD5eWA/py2ZVFhclgkAikaiRyaRwnAEKhUHKZZOBgUEqlVFEMYck\\nuWzZMsLg4CD1eojva8hyjaGh7TQaHnv3znD0aOyMiaKQdDomQ52cHCGdHmV8PM+WLVvWN1+ZjM7+\\n/Tlsu4UgCIgijI0VkOSFo5YAACAASURBVCSJatUiiiLy+SzpdB7fN4giEASRXC657mgASCYTqGoS\\nWX41FoMxwenFoCgKt956E5q2m7Nnl8nlcmzf3kehEPDIIyUeeWSRWq2JLAdYlkkiMYosV8jlcti2\\ni20HlEor7TQeC1XVaDSayPIA2WzAzp39DA0N4Hk+USSjKD7Dw/0MDaW44ootqKqA47g0Gh7LyzXS\\n6Szj4xqFgoYgyKytVVlbc1CULFNTEiMjA8iyTF+f3pZtdS9K9NjXl6GvL9Pm0hDp74/7XC8oikJf\\n3/Ob2zAMqdXiZ6jryRcdgvpCxPKPm0RkHSSTGskeIiOdiN1Lhe6qqsrOnVvb/+7tN8/n8+zaNYQk\\nXVxq+Vy8//0/iuclGRvrPSZzuTzvfe9tiKK/zsfUDTt3Xkkut5Vsdran3cjICJ/4xM/hukluvbX3\\nPc3MTPPe9+5Elm1Sqe4cGTHeCvwYsTOyO97//vfzj/+4C9C49trukSMAmpZkYCCJolw6xPqXfun/\\nwPf7GRnpXeYNN9zAu97lAgI7d472tL322uuYnt6ConSPRulg27bJS9pA3Kfid0zYxflZIJa4nAMO\\nMjY2SDqt0tenk0gEiKJPFKlIUkAikTivX7RacTTIRsKsu3V9WZbJ5y//0ur5dIbLXZNNnI9d7b/H\\nLndFXlWoqkqhoCNJ6jrv0e7d0+ze3fu6REJFllUE4VIRsBeO0c76YxObuBSqVcj35r8GYkdGufzq\\n12cTLx2vKUfG6wWdF/zs7BILC0XSaZX5+RMMDw/TbAsGT09P02w2GR4exnEcNE3j7NmzaJrOmTPL\\nDA72s3fvNhRFwbbtdfKxer3O4OAgjuOQTqfxfR9Zltc/bTtepGuahmmaPPtsGV0fxfcX2b17fJ0A\\nU1VVfN/H932azYgoAl0XkGVx3QZgeTne4Kqqun6K6Ps+8/NlTNNhamqIZFJFlmVarRbpdHpd5q4T\\nPt05KfN9f51s7dwNShiGmKZ5HsGiKIr4vk8qFechlkoNokglilwKBR1RfD60r1qt4vs+uq4ThuF5\\n99j5nJ2dJZFIMD09DcR50B3iv2azST6fJwzj0/JKpYKmPa9wESsTtNC0PpLJDEHQoq9PJ4oiXNdd\\nJ6DsSNCee6+SJBEEQVvKNNN21iTWCUk7tp36v5L5zhcjQXxhPpjruszNzWEYMcnq4OAg2WyWpaUl\\nZmdn14mzPM9bJyX1fa19T01mZ1dxXQ1d18lkrHVeElmG3bun6e/vp1qt43kKjiNSKKTI5TQGB/vW\\nn3EQBOtRI8lkElGUsO34N4OghqIoDA0NrRPABkGA7/vrBKC90Cl/I7av5LWbePE4t2++mGccc/zE\\nfacXfN/HNE1UVT2PTPiFiLluygQB9PVp9Pd3P1YplWqcPFlDFGHPnkJPjo4nnjjG0lKT/n6Nm27a\\n0/W+PM/jySePUK+3uOaaXRQKfV3L9DyPubk5UqkUo6PdN/1RFPHzP/8J/u3f7uenfuqn+PVf785R\\n4bout9/+dkDgW9/6Wk9emGKxxuJiiWRSZfv28Z7pW6VSiXq9zuTkZM+IGMuy+epX/5UoCvjAB97Z\\n00HUbDaZnZ1lbGyMgYGBrnYvBp15XRTF8+rZ6Z/T0zcwO/sc4PPtb3+Nq6++GkVRkCQJWZYxzTiK\\nKJ1OX/CMu5V9MZtOea9XdIitX+/1fDOg0zePHj3K7t37AYWJiT3Mzb1xnRm+71OtVtE0jcxGWBXb\\n6PTbDiHoxeC67nnruU28PLwZOTLuuCPmyXjb23rbVaswPb0pwXq5sJG++aZ+uw0N9ZHJ5CgWVxkc\\nfAueZzA8HDsTRkd1ZmZmqNVaqGqecnmNfP4KKpUFxsZ2Isseuh5vli0rQpI0Mhm5Z/gwcN6pXDKZ\\nJJFYpVxeZOvWBNl2nNO5i/j41NoEBDKZ54nINE2j1bIoFCZxXZMgSOC6kMtpeJ5HsWjjujJDQybD\\nw4ULfhu4INQ31vm+sEuIooiu6225V4cwjIgiB1GMw/GTSY1USsGyAhQlThHwPI9GwwMi8vn8BeV2\\n7lHTYnWSQmESQYjWnQu6rq9vOnK5XNc27KBQKLQJCk1SqefJ37otss+911gBJcI0TRSF9Zdnx8Hw\\nahF8bqRcVVXJ5foJgj4UJWBgYABJkpiZmWFmZgbP86jXY4dIIhHRbPqcPLkEKNj2PCMjBaDF9u1j\\n7NgxjmnaPPPMCs2mRaORIpVKsGXLFYRhhCjGajbZbAJVVajVLEAlmxVJJrU2kVasKqMoEY7jk04P\\nkUqJ5/XZjqNsI+jW517tazfx8rDRZxw7J0JAQJa9ngtPWZbX58BeiCVd0/g+62O9e5nxH3Q7vX8e\\nExODZLN9pNMXV8PoIAgCZDlDNpsjDC8dSbRtW3dVkQ4EQWBk5BruvPMeNK0370e1WuXnfu43iCKF\\n5eWldYnRiyGTSbbluy+tyFEoFC75/gKIopADB24CNhKhJjE6uv0VjWbrNa8D3HHH9Xz1q3chy0uY\\nJgiCTCqVakdxmUSRjKZdvD0uVfZGbV4PiNWmXv/1fDNhbW2N/v6fwvOGuOaaxctdnVcVsiz3JO3t\\nho3021ebdH0Tb3yUSrCB1x25HBgG+H5nLbGJ1xt+6B/Lo48+yi//8i8jiiIHDhzg85//PJ/97Ge5\\n77772Lp1K3/xF3/RdbOTSKgEgY3nBbhuCsPw0HURQdDwfb8t6ykgSQr1ukM6nadSMUkmNTIZ1iMO\\nTNNDFEWSSYFz59cgCLBtF0W5UPYvJgvz2bZtsh3Wf/EFsSiKaJpEFF2Y8+d5IaKYwHUjFEVel730\\nfZ8wlBAElUqlSTpdIZ/XX/bkH8tmSu0cRgtVFVEUkWQSUqkkmvb8ZiEIQkBab4eLPYNO+7iui2XF\\nobqKYqIoCoIAQRAhyyK+H5JIKJfctGaz6XVpzheLZFIjkXhp177asO0AWdbx/dZ5USS27RKGPkEQ\\nS+QWi0UsSyYIBBIJhSBQ2bJlBss6zdBQilptleVlB9MMEEWdet0nkXBJp0Oy2QQjI9l2lEXY3oC6\\nCIKMqkYkk/EzFUWF2KkWb/ZkOYXvW4RhiGU5SJKIpp2/CImiCNO0L/rdJt7YCIIAy4rJBlOp7qfc\\nHdtu8+W5EASBZFLBcTwUpfeckEhoTE2NrEdz9UJ/f5ZEwiKV6q18E5eVQJIULkU94Ps+lUoDVZXJ\\n57s7aUzTpFYTSKW2sLraXbEEwHEcTFNpR6v1TsPRtNgpuZF5zXVdPC9A03rL33qez+nTs0DE5GT3\\naJTYNkSWk20em4jXQkVgdTXCNGUEIU29blCvm2haHJERRSKiGHMjbWITrzWq1SqNhoLvKxSLb64T\\n8E1s4vWEchk2EiQoirHd2hqM9abk2sRlwutv1/YiMTU1xbe//W0eeOAB1tbW+N73vsd3vvMdHnjg\\nAfbv389Xv/rVrtc2GhYPPXSQRELj4MFvsWVLmqEhmXw+TiN48MEH0XWZM2eeZXBQ5+jRpxEEn2PH\\nFqnXfZaWloiiiDCM8P2AKIqoVqsAtFotmk2LWs2j2fTWU0ogXtwaRkC97iGKAVHUIpPRqLVjlzqh\\n2BCHJpsmlMvWeUzpjuOg6wl8v8HgYBpBsEin41A7WY4dI65rUK97VCoyi4uV9WsvRlDWqV8nhOeF\\nto7joCgKyWSEpoUoirp+z520g86n67okEiqaFqJp4Xp6yLnlxm1gU6/7NBqxeodlWbRa0GyGlEom\\n5bLN8nKdSsWh2bTX2+Xctuyg89sdUrNzf6cbOt91PjvXXowYrdMOnc9OO70WJGqZjEKtdhJFMdr9\\nLWRpaZVi0aRWc1henuP06VM89tgCJ06UKRaXiCILXTcpFg+ytLTI009X+Id/OMj8vEelchZVXSGf\\n99C0EMcxqdXqNJtNikWDSsWhXK7RbLaoVKptgliv7TRpIstxWLWmCYBFKiVjGCaWJdBoeOtOwNjx\\nFbWlU8Ewwp5qFL3Cx7p91/n/N1tY5OsBG23zIAgJw0vbGobdJsD1eo6rIAhoNn3KZZtWq/dGvjMP\\ndebGXmg2bc6eLdFoOD3vTVVVUimBhx76Bslkb8dcpdLg7NkWq6vuReet5+uZYP/+DH/4hx/g+ut7\\n807EvA4WkmRc0ukS1+HSTGVxm3osLtYwjN5teubMPKdOJTh2TObs2d58HplMglptmUxG2ZATY6MK\\nG73sNC3AdX8DQShjWQ1kOUmlEqeTJJMCsuyeRwzcScl7IV7KfPRysNEyN+e61z+6EcVKkoQgNIDf\\nIJl846vJBEFwQX/ttgY4d84/95pu/+6Glzs+NsfXGx9RtHFHBsCWLbD4xg6g+qHGG4oj42Mf+xjX\\nX389hmHwq7/6qzz55JP8zd/8DZ/73OfOs+vk3KjqVjzvWuCfgB8HjgIDgAU0gSuBxxCE24mivwfe\\nD3wXQbiHKHqGoaErSKUEstkUkpRD15eIot1MTCyRzV6PKJ7B97eRy9XYvfst2PYyx49X8LwQz7MQ\\nhEE0bRVJ2sLa2iMsL4/R3z/Pzp1vI5WqMzg4QatV5+mn5xGEJAcO5Bga2oFhzNNopPH9eRxniGSy\\nQS43ie+vsrzsYtsRV101TjY7xpEjP8A0C+RyRUZHd5NO2xQKWwhDC0FQMU2DJ588ThAk+NEf3cH4\\n+FaOHj3O8rJHOu1QKIzTaKxw7FgDRXHRdR1F0RgZ0VGUHCdOPEWlomGapzGMAvl8jYGBnWQycMst\\n+ymXS/zJn/wvGo2Iq6/WmZjYTyrVQlEKrKwc49gx0PUWN910PbZt8NBDR7GsgEplFs8bw3UPE0W7\\nGRhYoq9vH6nUKoIwSRRVMAyZRALyeQlB6GdiQkLXRwiCCoaRJJ02keUBBMEgDDWCwGF1tU4UyQwN\\nSajqAIrSwvPSCEKdKMqRSgUMDY0SRQ7NpksQRG1nk4okWQRBkkJBJZvtw/NMms0QRQnRdR1JEsnl\\nUhs+/VxcrBCGMDHRv34C3embQRBQrRp86Uv/xCc/+ec4jg8E7No1gqYlWFiwMAwD2+701RSxGskA\\ncch3Z7EgACagABpgA3mgCowD9fb/J1FVC01LIwgiQeAhSRAESfr7fVKpPJWKiyC4DA72MT6eZXp6\\nhkIh5stwHI+ZmTHy+QzT0wVyuQwQl1OtttC0VDvqo4/+fv2C0/HY2eGTSIjo+vmpQ67r0my6yHI8\\n1jobombTxHVDVBVclwu+38Qri3NzFQ3DxHFCUim5J0fD/Pw8f/mXD+J5Aj/7szcxOdmd0PE733mY\\nhx4qMzYm8dM/fU/X51ir1fiVX/ljlpYUPvrRKX7iJz7UtczDhw/zhS88jCyHfOpT72NoaKir7d13\\n/288/niaiYkizzxzX1e7+++/n3vu+W2gn/37T3Hw4MGutn/1V3/D7/3es+TzLl/60q8yPDzc1VYQ\\nRoklVb/SczEdt8stxBLhvW0/+9nP8ld/VWJszOTrX/+DrnZhGPIjP/ILLC2lede7+vnsZ3+tq+3k\\n5CTz89cBRT74wRHuvfferrb/8T9+lm98o8GVV3p88Yv/paud7/s89tgxDCPg6qvHeqa4lEolnnpq\\nCV2XOHBg5/pc8jy5pQC8B6gB3+XjH/89kkmdffsK7Nu3l2xWIYpU+vo0UqkEy8tVFhcr9PfrbN8+\\nsp7u2Gy6KIpAJnP+nNJtPnqpiKKIRsPE9yOy2URPbo6N2G3i8iEIAo4cmcOyQrZt61tP3z2/b94O\\n9HOpsfvDjnK5wuHDSyQSIvv2TZFIqDz22HMcO1bhiivyXH/9lSiKQhRFVCp1KhWbfD5BOq1hWQGq\\nKhBF4HkRmUzM29ZsOkiSQCaTvCi/zcsdH73WIa82giCg0bCIIsjlkhtOz30l8GbjyGg0YHw8lmDd\\nCN7zHvjoR+F973tVq7WJi2AjffOHPiKjg0OHDlEsFsnn8+t51tlsdj3K4YX49Kc/jeelgO3AW4id\\nFPuAu4idGlcC7wBuIp3+PeA2Bgf/GthNFH0M2IXvv51abT+VSh+Os5ujR0fI5z/CAw/4XHHFz/Lg\\ngzW2b38/hw+7eN4kBw+2WF3dweLiJLOzIqnU1Tz2mMPw8I/zwAMtdu78v3niCQVFuYnnntMol9Ms\\nL0c4zjYU5Toef7yEpu3g4YeLTEy8g+99r8TIyD0cPOhhmgOcPu2xsrIFQbiKYrHGwECGIBhg166b\\nOXLEZHj4Bo4caWHb6fYJoc7cnNFWDbmaJ5+cJYoUTpxoMjx8A4cP15DlEQ4eLOH7Ozh1SqJUShEE\\nWyiVWihKglOnBCYm3sG3vrXM6Oi7efRRkyCYYnU1RaXS4tSpeRYWtuF5N/PAAxVSqV08+ugaAwP7\\n+cEPigwN3UaplEcQFAzDxnW30moNMT+fR9dv5uBBkT17/j3f/36d0dF3893vGsBejh8XKRYHWV4e\\n58SJgCjazsGDdfL5PTzyyCrDwzfw2GMlEolpZmd9TDPH8jJUKllse5Rjxyw0bYYnnywyMLCfp54q\\nk8/vYW7Ox7ZlKhUXw0jRaKisrYUIQoHjx+vo+hSnT1dRVZ21NQtF6afZBNcVCENlwyeKtm3jeTpR\\nlMUwLpSw7PCRfPObp3Gcq4APAXeyuDjM2bNbMc192PbVxOz8twH3EG9u7iBeKF3X/nxn+//e2ba5\\nCbi7fc3twLb2/92O615No3ENhnEdhnEl9fp1GMbtlEqTLC5uxzBupNHYR7G4g4WFQYrFCY4eDSgW\\nRzGMHayuBgTBIKWSQKUClpWgUoEwHEKSkkSRhKLouO6FbWSaHqqq4zjRBafxtu0jSUl8XzwvMsZ1\\nQVHSVKtm+3tpw+2/iZeOMAxxnAhV1dfTRrpheXmZKNpDMnkVx4/3Vq44erTC+PhbqVQSlEqlrnZL\\nS0ssLQ0zMvIhvvnN0z3L/MEPThFFN2Caezhy5EhP24MHFaan/5TFxWnm5+e72n3qU58C3g78PIcO\\n9V5sfvObpxge/nc4ztU88sgjXe1iItS7gP8GvKtnmTF+AvjAJa3uu2+NwcFfYH5+G9///ve72j30\\n0EOUSjOMjPwCjzzSO4Ijbpt3AR/jy1/+ck/bb3xjmR07/l+efTbLyZMnu9rVajWaTR1Nm2FurjdF\\n/OJiDVmepNnUu7zf9wM/TTzvKczNpZHl/Zw+HeB5aRYWmihKP4YR0mhYmKaI5w1gmhqmGUejWJaH\\nLKfwPPGC0/WLzUcvBzGZtIQkJdsO6952oqj1tNvE5YNlWTSbKqo6xuqq0cXqFuCjwNWvXcVeI8Tv\\nZZcoilhZaQCjGEaWSqWJYRgsLkZo2l6WloJ10t04GixAVQdoNuMxqao6punjOCDLKSzLw3V9BEHD\\n86SLjrtzx5FtX1y69VLotQ55teF5PmGoEkUqnrc5vl9NlEobj8YAmJiAHkuCTVxm/NBzZEAcOvuL\\nv/iL3HvvvTz++OMsLCwA0Gg0yHfR1/n0pz/Nf/2v91KtngSeBv4ReAg41bbouOoeoNX6D8DDFIs/\\nBZwgkfjvBMFRCoUcolghnVYIQ59du1awrL/gjjs0Tpz4InfeOcTKyn3cfHOKZHKePXs0nn32WZLJ\\niMnJCEU5yIEDEadOfYUbbkhw7NhvctNNAlH0IHv22AwMtMjlZMJwBccps3//CLZ9nJtuGmR+/mvc\\nc88wKytf5/rrNdLpMjMzCsvLC4jiEtdfv4eBAZndu9MUi09z552TrK4+ys6dKWy7jK5HJJMNJid1\\nGo0zWFaFa6/diSB47NmTZW7uUQ4c6Mf3V7j66mGOHDnOtm0Bum6STC6ybdsgmiZw5ZVquy6TLC/f\\nxw03pFDVWQYHYWAgja7vYWLi76lWT3DHHQWWlw9x3XV9lMuHuPPOCY4efZjduwXGxzPousvRo08R\\nhhG7d9uE4cPcfXeO06f/jLvuKrC8fB+3364jy8+wY0eI664iCCL5vISinOTaa3PUas9x443DrK4+\\nyoEDBRznDFNTMmFYJ5mEZNLEtk0mJ5PY9mmuuWaQcvkQ1103TK32HDMzMqmUTyKh0myabU+gCJTY\\nsSOHYcwyM9OH6xqMjuo0GhWyWQFVjZCkePG7EaRSKRQl3qzpev8F38uyTC6X4Md+bCff+MYXMM3H\\ngIDJyTHS6RZzc3XCML6X+OQxB6wBOrEsWceDGRFHZ4jEw90FssRRGiNIUgOYB5Ikky6JRBpBkAC/\\nLXObolCISCTqlMsOkuS3IzIyjI7mGBrSaDaXEMWImZlhVLVCMqmTywUoiksyKWCaNTIZnYGBFFFk\\no6oXnuCnUgqmaaBp0gUnLcmkQrNpoSjC+imFJEkkEuA4Lfr7U7iuhSwLyPImB8erjZiMTcBxDNLp\\n3qdeO3bs4MEHvw3A3r039bTdt6+f7373IaampJ4n8rt27WJ8/CssLv4DH/1od6JLgOuv38bTTz9M\\nPh+we/f7e9oeOACPPPJ/snOnxcTERFe7X//1X+fd7/4c8Dh79vSWCHzf+67iD/7g7xgehttu+5Wu\\ndq1WC0maBP498M89y4zx9xuwgY98ZBt//Md/xPbtIbfc8h+62t1yyy1MTf1Pzpz5Uz7+8e09y7z1\\n1lt54IE/B+IoyF64++5RvvGN/4err5bYvr17ufl8nnx+lWazzu7dvRORt24tUCrNkclI5PPjF7E4\\nBPwl8bzoceWVNqp6lJ07+1FVk5GRHL5fIZfTSKc1TNNBUcptgul4Lo7nHBNFES6IHrvYfPRyEJMW\\nuwRBb4ngc+16qfps4vIhmUySzxdptZaYmuo2h/1P4DngqdewZq8+wjCkXrcJQ4lk0mZsLE+ptICu\\niwwMTKOqClu2iBw7dpidO/tJp2O5c0mSyGQkarUy2WwnIsNor6sjfN9E11VEUcRx7PaYvHCcxCnV\\nLr7vk06/tHVAr3XIqw1FkRFFq/3v3gpfm3h5KJc3RvTZwZYt0N5WbuJ1iB/61BLf93n3u9/NZz7z\\nGQ4cOMDa2ho/8zM/wz//8z/zu7/7u8zMzPDBD37wvGs6oSqnTp3i4YdPMjqaYfv2Pdi2QanUbE8o\\nDqKoMDlZQFE0FEXg0KHn2L9/D54XoaoSyaSG4zisrLSIIgFdj1BVjXQ6gW3bSJLE2loDTRPxffA8\\nB8OIm1vXYxJRSVJIJHQkySWKPDKZDK2W0ybR1NaJEsMwIp1+XnLUceI87rW1Btms1pNIDmirW7SA\\nCEFIAz75fAJRFNfLF0Xw/Yh0OkEURS8qNK9erzM/X6FQ0BkZOZ+pem2tguv6BAGEoUo2K5DLxS+x\\nVstBlgXCEDzPpdEIAPG8trQsC1EUKZWMDd1r53479Y/lWW06EcBRxHlt2ZHGvRRs28FxfBRFwHVD\\nkknlFWeFf2EYVRiGrKyUqdcbZDIpHCdAliU0TeLUqQrz84vMzp5ledlmeHiI22+fJpfLUKtZbflU\\nkZWVZRRFw3FMpqa2MjLSh2k2aTQMNC3FyEiefD6Labokkwqu61/QRoZhIYoCmqZiWQ6CIKCqMqbp\\nIkkQhgKe5yIIKaIoIJ9X2/nA54ddW5aN6wak04kL2vy1IgPcxEvDSwk/7TZ/XQz1eosgiCWc+/ou\\nDB3uIE67shFFFUXxyGRemRDger3OI488w4EDey5QdDoXhmHwd3/3KEGgcOONBa66ak9XW8uymJ8v\\nomkSk5MX23THCMOQL37x7/i5n/tFvvSlP+Mnf7J7DGupVOJ3fudLmGbAL/3Su9m+/dKqKBtBpVLD\\nMFyGhrI9N8qGYfD97z9DMqlw0037exKzdt4Lw8P6S1IwuBiCIMA0HURRIJ1+fsHf6Z/vec+Hue++\\nCrDA8eP/xJYtE22Fpd5pGxude4IgOO8dvYlNXAqdvrmwsMDExLuBAh/84Bbuvfe/X+6qvWIIgoBa\\nzQFkVNUnlUrQasUk3+eO027Xnjumu43HMAxpNk0sy0HXk6TTyVd8zfBGXof4vk+r5ZBIyOcRr7/Z\\nUku+9jX4/d+Hr399Y/Z//dfwL/8Cf/u33W08D975Tkgm4atfjUlCN/Hy8aZILelEYXzyk5/kbW97\\nG6dPn+a2227j1ltv5dChQ7z3ve/teu03v3kc09zPY49VMM0WlmXhuklWVmxOnjQpFvMcObJEFGms\\nrDTZvfsaVlaaBIGKaYYEQYAgCO0NW4RpRgSBSqvlkE6nWV6u4ftZTp4s02yqFIsujUYsS1oqedi2\\nju+7JBI+uq6Rz+dptRw8T8YwAnw/JllstSJME2z7+dO/RCLB8nKNMMyzumpj23bPh91sOihKti09\\nF5DJxBrdvu9jWQK2LVIuW0SRRqvlvOiTptOnS8A4c3PGeWROpmlSrYJpJmk0WqiqTCzFKNNqOfi+\\nQrlsrdchDMPz2tI0HXRdZ3W1QRjmKRbtnukDnXDATv3DMMS2HVxXptHwqdc9bFvEcZ6v40Y877Ez\\nxG/3hQaCkMIw/Fd98m80DKpVgWpV58yZOsWiQLkss7BQo1oNOXbM5+zZHHNzWUqlFEePljl+3KLR\\nGOTEiQqVik+1mubYMZtWa4SlJZNarUytluTYsZDlZYX5+RbVagtIUqm0cBwJ15X/f/bePMau6zrz\\n/Z15uvO9NXMURZEiZUryqHjupG23bbmd0Wmn8x6SF8BwgAgdJJ2gk26jEfcI5+WhE3QncV6cIMgA\\nJ3YbiePhyZYlW3Is0ZYlSqJEcRLJYs237nzm8f1xbpVYIusWKdGyJNYHECwW191nn3P3sPY6a33f\\n+nhzHI84VgkCEd/PZX2DQKLdHgAGzaZLHCtkmYKmxRSLErIsX+YM5M5Kuj7GXohRzsMaeeg2Xl0I\\nwxDfF4kimTAcne5bKOhoWkyppI6ck5IkUSop6HqCZV2/g+Tp06tMTb2Rs2c7I0sGBoMBSaIDlS1L\\nay5ebNLtFpmby+j1epvaOY7Dvfe2+fmff4B//Mezm9pBXgby5JMzXLx4mK9+9Vsjba8WYRjSasWk\\naYWVlf5I22effY7FxQanT1vMzo4uFzp9epU0neLcOXsk0e+1wPdDokjB854ndb4UX/ziLPCvgA/x\\n7/7dv8Nx8jXHuhL8rwAAIABJREFUdTe//rUcXFw3GO7z6XYp2zauCZ/61KfIS0t+hs9//tQPuzvX\\nFXlmhYxhpFiWju+HxLG66Ty9FC+c05vNxyAIse2UwUCl34+3bPfF4LUaxICc0Dr3v+Ib2p+an885\\nMq4Wt9wCW1Sm8vnP5zKtFy7A17720vq3jWvDq7605KMf/Sgf/ehHN/zurrvu4jd/8zev4tMDTpz4\\nPrVak1LJJIqEYY2siChmQJdaTSWOAwxDIo4DLEsmTSMkKZfqzLIMw5BJEpE0jUiSEF3PnXBBSFhZ\\naZKmAeAjSQlrfo8oQpa5FIvGhmh1kkS0Wg6KklKr6SRJwuzsEmkqcsstlQ0s9Zal0un0EYSQwSDC\\ndWMqlSuTTaqqSBAEqOrG6LgoiohiSJqCpgnEcUCS+MSxgKZx1YRHpZJCq9XENDdqfOfpsDFJklAu\\n62haLie7dq+rqy6C4GMYGrKcUShogHDZszQMmcHARtOuHHhYI3qKIsiyAFHUSdMAUVRJ0xBBUBGE\\nhMHARRRDTDO/r5wwki3vVRAEZBniOMQwZOI4QJZ/8JuepimIoo0sBxiGSBT5CEI0fGsQUipF2LZH\\no+EOSTSh0+kiih0MQySObRynRxAMaLcDDKMMGCwtLdBszgEe1WoVQSiwutpFknJnv9+PgBBN05Ak\\nkTT1KJUMZFler8/WdYUkCTFNiTSNkGWwrM3J70RRRJKy4Xy6+hjqpd+tZUnbb0FfRcgDigFpCpI0\\nWv5ZkqQt39ytQVVVXqKa9BWuH7O8PEexODqQWywWMc2csNmyRmeH6bpEu72CYURo2ua5rMViEdc9\\nx7lzsH9/Z2SblmXR73+HJFGYnt4/+qauEnladkoQ9KlWt5K51pmfP40kZajq60baalrC4uIcpVLw\\nkuW/n++riO9HiGJ2xb1AURaJoseAWQ4ffj2SlA73kutDnifLeSBcFF+Zct3beOUiJxt+BEiwrIUf\\ndneuOy5dl7eap5dCkkSyLLcdtfbKsoQgJECMJI0OeG/jcqiqhO/nWbSv5YDNVpibu7ZAxm23walT\\nOan8ZtvYZz8Lv/IruUzr5z4H/+JfXJ++bmNrvOoDGS8FN9+8D9tepFKp853v3Mfk5OQ6ediBAwfo\\n9XpUKtMsL1+kWi2ytNSiWi2yurpKpVLANHNJOcuSSFOBNBXwfR9ZLuL7wfCtdI+xsQqTkxpp2qDV\\nysnEymUJQQDT1PH9AMiGEeyUWq1ElsWcOXOGJEnQ9QZZJuK6IboeIEkiSZJSrRZRVZc0VRAEgzSN\\n8TwPRVGGxEd5yUQYxui6iu+7KEp+PUkSURQFSZKGB9EUWS4QBCH9fkYQRPh+NLST18sA1oicXlhS\\nsW/fLiYmbHQ9L7cRxbx9VVWp11XiOMYwSniejyxb+H5+uLEsDUGQEQQfTVMJw7zGUdM0HKeHZVXo\\ndvvUaiV03UNVdRYWFtB1fT39WdM0oijC82IEQWVxsYUkGaSpT6lUQ5JSKpUEVZWQ5Rog4HkuYRgP\\nAzZFwtAZOVYEIWeoT5IEWW4Qx/HLwiqdy5xGiOKaYoxCr9djdnaALEccOVJg796U+XmHJFmi2zUo\\nlUSiqItl1fD9gFIpptvtsrBwCkHYTRAsUy6Pc8cdBzCMDEWRaLc79Hohvu8yOVlBFAu02zHgMzU1\\nThT1abd7uK5OsVhAlhVU1WBlpYkk5Qzfuq5fcXPMx2KCqiqUy2vP8OqXnjRNiWNhOHZdjO3y0VcN\\nZFnGNKNrLlXbCq6bz99CwdxyLM3NzSHLMpOTkyPtpqbG0HWfYrEyMr1Y0zREsYltt6jVPjSyzWKx\\nSKMxQFVHs9CHYYiqJpw9ey9vfvMbRrZ5+PBhXPf/wbYdbr/9X420TdOUMIyQZWnkcxJFkVJJZmWl\\nTam0Y2SbjcY4Y2NPoarqphxUaxgbaxDHXcrlKmk6+uC/urqK7/tMTk6O7Kumachyng15pfbq9SmW\\nlr4ApNx++y9iGDLtdpcsk7Btm0LBGpKrXjvW3haXywpJkhBF0TWXF2ZZRhiGw/K86xyN28YrGm9/\\n+9uBPwAeRdev4ST1KoSqqhiGhyTJ62tfp9MhjmNqtdqG9VDXNRRl8zm9BkVRmJgorfsQ24GMa4Nl\\nGWhafMWy3xsJc3Pw5jdfvb1pwu7d8OyzcOTI5f8fhvDNb8Kf/ik0m/C//td16+o2rgI3dCDj2LGT\\nnD07xdNPf57p6R9ncfEBFKWOZZXZvfs+br75rbjuw+zf/xaOH/86Bw78KKdO3c+BA28lDGd5xzsM\\nFEUCcpmodruHopRoNucZH5/m2LGz+P4korhAoVAmDH2aTY80Tej3RUyzRqu1zNjYBLOz85jmOFnW\\np9GQuHjxIidPKoRhwMzMLOVyHd/Xse0Mx+lQKFSw7TbFYpUk8ZEkF0hxXZUgcOh2XWTZotu9QK22\\nk2bzDGNje1hYWGZqahJBiKlW81IO205JUwjDAaZZZGFhDkGokGVdDKOE6wZUqyJxHA85LAQguMyB\\nKxQKOI6H74tARKUiEkURy8s5P4Ztz1OrTbO0tMDU1Ay9noMgGAwGbTStxmDQxLZTskzGdReo12c4\\nefJpdu48iOfNsWPHDo4de5Z+v0oQzDE1VaJQKGCaLpZVZDAYIIomZ85cBHbT653h8OFDCEKALE8M\\nnfmQJElZXQ0QRQtJ6lOriVuSFsJaVkY+Za7lIP5ikWUZJ0/OceqUT6fTp1QaMDlZ5MSJC5w759Pp\\nDND1lNnZPvPzAUGwiqJUqFZtLMtDFB3Aod/3WF7Os3ZU9RT79tW47baE178+wDAsXFfHtjssLDhI\\nksHu3R6NhkkQ5KSfWZayutqn3zfIsg4331xmz54pVlfPs7KiEUUhBw+K7NihXnZYy7KMXs8HVILA\\no1y2rvnZSZKEYQgEgTvM2NnGqwVhGOI4IAgiknT5mvFiEMcx8/P2sFygy/T05pkO58/PceJEAgS8\\n6U2rI0lEez0b21ZJU5vp6dqmjt6jjz7KX/5lB0maYnX1c3zyk/ds2ubs7CIXLgjIss3OnTbVavWK\\ndufOneMf/uEMcDd//Mef54/+aNMm+a3f+i2efPINgMGHP/xLPP30Nza1dRyfKJLJsnwN38zx932f\\nRx9dQpJquO4FXv/6A5u2+eSTx3j8cQNBCLn11me46667NrWdm1um0ynguh12795cerbb7fLYY22y\\nzKTXO8/hw6MJR0cFhZaWzgC/DTzNT//0z/Lkkyfp9w3m5s4xM7OTSsXnllvGrzmIkMsjhoCMIDik\\n6dpYvrZx7fsBjgOQUKmIL8teso1XBt7//vcDPwccptX6rz/s7vxA4fsBnieRZQmSFOO6LidPDgAN\\n319h586pDfZX+2JIkqSXVZr0tYbt9SYPZPzkaO7vy/CmN8F3vnPlQMZ3vpOXn+QiBzmZ6PIyjFBb\\n38Z1xA09oh966Js89BC0Wg+Sqzs8BTSGzNNdSqUTVKtt6vUVWq1v8aUvLSOK3+Opp2wUZYVTp05h\\nmgK6nkuxqWqIJI2zcyfYtki/3yVJisiyhyRJ60RFcRwRxxJRpCBJNopiYdsDwtAkCJZYWFil01nC\\ncaYIQxfTLFEqWciygCAILC0tMxi0MM2QiQkFUQwplUx83+fYsTMEgU+73SYMLWq1GM9T6feb+L5B\\nHC/TbLqoakSpVAQSdN0CFGy7A9j4vouuV+h0Wnz72y7VqsShQ3uBlCxTEYSc/NF1I6LIx3FixsYK\\nFAqF9WcbRRGzsz183+fs2Ta+nzExoZKmKefOzfLYY/PU6wkHD95GGIY0m8vYdpe5uTZZJqNpMUGg\\nsrJyhhMnbCYn88yT5eUmvV6K562wvLyIaVrceus4QQBpGqOquXReHHvDkp48S+b48ecwDJnp6RJp\\nCv1+gChmTExIVKsFwjCk07FJ04h+PydDmpysbXD8V1fbDAbhZfeaJAm2nZOJFgqbkxReinws5AzV\\nV9JEh5zQ9dixk3z960+ztNSlUpEQRWi1PNLUoNVy6fcXaDZDXDcjTQcoSplCoYAoDsgygX7fHpYz\\nJcMx7nH6tMrFi7txnH2srDikaZGJiQL9fkSS6CwvZ5TLJiBjGBKuW+f8+SWeeqqPrscMBjexvLzK\\nysoS3a6JZclUKlNIUobnBUNuDYFqtUilYtHp5KSighCQphnFoo4sy8RxzGDgoyjilqRdpmlgvryy\\n7tu4DojjmLm5JgD79zdGHviiKMK2A1R16xKTixcX6fczDhwojgxk9Ps9vvvdsyhKwh13bH7gBnjo\\noe9x9GiXO+7QufXWXZvaLS8v841v3Esc1zCM0Zlc3W6Xp59uYpoJb33raDWOXDHrWZ5Xzroyvvzl\\nL5PLg1s888z9I20vXpzloYcuMjWlc/fdbxt99bMXcZwl7rhj9LN/+OGH+fSnjyKKIbfd9sGRgYzV\\n1SbHjy+xY0fMXXcd3NROkiQGgwFZlhHHo3mH1tYNWRYpFK60brSAR8lVSxLOn79As2nR661iGCa2\\nnTI2pjE+3rjss5euSZuVGsZxjG3bSFIytLlx32xu49rg+z7wNOCTj9PXLuI4pt32kWUolxXSNGVl\\npU0USTQaxZelD47jEYYJhcLmRL/buPHw3HOwZ8+1feZDH4LPfAY+/vHL/+/ee+F978t/FkW46y54\\n5BH48Idfcle3cRW4ofOyjh+Hqal7gJtQ1ZuAHZRKt2FZhxCEXVjWO1lYMNix482cPy+zf/+HWV4u\\nMT5+BysrMq67m4sXS8zOivj+TpaXM+r1/SwuhtTrY1QqdQ4cqHDTTdMUiwKlksb4eJ3x8TqNRonx\\ncYNSqUSxaNJoVJmeNuh0ImA3krSTm2+OeeMbK0xPT1MqWVQqBoaRkKYqY2O76fUSikWDNBXIMo1W\\nyybL6nheiU5HpV6/GddNmZyso6oFpqfrBIGEqtZpNgN6PQPbtoZlHzlZY6lUR1EMpqYMbDujVLqJ\\nZvN5kkZNi9G0GNBJEoX5eRtFGV/XTDdNnUIBBCHE9036fYEsU5mYmKFWM2g0YmxbZGLiDpaWEnRd\\nQddNKpUylmVRLNap1aYYHy+zY0eBICixc+etzM9HlEoNisUGN92kMz6uUirtolCYJMsSdF1GUXSK\\nRYO9e6c5fLjMvn17mJw00TQF05zA91VarYw0raAoArWash6QcJyQNFVZWrKJ4yK2LQ+djhxxHNNq\\nxSjKOM3mRn34MIwIQwnfF66a/C2KIuJYJo6VDcSjl2JlpYMkjRNFuzDNg/T7BTqdHWjaGxAEn1Kp\\nTBzvpVC4FVluUKu9AVkuoiglVPUQaVpDkm4GjgDTwH7gHYjimwnDSS5cSInjtxGGE4DEzMxOJicr\\nFAo7EIQZbLuApu1gdVUkioqY5iTF4t4heasJTFIulymXDTStTKeTsLoqMj+f0umo2LZMszmgXm+Q\\nph6KYiAIxjrpnueFCIKB7wsjyRW38epFXt5mIctFgmA0MZttB4iiie9nI8dDFEVIksXk5A6iaPQh\\nUhRlZmZuotHYuSUx3IkTXXbu/ACnTrFh7r8Qp06dQlUPYllv4Phxe1M7gDSV2LlzL9XqzMi1Ie/b\\nIeAWYM/INvO+vQf4lyPtAJ59dplC4QgXLwrrZZNXQhzHjI/XufnmPQjC6OyCL33pu0jSTwH/nIcf\\nfnikba8nsGfPPqLIIAguJ/hdg2EY3HbbOLfearBr1+iAj+9HgE4Yips80wngLeTPE6DGxITFwYM7\\nMc2Q3btvwravvFZfuia98P8lSaJc1hDFkFptEk2TMc3Lyyy3gq5rFIt5ecr229EbEW8EfpR8nL6W\\nIVIsGhhGPj8URWFqqsLOnVUM4wf/ViJJEjwvQxTNkUS/27ixEEUwOwv7rlHs6/3vh+9+NyfzfCEu\\nDWQA3HEHPPHES+vnNq4eN/Qu+oY3SHz3u59lbKyPICwSBAvoeglRTKnXByjKsxw6lOH7T3H77Qq9\\n3gPcfruKosxx8KBKsbiKpvUxTY1Coc30tIUkNTlwYIwoGrBv3xiiqGEYGpZlDVmd+6SpQqEgo6oG\\nWZaTF+URapnp6QLnzp3DMAIOHDiIJOWxJkkCw8ijyvW6Qbu9zM6dRSQppVRSgYhqtcDi4jKVSowg\\nSEjSKocPTyDLIXv2VBHFkEZDZTDoYFmgqg6SBJVKBVWVqdU0ksRlerqMrsvs21dneXmBRkNEVQXS\\n1CcIJARBHBJqKpTLEo6zSrWap+kKgoCmaZimzuzsKkkSUK+DJHlYlkmWGYyPKzSbJ9m3r4CuSzQa\\nFoNBiiBodLs2ghAwPV2kWjWZmlJZWTnLrl0F0nTAzp1FRLFAsQjNZkgcB4higSBIMAyQpJSZmSph\\nqNJoNCgWdUSxxPKyhyQlmGaKIDhomjTMVMiZm5MkotNZI9K0UZQMVX0+60KWZSwLPO/5e11DlqVD\\naduUcnl0zfil7QmCBwgoypUd4Wq1QKMhsHPngFbLxTQVbLtPlgUUixMsLg7odPqEYUi16pJl0rDW\\n3gNCCgWH1dXWsBbbQxAUPG+JUklm9+49HDhQYHb2SWRZoVwuUKsxDOyIeJ5LpRJTrYaYpoCmGfT7\\nc9TrVQ4fHkdRgmGtpcLYWJlSKU/17HbbpGlAkuTkfuWyTpIEVKsFkgSSxMcw8mVH02QGAx9JyhDF\\n7ZKR1yJ0XccwXEDENLcixpRxXQ9ZHq0klPPuSLjugPHx0W/26vUS9fo8kFAul0fa3nSTwbFjD3Dz\\nzck6GfCV8M53vpNK5Q8Igj4/+ZO3jmxz375xWq0mmhaPvP5tt90GLAAngdGqIYcOHeLo0UcBjV27\\nNs8cAWg0DB5//AS1Wq6ktRl0XWd62mAw8Nm9e3PpWYAPf/hHOHbsIaDNj/3YB0faTkzonD49x8RE\\nOvKZiqLI+HiVNBW2LPNTVWnIw5RtQiC7DDzOWmZLoyHR62WYpsbUVBUIMM0rp6erqoRt52+Rr/T/\\nsixTLJo4ToBhyC+Ka2Ntj9zGjYonAJt8nL52oWm5UpUo5sp+mqZRr+skiUilsvladL2Q87RlxLGH\\naW6Xomwjx/nzMD2dE/xfCwoF+NjH4L//dzaUfi4uwrlzeRbGGo4cgS984bp0dxtXASG7kcSDh1jT\\npe10bJ555jluuWUvx48/BojMzhokSczOnSmaZrFz5xRRlFCrmbRaKzQa43S7ufqHIOTSoEEQ4nke\\nU1MTJEnuBK8RbcZxjCiKeF5OsKmquZMWBOGQADEn4zTNnCjRtn0cJ0UQctWStXS4LNvI/JwfWNV1\\nArVctvT5N9tZlhFF0WV98bwQ1wVIKBalIXGduN7+mm2apmRZRrfbp1jMeQ2iKGIwyEnwDCNF17UN\\nn7kUnpfzgQA0GhqCIOA4KZKkIYoBiiJgmuaGfsdxTLeby0KVyzKqqjAYhPh+iq4LlEr6hmeapukw\\nHT1DECQMI8UwtKFUqodp6uukRvlb3Od/7vdjRFEhy1wMQ8N1AyTJIstCikVlUyKpK92r63q0WgFZ\\nljE5aV117fXa1Ls0vfmFmslRFBFFEWEYoigKvu/jeQFJknDyZJMkKaFpA6anSyws9Gm3Q6LIxzBU\\n4ljmwoVFkiTjllsm0LQMyKhUJMbHJ6nXy5w5M0cUWaSpwNSURa2mIUkCQRCtl3sMBgFZpuL7A+p1\\na4MTnpPEyuv302o5iKJGmnrU68X1kqq1MfbCcbz2/a89gyAIiOP8e9wm8npl4cVqza/JvF1tydXV\\n2CVJQpIkW861NE1ZWFhC0zTGxuojbdttm3bboVq1qNWskaVOZ86c5cKFWf7ZP3vXlv11XRdFUUam\\nNvu+z+/+7j9y9OgKb3lLjU984qOb2j7zzDP80i/9JZ3OLJ/+9D28612bl3b0eg79foRpqlQq2sja\\nctu2abf7TE42Rj5X23b41rceJ8tS3vveu0barqx06fdDCgWViYnylhLLL1wfNsML1w14fnx+8IP3\\n8JWvfBGw+cpX/op3v/vd9Pv5uDLNjDhO0DRlnSz6atp+MTbb2MYa1sbmV7/6VT7wgZ8FJnnPe36U\\nr33tj3/YXfuB4oXzJE3TDT7Dy9WHbV9iNF7s3v5qxBe+AH/2Z/ClL137Z1dX4dZbc2LPw4fz3/35\\nn8NXvwp/93fP2x0/Dj/1U3Dy5HXp8g2NqxmbN3RGRrGoMz1dRRQTDhy4kygKEIRFskxhZqaBplmY\\nZoqimIRhTKOxeyhDKQ/loop0u30cJ0VRKjiOR6WSv3W8lBTScTyCQCJNYyqV3JHMte1FOp0BhUIZ\\n2/YplUwsSyPLvKGM1fMO4qUbwZoiyKW/X1uoL90gXkhMmTtyGhAgy8oGGcu1di61zbMMLAaDiEpF\\nGrJQ+2RZimEYl33mhUz/aZr/ey0aLwgBQRBgWdqG66y1kWesZKSpuN63QkFBlvPSlyvdT/6zTxwn\\n65/p931Ap98PqFbNy9jhNU2jUMgDPb4v4PsSWQayHKGq8kjH/EobcJZlJEmGIHBNm8HVOMGKoiCK\\n4nqfHCcjDAUcx6NWqxMENvv27cAwVMJQpt9v0WhUMYyQNE0wjN0kicf+/Q0GgxhBKNPtLlOv1zh/\\nvsn09G5WVppUqxrlso5pakPpVYsoiimV8kwiz4uGmTvPP5ssy5Blef2eRVGkUtHx/RjDsNYPTWvf\\n8ZUc/0sdjLw+PUEUZSC4ainObbyy8YNwIiVJuqp2u90+rlvAcWIMw97AbfNCFIsacRxSLKoj52Yc\\nx1QqUwhCAc/bepzqur5lXxVFYc8eg5WVAocOjQ64VKtVPvKRdxDHCRMTo7MnclWoPNNgK9WUxUUX\\nKLO01GXXrvFNbft9G8vaNeT5GVCvj+pvhqLoZNnWJXfXEhQY9TwnJnTgTUxN1alUKmiahmX5QEaW\\nQZJo2HaMLF8elN6q7Wux2cY2XohiscjY2EdoNgeMjb04xZpRikqvJKwFJS/1iURxc8Lh63lduNw3\\n3sY2AI4evTbFkkvRaMAnPgH/5t/A178OggBf/jLcffdGuwMH8vIVz2NbZe9lwA0dyPj3//5T3Hef\\nzcTEPHfe+UFU1eXgwb2kKXz724+TJBZvfWudvXsPMRi0SRKLZvM0Z85E6PqAmZl9CEJEsVhBlj2C\\nIKDV8pma2kgGKYoCaZogCNl6dGmNhFFVE9I0IY4D2u08Da7fT1CUlEKhgCDkkyVN8zdmUSRSKknI\\nsoEoJqTpmq62RJJEDAZ5bXdeUqFQKuXlKO12l1bLp1iUmZx8nhxvjXQySbL1jIc1xHFIq+Wh6xnV\\nqjGUjUvIMjCMdINj7Hk+rhujaTlJWi7Jqa+37zgRgpCSpiJxfGX5zSRJWFnpEccpghCTZSpTUwVK\\npcsPH57n8eyzC2RZxvR0BVXVmZ9fxvcF0tRDEEooSv4sBCEhy0RysstcQaFU0jEMnaWlWbrdHmNj\\nEuPjV1cW8kIoikyhIABX9zbxahFFEd/+9mM88MCzGIbGO95xE66rYNs+ExNFyuUaCwsdHn30JLbd\\nQxA0giCm1wuRpJRqNePUqQGrqy1KJZVez6ZQGGNyUuOWW1bIsiaKMk69LnP48E6CQEDTepw712Ew\\niNi9u8jERANVVbAsFdsOkaSIYtEYBh1CgsAjCBIURWZ8vHRZAO5qkCQJvV6u5pNlImkqIIqvfEdt\\nG1sjDEPm59ukKezcWRs5NrrdPs2mi2XJIwk8syxjMHCJooxicfR4i+OQ8+dbyHLKzMzukX391reO\\n8r3vrXLwYJGf+IkfG9HPLr/wC/83vZ7IPffcyUc+8jOb2rquy+JiH0UR2bGjsen6IEkS/+N//AUX\\nLkxw5ozNT/3Uezdt88SJE/y3//ZZskzFMO7g4MFbNrXNs9YyIEbXN8+lzbMGXVw3ZGpq9Nxz3QHf\\n+MaTSFLGgQOjCVR1XUUUpfUSyc2Q74kucZyt71kvFn/+578P3M3i4kn+6q/+jnJ5CsMwmJ6ukKbZ\\nhr14G9t4OfGtb32LZvMMMMnf/M3/y1//9R9c0+fX/Cxd35oQ+YcJx/FotwesrvYRRZFdu8aoVAov\\nec6t+QqCAOWyuWE9XVtDBgMPWZYoFLRNCXu3cePi6FH4rd968Z//5V+GP/kT+Pu/h3e+E+67L//3\\npVAU2L8fnnkG3jBaTX0b1wE3dKjywQe7TE39Ok8+WaLXq7O6WqDXk+n3RTqdKopyiKef7mIY47Ra\\nEWNjUzz5ZItK5Uc4f15HEEoUCtNUKhpjYwZZVkTTGrTb7obrGIZOuSxTLuepvVmWYVkm1WqJarVI\\nuSwhSQqqWmBx0UZV6/T7EIYCYSgQBAJRJNLrZahqncXFAapaoN32UBSLwSAmTRVsO8L3dcLQpNsN\\nkCRjSIwGrZaLZU0yGKQbSMziOCaOZQRBv4yMTxQVarUiuq6vl6qkqQpohOFGW8+LURSLIMhI03TI\\nj6BQLEqATpoq9PsRimLheVcm3fN9H99XcRyJTife8CzzIEq4Xk7SanUJwyq+X6TVGuC6Ca1WjGGM\\nMxgk1OtFkkREkgwcJ8X3M1w3IQyFYf/zEpYkkRkfnySKXvxUyO9VplJRrysztuN4nDrlEASH6HZ3\\nc+xYiywrUixOo+s6uu4yGKj0etPMzzfw/WnCUCMI6iTJYc6dEwjD3XQ6+1hYmKDV2oPv78FxSiSJ\\niuc1gEkcp8j8fIAsV1hcdJGkMro+jueJ+L5Imio4jocg6EOC0hjPi5Akg34/JUlMokjd9HvdCvlY\\n0hAEA8uSKJflDdlC23j1wvd9oqhAlpWwbXekbafjYhjjOE4eANkMaZoShgKybG455kRRpdGoUqtd\\nWfb0Unz/+yuMj/9zjh93R5J9/tM//RPd7hGKxZ/nK185MbLNbtdFkmr4vjqyzccee4yFhRn27v1t\\nnn569Fr04IMPkiTvRlE+yIMPHh9p67oRsmwShqMJdQVBYGyswu7dFYrF0Vwm3a7L9PRBxsYO0Ov1\\nRtoWCjlBdbGob5nlEsfShj3rxeM2conLt/N3f/dZXNcgTa0h94WEZUGlom9LOG7jZcef/MmfAG8H\\nfgIYza/pXW/OAAAgAElEQVRzJbhujKoW8LzkFVsKkGUZvp/i+zKDgUYUlbHt6KqJ0EdhzVdIU/Uy\\n8ua1NSSKFNJUW/dFt7GNNbguPPYYvOUtL74NRYE/+AO45x74xV+En/s5qF0hMfJ1r4Onnnrx19nG\\n1eOGDmS8//1TLC7+HocOddH1JQqFATMzsGuXQq3WJwyPc/hwDc9bYd++KlHU5m1v20m3+zD796eM\\nj4uUSgHj42UqFYNSCYJglVrt8iiwomwsjdB1AUmKMc387ZNpKoShzdRUgTBsUSqBqmaoaoamZShK\\nSrksEIYtpqaKhKFNrWYQRQ7FoowoRhQKCrruo6oulUrOU6Dr+cG6XjdxnCWKRfGy8hNZjskyH03b\\neAg3TQVBiDCMPC05L3MIgWCd62MNhiETRQ6aJqxHyRUlr0NWlARRjCiVFKLIwTSvfNiXJAnH6REE\\nDoVCuuFZ2rZHv5+yuNim240ABUlaRVG6CIKI54WYZoznrTAzUwJC6nWTJPGQ5QjH8QmCAEmKhv3P\\nOTDqdZ0oatNovLS3G5d+v9cLlmVw4EARXX8GXT/N7t0VoEMQNBFFAUEoUCikKMpparVVLGuJQ4cK\\n7N3rUC6f4447LKamVti1a5Zq9SKVygJwnPFxME2bffsUisUmk5MxO3boxHGXmZkChUKEpnVpNDR0\\nPUUUc76MLPPX07ENQyFJvGEQzkVRQgzjxQVx8rEUIIrhcLxsy6S9VmCaJopiI0n9Ld+O1esmnrcy\\nJCLePMtCkiRUNSOO3S3HnKIIJIlHkgRbHlx37FA4fvzLNBr+pvwJkJN9Tkw8Qxj+DT/xE7ePbLNS\\nMUmSNroejmzz9ttvZ9euNufO/RcOHRrt8L/vfe/Dsr6JKH6Z973vTSNtTVMhjl1UNRt5/5IkUSio\\nqCpY1uiMqptu2oFlrVCtNrckG7Vtb3ig8UcevPJ9KNmwZ71YmOYZ4G+Ab/Oxj30c0/QQRQdZFuj1\\nYlw3e8UeArfx2sZv/MZvAN8G/pZS6fw1f940ZcLQxjCkV2xGkSAI6LqIrscUiwGK0huWCL90/+hS\\nX+GFfsLaGqKqEaIYbPBFt7ENgPvvh9e/HiovLvl6HT/6o/B7vwc335yTf14JR47Ak0++tOts4+pw\\nQ5eWvO1tRzh79mF27TpCpWKh6wbPPHMCXTdIEg+IeeCBh/nrv36EO+7QmJvTedObquzfv5fp6QJj\\nY2UkSRjyTggYRoU4zksu+n0XQUjo9QIKBZVKpUSSJMzOLgGwa9ckkiTh+wGe56IoArIsUCyWGRt7\\nnmgyyzJc1ydNMwyjQhSlqKpIFGWoqkoYpmiajq5r6wSdaZpSr5cRRXG9fV1XGRuTEYSEU6dm0TQR\\nRdGRZXGdWDEIQlw3RJYhjnMVgUtJ70RRpFrNyzza7S6+H6Oq4PusE2RmWcLCwiqatsaLISBJwjpz\\ndRSlBEFAszmgUJARRRVJyktnwjDANBXSVMA0MyRJpN1u88wz82SZzdISiKKNYVQxDMiyEElS8H0Y\\nDGJuuaVBuVzE931aLYdq1cCyLLrdmBMn5hDFlPFxA1nWkKQKopg/jzD0yTKFft9FlgWSJEOSBLIs\\nf/5BEBAECY1G6ZrLJjbD2lhI04Q9e2Y2ZcjfvXuCev007XaPp58+g+O4HDy4G4h55plzQIwoxliW\\nhG0vcvToeXRdRNNkzpwJcd0+mpaiKBJ79tRZXW2xvNxE00J27LgF01Tp95scP96j0bC4eLFPtarR\\naFRZWWkTBAFpmh8IV1d72LZDsWgxPT1JtWqiKCaVioiua9fkWGVZNszyEDBNnVptc+6Cbbzy0O/3\\n6fdDajVzpBqFJEmMj5fJsmzLAJWqqmiaj2luPcfuu+9Bzp4d8KEPHeHgwYOb2omihGHka8xW4zM/\\nJDgUi9WRBHHVapV//a/fxtxcjzvvvHNkm2ma0uv1KJW2Jq+dns544omLTE2Nlh8NgoCLF58ATBYX\\n9460DcOQ5eUW5bJGqTRaKUBRJNJ0dMAD8hLHJ598BFXV+eAHXz/S9tSpMxw71uTQoTI/8iNv3NRO\\nEAQuXpyj0/E4cmTvSIWXnMzZH+69lwegXXcAnAOOceedv0a1aiEIeW1+rriVrhOLuq4/zJA01kmn\\nXTfA9106HRfLUpmZmRx5jy/EWhv53np9M8tcNy8DtayteVe28crD3XffzT333APcSb/fuebPG4aO\\nrr/yy6Isy0CWRTzPR1EkqtXL1aXW5h+wTnZ/NZCkfF1znNxHkyRxff6WyxblsvWq4RHZxsuL//2/\\n4cMfvj5t/ezP5n82w+23w6c+dX2utY3RuKF3ws985lGy7CN88YvLnD1rcf/95/je96p84xsB3/xm\\nn6eesvjsZ5fodv8ln/rU9wmCH+d//s8n6HYnePTRLgsLAc1mQqvl4XnQbvskicbS0oA4Vjh9eoU4\\nLtFs+sRxTLvdZmVFZWVFpd1uDx2ymDTVWFzskWU6g0G4YQGOogjPE/B9gU7HJ001lpcHgMHiYp8s\\n07HtmCRJsG2bblem31fp9+0N7S8t9QGDEycWGQyqnDjRo9mMWVlJaLVcHCej1fKIY4Xl5cEV+7KG\\nPFCQ4jg6zz7bJI5LPPfcKlmmc+FCG8fRWVz06fUiBoOEXi/E90VWV12yTOfMmVXStMKZM22iSKbd\\n9vE8gcEgwXUzfD9jYSHA80wefvgiQTDDvfeeIIp28MQTTbpdk8cfX+bsWYVnn4UTJ5YJAot2O6/H\\nXFzskyRlFhdtsixjdbWD7xeYmws4c8an3da4cGGVIJB44oklsmyaJ56YJ001VlcdgkCi0wmGfY+Y\\nn/dxXZPV1dGyiNeCtbGwumrQbLavaON5Hv/0T2d48slxHnlE4r77Wpw9O8O3v73AmTMDTp0KeeSR\\nLo89pnH0qMC993Y4erTO17+u8uUvJ3zrWzpf+5rKgw+WOXaswgMP+Jw6Vefpp0s8/rjK/fe3eOQR\\nmwceCHjiCZO//dtnOHu2zv33Dzh6tM2JExHHjnW5cCHiscdWeeIJn6eeUvj+9x3m5nzOnl0limRc\\nN1k/GFxtKmcQhASBhO+LI8sItvHKQxzHLC/7xHGJ5eXRcyIM87kfBBJBMPp7Xlnpk6YVlpf9kWNi\\ndnaWhx4K6XTewBe+8OgW149IU4UwlLZMbb733vMIwl1885tLeJ63qd1jjz3G/fcLzM4e5r77vj+y\\nzVOn5mm365w+nbC6ujrynr72NQ1Z/rd87nPNkW3+6q/+KvB/Av8Xn/jEH460PX++ie/XmZ8PR95T\\nkiQ4TkIcKzhOMLLNz3/+/+PkyTdy9Ogu7rvvvpG2Dz98kSi6laNH2wwGg03tVldXefbZhGZzkief\\nPDeyTc8LiCIF1+Wy9PIcrwPuAX6bn/mZD3P8+BKOk5e+mWZGoZCTZV86Nn0/v2fXDYhjlZMnm3S7\\npS2f25Ww1obrZtclnX4NURQN71nB80Z/R9t4ZWLv3r3kY/NXyEugrh2vlgP6wsIqvV6ZpSWuWIK2\\nNv+uxQdw3QDHgW5XYmXFxXVzQsUXrgOvlme0jZcPg0HOa/FzP/fyXO/OO+Hxx2E7+e8Hjxs6kNFo\\nhMzPf5tKxaFU8tixQyJJlkjTJUolnzQdYBgdlpYepFIZMDd3P/V6iG0vI4p9DCMvD9E0CUFIURRI\\n04gkCVhd7SFJCVFkoyisK08Igo0g2MOfBSQJkiRE1yWSJESWNyo75JKhCYKQIst5+4YhE8cBhrH2\\nmUvb98kyH1WVEQQBWc7bNwyZJAkplxXCsEOhkCHLKaIY4jgu/b6DKMZkWU4kFce5ROqVkEuThqSp\\nT7EoEUU2pZJCkoRYlkya+qhqiiSBKKYoioAgJKhq3pdCQSII+pRKElmWZ3WIYoqqChQKMpYlY5qQ\\npj6lkojvLzMzYwEtpqdNNC1gfFxBEFxEcYCiZLhuD0XJh3Mc+ywvLxNFPqIoUiwayHKIaSbEsUOv\\n10ZRMiChUlFw3Sblct5/VRXJsnj4TPN7UNWUJHGv6k3x1SJXfbHJMhvDuHK7iqJQLILrniHLFtH1\\nPmk6S7kMmpagaS6SZBPHC6TpAoYxQBRnMc0lisUOkrSMpi2jqksYxipjYyGFQh/D6FKtuhSLMaVS\\nSKFgI0mrTE9LpOkyhYIDuMRxF10P0bQIy8rQtABZ7mIYIVnmkaYxvV4fUcwDGJ2OQ7frbXK42Ihc\\n9ScG4u1a9VcZZFlG0yCKbAxjdFJf/t3m68pWhI+6LhNFNpp2ZXWgNTQaDUyzw2DwNHv3js4yEEUI\\nAo8wdLd0bmdmFJLkLFNTKpa1ebu1Wg1JWqHbPUm9PrpcplaziKI2quqPVEzZu3cvqjqH799PsTia\\nS2RmZgZ4DHiCgwdH81lYlkoc91CUaGQ2WS6zPKDd7hNFow8V+/dP0W4fI4pOU62O5h6ZnraIoouM\\njUGptHlfTdNEVV3CcIWxscvf3l4KSRJJ0whR3Cxr5ingKPAMUKJYVMmyXJI6f6OtDdt5fmzKcr4G\\nybJIkoRUqwpR1EUURz+3K2GtDUFIrmvWRK74kJKm0ZZzaRuvTOQKP2eA7wOj+W1e7cgl4HsoSnjF\\nOZTPjXz+Xe08kWVx6A/npSP5HNuWV93G1vjc53JyzomJl+d6ExNgWXD+/MtzvRsZQnYDFouuKYc8\\n/vgp7r//EfbunSJNe4yNjaNpNZIkwnFcms0mqlpkcXGZvXt3kmUJ09MTVCo1VFUaHiY1HMcBcgc7\\nz6DIkCSdJHFRVdZro0VRxHVzJ9U0zXWN7SiK1gk1RVEkiqIhi7yHKIpDUsqEcrlMmualK0mSrP8t\\nCAKO4wxrBOX1e0zTFEVR1nW74zhGEASWl5cxTRNZlgnDEM+TAAFFCZFlEdM0ieMEVVXWa4n7/T6q\\nqq4fOnVdJ47jYXlLuP5vSZIIgmAoO+tssFUUhSiKkGUZz/OxLHNdkjMIgg3PR5ZlfN+nWCzS6/Up\\nlYosLS1Rq9VoNpuUSiXiOCaKIpJEx/dDxsYsdF1hMAhIEgVRjFHVFNM06ffzN8dRpJEkKePjFrqe\\nE/AtLTVpNPJ0csMw1iVP1+49DEPCMKRUKm04DK0RkF4qh3stWHvTZ1yiz3SpZnKWZZw+/RyPPvrc\\n8BloCELCkSN34Dh9ut0erpvhec4wlTLGdQeoaoooFhgMBkSRh+8HaJrEkSO3sri4iOcFHDx4Czt3\\n7hymKsfrY3AwGGAYFobRQJJEqtW8ZMh1vSHbv4AoSoRhgq5XGQy6jI1ZyLJMqxWQJCmqGq8HlfJA\\nnIBhGJcdTtfIB7cDGT9Y5OSYEbIsvaQ65UvHZt7maN6HNVzt97wm52kYxpaHx6WlJZaXlzly5MjI\\nAIXn+fR6CVmWUq9rI9ttNps8/PCjvPGNtzM9vXl5RxRFfOYzn2Vubo6Pf/wX2LFjalPbLMtYWVnB\\nNE2Kxc0P6EmS8Gu/9l/4whe+xi/8wo/zn/7Tv93UdnZ2lre//f9AEGS+/OU/5LbbDmxqm6vGzFMu\\nl6ldiZHskutfvLiK78fUaibj45sHKNrtLp/61GdI04T/8B8+PjJAEYYhTz31DPv27aGyRWFyr9fD\\ntm2mp6e3DDrFcXyZlOPa+HzDGz7MY499EYAvfelLvPe9713fWz3PW+duWrvvJElI0wxVzdf9tbZd\\n131RKkyb9e96IE3T9f18G68erI3NRx99lDe9Kee1ufPOD62P09cqbNseOYdejA8Qx/F6BmgURWia\\ntj0fXiIu3dtfq3jHO+DXfx1+/Mdfvmt+6EM5IehP/uTLd83XGq5mbN7QgYw//dN/4MKFKZ577uvs\\n3/9eBGGV97xnF5IkcezYKlEk0WyeYnLyduAUb3zjO1GUAbt3T2HbA1zXoN1eYn4+xDAKTE1F7N69\\nhzR1kGWVKAqR5QKO08cwCkSRjyDkC3aWJciyhu87WFYJSYooly1s2yUIJLrdNmGo4nkOggCmWWJi\\nQqRSudxpbLe7NJsAMbt3F5BlmV4vBCR0Pd0g07Wy0qbblfH9Ho1GGciwbZcsE3BdD8uqE8dtJiam\\ncZwullVmcXGOXs/A97tUqwUsq8DUlDzSge33+ywuxiRJjGmCaRaIIg/DKDIYdCgUqohiSLVawPN8\\nHAdc16bfTwAB3x9QLDaAHo3GJAsLcxjGOBcvPoeuTxAEHUolE0mSsO0BgmAgii6Nxk663Tmq1XHm\\n52dR1WkcZ5lGo4EkZQhChKZZTE2Z6LrG44+fJ0mqtNun2b//MILgsnt3bX1jjON402fpOB6+n79V\\nuF4s+JdO2sHA4bvfPc/jj8+xuNgly0RuummSSsVB06Z47rmloWMuIggB3a6C6w5IkmwYTItptz1s\\nW2RszKRScXFdC98vcOSIxOHD0whCEd8fIAgStp0hSSkzMxbT0yWmpiqUywU6nQGrq3lWkGEkQy4Y\\nZ1gqoGFZCqYpsrho0267ZFlEFMlEUYSqypRKFvW6ythYcTto8UNAr+cQxwqCEA75Al5c2u0P2tnp\\n9/N+QkSlYmx6CMyzfzxARlUTisXNsyLykjsfQYBi0Rx57//4j4/geTMoygJ33/36TYOTDz30EJ/8\\n5FNEUYN3vavF7/zOL2/a5vNrREK5rG7qcM/Pz3PHHb9JFN1NqfS3zM7+/aZtfvrTn+YTn2gDFj/9\\n00v84R/+101tT52apdPJs7/uuGNm0wNFGIZ85ztniWODqamEw4f3bdrmX/zFX/BnfyaSJD6//dsz\\nfOADH9jU9oknzuL7VSSpw5137tl0/o9aZ68Wa+NTEKaA3wCeZvfub3D+/HnSNGV5uUe/L6CqCdPT\\nBTRNI8syOh2HLFORpJBKZZurZxvXH2tjc9euXVy8+B7gMPC7ZNniD7trr0pcOm9lOfedt/Hi8VoP\\nZDz7LLzrXTA3l6uOvFz4j/8RkgT+83++OvvPfQ4++Un4nd/ZDn6s4WrG5g0dxsyfTUIc+7RabXTd\\nRpahVLIoFBzCUCaKSkxO1lleluj3HYrFy9Nu19JS12AYOoWCSbPZodu1iWMfXR+90AZBQKeTEcch\\novi8bZ4ZkPMObMU/kKbpsEREYjAISRKJsbHccc3JkUI8zwcKG+q2ct/+yg7+lRx/Qbg+b5nW+pAk\\nCf2+j++7ZJm64ZqX3nOaQpLEDAYOUeQA2VABRsWyiqyu9un3bQRBpFIpsLQk0u87hKE/vJ5AsWhh\\nWQUkafRhLn877BHHMWkqIYqbH8B/kOu/oihUKhXCUMB1Y0zTwLJSZFkly4ThWDNotVbw/YAoSvA8\\nh9VVB0FI8P2UKDIIQxnPC4kihTAMaDY9FhdVGg0Vz/NJEpEsUxHFDM8LEEVhmN3i0u3apKnGpWeQ\\nXGlHJQxlIM/0KZUs4ljAtgdcRXXJNl5mvIb9lE0hSdJ1d3LzQ3eLKFLodltb2na7IbKcUSpdjQeV\\nsFWcSRRzMl+QLlOPeikwTR1RLKEo9kg713VpNvsIgk+zef3K7a4W+X7hIcsihYJxhT0qAvpAiOfF\\nhGG4IVNxzT7L8iB+t2tjWSW2s9O38YNGEARACPSAbZ6TbWzjesLzwLZhbGzj73//9+HjH395gxiQ\\nK6T80R9dne3Fi/DLv5yroXzsY3kZTKPxg+3fawU3dEbG44+f5vTpJkGQv+2uVIq8+92vQ9d1Wq0W\\nrutiGAadzgDHydC0Bp7XZNeuHUCCYeRM/P1+nyiKmJ6e3lDO0e06JIlKmgYUCtI6L8ZaH9aY8ZMk\\nxbYDVLVIGDroen5QXCu3gPxgnZd8xJelc6dpim3bJElCHOtEUUwQ+BiGiWHkDOf9vg9oxLGHqqbr\\nZSJRFNHtxoCApsVDJvjnS0vSNEMQoNPpDIMG+bVH1XuvwbZzh1jTtA33Kkn534oirzNXOw5kWYqi\\nBMMafG39XsMwQlUVXNfH9wP6/ZgoCtE0HVVVMIyENE3xfQHfTykWJUxTJQhimk0bVRUplfJ0Yl3X\\nSdN0/e8oiuj1bCxLx3E8SqUCpmkSBAH9fkqWgWHkz+LS7w9eemnJlfDC0pJ+v8/KSpMkSQjDgCyD\\nPXt2s7y8SqvlkKY5X4ZtK1y82ME0Ay5eXMZxGjjOKrKc1+YbhsPrXnc7CwuzdDoOMzOHGBvLDy2l\\nUoMoigEHyzJJEh1dNzCMBEEokP7/7J15mBTV9b/frqX3dTY2QRAURBgFEcWNVcQFNEbECAhRUdEY\\nIkZjTFRcEpdgNPGrBlcQffiJUcQkRiMQxUQlMCqbJjEKIg7LMFtPr9W1/P6omYaBmZ61p2eg3ufh\\nYaam+txTVbdv3XvuvZ+jG6RSVfh8HtxuN/F4AsPYXx/NbAC22qCPjq6b98Vm27+1xEqrmjuysbUk\\nG+i6TiwWx+GwN1lXVNUUOD74O9kQdd/Rpq69oqKCbdt20q/fURm3YezatYvHHnuXWExj0qQBXHDB\\nWY2eGw5HqagwM2x07+5r9Lo0TeOhh/7A229vYubMCcyZM7VRm5FIhFtueZBEIsmjj/6CvLzGt2wo\\nikJ5eTlerzfj1hbT1zAVFdX07Nkt43aKf/xjPX/4w0Y0LcnPfnYGJ510UsbyS0v3UFiYl1F3BMxB\\nnqqquN2ZV86YKV0FDMPUOKq7p3X1c/Dg8/niiwrA4MUXf87EiWdTVBSqzT6l4HDYcTqdtW2/Cgio\\nag35+QFkWc7athCLI5e6uvnXv/6V889fADjp00fmm28yi+VaNI6qqul+6oHf1bp+iCzLluhnM8n1\\nigzDgF/+cn/A4Te/ocmA/sFs3gznnmsGMm66yVwFYbPBnj0waBB88QV0b1kCqjZTUQF9+0JZGTgc\\nmc+9/XYzA+Rjj8GcOdC7N9x1V4e42amxVmQ0QTIZxzB8pFL7cDp7ARqSJGG32+nRoweGYVBdHcPh\\nCLFz5w7i8SiJRIwdOypwOjWGDj26Nj2nDYfDvNkOh6N2KbeApqmIogToJJN2Uim1wSXTsgyqqpFI\\nxNC0JImEC0VJEQx66wUyvvlmL4oiUViYqNd5FQQBv99PIpFg584wmmYO5iXJQFFSqKpIVVUlmubC\\n5dIpKiqq99lkMoqm2QADp9NHZWUEu92Drqfw+z0oioIs+7HZDFyu5m+haCjYUdePl2UzlVwsZqDr\\nqXTwxzAcaJo54HK73el7aRgqwaC/1pcUup7CZhNrs8GkAImyst3IcoiKir0UFBxFZeVudN2Lqlai\\nqoUIQhyfT8PhcBIOVyFJTjwege7dC9m7t4KaGolEIkKfPs7atImmyr7P58PRQCtks9kaPN5e2Gw2\\nFEXDMELY7QqJRIzych1F2VObblXC6RQIBJxEIgkEQUXTvNjtMaqrYwhCkkTCgSCIeL1B/vvfGqJR\\nkby8Y4hEKigs9NGjR3cMw8auXSp+f4jCQh+7d9ewe3cVwaCdusyaougmlTL1XL75ppxIxKCwMMLR\\nRxfVpu01t6UEg3VbF6ylnp0FQRDSAoedmXg8STIpoKoKgYCUsRN6oB5QJiKRCLt2JQCd3r39GTU9\\n9u2LoGn5lJZWZwxk+Hw+TjyxD4mEnWOPLWz0PIBEIs7u3VEcDo1u3RoP/oqiiKI4GDRoEuFwNKPN\\n//73v1RV9cIwJDZv3sjo0aMbPTeV0pDlAKqqZ0xJqOs65eUxFMVFVVWEoqLGr18QUqiqqTvS1LvA\\nFA/1UF4ezRjIMINYKppmQxSVjPVV11UqKxO17c2hfiYSu4BRwNds2rSR4uKT0bQUDocfSRLTdUAU\\nRUQxiaIoGIZIdbWC3Z5AUUQEQScYdFvBDIt2ZevWrUAecDTJ5Pu5dqdL09A7IB5PsGdPBFU1yM93\\nNpj21aLzsXw5rFhhZvm47DJ4+mm47rrmf15V4fLL4YEH4MIL4bzzoLwcnngC5s+Hq6/u+CAGQF4e\\nDB4M//wnjBvX+HnRKDz3HKxbZ/4+dy5MnQp33tnygM6RyBEdyDAMAZfLTjTqoKAgH6dTPWgrg048\\nnkLXJTweH0cdVcCOHSqC4EEQEkQi0dpokZxebWAYBqpqIEkOdF3B4QBVlTAMGV03ZxEP7BylUikU\\nRcXlcuB224hEbGiaHU1T0qsYwIwyx2I6hiESDic4sJ9tGEbttgJztskwDIJBc7VCdbUNUbSj6wJu\\ntwNJ0g7p0Pr9fnTdIJmMIwgyiYSG2+0glYrV+qhhs8kYhkYsFq9dmeFodbRbVVWSyRThcIQ9e2K4\\nXDaCQXNPtCj6EASBbdu2E4vpFBbm4/Plp30x77MpXunzeYjH41RXG4BEIqFjt4uEwwq9evmJxb6j\\nZ898KioUbDZHrTiogd0uEI0qOBwyoqjhcjmJRhVkOY9Uqip934NBf+29Euo9K0kSUFUdu13K+iqD\\naFQhErERjVYSjabQND+qWoMsewkECkgkIpSXRxFFMzAEBi5XiJ49VRIJmXjcjWGoxOMpBEEjGpXp\\n1687uv4dfr+D6uoIggCplI1IRCWRSOL1etB1D3Y7OBw6lZU1uN1BdN1Mk5ZKSQiCm2QyWruqRUcU\\nzfqu63rtjIiBw2G3BgIWzcasR05UNZEWNW6MSCRCLKaQl+fPGNCIx5Ps2VONYRj06JE5w0hFRRxB\\n6E5Nze60mHJDOBwOiov7EIupFBYWNXhOHapqZirQtGRaHLkhEgkzBbXN5qOsLPN2lZqaGkSxF06n\\ni507G07dXIeiqCQSIIpaxntqzm5KOBx+EomqjDZ1XaKgwIEgBOoJFTdEZWWMeNxFLBajZ0+10We1\\n/10rIst6xkCGzWZut2xslqa01A24gQJKS/egKDa++WYvAwYEUFUj/f4z23gPyWSSSMR8tyQSEWTZ\\nha6n6r1/W4K5OtAUzm6NUKjF4UtVVRXgBwLs2ZM545BFw9T1d82VnvXbCVXV0XUJm01EUfRWf4ct\\nOo5EAn72M1i8GI49FpYtM4U5J06Efv2aZ+OZZ8wsIVdeaQ78V682RT2LikybixZl9RIyMmkSrFyZ\\nOZDx4otw5plwzDHm78OGgSjChg1Qqw1skYEj+hueSikkEgoul4zLFScYFOrN2NVt/1AUFa/XFAMr\\nKvN1oPMAACAASURBVPIgCCkMI0kyKZFMikiSgiyrOBzmMmefzw7EsdkMUik7qRSIYhK3m3oDX1PT\\nQiGZlGpn1AU8HgeiqODx1F8KLYoidruBYSTxePZ3juq2ZsTjApGITjKpoOsGgmBDksxUpjZbArdb\\nrm3ktXr3wFz5AG435Oe7kKQU3br5gHjtdYDL5UCWUwiCec3RKLVCj60jHE6gKDJffVVKZaWd//xn\\nD3v36lRWChhGlFisnI0bY3z5pYf//ncbhhHD5zO3p1RUhAmH7ezenUJRFDweD6EQuFwx3G4H8bhC\\n9+4+oILi4l643QmOOSZEIKBTWCiSn+9CllVkWUJVdcDsDPfoEUQQqigsdKZX5TidOi6XOSA/8FmV\\nllaiKDLhcDLry/FkGWpqIqRSUFBgJ5ncQ1GRh+7dvfj9USCBJPnZvHk71dUeSkuricercDgciKIN\\nny+Jx5MiEACnU6NPHyd2+3f06FHAt9/WsHNnih07klRUhInFkthsEsGgg1BIw++3UV2dAPIoKyvH\\n6TTw+Xz06GEnEAjTo4e5VL6uzrrdZqaXmhqNeFwgHrf2AFs0H6/XgSAk8XqljEEMMxNHjHDYyd69\\nmQfdsVicqqoU4XCSRCKR8dzu3b0kk3spLGxcaBTMLRBlZSrxuJvy8uqMNgsK/Hg8CYqKRNzuxgMp\\nZkA8RVnZdzidmbWQBg0axIABFeTn72DEiBMynmsGFnVUVct4nt1uJz9fQpLCFBVlHmBFozFqajxU\\nVwtUVlY2Ub6paaTrSpMDCk3Ta7NwZTyNaDROLGYjHE41mOo5P18DIkCYvn17UVZWiSSFKCvbW/s+\\n3F9A3ao6c5uoSijkq03VbWv1NixzC6RAOKyiqmqrbFgcnnTr1g2oAnaTl3fkvB/bs59krgC1EYmY\\nW1gPxO12EAza8HgUAgGnFcToAvzud+bAfcwY8/dBg+DWW83tFXXVxjDg7bfh+efN7RoHUlUFCxbA\\no4/uX73g88GqVfCvf8E//gHN2AmfNX74Q3jpJahupKugaabvN9+8/5jNZq4wWbasY3zs6hzRKzJ8\\nPg+JhINksgq73UzLWVFRU9uBMTAMG8lkAlU1lZFl2Yks2+nRw42iODAHwQaqanbAUqkaDEPE57MT\\nCHioqYmRTGpIkoDf76lV2zd1I/x+V622gIGua8iy+Q0URRG/3127ZSKCrqtEIglSKR2Px0ko5MHl\\nMr/diUSSaDSFosSIRGykUvEDsqKY5zidjnTUWlUlNC1GRUUEWRbSKv5NKcQLgnmuuac4Bdha9YKo\\nqYmhKDqapiCKMpJkIxKJoesKVVUR7HbQNBu6bsMwYhiGu1ZPA/btqyQS0YnHq0gmvQhCCpvNl97O\\nIwgSDkcYUZTw+WT69CmqXTKsYLNpiKKAKO6/Vl0X0HUzOGTeJyd9+tQPYnm99Qcedc9KkgR0XWuT\\nOJy5dcVcZeL3uxoduImija+//obS0nKGD+9Nnz6FyLKTioowVVUJdu8uR5Yj7NtXSXV1DK9XJZGQ\\nqK7ejaom6dOnBw6HQDKpI0k2evfuRt++HlRVorJyD4mEgc9nx+8PkJfnw26XcLmcuFzmvYhGk0Sj\\nGj6fk2RSI5WKUlAQolu3/SpEkiTh95tNiTkQMWrvj602sBEjlTLLsWYojyyaW8+hfj3KhCAI7N1b\\nRiRSycCBmdsut9tBfr4N0JpcPWUYdXo+DYscH1i+rqdIJmNIUuby7XY7PXs2T7Hrm2++YdeufPLz\\nM4ttmu8ac5WfKGZuhBwOGcMQEITMW3UA8vKCZNhRk8YMcCuA2ox7qpJI2HC5MgdSbDYbbrcDEJsU\\nZDPbKB1BoMFggyCEa/0z01t37x7EbpcJhaRGtwge+A5s6yq7ugkQm63xrTwWRybmwNvMzOZ0Zt5C\\ndrgQjyeIxVScTrFV2YgOxuxXaICR7u/WIYoieXmBNpdh0TGUlZl6GB99VP/4/Pnwxz/Cr38N48eb\\nv8fj5uqKu+82/3bqqea5d91lrr448cT6Nmw26N948q0O4+ijYdo089+CBWbQ5sDX0GuvQX6+uQrl\\nQC6/3FyVsnAhlhB1ExzRgYyjjupOKKRSWdkNuz1IIlFDLKYjSWYnyW53YBgCPp+TmpokHo8D0PD5\\nbEhSIC0WGY+70DSdWCyK1+skHk9ht9vxeJwIQhy73dyGkUqp6Lo5kEulVJxOB4GAC03TkOX6HSxF\\nUQEH0WiScBicTh82WwK/f7+wZDyeQpY9RCJJvF4HqZSAzSbWDhbrd6B8PhfxeJxkUkTTZBIJFbdb\\na9Gsk5lBwxycNqezl0qlsNnMmS1TrBJk2QPo+Hw2+vfvTWVlAlU9Cq/XTzxupp61212cfLKBYWj4\\nfAOw271s376bvLzeJJMxfD4bDocHQRBql0SLgITP58HhcOHxmC+3aDRZm+0jgdPpQpIEEokEsizj\\n9cqkUqkGl0anUikEQag36DKzcjhJJpMEg3kkk0kcDmerO6qpVApdt9fqYKRwuRoe4CmKhiTl0atX\\nX6LRPfTtK+Fyudm1K0kyGULXk2hagiFDhlJREUGSEgiCm/LyGiSpkGDQQ1XVHnr2PI5YrILu3QO4\\n3R4gwXHH9cBmsxEKSWnxV/O+RZEkU4g1P9+L328GjRTFjq6bdbfxwItIIOBI1xFzO4+AJDmIx+Pp\\nQIammUvdJanpAZZF16W59Ryo3ZZn1q1MgVJd1/H7/QQCTmy2zDPehYWF2O3ViKLYpECxpgkUFRWS\\nTFZkXJIsyzJ9+oTQNIP8/FBGm82luroaSerN0KGnAx9kPHfv3r1Eo/nYbDLbt+9mwIABjZ7rcjlQ\\n1ZraYG9TKyKa950sKurJ8OExZFmioAlZdVl2c8wxQVQ1nPGeHtxuZCIvL4goViOKDQdGvd7jgf6A\\nE1WNM2hQPoZh4HQ608JhdYKe2UgH7fG4sNtTCILc7vZVVW32+9ei8xGPx4FBwNH4/TUt/nxz28jO\\nQiqVIhxO4HD4qKkJ43a3vs9Uh91uJxDY37e06JoYBvzkJzBzphmgOBBJgv/3/+Cqq2DpUvO8a681\\nB/R/+hNMnmyu5AAzqLF5c8f73xIee8zU77juOvjySxg1CmbPhpEj4ZZbzK0lB38tBg82Axz/+IeZ\\nwcSicY7oVsBcyixis7nYs6caXU8Qi0nIsjkzaLOZ0fOamgSKEmPfvmq8XnA4zIivKRYmkkzG0HUN\\nVVWorKyhoMDsZESjCZJJkWQySShk2hUEc5ZIll31bByM3S6RSCSw2w2SyQjJZJxu3fLqddxcLplo\\nNIrPZz5Gh0PGDGDo2O31AyORSBxFEQmHq0il7DgcBvn5LY+ON/fFoSgK4bA5yAgEzI6X3Q6KEsXj\\ncWC325GkOCBjtxu43SKybGfnzjDxeJRu3dz4fCFUNYqiRPD5JPbu3UUyWYmmFSDLCj17BmrtKKiq\\njihCIqHjdpstQjQapbRURdOqCQRCiCLouhtRNNC0JHa7G0jWmyWIxxNEowY2m04wuF/Y1MwgkkDX\\nJaqqKnE4fChKglCodWvWJMmsC4YBdnvjz6GwMET37qXs2vUNRUV+JMlFKhWjqEhm48avqKzUCARs\\nFBbGEQSxNuizi6oqHUEop7LShqra2bHjM4YO7YGmKcRi5gxtJBLF73fg85lbROLxON9+G6GyMoos\\nmzPPgYCXvDwHHo85KID9dTfTtdUhiiKynCSViqW3KpnbdBJomojTmTpk5YvF4UNz6zmYWwYSCQFB\\nSBIKeRrt8JoZJdTadqJpUdlAoHkzdHl5LsrLKwiFMmu7mPU3iaqKhEJRfL62119TgHkHn38eY8SI\\nzNv2NE3jm28+J5WSUNWBGc8tL6+mslJAlmvo00fOqFHR/O+kSiymIIpNL40vLPRSXh4hFLI3+e5o\\n7rslHk+QStlRVR2XqyEtk2+B7sDX9O8/7JAtPc2tZ20hG4EGTdOork5iGAIej5ZeNWfRdTj66KMx\\nA5UaorirxZ8Ph2OkUqZIbWv7Hh1FXV8qkVCoqtqDy+UiFku0y6oMK5DXNamoMLOH7NkDr78O//mP\\nqW/REMccA++9d+jxyZPNbSY//KEZDHnzTXPA35mx282VJHffDbEYvPWWqQkydy7cey+MHdvw5+q2\\nl1iBjMwc0YEMl8uJ02ngdIq43TYUJV675UFClhUMwxTCFAQH4bBGIBDAMExdBMMwiEYTCIKNYNBT\\nO9skAxK6HiMcjpFMJpEkH7puCmyKonjIy2f37jLC4Tg9e+bVmzGUJIm8PC+plANZ9mCzCeltEIqi\\nkEiouFwyeXkeDMMgEokjy/ZGOzeaZiCKEoIgk5/vB7Qm9y2qqko8riDLYouzHphpW8X0vQLw+dz1\\nhEYNw4bdLiFJAqGQB01zIsu+2k51FZFIkkDA3KYjCJCfb2fHDoXdu2twu21pW4GAB03TAAGbzU4y\\nWcXOnfuIxxOEQoXE4xAK+TAMnUgkjiiqSBI4HCLhcCWVlVHy8ty43e5afRERXTeIRGK1GiLO2uuw\\nIYoS0WgCVXVgt6sZMwFkoqG60BAOh4PTTju+dq+7DUFwEo9X4fO5GTxYpXt3D/F4FYMH+xFFH6Wl\\ne6ipSSBJArFYJRUVcSTJT2GhiMfjZs+efXg8brxeP8GgH693f/pKTdPRdRHDkGqFawVAqK07jfsb\\njydQVR2320EkEiUSUcjP96ZnQP1+T1qgq6YmhsMhoWkgCGLtc7M4XGluPQezjRIEEcM4VJD4QGw2\\nG6Jobu84eOVZW/D5PAiChNudefuTYRh4vT4EwYHNdqhGQ2tQFIWTTx5J794B+vbNPFPrcDgYNuwU\\nJEkmQxIWACKRBPG4nUQiWU/I+mAMw2Dbtp1UVaXo3z+UMZAhCHaOOeboQ4SrG0IUbShKAru9aWHD\\nA9uRTCsZ9rfRNChgGgr1BfxI0lGEQoempm1uPWsMTdOIxZJIklDvfdtc/1uLmdVLqL12q93sing8\\nHiSpB6rqIxTq1eLP1/XjdL31fY+Oou576nS6sdtVHA5PbZtt0pb+pUXXIhYzt4k88QQMHAg9ekBx\\nMTz5JGSQjmqU4cNh48b297MjcLvh0kvNf01x+eXmqo3HHms6feuRzGEbyLj55pspKSlh+PDhPPbY\\nY42eZ7PZcLkcQBKv14wUp1IpkkmpNqgRw27X8HqDtarzjtqUq2b2BjNftbmVxOeTUBSVeFxAVWUg\\nhd1uphZtqMOnKAo7dkRwOLrz5ZelDBvWULpSGa9Xr10a66jVHFAQRRc1NXHy8mRiMVPHw8yokWow\\nWm1qHKTo0SOAoqjIstjkDFg0mkTXHbUpTxtX8W8IM3tKMn0NdcuKD37xSpJYu1JCrxWOTKKqOori\\nxAxM2GoHw2Y0XxDAZvOQSCTTQmp1L3SfTyaVUtm3T8FuL0RVEwQCUbp18yPLMoqioGmmAJTbrSMI\\nCcJhFVnOY9euKvr3d+NyOdD1OJqmoqpOVBUkScHhcOD321GUFD6fG10XOmRpp1nPzPvncmloWgJR\\ntJNMmntBk8l9FBT4EUU3EMcw7IiiD1WtxOFwcdRRXhKJBMccEySZNEgkXCQSGg5HikDAhssl1O75\\n13G7XXTrphII2NPZb2TZDOQcSF1gymaz1WbTMbDZZMLhKPv2JZDlILt2VdCv3/58V5qmEY0aCIKM\\nzabi98u126usWUULE6/XSSKhNNpe1qEoCpWVKQwjwO7dVfX0WhriwPqaiZqaJLouU1OjkJcnN3q+\\n0+mkWzcFRUmRl9c+mQcEQaB79wCqaiMUymyzb9++DBiwB1HUGDhwcMZzfT4XiUS8VpC38fY7Ho+z\\ne7eKIBTw7bdlHH1044Msr9dNXl44HejPxJdf7kMUu/O//+3l5JMDGbOm1LUjsVgy4yqXujZaksQG\\n33VebwgIo6pJPB7PIQO+5tazxojFkqiqnWTSFPmu2zoZjeqIop1IJEEg0P7pp+v6Apqm1fZZLLoa\\ndru9VsDQwGZr+QjO3N6aSm9X7szU9avdbhmbzdxmemDgry39S4uugWGYGTtuvhlOOw22boWePXPt\\nVdehXz8za8nixS1LR3ukcVgGMj755BOi0Shr167lhhtuYMOGDYwYMaLR881sIfuXu9ntcm0KwBRe\\nrzMdLXY4TIHNcFhB1xVsNjs2GwiCVPs5c89uVVUZsZiCzwceT+PLmkVRxOGAZLKC/PzGl8odHK2W\\nJBuplILdXicQas5OCoLRaMesLud2MplEUUDXNRyOzBF9U1MihSC0PIWVGSByous6VVVRdN2G32+v\\n1/E0hejMTmw4nMRmSxIMunE4zEwvui5gt5vlKkoKRTH3zoH54pMkKS0mqOtmIMPjceF2y0SjEUTR\\nwOHwYBjmPTQzlSRryzRnE2OxGpJJjbw8c7ZSVVUUxVydAErtMnZzhlaW5dqgjEEyaUOWbVnvTIii\\ngGGo2GxGbSYSkfLy3ZSVVeDxGAQCPqqrUxhGuHZViYjXC5GIjKrG6dWrCL/fgccjsm1bJZWVVTgc\\nZuDD5/MQDidqM5OALEsEgz5CocbrrKqq6fPrVMEFQUfTUrhcErIMqVQkvd2pjrrzdD2FJIm135Xs\\n3TeLrocoNk8Mzm6343DY0HUFvz/z+alUipoaBTAIBDKLjVZUVFJerhMIQH5+5lUkwWD7pk6UJIl4\\nPE4q5UDTMut+2Gw2Bg7sB4hNDmgTiQSRSApVTXDUUY2vv3U6nbjdpphvKJT5nnq9bvr27Q7ozdgu\\nolFWVorPp2S892aQu659yPyuqWujVVXF4Tj03dStm4Tf70OSEqiqQGVlDL/fkfa1ufWs8WsSSCaV\\neu9FM52r6b/Tmb0BmTVz3bXxer34fAKKIhAMtnw1V10/ritwcL/64BnltvQvLToHqRT85S+mjsWG\\nDaYgp99vpkLt1g3+9z8zmPHss6Zop0XLufNOuOwymDqVZolxH4kclq3HunXrmDhxIgATJkzgo4Ml\\ncZtAFEWCQRcbN350SMchmVSRJBc2mwOfTyIYdNZ7sZgrJ1wUFAQO0amo473ajV+iKDJkSB8GDw7Q\\nv3+fZvvn97sJBuX0rJXT6SAYtPPZZ+uajGonkxqi6ERVhSaX9Xs8LgIBmU8//VerB+yapqFpEoLg\\nqBUw3c/69esIBGTcbjuC4MQw5LQIWzDoJhjcv1UmkVCRZTdud4jBg4s4/vjeiKJYK1BXZ1/jvffe\\no2fPAo4+2k1hYRBJcqWvVZLM5+X1ithsDkTRid/v55hjCsjLM0X7FEVDEByIogu/XyYUciLLcvqZ\\nAXi9LoJBGb+/7Xvj32toE+ABfzOFrezpemaz2ZBliby8Qmw2D4LgwOVy4/EEsdns9OuXz6BB3Tjl\\nlCGMHFnMiBE9OfHEowgEQgwefDzx+DcMGzaA7t0LaoXjZBTF7FjV3f9MmGkc7YA9vbQ8EDC/Kx6P\\nmz59Cujd20337vVnyc1n6iIYtLeoM57p/rSWbNjsanaz5WtHlCeKIsXFvSkv30S/fpmXZ9fVV8OQ\\nm7GNSaJbtx6sX1+ScRtGa2jq+lVVpWfPoznttGEUFHTLeK4syxx1VCHffvs5DkfmFU2GIVJQ0A1Z\\n9ma8fkmSOO2040gmv+CEExoXDwVwu90cfbSfo48ONrmiqlevbpxwQnd27dqe8Z4e2D40pf1Q10av\\nXfthg9c0dOipjB49nCFDCnA6vYCdVKr90qC6XE6CQTvBoDs9AKt7ZwUCctr/jv6OHUwuyz+Srz1T\\n+cFgkNNOG84JJzgYMWJcxzrVBB19z+r6l4GAu8H+Za6fYUMc7j4119bmzfDTn0Lv3vDII3DhhbBm\\njXn8rbfgwQfhiivMrSQbN7YsiJHre5zr8g/2YdSo/RlM1qwxt+k0hqrCu++aW3bWr9+fvrYt5eeK\\nlvhwWAYyqqqq8Pl8gCn0VlVV1WIbgiDwwQeHqse73XZ0PYbTSXqG/EDMVG4Spohkw1POBz6g5qjp\\nH0ydWvOBjb8kSaxdu7bJz7rddgwjjsNhNCuy31y7mT5vt2tAAqez/v1477330hoUkECW96fzEwSh\\nnn8ejx1VjZKf78Tnk/F6pfTshN2u19o3Aw6CIOB0OvF6neh6rN61iqKI0+nE6QTDiFNY6EWSNLxe\\nc3DtdMpAArtdr/d8D3xmDd3/1tJUIAPMe3hgPcvP9yKKEUIhgUDAjs8HdruKx2NmH+nRowCPJ0VB\\ngUx+fj4ej4dQyIHdHmXHjq24XAI+nx2Hw4Esq7hcBi6XUe/+N4bdLiNJqXpbmA78rtTd+4Y4+Jk2\\nh640iO9KdrtyIAPMVRnr1q1rxnkygpBsVt0uKPCgaRVs2fKvdp8hbOr6JUni2GN9QCmDBxc1ea7H\\nI/Cvf32A3d7UNXmx2arIz5eaTH3sdDrZsGFDxnPqqFt92BR+vxOn08aGDR81eU+b2z7UtdEff/x+\\ng+eff/5wBgyoRJb/w5AhxyEIySbvU0sxRWzrX8/B/ue6M2gFMjpf+QMGDGDUKCe6vp4pUxpfJZwL\\ncnHPGvoe1ZHrZ9gQh7tPDdkyDPj6a1iyBObMMTUuzj8fZBnefx8++MAU3uzf31yFcdxxZirRSy81\\n/2/pqzTX9zjX5Tfkw8MPm1lbfvYzKCgwU8/ecQesWgXffAN/+5spHNqzJ/ziF/DZZzBjBpxwAvzf\\n/0E43Lbyc0FLfOgaa9RaSCAQIFz75KqrqwkGDxX8WrBgQfrnMWPGMGbMmGbZlmWZUChzp8jlctJA\\nVs9OgSRJHap2XSf2mInmCALa7Xby8hruODe2nzrTs2pMzK6j709rcLlc9Ou3v4IFAvX9dTgc9O1b\\nf1Y3GPQRDPrwej3k5/vSx5t6NgdjrsBo//3fFhbZwFxd17zvczBoCuDmKhvEgAF9yZBJNY0gCPj9\\nHpxOe5Mr8NxuN/365S4rkMPhqA2Ytl9Xo66Ndjga1jEZMmQIv/3tEBYs2EXPnpmDQhYWHYnX6+Wu\\nu65B13dy0kkn5dodC4tD0DRzZcUHH5ipPz/4wEwNeuaZZmDiRz+CoUNbHqCwaD02mxnIuPZaSCTg\\n44/NIMbdd8O330KfPnDBBebxY44xP2MYsHatuSrmrrtg2jQYPdoMOBUWmplevF7Tdk0NfPWVuRXo\\nf/8ztwtJEgwaZD7r/v3rtvVnF0Ux61VLyzosAxmjRo1i0aJFTJ06ldWrV/PDH/7wkHMODGRYWFhY\\nWFhYWFhYWFgcqRx/PIiiGbi44AJzm0jfvuaA1yL3OJ0wZoz5LxM2mxm4GD0aSkvNFTWvvWYGLMrL\\nYd8+apMZmEGPY46BY4+FAQPMIEckAi+9BFu2mJ/v3t3U6Dj4X34+BINmnakrt66uHPzzgccUxfTl\\nv//dH0CpqYHbboNf/apl98RmNJWDs4vyk5/8hE8++YRhw4bxu9/9rt7fxowZw/vvv58jzywsGicQ\\nCFBdXZ1rNywsDsGqmxadGat+WnRWrLpp0Zmx6qdFZ2X06NFNbjM5bAMZFhYWFhYWFhYWFhYWFhYW\\nhx/WLicLCwsLCwsLCwsLCwsLC4sugxXIsLCwsLCwsLCwsLCwsLCw6DJYgQwLCwsLCwsLCwsLCwsL\\nC4suw2GZtcTCoquxYcMGPvroI6qqqggGg4waNYoRIzpXnnkLi+ayZcsWJEli0KBB6WMff/wxp512\\nWruV8cQTT3DjjTe2+vOlpaX07NkTXddZuXIlX3zxBccccwyXXnopUitzjSmKwttvv01BQQGjRo3i\\npZdeIhwOM3369AbTgFu0D1b7aZFrampqqKqqIhQK4fXWT/ls1U+LzoZVJy0OFyyxz1pUVeWNN944\\n5It98cUXt7pTmw2blt3Dz+7atWtRVZUJEyak1aNXr16NJEmHZNxpDZk6WJ3JZleym0ql+Pe//522\\nOWjQoDbVja5qtyHmz5/P3r17kWWZsrIynn/+eYqKihg7dix///vfW2XzrLPOwmazceDrauvWrQwZ\\nMoS1a9e2yua4ceNYs2YNP/7xj3G73YwbN45PP/2UkpISli9f3iqbF198MSNHjqSqqoqSkhLOP/98\\n8vPzWbZsGe+8806rbEJ22iRd13nrrbeQJImJEyciCOYCzTfeeIOLL7641b4ezF133cW9997b6s9/\\n8sknDB8+nFgsxqJFi9IBp+uvv55gMMhPfvITFEXJWvvZFJs3b+bOO++kqqoKwzAQBAG/3899991H\\ncXFx1su3yC2rV6/m/vvvx+fzEQgECIfDhMNhfvGLXzBhwoSc18+D+e6773jggQfYunUrmqYhiiIn\\nnHACt99+O0cddVSH+2P51PF0hjqZrX52V/Ih1+V3Bh9qampYtGjRIeVfd911+Hy+ZtmwAhm1zJgx\\ng+LiYiZMmIDf7yccDrNq1So2bdrESy+91GlsWnYPP7sLFy5k7969h5x/9tlnt3qQBk13sDqLza5m\\nd+nSpTz77LOceOKJaZufffYZV199NVdeeWWrfe1qdhvjrLPO4oMPPgBg06ZN3HTTTSxcuJDbbrut\\n1YGMRx99lI0bNzJr1izGjh0LwHnnncdf//rXVvs5YcIEVq1alf6/jrYEXA787JAhQ9iyZUubbUJ2\\n2qTp06fTr18/ZFnm3Xff5dlnn2XQoEFt8rVPnz706dMHW13SeNov4HTllVdy+umnpwNOS5Ys4a23\\n3mq0nWxr+9lczjzzTJYvX07Pnj3Tx0pLS5k2bVr6e5BNch1IOdLLP+OMM/jb3/6Gx+NJH4tGo5xz\\nzjl8+OGHOa+fBzNu3DgefPBBRo4cmT72r3/9i5///OesXr26w/2xfGoe7RlY6Qx1Mlv97K7kQ67L\\n7ww+TJ48mZkzZx5S/tKlS/nTn/7UPCOGhWEYhnHmmWe26HiubFp2Dz+7PXv2NObMmWO8+uqrxttv\\nv20sX77cuO6664x58+a1qbzTTz/diEQi9Y5FIhFj1KhRncpmV7N7xhlnGJqm1Tumqqpx+umnt9pm\\nV7TbGKeffrqRTCbTv5eXlxvnnXeeUVhY2Ca7iUTCeOKJJ4zLLrvMeOONN4xzzz23TfaWLFliG4oH\\nqQAAIABJREFUXH311cbs2bON6dOnG4sWLTJuvPFG46c//WmrbU6ZMsW47777jFtuucUYO3assXDh\\nQuO5554zzjnnnDb5mo026eyzz07//N133xnnnHOO8cYbbxhjxoxptc3XXnvN+MEPfmA8//zzhqIo\\nhmEYxqRJk1ptzzAMY9y4cYamacbEiRMNXdfTx+v8/8lPfpKV9rO5nHHGGcbOnTvrHdu5c6dxxhln\\ndFj53333Xb1j3333XZvfV1b5zWPs2LHGhx9+WO/YRx99ZIwbN84wjNzXz4MZNWpUVt61bcHyqWnG\\njh1rrFu3rt6xdevWpetZS+gMdTJb/eyu5EOuy+8MPpx++umH9E81TWtR/9TSyKhlypQpXHDBBYwZ\\nMyYdFXr//feZPHlyp7Jp2e04u2PHjsXn82Xd7rx585gwYQLr1q3jyy+/JBAIcN111zFs2LA2ledw\\nONi0aROjRo1KH9u8eTMul6tT2exqdkOhEK+88grnnHMOfr8/vSwzLy+vTb52NbuN8dvf/pbKykq6\\ndesGQF5eHm+++Savvvpqm+w6HA5uuOEG5syZw9KlSznppJPaZO/KK69k/PjxvPPOO+zZswdN05gz\\nZw4nnnhiq20uX748rZHx8MMPs3TpUsLhMK+88kqbfM1GW2cYBjU1Nfh8Pnr27Mmf/vQnrr32WkpK\\nSlpt85JLLuGSSy7hrbfeYubMmYwaNQpFUVptD+D2229n2rRpBINBRo8ezZlnnskXX3zB9773PcBc\\nrfPJJ5+0e/vZXP7whz9w0003UVlZia7r2Gw28vLyeOqppzqkfKDelqu63w8+ZpWfHV566SUefPBB\\nfvGLX6BpGoIgUFxczIsvvgjkvn4ezP3338/kyZNxuVzptiQej3PfffflxB/Lp+aRSCQ44YQT6h07\\n4YQTiMfjLbbVGepktvrvXcmHXJd/oA/tPd5pLjfccANjxoxh6NCh6XuwZcsW5s6d22wb1taSA1i7\\ndi1bt24lGAwSCAQYMWIEX3/9dZsE6tavX8/69esJhUJ4PB6qqqqYPn06oii2ydeysjI2bNhAVVUV\\ngUCADRs2cNddd7XJJsDevXspKSlhw4YN9O/fnwEDBtRbWtcaSktLkSSJDRs2UF1dzbZt2+jduzeX\\nX345siy32u7KlSsZPnw4W7ZsSd+HESNGUFRU1CZ/FUVh2bJl7NixgwEDBpBKpdi+fTs//vGP2yzY\\nt3bt2rS/wWCQCy+8kD59+rTJZmOUlpby4IMPsmXLlnodrNtuu41evXp1GptdzW5NTQ3PPPMM69at\\nS9e7UaNGcc011zR7T9/hYNeiY9m7d2+6Da1r67Zv397q9nnbtm2EQqFD2rS1a9dy9tlnt4fLrFmz\\nhi1btjBq1ChOOeWUVtuJx+N89NFH7Nmzh2AwyIgRI9i2bVub302HA1u2bOGuu+46JJByzz33MHTo\\nUKt8iwaJx+Pp94Hb7c61O4DlUybWrFnD/ffff0hg5Re/+AXjx4/PmV9tIRtjrpaSrbFUc8jW2Kgl\\nZHO801xSqRT/+9//0s/guOOOa5E+hxXIqCUbAnVz584lmUwSj8dxOBz4/X78fj87d+5k8eLFrfY1\\nG8J3AJMmTeLtt9/mscceY9WqVVx44YX885//5KijjuKBBx5otd1siOoB9OzZkz59+tCtWzcuueQS\\npkyZQigUarW9OuoE+yorKykpKeGCCy5oF8G+n/3sZ8TjcU466STWrFmD0+lEFEXOOOOMrGgVWGSX\\nOgHRYDDYrgGBrmbXIvvoug7sn3mua/8nTZrEu+++2yabB5Itm+eee249HZLW2GzPa29PDmdRQIum\\naUijIxAIcO+993ZKsdf2ENezfMrd+7OzBFbaSjbGXC0lW2Op5pKtsVFLyNZ4p7m0h9iotbWklvXr\\n19cTqJs6dSoLFy5sk82tW7emvwxDhw5l8+bNAIwePbpNdi+55JJ2F74DSCaTALz++uv8/e9/RxRF\\nrr/+es4444w22a1Tw//888/TndmJEyemfW8tAwcO5O9//ztff/01r7/+Ot/73vew2+1cfPHF3HDD\\nDa22W11dzR133AGYgn233HILAEuWLGmTv+vXr2fNmjUAXHXVVWmRwfHjx2clkJGNDla2Om1dyW5X\\nEibNpl2LjsPj8TQ4S7Vx40bLZo6ZOXNmg6KAs2bN6hBRwFwHUo708ufOnZtTsdeWcsUVVzBz5kyu\\nuuqqeuJ6V1xxRfPF9SyfOtyfzhpYaS3ZGHO1lGyNpZpLtsZGLSFb453mMnv2bIqLi5k+fXq979ns\\n2bObLzbaKnWOw5BsCNQdKFaycuXK9M+jR49utc062lv4zjAMo6ioyJgxY4bRq1cvIxaLpY+ffPLJ\\nbbKbDVE9wzAaFKXbtWuXsWjRojbZzZZg37Rp04wHH3zQ+Mtf/mLcdtttxk033WQYRsPX0R5kQwQt\\nW8JqXcluVxImzaZdi45j2LBhRmVl5SHHx48fb9nMMbkWBWxPEUCr/JaTa7HXltIe4nrtjeVT01x4\\n4YXGK6+8YpSXlxupVMooLy83XnnlFePCCy/MiT9tJVui4C0lG2Op5pKtsVFLyNZ4p7m0h9iotSKj\\nlmwI1D399NOoqookSUyZMgUw9yPNnz+/zf62t/AdwLp169I/12l4RCKRNosbZUNUD0wRuIPp3r07\\n1157bZvsZkuw76WXXmLFihVs2bKF008/PS2m8/LLL7fJbiaMLIigZcNmV7LblYRJs2nXouP4y1/+\\n0uDzevvtty2bOSbXooDtKQJold9yOoPYa0toD3E9y6eOp6KigksvvTQ9i5+Xl8ell17K7373u5z4\\n01ayJQreUrIxlmou2RobtYRsjXeaS3sInloaGRYWhynZEEHLlrBaV7LblYRJs2nXwsJiP7nau55r\\nEcAjvfyuSFvF9SyfOp6XX36ZRYsWHRJYmTNnDjNmzMiJTxYW7UFbhcytQIaFhYWFhYWFRSvoLHvX\\ncy0CeKSWn2uNjpbSHuJ6lk+58akzBVYsLNqD9hDztgIZFhaHKdnoYGWr09aV7HYlYdJs2rWwsIDJ\\nkyczc+ZMJkyYUE+sbOnSpR0iCpjrQMqRXv64ceMaFHv9+c9/3iFiry1lxowZFBcXH1JfN23a1Hxx\\nPcunDvenMwZWLCzaisvlalTMu6KionlGWqzMYWFh0SXIhghatoTVupLdriRMmk27FhYWuRcFzLUI\\n4JFefq7FXltKe4jrtTeWT00zffp046GHHjJKSkqML7/80igpKTEeeughY/r06Tnxx8KiPWgPMW8r\\njGdhcZiSDRG0bAmrdTW7RhcRJs223cOJxYsXU1JSwuOPP95uNleuXMlxxx3H8ccfD8Ddd9/N2Wef\\nbe3dP4zItShgrkUAj/Tycy322lLaQ1zP8qnj+eabbw5ZCTJ8+HDOOuusnPhjYdEetIeYtxXIsLA4\\nTMlGBytbnbauZDdbKvVdza5F06xYsYLJkyenAxn33HNPjj2yaG+mT5/OZZddlrO967kOpBzp5Y8b\\nN45x48blXCOkudx6663MmjUrLa7Xu3dvZs2axfbt23Pq06mnnsrWrVvx+/1pn77++uuc+jRmzBjW\\nr1+P3++nR48edOvWjenTp+fEn84WWLGwaA969OjR4PGWvD8tjQyLJvne977Ht99+SyKRYN68ecyZ\\nM4fnnnuOhx9+mGAwSHFxMU6nk8cff5yysjLmzp3Ljh07AHjsscc4/fTTc3wFRzbZ6GBlq9PW1exa\\ndB1eeuklHn/8cRRF4dRTT+XJJ59kyZIlPPjggwSDQU488UQcDgePP/44s2fPZvLkyXz/+98HwOv1\\nEolEAHjooYd4+eWXEQSB888/n1//+tc888wzPPPMMyiKwoABA1i6dCmffvopkydPJhAIEAwG+eMf\\n/8i9996btrt69WpuvfVWVFXllFNO4amnnsJut9O3b19mz57Nn/70J1KpFK+++ioDBw485HoaO2/B\\nggX4fD5uueUWAIYMGcJbb72FrutMmjSJUaNG8eGHHzJixAhmzZrFPffcQ1lZGS+//DKnnHJKxz2Q\\nw4TOsHc91yKAR3L5udboaCntIa7X3syfP5+9e/ciyzJlZWU8//zzFBUVMXbsWP7+97/nxKe5c+eS\\nTCaJx+M4HA78fj9+v5+dO3eyePHinPhUVlbGhg0b0vV8w4YN3HXXXTnxxcKis2CtyLBokueff55Q\\nKEQ8HmfkyJFccMEF3H///Xz66ad4vV7GjRuXzr08b948br75Zs444wx27NjBpEmT+Pzzz3N8BUcm\\n2ehgZavT1pXsdiVh0mza7Up88cUXLF++nA8//BBRFLnxxhtZunQpCxYs4JNPPsHv9zN27FiGDx8O\\nmJ37A6n7/a9//Stvvvkm//rXv3A6nVRWVgLw/e9/nzlz5gBw55138txzz/GjH/2IKVOmMHnyZC65\\n5JK0HZvNRiKR4Ic//CFr1qxhwIABzJo1i6eeeop58+Zhs9koLCykpKSEp556ioULF/LMM88cck2N\\nndeY7wBfffUVr732GoMHD+aUU07hlVde4Z///Cdvvvkmv/71r1mxYkU73fEjh9mzZ1NcXMz06dPr\\niQLOnj27Q0QBVVVl5cqVOQukHOnlX3HFFcycOZOrrrqq3vO/4oorOkTstaV4PJ5GxfVyxfr16/ng\\ngw8A2LRpE1OnTmXhwoU58wdg69atrF27FoChQ4eyefNmAEaPHp0Tf84666x00OlAH1etWpX208Li\\nSMQKZFg0ye9+9zveeOMNAL799luWLl3KmDFjCAaDAEydOpX//ve/AKxatYovvvgi/dmamhpisZg1\\nC54DstHBylanrSvZnTlzZoMq9bNmzWqTSn1Xs9uVWL16NSUlJYwYMQIwV+h8+OGHjB07lvz8fACm\\nTZuWbscaY9WqVVx11VU4nU4AQqEQYGaG+eUvf0l1dTWRSIRJkyalP9OQPsl//vMf+vXrx4ABAwCY\\nNWsWTzzxBPPmzQNIBz6GDx/O66+/3qg/zT2vjn79+qU1Y0444QQmTJgAmKs2crm0vCuT673ruQ6k\\nHOnl51qjo6Ucf/zxrFixIt1/q6OuLcgFuq6jKAp2u53i4mJWrFjBjBkz2Lp1a8580jQt/fOvfvWr\\n9M8HB4o7iksuuYSNGzcya9Ysxo4dC8B5553HX//615z4Y2HRWbACGRYZee+991i9ejUff/wxTqeT\\nsWPHMmjQoHrBCsMw0o27YRisW7cOu92eK5ctaslGBytbnbauZLerCZNmy25XY9asWfz6179O/75y\\n5cp6g/8DAw6SJKWXYNd1soFDZsTqmD17Nm+++SZDhw5lyZIlvPfee+m/NdTxPfjYgW0ogMPhAEAU\\nRVRVBeDcc89l7969nHLKKTz99NONnneg72A+/4PtAgiCkG6nBUFIf96iZeR673quAylHevm51uho\\nKe0hrtfe/Pa3v6WyspJu3boB5nv6zTff5NVXX82ZT08//TSqqiJJElOmTAFAURTmz5+fE39uvvlm\\nkskkzz33HH/4wx+44oorjmjB7u3btzN58uT0ShmL/Ry8vfRwxwpkWGQkHA4TCoVwOp38+9//5uOP\\nPyYajfL+++9TVVWF1+vltdde48QTTwRg4sSJ/P73v+enP/0pAJ999ll624lFx5KNDla2Om1dyW5X\\nEibNpt2uxPjx47nooou4+eabKSwspKKigpNOOol58+ZRUVGBz+fj1VdfZdiwYYCpP1FSUsLUqVN5\\n8803SaVSAJxzzjnce++9TJ8+HZfLRWVlJaFQiEgkQvfu3UmlUrz00kv07t0bAJ/PRzgcrueLzWZj\\n4MCBbN++na+++or+/fuzdOnSJpcsv/POO8261r59+/LnP/8ZgE8++YRt27a16F5ZtIxcCxXmOpDS\\nWcofO3Zs+vvWkeXnWuy1pbSHuF57c+qppx5yTJIkfvCDH+TAG5ODg/8Adrs9HdTIBQ6HgxtuuIE5\\nc+awdOlSq2/dztQFrrKNruvpybVskKtVQ7mic7a0Fp2GSZMm8Yc//IHBgwczcOBARo0axVFHHcUd\\nd9zByJEjycvLY9CgQfj9fgB+//vfc+ONN3LiiSeiqiqjR4/mySefzPFVHJlko4OVrU5bV7KbLZX6\\nrma3K3H88cdz//33M3HiRHRdR5ZlnnjiCRYsWMCoUaMIBoPpIAbAnDlzuOiiizjppJOYNGkSXq8X\\nMFdFfPbZZ4wYMQK73Z7WC7rvvvs49dRTKSws5NRTT00Lg15++eXMmTOHxx9/vN7sosPh4IUXXmDq\\n1KmoqsrIkSO5/vrrgfqdkDpNjYZo7Lzvf//7vPjiiwwZMoRTTz21nlBoJv2MI63z0140JlQ4bdq0\\nDhEqvPXWW5k9e3ZaBLB3797E43Fuu+22rJcNZht7YBaM8vJypk2bxuWXX94h5c+bN4+ioiJ27NhB\\nr169cLlcjBw5kmuvvbZDys+1RofFkYcsy1x11VW5diPnaJrGtddey4cffkivXr1YuXIl//73v7n+\\n+uuJx+P079+f559/nmAwyJgxY3jkkUc4+eST2bdvH6eccgrbtm1j8eLFvP7660SjUXRdZ9myZVx2\\n2WXU1NSgqipPPfUUZ555Zr1yFy9ezIoVKwiHw3z33XfMmDEjLbrakKi4IAh4vV6uv/56Vq1axZNP\\nPplOgrB+/XoefPBBXnvtNVauXMkPfvADwuEwqqpywgkn8NVXX/HVV1/xox/9iLKyMtxuN8888wwD\\nBw7MmFyh7n3+zDPPsGLFCl5//fX0ltjDDStriUWriEajeDweVFXlkksu4eqrr+aiiy7KtVsWB5AN\\nNf1sKfR3JbtdSZg0m3YtLCxMEb4DhQpvuukmFi5cyG233dYhgYzGRACHDBnSISKA48aNY82aNfz4\\nxz/G7XYzbtw4Pv30U0pKSli+fHnWy7/44osZOXIklZWVlJSUcMEFF5Cfn8+yZcuavYqpLcyYMYPi\\n4mImTJhQT6Nj06ZNHaLRYWFxJLJ9+3aOPfZYSkpKKC4uZtq0aUyZMoWHH36Y//u//+Oss87i7rvv\\nJhwO8+ijjzJ27FgeeeQRhg8ffkgg484772Tz5s0Eg0EeeeQRkskkd9xxB4ZhEI1G0xMZdSxevJg7\\n7riDrVu34nK5OOWUU1i8eDFut5uf/exnrFixAlEUueGGGxg1ahQzZ85EEASWL1/OpZdeWs+WqqoM\\nHDiQr776ip/+9Kd88MEHPProo6RSKZ5++mlefvllxo8fz6JFixgwYADr1q3jjjvuYPXq1VxxxRXc\\neOONhyRXuOeee/B6vTgcDlavXs3y5cuRZbkjH0+HYoWLLVrFggULWLVqFYlEgnPPPdcKYnRCsiGC\\nli1hta5ktysJk2bTroWFRe6FCnMtAli3RPrzzz9n1apVgLnFtM6XbFNdXc0dd9wBmKK1dfvClyxZ\\n0iHl51qjw8LiSKVfv34UFxcDcPLJJ/PVV19RVVWV/u7NmjWLqVOnNmln4sSJafHbkSNHctVVV5FK\\npbj44ovT2+Yb+kyd2Pcll1zCP/7xD0RRPERUvHv37oCpY1WXzv1AJEmif//+/Pvf/2b9+vXMnz+f\\ntWvXomkaZ511FtFolA8//LDeddRpdjWUXCEajWIYBi+++CK9e/dm5cqViKLY5D3oyliBDItW8Zvf\\n/CbXLlg0QTY6WNnqtHUlu11JmDSbdi0sLHIvVJhrEcArr7ySa665ht69ezNjxgzOPvtsNm3alO7M\\nZxu/38/9999PVVUVRUVFPPLII4RCoQ6bgcy1RoiFxZHKgeLVoihSVVVV7++NCXgfKIAN1NtqW7fC\\n7s9//jOzZ89m/vz5+Hw+7rnnHgCeffbZjGLdB4uK1+F0OtPnTJo0iT179qSFu88++2zeeustZFlm\\n/PjxzJo1C13XWbhwIZqmEQqF+PTTTw+x2VhyBZvNxtChQ9m4cSPffvstffv2bfgGHiZYgQwLi8OU\\nbHSwstVpy7bd9hSC60rCpNm0a2Fh0TmECnMpAnjllVcyfvx43nnnHfbs2YOmacyZM6fRmcz2Zvny\\n5bz99tsUFBTw8MMPs3TpUsLhMK+88kqHlH/rrbfW0wipE3u10hm3jNLSUubNm8err77Kxo0bKS0t\\n5bzzzsv4mffee49HHnmkwZWFB2oiWBwZBAIB8vLy+Mc//sGZZ57J0qVLGTNmDGCKYG/YsIERI0bw\\nxz/+sVEbdVo711xzDclkkk8//ZTf/va3XHzxxelzNm/ezLvvvktlZSVOp5OVK1fywgsv4HK5DhEV\\nj0Qi9OnTp14ZB2cIOuuss5g5cyazZ8+moKCA8vJyysrK0oKz/fr1449//COXXnophmGwefNmiouL\\nD0musHHjxnS7O2zYMObOncuUKVN45513GhX5PRywNDIsLA5j1q5dy9atWwkGgwQCAUaMGMHXX3/N\\naaed1mqbZWVlaWG5QCDAhg0b0kJHraW0tBRJktKdwW3bttG7d28uv/zyNs2sKYrCsmXL2LFjBwMG\\nDCCVSrF9+3Z+/OMfp5cStoZUKpUVlfo6u5WVlQSDQY499th2mVlMpVJ8+eWXaY2Mzqyqb2FhYdFV\\nqJvlretK1+mVTJo0iXfffTeXrnVZFi9eTElJCY8//njG8zIFMg7URLA4/Ni+fTtTpkxh06ZNADzy\\nyCNEo1Euuugirr/+emKxGP379+eFF14gEAjwn//8h8suuwxRFLngggt4+eWX+frrr1myZAklJSX8\\n/ve/B+DFF1/kN7/5DbIs4/P5ePHFFzn66KPrlb1kyRLeeOMNqqur2blzJzNnzuTOO+8EzMDqAw88\\nkBYVf/LJJxk5cmR6Eqkh4vE4oVCIP//5z0yYMIHrrruOPXv28MYbb6Svde7cuezatYtUKsUPfvAD\\nfvnLX1JeXs6NN97IF198US+5wj333IPP52P+/Pn87W9/4/bbb2fVqlXk5eVl63HkFCuQYWFxmNKY\\nmv7YsWNbLUKXLWG5bAnGZUMILlvCpLqu89ZbbyFJEhMnTkxvBVm5cmW7a9Dcdddd3Hvvve1q08LC\\nwuJIw+VyNTgxsHHjRioqKnLgUW548cUXeeSRR7DZbBQXF3PZZZdx//33oygK+fn5vPzyyxQVFbFg\\nwYJ0JoZ9+/Zx2223cc0117B9+3YmT57MJ598Qv/+/UkkEvTq1Yuf//zn9OvXj3nz5pFIJHC5XLzw\\nwgscd9xxzQ5kLFu2jAceeADDMLjgggt48MEH0TSNq6++mpKSEmw2G1dffTXz5s3j97//PYsWLUKS\\nJAYPHsyyZctycDctOjPNDbRZdAzWlJyFxWHK+vXr66npT506lYULF7bJZraE5bIlGJcNIbhsCZPO\\nnDmTfv36Icsy999/P88++yyDBg3isccea1Mgo0+fPvTp06fevs6tW7fy3nvvdUhWAwsLC4vDleOP\\nP54VK1YcssJvwoQJOfKo49m6dSu/+tWv+Oijj8jLy6OyshKbzcbHH38MmLoCDz/8cLr/sWXLFj7+\\n+GMikQjDhg3jwgsvTNuSZZn77ruv3ix5TU0NH3zwAaIosmrVKu64446M2wMOpLS0lNtvv51PPvmE\\nYDDIxIkTWblyJb1796a0tJTNmzcDpGfLH3roIbZv344sy43OoFsc2WRKi27R8ViBDAuLw5RsqOln\\nS1guW4Jx2RCCy5Yw6c6dO3n55ZcBmDNnDrNnz+bGG29sk034/+3dX0zV9R/H8eeJHcgCEtQiWKvR\\nhhMGcgiaSXg4OMNAU3SypfFn1IjFOFBubuQgGLZZemG6lSgFWDlR5+bI/o0Ri61QwgaUlnNM2UJq\\ngcSfRJFOF/zO+YGiQgeUo6/HFRy+5/39nHNx+PLm8329R2aLHz58mGXLlvHSSy9hNBpv61QDEZG7\\n1bFjx5g1a9Z1j197H/zdrLa2luTkZMfWdR8fH1pbW0lOTqazs5MrV64QGBgIjPwRuGrVKjw8PPDw\\n8MBisXD8+PExmSo2m23MdUVPTw+pqamcPXsWg8HA0NDQhNZls9lobGwkNjaWOXPmALBhwwa+/fZb\\nCgoKaGtrw2q1kpiYyHPPPQdAWFgY69evZ/Xq1WNyEUTs0tLSSEtLu9PLkP+5704vQESmhz1N386e\\npu/stAp7sNwnn3xCV1fXlATLpaamUlxcTExMDCEhIY7AOGen4xw8eJDQ0FCSkpKoqalh7ty5DAwM\\nOBUEZw8Q3bZtG6WlpWzbto0VK1Y4HUxqs9no6+sDwN/fn+rqao4cOUJTU5NTddesWcP+/ft55JFH\\nSElJ4b333nOM7xIRkf/u0UcfHTM9we5eyiC69nZTgJycHKxWKy0tLZSWlnLp0qUbPt++I/NGCgoK\\nWLp0Ka2trVRXV183dQIgPj4ek8lEZmbmdWsbzb7O2bNn09zcTGxsLLt37+aVV14BRhpT2dnZnDx5\\nkqioKIaHh2+6NhG5s+6dT1qRe8x0p+kbjUYyMjKmpBZAQEDAlNaDkabL6NsypqKLPl0p9ZWVlWMu\\nmjw8PKisrOTll192csUjEhISSEhIoLa2FoPBQGNjI1FRUVNSW0RE7k1xcXEkJSXxxhtv4OvrS3d3\\nN729vfj7+wMjmQJ2NpuNo0ePkp+fT39/P3V1dbzzzjtjmhPe3t6Opj4wplZ5efm4axgv88pgMPD0\\n009jtVrp6upi9uzZHDhwwPG90WhkzZo1BAUFkZKSgs1mo729ndjYWKKjozlw4AADAwN4e3tPxdsk\\nItNAjQwRkUn4559/mDt3LvHx8cD//xu1YcMGp1Lq7cnY9hR8GLnoKykpcaru6HowMpbObDYTHx/v\\nyCMRERH5L4KDg9m8eTNmsxk3NzdMJhNFRUWsW7cOHx8f4uLiOH/+PIAjDNRisfDnn39SWFiIn58f\\n586dc+yesFgsbN26FZPJRH5+Pps2bSItLY0tW7aQmJg4ZpfFrbIK/Pz82Lp1KxaLBZvN5tg92dzc\\nTEZGhuP3oz0ANCUlhb/++gubzUZubq6aGCIznKaWiIhMwnSl1LtaXRERkckoLi7G09NVnLZ1AAAG\\nb0lEQVTTEbwtIuIM7cgQEZmE6Uqpd7W6IuI6Ojo6yM3N5dChQzQ3N9PR0cHzzz9/0+fcbLzlZDU1\\nNbFv3z6nM5rE9Wnig4hMFe3IEBGZhAsXLuDr63tdwNvVq1edCnhztboi4poqKipoampi165dNz1u\\nKhsZIiIiU01TS0REJmG6Uupdra6I3D779u1j4cKFhIeHk5qaymeffcaiRYuIiIhg2bJl/PHHHwAU\\nFRWRkpLC4sWLCQoKoqysDIBz584RGhrK0NAQhYWFVFVVYTKZOHjwII2NjSxevJiIiAiio6M5c+bM\\nLdfz+eefs2DBAiIjI7FarY6pTSdOnBi3Vl1dneOYoqIiMjIysFgsPPnkk7dsqIiIiIxHV7IiIiIi\\nM9TPP//M22+/zffff4+vry8XL17EYDDQ0NAAQFlZGe+++y7bt28H4KeffqKhoYH+/n5MJhMrVqxw\\n1DIajZSUlNDU1MTOnTsB6Ovro76+Hjc3N2pqanjzzTc5fPjwDdczODhIVlYW9fX1PP7446xfv95x\\nu8CCBQsmVOvMmTN888039Pb2Mn/+fF577TXc3Nym7D0TEZG7nxoZIiIiIjNUbW0tycnJ+Pr6AuDj\\n40NrayvJycl0dnZy5coVAgMDgZH8gVWrVuHh4YGHhwcWi4Xjx4+zcOFCRz2bzcbou4p7enpITU3l\\n7NmzGAwGhoaGbrqeX375hcDAQMekpRdffJE9e/ZMuJbBYCAxMRGj0cicOXN4+OGH+f333x0jNkVE\\nRCZCt5aIiIiIzFD2Ec+j5eTkYLVaaWlpobS0lEuXLt3w+ffdd/NLvYKCApYuXUprayvV1dUMDg5e\\nd0x8fDwmk4nMzMzrwhpHr20itQDc3d0dX7u5uXH16tWbrlFERORaamSIyIy2c+dOgoODSUlJueEx\\nnp6eTp+nsrKSCxcuOF1HRGQqxcXFcejQIce45O7ubnp7ex07GCoqKhzH2mw2jh49yuXLl+nq6qKu\\nro6oqKgx9by9venr63N8P7pWeXn5uGv46quv+PHHH9mzZw9BQUG0tbVx/vx5AKqqqhzNjYnUUsa8\\niIhMBTUyRGRG++CDD6ipqeHjjz++4TFTMc6toqKCjo4Op+uIiEyl4OBgNm/ejNlsJjw8nI0bN1JU\\nVMS6deuIjIxk3rx5js9Ag8FAWFgYFouFZ555hsLCQvz8/Bw/A7BYLJw6dcoR9rlp0yby8/OJiIhg\\neHh4zOfpeJ+ts2bN4v3332f58uVERkbi7e2Nt7c3wIRqGQwGjeAUERGnafyqiMxYWVlZlJeXM3/+\\nfNrb21m7di1tbW20t7eTl5dHTk4OAF5eXvT19ZGdnc3y5ctZuXIlSUlJ+Pr68uGHH/LRRx/R1tbG\\nli1bKCkp4dNPP2XevHk89thjPPXUUzzxxBOkp6cTEBDAAw88wHfffcf9999/h1+9iMjkFBcX4+np\\nycaNG6f1PAMDAzz44IMAZGdnExQURG5u7rSeU0REZDTtyBCRGWv37t34+/tTV1fH66+/zq+//srX\\nX3/NiRMnKC4uZnh4eMzxS5Ysob6+HoDffvuN06dPA1BfX4/ZbKaxsZEjR47Q0tLCF198wQ8//IDB\\nYGDt2rVERkayf/9+Tp48qSaGiLis27HbYe/evZhMJkJCQujt7eXVV1+d9nOKiIiMpqklIjLj2TeO\\n3Srp/tlnn2XHjh2cPn2akJAQenp66OzspKGhgV27dlFWVsbq1atxd3fH3d2dlStXjnseERFX9NZb\\nb92W8+Tl5ZGXl3dbziUiIjIeNTJExGXcKuk+ICCAnp4evvzyS5YsWUJ3dzdVVVV4eXnh6el5Xfr/\\ntY0L3bctIiIiIjLz6dYSEbmrLFq0iB07dmA2m4mJiWH79u3ExMQAEB0dTXV1NZcvX6a/v59jx445\\nnufl5UVvb++dWraIiIiIiEyQGhkiMqPdKkH/2sdjYmIYHh4mMDAQk8nExYsXHY2MyMhIXnjhBcLC\\nwkhISCA0NJSHHnoIgPT0dLKysoiIiGBwcHAaX5GIiIiIiDhDU0tE5J5iT9v/+++/MZvN7N27l/Dw\\n8Du9LBERERERmSBlZIjIPSUzM5NTp04xODhIenq6mhgiIiIiIi5GOzJERERERERExGUoI0NERERE\\nREREXIYaGSIiIiIiIiLiMtTIEBERERERERGXoUaGiIiIiIiIiLgMNTJERERERERExGX8C2KbJYEU\\ndnqOAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f845b60e310>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualizing Some Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We want to visualize the relationship between the number of years of education and age and income.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.scatter(\\n\",\n      \"    csv_data[csv_data['income']=='>50K']['education-num'],\\n\",\n      \"    csv_data[csv_data['income']=='>50K']['age'],\\n\",\n      \"    alpha=0.02,\\n\",\n      \"    label='>50K',\\n\",\n      \"    color='r'\\n\",\n      \"    )\\n\",\n      \"\\n\",\n      \"plt.scatter(\\n\",\n      \"    csv_data[csv_data['income']=='<=50K']['education-num'],\\n\",\n      \"    csv_data[csv_data['income']=='<=50K']['age'],\\n\",\n      \"    alpha=0.02,\\n\",\n      \"    label='<=50K',\\n\",\n      \"    color='b'\\n\",\n      \"    )\\n\",\n      \"\\n\",\n      \"plt.legend()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data['education-num_rnd'] = csv_data['education-num'] + np.random.uniform(-0.5,0.5, len(csv_data))\\n\",\n      \"plt.figure(figsize=(12,8))\\n\",\n      \"plt.scatter(\\n\",\n      \"    csv_data[csv_data['income']=='>50K']['education-num_rnd'],\\n\",\n      \"    csv_data[csv_data['income']=='>50K']['age'],\\n\",\n      \"    alpha=0.1,\\n\",\n      \"    label='>50K',\\n\",\n      \"    color='r'\\n\",\n      \"    )\\n\",\n      \"\\n\",\n      \"plt.scatter(\\n\",\n      \"    csv_data[csv_data['income']=='<=50K']['education-num_rnd'],\\n\",\n      \"    csv_data[csv_data['income']=='<=50K']['age'],\\n\",\n      \"    alpha=0.1,\\n\",\n      \"    label='<=50K',\\n\",\n      \"    color='b'\\n\",\n      \"    )\\n\",\n      \"\\n\",\n      \"plt.xlabel('education-num')\\n\",\n      \"plt.xticks(arange(17))\\n\",\n      \"plt.xlim(0,17)\\n\",\n      \"plt.ylabel('age')\\n\",\n      \"\\n\",\n      \"#plt.legend()\\n\",\n      \"plt.grid()\\n\",\n      \"plt.title(u'From Census Data of 1994 (education-num \\\\u00B10.5)')\\n\",\n      \"plt.show()\\n\",\n      \"csv_data = csv_data.drop('education-num_rnd', axis=1)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Processing Text Feature\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We have a lot of data that is not numerical. Most of machine learning algorithms use numerical data as features. We process text data according to what does the text represents. Commonly you will find:\\n\",\n      \"\\n\",\n      \"1. **None Organizable Categorical Text (City, Country, ...etc)**\\n\",\n      \"<br/>Process it using **Vectorizer**. This finds each value and makes it into a separate feature. For example if you have three values for city where users live (London, New York, Toronto) it will make three features each indicating if the user lives in that city with with 0=False and 1=True.\\n\",\n      \"\\n\",\n      \"2. **Organizable Categorical Text (Rating, Income Brackets, ...etc)**\\n\",\n      \"<br/>**Convert to a series of numbers** representing the value. For example (Excellence, Good, Average, Bad, Terrible) can be converted to (5,4,3,2,1).\\n\",\n      \"\\n\",\n      \"4. **Text Document**\\n\",\n      \"<br />Process it using **CountVectorizer**.\\n\",\n      \"\\n\",\n      \"5. **Special Values**\\n\",\n      \"<br /> Email, URL, Phone number, Usernames process using **Special Treatment**.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.feature_extraction import DictVectorizer\\n\",\n      \"vec = DictVectorizer()\\n\",\n      \"features = vec.fit_transform(csv_data\\\\\\n\",\n      \"                             .drop(\\\"capital-gain\\\", axis=1)\\\\\\n\",\n      \"                             .drop(\\\"capital-loss\\\", axis=1)\\\\\\n\",\n      \"                             .drop(\\\"income\\\", axis=1)\\\\\\n\",\n      \"                             .drop(\\\"fnlwgt\\\", axis=1).to_dict(outtype=\\\"records\\\")).toarray()\\n\",\n      \"results = csv_data[\\\"income\\\"] == \\\">50K\\\"\\n\",\n      \"\\n\",\n      \"# Wenow split the data set into a traing and testing\\n\",\n      \"X_train = features[1000:]\\n\",\n      \"Y_train = results[1000:]\\n\",\n      \"X_test = features[:1000]\\n\",\n      \"Y_test = results[:1000]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data.to_dict(outtype=\\\"records\\\")[8]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Train Classification Model using kNN Algorithm\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from  sklearn.neighbors import KNeighborsClassifier\\n\",\n      \"from sklearn.metrics import classification_report\\n\",\n      \"\\n\",\n      \"knn = KNeighborsClassifier().fit(X_train, Y_train)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Prediction\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"predictions = knn.predict(X_test)\\n\",\n      \"accuracy = predictions == Y_test\\n\",\n      \"print \\\"Accuracy: %s%%\\\" % (round(np.average(accuracy)* 100))\\n\",\n      \"\\n\",\n      \"print \\\"===============\\\\nSamsple Data\\\"\\n\",\n      \"for counter in range(10):\\n\",\n      \"    print \\\"Prediction: %s is %s\\\" % (predictions[counter], predictions[counter]==Y_test[counter])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Improving Accuracy\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"\\n\",\n      \"target_names = ['<=50K', '>50K']\\n\",\n      \"for kn in range(1,15):\\n\",\n      \"    for method in ['uniform', 'distance']:\\n\",\n      \"        knn = KNeighborsClassifier(n_neighbors=kn, weights=method).fit(X_train, Y_train)\\n\",\n      \"        predictions = knn.predict(X_test)\\n\",\n      \"        accuracy = predictions == Y_test\\n\",\n      \"        print \\\"Accuracy: %s%%, Neighbors: %s, Weights: %s\\\" % (round(np.average(accuracy)* 100), kn, method)\\n\",\n      \"        actual = pd.Series(Y_test).map({True: 1, False:0})\\n\",\n      \"        predictions = pd.Series(predictions).map({True: 1, False:0})\\n\",\n      \"        print(classification_report(actual.values, predictions.values, target_names=target_names))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Results Inspection\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"knn = KNeighborsClassifier(n_neighbors=11, weights='uniform').fit(X_train, Y_train)\\n\",\n      \"\\n\",\n      \"predictions = knn.predict(X_test)\\n\",\n      \"\\n\",\n      \"actual = pd.Series(Y_test).map({True: 1, False:0})\\n\",\n      \"predictions = pd.Series(predictions).map({True: 1, False:0})\\n\",\n      \"target_names = ['<=50K', '>50K']\\n\",\n      \"print(classification_report(actual.values, predictions.values, target_names=target_names))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data[\\\"prediction\\\"] = pd.Series(predictions)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Quick Prediction\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Select values for a person to predict if They make more or less than 50K\\n\",\n      \"person_features = {\\n\",\n      \" 'age': 35,\\n\",\n      \" 'education': 'Bachelors',\\n\",\n      \" 'education-num': 14,\\n\",\n      \" 'hours-per-week': 55,\\n\",\n      \" 'marital-status': 'Never-married',\\n\",\n      \" 'native-country': 'United-States',\\n\",\n      \" 'occupation': 'Sales',\\n\",\n      \" 'race': 'Black',\\n\",\n      \" 'relationship': 'Not-in-family',\\n\",\n      \" 'sex': 'Female',\\n\",\n      \" 'workclass': 'Private'\\n\",\n      \"}\\n\",\n      \"person_features = vec.transform(person_features).toarray()\\n\",\n      \"\\n\",\n      \"prediction = knn.predict(person_features)[0]\\n\",\n      \"\\n\",\n      \"labels={True: '>50K', False: '<=50K'}\\n\",\n      \"\\n\",\n      \"print labels[prediction]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"- **age**: continuous.\\n\",\n      \"- **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\\n\",\n      \"- **fnlwgt**: continuous.\\n\",\n      \"- **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\\n\",\n      \"- **education-num**: continuous.\\n\",\n      \"- **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\\n\",\n      \"- **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\\n\",\n      \"- **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\\n\",\n      \"- **race**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\\n\",\n      \"- **sex**: Female, Male.\\n\",\n      \"- **capital-gain**: continuous.\\n\",\n      \"- **capital-loss**: continuous.\\n\",\n      \"- **hours-per-week**: continuous.\\n\",\n      \"- **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"## Chart of Education Education in the United States\\n\",\n      \"From Wikipedia\\n\",\n      \"\\n\",\n      \"<img src=\\\"http://upload.wikimedia.org/wikipedia/commons/thumb/8/81/Education_in_the_United_States.svg/497px-Education_in_the_United_States.svg.png\\\" />\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### End of Lesson 2\\n\",\n      \"For questions please leave them on:\\n\",\n      \"\\n\",\n      \"- <a href=\\\"https://www.youtube.com/user/roshanRush\\\">YouTube</a>\\n\",\n      \"- <a href=\\\"https://twitter.com/twistedhardware\\\">Twitter</a>\\n\",\n      \"- <a href=\\\"https://plus.google.com/+Tcsaroshan/posts\\\">Google+</a>\\n\",\n      \"\\n\",\n      \"<a href=\\\"/notebooks/Lesson 1 - Understanding the tools.ipynb\\\">Previous Lesson - Introduction to Machine Learning</a>\\n\",\n      \"<br />\\n\",\n      \"<a href=\\\"/notebooks/Lesson 3 - Regression (1-3).ipynb\\\">Next Lesson - Regression (2/3)</a>\\n\",\n      \"\\n\",\n      \"In the next lesson:\\n\",\n      \"- Linear Regression\"\n     ]\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/Lesson 3 - Regression.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:e58f7a7825f497efef2ad61cad2e0a9ba4574ba781cfbb26ee67f06fabafba28\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Lesson Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"In this you will learn about the following:\\n\",\n      \"\\n\",\n      \"- <a href=\\\"#Why-do-we-use-regression?\\\">Why do we use regression?</a>\\n\",\n      \"- <a href=\\\"#Temperature-Data\\\">Temperature Data</a>\\n\",\n      \"- <a href=\\\"#Loading/Analyzing-Data\\\">Loading/Analyzing Data</a>\\n\",\n      \"- <a href=\\\"#Linear-Regression\\\">Linear Regression</a>\\n\",\n      \"- <a href=\\\"#SVM-Regression\\\">SVM (Support Vector Machine)</a>\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.display import HTML\\n\",\n      \"\\n\",\n      \"input_form = \\\"\\\"\\\"\\n\",\n      \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n      \"<p>User: root<br> Password: admin</p>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"javascript = \\\"\\\"\\\"\\n\",\n      \"<script type=\\\"text/Javascript\\\">\\n\",\n      \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n      \"</script>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"HTML(input_form + javascript)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"\\n\",\n        \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n        \"<p>User: root<br> Password: admin</p>\\n\",\n        \"\\n\",\n        \"<script type=\\\"text/Javascript\\\">\\n\",\n        \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n        \"</script>\\n\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 1,\n       \"text\": [\n        \"<IPython.core.display.HTML at 0x7f173abf6850>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Why do we use regression?\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"In classification, we train a predictive model to produce a class or a category. Regression is used when you need a predictive model that produces numeric values instead of classes. For example you would use a classification algorithm to predict the probability of rain but use a regression algorithm to predict the temperature.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Temperature Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will be using data from the Climatic Research Unit (CRU) of the University of East Anglia (UEA).\\n\",\n      \"<br />\\n\",\n      \"<br />\\n\",\n      \"<a href=\\\"http://www.uea.ac.uk/\\\">\\n\",\n      \"  <img src=\\\"http://www.cru.uea.ac.uk/UEALR04-theme/images/UEALR/logo.jpg\\\" />\\n\",\n      \"</a><br />\\n\",\n      \"<a href=\\\"http://www.cru.uea.ac.uk/\\\">\\n\",\n      \"  <img src=\\\"http://www.cru.uea.ac.uk/documents/421974/487107/cru+logo.gif/74cdd7b1-07f8-4abf-bd79-008ba17f33bd?t=1341910824330\\\" />\\n\",\n      \"</a>\\n\",\n      \"\\n\",\n      \"You can find the dataset of their website:<br />\\n\",\n      \"http://www.cru.uea.ac.uk/cru/data/temperature/<br />\\n\",\n      \"http://www.cru.uea.ac.uk/cru/data/temperature/CRUTEM4-gl.dat\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Data Sample\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \" 1851  0.823  0.357 -0.564  0.510  0.062  0.062  0.367  0.279  0.172  0.672 -0.132 -0.240  0.197\\n\",\n      \" 1851      3      3      3      3      3      3      3      3      3      3      3      3\\n\",\n      \" 1852  0.132  0.185 -0.016 -0.889  0.427  0.485  0.539  0.064  0.147 -0.252  0.126  1.834  0.232\\n\",\n      \" 1852      3      3      3      3      3      3      3      3      3      3      3      3\\n\",\n      \" 1853  0.711 -0.656 -1.348 -0.478 -0.213 -0.196  0.418  0.325 -0.333  0.082 -0.527 -1.230 -0.287\\n\",\n      \" 1853      3      3      3      3      3      3      3      3      3      3      3      3\\n\",\n      \" 1854 -0.444  0.256  0.200 -0.355  0.428  0.107  0.616  0.399  0.171  0.702 -0.501  0.346  0.161\\n\",\n      \" 1854      3      3      3      3      3      3      3      3      3      3      3      3\\n\",\n      \" 1855  0.059 -1.359 -0.521  0.444  0.243  0.197  0.277  0.127 -0.303  0.346 -0.431 -1.622 -0.212\\n\",\n      \" 1855      3      3      3      3      3      3      3      3      3      3      3      3\\n\",\n      \" 1856 -0.303 -0.751 -1.241 -0.022 -0.592  0.232 -0.413 -0.293 -0.601 -0.599 -1.486 -0.141 -0.518\\n\",\n      \" 1856      4      4      3      3      3      3      3      3      3      3      3      3\\n\",\n      \" 1857 -1.157  0.283 -0.708 -1.460 -1.028 -0.316 -0.007  0.147 -0.325 -0.412 -0.726  0.647 -0.422\\n\",\n      \" 1857      3      4      3      4      4      4      4      4      4      4      4      4\\n\",\n      \" 1858  0.054 -1.353 -0.734 -0.397 -0.365  0.146 -0.149 -0.344 -0.361 -0.059 -1.819 -0.382 -0.480\\n\",\n      \" 1858      4      4      4      4      4      4      4      4      4      4      4      4\\n\",\n      \" 1859 -0.167  0.153 -0.024 -0.149  0.021 -0.159  0.049  0.044 -0.551 -0.152 -0.310 -1.072 -0.193\\n\",\n      \" 1859      4      4      4      4      4      4      4      4      4      4      4      4\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Understanding the data structure\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### Columns:\\n\",\n      \"\\n\",\n      \"- Year\\n\",\n      \"- Jan\\n\",\n      \"- Feb\\n\",\n      \"- Mar\\n\",\n      \"- Apr\\n\",\n      \"- May\\n\",\n      \"- Jun\\n\",\n      \"- Jul\\n\",\n      \"- Aug\\n\",\n      \"- Sep\\n\",\n      \"- Oct\\n\",\n      \"- Nov\\n\",\n      \"- Dec\\n\",\n      \"\\n\",\n      \"### Rows\\n\",\n      \"\\n\",\n      \"- Odd rows: Change from previous month\\n\",\n      \"- Even rows: Percentage coverage of globe\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Processing data with Calc/Excel\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"This data was processed using OpenOffice Calc (Similar to Excel).\\n\",\n      \"\\n\",\n      \"### Download and Open file using Excel/Calc\\n\",\n      \"This file has fixed width columns. So we using fixed width columns when Opening/Importing this data.\\n\",\n      \"\\n\",\n      \"### Adding column names\\n\",\n      \"Column names are added to row number 1\\n\",\n      \"\\n\",\n      \"We add two new columns:\\n\",\n      \"\\n\",\n      \"- Average\\n\",\n      \"- Odd/Even\\n\",\n      \"\\n\",\n      \"#### Average\\n\",\n      \"This was calculated using this formula:\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"=AVERAGE(B2:M2)\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"#### Odd/Even\\n\",\n      \"This was calculated using this formula:\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"=MOD(ROW(),2)\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### Filter out odd rows\\n\",\n      \"You should filter out all columns with value of 1. This is what you end up with\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"Year\\tJan\\tFeb\\tMar\\tApr\\tMay\\tJun\\tJul\\tAug\\tSep\\tOct\\tNov\\tDec\\tAverage\\tOdd/Even\\n\",\n      \"1851\\t0.823\\t0.357\\t-0.564\\t0.51\\t0.062\\t0.062\\t0.367\\t0.279\\t0.172\\t0.672\\t-0.132\\t-0.24\\t0.1973333333\\t0\\n\",\n      \"1852\\t0.132\\t0.185\\t-0.016\\t-0.889\\t0.427\\t0.485\\t0.539\\t0.064\\t0.147\\t-0.252\\t0.126\\t1.834\\t0.2318333333\\t0\\n\",\n      \"1853\\t0.711\\t-0.656\\t-1.348\\t-0.478\\t-0.213\\t-0.196\\t0.418\\t0.325\\t-0.333\\t0.082\\t-0.527\\t-1.23\\t-0.2870833333\\t0\\n\",\n      \"1854\\t-0.444\\t0.256\\t0.2\\t-0.355\\t0.428\\t0.107\\t0.616\\t0.399\\t0.171\\t0.702\\t-0.501\\t0.346\\t0.1604166667\\t0\\n\",\n      \"1855\\t0.059\\t-1.359\\t-0.521\\t0.444\\t0.243\\t0.197\\t0.277\\t0.127\\t-0.303\\t0.346\\t-0.431\\t-1.622\\t-0.2119166667\\t0\\n\",\n      \"1856\\t-0.303\\t-0.751\\t-1.241\\t-0.022\\t-0.592\\t0.232\\t-0.413\\t-0.293\\t-0.601\\t-0.599\\t-1.486\\t-0.141\\t-0.5175\\t0\\n\",\n      \"1857\\t-1.157\\t0.283\\t-0.708\\t-1.46\\t-1.028\\t-0.316\\t-0.007\\t0.147\\t-0.325\\t-0.412\\t-0.726\\t0.647\\t-0.4218333333\\t0\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### Export as CSV\\n\",\n      \"Copy your data and past it in a new file. Then, remove Odd/Even column and save you file as a CSV file\"\n     ]\n    },\n    {\n     \"cell_type\": \"raw\",\n     \"metadata\": {},\n     \"source\": [\n      \"Year,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Average\\n\",\n      \"1851,0.823,0.357,-0.564,0.51,0.062,0.062,0.367,0.279,0.172,0.672,-0.132,-0.24,0.1973333333\\n\",\n      \"1852,0.132,0.185,-0.016,-0.889,0.427,0.485,0.539,0.064,0.147,-0.252,0.126,1.834,0.2318333333\\n\",\n      \"1853,0.711,-0.656,-1.348,-0.478,-0.213,-0.196,0.418,0.325,-0.333,0.082,-0.527,-1.23,-0.2870833333\\n\",\n      \"1854,-0.444,0.256,0.2,-0.355,0.428,0.107,0.616,0.399,0.171,0.702,-0.501,0.346,0.1604166667\\n\",\n      \"1855,0.059,-1.359,-0.521,0.444,0.243,0.197,0.277,0.127,-0.303,0.346,-0.431,-1.622,-0.2119166667\\n\",\n      \"1856,-0.303,-0.751,-1.241,-0.022,-0.592,0.232,-0.413,-0.293,-0.601,-0.599,-1.486,-0.141,-0.5175\\n\",\n      \".\\n\",\n      \".\\n\",\n      \".\\n\",\n      \"2009,0.779,0.792,0.633,0.829,0.581,0.659,0.587,0.876,0.976,0.724,0.845,0.492,0.7310833333\\n\",\n      \"2010,0.778,0.826,1.111,1.062,0.936,0.996,0.974,0.863,0.732,0.888,1.195,0.448,0.90075\\n\",\n      \"2011,0.467,0.382,0.585,0.83,0.523,0.778,0.775,0.766,0.852,0.941,0.609,0.827,0.6945833333\\n\",\n      \"2012,0.557,0.323,0.645,1.03,0.963,0.947,0.792,0.891,0.9,0.847,0.849,0.261,0.7504166667\\n\",\n      \"2013,0.935,0.949,0.669,0.581,0.773,0.827,0.671,0.706,0.776,0.821,0.984,0.795,0.7905833333\\n\",\n      \"2014,0.95,0.408,0.929,1.048,,,,,,,,,0.83375\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"The final file is available on your system in this relative path \\\"data/temp_data.csv\\\"\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Loading/Analyzing Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"import the required libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import numpy as np\\n\",\n      \"import pandas as pd\\n\",\n      \"import sklearn\\n\",\n      \"import matplotlib.pyplot as plt\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Load CSV data from file\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data = pd.read_csv(\\\"data/temp_data_features.csv\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualize data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.scatter(x=csv_data[\\\"Year\\\"], y=csv_data[\\\"Average\\\"], marker=\\\"o\\\", s=50, c=csv_data[\\\"Average\\\"])\\n\",\n      \"plt.plot(csv_data[\\\"Year\\\"], csv_data[\\\"Average\\\"], label=\\\"Annual Global Average Anomaly\\\", alpha=0.4, linewidth=2, c=\\\"grey\\\")\\n\",\n      \"plt.hlines(0,min(csv_data[\\\"Year\\\"])-3,max(csv_data[\\\"Year\\\"])+5)\\n\",\n      \"\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.xlim(min(csv_data[\\\"Year\\\"])-3, max(csv_data[\\\"Year\\\"])+5)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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X1s1AW1b2lpaZjN5kJfi6qqlrgbRkpKCgkJCbzwwgsADBgwAIvFwsaN\\nG7nxxhvdx138deH6ujl+/HiRWBw/ftx9PyQkxP2DyGQyAUW/Bl1fYxWJza233kqrVq0wm82sXbuW\\nXr16FUlOCSGEEEL8W3S/6ireXL+eHrm5qOd/53IGBeHw9uagohT63dFms5Gbm0uHDh246aabSE5O\\nds8fymPYsGHExMSwds0aTqam0rBBA75etIh27dpV+fsSl6Y8uznsAkpbXx4ZGVnoD70pKSmF5h8A\\nP/zwg3sFfNOmTWnSpAn79+8v9zyrOLJSoQbk5eWxdu1avv76a8LDwwkPD2fBggXs3r27SNfXyvDx\\n8cFsNrvvX7jzgdVqZejQoUyfPp3Tp0+TmZnJoEGDytVw8JprruHYsWNFSj5KOzciIoLk5GT3/aNH\\nj2IwGApNKo8ePVro35Hn67vuvfde4uLiOHToEOfOneOZZ54p9/aTy5Yt4+DBg+7Pd+rUqaSnp7tX\\nHIwePZpVq1axYcMG4uLiuOJ8J91GjRoxduxYMjMz3bfs7GymT5/ufu0LM4BlfZ7h4eFFvrFdQkND\\nMZlM7N27132ts2fPFuk54bJ8+XKcTieDBg0iPDycJk2aYLFYii2BKE5xsYiIiCjXuRerSGyioqLo\\n1q0b69ev54MPPtDUUjupZdU+iZG2SXy0TeKjfRKj6uV0OklJSSEtLa1cx1+u+IwZM4bT3t78oCg4\\nXUkF4OdGjWjYrBk9evRwH+sqwQ0MDHQnEyq6FWRISAhDhw1j8tSpjBg5sspWKKiqisViqZHG5XVR\\neVYmdAUmXnC7WOfOnTl48CDJycnYbDbWrFnDkCFDCh3TokULvvrqK6Cg/Hn//v3uuVBlSVKhBnzy\\nyScYDAaSkpLc9e1JSUlcddVV7iX6Ff3mvHAngvbt2/Ptt9+SkpLCuXPneO6559zH2Ww2bDYboaGh\\n6HQ6Nm/ezNatW8t1jebNmzNx4kRGjRrFV199RV5eHg6Hgx9++KHEc0aPHs3LL79McnIyOTk5PP74\\n44waNQqd7v+/9ObOnUteXh5//fUX77//PiNHjgQgJycHPz8/vL292bdvH2+99Va5xvnjjz/y999/\\n88svv7g/3z179jBmzBj35ztq1Ci++OIL3n77bW699Vb3ubfddhufffYZW7duxeFwYLFYSEhIKFSL\\ndGFsyvo8R4wYwZIlS9i3bx9ms9m9IgIKmm3efffdTJ061f0/t9TU1BLjsXTpUmbPnu1+T7t372bd\\nunVs2rSp0DK5kowePZq5c+eSnp5Oeno6c+bMqfQEv6Kxuf3225k3bx579uzhlltuqdQ1hRBCCCEq\\na+WKFcRERdGheXOuiIriP506XVJvtEvh4+NDwvffc7RtW7Z6evKdwcBGnQ7v9u1Z+8knhf6A5Uoq\\nhISE4OHhgV6vx263V6h5uWvVakBAAFDxpMTFzGYz06dNI8TPDz8fHxqHhfHKSy+V+49/onhV0ajR\\nYDCwcOFC+vfvT1xcHCNHjqRly5bEx8cTHx8PwOOPP86vv/5Ku3bt6Nu3L/Pnzyf4gkaglfGvLX+o\\nScuWLWPChAlFlqJMnjyZBx54gHnz5qEoSpGamAvvF/ec67HrrruOkSNH0rZtW+rVq8f06dP5/PPP\\nAfDz8+O1115jxIgRWK1WBg8eXGjpfHGvfaE33niD119/nWnTpnHo0CECAwNp1qwZa9euLVIWADBh\\nwgSOHz9Or169sFgsDBgwgNdff73QtXr37k1MTAxOp5NHHnmEvn37AvDiiy/y3//+l/nz59OhQwdG\\njRrF9u3byxznsmXLuOmmm2jVqlWhxx944AF69erF2bNnadCgAT169ODbb78ttEdvVFQUGzZsYPr0\\n6YwePRq9Xs+VV15ZaNJ84XXL+jwHDBjA/fffz9VXX41er2fWrFksX74cT09PAObNm8ecOXPo1q0b\\n6enpREZGct9999GvX79CY//pp59ISUlh0qRJhISEuB8fPHgwMTExrF69mkGDBpUau1mzZpGVleUu\\nzRgxYgSzZs0q8fMs7bUqGptbbrmF++67j1tuuQUvL68SX7e6SS2r9kmMtE3io20SH+2TGFWPD5Yv\\nZ/o993Cf2UxLwAF8u2sX/fr04ftffqFFixbFnnc54xMbG8uWr79mx44dmM1mrrjiCgwGQ6EJf05O\\nDlarFaPRiP/53RtcJdF5eXkYjWUvmHc6ne7XDAkJ4ezZs5eUVHA4HAy69lqciYk8bbEQDhxKS+Od\\n//2PwwcO8Prbb1f6tf/tqmpiPnDgQAYOHFjosYkT/39dQ2hoaKFS76qgqHVgvYqiKMX+Zb+kx4Wo\\nKUlJSbRp0wabzVZotca/QWxsLPHx8YX6U1xMvmeFEEIIUZWcTidNwsOZePo0LQBVUVDO/66xTqfD\\nOHIk71/QLL06paamcvLkSSIiIqhfvz579uzBbrcTExNDQEAAycnJnDlzhgYNGrjLg48ePUpaWhpR\\nUVHl6lGVk5PD/v37MZlMNGvWjN27d6PT6Wjfvn25mvpdbOPGjUwbNYq5OTnoAbvJhCEvj1zgfi8v\\nfk9KIjo6usKvWx51+fdERVHYW4nz4qj4CvfL4d81qxGiBnz88cdYrVYyMzOZMWMGQ4YM+dclFNav\\nX4+iKKUmFGqC1LJqn8RI2yQ+2ibx0T6J0eWXnJyMJSeH5oCq03G2TRuymzYFoIfTyZcXbHt9scsd\\nH1cPNJPJhF6vp0GDBqSmpjLr0Ufp2qoVs2fO5I8//ii0NN3VTLu8qw1cTbZ9fX0xGAx4eHjgdDqx\\nWq2VGvOGjz6i5/mEQl54OFktW2INCcEH6KwobN68uVKvK6pm94eaoqWxCFEnLVq0iPHjx6PX6+nT\\npw9vvvlmTQ+pWvXp04d9+/YV2r5SCCGEEKI6eHh4YHM6cQKqlxeq0Uj++VIC6/nna4orMeBKFPzw\\nww+88OyzdAFGWq1k7N/P+3v28NuPP/J6fDyKolQ6qeDj4+O+ls1mIy8vr1IlqYqioALW4GDyIiJA\\nVVHOb2+uFlO+LcqvPLs/aJWUPwghNEO+Z4UQQghR1TrFxdEnKYkugYHknF+lEPjHHyxWFNpOmcK8\\nF1+s9jHZ7fZCpQgWi4Wo+vV5ysuL4Au2APdMTmaqxcLKzZu56qqripxX1iT+jz/+ID8/n1atWuHl\\n5eUuuQgPD6/QLmCupvBffPEFM8aPZ3JkJDpFwfvoUbzS0sgFpnh58cf+/TRq1KiSn0rp6vLviYqi\\ncKwS50Uh5Q9CCCGEEEIIcVm99s47LPH2ZrunJzlABrDcx4f9oaE8PGNGjYzpwlUKiqKwZcsWYhSF\\nVmfOYDhfmqBzOAjKzOQGs5ml77wDFHT3NxqN5SphsNls5Ofno9fr3U3CXSsdLtx+vjTHjx9nwq23\\n4uvlhdFg4JlZs2jSsSNfGY1knTqFZ1oa+4Fnvb0Zd+edly2h8G9Qm8sfJKkghPjXklpW7ZMYaZvE\\nR9skPtonMaoePXv2ZPsPP5B3zTV87OnJVh8fmo0bx8+JidSrV6/E88oTn7Nnz7Jjxw7+/PPPCv3F\\n2JVU8Pb2BuDcuXMEO50oqorf+a3MTWfOoHM6CVFVzqanu88tbwnEhf0UXCsaXNcrT/lEWloaPTp2\\nxLFmDR/abGzX6xlgs5GVmYlXTAzPZWUxQlF4JzycyfPm8fIFO7yJijMaKn7TCg0NRQghhBBCCCGq\\nXrt27Xjy6ac5e/YsAOHh4aUmFMpit9uZ8eCDLH73Xa7w9CTNbicwLIzFq1bRtWtXzp07h6Io7q0g\\nL3ZxP4Urr7ySR5xO8gFTZibGPXvQ22wA/OLtTf9rr3WfazKZyMrKwmKxlDrGi/spAHh6eqIoCjab\\nDYfDgV6vL/H81156iY5nz3Kfw4FTpyMtJoYWnp74nz3Lgi+/5GRGBqqqYjDIlPLfrk73VAgODiYz\\nM7MGRiSEqIygoCAyMjJqehhCCCGEqIOSkpLcy/6Dg4Np0qRJpV/rwUmT+Pn993nGbCYEUIGtwFwv\\nL2Kjo0k6fBgV6NymDfMWLqR79+7FjqV58+b4+voCMKRfP/K//ZYpVivegAPYrCisCAxk7+HDBAUF\\nAXDmzBmSk5MJCgriiiuuKHGM+/btIzc3l9jY2ELJjeKuXZx2MTFMOXyY1sCZpk3JCwxEb7UStm8f\\nt3p58cl339GuXbtKfX6VUdd7KpyreN9MAiza6KlQp9NKMjkRQgghhBBCQEGPAZfKbqkIBXOMJe+9\\nxycWC8GA3WhEUVVC7XZUi4Ux+/ZxHeAEtuzaxeC+fdmckECXLl2Agkmga5WBa6UCwMr167n79tsZ\\ns3kzzTw9SbXbCWvYkG3r1rkTCheeU1oJg9PpdCdQLlyp4DrfbDZjNptLTSqoTmfB+42OJi8wEJ3d\\nTr1Dh9Db7ejq8AS/phhLXjSiedJToRaTOjztkxhpm8RH+yRG2ibx0TaJj/ZJjKqP0+nEfn7rQyhf\\nUqGk+CQmJtLM05NgwGoycaxNG462a8cnrVvzWHQ0PerVw2EyYQSGAFPMZmZPn17o2k6nEw8Pj0Ll\\nB76+vqxav56/Dh3i2Y8+YsuPP7IrKYm4uLhC1/fy8kJRFCwWC87zE/+L5eXloaoqXl5eRUocSuur\\nkJKSwuLFi1myZAlXDxzIt02aYA4JQXE4CD10CKPFwl+AzdOTNm3alPkZivIzGCp+0woNDUUIIYQQ\\nQgghqp5rlYKnpyf5+fnY7fYyewqUxN/fnwyHAxXIrlcPVVFwACc9PYnz9CQ9JKTgWrm5RO7bxw3A\\nMzt2oKoqiqIU6adwscjISCIjI0u8vk6nw9PTE4vFgsVicScJLpSTkwNQ7EqE4lY6OJ1OHpo8mfff\\ne49rDAacwJ+hoUSEh/OtzcZ1hw9jyM1lB/CStzfzX3qpUp+dKJmWGi9WVC0euujTp09ND0GUQWKk\\nbRIf7ZMYaZvER9skPtonMao+rqSCh4cHOp2OvLw8rFZrsRNyl5Li07FjR5SAAL4xm2kSHAxA+N69\\n7FMUAnx8UHx8yA0MxOrjQ56fH/nZ2ejP774ARXd+qAyTyYTFYiEvL6/Y1ymuSeOF57rG4Up0vLJg\\nAd8tXcr3VisBVisZ9evzd2gob6sqe729ecdiwaHT0aZZM956/nluvPHGSo9dlKAW52gkqSCEEEII\\nIYSo0y5MKuj1+nIlFUqi0+lYvHIl48eMYYjBQIucHI7k5WHS6fjKbObWtDQyw8PJiIggOzSUz3Ny\\nGDJwoHtbR1evg5JWKpSHyWQiMzOz0GoDVVXZvn0777/1Fqqq0qRFC+4YP57Q0NBC5xoMBoxGI/n5\\n+VitVjw9PXll/nwWmc0EAOeCgzndsCG+wJhDh3jB35+cvDzsdjuenp6VHrMoQy2emUtPhVpM6vC0\\nT2KkbRIf7ZMYaZvER9skPtonMao+FyYVXBPjsvoqlBafXr168e7y5eh69OADk4kNXbpw7xNPEO/r\\ny3uKQn56OjmqyqbgYFYGBjLnhRfc55ZV/lAeF5cwqKrKg/fdx39vHELrjRv4T0oy2V9+Qff27fnh\\nhx+KnH9hX4Xs7Gwyzp2jDWAxmTgRHQ1A/ZQUep05w1/Jye6SC3EZGSpx0wgNDUUIIYQQQgghqt6F\\nSQXXrgWXsgOE1WrF39+f28aNK9RfYNiwYTz9+OMM3rqVJhYLPXv04JM336R58+YAOBwObDbbJU/S\\nL04qfP3112z8YDnb83MhMJB/FOiWfY5u1hzGDh3KwdRUdDpdofPPnTtHXl4eYWFhGAwGUu12bNHR\\noCgEpqURfPo0+4EGgYHuVRbiMqrFM3NZqVCLSR2e9kmMtE3io30SI22T+GibxEf7JEbVpzIrFUqL\\nT3p6OgBBQUGFGha2atWK1Rs2kJWXxzfff8+d99xTqK+BKwng2sGhsly9IVxNJ5e88Qb3WHMJUMDs\\nU9Cc0WTOZYAOfM257Nixo9D5FyYl9Ho9t48dyxuNGpHn7Y3RaqX+sWM4gBe9vLjr3nsrPU5RAfpK\\n3DRCkgpCCCGEEEKIOq0ySYWSqKrKmTNnAAg5v9NDcQICAjAYDFgsFvduDFVR+gCgKEqhxED6yZM0\\nAs4GBpEeWh8A3+wsFAUa6RR3EsTFda6rv8Osp5/G3K4d8Z6efJ+czFKnkyG+vuR37Mhj//vfJY1V\\nlFMtLn+QpEItJnV42icx0jaJj/ZJjLRN4qNtEh/tkxhVD1VVCyUVPDw8UBQFm82G0+ks8byS4pOV\\nlUV+fj5RyOPsAAAgAElEQVSenp74+fmVeL6iKO4mia4kRFUlFZxOJ7t372b5e4uZdt+9mIKD+K5e\\nA1IaN0FVFOqfOoFvbg4WFX622mjfvn2h8+12Ozu+/ZZ33niDh6ZM4ZdffuGRmTO58Z572D9oEEdG\\nj+bplSvZ8u23eHl5XdJYRTnV4qSChoYihBBCCCGEEFXLbrejqioGg8HdV8DDwwOr1YrNZqvwpNn1\\nV/+Ld1UoTkhICCdPniQjI4OoqCj3yoBL2U7SZrMx/IbryTh8iCHhIZgy0thgcfJLeCStVehz6jhh\\np05gUeFhvSdX9epD06ZN3ecfPHiQ6/7Tk96NI+ngqedsdjYv7/qN7r1689QzzzB27NhKj038Oymq\\nq1NJLaYoCnXgbQghhBBCCCGqWG5uLvv27cPb25uWLVsCBRPrrKwsYmJiCAgIKPM19u/fT/zrr3Fo\\n7180a9OOXtdey8CBAzEajeU6Nycnh0aNGnHs2DGcTift2rXDYKjc33dfnD+fL5+bzaoQA0caN0Pv\\nsGPXG4i3KHyTcgqfc2dppIMfrfn8p1dvln74YaEVFd3btuHWlL+4oWkjzgQUJEZyVZVJe4+xYNly\\n+vXrV6lxXW51ec6nKApq+7KPK3JeIpr4TGSlghBCCCGEEKLOurD0waUifRXWrFrFlLvv4m6TjTH1\\nQjjgzOXpbV9xMjWV/5ajiWFwcDDfffcd8a+9Sr7NRtOWcTRu3LhcKx2Ks3jh67xnyMPPVjCVc+gN\\nKMDDmUdZc/osaz7+hJycHF5s357Y2NhC5+7Zs4fjyX9zr69KuiUPzudTrsg8zWTrad599RXNJhXq\\nvFo8M5eeCrWY1OFpn8RI2yQ+2icx0jaJj7ZJfLRPYlQ9XImDiiYVEhISyMjI4J677mRbkJln/O20\\naRDC9V4qi53HefShaRw9erTUazscDqbcfSefvvcOrY7u5T+nD3H8s7W0a96MpKSkSr2fE+npNDeA\\n0WHHI79g/I1O/kPzc6fxMehp164dw4cPL5JQAEhNTaWZpxG9Ar55uQWfhc1CRNpx4vRwPKX09yMu\\no1rcU0GSCkIIIYQQQog6Kz8/H6jcSoUPP/yQ/t4KbTwg18sHi4cJoz2fNtZzjPZxsmL58lLP/+CD\\nD/jn2695yXmc/5igvSc8bjjL/4xnuXvM6Eq9n9bNYtlxftixRw/SIjmJemfT2W8Hp95AvXr1Sjy3\\nRYsWJOZayVPBx5JLbMpBmv+zH53q5DuHjpbtKrEGX1SNKtpScsuWLbRo0YLY2FjmzZtX7DEJCQl0\\n6NCB1q1bV8nWtpJUqMVkb2Ptkxhpm8RH+yRG2ibx0TaJj/ZJjKpHZcsf+vTpQ1paGk0cBTs2nPMp\\nqBUIys5AQaWJ00baiROlXnvpG68zwyuX8Oz/39LRZMvjLj+VwwcPcOjQoQq/n6mznmC63ZtUB3jl\\nW/GxmMl2wn1Wbybdf3+pfR4aN25Mr969edjigUMF/9wsjA47e/Lh1XwvJj30cIXHI6pIFaxUcDgc\\nTJ48mS1btrB3715WrVpVZEXM2bNnmTRpEp999hl79uzho48+uuShS1JBCCGEEEIIUWeVVf5QWqO7\\nDh068LXiC4DZs2DHBl9LQdnANp0v7bt2LfXa6enpRBvB25aHb14Oiqrim5eLQYGGJg/3ThIVMWzY\\nMO6c8Tits7wYbvXldqs3Tc55EXPjUB5/cnaZ5y9etZr9LToSm+PDfRYvbrT60ivXm5fjFxXZelJU\\noypIKuzcuZOYmBiio6MxGo2MGjWKDRs2FDpm5cqVDB06lKioKKB8u5iURZIKtZjU4WmfxEjbJD7a\\nJzHSNomPtkl8tE9iVD2KW6mg0+kwGo2oquouj7hYQkICAwYMIDcwhHnZenLOJxVMFjOLchSS9N4M\\nHz681Gt37NKVrywFU66mJw4Rd3QvnnYrp+1wMNdK8+bNK/Weps+cycGjKQx+6Q16Pf8qv+5NIn7p\\nsnLtKBEYGMhXP/zImm3baTl7PsNfeYt/Tp5k9K23VmosoopUQVIhNTWVhg0buu9HRUWRmppa6JiD\\nBw+SkZHB1VdfTefOnVleRglPeYcuhBBCCCGEEHWOw+HA4XC4kwgX8vT0JD8/H6vVWijhcCG9Xs+m\\n7d8washgtlt8aKRX2XHaiEdoFF98vhGTyVTq9R949DEGbNzIVZ5mrvRyYHA6yHbCnVkmbh87lqCg\\noEq/t9DQUG6//fZKn9+lSxe6dOlS6fNFFSuhR0JFKIpS5jH5+fns2rWLbdu2YTab6d69O926dSu2\\nsWd5lSupkJSURHJyMjqdjsaNG9OiRYtKX1BUHanD0z6JkbZJfLRPYqRtEh9tk/hon8To8itulYKL\\np6cnOTk5WK1W/Pz8ijzvik+jRo3Y/M23bNu2jTNnzjC+dWu6detWrglchw4dWLRiJTeNu4Mr8lTq\\n6eHbLCtDh97MC6+/fmlvTtQt5ZiZJ6QX3EoSGRlJSkqK+35KSoq7zMGlYcOGhIaGYjKZMJlM9OrV\\ni927d1+epMKRI0d4+eWX2bRpE5GRkURERKCqKidOnODYsWPccMMNPPjgg0RHR1f64kIIIYQQQghx\\nuZSVVICyd4AAyMvLo3HjxnTp0qXQ8vLyuPHGGxl46jTbt28nOzubhd26FZnoCVGepEKfBgU3l6cO\\nFH6+c+fOHDx4kOTkZCIiIlizZg2rVq0qdMyNN97I5MmTcTgcWK1Wfv75Z6ZNm3ZJQy+xp8KMGTMY\\nPHgwSUlJfPPNN6xatYrVq1fzzTffsG/fPq6//nqmT59+SRcXl0bq8LRPYqRtEh/tkxhpm8RH2yQ+\\n2icxuvwuJalwYXzMZjNAmeUOJfHw8KB///4MGzZMEgqieFWwpaTBYGDhwoX079+fuLg4Ro4cScuW\\nLYmPjyc+Ph4o2FZ0wIABtG3bliuvvJK7776buLi4Sxp6ifmQtWvXlniS0WikX79+9OvX75IuLoQQ\\nQgghhBCXS1WuVADw9vauwtEJUfUGDhzIwIEDCz02ceLEQvcffvhhHn646rYPVdQS9lBZvnw5qqoW\\naf6xfPly9Ho9Y8aMqbJBXCpFUUrdCkYIIYQQQghRu6mqSmJiItnZ2bRr146AgIAyzzly5AgZGRlE\\nR0cTEhJS6Dm73c7u3bvR6/WlbqXocDhITExEURQ6dOhQrl4KourV5Tmfoiiooytx3io08ZmUWP7w\\n+uuvc/PNNxd5/Oabb+bFF1+8rIMSQgghhBBCCJfvvvuOVk0bM6JfLx67bQjRkQ147OFpOByOUs8r\\nbaWCwWBAr9fjcDiw2+0lvoZrlYKXl5ckFMTlUwVbStaUEpMK+fn5xXZB9fX1LXEvV1G9pA5P+yRG\\n2ibx0T6JkbZJfLRN4qN9EqPyOXDgADdfP4Dnw1I4cFUO33c5x95eFn5YHc//Hi29x1tpSQUovQTC\\nFR9XPwUpfRCXVRX0VKgpJSYVLBYLOTk5RR7Pzs6WpIIQQgghhBCiWrz6wjzua2hhSASoioJNMRJu\\nglVtzbz99ltkZWUVe56qqu55i9FoLPaY8vRVkH4KolrUxZUKd955J8OHDyc5Odn92JEjRxg5ciR3\\n3nlndYxNlEH2NtY+iZG2SXy0T2KkbRIfbZP4aJ/EqHx+3JHADfUKyhyO+DZhT1AbbDoPIkwQG2Bk\\nz549xZ6Xn5+PqqoYjUZ0uuKnPZ6enthsNjIzM4s854rPpe78IES51OKkQolDefjhh/H19aV3795k\\nZ2cDBaUPjz32GPfee2+1DVAIIYQQQgjx7+XvH8Cp8wsJcgy+qCjk6b0wOGycMjtKbNhYVunDn3/+\\nyVOPz+DY6XQyMzII8vHkqXkv0b9/f/cxqqrKSgVRPTSUJKioElcqANxzzz38888/JCcnk5yczNGj\\nRyWhoCFSh6d9EiNtk/hon8RI2yQ+2ibx0T6JUfmMuXMiL6V4Y1X12HUFZQz5Og/WpUJg/QbExcUV\\ne15pSYX9+/fTt1dPemV/x8udnXzc18CM4L2MG3kLmzZtAgriY7FYUFUVT09P9HoNFbGLuqcu9lSw\\n2+1s3LiRL7/8En9/f/z9/atzXEIIIYQQQgjBuPHj8YjpxOA/g0g8C3/nwEtHTUza70v80hUl7shQ\\nWlLh+TlP8EDTXO65woqHDux6T25uDIu7mJn50P3ubfqkSaOoNrW4/EFRS9jY8uabb2bw4MHk5OSw\\na9cu3n///WoeWvnV5T1LhRBCCCGE+Lez2WwsWbKEHdu2kpeXR5OmMdx3/1SuuOKKEs85evQoaWlp\\nNGzYkPr16xd6LizYn1/7ZhPlA7/7dkBFR/ucRBTVQf11Xvx54G/Cw8NJSUnh9OnTREREEB4efrnf\\npihFXZ7zKYqCOqMS581DE59JifmN5ORkRowYQV5eHitXrqzOMQkhhBBCCCGEm4eHB4MHD6Zz584A\\nBAQElJpQgNJXKuh1OvKdoAC+jlyy9X6kGUMJtZ7C4VTdpQ7ST0FUGw2VM1RUieUP8fHx3H///cyY\\nMYNFixZV55hEOUkdnvZJjLRN4qN9EiNtk/hom8RH+yRGFWOxWNz/Ls8W965tIotLKtx40828e7hg\\nFtfAdhKAUx5hrPtHoWXzWOrXr09CQoKUP4jqU4vLH0ocSteuXenatWt1jkUIIYQQQgghilWepIKq\\nqnywfDkLnnsKxcufIH9funXrxswnZuPj4+M+7rEnnqJH5w0YlXPcE5uFos9le6YPbx2N4p333wAK\\nVjo4HA4MBgNGo/HyvjkharESeyokJCSUuXfu9u3bufrqqy/HuCqkLtfXCCGEEEII8W+nqiqJiYk4\\nnU73Yx07dizSpPG5uU/xwVvzebm3lcAr2nMm18myr/eSamrFth0/FUoOJCcnM2fWo6z7eAMmHx8G\\n9e/PkKHDufHGG1EUhbNnz3L48GH8/f2JjY2ttvcqileX53yKoqDOqcR5T2ijp0KJ5Q+ff/45Xbt2\\n5fHHH2f9+vX8+OOPfP/996xbt47HHnuMLl26sHnz5ku6+JYtW2jRogWxsbHMmzevyPMJCQkEBATQ\\noUMHOnTowNy5cy/pekIIIYQQQojaJz8/H6fTWWjVwMWrFc6cOcP8ec/z5VAzVzXxQK9AI18bK663\\n4kzbz8cff1zo+OjoaN77YDXncvM4cSqNhx+bScOGDTlz5gwgOz+IalaLyx9KTCq8+OKLbNu2jbi4\\nOL788kuefvppnnnmGb766itat27N9u3bmT9/fqUv7HA4mDx5Mlu2bGHv3r2sWrWKpKSkIsf17t2b\\n33//nd9//51Zs2ZV+np1kdThaZ/ESNskPtonMdI2iY+2SXy0r67EKDMzk5UrV7JkyRKSk5MvyzVc\\npQ9eXl4lJhW+/PJLekcbifADGwV9FDxUKzoFxrfI5ZO1H5T4+oqi0KBBAwBOnjyJqqps374dAJPJ\\nVOXvR4gi9JW4aUSp+Q0/Pz9uu+02brvttiq/8M6dO4mJiSE6OhqAUaNGsWHDBlq2bFnoOC0s5xBC\\nCCGEEEIU9erLC3jyiVn0aWrAx6jy8AMOhg0bzhuL3sNgqLo/pbqaLnp5ebmTCRcnFVRVRX/+T6ZW\\n5XxSgYIdIPQ6UC8onShOUFAQx48fx2q18ueff3LkyBEaN24sKxVE9dDQyoOKqrGhp6am0rBhQ/f9\\nqKgofv7550LHKIrCDz/8QLt27YiMjOTFF18kLi6uuoeqWWX1vBA1T2KkbRIf7ZMYaZvER9skPtpX\\n22P02Wef8dq8J0i810J0UMFj2Va4Zc065jwZwZxnnq+ya7lWKnh6err7KLi2jHS59tprue+/+aTl\\ngs3Xs+B4bKgqLD/gy/j/jSr1GoqiYLFYWDD/WY7+8w/1A01s3vgpV3bpzLwFr0qzRnF51eKkQonl\\nD5fbxU1VitOxY0dSUlLYvXs3U6ZM4aabbqqGkQkhhBBCCCHK8vK8OTx3tZnoILCpHuSpJvw84e3r\\nzbz15hvu1QVVoTzlD/Xr12fSpMkM/Nib3ekeqCpkZFuZuNWDLK+GDB8+vNRrnDlzhpsHD6Czz2Fe\\nHqwwq7eFBddlsW/7+0yeeGeVvRchilVXyx8up8jISFJSUtz3U1JSiIqKKnSMn5+f+98DBw7kvvvu\\nIyMjg+Dg4CKvN27cOHcpRWBgIO3bt3dnf131anXtvusxrYxH7he9f3Gsano8cl/iU9vuv/LKK/+K\\nn+e19b7ER9v3JT7av5+YmMjUqVM1M56K3v8tcQ+9ewHAysPh2DByR9OjNA2xoVPtfPzxx4waNapK\\nrvfdd99hs9lo1aoVdrudX3/9lYCAgCKv//Rz84mIasTM+LfId+SQka4w9JaxPHnDTfz444+lXm/V\\nypVc08jM2OYn+fhQIPtPmJn6H4U1I/KIeG41fQcOdicmtPD51/X7iYmJnD17FuCy9erQlFq8UqHE\\nLSVdOnXqxIQJExgzZgxBQUFVdmG73U7z5s3Ztm0bERERdO3alVWrVhXqqXDq1Cnq16+Poijs3LmT\\nESNGFPsFVZe3FylNQkKC+xtPaJPESNskPtonMdI2iY+2SXy0r7bHqG2LJrzRK5ke0ToSnR0AaKCc\\nwMd6nMYveZJy/DT+/v6XfB2n08nvv/+Ooih06NCBrKwsDh06VOpWj4mJiZjNZjp16oSnp2e5rjO4\\nXy8mNNjBja10/OlozU9/Wxgak0GoLp2bV/sx5rHFZa52EJdPXZ7zKYqC+m4lzrtLGz0IdWUdsHr1\\nalJTU+nSpQujRo3iiy++qJKBGwwGFi5cSP/+/YmLi2PkyJG0bNmS+Ph44uPjAfjoo49o06YN7du3\\nZ+rUqaxevfqSr1uX1Ob/Cf1bSIy0TeKjfRIjbZP4aJvER/tqe4wmTJzCnB3eZNv/f9KerobyzLdG\\nrh84oEoSCvD/TRo9PDxQFAUPDw+gaE8FF4fDgcPhwGQylTuhAODrF8AZM+gUJ411/3BtUweBSiYA\\n6WYFX1/fS3wnQpSiFpc/lLlSwcXpdPL5559z7733otPpmDBhAg888ECxpQjVrS5nrYQQQgghhNCi\\n/Px8ht90Paf/2cvNXRrgaYBfT3qw71gWn3/xNfXr16+S65w9e5bDhw8TEBBATEwMdrud3bt3o9fr\\nad++fZHjzWYzSUlJmEymCjV537BhA09NvY0f787B84Kl6L8eg+tX+HP0+OkKJSlE1arLcz5FUVCX\\nV+K8sbVkpQLA7t27mTZtGo888ghDhw7lww8/xM/Pj2uuueZyj0+UwlV7JLRLYqRtEh/tkxhpm8RH\\n2yQ+2lfbY2Q0Gln/2RamzX6FYz5dSKItXfuP4f0Va6ssoQCFmzRCwYpnRVFwOBw4i9km8sKVDRUx\\nePBgmrbrxbXv+/DZXljyC7zyncINH3jzZvxiSSiIy8tQiZtGlDmUTp06ERAQwF133cXzzz/v/mbu\\n1q0b33///WUfoBBCCCGEEEKbdDodnTp1okmTJkRGRnL8+HHy8vKw2WwVntSX5MLtJF2MRiM2m438\\n/Pwik31XWURFkwA6nY7V6z5l6dKlvLDoNf5JSeU/Pf/D51tn0rlz50t8F0KUoYqSBFu2bGHq1Kk4\\nHA7uuusuZsyYUexxv/zyC927d2ft2rXccsstl3TNMssf/v77b6644opLusjlVpeXwgghhBBCCKFl\\nSUlJmM1mmjdvzunTp8nMzCQiIoLw8PAqef39+/eTk5NDs2bN3LvDFfeYy9GjR0lLSyMqKoqwsLAq\\nGYOoeXV5zqcoCuqHlThveOHyB4fDQfPmzfnqq6+IjIykS5cuRTZDcB133XXX4e3tzfjx4xk6dOgl\\njb/EfMiCBQv+f7AXBVBRFKZNm3ZJFxZCCCGEEELUfheWJ4SEhJCZmcmZM2do0KABiqJU2etfvFIB\\nCvo6XKyyKxWEqFFV0Hhx586dxMTEEB0dDcCoUaPYsGFDkaTC66+/zrBhw/jll18u/aKU0lMhOzub\\nnJwccnJyivw7Ozu7Si4uLk1tr8P7N5AYaZvER/skRtom8dE2iY/21YUY5efn43Q6MRgMGAwG/P39\\n8fDwwGq1VsmcwW63Y7fb0el0hcopSksqVLanwsXqQnxELVIFPRVSU1Np2LCh+35UVBSpqalFjtmw\\nYQP33nsvQJUk/kpcqTB79uxLfnEhhBBCCCFE3XXxKgJFUQgJCeHEiROcPHkSu91OYGAgOl25+sMX\\n4UoQuPq6uchKBVHnlKOnQsIfBbeSlCdBMHXqVJ5//nl3NUJVlJSUOfS8vDwWL17M3r17ycvLcw/0\\nvffeu+SLi0tT2/c2/jeQGGmbxEf7JEbaJvHRNomP9tWFGF28M4PLsvcX88uvv7IvaQ/+fr48PH0m\\nkybfX+G/ihZX+gAlJxUuXDmh11/aevK6EB9Ri5Tjy7VPh4Kby1MrCz8fGRlJSkqK+35KSgpRUVGF\\njvntt98YNWoUAOnp6WzevBmj0ciQIUMqPfQyU4Zjx47l1KlTbNmyhT59+pCSkoKvr2+lLyiEEEII\\nIYSoGy5OKpjNZvr3/Q/GrK95dpyTpCV+fDQjjffffJwnZhXfhb4ir+/iKm1wrUpwqarSByGqXRWU\\nP3Tu3JmDBw+SnJyMzWZjzZo1RZIFf//9N0eOHOHIkSMMGzaMt95665ISClCOpMKhQ4d4+umn8fX1\\n5Y477mDTpk38/PPPl3RRUTWkzkv7JEbaJvHRPomRtkl8tE3io31aj5GqquTl5eF0Oks85uLyhBUf\\nfECjgNM8fssp/LzhjCWUzs1g42wzr7/+Gunp6RUaQ0XLH6qy9EHr8RF1TBUkFQwGAwsXLqR///7E\\nxcUxcuRIWrZsSXx8PPHx8Zd16KVyZfkCAgL4888/adCgAWlpaZdtQEIIIYQQQoiao6oq7yyKZ8EL\\nT5OccgqTlwe3jx3LnLnzCAwMLHTsxeUJWzZ+yG19cgnyUkjJySfPbiI335uwIDNXtfUgISGBYcOG\\nlXssFS1/kJUKotaqgt0fAAYOHMjAgQMLPTZx4sRij12yZEmVXLPMlQp33303GRkZzJ07lyFDhhAX\\nF8f06dOr5OLi0kidl/ZJjLRN4qN9EiNtk/hom8RH+7QaozmzZ/Hmyw/z3kPHsWxz8OeSPMxH3+e6\\na3q4J+0ATqfTfd816dfrDeTbQVFUAjzOAWDO9wYg365UqM+BqqolrlTQ6/XodDqcTid2u939eFWu\\nVNBqfEQdVQUrFWpKuZIKwcHB9O7dmyNHjpCWlsY999xTHWMTQgghhBBCVKP09HRefmUBW+bn0rMt\\nKAo0DIN3ptsIMB5l7dq17mMvTCi4dne4cehtLP7KB6cTPPQFE3yb04MjJ+HnpHyuvfbaco/F1XTR\\naDQWm4xwrUa4cLWCrFQQtVZdTipkZmby6quv8uCDDzJlyhSmTJnC/fffXx1jE2WQOi/tkxhpm8RH\\n+yRG2ibx0TaJj/ZpMUZff/01vTt40CAELPle/HGiDadz6qEocEe/XD775P/bzRe3imD48OE4Tc0Y\\nM9+TlFM2bHb45k9P+v/Pmydnz8Hf37/cYymp9MGluBII6akgaq1anFQocyiDBg2ie/futG3b1r0F\\nTEW3ghFCCCGEEEJoX8He9QX/TjeHkO/w4HROfer7puFUC88Dipv0e3h4sOXLHTz/7NOMXbCCoBCF\\nsNCmPLtgfoV6KSQmJrJx40ZUVaVnz540b968yDEXJxVUVXUnFWSlgqh1qqinQk1QVNX1Y6N4HTt2\\nZNeuXdU1nkop+OFX6tsQQgghhBBClOHMmTM0vSKKv5ZaOOtshSW/YBVCXNgeBj1i4K6p8dx6660A\\nJCcnc+bMGRo1akS9evWKvJbVamXPnj0YjUbatm1bruvb7XbG3zGS7V9vZsyN9VGM9dj09Wk6d+7O\\nu++tKFQGkZqaysmTJ4mIiCA8PLxS1xO1R12e8ymKgvpnJc5rgyY+kzLLH8aMGcOiRYs4ceIEGRkZ\\n7psQQgghhBCien311VfcNKQvLZtH0f+67qxbt65KJxUhISFMf+RRrn8skMTDXqgqpJ2FKa+HYlGu\\nKLTaoKQmii4eHh4oiuLujVAe8+c9y8l/NnNwcx53j/BgxACVrYvOkbz/M15a8EKhYy9eqVCVpQ9C\\nVLtaXP5QZlLBy8uLRx55hG7dutGpUyc6depE586dq2NsogxS56V9EiNtk/hon8RI2yQ+2ibx0b7i\\nYlRaguDVVxZw1/gbGdx9Gx+9ksqEG37iyZl3MP3hqu139tjMJ5j04DzWfhvC3fPsPLfCg9CYUWzd\\n9n2hCXtZPQ8URSlx68fiqKrKm2++wkvT8zB5gSW/4HWD/Cy8+IiZN994qdDxF792VTdplO8hUa1q\\ncVKhzKEsWLCAw4cPExoaWh3jEUIIIYQQ4l8lOTmZp2bP4KN1G7BY8ul7TXdmPTGPnj17uo85deoU\\ns2fPYvenFhpFFDzWKhb6X5VLq+sXM/aOu6tsyb+iKPTq3ZuOnToRGRlJamoqiqLg4+PjPsZut2O3\\n29HpdKVO4j08PLDZbFit1jJXEFgsFtLPZNGmGTicOmx2DxRUPI02OsbBPylpOBwOdwmErFQQdUot\\n7qlQ5kqF2NhYTCZTdYxFVJDsnat9EiNtk/hon8RI2yQ+2ibx0b4+ffqQkpLCf3p2olHgRxz+1sq5\\nv5yM7P89N990Hdu2bXMfu379egZfo6NRBFhsnhxMjSXX4kOgP4y72caqlcurbFx2u52cnBwURaFe\\nvXr4+PigqirZ2dnuY1yrFEoqfXBxTfBdE/7SeHl5ERToS9JhMFt9AAWTpxlFUfljP0RFhhTqqXC5\\nVyrI95CoVnV5pYK3tzft27fn6quvdv9QUBSF11577bIPTgghhBBCiLps/rynGTMki6ce+v+eA+OG\\nQ1BAHo88fA+/7TqAoijk5OQQElAwec7MCSbL7I+n0YqPVy6hQQ7+PneuysZ07vxr+fn5odfr8ff3\\nJzc3l3PnzhEQEACUP6ngmuCXJ6mgKAoTJ07ikQUv8+bsglURvl65WG0wfYGJe+4pXOZxYVLhwp0f\\nZGENCVQAACAASURBVKWCqJU0lCSoqDJXKtx0003MnDmTnj170rlzZ3dfBVHzpM5L+yRG2ibx0T6J\\nkbZJfLRN4qN9CQkJfPrpeu4caQcg5XQUB1JiUVWFwX0hJeUYqampAFx11VV8luCBwwE2e8Fk2mY3\\noqrwyTY/evW+rsrGdfbsWQB3AsH133MXJC5cqwLKmsC7kgqu48vy+Mwn8fDvxW2PhfDp1xC/KpeW\\ng30ICLuGR6Y/VuhYnU6HwWBAVVXsdnu5x1Re8j0kqlVdXqkwbtw4rFYrBw4cAKBFixburKAQQggh\\nhBCi8pxOJ3o9OJ0KaZn1UVUFi80LL4889HrFvWvClVdeyRVN2/Hf//3GlPEFE/Ucs5GZLxnIzK3H\\nTTfdVGXjycrKAiAwMBAoWLlsMBiw2WxYLBa8vLwuy0oF1/HrPt7M+vXrSfx9F3g5WbX2Jrp27Yqi\\nKEWONxqN7oRCfn5+oeaQQtQmai3uqaCoZexBk5CQwB133EHjxo0BOHr0KEuXLqV3797VMsDyqMt7\\nlgohhBBCiLpr4n9vp4H3Sqbf58m+f1oCEBN1iJ93neOh5xrzx59H3JPp7OxsJt83gb37DtIwwo+T\\np83UrxdE/DsrCAsLq5LxnDt3jkOHDuHt7U3Lli3djx85coSMjAyioqIICwvjr7/+wmKx0LJlS7y9\\nvUt8PavVyp49e/Dw8KBNmzblGoPFYuGvv/7CaDSW2Xzy4MGDZGVlERUVxbFjx/D09KR169ble7Oi\\nVqnLcz5FUcivRAWTMaD0HWOqS5krFaZNm8bWrVtp3rw5AAcOHGDUqFHs2rXrsg9OCCGEEEKIumz6\\njCfp2WMDfgGe9OgKBgNs2u7BtDkmFr3zeqG/zvv5+bF0+Yfs2LGD06dPExwcTJ8+fYr9C35luUoc\\nXCUPLgEBAWRkZHDu3Dnq169f4fIHm82GqqrlGmtubi5Aod0mynr9nJycQveFqG0cGipnqKgyeyrY\\n7XZ3QgGgWbNm2O32yzooUT5S56V9EiNtk/hon8RI2yQ+2ibx0b6EhASaNm3K9oSf2H24F9Oe1nH/\\nkzqWbmjOkvfXM3jw4CLnOBwOvL29iY6Oxt/fv8p/L3f1U3CVPrj4+/sDBZP3vLw8VFXFaDQW2o2h\\nOBeWI5S3BMKVIPD19S3zWNdru86pyiaN8j0kqpNdr6vwTSvKzId06tSJu+66i9tuuw1VVVmxYgWd\\nO3eujrEJIYQQQghR57Vs2ZLHHp9Ddvb/sXfe4U1Vbxz/3KzuBbTQBW0pS0bZyJIh0wIiKuAAQVFE\\nkZ8CoqKiKCqIqCgulogDRVRUQJAhU5AhyCpQoJQWCt27aZKb+/sjJralbVpa0gDn8zx52iT33nNu\\n3tzcc77nHTnIskxgYCDh4eGlbltyYm40Gqsth0B+fr7teCVDGjQaDZ6enuTm5pKSkgLYz6dgxcXF\\nBaPRiMFgqNCkvzKeCtZzt4orwlNBcL0ia67GVaFiQt21xm5OBb1ez0cffcSuXbsAS+bZJ554wqlK\\ntdzI8TUCgUAgEAgEghsbs9nMwYMHbc89PT2LeQoXJTs7m9jYWNvzyMjIK0IVKtv2osWLWLjwXfR6\\nMy1aNGfggKGMGTPmim2TkpK4ePEiKpUKs9mMv78/9evXt9uGNR9DWFgYtWvXLndbWZY5dOgQkiTR\\nunVrVKryV2MzMzM5c+aM7Xl4eDi1atWy2yfB9ceNPOeTJIksU+UFMR+NwSk+E7tyiKurK1OmTGHK\\nlCmO6I9AIBAIBAKBQHBTkZ+fD1i8AUwmU7lhAqV5KlwtiqIw7tEHOXJ8DdPekvH2aELs8WTmfzid\\nc/GxvPrKG8W2j4+PZ8mSTzhxIhaNVk2rVi15btpLBAUFldtOZcpKWr0U3N3d7QoKRY9txZkWPgWC\\nyiDbCSVyZuxeqTt37qRv3740atSI8PBwwsPDiYiIcETfagSz2UxmZuZ1kTdCxHk5P8JGzo2wj/Mj\\nbOTcCPs4N8I+zo/VRtaJtNXjwGg0lrn6WJ2iwr59+9i85Ve+2yTRpYc7Li4etGoHy9fm8cEH75GY\\nmGjbdseOHQwa3IfQpjFMfV3DhGlg0Kymc5c2JCUlldtOZcpKVib0Abgi9KM6wx/ENSRwJDLqSj+c\\nBbuiwiOPPMLkyZPZuXMn+/btY9++fezdu9cRfXMosiwze84bhNb3J7R+Xer4+/DUpPHk5OTUdNcE\\nAoFAIBAIBDcwVk8FT09PtFotiqKUKRZYX3dzcyv2/Gr4ftUKho+RcfeQyMm0JGL09MnFv57EgKFa\\nVq9ebdt2ytTHeeNjiTvu0ePpJVHbX8W0NxT63pnP3HfeLLcdq/dARUSFyiRpBMv579u3j99//53D\\nhw9XayUMgcCRmFBX+uEs2BUVfH19GThwIHXr1qVOnTq2x43GU5Me46e1c1m0TsXh7NqsPexJUvZK\\n+g24zWm9Fnr27FnTXRDYQdjIuRH2cX6EjZwbYR/nRtjH+bHaqKjLv71Vfevr1pX8qogKen0+Hp4W\\nj4jcbMsk3tMn29IXDzN6vR6ACxcucOZMHAOH6fD0tkz6JUlBqzNy/2Nqfvzxu3LbqaingqIolfJU\\n2L59O2HhQfyxczmXs9fx06+f0KRpA06cOGF334ogriGBI5HRVPrhLNjtSa9evXj22WcZNmxYsRil\\ntm3bXtOOOZK4uDi++24Ff5z1xsvborMEhaqZs9SVe7ucY82aNQwdOrSGeykQCAQCgUAguNGQZZnC\\nwkJUKhVubm7odDry8vLsigrW6gxVERX69olm5hvf8ejTkJ/zr6jgnYPBoPDbTwoTf+0DWCor6HQq\\nVCrw8M5F62LA1b0ASQJXNwmjsfwFuKKigqIoZXoT6PV6ZFlGq9XaDWNIS0tj2N2DePcbLQ1CQZ+n\\nw8PHwK7t+UQPup2TJ+LRXFU2fYGgZnCmcIbKYtdTYc+ePezfv5/p06fbEjbeaEkbf//9d24f7IaX\\nt4qsVF9OH2pMdpoPKpXEkAfMrFn3Q013sVREnJfzI2zk3Aj7OD/CRs6NsI9zI+zj/GzdutW2Mu/m\\n5oYkSZX2VKhISEFZREdHo5Hq8/IkN3KywdW9gOTLRp64T6FTx+60bt0agPr16+Pt7cfubSZUajMN\\nW8QSHGHJt7D6GxP9+w8otx2VSmU3rAP+89ioSOjD8i+/4LYBWrr3dUGjtYgaWhcDwx92w9e/gN9+\\n+61Cn0F5iGtI4EiqK6fC+vXradq0KY0aNWLOnDlXvP/1118TFRVFq1at6Nq1K4cPH65y3+3Kd6Vd\\nTJcuXapyw87AyZMnSUpKIicnB6MR0i/VJvVCAAD52R54187CZFLQqIXKKRAIBAKBQCCofqz5FKwi\\nQXmigizLmM1mVCoVrq6ugMWLoLzV//JQq9VsWL+d559/hjkvxpCvz+LcWZmHxozhrTfn2baTJIlZ\\ns+bx9KiHeWepQvc+WgoLYdVyA59/ILFzxyt229LpdBiNRgwGQ5leCJUJfTh58ghRt5oAF7Q6i1Ch\\nc7F8Zm06mzl16pTdYwgEzkR1eCrIsszEiRPZtGkTwcHBdOjQgSFDhtCsWTPbNhEREWzfvh0fHx/W\\nr1/PY489xp49e6rUboVny5mZmaxatYoVK1YQExPDxYsXq9RwTRIbG8voMcOJO3ea0IbunPwnm4CA\\nAOJP1MHDy7KNbFJjNCr8tEzFvDkjarbDZSDivJwfYSPnRtjH+RE2cm6EfZwbYR/np2fPnpw5cwb4\\nL5yhPFHB+ppOp0OlUtlKUJpMpiuqIFQUX19fnnpqKpmZmfj5+REeHm5LAlmUe+8Zjkql5tUpz5KY\\nmITJaKZzl/Zs/P0jmjRpYrcda1hHYWFhmZ4I1iSNFREVQkMjiDlqmYT51k1DrTXhVTsLgNijavp2\\nCrF7DHuIa0jgSKoj8eLevXuJjIwkLCwMgJEjR/Lzzz8XExU6d+5s+79Tp07FqrxcLeWKCvn5+fz8\\n88+sWLGCQ4cOkZ2dzerVq+nevXuVG64pcnJy6N2nG/dPVfPBhBDUaonE441Z+JLMwrfzuOfRPAKD\\n6nLutMKrkwuoH9yafv361XS3BQKBQCAQCAQ3IFZPhcqKCmApp2gymTAajVctKhQWFqLX6/Hw8KBZ\\ns2blejzcPexuht01jJSUFFxcXGwlMCuCvQoQsiyj1+uRJMn2WZTHmIcepkXLt7h/gppmraBWvTQA\\ntm8o5MRhM3feeWeF+yYQOAPVkXjxwoULhIaG2p6HhITw119/lbn9kiVLuOOOO6rcbpk5Fe677z5a\\ntGjBtm3bePrpp4mLi8PPz4+ePXuiVl+/SSSWf7mcWzpKjHzKF7VKRcrZush6H8ZO9+XIkTjmv6Ln\\ntUk5LH5bQ49OT7P6pw2oVHZTT9QIIs7L+RE2cm6EfZwfYSPnRtjHuRH2cX42bdqEwWAoFs5QEVHB\\nKiBY/1YlWWN2tqXag7e3d4VCKCRJIiAgoFKCApR/XiaTiePHj5Obm4u7u3uFxt7BwcF8+ulSHuiZ\\nx8sT9CxfkMczDxQydZSRH1b9avs8q4K4hgSOpDpyKlQmDOqPP/5g6dKlpeZdqCxlyiExMTEEBATQ\\nrFkzmjVrdl0LCUXZ+edmug6y/FAln6lHQbYbKrWZwKZJdOyrYeCtz9GuXTvUarUtOY1AIBAIBDcL\\nRqMRjUYjar0LBA7AWrLR3d3dds1pNBpUKhWyLCPLcrExuFU8KOqpUPT1qyEryxIy4O3tfdXHqAjW\\nPhcWFtpeUxSFDz58nzlvv4F/QC08PWpRt54/s9+YR+PGje0ec8TwkXTr2p0vli8jMeYsvTq14vMF\\no/Hz87tm5yEQXCsqklNh/9Y89m/NL/P94OBgEhISbM8TEhIICbkyFOjw4cM8+uijrF+/vlqulzJF\\nhUOHDhETE8OKFSvo1asX/v7+5OTkcOnSJerVq1flhmsKX59apF0yA+BZJxujXktAZBI6NyNplxSb\\n6irLst2kN2lpaZjNZvz9/R3S95KIOC/nR9jIuRH2cX6EjRyDoih88unHzHtvNufOXMC3lhfjxj3K\\nKy+/Vq4bsrCPcyPs4/y0bduWpKSkK3II6HQ69Ho9BoOhWH6D0sIf4OpFBbPZTE5ODuA4UaGop8Jb\\nc2ax/Nv3eWtdXXzdwshNc2H/n6fo0asLf+8/QmBgoN3jBgcHM/2FF69Jn8U1JHA22vf0oH3P/34v\\nFs5MLf5++/bExsZy7tw5goKC+O6771ixYkWxbc6fP8+wYcP46quviIyMrJZ+letb1KxZM1577TVO\\nnDjBe++9x0MPPUTHjh3p0qVLtTReE4x64GF++qyA3GwZD788gpsnoHMzcuqwnqN/5TFo0CCbIizL\\ncqnH+PPPP+lyW3vCIkJo2Kg+bTs05/fff3fkaQgEAoFAUG1Me34yHy19hSnLvNhias+Hu8P4O+4b\\nBkT3LvNeKBAIqk7JfApWygoVqG5RITc3F7PZjJubW5kVGaqLojkVFEUhLy+PuXPf5vWfgmjY0h1D\\nng6NVuKuiZ50GerChx/Nv6b9EQicDRPqSj9KotFoWLBgAf379+eWW25hxIgRNGvWjM8++4zPPvsM\\ngNdee42MjAwmTJhAmzZt6NixY5X7XuFkAe3bt2fevHmcO3eOt956q8oN1xSdO3fmzsH38UiXZNYs\\nz+TovnyWvZ3BU/1S+PjjRXh4eKDRWBw4TCbTFfvv37+fwXf2p/fjWfyYGsWPaa0Z9rLMA6PvZtOm\\nTQ49FxHn5fwIGzk3wj7Oj7DRtefChQssWrSQORsa0KKLF5IkERLpyvSvQ0jPO8vatWvL3FfYx7kR\\n9nF+rDYqzVMBrhQVrOJBdeVUsOZTqGx+hKvBWq1CURRMJhMHDhygfmMPgsJdMBZoMcsqNDoZtU6m\\n9whPNm0u+7fHUYhrSOBIZDSVfpTGwIEDOXnyJKdPn+aFF14AYPz48YwfPx6AxYsXk5aWxsGDBzl4\\n8CB79+6tct8rnYFQpVLRo0ePKjdcU0iSxAfvf8LcN5axZ1Uz3p/oQmZMT9av28bIESMByvVUePX1\\nFxj9egC33++PWqNCpZLoOqQWEz8O4sVXnnXkqQgEAoFAUGXWr1/PrXfUwruWBmOBhstHgihId0et\\nlugz2p3Vv6ys6S4KBFXm8uXLHD16lLy8vJruig2DwWDLmWBdxbdSllhQ3Z4KjsqnYKVoXgUXFxcK\\nci0LeHkplprurj4Wz42CXPMVn4lAcKNTHYkaa4qq1624DpEkiTvvvLPMUjPleSps+n0b45dbEjim\\nx/pjNqmo0+wyXYfUYvaDB8jJycHLy+vadb4IIs7L+RE2cm6EfZwfYaNrjyV/kOV/fYY7skFNXqoH\\nbrXKTgRlRdjHgslk4uTJk2i1Who1auQ0SS6FfSyxw+OffJg/d+2mdj0P0i/l88i4ccx+Y+5Vl2Cs\\nKrIss27dOjZt2YC7mwfBwcFERUUV+96U5qlgMpkwm82o1WrbAlhVRAWDwYBer0etVuPp6VmVU6ow\\nLi4u5OfnYzAYaN++PQXZag5tzcffuz4S4Fk3B0VR+PnjHEbc+z+H9Kk8xDUkcCTOJBJUFueslVjD\\nlCcqaLUaDHozigKGHBdMBVrMJhUmo+W1G6VKhkAgEAhuDgYMGMDutelkp5swFlgmMsY8F2RZYfOX\\n+dw5+N4a7qFz88XyZTSICGbg0B7c1qcjzVpGsnHjxprulgDIycmhR++uBNx6ns8vdOTD462Yf7g1\\nO498z/gnHqmRPmVmZtK5W3umvfYohXX3k6X7h5dencqYRx7EbDbbtitNVCjppQDFRQVFUSrVF6uX\\ngpeXl8OEsKLnpVar+ejDRXz8lJGDW3PRm7I5fSSLmSMvoE8J4OGxDzukTwKBs3A9eyrYFRUuXbrE\\nI488woABAwA4fvw4S5YsueYdq0msokJp4Q933jWYNZ+lYDb999HJBjUbvkilZ++u5WbJrm5EnJfz\\nI2zk3Aj7OD/CRteekJAQxo17lOcHxHP2HxOKopB+2cTbo5PxdYsgOjq6zH2r2z45OTm2xHXXAyu+\\n/YYXXnmGyT+EsyC2HZ/Fd2T42z6MfOBu9uzZU9Pdu+mvny+Wf0FolMTwF+vj4qbGLEvUCXHl2e8b\\n8eOPPxIfH+/wPj0z9Sn8W2Uyd29zut8dinegzHO/hHPgxGYWLV5k2640UaFkPgWwhCWr1WpbnoKK\\nEB8fz9y5c/ngw/kcOXLEYV4KcGVZyejoaGa+8haXj9fj5ZHHmf1gHt1bPM4fm/906Ji6LG72a0jg\\nWKojUWNNYVdUGDNmDP369ePixYsANGrUiPfee++ad6wmsXoblPbj/Norb/Hbp7l89fplstNN5Gaa\\n+PnDNL6ckcqcN993dFcFAoFAIKgyc+e8x/jRL7Nhkcx7T5xnxew0Wobew/q1m21C+7Vk8+bNdOzS\\nhoC6dahV25c+A3rw999/X/N2q4KiKMyYOZ0nP4+kUQcfFDOARPs7/Bk5K5TX35pR01286dmybT2d\\n7rbkCsi76EnaoboY87S4eWpo29efHTt2OLQ/ubm5/LDqRx54IxRJkjDlWcQBjzoy970WxCcL/6t2\\nUFRUsHoglOapUPR5RUIg5sx9i6i2zdl8diGXVAdY+ctyBg7uS3JyctVPsAIUrQABkJqaSnh4OM9P\\ne4n4uIvEnjjPjJdfdajQIRA4C9WVqLEmsNuT1NRURowYwezZswGLOuqIAUZNUl74Q0REBLt37Wf2\\n27NY8cZJzGaFyEat2bHte5o1a+bQfoo4L+dH2Mi5EfZxfoSNHIMkSYwd8whdOnfDZDKhVqvx9/e/\\nIiN9SarDPps2bWLEA8MY81FjJg/ti2xS2L48kT79e7H9j120aNGiym1cC5KTk0lJTqFFj8YoZsg4\\nVge1i4xP4ww6Dwvgy2m7arqLN/314+7mQX6WZSxn/HcCb8rXovUwkpcpO3wlPCUlBU8fHT51dBjz\\nNSiyils610GtS6FBSw8S4s/YtrVWSjCZTJhMJrRabZmiglarpaCgwK6osGXLFt7/aC5zDnfGy9ub\\n3FM+dLrXxNrlFxkz7kHW/XLty6OXFEsuX74MQN26da9521fDzX4NCRyLM4UzVBa76oCnpydpaWm2\\n53v27HFI2ZmapLzqDwDh4eG89cZc4uLiAAgMDCQoKMhh/RMIBAKBoLopKCgALPd9vV7vsCz5z780\\nhYc/bULHuwIBUGugz2MN0OfKzHzjZb5f8ZND+lFZ3NzcMBpkDHozKtkVs0GN2aBCMUNuhhE3d9ea\\n7uJNz33DR/P09LH0fTgIs9EytjMbVSTE5HFqXwb9f+jv0P7Uq1eP/BwjKQl63PAHwMVPD8DJPdk0\\natKw2PY6nQ6TyYTBYCgmKpRMMFnRZI0ffvIug18IpVawG/mJ/5ak9DFwz6sNmRi6jfPnz1O/fv2q\\nn2g5FBUV0tPTMRqNuLm5Oaz6hEDgzFzPooLd8Id58+YxePBgzp49S5cuXRg1ahQffPCBI/pWY5Tn\\nqWCl6HtXW8anqog4L+dH2Mi5EfZxfoSNHIc1l0Ht2rWRJImCgoIyxXUrVbVPRkYGMcdO0n5IPRQz\\nZMf4knvGUkGp+4PBrF937VdOrxZvb2+63daFjYsvYsq3rtFImA1q1n5wkZEj7qvR/oG4fgYOHEjL\\nxp14tV8McQfzyUoxsOenNF7pe5x3582364lT3bi5uTF27FiW/O88eckW0Sn22AWyUgx8Pf0Czzz1\\nXLHtS+ZVsI43S/NUKPp+WZw5e5qIdpaFQVP2vyUpvY3o3NSENPXj3LlzVTi7ivHPP//w+RdLefPt\\nWUx/6XliY2OpW7eu01RMKcnNfg0JHMv1nFOhXE8FWZbZvn0727dv58SJEyiKQpMmTa74MbvRuF5E\\nBYFAIBAIqgurp4KHhwfu7u7k5eWRl5d3TVcQVSoVigJmWcGc54Kcr0HO16CYc5BNCmq1cxepev+d\\nj7itV1cKk/xocasOk0Hhp4XxHP9DZumuV2q6ezc9KpWKlSt+YtHiRWxcvZ6c7BT8AwJZ+fUsevTo\\nUaVjnz9/nm+//ZbMrAy6dunGgAEDKlQB7K035vLQ2At89XwioVFqYmLOc2pPLk9NnMTw4cOLbVtS\\nVCgv/AHsj0cbRkRy9kAC4a39kAs0ICloPI0YCmQSTmQQFhZWoXO/WpYuW8q0FyZz58QuNL7Vl4wL\\nqSz47H3OnT/L89OmX9O2BYLrAWfKkVBZJMVO/ZkOHTqwb98+R/XnqpAkqdJldMqjsLCQo0eP4uLi\\nUmYsZ3x8PKmpqQC4u7s7PJ+CQCAQCATVhaIoHDp0CLPZTFRUFElJSSQnJxMUFERgYOA1bbt7r1uJ\\nGltIu663YMy0JHHzapbB6ndO4hbflmVLvrqm7VeVc+fO8eGC+Rw7fhS1WkXbtu15etJkateuXdNd\\nE/yLwWDgyJEjgCW8p0mTJlU63tx332bWG6/TfkR9POtpiVmTiovRm03r/6hQboDTp09z9OhRTp06\\nhZubG0OHDiU0NPSK7S5fvkxiYiIBAQGEhoby999/oygKbdq0QaX6T3DLyMjg7Nmz+Pr60rBhwyuO\\nY2Xz5s2MGncvL23ogiYnAI2nEc/Gmax4Phb98VB++/XalUJNS0sjPLIB0/d0x1cTiCnTIoQU6jKZ\\n1Xsd+3YfJDIy8pq1L7gxqO45nzMhSRIrlcGV3m+49KtTfCZ25ZBu3boxceJERowYgYeHB4qiIEkS\\nbdu2dUT/aoTyqj9YEZ4KAoFAILhRKCwsxGw225IxW93CHZFX4Z3Z8xk0ZACqaaE06qhGNirseCeR\\nPz65zJ5dr13z9qtK/fr1eeD+UbZBXd26dYWg4GQUHadVdcy2bds25n0whxf/6YdfiOU6iX5Z4ZcX\\njzDq4fv5fe1mu33Jzs6mfv36REdHX5EfoShFPRWMRiOKoqBWq4sJClBxT4Xbb7+dSROm8sHQz+k0\\nUEbxzuSvX8/hYvZj0/qv7Z57VVi1ahVRdwQR2MQbfYLZ8qJaofYtcOuoUL76+ktefWXmNe2DQODs\\n3NA5FQ4ePMixY8eYMWMGU6ZMYerUqUyZMsURfasxiiZqLEv5KSkq1IRCJOK8nB9hI+dG2Mf5ETZy\\nDNbQB2s2/IqKCtVhn06dOrFq5WpyY/z4etpJvp9xBpfEFuzeuZeIiIgqH/9aU1BQUGwMUFhYWIO9\\nKY64fiwUnWyXt2BUEeZ//C63P9cIvxAPzHoVpgwNkiRxx4zm7Nu3z25egvT0dBRFwdvbG61WW66N\\nSooKRV8rSkVFBYDnp01n4adLaFy7G6HGXrw7czEH9x0hICDA7r5VIT09He9gy1qm2sNiA11AIZIa\\nfEN0pKanXNP2rxZxDQkciYy60g9nwa6nws14MUmSZCvjI8tyqSU0S96UjEbjDZ9rQiAQCAQ3JlZR\\nwc3NDbDUkrfeBwsLC2215a8VgYGBPDlhEl5eXuTk5ODn53ddCArwX4JLV1dX9Hq9Le5d4DwUtYks\\ny5jN5itW+yvKmbOniZ7SwHLcBFfkbDVS43y0XhDcrDbnzp0rNzeBtaJaRbxZiooKZeVTgOKigtWj\\nuCxkWcbT05OBAwYSFRVVoTwQ1UHHjh1Z9Mx8lDkK2lpGVG7ZqN0sHgsxv2Xy7JiuDumHQODMOFPi\\nxcpiV1SYOXOmLX6l6I/UjBkzrmnHahq1Wm2rDVyaqFBUMbYqyI4WFUTtXOdH2Mi5EfZxfoSNHENJ\\nUQEs3gpZWVnk5eWVKSpUh30MBgM5OTmoVCoCAwPJyclBr9dX+biOwioq+Pn5kZSUdM08FbKysvhs\\n4aes+uV7zGYzd95xF088/kS5k1Nx/VgouRBkMpmueszWMCKS+AOXCe9YB6XQIkzIWRrMWgMXYtLK\\nFRTy8/MpKChAo9Hg6+sLlG8jjcbiBWEymWzXRGn9VqlUqNVqZFkuczHMSl5eHoqi4OHh4TBBAaB3\\n7974utblx+nHuXNmUzRuYJbNbHz/NJlxZu655x6H9aUyiGtI4Eiu50SNdmVaDw8PPDw88PT0xBuR\\niAAAIABJREFURKVSsW7dOoeUnKlpyqsAoSgKsiwjSZJtACbyKggEAoHgesU6MbaGP0DFQyCqSnp6\\nOgA+Pj629vV6vVMknqoI1s/O29sblUqFLMtVdrEvSWpqKu07t2PlgeVEzQih3esNWHt6FW06RHHh\\nwoUKHSMxMZEXZ7xIn+je3P/QfWzZsuW6+YyrSskxWlXGbJMmPMOm2afISMxDMVkW20xZata9doz2\\n7TtUyEuhVq1aFSqhKEmSTUSwXodl5WCoaAhEbm4uYElY6UgkSWL9mo3kH6zDcw028uHA/TwfvpnT\\nP6r4Y+O2a+4NJRBcD1zP4Q92RQVrDoUpU6bw0ksvsW3bNs6cOeOIvtUo5YkK1tfUanWl4tiqm5sx\\nNOV6Q9jIuRH2cX6Eja49sixjMBhQqVTFBvYVERWqwz5WUaFWrVq2+6qiKNdFGIHZbC7m5WH9/Krb\\nW2HGazOo19uLEd/eTuO+9YnsHcrdn/egyf0hTH2h7DxXVvvs2rWLVm1bsiPrd4If9yK3w2UefPx+\\n/jdl0k0hLFjHaNaJfFXGbD179mTq/57nzdYb2bv8PIfXJLJhxmkubDTz1effXLF9Wloa78yby/AH\\n7+WDBfNJTEws5l1i7xqyigpWMaAsD4uS5SfLIicnB3C8qAAQEBDApvVb+XPbPmZO/IDN67azd9cB\\nwsPDHd6XiiLuQQJHcj2LCpX2scjLy6uwKn49UzRZY0msNyOtVlujooJAIBAIBFXFOil2dXUttnpq\\nFRXy8/OrFINur22rO7iPjw9gmZwbjUb0er3Tr15aPSpcXV1Rq9W4uLhQUFCAwWCwfX7VwTdff80T\\nB4cBYE5SoxglVCEmuj7TkrcbfIVxibHMFWxZlhk5agSDl9xGs8H/5aloO6opH0etIObECQ4fPoxG\\nq+HeYffw/LMvUK9ePcxmM2azuVxX+usF60Tb1dWVgoKCKnuSPDt5GsPuvJuVK1eSn59P90ERREdH\\nX5Hs8MCBA/SP7kdE//pE9GzAxcQE3nz7DY7GHOXF51+sUFtWscDa57JEhYqMR81ms00krAlRwUrj\\nxo1p3LhxjbUvEDgrziQSVBa7d4qWLVva/jebzSQnJ9/w+RSgYp4KGo2mRkUFEefl/AgbOTfCPs6P\\nsNG1p7TQB7CI625ubraJf2mT5Krax+ql4OfnZxM0XF1dyc7ORq/X24QGZ6XkZ3ctPBUURSE3Kxev\\neu4oBjBftIxP5DwVbhGWcMzvvvsOvV5Pp06dio3bevbsybZt29D4qGg2OALFDMoFNWgh/Xw2OVm5\\nmLsUMnLBXZj0JvZ+upvW7VvTsVMHNqzdgMkk075Le96Y8QZ9+vSptnNyNNYxmru7OwUFBdUyZqtX\\nrx79+vWzPS/p0WM2m7l75DD6fNiFFvc2QTmjRslUEXpvAO/1f5e+vfvSsWNHu9dQSRGhKuEP1nwK\\nbm5uN4RY5AjEPUjgSG7oRI1r1qyxucZpNBrq1q1bbk3dGwXrj21pngrOIioIBAKBQFBVSkvSaMXD\\nw4OCggJyc3OrbeVdURR27tzJvn37cHV1pVWrVtSqVcv2vqurK8B1kayxpKhQURf0yiBJEm1vbUPM\\nmjiad2tSpHGJf95KxNXDjbe+ehOPIA+ee/U52ka15YcVP+Dt7Q1Y8jH4hVn+V9JUKMmWQeup+Ze5\\n57VhRI5oAH4KaOHWZ9tz8Msj5EZl8sziJ9B5ajmxOpbho+7li4XLGTx4cLWdl6NQFMU2brN+t6oj\\n50VRzwGDwUB2dnaxpObbt29H8TDT/J7GKEZQslQggUdjF9o/1YLPln5Gx44d7bZTUlSoiqeCNYTC\\ny8vL/gkKBAKHc0MnanzppZcICwsjLCyMkJAQtFoto0aNckTfahRr+EN5ngparfaaDCAqiojzcn6E\\njZwbYR/nR9jo2mNPVID/Js8lqax9UlJS6NitIyMeHcHP8b+w/u8NTJ46mXW/rbNtU12iQmpqKrPe\\nnEWvgb0YfM8QVq1aVepCQVVwhKcCwCsvzOS3p/8i5YAlHl4VauL80Yuc2XiRMYtGc/eKuxiwtB+P\\nxz1KZmAGYx4bA1js07p1a87uSsCoN6FkWoZ9heZC0uMyCe/YABJUcFgNMSpOzD/DbY91pcf/uuHm\\n7opao6b5vU0ZtLwfk59/5rrMv1A0ZNU6ZquOhSDrWNDT0xOdTofJZLJdSwBJSUnUafJvQsY0FSgg\\neZuRtFCriS8XkhKBiudUAMtiVllhSBURFWoyn8L1irgHCRzJ9ZxTwa6ocPTo0WLPTSYTBw4cqJbG\\n169fT9OmTWnUqBFz5swpdZtJkybRqFEjoqKiOHjwYLW0WxGuh/AHgUAgEAiqgqIoFRIVrKuwVWXk\\n6JHouroyJuYRuk++jagJrWk3uwOPPfkYx44dA/4TFYpO0OxhMplYvXo1b7zxBsuWLePAgQM0b92c\\nn878Qu0nAjEPUjFlzrPcee/QaqvMoChKmZ4K1S0qREdH886seeybG8sfrx9kyeDVLH96NYH9A/Gt\\n74t0VgWXJNRaNbfP78WmjZtISEgAoGHDhtzWvTu/PbkLOV0BCeSgfI6eOIqqkQK+ltfIl8g9mk+L\\n7s3htAqOquGYGswQ0SeMjKwMzp49W63n5QiKigrlje2qclyrV0h2drbt/RYtWhD/ZyKy0YyS8u9w\\n298MQOL2S0Q1j6pQO4qicOjQIXbs2MH58+fLvA7tjUcVRXGKfAoCgaBsbkhR4c0338TLy4sjR47g\\n5eVlewQEBDBkyJAqNyzLMhMnTmT9+vUcP36cFStWEBMTU2ybdevWcfr0aWJjY1m4cCETJkyocrsV\\npbzwB+sPtkajKXaDcrSCL+K8HEdBQQEzZr5CUFgwOlcXWndqw8qVK+3uJ2zk3Aj7OD/CRqWjKAp7\\n9+7lo48+YsWKFTa35spSWFiI2WxGp9OVGmO9bds2Zs15k3HjxuHt680DYx+0TVahcvY5efIkfx86\\nSPdZtyEpElKGxU281q21af1kGz785EPAMjlSq9XIslwhwf7kyZNENG3I1Hef49fcjcz9+X269elO\\n6xfa03dJNJGDm9ByTBuG73qQE2knWPr50gr3uTysSRpdXFxs3o1WTwWDwVDtY4JB0YOZO3suT459\\niqXzvqRJsyb49vJCCba0I12QIBt0HjqC2wQTExNjs8/Xy1YQnB/Oxuf2sPODv/lm8G+gQNzBc9DQ\\nDFEyNJG5kHwBk5cBvBTLCLEQKOIwUpEyiM7GtUquXXSBqTRRoWXLljRrfAu7Xz6EUgi4KuCtcG5H\\nIke+PMmEx54Ayr+GNm/eTGSzRqza/BPb43bx6tuv0blHZ5KTk6/Y1t655eXlYTabcXV1vSnCmKsL\\ncQ8SCCpGmaLC9OnTycnJYerUqeTk5Nge6enpzJ49u8oN7927l8jISMLCwtBqtYwcOZKff/652Da/\\n/PILDz30EACdOnUiMzOTy5cvV7ntilCR8AeNRoMkScJb4QbHZDLRb1B/Vh3+md6/3MNj6S8SNiOK\\nSS8/zdvz5tZ09wQCwU1Geno63W7vTvTIISw9uoLXvp5NcIMQfvjxh0ofqzwvhR9+/IH7Hn4A94G1\\n6fnREEYd/B9ng5Po1O3Wq7oXnzhxgpD2wah1aqRUQAbFXQFXCOoSzNET/3lGVjQEQpZlBgwZSLPn\\n2jB0+yi6vdWHLu/djqSRaDWqHVIyqM6BlAlqnYY20zqy6ItFle57aVhXfYsmuFSpVLaSmNU9JsjM\\nzEStVtOlSxe6detGcFAwaafSoZ6CEvivgJEqYZbNpJ1OJTAw0Lavl5cXs2e9zfTnXuT+20ezdN5y\\nli38grVjNxG39TyKSkF2NaMOktj70z5obLYICwAGibObzuHn4+fUpf/K4lp5KhQdC1pzFOTm5hZb\\njPrx2x/RnfJk60t72L5gN9/0+YWf7tnId1+vJCwsrNzjJyQkcPfIe+j9zWCinutIk9Etuf2rQUhd\\nXBk2ctgV29sbi1qFR+GlIBA4LybUlX6URk1EA9gNf5g9ezYZGRns3buX7du32x5V5cKFC4SGhtqe\\nh4SEXFGqsrRtEhMTq9x2RahI+IP1B7ymRAUR5+UYfvrpJxLyL9J35XD8WwWiddcRHt2U6I2jeX3W\\n62RmZpa5r7CRcyPs4/wIG13JA2MfoLCFxAOnJ3HbJ4MYsOY+Bm0azSMTHrWFEFQUq6hQsvKD2Wzm\\nmWmT6fPtPQT2C0OlVuHu4s6ts/oSOCicefPfBSpnn9DQUFJiUlD0imVVHVD+nfemHkshNLi+bduK\\nigqbNm3C7KXQYlw7UBRUyQqqGBPtO7dHF6tGlQBSGqjiAQW8w3xJS0urcJ/Lo6zP7lrkVTCbzbZV\\ncF9fXwAmPDyBA3P/Rp+phzr/eitkSvyz+DCB/kG0bNnSZh/r/gEBAYwYMYJevXox/N7hfPTOAraN\\n380HwYt4v+4n6C66cerbOLa/vhu9vhCzbCb2l7OsGf077815/7r3VKhO79KiY0GNRoOHhweKotjy\\nFoDluzFj+gyeemwS90Tdx5sT53Dx/MViVSPKuoY+WfgJTR5oQWjPcND9+7m7qOg8qxcnTp/kn3/+\\nKba9SqVCrVYXS0xZFJGk8eoQ9yCBI5HRVPpxxTFqKBrAborJRYsW8cEHH5CQkECbNm3Ys2cPnTt3\\nZsuWLVVquKI3ppI/+mXtV903OpVKRZs2bZBlmUOHDhV7LywsDJ1Ox/nz59Hr9QQFBeHp6UlycnK5\\nE0zB9c0HmhepVasW3t7eJCYm2m7afn5+NdwzgUBwM3Lgw524urpiMBgwmy2x2i1atKjUMaz3r5SU\\nFDIyMq54P6HHQjw8PGjatCn5+fm2gcnf7GLuW29fVb+/aLiUwMBAMjKujNH/dvkKwFKuLzg4mPj4\\neFJTU+0e813VDAIDA20TJi8vL34d+wMpKSm4ublRq1YtTpw4YfMuqI4xQ5MmTVAUhYSEhGL5H+rW\\nrUtISAjnzp2rNgHD19eXhg0bkpycXCz8BGCun0XgadiwIWq12mbLoufo4+NDZGRkqfsX5Vi6RZTa\\nMmM7xz8+hbe3N+np6WSkZl6XlR8AAgICCA0N5fz586SkpBAREYGfnx+HDx+u0mJQ06ZNMZvNxMfH\\nU1hYaPv+ZWRkkJKSAli+Cz4+PuTn51/1otje+TttfT558qRNHGjduvUV2zZo0AAXF5crvpPW7fPz\\n8zlz5ky1JywVCATVQ3XkSCgaDQDYogGaNWtm26asaIC6detedbt2RYX58+ezb98+OnfuzB9//MGJ\\nEyd44YUXrrpBK8HBwcVubAkJCYSEhJS7TWJiIsHBwaUe71rkM/j7779RFIW2bdsWuzkfOnQIWZaJ\\niopCo9HYblT169fH39+/2vshqFnuGjmM/Ds03DK6LZpTMqocMDWQMNdRsfGe75k+bAr3339/TXdT\\nIBDcBKxZs4ZnP36RAevuQ8pT0J4wY/aVMDVUkbDlDImvH2XPH7vtHkev1/Prr79y7tw5AgMDGTZs\\nWLEV92PHjtH7zj7cFzsJyQzao2YkE0T7SlzMTGLP6PWcORZb6f7v37+faS9Mwy3EnYAO9QhMCObo\\n0sNM+d8UXp7+sm27zMxMzpw5g7e3N40aNSrzeL/++itPvz2Fu7aOQnNEQZJBDpXY/s4mUvalMWL7\\nQ7jnuCElQ5vUjqx66Fs2rd1Iu3btKt33oliT55nNZttYwMrFixdJSkoiMDCQoKCgKrVjJS4ujvT0\\ndIKDg6lXr16x93bv3s0XX39Bdk4WrVu0oWePnleUKrQKHEFBQcXCIsojNTWVs2fPUqdOHSIiIqrl\\nPGqC06dPk5WVRcOGDfH19eX48eMUFBTQrFmzK7xMKsORI0cwGAy0aNECFxcXcnNzOXnyJC4uLrRo\\n0QKj0ciRI0dQFMW2TWUYN+FRYhucp8Pz3aFAQZUOPQMBlcT37ZeydM4ibr/99mL7nDp1ipycHBo1\\naoS3tzc7duxg8vSpnDl5Ghd3V+o3bMDixYvp3r37VZ+3QFCTXI/eUpWhOkSF0jz9//rrL7vbJCYm\\nVklUsBv+4Orqaou11Ov1NG3alJMnT151g1bat29PbGws586dw2Aw8N13312RAHLIkCEsX74cgD17\\n9uDr61ulk60speVVUBQFWZaRJMn2vnUwIXIq3JgM6T+YuK+PWepPWz1xZShIy+Pc5lP07t27Rvsn\\nEAhuHoKDg0k7eRnFbEYqsIjpqmwFFIX0mGRCg0LtHMHizhscFsqLi99k5+VDLP1+OW1ubU98fLxt\\nm6ZNm6I1a0j6Mx7UEqZGKhQNqDIVUr88R9OIxgy+904G3BXNp599avMAKI3jx4+zdu1ajh49ilar\\nZdbMWUR3uIN6/wTQhVv5848/iwkKUPHwhwEDBpAfn0vit2eRZDB7SZgDJBrf15zkg5dZHLaA3/+3\\nlgNz9nDk9YN8/unSKgsK1n6VleCyusMfFEUhKysL+C/0oSidO3fm0wWf8vWybxjQfwBqtbqYC769\\n/cvC1dUVtVpdIyWzq5Oi4Q9F/1Z1zFbyuB4eHqjVagoLCyksLCQlJQVFUfD19a20oAAw/uHHOLrg\\nALlJOeAmYQ6WQCUR+9NxzOnGUhMInj17li1btrBy5Uo2bNjAoLuH4PnkLdx76CU6vjcM6Y56DLp7\\nCH/++WeVzl0gEFwbqqP6Q3VHA1QUu54KISEhZGRkMHToUPr27Yufn5/d5DIValijYcGCBfTv3x9Z\\nlnnkkUdo1qwZn332GQDjx4/njjvuYN26dURGRuLh4cHnn39e5XYr20eTyYTJZLLdNEomaYT/Skg5\\n+sa7detWkZXWAdx3333Mnf8OOyf9Rve7e6DzciXjeAqbp//KY48+esWqUVGEjZwbYR/nR9ioOK1b\\ntybAN4BjSw4QFf3v5NgM+oQ8jsz7i5VLVpS7/+XLlxl67zC6fPcgIR0a4XbKgOwuUbBuFwPuvIPj\\nB4/aRPN358zj8RFP0HXBQCIGNUVfp5BLn54m60Aa1NKhGxxO1unLvLNmIe988B6vv/Qq3//yAynp\\naXTt0JlhQ4Yy6dmnOXkmljqtQnFNlmnauCmTn3qaJ554otx+uri4IEmSLbRDpSp9DUSr1fLzqtVM\\nfnYKeY0zMDRVSD2ZTOyqYyz5dAm9evRk9+7dFBQUEBkZSVRUxcr42aNkKcmiVHdZyZycHGRZxs3N\\nzSa2lIYkSdSpU4ekpCRSU1Px8vJi69attGvXDpPJhIuLS6kJOcuipsY21U3JyX91JGuUZRlFUVCp\\nVLbvpiRJGI1GNvz+O4uXLSUstD6db+1M48aNyz1WWb9xHTp0YPLEZ5jX7l1uGd8G7whfkrYkEL/u\\nNOt//c22sAWWqhND7h1KcloqkT1aknzoMofW7abT+8NoOLI9mjMGJLWKekOaEaUaxAszX2Tbhj+u\\n+vxvJsQ9SOBIykq8WJSErXEkbI0r8/3qjgaoKHZFhdWrVwPw6quv0rNnT7KzsxkwYECVGrUycOBA\\nBg4cWOy18ePHF3u+YMGCamnraiitrGTRcpJWRPWHGxtXV1d2bN7Os9On8deLm5HUEnn5eTz1wAQm\\n/++Zmu6eQCC4iZAkiVVfr6Rn314YdmQT2i4MQ0YBB1ft5bFR4+jVq1e5+y9eupjQu1oS1LsJ6mTL\\npMrsruKWyb1Y9/l+tm3bZhtAD793OB7uHrz0+sv8du8K1Bo1t7RsTkDrMBo/1hVTbS1xoXHUmzmY\\nNe3eYsLLT9N0Wj88QlqyZt1fvHv7e7R6rh9DNj+ANh9cT+i5sP4Y454Yz+H9B4tNiko7TxcXF/R6\\nPXq9vlw39dDQUOa8OZv9+/cTGxdL78gujD72o83Nf9iwYbbwgaysrHIn5va4dOkSny1eyOHjR6lb\\nJ4A7+g0gIiKi2ApP0bKS1YE1V1NFvAxq165NUlISmZmZtkmzdf/K5v8pOrZRFKXG3Y6NRiNJSUn4\\n+Pjg4+NToX2KVuGoiKfCqVOnmDv/Xbb/uQMvLy8evm80jzz8yBWeBkUXmKz8+NOPTH52Ki0Hd0bT\\nwJPzcX/z/Y8/MG/OXHr06FH5EwZefP5FBg0cxOJli0laf4kRUcMYN/cR6tSpU2y7sY8/Qkp9mS7L\\nxuF20US92ir2fb+VxgPaoIszoMmy5FyRvVREjGzHV4+uwGQylVpGViAQ1BylJV4sSVDPRgT1/C8s\\ncPfM4gJh0WiAoKAgvvvuO1asKL7gMGTIEBYsWMDIkSOrLRqg3J6bTCZatGjBiRMngJuvVmtp4Q+l\\n3UhqSlS42exRk9SuXZt33ppLbGwser2ekJAQGjZsaHc/YSPnRtjH+RE2upKmTZsSe/wUixcv5tTp\\nWLw8/Bj37oJiGeXL4p+Yo/j1tlRZUGdbJhpmNwlJkvDvGk5MTEyxzzw6Opro6GgMBgNqtZqgsBAa\\nfhwNGhWadBMRIWEkfn8cnaSj79/PovW2TNjlQhOX952l5QsDUBkUXOMLkdQSAY+14cC6Xaxdu/aK\\nkMeSuLq62hUVZFnm8uXLuLq68vDDD5eZ2d7Hx4f09HQyMzOveuC0d+9e+g++g8C7omjarxWXkguY\\n+MIz/P7HJua/855t0q3Vam2r1uV5WdgjJSWFffv2kZeXR4MGDSokKri4uODt7U12djbp6en07NmT\\nI0eOAJULfQCLsKPT6TAYDBgMhqty4a8OzGYzb709m3fnv49ZDYXZ+fQd0I+P3/vQ7spaad6lZXkq\\n7Nq1izuGDib8idsIX3Q3+uRs5rz3GSt+WMmmtRuKnX9JoeLy5cuMGfcwPX6bSH31vxP+XqAeEMpd\\nw+8mMe58md9he79xUVFRfPjeh2W+n5SUxIb1Gxga/xqSyfJd02aYaR3VGt1pAxpXSx9lHxWKiwpz\\njgFJkq76e3mzIe5BAkdSHTkVaioaoFxRQaPR0KRJE+Lj42nQoEGVG7veKO3GU7KcZNH/hafCjY1e\\nr0ej0eDp6WnLtC4QCG4sFEVh165drP1tHWq1mrvuHFotMfiVITU1lczMTOrXr29zQS+Jl5cX/fr1\\n4zb9bQAVXnFsEBzK6Zh/UOWbUWeZQQWyn2UQkxOTTHB06ZM0az8yUzNwbVaHfEWDe6wBdbaMfmMi\\n3R4aQK1YGcWlALNOwu1AFl2je+J56L8M9GY3FYa6GmoNaMTefXsrJCpA+XkVkpOTkWUZT0/Pckvl\\n+fj4IEkSubm5V7VCazabuffBkbT4eDj1h7XD+1Aekhk6PtycFZ1nM2TgIPr06QP8Nxm3xtZXJuQA\\nLOOMSVOf4YsvvqB+l+YEufihT8/lpedeuMK7szTq1KlDdnY2Fy5cICMjg9zcXDw8PK4qKaFWq61x\\nUWHK88+yctc6Om55Bu9mQRizC4h9ZwOde3bj2N+Hy7V7ycl/0f+LjtkURWHM448QtfB+Qu9qa3s9\\nsH8Ldg/4kM+Xfc7j4x+3vV5ygemL5V8Qcldr/Do2wHysAJXejKKR8I1uTK0ODfjxxx958MEHq+HT\\nuJLY2Fj8bwlB6+WKOc8yNlGZoXbrUBJ2nSR4dGtMvioUV4uIcGrxbgbeGS1EBYHACakOUQFqJhrA\\n7i9Keno6zZs3p3fv3gwePJjBgwfbHQjcKFQ0/MGqgJtMJodONkXtXMdSdGBb0XJMwkbOjbCP8+NI\\nG+n1evoPuYO7HrmfXzQn+NF0mL7Dohk5+gGHlGCLi4ujz6AB1I8Mp1O/HtQNDWLW7DfLvK9Y3evV\\najUmk8luUkOAR8eO4/TSvzDtt5Q6NPqrUbQS59ceJf9sut1J6y2tW5C0+QRmTzV5t7gQdz6e1JQU\\nVHXdQQKp0Iw6R8bNwx1DRgGoJMyuKkw+agoidCBJGBOzqeVXy25fSxMVCgsLef3NWYREhuHu5cmj\\nTz7Ovn377FY0UKvVtsmnNWlhZdi9ezcGF4XQYe1QFSpIZjDrJLR1PAh7uhefLltcbPuqJGuc9uLz\\n/HpkO71Pv037z8YR8eIgak3uwciHHuCff/6xu79areaLr5bz5KSJRN81hKnPTWPp8mUV+n6UxCom\\n1dSiSXJyMosWLaLDLxPxbhYEioLW241bXhuKLqoey75YVu7+pYkKpS0YHT16lIy8bEKGtgFFwS1e\\njzbDhEqtIuzpniz55otixy25wBSXEI9HC4sHjMnHMikw+mtAJeHeom65ZTyr+hsXFBRE+ulLmI0y\\nZg8VhaE6Cuvr8HyqFRtmr2Tf0s3kZeaSfymbI7M3cmr2H8ye+WaV2ryZEOMEgSMxoa70w1mwK9W/\\n/vrrV7xW03F1jqKi4Q+SJKHRaDAajZhMpjJXlgTXN0UHZFVJ8CQQCJyTl2fOIFabQfejr6PSWn7j\\nm7w8lJ0D3+X9D+Yz5ZnJ16zttLQ0OvfoRsDEnty+cgEadxdyTl7k47GLycrOYu6bc4ptbxWx1Wo1\\n3t7ethVpe/kCGjduzJxZb/HVy99Qu3N9MiMgbUccSb+fZO3qNcUmX6Xx0tQXeHzyU/i1CMIrwh+T\\nn5r8IBWH9x6k/fBIVHoFyaig8Y3kj06zcP9fS7zCatv2zzmbQvwPhxj52rd2P5OSooIsy0QPG0Ks\\nlE7kt4/i71cHdsSz5KuvSclI4+UXXiz3eD4+PmRnZ5OVlUXt2rXL3bYkycnJeEb4IyngFm8RCkwe\\nljGCZ4Q/l385VGz7q82rkJOTw8KFi+hxdBYutb3QHbVUcfDoFUnYlP7Mef8dvvn8yzL3N5vN9L8z\\nGnOjWjSdeTd+sYnUbhjMxqVrOTZ8GBt//a1S/anpZI07duygXvemuNT2RJ0r4xWbj76uDn2QCwEj\\n2/HLl+t4auJTZe5fUU+FnJwc3P29LeO5bBO6VBPqXDNGPw2uAd5cKlJNo+i+1rFg8ybN2LLjawAK\\nA7XIHipMvpbvR/ae8zSZ1KSqH0WZREZG0vyW5sS8v4Xmz/bFGGDpk6vGG09fL9y3ZPPL228BED04\\nmuXb/6RJk2vXH4FAcPVUJKeCs2K35z179uTcuXOcPn2aPn36kJ+ff9NMqMoLfyjpOqnoOoGvAAAg\\nAElEQVTVajEajRiNRoeJCiLOy3EoinJVngrCRs6NsI/z4ygbmUwmFi1ezK17XkKl1aDJNll8+Tx1\\nNJp9D/NHL6hWUUFRFHbv3s33P/2A0WQiNysbr95NaDhtsHUDvJoE0Xr1//ikybO8MPU5atX6b3Xf\\nOsnT6XR4enraRIWSCdxKo0/vPoQ3CGfnn7uI33ueoW3vYcz8McWOXxZ333035y8kMKPdDOp2jECl\\nU5Ow7QRIEkH9mhPUvzm4gVrtgXe4P+s6zKHFc32p1TaU9AMJnHpvG3Pemk1QUJDdtoqKCoqisH79\\neo4mnaXT3ldRSSq8jmajalmfwE+GM7vji0x4dHy55+/r60tCQgJZWVmVznXQqlUrLv0Zi8upPDR5\\nYNZK6EMs9/rUzSfpG9W22PZXWwHixIkT+ETUxS24Fiq9jKrQ4kZv8lTjP6Alf335Rbn7b9myhbj0\\ni3R9/zG0MXkEtm+MopaIXDaWnc2eZ+/evXTs2LHC/alOUeHSpUusX78eRVHo169fhTKN63Q65HwD\\nKAruCXowgy7NiD7IBTmvEBdd+SEZFfVUaNmyJRmxSeQnpuOr9QRAZbB4CCX9/A+9uhVPtFhyLPjg\\nAw8y47VXufDbEYIHtsTkZ3n97Je7McZnMXjw4DL7WB2/cd8s/ZLb+vQkfWsc/oObYricQ9zivxg9\\n8kHee3veTbMYeC0Q4wSBoGLYFRUWLlzIokWLSE9P58yZMyQmJjJhwgQ2b97siP7VKKWFP5QlKuh0\\nOvLz8zEYDHh4eDiukwKHYDAYUBTFJh6ZTCanyIYtEFwLFEVh48aNfLpsMclpqXRu24GJjz9Ro7l1\\n8vLy+Orrr9iwbQsebu6MGn4fffv2rbZrMCcnB4PRiGfDukgmM16n81BUEplRXvi2acDFuLLdlyuL\\nyWRixOgH2Lp3F3VGdwedmoRv1xG1aBy6NAPul/SoDAqZt3jhEuCDf8fG/PnnnwwaNMh2jJKiAkBu\\nbq7dtnNzc8nOzqZevXq8OuOVq8r+/sykpxk39hE2b96MLMv0XGZJBnj/mAeJqbUGjxA/knadYsDA\\ngUx8ZwILv1hC7Jq9NGnYiI9+/o0OHTpUqB21Wm37zTUYDHz70/fUfbgbKo0at4t6VCYFo4cadSNf\\n6vVpxbp16xg9enSZx9PpdLi5uVFQUEBubi7e3t7ltr9jxw7e/3QBp+PO0iSyMd27diP50z0EP9iR\\n/EgPFJ2KS1tiOP/FHp7a+1Gxfa82/MHPz4/cpAzMJhmNwVJDXHZTgyRRkJiOnx3hZ/3GDdS6tx1m\\ndy1GTw3aXBMGHw0qnRb/u9uxceNGh4sKiqLwwoyX+HDBAgL6t0VSq5g45WnGPTKO999+p9xruHfv\\n3qSNGYVh/0XUKou9VAYF8kxcWLiLyZNeKbft8jwVit7Hvby8ePLJJ/lqxBJ6vD8GN5UbyAqJK/cT\\nv3AXP/z5V7HjlhwL+vr6suanXxhy91DiWm7Hs1U9sv46j3whl9/XrLfrAVRVwsLCOHkkhm+//ZY/\\n/txOLZ+6LPp5Jm3btrW/s0AgcBqqK6dCTWB3NPHRRx+xd+9ebr31VsDiOpmcnHzNO+YMlBb+UNoN\\nquhzR8Yditq5jsPqpeDq6orZbEaWZZvrcXkIGzk3wj5XoigKj096kh82riXg6TtwbdCMnzYdYWGH\\ntqz5YTXdu3d3aH+2bt1KZGQkXXv3QGlaF++7OyJn5bF+8uN0a9GOVV9/a/c6rAje3t64uLiQcyqJ\\nWgF1QAFJVlDnm0k9FEdIw+oTVOZ/+AF7Lp2k/dF3ULvqQFFgfwIBF814ncu3bafNMVHoosOYnX9F\\nWIN1kqfVanFzc0OtVlNYWIjRaCx1ApOUlER+fr7tHhUQEFClcnJeXl4MHTqUrVu3Urt2bXr27EnC\\nmXh27txJeno6bT9saxOhrracHoCbmxtGoxG9Xo/BaETl4o4u3YB7kuU3OT/YkgRR5aKpkBelr68v\\nBQUFZGZmlisqzH1vHm+8N5egaXfi+cQ9xP91Du3aC/jGS6wZ8S5eHULJj09Hn5DJj99+T3h4eLH9\\nrzb8ITIykogGYZxfvpPGQzoDltwNZpPM+bkbeHlU2a7+AC46F8z5FhvnB7mS8+MhPO9tbTlOngGd\\nf+U8KatDVFi8dAlLfl1Jx1ML0PlbSkGGpefw7cA3CP+wPk9P+l+Z+3p4ePDOnLf5fNqXaIa3x7NF\\nMMbLOZycs40I97oMGzas3LZLG7NJkoRarUaWZWRZtl0Hb86chSRJ/DbhO+o0qIchp4DMjEx++3kN\\nkZGRdo/bpUsXEuPO8/PPPxMfH0+TqU2444477AoK1XUfcnNzY+zYsYwdO7bKxxL8hxgnCBzJDS0q\\nuLi4FMv4azKZbprV2cqGP4CoAHGjUlRUKCwsRJZlTCZTtUxmBAJnYtOmTfywcR1Re+eg8bZki/cf\\n2A7v21swfNT9LF+0lLNnzxIeHs7tt9/ukGvg4SfH4/LArYS/MsL2WvBj/dnT9zWWLF3CY48+VuU2\\n1Go1E8Y/zjeTv6Xrh4/aXpdS8omd9j3Tnpx01cdOSUnhk4WfsX7rZjw9PDh08CANv52I2kWLa2oh\\nHkkFaHt2JGNLDH6dG2F2VaPLNqEulMn8O478uBRuu+22Yse03mt0Oh2SJOHh4UF2djY5OTnFwhgO\\nHjzIY09P5PiRo/gG+NMotAFDogcxqZw49KtFrVZXSUAoDVdXV7Kzs9Hr9QwdMIhXPn8Xj3+rceSF\\nuGLy0mDMzifpt0P0+T975x0dRdXG4We2bypJCCWUJPReBAtFQHpXBASlSRGlBOlFRKQJqICKNGmx\\nAEoTUCki7VMQBRGB0Esg1ADp28t8fyy7bpJN38QE9jlnz1Ey5e7cnTtzf/d9f+8HX2V5PD8/Pw4d\\nOsSGjd+i1evp2qETbdu2TZUKER0dzYw5s2lwcgGqssVRJhjwVwVjqlWV395axIbIb7h58yZBQUG0\\nbNnS5aQxL0aNX32xhuZtWiI/+YDQp6vz0JTC2ZU/U7N4GANfz3zC2LN7D5Z2WUnld14EXxUpoV6o\\nvWQY41O4u+lPXv59Sab7p8UdosK8RR8T9vnrKIL9kerMCACBvoQvHsSHvRfydsSoTN8ru3Tqgkqh\\nYtvOHzi4cDtVqlalZ+O2jBs7NssJe2YLQRaLBZPJ5Hifk0gkzJ05hxc7diEmJga1Wk3jxo1dpgVl\\n9C6oUqno1atXuu09ePDgITs81qJC8+bNmTNnDlqtlr1797J06dJMc8MeJ3KS/vBfiAoe5bTgcBYV\\nNBoNRqMxW74Knj4q3Hj6Jz0rvlxNiVHtkfl5IdeYUD00YCimwKtSaR4mJtB73DB8n62GfvU1ZMO1\\n7Pp+B7Vr18639tSoUYPDv/5Gow02d311rA6LSorRT0HI1JdZMmOlW0QFgPfffY+ovme5MCCSUk2r\\nIVpEru87SdsGjYkYMTJXx7xw4QJNWrbAp0N9/Ma8QEKChgc79/BsaGn8zyYh1dvGEZ/nq3Bm12+c\\nH/s59Sf1oCQ+3P89ihMffMuKTxen8+pxTn8A8PHxISkpCY1G45gEXblyhRfat6X03L407DeF4GvJ\\nWM/c5IuPvkGhVDBy2IhcXql/yc976M6dO3zw0XxO/XOKe3fuUrZSOKGyIO58cwSf/g0xlyxGUlQM\\n54d/xWuvvkr58uUzPZ7JZOKV/n1I0CTj93xNbgQZ2DzlbSp9WIq9P+x0pC6u37Cekr2boipbHJnW\\njP+1FATAWLsUipbVOXHiBKNHj870XFKp1LEanlH0SEbUrl2bqL9PsXT5Us7vvoiXycRHEdN45ZVX\\nsowuqVevHt06dWVXq3lUmP0y/vXCuLvzb65O3cLg1wdSsWLFbLcDbO86EonEsaqfUxFRFEWuRl2g\\nYrOaSHVmgs7bKm/crxOA39OVeXDnHjqdLsNylyaTibt371KzZk26deuGQqHgzJkzSKXSbJW4zEhU\\ncLVoBLb7Si6XU6FCBcf/u8JVefHc4nkOFW48/eOhIClM1RxySpaiwrx581i9ejW1a9dmxYoVdOzY\\nkSFDhhRE2/5z0qY/2MPe7dUenPFEKjzeOIsKrtJiPHh4XIh9+ABVaBUAvG9rUSaZUN3VcnPSN9Sa\\nNQSvYe0RpLZV3fvr9tGyQzuuX7yc4aQgz+2JjcW7VBBSLyUyrRm/GA0WuYQHdQLxqhLCpbv33HYu\\nhULB999uZvv27Zw+fRqJRELE7N60b98+18d8fdgbBE7uRpkIWylmucaEvNXvKA5EI61WFotSgiZE\\njSFAQcj8VzjeeBoxE7eiCwikTNky/PjdVpcpJ65EBbB5Q9iZt/Ajgt5sS6lB7VAkG1Ekm7BWKIXf\\n8sFM7zCTN4cMzfdc79xy//59GjZthO/Ljan5bm+CA7zQ/HEBzabDlLlrYGfzqQhyKSqFkrcjRvHO\\nxMlZHnP+xx9xUnuXhktH45NgQl7amxLjXuRKv4VMfu9dFi9YBEBcQjySkGIAqB/oEawiuiAlmtJe\\nCCH+xMXHZes7KJVKh9dSTq9z6dKlGdBvAImJiVSsWJFixYple99VS1ewes1qPpnyOaev3SC8cgU+\\nHjudvn375qgNdhQKhS39xGhErVbnaF9BEAgsFYzuwi3KiT4IVptPhCLRREJyIgqVMlVqjyiKbNu2\\njYXLP+f69Rs0rP8UL3XoRJMmTRwlQZVKJQaDAa1Wm6mHlSiKOU5ZTVt205Wo4HzcvKQQefDgwUNa\\ninL1hyytj6VSKQMGDGDatGm89957DBgw4IlJf5BKpQiCgNVqxWq1ZhilAP+dp4KHgsEewqpSqVxG\\nsGSEp48KN57+SU/jBs+QtPcUALJHq+iJp67hXSqIKo2eonTUQ3zvpCBYRYL7tEJRL5yNGzfmW3ti\\nYmLQ3o1Df/shco1tfJWarEiMFuIPnqF27VpuPZ9Go6F8+fL06NGDTp06ERwcnOtx/ebNm5w+dZrS\\nb3YAUaTYtUSKn4+nbPvnuPXtbyT4Q1wNfwyBSiwGE9cmbmDM6NGcPnaCRR9+zMTR4zL0sEgrKnh7\\neyMIAjqdzjE27dn3C4E9m4Io4nfLZuKoKemFd90KSPzVnDt3Llffy5n8uoc+WrQAaZs6lPygP/JA\\nX1SClKDnauAztC1//fMPD+7Gcu3cJe7F3GbalKnZWkFfunIFIXP6YixuE8BUiQYEqZSyc/sTGRnp\\neMY3frYRmp3/AP/eA/oAJaIootn5D42ea5St75CXFAjIeJU9KyQSCW8MeYOo4/+wY8v3/HP0L/r1\\n65frd7e8pkAMGTQY/fzdSDUmxEdNUMbrufHet7z++uupUk/GT5nEkHfHc29gY0ptmIS2cUW+WPcl\\n323Z5NjG7oWRlJSU6XktFguiKCKVStNV+sjonU2n0wGZf2f7/WV/R8wrnudQ4cbTPx4KEgvSHH8K\\nC1mKCj/99BOVKlVi1KhRREREULFiRXbu3FkQbSsUOE8gC5uo4KFgMJvNmEwmJBIJcrncE6ng4bFm\\n5FvDub/+V+7v+BOJwYIowNnoS8RXDMCkliM1WfC7nYLPPQ0AiuY1OHnmVL61x2Y+9jpXhq5AGqdz\\n/Lsp6jY339/IlNHj3Xo+ewUFHx8fxyqoRqPJ1bHi4+NRlwxEopAj05lRxxkQBZB0f5qbJeXsrB/B\\nhWEruThqDUcrjuK5gHBmTJuOVCpFJpNhtVqzXCm1P3skEokjWsT+HVQqFZZkHd73tMg1ZixyCZoS\\nakSrFVOKLp35Y2Fi47atBL3RDqtcivVRZIxZIUXfowFxyYnExMQQHByco3D82JjbeNcKxeCrQJSA\\nXGtGMFtRhZbEbDY7rlvXrl3xSjARPfM7pMk2QcAoEbn27noCrUratWuXrfPltqykndyKCu7Gfv6c\\niArx8fEcOHCAY8eOETFsBOEmH2Lmfc+5w38Rd+gM9yd/R+ClZObOnO3YJyoqipVfrqXCrwsp3rsl\\npb2L4de4Fqp5/Zkzbx63bt0CcEQsOEfluCKz65dR+oM9UsEuXLjqO3emPnjw4MGDM0VZVMgyxmLs\\n2LEcOHDA4Xx75coVOnbsSMeOHfO9cYUBqVTqKCGYmaggk8kQBAGz2Zzj+te5xZPnVTA4Ryk4p754\\nPBWKPp7+SU/ZsmXZte0H+g0ZyM0qFcFfxc0tu/FrWY/A2X3xuavB/1YyMt2jtLALtylb/YV8a0+L\\nFi1o3LgxD4YO5uKApRRvUAVRayD66CnmT5tBy5Yt3Xo+u4Dg4+ODVColOTkZjUaTo/BzOxUrVkR/\\nNw5d9D2KBdj2N/oqSCnri1edMFrcfYFO1dtiNptpt3tuKm8KpVKJ2WzGYDCk81MwmUyOErfOzxpf\\nX180Gg0pKSn4+/vTt2dvvlz2M1XHvAZAYqgvolTCw+2/UyKwOJUrV87xd0pLft1DFosVQW4ba3UB\\nSpRJRh5WDkBUyJDIZdkaf9MSWrUSSUfPU6x5HUxqGQqNGbnOTPyNO3h5ezkmqzKZjEN7fqH3wH5E\\nH/wGZaliHN//G0/Vr893u37O9vPdZDJx7NgxRFGkbdu2OfIzyCx0Pye4o3/sv7/sLJpYLBbGTZnE\\nypWr8K1dCTE+mZplQhnUtz8ymYwfft6Ft9pCwzZd6d69u+OaA6z/7luK9WuNPNAPVYIeZbIRq0yC\\nrmZZArs1ZcuWLYwaNcqxT0pKSqbvW84VUtJif45nFKng7+/PgwcPXAop7k598DyHCjee/vFQkDzW\\nngp+fn6pSulUqFAhy9rOjxPOanZmD3hBEJDL5RiNRkwmU7YMhDwUDZz9FMB1qVEPHh4nGjduzJ+/\\nHmHv3r0YjUY+GTudJi80J/n4RRTVwgCQGSxozlzj4dbD9D27Ol/bo1Ao+GZ1JHv37uXSpUsoFAqe\\nfXsq9erVc+t5RFFMFalgn6xkFKlgMpnYs2cPt27dokaNGjRt2jRVOLSXlxcjhg8n8vVPqLNqLAAW\\nuYSkP85z94NNfLNjJ88884zLY9tNYQ0GQ6qJF2Q8WbL7Kti/w6gRIznY+388WLMX4cWnSIoXebj6\\nMHfnbubHzd8X6lTGLh06snPdAbzrVCAh1N9WdlMQSPr9LCpRQpUqVXJ8zPERbzN9/Kd4752F2UuO\\nQmNGEqch5u2VjBoxMlXUQ5kyZfhp8zYOHz5MYmIiK2ctyJEosOyLFcyYPZsarZ9HKxcY/967dO7Y\\nka9Xrk4nErnC+X3jv+6nnKQ/TJo2lXV/HiT8/HrkJQIodSEW8Y/zzFu4gE3r1tO7d29u377NnTt3\\n0Gg0BAUFOfZN1qQglLD91n0fRUIllfJBlEkgyMfxu5bJZHh7e6PRaEhOTsbf399lW5wrpKTFfu9k\\nFKlgFxVdGW1mtsDkwYMHD3nhsfZUaNCgAR07diQyMpLIyEg6d+5Mw4YN2bp1K1u3bi2INv6nOIsK\\nWT1ICjoFwpPnVTDYXzLsQpH9xdPjqVD08fRPxphMJipXrkzLli2pW7cu6yO/4kr7aVx7Zw1JR8+R\\n9OU+LraYxBdLl1GqVKl8a4e9j7RaLcHBwbRo0YKGDRsiiiKiKLr1XHq9HovFgkKhQKFQpEp/SHuu\\nI0eOEBIeyqD57/P+8b28+OZAajZ8ipiYmFTbzXl/Jj0bNOdyx5nc//QHrg5dTHS3eUQu+yJDQQH+\\nHW/SGsdBej8FO97e3sTFxbFnzx4iIyO5desWM6ZO49mgMC5ELOVy08nUPp7Ir3v3u630Y37dQ5PH\\njifpqwPcXrgFS4oOURSJ//k413vP58PZH+SqlOlbQ9+kV7P2nKz0BtfmfsuDdQe43m0eLcvXZOqk\\nKem21+l0lChRgsaNG+dIUNi3bx+TZs8gaNcCfEZ0J3DkK4Rd38aBuBuMeydrQ0lwX+qDO/onu6JC\\ncnIyy5evoMS695CXDKTY7SSUGiPypyqT9NIzzP9kIYBDBEhISEi1f8vnm6PfdhSZxogyxYhVKqAp\\nbkvX0Ww/mspfJDspEJldQ1fva/aqTjKZDJlM5rgH035vd6eleJ5DhRtP/3goSB7r9Ae9Xk+JEiU4\\ndOgQAMHBwej1en744QcAXn755fxt4X+M8wSysIkKHgqGtJEKGeVieij6WCwWNBpNqlXqJxV72o/9\\nxbpr165EnTjJitUruXvmHiWLl2TZ73+6JYQ+O9ijBfz9/UlMTMRoNKLX63PsRp8ZzlEKYBvT7U7z\\nzmXvHjx4QIeXXsRv7TR8O9kmOqIoEj8/kjZdO3PuxEnH6rJUKmXxgk94Y8Ag/vrrL4oXL0779u2z\\nnJBkZvLnSlSwWq0MH/M2x/78k2INanE3JRH/Vato17YNE8aOZ/aMWXm5NAVO+fLl+f3g/3h78ngO\\nvNsLQSqhXIVwVi38jB7de+TqmIIgsHjBIsaOHMWOHTswGAzUn/8abdq0cbm9PRQ+p7+xOZ8swG/G\\nECQ1wuBEDDKjGYlaSeDyiayt3Ze57890/MYyIiPh6L8gu6JCVFQU3hXLoihTArnOiP9dm5Hi/bAg\\n1F0a8+uADwGb+CWXyzGZTGg0God417lzZ4JmTscw8zusLzZDUy4AU7KWOxNXUrVMKE2bNnWcy8/P\\nj7t372Zq1phZmoKr53ja/lYoFGi1WgwGQ6oqE55IBQ8ePOQXhUkkyClZjoiRkZEF0IzCS2GOVMht\\nnpdGo+HXX3/FarXStGnTJyqdJTdklP7g8VQo+tj7R6vVMmX6NNasWYvRYMDH358xERFMmTAxVyui\\njwNpI3TANtGbM2MWZ8+eRafTERISku/tsPeRXVTw9vbGbDZjNBrRaDT5KirYz2cwGNBoNA5RYe2X\\nkag7Nsa30/MIVhFVih6dr4pik17n3je7OXToULp738vLizp16lCxYsVsrXDaxxtXooKrsO65H33I\\nlpN/UGXtDNQ6M+GANTGFTTOWUqViJfr06ZOja5Fd8nOMq1q1Kru//8FRltHf398tqQDh4eFERETw\\n999/Oyo8uRIRcysqRJ0+Q7FPh4IgYFbIkBnN+N9LJrFMCZQlA7l+/To1a9bM9BjuWg13p6eC0WhE\\nFMUM+6BYsWLo7z1EtFrxi7XdS8nBPhh8lZhuPyAgICDVtvfv3ycxMdExYZfJZOz7aTeTp03l0uTV\\nxOgTeXjpOp27dGbl91+nOq+3tzcSiQSdTpcuPcFOTiMV0j7rM4pUcLeo4HlPKNx4+seDh+yR5Yh4\\n9epVFi9eTHR0tGMgFQSBHTt25HvjCgOuqj9k9JAvCpEKi5cuYcq0aahrVwGpFG3fs7wzeTLvTJj4\\nn+dtFkZEUUxl1AieSIXHDavVSusunbgY7EXg8Q3Iw8tiOH2RhaPmc+HKZb5emb9+AYWVtL97Z5RK\\nJTqdLt0KXn7iLCqYTCbi4+PRarVuPUdGokJcXBwajYbg4GAAjp85jaSZzc+h+I2H+D5M4V6FYDQB\\n3iib1ScqKirdi2hOJ4nOkQppJ3JpV7EtFgsLP/sM392LMZYKgGv3AbCWCCB5Uh9mv7cg30SFgsDL\\ny8sh6LgLiUSCSqVCr9ej1+vTHV8URcckM6eiQolSJUm+eANFhTLEh/gRHB1H4K0E0Oi5fOc+UVFR\\n/PXXX9SuXZv69eu7PEZhqfwAtmslk8kciysZtalq1aqEBJcgaf1eQh+JJkklfBDNZlIWbmRSn0GO\\nbe2iQkJCQipxUhAERg0bgcViQSKREBYWRvHixV22ycfHh6SkJJKTkwkMDEy3TWaeChKJBIlEgtVq\\nxWKxIJVK0/V3RhEanuoPHjx4yC+KslFjlvG9L730kkPVHzdunOPzpOBsypeV46+7RQW9Xs/yFct5\\numULajZ6lvFTJjtKKkHO87y2bNnC1AUf4v/7OrwPRuK9bzUBf2/mw6/WsHrtGre0+XHD/kKvUCgc\\nK1keT4Wiz6VLl5g5exavvNqbGTNmcD4uFv91c5GHlwVAWbsKxX5czPc7dnDx4sX/uLUFj92czF5G\\nNS2Zhea7m4MHDzoMcKVSKUqlMs+lHl1hNBoxGo1IpdJUQoqrc5UvHYL1wg2kRjO+cY9Ka+ps4771\\nwg2XHhM5nSRKpVLkcjlWqzXdMyWtqJCQkIBWq0VZuzI6n3/bfi+0OKoWDbgSdS5b58wNRXmMswsJ\\nrsQpg8GA1WpFoVDkOFpp5KAhpMyOxKo3kBLkw/0w24RX+ukWSpYqzVsff8C4n7fS7KUuPNOiGbGx\\nsemOUZg8FSB7KRCCIPD1ilUIczaQsnE/8TG3efDTb9xq+TY1FP4MfH2gY1tfX1+kUqlDnASbkHP/\\nvk0Qq1q1Kg0bNnQpKNixR1lmlAKR1TVM+85mj0yx3/8ZlQR1d/WHonwPPQl4+sdDQWJBluNPYSHL\\nlqhUKkaNGlUQbSmU/FfpD1qtlmbt23HFS4owti8Sf1/WbNnD6oYN+O2XfVmGTrrivQ/nofp0MvIq\\n4UhNZgRRhNAyqJe/x4whMxg8cJAnWiENacMhwROpUNSZPnsWH336KYo+XTF6i+xYuxLviFdR600E\\n3LmPWmPgVqVSGLy98OrWil27duXKab4o4+yn4GpMyCgsOL+wT/q8vb0RBMExGdTpdG4r4escpZC2\\ngoNEIkGv12M2m5HJZLzx+kCWNmlEWPd2ILVdC5nRjGbfHxijrtK5c+dUx85teUClUonJZEKv16da\\nbU0rKvj6+iJYrZjv3IfSwcSGFkeUCOh9VRj/Pkdg6ZK5uCKPP/YVaftk0pncpj4ADBk8hN0H9nOo\\nwUBUQ7sSX8yH2M2/EvBAS+ikt0hp/RyxZYujtFq5PHUhHXu8zLFDv2YajfJfY/cXMBqNmUYnNWjQ\\ngFVLl7Hr5z0c/GwLIXIFQwaOpG/fvql++4Ig4O/vT1xcHAkJCZQsWZKEhASMRiNKpTJbaZlqtZqT\\nJ09y6txZRFGk18vdady4seM6ZnXPyWQyDAaD41meNlKhoNIfPHjw4MHOY+2pEBERwfvvv0+7du1S\\n5dY+9dRT+dqwwkJO0x8SExOJi4ujfPnyeSoruXjJ51wJUKP6fhmC/YX5+WfQVSSW0bEAACAASURB\\nVK/I6yOGcezg/3KU5yWKIueOn6BcO1tEQvnzt5BYrVypE4ayyVPciblJSkpKutJlTzquRAWpVOrI\\nw80svxQ8uXiFjT179rAocg2+Z3YiLRmMF2AZPYeyRoES5287tvNJ0GDwVoLZnC+GjTdv3uTq1auU\\nL1+esLAwtx8/r7jyU3CmICMVWrRo4YjQsosJUqkUtVqNTqdDq9VmaXqXHeyiQtoJk13ESElJQaPR\\n4O/vT5UqVZg/cxZfj/8cTbOnkJQujv5CNPEbdrBj0+Z01815EpIT4VapVJKSkpLqOlutVsxmM4Ig\\nOJ5PCoWCXq+9yo/TlxOw4l2Si9vGcdFsRjN9BRGDBuf8gmSTojzGZRapkBdRQSqVsnX9t+zbt48v\\nv9tAivYWKAL4u04I/nWrExCbgMRq5W75EnjNGcvFym05fvw4Tz/9tOMYhclTAbJv1piYmIifnx/9\\n+/Rl/gdzM/29pxUV7FEKJUqUyPI+SUhIoFn7dniXLonk2XpcMGmJHNCXdo2a8G3kl47qMFKpNMMx\\n3HkhKG3lh8y+s7tTU4ryPfQk4OkfDwXJYy0qREVF8fXXX3PgwIFUA/OBAwfytWGFBXvYo/2BIwiC\\ny1DICxcuMChiJBa9HkGtIqrPa4x7ezTTpkzJ1aRk5bpvkC6eiiCR4P8gEbVGz93yJVAN7EHU1IXc\\nunWLMmXKZPt4giDgGxSI+cYdFOFlkRttL7lSswVjfCISicSthmePCxnllUul0izzSz0UPj5atgTJ\\n1GFISwaj1OopdSMW+dNPYdiwA0u7Zuj9vfBO0qHQm7AkJKHZtp/OUz9y2/nj4uJ47Y0h/O/gQdTV\\nK6O7eJWGDRrw7eo1BWJ6mF0y81OAghUVILWfgh1vb+98ERVcHcvb2zuVqADQ8+XuhJcP5X+Hf+Nh\\n1G3Kh4Sz/fQZl6kPuV11dmXWaD+WXC5PNfFaNHc+x9q25m7zIUj7dkA0mjCv2UHtEmWYPGFijs77\\npGB/5mm12nQCsStBOScIgkDr1q1p3bo1AJ17v0Jy86e4WSmEslfu4P8giRR/b1KK+aBs8SynTp3K\\nF1HBXWQ3EtOeyhEcHJylMODv78/t27c58scfPIiPp2716jzzzDMEBQVl2Z4R48dxo25lwieNpFh8\\nMsXLlSBu/FD2tuvP8hXLGTRwUKp2u8IuHphMJpciklQqdXhJ2M0gRVF0pD4+qSa+Hjx4yD+KsqiQ\\n5Wx306ZNXLt2jUOHDnHgwAHH50nB/tCxv8i5Cne7d+8ejVu+wKmOTVF8+C6KOZORHdrCwh+/Z9K0\\nabk6b1JiEpLSJQAIvv2QgAeJKPVGBIUCRVAAiYmJOc7zGtC/H7o5K5CY/vUCkFqsaOetpGfvXp5Q\\nPhdk9GKZXV8FTy5e4eJadDTyejUACIm+h+HQHwg1K3PPV8Xvk+dxO/4hosWKePICCW3fYmD//oSH\\nh7vl3KIo0qpLZ46UCUR9/U+E/21Fff0PTj5dg6Zt2xQqg9esIhUUCgWCIGA0GrFarfnalgMHDqRK\\nf7BjX2V2h6+CxWJBp9MhCILL0O60vgqiKBIbG0upUqWYNH4Cwwa/QeeOnShZ0nWaQW4niPbrb+8P\\nyFigKFasGCd+O8KSEeNpcSSaNifv8fWsD9n/0648Rc1lRVEe4+RyucO3Iu1qdF4iFVxRtmQpxCs3\\n0Pp5EVeiGAAq7SMvgcvXKVGihGNbi8XiMCrM63O5ID0VdDodycnJSCSSTL0Q7Lw7YwZTZs3ikNXA\\nidBSfPfn77z19ihiYmIy3U+j0bB102bks8eh9bPdmz5JWgS1CmHWWBZ9sSJb95z9b2azOcNnfVpf\\nhdxGHWVGUb6HngQ8/eOhIDEjzfGnsJClqFC7dm3i4+MLoi2FEvsDXRTFVP/vzJLlyzF3aoVy1BDM\\n6keliMLKId38BUuWLiUhISHH52303HMYf9yPYLUiNz16iJnMmC9eRUxIomLFijk+5qxp0yn1z1X0\\nPcdg/CsK48lzaPu/g9/uoyyYMzfHx3sSyOhFw+OrUDSpUqkSpmOnkBlNqHQGrFIJV2pXQLf0ffR/\\nnCK553gejJiNas0PzB74JosXLHLbuQ8ePMjVhDjki95H8FIjNxgRlEoU748jPsCX7du3u+1cecXZ\\nU8EVgiBkOxw6r5hMJiwWCwqFItUEwV1mjXq9nhMnTpCQkODwT0iL87lEUSQ+Ph6j0YhKpSIgIACZ\\nTIYoihmOB3kVFZwjFTJztFcoFPTq1YvvIr9i3ao1dOrUybOamgWuUiCsVit6vR5BEHIdqZCWtwYO\\nwrjsWyx3YjGqbX2n0BvR7zuC9fIN2rVr59i2sEUpQOaigiiK6HQ6R5RCUFBQlr+7vXv38vn6b0hZ\\n8wnSFzvg1bwJjBhMdJe29BjQP9N9Hz58iMzXG0lwECl+XoiAb2IKxR4kIqtZhbsxN7N1DZ2f4xmJ\\nSGl9FQpj33jw4OHxoSgbNWYpKsTHx1OtWjXatm1Lly5d6NKlC127di2IthUK7GWH7Lh6kPx4YB9i\\n944AmOW2zpWbzEhKl0RdryZ//vlnjs/73vgJmD9YjvWXww5BQ7hxB0OfcUwYOxalUpnjPC9/f3+O\\n/3qYqR1eptSRKEr+7x/GN2nNqT+OpVol8WDDnt5gd2F3JruRCp5cvMLFhOEjsXywHK+L0QDIX2iE\\nRSJgnPEZrdu24d71GNasXs38mbMYMmiwW41Ljxw5gqVTS5tBWVwiVaKuEhhrE2yNnVtx6MgRt50r\\nr2Qn9LugUiAaNGgAkK7kn1qtRiKRpDJaywlWq5VpM2cSXK4cfUZFMHXmTEZNnMj58+fTbatQKFAo\\nFFgsFvR6Pffu3QP+zf22jw8ZCSy5nYg4pz/YnwOFzcCvqI9xrswanSN13OWpUq9ePSa/PZqkht2J\\nX/oNxr/OwOfr0Pcey9b1G1L1pzsnrvnpqWC1WvlowQJKhodTLCiI3v36sWnr1mx5My36YgWWccNJ\\nCSvn+LdkXx8so4dy/vJlLly4kOG+JUqUQNTpsUTHYFbIuVfWVuq19PW7eB/4g7DKlTh69ChRUVGp\\nonzS4pzSkVWkgv1754dJY1G/hx53PP3joSCxIM3xp7CQ5ag4Y8YMwLYy5ZjcPmEVAqRSqSPE19WD\\nxEutRkxKBsCkkKPW6pE98iywJiXnKnyyQYMGbPryKyLGTcAUVh6JWo3x5ClG9unD1ImTcv1dvLy8\\n6N2rN88+8ywAYWFhHnNGFxgMBn755RdiY2OpVatWur97IhWKJi1btmTaqLeJ7DsBY4tG3PdRotux\\nl1BvP7758SfA9lJpf8l0Z9i4n58fsounAPBOtq2KqvS2Cbn03gMC/Eq77Vx5wWw2O0KvM5vUFJSo\\n4MpPAVIbKGq12my5xTsz/p13WHnwIKa9e1ACJCZy8s8/adSyJedOnEjljWAwGNi/fz8Hjx7lflwc\\nz9WrR4e2balfvz5gm3jodLoMnfFzO0m094HdSE6pVBY6UaGo4ypSwd2pD3benTSZDq3bsHztGgzX\\nLxBapgxb/j5J2bJlU21XGPvY7uFhMpkcFVfeiIhg44kTmL5cS6myZRFPnean77fxa8+e/Przz5kK\\nMtExMUhqDMCskKPx8cI7RcuDEoEIMhnKKhW5efMmVatWdbmvSqViyJAhRI6agWTTEuJKBiKIUOL8\\nNUp9sZGbD+IYv3ARxYODeXfWLHr36sW8mTPTtceVqJC2zzNKf/BEKnjw4CE/yG+RIC4ujl69enH9\\n+nXCwsLYuHEjxYoVS7VNTEwM/fv3JzY2FkEQGDp0aLYqQWYpwbdo0YKwsDBMJhMtWrTgmWeecbxI\\nPSk4CwmuRIXBr/RGuuwrRKsVo8L2oFEYTZiOHEca+5BGjRrl6rwdOnTgt72/8PaAQbzVrQd7fviR\\nOdPfd4g6uc3zcp4IeybF6Vm/YQPB5cszYsEi1vyyj76DBjF64sRUueMeT4Wiy4QxY/l84SI6lw6n\\nxc1ENi/8jJNHfneYg9lXqjJb4coNPXr0wLBjD5brN1HqbZMGqcmMNfYB5m+20ufVV916vtySXYO6\\nghIV7PeQq8l6bn0V4uPjWbZ8OeavI5GEh+Gl1SJIZegGD8LUoR2fL1vm2Fav19O0bTsW7f2FmKYv\\noH+1P79a4M2IUfz9999A1vnmzuaKOSWtWWNhm3AW9TGuIEUFsC0YrPx8CRNGvc1LXboSHBycbht3\\nRiq4q3+cI3JMJhNXrlxhw8aNmDd9i6ReXYrHxSEEBxM7631Ox8by888/Z3q8WtWqIR79C4AboWW4\\nVrE8KX4+iFotutNnqVy5cqb7fzhrNs29AtBWaIZh5HvcnTKXq0PGIxpMhEyfgWrOBxiHj8Cw7luW\\n7dnDBx9+mO4Y9vc5nU6XrvKDnYzSH9wZqVDU76HHHU//eChI8ttTYd68ebRp04aLFy/SqlUr5s2b\\nl24buVzOokWLiIqK4ujRoyxZsoRz585leewsRYUvvviCnj178uabbwK2UmjdunXL0Rco6mQlKrz2\\n2mtUFWWYXxqM7sw5rA/iYOMOLD2Gsmrx53l6+JjNZsLDw6lSpYrbXiKdhYSsJsVPGv/73/94Y+w4\\nUtZ9j/WT5eiHDCd5WSSr/vcbMz/4wLGdJ1Kh6JKSkoK/vz9dunTh9ddfp3Xr1qmir1w57ruD0qVL\\nM3fmLMzNuyPduR9rdAzW3QcwNe3G2BEjM1yVK2iy8lOwUxCigj23HdKnP0DGvgoGg4EdO3awdu1a\\nTp06lW6/Y8eOoapbB6FkSby0OiRWEYNSgUUmw9SlCzsPHXJsu2LFCs7KlTz8dCVCjVoIoeGYO3fj\\nxvAx9Bs2DPh3gp+R2WZmPghZkdasMS8ChYf0KBQKJBIJJpPJ0U/5KSrYyUy8LKx5+87i2a5du5B2\\n6ojg54dfUjIKgxGjUkFysWLoevVkyw8/ZHqsCSNGIny6AsuFy5gVcpugYLVinfoBzzdrRvny5bNs\\ny/Zvv+Ponr1Mr1iXWQ2bEVapCre7vogQGorCnq5QqjSGxcv4aNGidKKf/fraFwxc9XdmRo0ePHjw\\n4G7y21Nhx44dDBgwAIABAwawbdu2dNuUKlWKevXqAbaKWNWrV+f27dvptktLlqLCkiVL+O233xyh\\npVWqVHGY8TwpOBsOuXrIK5VKDu3azdTmbfCZ9RmSBSuo/89lftm2nZdeeilP53Zl0GUnt3lezkKC\\nZ1Kcmhkff4xuwlSE2nVRGW0ve4aSpdAuWsbCTz9z9IfHU6HokpiYCNjSEVz1jyvHfXcxOiKC7ZFf\\nUf1SDD7rtlHz7/NsXrKM2dOnu/1cuSWrcpJ2CkJU0Ol0NGjQAJVK5dL4zZWosHv3bkqUD6Xf/IVE\\n7NxPow6deL5d+1SGuWq1GmtiEgDFkmy/h2R7GlhiIt5OAsaK9evRvRmBzssb8ZH4FFcsAPPLrxAd\\nc5PLly9nGamQl0li2utc2CIVivoYZ0+jgX/FhMdJVHBn/zj/zgVBAFEEUSTk7l0A7gcFgSAgWK1Z\\npsk+/fTTfDbnAyzNusBrb2IZ9x5i7WZUPXORDatWZ7tNtWrVYsyYMYwYMYLTp09xr2177gb/6xFl\\nkssRqlTB6uXF9evXU+0rlUpdCsoZfWdnM1aPp8KTg6d/PBQk+e2pcO/ePUelqpIlSzo8ojIiOjqa\\nv//+m2effTbLY2c5KiqVylQrVmaz+YnzVMgqUgFsD6OJEyYQMXIkZ8+eRaVSUbNmzTyfOzNRIbd4\\n0h8y5sTxv+D9jwBQPrr2eqUKoUIlRB8fbty4QeXKlT2RCkWYpCTbZNLf39/l3/Mr/cFOgwYNHOeW\\nSqUONbiwkFU5STvOk11RFPPluZCRn4JzG+x15I1GIzdu3KB7335oV2yCp5vaNjKb+XPGGHr0G8Av\\nP9gqbDRq1AjZgwfof/8d/4BAAOL9/REtFpSr1jBkyBDHOZKSkiE4GFEiIcnbF19tCvcCgxGkUuRB\\nxUlKSnL4L7gSFaxWq+O5mZuJiHPkjNlsduSze1ZK3YdarXZ4c3h5eWEymZBIJPkq3GQ2zhQ24ciO\\n8wS7Y8eOjJ8+neCxY1EajBiUCh4EBSKazai+20SPhQuzPN6ggQN56cUX+f7770lISOC5yAE0btw4\\n12OJf0AAKXfucPfpZzDLZChMJgxKJaJOhyk+Pl3eMNiEG/v1diUiSaVSxxhjNpsLbRSJBw8enhwM\\nB49iPHg0w7+3adOGu4/EXmfmzJmT6v8FQch0vE1JSaFHjx58+umn+Pj4ZNmuDCMVPv/8cwCaN2/O\\nnDlz0Gq17N27l549e9KlS5csD/w4kR1RwY67y6w5HyetqOAOTwVP+kNqigUFwq0YBKsVpdGIKAgY\\n5ApErRZTfJzjpeRx9VQwGAxs2LCBN0ZGMOmdqURFRf3XTXIrRqMRnU6HRCLBx8fHZf8oFAqHIVl+\\n3B/OLvMWiyWVV0dhILuRCnYTQVEU3SZ42tmyZQt1Gzfl+RYv8Maw4axbv8HlmHr79m3WfPU1YyZN\\npnLNWrTv9jLGnq/D002Rm4z4aFNAJsP47sccPvo7ly5dAmzj+NqlS/F/cziyX/ZhiI8j+fhxpK+8\\nSk2Fgled/C2aNXoOyd7dAFwtG8qZStVsE5Ub17HcvU21atUyHffzWtfeOXImL2kU+UVRG+Nc4Ryp\\nYL8/VSpVvi6gFFSkgjv7x9lTITw8nAF9+hDy/gys0de4VaoU1gsXkA4YSP2yZWndunW2jhkYGMjg\\nwYMZN24cTZo0ydM1H9q3L8pPFyFarTwIKs7tUo/Mb9eu5unnnkvnX5GSksLBgwf5btNm9u3bl8pX\\nwxlnATU/IhUeh3voccbTPx4KkuxEJshaNMHr/XGOT1r27t3L6dOn0326du1KyZIlHYLDnTt3Mqz+\\nZzKZ6N69O3379s121H2GosLq1bbws3nz5hEcHEzt2rVZsWIFHTt2ZPbs2dk6+ONAYmIi3323kU+X\\nreCLtZEcOnTIUQXDFVKp1FEtIq+r2M4u7IIguG0C4olUyJhh/fujXrwApU6LgGgTFCQSJGtW8FyT\\nJo6XkscxUuHmzZtUrl2XoZ+vZpVPRRbct/J0y9ZMnDrtv26a27BHKfj5+WX48upcmz4/ohWcRQUo\\nfL+h7EYqOG/jzhSIuR99TP8JUzj16njkc9agadqRhft/p92L3VKJPLdu3aLec43Ymiyg7Tsay4fr\\nuWKwInumKWF3rlP7WhRVYy5SLDkBlEoUTz2XSiTr2rUrX69YQY3rNzDOnE356TOY0aEDB376KdWk\\nfeq4cShXfI74yx6sgoBZJke8fQuv4QMZM2oUXl5eqSZbacfovK46OxvF2a9zYRIVHgeczRozqgLg\\nbjIaY5xFusK2Gp5WPJsydizdGzeGlau4HxqO6sWXiahXnz3btrmtFGdOGPP221TXpKDs/hLWHduw\\nHtiHbPQofJcvZe2jhTI7x44do2ylyqz49Rj78eX7izeo3fBpfnDhBeH8vT3VHzx48JCfWKzSHH9y\\nQteuXfnyyy8B+PLLL10KBqIoMnjwYGrUqMHo0aOzfWxBzGCGXL9+fYezdWHHudylO7l69SrPtWiJ\\nssHzlGzYGHQaLm7+kjZP1WXTN19l+NA8e/YsOp2OatWqZRi2mx00Gg3nz59HrVY7wu5q1aqV5zJ3\\np0+fdrwUKJVKlyUT8wOdTsfcDz9i+Zq1xMfepXLNOkyfMJZevXpl+xgxMTHs3LkTURRp164d4eHh\\nbm2jXq+nZefO3LAKlOrclQRvX27v34v377/xx8EDVKhQwfFd3JnmUhho1KoNx2o1x/Lmu//+Y/wD\\nvPs1ZtOST+nQocN/1zg3ceXKFRISEihfvrxL13U7V69eJT4+nvDwcAIDA93aBvv4YB+3qlev7tKE\\n8L/AZDJx6tQpZDIZdevWzXL76OhoHj58SGhoKMWLF8/z+R8+fEjZChXRbz2NtERp6l3/B6sgcLJM\\nLbz7Pse382fRqVMnAAYNG87XRh+8hk2l8t3L6BRq9F99SrHKFREat3AcU6Py5nz5Kvi0rcvPkasd\\n1XhEUeTUqVOYzeYs++DAgQP0f2sYCRYL0oBAjFcu8XZEBHOmT3c8B+zjatoxOj4+nqtXr1KsWDEq\\nVqyYq+tiP7Y9/7F48eKEhobm6lge0mO1Wjl58iSiKBIUFGT7HZYt68g7zS9OnjyJxWKhbt26DqE6\\np/dgQaLVajl37hxqtZrw8HDOnj2LIAhUr14dhULh0vekoDEYDKxfv57VGzei1el4sVUrhr/1Vqrx\\n3mAwUDq8AvHvLCW0dl2KJz/EJJVxKsmI17D2XDkblaqk7M2bN7l37x4hISHcvXsXq9VKvXr1CsX3\\n9eDhSSO/5nyFAUEQ8DfcyfF+icrS2b4mcXFxvPLKK9y4cSNVScnbt2/zxhtv8NNPP/Hbb7/RrFkz\\n6tSp41iAmzt3Lu3bt8/02BnGb506dQpfu3FVGgRBcKz4Pc70eeNNHvaMwK/HYErGXkEUBJJfHMme\\noS/w9ddfO9wz06JUKjOtWZ5dnCf+9nBss9mcZ1Hhv4hUMJvNtOrUhb8FX/TzvofylTl3/CCDpozj\\ncvR1pk6amOn+oijy9viJfLF6NZJmXUAqRXznXfq++iorFn/qtlURlUrFoV27+Oqrr9h/+DD3ExIY\\n8uyzDF22ONXk8nGLVLh69Sr/nDqFZcFPAAQlP0Cr9EYXUBzN4CksWP5FkRcVRFEkOTkZyNhPwU5+\\nmTWKopiqmoFGo3F76kBeyEmUgvN27opU2L17N7JnW0LpcvilxAOgVXghKhSkvDSYrzdtcYgKm7ds\\nwfzNUbRK2xirNupQP9sc6/YvefB8W2LLVaRqzCW89Rp8tq0jQBB57rnnHOdKTk7GbDajUqmyFHVe\\neOEFbpw/x8mTJ0lJSaFevXrpno8KhQKj0YjRaEx1/dyx6qxUKjEajY7frydSwb1IJBJUKhU6nc5h\\n6JnfkQpge95oNBr0er0jX7WwRimYzWZWrVrNrv0HSEqIJ7hkKbp37kjz5s0L5FplF6VSycCBAxk4\\ncGCG2/zwww+Yw6pByxcxx90CQC9XQ+26WFu/zNrIL5kyeVKqY4JtfLQ+MqH0CAoePHjIDyzm/PVL\\nCgwM5Jdffkn37yEhIfz0k20O0LRp01xFxmc4E6tTpw7JyckuP0+CoHDjxg1OnjyJ9bUIzI8eHiap\\nDJQqNAOn8MnKNRnu6y5fBefSbs7htXZyk+dltVodD0Ww5XQXhOK3Y8cOTj9MRj9vM1StB2pveL4T\\n2s/3MvuDD4iLi8t0/2XLV7B6zwEM319G9/6X6KatQb/tKuuP/s2CRZ+4ta1yuZwWLVowdsQIvl21\\nismTJqVbrX7cPBVu3ryJIqwyKBR4GTSE3b9OWGy07Y+VanE9JuY/bZ87SElJwWKxoFKpHPdoRv2T\\nX+kPdlNDhULheFEtTMJUdv0U7LhbVDCZTIhKFd76FELvRwMQe/5RyoJKjcFp/DMbDKD2xiyVkaT2\\nxSKRcrd+c85UqM+Nnm0wrv6M2H/+Rr75Kyp8s5TtG9anSnmJj7eJFgEBAdlqmyAI1K9fn+eff96l\\n4J7RuO+OSaK9P+w534VpwllUxrissE+M7WN6QYkKkHqccbeo4I7+sVqtdO7xClO++Z57z/fA0P8d\\nblZvyuLVkfzw4868N7KAiY6ORlfFZpBrkNnuW63SJizqq9TjwrXoVNvb7227cay777/H5R56XPH0\\nj4eCxGKW5vhTWPDYR2dAbGwsitLl0MsVGAUQAYPs0Yt2uUrE3s+4rKa7RAXnXFz7i05eVzWdTYbs\\n5ZEsFku+O4mv27qNlM6DQCqldMIdArVxXA6uiKFUOeQNW/Dzzz/Tu3fvDPef+8mnaCeuBP9A1EYd\\nIKLz8UM79jM+nNyN8WPHuNVUy/6Sl9HkSiKRIJFIsFqtWCyWIr9qUbFiRfSXz4JWg9JqmyCqjVok\\nVgviqaPUqFLlP25haqxWKwcOHODixYuEhobStm3bLH/DWVV9cCa/RAXnUnWuhML/GmchMzu4Q1SI\\njo5m1dpIrsXcIqx0SRTHDxEafRqpQsFDnyDifZJAFPHZtZ6X3/o3Oqx5q9bs3v0d9IngUmmn32fD\\nFwjZv4V2ibe4d/U0jevVoelrH1GtWjXHJlar1SEquCu9JT9FhbT94YlUcC9Go5Ht23fwy/9+Q6fT\\nEBYaxsg336B+/fr5el5X40xhrPywa9cuDp+/gnbVMYwPLqEy66HWc1yv+AwTItrSr+9rjrLjRYFK\\nlSqh3riDZCDOJwhREEj0spkwq88eo2bj2qm2TzvOeSqvePDgIb8oTCJBTskwUqFnz54F2Y5CR+XK\\nlTHevAYPYzHKFJwPqcbVErb8feHP/dSvUyfDfe0vA3ldvcsqUiE3tXOdRQX7RDg/V0pv3rTVcTea\\nzCCXU/7hDUISb6My6QnQ2l7qRbki0zaIosjNi+ehbmMQRarcu0j1O+dRGXVQ/SkS7semM7/LC1ar\\nNVsrttmJVigq9Y3LlClDy5atUCyciMJk++4C4H3tHOo185kYMeK/baATly9fpkLN2rw0Yhzj9pyk\\n95TZlKlQiRMnTrjc3mq1cuHCBc6fP48oiqlefjPqH+cyfu6M5HEWFQpjCk1Bpz9EfvkV1es34KNz\\nCawPbMjy07cIDwtDue4z4pJSiA4OhSq1kc8fTcmk+6meSx9Mm4rXipnw0wYwm0EU4eg+vKYPZsnH\\nH7Nm2VJ+2vgt/fr2xcfHJ1Ut5qSkJCwWC2q1OttRGVlhH6OfNFGhqIxxGWEymWjVqStzv9/L3We6\\nkthpOIfVoTRt3Y6dO/N3Fb4gIhXc0T9fbdxMSpehIFdgfLSyr5epuF+5IbI6jdm9e3eez1GQdOrU\\nCfW9G/DTBqwSCQ99i2OWyuCvX5Ec+pHXB/RPtX3a+83dokJRv4cedzz946EgMZukOf4UFjIcGd95\\n552CbEehw9/fn359+/HNrDfQzd2AVv3IG+HSadRrPmDqti0Z7uvs1p0XNuIVKQAAIABJREFUnJ2+\\n7cdyZ6SCffKcH5OaP/74g8ERo7ly+TJStTcSfQpVE7X41KkDjyIKvA0aSIzDdHQvLddknMIgCALF\\nSoWQcO0c8tDKyKy29obG3eCCSYbSy8ttEwP4dyKpVCoz9WqQyWQOn4vcvuT/+eefrPl6HQ8TEmnV\\npBF9+/bJVi3Y/GDdqi9o+2I3Esf2QPJMU2SaZAJ/28fE6dNo0qTJf9KmtJjNZlq078jtLqMRXxrm\\n+C0lH9hEq46dibl8MdX127JlKxETJqGziFSvVAGVXMaot4ZmWR7HuTa5yWRy2yTOWVSw56sVJlEh\\np+kPdnHSYrFgNptz9LJ97do1ho8Zi/6zwxBaDbVJS7mnn0W4cpKEbxfxcHQv1IKAVaflxZe6sfTg\\n/lTtql+/Pj9v38aw8RO5+MEIJDI5wcWLs2jJYl566UXHdiVLluT+/fvExcUREhKCQqFwe5QC/Dvx\\nSDtGu2Pl2f69RVFEEIRCJSoUdTZs2MDfDzToP/6FcvfPAKANrYs2/Dn6D+3HvevX8i0SrSBEBXeg\\n0enBy5byk6T0xceQzI2AcoiCgOjlly9VcvITuVzO3h920KpTZwzb15BcuxHeV6Pg+CG2ffdtOhNf\\niUTieB7Y9/fgwYOH/MBqKbqRUAVf86cIsXjBR3QuG4CqQ3l8pryK3/C2eA99gRULP3Y4iLvCHekP\\noiimMmp0taqZmzwvZ1HBfsysfAHSYjQaiY6OdryYp+XcuXO06tSFqJYj0a+7g271FUpNWoVP8kOs\\nu9YRLbVN+ryvn8NrbGcGDxxESEhIpucc9sYQVMumojT8W0faR5dEyJczGfj6QLeWr8puSbHsRCpk\\n1EeiKDJyzDheeKknK1NKsDn4ecav303FGrW4cuVK7hqeRwICAvjz0AE+njqJbiHFeKVOFTavX8fw\\nt978T9rjip07d5LkE4zYbTgA/voEBNEKL/TEVP051q1b79h2165d9B8ewZ3hq5B++jvGAR9wo/lg\\nXntjGHv37gUyv4fyIwXCOa3G/mJaWEQFZxPJnJjB5jZaYdXaSCxt+kFoNVQmPVUeXkImWkio8Ty3\\nVcVZ+flnXP77L7Zt/I7vvookKCgo3TGaNGnCqd8PE33uLBdOHCf67BlefrlbuvYFBAQgiiKxsbEY\\njUaHGV92/RSyQ36lP1gsFj75bDHjJr/DW2+9xegJk/jw4wWF5ndT1PONV677Ds2LEVjkSoxSWx/q\\nZCqo1xyjTyC///57vp3bbsJsMBgcImNh9FTo2rol3gc3AXDPrxQny9QjWeUHmmTMf/xM8+bN83yO\\ngqZOnTrcvHyJL0YO5v1SUj7r1Yk70ddo3bq1y+2dx0R3RyoU9XvoccfTPx4KFLM0559CQtGVQwoA\\npVLJxq8iuXr1KocPH8bHx4d27dpl6RQuk8mQSCRYLJZc59ubTCZEUUQulyORSNyWf+0sKtgnw9l9\\nObVarcyeO58Fn3yKRabAlJxA85atWb3kU8qVK+fYbua8j9B1HQMt+yCzmqmUcAnv0uUwdRrOjcUT\\n0H23BmrWxN/Li3df7cak8eOyPPd770zh164vET2mI7TqikmQ4BN1lGfUCma9926W++cE+2pyVqu1\\nGYWv//nnn7z3wYf8fuQwMqmUYW8MYeL4sanC7n/88Ucit+9C+/k/4GPL5dS0H4xu26f06Pc6fx/5\\n1Z1fKdsIgkCVKlUoU6YMYBNO7KujhYEzZ86grfk8AKVS7lEm+RYP1EFcDwhDU6sZf5+Jcmw7cfos\\ntMOWQJ3m+CdcBUEgqXozdG98wqT3Z9OmTZtMz6VSqUhJSUGv17slX9hqtaLX6xEEAZVK5UircIen\\nwtWrV1m5JpKrN25Sr2Y1Bg98nRIlSuToGM5jTk7GLKVSiVarxWAw5KjazZUbNzGWt1VjKJ1yB5nV\\nTKLSn6sB4SjK1+Du3buEhIRw8eLFLI/lXP7NFUFBQazf8C179u3n/Ol/qF6nLq1atKBOJmlsOcWV\\nqGD3XBEEIdcTkVcHDOKnqBtUeG00yuJlePjgLrO+XcSxk6fYsv5rt7T9SSZZowE/W8RKgtKf4rqH\\nJCtt97vEP8hhzpcfCIKAUqlEr9djMBhQq9WF0lOhT5/XmPXRxxi+mIa5z0REb1+4HY3XR2/ycvfu\\nRba8qVKpzNTLyRmFQuH4LXg8FTx48JBvFCKRIKfkaHm3f//+WW/0GFKhQgX69etHt27dsl1PPq++\\nCs6pD4DbPBXsQoJzpEJ2RYXREyYzf8OPJM04iGbFDYyrbrHftx5PN21OYmKiY7tf9u/H+nwPAMIT\\nr+Ft0mCQKrhQrxsmiZzjRw7zxZLPWTBvLm8OGZytKAOVSsWh3Tv5ZMo4Ghvv82zKTYb37s47E8aR\\nkJCAyWQiJSXFLfnvWZk02nEVqbBr1y5e6NCFPaVbkTTvGHETd/Dxr5d4tnlLUlJSHNt9smI1mu6T\\nwacYXiYNJTS2fG9rlxFcvHKVCxcu5Pl75Bb7b8wujBWm0NaQkBBUty6AKFJcex+AIN1D1CYdypsX\\nKB9SGrDdP2dPHodnOyO1mvEz2kwaE5V+0KQbJ4/+htVqzfQecvZVcAfOUQDOQmFeV5zXRn5JrQbP\\nsCAqhY1+jZm5/zwVqtfk0KFDuW5fTshtpMJTNaujPncERBE/g61/YvzLIiKgiDpM9erVAffksw4d\\n+TYbfz2Brv0Iys3fg7Z9BKv3/0X31/rl+dh20orJkPdV55MnT/LTL/vRTvsJQ5kaIAgYy1ZH++6P\\n7D5wKEMfkYKkqOcbd2jRDOXh7wGI8S/HPyXrYJApIT4Ww7m/ePrpp/P1/Gkjogqjp4K3tzd/HDpA\\nq4dnUXYrh88rlfAa3IC3mjVgzbIleW9kEcHuzZPT6M6sKOr30OOOp388FChmIeefQkKGcmuXLl0Q\\nBCHVJG3//v3Ex8cjCAI7duwokAYWVeyrD0ajMdtChDPOqQ/gWlTIDfYJjPNKZHYekPfv3+eLlV9g\\nWHIJ/IMRRCuily+WXu+RfOssa9ZGMmb02wAoVCrQJiGIVnyNyYjAhYCqmJAi6DX4+/sjk8m4ffs2\\nGo0m23nNEomEhg0bEhYWRvny5fHz8+PIkSOsivyKzZs2kpSURJmwisyZ9g59+ryW84vzCOe898xI\\nK8pYrVYGDY9AO2YD1Glp26h4WQyjv+H6hy/zxcpVjB0zGoCYW7egbVUAwhOjUVn0aOVepCh8kZep\\nxJ07d6hatWquv0NuEUXR8Rvz8/MjISGBlJSUQlOHvHv37owcOx7fUwdRBttWEwWgzKXfuXngOwZ8\\nfhqwT/CkWLXJlBGTkIoWkhW+GKVKSL6PTKHMMvrCuTa5O0j7u7L/fvJyT0dHRzN89Fj0HxyBsrbf\\ni77tEPhnH117vMK9mOtZimPXrl1j8nsz+e1/ByhTuhRlwyryzvgxNGzYMFttyK2AOmjg68yaVx2v\\nY7uQlS2JQarEIFUi+X4JgRho1apVjo6XEf/88w8//vwLskXHqay/hQQRMagsd4d9xf4xdTl27Jjb\\nJo4KhcIx7qvV6jxPELdv34Hh+VdB5YXB9MirR6IApRr986+xffsOnnrqKbe0/Ull1IhhLKv3FMbw\\nuogdB2KVyeHeDbzm9mPw0KFu9d1whbOoYLVaMZvNeYpsyS9CQkLYvW0LcXFxPHz4kLJlyxaa50J+\\ns3TZCuZ/vIByNWxlKMdPnsrkiRN4uxAZGHvw4MHDf02GS8Q3b97E19eXsWPHMn78eMaNG4efn5/j\\nvz1kTl59FdKWdpNIJI5QdPsk1l2eChmtlF69epWJU6byYq++DBsxEnn1Rgh+QYRob1P/4UnKaG4B\\noG3Ui6279jr269/rFZQ/LkFt1iMgYpCqMEkV8L+NVKxQkXLlyjnM9HIaWuocwaHVahk1fiInDP6U\\nfnc7lrUpXO/5GUMnTWfxkmU5uzCPEEUx22Z1aSMV/vnnH1JEGdR+AYlopVLiJYod/x4EAV274azZ\\nsMmxb71aNZFE/YbSbEBleRQZYdaDNhnD1dNU+Y9KOJrNZkRRRCaT4etrM+bKz/DfnOLr68umdV9T\\nZtUYZL+sJ/b0cTiwieLrZ7D0k0Wp0jY6vtgN352fE6x7gIjADd/yAEh2LKZbj1cQBKFAPRXSigr2\\nsqSiKOZ65Wv12i+xNu8LZasitxgprb2DRLRA3VaIYfWyFH+jo6Np0Kgpmw1hSMZvx9rnY44ENqJ5\\n2w4cPnw4W23IbaRC8eLF2bltK2XWTES5ZRHaH9fiO/xpyu1eyr4fdzgimPKaz/rjjz9iaNybJJ9S\\n6KS2a58i98Wk8kXb5FW2bXefQJ523M9rKLsoioiC7TokKPwxS2QkKmximihIsLqxMkluKer5xqVL\\nl+a3fXupc+Rr1K+UxW9IHbyG1mdkx+Ysmj8338/vPM44i1DuSjlzd/8EBgZSuXLlJ0ZQ2Lx5MxNm\\nf0jCsA3oO09C33kSSaO38878T9iw4Vu3nKOo30OPO57+8VCgmHPxKSRkKCocP36cBg0aMGfOHPz8\\n/GjRogWq/7N33mFOlF0fvic92d4pi3QQFKQsgkoTBQSRjiKoYAeUJqjoZwdpNhDFrjRFwAZiQXxl\\nqSJdei+yCyxs3/Qy8/0RErMlu8kWCDj3deWCJPPMPJkzk805zzm/o9PRqVOnK1KU51JTWUEF3x+j\\nlZGtEKhQ41eLv+b61jcya4+VFbHdWX7cgliQQ5Pcg1Q3n0VAItqee3GyJvS6f1Omn5kwnmon/iLy\\ns/Fw/h/Mebkols4g7OMxfDrnHQBv9obZbPYKVAWCbwbH3A8/4mhkE8xt70WnVVPNngHXd8H81I88\\n/9LL5XIGA+38ABQ7f1arFYU+HASBCEcBUY584mxZCJIEunCstn/n8+y40Wi/fYPIE9u9r2kdZrQf\\nj6Nb9zvKFK6sKnydIE/gx7dsIxTo2rUr7739Ft1rRdHm5EZuj1fx8vPP0a7tjYUyq96ZNoUmh35D\\nuW4p53ILsJ46iPrDscSmzuON118r8zgeETW73R7UNeqPkspqKloCcSItHXsNd5lATXM6NcxnqGZx\\nl9LYajYhPT291PEvTZlG/i0PIg54GZ0hDDR6bDcNxXzfuzwxYVJAc6hImUiHDh34dslihnXtyBM3\\n1GTZ269zfP9e6tevH/S+/CFJkrdLSFpYTZyCigx9kvtNQVGpLUOLfu9XNFOhV6870W34Ghw28rVR\\n/B1/A/naKHDYMGz8mrt63Vk5E/+Pc/3117Nr03r2b/uL1CXzOZ/2DzOmvFZlXR988RdUkAkN/m/y\\ndMz3z8Fe4zrva84aTTAPm8sLr0+/jDOTkZG5KrkagwpKpZKnnnqKefPmMXXqVJ544omQUZu+Eqio\\npkLR8gconm5fnjov36CC5wdTUbueO3eOR0aMwjIpFceQtxDaDyXx0VlcG61Bf/4oVqUWCQGdy4rC\\naSds9Yc8OHigd3xMTAw7Nq3nweuSiPr9E9Rzn2CA7QB/rUulXTu3MJtSqUSv1yNJEmazmUDw7Yih\\n0WhYuuJnrLc8wD8Gt0hUNes5NC4b1LwWRWJdtmzZEvT5CVSk0fMZ4N/z16JFC8Tzp+DMEfRO92eK\\nbtySCEc+mk1Luavbv6rSrVu35uPZb5P48RNoV76P+o+FRM0cTDvxHAs/+TDoeVcWvj9q9Xo9CoUC\\nm81WKWKClUVWVhbR0dHcP3QI3365gKmTX6NGjRqYzeZCHUn0ej1TX32F9rECvPs4Nd4azKh6WnZv\\n/YtrrnFnLZR2D3lE1KByshVKKqupaAlEq+ubYjiyESSJqIu6EbG2bJAkNAfX07Rp01LHL1++HFfn\\nRwDQiu7PaFPqoN0gDuzdTVZWVplz8KyqOhyOoIMvTqcTu91OSkoKY8eOpXv37sWCeRWtZ+3Vqxfa\\nTUvAbiVfE8XfcTeQp/E45l/Rp/ddFdq/L5UdVEhJSaHLzW3RT+0L/+x3v/jPAfTT+tOxTasqr/cP\\nhKup3rhOnTq0bNkyKMHRiuIbVPBcN5UZVLia7HOpcblcHNm7E27ojk2hwRN+dAgquP42Th7aXyma\\nO7KNQhvZPjKXlCs4qFBm0V5ycjLLli1j5cqVREVFXYo5XRV4nJHKKn+Ays9UKPqah0WLvkRq0x9q\\nXY9adNDQdAS9aEFqezcZn/8fZ7pPpFGjhoTlphM3/3Xqx+sYOHBgoX3ExsYy7L6hDOjbh4YNG5ao\\nnh8WFobFYsFkMnlXxUujaEcMQRBAdGFUhZOtjiHWkUOcI5uzyuogieVKHw20nSQUz1TQ6/W88Nxz\\nvPZGf/Qj34bYWHA6iFn7BbmbvmbCe1sLjR9y72Dq16vLrl27sFgsNB7YlTvvvLwrj75BG0EQCAsL\\no6CgAJPJRHR09GWdm4fMzEzAnT4P7jKCGjVqsHnzZj79YgG//P4HsbGxDB3Qh/btb+Hl/3uet2aU\\nb0VJp9N5ldnLo43iweVyYbfbUSgUpQYKg2X4sAd45fWpGLavQFUnGQCt00r4r+8SJ1nK7HAhulyg\\nUqMW7ehEGyIKrAotSCBcFB0sC09LvGPHjmG322nXrl3A915BQQEA4eHhVbYq3KJFC+64tSO/Tu+N\\n+YE3oU5z+Gcf+gVP07FNS2688cZKO1ZlBxUAvlu8iFenTOW9l2/DlJeLITKKJ0eM4OUXng+Zriwy\\n5UepVKJWq3E4HN5Ss1Dq/PBfRqFQoDWEY805ixSXTLquJgIXS5LyzqHWaOWsEhkZmcoldNbwgsZv\\npkJ2dnahx80338yECRO8z2VKpyLlD6Io4nA4EASh0B+sokGFytJUKOo4/JN+BmuiW/Qt0X4evWjB\\nptByuOVw0mKbk/zLTPhgJDEbFjC2exvW/LKy2B9W3wwEf86YZzUo0Jr9oiUh9w3og37d5wBkadw9\\n7GPt2XDqb4TsNNq2bRvQfn0JtPMDFM9UAHfpx7SnRpLww2Q0iyZhfvdRUjK3sSn1fyQnJxcaX1BQ\\ngEajoWPHjnTq1Inq1atXaip2eSjqBJVX+6Kq8LR4VKvVhYKcu3fv5sVXp7DNEodl0PsYbxrJt+t2\\nMHn6G6Xasqx7qLIyFXwzYHwdwYoGCmNjY/l1xQ8kf/cC2hVvolz/Fdpvp3Dt7m/5388/llnC0/2O\\nHig2LCLK4e7ekq+OdP9g3vkTtevWJyEhodTxdrudhx5/gocfG8FHS39m4IOjqN2wKZs2bQpo/vn5\\n7uyK0lp2VkY965KF83i2b2dipvVEMUBD1Gu3M6FHW5YvXVypjnlVBBXUajVTXn2ZnHNnyM3KJDfj\\nLK+/9krIOJ5yvXHF8XxHeYJslemoyvYpP4IgcO+QoahXzgAgQ1eNczp3hyHVjzO4e/C9AXWvKgvZ\\nRqGNbB+ZS4qrHI8QwW+mQnx8PMnJySWuHgmCwPHjx6t0Ylc6KpUKQRBwOp2IohjUH56iq8UeKuqA\\niKKIKLpX8D2ij1B8lbTVDc0I/+A7jDyNRnQ78me0NTCqwokoSOOdaZPp2LEjp06dIiYmpkSnzWaz\\nIYoiarXar4p1sEGFoiUhjz36CO9/8hnp854kv+dEHCrQndxM3OIZzJw+rVw/uoMpfygpKCMIAk+O\\nHMFNN7YhOzubw4cPc/PNN3vT7X3xtOGMiYkhMzMTh8OBw+G4rM5CUWE5j41CRVfBk6UQFxfnvTdE\\nUeT+R0ZgumsGDepeQzICQlxDzMkT2PvFRBYuXMjDDz9cruNVllijv44iFc1UALj55pv5bsnX/Pnn\\nnxQUFJCQ0JLrr7+eevXqlTl28ovPsap9J6KjtdC0LbnacNjyHfp5o5j95bwyHe4Ro8fz9V8niR/6\\nFQa1FedtNTmzfzPdevVhz/Yt1K1bt9TxnnugtKBCZaBSqXjphed58f+ew2azefUyKht/Qo2V4SR6\\nModkrj50Oh0FBQXe7wl59Tt0mPn6a6xp34mMdwdiaf8gCAr0G+YRn/E3b36+7nJPT0ZG5mojhMoZ\\ngsWvpztmzBiio6Pp0aMH8+fP5/jx45w4cYITJ07IAYUAEASh3LoKJZU+QPGgQrB1XkVLH3y7F/iu\\nkN99991o/tkBG79CI7qPZRdUCH98TFh+GnfddVeZAYGyshTA/UNKqVRit9sDyugomqkQGRnJ1g1r\\nGV5XwPBiK3Km96fG4VW8N/01HnpoeJn7K4okSRXOVICLgo0KBcnJyfTr1w+gUK2/B88qbVRUVKW3\\nLywvRVdWfe1cGWKF5cVisXD27FlvjX9cXJz3vZ07d5LvUJDXtC8FigiEi5WvadpaFLQfw0fzF/vd\\nb1n3UGUFFfxdV5URVHC5XNhsNlq1asWYMWNo0aIFLpcroEBQkyZNWP+/32idvxPFwvGYRjfkurVv\\nsPzrhdxxxx2ljr1w4QKLF3+F5b5F2AzuUhQtdrihL/Y2D/LOu6X3r7dYLDgcDtRqdanfE5VZzyoI\\nQrFskcrE9zvatz1rqGQVVAVyvXHFKfq9IGsqhA7x8fHs3rqZ1wd1oPWfb9Nq4xtM7t+OPdv+IjEx\\nsVKOIdsotJHtI3NJuRo1FWbNmoUoiqSmprJo0SJGjx5Nt27dGDVqVJmrTzJutFotNpvN27M8UErq\\n/AClOyCSJLF9+3YOHDhAzZo16dy5c7HsiKJBBU/GgsvlwuVyeV83GAysWfUz3Xv3I/LI9agS66LZ\\nvoFYp4nfVv3szT5QKBTY7XacTmexbATPiktpzoJn5S0/Px+TyVTmD++SxCvj4+P5eO4cPp47B6PR\\nyKFDh1Cr1UiSFLTj4On8oNFoAqrvLhqU8RzPd1U6OjqajIwMcnNzqVWrlnesp05fpVJhMBjQarUY\\njcZKEX2qCEUzFVQqlVdXwGKxXPKV0vPnz/Pk+GdY/sO3xCckUb9eHXp270arVq282xiNRpQR8SAI\\npKlr0th2CKMigixVPITHU1CBLIuKdDbwxV+mQmXopBQUFCBJkleXIDY2lrNnz5Kdne1tC1oaderU\\n4elxY1CpVNSrVy+gMeAO5mjrtMJqiMHmcneC0V7MbHI0vZM1G/+v1PGXKkvhUqJQKFCpVDidTm+2\\nlqcdsIyMP4oGFa7mINSVSEREBOPHjWX8uLGXeyoyMjJXOyEUJAiWUnPyFQoFXbp0YebMmYwYMYJ5\\n8+axevXqSzW3Kx5/ugqbNm3ijt4DSahZh4bXt2bWrNmFtinJeQb/mgpnzpyhxY3t6dzrHkbNWUXf\\nhyeSXLcRO3fuLDS+JJFGf4GK5s2bc/roIUbfP5BBrWuzZM50ThzaR+PGbq0FQRC8AYOSshU8mQpl\\nBVOCKYHwF2zxEB4ejlarxeFweGtTgyGYLAX4NygDhUsgfB3IrVu3olarsdvthbpc+DpUvl0GLndQ\\noaQa8MvVWtJkMnFj+1v5Pj0O+4STRD72I+aUJ5nz1c9MfPZfh7VFixbY0vZB3lnMyjD26JtxVOtu\\nSaje8wNdO7X3e4yyaiX37dvHJ5/P57mXXuPW7r359ttvy6V7UZXlD0V1CWJiYgB3dkwg2SWeazEx\\nMTHggAJAdHQ0rtwzIEnYBff1q+Hi91jeGWLLEPYMRE8Brrx6Vs/3k+c77WpPZb/S7BOK6HQ6XC4X\\neXl52O12WVPhP4Zso9BGto/MJeVqzFQwGo0sX76cJUuWcOHCBfr378/27dtLrA2XKZmSggpLlizl\\noZFjMXd4Ge6dSWbuKZ7/aBrfrfyVP379EZVK5dd5LmlVU5Ikbu/ZhyMJvXD2eREUCpAkCvYu49Zu\\nPTl55IBXtd9fUMFms5Wo8i5JEk2bNkWlUnHDDTcUez8sLAyj0YjZbC7WGSSQ8gfPPiCwoIK/YIsv\\nvqu0wa6A+nP8SkOlUhXL9PDdjyAIREdHc+HCBXJzc73nw7f0ASpvRbwiePQ/lEploZXVsLAwMjMz\\nL7lY48KFi8jU1cPZ4y30kpkwlxlndB3O9l7C+7MaM+np8SQkJBAVFcXIESP46PO7MQ/7CmdsLRBd\\nsOVLtFvm89QHwbcWBfjuu++5/+ERXNNjLGENOnIow8ywpybz7fKf6dLxJn5ctQaDXsewIYPo1q1b\\nscyggoICXp08lXkLFlH3mhokVKvJQw8MKdQpxXNPVySoUHTFX6/Xo9frsVgs5Ofnl9q1Q5IkcnPd\\nWQbBdvdISUkhSi1i3PMj9mY9AdBIdnDaCVv/DiMnP+V3rG95xtWUqQDu722z2fyfCSrIVAyHw8HL\\nr73O2tRUJEGB02GjSdNmzHlnptxxS0ZGRua/RggFCYLFb1AhKSmJhg0bcs8999CoUSMAtm3bxtat\\nWxEEgf79+1+ySV6pFNVUsNlsPDZqNOZ7f4aard0bxdbDUrsDO+Z35JtvvmHw4MF+nWffVU1Jkujc\\nuTOpqamczrLgHPQSCAJhkhEzBqRmd+M4/B0LFixkzJjRwL+r6b5BBX+6AFA8Fb4o/jIVHA4HTqcT\\npVJZagAA/g0qmM3mUksWJEkqcz7wb1AhNzc3aIHMYDMVoPD583xW36BC586dyc/P58KFC+Tk5FCj\\nRg1cLpc3k8LjUIWCpoI/UbnLlanwzY+rMF03BIB4ya2lkC3EIYUnoanfkdTUVAYNGgTAG9OmoFa/\\nypxpN6BOqIMz7zy1alRj0aqfqVOnjt9j+KuVtFqtDH/kccz3/oy1RjxhZKGLu4bMpJv5em5rvt+S\\njrXxYCgoYOWjz9Ch1aes+PZr771ls9m45dZuHBbroblnOVKUneMZxxj25HOc+iedCU+502g925e3\\n/MHT295TRuMhNjaW9PR0srOzSw0WmM1mnE4nGo0mqGAauDPZvp7/GXfc1Q/7yYdx3dASpSWHiFUj\\n6XB9ba9tSsJoNCJJEmFhYX6FXD1cafWsnu8nz/1ytaeyX2n2CTUeeOhxVmxLo/adL2OIiMJqtbLk\\nj/ns7NKdnX9tKPP+KAvZPqGPbKPQRraPzCXlCg4q+PW4Bg0aRMuWLTl8+DArV65k5cqV/Pjjj95/\\nZcrG4yh6nLV169ZBXAOo2RqV5KCReJg4KROUKkwtR/LZoqWAf6Fe8hpJAAAgAElEQVRG3xaTHifk\\n77//xlHnVhAEYqRsrpUOUY1zAJivuY0/t//tHR9M+YPvMfyttHmcGN+0ft/nZWUpeI6v1WoRRdHr\\njJeEv44YRdHpdBgMBm8qaTB4HPpgMxXg34CN0+nE4XCgUCi8zkRERARKpRKr1YrVavXWwPs6VL7l\\nD5erraQ/UTmtVsuxY8f48quvGT/hGdatW3dJ5qhWq8DltnuE5A7CZAvu1H6ctkLXpVKpZMbrr3E+\\n/R9+X/wROzf+wcG/t5GSklKuY69atQqhWjNITsEquINM8VIW8ZteRtd2JNa7f4EbhsONozHev5W1\\n+zP58MOPvOOXLFnC8QI9tj6L0MfWBKUaS40OmAev4sVXXvUGlYoGCoPFt4TA976IjY0F3FkMJWUh\\neShvloKH9u3bs3v7Xzza2E7i/iU0yPmLuS8+yYpvvi5VR+Bq1FPw4Ll/ZCV/mbI4fPgwy1f+hHnA\\nD1gj3FpVdl0C9p4fcTJPIf/WkpGRkZG5YvAbAp83b94lnMbVSdHyB4vFAjp3OmOidIEICgiTjBgJ\\nx6aLxpRlwel04nK5vIJfRVGr1d7Wg5s2bSIpKQlN7ipsQKTkdjDCJRMIoM49Sq3GSd6xnsCB74/9\\nktoieigrM8DTvcHhcGC3273bBRNUAPdKuM1mw2Qy+R1Tlp6CL7GxsZjNZrKzs7315WURbOcHD0Uz\\nPYqWPqSmptK5c2eio6PJysoiNzfX+1l8U1uVSmUh216O1c2S7G232+nV927OXMhFV+9WTmRr+eTL\\nR7m5VRNW/rC0Suf5wD39WP/yXKwthqCTLIgImDFA1nGcp7dy223fFBsTHh5OmzZtAj6Gxz5Fyc/P\\nRwxz3zsm3Nk0YbbzhHEKbhmFS9iFSQrjAgnkqmIwt3uR2R8+w5NPPgHAoqXLMTV7GAQBveS+rizo\\nIDYRda02/PHHH/Tp0wdBELzCfr4lNIHiT5dAo9Fgt9v59bfVvPL6myTGxzLi0eG0bt260HYe574i\\nadb16tXj/dlvc+TIEfLz82nQoEGJAYWMjAyef/E1vv56MQ3qXkODa6/jkeH3UaNGjVL3789GoYq/\\nsrWrlSvNPqHE6tWroXFv0BiwSu6/Ow7UIAgYG93L9z/+6u0gVF5k+4Q+so1CG9k+MpeU8ut2X3b8\\nZiqMGzfO+//Zs2cXem/48OFVNqGrCbVajSAIOBwORFHkpptuwn7iTzBnEkcmAAokakun0B/6hl5d\\nO5epG1A0U6F3795IaVvh1CbCcZchaLFC7j+odn7BIw8N844tKVOhtPKHsjIVoHD5godARRp99+Fy\\nufjnn3/8ZisEoqfgwbNKu2vXLpYsWcKuXbvKHGO32xFFEbVaHZRSe9GgjL/PHhUVxbFjx5g1ew4f\\nfvgxR48eLeYIXu4SiJLs/dqUaWw47iKr0yKo14uwVg9iGrKHDcddvDZlWpXOZ8CAATRJUBL74/0I\\neWmYnQLS/hUYFnbj9ddeDUpUMFjatWuH6+j/wGGlQIhgv9CE0xYd2WawqSNRIhIpFFBbOAVIENuI\\nzAsZ3vFOlwsU7vMYjjsN3szFgJlCVSiIV94SCFEUi5XReFi/fj2D7xvOqr9z2a/pwud7k+jYtTeT\\nX5/u3cZms2GxWFAqlZVyLv0J0wJkZWXRqm17FuzS4BqyFXX3Dzim68DAoQ/z7bffVfjYocR/Lagg\\nU37UajWC0/19n0M0FnTk4MnGsqDVyNeOjIyMzH8KVzkeIYLfoMLatWu9/y+atfD3338jUza+5Qp2\\nu52EhAQefPBBkn4aisZ0FhtaHC6JyKPfU7NgB48/9ojf0gcPvunSnTt3xmAwsGzxQiK+7UfY3gWQ\\nvh3dwe8wfNaOKS+/4NXD8Izx3Yfv/8uTqQAl6yoE0k7Sg8Ph4M2332X8hGd4dOQYYhOqM+zhEcVK\\nFwKZi4eTJ08y4dkXePu9z3h21nJuub0vLdq0Jy0tze+Y8og0QvGgTNESis6dO+NwOBg67FHefv9z\\nfj+qZvVRNW+99xn9Bg0t5IBdbrHGokEFSZJ4/4OPsNw0A6PCvZIdLphAqcFy0wze/+CjCpVBuFwu\\njhw5wunTp0t8X6PRsHb1zzzerQnhO+ZiXTCI5gffZNHcNxg/dnS5j+uLv9WHhg0b0uXWzuhWPAiW\\nXCyCgfNhTTlxeA97cyL5W2qODS0qXIRhhlOpNG16vXf83X16EHZgIWrJhg4rLpSYCYOCs9hPbuLW\\nW2/1bltesUaj0Ygoiuj1+kKOq8PhoO/AeznTYhaOhvcRWaslwo2TMA/cxrQ332XHjh1A8Q4kFaW0\\n9piz3n2PrJgOODu9Q2RUBCg15Ffvhrnb14waO7HULhVX2grRfy2ocKXZJ5To1asXrkMrwZiBVdCz\\nX7iOPCEanDbC9s1jyD0DKnwM2T6hj2yj0Ea2j8wlpYq7P2RnZ9O1a1caNWpEt27dvGWwJeFyuWjZ\\nsiV33XVXQPsOXMVOplwU1VWY884bPNyrHZoNUzEvfYSMTwdS176f92a9SWRkZJlp/iX9cL/jjjvY\\nvG4NtzXQcK11K+2TjKT+upynxo8pNLYqMhWK6iq4XC5sNhuCIARURjDkgUeY881WLM2fQ9HpXRz3\\n7GPJNicdb+tRaE5lBVs8WK1W2t/ajX2aHthav4b25lcwDz7GXk0POt3es1jwxG63s2TJEsZPfI75\\nC79i7969QTnKRVtKlpSp8PrUGaQeMHGh5WyocxfUuYvzreey4ZiLVydP9W53udtKFg3cOBwO8rLP\\nQ3xTzFIYEgJ6wYwKJ8Q1IS/7fLkFBj/7fB7VatWn5U1daXR9a5re0JaNGzcW285gMPDAfUN4a8ZU\\ndm7byt9bNlQ4HThQli76gr7XGdDNqkvUgo4Y5jYhLjYa3apHcdrt5Evu7IDI/H0YNr7IS5PGe8fe\\nf/99JNpPELthEtjyKSAMKe0vDEt68tT4cYXKcsqbqeCv9GH16tU4wmrjrN2DfCkCAYk4RRaEV8d+\\n3Ug++nQeUDmlD76Ulqmw+JsV2Jo87J6v4J53vhgJyR0wO1Xs3bu3UuYQCqhUqkJBmqs9qCBTfmrU\\nqMGE8eMwfHUbHP4ZrHlw+k8MS3vRMaWp7MzIyMjI/Neo4qDC9OnT6dq1K4cPH+a2225j+vTpfred\\nPXs2TZs2DXjhyW9QweVykZ2dTVZWlvf/vs9lAqPoD21RFBnQrw9vvzGDH778hF1bNzP1tZcICwvj\\nnVmzePjxMbw1631++eWXEp1L36CCb+/c6OhoBvTry/gnR3DfkME0aNCg2NhghRoDyQ4o2hLS16ku\\n6yI8cOAAP/2yCkvX7zHp6rj3FxGOrePHHL/g4qeffgpqLgDLli3DbGhMToMnEAUF4YoC1AoXrlbP\\nc96sYdWqVd5ts7KyaN7qJh6ZNJfUc43ZlJ7A2Oem06vv3QGvGhcV2iuaqZCamsqcuR9haTODXBK8\\n4/KJw9JmJu/N/dAbxLjc5Q9Fz7FarSYuKRkydiCioECKQACihDw4v5O4pORyaSrMm7eAMc9OJvOm\\npZjuPon13rMcSHqKbj37snv37kLbSpLkvbY8XSgqk9L6TxsMBhYv+IyTRw+y/KPJbFv3G2dPHaVf\\n21roPqiNc81LaHe+T9yaEcx45Vm6d+/uHRsWFsaWjanc1cCBas0LWD7rRtKqocx49jGmvPpSoeOU\\ndg8WxWKx8Nbb73Btsxu59/6H+OyLhWRkZBTaJiMjAzHKff9ni3EAJCvTqK44g8tQnbXr1nNr9768\\nPu1NduzY4b2HK0ppQQXRpxzEILizgoxSBAgCglJV6t+UK7FH+LFjx0hNTWXnzp2XTXj1UnEl2ieU\\nmPLqS3wy83ma7H0V7exaJP/xEK882oMV335dKRlEsn1CH9lGoY1sH5lLShUHFVasWMGwYe7S+GHD\\nhvHDDz+UuF1aWho///wzjzzySMC/Y/yqguXn53tFvSRJKibwJRMYRX9oZ2VlIUkSCQkJ1K9fH4BT\\np07x4suTsenqYI68gwynnTXv/sAHnyxgQ+qqQj/6/aUYexwvj0BbSQGJYMsfAslU0Gg0XoFBm80W\\nlEjj6tWrker2BZUekxRGBEbCBRP5QhTG2veyfOUq+vTpAwSeqfDX1p0YE28HlOSJ0cQocohW5nLB\\nlYg5qSs7d+6kZ8+eAIwcPYHjyptx3PYuyfqDoDSTHfUYqb8P5L335zJu7JhSjwWFMxVsNhuiKKLR\\naAqd1+wL6RDXjDxRRLwYx8sXIyE2BmN+NjabDZ1Od9kzFYraWxAEJox7kskfPYW510/kaKOJVOYT\\n7TqHZeNTTBz3ZNDHEEWRZ194BXOHLyHpRgQkUCiQGtyDxXSaV6bM5Luli7zbW61WXC6X9zq7HCQl\\nJZGU9K/g6VcLPuPEiRdYs2YNdrudZs0epl27dsXGxcfHM3rkY1itVmrXrk1iYmKJTkKgQQWLxcIt\\nnbpxMDsaZ6PXCa9u4a/0XSy4rQe//PgtHTp0AKB58+aQ9hqILrKJRYuV6sqz1HDsJTz9A07aG3Be\\n2Z8C0wV2frGcxUuX8+tP31VYdNO31Kso/Xv34N01C3FWT0GNHQkBOxo4tx21q4BmzZpV6NihQlpa\\nGl179EVjiMKQ1Bws23j6uZdY/u3XdOzY8XJPTyYEEQSBIUOGMGTIkMs9FRkZGRmZy00Vt5TMyMjw\\n/qZNSkoqtjDlYfz48bzxxhverNhA8BtUWLt2LbVr1w5yqoGRnZ3NPffcw6lTp6hTpw5Lly4tsaVZ\\nnTp1iIyM9Crjb9mypUrmU5V4fqh7HMXMTLdAY3x8vHebMeMnccDZhuT6/bwGyW17P7nrH2La9DeY\\nMvkV77a+QQVPaqTvam5cXBxnz54tttotiiKiKCIIQiEhQn/lD06nE1EUUSqVZQoXGgwG8vLyMJlM\\nQekpqNVqFKJ7nkYxApQZRCryOeOqgeAjUiWKIg6Ho5BGhT+SEuLQWE5hB8yigRhFDhrB7eToLCeJ\\nj+/sPp7RyIrl3+PofxwE0Cvc87AKkbiavcrs958IKKjg6xCWpMtw2223kVC9NhfOb8eVlMJheyNA\\nwoUSLvxNdGySN5hQtK1kZaxSBYrL5Sqx68jTE8azZ99Bvp9fH1PjQSiTtcSd20bb9g2YOGF8KXss\\nmVOnTmE02yGpHQpcXKfbh1XUc8TeEKnuQNaseqfQ9kajW+SwKrIUoPy1knXr1qVu3bocPHgQk8mE\\n0WgsVkZgsVhwOBzodLpCQYmi+AsUSpLE//73P35Y8bP7WpCcHMyKxNJxBXHqbNCdJF/bFFPr5jzw\\n8CiOH9qNIAi0bt2aa+tfw+7N/4ej3eucpQYmKZy6u6YR2bgPjWr2xS5qQWkkI6o/R1c/yIcffsSY\\nMRXTqfAtmynKhPFjmLfgRszbkxBad8amjEQ6uRpD6uO8MWNyqV0vrpQUcEmS6NazH0d0/anV6l7U\\n6mysoo5cxUP07D2AY4f2lnodXKlcKfb5ryLbJ/SRbRTayPaRuaQEElQ4kgpHU/2+3bVrV86dO1fs\\n9ddff73Qc0EQSvQ1Vq5cSWJiIi1btgwqU8dv+UNV1i0HWs/hacm3c+fOKzKgAIUzFQoKCrDZbKjV\\nam8dtMlk4tdfVpKRPAGj61/HyS7psTZ9no8/m19ofyWtavqu5npU3IuudpeUpeBvf575+s6/NHx1\\nFYLJVLjrrrsQj68AywUKxAhEFIQpTKhcRgzH5zP47v7F5lKWo/3AA/ehOPIV5J/EIbk/mxonZO1D\\nPPUrgwYNAtyBLaU2AnRxqAUHCkHEIalwoYLoJpw/l17m/KFwpoI/sccJY5/AsHUiOEyYpDBMUjg4\\nzBi2PMX4MU94P5MneCZJUomrvVWJv6wUpVLJl/M/ZdumP3h14DX0TYnjxecm8t6sN4LqkuFBr9fj\\nsptBdGJQWNAIDiKV+egEKzjy0eoKnztPsKyyUvQrG899XFIk15/mQVFKugctFgudbutJv/vG8/66\\nRN5PjeX9TxdjaTAWvdJKgvoCAHmuSLimDxeyjezfv987/ucVy2gubMbwZX3C/3gA17Iu7N/6O8bE\\nvmgEB+FK48Xx8ZivfZ45H84r/0m4iCcAKYpise+T6tWrs2XTWrrHHUC5/mkcPw6l7p6n+eL9mTz0\\n0PAKHzsU2LRpE6fPG3Fd/xx26aKWjqSGml1x1erHJ59+fplnKCMjIyMjIxPSBFLuULczdH3l30cR\\nVq9ezZ49e4o9evfuTVJSkjfgcPbsWRITE4uN37RpEytWrKBu3brce++9/PHHHzzwwANlTt1vUKEq\\n60ADreeo6nlcCnyFGn2zFDyOZEFBAQq1HrQxnLLWRpQUWESdO00+rDb5eVmF9leSpoKv4+WvLt9f\\nUMHXKfY914GUPnjwOHxGo7GYpkBpJCcnM3b0k4T91AXx5K8U2ATIPUrSxsF0btfcmy4cTICjdu3a\\nzJw6Gf3ym3Dt+Qgy96A58gX6lbfy6Ydzve0mk5KSUIg2yD+OWnB/Vod4cf/nNtCwcdMyjwVlZyqk\\npqYyccJ4+rSvj35xA9Qbx6LaOB79kobceWMyk56dWGh/l6sEoqxz3LRpU55++mkefPBBqlevXqpa\\nbGlUq1aNa5tcB0cXoxP+bR8ao8xGc+A97rt3UKHtqzpToaK1kqUFFTztHstq11hS94cXXnqNrf+E\\nYey8E5pOQrrueSLj69Eo0UlTw37ClCZckpI8ZzQIAkp9rPdcASQmJrLtz1TWr/qO98bfxjsvPo42\\nIpHD9macs7tXyy2iHqukg/C6ZGddqNB58FCarkK9evX48L23eXf2LL7/ZinHDuzi7rsHFduuKFdK\\nPevevXsRE9qDIGC7+F1iv/ivNaYDW3fuu5zTqzKuFPv8V5HtE/rINgptZPvIXFIc5XgEQe/evZk/\\n371gPX/+fPr27Vtsm6lTp3L69GlOnDjB119/TZcuXViwYEGZ+/abc5qens6YMWNKdOoFQeDdd98N\\n5jMUItB6DkEQuP3221EqlTz++OM8+uij5T7m5cJisbBu3TrOn8+kWrVEUlJSiIuL876fkJBAmEGH\\nNWsX1rgW7DM39dbdc+Y3rm9eWMtCqVSiUCgQRdGrg+AbVNBoNCgUCpxOJ06ns9gqaNGggqccwpP+\\n7nm/PJkKnnnodDoUisAai0x7/VWaXdeYKTNewfh3JrWbNGXQkE5MeGq8N/ASqJ6Ch9FPjqL9LTcx\\n54NPMBYcpnqDBB6fkUrTpv8GCrRaLSNGPM7cZaNQ3zoPAAcqMJ0hbPckXpz7up+9FyaQTAWlUslX\\nCz5j3759rFy5EkmSuPPOX0usI9fpdBiNxkseVAg0iBQdHU16ejp5eXnlLtH49IN3uLVrTwyaf6BO\\na3BZSTw9D5VxHZOe2VBoTjabDYVCEXSrz0tFWFgYCoUCq9WK3W733i+SJHmDCoFmKnhsIIoiH3/y\\nKdYOf4JCRYw6h5q6dLTXt4f8P3FF9yfLGc95eyJ2SQMFJ3DlnyrxemrVqhWtWrXCYrEwfuJzSAWn\\nSI+oQ7YzDufFTB7O/k7Llq0q5XxoNBosFgt2u73EbCW73Y5KpSI2NvaSlvdcCmrWrInK+CUAOc4Y\\ntHY7WQ53EFNlPEjdlBqXc3oyMjIyMjIyoU4V90KYNGkSd999N5999plXggDgzJkzPProo4VE8j0E\\n+nvNb1BBr9fTunXrYo5DoI5ERes5ADZu3Ej16tW5cOECXbt25dprr/WKkV0JrFmzht597+balL5I\\n+tpoduzli3mLeG/OO7Rv3x5wO5zPPzuRF998GHOHldgN1d2Dc/dj2P00kxd9WGy/arUam83GLbfc\\nAhRPEdfpdJjNZmw2WzEhxpJql1Uqt/q6bxAimEyFnJwcflj+I7v+3o9SpaT9TSnUqVMnoJR1QRAY\\nOnQoQ4cOxWazsXfv3mI6DsEEODy0bNmSD9+bxZ49e1Cr1YUCCh6mTn6Zf/55lA2rO6K+4Q6UeUa0\\n277j2ecnMWBAYP3BlUolgiAgimKJrTR9a/Guu+46rrvuulL3F6qZCh50Oh06nQ6r1UpBQUGZDnNJ\\npKSksPXPdbz1zhyOHHoHpUpNuxtbM/KT3wtpjfhe11XlgFa0VlIQBCIiIsjLyyM/P987f5PJhCiK\\n6HS6Mu+hooE/i8WC1WyEiAYoBSd1DScQkLDF9+D8mifIym+Cq/Y9oFBCwXEMm+5h4oSnSi050uv1\\nPDFqFO8tfgjzLd9h0V7UsMneg2Hfy7y0fHGFzoOH0sQaIfgAIVw59azdu3dHZR4Bab8gJvfgjP1i\\nEKHgJOpjn/L4l6mXdX5VxZVin/8qsn1CH9lGoY1sH5lLShULNcbGxvL7778Xe71GjRolBhQ6depE\\np06dAtq336BCbGyst0ShPKxevdrve556jmrVqvmt5wB3HS64V/P79evHli1b/AYVhg8fTp06dQD3\\namqLFi28XwSe1KVL+dxoNHLv0AcxXreMDMU/6F0WImr24B97Gt3vuItvln1Fjx49AGjZohl9uzTj\\nu++boKnWFqfxLGLBKWbNepsePXoU2/+OHTuwWCw0atQItVrN+vXrEQSBli1bArBz504KCgq8jn1q\\naiq5ubnUqlULlUpVbH/bt2/HarVy7bXXeud/7tw5GjZsiEajKfXzHj9+nJat2hJVqwOJjQeC5OSN\\nD+fx7nsfsefvrURFRQV1/nQ6HRs2bODMmTPeLg1r166loKCAgQMHBmUPT/nE5s2bycrK4tZbby22\\n/ZKv5jF37lz27t1Lsx5tGLBsOvv37yc1NTVge+/YsQOXy0VKSgo6nY61a9cG/HmLPtdqtWzbto3w\\n8HCGDh0a9PjyPs/IyKBBgwao1eoytz9w4ABZWVkkJCQQGRlZ7uOPeXIEdrudAwcOYDKZvIEDz/sN\\nGzYE4O+//+bMmTOX9X4u7fmePXs4f/48sbGxxMfHk5qaSmZmJnXr1g3o/GzYsIHDhw/TunVrRFFk\\ny5Yt6AzhGHN2E5FwDcb0rVhcek5H3Q/VJ6LYNALt1jFoo2ogms4yoH9fOnW4CQ/+jjd1ysvk5I5n\\n3rxkVPE3oFZrELP38uTIxxBFsczxgTzXaDRs27aN48ePe/VLfN+3Wq1s27aNzMxMbwvOy22/ynz+\\n4/dLua1bT8T4dthrDUZlPITi8Ac88siDNGnS5LLPT34uP5efy8/l5/LzK+n5rl27vCW3J0+eRCZ0\\nESQ/ogXt2rVj8+bNVXLQZ555hri4OJ599lmmT59Obm5uMbFGs9mMy+UiIiICk8lEt27dePnll+nW\\nrVvxDyEIIae98MEHHzDxjVTM1y+hbsQJYrXZOEUVu7ObEbanH3Ne7sfw4cMLjcnJyWHDhg2o1Wo6\\nderkN+X7+PHj5OTkkJ6eTufOnTly5AhhYWHeoMCZM2c4e/Ys1atXp0YN92rZ2bNnOXPmTKHXPBw5\\ncoT8/HwaNGjgVbAv6bWSuL17X9b8cxMJ1w0jOcwtbng4pwH23aMZc08tZs4IrIzAQ1pamrc8Jjk5\\nGcCrsN+4ceOga+t37dqFy+Xihhtu8Kswf/r0ac6fP0+tWrX8BrhKY9++fV4tidjYWOrWret9LzU1\\n1fvlGAgWi4X9+/ej0+nKzGooCavVyoIFC/liwTdYrFb69LqdJ0Y9XubnOnr0KHl5edSvX7/ETiy+\\nmEwmDh48iFqtplmzZuXKInC5XOzatQtBEGjYsCGHDx9Go9EUSuE/dOgQRqOxzGuwIgRrn5KwWq3s\\n27cPlUpF8+bNEQTBO/dAzifA7t27cTgcNGvWDI1Gw5TXpzPto9+IvfUjkgz5nLFW56wxHMOfdzD1\\nmXvp2eMOCgoKaNKkSdClIWfPnmXjxo3o9Xq6dOlSqaUlWVlZnDx5sth9AO4st507dyJJEi1btgy4\\nRKoybHQpuXDhAp9/Po8tO/ZS55rqPPrwcO9389XIlWaf/xqyfUIf2UahjWyf0CIUfb7KQhAEeLEc\\nn21yaJwTv5kK77//Pjt27PA+FwSB+Ph4atWqVeGDBlLPce7cOfr3d6v/O51Ohg4dWmJAIVQ5cvQE\\nZp27Ttnq1IEWsmxxSCgwalpy7NjxYmNiYmK46667yty3r7BbSer4JYk1elKrS1LsL0l9PpB0+Pz8\\nfNav+x9ih0WYHf9ezGZXGK5aTzNvQe+ggwpRUVFkZGSQl5fnDSp4UqaDKX/woFarcblcOBwOv0EF\\nT6lHaW3tSsP3nFbUQatIW0mz2cwtHbtx+IwBc9woUIZzcN5S3p/bms2b1tCgQQO/Y4MV5tRoNNjt\\ndsxmc7k6M3iuTZ1OR3h4OGq1GrvdjslkIiwsDEmSvJ1EQrXzgwedTuc9HxaLBa1W6828KEuk0YNa\\nrcbhcOB0OtFoNDw36WkOHDrKvr0PoKx+M/YzVnRHlzHk3nsY/eQTATvkJVG9enVv1k9lU5pQo91u\\nR5IkdyvZCsw/1ElISODZZ5++3NOQkZGRkZGRudKo4vKHqsSvFzVx4sRir2VnZ2O321m8eDEtWrQo\\n90EDqeeoV68eu3btKvcxLjeNGtbD8P3/MAMZlkTsoppsm1u0K9y2nYYNy1Y994fH+W3btm2JQQVP\\nTb9vXb4/oUYoLDboIZCggtFoRKk2gCock9OFQ1Jhd2lxSSrQVMNkzAv6s4WHh6NUKrFard72m06n\\nE0EQAnJ4i6JWq7FarTgcDr8OfzAOdVHOnz/Pd999zz+nz5IQH8vgwYOoVq2a9/1go9sKhcLrYNrt\\n9qBqz9988x0OnkvC2ngZCG6nzRZ7O460t3j4sbGs/aN4rZSHYHUroqOjOX/+PLm5uRUKKuj1egRB\\nICYmhvPnz5OdnU1YWBhms9mrSVDeYE8gVNbqQ2RkJJmZmeTn56PT6ZAkyXstB0JRLROlUsmCLz7i\\n119/Zc+ePWg0Gnr33lRqYCgUKE1ToTx6ClB5NpKpGmT7hDayfUIf2UahjWwfmUtKkN0cQgm/v9bX\\nrFlT4uvbtm1jzJgxrFu3rsomdTUwZMgQnpn0Elz4HTHhdofqo/UAACAASURBVLJsFwXoMn5Gmb+V\\ngQOXlHvfvm0lPUEF37IA30wFz2p3aUGFopkKLpcLURRRKBSlOkXVqlUjzKDHkrsVMboN+7OuQ+Ti\\nyvqFn2id0i7ozyYIApGRkeTk5JCXl+dd6dVoNOVKsy8pC6Mo5Q0qrFmzhrv63E3NawcSEdsY5bEz\\nzJ7ThffnvM399w8Neq4edDqdt/NBMA7Yx58twpo0DwQF8YYL6FUW0gtqIlYfyeY/XyMrK6tQ5xEP\\noih6AzeBOvC+QYUaNWoEbRtPpwxPACw2Npbz58+Tk5NDcnJyicGyUCYiIoLMzEwKCgq811OgWQpQ\\n8nVqNBqpVq0aDRo0oHHjxpU74SrCE5RyOBzFMm3KG1SQkZGRkZGRkflPUMXdH6qSoHNQU1JSvK3S\\nZPwTGRnJyhXfEHFgCGF7+sHh1wjf3ZuoIw/x688/lKrUXhYe53fNmjU4nU7UanWhFWaVSoVKpUIU\\nRa+DE0ymQqCr1gqFgldenIThyINgOoZTUiFKSsjZjOHU80x+5dlyfT5P/XxeXp53LuV1RHwDMP4o\\nT1DBbDbTp989mGotxRL/PER2wBY5lII6a3h85BhOnz4N/Cs6Ewwlla8EQkFBLmhrIiBSKzKNxLAL\\nNIo9gkqlQa2LJi+v5MwR388faHDAbDYzb/6XPPLoCAyGMG5o2b5E1Vh/+JY/wL8lFQ6HA6PRiNFo\\nBAhaQyNYymOfkoiMjMRms7F+/XpWrVpFfn5+UJ0xfEuaPHi+Z6v6HFQmCoUClUqFJEnFAnnlDSpU\\nlo1kqgbZPqGNbJ/QR7ZRaCPbR+aS4izHI0QIOqiQkZFxVdfDViYdO3bkTNpxZr/Qi+cH2JnzSn/S\\nTx+jXbvgV/B9KZpiXNJqbtESiGAyFYJxskeNepwXJz5I2M62RP7djogdNxB7bBDzPn034BYkRfEE\\nFQoKCrwr2uXRU4CyMxVEUUQURQRBCDhVHWD58uVIYa0h6laconucxaEHQ1PE2MF8/sX8cs0Xyt9W\\nMiXlRsj+hQhtAQrBreYfpjHR2PAziBZapbRHoVDS6NrWLF78bwvBYIMqOTk5pNzYkV82KnDEvUx0\\n28PszpvA3feOYOHCLwPah2/5g4fY2Fjv/q+0TIVPPvmcUU+M5fNFa/hi8Z88O+kFnvu/Vwt1VSiN\\nouUPgDewEkzGQyjgT1dBzlSQkZGRkZGRkSmFKzio4DfXefTo0cVey8nJYePGjcyePbtKJ3U1ER4e\\nzsMPP1yp+/Q4fykpKUDJjpdWq8VoNGK1WomIiAgqqBBMfb0gCEx6dgJjRo9k27ZtqNVq2rRpU6E6\\neJVKRVhYGCaTiaysLO/nKQ9lZSqUt/QhPT0dm9LdIs7udJ8nk91tB5uqKSdP7gPKV4tX3qDCqy89\\nzeaeg4hKrgUkcMEcT7h0HP35t2nQtDeHCsYg1WzCEeMfPDJyLCdOpvH8c08Hracwd+5HZNrboNO9\\nQLTqGNEGI+ei+2HW1GLs+L4MHnx3qedTFEVsNhuCIBSyq8Ph4MuvlrBlyzYkSaRRo2sZN3YkN9xw\\nQ1DnIRgqo1byl19+YeKkqcQ0W4wqyn0O8xQCn385msT4mbz44qQy91FSCZLZbEYQhCsmsOLBN+jp\\nO3dZU+HqRLZPaCPbJ/SRbRTayPaRuaRcjZoKrVu39rbtEAQBQRCIi4vjrbfeIikp6VLOUaYIBw4c\\n4KvFS8nOzqdunZrcd9+QYtv4ZiqUtRpf3vIHXwwGAx07dgz6s/jD0wXC016xdu3a5dpPSSvAvpQ3\\nqNCkSRN0tm9wANnmOFyiinybe0XZ4NhIyxY3lWu+4LZdZmYmJ06cQK/XU7NmzYDGtW/fnvmfv8eb\\nb09FdSYe++kCjp35i/qNO6OvNYLGcRJHM+yYhO6YtauZPKU5o0Y+GvQ5+GrJCqzhU7BbIxElBWFa\\nExqVDbshBSdx7Nixg7Zt2/od78lS0Gq13nKLc+fO0e7mLiTWGYgmciIIWjbu3c6yDl35/bcVFc7u\\nqUpeee0tzLEzUIrXUY0jAOTba2JO/JS33unCpEkTyjy3RcsfTCYTkiQRFhYWVAZNKCBnKsjIyMjI\\nyMjIlIOrUVNh+PDhDBs2zPvvAw88wJ133klSUhJbtmy5lHOU8WHmG+/Q7uZu/LFZx6YdWn763wVa\\ntLyJDRs2FNrOty6/tCwF39fLU/5QFTidTqa8/ibPTnqBjz/7lVlzFtKmTccSO4aURUm16r6U97Pe\\ncccdROlyEM5/gCRBrjX6op7ETygL/sf9998HBF+Ll56eTtfufXn51WnMmrOMBg2b06PnQLKzswMa\\n36vXnbw7603GjBzM/LlP06lTNw5m9SfXEo1K6aRRtcMYNGbQ1EIdeQu///570EEkd0q/ElFSkGOK\\nAaBWTJr7TUFRZq/ckkofXps8g2xXbzKFcaBKAmU0RvX9mCLeYcSoqmvPVxm1knv37oTwLhit4YiS\\n+yu1wBoJuiY4nArOnTtX5j6KBr+uRD0FD75ijR4cDgeiKKJUKoPOYpLrWUMb2T6hjWyf0Ee2UWgj\\n20fmknIFlz/4DSqIosi3337LzJkz+fnnnwF354du3brx2GOPXbIJyvzL7t27eeXVmVgStmHT3AOa\\nJhQoR1AQNp8+fQcX+hHvyVQIJqjgyVTw7Ke8OgYVZcLE/2PeV7uw6Kdh0T6FTf8SaY636dNvCPv2\\n7QtqX2WVP5R1bvyhVCr54/eV1HK9S8SxFLRpTxJxojMxFx5j1S8/EBMTE9T+wL2ye0v7rvx14BbM\\n+rex6p5BrHGUP7ZUp2u3vmU66+AWt1QqlbRp04Zu3bohCAKipOJ4Rn2yjbEoFCJ1Ek4gIIKgwuVy\\nBR1YuXvgnehMCwBIz62BS1QSbcglSvgLhescrVq1KnV80c4PAEuXfYcjfAQ5xljva0ZrGETew8GD\\n+8jIyAhobpeDmJgEsJ9AQsHJzDqczk5262u48nA6CrwaIaVRNLB3peopQMmZCnKWgoyMjIyMjIxM\\nGVyNQYXHHnuMuXPnkpOTw5QpUxgwYADDhg1j1KhR7Nq161LOUeYiH370BXb946BKxulSExGX4na8\\nwu7AKdTht99+827r+fFut9u9TqM/x9mTXu10OpEkyesMXI5Mhfz8fD7++GPM4QvJs9QCQJSUODV3\\nYNONZfqM4PQ8fJ21kpzyimRlNGzYkBPH9rFs4VRmTmzE/LljOXfmBDfd9G/pQzC1eN9//z1ZBUk4\\nI17C5nCvUOt0SuwR73L46AXWr19f5j7y8/OBf8Uu7xnUkzD7QiQJTmXWxmrXoddYqRG5D0fuWrp0\\n6RJ0psLoJ0cSKf2BMuMFHLYCzuRUA+t+GqieZ/rU18rcT9HODwAOhx0UBqwOHXnmKGwOLSZrOKBE\\nodAUS6WvLCqjVnLk48PR504ByUWOOYbzBe7yMFXWTLp16xlQFwjf4JcoiiW2ir1SKCokCxULKsj1\\nrKGNbJ/QRrZP6CPbKLSR7SNzSXGU4xEi+F2e3bx5M7t370ahUGC1WqlWrRrHjh0rsc+9zKXh1D9n\\ncSnbAGCx64k05FNgdjssLmWjQmnWCoUCjcbtjJnNZsB/UMGjteByuXC5XOXSVKgs9u/fj9bQAKuq\\nBnmmHOIjM7E53PNwaXqyfsOwoPanUCgKfbai56CipR4KhYLu3bvTvXv3co33JXXtnxilXgBY7Voi\\n9AXoNBbyzZFYlL3YtGlTqboVLpfLmzbvcWQHDx7MtBnvcurCE9hjXuTkhdo0jl9FLWERQ558goSE\\nBO91E+g5iI+PZ/vW9Yx76nlWLL+GLNFF0w63Mvihu+nVq2eZ40sKKtx+++38sOFrxJhnOHquPnCx\\ntaU5lbi4GJKTkwOa2+VgwoRx/PTL/9h9qAMmw6OgCMNg/ZpYzT4+/jA1oH0oFAoUCgWiKJKfn48k\\nSRgMhitOTwHkTAUZGRkZGRkZmf8afjMV1Gq1t3WkTqejbt26ckDhMtO2TTN04joAzmRVZ9/B8+Sa\\nokESwbKO66+/vtD2HqfNk0pdmoPicbbtdjsulwtBECrUwaG8REdH47CdA8lFnjGajNxEzmReFCp0\\npZerrKC0EghPunlVZWUEU4sXGxOJSnCn+RdY3GnvsRE5AGgUGWWm0RcUFHjF/Ty20+v1bN70P+7t\\nIaI92Rj7nmrYz0ynf+/2PPDAYA4dOsT27dtJT08PKoiUnJzMN0sXYDblkZ+XzcofvyElJYUTJ07w\\n/vvvM2nS88yfP98b0PLg6fwAhYMKk199Dr3pTcidB5IDJAlMf6DPvp+335zsFXSsbCqjVlKn07Eu\\n9Re+mDuens1/pUvDxbzxYlcO7NtO9erVA96P5xrMyXHb/ErMUoDCmgqe7KCKBBXketbQRrZPaCPb\\nJ/SRbRTayPaRuaS4yvEIEfx6jQcPHqRZs2be58eOHfM+FwSB3bt3V/3sZArx2GMPM/PNZqDqhxjW\\nHatdD0oRVf6r1K+byI033lhoe51OR35+vjeVurQggSfg4Kl3v1x6Co0bN6ZWchKHsr5CCr+ftAvu\\nEggkJwb7m4wacV/Q+yxar+5LWaUhl5L77ruXd2bfijPsafKMcbhEJWE6EzrhEJLpRwYMeLPU8Xl5\\neQDFgg+xsbHM++JDPv3kPcxmM2FhYWzZsoUpU97g7917iY+/Hqv1NP/3wjSW//Al9erVC3jOKpXK\\ne+6OHz/OO++8h8nSkN37axOuX8a48c/x++ofad26NeB2LiVJQqvVeoOWAE2bNiX1j58ZMepp9uwZ\\nh0KhITExgbe+mMXAgQMCns/lQqVSMWjQIAYNGlShfdhsNq8dr0Q9BXD/fVCr1TgcDhwOBxqNRs5U\\nkJGRkZGRkZEpixDSSAgWv57UgQMHLuU8ZAKgWrVq/PLTd/TpOxinox4uZSOEzEepXyeBX3/5tthq\\nrucHvEeAsTTH2fPe5Q4qCILAV19+TOdbe2CTtmNX9QHXecKc73JjyxiGDx8e9D5Ly1So6k4XwdTi\\nNWnShLGjH2fO3JsxaSaRk51MfMQeaii+YvzMqSQmJpY6vqieQlFUKhWRkZFIksSTTz6DUtOBsJhR\\nWFwqzE4dh46u5pb2XTlxfF+hLIJAuHDhAg8//AR1G89FbahLXLXaZOW8CLbv6da9D2fPHEej0ZTY\\n+cFDSkoK27asITMzE7vdTvXq1assQ8FDKNVKFhVMvVIzFcD9/eFwOLDb7RUOKoSSjWSKI9sntJHt\\nE/rINgptZPvIXFKu4KCC3/IHh8NBWloaderUKfRIS0vz/uiVufR06NCBjHMn+eqLZ5k95SZ+/3Uh\\nu3ZuLDHNuqhjGEymwuVqJwnQqlUr9u3dxuhhepolvUz7Jgv4YPYIflv1Q7nmdTmDCsEybdqrfLNk\\nDl1b/US441Wa1TvKjGnP88QTI0odZ7FYsNvtqNXqEh12X1JTUzl8NIe080/iiSvaHVpE1TiM5oYs\\nW7Ys6HnPm7cAJ3eSltEFgOTq6aiUTlD3w+FqzPLly73zhOLXpi/x8fHUqFGjygMKoUZBQQF79uzh\\n2LFjaLXakMieKS++Yo0ulwun04lCoQiZ+0xGRkZGRkZGJuS4goUa/QYVxo0bV6JqeWRkJOPGjavS\\nScmUjlqtplevXjRo0IB27dr5db6KrgpeCZkKHmrVqsWbb05j9651rF/7E/fff3+5nSx/5Q+ejhBK\\npbJQKn5lUp5avDvuuIPfVn3H3t2bmPDUk9StW9crwOgPT8p8ZGRkmc741q1bsTq7c+5CdUwWAwD2\\ni2KYRmsP1q79K+g5Hzh4HIu9Ndm5seQbI1ApnVRLPAuAxd6K48ePAyWLNF5OQqFW0m63M2zYCPr3\\nv5fPPvuD2bMXcf/9j7J27drLPbVy46ur4JulUJ5AUSjYSMY/sn1CG9k+oY9so9BGto/MJeUK1lTw\\n60llZGTQvHnzYq83b96cEydOVOmkZCoHjUZT6Ed8IEGFUFu5rwz8ZSp4ggyhvCLsEUfNysoqdTt/\\negolERMTg1adjoTA8VP1yMqJ40JWAgBqZRqJibFBz7PJtfXQa7YDkHbW3akhIS4TtcqBXrPDq9MQ\\nakGFUGDEiPEsW5aO2bwSi2UsNttLnDr1DHfeOYijR49e7umVC98OELKegoyMjIyMjIxMADjL8QgR\\n/AYVcnNz/Q7yOAYyl5ey6rwEQcBsNrNz507279+PKIp+ty3aGeJyZypUJv4yFS5FAKWitXixsW4H\\nPzc3t1jZUX5+PnPmvMdtt/Vj2rS32bZtGwaDocx9DhgwAJd9FbgOYndoOZlW5//bu/f4qKp77+Of\\nyY2QBISEkEACRoEx5AoSI9gKoRCoopTbU1EsIKhHxQrFA8RWn0NrlaBSQXtsFcVyULG1HJQ+KjUU\\nAT0cIBIEAipUEy5JiHkRgrlPJqznj0gqhJCLmZmd5Pt+vealmdl7s2a+DNl77bV+i6rqrnCuAB+z\\nltmzW14Mc/bsmXid+xs4/4fKqgDOfNMDL9s5woPfxNf7C37yk59gjLFcp4Kn50oWFRWxfv0bVFau\\nxek835ljo7TsFqod9/LMM897tH2t1ZadCp7OSC5P+Vib8rE+ZWRtykfcqiN2KiQlJfHSSy81eH71\\n6tX1ldzFumpqapg16z7uuedB/vSnnbz44t+48sro+rntF7v4bn1nGKnQHkZldOnShW7dunHu3Ln6\\npQYB8vLyGDx4GGlp29i3byrZ2XG88MK7jB07sf4irjHBwcH84YVVBJCCl3MZOD/EVvMcAQznkUcW\\nYrfbW9zO0NBQNmx4nUCvnxBgm07B8Tfo4vV7+vdZw3vvbqgv1meMwc/P77LLm3YmBw4coEuXRLAF\\nU1NT9/ewqsofp9MXZ82P2bFjj4db2DrframgkQoiIiIizdARayqsXLmSV199lVGjRrFw4UIWLlzI\\nqFGjeOWVV1i5cqU72yiNuNw8r/nzl/DWW8coLf0LVVXzqKxM48yZDdxxx71kZWU12F4jFVyjLebi\\nnR+t8N0pEPfcs4DCwjuoqPgr3bv/ELiBwsI/snevDytXPtfkMWfPnsnHH7/PHdNySLQvZdLNmbz/\\n3joeeyyt1e0cP348eXlfsmJ5CvMfqGLmncN5+qkn6du3L2DNqQ+enisZHBxMbW0eGENpWRCFX/fm\\nxIlvl1HlJCEhLZ+KYgWN1VRoDU9nJJenfKxN+VifMrI25SNu1Y5rKjQ6mTw8PJydO3fy4Ycfkp2d\\njc1m45ZbbuFHP/qRO9snrXD27FleffVVqqo+o6rKByjF6fQBrqWqajFPPrmSv/71vy7Y57sjFWw2\\nm6XrDLRUe66pAHU1EE6cOEFRURFnzpwhMDCQrVs/oLZ2Dd7etVxxRd1SkmfP9qSq6lH+8z/vZsmS\\nRU0ed+jQoaz7r4ajkb6PK664gvvuq1uporKyksOHD1NUVERYWFh9EdCmVqfoTIYMGUKvXl0oK9sA\\nTOPkyW87FEw1gYG/Y968hR5tX2td6junkQoiIiIil+Hi6QzFxcXcdtttHDt2jKioKP7yl7/Qo0eP\\nBtuVlJRw9913c+jQIWw2G2vWrGH48OGXPbbNGGNc1XB3sdlsdIC30WYyMzMZO/Y+vvlmL4GBZURH\\nf0FlZVcOH44BDtCv3x0cP559wT5VVVUcOnQIqLvLGB8f74GWu86nn35KbW0tiYmJ9Z0Iubm5nD59\\nmiuvvJJevXp5uIWNczgcLFr0K3bv3sXp014cP34Qp9OHoKCjXHnlMfz8aqiq8ufQoVjgFEFBCZSW\\nfu3pZgPw1VdfcebMGa644gpOnTrFuXPniImJsfTn7W579uxh7NhbqXbMwFE9AcgnMGgVY340iP/+\\n79fa7VSRgwcP4nA4gLp/o4cOHdrplgkVERGRttORr/lsNhsMbsV7+6z5n8nixYvp1asXixcvZvny\\n5Zw5c4b09PQG282aNYtRo0YxZ84cnE4n5eXlTRaDt/YtWmmVkJAQamrygBrKywMpLAyjtDTo21dz\\nL3lB990Ll4409eE8Hx8famtrcTqd7W6li7vuup+MjCL6938Uf/9e1NZG0K/fWHr1+h8gjPLyQHJy\\nor7degvx8UM92NoLhYaGsmbNWv7xjw9xOn3w9q4hJCScl15aSUREhKebZwnJyclkZ2eyatULbN36\\nOMHBPbj//jSmTJnisqVO3cHX17e+U+HilWhERERExL02bdpUv2T5rFmzSElJadCpcPbsWT766CPW\\nrl0L1F1DNWd1ufZ7xiqNzvO6+uqrsdsHYbO9DNg4eTKSs2d7AA4CA5/iwQdnXrC9w+Hg5Zdf5je/\\neYpHHlnK00+vIjs7+5LHbq8uNRz7/PQHK9dUOHHiBBs2bKSoaDUORy+6dHEQF3eCXr2mce7cK5w8\\nGcTnn19DdbU/8DkBAb9k6dKH26TtbWHBgjQ2bNhPTc3DGPMbnM6lbNkygOTkFEpLSz3dPMvMlezf\\nvz8rVqSzb982/vGPt5k2bVq77lCACzsnv8/UB6tkJJemfKxN+VifMrI25SNu5eJCjYWFhYSFhQEQ\\nFhZGYWFhg21ycnIIDQ3lrrvu4tprr+Wee+6hoqKiyWO377NWadT69S/Ro8dv8fefA2wC1hAUNIJR\\no/owc+a/OhVqampITZ3Iv//7Gxw/nkJx8XQyMnpy/fUpZGRkeKr5be5SnQrn/9/KNRV27tyJn99I\\noDunT/cEwMfHSXn5JD77rJavvx5Gt26T6d49lYCAH/Dss//BuHHjPNvob+Xl5fH662+Qm/sMxtSN\\njqmpCcLh+CVnzw5m3bp1Hm6huJKfnx+VlZVUV1ernoKIiIhIU9qgUGNqairx8fENHps2bbpgO5vN\\ndslRpE6nk6ysLB544AGysrIIDAy85BSJi1n3akqadLm1cwcPHsyRI/v54x9X8/77f6RHj27cffej\\nTJw48YKpDuvXr2fv3rNUVPwVpzMHb+8aqqqGUlGRyM9+9m/k5/+z3d8xhYYrQBhj3DL94fuubxwY\\nGAgUA/D1173o2rWKsrJACgtDgTGMH+/F3Lm306VLF8aMGfPt9tawY8cO/PxGUVramzNnqggOLqGy\\nsu7isrx8Ehs3/j8eeOABj7ZR60+7xrZt23jssWU4HHWjUYKCglm27P+SnJzc4mMpI2tTPtamfKxP\\nGVmb8hG3ak6hxqptUL2t0Zcvd1M4LCyMU6dOER4eTkFBAb17926wTWRkJJGRkVx33XUATJs2TZ0K\\nnV2vXr149NFHePTRRxrd5sUX36C8fA7gQ22tN1BDTY0vMJKKii7s2bOnyWqf7cHFIxW+u/KDled6\\njx07FmPuAj7F6RzCl19e9e0r1QQGrmbevCe49dZbPdnERvn7+2OzlQGQlxeOr28NRUUh375aRmCg\\ndZaWlLazY8cObr75/9Cly38wYIAdOEdW1iFGj76Zjz76gGuvvdbTTRQRERGxnuZ0Kvik1D3O++bX\\nzT78xIkTWbt2LUuWLGHt2rVMmjSpwTbh4eH069ePI0eOYLfb2bJlC7GxsU0eu/3fgu7E2mKe1zff\\nlAF1F3oVFf4YY6Oiwh+wYbOFUFZW9r3/DCu4eKSCu4o0ft+M/P39efnl3xMQMAWb7QXgMPAeAQE3\\nkZJyDRMmTGiLZrpEamoqTmcWcBSHw48jRwZQUnIFUENQ0J+4667bPN1EzZV0gQULHqWycikOx3jq\\nfsX4UF09iYqKhSxZ0vxffOcpI2tTPtamfKxPGVmb8hG3cnFNhbS0NDIyMrDb7WzdupW0tDQA8vPz\\nL7imeP7555kxYwaJiYkcOHCAX/7yl00eWyMVOrnx43/IkSN/x+H4AceO9SEvrzdOpw9QhMNxqMPc\\nVbzcSAWru+2224iKiuKJJ55l796XCQ3txUMP3cOsWbMsPTUlKCiI3/1uOQsXTqKi4lFgJJBDQMAK\\nkpP7csstt3i6idLGKioqOHBgD7AWh+NfI4Cqq32BKXz44a8xxlh6dJCIiIiIR1yiRkJbCg4OZsuW\\nLQ2e79u3L++++279z4mJiWRmZrbo2DbTARb77MhrlrraiRMniI0dRmnpY8BUwBvIJyDgQebMGcHz\\nzz/j4Ra2jbKyMr744gsCAgIYPHgwp0+fJjc3l+DgYK666qqmDyCtlpGRwa9/vYLs7AOEhIQyb95s\\nHnxwXodcurSzq6qqIiioB7W1B4GuDBx4ktpaL3Jy+gJn8PMbTnV1uaebKSIiIu1QR77ms9ls0K0V\\n763UGp+J9W/Tikv169eP7dv/zowZ95Kbuwxf397U1Bznvvv+jaee+q2nm9dmzo9UcPf0B6mbBpGa\\nmurpZogb+Pv7c8MNo/noow3Anfzzn5H1r9ls67n55omea5yIiIiIlTWnpoJFWXfstDSpreZ5DR06\\nlMOHM/n0021kZLxEYeExVqxYdsEqEe1dY9MfrF5TQVxL+bS9555bRmDgCmy21cAZoAgvr+fp1u0V\\nli9XTYWORvlYm/KxPmVkbcpH3MrFNRVcSZ0KUs9ut5OcnEy3bt083ZQ25+XlhZeXF8YYamtr6zsX\\n2kNNBZH2ZMiQIfzv/25lwoTP8fMbgb9/CpMn5/PJJx9jt9s93TwRERERa6ptxcMiVFNBOo3s7Gyq\\nq6uJjY3l+PHjlJaWMmjQILp37+7ppomIiIiIyGV05Gu+uiLWrXlv1vhMNFJBOo3vToFQTQURERER\\nEZHvT50K7ZjmebXM+akOTqdTNRUEUD7tgTKyNuVjbcrH+pSRtSkfkeZRp4J0Guc7EBwOB06nE5vN\\n1qGKUYqIiIiIiLibaipIp5Gfn09BQQEhISGcPn0aX19fEhISPN0sERERERFpQke+5mvvNRVU+l46\\njfMjFSoqKi74WURERERExLMstEZkC2n6QzumeV4tc74Toaqq6oKfXUkZWZvysT5lZG3Kx9qUj/Up\\nI2tTPuJezlY8rEEjFaTTOF+o8fwQofM/i4iIiIiIeFb7HamgmgrSaVRXV5OdnV3/c3h4OBERER5s\\nkYiIiIiINEdHvuarq6lwuhV7hljiM9GtWuk0Lh6Z3NoxgAAADixJREFUoJoKIiIiIiJiDe13pIJq\\nKrRjmufVMt7e3nh5/euvvGoqiPKxPmVkbcrH2pSP9Skja1M+4l41rXhYg0YqSKfi6+tLdXU1oJoK\\nIiIiIiJiFdYpvNhSqqkgncrnn39OeXk5ALGxsfj7+3u4RSIiIiIi0pSOfM1XV1PhSCv2tFviM9Gt\\nWuk0HA4HO3fu5OjRf9K9ezfCwsKIjIz0dLNERERERETaLdVUaMc0z6v5vvzyS6KiBvH002vIyDjG\\nxo2fMGhQDKtXv+zSP1cZWZvysT5lZG3Kx9qUj/UpI2tTPuJezlY8rEEjFaTDM8YwYcJkCgvjgGQC\\nA6GyEqqqIpg/fxHDh19PfHy8p5spIiIiIiKdlnUKL7aUaipIh5eZmcno0RMpL7+X0FAv+veHsjL4\\n4gvw9t7BXXfZWb36D55upoiIiIiINKIjX/PV1VTY24o9h1niM9FIBenwvvrqK7y8+gBefLvwAw5H\\n3X9ra8P47LPWFEURERERERFpK+13pIJqKrRjmufVPAMGDODcuXzgHN98A7m5kJdX95q39yliY69x\\n2Z+tjKxN+VifMrI25WNtysf6lJG1KR9xL9fWVCguLiY1NRW73c64ceMoKSm55HbLli0jNjaW+Ph4\\n7rjjDqrP35W9DHUqSIc3bNgwrrwyHC+v3QCcPn1+pEIRfn77eOiheR5tn4iIiIiIdHY1rXg0X3p6\\nOqmpqRw5coQxY8aQnp7eYJvc3FxWr15NVlYWBw8epLa2ljfffLPJY6umgnQKubm5jBw5hpISL0pL\\nI/D3LwO+4A9/eJ7Zs2d7unkiIiIiInIZHfmar66mQkYr9kxt9mcSHR3N9u3bCQsL49SpU6SkpPD5\\n559fsE1xcTEjRoxg165ddOvWjcmTJzN//nzGjh172WOrpoJ0ClFRUXz11Rf87W9/Iysri9DQUKZP\\nn07v3r093TQREREREen0XFtTobCwkLCwMADCwsIoLCxssE1wcDAPP/ww/fv3p2vXrowfP77JDgXQ\\n9Id2TfO8WsbHx4fJkyfz+OOP89BDD7mlQ0EZWZvysT5lZG3Kx9qUj/UpI2tTPuJe37+mQmpqKvHx\\n8Q0emzZtumA7m8327eiIC3355ZesXLmS3Nxc8vPzKSsr4/XXX2+y5RqpICIiIiIiIuJRzRmpkA0c\\navTVjIzGp1Ccn/YQHh5OQUHBJW+wfvLJJ9xwww2EhIQAMGXKFHbu3MmMGTMu2yrVVBARERERERFL\\n68jXfHWjBpouiNjQ9GZ/JosXLyYkJIQlS5aQnp5OSUlJg2KN+/fvZ8aMGWRmZuLv78/s2bNJTk5m\\n3rzLF7bX9AcRERERERERj3LtkpJpaWlkZGRgt9vZunUraWlpAOTn5zNhwgQAEhMTmTlzJklJSSQk\\nJABw7733NnlsjVRox7Zt20ZKSoqnmyGXoYysTflYnzKyNuVjbcrH+pSRtSkfa+nI13x1IxVea8We\\nd1riM1FNBRERERERERGPcu3qD66kkQoiIiIiIiJiaR35mq9upMLqVux5jyU+E41UEBEREREREfGo\\n9jtSwSOFGt966y1iY2Px9vYmKyur0e02b95MdHQ0gwYNYvny5W5sYfugtXOtTxlZm/KxPmVkbcrH\\n2pSP9Skja1M+4l6uLdToSh7pVIiPj2fjxo2MHDmy0W1qa2t58MEH2bx5M4cPH2b9+vV89tlnbmyl\\n9X366aeeboI0QRlZm/KxPmVkbcrH2pSP9Skja1M+4l41rXhYg0emP0RHRze5zZ49exg4cCBRUVEA\\nTJ8+nXfeeYfBgwe7uHXtR0lJiaebIE1QRtamfKxPGVmb8rE25WN9ysjalI+4l3VGHrSUR0YqNEde\\nXh79+vWr/zkyMpK8vDwPtkhERERERETEFTRSoYHU1FROnTrV4Pknn3ySW2+9tcn96ypgyuXk5uZ6\\nugnSBGVkbcrH+pSRtSkfa1M+1qeMrE35iHu135EKHl1ScvTo0axYsYJrr722wWu7du1i6dKlbN68\\nGYBly5bh5eXFkiVLGmw7ZMgQ9u/f7/L2ioiIiIiIiPslJiZ22DoXrb2h3rNnT4qLi9u4NS3n8SUl\\nG+vTSEpK4ujRo+Tm5tK3b1/+/Oc/s379+ktu21H/comIiIiIiEjH5sH7/G3CIzUVNm7cSL9+/di1\\naxcTJkzgpptuAiA/P58JEyYA4OPjw+9//3vGjx9PTEwMt912m4o0ioiIiIiIiFiIR6c/iIiIiIiI\\niEj7ZdnVHzqrOXPmEBYWRnx8fP1ze/bsITk5maFDh3LdddeRmZlZ/9qBAwcYMWIEcXFxJCQk4HA4\\nANi7dy/x8fEMGjSI+fPnu/19dFQtyaeqqorbb7+dhIQEYmJiSE9Pr99H+bjGpfLZv38/I0aMICEh\\ngYkTJ1JaWlr/2rJlyxg0aBDR0dF88MEH9c8rH9dpSUYZGRkkJSWRkJBAUlISH374Yf0+ysg1Wvod\\nAjh+/DhBQUGsWLGi/jnl4zotzUjnCe7Vknx0nuB+J06cYPTo0cTGxhIXF8dzzz0HQHFxMampqdjt\\ndsaNG3fBUpI6VxBpBiOWsmPHDpOVlWXi4uLqnxs1apTZvHmzMcaY9957z6SkpBhjjKmpqTEJCQnm\\nwIEDxhhjiouLTW1trTHGmOuuu87s3r3bGGPMTTfdZN5//313vo0OqyX5vPrqq2b69OnGGGMqKipM\\nVFSUOXbsmDFG+bjKpfJJSkoyO3bsMMYYs2bNGvPYY48ZY4w5dOiQSUxMNA6Hw+Tk5JgBAwaYc+fO\\nGWOUjyu1JKN9+/aZgoICY4wx2dnZJiIion4fZeQaLcnnvKlTp5qf/vSn5plnnql/Tvm4Tksy0nmC\\n+7UkH50nuF9BQYHZt2+fMcaY0tJSY7fbzeHDh82iRYvM8uXLjTHGpKenmyVLlhhjdK4g0lwaqWAx\\nN954Iz179rzguT59+nD27FkASkpKiIiIAOCDDz4gISGhvje8Z8+eeHl5UVBQQGlpKcnJyQDMnDmT\\nt99+243vouNqST59+vShvLyc2tpaysvL8fPzo3v37srHhS6Vz9GjR7nxxhsBGDt2LBs2bADgnXfe\\n4fbbb8fX15eoqCgGDhzI7t27lY+LtSSjIUOGEB4eDkBMTAyVlZXU1NQoIxdqST4Ab7/9NldffTUx\\nMTH1zykf12pJRjpPcL+W5KPzBPcLDw9nyJAhAAQFBTF48GDy8vLYtGkTs2bNAmDWrFn1n7fOFUSa\\nR50K7UB6ejoPP/ww/fv3Z9GiRSxbtgyo+yVls9n48Y9/zLBhw3j66acByMvLIzIysn7/iIgI8vLy\\nPNL2zuDifJ588kkAxo8fT/fu3enTpw9RUVEsWrSIHj16KB83i42N5Z133gHgrbfe4sSJE0BdYdjv\\n5hAZGUleXl6D55WP6zWW0Xdt2LCBYcOG4evrq++QmzWWT1lZGU899RRLly69YHvl436NZXTkyBGd\\nJ1hAY/noPMGzcnNz2bdvH9dffz2FhYWEhYUBEBYWRmFhIaBzBZHmUqdCOzB37lyee+45jh8/zrPP\\nPsucOXMAqKmp4eOPP+aNN97g448/ZuPGjWzdurXV65xK61ycz9y5cwF47bXXqKyspKCggJycHJ55\\n5hlycnI83NrOZ82aNbzwwgskJSVRVlaGn5+fp5skF2kqo0OHDpGWlsaLL77ooRZ2bo3ls3TpUn7x\\ni18QEBDQ7pfCau8ay8jpdOo8wQIay0fnCZ5TVlbG1KlTWbVqFd26dbvgNZvNpu+ISAv5eLoB0rQ9\\ne/awZcsWAKZNm8bdd98NQL9+/Rg5ciTBwcEA3HzzzWRlZXHnnXdy8uTJ+v1PnjxZPyRf2l5j+ezc\\nuZPJkyfj7e1NaGgoP/jBD9i7dy8//OEPlY8bXXPNNfz9738H6u7avfvuu0DdXYXv3hE/efIkkZGR\\nREREKB83aywjqPv8p0yZwrp167jqqqsAlJGbXZzPe++9B9T927dhwwYWL15MSUkJXl5edO3alSlT\\npigfN2vsO6TzBGto7Duk8wTPqKmpYerUqfzsZz9j0qRJQN3ohFOnThEeHk5BQQG9e/cGdK4g0lwa\\nqdAODBw4kO3btwOwdetW7HY7AOPGjePgwYNUVlbidDrZvn07sbGxhIeH0717d3bv3o0xhnXr1tX/\\noyltr7F8oqOj2bp1KwDl5eXs2rWL6Oho5eNmRUVFAJw7d47f/va33H///QBMnDiRN998E4fDQU5O\\nDkePHiU5OVn5eEBjGZWUlDBhwgSWL1/OiBEj6rfv06ePMnKji/O57777ANixYwc5OTnk5OSwYMEC\\nfvWrX/HAAw/oO+QBjX2Hxo8fr/MEC2jsO6TzBPczxjB37lxiYmJYsGBB/fMTJ05k7dq1AKxdu7b+\\n89a5gkgzeapCpFza9OnTTZ8+fYyvr6+JjIw0a9asMZmZmSY5OdkkJiaa4cOHm6ysrPrtX3vtNRMb\\nG2vi4uLqK9UaY8wnn3xi4uLizIABA8zPf/5zT7yVDqkl+VRVVZkZM2aYuLg4ExMTc0FldOXjGhfn\\n88orr5hVq1YZu91u7Ha7eeSRRy7Y/oknnjADBgww11xzTf0KHsYoH1dqSUaPP/64CQwMNEOGDKl/\\nFBUVGWOUkau09Dt03tKlS82KFSvqf1Y+rtPSjHSe4F4tyUfnCe730UcfGZvNZhITE+t/r7z//vvm\\n9OnTZsyYMWbQoEEmNTXVnDlzpn4fnSuINM1mjCZCioiIiIiIiEjLafqDiIiIiIiIiLSKOhVERERE\\nREREpFXUqSAiIiIiIiIiraJOBRERERERERFpFXUqiIiIiIiIiEirqFNBRERERERERFpFnQoiIiIi\\nIiIi0irqVBARERERERGRVvn/sEc9qPB1GfQAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f1730f20a10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(\\n\",\n      \"    csv_data[\\\"Year\\\"],\\n\",\n      \"    csv_data[\\\"Average\\\"],\\n\",\n      \"    width=0.7,\\n\",\n      \"    edgecolor=\\\"none\\\",\\n\",\n      \"    color=(csv_data[\\\"Average\\\"]>0).map({True: 'r', False: 'b'}),\\n\",\n      \"    label=\\\"Annual Average Global Anomaly\\\",\\n\",\n      \"    )\\n\",\n      \"plt.hlines(0,min(csv_data[\\\"Year\\\"])-3,max(csv_data[\\\"Year\\\"])+5)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.xlim(min(csv_data[\\\"Year\\\"])-3, max(csv_data[\\\"Year\\\"])+5)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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ZG9vw+mab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\\nqYqPjw9psQA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      \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f1730b87650>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Final Record is not complete so average of the last year is not reliable\\n\",\n      \"csv_data[-1:]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Year</th>\\n\",\n        \"      <th>Jan</th>\\n\",\n        \"      <th>Feb</th>\\n\",\n        \"      <th>Mar</th>\\n\",\n        \"      <th>Apr</th>\\n\",\n        \"      <th>May</th>\\n\",\n        \"      <th>Jun</th>\\n\",\n        \"      <th>Jul</th>\\n\",\n        \"      <th>Aug</th>\\n\",\n        \"      <th>Sep</th>\\n\",\n        \"      <th>Oct</th>\\n\",\n        \"      <th>Nov</th>\\n\",\n        \"      <th>Dec</th>\\n\",\n        \"      <th>Average</th>\\n\",\n        \"      <th>TSI</th>\\n\",\n        \"      <th>CO2</th>\\n\",\n        \"      <th>CH4</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>163</th>\\n\",\n        \"      <td> 2014</td>\\n\",\n        \"      <td> 0.95</td>\\n\",\n        \"      <td> 0.408</td>\\n\",\n        \"      <td> 0.929</td>\\n\",\n        \"      <td> 1.048</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td> 0.83375</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"      <td>NaN</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>1 rows \\u00d7 17 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"     Year   Jan    Feb    Mar    Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec  \\\\\\n\",\n        \"163  2014  0.95  0.408  0.929  1.048  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \\n\",\n        \"\\n\",\n        \"     Average  TSI  CO2  CH4  \\n\",\n        \"163  0.83375  NaN  NaN  NaN  \\n\",\n        \"\\n\",\n        \"[1 rows x 17 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualize monthly temperature\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Prepare monthly data\\n\",\n      \"monthly_temp = csv_data.drop(\\\"Year\\\", 1).drop(\\\"Average\\\", 1).drop(\\\"TSI\\\", 1).drop(\\\"CO2\\\", 1).drop(\\\"CH4\\\", 1)\\n\",\n      \"monthly_temp = pd.Series(np.ravel(monthly_temp)).dropna()\\n\",\n      \"month_index = list((monthly_temp.index/12.) + 1851)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(15,8))\\n\",\n      \"plt.scatter(\\n\",\n      \"    x=month_index,\\n\",\n      \"    y=monthly_temp,\\n\",\n      \"    marker=\\\"o\\\",\\n\",\n      \"    label=\\\"Monthly Average Global Anomaly\\\",\\n\",\n      \"    c=monthly_temp,\\n\",\n      \"    alpha=0.6\\n\",\n      \"    )\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.legend(loc=\\\"lower right\\\")\\n\",\n      \"plt.xlim(min(month_index)-3,max(month_index)+5)\\n\",\n      \"plt.ylim(min(monthly_temp),max(monthly_temp))\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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b3VLLVUV1ezZfNmMlNTCAoLp0uXLiiKctlj/cMiOHRmGw3cnamqNnK0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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f1730e07e90>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(month_index, monthly_temp,  width=0.1, edgecolor=\\\"none\\\", color=(monthly_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Monthly Average Global Anomaly\\\")\\n\",\n      \"plt.hlines(0,min(month_index)-1,max(month_index)+1)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.xlim(min(month_index)-1, max(month_index)+1)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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yZ5LsC9d+9e/fa3v9WXvvQl1dXV6Ytf/KK+/OUvq2fPnl42j5RxMCnI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\\nddppp0mSNm3apKlTp0qSNm/erAkTJmjkyJ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y/+/frrr2vTpk3Fvnr0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\\nYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f17309e5cd0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Linear Regression\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Predicting Annual Temperature\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"annual_temp = csv_data[\\\"Average\\\"]\\n\",\n      \"annual_index = list(csv_data[\\\"Year\\\"].values)\\n\",\n      \"annual_index_feature = list(csv_data[[\\\"Year\\\"]].values)\\n\",\n      \"prediction_annual_index = [[item] for item in range(min(annual_index_feature),max(annual_index_feature)+10)]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Code source: Jaques Grobler\\n\",\n      \"# License: BSD 3 clause\\n\",\n      \"\\n\",\n      \"from sklearn import linear_model\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"# Create linear regression object\\n\",\n      \"regr = linear_model.LinearRegression()\\n\",\n      \"\\n\",\n      \"# Train the model using the training sets\\n\",\n      \"regr.fit(annual_index_feature, annual_temp)\\n\",\n      \"\\n\",\n      \"# The coefficients\\n\",\n      \"print 'Coefficients:', regr.coef_\\n\",\n      \"# The mean square error\\n\",\n      \"print(\\\"Residual sum of squares: %.2f\\\"\\n\",\n      \"      % np.mean((regr.predict(annual_index_feature) - annual_temp) ** 2))\\n\",\n      \"# Explained variance score: 1 is perfect prediction\\n\",\n      \"print('Variance score: %.2f' % regr.score(annual_index_feature, annual_temp))\\n\",\n      \"\\n\",\n      \"# Plot outputs\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\\\"none\\\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.3)\\n\",\n      \"\\n\",\n      \"plt.plot(prediction_annual_index[:], regr.predict(prediction_annual_index[:]), color='green',\\n\",\n      \"        linewidth=3, alpha=1.0, label=\\\"Linear Regression\\\")\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlim(np.min(annual_index_feature), np.max(annual_index_feature)+5)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Coefficients: [ 0.00651672]\\n\",\n        \"Residual sum of squares: 0.06\\n\",\n        \"Variance score: 0.60\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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27EgAEDEB8fz2ISmcX57CRiJkjETJCImSARM0EiWzIhvv+uf7/cVqd2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\\nAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f172867dad0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Predicting Monthly Temperature\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"month_index_feature = [[item] for item in month_index]\\n\",\n      \"prediction_month_index = [[item[0] + 5] for item in month_index_feature]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Code source: Jaques Grobler\\n\",\n      \"# License: BSD 3 clause\\n\",\n      \"\\n\",\n      \"from sklearn import linear_model\\n\",\n      \"\\n\",\n      \"regr = linear_model.LinearRegression()\\n\",\n      \"\\n\",\n      \"# Train the model using the training sets\\n\",\n      \"regr.fit(month_index_feature, monthly_temp)\\n\",\n      \"\\n\",\n      \"# The coefficients\\n\",\n      \"print 'Coefficients:', regr.coef_\\n\",\n      \"# The mean square error\\n\",\n      \"print(\\\"Residual sum of squares: %.2f\\\"\\n\",\n      \"      % np.mean((regr.predict(month_index_feature) - monthly_temp) ** 2))\\n\",\n      \"# Explained variance score: 1 is perfect prediction\\n\",\n      \"print('Variance score: %.2f' % regr.score(month_index_feature, monthly_temp))\\n\",\n      \"\\n\",\n      \"# Plot outputs\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(month_index, monthly_temp,  width=0.1, edgecolor=\\\"none\\\", color=(monthly_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Monthly Average Global Anomaly\\\", alpha=0.1)\\n\",\n      \"\\n\",\n      \"plt.plot(month_index, regr.predict(month_index_feature), color='black',\\n\",\n      \"        linewidth=3, alpha=0.5, label=\\\"Linear Regression\\\")\\n\",\n      \"plt.plot(prediction_month_index[-5*12:], regr.predict(prediction_month_index[-5*12:]), color='green',\\n\",\n      \"        linewidth=3, alpha=1.0, label=\\\"Linear Regression Prediction\\\")\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlim(np.min(month_index_feature), np.max(month_index_feature)+5)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Coefficients: [ 0.00645693]\\n\",\n        \"Residual sum of squares: 0.16\\n\",\n        \"Variance score: 0.37\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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9Hr9cr7utrxB2jnWzR7JuVyEHnppZdwyy23FNyoj33sY3j55ZcxMzOD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\\n6IislMYAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f172890e850>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"SVM Regression\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.svm import SVR\\n\",\n      \"\\n\",\n      \"# Create linear regression object\\n\",\n      \"regr_linear = SVR(kernel=\\\"linear\\\")\\n\",\n      \"regr_rbf_1 = SVR(kernel=\\\"rbf\\\", C=100.0, gamma=0.004, epsilon=0.01)\\n\",\n      \"regr_rbf_2 = SVR(kernel=\\\"rbf\\\", C=10.0, gamma=0.0001, epsilon=0.01)\\n\",\n      \"regr_rbf_3 = SVR(kernel=\\\"rbf\\\", C=1.0, gamma=0.0002, epsilon=0.1)\\n\",\n      \"\\n\",\n      \"# Train the model using the training sets\\n\",\n      \"regr_linear.fit(annual_index_feature, annual_temp)\\n\",\n      \"regr_rbf_1.fit(annual_index_feature, annual_temp)\\n\",\n      \"regr_rbf_2.fit(annual_index_feature, annual_temp)\\n\",\n      \"regr_rbf_3.fit(annual_index_feature, annual_temp)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"# The coefficients\\n\",\n      \"#print 'Coefficients:', regr.coef_\\n\",\n      \"# The mean square error\\n\",\n      \"print(\\\"Residual sum of squares: %.2f\\\"\\n\",\n      \"      % np.mean((regr_rbf_1.predict(annual_index_feature) - annual_temp) ** 2))\\n\",\n      \"# Explained variance score: 1 is perfect prediction\\n\",\n      \"print('score1: %.2f' % regr_rbf_1.score(annual_index_feature, annual_temp))\\n\",\n      \"print('score2: %.2f' % regr_rbf_2.score(annual_index_feature, annual_temp))\\n\",\n      \"print('score3: %.2f' % regr_rbf_3.score(annual_index_feature, annual_temp))\\n\",\n      \"\\n\",\n      \"# Plot outputs\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\\\"none\\\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.3)\\n\",\n      \"\\n\",\n      \"plt.plot(prediction_annual_index[:], regr_linear.predict(prediction_annual_index[:]), color='green',\\n\",\n      \"        linewidth=3, alpha=0.5, label=\\\"Linear Prediction\\\")\\n\",\n      \"plt.plot(prediction_annual_index[:], regr_rbf_1.predict(prediction_annual_index[:]), color='blue',\\n\",\n      \"        linewidth=3, alpha=0.5, label=\\\"RBF1 Prediction\\\")\\n\",\n      \"plt.plot(prediction_annual_index[:], regr_rbf_2.predict(prediction_annual_index[:]), color='orange',\\n\",\n      \"        linewidth=3, alpha=0.5, label=\\\"RBF2 Prediction\\\")\\n\",\n      \"plt.plot(prediction_annual_index[:], regr_rbf_3.predict(prediction_annual_index[:]), color='red',\\n\",\n      \"        linewidth=3, alpha=0.5, label=\\\"RBF3 Prediction\\\")\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlim(np.min(annual_index_feature), np.max(annual_index_feature)+10)\\n\",\n      \"plt.xticks(np.arange(np.min(annual_index_feature), np.max(annual_index_feature)+10, 10))\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Residual sum of squares: 0.02\\n\",\n        \"score1: 0.87\\n\",\n        \"score2: 0.82\\n\",\n        \"score3: 0.82\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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wfA2tqasWPH8vbbb5e6PVevXiUrK4s1a9YQHh7Otm3b7jknLy/vIRIL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\\nYAAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f171c64ed50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.svm import SVR\\n\",\n      \"from sklearn.grid_search import GridSearchCV\\n\",\n      \"\\n\",\n      \"regr_rbf = SVR(kernel=\\\"rbf\\\")\\n\",\n      \"C = [100, 10, 1]\\n\",\n      \"gamma = [0.005, 0.004, 0.003, 0.002, 0.001]\\n\",\n      \"epsilon=[0.01]\\n\",\n      \"parameters = {\\\"C\\\":C, \\\"gamma\\\":gamma, \\\"epsilon\\\":epsilon}\\n\",\n      \"\\n\",\n      \"gs = GridSearchCV(regr_rbf, parameters, scoring=\\\"r2\\\")\\n\",\n      \"\\n\",\n      \"gs.fit(annual_index_feature, annual_temp)\\n\",\n      \"\\n\",\n      \"print \\\"Best Estimator:\\\\n%s\\\"  % gs.best_estimator_\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Best Estimator:\\n\",\n        \"SVR(C=1, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.001,\\n\",\n        \"  kernel=rbf, max_iter=-1, probability=False, random_state=None,\\n\",\n        \"  shrinking=True, tol=0.001, verbose=False)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.grid_search import GridSearchCV\\n\",\n      \"\\n\",\n      \"regr_rbf = SVR(kernel=\\\"rbf\\\")\\n\",\n      \"C = np.arange(gs.best_estimator_.C * 0.9, gs.best_estimator_.C * 1.1, gs.best_estimator_.C * 0.01)\\n\",\n      \"gamma = np.arange(gs.best_estimator_.gamma * 0.9, gs.best_estimator_.gamma * 1.1, gs.best_estimator_.gamma * 0.01)\\n\",\n      \"parameters = {\\\"C\\\":C, \\\"gamma\\\":gamma}\\n\",\n      \"\\n\",\n      \"gs = GridSearchCV(regr_rbf, parameters, scoring=\\\"r2\\\")\\n\",\n      \"\\n\",\n      \"gs.fit(annual_index_feature, annual_temp)\\n\",\n      \"\\n\",\n      \"print \\\"Best Estimator:\\\\n%s\\\"  % gs.best_estimator_\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Best Estimator:\\n\",\n        \"SVR(C=0.93, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.0009,\\n\",\n        \"  kernel=rbf, max_iter=-1, probability=False, random_state=None,\\n\",\n        \"  shrinking=True, tol=0.001, verbose=False)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from sklearn.svm import SVR\\n\",\n      \"\\n\",\n      \"# Create linear regression object\\n\",\n      \"regr_rbf = gs.best_estimator_\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"# The coefficients\\n\",\n      \"#print 'Coefficients:', regr.coef_\\n\",\n      \"# The mean square error\\n\",\n      \"print(\\\"Residual sum of squares: %.2f\\\"\\n\",\n      \"      % np.mean((regr_rbf.predict(annual_index_feature) - annual_temp) ** 2))\\n\",\n      \"# Explained variance score: 1 is perfect prediction\\n\",\n      \"print('score: %.2f' % regr_rbf.score(annual_index_feature, annual_temp))\\n\",\n      \"\\n\",\n      \"# Plot outputs\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\\\"none\\\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.3)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"plt.plot(prediction_annual_index[:], regr_rbf.predict(prediction_annual_index[:]), color='black',\\n\",\n      \"        linewidth=3, alpha=0.7, label=\\\"Best RBF Prediction\\\")\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.title(regr_rbf)\\n\",\n      \"plt.xlim(np.min(annual_index_feature)+1, np.max(annual_index_feature)+10)\\n\",\n      \"plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature)+10, 10))\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Residual sum of squares: 0.02\\n\",\n        \"score: 0.84\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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X0VFRX4+OOP4e7uDolEAldXVyFPbfkq2dnZCX+bmJgIM2GSk5MxZMgQ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\\nlPRNNKIAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f1730da5110>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Examining Extra Features\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"There are three features that we can add to the tempreture data to build a better predective model. We will look at greenhouse gases (CO2 and CH4) and solar activity. So we will use the following extra features:\\n\",\n      \"\\n\",\n      \"- CO2 Level\\n\",\n      \"- CH4 Level\\n\",\n      \"- Total Solar Irradiance (TSI)\\n\",\n      \"\\n\",\n      \"This data is from the following sources:\\n\",\n      \"\\n\",\n      \"###CO2 and CH4 Data:\\n\",\n      \"Dr. Pieter Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/).\\n\",\n      \"\\n\",\n      \"- <a href=\\\"ftp://aftp.cmdl.noaa.gov/data/greenhouse_gases/co2/in-situ/surface/spo/co2_spo_surface-insitu_1_ccgg_month.txt\\\">CO2 Dataset</a>\\n\",\n      \"- <a href=\\\"ftp://aftp.cmdl.noaa.gov/data/greenhouse_gases/ch4/flask/surface/ch4_brw_surface-flask_1_ccgg_month.txt\\\">CH4 Dataset</a>\\n\",\n      \"\\n\",\n      \"<a href=\\\"http://www.esrl.noaa.gov/gmd/ccgg/trends/\\\"><img src=\\\"http://www.esrl.noaa.gov/gmd/images/noaalogo2.png\\\" /></a>\\n\",\n      \"\\n\",\n      \"###Total Solar Irradiance:\\n\",\n      \"The Solar Radiation and Climate Experiment (SORCE) is a NASA-sponsored satellite mission that is providing state-of-the-art measurements of incoming x-ray, ultraviolet, visible, near-infrared, and total solar radiation. The measurements provided by SORCE specifically address long-term climate change, natural variability and enhanced climate prediction, and atmospheric ozone and UV-B radiation. These measurements are critical to studies of the Sun; its effect on our Earth system; and its influence on humankind.\\n\",\n      \"\\n\",\n      \"- <a href=\\\"http://lasp.colorado.edu/data/sorce/tsi_data/TSI_TIM_Reconstruction.txt\\\">TSI Dataset</a>\\n\",\n      \"\\n\",\n      \"<a href=\\\"http://www.colorado.edu/\\\"><img src=\\\"http://lasp.colorado.edu/home/sorce/files/2011/09/CU_logo.png\\\" /></a><a href=\\\"http://lasp.colorado.edu/home/sorce/\\\"><img src=\\\"http://lasp.colorado.edu/home/sorce/files/2013/01/SORCE10Years.jpg\\\" /></a>\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import math\\n\",\n      \"\\n\",\n      \"def average(x):\\n\",\n      \"    assert len(x) > 0\\n\",\n      \"    return float(sum(x)) / len(x)\\n\",\n      \"\\n\",\n      \"def pearson_def(x, y):\\n\",\n      \"    assert len(x) == len(y)\\n\",\n      \"    n = len(x)\\n\",\n      \"    assert n > 0\\n\",\n      \"    avg_x = average(x)\\n\",\n      \"    avg_y = average(y)\\n\",\n      \"    diffprod = 0\\n\",\n      \"    xdiff2 = 0\\n\",\n      \"    ydiff2 = 0\\n\",\n      \"    for idx in range(n):\\n\",\n      \"        xdiff = x[idx] - avg_x\\n\",\n      \"        ydiff = y[idx] - avg_y\\n\",\n      \"        diffprod += xdiff * ydiff\\n\",\n      \"        xdiff2 += xdiff * xdiff\\n\",\n      \"        ydiff2 += ydiff * ydiff\\n\",\n      \"\\n\",\n      \"    return diffprod / math.sqrt(xdiff2 * ydiff2)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import scipy as sp\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\\\"none\\\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.2)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"\\n\",\n      \"tsi_ax = plt.twinx()\\n\",\n      \"tsi_ax.plot(csv_data[\\\"Year\\\"], csv_data[\\\"TSI\\\"], linewidth=3, c=\\\"orange\\\", alpha=1.0)\\n\",\n      \"plt.ylabel(u\\\"TSI Reconstruction from IPCC AR5\\\")\\n\",\n      \"\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlim(np.min(annual_index_feature)+1, np.max(annual_index_feature)+10)\\n\",\n      \"\\n\",\n      \"plt.show()\\n\",\n      \"print \\\"Correlation between TSI and Temperature: %s%%\\\" % (round(1000*pearson_def(\\n\",\n      \"                                        csv_data[[\\\"Average\\\",\\\"TSI\\\"]].dropna()[\\\"Average\\\"].values,\\n\",\n      \"                                        csv_data[[\\\"Average\\\",\\\"TSI\\\"]].dropna()[\\\"TSI\\\"].values))/10)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stderr\",\n       \"text\": [\n        \"/usr/lib/pymodules/python2.7/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\\n\",\n        \"  warnings.warn(\\\"No labeled objects found. \\\"\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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2WlEB0fmjmKiM9yVq/GVlra2I5OSSFzypQ27mjdhmXLyEpL8zxXUNAY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\\niIhI2FFQS0REREREREREws7/A6bCXyE34A49AAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f1730da5610>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Correlation between TSI and Temperature: 35.2%\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CO2\\\"]].dropna()[\\\"Year\\\"],\\n\",\n      \"        csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CO2\\\"]].dropna()[\\\"Average\\\"],\\n\",\n      \"        width=0.7, edgecolor=\\\"none\\\",\\n\",\n      \"        color=(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CO2\\\"]].dropna()[\\\"Average\\\"]>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.2)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"co2_ax = plt.twinx()\\n\",\n      \"co2_ax.plot(csv_data[\\\"Year\\\"], csv_data[\\\"CO2\\\"], linewidth=3, c=\\\"g\\\", alpha=.8)\\n\",\n      \"plt.ylabel(u\\\"CO2 CCGG (In Situ) ppm\\\")\\n\",\n      \"\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlim(np.min(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CO2\\\"]].dropna()[\\\"Year\\\"]),\\n\",\n      \"         np.max(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CO2\\\"]].dropna()[\\\"Year\\\"]))\\n\",\n      \"\\n\",\n      \"plt.show()\\n\",\n      \"print \\\"Correlation between TSI and Temperature: %s%%\\\" % (round(1000*pearson_def(\\n\",\n      \"                                        csv_data[[\\\"Average\\\",\\\"CO2\\\"]].dropna()[\\\"Average\\\"].values,\\n\",\n      \"                                        csv_data[[\\\"Average\\\",\\\"CO2\\\"]].dropna()[\\\"CO2\\\"].values))/10)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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J8+HVOnTkVBQQHCwsJ6jbd7927947i4OMTFxZmlbiIiIiIiIiIyP61O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\\nIiIiIiKiEe3/A0REcJKJvCSoAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f17300250d0>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Correlation between TSI and Temperature: 91.0%\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CH4\\\"]].dropna()[\\\"Year\\\"],\\n\",\n      \"        csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CH4\\\"]].dropna()[\\\"Average\\\"],\\n\",\n      \"        width=0.7, edgecolor=\\\"none\\\",\\n\",\n      \"        color=(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CH4\\\"]].dropna()[\\\"Average\\\"]>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.3)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"\\n\",\n      \"ch4_ax = plt.twinx()\\n\",\n      \"ch4_ax.plot(csv_data[\\\"Year\\\"], csv_data[\\\"CH4\\\"], linewidth=3, c=\\\"b\\\", alpha=.8)\\n\",\n      \"plt.ylabel(u\\\"CH4 CCGG (Individual Flasks) ppb\\\")\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlim(np.min(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CH4\\\"]].dropna()[\\\"Year\\\"]),\\n\",\n      \"         np.max(csv_data[[\\\"Year\\\", \\\"Average\\\",\\\"CH4\\\"]].dropna()[\\\"Year\\\"]))\\n\",\n      \"\\n\",\n      \"plt.show()\\n\",\n      \"print \\\"Correlation between TSI and Temperature: %s%%\\\" % (round(1000*pearson_def(\\n\",\n      \"                                        csv_data[[\\\"Average\\\",\\\"CH4\\\"]].dropna()[\\\"Average\\\"].values,\\n\",\n      \"                                        csv_data[[\\\"Average\\\",\\\"CH4\\\"]].dropna()[\\\"CH4\\\"].values))/10)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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IiIiIhcfvLzYft22LABtmyBY/G38lcvN8wmKy4uVlzMxmY2YXvvYrZi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\\n8D7FxdJbb0kLF0qFhWXlzZtb9/KKjHRfbAAAAAAAwPs9+OCDGjJkiCZPn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      \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f1728bd16d0>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Correlation between TSI and Temperature: 80.6%\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Forecasting TSI, CO2 and CH4\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"regr_rbf = SVR(kernel=\\\"rbf\\\")\\n\",\n      \"C = [30]\\n\",\n      \"gamma_1 = [0.015]\\n\",\n      \"gamma_2 = [0.005, 0.004, 0.003, 0.002, 0.001, 0.0009, 0.0008, 0.0007, 0.0006, 0.0005, 0.0004, 0.0003, 0.0002, 0.0001]\\n\",\n      \"epsilon=[0.01, 0.001]\\n\",\n      \"parameters_1 = {\\\"C\\\":C, \\\"gamma\\\":gamma_1, \\\"epsilon\\\":epsilon}\\n\",\n      \"parameters_2 = {\\\"C\\\":C, \\\"gamma\\\":gamma_2, \\\"epsilon\\\":epsilon}\\n\",\n      \"\\n\",\n      \"gs_1 = GridSearchCV(regr_rbf, parameters_1, scoring=\\\"r2\\\")\\n\",\n      \"gs_2 = GridSearchCV(regr_rbf, parameters_2, scoring=\\\"r2\\\")\\n\",\n      \"\\n\",\n      \"gs_1.fit(csv_data[[\\\"Year\\\",\\\"TSI\\\"]].dropna()[[\\\"Year\\\"]], csv_data[[\\\"Year\\\",\\\"TSI\\\"]].dropna()[\\\"TSI\\\"])\\n\",\n      \"gs_2.fit(csv_data[[\\\"Year\\\",\\\"TSI\\\"]].dropna()[[\\\"Year\\\"]], csv_data[[\\\"Year\\\",\\\"TSI\\\"]].dropna()[\\\"TSI\\\"])\\n\",\n      \"\\n\",\n      \"print \\\"Best Estimator:\\\\n%s\\\"  % gs_1.best_estimator_\\n\",\n      \"print \\\"Best Estimator:\\\\n%s\\\"  % gs_2.best_estimator_\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Best Estimator:\\n\",\n        \"SVR(C=30, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.015,\\n\",\n        \"  kernel=rbf, max_iter=-1, probability=False, random_state=None,\\n\",\n        \"  shrinking=True, tol=0.001, verbose=False)\\n\",\n        \"Best Estimator:\\n\",\n        \"SVR(C=30, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.0001,\\n\",\n        \"  kernel=rbf, max_iter=-1, probability=False, random_state=None,\\n\",\n        \"  shrinking=True, tol=0.001, verbose=False)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"annual_index_feature = np.arange(np.min(csv_data[[\\\"Year\\\",\\\"TSI\\\"]].dropna()[\\\"Year\\\"]),\\n\",\n      \"                                 np.max(csv_data[[\\\"Year\\\",\\\"TSI\\\"]].dropna()[\\\"Year\\\"])+10)\\n\",\n      \"annual_index_feature = [[item] for item in annual_index_feature]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\\\"none\\\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.3)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"\\n\",\n      \"tsi_ax = plt.twinx()\\n\",\n      \"tsi_ax.plot(csv_data[\\\"Year\\\"], csv_data[\\\"TSI\\\"], \\\"--\\\", linewidth=3, c=\\\"gray\\\", label=\\\"TSI\\\")\\n\",\n      \"tsi_ax.plot(annual_index_feature,\\n\",\n      \"            gs_1.best_estimator_.predict(annual_index_feature),\\n\",\n      \"            linewidth=3, c=\\\"green\\\", label=\\\"TSI Short-term Trend\\\", alpha=0.4)\\n\",\n      \"tsi_ax.plot(annual_index_feature,\\n\",\n      \"            gs_2.best_estimator_.predict(annual_index_feature),\\n\",\n      \"            linewidth=3, c=\\\"blue\\\", label=\\\"TSI Long-term Trend\\\", alpha=0.4)\\n\",\n      \"plt.ylabel(u\\\"TSI Reconstruction from IPCC AR5\\\")\\n\",\n      \"\\n\",\n      \"plt.legend(loc=\\\"upper left\\\")\\n\",\n      \"\\n\",\n      \"plt.xlim(np.min(annual_index_feature)-1, np.max(annual_index_feature))\\n\",\n      \"plt.title(\\\"Total Solar Irradiance (TSI) with a short term and long term predictions\\\")\\n\",\n      \"plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature), 10))\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"st_prediction = gs_1.best_estimator_.predict(annual_index_feature)\\n\",\n      \"lt_prediction = gs_2.best_estimator_.predict(annual_index_feature)\\n\",\n      \"print u\\\"Long-Term Wave Length \\\\u2248 (%s - %s) * 2 \\\\u2248 %s\\\" % (annual_index_feature[np.argmax(lt_prediction)][0],\\n\",\n      \"                        annual_index_feature[np.argmin(lt_prediction)][0],\\n\",\n      \"                        (annual_index_feature[np.argmax(lt_prediction)][0]-annual_index_feature[np.argmin(lt_prediction)][0])*2\\n\",\n      \"                        )\\n\",\n      \"st_min = csv_data[\\\"Year\\\"][np.argmin(csv_data[\\\"Year\\\"][5:13])]\\n\",\n      \"st_max = csv_data[\\\"Year\\\"][np.argmax(csv_data[\\\"Year\\\"][5:13])]\\n\",\n      \"print u\\\"Short-Term Wave Length \\\\u2248 (%s - %s) * 2 \\\\u2248 %s\\\" % (st_max,\\n\",\n      \"                        st_min,\\n\",\n      \"                        (st_max-st_min)*2\\n\",\n      \"                        )\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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jT69euHFi1awMzMDOvXr68SQ10ngtdUvvKxlStXwt3dHWZmZlAqlcjJ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\\n03lSkQAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f17288f2290>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Long-Term Wave Length \\u2248 (1973 - 1882) * 2 \\u2248 182\\n\",\n        \"Short-Term Wave Length \\u2248 (1863 - 1856) * 2 \\u2248 14\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"regr_rbf = SVR(kernel=\\\"rbf\\\")\\n\",\n      \"C = [1,10,20,30,50,100,1000]\\n\",\n      \"gamma_2 = [0.01, 0.005, 0.004, 0.003, 0.002, 0.001, 0.0009, 0.0008, 0.0007, 0.0006, 0.0005, 0.0004, 0.0003, 0.0002, 0.0001,\\n\",\n      \"           0.00009,0.00008,0.00007,0.00006,0.00005,0.00004,0.00003,0.00002,0.00001]\\n\",\n      \"epsilon=[0.01, 0.001]\\n\",\n      \"parameters_2 = {\\\"C\\\":C, \\\"gamma\\\":gamma_2, \\\"epsilon\\\":epsilon}\\n\",\n      \"\\n\",\n      \"gs_2 = GridSearchCV(regr_rbf, parameters_2, scoring=\\\"r2\\\")\\n\",\n      \"\\n\",\n      \"gs_2.fit(csv_data[[\\\"Year\\\",\\\"CO2\\\"]].dropna()[[\\\"Year\\\"]], csv_data[[\\\"Year\\\",\\\"CO2\\\"]].dropna()[\\\"CO2\\\"])\\n\",\n      \"\\n\",\n      \"print \\\"Best Estimator:\\\\n%s\\\"  % gs_2.best_estimator_\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Best Estimator:\\n\",\n        \"SVR(C=1000, cache_size=200, coef0=0.0, degree=3, epsilon=0.001, gamma=5e-05,\\n\",\n        \"  kernel=rbf, max_iter=-1, probability=False, random_state=None,\\n\",\n        \"  shrinking=True, tol=0.001, verbose=False)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 24\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"annual_index_feature = np.arange(np.min(csv_data[[\\\"Year\\\",\\\"CO2\\\"]].dropna()[\\\"Year\\\"]),\\n\",\n      \"                                 np.max(csv_data[[\\\"Year\\\",\\\"CO2\\\"]].dropna()[\\\"Year\\\"])+10)\\n\",\n      \"annual_index_feature = [[item] for item in annual_index_feature]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\\\"none\\\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.3)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"\\n\",\n      \"tsi_ax = plt.twinx()\\n\",\n      \"tsi_ax.plot(csv_data[\\\"Year\\\"], csv_data[\\\"CO2\\\"], \\\"--\\\", linewidth=3, c=\\\"gray\\\", label=\\\"CO2\\\")\\n\",\n      \"\\n\",\n      \"tsi_ax.plot(annual_index_feature,\\n\",\n      \"            gs_2.best_estimator_.predict(annual_index_feature),\\n\",\n      \"            linewidth=3, c=\\\"blue\\\", label=\\\"CO2 Trend\\\", alpha=0.4)\\n\",\n      \"plt.ylabel(u\\\"CO2 CCGG (In Situ) ppm\\\")\\n\",\n      \"\\n\",\n      \"plt.legend(loc=\\\"upper left\\\")\\n\",\n      \"\\n\",\n      \"plt.xlim(np.min(annual_index_feature)-1, np.max(annual_index_feature))\\n\",\n      \"plt.title(\\\"CO2 with trend\\\")\\n\",\n      \"plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature), 10))\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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9BKSIAfrYDUY53VAVdXCQ0NZd68eUyfPh2TycTYsWMxm81s2LCBjIwM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\\nahxwERERERERERGRqv0PufDpylIP+i0AAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f1728d11490>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 25\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"regr_rbf = SVR(kernel=\\\"rbf\\\")\\n\",\n      \"C = [1,10,20,30,40,50,60,70,80,90,100,1000]\\n\",\n      \"gamma_2 = [0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09, 0.005, 0.004, 0.003, 0.002, 0.001]\\n\",\n      \"epsilon=[0.01, 0.001, 0.0001, 0.00001]\\n\",\n      \"parameters_2 = {\\\"C\\\":C, \\\"gamma\\\":gamma_2, \\\"epsilon\\\":epsilon}\\n\",\n      \"\\n\",\n      \"gs_2 = GridSearchCV(regr_rbf, parameters_2, scoring=\\\"r2\\\")\\n\",\n      \"\\n\",\n      \"gs_2.fit(csv_data[[\\\"Year\\\",\\\"CH4\\\"]].dropna()[[\\\"Year\\\"]], csv_data[[\\\"Year\\\",\\\"CH4\\\"]].dropna()[\\\"CH4\\\"])\\n\",\n      \"\\n\",\n      \"print \\\"Best Estimator:\\\\n%s\\\"  % gs_2.best_estimator_\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Best Estimator:\\n\",\n        \"SVR(C=1000, cache_size=200, coef0=0.0, degree=3, epsilon=0.0001, gamma=0.004,\\n\",\n        \"  kernel=rbf, max_iter=-1, probability=False, random_state=None,\\n\",\n        \"  shrinking=True, tol=0.001, verbose=False)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 26\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"annual_index_feature = np.arange(np.min(csv_data[[\\\"Year\\\",\\\"CH4\\\"]].dropna()[\\\"Year\\\"]),\\n\",\n      \"                                 np.max(csv_data[[\\\"Year\\\",\\\"CH4\\\"]].dropna()[\\\"Year\\\"])+10)\\n\",\n      \"annual_index_feature = [[item] for item in annual_index_feature]\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"plt.figure(figsize=(20,5))\\n\",\n      \"plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\\\"none\\\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\\n\",\n      \"        label=\\\"Annual Average Global Anomaly\\\", alpha=0.3)\\n\",\n      \"plt.ylabel(u\\\"CRUTEM4 Temperature Anomaly (\\\\u00B0C)\\\")\\n\",\n      \"\\n\",\n      \"tsi_ax = plt.twinx()\\n\",\n      \"tsi_ax.plot(csv_data[\\\"Year\\\"], csv_data[\\\"CH4\\\"], \\\"--\\\", linewidth=3, c=\\\"gray\\\", label=\\\"CH4\\\")\\n\",\n      \"\\n\",\n      \"tsi_ax.plot(annual_index_feature,\\n\",\n      \"            gs_2.best_estimator_.predict(annual_index_feature),\\n\",\n      \"            linewidth=3, c=\\\"blue\\\", label=\\\"CH4 Trend\\\", alpha=0.4)\\n\",\n      \"plt.ylabel(u\\\"CH4 CCGG (Individual Flasks) ppb\\\")\\n\",\n      \"\\n\",\n      \"plt.legend(loc=\\\"upper left\\\")\\n\",\n      \"\\n\",\n      \"plt.xlim(np.min(annual_index_feature)-1, np.max(annual_index_feature))\\n\",\n      \"plt.title(\\\"Methane (CH4) with trend\\\")\\n\",\n      \"plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature), 10))\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": \"iVBORw0KGgoAAAANSUhEUgAABL4AAAFCCAYAAAD/ihTJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nrm0j5fzZbA7eeSef7GwHY8dO8vksJQUuuSSK7OxkRo++mmH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      \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f17280cda50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 27\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### End of Lesson 3\\n\",\n      \"For questions please leave them on:\\n\",\n      \"\\n\",\n      \"- <a href=\\\"https://www.youtube.com/user/roshanRush\\\">YouTube</a>\\n\",\n      \"- <a href=\\\"https://twitter.com/twistedhardware\\\">Twitter</a>\\n\",\n      \"- <a href=\\\"https://plus.google.com/+Tcsaroshan/posts\\\">Google+</a>\\n\",\n      \"\\n\",\n      \"<a href=\\\"/notebooks/Lesson 2 - Intoroduction to Machine Learning.ipynb\\\">Previous Lesson - Introduction to Machine Learning</a>\\n\",\n      \"<br />\\n\",\n      \"<a href=\\\"/notebooks/Lesson 4 - Clustering.ipynb\\\">Next Lesson - Clustering</a>\\n\",\n      \"\\n\",\n      \"In the next lesson:\\n\",\n      \"- Why do we use Clustering?\\n\",\n      \"- Finding Certoids\\n\",\n      \"- K-Means Algorithm\"\n     ]\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/Lesson 4 - Clustering.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:42aadde3139597ff8f8143e835f3473a4584a11d644f24156e679f8237ba3244\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Lesson Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"In this you will learn about the following:\\n\",\n      \"\\n\",\n      \"- <a href=\\\"#Why-do-we-use-clustering?\\\">Why do we use clustering?</a>\\n\",\n      \"- <a href=\\\"#Clustering-Examples\\\">Clustering Examples</a>\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.display import HTML\\n\",\n      \"\\n\",\n      \"input_form = \\\"\\\"\\\"\\n\",\n      \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n      \"<p>User: root<br> Password: admin</p>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"javascript = \\\"\\\"\\\"\\n\",\n      \"<script type=\\\"text/Javascript\\\">\\n\",\n      \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n      \"</script>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"HTML(input_form + javascript)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"\\n\",\n        \"<a id=\\\"admin_link\\\" target=\\\"_blank\\\" href=\\\"#\\\">Ajenti Administration Interface</a>\\n\",\n        \"<p>User: root<br> Password: admin</p>\\n\",\n        \"\\n\",\n        \"<script type=\\\"text/Javascript\\\">\\n\",\n        \"document.getElementById('admin_link').href = \\\"https://\\\" + window.location.hostname + \\\":8000\\\"\\n\",\n        \"</script>\\n\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 1,\n       \"text\": [\n        \"<IPython.core.display.HTML at 0x7f6c9c2f87d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Why do we use clustering?\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Unlike classification and regression, clustering is unsupervised. That means the algorithm will classify your data not based on predeined categories. The algorithm will look for similar data points in the dataset and cluster them together. Different algorithms can solve specific problems but k-means is a general use algorithm and that what we will cover in this tutorial.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Clustering Examples\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"- Finding similarities between items with no categories\\n\",\n      \" - Categorizing text documents\\n\",\n      \" - Counting objects in an image\\n\",\n      \" - Grouping images based on content\\n\",\n      \"- Finding new patterns in categorized data\\n\",\n      \" - Looking at unkown features\\n\",\n      \" - Finding correlated stocks and commodities (possitive/negative)\\n\",\n      \"- Recommendation systems\\n\",\n      \" - Finding related news\\n\",\n      \" - Finding products/information to a user's taste\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pandas as pd\\n\",\n      \"import numpy as np\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"\\n\",\n      \"from matplotlib.finance import candlestick, quotes_historical_yahoo, date2num\\n\",\n      \"from sklearn.cluster import KMeans\\n\",\n      \"from sklearn.preprocessing import normalize\\n\",\n      \"\\n\",\n      \"from datetime import datetime, timedelta\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Configure Pandas\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"pd.options.display.max_columns=50\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Retreiving Stock Price for Google\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def download_data(symbol, days_delta=30):\\n\",\n      \"    # Set Start and End Date\\n\",\n      \"    finish_date = datetime.today()\\n\",\n      \"    start_date = finish_date - timedelta(days=days_delta)\\n\",\n      \"\\n\",\n      \"    # Read from Yahoo! Finance\\n\",\n      \"    stocks_raw = quotes_historical_yahoo(symbol, start_date, finish_date)\\n\",\n      \"    stocks_df = pd.DataFrame(stocks_raw, columns=[\\\"n_date\\\", \\\"open\\\", \\\"close\\\", \\\"high\\\", \\\"low\\\", \\\"volume\\\"])\\n\",\n      \"    return stocks_df\\n\",\n      \"\\n\",\n      \"stocks_df = download_data(\\\"GOOG\\\")\\n\",\n      \"stocks_df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>n_date</th>\\n\",\n        \"      <th>open</th>\\n\",\n        \"      <th>close</th>\\n\",\n        \"      <th>high</th>\\n\",\n        \"      <th>low</th>\\n\",\n        \"      <th>volume</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> 735456</td>\\n\",\n        \"      <td> 569.99</td>\\n\",\n        \"      <td> 567.88</td>\\n\",\n        \"      <td> 570.49</td>\\n\",\n        \"      <td> 566.00</td>\\n\",\n        \"      <td> 1211400</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> 735457</td>\\n\",\n        \"      <td> 564.52</td>\\n\",\n        \"      <td> 562.73</td>\\n\",\n        \"      <td> 565.90</td>\\n\",\n        \"      <td> 560.88</td>\\n\",\n        \"      <td> 1537800</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> 735458</td>\\n\",\n        \"      <td> 567.31</td>\\n\",\n        \"      <td> 574.78</td>\\n\",\n        \"      <td> 575.00</td>\\n\",\n        \"      <td> 565.75</td>\\n\",\n        \"      <td> 1435300</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> 735459</td>\\n\",\n        \"      <td> 576.18</td>\\n\",\n        \"      <td> 574.65</td>\\n\",\n        \"      <td> 577.90</td>\\n\",\n        \"      <td> 570.88</td>\\n\",\n        \"      <td>  982800</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> 735460</td>\\n\",\n        \"      <td> 577.86</td>\\n\",\n        \"      <td> 573.48</td>\\n\",\n        \"      <td> 579.38</td>\\n\",\n        \"      <td> 570.52</td>\\n\",\n        \"      <td> 1515000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> 735463</td>\\n\",\n        \"      <td> 576.11</td>\\n\",\n        \"      <td> 582.16</td>\\n\",\n        \"      <td> 584.51</td>\\n\",\n        \"      <td> 576.00</td>\\n\",\n        \"      <td> 1280600</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> 735464</td>\\n\",\n        \"      <td> 585.00</td>\\n\",\n        \"      <td> 586.86</td>\\n\",\n        \"      <td> 587.34</td>\\n\",\n        \"      <td> 584.00</td>\\n\",\n        \"      <td>  976000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> 735465</td>\\n\",\n        \"      <td> 585.88</td>\\n\",\n        \"      <td> 584.49</td>\\n\",\n        \"      <td> 586.70</td>\\n\",\n        \"      <td> 582.57</td>\\n\",\n        \"      <td> 1033900</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> 735466</td>\\n\",\n        \"      <td> 583.82</td>\\n\",\n        \"      <td> 583.37</td>\\n\",\n        \"      <td> 584.50</td>\\n\",\n        \"      <td> 581.14</td>\\n\",\n        \"      <td>  912300</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> 735467</td>\\n\",\n        \"      <td> 583.59</td>\\n\",\n        \"      <td> 582.56</td>\\n\",\n        \"      <td> 585.24</td>\\n\",\n        \"      <td> 580.64</td>\\n\",\n        \"      <td>  786900</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> 735470</td>\\n\",\n        \"      <td> 584.72</td>\\n\",\n        \"      <td> 580.20</td>\\n\",\n        \"      <td> 585.00</td>\\n\",\n        \"      <td> 579.00</td>\\n\",\n        \"      <td> 1357700</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 735471</td>\\n\",\n        \"      <td> 581.26</td>\\n\",\n        \"      <td> 577.86</td>\\n\",\n        \"      <td> 581.80</td>\\n\",\n        \"      <td> 576.58</td>\\n\",\n        \"      <td> 1635200</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> 735472</td>\\n\",\n        \"      <td> 577.27</td>\\n\",\n        \"      <td> 571.00</td>\\n\",\n        \"      <td> 578.49</td>\\n\",\n        \"      <td> 570.10</td>\\n\",\n        \"      <td> 1698700</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> 735473</td>\\n\",\n        \"      <td> 569.56</td>\\n\",\n        \"      <td> 569.20</td>\\n\",\n        \"      <td> 573.25</td>\\n\",\n        \"      <td> 567.10</td>\\n\",\n        \"      <td> 1289400</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> 735474</td>\\n\",\n        \"      <td> 571.33</td>\\n\",\n        \"      <td> 571.60</td>\\n\",\n        \"      <td> 572.04</td>\\n\",\n        \"      <td> 567.07</td>\\n\",\n        \"      <td> 1080800</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> 735478</td>\\n\",\n        \"      <td> 571.85</td>\\n\",\n        \"      <td> 577.33</td>\\n\",\n        \"      <td> 577.83</td>\\n\",\n        \"      <td> 571.19</td>\\n\",\n        \"      <td> 1574100</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> 735479</td>\\n\",\n        \"      <td> 580.00</td>\\n\",\n        \"      <td> 577.94</td>\\n\",\n        \"      <td> 582.99</td>\\n\",\n        \"      <td> 575.00</td>\\n\",\n        \"      <td> 1211800</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> 735480</td>\\n\",\n        \"      <td> 580.00</td>\\n\",\n        \"      <td> 581.98</td>\\n\",\n        \"      <td> 586.00</td>\\n\",\n        \"      <td> 579.22</td>\\n\",\n        \"      <td> 1454200</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> 735481</td>\\n\",\n        \"      <td> 583.98</td>\\n\",\n        \"      <td> 586.08</td>\\n\",\n        \"      <td> 586.55</td>\\n\",\n        \"      <td> 581.95</td>\\n\",\n        \"      <td> 1627900</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>19 rows \\u00d7 6 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"    n_date    open   close    high     low   volume\\n\",\n        \"0   735456  569.99  567.88  570.49  566.00  1211400\\n\",\n        \"1   735457  564.52  562.73  565.90  560.88  1537800\\n\",\n        \"2   735458  567.31  574.78  575.00  565.75  1435300\\n\",\n        \"3   735459  576.18  574.65  577.90  570.88   982800\\n\",\n        \"4   735460  577.86  573.48  579.38  570.52  1515000\\n\",\n        \"5   735463  576.11  582.16  584.51  576.00  1280600\\n\",\n        \"6   735464  585.00  586.86  587.34  584.00   976000\\n\",\n        \"7   735465  585.88  584.49  586.70  582.57  1033900\\n\",\n        \"8   735466  583.82  583.37  584.50  581.14   912300\\n\",\n        \"9   735467  583.59  582.56  585.24  580.64   786900\\n\",\n        \"10  735470  584.72  580.20  585.00  579.00  1357700\\n\",\n        \"11  735471  581.26  577.86  581.80  576.58  1635200\\n\",\n        \"12  735472  577.27  571.00  578.49  570.10  1698700\\n\",\n        \"13  735473  569.56  569.20  573.25  567.10  1289400\\n\",\n        \"14  735474  571.33  571.60  572.04  567.07  1080800\\n\",\n        \"15  735478  571.85  577.33  577.83  571.19  1574100\\n\",\n        \"16  735479  580.00  577.94  582.99  575.00  1211800\\n\",\n        \"17  735480  580.00  581.98  586.00  579.22  1454200\\n\",\n        \"18  735481  583.98  586.08  586.55  581.95  1627900\\n\",\n        \"\\n\",\n        \"[19 rows x 6 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Processing the Date\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def process_date(stocks_df):\\n\",\n      \"    stocks_df[\\\"n_date\\\"] = stocks_df[\\\"n_date\\\"].astype(np.int32)\\n\",\n      \"    stocks_df[\\\"date\\\"] = stocks_df[\\\"n_date\\\"].apply(datetime.fromordinal)\\n\",\n      \"    return stocks_df\\n\",\n      \"\\n\",\n      \"process_date(stocks_df)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>n_date</th>\\n\",\n        \"      <th>open</th>\\n\",\n        \"      <th>close</th>\\n\",\n        \"      <th>high</th>\\n\",\n        \"      <th>low</th>\\n\",\n        \"      <th>volume</th>\\n\",\n        \"      <th>date</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> 735456</td>\\n\",\n        \"      <td> 569.99</td>\\n\",\n        \"      <td> 567.88</td>\\n\",\n        \"      <td> 570.49</td>\\n\",\n        \"      <td> 566.00</td>\\n\",\n        \"      <td> 1211400</td>\\n\",\n        \"      <td>2014-08-11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> 735457</td>\\n\",\n        \"      <td> 564.52</td>\\n\",\n        \"      <td> 562.73</td>\\n\",\n        \"      <td> 565.90</td>\\n\",\n        \"      <td> 560.88</td>\\n\",\n        \"      <td> 1537800</td>\\n\",\n        \"      <td>2014-08-12</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> 735458</td>\\n\",\n        \"      <td> 567.31</td>\\n\",\n        \"      <td> 574.78</td>\\n\",\n        \"      <td> 575.00</td>\\n\",\n        \"      <td> 565.75</td>\\n\",\n        \"      <td> 1435300</td>\\n\",\n        \"      <td>2014-08-13</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> 735459</td>\\n\",\n        \"      <td> 576.18</td>\\n\",\n        \"      <td> 574.65</td>\\n\",\n        \"      <td> 577.90</td>\\n\",\n        \"      <td> 570.88</td>\\n\",\n        \"      <td>  982800</td>\\n\",\n        \"      <td>2014-08-14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> 735460</td>\\n\",\n        \"      <td> 577.86</td>\\n\",\n        \"      <td> 573.48</td>\\n\",\n        \"      <td> 579.38</td>\\n\",\n        \"      <td> 570.52</td>\\n\",\n        \"      <td> 1515000</td>\\n\",\n        \"      <td>2014-08-15</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> 735463</td>\\n\",\n        \"      <td> 576.11</td>\\n\",\n        \"      <td> 582.16</td>\\n\",\n        \"      <td> 584.51</td>\\n\",\n        \"      <td> 576.00</td>\\n\",\n        \"      <td> 1280600</td>\\n\",\n        \"      <td>2014-08-18</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> 735464</td>\\n\",\n        \"      <td> 585.00</td>\\n\",\n        \"      <td> 586.86</td>\\n\",\n        \"      <td> 587.34</td>\\n\",\n        \"      <td> 584.00</td>\\n\",\n        \"      <td>  976000</td>\\n\",\n        \"      <td>2014-08-19</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> 735465</td>\\n\",\n        \"      <td> 585.88</td>\\n\",\n        \"      <td> 584.49</td>\\n\",\n        \"      <td> 586.70</td>\\n\",\n        \"      <td> 582.57</td>\\n\",\n        \"      <td> 1033900</td>\\n\",\n        \"      <td>2014-08-20</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> 735466</td>\\n\",\n        \"      <td> 583.82</td>\\n\",\n        \"      <td> 583.37</td>\\n\",\n        \"      <td> 584.50</td>\\n\",\n        \"      <td> 581.14</td>\\n\",\n        \"      <td>  912300</td>\\n\",\n        \"      <td>2014-08-21</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> 735467</td>\\n\",\n        \"      <td> 583.59</td>\\n\",\n        \"      <td> 582.56</td>\\n\",\n        \"      <td> 585.24</td>\\n\",\n        \"      <td> 580.64</td>\\n\",\n        \"      <td>  786900</td>\\n\",\n        \"      <td>2014-08-22</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> 735470</td>\\n\",\n        \"      <td> 584.72</td>\\n\",\n        \"      <td> 580.20</td>\\n\",\n        \"      <td> 585.00</td>\\n\",\n        \"      <td> 579.00</td>\\n\",\n        \"      <td> 1357700</td>\\n\",\n        \"      <td>2014-08-25</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 735471</td>\\n\",\n        \"      <td> 581.26</td>\\n\",\n        \"      <td> 577.86</td>\\n\",\n        \"      <td> 581.80</td>\\n\",\n        \"      <td> 576.58</td>\\n\",\n        \"      <td> 1635200</td>\\n\",\n        \"      <td>2014-08-26</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> 735472</td>\\n\",\n        \"      <td> 577.27</td>\\n\",\n        \"      <td> 571.00</td>\\n\",\n        \"      <td> 578.49</td>\\n\",\n        \"      <td> 570.10</td>\\n\",\n        \"      <td> 1698700</td>\\n\",\n        \"      <td>2014-08-27</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> 735473</td>\\n\",\n        \"      <td> 569.56</td>\\n\",\n        \"      <td> 569.20</td>\\n\",\n        \"      <td> 573.25</td>\\n\",\n        \"      <td> 567.10</td>\\n\",\n        \"      <td> 1289400</td>\\n\",\n        \"      <td>2014-08-28</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> 735474</td>\\n\",\n        \"      <td> 571.33</td>\\n\",\n        \"      <td> 571.60</td>\\n\",\n        \"      <td> 572.04</td>\\n\",\n        \"      <td> 567.07</td>\\n\",\n        \"      <td> 1080800</td>\\n\",\n        \"      <td>2014-08-29</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> 735478</td>\\n\",\n        \"      <td> 571.85</td>\\n\",\n        \"      <td> 577.33</td>\\n\",\n        \"      <td> 577.83</td>\\n\",\n        \"      <td> 571.19</td>\\n\",\n        \"      <td> 1574100</td>\\n\",\n        \"      <td>2014-09-02</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> 735479</td>\\n\",\n        \"      <td> 580.00</td>\\n\",\n        \"      <td> 577.94</td>\\n\",\n        \"      <td> 582.99</td>\\n\",\n        \"      <td> 575.00</td>\\n\",\n        \"      <td> 1211800</td>\\n\",\n        \"      <td>2014-09-03</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> 735480</td>\\n\",\n        \"      <td> 580.00</td>\\n\",\n        \"      <td> 581.98</td>\\n\",\n        \"      <td> 586.00</td>\\n\",\n        \"      <td> 579.22</td>\\n\",\n        \"      <td> 1454200</td>\\n\",\n        \"      <td>2014-09-04</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> 735481</td>\\n\",\n        \"      <td> 583.98</td>\\n\",\n        \"      <td> 586.08</td>\\n\",\n        \"      <td> 586.55</td>\\n\",\n        \"      <td> 581.95</td>\\n\",\n        \"      <td> 1627900</td>\\n\",\n        \"      <td>2014-09-05</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>19 rows \\u00d7 7 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 5,\n       \"text\": [\n        \"    n_date    open   close    high     low   volume       date\\n\",\n        \"0   735456  569.99  567.88  570.49  566.00  1211400 2014-08-11\\n\",\n        \"1   735457  564.52  562.73  565.90  560.88  1537800 2014-08-12\\n\",\n        \"2   735458  567.31  574.78  575.00  565.75  1435300 2014-08-13\\n\",\n        \"3   735459  576.18  574.65  577.90  570.88   982800 2014-08-14\\n\",\n        \"4   735460  577.86  573.48  579.38  570.52  1515000 2014-08-15\\n\",\n        \"5   735463  576.11  582.16  584.51  576.00  1280600 2014-08-18\\n\",\n        \"6   735464  585.00  586.86  587.34  584.00   976000 2014-08-19\\n\",\n        \"7   735465  585.88  584.49  586.70  582.57  1033900 2014-08-20\\n\",\n        \"8   735466  583.82  583.37  584.50  581.14   912300 2014-08-21\\n\",\n        \"9   735467  583.59  582.56  585.24  580.64   786900 2014-08-22\\n\",\n        \"10  735470  584.72  580.20  585.00  579.00  1357700 2014-08-25\\n\",\n        \"11  735471  581.26  577.86  581.80  576.58  1635200 2014-08-26\\n\",\n        \"12  735472  577.27  571.00  578.49  570.10  1698700 2014-08-27\\n\",\n        \"13  735473  569.56  569.20  573.25  567.10  1289400 2014-08-28\\n\",\n        \"14  735474  571.33  571.60  572.04  567.07  1080800 2014-08-29\\n\",\n        \"15  735478  571.85  577.33  577.83  571.19  1574100 2014-09-02\\n\",\n        \"16  735479  580.00  577.94  582.99  575.00  1211800 2014-09-03\\n\",\n        \"17  735480  580.00  581.98  586.00  579.22  1454200 2014-09-04\\n\",\n        \"18  735481  583.98  586.08  586.55  581.95  1627900 2014-09-05\\n\",\n        \"\\n\",\n        \"[19 rows x 7 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Calculate the Daily Average, Daily Change and Rage\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We'll be calculating the daily average using this equation to compile the daily price into one number:\\n\",\n      \"\\n\",\n      \"$Average = \\\\frac{(Close + High + Low)}{3}$\\n\",\n      \"\\n\",\n      \"Then we will be calculating the daily change amount and percentage using these equations:\\n\",\n      \"\\n\",\n      \"$Change Amount = Close - Open$\\n\",\n      \"\\n\",\n      \"$Change Percentage = \\\\frac{Change Amount}{Average}$\\n\",\n      \"\\n\",\n      \"$Range = \\\\frac{(High - Low)}{Average}$\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def calculate_stats(stocks_df):\\n\",\n      \"    stocks_df[\\\"average\\\"] = (stocks_df[\\\"close\\\"] + stocks_df[\\\"high\\\"] + stocks_df[\\\"low\\\"]) / 3.0\\n\",\n      \"    stocks_df[\\\"change_amount\\\"] = stocks_df[\\\"close\\\"] - stocks_df[\\\"open\\\"]\\n\",\n      \"    stocks_df[\\\"change_per\\\"] = stocks_df[\\\"change_amount\\\"] / stocks_df[\\\"average\\\"]\\n\",\n      \"    stocks_df[\\\"range\\\"] = (stocks_df[\\\"high\\\"] - stocks_df[\\\"low\\\"]) / stocks_df[\\\"average\\\"]\\n\",\n      \"    stocks_df[\\\"change_1_amount\\\"] = pd.Series(0.0)\\n\",\n      \"    stocks_df[\\\"change_1_amount\\\"][1:] = stocks_df[\\\"average\\\"][1:].values - stocks_df[\\\"average\\\"][:-1].values\\n\",\n      \"    stocks_df[\\\"change_1_per\\\"] = stocks_df[\\\"change_1_amount\\\"] / stocks_df[\\\"average\\\"]\\n\",\n      \"    return stocks_df\\n\",\n      \"\\n\",\n      \"calculate_stats(stocks_df)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>n_date</th>\\n\",\n        \"      <th>open</th>\\n\",\n        \"      <th>close</th>\\n\",\n        \"      <th>high</th>\\n\",\n        \"      <th>low</th>\\n\",\n        \"      <th>volume</th>\\n\",\n        \"      <th>date</th>\\n\",\n        \"      <th>average</th>\\n\",\n        \"      <th>change_amount</th>\\n\",\n        \"      <th>change_per</th>\\n\",\n        \"      <th>range</th>\\n\",\n        \"      <th>change_1_amount</th>\\n\",\n        \"      <th>change_1_per</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> 735456</td>\\n\",\n        \"      <td> 569.99</td>\\n\",\n        \"      <td> 567.88</td>\\n\",\n        \"      <td> 570.49</td>\\n\",\n        \"      <td> 566.00</td>\\n\",\n        \"      <td> 1211400</td>\\n\",\n        \"      <td>2014-08-11</td>\\n\",\n        \"      <td> 568.123333</td>\\n\",\n        \"      <td>-2.11</td>\\n\",\n        \"      <td>-0.003714</td>\\n\",\n        \"      <td> 0.007903</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> 735457</td>\\n\",\n        \"      <td> 564.52</td>\\n\",\n        \"      <td> 562.73</td>\\n\",\n        \"      <td> 565.90</td>\\n\",\n        \"      <td> 560.88</td>\\n\",\n        \"      <td> 1537800</td>\\n\",\n        \"      <td>2014-08-12</td>\\n\",\n        \"      <td> 563.170000</td>\\n\",\n        \"      <td>-1.79</td>\\n\",\n        \"      <td>-0.003178</td>\\n\",\n        \"      <td> 0.008914</td>\\n\",\n        \"      <td>-4.953333</td>\\n\",\n        \"      <td>-0.008795</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> 735458</td>\\n\",\n        \"      <td> 567.31</td>\\n\",\n        \"      <td> 574.78</td>\\n\",\n        \"      <td> 575.00</td>\\n\",\n        \"      <td> 565.75</td>\\n\",\n        \"      <td> 1435300</td>\\n\",\n        \"      <td>2014-08-13</td>\\n\",\n        \"      <td> 571.843333</td>\\n\",\n        \"      <td> 7.47</td>\\n\",\n        \"      <td> 0.013063</td>\\n\",\n        \"      <td> 0.016176</td>\\n\",\n        \"      <td> 8.673333</td>\\n\",\n        \"      <td> 0.015167</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> 735459</td>\\n\",\n        \"      <td> 576.18</td>\\n\",\n        \"      <td> 574.65</td>\\n\",\n        \"      <td> 577.90</td>\\n\",\n        \"      <td> 570.88</td>\\n\",\n        \"      <td>  982800</td>\\n\",\n        \"      <td>2014-08-14</td>\\n\",\n        \"      <td> 574.476667</td>\\n\",\n        \"      <td>-1.53</td>\\n\",\n        \"      <td>-0.002663</td>\\n\",\n        \"      <td> 0.012220</td>\\n\",\n        \"      <td> 2.633333</td>\\n\",\n        \"      <td> 0.004584</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> 735460</td>\\n\",\n        \"      <td> 577.86</td>\\n\",\n        \"      <td> 573.48</td>\\n\",\n        \"      <td> 579.38</td>\\n\",\n        \"      <td> 570.52</td>\\n\",\n        \"      <td> 1515000</td>\\n\",\n        \"      <td>2014-08-15</td>\\n\",\n        \"      <td> 574.460000</td>\\n\",\n        \"      <td>-4.38</td>\\n\",\n        \"      <td>-0.007625</td>\\n\",\n        \"      <td> 0.015423</td>\\n\",\n        \"      <td>-0.016667</td>\\n\",\n        \"      <td>-0.000029</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> 735463</td>\\n\",\n        \"      <td> 576.11</td>\\n\",\n        \"      <td> 582.16</td>\\n\",\n        \"      <td> 584.51</td>\\n\",\n        \"      <td> 576.00</td>\\n\",\n        \"      <td> 1280600</td>\\n\",\n        \"      <td>2014-08-18</td>\\n\",\n        \"      <td> 580.890000</td>\\n\",\n        \"      <td> 6.05</td>\\n\",\n        \"      <td> 0.010415</td>\\n\",\n        \"      <td> 0.014650</td>\\n\",\n        \"      <td> 6.430000</td>\\n\",\n        \"      <td> 0.011069</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> 735464</td>\\n\",\n        \"      <td> 585.00</td>\\n\",\n        \"      <td> 586.86</td>\\n\",\n        \"      <td> 587.34</td>\\n\",\n        \"      <td> 584.00</td>\\n\",\n        \"      <td>  976000</td>\\n\",\n        \"      <td>2014-08-19</td>\\n\",\n        \"      <td> 586.066667</td>\\n\",\n        \"      <td> 1.86</td>\\n\",\n        \"      <td> 0.003174</td>\\n\",\n        \"      <td> 0.005699</td>\\n\",\n        \"      <td> 5.176667</td>\\n\",\n        \"      <td> 0.008833</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> 735465</td>\\n\",\n        \"      <td> 585.88</td>\\n\",\n        \"      <td> 584.49</td>\\n\",\n        \"      <td> 586.70</td>\\n\",\n        \"      <td> 582.57</td>\\n\",\n        \"      <td> 1033900</td>\\n\",\n        \"      <td>2014-08-20</td>\\n\",\n        \"      <td> 584.586667</td>\\n\",\n        \"      <td>-1.39</td>\\n\",\n        \"      <td>-0.002378</td>\\n\",\n        \"      <td> 0.007065</td>\\n\",\n        \"      <td>-1.480000</td>\\n\",\n        \"      <td>-0.002532</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> 735466</td>\\n\",\n        \"      <td> 583.82</td>\\n\",\n        \"      <td> 583.37</td>\\n\",\n        \"      <td> 584.50</td>\\n\",\n        \"      <td> 581.14</td>\\n\",\n        \"      <td>  912300</td>\\n\",\n        \"      <td>2014-08-21</td>\\n\",\n        \"      <td> 583.003333</td>\\n\",\n        \"      <td>-0.45</td>\\n\",\n        \"      <td>-0.000772</td>\\n\",\n        \"      <td> 0.005763</td>\\n\",\n        \"      <td>-1.583333</td>\\n\",\n        \"      <td>-0.002716</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> 735467</td>\\n\",\n        \"      <td> 583.59</td>\\n\",\n        \"      <td> 582.56</td>\\n\",\n        \"      <td> 585.24</td>\\n\",\n        \"      <td> 580.64</td>\\n\",\n        \"      <td>  786900</td>\\n\",\n        \"      <td>2014-08-22</td>\\n\",\n        \"      <td> 582.813333</td>\\n\",\n        \"      <td>-1.03</td>\\n\",\n        \"      <td>-0.001767</td>\\n\",\n        \"      <td> 0.007893</td>\\n\",\n        \"      <td>-0.190000</td>\\n\",\n        \"      <td>-0.000326</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> 735470</td>\\n\",\n        \"      <td> 584.72</td>\\n\",\n        \"      <td> 580.20</td>\\n\",\n        \"      <td> 585.00</td>\\n\",\n        \"      <td> 579.00</td>\\n\",\n        \"      <td> 1357700</td>\\n\",\n        \"      <td>2014-08-25</td>\\n\",\n        \"      <td> 581.400000</td>\\n\",\n        \"      <td>-4.52</td>\\n\",\n        \"      <td>-0.007774</td>\\n\",\n        \"      <td> 0.010320</td>\\n\",\n        \"      <td>-1.413333</td>\\n\",\n        \"      <td>-0.002431</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 735471</td>\\n\",\n        \"      <td> 581.26</td>\\n\",\n        \"      <td> 577.86</td>\\n\",\n        \"      <td> 581.80</td>\\n\",\n        \"      <td> 576.58</td>\\n\",\n        \"      <td> 1635200</td>\\n\",\n        \"      <td>2014-08-26</td>\\n\",\n        \"      <td> 578.746667</td>\\n\",\n        \"      <td>-3.40</td>\\n\",\n        \"      <td>-0.005875</td>\\n\",\n        \"      <td> 0.009019</td>\\n\",\n        \"      <td>-2.653333</td>\\n\",\n        \"      <td>-0.004585</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> 735472</td>\\n\",\n        \"      <td> 577.27</td>\\n\",\n        \"      <td> 571.00</td>\\n\",\n        \"      <td> 578.49</td>\\n\",\n        \"      <td> 570.10</td>\\n\",\n        \"      <td> 1698700</td>\\n\",\n        \"      <td>2014-08-27</td>\\n\",\n        \"      <td> 573.196667</td>\\n\",\n        \"      <td>-6.27</td>\\n\",\n        \"      <td>-0.010939</td>\\n\",\n        \"      <td> 0.014637</td>\\n\",\n        \"      <td>-5.550000</td>\\n\",\n        \"      <td>-0.009683</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> 735473</td>\\n\",\n        \"      <td> 569.56</td>\\n\",\n        \"      <td> 569.20</td>\\n\",\n        \"      <td> 573.25</td>\\n\",\n        \"      <td> 567.10</td>\\n\",\n        \"      <td> 1289400</td>\\n\",\n        \"      <td>2014-08-28</td>\\n\",\n        \"      <td> 569.850000</td>\\n\",\n        \"      <td>-0.36</td>\\n\",\n        \"      <td>-0.000632</td>\\n\",\n        \"      <td> 0.010792</td>\\n\",\n        \"      <td>-3.346667</td>\\n\",\n        \"      <td>-0.005873</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> 735474</td>\\n\",\n        \"      <td> 571.33</td>\\n\",\n        \"      <td> 571.60</td>\\n\",\n        \"      <td> 572.04</td>\\n\",\n        \"      <td> 567.07</td>\\n\",\n        \"      <td> 1080800</td>\\n\",\n        \"      <td>2014-08-29</td>\\n\",\n        \"      <td> 570.236667</td>\\n\",\n        \"      <td> 0.27</td>\\n\",\n        \"      <td> 0.000473</td>\\n\",\n        \"      <td> 0.008716</td>\\n\",\n        \"      <td> 0.386667</td>\\n\",\n        \"      <td> 0.000678</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> 735478</td>\\n\",\n        \"      <td> 571.85</td>\\n\",\n        \"      <td> 577.33</td>\\n\",\n        \"      <td> 577.83</td>\\n\",\n        \"      <td> 571.19</td>\\n\",\n        \"      <td> 1574100</td>\\n\",\n        \"      <td>2014-09-02</td>\\n\",\n        \"      <td> 575.450000</td>\\n\",\n        \"      <td> 5.48</td>\\n\",\n        \"      <td> 0.009523</td>\\n\",\n        \"      <td> 0.011539</td>\\n\",\n        \"      <td> 5.213333</td>\\n\",\n        \"      <td> 0.009060</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> 735479</td>\\n\",\n        \"      <td> 580.00</td>\\n\",\n        \"      <td> 577.94</td>\\n\",\n        \"      <td> 582.99</td>\\n\",\n        \"      <td> 575.00</td>\\n\",\n        \"      <td> 1211800</td>\\n\",\n        \"      <td>2014-09-03</td>\\n\",\n        \"      <td> 578.643333</td>\\n\",\n        \"      <td>-2.06</td>\\n\",\n        \"      <td>-0.003560</td>\\n\",\n        \"      <td> 0.013808</td>\\n\",\n        \"      <td> 3.193333</td>\\n\",\n        \"      <td> 0.005519</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> 735480</td>\\n\",\n        \"      <td> 580.00</td>\\n\",\n        \"      <td> 581.98</td>\\n\",\n        \"      <td> 586.00</td>\\n\",\n        \"      <td> 579.22</td>\\n\",\n        \"      <td> 1454200</td>\\n\",\n        \"      <td>2014-09-04</td>\\n\",\n        \"      <td> 582.400000</td>\\n\",\n        \"      <td> 1.98</td>\\n\",\n        \"      <td> 0.003400</td>\\n\",\n        \"      <td> 0.011641</td>\\n\",\n        \"      <td> 3.756667</td>\\n\",\n        \"      <td> 0.006450</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> 735481</td>\\n\",\n        \"      <td> 583.98</td>\\n\",\n        \"      <td> 586.08</td>\\n\",\n        \"      <td> 586.55</td>\\n\",\n        \"      <td> 581.95</td>\\n\",\n        \"      <td> 1627900</td>\\n\",\n        \"      <td>2014-09-05</td>\\n\",\n        \"      <td> 584.860000</td>\\n\",\n        \"      <td> 2.10</td>\\n\",\n        \"      <td> 0.003591</td>\\n\",\n        \"      <td> 0.007865</td>\\n\",\n        \"      <td> 2.460000</td>\\n\",\n        \"      <td> 0.004206</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>19 rows \\u00d7 13 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"    n_date    open   close    high     low   volume       date     average  \\\\\\n\",\n        \"0   735456  569.99  567.88  570.49  566.00  1211400 2014-08-11  568.123333   \\n\",\n        \"1   735457  564.52  562.73  565.90  560.88  1537800 2014-08-12  563.170000   \\n\",\n        \"2   735458  567.31  574.78  575.00  565.75  1435300 2014-08-13  571.843333   \\n\",\n        \"3   735459  576.18  574.65  577.90  570.88   982800 2014-08-14  574.476667   \\n\",\n        \"4   735460  577.86  573.48  579.38  570.52  1515000 2014-08-15  574.460000   \\n\",\n        \"5   735463  576.11  582.16  584.51  576.00  1280600 2014-08-18  580.890000   \\n\",\n        \"6   735464  585.00  586.86  587.34  584.00   976000 2014-08-19  586.066667   \\n\",\n        \"7   735465  585.88  584.49  586.70  582.57  1033900 2014-08-20  584.586667   \\n\",\n        \"8   735466  583.82  583.37  584.50  581.14   912300 2014-08-21  583.003333   \\n\",\n        \"9   735467  583.59  582.56  585.24  580.64   786900 2014-08-22  582.813333   \\n\",\n        \"10  735470  584.72  580.20  585.00  579.00  1357700 2014-08-25  581.400000   \\n\",\n        \"11  735471  581.26  577.86  581.80  576.58  1635200 2014-08-26  578.746667   \\n\",\n        \"12  735472  577.27  571.00  578.49  570.10  1698700 2014-08-27  573.196667   \\n\",\n        \"13  735473  569.56  569.20  573.25  567.10  1289400 2014-08-28  569.850000   \\n\",\n        \"14  735474  571.33  571.60  572.04  567.07  1080800 2014-08-29  570.236667   \\n\",\n        \"15  735478  571.85  577.33  577.83  571.19  1574100 2014-09-02  575.450000   \\n\",\n        \"16  735479  580.00  577.94  582.99  575.00  1211800 2014-09-03  578.643333   \\n\",\n        \"17  735480  580.00  581.98  586.00  579.22  1454200 2014-09-04  582.400000   \\n\",\n        \"18  735481  583.98  586.08  586.55  581.95  1627900 2014-09-05  584.860000   \\n\",\n        \"\\n\",\n        \"    change_amount  change_per     range  change_1_amount  change_1_per  \\n\",\n        \"0           -2.11   -0.003714  0.007903         0.000000      0.000000  \\n\",\n        \"1           -1.79   -0.003178  0.008914        -4.953333     -0.008795  \\n\",\n        \"2            7.47    0.013063  0.016176         8.673333      0.015167  \\n\",\n        \"3           -1.53   -0.002663  0.012220         2.633333      0.004584  \\n\",\n        \"4           -4.38   -0.007625  0.015423        -0.016667     -0.000029  \\n\",\n        \"5            6.05    0.010415  0.014650         6.430000      0.011069  \\n\",\n        \"6            1.86    0.003174  0.005699         5.176667      0.008833  \\n\",\n        \"7           -1.39   -0.002378  0.007065        -1.480000     -0.002532  \\n\",\n        \"8           -0.45   -0.000772  0.005763        -1.583333     -0.002716  \\n\",\n        \"9           -1.03   -0.001767  0.007893        -0.190000     -0.000326  \\n\",\n        \"10          -4.52   -0.007774  0.010320        -1.413333     -0.002431  \\n\",\n        \"11          -3.40   -0.005875  0.009019        -2.653333     -0.004585  \\n\",\n        \"12          -6.27   -0.010939  0.014637        -5.550000     -0.009683  \\n\",\n        \"13          -0.36   -0.000632  0.010792        -3.346667     -0.005873  \\n\",\n        \"14           0.27    0.000473  0.008716         0.386667      0.000678  \\n\",\n        \"15           5.48    0.009523  0.011539         5.213333      0.009060  \\n\",\n        \"16          -2.06   -0.003560  0.013808         3.193333      0.005519  \\n\",\n        \"17           1.98    0.003400  0.011641         3.756667      0.006450  \\n\",\n        \"18           2.10    0.003591  0.007865         2.460000      0.004206  \\n\",\n        \"\\n\",\n        \"[19 rows x 13 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualizing the Price\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Plotting the Average Price\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(10,6))\\n\",\n      \"plt.plot_date(stocks_df[\\\"n_date\\\"], stocks_df[\\\"average\\\"], fmt=\\\"-b\\\", linewidth=3, alpha=.4, label=\\\"Average\\\")\\n\",\n      \"plt.gcf().autofmt_xdate()\\n\",\n      \"\\n\",\n      \"plt.title(\\\"Google Average Stock Price - Daily\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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y0tDcOGDTN8/8knnzTM6vTt2xcTJkyAq6urYeZpxYoVqKqqQlxcHPz9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\\n82IAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84cd6510>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Plotting More Data on a Different Y-Scale\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure(figsize=(10,6))\\n\",\n      \"\\n\",\n      \"axes_1 = fig.add_subplot(111)\\n\",\n      \"#axes_2 = fig.add_subplot(111)\\n\",\n      \"axes_2 = axes_1.twinx()\\n\",\n      \"\\n\",\n      \"line1 = axes_1.plot_date(stocks_df[\\\"n_date\\\"], stocks_df[\\\"average\\\"], \\\"o-b\\\", \\n\",\n      \"                    linewidth=3, alpha=.4, label=\\\"Average\\\")\\n\",\n      \"line2 = axes_2.plot_date(stocks_df[\\\"n_date\\\"], stocks_df[\\\"change_1_per\\\"], \\\".-r\\\",\\n\",\n      \"                    linewidth=2, alpha=.4, label=\\\"Change from 1 day\\\")\\n\",\n      \"\\n\",\n      \"axes_2.plot_date(stocks_df[\\\"n_date\\\"], np.zeros(len(stocks_df)), fmt=\\\"-k\\\", linewidth=1, alpha=.4)\\n\",\n      \"\\n\",\n      \"plt.gcf().autofmt_xdate()\\n\",\n      \"\\n\",\n      \"axes_1.set_title(\\\"Google Average Stock Price - Daily\\\")\\n\",\n      \"axes_1.grid()\\n\",\n      \"axes_1.legend(loc=\\\"upper right\\\")\\n\",\n      \"axes_2.legend(loc=\\\"lower right\\\")\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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8Hb2xv+/v44ceIEDh06BEdHRwC8iPHSpUvh7u6O7777DuPGjcNbb72F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\\ngjwGg8FgMBgMI4QFeQwGg8FgMBhGyP8Boh8zPu/fPRUAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84c43650>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Using Candle Stick Chart\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"```\\n\",\n      \"candlestick(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0)\\n\",\n      \"\\n\",\n      \"quotes is a sequence of (time, open, close, high, low, ...) sequences.\\n\",\n      \"\\n\",\n      \"ax          : an Axes instance to plot to\\n\",\n      \"width       : fraction of a day for the rectangle width\\n\",\n      \"colorup     : the color of the rectangle where close >= open\\n\",\n      \"colordown   : the color of the rectangle where close <  open\\n\",\n      \"alpha       : the rectangle alpha level\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure(figsize=(10,6))\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"\\n\",\n      \"candlestick(axes, stocks_df[[\\\"n_date\\\", \\\"open\\\", \\\"close\\\", \\\"high\\\", \\\"low\\\"]].values,\\n\",\n      \"            width=0.6, colorup='g', colordown='r')\\n\",\n      \"\\n\",\n      \"axes.xaxis_date()\\n\",\n      \"plt.gcf().autofmt_xdate()\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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xsZGYnDhw97a+heob0XgfnMpSpbSEiVO0vYa1tWAVXbrxra87EHTyej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\\nRKTM/wd0H6c1uZEHbwAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84cc0f50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure(figsize=(10,6))\\n\",\n      \"axes_1 = fig.add_subplot(111)\\n\",\n      \"axes_2 = axes_1.twinx()\\n\",\n      \"\\n\",\n      \"candlestick(axes_1, stocks_df[[\\\"n_date\\\", \\\"open\\\", \\\"close\\\", \\\"high\\\", \\\"low\\\"]].values,\\n\",\n      \"            width=0.6, colorup='g', colordown='r')\\n\",\n      \"\\n\",\n      \"axes_2.plot_date(stocks_df[\\\"n_date\\\"], stocks_df[\\\"change_1_per\\\"], \\\"o--b\\\", linewidth=3, alpha=0.4)\\n\",\n      \"\\n\",\n      \"axes_1.xaxis_date()\\n\",\n      \"plt.gcf().autofmt_xdate()\\n\",\n      \"\\n\",\n      \"axes_1.grid()\\n\",\n      \"\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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/77uf/++4PRtLDz+efu242NEAGdvkKEhiZLmgohzLR8+XKWL1/utm/9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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84b94510>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Working with many stocks\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Now we will download 50 stocks and process their stats. We will be working with these stocks:\\n\",\n      \"\\n\",\n      \"1. GOOG: Google\\n\",\n      \"2. FB: Facebook, Inc.\\n\",\n      \"3. AAPL: Apple Inc.\\n\",\n      \"4. MSFT: Microsoft Corporation\\n\",\n      \"5. HPQ: Hewlett-Packard Company\\n\",\n      \"6. INTC: Intel Corporation\\n\",\n      \"7. NVDA: NVIDIA Corporation\\n\",\n      \"8. TXN: Texas Instruments Incorporated\\n\",\n      \"9. IBM: International Business Machines Corp. (IBM)\\n\",\n      \"10. SAP: SAP SE (ADR)\\n\",\n      \"11. ADBE: Adobe Systems Incorporated\\n\",\n      \"12. ADSK: Autodesk, Inc.\\n\",\n      \"13. CRM: salesforce.com, inc.\\n\",\n      \"14. N: NetSuite Inc\\n\",\n      \"15. VMW: VMware, Inc.\\n\",\n      \"16. CTXS: Citrix Systems, Inc.\\n\",\n      \"17. RHT: Red Hat Inc\\n\",\n      \"18. RAX: Rackspace Hosting, Inc.\\n\",\n      \"19. AMZN: Amazon.com, Inc.\\n\",\n      \"20. NWSA: News Corp\\n\",\n      \"21. EBAY: eBay Inc\\n\",\n      \"22. CBS: CBS Corporation\\n\",\n      \"23. CMCSA: Comcast Corporation\\n\",\n      \"24. VIAB: Viacom, Inc.\\n\",\n      \"25. NFLX: Netflix, Inc.\\n\",\n      \"26. TWX: Time Warner Inc\\n\",\n      \"27. FOXA: Twenty-First Century Fox Inc\\n\",\n      \"28. NYT: The New York Times Company\\n\",\n      \"29. TRI: Thomson Reuters Corporation (USA)\\n\",\n      \"30. DIS: The Walt Disney Company\\n\",\n      \"31. SNE: Sony Corp (ADR)\\n\",\n      \"32. PCRFY: Panasonic Corporation (ADR)\\n\",\n      \"33. CAJ: Canon Inc (ADR)\\n\",\n      \"34. TOSYY: Toshiba Corp (USA)\\n\",\n      \"35. BBRY: BlackBerry Ltd\\n\",\n      \"36. CSC: Computer Sciences Corporation\\n\",\n      \"37. GE: General Electric Company\\n\",\n      \"38. HTHIY: Hitachi, Ltd. (ADR)\\n\",\n      \"39. SIEGY: Siemens AG (ADR)\\n\",\n      \"40. CVX: Chevron Corporation\\n\",\n      \"41. XOM: Exxon Mobil Corporation\\n\",\n      \"42. BP: BP plc (ADR)\\n\",\n      \"43. CAT: Caterpillar Inc.\\n\",\n      \"44. LXK: Lexmark International Inc\\n\",\n      \"45. BKS: Barnes & Noble, Inc.\\n\",\n      \"46. FJTSY: Fujitsu Ltd (ADR)\\n\",\n      \"47. EMC: EMC Corporation\\n\",\n      \"48. ORCL: Oracle Corporation\\n\",\n      \"49. CSCO: Cisco Systems, Inc.\\n\",\n      \"50. XRX: Xerox Corp\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"stock_dict={\\\"GOOG\\\": \\\"Google\\\",\\n\",\n      \"            \\\"FB\\\": \\\"Facebook, Inc.\\\",\\n\",\n      \"            \\\"AAPL\\\": \\\"Apple Inc.\\\",\\n\",\n      \"            \\\"MSFT\\\": \\\"Microsoft Corporation\\\",\\n\",\n      \"            \\\"HPQ\\\": \\\"Hewlett-Packard Company\\\",\\n\",\n      \"            \\\"INTC\\\": \\\"Intel Corporation\\\",\\n\",\n      \"            \\\"NVDA\\\": \\\"NVIDIA Corporation\\\",\\n\",\n      \"            \\\"TXN\\\": \\\"Texas Instruments Incorporated\\\",\\n\",\n      \"            \\\"IBM\\\": \\\"International Business Machines Corp. (IBM)\\\",\\n\",\n      \"            \\\"SAP\\\": \\\"SAP SE (ADR)\\\",\\n\",\n      \"            \\\"ADBE\\\": \\\"Adobe Systems Incorporated\\\",\\n\",\n      \"            \\\"ADSK\\\": \\\"Autodesk, Inc.\\\",\\n\",\n      \"            \\\"CRM\\\": \\\"salesforce.com, inc.\\\",\\n\",\n      \"            \\\"N\\\": \\\"NetSuite Inc\\\",\\n\",\n      \"            \\\"VMW\\\": \\\"VMware, Inc.\\\",\\n\",\n      \"            \\\"CTXS\\\": \\\"Citrix Systems, Inc.\\\",\\n\",\n      \"            \\\"RHT\\\": \\\"Red Hat Inc\\\",\\n\",\n      \"            \\\"RAX\\\": \\\"Rackspace Hosting, Inc.\\\",\\n\",\n      \"            \\\"AMZN\\\": \\\"Amazon.com, Inc.\\\",\\n\",\n      \"            \\\"NWSA\\\": \\\"News Corp\\\",\\n\",\n      \"            \\\"EBAY\\\": \\\"eBay Inc\\\",\\n\",\n      \"            \\\"CBS\\\": \\\"CBS Corporation\\\",\\n\",\n      \"            \\\"CMCSA\\\": \\\"Comcast Corporation\\\",\\n\",\n      \"            \\\"VIAB\\\": \\\"Viacom, Inc.\\\",\\n\",\n      \"            \\\"NFLX\\\": \\\"Netflix, Inc.\\\",\\n\",\n      \"            \\\"TWX\\\": \\\"Time Warner Inc\\\",\\n\",\n      \"            \\\"FOXA\\\": \\\"Twenty-First Century Fox Inc\\\",\\n\",\n      \"            \\\"NYT\\\": \\\"The New York Times Company\\\",\\n\",\n      \"            \\\"TRI\\\": \\\"Thomson Reuters Corporation (USA)\\\",\\n\",\n      \"            \\\"DIS\\\": \\\"The Walt Disney Company\\\",\\n\",\n      \"            \\\"SNE\\\": \\\"Sony Corp (ADR)\\\",\\n\",\n      \"            \\\"PCRFY\\\": \\\"Panasonic Corporation (ADR)\\\",\\n\",\n      \"            \\\"CAJ\\\": \\\"Canon Inc (ADR)\\\",\\n\",\n      \"            \\\"TOSYY\\\": \\\"Toshiba Corp (USA)\\\",\\n\",\n      \"            \\\"BBRY\\\": \\\"BlackBerry Ltd\\\",\\n\",\n      \"            \\\"CSC\\\": \\\"Computer Sciences Corporation\\\",\\n\",\n      \"            \\\"GE\\\": \\\"General Electric Company\\\",\\n\",\n      \"            \\\"HTHIY\\\": \\\"Hitachi, Ltd. (ADR)\\\",\\n\",\n      \"            \\\"SIEGY\\\": \\\"Siemens AG (ADR)\\\",\\n\",\n      \"            \\\"CVX\\\": \\\"Chevron Corporation\\\",\\n\",\n      \"            \\\"XOM\\\": \\\"Exxon Mobil Corporation\\\",\\n\",\n      \"            \\\"BP\\\": \\\"BP plc (ADR)\\\",\\n\",\n      \"            \\\"CAT\\\": \\\"Caterpillar Inc.\\\",\\n\",\n      \"            \\\"LXK\\\": \\\"Lexmark International Inc\\\",\\n\",\n      \"            \\\"BKS\\\": \\\"Barnes & Noble, Inc.\\\",\\n\",\n      \"            \\\"FJTSY\\\": \\\"Fujitsu Ltd (ADR)\\\",\\n\",\n      \"            \\\"EMC\\\": \\\"EMC Corporation\\\",\\n\",\n      \"            \\\"ORCL\\\": \\\"Oracle Corporation\\\",\\n\",\n      \"            \\\"CSCO\\\": \\\"Cisco Systems, Inc.\\\",\\n\",\n      \"            \\\"XRX\\\": \\\"Xerox Corp\\\",\\n\",\n      \"           }\\n\",\n      \"symbols = stock_dict.keys()\\n\",\n      \"names = stock_dict.values()\\n\",\n      \"\\n\",\n      \"stocks_data = pd.DataFrame(symbols, columns=[\\\"symbol\\\"])\\n\",\n      \"stocks_data[\\\"name\\\"] = names\\n\",\n      \"stocks_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>symbol</th>\\n\",\n        \"      <th>name</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td>  NFLX</td>\\n\",\n        \"      <td>                               Netflix, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>  AAPL</td>\\n\",\n        \"      <td>                                  Apple Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>   NYT</td>\\n\",\n        \"      <td>                  The New York Times Company</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>    FB</td>\\n\",\n        \"      <td>                              Facebook, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>    BP</td>\\n\",\n        \"      <td>                                BP plc (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>  ADSK</td>\\n\",\n        \"      <td>                              Autodesk, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>  MSFT</td>\\n\",\n        \"      <td>                       Microsoft Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>   EMC</td>\\n\",\n        \"      <td>                             EMC Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td>   BKS</td>\\n\",\n        \"      <td>                        Barnes &amp; Noble, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>   SAP</td>\\n\",\n        \"      <td>                                SAP SE (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>   RAX</td>\\n\",\n        \"      <td>                     Rackspace Hosting, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td>     N</td>\\n\",\n        \"      <td>                                NetSuite Inc</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>    GE</td>\\n\",\n        \"      <td>                    General Electric Company</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td>  AMZN</td>\\n\",\n        \"      <td>                            Amazon.com, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>   SNE</td>\\n\",\n        \"      <td>                             Sony Corp (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>  VIAB</td>\\n\",\n        \"      <td>                                Viacom, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>  FOXA</td>\\n\",\n        \"      <td>                Twenty-First Century Fox Inc</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> PCRFY</td>\\n\",\n        \"      <td>                 Panasonic Corporation (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td>   RHT</td>\\n\",\n        \"      <td>                                 Red Hat Inc</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td>  EBAY</td>\\n\",\n        \"      <td>                                    eBay Inc</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td>   VMW</td>\\n\",\n        \"      <td>                                VMware, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>   LXK</td>\\n\",\n        \"      <td>                   Lexmark International Inc</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td> CMCSA</td>\\n\",\n        \"      <td>                         Comcast Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td>   TRI</td>\\n\",\n        \"      <td>           Thomson Reuters Corporation (USA)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>  ADBE</td>\\n\",\n        \"      <td>                  Adobe Systems Incorporated</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>   IBM</td>\\n\",\n        \"      <td> International Business Machines Corp. (IBM)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td>   CVX</td>\\n\",\n        \"      <td>                         Chevron Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td> FJTSY</td>\\n\",\n        \"      <td>                           Fujitsu Ltd (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td>   CRM</td>\\n\",\n        \"      <td>                        salesforce.com, inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td>   CAT</td>\\n\",\n        \"      <td>                            Caterpillar Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td>   CAJ</td>\\n\",\n        \"      <td>                             Canon Inc (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td>  NVDA</td>\\n\",\n        \"      <td>                          NVIDIA Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td>   TWX</td>\\n\",\n        \"      <td>                             Time Warner Inc</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td>   DIS</td>\\n\",\n        \"      <td>                     The Walt Disney Company</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>   TXN</td>\\n\",\n        \"      <td>              Texas Instruments Incorporated</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td>  NWSA</td>\\n\",\n        \"      <td>                                   News Corp</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td>  ORCL</td>\\n\",\n        \"      <td>                          Oracle Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td>  CTXS</td>\\n\",\n        \"      <td>                        Citrix Systems, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td> HTHIY</td>\\n\",\n        \"      <td>                         Hitachi, Ltd. (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td>  GOOG</td>\\n\",\n        \"      <td>                                      Google</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td>  INTC</td>\\n\",\n        \"      <td>                           Intel Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td>  BBRY</td>\\n\",\n        \"      <td>                              BlackBerry Ltd</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td> TOSYY</td>\\n\",\n        \"      <td>                          Toshiba Corp (USA)</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td>   HPQ</td>\\n\",\n        \"      <td>                     Hewlett-Packard Company</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td>   CSC</td>\\n\",\n        \"      <td>               Computer Sciences Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td>   XRX</td>\\n\",\n        \"      <td>                                  Xerox Corp</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td>   XOM</td>\\n\",\n        \"      <td>                     Exxon Mobil Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47</th>\\n\",\n        \"      <td>  CSCO</td>\\n\",\n        \"      <td>                         Cisco Systems, Inc.</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>48</th>\\n\",\n        \"      <td>   CBS</td>\\n\",\n        \"      <td>                             CBS Corporation</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td> SIEGY</td>\\n\",\n        \"      <td>                            Siemens AG (ADR)</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>50 rows \\u00d7 2 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"   symbol                                         name\\n\",\n        \"0    NFLX                                Netflix, Inc.\\n\",\n        \"1    AAPL                                   Apple Inc.\\n\",\n        \"2     NYT                   The New York Times Company\\n\",\n        \"3      FB                               Facebook, Inc.\\n\",\n        \"4      BP                                 BP plc (ADR)\\n\",\n        \"5    ADSK                               Autodesk, Inc.\\n\",\n        \"6    MSFT                        Microsoft Corporation\\n\",\n        \"7     EMC                              EMC Corporation\\n\",\n        \"8     BKS                         Barnes & Noble, Inc.\\n\",\n        \"9     SAP                                 SAP SE (ADR)\\n\",\n        \"10    RAX                      Rackspace Hosting, Inc.\\n\",\n        \"11      N                                 NetSuite Inc\\n\",\n        \"12     GE                     General Electric Company\\n\",\n        \"13   AMZN                             Amazon.com, Inc.\\n\",\n        \"14    SNE                              Sony Corp (ADR)\\n\",\n        \"15   VIAB                                 Viacom, Inc.\\n\",\n        \"16   FOXA                 Twenty-First Century Fox Inc\\n\",\n        \"17  PCRFY                  Panasonic Corporation (ADR)\\n\",\n        \"18    RHT                                  Red Hat Inc\\n\",\n        \"19   EBAY                                     eBay Inc\\n\",\n        \"20    VMW                                 VMware, Inc.\\n\",\n        \"21    LXK                    Lexmark International Inc\\n\",\n        \"22  CMCSA                          Comcast Corporation\\n\",\n        \"23    TRI            Thomson Reuters Corporation (USA)\\n\",\n        \"24   ADBE                   Adobe Systems Incorporated\\n\",\n        \"25    IBM  International Business Machines Corp. (IBM)\\n\",\n        \"26    CVX                          Chevron Corporation\\n\",\n        \"27  FJTSY                            Fujitsu Ltd (ADR)\\n\",\n        \"28    CRM                         salesforce.com, inc.\\n\",\n        \"29    CAT                             Caterpillar Inc.\\n\",\n        \"30    CAJ                              Canon Inc (ADR)\\n\",\n        \"31   NVDA                           NVIDIA Corporation\\n\",\n        \"32    TWX                              Time Warner Inc\\n\",\n        \"33    DIS                      The Walt Disney Company\\n\",\n        \"34    TXN               Texas Instruments Incorporated\\n\",\n        \"35   NWSA                                    News Corp\\n\",\n        \"36   ORCL                           Oracle Corporation\\n\",\n        \"37   CTXS                         Citrix Systems, Inc.\\n\",\n        \"38  HTHIY                          Hitachi, Ltd. (ADR)\\n\",\n        \"39   GOOG                                       Google\\n\",\n        \"40   INTC                            Intel Corporation\\n\",\n        \"41   BBRY                               BlackBerry Ltd\\n\",\n        \"42  TOSYY                           Toshiba Corp (USA)\\n\",\n        \"43    HPQ                      Hewlett-Packard Company\\n\",\n        \"44    CSC                Computer Sciences Corporation\\n\",\n        \"45    XRX                                   Xerox Corp\\n\",\n        \"46    XOM                      Exxon Mobil Corporation\\n\",\n        \"47   CSCO                          Cisco Systems, Inc.\\n\",\n        \"48    CBS                              CBS Corporation\\n\",\n        \"49  SIEGY                             Siemens AG (ADR)\\n\",\n        \"\\n\",\n        \"[50 rows x 2 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Download Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"temp_list = []\\n\",\n      \"for symbol in stocks_data[\\\"symbol\\\"]:\\n\",\n      \"    temp_data = download_data(symbol)\\n\",\n      \"    process_date(temp_data)\\n\",\n      \"    calculate_stats(temp_data)\\n\",\n      \"    temp_data[\\\"symbol\\\"] = symbol\\n\",\n      \"    temp_list.append(temp_data)\\n\",\n      \"\\n\",\n      \"stocks_df = pd.concat(temp_list)\\n\",\n      \"stocks_df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>n_date</th>\\n\",\n        \"      <th>open</th>\\n\",\n        \"      <th>close</th>\\n\",\n        \"      <th>high</th>\\n\",\n        \"      <th>low</th>\\n\",\n        \"      <th>volume</th>\\n\",\n        \"      <th>date</th>\\n\",\n        \"      <th>average</th>\\n\",\n        \"      <th>change_amount</th>\\n\",\n        \"      <th>change_per</th>\\n\",\n        \"      <th>range</th>\\n\",\n        \"      <th>change_1_amount</th>\\n\",\n        \"      <th>change_1_per</th>\\n\",\n        \"      <th>symbol</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> 735456</td>\\n\",\n        \"      <td> 449.22</td>\\n\",\n        \"      <td> 451.54</td>\\n\",\n        \"      <td> 457.65</td>\\n\",\n        \"      <td> 448.71</td>\\n\",\n        \"      <td>   1892400</td>\\n\",\n        \"      <td>2014-08-11</td>\\n\",\n        \"      <td> 452.633333</td>\\n\",\n        \"      <td> 2.32</td>\\n\",\n        \"      <td> 0.005126</td>\\n\",\n        \"      <td> 0.019751</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> 735457</td>\\n\",\n        \"      <td> 451.35</td>\\n\",\n        \"      <td> 446.41</td>\\n\",\n        \"      <td> 453.00</td>\\n\",\n        \"      <td> 443.41</td>\\n\",\n        \"      <td>   1549300</td>\\n\",\n        \"      <td>2014-08-12</td>\\n\",\n        \"      <td> 447.606667</td>\\n\",\n        \"      <td>-4.94</td>\\n\",\n        \"      <td>-0.011036</td>\\n\",\n        \"      <td> 0.021425</td>\\n\",\n        \"      <td>-5.026667</td>\\n\",\n        \"      <td>-0.011230</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> 735458</td>\\n\",\n        \"      <td> 448.60</td>\\n\",\n        \"      <td> 451.53</td>\\n\",\n        \"      <td> 454.22</td>\\n\",\n        \"      <td> 446.82</td>\\n\",\n        \"      <td>   1294000</td>\\n\",\n        \"      <td>2014-08-13</td>\\n\",\n        \"      <td> 450.856667</td>\\n\",\n        \"      <td> 2.93</td>\\n\",\n        \"      <td> 0.006499</td>\\n\",\n        \"      <td> 0.016413</td>\\n\",\n        \"      <td> 3.250000</td>\\n\",\n        \"      <td> 0.007208</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> 735459</td>\\n\",\n        \"      <td> 452.21</td>\\n\",\n        \"      <td> 450.87</td>\\n\",\n        \"      <td> 455.00</td>\\n\",\n        \"      <td> 448.22</td>\\n\",\n        \"      <td>    951500</td>\\n\",\n        \"      <td>2014-08-14</td>\\n\",\n        \"      <td> 451.363333</td>\\n\",\n        \"      <td>-1.34</td>\\n\",\n        \"      <td>-0.002969</td>\\n\",\n        \"      <td> 0.015021</td>\\n\",\n        \"      <td> 0.506667</td>\\n\",\n        \"      <td> 0.001123</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> 735460</td>\\n\",\n        \"      <td> 451.49</td>\\n\",\n        \"      <td> 459.09</td>\\n\",\n        \"      <td> 462.00</td>\\n\",\n        \"      <td> 448.60</td>\\n\",\n        \"      <td>   3148100</td>\\n\",\n        \"      <td>2014-08-15</td>\\n\",\n        \"      <td> 456.563333</td>\\n\",\n        \"      <td> 7.60</td>\\n\",\n        \"      <td> 0.016646</td>\\n\",\n        \"      <td> 0.029350</td>\\n\",\n        \"      <td> 5.200000</td>\\n\",\n        \"      <td> 0.011389</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> 735463</td>\\n\",\n        \"      <td> 462.06</td>\\n\",\n        \"      <td> 466.00</td>\\n\",\n        \"      <td> 469.50</td>\\n\",\n        \"      <td> 461.25</td>\\n\",\n        \"      <td>   1928000</td>\\n\",\n        \"      <td>2014-08-18</td>\\n\",\n        \"      <td> 465.583333</td>\\n\",\n        \"      <td> 3.94</td>\\n\",\n        \"      <td> 0.008463</td>\\n\",\n        \"      <td> 0.017720</td>\\n\",\n        \"      <td> 9.020000</td>\\n\",\n        \"      <td> 0.019374</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> 735464</td>\\n\",\n        \"      <td> 467.09</td>\\n\",\n        \"      <td> 468.15</td>\\n\",\n        \"      <td> 470.50</td>\\n\",\n        \"      <td> 462.43</td>\\n\",\n        \"      <td>   1556800</td>\\n\",\n        \"      <td>2014-08-19</td>\\n\",\n        \"      <td> 467.026667</td>\\n\",\n        \"      <td> 1.06</td>\\n\",\n        \"      <td> 0.002270</td>\\n\",\n        \"      <td> 0.017280</td>\\n\",\n        \"      <td> 1.443333</td>\\n\",\n        \"      <td> 0.003090</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> 735465</td>\\n\",\n        \"      <td> 467.00</td>\\n\",\n        \"      <td> 472.19</td>\\n\",\n        \"      <td> 473.75</td>\\n\",\n        \"      <td> 464.54</td>\\n\",\n        \"      <td>   1737700</td>\\n\",\n        \"      <td>2014-08-20</td>\\n\",\n        \"      <td> 470.160000</td>\\n\",\n        \"      <td> 5.19</td>\\n\",\n        \"      <td> 0.011039</td>\\n\",\n        \"      <td> 0.019589</td>\\n\",\n        \"      <td> 3.133333</td>\\n\",\n        \"      <td> 0.006664</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> 735466</td>\\n\",\n        \"      <td> 471.28</td>\\n\",\n        \"      <td> 472.05</td>\\n\",\n        \"      <td> 476.15</td>\\n\",\n        \"      <td> 467.67</td>\\n\",\n        \"      <td>   1678100</td>\\n\",\n        \"      <td>2014-08-21</td>\\n\",\n        \"      <td> 471.956667</td>\\n\",\n        \"      <td> 0.77</td>\\n\",\n        \"      <td> 0.001632</td>\\n\",\n        \"      <td> 0.017968</td>\\n\",\n        \"      <td> 1.796667</td>\\n\",\n        \"      <td> 0.003807</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> 735467</td>\\n\",\n        \"      <td> 470.94</td>\\n\",\n        \"      <td> 479.19</td>\\n\",\n        \"      <td> 479.55</td>\\n\",\n        \"      <td> 470.05</td>\\n\",\n        \"      <td>   1962200</td>\\n\",\n        \"      <td>2014-08-22</td>\\n\",\n        \"      <td> 476.263333</td>\\n\",\n        \"      <td> 8.25</td>\\n\",\n        \"      <td> 0.017322</td>\\n\",\n        \"      <td> 0.019947</td>\\n\",\n        \"      <td> 4.306667</td>\\n\",\n        \"      <td> 0.009043</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> 735470</td>\\n\",\n        \"      <td> 481.55</td>\\n\",\n        \"      <td> 480.93</td>\\n\",\n        \"      <td> 485.30</td>\\n\",\n        \"      <td> 477.53</td>\\n\",\n        \"      <td>   1926400</td>\\n\",\n        \"      <td>2014-08-25</td>\\n\",\n        \"      <td> 481.253333</td>\\n\",\n        \"      <td>-0.62</td>\\n\",\n        \"      <td>-0.001288</td>\\n\",\n        \"      <td> 0.016145</td>\\n\",\n        \"      <td> 4.990000</td>\\n\",\n        \"      <td> 0.010369</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 735471</td>\\n\",\n        \"      <td> 478.50</td>\\n\",\n        \"      <td> 479.36</td>\\n\",\n        \"      <td> 481.85</td>\\n\",\n        \"      <td> 474.55</td>\\n\",\n        \"      <td>   1448200</td>\\n\",\n        \"      <td>2014-08-26</td>\\n\",\n        \"      <td> 478.586667</td>\\n\",\n        \"      <td> 0.86</td>\\n\",\n        \"      <td> 0.001797</td>\\n\",\n        \"      <td> 0.015253</td>\\n\",\n        \"      <td>-2.666667</td>\\n\",\n        \"      <td>-0.005572</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> 735472</td>\\n\",\n        \"      <td> 479.40</td>\\n\",\n        \"      <td> 474.70</td>\\n\",\n        \"      <td> 480.25</td>\\n\",\n        \"      <td> 473.63</td>\\n\",\n        \"      <td>   1484900</td>\\n\",\n        \"      <td>2014-08-27</td>\\n\",\n        \"      <td> 476.193333</td>\\n\",\n        \"      <td>-4.70</td>\\n\",\n        \"      <td>-0.009870</td>\\n\",\n        \"      <td> 0.013902</td>\\n\",\n        \"      <td>-2.393333</td>\\n\",\n        \"      <td>-0.005026</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> 735473</td>\\n\",\n        \"      <td> 472.65</td>\\n\",\n        \"      <td> 475.21</td>\\n\",\n        \"      <td> 477.48</td>\\n\",\n        \"      <td> 470.81</td>\\n\",\n        \"      <td>   1083500</td>\\n\",\n        \"      <td>2014-08-28</td>\\n\",\n        \"      <td> 474.500000</td>\\n\",\n        \"      <td> 2.56</td>\\n\",\n        \"      <td> 0.005395</td>\\n\",\n        \"      <td> 0.014057</td>\\n\",\n        \"      <td>-1.693333</td>\\n\",\n        \"      <td>-0.003569</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> 735474</td>\\n\",\n        \"      <td> 476.85</td>\\n\",\n        \"      <td> 477.64</td>\\n\",\n        \"      <td> 480.50</td>\\n\",\n        \"      <td> 474.14</td>\\n\",\n        \"      <td>   1405500</td>\\n\",\n        \"      <td>2014-08-29</td>\\n\",\n        \"      <td> 477.426667</td>\\n\",\n        \"      <td> 0.79</td>\\n\",\n        \"      <td> 0.001655</td>\\n\",\n        \"      <td> 0.013321</td>\\n\",\n        \"      <td> 2.926667</td>\\n\",\n        \"      <td> 0.006130</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> 735478</td>\\n\",\n        \"      <td> 478.50</td>\\n\",\n        \"      <td> 476.60</td>\\n\",\n        \"      <td> 478.79</td>\\n\",\n        \"      <td> 474.59</td>\\n\",\n        \"      <td>   1258100</td>\\n\",\n        \"      <td>2014-09-02</td>\\n\",\n        \"      <td> 476.660000</td>\\n\",\n        \"      <td>-1.90</td>\\n\",\n        \"      <td>-0.003986</td>\\n\",\n        \"      <td> 0.008811</td>\\n\",\n        \"      <td>-0.766667</td>\\n\",\n        \"      <td>-0.001608</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> 735479</td>\\n\",\n        \"      <td> 480.52</td>\\n\",\n        \"      <td> 477.39</td>\\n\",\n        \"      <td> 487.60</td>\\n\",\n        \"      <td> 476.53</td>\\n\",\n        \"      <td>   2317000</td>\\n\",\n        \"      <td>2014-09-03</td>\\n\",\n        \"      <td> 480.506667</td>\\n\",\n        \"      <td>-3.13</td>\\n\",\n        \"      <td>-0.006514</td>\\n\",\n        \"      <td> 0.023038</td>\\n\",\n        \"      <td> 3.846667</td>\\n\",\n        \"      <td> 0.008005</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> 735480</td>\\n\",\n        \"      <td> 479.11</td>\\n\",\n        \"      <td> 472.67</td>\\n\",\n        \"      <td> 481.69</td>\\n\",\n        \"      <td> 472.13</td>\\n\",\n        \"      <td>   1708400</td>\\n\",\n        \"      <td>2014-09-04</td>\\n\",\n        \"      <td> 475.496667</td>\\n\",\n        \"      <td>-6.44</td>\\n\",\n        \"      <td>-0.013544</td>\\n\",\n        \"      <td> 0.020105</td>\\n\",\n        \"      <td>-5.010000</td>\\n\",\n        \"      <td>-0.010536</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> 735481</td>\\n\",\n        \"      <td> 473.23</td>\\n\",\n        \"      <td> 475.68</td>\\n\",\n        \"      <td> 477.35</td>\\n\",\n        \"      <td> 470.07</td>\\n\",\n        \"      <td>   1707600</td>\\n\",\n        \"      <td>2014-09-05</td>\\n\",\n        \"      <td> 474.366667</td>\\n\",\n        \"      <td> 2.45</td>\\n\",\n        \"      <td> 0.005165</td>\\n\",\n        \"      <td> 0.015347</td>\\n\",\n        \"      <td>-1.130000</td>\\n\",\n        \"      <td>-0.002382</td>\\n\",\n        \"      <td> NFLX</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> 735456</td>\\n\",\n        \"      <td>  95.27</td>\\n\",\n        \"      <td>  95.99</td>\\n\",\n        \"      <td>  96.08</td>\\n\",\n        \"      <td>  94.84</td>\\n\",\n        \"      <td>  36585000</td>\\n\",\n        \"      <td>2014-08-11</td>\\n\",\n        \"      <td>  95.636667</td>\\n\",\n        \"      <td> 0.72</td>\\n\",\n        \"      <td> 0.007528</td>\\n\",\n        \"      <td> 0.012966</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> 735457</td>\\n\",\n        \"      <td>  96.04</td>\\n\",\n        \"      <td>  95.97</td>\\n\",\n        \"      <td>  96.88</td>\\n\",\n        \"      <td>  95.61</td>\\n\",\n        \"      <td>  33795000</td>\\n\",\n        \"      <td>2014-08-12</td>\\n\",\n        \"      <td>  96.153333</td>\\n\",\n        \"      <td>-0.07</td>\\n\",\n        \"      <td>-0.000728</td>\\n\",\n        \"      <td> 0.013208</td>\\n\",\n        \"      <td> 0.516667</td>\\n\",\n        \"      <td> 0.005373</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> 735458</td>\\n\",\n        \"      <td>  96.15</td>\\n\",\n        \"      <td>  97.24</td>\\n\",\n        \"      <td>  97.24</td>\\n\",\n        \"      <td>  96.04</td>\\n\",\n        \"      <td>  31916000</td>\\n\",\n        \"      <td>2014-08-13</td>\\n\",\n        \"      <td>  96.840000</td>\\n\",\n        \"      <td> 1.09</td>\\n\",\n        \"      <td> 0.011256</td>\\n\",\n        \"      <td> 0.012392</td>\\n\",\n        \"      <td> 0.686667</td>\\n\",\n        \"      <td> 0.007091</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> 735459</td>\\n\",\n        \"      <td>  97.33</td>\\n\",\n        \"      <td>  97.50</td>\\n\",\n        \"      <td>  97.57</td>\\n\",\n        \"      <td>  96.80</td>\\n\",\n        \"      <td>  28116000</td>\\n\",\n        \"      <td>2014-08-14</td>\\n\",\n        \"      <td>  97.290000</td>\\n\",\n        \"      <td> 0.17</td>\\n\",\n        \"      <td> 0.001747</td>\\n\",\n        \"      <td> 0.007914</td>\\n\",\n        \"      <td> 0.450000</td>\\n\",\n        \"      <td> 0.004625</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> 735460</td>\\n\",\n        \"      <td>  97.90</td>\\n\",\n        \"      <td>  97.98</td>\\n\",\n        \"      <td>  98.19</td>\\n\",\n        \"      <td>  96.86</td>\\n\",\n        \"      <td>  48951000</td>\\n\",\n        \"      <td>2014-08-15</td>\\n\",\n        \"      <td>  97.676667</td>\\n\",\n        \"      <td> 0.08</td>\\n\",\n        \"      <td> 0.000819</td>\\n\",\n        \"      <td> 0.013616</td>\\n\",\n        \"      <td> 0.386667</td>\\n\",\n        \"      <td> 0.003959</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> 735463</td>\\n\",\n        \"      <td>  98.49</td>\\n\",\n        \"      <td>  99.16</td>\\n\",\n        \"      <td>  99.37</td>\\n\",\n        \"      <td>  97.98</td>\\n\",\n        \"      <td>  47572000</td>\\n\",\n        \"      <td>2014-08-18</td>\\n\",\n        \"      <td>  98.836667</td>\\n\",\n        \"      <td> 0.67</td>\\n\",\n        \"      <td> 0.006779</td>\\n\",\n        \"      <td> 0.014064</td>\\n\",\n        \"      <td> 1.160000</td>\\n\",\n        \"      <td> 0.011737</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> 735464</td>\\n\",\n        \"      <td>  99.41</td>\\n\",\n        \"      <td> 100.53</td>\\n\",\n        \"      <td> 100.68</td>\\n\",\n        \"      <td>  99.32</td>\\n\",\n        \"      <td>  69274700</td>\\n\",\n        \"      <td>2014-08-19</td>\\n\",\n        \"      <td> 100.176667</td>\\n\",\n        \"      <td> 1.12</td>\\n\",\n        \"      <td> 0.011180</td>\\n\",\n        \"      <td> 0.013576</td>\\n\",\n        \"      <td> 1.340000</td>\\n\",\n        \"      <td> 0.013376</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> 735465</td>\\n\",\n        \"      <td> 100.44</td>\\n\",\n        \"      <td> 100.57</td>\\n\",\n        \"      <td> 101.09</td>\\n\",\n        \"      <td>  99.95</td>\\n\",\n        \"      <td>  52612800</td>\\n\",\n        \"      <td>2014-08-20</td>\\n\",\n        \"      <td> 100.536667</td>\\n\",\n        \"      <td> 0.13</td>\\n\",\n        \"      <td> 0.001293</td>\\n\",\n        \"      <td> 0.011339</td>\\n\",\n        \"      <td> 0.360000</td>\\n\",\n        \"      <td> 0.003581</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> 735466</td>\\n\",\n        \"      <td> 100.57</td>\\n\",\n        \"      <td> 100.58</td>\\n\",\n        \"      <td> 100.94</td>\\n\",\n        \"      <td> 100.11</td>\\n\",\n        \"      <td>  33421900</td>\\n\",\n        \"      <td>2014-08-21</td>\\n\",\n        \"      <td> 100.543333</td>\\n\",\n        \"      <td> 0.01</td>\\n\",\n        \"      <td> 0.000099</td>\\n\",\n        \"      <td> 0.008255</td>\\n\",\n        \"      <td> 0.006667</td>\\n\",\n        \"      <td> 0.000066</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> 735467</td>\\n\",\n        \"      <td> 100.29</td>\\n\",\n        \"      <td> 101.32</td>\\n\",\n        \"      <td> 101.47</td>\\n\",\n        \"      <td> 100.19</td>\\n\",\n        \"      <td>  44102400</td>\\n\",\n        \"      <td>2014-08-22</td>\\n\",\n        \"      <td> 100.993333</td>\\n\",\n        \"      <td> 1.03</td>\\n\",\n        \"      <td> 0.010199</td>\\n\",\n        \"      <td> 0.012674</td>\\n\",\n        \"      <td> 0.450000</td>\\n\",\n        \"      <td> 0.004456</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> 735470</td>\\n\",\n        \"      <td> 101.79</td>\\n\",\n        \"      <td> 101.54</td>\\n\",\n        \"      <td> 102.17</td>\\n\",\n        \"      <td> 101.28</td>\\n\",\n        \"      <td>  40144700</td>\\n\",\n        \"      <td>2014-08-25</td>\\n\",\n        \"      <td> 101.663333</td>\\n\",\n        \"      <td>-0.25</td>\\n\",\n        \"      <td>-0.002459</td>\\n\",\n        \"      <td> 0.008754</td>\\n\",\n        \"      <td> 0.670000</td>\\n\",\n        \"      <td> 0.006590</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 735471</td>\\n\",\n        \"      <td> 101.42</td>\\n\",\n        \"      <td> 100.89</td>\\n\",\n        \"      <td> 101.50</td>\\n\",\n        \"      <td> 100.86</td>\\n\",\n        \"      <td>  33119800</td>\\n\",\n        \"      <td>2014-08-26</td>\\n\",\n        \"      <td> 101.083333</td>\\n\",\n        \"      <td>-0.53</td>\\n\",\n        \"      <td>-0.005243</td>\\n\",\n        \"      <td> 0.006331</td>\\n\",\n        \"      <td>-0.580000</td>\\n\",\n        \"      <td>-0.005738</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> 735472</td>\\n\",\n        \"      <td> 101.02</td>\\n\",\n        \"      <td> 102.13</td>\\n\",\n        \"      <td> 102.57</td>\\n\",\n        \"      <td> 100.70</td>\\n\",\n        \"      <td>  46827400</td>\\n\",\n        \"      <td>2014-08-27</td>\\n\",\n        \"      <td> 101.800000</td>\\n\",\n        \"      <td> 1.11</td>\\n\",\n        \"      <td> 0.010904</td>\\n\",\n        \"      <td> 0.018369</td>\\n\",\n        \"      <td> 0.716667</td>\\n\",\n        \"      <td> 0.007040</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> 735473</td>\\n\",\n        \"      <td> 101.59</td>\\n\",\n        \"      <td> 102.25</td>\\n\",\n        \"      <td> 102.78</td>\\n\",\n        \"      <td> 101.56</td>\\n\",\n        \"      <td>  68389800</td>\\n\",\n        \"      <td>2014-08-28</td>\\n\",\n        \"      <td> 102.196667</td>\\n\",\n        \"      <td> 0.66</td>\\n\",\n        \"      <td> 0.006458</td>\\n\",\n        \"      <td> 0.011938</td>\\n\",\n        \"      <td> 0.396667</td>\\n\",\n        \"      <td> 0.003881</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> 735474</td>\\n\",\n        \"      <td> 102.86</td>\\n\",\n        \"      <td> 102.50</td>\\n\",\n        \"      <td> 102.90</td>\\n\",\n        \"      <td> 102.20</td>\\n\",\n        \"      <td>  44567000</td>\\n\",\n        \"      <td>2014-08-29</td>\\n\",\n        \"      <td> 102.533333</td>\\n\",\n        \"      <td>-0.36</td>\\n\",\n        \"      <td>-0.003511</td>\\n\",\n        \"      <td> 0.006827</td>\\n\",\n        \"      <td> 0.336667</td>\\n\",\n        \"      <td> 0.003283</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> 735478</td>\\n\",\n        \"      <td> 103.06</td>\\n\",\n        \"      <td> 103.30</td>\\n\",\n        \"      <td> 103.74</td>\\n\",\n        \"      <td> 102.72</td>\\n\",\n        \"      <td>  53491400</td>\\n\",\n        \"      <td>2014-09-02</td>\\n\",\n        \"      <td> 103.253333</td>\\n\",\n        \"      <td> 0.24</td>\\n\",\n        \"      <td> 0.002324</td>\\n\",\n        \"      <td> 0.009879</td>\\n\",\n        \"      <td> 0.720000</td>\\n\",\n        \"      <td> 0.006973</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> 735479</td>\\n\",\n        \"      <td> 103.10</td>\\n\",\n        \"      <td>  98.94</td>\\n\",\n        \"      <td> 103.20</td>\\n\",\n        \"      <td>  98.58</td>\\n\",\n        \"      <td> 125233100</td>\\n\",\n        \"      <td>2014-09-03</td>\\n\",\n        \"      <td> 100.240000</td>\\n\",\n        \"      <td>-4.16</td>\\n\",\n        \"      <td>-0.041500</td>\\n\",\n        \"      <td> 0.046089</td>\\n\",\n        \"      <td>-3.013333</td>\\n\",\n        \"      <td>-0.030061</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> 735480</td>\\n\",\n        \"      <td>  98.85</td>\\n\",\n        \"      <td>  98.12</td>\\n\",\n        \"      <td> 100.09</td>\\n\",\n        \"      <td>  97.79</td>\\n\",\n        \"      <td>  85594800</td>\\n\",\n        \"      <td>2014-09-04</td>\\n\",\n        \"      <td>  98.666667</td>\\n\",\n        \"      <td>-0.73</td>\\n\",\n        \"      <td>-0.007399</td>\\n\",\n        \"      <td> 0.023311</td>\\n\",\n        \"      <td>-1.573333</td>\\n\",\n        \"      <td>-0.015946</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> 735481</td>\\n\",\n        \"      <td>  98.80</td>\\n\",\n        \"      <td>  98.97</td>\\n\",\n        \"      <td>  99.39</td>\\n\",\n        \"      <td>  98.31</td>\\n\",\n        \"      <td>  58353200</td>\\n\",\n        \"      <td>2014-09-05</td>\\n\",\n        \"      <td>  98.890000</td>\\n\",\n        \"      <td> 0.17</td>\\n\",\n        \"      <td> 0.001719</td>\\n\",\n        \"      <td> 0.010921</td>\\n\",\n        \"      <td> 0.223333</td>\\n\",\n        \"      <td> 0.002258</td>\\n\",\n        \"      <td> AAPL</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> 735456</td>\\n\",\n        \"      <td>  12.58</td>\\n\",\n        \"      <td>  12.48</td>\\n\",\n        \"      <td>  12.68</td>\\n\",\n        \"      <td>  12.46</td>\\n\",\n        \"      <td>    624200</td>\\n\",\n        \"      <td>2014-08-11</td>\\n\",\n        \"      <td>  12.540000</td>\\n\",\n        \"      <td>-0.10</td>\\n\",\n        \"      <td>-0.007974</td>\\n\",\n        \"      <td> 0.017544</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> 735457</td>\\n\",\n        \"      <td>  12.39</td>\\n\",\n        \"      <td>  12.26</td>\\n\",\n        \"      <td>  12.50</td>\\n\",\n        \"      <td>  12.18</td>\\n\",\n        \"      <td>    704400</td>\\n\",\n        \"      <td>2014-08-12</td>\\n\",\n        \"      <td>  12.313333</td>\\n\",\n        \"      <td>-0.13</td>\\n\",\n        \"      <td>-0.010558</td>\\n\",\n        \"      <td> 0.025988</td>\\n\",\n        \"      <td>-0.226667</td>\\n\",\n        \"      <td>-0.018408</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> 735458</td>\\n\",\n        \"      <td>  12.27</td>\\n\",\n        \"      <td>  12.26</td>\\n\",\n        \"      <td>  12.33</td>\\n\",\n        \"      <td>  12.23</td>\\n\",\n        \"      <td>    593000</td>\\n\",\n        \"      <td>2014-08-13</td>\\n\",\n        \"      <td>  12.273333</td>\\n\",\n        \"      <td>-0.01</td>\\n\",\n        \"      <td>-0.000815</td>\\n\",\n        \"      <td> 0.008148</td>\\n\",\n        \"      <td>-0.040000</td>\\n\",\n        \"      <td>-0.003259</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> 735459</td>\\n\",\n        \"      <td>  12.25</td>\\n\",\n        \"      <td>  12.27</td>\\n\",\n        \"      <td>  12.35</td>\\n\",\n        \"      <td>  12.16</td>\\n\",\n        \"      <td>    514800</td>\\n\",\n        \"      <td>2014-08-14</td>\\n\",\n        \"      <td>  12.260000</td>\\n\",\n        \"      <td> 0.02</td>\\n\",\n        \"      <td> 0.001631</td>\\n\",\n        \"      <td> 0.015498</td>\\n\",\n        \"      <td>-0.013333</td>\\n\",\n        \"      <td>-0.001088</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> 735460</td>\\n\",\n        \"      <td>  12.41</td>\\n\",\n        \"      <td>  12.27</td>\\n\",\n        \"      <td>  12.41</td>\\n\",\n        \"      <td>  12.06</td>\\n\",\n        \"      <td>    688600</td>\\n\",\n        \"      <td>2014-08-15</td>\\n\",\n        \"      <td>  12.246667</td>\\n\",\n        \"      <td>-0.14</td>\\n\",\n        \"      <td>-0.011432</td>\\n\",\n        \"      <td> 0.028579</td>\\n\",\n        \"      <td>-0.013333</td>\\n\",\n        \"      <td>-0.001089</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> 735463</td>\\n\",\n        \"      <td>  12.42</td>\\n\",\n        \"      <td>  12.38</td>\\n\",\n        \"      <td>  12.47</td>\\n\",\n        \"      <td>  12.34</td>\\n\",\n        \"      <td>    548200</td>\\n\",\n        \"      <td>2014-08-18</td>\\n\",\n        \"      <td>  12.396667</td>\\n\",\n        \"      <td>-0.04</td>\\n\",\n        \"      <td>-0.003227</td>\\n\",\n        \"      <td> 0.010487</td>\\n\",\n        \"      <td> 0.150000</td>\\n\",\n        \"      <td> 0.012100</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> 735464</td>\\n\",\n        \"      <td>  12.38</td>\\n\",\n        \"      <td>  12.29</td>\\n\",\n        \"      <td>  12.44</td>\\n\",\n        \"      <td>  12.27</td>\\n\",\n        \"      <td>    412100</td>\\n\",\n        \"      <td>2014-08-19</td>\\n\",\n        \"      <td>  12.333333</td>\\n\",\n        \"      <td>-0.09</td>\\n\",\n        \"      <td>-0.007297</td>\\n\",\n        \"      <td> 0.013784</td>\\n\",\n        \"      <td>-0.063333</td>\\n\",\n        \"      <td>-0.005135</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> 735465</td>\\n\",\n        \"      <td>  12.25</td>\\n\",\n        \"      <td>  12.29</td>\\n\",\n        \"      <td>  12.33</td>\\n\",\n        \"      <td>  12.20</td>\\n\",\n        \"      <td>    426200</td>\\n\",\n        \"      <td>2014-08-20</td>\\n\",\n        \"      <td>  12.273333</td>\\n\",\n        \"      <td> 0.04</td>\\n\",\n        \"      <td> 0.003259</td>\\n\",\n        \"      <td> 0.010592</td>\\n\",\n        \"      <td>-0.060000</td>\\n\",\n        \"      <td>-0.004889</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> 735466</td>\\n\",\n        \"      <td>  12.27</td>\\n\",\n        \"      <td>  12.41</td>\\n\",\n        \"      <td>  12.45</td>\\n\",\n        \"      <td>  12.24</td>\\n\",\n        \"      <td>    664100</td>\\n\",\n        \"      <td>2014-08-21</td>\\n\",\n        \"      <td>  12.366667</td>\\n\",\n        \"      <td> 0.14</td>\\n\",\n        \"      <td> 0.011321</td>\\n\",\n        \"      <td> 0.016981</td>\\n\",\n        \"      <td> 0.093333</td>\\n\",\n        \"      <td> 0.007547</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> 735467</td>\\n\",\n        \"      <td>  12.41</td>\\n\",\n        \"      <td>  12.38</td>\\n\",\n        \"      <td>  12.46</td>\\n\",\n        \"      <td>  12.32</td>\\n\",\n        \"      <td>    556300</td>\\n\",\n        \"      <td>2014-08-22</td>\\n\",\n        \"      <td>  12.386667</td>\\n\",\n        \"      <td>-0.03</td>\\n\",\n        \"      <td>-0.002422</td>\\n\",\n        \"      <td> 0.011302</td>\\n\",\n        \"      <td> 0.020000</td>\\n\",\n        \"      <td> 0.001615</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> 735470</td>\\n\",\n        \"      <td>  12.44</td>\\n\",\n        \"      <td>  12.32</td>\\n\",\n        \"      <td>  12.46</td>\\n\",\n        \"      <td>  12.24</td>\\n\",\n        \"      <td>    350900</td>\\n\",\n        \"      <td>2014-08-25</td>\\n\",\n        \"      <td>  12.340000</td>\\n\",\n        \"      <td>-0.12</td>\\n\",\n        \"      <td>-0.009724</td>\\n\",\n        \"      <td> 0.017828</td>\\n\",\n        \"      <td>-0.046667</td>\\n\",\n        \"      <td>-0.003782</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> 735471</td>\\n\",\n        \"      <td>  12.34</td>\\n\",\n        \"      <td>  12.39</td>\\n\",\n        \"      <td>  12.42</td>\\n\",\n        \"      <td>  12.27</td>\\n\",\n        \"      <td>    532000</td>\\n\",\n        \"      <td>2014-08-26</td>\\n\",\n        \"      <td>  12.360000</td>\\n\",\n        \"      <td> 0.05</td>\\n\",\n        \"      <td> 0.004045</td>\\n\",\n        \"      <td> 0.012136</td>\\n\",\n        \"      <td> 0.020000</td>\\n\",\n        \"      <td> 0.001618</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> 735472</td>\\n\",\n        \"      <td>  12.40</td>\\n\",\n        \"      <td>  12.40</td>\\n\",\n        \"      <td>  12.44</td>\\n\",\n        \"      <td>  12.35</td>\\n\",\n        \"      <td>    586200</td>\\n\",\n        \"      <td>2014-08-27</td>\\n\",\n        \"      <td>  12.396667</td>\\n\",\n        \"      <td> 0.00</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.007260</td>\\n\",\n        \"      <td> 0.036667</td>\\n\",\n        \"      <td> 0.002958</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> 735473</td>\\n\",\n        \"      <td>  12.33</td>\\n\",\n        \"      <td>  12.35</td>\\n\",\n        \"      <td>  12.41</td>\\n\",\n        \"      <td>  12.25</td>\\n\",\n        \"      <td>    469100</td>\\n\",\n        \"      <td>2014-08-28</td>\\n\",\n        \"      <td>  12.336667</td>\\n\",\n        \"      <td> 0.02</td>\\n\",\n        \"      <td> 0.001621</td>\\n\",\n        \"      <td> 0.012969</td>\\n\",\n        \"      <td>-0.060000</td>\\n\",\n        \"      <td>-0.004864</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> 735474</td>\\n\",\n        \"      <td>  12.36</td>\\n\",\n        \"      <td>  12.38</td>\\n\",\n        \"      <td>  12.41</td>\\n\",\n        \"      <td>  12.17</td>\\n\",\n        \"      <td>    342000</td>\\n\",\n        \"      <td>2014-08-29</td>\\n\",\n        \"      <td>  12.320000</td>\\n\",\n        \"      <td> 0.02</td>\\n\",\n        \"      <td> 0.001623</td>\\n\",\n        \"      <td> 0.019481</td>\\n\",\n        \"      <td>-0.016667</td>\\n\",\n        \"      <td>-0.001353</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> 735478</td>\\n\",\n        \"      <td>  12.37</td>\\n\",\n        \"      <td>  12.33</td>\\n\",\n        \"      <td>  12.45</td>\\n\",\n        \"      <td>  12.19</td>\\n\",\n        \"      <td>    810500</td>\\n\",\n        \"      <td>2014-09-02</td>\\n\",\n        \"      <td>  12.323333</td>\\n\",\n        \"      <td>-0.04</td>\\n\",\n        \"      <td>-0.003246</td>\\n\",\n        \"      <td> 0.021098</td>\\n\",\n        \"      <td> 0.003333</td>\\n\",\n        \"      <td> 0.000270</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> 735479</td>\\n\",\n        \"      <td>  12.38</td>\\n\",\n        \"      <td>  12.33</td>\\n\",\n        \"      <td>  12.43</td>\\n\",\n        \"      <td>  12.29</td>\\n\",\n        \"      <td>    968100</td>\\n\",\n        \"      <td>2014-09-03</td>\\n\",\n        \"      <td>  12.350000</td>\\n\",\n        \"      <td>-0.05</td>\\n\",\n        \"      <td>-0.004049</td>\\n\",\n        \"      <td> 0.011336</td>\\n\",\n        \"      <td> 0.026667</td>\\n\",\n        \"      <td> 0.002159</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> 735480</td>\\n\",\n        \"      <td>  12.38</td>\\n\",\n        \"      <td>  12.35</td>\\n\",\n        \"      <td>  12.46</td>\\n\",\n        \"      <td>  12.26</td>\\n\",\n        \"      <td>    750900</td>\\n\",\n        \"      <td>2014-09-04</td>\\n\",\n        \"      <td>  12.356667</td>\\n\",\n        \"      <td>-0.03</td>\\n\",\n        \"      <td>-0.002428</td>\\n\",\n        \"      <td> 0.016186</td>\\n\",\n        \"      <td> 0.006667</td>\\n\",\n        \"      <td> 0.000540</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> 735481</td>\\n\",\n        \"      <td>  12.31</td>\\n\",\n        \"      <td>  12.37</td>\\n\",\n        \"      <td>  12.45</td>\\n\",\n        \"      <td>  12.27</td>\\n\",\n        \"      <td>    559800</td>\\n\",\n        \"      <td>2014-09-05</td>\\n\",\n        \"      <td>  12.363333</td>\\n\",\n        \"      <td> 0.06</td>\\n\",\n        \"      <td> 0.004853</td>\\n\",\n        \"      <td> 0.014559</td>\\n\",\n        \"      <td> 0.006667</td>\\n\",\n        \"      <td> 0.000539</td>\\n\",\n        \"      <td>  NYT</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> 735456</td>\\n\",\n        \"      <td>  73.46</td>\\n\",\n        \"      <td>  73.44</td>\\n\",\n        \"      <td>  73.91</td>\\n\",\n        \"      <td>  73.06</td>\\n\",\n        \"      <td>  24591000</td>\\n\",\n        \"      <td>2014-08-11</td>\\n\",\n        \"      <td>  73.470000</td>\\n\",\n        \"      <td>-0.02</td>\\n\",\n        \"      <td>-0.000272</td>\\n\",\n        \"      <td> 0.011569</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td>   FB</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> 735457</td>\\n\",\n        \"      <td>  73.09</td>\\n\",\n        \"      <td>  72.83</td>\\n\",\n        \"      <td>  73.33</td>\\n\",\n        \"      <td>  72.22</td>\\n\",\n        \"      <td>  27419000</td>\\n\",\n        \"      <td>2014-08-12</td>\\n\",\n        \"      <td>  72.793333</td>\\n\",\n        \"      <td>-0.26</td>\\n\",\n        \"      <td>-0.003572</td>\\n\",\n        \"      <td> 0.015249</td>\\n\",\n        \"      <td>-0.676667</td>\\n\",\n        \"      <td>-0.009296</td>\\n\",\n        \"      <td>   FB</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> 735458</td>\\n\",\n        \"      <td>  73.12</td>\\n\",\n        \"      <td>  73.77</td>\\n\",\n        \"      <td>  74.25</td>\\n\",\n        \"      <td>  73.05</td>\\n\",\n        \"      <td>  29198500</td>\\n\",\n        \"      <td>2014-08-13</td>\\n\",\n        \"      <td>  73.690000</td>\\n\",\n        \"      <td> 0.65</td>\\n\",\n        \"      <td> 0.008821</td>\\n\",\n        \"      <td> 0.016284</td>\\n\",\n        \"      <td> 0.896667</td>\\n\",\n        \"      <td> 0.012168</td>\\n\",\n        \"      <td>   FB</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>950 rows \\u00d7 14 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"    n_date    open   close    high     low     volume       date     average  \\\\\\n\",\n        \"0   735456  449.22  451.54  457.65  448.71    1892400 2014-08-11  452.633333   \\n\",\n        \"1   735457  451.35  446.41  453.00  443.41    1549300 2014-08-12  447.606667   \\n\",\n        \"2   735458  448.60  451.53  454.22  446.82    1294000 2014-08-13  450.856667   \\n\",\n        \"3   735459  452.21  450.87  455.00  448.22     951500 2014-08-14  451.363333   \\n\",\n        \"4   735460  451.49  459.09  462.00  448.60    3148100 2014-08-15  456.563333   \\n\",\n        \"5   735463  462.06  466.00  469.50  461.25    1928000 2014-08-18  465.583333   \\n\",\n        \"6   735464  467.09  468.15  470.50  462.43    1556800 2014-08-19  467.026667   \\n\",\n        \"7   735465  467.00  472.19  473.75  464.54    1737700 2014-08-20  470.160000   \\n\",\n        \"8   735466  471.28  472.05  476.15  467.67    1678100 2014-08-21  471.956667   \\n\",\n        \"9   735467  470.94  479.19  479.55  470.05    1962200 2014-08-22  476.263333   \\n\",\n        \"10  735470  481.55  480.93  485.30  477.53    1926400 2014-08-25  481.253333   \\n\",\n        \"11  735471  478.50  479.36  481.85  474.55    1448200 2014-08-26  478.586667   \\n\",\n        \"12  735472  479.40  474.70  480.25  473.63    1484900 2014-08-27  476.193333   \\n\",\n        \"13  735473  472.65  475.21  477.48  470.81    1083500 2014-08-28  474.500000   \\n\",\n        \"14  735474  476.85  477.64  480.50  474.14    1405500 2014-08-29  477.426667   \\n\",\n        \"15  735478  478.50  476.60  478.79  474.59    1258100 2014-09-02  476.660000   \\n\",\n        \"16  735479  480.52  477.39  487.60  476.53    2317000 2014-09-03  480.506667   \\n\",\n        \"17  735480  479.11  472.67  481.69  472.13    1708400 2014-09-04  475.496667   \\n\",\n        \"18  735481  473.23  475.68  477.35  470.07    1707600 2014-09-05  474.366667   \\n\",\n        \"0   735456   95.27   95.99   96.08   94.84   36585000 2014-08-11   95.636667   \\n\",\n        \"1   735457   96.04   95.97   96.88   95.61   33795000 2014-08-12   96.153333   \\n\",\n        \"2   735458   96.15   97.24   97.24   96.04   31916000 2014-08-13   96.840000   \\n\",\n        \"3   735459   97.33   97.50   97.57   96.80   28116000 2014-08-14   97.290000   \\n\",\n        \"4   735460   97.90   97.98   98.19   96.86   48951000 2014-08-15   97.676667   \\n\",\n        \"5   735463   98.49   99.16   99.37   97.98   47572000 2014-08-18   98.836667   \\n\",\n        \"6   735464   99.41  100.53  100.68   99.32   69274700 2014-08-19  100.176667   \\n\",\n        \"7   735465  100.44  100.57  101.09   99.95   52612800 2014-08-20  100.536667   \\n\",\n        \"8   735466  100.57  100.58  100.94  100.11   33421900 2014-08-21  100.543333   \\n\",\n        \"9   735467  100.29  101.32  101.47  100.19   44102400 2014-08-22  100.993333   \\n\",\n        \"10  735470  101.79  101.54  102.17  101.28   40144700 2014-08-25  101.663333   \\n\",\n        \"11  735471  101.42  100.89  101.50  100.86   33119800 2014-08-26  101.083333   \\n\",\n        \"12  735472  101.02  102.13  102.57  100.70   46827400 2014-08-27  101.800000   \\n\",\n        \"13  735473  101.59  102.25  102.78  101.56   68389800 2014-08-28  102.196667   \\n\",\n        \"14  735474  102.86  102.50  102.90  102.20   44567000 2014-08-29  102.533333   \\n\",\n        \"15  735478  103.06  103.30  103.74  102.72   53491400 2014-09-02  103.253333   \\n\",\n        \"16  735479  103.10   98.94  103.20   98.58  125233100 2014-09-03  100.240000   \\n\",\n        \"17  735480   98.85   98.12  100.09   97.79   85594800 2014-09-04   98.666667   \\n\",\n        \"18  735481   98.80   98.97   99.39   98.31   58353200 2014-09-05   98.890000   \\n\",\n        \"0   735456   12.58   12.48   12.68   12.46     624200 2014-08-11   12.540000   \\n\",\n        \"1   735457   12.39   12.26   12.50   12.18     704400 2014-08-12   12.313333   \\n\",\n        \"2   735458   12.27   12.26   12.33   12.23     593000 2014-08-13   12.273333   \\n\",\n        \"3   735459   12.25   12.27   12.35   12.16     514800 2014-08-14   12.260000   \\n\",\n        \"4   735460   12.41   12.27   12.41   12.06     688600 2014-08-15   12.246667   \\n\",\n        \"5   735463   12.42   12.38   12.47   12.34     548200 2014-08-18   12.396667   \\n\",\n        \"6   735464   12.38   12.29   12.44   12.27     412100 2014-08-19   12.333333   \\n\",\n        \"7   735465   12.25   12.29   12.33   12.20     426200 2014-08-20   12.273333   \\n\",\n        \"8   735466   12.27   12.41   12.45   12.24     664100 2014-08-21   12.366667   \\n\",\n        \"9   735467   12.41   12.38   12.46   12.32     556300 2014-08-22   12.386667   \\n\",\n        \"10  735470   12.44   12.32   12.46   12.24     350900 2014-08-25   12.340000   \\n\",\n        \"11  735471   12.34   12.39   12.42   12.27     532000 2014-08-26   12.360000   \\n\",\n        \"12  735472   12.40   12.40   12.44   12.35     586200 2014-08-27   12.396667   \\n\",\n        \"13  735473   12.33   12.35   12.41   12.25     469100 2014-08-28   12.336667   \\n\",\n        \"14  735474   12.36   12.38   12.41   12.17     342000 2014-08-29   12.320000   \\n\",\n        \"15  735478   12.37   12.33   12.45   12.19     810500 2014-09-02   12.323333   \\n\",\n        \"16  735479   12.38   12.33   12.43   12.29     968100 2014-09-03   12.350000   \\n\",\n        \"17  735480   12.38   12.35   12.46   12.26     750900 2014-09-04   12.356667   \\n\",\n        \"18  735481   12.31   12.37   12.45   12.27     559800 2014-09-05   12.363333   \\n\",\n        \"0   735456   73.46   73.44   73.91   73.06   24591000 2014-08-11   73.470000   \\n\",\n        \"1   735457   73.09   72.83   73.33   72.22   27419000 2014-08-12   72.793333   \\n\",\n        \"2   735458   73.12   73.77   74.25   73.05   29198500 2014-08-13   73.690000   \\n\",\n        \"       ...     ...     ...     ...     ...        ...        ...         ...   \\n\",\n        \"\\n\",\n        \"    change_amount  change_per     range  change_1_amount  change_1_per symbol  \\n\",\n        \"0            2.32    0.005126  0.019751         0.000000      0.000000   NFLX  \\n\",\n        \"1           -4.94   -0.011036  0.021425        -5.026667     -0.011230   NFLX  \\n\",\n        \"2            2.93    0.006499  0.016413         3.250000      0.007208   NFLX  \\n\",\n        \"3           -1.34   -0.002969  0.015021         0.506667      0.001123   NFLX  \\n\",\n        \"4            7.60    0.016646  0.029350         5.200000      0.011389   NFLX  \\n\",\n        \"5            3.94    0.008463  0.017720         9.020000      0.019374   NFLX  \\n\",\n        \"6            1.06    0.002270  0.017280         1.443333      0.003090   NFLX  \\n\",\n        \"7            5.19    0.011039  0.019589         3.133333      0.006664   NFLX  \\n\",\n        \"8            0.77    0.001632  0.017968         1.796667      0.003807   NFLX  \\n\",\n        \"9            8.25    0.017322  0.019947         4.306667      0.009043   NFLX  \\n\",\n        \"10          -0.62   -0.001288  0.016145         4.990000      0.010369   NFLX  \\n\",\n        \"11           0.86    0.001797  0.015253        -2.666667     -0.005572   NFLX  \\n\",\n        \"12          -4.70   -0.009870  0.013902        -2.393333     -0.005026   NFLX  \\n\",\n        \"13           2.56    0.005395  0.014057        -1.693333     -0.003569   NFLX  \\n\",\n        \"14           0.79    0.001655  0.013321         2.926667      0.006130   NFLX  \\n\",\n        \"15          -1.90   -0.003986  0.008811        -0.766667     -0.001608   NFLX  \\n\",\n        \"16          -3.13   -0.006514  0.023038         3.846667      0.008005   NFLX  \\n\",\n        \"17          -6.44   -0.013544  0.020105        -5.010000     -0.010536   NFLX  \\n\",\n        \"18           2.45    0.005165  0.015347        -1.130000     -0.002382   NFLX  \\n\",\n        \"0            0.72    0.007528  0.012966         0.000000      0.000000   AAPL  \\n\",\n        \"1           -0.07   -0.000728  0.013208         0.516667      0.005373   AAPL  \\n\",\n        \"2            1.09    0.011256  0.012392         0.686667      0.007091   AAPL  \\n\",\n        \"3            0.17    0.001747  0.007914         0.450000      0.004625   AAPL  \\n\",\n        \"4            0.08    0.000819  0.013616         0.386667      0.003959   AAPL  \\n\",\n        \"5            0.67    0.006779  0.014064         1.160000      0.011737   AAPL  \\n\",\n        \"6            1.12    0.011180  0.013576         1.340000      0.013376   AAPL  \\n\",\n        \"7            0.13    0.001293  0.011339         0.360000      0.003581   AAPL  \\n\",\n        \"8            0.01    0.000099  0.008255         0.006667      0.000066   AAPL  \\n\",\n        \"9            1.03    0.010199  0.012674         0.450000      0.004456   AAPL  \\n\",\n        \"10          -0.25   -0.002459  0.008754         0.670000      0.006590   AAPL  \\n\",\n        \"11          -0.53   -0.005243  0.006331        -0.580000     -0.005738   AAPL  \\n\",\n        \"12           1.11    0.010904  0.018369         0.716667      0.007040   AAPL  \\n\",\n        \"13           0.66    0.006458  0.011938         0.396667      0.003881   AAPL  \\n\",\n        \"14          -0.36   -0.003511  0.006827         0.336667      0.003283   AAPL  \\n\",\n        \"15           0.24    0.002324  0.009879         0.720000      0.006973   AAPL  \\n\",\n        \"16          -4.16   -0.041500  0.046089        -3.013333     -0.030061   AAPL  \\n\",\n        \"17          -0.73   -0.007399  0.023311        -1.573333     -0.015946   AAPL  \\n\",\n        \"18           0.17    0.001719  0.010921         0.223333      0.002258   AAPL  \\n\",\n        \"0           -0.10   -0.007974  0.017544         0.000000      0.000000    NYT  \\n\",\n        \"1           -0.13   -0.010558  0.025988        -0.226667     -0.018408    NYT  \\n\",\n        \"2           -0.01   -0.000815  0.008148        -0.040000     -0.003259    NYT  \\n\",\n        \"3            0.02    0.001631  0.015498        -0.013333     -0.001088    NYT  \\n\",\n        \"4           -0.14   -0.011432  0.028579        -0.013333     -0.001089    NYT  \\n\",\n        \"5           -0.04   -0.003227  0.010487         0.150000      0.012100    NYT  \\n\",\n        \"6           -0.09   -0.007297  0.013784        -0.063333     -0.005135    NYT  \\n\",\n        \"7            0.04    0.003259  0.010592        -0.060000     -0.004889    NYT  \\n\",\n        \"8            0.14    0.011321  0.016981         0.093333      0.007547    NYT  \\n\",\n        \"9           -0.03   -0.002422  0.011302         0.020000      0.001615    NYT  \\n\",\n        \"10          -0.12   -0.009724  0.017828        -0.046667     -0.003782    NYT  \\n\",\n        \"11           0.05    0.004045  0.012136         0.020000      0.001618    NYT  \\n\",\n        \"12           0.00    0.000000  0.007260         0.036667      0.002958    NYT  \\n\",\n        \"13           0.02    0.001621  0.012969        -0.060000     -0.004864    NYT  \\n\",\n        \"14           0.02    0.001623  0.019481        -0.016667     -0.001353    NYT  \\n\",\n        \"15          -0.04   -0.003246  0.021098         0.003333      0.000270    NYT  \\n\",\n        \"16          -0.05   -0.004049  0.011336         0.026667      0.002159    NYT  \\n\",\n        \"17          -0.03   -0.002428  0.016186         0.006667      0.000540    NYT  \\n\",\n        \"18           0.06    0.004853  0.014559         0.006667      0.000539    NYT  \\n\",\n        \"0           -0.02   -0.000272  0.011569         0.000000      0.000000     FB  \\n\",\n        \"1           -0.26   -0.003572  0.015249        -0.676667     -0.009296     FB  \\n\",\n        \"2            0.65    0.008821  0.016284         0.896667      0.012168     FB  \\n\",\n        \"              ...         ...       ...              ...           ...    ...  \\n\",\n        \"\\n\",\n        \"[950 rows x 14 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualizing Change\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"fig = plt.figure(figsize=(10,6))\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"for symbol in stocks_df[\\\"symbol\\\"].unique():\\n\",\n      \"    x = stocks_df[stocks_df[\\\"symbol\\\"] == symbol][\\\"n_date\\\"]\\n\",\n      \"    y = stocks_df[stocks_df[\\\"symbol\\\"] == symbol][\\\"change_1_per\\\"]\\n\",\n      \"    \\n\",\n      \"    axes.plot_date(x=x, y=y, fmt=\\\"-\\\", alpha=0.2, linewidth=2)\\n\",\n      \"    \\n\",\n      \"    axes.xaxis_date()\\n\",\n      \"    plt.gcf().autofmt_xdate()\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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StrPudLX/oSJ0+e5OzZs7xVnX+ocByHhx56iJ/8yZ9s9lCEEPtoP+q5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\\nQgghAUCDLkIIIYSQAPh/5q89N6uoYIcAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84655990>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Finding Similar Stocks\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Preparing Data for Clustering\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def pivot_data(stocks_df, values=\\\"change_1_per\\\"):\\n\",\n      \"    clustering_data = stocks_df.pivot(index=\\\"symbol\\\", columns=\\\"n_date\\\", values=values)\\n\",\n      \"    return clustering_data\\n\",\n      \"clustering_data = pivot_data(stocks_df, values=\\\"change_1_per\\\")\\n\",\n      \"clustering_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th>n_date</th>\\n\",\n        \"      <th>735456</th>\\n\",\n        \"      <th>735457</th>\\n\",\n        \"      <th>735458</th>\\n\",\n        \"      <th>735459</th>\\n\",\n        \"      <th>735460</th>\\n\",\n        \"      <th>735463</th>\\n\",\n        \"      <th>735464</th>\\n\",\n        \"      <th>735465</th>\\n\",\n        \"      <th>735466</th>\\n\",\n        \"      <th>735467</th>\\n\",\n        \"      <th>735470</th>\\n\",\n        \"      <th>735471</th>\\n\",\n        \"      <th>735472</th>\\n\",\n        \"      <th>735473</th>\\n\",\n        \"      <th>735474</th>\\n\",\n        \"      <th>735478</th>\\n\",\n        \"      <th>735479</th>\\n\",\n        \"      <th>735480</th>\\n\",\n        \"      <th>735481</th>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>symbol</th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AAPL</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.005373</td>\\n\",\n        \"      <td> 0.007091</td>\\n\",\n        \"      <td> 0.004625</td>\\n\",\n        \"      <td> 0.003959</td>\\n\",\n        \"      <td> 0.011737</td>\\n\",\n        \"      <td> 0.013376</td>\\n\",\n        \"      <td> 0.003581</td>\\n\",\n        \"      <td> 0.000066</td>\\n\",\n        \"      <td> 0.004456</td>\\n\",\n        \"      <td> 0.006590</td>\\n\",\n        \"      <td>-0.005738</td>\\n\",\n        \"      <td> 0.007040</td>\\n\",\n        \"      <td> 0.003881</td>\\n\",\n        \"      <td> 0.003283</td>\\n\",\n        \"      <td> 0.006973</td>\\n\",\n        \"      <td>-0.030061</td>\\n\",\n        \"      <td>-0.015946</td>\\n\",\n        \"      <td> 0.002258</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ADBE</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.002185</td>\\n\",\n        \"      <td> 0.010283</td>\\n\",\n        \"      <td> 0.007582</td>\\n\",\n        \"      <td> 0.004037</td>\\n\",\n        \"      <td> 0.009363</td>\\n\",\n        \"      <td> 0.012268</td>\\n\",\n        \"      <td>-0.006643</td>\\n\",\n        \"      <td> 0.003217</td>\\n\",\n        \"      <td> 0.006578</td>\\n\",\n        \"      <td> 0.000648</td>\\n\",\n        \"      <td>-0.003437</td>\\n\",\n        \"      <td>-0.001256</td>\\n\",\n        \"      <td>-0.004954</td>\\n\",\n        \"      <td> 0.006732</td>\\n\",\n        \"      <td> 0.005219</td>\\n\",\n        \"      <td> 0.003177</td>\\n\",\n        \"      <td> 0.003212</td>\\n\",\n        \"      <td> 0.003430</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ADSK</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.004898</td>\\n\",\n        \"      <td> 0.017504</td>\\n\",\n        \"      <td> 0.013646</td>\\n\",\n        \"      <td>-0.033605</td>\\n\",\n        \"      <td>-0.015570</td>\\n\",\n        \"      <td> 0.004711</td>\\n\",\n        \"      <td>-0.004108</td>\\n\",\n        \"      <td> 0.003350</td>\\n\",\n        \"      <td>-0.002924</td>\\n\",\n        \"      <td>-0.001059</td>\\n\",\n        \"      <td> 0.010964</td>\\n\",\n        \"      <td>-0.002099</td>\\n\",\n        \"      <td>-0.002661</td>\\n\",\n        \"      <td>-0.000619</td>\\n\",\n        \"      <td>-0.007361</td>\\n\",\n        \"      <td> 0.011897</td>\\n\",\n        \"      <td> 0.007039</td>\\n\",\n        \"      <td>-0.006158</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AMZN</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.002078</td>\\n\",\n        \"      <td> 0.026988</td>\\n\",\n        \"      <td> 0.009929</td>\\n\",\n        \"      <td> 0.002949</td>\\n\",\n        \"      <td> 0.009095</td>\\n\",\n        \"      <td>-0.002142</td>\\n\",\n        \"      <td> 0.002989</td>\\n\",\n        \"      <td>-0.005231</td>\\n\",\n        \"      <td>-0.005269</td>\\n\",\n        \"      <td> 0.007005</td>\\n\",\n        \"      <td> 0.016890</td>\\n\",\n        \"      <td> 0.010134</td>\\n\",\n        \"      <td>-0.010366</td>\\n\",\n        \"      <td>-0.002032</td>\\n\",\n        \"      <td> 0.003425</td>\\n\",\n        \"      <td>-0.002177</td>\\n\",\n        \"      <td> 0.018375</td>\\n\",\n        \"      <td>-0.003090</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BBRY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.006726</td>\\n\",\n        \"      <td>-0.002840</td>\\n\",\n        \"      <td> 0.009494</td>\\n\",\n        \"      <td> 0.010783</td>\\n\",\n        \"      <td> 0.014736</td>\\n\",\n        \"      <td> 0.009504</td>\\n\",\n        \"      <td> 0.027402</td>\\n\",\n        \"      <td>-0.013043</td>\\n\",\n        \"      <td>-0.004030</td>\\n\",\n        \"      <td> 0.007333</td>\\n\",\n        \"      <td> 0.003322</td>\\n\",\n        \"      <td> 0.018265</td>\\n\",\n        \"      <td> 0.001628</td>\\n\",\n        \"      <td> 0.000651</td>\\n\",\n        \"      <td> 0.012849</td>\\n\",\n        \"      <td> 0.032027</td>\\n\",\n        \"      <td>-0.009733</td>\\n\",\n        \"      <td>-0.009189</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BKS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.004016</td>\\n\",\n        \"      <td> 0.001040</td>\\n\",\n        \"      <td> 0.009566</td>\\n\",\n        \"      <td>-0.002360</td>\\n\",\n        \"      <td> 0.014967</td>\\n\",\n        \"      <td> 0.008071</td>\\n\",\n        \"      <td>-0.018048</td>\\n\",\n        \"      <td>-0.007093</td>\\n\",\n        \"      <td> 0.002947</td>\\n\",\n        \"      <td>-0.008769</td>\\n\",\n        \"      <td> 0.013200</td>\\n\",\n        \"      <td> 0.027528</td>\\n\",\n        \"      <td> 0.008626</td>\\n\",\n        \"      <td> 0.001553</td>\\n\",\n        \"      <td>-0.010558</td>\\n\",\n        \"      <td> 0.016419</td>\\n\",\n        \"      <td> 0.002240</td>\\n\",\n        \"      <td>-0.004501</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BP</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001125</td>\\n\",\n        \"      <td>-0.005802</td>\\n\",\n        \"      <td> 0.005278</td>\\n\",\n        \"      <td>-0.000775</td>\\n\",\n        \"      <td> 0.009557</td>\\n\",\n        \"      <td> 0.007821</td>\\n\",\n        \"      <td>-0.000138</td>\\n\",\n        \"      <td> 0.001797</td>\\n\",\n        \"      <td>-0.001661</td>\\n\",\n        \"      <td> 0.006054</td>\\n\",\n        \"      <td>-0.004214</td>\\n\",\n        \"      <td> 0.002206</td>\\n\",\n        \"      <td>-0.007850</td>\\n\",\n        \"      <td>-0.002996</td>\\n\",\n        \"      <td>-0.011132</td>\\n\",\n        \"      <td> 0.010251</td>\\n\",\n        \"      <td>-0.041773</td>\\n\",\n        \"      <td>-0.001601</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CAJ</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.002323</td>\\n\",\n        \"      <td> 0.003221</td>\\n\",\n        \"      <td> 0.004908</td>\\n\",\n        \"      <td> 0.000501</td>\\n\",\n        \"      <td>-0.000501</td>\\n\",\n        \"      <td> 0.001900</td>\\n\",\n        \"      <td>-0.008164</td>\\n\",\n        \"      <td>-0.000403</td>\\n\",\n        \"      <td>-0.007006</td>\\n\",\n        \"      <td>-0.000102</td>\\n\",\n        \"      <td> 0.003441</td>\\n\",\n        \"      <td>-0.002638</td>\\n\",\n        \"      <td> 0.000608</td>\\n\",\n        \"      <td>-0.004584</td>\\n\",\n        \"      <td> 0.003351</td>\\n\",\n        \"      <td> 0.003541</td>\\n\",\n        \"      <td> 0.006134</td>\\n\",\n        \"      <td>-0.002217</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CAT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001084</td>\\n\",\n        \"      <td> 0.004349</td>\\n\",\n        \"      <td> 0.005242</td>\\n\",\n        \"      <td> 0.002017</td>\\n\",\n        \"      <td> 0.009583</td>\\n\",\n        \"      <td> 0.004691</td>\\n\",\n        \"      <td> 0.003653</td>\\n\",\n        \"      <td> 0.003178</td>\\n\",\n        \"      <td>-0.007492</td>\\n\",\n        \"      <td> 0.007283</td>\\n\",\n        \"      <td> 0.002002</td>\\n\",\n        \"      <td> 0.002672</td>\\n\",\n        \"      <td>-0.000861</td>\\n\",\n        \"      <td> 0.003523</td>\\n\",\n        \"      <td> 0.003237</td>\\n\",\n        \"      <td>-0.004016</td>\\n\",\n        \"      <td> 0.000919</td>\\n\",\n        \"      <td>-0.001718</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CBS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.010617</td>\\n\",\n        \"      <td>-0.002817</td>\\n\",\n        \"      <td> 0.003984</td>\\n\",\n        \"      <td> 0.007462</td>\\n\",\n        \"      <td> 0.008284</td>\\n\",\n        \"      <td> 0.004180</td>\\n\",\n        \"      <td>-0.002702</td>\\n\",\n        \"      <td>-0.001215</td>\\n\",\n        \"      <td> 0.002478</td>\\n\",\n        \"      <td> 0.004387</td>\\n\",\n        \"      <td>-0.009521</td>\\n\",\n        \"      <td> 0.002485</td>\\n\",\n        \"      <td>-0.009082</td>\\n\",\n        \"      <td>-0.007353</td>\\n\",\n        \"      <td>-0.005758</td>\\n\",\n        \"      <td>-0.006992</td>\\n\",\n        \"      <td> 0.011908</td>\\n\",\n        \"      <td>-0.002816</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CMCSA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000310</td>\\n\",\n        \"      <td> 0.005419</td>\\n\",\n        \"      <td> 0.002886</td>\\n\",\n        \"      <td> 0.004888</td>\\n\",\n        \"      <td> 0.003895</td>\\n\",\n        \"      <td>-0.003114</td>\\n\",\n        \"      <td>-0.000366</td>\\n\",\n        \"      <td>-0.000733</td>\\n\",\n        \"      <td>-0.005716</td>\\n\",\n        \"      <td> 0.005441</td>\\n\",\n        \"      <td> 0.000367</td>\\n\",\n        \"      <td> 0.001099</td>\\n\",\n        \"      <td>-0.002202</td>\\n\",\n        \"      <td> 0.003779</td>\\n\",\n        \"      <td> 0.000122</td>\\n\",\n        \"      <td> 0.002674</td>\\n\",\n        \"      <td> 0.003392</td>\\n\",\n        \"      <td> 0.007574</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CRM</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.029861</td>\\n\",\n        \"      <td>-0.004338</td>\\n\",\n        \"      <td> 0.006434</td>\\n\",\n        \"      <td> 0.003858</td>\\n\",\n        \"      <td> 0.013566</td>\\n\",\n        \"      <td> 0.010267</td>\\n\",\n        \"      <td> 0.005979</td>\\n\",\n        \"      <td> 0.006361</td>\\n\",\n        \"      <td> 0.066805</td>\\n\",\n        \"      <td>-0.006311</td>\\n\",\n        \"      <td> 0.002361</td>\\n\",\n        \"      <td> 0.009025</td>\\n\",\n        \"      <td>-0.025011</td>\\n\",\n        \"      <td> 0.010118</td>\\n\",\n        \"      <td> 0.012062</td>\\n\",\n        \"      <td>-0.004713</td>\\n\",\n        \"      <td>-0.002418</td>\\n\",\n        \"      <td> 0.003642</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CSC</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.003303</td>\\n\",\n        \"      <td> 0.002547</td>\\n\",\n        \"      <td> 0.006370</td>\\n\",\n        \"      <td> 0.002154</td>\\n\",\n        \"      <td> 0.010804</td>\\n\",\n        \"      <td> 0.004196</td>\\n\",\n        \"      <td> 0.002791</td>\\n\",\n        \"      <td> 0.006711</td>\\n\",\n        \"      <td>-0.004814</td>\\n\",\n        \"      <td>-0.001434</td>\\n\",\n        \"      <td> 0.000394</td>\\n\",\n        \"      <td> 0.004502</td>\\n\",\n        \"      <td>-0.004918</td>\\n\",\n        \"      <td>-0.000559</td>\\n\",\n        \"      <td> 0.006930</td>\\n\",\n        \"      <td> 0.002213</td>\\n\",\n        \"      <td>-0.002162</td>\\n\",\n        \"      <td> 0.000166</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CSCO</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.002519</td>\\n\",\n        \"      <td>-0.000265</td>\\n\",\n        \"      <td>-0.021680</td>\\n\",\n        \"      <td>-0.005998</td>\\n\",\n        \"      <td> 0.004883</td>\\n\",\n        \"      <td> 0.002841</td>\\n\",\n        \"      <td> 0.000676</td>\\n\",\n        \"      <td> 0.007246</td>\\n\",\n        \"      <td>-0.003501</td>\\n\",\n        \"      <td>-0.000404</td>\\n\",\n        \"      <td> 0.002419</td>\\n\",\n        \"      <td>-0.000538</td>\\n\",\n        \"      <td> 0.000269</td>\\n\",\n        \"      <td> 0.004683</td>\\n\",\n        \"      <td>-0.001474</td>\\n\",\n        \"      <td> 0.006126</td>\\n\",\n        \"      <td>-0.002804</td>\\n\",\n        \"      <td> 0.000667</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CTXS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.004566</td>\\n\",\n        \"      <td> 0.005074</td>\\n\",\n        \"      <td> 0.001158</td>\\n\",\n        \"      <td>-0.001741</td>\\n\",\n        \"      <td> 0.009672</td>\\n\",\n        \"      <td>-0.001151</td>\\n\",\n        \"      <td>-0.000288</td>\\n\",\n        \"      <td> 0.004345</td>\\n\",\n        \"      <td> 0.006168</td>\\n\",\n        \"      <td>-0.003571</td>\\n\",\n        \"      <td>-0.000286</td>\\n\",\n        \"      <td>-0.002100</td>\\n\",\n        \"      <td>-0.004555</td>\\n\",\n        \"      <td> 0.009922</td>\\n\",\n        \"      <td> 0.005148</td>\\n\",\n        \"      <td> 0.001368</td>\\n\",\n        \"      <td> 0.000801</td>\\n\",\n        \"      <td> 0.001553</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CVX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.008605</td>\\n\",\n        \"      <td> 0.005390</td>\\n\",\n        \"      <td>-0.003951</td>\\n\",\n        \"      <td> 0.001565</td>\\n\",\n        \"      <td>-0.000132</td>\\n\",\n        \"      <td> 0.008799</td>\\n\",\n        \"      <td>-0.000026</td>\\n\",\n        \"      <td> 0.003705</td>\\n\",\n        \"      <td>-0.005799</td>\\n\",\n        \"      <td> 0.005766</td>\\n\",\n        \"      <td> 0.004183</td>\\n\",\n        \"      <td> 0.001945</td>\\n\",\n        \"      <td> 0.000648</td>\\n\",\n        \"      <td> 0.004078</td>\\n\",\n        \"      <td>-0.009115</td>\\n\",\n        \"      <td> 0.001119</td>\\n\",\n        \"      <td>-0.007733</td>\\n\",\n        \"      <td>-0.001207</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>DIS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.003517</td>\\n\",\n        \"      <td> 0.005172</td>\\n\",\n        \"      <td> 0.008895</td>\\n\",\n        \"      <td> 0.007890</td>\\n\",\n        \"      <td> 0.009188</td>\\n\",\n        \"      <td> 0.000370</td>\\n\",\n        \"      <td>-0.001706</td>\\n\",\n        \"      <td> 0.004910</td>\\n\",\n        \"      <td> 0.002284</td>\\n\",\n        \"      <td> 0.001140</td>\\n\",\n        \"      <td>-0.004397</td>\\n\",\n        \"      <td> 0.000480</td>\\n\",\n        \"      <td>-0.000296</td>\\n\",\n        \"      <td>-0.004043</td>\\n\",\n        \"      <td> 0.008423</td>\\n\",\n        \"      <td> 0.003263</td>\\n\",\n        \"      <td>-0.005233</td>\\n\",\n        \"      <td> 0.002316</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>EBAY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.009406</td>\\n\",\n        \"      <td>-0.012296</td>\\n\",\n        \"      <td> 0.001951</td>\\n\",\n        \"      <td>-0.001892</td>\\n\",\n        \"      <td> 0.003080</td>\\n\",\n        \"      <td> 0.009711</td>\\n\",\n        \"      <td>-0.001871</td>\\n\",\n        \"      <td> 0.036590</td>\\n\",\n        \"      <td> 0.001560</td>\\n\",\n        \"      <td> 0.001737</td>\\n\",\n        \"      <td> 0.006958</td>\\n\",\n        \"      <td> 0.001484</td>\\n\",\n        \"      <td>-0.012566</td>\\n\",\n        \"      <td>-0.000722</td>\\n\",\n        \"      <td>-0.006297</td>\\n\",\n        \"      <td>-0.005785</td>\\n\",\n        \"      <td>-0.002014</td>\\n\",\n        \"      <td>-0.014046</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>EMC</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000455</td>\\n\",\n        \"      <td> 0.005656</td>\\n\",\n        \"      <td> 0.006630</td>\\n\",\n        \"      <td> 0.001683</td>\\n\",\n        \"      <td> 0.007350</td>\\n\",\n        \"      <td>-0.001897</td>\\n\",\n        \"      <td>-0.007079</td>\\n\",\n        \"      <td> 0.002018</td>\\n\",\n        \"      <td>-0.004166</td>\\n\",\n        \"      <td> 0.002919</td>\\n\",\n        \"      <td>-0.003832</td>\\n\",\n        \"      <td>-0.003393</td>\\n\",\n        \"      <td>-0.003176</td>\\n\",\n        \"      <td> 0.003054</td>\\n\",\n        \"      <td>-0.004202</td>\\n\",\n        \"      <td> 0.006880</td>\\n\",\n        \"      <td>-0.010486</td>\\n\",\n        \"      <td>-0.009782</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>FB</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.009296</td>\\n\",\n        \"      <td> 0.012168</td>\\n\",\n        \"      <td> 0.005846</td>\\n\",\n        \"      <td>-0.004926</td>\\n\",\n        \"      <td> 0.008913</td>\\n\",\n        \"      <td> 0.009362</td>\\n\",\n        \"      <td>-0.003428</td>\\n\",\n        \"      <td>-0.001963</td>\\n\",\n        \"      <td>-0.005833</td>\\n\",\n        \"      <td> 0.009863</td>\\n\",\n        \"      <td> 0.007014</td>\\n\",\n        \"      <td>-0.009351</td>\\n\",\n        \"      <td>-0.011530</td>\\n\",\n        \"      <td> 0.007288</td>\\n\",\n        \"      <td> 0.019939</td>\\n\",\n        \"      <td> 0.003102</td>\\n\",\n        \"      <td>-0.002189</td>\\n\",\n        \"      <td> 0.007948</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>FJTSY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.015135</td>\\n\",\n        \"      <td> 0.022376</td>\\n\",\n        \"      <td>-0.003667</td>\\n\",\n        \"      <td>-0.000367</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td>-0.007576</td>\\n\",\n        \"      <td>-0.008573</td>\\n\",\n        \"      <td> 0.002510</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td>-0.022526</td>\\n\",\n        \"      <td>-0.021060</td>\\n\",\n        \"      <td> 0.000679</td>\\n\",\n        \"      <td> 0.016783</td>\\n\",\n        \"      <td>-0.032388</td>\\n\",\n        \"      <td> 0.002945</td>\\n\",\n        \"      <td> 0.000883</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>FOXA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.010004</td>\\n\",\n        \"      <td> 0.008595</td>\\n\",\n        \"      <td> 0.009264</td>\\n\",\n        \"      <td> 0.007339</td>\\n\",\n        \"      <td> 0.003148</td>\\n\",\n        \"      <td>-0.003998</td>\\n\",\n        \"      <td>-0.002143</td>\\n\",\n        \"      <td>-0.002522</td>\\n\",\n        \"      <td> 0.000560</td>\\n\",\n        \"      <td>-0.001777</td>\\n\",\n        \"      <td>-0.001123</td>\\n\",\n        \"      <td> 0.003359</td>\\n\",\n        \"      <td>-0.001776</td>\\n\",\n        \"      <td>-0.004979</td>\\n\",\n        \"      <td> 0.008014</td>\\n\",\n        \"      <td> 0.010968</td>\\n\",\n        \"      <td> 0.002666</td>\\n\",\n        \"      <td>-0.004432</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>GE</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005840</td>\\n\",\n        \"      <td> 0.005036</td>\\n\",\n        \"      <td> 0.001418</td>\\n\",\n        \"      <td>-0.005705</td>\\n\",\n        \"      <td> 0.011155</td>\\n\",\n        \"      <td> 0.003450</td>\\n\",\n        \"      <td> 0.006475</td>\\n\",\n        \"      <td> 0.005178</td>\\n\",\n        \"      <td>-0.006995</td>\\n\",\n        \"      <td>-0.001528</td>\\n\",\n        \"      <td>-0.002682</td>\\n\",\n        \"      <td> 0.000638</td>\\n\",\n        \"      <td>-0.003715</td>\\n\",\n        \"      <td>-0.001154</td>\\n\",\n        \"      <td>-0.004121</td>\\n\",\n        \"      <td> 0.001543</td>\\n\",\n        \"      <td> 0.002693</td>\\n\",\n        \"      <td> 0.000256</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>GOOG</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.008795</td>\\n\",\n        \"      <td> 0.015167</td>\\n\",\n        \"      <td> 0.004584</td>\\n\",\n        \"      <td>-0.000029</td>\\n\",\n        \"      <td> 0.011069</td>\\n\",\n        \"      <td> 0.008833</td>\\n\",\n        \"      <td>-0.002532</td>\\n\",\n        \"      <td>-0.002716</td>\\n\",\n        \"      <td>-0.000326</td>\\n\",\n        \"      <td>-0.002431</td>\\n\",\n        \"      <td>-0.004585</td>\\n\",\n        \"      <td>-0.009683</td>\\n\",\n        \"      <td>-0.005873</td>\\n\",\n        \"      <td> 0.000678</td>\\n\",\n        \"      <td> 0.009060</td>\\n\",\n        \"      <td> 0.005519</td>\\n\",\n        \"      <td> 0.006450</td>\\n\",\n        \"      <td> 0.004206</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>HPQ</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005128</td>\\n\",\n        \"      <td> 0.004443</td>\\n\",\n        \"      <td> 0.003673</td>\\n\",\n        \"      <td>-0.004162</td>\\n\",\n        \"      <td> 0.003487</td>\\n\",\n        \"      <td> 0.004130</td>\\n\",\n        \"      <td>-0.007852</td>\\n\",\n        \"      <td> 0.036108</td>\\n\",\n        \"      <td> 0.008050</td>\\n\",\n        \"      <td> 0.006559</td>\\n\",\n        \"      <td> 0.015742</td>\\n\",\n        \"      <td> 0.007287</td>\\n\",\n        \"      <td>-0.001935</td>\\n\",\n        \"      <td> 0.002807</td>\\n\",\n        \"      <td>-0.001934</td>\\n\",\n        \"      <td> 0.005072</td>\\n\",\n        \"      <td>-0.008555</td>\\n\",\n        \"      <td>-0.008718</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>HTHIY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001694</td>\\n\",\n        \"      <td> 0.018982</td>\\n\",\n        \"      <td>-0.004305</td>\\n\",\n        \"      <td>-0.004058</td>\\n\",\n        \"      <td> 0.007181</td>\\n\",\n        \"      <td> 0.003708</td>\\n\",\n        \"      <td>-0.015190</td>\\n\",\n        \"      <td>-0.000753</td>\\n\",\n        \"      <td>-0.007772</td>\\n\",\n        \"      <td> 0.012744</td>\\n\",\n        \"      <td>-0.001325</td>\\n\",\n        \"      <td> 0.000618</td>\\n\",\n        \"      <td>-0.003943</td>\\n\",\n        \"      <td> 0.007300</td>\\n\",\n        \"      <td> 0.016224</td>\\n\",\n        \"      <td> 0.001598</td>\\n\",\n        \"      <td>-0.011005</td>\\n\",\n        \"      <td>-0.003198</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IBM</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001297</td>\\n\",\n        \"      <td> 0.001703</td>\\n\",\n        \"      <td>-0.001457</td>\\n\",\n        \"      <td> 0.000124</td>\\n\",\n        \"      <td> 0.006844</td>\\n\",\n        \"      <td> 0.004827</td>\\n\",\n        \"      <td> 0.000105</td>\\n\",\n        \"      <td> 0.007076</td>\\n\",\n        \"      <td>-0.002131</td>\\n\",\n        \"      <td> 0.001917</td>\\n\",\n        \"      <td> 0.006684</td>\\n\",\n        \"      <td> 0.000692</td>\\n\",\n        \"      <td>-0.005183</td>\\n\",\n        \"      <td> 0.002135</td>\\n\",\n        \"      <td>-0.001269</td>\\n\",\n        \"      <td> 0.002254</td>\\n\",\n        \"      <td>-0.005807</td>\\n\",\n        \"      <td>-0.000681</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INTC</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001112</td>\\n\",\n        \"      <td> 0.024832</td>\\n\",\n        \"      <td> 0.004219</td>\\n\",\n        \"      <td> 0.001763</td>\\n\",\n        \"      <td> 0.007486</td>\\n\",\n        \"      <td> 0.002231</td>\\n\",\n        \"      <td> 0.000969</td>\\n\",\n        \"      <td> 0.015269</td>\\n\",\n        \"      <td> 0.002475</td>\\n\",\n        \"      <td>-0.004206</td>\\n\",\n        \"      <td>-0.000670</td>\\n\",\n        \"      <td>-0.001725</td>\\n\",\n        \"      <td>-0.002787</td>\\n\",\n        \"      <td> 0.004401</td>\\n\",\n        \"      <td>-0.005967</td>\\n\",\n        \"      <td>-0.000096</td>\\n\",\n        \"      <td> 0.005932</td>\\n\",\n        \"      <td> 0.004097</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>LXK</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.007975</td>\\n\",\n        \"      <td> 0.008664</td>\\n\",\n        \"      <td> 0.010324</td>\\n\",\n        \"      <td>-0.007590</td>\\n\",\n        \"      <td> 0.000110</td>\\n\",\n        \"      <td> 0.003788</td>\\n\",\n        \"      <td>-0.005231</td>\\n\",\n        \"      <td>-0.002781</td>\\n\",\n        \"      <td>-0.008994</td>\\n\",\n        \"      <td>-0.001651</td>\\n\",\n        \"      <td> 0.001430</td>\\n\",\n        \"      <td>-0.000267</td>\\n\",\n        \"      <td> 0.001469</td>\\n\",\n        \"      <td> 0.009064</td>\\n\",\n        \"      <td>-0.008811</td>\\n\",\n        \"      <td>-0.008414</td>\\n\",\n        \"      <td>-0.004055</td>\\n\",\n        \"      <td>-0.000609</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>MSFT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.003428</td>\\n\",\n        \"      <td> 0.012751</td>\\n\",\n        \"      <td> 0.006959</td>\\n\",\n        \"      <td> 0.010214</td>\\n\",\n        \"      <td> 0.006049</td>\\n\",\n        \"      <td> 0.010608</td>\\n\",\n        \"      <td>-0.001848</td>\\n\",\n        \"      <td> 0.000370</td>\\n\",\n        \"      <td> 0.002874</td>\\n\",\n        \"      <td>-0.000295</td>\\n\",\n        \"      <td>-0.002216</td>\\n\",\n        \"      <td>-0.005348</td>\\n\",\n        \"      <td>-0.001190</td>\\n\",\n        \"      <td> 0.009283</td>\\n\",\n        \"      <td>-0.002437</td>\\n\",\n        \"      <td>-0.005944</td>\\n\",\n        \"      <td> 0.004806</td>\\n\",\n        \"      <td> 0.012413</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>N</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.003774</td>\\n\",\n        \"      <td>-0.002940</td>\\n\",\n        \"      <td>-0.001937</td>\\n\",\n        \"      <td>-0.004743</td>\\n\",\n        \"      <td> 0.010588</td>\\n\",\n        \"      <td> 0.011145</td>\\n\",\n        \"      <td>-0.001350</td>\\n\",\n        \"      <td>-0.000040</td>\\n\",\n        \"      <td> 0.012627</td>\\n\",\n        \"      <td>-0.002477</td>\\n\",\n        \"      <td> 0.012155</td>\\n\",\n        \"      <td> 0.013787</td>\\n\",\n        \"      <td>-0.014610</td>\\n\",\n        \"      <td> 0.013834</td>\\n\",\n        \"      <td> 0.015171</td>\\n\",\n        \"      <td> 0.001545</td>\\n\",\n        \"      <td>-0.006065</td>\\n\",\n        \"      <td>-0.003195</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NFLX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.011230</td>\\n\",\n        \"      <td> 0.007208</td>\\n\",\n        \"      <td> 0.001123</td>\\n\",\n        \"      <td> 0.011389</td>\\n\",\n        \"      <td> 0.019374</td>\\n\",\n        \"      <td> 0.003090</td>\\n\",\n        \"      <td> 0.006664</td>\\n\",\n        \"      <td> 0.003807</td>\\n\",\n        \"      <td> 0.009043</td>\\n\",\n        \"      <td> 0.010369</td>\\n\",\n        \"      <td>-0.005572</td>\\n\",\n        \"      <td>-0.005026</td>\\n\",\n        \"      <td>-0.003569</td>\\n\",\n        \"      <td> 0.006130</td>\\n\",\n        \"      <td>-0.001608</td>\\n\",\n        \"      <td> 0.008005</td>\\n\",\n        \"      <td>-0.010536</td>\\n\",\n        \"      <td>-0.002382</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NVDA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.004594</td>\\n\",\n        \"      <td> 0.007043</td>\\n\",\n        \"      <td>-0.006938</td>\\n\",\n        \"      <td> 0.004801</td>\\n\",\n        \"      <td> 0.011358</td>\\n\",\n        \"      <td> 0.012215</td>\\n\",\n        \"      <td>-0.002593</td>\\n\",\n        \"      <td>-0.007490</td>\\n\",\n        \"      <td>-0.002795</td>\\n\",\n        \"      <td> 0.003828</td>\\n\",\n        \"      <td> 0.009650</td>\\n\",\n        \"      <td>-0.002072</td>\\n\",\n        \"      <td> 0.000690</td>\\n\",\n        \"      <td> 0.004124</td>\\n\",\n        \"      <td> 0.002229</td>\\n\",\n        \"      <td> 0.009847</td>\\n\",\n        \"      <td> 0.015379</td>\\n\",\n        \"      <td> 0.001502</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NWSA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000192</td>\\n\",\n        \"      <td>-0.021825</td>\\n\",\n        \"      <td> 0.017388</td>\\n\",\n        \"      <td> 0.000386</td>\\n\",\n        \"      <td>-0.000386</td>\\n\",\n        \"      <td>-0.005830</td>\\n\",\n        \"      <td> 0.010955</td>\\n\",\n        \"      <td> 0.010272</td>\\n\",\n        \"      <td>-0.001715</td>\\n\",\n        \"      <td> 0.005872</td>\\n\",\n        \"      <td> 0.002457</td>\\n\",\n        \"      <td>-0.000378</td>\\n\",\n        \"      <td> 0.000567</td>\\n\",\n        \"      <td>-0.000756</td>\\n\",\n        \"      <td>-0.005705</td>\\n\",\n        \"      <td> 0.003222</td>\\n\",\n        \"      <td> 0.003965</td>\\n\",\n        \"      <td> 0.001320</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NYT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.018408</td>\\n\",\n        \"      <td>-0.003259</td>\\n\",\n        \"      <td>-0.001088</td>\\n\",\n        \"      <td>-0.001089</td>\\n\",\n        \"      <td> 0.012100</td>\\n\",\n        \"      <td>-0.005135</td>\\n\",\n        \"      <td>-0.004889</td>\\n\",\n        \"      <td> 0.007547</td>\\n\",\n        \"      <td> 0.001615</td>\\n\",\n        \"      <td>-0.003782</td>\\n\",\n        \"      <td> 0.001618</td>\\n\",\n        \"      <td> 0.002958</td>\\n\",\n        \"      <td>-0.004864</td>\\n\",\n        \"      <td>-0.001353</td>\\n\",\n        \"      <td> 0.000270</td>\\n\",\n        \"      <td> 0.002159</td>\\n\",\n        \"      <td> 0.000540</td>\\n\",\n        \"      <td> 0.000539</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ORCL</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001671</td>\\n\",\n        \"      <td> 0.007382</td>\\n\",\n        \"      <td> 0.000332</td>\\n\",\n        \"      <td> 0.002069</td>\\n\",\n        \"      <td> 0.007881</td>\\n\",\n        \"      <td> 0.014721</td>\\n\",\n        \"      <td> 0.002340</td>\\n\",\n        \"      <td> 0.005058</td>\\n\",\n        \"      <td> 0.001203</td>\\n\",\n        \"      <td> 0.005741</td>\\n\",\n        \"      <td> 0.001592</td>\\n\",\n        \"      <td>-0.006087</td>\\n\",\n        \"      <td>-0.005719</td>\\n\",\n        \"      <td> 0.002811</td>\\n\",\n        \"      <td> 0.002404</td>\\n\",\n        \"      <td> 0.005340</td>\\n\",\n        \"      <td>-0.003680</td>\\n\",\n        \"      <td>-0.005793</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>PCRFY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001397</td>\\n\",\n        \"      <td> 0.016763</td>\\n\",\n        \"      <td> 0.006823</td>\\n\",\n        \"      <td>-0.001093</td>\\n\",\n        \"      <td> 0.005975</td>\\n\",\n        \"      <td> 0.000814</td>\\n\",\n        \"      <td>-0.015431</td>\\n\",\n        \"      <td> 0.001101</td>\\n\",\n        \"      <td>-0.015372</td>\\n\",\n        \"      <td> 0.005006</td>\\n\",\n        \"      <td> 0.003602</td>\\n\",\n        \"      <td> 0.005237</td>\\n\",\n        \"      <td> 0.001651</td>\\n\",\n        \"      <td> 0.010887</td>\\n\",\n        \"      <td> 0.007295</td>\\n\",\n        \"      <td> 0.014643</td>\\n\",\n        \"      <td>-0.002134</td>\\n\",\n        \"      <td>-0.010515</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RAX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.036414</td>\\n\",\n        \"      <td>-0.004699</td>\\n\",\n        \"      <td> 0.033312</td>\\n\",\n        \"      <td> 0.009003</td>\\n\",\n        \"      <td> 0.017585</td>\\n\",\n        \"      <td> 0.035446</td>\\n\",\n        \"      <td>-0.000203</td>\\n\",\n        \"      <td> 0.012440</td>\\n\",\n        \"      <td> 0.021210</td>\\n\",\n        \"      <td>-0.007319</td>\\n\",\n        \"      <td> 0.020159</td>\\n\",\n        \"      <td>-0.008504</td>\\n\",\n        \"      <td>-0.005109</td>\\n\",\n        \"      <td> 0.012419</td>\\n\",\n        \"      <td> 0.020992</td>\\n\",\n        \"      <td> 0.031463</td>\\n\",\n        \"      <td> 0.019219</td>\\n\",\n        \"      <td> 0.009385</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RHT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000507</td>\\n\",\n        \"      <td> 0.007825</td>\\n\",\n        \"      <td> 0.002064</td>\\n\",\n        \"      <td> 0.001281</td>\\n\",\n        \"      <td> 0.014115</td>\\n\",\n        \"      <td> 0.021549</td>\\n\",\n        \"      <td>-0.002640</td>\\n\",\n        \"      <td>-0.002376</td>\\n\",\n        \"      <td> 0.004356</td>\\n\",\n        \"      <td>-0.005406</td>\\n\",\n        \"      <td> 0.003287</td>\\n\",\n        \"      <td>-0.007984</td>\\n\",\n        \"      <td>-0.015275</td>\\n\",\n        \"      <td> 0.007824</td>\\n\",\n        \"      <td> 0.004141</td>\\n\",\n        \"      <td> 0.006711</td>\\n\",\n        \"      <td>-0.004622</td>\\n\",\n        \"      <td>-0.003985</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SAP</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.006488</td>\\n\",\n        \"      <td>-0.001993</td>\\n\",\n        \"      <td> 0.002852</td>\\n\",\n        \"      <td>-0.007576</td>\\n\",\n        \"      <td> 0.008291</td>\\n\",\n        \"      <td> 0.009113</td>\\n\",\n        \"      <td>-0.008675</td>\\n\",\n        \"      <td> 0.006645</td>\\n\",\n        \"      <td>-0.005908</td>\\n\",\n        \"      <td> 0.014241</td>\\n\",\n        \"      <td> 0.005454</td>\\n\",\n        \"      <td> 0.003329</td>\\n\",\n        \"      <td>-0.015968</td>\\n\",\n        \"      <td>-0.000986</td>\\n\",\n        \"      <td> 0.008287</td>\\n\",\n        \"      <td>-0.008875</td>\\n\",\n        \"      <td>-0.002407</td>\\n\",\n        \"      <td> 0.001502</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SIEGY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.012527</td>\\n\",\n        \"      <td> 0.014653</td>\\n\",\n        \"      <td> 0.014548</td>\\n\",\n        \"      <td>-0.004600</td>\\n\",\n        \"      <td> 0.004551</td>\\n\",\n        \"      <td> 0.005068</td>\\n\",\n        \"      <td>-0.001160</td>\\n\",\n        \"      <td> 0.014180</td>\\n\",\n        \"      <td>-0.008505</td>\\n\",\n        \"      <td> 0.016414</td>\\n\",\n        \"      <td> 0.007543</td>\\n\",\n        \"      <td>-0.000786</td>\\n\",\n        \"      <td>-0.015761</td>\\n\",\n        \"      <td>-0.002001</td>\\n\",\n        \"      <td> 0.008176</td>\\n\",\n        \"      <td> 0.011301</td>\\n\",\n        \"      <td> 0.000680</td>\\n\",\n        \"      <td>-0.000131</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SNE</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.029412</td>\\n\",\n        \"      <td> 0.005369</td>\\n\",\n        \"      <td> 0.019070</td>\\n\",\n        \"      <td> 0.007033</td>\\n\",\n        \"      <td> 0.016146</td>\\n\",\n        \"      <td> 0.006522</td>\\n\",\n        \"      <td>-0.002651</td>\\n\",\n        \"      <td>-0.000707</td>\\n\",\n        \"      <td> 0.007548</td>\\n\",\n        \"      <td>-0.005826</td>\\n\",\n        \"      <td>-0.001414</td>\\n\",\n        \"      <td> 0.002293</td>\\n\",\n        \"      <td> 0.010991</td>\\n\",\n        \"      <td> 0.013425</td>\\n\",\n        \"      <td> 0.020566</td>\\n\",\n        \"      <td>-0.006789</td>\\n\",\n        \"      <td>-0.022029</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TOSYY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.012976</td>\\n\",\n        \"      <td>-0.004764</td>\\n\",\n        \"      <td>-0.001632</td>\\n\",\n        \"      <td> 0.010191</td>\\n\",\n        \"      <td> 0.012761</td>\\n\",\n        \"      <td>-0.008040</td>\\n\",\n        \"      <td> 0.007245</td>\\n\",\n        \"      <td>-0.008420</td>\\n\",\n        \"      <td> 0.001731</td>\\n\",\n        \"      <td>-0.002230</td>\\n\",\n        \"      <td>-0.015474</td>\\n\",\n        \"      <td> 0.001005</td>\\n\",\n        \"      <td>-0.005942</td>\\n\",\n        \"      <td> 0.013593</td>\\n\",\n        \"      <td>-0.010841</td>\\n\",\n        \"      <td>-0.004177</td>\\n\",\n        \"      <td> 0.005038</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TRI</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.004010</td>\\n\",\n        \"      <td>-0.004197</td>\\n\",\n        \"      <td> 0.009253</td>\\n\",\n        \"      <td> 0.001130</td>\\n\",\n        \"      <td> 0.005721</td>\\n\",\n        \"      <td>-0.004335</td>\\n\",\n        \"      <td>-0.000177</td>\\n\",\n        \"      <td> 0.001681</td>\\n\",\n        \"      <td>-0.000531</td>\\n\",\n        \"      <td>-0.001064</td>\\n\",\n        \"      <td> 0.001328</td>\\n\",\n        \"      <td> 0.006857</td>\\n\",\n        \"      <td> 0.000352</td>\\n\",\n        \"      <td>-0.002025</td>\\n\",\n        \"      <td> 0.000968</td>\\n\",\n        \"      <td> 0.003943</td>\\n\",\n        \"      <td> 0.003232</td>\\n\",\n        \"      <td>-0.003242</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TWX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.008865</td>\\n\",\n        \"      <td> 0.006782</td>\\n\",\n        \"      <td> 0.026300</td>\\n\",\n        \"      <td> 0.007408</td>\\n\",\n        \"      <td> 0.005309</td>\\n\",\n        \"      <td> 0.001090</td>\\n\",\n        \"      <td> 0.000651</td>\\n\",\n        \"      <td>-0.002313</td>\\n\",\n        \"      <td> 0.002966</td>\\n\",\n        \"      <td> 0.002401</td>\\n\",\n        \"      <td>-0.002507</td>\\n\",\n        \"      <td> 0.002647</td>\\n\",\n        \"      <td>-0.000390</td>\\n\",\n        \"      <td> 0.000866</td>\\n\",\n        \"      <td> 0.003151</td>\\n\",\n        \"      <td> 0.005495</td>\\n\",\n        \"      <td>-0.004095</td>\\n\",\n        \"      <td>-0.006769</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TXN</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001002</td>\\n\",\n        \"      <td> 0.010276</td>\\n\",\n        \"      <td> 0.003460</td>\\n\",\n        \"      <td> 0.004779</td>\\n\",\n        \"      <td> 0.002664</td>\\n\",\n        \"      <td> 0.002796</td>\\n\",\n        \"      <td> 0.003344</td>\\n\",\n        \"      <td> 0.004577</td>\\n\",\n        \"      <td> 0.000901</td>\\n\",\n        \"      <td>-0.008384</td>\\n\",\n        \"      <td>-0.004774</td>\\n\",\n        \"      <td> 0.002800</td>\\n\",\n        \"      <td> 0.005708</td>\\n\",\n        \"      <td> 0.005124</td>\\n\",\n        \"      <td>-0.002917</td>\\n\",\n        \"      <td> 0.004425</td>\\n\",\n        \"      <td> 0.002345</td>\\n\",\n        \"      <td> 0.002820</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>VIAB</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005008</td>\\n\",\n        \"      <td>-0.000787</td>\\n\",\n        \"      <td> 0.002314</td>\\n\",\n        \"      <td> 0.007547</td>\\n\",\n        \"      <td> 0.009547</td>\\n\",\n        \"      <td>-0.006172</td>\\n\",\n        \"      <td>-0.002993</td>\\n\",\n        \"      <td>-0.004944</td>\\n\",\n        \"      <td>-0.001899</td>\\n\",\n        \"      <td>-0.000950</td>\\n\",\n        \"      <td>-0.002112</td>\\n\",\n        \"      <td> 0.001860</td>\\n\",\n        \"      <td> 0.001980</td>\\n\",\n        \"      <td> 0.000948</td>\\n\",\n        \"      <td> 0.007079</td>\\n\",\n        \"      <td>-0.002872</td>\\n\",\n        \"      <td>-0.000739</td>\\n\",\n        \"      <td>-0.000041</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>VMW</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.007897</td>\\n\",\n        \"      <td> 0.004582</td>\\n\",\n        \"      <td> 0.009869</td>\\n\",\n        \"      <td> 0.005238</td>\\n\",\n        \"      <td> 0.015535</td>\\n\",\n        \"      <td> 0.000745</td>\\n\",\n        \"      <td> 0.003810</td>\\n\",\n        \"      <td>-0.003434</td>\\n\",\n        \"      <td>-0.003283</td>\\n\",\n        \"      <td>-0.014676</td>\\n\",\n        \"      <td>-0.028492</td>\\n\",\n        \"      <td>-0.005011</td>\\n\",\n        \"      <td>-0.001810</td>\\n\",\n        \"      <td> 0.008902</td>\\n\",\n        \"      <td> 0.002936</td>\\n\",\n        \"      <td>-0.002571</td>\\n\",\n        \"      <td>-0.017769</td>\\n\",\n        \"      <td>-0.010790</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>XOM</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005729</td>\\n\",\n        \"      <td> 0.006333</td>\\n\",\n        \"      <td> 0.000303</td>\\n\",\n        \"      <td>-0.001247</td>\\n\",\n        \"      <td> 0.005199</td>\\n\",\n        \"      <td> 0.002409</td>\\n\",\n        \"      <td>-0.000770</td>\\n\",\n        \"      <td>-0.000100</td>\\n\",\n        \"      <td>-0.010319</td>\\n\",\n        \"      <td> 0.001992</td>\\n\",\n        \"      <td> 0.009432</td>\\n\",\n        \"      <td>-0.001306</td>\\n\",\n        \"      <td>-0.000872</td>\\n\",\n        \"      <td>-0.001578</td>\\n\",\n        \"      <td>-0.006624</td>\\n\",\n        \"      <td> 0.005612</td>\\n\",\n        \"      <td>-0.006018</td>\\n\",\n        \"      <td> 0.003000</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>XRX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.005475</td>\\n\",\n        \"      <td> 0.010589</td>\\n\",\n        \"      <td>-0.002964</td>\\n\",\n        \"      <td>-0.007214</td>\\n\",\n        \"      <td> 0.005935</td>\\n\",\n        \"      <td>-0.002976</td>\\n\",\n        \"      <td> 0.006162</td>\\n\",\n        \"      <td> 0.003684</td>\\n\",\n        \"      <td> 0.001471</td>\\n\",\n        \"      <td> 0.002202</td>\\n\",\n        \"      <td> 0.003657</td>\\n\",\n        \"      <td> 0.002189</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.007005</td>\\n\",\n        \"      <td> 0.003850</td>\\n\",\n        \"      <td> 0.009061</td>\\n\",\n        \"      <td>-0.013778</td>\\n\",\n        \"      <td>-0.013971</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>50 rows \\u00d7 19 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 14,\n       \"text\": [\n        \"n_date  735456    735457    735458    735459    735460    735463    735464  \\\\\\n\",\n        \"symbol                                                                       \\n\",\n        \"AAPL         0  0.005373  0.007091  0.004625  0.003959  0.011737  0.013376   \\n\",\n        \"ADBE         0 -0.002185  0.010283  0.007582  0.004037  0.009363  0.012268   \\n\",\n        \"ADSK         0  0.004898  0.017504  0.013646 -0.033605 -0.015570  0.004711   \\n\",\n        \"AMZN         0  0.002078  0.026988  0.009929  0.002949  0.009095 -0.002142   \\n\",\n        \"BBRY         0 -0.006726 -0.002840  0.009494  0.010783  0.014736  0.009504   \\n\",\n        \"BKS          0 -0.004016  0.001040  0.009566 -0.002360  0.014967  0.008071   \\n\",\n        \"BP           0 -0.001125 -0.005802  0.005278 -0.000775  0.009557  0.007821   \\n\",\n        \"CAJ          0 -0.002323  0.003221  0.004908  0.000501 -0.000501  0.001900   \\n\",\n        \"CAT          0  0.001084  0.004349  0.005242  0.002017  0.009583  0.004691   \\n\",\n        \"CBS          0 -0.010617 -0.002817  0.003984  0.007462  0.008284  0.004180   \\n\",\n        \"CMCSA        0  0.000310  0.005419  0.002886  0.004888  0.003895 -0.003114   \\n\",\n        \"CRM          0 -0.029861 -0.004338  0.006434  0.003858  0.013566  0.010267   \\n\",\n        \"CSC          0 -0.003303  0.002547  0.006370  0.002154  0.010804  0.004196   \\n\",\n        \"CSCO         0 -0.002519 -0.000265 -0.021680 -0.005998  0.004883  0.002841   \\n\",\n        \"CTXS         0 -0.004566  0.005074  0.001158 -0.001741  0.009672 -0.001151   \\n\",\n        \"CVX          0 -0.008605  0.005390 -0.003951  0.001565 -0.000132  0.008799   \\n\",\n        \"DIS          0 -0.003517  0.005172  0.008895  0.007890  0.009188  0.000370   \\n\",\n        \"EBAY         0 -0.009406 -0.012296  0.001951 -0.001892  0.003080  0.009711   \\n\",\n        \"EMC          0  0.000455  0.005656  0.006630  0.001683  0.007350 -0.001897   \\n\",\n        \"FB           0 -0.009296  0.012168  0.005846 -0.004926  0.008913  0.009362   \\n\",\n        \"FJTSY        0 -0.015135  0.022376 -0.003667 -0.000367  0.000000 -0.007576   \\n\",\n        \"FOXA         0  0.010004  0.008595  0.009264  0.007339  0.003148 -0.003998   \\n\",\n        \"GE           0 -0.005840  0.005036  0.001418 -0.005705  0.011155  0.003450   \\n\",\n        \"GOOG         0 -0.008795  0.015167  0.004584 -0.000029  0.011069  0.008833   \\n\",\n        \"HPQ          0 -0.005128  0.004443  0.003673 -0.004162  0.003487  0.004130   \\n\",\n        \"HTHIY        0  0.001694  0.018982 -0.004305 -0.004058  0.007181  0.003708   \\n\",\n        \"IBM          0  0.001297  0.001703 -0.001457  0.000124  0.006844  0.004827   \\n\",\n        \"INTC         0 -0.001112  0.024832  0.004219  0.001763  0.007486  0.002231   \\n\",\n        \"LXK          0  0.007975  0.008664  0.010324 -0.007590  0.000110  0.003788   \\n\",\n        \"MSFT         0  0.003428  0.012751  0.006959  0.010214  0.006049  0.010608   \\n\",\n        \"N            0 -0.003774 -0.002940 -0.001937 -0.004743  0.010588  0.011145   \\n\",\n        \"NFLX         0 -0.011230  0.007208  0.001123  0.011389  0.019374  0.003090   \\n\",\n        \"NVDA         0 -0.004594  0.007043 -0.006938  0.004801  0.011358  0.012215   \\n\",\n        \"NWSA         0  0.000192 -0.021825  0.017388  0.000386 -0.000386 -0.005830   \\n\",\n        \"NYT          0 -0.018408 -0.003259 -0.001088 -0.001089  0.012100 -0.005135   \\n\",\n        \"ORCL         0 -0.001671  0.007382  0.000332  0.002069  0.007881  0.014721   \\n\",\n        \"PCRFY        0 -0.001397  0.016763  0.006823 -0.001093  0.005975  0.000814   \\n\",\n        \"RAX          0 -0.036414 -0.004699  0.033312  0.009003  0.017585  0.035446   \\n\",\n        \"RHT          0  0.000507  0.007825  0.002064  0.001281  0.014115  0.021549   \\n\",\n        \"SAP          0 -0.006488 -0.001993  0.002852 -0.007576  0.008291  0.009113   \\n\",\n        \"SIEGY        0 -0.012527  0.014653  0.014548 -0.004600  0.004551  0.005068   \\n\",\n        \"SNE          0  0.000000  0.029412  0.005369  0.019070  0.007033  0.016146   \\n\",\n        \"TOSYY        0  0.000000  0.012976 -0.004764 -0.001632  0.010191  0.012761   \\n\",\n        \"TRI          0  0.004010 -0.004197  0.009253  0.001130  0.005721 -0.004335   \\n\",\n        \"TWX          0 -0.008865  0.006782  0.026300  0.007408  0.005309  0.001090   \\n\",\n        \"TXN          0  0.001002  0.010276  0.003460  0.004779  0.002664  0.002796   \\n\",\n        \"VIAB         0 -0.005008 -0.000787  0.002314  0.007547  0.009547 -0.006172   \\n\",\n        \"VMW          0 -0.007897  0.004582  0.009869  0.005238  0.015535  0.000745   \\n\",\n        \"XOM          0 -0.005729  0.006333  0.000303 -0.001247  0.005199  0.002409   \\n\",\n        \"XRX          0  0.005475  0.010589 -0.002964 -0.007214  0.005935 -0.002976   \\n\",\n        \"\\n\",\n        \"n_date    735465    735466    735467    735470    735471    735472    735473  \\\\\\n\",\n        \"symbol                                                                         \\n\",\n        \"AAPL    0.003581  0.000066  0.004456  0.006590 -0.005738  0.007040  0.003881   \\n\",\n        \"ADBE   -0.006643  0.003217  0.006578  0.000648 -0.003437 -0.001256 -0.004954   \\n\",\n        \"ADSK   -0.004108  0.003350 -0.002924 -0.001059  0.010964 -0.002099 -0.002661   \\n\",\n        \"AMZN    0.002989 -0.005231 -0.005269  0.007005  0.016890  0.010134 -0.010366   \\n\",\n        \"BBRY    0.027402 -0.013043 -0.004030  0.007333  0.003322  0.018265  0.001628   \\n\",\n        \"BKS    -0.018048 -0.007093  0.002947 -0.008769  0.013200  0.027528  0.008626   \\n\",\n        \"BP     -0.000138  0.001797 -0.001661  0.006054 -0.004214  0.002206 -0.007850   \\n\",\n        \"CAJ    -0.008164 -0.000403 -0.007006 -0.000102  0.003441 -0.002638  0.000608   \\n\",\n        \"CAT     0.003653  0.003178 -0.007492  0.007283  0.002002  0.002672 -0.000861   \\n\",\n        \"CBS    -0.002702 -0.001215  0.002478  0.004387 -0.009521  0.002485 -0.009082   \\n\",\n        \"CMCSA  -0.000366 -0.000733 -0.005716  0.005441  0.000367  0.001099 -0.002202   \\n\",\n        \"CRM     0.005979  0.006361  0.066805 -0.006311  0.002361  0.009025 -0.025011   \\n\",\n        \"CSC     0.002791  0.006711 -0.004814 -0.001434  0.000394  0.004502 -0.004918   \\n\",\n        \"CSCO    0.000676  0.007246 -0.003501 -0.000404  0.002419 -0.000538  0.000269   \\n\",\n        \"CTXS   -0.000288  0.004345  0.006168 -0.003571 -0.000286 -0.002100 -0.004555   \\n\",\n        \"CVX    -0.000026  0.003705 -0.005799  0.005766  0.004183  0.001945  0.000648   \\n\",\n        \"DIS    -0.001706  0.004910  0.002284  0.001140 -0.004397  0.000480 -0.000296   \\n\",\n        \"EBAY   -0.001871  0.036590  0.001560  0.001737  0.006958  0.001484 -0.012566   \\n\",\n        \"EMC    -0.007079  0.002018 -0.004166  0.002919 -0.003832 -0.003393 -0.003176   \\n\",\n        \"FB     -0.003428 -0.001963 -0.005833  0.009863  0.007014 -0.009351 -0.011530   \\n\",\n        \"FJTSY  -0.008573  0.002510  0.000000  0.000000  0.000000 -0.022526 -0.021060   \\n\",\n        \"FOXA   -0.002143 -0.002522  0.000560 -0.001777 -0.001123  0.003359 -0.001776   \\n\",\n        \"GE      0.006475  0.005178 -0.006995 -0.001528 -0.002682  0.000638 -0.003715   \\n\",\n        \"GOOG   -0.002532 -0.002716 -0.000326 -0.002431 -0.004585 -0.009683 -0.005873   \\n\",\n        \"HPQ    -0.007852  0.036108  0.008050  0.006559  0.015742  0.007287 -0.001935   \\n\",\n        \"HTHIY  -0.015190 -0.000753 -0.007772  0.012744 -0.001325  0.000618 -0.003943   \\n\",\n        \"IBM     0.000105  0.007076 -0.002131  0.001917  0.006684  0.000692 -0.005183   \\n\",\n        \"INTC    0.000969  0.015269  0.002475 -0.004206 -0.000670 -0.001725 -0.002787   \\n\",\n        \"LXK    -0.005231 -0.002781 -0.008994 -0.001651  0.001430 -0.000267  0.001469   \\n\",\n        \"MSFT   -0.001848  0.000370  0.002874 -0.000295 -0.002216 -0.005348 -0.001190   \\n\",\n        \"N      -0.001350 -0.000040  0.012627 -0.002477  0.012155  0.013787 -0.014610   \\n\",\n        \"NFLX    0.006664  0.003807  0.009043  0.010369 -0.005572 -0.005026 -0.003569   \\n\",\n        \"NVDA   -0.002593 -0.007490 -0.002795  0.003828  0.009650 -0.002072  0.000690   \\n\",\n        \"NWSA    0.010955  0.010272 -0.001715  0.005872  0.002457 -0.000378  0.000567   \\n\",\n        \"NYT    -0.004889  0.007547  0.001615 -0.003782  0.001618  0.002958 -0.004864   \\n\",\n        \"ORCL    0.002340  0.005058  0.001203  0.005741  0.001592 -0.006087 -0.005719   \\n\",\n        \"PCRFY  -0.015431  0.001101 -0.015372  0.005006  0.003602  0.005237  0.001651   \\n\",\n        \"RAX    -0.000203  0.012440  0.021210 -0.007319  0.020159 -0.008504 -0.005109   \\n\",\n        \"RHT    -0.002640 -0.002376  0.004356 -0.005406  0.003287 -0.007984 -0.015275   \\n\",\n        \"SAP    -0.008675  0.006645 -0.005908  0.014241  0.005454  0.003329 -0.015968   \\n\",\n        \"SIEGY  -0.001160  0.014180 -0.008505  0.016414  0.007543 -0.000786 -0.015761   \\n\",\n        \"SNE     0.006522 -0.002651 -0.000707  0.007548 -0.005826 -0.001414  0.002293   \\n\",\n        \"TOSYY  -0.008040  0.007245 -0.008420  0.001731 -0.002230 -0.015474  0.001005   \\n\",\n        \"TRI    -0.000177  0.001681 -0.000531 -0.001064  0.001328  0.006857  0.000352   \\n\",\n        \"TWX     0.000651 -0.002313  0.002966  0.002401 -0.002507  0.002647 -0.000390   \\n\",\n        \"TXN     0.003344  0.004577  0.000901 -0.008384 -0.004774  0.002800  0.005708   \\n\",\n        \"VIAB   -0.002993 -0.004944 -0.001899 -0.000950 -0.002112  0.001860  0.001980   \\n\",\n        \"VMW     0.003810 -0.003434 -0.003283 -0.014676 -0.028492 -0.005011 -0.001810   \\n\",\n        \"XOM    -0.000770 -0.000100 -0.010319  0.001992  0.009432 -0.001306 -0.000872   \\n\",\n        \"XRX     0.006162  0.003684  0.001471  0.002202  0.003657  0.002189  0.000000   \\n\",\n        \"\\n\",\n        \"n_date    735474    735478    735479    735480    735481  \\n\",\n        \"symbol                                                    \\n\",\n        \"AAPL    0.003283  0.006973 -0.030061 -0.015946  0.002258  \\n\",\n        \"ADBE    0.006732  0.005219  0.003177  0.003212  0.003430  \\n\",\n        \"ADSK   -0.000619 -0.007361  0.011897  0.007039 -0.006158  \\n\",\n        \"AMZN   -0.002032  0.003425 -0.002177  0.018375 -0.003090  \\n\",\n        \"BBRY    0.000651  0.012849  0.032027 -0.009733 -0.009189  \\n\",\n        \"BKS     0.001553 -0.010558  0.016419  0.002240 -0.004501  \\n\",\n        \"BP     -0.002996 -0.011132  0.010251 -0.041773 -0.001601  \\n\",\n        \"CAJ    -0.004584  0.003351  0.003541  0.006134 -0.002217  \\n\",\n        \"CAT     0.003523  0.003237 -0.004016  0.000919 -0.001718  \\n\",\n        \"CBS    -0.007353 -0.005758 -0.006992  0.011908 -0.002816  \\n\",\n        \"CMCSA   0.003779  0.000122  0.002674  0.003392  0.007574  \\n\",\n        \"CRM     0.010118  0.012062 -0.004713 -0.002418  0.003642  \\n\",\n        \"CSC    -0.000559  0.006930  0.002213 -0.002162  0.000166  \\n\",\n        \"CSCO    0.004683 -0.001474  0.006126 -0.002804  0.000667  \\n\",\n        \"CTXS    0.009922  0.005148  0.001368  0.000801  0.001553  \\n\",\n        \"CVX     0.004078 -0.009115  0.001119 -0.007733 -0.001207  \\n\",\n        \"DIS    -0.004043  0.008423  0.003263 -0.005233  0.002316  \\n\",\n        \"EBAY   -0.000722 -0.006297 -0.005785 -0.002014 -0.014046  \\n\",\n        \"EMC     0.003054 -0.004202  0.006880 -0.010486 -0.009782  \\n\",\n        \"FB      0.007288  0.019939  0.003102 -0.002189  0.007948  \\n\",\n        \"FJTSY   0.000679  0.016783 -0.032388  0.002945  0.000883  \\n\",\n        \"FOXA   -0.004979  0.008014  0.010968  0.002666 -0.004432  \\n\",\n        \"GE     -0.001154 -0.004121  0.001543  0.002693  0.000256  \\n\",\n        \"GOOG    0.000678  0.009060  0.005519  0.006450  0.004206  \\n\",\n        \"HPQ     0.002807 -0.001934  0.005072 -0.008555 -0.008718  \\n\",\n        \"HTHIY   0.007300  0.016224  0.001598 -0.011005 -0.003198  \\n\",\n        \"IBM     0.002135 -0.001269  0.002254 -0.005807 -0.000681  \\n\",\n        \"INTC    0.004401 -0.005967 -0.000096  0.005932  0.004097  \\n\",\n        \"LXK     0.009064 -0.008811 -0.008414 -0.004055 -0.000609  \\n\",\n        \"MSFT    0.009283 -0.002437 -0.005944  0.004806  0.012413  \\n\",\n        \"N       0.013834  0.015171  0.001545 -0.006065 -0.003195  \\n\",\n        \"NFLX    0.006130 -0.001608  0.008005 -0.010536 -0.002382  \\n\",\n        \"NVDA    0.004124  0.002229  0.009847  0.015379  0.001502  \\n\",\n        \"NWSA   -0.000756 -0.005705  0.003222  0.003965  0.001320  \\n\",\n        \"NYT    -0.001353  0.000270  0.002159  0.000540  0.000539  \\n\",\n        \"ORCL    0.002811  0.002404  0.005340 -0.003680 -0.005793  \\n\",\n        \"PCRFY   0.010887  0.007295  0.014643 -0.002134 -0.010515  \\n\",\n        \"RAX     0.012419  0.020992  0.031463  0.019219  0.009385  \\n\",\n        \"RHT     0.007824  0.004141  0.006711 -0.004622 -0.003985  \\n\",\n        \"SAP    -0.000986  0.008287 -0.008875 -0.002407  0.001502  \\n\",\n        \"SIEGY  -0.002001  0.008176  0.011301  0.000680 -0.000131  \\n\",\n        \"SNE     0.010991  0.013425  0.020566 -0.006789 -0.022029  \\n\",\n        \"TOSYY  -0.005942  0.013593 -0.010841 -0.004177  0.005038  \\n\",\n        \"TRI    -0.002025  0.000968  0.003943  0.003232 -0.003242  \\n\",\n        \"TWX     0.000866  0.003151  0.005495 -0.004095 -0.006769  \\n\",\n        \"TXN     0.005124 -0.002917  0.004425  0.002345  0.002820  \\n\",\n        \"VIAB    0.000948  0.007079 -0.002872 -0.000739 -0.000041  \\n\",\n        \"VMW     0.008902  0.002936 -0.002571 -0.017769 -0.010790  \\n\",\n        \"XOM    -0.001578 -0.006624  0.005612 -0.006018  0.003000  \\n\",\n        \"XRX     0.007005  0.003850  0.009061 -0.013778 -0.013971  \\n\",\n        \"\\n\",\n        \"[50 rows x 19 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Normalize the data\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Assuming $X_{i,j}$ is a matrix representing our dataset where $i$ is th number of rows and $j$ is the number of columns. This is the function to return $X_{scaled}$ which is the normalized version of the dataset:\\n\",\n      \"\\n\",\n      \"$X_{std} = \\\\frac{X - min(X_j)}{max(X_j) - min(X_j)}$\\n\",\n      \"\\n\",\n      \"$X_{scaled} = X_{std} * (max - min) + min$\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"**The most common Max and Min are:**\\n\",\n      \"\\n\",\n      \"$max  = 1$\\n\",\n      \"\\n\",\n      \"$min  = -1$\\n\",\n      \"\\n\",\n      \"$X_{scaled} = X_{std} * (1 - (-1)) + (-1) = X_{std} * 2 - 1$\\n\",\n      \"\\n\",\n      \"**And:**\\n\",\n      \"\\n\",\n      \"$max  = 1$\\n\",\n      \"\\n\",\n      \"$min  = 0$\\n\",\n      \"\\n\",\n      \"$X_{scaled} = X_{std} * (1 - 0) + 0 = X_{std}$\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for item in clustering_data.values:\\n\",\n      \"    plt.plot(item)\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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xmVb2ulpkbj9PXpWGoqcRXcdE2tSVQRgbqAtKy1qDiCuYr+kwBU+PY6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\\n2Z7VAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c847397d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"norm_data = normalize(clustering_data.values, axis=1)\\n\",\n      \"norm_data = pd.DataFrame(norm_data)\\n\",\n      \"for item in norm_data.values:\\n\",\n      \"    plt.plot(item)\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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w8+uJVT3nY0Gg1xcXEEBQUJfr6zM5mZmYSnpbH94EEKymyAV3x9+TUt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\\nPoVCoQwhqNOnUCiUIQR1+hQKhTKE+P9AyyujOGPE4gAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84bd34d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Perform Clustering\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def cluster_data(data, n_clusters=8, normalize_data=False):\\n\",\n      \"    if normalize_data:\\n\",\n      \"        data = normalize(data.values, norm='l2', axis=1, copy=True)\\n\",\n      \"    cluster_model = KMeans(n_clusters=n_clusters)\\n\",\n      \"    prediction = cluster_model.fit_predict(data)\\n\",\n      \"    return prediction, cluster_model, data\\n\",\n      \"\\n\",\n      \"prediction, model, data = cluster_data(clustering_data, n_clusters=8, normalize_data=True)\\n\",\n      \"print \\\"Cluster Count: %s\\\" % len(np.unique(prediction))\\n\",\n      \"clustering_data[\\\"Cluster\\\"] = prediction\\n\",\n      \"clustering_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster Count: 8\\n\"\n       ]\n      },\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th>n_date</th>\\n\",\n        \"      <th>735456</th>\\n\",\n        \"      <th>735457</th>\\n\",\n        \"      <th>735458</th>\\n\",\n        \"      <th>735459</th>\\n\",\n        \"      <th>735460</th>\\n\",\n        \"      <th>735463</th>\\n\",\n        \"      <th>735464</th>\\n\",\n        \"      <th>735465</th>\\n\",\n        \"      <th>735466</th>\\n\",\n        \"      <th>735467</th>\\n\",\n        \"      <th>735470</th>\\n\",\n        \"      <th>735471</th>\\n\",\n        \"      <th>735472</th>\\n\",\n        \"      <th>735473</th>\\n\",\n        \"      <th>735474</th>\\n\",\n        \"      <th>735478</th>\\n\",\n        \"      <th>735479</th>\\n\",\n        \"      <th>735480</th>\\n\",\n        \"      <th>735481</th>\\n\",\n        \"      <th>Cluster</th>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>symbol</th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AAPL</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.005373</td>\\n\",\n        \"      <td> 0.007091</td>\\n\",\n        \"      <td> 0.004625</td>\\n\",\n        \"      <td> 0.003959</td>\\n\",\n        \"      <td> 0.011737</td>\\n\",\n        \"      <td> 0.013376</td>\\n\",\n        \"      <td> 0.003581</td>\\n\",\n        \"      <td> 0.000066</td>\\n\",\n        \"      <td> 0.004456</td>\\n\",\n        \"      <td> 0.006590</td>\\n\",\n        \"      <td>-0.005738</td>\\n\",\n        \"      <td> 0.007040</td>\\n\",\n        \"      <td> 0.003881</td>\\n\",\n        \"      <td> 0.003283</td>\\n\",\n        \"      <td> 0.006973</td>\\n\",\n        \"      <td>-0.030061</td>\\n\",\n        \"      <td>-0.015946</td>\\n\",\n        \"      <td> 0.002258</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ADBE</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.002185</td>\\n\",\n        \"      <td> 0.010283</td>\\n\",\n        \"      <td> 0.007582</td>\\n\",\n        \"      <td> 0.004037</td>\\n\",\n        \"      <td> 0.009363</td>\\n\",\n        \"      <td> 0.012268</td>\\n\",\n        \"      <td>-0.006643</td>\\n\",\n        \"      <td> 0.003217</td>\\n\",\n        \"      <td> 0.006578</td>\\n\",\n        \"      <td> 0.000648</td>\\n\",\n        \"      <td>-0.003437</td>\\n\",\n        \"      <td>-0.001256</td>\\n\",\n        \"      <td>-0.004954</td>\\n\",\n        \"      <td> 0.006732</td>\\n\",\n        \"      <td> 0.005219</td>\\n\",\n        \"      <td> 0.003177</td>\\n\",\n        \"      <td> 0.003212</td>\\n\",\n        \"      <td> 0.003430</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ADSK</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.004898</td>\\n\",\n        \"      <td> 0.017504</td>\\n\",\n        \"      <td> 0.013646</td>\\n\",\n        \"      <td>-0.033605</td>\\n\",\n        \"      <td>-0.015570</td>\\n\",\n        \"      <td> 0.004711</td>\\n\",\n        \"      <td>-0.004108</td>\\n\",\n        \"      <td> 0.003350</td>\\n\",\n        \"      <td>-0.002924</td>\\n\",\n        \"      <td>-0.001059</td>\\n\",\n        \"      <td> 0.010964</td>\\n\",\n        \"      <td>-0.002099</td>\\n\",\n        \"      <td>-0.002661</td>\\n\",\n        \"      <td>-0.000619</td>\\n\",\n        \"      <td>-0.007361</td>\\n\",\n        \"      <td> 0.011897</td>\\n\",\n        \"      <td> 0.007039</td>\\n\",\n        \"      <td>-0.006158</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>AMZN</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.002078</td>\\n\",\n        \"      <td> 0.026988</td>\\n\",\n        \"      <td> 0.009929</td>\\n\",\n        \"      <td> 0.002949</td>\\n\",\n        \"      <td> 0.009095</td>\\n\",\n        \"      <td>-0.002142</td>\\n\",\n        \"      <td> 0.002989</td>\\n\",\n        \"      <td>-0.005231</td>\\n\",\n        \"      <td>-0.005269</td>\\n\",\n        \"      <td> 0.007005</td>\\n\",\n        \"      <td> 0.016890</td>\\n\",\n        \"      <td> 0.010134</td>\\n\",\n        \"      <td>-0.010366</td>\\n\",\n        \"      <td>-0.002032</td>\\n\",\n        \"      <td> 0.003425</td>\\n\",\n        \"      <td>-0.002177</td>\\n\",\n        \"      <td> 0.018375</td>\\n\",\n        \"      <td>-0.003090</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BBRY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.006726</td>\\n\",\n        \"      <td>-0.002840</td>\\n\",\n        \"      <td> 0.009494</td>\\n\",\n        \"      <td> 0.010783</td>\\n\",\n        \"      <td> 0.014736</td>\\n\",\n        \"      <td> 0.009504</td>\\n\",\n        \"      <td> 0.027402</td>\\n\",\n        \"      <td>-0.013043</td>\\n\",\n        \"      <td>-0.004030</td>\\n\",\n        \"      <td> 0.007333</td>\\n\",\n        \"      <td> 0.003322</td>\\n\",\n        \"      <td> 0.018265</td>\\n\",\n        \"      <td> 0.001628</td>\\n\",\n        \"      <td> 0.000651</td>\\n\",\n        \"      <td> 0.012849</td>\\n\",\n        \"      <td> 0.032027</td>\\n\",\n        \"      <td>-0.009733</td>\\n\",\n        \"      <td>-0.009189</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BKS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.004016</td>\\n\",\n        \"      <td> 0.001040</td>\\n\",\n        \"      <td> 0.009566</td>\\n\",\n        \"      <td>-0.002360</td>\\n\",\n        \"      <td> 0.014967</td>\\n\",\n        \"      <td> 0.008071</td>\\n\",\n        \"      <td>-0.018048</td>\\n\",\n        \"      <td>-0.007093</td>\\n\",\n        \"      <td> 0.002947</td>\\n\",\n        \"      <td>-0.008769</td>\\n\",\n        \"      <td> 0.013200</td>\\n\",\n        \"      <td> 0.027528</td>\\n\",\n        \"      <td> 0.008626</td>\\n\",\n        \"      <td> 0.001553</td>\\n\",\n        \"      <td>-0.010558</td>\\n\",\n        \"      <td> 0.016419</td>\\n\",\n        \"      <td> 0.002240</td>\\n\",\n        \"      <td>-0.004501</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>BP</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001125</td>\\n\",\n        \"      <td>-0.005802</td>\\n\",\n        \"      <td> 0.005278</td>\\n\",\n        \"      <td>-0.000775</td>\\n\",\n        \"      <td> 0.009557</td>\\n\",\n        \"      <td> 0.007821</td>\\n\",\n        \"      <td>-0.000138</td>\\n\",\n        \"      <td> 0.001797</td>\\n\",\n        \"      <td>-0.001661</td>\\n\",\n        \"      <td> 0.006054</td>\\n\",\n        \"      <td>-0.004214</td>\\n\",\n        \"      <td> 0.002206</td>\\n\",\n        \"      <td>-0.007850</td>\\n\",\n        \"      <td>-0.002996</td>\\n\",\n        \"      <td>-0.011132</td>\\n\",\n        \"      <td> 0.010251</td>\\n\",\n        \"      <td>-0.041773</td>\\n\",\n        \"      <td>-0.001601</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CAJ</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.002323</td>\\n\",\n        \"      <td> 0.003221</td>\\n\",\n        \"      <td> 0.004908</td>\\n\",\n        \"      <td> 0.000501</td>\\n\",\n        \"      <td>-0.000501</td>\\n\",\n        \"      <td> 0.001900</td>\\n\",\n        \"      <td>-0.008164</td>\\n\",\n        \"      <td>-0.000403</td>\\n\",\n        \"      <td>-0.007006</td>\\n\",\n        \"      <td>-0.000102</td>\\n\",\n        \"      <td> 0.003441</td>\\n\",\n        \"      <td>-0.002638</td>\\n\",\n        \"      <td> 0.000608</td>\\n\",\n        \"      <td>-0.004584</td>\\n\",\n        \"      <td> 0.003351</td>\\n\",\n        \"      <td> 0.003541</td>\\n\",\n        \"      <td> 0.006134</td>\\n\",\n        \"      <td>-0.002217</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CAT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001084</td>\\n\",\n        \"      <td> 0.004349</td>\\n\",\n        \"      <td> 0.005242</td>\\n\",\n        \"      <td> 0.002017</td>\\n\",\n        \"      <td> 0.009583</td>\\n\",\n        \"      <td> 0.004691</td>\\n\",\n        \"      <td> 0.003653</td>\\n\",\n        \"      <td> 0.003178</td>\\n\",\n        \"      <td>-0.007492</td>\\n\",\n        \"      <td> 0.007283</td>\\n\",\n        \"      <td> 0.002002</td>\\n\",\n        \"      <td> 0.002672</td>\\n\",\n        \"      <td>-0.000861</td>\\n\",\n        \"      <td> 0.003523</td>\\n\",\n        \"      <td> 0.003237</td>\\n\",\n        \"      <td>-0.004016</td>\\n\",\n        \"      <td> 0.000919</td>\\n\",\n        \"      <td>-0.001718</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CBS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.010617</td>\\n\",\n        \"      <td>-0.002817</td>\\n\",\n        \"      <td> 0.003984</td>\\n\",\n        \"      <td> 0.007462</td>\\n\",\n        \"      <td> 0.008284</td>\\n\",\n        \"      <td> 0.004180</td>\\n\",\n        \"      <td>-0.002702</td>\\n\",\n        \"      <td>-0.001215</td>\\n\",\n        \"      <td> 0.002478</td>\\n\",\n        \"      <td> 0.004387</td>\\n\",\n        \"      <td>-0.009521</td>\\n\",\n        \"      <td> 0.002485</td>\\n\",\n        \"      <td>-0.009082</td>\\n\",\n        \"      <td>-0.007353</td>\\n\",\n        \"      <td>-0.005758</td>\\n\",\n        \"      <td>-0.006992</td>\\n\",\n        \"      <td> 0.011908</td>\\n\",\n        \"      <td>-0.002816</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CMCSA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000310</td>\\n\",\n        \"      <td> 0.005419</td>\\n\",\n        \"      <td> 0.002886</td>\\n\",\n        \"      <td> 0.004888</td>\\n\",\n        \"      <td> 0.003895</td>\\n\",\n        \"      <td>-0.003114</td>\\n\",\n        \"      <td>-0.000366</td>\\n\",\n        \"      <td>-0.000733</td>\\n\",\n        \"      <td>-0.005716</td>\\n\",\n        \"      <td> 0.005441</td>\\n\",\n        \"      <td> 0.000367</td>\\n\",\n        \"      <td> 0.001099</td>\\n\",\n        \"      <td>-0.002202</td>\\n\",\n        \"      <td> 0.003779</td>\\n\",\n        \"      <td> 0.000122</td>\\n\",\n        \"      <td> 0.002674</td>\\n\",\n        \"      <td> 0.003392</td>\\n\",\n        \"      <td> 0.007574</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CRM</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.029861</td>\\n\",\n        \"      <td>-0.004338</td>\\n\",\n        \"      <td> 0.006434</td>\\n\",\n        \"      <td> 0.003858</td>\\n\",\n        \"      <td> 0.013566</td>\\n\",\n        \"      <td> 0.010267</td>\\n\",\n        \"      <td> 0.005979</td>\\n\",\n        \"      <td> 0.006361</td>\\n\",\n        \"      <td> 0.066805</td>\\n\",\n        \"      <td>-0.006311</td>\\n\",\n        \"      <td> 0.002361</td>\\n\",\n        \"      <td> 0.009025</td>\\n\",\n        \"      <td>-0.025011</td>\\n\",\n        \"      <td> 0.010118</td>\\n\",\n        \"      <td> 0.012062</td>\\n\",\n        \"      <td>-0.004713</td>\\n\",\n        \"      <td>-0.002418</td>\\n\",\n        \"      <td> 0.003642</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CSC</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.003303</td>\\n\",\n        \"      <td> 0.002547</td>\\n\",\n        \"      <td> 0.006370</td>\\n\",\n        \"      <td> 0.002154</td>\\n\",\n        \"      <td> 0.010804</td>\\n\",\n        \"      <td> 0.004196</td>\\n\",\n        \"      <td> 0.002791</td>\\n\",\n        \"      <td> 0.006711</td>\\n\",\n        \"      <td>-0.004814</td>\\n\",\n        \"      <td>-0.001434</td>\\n\",\n        \"      <td> 0.000394</td>\\n\",\n        \"      <td> 0.004502</td>\\n\",\n        \"      <td>-0.004918</td>\\n\",\n        \"      <td>-0.000559</td>\\n\",\n        \"      <td> 0.006930</td>\\n\",\n        \"      <td> 0.002213</td>\\n\",\n        \"      <td>-0.002162</td>\\n\",\n        \"      <td> 0.000166</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CSCO</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.002519</td>\\n\",\n        \"      <td>-0.000265</td>\\n\",\n        \"      <td>-0.021680</td>\\n\",\n        \"      <td>-0.005998</td>\\n\",\n        \"      <td> 0.004883</td>\\n\",\n        \"      <td> 0.002841</td>\\n\",\n        \"      <td> 0.000676</td>\\n\",\n        \"      <td> 0.007246</td>\\n\",\n        \"      <td>-0.003501</td>\\n\",\n        \"      <td>-0.000404</td>\\n\",\n        \"      <td> 0.002419</td>\\n\",\n        \"      <td>-0.000538</td>\\n\",\n        \"      <td> 0.000269</td>\\n\",\n        \"      <td> 0.004683</td>\\n\",\n        \"      <td>-0.001474</td>\\n\",\n        \"      <td> 0.006126</td>\\n\",\n        \"      <td>-0.002804</td>\\n\",\n        \"      <td> 0.000667</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CTXS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.004566</td>\\n\",\n        \"      <td> 0.005074</td>\\n\",\n        \"      <td> 0.001158</td>\\n\",\n        \"      <td>-0.001741</td>\\n\",\n        \"      <td> 0.009672</td>\\n\",\n        \"      <td>-0.001151</td>\\n\",\n        \"      <td>-0.000288</td>\\n\",\n        \"      <td> 0.004345</td>\\n\",\n        \"      <td> 0.006168</td>\\n\",\n        \"      <td>-0.003571</td>\\n\",\n        \"      <td>-0.000286</td>\\n\",\n        \"      <td>-0.002100</td>\\n\",\n        \"      <td>-0.004555</td>\\n\",\n        \"      <td> 0.009922</td>\\n\",\n        \"      <td> 0.005148</td>\\n\",\n        \"      <td> 0.001368</td>\\n\",\n        \"      <td> 0.000801</td>\\n\",\n        \"      <td> 0.001553</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>CVX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.008605</td>\\n\",\n        \"      <td> 0.005390</td>\\n\",\n        \"      <td>-0.003951</td>\\n\",\n        \"      <td> 0.001565</td>\\n\",\n        \"      <td>-0.000132</td>\\n\",\n        \"      <td> 0.008799</td>\\n\",\n        \"      <td>-0.000026</td>\\n\",\n        \"      <td> 0.003705</td>\\n\",\n        \"      <td>-0.005799</td>\\n\",\n        \"      <td> 0.005766</td>\\n\",\n        \"      <td> 0.004183</td>\\n\",\n        \"      <td> 0.001945</td>\\n\",\n        \"      <td> 0.000648</td>\\n\",\n        \"      <td> 0.004078</td>\\n\",\n        \"      <td>-0.009115</td>\\n\",\n        \"      <td> 0.001119</td>\\n\",\n        \"      <td>-0.007733</td>\\n\",\n        \"      <td>-0.001207</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>DIS</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.003517</td>\\n\",\n        \"      <td> 0.005172</td>\\n\",\n        \"      <td> 0.008895</td>\\n\",\n        \"      <td> 0.007890</td>\\n\",\n        \"      <td> 0.009188</td>\\n\",\n        \"      <td> 0.000370</td>\\n\",\n        \"      <td>-0.001706</td>\\n\",\n        \"      <td> 0.004910</td>\\n\",\n        \"      <td> 0.002284</td>\\n\",\n        \"      <td> 0.001140</td>\\n\",\n        \"      <td>-0.004397</td>\\n\",\n        \"      <td> 0.000480</td>\\n\",\n        \"      <td>-0.000296</td>\\n\",\n        \"      <td>-0.004043</td>\\n\",\n        \"      <td> 0.008423</td>\\n\",\n        \"      <td> 0.003263</td>\\n\",\n        \"      <td>-0.005233</td>\\n\",\n        \"      <td> 0.002316</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>EBAY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.009406</td>\\n\",\n        \"      <td>-0.012296</td>\\n\",\n        \"      <td> 0.001951</td>\\n\",\n        \"      <td>-0.001892</td>\\n\",\n        \"      <td> 0.003080</td>\\n\",\n        \"      <td> 0.009711</td>\\n\",\n        \"      <td>-0.001871</td>\\n\",\n        \"      <td> 0.036590</td>\\n\",\n        \"      <td> 0.001560</td>\\n\",\n        \"      <td> 0.001737</td>\\n\",\n        \"      <td> 0.006958</td>\\n\",\n        \"      <td> 0.001484</td>\\n\",\n        \"      <td>-0.012566</td>\\n\",\n        \"      <td>-0.000722</td>\\n\",\n        \"      <td>-0.006297</td>\\n\",\n        \"      <td>-0.005785</td>\\n\",\n        \"      <td>-0.002014</td>\\n\",\n        \"      <td>-0.014046</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>EMC</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000455</td>\\n\",\n        \"      <td> 0.005656</td>\\n\",\n        \"      <td> 0.006630</td>\\n\",\n        \"      <td> 0.001683</td>\\n\",\n        \"      <td> 0.007350</td>\\n\",\n        \"      <td>-0.001897</td>\\n\",\n        \"      <td>-0.007079</td>\\n\",\n        \"      <td> 0.002018</td>\\n\",\n        \"      <td>-0.004166</td>\\n\",\n        \"      <td> 0.002919</td>\\n\",\n        \"      <td>-0.003832</td>\\n\",\n        \"      <td>-0.003393</td>\\n\",\n        \"      <td>-0.003176</td>\\n\",\n        \"      <td> 0.003054</td>\\n\",\n        \"      <td>-0.004202</td>\\n\",\n        \"      <td> 0.006880</td>\\n\",\n        \"      <td>-0.010486</td>\\n\",\n        \"      <td>-0.009782</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>FB</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.009296</td>\\n\",\n        \"      <td> 0.012168</td>\\n\",\n        \"      <td> 0.005846</td>\\n\",\n        \"      <td>-0.004926</td>\\n\",\n        \"      <td> 0.008913</td>\\n\",\n        \"      <td> 0.009362</td>\\n\",\n        \"      <td>-0.003428</td>\\n\",\n        \"      <td>-0.001963</td>\\n\",\n        \"      <td>-0.005833</td>\\n\",\n        \"      <td> 0.009863</td>\\n\",\n        \"      <td> 0.007014</td>\\n\",\n        \"      <td>-0.009351</td>\\n\",\n        \"      <td>-0.011530</td>\\n\",\n        \"      <td> 0.007288</td>\\n\",\n        \"      <td> 0.019939</td>\\n\",\n        \"      <td> 0.003102</td>\\n\",\n        \"      <td>-0.002189</td>\\n\",\n        \"      <td> 0.007948</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>FJTSY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.015135</td>\\n\",\n        \"      <td> 0.022376</td>\\n\",\n        \"      <td>-0.003667</td>\\n\",\n        \"      <td>-0.000367</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td>-0.007576</td>\\n\",\n        \"      <td>-0.008573</td>\\n\",\n        \"      <td> 0.002510</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td>-0.022526</td>\\n\",\n        \"      <td>-0.021060</td>\\n\",\n        \"      <td> 0.000679</td>\\n\",\n        \"      <td> 0.016783</td>\\n\",\n        \"      <td>-0.032388</td>\\n\",\n        \"      <td> 0.002945</td>\\n\",\n        \"      <td> 0.000883</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>FOXA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.010004</td>\\n\",\n        \"      <td> 0.008595</td>\\n\",\n        \"      <td> 0.009264</td>\\n\",\n        \"      <td> 0.007339</td>\\n\",\n        \"      <td> 0.003148</td>\\n\",\n        \"      <td>-0.003998</td>\\n\",\n        \"      <td>-0.002143</td>\\n\",\n        \"      <td>-0.002522</td>\\n\",\n        \"      <td> 0.000560</td>\\n\",\n        \"      <td>-0.001777</td>\\n\",\n        \"      <td>-0.001123</td>\\n\",\n        \"      <td> 0.003359</td>\\n\",\n        \"      <td>-0.001776</td>\\n\",\n        \"      <td>-0.004979</td>\\n\",\n        \"      <td> 0.008014</td>\\n\",\n        \"      <td> 0.010968</td>\\n\",\n        \"      <td> 0.002666</td>\\n\",\n        \"      <td>-0.004432</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>GE</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005840</td>\\n\",\n        \"      <td> 0.005036</td>\\n\",\n        \"      <td> 0.001418</td>\\n\",\n        \"      <td>-0.005705</td>\\n\",\n        \"      <td> 0.011155</td>\\n\",\n        \"      <td> 0.003450</td>\\n\",\n        \"      <td> 0.006475</td>\\n\",\n        \"      <td> 0.005178</td>\\n\",\n        \"      <td>-0.006995</td>\\n\",\n        \"      <td>-0.001528</td>\\n\",\n        \"      <td>-0.002682</td>\\n\",\n        \"      <td> 0.000638</td>\\n\",\n        \"      <td>-0.003715</td>\\n\",\n        \"      <td>-0.001154</td>\\n\",\n        \"      <td>-0.004121</td>\\n\",\n        \"      <td> 0.001543</td>\\n\",\n        \"      <td> 0.002693</td>\\n\",\n        \"      <td> 0.000256</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>GOOG</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.008795</td>\\n\",\n        \"      <td> 0.015167</td>\\n\",\n        \"      <td> 0.004584</td>\\n\",\n        \"      <td>-0.000029</td>\\n\",\n        \"      <td> 0.011069</td>\\n\",\n        \"      <td> 0.008833</td>\\n\",\n        \"      <td>-0.002532</td>\\n\",\n        \"      <td>-0.002716</td>\\n\",\n        \"      <td>-0.000326</td>\\n\",\n        \"      <td>-0.002431</td>\\n\",\n        \"      <td>-0.004585</td>\\n\",\n        \"      <td>-0.009683</td>\\n\",\n        \"      <td>-0.005873</td>\\n\",\n        \"      <td> 0.000678</td>\\n\",\n        \"      <td> 0.009060</td>\\n\",\n        \"      <td> 0.005519</td>\\n\",\n        \"      <td> 0.006450</td>\\n\",\n        \"      <td> 0.004206</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>HPQ</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005128</td>\\n\",\n        \"      <td> 0.004443</td>\\n\",\n        \"      <td> 0.003673</td>\\n\",\n        \"      <td>-0.004162</td>\\n\",\n        \"      <td> 0.003487</td>\\n\",\n        \"      <td> 0.004130</td>\\n\",\n        \"      <td>-0.007852</td>\\n\",\n        \"      <td> 0.036108</td>\\n\",\n        \"      <td> 0.008050</td>\\n\",\n        \"      <td> 0.006559</td>\\n\",\n        \"      <td> 0.015742</td>\\n\",\n        \"      <td> 0.007287</td>\\n\",\n        \"      <td>-0.001935</td>\\n\",\n        \"      <td> 0.002807</td>\\n\",\n        \"      <td>-0.001934</td>\\n\",\n        \"      <td> 0.005072</td>\\n\",\n        \"      <td>-0.008555</td>\\n\",\n        \"      <td>-0.008718</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>HTHIY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001694</td>\\n\",\n        \"      <td> 0.018982</td>\\n\",\n        \"      <td>-0.004305</td>\\n\",\n        \"      <td>-0.004058</td>\\n\",\n        \"      <td> 0.007181</td>\\n\",\n        \"      <td> 0.003708</td>\\n\",\n        \"      <td>-0.015190</td>\\n\",\n        \"      <td>-0.000753</td>\\n\",\n        \"      <td>-0.007772</td>\\n\",\n        \"      <td> 0.012744</td>\\n\",\n        \"      <td>-0.001325</td>\\n\",\n        \"      <td> 0.000618</td>\\n\",\n        \"      <td>-0.003943</td>\\n\",\n        \"      <td> 0.007300</td>\\n\",\n        \"      <td> 0.016224</td>\\n\",\n        \"      <td> 0.001598</td>\\n\",\n        \"      <td>-0.011005</td>\\n\",\n        \"      <td>-0.003198</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>IBM</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001297</td>\\n\",\n        \"      <td> 0.001703</td>\\n\",\n        \"      <td>-0.001457</td>\\n\",\n        \"      <td> 0.000124</td>\\n\",\n        \"      <td> 0.006844</td>\\n\",\n        \"      <td> 0.004827</td>\\n\",\n        \"      <td> 0.000105</td>\\n\",\n        \"      <td> 0.007076</td>\\n\",\n        \"      <td>-0.002131</td>\\n\",\n        \"      <td> 0.001917</td>\\n\",\n        \"      <td> 0.006684</td>\\n\",\n        \"      <td> 0.000692</td>\\n\",\n        \"      <td>-0.005183</td>\\n\",\n        \"      <td> 0.002135</td>\\n\",\n        \"      <td>-0.001269</td>\\n\",\n        \"      <td> 0.002254</td>\\n\",\n        \"      <td>-0.005807</td>\\n\",\n        \"      <td>-0.000681</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>INTC</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001112</td>\\n\",\n        \"      <td> 0.024832</td>\\n\",\n        \"      <td> 0.004219</td>\\n\",\n        \"      <td> 0.001763</td>\\n\",\n        \"      <td> 0.007486</td>\\n\",\n        \"      <td> 0.002231</td>\\n\",\n        \"      <td> 0.000969</td>\\n\",\n        \"      <td> 0.015269</td>\\n\",\n        \"      <td> 0.002475</td>\\n\",\n        \"      <td>-0.004206</td>\\n\",\n        \"      <td>-0.000670</td>\\n\",\n        \"      <td>-0.001725</td>\\n\",\n        \"      <td>-0.002787</td>\\n\",\n        \"      <td> 0.004401</td>\\n\",\n        \"      <td>-0.005967</td>\\n\",\n        \"      <td>-0.000096</td>\\n\",\n        \"      <td> 0.005932</td>\\n\",\n        \"      <td> 0.004097</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>LXK</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.007975</td>\\n\",\n        \"      <td> 0.008664</td>\\n\",\n        \"      <td> 0.010324</td>\\n\",\n        \"      <td>-0.007590</td>\\n\",\n        \"      <td> 0.000110</td>\\n\",\n        \"      <td> 0.003788</td>\\n\",\n        \"      <td>-0.005231</td>\\n\",\n        \"      <td>-0.002781</td>\\n\",\n        \"      <td>-0.008994</td>\\n\",\n        \"      <td>-0.001651</td>\\n\",\n        \"      <td> 0.001430</td>\\n\",\n        \"      <td>-0.000267</td>\\n\",\n        \"      <td> 0.001469</td>\\n\",\n        \"      <td> 0.009064</td>\\n\",\n        \"      <td>-0.008811</td>\\n\",\n        \"      <td>-0.008414</td>\\n\",\n        \"      <td>-0.004055</td>\\n\",\n        \"      <td>-0.000609</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>MSFT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.003428</td>\\n\",\n        \"      <td> 0.012751</td>\\n\",\n        \"      <td> 0.006959</td>\\n\",\n        \"      <td> 0.010214</td>\\n\",\n        \"      <td> 0.006049</td>\\n\",\n        \"      <td> 0.010608</td>\\n\",\n        \"      <td>-0.001848</td>\\n\",\n        \"      <td> 0.000370</td>\\n\",\n        \"      <td> 0.002874</td>\\n\",\n        \"      <td>-0.000295</td>\\n\",\n        \"      <td>-0.002216</td>\\n\",\n        \"      <td>-0.005348</td>\\n\",\n        \"      <td>-0.001190</td>\\n\",\n        \"      <td> 0.009283</td>\\n\",\n        \"      <td>-0.002437</td>\\n\",\n        \"      <td>-0.005944</td>\\n\",\n        \"      <td> 0.004806</td>\\n\",\n        \"      <td> 0.012413</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>N</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.003774</td>\\n\",\n        \"      <td>-0.002940</td>\\n\",\n        \"      <td>-0.001937</td>\\n\",\n        \"      <td>-0.004743</td>\\n\",\n        \"      <td> 0.010588</td>\\n\",\n        \"      <td> 0.011145</td>\\n\",\n        \"      <td>-0.001350</td>\\n\",\n        \"      <td>-0.000040</td>\\n\",\n        \"      <td> 0.012627</td>\\n\",\n        \"      <td>-0.002477</td>\\n\",\n        \"      <td> 0.012155</td>\\n\",\n        \"      <td> 0.013787</td>\\n\",\n        \"      <td>-0.014610</td>\\n\",\n        \"      <td> 0.013834</td>\\n\",\n        \"      <td> 0.015171</td>\\n\",\n        \"      <td> 0.001545</td>\\n\",\n        \"      <td>-0.006065</td>\\n\",\n        \"      <td>-0.003195</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NFLX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.011230</td>\\n\",\n        \"      <td> 0.007208</td>\\n\",\n        \"      <td> 0.001123</td>\\n\",\n        \"      <td> 0.011389</td>\\n\",\n        \"      <td> 0.019374</td>\\n\",\n        \"      <td> 0.003090</td>\\n\",\n        \"      <td> 0.006664</td>\\n\",\n        \"      <td> 0.003807</td>\\n\",\n        \"      <td> 0.009043</td>\\n\",\n        \"      <td> 0.010369</td>\\n\",\n        \"      <td>-0.005572</td>\\n\",\n        \"      <td>-0.005026</td>\\n\",\n        \"      <td>-0.003569</td>\\n\",\n        \"      <td> 0.006130</td>\\n\",\n        \"      <td>-0.001608</td>\\n\",\n        \"      <td> 0.008005</td>\\n\",\n        \"      <td>-0.010536</td>\\n\",\n        \"      <td>-0.002382</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NVDA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.004594</td>\\n\",\n        \"      <td> 0.007043</td>\\n\",\n        \"      <td>-0.006938</td>\\n\",\n        \"      <td> 0.004801</td>\\n\",\n        \"      <td> 0.011358</td>\\n\",\n        \"      <td> 0.012215</td>\\n\",\n        \"      <td>-0.002593</td>\\n\",\n        \"      <td>-0.007490</td>\\n\",\n        \"      <td>-0.002795</td>\\n\",\n        \"      <td> 0.003828</td>\\n\",\n        \"      <td> 0.009650</td>\\n\",\n        \"      <td>-0.002072</td>\\n\",\n        \"      <td> 0.000690</td>\\n\",\n        \"      <td> 0.004124</td>\\n\",\n        \"      <td> 0.002229</td>\\n\",\n        \"      <td> 0.009847</td>\\n\",\n        \"      <td> 0.015379</td>\\n\",\n        \"      <td> 0.001502</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NWSA</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000192</td>\\n\",\n        \"      <td>-0.021825</td>\\n\",\n        \"      <td> 0.017388</td>\\n\",\n        \"      <td> 0.000386</td>\\n\",\n        \"      <td>-0.000386</td>\\n\",\n        \"      <td>-0.005830</td>\\n\",\n        \"      <td> 0.010955</td>\\n\",\n        \"      <td> 0.010272</td>\\n\",\n        \"      <td>-0.001715</td>\\n\",\n        \"      <td> 0.005872</td>\\n\",\n        \"      <td> 0.002457</td>\\n\",\n        \"      <td>-0.000378</td>\\n\",\n        \"      <td> 0.000567</td>\\n\",\n        \"      <td>-0.000756</td>\\n\",\n        \"      <td>-0.005705</td>\\n\",\n        \"      <td> 0.003222</td>\\n\",\n        \"      <td> 0.003965</td>\\n\",\n        \"      <td> 0.001320</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>NYT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.018408</td>\\n\",\n        \"      <td>-0.003259</td>\\n\",\n        \"      <td>-0.001088</td>\\n\",\n        \"      <td>-0.001089</td>\\n\",\n        \"      <td> 0.012100</td>\\n\",\n        \"      <td>-0.005135</td>\\n\",\n        \"      <td>-0.004889</td>\\n\",\n        \"      <td> 0.007547</td>\\n\",\n        \"      <td> 0.001615</td>\\n\",\n        \"      <td>-0.003782</td>\\n\",\n        \"      <td> 0.001618</td>\\n\",\n        \"      <td> 0.002958</td>\\n\",\n        \"      <td>-0.004864</td>\\n\",\n        \"      <td>-0.001353</td>\\n\",\n        \"      <td> 0.000270</td>\\n\",\n        \"      <td> 0.002159</td>\\n\",\n        \"      <td> 0.000540</td>\\n\",\n        \"      <td> 0.000539</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>ORCL</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001671</td>\\n\",\n        \"      <td> 0.007382</td>\\n\",\n        \"      <td> 0.000332</td>\\n\",\n        \"      <td> 0.002069</td>\\n\",\n        \"      <td> 0.007881</td>\\n\",\n        \"      <td> 0.014721</td>\\n\",\n        \"      <td> 0.002340</td>\\n\",\n        \"      <td> 0.005058</td>\\n\",\n        \"      <td> 0.001203</td>\\n\",\n        \"      <td> 0.005741</td>\\n\",\n        \"      <td> 0.001592</td>\\n\",\n        \"      <td>-0.006087</td>\\n\",\n        \"      <td>-0.005719</td>\\n\",\n        \"      <td> 0.002811</td>\\n\",\n        \"      <td> 0.002404</td>\\n\",\n        \"      <td> 0.005340</td>\\n\",\n        \"      <td>-0.003680</td>\\n\",\n        \"      <td>-0.005793</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>PCRFY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.001397</td>\\n\",\n        \"      <td> 0.016763</td>\\n\",\n        \"      <td> 0.006823</td>\\n\",\n        \"      <td>-0.001093</td>\\n\",\n        \"      <td> 0.005975</td>\\n\",\n        \"      <td> 0.000814</td>\\n\",\n        \"      <td>-0.015431</td>\\n\",\n        \"      <td> 0.001101</td>\\n\",\n        \"      <td>-0.015372</td>\\n\",\n        \"      <td> 0.005006</td>\\n\",\n        \"      <td> 0.003602</td>\\n\",\n        \"      <td> 0.005237</td>\\n\",\n        \"      <td> 0.001651</td>\\n\",\n        \"      <td> 0.010887</td>\\n\",\n        \"      <td> 0.007295</td>\\n\",\n        \"      <td> 0.014643</td>\\n\",\n        \"      <td>-0.002134</td>\\n\",\n        \"      <td>-0.010515</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RAX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.036414</td>\\n\",\n        \"      <td>-0.004699</td>\\n\",\n        \"      <td> 0.033312</td>\\n\",\n        \"      <td> 0.009003</td>\\n\",\n        \"      <td> 0.017585</td>\\n\",\n        \"      <td> 0.035446</td>\\n\",\n        \"      <td>-0.000203</td>\\n\",\n        \"      <td> 0.012440</td>\\n\",\n        \"      <td> 0.021210</td>\\n\",\n        \"      <td>-0.007319</td>\\n\",\n        \"      <td> 0.020159</td>\\n\",\n        \"      <td>-0.008504</td>\\n\",\n        \"      <td>-0.005109</td>\\n\",\n        \"      <td> 0.012419</td>\\n\",\n        \"      <td> 0.020992</td>\\n\",\n        \"      <td> 0.031463</td>\\n\",\n        \"      <td> 0.019219</td>\\n\",\n        \"      <td> 0.009385</td>\\n\",\n        \"      <td> 5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>RHT</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000507</td>\\n\",\n        \"      <td> 0.007825</td>\\n\",\n        \"      <td> 0.002064</td>\\n\",\n        \"      <td> 0.001281</td>\\n\",\n        \"      <td> 0.014115</td>\\n\",\n        \"      <td> 0.021549</td>\\n\",\n        \"      <td>-0.002640</td>\\n\",\n        \"      <td>-0.002376</td>\\n\",\n        \"      <td> 0.004356</td>\\n\",\n        \"      <td>-0.005406</td>\\n\",\n        \"      <td> 0.003287</td>\\n\",\n        \"      <td>-0.007984</td>\\n\",\n        \"      <td>-0.015275</td>\\n\",\n        \"      <td> 0.007824</td>\\n\",\n        \"      <td> 0.004141</td>\\n\",\n        \"      <td> 0.006711</td>\\n\",\n        \"      <td>-0.004622</td>\\n\",\n        \"      <td>-0.003985</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SAP</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.006488</td>\\n\",\n        \"      <td>-0.001993</td>\\n\",\n        \"      <td> 0.002852</td>\\n\",\n        \"      <td>-0.007576</td>\\n\",\n        \"      <td> 0.008291</td>\\n\",\n        \"      <td> 0.009113</td>\\n\",\n        \"      <td>-0.008675</td>\\n\",\n        \"      <td> 0.006645</td>\\n\",\n        \"      <td>-0.005908</td>\\n\",\n        \"      <td> 0.014241</td>\\n\",\n        \"      <td> 0.005454</td>\\n\",\n        \"      <td> 0.003329</td>\\n\",\n        \"      <td>-0.015968</td>\\n\",\n        \"      <td>-0.000986</td>\\n\",\n        \"      <td> 0.008287</td>\\n\",\n        \"      <td>-0.008875</td>\\n\",\n        \"      <td>-0.002407</td>\\n\",\n        \"      <td> 0.001502</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SIEGY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.012527</td>\\n\",\n        \"      <td> 0.014653</td>\\n\",\n        \"      <td> 0.014548</td>\\n\",\n        \"      <td>-0.004600</td>\\n\",\n        \"      <td> 0.004551</td>\\n\",\n        \"      <td> 0.005068</td>\\n\",\n        \"      <td>-0.001160</td>\\n\",\n        \"      <td> 0.014180</td>\\n\",\n        \"      <td>-0.008505</td>\\n\",\n        \"      <td> 0.016414</td>\\n\",\n        \"      <td> 0.007543</td>\\n\",\n        \"      <td>-0.000786</td>\\n\",\n        \"      <td>-0.015761</td>\\n\",\n        \"      <td>-0.002001</td>\\n\",\n        \"      <td> 0.008176</td>\\n\",\n        \"      <td> 0.011301</td>\\n\",\n        \"      <td> 0.000680</td>\\n\",\n        \"      <td>-0.000131</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>SNE</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.029412</td>\\n\",\n        \"      <td> 0.005369</td>\\n\",\n        \"      <td> 0.019070</td>\\n\",\n        \"      <td> 0.007033</td>\\n\",\n        \"      <td> 0.016146</td>\\n\",\n        \"      <td> 0.006522</td>\\n\",\n        \"      <td>-0.002651</td>\\n\",\n        \"      <td>-0.000707</td>\\n\",\n        \"      <td> 0.007548</td>\\n\",\n        \"      <td>-0.005826</td>\\n\",\n        \"      <td>-0.001414</td>\\n\",\n        \"      <td> 0.002293</td>\\n\",\n        \"      <td> 0.010991</td>\\n\",\n        \"      <td> 0.013425</td>\\n\",\n        \"      <td> 0.020566</td>\\n\",\n        \"      <td>-0.006789</td>\\n\",\n        \"      <td>-0.022029</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TOSYY</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.012976</td>\\n\",\n        \"      <td>-0.004764</td>\\n\",\n        \"      <td>-0.001632</td>\\n\",\n        \"      <td> 0.010191</td>\\n\",\n        \"      <td> 0.012761</td>\\n\",\n        \"      <td>-0.008040</td>\\n\",\n        \"      <td> 0.007245</td>\\n\",\n        \"      <td>-0.008420</td>\\n\",\n        \"      <td> 0.001731</td>\\n\",\n        \"      <td>-0.002230</td>\\n\",\n        \"      <td>-0.015474</td>\\n\",\n        \"      <td> 0.001005</td>\\n\",\n        \"      <td>-0.005942</td>\\n\",\n        \"      <td> 0.013593</td>\\n\",\n        \"      <td>-0.010841</td>\\n\",\n        \"      <td>-0.004177</td>\\n\",\n        \"      <td> 0.005038</td>\\n\",\n        \"      <td> 4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TRI</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.004010</td>\\n\",\n        \"      <td>-0.004197</td>\\n\",\n        \"      <td> 0.009253</td>\\n\",\n        \"      <td> 0.001130</td>\\n\",\n        \"      <td> 0.005721</td>\\n\",\n        \"      <td>-0.004335</td>\\n\",\n        \"      <td>-0.000177</td>\\n\",\n        \"      <td> 0.001681</td>\\n\",\n        \"      <td>-0.000531</td>\\n\",\n        \"      <td>-0.001064</td>\\n\",\n        \"      <td> 0.001328</td>\\n\",\n        \"      <td> 0.006857</td>\\n\",\n        \"      <td> 0.000352</td>\\n\",\n        \"      <td>-0.002025</td>\\n\",\n        \"      <td> 0.000968</td>\\n\",\n        \"      <td> 0.003943</td>\\n\",\n        \"      <td> 0.003232</td>\\n\",\n        \"      <td>-0.003242</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TWX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.008865</td>\\n\",\n        \"      <td> 0.006782</td>\\n\",\n        \"      <td> 0.026300</td>\\n\",\n        \"      <td> 0.007408</td>\\n\",\n        \"      <td> 0.005309</td>\\n\",\n        \"      <td> 0.001090</td>\\n\",\n        \"      <td> 0.000651</td>\\n\",\n        \"      <td>-0.002313</td>\\n\",\n        \"      <td> 0.002966</td>\\n\",\n        \"      <td> 0.002401</td>\\n\",\n        \"      <td>-0.002507</td>\\n\",\n        \"      <td> 0.002647</td>\\n\",\n        \"      <td>-0.000390</td>\\n\",\n        \"      <td> 0.000866</td>\\n\",\n        \"      <td> 0.003151</td>\\n\",\n        \"      <td> 0.005495</td>\\n\",\n        \"      <td>-0.004095</td>\\n\",\n        \"      <td>-0.006769</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>TXN</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.001002</td>\\n\",\n        \"      <td> 0.010276</td>\\n\",\n        \"      <td> 0.003460</td>\\n\",\n        \"      <td> 0.004779</td>\\n\",\n        \"      <td> 0.002664</td>\\n\",\n        \"      <td> 0.002796</td>\\n\",\n        \"      <td> 0.003344</td>\\n\",\n        \"      <td> 0.004577</td>\\n\",\n        \"      <td> 0.000901</td>\\n\",\n        \"      <td>-0.008384</td>\\n\",\n        \"      <td>-0.004774</td>\\n\",\n        \"      <td> 0.002800</td>\\n\",\n        \"      <td> 0.005708</td>\\n\",\n        \"      <td> 0.005124</td>\\n\",\n        \"      <td>-0.002917</td>\\n\",\n        \"      <td> 0.004425</td>\\n\",\n        \"      <td> 0.002345</td>\\n\",\n        \"      <td> 0.002820</td>\\n\",\n        \"      <td> 3</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>VIAB</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005008</td>\\n\",\n        \"      <td>-0.000787</td>\\n\",\n        \"      <td> 0.002314</td>\\n\",\n        \"      <td> 0.007547</td>\\n\",\n        \"      <td> 0.009547</td>\\n\",\n        \"      <td>-0.006172</td>\\n\",\n        \"      <td>-0.002993</td>\\n\",\n        \"      <td>-0.004944</td>\\n\",\n        \"      <td>-0.001899</td>\\n\",\n        \"      <td>-0.000950</td>\\n\",\n        \"      <td>-0.002112</td>\\n\",\n        \"      <td> 0.001860</td>\\n\",\n        \"      <td> 0.001980</td>\\n\",\n        \"      <td> 0.000948</td>\\n\",\n        \"      <td> 0.007079</td>\\n\",\n        \"      <td>-0.002872</td>\\n\",\n        \"      <td>-0.000739</td>\\n\",\n        \"      <td>-0.000041</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>VMW</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.007897</td>\\n\",\n        \"      <td> 0.004582</td>\\n\",\n        \"      <td> 0.009869</td>\\n\",\n        \"      <td> 0.005238</td>\\n\",\n        \"      <td> 0.015535</td>\\n\",\n        \"      <td> 0.000745</td>\\n\",\n        \"      <td> 0.003810</td>\\n\",\n        \"      <td>-0.003434</td>\\n\",\n        \"      <td>-0.003283</td>\\n\",\n        \"      <td>-0.014676</td>\\n\",\n        \"      <td>-0.028492</td>\\n\",\n        \"      <td>-0.005011</td>\\n\",\n        \"      <td>-0.001810</td>\\n\",\n        \"      <td> 0.008902</td>\\n\",\n        \"      <td> 0.002936</td>\\n\",\n        \"      <td>-0.002571</td>\\n\",\n        \"      <td>-0.017769</td>\\n\",\n        \"      <td>-0.010790</td>\\n\",\n        \"      <td> 7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>XOM</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>-0.005729</td>\\n\",\n        \"      <td> 0.006333</td>\\n\",\n        \"      <td> 0.000303</td>\\n\",\n        \"      <td>-0.001247</td>\\n\",\n        \"      <td> 0.005199</td>\\n\",\n        \"      <td> 0.002409</td>\\n\",\n        \"      <td>-0.000770</td>\\n\",\n        \"      <td>-0.000100</td>\\n\",\n        \"      <td>-0.010319</td>\\n\",\n        \"      <td> 0.001992</td>\\n\",\n        \"      <td> 0.009432</td>\\n\",\n        \"      <td>-0.001306</td>\\n\",\n        \"      <td>-0.000872</td>\\n\",\n        \"      <td>-0.001578</td>\\n\",\n        \"      <td>-0.006624</td>\\n\",\n        \"      <td> 0.005612</td>\\n\",\n        \"      <td>-0.006018</td>\\n\",\n        \"      <td> 0.003000</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>XRX</th>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0.005475</td>\\n\",\n        \"      <td> 0.010589</td>\\n\",\n        \"      <td>-0.002964</td>\\n\",\n        \"      <td>-0.007214</td>\\n\",\n        \"      <td> 0.005935</td>\\n\",\n        \"      <td>-0.002976</td>\\n\",\n        \"      <td> 0.006162</td>\\n\",\n        \"      <td> 0.003684</td>\\n\",\n        \"      <td> 0.001471</td>\\n\",\n        \"      <td> 0.002202</td>\\n\",\n        \"      <td> 0.003657</td>\\n\",\n        \"      <td> 0.002189</td>\\n\",\n        \"      <td> 0.000000</td>\\n\",\n        \"      <td> 0.007005</td>\\n\",\n        \"      <td> 0.003850</td>\\n\",\n        \"      <td> 0.009061</td>\\n\",\n        \"      <td>-0.013778</td>\\n\",\n        \"      <td>-0.013971</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>50 rows \\u00d7 20 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 17,\n       \"text\": [\n        \"n_date  735456    735457    735458    735459    735460    735463    735464  \\\\\\n\",\n        \"symbol                                                                       \\n\",\n        \"AAPL         0  0.005373  0.007091  0.004625  0.003959  0.011737  0.013376   \\n\",\n        \"ADBE         0 -0.002185  0.010283  0.007582  0.004037  0.009363  0.012268   \\n\",\n        \"ADSK         0  0.004898  0.017504  0.013646 -0.033605 -0.015570  0.004711   \\n\",\n        \"AMZN         0  0.002078  0.026988  0.009929  0.002949  0.009095 -0.002142   \\n\",\n        \"BBRY         0 -0.006726 -0.002840  0.009494  0.010783  0.014736  0.009504   \\n\",\n        \"BKS          0 -0.004016  0.001040  0.009566 -0.002360  0.014967  0.008071   \\n\",\n        \"BP           0 -0.001125 -0.005802  0.005278 -0.000775  0.009557  0.007821   \\n\",\n        \"CAJ          0 -0.002323  0.003221  0.004908  0.000501 -0.000501  0.001900   \\n\",\n        \"CAT          0  0.001084  0.004349  0.005242  0.002017  0.009583  0.004691   \\n\",\n        \"CBS          0 -0.010617 -0.002817  0.003984  0.007462  0.008284  0.004180   \\n\",\n        \"CMCSA        0  0.000310  0.005419  0.002886  0.004888  0.003895 -0.003114   \\n\",\n        \"CRM          0 -0.029861 -0.004338  0.006434  0.003858  0.013566  0.010267   \\n\",\n        \"CSC          0 -0.003303  0.002547  0.006370  0.002154  0.010804  0.004196   \\n\",\n        \"CSCO         0 -0.002519 -0.000265 -0.021680 -0.005998  0.004883  0.002841   \\n\",\n        \"CTXS         0 -0.004566  0.005074  0.001158 -0.001741  0.009672 -0.001151   \\n\",\n        \"CVX          0 -0.008605  0.005390 -0.003951  0.001565 -0.000132  0.008799   \\n\",\n        \"DIS          0 -0.003517  0.005172  0.008895  0.007890  0.009188  0.000370   \\n\",\n        \"EBAY         0 -0.009406 -0.012296  0.001951 -0.001892  0.003080  0.009711   \\n\",\n        \"EMC          0  0.000455  0.005656  0.006630  0.001683  0.007350 -0.001897   \\n\",\n        \"FB           0 -0.009296  0.012168  0.005846 -0.004926  0.008913  0.009362   \\n\",\n        \"FJTSY        0 -0.015135  0.022376 -0.003667 -0.000367  0.000000 -0.007576   \\n\",\n        \"FOXA         0  0.010004  0.008595  0.009264  0.007339  0.003148 -0.003998   \\n\",\n        \"GE           0 -0.005840  0.005036  0.001418 -0.005705  0.011155  0.003450   \\n\",\n        \"GOOG         0 -0.008795  0.015167  0.004584 -0.000029  0.011069  0.008833   \\n\",\n        \"HPQ          0 -0.005128  0.004443  0.003673 -0.004162  0.003487  0.004130   \\n\",\n        \"HTHIY        0  0.001694  0.018982 -0.004305 -0.004058  0.007181  0.003708   \\n\",\n        \"IBM          0  0.001297  0.001703 -0.001457  0.000124  0.006844  0.004827   \\n\",\n        \"INTC         0 -0.001112  0.024832  0.004219  0.001763  0.007486  0.002231   \\n\",\n        \"LXK          0  0.007975  0.008664  0.010324 -0.007590  0.000110  0.003788   \\n\",\n        \"MSFT         0  0.003428  0.012751  0.006959  0.010214  0.006049  0.010608   \\n\",\n        \"N            0 -0.003774 -0.002940 -0.001937 -0.004743  0.010588  0.011145   \\n\",\n        \"NFLX         0 -0.011230  0.007208  0.001123  0.011389  0.019374  0.003090   \\n\",\n        \"NVDA         0 -0.004594  0.007043 -0.006938  0.004801  0.011358  0.012215   \\n\",\n        \"NWSA         0  0.000192 -0.021825  0.017388  0.000386 -0.000386 -0.005830   \\n\",\n        \"NYT          0 -0.018408 -0.003259 -0.001088 -0.001089  0.012100 -0.005135   \\n\",\n        \"ORCL         0 -0.001671  0.007382  0.000332  0.002069  0.007881  0.014721   \\n\",\n        \"PCRFY        0 -0.001397  0.016763  0.006823 -0.001093  0.005975  0.000814   \\n\",\n        \"RAX          0 -0.036414 -0.004699  0.033312  0.009003  0.017585  0.035446   \\n\",\n        \"RHT          0  0.000507  0.007825  0.002064  0.001281  0.014115  0.021549   \\n\",\n        \"SAP          0 -0.006488 -0.001993  0.002852 -0.007576  0.008291  0.009113   \\n\",\n        \"SIEGY        0 -0.012527  0.014653  0.014548 -0.004600  0.004551  0.005068   \\n\",\n        \"SNE          0  0.000000  0.029412  0.005369  0.019070  0.007033  0.016146   \\n\",\n        \"TOSYY        0  0.000000  0.012976 -0.004764 -0.001632  0.010191  0.012761   \\n\",\n        \"TRI          0  0.004010 -0.004197  0.009253  0.001130  0.005721 -0.004335   \\n\",\n        \"TWX          0 -0.008865  0.006782  0.026300  0.007408  0.005309  0.001090   \\n\",\n        \"TXN          0  0.001002  0.010276  0.003460  0.004779  0.002664  0.002796   \\n\",\n        \"VIAB         0 -0.005008 -0.000787  0.002314  0.007547  0.009547 -0.006172   \\n\",\n        \"VMW          0 -0.007897  0.004582  0.009869  0.005238  0.015535  0.000745   \\n\",\n        \"XOM          0 -0.005729  0.006333  0.000303 -0.001247  0.005199  0.002409   \\n\",\n        \"XRX          0  0.005475  0.010589 -0.002964 -0.007214  0.005935 -0.002976   \\n\",\n        \"\\n\",\n        \"n_date    735465    735466    735467    735470    735471    735472    735473  \\\\\\n\",\n        \"symbol                                                                         \\n\",\n        \"AAPL    0.003581  0.000066  0.004456  0.006590 -0.005738  0.007040  0.003881   \\n\",\n        \"ADBE   -0.006643  0.003217  0.006578  0.000648 -0.003437 -0.001256 -0.004954   \\n\",\n        \"ADSK   -0.004108  0.003350 -0.002924 -0.001059  0.010964 -0.002099 -0.002661   \\n\",\n        \"AMZN    0.002989 -0.005231 -0.005269  0.007005  0.016890  0.010134 -0.010366   \\n\",\n        \"BBRY    0.027402 -0.013043 -0.004030  0.007333  0.003322  0.018265  0.001628   \\n\",\n        \"BKS    -0.018048 -0.007093  0.002947 -0.008769  0.013200  0.027528  0.008626   \\n\",\n        \"BP     -0.000138  0.001797 -0.001661  0.006054 -0.004214  0.002206 -0.007850   \\n\",\n        \"CAJ    -0.008164 -0.000403 -0.007006 -0.000102  0.003441 -0.002638  0.000608   \\n\",\n        \"CAT     0.003653  0.003178 -0.007492  0.007283  0.002002  0.002672 -0.000861   \\n\",\n        \"CBS    -0.002702 -0.001215  0.002478  0.004387 -0.009521  0.002485 -0.009082   \\n\",\n        \"CMCSA  -0.000366 -0.000733 -0.005716  0.005441  0.000367  0.001099 -0.002202   \\n\",\n        \"CRM     0.005979  0.006361  0.066805 -0.006311  0.002361  0.009025 -0.025011   \\n\",\n        \"CSC     0.002791  0.006711 -0.004814 -0.001434  0.000394  0.004502 -0.004918   \\n\",\n        \"CSCO    0.000676  0.007246 -0.003501 -0.000404  0.002419 -0.000538  0.000269   \\n\",\n        \"CTXS   -0.000288  0.004345  0.006168 -0.003571 -0.000286 -0.002100 -0.004555   \\n\",\n        \"CVX    -0.000026  0.003705 -0.005799  0.005766  0.004183  0.001945  0.000648   \\n\",\n        \"DIS    -0.001706  0.004910  0.002284  0.001140 -0.004397  0.000480 -0.000296   \\n\",\n        \"EBAY   -0.001871  0.036590  0.001560  0.001737  0.006958  0.001484 -0.012566   \\n\",\n        \"EMC    -0.007079  0.002018 -0.004166  0.002919 -0.003832 -0.003393 -0.003176   \\n\",\n        \"FB     -0.003428 -0.001963 -0.005833  0.009863  0.007014 -0.009351 -0.011530   \\n\",\n        \"FJTSY  -0.008573  0.002510  0.000000  0.000000  0.000000 -0.022526 -0.021060   \\n\",\n        \"FOXA   -0.002143 -0.002522  0.000560 -0.001777 -0.001123  0.003359 -0.001776   \\n\",\n        \"GE      0.006475  0.005178 -0.006995 -0.001528 -0.002682  0.000638 -0.003715   \\n\",\n        \"GOOG   -0.002532 -0.002716 -0.000326 -0.002431 -0.004585 -0.009683 -0.005873   \\n\",\n        \"HPQ    -0.007852  0.036108  0.008050  0.006559  0.015742  0.007287 -0.001935   \\n\",\n        \"HTHIY  -0.015190 -0.000753 -0.007772  0.012744 -0.001325  0.000618 -0.003943   \\n\",\n        \"IBM     0.000105  0.007076 -0.002131  0.001917  0.006684  0.000692 -0.005183   \\n\",\n        \"INTC    0.000969  0.015269  0.002475 -0.004206 -0.000670 -0.001725 -0.002787   \\n\",\n        \"LXK    -0.005231 -0.002781 -0.008994 -0.001651  0.001430 -0.000267  0.001469   \\n\",\n        \"MSFT   -0.001848  0.000370  0.002874 -0.000295 -0.002216 -0.005348 -0.001190   \\n\",\n        \"N      -0.001350 -0.000040  0.012627 -0.002477  0.012155  0.013787 -0.014610   \\n\",\n        \"NFLX    0.006664  0.003807  0.009043  0.010369 -0.005572 -0.005026 -0.003569   \\n\",\n        \"NVDA   -0.002593 -0.007490 -0.002795  0.003828  0.009650 -0.002072  0.000690   \\n\",\n        \"NWSA    0.010955  0.010272 -0.001715  0.005872  0.002457 -0.000378  0.000567   \\n\",\n        \"NYT    -0.004889  0.007547  0.001615 -0.003782  0.001618  0.002958 -0.004864   \\n\",\n        \"ORCL    0.002340  0.005058  0.001203  0.005741  0.001592 -0.006087 -0.005719   \\n\",\n        \"PCRFY  -0.015431  0.001101 -0.015372  0.005006  0.003602  0.005237  0.001651   \\n\",\n        \"RAX    -0.000203  0.012440  0.021210 -0.007319  0.020159 -0.008504 -0.005109   \\n\",\n        \"RHT    -0.002640 -0.002376  0.004356 -0.005406  0.003287 -0.007984 -0.015275   \\n\",\n        \"SAP    -0.008675  0.006645 -0.005908  0.014241  0.005454  0.003329 -0.015968   \\n\",\n        \"SIEGY  -0.001160  0.014180 -0.008505  0.016414  0.007543 -0.000786 -0.015761   \\n\",\n        \"SNE     0.006522 -0.002651 -0.000707  0.007548 -0.005826 -0.001414  0.002293   \\n\",\n        \"TOSYY  -0.008040  0.007245 -0.008420  0.001731 -0.002230 -0.015474  0.001005   \\n\",\n        \"TRI    -0.000177  0.001681 -0.000531 -0.001064  0.001328  0.006857  0.000352   \\n\",\n        \"TWX     0.000651 -0.002313  0.002966  0.002401 -0.002507  0.002647 -0.000390   \\n\",\n        \"TXN     0.003344  0.004577  0.000901 -0.008384 -0.004774  0.002800  0.005708   \\n\",\n        \"VIAB   -0.002993 -0.004944 -0.001899 -0.000950 -0.002112  0.001860  0.001980   \\n\",\n        \"VMW     0.003810 -0.003434 -0.003283 -0.014676 -0.028492 -0.005011 -0.001810   \\n\",\n        \"XOM    -0.000770 -0.000100 -0.010319  0.001992  0.009432 -0.001306 -0.000872   \\n\",\n        \"XRX     0.006162  0.003684  0.001471  0.002202  0.003657  0.002189  0.000000   \\n\",\n        \"\\n\",\n        \"n_date    735474    735478    735479    735480    735481  Cluster  \\n\",\n        \"symbol                                                             \\n\",\n        \"AAPL    0.003283  0.006973 -0.030061 -0.015946  0.002258        3  \\n\",\n        \"ADBE    0.006732  0.005219  0.003177  0.003212  0.003430        3  \\n\",\n        \"ADSK   -0.000619 -0.007361  0.011897  0.007039 -0.006158        4  \\n\",\n        \"AMZN   -0.002032  0.003425 -0.002177  0.018375 -0.003090        1  \\n\",\n        \"BBRY    0.000651  0.012849  0.032027 -0.009733 -0.009189        6  \\n\",\n        \"BKS     0.001553 -0.010558  0.016419  0.002240 -0.004501        2  \\n\",\n        \"BP     -0.002996 -0.011132  0.010251 -0.041773 -0.001601        0  \\n\",\n        \"CAJ    -0.004584  0.003351  0.003541  0.006134 -0.002217        1  \\n\",\n        \"CAT     0.003523  0.003237 -0.004016  0.000919 -0.001718        6  \\n\",\n        \"CBS    -0.007353 -0.005758 -0.006992  0.011908 -0.002816        5  \\n\",\n        \"CMCSA   0.003779  0.000122  0.002674  0.003392  0.007574        3  \\n\",\n        \"CRM     0.010118  0.012062 -0.004713 -0.002418  0.003642        5  \\n\",\n        \"CSC    -0.000559  0.006930  0.002213 -0.002162  0.000166        6  \\n\",\n        \"CSCO    0.004683 -0.001474  0.006126 -0.002804  0.000667        0  \\n\",\n        \"CTXS    0.009922  0.005148  0.001368  0.000801  0.001553        5  \\n\",\n        \"CVX     0.004078 -0.009115  0.001119 -0.007733 -0.001207        0  \\n\",\n        \"DIS    -0.004043  0.008423  0.003263 -0.005233  0.002316        6  \\n\",\n        \"EBAY   -0.000722 -0.006297 -0.005785 -0.002014 -0.014046        0  \\n\",\n        \"EMC     0.003054 -0.004202  0.006880 -0.010486 -0.009782        0  \\n\",\n        \"FB      0.007288  0.019939  0.003102 -0.002189  0.007948        4  \\n\",\n        \"FJTSY   0.000679  0.016783 -0.032388  0.002945  0.000883        4  \\n\",\n        \"FOXA   -0.004979  0.008014  0.010968  0.002666 -0.004432        2  \\n\",\n        \"GE     -0.001154 -0.004121  0.001543  0.002693  0.000256        6  \\n\",\n        \"GOOG    0.000678  0.009060  0.005519  0.006450  0.004206        4  \\n\",\n        \"HPQ     0.002807 -0.001934  0.005072 -0.008555 -0.008718        0  \\n\",\n        \"HTHIY   0.007300  0.016224  0.001598 -0.011005 -0.003198        4  \\n\",\n        \"IBM     0.002135 -0.001269  0.002254 -0.005807 -0.000681        0  \\n\",\n        \"INTC    0.004401 -0.005967 -0.000096  0.005932  0.004097        3  \\n\",\n        \"LXK     0.009064 -0.008811 -0.008414 -0.004055 -0.000609        3  \\n\",\n        \"MSFT    0.009283 -0.002437 -0.005944  0.004806  0.012413        3  \\n\",\n        \"N       0.013834  0.015171  0.001545 -0.006065 -0.003195        5  \\n\",\n        \"NFLX    0.006130 -0.001608  0.008005 -0.010536 -0.002382        6  \\n\",\n        \"NVDA    0.004124  0.002229  0.009847  0.015379  0.001502        1  \\n\",\n        \"NWSA   -0.000756 -0.005705  0.003222  0.003965  0.001320        2  \\n\",\n        \"NYT    -0.001353  0.000270  0.002159  0.000540  0.000539        5  \\n\",\n        \"ORCL    0.002811  0.002404  0.005340 -0.003680 -0.005793        0  \\n\",\n        \"PCRFY   0.010887  0.007295  0.014643 -0.002134 -0.010515        1  \\n\",\n        \"RAX     0.012419  0.020992  0.031463  0.019219  0.009385        5  \\n\",\n        \"RHT     0.007824  0.004141  0.006711 -0.004622 -0.003985        4  \\n\",\n        \"SAP    -0.000986  0.008287 -0.008875 -0.002407  0.001502        4  \\n\",\n        \"SIEGY  -0.002001  0.008176  0.011301  0.000680 -0.000131        4  \\n\",\n        \"SNE     0.010991  0.013425  0.020566 -0.006789 -0.022029        1  \\n\",\n        \"TOSYY  -0.005942  0.013593 -0.010841 -0.004177  0.005038        4  \\n\",\n        \"TRI    -0.002025  0.000968  0.003943  0.003232 -0.003242        2  \\n\",\n        \"TWX     0.000866  0.003151  0.005495 -0.004095 -0.006769        6  \\n\",\n        \"TXN     0.005124 -0.002917  0.004425  0.002345  0.002820        3  \\n\",\n        \"VIAB    0.000948  0.007079 -0.002872 -0.000739 -0.000041        7  \\n\",\n        \"VMW     0.008902  0.002936 -0.002571 -0.017769 -0.010790        7  \\n\",\n        \"XOM    -0.001578 -0.006624  0.005612 -0.006018  0.003000        0  \\n\",\n        \"XRX     0.007005  0.003850  0.009061 -0.013778 -0.013971        0  \\n\",\n        \"\\n\",\n        \"[50 rows x 20 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualize Clusters\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def visualize_clusters(data_df, values=\\\"change_1_per\\\", n_clusters=8, normalize_data=False):\\n\",\n      \"    data = pivot_data(data_df, values)\\n\",\n      \"    prediction, model, c_data = cluster_data(data, n_clusters=n_clusters, normalize_data=normalize_data)\\n\",\n      \"    c_data = pd.DataFrame(c_data, index=data.index,columns=data.columns)\\n\",\n      \"    data[\\\"Cluster\\\"] = prediction\\n\",\n      \"    c_data[\\\"Cluster\\\"] = prediction\\n\",\n      \"    plt.figure\\n\",\n      \"    for cluster in np.unique(prediction):\\n\",\n      \"        plt.plot(model.cluster_centers_[cluster], \\\"o-\\\", alpha=0.5, linewidth=2)\\n\",\n      \"    plt.show()\\n\",\n      \"    for cluster in np.unique(prediction):\\n\",\n      \"        temp_cluster_data = c_data[c_data[\\\"Cluster\\\"]==cluster]\\n\",\n      \"        print \\\"Cluster: %s\\\" % cluster\\n\",\n      \"        print \\\"Members: %s\\\" % [\\\"%s: %s\\\"% (symbol, stock_dict[symbol]) for symbol in list(temp_cluster_data.index)]\\n\",\n      \"        plt.figure()\\n\",\n      \"        plt.title(\\\"Cluster#: %s\\\" % cluster)\\n\",\n      \"        plt.plot(model.cluster_centers_[cluster], \\\"o--\\\", alpha=0.5, linewidth=2)\\n\",\n      \"        for symbol in temp_cluster_data.index:\\n\",\n      \"            plt.plot(np.ravel(temp_cluster_data.loc[[symbol]].drop(\\\"Cluster\\\", 1).values),\\n\",\n      \"                     alpha=0.2, linewidth=2)\\n\",\n      \"            \\n\",\n      \"        plt.grid()\\n\",\n      \"        plt.show();\\n\",\n      \"    return prediction, model, c_data\\n\",\n      \"\\n\",\n      \"prediction, model, c_data = visualize_clusters(stocks_df, values=\\\"average\\\", n_clusters=3, normalize_data=True);\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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0pKCs8//7z9PVlZWWi1WtLS0khLSyM7O3sCv5IgCKNltpo52XCSncd3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\\nkPsf/t+jUPGrHjEAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84690e50>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 0\\n\",\n        \"Members: ['ADSK: Autodesk, Inc.', 'BP: BP plc (ADR)', 'CAJ: Canon Inc (ADR)', 'CBS: CBS Corporation', 'CSCO: Cisco Systems, Inc.', 'EMC: EMC Corporation', 'FJTSY: Fujitsu Ltd (ADR)', 'GE: General Electric Company', 'GOOG: Google', 'LXK: Lexmark International Inc', 'NYT: The New York Times Company', 'TOSYY: Toshiba Corp (USA)', 'VIAB: Viacom, Inc.', 'VMW: VMware, Inc.', 'XOM: Exxon Mobil Corporation']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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yWz8PXkcuIcjhwR51FeexnHZbMV/cyBTCtjK8q0FgKLYAXYtrAIJAm6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\\nAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84979e10>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 1\\n\",\n        \"Members: ['BBRY: BlackBerry Ltd', 'CRM: salesforce.com, inc.', 'HPQ: Hewlett-Packard Company', 'RAX: Rackspace Hosting, Inc.', 'SNE: Sony Corp (ADR)']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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QwR08wpxrjmMybP8/xOWUCv1vPzxhbj5zRbhNgLBAJBDvOUma9Lr1fr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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84442950>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 2\\n\",\n        \"Members: ['AAPL: Apple Inc.', 'ADBE: Adobe Systems Incorporated', 'AMZN: Amazon.com, Inc.', 'BKS: Barnes & Noble, Inc.', 'CAT: Caterpillar Inc.', 'CMCSA: Comcast Corporation', 'CSC: Computer Sciences Corporation', 'CTXS: Citrix Systems, Inc.', 'CVX: Chevron Corporation', 'DIS: The Walt Disney Company', 'EBAY: eBay Inc', 'FB: Facebook, Inc.', 'FOXA: Twenty-First Century Fox Inc', 'HTHIY: Hitachi, Ltd. (ADR)', 'IBM: International Business Machines Corp. (IBM)', 'INTC: Intel Corporation', 'MSFT: Microsoft Corporation', 'N: NetSuite Inc', 'NFLX: Netflix, Inc.', 'NVDA: NVIDIA Corporation', 'NWSA: News Corp', 'ORCL: Oracle Corporation', 'PCRFY: Panasonic Corporation (ADR)', 'RHT: Red Hat Inc', 'SAP: SAP SE (ADR)', 'SIEGY: Siemens AG (ADR)', 'TRI: Thomson Reuters Corporation (USA)', 'TWX: Time Warner Inc', 'TXN: Texas Instruments Incorporated', 'XRX: Xerox Corp']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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wqaqaA1aaR6U0Re7EQOayHuuIs346G362gt2gXxcyQ+goSEhJcEkYgY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\\nNQAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c8477cd10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Measuring Performance\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def measure_error(prediction, model, c_data):\\n\",\n      \"    error_score = []\\n\",\n      \"    for counter in range(len(c_data)):\\n\",\n      \"        true_val = c_data.drop(\\\"Cluster\\\",1).values[counter]\\n\",\n      \"        center_val = model.cluster_centers_[c_data[\\\"Cluster\\\"][counter]]\\n\",\n      \"\\n\",\n      \"        error_score.append(np.average(np.abs(true_val - center_val)) / np.average(center_val))\\n\",\n      \"    \\n\",\n      \"    cluster_counts = c_data[\\\"Cluster\\\"].value_counts()\\n\",\n      \"    \\n\",\n      \"    return np.average(error_score), len(cluster_counts[cluster_counts==1])\\n\",\n      \"\\n\",\n      \"measure_error(prediction, model, c_data)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 19,\n       \"text\": [\n        \"(0.008035569568482899, 0)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Tuning Performance using Widgets\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.html import widgets\\n\",\n      \"from IPython.html.widgets import interact\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def visualize_clusters_widget(values=\\\"change_1_per\\\", n_clusters=8, normalize_data=False):\\n\",\n      \"    prediction, model, c_data = visualize_clusters(data_df=stocks_df, \\n\",\n      \"                                                   values=values, \\n\",\n      \"                                                   n_clusters=n_clusters, \\n\",\n      \"                                                   normalize_data=normalize_data\\n\",\n      \"                                                   )\\n\",\n      \"    print measure_error(prediction, model, c_data)\\n\",\n      \"interact(visualize_clusters_widget, \\n\",\n      \"         values=[\\\"change_1_per\\\",\\\"close\\\",\\\"average\\\",\\\"change_per\\\"],\\n\",\n      \"         n_clusters=(2,50),\\n\",\n      \"         normalize=False\\n\",\n      \"         );\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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UIMrl2DBx4Y7Q6ai0wiEKmrkZFCDPr6RiwFyccLk8lEcXExLpcLvV7P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\\nt07cqnIzPwAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c82bc4e50>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 0\\n\",\n        \"Members: ['CRM: salesforce.com, inc.']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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wzSkrwtg7ZOaRmIyCXFIABq7R9UY66JksnpduL2uNFH6dFph8/ITks+\\nXQy6Ai8GE+U8jQc15lJjJn/JNZCF8MGAy7sn0dmtAoDJ2UbmzYXJWdIyEJFLxgyE8ME/32nlX/vr\\n+OSCNK69fPKw37k8Lg40HyBKG0VJZkmYEgoxnIwZCBECp5oGaG0DjSvmnN/ptDqitFG4PW5cHlcY\\n0gkRPCkGAVBr/6Aac02UTM1t3sHjrIxzu4kAjFGnt6VwBdZVNFHO03hQYy41ZvKXFAMhLkBRoKXz\\n9NXNJp3bMgDZo0hEPhkzEOICOrqc3P/bg5iidfzXhnmMdBmPhu4GmnubyYrPYlL8pPEPKcRZZMxA\\niDFW1+xtFWQkx4xYCAB6LEZ2vQ/vfygtAxGZpBgEQK39g2rMNREyTcq1cdNNcM3VI48XADgGjBw4\\nABWVMmYQamrMpcZM/pJ1BkJcgN1tIyEeJptHHi8AyEz1jhl0WKVlICKTjBkIcQGV7ZX0OnqZnjKd\\neGP8iMd4PPCdX+7F7VH43Y/mYzRIo1uEl4wZCDHGBvckitGP3jLQaiEp/vTupe2Bb2UtRLhIMQiA\\nWvsH1Zgr0jM53U5cHhc6re6cPYnOlpLoLQatAWxlHennaTypMZcaM/lLxgyEOI8+uw1FGXlPorNd\\nMs9IZh4kpsq4gYg8MmYgxHmUvtXKq/+u46pL07hx+eTzHtva10qdtY702HRyzbnjlFCIkcmYgRBj\\nqLndhtMJscYLtwyGtqSQVcgiAkkxCIBa+wfVmCvSMzW3execZWeMPng8aGhLigD2J4r08zSe1JhL\\njZn8JcVAiFF49yTyziTKGWWDujNJy0BEMhkzEGIUlm4X9z16gGhDFL+9v2TUrSjOdKjlEA63gzkZ\\nczBEGUIfUohRyJiBEGOkuX0Ane78exKdrbLCSFkZVNdK60BEFikGAVBr/6Aac0VyppQMG9/8Btx4\\nw4W7iAZ1thmpPQUNzf4Vg0g+T+NNjbnUmMlfUgyEGMWAawCNBpLiLzx4PCgtyTtu0NYpLQMRWWTM\\nQIhRHOs4Ro+9h8KUQhKMCT495p0Pu/jjKyeYnZ/E3bdMC3FCIUYnYwZCjJEBp3daaYzO95ZBhuxe\\nKiJU0MWgrKyMGTNmUFhYyCOPPDLiMXfffTeFhYXMmzePffv2Dfud2+1m/vz5fO5znws2yrhRa/+g\\nGnNFaiaXx4XL4yJKG4U+Su/zc2ekfFwM/GnQRup5Cgc15lJjJn8FVQzcbjd33XUXZWVlVFRUsGXL\\nFo4cOTLsmNLSUo4fP05VVRVPP/00d9xxx7Df//a3v2XmzJlofJ2uIcQ46Oy20dMD0VG+twoA4mKj\\nuGapjutWunF5XCFKJ8TYC6oY7N69m4KCAvLy8tDr9axevZqtW7cOO2bbtm2sWbMGgMWLF2OxWGhp\\naQGgvr6e0tJSbrvttogaF1iyZEm4I4xIjbkiNdP+jwZ4YQu8+y/fZxIBaDQwu9hIZgY4/Fh8Fqnn\\nKRzUmEuNmfwVVDFoaGggN/fjDblycnJoaGjw+Zh7772XX//612i1MnQh1KWxzbvyOCPFv2IAshJZ\\nRKagtrD2tWvn7G/9iqLwj3/8g/T0dObPn3/B/ra1a9eSl5cHQGJiIiUlJUOVePCx43l7//79rF+/\\nPmyvP9rtM8+jGvIAPPbYY2F/v86+7cv719yeBUBj7R7Ky2P9ev72/namlkzF7rL7nG/wPjWcn7Oz\\nqCXP4G01/v83eF+436/NmzcDDH1e+kUJwnvvvaesWLFi6PZDDz2kbNy4cdgx69atU7Zs2TJ0u6io\\nSGlqalLuv/9+JScnR8nLy1MyMzMVk8mk3HLLLee8RpARQ+LNN98Md4QRqTFXpGa6d+MB5fYH9iit\\nHXa/n7+9r13Z07BHOdl1ckwzjTc1ZlIUdeZSYyZ/PzuDWmfgcrkoKipix44dZGVlcemll7JlyxaK\\ni4uHjiktLWXTpk2Ulpaya9cu1q9fz65du4Y9z1tvvcV//ud/8sorr5zzGrLOQIw3a4+LH/zmAAZ9\\nFL/7kW97Ep2p19FLZXslcYY4ilKLQhNSiAvw97MzqG4inU7Hpk2bWLFiBW63m1tvvZXi4mKeeuop\\nANatW8fKlSspLS2loKCA2NhYnn322VGDC6EGXT020tIgzhDtdyEAUJxGXi0Fj9POz+648PFCqEII\\nWidjSo0R1dgkVBR15orETK29rcqehj1KTVdNQM/v8SjKHT/fq9z+wB5lwOYek0zhoMZMiqLOXGrM\\n5O9np0zjEeIsNpd3JpEv1z0eiXc/I+/21c3tMqNIRAbZm0iIs1R1VNFt7/ZrT6KzPbr5OEdrrNz6\\n+XwWz0sc44RCXJjsTSREkAZc3j2JAm0ZAKQmyu6lIrJIMQjAmXOL1USNuSItk9vjxul2otVog7pS\\nWVqytxi0dvlWDCLtPIWTGnOpMZO/gppNJMRE09wxwKk6yM3wb0+is82ZaUSbCNmp0jIQkUHGDIQ4\\nw/Z323npn7UsnJnCt76cF/Dz2Fw2DrcexqgzMjt99tgFFMJHMmYgRBAaW73jBZNSg2sZGKOMaDQa\\nHG6HfJkREUGKQQDU2j+oxlyRlqm5wzutNCs98MFj8H4r02v1KIqCw+0IKlO4qDETqDOXGjP5S4qB\\nEGdo6fC2DHIyg2sZABh1snupiBwyZiDEab19br736/3odVo2/Xh+QFtRnKnWUkt7fzu5CZNJj0sb\\nm5BC+EjGDIQIUK/dxvRCKJoW2J5EZ6s+ZuT5P8P2cmkZCPWTYhAAtfYPqjFXJGXSRQ9w1VWwamXw\\nXUQA0XojfX2+LTyLpPMUbmrMpcZM/pJiIMRpwe5JdLaMFO+YQYdVWgZC/WTMQIjTBvckKkguwBxt\\nDvr5+vrd3Pur/QFfF0GIYMiYgRABGuuWQawpihijDofTjbXHNSbPKUSoSDEIgFr7B9WYK1IyuT1u\\nHG4HWo12aEroWEgxn96jqOP8XUWRcp7UQI251JjJX7I3kRBAQ6uN/QcgLzsaJo3d897wOSMDSh+p\\naXYgduyeWIgxJmMGQgA7d3Xwl7Ia5s9I5o7VU8fseRt7GmnqaSIrPotJ8WNYZYS4ABkzECIAY7Un\\n0dmMUbIKWUQGKQYBUGv/oBpzRUqmljHak+hsQ1tSuGTMYKyoMZcaM/lLioEQeK9jAGOzJ9GZpGUg\\nIoWMGYiL3oDNwz0b96GL8u5JpB3jr0j7mvZhc3hYlDMfXZR8/xLjw9/PTplNJC56/Y4BFi8GrTt6\\nzAsBwKvbjJxqGiDzNjtTc8a25SHEWJGvKQFQa/+gGnNFRCadjZJ5sOQTYzteMCgu2ttV1NI+eldR\\nRJwnlVBjLjVm8pcUA3HRG1x5HKMPzbf2lERvMfBlwzohwkXGDMRF73jncaw2K/nJ+SRGJ47587/+\\ndht/23GKy+ak8c0vTh7z5xdiJLLOQAg/jfWeRGdLP717aVuXtAyEekkxCIBa+wfVmEvtmTyKB7vL\\njkajCVkxyEg1otGcf62B2s+Tmqgxlxoz+SvoYlBWVsaMGTMoLCzkkUceGfGYu+++m8LCQubNm8e+\\nffsAqKur46qrrmLWrFnMnj2bxx9/PNgoQvitrslGeTmcrApNIQDITDVw260aPnu9Q7o8hWoFNWbg\\ndrspKipi+/btZGdns2jRIrZs2UJxcfHQMaWlpWzatInS0lLef/997rnnHnbt2kVzczPNzc2UlJTQ\\n29vLJZdcwssvvzzssSBjBiK0ynd38EJpDfOmJ/Odm8duT6KzfdT6EXaXndnps8d0V1QhRjOuYwa7\\nd++moKCAvLw89Ho9q1evZuvWrcOO2bZtG2vWrAFg8eLFWCwWWlpayMzMpKSkBIC4uDiKi4tpbGwM\\nJo4Qfmtq9Y4XTEoNXcsAZCWyUL+gikFDQwO5ublDt3NycmhoaLjgMfX19cOOqampYd++fSxevDiY\\nOONGrf2Dasyl9kxN7d5tKLLSQ7sY7EJ7FKn9PKmJGnOpMZO/glqBrPHxOn5nN1XOfFxvby833ngj\\nv/3tb4mLixvx8WvXriUvLw+AxMRESkpKWLJkCfDxmzCet/fv3x/W14+k2/v371dVnrPfvz2736a7\\n30F2xqyQvn7xwuKh22mxaef8fpAazo/ab6vx/79B4cxTXl7O5s2bAYY+L/0R1JjBrl27+NnPfkZZ\\nWRkADz/8MFqtlg0bNgwd8+1vf5slS5awevVqAGbMmMFbb71FRkYGTqeTz372s1x33XWsX79+5IAy\\nZiBCxGb3cM/D+9Fq4Xc/mo9OF7qLFHcNWKhoqsZIIgun5YfsdYQYNK5jBgsXLqSqqoqamhocDgcv\\nvvgiq1atGnbMqlWreO655wBv8UhMTCQjIwNFUbj11luZOXPmqIVAiFByeGysuFZh6aejQ1oIACwd\\nRv74HLz8iowZCHUKqhjodDo2bdrEihUrmDlzJl/5ylcoLi7mqaee4qmnngJg5cqVTJs2jYKCAtat\\nW8eTTz4JwDvvvMPzzz/Pm2++yfz585k/f/5QC0Ptzm4aqoUac6k5kwsbUybDgrmhHTwGyEz1jhl0\\ndtsZ6cuams+T2qgxlxoz+SvoXUuvu+46rrvuumH3rVu3btjtTZs2nfO4T37yk3g8nmBfXoiADTi9\\ng8cxutDvJBoTrcUUraPf5qLT4iQlSR/y1xTCH7I3kbhoVXdWY7FZmJY0jaSYpJC/3i/+71HqWvpY\\n/7UiZhaMPFlCiLEiexMJ4aMB1+mWQYh2Kz3b4O6lLR0ybiDUR4pBANTaP6jGXGrNpCgKDrcDjUYz\\ntCAs1DJTjZjN4PScWwzUep7USI251JjJX3KlM3FRqm208b8vK0ybHM2Cz4V2JtGgT3/CyJRZkBwj\\nLQOhPjJmIIZUVtayfXs1TqcWvd7DsmX5FBVNCXeskHh7Tyd/+sdJ5hQk8d2vTRuX1+x19FLZXkms\\nIZYZqTPG5TXFxUuugSwCUllZy+bNxzEalw7dt3nzDtauZUIWhMbTexJlhnhPojMN7U90nq2shQgX\\nGTMIgFr7B4PJtX17NQbDUirqmvj3kWNU1rfg9FzJjh3VYcsUKuXl5TS1jc+eRGfSR+nRarS4PC7c\\nHvc5mdRGjZlAnbnUmMlf0jIQADidWg7W1nGqvRWArr4ejjXXc6i+nqXXN5OXkTShtl5u6fS2DLIz\\nxq9lAN4N6wacA9jddkxa07i+tvC6mLpD/SFjBgKAXzy+hQNV06mt1VKQnkX3QD9tPVZMsbv41a8X\\nEaUFk95EUkwSSdGRXRgcDoXvPrwPDd49ifT68RlABviooZqaZguzs6eRlxn6tQ1iuJG6Q+32Haxd\\nWzDhCoKMGQi/1VhqmLkonhPNH/Cp2TcTb0gAoH9gO1dd+wnS4+Kx2Cz0O/vpd/bT0N2A4ozhVGUS\\ni+YkkTtpfL9dB8uFjZtWK9j7o8e1EADs/9DIv/aC65N28jLH9aUvWooCTW12ahr6+cPmXfQ7FpCU\\ncQANGlKZgdG4lB07dk64YuAvKQYBKC8vH9pCVk38zaUoCjWWGjoHOsmfmsN3b57Grn/tweHQYjB4\\nWLq0cOh/EEVR6LZ302XrwmKzcOj4AG+/O0DZu42kJ8Uwf8bHhUGj+bgpfuTIQYqL56qqKb7jze1k\\nzcli8jiOFwxKT/a2qNotwweR1fg3pcZMcOFcNpeNfmc/J+r6eaO8n8a2fuxO7xjN0XoLRkM35gzv\\nsVbqSCYfhyO44VO1nit/SDG4SCmKwknLSboGuojSRlGYXEhsZiwLZs8c8XiNRoM52ow52oyiKBj7\\ne7B1dvHRcQutXQO8/t4Ar7/XyGULoynI6WPbi+3EGVfS26ulrW2JqmYmDV5tLFo3/i2atJTTxaBL\\nZhT568wvGIcPe/j0p6cRk5BOu7WfSZO9rdYB5wAexbvnWZcTTp6+eGKCyUB2ugmPJRmtp4A09HRw\\nDBsW7HRjMMg+aTJmcBFyOhV+/1I104qt5GTpKEwpxKQPbDDT5VI4VNXDh4e7OFRlYelyF++88wFd\\nXYvQEU0MycSShhYd6ek7ufPOq8f4X+O/E10n6BroGrc9ic7U1GbngSc+whxn4NffnzOurx3JKitr\\neebZKposJXT19tFt66dn4C0KCnOYNGkSt3wNBq+ZZdQZMelNREeZaKk3MTXHRGKCbuh5BscMemmh\\nm3o8jv18/+tLmTEjL3z/wBCQMQNxXnaHh8efr6bqVDd1LTp+eud0TEHszaPTaZhfnMD84gRcrsn0\\nOXv58F9v5uCIAAAgAElEQVTH0aLDhY0eGumlGUtTEk6NYwz/JYEb3K00LC2DJANajYbuPidOpzLu\\nYxa+UONsm+3bq2m2lnC4vnboPo3mUqwd+1myeBaZJhNmkwmT3kSUNmromKyzGrpFRVNYuxZ27NiJ\\n2a4hOuokk6dl0NgdzcW+DFDWGQRArXOKL5TLZvfwX388TtWpbuJi9Hxn9XTioseu31yn02COiSfd\\nmEYm80ihkPaao9hsCscbOnjroxoe/H0l7+ztwuUKT2vP41H497/eQaPRhKUY6HQapuQYmDJFoWfg\\n464itfxNDX5zbmn7NLv399PWdjWbNx+nsrL2wg8OIadTS0K6hZmzINZ9nMsLZ3Dd/BKuu2Qad940\\nlZykDOKN8cMKwWiKiqZw551Xc++9V3HbjV/iww8n8bd/NtHe6Qw4n1rev2BIMbhI9PW7+c2zVZxo\\n6CHepOd7a6YzJSs0A6jLluVjt+/ASAIJZJOhmU12YhVT8yZT29zLH7ed4IePfsSrbzXj8rhCkmE0\\np5psvPaawpvbjT5fw3us3XiDkRXXQJRBfeMG27dXoxgvoY3DdFNHPx2nZ9sEt/gwWDqdG4emh5gY\\nmJKRQqo5Fl2UNui+/ryseGbnJ+F0efjLa3VjlDYySTEIgFpnDYyWy+1x825lFXUtvZjjDHz/G0Xk\\nZIbuW7G3KV5AevpOSkogL/ff/Pr/XM4ffnktX75mCulJMXT3OzjZ3sChlkPUWGrod/aHLM+ZGlps\\npOcuRKeEbzrs4BqNwYFsUMfflMvjotneQifVuHGSmbuQtp4ugKBn2wTr8iUZOBy70GMiP28Z4F0f\\nsHRp8NeTXr0yB71Oy8GqLg5W9gT0HGp4/4IlYwZhMl79si6Pi6qOKpIz+lm5wshlhdNJTzGM+euc\\nrahoyoj/nmVXpLL08lQOHevBE92KBysd/R109HcQZ4gjPTadxOjEkH1rb2z1jhdkpo7/tNJBatyj\\naNd+C67YUziirGiJItqVyTsHG4AesucpYZ9tkzE5kc9+LpfKPYcwuVtOT30em4ViackGll82idJ/\\nN/BSWR0z84tDfk1sNZKWQQCC7R8c7Jdta7sai2XJmPXLnp3L5XFxrOMY/c5+onXRXLeoaFwKwfky\\ngXfWx9yieEqm5DM7fTYZcRlEaaPodfRyousE//ncR/z9jWasPS4qK2t54omdPPZYOU88sTOoc1RZ\\nWctL/7uTf+/8M++9vTts/eAjtQzC1eds7XHxxJ9P8szL1ex8y8mSK4pIsDeTqMvEaT2Mx+Ohsf2V\\nMfkGHlROm5W8KZO4e91KSkrgzjuvHtMvT5/5dAap5mis/QMcOdXq9+MnwpiBtAzCYPv2aozGpdS2\\ndmJzOMlOjyNujFdBOt1OjnUcw+ayEaOPoTC5EH2U+q67a4gykJOQQ1Z8Fh39HVTUtlF1coCqkw28\\nVPohliYrhdk3kBzvnfo60noFRfFOcbU7PN4fpwe93oMp1oNH8f5UVtXywgvHqWueh8vZh8exgs2b\\n3w3L2ge1tAx27bfw0uun6B1wYtBHcdmMbJYvTGNaQi07duwkM/kkvbp+CmbPC+tsIkuPg/oWG+mp\\nUcTqY0PyGnq9hm98MZdWVxVuUxNOd7Iq/38JJVlnEAaPPVbOBxUzOdroHbCaMweijVGkxh/m7m8v\\nJ94QH9SlGNs6HeytPUZqhp0YfQzTU6aj00ZG3VcUOHSsh527Wvmff7zBwMAiAKZkmcjK0qLgISn5\\nPb7ylUV4FA9793nYtVs5529k/ny4dNHHt1/6q3ftQ10dtLVquLakBF2UNixrHzyKh398uI+ebi03\\nXzWf8R7Hdrpd/Pf/1LH3SCcA07Lj+cbn88hIHd5qPF7bz6+ePYI5zsCv/mPOuOcc9M9/t/E/20+x\\ncFYS3/pSaK89MXhd7OSYZKYmTQ3pa4WarDOIADUtlqFCkGxKwmQcwIUNl66HOqv3fi063vtXPAU5\\n8cwqTCAr3ejT/4wt7Q4e/eMxegbsfOkGEyUzp/s03U4tBruQ5hbFY62r5sDxDBq62hlw9zO4SmHA\\n6cThPn1L6y0guigtep0WfZQWvV5LfLSWWIMGrUaLVqNF707ARAqTErRkxySgi/L2kIZjYFSr0VK+\\nQ0/vgJMVJU5Sk8fvG6jFZuGU9RRRcd7WwKpPZ7P8E2kj/m3lTzYRF6PH2uugrsnG5KzwDLpXVHcD\\nUJBrDvlr5Zpz6bZ30znQSVpsGnGGuJC/plpIMQhAMPuQ7DlkpWlAj9u9m5k5X2R6djoA/fbX+fxV\\nl5NiMtNj76Gh2cH+o13sP9rF/2z3LqcvmBzPzPx4LlsQjyHq429xg4PRH3z4IXW2JFLSM5lTVMCl\\n0wrDXgiCOVepSTrm5uUwMzeLXnsfJjRo0JIe3cacjDloNVrmZWhZs1yL9gKf6ZOMp2gjj0Qz1NSU\\nQ6o3U7gGRlPMRnoHnLR02ElN1od8bxuXx0WdtY7OAW9r4JOL4rn+srzzjiG99VY582ZMpbm7nW6H\\nFRj/YuB0Khyv9xaDeUXeDRRDea4MUQYy4zJp7GnklPUUM9NG3p7lbLI3kfBLj72XbW+fwJyYyZov\\npmF0fHTGpnAzhvXL5sTY0a/o4ejJHqrreujud7D3aAct3R3EZnsHIeMN8bTUWfnbC804XJ9i97GT\\nxCSW4LJ/xI++MYtYU+S0CEaybFk+mzfvwGhcSqIpHvBOJ7x2WfGwYujvcw3yTk0sGNPMvkpJNFLb\\n3EtLh51ZhaH99jnYGnC6nURpo8iOzyYtNs2nx16/wkx1ZzsGoxXICGnOkRw50YvD6SEzxURK0vi0\\noDLjMukY6KDPMcDhmlZm5aWPy+uOlcEvh/6SMYNx0u/s987ssblpPZnG9Usm+9wHqyhQ12SjoroH\\njN1Mmtw7tFjrpb9+QGfnIpoatDQ1e0iKTeCy6flkTSpXxT5AwaqsrGXHjuozimbgU3DH8rmC9bfX\\nm3j9vUaWXjqJr6zMCslrWHtc/GnbKSZN7SIvD+KN8eQl5vlVSN0eNwdaDgAwL2PeuLc0n99Wz7/2\\ntnD1okxWfyZ73F63xWrl8eeP022J4hffnT20t5HaDc5UdEct5tcPxsuYgdrYXDaqOqpwe9xMMidz\\nxVWT/Xq8RgOTs6JP99l6v9H1O/vpsfegcx0lShOFy+0mJc7MpYX56KI0YV8kNFZGW68Q7ucKVlry\\n2O9eeubalbp2K20OA9GmdE61RPHJOdlkxPvWGjhTlDaKOEMcPfYeuu3d476xnyHBSkYGzC0K/XjB\\nmdITzCSbzLS1WXmprJ5vfTlvXF8/UK+/cYx+QyEHqyr9fuzE+MQYZ/7MKXa4HVR1VOHyuDBHm8lL\\nzBuTDCa9iYy4DLKiJ5HBPGalz2KSsR5dlLe5Ee5FQoPUOP9aDZlyJhnJnwbpWd5iMFZrV+oaPsXr\\nuydTvj+Hg4dqSDD08P01MwMqBIOZEqMTAbDarUFl9JfD7SB/uo0vfj6K4vyPp5SOx/un0cDNn81F\\np9Wyp6KDyhN95z1eDX9TbX1t1NlrGdB0kJnp/0e7FIMQ6u13UdlWhcPtIM4Qx7SkaWO+snbZsnwc\\n9p3Ex0QPdTuN1TJ9ETqTs4wsWwbTi8emZbB9ezUYLuOdYxW0dnuvUTF78o1MNXuCXmhoNnq/lVtt\\n41sMuu3egeMEY0JY9pGalGZkySLvOMmW107hUcf3q3P0Ofo40naEU9ZTaHUuojFTkDDL7+cJuhiU\\nlZUxY8YMCgsLeeSRR0Y85u6776awsJB58+axb98+vx6rRr7MGujrd/Ofz1ZR+oYNY5SJguQCtJqx\\nr71n7wOUnr5TVddzVeMMCzVk0ml1RGmjcHlcuD3uoDNZHX10ao4xOc9Jclw8n545k4JJaTidgf/N\\nDWYy6ow0nDLyapmLqprzf0MeS4PFJ8GYMGKu8XD90kzMcQYa2/p5e2/bqMeF42+qu9fFn16p5VDT\\nUfqd/Rh1Rr509SeItdcShf9fAIIaM3C73dx1111s376d7OxsFi1axKpVqyguLh46prS0lOPHj1NV\\nVcX777/PHXfcwa5du3x67KAnntipij3VfeVwKDz+fDWNbf2kOKLJNYV2iqea+sKF74xRRvo9/djd\\ndkzawC4uBNDS24JV24BCFulxaRTO+HhMaqy6Cy3NZmpqWtlbYaUwLzSrgM+kKAo9Du+mcYMtk3Aw\\nGrR85dpcjrZUY85uxOVJCvsCTkWBnbva2VbewIDdRa9bw/VLM8mMy0SbriV+rXcFub+C+qq6e/du\\nCgoKyMvLQ6/Xs3r1arZu3TrsmG3btrFmzRoAFi9ejMViobm52afHDlLLnuqDztc/6HIpPPHCCU42\\n9pBgMnDv1wsxx4/PH48a+i3PJplGN7RHkcsecKZT1lPUd9dz6aU5RNtPYubjQhBsd+GZmQYHcA9X\\nj09XUa+jF7fHTYw+5pxtIcb7/Vs4O5HFJQm4FRcN3Q0jHjNemarr+vnl74/y4uu1DNhdFOQmsOry\\nWWTFZw31PAxer8FfQX1KNTQ0kJubO3Q7JyeH999//4LHNDQ00NjYeMHHDlJQTu+pPnZ794SCosD/\\n+59ajtRYiDHquOeWwnHfGE5EjqE9itz+jxsM2Dy8uf8EGZOtaDVari75BLOTu9mxY+cZU2fHrrtw\\nVkE8Bn0UzR39tHeGftX0znetnOqEJZeYByfQhdVk82QOtx2mY6CDtNi0gC8TGyi3x81HtY08+Vwb\\niqKQYDLw+WU5XDE/acy2CQmqGPg6qBPsOoG/vPhVJmVMJzq6BofjICUlJUN9dIMVebxvDzrz99Xt\\ndbzz/uv0dkfx65/eTO6k6HHNt2TJkrCdj9FuD96nljzne//G+7a938hfX9hDiqmKH9y52ufHW3tc\\n7K3Npqm9n+zkfVxanM385fNJKkqiqenk6eOvDjrf2X9PhbnxvLFzO398oZ7/uOv6kJ6fDw6m09oF\\nsf0H6KipUsX7lxGbwT/++Q8+0n/E16//+ri9fre9m7ySPFxGJxrHh6SakvjJ3V8kJlo77Pjy8nI2\\nb94MQF5eHv4KatHZrl27+NnPfkZZWRkADz/8MFqtlg0bNgwd8+1vf5slS5awerX3j33GjBm89dZb\\nnDx58oKPBW/B+ezXTnJJfp5qLqg+kqaeJhp7GnG7tJjdBRRNjQ93JKFyB4728MRfjjE1K577vzXd\\np8fUNAzwxAvHsfY5SDFHc/fXCpiUZgxxUq8d77Xz4uu1zMpP5J5bQjdbra3TwY8fP4RBH8VjG+ap\\n5toCHsXD4dbDONwOchOmkB6XGtLXs7lsnLKeosfuHTuJM8SRmzAZk8G3TSz9XbAb1JjBwoULqaqq\\noqamBofDwYsvvsiqVauGHbNq1Sqee+45wFs8EhMTycjI8Omxg1q7LQzYtqtmuuTZ305a+1pp7GlE\\no9EwPX1q2ArB2bnUQDKNLjPV+yHeafVtzGD/kW5+s7kSa5+DvElx/Oj2GSEtBGdnWjjbzMrr4Iqr\\nukO6K8DBSu+U0sLc+BELQbjeP61GS3Z8Docr4NE/NNDb5x6zTGdet+PR/9rOn7buoqKtgh57D/oo\\nPVOTplKUWuRzIQhEUN1EOp2OTZs2sWLFCtxuN7feeivFxcU89dRTAKxbt46VK1dSWlpKQUEBsbGx\\nPPvss+d97Ej0hl1c+slPqHK8oHOgc2in0SnmKUMLdIS4kLRkA1qNBmufA6fz/B+urT0dPF9ai92p\\nMG96Mt/6Uh56/fh+YzbH6ymaZvKufnf0nDPlc6x8VOUdpJ5dGL5ZRKNJikmiqSaeTmsPf329gW98\\nwb/dBEYyuGDQYFhKfXsXFQ312Lb/m7uNk1l2+Vyy4rPGZRuQiNib6PYH9rBwZorqloQfrbHSo6tG\\nq1XIScghI278N/ISke3+/zpMh9XG/1k3i9xJI+8K2tjTSFNPEx2dYKnP5EsrssN2bYHBLOmx6eSa\\ncy/8AD+53QrrNx7A7nTz4N1zSEtW3wSMU402HvpDBQpw3zdnkJ8b2GCyoij0Ofv4yc/LOFI9H+tA\\nL06Xd8+xhJhYrlpwgp/evzLgnBP2egZVdRY8HgWtNnz9h2fu/dJm7eOEFWbPyeTrn58khUAEJMVs\\npMNqo6Xdfk4xUBSFGksNnQOdaDQaFkybTOrs0PZTX4jZaKappwmr3UouY18M+l293PglN5b2GFUW\\nAvDuE/bJ+en8a28Lf3m1jh+tK/KpODtcLvpdvfQ6eulz9NHn7ENRFJp6O2jvsQBg1BsoyJjE1IxU\\nEmKaQ/wvGS4iisGXrjdhTuunx9GNOTo8TcfBppzRuJSDFWXU27JxON9jTn4SOebQ7DrprzNn7aiF\\nZDq/2cVG4tPgo8M7WTjn80P3uz1uqruq6bH3EKWNYlrStJB1y4xmpPMUa4hFH6XH7rJjc9mI1o3t\\nNQ667d3ExUF+5uj/VjW8f1+8Jou9Rzs5cLSKe39UgaunluLiuUOLYwd3Gj5W00t1XS8nG3vJyLLz\\nqSs/fg6NRoNJb2JaZgImx1RS4uOIjTYMFZbx3l8sIorBnMIkGrr76bJ1ha0YDF63uLvfztHGemIS\\n08hOvpYE5WTYmuwi8i2YayTNCtV7nUP3NbXZ2bn/ODNm2TBEGShILgjqMqhjLcGYQENXB9UNVmZN\\nGdtiMLgZXjhXHfsiJlrLgnyF93fXET31Eyi98dS3LWLT5jIu/WQNHxwwY3O4hz1Gb4giwRhLnCGO\\nWEMssfpYorRRrP28ic2b94X9WhsRUQySopNo6G7AYrOgKEpYNq0a3OOlor6emMT5pMSZWZCfh8ul\\njlXRoI49d84mmc5vcOHZgisWAHDsZB9PvnicfpuLOJOJlZcVhO3C7KOdp64mM8+91EHeJCuzvjV2\\n3aNOt5MB58DQttn+5hpvlqYOSmZdjVPTC7FJdHAMjNPYe/gDbI5FmOMM5GXFUZAbR0FeLFOzTSNe\\nkc+7vxghWzDoq4goBkadEZPeO4uh2x6eriK93oNLcdLvtp7uv80jSqtRzVbRIjLVnmjmpTc+AJcR\\nd1cFDf1RxCdkUjTFzDWXTEMfpb6NhadPSUCDhtqmXnr73MTFjs1Ml8FWQbwhPixf+PzlcmlJ1Eym\\nnaOAgo4YDMSRps/mJ/fM9evKbGrYX0x9f2mjGLyoRpetKyyvv2xZPlbHy8woVsiKP0q0Qae6raLV\\nMn/+TJJpdJWVtbzwp1N0di7i3285eXN/DkeOnGJKqp17bsknJjq8/3uOdp5iTVHkZcXhURQOnF4T\\nMBZqm7vxKFzwy55a3j+93oOeGDKYi63GQhrFmMkl0Rg7bpfoHEuRUwyik7DZ4L19lgvOyQ6FoqIp\\nXHODmeSkD8hMqlXdVtEi8mzfXk20cTkajx6LVQEUirO/QKLGpppVt6OZXeD9wD50bGw2rnO5FDZv\\n6ea550DvGd+B8kAtW5aP3b4DLVFoTn+Uqu0Loj8iopsIvF1FO8pM1Lf2U5jazcI549tV1GPvISs3\\nha/mTWJOxpxxfW1fqaUv9UySaXSD41CmqAQWXn4pup48ctOScDqPhDmZ1/nOU0mxmW1v1XO0xorH\\nw4h94f6oPNmH3ekmLTEac9z5p5Sq5f07s68/MVGLwbAzLH39YyViigHAvKIk6lv72XO4a9yLQXt/\\nOwCppvDO8xYTh17vHW9KJA9z9GQ0p7uFImEcKjsjmpxMI3GJdrp6+0hJCO4aB4MtjOJp6p5FdDY1\\n9PWPlYjpJgK4dK533OBw9fh2Fbk9biw2CxqNhhRTimr6LM+mxlySaXSD3QwAtTX/AtTVzXC+86TR\\nwDduMvPpT4FdE3xX0ZGT3rGHOdMvXAzU8v6dSY2Z/BVRxWBSmpGsNBN2p5t9R8bveqzluzt4510P\\nnoEEDFHqXBUpIs+ZlyyNi9sfceNQgwO9wV4budPipKm9H71Oy8z80aeUitCKiL2Jzoy4bUcL/3i7\\nnpKiZO68aWrIX19R4Ke/q6Clc4Bv3pDPZSWyEZ0Q4N0uY3/zfjyKh7kZcwNeD3H0VAcvvl5DUkwi\\nd39NHa2iiWDC7k00aPG8JI631TOl0DouC9Cqavpo6RwgLkbPwtmR1Z8pRChpNBoSjAlYbBasdmvA\\n42mGeCuf/QzkJkTGLKKJKqK6iQAyUg2sXBZL5iT30CKVUPrXh96B44UzU4am+6m1f1CNuSSTbyI1\\n01h0FXXbveMFiTG+fdmK1HOldhFXDMC75gCgayC0C9AGbB72V3pf41OLUkL6WkJEogSDmY8+ghdf\\n7g5oUsfghe+jddEyHhdmETdmAOBwOzjUcogobRTzMuaFrKvo/UPtPPO/teRN8v2yhEJcbB7YdISm\\n9n7u+Eoh84v96+oZvD5CRlwGOQk5IUp4cRrXy16GiyHKQKwhFrcntF1F5knt3LQabrhG1hYIMZpZ\\n+d7unYNH/f9/cbB7aby35xbnishiAB93FbV0h6araMA5QJ+jj0RzFEVThs8gUmv/oBpzSSbfRHKm\\nkhneYnDkpBV/+hk6LU7KdvZTd0pLvMH364ZH8rlSs4gtBvH6JP7xD9j0Bwt2x9iv2BxccZwSk4JW\\nE7GnSYiQK5gSiylaR2e3nfpmm8+PO1DZzZEjUH0kMnYpnegi9lPOZDSgccVid3rYVzF2OyeCd/50\\n50AnMPL2E2rZG+VsaswlmXwTyZm0WiieerqrqNL3rqLDx09f+L7AvynbkXyu1CxiiwHAgmJvV9GH\\nh8e2q8his+DyuDDpTaq6wpQQavXpy8xcvwryZ/lWDDweqKrtAWDuDBkvUIOILgaDexVVnBzbrqLX\\n3m6juQVSTWkj/l6t/YNqzCWZfBPpmQpzE5g0SUO/yztV9EKOnexjwOEi1RzNpDRjyHKNFzVm8ldE\\nF4P0FAO5GbE4XR72VozNrKKmNjvl7/ZQ+qoWkyZpTJ5TiIlu8FKViqIMLSI7n4OndymdMVVaBWoR\\nkesMzvRqeSv/fL+OpVckserKaUG/3l9ebWTnB00snJnCt76cF/TzCXGxaOltob67nhRTCnmJeec9\\n9sNTR6g62c8lUwspnCwFIRQm/N5EZ1tyWSLphXXotFY8iieomT9uN+w+7J1F9KlLRu4iEkKMzBxt\\npr67/oItA5fHBbp+iqZrKcj0fUqpCK2I7iYCiI02kGCMw6N4gt5Kd+9hK739TtISoymaNvrFOtTa\\nP6jGXJLJNxMhU7QuGqPOSFe3k5auvlGPG/z/NN4Y2JTSiXCu1CjiiwFAUszpvYpswc0qene/t1Vw\\n+bxUZNqzEP47esDMn/8Mb+0a/YvZYMtBVh2rS8SPGQA43U4OthxEq9EyL3NeQF1FTreT3acOcbwK\\nbrh8Lub4iO9BE2Lc7TvSzf99sYqsNBM/+07xiMccaD6Ay+NidvpsjDr/ZhIJ343r3kSdnZ0sX76c\\n6dOnc80112CxWEY8rqysjBkzZlBYWMgjjzwydP8PfvADiouLmTdvHl/4whewWgPr5tFH6YkzBNdV\\n1DHQQbRR4cpFiVIIhAjQ7IJ49DotjW39dFqc5/y+o7sPm9OFUWeUQqAyQRWDjRs3snz5co4dO8bS\\npUvZuHHjOce43W7uuusuysrKqKioYMuWLRw5cgSAa665hsOHD3PgwAGmT5/Oww8/HHCWGE0SHx2G\\n18oD6yry54L3au0fVGMuyeSbiZJJr9dQmOvt/hnp0rQ737Xy3HNQUxn4haImyrlSm6CKwbZt21iz\\nZg0Aa9as4eWXXz7nmN27d1NQUEBeXh56vZ7Vq1ezdetWAJYvX45W642wePFi6uvrA84Sq03inXfg\\n33us2Oz+LUDrsfdgd9kx6ozSjylEkAYvav9R1bnF4MiJbpxOyEqWqwaqTVDFoKWlhYyMDAAyMjJo\\naWk555iGhgZyc3OHbufk5NDQ0HDOcc888wwrV64MOEtKkp68SXG43B4+POxfV9GZm9L5Qq37kKgx\\nl2TyzUTKVDLDjDkBjAk9w/qsrT0u6lv70EVpmVUY+IXvJ9K5UpMLdo4vX76c5ubmc+5/8MEHh93W\\naDQjThPzZerYgw8+iMFg4Oabb77gsedTMiOJmqZePjzcxScW+LZ6uLvXxb6jXUyerAn4Gq5CiI+l\\nJOn55i0m+p399Dh6hlrbByu9s4imZsdhNEyIiYwTygWLwRtvvDHq7zIyMmhubiYzM5OmpibS09PP\\nOSY7O5u6urqh23V1deTkfHxFo82bN1NaWsqOHTtGfZ21a9eSl5cHQGJiIiUlJUOVeLCvbsmSJSye\\nm8STz/6dllNabv9SHjHR2mG/P/t4gCf+3yu8s7+V61YsZeFN+gseX15ezv79+1m/fv2ovw/X7TP7\\nLdWQB+Cxxx4b9f0K1201vn+D96klT7B/T9MvmU6/s5+yN8pIj0tnyZIlHKqy0lizh2mJ6UBhwPnk\\n/Rv9/dq8eTPA0OelX5Qg/OAHP1A2btyoKIqiPPzww8qGDRvOOcbpdCrTpk1TTp48qdjtdmXevHlK\\nRUWFoiiK8tprrykzZ85U2traRn0NfyM++Pujyu0P7FHe2dt5wWM9HkX58WOHldsf2KO8f6DL59d4\\n8803/co0XtSYSzL5ZqJl6rX3Knsa9iiHWg4N3fdfW/Yrd/5yj1LfbAtbrlBRYyZ/PzuDWmfQ2dnJ\\nl7/8ZU6dOkVeXh4vvfQSiYmJNDY2cvvtt/Pqq68C8Nprr7F+/Xrcbje33nor999/PwCFhYU4HA6S\\nk5MBuPzyy3nyySeHvYa/c2X3HGmlub+OGXmJFKTkn/fYyhN9/Oa5o8SZ9Pzqe3PQ6WSlmRBjZXA9\\nwaz0Wbg9bo62HyUKI/MmzZZFnePA38/OCbHo7ExOt5NDrYfQoLngArSnX6plT0U7Vy/KZPVnssci\\nrhDitBpLDR39HeQk5OBRPDT2NJIem06uOffCDxZBG9dFZ2p05gI0i23kRXAA/QMeDhzzXs3sU4v8\\nGzg+s59QTdSYSzL5ZiJmMihm9u2Dbf+0YrWP3YXvJ+K5UoMJudQ2KTqJHnsPXQNdJMckj3hMt7OT\\nK7SVBCkAAAtbSURBVK/00NcVT1a6rIQUYqwlGBLYs0cD9DJ7AUQbtcQbZZdStZpw3UTgW1fR0faj\\n9Dn6mJo0ddSCIYQIzvcefJP391VSUKAhLyuGm1d8mqKiKeGOdVG46LuJ4OOuou4eD8frz+0qGnAO\\n0OfoQ6fVkRQtVzMTIhQqK2s5VdWBzbaIw4cX0t16LZs3H6eysjbc0cQIJmQxAGipTeLPL0DpznP3\\nKhpccZwckzxh9lMHdeaSTL6ZiJm2b68mN+2zACgKYE/AaFzKjh3VYc0VCmrM5K8JOWYAMKcgCY2m\\njmO13fT1u4k1RQGgKAodAx2Ab5vSCSEC43RqSTBFMynRu81LgikaAIdjwn4HjWgTcsxg0MY/HONE\\nQw+3fHYqVy70jgucaOqkzXkSsymWGakzxjKqEOIMTzyxk7a2q8+5Pz19J3feee79YmzJmMEZFhR7\\nxwM+PPxxV9GLr7Tzpz+BrVNaBUKE0rJl+djtw7eZsdt3sHTp+ReDivCY0MXg0rlJaDSaoa6ixlY7\\nJxt7gCiKJgc+g0it/YNqzCWZfDMRMxUVTWHt2gLS03eSmFhOevpO1q4tCHo20UQ8V2owYccMABIT\\ndMyZHgeGHjr6rLyzewCAedOTiIme0HVQCFUoKpoiU0kjxIQeMwBo62vjlPUUsfoEnn5mgN4BJ99f\\nM4PpU2PHMKUQQqiLv5+dE7plAJAUk8TbB3fz6qsfUHFEg9mkx2NPBaQYCCHEoAnfV1Jd1cDrr7Rj\\nsy3CFLOQVNNn+eMfg1v4otb+QTXmkky+kUy+U2MuNWby14QvBtu3V2M2fIbYOCgo0DJtUsqYLHwR\\nQoiJZMKPGTz2WDmdlivp5DhGEohnEgCJieWsX79kjFIKIYS6yJjBWfR6D1qiSKVo2P0GgydMiYQQ\\nQn0mfDdRKBa+qLV/UI25JJNvJJPv1JhLjZn8NeFbBt6FL7Bjx04cDi0Gg4elS4Nf+CKEEBPJhB8z\\nEEKIi5HsTSSEEMJvUgwCoNb+QTXmkky+kUy+U2MuNWbylxQDIYQQMmYghBATkYwZCCGE8JsUgwCo\\ntX9Qjbkkk28kk+/UmEuNmfwlxUAIIYSMGQghxEQkYwZCCCH8FnAx6OzsZPny5UyfPp1rrrkGi8Uy\\n4nFlZWXMmDGDwsJCHnnkkXN+/5vf/AatVktnZ2egUcadWvsH1ZhLMvlGMvlOjbnUmMlfAReDjRs3\\nsnz5co4dO8bSpUvZuHHjOce43W7uuusuysrKqKioYMuWLRw5cmTo93V1dbzxxhtMmRJZ+wTt378/\\n3BFGpMZcksk3ksl3asylxkz+CrgYbNu2jTVr1gCwZs0aXn755XOO2b17NwUFBeTl5aHX61m9ejVb\\nt24d+v33vvc9fvWrXwUaIWxGawWFmxpzSSbfSCbfqTGXGjP5K+Bi0NLSQkZGBgAZGRm0tLScc0xD\\nQwO5ublDt3NycmhoaABg69at5OTkMHfu3EAjCCGEGCPn3cJ6+fLlNDc3n3P/gw8+OOy2RqNBo9Gc\\nc9xI9wEMDAzw0EMP8cYbbwzdF0kzhmpqasIdYURqzCWZfCOZfKfGXGrM5DclQEVFRUpTU5OiKIrS\\n2NioFBUVnXPMe++9p6xYsWLo9kMPPaRs3LhROXTokJKenq7k5eUpeXl5ik6nU6ZMmaK0tLSc8xz5\\n+fkKID/yIz/yIz9+/OTn5/v1mR7wOoP77ruPlJQUNmzYwMaNG7FYLOcMIrtcLoqKitixYwdZWVlc\\neumlbNmyheLi4mHHTZ06lQ8//JDk5ORAogghhAhSwGMGP/zhD3njjTeYPn06O3fu5Ic//CEAjY2N\\nfOYznwFAp9OxadMmVqxYwcyZM/nKV75yTiGA0buThBBCjA/Vr0AWQggReqpegXyhBWvjra6ujquu\\nuopZs2Yxe/ZsHn/88XBHGuJ2u///du4vpKk+juP4J2kSJP0T/+VJGtpoc2ubOZUgklKCQNE1pIYM\\n0rqoqyLG6LKLZjKifxddKRoEdVnEkpBVCjJUjl11ocgZbigJQyVZNRef52I4fCB9euB59jsXv9ed\\nhzneyNm+np3fb3A6nWhraxOdAiC71M7j8cBsNsNisSAajYpOQl9fH2pra2Gz2eD1evHz508hHT09\\nPSgrK4PNZssd+9NNnPls8vv9MJvNsNvtcLvdWFtbE960SdRm1e2anj59CrPZDKvVikAgkNem7bom\\nJyfR0NAAp9MJl8uFqampnZ/kX91hyKNMJsPq6mpqmsZ0Ok273c4vX74IbVpaWuLMzAxJ8tu3bzSZ\\nTMKbNj148IBer5dtbW2iU0iSPp+PAwMDJMmNjQ2urq4K7dE0jUajkT9+/CBJdnV1cWhoSEjL2NgY\\nVVWl1WrNHfP7/ezv7ydJ3r9/n4FAQHjT+/fv+evXL5JkIBDQRRNJLiws8Pz58zx69CiTyaTwpkgk\\nwpaWFqbTaZLk8vJyXpu26zpz5gxHRkZIkuFwmM3NzTs+h26vDP5pw5oI5eXlcDgcAICioiKYzWYs\\nLi4KbQKARCKBcDiMq1ev6mKJ7traGsbHx9HT0wMge+9o//79Qpv27dsHg8GAVCqFTCaDVCqFyspK\\nIS2nT5/GwYMH/3bsTzZx5ruptbUVBQXZt4jGxkYkEgnhTYDYzaq/a3r27Bnu3LkDg8EAACgpKdFF\\nV0VFRe5qbnV19R/Pd90Og502rOlBLBbDzMwMGhsbRafg1q1bCIVCuReuaJqmoaSkBFeuXEFdXR2u\\nXbuGVColtOnQoUO4ffs2qqqqcPjwYRw4cAAtLS1Cm7b6k02cIg0ODuLChQuiM3S5WXVubg5jY2No\\nampCc3MzpqenRScByH5l0OY57/f70dfXt+Pj9fHu8Rt6XmG0vr4Oj8eDx48fo6ioSGjL27dvUVpa\\nCqfTqYurAiC7pFhVVdy4cQOqqmLv3r2//e6qfJqfn8ejR48Qi8WwuLiI9fV1vHjxQmjTdrbbxCnK\\nvXv3UFhYCK/XK7QjlUohGAzi7t27uWN6OOczmQxWVlYQjUYRCoXQ1dUlOgkA0NvbiydPnmBhYQEP\\nHz7MXalvR7fDoLKyEvF4PPdzPB6HoigCi7I2NjZw8eJFdHd3o6OjQ3QOJiYm8ObNGxiNRly+fBmR\\nSAQ+n09ok6IoUBQFLpcLAODxeKCqqtCm6elpnDp1CsXFxdi9ezfcbjcmJiaENm1VVlaW2+2/tLSE\\n0tJSwUVZQ0NDCIfDuhic8/PziMVisNvtMBqNSCQSOHnyJJaXl4V2KYoCt9sNAHC5XCgoKEAymRTa\\nBGQ/au/s7ASQfQ1OTk7u+HjdDoP6+nrMzc0hFoshnU7j1atXaG9vF9pEEr29vbBYLLh586bQlk3B\\nYBDxeByapuHly5c4e/Ysnj9/LrSpvLwcR44cwezsLABgdHQUtbW1QpuOHz+OaDSK79+/gyRGR0dh\\nsViENm3V3t6O4eFhAMDw8LAu/tEYGRlBKBTC69evsWfPHtE5sNls+Pr1KzRNg6ZpUBQFqqoKH5wd\\nHR2IRCIAgNnZWaTTaRQXFwttAoCamhp8+vQJABCJRGAymXb+hf/r7vZ/IRwO02Qysbq6msFgUHQO\\nx8fHuWvXLtrtdjocDjocDr579050Vs7Hjx91s5ro8+fPrK+v54kTJ9jZ2Sl8NRFJ9vf302Kx0Gq1\\n0ufz5VZ/5NulS5dYUVFBg8FARVE4ODjIZDLJc+fO8dixY2xtbeXKyorQpoGBAdbU1LCqqip3rl+/\\nfl1IU2FhYe7vtJXRaMz7aqLfNaXTaXZ3d9NqtbKuro4fPnzIa9PWrq3n1NTUFBsaGmi329nU1ERV\\nVXd8DrnpTJIkSdLvx0SSJElS/shhIEmSJMlhIEmSJMlhIEmSJEEOA0mSJAlyGEiSJEmQw0CSJEmC\\nHAaSJEkSgL8ADzourl11UvoAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84c9e790>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 1\\n\",\n        \"Members: ['AAPL: Apple Inc.', 'ADBE: Adobe Systems Incorporated', 'CTXS: Citrix Systems, Inc.', 'DIS: The Walt Disney Company', 'FB: Facebook, Inc.', 'FJTSY: Fujitsu Ltd (ADR)', 'GOOG: Google', 'HTHIY: Hitachi, Ltd. (ADR)', 'INTC: Intel Corporation', 'MSFT: Microsoft Corporation', 'N: NetSuite Inc', 'NFLX: Netflix, Inc.', 'NVDA: NVIDIA Corporation', 'ORCL: Oracle Corporation', 'RHT: Red Hat Inc', 'TOSYY: Toshiba Corp (USA)', 'TXN: Texas Instruments Incorporated', 'VIAB: Viacom, Inc.', 'VMW: VMware, Inc.']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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v3Tb2PLflZRrVZbMXGDC03WxaLrg3ie/7ksq7CgcwZnBjFsg6gSZU1q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\\n/wPeVJ7Lk8PbaQAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84505e10>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 2\\n\",\n        \"Members: ['RAX: Rackspace Hosting, Inc.']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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57ZNbaY+OXvqkiKi+Dnz0yv3ychcTmvvNFEx2Afq1dGszh/1ujnf9jT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\\nEEIIgP8HohoWxr+aKTYAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c82c01cd0>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 3\\n\",\n        \"Members: ['AMZN: Amazon.com, Inc.', 'FOXA: Twenty-First Century Fox Inc', 'PCRFY: Panasonic Corporation (ADR)', 'SNE: Sony Corp (ADR)', 'TWX: Time Warner Inc']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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Sn7PProo5w+fZpTp07x3e9+ly9+8YsAaJrGP/zDP/Dmm2/y4osv8p3v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\\nDqRqAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84cb2ed0>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 4\\n\",\n        \"Members: ['ADSK: Autodesk, Inc.', 'BP: BP plc (ADR)', 'CAJ: Canon Inc (ADR)', 'CAT: Caterpillar Inc.', 'CBS: CBS Corporation', 'CMCSA: Comcast Corporation', 'CSC: Computer Sciences Corporation', 'CSCO: Cisco Systems, Inc.', 'CVX: Chevron Corporation', 'EMC: EMC Corporation', 'GE: General Electric Company', 'IBM: International Business Machines Corp. (IBM)', 'LXK: Lexmark International Inc', 'NWSA: News Corp', 'NYT: The New York Times Company', 'SAP: SAP SE (ADR)', 'SIEGY: Siemens AG (ADR)', 'TRI: Thomson Reuters Corporation (USA)', 'XOM: Exxon Mobil Corporation', 'XRX: Xerox Corp']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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EDdgWWwpgyCQQjIASIi2I5FzXEQJI943K9ELpd3eCJXied52O6raxZq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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c847a0290>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 5\\n\",\n        \"Members: ['BKS: Barnes & Noble, Inc.']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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JqUgFJ1BUNYxqC1tZW8vLyxz7m5ubS2ts64TUtLy4RtGhsbOX78OOvW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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84847790>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 6\\n\",\n        \"Members: ['BBRY: BlackBerry Ltd']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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6F2AIdDQbJupDFoaTiOQgH91tg1BrHp3BJMCTQaN93mASSFJyg3YLWR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\\ngwFjjDHwYMAYYwzAfwHe92wgsHVYnQAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84c309d0>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Cluster: 7\\n\",\n        \"Members: ['EBAY: eBay Inc', 'HPQ: Hewlett-Packard Company']\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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rvvpqysjB07dgCwbds2Nm3axO7duykuLiYlJYUnn3wSgObmZrZs2UIg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\\n0qQGJ4Le9csAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84655a50>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"(5.4891499235401122, 4)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Searching for the Best Number of Cluster\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"max_clusters = 30\\n\",\n      \"feature = \\\"average\\\"\\n\",\n      \"clustering_data = pivot_data(stocks_df, values=feature)\\n\",\n      \"clustering_data[\\\"Cluster\\\"] = pd.Series()\\n\",\n      \"for normalize_data in [True, False]:\\n\",\n      \"    fig = plt.figure(figsize=(10,6))\\n\",\n      \"    plt.title(\\\"K-Means - Feature: %s Normalized: %s\\\" % (feature, normalize_data))\\n\",\n      \"    axes_1 = fig.add_subplot(111)\\n\",\n      \"    axes_2 = axes_1.twinx()\\n\",\n      \"    score_error_list = []\\n\",\n      \"    failed_clusters_list = []\\n\",\n      \"    \\n\",\n      \"    for n_clusters in range(2,max_clusters):\\n\",\n      \"        prediction, model, data = cluster_data(clustering_data.drop(\\\"Cluster\\\",1), n_clusters=n_clusters,\\n\",\n      \"                                               normalize_data=normalize_data)\\n\",\n      \"        data = pd.DataFrame(data, index=clustering_data.index,columns=clustering_data.drop(\\\"Cluster\\\",1).columns)\\n\",\n      \"        data[\\\"Cluster\\\"] = prediction\\n\",\n      \"        score_error, failed_clusters =  measure_error(prediction, model, data)\\n\",\n      \"        score_error_list.append(score_error)\\n\",\n      \"        failed_clusters_list.append(failed_clusters)\\n\",\n      \"    axes_1.plot(range(2,max_clusters), score_error_list, \\\"ro-\\\", label = \\\"Average Error\\\")\\n\",\n      \"    axes_2.plot(range(2,max_clusters), failed_clusters_list, \\\"bo-\\\", label = \\\"Failed Cluster\\\")\\n\",\n      \"    \\n\",\n      \"    axes_1.grid()\\n\",\n      \"    axes_1.legend(loc = \\\"lower center\\\")\\n\",\n      \"    axes_2.legend(loc = \\\"upper center\\\")\\n\",\n      \"    axes_1.set_ylabel(\\\"Average Error\\\")\\n\",\n      \"    axes_2.set_ylabel(\\\"Failed Cluster\\\")\\n\",\n      \"    axes_1.set_xlabel(\\\"Clusters\\\")\\n\",\n      \"    plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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zN+/HiGDh1q7VCEEDYkLS2NIUOGcO7cOZycnBg9ejTR0dGa1kEWu5Ko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\\nRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c82b6e810>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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cTExGD37t1wcCi6I6ekGb9nHzfmM/tsrrZt24arV6+iZs2a2jN1Fy1a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\\nRERE5OQY+IiIiIic3P8DzlrR6PL7yi4AAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f6c84468a10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### End of Lesson 4\\n\",\n      \"For questions please leave them on:\\n\",\n      \"\\n\",\n      \"- <a href=\\\"https://www.youtube.com/user/roshanRush\\\">YouTube</a>\\n\",\n      \"- <a href=\\\"https://twitter.com/twistedhardware\\\">Twitter</a>\\n\",\n      \"- <a href=\\\"https://plus.google.com/+Tcsaroshan/posts\\\">Google+</a>\\n\",\n      \"\\n\",\n      \"<a href=\\\"/notebooks/Lesson 3 - Regression.ipynb\\\">Previous Lesson - Regression</a>\\n\",\n      \"<br />\\n\",\n      \"<a href=\\\"/notebooks/Lesson 5 - Preprocessing.ipynb\\\">Next Lesson - Preprocessing</a>\\n\",\n      \"\\n\",\n      \"In the next lesson:\\n\",\n      \"- Normalization\\n\",\n      \"- Features Matrix\\n\",\n      \"- Processing Text\"\n     ]\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/Lesson 5 - Preprocesing.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:64256673831ed946f068d3d427d056a3cf298c61336ac2e60a7d7369a958765d\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Preprocessing is the transformations to your data the happens before training your model. This includes converting your data to its basic numerical components.\\n\",\n      \"\\n\",\n      \"**Video Tutorial**:\\n\",\n      \"\\n\",\n      \"http://youtu.be/NWp6DFtnqYk\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Data Set\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"My Youtube stats as of Sep 29, 2014.\\n\",\n      \"\\n\",\n      \"###Columns:\\n\",\n      \"\\n\",\n      \"- Video\\n\",\n      \"- Upload Time\\tVideo length (minutes)\\n\",\n      \"- Views\\tEstimated minutes watched\\n\",\n      \"- Average view duration (minutes)\\n\",\n      \"- Average percentage viewed\\n\",\n      \"- Subscriber views\\n\",\n      \"- Subscriber minutes watched\\n\",\n      \"- Likes\\n\",\n      \"- Dislikes\\n\",\n      \"- Shares\\n\",\n      \"- Comments\\n\",\n      \"- Favorites\\n\",\n      \"- Subscribers\\n\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Core Libraries\\n\",\n      \"import os\\n\",\n      \"from fnmatch import fnmatch\\n\",\n      \"import numpy as np\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"import pandas as pd\\n\",\n      \"from IPython.html import widgets\\n\",\n      \"from IPython.html.widgets import interact\\n\",\n      \"from IPython.display import display\\n\",\n      \"import urllib2\\n\",\n      \"from datetime import datetime\\n\",\n      \"\\n\",\n      \"# Feature Extraction\\n\",\n      \"from sklearn.feature_extraction import DictVectorizer\\n\",\n      \"\\n\",\n      \"# Preprocessing\\n\",\n      \"from sklearn import preprocessing\\n\",\n      \"\\n\",\n      \"# External\\n\",\n      \"from sklearn.externals import joblib\\n\",\n      \"\\n\",\n      \"# Hide Warnings\\n\",\n      \"import warnings\\n\",\n      \"warnings.filterwarnings('ignore')\\n\",\n      \"\\n\",\n      \"# Configure Pandas\\n\",\n      \"pd.options.display.max_columns = 100\\n\",\n      \"pd.options.display.width = 120\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Loading Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"success_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-success\\\" role=\\\"alert\\\">Loading data from %s was successful.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"error_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-danger\\\" role=\\\"alert\\\">Error loading data from %s. %s</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"def load_data(widget):\\n\",\n      \"    global csv_data\\n\",\n      \"    path = wgt_file_location.value\\n\",\n      \"    try:\\n\",\n      \"        if wgt_header.value:\\n\",\n      \"            csv_data = pd.read_csv(path, sep=wgt_separator.value)\\n\",\n      \"        else:\\n\",\n      \"            csv_data = pd.read_csv(path, sep=wgt_separator.value, names=wgt_manual_header.value.split(\\\",\\\"))\\n\",\n      \"        wgt_alert.value = success_alert % path\\n\",\n      \"    except Exception as ex:\\n\",\n      \"        print ex\\n\",\n      \"        print path\\n\",\n      \"        wgt_alert.value = error_alert % (path, ex)\\n\",\n      \"    wgt_alert.visible = True\\n\",\n      \"    \\n\",\n      \"\\n\",\n      \"def preview_file(widget):\\n\",\n      \"    path = wgt_file_location.value\\n\",\n      \"    if path.startswith(\\\"http://\\\") or path.startswith(\\\"https://\\\") or path.startswith(\\\"ftp://\\\"):\\n\",\n      \"        raw_file = urllib2.urlopen(path)\\n\",\n      \"        wgt_file_preview.value = \\\"<pre>%s</pre>\\\" % raw_file.read(1000)\\n\",\n      \"        raw_file.close()\\n\",\n      \"    else:\\n\",\n      \"        raw_file = open(path)\\n\",\n      \"        wgt_file_preview.value = \\\"<pre>%s</pre>\\\" % raw_file.read(1000)\\n\",\n      \"        raw_file.close()\\n\",\n      \"    \\n\",\n      \"\\n\",\n      \"def manual_columns(name,old,new):\\n\",\n      \"    wgt_manual_header.visible = old\\n\",\n      \"    \\n\",\n      \"def update_path(name,old,new):\\n\",\n      \"    wgt_file_location.value = new\\n\",\n      \"    \\n\",\n      \"    \\n\",\n      \"def load_files(widget):\\n\",\n      \"    files_list = {}\\n\",\n      \"    root = os.curdir\\n\",\n      \"    patterns = [\\\"*.txt\\\", \\\"*.csv\\\"]\\n\",\n      \"\\n\",\n      \"    for path, subdirs, files in os.walk(root):\\n\",\n      \"        for name in files:\\n\",\n      \"            for pattern in patterns:\\n\",\n      \"                if fnmatch(name, pattern):\\n\",\n      \"                    files_list[os.path.join(path, name)] = os.path.join(path, name)\\n\",\n      \"    widget.values = files_list\\n\",\n      \"\\n\",\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"wgt_alert = widgets.HTMLWidget()\\n\",\n      \"wgt_file_location = widgets.TextWidget(description=\\\"Path/URL:\\\")\\n\",\n      \"wgt_file_path = widgets.DropdownWidget(description=\\\"Files List\\\")\\n\",\n      \"wgt_separator = widgets.TextWidget(description=\\\"Separator\\\", value=\\\",\\\")\\n\",\n      \"wgt_header = widgets.CheckboxWidget(description=\\\"First columns is a header?\\\", value=True)\\n\",\n      \"wgt_manual_header = widgets.TextWidget(description=\\\"Columns seperated by commas\\\", visible=False)\\n\",\n      \"wgt_load_data = widgets.ButtonWidget(description=\\\"Load Data\\\")\\n\",\n      \"wgt_preview_file = widgets.ButtonWidget(description=\\\"Preview File\\\")\\n\",\n      \"wgt_file_preview = widgets.HTMLWidget()\\n\",\n      \"\\n\",\n      \"wgt_alert.visible = False\\n\",\n      \"\\n\",\n      \"wgt_load_data.on_click(load_data)\\n\",\n      \"wgt_preview_file.on_click(preview_file)\\n\",\n      \"wgt_file_path.on_displayed(load_files)\\n\",\n      \"wgt_file_path.on_trait_change(update_path, \\\"value\\\")\\n\",\n      \"wgt_header.on_trait_change(manual_columns, \\\"value\\\")\\n\",\n      \"\\n\",\n      \"container.children = (wgt_alert, wgt_file_path, wgt_file_location, wgt_separator, wgt_header, wgt_manual_header,\\n\",\n      \"                      wgt_load_data, wgt_preview_file, wgt_file_preview)\\n\",\n      \"\\n\",\n      \"display(container)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Type Conversion\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"success_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-success\\\" role=\\\"alert\\\">Type conversion was successful.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"error_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-danger\\\" role=\\\"alert\\\">Error in type conversion. %s.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"def get_stats(column):\\n\",\n      \"    series = pd.Series(csv_data[column].values.ravel())\\n\",\n      \"    stats = \\\"Max:<span class='badge'>%s</span>, Min:<span class='badge'>%s</span>,\\\"\\n\",\n      \"    stats += \\\"Avg:<span class='badge'>%s</span>, Median:<span class='badge'>%s</span>\\\"\\n\",\n      \"    if str(series.dtype) in [\\\"int32\\\", \\\"int64\\\", \\\"float32\\\", \\\"float64\\\"]:\\n\",\n      \"        return stats % (series.max(), series.min(), series.mean(), series.median())\\n\",\n      \"    else:\\n\",\n      \"        return \\\"Not numerical\\\"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"def get_column_type(column):\\n\",\n      \"    column_type = str(pd.Series(csv_data[column].values.ravel()).dtype)\\n\",\n      \"    if column_type == \\\"int64\\\":\\n\",\n      \"        return \\\"Int\\\"\\n\",\n      \"    elif column_type == \\\"int32\\\":\\n\",\n      \"        return \\\"Int\\\"\\n\",\n      \"    elif column_type == \\\"float32\\\":\\n\",\n      \"        return \\\"Float\\\"\\n\",\n      \"    elif column_type == \\\"float64\\\":\\n\",\n      \"        return \\\"Float\\\"\\n\",\n      \"    elif column_type == \\\"object\\\":\\n\",\n      \"        return \\\"Object\\\"\\n\",\n      \"\\n\",\n      \"def process_column(column):\\n\",\n      \"    column_name = column.children[1].value\\n\",\n      \"    data_type = column.children[2].value\\n\",\n      \"    if data_type == \\\"Float\\\":\\n\",\n      \"        csv_data[column_name] = csv_data[column_name].astype(np.float64)\\n\",\n      \"    elif data_type == \\\"Int\\\":\\n\",\n      \"        csv_data[column_name] = csv_data[column_name].astype(np.int64)\\n\",\n      \"    elif data_type == \\\"Ordinal Date\\\":\\n\",\n      \"        csv_data[column_name + \\\"_date\\\"] = csv_data[column_name].apply(datetime.fromordinal)\\n\",\n      \"    elif data_type == \\\"Text Date\\\":\\n\",\n      \"        csv_data[column_name + \\\"_date\\\"] = csv_data[column_name].astype(str).apply(datetime.strptime,\\n\",\n      \"                                                                                       args=(wgt_date_format.value,))\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"def process_columns(widget):\\n\",\n      \"    try:\\n\",\n      \"        for column in main_container.children:\\n\",\n      \"            if isinstance(column, widgets.ContainerWidget):\\n\",\n      \"                process_column(column)\\n\",\n      \"        \\n\",\n      \"        wgt_alert.value = success_alert\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"    except Exception as ex:\\n\",\n      \"        wgt_alert.value = error_alert % ex\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"main_container = widgets.ContainerWidget()\\n\",\n      \"display(main_container)\\n\",\n      \"\\n\",\n      \"columns = []\\n\",\n      \"\\n\",\n      \"wgt_alert = widgets.HTMLWidget(visible=False)\\n\",\n      \"wgt_date_format = widgets.TextWidget(description=\\\"Text Date Format:\\\" ,value=\\\"%Y%m%d\\\")\\n\",\n      \"wgt_process = widgets.ButtonWidget(description=\\\"Process Columns\\\")\\n\",\n      \"wgt_process.on_click(process_columns)\\n\",\n      \"\\n\",\n      \"main_container.children = (wgt_alert, wgt_date_format,)\\n\",\n      \"\\n\",\n      \"for column in csv_data.columns:\\n\",\n      \"    temp_container = widgets.ContainerWidget()\\n\",\n      \"    \\n\",\n      \"    main_container.children += (temp_container,)\\n\",\n      \"    \\n\",\n      \"    temp_container.remove_class('vbox')\\n\",\n      \"    temp_container.add_class('hbox')\\n\",\n      \"    temp_container.add_class('start')\\n\",\n      \"    \\n\",\n      \"    w1 = widgets.CheckboxWidget(value=True)\\n\",\n      \"    w2 = widgets.TextWidget(value=column, disabled=True)\\n\",\n      \"    w3 = widgets.DropdownWidget(values=[\\\"Float\\\", \\\"Int\\\", \\\"Ordinal Date\\\", \\\"Text Date\\\", \\\"Boolean\\\", \\\"Object\\\"],\\n\",\n      \"                                value = get_column_type(column))\\n\",\n      \"    w4 = widgets.HTMLWidget()\\n\",\n      \"    \\n\",\n      \"    w2.set_css(\\\"width\\\",\\\"200px\\\")\\n\",\n      \"    \\n\",\n      \"    stats = get_stats(column)\\n\",\n      \"    w4.value = \\\"<pre>%s</pre>\\\" % stats\\n\",\n      \"    \\n\",\n      \"    children = [w1, w2, w3, w4]\\n\",\n      \"\\n\",\n      \"    temp_container.children = children\\n\",\n      \"\\n\",\n      \"main_container.children += (wgt_process,)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"##Text Date Format:\\n\",\n      \"\\n\",\n      \"ref: https://docs.python.org/2/library/datetime.html\\n\",\n      \"\\n\",\n      \"<table>\\n\",\n      \"<colgroup>\\n\",\n      \"<col width=\\\"15%\\\">\\n\",\n      \"<col width=\\\"43%\\\">\\n\",\n      \"<col width=\\\"32%\\\">\\n\",\n      \"<col width=\\\"9%\\\">\\n\",\n      \"</colgroup>\\n\",\n      \"<thead valign=\\\"bottom\\\">\\n\",\n      \"<tr class=\\\"row-odd\\\"><th class=\\\"head\\\">Directive</th>\\n\",\n      \"<th class=\\\"head\\\">Meaning</th>\\n\",\n      \"<th class=\\\"head\\\">Example</th>\\n\",\n      \"<th class=\\\"head\\\">Notes</th>\\n\",\n      \"</tr>\\n\",\n      \"</thead>\\n\",\n      \"<tbody valign=\\\"top\\\">\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%a</span></tt></td>\\n\",\n      \"<td>Weekday as locale\\u2019s\\n\",\n      \"abbreviated name.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">Sun, Mon, ..., Sat\\n\",\n      \"(en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">So, Mo, ..., Sa\\n\",\n      \"(de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%A</span></tt></td>\\n\",\n      \"<td>Weekday as locale\\u2019s full name.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">Sunday, Monday, ...,\\n\",\n      \"Saturday (en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">Sonntag, Montag, ...,\\n\",\n      \"Samstag (de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%w</span></tt></td>\\n\",\n      \"<td>Weekday as a decimal number,\\n\",\n      \"where 0 is Sunday and 6 is\\n\",\n      \"Saturday.</td>\\n\",\n      \"<td>0, 1, ..., 6</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%d</span></tt></td>\\n\",\n      \"<td>Day of the month as a\\n\",\n      \"zero-padded decimal number.</td>\\n\",\n      \"<td>01, 02, ..., 31</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%b</span></tt></td>\\n\",\n      \"<td>Month as locale\\u2019s abbreviated\\n\",\n      \"name.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">Jan, Feb, ..., Dec\\n\",\n      \"(en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">Jan, Feb, ..., Dez\\n\",\n      \"(de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%B</span></tt></td>\\n\",\n      \"<td>Month as locale\\u2019s full name.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">January, February,\\n\",\n      \"..., December (en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">Januar, Februar, ...,\\n\",\n      \"Dezember (de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%m</span></tt></td>\\n\",\n      \"<td>Month as a zero-padded\\n\",\n      \"decimal number.</td>\\n\",\n      \"<td>01, 02, ..., 12</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%y</span></tt></td>\\n\",\n      \"<td>Year without century as a\\n\",\n      \"zero-padded decimal number.</td>\\n\",\n      \"<td>00, 01, ..., 99</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%Y</span></tt></td>\\n\",\n      \"<td>Year with century as a decimal\\n\",\n      \"number.</td>\\n\",\n      \"<td>1970, 1988, 2001, 2013</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%H</span></tt></td>\\n\",\n      \"<td>Hour (24-hour clock) as a\\n\",\n      \"zero-padded decimal number.</td>\\n\",\n      \"<td>00, 01, ..., 23</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%I</span></tt></td>\\n\",\n      \"<td>Hour (12-hour clock) as a\\n\",\n      \"zero-padded decimal number.</td>\\n\",\n      \"<td>01, 02, ..., 12</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%p</span></tt></td>\\n\",\n      \"<td>Locale\\u2019s equivalent of either\\n\",\n      \"AM or PM.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">AM, PM (en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">am, pm (de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1),\\n\",\n      \"(2)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%M</span></tt></td>\\n\",\n      \"<td>Minute as a zero-padded\\n\",\n      \"decimal number.</td>\\n\",\n      \"<td>00, 01, ..., 59</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%S</span></tt></td>\\n\",\n      \"<td>Second as a zero-padded\\n\",\n      \"decimal number.</td>\\n\",\n      \"<td>00, 01, ..., 59</td>\\n\",\n      \"<td>(3)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%f</span></tt></td>\\n\",\n      \"<td>Microsecond as a decimal\\n\",\n      \"number, zero-padded on the\\n\",\n      \"left.</td>\\n\",\n      \"<td>000000, 000001, ...,\\n\",\n      \"999999</td>\\n\",\n      \"<td>(4)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%z</span></tt></td>\\n\",\n      \"<td>UTC offset in the form +HHMM\\n\",\n      \"or -HHMM (empty string if the\\n\",\n      \"the object is naive).</td>\\n\",\n      \"<td>(empty), +0000, -0400,\\n\",\n      \"+1030</td>\\n\",\n      \"<td>(5)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%Z</span></tt></td>\\n\",\n      \"<td>Time zone name (empty string\\n\",\n      \"if the object is naive).</td>\\n\",\n      \"<td>(empty), UTC, EST, CST</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%j</span></tt></td>\\n\",\n      \"<td>Day of the year as a\\n\",\n      \"zero-padded decimal number.</td>\\n\",\n      \"<td>001, 002, ..., 366</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%U</span></tt></td>\\n\",\n      \"<td>Week number of the year\\n\",\n      \"(Sunday as the first day of\\n\",\n      \"the week) as a zero padded\\n\",\n      \"decimal number. All days in a\\n\",\n      \"new year preceding the first\\n\",\n      \"Sunday are considered to be in\\n\",\n      \"week 0.</td>\\n\",\n      \"<td>00, 01, ..., 53</td>\\n\",\n      \"<td>(6)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%W</span></tt></td>\\n\",\n      \"<td>Week number of the year\\n\",\n      \"(Monday as the first day of\\n\",\n      \"the week) as a decimal number.\\n\",\n      \"All days in a new year\\n\",\n      \"preceding the first Monday\\n\",\n      \"are considered to be in\\n\",\n      \"week 0.</td>\\n\",\n      \"<td>00, 01, ..., 53</td>\\n\",\n      \"<td>(6)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%c</span></tt></td>\\n\",\n      \"<td>Locale\\u2019s appropriate date and\\n\",\n      \"time representation.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">Tue Aug 16 21:30:00\\n\",\n      \"1988 (en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">Di 16 Aug 21:30:00\\n\",\n      \"1988 (de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%x</span></tt></td>\\n\",\n      \"<td>Locale\\u2019s appropriate date\\n\",\n      \"representation.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">08/16/88 (None);</div>\\n\",\n      \"<div class=\\\"line\\\">08/16/1988 (en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">16.08.1988 (de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-even\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%X</span></tt></td>\\n\",\n      \"<td>Locale\\u2019s appropriate time\\n\",\n      \"representation.</td>\\n\",\n      \"<td><div class=\\\"first last line-block\\\">\\n\",\n      \"<div class=\\\"line\\\">21:30:00 (en_US);</div>\\n\",\n      \"<div class=\\\"line\\\">21:30:00 (de_DE)</div>\\n\",\n      \"</div>\\n\",\n      \"</td>\\n\",\n      \"<td>(1)</td>\\n\",\n      \"</tr>\\n\",\n      \"<tr class=\\\"row-odd\\\"><td><tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">%%</span></tt></td>\\n\",\n      \"<td>A literal <tt class=\\\"docutils literal\\\"><span class=\\\"pre\\\">'%'</span></tt> character.</td>\\n\",\n      \"<td>%</td>\\n\",\n      \"<td>&nbsp;</td>\\n\",\n      \"</tr>\\n\",\n      \"</tbody>\\n\",\n      \"</table>\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Feature Extraction\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Text\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"success_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-success\\\" role=\\\"alert\\\">Text features extraction was successful.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"error_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-danger\\\" role=\\\"alert\\\">Error in features extraction. %s.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"def get_map_dict(column):\\n\",\n      \"    keys = csv_data[column].unique()\\n\",\n      \"    values = xrange(len(keys))\\n\",\n      \"    return {key:value for key,value in zip(keys,values)}\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"def process_text_column(column):\\n\",\n      \"    column_name = column.children[0].value\\n\",\n      \"    text_process = column.children[1].value\\n\",\n      \"    map_dict = eval(column.children[2].value)\\n\",\n      \"    if text_process == \\\"Map\\\":\\n\",\n      \"        csv_data[column_name + \\\"_mapped\\\"] = csv_data[column_name].map(map_dict)\\n\",\n      \"    elif text_process == \\\"Binary Vectorize\\\":\\n\",\n      \"        temp_dict = [{column_name: item} for item in csv_data[column_name]]\\n\",\n      \"        vec = DictVectorizer(separator=\\\"_is_\\\")\\n\",\n      \"        vec_list = vec.fit_transform(temp_dict).toarray()\\n\",\n      \"        columns = vec.get_feature_names()\\n\",\n      \"        for counter in range(len(columns)):\\n\",\n      \"            column = columns[counter]\\n\",\n      \"            values = vec_list[:,counter]\\n\",\n      \"            csv_data[column] = values\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"def process_text_columns(widget):\\n\",\n      \"    try:\\n\",\n      \"        for column in main_container.children:\\n\",\n      \"            if isinstance(column, widgets.ContainerWidget):\\n\",\n      \"                process_text_column(column)\\n\",\n      \"        wgt_alert.value = success_alert\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"    except Exception as ex:\\n\",\n      \"        wgt_alert.value = error_alert % ex\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"\\n\",\n      \"    \\n\",\n      \"main_container = widgets.ContainerWidget()\\n\",\n      \"display(main_container)\\n\",\n      \"\\n\",\n      \"wgt_alert = widgets.HTMLWidget(visible=False)\\n\",\n      \"wgt_process = widgets.ButtonWidget(description=\\\"Process Columns\\\")\\n\",\n      \"wgt_process.on_click(process_text_columns)\\n\",\n      \"\\n\",\n      \"main_container.children = (wgt_alert,)\\n\",\n      \"\\n\",\n      \"columns = []\\n\",\n      \"\\n\",\n      \"for column in csv_data.columns:\\n\",\n      \"    if str(pd.Series(csv_data[column].values).dtype) == \\\"object\\\":\\n\",\n      \"        temp_container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"        main_container.children += (temp_container,)\\n\",\n      \"\\n\",\n      \"        temp_container.remove_class('vbox')\\n\",\n      \"        temp_container.add_class('hbox')\\n\",\n      \"        temp_container.add_class('start')\\n\",\n      \"\\n\",\n      \"        w1 = widgets.TextWidget(value=column, disabled=True)\\n\",\n      \"        w2 = widgets.DropdownWidget(values=[\\\"Map\\\", \\\"Binary Vectorize\\\", \\\"Don't Process\\\"])\\n\",\n      \"        w3 = widgets.TextWidget(description=\\\"Dict: {'m': 0, 'f': 1}\\\")\\n\",\n      \"\\n\",\n      \"        w1.set_css(\\\"width\\\",\\\"200px\\\")\\n\",\n      \"\\n\",\n      \"        w3.value = str(get_map_dict(column))\\n\",\n      \"\\n\",\n      \"        children = [w1, w2, w3]\\n\",\n      \"\\n\",\n      \"        temp_container.children = children;\\n\",\n      \"\\n\",\n      \"main_container.children += (wgt_process,)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Data Preview\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def print_preview():\\n\",\n      \"    print \\\"Data Sample:\\\"\\n\",\n      \"    print csv_data.head(5)\\n\",\n      \"    print \\\".\\\\n\\\" * 3\\n\",\n      \"    print csv_data.tail(5)\\n\",\n      \"    \\n\",\n      \"print_preview()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Data Sample:\\n\",\n        \"                                               Video                  Upload Time  Video length (minutes)  Views  \\\\\\n\",\n        \"0  1. Notebooks and Cells - IPython Notebook Tuto...       July 26, 2014 02:47 AM                    7.55    327   \\n\",\n        \"1        4. NumPy Basics - IPython Notebook Tutorial     August 07, 2014 06:04 AM                   30.72    249   \\n\",\n        \"2     Load Balancing - IPython Parallel Computing #1  September 10, 2014 01:58 AM                    8.57    244   \\n\",\n        \"3     6. IPython Widgets - IPython Notebook Tutorial     August 10, 2014 10:59 AM                   10.07    234   \\n\",\n        \"4    2. Markdown & LaTeX - IPython Notebook Tutorial       July 31, 2014 02:39 AM                   10.77    230   \\n\",\n        \"\\n\",\n        \"   Estimated minutes watched  Average view duration (minutes)  Average percentage viewed  Subscriber views  \\\\\\n\",\n        \"0                        858                             2.62                      34.76                16   \\n\",\n        \"1                       1829                             7.34                      23.91                26   \\n\",\n        \"2                        712                             2.92                      34.06                 6   \\n\",\n        \"3                        823                             3.52                      34.93                15   \\n\",\n        \"4                        821                             3.57                      33.15                12   \\n\",\n        \"\\n\",\n        \"   Subscriber minutes watched  Likes  Dislikes  Shares  Comments  Favorites  Subscribers    Upload Time_date  \\\\\\n\",\n        \"0                          63      3         1       2         2          0            8 2014-07-26 02:47:00   \\n\",\n        \"1                         159      4         0       2         3          0            4 2014-08-07 06:04:00   \\n\",\n        \"2                          10      6         0       1         3          1            4 2014-09-10 01:58:00   \\n\",\n        \"3                          53      6         0       1         8          0            1 2014-08-10 10:59:00   \\n\",\n        \"4                          66      3         1       3         2          1            3 2014-07-31 02:39:00   \\n\",\n        \"\\n\",\n        \"   Video_mapped  Upload Time_mapped  Video_is_1. Notebooks and Cells - IPython Notebook Tutorial  \\\\\\n\",\n        \"0             0                   0                                                  1             \\n\",\n        \"1             1                   1                                                  0             \\n\",\n        \"2             2                   2                                                  0             \\n\",\n        \"3             3                   3                                                  0             \\n\",\n        \"4             4                   4                                                  0             \\n\",\n        \"\\n\",\n        \"   Video_is_2. Markdown & LaTeX - IPython Notebook Tutorial  Video_is_3. Basic Python - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0                                                         0      \\n\",\n        \"1                                                  0                                                         0      \\n\",\n        \"2                                                  0                                                         0      \\n\",\n        \"3                                                  0                                                         0      \\n\",\n        \"4                                                  1                                                         0      \\n\",\n        \"\\n\",\n        \"   Video_is_4. NumPy Basics - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0      \\n\",\n        \"1                                                  1      \\n\",\n        \"2                                                  0      \\n\",\n        \"3                                                  0      \\n\",\n        \"4                                                  0      \\n\",\n        \"\\n\",\n        \"   Video_is_5. Plotting Charts with Matplotlib - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0                         \\n\",\n        \"1                                                  0                         \\n\",\n        \"2                                                  0                         \\n\",\n        \"3                                                  0                         \\n\",\n        \"4                                                  0                         \\n\",\n        \"\\n\",\n        \"   Video_is_6. IPython Widgets - IPython Notebook Tutorial  Video_is_7. Pandas - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0                                                     0   \\n\",\n        \"1                                                  0                                                     0   \\n\",\n        \"2                                                  0                                                     0   \\n\",\n        \"3                                                  1                                                     0   \\n\",\n        \"4                                                  0                                                     0   \\n\",\n        \"\\n\",\n        \"   Video_is_8. SymPy - IPython Notebook Tutorial  Video_is_Can computers DISCRIMINATE  against race and gender?  \\\\\\n\",\n        \"0                                              0                                                  0               \\n\",\n        \"1                                              0                                                  0               \\n\",\n        \"2                                              0                                                  0               \\n\",\n        \"3                                              0                                                  0               \\n\",\n        \"4                                              0                                                  0               \\n\",\n        \"\\n\",\n        \"   Video_is_Container Widgets - IPython Widgets #1  Video_is_Folder Management - IPython Notebook Tips  \\\\\\n\",\n        \"0                                                0                                                  0    \\n\",\n        \"1                                                0                                                  0    \\n\",\n        \"2                                                0                                                  0    \\n\",\n        \"3                                                0                                                  0    \\n\",\n        \"4                                                0                                                  0    \\n\",\n        \"\\n\",\n        \"   Video_is_Handling Events - IPython Widgets #2  Video_is_How does SVM work?  \\\\\\n\",\n        \"0                                              0                            0   \\n\",\n        \"1                                              0                            0   \\n\",\n        \"2                                              0                            0   \\n\",\n        \"3                                              0                            0   \\n\",\n        \"4                                              0                            0   \\n\",\n        \"\\n\",\n        \"   Video_is_How does kNN (k-Nearest Neighbors) work?  \\\\\\n\",\n        \"0                                                  0   \\n\",\n        \"1                                                  0   \\n\",\n        \"2                                                  0   \\n\",\n        \"3                                                  0   \\n\",\n        \"4                                                  0   \\n\",\n        \"\\n\",\n        \"   Video_is_Indian Elections 2014 - IPython Notebook Tutorial (Exercise)  \\\\\\n\",\n        \"0                                                  0                       \\n\",\n        \"1                                                  0                       \\n\",\n        \"2                                                  0                       \\n\",\n        \"3                                                  0                       \\n\",\n        \"4                                                  0                       \\n\",\n        \"\\n\",\n        \"   Video_is_Load Balancing - IPython Parallel Computing #1  \\\\\\n\",\n        \"0                                                  0         \\n\",\n        \"1                                                  0         \\n\",\n        \"2                                                  1         \\n\",\n        \"3                                                  0         \\n\",\n        \"4                                                  0         \\n\",\n        \"\\n\",\n        \"   Video_is_Machine Learning 1 - Setup Development Environment  Video_is_Machine Learning 2 - Introduction to ML  \\\\\\n\",\n        \"0                                                  0                                                           0   \\n\",\n        \"1                                                  0                                                           0   \\n\",\n        \"2                                                  0                                                           0   \\n\",\n        \"3                                                  0                                                           0   \\n\",\n        \"4                                                  0                                                           0   \\n\",\n        \"\\n\",\n        \"   Video_is_Machine Learning 3 - Clustering  Video_is_Machine Learning 3 - Regression  \\\\\\n\",\n        \"0                                         0                                         0   \\n\",\n        \"1                                         0                                         0   \\n\",\n        \"2                                         0                                         0   \\n\",\n        \"3                                         0                                         0   \\n\",\n        \"4                                         0                                         0   \\n\",\n        \"\\n\",\n        \"   Video_is_Styling Widgets - IPython Widgets #3  Video_is_Web DICOM Viewer  \\\\\\n\",\n        \"0                                              0                          0   \\n\",\n        \"1                                              0                          0   \\n\",\n        \"2                                              0                          0   \\n\",\n        \"3                                              0                          0   \\n\",\n        \"4                                              0                          0   \\n\",\n        \"\\n\",\n        \"   Video_is_Widgets Alignment - IPython Widgets #4  \\n\",\n        \"0                                                0  \\n\",\n        \"1                                                0  \\n\",\n        \"2                                                0  \\n\",\n        \"3                                                0  \\n\",\n        \"4                                                0  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \".\\n\",\n        \".\\n\",\n        \".\\n\",\n        \"\\n\",\n        \"                                                Video                  Upload Time  Video length (minutes)  Views  \\\\\\n\",\n        \"18               Handling Events - IPython Widgets #2  September 11, 2014 09:54 PM                    5.63     31   \\n\",\n        \"19                                   Web DICOM Viewer        May 30, 2014 11:56 PM                    1.72     28   \\n\",\n        \"20  Indian Elections 2014 - IPython Notebook Tutor...  September 06, 2014 03:08 PM                   30.58     23   \\n\",\n        \"21               Styling Widgets - IPython Widgets #3  September 13, 2014 04:26 AM                    8.25     23   \\n\",\n        \"22             Widgets Alignment - IPython Widgets #4  September 27, 2014 10:39 PM                    3.18      4   \\n\",\n        \"\\n\",\n        \"    Estimated minutes watched  Average view duration (minutes)  Average percentage viewed  Subscriber views  \\\\\\n\",\n        \"18                         89                             2.87                      50.88                 4   \\n\",\n        \"19                         32                             1.13                      71.10                10   \\n\",\n        \"20                         90                             3.93                      12.86                 6   \\n\",\n        \"21                         87                             3.79                      45.99                 9   \\n\",\n        \"22                          5                             1.14                      35.68                 2   \\n\",\n        \"\\n\",\n        \"    Subscriber minutes watched  Likes  Dislikes  Shares  Comments  Favorites  Subscribers    Upload Time_date  \\\\\\n\",\n        \"18                          12      2         0       2         2          1            1 2014-09-11 21:54:00   \\n\",\n        \"19                          13      3         0       2         2          0            0 2014-05-30 23:56:00   \\n\",\n        \"20                          21      3         0       1         1          0            2 2014-09-06 15:08:00   \\n\",\n        \"21                          35      2         0       0         1          0            0 2014-09-13 04:26:00   \\n\",\n        \"22                           1      2         0       0         1          0            0 2014-09-27 22:39:00   \\n\",\n        \"\\n\",\n        \"    Video_mapped  Upload Time_mapped  Video_is_1. Notebooks and Cells - IPython Notebook Tutorial  \\\\\\n\",\n        \"18            18                  18                                                  0             \\n\",\n        \"19            19                  19                                                  0             \\n\",\n        \"20            20                  20                                                  0             \\n\",\n        \"21            21                  21                                                  0             \\n\",\n        \"22            22                  22                                                  0             \\n\",\n        \"\\n\",\n        \"    Video_is_2. Markdown & LaTeX - IPython Notebook Tutorial  Video_is_3. Basic Python - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0                                                         0      \\n\",\n        \"19                                                  0                                                         0      \\n\",\n        \"20                                                  0                                                         0      \\n\",\n        \"21                                                  0                                                         0      \\n\",\n        \"22                                                  0                                                         0      \\n\",\n        \"\\n\",\n        \"    Video_is_4. NumPy Basics - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0      \\n\",\n        \"19                                                  0      \\n\",\n        \"20                                                  0      \\n\",\n        \"21                                                  0      \\n\",\n        \"22                                                  0      \\n\",\n        \"\\n\",\n        \"    Video_is_5. Plotting Charts with Matplotlib - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0                         \\n\",\n        \"19                                                  0                         \\n\",\n        \"20                                                  0                         \\n\",\n        \"21                                                  0                         \\n\",\n        \"22                                                  0                         \\n\",\n        \"\\n\",\n        \"    Video_is_6. IPython Widgets - IPython Notebook Tutorial  Video_is_7. Pandas - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0                                                     0   \\n\",\n        \"19                                                  0                                                     0   \\n\",\n        \"20                                                  0                                                     0   \\n\",\n        \"21                                                  0                                                     0   \\n\",\n        \"22                                                  0                                                     0   \\n\",\n        \"\\n\",\n        \"    Video_is_8. SymPy - IPython Notebook Tutorial  Video_is_Can computers DISCRIMINATE  against race and gender?  \\\\\\n\",\n        \"18                                              0                                                  0               \\n\",\n        \"19                                              0                                                  0               \\n\",\n        \"20                                              0                                                  0               \\n\",\n        \"21                                              0                                                  0               \\n\",\n        \"22                                              0                                                  0               \\n\",\n        \"\\n\",\n        \"    Video_is_Container Widgets - IPython Widgets #1  Video_is_Folder Management - IPython Notebook Tips  \\\\\\n\",\n        \"18                                                0                                                  0    \\n\",\n        \"19                                                0                                                  0    \\n\",\n        \"20                                                0                                                  0    \\n\",\n        \"21                                                0                                                  0    \\n\",\n        \"22                                                0                                                  0    \\n\",\n        \"\\n\",\n        \"    Video_is_Handling Events - IPython Widgets #2  Video_is_How does SVM work?  \\\\\\n\",\n        \"18                                              1                            0   \\n\",\n        \"19                                              0                            0   \\n\",\n        \"20                                              0                            0   \\n\",\n        \"21                                              0                            0   \\n\",\n        \"22                                              0                            0   \\n\",\n        \"\\n\",\n        \"    Video_is_How does kNN (k-Nearest Neighbors) work?  \\\\\\n\",\n        \"18                                                  0   \\n\",\n        \"19                                                  0   \\n\",\n        \"20                                                  0   \\n\",\n        \"21                                                  0   \\n\",\n        \"22                                                  0   \\n\",\n        \"\\n\",\n        \"    Video_is_Indian Elections 2014 - IPython Notebook Tutorial (Exercise)  \\\\\\n\",\n        \"18                                                  0                       \\n\",\n        \"19                                                  0                       \\n\",\n        \"20                                                  1                       \\n\",\n        \"21                                                  0                       \\n\",\n        \"22                                                  0                       \\n\",\n        \"\\n\",\n        \"    Video_is_Load Balancing - IPython Parallel Computing #1  \\\\\\n\",\n        \"18                                                  0         \\n\",\n        \"19                                                  0         \\n\",\n        \"20                                                  0         \\n\",\n        \"21                                                  0         \\n\",\n        \"22                                                  0         \\n\",\n        \"\\n\",\n        \"    Video_is_Machine Learning 1 - Setup Development Environment  Video_is_Machine Learning 2 - Introduction to ML  \\\\\\n\",\n        \"18                                                  0                                                           0   \\n\",\n        \"19                                                  0                                                           0   \\n\",\n        \"20                                                  0                                                           0   \\n\",\n        \"21                                                  0                                                           0   \\n\",\n        \"22                                                  0                                                           0   \\n\",\n        \"\\n\",\n        \"    Video_is_Machine Learning 3 - Clustering  Video_is_Machine Learning 3 - Regression  \\\\\\n\",\n        \"18                                         0                                         0   \\n\",\n        \"19                                         0                                         0   \\n\",\n        \"20                                         0                                         0   \\n\",\n        \"21                                         0                                         0   \\n\",\n        \"22                                         0                                         0   \\n\",\n        \"\\n\",\n        \"    Video_is_Styling Widgets - IPython Widgets #3  Video_is_Web DICOM Viewer  \\\\\\n\",\n        \"18                                              0                          0   \\n\",\n        \"19                                              0                          1   \\n\",\n        \"20                                              0                          0   \\n\",\n        \"21                                              1                          0   \\n\",\n        \"22                                              0                          0   \\n\",\n        \"\\n\",\n        \"    Video_is_Widgets Alignment - IPython Widgets #4  \\n\",\n        \"18                                                0  \\n\",\n        \"19                                                0  \\n\",\n        \"20                                                0  \\n\",\n        \"21                                                0  \\n\",\n        \"22                                                1  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Date\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"success_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-success\\\" role=\\\"alert\\\">Date features extraction was successful.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"error_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-danger\\\" role=\\\"alert\\\">Error in date extraction. %s.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"def process_date_column(column):\\n\",\n      \"    column_name = column.children[0].value\\n\",\n      \"    if column.children[1].value: # Year\\n\",\n      \"        csv_data[column_name + \\\"_year\\\"] = csv_data[column_name].apply(lambda x: x.year)\\n\",\n      \"    if column.children[2].value: # Month\\n\",\n      \"        csv_data[column_name + \\\"_month\\\"] = csv_data[column_name].apply(lambda x: x.month)\\n\",\n      \"    if column.children[3].value: # Day\\n\",\n      \"        csv_data[column_name + \\\"_day\\\"] = csv_data[column_name].apply(lambda x: x.day)\\n\",\n      \"    if column.children[4].value: # Day of week\\n\",\n      \"        csv_data[column_name + \\\"_dayofweek\\\"] = csv_data[column_name].apply(lambda x: x.dayofweek)\\n\",\n      \"    if column.children[5].value: # Hour\\n\",\n      \"        csv_data[column_name + \\\"_hour\\\"] = csv_data[column_name].apply(lambda x: x.hour)\\n\",\n      \"    if column.children[6].value: # Minute\\n\",\n      \"        csv_data[column_name + \\\"_minute\\\"] = csv_data[column_name].apply(lambda x: x.minute)\\n\",\n      \"    if column.children[7].value: # Second\\n\",\n      \"        csv_data[column_name + \\\"_second\\\"] = csv_data[column_name].apply(lambda x: x.second)\\n\",\n      \"    #if column.children[8].value: # Micro Second\\n\",\n      \"    #    csv_data[column_name + \\\"_microsecond\\\"] = csv_data[column_name].apply(lambda x: datetime.microsecond)\\n\",\n      \"\\n\",\n      \"def process_date_columns(widget):\\n\",\n      \"    try:\\n\",\n      \"        for column in main_container.children:\\n\",\n      \"            if isinstance(column, widgets.ContainerWidget):\\n\",\n      \"                process_date_column(column)\\n\",\n      \"        wgt_alert.value = success_alert\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"    except Exception as ex:\\n\",\n      \"        wgt_alert.value = error_alert % ex\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"main_container = widgets.ContainerWidget()\\n\",\n      \"display(main_container)\\n\",\n      \"\\n\",\n      \"wgt_alert = widgets.HTMLWidget(visible=False)\\n\",\n      \"wgt_process = widgets.ButtonWidget(description=\\\"Process Columns\\\")\\n\",\n      \"wgt_process.on_click(process_date_columns)\\n\",\n      \"\\n\",\n      \"main_container.children = (wgt_alert,)\\n\",\n      \"\\n\",\n      \"columns = []\\n\",\n      \"\\n\",\n      \"for column in csv_data.columns:\\n\",\n      \"    if str(pd.Series(csv_data[column].values).dtype) == \\\"datetime64[ns]\\\":\\n\",\n      \"        temp_container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"        main_container.children += (temp_container,)\\n\",\n      \"\\n\",\n      \"        temp_container.remove_class('vbox')\\n\",\n      \"        temp_container.add_class('hbox')\\n\",\n      \"        temp_container.add_class('start')\\n\",\n      \"\\n\",\n      \"        w1 = widgets.TextWidget(value=column, disabled=True)\\n\",\n      \"        w2 = widgets.CheckboxWidget(description=\\\"Year\\\", value=True)\\n\",\n      \"        w3 = widgets.CheckboxWidget(description=\\\"Month\\\", value=True)\\n\",\n      \"        w4 = widgets.CheckboxWidget(description=\\\"Day\\\", value=True)\\n\",\n      \"        w5 = widgets.CheckboxWidget(description=\\\"DayOfWeek\\\", value=True)\\n\",\n      \"        w6 = widgets.CheckboxWidget(description=\\\"Hour\\\", value=True)\\n\",\n      \"        w7 = widgets.CheckboxWidget(description=\\\"Minute\\\", value=True)\\n\",\n      \"        w8 = widgets.CheckboxWidget(description=\\\"Second\\\", value=True)\\n\",\n      \"        #w9 = widgets.CheckboxWidget(description=\\\"MS\\\", value=True)\\n\",\n      \"\\n\",\n      \"        w1.set_css(\\\"width\\\",\\\"200px\\\")\\n\",\n      \"\\n\",\n      \"        children = [w1, w2, w3, w4, w5, w6, w7, w8]\\n\",\n      \"\\n\",\n      \"        temp_container.children = children;\\n\",\n      \"\\n\",\n      \"main_container.children += (wgt_process,)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print_preview()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Data Sample:\\n\",\n        \"                                               Video                  Upload Time  Video length (minutes)  Views  \\\\\\n\",\n        \"0  1. Notebooks and Cells - IPython Notebook Tuto...       July 26, 2014 02:47 AM                    7.55    327   \\n\",\n        \"1        4. NumPy Basics - IPython Notebook Tutorial     August 07, 2014 06:04 AM                   30.72    249   \\n\",\n        \"2     Load Balancing - IPython Parallel Computing #1  September 10, 2014 01:58 AM                    8.57    244   \\n\",\n        \"3     6. IPython Widgets - IPython Notebook Tutorial     August 10, 2014 10:59 AM                   10.07    234   \\n\",\n        \"4    2. Markdown & LaTeX - IPython Notebook Tutorial       July 31, 2014 02:39 AM                   10.77    230   \\n\",\n        \"\\n\",\n        \"   Estimated minutes watched  Average view duration (minutes)  Average percentage viewed  Subscriber views  \\\\\\n\",\n        \"0                        858                             2.62                      34.76                16   \\n\",\n        \"1                       1829                             7.34                      23.91                26   \\n\",\n        \"2                        712                             2.92                      34.06                 6   \\n\",\n        \"3                        823                             3.52                      34.93                15   \\n\",\n        \"4                        821                             3.57                      33.15                12   \\n\",\n        \"\\n\",\n        \"   Subscriber minutes watched  Likes  Dislikes  Shares  Comments  Favorites  Subscribers    Upload Time_date  \\\\\\n\",\n        \"0                          63      3         1       2         2          0            8 2014-07-26 02:47:00   \\n\",\n        \"1                         159      4         0       2         3          0            4 2014-08-07 06:04:00   \\n\",\n        \"2                          10      6         0       1         3          1            4 2014-09-10 01:58:00   \\n\",\n        \"3                          53      6         0       1         8          0            1 2014-08-10 10:59:00   \\n\",\n        \"4                          66      3         1       3         2          1            3 2014-07-31 02:39:00   \\n\",\n        \"\\n\",\n        \"   Video_mapped  Upload Time_mapped  Video_is_1. Notebooks and Cells - IPython Notebook Tutorial  \\\\\\n\",\n        \"0             0                   0                                                  1             \\n\",\n        \"1             1                   1                                                  0             \\n\",\n        \"2             2                   2                                                  0             \\n\",\n        \"3             3                   3                                                  0             \\n\",\n        \"4             4                   4                                                  0             \\n\",\n        \"\\n\",\n        \"   Video_is_2. Markdown & LaTeX - IPython Notebook Tutorial  Video_is_3. Basic Python - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0                                                         0      \\n\",\n        \"1                                                  0                                                         0      \\n\",\n        \"2                                                  0                                                         0      \\n\",\n        \"3                                                  0                                                         0      \\n\",\n        \"4                                                  1                                                         0      \\n\",\n        \"\\n\",\n        \"   Video_is_4. NumPy Basics - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0      \\n\",\n        \"1                                                  1      \\n\",\n        \"2                                                  0      \\n\",\n        \"3                                                  0      \\n\",\n        \"4                                                  0      \\n\",\n        \"\\n\",\n        \"   Video_is_5. Plotting Charts with Matplotlib - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0                         \\n\",\n        \"1                                                  0                         \\n\",\n        \"2                                                  0                         \\n\",\n        \"3                                                  0                         \\n\",\n        \"4                                                  0                         \\n\",\n        \"\\n\",\n        \"   Video_is_6. IPython Widgets - IPython Notebook Tutorial  Video_is_7. Pandas - IPython Notebook Tutorial  \\\\\\n\",\n        \"0                                                  0                                                     0   \\n\",\n        \"1                                                  0                                                     0   \\n\",\n        \"2                                                  0                                                     0   \\n\",\n        \"3                                                  1                                                     0   \\n\",\n        \"4                                                  0                                                     0   \\n\",\n        \"\\n\",\n        \"   Video_is_8. SymPy - IPython Notebook Tutorial  Video_is_Can computers DISCRIMINATE  against race and gender?  \\\\\\n\",\n        \"0                                              0                                                  0               \\n\",\n        \"1                                              0                                                  0               \\n\",\n        \"2                                              0                                                  0               \\n\",\n        \"3                                              0                                                  0               \\n\",\n        \"4                                              0                                                  0               \\n\",\n        \"\\n\",\n        \"   Video_is_Container Widgets - IPython Widgets #1  Video_is_Folder Management - IPython Notebook Tips  \\\\\\n\",\n        \"0                                                0                                                  0    \\n\",\n        \"1                                                0                                                  0    \\n\",\n        \"2                                                0                                                  0    \\n\",\n        \"3                                                0                                                  0    \\n\",\n        \"4                                                0                                                  0    \\n\",\n        \"\\n\",\n        \"   Video_is_Handling Events - IPython Widgets #2  Video_is_How does SVM work?  \\\\\\n\",\n        \"0                                              0                            0   \\n\",\n        \"1                                              0                            0   \\n\",\n        \"2                                              0                            0   \\n\",\n        \"3                                              0                            0   \\n\",\n        \"4                                              0                            0   \\n\",\n        \"\\n\",\n        \"   Video_is_How does kNN (k-Nearest Neighbors) work?  \\\\\\n\",\n        \"0                                                  0   \\n\",\n        \"1                                                  0   \\n\",\n        \"2                                                  0   \\n\",\n        \"3                                                  0   \\n\",\n        \"4                                                  0   \\n\",\n        \"\\n\",\n        \"   Video_is_Indian Elections 2014 - IPython Notebook Tutorial (Exercise)  \\\\\\n\",\n        \"0                                                  0                       \\n\",\n        \"1                                                  0                       \\n\",\n        \"2                                                  0                       \\n\",\n        \"3                                                  0                       \\n\",\n        \"4                                                  0                       \\n\",\n        \"\\n\",\n        \"   Video_is_Load Balancing - IPython Parallel Computing #1  \\\\\\n\",\n        \"0                                                  0         \\n\",\n        \"1                                                  0         \\n\",\n        \"2                                                  1         \\n\",\n        \"3                                                  0         \\n\",\n        \"4                                                  0         \\n\",\n        \"\\n\",\n        \"   Video_is_Machine Learning 1 - Setup Development Environment  Video_is_Machine Learning 2 - Introduction to ML  \\\\\\n\",\n        \"0                                                  0                                                           0   \\n\",\n        \"1                                                  0                                                           0   \\n\",\n        \"2                                                  0                                                           0   \\n\",\n        \"3                                                  0                                                           0   \\n\",\n        \"4                                                  0                                                           0   \\n\",\n        \"\\n\",\n        \"   Video_is_Machine Learning 3 - Clustering  Video_is_Machine Learning 3 - Regression  \\\\\\n\",\n        \"0                                         0                                         0   \\n\",\n        \"1                                         0                                         0   \\n\",\n        \"2                                         0                                         0   \\n\",\n        \"3                                         0                                         0   \\n\",\n        \"4                                         0                                         0   \\n\",\n        \"\\n\",\n        \"   Video_is_Styling Widgets - IPython Widgets #3  Video_is_Web DICOM Viewer  \\\\\\n\",\n        \"0                                              0                          0   \\n\",\n        \"1                                              0                          0   \\n\",\n        \"2                                              0                          0   \\n\",\n        \"3                                              0                          0   \\n\",\n        \"4                                              0                          0   \\n\",\n        \"\\n\",\n        \"   Video_is_Widgets Alignment - IPython Widgets #4  Upload Time_date_month  Upload Time_date_day  \\\\\\n\",\n        \"0                                                0                       7                    26   \\n\",\n        \"1                                                0                       8                     7   \\n\",\n        \"2                                                0                       9                    10   \\n\",\n        \"3                                                0                       8                    10   \\n\",\n        \"4                                                0                       7                    31   \\n\",\n        \"\\n\",\n        \"   Upload Time_date_dayofweek  Upload Time_date_hour  Upload Time_date_minute  \\n\",\n        \"0                           5                      2                       47  \\n\",\n        \"1                           3                      6                        4  \\n\",\n        \"2                           2                      1                       58  \\n\",\n        \"3                           6                     10                       59  \\n\",\n        \"4                           3                      2                       39  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \".\\n\",\n        \".\\n\",\n        \".\\n\",\n        \"\\n\",\n        \"                                                Video                  Upload Time  Video length (minutes)  Views  \\\\\\n\",\n        \"18               Handling Events - IPython Widgets #2  September 11, 2014 09:54 PM                    5.63     31   \\n\",\n        \"19                                   Web DICOM Viewer        May 30, 2014 11:56 PM                    1.72     28   \\n\",\n        \"20  Indian Elections 2014 - IPython Notebook Tutor...  September 06, 2014 03:08 PM                   30.58     23   \\n\",\n        \"21               Styling Widgets - IPython Widgets #3  September 13, 2014 04:26 AM                    8.25     23   \\n\",\n        \"22             Widgets Alignment - IPython Widgets #4  September 27, 2014 10:39 PM                    3.18      4   \\n\",\n        \"\\n\",\n        \"    Estimated minutes watched  Average view duration (minutes)  Average percentage viewed  Subscriber views  \\\\\\n\",\n        \"18                         89                             2.87                      50.88                 4   \\n\",\n        \"19                         32                             1.13                      71.10                10   \\n\",\n        \"20                         90                             3.93                      12.86                 6   \\n\",\n        \"21                         87                             3.79                      45.99                 9   \\n\",\n        \"22                          5                             1.14                      35.68                 2   \\n\",\n        \"\\n\",\n        \"    Subscriber minutes watched  Likes  Dislikes  Shares  Comments  Favorites  Subscribers    Upload Time_date  \\\\\\n\",\n        \"18                          12      2         0       2         2          1            1 2014-09-11 21:54:00   \\n\",\n        \"19                          13      3         0       2         2          0            0 2014-05-30 23:56:00   \\n\",\n        \"20                          21      3         0       1         1          0            2 2014-09-06 15:08:00   \\n\",\n        \"21                          35      2         0       0         1          0            0 2014-09-13 04:26:00   \\n\",\n        \"22                           1      2         0       0         1          0            0 2014-09-27 22:39:00   \\n\",\n        \"\\n\",\n        \"    Video_mapped  Upload Time_mapped  Video_is_1. Notebooks and Cells - IPython Notebook Tutorial  \\\\\\n\",\n        \"18            18                  18                                                  0             \\n\",\n        \"19            19                  19                                                  0             \\n\",\n        \"20            20                  20                                                  0             \\n\",\n        \"21            21                  21                                                  0             \\n\",\n        \"22            22                  22                                                  0             \\n\",\n        \"\\n\",\n        \"    Video_is_2. Markdown & LaTeX - IPython Notebook Tutorial  Video_is_3. Basic Python - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0                                                         0      \\n\",\n        \"19                                                  0                                                         0      \\n\",\n        \"20                                                  0                                                         0      \\n\",\n        \"21                                                  0                                                         0      \\n\",\n        \"22                                                  0                                                         0      \\n\",\n        \"\\n\",\n        \"    Video_is_4. NumPy Basics - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0      \\n\",\n        \"19                                                  0      \\n\",\n        \"20                                                  0      \\n\",\n        \"21                                                  0      \\n\",\n        \"22                                                  0      \\n\",\n        \"\\n\",\n        \"    Video_is_5. Plotting Charts with Matplotlib - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0                         \\n\",\n        \"19                                                  0                         \\n\",\n        \"20                                                  0                         \\n\",\n        \"21                                                  0                         \\n\",\n        \"22                                                  0                         \\n\",\n        \"\\n\",\n        \"    Video_is_6. IPython Widgets - IPython Notebook Tutorial  Video_is_7. Pandas - IPython Notebook Tutorial  \\\\\\n\",\n        \"18                                                  0                                                     0   \\n\",\n        \"19                                                  0                                                     0   \\n\",\n        \"20                                                  0                                                     0   \\n\",\n        \"21                                                  0                                                     0   \\n\",\n        \"22                                                  0                                                     0   \\n\",\n        \"\\n\",\n        \"    Video_is_8. SymPy - IPython Notebook Tutorial  Video_is_Can computers DISCRIMINATE  against race and gender?  \\\\\\n\",\n        \"18                                              0                                                  0               \\n\",\n        \"19                                              0                                                  0               \\n\",\n        \"20                                              0                                                  0               \\n\",\n        \"21                                              0                                                  0               \\n\",\n        \"22                                              0                                                  0               \\n\",\n        \"\\n\",\n        \"    Video_is_Container Widgets - IPython Widgets #1  Video_is_Folder Management - IPython Notebook Tips  \\\\\\n\",\n        \"18                                                0                                                  0    \\n\",\n        \"19                                                0                                                  0    \\n\",\n        \"20                                                0                                                  0    \\n\",\n        \"21                                                0                                                  0    \\n\",\n        \"22                                                0                                                  0    \\n\",\n        \"\\n\",\n        \"    Video_is_Handling Events - IPython Widgets #2  Video_is_How does SVM work?  \\\\\\n\",\n        \"18                                              1                            0   \\n\",\n        \"19                                              0                            0   \\n\",\n        \"20                                              0                            0   \\n\",\n        \"21                                              0                            0   \\n\",\n        \"22                                              0                            0   \\n\",\n        \"\\n\",\n        \"    Video_is_How does kNN (k-Nearest Neighbors) work?  \\\\\\n\",\n        \"18                                                  0   \\n\",\n        \"19                                                  0   \\n\",\n        \"20                                                  0   \\n\",\n        \"21                                                  0   \\n\",\n        \"22                                                  0   \\n\",\n        \"\\n\",\n        \"    Video_is_Indian Elections 2014 - IPython Notebook Tutorial (Exercise)  \\\\\\n\",\n        \"18                                                  0                       \\n\",\n        \"19                                                  0                       \\n\",\n        \"20                                                  1                       \\n\",\n        \"21                                                  0                       \\n\",\n        \"22                                                  0                       \\n\",\n        \"\\n\",\n        \"    Video_is_Load Balancing - IPython Parallel Computing #1  \\\\\\n\",\n        \"18                                                  0         \\n\",\n        \"19                                                  0         \\n\",\n        \"20                                                  0         \\n\",\n        \"21                                                  0         \\n\",\n        \"22                                                  0         \\n\",\n        \"\\n\",\n        \"    Video_is_Machine Learning 1 - Setup Development Environment  Video_is_Machine Learning 2 - Introduction to ML  \\\\\\n\",\n        \"18                                                  0                                                           0   \\n\",\n        \"19                                                  0                                                           0   \\n\",\n        \"20                                                  0                                                           0   \\n\",\n        \"21                                                  0                                                           0   \\n\",\n        \"22                                                  0                                                           0   \\n\",\n        \"\\n\",\n        \"    Video_is_Machine Learning 3 - Clustering  Video_is_Machine Learning 3 - Regression  \\\\\\n\",\n        \"18                                         0                                         0   \\n\",\n        \"19                                         0                                         0   \\n\",\n        \"20                                         0                                         0   \\n\",\n        \"21                                         0                                         0   \\n\",\n        \"22                                         0                                         0   \\n\",\n        \"\\n\",\n        \"    Video_is_Styling Widgets - IPython Widgets #3  Video_is_Web DICOM Viewer  \\\\\\n\",\n        \"18                                              0                          0   \\n\",\n        \"19                                              0                          1   \\n\",\n        \"20                                              0                          0   \\n\",\n        \"21                                              1                          0   \\n\",\n        \"22                                              0                          0   \\n\",\n        \"\\n\",\n        \"    Video_is_Widgets Alignment - IPython Widgets #4  Upload Time_date_month  Upload Time_date_day  \\\\\\n\",\n        \"18                                                0                       9                    11   \\n\",\n        \"19                                                0                       5                    30   \\n\",\n        \"20                                                0                       9                     6   \\n\",\n        \"21                                                0                       9                    13   \\n\",\n        \"22                                                1                       9                    27   \\n\",\n        \"\\n\",\n        \"    Upload Time_date_dayofweek  Upload Time_date_hour  Upload Time_date_minute  \\n\",\n        \"18                           3                     21                       54  \\n\",\n        \"19                           4                     23                       56  \\n\",\n        \"20                           5                     15                        8  \\n\",\n        \"21                           5                      4                       26  \\n\",\n        \"22                           5                     22                       39  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Normalization\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"success_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-success\\\" role=\\\"alert\\\">Numbers processing was successful.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"error_alert = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-danger\\\" role=\\\"alert\\\">Error in numbers processing. %s.</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"def process_number_column(column):\\n\",\n      \"    column_name = column.children[0].value\\n\",\n      \"    number_process = column.children[1].value\\n\",\n      \"    scale_min = column.children[2].value\\n\",\n      \"    scale_max = column.children[3].value\\n\",\n      \"    if number_process == \\\"Scale\\\":\\n\",\n      \"        pre_process = preprocessing.MinMaxScaler(feature_range=(scale_min, scale_max))\\n\",\n      \"        csv_data[column_name + \\\"_scaled\\\"] = pre_process.fit_transform(csv_data[[column_name]].astype(np.float64).values)\\n\",\n      \"    elif number_process == \\\"Standard Scaler\\\":\\n\",\n      \"        pre_process = preprocessing.StandardScaler()\\n\",\n      \"        csv_data[column_name + \\\"_standardscaler\\\"] = pre_process.fit_transform(csv_data[column_name].copy().astype(np.float64))\\n\",\n      \"\\n\",\n      \"def process_number_columns(widget):\\n\",\n      \"    if True: #try:\\n\",\n      \"        for column in main_container.children:\\n\",\n      \"            if isinstance(column, widgets.ContainerWidget):\\n\",\n      \"                process_number_column(column)\\n\",\n      \"        wgt_alert.value = success_alert\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"    else: #except Exception as ex:\\n\",\n      \"        raise ex\\n\",\n      \"        wgt_alert.value = error_alert % ex\\n\",\n      \"        wgt_alert.visible = True\\n\",\n      \"\\n\",\n      \"    \\n\",\n      \"main_container = widgets.ContainerWidget()\\n\",\n      \"display(main_container)\\n\",\n      \"\\n\",\n      \"wgt_alert = widgets.HTMLWidget(visible=False)\\n\",\n      \"wgt_process = widgets.ButtonWidget(description=\\\"Process Columns\\\")\\n\",\n      \"wgt_process.on_click(process_number_columns)\\n\",\n      \"\\n\",\n      \"main_container.children = (wgt_alert,)\\n\",\n      \"\\n\",\n      \"columns = []\\n\",\n      \"\\n\",\n      \"for column in csv_data.columns:\\n\",\n      \"    data_type = str(pd.Series(csv_data[column].values).dtype)\\n\",\n      \"    if data_type in [\\\"float32\\\", \\\"float64\\\", \\\"int32\\\", \\\"int64\\\"]:\\n\",\n      \"        temp_container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"        main_container.children += (temp_container,)\\n\",\n      \"\\n\",\n      \"        temp_container.remove_class('vbox')\\n\",\n      \"        temp_container.add_class('hbox')\\n\",\n      \"        temp_container.add_class('start')\\n\",\n      \"\\n\",\n      \"        w1 = widgets.TextWidget(value=column, disabled=True)\\n\",\n      \"        w2 = widgets.DropdownWidget(values=[\\\"Scale\\\", \\\"Standard Scaler\\\", \\\"Don't Process\\\"], value=\\\"Don't Process\\\")\\n\",\n      \"        w3 = widgets.FloatTextWidget(description=\\\"Scale Min:\\\", value=0)\\n\",\n      \"        w4 = widgets.FloatTextWidget(description=\\\"Scale Max:\\\", value=1)\\n\",\n      \"\\n\",\n      \"        w1.set_css(\\\"width\\\",\\\"200px\\\")\\n\",\n      \"        w3.set_css(\\\"width\\\",\\\"50px\\\")\\n\",\n      \"        w4.set_css(\\\"width\\\",\\\"50px\\\")\\n\",\n      \"\\n\",\n      \"        children = [w1, w2, w3, w4]\\n\",\n      \"\\n\",\n      \"        temp_container.children = children;\\n\",\n      \"\\n\",\n      \"main_container.children += (wgt_process,)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualize Preprocessing\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def display_feature(feature_name):\\n\",\n      \"    plt.figure()\\n\",\n      \"    plt.scatter(list(csv_data.index), csv_data[feature_name])\\n\",\n      \"    plt.grid()\\n\",\n      \"    plt.ylabel(feature_name)\\n\",\n      \"    plt.show()\\n\",\n      \"    print \\\"Mean: %4f\\\" % np.mean(csv_data[feature_name])\\n\",\n      \"    print \\\"Std : %4f\\\" % np.std(csv_data[feature_name])\\n\",\n      \"\\n\",\n      \"wgt_column = widgets.SelectWidget(values=list(csv_data._get_numeric_data().columns))\\n\",\n      \"\\n\",\n      \"interact(display_feature, feature_name=wgt_column);\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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MU2V7RMNA94CzCY7xoHLpiL+o3LRGZm5VU6N1EnuDEwMyuvrX0GEXFJ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\\n4cbAzMxwY2BmZrgxMDMz3BiYmRnw/wFyE5UEyKXgaAAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7fec0ca24610>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Mean: 0.000000\\n\",\n        \"Std : 1.000000\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###Further Preprocessing:\\n\",\n      \"\\n\",\n      \"- Missing Values:\\n\",\n      \" - Drop Columns (Features/Attributes) \\n\",\n      \" - Drop Rows (Samples/Data Points)\\n\",\n      \" - Impute Values (Mean/Median/Mode)\\n\",\n      \"- Denoising Time Series\\n\",\n      \"- Counting Vectorizer For Long Text\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Save Dataset\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"csv_data.save(\\\"processed_data.csv\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"joblib.dump(csv_data, \\\"processed_data.pkl\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 15,\n       \"text\": [\n        \"['processed_data.pkl',\\n\",\n        \" 'processed_data.pkl_01.npy',\\n\",\n        \" 'processed_data.pkl_02.npy',\\n\",\n        \" 'processed_data.pkl_03.npy',\\n\",\n        \" 'processed_data.pkl_04.npy',\\n\",\n        \" 'processed_data.pkl_05.npy']\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/Lesson 6 - Features Extraction.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:2f47664872cfaa34ab4161cd438a77f10d2905ddc7b40effff295fc6a1989117\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"<h3>This Tutorial is also available on <a href=\\\"https://www.youtube.com/user/roshanRush\\\">YouTube</a></h3>\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.display import YouTubeVideo\\n\",\n      \"# a talk about IPython at Sage Days at U. Washington, Seattle.\\n\",\n      \"# Video credit: William Stein.\\n\",\n      \"YouTubeVideo('http://youtu.be/HuDIbSCnsqo')\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"\\n\",\n        \"        <iframe\\n\",\n        \"            width=\\\"400\\\"\\n\",\n        \"            height=300\\\"\\n\",\n        \"            src=\\\"https://www.youtube.com/embed/http://youtu.be/HuDIbSCnsqo\\\"\\n\",\n        \"            frameborder=\\\"0\\\"\\n\",\n        \"            allowfullscreen\\n\",\n        \"        ></iframe>\\n\",\n        \"        \"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 22,\n       \"text\": [\n        \"<IPython.lib.display.YouTubeVideo at 0x7f41bca4e990>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Extracting Features from Text\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"One of the most common ways to analyze is to split it in to words and count how many times each word was used in a text. This may not sound very useful, but with large amounts of text you can build a fairly accurate classifier and this is what we will do in this tutorial.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Required Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pandas as pd\\n\",\n      \"import lxml\\n\",\n      \"import requests\\n\",\n      \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n      \"from sklearn.cross_validation import train_test_split\\n\",\n      \"from sklearn.naive_bayes import MultinomialNB\\n\",\n      \"import warnings\\n\",\n      \"\\n\",\n      \"warnings.filterwarnings(\\\"ignore\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Fetch the RSS news feed from Reuters\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"url_list = [\\\"http://feeds.reuters.com/reuters/businessNews\\\",\\n\",\n      \"            \\\"http://feeds.reuters.com/reuters/technologyNews\\\",\\n\",\n      \"            \\\"http://feeds.reuters.com/reuters/sportsNews\\\",\\n\",\n      \"            ]\\n\",\n      \"documents = []\\n\",\n      \"\\n\",\n      \"for url in url_list:\\n\",\n      \"    response = requests.get(url)\\n\",\n      \"    xml_page = response.text\\n\",\n      \"    parser = lxml.etree.XMLParser(recover=True, encoding='utf-8')\\n\",\n      \"    documents.append(lxml.etree.fromstring(xml_page.encode(\\\"utf-8\\\"), parser=parser))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Extracting Articles from the XML Feed\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Examining the XML Feed Format\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def print_tag(node):\\n\",\n      \"    print \\\"<%s %s>%s\\\" % (node.tag, \\\" \\\".join([\\\"%s=%s\\\" % (k,v)for k,v in node.attrib.iteritems()]), node.text)\\n\",\n      \"    for item in node[:25]:\\n\",\n      \"        print \\\"  <%s %s>%s</%s>\\\" % (item.tag, \\\" \\\".join([\\\"%s=%s\\\" % (k,v)for k,v in item.attrib.iteritems()]), item.text, item.tag)\\n\",\n      \"    print \\\"</%s>\\\" % node.tag\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"temp_node = documents[0]\\n\",\n      \"print_tag(temp_node)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<rss version=2.0>None\\n\",\n        \"  <channel >None</channel>\\n\",\n        \"</rss>\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"temp_node = temp_node[0]\\n\",\n      \"print_tag(temp_node)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<channel >None\\n\",\n        \"  <title >Reuters: Business News</title>\\n\",\n        \"  <link >http://www.reuters.com</link>\\n\",\n        \"  <description >Reuters.com is your source for breaking news, business, financial and investing news, including personal finance and stocks. Reuters is the leading global provider of news, financial information and technology solutions to the world's media, financial institutions, businesses and individuals.</description>\\n\",\n        \"  <language >en-us</language>\\n\",\n        \"  <copyright >All rights reserved. Users may download and print extracts of content from this website for their own personal and non-commercial use only. Republication or redistribution of Reuters content, including by framing or similar means, is expressly prohibited without the prior written consent of Reuters. Reuters and the Reuters sphere logo are registered trademarks or trademarks of the Reuters group of companies around the world. \\u00a9 Reuters 2015</copyright>\\n\",\n        \"  <pubDate >Sat, 07 Feb 2015 14:34:41 GMT</pubDate>\\n\",\n        \"  <lastBuildDate >Sat, 07 Feb 2015 14:34:41 GMT</lastBuildDate>\\n\",\n        \"  <ttl >5</ttl>\\n\",\n        \"  <image >None</image>\\n\",\n        \"  <{http://www.w3.org/2005/Atom}link rel=self type=application/rss+xml href=http://feeds.reuters.com/reuters/businessNews>None</{http://www.w3.org/2005/Atom}link>\\n\",\n        \"  <{http://rssnamespace.org/feedburner/ext/1.0}info uri=reuters/businessnews>None</{http://rssnamespace.org/feedburner/ext/1.0}info>\\n\",\n        \"  <{http://www.w3.org/2005/Atom}link rel=hub href=http://pubsubhubbub.appspot.com/>None</{http://www.w3.org/2005/Atom}link>\\n\",\n        \"  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://add.my.yahoo.com/rss?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://us.i1.yimg.com/us.yimg.com/i/us/my/addtomyyahoo4.gif>Subscribe with My Yahoo!</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\\n\",\n        \"  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://www.newsgator.com/ngs/subscriber/subext.aspx?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://www.newsgator.com/images/ngsub1.gif>Subscribe with NewsGator</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\\n\",\n        \"  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://feeds.my.aol.com/add.jsp?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://o.aolcdn.com/favorites.my.aol.com/webmaster/ffclient/webroot/locale/en-US/images/myAOLButtonSmall.gif>Subscribe with My AOL</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\\n\",\n        \"  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare 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href=http://www.pageflakes.com/subscribe.aspx?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://www.pageflakes.com/ImageFile.ashx?instanceId=Static_4&fileName=ATP_blu_91x17.gif>Subscribe with Pageflakes</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\\n\",\n        \"  <item >None</item>\\n\",\n        \"  <item >None</item>\\n\",\n        \"  <item >None</item>\\n\",\n        \"  <item >None</item>\\n\",\n        \"  <item >None</item>\\n\",\n        \"  <item >None</item>\\n\",\n        \"</channel>\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"temp_node = temp_node.xpath(\\\"item\\\")[0]\\n\",\n      \"print_tag(temp_node)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<item >None\\n\",\n        \"  <title >Exclusive: Top Fox investors seek to convert voting shares, Murdoch may benefit</title>\\n\",\n        \"  <link >http://feeds.reuters.com/~r/reuters/businessNews/~3/Ti_4toCmS6w/story01.htm</link>\\n\",\n        \"  <description >NEW YORK (Reuters) - Several top investors in Twenty-First Century Fox Inc are pressing for the right to swap their voting shares for ordinary shares, which are trading at an unusual premium, even though the move could hand even more control of the company to Rupert Murdoch, according to people familiar with the matter.<img width='1' height='1' src='http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/mf.gif' border='0'/><br clear='all'/><div class=\\\"feedflare\\\">\\n\",\n        \"<a href=\\\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:yIl2AUoC8zA\\\"><img src=\\\"http://feeds.feedburner.com/~ff/reuters/businessNews?d=yIl2AUoC8zA\\\" border=\\\"0\\\"></img></a> <a href=\\\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\\\"><img src=\\\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\\\" border=\\\"0\\\"></img></a> <a href=\\\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\\\"><img src=\\\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\\\" border=\\\"0\\\"></img></a>\\n\",\n        \"</div><img src=\\\"//feeds.feedburner.com/~r/reuters/businessNews/~4/Ti_4toCmS6w\\\" height=\\\"1\\\" width=\\\"1\\\" alt=\\\"\\\"/></description>\\n\",\n        \"  <category domain=>businessNews</category>\\n\",\n        \"  <pubDate >Sat, 07 Feb 2015 14:30:10 GMT</pubDate>\\n\",\n        \"  <guid isPermaLink=false>http://www.reuters.com/article/2015/02/07/us-fox-votingshares-exclusive-idUSKBN0LA2HP20150207?feedType=RSS&feedName=businessNews</guid>\\n\",\n        \"  <{http://rssnamespace.org/feedburner/ext/1.0}origLink >http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/l/0L0Sreuters0N0Carticle0C20A150C0A20C0A70Cus0Efox0Evotingshares0Eexclusive0EidUSKBN0ALA2HP20A150A20A70DfeedType0FRSS0GfeedName0FbusinessNews/story01.htm</{http://rssnamespace.org/feedburner/ext/1.0}origLink>\\n\",\n        \"</item>\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Processing All Feeds to a DataFrame\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"title_list = []\\n\",\n      \"description_list = []\\n\",\n      \"category_list = []\\n\",\n      \"\\n\",\n      \"for xml_doc in documents:\\n\",\n      \"    articles = xml_doc.xpath(\\\"//item\\\")\\n\",\n      \"    for article in articles:\\n\",\n      \"        title_list.append(article[0].text)\\n\",\n      \"        description_list.append(article[2].text)\\n\",\n      \"        category_list.append(article[3].text)\\n\",\n      \"        \\n\",\n      \"\\n\",\n      \"news_data = pd.DataFrame(title_list, columns=[\\\"title\\\"])\\n\",\n      \"news_data[\\\"description\\\"] = description_list\\n\",\n      \"news_data[\\\"category\\\"] = category_list\\n\",\n      \"print len(news_data)\\n\",\n      \"news_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"60\\n\"\n       ]\n      },\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>title</th>\\n\",\n        \"      <th>description</th>\\n\",\n        \"      <th>category</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> Exclusive: Top Fox investors seek to convert v...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Several top investors in ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> Different delivery, one message to Greece's ne...</td>\\n\",\n        \"      <td> ROME/PARIS (Reuters) - In Paris and Rome, it w...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> Isolated Greece wants no more bailout money wi...</td>\\n\",\n        \"      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> Valuations may hurt small caps, despite job gr...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - The good news from Friday...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> Shippers suspend weekend cargo loading at U.S....</td>\\n\",\n        \"      <td> LOS ANGELES (Reuters) - The loading and unload...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> Building unions return to Kentucky refinery de...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Workers represented by th...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> Wall St. ends down on interest rate, Greece ji...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> Strong U.S. job, wage gains open door to mid-y...</td>\\n\",\n        \"      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> Building unions return to Kentucky refinery de...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Workers represented by th...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> JPMorgan under scrutiny over hiring of Chinese...</td>\\n\",\n        \"      <td> (Reuters) - JPMorgan Chase &amp; Co is under feder...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> Exclusive: Top Fox investors seek to convert v...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Several top investors in ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> Isolated Greece wants no more bailout money wi...</td>\\n\",\n        \"      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> Wall St. ends down on interest rate, Greece ji...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>  RadioShack would accept liquidation bids: lawyer</td>\\n\",\n        \"      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> Strong U.S. job, wage gains open door to mid-y...</td>\\n\",\n        \"      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> Wall Street firms waver over June 2015 rate hi...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Economists at Wall Street...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> Oil climbs, Brent posts best two weeks since 1998</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Oil rallied again on Frid...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td> Brazil's Petrobras taps state banker as CEO; s...</td>\\n\",\n        \"      <td> BRASILIA (Reuters) - Brazil's President Dilma ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td> Anthem warns U.S. customers of email scam afte...</td>\\n\",\n        \"      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td> U.S. court orders Symantec to pay $17 mln for ...</td>\\n\",\n        \"      <td> (Reuters) - Symantec Corp, maker of the popula...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>  RadioShack would accept liquidation bids: lawyer</td>\\n\",\n        \"      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td> Anthem warns U.S. customers of email scam afte...</td>\\n\",\n        \"      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td> Google can disrupt car industry but is no auto...</td>\\n\",\n        \"      <td> FRANKFURT (Reuters) - Technology companies suc...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td> Chipmaker Intel promotes senior executives, du...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td> Google panel backs firm on EU limit to 'right ...</td>\\n\",\n        \"      <td> BRUSSELS (Reuters) - A panel of experts appoin...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td> Family money puts its faith in European techno...</td>\\n\",\n        \"      <td> LONDON (Reuters) - Some of Europe's wealthiest...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td> U.S. states probe massive data breach at healt...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Several U.S. states are i...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td> Health insurer Anthem hit by massive cybersecu...</td>\\n\",\n        \"      <td> (Reuters) - Health insurer Anthem Inc , which ...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>        China to have most robots in world by 2017</td>\\n\",\n        \"      <td> FRANKFURT (Reuters) - China will have more rob...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td> U.S. businesses ask White House to help on Chi...</td>\\n\",\n        \"      <td> WASHINGTON (Reuters) - U.S. business lobbies c...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td> Sharp says not considering sale of any oversea...</td>\\n\",\n        \"      <td> TOKYO (Reuters) - Japanese electronics maker S...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td> RadioShack files for bankruptcy; Sprint to tak...</td>\\n\",\n        \"      <td> (Reuters) - Electronics retailer RadioShack Co...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td> SEC probes Blackberry options trading ahead of...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - The U.S. Securities and E...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td> Twitter tops Wall Street revenue target, user ...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Twitter Inc said on ...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td> U.S. Golf Association to launch Senior Women's...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - The United States Golf As...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td> In-form Wiesberger goes two shots clear in Kua...</td>\\n\",\n        \"      <td> KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td> Jansrud signals downhill ambition in final tra...</td>\\n\",\n        \"      <td> BEAVER CREEK, Colorado (Reuters) - Unheralded ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td> NFL, players union spar in court over Peterson...</td>\\n\",\n        \"      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td> English takes charge as Mickelson exits Torrey...</td>\\n\",\n        \"      <td> LA JOLLA, California (Reuters) - Local favorit...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td>        No pressure for double medallist Fenninger</td>\\n\",\n        \"      <td> BEAVER CREEK, Colorado (Reuters) - A Super-G 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     \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td> NFL and players union spar in court over Peter...</td>\\n\",\n        \"      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50</th>\\n\",\n        \"      <td>           No sharing world downhill gold for Maze</td>\\n\",\n        \"      <td> BEAVER CREEK, Colorado (Reuters) - Tina Maze h...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>51</th>\\n\",\n        \"      <td>   Doping should be criminal offense: Russia chief</td>\\n\",\n        \"      <td> MOSCOW (Reuters) - Russia's Athletics Federati...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52</th>\\n\",\n        \"      <td> Kansas police 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need to take a longer...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56</th>\\n\",\n        \"      <td> European Games will convince skeptics, attract...</td>\\n\",\n        \"      <td> (Reuters) - The inaugural European Games this ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>57</th>\\n\",\n        \"      <td> Departing VFLA President to continue fight aga...</td>\\n\",\n        \"      <td> MOSCOW (Reuters) - Russian Athletics Federatio...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>58</th>\\n\",\n        \"      <td> Canizares joins champion Westwood at the top i...</td>\\n\",\n        \"      <td> KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>59</th>\\n\",\n        \"      <td> Force India opposed Marussia's bid to use old car</td>\\n\",\n        \"      <td> LONDON (Reuters) - Marussia's hopes of rising ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 7,\n       \"text\": [\n        \"                                                title  \\\\\\n\",\n        \"0   Exclusive: Top Fox investors seek to convert v...   \\n\",\n        \"1   Different delivery, one message to Greece's ne...   \\n\",\n        \"2   Isolated Greece wants no more bailout money wi...   \\n\",\n        \"3   Valuations may hurt small caps, despite job gr...   \\n\",\n        \"4   Shippers suspend weekend cargo loading at U.S....   \\n\",\n        \"5   Building unions 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\\n\",\n        \"19  Brazil's Petrobras taps state banker as CEO; s...   \\n\",\n        \"20  Feds can't indefinitely gag Yahoo about subpoe...   \\n\",\n        \"21        Motorola exploring possible sale: Bloomberg   \\n\",\n        \"22  Anthem warns U.S. customers of email scam afte...   \\n\",\n        \"23  U.S. court orders Symantec to pay $17 mln for ...   \\n\",\n        \"24        Motorola exploring possible sale: Bloomberg   \\n\",\n        \"25   RadioShack would accept liquidation bids: lawyer   \\n\",\n        \"26  Anthem warns U.S. customers of email scam afte...   \\n\",\n        \"27  Google can disrupt car industry but is no auto...   \\n\",\n        \"28  Chipmaker Intel promotes senior executives, du...   \\n\",\n        \"29  Feds can't indefinitely gag Yahoo about subpoe...   \\n\",\n        \"30  Google panel backs firm on EU limit to 'right ...   \\n\",\n        \"31  Family money puts its faith in European techno...   \\n\",\n        \"32  U.S. states probe massive data breach at healt...   \\n\",\n        \"33  Health insurer Anthem hit by massive cybersecu...   \\n\",\n        \"34         China to have most robots in world by 2017   \\n\",\n        \"35  U.S. businesses ask White House to help on Chi...   \\n\",\n        \"36  Sharp says not considering sale of any oversea...   \\n\",\n        \"37  RadioShack files for bankruptcy; Sprint to tak...   \\n\",\n        \"38  SEC probes Blackberry options trading ahead of...   \\n\",\n        \"39  Twitter tops Wall Street revenue target, user ...   \\n\",\n        \"40  U.S. Golf Association to launch Senior Women's...   \\n\",\n        \"41  In-form Wiesberger goes two shots clear in Kua...   \\n\",\n        \"42  Jansrud signals downhill ambition in final tra...   \\n\",\n        \"43  NFL, players union spar in court over Peterson...   \\n\",\n        \"44  English takes charge as Mickelson exits Torrey...   \\n\",\n        \"45         No pressure for double medallist Fenninger   \\n\",\n        \"46  Referees association backs rookie in flare-up ...   \\n\",\n        \"47  Rangers goalie Lundqvist out for three weeks w...   \\n\",\n        \"48          Miracle now needed to win gold, says Vonn   \\n\",\n        \"49  NFL and players union spar in court over Peter...   \\n\",\n        \"50            No sharing world downhill gold for Maze   \\n\",\n        \"51    Doping should be criminal offense: Russia chief   \\n\",\n        \"52  Kansas police drop drug charges against Dallas...   \\n\",\n        \"53       Thompson leads by one stroke at Torrey Pines   \\n\",\n        \"54  Borzakovskiy takes over as Russia's caretaker ...   \\n\",\n        \"55    Clubs need long-term mental health plan: FIFPro   \\n\",\n        \"56  European Games will convince skeptics, attract...   \\n\",\n        \"57  Departing VFLA President to continue fight aga...   \\n\",\n        \"58  Canizares joins champion Westwood at the top i...   \\n\",\n        \"59  Force India 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technologyNews  \\n\",\n        \"21  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews  \\n\",\n        \"22  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews  \\n\",\n        \"23  (Reuters) - Symantec Corp, maker of the popula...  technologyNews  \\n\",\n        \"24  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews  \\n\",\n        \"25  (Reuters) - A lawyer for RadioShack on Friday ...  technologyNews  \\n\",\n        \"26  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews  \\n\",\n        \"27  FRANKFURT (Reuters) - Technology companies suc...  technologyNews  \\n\",\n        \"28  SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...  technologyNews  \\n\",\n        \"29  SAN FRANCISCO (Reuters) - Law enforcement cann...  technologyNews  \\n\",\n        \"30  BRUSSELS (Reuters) - A panel of experts appoin...  technologyNews  \\n\",\n        \"31  LONDON (Reuters) - Some of Europe's wealthiest...  technologyNews  \\n\",\n        \"32  NEW YORK (Reuters) - Several U.S. states are i...  technologyNews  \\n\",\n        \"33  (Reuters) - Health insurer Anthem Inc , which ...  technologyNews  \\n\",\n        \"34  FRANKFURT (Reuters) - China will have more rob...  technologyNews  \\n\",\n        \"35  WASHINGTON (Reuters) - U.S. business lobbies c...  technologyNews  \\n\",\n        \"36  TOKYO (Reuters) - Japanese electronics maker S...  technologyNews  \\n\",\n        \"37  (Reuters) - Electronics retailer RadioShack Co...  technologyNews  \\n\",\n        \"38  NEW YORK (Reuters) - The U.S. Securities and E...  technologyNews  \\n\",\n        \"39  SAN FRANCISCO (Reuters) - Twitter Inc said on ...  technologyNews  \\n\",\n        \"40  NEW YORK (Reuters) - The United States Golf As...      sportsNews  \\n\",\n        \"41  KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...      sportsNews  \\n\",\n        \"42  BEAVER CREEK, Colorado (Reuters) - Unheralded ...      sportsNews  \\n\",\n        \"43  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews  \\n\",\n        \"44  LA JOLLA, California (Reuters) - Local favorit...      sportsNews  \\n\",\n        \"45  BEAVER CREEK, Colorado (Reuters) - A Super-G v...      sportsNews  \\n\",\n        \"46  (Reuters) - The National Basketball Referees A...      sportsNews  \\n\",\n        \"47  (Reuters) - New York Rangers goalie Henrik Lun...      sportsNews  \\n\",\n        \"48  BEAVER CREEK, Colorado (Reuters) - Denied gold...      sportsNews  \\n\",\n        \"49  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews  \\n\",\n        \"50  BEAVER CREEK, Colorado (Reuters) - Tina Maze h...      sportsNews  \\n\",\n        \"51  MOSCOW (Reuters) - Russia's Athletics Federati...      sportsNews  \\n\",\n        \"52  DALLAS, Texas (Reuters) - Authorities have dro...      sportsNews  \\n\",\n        \"53  LA JOLLA, California (Reuters - American Nicho...      sportsNews  \\n\",\n        \"54  MOSCOW (Reuters) - Former Olympic 800 meters c...      sportsNews  \\n\",\n        \"55  (Reuters) - Soccer clubs need to take a longer...      sportsNews  \\n\",\n        \"56  (Reuters) - The inaugural European Games this ...      sportsNews  \\n\",\n        \"57  MOSCOW (Reuters) - Russian Athletics Federatio...      sportsNews  \\n\",\n        \"58  KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...      sportsNews  \\n\",\n        \"59  LONDON (Reuters) - Marussia's hopes of rising ...      sportsNews  \"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Clean up data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"news_data[\\\"description\\\"].head()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 8,\n       \"text\": [\n        \"0    NEW YORK (Reuters) - Several top investors in ...\\n\",\n        \"1    ROME/PARIS (Reuters) - In Paris and Rome, it w...\\n\",\n        \"2    ATHENS/BRUSSELS (Reuters) - Greece's new lefti...\\n\",\n        \"3    NEW YORK (Reuters) - The good news from Friday...\\n\",\n        \"4    LOS ANGELES (Reuters) - The loading and unload...\\n\",\n        \"Name: description, dtype: object\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print news_data[\\\"description\\\"][0]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"NEW YORK (Reuters) - Several top investors in Twenty-First Century Fox Inc are pressing for the right to swap their voting shares for ordinary shares, which are trading at an unusual premium, even though the move could hand even more control of the company to Rupert Murdoch, according to people familiar with the matter.<img width='1' height='1' src='http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/mf.gif' border='0'/><br clear='all'/><div class=\\\"feedflare\\\">\\n\",\n        \"<a href=\\\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:yIl2AUoC8zA\\\"><img src=\\\"http://feeds.feedburner.com/~ff/reuters/businessNews?d=yIl2AUoC8zA\\\" border=\\\"0\\\"></img></a> <a href=\\\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\\\"><img src=\\\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\\\" border=\\\"0\\\"></img></a> <a href=\\\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\\\"><img 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- Several top investors in ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Several top investors in Twenty-First Century ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> Different delivery, one message to Greece's ne...</td>\\n\",\n        \"      <td> ROME/PARIS (Reuters) - In Paris and Rome, it w...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> In Paris and Rome, it was sugar coated; in Ber...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> Isolated Greece wants no more bailout money wi...</td>\\n\",\n        \"      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Greece's new leftist-led government, isolated ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> Valuations may hurt small caps, despite job gr...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - The good news from Friday...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> The good news from Friday's jobs report may al...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> Shippers suspend weekend cargo loading at U.S....</td>\\n\",\n        \"      <td> LOS ANGELES (Reuters) - The loading and unload...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> The loading and unloading of freighters will b...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> Building unions return to Kentucky refinery de...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Workers represented by th...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Workers represented by the Building Trades Uni...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Walkie-talkie and radio systems maker Motorola...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> Wall St. ends down on interest rate, Greece ji...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Wall Street stocks fell on Friday as a better-...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> Strong U.S. job, wage gains open door to mid-y...</td>\\n\",\n        \"      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> U.S. job growth rose solidly in January and wa...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> Building unions return to Kentucky refinery de...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Workers represented by th...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Workers represented by the Building Trades Uni...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Walkie-talkie and radio systems maker Motorola...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> JPMorgan under scrutiny over hiring of Chinese...</td>\\n\",\n        \"      <td> (Reuters) - JPMorgan Chase &amp; Co is under feder...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> JPMorgan Chase &amp; Co is under federal scrutiny ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> Exclusive: Top Fox investors seek to convert v...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Several top investors in ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Several top investors in Twenty-First Century ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> Isolated Greece wants no more bailout money wi...</td>\\n\",\n        \"      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Greece's new leftist-led government, isolated ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> Wall St. ends down on interest rate, Greece ji...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Wall Street stocks fell on Friday as a better-...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td>  RadioShack would accept liquidation bids: lawyer</td>\\n\",\n        \"      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> A lawyer for RadioShack on Friday said the ban...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> Strong U.S. job, wage gains open door to mid-y...</td>\\n\",\n        \"      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> U.S. job growth rose solidly in January and wa...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> Wall Street firms waver over June 2015 rate hi...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Economists at Wall Street...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Economists at Wall Street's biggest banks are ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> Oil climbs, Brent posts best two weeks since 1998</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Oil rallied again on Frid...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Oil rallied again on Friday, with benchmark Br...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td> Brazil's Petrobras taps state banker as CEO; s...</td>\\n\",\n        \"      <td> BRASILIA (Reuters) - Brazil's President Dilma ...</td>\\n\",\n        \"      <td>   businessNews</td>\\n\",\n        \"      <td> Brazil's President Dilma Rousseff tapped a con...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Law enforcement cannot indefinitely forbid Yah...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Walkie-talkie and radio systems maker Motorola...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td> Anthem warns U.S. customers of email scam afte...</td>\\n\",\n        \"      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Health insurer Anthem Inc on Friday warned U.S...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td> U.S. court orders Symantec to pay $17 mln for ...</td>\\n\",\n        \"      <td> (Reuters) - Symantec Corp, maker of the popula...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Symantec Corp, maker of the popular Norton ant...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td>       Motorola exploring possible sale: Bloomberg</td>\\n\",\n        \"      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Walkie-talkie and radio systems maker Motorola...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td>  RadioShack would accept liquidation bids: lawyer</td>\\n\",\n        \"      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> A lawyer for RadioShack on Friday said the ban...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td> Anthem warns U.S. customers of email scam afte...</td>\\n\",\n        \"      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Health insurer Anthem Inc on Friday warned U.S...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td> Google can disrupt car industry but is no auto...</td>\\n\",\n        \"      <td> FRANKFURT (Reuters) - Technology companies suc...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Technology companies such as Google are unlike...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td> Chipmaker Intel promotes senior executives, du...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Chipmaker Intel Corp said on Friday it promote...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Law enforcement cannot indefinitely forbid Yah...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td> Google panel backs firm on EU limit to 'right ...</td>\\n\",\n        \"      <td> BRUSSELS (Reuters) - A panel of experts appoin...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> A panel of experts appointed by Google to advi...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td> Family money puts its faith in European techno...</td>\\n\",\n        \"      <td> LONDON (Reuters) - Some of Europe's wealthiest...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Some of Europe's wealthiest families are inves...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td> U.S. states probe massive data breach at healt...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - Several U.S. states are i...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Several U.S. states are investigating a massiv...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td> Health insurer Anthem hit by massive cybersecu...</td>\\n\",\n        \"      <td> (Reuters) - Health insurer Anthem Inc , which ...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Health insurer Anthem Inc , which has nearly 4...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td>        China to have most robots in world by 2017</td>\\n\",\n        \"      <td> FRANKFURT (Reuters) - China will have more rob...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> China will have more robots operating in its p...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td> U.S. businesses ask White House to help on Chi...</td>\\n\",\n        \"      <td> WASHINGTON (Reuters) - U.S. business lobbies c...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> U.S. business lobbies called on the White Hous...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td> Sharp says not considering sale of any oversea...</td>\\n\",\n        \"      <td> TOKYO (Reuters) - Japanese electronics maker S...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Japanese electronics maker Sharp Corp said a s...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td> RadioShack files for bankruptcy; Sprint to tak...</td>\\n\",\n        \"      <td> (Reuters) - Electronics retailer RadioShack Co...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Electronics retailer RadioShack Corp filed for...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td> SEC probes Blackberry options trading ahead of...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - The U.S. Securities and E...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> The U.S. Securities and Exchange Commission is...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td> Twitter tops Wall Street revenue target, user ...</td>\\n\",\n        \"      <td> SAN FRANCISCO (Reuters) - Twitter Inc said on ...</td>\\n\",\n        \"      <td> technologyNews</td>\\n\",\n        \"      <td> Twitter Inc said on Thursday the social media ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td> U.S. Golf Association to launch Senior Women's...</td>\\n\",\n        \"      <td> NEW YORK (Reuters) - The United States Golf As...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> The United States Golf Association will launch...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td> In-form Wiesberger goes two shots clear in Kua...</td>\\n\",\n        \"      <td> KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Austrian Bernd Wiesberger will take a two-shot...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td> Jansrud signals downhill ambition in final tra...</td>\\n\",\n        \"      <td> BEAVER CREEK, Colorado (Reuters) - Unheralded ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Unheralded Frenchman Brice Roger posted the fa...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td> NFL, players union spar in court over Peterson...</td>\\n\",\n        \"      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> The National Football League and its players u...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td> English takes charge as Mickelson exits Torrey...</td>\\n\",\n        \"      <td> LA JOLLA, California (Reuters) - Local favorit...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Local favorite Phil Mickelson has joined Tiger...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td>        No pressure for double medallist Fenninger</td>\\n\",\n        \"      <td> BEAVER CREEK, Colorado (Reuters) - A Super-G v...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> A Super-G victory at last year's Winter Olympi...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td> Referees association backs rookie in flare-up ...</td>\\n\",\n        \"      <td> (Reuters) - The National Basketball Referees A...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> The National Basketball Referees Association c...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47</th>\\n\",\n        \"      <td> Rangers goalie Lundqvist out for three weeks w...</td>\\n\",\n        \"      <td> (Reuters) - New York Rangers goalie Henrik Lun...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> New York Rangers goalie Henrik Lundqvist will ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>48</th>\\n\",\n        \"      <td>         Miracle now needed to win gold, says Vonn</td>\\n\",\n        \"      <td> BEAVER CREEK, Colorado (Reuters) - Denied gold...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Denied gold medals in her best events, America...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td> NFL and players union spar in court over Peter...</td>\\n\",\n        \"      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> The National Football League and its players u...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50</th>\\n\",\n        \"      <td>           No sharing world downhill gold for Maze</td>\\n\",\n        \"      <td> BEAVER CREEK, Colorado (Reuters) - Tina Maze h...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Tina Maze had to share Olympic downhill gold b...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>51</th>\\n\",\n        \"      <td>   Doping should be criminal offense: Russia chief</td>\\n\",\n        \"      <td> MOSCOW (Reuters) - Russia's Athletics Federati...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Russia's Athletics Federation (VFLA) president...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52</th>\\n\",\n        \"      <td> Kansas police drop drug charges against Dallas...</td>\\n\",\n        \"      <td> DALLAS, Texas (Reuters) - Authorities have dro...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Authorities have dropped drug charges against ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53</th>\\n\",\n        \"      <td>      Thompson leads by one stroke at Torrey Pines</td>\\n\",\n        \"      <td> LA JOLLA, California (Reuters - American Nicho...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> American Nicholas Thompson has a one-shot lead...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54</th>\\n\",\n        \"      <td> Borzakovskiy takes over as Russia's caretaker ...</td>\\n\",\n        \"      <td> MOSCOW (Reuters) - Former Olympic 800 meters c...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Former Olympic 800 meters champion Yury Borzak...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>55</th>\\n\",\n        \"      <td>   Clubs need long-term mental health plan: FIFPro</td>\\n\",\n        \"      <td> (Reuters) - Soccer clubs need to take a longer...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Soccer clubs need to take a longer-term approa...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56</th>\\n\",\n        \"      <td> European Games will convince skeptics, attract...</td>\\n\",\n        \"      <td> (Reuters) - The inaugural European Games this ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> The inaugural European Games this year will co...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>57</th>\\n\",\n        \"      <td> Departing VFLA President to continue fight aga...</td>\\n\",\n        \"      <td> MOSCOW (Reuters) - Russian Athletics Federatio...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Russian Athletics Federation (VFLA) President ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>58</th>\\n\",\n        \"      <td> Canizares joins champion Westwood at the top i...</td>\\n\",\n        \"      <td> KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Spaniard Alejandro Canizares picked up three s...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>59</th>\\n\",\n        \"      <td> Force India opposed Marussia's bid to use old car</td>\\n\",\n        \"      <td> LONDON (Reuters) - Marussia's hopes of rising ...</td>\\n\",\n        \"      <td>     sportsNews</td>\\n\",\n        \"      <td> Marussia's hopes of rising from the dead to ra...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"                                                title  \\\\\\n\",\n        \"0   Exclusive: Top Fox investors seek to convert v...   \\n\",\n        \"1   Different delivery, one message to Greece's ne...   \\n\",\n        \"2   Isolated Greece wants no more bailout money wi...   \\n\",\n        \"3   Valuations may hurt small caps, despite job gr...   \\n\",\n        \"4   Shippers suspend weekend cargo loading at U.S....   \\n\",\n        \"5   Building unions return to Kentucky refinery de...   \\n\",\n        \"6         Motorola exploring possible sale: Bloomberg   \\n\",\n        \"7   Wall St. ends down on interest rate, Greece ji...   \\n\",\n        \"8   Strong U.S. job, wage gains open door to mid-y...   \\n\",\n        \"9   Building unions return to Kentucky refinery de...   \\n\",\n        \"10        Motorola exploring possible sale: Bloomberg   \\n\",\n        \"11  JPMorgan under scrutiny over hiring of Chinese...   \\n\",\n        \"12  Exclusive: Top Fox investors seek to convert v...   \\n\",\n        \"13  Isolated Greece wants no more bailout money wi...   \\n\",\n        \"14  Wall St. ends down on interest rate, Greece ji...   \\n\",\n        \"15   RadioShack would accept liquidation bids: lawyer   \\n\",\n        \"16  Strong U.S. job, wage gains open door to mid-y...   \\n\",\n        \"17  Wall Street firms waver over June 2015 rate hi...   \\n\",\n        \"18  Oil climbs, Brent posts best two weeks since 1998   \\n\",\n        \"19  Brazil's Petrobras taps state banker as CEO; s...   \\n\",\n        \"20  Feds can't indefinitely gag Yahoo about subpoe...   \\n\",\n        \"21        Motorola exploring possible sale: Bloomberg   \\n\",\n        \"22  Anthem warns U.S. customers of email scam afte...   \\n\",\n        \"23  U.S. court orders Symantec to pay $17 mln for ...   \\n\",\n        \"24        Motorola exploring possible sale: Bloomberg   \\n\",\n        \"25   RadioShack would accept liquidation bids: lawyer   \\n\",\n        \"26  Anthem warns U.S. customers of email scam afte...   \\n\",\n        \"27  Google can disrupt car industry but is no auto...   \\n\",\n        \"28  Chipmaker Intel promotes senior executives, du...   \\n\",\n        \"29  Feds can't indefinitely gag Yahoo about subpoe...   \\n\",\n        \"30  Google panel backs firm on EU limit to 'right ...   \\n\",\n        \"31  Family money puts its faith in European techno...   \\n\",\n        \"32  U.S. states probe massive data breach at healt...   \\n\",\n        \"33  Health insurer Anthem hit by massive cybersecu...   \\n\",\n        \"34         China to have most robots in world by 2017   \\n\",\n        \"35  U.S. businesses ask White House to help on Chi...   \\n\",\n        \"36  Sharp says not considering sale of any oversea...   \\n\",\n        \"37  RadioShack files for bankruptcy; Sprint to tak...   \\n\",\n        \"38  SEC probes Blackberry options trading ahead of...   \\n\",\n        \"39  Twitter tops Wall Street revenue target, user ...   \\n\",\n        \"40  U.S. Golf Association to launch Senior Women's...   \\n\",\n        \"41  In-form Wiesberger goes two shots clear in Kua...   \\n\",\n        \"42  Jansrud signals downhill ambition in final tra...   \\n\",\n        \"43  NFL, players union spar in court over Peterson...   \\n\",\n        \"44  English takes charge as Mickelson exits Torrey...   \\n\",\n        \"45         No pressure for double medallist Fenninger   \\n\",\n        \"46  Referees association backs rookie in flare-up ...   \\n\",\n        \"47  Rangers goalie Lundqvist out for three weeks w...   \\n\",\n        \"48          Miracle now needed to win gold, says Vonn   \\n\",\n        \"49  NFL and players union spar in court over Peter...   \\n\",\n        \"50            No sharing world downhill gold for Maze   \\n\",\n        \"51    Doping should be criminal offense: Russia chief   \\n\",\n        \"52  Kansas police drop drug charges against Dallas...   \\n\",\n        \"53       Thompson leads by one stroke at Torrey Pines   \\n\",\n        \"54  Borzakovskiy takes over as Russia's caretaker ...   \\n\",\n        \"55    Clubs need long-term mental health plan: FIFPro   \\n\",\n        \"56  European Games will convince skeptics, attract...   \\n\",\n        \"57  Departing VFLA President to continue fight aga...   \\n\",\n        \"58  Canizares joins champion Westwood at the top i...   \\n\",\n        \"59  Force India opposed Marussia's bid to use old car   \\n\",\n        \"\\n\",\n        \"                                          description        category  \\\\\\n\",\n        \"0   NEW YORK (Reuters) - Several top investors in ...    businessNews   \\n\",\n        \"1   ROME/PARIS (Reuters) - In Paris and Rome, it w...    businessNews   \\n\",\n        \"2   ATHENS/BRUSSELS (Reuters) - Greece's new lefti...    businessNews   \\n\",\n        \"3   NEW YORK (Reuters) - The good news from Friday...    businessNews   \\n\",\n        \"4   LOS ANGELES (Reuters) - The loading and unload...    businessNews   \\n\",\n        \"5   NEW YORK (Reuters) - Workers represented by th...    businessNews   \\n\",\n        \"6   (Reuters) - Walkie-talkie and radio systems ma...    businessNews   \\n\",\n        \"7   NEW YORK (Reuters) - Wall Street stocks fell o...    businessNews   \\n\",\n        \"8   WASHINGTON (Reuters) - U.S. job growth rose so...    businessNews   \\n\",\n        \"9   NEW YORK (Reuters) - Workers represented by th...    businessNews   \\n\",\n        \"10  (Reuters) - Walkie-talkie and radio systems ma...    businessNews   \\n\",\n        \"11  (Reuters) - JPMorgan Chase & Co is under feder...    businessNews   \\n\",\n        \"12  NEW YORK (Reuters) - Several top investors in ...    businessNews   \\n\",\n        \"13  ATHENS/BRUSSELS (Reuters) - Greece's new lefti...    businessNews   \\n\",\n        \"14  NEW YORK (Reuters) - Wall Street stocks fell o...    businessNews   \\n\",\n        \"15  (Reuters) - A lawyer for RadioShack on Friday ...    businessNews   \\n\",\n        \"16  WASHINGTON (Reuters) - U.S. job growth rose so...    businessNews   \\n\",\n        \"17  NEW YORK (Reuters) - Economists at Wall Street...    businessNews   \\n\",\n        \"18  NEW YORK (Reuters) - Oil rallied again on Frid...    businessNews   \\n\",\n        \"19  BRASILIA (Reuters) - Brazil's President Dilma ...    businessNews   \\n\",\n        \"20  SAN FRANCISCO (Reuters) - Law enforcement cann...  technologyNews   \\n\",\n        \"21  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews   \\n\",\n        \"22  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews   \\n\",\n        \"23  (Reuters) - Symantec Corp, maker of the popula...  technologyNews   \\n\",\n        \"24  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews   \\n\",\n        \"25  (Reuters) - A lawyer for RadioShack on Friday ...  technologyNews   \\n\",\n        \"26  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews   \\n\",\n        \"27  FRANKFURT (Reuters) - Technology companies suc...  technologyNews   \\n\",\n        \"28  SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...  technologyNews   \\n\",\n        \"29  SAN FRANCISCO (Reuters) - Law enforcement cann...  technologyNews   \\n\",\n        \"30  BRUSSELS (Reuters) - A panel of experts appoin...  technologyNews   \\n\",\n        \"31  LONDON (Reuters) - Some of Europe's wealthiest...  technologyNews   \\n\",\n        \"32  NEW YORK (Reuters) - Several U.S. states are i...  technologyNews   \\n\",\n        \"33  (Reuters) - Health insurer Anthem Inc , which ...  technologyNews   \\n\",\n        \"34  FRANKFURT (Reuters) - China will have more rob...  technologyNews   \\n\",\n        \"35  WASHINGTON (Reuters) - U.S. business lobbies c...  technologyNews   \\n\",\n        \"36  TOKYO (Reuters) - Japanese electronics maker S...  technologyNews   \\n\",\n        \"37  (Reuters) - Electronics retailer RadioShack Co...  technologyNews   \\n\",\n        \"38  NEW YORK (Reuters) - The U.S. Securities and E...  technologyNews   \\n\",\n        \"39  SAN FRANCISCO (Reuters) - Twitter Inc said on ...  technologyNews   \\n\",\n        \"40  NEW YORK (Reuters) - The United States Golf As...      sportsNews   \\n\",\n        \"41  KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...      sportsNews   \\n\",\n        \"42  BEAVER CREEK, Colorado (Reuters) - Unheralded ...      sportsNews   \\n\",\n        \"43  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews   \\n\",\n        \"44  LA JOLLA, California (Reuters) - Local favorit...      sportsNews   \\n\",\n        \"45  BEAVER CREEK, Colorado (Reuters) - A Super-G v...      sportsNews   \\n\",\n        \"46  (Reuters) - The National Basketball Referees A...      sportsNews   \\n\",\n        \"47  (Reuters) - New York Rangers goalie Henrik Lun...      sportsNews   \\n\",\n        \"48  BEAVER CREEK, Colorado (Reuters) - Denied gold...      sportsNews   \\n\",\n        \"49  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews   \\n\",\n        \"50  BEAVER CREEK, Colorado (Reuters) - Tina Maze h...      sportsNews   \\n\",\n        \"51  MOSCOW (Reuters) - Russia's Athletics Federati...      sportsNews   \\n\",\n        \"52  DALLAS, Texas (Reuters) - Authorities have dro...      sportsNews   \\n\",\n        \"53  LA JOLLA, California (Reuters - American Nicho...      sportsNews   \\n\",\n        \"54  MOSCOW (Reuters) - Former Olympic 800 meters c...      sportsNews   \\n\",\n        \"55  (Reuters) - Soccer clubs need to take a longer...      sportsNews   \\n\",\n        \"56  (Reuters) - The inaugural European Games this ...      sportsNews   \\n\",\n        \"57  MOSCOW (Reuters) - Russian Athletics Federatio...      sportsNews   \\n\",\n        \"58  KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...      sportsNews   \\n\",\n        \"59  LONDON (Reuters) - Marussia's hopes of rising ...      sportsNews   \\n\",\n        \"\\n\",\n        \"                                    short_description  \\n\",\n        \"0   Several top investors in Twenty-First Century ...  \\n\",\n        \"1   In Paris and Rome, it was sugar coated; in Ber...  \\n\",\n        \"2   Greece's new leftist-led government, isolated ...  \\n\",\n        \"3   The good news from Friday's jobs report may al...  \\n\",\n        \"4   The loading and unloading of freighters will b...  \\n\",\n        \"5   Workers represented by the Building Trades Uni...  \\n\",\n        \"6   Walkie-talkie and radio systems maker Motorola...  \\n\",\n        \"7   Wall Street stocks fell on Friday as a better-...  \\n\",\n        \"8   U.S. job growth rose solidly in January and wa...  \\n\",\n        \"9   Workers represented by the Building Trades Uni...  \\n\",\n        \"10  Walkie-talkie and radio systems maker Motorola...  \\n\",\n        \"11  JPMorgan Chase & Co is under federal scrutiny ...  \\n\",\n        \"12  Several top investors in Twenty-First Century ...  \\n\",\n        \"13  Greece's new leftist-led government, isolated ...  \\n\",\n        \"14  Wall Street stocks fell on Friday as a better-...  \\n\",\n        \"15  A lawyer for RadioShack on Friday said the ban...  \\n\",\n        \"16  U.S. job growth rose solidly in January and wa...  \\n\",\n        \"17  Economists at Wall Street's biggest banks are ...  \\n\",\n        \"18  Oil rallied again on Friday, with benchmark Br...  \\n\",\n        \"19  Brazil's President Dilma Rousseff tapped a con...  \\n\",\n        \"20  Law enforcement cannot indefinitely forbid Yah...  \\n\",\n        \"21  Walkie-talkie and radio systems maker Motorola...  \\n\",\n        \"22  Health insurer Anthem Inc on Friday warned U.S...  \\n\",\n        \"23  Symantec Corp, maker of the popular Norton ant...  \\n\",\n        \"24  Walkie-talkie and radio systems maker Motorola...  \\n\",\n        \"25  A lawyer for RadioShack on Friday said the ban...  \\n\",\n        \"26  Health insurer Anthem Inc on Friday warned U.S...  \\n\",\n        \"27  Technology companies such as Google are unlike...  \\n\",\n        \"28  Chipmaker Intel Corp said on Friday it promote...  \\n\",\n        \"29  Law enforcement cannot indefinitely forbid Yah...  \\n\",\n        \"30  A panel of experts appointed by Google to advi...  \\n\",\n        \"31  Some of Europe's wealthiest families are inves...  \\n\",\n        \"32  Several U.S. states are investigating a massiv...  \\n\",\n        \"33  Health insurer Anthem Inc , which has nearly 4...  \\n\",\n        \"34  China will have more robots operating in its p...  \\n\",\n        \"35  U.S. business lobbies called on the White Hous...  \\n\",\n        \"36  Japanese electronics maker Sharp Corp said a s...  \\n\",\n        \"37  Electronics retailer RadioShack Corp filed for...  \\n\",\n        \"38  The U.S. Securities and Exchange Commission is...  \\n\",\n        \"39  Twitter Inc said on Thursday the social media ...  \\n\",\n        \"40  The United States Golf Association will launch...  \\n\",\n        \"41  Austrian Bernd Wiesberger will take a two-shot...  \\n\",\n        \"42  Unheralded Frenchman Brice Roger posted the fa...  \\n\",\n        \"43  The National Football League and its players u...  \\n\",\n        \"44  Local favorite Phil Mickelson has joined Tiger...  \\n\",\n        \"45  A Super-G victory at last year's Winter Olympi...  \\n\",\n        \"46  The National Basketball Referees Association c...  \\n\",\n        \"47  New York Rangers goalie Henrik Lundqvist will ...  \\n\",\n        \"48  Denied gold medals in her best events, America...  \\n\",\n        \"49  The National Football League and its players u...  \\n\",\n        \"50  Tina Maze had to share Olympic downhill gold b...  \\n\",\n        \"51  Russia's Athletics Federation (VFLA) president...  \\n\",\n        \"52  Authorities have dropped drug charges against ...  \\n\",\n        \"53  American Nicholas Thompson has a one-shot lead...  \\n\",\n        \"54  Former Olympic 800 meters champion Yury Borzak...  \\n\",\n        \"55  Soccer clubs need to take a longer-term approa...  \\n\",\n        \"56  The inaugural European Games this year will co...  \\n\",\n        \"57  Russian Athletics Federation (VFLA) President ...  \\n\",\n        \"58  Spaniard Alejandro Canizares picked up three s...  \\n\",\n        \"59  Marussia's hopes of rising from the dead to ra...  \"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Extract Features with CountVictrizer\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"corpus = news_data[\\\"short_description\\\"]\\n\",\n      \"vectorizer = CountVectorizer(min_df=1)\\n\",\n      \"X = vectorizer.fit_transform(corpus).toarray()\\n\",\n      \"print X.shape\\n\",\n      \"X\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"(60, 809)\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"array([[0, 0, 0, ..., 0, 0, 0],\\n\",\n        \"       [0, 0, 0, ..., 1, 0, 0],\\n\",\n        \"       [0, 0, 0, ..., 0, 0, 1],\\n\",\n        \"       ..., \\n\",\n        \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n        \"       [0, 0, 0, ..., 0, 0, 0],\\n\",\n        \"       [0, 0, 0, ..., 0, 0, 0]])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"vectorizer.get_feature_names()[:25]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 13,\n       \"text\": [\n        \"[u'11',\\n\",\n        \" u'14',\\n\",\n        \" u'17',\\n\",\n        \" u'18',\\n\",\n        \" u'2012',\\n\",\n        \" u'2017',\\n\",\n        \" u'2018',\\n\",\n        \" u'29',\\n\",\n        \" u'40',\\n\",\n        \" u'400',\\n\",\n        \" u'65',\\n\",\n        \" u'800',\\n\",\n        \" u'about',\\n\",\n        \" u'abroad',\\n\",\n        \" u'abuse',\\n\",\n        \" u'accept',\\n\",\n        \" u'according',\\n\",\n        \" u'account',\\n\",\n        \" u'added',\\n\",\n        \" u'admitted',\\n\",\n        \" u'adrian',\\n\",\n        \" u'advise',\\n\",\n        \" u'affiliate',\\n\",\n        \" u'after',\\n\",\n        \" u'again']\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"categories = news_data[\\\"category\\\"].unique()\\n\",\n      \"category_dict = {value:index for index, value in enumerate(categories)}\\n\",\n      \"results = news_data[\\\"category\\\"].map(category_dict)\\n\",\n      \"category_dict\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 14,\n       \"text\": [\n        \"{'businessNews': 0, 'sportsNews': 2, 'technologyNews': 1}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print \\\"corpus size: %s\\\" % len(vectorizer.get_feature_names())\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"corpus size: 809\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Multinomial Naive Bayes Cassifier\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x_train,x_test, y_train,y_test = train_test_split(X, results, test_size=0.2, random_state=1, )\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"clf = MultinomialNB()\\n\",\n      \"clf.fit(x_train, y_train)\\n\",\n      \"clf.score(x_test, y_test)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 17,\n       \"text\": [\n        \"0.83333333333333337\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"y_test\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 18,\n       \"text\": [\n        \"array([1, 2, 0, 2, 2, 2, 1, 1, 2, 1, 1, 2])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"clf.predict(x_test)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 19,\n       \"text\": [\n        \"array([1, 2, 0, 2, 2, 2, 1, 0, 2, 1, 0, 2])\"\n       ]\n      }\n     ],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"category_dict\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 20,\n       \"text\": [\n        \"{'businessNews': 0, 'sportsNews': 2, 'technologyNews': 1}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Manual Testing\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"text = [\\\"Who won the Superbawl?\\\"]\\n\",\n      \"vec_text = vectorizer.transform(text).toarray()\\n\",\n      \"category_dict.keys()[category_dict.values().index(clf.predict(vec_text)[0])]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 21,\n       \"text\": [\n        \"'sportsNews'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 21\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/data/temp_data_features.csv",
    "content": "Year,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Average,TSI,CO2,CH4\n1851,0.823,0.357,-0.564,0.51,0.062,0.062,0.367,0.279,0.172,0.672,-0.132,-0.24,0.1973333333,1360.8289,,\n1852,0.132,0.185,-0.016,-0.889,0.427,0.485,0.539,0.064,0.147,-0.252,0.126,1.834,0.2318333333,1360.8634,,\n1853,0.711,-0.656,-1.348,-0.478,-0.213,-0.196,0.418,0.325,-0.333,0.082,-0.527,-1.23,-0.2870833333,1360.7908,,\n1854,-0.444,0.256,0.2,-0.355,0.428,0.107,0.616,0.399,0.171,0.702,-0.501,0.346,0.1604166667,1360.642,,\n1855,0.059,-1.359,-0.521,0.444,0.243,0.197,0.277,0.127,-0.303,0.346,-0.431,-1.622,-0.2119166667,1360.5151,,\n1856,-0.303,-0.751,-1.241,-0.022,-0.592,0.232,-0.413,-0.293,-0.601,-0.599,-1.486,-0.141,-0.5175,1360.4386,,\n1857,-1.157,0.283,-0.708,-1.46,-1.028,-0.316,-0.007,0.147,-0.325,-0.412,-0.726,0.647,-0.4218333333,1360.4407,,\n1858,0.054,-1.353,-0.734,-0.397,-0.365,0.146,-0.149,-0.344,-0.361,-0.059,-1.819,-0.382,-0.48025,1360.5687,,\n1859,-0.167,0.153,-0.024,-0.149,0.021,-0.159,0.049,0.044,-0.551,-0.152,-0.31,-1.072,-0.1930833333,1360.6651,,\n1860,0.156,-1.111,-1.565,-0.538,-0.291,0.056,-0.252,-0.011,-0.268,-0.335,-1.291,-1.61,-0.5883333333,1360.9295,,\n1861,-2.001,0.241,0.101,-0.782,-1.015,0.173,-0.099,0.079,-0.3,-0.264,-0.705,-0.448,-0.4183333333,1360.9756,,\n1862,-1.255,-1.314,-0.414,-0.308,-0.154,-0.36,-0.433,-0.725,-0.359,-0.535,-1.006,-1.171,-0.6695,1360.8084,,\n1863,1.155,0.468,-0.366,-0.302,-0.214,-0.374,-0.457,-0.249,-0.485,-0.604,-0.381,-0.377,-0.1821666667,1360.7553,,\n1864,-1.422,-0.618,-0.505,-0.762,-0.627,0.165,-0.034,-0.465,-0.279,-1.356,-1.319,-1.285,-0.7089166667,1360.588,,\n1865,-0.131,-1.352,-1.429,-0.157,-0.161,-0.172,0.161,-0.331,0.282,-0.597,0.057,-0.564,-0.3661666667,1360.5792,,\n1866,0.599,-0.528,-0.918,-0.214,-0.729,0.077,0.011,-0.492,-0.009,-0.857,-0.654,-0.38,-0.3411666667,1360.5595,,\n1867,-0.729,0.612,-1.931,-0.499,-1.195,-0.259,-0.321,-0.171,-0.079,-0.079,-0.641,-0.982,-0.5228333333,1360.445,,\n1868,-1.426,-0.888,-0.197,-0.682,0.195,0.136,0.496,0.142,-0.355,-0.116,-1.024,-0.201,-0.3266666667,1360.43,,\n1869,-0.154,0.891,-1.167,-0.363,-0.449,-0.313,-0.14,0.28,-0.04,-1.005,-0.622,-0.424,-0.2921666667,1360.6744,,\n1870,0.136,-0.968,-1.156,-0.458,0.013,0.244,0.166,-0.747,-0.317,-0.485,-0.133,-1.854,-0.46325,1360.5065,,\n1871,-1.03,-1.2,-0.007,-0.34,-0.571,-0.382,0.059,0.163,-0.604,-0.616,-1.33,-1.234,-0.591,1360.8971,,\n1872,-0.105,-0.507,-0.617,-0.109,-0.004,-0.061,-0.109,-0.426,-0.163,-0.142,-0.5,-0.809,-0.296,1360.8028,,\n1873,-0.004,-0.64,-0.535,-1.022,-0.578,0.277,-0.13,0.05,-0.378,-0.4,-0.945,0.032,-0.3560833333,1360.8236,,\n1874,0.291,-0.481,-1.132,-0.681,-0.871,-0.407,-0.247,-0.374,-0.192,-0.244,-0.833,-0.368,-0.4615833333,1360.7149,,\n1875,-1.263,-1.638,-1.709,-1.093,-0.251,-0.177,-0.316,-0.296,-0.665,-0.836,-1.327,-0.94,-0.8759166667,1360.6017,,\n1876,-0.265,-0.224,-0.451,-0.383,-0.769,-0.055,0.128,-0.163,-0.199,-0.45,-1.34,-1.515,-0.4738333333,1360.5014,,\n1877,-0.308,-0.026,-0.636,-0.765,-0.924,-0.038,0.211,0.25,-0.367,-0.57,0.094,0.119,-0.2466666667,1360.4721,,\n1878,-0.343,0.442,0.675,0.428,-0.446,-0.076,-0.061,0,0.301,0.156,-0.13,-1.011,-0.0054166667,1360.4132,,\n1879,-0.485,-0.498,-0.47,-0.648,-0.535,-0.623,-0.377,-0.381,-0.348,-0.118,-0.912,-1.238,-0.55275,1360.3903,,\n1880,0.108,-0.269,-0.498,-0.346,-0.089,-0.103,-0.19,0.172,-0.165,-1.021,-1.088,-0.624,-0.34275,1360.4251,,\n1881,-1.303,-0.626,-0.737,-0.46,-0.02,-0.609,-0.283,-0.138,-0.112,-0.663,-0.709,0.14,-0.46,1360.6637,,\n1882,0.682,0.102,0.137,-0.714,-0.74,-0.679,-0.532,-0.074,-0.082,-0.379,-0.768,-0.918,-0.3304166667,1360.5734,,\n1883,-1.103,-0.947,-1.128,-0.722,-0.559,0.076,-0.242,-0.43,-0.542,-0.559,-0.584,-0.355,-0.59125,1360.4887,,\n1884,-0.615,-0.416,-0.83,-1.099,-1.05,-0.698,-0.736,-0.239,-0.422,-0.526,-1.031,-0.641,-0.6919166667,1360.8455,,\n1885,-1.362,-0.808,-1.074,-0.923,-0.91,-0.715,-0.344,-0.621,-0.342,-0.501,-0.439,0.037,-0.6668333333,1360.7314,,\n1886,-0.783,-1.071,-0.98,-0.301,-0.411,-0.66,-0.319,-0.393,-0.197,-0.572,-0.484,-0.277,-0.5373333333,1360.6114,,\n1887,-0.71,-0.796,-0.576,-0.676,-0.439,-0.421,-0.292,-0.458,-0.297,-0.894,-0.687,-0.613,-0.5715833333,1360.475,,\n1888,-1.206,-1.035,-1.484,-0.33,-0.771,-0.47,-0.405,-0.443,-0.209,-0.387,-0.485,-0.296,-0.62675,1360.4428,,\n1889,-0.437,-0.428,-0.275,0.029,0.161,-0.139,-0.351,-0.386,-0.564,-0.332,-0.699,0.054,-0.2805833333,1360.4102,,\n1890,-0.138,-0.364,-0.506,-0.255,-0.628,-0.285,-0.426,-0.43,-0.236,-0.528,-0.83,-0.721,-0.4455833333,1360.4097,,\n1891,-0.979,-0.867,-0.699,-0.745,-0.471,-0.503,-0.511,-0.529,-0.217,-0.508,-0.934,0.191,-0.5643333333,1360.5689,,\n1892,-0.726,-0.049,-0.885,-0.882,-0.82,-0.421,-0.482,-0.342,-0.269,-0.468,-0.787,-1.37,-0.6250833333,1360.7463,,\n1893,-2.125,-1.556,-0.601,-0.683,-0.772,-0.485,-0.169,-0.372,-0.486,-0.241,-0.518,-0.386,-0.6995,1360.972,,\n1894,-0.557,-0.452,-0.205,-0.429,-0.549,-0.603,-0.424,-0.387,-0.758,-0.425,-0.434,-0.417,-0.47,1361.2425,,\n1895,-1.115,-1.555,-0.773,-0.47,-0.49,-0.367,-0.656,-0.344,-0.165,-0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028,0.037,-0.046,0.041,-0.03325,1360.7718,346.04,1754.435\n1986,0.452,0.207,0.233,0.298,0.201,0.153,-0.044,0.022,-0.066,-0.003,-0.036,0.028,0.1204166667,1360.7799,347.39,1776.4766666667\n1987,0.179,0.676,-0.122,0.185,0.167,0.207,0.368,0.188,0.305,0.237,0.106,0.531,0.25225,1360.7853,349.16,1780.5516666667\n1988,0.641,0.261,0.414,0.331,0.369,0.429,0.345,0.325,0.389,0.378,0.109,0.509,0.375,1361.2885,351.56,1796.0975\n1989,0.15,0.459,0.415,0.244,0.178,0.155,0.216,0.219,0.218,0.308,0.062,0.32,0.2453333333,1361.7128,353.07,1810.4516666667\n1990,0.39,0.527,1.186,0.619,0.413,0.436,0.286,0.356,0.195,0.491,0.589,0.419,0.49225,1361.7209,354.35,1819.6\n1991,0.522,0.533,0.255,0.599,0.33,0.583,0.455,0.368,0.388,0.363,0.259,0.106,0.39675,1361.814,355.57,1824.685\n1992,0.73,0.561,0.432,0.071,0.155,0.047,-0.258,-0.086,-0.241,-0.222,-0.247,0.179,0.0934166667,1361.7312,356.38,1828.8966666667\n1993,0.485,0.459,0.361,0.208,0.25,0.198,0.17,0.114,-0.125,0.105,-0.366,0.241,0.175,1361.1668,357.07,1828.9458333333\n1994,0.349,-0.184,0.333,0.428,0.397,0.444,0.274,0.279,0.342,0.514,0.515,0.419,0.3425,1360.9068,358.82,1843.7925\n1995,0.89,1.252,0.511,0.528,0.364,0.536,0.427,0.616,0.486,0.691,0.587,0.211,0.5915833333,1360.8431,360.8,1845.2466666667\n1996,0.195,0.458,0.136,-0.013,0.295,0.267,0.289,0.217,0.078,0.166,0.234,0.408,0.2275,1360.7208,362.59,1846.25\n1997,0.423,0.651,0.55,0.41,0.345,0.525,0.397,0.497,0.574,0.664,0.546,0.635,0.5180833333,1360.8221,363.71,1847.0366666667\n1998,0.725,1.355,0.785,0.965,0.831,0.889,0.984,0.919,0.701,0.7,0.285,0.874,0.8344166667,1361.1838,366.65,1859.6883333333\n1999,0.773,1.18,0.211,0.488,0.393,0.466,0.501,0.43,0.571,0.546,0.372,0.808,0.5615833333,1361.4072,368.33,1863.75\n2000,0.321,0.985,0.771,0.953,0.505,0.427,0.394,0.547,0.395,0.263,0.05,0.194,0.48375,1361.613,369.52,1861.4525\n2001,0.718,0.523,0.798,0.667,0.702,0.565,0.646,0.808,0.55,0.615,1.032,0.546,0.6808333333,1361.553,371.13,1860.1308333333\n2002,1.212,1.368,1.174,0.697,0.598,0.659,0.788,0.622,0.7,0.501,0.744,0.272,0.7779166667,1361.5834,373.22,1862.225\n2003,1.002,0.576,0.572,0.612,0.853,0.71,0.628,0.796,0.757,0.933,0.676,1.12,0.7695833333,1361.0274,375.77,1877.5841666667\n2004,0.81,1.073,0.916,0.705,0.345,0.527,0.388,0.465,0.549,0.772,1.076,0.459,0.67375,1360.9153,377.49,1866.2883333333\n2005,0.96,0.566,0.872,1.022,0.762,0.869,0.828,0.709,0.91,1.093,1.205,0.787,0.8819166667,1360.746,379.8,1867.5908333333\n2006,0.608,0.93,0.775,0.574,0.506,0.867,0.792,0.732,0.77,0.974,0.884,1.313,0.8104166667,1360.6647,381.9,1865.4491666667\n2007,1.697,0.904,1.01,1.126,0.786,0.582,0.622,0.784,0.827,0.989,0.778,0.798,0.9085833333,1360.5636,383.76,1872.7608333333\n2008,0.385,0.499,1.204,0.557,0.479,0.605,0.73,0.569,0.563,1.011,1.065,0.67,0.69475,1360.5324,385.59,1883.7858333333\n2009,0.779,0.792,0.633,0.829,0.581,0.659,0.587,0.876,0.976,0.724,0.845,0.492,0.7310833333,1360.5516,387.37,1885.5558333333\n2010,0.778,0.826,1.111,1.062,0.936,0.996,0.974,0.863,0.732,0.888,1.195,0.448,0.90075,1360.7969,389.85,1896.4875\n2011,0.467,0.382,0.585,0.83,0.523,0.778,0.775,0.766,0.852,0.941,0.609,0.827,0.6945833333,1361.068,391.63,1896.0341666667\n2012,0.557,0.323,0.645,1.03,0.963,0.947,0.792,0.891,0.9,0.847,0.849,0.261,0.7504166667,1361.2332,393.82,1899.78\n2013,0.935,0.949,0.669,0.581,0.773,0.827,0.671,0.706,0.776,0.821,0.984,0.795,0.7905833333,1361.2924,396.48,1906.75\n2014,0.95,0.408,0.929,1.048,,,,,,,,,0.83375\n"
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  {
    "path": "notebooks/data-mining/1. Web Scraping.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:bda576f544c5c3c928cf09474fc234c8a15aebe832608fcd49802d53914e2869\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Web scrpping a one of the most common method of collecting data. This tutorial covers the basics of web scraping.\\n\",\n      \"\\n\",\n      \"**Video Tutorial**:\\n\",\n      \"\\n\",\n      \"http://youtu.be/wT66i7jeyL8\\n\",\n      \"\\n\",\n      \"More About LXML:\\n\",\n      \"\\n\",\n      \"http://lxml.de/\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from datetime import datetime\\n\",\n      \"\\n\",\n      \"from lxml import html\\n\",\n      \"import requests\\n\",\n      \"\\n\",\n      \"import numpy as np\\n\",\n      \"import pandas as pd\\n\",\n      \"import matplotlib.pylab as plt\\n\",\n      \"\\n\",\n      \"pd.options.display.max_columns=50\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Data and Data Source\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will be working with the Nobel Prize data. We will get that from Wikipedia\\n\",\n      \"![](http://upload.wikimedia.org/wikipedia/en/e/ed/Nobel_Prize.png)\\n\",\n      \"\\n\",\n      \"Webpage URL:\\n\",\n      \"\\n\",\n      \"http://en.wikipedia.org/wiki/List_of_Nobel_laureates\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Before you Web Scrape - Read the ToS\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Please make sure that you read the ToS (Terms of Service) of the website before you web scrape any data. Most websites don't allows web scraping of their content. Some allow moderate use.\\n\",\n      \"\\n\",\n      \"For Wikipedia:\\n\",\n      \"\\n\",\n      \"> ## 4. Refraining from Certain Activities\\n\",\n      \"> ### Engaging in Disruptive and Illegal Misuse of Facilities\\n\",\n      \">  Engaging in automated uses of the site that are abusive or disruptive of the services and have not been approved by the Wikimedia community;\\n\",\n      \"> ##### [Wikimedia Foundation Terms of Use](wikimediafoundation.org/wiki/Terms_of_Use#4._Refraining_from_Certain_Activities)\\n\",\n      \"\\n\",\n      \"**From my basic technical understanding**:\\n\",\n      \"\\n\",\n      \"This means \\\"automated uses\\\" like web scraping are not allowed if they are \\\"abusive or disruptive of the services\\\" but I would argue web scraping a single page is not \\\"abusive or disruptive of the services\\\". Web scraping will actually consume less servers time and bandwidth than reading the page in a browser. This is due to the fact that we will only request the page and none of the static files linked to it like CSS, images, javascript ... etc.\\n\",\n      \"\\n\",\n      \"**DISCLAIMERL**: I'm not a lawyer\\n\",\n      \"\\n\",\n      \"##Basic Rules:\\n\",\n      \"\\n\",\n      \"- Check if there is an API use it. It will make your life easier.\\n\",\n      \"- Don't use scrape too much in a short time. It slows down the servers and might gets you banned from the website.\\n\",\n      \"- Never scrape anything that is not public.\\n\",\n      \"- Check /robots.txt for allowed paths\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Fetch page & build HTML tree\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def print_element(element):\\n\",\n      \"    print \\\"<%s %s>%s ...\\\" % (element.tag, element.attrib, element.text_content()[:200].replace(\\\"\\\\n\\\", \\\" \\\"))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"page = requests.get('http://en.wikipedia.org/wiki/List_of_Nobel_laureates')\\n\",\n      \"tree = html.fromstring(page.text)\\n\",\n      \"print_element(tree)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<html {'lang': 'en', 'class': 'client-nojs', 'dir': 'ltr'}>   List of Nobel laureates - Wikipedia, the free encyclopedia              a:lang(ar),a:lang(kk-arab),a:lang(mzn),a:lang(ps),a:lang(ur){text-decoration:none} /* cache key: enwiki:resourceloader:filter ...\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Locate The table\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"First we find all tables\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"tables = tree.xpath('//table')\\n\",\n      \"for table in tables:\\n\",\n      \"    print_element(table)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<table {'class': 'wikitable sortable'}>  Year Physics Chemistry Physiology or Medicine Literature Peace Economics   1901 R\\u00f6ntgen, WilhelmWilhelm R\\u00f6ntgen Hoff, Jacobus Henricus van 'tJacobus Henricus van 't Hoff von Behring, Emil AdolfEmil  ...\\n\",\n        \"<table {'style': 'border:1px solid #aaa;background-color:#f9f9f9', 'class': 'mbox-small plainlinks'}>   Wikimedia Commons has media related to Nobel laureates.   ...\\n\",\n        \"<table {'style': 'border-spacing:0;', 'cellspacing': '0', 'class': 'navbox'}>        v t e   Nobel Prizes       Prizes    Chemistry Economics1 Literature Peace Physics Physiology or Medicine         Laureates     by subject    Chemistry Economics Literature Peace Physics Physi ...\\n\",\n        \"<table {'style': 'border-spacing:0;background:transparent;color:inherit;', 'cellspacing': '0', 'class': 'nowraplinks hlist collapsible collapsed navbox-inner'}>     v t e   Nobel Prizes       Prizes    Chemistry Economics1 Literature Peace Physics Physiology or Medicine         Laureates     by subject    Chemistry Economics Literature Peace Physics Physiolo ...\\n\",\n        \"<table {'style': 'border-spacing:0;', 'cellspacing': '0', 'class': 'nowraplinks navbox-subgroup'}>  by subject    Chemistry Economics Literature Peace Physics Physiology or Medicine         by criterion    Country University affiliation Female Black Christians Chinese Indian Muslim Japanese Jewish ...\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"**When locating the table watchout for client side javascript alteration to the HTML code**\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"table = tree.xpath('//table[@class=\\\"wikitable sortable\\\"]')[0]\\n\",\n      \"print_element(table)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<table {'class': 'wikitable sortable'}>  Year Physics Chemistry Physiology or Medicine Literature Peace Economics   1901 R\\u00f6ntgen, WilhelmWilhelm R\\u00f6ntgen Hoff, Jacobus Henricus van 'tJacobus Henricus van 't Hoff von Behring, Emil AdolfEmil  ...\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Extract the Subjects & Years\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"subjects = [subject[0].text_content().replace(\\\"\\\\n\\\",\\\" \\\") for subject in table.xpath('tr')[0][1:]]\\n\",\n      \"subjects\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"['Physics',\\n\",\n        \" 'Chemistry',\\n\",\n        \" 'Physiology or Medicine',\\n\",\n        \" 'Literature',\\n\",\n        \" 'Peace',\\n\",\n        \" 'Economics']\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"years = [item[0].text for item in table.xpath('tr')[1:-1]]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Extract Winners Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Testing for a sigle years\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for index, item in enumerate(table.xpath('tr')[1][1:]):\\n\",\n      \"    subject = subjects[index]\\n\",\n      \"    print \\\"%s:\\\" % subject\\n\",\n      \"    for winner in item.xpath('span[@class=\\\"vcard\\\"]/span/a'):\\n\",\n      \"        winner_name = winner.attrib[\\\"title\\\"]\\n\",\n      \"        winner_url = winner.attrib[\\\"href\\\"]\\n\",\n      \"        print \\\" - %s\\\" % winner_name\\n\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Physics:\\n\",\n        \" - Wilhelm R\\u00f6ntgen\\n\",\n        \"Chemistry:\\n\",\n        \" - Jacobus Henricus van 't Hoff\\n\",\n        \"Physiology or Medicine:\\n\",\n        \" - Emil Adolf von Behring\\n\",\n        \"Literature:\\n\",\n        \" - Sully Prudhomme\\n\",\n        \"Peace:\\n\",\n        \" - Henry Dunant\\n\",\n        \" - Fr\\u00e9d\\u00e9ric Passy\\n\",\n        \"Economics:\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Extract The complete table\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"year_list = []\\n\",\n      \"subject_list = []\\n\",\n      \"name_list = []\\n\",\n      \"url_list = []\\n\",\n      \"for y_index, year in enumerate(years):\\n\",\n      \"    #print year\\n\",\n      \"    for index, item in enumerate(table.xpath('tr')[y_index + 1][1:]):\\n\",\n      \"        subject = subjects[index]\\n\",\n      \"        #print \\\"%s:\\\" % subject\\n\",\n      \"        for winner in item.xpath('span[@class=\\\"vcard\\\"]/span/a'):\\n\",\n      \"            winner_name = winner.attrib[\\\"title\\\"]\\n\",\n      \"            winner_url = winner.attrib[\\\"href\\\"]\\n\",\n      \"            #print \\\" - %s\\\" % winner_name\\n\",\n      \"            year_list.append(year)\\n\",\n      \"            subject_list.append(subject)\\n\",\n      \"            name_list.append(winner_name)\\n\",\n      \"            url_list.append(winner_url)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Post Processing in Pandas\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"data_set = pd.DataFrame(name_list, columns=[\\\"winner_name\\\"])\\n\",\n      \"data_set[\\\"subject\\\"] = subject_list\\n\",\n      \"data_set[\\\"year\\\"] = year_list\\n\",\n      \"data_set[\\\"year\\\"] = data_set[\\\"year\\\"].astype(np.int32)\\n\",\n      \"data_set[\\\"url\\\"] = url_list\\n\",\n      \"data_set.head(5)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>winner_name</th>\\n\",\n        \"      <th>subject</th>\\n\",\n        \"      <th>year</th>\\n\",\n        \"      <th>url</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0</th>\\n\",\n        \"      <td>              Wilhelm R\\u00f6ntgen</td>\\n\",\n        \"      <td>                Physics</td>\\n\",\n        \"      <td> 1901</td>\\n\",\n        \"      <td>           /wiki/Wilhelm_R%C3%B6ntgen</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1</th>\\n\",\n        \"      <td> Jacobus Henricus van 't Hoff</td>\\n\",\n        \"      <td>              Chemistry</td>\\n\",\n        \"      <td> 1901</td>\\n\",\n        \"      <td> /wiki/Jacobus_Henricus_van_%27t_Hoff</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2</th>\\n\",\n        \"      <td>       Emil Adolf von Behring</td>\\n\",\n        \"      <td> Physiology or Medicine</td>\\n\",\n        \"      <td> 1901</td>\\n\",\n        \"      <td>         /wiki/Emil_Adolf_von_Behring</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3</th>\\n\",\n        \"      <td>              Sully Prudhomme</td>\\n\",\n        \"      <td>             Literature</td>\\n\",\n        \"      <td> 1901</td>\\n\",\n        \"      <td>                /wiki/Sully_Prudhomme</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4</th>\\n\",\n        \"      <td>                 Henry Dunant</td>\\n\",\n        \"      <td>                  Peace</td>\\n\",\n        \"      <td> 1901</td>\\n\",\n        \"      <td>                   /wiki/Henry_Dunant</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 10,\n       \"text\": [\n        \"                    winner_name                 subject  year                                   url\\n\",\n        \"0               Wilhelm R\\u00f6ntgen                 Physics  1901            /wiki/Wilhelm_R%C3%B6ntgen\\n\",\n        \"1  Jacobus Henricus van 't Hoff               Chemistry  1901  /wiki/Jacobus_Henricus_van_%27t_Hoff\\n\",\n        \"2        Emil Adolf von Behring  Physiology or Medicine  1901          /wiki/Emil_Adolf_von_Behring\\n\",\n        \"3               Sully Prudhomme              Literature  1901                 /wiki/Sully_Prudhomme\\n\",\n        \"4                  Henry Dunant                   Peace  1901                    /wiki/Henry_Dunant\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Looking at the data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"years_df = data_set[\\\"year\\\"].value_counts().sort_index()\\n\",\n      \"years_df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"1901    6\\n\",\n        \"1902    7\\n\",\n        \"1903    7\\n\",\n        \"1904    5\\n\",\n        \"1905    5\\n\",\n        \"1906    6\\n\",\n        \"1907    6\\n\",\n        \"1908    7\\n\",\n        \"1909    7\\n\",\n        \"1910    4\\n\",\n        \"1911    6\\n\",\n        \"1912    6\\n\",\n        \"1913    5\\n\",\n        \"1914    3\\n\",\n        \"1915    4\\n\",\n        \"...\\n\",\n        \"1999     6\\n\",\n        \"2000    13\\n\",\n        \"2001    14\\n\",\n        \"2002    13\\n\",\n        \"2003    11\\n\",\n        \"2004    12\\n\",\n        \"2005    12\\n\",\n        \"2006     8\\n\",\n        \"2007    11\\n\",\n        \"2008    12\\n\",\n        \"2009    13\\n\",\n        \"2010    11\\n\",\n        \"2011    13\\n\",\n        \"2012    10\\n\",\n        \"2013    13\\n\",\n        \"Length: 110, dtype: int64\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Number of Prizes per Year\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(15,5))\\n\",\n      \"plt.plot(years_df.index, years_df.values, linewidth=2, alpha=.6)\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlabel(\\\"Year\\\")\\n\",\n      \"plt.ylabel(\\\"Number of Prizes\\\")\\n\",\n      \"plt.show();\\n\",\n      \"print \\\"Total Prizes: %s\\\" % len(data_set)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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QkhhBCSbU6ckAITdXXA+vXRt7ft2UVqHyM2Lr8c2LpVbGLPHu9cTyMw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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7fc3daf60dd0>\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Total Prizes: 853\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"years_df.value_counts()\\n\",\n      \"plt.bar(years_df.value_counts().index, years_df.value_counts())\\n\",\n      \"plt.box(on=\\\"off\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlabel(\\\"Number of Nober Prizes/Year\\\")\\n\",\n      \"plt.xlabel(\\\"\\\")\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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HuX7UdtP2P7advv7zrTVmyfZvtJ2w93nWUzts+0/YDt52w/a/uSrjNt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\\nRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7fc3da7bb910>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"By Subject\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(13,5))\\n\",\n      \"\\n\",\n      \"for subject in subjects:\\n\",\n      \"    df = data_set[data_set[\\\"subject\\\"]==subject][\\\"year\\\"].value_counts().sort_index().cumsum()\\n\",\n      \"    plt.plot(df.index, df, label=subject, linewidth=2, alpha=.6)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.xlabel(\\\"Year\\\")\\n\",\n      \"plt.ylabel(\\\"Cumulative Sum of Given Nobel Prizes\\\")\\n\",\n      \"plt.xticks(np.arange(1900, 2020, 10))\\n\",\n      \"\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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Vimtc4ApO+DEEIIIYQQTuiONQhKqZeA14D/AX2BYGCu1rqz45uXa3uk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\\nzlIzAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7fc3daf01710>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"The effects of WW I and WW II\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"plt.figure(figsize=(13,5))\\n\",\n      \"\\n\",\n      \"for subject in subjects:\\n\",\n      \"    df = data_set[(data_set[\\\"subject\\\"]==subject) &\\n\",\n      \"                  (data_set[\\\"year\\\"].astype(np.int32)<1950)][\\\"year\\\"].value_counts().sort_index().cumsum()\\n\",\n      \"    plt.plot(df.index, df, label=subject, linewidth=2, alpha=.6)\\n\",\n      \"\\n\",\n      \"plt.grid()\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.xlabel(\\\"Year\\\")\\n\",\n      \"plt.ylabel(\\\"Cumulative Sum of Given Nobel Prizes\\\")\\n\",\n      \"plt.xticks(np.arange(1900, 1950, 5))\\n\",\n      \"\\n\",\n      \"gca = plt.gca()\\n\",\n      \"\\n\",\n      \"gca.add_patch(plt.Rectangle((1914,0), 4, 60, alpha=.3, color=\\\"orange\\\"))\\n\",\n      \"gca.add_patch(plt.Rectangle((1939,0), (45-39), 60, alpha=.3, color=\\\"orange\\\"))\\n\",\n      \"\\n\",\n      \"plt.annotate(s=\\\"WW I\\\", xy=(1915,55))\\n\",\n      \"plt.annotate(s=\\\"WW II\\\", xy=(1941,55))\\n\",\n      \"plt.show();\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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ddddO3alZUrV1qvnZ+fT3BwMDt37nRcZgohhBBC1Cbnz8NLLxmVgBYt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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7fc3d2e79390>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/data-mining/2. YouTube Data.ipynb",
    "content": "  {\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:3dd2f282369fba3f33bc12f052ab125052158b319a86dc7b60158f9eac201e91\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will cover some functions from YouTube Data API v3 from Google Developer Console.\\n\",\n      \"\\n\",\n      \"**Important Links**:\\n\",\n      \"\\n\",\n      \"- [Google Developer Console](https://console.developers.google.com)\\n\",\n      \"- [YouTube Data API v3 Documentation](https://developers.google.com/youtube/v3/)\\n\",\n      \"\\n\",\n      \"**We will use the following function**:\\n\",\n      \"\\n\",\n      \"- youtube.search.list [Doc](https://developers.google.com/youtube/v3/docs/search/list)\\n\",\n      \"- youtube.videos.list [Doc](https://developers.google.com/youtube/v3/docs/videos/list)\\n\",\n      \"\\n\",\n      \"**Video Tutorial**:\\n\",\n      \"\\n\",\n      \"https://www.youtube.com/watch?v=bCrCkfSyuNE\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Google APIs\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"There is a Python Google Library. But we will be using HTTP requests to access the API.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"api_key = \\\"\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 73\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Libraries\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from __future__ import division\\n\",\n      \"from datetime import datetime \\n\",\n      \"import requests\\n\",\n      \"from lxml import html, etree\\n\",\n      \"import json\\n\",\n      \"from textblob import TextBlob\\n\",\n      \"\\n\",\n      \"import pandas as pd\\n\",\n      \"\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"\\n\",\n      \"import warnings\\n\",\n      \"warnings.filterwarnings('ignore')\\n\",\n      \"\\n\",\n      \"pd.options.display.max_columns = 100\\n\",\n      \"pd.options.display.max_rows = 35\\n\",\n      \"pd.options.display.width = 120\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 57\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Searching YouTube Using `youtube.search.list`\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### Documentation:\\n\",\n      \"\\n\",\n      \"https://developers.google.com/youtube/v3/docs/search\\n\",\n      \"\\n\",\n      \"### HTTPS Request:\\n\",\n      \"\\n\",\n      \"```\\n\",\n      \"GET https://www.googleapis.com/youtube/v3/search\\n\",\n      \"```\\n\",\n      \"\\n\",\n      \"### Parameters:\\n\",\n      \"\\n\",\n      \"<table class=\\\"matchpre\\\">\\n\",\n      \"  <thead>\\n\",\n      \"    <tr>\\n\",\n      \"      <th>Parameter name</th>\\n\",\n      \"      <th>Value</th>\\n\",\n      \"      <th>Description</th>\\n\",\n      \"    </tr>\\n\",\n      \"  </thead>\\n\",\n      \"  <tbody>\\n\",\n      \"        <tr id=\\\"required-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Required parameters</b></td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"part\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">part</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>part</strong></code> parameter specifies a comma-separated list of one or more <code>search</code> resource properties that the API response will include. Set the parameter value to <code>snippet</code>. The <code>snippet</code> part has a quota cost of 1 unit.\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"filter-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Filters <span style=\\\"color:#555\\\">(specify 0 or 1 of the following parameters)</span></b></td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"forContentOwner\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">forContentOwner</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">boolean</code></td>\\n\",\n      \"      <td>This parameter can only be used in a properly <a href=\\\"/youtube/v3/guides/authentication\\\">authorized request</a>. <strong>Note:</strong> This parameter is intended exclusively for YouTube content partners.<br><br>The <code><strong>forContentOwner</strong></code> parameter restricts the search to only retrieve resources owned by the content owner specified by the <code><strong>onBehalfOfContentOwner</strong></code> parameter. The user must be authenticated using a CMS account linked to the specified content owner and <code><strong>onBehalfOfContentOwner</strong></code> must be provided.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"forMine\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">forMine</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">boolean</code></td>\\n\",\n      \"      <td>This parameter can only be used in a properly <a href=\\\"/youtube/v3/guides/authentication\\\">authorized request</a>. The <code><strong>forMine</strong></code> parameter restricts the search to only retrieve videos owned by the authenticated user. If you set this parameter to <code>true</code>, then the <code>type</code> parameter's value must also be set to <code>video</code>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"relatedToVideoId\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">relatedToVideoId</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>relatedToVideoId</strong></code> parameter retrieves a list of videos that are related to the video that the parameter value identifies. The parameter value must be set to a YouTube video ID and, if you are using this parameter, the <code>type</code> parameter must be set to <code>video</code>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"optional-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Optional parameters</b></td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"channelId\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">channelId</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>channelId</strong></code> parameter indicates that the API response should only contain resources created by the channel</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"channelType\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">channelType</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>channelType</strong></code> parameter lets you restrict a search to a particular type of channel.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Return all channels.</li>\\n\",\n      \"<li><code><strong>show</strong></code> \\u2013 Only retrieve shows.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"eventType\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">eventType</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>eventType</strong></code> parameter restricts a search to broadcast events. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>completed</strong></code> \\u2013 Only include completed broadcasts.</li>\\n\",\n      \"<li><code><strong>live</strong></code> \\u2013 Only include active broadcasts.</li>\\n\",\n      \"<li><code><strong>upcoming</strong></code> \\u2013 Only include upcoming broadcasts.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"location\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">location</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>location</strong></code> parameter, in conjunction with the <code>locationRadius</code> parameter, defines a circular geographic area and also restricts a search to videos that specify, in their metadata, a geographic location that falls within that area. The parameter value is a string that specifies latitude/longitude coordinates e.g. (<code>37.42307,-122.08427</code>).<br><br><ul><li>The <code>location</code> parameter value identifies the point at the center of the area.</li><li>The <code>locationRadius</code> parameter specifies the maximum distance that the location associated with a video can be from that point for the video to still be included in the search results.</li></ul>The API returns an error if your request specifies a value for the <code>location</code> parameter but does not also specify a value for the <code>locationRadius</code> parameter.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"locationRadius\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">locationRadius</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>locationRadius</strong></code> parameter, in conjunction with the <code>location</code> parameter, defines a circular geographic area.<br><br>The parameter value must be a floating point number followed by a measurement unit. Valid measurement units are <code>m</code>, <code>km</code>, <code>ft</code>, and <code>mi</code>. For example, valid parameter values include <code>1500m</code>, <code>5km</code>, <code>10000ft</code>, and <code>0.75mi</code>. The API does not support <code>locationRadius</code> parameter values larger than 1000 kilometers.<br><br><strong>Note:</strong> See the definition of the <code><a href=\\\"#location\\\">location</a></code> parameter for more information.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"maxResults\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">maxResults</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">unsigned integer</code></td>\\n\",\n      \"      <td>The <code><strong>maxResults</strong></code> parameter specifies the maximum number of items that should be returned in the result set. Acceptable values are <code>0</code> to <code>50</code>, inclusive. The default value is <code>5</code>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"onBehalfOfContentOwner\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">onBehalfOfContentOwner</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>This parameter can only be used in a properly <a href=\\\"/youtube/v3/guides/authentication\\\">authorized request</a>. <strong>Note:</strong> This parameter is intended exclusively for YouTube content partners.<br><br>The <code><strong>onBehalfOfContentOwner</strong></code> parameter indicates that the request's authorization credentials identify a YouTube CMS user who is acting on behalf of the content owner specified in the parameter value. This parameter is intended for YouTube content partners that own and manage many different YouTube channels. It allows content owners to authenticate once and get access to all their video and channel data, without having to provide authentication credentials for each individual channel. The CMS account that the user authenticates with must be linked to the specified YouTube content owner.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"order\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">order</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>order</strong></code> parameter specifies the method that will be used to order resources in the API response. The default value is <code>relevance</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>date</strong></code> \\u2013 Resources are sorted in reverse chronological order based on the date they were created.</li>\\n\",\n      \"<li><code><strong>rating</strong></code> \\u2013 Resources are sorted from highest to lowest rating.</li>\\n\",\n      \"<li><code><strong>relevance</strong></code> \\u2013 Resources are sorted based on their relevance to the search query. This is the default value for this parameter.</li>\\n\",\n      \"<li><code><strong>title</strong></code> \\u2013 Resources are sorted alphabetically by title.</li>\\n\",\n      \"<li><code><strong>videoCount</strong></code> \\u2013 Channels are sorted in descending order of their number of uploaded videos.</li>\\n\",\n      \"<li><code><strong>viewCount</strong></code> \\u2013 Resources are sorted from highest to lowest number of views.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"pageToken\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">pageToken</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>pageToken</strong></code> parameter identifies a specific page in the result set that should be returned. In an API response, the <code>nextPageToken</code> and <code>prevPageToken</code> properties identify other pages that could be retrieved.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"publishedAfter\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">publishedAfter</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">datetime</code></td>\\n\",\n      \"      <td>The <code><strong>publishedAfter</strong></code> parameter indicates that the API response should only contain resources created after the specified time. The value is an RFC 3339 formatted date-time value (1970-01-01T00:00:00Z).</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"publishedBefore\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">publishedBefore</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">datetime</code></td>\\n\",\n      \"      <td>The <code><strong>publishedBefore</strong></code> parameter indicates that the API response should only contain resources created before the specified time. The value is an RFC 3339 formatted date-time value (1970-01-01T00:00:00Z).</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"q\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">q</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>q</strong></code> parameter specifies the query term to search for.<br><br>Your request can also use the Boolean NOT (<code>-</code>) and OR (<code>|</code>) operators to exclude videos or to find videos that are associated with one of several search terms. For example, to search for videos matching either \\\"boating\\\" or \\\"sailing\\\", set the <code>q</code> parameter value to <code>boating|sailing</code>. Similarly, to search for videos matching either \\\"boating\\\" or \\\"sailing\\\" but not \\\"fishing\\\", set the <code>q</code> parameter value to <code>boating|sailing -fishing</code>. Note that the pipe character must be URL-escaped when it is sent in your API request. The URL-escaped value for the pipe character is <code>%7C</code>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"regionCode\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">regionCode</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>regionCode</strong></code> parameter instructs the API to return search results for the specified country. The parameter value is an <a href=\\\"http://www.iso.org/iso/country_codes/iso_3166_code_lists/country_names_and_code_elements.htm\\\">ISO 3166-1 alpha-2</a> country code.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"safeSearch\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">safeSearch</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>safeSearch</strong></code> parameter indicates whether the search results should include restricted content as well as standard content.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>moderate</strong></code> \\u2013 YouTube will filter some content from search results and, at the least, will filter content that is restricted in your locale. Based on their content, search results could be removed from search results or demoted in search results. This is the default parameter value.</li>\\n\",\n      \"<li><code><strong>none</strong></code> \\u2013 YouTube will not filter the search result set.</li>\\n\",\n      \"<li><code><strong>strict</strong></code> \\u2013 YouTube will try to exclude all restricted content from the search result set. Based on their content, search results could be removed from search results or demoted in search results.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"topicId\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">topicId</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>topicId</strong></code> parameter indicates that the API response should only contain resources associated with the specified topic. The value identifies a <a href=\\\"//wiki.freebase.com/wiki/Freebase_API\\\">Freebase topic ID</a>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"type\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">type</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>type</strong></code> parameter restricts a search query to only retrieve a particular type of resource. The value is a comma-separated list of resource types. The default value is <code>video,channel,playlist</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>channel</strong></code></li><li><code><strong>playlist</strong></code></li><li><code><strong>video</strong></code></li></ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoCaption\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoCaption</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoCaption</strong></code> parameter indicates whether the API should filter video search results based on whether they have captions. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Do not filter results based on caption availability.</li>\\n\",\n      \"<li><code><strong>closedCaption</strong></code> \\u2013 Only include videos that have captions.</li>\\n\",\n      \"<li><code><strong>none</strong></code> \\u2013 Only include videos that do not have captions.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoCategoryId\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoCategoryId</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoCategoryId</strong></code> parameter filters video search results based on their <a href=\\\"/youtube/v3/docs/videoCategories\\\">category</a>. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoDefinition\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoDefinition</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoDefinition</strong></code> parameter lets you restrict a search to only include either high definition (HD) or standard definition (SD) videos. HD videos are available for playback in at least 720p, though higher resolutions, like 1080p, might also be available. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Return all videos, regardless of their resolution.</li>\\n\",\n      \"<li><code><strong>high</strong></code> \\u2013 Only retrieve HD videos.</li>\\n\",\n      \"<li><code><strong>standard</strong></code> \\u2013 Only retrieve videos in standard definition.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoDimension\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoDimension</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoDimension</strong></code> parameter lets you restrict a search to only retrieve 2D or 3D videos. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>2d</strong></code> \\u2013 Restrict search results to exclude 3D videos.</li>\\n\",\n      \"<li><code><strong>3d</strong></code> \\u2013 Restrict search results to only include 3D videos.</li>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Include both 3D and non-3D videos in returned results. This is the default value.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoDuration\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoDuration</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoDuration</strong></code> parameter filters video search results based on their duration. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Do not filter video search results based on their duration. This is the default value.</li>\\n\",\n      \"<li><code><strong>long</strong></code> \\u2013 Only include videos longer than 20 minutes.</li>\\n\",\n      \"<li><code><strong>medium</strong></code> \\u2013 Only include videos that are between four and 20 minutes long (inclusive).</li>\\n\",\n      \"<li><code><strong>short</strong></code> \\u2013 Only include videos that are less than four minutes long.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoEmbeddable\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoEmbeddable</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoEmbeddable</strong></code> parameter lets you to restrict a search to only videos that can be embedded into a webpage. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Return all videos, embeddable or not.</li>\\n\",\n      \"<li><code><strong>true</strong></code> \\u2013 Only retrieve embeddable videos.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoLicense\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoLicense</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoLicense</strong></code> parameter filters search results to only include videos with a particular license. YouTube lets video uploaders choose to attach either the Creative Commons license or the standard YouTube license to each of their videos. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Return all videos, regardless of which license they have, that match the query parameters.</li>\\n\",\n      \"<li><code><strong>creativeCommon</strong></code> \\u2013 Only return videos that have a Creative Commons license. Users can reuse videos with this license in other videos that they create. <a href=\\\"http://www.google.com/support/youtube/bin/answer.py?answer=1284989\\\">Learn more</a>.</li>\\n\",\n      \"<li><code><strong>youtube</strong></code> \\u2013 Only return videos that have the standard YouTube license.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoSyndicated\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoSyndicated</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoSyndicated</strong></code> parameter lets you to restrict a search to only videos that can be played outside youtube.com. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Return all videos, syndicated or not.</li>\\n\",\n      \"<li><code><strong>true</strong></code> \\u2013 Only retrieve syndicated videos.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoType\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoType</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoType</strong></code> parameter lets you restrict a search to a particular type of videos. If you specify a value for this parameter, you must also set the <code><a href=\\\"#type\\\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>any</strong></code> \\u2013 Return all videos.</li>\\n\",\n      \"<li><code><strong>episode</strong></code> \\u2013 Only retrieve episodes of shows.</li>\\n\",\n      \"<li><code><strong>movie</strong></code> \\u2013 Only retrieve movies.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"\\n\",\n      \"  </tbody>\\n\",\n      \"</table>\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### The important parameters:\\n\",\n      \"\\n\",\n      \"- **`part`**:\\n\",\n      \" - `id`: Returns only resource ID data\\n\",\n      \" - `snippet`: Returns some basic meta data about the resource\\n\",\n      \"\\n\",\n      \"- **`channelId`**:\\n\",\n      \" - Filter results to a single channelId.\\n\",\n      \"\\n\",\n      \"- **`maxResults`**:\\n\",\n      \" - Between 0 and 50 results per page. The default is 5.\\n\",\n      \"\\n\",\n      \"- **`order`**:\\n\",\n      \" - date: Resources are sorted in reverse chronological order based on the date they were uploaded.\\n\",\n      \" - rating: Resources are sorted from highest to lowest rating.\\n\",\n      \" - **relevance**: Resources are sorted based on their relevance to the search query. This is the default value for this parameter.\\n\",\n      \" - title: Resources are sorted alphabetically by title.\\n\",\n      \" - videoCount: Channels are sorted in descending order of their number of uploaded videos.\\n\",\n      \" - viewCount: Resources are sorted from highest to lowest number of views.\\n\",\n      \"\\n\",\n      \"- **`pageToken`**:\\n\",\n      \" - A string token to select results page\\n\",\n      \"\\n\",\n      \"- **`publishedAfter`**:\\n\",\n      \" - Use RFC 3339 format for Date Time 2000-12-31T23:59:59\\n\",\n      \"\\n\",\n      \"- **`publishedBefore`**:\\n\",\n      \" - Use RFC 3339 format for Date Time 2000-12-31T23:59:59\\n\",\n      \"\\n\",\n      \"- **`q`**:\\n\",\n      \" - Query term(s)\\n\",\n      \" - You can use multiple search terms\\n\",\n      \" - For OR operator use `|`\\n\",\n      \" - For NOT operator use `-`\\n\",\n      \"\\n\",\n      \"- **`key`**:\\n\",\n      \" - You API Key code\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Preparing The HTTP Request\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"parameters = {\\\"part\\\": \\\"snippet\\\",\\n\",\n      \"              \\\"maxResults\\\": 5,\\n\",\n      \"              \\\"order\\\": \\\"date\\\",\\n\",\n      \"              \\\"pageToken\\\": \\\"\\\",\\n\",\n      \"              \\\"publishedAfter\\\": \\\"2008-08-04T00:00:00Z\\\",\\n\",\n      \"              \\\"publishedBefore\\\": \\\"2008-11-04T00:00:00Z\\\",\\n\",\n      \"              \\\"q\\\": \\\"\\\",\\n\",\n      \"              \\\"key\\\": api_key,\\n\",\n      \"              \\\"type\\\": \\\"video\\\",\\n\",\n      \"              }\\n\",\n      \"url = \\\"https://www.googleapis.com/youtube/v3/search\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Fetch Results for a Single Page\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"parameters[\\\"q\\\"] = \\\"Mark Udall\\\"\\n\",\n      \"page = requests.request(method=\\\"get\\\", url=url, params=parameters)\\n\",\n      \"j_results = json.loads(page.text)\\n\",\n      \"print page.text\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"{\\n\",\n        \" \\\"kind\\\": \\\"youtube#searchListResponse\\\",\\n\",\n        \" \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/_2hFMhP6zvFl7CAy5D9Ir40dMWE\\\\\\\"\\\",\\n\",\n        \" \\\"nextPageToken\\\": \\\"CAUQAA\\\",\\n\",\n        \" \\\"pageInfo\\\": {\\n\",\n        \"  \\\"totalResults\\\": 2325,\\n\",\n        \"  \\\"resultsPerPage\\\": 5\\n\",\n        \" },\\n\",\n        \" \\\"items\\\": [\\n\",\n        \"  {\\n\",\n        \"   \\\"kind\\\": \\\"youtube#searchResult\\\",\\n\",\n        \"   \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/tmAMwya2pvXlrX05odd04vzKBSQ\\\\\\\"\\\",\\n\",\n        \"   \\\"id\\\": {\\n\",\n        \"    \\\"kind\\\": \\\"youtube#video\\\",\\n\",\n        \"    \\\"videoId\\\": \\\"5Q98TvXjIZg\\\"\\n\",\n        \"   },\\n\",\n        \"   \\\"snippet\\\": {\\n\",\n        \"    \\\"publishedAt\\\": \\\"2008-11-03T15:31:30.000Z\\\",\\n\",\n        \"    \\\"channelId\\\": \\\"UC52X5wxOL_s5yw0dQk7NtgA\\\",\\n\",\n        \"    \\\"title\\\": \\\"Cousins Vying to Ride Democratic Wave to Senate\\\",\\n\",\n        \"    \\\"description\\\": \\\"Cousins Tom and Mark Udall are vying to become U.S. Senators in New Mexico and Colorado. The two are hoping to ride an emerging Democratic wave in the ...\\\",\\n\",\n        \"    \\\"thumbnails\\\": {\\n\",\n        \"     \\\"default\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/5Q98TvXjIZg/default.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"medium\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/5Q98TvXjIZg/mqdefault.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"high\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/5Q98TvXjIZg/hqdefault.jpg\\\"\\n\",\n        \"     }\\n\",\n        \"    },\\n\",\n        \"    \\\"channelTitle\\\": \\\"AssociatedPress\\\",\\n\",\n        \"    \\\"liveBroadcastContent\\\": \\\"none\\\"\\n\",\n        \"   }\\n\",\n        \"  },\\n\",\n        \"  {\\n\",\n        \"   \\\"kind\\\": \\\"youtube#searchResult\\\",\\n\",\n        \"   \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/Pqtrk7f6rZM5jwPLKtXoJ98nNtg\\\\\\\"\\\",\\n\",\n        \"   \\\"id\\\": {\\n\",\n        \"    \\\"kind\\\": \\\"youtube#video\\\",\\n\",\n        \"    \\\"videoId\\\": \\\"nnghUTeSKW0\\\"\\n\",\n        \"   },\\n\",\n        \"   \\\"snippet\\\": {\\n\",\n        \"    \\\"publishedAt\\\": \\\"2008-11-03T00:06:40.000Z\\\",\\n\",\n        \"    \\\"channelId\\\": \\\"UC9ZGcEDoHfuY8lB5_SknuLA\\\",\\n\",\n        \"    \\\"title\\\": \\\"mark udall\\\",\\n\",\n        \"    \\\"description\\\": \\\"gov project.\\\",\\n\",\n        \"    \\\"thumbnails\\\": {\\n\",\n        \"     \\\"default\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/nnghUTeSKW0/default.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"medium\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/nnghUTeSKW0/mqdefault.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"high\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/nnghUTeSKW0/hqdefault.jpg\\\"\\n\",\n        \"     }\\n\",\n        \"    },\\n\",\n        \"    \\\"channelTitle\\\": \\\"g072091\\\",\\n\",\n        \"    \\\"liveBroadcastContent\\\": \\\"none\\\"\\n\",\n        \"   }\\n\",\n        \"  },\\n\",\n        \"  {\\n\",\n        \"   \\\"kind\\\": \\\"youtube#searchResult\\\",\\n\",\n        \"   \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/ay0GP1CevugYOb4FvBtJzXG_A0c\\\\\\\"\\\",\\n\",\n        \"   \\\"id\\\": {\\n\",\n        \"    \\\"kind\\\": \\\"youtube#video\\\",\\n\",\n        \"    \\\"videoId\\\": \\\"Pq-KnAMpDHs\\\"\\n\",\n        \"   },\\n\",\n        \"   \\\"snippet\\\": {\\n\",\n        \"    \\\"publishedAt\\\": \\\"2008-11-01T00:55:26.000Z\\\",\\n\",\n        \"    \\\"channelId\\\": \\\"UC5QhjJAjxtRvJ9ujFxNiJbA\\\",\\n\",\n        \"    \\\"title\\\": \\\"Eden Lane One on One with Congressman Mark Udall\\\",\\n\",\n        \"    \\\"description\\\": \\\"Senate candidate, Congressman Mark Udall spoke with me at a campaign event. Congressional candidate Jared Polis, and State Senate Candidate Joe ...\\\",\\n\",\n        \"    \\\"thumbnails\\\": {\\n\",\n        \"     \\\"default\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/Pq-KnAMpDHs/default.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"medium\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/Pq-KnAMpDHs/mqdefault.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"high\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/Pq-KnAMpDHs/hqdefault.jpg\\\"\\n\",\n        \"     }\\n\",\n        \"    },\\n\",\n        \"    \\\"channelTitle\\\": \\\"missedenlane\\\",\\n\",\n        \"    \\\"liveBroadcastContent\\\": \\\"none\\\"\\n\",\n        \"   }\\n\",\n        \"  },\\n\",\n        \"  {\\n\",\n        \"   \\\"kind\\\": \\\"youtube#searchResult\\\",\\n\",\n        \"   \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/y4ELzTNng5HKENL9FoHgkDV304k\\\\\\\"\\\",\\n\",\n        \"   \\\"id\\\": {\\n\",\n        \"    \\\"kind\\\": \\\"youtube#video\\\",\\n\",\n        \"    \\\"videoId\\\": \\\"aITDlrkKOoY\\\"\\n\",\n        \"   },\\n\",\n        \"   \\\"snippet\\\": {\\n\",\n        \"    \\\"publishedAt\\\": \\\"2008-11-01T00:42:17.000Z\\\",\\n\",\n        \"    \\\"channelId\\\": \\\"UCT3P1V7_N5HzV1vEZUSdNNQ\\\",\\n\",\n        \"    \\\"title\\\": \\\"CO: AFGE, APWU, NALC, and NPMHU leaflet with Mark Udall\\\",\\n\",\n        \"    \\\"description\\\": \\\"APWU, NALC, and NPMHU are out at the worksite when it matters most!\\\",\\n\",\n        \"    \\\"thumbnails\\\": {\\n\",\n        \"     \\\"default\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/aITDlrkKOoY/default.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"medium\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/aITDlrkKOoY/mqdefault.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"high\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/aITDlrkKOoY/hqdefault.jpg\\\"\\n\",\n        \"     }\\n\",\n        \"    },\\n\",\n        \"    \\\"channelTitle\\\": \\\"shubi10\\\",\\n\",\n        \"    \\\"liveBroadcastContent\\\": \\\"none\\\"\\n\",\n        \"   }\\n\",\n        \"  },\\n\",\n        \"  {\\n\",\n        \"   \\\"kind\\\": \\\"youtube#searchResult\\\",\\n\",\n        \"   \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/JgS_14GwklWRyGGiyMW8NbsyiQA\\\\\\\"\\\",\\n\",\n        \"   \\\"id\\\": {\\n\",\n        \"    \\\"kind\\\": \\\"youtube#video\\\",\\n\",\n        \"    \\\"videoId\\\": \\\"JAHI1pSiEPM\\\"\\n\",\n        \"   },\\n\",\n        \"   \\\"snippet\\\": {\\n\",\n        \"    \\\"publishedAt\\\": \\\"2008-10-30T04:43:12.000Z\\\",\\n\",\n        \"    \\\"channelId\\\": \\\"UCxdp8upAlGFfB4jjTH3wAHw\\\",\\n\",\n        \"    \\\"title\\\": \\\"[SEN-CO] Udall: Reason\\\",\\n\",\n        \"    \\\"description\\\": \\\"http://politicalrealm.blogspot.com A new campaign ad from Democrat Mark Udall.\\\",\\n\",\n        \"    \\\"thumbnails\\\": {\\n\",\n        \"     \\\"default\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/JAHI1pSiEPM/default.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"medium\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/JAHI1pSiEPM/mqdefault.jpg\\\"\\n\",\n        \"     },\\n\",\n        \"     \\\"high\\\": {\\n\",\n        \"      \\\"url\\\": \\\"https://i.ytimg.com/vi/JAHI1pSiEPM/hqdefault.jpg\\\"\\n\",\n        \"     }\\n\",\n        \"    },\\n\",\n        \"    \\\"channelTitle\\\": \\\"PoliticalRealm\\\",\\n\",\n        \"    \\\"liveBroadcastContent\\\": \\\"none\\\"\\n\",\n        \"   }\\n\",\n        \"  }\\n\",\n        \" ]\\n\",\n        \"}\\n\",\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"YouTube Video Meta Data Using youtube.video.list\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"### Documentation:\\n\",\n      \"\\n\",\n      \"https://developers.google.com/youtube/v3/docs/videos/list\\n\",\n      \"\\n\",\n      \"### HTTPS Request:\\n\",\n      \"\\n\",\n      \"```\\n\",\n      \"GET https://www.googleapis.com/youtube/v3/videos\\n\",\n      \"```\\n\",\n      \"\\n\",\n      \"### Parameters:\\n\",\n      \"\\n\",\n      \"<table class=\\\"matchpre\\\">\\n\",\n      \"  <thead>\\n\",\n      \"    <tr>\\n\",\n      \"      <th>Parameter name</th>\\n\",\n      \"      <th>Value</th>\\n\",\n      \"      <th>Description</th>\\n\",\n      \"    </tr>\\n\",\n      \"  </thead>\\n\",\n      \"  <tbody>\\n\",\n      \"        <tr id=\\\"required-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Required parameters</b></td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"part\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">part</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>part</strong></code> parameter specifies a comma-separated list of one or more <code>video</code> resource properties that the API response will include.<br><br>If the parameter identifies a property that contains child properties, the child properties will be included in the response. For example, in a <code>video</code> resource, the <code>snippet</code> property contains the <code>channelId</code>, <code>title</code>, <code>description</code>, <code>tags</code>, and <code>categoryId</code> properties. As such, if you set <code><strong>part=snippet</strong></code>, the API response will contain all of those properties.<br><br>The list below contains the <code>part</code> names that you can include in the parameter value and the <a href=\\\"/youtube/v3/getting-started#quota\\\">quota cost</a> for each part:<br><ul>\\n\",\n      \"<li><code>contentDetails</code>: 2</li>\\n\",\n      \"<li><code>fileDetails</code>: 1</li>\\n\",\n      \"<li><code>id</code>: 0</li>\\n\",\n      \"<li><code>liveStreamingDetails</code>: 2</li>\\n\",\n      \"<li><code>player</code>: 0</li>\\n\",\n      \"<li><code>processingDetails</code>: 1</li>\\n\",\n      \"<li><code>recordingDetails</code>: 2</li>\\n\",\n      \"<li><code>snippet</code>: 2</li>\\n\",\n      \"<li><code>statistics</code>: 2</li>\\n\",\n      \"<li><code>status</code>: 2</li>\\n\",\n      \"<li><code>suggestions</code>: 1</li>\\n\",\n      \"<li><code>topicDetails</code>: 2</li>\\n\",\n      \"\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"filter-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Filters <span style=\\\"color:#555\\\">(specify exactly one of the following parameters)</span></b></td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"chart\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">chart</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>chart</strong></code> parameter identifies the chart that you want to retrieve.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>mostPopular</strong></code> \\u2013 Return the most popular videos for the specified <a href=\\\"#regionCode\\\">content region</a> and <a href=\\\"#videoCategoryId\\\">video category</a>.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"id\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">id</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>id</strong></code> parameter specifies a comma-separated list of the YouTube video ID(s) for the resource(s) that are being retrieved. In a <code>video</code> resource, the <code>id</code> property specifies the video's ID.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"myRating\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">myRating</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>This parameter can only be used in a properly <a href=\\\"/youtube/v3/guides/authentication\\\">authorized request</a>. Set this parameter's value to <code>like</code> or <code>dislike</code> to instruct the API to only return videos liked or disliked by the authenticated user.<br><br>Acceptable values are:\\n\",\n      \"<ul>\\n\",\n      \"<li><code><strong>dislike</strong></code> \\u2013 Returns only videos disliked by the authenticated user.</li>\\n\",\n      \"<li><code><strong>like</strong></code> \\u2013 Returns only video liked by the authenticated user.</li>\\n\",\n      \"</ul>\\n\",\n      \"</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"optional-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Optional parameters</b></td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"maxResults\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">maxResults</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">unsigned integer</code></td>\\n\",\n      \"      <td>The <code><strong>maxResults</strong></code> parameter specifies the maximum number of items that should be returned in the result set.<br><br><strong>Note:</strong> This parameter is supported for use in conjunction with the <code><a href=\\\"#myRating\\\">myRating</a></code> parameter, but it is not supported for use in conjunction with the <code><a href=\\\"#id\\\">id</a></code> parameter. Acceptable values are <code>1</code> to <code>50</code>, inclusive. The default value is <code>5</code>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"onBehalfOfContentOwner\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">onBehalfOfContentOwner</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>This parameter can only be used in a properly <a href=\\\"/youtube/v3/guides/authentication\\\">authorized request</a>. <strong>Note:</strong> This parameter is intended exclusively for YouTube content partners.<br><br>The <code><strong>onBehalfOfContentOwner</strong></code> parameter indicates that the request's authorization credentials identify a YouTube CMS user who is acting on behalf of the content owner specified in the parameter value. This parameter is intended for YouTube content partners that own and manage many different YouTube channels. It allows content owners to authenticate once and get access to all their video and channel data, without having to provide authentication credentials for each individual channel. The CMS account that the user authenticates with must be linked to the specified YouTube content owner.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"pageToken\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">pageToken</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>pageToken</strong></code> parameter identifies a specific page in the result set that should be returned. In an API response, the <code>nextPageToken</code> and <code>prevPageToken</code> properties identify other pages that could be retrieved.<br><br><strong>Note:</strong> This parameter is supported for use in conjunction with the <code><a href=\\\"#myRating\\\">myRating</a></code> parameter, but it is not supported for use in conjunction with the <code><a href=\\\"#id\\\">id</a></code> parameter.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"regionCode\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">regionCode</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>regionCode</strong></code> parameter instructs the API to select a video chart available in the specified region. This parameter can only be used in conjunction with the <code><a href=\\\"#chart\\\">chart</a></code> parameter. The parameter value is an ISO 3166-1 alpha-2 country code.</td>\\n\",\n      \"    </tr>\\n\",\n      \"    <tr id=\\\"videoCategoryId\\\">\\n\",\n      \"      <td><code itemprop=\\\"property\\\">videoCategoryId</code></td>\\n\",\n      \"      <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"      <td>The <code><strong>videoCategoryId</strong></code> parameter identifies the <a href=\\\"/youtube/v3/docs/videoCategories\\\">video category</a> for which the chart should be retrieved. This parameter can only be used in conjunction with the <code><a href=\\\"#chart\\\">chart</a></code> parameter. By default, charts are not restricted to a particular category. The default value is <code>0</code>.</td>\\n\",\n      \"    </tr>\\n\",\n      \"\\n\",\n      \"  </tbody>\\n\",\n      \"</table>\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Preparing The HTTP Request\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"parameters = {\\\"part\\\": \\\"statistics\\\",\\n\",\n      \"              \\\"id\\\": \\\"5Q98TvXjIZg\\\",\\n\",\n      \"              \\\"key\\\": api_key,\\n\",\n      \"              }\\n\",\n      \"url = \\\"https://www.googleapis.com/youtube/v3/videos\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"page = requests.request(method=\\\"get\\\", url=url, params=parameters)\\n\",\n      \"j_results = json.loads(page.text)\\n\",\n      \"print page.text\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"{\\n\",\n        \" \\\"kind\\\": \\\"youtube#videoListResponse\\\",\\n\",\n        \" \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/RI2HLqoe4gS1QbNV867B5089lmY\\\\\\\"\\\",\\n\",\n        \" \\\"pageInfo\\\": {\\n\",\n        \"  \\\"totalResults\\\": 1,\\n\",\n        \"  \\\"resultsPerPage\\\": 1\\n\",\n        \" },\\n\",\n        \" \\\"items\\\": [\\n\",\n        \"  {\\n\",\n        \"   \\\"kind\\\": \\\"youtube#video\\\",\\n\",\n        \"   \\\"etag\\\": \\\"\\\\\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/HdPfiQBFpxUe-eEq-EYCkg3p4b8\\\\\\\"\\\",\\n\",\n        \"   \\\"id\\\": \\\"5Q98TvXjIZg\\\",\\n\",\n        \"   \\\"statistics\\\": {\\n\",\n        \"    \\\"viewCount\\\": \\\"58\\\",\\n\",\n        \"    \\\"likeCount\\\": \\\"0\\\",\\n\",\n        \"    \\\"dislikeCount\\\": \\\"0\\\",\\n\",\n        \"    \\\"favoriteCount\\\": \\\"0\\\",\\n\",\n        \"    \\\"commentCount\\\": \\\"0\\\"\\n\",\n        \"   }\\n\",\n        \"  }\\n\",\n        \" ]\\n\",\n        \"}\\n\",\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Process Data Range\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"I'll check the coorelation between the results of 2008 Senate elections results and YouTube Stats.\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Colorado Senate - Gardner vs. Udall\\n\",\n      \"Cory Gardner (R)\\n\",\n      \"Mark Udall (D)\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def _search_list(q=\\\"\\\", publishedAfter=None, publishedBefore=None, pageToken=\\\"\\\"):\\n\",\n      \"    parameters = {\\\"part\\\": \\\"id\\\",\\n\",\n      \"                  \\\"maxResults\\\": 50,\\n\",\n      \"                  \\\"order\\\": \\\"viewCount\\\",\\n\",\n      \"                  \\\"pageToken\\\": pageToken,\\n\",\n      \"                  \\\"q\\\": q,\\n\",\n      \"                  \\\"type\\\": \\\"video\\\",\\n\",\n      \"                  \\\"key\\\": api_key,\\n\",\n      \"                  }\\n\",\n      \"    url = \\\"https://www.googleapis.com/youtube/v3/search\\\"\\n\",\n      \"    \\n\",\n      \"    if publishedAfter: parameters[\\\"publishedAfter\\\"] = publishedAfter\\n\",\n      \"    if publishedBefore: parameters[\\\"publishedBefore\\\"] = publishedBefore\\n\",\n      \"    \\n\",\n      \"    page = requests.request(method=\\\"get\\\", url=url, params=parameters)\\n\",\n      \"    return json.loads(page.text)\\n\",\n      \"\\n\",\n      \"def search_list(q=\\\"\\\", publishedAfter=None, publishedBefore=None, max_requests=10):\\n\",\n      \"    more_results = True\\n\",\n      \"    pageToken=\\\"\\\"\\n\",\n      \"    results = []\\n\",\n      \"    \\n\",\n      \"    for counter in range(max_requests):\\n\",\n      \"        j_results = _search_list(q=q, publishedAfter=publishedAfter, publishedBefore=publishedBefore, pageToken=pageToken)\\n\",\n      \"        items = j_results.get(\\\"items\\\", None)\\n\",\n      \"        if items:\\n\",\n      \"            results += [item[\\\"id\\\"][\\\"videoId\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"            if j_results.has_key(\\\"nextPageToken\\\"):\\n\",\n      \"                pageToken = j_results[\\\"nextPageToken\\\"]\\n\",\n      \"            else:\\n\",\n      \"                return results\\n\",\n      \"        else:\\n\",\n      \"            return results\\n\",\n      \"    return results\\n\",\n      \"\\n\",\n      \"def _video_list(video_id_list):\\n\",\n      \"    parameters = {\\\"part\\\": \\\"statistics\\\",\\n\",\n      \"                  \\\"id\\\": \\\",\\\".join(video_id_list),\\n\",\n      \"                  \\\"key\\\": api_key,\\n\",\n      \"                  \\\"maxResults\\\": 50\\n\",\n      \"                  }\\n\",\n      \"    url = \\\"https://www.googleapis.com/youtube/v3/videos\\\"\\n\",\n      \"    page = requests.request(method=\\\"get\\\", url=url, params=parameters)\\n\",\n      \"    j_results = json.loads(page.text)\\n\",\n      \"    df = pd.DataFrame([item[\\\"statistics\\\"] for item in j_results[\\\"items\\\"]], dtype=np.int64)\\n\",\n      \"    df[\\\"video_id\\\"] = [item[\\\"id\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"    \\n\",\n      \"    parameters[\\\"part\\\"] = \\\"snippet\\\"\\n\",\n      \"    page = requests.request(method=\\\"get\\\", url=url, params=parameters)\\n\",\n      \"    j_results = json.loads(page.text)\\n\",\n      \"    df[\\\"publishedAt\\\"] = [item[\\\"snippet\\\"][\\\"publishedAt\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"    df[\\\"publishedAt\\\"] = df[\\\"publishedAt\\\"].apply(lambda x: datetime.strptime(x, \\\"%Y-%m-%dT%H:%M:%S.000Z\\\"))\\n\",\n      \"    df[\\\"date\\\"] = df[\\\"publishedAt\\\"].apply(lambda x: x.date())\\n\",\n      \"    df[\\\"week\\\"] = df[\\\"date\\\"].apply(lambda x: x.isocalendar()[1])\\n\",\n      \"    df[\\\"channelId\\\"] = [item[\\\"snippet\\\"][\\\"channelId\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"    df[\\\"title\\\"] = [item[\\\"snippet\\\"][\\\"title\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"    df[\\\"description\\\"] = [item[\\\"snippet\\\"][\\\"description\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"    df[\\\"channelTitle\\\"] = [item[\\\"snippet\\\"][\\\"channelTitle\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"    df[\\\"categoryId\\\"] = [item[\\\"snippet\\\"][\\\"categoryId\\\"] for item in j_results[\\\"items\\\"]]\\n\",\n      \"    return df\\n\",\n      \"\\n\",\n      \"def video_list(video_id_list):\\n\",\n      \"    values = []\\n\",\n      \"    for index, item in enumerate(video_id_list[::50]):\\n\",\n      \"        t_index = index * 50\\n\",\n      \"        values.append(_video_list(video_id_list[t_index:t_index+50]))\\n\",\n      \"    return pd.concat(values)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Get Data for Two Candidates\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_data(candidates, publishedAfter, publishedBefore):\\n\",\n      \"    results_list = []\\n\",\n      \"    for q in candidates:\\n\",\n      \"        results = search_list(q=q,\\n\",\n      \"                              publishedAfter=publishedAfter,\\n\",\n      \"                              publishedBefore=publishedBefore,\\n\",\n      \"                              max_requests=50)\\n\",\n      \"\\n\",\n      \"        stat_data_set = video_list(results)\\n\",\n      \"        stat_data_set[\\\"candidate_name\\\"] = q\\n\",\n      \"        results_list.append(stat_data_set)\\n\",\n      \"    data_set = pd.concat(results_list)\\n\",\n      \"    return data_set\\n\",\n      \"\\n\",\n      \"def get_2008_data(candidates):\\n\",\n      \"    return get_data(candidates, publishedAfter=\\\"2008-08-04T00:00:00Z\\\", publishedBefore=\\\"2008-11-04T00:00:00Z\\\")\\n\",\n      \"\\n\",\n      \"def get_2010_data(candidates):\\n\",\n      \"    return get_data(candidates, publishedAfter=\\\"2010-08-04T00:00:00Z\\\", publishedBefore=\\\"2010-11-04T00:00:00Z\\\")\\n\",\n      \"\\n\",\n      \"def get_2012_data(candidates):\\n\",\n      \"    return get_data(candidates, publishedAfter=\\\"2012-08-04T00:00:00Z\\\", publishedBefore=\\\"2012-11-04T00:00:00Z\\\")\\n\",\n      \"\\n\",\n      \"def get_2014_data(candidates):\\n\",\n      \"    return get_data(candidates, publishedAfter=\\\"2014-08-04T00:00:00Z\\\", publishedBefore=\\\"2014-11-04T00:00:00Z\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Analyzing Colorado Senate Race for 2014\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"candidates = [\\\"Cory Gardner\\\", \\\"Mark Udall\\\"] # Cory Gardner (R), Mark Udall (D)*\\n\",\n      \"colorado_2014_ds = get_2014_data(candidates)\\n\",\n      \"pd.pivot_table(colorado_2014_ds, values=[\\\"commentCount\\\", \\\"favoriteCount\\\", \\\"dislikeCount\\\", \\\"likeCount\\\", \\\"viewCount\\\"],\\n\",\n      \"               aggfunc='sum', rows=\\\"candidate_name\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>commentCount</th>\\n\",\n        \"      <th>dislikeCount</th>\\n\",\n        \"      <th>favoriteCount</th>\\n\",\n        \"      <th>likeCount</th>\\n\",\n        \"      <th>viewCount</th>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>candidate_name</th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Cory Gardner</th>\\n\",\n        \"      <td> 304</td>\\n\",\n        \"      <td> 167</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 437</td>\\n\",\n        \"      <td> 234669</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Mark Udall</th>\\n\",\n        \"      <td> 195</td>\\n\",\n        \"      <td> 470</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 450</td>\\n\",\n        \"      <td> 144744</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount\\n\",\n        \"candidate_name                                                                 \\n\",\n        \"Cory Gardner             304           167              0        437     234669\\n\",\n        \"Mark Udall               195           470              0        450     144744\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = colorado_2014_ds[colorado_2014_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = cand[\\\"week\\\"].value_counts()\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos Published\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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pr/MurVKwgqFhFhxIgRvPbaa36rlABWrVrFRRddxIknnkjTpk3529/+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\\nDEsOxhhjyrDkYIwxpgxLDsYYY8r4/4K9BcySN94FAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff0805e8e50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = colorado_2014_ds[colorado_2014_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"viewCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos viewCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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sAgYLrPOZ6+rsE4+f1i/SixR1raQoYNm8PcueNYsCCVuXPHMWzYHNLS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\\nRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff0804fa310>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = colorado_2014_ds[colorado_2014_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"likeCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos likeCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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sZ5afeE5z5ycvzpQxk2bBhvvPEG8+fPp0OHDpx4YvF1fn379qVjx44A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\\nZmbWxMXBzMyauDiYmVkTFwczM2vyfzXse0CGFIOvAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078bd3c50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = colorado_2014_ds[colorado_2014_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"dislikeCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos dislikeCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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UuWcMYZZ9CuXTtatGjBk08+ybZt20ptE2j6zIcffpgbb7yx0sSQmJjI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\\nAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078b29890>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"How Predective Was It in 2012?\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Virginia Senate - Allen vs. Kaine\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"candidates = [\\\"George Allen\\\", \\\"Tim Kaine\\\"] # George Allen (R), Tim Kaine (D)Winner\\n\",\n      \"va_2012_ds = get_2012_data(candidates)\\n\",\n      \"pd.pivot_table(va_2012_ds, values=[\\\"commentCount\\\", \\\"favoriteCount\\\", \\\"dislikeCount\\\", \\\"likeCount\\\", \\\"viewCount\\\"],\\n\",\n      \"               aggfunc='sum', rows=\\\"candidate_name\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>commentCount</th>\\n\",\n        \"      <th>dislikeCount</th>\\n\",\n        \"      <th>favoriteCount</th>\\n\",\n        \"      <th>likeCount</th>\\n\",\n        \"      <th>viewCount</th>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>candidate_name</th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>George Allen</th>\\n\",\n        \"      <td> 297</td>\\n\",\n        \"      <td> 352</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 475</td>\\n\",\n        \"      <td> 203297</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Tim Kaine</th>\\n\",\n        \"      <td> 174</td>\\n\",\n        \"      <td>  97</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 553</td>\\n\",\n        \"      <td> 248367</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 14,\n       \"text\": [\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount\\n\",\n        \"candidate_name                                                                 \\n\",\n        \"George Allen             297           352              0        475     203297\\n\",\n        \"Tim Kaine                174            97              0        553     248367\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = va_2012_ds[va_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = cand[\\\"week\\\"].value_counts()\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos Published\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": \"iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nJQAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078b29050>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = va_2012_ds[va_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"viewCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos viewCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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nR1tnsAmzzHbQJ6+ijf7JTjPG8EUNUKYK+IdGygrrDw59OHYHz6sR0X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\\nGFMrfwBiJuwHHym/UgAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078959c10>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = va_2012_ds[va_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"likeCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos likeCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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zJatiwLRTS/dK3yhKWmptK5c+eKr/rU1FQAv1NpiggzZ87km2++4Yor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\\nf/oeH1XVkxNPAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff07891d5d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = va_2012_ds[va_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"dislikeCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos dislikeCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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mp78ODB5OTkMG3aNEaPHk3Tpk1ZuXIlpaWlHHHEEQn+JEajQbW2crCR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\\nUQtTDoZhGEYt/j+5tXDtl8d7oAAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078bdb110>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Nevada Senate - Heller vs. Berkley\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"candidates = [\\\"Dean Heller\\\", \\\"Shelley Berkley\\\"] # Dean Heller (R)*Winnner, Shelley Berkley (D)\\n\",\n      \"nv_2012_ds = get_2012_data(candidates)\\n\",\n      \"print pd.pivot_table(nv_2012_ds, values=[\\\"commentCount\\\", \\\"favoriteCount\\\", \\\"dislikeCount\\\", \\\"likeCount\\\", \\\"viewCount\\\"],\\n\",\n      \"               aggfunc='sum', rows=\\\"candidate_name\\\")\\n\",\n      \"\\n\",\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = nv_2012_ds[nv_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = cand[\\\"week\\\"].value_counts()\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos Published\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = nv_2012_ds[nv_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"viewCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos viewCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = nv_2012_ds[nv_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"likeCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos likeCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"for candidate, color in zip(candidates, [\\\"r\\\", \\\"b\\\"]):\\n\",\n      \"    cand = nv_2012_ds[nv_2012_ds[\\\"candidate_name\\\"]==candidate]\\n\",\n      \"    by_date = pd.pivot_table(cand, rows=[\\\"week\\\"], values=[\\\"dislikeCount\\\"], aggfunc=\\\"sum\\\")\\n\",\n      \"    by_date = by_date.sort_index()\\n\",\n      \"    dates = by_date.index\\n\",\n      \"    plt.plot(dates, by_date.values, \\\"-o\\\", label=candidate, c=color, linewidth=2)\\n\",\n      \"plt.legend(loc=\\\"best\\\")\\n\",\n      \"plt.ylabel(\\\"Videos dislikeCount\\\")\\n\",\n      \"plt.xlabel(\\\"Week\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount\\n\",\n        \"candidate_name                                                                  \\n\",\n        \"Dean Heller               248           644              0        926     870677\\n\",\n        \"Shelley Berkley           222           206              0        472     679636\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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GbPnh1UkLmCFYllNAFXMY7hJEf3Px/qWWDKFPuZ+ZMmZVNcPNXHpKO1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\\nYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078bdb450>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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2uXLl30ww8/VFXVvLw8TUtL0+eee06Li4v1888/13bt2unKlStVVXXU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\\nY2ZmtfsTRQ04sUhF/P4AAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078c13850>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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Y2JiXF6zoQJE/D222+jrKwMAwYMcHleTZo3b47x48fjxhtvBKBKgw4d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\\nIEkaMBwkSQOGgyRp4G9RkJPr3B2HnwAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff07880f210>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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kqKi4tl+/btkpycLIcOHar+/O2335Zzzz23+lx3yqGqqkqaN29erShE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\\nTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078778450>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Current Senate $113^{th}$\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Data Sources:\\n\",\n      \"\\n\",\n      \"- http://www.fec.gov/data/CandidateSummary.do?format=html\\n\",\n      \"- http://www.senate.gov/general/contact_information/senators_cfm.cfm\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Get Current Senate Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"url = \\\"http://www.senate.gov/general/contact_information/senators_cfm.xml\\\"\\n\",\n      \"response = requests.get(url)\\n\",\n      \"tree = etree.fromstring(str(response.text))\\n\",\n      \"print tree\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<Element contact_information at 0x7ff0804d4170>\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Store Data In Pandas Data Frame\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"member_full = [member.xpath(\\\"member_full\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators = pd.DataFrame(member_full, columns=[\\\"member_full\\\"])\\n\",\n      \"\\n\",\n      \"senators[\\\"member_full\\\"] = member_full\\n\",\n      \"senators[\\\"last_name\\\"] = [member.xpath(\\\"last_name\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"first_name\\\"] = [member.xpath(\\\"first_name\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"party\\\"] = [member.xpath(\\\"party\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"state\\\"] = [member.xpath(\\\"state\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"address\\\"] = [member.xpath(\\\"address\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"phone\\\"] = [member.xpath(\\\"phone\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"website\\\"] = [member.xpath(\\\"website\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"bioguide_id\\\"] = [member.xpath(\\\"bioguide_id\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"senators[\\\"class\\\"] = [member.xpath(\\\"class\\\")[0].text for member in tree.xpath(\\\"//member\\\")]\\n\",\n      \"\\n\",\n      \"senators\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<pre>\\n\",\n        \"&lt;class 'pandas.core.frame.DataFrame'&gt;\\n\",\n        \"Int64Index: 100 entries, 0 to 99\\n\",\n        \"Data columns (total 10 columns):\\n\",\n        \"member_full    100  non-null values\\n\",\n        \"last_name      100  non-null values\\n\",\n        \"first_name     100  non-null values\\n\",\n        \"party          100  non-null values\\n\",\n        \"state          100  non-null values\\n\",\n        \"address        100  non-null values\\n\",\n        \"phone          100  non-null values\\n\",\n        \"website        100  non-null values\\n\",\n        \"bioguide_id    100  non-null values\\n\",\n        \"class          100  non-null values\\n\",\n        \"dtypes: object(10)\\n\",\n        \"</pre>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 21,\n       \"text\": [\n        \"<class 'pandas.core.frame.DataFrame'>\\n\",\n        \"Int64Index: 100 entries, 0 to 99\\n\",\n        \"Data columns (total 10 columns):\\n\",\n        \"member_full    100  non-null values\\n\",\n        \"last_name      100  non-null values\\n\",\n        \"first_name     100  non-null values\\n\",\n        \"party          100  non-null values\\n\",\n        \"state          100  non-null values\\n\",\n        \"address        100  non-null values\\n\",\n        \"phone          100  non-null values\\n\",\n        \"website        100  non-null values\\n\",\n        \"bioguide_id    100  non-null values\\n\",\n        \"class          100  non-null values\\n\",\n        \"dtypes: object(10)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 21\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Control By Party\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"by_party = senators[\\\"party\\\"].value_counts()\\n\",\n      \"by_party.sort(ascending=False)\\n\",\n      \"print by_party\\n\",\n      \"\\n\",\n      \"color_dict = {\\\"D\\\": \\\"b\\\",\\n\",\n      \"              \\\"R\\\": \\\"r\\\",\\n\",\n      \"              \\\"I\\\": \\\"g\\\"}\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"labels = [\\\"%s: %s\\\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\\n\",\n      \"colors = list(pd.Series(by_party.index).map(color_dict))\\n\",\n      \"\\n\",\n      \"plt.figure()\\n\",\n      \"plt.axis(\\\"equal\\\")\\n\",\n      \"plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"axes.barh(range(len(by_party.index)), by_party.values, color=colors)\\n\",\n      \"plt.box(on=\\\"off\\\")\\n\",\n      \"axes.axvline(x=50, color=\\\"black\\\", alpha=0.7, linewidth=2)\\n\",\n      \"axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\\n\",\n      \"axes.yaxis.set_ticklabels(by_party.index, minor=False)\\n\",\n      \"plt.xlabel(\\\"$113^{th}$ Senate Seats Controlled by Party\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"D    53\\n\",\n        \"R    45\\n\",\n        \"I     2\\n\",\n        \"dtype: int64\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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xyR/bhZEbA3jPOjChtbmTtAxxaatjJ/gKcdYM6o4jXvuBh5TbX76NuQ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\\npvWdDJ7da9YAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff0786b7e10>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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sSG9v61NIEPmx/31Tx/3Lt7fa6mzPzegHNqCQzs5ese4O00PxIPLDOf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\\nAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff07874a990>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 22\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Who is up for Re-election?\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Class II senators are up for re-election.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"class_2_senators = senators[senators[\\\"class\\\"]==\\\"Class II\\\"]\\n\",\n      \"by_party =class_2_senators[\\\"party\\\"].value_counts()\\n\",\n      \"by_party.sort(ascending=False)\\n\",\n      \"print by_party\\n\",\n      \"\\n\",\n      \"labels = [\\\"%s: %s\\\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\\n\",\n      \"colors = list(pd.Series(by_party.index).map(color_dict))\\n\",\n      \"\\n\",\n      \"plt.figure()\\n\",\n      \"plt.axis(\\\"equal\\\")\\n\",\n      \"plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"color_dict = {\\\"D\\\": \\\"b\\\",\\n\",\n      \"              \\\"R\\\": \\\"r\\\",\\n\",\n      \"              \\\"I\\\": \\\"g\\\"}\\n\",\n      \"\\n\",\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"axes.barh(range(len(by_party.index)), by_party.values, color=colors)\\n\",\n      \"plt.box(on=\\\"off\\\")\\n\",\n      \"axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\\n\",\n      \"axes.yaxis.set_ticklabels(by_party.index, minor=False)\\n\",\n      \"plt.xlabel(\\\"$113^{th}$ Senate Seats of $Class II$ Controlled by Party\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"D    20\\n\",\n        \"R    13\\n\",\n        \"dtype: int64\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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tlZmn/MjjYgAhVtsFmPjMiKWx0ckoTKP8Ieh6hwpVhopJsdCSANlW2w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\\nKyISIpWuiEiIVLoiIiFS6YqIhOh/Aa5lRiyJeaDFAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff0786b7b10>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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59er+GpPcrpJbu26Vz+HHA8zVj9BuP6HX6WzRylnXXP0L//PMpgw9Q5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\\nSUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078adc510>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 23\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Looking at the other classes\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"class_3_senators = senators[senators[\\\"class\\\"]==\\\"Class III\\\"]\\n\",\n      \"by_party =class_3_senators[\\\"party\\\"].value_counts()\\n\",\n      \"by_party.sort(ascending=False)\\n\",\n      \"print by_party\\n\",\n      \"\\n\",\n      \"labels = [\\\"%s: %s\\\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\\n\",\n      \"colors = list(pd.Series(by_party.index).map(color_dict))\\n\",\n      \"\\n\",\n      \"plt.figure()\\n\",\n      \"plt.axis(\\\"equal\\\")\\n\",\n      \"plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"color_dict = {\\\"D\\\": \\\"b\\\",\\n\",\n      \"              \\\"R\\\": \\\"r\\\",\\n\",\n      \"              \\\"I\\\": \\\"g\\\"}\\n\",\n      \"\\n\",\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"axes.barh(range(len(by_party.index)), by_party.values, color=colors)\\n\",\n      \"plt.box(on=\\\"off\\\")\\n\",\n      \"axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\\n\",\n      \"axes.yaxis.set_ticklabels(by_party.index, minor=False)\\n\",\n      \"plt.xlabel(\\\"$113^{th}$ Senate Seats of $Class III$ Controlled by Party\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"R    24\\n\",\n        \"D    10\\n\",\n        \"dtype: int64\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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Kh082Yh9LbDr3Y8bmZnoE234/B7d38sdIhSoNLtp6R7OmGWVunm113Q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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078ae45d0>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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F2PX8m6R21D7T8z74iI9TRz5H8ZEV/KzM4/CCbb/xAG88+dbqV5W9xZ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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff0787413d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 24\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"class_1_senators = senators[senators[\\\"class\\\"]==\\\"Class I\\\"]\\n\",\n      \"by_party =class_1_senators[\\\"party\\\"].value_counts()\\n\",\n      \"by_party.sort(ascending=False)\\n\",\n      \"print by_party\\n\",\n      \"\\n\",\n      \"labels = [\\\"%s: %s\\\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\\n\",\n      \"colors = list(pd.Series(by_party.index).map(color_dict))\\n\",\n      \"\\n\",\n      \"plt.figure()\\n\",\n      \"plt.axis(\\\"equal\\\")\\n\",\n      \"plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\\n\",\n      \"plt.show()\\n\",\n      \"\\n\",\n      \"color_dict = {\\\"D\\\": \\\"b\\\",\\n\",\n      \"              \\\"R\\\": \\\"r\\\",\\n\",\n      \"              \\\"I\\\": \\\"g\\\"}\\n\",\n      \"\\n\",\n      \"fig = plt.figure()\\n\",\n      \"axes = fig.add_subplot(111)\\n\",\n      \"axes.barh(range(len(by_party.index)), by_party.values, color=colors)\\n\",\n      \"plt.box(on=\\\"off\\\")\\n\",\n      \"axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\\n\",\n      \"axes.yaxis.set_ticklabels(by_party.index, minor=False)\\n\",\n      \"plt.xlabel(\\\"$113^{th}$ Senate Seats of $Class I$ Controlled by Party\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"D    23\\n\",\n        \"R     8\\n\",\n        \"I     2\\n\",\n        \"dtype: int64\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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ED/yN9JKSVNzpO0lFTchJKan2OUbNDjeSD/n9pWM/FE6FZHhcZ+umNM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\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff0787789d0>\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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9DFhre2fj9Pd67rM9vy3n3lMLp9ZdBz3a2eCfrd8LbMuoDirqM7ertk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\\nDzGUd1HHiQTdAAAAAElFTkSuQmCC\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff078729c50>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 25\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Forecasting Results of Senate Elections 2014\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Start with listing all seat in $Class II$\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"class_2_senators = senators[senators[\\\"class\\\"]==\\\"Class II\\\"].sort(\\\"state\\\")\\n\",\n      \"class_2_senators\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<pre>\\n\",\n        \"&lt;class 'pandas.core.frame.DataFrame'&gt;\\n\",\n        \"Int64Index: 33 entries, 4 to 29\\n\",\n        \"Data columns (total 10 columns):\\n\",\n        \"member_full    33  non-null values\\n\",\n        \"last_name      33  non-null values\\n\",\n        \"first_name     33  non-null values\\n\",\n        \"party          33  non-null values\\n\",\n        \"state          33  non-null values\\n\",\n        \"address        33  non-null values\\n\",\n        \"phone          33  non-null values\\n\",\n        \"website        33  non-null values\\n\",\n        \"bioguide_id    33  non-null values\\n\",\n        \"class          33  non-null values\\n\",\n        \"dtypes: object(10)\\n\",\n        \"</pre>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 26,\n       \"text\": [\n        \"<class 'pandas.core.frame.DataFrame'>\\n\",\n        \"Int64Index: 33 entries, 4 to 29\\n\",\n        \"Data columns (total 10 columns):\\n\",\n        \"member_full    33  non-null values\\n\",\n        \"last_name      33  non-null values\\n\",\n        \"first_name     33  non-null values\\n\",\n        \"party          33  non-null values\\n\",\n        \"state          33  non-null values\\n\",\n        \"address        33  non-null values\\n\",\n        \"phone          33  non-null values\\n\",\n        \"website        33  non-null values\\n\",\n        \"bioguide_id    33  non-null values\\n\",\n        \"class          33  non-null values\\n\",\n        \"dtypes: object(10)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 26\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Get Competitors\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Fetch Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"url = \\\"http://www.fec.gov/data/CandidateSummary.do?format=xml\\\"\\n\",\n      \"response = requests.get(url)\\n\",\n      \"page = html.fromstring(str(response.text))\\n\",\n      \"print response.text[:1000]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<data.fec.gov xmlns:fecdc=\\\"http://www.w3.org/2001/XMLSchema-instance\\\" fecdc:schemaLocation=\\\"/data /finance/disclosure/schema/CandidateSummary.xsd\\\"><title>Candidate Summary</title><description>This file contains information for each candidate who has registered with the FEC or appears on an official state ballot for an election to the U.S. House of Representatives, U.S. Senate or U.S. President. The table is available for the current election cycle and for election cycles through 2008.</description><timestamp>2014-10-09T05:06:27-05:00</timestamp><copyright>Copyright 2014, Federal Election Commission.</copyright><can_sum><lin_ima>http://www.fec.gov/fecviewer/CandidateCommitteeDetail.do?candidateCommitteeId=H4UT04052&amp;tabIndex=1</lin_ima><can_id>H4UT04052</can_id><can_nam>AALDERS, TIM</can_nam><can_off>H</can_off><can_off_sta>UT</can_off_sta><can_off_dis>04</can_off_dis><can_par_aff>IAP</can_par_aff><can_inc_cha_ope_sea>OPEN</can_inc_cha_ope_sea><can_str1>5306 WEST 10320 NORTH</can_str\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 63\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Process the data into an XML Tree\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for item in page[:10]:\\n\",\n      \"    print item.tag\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"title\\n\",\n        \"description\\n\",\n        \"timestamp\\n\",\n        \"copyright\\n\",\n        \"can_sum\\n\",\n        \"can_sum\\n\",\n        \"can_sum\\n\",\n        \"can_sum\\n\",\n        \"can_sum\\n\",\n        \"can_sum\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 64\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Notice `<can_sum>` encapsulates the candidates data.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for item in page.xpath(\\\"//can_sum\\\")[0]:\\n\",\n      \"    print \\\"<%s>%s</%s>\\\" % (item.tag, str(item.text), item.tag)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<lin_ima>http://www.fec.gov/fecviewer/CandidateCommitteeDetail.do?candidateCommitteeId=H4UT04052&tabIndex=1</lin_ima>\\n\",\n        \"<can_id>H4UT04052</can_id>\\n\",\n        \"<can_nam>AALDERS, TIM</can_nam>\\n\",\n        \"<can_off>H</can_off>\\n\",\n        \"<can_off_sta>UT</can_off_sta>\\n\",\n        \"<can_off_dis>04</can_off_dis>\\n\",\n        \"<can_par_aff>IAP</can_par_aff>\\n\",\n        \"<can_inc_cha_ope_sea>OPEN</can_inc_cha_ope_sea>\\n\",\n        \"<can_str1>5306 WEST 10320 NORTH</can_str1>\\n\",\n        \"<can_str2>None</can_str2>\\n\",\n        \"<can_cit>HIGHLAND</can_cit>\\n\",\n        \"<can_sta>UT</can_sta>\\n\",\n        \"<can_zip>84003</can_zip>\\n\",\n        \"<ind_ite_con>None</ind_ite_con>\\n\",\n        \"<ind_uni_con>None</ind_uni_con>\\n\",\n        \"<ind_con>None</ind_con>\\n\",\n        \"<par_com_con>None</par_com_con>\\n\",\n        \"<oth_com_con>None</oth_com_con>\\n\",\n        \"<can_con>None</can_con>\\n\",\n        \"<tot_con>None</tot_con>\\n\",\n        \"<tra_fro_oth_aut_com>None</tra_fro_oth_aut_com>\\n\",\n        \"<can_loa>None</can_loa>\\n\",\n        \"<oth_loa>None</oth_loa>\\n\",\n        \"<tot_loa>None</tot_loa>\\n\",\n        \"<off_to_ope_exp>None</off_to_ope_exp>\\n\",\n        \"<off_to_fun>None</off_to_fun>\\n\",\n        \"<off_to_leg_acc>None</off_to_leg_acc>\\n\",\n        \"<oth_rec>None</oth_rec>\\n\",\n        \"<tot_rec>None</tot_rec>\\n\",\n        \"<ope_exp>None</ope_exp>\\n\",\n        \"<exe_leg_acc_dis>None</exe_leg_acc_dis>\\n\",\n        \"<fun_dis>None</fun_dis>\\n\",\n        \"<tra_to_oth_aut_com>None</tra_to_oth_aut_com>\\n\",\n        \"<can_loa_rep>None</can_loa_rep>\\n\",\n        \"<oth_loa_rep>None</oth_loa_rep>\\n\",\n        \"<tot_loa_rep>None</tot_loa_rep>\\n\",\n        \"<ind_ref>None</ind_ref>\\n\",\n        \"<par_com_ref>None</par_com_ref>\\n\",\n        \"<oth_com_ref>None</oth_com_ref>\\n\",\n        \"<tot_con_ref>None</tot_con_ref>\\n\",\n        \"<oth_dis>None</oth_dis>\\n\",\n        \"<tot_dis>None</tot_dis>\\n\",\n        \"<cas_on_han_beg_of_per>None</cas_on_han_beg_of_per>\\n\",\n        \"<cas_on_han_clo_of_per>None</cas_on_han_clo_of_per>\\n\",\n        \"<net_con>None</net_con>\\n\",\n        \"<net_ope_exp>None</net_ope_exp>\\n\",\n        \"<deb_owe_by_com>None</deb_owe_by_com>\\n\",\n        \"<deb_owe_to_com>None</deb_owe_to_com>\\n\",\n        \"<cov_sta_dat>None</cov_sta_dat>\\n\",\n        \"<cov_end_dat>None</cov_end_dat>\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 65\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cand_list = [cand for cand in page.xpath(\\\"//can_sum\\\") if cand.xpath(\\\"can_off\\\")[0].text==\\\"S\\\"]\\n\",\n      \"lin_ima = [cand.xpath(\\\"lin_ima\\\")[0].text for cand in cand_list]\\n\",\n      \"len(lin_ima)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 66,\n       \"text\": [\n        \"412\"\n       ]\n      }\n     ],\n     \"prompt_number\": 66\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Store data into Pandas Data Frame\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"senate_cadidate = pd.DataFrame(lin_ima, columns=[\\\"lin_ima\\\"])\\n\",\n      \"senate_cadidate[\\\"can_id\\\"] = [cand.xpath(\\\"can_id\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_nam\\\"] = [cand.xpath(\\\"can_nam\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_off\\\"] = [cand.xpath(\\\"can_off\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_off_sta\\\"] = [cand.xpath(\\\"can_off_sta\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_par_aff\\\"] = [cand.xpath(\\\"can_par_aff\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_inc_cha_ope_sea\\\"] = [cand.xpath(\\\"can_inc_cha_ope_sea\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"ind_ite_con\\\"] = [cand.xpath(\\\"ind_ite_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"ind_uni_con\\\"] = [cand.xpath(\\\"ind_uni_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"ind_con\\\"] = [cand.xpath(\\\"ind_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"par_com_con\\\"] = [cand.xpath(\\\"par_com_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"oth_com_con\\\"] = [cand.xpath(\\\"oth_com_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_con\\\"] = [cand.xpath(\\\"can_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tot_con\\\"] = [cand.xpath(\\\"tot_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tra_fro_oth_aut_com\\\"] = [cand.xpath(\\\"tra_fro_oth_aut_com\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_loa\\\"] = [cand.xpath(\\\"can_loa\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"oth_loa\\\"] = [cand.xpath(\\\"oth_loa\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tot_loa\\\"] = [cand.xpath(\\\"tot_loa\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"off_to_ope_exp\\\"] = [cand.xpath(\\\"off_to_ope_exp\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"off_to_fun\\\"] = [cand.xpath(\\\"off_to_fun\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"off_to_leg_acc\\\"] = [cand.xpath(\\\"off_to_leg_acc\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"oth_rec\\\"] = [cand.xpath(\\\"oth_rec\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tot_rec\\\"] = [cand.xpath(\\\"tot_rec\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"ope_exp\\\"] = [cand.xpath(\\\"ope_exp\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"fun_dis\\\"] = [cand.xpath(\\\"fun_dis\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"exe_leg_acc_dis\\\"] = [cand.xpath(\\\"exe_leg_acc_dis\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tra_to_oth_aut_com\\\"] = [cand.xpath(\\\"tra_to_oth_aut_com\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"can_loa_rep\\\"] = [cand.xpath(\\\"can_loa_rep\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"oth_loa_rep\\\"] = [cand.xpath(\\\"oth_loa_rep\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tot_loa_rep\\\"] = [cand.xpath(\\\"tot_loa_rep\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"ind_ref\\\"] = [cand.xpath(\\\"ind_ref\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"par_com_ref\\\"] = [cand.xpath(\\\"par_com_ref\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"oth_com_ref\\\"] = [cand.xpath(\\\"oth_com_ref\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tot_con_ref\\\"] = [cand.xpath(\\\"tot_con_ref\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"oth_dis\\\"] = [cand.xpath(\\\"oth_dis\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"tot_dis\\\"] = [cand.xpath(\\\"tot_dis\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"cas_on_han_beg_of_per\\\"] = [cand.xpath(\\\"cas_on_han_beg_of_per\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"cas_on_han_clo_of_per\\\"] = [cand.xpath(\\\"cas_on_han_clo_of_per\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"net_con\\\"] = [cand.xpath(\\\"net_con\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"net_ope_exp\\\"] = [cand.xpath(\\\"net_ope_exp\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"deb_owe_by_com\\\"] = [cand.xpath(\\\"deb_owe_by_com\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"deb_owe_to_com\\\"] = [cand.xpath(\\\"deb_owe_to_com\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"cov_sta_dat\\\"] = [cand.xpath(\\\"cov_sta_dat\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate[\\\"cov_end_dat\\\"] = [cand.xpath(\\\"cov_end_dat\\\")[0].text for cand in cand_list]\\n\",\n      \"senate_cadidate\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<pre>\\n\",\n        \"&lt;class 'pandas.core.frame.DataFrame'&gt;\\n\",\n        \"Int64Index: 412 entries, 0 to 411\\n\",\n        \"Data columns (total 44 columns):\\n\",\n        \"lin_ima                  412  non-null values\\n\",\n        \"can_id                   412  non-null values\\n\",\n        \"can_nam                  412  non-null values\\n\",\n        \"can_off                  412  non-null values\\n\",\n        \"can_off_sta              412  non-null values\\n\",\n        \"can_par_aff              412  non-null values\\n\",\n        \"can_inc_cha_ope_sea      411  non-null values\\n\",\n        \"ind_ite_con              197  non-null values\\n\",\n        \"ind_uni_con              186  non-null values\\n\",\n        \"ind_con                  204  non-null values\\n\",\n        \"par_com_con              37  non-null values\\n\",\n        \"oth_com_con              116  non-null values\\n\",\n        \"can_con                  101  non-null values\\n\",\n        \"tot_con                  220  non-null values\\n\",\n        \"tra_fro_oth_aut_com      61  non-null values\\n\",\n        \"can_loa                  103  non-null values\\n\",\n        \"oth_loa                  11  non-null values\\n\",\n        \"tot_loa                  102  non-null values\\n\",\n        \"off_to_ope_exp           104  non-null values\\n\",\n        \"off_to_fun               0  non-null values\\n\",\n        \"off_to_leg_acc           0  non-null values\\n\",\n        \"oth_rec                  76  non-null values\\n\",\n        \"tot_rec                  223  non-null values\\n\",\n        \"ope_exp                  221  non-null values\\n\",\n        \"fun_dis                  0  non-null values\\n\",\n        \"exe_leg_acc_dis          0  non-null values\\n\",\n        \"tra_to_oth_aut_com       22  non-null values\\n\",\n        \"can_loa_rep              34  non-null values\\n\",\n        \"oth_loa_rep              4  non-null values\\n\",\n        \"tot_loa_rep              38  non-null values\\n\",\n        \"ind_ref                  117  non-null values\\n\",\n        \"par_com_ref              4  non-null values\\n\",\n        \"oth_com_ref              47  non-null values\\n\",\n        \"tot_con_ref              121  non-null values\\n\",\n        \"oth_dis                  86  non-null values\\n\",\n        \"tot_dis                  221  non-null values\\n\",\n        \"cas_on_han_beg_of_per    69  non-null values\\n\",\n        \"cas_on_han_clo_of_per    192  non-null values\\n\",\n        \"net_con                  215  non-null values\\n\",\n        \"net_ope_exp              217  non-null values\\n\",\n        \"deb_owe_by_com           108  non-null values\\n\",\n        \"deb_owe_to_com           4  non-null values\\n\",\n        \"cov_sta_dat              231  non-null values\\n\",\n        \"cov_end_dat              231  non-null values\\n\",\n        \"dtypes: object(44)\\n\",\n        \"</pre>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 67,\n       \"text\": [\n        \"<class 'pandas.core.frame.DataFrame'>\\n\",\n        \"Int64Index: 412 entries, 0 to 411\\n\",\n        \"Data columns (total 44 columns):\\n\",\n        \"lin_ima                  412  non-null values\\n\",\n        \"can_id                   412  non-null values\\n\",\n        \"can_nam                  412  non-null values\\n\",\n        \"can_off                  412  non-null values\\n\",\n        \"can_off_sta              412  non-null values\\n\",\n        \"can_par_aff              412  non-null values\\n\",\n        \"can_inc_cha_ope_sea      411  non-null values\\n\",\n        \"ind_ite_con              197  non-null values\\n\",\n        \"ind_uni_con              186  non-null values\\n\",\n        \"ind_con                  204  non-null values\\n\",\n        \"par_com_con              37  non-null values\\n\",\n        \"oth_com_con              116  non-null values\\n\",\n        \"can_con                  101  non-null values\\n\",\n        \"tot_con                  220  non-null values\\n\",\n        \"tra_fro_oth_aut_com      61  non-null values\\n\",\n        \"can_loa                  103  non-null values\\n\",\n        \"oth_loa                  11  non-null values\\n\",\n        \"tot_loa                  102  non-null values\\n\",\n        \"off_to_ope_exp           104  non-null values\\n\",\n        \"off_to_fun               0  non-null values\\n\",\n        \"off_to_leg_acc           0  non-null values\\n\",\n        \"oth_rec                  76  non-null values\\n\",\n        \"tot_rec                  223  non-null values\\n\",\n        \"ope_exp                  221  non-null values\\n\",\n        \"fun_dis                  0  non-null values\\n\",\n        \"exe_leg_acc_dis          0  non-null values\\n\",\n        \"tra_to_oth_aut_com       22  non-null values\\n\",\n        \"can_loa_rep              34  non-null values\\n\",\n        \"oth_loa_rep              4  non-null values\\n\",\n        \"tot_loa_rep              38  non-null values\\n\",\n        \"ind_ref                  117  non-null values\\n\",\n        \"par_com_ref              4  non-null values\\n\",\n        \"oth_com_ref              47  non-null values\\n\",\n        \"tot_con_ref              121  non-null values\\n\",\n        \"oth_dis                  86  non-null values\\n\",\n        \"tot_dis                  221  non-null values\\n\",\n        \"cas_on_han_beg_of_per    69  non-null values\\n\",\n        \"cas_on_han_clo_of_per    192  non-null values\\n\",\n        \"net_con                  215  non-null values\\n\",\n        \"net_ope_exp              217  non-null values\\n\",\n        \"deb_owe_by_com           108  non-null values\\n\",\n        \"deb_owe_to_com           4  non-null values\\n\",\n        \"cov_sta_dat              231  non-null values\\n\",\n        \"cov_end_dat              231  non-null values\\n\",\n        \"dtypes: object(44)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 67\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Retrieive YouTube Data for All Candidates\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_state_data(candidates):\\n\",\n      \"    data_set = get_2014_data(candidates)\\n\",\n      \"    t_ds = pd.pivot_table(data_set, values=[\\\"commentCount\\\", \\\"favoriteCount\\\", \\\"dislikeCount\\\", \\\"likeCount\\\", \\\"viewCount\\\"],\\n\",\n      \"                   aggfunc='sum', rows=\\\"candidate_name\\\")\\n\",\n      \"    t_ds[\\\"like_dislike_r\\\"] = t_ds[\\\"likeCount\\\"] / (t_ds[\\\"dislikeCount\\\"] + t_ds[\\\"likeCount\\\"])\\n\",\n      \"    t_ds[\\\"views_share\\\"] = t_ds[\\\"viewCount\\\"] / t_ds[\\\"viewCount\\\"].sum()\\n\",\n      \"    t_ds[\\\"msgs_share\\\"] = t_ds[\\\"commentCount\\\"] / t_ds[\\\"commentCount\\\"].sum()\\n\",\n      \"    t_ds[\\\"likes_share\\\"] = t_ds[\\\"likeCount\\\"] / t_ds[\\\"likeCount\\\"].sum()\\n\",\n      \"    t_ds[\\\"dislikes_share\\\"] = t_ds[\\\"dislikeCount\\\"] / t_ds[\\\"dislikeCount\\\"].sum()\\n\",\n      \"    print t_ds\\n\",\n      \"    return t_ds\\n\",\n      \"\\n\",\n      \"def fix_name(val_name):\\n\",\n      \"    val_names = val_name.split(\\\", \\\")\\n\",\n      \"    return \\\"%s %s\\\" % (val_names[1].split(\\\" \\\")[0].capitalize(), val_names[0].capitalize())\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 69\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"values_list = []\\n\",\n      \"for index, state in zip(class_2_senators.index, class_2_senators[\\\"state\\\"]):\\n\",\n      \"    print \\\"%s: %s\\\" % (state,\\n\",\n      \"                           class_2_senators[\\\"member_full\\\"][index])\\n\",\n      \"    candidates = senate_cadidate[senate_cadidate[\\\"can_off_sta\\\"]==state]\\n\",\n      \"    candidates = candidates[~senate_cadidate[\\\"tot_rec\\\"].isnull()]\\n\",\n      \"    candidates[\\\"tot_rec_num\\\"] = candidates[\\\"tot_rec\\\"].apply(lambda x: x[1:].replace(\\\",\\\",\\\"\\\")).astype(np.float64)\\n\",\n      \"    top_candidates = candidates.sort(\\\"tot_rec_num\\\", ascending=False)[:2][[\\\"can_nam\\\",\\n\",\n      \"                                                                      \\\"can_par_aff\\\",\\n\",\n      \"                                                                      \\\"can_inc_cha_ope_sea\\\",\\n\",\n      \"                                                                      \\\"tot_rec_num\\\",\\n\",\n      \"                                                                      \\\"can_off_sta\\\"]]\\n\",\n      \"    top_candidates[\\\"full_name\\\"] = [fix_name(name) for name in top_candidates.values[:,0]]\\n\",\n      \"    top_candidates = top_candidates.sort(\\\"full_name\\\")\\n\",\n      \"    print top_candidates[\\\"full_name\\\"]\\n\",\n      \"    try:\\n\",\n      \"        ds = get_state_data([fix_name(name) for name in top_candidates.values[:,0]])\\n\",\n      \"        ds[\\\"state\\\"] = state\\n\",\n      \"        ds[\\\"party\\\"] = top_candidates[\\\"can_par_aff\\\"].values\\n\",\n      \"        ds[\\\"donations\\\"] = top_candidates[\\\"tot_rec_num\\\"].values\\n\",\n      \"        values_list.append(ds)\\n\",\n      \"    except:\\n\",\n      \"        print \\\"NA\\\"\\n\",\n      \"        \\n\",\n      \"        \\n\",\n      \"sentate_2014 = pd.concat(values_list)\\n\",\n      \"sentate_2014\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"AK: Begich (D-AK)\\n\",\n        \"359    Dan Sullivan\\n\",\n        \"27      Mark Begich\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Dan Sullivan             228            90              0        496     189278        0.846416     0.644151   \\n\",\n        \"Mark Begich               65            96              0        157     104563        0.620553     0.355849   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Dan Sullivan      0.778157     0.759571        0.483871  \\n\",\n        \"Mark Begich       0.221843     0.240429        0.516129  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"AL: Sessions (R-AL)\\n\",\n        \"335    Jeff Sessions\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Jeff Sessions            137            10              0         29       4800         0.74359            1   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Jeff Sessions            1            1               1  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"AR: Pryor (D-AR)\\n\",\n        \"290       Mark Pryor\\n\",\n        \"89     Thomas Cotton\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Mark Pryor               139            37              0        175     228610        0.825472     0.263103   \\n\",\n        \"Thomas Cotton            152            73              0        270     640288        0.787172     0.736897   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Mark Pryor        0.477663     0.393258        0.336364  \\n\",\n        \"Thomas Cotton     0.522337     0.606742        0.663636  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"CO: Udall (D-CO)\\n\",\n        \"137    Cory Gardner\\n\",\n        \"375      Mark Udall\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Cory Gardner             327           157              0        467     282673        0.748397     0.659577   \\n\",\n        \"Mark Udall               263           455              0        411     145894        0.474596     0.340423   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Cory Gardner      0.554237     0.531891        0.256536  \\n\",\n        \"Mark Udall        0.445763     0.468109        0.743464  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"DE: Coons (D-DE)\\n\",\n        \"85     Christopher Coons\\n\",\n        \"379           Kevin Wade\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                   commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                    \\n\",\n        \"Christopher Coons           200            20              0        110      20040        0.846154      0.54844   \\n\",\n        \"Kevin Wade                  110            30              0        190      16500        0.863636      0.45156   \\n\",\n        \"\\n\",\n        \"                   msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                              \\n\",\n        \"Christopher Coons    0.645161     0.366667             0.4  \\n\",\n        \"Kevin Wade           0.354839     0.633333             0.6  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"GA: Chambliss (R-GA)\\n\",\n        \"197    John Kingston\\n\",\n        \"261        Mary Nunn\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"John Kingston            113             4              0         60       2597        0.937500     0.727247   \\n\",\n        \"Mary Nunn                 70             8              0         18        974        0.692308     0.272753   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"John Kingston     0.617486     0.769231        0.333333  \\n\",\n        \"Mary Nunn         0.382514     0.230769        0.666667  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"IA: Harkin (D-IA)\\n\",\n        \"43     Bruce Braley\\n\",\n        \"180     Mark Jacobs\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Bruce Braley             260            70              0        278      87802        0.798851     0.056535   \\n\",\n        \"Mark Jacobs             7341          1053              0      66086    1465259        0.984316     0.943465   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Bruce Braley      0.034206     0.004189        0.062333  \\n\",\n        \"Mark Jacobs       0.965794     0.995811        0.937667  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"ID: Risch (R-ID)\\n\",\n        \"253    Briane Mitchell\\n\",\n        \"306        James Risch\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"NA\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"IL: Durbin (D-IL)\\n\",\n        \"264    James Oberweis\\n\",\n        \"115    Richard Durbin\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"NA\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"KS: Roberts (R-KS)\\n\",\n        \"403    Milton Wolf\\n\",\n        \"307    Pat Roberts\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Milton Wolf               20            20              0         20       5210        0.500000     0.039943   \\n\",\n        \"Pat Roberts              488            98              0       1000     125227        0.910747     0.960057   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Milton Wolf        0.03937     0.019608        0.169492  \\n\",\n        \"Pat Roberts        0.96063     0.980392        0.830508  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"KY: McConnell (R-KY)\\n\",\n        \"152      Alison Grimes\\n\",\n        \"239    Mitch Mcconnell\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Alison Grimes            1362           376              0       2797     706090        0.881500     0.432752   \\n\",\n        \"Mitch Mcconnell          2247           291              0       4839     925538        0.943275     0.567248   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                            \\n\",\n        \"Alison Grimes       0.37739     0.366291        0.563718  \\n\",\n        \"Mitch Mcconnell     0.62261     0.633709        0.436282  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"LA: Landrieu (D-LA)\\n\",\n        \"206      Mary Landrieu\\n\",\n        \"69     William Cassidy\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Mary Landrieu             744            79              0       2113     407306        0.963960     0.984916   \\n\",\n        \"William Cassidy            84             1              0         94       6238        0.989474     0.015084   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                            \\n\",\n        \"Mary Landrieu      0.898551     0.957408          0.9875  \\n\",\n        \"William Cassidy    0.101449     0.042592          0.0125  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MA: Markey (D-MA)\\n\",\n        \"231    Edward Markey\\n\",\n        \"146    Gabriel Gomez\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Edward Markey              0            10              0         70       7994        0.875000     0.004272   \\n\",\n        \"Gabriel Gomez            920           515              0       1235    1863296        0.705714     0.995728   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Edward Markey            0      0.05364        0.019048  \\n\",\n        \"Gabriel Gomez            1      0.94636        0.980952  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"ME: Collins (R-ME)\\n\",\n        \"29    Shenna Bellows\\n\",\n        \"80     Susan Collins\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Shenna Bellows            15             0              0          6      57891         1.00000     0.163068   \\n\",\n        \"Susan Collins             90            18              0        351     297121         0.95122     0.836932   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Shenna Bellows    0.142857     0.016807               0  \\n\",\n        \"Susan Collins     0.857143     0.983193               1  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MI: Levin (D-MI)\\n\",\n        \"280    Gary Peters\\n\",\n        \"205     Terri Land\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Gary Peters               89            45              0        424      37748        0.904051     0.012098   \\n\",\n        \"Terri Land               130           558              0       1784    3082566        0.761742     0.987902   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Gary Peters       0.406393     0.192029        0.074627  \\n\",\n        \"Terri Land        0.593607     0.807971        0.925373  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MN: Franken (D-MN)\\n\",\n        \"133          Al Franken\\n\",\n        \"242    Michael Mcfadden\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                   \\n\",\n        \"Al Franken                 186            79              0        235     262735        0.748408     0.894219   \\n\",\n        \"Michael Mcfadden            57            40              0        106      31080        0.726027     0.105781   \\n\",\n        \"\\n\",\n        \"                  msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                             \\n\",\n        \"Al Franken          0.765432      0.68915        0.663866  \\n\",\n        \"Michael Mcfadden    0.234568      0.31085        0.336134  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MS: Cochran (R-MS)\\n\",\n        \"241    Christopher Mcdaniel\\n\",\n        \"79             Thad Cochran\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                      commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                       \\n\",\n        \"Christopher Mcdaniel             0             0              0          6       1062        1.000000     0.061698   \\n\",\n        \"Thad Cochran                   109            10              0         55      16151        0.846154     0.938302   \\n\",\n        \"\\n\",\n        \"                      msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                                 \\n\",\n        \"Christopher Mcdaniel           0     0.098361               0  \\n\",\n        \"Thad Cochran                   1     0.901639               1  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MT: Walsh (D-MT)\\n\",\n        \"384       John Walsh\\n\",\n        \"104    Steven Daines\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"NA\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NC: Hagan (D-NC)\\n\",\n        \"155      Kay Hagan\\n\",\n        \"370    Thom Tillis\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Kay Hagan                278           128              0        271      51704        0.679198     0.056229   \\n\",\n        \"Thom Tillis              231           206              0        561     867825        0.731421     0.943771   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Kay Hagan         0.546169     0.325721        0.383234  \\n\",\n        \"Thom Tillis       0.453831     0.674279        0.616766  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NE: Johanns (R-NE)\\n\",\n        \"325    Benjamin Sasse\\n\",\n        \"111      Sid Dinsdale\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"NA\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NH: Shaheen (D-NH)\\n\",\n        \"336    Jeanne Shaheen\\n\",\n        \"50        Scott Brown\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Jeanne Shaheen            30            34              0         84      37123        0.711864     0.085959   \\n\",\n        \"Scott Brown              721            96              0       2465     394746        0.962515     0.914041   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Jeanne Shaheen    0.039947     0.032954        0.261538  \\n\",\n        \"Scott Brown       0.960053     0.967046        0.738462  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NJ: Booker (D-NJ)\\n\",\n        \"36       Cory Booker\\n\",\n        \"272    Frank Pallone\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Cory Booker              299            43              0        688      93738        0.941176     0.997956   \\n\",\n        \"Frank Pallone              1             0              0          0        192             inf     0.002044   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Cory Booker       0.996667            1               1  \\n\",\n        \"Frank Pallone     0.003333            0               0  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NM: Udall (D-NM)\\n\",\n        \"391    Allen Weh\\n\",\n        \"376    Tom Udall\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Allen Weh                800            70              0        250    1950780        0.781250     0.987187   \\n\",\n        \"Tom Udall                700            40              0        670      25320        0.943662     0.012813   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Allen Weh         0.533333     0.271739        0.636364  \\n\",\n        \"Tom Udall         0.466667     0.728261        0.363636  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"OK: Inhofe (R-OK)\\n\",\n        \"178      James Inhofe\\n\",\n        \"207    James Lankford\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"James Inhofe            1927           412              0       2116     172284        0.837025     0.985471   \\n\",\n        \"James Lankford            30            10              0         10       2540        0.500000     0.014529   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"James Inhofe       0.98467     0.995296        0.976303  \\n\",\n        \"James Lankford     0.01533     0.004704        0.023697  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"OR: Merkley (D-OR)\\n\",\n        \"249    Jeffrey Merkley\\n\",\n        \"392       Monica Wehby\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Jeffrey Merkley           156            36              0        392      43632        0.915888     0.310716   \\n\",\n        \"Monica Wehby              135           305              0        660      96792        0.683938     0.689284   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                            \\n\",\n        \"Jeffrey Merkley    0.536082     0.372624        0.105572  \\n\",\n        \"Monica Wehby       0.463918     0.627376        0.894428  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"RI: Reed (D-RI)\\n\",\n        \"301        Jack Reed\\n\",\n        \"410    Mark Zaccaria\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Jack Reed                 70             9              0        180      41526        0.952381     0.945277   \\n\",\n        \"Mark Zaccaria             10             0              0          0       2404             inf     0.054723   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Jack Reed            0.875            1               1  \\n\",\n        \"Mark Zaccaria        0.125            0               0  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"SC: Graham (R-SC)\\n\",\n        \"148    Lindsey Graham\\n\",\n        \"333     Timothy Scott\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Lindsey Graham          1274           184              0       1555     125933        0.894192     0.877735   \\n\",\n        \"Timothy Scott            198             0              0        332      17542        1.000000     0.122265   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"Lindsey Graham    0.865489     0.824059               1  \\n\",\n        \"Timothy Scott     0.134511     0.175941               0  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"SD: Johnson (D-SD)\\n\",\n        \"40     Annette Bosworth\\n\",\n        \"315       Marion Rounds\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"NA\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"TN: Alexander (R-TN)\\n\",\n        \"131       George Flinn\\n\",\n        \"8      Lamar Alexander\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"George Flinn              279            27              0         51       5247        0.653846     0.437505   \\n\",\n        \"Lamar Alexander            37             4              0         49       6746        0.924528     0.562495   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                            \\n\",\n        \"George Flinn       0.882911         0.51        0.870968  \\n\",\n        \"Lamar Alexander    0.117089         0.49        0.129032  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"TX: Cornyn (R-TX)\\n\",\n        \"7     David Alameel\\n\",\n        \"88      John Cornyn\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"David Alameel              3             2              0          2        105        0.500000     0.004871   \\n\",\n        \"John Cornyn              166            20              0        260      21450        0.928571     0.995129   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                           \\n\",\n        \"David Alameel     0.017751     0.007634        0.090909  \\n\",\n        \"John Cornyn       0.982249     0.992366        0.909091  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"VA: Warner (D-VA)\\n\",\n        \"140    Edward Gillespie\\n\",\n        \"386         Mark Warner\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                   \\n\",\n        \"Edward Gillespie             2             0              0          8        434        1.000000     0.021218   \\n\",\n        \"Mark Warner                 53             9              0         44      20020        0.830189     0.978782   \\n\",\n        \"\\n\",\n        \"                  msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                             \\n\",\n        \"Edward Gillespie    0.036364     0.153846               0  \\n\",\n        \"Mark Warner         0.963636     0.846154               1  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"WV: Rockefeller (D-WV)\\n\",\n        \"366    Natalie Tennant\\n\",\n        \"61      Shelley Capito\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Natalie Tennant           350           271              0        830     149271        0.753860     0.945921   \\n\",\n        \"Shelley Capito             48            24              0        152       8534        0.863636     0.054079   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                            \\n\",\n        \"Natalie Tennant    0.879397     0.845214        0.918644  \\n\",\n        \"Shelley Capito     0.120603     0.154786        0.081356  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"WY: Enzi (R-WY)\\n\",\n        \"72     Elizabeth Cheney\\n\",\n        \"121        Michael Enzi\\n\",\n        \"Name: full_name, dtype: object\\n\",\n        \"                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                   \\n\",\n        \"Elizabeth Cheney            48             0              0         30       4482               1     0.749875   \\n\",\n        \"Michael Enzi                25             0              0         70       1495               1     0.250125   \\n\",\n        \"\\n\",\n        \"                  msgs_share  likes_share  dislikes_share  \\n\",\n        \"candidate_name                                             \\n\",\n        \"Elizabeth Cheney    0.657534          0.3             inf  \\n\",\n        \"Michael Enzi        0.342466          0.7             inf  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      },\n      {\n       \"html\": [\n        \"<pre>\\n\",\n        \"&lt;class 'pandas.core.frame.DataFrame'&gt;\\n\",\n        \"Index: 55 entries, Dan Sullivan to Michael Enzi\\n\",\n        \"Data columns (total 13 columns):\\n\",\n        \"commentCount      55  non-null values\\n\",\n        \"dislikeCount      55  non-null values\\n\",\n        \"favoriteCount     55  non-null values\\n\",\n        \"likeCount         55  non-null values\\n\",\n        \"viewCount         55  non-null values\\n\",\n        \"like_dislike_r    55  non-null values\\n\",\n        \"views_share       55  non-null values\\n\",\n        \"msgs_share        55  non-null values\\n\",\n        \"likes_share       55  non-null values\\n\",\n        \"dislikes_share    55  non-null values\\n\",\n        \"state             55  non-null values\\n\",\n        \"party             55  non-null values\\n\",\n        \"donations         55  non-null values\\n\",\n        \"dtypes: float64(6), int64(5), object(2)\\n\",\n        \"</pre>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 79,\n       \"text\": [\n        \"<class 'pandas.core.frame.DataFrame'>\\n\",\n        \"Index: 55 entries, Dan Sullivan to Michael Enzi\\n\",\n        \"Data columns (total 13 columns):\\n\",\n        \"commentCount      55  non-null values\\n\",\n        \"dislikeCount      55  non-null values\\n\",\n        \"favoriteCount     55  non-null values\\n\",\n        \"likeCount         55  non-null values\\n\",\n        \"viewCount         55  non-null values\\n\",\n        \"like_dislike_r    55  non-null values\\n\",\n        \"views_share       55  non-null values\\n\",\n        \"msgs_share        55  non-null values\\n\",\n        \"likes_share       55  non-null values\\n\",\n        \"dislikes_share    55  non-null values\\n\",\n        \"state             55  non-null values\\n\",\n        \"party             55  non-null values\\n\",\n        \"donations         55  non-null values\\n\",\n        \"dtypes: float64(6), int64(5), object(2)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 79\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"class_2_senators[\\\"state\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 94,\n       \"text\": [\n        \"4     AK\\n\",\n        \"84    AL\\n\",\n        \"73    AR\\n\",\n        \"91    CO\\n\",\n        \"22    DE\\n\",\n        \"17    GA\\n\",\n        \"38    IA\\n\",\n        \"76    ID\\n\",\n        \"28    IL\\n\",\n        \"77    KS\\n\",\n        \"62    KY\\n\",\n        \"54    LA\\n\",\n        \"59    MA\\n\",\n        \"21    ME\\n\",\n        \"57    MI\\n\",\n        \"33    MN\\n\",\n        \"20    MS\\n\",\n        \"94    MT\\n\",\n        \"37    NC\\n\",\n        \"47    NE\\n\",\n        \"85    NH\\n\",\n        \"8     NJ\\n\",\n        \"92    NM\\n\",\n        \"45    OK\\n\",\n        \"64    OR\\n\",\n        \"74    RI\\n\",\n        \"35    SC\\n\",\n        \"49    SD\\n\",\n        \"0     TN\\n\",\n        \"24    TX\\n\",\n        \"95    VA\\n\",\n        \"78    WV\\n\",\n        \"29    WY\\n\",\n        \"Name: state, dtype: object\"\n       ]\n      }\n     ],\n     \"prompt_number\": 94\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x_column = \\\"views_share\\\"\\n\",\n      \"y_column = \\\"viewCount\\\"\\n\",\n      \"s_column = \\\"donations\\\"\\n\",\n      \"\\n\",\n      \"color_dict = {\\\"DEM\\\": \\\"b\\\", \\\"REP\\\": \\\"r\\\", \\\"IND\\\":\\\"g\\\", \\\"NPA\\\": \\\"g\\\", \\\"DFL\\\": \\\"g\\\"}\\n\",\n      \"            \\n\",\n      \"plt.figure(figsize=(18,12))\\n\",\n      \"\\n\",\n      \"for party in sentate_2014[\\\"party\\\"].unique():\\n\",\n      \"    cands = sentate_2014[sentate_2014[\\\"party\\\"]==party]\\n\",\n      \"    x = cands[x_column]\\n\",\n      \"    y = cands[y_column]\\n\",\n      \"    size = sentate_2014[sentate_2014[\\\"party\\\"]==party][s_column] / 3000000\\n\",\n      \"    plt.scatter(x,y, s=(np.array(size)) * 1000, c=color_dict[party], alpha=0.5)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"    \\n\",\n      \"print plt.ylim()[1]\\n\",\n      \"plt.vlines(0.5, ymin=1, ymax=plt.ylim()[1]*0.9)\\n\",\n      \"\\n\",\n      \"prejected_winners = sentate_2014[sentate_2014[x_column]>0.5][\\\"party\\\"].value_counts()\\n\",\n      \"\\n\",\n      \"result_text = []\\n\",\n      \"for item in sentate_2014.iterrows():#[sentate_2014[x_column]>0.5].iterrows():\\n\",\n      \"    plt.annotate(item[1][\\\"state\\\"], xy=(item[1][x_column], item[1][y_column]))\\n\",\n      \"\\n\",\n      \"for item in sentate_2014[sentate_2014[x_column]>0.5].iterrows():\\n\",\n      \"    result_text += [\\\"%s: %s (%s) - %0.1f%%\\\" % (item[1][\\\"state\\\"], item[0], item[1][\\\"party\\\"], item[1][\\\"views_share\\\"] * 100.)]\\n\",\n      \"result_text = \\\"\\\\n\\\".join(result_text)\\n\",\n      \"prejected_winners = \\\"\\\\n\\\".join([\\\"%s:%s\\\" % (party, value) for party, value in zip(prejected_winners.index, prejected_winners.values)])\\n\",\n      \"\\n\",\n      \"plt.annotate(prejected_winners, xy=(.65,plt.ylim()[1]*0.8))\\n\",\n      \"\\n\",\n      \"plt.annotate(result_text, xy=(.8, 1.5))\\n\",\n      \"\\n\",\n      \"plt.xlabel(x_column)\\n\",\n      \"plt.ylabel(y_column + \\\" (Log Scale)\\\")\\n\",\n      \"plt.grid()\\n\",\n      \"plt.yscale(\\\"log\\\")\\n\",\n      \"#plt.axis(\\\"tight\\\")\\n\",\n      \"plt.title(\\\"Senate 2014 Elections Forecast (Size is relative and represents the amount of donations)\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"3500000.0\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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TAG+yyTYIyZgn++8lkZY/5ijJmBdad9LO0P/uvP59x6+/yNr+8BF2N1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\\nERGRoPpfPa5xYbvut9QAAAAASUVORK5CYII=\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff0758e6cd0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 97\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sentate_2014[sentate_2014[x_column]>0.5]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>commentCount</th>\\n\",\n        \"      <th>dislikeCount</th>\\n\",\n        \"      <th>favoriteCount</th>\\n\",\n        \"      <th>likeCount</th>\\n\",\n        \"      <th>viewCount</th>\\n\",\n        \"      <th>like_dislike_r</th>\\n\",\n        \"      <th>views_share</th>\\n\",\n        \"      <th>msgs_share</th>\\n\",\n        \"      <th>likes_share</th>\\n\",\n        \"      <th>dislikes_share</th>\\n\",\n        \"      <th>state</th>\\n\",\n        \"      <th>party</th>\\n\",\n        \"      <th>donations</th>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>candidate_name</th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"      <th></th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Dan Sullivan</th>\\n\",\n        \"      <td>  360</td>\\n\",\n        \"      <td>   94</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   623</td>\\n\",\n        \"      <td>  165715</td>\\n\",\n        \"      <td> 0.868898</td>\\n\",\n        \"      <td> 0.613568</td>\\n\",\n        \"      <td> 0.814480</td>\\n\",\n        \"      <td> 0.774876</td>\\n\",\n        \"      <td> 0.494737</td>\\n\",\n        \"      <td> AK</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  6340422.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Jeff Sessions</th>\\n\",\n        \"      <td>  142</td>\\n\",\n        \"      <td>   10</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>    36</td>\\n\",\n        \"      <td>    5114</td>\\n\",\n        \"      <td> 0.782609</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> AL</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  1115688.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Thomas Cotton</th>\\n\",\n        \"      <td>  100</td>\\n\",\n        \"      <td>   91</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   195</td>\\n\",\n        \"      <td>  797919</td>\\n\",\n        \"      <td> 0.681818</td>\\n\",\n        \"      <td> 0.814496</td>\\n\",\n        \"      <td> 0.434783</td>\\n\",\n        \"      <td> 0.537190</td>\\n\",\n        \"      <td> 0.722222</td>\\n\",\n        \"      <td> AR</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  7097224.06</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Cory Gardner</th>\\n\",\n        \"      <td>  316</td>\\n\",\n        \"      <td>  167</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   450</td>\\n\",\n        \"      <td>  234223</td>\\n\",\n        \"      <td> 0.729335</td>\\n\",\n        \"      <td> 0.628876</td>\\n\",\n        \"      <td> 0.633267</td>\\n\",\n        \"      <td> 0.530660</td>\\n\",\n        \"      <td> 0.268058</td>\\n\",\n        \"      <td> CO</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td> 10420571.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Christopher Coons</th>\\n\",\n        \"      <td>  190</td>\\n\",\n        \"      <td>   20</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   110</td>\\n\",\n        \"      <td>   19950</td>\\n\",\n        \"      <td> 0.846154</td>\\n\",\n        \"      <td> 0.559278</td>\\n\",\n        \"      <td> 0.627063</td>\\n\",\n        \"      <td> 0.379310</td>\\n\",\n        \"      <td> 0.400000</td>\\n\",\n        \"      <td> DE</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  4173447.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>John Kingston</th>\\n\",\n        \"      <td>   37</td>\\n\",\n        \"      <td>    2</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>    17</td>\\n\",\n        \"      <td>     717</td>\\n\",\n        \"      <td> 0.894737</td>\\n\",\n        \"      <td> 0.540724</td>\\n\",\n        \"      <td> 0.451220</td>\\n\",\n        \"      <td> 0.447368</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> GA</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  9211931.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Mark Jacobs</th>\\n\",\n        \"      <td> 7334</td>\\n\",\n        \"      <td> 1052</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 66064</td>\\n\",\n        \"      <td> 1464016</td>\\n\",\n        \"      <td> 0.984326</td>\\n\",\n        \"      <td> 0.943058</td>\\n\",\n        \"      <td> 0.959571</td>\\n\",\n        \"      <td> 0.995765</td>\\n\",\n        \"      <td> 0.932624</td>\\n\",\n        \"      <td> IA</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  4810813.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Pat Roberts</th>\\n\",\n        \"      <td>  256</td>\\n\",\n        \"      <td>   83</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   374</td>\\n\",\n        \"      <td>   97678</td>\\n\",\n        \"      <td> 0.818381</td>\\n\",\n        \"      <td> 0.943867</td>\\n\",\n        \"      <td> 0.733524</td>\\n\",\n        \"      <td> 0.869767</td>\\n\",\n        \"      <td> 0.768519</td>\\n\",\n        \"      <td> KS</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  1068018.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Mitch Mcconnell</th>\\n\",\n        \"      <td> 2197</td>\\n\",\n        \"      <td>  272</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>  4760</td>\\n\",\n        \"      <td>  893077</td>\\n\",\n        \"      <td> 0.945946</td>\\n\",\n        \"      <td> 0.569208</td>\\n\",\n        \"      <td> 0.632230</td>\\n\",\n        \"      <td> 0.640387</td>\\n\",\n        \"      <td> 0.428346</td>\\n\",\n        \"      <td> KY</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td> 11353760.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Mary Landrieu</th>\\n\",\n        \"      <td>  745</td>\\n\",\n        \"      <td>   84</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>  2015</td>\\n\",\n        \"      <td>  395161</td>\\n\",\n        \"      <td> 0.959981</td>\\n\",\n        \"      <td> 0.986888</td>\\n\",\n        \"      <td> 0.937107</td>\\n\",\n        \"      <td> 0.971084</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> LA</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td> 10190144.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Gabriel Gomez</th>\\n\",\n        \"      <td>  899</td>\\n\",\n        \"      <td>  514</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>  1239</td>\\n\",\n        \"      <td> 1855996</td>\\n\",\n        \"      <td> 0.706788</td>\\n\",\n        \"      <td> 0.995771</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> 0.946524</td>\\n\",\n        \"      <td> 0.980916</td>\\n\",\n        \"      <td> MA</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  4755654.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Susan Collins</th>\\n\",\n        \"      <td>   81</td>\\n\",\n        \"      <td>    9</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   351</td>\\n\",\n        \"      <td>  291078</td>\\n\",\n        \"      <td> 0.975000</td>\\n\",\n        \"      <td> 0.834797</td>\\n\",\n        \"      <td> 0.658537</td>\\n\",\n        \"      <td> 0.983193</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> ME</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  1333016.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Terri Land</th>\\n\",\n        \"      <td>  110</td>\\n\",\n        \"      <td>  560</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>  1660</td>\\n\",\n        \"      <td> 3077280</td>\\n\",\n        \"      <td> 0.747748</td>\\n\",\n        \"      <td> 0.988351</td>\\n\",\n        \"      <td> 0.578947</td>\\n\",\n        \"      <td> 0.783019</td>\\n\",\n        \"      <td> 0.918033</td>\\n\",\n        \"      <td> MI</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  6994603.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Al Franken</th>\\n\",\n        \"      <td>  141</td>\\n\",\n        \"      <td>   71</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   208</td>\\n\",\n        \"      <td>  260800</td>\\n\",\n        \"      <td> 0.745520</td>\\n\",\n        \"      <td> 0.897435</td>\\n\",\n        \"      <td> 0.762162</td>\\n\",\n        \"      <td> 0.753623</td>\\n\",\n        \"      <td> 0.639640</td>\\n\",\n        \"      <td> MN</td>\\n\",\n        \"      <td> DFL</td>\\n\",\n        \"      <td> 15126268.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Thad Cochran</th>\\n\",\n        \"      <td>   74</td>\\n\",\n        \"      <td>   10</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>    35</td>\\n\",\n        \"      <td>   15199</td>\\n\",\n        \"      <td> 0.777778</td>\\n\",\n        \"      <td> 0.955972</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> 0.897436</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> MS</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  2727209.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Thom Tillis</th>\\n\",\n        \"      <td>  253</td>\\n\",\n        \"      <td>  194</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   586</td>\\n\",\n        \"      <td>  797943</td>\\n\",\n        \"      <td> 0.751282</td>\\n\",\n        \"      <td> 0.950310</td>\\n\",\n        \"      <td> 0.496078</td>\\n\",\n        \"      <td> 0.716381</td>\\n\",\n        \"      <td> 0.619808</td>\\n\",\n        \"      <td> NC</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  4764110.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Scott Brown</th>\\n\",\n        \"      <td>  708</td>\\n\",\n        \"      <td>   92</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>  2456</td>\\n\",\n        \"      <td>  388637</td>\\n\",\n        \"      <td> 0.963893</td>\\n\",\n        \"      <td> 0.916956</td>\\n\",\n        \"      <td> 0.964578</td>\\n\",\n        \"      <td> 0.967310</td>\\n\",\n        \"      <td> 0.736000</td>\\n\",\n        \"      <td> NH</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  3686708.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Cory Booker</th>\\n\",\n        \"      <td>  355</td>\\n\",\n        \"      <td>   60</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   894</td>\\n\",\n        \"      <td>  130143</td>\\n\",\n        \"      <td> 0.937107</td>\\n\",\n        \"      <td> 0.998527</td>\\n\",\n        \"      <td> 0.997191</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> NJ</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td> 16167874.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Allen Weh</th>\\n\",\n        \"      <td>  821</td>\\n\",\n        \"      <td>   71</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   256</td>\\n\",\n        \"      <td> 1949518</td>\\n\",\n        \"      <td> 0.782875</td>\\n\",\n        \"      <td> 0.987909</td>\\n\",\n        \"      <td> 0.539776</td>\\n\",\n        \"      <td> 0.279476</td>\\n\",\n        \"      <td> 0.702970</td>\\n\",\n        \"      <td> NM</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  5050539.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>James Inhofe</th>\\n\",\n        \"      <td> 1905</td>\\n\",\n        \"      <td>  406</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>  2094</td>\\n\",\n        \"      <td>  170226</td>\\n\",\n        \"      <td> 0.837600</td>\\n\",\n        \"      <td> 0.981288</td>\\n\",\n        \"      <td> 0.968480</td>\\n\",\n        \"      <td> 0.984023</td>\\n\",\n        \"      <td> 0.966667</td>\\n\",\n        \"      <td> OK</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  2811701.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Monica Wehby</th>\\n\",\n        \"      <td>  115</td>\\n\",\n        \"      <td>  295</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   660</td>\\n\",\n        \"      <td>   95280</td>\\n\",\n        \"      <td> 0.691099</td>\\n\",\n        \"      <td> 0.702572</td>\\n\",\n        \"      <td> 0.481172</td>\\n\",\n        \"      <td> 0.662651</td>\\n\",\n        \"      <td> 0.891239</td>\\n\",\n        \"      <td> OR</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  2049732.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Jack Reed</th>\\n\",\n        \"      <td>   90</td>\\n\",\n        \"      <td>    4</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   128</td>\\n\",\n        \"      <td>   19412</td>\\n\",\n        \"      <td> 0.969697</td>\\n\",\n        \"      <td> 0.910763</td>\\n\",\n        \"      <td> 0.865385</td>\\n\",\n        \"      <td> 0.969697</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> RI</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  2833802.97</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Lindsey Graham</th>\\n\",\n        \"      <td> 1089</td>\\n\",\n        \"      <td>  135</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>  1412</td>\\n\",\n        \"      <td>  119855</td>\\n\",\n        \"      <td> 0.912734</td>\\n\",\n        \"      <td> 0.891586</td>\\n\",\n        \"      <td> 0.840927</td>\\n\",\n        \"      <td> 0.811494</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> SC</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  6788544.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Lamar Alexander</th>\\n\",\n        \"      <td>   34</td>\\n\",\n        \"      <td>    4</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>    48</td>\\n\",\n        \"      <td>    6291</td>\\n\",\n        \"      <td> 0.923077</td>\\n\",\n        \"      <td> 0.590538</td>\\n\",\n        \"      <td> 0.161905</td>\\n\",\n        \"      <td> 0.578313</td>\\n\",\n        \"      <td> 0.173913</td>\\n\",\n        \"      <td> TN</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  1812250.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>John Cornyn</th>\\n\",\n        \"      <td>  166</td>\\n\",\n        \"      <td>   20</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   260</td>\\n\",\n        \"      <td>   21110</td>\\n\",\n        \"      <td> 0.928571</td>\\n\",\n        \"      <td> 0.995943</td>\\n\",\n        \"      <td> 0.982249</td>\\n\",\n        \"      <td> 0.996169</td>\\n\",\n        \"      <td> 0.909091</td>\\n\",\n        \"      <td> TX</td>\\n\",\n        \"      <td> DEM</td>\\n\",\n        \"      <td>  9673572.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Natalie Tennant</th>\\n\",\n        \"      <td>  210</td>\\n\",\n        \"      <td>  110</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   690</td>\\n\",\n        \"      <td>  159430</td>\\n\",\n        \"      <td> 0.862500</td>\\n\",\n        \"      <td> 0.969238</td>\\n\",\n        \"      <td> 0.860656</td>\\n\",\n        \"      <td> 0.873418</td>\\n\",\n        \"      <td> 0.873016</td>\\n\",\n        \"      <td> WV</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  5482547.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>Elizabeth Cheney</th>\\n\",\n        \"      <td>   30</td>\\n\",\n        \"      <td>    0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>    20</td>\\n\",\n        \"      <td>    3900</td>\\n\",\n        \"      <td> 1.000000</td>\\n\",\n        \"      <td> 0.722892</td>\\n\",\n        \"      <td> 0.545455</td>\\n\",\n        \"      <td> 0.222222</td>\\n\",\n        \"      <td>      inf</td>\\n\",\n        \"      <td> WY</td>\\n\",\n        \"      <td> REP</td>\\n\",\n        \"      <td>  3016825.00</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 58,\n       \"text\": [\n        \"                   commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                    \\n\",\n        \"Dan Sullivan                360            94              0        623     165715        0.868898     0.613568   \\n\",\n        \"Jeff Sessions               142            10              0         36       5114        0.782609     1.000000   \\n\",\n        \"Thomas Cotton               100            91              0        195     797919        0.681818     0.814496   \\n\",\n        \"Cory Gardner                316           167              0        450     234223        0.729335     0.628876   \\n\",\n        \"Christopher Coons           190            20              0        110      19950        0.846154     0.559278   \\n\",\n        \"John Kingston                37             2              0         17        717        0.894737     0.540724   \\n\",\n        \"Mark Jacobs                7334          1052              0      66064    1464016        0.984326     0.943058   \\n\",\n        \"Pat Roberts                 256            83              0        374      97678        0.818381     0.943867   \\n\",\n        \"Mitch Mcconnell            2197           272              0       4760     893077        0.945946     0.569208   \\n\",\n        \"Mary Landrieu               745            84              0       2015     395161        0.959981     0.986888   \\n\",\n        \"Gabriel Gomez               899           514              0       1239    1855996        0.706788     0.995771   \\n\",\n        \"Susan Collins                81             9              0        351     291078        0.975000     0.834797   \\n\",\n        \"Terri Land                  110           560              0       1660    3077280        0.747748     0.988351   \\n\",\n        \"Al Franken                  141            71              0        208     260800        0.745520     0.897435   \\n\",\n        \"Thad Cochran                 74            10              0         35      15199        0.777778     0.955972   \\n\",\n        \"Thom Tillis                 253           194              0        586     797943        0.751282     0.950310   \\n\",\n        \"Scott Brown                 708            92              0       2456     388637        0.963893     0.916956   \\n\",\n        \"Cory Booker                 355            60              0        894     130143        0.937107     0.998527   \\n\",\n        \"Allen Weh                   821            71              0        256    1949518        0.782875     0.987909   \\n\",\n        \"James Inhofe               1905           406              0       2094     170226        0.837600     0.981288   \\n\",\n        \"Monica Wehby                115           295              0        660      95280        0.691099     0.702572   \\n\",\n        \"Jack Reed                    90             4              0        128      19412        0.969697     0.910763   \\n\",\n        \"Lindsey Graham             1089           135              0       1412     119855        0.912734     0.891586   \\n\",\n        \"Lamar Alexander              34             4              0         48       6291        0.923077     0.590538   \\n\",\n        \"John Cornyn                 166            20              0        260      21110        0.928571     0.995943   \\n\",\n        \"Natalie Tennant             210           110              0        690     159430        0.862500     0.969238   \\n\",\n        \"Elizabeth Cheney             30             0              0         20       3900        1.000000     0.722892   \\n\",\n        \"\\n\",\n        \"                   msgs_share  likes_share  dislikes_share state party    donations  \\n\",\n        \"candidate_name                                                                       \\n\",\n        \"Dan Sullivan         0.814480     0.774876        0.494737    AK   DEM   6340422.00  \\n\",\n        \"Jeff Sessions        1.000000     1.000000        1.000000    AL   REP   1115688.00  \\n\",\n        \"Thomas Cotton        0.434783     0.537190        0.722222    AR   REP   7097224.06  \\n\",\n        \"Cory Gardner         0.633267     0.530660        0.268058    CO   DEM  10420571.00  \\n\",\n        \"Christopher Coons    0.627063     0.379310        0.400000    DE   DEM   4173447.00  \\n\",\n        \"John Kingston        0.451220     0.447368        1.000000    GA   DEM   9211931.00  \\n\",\n        \"Mark Jacobs          0.959571     0.995765        0.932624    IA   REP   4810813.00  \\n\",\n        \"Pat Roberts          0.733524     0.869767        0.768519    KS   REP   1068018.00  \\n\",\n        \"Mitch Mcconnell      0.632230     0.640387        0.428346    KY   DEM  11353760.00  \\n\",\n        \"Mary Landrieu        0.937107     0.971084        1.000000    LA   DEM  10190144.00  \\n\",\n        \"Gabriel Gomez        1.000000     0.946524        0.980916    MA   REP   4755654.00  \\n\",\n        \"Susan Collins        0.658537     0.983193        1.000000    ME   DEM   1333016.00  \\n\",\n        \"Terri Land           0.578947     0.783019        0.918033    MI   DEM   6994603.00  \\n\",\n        \"Al Franken           0.762162     0.753623        0.639640    MN   DFL  15126268.00  \\n\",\n        \"Thad Cochran         1.000000     0.897436        1.000000    MS   REP   2727209.00  \\n\",\n        \"Thom Tillis          0.496078     0.716381        0.619808    NC   REP   4764110.00  \\n\",\n        \"Scott Brown          0.964578     0.967310        0.736000    NH   REP   3686708.00  \\n\",\n        \"Cory Booker          0.997191     1.000000        1.000000    NJ   DEM  16167874.00  \\n\",\n        \"Allen Weh            0.539776     0.279476        0.702970    NM   DEM   5050539.00  \\n\",\n        \"James Inhofe         0.968480     0.984023        0.966667    OK   REP   2811701.00  \\n\",\n        \"Monica Wehby         0.481172     0.662651  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    TX\\n\",\n        \"party                   DEM\\n\",\n        \"donations           9673572\\n\",\n        \"Name: John Cornyn, dtype: object\\n\",\n        \"commentCount            210\\n\",\n        \"dislikeCount            110\\n\",\n        \"favoriteCount             0\\n\",\n        \"likeCount               690\\n\",\n        \"viewCount            159430\\n\",\n        \"like_dislike_r       0.8625\\n\",\n        \"views_share       0.9692383\\n\",\n        \"msgs_share        0.8606557\\n\",\n        \"likes_share       0.8734177\\n\",\n        \"dislikes_share    0.8730159\\n\",\n        \"state                    WV\\n\",\n        \"party                   REP\\n\",\n        \"donations           5482547\\n\",\n        \"Name: Natalie Tennant, dtype: object\\n\",\n        \"commentCount             30\\n\",\n        \"dislikeCount              0\\n\",\n        \"favoriteCount             0\\n\",\n        \"likeCount                20\\n\",\n        \"viewCount              3900\\n\",\n        \"like_dislike_r            1\\n\",\n        \"views_share       0.7228916\\n\",\n        \"msgs_share        0.5454545\\n\",\n        \"likes_share       0.2222222\\n\",\n        \"dislikes_share          inf\\n\",\n        \"state                    WY\\n\",\n        \"party                   REP\\n\",\n        \"donations           3016825\\n\",\n        \"Name: Elizabeth Cheney, dtype: object\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 52\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"len(sentate_2014[\\\"state\\\"].unique())\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 46,\n       \"text\": [\n        \"27\"\n       ]\n      }\n     ],\n     \"prompt_number\": 46\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Check 2012 Senate Elections\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": []\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def get_state_data(candidates):\\n\",\n      \"    data_set = get_2012_data(candidates)\\n\",\n      \"    t_ds = pd.pivot_table(data_set, values=[\\\"commentCount\\\", \\\"favoriteCount\\\", \\\"dislikeCount\\\", \\\"likeCount\\\", \\\"viewCount\\\"],\\n\",\n      \"                   aggfunc='sum', rows=\\\"candidate_name\\\")\\n\",\n      \"    t_ds[\\\"like_dislike_r\\\"] = t_ds[\\\"likeCount\\\"] / (t_ds[\\\"dislikeCount\\\"] + t_ds[\\\"likeCount\\\"])\\n\",\n      \"    t_ds[\\\"views_share\\\"] = t_ds[\\\"viewCount\\\"] / t_ds[\\\"viewCount\\\"].sum()\\n\",\n      \"    t_ds[\\\"msgs_share\\\"] = t_ds[\\\"commentCount\\\"] / t_ds[\\\"commentCount\\\"].sum()\\n\",\n      \"    t_ds[\\\"likes_share\\\"] = t_ds[\\\"likeCount\\\"] / t_ds[\\\"likeCount\\\"].sum()\\n\",\n      \"    t_ds[\\\"dislikes_share\\\"] = t_ds[\\\"dislikeCount\\\"] / t_ds[\\\"dislikeCount\\\"].sum()\\n\",\n      \"    # Sentemate Analysis of the title\\n\",\n      \"    t_ds[\\\"sentiment\\\"] = pd.Series()\\n\",\n      \"    for cand in candidates:\\n\",\n      \"        t_ds[\\\"sentiment\\\"][cand] = np.mean(\\n\",\n      \"                                    [TextBlob(title).polarity for title in data_set[data_set[\\\"candidate_name\\\"]==cand][\\\"title\\\"]]\\n\",\n      \"                                         )\\n\",\n      \"    \\n\",\n      \"    print t_ds\\n\",\n      \"    return t_ds\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 35\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"senate_2012 = pd.read_csv(\\\"data/2012_senate_results.csv\\\")\\n\",\n      \"senate_2012[\\\"Full Name\\\"] = senate_2012[\\\"First Name\\\"] + \\\" \\\"  + senate_2012[\\\"Last Name\\\"]\\n\",\n      \"senate_2012\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<pre>\\n\",\n        \"&lt;class 'pandas.core.frame.DataFrame'&gt;\\n\",\n        \"Int64Index: 126 entries, 0 to 125\\n\",\n        \"Data columns (total 9 columns):\\n\",\n        \"State Postal    126  non-null values\\n\",\n        \"County Name     126  non-null values\\n\",\n        \"Party           126  non-null values\\n\",\n        \"First Name      126  non-null values\\n\",\n        \"Last Name       126  non-null values\\n\",\n        \"Incumbent       126  non-null values\\n\",\n        \"Vote Count      126  non-null values\\n\",\n        \"Winner          33  non-null values\\n\",\n        \"Full Name       126  non-null values\\n\",\n        \"dtypes: int64(2), object(7)\\n\",\n        \"</pre>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 36,\n       \"text\": [\n        \"<class 'pandas.core.frame.DataFrame'>\\n\",\n        \"Int64Index: 126 entries, 0 to 125\\n\",\n        \"Data columns (total 9 columns):\\n\",\n        \"State Postal    126  non-null values\\n\",\n        \"County Name     126  non-null values\\n\",\n        \"Party           126  non-null values\\n\",\n        \"First Name      126  non-null values\\n\",\n        \"Last Name       126  non-null values\\n\",\n        \"Incumbent       126  non-null values\\n\",\n        \"Vote Count      126  non-null values\\n\",\n        \"Winner          33  non-null values\\n\",\n        \"Full Name       126  non-null values\\n\",\n        \"dtypes: int64(2), object(7)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 36\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"senate_2012[\\\"commentCount\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"dislikeCount\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"favoriteCount\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"likeCount\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"viewCount\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"like_dislike_r\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"views_share\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"msgs_share\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"likes_share\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"dislikes_share\\\"] = pd.Series()\\n\",\n      \"senate_2012[\\\"sentiment\\\"] = pd.Series()\\n\",\n      \"\\n\",\n      \"for state in np.unique(senate_2012[\\\"State Postal\\\"]):\\n\",\n      \"    print state + \\\":\\\"\\n\",\n      \"    cands = senate_2012[senate_2012[\\\"State Postal\\\"] == state]\\n\",\n      \"    top_cands = cands.sort(\\\"Vote Count\\\",ascending=False)[:2]\\n\",\n      \"    #print top_cands\\n\",\n      \"    try:\\n\",\n      \"        youtube_stats = get_state_data(top_cands[\\\"Full Name\\\"].values)\\n\",\n      \"        #print youtube_stats\\n\",\n      \"        # Store Data Back\\n\",\n      \"\\n\",\n      \"        for item in youtube_stats.iterrows():\\n\",\n      \"            cand = item[0]\\n\",\n      \"            stats = item[1]\\n\",\n      \"            index = int(senate_2012[senate_2012[\\\"Full Name\\\"] == cand].index)\\n\",\n      \"            senate_2012[\\\"commentCount\\\"][index] = stats[\\\"commentCount\\\"]\\n\",\n      \"            senate_2012[\\\"dislikeCount\\\"][index] = stats[\\\"dislikeCount\\\"]\\n\",\n      \"            senate_2012[\\\"favoriteCount\\\"][index] = stats[\\\"favoriteCount\\\"]\\n\",\n      \"            senate_2012[\\\"likeCount\\\"][index] = stats[\\\"likeCount\\\"]\\n\",\n      \"            senate_2012[\\\"viewCount\\\"][index] = stats[\\\"viewCount\\\"]\\n\",\n      \"            senate_2012[\\\"like_dislike_r\\\"][index] = stats[\\\"like_dislike_r\\\"]\\n\",\n      \"            senate_2012[\\\"views_share\\\"][index] = stats[\\\"views_share\\\"]\\n\",\n      \"            senate_2012[\\\"msgs_share\\\"][index] = stats[\\\"msgs_share\\\"]\\n\",\n      \"            senate_2012[\\\"likes_share\\\"][index] = stats[\\\"likes_share\\\"]\\n\",\n      \"            senate_2012[\\\"dislikes_share\\\"][index] = stats[\\\"dislikes_share\\\"]\\n\",\n      \"            senate_2012[\\\"sentiment\\\"][index] = stats[\\\"sentiment\\\"]\\n\",\n      \"    except:\\n\",\n      \"        pass\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"AZ:\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Jeff Flake                542          1240              0       2927    1829090        0.702424      0.58453   \\n\",\n        \"Richard Carmona           513          2112              0       4590    1300075        0.684870      0.41547   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                       \\n\",\n        \"Jeff Flake         0.513744     0.389384        0.369928   0.016247  \\n\",\n        \"Richard Carmona    0.486256     0.610616        0.630072   0.044413  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"CA:\\n\",\n        \"                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                   \\n\",\n        \"Dianne Feinstein         12130          1690              0      16760    3165370        0.908401       0.7182   \\n\",\n        \"Elizabeth Emken           4492           437              0       6777    1241994        0.939423       0.2818   \\n\",\n        \"\\n\",\n        \"                  msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                        \\n\",\n        \"Dianne Feinstein    0.729756      0.71207        0.794546  -0.000463  \\n\",\n        \"Elizabeth Emken     0.270244      0.28793        0.205454  -0.006897  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"CT:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Chris Murphy             233            88              0        447     159171        0.835514     0.371103   \\n\",\n        \"Linda McMahon           3962           418              0       4062     269742        0.906696     0.628897   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Chris Murphy      0.055542     0.099135        0.173913   0.004893  \\n\",\n        \"Linda McMahon     0.944458     0.900865        0.826087   0.029649  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"DE:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Kevin Wade                60            10              0         70      11400           0.875     0.276123   \\n\",\n        \"Thomas Carper             28            10              0         70      29886           0.875     0.723877   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Kevin Wade        0.681818          0.5             0.5  -0.031250  \\n\",\n        \"Thomas Carper     0.318182          0.5             0.5   0.018583  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"FL:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Bill Nelson              376            59              0        937     511466        0.940763     0.865892   \\n\",\n        \"Connie Mack              164            67              0        249      79215        0.787975     0.134108   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Bill Nelson       0.696296     0.790051        0.468254   0.021819  \\n\",\n        \"Connie Mack       0.303704     0.209949        0.531746   0.015071  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"HI:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Linda Lingle             343           451              0        672     480724        0.598397     0.476687   \\n\",\n        \"Mazie Hirono             367           577              0        924     527744        0.615590     0.523313   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Linda Lingle      0.483099     0.421053        0.438716   0.023237  \\n\",\n        \"Mazie Hirono      0.516901     0.578947        0.561284   0.065901  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"IN:\\n\",\n        \"                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                   \\n\",\n        \"Joe Donnelly              3011           931              0       1203    2646352        0.563730     0.756489   \\n\",\n        \"Richard Mourdock          7869          1467              0       8777     851850        0.856794     0.243511   \\n\",\n        \"\\n\",\n        \"                  msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                        \\n\",\n        \"Joe Donnelly        0.276746     0.120541         0.38824  -0.009662  \\n\",\n        \"Richard Mourdock    0.723254     0.879459         0.61176   0.043522  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MA:\\n\",\n        \"                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                   \\n\",\n        \"Elizabeth Warren         10007          2208              0      15744    2458138        0.877005     0.718429   \\n\",\n        \"Scott Brown               5226          1259              0       6490     963410        0.837527     0.281571   \\n\",\n        \"\\n\",\n        \"                  msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                        \\n\",\n        \"Elizabeth Warren    0.656929     0.708105        0.636862   0.014748  \\n\",\n        \"Scott Brown         0.343071     0.291895        0.363138   0.019217  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MD:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Ben Cardin               301            90              0        386      61822        0.810924      0.82694   \\n\",\n        \"Daniel Bongino           162            10              0        162      12938        0.941860      0.17306   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Ben Cardin        0.650108      0.70438             0.9   0.008083  \\n\",\n        \"Daniel Bongino    0.349892      0.29562             0.1  -0.018750  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"ME:\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Angus King                 91           133              0        385      39436        0.743243     0.911689   \\n\",\n        \"Charles Summers             0            10              0         40       3820        0.800000     0.088311   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                       \\n\",\n        \"Angus King                1     0.905882         0.93007   0.053315  \\n\",\n        \"Charles Summers           0     0.094118         0.06993   0.016667  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MI:\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Debbie Stabenow            63            83              0        595     428223        0.877581     0.870664   \\n\",\n        \"Pete Hoekstra              57           187              0        398      63612        0.680342     0.129336   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                       \\n\",\n        \"Debbie Stabenow       0.525     0.599194        0.307407   0.062909  \\n\",\n        \"Pete Hoekstra         0.475     0.400806        0.692593   0.121008  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MN:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Amy Klobuchar            264           242              0        430      97354        0.639881     0.659651   \\n\",\n        \"Kurt Bills               300           100              0        460      50230        0.821429     0.340349   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Amy Klobuchar     0.468085     0.483146        0.707602   0.030468  \\n\",\n        \"Kurt Bills        0.531915     0.516854        0.292398   0.160000  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MO:\\n\",\n        \"                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                   \\n\",\n        \"Claire McCaskill           525           438              0        765     141427        0.635910     0.036271   \\n\",\n        \"Todd Akin                44285          5817              0      66830    3757741        0.919928     0.963729   \\n\",\n        \"\\n\",\n        \"                  msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                        \\n\",\n        \"Claire McCaskill    0.011716     0.011317        0.070024   0.009500  \\n\",\n        \"Todd Akin           0.988284     0.988683        0.929976  -0.016794  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MS:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Albert Gore              370             0              0        260      75470        1.000000      0.70684   \\n\",\n        \"Roger Wicker             120            93              0        203      31301        0.685811      0.29316   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Albert Gore       0.755102     0.561555               0  -0.050000  \\n\",\n        \"Roger Wicker      0.244898     0.438445               1   0.029821  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"MT:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Denny Rehberg           4500           351              0      10651     390033        0.968097     0.807661   \\n\",\n        \"Jon Tester               480            72              0       1172      92884        0.942122     0.192339   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Denny Rehberg     0.903614     0.900871        0.829787   0.003883  \\n\",\n        \"Jon Tester        0.096386     0.099129        0.170213   0.023900  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"ND:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Heidi Heitkamp           315           289              0        726     773197        0.715271     0.574998   \\n\",\n        \"Rick Berg                331           200              0        573     571499        0.741268     0.425002   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Heidi Heitkamp    0.487616     0.558891        0.591002   0.007323  \\n\",\n        \"Rick Berg         0.512384     0.441109        0.408998   0.033317  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NE:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Bob Kerrey               328           179              0       1526     526859        0.895015     0.616558   \\n\",\n        \"Deb Fischer              441           272              0       2076     327657        0.884157     0.383442   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Bob Kerrey        0.426528     0.423654        0.396896   0.015867  \\n\",\n        \"Deb Fischer       0.573472     0.576346        0.603104   0.011278  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NJ:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Bob Menendez             601           232              0        819     132137        0.779258     0.777057   \\n\",\n        \"Joe Kyrillos              87           189              0        198      37911        0.511628     0.222943   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Bob Menendez      0.873547      0.80531        0.551069  -0.017692  \\n\",\n        \"Joe Kyrillos      0.126453      0.19469        0.448931   0.083939  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NM:\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Heather Wilson            170           330              0        460     112560        0.582278     0.116505   \\n\",\n        \"Martin Heinrich           380           790              0       2580     853580        0.765579     0.883495   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                       \\n\",\n        \"Heather Wilson     0.309091     0.151316        0.294643   0.064444  \\n\",\n        \"Martin Heinrich    0.690909     0.848684        0.705357   0.058437  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NV:\\n\",\n        \"                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                  \\n\",\n        \"Dean Heller               286           748              0       1077    1014221        0.590137     0.429432   \\n\",\n        \"Shelley Berkley           434           402              0        884    1347552        0.687403     0.570568   \\n\",\n        \"\\n\",\n        \"                 msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                       \\n\",\n        \"Dean Heller        0.397222      0.54921        0.650435  -0.003463  \\n\",\n        \"Shelley Berkley    0.602778      0.45079        0.349565   0.005413  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"NY:\\n\",\n        \"                    commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                     \\n\",\n        \"Kirsten Gillibrand           684           167              0       1452     399903        0.896850     0.638157   \\n\",\n        \"Wendy Long                   494            83              0        740     226750        0.899149     0.361843   \\n\",\n        \"\\n\",\n        \"                    msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                          \\n\",\n        \"Kirsten Gillibrand    0.580645     0.662409           0.668   0.030444  \\n\",\n        \"Wendy Long            0.419355     0.337591           0.332   0.074461  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"OH:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Josh Mandel              484           311              0        423     163496        0.576294     0.317986   \\n\",\n        \"Sherrod Brown            292            70              0        331     350665        0.825436     0.682014   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Josh Mandel       0.623711     0.561008        0.816273   0.002392  \\n\",\n        \"Sherrod Brown     0.376289     0.438992        0.183727   0.034265  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"PA:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Bob Casey                117           139              0        316      57281        0.694505     0.203635   \\n\",\n        \"Tom Smith                793           209              0       1648     224012        0.887453     0.796365   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Bob Casey         0.128571     0.160896        0.399425   0.014105  \\n\",\n        \"Tom Smith         0.871429     0.839104        0.600575   0.048067  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"RI:\\n\",\n        \"                    commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                     \\n\",\n        \"Barry Hinckley                12            12              0        138      21111        0.920000     0.404712   \\n\",\n        \"Sheldon Whitehouse            10            10              0        134      31052        0.930556     0.595288   \\n\",\n        \"\\n\",\n        \"                    msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                          \\n\",\n        \"Barry Hinckley        0.545455     0.507353        0.545455   0.138462  \\n\",\n        \"Sheldon Whitehouse    0.454545     0.492647        0.454545   0.033625  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"TN:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Bob Corker               155            20              0        162      17499        0.890110     0.258238   \\n\",\n        \"Mark Clayton             230            50              0        230      50264        0.821429     0.741762   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Bob Corker        0.402597     0.413265        0.285714   0.144643  \\n\",\n        \"Mark Clayton      0.597403     0.586735        0.714286   0.100538  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"TX:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Paul Sadler              840           257              0       1937     250148        0.882862     0.462666   \\n\",\n        \"Ted Cruz                1676           255              0       2410     290519        0.904315     0.537334   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Paul Sadler       0.333863     0.445595        0.501953   0.015821  \\n\",\n        \"Ted Cruz          0.666137     0.554405        0.498047   0.009659  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"UT:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Orrin Hatch              495           336              0        284      38701        0.458065     0.511722   \\n\",\n        \"Scott Howell              19            13              0        175      36928        0.930851     0.488278   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Orrin Hatch       0.963035     0.618736        0.962751   0.084909  \\n\",\n        \"Scott Howell      0.036965     0.381264        0.037249   0.050492  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"VA:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"George Allen             324           443              0        500     206940        0.530223     0.718385   \\n\",\n        \"Timothy Kaine            174            90              0        272      81123        0.751381     0.281615   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"George Allen      0.650602     0.647668        0.831144   0.023067  \\n\",\n        \"Timothy Kaine     0.349398     0.352332        0.168856   0.022738  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"VT:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Bernie Sanders          2131           129              0       4878     223002        0.974236     0.992571   \\n\",\n        \"John MacGovern             6             0              0          2       1669        1.000000     0.007429   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Bernie Sanders    0.997192      0.99959               1   0.011692  \\n\",\n        \"John MacGovern    0.002808      0.00041               0   0.000000  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"WA:\\n\",\n        \"                     commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                      \\n\",\n        \"Maria Cantwell                234            52              0        244      62934        0.824324     0.736725   \\n\",\n        \"Michael Baumgartner           112            70              0        137      22490        0.661836     0.263275   \\n\",\n        \"\\n\",\n        \"                     msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                           \\n\",\n        \"Maria Cantwell         0.676301      0.64042         0.42623   0.107853  \\n\",\n        \"Michael Baumgartner    0.323699      0.35958         0.57377   0.031234  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"WI:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Tammy Baldwin            393           169              0        944     488400        0.848158     0.602401   \\n\",\n        \"Tommy Thompson          1494           550              0       2332     322355        0.809160     0.397599   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Tammy Baldwin     0.208267     0.288156        0.235049   0.022898  \\n\",\n        \"Tommy Thompson    0.791733     0.711844        0.764951   0.010391  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"WV:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"Joe Manchin              675           545              0       3005     604285        0.846479     0.964792   \\n\",\n        \"John Raese                74            68              0        118      22052        0.634409     0.035208   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"Joe Manchin       0.901202     0.962216         0.88907   0.006897  \\n\",\n        \"John Raese        0.098798     0.037784         0.11093  -0.057143  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\",\n        \"WY:\\n\",\n        \"                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\\\\n\",\n        \"candidate_name                                                                                                 \\n\",\n        \"John Barrasso            630           290              0        380      74030        0.567164     0.996943   \\n\",\n        \"Tim Chesnut                4             2              0          2        227        0.500000     0.003057   \\n\",\n        \"\\n\",\n        \"                msgs_share  likes_share  dislikes_share  sentiment  \\n\",\n        \"candidate_name                                                      \\n\",\n        \"John Barrasso     0.993691     0.994764        0.993151       0.09  \\n\",\n        \"Tim Chesnut       0.006309     0.005236        0.006849       0.00  \"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 37\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cands_with_stats = senate_2012[~senate_2012[\\\"viewCount\\\"].isnull()]\\n\",\n      \"cands_with_stats[\\\"VotesShare\\\"] = cands_with_stats[[\\\"Vote Count\\\", \\\"State Postal\\\"]].apply(lambda x:x[0]/senate_2012[senate_2012[\\\"State Postal\\\"]==x[1]][\\\"Vote Count\\\"].sum(), axis=1)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 38\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x_col = \\\"views_share\\\"\\n\",\n      \"y_col = \\\"VotesShare\\\"\\n\",\n      \"\\n\",\n      \"plt.figure(figsize=(15,10))\\n\",\n      \"color_dict = {\\\"Dem\\\": \\\"b\\\", \\\"GOP\\\": \\\"r\\\", \\\"Ind\\\":\\\"g\\\", \\\"NPA\\\": \\\"orange\\\"}\\n\",\n      \"shape_dict = {\\\"X\\\": \\\"*\\\", \\\"nan\\\": \\\".\\\"}\\n\",\n      \"\\n\",\n      \"wl_dp = [len(cands_with_stats[(cands_with_stats[x_col]>=0.5) &\\n\",\n      \"                          (cands_with_stats[\\\"Winner\\\"]==\\\"X\\\")]),\\n\",\n      \"                    len(cands_with_stats[(cands_with_stats[x_col]>=0.5)])]\\n\",\n      \"wl_dm = [len(cands_with_stats[(cands_with_stats[x_col]<0.5) &\\n\",\n      \"                          (cands_with_stats[\\\"Winner\\\"]==\\\"X\\\")]),\\n\",\n      \"                    len(cands_with_stats[(cands_with_stats[x_col]<0.5)])]\\n\",\n      \"\\n\",\n      \"wl_50p = \\\"Winning Ratio %s/%s ($%0.1f \\\\%%$)\\\" % (wl_dp[0], wl_dp[1], wl_dp[0]/wl_dp[1]*100)\\n\",\n      \"wl_50m = \\\"Winning Ratio %s/%s ($%0.1f \\\\%%$)\\\" % (wl_dm[0], wl_dm[1], wl_dm[0]/wl_dm[1]*100)\\n\",\n      \"\\n\",\n      \"for cand in cands_with_stats.iterrows():\\n\",\n      \"    stats = cand[1]\\n\",\n      \"    x = stats[x_col]\\n\",\n      \"    y = stats[y_col]\\n\",\n      \"    c = color_dict[stats[\\\"Party\\\"]]\\n\",\n      \"    m = shape_dict[str(stats[\\\"Winner\\\"])]\\n\",\n      \"    plt.scatter(x, y, c=c, marker=m, s=500, alpha=0.5)\\n\",\n      \"    if stats[x_col] > 0.9:\\n\",\n      \"        plt.annotate(stats[\\\"Full Name\\\"],xytext=(8,20), xy=(x,y),\\n\",\n      \"                     textcoords='offset points', arrowprops=dict(arrowstyle='-|>'))\\n\",\n      \"\\n\",\n      \"plt.xlabel(\\\"Youtube \\\" + x_col + \\\" Between Competing Candidates in a State Race\\\")\\n\",\n      \"plt.ylabel(\\\"Actual \\\" + y_col)\\n\",\n      \"plt.vlines(.5, ymin=0, ymax=1)\\n\",\n      \"\\n\",\n      \"plt.annotate(s=wl_50p, xy=(0.7, 1))\\n\",\n      \"plt.annotate(s=wl_50m, xy=(0.2, 1))\\n\",\n      \"plt.title(\\\"Youtube Video Views for Candidate from 2012-08-04 to 2012-11-04 and Actual Votes\\\")\\n\",\n      \"plt.annotate(\\\"Start Represent Winning Candidates\\\\nCircles Represent Loosing Candidate\\\", xy=(0.03, 0.85))\\n\",\n      \"plt.annotate(\\\"Red: GOP\\\\nBlue: Dem\\\\nGreen: Ind\\\\nYellow: NPA\\\", xy=(0.03, 0.7))\\n\",\n      \"axis(\\\"tight\\\")\\n\",\n      \"plt.box(on=\\\"off\\\")\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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SgDxdwLrEP5jG+R9DSli+rKKqORzjHiK40/o87s/9VtsQblfrXvR0T9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\\nNoqN8Opk9wAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7ff080672950>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 39\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cands_with_stats[cands_with_stats[\\\"State Postal\\\"]==\\\"MO\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>State Postal</th>\\n\",\n        \"      <th>County Name</th>\\n\",\n        \"      <th>Party</th>\\n\",\n        \"      <th>First Name</th>\\n\",\n        \"      <th>Last Name</th>\\n\",\n        \"      <th>Incumbent</th>\\n\",\n        \"      <th>Vote Count</th>\\n\",\n        \"      <th>Winner</th>\\n\",\n        \"      <th>Full Name</th>\\n\",\n        \"      <th>commentCount</th>\\n\",\n        \"      <th>dislikeCount</th>\\n\",\n        \"      <th>favoriteCount</th>\\n\",\n        \"      <th>likeCount</th>\\n\",\n        \"      <th>viewCount</th>\\n\",\n        \"      <th>like_dislike_r</th>\\n\",\n        \"      <th>views_share</th>\\n\",\n        \"      <th>msgs_share</th>\\n\",\n        \"      <th>likes_share</th>\\n\",\n        \"      <th>dislikes_share</th>\\n\",\n        \"      <th>sentiment</th>\\n\",\n        \"      <th>VotesShare</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> MO</td>\\n\",\n        \"      <td> Missouri</td>\\n\",\n        \"      <td> Dem</td>\\n\",\n        \"      <td> Claire</td>\\n\",\n        \"      <td> McCaskill</td>\\n\",\n        \"      <td> 1</td>\\n\",\n        \"      <td> 1484683</td>\\n\",\n        \"      <td>   X</td>\\n\",\n        \"      <td> Claire McCaskill</td>\\n\",\n        \"      <td>   525</td>\\n\",\n        \"      <td>  438</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   765</td>\\n\",\n        \"      <td>  141427</td>\\n\",\n        \"      <td> 0.635910</td>\\n\",\n        \"      <td> 0.036271</td>\\n\",\n        \"      <td> 0.011716</td>\\n\",\n        \"      <td> 0.011317</td>\\n\",\n        \"      <td> 0.070024</td>\\n\",\n        \"      <td> 0.009500</td>\\n\",\n        \"      <td> 0.547173</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td> MO</td>\\n\",\n        \"      <td> Missouri</td>\\n\",\n        \"      <td> GOP</td>\\n\",\n        \"      <td>   Todd</td>\\n\",\n        \"      <td>      Akin</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 1063698</td>\\n\",\n        \"      <td> NaN</td>\\n\",\n        \"      <td>        Todd Akin</td>\\n\",\n        \"      <td> 44285</td>\\n\",\n        \"      <td> 5817</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 66830</td>\\n\",\n        \"      <td> 3757741</td>\\n\",\n        \"      <td> 0.919928</td>\\n\",\n        \"      <td> 0.963729</td>\\n\",\n        \"      <td> 0.988284</td>\\n\",\n        \"      <td> 0.988683</td>\\n\",\n        \"      <td> 0.929976</td>\\n\",\n        \"      <td>-0.016794</td>\\n\",\n        \"      <td> 0.392021</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 40,\n       \"text\": [\n        \"   State Postal County Name Party First Name  Last Name  Incumbent  Vote Count Winner         Full Name  commentCount  \\\\\\n\",\n        \"13           MO    Missouri   Dem     Claire  McCaskill          1     1484683      X  Claire McCaskill           525   \\n\",\n        \"46           MO    Missouri   GOP       Todd       Akin          0     1063698    NaN         Todd Akin         44285   \\n\",\n        \"\\n\",\n        \"    dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  msgs_share  likes_share  \\\\\\n\",\n        \"13           438              0        765     141427        0.635910     0.036271    0.011716     0.011317   \\n\",\n        \"46          5817              0      66830    3757741        0.919928     0.963729    0.988284     0.988683   \\n\",\n        \"\\n\",\n        \"    dislikes_share  sentiment  VotesShare  \\n\",\n        \"13        0.070024   0.009500    0.547173  \\n\",\n        \"46        0.929976  -0.016794    0.392021  \"\n       ]\n      }\n     ],\n     \"prompt_number\": 40\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/data-mining/3. Twitter API.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:cf456697bea046551257aa9fb7eaf09f0280024ac5f7db881b965d58e72cda37\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will be using tweepy library to perform data mine on twitter.\\n\",\n      \"\\n\",\n      \"##Twitter API\\n\",\n      \"\\n\",\n      \"https://apps.twitter.com/\\n\",\n      \"\\n\",\n      \"###Topics:\\n\",\n      \"\\n\",\n      \"**Topic 1**: Register You App.\\n\",\n      \"\\n\",\n      \"**Topic 2**: Using REST API\\n\",\n      \"\\n\",\n      \"**Video Tutorial**:\\n\",\n      \"\\n\",\n      \"https://www.youtube.com/user/roshanRush\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import the library\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import tweepy\\n\",\n      \"import pandas as pd\\n\",\n      \"import matplotlib.pyplot as plt\\n\",\n      \"\\n\",\n      \"pd.options.display.max_columns = 50\\n\",\n      \"pd.options.display.max_rows= 50\\n\",\n      \"pd.options.display.width= 120\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Authentication\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"consumer_key = \\\"\\\" # Use your own key. To get a key https://apps.twitter.com/\\n\",\n      \"consumer_secret = \\\"\\\"\\n\",\n      \"\\n\",\n      \"auth = tweepy.OAuthHandler(consumer_key=consumer_key, consumer_secret=consumer_secret)\\n\",\n      \"\\n\",\n      \"api = tweepy.API(auth)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"REST API\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Searching for Tweets\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"###`API.search(q[, lang][, locale][, rpp][, page][, since_id][, geocode][, show_user])`\\n\",\n      \"```\\n\",\n      \"\\n\",\n      \"    Returns tweets that match a specified query.\\n\",\n      \"    Parameters:\\t\\n\",\n      \"\\n\",\n      \"        q \\u2013 the search query string\\n\",\n      \"        lang \\u2013 Restricts tweets to the given language, given by an ISO 639-1 code.\\n\",\n      \"        locale \\u2013 Specify the language of the query you are sending. This is intended for language-specific clients and the default should work in the majority of cases.\\n\",\n      \"        rpp \\u2013 The number of tweets to return per page, up to a max of 100.\\n\",\n      \"        page \\u2013 The page number (starting at 1) to return, up to a max of roughly 1500 results (based on rpp * page.\\n\",\n      \"        geocode \\u2013 Returns tweets by users located within a given radius of the given latitude/longitude. The location is preferentially taking from the Geotagging API, but will fall back to their Twitter profile. The parameter value is specified by \\u201clatitide,longitude,radius\\u201d, where radius units must be specified as either \\u201cmi\\u201d (miles) or \\u201ckm\\u201d (kilometers). Note that you cannot use the near operator via the API to geocode arbitrary locations; however you can use this geocode parameter to search near geocodes directly.\\n\",\n      \"        show_user \\u2013 When true, prepends \\u201c<user>:\\u201d to the beginning of the tweet. This is useful for readers that do not display Atom\\u2019s author field. The default is false.\\n\",\n      \"\\n\",\n      \"    Return type:\\t\\n\",\n      \"\\n\",\n      \"    list of SearchResult objects\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results = api.search(q=\\\"IPython\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Inspecting Results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"len(results)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"15\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def print_tweet(tweet):\\n\",\n      \"    print \\\"@%s - %s (%s)\\\" % (tweet.user.screen_name, tweet.user.name, tweet.created_at)\\n\",\n      \"    print tweet.text\\n\",\n      \"\\n\",\n      \"tweet=results[1]\\n\",\n      \"print_tweet(tweet)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"@josephmisiti - joseph misiti (2014-10-12 16:31:53)\\n\",\n        \"RT @datamusing: Easy sort and slice of pandas dataframe views.  http://t.co/68KyqDidkO\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Inspecting a Status Object\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"tweet=results[2]\\n\",\n      \"\\n\",\n      \"for param in dir(tweet):\\n\",\n      \"    if not param.startswith(\\\"_\\\"):\\n\",\n      \"        print \\\"%s : %s\\\" % (param, eval(\\\"tweet.\\\" + param))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"author : <tweepy.models.User object at 0x7f854492d650>\\n\",\n        \"contributors : None\\n\",\n        \"coordinates : None\\n\",\n        \"created_at : 2014-10-12 16:26:20\\n\",\n        \"destroy : <bound method Status.destroy of <tweepy.models.Status object at 0x7f854492d4d0>>\\n\",\n        \"entities : {'symbols': [], 'user_mentions': [{'id': 20167623, 'indices': [3, 13], 'id_str': '20167623', 'screen_name': 'kdnuggets', 'name': 'Gregory Piatetsky'}], 'hashtags': [], 'urls': [{'url': 'http://t.co/hVnBWL0R72', 'indices': [77, 99], 'expanded_url': 'http://buff.ly/1s5OuRe', 'display_url': 'buff.ly/1s5OuRe'}]}\\n\",\n        \"favorite : <bound method Status.favorite of <tweepy.models.Status object at 0x7f854492d4d0>>\\n\",\n        \"favorite_count : 0\\n\",\n        \"favorited : False\\n\",\n        \"geo : None\\n\",\n        \"id : 521336071288680449\\n\",\n        \"id_str : 521336071288680449\\n\",\n        \"in_reply_to_screen_name : None\\n\",\n        \"in_reply_to_status_id : None\\n\",\n        \"in_reply_to_status_id_str : None\\n\",\n        \"in_reply_to_user_id : None\\n\",\n        \"in_reply_to_user_id_str : None\\n\",\n        \"lang : en\\n\",\n        \"metadata : {'iso_language_code': 'en', 'result_type': 'recent'}\\n\",\n        \"parse : <bound method type.parse of <class 'tweepy.models.Status'>>\\n\",\n        \"parse_list : <bound method type.parse_list of <class 'tweepy.models.Status'>>\\n\",\n        \"place : None\\n\",\n        \"possibly_sensitive : False\\n\",\n        \"retweet : <bound method Status.retweet of <tweepy.models.Status object at 0x7f854492d4d0>>\\n\",\n        \"retweet_count : 6\\n\",\n        \"retweeted : False\\n\",\n        \"retweeted_status : <tweepy.models.Status object at 0x7f854492d610>\\n\",\n        \"retweets : <bound method Status.retweets of <tweepy.models.Status object at 0x7f854492d4d0>>\\n\",\n        \"source : Twitter for iPhone\\n\",\n        \"source_url : http://twitter.com/download/iphone\\n\",\n        \"text : RT @kdnuggets: Using Python to detect Algorithmically Generated Domain Names http://t.co/hVnBWL0R72\\n\",\n        \"truncated : False\\n\",\n        \"user : <tweepy.models.User object at 0x7f854492d650>\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Inspecting a User Object\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"user=tweet.author\\n\",\n      \"\\n\",\n      \"for param in dir(user):\\n\",\n      \"    if not param.startswith(\\\"_\\\"):\\n\",\n      \"        print \\\"%s : %s\\\" % (param, eval(\\\"user.\\\" + param))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"contributors_enabled : False\\n\",\n        \"created_at : 2010-05-22 09:03:02\\n\",\n        \"default_profile : True\\n\",\n        \"default_profile_image : False\\n\",\n        \"description : Data science, javascript, nodejs, iOS & Sencha touch\\n\",\n        \"entities : {'url': {'urls': [{'url': 'http://t.co/gzMlBYVCcv', 'indices': [0, 22], 'expanded_url': 'http://in.linkedin.com/in/sandeepu', 'display_url': 'in.linkedin.com/in/sandeepu'}]}, 'description': {'urls': []}}\\n\",\n        \"favourites_count : 141\\n\",\n        \"follow : <bound method User.follow of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"follow_request_sent : None\\n\",\n        \"followers : <bound method User.followers of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"followers_count : 67\\n\",\n        \"followers_ids : <bound method User.followers_ids of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"following : False\\n\",\n        \"friends : <bound method User.friends of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"friends_count : 297\\n\",\n        \"geo_enabled : True\\n\",\n        \"id : 146775585\\n\",\n        \"id_str : 146775585\\n\",\n        \"is_translation_enabled : False\\n\",\n        \"is_translator : False\\n\",\n        \"lang : en\\n\",\n        \"listed_count : 4\\n\",\n        \"lists : <bound method User.lists of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"lists_memberships : <bound method User.lists_memberships of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"lists_subscriptions : <bound method User.lists_subscriptions of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"location : Cochin, Kerala, India\\n\",\n        \"name : Sandeep Unni\\n\",\n        \"notifications : None\\n\",\n        \"parse : <bound method type.parse of <class 'tweepy.models.User'>>\\n\",\n        \"parse_list : <bound method type.parse_list of <class 'tweepy.models.User'>>\\n\",\n        \"profile_background_color : C0DEED\\n\",\n        \"profile_background_image_url : http://abs.twimg.com/images/themes/theme1/bg.png\\n\",\n        \"profile_background_image_url_https : https://abs.twimg.com/images/themes/theme1/bg.png\\n\",\n        \"profile_background_tile : False\\n\",\n        \"profile_image_url : http://pbs.twimg.com/profile_images/378800000096874830/9122fec9b60fcaf9b826731473bf4b17_normal.jpeg\\n\",\n        \"profile_image_url_https : https://pbs.twimg.com/profile_images/378800000096874830/9122fec9b60fcaf9b826731473bf4b17_normal.jpeg\\n\",\n        \"profile_link_color : 0084B4\\n\",\n        \"profile_sidebar_border_color : C0DEED\\n\",\n        \"profile_sidebar_fill_color : DDEEF6\\n\",\n        \"profile_text_color : 333333\\n\",\n        \"profile_use_background_image : True\\n\",\n        \"protected : False\\n\",\n        \"screen_name : psandeepu\\n\",\n        \"statuses_count : 358\\n\",\n        \"time_zone : None\\n\",\n        \"timeline : <bound method User.timeline of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"unfollow : <bound method User.unfollow of <tweepy.models.User object at 0x7f854492d650>>\\n\",\n        \"url : http://t.co/gzMlBYVCcv\\n\",\n        \"utc_offset : None\\n\",\n        \"verified : False\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Using Cursor for Pagination\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"For data mining you will be dealing with a large amount of results. Cursor is a simple way to handle interation and results pages.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results = []\\n\",\n      \"for tweet in tweepy.Cursor(api.search, q=\\\"IPython\\\").items(100):\\n\",\n      \"    results.append(tweet)\\n\",\n      \"\\n\",\n      \"print len(results)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"100\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Store Results in a Data Frame\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def process_results(results):\\n\",\n      \"    id_list = [tweet.id for tweet in results]\\n\",\n      \"    data_set = pd.DataFrame(id_list, columns=[\\\"id\\\"])\\n\",\n      \"\\n\",\n      \"    # Processing Tweet Data\\n\",\n      \"\\n\",\n      \"    data_set[\\\"text\\\"] = [tweet.text for tweet in results]\\n\",\n      \"    data_set[\\\"created_at\\\"] = [tweet.created_at for tweet in results]\\n\",\n      \"    data_set[\\\"retweet_count\\\"] = [tweet.retweet_count for tweet in results]\\n\",\n      \"    data_set[\\\"favorite_count\\\"] = [tweet.favorite_count for tweet in results]\\n\",\n      \"    data_set[\\\"source\\\"] = [tweet.source for tweet in results]\\n\",\n      \"\\n\",\n      \"    # Processing User Data\\n\",\n      \"    data_set[\\\"user_id\\\"] = [tweet.author.id for tweet in results]\\n\",\n      \"    data_set[\\\"user_screen_name\\\"] = [tweet.author.screen_name for tweet in results]\\n\",\n      \"    data_set[\\\"user_name\\\"] = [tweet.author.name for tweet in results]\\n\",\n      \"    data_set[\\\"user_created_at\\\"] = [tweet.author.created_at for tweet in results]\\n\",\n      \"    data_set[\\\"user_description\\\"] = [tweet.author.description for tweet in results]\\n\",\n      \"    data_set[\\\"user_followers_count\\\"] = [tweet.author.followers_count for tweet in results]\\n\",\n      \"    data_set[\\\"user_friends_count\\\"] = [tweet.author.friends_count for tweet in results]\\n\",\n      \"    data_set[\\\"user_location\\\"] = [tweet.author.location for tweet in results]\\n\",\n      \"\\n\",\n      \"    return data_set\\n\",\n      \"data_set = process_results(results)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Looking at the Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"data_set.head(5)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>id</th>\\n\",\n        \"      <th>text</th>\\n\",\n        \"      <th>created_at</th>\\n\",\n        \"      <th>retweet_count</th>\\n\",\n        \"      <th>favorite_count</th>\\n\",\n        \"      <th>source</th>\\n\",\n        \"      <th>user_id</th>\\n\",\n        \"      <th>user_screen_name</th>\\n\",\n        \"      <th>user_name</th>\\n\",\n        \"      <th>user_created_at</th>\\n\",\n        \"      <th>user_description</th>\\n\",\n        \"      <th>user_followers_count</th>\\n\",\n        \"      <th>user_friends_count</th>\\n\",\n        \"      <th>user_location</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0</th>\\n\",\n        \"      <td> 521338248673501184</td>\\n\",\n        \"      <td> RT @kdnuggets: Using Python to detect Algorith...</td>\\n\",\n        \"      <td>2014-10-12 16:34:59</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   Twitter for iPhone</td>\\n\",\n        \"      <td>    7534972</td>\\n\",\n        \"      <td>          riyaaz</td>\\n\",\n        \"      <td>             Riyaz</td>\\n\",\n        \"      <td>2007-07-17 15:59:49</td>\\n\",\n        \"      <td> Professional Heretic preaching how to OR how n...</td>\\n\",\n        \"      <td>  215</td>\\n\",\n        \"      <td> 478</td>\\n\",\n        \"      <td>               Chennai, India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1</th>\\n\",\n        \"      <td> 521337469640249345</td>\\n\",\n        \"      <td> RT @datamusing: Easy sort and slice of pandas ...</td>\\n\",\n        \"      <td>2014-10-12 16:31:53</td>\\n\",\n        \"      <td> 2</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   Twitter Web Client</td>\\n\",\n        \"      <td>   58631564</td>\\n\",\n        \"      <td>    josephmisiti</td>\\n\",\n        \"      <td>     joseph misiti</td>\\n\",\n        \"      <td>2009-07-20 23:42:13</td>\\n\",\n        \"      <td> I write about Bayesian Inference, Django, Scal...</td>\\n\",\n        \"      <td> 2092</td>\\n\",\n        \"      <td> 306</td>\\n\",\n        \"      <td>    Lower East Side, New York</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2</th>\\n\",\n        \"      <td> 521336071288680449</td>\\n\",\n        \"      <td> RT @kdnuggets: Using Python to detect Algorith...</td>\\n\",\n        \"      <td>2014-10-12 16:26:20</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   Twitter for iPhone</td>\\n\",\n        \"      <td>  146775585</td>\\n\",\n        \"      <td>       psandeepu</td>\\n\",\n        \"      <td>      Sandeep Unni</td>\\n\",\n        \"      <td>2010-05-22 09:03:02</td>\\n\",\n        \"      <td> Data science, javascript, nodejs, iOS &amp; Sencha...</td>\\n\",\n        \"      <td>   67</td>\\n\",\n        \"      <td> 297</td>\\n\",\n        \"      <td>        Cochin, Kerala, India</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3</th>\\n\",\n        \"      <td> 521335988548022272</td>\\n\",\n        \"      <td> RT @kdnuggets: Using Python to detect Algorith...</td>\\n\",\n        \"      <td>2014-10-12 16:26:00</td>\\n\",\n        \"      <td> 6</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td>   Twitter for iPhone</td>\\n\",\n        \"      <td> 1318985240</td>\\n\",\n        \"      <td> DataScienceDojo</td>\\n\",\n        \"      <td> Data Science Dojo</td>\\n\",\n        \"      <td>2013-03-31 19:25:57</td>\\n\",\n        \"      <td> Visit Our Website: http://t.co/mRpEkKrS55\\\\r\\\\nF...</td>\\n\",\n        \"      <td>  511</td>\\n\",\n        \"      <td> 232</td>\\n\",\n        \"      <td> Seattle, WA and Stanford, CA</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4</th>\\n\",\n        \"      <td> 521335809987723265</td>\\n\",\n        \"      <td> @davidwhogg @exoplaneteer @kylebarbary http://...</td>\\n\",\n        \"      <td>2014-10-12 16:25:17</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> Twitter for Websites</td>\\n\",\n        \"      <td>  244991150</td>\\n\",\n        \"      <td>      fperez_org</td>\\n\",\n        \"      <td>    Fernando Perez</td>\\n\",\n        \"      <td>2011-01-30 16:28:09</td>\\n\",\n        \"      <td> Physicist, applied mathematician, scientific P...</td>\\n\",\n        \"      <td> 5174</td>\\n\",\n        \"      <td> 335</td>\\n\",\n        \"      <td>                 Berkeley, CA</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 10,\n       \"text\": [\n        \"                   id                                               text          created_at  retweet_count  \\\\\\n\",\n        \"0  521338248673501184  RT @kdnuggets: Using Python to detect Algorith... 2014-10-12 16:34:59              6   \\n\",\n        \"1  521337469640249345  RT @datamusing: Easy sort and slice of pandas ... 2014-10-12 16:31:53              2   \\n\",\n        \"2  521336071288680449  RT @kdnuggets: Using Python to detect Algorith... 2014-10-12 16:26:20              6   \\n\",\n        \"3  521335988548022272  RT @kdnuggets: Using Python to detect Algorith... 2014-10-12 16:26:00              6   \\n\",\n        \"4  521335809987723265  @davidwhogg 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  \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>96</th>\\n\",\n        \"      <td> 521104249279635456</td>\\n\",\n        \"      <td> Q for CS: Does iPython support version control...</td>\\n\",\n        \"      <td>2014-10-12 01:05:09</td>\\n\",\n        \"      <td>  1</td>\\n\",\n        \"      <td> 0</td>\\n\",\n        \"      <td> Twitter Web Client</td>\\n\",\n        \"      <td>   50933459</td>\\n\",\n        \"      <td>     ncloonan</td>\\n\",\n        \"      <td> Nicole Cloonan</td>\\n\",\n        \"      <td>2009-06-26 05:15:46</td>\\n\",\n        \"      <td> New lab head at  QIMR Berghofer Medical Resear...</td>\\n\",\n        \"      <td>  841</td>\\n\",\n        \"      <td> 1232</td>\\n\",\n        \"      <td> Brisbane, QLD, Australia </td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>97</th>\\n\",\n        \"      <td> 521103719120011264</td>\\n\",\n        \"      <td> RT @ramialison_lab: Clare Sloggett 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sources.index)\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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/zhALkBuCDJ3sBPGLzvd8GkOyd7MujB7ldVv27LTmcQjl+eZNelwKnA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\\nrXytgLuy2QAAAABJRU5ErkJggg==\\n\",\n       \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f85441f5690>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/data-mining/4. Google Custom Search.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:165cb1198a163a54ba91b1e493f98feb89ae0ae4d3d8bd78adcf43efaf80056c\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"In this tutorial we will cover the basics of using Google Custom Search to search the Interner.\\n\",\n      \"\\n\",\n      \"Links:\\n\",\n      \"\\n\",\n      \"- https://www.google.com/cse/all\\n\",\n      \"- https://console.developers.google.com/\\n\",\n      \"- https://developers.google.com/custom-search/json-api/v1/reference/cse/list\\n\",\n      \"\\n\",\n      \"Video Tutorial:\\n\",\n      \"\\n\",\n      \"http://youtu.be/ewPJs4N8d8M\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import json\\n\",\n      \"import requests\\n\",\n      \"import pandas as pd\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Understanding Google Custom Search\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Google Custom Search replaces the depreciated Google Search API. It is designed to search one or more website and to embedded with in the website.\\n\",\n      \"\\n\",\n      \"There is still an options to search the complete web. This options combined with no specified website to search retuen results which are very close to what you get when you search Google. The difference in results is due to personalized and localized search results that Google search returns.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"key = \\\"\\\"\\n\",\n      \"cx = \\\"\\\"\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"<table class=\\\"matchpre\\\">\\n\",\n      \"    <thead>\\n\",\n      \"      <tr>\\n\",\n      \"        <th>Parameter name</th>\\n\",\n      \"        <th>Value</th>\\n\",\n      \"        <th>Description</th>\\n\",\n      \"      </tr>\\n\",\n      \"    </thead>\\n\",\n      \"    <tbody>\\n\",\n      \"    <tr id=\\\"required-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Required parameters</b></td>\\n\",\n      \"    </tr>\\n\",\n      \"      <tr id=\\\"q\\\">\\n\",\n      \"        <td><code>q</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          The search expression.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"\\n\",\n      \"    <tr id=\\\"optional-parameters\\\" class=\\\"alt\\\">\\n\",\n      \"      <td colspan=\\\"3\\\"><b>Optional parameters</b></td>\\n\",\n      \"    </tr>\\n\",\n      \"      <tr id=\\\"c2coff\\\">\\n\",\n      \"        <td><code>c2coff</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <span>Enables or disables&nbsp;</span><a href=\\\"https://developers.google.com/custom-search/docs/xml_results#chineseSearch\\\">Simplified and Traditional Chinese Search</a>.&nbsp;<ul><li>The default value for this parameter is&nbsp;<code>0</code>&nbsp;(zero), meaning that the feature is enabled. Supported values are:<ul><li><code>1</code>: Disabled</li><li><code>0</code>: Enabled (default)</li></ul></li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"cr\\\">\\n\",\n      \"        <td><code>cr</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <span>R</span><span>estricts search results to documents originating in a particular country. <br></span><ul><li><span>You may use&nbsp;</span><a href=\\\"https://developers.google.com/custom-search/docs/xml_results#booleanOperators\\\">Boolean operators</a><span>&nbsp;</span><span>in the</span><span>&nbsp;</span><code>cr</code><span>&nbsp;</span><span>parameter's value.</span><br></li><li>Google Search determines the country of a document by analyzing:<ul><li>the top-level domain (TLD) of the document's URL</li><li>the geographic location of the Web server's IP address</li></ul></li><li>See the&nbsp;<a href=\\\"https://developers.google.com/custom-search/docs/xml_results#countryCollections\\\">Country Parameter Values</a>&nbsp;page for a list of valid values for this parameter.</li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"cref\\\">\\n\",\n      \"        <td><code>cref</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <span>The URL of a&nbsp;</span><a href=\\\"http://www.google.com/cse/docs/cref.html\\\">linked</a><span><font color=\\\"#222222\\\">&nbsp;custom search engine specification to use for this request.&nbsp;<br><ul><li><span>Does not apply for Google Site Search</span></li><li><font color=\\\"#222222\\\" face=\\\"Arial\\\"><span>If both&nbsp;</span></font><code>cx</code><span>&nbsp;and&nbsp;</span><code>cref</code><span>&nbsp;are specified, the&nbsp;</span><code>cx</code><span>&nbsp;value is used</span><br></li></ul></font></span>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"cx\\\">\\n\",\n      \"        <td><code>cx</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          The custom search engine ID to use for this request.<br><ul><li>If both&nbsp;<code>cx</code>&nbsp;and&nbsp;<code>cref</code>&nbsp;are specified, the&nbsp;<code>cx</code>&nbsp;value is used.</li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"dateRestrict\\\">\\n\",\n      \"        <td><code>dateRestrict</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <span>Restricts results to URLs based on date. Supported values include:</span><ul><li><code>d[number]</code>: requests results from the specified number of past days.</li><li><code>w[number]</code>: requests results from the specified number of past weeks.</li><li><code>m[number]</code>: requests results from the specified number of past months.</li><li><code>y[number]</code>: requests results from the specified number of past years.</li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"exactTerms\\\">\\n\",\n      \"        <td><code>exactTerms</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Identifies a phrase that all documents in the search results must contain.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"excludeTerms\\\">\\n\",\n      \"        <td><code>excludeTerms</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Identifies a word or phrase that should not appear in any documents in the search results.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"fileType\\\">\\n\",\n      \"        <td><code>fileType</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Restricts results to files of a specified extension. A list of file types indexable by Google<span>&nbsp;can be found in Webmaster Tools <a href=\\\"https://support.google.com/webmasters/answer/35287?hl=en\\\">Help Center</a>.</span><span><br></span>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"filter\\\">\\n\",\n      \"        <td><code>filter</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Controls turning on or off the duplicate content filter.<ul><li>See&nbsp;<a href=\\\"https://developers.google.com/custom-search/docs/xml_results#automaticFiltering\\\">Automatic Filtering</a>&nbsp;for more information about Google's search results filters. Note that host crowding filtering applies only to multi-site searches.</li><li>By default, Google applies filtering to all search results to improve the quality of those results.</li></ul>\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>0</code>\\\": Turns off duplicate content filter.\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>1</code>\\\": Turns on duplicate content filter.\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"gl\\\">\\n\",\n      \"        <td><code>gl</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Geolocation of end user.&nbsp;<ul><li>The&nbsp;<code>gl</code>&nbsp;parameter value is a two-letter country code. The&nbsp;<code>gl</code>&nbsp;parameter boosts search results whose country of origin matches the parameter value. See the&nbsp;<a href=\\\"https://developers.google.com/custom-search/docs/xml_results#countryCodes\\\">Country Codes</a>&nbsp;page for a list of valid values.</li><li>Specifying a&nbsp;<code>gl</code>&nbsp;parameter value should lead to more relevant results. This is particularly true for international customers and, even more specifically, for customers in English- speaking countries other than the United States.</li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"googlehost\\\">\\n\",\n      \"        <td><code>googlehost</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          The local Google domain&nbsp;<span>(for example, google.com, google.de, or google.fr) </span>to use to perform the search.&nbsp;\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"highRange\\\">\\n\",\n      \"        <td><code>highRange</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <ul><li><span>Specifies the ending value for a search range.</span><br></li><li>Use&nbsp;<code>lowRange</code>&nbsp;and&nbsp;<code>highRange</code>&nbsp;to append an inclusive search range of&nbsp;<code>lowRange...highRange&nbsp;</code>&nbsp;to the query.</li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"hl\\\">\\n\",\n      \"        <td><code>hl</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Sets the user interface language.&nbsp;<ul><li>Explicitly setting this parameter improves the performance and the quality of your search results.</li><li>See the&nbsp;<a href=\\\"https://developers.google.com/custom-search/docs/xml_results#wsInterfaceLanguages\\\">Interface Languages</a>&nbsp;section of&nbsp;<a href=\\\"https://developers.google.com/custom-search/docs/xml_results#wsInternationalizing\\\">Internationalizing Queries and Results Presentation</a>&nbsp;for more information, and&nbsp;<a href=\\\"https://developers.google.com/custom-search/docs/xml_results#interfaceLanguages\\\">Supported Interface Languages</a>&nbsp;for a list of supported languages.</li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"hq\\\">\\n\",\n      \"        <td><code>hq</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <span>Appends the specified query terms to the query, as if they were combined with a logical&nbsp;</span><code>AND</code><span>&nbsp;</span><span>operator.</span>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"imgColorType\\\">\\n\",\n      \"        <td><code>imgColorType</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Returns black and white, grayscale, or color images: <code>mono</code>, <code>gray</code>, and <code>color</code>.\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>color</code>\\\": color\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>gray</code>\\\": gray\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>mono</code>\\\": mono\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"imgDominantColor\\\">\\n\",\n      \"        <td><code>imgDominantColor</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Returns images of a specific dominant color.\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>black</code>\\\": black\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>blue</code>\\\": blue\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>brown</code>\\\": brown\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>gray</code>\\\": gray\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>green</code>\\\": green\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>pink</code>\\\": pink\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>purple</code>\\\": purple\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>teal</code>\\\": teal\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>white</code>\\\": white\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>yellow</code>\\\": yellow\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"imgSize\\\">\\n\",\n      \"        <td><code>imgSize</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Returns images of a specified size.\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>huge</code>\\\": huge\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>icon</code>\\\": icon\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>large</code>\\\": large\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>medium</code>\\\": medium\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>small</code>\\\": small\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>xlarge</code>\\\": xlarge\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>xxlarge</code>\\\": xxlarge\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"imgType\\\">\\n\",\n      \"        <td><code>imgType</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Returns images of a type.<code></code>\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>clipart</code>\\\": clipart\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>face</code>\\\": face\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lineart</code>\\\": lineart\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>news</code>\\\": news\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>photo</code>\\\": photo\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"linkSite\\\">\\n\",\n      \"        <td><code>linkSite</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Specifies that all search results should contain a link to a particular URL\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"lowRange\\\">\\n\",\n      \"        <td><code>lowRange</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <span>Specifies the starting value for a search range. <br></span><span>Use&nbsp;</span><code>lowRange&nbsp;</code><span>and</span><span>&nbsp;</span><code>highRange</code><span>&nbsp;</span><span>to append an inclusive search range of&nbsp;</span><code>lowRange...highRange</code><span>&nbsp;to the query.</span><span><br></span>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"lr\\\">\\n\",\n      \"        <td><code>lr</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          <span>Restricts the search to documents written in a particular language (e.g.,&nbsp;</span><code>lr=lang_ja</code><span>).</span>\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>lang_ar</code>\\\": Arabic\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_bg</code>\\\": Bulgarian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_ca</code>\\\": Catalan\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_cs</code>\\\": Czech\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_da</code>\\\": Danish\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_de</code>\\\": German\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_el</code>\\\": Greek\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_en</code>\\\": English\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_es</code>\\\": Spanish\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_et</code>\\\": Estonian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_fi</code>\\\": Finnish\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_fr</code>\\\": French\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_hr</code>\\\": Croatian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_hu</code>\\\": Hungarian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_id</code>\\\": Indonesian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_is</code>\\\": Icelandic\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_it</code>\\\": Italian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_iw</code>\\\": Hebrew\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_ja</code>\\\": Japanese\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_ko</code>\\\": Korean\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_lt</code>\\\": Lithuanian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_lv</code>\\\": Latvian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_nl</code>\\\": Dutch\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_no</code>\\\": Norwegian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_pl</code>\\\": Polish\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_pt</code>\\\": Portuguese\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_ro</code>\\\": Romanian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_ru</code>\\\": Russian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_sk</code>\\\": Slovak\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_sl</code>\\\": Slovenian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_sr</code>\\\": Serbian\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_sv</code>\\\": Swedish\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_tr</code>\\\": Turkish\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_zh-CN</code>\\\": Chinese (Simplified)\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>lang_zh-TW</code>\\\": Chinese (Traditional)\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"num\\\">\\n\",\n      \"        <td><code>num</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">unsigned integer</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Number of search results to return.<br><ul><li>Valid values are integers between 1 and 10, inclusive.</li></ul>\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"orTerms\\\">\\n\",\n      \"        <td><code>orTerms</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Provides additional search terms to check for in a document, where each document in the search results must contain at least one of the additional search terms.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"relatedSite\\\">\\n\",\n      \"        <td><code>relatedSite</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Specifies that all search results should be pages that are related to the specified URL.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"rights\\\">\\n\",\n      \"        <td><code>rights</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Filters based on licensing. Supported values include: <code>cc_publicdomain</code>, <code>cc_attribute</code>, <code>cc_sharealike</code>, <code>cc_noncommercial</code>, <code>cc_nonderived,</code> and combinations of these.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"safe\\\">\\n\",\n      \"        <td><code>safe</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Search safety level.\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>high</code>\\\": Enables highest level of SafeSearch filtering.\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>medium</code>\\\": Enables moderate SafeSearch filtering.\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>off</code>\\\": Disables SafeSearch filtering.\\n\",\n      \"              (default)\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"searchType\\\">\\n\",\n      \"        <td><code>searchType</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Specifies the search type: <code>image</code>.&nbsp;<span>&nbsp;If unspecified, results are limited to webpages.</span>\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>image</code>\\\": custom image search.\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"siteSearch\\\">\\n\",\n      \"        <td><code>siteSearch</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Specifies all search results should be pages from a given site.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"siteSearchFilter\\\">\\n\",\n      \"        <td><code>siteSearchFilter</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          Controls whether to include or exclude results from the site named in the <code>siteSearch</code> parameter.\\n\",\n      \"\\n\",\n      \"          <br><br>Acceptable values are:\\n\",\n      \"          <ul>\\n\",\n      \"            <li>\\\"<code>e</code>\\\": exclude\\n\",\n      \"            </li>\\n\",\n      \"            <li>\\\"<code>i</code>\\\": include\\n\",\n      \"            </li>\\n\",\n      \"          </ul>\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"sort\\\">\\n\",\n      \"        <td><code>sort</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">string</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          The sort expression to apply to the results.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"      <tr id=\\\"start\\\">\\n\",\n      \"        <td><code>start</code></td>\\n\",\n      \"        <td><code class=\\\"apitype\\\">unsigned integer</code></td>\\n\",\n      \"        <td>\\n\",\n      \"          The index of the first result to return.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"        </td>\\n\",\n      \"      </tr>\\n\",\n      \"    </tbody>\\n\",\n      \"  </table>\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Prepare The request\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"url = \\\"https://www.googleapis.com/customsearch/v1\\\"\\n\",\n      \"parameters = {\\\"q\\\": \\\"halloween\\\",\\n\",\n      \"              \\\"cx\\\": cx,\\n\",\n      \"              \\\"key\\\": key,\\n\",\n      \"              }\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Make the request\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"page = requests.request(\\\"GET\\\", url, params=parameters)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Process Results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results = json.loads(page.text)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Inspecting Results\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results.keys()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"[u'kind', u'url', u'items', u'context', u'queries', u'searchInformation']\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Inspecting Search Meta Data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results[\\\"kind\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 7,\n       \"text\": [\n        \"u'customsearch#search'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results[\\\"url\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 8,\n       \"text\": [\n        \"{u'template': u'https://www.googleapis.com/customsearch/v1?q={searchTerms}&num={count?}&start={startIndex?}&lr={language?}&safe={safe?}&cx={cx?}&cref={cref?}&sort={sort?}&filter={filter?}&gl={gl?}&cr={cr?}&googlehost={googleHost?}&c2coff={disableCnTwTranslation?}&hq={hq?}&hl={hl?}&siteSearch={siteSearch?}&siteSearchFilter={siteSearchFilter?}&exactTerms={exactTerms?}&excludeTerms={excludeTerms?}&linkSite={linkSite?}&orTerms={orTerms?}&relatedSite={relatedSite?}&dateRestrict={dateRestrict?}&lowRange={lowRange?}&highRange={highRange?}&searchType={searchType}&fileType={fileType?}&rights={rights?}&imgSize={imgSize?}&imgType={imgType?}&imgColorType={imgColorType?}&imgDominantColor={imgDominantColor?}&alt=json',\\n\",\n        \" u'type': u'application/json'}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"len(results[\\\"items\\\"])\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"10\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results[\\\"queries\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 10,\n       \"text\": [\n        \"{u'nextPage': [{u'count': 10,\\n\",\n        \"   u'cx': u'013297447421619698040:gnhhn4xeyns',\\n\",\n        \"   u'inputEncoding': u'utf8',\\n\",\n        \"   u'outputEncoding': u'utf8',\\n\",\n        \"   u'safe': u'off',\\n\",\n        \"   u'searchTerms': u'halloween',\\n\",\n        \"   u'startIndex': 11,\\n\",\n        \"   u'title': u'Google Custom Search - halloween',\\n\",\n        \"   u'totalResults': u'117000000'}],\\n\",\n        \" u'request': [{u'count': 10,\\n\",\n        \"   u'cx': u'013297447421619698040:gnhhn4xeyns',\\n\",\n        \"   u'inputEncoding': u'utf8',\\n\",\n        \"   u'outputEncoding': u'utf8',\\n\",\n        \"   u'safe': u'off',\\n\",\n        \"   u'searchTerms': u'halloween',\\n\",\n        \"   u'startIndex': 1,\\n\",\n        \"   u'title': u'Google Custom Search - halloween',\\n\",\n        \"   u'totalResults': u'117000000'}]}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results[\\\"searchInformation\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"{u'formattedSearchTime': u'0.53',\\n\",\n        \" u'formattedTotalResults': u'117,000,000',\\n\",\n        \" u'searchTime': 0.531343,\\n\",\n        \" u'totalResults': u'117000000'}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Inspecting a single result\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"results[\\\"items\\\"][0]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 12,\n       \"text\": [\n        \"{u'cacheId': u'0b5Oki2-f4EJ',\\n\",\n        \" u'displayLink': u'en.wikipedia.org',\\n\",\n        \" u'formattedUrl': u'en.wikipedia.org/wiki/Halloween',\\n\",\n        \" u'htmlFormattedUrl': u'en.wikipedia.org/wiki/<b>Halloween</b>',\\n\",\n        \" u'htmlSnippet': u'<b>Halloween</b> or Hallowe&#39;en <sup>6]</sup> also known as Allhalloween, All Hallows&#39; Eve, or All <br>\\\\nSaints&#39; Eve, is a yearly celebration observed in a number of countries on 31&nbsp;...',\\n\",\n        \" u'htmlTitle': u'<b>Halloween</b> - Wikipedia, the free encyclopedia',\\n\",\n        \" u'kind': u'customsearch#result',\\n\",\n        \" u'link': u'http://en.wikipedia.org/wiki/Halloween',\\n\",\n        \" u'pagemap': {u'cse_image': [{u'src': u\\\"http://upload.wikimedia.org/wikipedia/commons/thumb/a/a2/Jack-o'-Lantern_2003-10-31.jpg/240px-Jack-o'-Lantern_2003-10-31.jpg\\\"}],\\n\",\n        \"  u'cse_thumbnail': [{u'height': u'188',\\n\",\n        \"    u'src': u'https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcQDphQXJ1JeGZS30rhMAfcAhklES25AKZ-9TS9YwUdycnIqLa7W96Maxjg',\\n\",\n        \"    u'width': u'192'}],\\n\",\n        \"  u'event': [{u'dtstart': u'2014-10-31',\\n\",\n        \"    u'summary': u'First day of Allhallowtide'}],\\n\",\n        \"  u'hcalendar': [{u'dtstart': u'2014-10-31',\\n\",\n        \"    u'summary': u'First day of Allhallowtide'}]},\\n\",\n        \" u'snippet': u\\\"Halloween or Hallowe'en 6] also known as Allhalloween, All Hallows' Eve, or All \\\\nSaints' Eve, is a yearly celebration observed in a number of countries on 31\\\\xa0...\\\",\\n\",\n        \" u'title': u'Halloween - Wikipedia, the free encyclopedia'}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Process Results Into a Pandas Data Frame\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def process_search(results):\\n\",\n      \"    link_list = [item[\\\"link\\\"] for item in results[\\\"items\\\"]]\\n\",\n      \"    df = pd.DataFrame(link_list, columns=[\\\"link\\\"])\\n\",\n      \"    df[\\\"title\\\"] = [item[\\\"title\\\"] for item in results[\\\"items\\\"]]\\n\",\n      \"    df[\\\"snippet\\\"] = [item[\\\"snippet\\\"] for item in results[\\\"items\\\"]]\\n\",\n      \"    return df\\n\",\n      \"df = process_search(results)\\n\",\n      \"df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>link</th>\\n\",\n        \"      <th>title</th>\\n\",\n        \"      <th>snippet</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0</th>\\n\",\n        \"      <td>            http://en.wikipedia.org/wiki/Halloween</td>\\n\",\n        \"      <td>      Halloween - Wikipedia, the free encyclopedia</td>\\n\",\n        \"      <td> Halloween or Hallowe'en 6] also known as Allha...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1</th>\\n\",\n        \"      <td>           http://www.history.com/topics/halloween</td>\\n\",\n        \"      <td> Halloween - Videos, Facts, Origin &amp; Meaning - ...</td>\\n\",\n        \"      <td> Find out more about the history of Halloween, ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2</th>\\n\",\n        \"      <td>              http://www.imdb.com/title/tt0077651/</td>\\n\",\n        \"      <td>                           Halloween (1978) - IMDb</td>\\n\",\n        \"      <td> Halloween II. A Nightmare on Elm Street. Hallo...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3</th>\\n\",\n        \"      <td>                         http://www.halloween.com/</td>\\n\",\n        \"      <td>                    Halloween 2014 | Halloween.com</td>\\n\",\n        \"      <td> Halloween fun on the internet, the one source ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4</th>\\n\",\n        \"      <td>                       http://halloweenmovies.com/</td>\\n\",\n        \"      <td>                                  HalloweenMovies\\u2122</td>\\n\",\n        \"      <td> Tickets now on sale for Halloween - ODEON Cine...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5</th>\\n\",\n        \"      <td>        http://www.loc.gov/folklife/halloween.html</td>\\n\",\n        \"      <td> Halloween: The Fantasy and Folklore of All Hal...</td>\\n\",\n        \"      <td> Beginnings of Halloween celebration; First ori...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6</th>\\n\",\n        \"      <td>                   http://www.spirithalloween.com/</td>\\n\",\n        \"      <td> Halloween Costumes - Childrens &amp; Adult Hallowe...</td>\\n\",\n        \"      <td> Spirit Halloween - Halloween Stores nationwide...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7</th>\\n\",\n        \"      <td>              http://www.cdc.gov/family/halloween/</td>\\n\",\n        \"      <td> CDC - Halloween Health and Safety - Family Health</td>\\n\",\n        \"      <td> 5 days ago ... Fall celebrations like Hallowee...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8</th>\\n\",\n        \"      <td> http://www.partycity.com/category/halloween+co...</td>\\n\",\n        \"      <td> Halloween Costumes for Kids &amp; Adults - Costume...</td>\\n\",\n        \"      <td> Halloween costumes for all ages and sizes. Sho...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9</th>\\n\",\n        \"      <td>        http://www.overkillsoftware.com/halloween/</td>\\n\",\n        \"      <td>                     PAYDAY HALLOWEEN SPECIAL 2014</td>\\n\",\n        \"      <td>                                                  </td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 13,\n       \"text\": [\n        \"                                                link  \\\\\\n\",\n        \"0             http://en.wikipedia.org/wiki/Halloween   \\n\",\n        \"1            http://www.history.com/topics/halloween   \\n\",\n        \"2               http://www.imdb.com/title/tt0077651/   \\n\",\n        \"3                          http://www.halloween.com/   \\n\",\n        \"4                        http://halloweenmovies.com/   \\n\",\n        \"5         http://www.loc.gov/folklife/halloween.html   \\n\",\n        \"6                    http://www.spirithalloween.com/   \\n\",\n        \"7               http://www.cdc.gov/family/halloween/   \\n\",\n        \"8  http://www.partycity.com/category/halloween+co...   \\n\",\n        \"9         http://www.overkillsoftware.com/halloween/   \\n\",\n        \"\\n\",\n        \"                                               title  \\\\\\n\",\n        \"0       Halloween - Wikipedia, the free encyclopedia   \\n\",\n        \"1  Halloween - Videos, Facts, Origin & Meaning - ...   \\n\",\n        \"2                            Halloween (1978) - IMDb   \\n\",\n        \"3                     Halloween 2014 | Halloween.com   \\n\",\n        \"4                                   HalloweenMovies\\u2122   \\n\",\n        \"5  Halloween: The Fantasy and Folklore of All Hal...   \\n\",\n        \"6  Halloween Costumes - Childrens & Adult Hallowe...   \\n\",\n        \"7  CDC - Halloween Health and Safety - Family Health   \\n\",\n        \"8  Halloween Costumes for Kids & Adults - Costume...   \\n\",\n        \"9                      PAYDAY HALLOWEEN SPECIAL 2014   \\n\",\n        \"\\n\",\n        \"                                             snippet  \\n\",\n        \"0  Halloween or Hallowe'en 6] also known as Allha...  \\n\",\n        \"1  Find out more about the history of Halloween, ...  \\n\",\n        \"2  Halloween II. A Nightmare on Elm Street. Hallo...  \\n\",\n        \"3  Halloween fun on the internet, the one source ...  \\n\",\n        \"4  Tickets now on sale for Halloween - ODEON Cine...  \\n\",\n        \"5  Beginnings of Halloween celebration; First ori...  \\n\",\n        \"6  Spirit Halloween - Halloween Stores nationwide...  \\n\",\n        \"7  5 days ago ... Fall celebrations like Hallowee...  \\n\",\n        \"8  Halloween costumes for all ages and sizes. Sho...  \\n\",\n        \"9                                                     \"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Getting Results from more pages\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Use `\\\"start\\\"` parameter to skip results from previous pages. To get the next `\\\"start\\\"` index look it up in `\\\"queries.nextPage[0].startIndex\\\"`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"next_index = results[\\\"queries\\\"][\\\"nextPage\\\"][0][\\\"startIndex\\\"]\\n\",\n      \"search_terms = results[\\\"queries\\\"][\\\"nextPage\\\"][0][\\\"searchTerms\\\"]\\n\",\n      \"\\n\",\n      \"url = \\\"https://www.googleapis.com/customsearch/v1\\\"\\n\",\n      \"parameters = {\\\"q\\\": search_terms,\\n\",\n      \"              \\\"cx\\\": cx,\\n\",\n      \"              \\\"key\\\": key,\\n\",\n      \"              \\\"start\\\": next_index\\n\",\n      \"              }\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"page = requests.request(\\\"GET\\\", url, params=parameters)\\n\",\n      \"results = json.loads(page.text)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def process_search(results):\\n\",\n      \"    link_list = [item[\\\"link\\\"] for item in results[\\\"items\\\"]]\\n\",\n      \"    df = pd.DataFrame(link_list, columns=[\\\"link\\\"])\\n\",\n      \"    df[\\\"title\\\"] = [item[\\\"title\\\"] for item in results[\\\"items\\\"]]\\n\",\n      \"    df[\\\"snippet\\\"] = [item[\\\"snippet\\\"] for item in results[\\\"items\\\"]]\\n\",\n      \"    return df\\n\",\n      \"temp_df = process_search(results)\\n\",\n      \"df = pd.concat([df, temp_df], ignore_index=True)\\n\",\n      \"df\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>link</th>\\n\",\n        \"      <th>title</th>\\n\",\n        \"      <th>snippet</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td>            http://en.wikipedia.org/wiki/Halloween</td>\\n\",\n        \"      <td>      Halloween - Wikipedia, the free encyclopedia</td>\\n\",\n        \"      <td> Halloween or Hallowe'en 6] also known as Allha...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td>           http://www.history.com/topics/halloween</td>\\n\",\n        \"      <td> Halloween - Videos, Facts, Origin &amp; Meaning - ...</td>\\n\",\n        \"      <td> Find out more about the history of Halloween, ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td>              http://www.imdb.com/title/tt0077651/</td>\\n\",\n        \"      <td>                           Halloween (1978) - IMDb</td>\\n\",\n        \"      <td> Halloween II. A Nightmare on Elm Street. Hallo...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td>                         http://www.halloween.com/</td>\\n\",\n        \"      <td>                    Halloween 2014 | Halloween.com</td>\\n\",\n        \"      <td> Halloween fun on the internet, the one source ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td>                       http://halloweenmovies.com/</td>\\n\",\n        \"      <td>                                  HalloweenMovies\\u2122</td>\\n\",\n        \"      <td> Tickets now on sale for Halloween - ODEON Cine...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td>        http://www.loc.gov/folklife/halloween.html</td>\\n\",\n        \"      <td> Halloween: The Fantasy and Folklore of All Hal...</td>\\n\",\n        \"      <td> Beginnings of Halloween celebration; First ori...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td>                   http://www.spirithalloween.com/</td>\\n\",\n        \"      <td> Halloween Costumes - Childrens &amp; Adult Hallowe...</td>\\n\",\n        \"      <td> Spirit Halloween - Halloween Stores nationwide...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td>              http://www.cdc.gov/family/halloween/</td>\\n\",\n        \"      <td> CDC - Halloween Health and Safety - Family Health</td>\\n\",\n        \"      <td> 5 days ago ... Fall celebrations like Hallowee...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> http://www.partycity.com/category/halloween+co...</td>\\n\",\n        \"      <td> Halloween Costumes for Kids &amp; Adults - Costume...</td>\\n\",\n        \"      <td> Halloween costumes for all ages and sizes. Sho...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td>        http://www.overkillsoftware.com/halloween/</td>\\n\",\n        \"      <td>                     PAYDAY HALLOWEEN SPECIAL 2014</td>\\n\",\n        \"      <td>                                                  </td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> http://www.popsugar.com/moms/Ultimate-Hallowee...</td>\\n\",\n        \"      <td>          Ultimate Halloween Guide | POPSUGAR Moms</td>\\n\",\n        \"      <td> Download our Halloween app! ... Felicity to Sc...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> http://www.yandy.com/Shopping/products/categor...</td>\\n\",\n        \"      <td>                           Sexy Halloween Costumes</td>\\n\",\n        \"      <td> Sexy Halloween costumes up to 75% off! Free sh...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td>       http://www.grandinroad.com/halloween-haven/</td>\\n\",\n        \"      <td> Halloween Decorations - Halloween Decor - Gran...</td>\\n\",\n        \"      <td> Shop Grandin Road's collection of outdoor Hall...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> http://www.amazon.com/Halloween-Donald-Pleasen...</td>\\n\",\n        \"      <td> Amazon.com: Halloween: Donald Pleasence, Jamie...</td>\\n\",\n        \"      <td> Halloween stars Jamie Lee Curtis (A Fish Calle...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td>            http://www.instructables.com/halloween</td>\\n\",\n        \"      <td>                           Halloween Instructables</td>\\n\",\n        \"      <td> DIY Halloween costumes for adults, kids and pe...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> http://www.huffingtonpost.com/2014/10/27/hallo...</td>\\n\",\n        \"      <td> These Are The Most Googled Halloween Costumes ...</td>\\n\",\n        \"      <td> 1 day ago ... Some states are making a topical...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td>       http://www.wilstar.com/holidays/hallown.htm</td>\\n\",\n        \"      <td> Halloween - The History, Traditions, and Custo...</td>\\n\",\n        \"      <td> The history of Halloween and its customs start...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td>             https://www.etsy.com/browse/halloween</td>\\n\",\n        \"      <td>        Halloween Costumes, Decor &amp; Treats on Etsy</td>\\n\",\n        \"      <td> Find one-of-a-kind costumes for kids, adults a...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> https://nrf.com/media/press-releases/record-nu...</td>\\n\",\n        \"      <td> Record Number of Americans to Buy Halloween Co...</td>\\n\",\n        \"      <td> Sep 24, 2014 ... More than two-thirds (67.4%) ...</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td> http://www.accuweather.com/en/weather-news/eas...</td>\\n\",\n        \"      <td> Halloween Forecast: Snow, Cold to Blast Northe...</td>\\n\",\n        \"      <td> 1 day ago ... A shocking blast of cold air and...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 16,\n       \"text\": [\n        \"                                                 link  \\\\\\n\",\n        \"0              http://en.wikipedia.org/wiki/Halloween   \\n\",\n        \"1             http://www.history.com/topics/halloween   \\n\",\n        \"2                http://www.imdb.com/title/tt0077651/   \\n\",\n        \"3                           http://www.halloween.com/   \\n\",\n        \"4                         http://halloweenmovies.com/   \\n\",\n        \"5          http://www.loc.gov/folklife/halloween.html   \\n\",\n        \"6                     http://www.spirithalloween.com/   \\n\",\n        \"7                http://www.cdc.gov/family/halloween/   \\n\",\n        \"8   http://www.partycity.com/category/halloween+co...   \\n\",\n        \"9          http://www.overkillsoftware.com/halloween/   \\n\",\n        \"10  http://www.popsugar.com/moms/Ultimate-Hallowee...   \\n\",\n        \"11  http://www.yandy.com/Shopping/products/categor...   \\n\",\n        \"12        http://www.grandinroad.com/halloween-haven/   \\n\",\n        \"13  http://www.amazon.com/Halloween-Donald-Pleasen...   \\n\",\n        \"14             http://www.instructables.com/halloween   \\n\",\n        \"15  http://www.huffingtonpost.com/2014/10/27/hallo...   \\n\",\n        \"16        http://www.wilstar.com/holidays/hallown.htm   \\n\",\n        \"17              https://www.etsy.com/browse/halloween   \\n\",\n        \"18  https://nrf.com/media/press-releases/record-nu...   \\n\",\n        \"19  http://www.accuweather.com/en/weather-news/eas...   \\n\",\n        \"\\n\",\n        \"                                                title  \\\\\\n\",\n        \"0        Halloween - Wikipedia, the free encyclopedia   \\n\",\n        \"1   Halloween - Videos, Facts, Origin & Meaning - ...   \\n\",\n        \"2                             Halloween (1978) - IMDb   \\n\",\n        \"3                      Halloween 2014 | Halloween.com   \\n\",\n        \"4                                    HalloweenMovies\\u2122   \\n\",\n        \"5   Halloween: The Fantasy and Folklore of All Hal...   \\n\",\n        \"6   Halloween Costumes - Childrens & Adult Hallowe...   \\n\",\n        \"7   CDC - Halloween Health and Safety - Family Health   \\n\",\n        \"8   Halloween Costumes for Kids & Adults - Costume...   \\n\",\n        \"9                       PAYDAY HALLOWEEN SPECIAL 2014   \\n\",\n        \"10           Ultimate Halloween Guide | POPSUGAR Moms   \\n\",\n        \"11                            Sexy Halloween Costumes   \\n\",\n        \"12  Halloween Decorations - Halloween Decor - Gran...   \\n\",\n        \"13  Amazon.com: Halloween: Donald Pleasence, Jamie...   \\n\",\n        \"14                            Halloween Instructables   \\n\",\n        \"15  These Are The Most Googled Halloween Costumes ...   \\n\",\n        \"16  Halloween - The History, Traditions, and Custo...   \\n\",\n        \"17         Halloween Costumes, Decor & Treats on Etsy   \\n\",\n        \"18  Record Number of Americans to Buy Halloween Co...   \\n\",\n        \"19  Halloween Forecast: Snow, Cold to Blast Northe...   \\n\",\n        \"\\n\",\n        \"                                              snippet  \\n\",\n        \"0   Halloween or Hallowe'en 6] also known as Allha...  \\n\",\n        \"1   Find out more about the history of Halloween, ...  \\n\",\n        \"2   Halloween II. A Nightmare on Elm Street. Hallo...  \\n\",\n        \"3   Halloween fun on the internet, the one source ...  \\n\",\n        \"4   Tickets now on sale for Halloween - ODEON Cine...  \\n\",\n        \"5   Beginnings of Halloween celebration; First ori...  \\n\",\n        \"6   Spirit Halloween - Halloween Stores nationwide...  \\n\",\n        \"7   5 days ago ... Fall celebrations like Hallowee...  \\n\",\n        \"8   Halloween costumes for all ages and sizes. Sho...  \\n\",\n        \"9                                                      \\n\",\n        \"10  Download our Halloween app! ... Felicity to Sc...  \\n\",\n        \"11  Sexy Halloween costumes up to 75% off! Free sh...  \\n\",\n        \"12  Shop Grandin Road's collection of outdoor Hall...  \\n\",\n        \"13  Halloween stars Jamie Lee Curtis (A Fish Calle...  \\n\",\n        \"14  DIY Halloween costumes for adults, kids and pe...  \\n\",\n        \"15  1 day ago ... Some states are making a topical...  \\n\",\n        \"16  The history of Halloween and its customs start...  \\n\",\n        \"17  Find one-of-a-kind costumes for kids, adults a...  \\n\",\n        \"18  Sep 24, 2014 ... More than two-thirds (67.4%) ...  \\n\",\n        \"19  1 day ago ... A shocking blast of cold air and...  \"\n       ]\n      }\n     ],\n     \"prompt_number\": 16\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/data-mining/5. MongoDB.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:09e16c4bfbc9ec74cf600280655acc86f44886215a4de940faa26d2db6c7273e\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Video Tutorial: http://youtu.be/2b32-KVzBXQ\\n\",\n      \"\\n\",\n      \"In many cases you don't have to go and collect the data because you have data in a local NoSQL DB that requires analysis.This could be a URL audit-trail for your website where you want to study visitors and trends. One common NoSQL Database is MongoDB which is the subject of this tutorial.\\n\",\n      \"\\n\",\n      \"- [Why to use NoSQL?](#Why-to-use-NoSQL?)\\n\",\n      \"- [Common NoSQL Databases](#Common-NoSQL-Databses)\\n\",\n      \"- [PyMongo](#PyMongo)\\n\",\n      \"- [Single document with nested documents](#Finding-a-Single-Document)\\n\",\n      \"- [Working with multiple documents](#Finding-Multiple-Documents)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Why to use NoSQL?\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"NoSQL was designed to deal with the problems that came up when developers started dealing with large amount of data using the existing Relational Databases (also referred to as SQL Databases). The three main issues with SQL Databases were:\\n\",\n      \"\\n\",\n      \"###Changing Schema of Data\\n\",\n      \"SQL Databases had fixed schema of tables which limited the ability to store new fields in your table. You have to change the table schema when ever you need a new field.\\n\",\n      \"\\n\",\n      \"###The Volume of Data in Storage\\n\",\n      \"SQL Databases can store virtually billions of records in a single table but the problem was the overhead cost to your CPU and memory to be able to access this data efficiently.\\n\",\n      \"\\n\",\n      \"###The Frequency of Adding New Data\\n\",\n      \"SQL databases are not designed to insert large amount of records in short time specially to large tables.\\n\",\n      \"\\n\",\n      \"##What did NoSQL Do?\\n\",\n      \"- Document based archives use a JSON-like format to store rows in a collection. This allows the collection to store data in any schema without changing the database.\\n\",\n      \"- NoSQL databases use different distributed clustering systems to store data in multiple machines and distribute the processing power over multiple machines. MongoDB uses GridFS and some other databases use Hadoop.\\n\",\n      \"- NoSQL dropped the support for relational data which required multiple read operations to ensure PK integrity in a relation. This reduced the cost of storing new data to simply storing the data on disk and generating PK in some systems.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Common NoSQL Databases\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"##MongoDB\\n\",\n      \"\\n\",\n      \"![MongoDB](http://www.mongodb.com/sites/mongodb.com/files/media/mongodb-logo-web.png)\\n\",\n      \"\\n\",\n      \"> <p><b>MongoDB</b> (from \\\"hu<b>mongo</b>us\\\") is a cross-platform <a href=\\\"http://en.wikipedia.org/wiki/Document-oriented_database\\\" title=\\\"Document-oriented database\\\">document-oriented database</a>. Classified as a <a href=\\\"http://en.wikipedia.org/wiki/NoSQL\\\" title=\\\"NoSQL\\\">NoSQL</a> database, MongoDB eschews the traditional table-based <a href=\\\"http://en.wikipedia.org/wiki/Relational_database\\\" title=\\\"Relational database\\\">relational database</a> structure in favor of <a href=\\\"http://en.wikipedia.org/wiki/JSON\\\" title=\\\"JSON\\\">JSON</a>-like documents with dynamic <a href=\\\"http://en.wikipedia.org/wiki/Database_schema\\\" title=\\\"Database schema\\\">schemas</a> (MongoDB calls the format <a href=\\\"http://en.wikipedia.org/wiki/BSON\\\" title=\\\"BSON\\\">BSON</a>), making the integration of data in certain types of applications easier and faster. Released under a combination of the <a href=\\\"http://en.wikipedia.org/wiki/GNU_Affero_General_Public_License\\\" title=\\\"GNU Affero General Public License\\\" class=\\\"mw-redirect\\\">GNU Affero General Public License</a> and the <a href=\\\"http://en.wikipedia.org/wiki/Apache_License\\\" title=\\\"Apache License\\\">Apache License</a>, MongoDB is <a href=\\\"http://en.wikipedia.org/wiki/Free_and_open_source_software\\\" title=\\\"Free and open source software\\\" class=\\\"mw-redirect\\\">free and open-source software</a>.</p>\\n\",\n      \"> ####from [Wikipedia](http://en.wikipedia.org/wiki/MongoDB)\\n\",\n      \"\\n\",\n      \"##Apache Casandra\\n\",\n      \"\\n\",\n      \"<img alt=\\\"Cassandra logo\\\" src=\\\"//upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Cassandra_logo.svg/220px-Cassandra_logo.svg.png\\\" srcset=\\\"//upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Cassandra_logo.svg/330px-Cassandra_logo.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Cassandra_logo.svg/440px-Cassandra_logo.svg.png 2x\\\" data-file-width=\\\"279\\\" data-file-height=\\\"187\\\" height=\\\"147\\\" width=\\\"220\\\">\\n\",\n      \"\\n\",\n      \"> <p><b>Apache Cassandra</b> is an <a href=\\\"http://en.wikipedia.org/wiki/Open_source_software\\\" title=\\\"Open source software\\\" class=\\\"mw-redirect\\\">open source</a> <a href=\\\"http://en.wikipedia.org/wiki/Distributed_database\\\" title=\\\"Distributed database\\\">distributed</a> <a href=\\\"http://en.wikipedia.org/wiki/Database_management_system\\\" title=\\\"Database management system\\\" class=\\\"mw-redirect\\\">database management system</a> designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters,<sup id=\\\"cite_ref-1\\\" class=\\\"reference\\\"><a href=\\\"http://en.wikipedia.org/wiki/Apache_Cassandra#cite_note-1\\\"><span>[</span>1<span>]</span></a></sup> with asynchronous masterless replication allowing low latency operations for all clients.</p>\\n\",\n      \"> ####from [Wikipedia](http://en.wikipedia.org/wiki/Apache_Cassandra)\\n\",\n      \"\\n\",\n      \"##Apache HBase\\n\",\n      \"\\n\",\n      \"![HBase](http://upload.wikimedia.org/wikipedia/en/e/e7/HBase_Logo.png)\\n\",\n      \"\\n\",\n      \"><p><b>HBase</b> is an <a href=\\\"http://en.wikipedia.org/wiki/Open_source\\\" title=\\\"Open source\\\">open source</a>, <a href=\\\"http://en.wikipedia.org/wiki/Non-relational_database\\\" title=\\\"Non-relational database\\\" class=\\\"mw-redirect\\\">non-relational</a>, <a href=\\\"http://en.wikipedia.org/wiki/Distributed_database\\\" title=\\\"Distributed database\\\">distributed database</a> modeled after <a href=\\\"http://en.wikipedia.org/wiki/Google\\\" title=\\\"Google\\\">Google's</a> <a href=\\\"http://en.wikipedia.org/wiki/BigTable\\\" title=\\\"BigTable\\\">BigTable</a> and written in <a href=\\\"http://en.wikipedia.org/wiki/Java_(programming_language)\\\" title=\\\"Java (programming language)\\\">Java</a>. It is developed as part of <a href=\\\"http://en.wikipedia.org/wiki/Apache_Software_Foundation\\\" title=\\\"Apache Software Foundation\\\">Apache Software Foundation</a>'s <a href=\\\"http://en.wikipedia.org/wiki/Hadoop\\\" title=\\\"Hadoop\\\" class=\\\"mw-redirect\\\">Apache Hadoop</a> project and runs on top of <a href=\\\"http://en.wikipedia.org/wiki/Hadoop_Distributed_Filesystem\\\" title=\\\"Hadoop Distributed Filesystem\\\" class=\\\"mw-redirect\\\">HDFS (Hadoop Distributed Filesystem)</a>, providing BigTable-like capabilities for Hadoop. That is, it provides a <a href=\\\"http://en.wikipedia.org/wiki/Fault-tolerant\\\" title=\\\"Fault-tolerant\\\" class=\\\"mw-redirect\\\">fault-tolerant</a> way of storing large quantities of <a href=\\\"http://en.wikipedia.org/wiki/Sparse_file\\\" title=\\\"Sparse file\\\">sparse</a> data (small amounts of information caught within a large collection of empty or unimportant data, such as finding the 50 largest items in a group of 2 billion records, or finding the non-zero items representing less than 0.1% of a huge collection).</p>\\n\",\n      \"> ####from [Wikipedia](http://en.wikipedia.org/wiki/Apache_HBase)\\n\",\n      \"\\n\",\n      \"##CouchDB\\n\",\n      \"\\n\",\n      \"![CouchDB](http://couchdb.apache.org/image/couch.png)\\n\",\n      \"\\n\",\n      \"><p><b>Apache CouchDB</b>, commonly referred to as <b>CouchDB</b>, is an <a href=\\\"http://en.wikipedia.org/wiki/Open-source_software\\\" title=\\\"Open-source software\\\">open source</a> database that focuses on ease of use and on being \\\"a database that completely embraces the web\\\".<sup id=\\\"cite_ref-official-website_1-0\\\" class=\\\"reference\\\"><a href=\\\"http://en.wikipedia.org/wiki/CouchDB#cite_note-official-website-1\\\"><span>[</span>1<span>]</span></a></sup> It is a <a href=\\\"http://en.wikipedia.org/wiki/NoSQL\\\" title=\\\"NoSQL\\\">NoSQL</a> database that uses <a href=\\\"http://en.wikipedia.org/wiki/JSON\\\" title=\\\"JSON\\\">JSON</a> to store data, <a href=\\\"http://en.wikipedia.org/wiki/JavaScript\\\" title=\\\"JavaScript\\\">JavaScript</a> as its query language using <a href=\\\"http://en.wikipedia.org/wiki/MapReduce\\\" title=\\\"MapReduce\\\">MapReduce</a>, and <a href=\\\"http://en.wikipedia.org/wiki/HTTP\\\" title=\\\"HTTP\\\" class=\\\"mw-redirect\\\">HTTP</a> for an <a href=\\\"http://en.wikipedia.org/wiki/API\\\" title=\\\"API\\\" class=\\\"mw-redirect\\\">API</a>.<sup id=\\\"cite_ref-official-website_1-1\\\" class=\\\"reference\\\"><a href=\\\"http://en.wikipedia.org/wiki/CouchDB#cite_note-official-website-1\\\"><span>[</span>1<span>]</span></a></sup> One of its distinguishing features is <a href=\\\"http://en.wikipedia.org/wiki/Multi-master_replication\\\" title=\\\"Multi-master replication\\\">multi-master replication</a>. CouchDB was first released in 2005 and later became an <a href=\\\"http://en.wikipedia.org/wiki/Apache_Software_Foundation\\\" title=\\\"Apache Software Foundation\\\">Apache</a> project in 2008.</p>\\n\",\n      \">#### from [Wikipedia](http://en.wikipedia.org/wiki/CouchDB)\\n\",\n      \"\\n\",\n      \"##Cloud Only NoSQL (Managed Services)\\n\",\n      \"\\n\",\n      \"- [Amazon Dynamo DB](http://aws.amazon.com/dynamodb/)\\n\",\n      \"- [Google DataStore](https://cloud.google.com/datastore/)\\n\",\n      \"\\n\",\n      \"##Other Options:\\n\",\n      \"\\n\",\n      \"- [MySQL Cluter (Suports both SQL and NoSQL)](http://www.mysql.com/products/cluster/nosql.html)\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"PyMongo\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Documentation: http://api.mongodb.org/python/current/\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Importing the Library\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pymongo\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Creating a Connection\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"client_con = pymongo.MongoClient()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Exploring MongoDB\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Listing Available Databases\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"client_con.database_names()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 3,\n       \"text\": [\n        \"[u'mydb', u'local', u'roshan', u'admin']\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Listing Available Collections\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"roshan_db = client_con[\\\"roshan\\\"]\\n\",\n      \"roshan_db.collection_names()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"[u'system.indexes',\\n\",\n        \" u'Twitter',\\n\",\n        \" u'SearchTweets',\\n\",\n        \" u'TwitterFollowers',\\n\",\n        \" u'TwitterUserSearch',\\n\",\n        \" u'TCelebrities',\\n\",\n        \" u'TwitterFriendsIds',\\n\",\n        \" u'TwitterFollowersIds',\\n\",\n        \" u'TwitterIds',\\n\",\n        \" u'TwiiterTemp',\\n\",\n        \" u'test youtube']\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Connecting to a Collection\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"twitter_col = roshan_db[\\\"Twitter\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Counting Documents in a Collection\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"twitter_col.count()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 6,\n       \"text\": [\n        \"1045260\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Finding a Single Document\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"doc = twitter_col.find_one()\\n\",\n      \"doc\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 7,\n       \"text\": [\n        \"{u'_id': ObjectId('543d819c105f1913c74ab20b'),\\n\",\n        \" u'contributors': None,\\n\",\n        \" u'coordinates': None,\\n\",\n        \" u'created_at': u'Tue Oct 14 20:04:05 +0000 2014',\\n\",\n        \" u'entities': {u'hashtags': [],\\n\",\n        \"  u'symbols': [],\\n\",\n        \"  u'trends': [],\\n\",\n        \"  u'urls': [],\\n\",\n        \"  u'user_mentions': []},\\n\",\n        \" u'favorite_count': 0,\\n\",\n        \" u'favorited': False,\\n\",\n        \" u'filter_level': u'medium',\\n\",\n        \" u'geo': None,\\n\",\n        \" u'id': 522115647623692288L,\\n\",\n        \" u'id_str': u'522115647623692288',\\n\",\n        \" u'in_reply_to_screen_name': None,\\n\",\n        \" u'in_reply_to_status_id': None,\\n\",\n        \" u'in_reply_to_status_id_str': None,\\n\",\n        \" u'in_reply_to_user_id': None,\\n\",\n        \" u'in_reply_to_user_id_str': None,\\n\",\n        \" u'lang': u'ar',\\n\",\n        \" u'place': None,\\n\",\n        \" u'possibly_sensitive': False,\\n\",\n        \" u'retweet_count': 0,\\n\",\n        \" u'retweeted': False,\\n\",\n        \" u'source': u'<a href=\\\"http://w.drr-alkalam.com/\\\" rel=\\\"nofollow\\\">\\\\u062f\\\\u0631\\\\u0631 \\\\u0627\\\\u0644\\\\u0643\\\\u0644\\\\u0627\\\\u0645 3</a>',\\n\",\n        \" u'text': u'\\\\u0627\\\\u0644\\\\u0632\\\\u0646\\\\u0643 \\\\u064a\\\\u0648\\\\u062c\\\\u062f \\\\u0628\\\\u0648\\\\u0641\\\\u0631\\\\u0629 \\\\u0627\\\\u0644\\\\u0644\\\\u062d\\\\u0645 \\\\u0648\\\\u0627\\\\u0644\\\\u0633\\\\u0645\\\\u0643 \\\\u0648\\\\u0627\\\\u0644\\\\u062d\\\\u0644\\\\u064a\\\\u0628 \\\\u0627\\\\u0644\\\\u0637\\\\u0627\\\\u0632\\\\u062c \\\\u0648 \\\\u0627\\\\u0644\\\\u062c\\\\u0628\\\\u0646 \\\\u0627\\\\u0644\\\\u0637\\\\u0627\\\\u0632\\\\u062c \\\\u0648 \\\\u0627\\\\u0644\\\\u0628\\\\u0642\\\\u0648\\\\u0644\\\\u064a\\\\u0627\\\\u062a \\\\u0648 \\\\u0627\\\\u0644\\\\u0644\\\\u0628\\\\u0646, \\\\u0643\\\\u0645\\\\u0627 \\\\u064a\\\\u0645\\\\u0643\\\\u0646 \\\\u062a\\\\u0646\\\\u0627\\\\u0648\\\\u0644\\\\u0647 \\\\u0643\\\\u0645\\\\u0643\\\\u0645\\\\u0644\\\\u0627\\\\u062a \\\\u063a\\\\u0630\\\\u0627\\\\u0626\\\\u064a\\\\u0629.',\\n\",\n        \" u'timestamp_ms': u'1413317045494',\\n\",\n        \" u'truncated': False,\\n\",\n        \" u'user': {u'contributors_enabled': False,\\n\",\n        \"  u'created_at': u'Mon Sep 15 09:21:34 +0000 2014',\\n\",\n        \"  u'default_profile': True,\\n\",\n        \"  u'default_profile_image': False,\\n\",\n        \"  u'description': u'\\\\u062a\\\\u0631\\\\u0628\\\\u064a\\\\u0647\\\\u06c1 \\\\u0629 \\\\u0634\\\\u0627\\\\u064a\\\\u0628 \\\\u0648\\\\u0648\\\\u0644\\\\u0648 \\\\ufe8e\\\\u0646\\\\u0646\\\\u064a\\\\u064a\\\\u064e \\\\u0641\\\\u0642\\\\u064a\\\\u0631 \\\\ufe8e\\\\u0639\\\\u0637\\\\u064a\\\\u064a\\\\u0621 \\\\ufe8e\\\\u0644\\\\u062d\\\\u0627\\\\u062c\\\\u0647\\\\u06c1 \\\\u0648\\\\ufe8e\\\\u0646\\\\u0627 \\\\u0645\\\\u062d\\\\u062a\\\\u0627\\\\u062c\\\\u0647\\\\u0627\\\\u0627 \\\\u060c PIN:264400B1',\\n\",\n        \"  u'favourites_count': 0,\\n\",\n        \"  u'follow_request_sent': None,\\n\",\n        \"  u'followers_count': 133,\\n\",\n        \"  u'following': None,\\n\",\n        \"  u'friends_count': 119,\\n\",\n        \"  u'geo_enabled': False,\\n\",\n        \"  u'id': 2811023737L,\\n\",\n        \"  u'id_str': u'2811023737',\\n\",\n        \"  u'is_translator': False,\\n\",\n        \"  u'lang': u'en',\\n\",\n        \"  u'listed_count': 0,\\n\",\n        \"  u'location': u'',\\n\",\n        \"  u'name': u'\\\\u0639\\\\u0640\\\\u0627\\\\u0645\\\\u0640\\\\u062f \\\\u0627\\\\u0644\\\\u0640\\\\u0639\\\\u0640\\\\u0637\\\\u0640\\\\u0627\\\\u0648\\\\u064a',\\n\",\n        \"  u'notifications': None,\\n\",\n        \"  u'profile_background_color': u'C0DEED',\\n\",\n        \"  u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme1/bg.png',\\n\",\n        \"  u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme1/bg.png',\\n\",\n        \"  u'profile_background_tile': False,\\n\",\n        \"  u'profile_image_url': u'http://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\\n\",\n        \"  u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\\n\",\n        \"  u'profile_link_color': u'0084B4',\\n\",\n        \"  u'profile_sidebar_border_color': u'C0DEED',\\n\",\n        \"  u'profile_sidebar_fill_color': u'DDEEF6',\\n\",\n        \"  u'profile_text_color': u'333333',\\n\",\n        \"  u'profile_use_background_image': True,\\n\",\n        \"  u'protected': False,\\n\",\n        \"  u'screen_name': u'Aamdaladawe_',\\n\",\n        \"  u'statuses_count': 209,\\n\",\n        \"  u'time_zone': None,\\n\",\n        \"  u'url': None,\\n\",\n        \"  u'utc_offset': None,\\n\",\n        \"  u'verified': False}}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Examining a Document\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"ObjectId\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Documentation: http://docs.mongodb.org/manual/reference/object-id/\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"document_id = doc[\\\"_id\\\"]\\n\",\n      \"print type(document_id)\\n\",\n      \"document_id\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"<class 'bson.objectid.ObjectId'>\\n\"\n       ]\n      },\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 8,\n       \"text\": [\n        \"ObjectId('543d819c105f1913c74ab20b')\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print document_id.generation_time.strftime(\\\"%Y-%m-%d %H:%M:%SZ%z\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2014-10-14 20:03:40Z+0000\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Other Fields\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"doc[\\\"created_at\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 10,\n       \"text\": [\n        \"u'Tue Oct 14 20:04:05 +0000 2014'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"doc[\\\"text\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 11,\n       \"text\": [\n        \"u'\\\\u0627\\\\u0644\\\\u0632\\\\u0646\\\\u0643 \\\\u064a\\\\u0648\\\\u062c\\\\u062f \\\\u0628\\\\u0648\\\\u0641\\\\u0631\\\\u0629 \\\\u0627\\\\u0644\\\\u0644\\\\u062d\\\\u0645 \\\\u0648\\\\u0627\\\\u0644\\\\u0633\\\\u0645\\\\u0643 \\\\u0648\\\\u0627\\\\u0644\\\\u062d\\\\u0644\\\\u064a\\\\u0628 \\\\u0627\\\\u0644\\\\u0637\\\\u0627\\\\u0632\\\\u062c \\\\u0648 \\\\u0627\\\\u0644\\\\u062c\\\\u0628\\\\u0646 \\\\u0627\\\\u0644\\\\u0637\\\\u0627\\\\u0632\\\\u062c \\\\u0648 \\\\u0627\\\\u0644\\\\u0628\\\\u0642\\\\u0648\\\\u0644\\\\u064a\\\\u0627\\\\u062a \\\\u0648 \\\\u0627\\\\u0644\\\\u0644\\\\u0628\\\\u0646, \\\\u0643\\\\u0645\\\\u0627 \\\\u064a\\\\u0645\\\\u0643\\\\u0646 \\\\u062a\\\\u0646\\\\u0627\\\\u0648\\\\u0644\\\\u0647 \\\\u0643\\\\u0645\\\\u0643\\\\u0645\\\\u0644\\\\u0627\\\\u062a \\\\u063a\\\\u0630\\\\u0627\\\\u0626\\\\u064a\\\\u0629.'\"\n       ]\n      }\n     ],\n     \"prompt_number\": 11\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Unicode Trick\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print doc[\\\"text\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"\\u0627\\u0644\\u0632\\u0646\\u0643 \\u064a\\u0648\\u062c\\u062f \\u0628\\u0648\\u0641\\u0631\\u0629 \\u0627\\u0644\\u0644\\u062d\\u0645 \\u0648\\u0627\\u0644\\u0633\\u0645\\u0643 \\u0648\\u0627\\u0644\\u062d\\u0644\\u064a\\u0628 \\u0627\\u0644\\u0637\\u0627\\u0632\\u062c \\u0648 \\u0627\\u0644\\u062c\\u0628\\u0646 \\u0627\\u0644\\u0637\\u0627\\u0632\\u062c \\u0648 \\u0627\\u0644\\u0628\\u0642\\u0648\\u0644\\u064a\\u0627\\u062a \\u0648 \\u0627\\u0644\\u0644\\u0628\\u0646, \\u0643\\u0645\\u0627 \\u064a\\u0645\\u0643\\u0646 \\u062a\\u0646\\u0627\\u0648\\u0644\\u0647 \\u0643\\u0645\\u0643\\u0645\\u0644\\u0627\\u062a \\u063a\\u0630\\u0627\\u0626\\u064a\\u0629.\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 12\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Nested Documents\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"doc[\\\"user\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 13,\n       \"text\": [\n        \"{u'contributors_enabled': False,\\n\",\n        \" u'created_at': u'Mon Sep 15 09:21:34 +0000 2014',\\n\",\n        \" u'default_profile': True,\\n\",\n        \" u'default_profile_image': False,\\n\",\n        \" u'description': u'\\\\u062a\\\\u0631\\\\u0628\\\\u064a\\\\u0647\\\\u06c1 \\\\u0629 \\\\u0634\\\\u0627\\\\u064a\\\\u0628 \\\\u0648\\\\u0648\\\\u0644\\\\u0648 \\\\ufe8e\\\\u0646\\\\u0646\\\\u064a\\\\u064a\\\\u064e \\\\u0641\\\\u0642\\\\u064a\\\\u0631 \\\\ufe8e\\\\u0639\\\\u0637\\\\u064a\\\\u064a\\\\u0621 \\\\ufe8e\\\\u0644\\\\u062d\\\\u0627\\\\u062c\\\\u0647\\\\u06c1 \\\\u0648\\\\ufe8e\\\\u0646\\\\u0627 \\\\u0645\\\\u062d\\\\u062a\\\\u0627\\\\u062c\\\\u0647\\\\u0627\\\\u0627 \\\\u060c PIN:264400B1',\\n\",\n        \" u'favourites_count': 0,\\n\",\n        \" u'follow_request_sent': None,\\n\",\n        \" u'followers_count': 133,\\n\",\n        \" u'following': None,\\n\",\n        \" u'friends_count': 119,\\n\",\n        \" u'geo_enabled': False,\\n\",\n        \" u'id': 2811023737L,\\n\",\n        \" u'id_str': u'2811023737',\\n\",\n        \" u'is_translator': False,\\n\",\n        \" u'lang': u'en',\\n\",\n        \" u'listed_count': 0,\\n\",\n        \" u'location': u'',\\n\",\n        \" u'name': u'\\\\u0639\\\\u0640\\\\u0627\\\\u0645\\\\u0640\\\\u062f \\\\u0627\\\\u0644\\\\u0640\\\\u0639\\\\u0640\\\\u0637\\\\u0640\\\\u0627\\\\u0648\\\\u064a',\\n\",\n        \" u'notifications': None,\\n\",\n        \" u'profile_background_color': u'C0DEED',\\n\",\n        \" u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme1/bg.png',\\n\",\n        \" u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme1/bg.png',\\n\",\n        \" u'profile_background_tile': False,\\n\",\n        \" u'profile_image_url': u'http://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\\n\",\n        \" u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\\n\",\n        \" u'profile_link_color': u'0084B4',\\n\",\n        \" u'profile_sidebar_border_color': u'C0DEED',\\n\",\n        \" u'profile_sidebar_fill_color': u'DDEEF6',\\n\",\n        \" u'profile_text_color': u'333333',\\n\",\n        \" u'profile_use_background_image': True,\\n\",\n        \" u'protected': False,\\n\",\n        \" u'screen_name': u'Aamdaladawe_',\\n\",\n        \" u'statuses_count': 209,\\n\",\n        \" u'time_zone': None,\\n\",\n        \" u'url': None,\\n\",\n        \" u'utc_offset': None,\\n\",\n        \" u'verified': False}\"\n       ]\n      }\n     ],\n     \"prompt_number\": 13\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"doc[\\\"user\\\"][\\\"verified\\\"]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 14,\n       \"text\": [\n        \"False\"\n       ]\n      }\n     ],\n     \"prompt_number\": 14\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Unicode Trick for dict\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"for k,v in doc[\\\"user\\\"].iteritems():\\n\",\n      \"    print \\\"%s: %s\\\" % (k,v)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"follow_request_sent: None\\n\",\n        \"profile_use_background_image: True\\n\",\n        \"id: 2811023737\\n\",\n        \"verified: False\\n\",\n        \"profile_image_url_https: https://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png\\n\",\n        \"profile_sidebar_fill_color: DDEEF6\\n\",\n        \"is_translator: False\\n\",\n        \"geo_enabled: False\\n\",\n        \"profile_text_color: 333333\\n\",\n        \"followers_count: 133\\n\",\n        \"protected: False\\n\",\n        \"location: \\n\",\n        \"default_profile_image: False\\n\",\n        \"id_str: 2811023737\\n\",\n        \"utc_offset: None\\n\",\n        \"statuses_count: 209\\n\",\n        \"description: \\u062a\\u0631\\u0628\\u064a\\u0647\\u06c1 \\u0629 \\u0634\\u0627\\u064a\\u0628 \\u0648\\u0648\\u0644\\u0648 \\ufe8e\\u0646\\u0646\\u064a\\u064a\\u064e \\u0641\\u0642\\u064a\\u0631 \\ufe8e\\u0639\\u0637\\u064a\\u064a\\u0621 \\ufe8e\\u0644\\u062d\\u0627\\u062c\\u0647\\u06c1 \\u0648\\ufe8e\\u0646\\u0627 \\u0645\\u062d\\u062a\\u0627\\u062c\\u0647\\u0627\\u0627 \\u060c PIN:264400B1\\n\",\n        \"friends_count: 119\\n\",\n        \"profile_link_color: 0084B4\\n\",\n        \"profile_image_url: http://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png\\n\",\n        \"notifications: None\\n\",\n        \"profile_background_image_url_https: https://abs.twimg.com/images/themes/theme1/bg.png\\n\",\n        \"profile_background_color: C0DEED\\n\",\n        \"profile_background_image_url: http://abs.twimg.com/images/themes/theme1/bg.png\\n\",\n        \"screen_name: Aamdaladawe_\\n\",\n        \"lang: en\\n\",\n        \"profile_background_tile: False\\n\",\n        \"favourites_count: 0\\n\",\n        \"name: \\u0639\\u0640\\u0627\\u0645\\u0640\\u062f \\u0627\\u0644\\u0640\\u0639\\u0640\\u0637\\u0640\\u0627\\u0648\\u064a\\n\",\n        \"url: None\\n\",\n        \"created_at: Mon Sep 15 09:21:34 +0000 2014\\n\",\n        \"contributors_enabled: False\\n\",\n        \"time_zone: None\\n\",\n        \"profile_sidebar_border_color: C0DEED\\n\",\n        \"default_profile: True\\n\",\n        \"following: None\\n\",\n        \"listed_count: 0\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Finding Multiple Documents\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"verified_users = twitter_col.find({\\\"user.verified\\\":True})\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"verified_users.count()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 17,\n       \"text\": [\n        \"672\"\n       ]\n      }\n     ],\n     \"prompt_number\": 17\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pandas\\n\",\n      \"\\n\",\n      \"users = pandas.DataFrame([msg[\\\"user\\\"] for msg in verified_users])\\n\",\n      \"users\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<pre>\\n\",\n        \"&lt;class 'pandas.core.frame.DataFrame'&gt;\\n\",\n        \"Int64Index: 672 entries, 0 to 671\\n\",\n        \"Data columns (total 38 columns):\\n\",\n        \"contributors_enabled                  672  non-null values\\n\",\n        \"created_at                            672  non-null values\\n\",\n        \"default_profile                       672  non-null values\\n\",\n        \"default_profile_image                 672  non-null values\\n\",\n        \"description                           672  non-null values\\n\",\n        \"favourites_count                      672  non-null values\\n\",\n        \"follow_request_sent                   0  non-null values\\n\",\n        \"followers_count                       672  non-null values\\n\",\n        \"following                             0  non-null values\\n\",\n        \"friends_count                         672  non-null values\\n\",\n        \"geo_enabled                           672  non-null values\\n\",\n        \"id                                    672  non-null values\\n\",\n        \"id_str                                672  non-null values\\n\",\n        \"is_translator                         672  non-null values\\n\",\n        \"lang                                  672  non-null values\\n\",\n        \"listed_count                          672  non-null values\\n\",\n        \"location                              672  non-null values\\n\",\n        \"name                                  672  non-null values\\n\",\n        \"notifications                         0  non-null values\\n\",\n        \"profile_background_color              672  non-null values\\n\",\n        \"profile_background_image_url          672  non-null values\\n\",\n        \"profile_background_image_url_https    672  non-null values\\n\",\n        \"profile_background_tile               672  non-null values\\n\",\n        \"profile_banner_url                    578  non-null values\\n\",\n        \"profile_image_url                     672  non-null values\\n\",\n        \"profile_image_url_https               672  non-null values\\n\",\n        \"profile_link_color                    672  non-null values\\n\",\n        \"profile_sidebar_border_color          672  non-null values\\n\",\n        \"profile_sidebar_fill_color            672  non-null values\\n\",\n        \"profile_text_color                    672  non-null values\\n\",\n        \"profile_use_background_image          672  non-null values\\n\",\n        \"protected                             672  non-null values\\n\",\n        \"screen_name                           672  non-null values\\n\",\n        \"statuses_count                        672  non-null values\\n\",\n        \"time_zone                             656  non-null values\\n\",\n        \"url                                   631  non-null values\\n\",\n        \"utc_offset                            656  non-null values\\n\",\n        \"verified                              672  non-null values\\n\",\n        \"dtypes: bool(9), float64(1), int64(6), object(22)\\n\",\n        \"</pre>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 18,\n       \"text\": [\n        \"<class 'pandas.core.frame.DataFrame'>\\n\",\n        \"Int64Index: 672 entries, 0 to 671\\n\",\n        \"Data columns (total 38 columns):\\n\",\n        \"contributors_enabled                  672  non-null values\\n\",\n        \"created_at                            672  non-null values\\n\",\n        \"default_profile                       672  non-null values\\n\",\n        \"default_profile_image                 672  non-null values\\n\",\n        \"description                           672  non-null values\\n\",\n        \"favourites_count                      672  non-null values\\n\",\n        \"follow_request_sent                   0  non-null values\\n\",\n        \"followers_count                       672  non-null values\\n\",\n        \"following                             0  non-null values\\n\",\n        \"friends_count                         672  non-null values\\n\",\n        \"geo_enabled                           672  non-null values\\n\",\n        \"id                                    672  non-null values\\n\",\n        \"id_str                                672  non-null values\\n\",\n        \"is_translator                         672  non-null values\\n\",\n        \"lang                                  672  non-null values\\n\",\n        \"listed_count                          672  non-null values\\n\",\n        \"location                              672  non-null values\\n\",\n        \"name                                  672  non-null values\\n\",\n        \"notifications                         0  non-null values\\n\",\n        \"profile_background_color              672  non-null values\\n\",\n        \"profile_background_image_url          672  non-null values\\n\",\n        \"profile_background_image_url_https    672  non-null values\\n\",\n        \"profile_background_tile               672  non-null values\\n\",\n        \"profile_banner_url                    578  non-null values\\n\",\n        \"profile_image_url                     672  non-null values\\n\",\n        \"profile_image_url_https               672  non-null values\\n\",\n        \"profile_link_color                    672  non-null values\\n\",\n        \"profile_sidebar_border_color          672  non-null values\\n\",\n        \"profile_sidebar_fill_color            672  non-null values\\n\",\n        \"profile_text_color                    672  non-null values\\n\",\n        \"profile_use_background_image          672  non-null values\\n\",\n        \"protected                             672  non-null values\\n\",\n        \"screen_name                           672  non-null values\\n\",\n        \"statuses_count                        672  non-null values\\n\",\n        \"time_zone                             656  non-null values\\n\",\n        \"url                                   631  non-null values\\n\",\n        \"utc_offset                            656  non-null values\\n\",\n        \"verified                              672  non-null values\\n\",\n        \"dtypes: bool(9), float64(1), int64(6), object(22)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 18\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Filtering out duplicates\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"users_unique = users.drop_duplicates(cols=[\\\"id\\\"])\\n\",\n      \"print len(users_unique)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"222\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 19\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Visualizing the realtion between number of tweets and the number of followers\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"x = users_unique[\\\"followers_count\\\"]\\n\",\n      \"y = users_unique[\\\"statuses_count\\\"]\\n\",\n      \"z = users_unique[\\\"friends_count\\\"]\\n\",\n      \"plt.scatter(x, y, c=z, alpha=0.4, s=200, cmap=plt.cm.Accent)\\n\",\n      \"plt.yscale(\\\"symlog\\\")\\n\",\n      \"plt.xscale(\\\"symlog\\\")\\n\",\n      \"plt.ylim(y.min(),y.max())\\n\",\n      \"plt.xlim(x.min(),x.max())\\n\",\n      \"plt.grid()\\n\",\n      \"plt.xlabel(\\\"followers_count\\\")\\n\",\n      \"plt.ylabel(\\\"statuses_count\\\")\\n\",\n      \"plt.colorbar()\\n\",\n      \"plt.show()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"png\": 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21IjUQXHVQJuW1uvHYvRUVF6Kv1REdHL7re+Pg42mgtEbqIkPug0Wo4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\\n6xkcHMSUZuJ3vvY7SCmpqKgAybQxWEh/z58/z3e/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      \"text\": [\n        \"<matplotlib.figure.Figure at 0x7f5c26c91a90>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 20\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/data-mining/6. Twitter Streaming API.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial  Outline\\n\",\n    \"\\n\",\n    \"- [Introduction](#Introduction)\\n\",\n    \"- [Preprerequisites](#Preprerequisites)\\n\",\n    \"- [How does it work?](#How-does-it-work?)\\n\",\n    \"- [Authentication](#Authentication)\\n\",\n    \" - [Authentication keys](#Authentication-keys)\\n\",\n    \"- [MongoDB Collection](#MongoDB-Collection)\\n\",\n    \"- [Starting a Stream](#Starting-a-Stream)\\n\",\n    \" - [Stream Listener](#Stream-Listener)\\n\",\n    \" - [Connect to a streaming API](#Connect-to-a-streaming-API)\\n\",\n    \"- [Data Access and Analysis](#Data-Access-and-Analysis)\\n\",\n    \" - [Load results to a DataFrame](#Load-results-to-a-DataFrame)\\n\",\n    \"- [Visualization](#Visualization)\\n\",\n    \"\\n\",\n    \"# Introduction\\n\",\n    \"\\n\",\n    \"Twitter provides two types of API to access their data:\\n\",\n    \"\\n\",\n    \"- RESTful API: Used to get data about existing data objects like statuses \\\"tweets\\\", user, ... etc\\n\",\n    \"- Streaming API: Used to get live statuses \\\"tweets\\\" as they are sent\\n\",\n    \"\\n\",\n    \"The reason why you would like to use streaming API:\\n\",\n    \"\\n\",\n    \"- Capture large amount of data because RESTful API has limited access to older data\\n\",\n    \"- Real-time analysis like monitoring social discussion about a live event\\n\",\n    \"- In house archive like archiving social discussion about your brand(s)\\n\",\n    \"- AI response system for a twitter account like automated reply and filing questions or providing answers\\n\",\n    \"\\n\",\n    \"# Preprerequisites\\n\",\n    \"\\n\",\n    \"- Python 2 or 3\\n\",\n    \"- Jupyter /w IPyWidgets\\n\",\n    \"- Pandas\\n\",\n    \"- Numpy\\n\",\n    \"- Matplotlib\\n\",\n    \"- MogoDB Installtion\\n\",\n    \"- Pymongo\\n\",\n    \"- Scikit-learn\\n\",\n    \"- Tweepy\\n\",\n    \"- Twitter account\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# How does it work?\\n\",\n    \"\\n\",\n    \"Twitter streaming API can provide data through a streaming HTTP response. This is very similar to downloading a file where you read a number of bytes and store it to disk and repeat until the end of file. The only difference is this stream is endless. The only things that could stop this stream are:\\n\",\n    \"\\n\",\n    \"- If you closed your connection to the streaming response\\n\",\n    \"- If your connection speed is not capable of receiving data and the servers buffer is filling up\\n\",\n    \"\\n\",\n    \"This means that this process will be using the thread that it was launched from until it is stopped. In production, you should always start this in a different thread or process to make sure your software doesn't freeze until you stop the stream.\\n\",\n    \"\\n\",\n    \"# Authentication\\n\",\n    \"\\n\",\n    \"You will need four numbers from twitter development to start using streaming API. First, let's import some important libraries for dealing with twitter API, data analysis, data storage ... etc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import tweepy\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import pymongo\\n\",\n    \"import ipywidgets as wgt\\n\",\n    \"from IPython.display import display\\n\",\n    \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n    \"import re\\n\",\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Authentication keys\\n\",\n    \"\\n\",\n    \"1. Go to https://apps.twitter.com/\\n\",\n    \"2. Create an App (if you don't have one yet)\\n\",\n    \"3. Grant read-only access to your account\\n\",\n    \"4. Copy the four keys and paste them here:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"api_key = \\\"yP0yoCitoUNgD63ebMerGyJaE\\\" # <---- Add your API Key\\n\",\n    \"api_secret = \\\"kLO5YUtlth3cd4lOHLy8nlLHW5npVQgUfO4FhsyCn6wCMIz5E6\\\" # <---- Add your API Secret\\n\",\n    \"access_token = \\\"259862037-iMXNjfL8JBApm4LVcdfwc3FcMm7Xta4TKg5cd44K\\\" # <---- Add your access token\\n\",\n    \"access_token_secret = \\\"UIgh08dtmavzlvlWWukIXwN5HDIQD0wNwyn5sPzhrynBf\\\" # <---- Add your access token secret\\n\",\n    \"\\n\",\n    \"auth = tweepy.OAuthHandler(api_key, api_secret)\\n\",\n    \"auth.set_access_token(access_token, access_token_secret)\\n\",\n    \"\\n\",\n    \"api = tweepy.API(auth)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# MongoDB Collection\\n\",\n    \"\\n\",\n    \"Connect to MongoDB and create/get a collection.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2251\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"col = pymongo.MongoClient()[\\\"tweets\\\"][\\\"StreamingTutorial\\\"]\\n\",\n    \"col.count()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Starting a Stream\\n\",\n    \"\\n\",\n    \"We need a listener which should extend `tweepy.StreamListener` class. There is a number of methods that you can extend to instruct the listener class to perform functionality. Some of the important methods are:\\n\",\n    \"\\n\",\n    \"- `on_status(self, status)`: This will pass a status \\\"tweet\\\" object when a tweet is received\\n\",\n    \"- `on_data(self, raw_data)`: Called when any any data is received and the raw data will be passed\\n\",\n    \"- `on_error(self, status_code)`: Called when you get a response with code other than 200 (ok)\\n\",\n    \"\\n\",\n    \"## Stream Listener\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class MyStreamListener(tweepy.StreamListener):\\n\",\n    \"    \\n\",\n    \"    counter = 0\\n\",\n    \"    \\n\",\n    \"    def __init__(self, max_tweets=1000, *args, **kwargs):\\n\",\n    \"        self.max_tweets = max_tweets\\n\",\n    \"        self.counter = 0\\n\",\n    \"        super().__init__(*args, **kwargs)\\n\",\n    \"    \\n\",\n    \"    def on_connect(self):\\n\",\n    \"        self.counter = 0\\n\",\n    \"        self.start_time = datetime.now()\\n\",\n    \"    \\n\",\n    \"    def on_status(self, status):\\n\",\n    \"        # Increment counter\\n\",\n    \"        self.counter += 1\\n\",\n    \"        \\n\",\n    \"        # Store tweet to MongoDB\\n\",\n    \"        col.insert_one(status._json)\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        if self.counter % 1 == 0:\\n\",\n    \"            value = int(100.00 * self.counter / self.max_tweets)\\n\",\n    \"            mining_time = datetime.now() - self.start_time\\n\",\n    \"            progress_bar.value = value\\n\",\n    \"            html_value = \\\"\\\"\\\"<span class=\\\"label label-primary\\\">Tweets/Sec: %.1f</span>\\\"\\\"\\\" % (self.counter / max([1,mining_time.seconds]))\\n\",\n    \"            html_value += \\\"\\\"\\\" <span class=\\\"label label-success\\\">Progress: %.1f%%</span>\\\"\\\"\\\" % (self.counter / self.max_tweets * 100.0)\\n\",\n    \"            html_value += \\\"\\\"\\\" <span class=\\\"label label-info\\\">ETA: %.1f Sec</span>\\\"\\\"\\\" % ((self.max_tweets - self.counter) / (self.counter / max([1,mining_time.seconds])))\\n\",\n    \"            wgt_status.value = html_value\\n\",\n    \"            #print(\\\"%s/%s\\\" % (self.counter, self.max_tweets))\\n\",\n    \"            if self.counter >= self.max_tweets:\\n\",\n    \"                myStream.disconnect()\\n\",\n    \"                print(\\\"Finished\\\")\\n\",\n    \"                print(\\\"Total Mining Time: %s\\\" % (mining_time))\\n\",\n    \"                print(\\\"Tweets/Sec: %.1f\\\" % (self.max_tweets / mining_time.seconds))\\n\",\n    \"                progress_bar.value = 0\\n\",\n    \"                \\n\",\n    \"    \\n\",\n    \"myStreamListener = MyStreamListener(max_tweets=100)\\n\",\n    \"myStream = tweepy.Stream(auth = api.auth, listener=myStreamListener)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Connect to a streaming API\\n\",\n    \"\\n\",\n    \"There are two methods to connect to a stream:\\n\",\n    \"\\n\",\n    \"- `filter(follow=None, track=None, async=False, locations=None, stall_warnings=False, languages=None, encoding='utf8', filter_level=None)`\\n\",\n    \"- `firehose(count=None, async=False)`\\n\",\n    \"\\n\",\n    \"Firehose captures everything. You should make sure that you have connection speed that can handle the stream and you have the storage capacity that can store these tweets at the same rate. We cannot really use firehose for this tutorial but we'll be using `filter`.\\n\",\n    \"\\n\",\n    \"You have to specify one of two things to filter:\\n\",\n    \"\\n\",\n    \"- `follow`: A list of user ID to follow. This will stream all their tweets, retweets, and others retweeting their tweets. This doesn't include mentions and manual retweets where the user doesn't press the retweet button.\\n\",\n    \"- `track`: A string or list of string to be used for filtering. If you use multiple words separated by spaces, this will be used for AND operator. If you use multiple words in a string separated by commas or pass a list of words this will be treated as OR operator.\\n\",\n    \"\\n\",\n    \"**Note**: `track` is case insensitive.\\n\",\n    \"\\n\",\n    \"### What to track?\\n\",\n    \"I want to collect all tweets that contains any of these words:\\n\",\n    \"\\n\",\n    \"- Jupyter\\n\",\n    \"- Python\\n\",\n    \"- Data Mining\\n\",\n    \"- Machine Learning\\n\",\n    \"- Data Science\\n\",\n    \"- Big Data\\n\",\n    \"- IoT\\n\",\n    \"- #R\\n\",\n    \"\\n\",\n    \"This could be done with a string or a list. It is easier to to it with a list to make your code clear to read.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Finished\\n\",\n      \"Total Mining Time: 0:01:21.477351\\n\",\n      \"Tweets/Sec: 1.2\\n\",\n      \"Tweets collected: 100\\n\",\n      \"Total tweets in collection: 2351\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"keywords = [\\\"Jupyter\\\",\\n\",\n    \"            \\\"Python\\\",\\n\",\n    \"            \\\"Data Mining\\\",\\n\",\n    \"            \\\"Machine Learning\\\",\\n\",\n    \"            \\\"Data Science\\\",\\n\",\n    \"            \\\"Big Data\\\",\\n\",\n    \"            \\\"DataMining\\\",\\n\",\n    \"            \\\"MachineLearning\\\",\\n\",\n    \"            \\\"DataScience\\\",\\n\",\n    \"            \\\"BigData\\\",\\n\",\n    \"            \\\"IoT\\\",\\n\",\n    \"            \\\"#R\\\",\\n\",\n    \"           ]\\n\",\n    \"\\n\",\n    \"# Visualize a progress bar to track progress\\n\",\n    \"progress_bar = wgt.IntProgress(value=0)\\n\",\n    \"display(progress_bar)\\n\",\n    \"wgt_status = wgt.HTML(value=\\\"\\\"\\\"<span class=\\\"label label-primary\\\">Tweets/Sec: 0.0</span>\\\"\\\"\\\")\\n\",\n    \"display(wgt_status)\\n\",\n    \"\\n\",\n    \"# Start a filter with an error counter of 20\\n\",\n    \"for error_counter in range(20):\\n\",\n    \"    try:\\n\",\n    \"        myStream.filter(track=keywords)\\n\",\n    \"        print(\\\"Tweets collected: %s\\\" % myStream.listener.counter)\\n\",\n    \"        print(\\\"Total tweets in collection: %s\\\" % col.count())\\n\",\n    \"        break\\n\",\n    \"    except:\\n\",\n    \"        print(\\\"ERROR# %s\\\" % (error_counter + 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Data Access and Analysis\\n\",\n    \"\\n\",\n    \"Now that we have stored all these tweets in a MongoDB collection, let's take a look at one of these tweets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'_id': ObjectId('56937d2e105f1970314720e2'),\\n\",\n       \" 'contributors': None,\\n\",\n       \" 'coordinates': None,\\n\",\n       \" 'created_at': 'Mon Jan 11 10:00:14 +0000 2016',\\n\",\n       \" 'entities': {'hashtags': [{'indices': [22, 27], 'text': 'Rの法則'}],\\n\",\n       \"  'symbols': [],\\n\",\n       \"  'urls': [],\\n\",\n       \"  'user_mentions': []},\\n\",\n       \" 'favorite_count': 0,\\n\",\n       \" 'favorited': False,\\n\",\n       \" 'filter_level': 'low',\\n\",\n       \" 'geo': None,\\n\",\n       \" 'id': 686487772970942466,\\n\",\n       \" 'id_str': '686487772970942466',\\n\",\n       \" 'in_reply_to_screen_name': None,\\n\",\n       \" 'in_reply_to_status_id': None,\\n\",\n       \" 'in_reply_to_status_id_str': None,\\n\",\n       \" 'in_reply_to_user_id': None,\\n\",\n       \" 'in_reply_to_user_id_str': None,\\n\",\n       \" 'is_quote_status': False,\\n\",\n       \" 'lang': 'ja',\\n\",\n       \" 'place': None,\\n\",\n       \" 'retweet_count': 0,\\n\",\n       \" 'retweeted': False,\\n\",\n       \" 'source': '<a href=\\\"http://twitter.com/download/iphone\\\" rel=\\\"nofollow\\\">Twitter for iPhone</a>',\\n\",\n       \" 'text': '体力落ちてきておばさんみたいになってきた。\\\\n#Rの法則',\\n\",\n       \" 'timestamp_ms': '1452506414059',\\n\",\n       \" 'truncated': False,\\n\",\n       \" 'user': {'contributors_enabled': False,\\n\",\n       \"  'created_at': 'Tue Aug 18 16:19:16 +0000 2015',\\n\",\n       \"  'default_profile': True,\\n\",\n       \"  'default_profile_image': False,\\n\",\n       \"  'description': '☮ 関ジャニ∞ ＆ 山田涼介 ＆ Justin Bieber ＆ Benjamin Lasnier ＆ Selena Gomez ☮',\\n\",\n       \"  'favourites_count': 1121,\\n\",\n       \"  'follow_request_sent': None,\\n\",\n       \"  'followers_count': 121,\\n\",\n       \"  'following': None,\\n\",\n       \"  'friends_count': 92,\\n\",\n       \"  'geo_enabled': True,\\n\",\n       \"  'id': 3318871652,\\n\",\n       \"  'id_str': '3318871652',\\n\",\n       \"  'is_translator': False,\\n\",\n       \"  'lang': 'en',\\n\",\n       \"  'listed_count': 0,\\n\",\n       \"  'location': 'The land of dreams',\\n\",\n       \"  'name': 'rena',\\n\",\n       \"  'notifications': None,\\n\",\n       \"  'profile_background_color': 'C0DEED',\\n\",\n       \"  'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\\n\",\n       \"  'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\\n\",\n       \"  'profile_background_tile': False,\\n\",\n       \"  'profile_banner_url': 'https://pbs.twimg.com/profile_banners/3318871652/1452436374',\\n\",\n       \"  'profile_image_url': 'http://pbs.twimg.com/profile_images/683964013558931456/Q1rx1s5b_normal.jpg',\\n\",\n       \"  'profile_image_url_https': 'https://pbs.twimg.com/profile_images/683964013558931456/Q1rx1s5b_normal.jpg',\\n\",\n       \"  'profile_link_color': '0084B4',\\n\",\n       \"  'profile_sidebar_border_color': 'C0DEED',\\n\",\n       \"  'profile_sidebar_fill_color': 'DDEEF6',\\n\",\n       \"  'profile_text_color': '333333',\\n\",\n       \"  'profile_use_background_image': True,\\n\",\n       \"  'protected': False,\\n\",\n       \"  'screen_name': 'Q2HpiJwCX1huBwf',\\n\",\n       \"  'statuses_count': 497,\\n\",\n       \"  'time_zone': None,\\n\",\n       \"  'url': None,\\n\",\n       \"  'utc_offset': None,\\n\",\n       \"  'verified': False}}\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"col.find_one()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load results to a DataFrame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>created_at</th>\\n\",\n       \"      <th>source</th>\\n\",\n       \"      <th>text</th>\\n\",\n       \"      <th>user</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:14 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/iphone\\\" r...</td>\\n\",\n       \"      <td>体力落ちてきておばさんみたいになってきた。\\\\n#Rの法則</td>\\n\",\n       \"      <td>@Q2HpiJwCX1huBwf</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>Mon Jan 11 10:09:26 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/android\\\" ...</td>\\n\",\n       \"      <td>皆におばさんと言われてうれしがってる #Rの法則</td>\\n\",\n       \"      <td>@Tamutamu1017</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:10 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://trendkeyword.blog.jp/\\\" rel=\\\"no...</td>\\n\",\n       \"      <td>【R.I.P】急上昇ワード「R.I.Pã€ã®ã¾ã¨ã‚é€Ÿå ± https://t.co/yi1yfC...</td>\\n\",\n       \"      <td>@pickword_matome</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:10 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>#Rの法則 \\\\nどれもおばさん臭いけれどやっぱり黄色が一番だなぁ</td>\\n\",\n       \"      <td>@kakinotise</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:10 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://bufferapp.com\\\" rel=\\\"nofollow\\\"&gt;...</td>\\n\",\n       \"      <td>The New Best Thing HP ATP - Vertica Big Data S...</td>\\n\",\n       \"      <td>@DataCentreNews1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:11 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>IoT Now: l’Internet of Things è qui, ora https...</td>\\n\",\n       \"      <td>@datamanager_it</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:11 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://trendkeyword.doorblog.jp/\\\" rel...</td>\\n\",\n       \"      <td>今話題の「R.I.P」まとめ https://t.co/VOc5cwK5hg #R.I.P ...</td>\\n\",\n       \"      <td>@buzz_wadai</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:11 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitterfeed.com\\\" rel=\\\"nofollow...</td>\\n\",\n       \"      <td>#oldham #stockport VIDEO: Snake thief hides py...</td>\\n\",\n       \"      <td>@Labour_is_PIE</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:11 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"https://about.twitter.com/products/tw...</td>\\n\",\n       \"      <td>Las #startup pioneras de #machinelearning ofre...</td>\\n\",\n       \"      <td>@techreview_es</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:12 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.linkedin.com/\\\" rel=\\\"nofoll...</td>\\n\",\n       \"      <td>Lets talk about how to harness the power of ma...</td>\\n\",\n       \"      <td>@jansmit1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:13 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://catalystfive.com\\\" rel=\\\"nofollo...</td>\\n\",\n       \"      <td>Business Intelligence and Big Data Consulting ...</td>\\n\",\n       \"      <td>@Catalyst5Jobs</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>Mon Jan 11 10:00:13 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/NewsICT\\\" rel=\\\"nofo...</td>\\n\",\n       \"      <td>[æƒ…å ±é€šä¿¡]2016年台北国際コンピューター見本市が新しい位置づけと新しい展示で装い新たに！...</td>\\n\",\n       \"      <td>@NewsICT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:10 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>#bonplan Parties de Laser Quest entre amis à  2...</td>\\n\",\n       \"      <td>@Bons_Plans_</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:10 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>Parties de Laser Quest entre amis à  22.00€ au ...</td>\\n\",\n       \"      <td>@keepmymindfree</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:10 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @jose_garde: Why a Simple Data Analytics St...</td>\\n\",\n       \"      <td>@martingeldish</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:11 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/iphone\\\" r...</td>\\n\",\n       \"      <td>芸能人の人たくさん手たたき笑いしてるからおばさんたくさんになっちゃうよwww\\\\n\\\\n#Rの法則</td>\\n\",\n       \"      <td>@YK__0704</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:12 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>Découvrez le jeu Pure Mission entre amis à  22....</td>\\n\",\n       \"      <td>@keepmymindfree</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:12 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>#bonplan Parties de bowling pour 4 à  #POINCY :...</td>\\n\",\n       \"      <td>@Bons_Plans_</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:12 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>20 min de vol découverte ULM pour 1 ou 2 à  79....</td>\\n\",\n       \"      <td>@CrationSiteWeb</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:12 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @hortonworks: Paris is the city of love but...</td>\\n\",\n       \"      <td>@bigdataparis</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:12 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @hynek: So #emacs / @spacemacs nerds: is th...</td>\\n\",\n       \"      <td>@fdiesch</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:12 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.hootsuite.com\\\" rel=\\\"nofoll...</td>\\n\",\n       \"      <td>.@QonexCyber founder member of @IoT_SF is orga...</td>\\n\",\n       \"      <td>@QonexCyber</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:13 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>Parties de bowling pour 4 à  #POINCY : 35.00€ a...</td>\\n\",\n       \"      <td>@keepmymindfree</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:13 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>#bonplan 30 séances de Squash à  #LISSES : 39.9...</td>\\n\",\n       \"      <td>@Bons_Plans_</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:13 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>App: ExZeus 2 – free to play   https://t.co/ZT...</td>\\n\",\n       \"      <td>@UniversalConsol</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:13 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\"&gt;dlvr.i...</td>\\n\",\n       \"      <td>#discount Parties de bowling pour 4 à  #POINCY ...</td>\\n\",\n       \"      <td>@PromosPromos</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>Mon Jan 11 10:02:13 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.google.com/\\\" rel=\\\"nofollow...</td>\\n\",\n       \"      <td>spiegel.de :  Tier macht Sachen: Python beißt ...</td>\\n\",\n       \"      <td>@arminfischer_de</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>Mon Jan 11 10:09:02 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/android\\\" ...</td>\\n\",\n       \"      <td>若いっていいねえ…ってよく言うw #Rの法則</td>\\n\",\n       \"      <td>@naco75x</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>Mon Jan 11 10:09:17 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/iphone\\\" r...</td>\\n\",\n       \"      <td>最近の若い子は最近使った\\\\n#Rの法則</td>\\n\",\n       \"      <td>@K1224West</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>Mon Jan 11 10:09:19 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/android\\\" ...</td>\\n\",\n       \"      <td>#Rの法則\\\\n自分も若者なのに笑</td>\\n\",\n       \"      <td>@V6ZRRT7Q22BZ1cF</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2221</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:40 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://201512291327-7430af.bitnamiapp...</td>\\n\",\n       \"      <td>https://t.co/BTAAq6HuuJ - pcgamer - #machinele...</td>\\n\",\n       \"      <td>@vinceyue</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2222</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:40 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @jose_garde: The Big Data Analytics Softwar...</td>\\n\",\n       \"      <td>@LJ_Blanchard</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2223</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:41 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.linkedin.com/\\\" rel=\\\"nofoll...</td>\\n\",\n       \"      <td>What is data mining? Do you have to be a mathe...</td>\\n\",\n       \"      <td>@ednuwan</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2224</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:41 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @AgroKnow: A #BigData platform for the futu...</td>\\n\",\n       \"      <td>@albertspijkers</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2225</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:42 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.linkedin.com/\\\" rel=\\\"nofoll...</td>\\n\",\n       \"      <td>Big Data: Is It A Tsunami, The New Oil, Or Sim...</td>\\n\",\n       \"      <td>@Summerlovegrove</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2226</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:43 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"https://www.jobfindly.com/php-jobs.ht...</td>\\n\",\n       \"      <td>Sr Software Engineer C Php Python Linux Jobs i...</td>\\n\",\n       \"      <td>@jobfindlyphpdev</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2227</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:43 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/iphone\\\" r...</td>\\n\",\n       \"      <td>RT @bigdataparis: #Bigdata bang : un marché en...</td>\\n\",\n       \"      <td>@LifeIsWeb</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2228</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:43 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>Learn from the best professors in India .. cou...</td>\\n\",\n       \"      <td>@ashwaniapex</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2229</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:45 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://getsmoup.com\\\" rel=\\\"nofollow\\\"&gt;S...</td>\\n\",\n       \"      <td>RT @rebrandtoday: #startup or #rebrand -Buy Cr...</td>\\n\",\n       \"      <td>@SmartData_Fr</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2230</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:45 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.ajaymatharu.com/\\\" rel=\\\"nof...</td>\\n\",\n       \"      <td>¿Cómo será el futuro del Big Data? https://t.c...</td>\\n\",\n       \"      <td>@eduardogarsanch</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2231</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:46 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @jose_garde: 3 Ways to Transform Your Compa...</td>\\n\",\n       \"      <td>@LJ_Blanchard</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2232</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:46 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://publicize.wp.com/\\\" rel=\\\"nofoll...</td>\\n\",\n       \"      <td>Woman Tries To Kiss Python, Gets Bitten In The...</td>\\n\",\n       \"      <td>@NAIJA_VIBEZ</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2233</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:46 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.itknowingness.com\\\" rel=\\\"no...</td>\\n\",\n       \"      <td>RT @jose_garde: 3 Ways to Transform Your Compa...</td>\\n\",\n       \"      <td>@itknowingness</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2234</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:48 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/iphone\\\" r...</td>\\n\",\n       \"      <td>RT @ErikaPauwels: Building a #BigData platform...</td>\\n\",\n       \"      <td>@impulsater</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2235</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:49 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com/download/android\\\" ...</td>\\n\",\n       \"      <td>En 2016 j'aimerais moins râler. #résolution. S...</td>\\n\",\n       \"      <td>@ce1ce2makarenko</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2236</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:51 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @jose_garde: How Marketing Can Be Better Au...</td>\\n\",\n       \"      <td>@LJ_Blanchard</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2237</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:51 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>Hey @Pontifex accueille ces réfugiées dans ta ...</td>\\n\",\n       \"      <td>@Atmosfive</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2238</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:51 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @Ubixr: .@GroupeLaPoste choisit le toulousa...</td>\\n\",\n       \"      <td>@The_Nextwork</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2239</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:56 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitterfeed.com\\\" rel=\\\"nofollow...</td>\\n\",\n       \"      <td>Thanks @hackplayers Blade: un webshell en Pyth...</td>\\n\",\n       \"      <td>@Navarmedia</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2240</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:56 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @jose_garde: Which big data personality are...</td>\\n\",\n       \"      <td>@LJ_Blanchard</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2241</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:56 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://ifttt.com\\\" rel=\\\"nofollow\\\"&gt;IFTT...</td>\\n\",\n       \"      <td>Cybersecurity Forum tackles challenges with th...</td>\\n\",\n       \"      <td>@wulfsec</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2242</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:56 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://publicize.wp.com/\\\" rel=\\\"nofoll...</td>\\n\",\n       \"      <td>Woman Tries To Kiss Python, Gets Bitten In The...</td>\\n\",\n       \"      <td>@Lola2Records</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2243</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:56 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @TeamAnodot: Join David Drai, CEO of Anodot...</td>\\n\",\n       \"      <td>@iottechexpo</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2244</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:57 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://getsmoup.com\\\" rel=\\\"nofollow\\\"&gt;A...</td>\\n\",\n       \"      <td>RT @rebrandtoday: #startup or #rebrand -Buy Cr...</td>\\n\",\n       \"      <td>@AI__news</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2245</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:57 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.twitter.com\\\" rel=\\\"nofollow...</td>\\n\",\n       \"      <td>RT @Matthis__VERNON: \\\"@Fred_Poquet Sans #confi...</td>\\n\",\n       \"      <td>@sibueta</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2246</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:57 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"https://social.zoho.com\\\" rel=\\\"nofollo...</td>\\n\",\n       \"      <td>The right place for #BigData is #Cloud #Storag...</td>\\n\",\n       \"      <td>@TyroneSystems</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2247</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:59 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @PEBlanrue: Il a toujours le mot pour rire,...</td>\\n\",\n       \"      <td>@lesroisduring</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2248</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:58 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>RT @DigitalAgendaEU: €15 million for a #IoT so...</td>\\n\",\n       \"      <td>@ImproveNPA</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2249</th>\\n\",\n       \"      <td>Mon Jan 11 10:29:59 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\"&gt;Tw...</td>\\n\",\n       \"      <td>Neat IoT innovation https://t.co/atARX0m5Bj</td>\\n\",\n       \"      <td>@sherwinnovator</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2250</th>\\n\",\n       \"      <td>Mon Jan 11 10:30:00 +0000 2016</td>\\n\",\n       \"      <td>&lt;a href=\\\"http://www.hubspot.com/\\\" rel=\\\"nofollo...</td>\\n\",\n       \"      <td>Check out our #Mobile App Predicitions for 201...</td>\\n\",\n       \"      <td>@B60uk</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>2251 rows × 4 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                          created_at  \\\\\\n\",\n       \"0     Mon Jan 11 10:00:14 +0000 2016   \\n\",\n       \"1     Mon Jan 11 10:09:26 +0000 2016   \\n\",\n       \"2     Mon Jan 11 10:00:10 +0000 2016   \\n\",\n       \"3     Mon Jan 11 10:00:10 +0000 2016   \\n\",\n       \"4     Mon Jan 11 10:00:10 +0000 2016   \\n\",\n       \"5     Mon Jan 11 10:00:11 +0000 2016   \\n\",\n       \"6     Mon Jan 11 10:00:11 +0000 2016   \\n\",\n       \"7     Mon Jan 11 10:00:11 +0000 2016   \\n\",\n       \"8     Mon Jan 11 10:00:11 +0000 2016   \\n\",\n       \"9     Mon Jan 11 10:00:12 +0000 2016   \\n\",\n       \"10    Mon Jan 11 10:00:13 +0000 2016   \\n\",\n       \"11    Mon Jan 11 10:00:13 +0000 2016   \\n\",\n       \"12    Mon Jan 11 10:02:10 +0000 2016   \\n\",\n       \"13    Mon Jan 11 10:02:10 +0000 2016   \\n\",\n       \"14    Mon Jan 11 10:02:10 +0000 2016   \\n\",\n       \"15    Mon Jan 11 10:02:11 +0000 2016   \\n\",\n       \"16    Mon Jan 11 10:02:12 +0000 2016   \\n\",\n       \"17    Mon Jan 11 10:02:12 +0000 2016   \\n\",\n       \"18    Mon Jan 11 10:02:12 +0000 2016   \\n\",\n       \"19    Mon Jan 11 10:02:12 +0000 2016   \\n\",\n       \"20    Mon Jan 11 10:02:12 +0000 2016   \\n\",\n       \"21    Mon Jan 11 10:02:12 +0000 2016   \\n\",\n       \"22    Mon Jan 11 10:02:13 +0000 2016   \\n\",\n       \"23    Mon Jan 11 10:02:13 +0000 2016   \\n\",\n       \"24    Mon Jan 11 10:02:13 +0000 2016   \\n\",\n       \"25    Mon Jan 11 10:02:13 +0000 2016   \\n\",\n       \"26    Mon Jan 11 10:02:13 +0000 2016   \\n\",\n       \"27    Mon Jan 11 10:09:02 +0000 2016   \\n\",\n       \"28    Mon Jan 11 10:09:17 +0000 2016   \\n\",\n       \"29    Mon Jan 11 10:09:19 +0000 2016   \\n\",\n       \"...                              ...   \\n\",\n       \"2221  Mon Jan 11 10:29:40 +0000 2016   \\n\",\n       \"2222  Mon Jan 11 10:29:40 +0000 2016   \\n\",\n       \"2223  Mon Jan 11 10:29:41 +0000 2016   \\n\",\n       \"2224  Mon Jan 11 10:29:41 +0000 2016   \\n\",\n       \"2225  Mon Jan 11 10:29:42 +0000 2016   \\n\",\n       \"2226  Mon Jan 11 10:29:43 +0000 2016   \\n\",\n       \"2227  Mon Jan 11 10:29:43 +0000 2016   \\n\",\n       \"2228  Mon Jan 11 10:29:43 +0000 2016   \\n\",\n       \"2229  Mon Jan 11 10:29:45 +0000 2016   \\n\",\n       \"2230  Mon Jan 11 10:29:45 +0000 2016   \\n\",\n       \"2231  Mon Jan 11 10:29:46 +0000 2016   \\n\",\n       \"2232  Mon Jan 11 10:29:46 +0000 2016   \\n\",\n       \"2233  Mon Jan 11 10:29:46 +0000 2016   \\n\",\n       \"2234  Mon Jan 11 10:29:48 +0000 2016   \\n\",\n       \"2235  Mon Jan 11 10:29:49 +0000 2016   \\n\",\n       \"2236  Mon Jan 11 10:29:51 +0000 2016   \\n\",\n       \"2237  Mon Jan 11 10:29:51 +0000 2016   \\n\",\n       \"2238  Mon Jan 11 10:29:51 +0000 2016   \\n\",\n       \"2239  Mon Jan 11 10:29:56 +0000 2016   \\n\",\n       \"2240  Mon Jan 11 10:29:56 +0000 2016   \\n\",\n       \"2241  Mon Jan 11 10:29:56 +0000 2016   \\n\",\n       \"2242  Mon Jan 11 10:29:56 +0000 2016   \\n\",\n       \"2243  Mon Jan 11 10:29:56 +0000 2016   \\n\",\n       \"2244  Mon Jan 11 10:29:57 +0000 2016   \\n\",\n       \"2245  Mon Jan 11 10:29:57 +0000 2016   \\n\",\n       \"2246  Mon Jan 11 10:29:57 +0000 2016   \\n\",\n       \"2247  Mon Jan 11 10:29:59 +0000 2016   \\n\",\n       \"2248  Mon Jan 11 10:29:58 +0000 2016   \\n\",\n       \"2249  Mon Jan 11 10:29:59 +0000 2016   \\n\",\n       \"2250  Mon Jan 11 10:30:00 +0000 2016   \\n\",\n       \"\\n\",\n       \"                                                 source  \\\\\\n\",\n       \"0     <a href=\\\"http://twitter.com/download/iphone\\\" r...   \\n\",\n       \"1     <a href=\\\"http://twitter.com/download/android\\\" ...   \\n\",\n       \"2     <a href=\\\"http://trendkeyword.blog.jp/\\\" rel=\\\"no...   \\n\",\n       \"3     <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"4     <a href=\\\"http://bufferapp.com\\\" rel=\\\"nofollow\\\">...   \\n\",\n       \"5     <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"6     <a href=\\\"http://trendkeyword.doorblog.jp/\\\" rel...   \\n\",\n       \"7     <a href=\\\"http://twitterfeed.com\\\" rel=\\\"nofollow...   \\n\",\n       \"8     <a href=\\\"https://about.twitter.com/products/tw...   \\n\",\n       \"9     <a href=\\\"http://www.linkedin.com/\\\" rel=\\\"nofoll...   \\n\",\n       \"10    <a href=\\\"http://catalystfive.com\\\" rel=\\\"nofollo...   \\n\",\n       \"11    <a href=\\\"http://twitter.com/NewsICT\\\" rel=\\\"nofo...   \\n\",\n       \"12    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"13    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"14    <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"15    <a href=\\\"http://twitter.com/download/iphone\\\" r...   \\n\",\n       \"16    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"17    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"18    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"19    <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"20    <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"21    <a href=\\\"http://www.hootsuite.com\\\" rel=\\\"nofoll...   \\n\",\n       \"22    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"23    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"24    <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"25    <a href=\\\"http://dlvr.it\\\" rel=\\\"nofollow\\\">dlvr.i...   \\n\",\n       \"26    <a href=\\\"http://www.google.com/\\\" rel=\\\"nofollow...   \\n\",\n       \"27    <a href=\\\"http://twitter.com/download/android\\\" ...   \\n\",\n       \"28    <a href=\\\"http://twitter.com/download/iphone\\\" r...   \\n\",\n       \"29    <a href=\\\"http://twitter.com/download/android\\\" ...   \\n\",\n       \"...                                                 ...   \\n\",\n       \"2221  <a href=\\\"http://201512291327-7430af.bitnamiapp...   \\n\",\n       \"2222  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2223  <a href=\\\"http://www.linkedin.com/\\\" rel=\\\"nofoll...   \\n\",\n       \"2224  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2225  <a href=\\\"http://www.linkedin.com/\\\" rel=\\\"nofoll...   \\n\",\n       \"2226  <a href=\\\"https://www.jobfindly.com/php-jobs.ht...   \\n\",\n       \"2227  <a href=\\\"http://twitter.com/download/iphone\\\" r...   \\n\",\n       \"2228  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2229  <a href=\\\"http://getsmoup.com\\\" rel=\\\"nofollow\\\">S...   \\n\",\n       \"2230  <a href=\\\"http://www.ajaymatharu.com/\\\" rel=\\\"nof...   \\n\",\n       \"2231  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2232  <a href=\\\"http://publicize.wp.com/\\\" rel=\\\"nofoll...   \\n\",\n       \"2233  <a href=\\\"http://www.itknowingness.com\\\" rel=\\\"no...   \\n\",\n       \"2234  <a href=\\\"http://twitter.com/download/iphone\\\" r...   \\n\",\n       \"2235  <a href=\\\"http://twitter.com/download/android\\\" ...   \\n\",\n       \"2236  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2237  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2238  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2239  <a href=\\\"http://twitterfeed.com\\\" rel=\\\"nofollow...   \\n\",\n       \"2240  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2241  <a href=\\\"http://ifttt.com\\\" rel=\\\"nofollow\\\">IFTT...   \\n\",\n       \"2242  <a href=\\\"http://publicize.wp.com/\\\" rel=\\\"nofoll...   \\n\",\n       \"2243  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2244  <a href=\\\"http://getsmoup.com\\\" rel=\\\"nofollow\\\">A...   \\n\",\n       \"2245  <a href=\\\"http://www.twitter.com\\\" rel=\\\"nofollow...   \\n\",\n       \"2246  <a href=\\\"https://social.zoho.com\\\" rel=\\\"nofollo...   \\n\",\n       \"2247  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2248  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2249  <a href=\\\"http://twitter.com\\\" rel=\\\"nofollow\\\">Tw...   \\n\",\n       \"2250  <a href=\\\"http://www.hubspot.com/\\\" rel=\\\"nofollo...   \\n\",\n       \"\\n\",\n       \"                                                   text              user  \\n\",\n       \"0                          体力落ちてきておばさんみたいになってきた。\\\\n#Rの法則  @Q2HpiJwCX1huBwf  \\n\",\n       \"1                              皆におばさんと言われてうれしがってる #Rの法則     @Tamutamu1017  \\n\",\n       \"2     【R.I.P】急上昇ワード「R.I.Pã€ã®ã¾ã¨ã‚é€Ÿå ± https://t.co/yi1yfC...  @pickword_matome  \\n\",\n       \"3                      #Rの法則 \\\\nどれもおばさん臭いけれどやっぱり黄色が一番だなぁ       @kakinotise  \\n\",\n       \"4     The New Best Thing HP ATP - Vertica Big Data S...  @DataCentreNews1  \\n\",\n       \"5     IoT Now: l’Internet of Things è qui, ora https...   @datamanager_it  \\n\",\n       \"6     今話題の「R.I.P」まとめ https://t.co/VOc5cwK5hg #R.I.P ...       @buzz_wadai  \\n\",\n       \"7     #oldham #stockport VIDEO: Snake thief hides py...    @Labour_is_PIE  \\n\",\n       \"8     Las #startup pioneras de #machinelearning ofre...    @techreview_es  \\n\",\n       \"9     Lets talk about how to harness the power of ma...         @jansmit1  \\n\",\n       \"10    Business Intelligence and Big Data Consulting ...    @Catalyst5Jobs  \\n\",\n       \"11    [æƒ…å ±é€šä¿¡]2016年台北国際コンピューター見本市が新しい位置づけと新しい展示で装い新たに！...          @NewsICT  \\n\",\n       \"12    #bonplan Parties de Laser Quest entre amis à  2...      @Bons_Plans_  \\n\",\n       \"13    Parties de Laser Quest entre amis à  22.00€ au ...   @keepmymindfree  \\n\",\n       \"14    RT @jose_garde: Why a Simple Data Analytics St...    @martingeldish  \\n\",\n       \"15      芸能人の人たくさん手たたき笑いしてるからおばさんたくさんになっちゃうよwww\\\\n\\\\n#Rの法則         @YK__0704  \\n\",\n       \"16    Découvrez le jeu Pure Mission entre amis à  22....   @keepmymindfree  \\n\",\n       \"17    #bonplan Parties de bowling pour 4 à  #POINCY :...      @Bons_Plans_  \\n\",\n       \"18    20 min de vol découverte ULM pour 1 ou 2 à  79....   @CrationSiteWeb  \\n\",\n       \"19    RT @hortonworks: Paris is the city of love but...     @bigdataparis  \\n\",\n       \"20    RT @hynek: So #emacs / @spacemacs nerds: is th...          @fdiesch  \\n\",\n       \"21    .@QonexCyber founder member of @IoT_SF is orga...       @QonexCyber  \\n\",\n       \"22    Parties de bowling pour 4 à  #POINCY : 35.00€ a...   @keepmymindfree  \\n\",\n       \"23    #bonplan 30 séances de Squash à  #LISSES : 39.9...      @Bons_Plans_  \\n\",\n       \"24    App: ExZeus 2 – free to play   https://t.co/ZT...  @UniversalConsol  \\n\",\n       \"25    #discount Parties de bowling pour 4 à  #POINCY ...     @PromosPromos  \\n\",\n       \"26    spiegel.de :  Tier macht Sachen: Python beißt ...  @arminfischer_de  \\n\",\n       \"27                               若いっていいねえ…ってよく言うw #Rの法則          @naco75x  \\n\",\n       \"28                                  最近の若い子は最近使った\\\\n#Rの法則        @K1224West  \\n\",\n       \"29                                     #Rの法則\\\\n自分も若者なのに笑  @V6ZRRT7Q22BZ1cF  \\n\",\n       \"...                                                 ...               ...  \\n\",\n       \"2221  https://t.co/BTAAq6HuuJ - pcgamer - #machinele...         @vinceyue  \\n\",\n       \"2222  RT @jose_garde: The Big Data Analytics Softwar...     @LJ_Blanchard  \\n\",\n       \"2223  What is data mining? Do you have to be a mathe...          @ednuwan  \\n\",\n       \"2224  RT @AgroKnow: A #BigData platform for the futu...   @albertspijkers  \\n\",\n       \"2225  Big Data: Is It A Tsunami, The New Oil, Or Sim...  @Summerlovegrove  \\n\",\n       \"2226  Sr Software Engineer C Php Python Linux Jobs i...  @jobfindlyphpdev  \\n\",\n       \"2227  RT @bigdataparis: #Bigdata bang : un marché en...        @LifeIsWeb  \\n\",\n       \"2228  Learn from the best professors in India .. cou...      @ashwaniapex  \\n\",\n       \"2229  RT @rebrandtoday: #startup or #rebrand -Buy Cr...     @SmartData_Fr  \\n\",\n       \"2230  ¿Cómo será el futuro del Big Data? https://t.c...  @eduardogarsanch  \\n\",\n       \"2231  RT @jose_garde: 3 Ways to Transform Your Compa...     @LJ_Blanchard  \\n\",\n       \"2232  Woman Tries To Kiss Python, Gets Bitten In The...      @NAIJA_VIBEZ  \\n\",\n       \"2233  RT @jose_garde: 3 Ways to Transform Your Compa...    @itknowingness  \\n\",\n       \"2234  RT @ErikaPauwels: Building a #BigData platform...       @impulsater  \\n\",\n       \"2235  En 2016 j'aimerais moins râler. #résolution. S...  @ce1ce2makarenko  \\n\",\n       \"2236  RT @jose_garde: How Marketing Can Be Better Au...     @LJ_Blanchard  \\n\",\n       \"2237  Hey @Pontifex accueille ces réfugiées dans ta ...        @Atmosfive  \\n\",\n       \"2238  RT @Ubixr: .@GroupeLaPoste choisit le toulousa...     @The_Nextwork  \\n\",\n       \"2239  Thanks @hackplayers Blade: un webshell en Pyth...       @Navarmedia  \\n\",\n       \"2240  RT @jose_garde: Which big data personality are...     @LJ_Blanchard  \\n\",\n       \"2241  Cybersecurity Forum tackles challenges with th...          @wulfsec  \\n\",\n       \"2242  Woman Tries To Kiss Python, Gets Bitten In The...     @Lola2Records  \\n\",\n       \"2243  RT @TeamAnodot: Join David Drai, CEO of Anodot...      @iottechexpo  \\n\",\n       \"2244  RT @rebrandtoday: #startup or #rebrand -Buy Cr...         @AI__news  \\n\",\n       \"2245  RT @Matthis__VERNON: \\\"@Fred_Poquet Sans #confi...          @sibueta  \\n\",\n       \"2246  The right place for #BigData is #Cloud #Storag...    @TyroneSystems  \\n\",\n       \"2247  RT @PEBlanrue: Il a toujours le mot pour rire,...    @lesroisduring  \\n\",\n       \"2248  RT @DigitalAgendaEU: €15 million for a #IoT so...       @ImproveNPA  \\n\",\n       \"2249        Neat IoT innovation https://t.co/atARX0m5Bj   @sherwinnovator  \\n\",\n       \"2250  Check out our #Mobile App Predicitions for 201...            @B60uk  \\n\",\n       \"\\n\",\n       \"[2251 rows x 4 columns]\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataset = [{\\\"created_at\\\": item[\\\"created_at\\\"],\\n\",\n    \"            \\\"text\\\": item[\\\"text\\\"],\\n\",\n    \"            \\\"user\\\": \\\"@%s\\\" % item[\\\"user\\\"][\\\"screen_name\\\"],\\n\",\n    \"            \\\"source\\\": item[\\\"source\\\"],\\n\",\n    \"           } for item in col.find()]\\n\",\n    \"\\n\",\n    \"dataset = pd.DataFrame(dataset)\\n\",\n    \"dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Checking the highest used words\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>word</th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>https</td>\\n\",\n       \"      <td>1986</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>co</td>\\n\",\n       \"      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     \"      <th>8</th>\\n\",\n       \"      <td>00</td>\\n\",\n       \"      <td>275</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>data</td>\\n\",\n       \"      <td>250</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>in</td>\\n\",\n       \"      <td>234</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>python</td>\\n\",\n       \"      <td>219</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>to</td>\\n\",\n       \"      <td>212</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>au</td>\\n\",\n       \"      <td>199</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>of</td>\\n\",\n       \"      <td>188</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>lieu</td>\\n\",\n       \"      <td>168</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>réduction</td>\\n\",\n       \"      <td>166</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>big</td>\\n\",\n       \"      <td>157</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>on</td>\\n\",\n       \"      <td>143</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>is</td>\\n\",\n       \"      <td>142</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>and</td>\\n\",\n       \"      <td>140</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>for</td>\\n\",\n       \"      <td>136</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>analytics</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>le</td>\\n\",\n       \"      <td>89</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>via</td>\\n\",\n       \"      <td>86</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>you</td>\\n\",\n       \"      <td>86</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>thingsexpo</td>\\n\",\n       \"      <td>86</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>by</td>\\n\",\n       \"      <td>85</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>2016</td>\\n\",\n       \"      <td>84</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>snake</td>\\n\",\n       \"      <td>80</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>30</th>\\n\",\n       \"      <td>en</td>\\n\",\n       \"      <td>76</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>31</th>\\n\",\n       \"      <td>bowie</td>\\n\",\n       \"      <td>75</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>32</th>\\n\",\n       \"      <td>la</td>\\n\",\n       \"      <td>74</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>33</th>\\n\",\n       \"      <td>thief</td>\\n\",\n       \"      <td>74</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>34</th>\\n\",\n       \"      <td>video</td>\\n\",\n       \"      <td>73</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>35</th>\\n\",\n       \"      <td>m2m</td>\\n\",\n       \"      <td>70</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>36</th>\\n\",\n       \"      <td>jose_garde</td>\\n\",\n       \"      <td>68</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>37</th>\\n\",\n       \"      <td>19</td>\\n\",\n       \"      <td>67</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>38</th>\\n\",\n       \"      <td>david</td>\\n\",\n       \"      <td>66</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39</th>\\n\",\n       \"      <td>with</td>\\n\",\n       \"      <td>63</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>40</th>\\n\",\n       \"      <td>how</td>\\n\",\n       \"      <td>61</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>41</th>\\n\",\n       \"      <td>it</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42</th>\\n\",\n       \"      <td>will</td>\\n\",\n       \"      <td>55</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>43</th>\\n\",\n       \"      <td>un</td>\\n\",\n       \"      <td>54</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>44</th>\\n\",\n       \"      <td>amp</td>\\n\",\n       \"      <td>53</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>45</th>\\n\",\n       \"      <td>des</td>\\n\",\n       \"      <td>53</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>46</th>\\n\",\n       \"      <td>réparation</td>\\n\",\n       \"      <td>53</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>47</th>\\n\",\n       \"      <td>new</td>\\n\",\n       \"      <td>52</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48</th>\\n\",\n       \"      <td>39</td>\\n\",\n       \"      <td>52</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>49</th>\\n\",\n       \"      <td>at</td>\\n\",\n       \"      <td>51</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          word  count\\n\",\n       \"0        https   1986\\n\",\n       \"1           co   1907\\n\",\n       \"2           rt    804\\n\",\n       \"3           de    550\\n\",\n       \"4         rの法則    408\\n\",\n       \"5          iot    374\\n\",\n       \"6          the    358\\n\",\n       \"7      bigdata    293\\n\",\n       \"8           00    275\\n\",\n       \"9         data    250\\n\",\n       \"10          in    234\\n\",\n       \"11      python    219\\n\",\n       \"12          to    212\\n\",\n       \"13          au    199\\n\",\n       \"14          of    188\\n\",\n       \"15        lieu    168\\n\",\n       \"16   réduction    166\\n\",\n       \"17         big    157\\n\",\n       \"18          on    143\\n\",\n       \"19          is    142\\n\",\n       \"20         and    140\\n\",\n       \"21         for    136\\n\",\n       \"22   analytics    107\\n\",\n       \"23          le     89\\n\",\n       \"24         via     86\\n\",\n       \"25         you     86\\n\",\n       \"26  thingsexpo     86\\n\",\n       \"27          by     85\\n\",\n       \"28        2016     84\\n\",\n       \"29       snake     80\\n\",\n       \"30          en     76\\n\",\n       \"31       bowie     75\\n\",\n       \"32          la     74\\n\",\n       \"33       thief     74\\n\",\n       \"34       video     73\\n\",\n       \"35         m2m     70\\n\",\n       \"36  jose_garde     68\\n\",\n       \"37          19     67\\n\",\n       \"38       david     66\\n\",\n       \"39        with     63\\n\",\n       \"40         how     61\\n\",\n       \"41          it     60\\n\",\n       \"42        will     55\\n\",\n       \"43          un     54\\n\",\n       \"44         amp     53\\n\",\n       \"45         des     53\\n\",\n       \"46  réparation     53\\n\",\n       \"47         new     52\\n\",\n       \"48          39     52\\n\",\n       \"49          at     51\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cv = CountVectorizer()\\n\",\n    \"count_matrix = cv.fit_transform(dataset.text)\\n\",\n    \"\\n\",\n    \"word_count = pd.DataFrame(cv.get_feature_names(), columns=[\\\"word\\\"])\\n\",\n    \"word_count[\\\"count\\\"] = count_matrix.sum(axis=0).tolist()[0]\\n\",\n    \"word_count = word_count.sort_values(\\\"count\\\", ascending=False).reset_index(drop=True)\\n\",\n    \"word_count[:50]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_source_name(x):\\n\",\n    \"    value = re.findall(pattern=\\\"<[^>]+>([^<]+)</a>\\\", string=x)\\n\",\n    \"    if len(value) > 0:\\n\",\n    \"        return value[0]\\n\",\n    \"    else:\\n\",\n    \"        return \\\"\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Facebook                25\\n\",\n       \"TweetDeck               37\\n\",\n       \"RoundTeam               41\\n\",\n       \"Hootsuite               46\\n\",\n       \"twitterfeed             81\\n\",\n       \"IFTTT                  134\\n\",\n       \"dlvr.it                200\\n\",\n       \"Twitter Web Client     388\\n\",\n       \"Twitter for Android    392\\n\",\n       \"Twitter for iPhone     515\\n\",\n       \"Name: source, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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sD2T5o/DEmP1rZPqp1vQknbvIxkvynpNklXSFpb0sG1z2aGpLXr9dq+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\\nOlACdERERAdKgI6IiOhA/z8aaHKp8XyaNwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fec53d81550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dataset.source_name = dataset.source.apply(get_source_name)\\n\",\n    \"\\n\",\n    \"source_counts = dataset.source_name.value_counts().sort_values()[-10:]\\n\",\n    \"\\n\",\n    \"bottom = [index for index, item in enumerate(source_counts.index)]\\n\",\n    \"plt.barh(bottom, width=source_counts, color=\\\"orange\\\", linewidth=0)\\n\",\n    \"\\n\",\n    \"y_labels = [\\\"%s %.1f%%\\\" % (item, 100.0*source_counts[item]/len(dataset)) for index,item in enumerate(source_counts.index)]\\n\",\n    \"plt.yticks(np.array(bottom)+0.4, y_labels)\\n\",\n    \"\\n\",\n    \"source_counts\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/ipython_widgets/2. Events.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:c95d641333791fd278e7097c70c834da61dbd26a43aa874668dfcdd14869cd67\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Handling the wivents of widgets using:\\n\",\n      \"\\n\",\n      \"- on_click()\\n\",\n      \"- on_trait_change()\\n\",\n      \"\\n\",\n      \"**Video Tutorial**: http://youtu.be/hXS4xhyx0a8\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.html import widgets\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widgets.on_click()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def print_msg(widget):\\n\",\n      \"    print \\\"You have clicked (%s)\\\" % widget.description\\n\",\n      \"\\n\",\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"button_1 = widgets.ButtonWidget(description=\\\"Click Me\\\")\\n\",\n      \"button_2 = widgets.ButtonWidget(description=\\\"Don't Click Me\\\")\\n\",\n      \"\\n\",\n      \"button_1.on_click(print_msg)\\n\",\n      \"button_2.on_click(print_msg)\\n\",\n      \"\\n\",\n      \"container.children = [button_1, button_2]\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"You have clicked (Click Me)\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"You have clicked (Don't Click Me)\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widget.on_trait_change()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"control_1 = widgets.FloatTextWidget(description=\\\"X: \\\")\\n\",\n      \"control_2 = widgets.FloatTextWidget(description=\\\"Y: \\\")\\n\",\n      \"control_3 = widgets.SelectWidget(description= \\\"Operation\\\", values=[\\\"+\\\",\\\"-\\\",\\\"*\\\",\\\"/\\\"])\\n\",\n      \"control_4 = widgets.LatexWidget(value=\\\"Formula: NA\\\")\\n\",\n      \"control_5 = widgets.LatexWidget(value=\\\"Result: 0.0\\\")\\n\",\n      \"\\n\",\n      \"container.children = [control_1, control_2, control_3, control_4, control_5]\\n\",\n      \"\\n\",\n      \"def calculate_form(name):\\n\",\n      \"    # Retreive values from form\\n\",\n      \"    form = container.children\\n\",\n      \"    X = form[0].value\\n\",\n      \"    Y = form[1].value\\n\",\n      \"    operation = form[2].value\\n\",\n      \"    \\n\",\n      \"    # Build Fomula\\n\",\n      \"    formula = str(X) + operation + str(Y)\\n\",\n      \"    form[3].value = \\\"Formula: %s\\\" % formula\\n\",\n      \"    \\n\",\n      \"    # Check if Divive by Zero\\n\",\n      \"    if Y == 0.0 and operation == \\\"/\\\":\\n\",\n      \"        form[4].value = \\\"Div by Zero\\\"\\n\",\n      \"    else:\\n\",\n      \"        # Evaluate fumula\\n\",\n      \"        form[4].value = str(eval(formula))\\n\",\n      \"    print \\\"%s = %s\\\" % (formula, control_5.value)\\n\",\n      \"\\n\",\n      \"# Add Handlers\\n\",\n      \"control_1.on_trait_change(calculate_form, \\\"value\\\")\\n\",\n      \"control_2.on_trait_change(calculate_form, \\\"value\\\")\\n\",\n      \"control_3.on_trait_change(calculate_form, \\\"value\\\")\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"0.0-0.0 = 0.0\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"0.0*0.0 = 0.0\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"0.0/0.0 = Div by Zero\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"0.0/4.0 = 0.0\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.0/4.0 = 0.5\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.0+4.0 = 6.0\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.0/4.0 = 0.5\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.2/4.0 = 0.55\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25/4.0 = 0.5625\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.254/4.0 = 0.5635\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.2547/4.0 = 0.563675\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478/4.0 = 0.563695\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.254785/4.0 = 0.56369625\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.2547854/4.0 = 0.56369635\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546/4.0 = 0.563696365\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.254785465/4.0 = 0.56369636625\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.2547854658/4.0 = 0.56369636645\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546587/4.0 = 0.563696366467\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.0 = 0.56369636647\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.0 = 0.56369636647\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.0 = 0.56369636647\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.0 = 0.56369636647\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.4 = 0.512451242245\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.45 = 0.506693363119\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.458 = 0.505784088354\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.4585 = 0.505727367025\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.45854 = 0.505722829868\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.458542 = 0.505722603012\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.4585426 = 0.505722534956\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.45854264 = 0.505722530419\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.458542648 = 0.505722529511\\n\"\n       ]\n      },\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2.25478546588/4.4585426481 = 0.5057225295\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/ipython_widgets/3. Styling Widgets.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:dd82781b00f105187cdb9e8112116d56f6f0c50345ecf58c019b0cc5dc68f416\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Controlling the style of your widgets using the following:\\n\",\n      \"\\n\",\n      \"- widget.visible()\\n\",\n      \"- widget.set_css()\\n\",\n      \"- widget.add_class()\\n\",\n      \"\\n\",\n      \"**Video Tutorial:**\\n\",\n      \"\\n\",\n      \"http://youtu.be/sER3W_ChuNQ\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.html import widgets\\n\",\n      \"import re\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widget.visible()`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"widget_1 = widgets.TextWidget(description=\\\"Username: \\\")\\n\",\n      \"widget_2 = widgets.ButtonWidget(description=\\\"Hide\\\")\\n\",\n      \"widget_3 = widgets.ButtonWidget(description=\\\"Show\\\")\\n\",\n      \"\\n\",\n      \"container.children = (widget_1, widget_2, widget_3)\\n\",\n      \"\\n\",\n      \"def hide_user(widget):\\n\",\n      \"    widget_1.visible = False\\n\",\n      \"\\n\",\n      \"def show_user(widget):\\n\",\n      \"    widget_1.visible = True\\n\",\n      \"    \\n\",\n      \"widget_2.on_click(hide_user)\\n\",\n      \"widget_3.on_click(show_user)\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widget.set_css(self, dict_or_key, value=None, selector='')`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"widget_1 = widgets.TextWidget(description=\\\"Name\\\")\\n\",\n      \"widget_2 = widgets.TextWidget(description=\\\"Email\\\")\\n\",\n      \"\\n\",\n      \"container.children = (widget_1, widget_2)\\n\",\n      \"\\n\",\n      \"def check_email(name, old, new):\\n\",\n      \"    new_check = re.match(r\\\"[^@]+@[^@]+\\\\.[^@]+\\\", new)\\n\",\n      \"    old_check = re.match(r\\\"[^@]+@[^@]+\\\\.[^@]+\\\", old)\\n\",\n      \"    \\n\",\n      \"    if new_check <> old_check:\\n\",\n      \"        style_email()\\n\",\n      \"\\n\",\n      \"def style_email(*args):\\n\",\n      \"    new_check = re.match(r\\\"[^@]+@[^@]+\\\\.[^@]+\\\", widget_2.value)\\n\",\n      \"    if new_check:\\n\",\n      \"        widget_2.set_css(\\\"color\\\", \\\"#228822\\\")\\n\",\n      \"        widget_2.set_css(\\\"background-color\\\", \\\"#fff\\\")\\n\",\n      \"        widget_2.set_css(\\\"font-weight\\\", \\\"400\\\")\\n\",\n      \"    else:\\n\",\n      \"        widget_2.set_css(\\\"color\\\", \\\"#ff0000\\\")\\n\",\n      \"        widget_2.set_css(\\\"background-color\\\", \\\"#ffbbbb\\\")\\n\",\n      \"        widget_2.set_css(\\\"font-weight\\\", \\\"800\\\")\\n\",\n      \"            \\n\",\n      \"widget_2.on_trait_change(check_email, \\\"value\\\")\\n\",\n      \"widget_2.on_displayed(style_email)\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widget.add_class(self, class_names, selector='')`\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"You can define your own CSS calsses or use the Bootstrap library that is available\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Bootstrap Classes\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Button Style\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"We will add a list of buttons\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"widget_1 = widgets.ButtonWidget(description=\\\"btn-default\\\")\\n\",\n      \"widget_2 = widgets.ButtonWidget(description=\\\"btn-primary\\\")\\n\",\n      \"widget_3 = widgets.ButtonWidget(description=\\\"btn-success\\\")\\n\",\n      \"widget_4 = widgets.ButtonWidget(description=\\\"btn-info\\\")\\n\",\n      \"widget_5 = widgets.ButtonWidget(description=\\\"btn-warning\\\")\\n\",\n      \"widget_6 = widgets.ButtonWidget(description=\\\"btn-danger\\\")\\n\",\n      \"widget_7 = widgets.ButtonWidget(description=\\\"btn-link\\\")\\n\",\n      \"\\n\",\n      \"container.children = (widget_1, widget_2, widget_3, widget_4, widget_5,\\n\",\n      \"                       widget_6, widget_7)\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Works for already displayed widgets\\n\",\n      \"widget_1.add_class(\\\"btn-default\\\")\\n\",\n      \"widget_2.add_class(\\\"btn-primary\\\")\\n\",\n      \"widget_3.add_class(\\\"btn-success\\\")\\n\",\n      \"widget_4.add_class(\\\"btn-info\\\")\\n\",\n      \"widget_5.add_class(\\\"btn-warning\\\")\\n\",\n      \"widget_6.add_class(\\\"btn-danger\\\")\\n\",\n      \"widget_7.add_class(\\\"btn-link\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 4,\n     \"metadata\": {},\n     \"source\": [\n      \"Block Button\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"widget_1 = widgets.TextWidget(description=\\\"Name\\\")\\n\",\n      \"widget_2 = widgets.ButtonWidget(description=\\\"btn-block\\\")\\n\",\n      \"\\n\",\n      \"container.children = (widget_1, widget_2)\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"# Works for already displayed widgets\\n\",\n      \"widget_2.add_class(\\\"btn-primary\\\")\\n\",\n      \"widget_2.add_class(\\\"btn-block\\\")\\n\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"HTMLWidget\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Labels\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%html\\n\",\n      \"<span class=\\\"label label-default\\\">Default</span>\\n\",\n      \"<span class=\\\"label label-success\\\">Success</span>\\n\",\n      \"<span class=\\\"label label-info\\\">Info</span>\\n\",\n      \"<span class=\\\"label label-warning\\\">Warning</span>\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<span class=\\\"label label-default\\\">Default</span>\\n\",\n        \"<span class=\\\"label label-success\\\">Success</span>\\n\",\n        \"<span class=\\\"label label-info\\\">Info</span>\\n\",\n        \"<span class=\\\"label label-warning\\\">Warning</span>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"text\": [\n        \"<IPython.core.display.HTML at 0x7fa25c094650>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"html_code = \\\"\\\"\\\"\\n\",\n      \"<span class=\\\"label label-default\\\">Default</span>\\n\",\n      \"<span class=\\\"label label-success\\\">Success</span>\\n\",\n      \"<span class=\\\"label label-info\\\">Info</span>\\n\",\n      \"<span class=\\\"label label-warning\\\">Warning</span>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"widget_1 = widgets.HTMLWidget(value = html_code)\\n\",\n      \"\\n\",\n      \"container.children = (widget_1,)\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Alerts\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%html\\n\",\n      \"<div class=\\\"alert alert-success alert-dismissible\\\" role=\\\"alert\\\">\\n\",\n      \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n      \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n      \"  </button>\\n\",\n      \"  <strong>Calss: </strong>alert-success\\n\",\n      \"</div>\\n\",\n      \"<div class=\\\"alert alert-info\\\" role=\\\"alert\\\">\\n\",\n      \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n      \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n      \"  </button>\\n\",\n      \"  <strong>Calss: </strong>alert-info\\n\",\n      \"</div>\\n\",\n      \"<div class=\\\"alert alert-warning\\\" role=\\\"alert\\\">\\n\",\n      \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n      \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n      \"  </button>\\n\",\n      \"  <strong>Calss: </strong>alert-warning\\n\",\n      \"</div>\\n\",\n      \"<div class=\\\"alert alert-danger\\\" role=\\\"alert\\\">\\n\",\n      \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n      \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n      \"  </button>\\n\",\n      \"  <strong>Calss: </strong>alert-danger\\n\",\n      \"</div>\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div class=\\\"alert alert-success alert-dismissible\\\" role=\\\"alert\\\">\\n\",\n        \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n        \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n        \"  </button>\\n\",\n        \"  <strong>Calss: </strong>alert-success\\n\",\n        \"</div>\\n\",\n        \"<div class=\\\"alert alert-info\\\" role=\\\"alert\\\">\\n\",\n        \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n        \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n        \"  </button>\\n\",\n        \"  <strong>Calss: </strong>alert-info\\n\",\n        \"</div>\\n\",\n        \"<div class=\\\"alert alert-warning\\\" role=\\\"alert\\\">\\n\",\n        \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n        \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n        \"  </button>\\n\",\n        \"  <strong>Calss: </strong>alert-warning\\n\",\n        \"</div>\\n\",\n        \"<div class=\\\"alert alert-danger\\\" role=\\\"alert\\\">\\n\",\n        \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n        \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n        \"  </button>\\n\",\n        \"  <strong>Calss: </strong>alert-danger\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"display_data\",\n       \"text\": [\n        \"<IPython.core.display.HTML at 0x7fa25c0946d0>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Example Using HTML Widget\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"alert_code = \\\"\\\"\\\"\\n\",\n      \"<div class=\\\"alert alert-{type} alert-dismissible\\\" role=\\\"alert\\\">\\n\",\n      \"  <button type=\\\"button\\\" class=\\\"close\\\" data-dismiss=\\\"alert\\\">\\n\",\n      \"    <span aria-hidden=\\\"true\\\">&times;</span>\\n\",\n      \"  </button>\\n\",\n      \"  <strong>{title}: </strong>{message}\\n\",\n      \"</div>\\n\",\n      \"\\\"\\\"\\\"\\n\",\n      \"\\n\",\n      \"widget_1 = widgets.HTMLWidget()\\n\",\n      \"widget_2 = widgets.TextWidget(description=\\\"Name\\\")\\n\",\n      \"widget_3 = widgets.ButtonWidget(description=\\\"Save\\\")\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"def save_form(widget):\\n\",\n      \"    widget_1.value = alert_code.format(type=\\\"success\\\",\\n\",\n      \"                                       title=\\\"Saved\\\",\\n\",\n      \"                                       message=\\\"Your form has been saved\\\",\\n\",\n      \"                                        )\\n\",\n      \"\\n\",\n      \"widget_3.on_click(save_form)\\n\",\n      \"\\n\",\n      \"container.children = (widget_1, widget_2, widget_3)\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 11\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/ipython_widgets/4. Widges Layout.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:c05a3fedc7549d209ef933f31aef324d989659f8f1e3525e2d32fd0854902ed6\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"This tutorial covers the basics of contrlling the layout of IPython widgets.\\n\",\n      \"\\n\",\n      \"We will be using the following functions to do that.\\n\",\n      \"\\n\",\n      \"- display()\\n\",\n      \"- widget.add_class()\\n\",\n      \"- widget.set_css()\\n\",\n      \"\\n\",\n      \"Video Tutorial:\\n\",\n      \"\\n\",\n      \"https://www.youtube.com/watch?v=8v7gVQlHJ-g\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import the library\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.html import widgets\\n\",\n      \"from IPython.display import display\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Default Layout `\\\"vbox\\\"` class\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"By default a container will have a `\\\"vbox\\\"` css class added to it.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"v_container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"# Define Widgets\\n\",\n      \"for counter in range(5):\\n\",\n      \"    wgt_check = widgets.CheckboxWidget()\\n\",\n      \"    wgt_text = widgets.TextWidget(description=\\\"Name\\\")\\n\",\n      \"    wgt_select = widgets.DropdownWidget(values=[\\\"Guest\\\", \\\"User\\\", \\\"Admin\\\"])\\n\",\n      \"\\n\",\n      \"    # Add Widgets\\n\",\n      \"    v_container.children += (wgt_check, wgt_text, wgt_select)\\n\",\n      \"\\n\",\n      \"wgt_button = widgets.ButtonWidget(description=\\\"Submit\\\")\\n\",\n      \"v_container.children += (wgt_button,)\\n\",\n      \"\\n\",\n      \"# Display the container\\n\",\n      \"display(v_container)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Horizontal Layout with `\\\"hbox\\\"` class\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"v_container = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"# Display the container\\n\",\n      \"display(v_container)\\n\",\n      \"\\n\",\n      \"# Define Widgets\\n\",\n      \"for counter in range(5):\\n\",\n      \"    wgt_check = widgets.CheckboxWidget()\\n\",\n      \"    wgt_text = widgets.TextWidget(description=\\\"Name\\\")\\n\",\n      \"    wgt_select = widgets.DropdownWidget(values=[\\\"Guest\\\", \\\"User\\\", \\\"Admin\\\"])\\n\",\n      \"\\n\",\n      \"    # Add Widgets\\n\",\n      \"    h_container = widgets.ContainerWidget()\\n\",\n      \"    h_container.children += (wgt_check, wgt_text, wgt_select)\\n\",\n      \"    v_container.children += (h_container,)\\n\",\n      \"    \\n\",\n      \"    # Change css class of the container\\n\",\n      \"    h_container.remove_class('vbox')\\n\",\n      \"    h_container.add_class('hbox')\\n\",\n      \"\\n\",\n      \"wgt_button = widgets.ButtonWidget(description=\\\"Submit\\\")\\n\",\n      \"v_container.children += (wgt_button,)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/ipython_widgets/Widgets Containers.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:86b177a1169f7657f8166ad3eb039a5ba0ff60065882d4645ac3858d2bfa275f\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"In this tutorial we will cover the following containers and how to use them:\\n\",\n      \"\\n\",\n      \"- ContainerWidget\\n\",\n      \"- AccordionWidget\\n\",\n      \"- TabWidget\\n\",\n      \"- PopupWidget\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import the Library\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.html import widgets\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widgets.ContainerWidget`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container = widgets.ContainerWidget()\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"control_1 = widgets.TextWidget(description=\\\"Name: \\\")\\n\",\n      \"control_2 = widgets.TextWidget(description=\\\"Email: \\\")\\n\",\n      \"control_3 = widgets.ButtonWidget(description=\\\"Submit\\\")\\n\",\n      \"\\n\",\n      \"container.children = [control_1, control_2, control_3]\\n\",\n      \"\\n\",\n      \"container\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widgets.AccordionWidget`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"container_1 = container\\n\",\n      \"\\n\",\n      \"container_2 = widgets.ContainerWidget()\\n\",\n      \"\\n\",\n      \"control_1 = widgets.TextWidget(description=\\\"Country: \\\")\\n\",\n      \"control_2 = widgets.TextWidget(description=\\\"State: \\\")\\n\",\n      \"control_3 = widgets.TextWidget(description=\\\"City: \\\")\\n\",\n      \"control_4 = widgets.ButtonWidget(description=\\\"Save\\\")\\n\",\n      \"\\n\",\n      \"container_2.children = [control_1, control_2, control_3, control_4]\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"accordion = widgets.AccordionWidget()\\n\",\n      \"accordion.children = [container_1, container_2]\\n\",\n      \"accordion\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"accordion.set_title(0, \\\"Personal Information\\\")\\n\",\n      \"accordion.set_title(1, \\\"Location\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widgets.TabWidget`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"tabs = widgets.TabWidget()\\n\",\n      \"tabs.children = [container_1, container_2]\\n\",\n      \"\\n\",\n      \"tabs\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"tabs.set_title(0, \\\"Personal Information\\\")\\n\",\n      \"tabs.set_title(1, \\\"Location\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"`widgets.PopupWidget`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"popup = widgets.PopupWidget(description=\\\"Register Now!\\\")\\n\",\n      \"popup.children = [accordion]\\n\",\n      \"popup\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 9\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/jupyter/1. Introduction.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# IPython 3 (jupyter)\\n\",\n    \"\\n\",\n    \"**Video Toturial:** https://www.youtube.com/user/roshanRush\\n\",\n    \"\\n\",\n    \"Jupyter is an web-based interactive development environment. It support multiple programming languages like Julia, Octave, Python and R (In alphabetical order).\\n\",\n    \"\\n\",\n    \"## Upgrade to the new version\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"sudo pip install --upgrade \\\"ipython[all]\\\"\\n\",\n    \"```\\n\",\n    \"If you use `sudo pip install --upgrade ipython`, you might get **\\\"Terminal Unavailable\\\"** on **`New`** menu.\\n\",\n    \"\\n\",\n    \"## Enabling Python3\\n\",\n    \"\\n\",\n    \"You can add Python 3 kernel to jupyter by using this command:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"sudo ipython3 kernelspec install-self\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Why is it called jupyter?\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/jupyter-main-logo.svg\\\" style=\\\"width: 400px\\\"> \\n\",\n    \"\\n\",\n    \"The name represents the new direction of IPython development. They are moving away from linking the IPython platform with a single language. The way it currently works is as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](images/kernel.png)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"$$Julia + Python + R = jupyter$$\\n\",\n    \"\\n\",\n    \"This is not to indicate that jupyter only supports these languages. But it is a reference to a [talk](http://youtu.be/JDrhn0-r9Eg?t=6m14s) by [Fernando Perez](https://twitter.com/fperez_org). The complete list is available [here](https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### But there is another reason\\n\",\n    \"\\n\",\n    \"Galileo documented his observations about Jupiter's moons and published them in the Sidereus Nuncius (Starry Messenger) back in 1610 which was a pamphlet that he published. This was the first paper ever published about astronomy that used observations using a telescope. He proved that the Earth orbits the Sun like Jupiter's moons orbit Jupiter.\\n\",\n    \"\\n\",\n    \"> \\\"I therefore concluded and decided unhesitatingly, that there are three stars in the heavens moving about Jupiter, as Venus and Mercury round the Sun; which at length was established as clear as daylight by numerous subsequent observations. These observations also established that there are not only three, but four, erratic sidereal bodies performing their revolutions round Jupiter...the revolutions are so swift that an observer may generally get differences of position every hour.\\\"\\n\",\n    \">\\n\",\n    \"> Galileo trans Carlos, 1880, p47. \\n\",\n    \"\\n\",\n    \"<img src=\\\"http://upload.wikimedia.org/wikipedia/commons/d/d0/Sidereus_Nuncius_Medicean_Stars.jpg\\\">\\n\",\n    \"<center>Image(s) courtesy of the History of Science Collections, University of Oklahoma Libraries.</center>\\n\",\n    \"\\n\",\n    \"Sharing these observations and conclusions in a way that can be replicated and verified inspired the modern scientific method. This is what IPython Notebooks are used for; sharing the complete process with others to verify your process and replicate the results.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# So what's new?\\n\",\n    \"\\n\",\n    \"http://ipython.org/ipython-doc/3/whatsnew/version3.html\\n\",\n    \"\\n\",\n    \"> 3.x will be the last monolithic release of IPython, as the next release cycle will see the growing project split into its Python-specific and language-agnostic components.\\n\",\n    \">\\n\",\n    \"> **http://ipython.org/ipython-doc/3/whatsnew/version3.html**\\n\",\n    \"\\n\",\n    \"## 1. Notebooks with different kernels\\n\",\n    \"https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages\\n\",\n    \"\\n\",\n    \"![](http://ipython.org/ipython-doc/3/_images/kernel_selector_screenshot.png)\\n\",\n    \"## 2. Unicode Identifiers\\n\",\n    \"**Works with Python3 and Julia**\\n\",\n    \"\\n\",\n    \"To get `α` variable, type \\\\alpha Then Press Tab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.5\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"α = 1 #\\\\alpha\\n\",\n    \"β = 2 #\\\\beta\\n\",\n    \"λ = 0 #\\\\lambda\\n\",\n    \"Λ = 0 #\\\\Lambda\\n\",\n    \"𝔞 = 0 #\\\\mfraka - Notice it is not monospaced - Thinner\\n\",\n    \"𝖰 = 0 #\\\\msansQ - Notice it is not monospaced - Wider\\n\",\n    \"α / β\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"slideshow\": {\n     \"slide_type\": \"slide\"\n    }\n   },\n   \"source\": [\n    \"## 3. Widgets are **NOT** Backwards Compatible\\n\",\n    \"\\n\",\n    \"http://ipython.org/ipython-doc/3/whatsnew/version3_widget_migration.html\\n\",\n    \"\\n\",\n    \"## 4. No Header Cells\\n\",\n    \"You can now use heading inside a markdown cell and you will get a bookmark link on them.\\n\",\n    \"\\n\",\n    \"## 5. Text Editor\\n\",\n    \"A multilingual text editor has been added with highlighting for many programming and scripting languages.\\n\",\n    \"\\n\",\n    \"## 6. Static File Server\\n\",\n    \"You can serve static files using https://url:port/files/%FILE_PATH% like https://0.0.0.0:8888/files/myproject/README.html\\n\",\n    \"\\n\",\n    \"**<span style=\\\"color:red;\\\">Security Warning:</span>** Because Jupyter uses a static file server, it is advised that you use https even if you are running on localhost.\\n\",\n    \"\\n\",\n    \"But on the upside, you can start with exploring the data and end up with a running web-based application without leaving jupyter. You might not want to use this setup for production but it is perfect for development.\\n\",\n    \"\\n\",\n    \"## 7. Build-it Terminal\\n\",\n    \"You can start a terminal from jupyter to access your machine. Thanks to the development team for making it black background with white text.\\n\",\n    \"\\n\",\n    \"## 8. Jupyter Drive\\n\",\n    \"You can share jupyter notebooks from your Google Drive using the new [Jupyter Drive](https://github.com/jupyter/jupyter-drive). This allows you share jupyter notebooks like NBViewr with all the access control that Google Drive provides.\\n\",\n    \"\\n\",\n    \"You can also install [Jupyter Drive](https://github.com/jupyter/jupyter-drive) locally. This allows you to access Notebooks on your Google Drive or other Notebooks shared with you. It also allows you to access notebooks on you local machines.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/jupyter/2. Markdown & LaTeX.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#Tutorial Brief\\n\",\n    \"\\n\",\n    \"**Video Tutorial:** https://www.youtube.com/user/roshanRush\\n\",\n    \"\\n\",\n    \"Jupyter has an implementation of markdown language that can be used in in markdown cells to create formatted text and media documentation in your notebook. LaTeX is also implemented to create high quality mathematical typeset.\\n\",\n    \"\\n\",\n    \"#Markdown\\n\",\n    \"\\n\",\n    \"##Headings\\n\",\n    \"\\n\",\n    \"#H1\\n\",\n    \"##H2\\n\",\n    \"###H3\\n\",\n    \"####H4\\n\",\n    \"#####H5\\n\",\n    \"######H6\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"#H1\\n\",\n    \"##H2\\n\",\n    \"###H3\\n\",\n    \"####H4\\n\",\n    \"#####H5\\n\",\n    \"######H6\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Alternative Headings\\n\",\n    \"\\n\",\n    \"Heading 1\\n\",\n    \"=========\\n\",\n    \"\\n\",\n    \"Heading 2\\n\",\n    \"----------\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"Heading 1\\n\",\n    \"=========\\n\",\n    \"\\n\",\n    \"Heading 2\\n\",\n    \"---------\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Font Styles\\n\",\n    \"\\n\",\n    \"**Bold Font** or __Bold Font__\\n\",\n    \"\\n\",\n    \"*Italic* or _Italic Font_\\n\",\n    \"\\n\",\n    \"~~Scratched Text~~\\n\",\n    \"\\n\",\n    \"Markdown doesn't support underline. but you can do that using HTML <u>Text</u>\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"**Bold Font** or __Bold Font__\\n\",\n    \"\\n\",\n    \"*Italic* or _Italic Font_\\n\",\n    \"\\n\",\n    \"~~Scratched Text~~\\n\",\n    \"\\n\",\n    \"Markdown doesn't support underline. but you can do that using HTML <u>Text</u>\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Lists\\n\",\n    \"\\n\",\n    \"- item\\n\",\n    \"- item\\n\",\n    \" - subitem\\n\",\n    \" - subitem\\n\",\n    \"- item\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"1. item\\n\",\n    \"2. item\\n\",\n    \" 1. sub item\\n\",\n    \" 2. sub item\\n\",\n    \"3. item\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"- item\\n\",\n    \"- item\\n\",\n    \" - subitem\\n\",\n    \" - subitem\\n\",\n    \"- item\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"1. item\\n\",\n    \"2. item\\n\",\n    \" 1. sub item\\n\",\n    \" 2. sub item\\n\",\n    \"3. item\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Links\\n\",\n    \"\\n\",\n    \"http://www.github.com/\\n\",\n    \"\\n\",\n    \"[Github](http://www.github.com/)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```\\n\",\n    \"http://www.github.com/\\n\",\n    \"\\n\",\n    \"[Github](http://www.github.com/)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Images\\n\",\n    \"![Turing's Device](http://www.google.com/logos/2012/turing-doodle-static.jpg \\\"Alan Turing's 100th Birthday\\\")\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"![Turing's Device](http://www.google.com/logos/2012/turing-doodle-static.jpg \\\"Alan Turing's 100th Birthday\\\")\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Quotes\\n\",\n    \"\\n\",\n    \"> Why, oh why, Javascript??? Wars, famine, planetary destruction... I guess as a species, we deserve this abomination...\\n\",\n    \">\\n\",\n    \"> [Fernando Perez](https://twitter.com/fperez_org)\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```\\n\",\n    \"> Why, oh why, Javascript??? Wars, famine, planetary destruction... I guess as a species, we deserve this abomination...\\n\",\n    \">\\n\",\n    \"> [Fernando Perez](https://twitter.com/fperez_org)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Horizontal Line\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"---\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Tables\\n\",\n    \"\\n\",\n    \"| Tables        | Are           | Cool  |\\n\",\n    \"| ------------- |:-------------:| -----:|\\n\",\n    \"| col 3 is      | right-aligned |  1600 |\\n\",\n    \"| col 2 is      | centered      |    12 |\\n\",\n    \"| zebra stripes | are neat      |     1 |\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"| Tables        | Are           | Cool  |\\n\",\n    \"| ------------- |:-------------:| -----:|\\n\",\n    \"| col 3 is      | right-aligned |  1600 |\\n\",\n    \"| col 2 is      | centered      |    12 |\\n\",\n    \"| zebra stripes | are neat      |     1 |\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##HTML\\n\",\n    \"\\n\",\n    \"<b>You</b> can <i>render</i> almost any <span style=\\\"color:red;\\\">HTML</span> code you <u>like</u>.\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"<b>You</b> can <i>render</i> almost any <span style=\\\"color:red;\\\">HTML</span> code you <u>like</u>.\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Code\\n\",\n    \"\\n\",\n    \"You can add in line code like this `import numpy as np`\\n\",\n    \"\\n\",\n    \"Or block code:\\n\",\n    \"\\n\",\n    \"Python Code:\\n\",\n    \"```python\\n\",\n    \"x = 5\\n\",\n    \"print \\\"%.2f\\\" % x\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Java Script Code:\\n\",\n    \"```javascript\\n\",\n    \"x = 5\\n\",\n    \"alert(x);\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"\\n\",\n    \"<pre>\\n\",\n    \"Python Code:\\n\",\n    \"```python\\n\",\n    \"x = 5\\n\",\n    \"print \\\"%.2f\\\" % x\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Java Script Code:\\n\",\n    \"```javascript\\n\",\n    \"x = 5\\n\",\n    \"alert(x);\\n\",\n    \"```\\n\",\n    \"</pre>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#LaTeX\\n\",\n    \"\\n\",\n    \"**References:**\\n\",\n    \"\\n\",\n    \"- [LaTeX Wiki](http://en.wikibooks.org/wiki/LaTeX/Mathematics)\\n\",\n    \"- [Duke University, Department of  Statistical Science](https://stat.duke.edu/resources/computing/latex)\\n\",\n    \"- [Equation Sheet](http://www.equationsheet.com/)\\n\",\n    \"\\n\",\n    \"LaTeX is a large typeset system for scientific documentation which symbols for mathematics, statistics, physics, quantum mechanics, and computer science. It is beyond the scope of this tutorial to cover everything, but we will go over the basics of writing high quality mathematical equations using LaTeX.\\n\",\n    \"\\n\",\n    \"You can use LaTeX in line by like this $y = x^2$ or in block like this $$y = x^2$$\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"You can use LaTeX in line by like this $y = x^2$ or in block like this $$y = x^2$$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Operators:\\n\",\n    \"\\n\",\n    \"- Add:\\n\",\n    \" - $x + y$\\n\",\n    \"- Subtract:\\n\",\n    \" - $x - y$\\n\",\n    \"- Multiply\\n\",\n    \" - $x * y$\\n\",\n    \" - $x \\\\times y$ \\n\",\n    \" - $x . y$ \\n\",\n    \"- Divide\\n\",\n    \" - $x / y$\\n\",\n    \" - $x \\\\div y$\\n\",\n    \" - $\\\\frac{x}{y}$\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"- Add:\\n\",\n    \" - $x + y$\\n\",\n    \"- Subtract:\\n\",\n    \" - $x - y$\\n\",\n    \"- Multiply\\n\",\n    \" - $x * y$\\n\",\n    \" - $x \\\\times y$ \\n\",\n    \" - $x . y$ \\n\",\n    \"- Divide\\n\",\n    \" - $x / y$\\n\",\n    \" - $x \\\\div y$\\n\",\n    \" - $\\\\frac{x}{y}$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Relations\\n\",\n    \"\\n\",\n    \"- $\\\\pi \\\\approx 3.14159$\\n\",\n    \"- ${1 \\\\over 0} \\\\neq \\\\inf$\\n\",\n    \"- $0 < x > 1$\\n\",\n    \"- $0 \\\\leq x \\\\geq 1$\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```\\n\",\n    \"- $\\\\pi \\\\approx 3.14159$\\n\",\n    \"- ${1 \\\\over 0} \\\\neq \\\\inf$\\n\",\n    \"- $0 < x > 1$\\n\",\n    \"- $0 \\\\leq x \\\\geq 1$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Fractions\\n\",\n    \"\\n\",\n    \"- $^1/_2$\\n\",\n    \"- $\\\\frac{1}{2x}$\\n\",\n    \"- ${3 \\\\over 4}$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```\\n\",\n    \"- $^1/_2$\\n\",\n    \"- $\\\\frac{1}{2x}$\\n\",\n    \"- ${3 \\\\over 4}$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Greek Alphabet\\n\",\n    \"\\n\",\n    \"| Small Letter          | Capical Letter       | Alervative                  |\\n\",\n    \"| --------------------- | -------------------- | --------------------------- |\\n\",\n    \"| $\\\\alpha$   `\\\\alpha`   | $A$ `A`              |                             |\\n\",\n    \"| $\\\\beta$   `\\\\beta`     | $B$ `B`              |                             |\\n\",\n    \"| $\\\\gamma$   `\\\\gamma`   | $\\\\Gamma$ `\\\\Gamma`    |                             |\\n\",\n    \"| $\\\\delta$   `\\\\delta`   | $\\\\Delta$ `\\\\Delta`    |                             |\\n\",\n    \"| $\\\\epsilon$ `\\\\epsilon` | $E$ `E`              | $\\\\varepsilon$ `\\\\varepsilon` |\\n\",\n    \"| $\\\\zeta$   `\\\\zeta`     | $Z$ `Z`              |                             |\\n\",\n    \"| $\\\\eta$   `\\\\eta`       | $H$ `H`              |                             |\\n\",\n    \"| $\\\\theta$ `\\\\theta`     | $\\\\Theta$ `\\\\Theta`    | $\\\\vartheta$ `\\\\vartheta`     |\\n\",\n    \"| $\\\\iota$   `\\\\zeta`     | $I$ `I`              |                             |\\n\",\n    \"| $\\\\kappa$ `\\\\kappa`     | $K$ `K`              | $\\\\varkappa$ `\\\\varkappa`     |\\n\",\n    \"| $\\\\lambda$   `\\\\lambda` | $\\\\Lambda$ `\\\\Lambda`  |                             |\\n\",\n    \"| $\\\\mu$   `\\\\mu`         | $M$ `M`              |                             |\\n\",\n    \"| $\\\\nu$   `\\\\nu`         | $N$ `N`              |                             |\\n\",\n    \"| $\\\\xi$   `\\\\xi`         | $Xi$ `\\\\Xi`           |                             |\\n\",\n    \"| $\\\\omicron$ `\\\\omicron` | $O$ `O`              |                             |\\n\",\n    \"| $\\\\pi$ `\\\\pi`           | $\\\\Pi$ `\\\\Pi`          | $\\\\varpi$ `\\\\varpi`           |\\n\",\n    \"| $\\\\rho$ `\\\\rho`         | $P$ `P`              | $\\\\varrho$ `\\\\varrho`         |\\n\",\n    \"| $\\\\sigma$ `\\\\sigma`     | $\\\\Sigma$ `\\\\Sigma`    | $\\\\varsigma$ `\\\\varsigma`     |\\n\",\n    \"| $\\\\tau$   `\\\\tau`       | $T$ `T`              |                             |\\n\",\n    \"| $\\\\upsilon$ `\\\\upsilon` | $\\\\Upsilon$ `\\\\Upsilon`|                             |\\n\",\n    \"| $\\\\phi$ `\\\\phi`         | $\\\\Phi$ `\\\\Phi`        | $\\\\varphi$ `\\\\varphi`         |\\n\",\n    \"| $\\\\chi$   `\\\\chi`       | $X$ `X`              |                             |\\n\",\n    \"| $\\\\psi$ `\\\\psi`         | $\\\\Psi$ `\\\\Psi`        |                             |\\n\",\n    \"| $\\\\omega$   `\\\\omega`   | $\\\\Omega$ `\\\\Omega`    |                             |\\n\",\n    \"\\n\",\n    \"##Power & Index\\n\",\n    \"\\n\",\n    \"You can add power using the carrot `^` symbol. If you have more than one character you have to enclose them in a curly brackets.\\n\",\n    \"\\n\",\n    \" $$f(x) = x^2 - x^{1 \\\\over \\\\pi}$$\\n\",\n    \"\\n\",\n    \"For index you can use the underscore symbol:\\n\",\n    \"\\n\",\n    \"$$f(X,n) = X_n + X_{n-1}$$\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"$$f(x) = x^2 - x^{1 \\\\over \\\\pi}$$\\n\",\n    \"$$f(X,n) = X_n + X_{n-1}$$\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Roots & Log\\n\",\n    \"\\n\",\n    \"You can express a square root in LaTeX using the `\\\\sqrt` and to change the level of the root you can use `\\\\sqrt[n]` where `n` is the level of the root.\\n\",\n    \"\\n\",\n    \"$$f(x) = \\\\sqrt[3]{2x} + \\\\sqrt{x-2}$$\\n\",\n    \"\\n\",\n    \"To represent a log use `\\\\log[base]` where `base` is the base of the logarithmic term.\\n\",\n    \"\\n\",\n    \"$$\\\\log[x] x = 1$$\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"$$f(x) = \\\\sqrt[3]{2x} + \\\\sqrt{x-2}$$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Sums & Products\\n\",\n    \"\\n\",\n    \"You can represent a sum with a sigma using `\\\\sum\\\\limits_{a}^{b}` where a and b are the lower and higher limits of the sum.\\n\",\n    \"\\n\",\n    \"$$\\\\sum\\\\limits_{x=1}^{\\\\infty} {1 \\\\over x} = 2$$\\n\",\n    \"\\n\",\n    \"Also you can represent a product with `\\\\prod\\\\limits_{a}^{a}` where a and b are the lower and higher limits.\\n\",\n    \"\\n\",\n    \"$$\\\\prod\\\\limits_{i=1}^{n} x_i - 1$$\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```\\n\",\n    \"$$\\\\sum\\\\limits_{x=1}^{\\\\infty} {1 \\\\over x} = 2$$\\n\",\n    \"$$\\\\prod\\\\limits_{i=1}^{n} x_i - 1$$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Statistics\\n\",\n    \"\\n\",\n    \"To represent basic concepts in statistics about sample space `S`, you can represent a maximum:\\n\",\n    \"\\n\",\n    \"$$max(S) = \\\\max\\\\limits_{i: S_i \\\\in S} S_i$$\\n\",\n    \"\\n\",\n    \"In the same way you can get the minimum:\\n\",\n    \"\\n\",\n    \"$$min(S) = \\\\min\\\\limits_{i: S_i \\\\in S} S_i$$\\n\",\n    \"\\n\",\n    \"To represent a [binomial coefficient](http://en.wikipedia.org/wiki/Binomial_coefficient) with n choose k, use the following:\\n\",\n    \"\\n\",\n    \"$$\\\\frac{n!}{k!(n-k)!} = {n \\\\choose k}$$\\n\",\n    \"\\n\",\n    \"for :\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```\\n\",\n    \"$$max(S) = \\\\max\\\\limits_{i: x_i \\\\in \\\\{S\\\\}} x_i$$\\n\",\n    \"$$min (S) = \\\\min\\\\limits_{i: x_i \\\\in \\\\{S\\\\}} x_i$$\\n\",\n    \"$$\\\\frac{n!}{k!(n-k)!} = {n \\\\choose k}$$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Calculus\\n\",\n    \"\\n\",\n    \"Limits are represented using `\\\\lim\\\\limits_{x \\\\to a}` as `x` approaches `a`.\\n\",\n    \"\\n\",\n    \"$$\\\\lim\\\\limits_{x \\\\to 0^+} {1 \\\\over 0} = \\\\infty$$\\n\",\n    \"\\n\",\n    \"For integral equations use `\\\\int\\\\limits_{a}^{b}` where `a` and `b` are the lower and higher limits.\\n\",\n    \"\\n\",\n    \"$$\\\\int\\\\limits_a^b 2x \\\\, dx$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```markdown\\n\",\n    \"$$\\\\lim\\\\limits_{x \\\\to 0^+} {1 \\\\over 0} = \\\\inf$$\\n\",\n    \"$$\\\\int\\\\limits_a^b 2x \\\\, dx$$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"##Function definition over periods\\n\",\n    \"\\n\",\n    \"Defining a function that is calculated differently over a number of period can done using LaTeX. There are a few tricks that we will use to do that:\\n\",\n    \"\\n\",\n    \"- The large curly bracket `\\\\left\\\\{ ... \\\\right.` Notice it you want to use `(` or `[` you don't have to add a back slash(`\\\\`). You can also place a right side matching bracket by replacing the `.` after `\\\\right` like this `.right}`\\n\",\n    \"- Array to hold the definitions in place. it has two columns with left alignment. `\\\\begin{array}{ll} ... \\\\end{array}`\\n\",\n    \"- Line Breaker `\\\\\\\\`\\n\",\n    \"- Text alignment box ` \\\\mbox{Text}`\\n\",\n    \"\\n\",\n    \"$f(x) =\\\\left\\\\{\\\\begin{array}{ll}0  & \\\\mbox{if } x = 0 \\\\\\\\{1 \\\\over x} & \\\\mbox{if } x \\\\neq 0\\\\end{array}\\\\right.$\\n\",\n    \"\\n\",\n    \"**Code:**\\n\",\n    \"```\\n\",\n    \"$f(x) =\\n\",\n    \"\\\\left\\\\{\\n\",\n    \"\\t\\\\begin{array}{ll}\\n\",\n    \"\\t\\t0  & \\\\mbox{if } x = 0 \\\\\\\\\\n\",\n    \"\\t\\t{1 \\\\over x} & \\\\mbox{if } x \\\\neq 0\\n\",\n    \"\\t\\\\end{array}\\n\",\n    \"\\\\right.$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**Note:** If you are planning to show your notebook in NBViewer write your latex code in one line. For example you can write the code above like this:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"$f(x) =\\\\left\\\\{\\\\begin{array}{ll}0  & \\\\mbox{if } x = 0 \\\\\\\\{1 \\\\over x} & \\\\mbox{if } x \\\\neq 0\\\\end{array}\\\\right.$\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"#Quick Quiz (Normal Distribution)\\n\",\n    \"\\n\",\n    \"Try to replicate the [Normal Distribution](http://en.wikipedia.org/wiki/Normal_distribution) formula using LaTeX. If you solve it, leave the LaTeX code in the comments below. $Don't\\\\ cheat$.\\n\",\n    \"\\n\",\n    \"$$P(x,\\\\sigma,\\\\mu) = \\\\frac{1}{{\\\\sigma \\\\sqrt {2\\\\pi } }}e^{{-(x - \\\\mu)^2 } / {2\\\\sigma ^2}}$$\\n\",\n    \"\\n\",\n    \"Tips to help with the quiz:\\n\",\n    \"\\n\",\n    \"- $\\\\mu$ is `\\\\mu`\\n\",\n    \"- $\\\\sigma$ is `\\\\sigma`\\n\",\n    \"- $e$ is `e`\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/jupyter/3. Python Basics.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# Tutorial Brief\\n\",\n    \"This tutorial is an introduction to Python 3. This should give you the set of `pythonic` skills that you will need to proceed with this tutorial series.\\n\",\n    \"\\n\",\n    \"If you don't have the Jupyter installed, shame on you. No just kidding you can follow this tutorial using an online jupyter service:\\n\",\n    \"\\n\",\n    \"https://try.jupyter.org/\\n\",\n    \"\\n\",\n    \"##Cell Input and Output\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1+2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1+1\\n\",\n    \"1+2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##The `print` function\\n\",\n    \"\\n\",\n    \"**Notice:** `print` is a function in Python 3. You should use parentheses around your parameter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(1+2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Variables\\n\",\n    \"\\n\",\n    \"There are many variable types in Python 3. Here is a list of the most common types:\\n\",\n    \"\\n\",\n    \"###Numerical Types:\\n\",\n    \"- bool (Boolean)\\n\",\n    \"- int (Integer/Long)\\n\",\n    \"- float\\n\",\n    \"- complex\\n\",\n    \"\\n\",\n    \"**Notice:** In Python 3 integer represents integer and long. Because there is no more long data type, you will not get `L` at the end of long integers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"a = 4\\n\",\n    \"b = 1.5\\n\",\n    \"c = 121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212\\n\",\n    \"d = 1j\\n\",\n    \"e = 1/3\\n\",\n    \"f = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5.5\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a+b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a*c\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(6+4j)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"(b+d)*a\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a+f\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"float\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(1.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###Other Value Types:\\n\",\n    \"- str (String)\\n\",\n    \"- list (Ordered Array)\\n\",\n    \"- tuple (Ordered Immutable Array)\\n\",\n    \"- dict (Unordered list of keys and values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Roshan\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"my_name = \\\"Roshan\\\"\\n\",\n    \"print(my_name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 2, 3, 4, 5]\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list = [1,2,3,4,5]\\n\",\n    \"my_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 2, 3, 4, 5, 6]\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list + [6]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 2, 3, 4, 5]\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 2, 3, 4, 5, 6, 7, 8]\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list += [6,7,8]\\n\",\n    \"my_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 2, 3, 4, 5, 6, 7, 8, 9]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list.append(9)\\n\",\n    \"my_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1, 2, 3)\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_tuple = (1,2,3)\\n\",\n    \"my_tuple\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1, 2, 3, 4, 5, 6)\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_tuple + (4,5,6)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'credit': 100, 'name': 'Roshan'}\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_dict = {\\\"name\\\":\\\"Roshan\\\", \\\"credit\\\":100}\\n\",\n    \"my_dict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Roshan'\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_dict[\\\"name\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'credit': 100, 'level': 4, 'name': 'Roshan'}\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_dict[\\\"level\\\"] = 4\\n\",\n    \"my_dict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dict_values([100, 'Roshan', 4])\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_dict.values()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dict_keys(['credit', 'name', 'level'])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_dict.keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###Selecting / Slicing\\n\",\n    \"\\n\",\n    \"Use `len` function to measure the length of a list.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(my_list)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To access a single value in a list use this syntax:\\n\",\n    \"```python\\n\",\n    \"list_name[index]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select multiple value from a list use this syntax:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"index[start:end:step]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[2]\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[1:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 2, 3]\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[:3]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[4, 5, 6, 7, 8, 9]\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[3:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Notice:** negative index selected from the end of the list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[-2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can use negative indexing in selecting multiple values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[8, 9]\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[-2:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 2, 3, 4, 5, 6, 7]\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[:-2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[4, 5, 6, 7, 8]\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[3:-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The third location in the index is the `step`. If the `step` is negative the the list is returned in descending order.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1, 3, 5, 7, 9]\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[::2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[4, 6, 8]\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[3::2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[9, 8, 7, 6, 5, 4, 3, 2, 1]\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_list[::-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###Working with Strings\\n\",\n    \"\\n\",\n    \"You can select from a string like a list suing this syntax:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"my_string[star:end:step]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Roshan'\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_name\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'R'\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_name[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Notice:** You can also use negative indexing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Ro'\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"my_name[:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Unicode\\n\",\n    \"\\n\",\n    \"**Notice:** You can use unicode inside your string variables. Unlike Python 2, no need to use `u\\\"\\\"` to use unicode.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"आप शून्य से विभाजित नहीं किया जा सकता ≈\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"str\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Sorted by most spoken languages in order\\n\",\n    \"divide_by_zero = {\\\"zho\\\":\\\"你不能除以零\\\",\\n\",\n    \"                  \\\"eng\\\":\\\"You cannot divide by zero\\\",\\n\",\n    \"                  \\\"esp\\\":\\\"No se puede dividir por cero\\\",\\n\",\n    \"                  \\\"hin\\\":\\\"आप शून्य से विभाजित नहीं किया जा सकता \\\\u2248\\\",\\n\",\n    \"                  \\\"arb\\\":\\\"لا يمكن القسمة على صفر\\\"}\\n\",\n    \"\\n\",\n    \"print(divide_by_zero[\\\"hin\\\"])\\n\",\n    \"type(divide_by_zero[\\\"hin\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##String Formatting\\n\",\n    \"\\n\",\n    \"You can use this syntax to format a string:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"some_variable = 50\\n\",\n    \"x = \\\"Value: %s\\\" % some_variable\\n\",\n    \"print(x) # Value: 50\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Rush, R.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"first_name = \\\"Roshan\\\"\\n\",\n    \"last_name = \\\"Rush\\\"\\n\",\n    \"formatted_name = \\\"%s, %s.\\\" % (last_name, first_name[0])\\n\",\n    \"print(formatted_name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Other formatters could be used to format numbers:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"π ≈ 3.14\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"π ≈ %.2f\\\" % 3.14159)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To find unicode symbols:\\n\",\n    \"\\n\",\n    \"http://www.fileformat.info/info/unicode/char/search.htm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Homeworks: 15.75\\n\",\n      \"Mid-term: 22.00\\n\",\n      \"Final: 51.00\\n\",\n      \"Total: 88.75/100\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"homeworks = 15.75\\n\",\n    \"midterm = 22\\n\",\n    \"final = 51\\n\",\n    \"total = homeworks + midterm + final\\n\",\n    \"\\n\",\n    \"print(\\\"Homeworks: %.2f\\\\nMid-term: %.2f\\\\nFinal: %.2f\\\\nTotal: %.2f/100\\\" % (homeworks, midterm, final, total))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###Using `format(*args, **kwargs)` function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'http://en.wikipedia.org/'\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"url = \\\"http://{language}.wikipedia.org/\\\"\\n\",\n    \"url = url.format(language=\\\"en\\\")\\n\",\n    \"url\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Mathematics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1+1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"-1\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"4-5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Notice:** The default behavior of division in Python 3 is float division. To use integer division like Python 2, use `//`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2.8\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"14/5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"14//5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"10\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"2*5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To raise a number to any power use down asterisk `**`. To represent $a^{n}$:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"a**n\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"2**3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To calculate the remainder (modulo operator) use `%`. To represent $a \\\\mod b = r$:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"a % b # Returns r\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"10 % 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can use the `math` library to access a varaity of tools for algebra and geometry. To import a library, you can use one of these syntaxes:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"import library_name\\n\",\n    \"import library_name as alias\\n\",\n    \"from module_name import some_class\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import math\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"52.0\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"n=52\\n\",\n    \"k=1\\n\",\n    \"math.factorial(n) / (math.factorial(k) * math.factorial(n-k))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Loops\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"3\\n\",\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for counter in [1,2,3,4]:\\n\",\n    \"    print(counter)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###range\\n\",\n    \"\\n\",\n    \"In Python 3 `range` is a data type that generates a list of numbers.\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"range(stop)\\n\",\n    \"range(start,stop[ ,step])\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**Notice:** In Python 2 `range` is a function that returns a list. In Python 3, `range` returns an iterable of type `range`. If you need to get a list you can use the `list()` function:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"list(range(start,stop[, step]))\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\",\n      \"1\\n\",\n      \"2\\n\",\n      \"3\\n\",\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for counter in range(5):\\n\",\n    \"    print(counter)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Notice:** The list doesn't reach the `stop` value and stops one `step` before. The reason behind that is to make this syntax possible:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 55,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"list(range(1,10)) == list(range(1,5)) + list(range(5,10))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Notice:** In Python 3 use use `==` to check if two values are equal. To check if two values are not equal use `!=` and don't use `<>` from Python 2 because it is not supported any more in Python 3.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"3\\n\",\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for counter in range(1,5):\\n\",\n    \"    print(counter)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2\\n\",\n      \"4\\n\",\n      \"6\\n\",\n      \"8\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for counter in range(2,10,2):\\n\",\n    \"    print(counter)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###While Loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1\\n\",\n      \"2\\n\",\n      \"3\\n\",\n      \"4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"counter =1\\n\",\n    \"while counter < 5:\\n\",\n    \"    print(counter)\\n\",\n    \"    counter += 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##If .. Else\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"π is irrational\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if math.pi == 3.2:\\n\",\n    \"    print(\\\"Edward J. Goodwin was right!\\\")\\n\",\n    \"else:\\n\",\n    \"    print(\\\"π is irrational\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Square root of 2 is irrational\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if math.sqrt(2) == (10/7):\\n\",\n    \"    print(\\\"Edward J. Goodwin was right!\\\")\\n\",\n    \"elif math.sqrt(2) != (10/7):\\n\",\n    \"    print(\\\"Square root of 2 is irrational\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###If you like Math:\\n\",\n    \"Fun story about pi where it was *almost* set by law to be equal to 3.2!\\n\",\n    \"\\n\",\n    \"If you don't what is the \\\"pi bill\\\" you can read about it here:\\n\",\n    \"http://en.wikipedia.org/wiki/Indiana_Pi_Bill\\n\",\n    \"\\n\",\n    \"Or watch Numberphile video about it:\\n\",\n    \"https://www.youtube.com/watch?v=bFNjA9LOPsg\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Unusual\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"probability = 0.3\\n\",\n    \"\\n\",\n    \"if probability >= 0.75:\\n\",\n    \"    print(\\\"Sure thing\\\")\\n\",\n    \"elif probability >= 0.5:\\n\",\n    \"    print(\\\"Maybe\\\")\\n\",\n    \"elif probability >= 0.25:\\n\",\n    \"    print(\\\"Unusual\\\")\\n\",\n    \"else:\\n\",\n    \"    print(\\\"No way\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##Functions\\n\",\n    \"\\n\",\n    \"Functions are defined in Python using `def` keyword.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_circumference(r):\\n\",\n    \"    return math.pi * r * 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"31.41592653589793\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"get_circumference(5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1326.0\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def binomilal_coef(n,k):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    This function returns the binominal coef\\n\",\n    \"    \\n\",\n    \"    Parameters:\\n\",\n    \"    ===========\\n\",\n    \"    n, k int\\n\",\n    \"    \\n\",\n    \"    return n!/(k!*(n-k)!)\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    value = math.factorial(n)/(math.factorial(k)*math.factorial(n-k))\\n\",\n    \"    return value\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"binomilal_coef(52,2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Notice:** Use can use SHIFT+TAB to show the docstring of a function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###If You Like Math\\n\",\n    \"\\n\",\n    \"This was the binomial function which tells you how many ways are there to select k number of samples from a sample space with n items if order didn't matter and chosen samples could not be repeated (chosen again).\\n\",\n    \"\\n\",\n    \"As an example, How many ways to select 2 card from a deck of 52 cards if order of selection didn't matter and you where not allowed to choose the same card twice?\\n\",\n    \"\\n\",\n    \"$${n \\\\choose k} = {n! \\\\over {k!(n-k)!}}$$\\n\",\n    \"\\n\",\n    \"$${52 \\\\choose 2} = {52! \\\\over {2!(52-2)!}} = {52! \\\\over {2!(50)!}} =  1326$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"#Quick Quiz:\\n\",\n    \"\\n\",\n    \"How many ways to select 2 cards from a deck of 52 cards if order didn't matter and you could choose the same card twice?\\n\",\n    \"\\n\",\n    \"**hints:**\\n\",\n    \"\\n\",\n    \"- Read about k-combination with repetitions:\\n\",\n    \"http://en.wikipedia.org/wiki/Combination#Number_of_combinations_with_repetition\\n\",\n    \"\\n\",\n    \"- The equation\\n\",\n    \"$$\\\\left(\\\\!\\\\!\\\\!\\\\binom{n}{k}\\\\!\\\\!\\\\!\\\\right) = {{n+k-1} \\\\choose k}$$\\n\",\n    \"\\n\",\n    \"- To make your life easier:\\n\",\n    \"\\n\",\n    \"$$\\\\left(\\\\!\\\\!\\\\!\\\\binom{52}{2}\\\\!\\\\!\\\\!\\\\right) = {{52+2-1} \\\\choose 2} = {{53} \\\\choose 2} = \\\\frac{53!}{2!(51)!}$$\\n\",\n    \"\\n\",\n    \"- **If you got 1326 then you got it <span style=\\\"color:red\\\">WRONG</span>.**\\n\",\n    \"\\n\",\n    \"#Last quiz was solved by:\\n\",\n    \"[pacrii](https://www.youtube.com/user/pacrii)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/jupyter/4. Numpy.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tutorial Outline\\n\",\n    \"\\n\",\n    \"- [What is Numpy?](#What-is-Numpy)\\n\",\n    \"- [`ndarray` object](#ndarray-object)\\n\",\n    \"- [Mathematical & Logical Operation](#Mathematical-&-Logical-Operation)\\n\",\n    \"- Benchmarking Performance\\n\",\n    \"- [Multi-Dimensional Arrays Operations](#Multi-Dimensional-Arrays-Operations)\\n\",\n    \"- [Slicing](#Slicing)\\n\",\n    \"- [Filtering](#Filtering)\\n\",\n    \"- [Exploring `ndarray` object](#Exploring-ndarray-object)\\n\",\n    \"- [Array Creation](#Array-Creation)\\n\",\n    \"- [Numpy array as an image](#Numpy-array-as-an-image)\\n\",\n    \"\\n\",\n    \"# What is Numpy\\n\",\n    \"\\n\",\n    \"Numpy is a fundamental library for Python which is used for scientific computing and manipulation of large arrays of data. Numpy performs mathematical, statistical and data manipulation on arrays much faster that regular Python operations. This difference is very important when you are performing large amount of calculations of arrays.\\n\",\n    \"\\n\",\n    \"The major advantage of using Numpy for handling arrays, is that Numpy implements all the heavy lifting in C language which is much faster. This allows you to tap into the powerful C language performance from the comfort of Python.\\n\",\n    \"\\n\",\n    \"Numpy is a part of the Scipy ecosystem of libraries which is used for mathimatics, science and engineering. This ecosystem includes:\\n\",\n    \"\\n\",\n    \"- Scipy (Fundamental librariy for science, mathematics and engineering)\\n\",\n    \"- Numpy (Multi-dimensional array for effecient math and logic operations)\\n\",\n    \"- Pandas (Data analysis and DataFrame & Series objects)\\n\",\n    \"- Jupyter (Web-based interactive development environment)\\n\",\n    \"- Matplotlib (Charting library)\\n\",\n    \"- Sympy (Symbolic computation library)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# `ndarray` object\\n\",\n    \"\\n\",\n    \"The `ndarray` (pronounced *N D array*) object is the main object for representing your array. This object can handle multi-dimensional array of any size that your memory can store. The biggest differences between a Python `list` and a `ndarray` object are these:\\n\",\n    \"\\n\",\n    \"- `ndarray` is a fixed size array while `list` has a dynamic size. When you reshare a `ndarray` a new object is created with the new shape and the old object is deleted from memory.\\n\",\n    \"- `ndarray` allows mathematical and logical operations on complete multi-dimensional arrays. With a Python `list` you will have to iterate over the sequence which take more time and code.\\n\",\n    \"- `ndarray` has homogeneous data type for the complete array while Python `list` can contain multiple data types within a single array.*\\n\",\n    \"\\n\",\n    \"**Note**: You could have multiple data types in a single object that has multiple dimensions.\\n\",\n    \"\\n\",\n    \"# Mathematical & Logical Operation\\n\",\n    \"\\n\",\n    \"## Import the library\\n\",\n    \"\\n\",\n    \"You will have to import Numpy in your code to use it. A common alias for Numpy is `np`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Create arrays\\n\",\n    \"\\n\",\n    \"We will perform a number of mathematical operations on a large list with 5 millions variables to compare Python `list` to Numpy `ndarray`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#%%timeit\\n\",\n    \"python_list_1 = list(range(5000000))\\n\",\n    \"python_list_2 = list(range(5000000))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#%%timeit\\n\",\n    \"np_array_1 = np.arange(5000000)\\n\",\n    \"np_array_2 = np.arange(5000000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adding a fixed number to an array\\n\",\n    \"\\n\",\n    \"We will define a fixed number that we will add to every item in our array. In Numpy it is very simple to do that, just add the two.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100 loops, best of 3: 11.4 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"np_array_1 + 7\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In python it is much more complicated because you will have to do that manually with a loop. It is also much slower.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loops, best of 3: 626 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"python_output = []\\n\",\n    \"for i in python_list_1:\\n\",\n    \"    python_output.append(i + 7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loops, best of 3: 373 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"python_output = [i + 7 for i in python_list_1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adding two lists to each other\\n\",\n    \"\\n\",\n    \"You will see the same tpattern here. In it very simple and fast to add two arrays in Numpy, just add them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100 loops, best of 3: 14.3 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"np_array_1 + np_array_2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loops, best of 3: 1.16 s per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"python_output = []\\n\",\n    \"for i in range(len(python_list_1)):\\n\",\n    \"    python_output.append(python_list_1[i] + python_list_2[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loops, best of 3: 874 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"python_output = [python_list_1[i] + python_list_2[i] for i in range(len(python_list_1))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Multiplying two lists\\n\",\n    \"\\n\",\n    \"This is pointwise product multiplication, meaning that we multiply each value in the fist array by the value of the second array. If we had two arrays $A$ and $B$. Both matricies should have the same size.\\n\",\n    \"\\n\",\n    \"$$ C = A \\\\circ B$$\\n\",\n    \"$$ C_{ij} = A_{ij} \\\\times B_{ij}$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100 loops, best of 3: 14.7 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"np_array_1 * np_array_2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loops, best of 3: 1.18 s per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"python_output = []\\n\",\n    \"for i in range(len(python_list_1)):\\n\",\n    \"    python_output.append(python_list_1[i] * python_list_2[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loops, best of 3: 914 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"python_output = [python_list_1[i] * python_list_2[i] for i in range(len(python_list_1))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix Multiplication\\n\",\n    \"\\n\",\n    \"This is different that the previous pointwise product multiplication. If we had matrix $A$ and matrix $B$ and we wanted to multiply them the following must be true.\\n\",\n    \"\\n\",\n    \"$A$ has size of $m,n$ *(rows, columns)* and $B$ has a size of $o,p$. To multiply:\\n\",\n    \"\\n\",\n    \"$$A_{m,n} . B_{o,p} = C_{m,p}$$\\n\",\n    \"\\n\",\n    \"if $n=o$ *(the inner dimension)* resulting in a matrix of the size $(m,p)$ *(the outer dimension)*.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"A:\\n\",\n      \"==\\n\",\n      \"[[4]\\n\",\n      \" [3]\\n\",\n      \" [6]]\\n\",\n      \"B:\\n\",\n      \"==\\n\",\n      \"[[8 8 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"A = np.random.randint(1,10,(3,1))\\n\",\n    \"B = np.random.randint(1,10,(1,3))\\n\",\n    \"print(\\\"A:\\\\n==\\\")\\n\",\n    \"print(A)\\n\",\n    \"print(\\\"B:\\\\n==\\\")\\n\",\n    \"print(B)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[32 32 28]\\n\",\n      \" [24 24 21]\\n\",\n      \" [48 48 42]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"C = A.dot(B)\\n\",\n    \"print(C)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[98]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"C = B.dot(A)\\n\",\n    \"print(C)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Odd or even\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ True, False,  True, ..., False,  True, False], dtype=bool)\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1 % 2 == 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"python_output = [python_list_1[i] % 2 == 0 for i in python_list_1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Multi-Dimensional Arrays Operations\\n\",\n    \"\\n\",\n    \"Starting with 2D array which is 2000 * 2000 = 4M with values like this:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"0 0 0 ... 0\\n\",\n    \"1 1 1 ... 1\\n\",\n    \"...\\n\",\n    \"...\\n\",\n    \"1998 1998 1998 ... 1998\\n\",\n    \"1999 1999 1999 ... 1999\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"python_list_1 = [[i for l in range(2000)] for i in range(2000)]\\n\",\n    \"python_list_2 = [[i for l in range(2000)] for i in range(2000)]\\n\",\n    \"\\n\",\n    \"np_array_1 = np.array(python_list_1)\\n\",\n    \"np_array_2 = np.array(python_list_2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[   0,    0,    0, ...,    0,    0,    0],\\n\",\n       \"       [   1,    1,    1, ...,    1,    1,    1],\\n\",\n       \"       [   2,    2,    2, ...,    2,    2,    2],\\n\",\n       \"       ..., \\n\",\n       \"       [1997, 1997, 1997, ..., 1997, 1997, 1997],\\n\",\n       \"       [1998, 1998, 1998, ..., 1998, 1998, 1998],\\n\",\n       \"       [1999, 1999, 1999, ..., 1999, 1999, 1999]])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Multiply 2D Arrays\\n\",\n    \"\\n\",\n    \"This is pointwise product multiplication, meaning that we multiply each value in the fist array by the value of the second array. If we had two arrays $A$ and $B$. Both matricies should have the same size.\\n\",\n    \"\\n\",\n    \"$$ C = A \\\\circ B$$\\n\",\n    \"$$ C_{ij} = A_{ij} \\\\times B_{ij}$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100 loops, best of 3: 8.55 ms per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"np_array_1 * np_array_2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 loops, best of 3: 1.95 s per loop\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%timeit\\n\",\n    \"python_output = []\\n\",\n    \"for i in range(len(python_list_1)):\\n\",\n    \"    python_output.append([])\\n\",\n    \"    for l in range(len(python_list_2)):\\n\",\n    \"        python_output[-1:].append(python_list_1[i][l] * python_list_2[i][l])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Indexing\\n\",\n    \"\\n\",\n    \"## 1D Indexing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np_array_1 = np.array(range(5000000))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4999999\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[-1]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[1:10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 3, 5, 7, 9])\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[1:10:2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2D Indexing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[   0,    1,    2, ..., 1997, 1998, 1999],\\n\",\n       \"       [   1,    2,    3, ..., 1998, 1999, 2000],\\n\",\n       \"       [   2,    3,    4, ..., 1999, 2000, 2001],\\n\",\n       \"       ..., \\n\",\n       \"       [1997, 1998, 1999, ..., 3994, 3995, 3996],\\n\",\n       \"       [1998, 1999, 2000, ..., 3995, 3996, 3997],\\n\",\n       \"       [1999, 2000, 2001, ..., 3996, 3997, 3998]])\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"python_list_1 = [[i+l for l in range(2000)] for i in range(2000)]\\n\",\n    \"np_array_1 = np.array(python_list_1)\\n\",\n    \"np_array_1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([   2,    3,    4, ..., 1999, 2000, 2001])\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[2][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[2,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3, 4])\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[1:5,0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Filtering\\n\",\n    \"\\n\",\n    \"You find find items in a numpy array using a mask of booleans. The mask \\\"or filter\\\" should be the same shape as data and returns values where the mask is equal to `True`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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83VxXrpi3lxcmfMXXSDso9TaiGiDRrAT0w5NwyjUtGv8DWn3vrPI69f+/g3+d35929s+dg78y4efO5Lc3u7yqLn9g6pjGk02be31bEnNuWkjM8iVZhHkozs/j2uyAGj23F568U4t2vS9jaNp3rxhfzr6ey2PT8Ih54rxVpncOxuWrJW59PfhUQlUzGPanEalYHkZNeQAeqt3Y3GzZsIbuRdVW7trJl37/q2F2rW9Cc1GzRDH3qHCzT1/LVinxWvJkP1mAS+6Ry2bTTOK1iJV8DdW43HpO9s0RYiD/nTCamtuXT2VtYu6aIDV8WAhDSKo7ThqcxNKMjHWJ0+zgRCfBADen5FFnmU/6uhvyegqIY+vIYhh5hs9CB5zDts8NvY4lJYOgD5x2irMH847PBjTxuENYplQv+nsoF/n4vROSEpjFUERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqInISM4tmt/6DYZQahmEeerGbsR1O3zXir0+/vGhbTQ9z79QnJz6vUfDGgEsNwzCN019cv9tD+O/5bApUEREBILRt54K0tLQt+y9dOnfYlhTlLi/PWpP80Yvjx47s3HPNze/nj/EETKg2ptqyfGzc00bkZWWfVdLWV6UqUEVEBEj0Xj9//U2bN29O33/Z8tOOzrkOZ4uyDXNPueuM4M9ha9DMK2947t18T7q/a3zUXHm2b1eX9vF1sQpUERE5giAz5tTRm55+/5Wrzw0lj+oPo579KP+iQG2lmpWZkZ9vpauvy1WgiohIk1haDS6+pBtrALLX7j7FeUCGeIzS718/7f7RA9/sGGsrahh7DTOTTjsv87YZX99Z6Cb01yV6jYoNb3d9MGPQa11ahuT/PGYbHN+5bFDGxPmzf6zo790X2i5j+zPdbzQMwzTOnLu6xCT4wLLcRta0nmMPvR6gwvjoPPssS9yook9cJFA1L2ZolJFjGOHei5dWXnus709AT44vIiLHkddt1LuxAdhCbfW/NE9dRtbrV10y4Np5b+ZDcEy3P20dNSL50+CyzMgvPlo87PlbFj/z1oczz181/6ZLOgdT3rCPaVSunNhn4IAnv8iEiKj0wTtGntVmfrir1Ni57qseK96ZfPGKdxZc8OM33148ZUDUIt80hYPNjqPvXXxr9IehL85Zn+EkxXvhzRe93j44qvz09vb/HWvpClQREWkS9+7FSbM3cAaE0H1Q+++CwQvg2jYjNWPsvBfzibGPnLliwpwbT/lnmIEbwJn9Trvr+md8/NaHN5919f87666vb+/0iA28mEWWjx78552ZEJH+9++f/+6RPndFGLgAMCuta58+928D7vtm2tR75j48btkNnydbqTn2VxBM2tgn3n26l/unD+asvyg7om/9bU89+/ezI9nti/enSYHatm3bmpdeeomzzz7b2ZTtpWmcTmeIw+EI2Lueh9WaZv/6IOZf/qGOCx/bUlbew2Kx6Ca+coLwGhUb53Z5IOPWWd94iKPt9bvvOyt2kQEm1FjW/GvKtau8xAYPe+HrmTec8tzPYQpgTxmd88xzr93338sWL1j97EtjMv/65HM9QyjGVWTfvMvVDqLMnkPSVu4LUwAj0tP7jnkvLTtj54bgdmn5rSzU+fsdaIomBeru3bvDiouLmTBhQsB++Z+ISktLnV9//bW/q3HUDJfHWOxwMDc1VceFj11WXILX69U5DnIc7bG8clmfGV9EWCYf8LDpslQXZcVlFTlbARAzrPyfi568ZlCkkQdA/Y7gxUvyzwLodfVZH7T+VfhZzPjBV33fm8WOFVlLk1YWPZHcM9lajK21s3unkO1srRj07l0TH7zozUe3XnpqzHdWMAGwJ9X3+1PSsv0KOuFPgGpyl++IESMYPXq0v+vbrDgcjrr77ruv1t/1OBbt7HZGx8b6uxrNzqBevX70dx3k5FOdvSkpE5IaXdnizLLr7xo37dabL53Zs4U1b9/jzvzgzD0N13Jue/HmjIxFttN/ta+7wLYdwiDXuj63PoXksHUY8Z7zpz3/2IX9bxi0cP1z6Rndn1sVmtIvd9ifz1ly7vCRiy/48+mftAk1Kvz9nvwWGkMVEREg0Xvrqu2jpvUNff+XxzxG7lsXjEq7+qO51bWhtj9cc8l7B4QpgFlvVDsbzqgtXrGg1zzodejnqDFLq70RP//L3vH6HfN/6n3GO/96+ubpby64dsXOVV3ef3HV2PdffHzsLZYU97nj//XCCw+Pejg1BIe/352mUJeSiIgcgtVsc9m0D6cOsn1A7ZKI/7vlnWdyPYQdsIklxBsVQi1EmRmfV15lmqZx6KXC8s7ZEW8e8AyxvcqufGz25K93VKVX7vq+7aKXHv/L34anvR/izbZ8/OTFtw/460evFHsJaUptvV7Tr5mmQBURkUOzpdZfP/OxB3pCRdWHt/zxtvl7/nLAhA72NvU9ksiBCmPnzooU71GPdVrMiOQ+uSOuf/C1GR9lXrTro+vPSYT6wjeePO/jQm9HMDAsRsP4qtdj/PqMPTcVeY6W/nyrFKgiInJYwafctuU/93WaAhXGe3+747H3C72d9620p9QPG5b4JcCa1z65MO/gFixAzZbwhbM/Gr0ur7b9zxPre0rXxy565Zlrnn5ry5g6sB64g9VsOeiyNf1CKcQstuZWeGLAQlhsWMOYasWe8Ar3QRM3uPPsy5ZmD/lNL8z0Gr6c6F+BKiIiRxDu7XP/f2b+LYkfKZsbd9Pdi6f+0g0bava87e43+xg43Mtu73vD9B/uqjQbJn8AoD4rZN5tf5500VUj5va94s13ctwNd3zxln3V4pHr7p42/torX5y8vGTEgdMYuo3cJa8OWl5LAjE9HX0SbLvAYkZ3PWN7EtSz8d3UpXnuX0Idp5E9557zH1vH2Q3/NveeKtw4iy3UbQOT6qywbWUen7VqFagiInJERvTg8sdnXnFPC3AVv3n9yPs+Lb3k5+5de5fbts195bIbE6l0fvJ/vR5LTBu89YLLr3o346I/LeiW0CH7spez7yDugpIZs8bc2i6IKgBb6tisFx7s+YTFszb48cHxC5N6/HHjyEsz/psxeuS8P/VIWtv20rc/KKFF0IXPTnliSBS5AKGn3bzx1p4sx/tt8M0D//zRTQ899dQ/n350/P+N6jX3lBtzXrnu8T/8ywo/dwkfsuVpb9M/v1ckBbDGdnPf05cOHnzm8kumbbnfeYyZqEAVEZEmsJjxw//x5bQR4bOhwPLydROf+qrcTG5YZzdTr539bubaN86YMHrgm62Lvg3/YO7sS95Z+NWFOVGnu0bd/dK0bzfN731Dp+Dv9hVnRHr6Pfb1PzcteGL42KHdP7LtXJa4aP47o955d9GlX++OSD3jojs/nLli0znzrk35t23vjEwEn1J77ydfXDX1qn7/SXZ8HvyfSRPG3/PA8098Xj8s5oVvPx0+cUCbj8LAg7PG5vIeJt9izi775yvjbu0bz24KfmixfHXWGXVB1mPu+zVM0zziRjNmzHh7w4YNGdOnT/f3J9qsOByOipSUFLO8vDzG33U5GrvWrOl/5sCB32Z37+7vqjQ7Z3q9P0557rlxgwYNCtyZP0ROMmqhioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPNIPJ8b041r/N1CdeYM6S1exweIBQWncbyIgxd/Hg/w2nQ8gJf9efk5LpLmXQ+p1809QdwjtQkN6CVkf5fAUlm0nIqoawFHLS42mrw0JEfCjAA9XJzjeuYdCYueQBGDEkp7clrGIbWzI/4+X7PuON+Y+x4tMHOSNS354nMmuQjdZH+Igigq0Hz08mInLCCOhAdW37f1w5di55WOhx5wLen3w+KSEG4CL/i8e58M+Ps3r13xk77VLWPtAV3QX7RGXj5k6nMi1cIxAiErgCOFDr2fjys6z0AN2nMPfJkaTsS0wbCX+ayEv3zabHpO1kvvUB2fd2pbPtGJ5ORCRArFy58qUFCxZ083c9mpuYmJh1EyZMuOVQ6wM4UA2i+t7Gw+N3Y+l/Bam/an4GkzIwDTvbcZZmU+oCFKjNi+lmRfEephaX82WNk4bbUFhIDI3govhEHm4ZccRuZICK2lKm7ilibmU1W90NM4fZg4I5IzKWWxITyAi1HnT2nklpTSlP5xcxt6KGHR5z7/NGcknLBB6Kj6CVRhjEjwYMGHD9/fffT3R0tL+r0qxMmDChf3R09Iabb755RmPrAzhQ7XS46E4euehQ671U5ZfgBIhPJU5h2ryYLt7N2sToUhdgJT0immE2Cy53HcsrK5iRU8F/qzvyQ/sYEg4TbpXVuQzcnE8mEBUSwchIO+Gmm501Vawoy2dFWTk/pqczJfzneT5Nskp2MiCrjHwgJjSSUaF2gj21fOFw8PwuB285UljVMZ7OClXxk+joaPO+++4zFKi+lZ2dvexw6wM4UI/AuZW3pq0CoMeYkaQoUJuV2pp87ih1ASE8lJ7Oo+G/TGxdXbOHQZvy+KE0h2daRzE17FBjs24+yi0gE0hP7Mp3SWFE7FvnYW3+NgbkVjF1dxnjusSTbICrvoiMrDLysTIyJZ058SH7bv7odJZx3eYdvOXI5uqiSL5uFaxOEZGTSPM8C8SsZPXkDCasA1JuZdpfO+uLrZmx2FswI7U9s1Lbc3vYgXeJCA+LZ1wYgJPPqt14DlWI6WKz0wSs9IwM2S9MAaz0bp3Ksi5prG0fs7cL18uagnxWAcFR7Zi5X5gC2O2xPJMcTSiwuqCYTK+/3yUROZ6aXwvVW863Uy5g6KM/4A0exNT3n+LMKPW9ndhcTN+6gbmH/Zgs9G2bxoI4O1Yg2BbOyNjwfWudHjcFbg+1pgl4ce0tq9rtxQONX25j2OgeYoF6D+/m5HJRhyQuDd3v0hzDRr/I/X6KmfUsrnAB0KtFJK0bKTI+ogW9cbDCWcFKdxI97Tr2RE4WzStQ67bz1s1/5upXt0P0MP7x+Xzu6hF2zPe4k9+f1+Om8Ajb5Hhg/5sNVtaWMikvn1cdtRQe4i6Eh785YRDnJydzYXU2C2sLydhYSKg9nGFRUZwbHc0FUeG02b8Px+sisyFP2Va8iwxHI0eW6WI7AC7Wu0xQoIqcNJpNoHpLlzPpouE8vLwaUv/CW0tmcGXHEH9XS5rExq3pv+061KqaPQzZlMc6ICQkhnGxEXS1BxFrMTDw8v2ebJ6pO3I59uB45p8axjsFBUwvLWdFfTXvF1fzfvEebsHOuQnJvJAYQ6oFwKR6b0IXV5Uz77AleylVl6/ISaVZBKp7zwfcNvgC/t82iDprCh+/O54Bsc1zeFgA08nbuxrCNDyqPRs6xdHhgIagh+DipgUqgNUaxpVJHbgyCaqcNXxV4WBReSmvOur4OH87A1ydyGwfTbxhIcoCeKxkdOnB25E6xkTkFwH/jeAtX87EoQ1h2vaqN/nhowkK0+bOW8cntQBWRrSOOShMAW89q+uPrugIexgj4hOZ0ekUdnWKJxEoLMnnYxdg2OhhA/Cws96DGqAisr/ATh5PPu/99UKmboTYUa+z/OWr6BDs70rJ8eAGwCDG+usxypLKQmY5G/4+3Biqx13LopICni6t49eNWYOWEbH0szQ8W67HBMPOsKiGk5TWlFSQ11jh3joWljpY5/IeYfxWRJqbAA5Uk9Ild3PT3DJodR1zXrqK9pqs9+RgsdPPDuBmSVktVftWmJRU5XN5VsXeOZ2huN6F8xDFeD2VPJK1m/FZO5lcdfDlNSa5FSUs9wLWMPrYDMBCz5at6QO4q3K4oaiGygN2cTIvZysX7dxG3x2l5ChRRU4qgTuG6slh3qOzKQUo/5hxA0859J1Igk/h7vfe4cZUXY3aLBjBXJ0QxaSsCrIKttCjJpazgg2K6yr5sMpFt8QuvG3PpVt2FWUlWVxMDCNatSHjoGJswXG8kFjCwD01PL5lPTNDI+kbEkQ4Xgrqqvmy1g0EcWFyG4bsPbjsIa2Y276aQVllfJKzicTCCM4KsxNmuthQWclGDxAUw4z2cbTTCb4iJ5UADtQaisv3/u3MY9uWw23sZU+dmgvNh0FyXCorzFzuzC/ly8pSZlUatA6N4s4OnXioRSgRnrZMLtvBgxVOlpY6aB+X1Eg5VvolpbEp+DfG2gAAHb5JREFUrJAni0pZUlXJotqGNUHWYM6IieOG1gmMjQjab2IQg9S4DmSGxjA1v4i5FdV8UNbQRo60hzEqviXjE+LoH6Q0FTnZBG6g2tOZuNlkor/rIUfNCGrBij4tjnJvKz3j2/FFfLtDrA5nQufuTDjgwXTMuIM3tNAlJoGXYxJ+S82JDWvBlNQWTPHTeyciJ54AHkMVERE5cShQRUTk+HPuZvGDfyTWMDCMjjy44SivdTuBBG6Xr4iIBCCT2q3zGJ9xLc+vbeLsKwFCLVQRETlOnOx4Yyw90y7n+bUh/PGOuzi9GaVQM3opIiJyQjMr+f7F1/gpdiiPf76FpY8OpVUz6idtRi9FREROaIaN5EueZ9l7f+PMeCtU+LtCvqVAFRGR4ySKAXeM83clfjfq8hUREfEBBaqIiIgPKFBFRER8QIEqIiLiAwpUERERH1CgioiI+IACVURExAeadB1qu3btaqdPn07//v09/q5wc+J0OsMdDof12EvyD6vFYnYLDaX/1q06LnxsU3V1D4vF4vV3PUSk6ZoUqLt27Qo1DINJkyYF7Jf/iai0tLT+8ssvD9j31OP1GosdDpZ27hywr+FEdWFOTr3X61UPkkgAafJMSYMGDWLo0KH+rm+z4nA46qOjowP6dgvt7HaGRkX5uxrNTu+uXbf4uw4ivubcMo1LRr/AVufeBzyOvX/v4N/nd+fd0L2/Ie2dGTdvPrel2f1d5d9EUw+KiMhx4a3dzYYNW8huZF3Vrq388iuyjt21gTfioUAVEZHjIqTnU2SZT/m7Gr8bjdGIiIj4gAJVRETEBxSoIiIiPqBAFRER8QEFqoiIiA/oLF8R+V158uYlXX/JE2/8UEucv+tysJDu4zfNe+XKG5ODqPJ3XSTwKVBF5Hdl1ucFr1u5/owfIdLfdfkV1+6QOhPN9CU+oS5fERERH1CgioiI+IACVURExAcUqCIiIj4Q+CclmXXs+nQGU/71Oh8s+4HcaoBQEk8dxIgxd3D/rcNJDTX8XUtphOkuZdD6nXzT1B3CO1CQ3oJWR/l8BSWbSciqhrAUctLjaavD4rgI6nD7zvXm7bolkTR7gR2o3lK+emAoQ59ahxsgKoWuPSJxF25l6/+W8tL4pbz6zkMs/+xR+kfr2/NEZg2y0foIH1FEsFWnY4rICSuAA9WkdMntXPLUOtykcM2rH/LCNd2ItAA42fPZ41w4fBLfrXmcvzx7Oesf7kawv6ssh2Dj5k6nMi1cIxAiErgCN1DNUr58di4lQOIts5l+bTci9q20k3j2g7x072xOm7yDLfOXkjuxG6mB+2pFRJps9OjRxk033VQVFBQUeDcVPYF9/fXXg7t37z7nUOsDOGIiGPjUUj4ZV05I79P3C9OfBdO2V1tgB9SUUuMJ7FcrjTDdrCjew9Ticr6scVIBgIXE0Aguik/k4ZYRR+xGBqioLWXqniLmVlaz1W0CYA8K5ozIWG5JTCAj1HrQ2XsmpTWlPJ1fxNyKGnZ4zL3PG8klLRN4KD6CVhphED966aWXuP/++yOio6P9XZVmZfbs2YddH7gRYwSTcNpgEk471AZVbPpiGwDh3fvRxu7vCotPmS7ezdrE6FIXYCU9IpphNgsudx3LKyuYkVPBf6s78kP7GBIOE26V1bkM3JxPJhAVEsHISDvhppudNVWsKMtnRVk5P6anMyXcSkMxJlklOxmQVUY+EBMayahQO8GeWr5wOHh+l4O3HCms6hhPZ4Wq+El0dLR53333GQpU38rOzl52uPWBG6iH4ancztJpt3HN9DywDeDhSWcTqy+3ZqW2Jp87Sl1ACA+lp/PovsCD6po9DNqUxw+lOTzTOoqpYYcam3XzUW4BmUB6Yle+Swrbr6fDw9r8bQzIrWLq7jLGdYkn2QBXfREZWWXkY2VkSjpz4kMI27uH01nGdZt38JYjm6uLIvm6VTA2f79RInLcNJuzQFw7X+S8jkm0jgkhKKoTwyeuJvHCCcxZv4R7uoX4u3riYxZ7C2aktmdWantuD/slTAHCw+IZFwbg5LNqN55DFWK62Ow0ASs9I0MOGjaw0rt1Ksu6pLG2fczeLlwvawryWQUER7Vj5n5hCmC3x/JMcjShwOqCYjI1eiVyUmk2LVTTWcLOHXso/PkBq4Grsojdu8twdo3QGb4nNBfTt25g7mF7ESz0bZvGgjg7ViDYFs7I2PB9a50eNwVuD7WmCXhx7S2r2u3FA41fbmPY6B5igXoP7+bkclGHJC4N3e/SHMNGv8j92phmPYsrXAD0ahFJ60aKjI9oQW8crHBWsNKdRE+7ukZEThbNJlDtafezybwfr7OSwh3r+Oytp7l/0izu+XwuS6av4f2bOytUT2Bej/uXH0OHkOMBc79/V9aWMikvn1cdtRSaje9jHrbEIM5PTubC6mwW1haSsbGQUHs4w6KiODc6mguiwmmzfx+O10VmQ56yrXgXGY5GwtJ0sR0AF+tdJihQRU4azSZQf2axR5KQPpirHv8DZ/W4nFMum8+Se+7mo9ELGRWvL7cTk41b03/bdahVNXsYsimPdUBISAzjYiPoag8i1mJg4OX7Pdk8U3fkcuzB8cw/NYx3CgqYXlrOivpq3i+u5v3iPdyCnXMTknkhMYZUC4BJ9d6ELq4qZ95hS/ZSqi5fkZNKswvUX1hJPPdmhkXOZ17lChZurGHU4PBjL1b8z3Ty9q6GMA2Pas+GTnF0OOC3kofg4qYFKoDVGsaVSR24MgmqnDV8VeFgUXkprzrq+Dh/OwNcnchsH028YSHKAnisZHTpwduRzeYUBBHxgcD9RqhcxeOXDWPQwFE8t9nZ+DYGe09WcVLtVHOh2fDW8UktgJURrWMOClPAW8/q+qMrOsIexoj4RGZ0OoVdneJJBApL8vnYBRg2etgAPOys96AjSkT2F7iBagum+rtPWfHtAp57cxONNUZqNy9mZSVAMn2SdaZvc+IGwCDG+utu/JLKQmbt/Y11uDFUj7uWRSUFPF1a18jxY9AyIpZ+loZny/WYYNgZFtVwktKakgryGivcW8fCUgfrXN4jjN+KSHMTuF2+Id244fYzmHrnd2x/YhR/6/I+z119akOXHF4qN77NnRn/ZBdgnH4Tl6bqisBmw2Knnx0W1rlZUlZLVXj43kteTEqqCrg8q4KUEIPiOpPiehdOQmhsXg+vp5JHsnazxiil0t6ZhyOC9jsb2CS3ooTlXsAaRh+bARj0bNmaPoW7WVOVww1FocxrFUbkvl2czMvZymXFToIiUtjeJZ52GrbHk/dO0rUXPv72D7XE+bsuBwvpPmHje29cfV1yEFX+rosEvsANVGx0umUOL389kLHzd/Latd15bVwyXVNjoXwnm3ZVNmwWO4Lpb/6NTsrT5sMI5uqEKCZlVZBVsIUeNbGcFWxQXFfJh1UuuiV24W17Lt2yqygryeJiYhjRqg0ZBxVjC47jhcQSBu6p4fEt65kZGknfkCDC8VJQV82XtW4giAuT2zBkb9LaQ1oxt301g7LK+CRnE4mFEZwVZifMdLGhspKNHiAohhnt4xSme5n1+cEbvs/slckvvz1OGGZeUJ2pmxiJbwRuly+APZW/zN3CD7MfJOMPnWhRn8OmH39k0y4n8V3+wOh7Z7HypwX8LU3dvc2LQXJcKitSWvLHYAs7K0uZVVzKSk8od3boyvKkCNJj2zI5yo4VJ0tLHWR6GuuAtdIvKY1NHdswNioUW30li8rKeKfMwddOK2fEtGZmWjfmxdn3m/HIIDWuA5ldOzAhNoLW7mo+KCvlnfJKcqxhjGqdwrfdUrkhWGkqcrIJ4BbqXpZoTrvicd6+4nF/10R+IyOoBSv6tDjKva30jG/HF/HtDrE6nAmduzPhgAfTMX/V6WihS0wCL8ck/JaaExvWgimpLZjip/dORE48gd1CFREROUEoUEVE5Lgwi2bzB8PAOMIy4I2CgLwsLfC7fEXkhBbU4fad683bo/xdD/E/T1Xx3tOp7SR16kBko6eDhZMeF0QgnoWgQBURkePCU1VENUDr65i/fgb9w461xBOLunxFROS42NdCjYgnvBlerKRAFRGR48JTXdQQqOHxRChQRUREjo67soQagPDm2ULVGKqIiBwHHqqLqzABW3QY5d/N5qU3FvHNpjyqjEgS0/oz/MrryBiY2OhUoYFAgSoiIseBh6qihnN8XUuuJO3jg25J8eki5rzwII+OeYMlL15Nx2B/1/e3U5eviIgcBw0tVAC8kZx110t8vqWYWlcdZTu/ZfYDw2gJ7Hj9Gs59cHXD2cABRi1UERE5DkLoes9SfvyLE2tsKunJEftadCHt+3PFE4s4vdUQut6xkm3/foCFdy/hyoTAavMFVm1FRCRAGYQlpdO9Rw9O2S9Mf2Gn0zX38+cQwLWahZk1/q7wb6ZAFRGRE4IRmkinFgBVFFW5/V2d30yBKiIix4e3jrI9uZQ5D7G+voisMoBokqIDb0SySTVOTk6u+de//kX//v09/q5wc+J0OsMrKioC9keN1TC87e12+m/dquPCxzKrq3tYrVa9r9J8VK/gpo6D+E9BMEPf2MHHVycddGd3LwWfzuTzWsDWmxFdA29ewiYFak5OTlhISAiTJk1qhpfi+k9paWn95ZdfHoAnhzfwmKZlWVUVSzt31nHhYxfm5NR7PB69r9J8hPfk+iva8Z9nd/HpTRlMSprD/Wcl7b3mtJ7dS6dwxbXvUwUkXf93RrQKvLZGk9vUgwYNYujQof6ub7PicDjqo6Oj6469JP9pZ7czNEo3EvG13l27bvF3HUR8K5y+j87j8dVDeOib5TxydhsmJ6bTLSmEmt2ZbClwARD5p6f4YOpgogLwdjOB9xNAREQCkhHVlwe/2MGXL9zG+b2TCS7YzLo1P7ClMJSUM0Zx90vfsn3JeHpHBmCaoutQRUTkeLInMuSW5xhyy3P+ronPqYUqIiLiAwpUERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqIiLiAwpUERERH1CgioiI+EAznMvXpPyLcXQ/awa7MThrYRmfXRDt70pJI0x3KYPW7+Sbpu4Q3oGC9Ba0OsrnKyjZTEJWNYSlkJMeT9vAnH9bRE5QzS5QzfIveWDMDHb7uyLym1iDbLQ+QsBFBFvRDUJF5ETVvALVLOfLB8YwYzdEAFX+ro80kY2bO53KtHCNQIhI4GpGgWpS/uUDjJmxG0vfv/NQ0GPc1+S+RBGR5sPhcBj333//yqioqHp/16U5mTFjxpBZs2a9dqj1zSZQzfKvmHjtDHYbvZny8t/oPO4xf1dJfm+mmxXFe5haXM6XNU4qALCQGBrBRfGJPNwy4ojdyAAVtaVM3VPE3MpqtrpNAOxBwZwRGcstiQlkhFoPOnvPpLSmlKfzi5hbUcMOj7n3eSO5pGUCD8VH0Erjs+JHa9eu7b1kyZJz/F2P5mb69OnvXHfddS8fan3zCFSznK8mjmF6jkGvya9xR7dQFvu7TvL7Ml28m7WJ0aUuwEp6RDTDbBZc7jqWV1YwI6eC/1Z35If2MSQcJtwqq3MZuDmfTCAqJIKRkXbCTTc7a6pYUZbPirJyfkxPZ0q4lYZiTLJKdjIgq4x8ICY0klGhdoI9tXzhcPD8LgdvOVJY1TGezgpV8ZNevXqt69Wr1zp/1+Nk0wwC1aT8q4mMmZ6D0Wsyr995KiGU+7tS8jurrcnnjlIXEMJD6ek8ui/woLpmD4M25fFDaQ7PtI5iatihxmbdfJRbQCaQntiV75LCiNi3zsPa/G0MyK1i6u4yxnWJJ9kAV30RGVll5GNlZEo6c+JDCNu7h9NZxnWbd/CWI5uriyL5ulUwNn+/USJy3AT8WSCm4ysmjplOjtGLJ16/k1ND/F0jOR4s9hbMSG3PrNT23B72S5gChIfFMy4MwMln1W48hyrEdLHZaQJWekaG7BemAFZ6t05lWZc01raP2duF62VNQT6rgOCodszcL0wB7PZYnkmOJhRYXVBMptff75KIHE+B3UI1HSybOIbpOdBr8uvcqTQNUC6mb93A3MN2kVro2zaNBXF2rECwLZyRseH71jo9bgrcHmpNE/Di2ltWtduLBxq/3Maw0T3EAvUe3s3J5aIOSVwaut+lOYaNfpH7tTHNehZXuADo1SKS1o0UGR/Rgt44WOGsYKU7iZ529fuKnCwCOFBNHMsmMuaFHNjX1SuByutxU3iEbXI8YO7378raUibl5fOqo5ZCs/F9zMOWGMT5yclcWJ3NwtpCMjYWEmoPZ1hUFOdGR3NBVDht9u/D8brIbMhTthXvIsPRSFiaLrYD4GK9ywQFqshJI2AD1XQsY+KYF9hl6cOUN9TVG9hs3Jr+265DrarZw5BNeawDQkJiGBcbQVd7ELEWAwMv3+/J5pm6I5djD45n/qlhvFNQwPTSclbUV/N+cTXvF+/hFuycm5DMC4kxpFoATKr3JnRxVTnzDluyl1J1+YqcVAI0UN1kv3k3L+wCor18du/FLDtgvZPc7wBMVv39Us77f3aiBj3OKw/0JtTfVZdjZzp5e1dDmIZHtWdDpzg6HNAQ9BBc3LRABbBaw7gyqQNXJkGVs4avKhwsKi/lVUcdH+dvZ4CrE5nto4k3LERZAI+VjC49eDsy4E9BEBEfCtBANXFW1TT86VjHp4e5RqZ6/acsXg82xvEiKFCbA28dn9QCWBnROuagMAW89aw+ysvZI+xhjIgPY0R8Ao85dnHatmL2lOTzcZtorg6y0cMGcz0edtZ78EZaAv+sPhHxmQD9PrDR5b6NmKZ5iKWM/w4BMDhrYTmmaeL86Dyi/F1t8Rk3AAYx1l+PUZZUFjLL2fD34cZQPe5aFpUU8HRpHb9uzBq0jIiln6Xh2XI9Jhh2hkU1nKS0pqSCvMYK99axsNTBOpf3COO3ItLcBGigyknNYqefHcDNkrLa/eZsNimpyufyrApSQhqCtrjehfMQxXg9lTyStZvxWTuZXHXw5TUmuRUlLPcC1jD62AzAQs+WrekDuKtyuKGohsoDdnEyL2crF+3cRt8dpeQoUUVOKgHa5SsnNSOYqxOimJRVQVbBFnrUxHJWsEFxXSUfVrnoltiFt+25dMuuoqwki4uJYUSrNmQcVIwtOI4XEksYuKeGx7esZ2ZoJH1DggjHS0FdNV/WuoEgLkxuw5C919LYQ1oxt301g7LK+CRnE4mFEZwVZifMdLGhspKNHiAohhnt42inE3xFTioKVAlABslxqawwc7kzv5QvK0uZVWnQOjSKOzt04qEWoUR42jK5bAcPVjhZWuqgfVxSI+VY6ZeUxqawQp4sKmVJVSWLahvWBFmDOSMmjhtaJzA2Imi/GY8MUuM6kBkaw9T8IuZWVPNBWUMbOdIexqj4loxPiKN/kNJU5GTTTAM1hlFfmhrDOsEZQS1Y0afFUe5tpWd8O76Ib3eI1eFM6NydCQc8mI4Zd/CGFrrEJPByTMJvqTmxYS2YktqCKX5670TkxKMxVBERER9QoIqIiPiAAlVERMQHFKgiIiI+oEAVERHxAQWqiIiIDyhQRUREfECBKiIi4gMKVBERER9QoIqIiPiAAlVERMQHFKgiIiI+oEAVERHxAQWqiIiIDzTp9m0pKSm1kydPZtmyZV5/V7g5cTqdkQ6HI2BvnFkfZfPausTQpmSHjgsfK86v7GG1Wj3+roeINF2TAjU7Ozu0ffv2PPvss2rR+lBZWVntsGHDQv1dj6PlxrRs/18h42eM0HHhY7Me+rbc4/FY/V0PEWm6Jt9gvHv37vTp08ff9W1WHA6HKzo62unvehyL2FbhtOsSd+wFyQE6duqwy991EJHfRi0LERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqIiLiAwpUERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfGBJt9t5kRkFs1mUKur+OYI2/V/PZ8V17TWr4cTmpfCOR/w2H8cv3G/GEa9fj5nt9GnKyL+FdCB6qkqpgoAO0mdOhDZ6N0jw0mPCyJg7+J90jCwRkYQH+fCdcDjHmpK6hseCw4hOuKg4AyOICxIn66I+F+AB2oR1QCtr2P++hn0D/N3jeToGcSNOJtHRhz0sLuCT29awIJsSLpuOPddGonuui0iJ6KA7ifb10KNiCdc37JyMNPEVe/F9Hc9ROSkENgt1OqihkANjydCgXoSM3EsXszEfxRjP3Moj46t5r0n1rJ6ez0hfzqHJx5sReW7i3hkRjmcOpinnm1PuHHg/iXvLeLh58saWW9Ss2UHn875ibVriymuMgEr0akJ9BzZnXNHtDrEUIOInGwCOlDdlSXUAISrhXpyM7CGNnS2uMpKWProOlbnR5DcLZqotrZjGD/3UrJkOf98KpsKILR9Aqf1CyeoqpyfVuXy1b9z+W5Vf+59pAstbf5+D0TE3wI4UD1UF1dhArboMMq/m81Lbyzim015VBmRJKb1Z/iV15ExMBG7v6sqvzurrSFQzc2bWNO1N+OfP5V2+8bUvUdVpid3C69MzaYCO93vHM7YEdHY96azpyCLN29dxncrV/HqB4ncdbHGdkVOdgEdqFVFDef4upZcSdrHdQeu/nQRc154kEfHvMGSF6+mY7C/6yu/KwMMwHRb6XNz1/3C9Gi52TX/f2SZENSnHxn7hSmAtXUKF9/ahh8eyyV7/lb2nN+btvrlJnJSC+CTkhpaqAB4Iznrrpf4fEsxta46ynZ+y+wHhtES2PH6NZz74OqGs4Gl+QtvxantfNBWdFWx8ftaAJKHJhD1q35jg4geHUgGyM8jy6FTn0ROdgHcQg2h6z1L+fEvTqyxqaQnR+z7dR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\\\" alt=\\\"Array Filter\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3 4 5 6 7 8 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np_array_1 = np.array(range(10))\\n\",\n    \"print(np_array_1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mask = np.array([True, False, False, False, False, False, False, False, False, True])\\n\",\n    \"print(np_array_1[mask])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4])\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[np_array_1 < 5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 2, 4, 6, 8])\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[np_array_1 % 2 == 0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Applying a function\\n\",\n    \"\\n\",\n    \"To apply a function to a numpy array, you have to vectorize the function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def isprime(n):\\n\",\n    \"    '''check if integer n is a prime'''\\n\",\n    \"    # make sure n is a positive integer\\n\",\n    \"    n = abs(int(n))\\n\",\n    \"    # 0 and 1 are not primes\\n\",\n    \"    if n < 2:\\n\",\n    \"        return False\\n\",\n    \"    # 2 is the only even prime number\\n\",\n    \"    if n == 2: \\n\",\n    \"        return True    \\n\",\n    \"    # all other even numbers are not primes\\n\",\n    \"    if not n & 1: \\n\",\n    \"        return False\\n\",\n    \"    # range starts with 3 and only needs to go up the squareroot of n\\n\",\n    \"    # for all odd numbers\\n\",\n    \"    for x in range(3, int(n**0.5)+1, 2):\\n\",\n    \"        if n % x == 0:\\n\",\n    \"            return False\\n\",\n    \"    return True\\n\",\n    \"\\n\",\n    \"visprime = np.vectorize(isprime)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2, 3, 5, 7])\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1[visprime(np_array_1)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Exploring `ndarray` object\\n\",\n    \"\\n\",\n    \"## Exploring booleans with `all()` and `any()`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([6, 7, 4, ..., 6, 8, 1])\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1 = np.random.randint(1,10, size=(500000,))\\n\",\n    \"np_array_1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ True, False,  True, ...,  True,  True, False], dtype=bool)\"\n      ]\n     },\n     \"execution_count\": 40,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1 % 2 == 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"False\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"(np_array_1 % 2 == 0).all()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"True\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"(np_array_1 % 2 == 0).any()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Exploring Basic Stats\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9\"\n      ]\n     },\n     \"execution_count\": 43,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.max()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.min()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"5.0037859999999998\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2.580650240967187\"\n      ]\n     },\n     \"execution_count\": 46,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.std()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Exploring data type shape and memory use\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dtype('int64')\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(500000,)\"\n      ]\n     },\n     \"execution_count\": 48,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 49,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"4000000\"\n      ]\n     },\n     \"execution_count\": 50,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.size * np_array_1.itemsize\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'MB: 3.8'\"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"\\\"MB: %0.1f\\\" % (_ / 1024 / 1024)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Manuplating data type and shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 6.,  7.,  4., ...,  6.,  8.,  1.])\"\n      ]\n     },\n     \"execution_count\": 52,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.astype(float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[6, 7, 4, ..., 5, 8, 3],\\n\",\n       \"       [4, 7, 6, ..., 1, 6, 1],\\n\",\n       \"       [6, 6, 3, ..., 5, 5, 8],\\n\",\n       \"       ..., \\n\",\n       \"       [5, 1, 8, ..., 9, 2, 5],\\n\",\n       \"       [1, 9, 5, ..., 2, 8, 1],\\n\",\n       \"       [7, 8, 1, ..., 6, 8, 1]])\"\n      ]\n     },\n     \"execution_count\": 53,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.reshape(500, 1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[6, 7, 4, 2, 9],\\n\",\n       \"        [7, 9, 5, 7, 5],\\n\",\n       \"        [1, 2, 3, 5, 3],\\n\",\n       \"        ..., \\n\",\n       \"        [5, 7, 1, 9, 7],\\n\",\n       \"        [3, 9, 8, 5, 8],\\n\",\n       \"        [5, 6, 4, 4, 9]],\\n\",\n       \"\\n\",\n       \"       [[6, 4, 8, 7, 8],\\n\",\n       \"        [6, 3, 5, 2, 5],\\n\",\n       \"        [5, 5, 1, 4, 6],\\n\",\n       \"        ..., \\n\",\n       \"        [4, 4, 1, 5, 5],\\n\",\n       \"        [4, 3, 9, 6, 4],\\n\",\n       \"        [8, 1, 2, 1, 9]],\\n\",\n       \"\\n\",\n       \"       [[7, 2, 3, 1, 9],\\n\",\n       \"        [8, 7, 5, 2, 8],\\n\",\n       \"        [9, 3, 3, 7, 3],\\n\",\n       \"        ..., \\n\",\n       \"        [7, 4, 4, 8, 7],\\n\",\n       \"        [4, 6, 7, 4, 5],\\n\",\n       \"        [9, 9, 8, 9, 2]],\\n\",\n       \"\\n\",\n       \"       ..., \\n\",\n       \"       [[3, 4, 1, 7, 4],\\n\",\n       \"        [8, 9, 3, 4, 8],\\n\",\n       \"        [9, 8, 4, 1, 1],\\n\",\n       \"        ..., \\n\",\n       \"        [6, 6, 4, 5, 9],\\n\",\n       \"        [7, 1, 4, 1, 9],\\n\",\n       \"        [2, 1, 5, 5, 7]],\\n\",\n       \"\\n\",\n       \"       [[2, 9, 1, 8, 5],\\n\",\n       \"        [7, 2, 5, 5, 4],\\n\",\n       \"        [3, 4, 9, 6, 4],\\n\",\n       \"        ..., \\n\",\n       \"        [7, 9, 8, 2, 6],\\n\",\n       \"        [4, 6, 3, 3, 8],\\n\",\n       \"        [9, 2, 6, 9, 9]],\\n\",\n       \"\\n\",\n       \"       [[3, 8, 5, 5, 4],\\n\",\n       \"        [7, 9, 8, 3, 9],\\n\",\n       \"        [5, 1, 4, 4, 4],\\n\",\n       \"        ..., \\n\",\n       \"        [5, 5, 8, 9, 9],\\n\",\n       \"        [2, 3, 5, 5, 3],\\n\",\n       \"        [6, 4, 6, 8, 1]]])\"\n      ]\n     },\n     \"execution_count\": 54,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np_array_1.reshape(100, 1000, 5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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\\\" alt=\\\"3D Array\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Array Creation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 56,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.array(range(10))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\"\n      ]\n     },\n     \"execution_count\": 57,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.zeros((10,), dtype=int)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])\"\n      ]\n     },\n     \"execution_count\": 58,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.ones((10,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13])\"\n      ]\n     },\n     \"execution_count\": 59,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.arange(4, 14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  1.        ,   1.18367347,   1.36734694,   1.55102041,\\n\",\n       \"         1.73469388,   1.91836735,   2.10204082,   2.28571429,\\n\",\n       \"         2.46938776,   2.65306122,   2.83673469,   3.02040816,\\n\",\n       \"         3.20408163,   3.3877551 ,   3.57142857,   3.75510204,\\n\",\n       \"         3.93877551,   4.12244898,   4.30612245,   4.48979592,\\n\",\n       \"         4.67346939,   4.85714286,   5.04081633,   5.2244898 ,\\n\",\n       \"         5.40816327,   5.59183673,   5.7755102 ,   5.95918367,\\n\",\n       \"         6.14285714,   6.32653061,   6.51020408,   6.69387755,\\n\",\n       \"         6.87755102,   7.06122449,   7.24489796,   7.42857143,\\n\",\n       \"         7.6122449 ,   7.79591837,   7.97959184,   8.16326531,\\n\",\n       \"         8.34693878,   8.53061224,   8.71428571,   8.89795918,\\n\",\n       \"         9.08163265,   9.26530612,   9.44897959,   9.63265306,\\n\",\n       \"         9.81632653,  10.        ])\"\n      ]\n     },\n     \"execution_count\": 60,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.linspace(1, 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 61,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  1.  ,   1.25,   1.5 ,   1.75,   2.  ,   2.25,   2.5 ,   2.75,\\n\",\n       \"         3.  ,   3.25,   3.5 ,   3.75,   4.  ,   4.25,   4.5 ,   4.75,\\n\",\n       \"         5.  ,   5.25,   5.5 ,   5.75,   6.  ,   6.25,   6.5 ,   6.75,\\n\",\n       \"         7.  ,   7.25,   7.5 ,   7.75,   8.  ,   8.25,   8.5 ,   8.75,\\n\",\n       \"         9.  ,   9.25,   9.5 ,   9.75,  10.  ])\"\n      ]\n     },\n     \"execution_count\": 61,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.linspace(1, 10, 37)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Log Space\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Log space creates `num` values between $base^{start}$ and $base^{stop}$. By default, the base is 10.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 62,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  1.00000000e+01,   1.00000000e+02,   1.00000000e+03,\\n\",\n       \"         1.00000000e+04,   1.00000000e+05])\"\n      ]\n     },\n     \"execution_count\": 62,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.logspace(1, 5, 5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 63,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  1.00000000e-01,   1.00000000e+00,   1.00000000e+01,\\n\",\n       \"         1.00000000e+02,   1.00000000e+03,   1.00000000e+04,\\n\",\n       \"         1.00000000e+05])\"\n      ]\n     },\n     \"execution_count\": 63,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.logspace(-1, 5, 7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  0.5,   1. ,   2. ,   4. ,   8. ,  16. ,  32. ])\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.logspace(-1, 5, 7, base=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Numpy array as an image\\n\",\n    \"\\n\",\n    \"## Import `matplotlib`\\n\",\n    \"First we will import a library for showing images and charts called `matplotlib` (The next tutorial in this series is about it). \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Creating an RGB image\\n\",\n    \"Now we will create a 3D array of size 50 x 50 x 3 which will represent an image os the size 50px by 50px with RGB (Red Green Blue) values for each pixel.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f07d2cafc50>\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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3yWr58cC2TGJW9ImuHhcJ/YoTI3ndQ02FixrEYF0lTbkwVi74TsUNt8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\\nvsBKAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f07d2b46320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"randim_image = np.random.rand(50,50,3)\\n\",\n    \"plt.imshow(randim_image, interpolation=\\\"nearest\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set Red to 0 (remove the red color)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 67,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f07d2b8e0b8>\"\n      ]\n     },\n     \"execution_count\": 67,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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XZW4JrXoRAot+CGmzCLGy8SRpcNhtde7iTXBMD7UfgA12yCkVlVhNzA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\\nolcUj6GLXlE8xv8D44UJ9bJR5jcAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f07d2cdd550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"randim_image[:,:,0] = 0\\n\",\n    \"plt.imshow(randim_image, interpolation=\\\"nearest\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Divide Blue by 3 (reduce blue to third)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 68,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f07d2bba208>\"\n      ]\n     },\n     \"execution_count\": 68,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Pq9VqI4iw47rhevU5v0ghuEu/XjZMPVqPD5BHanN4yYYYPeMGKGDXrD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\\n6A0jZvw/Xkr/GokFjGAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f07d2ba1c50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"randim_image[:,:,2] /= 3\\n\",\n    \"plt.imshow(randim_image, interpolation=\\\"nearest\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set green in every other row to 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 69,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f07d0a98240>\"\n      ]\n     },\n     \"execution_count\": 69,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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1cIV5x+JLS5gJ2BARxArkHnlsYxfaiA6xnxXo++ET8m8Du942QMn/SO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\\nVYfXrAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f07d2bd93c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"randim_image[::2,:,1] = 1\\n\",\n    \"\\n\",\n    \"plt.imshow(randim_image, interpolation=\\\"nearest\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# There is more\\n\",\n    \"\\n\",\n    \"Numpy is a large library with many useful out of the box functions like:\\n\",\n    \"\\n\",\n    \"- [Statistics](https://docs.scipy.org/doc/numpy/reference/routines.statistics.html#order-statistics)\\n\",\n    \"- [Linear algebra](https://docs.scipy.org/doc/numpy/reference/routines.dual.html#linear-algebra)\\n\",\n    \"- [Fast Fourier Transform (FFT) algorithm](https://docs.scipy.org/doc/numpy/reference/routines.fft.html)\\n\",\n    \"- [Financial](https://docs.scipy.org/doc/numpy/reference/routines.financial.html)\\n\",\n    \"- [Business Days](https://docs.scipy.org/doc/numpy/reference/routines.datetime.html#business-day-functions)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/jupyter/BibTex/Compile Article.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%bash\\n\",\n    \"ipython nbconvert mynotebook.ipynb --to latex --template citations.tplx\\n\",\n    \"latex mynotebook.tex\\n\",\n    \"bibtex mynotebook.aux\\n\",\n    \"pdflatex mynotebook.tex\\n\",\n    \"pdflatex mynotebook.tex\\n\",\n    \"pdflatex mynotebook.tex\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/jupyter/BibTex/citations.tplx",
    "content": "((*- extends 'article.tplx' -*))\n\n((* block author *))\n\\author{Your Name}\n((* endblock author *))\n\n((* block title *))\n\\title{Title of your paper}\n((* endblock title *))\n\n((* block bibliography *))\n\\bibliographystyle{plain}\n\\bibliography{ref}\n((* endblock bibliography *))\n"
  },
  {
    "path": "notebooks/jupyter/BibTex/mynotebook.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#My Paper\\n\",\n    \"\\n\",\n    \"Depndincies\\n\",\n    \"```bash\\n\",\n    \"sudo apt-get install texlive-latex-extra\\n\",\n    \"sudo apt-get install texlive-bibtex-extra\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Machine learning is used to in finger print images classification<cite data-cite=\\\"riesen2008iam\\\"></cite>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"1+1\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/jupyter/BibTex/ref.bib",
    "content": "@incollection{riesen2008iam,\n  title={IAM graph database repository for graph based pattern recognition and machine learning},\n  author={Riesen, Kaspar and Bunke, Horst},\n  booktitle={Structural, Syntactic, and Statistical Pattern Recognition},\n  pages={287--297},\n  year={2008},\n  publisher={Springer}\n}\n"
  },
  {
    "path": "notebooks/live/.ipynb_checkpoints/1. painting-checkpoint.ipynb",
    "content": "{\n \"cells\": [],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/live/1. painting.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Neural Network Painting an Image\\n\",\n    \"\\n\",\n    \"Let's do this one more time. This time we will get it to work and from scratch! I'm mean really from scratch.\\n\",\n    \"\\n\",\n    \"To understand what we are about to do, let's go over some basic concepts of neural networks.\\n\",\n    \"\\n\",\n    \"## What is a neural network?\\n\",\n    \"\\n\",\n    \"This is the most basic neural network that you can use:\\n\",\n    \"\\n\",\n    \"![](images/simple_net.png)\\n\",\n    \"\\n\",\n    \"So what is this exactly? It is a representation of a mathematical function. We can write in mathematical notation as:\\n\",\n    \"\\n\",\n    \"$ y_1 = W_1 x_1 + b_1 $\\n\",\n    \"\\n\",\n    \"- $x_i$ is input to the network, that is your input data.\\n\",\n    \"- $W_i$ is called a weight. A higher weight will give the input more prevalence in calculating the output.\\n\",\n    \"- $B_i$ is called the bias. It is a values that is optionally added to your function.\\n\",\n    \"\\n\",\n    \"So let's visualize this function:\\n\",\n    \"\\n\",\n    \"We will use `mathplotlib` for that, so let's impport it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"tf.set_random_seed(2)\\n\",\n    \"tf.logging.set_verbosity(tf.logging.WARN)\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x_1 = np.arange(-5,5)\\n\",\n    \"W_1 = 2\\n\",\n    \"b_1 = 5\\n\",\n    \"\\n\",\n    \"y_1 = W_1 * x_1 + b_1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(x_1, y_1)\\n\",\n    \"plt.grid()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Activation Functions\\n\",\n    \"\\n\",\n    \"One more thing you should know about neural network which is activation functions. The network we just made has a single neuron which produced an output of what ever the input was. This is called a linear neuron.\\n\",\n    \"\\n\",\n    \"There are other types of neurons that perform applies some function to their input. Here is a short list of some really commonly used activation functions for neurons:\\n\",\n    \"\\n\",\n    \"### Rectifier\\n\",\n    \"\\n\",\n    \"It is a very common activation function commonly refered to as (ReLU) which is sort for \\\"Rectifier Linear Unit\\\". The function is:\\n\",\n    \"\\n\",\n    \"$$y = max(0, x)$$\\n\",\n    \"\\n\",\n    \"It is represented in a neural network as:\\n\",\n    \"\\n\",\n    \"![](images/relu.png)\\n\",\n    \"\\n\",\n    \"Let's visualize that and see how it looks:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7fc99058dcc0>]\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(-5, 5)\\n\",\n    \"y = np.maximum(0, x)\\n\",\n    \"\\n\",\n    \"plt.plot(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Sigmoid\\n\",\n    \"\\n\",\n    \"Sigmoid is another commonly used activation function. The output applies this mathematical function to its input:\\n\",\n    \"\\n\",\n    \"$$y = \\\\frac{e^x}{e^x + 1}$$\\n\",\n    \"\\n\",\n    \"It is represented in neural networks as:\\n\",\n    \"\\n\",\n    \"![](images/sigmoid.png)\\n\",\n    \"\\n\",\n    \"Let's visualize that and see how it looks:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7fc990552400>]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(-5,5)\\n\",\n    \"y = np.exp(x)/(np.exp(x)+1)\\n\",\n    \"\\n\",\n    \"plt.plot(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There are so many more, but let's get to the fun stuff. Let's have another go at painting `Tensy`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"What we want to build a a network that looks like this:\\n\",\n    \"\\n\",\n    \"![](images/painter.png)\\n\",\n    \"\\n\",\n    \"The input of this network will be a position of a pixel in the image (x,y) and the output will be the color in (RGB).\\n\",\n    \"\\n\",\n    \"So Let's get building!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7fc990505e10>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"img = plt.imread(\\\"images/bird.png\\\")\\n\",\n    \"img = img[:,:,:3]\\n\",\n    \"\\n\",\n    \"plt.imshow(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_x = []\\n\",\n    \"train_y = []\\n\",\n    \"\\n\",\n    \"for h in range(img.shape[0]):\\n\",\n    \"    for w in range(img.shape[1]):\\n\",\n    \"        train_x += [[h,w]]\\n\",\n    \"        train_y += [img[h,w]]\\n\",\n    \"\\n\",\n    \"train_x = np.array(train_x)\\n\",\n    \"train_y = np.array(train_y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with tf.variable_scope(\\\"placeholders\\\"):\\n\",\n    \"    x = tf.placeholder(tf.float32, shape=(None, 2), name=\\\"X\\\")\\n\",\n    \"    y = tf.placeholder(tf.float32, shape=(None, 3), name=\\\"Y\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"hidden_size = 250\\n\",\n    \"with tf.variable_scope(\\\"dense_1\\\"):\\n\",\n    \"    w_1 = tf.get_variable(\\\"W_1\\\", shape=(2,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_1 = tf.get_variable(\\\"b_1\\\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\\n\",\n    \"    \\n\",\n    \"with tf.variable_scope(\\\"dense_2\\\"):\\n\",\n    \"    w_2 = tf.get_variable(\\\"W_2\\\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_2 = tf.get_variable(\\\"b_2\\\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\\n\",\n    \"    \\n\",\n    \"with tf.variable_scope(\\\"dense_3\\\"):\\n\",\n    \"    w_3 = tf.get_variable(\\\"W_3\\\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_3 = tf.get_variable(\\\"b_3\\\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\\n\",\n    \"    \\n\",\n    \"with tf.variable_scope(\\\"dense_4\\\"):\\n\",\n    \"    w_4 = tf.get_variable(\\\"W_4\\\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_4 = tf.get_variable(\\\"b_4\\\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\\n\",\n    \"    \\n\",\n    \"with tf.variable_scope(\\\"dense_5\\\"):\\n\",\n    \"    w_5 = tf.get_variable(\\\"W_5\\\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_5 = tf.get_variable(\\\"b_5\\\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\\n\",\n    \"    \\n\",\n    \"with tf.variable_scope(\\\"dense_6\\\"):\\n\",\n    \"    w_6 = tf.get_variable(\\\"W_6\\\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_6 = tf.get_variable(\\\"b_6\\\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\\n\",\n    \"\\n\",\n    \"with tf.variable_scope(\\\"dense_7\\\"):\\n\",\n    \"    w_7 = tf.get_variable(\\\"W_7\\\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_7 = tf.get_variable(\\\"b_7\\\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\\n\",\n    \"\\n\",\n    \"with tf.variable_scope(\\\"dense_8\\\"):\\n\",\n    \"    w_8 = tf.get_variable(\\\"W_8\\\", shape=(hidden_size,3), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\\n\",\n    \"    b_8 = tf.get_variable(\\\"b_8\\\", shape=(3,), initializer=tf.truncated_normal_initializer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"l_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)\\n\",\n    \"l_2 = tf.nn.relu(tf.matmul(l_1, w_2) + b_2)\\n\",\n    \"l_3 = tf.nn.relu(tf.matmul(l_2, w_3) + b_3)\\n\",\n    \"l_4 = tf.nn.relu(tf.matmul(l_3, w_4) + b_4)\\n\",\n    \"l_5 = tf.nn.relu(tf.matmul(l_4, w_5) + b_5)\\n\",\n    \"l_6 = tf.nn.relu(tf.matmul(l_5, w_6) + b_6)\\n\",\n    \"l_7 = tf.nn.relu(tf.matmul(l_6, w_7) + b_7)\\n\",\n    \"out = tf.matmul(l_7, w_8) + b_8\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"loss = tf.losses.mean_squared_error(y, out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = tf.placeholder(tf.float32, shape=[])\\n\",\n    \"optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sess = tf.Session()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.global_variables_initializer().run(session=sess)\\n\",\n    \"seq = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 0: 0.30167004466056824\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 10: 28974.66015625\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 20: 2736.043701171875\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 30: 1560899.625\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 40: 400.8169860839844\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 50: 1.6070836782455444\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 60: 1.8041610717773438\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 70: 0.3010651171207428\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 80: 0.0904325321316719\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 90: 0.07690518349409103\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 100: 0.05362841859459877\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 200: 0.009619979187846184\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 300: 0.008745246566832066\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 400: 0.00811453815549612\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 500: 0.007663724943995476\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 600: 0.007481598760932684\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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IROcRC43zdAHNsEiSI93WWjYkyEuaI0+/uSbiwqzHqvLDb5nUJ8rFvqlLpciR9bW7RempCiz1NU5sBRciaHIiubUXfBlm7plo/hB1joqPLiGsq4eXMsrldVd8PvL96fBh4ag67hmEYpTHTO4IqRtMuW2xlYUDtEnrpTP4owHF8xCrmeEyVisfMPaR2qWuDRWqXXe0ttP6Z4ePn6TRXf19AKu7rGjbRAMQ4zMgLJfU7eyDCYbou6sAxs6F07dJhyvseiTgenYovh66PJX0ID6GHBwchbuwfdhiGYUQwr0gzpeDTf6KMBNhNifQ2xidUEqM2Fg+YNhVD7uiyw25SSu7Z+JWr4LPqlkPiCRjjXFbulLw5LuYVn4/T7HSYObTLod/cOSrjjmNZdabAuSfaIzcsQzjLXI6yvU9Tuwyx1aFdLu10SkOxEk/AmMayxtEuU15xS88NwzAiKDvSjK2Ou7rFTZSRMLv57x3329KUFKhPNFw8VvBZV8iHlnhajufOXDrs9X2ly3Sao20lms5Z2m2QJTCSo1ySwWHm3UpEFu2yfYYc2mXCNZWsXYbPZem5YRhGBOVFmp2fnqVHmH7b5XwzZK6Cj89Wqcw0Hd94kGortODjandlLhNeUx1Ra/dZir92y3CaQsCFMPOtRIQ6zDadNkW7ZOCtRL5jpVLy3T2lbyXCcU15xoxWIY/VLtOuX0vPDcMwIigj0gSGLfi02MrCgAUfyJyOB9pKStdKZ6T9l0u8hUy/vbz7L6Hs/3vZ1d5Cr/2X/a/fQpymZ7N6W5cgptMuoc9WooQx0TpTTBXT114yM9YuV0+6bAVuJUpJxZfHo7TL2vGZa5cuCnGadXaxdgke/TJGu3T1N+0yq80drF22z1Cedtk4nGLfthwZhmEMQyGR5kb5vEDt0m93vtqlq79Fl502k9LxZr/s2iUsosysEk/AmMay5qJdznXLUZ0dqV062kfZI2faZVZ7ubXLYEfp66NrWym3QTo/hHOl4320y5itRMOk5HUsPTcMw4igjEhTKSzCnLLg0y3OxwneA1Yxi2JeUWY5BZ+eEk9jYO4KeWjBJ2aO/td5r0hTRE4TkbeLyCdF5FYR+QEROUNE3iMin65+n957le2zD2M2QLtcf42u1H4CbCmsvxmyra+jffnNkK39PbZ2rMNUenzahttrnObQeZt9VmZWDzI4zNVyYiUeWUk8YQ5z65paDXRdh773xhaN09pMyd0BQ8gcEWvx0Dc9/z3g/6jqPwS+F7gVuBq4TlUvBK6rnhuGYewIkp2miJwKPIvqe81V9QFV/QpwKXBN1e0a4LK+i6zNSs5PjHbbbsLT8RZbPSrkm5+yvuhSHJ/aMdFtyeSMLjvstZ6a0OiyK8IMiVS7+ujChijr77KPifLW10dc5lJbWlu7q7+P1utUNg6lp+QR/iLikuoTaV4AfBH4HyLyERH5IxF5KHC2qt5Z9bkLOLvHHDWGdJR+25rqgFbpeMSFvUzHe1fIM6RNRZA7Fa/bbaHVWaZtA4pLxz3zSFUh7yPxpHwIN5aW6UM4qUIeev1GygIR9HGaJwAXAa9V1acA97GViquqc0kicpWIHBSRg18/en+PZRiGYYxHH6d5BDiiqjdUz9/OwoneLSLnAFS/72kbrKoHVHW/qu5/2N5THFMMnY530170CbDVKc57PqWrgk8zEggU571zzI0hostcBZ/udHwVZfaIVDfTcSILPlV7VMFnq32Ugk9IlBk6R0C/nolLstNU1buA20Xk8VXTxcAngGuBK6q2K4B3xFsf0lH6ba8dZddaHLacOhOOtqrdqTN1zBtUxez6G0pmpHS89dSkVbU3HOXS0fXRLtlOx3NJPCkfwm1EpuIO7bJ+uEPk8BB4jTtfjvC/pe8+zV8A3iQiJwGHgRezcMRvE5Ergc8DL+w5h2EYRjH0cpqqehOwv+XQxelWh4qI/NFl2Ji2T/Uu+x3zRv8nma20Kab/LMgdWXpsZoowV6aku0/wPN4N657XuxFdBoxxLqstuo0kueATQoQs0GueNWXcEQRM5Sxh22F29W/RLjvHdFzwwZXxrWM70lnCtJXxNFvSeJJua5WKr7qkaZfNGXJdV5HXVOPPbI4f3GFmdJR1CnKaOQk7KWX/38tae7BTDzleIjN1mL0dJQFR5Woih+nYbGXr2BAfwh3BRPfZCtUuY9aQOL4D+4cdhmEYEezASDMmHY+M2lLTpmqsaZd1RoouwXF64nXHvOk4+P/vpau965ryjHEuqad26UnH56hduthBTjOXdulKrRPGtOpMKYK8Octom5kcZcNUULHHc8yrXXpS60G1S5+tLTocsP+Vzugwgwu5/bH03DAMI4KZR5rhnyTp3wyZVvCBIdNx37FSmXPBJ8RO4Fai1G+GbC0kBkpMgxZ8mmN3Ujq+zYyd5lRbiVK0ywBbpl32t7dxekLnHUm7XHXJtZUo0FbOu3paFxFaIY+Zp4/DHP49Yum5YRhGBDONNLs/Tcoo+Lj6x1Yxu+YtmRHv7smUjsvGg57R5fKYkJiOuzKXEgo+Q+y/DOw3QTq+zYycZthJSf9myIQLy26DdLDTtUtPn+TbINdjkiSe1fiEMZ3rcY+dv3YZP97Sc8MwjAhmEGlOVfBxtacUfDbHBfedFVNFlzFzdyS6O6bg4+o/VsEnZq75RZlQvNOcs3a51W4b1vvbGyQdN+2yQVI6HjPP1NplPxuWnhuGYURQYKQ5cMEHMorzu7ngM2V1PGR+j3iSMx2HyCizp8TTurRhK+SjpeLOyaZJxdsoyGkOqF1CxnTct84BdaYiGMlZDuEoN56k3t1TT8cb1l2zbrYPql12tbcQ4KBGcZidL0c5DhOKcprdjHobZLTOFHtR+46Vypy1yxA7AceXthKzleYsKR/Cc9Aupy7y5LLRxDRNwzCMCMqONFMivWpcVP+NsYkbi0277G8veTvRwHf39Prv6jnT8bYxCddU0t09MfNMGWUO/x7rFWmKyH8QkY+LyC0i8mYROUVELhCRG0TkkIi8tfqmyihW3ze+mKX201hBs10d7a7+y/blV5629t+NDlMZNR2X7X6++Zt9Nl6t1YOQlLytT9W++r5xOlLyjnZtmyHg+twY5LoOI7XLxkKaKXk/h+n6u3zrqI8t22FCD6cpIucC/x7Yr6pPAvYAlwOvAX5XVb8b+DJwZY6FGoZhlEBfTfME4DtF5ATgIcCdwA8Db6+OXwNcFmMwfCvRdoRZRYsxkd4yumxEA75IQLY+MSOj4eIZIsJ0VMhbI8x4WxvRpcREqo5jS1vqem07XtflNdUqL+UoJkZeUw176/H+MxVSqIqIMJ3jU8kRocaRrGmq6h0i8p+BLwDfBN4NfBj4iqoeq7odAc712oLA7+3JpV2u06aoMRtzpsxbMiNql9ByiuJ1R2k82cnaZZedFgLsjbKVqHOivu+Tad5nfdLz04FLgQuAfwA8FLgkYvxVInJQRA4ePXp/6jIMwzBGpU96/iPAZ1X1i6r6beDPgKcDp1XpOsA+4I62wap6QFX3q+r+vXtPISgl3jBAezsB7c60KUCc32hvw6JMb8FH6v3iCz5LU5tPUqPMkIJPwPXhlHhyFXzyRJndZzx0nhIKPtO9z/o4zS8ATxORh4iIABcDnwDeB/xk1ecK4B3pU7g1o2in1aozmXY5isNsPTXp2uVav9Safumz07auUO3SZbpLu3QRo136bG2vB6cD9r/SmbTL5WTO8amMr126SHaaqnoDi4LPjcDHKlsHgF8FXi4ih4BHAK/LsE7DMIwi6LW5XVVfDbx6q/kw8NQ+dls/TdTR7uq/bM8mzvcU5oshd2TpsblxikLn9qXiIbZ8hShNv6Yq88NcU13tLTijOk+XqHl2Z8HHRUF3BLnS6q7jsRd2ripmV3vJjKhdRs/bsZchl6NcbiUCR3U8IEVtfAiHpLXby8t0TXU44H6peETfwRxlLhv5sXvPDcMwIigo0mwh8cvOwvdfxqZNvmOlMmJKninKlI0HU+6/XI8JT8e3jg2akjfHxe0/7tF3l6Tj25TnNIvULrvaS6bkdLzZL0279PRZaZeNGTraau2t2mWArSGuKY90ZNrlOJTjNJP0HtMu25nKWYbMnUO79PXxaZcbk7Ufa/0QzhVZjqVdxsw1pXaZ087wmKZpGIYRQSGRpmvTai7tcqs9dxWzCIbQLTvsDlIhn1K77CnxNAZOWSG3dHxICnGa2+TaSrR1zLTLfvYyaZcNU70dZpWOZ5V4Am3tZO3SOdnudJZLLD03DMOIoMBIMyZ9Sin4uMbN8dNvpK1EuQs+qwcZCj5LWz22EjVnmdNWotB5po4uc9uZjoKcpuNkRu+RG7CKWQQ7QbsMseM57tUvU7YSTbjrwrTL2WDpuWEYRgQFRZp1rODTTskFn4AySq6Cz6rLyBXyQfdfto8f/O4e78vR530yx/eYnwKdpmx9T9C6vbMt+DbIkOOlsctvg8ykXc77NsiYeaZOx3PZGJPw9Vp6bhiGEUE5keagBZ+u9pIZKR2HgSLMdDtrg6kb1lP2X8buuki4pqzgUxBp6y3GaYZrl7Xj5iz72RtiK9HqSYnaZQnpePvYwR2m3Tteo2WtEW85S88NwzAiKCbSXBOTju+0gg/MtkKeY//lTvu/lx6b8y/4zPH95ZNg/BTkNE27HMXexukp5d5x8P/fS0+73TseuJbI8X3nL4Kt9fZ8u3nTcxF5vYjcIyK31NrOEJH3iMinq9+nV+0iIr8vIodE5KMiclHasmT9o6x/No67xsyJxh+WyeYWtdPZ2a9hp7m+DTOrJ77tQq6/s2oXpfsrdF2v7foaaV+l5zoZQrvscJjdr3ZGh+l8z+R4j8zpPdby92Z4u4Vomm8ALtlquxq4TlUvBK6rngM8F7iw+rkKeG3/JRqGYZSD12mq6geAL201XwpcUz2+Bris1v5GXXA9cJqInBO3pK50vCt6mBtDRJiOKDOkX6OPw9TqZeiKIAPmEV3bct473q1fqrL4CR3j1MUzZC4d16r7TIRGf4H9nBPliC7nksm1rNV3qa7GhZGqaZ6tqndWj+8Czq4enwvcXut3pGq7Ey+mXWazl1Tkae87iHa56pKmXbbPkEMTz6tdtnaJnseKPeHEpuJpf1vvLUeqmiTMichVInJQRA4ePXp/32UYhmGMQqrTvHuZdle/76na7wDOq/XbV7U1UNUDqrpfVffv3XtKRMGnq71kSo4ymyd/leAsU3HxfTZGFHx0K33amNFf8GmuMqDgE5SOR9Cw1yz49IsyI9Lx1oi3bzo9l3R8SUyU2e9vS3Wa1wJXVI+vAN5Ra//pqor+NOCrtTQ+kJQqasnkrpB3aJfRDrPd1oZ2mSMd79QuVzO2tIlDu/SMiU7JI7VLx9aoMrTLHI5yLu+xrfVm1i5deDVNEXkz8IPAmSJyBHg18JvA20TkSuDzwAur7u8EngccAr4BvLj3Cg3DMArC6zRV9UWOQxe39FXgpWlLsVQ82l7rqQmdezcXfLraWwioSI+y/7Jzol1c8IHBo8s6hdwRZA4z2l6ydukwJd19wufRhFR861hQKt4+zj+mT4U81FGGzjO1o8xlY0xitcv82D/sMAzDiKCQSLPO3D75IH902WE3KSX31HFzpuOQEGVKS3QZMMa5tNwpeXNc/8p4yjoSxvedvwjGiC7DxxXkNOf2QsIwzjKXo2zv09QuQ2y5juvajqOi7G6rtbc6zF2sXQ6acs7pfTa0dpl2Liw9NwzDiKCgSHNuWMFnZSvqe8fXY8Kjy5bjQfsvI7CCT2HEpOQt/bP1bWJOM4qRHOWSTA6zNO2yfYbYdLxtTMKbIUm7jJlraoc5J2c5pKPsM2YTc5rBzDSy3HiQaitUu3S1p2wlGlC7bNhsjp9Wu7TIsgTt0oVpmoZhGBFYpOml5Lt7St9KhEO79IwZrUIeo12GzGOpeDxd8ounf9a+4ZjTbGWkrURLMqTkebcSQTm3QbrGzHArUedEu8lhDr2VKGVMeH9Lzw3DMCKwSLPBjAs+qyddtkLvHW+13vK8ZTXF3ju+Ob7/VqLAflbwqVFSwSdWGlhgTnNFyXf3zEO7bJ+hPO2ycTjFfq90fDel4hC/lahlTNb+ac5yiaXnhmHvClGkAAAOJklEQVQYEezySHPKgk/o3AMXfGARZdq944GTWMEnnKELPokFop5v+13sNGeqXfbeqF47trSVchuks0KeKx3vo13GbCXKmJIPql3mtDMGsSn5kFuJ+qXj21h6bhiGEcEujTTnFWXOu+DTFXHkrpCHFnxi5rB0PJydVfBxsYucZsmOsr2fNB5kcJhZ7x0PGNNY0vBbiVoPR89jzjKOkrYSVWOi3vIZN7eLyOtF5B4RuaXW9tsi8kkR+aiI/LmInFY79koROSQit4nIc2KWPQxKXofpsCckOEylzd7KlMD6+8Z9ey9dfZTV95avvm+8be9l6x/A6uJr3XvZMaa+tLZ2V38fG3/m5hr8Zyrz3stWx+06JzHMyWG2/L3ZHGbsuayd/+AoN/71CtE03wBcstX2HuBJqvo9wKeAVwKIyBOBy4F/VI357yKyJ2pFhmEYBeN1mqr6AeBLW23vVtVj1dPrgX3V40uBt6jqt1T1syy+//ypGdcbSEh0lmq3hdYIs8eG9dWHX2ik6kCWd/dIR4XcFV0uflS3Z+mKLmvHWiPC0PkdJFXIQ6OJwH7OU54juswRpY5By1q9b7mUqDFyTcFv+37nOUf1/CXAu6rH5wK3144dqdpGZAhH2fJKNK6b9DR6w5TQ2qfdlqN9lY6TUOyRVSqevPdSl22xUkALjVPWHOv++AmZI8JROp1lH0c3F0e5pCUV9zrKGFkkMSUPftu3247xGr2cpoi8CjgGvClh7FUiclBEDh49+s0+yzAMwxiN5Oq5iPwM8HzgYlVdOuo7gPNq3fZVbQ1U9QBwAODRj3pkhvAwd4TpsNn6QVXAVqLlsWU63krHJ/iOuHc8Zp6ISLfP+BxrKIKWtU5WIXddd/G2U7xGUqQpIpcArwBeoKrfqB26FrhcRE4WkQuAC4EPpswRxhDaZYfNZO3S4Ypya5dOh+nRFKt/FOyo4TtsuTSknuk43fbcZyJWuwxYw6Dp+FwcZox22SO9junfU7vs6zW8kaaIvBn4QeBMETkCvJpFtfxk4D0iAnC9qv6cqn5cRN4GfIJF2v5SVX0wcW2GYRjF4XWaqvqilubXdfT/DeA3+iwqjCkr42m2pPGkT0qua4Orwot31s12dc0QWPDp7B8ZSbXuefR0iZ4roODTy37P+YuipeAT0z9b363+IxZ8XMz0jqCZOkzTLh1rco+bv3Y5J0e5JMZh7lzt0oX9ww7DMIwIZhRpjhRdQmKF3BO75Ygyk//ZhqxMBwgHzTan6O+z0YEnHR88FfdOtNsizCGjy5QxMXsv2+0PsZ8GZuE0S95KFKpdptsK0y49qXWrdlnCVqLN8f5XOqPDDPogSGVODnPIrURz0i7D12rpuWEYRgSFR5pzLviE2PEcX333eGKEV+2/jBpTS+X9Y/qk5KHpeOg8lo6HExtdOsYM1T9xLWneIv51K9BpTqVdhs47onY56VaiIRxl+/hwndVFH4e5m5wlDLuVKGVM24d0vO2xHCYU5TRH0i4hW2QpGw8m3krkLPaUoF0OsZUosN+gey/n5DCH1C5j+89Du3RhmqZhGEYEBUWaOSlNu/T08W4lStEufeNioswxtEvbSjQMsel4y5hsfbf6F65duthBTjPUUXb09fTbmdqlq/9Y2mXMXOYwwxlSu+yhc85Iu3Rh6blhGEYEOyTS3EX3jpdW8PHYtILPFMREmSUVfNy2pyj4uJi508yZkntUwZzpOIxwG2RL+4gV8tFScedkuykVhyK1y0lugxz+dbP03DAMI4KZRpq57h1v79cs+ITY6rh3fGmnyIJPV3sLAVHdKFFm1pQz9/gxKWn/ZW3MDPdfhjIzp1naVqKA40tbiVuJmrP4Lo42hzkH7XLqynguG2NSina51b9npT7OYY7/mll6bhiGEcGMIs2cKfnAt0QmF3yq9mzpeOAYH0m3RA5QZBgkytzJ0WVL/5Q5QvrugP2XoRTuNEe6d3zjSS7tsjFDR1utvbVCbtplv3mGGj82JaXj1ZgdrF268KbnIvJ6EblHRG5pOfZLIqIicmb1XETk90XkkIh8VEQuGmLRhmEYUxGiab4BuGS7UUTOA54NfKHW/FwW33V+IXAV8Nq0ZSnOdFwC+gXYWhV8hCqd9tnqOq5rW7oy6lt8bbigyuIndExUlNllp209bWPXRanuMxVSqApYj3OSwPF95y+KrfV6L/vY9Dqlf0iU2X2u4ws+ZbxmXqepqh8AvtRy6HeBV7D5t18KvFEXXA+cJiLnhC3F83bMkJJvvIQr7TLN6S4MavXDwlk6K+SO9mpMu0vvsiUtpyvSUbvosNftKEPmmrpCXsabLoyWc9p5uaZ8GCQ4y5D4osN28PAAW1ORpGmKyKXAHap6s8jGH3QucHvt+ZGq7c5uiyNtJdp4kmorRLvsepFd2mXAmMaSMmiXDZvN8dNqlzneMGW96fyUpF2ur9c+c8xRu3QR7TRF5CHAr7FIzZMRkatYpPCccfrePqYMwzBGI2Wf5mOBC4CbReRzwD7gRhH5LuAO4Lxa331VWwNVPaCq+1V1/969p2webM0y0rXLtX6pNf3SZ6dNUw3VLl2mu7RLFwNGma3p/cja5XIy5/hUZq5dBuWxY2iXvjVs91/PkZaKL22VS3SkqaofAx65fF45zv2qeq+IXAv8vIi8Bfh+4Kuq6knNt9g4X6Gnu8MFSXsfn40GsvR0sc7KlY4H2tqpW4k6J9pt2uUW2bdZJaTku2jfZSwhW47eDPwt8HgROSIiV3Z0fydwGDgE/CHw77Ks0jAMoxC8kaaqvshz/PzaYwVemrSS6AgzR8EnIPpcGky5d7z2ia31Nt+YxvKGKPiERpex83j6WsGnRkzBp6V/tr5b/UeNMuf2mhV5R1Caw5SNB+lOd2GjdizlHwW3/qMNz5jRUvLQyuYAb1BLx4l3lC1jsvaPdZa7Lx3fxv5hh2EYRgQFRZppm9WbTzJEmZ1pcUrBJ8DWTt1/2TnRboowoaz9l9UYK/hEU5DTdNHhgnI5Sq92uTFZ+7GGdhkwpnVpKdrpFsnaZcxcU2qXOe2MwZy1S7f9na5durD03DAMI4LCI01flDllwccVXXrGOJc2ZYXc0vH8xO6/dIzJ1j9mo7rb9m5Mx7cp1Gl63FAuh5m0pSdFu9wcF9w3lFK0S+dku8lZwry1y/Y5dtpdPX2w9NwwDCOCgiJNT9yWI7rc+A9FJRZ8fLa2SNp/GTrP1NFlbjtjEJuSl7//Mnh4oK2dgCxu4pl4ESJfBO4D7p16LTXOxNbjo7Q12Xq6KW09UNaaHq2qZ/k6FeE0AUTkoKrun3odS2w9fkpbk62nm9LWA2WuyYdpmoZhGBGY0zQMw4igJKd5YOoFbGHr8VPammw93ZS2HihzTZ0Uo2kahmHMgZIiTcMwjOKZ3GmKyCUicpuIHBKRqydaw3ki8j4R+YSIfFxEXla1/7qI3CEiN1U/zxtxTZ8TkY9V8x6s2s4QkfeIyKer36ePtJbH187BTSLyNRH5xbHPj4i8XkTuEZFbam2t50QW/H51XX1URC4aaT2/LSKfrOb8cxE5rWo/X0S+WTtXfzDSepyvkYi8sjo/t4nIc0Zaz1tra/mciNxUtQ9+frKhqpP9AHuAzwCPAU4CbgaeOME6zgEuqh4/DPgU8ETg14FfnujcfA44c6vtt4Crq8dXA6+Z6DW7C3j02OcHeBZwEXCL75wAzwPexWKn9dOAG0Zaz7OBE6rHr6mt5/x6vxHPT+trVF3fNwMns/iixM8Ae4Zez9bx/wL8x7HOT66fqSPNpwKHVPWwqj4AvAW4dOxFqOqdqnpj9fjrwK0svq+9NC4FrqkeXwNcNsEaLgY+o6qfH3tiVf0A8KWtZtc5uRR4oy64HjhNRM4Zej2q+m5VPVY9vZ7FN7KOguP8uLgUeIuqfktVP8vie72eOtZ6RESAFwJvzjnnGEztNM8Fbq89P8LEzkpEzgeeAtxQNf18lWq9fqx0uEKBd4vIh6vviAc4W9ff7nkXcPaI61lyOZsX+lTnZ4nrnJRwbb2ERbS75AIR+YiI/D8ReeaI62h7jaY+P88E7lbVT9fapjo/UUztNItCRPYCfwr8oqp+DXgti+95fzJwJ4t0YiyeoaoXAc8FXioiz6of1EVOM+rWBxE5CXgB8L+qpinPT4MpzokLEXkVcAx4U9V0J/AoVX0K8HLgT0Tk4SMspajXqMaL2Pzwner8RDO107wDOK/2fF/VNjoiciILh/kmVf0zAFW9W1UfVNXjLL6SOGv60oWq3lH9vgf482ruu5cpZvX7nrHWU/Fc4EZVvbta22Tnp4brnEx2bYnIzwDPB36qcuRUafDfV48/zEJDfNzQa+l4jaY8PycAPwG8tbbOSc5PClM7zQ8BF4rIBVUUczlw7diLqPSV1wG3qurv1NrrGtg/BW7ZHjvQeh4qIg9bPmZRXLiFxbm5oup2BfCOMdZTYyM6mOr8bOE6J9cCP11V0Z8GfLWWxg+GiFwCvAJ4gap+o9Z+lojsqR4/BrgQODzCelyv0bXA5SJysohcUK3ng0Ovp+JHgE+q6pHaOic5P0lMXYliUeX8FItPlldNtIZnsEjrPgrcVP08D/hj4GNV+7XAOSOt5zEsKps3Ax9fnhfgEcB1wKeBvwTOGPEcPRT4e+DUWtuo54eFw74T+DYLDe5K1zlhUTX/b9V19TFg/0jrOcRCK1xeR39Q9f1n1Wt5E3Aj8OMjrcf5GgGvqs7PbcBzx1hP1f4G4Oe2+g5+fnL92B1BhmEYEUydnhuGYcwKc5qGYRgRmNM0DMOIwJymYRhGBOY0DcMwIjCnaRiGEYE5TcMwjAjMaRqGYUTw/wEVbEtfp7TT/AAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 700: 0.00793477613478899\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 800: 0.007531074807047844\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 900: 0.007441612426191568\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1000: 0.007371342740952969\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1100: 0.007531122304499149\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1200: 0.007371849380433559\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1300: 0.007416746579110622\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1400: 0.00753899896517396\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1500: 0.007307794876396656\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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zDgyXyNK3gk6NdMZXxKaMr+JRaHYfmd9N3OD4nk93LjFvVIzealrvM1SZv7jJQ10C5y3RbiWD41dnXViIXfcPmLkNXyYiN5thzl30aSr+2lruM1biac5eeffVyGWSKMZ/EcpqGYRgejNTTHJ+XGe9hxrS13GV+basrHK8/OrbcpQR9PCMymuMr9MBq30rkqWt0hrKt1dCrM4WxXJRtbunRX/TVPX7GuXF9B35EFp4bhmF4MAJPc5weJky/z8oMxxelLByPb1XKyoz1MjNUx3u5dtyx4BP5MUV5miKyWUQ+JSL3isg9IvJiETlLRG4SkQeq/88M76H+zpAlIzOPOKPn23Zlz35SYzGYbu/RB39tTWMYemW2zY3rvNXL1B/pMl4+uUtXQ95tMBvXt04fDl11EBue/znwf1T1F4BfBO4BrgVuVtULgZur14ZhGEtBsNEUkWcCL6W6r7mqPq6qPwJ2AXsrsb3AFX6a629YPPT3eBcrv93yeYrNbUOkUnptnrq8btmd1rsM11jnXQ4d/7h5YG56FrX6FQlnWjhNjavn69Zn4/pO/PHEeJrnAw8D/11EviYifykipwFbVfVQJXMY2Oqmrt5QDr0k21gMxfMaveYR+EilDHEDdHl/oKUYy9LCcfAKkVtlupI1rsarwikc98iHOuioHbPXWnNfFTFGcx1wMfABVX0R8BPmQnFVbRy2iOwWkX0isu/YseMRwzAMw+iPGKN5ADigqrdWrz/FxIgeEZFzAKr/j9Y1VtU9qrpTVXdu2rRh5TnK+B5vI00hJW8YbwWfRa1+0k0eZgnheJ6CT1g4PheSe/TZLNt9rtbf9C74+K+zYKOpqoeBh0Tk+dWhy4C7gRuBq6pjVwE3OOlj+OXowlhyl/UZqhSMM3fpbzBnKWV19pW77Jq11LnLrj7rE2ELMj19Kcfu0/x3wMdE5BTgQeC3mRji60XkauA7wOsj+zAMwyiGKKOpqrcDO2tOXRajt0TaUs4hWnK1yxOO5wzFI/pIrq2u1dDeJcR7mPUyfqF4jczg144zCcW9iF9nI7giaFjSGaG84Xh8X3FjWMGA9+zx19okPbSxTBGK18u6mtDGcz1f3dP4DpLuwnCfUzOaLcQbzLyepf/iTz+GEyyFZzm0oZySJ3cZpsvVQKX3LBvHnPRL2X/l2A92GIZheGCe5hxpwvH8YXye3GWgrlH9unqp4Ti0V6vD25efu1w8v/AqSyQzTPV8abDc5bhyl8tT6EmVa0thMGvO9Vjsic9d5jOUs1h4bhiG4YF5mqzmgo9tJRqW9AUf1yOtekrYSuQdkqerjnexao3mGHKX4w3HA/Un1Wi5SzedqY1ll2xH7rJzHD59+ci4Y+G5YRiGB6vS02xLPfu0ztW2KA9zacJxGN7L7Gv/ZUBfgxZ8qqOFFXyaWFVGM85YxrRzb5vHYFruclhSGMtF2aXJXXaOxbU/l/PxWHhuGIbhwdJ7mrb/cjV4mSV6mHm8y+bWHv0t5bXjrjLxLLXRXL1biQJ0jc5QtrVaBoOZwljWnE/yu5duMmPPXTZh4blhGIYHS+dpjmH/5aLUmMLxofdfNrUa2ruE+CKFqy9ZWsFn8Xz+go+rTMr+JiyN0bTcZU5jmScESpO7hOHv2RNzvl3Ob82UtpUoJHfp3l88YbqWwmiWnrusl7TcZVyr0j3L1LlLj/6SFHoSbCUq9DLIWF2W0zQMw/BgtJ6m5S4tdzksMV6mTx3cQ9fg145Px5GqLx+ZPnRMiPI0ReQ/iMhdInKniHxcRDaIyPkicquI7BeRT1Z3qkzKyixKnwZTcOmzXirVhxbwnr3uPhszp6k0Ns3z0Aaz7fN3Wxt+ucsmXTV9JctddvXZsr6n9xx3pss4p1qLaddzsNEUkW3Avwd2quoLgbXAlcB7gPep6vOAHwJXpxioYRhGCcTmNNcBp4rIOmAjcAh4OfCp6vxe4IrIPk6w8nsnn6fY3DZEKqXnNh4PM2ymm7xLrzeSATcPzE3PYsvFyMmj4NM4NT6er/v7a1zf3ussJr3h21dago2mqh4E/jPwXSbG8hHgq8CPVPXJSuwAsC1mgCunOL/Rax6Bj1Tq0MJTl7eNSR+Ox7ca2lBOif3jbjaNYbpwmJowI754rmnMU0MpHh+TjwGPIeXfXj0x4fmZwC7gfOAfAacBl3u03y0i+0Rk37Fjx0OHYRiG0Ssx4fmvAt9S1YdV9QngM8BLgM1VuA6wHThY11hV96jqTlXduWnThtoOFoOZEEK+ddy+rRoT4skI0JU0TPInLByv8zKHpG1ufMPexaMuco39dXqYPt5v2/kmSR/vclFf81hSRWX5iTGa3wUuFZGNIiLAZcDdwJeA11UyVwE3+Cp2CWbcNOQL4/0Xv0//geF4sjDJj7DZbgrHS81d0nGuWa45QHcMj3vNXcp87yvPF5m7TK2rnZic5q1MCj63AXdUuvYAfwC8TUT2A88CPpRgnIZhGEUQtbldVd8FvGvu8IPAJb660oW4MWG8r8RYQvGIPpJqa2oxdDgO7R5YePuwNTMjM/ivq/t4ly79uZxP1Y8HHu+xwCuC8hm9mDbjNZhDG8q2VkMby/SheHtLj/56vrqn8V0UeRuKHMbSXadde24YhuFBQZ5mXk8xtG0+D3M1hON1rYb2LqfkKfj463INxV10ucg5jnkJfveykRXvzV9fQUbTlz4r46H9JdIzOmO5usLx+qPl5i7jDaVLfy7nU/XjQaTBhNEZzTF7lgG6RndVT1PLZTCWi7L5c5ddulzOr5TJn7t0lUnVlyMBucsmLKdpGIbhwYg8zbxV9XRbnsL6X8HowvG6VkN7l1NivcwM4bjlLvvRlSAUr2MERnOsuctAXaO6b4/lLr3D555vclY73iW7Z08tmljfDBaeG4ZheFCop2kFn2S6s2ksMRxPVc1NXPBx/k2AGJmuMZdc8EmkK1M4Pk+BRjNv7nKl5FhC8Yg+kmsrsToOReYuoYDLIKsjS3pnyBMkrI53YeG5YRiGBwV5mvkKPvWSY/EyreDTTJ8FH8/+RnftuHtf8ZS5/9KVgoymLyVUyHPmLgP0Z9FWYu4SchjMsLXik7u0rURjyl02YeG5YRiGByP1NEO8zIG8yymjuyTSCj5eunref1n7Kkskk2It5gjHE+v10DUioznCrUQwstxlU4uhjWWerUTNLVPlLrt0+feZP3fpcj5VPx5kyV2G6bLw3DAMw4MReJpDF3xW6/7Lob3LKXkq5OWH46n3X1rBJ5Wuwo1m+5vLWxkP1DWqq3tK3UoEzWNLvZWoNGO5eN5yl+UYTHAIz0XkOhE5KiJ3zhw7S0RuEpEHqv/PrI6LiLxfRPaLyDdE5GL/IcnMo12qvl0KAnR530I33UII01iiwex6J+Fbieq1dnl6c6168S6lqXf8PcuuVZFyLaY2mKnHlu5vziWn+WHg8rlj1wI3q+qFwM3Va4BXAhdWj93AB5KM0jAMoxA6jaaqfhn4wdzhXcDe6vle4IqZ4x/RCbcAm0XkHLehuH8T5KmQB3wbKR4e5rSPdIR9d9a18n4jGXDzwLp1tIW2szKO+cTWqXFdM21yi+cax5z0zpADRmV1rJjnIcbm3mdoTnOrqh6qnh8GtlbPtwEPzcgdqI4dIpI8hjJC16iKPSWG4uBsuAL0hOW7XUNxF10ucl2G0mUcrn35yPSho2Lw3KV/n9FbjlQ1yEURkd0isk9E9h07djx2GIZhGL0QajSPTMPu6v+j1fGDwLkzcturYwuo6h5V3amqOzdt2tDYkX8C34c+wvF0odDyFHwgjZe5GNY2r5e24pJvwScmHF+po3G8SQuLVvAJk6sn1GjeCFxVPb8KuGHm+BurKvqlwCMzYbw3i9mp1JPrgXeYlG5xhWsrNXfZbUy625ecu3Q7nz936Srj2lepuUsfXfF9duY0ReTjwMuALSJyAHgX8G7gehG5GvgO8PpK/HPAq4D9wE+B344eoWEYRkF0Gk1VfUPDqctqZBW4JmZA4y34pC30hGuta1FyKO5yvl3Ob83MnbOCTz96RljwaaKYK4Jc/xxS91DLqCrjXa1KNpjhCz5svbjmLV10+cs05i6dSfXlk6IvD0YQivuMzn6wwzAMw4NiPM2TjCUcD9SfRWNdwWdoYr0iH18ylYfpM/vuHnR8SN5nOJ5I10jCcQkYW0FGcyzGsuTcJQxvMPOE4/VHPfvq8ZeJGt9B0tRPYYZyymDhuJ9siMEEC88NwzC8KMTTtIJPXKuhvUvor+DjGapmL/h4jDnpteOpKLk6nt7DFG+9ixRiNGPJnbsM7CO5thJzlxBvMEutjnfJteUuS95KlDoUz6C379ylx+c0cqMZ+CENmL+03GW3nL93OXe+xx8KttzlkGNLYDAD/owsp2kYhuHBiD1Nz28l20qUiFT5NsdtOa46Br8NxcyRIrcSlZy79NGVIHfZ+PsCbozMaK6G3GWphR5ofjd9h+NzMoPe5Kzk3GVCPaMxlj65S9tyZBiGkZ0ReZo5vcyhvcu2VkN7mVbwaZb0LfiMcCsRLGHBZ6m3HOUOxy132UxfuUvPvoq4DLI6WmTuMpGuZQjHI3OXTVh4bhiG4UGhnuZqLfhA2V7mai74VK+Sr7PCCj4woqt7+gnH5ynQaHq8wdEZyrZWQxvLVJurXUJbz/5G90PBY85dptSZPnfpt5UovaEGC88NwzC8KMjT9PxWWgovc2jvckqsl+kaeKcq+LjocpGzgs9SFHwgwsv0f88FGU0PBqyQL0/u0rYSNUvlyF26yvSho2IZDGbUewh7z53huYhcJyJHReTOmWPvFZF7ReQbIvJZEdk8c+4dIrJfRO4TkV8LGlUdXneflZlHGsK01bUo+Ra6dJxrlmue8a6+5rzLKIPp8rmfPF8/Vl/P0rW/ggzmilvophybiy43uYmE1BvMWlvgs27j3rNLTvPDwOVzx24CXqiq/wS4H3gHgIhcBFwJvKBq899EZG3w6AzDMAqj02iq6peBH8wd+6KqPlm9vAXYXj3fBXxCVX+mqt9icv/zS6JG6O2YpQ/HwzzMuvxl6SF5mJfQ6LG5erStU+PjwbjrqB2z10fkOlcpSKBrhXc2xNjc+5Q22drcZdr+u0hRPf8d4PPV823AQzPnDlTHwkgaJvnjp61pDEMby7a58Q2pXI86jsXJWLrgZpxrTadOH45dtY5riHDXgcFC8Vl5F6mGcBzm1kraVIAvUUZTRN4JPAl8LKDtbhHZJyL7jh07HjMMwzCM3giunovIbwGvAS5T1el3wEHg3Bmx7dWxBVR1D7AH4LnnnX3ye96uHU+IY3jsoaO5lUco7tlnVH+Nkr7eZQqZlP11MJrKeIt81LXjqeVOEuRpisjlwNuB16rqT2dO3QhcKSLrReR84ELgH5yULlXucmhcQkiX9hlyl559Nsu5nVuQDArHQ8fjQ+pwfFZvCvKE4416e8ldhs1Np6cpIh8HXgZsEZEDwLuYVMvXAzeJCMAtqvomVb1LRK4H7mYStl+jqk8FjcwwDKNAOo2mqr6h5vCHWuT/BPiTmEE1kz4c99faJD20h5kiFK+XbQxvXXWM7trxrr5czqfqx4Pk14776vLxMFsIeh+5ZBcZ0RVB6cPx+FZDG8opfeUuPXJ7dhlkP7qWIXcJPVwGmW5u7Ac7DMMwPBiBp2kV8mZiQ0hXf9LTAxv8zpA+3qVrf64yfeioGI2X2SLXy7XjaW1IoUazhNxlU4uhjWWqXFsKg+lS9XTV5d/nuHKXCXUtXe7Sp//+cpdNWHhuGIbhQYGeZokFHxjew4QcBR/XI616Br8NRXXEPMwA0nt4fjc7K7fg00RBRnPo3GWpW4mgeWypQ3EXnamNZZdsR+6ycxw+ffnI9KGjwnKXieXi2hZkNNOQzrOE4Q1mX1uJAvoaNHdZHV3mrURTCr/neOdWIsicu4ydG//2ltM0DMPwYGk8TctddssuTe6ycyyu/bmc9yF17jKhzr6r473cGTJ0buLmdCmM5vJvJYpbcHlzly66XOT63krkKtOHjoplyF1CRMGqrNxlExaeG4ZheDBaT9MKPt1y0R4mJAjJ3WWs4FOyd9khX/xWonRzO0qjmcZgDm0oIT7f5moWS8tdLp7Pn7t0lUnZXwfKqlmCAAAIsUlEQVTLEI4X+kPBce3asfDcMAzDg1F5mmkKPjD8jc5izrfL+XmZpe2/DCn4uPcXT+rqeGpPKL3nln7Deo4qeoK2HmuueKNpW4m65ZpbOvaXpDqeYCvRsl8GCcXnLk9KDpW79JWNbBdgFiw8NwzD8KBoT3N5Cj4Q52X61MFT7b9M5Rl0FXyqo1bwCcAKPlFtA01DkUZzOXKXkCscrz86ptxldXTZc5dQ/LXjJ6WGzF2GyAe2S2ASOsNzEblORI6KyJ01535PRFREtlSvRUTeLyL7ReQbInJx/BANwzDKwSWn+WHg8vmDInIu8ArguzOHX8nkXucXAruBD7gORGYe7tS10JnHULS9E593uRjarmztMmtz4Xjj1Lh+Al1yiyOsPe8Vjnu8xyj8V2AtK+Z5qHC8+71MJKTey1xYK+HrtmsEYV6pZ7uEJqHTaKrql4Ef1Jx6H/D2uaHsAj6iE24BNovIOUlGukCTsRya2D/uZtMYpguHqUkVjjeNeWooxeNj8jHgMYT+4dYQbGS6cNXlJnfSUDbILoTjvkYwlVxTW08Sm4Wg6rmI7AIOqurX505tAx6aeX2gOpaIpgU+tLFs+8Pz9eAWj7rINfbXaSxTLPI2A+9jKBf1NY8lhUFK6AWe8CpTji29d9eZu/Q2+nnG2dyPIxkDTu9CkIhsBP6QSWgejIjsZhLCc9aZm2JUGYZh9EaIp/lzwPnA10Xk28B24DYReQ5wEDh3RnZ7dWwBVd2jqjtVdeemTRscum0Kx0vNXdJxrlmuOUB3DI97zV3KfO8rzxeZu0ykaylzl779u8qFeOAB7XowCd5GU1XvUNVnq+oOVd3BJAS/WFUPAzcCb6yq6JcCj6jqofDhlRqOg6sxaW9fb2rc+mmQ6Tl3uSgtnrlLl3ENFfK2MILc5USyRbY2d5my/5i5CWjXk1lw2XL0ceDvgeeLyAERubpF/HPAg8B+4C+A300ySsMwjELozGmq6hs6zu+Yea7ANfHDavqWGdrDTB+Kt7f06K/nq3sa38Uy34YCMvzYRkjI6iLRUfDx7j/9OJO0TWYS3Pss8IqgEkNxyJW79NflGoq76HKRcxyzXQYZQFqDZZdB9tOv/WCHYRiGBwV5mnUWf2gvM084Xn+0xIJPm5SPd+nSn8v5VP14sHR3hswVZo+p4BP3ORZkNGdZBmO5KJs/d9mly+X8Spn8uUtXmVR9OZLlh4JTh+MtskXfQjeg/QC5yyYsPDcMw/CgIE9zaO9ySqyXmSEct4JPP7qWoeADhd8Zcnzh+DwFGc0hKSl3mWMrkXufteNNnrt0lelDR8UyGMxlyl0OVBl3wcJzwzAMD1a5p5mqmpu44OP802mxMmMt+CTUNZqCT4N88fsvA9oX7GUCyOQinmERkYeBnwDfG3osM2zBxtNFaWOy8bRT2nigrDE9V1XP7hIqwmgCiMg+Vd059Dim2Hi6KW1MNp52ShsPlDmmLiynaRiG4YEZTcMwDA9KMpp7hh7AHDaebkobk42nndLGA2WOqZVicpqGYRhjoCRP0zAMo3gGN5oicrmI3Cci+0Xk2oHGcK6IfElE7haRu0TkLdXxPxKRgyJye/V4VY9j+raI3FH1u686dpaI3CQiD1T/n9nTWJ4/Mwe3i8ijIvLWvudHRK4TkaMicufMsdo5qW658v5qXX1DRC7uaTzvFZF7qz4/KyKbq+M7ROSxmbn6YE/jafyMROQd1fzcJyK/1tN4Pjkzlm+LyO3V8ezzkwxVHewBrAW+CVwAnAJ8HbhogHGcw+Q+RwCnA/cDFwF/BPz+QHPzbWDL3LH/BFxbPb8WeM9An9lh4Ll9zw/wUuBi4M6uOQFeBXyeyS7nS4FbexrPK4B11fP3zIxnx6xcj/NT+xlV6/vrwHomN0r8JrA293jmzv8p8B/7mp9Uj6E9zUuA/ar6oKo+DnwC2NX3IFT1kKreVj3/MXAPSe/XnoxdwN7q+V7gigHGcBnwTVX9Tt8dq+qXgR/MHW6ak13AR3TCLcBmETkn93hU9Yuq+mT18hYmd2TthYb5aWIX8AlV/ZmqfovJfb0u6Ws8IiLA64GPp+yzD4Y2mtuAh2ZeH2BgYyUiO4AXAbdWh95chVrX9RUOVyjwRRH5anWPeICtevLunoeBrT2OZ8qVrFzoQ83PlKY5KWFt/Q4Tb3fK+SLyNRH5WxH5lR7HUfcZDT0/vwIcUdUHZo4NNT9eDG00i0JENgGfBt6qqo8CH2Byn/dfAg4xCSf64pdV9WLglcA1IvLS2ZM6iWl63fogIqcArwX+qjo05PwsMMScNCEi7wSeBD5WHToEnKeqLwLeBvwPETmjh6EU9RnN8AZWfvkONT/eDG00DwLnzrzeXh3rHRF5BhOD+TFV/QyAqh5R1adU9WkmtyROGr60oaoHq/+PAp+t+j4yDTGr/4/2NZ6KVwK3qeqRamyDzc8MTXMy2NoSkd8CXgP8ZmXIqcLg71fPv8okh/jzucfS8hkNOT/rgF8HPjkzzkHmJ4ShjeZXgAtF5PzKi7kSuLHvQVT5lQ8B96jqn80cn82B/Uvgzvm2mcZzmoicPn3OpLhwJ5O5uaoSuwq4oY/xzLDCOxhqfuZompMbgTdWVfRLgUdmwvhsiMjlwNuB16rqT2eOny0ia6vnFwAXAg/2MJ6mz+hG4EoRWS8i51fj+Yfc46n4VeBeVT0wM85B5ieIoStRTKqc9zP5ZnnnQGP4ZSZh3TeA26vHq4CPAndUx28EzulpPBcwqWx+HbhrOi/As4CbgQeA/wuc1eMcnQZ8H3jmzLFe54eJwT4EPMEkB3d105wwqZr/12pd3QHs7Gk8+5nkCqfr6IOV7G9Un+XtwG3Av+hpPI2fEfDOan7uA17Zx3iq4x8G3jQnm31+Uj3siiDDMAwPhg7PDcMwRoUZTcMwDA/MaBqGYXhgRtMwDMMDM5qGYRgemNE0DMPwwIymYRiGB2Y0DcMwPPj/2oLSuuegDq4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-33-a0ed6d32edb7>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m     19\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m     20\\u001b[0m     _ = sess.run(\\n\\u001b[0;32m---> 21\\u001b[0;31m         \\u001b[0;34m[\\u001b[0m\\u001b[0moptimizer\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m     22\\u001b[0m     )\\n\\u001b[1;32m     23\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36mrun\\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m    903\\u001b[0m     \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    904\\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\\n\\u001b[0;32m--> 905\\u001b[0;31m                          run_metadata_ptr)\\n\\u001b[0m\\u001b[1;32m    906\\u001b[0m       \\u001b[0;32mif\\u001b[0m \\u001b[0mrun_metadata\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    907\\u001b[0m         \\u001b[0mproto_data\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtf_session\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mTF_GetBuffer\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrun_metadata_ptr\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_run\\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m   1138\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0mfinal_fetches\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0mfinal_targets\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mhandle\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0mfeed_dict_tensor\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1139\\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\\n\\u001b[0;32m-> 1140\\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\\n\\u001b[0m\\u001b[1;32m   1141\\u001b[0m     \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1142\\u001b[0m       \\u001b[0mresults\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_do_run\\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m   1319\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0mhandle\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1320\\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\\n\\u001b[0;32m-> 1321\\u001b[0;31m                            run_metadata)\\n\\u001b[0m\\u001b[1;32m   1322\\u001b[0m     \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1323\\u001b[0m       \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_do_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0m_prun_fn\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mhandle\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeeds\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfetches\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_do_call\\u001b[0;34m(self, fn, *args)\\u001b[0m\\n\\u001b[1;32m   1325\\u001b[0m   \\u001b[0;32mdef\\u001b[0m \\u001b[0m_do_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfn\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1326\\u001b[0m     \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1327\\u001b[0;31m       \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfn\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m   1328\\u001b[0m     \\u001b[0;32mexcept\\u001b[0m \\u001b[0merrors\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mOpError\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0me\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1329\\u001b[0m       \\u001b[0mmessage\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcompat\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mas_text\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0me\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmessage\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_run_fn\\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\\u001b[0m\\n\\u001b[1;32m   1310\\u001b[0m       \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_extend_graph\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1311\\u001b[0m       return self._call_tf_sessionrun(\\n\\u001b[0;32m-> 1312\\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\\n\\u001b[0m\\u001b[1;32m   1313\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1314\\u001b[0m     \\u001b[0;32mdef\\u001b[0m \\u001b[0m_prun_fn\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mhandle\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfetch_list\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\\u001b[0m in \\u001b[0;36m_call_tf_sessionrun\\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\\u001b[0m\\n\\u001b[1;32m   1418\\u001b[0m         return tf_session.TF_Run(\\n\\u001b[1;32m   1419\\u001b[0m             \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_session\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moptions\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfetch_list\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtarget_list\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1420\\u001b[0;31m             status, run_metadata)\\n\\u001b[0m\\u001b[1;32m   1421\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1422\\u001b[0m   \\u001b[0;32mdef\\u001b[0m \\u001b[0m_call_tf_sessionprun\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mhandle\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfeed_dict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfetch_list\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"feed_dict = {x : train_x, y : train_y, learning_rate: 0.1}\\n\",\n    \"for step in range(30000):\\n\",\n    \"    if (step < 100 and step % 10 == 0) or step % 100 == 0:\\n\",\n    \"        l, predictions = sess.run(\\n\",\n    \"            [loss, out], feed_dict = feed_dict\\n\",\n    \"        )\\n\",\n    \"        print('loss at step {0}: {1}'.format(step, l))\\n\",\n    \"        painting = predictions.reshape((img.shape[0], img.shape[1], 3))\\n\",\n    \"        painting[painting>1] = 1\\n\",\n    \"        painting[painting<0] = 0\\n\",\n    \"        plt.imshow(painting)\\n\",\n    \"        fname = \\\"image_seq/%04d.png\\\" % seq\\n\",\n    \"        plt.imsave(fname, painting)\\n\",\n    \"        plt.show()\\n\",\n    \"        seq += 1\\n\",\n    \"        #plt.hist(predictions)\\n\",\n    \"        #plt.show()\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"    _ = sess.run(\\n\",\n    \"        [optimizer], feed_dict = feed_dict\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So While is is training, let's go over some other concepts that we skipped.\\n\",\n    \"\\n\",\n    \"## Loss Function\\n\",\n    \"\\n\",\n    \"Loss function is the function that measures how far are you from a correct prediction. To make your life easy, there are two main functions that you will have to deal with. \\n\",\n    \"\\n\",\n    \"- For classification, use cross entropy\\n\",\n    \"- For regression, use mean squared\\n\",\n    \"\\n\",\n    \"## Optimizer\\n\",\n    \"\\n\",\n    \"There are so many optimization functions that updates your weight values and biases. The most famous one (although not really used in production much) is Gradient Descent. The function that we are using here is called Adam Optimizer.\\n\",\n    \"\\n\",\n    \"## Calculation of Multiple Inputs\\n\",\n    \"\\n\",\n    \"The way you calculate multiple inputs is this:\\n\",\n    \"\\n\",\n    \"![](images/painter.png)\\n\",\n    \"\\n\",\n    \"$ Node_{1,1} = (w_{(1,1)} x_1 + b_{(1,1)}) + (w_{(2,1)} x_2 + b_{(1,1)})$\\n\",\n    \"\\n\",\n    \"Then you can apply the activation function to that.\\n\",\n    \"\\n\",\n    \"## Initialization of wieght values\\n\",\n    \"\\n\",\n    \"We initialized weight values with a standard deviation of 0.01 and the reason for that is we know that the values for output should be between 0,1 (which is the stadard format for GRB in here). Initializing the network's weights with values very close to 0 but to quite 0 will allow the output to be small enough that it will be around the desired output that we want.\\n\",\n    \"\\n\",\n    \"## Why ReLU?\\n\",\n    \"\\n\",\n    \"One more thing that we skipped through is why did we choose rectifier activation. The reason is first, we don't want negative output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 0: 0.0022454524878412485\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 500: 0.002034383360296488\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1000: 0.0019454010762274265\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1500: 0.0018444970482960343\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feed_dict = {x : train_x, y : train_y, learning_rate: 0.0001}\\n\",\n    \"for step in range(2000):\\n\",\n    \"    _ = sess.run(\\n\",\n    \"        [optimizer], feed_dict = feed_dict\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    if step % 500 == 0:\\n\",\n    \"        l, predictions = sess.run(\\n\",\n    \"            [loss, out], feed_dict = feed_dict\\n\",\n    \"        )\\n\",\n    \"        print('loss at step {0}: {1}'.format(step, l))\\n\",\n    \"        plt.imshow(predictions.reshape((img.shape[0], img.shape[1], 3)))\\n\",\n    \"        fname = \\\"image_seq/%04d.png\\\" % seq\\n\",\n    \"        plt.imsave(fname, predictions.reshape((img.shape[0], img.shape[1], 3)))\\n\",\n    \"        plt.show()\\n\",\n    \"        seq += 1\\n\",\n    \"        #plt.hist(predictions)\\n\",\n    \"        #plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 0: 0.0012141595361754298\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 500: 0.001206237473525107\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1000: 0.001196705037727952\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 1500: 0.001184494118206203\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feed_dict = {x : train_x, y : train_y, learning_rate: 0.00001}\\n\",\n    \"for step in range(2000):\\n\",\n    \"    _ = sess.run(\\n\",\n    \"        [optimizer], feed_dict = feed_dict\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    if step % 500 == 0:\\n\",\n    \"        l, predictions = sess.run(\\n\",\n    \"            [loss, out], feed_dict = feed_dict\\n\",\n    \"        )\\n\",\n    \"        print('loss at step {0}: {1}'.format(step, l))\\n\",\n    \"        plt.imshow(predictions.reshape((img.shape[0], img.shape[1], 3)))\\n\",\n    \"        fname = \\\"image_seq/%04d.png\\\" % seq\\n\",\n    \"        plt.imsave(fname, predictions.reshape((img.shape[0], img.shape[1], 3)))\\n\",\n    \"        plt.show()\\n\",\n    \"        seq += 1\\n\",\n    \"        #plt.hist(predictions)\\n\",\n    \"        #plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 0: 0.0011687794467434287\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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N9pJkJ38oIwIvEINaeJIJykYqADUjyjGBSlpi4sbHMwrYn0zIRNzjDqJRG6a00SgVTq9Imml0R235U/dgYYxmDbZtgibGiUOAWUO66tfVNXLhwER9//ccBAO//8AeZl+OjOI0PlvZtfSCiZQD/F4D/yhizxo8Zw9famet+mYi+RUTfunHjxn670VNPPR0Q7QspEFEOyxD+jTHm37rd14jorDHmKhGdBXC97VpjzG8C+E0A+MQnPtGlZ7p/1NEDv/vGjZt49pvfAgBMt6ehkIlHCVFRGPXkZNrXHb8uZ+5gISUGzu02lzIo/YpMxnqPQiBz50gpkEmJ3K1ugyLHskMEq0sjHFlacu1xAp+b4gLf5ihAiPg2sQaEmRkj7zykWDEapWLEolI6JKudVhUmU4cgyhKTaZWIHBMnipRVHVBDWalwfaU0Ezc0pkqjYugsWCI4OmCjr020UJRVjYuvvYaLFy4CAN7z/vdFpPCoYv4DpP1YHwjAPwNwwRjzv7BDvwvgFwH8uvv3d/bVwwdIBgiT/9bNW3jbVW7SRscS7cG8yESGLo9ER4UgLOU5Tjp5fzwsAiMAEBhBkcnghVfkWRAR8ixz27k7LwtFWZdGQ6yMx+6ZFKo1cRFFiBQgckBnzKwnZhe1FZBXWkWxQuswfkor1Gw/Zx6TaYltzyCmJbYnvj3FthM/pmUVdBJ1rVDXGlvu2PXtCaQbYcW8JQ2PogLCb1Zrwr31DXzvpQsAgL/0Mz+Nk64SVTOI7J1I+0EKPwPg7wB4gYi+4/b997DM4LeI6DMA3gDw6f118cFS7eTg6zduhXh+8ArM4cxZdNBkCh4NHBkOcGxljOPLlimsjIcYuGSlgyLHwE3wTMrACDgTyLMMeZYhcwxDEIHcRB8NBszlN05oW0EacT9X6JmYO8kwm2w3OzCdm1LKwC3mMQjOFAZ5jiVlE6goFa+pVB0QxPakDPUwtsvSMgmnkxhvFHj97lroi3enNs6h2rfD76EVqopw8bXXAACvfv9VHDtlK2JnRT7jTr4fOsBb3Tfaj/XhK+h+15/b63176qmnB0u914ajuIqm66Avinrv7t2ADow2Yb+3Nvh0Xk3To0fqIylxYtnL+gMcX1nCkWVvLsxakcIwz1G4/UWeI3NihRACUgoIwfUNri1lUn7ekxCi4ZQUPS9NB2qYAxU6iV+SIQvPUEolOgibZ1GF7dBWUY9QqAxDh46WR0N2jrVWTCYWOZw6WmI0tOddfPtmEB+0iR5OhkxwdtKGoLTBmivA8/blK9C1070U/C32uL4/ivCAUc8U0PLtm/ivFx/W1taj++yM3iDegRADd4QgLDmIf3J1CSvjgW0fWcGJ1eUgDgyKnIkJOQp3jZRR0SiYbOzVg4l44riPzOJEFGzi224yvQFSphBPSUdjnk4hOa9ti+3MsywwKc8ggjihFGMQKpn8NWMWfn+tNAqVYeSY54pSiWj18lWr295QJnFPNSznggagXIKGtXt3UTnRsBgNut0oHyKa96vst8t9QFRPPfWUUI8UdiC/Ut24dj2sbM3Vsxm74EWGoZQ4vmItAUeWRzjtipQ8cXQVRZEH56NMymgSFDFGwdLsat50dRRCJNfzpS4kTiVKxQWOIgx7Dr9xh3dh2/5FEQUAIM9TcaITKTB0UCvUrlqUqBVq5mQFAEeWrEl2vLaBFSdybW9vQ/l1j70jQCirOjz/2vUb2Fizisqlo0f4W+GgoMJBgw4eINb2rP1QzxR2oLU168585crlkI1ZszyKXlzw25III2fzPrGyhCNLVqt+cnUFp4/bD248GEBIEYOQkklNIOcnYP+NAkrzxyZmYhQhoClaImBY0BFjCmFSMx0Dp84J3rRKcB1Fcl7HfnZcCAHhJnzTzTswL22gnSwmhIbQfozMzLYfy5XhAEcdg7g7nWKbBWzyQCki4I5jBC+9+gNcdzk2T545A+nEEjvzuJy4U/yqu/9DKnIsSr340FNPPSXUIwWgAS3T3W++YYvArm9uoPJFULe30OUpnwnCydVlAMDKaIBTR1cBAGdOHMXIwVprORBJLENMeyZC6LEg0SpWBEUiOyY46nDKSWNiajTeURNMLGzlb0MNiSWmbX+6zze6UEPyfI2AvIQQQQkpTEQARhgIn6VZCwjhztfk3pld45HC0girY4vORvckptqKHKpR9Ja/8/rmFt6+apHCh9SPQba5ZRlgoQqzdP90k1wfepD0jmcKbQPq900mE7z+xpsAbDSk1ymkbs0p/M2FCB/lySPLgSkMitxG4yHmL+CuxcQmdZL3oCVHohAivb4pPpC/Vxr9GN7PMYUoJphWnpBO5Jb9yT9tDKLBIrypVGkQmfDOwhgIr6+hONm14SKChjZMfDA6uGMLHa9ZHg6wNLRWnqWiwL3gZJbmbOATt65rXHvbeqtWZYl8NEQrGYSqWJ2CxENssViUevGhp556SugdjxSAbvi1tbWNjXtWGVVVNTa3bM6EeZw0lwLLI7tSnTq6itHAigzDIsY3eNFBNLZD2ysaSaQIgp0vpQzOS0KIaJEgrqjkqCMVP7yrs31/076674gaZtFBF2owMCHjsw/G8u9vdIT/mqEGQQLGiwxG2OK7iErHID4IDaHj+K24lf7YyhJubdsCtVvaBA1g0zV9fWMDb1+3kbrlZILxkVXsi6KR477QPEvEXqhnCo7a6kNubW/jlqsUrZVKfuMgPphUfCikDM5Ho8EAIwdlvR4BgEuF1rQ+MJ1CErE4q1PwooN0sRQiYRAyMg8p25mCe+F2x6amG1Ibg3BH5ogU/DkAoLWCCWKNARkWpSkEhNcpaA3DxIogMmgTdQrGMgTBdBJcv+CZ8rDIYsKaNl2H+9HqusadezZVWzWZsI+hW0Fg0miKrpPuK2Pwj9wv9eJDTz31lFCPFAB06NixtbmF19+y1oetyfbcO/jUaEWWhTiEEQuJThSFDg1Qp/gwu5+Igstz5iIkpY+STNCBaEUNPH8CkCICLkrYMWhHB52oYY6Tk3Z5FsgIkFPSGacM9duCBAztZInQEAw1SKGh/TtrzXJDUEhHl0uBzKMT6MZqahLVo49lWbt3DyfUk/a+2QFMjwcgSszrxiL0jmcKM/IY25hOplh3QTNWfIgyKTVG2RddyTLJApEi/KeWyZ5YFlosCVKIYEkQgkKodJblyPMsfLRcfKCm+NDGFFzKZu7RGCd8GhzVRfMcnPwRrVS0hJCJug4jYMg0skvPt0SYhk5Ba0qu56nlvChmE9bMeoc2iWBC/sbN9Q1oF7otHqPZsRue9Bi99sGR/3Tu3r4TAqK6zFkEW9Q19x8iYwq1Uhg6RaMQjY+4w6TozY2AYyScKQSkICGyLAZLSQkhGYPg+0WbhEgwxMyUBkgK2bZM+DbX7s68C74SlhDhnMRU6hmEV/wJAplZBqmNBumWid+WY5IrWin+FgPHOKksfa9bFXPbW5sAgOlkAj2nyhVf+XcFAh4hU2WvU+ipp54S6pFCg/jaV5UxFyOnpq++IELeuiJzJ6dZr8UoGjREhqCHiFYJmSCAzOkkOFJob3cRsaXOME897rTHrQepPsIdZTkeOWrgiMjQLBrwbcORA7NEUKi5yYOeiJld3VG2zbNOeyoyiaFzGGsu0oQ0fkU5J6e6Km3a/sOgLrj5kFHPFAAAlMjX2smudVmyJKbuoDufk023HkuQdUux7KMmNGTiCH+j0jFC6SS3gmMcUV/BoiwZU5jrc2vlBHcWMSscZxAmTGp7jWcQ9qrUX2HWKMbfyzDFohclguKRTXhCY1wQJ3tsU7rNjIPEfss8yzD0qe2I4IUCHhjm7126HJFVyZnCYph/T5LBQyxO9OJDTz31lFCPFFrIBz5lWcZWY+IVTRNKNdyIqx5fZRvx/9TYXoRSZZqI2vxG7EQC6+PVaG7yhZ4ooqBW350W1BD0lO1AIekzFyUSxNTsGjUL0+xO0UgUDY1FFhPf5kKEwremoWkkAmr3m0+2t6EdUmhTSB4oPaTixEEUg5FE9G0i+j23/R4i+joRvUpEnyeiYv/dPFxqwn3tUoXxtOveCsCv8ZYHAYI2xlod3Ecd5ogz/TFw3EiiMksGsx8j955L5Gwuazf6lp6Dmf/J/0fuHuT755uUPMP/1+gE0hGcQ6y7xO4XJ7m7O8V3ivvZOb7vfJu1XZ56EJFlDJlELsSMaOdJIPqRrN25A60UtFI2/V7bj9FCC5628w0OlQstRgchPvyXAC6w7X8M4J8YY94P4A6AzxzAM3rqqaf7RPtiCkR0HsB/BOB/d9sE4K8B+G13ymcB/Kf7ecZ9ocby4R38fLwBEYW0YE3ySsEEEbglybjVimvGdysypH1ka92MOj2u5p0v1rxhC2qARwhhdWaoofUZe36ZFLmw9yD2fzyngVrYeKRIJ/5+MAaDTGKQSYzyDOS8GO2lqULUI4Wtza2IFOalmPMWmMNY2h8wYtivTuF/BfDfAVhx2ycA3DXGePXtJdhK1A8dzYy5/05NPCrFrMjA/03uxz4gXwlpUlVYobG7aPcTacZyMSM2tPSHGntmD7ZsG9ZkkjT5bd+bDmelvXzBST9TbpnoFLzgRUw/ERhD3I7iWvTONECwPiwNCmTbNmOzL1QLpkfxeqHpdBLMk2Bp9xZ9wwM1KjwgncOekQIR/U0A140xz+7x+r7AbE89PYS037Jx/wkR/Q0AQwCrAH4DwFEiyhxaOA/gctvF97vA7E4PSNbD4GPPLQQmOt+wC/y6qrxvg9KofIHUsp6rVNztSyfKRt7rrmfMQwkMEPANIoT6CAmObQaImO6Ykd2SlxJsT5riFkMD4XW7rQ+GhXRLITEe2DDq8XAQXNGnSiejQexlyrJEVdoiM7vKUM1oV+7PD/Sm7bRnpGCM+TVjzHljzNMAfgHAHxlj/jMAfwzg591pv4gFC8wehhiVKHTN/P+5DKu0gtJqJm6Ai78CUWsNGNRG2/+VtpWTqxrTqobWJjhD7Y1SUSHsap4VJgalHW3TuVP3IcMHK1G+NzXx6bYBq0eJvfyWs+/JdRqJtYIi82haH0KhGxhIKTAeFhgPCywNBxhIiYGUrfMq/OYEbK2vY2s9Fv95qOg+WCkOw0/hVwB8joj+RwDfhq1MvTAdhEzWtoItOoYEhIhDw5RNTqVl2xTvp4wGtEAu4zGvUyirCtPg8yA7+zC3bwGRNHQKRO3jNG/wqNnYaVRSOMBDp7u0C2lFpkYVKsZA5hLzx0iVknG/ZwzxPH/zmPzEltOzjH08KILLsyhLNNXG3p1da41tFxnLmUICrBakA9UvdD3A0wE+6ECYgjHmTwD8iWu/BuCTB3Hfnnrq6f7TQ+nRuB8G2CL67o4IWHaFX+u6DmnIrYJ+dn1UBhBkgmlSChmsFrXWoZT68mg0+6BWHN/VRmjHBX8HnUKnFcK9BRMFkjiGBF35lb5xLd9qyt6NSlR+X5K/wTDBpONHsuCgRafgrRVsmxe78mnqjIm/y3hYYJSzOAiTBrr556i6xnTirBSqRg7ve7e3pfi+qQIOEDU8lEyB06Lvuh+RgROfhnVdhy+WgJDvT8GgaazyHo+ZFMhchagiz0JilJ1+qK6+tqoG2u7FzXVdzMDMNDr60q1cDLubt4hzv1Gezpfa00k7zRG5c6XrtuAorpDkz/RM2RgT9EJjlvo9XxeYqpQpSPf71XWNDZevcbq5idHyOHRrRoS4j8q/XdE+GUQfENVTTz0l9HAjhYamposBHqwiluCDg3Ipw90JMQAIJq6mfpdfnTIpQwbn5dEIK66uITGl2a76y6ACDxBuc/aZ3djpYRy+t6ODbrGh/ZgxOohcRuugrDPazLRbj6GhnGTPbAZH8VBqf5YgAslYS9OHvo8HAxxZsWLh+O49bKo6Weh9cNm0LFE68WG6uQGtT9pzvCKZv/EuVuFDVzrOezB29/CHmykAnZyg81tfRI9gZptcPPcf+3A0DFCKg/LZH5hifYdhgRVXIWplaQTpLRmwwVP2GdT5I832ffZjDNtEyTkAQvHUnWhGjueWgQ5ZbFb6aOoIZi0O3W3dIVqYpJCvN+dqo9n19p39e3qXZsDqE/i7BaYwFDiyZEWBUZ5DTCbwAgR/Xa1VYApba2swiqWWa4zrbhchGQbGAAAgAElEQVSmB8YY/MMXpF586KmnnhJ6aJDCTpZzA6QZlDtg8jyG2KblnqOzw3AwCKtWqr5LeX4mCCtjKyYsjQbB0rAyHrc+jTveLEyp219CxpiQgRgGSep43uNZmjfa8ywOqehgjA52fisyuL7oLjSgO1GE1jokvrVt7z9g4jP89fCPUeEaKWQi2vHCOmMn1g3yDLkgTEOqvQgPldKYuixMa3fvhixMIp8/VRb1/HgU6KFhCq3UUPcmPMGk5+z0Y/CPmMKfrsfag0ePHcXK0IoC25vbrb+8IEIhJYYDa+5aHo+w7BiElDLVCTAZRSkNODk1EyKB/Dt/WOQYgQovR8bL0SK0jWHZnHfQNeze4mAaugMG//2kNh16g6ZOocEUavdeqq5RKzsp61ql7bpGxbbDe7KMLzzduyDr3QgAwzzDSGYodRnGxr+aUiowgun2BKUrFViMBszi1E07OTk9rAYLTr340FNPPSX0cCOFORQ47g4oIUTJhj/ziRC1yidOHsePvf+9AIA/vfudhsOLg6skcGJ1BSO3Ci2PhsHi0N0nA6V1kFNsbUmXdZhSISW5zivdlHLoIDoHEIPivi4jGQMYXuLerwEtjsZMeThPbPDP4JYFzZWDDDX4vASAXYF9tSitNJSqIyJQKuSrUEol+327ruP5VV1bxzLXNZkkuBXB54BEWnDHB0edOLqKe1vbWKvL5ihA+98GQDmdYOJcnpdPHJs5t2vZf9RFiYeOKXTBr679OzGExPlnh+c22ytHjuD8u94FABh990VsuOg5IEKsjAROHDsSdAqrS2NIGYc1sW64Gyujk0InShNEqEokUsbgvnw7CRwj0tpVnfYl1dLq1DowGxFKqyV5HEnYnrHzIk8wiX6gzTvRpirjegQVdBqWKajQVnxS12xSqzphBOE83q4ZU1BWZLDXK0hBLN+FjJYdUJoWnzEFHwexPBpZvYJnkg3zrn9+VZYot7fcO2tQ1p0yf7c0Tz32oKkXH3rqqaeEHjqkMEP7xWILigz+/sREBFIKx44dBYCwyvjz/Wo+zDMsjQY44txhV13chL9lNBhQSAattYFScRWtasIkrGgicdLJWFHZPIuFY6UUwQdCsqKytj5ERAQcQSQFV4QIBWqRZQERaK1nEIFvB2Tg2hwpKIcUlLIhyL6diAK1VwzWQQSw758iAlUzdMBQQxDZpACRBKtu0erBRURhLCSJUDeDyIpswR2amKWGKPRfK4XJti0srKoauZS7cljaSenoqXnOg0YODyVT2C8fCHqEhRmCcbUGAVWWgGMMdVlieXkZADDKJe62FJ4eDQosj0dYXVpyz+RpwuI7GCDI17WqE2isFJtgLIiHBDmvSiBncRRFlieTX7AgrIRBCAHhJ4WIxWRkJpHlRcJ8/DVa6YZ+IE4QP4lVVaGqGfzXakdRwDILe9+qskyhCkyhRlXFdrw+6i2yTAQGmWSVDj8i26S4OzBlxgSIbDuk5hciFPPhVcONMcGRqZpMkDuT5m5oL9/ygxYtHkqm0EoUzZBzB3jB0U90CGWJ9du3AQBrt++EiSClRMHKmvMAnnAtEfIsw7JXLnLvPoBdA7Yy1qhqNmHYqqu1DlmctI4ZgvI8Q+6YQp5J5HkekIMUkk3+lCnIBGk4plLkGJEIlaSaPhOa6QSMUmGfYRO8qqtgzy/LsqEo1KzdjhqqWgWmULvx8GPjdQ+ZjP1vnR4tORpnF4K4w3udFllmx4li9a2By+WY53nQCcksC2jK14LYKy2KGpp0cChi8af3OoWeeuopoUcHKSxKTczetsDwDa2hyhLXL18BANy4ehWqttmSijyHmlqLw3hQJL72fm3Ks8yFSpN7ZIS8vgtAlKNt266SiqGDiBRMY7+9Q1nXrMakhdI+RDtnkNfqGiyv5yJHJiNSAMYo8jqsgiFFeoO0UgwpRO/C2okLXEzwtRgnZRUqbNVMb5KKEjpBDolOoVZBf5PqZIDZgChGC8iKA+eVeOLoKoorWXhOkWURETJ0ILMcma8R2qwC9oBo96LF7vHJQ80Udmue5MeB7gCUZL9SmGxt497tWwCAjbt3gkxdMQXc8ZUVZNds1ulpuItVWOXsPO5daYxpiAxRsWYn1SxTUMydl++3cn+UiStRQ1a8+GwUHwIjqCSKIHJkGPp8AlWOqqpamUKzUrVmisam3iCd5FFk8O85LStMyyqeozmD0InuwLdzbvYz7ZPA6gp44lbsTCb6n4yHA5ueTUblrGcYeZ7H8cuzyBRo/6B6ryJEFx2W7qEXH3rqqaeEHmqkMEOLqnL5eTs5m2cZZCZjCq66hnbw11QlMrc6+2zAALCpotJJKed3H1YSjRDdz1Y6pTTK0okPfqXtUC5G1BDbQMzuZFPPMy8+UUfLghCQbqWWUqKUPgtUHtBEWZUoqxx5gMasKG3HsmsdkTwa0Ck60E2FYjzP958nsa2qGkrFGAfBTIehuhOc3yVX2vIONeyQ3PTbRjwkfDwoMBoUGHglIlEYvzzLmKIxh8zy0MfZW+9+fT4sb8d2RL23p+yLKRDRUdiScc/A9uvvAXgZwOcBPA3gdQCfNsbc2fMz3L97eT2eG6H75yMMx2OcPnceALB++3Yyqb0cvzweYeQj5coq3FEQWcjtJ5VBDJwhJOZFbyEwzsU3TCSmO9BKN5gFj35kzxQUIK11k/YWE8FEiShWAAhQvsgrlGWFIrfbQspEXu8YpsT/wKdE59uAFwUiU2tre/HBdNqOZ5kq5xbN4KwmI0i3vEdm3JtnEsujIQonqlRgv5P/PQFkOdcpLA6qF/lmD1qUSImNE3veorRf8eE3APyhMeZDAD4GW2j2VwF82RjzIwC+7LZ76qmnR4T2jBSI6AiAvwLg7wKAMaYEUBLRpwD8rDvts7Cp339lP52cffiCPgucGlrHJCYhz3H0xAkAQDEcYdvFOBCA2kHepfEYqyMbRn15YytIC1kmQULCcM04W+n8yusLmNq2sNDfKxqVDnZ61RQf/DkNpyZBooEcHGqooxfkYGCdfvz1HikMigplVaGsrMUgz7LUkSk4CaXkV2Rj9Iyikfsm6ARBRNSTBQQzaGRzbgZhMTItTWMaokW7NZ8Adt+YkUqQwLHVFQwcUlBKh7EFURxXGZW2JHe/fu5GIb5/mn+X3TxjP+LDewDcAPB/ENHHADwLW5b+tDHmqjvnbQCn9/GMfVNqRmwnIzMsr9oauUdPHMdkfc1dE918h4MCqy5pSi5uh3sNCier84/KQ1WK6cDS6tNeJ2C3pZRh8tTKBO29aogSqQeiCB9ylknk/n2zLIgpBEpgse9iVddBrwAARV4vBI8DgxPCRhMm4kBkan4/EaHIo8MVd+RKk6wwh6+Z1O9s4pu0Ha087f1NnMeIixAGR5aXgmVmYqpUpxLEMpEEkR0u5N8LHU5v9iM+ZAB+AsA/NcZ8HMAmGqKCaWbeZNQXmO2pp4eT9oMULgG4ZIz5utv+bVimcI2IzhpjrhLRWQDX2y7ebYHZvfospA9lFzVukrtY+1OnT+PmZVsTt57UIfZgMBjguEMT4yxD6fR/WZZBsGzCTUcgYqsOD+NtIocA/4UIba60q1XMLpRJu2oFF2YpA8zNMxn6bOG6fyazXpB12S6dOFHmJXMnTp2E4hClBV0zKYMzE/e5kEKEbNYzZeP8z+D3MytDUiiGiRX8nFBE1qToAjPPYWJJgIqAYfn8xqNhdBPn9T1oNnDMHcBeaPdKx3mOzfcHp+yZKRhj3iait4jog8aYlwH8HIDvuf9/EcCvYxcFZndNbLQXYQzUOIkPNQEg5/s+XlnB8pEjAIA7k20WXBXzMI4yibqyk8Ca+iSzOAgQuBmRwXcvSogmU4jyunCxFLYdg4AUr2vpJmWMoIzejWmbRVZyHYRIxReldAjWIgJIObEma/88yOlHMub8szTyWavZRGaT1W+7RgrtE1GgaWWIE1yz9xdgoiFnKkgZRPr8+KvLLDp2CaoSSwgXk7jDWGMUWsdmr9TNPO6/wLJfP4X/AsC/IaICwGsAfglWJPktIvoMgDcAfHqfz+ipp57uI+2LKRhjvgPgEy2Hfm4/990VOQSwkCaXkn/StnNSKUYjLK+uAgDu3byRuBn7smNLRYGN2loohoMCg6LgcZOJI5Mnbn0IJdS5y41btYsiTyL+oviQujxbKcWt6ELEEGuZJQgiREbm0de/qagUggJk10qDfE3mxuoYiq/AQmv/nIIhElsOnq/artVEDAuig666EWDiTGJ9YTEKXUjFIjOBYWGjJsXWdiL+JcrFRuLbRb6zNin14VNSdtMj5dHY+YOwEd/r4HvZsxgOsLRicyiILIN2AVFSSgxdZucj4xHuTmPQVCazoH3XAiFOn1i9czuRY7pxwawBUgqWxCWV3Ym9dB0gPrUECMW2n6BFliEPPv0ZiiwG/Qj28YvG5OdJVpJAIIoNQkxMkudZkveB2C/FTYJNvUG3aMGsEi0Zn5uTnXc/0YOkB1iWaUJdVTGdHTjDaNfvtHpK7lKCWEjMXeCcw6ZHiinMpYaOAXFzV1QMhhi6hCl5UaB0fgqyKMKqOx4UKGQ0J5KgAAoMQwpEDCkISsyTQogwYQvOCNDQNTSUk2E/KK5e/FiLQhBoMIgss3kDkpyNXUQzLX5/f78kQxTPUdkwI/p9iblxHiJg1zfPSStGtfR8DoOoqwoTF9nJ0QE4UhBRD+OHe5HvaR6vWFTxuNM5h0l9QFRPPfWU0COJFOZy7AZM2InrmsYlyCQGrsJTMRyicjn6dB1DjZeHQ4xcu6zrRD43LAyFhACcHkAIiTxj1oNMRicfNEUBe32KDggxe5hAkecJOkhQAyKiADvHI5U8yy1acOJExsyRncscpQe9CGPfk6JJVEbnKUFplugUNSBd+dGCDuYgiHniRJT/O5CCEKAqFpgtshyDYhDGJuRyZGnxm2baTg9MdFu+d0sPSpR4JJkCsKCI0DGqTUaQbBOhGFqmMF5ewdaa9W6EViE45ujyMgp5EwBQKZ1AThgTdQeMKUkhggnMpyvnOoI2z8MusUAQIc9zDLyijCdlZeehwSDixLDnZA7mkxAtQkJzKNN+hZu6K7h5kusquKdkUyzgisZWkaE58ZviRqcSsqX/zH9Eu3f2wt2gyIO+aDQaYeTEx+FoFM2yTZPkfmf8DHX0e+7Rw6FefOipp54SemSRAqf9KBab1xEJFG7VGI7HYaVQqkbuVuaV5aWQZ2E8HEBpEyCn0SZYHwQEhCv4YhGEg+95jrquQ7hxqlxsRwqioYD0IgQADIoiTXDaAp/B4L5/QJvzELHjnJLsTGTFJWaMSLwAY1FX7jA1K0q0wv+GmMFNmjNIYwdEkWB81n9jgFqbgBTyosDQocPR0lJACqOlJQjv4NSIDbnf0P5+Pu+xYAqcFh28TrlPEHKX9XfMPgpdSUA6L8ZBESLsJpubqLRG7jwiDemY8IQxAoJmUJ6Q59GMOcsU/ORjk62FcXidgJRZnIgMvqc2d5qZeLyobBt85+NjEk8/Ssx1/j1sn1OTHnfz5nK9mZn8LUxhB6tE2/Wz57EXcX3xeTB9Ks2l4QhDFwHbZAo+OpKk2FXNh8Ogg1j8FqFefOipp54SeuyQwgztUlNjEPMJjJaXkTmRQZdlyBOQFwMMHTK4fv0a1jY2sXzyuD3PxOSnEgj1IgWvPBVWeqfo6xIf5okVycuBOQ80FIFtCjiyYlLnwtfmXQgkSIdbXKYshwKIWNq4BlJg9v8uRLCIWLCj+NDq5wAYb0mAslYi1//hcIjROKIDjxQGozFzXgIWKUXvz+2ig1rhD1OceCyZwm74wIwlgkHu4dIYuTNVVXIbQtvhGi0t4aRzhb67dQPaxGAdIYjlSyRI7cCYqhOZftYMyawEmMcI2Bs2rQF+ZxgAwxyt45sScxyaS0wOl1kWgqag6hkxwVtStFK2DB0s4xGMEfC2Zwy2mwtaH5rizwLMw/fZAKHwrq5raCDkqBwOhxgv2VwZ46UljB1TEJkM4sOiE3DvEsbu7QyHZZnoxYeeeuopoccSKbTRPLg148DkYGY+GGDoHJnK7e2w0kBrrKzY3ArZjVu48fbbOOkK0Q6zLGRLGhRDZE5sIBEVfeSf14kOZvfPvAzSY1EhGV80gf/GhANt40BJI1pMOHk/DV+GnpwLuBAUYz9YOrksY/EVLf4LiQ9DEuPgfcZ3UjT6+ptMUapZPQkdHZRYjm1bfEYbDAfeN2GcKBd9W8iIbnZajR+kDvKgRYnHkim0DtBuRi7k6JMYu8k/2dpCSOyuNU6dOQMAGL11CZsb66jcvTOtwmRSWofCpYIo1Kj0DCEB/Q2PuXBeskEdm6luoc2qYLejtr7JCYO8zESLmQ/d7ciKHFprjAYxyUrp8loqXp3aaBDFgjOJKJFUxG5YJXQ6+f29ZhiEH2dWlFabNIWdf3sDxHqVSkFpE8yQ43HKFAYjK0okZlQ+PLuhBsbfGfLfb2PnLPXiQ0899ZTQY4MU9uO0xHmzXYFd1uEsw6mzZwEA5WSCjbACayw5BLG6tIRb3hUabvFyEZST6TQUHMkyGRyZDPNfaCNqQwBsc0akaKCI6LuTooQZBJEM2uwIJnsIMSEtgLzIA3wfD4dBoVcrhZxlec5YvAfPaMTFB44a0PQ5aEENWqsUHTSqdmsmVmi2wlesjwbAqlMWW5FhObSlzw2RZHDeg4DQ0P8eJgA4SKXjI8sU9vTyC46cYRr30bKFlSfOnEHlQm2hNaTLs3Di5AncfuMt/ODiRQDAh3/0I+Hjy4TAxMnd+aAIsIznQ2x7n1RsmP0YqWV/YkFhzIBr7NN9aa6DOR1gp6RiReacp7TWGDvnn82t7eBpqVQNpVy1paJIg7saJsqY9kwg6D6YHkHVNZTLcu2ZQGAEpsEUvPhC8bUMYj6KSinILMeJI8cAAMurq8HiMF5aYt6LezRBLnDZfB6x+yn+jtcp7HUAuobazBxzTCHPMXQf8rGTJzHZ2gIA3K5r5EPLIJ548km8fukyNtbXAQClNhiGvAkCE8c8RnmOjLkX8wi8hSzg1KJy5IyhTaFo0MIM7PP4seRvvNw9j40WW3W5n0Se52EijoYGtavaLTMJ5UrsKVbQ1ps6SXAfBpdLUlBAaqquAgKZQQMd6EDpyEg0URjZmtXW0MZm2DrxxBMA0rycxWgYEULbj9L1ER2KpnExeLHQfNjFpOl1Cj311FNCjwxSOFR97Iwm3pIWEsKN0HBpjJNnbF2bjXv3grZ9ZfUIVpdXsOmg7drduxiesN6NtVIhu9F2WWLgcjzCWSjIpRxfqBALMCtKtDn2gIGDFpEh7DasrhJv8yCkhkzBMqTbI77/APK8CM+ZsGK9tfTFbiP8l1mGZir1kJVKxKIxWptWsWAWKZhElOAiQzBDKhXEOg3g+PHjOOmQQjEaYcUhhbzIGynomhBgj9K7mbs5h/ahLdjjpNlvgdn/GsB/7h7/Amw257MAPgfgBGzVqL9jbEm5XdOhMQKGykKz5WGaRMBSIgNWjlpfhGNPPBGqVBejKU6efgLbV2xRrJtvv42TjilYe7z9UbenJZZcoFXecNOFMVGOpZQ7tekjm/kI2o6FV+JegPGkGRfm9jZ/QpNFAD5CyLBu53keqmptTydBf1JLCVm74LI6s4yhJUGqTW0Xn6RbqnEHpmBa9AjGBJGhyRR8abu8KHD85EmcOO2YwnCEkfNoFC36njACXSLCfXFS6BYlDlqHuWfxgYjOAfiHAD5hjHkG1tX/FwD8YwD/xBjzfgB3AHzmIDraU0893R/ar/iQARgRUQVgDOAqgL8G4G+7458F8D8A+Ke7uel9d92guKA2mb52Si8jCKKwR8+/9+lgiVBa48mn34Otqds2BlO3Io0y2WqJKPJsBil40xsJgIc7c8ifgnykykWmRIwntaMG306RQ5AZ2tvNWxOYjyDgC60QKMQUGGNQO5FBiCpWtMpySGcBsMe40lFCec9PFvuhjZlRLCbWB/ZuPt7DgFiNThWsD8srR3DsxEkcP3UKgBVnZO6zUFE7PHNv1721GN33b3sPtJ8KUZeJ6H8G8CaAbQBfhBUX7hpjvPPfJQDnFrrfXjuyIC16/4Z6Ie4ngnLeeaIQeOp97wnna5LhQ9za3AzwmSiLk4UEtiZWDzHOJCSbsNSYlGCTImEDTTfftvczSJlN2N/CBLjuYJE2ux4GsdI264HVNdg9WZ5Du/p6qq5DBe9qOoXIssAUZJYlkZVaBYeMcC9CfC/PEBLRgjGFyB5NEBlqpQKzGS2vYOXokeC2LaScKzZ00n33bd6loLBHuWI/4sMxAJ8C8B4ATwJYAvDXd3F9X2C2p54eQtqP+PDvA/ihMeYGABDRvwXwMwCOElHm0MJ5AJfbLja7LDC7F5p7U6bUJTAlUlwcOxcCTQRast5wT7//fTh67BiOnjwJALh+5TLWNzbtrZjnYqVUSOE2rRWGFIuwGG0A4YOABIgp+pLUYk0k3xZu3CVitCCAHUWGRjtBAw3RpM1DkyiKEuV0irp2vghVCTHNgt+CzDNIxCSyYE5OSW4I5ufRVDT6t9aIw2QM92LUIc3esZMnMFhaCs9PrA2NOJIwlgeACg4TDXfaKPZgvNgPU3gTwE8R0RhWfPg5AN8C8McAfh7WAvGLOKwCsw3qeuednYLsx9PqWTxnIP1HQuMlHC8KrDr59MixY3j9+98HANy+dTM4LMGYaImoKgwGRXTn5ZwoMS9GMDwT2NQ0Q7JGKj7Mmez7EB84w2n2Ly3C4qtIFVDOqUlJiXo6ReWrPkvGIEwW5yJjBM0qWMaA6RRYZmhQ+G00ohejMhqjoXVNP3biBAajcVLoJfzunR8Mzd186GkX/d2z+GBsCfrfBvAcrDlSwK78vwLgvyGiV2HNkv9sr8/oqaee7j/tt8DsPwLwjxq7XwPwyf3cd1d9aGzvhYFTAx2Ee6YBBa3XaiKYLAdcDYWjZ87giQ3r8nz7zh2Qg7hCyqAYm2qNMtMo/EKVRSd96kAK3c5GSKF8Ez3MQQD7Fh+Q3m9mrCj8sWHIxq5BdV1Z5Z6v05kxpCCz8A5JEV6GGuBQQxAZjAlKT4PY51qZID4YEEbLLuhpeTkEPe1E/LVSvd3utHiHKTocND0yHo2cDBZAe3OoeY1pHCR2IDIIQhdjMIyrFMMBll2B2mIwhJ5suZNMsEoYYzBVKhai1Tom85gxL7ZN0CYDSPuVxDgk73J44gM/5i0xZPwfexvh4huUst6Nym1X02k0T2ZZjLHQGpEnpIVxmrEgnuHaICh7pKyrwBTywRBHT5wAAAxYlmZ39/Qd+CpBrWc0rrufU94zv8N7Zh/70FNPPSX0SCKF3aCDRc7t1C1SWOjsfh7l2HkzwspJ6z577NhR3LhqkYLU2sY8wCY3VZJgXEZorVRYBY0xwc2XhE5W+hml4w6uySaB/LOh0rsSHzqeFw8l8kM83e+muF8ICVVXUM43QFUVKiZKeJ8BrQ3z+WAQzjk1pdmq7L3LuoavQlFWNZR7/qAY4LizEA3HY9YnT7O/aNtavFtscNDr+f3AJI8kU2ijw1YGcwmy2eYtA4PC5RY4fvwYrl+1MRFG1fEDNzbLcK1cMpc8C5r0phejp2QizoldsE0mPiQSBr+vab1+Rg8xc09+LueYrKfJbrehES02bl+wRgiJWlqP0CrLYlp9JnJprVMmwHUMSH//ynlRVtqEMIp8MEDh8m3KLHPXtssGqR5h0WXl8Kfr/RJSHlmmcN8sQg1/BsDqEMgkn3x6iZNXl1ZWQL6cnDEBKVivvRrOwQ9SEPIs6hR89GRTZJ9NntK+us9MfqT6AcssWnQEM+05SKGpXGx5JpKmmXmf4AItawjHIOpyGkyFFjS5+yoF47wTvaIx8Y2glglO0eV5OBohHwzc81qSsR7CB3U/NQ0HSb1OoaeeekrokUEK9wsZHAQQ9NcvnTiJkZNfy7qE2bKrIRlbV7J0QVWCACmiiawrf+O81b0VHaSywIz1YufQ6eThjed19KuBINoCqpqa86qchmApVdUQLgeDMQZeq2IDopx5UasoQsBZJlocubTWyFygk60Lmofj3d/TXr+0Dm3DAcGF+4k6Hmqm8Cg4jbUzEdvzwWiIE8dtHsCrG+txMmkDTRTSlgmikIxlMBhA+1JzzOV3VmfQnmNxVkzg7UVEhoajdCKV8CPt4kO6O+0Le0SyrZQKUadSSohahvNioBNLs6a0Vc6662etxdFgnTn/h2JQhEhIr9UwjbN3QwsvHg/CarlP6sWHnnrqKaGHDik8CuiglVrsmkLKUDTm8ltvAaHILEFpFVa+WtWofLCQEMhzBov9LdusC22xEzPogHWqCd93EB/Yq3QiiNbnJE3TbIJ1JPRnOrWZrCQLYyYhYuJWEy0JxuhQpcoSsbFNyZ9jPSpjX1IL0sF+dQcJCrrvdXgWj4eGKTwoZnAYwxo+NgLGx2xqtjzPUTlXaFWV7mP1TEGhdpmGM6khRO2uJ1a2rG3it+sL2mT/VHRoyvo7uFAjmdrxnxbfhPS0dgYxe218zvb2VkzGUhSBKSgdK0Rp5wruK25prYIlg39JhuJzhBDBvNnoLWwCt64vcLdCxv0xTx4m9eJDTz31lNBDgxTuJx0uHyfmZ0AYr9pw3ZOnTuHa1gYAoJ5u2xwAfhUjQu3qI9RKssIoKrFEzK34lGoE/Smt7Xg9km12NrtvetzublEyJiBiti9JqwUl+D1GqyBKCCmj8xKrUalhEscmpVRQ2tY8DtogOoyxgjNN6sIJM/saIuJh6xB3vu/h9OAdwxQO/Idr/h4dD/COOCdOn8bNKzbfTKltEVR/C6U1iJU0y6SHxWKOp2NTX7CAfmBGpk+mMLsXO6vJDJILuhjOzBUzDKKLKQFA5by6srKMruWsmrTPyegtE3Udxa9SKWgnphFiFas096LXKsx2ea6QMBMCY3kAACAASURBVHtZBz2CJgdGvfjQU089JfRYI4UD5dNMf2Sbi64G9ryVk6eQheSglARBCUFRoVbXIVCIhAB5nwUSjQWIr+jt7XSlXmyV7t5vGmAgfQ6/QZvDUmdfWvrji8bUdQXpVn2TiAs1lDFJwVgvfpXTEto5PykhUVbuXlUF5RBI8xfbk5J75nuYvZ+Zd9IcetD44rFkCg96UAOxD2G0vIzVJ6x5cuPmDehyCuE+H60NiGZTkQshod1+yLRCkyUP3zsm/IzoMIcxzIgG3fqBphiSzvHFmM9Mv1ueX1dVGj3K3nc6naJqSd9eqxqVYwQmy7A+tYzgjdffxPEztoL4EeyDuJyx0LrQCJ45lI/zYG/ciw899dRTQo8dUngQKIGAJBt02xJCAJZXbUYm4VKOGbf087DgWtUh63EmJZSOjkwx9Lhxfw7Z+YoONNDCLGwPTd5qGcSDEB86+9FUbrq/VR0rVWtWTRqw2ZynLgdDrRQqVt9h6sSEckLYKC1qeOWHP8R73vdeAMDxM2dRuBJ+/mlBhDg0hxm3mj8COsjHginct/Hdxw9aVxVG4yUAwHh1Fet3b0VoDIR05aSJQWEVKidpoUG+diO1JHlpyYfQLcc3w5hbJjw7NPOocH5TPGlcMIdBzfS5o59ev2DHKL3eW2amZRn0C5XS2HbVum5vTbBR2f2bN27juy+8AAA4cfYsTp3jNYpSs8JCfIFfQo1kPOw+aY/3pmNYjA6O2+zIFIjonwP4mwCuu5qRIKLjAD4P4GkArwP4tDHmDtmv/DcA/A0AWwD+rjHmuX33cg61DUGLx/EDIWK//HSyHT7iu5MS1jU3TgoTcgyaxGfBB0ppEsG8SYZc2bYW6tIpNLa7VufmgHUziPnmxtnTd9AntDElg+B/QEIGpqAbLs1am6BQnJQV7m1uAwAu37qLDbd/uza4+NrrAICPXr6ME2fPBs/JxShlHDPUMSfbMaPbs/A195cW0Sn8C8xWfvpVAF82xvwIgC+7bQD4DwH8iPv/l7HLGpI99dTTg6cdkYIx5k+J6OnG7k8B+FnX/iyAP4Gt9/ApAP/S2GXga0R0lIjOGmOuHlSHm/SgYiZ4FqYZWMzIO9xMNjbxwx+8BgB46603cUwaCJd2jBBXTq5fUEpDSS9WKAg4UyUZkEGjliOjDueleDjK5vwf21wAGcSbz5w7k2V4AeenFCikugqfpVmAYHhFKBOdv3zFKMDmZbzrKnRdv3MPm7UbvyzHC6/8EADwoZe+h1PnzuOJ80+6O8R08clY7vRxtVy0w+fQuPlhiBL7v9ledQqn2UR/G8Bp1z4H4C12ni8we2hM4WGnysm3N65fx9e+/V0AQLm+gaXjR0Du2IArvdgHXtd18G0QQkSXZy8+hMkfqzO7m8Rmyz6/3a1cbIP/c453iA+LP5+dOaOHsLu1VkF8UEoFZgvYaeDHTGmNDVfId2N7gk2ngByOBCZOlHjh+z/AR3/0Ek6ctSbiLKkBMU9M6Di2g2Th+zg7VXcWJfZG+xOg922SNEmywMWpLzDbU08PJ+0VKVzzYgERnQVw3e2/DOBd7LwHWmB27zSriW5ZO1uvMGxJ0FrjjR++AQD41nPfxY11WzmqXNvA3Zs38cxTFr42kYKH90orKBceLLUIlghCU3yI1oQ0lVu3Ai95qw5RoPPNm+cnx+Y7LPl3TB7U7GPL87WOCMojBaVqNGlSltjctkih0jrkYChrBeHiI15+4wpeePElPHH+PADg1LlzMcEVuxdfMWexTzc0aEmWFa5oH7WHyyqxV6bwu7DFY38daRHZ3wXwD4jocwB+EsC9w9QnHCzNN0nt5be6d+cevvO8NYO98tYlTN1HeWd7gsn16/jY09YsZgumss/OiwXahHTlmcxAPmGIK5nW1s/Z3ACzEw/oNknO6gRmGown7DAiBxBZGSIjWVKVsqpQVRXK0lomqroOx9a3JljfstYHzZ6nlA6+DFuTEn/2/Et45pmPAACOnzkDIqevQTTr8DGe/f0bv0Abr9jVRzMrSlBzyPZFi2vfFjFJ/p+wSsWTRHQJtnbkrwP4LSL6DIA3AHzanf77sObIV2FNkr+0m2731FNPD54WsT78rY5DP9dyrgHw9/fbqftLNGer49w5LJwrvb79nRfw8g+sxntje4LSacJLpbFWlrjw5hUAwMfe924MnCXCICIFbUxQqCmtILR3ZHKFVz2i4HUVDaInTRMx7IQWwj+7FRlaEEY41rZ/nvMUz63AwqW1DqipLCtMqyp6LpZ1CJ1e35qg9jU7AfgxqJWG9A5OtcKN2+v49nctijt9/l3Bmcn6h7krTfQi3QvNerouAuYPLUBiYXosPBot7UZ2OmhDZvwh61rh7SvXAADfeu7b2HZZisu6jjkZawVRFLi3bk1nk2mJwpsnk7wJJjg11XUd0sAL2IjJOPVTKNu2n9iR2bwKjUndxiyQzNvmSSl12OQWc1gyicjgmUJZ1yHPQlnVKMsqOCxNqwobW5NwHxVfK/ksgsWCCNNa4y9euAAA+PGPPoPV47b47HA8DOJXp4MYfwfwh2BGqgjUmOvzp/6hyA8LUx8Q1VNPPSX0CCOFLh5+cChgUSZtYFC7VeuN19/Cs89Zf4T17e2wmiltoIIt3eUCcNaEPJPY2LYr3bDIUeQsh4BbXau6Ru6zCBGlCkUDFiyFxgLmRQFKsES62PMlu81JaXY0jDt3p5FpH0TTOMRqOmgdckvUSkWRoapRMqQwrSJSsPtnFapElAyMd4QCEUSWY90V5/nKN5/F0RM2we75D3yAWXCaA9tOM2pfNv77cz/oCKo4ZHqEmMLeJvtuf5Rdj70BVK1x+ZI1snzjm8/hVedjP5mWqGr/gWtMnShh4/91EC3WNjZx7MgqAGBzewKlrCgxKPLgsEM6xkSQEPZbIT7hZyf/rFDR7tTSlmuhdTzmMYEdGMTczM46Vn9SWofMzGVVM6bAmEBdO72Ct8wITPw4sV4TRSBMhkKNT1+ctnJdeP77r+OZD74OAFg5dhxHnzgFAJCgEMkK48a103epTY/ToBYRIh2Jtku8KHP/RIlefOipp54SesiRwoOKbFictNG4e+cuLr7yKgDg+6+9js1tayevlWYpwzQmLgtQ7RxxVMgcpGHcirg9Ldl+hUFhRYZhUYSMQlJKCBKJQtE4mGszSbePm1+tZ/IxsGZblWnMnj0HGXQpLGePaRbLoINloE4RgRMZqirdP60qFC7XQq11FA2YXGJgAlqQMkOW5+4cW7Fauu2NaY0vfvWbAICnnnoKK65WB+USgokShmt37W1m3kxwRMaaASTsWZ6g+6Z4fAiZwsEygsMaw+iHr/HmpSv47ndfBABsTrbDh6y0QenFB61DngSlDZQxKJmHns8/OCxy3NvYstcMNdNDaIxcKfVCZSCZmB8SCpO/O74aXaIEh/Y7MYFZD8m258RmcMpynvHByqAUSi4yhMnP9QacKVQoZIbcxYVUbPyS/rM0+HmWpZmdYYI1xxjC2zfuAAC++ey3sXrcMoVjZ04HBygrOjSsOS3vrQGIVpNwg/bkxHh/dAwPEVN4+FGBJ2MiU7h18za+9o1ncXfD1XRQGkr5+gRRuaW0hs8CUCsNpQ02nU6BZxVaGQ5i3oXNrWBGq5UKDEJrjZXlJZvMFalMa0waHNX2ecbzdwicmdEtdH2QDf1ECNRi2ZKY0tQ4/4vA8JROFYp1Czpg7UwK5FlcxY2O6d75ZDXGQDg9ghAiFJuVeQHDmJSUGWo3FF/5zkv40Q9/EACwcvw4ssKjC3t3YnqcLvImUUEmJMbxSsf9z+XDN1f2OoWeeuopoYcIKRw8HSwjjV54ShuUTj/wF19/FpevXkOt4qqnQkBTXI2VjrkByqqy2YLcNdx0paoKy6MhAIsovKmyqlVEIG5l9OJElsWqUnb19Dr4dKVqeyd/MIrOCwQ0hf0MpgcUEK8xxgS9QRMZKOaYZNtRj8JNjxwpCJd5qsgz5JkM4pjWOjh5xbe3/QjjIuIY+WxWvs95lsXYie0SX/rzrwEATp4+jZM+bZsgNzZefDBAVz4LR9pEUYKQntvUL+xe1XB4osRjyRT2O0a8OnFb7sOqqvHVrz8LALj48vdRVlX4QG0+BHtiXavQF4MockgpUYKCEpKburTWKNxHuzoehom0NZkGuVsZGxw0dQFBgyIPeRcyKUN+AClEnACYDZNK/BaCohKYFRtcK8By3WAEPLIzTnClFWun+7XSDSahQtuPJWcKADBwOoE8k8hkrOnAc1BwpmAL9LJ8FKEcHyWKWiFEmOAik3jldRvY++KL38NPjEYAgPHKCmSRszFrKAU6WIPXSQiYJII1ESX2LFccjijRiw899dRTQo8NUtgfo4yigTFRq86deoxBWNmefe55fMVBzPXNrUQJqLRmCUajAswwZi6khJAyQQGALUQrAGjnpFRkEkfGw9BLnyfgztom1JIOQVSTskTulGgcKeRShP15JoO2HbCrJSXwt10lGRLKGhPSoXk05Ff3urHSB4Vox36PDJTSLcfiWNYqFuEdDYrg6ZlLaRfX8KPFNn8vwYrK2sxVHinYLFaheK0xYZy00agcavrS176JVVcg+Kl3P4Xh0jIG47Ed5yKLjkXMeSzNZxFHVRn72wp2eDciw46xEgcoSjwWTGFP48AzKTM5mmultTHQ/sOtFS5deRsA8Gd//jWsuTyAZVUDREFksJOFTyT/wPg1FMUAZjgESmt63J6W4Zkik4lMPnTab6+zAGzE5e21DYyHTqcgJaTTsudSIvdMIcuQ+f1ZhiKIFRJCUNDeW5jtGQRjFsREqcSvwnpkBhOrak7wbkbA97fpFBQz3Q7yDAPnSzDIY/9zKVEp1ZrdWfgfEW1MIb4vLzhb1zUK9xwBASHt/jtrm/jCV74KAPjkvXW869xZHHPu0MOlJQxdyn5Z5EEnQ5hlDJ5sfx3DajAHs29J4OBEiV586KmnnhJ6ZJHCfphhU1tuuGVB6RCbv7a2jpu3rFPL9Zs38eqrNhvzjZu3Qyy/Nh48RotDRBrMkYiYBlpKUJbDlHZ7y1kjAKsc1Awp+JVudWmUaM2NQcgwJIhCDQNBFM7LMxm8/vJcJquuIEosFh4pCIoIQggR2rWO5d5rZds189ZsX/U7FI1OREiP2euLPMfIpacb5nlASoM8Y8pUgbKuUkWnz/pMBJ/SlSOFVKygoGz034D3kyjyPI7FcAlXr9vf//979tt46tIlnD9r61GeP3cWT7gYidHSEgZLFjVkOUMNlKIGrmzmfqXErE88nV+TdvAqiWftU5R4pJjCQekNtEknrqoVKqfJf/vGLVy+bIObrl27jus3bwKwTkobmxHuayZ+EEz4EBI9AnsmNapGCymh3Ae7XVXho5RiCKOi7G4YFF5ZsprwPJPJsXubW6iVNV1mUjL4TIlOwTOIQZ5b5x/OCDiDYJOHM4iYZVqh4kVda5WKBi2TvU1E8IxwkGdBFBoUGYa5d+3OAyMrWH+1M+9ykc2LEHVdt5ohvR4htkUyYT3DIyLGfDKY3Pbr3r1NXNic4IdXba6M85cu47zLBn3uyTM459qj5WUMvd5hUCSWEAKCA5sxJo5ttG4mhox5MWY76hjCSbufNb340FNPPSX00COFvaEDbj1AaHNY7leGjY0tXH37Gi5dsejg8uWruOLaG5tb4TyldQiDrpj/AdzKyqEdA4ahLx6y+ucLIaHdirRdqeDvD0QllLZaz3CNFwuWxkNkmcTNu2v2PK1DAZS1za0ZezhgEYRX1BVOgSfDitqNDvj+UMJOGydC2D5XCykauZu2wSDPsDR0YkKRB3QwKFKRwStNM2dxAKxylyMlrU34bbbLKrg2SymjgxERsz5QSH7rxy9aLyLkr+o6BKRVADa3t4JvxPc3L+H1qzaJ+ZNvXcb5s7b0ybmzZ/DkkxY1HD16FMNlW1TYixVp/dAAIyFiM35LlH7DVlRl1KHQROs5i8+kh5opLPYaab6/Locjg5g7sSprvPa6rVnzyqs/wOXLV3Htmv2BtyfTRFbmRUa8BcAA4Jps3k8hRMgRyIkoyrcaQFbkyIyFmZvM918pFXz0da2i2U1rGA/3kWE0GODMiWMA7ISJ0Frj9trGzFj484Do/ONNfHKefiG0BaRrZ1LanIlMfIiWiAYjYGbDQW6fPxoWlhEU9vmJ7qDIMWDm1fB8kWa8JrDfs455F6ZVFX4PKWRIZEOC0t+MOTyB6RvqOjJ1o3UYv8GggFxewWRixbTpZIrCMaLXtq/grWu2dsnpty7j3BnHIJ48g6fOW4/Ik6dOYjhegsxns0ZrE+UHMnFRmBEl5nmbsndrpwUYiKO9Fpj9nwD8xwBKAD8A8EvGmLvu2K8B+AwABeAfGmO+sHBvsCAjaJgQozqviQ7iflUrrLnJcvGVV/HCSzY/35tvXkJZVmHC14kcbJgtG+ED5yto4MPM9OgnmF094wrkFVskACELTEvrdzA1KlRKLqu4OtWUopvgMyA0hJBBX/DE8aOhXTDdwa21jTBZ+MTZ3DYhqAiIJkrAMoKgkOvQL2RSIpMiyPSVUgmi4pPXo5NhkbEw8DxBB5xBZDILfbFMKKKr4Apd16jqGFlZ1SoghU2lMHURSYOhgHGMkNhvZozNghVkZ2+iBAAdlY6gLCiUDaxZd+Bcy6XMAoNAVSFz1a3f3L6GK9dvAQBeu3QZ59+8BAB497vO4z1PP4VTp07acRmNghmZFyK22aLgxj9FDUKIpDJWQv47oZT57YX2WmD2/wXwjDHmxwC8AuDXXCc+AuAXAPyou+Z/I59Qv6eeenokaE8FZo0xX2SbXwPw8679KQCfM8ZMAfyQiF4F8EkAX93xOTt3hKEB5oHo/nKPRM0cZnwG4MtXr+HChVcAABcuvozbd+4BsCtzEx0ErXbDI5G4xwnzAOR9J4BpvwkhzN/E/cZBRB1kV42bLofCE8dWYYz1YhTMKWomHtogohUpcOKI9bzLM5lYHHxAFYf4WmtMygrbLqiLqErQQXs7rjxe3Ag6BhO9OGEiOhjkDUtCQ1zgugMuvkTdCx/dmO7eo54Qbl3XoU5kqQ20Q2RlVWNYWIsNCRl+P6U1S55iV1fh10dKkY53/iqduBFWd6KAGmpVY9uhBkmEzKGWcnIT129ak+Zrly7jjUtX8J532wJqT7/7XTh10qOGYRANJeJ3wWKuvJddiN+wCLYFCRjmcEbRwrGQ/sG/88JndtPfA/B51z4HyyQ8+QKze6Jm1aT43f3/7V1brCVZWf7+VbUv53T3dM90z8AgDMPgiKIPggR5QH0gIUgUNL5gTJRIYoyQSNQYlBdeeEAjJkYjkUC8BCEaNc6DJqIx+qCDAg4OeAGEoadvMzTnvi9Vtdb6fVi3f9Wufc4+p7vP2ZD6ku5Tu66rVq31r//+cww04ZDFyLOW83mFnV2ngNva3sHWlv8oX/0anvnaVQDAbF4LttpkrsnOz2BZg5btE+0ULF9WFBaIbHHgAJXy3a8K3PaTt250TAKbK/qEzV0pkEq+CY7guDZcuu8Czm+6iXDfuQ18PSojZep0tx2iEQ9m8xhc5SZ7YN/TMwuhaFREGTuuiKJJcF7Vkf2UE1+aF4O4UArfitA3YfC3u9za5D/ixAWNpklEYuK/pxYKReknUhQyU1XIc+GDqNoEQogZzK6NYYwoI5WzoW8KjIaeQGiNA+8/MhyUKLW7vq40trb28Mx1V+vjsWvX8egjnkA88mJcueJSzI82NhBSSTKSywH5RTGLRhUKs2xhEoTERpqwuqLxjkySRPReABrAx05wbV9gtkePNcSJOQUiejucAvINnJb0Oy8wu1RMSNyB0Umxtb9/gJ3dPWxt7wAAtra24/Y3traxu+PEhL2DSTQnNVonxaKx3pkpPEe845J3b9NclucKql0oFR2UiGxawayGKghlCPBRY0yMVzrWNeY+I9O5jXEnp6AUeS7C03SVFFWWU76AF1x+APf7gJ796SwqKm0r9LmqG0x9sBWDc+6AUuxAoWRbWrETkWVvMPNcT1VXnabGoc//EDkf5Fyh7ODQr7L8fOMVjYHbmzUa8/BuRKAQ+KUUas9NDMfJ09G1OWXFkqsoObth3I7h7kqBOYU/Z99DillFEQv7aK0x9xzYoCxRNgVuXncL4O1v7OKZa45reNm1G3j0EVfs9qEHr+Bhb73Y3NzMLBEECBO1UK/z4pgM7xh68Dg6xxMRBSJ6E4BfBfBDzDwVh54A8KdE9EEALwLwOIB/W+WeeWy+22eFrkA3JuoHbtx8HtdvuA7d2t7B9vYutredmLC/vx8TpBpjonmw0dJN18b99hCuKpvsC+31Gz4ARZYXk34KUe5WFFN+WbYAlRj4wWPnGo2/5mBWxSQrm6NRSifmWXa37T3yRLBPaI5ihg37hZv0cDjMfB4kUQh9BbiSbKVIYdalX3A2f0AOOBJWAq1DObca1bxCOLMoZPtTJ8r/KW36vRzbZ4QJtPFelQAw1Romys4qpnJXgyHgRbTpbI6Rz40QkCxHIh9DiyjEdljrRYlASJQwl+bu1EGss8xQkfDWqEApWM0YXPfj9Lnb23jmmls7H33kxXjI6xouXbyIl7zYVSa/8sD9gLVxPIHbpvjFVc29S+jXu2uS7Cow+2sARgA+6TvuSWb+eWb+AhH9GYD/ghMr3snMS2woPXr0WEectMDsRw45//0A3n+cRjCQafwjd6BNLDd+4+YtXPXU9Nlnr+OGD2OezedoGp372ItkqTLNV9q/tBzqig1OamHJMMTDlFY9GbqrRHGSQhUofIjutFIoPNfw9YMp7j+/Gds8jKuruH/822J73QaKaIlJnIJLDisKr7aUuMFhajgcdLLvMmUcsMgpiHU3sqqFSvkcjE7fKKxy+dol+YW0SzqP5aHbGjM/NuaWYSKjoFD4eIViNIbh5DAWnq+KMn85qbF3ZiZ/XPQvXB8mw4BZKtpJz8mgTAU7sWfqRauy0dA+8GtoLK4/62Iqnt/awUu8d+SDVy7jWe9d+9CDD+LhFz6EC56LvHj+fOIakPvnyA6MzM4pWx/uHJxKqmltYv7D6zdv4eqzjhBcvXYdt3wwysFkEjXPYbDITMdhm30qdXceHyoqLG2a/0tANyHxMoY8T3JymQwq2HJVCMsEKRQDN0BuT/fxUlE4deD1Du3nsxAkGRAEh7PBHuQaVaSUa+lGYiJEqKh7APKxRBlZSAcpO5Gh/HlWuH8rcYrRGrxAmDt6lwGZjyHogRptUDcG02BxAEBeTBiMxiiGbuJQOYy3JaScFAPVTo1GGYGN+Rky4kggshklC36rytpEFAqFQiURIzxnMCizvBVaG0y8lcKMLKz1DmuW8dW5c3i6dXsLD1y6CAC4fut5fPXqNTzwgPNivXThPL7n8ZcDAMajkRgLOSFL3+iUrA89evT41sNacArMwGTiqOa1G7fwrOcOrl2/gVs+JmF/f5L5FkhHHCMqBMnAp9yq0KaUJ3MBXWg7uCU65E8Iz5fOPs6VVwF+RSKlEEKs5kDUWFd1g3OeXVRIK5N/cCeIVHxXt7qFfkmrYapLKNl/weuE6Bzpky+f4f/LOAeplEMIQ07cQZbPAAQtxQnxQtlXYQixMM/nMGsazAJ3qQqUnjtQww2Uow3/nkVKLcAM69/LWhNX9ti2DuuTtDYk9X86Fj6uBlBIJa5/TlGk64kUiiI5UBGpGFA2m81TPonhENZ6kctM0MzdWNjbPMDW9i5u3HLc8qAs8YyvX/qq7/5OPPqih9GNwLV8k4kP86rCvzz5aQBOdxCCk/b2D6IZUQttsxZReYEIdOVCdFjGNmUM/4rovkY+oU0gkkiXtNKlT7kWCUZRwHCSd/e9xv6+eiP69I/FBGs/N3s+idFLwiwit5nBJCLuBIHI3k+eE/UI8ow0YbKrvR6BTE4IkgkzJxJG607izcLhSmZ5rhqNaaNReQ/6cjCEGjmiUA7HKLwjkWFJ30TGaeNiHYxYWDLLin/+YlhbSzIL28yxbUU2FmWKeV4QJ6RJNxbVrWtseCvJUDjVERNgAD2v/f0UDrxj2t72Lq49/hgA4Pte+V0x8MzRdNni1dCLDz169MiwFpzCZDLFU597GgCwt5e4g8ZoaC2sB4GVZOHm3AonXY1LaJ9zNLeweFbiTKTyPTuPRYFySpaIoihgjYlK7tFohGY+89tj3PYFai83TXQ/Ps+8uEoJjyvJNaTVIdmpwS1RQvRb5gKbsx1JUdfBJXRxpFnEX1HEttjMzyH4PaR7Gx1W7SRSsHxFsdJrYzCzNvljlAMUviZEMRhCCY1/l/8LKbcyB4uLMSJEXfSfQro+8F/xHdrjzp/XaB0tDswsxAoVuYXwjKB4HtIAOnBN1sboy6ZpnBIRzvrEYNQqRLYmpeXs6tWYrauZVXj82x3X8NADl5aZJQ7FWhCFqqpwe8t5IUp9gdYiMQenQKVssIRBeJSUABwy91cjDIde3aFXkBpfGRBVKIWqqrDhk4yURYHKEwI1GGJ/5hKmzOsmhu42jcbIm9J82Rl5d8gXJWGJ4IzRF6SDSAwYqRsQfdmSo3N9fdrR1jpkokQIXSaKgT5BdJAse8wFqZMZkoQlpe18c9jXyglJfGOha8mfWQwGmUk7f1D3k5weIo1HiSDyFYVIp8eMwhMGd720PgED7/lJJpk6jbXY9zVKG91gVFUYeTOmNGkrItx87pZvi41xGBujDVzx1ovjGOHXgihYy5j7fAJ5IdXEEVjOp8BKaJ/YdSG1Dy4ZBK3bxFUn7JOTRyqtxMSR+RZdzkN/Yklx8hhmGH/R/rzCxTqZJ8djb2qTM9K3JVsQ4jNVei9KjSTijFPIu4WQT/buPsibsGzKciSMpFScXxZYIApyO3iuGmNzP4mV6XYH7yiUTQxnwg2K6/FoiBDhT5ZiWv/8jq0aW7LNLa4hnOWqifsjBbwe6faeowAAFVVJREFUKdcxhHcOvi2FzNUhdB113Ti3ac9FjEbD6HlaFCp6ix5MDnDzOaeAvHz5fly66IjCxftS/ZCj0OsUevTokWE9OAXmmEUnFE8N24H9jWKC+3EEt3B8OWoldNw2tlAwGhnPIVanmI+gUD6lWZJ3Q/UmAxu15/tCfKhqHftoMBzmOgUhPkinJigSqxsjY2tIsBeHyJ1di3OyqHSftWxBD6txAYIlk5krA4y18ZtL3ZG8czy/w2IBtEUGZMfiH0oOX9PZHOc2NxZOk7kbYwCSEGdYKEVIcrKiy0O+SssWZVkK7oxSsJPgOpxVyG2XRZF5TYI5cgTWWmh/N0UE46uKwRoob+re29nG7u4WAOD8uWUmy0WsBVEAGMan0LIZW7sKv8grEIm7jJYSIWMdW7qFZcJIURaofTSkTN5htMWmrzw03dtOPgsiDfwwEIUwkOTo5zRJZKASBFF1c0IqHts5JBZ7s1uUSHLGUV9KKiChFAqhY7DMcYKxmGwMZK7pC/J+N5VGNvk7TdXuR/AZUKwwmbq4vnObm3mgUfCO9aZGFZpA6d5OMhM90EFkrWU0jY4l/cAq8ulO15SWkriVmTSVSw4Tcl0UKWoVSCnqnW6E453qypm3p5PJYl8tQS8+9OjRI8NacArSC3GR5buD++IkNoXVrmorGrtNkuJdxMoCOO1xUEK5DM5uBamMQVCvzRnY845MF+sGtV8NRsbErMDhQSy4gMzIIFaTaIloeTS6Ezu0o2mHf4VlmvjW785fnO+hoAgN6sjEikuRUfs+mzUa+95x56BuFsfIkYMmVzTK37LQzWw+x3g0zq8DACYnTsgjMohKvNuC4ha+z5FKC7iotfCetuXYlZ4cuCPlg6uUyIoVrQ/CO9OIal1gC0Xu+pAYdxWsBVEAIAZCpxSIFb56Ou84VGVFypGfJj9oS6cACJNk+wYZ0x0Db4ylaJJSqoguz6YoYpqxqtGxNkTdaGwOhvlrRkLA6LQrkJzSQacQDqX06Uzt/lveOZllYOkZLWLgt1jmdRRnWbHdWI6EcFY32PWJYHaqBhOmmL5dikyZ9LBgYWFxvgCniaW1Rq0c8XFFZ1NeTSCZS91l8pnZE/xbEqSfiDzDBWe571lSmbcnXC/Mxozgeen1MqKocKFSBnHp7WusjQtPHxDVo0ePE2NtOIWV0Gb/xJ978qDDXWQ6tloWh2UrKAU2UVRcCrUGgCg+lKMNTCunAJs3DSrPKQy1wdAYlJ67cCz3IncglWuqzeO3RYTIs/IS+Wc5cp8FKWa0L077s1qQzp0ontP41bg2FpVf9WaNwUFINMsMrVyRXgCgoj2MJdvWxR2EH4uikVIKcy+yKVJRrGN/fgg/FzrMLDhqGdcQual4PcdQbkCjKFNhnjjCW58FENYMW8Sw+qIoUJYp2C4l6E3FjPgYeQPWhih0ssInuVbgWL4ux1Q+HFen4DhBSWwofjBFqQAJAfCpIrC5cQ61JwoHVYNLIsdko3UkCnJKsRytmYamwy1ZNj5o0onydmY3Xo5l5kn5sMDCGusS3oRcF5ZtFLkMM2o/WSptUHnxYdpoTEM2ZyhQOYDyREGVQ6hiINq/+PZdBK6LrMuEKZPpFBd82bdgIYiT05g0+dtE4TAIk2Y4WYoSKFOWa3lBNDV7OqK1QVn6/BBliUFw8y4LFMHzVVbr6qhatgy9+NCjR48Ma8Mp3As54FgMQKfEcPQdVlU0Mi+u1CHV3HBYgnWKaCg9pW8so/GKpYnWMXS8bhynMPC/nZIyV4Mu/khutZ3IxIcWzyqPI3E82esITmGZ9SGtWs7mn/sguO3GMLQJ2xazRmRs9vsNgEE5gCpdHABTASNX8GjIaH87zv/vkkAZ0ZHMWouJt+9fuHAeihRsLMQgGbI8AzkldiDblm2i7HuJ662N/huFKKpL5JyXZODVwPiaoywzcBdR5Ak1P8O7rIo1IgrLcLTuYNEMdIePWyAM7imcnbKCTiHuCOenCUKE6OMPShWlR8NhHDjaMgZj52m3Xc0w9fEhG+Mx6sbEAqdlWcbMzm6wJ5k0RzJ7dR+Bq0UY2y9v0H7Llp6d8qPtKyynRDiOICTrQ2NsrPDUGBN1Co21mPrYj2mt0fgbWiLUjYEKvDQhJU6wHJ2yZMGarIGckwdpvZBQKiVCOZhMcG5zM3lhegvKYs9QRgASraT8mOgzlxgntC1NfGaOEZ/BEiGfZYRjVXpMskQUZZk8Ze+m+EBEHyWi54no8x3HfpmImIiu+N9ERL9DRF8mov8kolev3JIePXqsBVbhFP4QwO8C+GO5k4heAuCNAK6K3T8MV+vhcQDfD+D3/d97ijtRUp70gQvSRpeiUbanpexvXM1zAK6s+YZPu6a1jqHjajzGee/yPKkr3PL1Ji9eOOdW1BALYYdpdRQiwlLBR2ZnyhqZWxJIpHBbCAPl/O6d3AcRslJ1nIsPQRyaNxpzzxHMG42mQ9E40RqzEFLPBrCEwrehKAepgIdKnIKSORfECho+4DIxK0CRgvIrbV1rME+xESNVU+i1dM13liXhjyLEBdft6Vie7clvZ8t04vqUUlBlEbkCWQaw/S7RkkIqKh0Dx7AKTlRg1uO34QrC/LXY91YAf+wrRj1JRJeI6GFmvrlyi048qbvZ+U5Vwaq3WqpfkM/yk/EInYK1rjJUgNY6igzT6UyYvhiVj4kYDYeA/5iDjU3sVC7nwt50juFwGOP2tUk5Bw9l/TK5lhYPtN7N+d547bdMd8606OSUvbO/hpNJTOoQLFs0xqASBWJr4aQVHJYqbXDgzYP7VR3Nk4YJRlcofdvKIaBCCjjL0YvPKgVl5XaYYMuJYlvkCt+ICxV1QIALt7acCrPEgKgukQGIBCHpCCgRCPE9qEVUkoOU+xtS5rPMLyLDwrNvkgKtiuIuEoUuENFbAVxn5s+1lDnfBuBZ8TsUmF2dKHwToEv9mNGRoPSyFtpHrxnhaeZMWxx/K0Uxn8QGJTl4XteY1U6ZRlSg9tq0q9t7KMoyekEOGh0/OlGqJOV0GnEYigHanhQSrcmSlA0db9txiTgsc2fKbW0tap+mHcg5glonYrE/m2PXJ5+ZNQ1q332GnXJRs6+hwMDAP7MoVKw6nXEKzOBALFhlFbcWsbgyOeVvyvVAhGgGZFFdXHIAiwRCKj9zhayMGJUZquJZ3uwZOAXnmyA4FWGGzukCZX9XwbGJAhFtAvh1ONHhxCCinwPwcwCwef7CndyqR48edxEn4RReDuBlAAKX8GIAnyWi1+KEBWYvP/jCYwoNbYFwpSuO65+0XEEgZO22TkEbEzXWUj9gjIE1IpEaJ+80pVRcgYqiwMgXYp3P59iLK51F7QOCdA0o2okr4gtVkbTUSoH8slUUqZPyhYJWXDmSlUG44acfneXNSeQQyDkFw6KYi+AI6kbHvJyzusbWnktBtrU/iSZJwxzNjtoATCZxZFxFR6jRcATl45stqZhbwFpRxclzEEGcoCzvxHJIp6K6buI1RVFELiTXI7Q4gyU6hfbIlCJGZrpsWSaCaKMURX2NZfGdWlaJVXFsosDMTwN4SDzsGQCvYebbRPQEgHcR0SfgFIy7x9In3AnulYJxqX4hbWmtEyEwGiZuJwIhC6QC7qMGPYJSBcrS3dNoDfbuq1VVYeJ1Coot5lMvX+satmkiIVCqAMdSZ4Tx0A9WZZF5AEtd4YqDhEXGkMVLKPZBOGRFoFNGFCxHHUitjdcjpN8TLz7t7E8iUdidzjDzxNJyKi3IrjUpoMoYGJ83gC3Hqs9KWVib0p9FD1Kva4iihU3FYg/z5ZBl4AiISXXH45REVXqJZ1WoqP17mR4h6RrQMm8ykMygLCJLodJ6Za04J43UIxxSM6xikvw4gH8F8AoiukZE7zjk9L8B8BUAXwbwYQC/sHpTevTosQ44aYFZefxRsc0A3nnnzTouViODJ7JEdMAyR1HAWOOTarpVQ2uTcw1BZMhS/yRlEeAWhpBunK3NRI6ZT9RZEmHii5MWYOzO5rix7TJgj4cDjEdhdcxXo6CMhFeUhf1h5TkUDKT0761ey+QJcYlNryk5hdrknEH4BwAHszl29x13sLM/wZbPgjSpa9ShYAtU1l6O/zlxLAT8hGxWgOtTFatCpXqP3BIfrLJQIaEqsehDwbrH1/aBR2VaxauqjqZKULdHY1vht8g1LDzS/8zFDxmElTgfi7px7900jbD4WPBxWASPNfdo5EN+Ld93NyGrHqdqRaZFFOroM2BtyuCbEwJ0WTTj7qDJttZE+bAoiiR+IFUsYmsxqSys3QfgZN2xZ5kHZZkNvBB9dz4QB/HUI4kjJcGg3c/S9BisKeE8mdcweNI1osJXbRxB2J+4yb97MMHOgXMn3ppMoigxb5rkvpxp1XObr7O4pHeO1aVV98RzXpucqoMzxW9mlYWKIofKJ6yQDZRSKBGK/1KMrNzYGAs3cFqY/F3EINNDHCrWJS9I5kQIZTRk3TSY+cUji5I8BnHoA6J69OiRYa05Bckiij2Hsger0MPDLBFG1J0wxmS1DEP1InmONabFplGnVt45AfkVlIJSSjrNuO1yUMZkoQRgz6+ghVJQXmtorXP+CQqlW9vbGHil5Xg0jJp4pVQUUQaDATal1pFyXoE7tmQvdepZs0U0cUcsTovl400SF+Z1g92DCXb9u+0cTLA7CSHiFeZSudj1pbx3IqtkcVGihkKXsjDnd8KvwCkI6z4DNvBktsXut4LCokOQcPuYV1Ws6tRqcut3y4dhiRdk6wXkhvBuFJxCXWM6c31ZVVW0ZF24sHrdh7UmCivjDmQIhpvkqQCJyaoFBQcja022HT7IcDgAmfY9O+QEQnQAiom5RFKG8FHH5TDTko9HTbxBKsEG6HoeCdOkqnFzaxsAMCgKvPQFD7rzhAlrUJYY+tTxQzmY0d5cQi6lJLQg9+Y7k8ZbOCwZi5kXCwJB2PFu27uTKfa97mRaNynPgrwXUr+qonCensEyo1QkBCoTJURhlZb7t5jHC+8ZN1vEfUHg8ofLoozHLJmo1whWkPTUFjI357Sv7QXZfYek05D6hZBKHgDG4zHOeTf5Y1gke/GhR48eOb4FOIWTsQlSMdM0TUyKKgvZWmuzbbmCh6SZQaNtRDq1btrOwlPYiRLc5iTgdFmB0ltrIytqmWOef6MIihi68golBva8xv7G1nbkIKq6ibb90XCAgV+5isI5O3UptZbqo6iLF0gHpWtvuEkIkQaca3OwngROYTdYGaoK86CoZSu4g9ba6J/hgoPKjFOIHIFQDmYuw5n9vwuBaxO+AUjv3HVlEj9IFKslEAXlqvbJX5ffQ7xceialfcL2kN+B5B1Tm1073I+N8bjFrayGbwGicBy4j9g0OooLiSh4k6KIUWBrlw6kGGiiFAiI9R87TQsAwCkdWTzQMQO11hiPHMtnrQF7omCMgQkZe5UCUZoyTTWD9RRn5+AgerppY0SxUxUHqPP6UzFyLmvV0pErtQ60+JpKTD5xSQiCqhuNAx/HcDCd4WBexSAobW0UGZz1YrFBRMkjMRC1EATlohm7RAaCZNFluw5bSo7BaQMA5tU8xaGUZXSKciJnynlx3BtL/UK7dZJIECEWlT23eS6+62xedeTmPBq9+NCjR48Ma8op3Kn3gdQyB/dhg1qUdU/bDeqmydh/4aea3bVDAZxs2dSIg8KiHpTakt2L9u5FKu6sHI5TKctBpPpN00QOIFQmjtkBjIYJnI5l7PoUYjK7kbE2ht2ORiMUZQmlxCqGtFJn6NAudjU9av9VWp2leKSNwbzyJfC0RmMNtLfmaGuFUw4yMSsTC4Jbd1H4KMHk5i2fn3EKLeeh9Fo5pyb9ylYdfeGO49EIB956UhUqfifX98kSVJbl0f7GS8ZcEIW6Fv6yGGDDZ+ja3BjH6uQ7e3vY2dsDAFy+fH7FtwLoJB5PdxtE9HUAEwC3z7otAlfQt+corFub+vYcjpcy84NHnbQWRAEAiOjTzPyas25HQN+eo7Fuberbc3fQ6xR69OiRoScKPXr0yLBOROEPzroBLfTtORrr1qa+PXcBa6NT6NGjx3pgnTiFHj16rAHOnCgQ0ZuI6H99AZn3nFEbXkJE/0hE/0VEXyCiX/T730dE14noKf/vzafYpmeI6Gn/3E/7fQ8Q0SeJ6Ev+7/2n1JZXiD54ioj2iOjdp90/1FGYaFmfkMM9LUy0pD2/SUT/45/5V0R0ye9/lIhmoq8+dLfbc9cQkkGexT8ABYD/A/AYgCGAzwF45Rm042EAr/bbFwB8EcArAbwPwK+cUd88A+BKa99vAHiP334PgA+c0Te7BeClp90/AH4QwKsBfP6oPgHwZgB/C+f/8zoAnzql9rwRQOm3PyDa86g8b53/nTWn8FoAX2bmrzBzDeATcAVlThXMfJOZP+u39wH8N1y9inXDWwH8kd/+IwA/dgZteAOA/2Pmr532g5n5nwFstXYv65NYmIiZnwRwiYgevtftYea/Y+bgHvskXEbzbyqcNVFYVjzmzEBEjwJ4FYBP+V3v8qzgR0+LXfdgAH9HRJ8hVyMDAF7AKTv2LQAvOMX2BLwNwMfF77Pqn4BlfbIOY+tn4biVgJcR0X8Q0T8R0Q+ccltWxlkThbUCEZ0H8BcA3s3Me3C1MF8O4Hvhqlz91ik25/XM/Gq4+pzvJKIflAfZ8aSnajoioiGAtwD4c7/rLPtnAWfRJ8tARO8FoAF8zO+6CeARZn4VgF8C8KdEdN9Zte8wnDVRWLl4zL0GEQ3gCMLHmPkvAYCZn2Nmwy7v2YfhxJ1TATNf93+fB/BX/tnPBRbY/33+tNrj8cMAPsvMz/m2nVn/CCzrkzMbW0T0dgA/AuCnPKECM1fM/A2//Rk4Xdp3nEZ7jouzJgr/DuBxInqZX4XeBuCJ024EuRC6jwD4b2b+oNgvZdAfB/D59rX3qD3niOhC2IZTXn0erm9+xp/2M8iL+54GfhJCdDir/mlhWZ88AeCnvRXidTilwkRE9Ca4wstvYeap2P8gERV++zG4yuxfudftORHOWtMJpyX+IhzlfO8ZteH1cGznfwJ4yv97M4A/AfC03/8EgIdPqT2PwVliPgfgC6FfAFwG8A8AvgTg7wE8cIp9dA7ANwBcFPtOtX/gCNJNAA2cjuAdy/oEzurwe35cPQ1Xxew02vNlOF1GGEcf8uf+hP+WTwH4LIAfPYuxvsq/3qOxR48eGc5afOjRo8eaoScKPXr0yNAThR49emToiUKPHj0y9EShR48eGXqi0KNHjww9UejRo0eGnij06NEjw/8DjrFYFWD67s0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 10: 0.001168742193840444\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 20: 0.0011687043588608503\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 30: 0.0011686686193570495\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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3rI7UmfCcjAC8SgyAWCEFQLlIRsAFJnhFMyxLTmR2b7bLE1I3TdFZiysQNyzDs9ZVSKL2VRqlgalXaRLMrYtuPqh8bYyxjsG0TLDHWShG4BZS7bm19E9996QJ++PUfBgC8/8MfZF6Oj+I0Pljat/WBiFYA/N8A/itjzBo/Zgxfa1vX/TwRfYOIvnHjxo39dmOggQY6INoXUiCiHJYh/BtjzL91u68R0VljzBUiOgvgete1xphfBfCrAPCpT32qT890/6inB373jRs38ezXvwEAmG7PQiETjxKiojDqycl0rzt+Xc7cwUJKjJzbbS4kpGsXmYz1HoVA5vZLKZBJidytbqMix4pDBEeWJzi6vOzaSwl8booLfDsqGqMCEoi4nCsaPXnnIcWK0SgVIxaV0iFZ7ayqUgQxqxKRY+pEkbKqA2ooKxWur5Rm4obGTGlUDJ0FSwRHB2z0tYkWirKqceG113DhpQsAgPe8/30RKTyqmP8AaT/WBwLwzwG8ZIz5X9ih3wLwswB+2f39zX318AGSAcLkv3XzFq66yk3G6LDfT/xEZOjzSHRUCMJynuOUk/eXxgWkiKDNM4Iik8ELr8izICLkWea287A9chr05ckEq0tL7pkUqjVxEUWIFCByQGdM2xOzj7oKyCutolih4zgprVCz/Zx5TGcltj2DmJXYnvr2DNtO/JiVVdBJ1LVCXWtsuWPXt6eQboQV85Y0PIoKCAyy1oR76xv47osvAQD+0k/8OE65SlTNILJ3Iu0HKfwEgL8L4Hki+rbb99/DMoNfJ6LPAXgDwGf318UHS7WTg6/fuBXi+cFcluPn00YHTabg0cDR8QjHV5dwYsUyhdWlMUYuWemoyDFyisJMysAIijxD7phAkWXIswyZYxiCCOQm+mQ0Yi6/cULbCtKI+7lCz8TcSYbZZPvZgendlFIGbjGPQXCmMMpzLCubQEWpeE2l6oAgtqdlqIexXZaWSTgl5tJGgdfvroW+eHdq4xyqLTGzp1aoKsKF12ypule/9yqOn7YVsbMib7mT74cO8Fb3jfZjffgK+t/1p/Z634EGGujB0uC14Siuouk66Iui3rt7F6XPjaBN2O+tDT6dV9P06JH6REqcXPGy/ggnVpdxdMWbC7NOpDDOcxRuf5HnyJxYIYSAlAJCcH2Da0uZlJ/3JIRoOCVFz0vTgxrmQIVe4pdkyMIzlFKJDsLmWVRhO7RV1CMUKsPYoaOVyZido1HWNaZTixxOHysxGdvzLly9GcQHbaKHk2EQThuC0gZrrgDP1UuXoZ0lBAV/i0dpfT84GpgCOr59E/968WFtbR1GR6VbqjeIdyDEwB0hCMsO4p86sozVpZFtH13FySMrQScwKnImJuQo3DVSRkWjYLKxVw8m4onjPjKLE1GwiW+7yfQGSJlCPCUdjXk6heS8ri22M8+ywKQ8gwjihFKMQahk8teMWfj9tdIoVIaJY56rSiX6lZevWN32hjKJe6phORc0AOUSNKzfu4vKiYbFZNTvRrko3QeeMu9X2e9jh4CogQYaKKEBKexAfqW6ce16WNmaq2czdsGLDGMpcWLVWgKOrkxwxhUpeezYERRFHpyPMimjSVDEGAVL7dW86eoohEiu50tdSJzKA4VMKj7wILDkzXq8C7v2L4ooAAB5nooTvUiBoYNaoXbVokStUDMnKwA4umxNsktrG1h1Itf29jaUX/fYOwKEsqrD869ev4HNNauoXD52lL8VDmq5P2hLJw8Q63rWfmhgCjvQ2pp1Z758+VLIxqxZHkUvLvhtSYSJs3mfXF3G0WWrVT91ZBVnTtgPbmk0gpAimCEpmdQEcn4C9m8UUJo/NjETo7+emCUChgUdMaYQJjXTMXDqneBNqwTXUSTn7bTf9dlN+Kabd2Be2kA7WUwIDaH9GJnWth/L1fEIxxyDuDubYZsFbPJAKSLgjmMEL776fVxzOTZPPv44pBNL7MzjcuJO8avu/o+4KmIQHwYaaKCEBqQANKBluvvNN2wR2PXNDVS+COr2Fvo85TNBOHVkBQCwOhnh9LEjAIDHTx7DxMFaazkQSSxDTHsmYowBiU6xIigS2THBUYdTThoTU6PxjppgYmErfxdqSCwxzf19vgr9qCF5vkZAXkKIoIQUJiIAIwyEz9KsBYRw52ty78yu8UhhaYIjSxadTe5JzLQVOVSj6C1/5/XNLVy9YpHCh9THIbvcsgywUIVZun82C64PPUh6xzOFrgH1+6bTKV5/400ANhrS6xRSt+YU/uZChI/y1NGVwBRGRW6j8eACmrjIwCA/iXbeA38O90iUDT1CIj6Qv1ca/RjezzGFKCaYTp6QTuSO/ckf0zrcuHO4iVYaRCa8szAGwutrKE52bbiIoKENEx+MjvkgdLxmZTLC8thaeZaLAvcq72SW5mzgE7eua1y7ar1Vq7JEPhmjkwxCVaxeQeKglQcPgAbxYaCBBkroHY8UgH74tbW1jY17VhlVVTU2t2zOhHmcNJcCKxO7Up0+dgSTkRUZxkWMb/Cig2hsh7ZXNJJIEQQ7X0oZnJeEENEiQVxRyVFHKn54V2f7/qZ7dd8RNbTRQR9qMDAh47MPxvLvb3SE/5qhBkECxosMRtjiu4hKxyA+CA2h4/itupX++Ooybm3bArVb2gQNIPl+uVV/fWMDV6/bSN1yOsXS0SPYF90HPwVO8ywRe6GBKTjqqg+5tbWNW65StFYq+Y2D+GBS8aGQMjgfTUYjTByU9XoEwFoYBDWtD0ynkEQstnUKXnTw0ZQiYRAyMg8pu5mCe+Fux6amG1IXg3BH5ogU/DkAoLWCCWKNARkWpSkEhNcpaA3DxIogMmgTdQrGMgTBdBJcv+CZ8rjIYsKathYkUF3XuLNmU7VV0yn7GPoVBDbsaodZfx9FiYPULwziw0ADDZTQgBQA9OjYsbW1hdffstaHren23Dv41GhFloU4hAkLiU4UhQ4NUK/40N5PRMHlOXMRktJHSSboQHSiBp4/AUgRARcl7Bh0o4Ne1DDHyUm7PAtkBMjBdeOUoX5bkIChnSwRGoKhBik0tH9nrVluCMLIuzxLgcyjE+jGamoS1aO3LK3du4eT6gl73+wApscDECXmdWMResczhZY8xjZm0xnWXdCMFR+iTNo2RdqPL8skC0SK8J86JntiWeiwJEghgiVBCAqh0lmWI8+z8NFy8YGa4kMXU3DVVLhHY5zwaXBUH81zcPJHtFLREkIm6jqMgCGTWll6LRHeizTVKWhNyfU8tZwXxXIpA7MOL9pBBBPyN26ub0C70G3xNpodu+FJb6PXPjjyn87d23dCQFTLnMUc3QTZ3IoAkDOmUCuFsVM0cl8E73XYxQi8uRFwjIQzhYAUJESWxWApKSEkYxB8v+iSEAmGmJnSxGwDVv/W4bPR4drdm3fBJ6ARIpyTmEo9g/CKP0Eg02aQ2miQ9tfoVu7IJMdkPBjaeSYxcoyTytL3ulMxt721CQCYTafQc6pc8ZV/VyDgETJVDjqFgQYaKKEBKTSIr31VGXMxcmqKD4IIeeeKzKwUHV6LUTRoiAxBDxGtEjJBAJnTSXCk0N3uI2JLnWGeetxpj1sPUn2EO8pyPHLUwBGRoTYa8G3DkQOzRFCoucmDnoiZXd3RDnTA+1lkEmPnMNZcpAlp/IpyOoW6Km3a/sOgPu+ph4wGpgAAoES+1k52rcuSJTF1B935nGy69ViCrF+KZR9140Pm8n5UOkYoneRWcIwj6itYlCVjCnN9bpmdnkDMCscZhAmT2l7jGYS9KvVXaBvF+HsZplj0okRQPLIJT2iMi+8LgbUp3WbGQWK/ZZ5loQZGRgQvFPDAMH/v0uWIrErOFBbD/HuSDB5icWIQHwYaaKCEBqTQQd48lWUZW42JVzRNKNVws1WPr7KN+H9qbC9CHE1Yb8dUcWn7KZJVN+lXY5Mv9H7VThAABxodqCHoKbuBQtJnLkokiKnZNWoWpumwMMxRNBJFQ2ORxcS3uRCh8G1TyUgE1O43n25vQzuk0KWQPFB6SMWJgygGI4noW0T02277PUT0DBG9SkS/RkTF/rt5uNSE+9qlCuNp170VgF/jLQ8CBG2MtTpQKkJ4l2IGjhtJVNpk0PHhsl4mcjaXtRt9S89B63/y/5G7B/n++SYlz/D/NTqBdATnEOsusfvFSe7uTvGd4n52ju8732Ztl6ceRGQZQyaRCxHHpdFdgehHsnbnDrRS0ErZ9HtdP0YHLXjazjc4VC60GB2E+PBfAniJbf8TAP/UGPN+AHcAfO4AnjHQQAPdJ9oXUyCi8wD+IwD/u9smAH8VwG+4Uz4P4D/dzzPuCzVWDr+6+3gDIgppwZrklYIJInBLknGrFa/HuFuRIe0jwwAtdXpczXtfLNmfooeAAjxCCKszQw1+/55fotEvjlzYexD7P57TQC1sPFKkE4ACYExACuMsAzkvRntpqhD1SGFrcysihXkp5rwF5jCW9geMGParU/hfAfx3AFbd9kkAd40xXn17EbYS9UNHrTH33yk7IEVbZOB/k/uxD8hXQppWFVZpyV20+4nUsly0xIaO/jTxcftgx7ZhTSZJk9/2vXHWgtbg7eELTvqZcstEp+AFLyci2OOcMXiGhtC3UNgGCBmfV8YFbk9txmZfqBZMj+L1QrPZNJgnwdLuLfqGB2pUeEA6hz0jBSL6WwCuG2Oe3eP1Q4HZgQZ6CGm/ZeP+EyL6mwDGAI4A+BUAx4goc2jhPIBLXRff7wKzOz0gWQ+Djz23EJjofMMu8Ouq8r4NSqPyBVLLei7U3u1LJ8pG3uu+Z8xDCQwQ8A0ihPoICY5tBoiY/piR3ZKXEmxPmuIWsf3+fCZesG3bjRjSLYXE0siGUS+NR8EVfaZ0MhrEXqYsS1TOZ2FXGaoZ7cr9+YHetJv2jBSMMb9kjDlvjHkKwM8A+ENjzH8G4I8A/LQ77WexYIHZwxCjEoWumf8/l2GVVlBateIGuPgrELXWgEFttP1faVs5uaoxq2pobYIz1N4oFRXCruZZYWJQ2tGW0A4+Azv0KUAYlET53tTEp9sGrB4l9vJbtt+T6zQSawVF5tG0PoRCNzCQUmBpXGBpXGB5PMJISoyk7JxX4TcnYGt9DVvra2FxeKjoPlgpDsNP4RcAfIGI/kcA34KtTL0wHYRM1rWCLTqGBISIQ8OUTU6lZdsU76eMBrRALuMxr1Moqwqz4PMge/swt29Mbk4mOlH3OM0bPGo2dhqVFA7w0Ok4/ZuXGNZsVKFiDGQuMX+MVCkZ93vGEM/zN4/JT2w5PcvYl0ZFcHkWZYmm2ti7s2utse0iYzlTSIDVgnSg+oW+B3g6wAcdCFMwxvwxgD927dcAfPog7jvQQAPdf3ooPRr3wwA7RN/dEQErrvBrXdchDblV0LfXR2UAQSaYJqWQwWpRax1Kqa9MJu0HdeL4vjZCOy74O+gUeq0Q7i2YKJDEMSToqsvikI5qS/ZuVKLy+5L8DWaeNSN2uVOn4K0VbJsXu/Jp6oyJv8vSuMAkZ3EQJg10889RdY2Zt1KoGjm8793eluL7pgo4QNTwUDIFTou+635EBk58GtZ1Hb5YAkK+PwWDprHKezxmUiBzFaKKPAuJUXb6ofr62qka6LoXN9f1MQPTavT0pV+5GHY3bxHnfqM8nU+SopN2miNy50rXXcFRXCHJn+mZsvc1Aayi0ad+z9cFZiplCtL9fnVdY+Oezdc429zEZGUpdKslQtxH5d+uaJ/9GgKiBhpooIQebqTQ0NT0oYaDVcQSfHBQLmW4OyEGAMHE1dTv8qtTJmXI4LwymWDV1TUkpjTbVX8ZVOABwl3OPu2NnR7G4Xs3OugXG7qPGaODyGW0Dso6o02r3XkMDeUke2YzOIqHUvuzBBFIxlqaPvR9aTTCUScWLt29h01VJwuqDy6blSVKJz7MNjeg9Sl7jlck8zfexUp86ErHvod62sXDH26mAPS+WO+3vogewbSbXDz3H/t4Mg5QioPy9g9Msb7DuMCqqxC1ujyB9JYM2OAp+wzq/ZHafW9/jGGbKDkHQCieuhO15HhuGeiRxdrSR1NH0LY49Ld1j2hhkkK+3pyrjWbX23f27+ldmgGrT+DvFpjCWOCoEwUmeQYxBbwAwV9XaxVqlxlPAAAgAElEQVSYwtbaGoxiqeUa47rbhemBMAb+8AVpEB8GGmighB4apLCT5dwASVxCH0yexxC7tNxzdHYYj0Zh1UrVdynPzwRhdcmKCcuTUbA0rC4t9Twt9chbiFK3v4SMMSEDMQyS1PG8x22aN9rzLA6p6GCMDnZ+KzK4vug+NKB7UYTWOiS+tW3vP2DiM/z18I9R4RopZCLa8cI6S06sG+UZckGYhVR7ER4qpTFzHo1rd++GLEwinz9VFvX8eBTooWEKndRQ9yY8waTn7PRj8I+Ywj99j7UHjx0/htWxFQW2N7c7f3lBhEJKjEcu8GZpghXHIKSUqU6AycRKacDJqZkQCeTf+cMixwhUeDkyXo4WoW0My+a8g65h9xYH09AdMPjvJ7Xp0Rs0dQoNplC791J1jVrZSVnXKm3XNSq2Hd6TZXzh6d4FWe9GABjnOcYyQ6nLMDb+1ZRSgRHMtqcoXanAYjJiFqd+2snJ6WE1WHAaxIeBBhoooYcbKcyhwHF3QAkhSjb8M58IUat88tQJfPz97wUA/MndbzccXhxcJYGTR1YxcavQymQcLA79fTJQWgc5xdaWdFmHKRVSkuu80k0phw6icwAxKO7rMpIxgOEl7v0a0OFozJSH88QG/wxuWdBcOchQg89LANgV2FeL0kpDqToiAqVCvgqlVLLft+s6nl/VtXUsc12TSYJbEXwOSKQFdyYuOOrksSO4t7WN9bpsjgK0/20AlLMpps7leeXk8da5fcv+oy5KPHRMoQ9+9e3fiSEkzj87PLfZXj16FOff9S4AwOQ7L2CjnIVz/PTKSODk8aNBp3BkeQlSxmFNrBvuxspoV+jEOUNpgghViUTKGNyXbyeBY0Rau6rTvqRaWp1aB2YjQmm1JI8jCdszdl7kCSbRD3R5J9pUZVyPoIJOwzIFFdqKT+qaTWpVJ4wgnMfbNWMKyooM9noFKYjlu5DRsgNK0+IzppA7U+XKZGL1Cp5JNsy7/vlVWaLc3nLvrEFZf8r83VLzu32YxIlBfBhooIESeuiQQov2i8UWFBn8/YmJCKQUjh8/BgAh2s6f71fzcZ5heTIKNvAjzkHG35LZG0IyaK0NlIqraFUTpmFFE4lCMmNFZfMsFo6VUgQfCMmKytr6EBERcASRFFwRIhSoRZYFRKC1biEC3w7IwLU5UlAOKShlQ5B9OxEFaq8YrIMIYN8/RQSqZuiAoYYgskkBIglW3aLTg4uIwlhIEqFuBpEV2YI7NDFLDVHov1YK021bWFhVNXIpd+WwtJPSkdMe/YwOhR5KprBfPhD0CAszBONqDQKqLAHHGOqyxMrKCgBgkkvc7Sg8PRkVWFma4MjysnsmszKwdzBAkK9rVSfQWCk2wVgQDwlyXpVAzuIoiixPJr9gQVgJgxACwk8KEYvJyEwiy4uE+fhrtNIN/UCcIH4Sq6pCVTP4r9WOogBnFlWlUDnGAFhxoqpiO14f9RZZJgKDTLJKhx+RbVLcHZgyYwJEth1S8wsRivkUrEanMSY4MlXTKXJn0twN7eVbftAM4qFkCp1E0Qw5d4AXHP1Eh1CWWL99GwCwdvtOmAhSShSsrDkP4AnXEiHPMqx45SL37gPYNWArY42qZhOGrbpa65DFSeuYISjPM+SOKeSZRJ7nATlIIdnkT5mCTJCGYypFjgmJUEmq6TOhmU7AqMgUDJvgVV0Fe35Zlg1FoWbtbtRQ1SowhdqNhx8br3vIZOx/5/TgEZTpLn5SaHmv0yLL7DhRrL41crkc8zxH5nRCMssCmvK1IPZKu0ENnA5O97D40wedwkADDZTQo4MUFqUmZu9aYPiG1lBlieuXLgMAbly5AlXbbElFnkPNrMVhaVQkvvZ+bcqzzIVKk3tkhLy+C0CUo23brpKKoYOIFExjv71DWdesxqSF0j5EO8+yAK2trsHyei5yZDIiBWAJRV6HVTCkSG8QRwfcu7B24gIXE3wtxmlZhQpbNdObpKKETpBDolOoVdDfpDoZoB0QxWgBWXHkvBJPHjuC4nIWxK9RlsVKUgwdyCxH5muEigcB5Nu0e9Fi9/jkoWYKuzVP8uNAfwBKsl8pTLe2ce/2LQDAxt07QaaumALuxOoqsms26/Qs3MUqrHJ2HveuNMY0RIaoWLOTqs0UFHPn5fut3B9l4krUkBUvPhvFh8AIKokiix/72OcTqHJUVdXJFJqVqjVTNDb1BukkjyKDf89ZWWFWVvEczRmETnQHvp1zs5/pngRWV8ATuWJnMtH/ZGk8wtKowERG5azXKeR5HscvzyJToP2D6r2KEH10WLqHQXwYaKCBEnqokUKLFlXl8vN2cjbPMshMxhRcdQ3t4K+pSmRudfbZgAFgU0Wlk1LO7z6sJBohup+tdEpplKUTH/xK26NcjKghtoGY3cmmnmdefKKOlgUhIN1KLaVEKX0WqDygibKqUFYl8gCNWVHanmXXOiJ5NKBTdKCbCkV2nvcOZElsq6qGUjHGQTDTYajuBOd3yZW2vEMNOyQ3/XYRDwlfGhWYjAqMvBKRKIxfnmXB+UxmOWSWhz62b7379fmwvB27EfXenrIvpkBEx2BLxj0N26+/D+BlAL8G4CkArwP4rDHmzp6f4f7u5fV4boT+n48wXlrCmXPnAQDrt28nk9rL8StLE0x8pFxZhTsKIgu5/aQyiIEzhMS86C0Exrn4honEdAeaTSTLLHj0I3umoABprZu0t5gIJkownwUgQPkiL1GWOYrcbgspE3m9Z5gS/wOfEp1vA14UYEyNtf1+Lz6YXttxm6lybtEMzmoygnTLe2TGvXkmsTwZo3CiSgX2O/nfE0CWc53C4qB6kW/2oEWJlNg4sectSvsVH34FwO8bYz4E4BOwhWZ/EcCXjTEfAPBltz3QQAM9IrRnpEBERwH8ZQB/DwCMMSWAkog+A+An3Wmfh039/gv76WT74Qv6LHBqaB2TmIQ8x7GTJwEAxXiCbRfjQABqB3mXl5ZwZGLDqC9tbAVpIcskSEgYrhlnK51feX0BU9sWFvp7RaPSwU6vmuKDP6fh1CRINJCDQw119IIcjazTD2DrJ3qkMCqqIEIAFjJTx0rZXF38imyMbikauW+CThBERD1ZQDCjRjbnZhAWI9PRNKYhWnRb8wlg940ZqQQJnDiygpFDCkrpMLYgiuMqo9KW5O7Xz90oxPdP8++ym2fsR3x4D4AbAP4PIvoEgGdhy9KfMcZccedcBXBmH8/YN6VmxG4yMsPKEVsj99jJE5iur7lropvveFTgiEuakovb4V6jwsnq/KPyUJViOrCuMmc8cMdPnlqZoL1XDVEi9UAU4UPOMoncv2+WBTGFkEYJ+i5WdY2yKlFWzosvryM8noMzA4MTwkYTJuJAZGp+PxExU59MJmiaZIU5fLVSv7OJb9J2tPJ09zdxHiMuQhgcXVkJlpmprlKdShDLRBJEdriQfy90OL3Zj/iQAfgRAP/MGPNJAJtoiAqmmXmT0VBgdqCBHk7aD1K4COCiMeYZt/0bsEzhGhGdNcZcIaKzAK53XbzbArN79VlIH8ouatwkd7H2p8+cwc1LtiZuPa1D7MFoNMIJhyaWsgyl0/9lWQbBsgk3HYGIrTrpqk2tlT+c54vJqFQ55/dn0q5awYVZygBz80yGPlu47p/BrBdkXbZLJ05UecnciaOYwJWOzQKvmZTBmYn7XEgpQjbrVtk4/zP4/czKkBSKYWIFPycUkTUpukDrOUwsCVARMCyf39JkHN3EeX0PageOhQHcA+1e6TjPsfn+4JQ9MwVjzFUieouIPmiMeRnATwH4rvv/ZwH8MnZRYHbXxEZ7EcZAjZP4UBMAcr7vS6urWDl6FABwZ7rNgqtiHsZJJlFXdhJYU59kFgcBAjcjMpHBixKiyRTiRBQulsK2YxCQ4nUt3aSMEZTRuzFts8hKroMQqfhSKx2CtYgAUk6sybo/D3L6kYw5/yxPfNZqNpHZZPXbrpFC+0QUSBkBz+eg2fsLMNGQMxWkDCJ9fvzVZRYduwRViSWEi0ncYawxCp1js1fqZx73X2DZr5/CfwHg3xBRAeA1AD8HK5L8OhF9DsAbAD67z2cMNNBA95H2xRSMMd8G8KmOQz+1n/vuihwCWEiTS8mftO2cVIrJBCtHjgAA7t28kbgZ+7Jjy0WBjdpaKMajAqOi4HGTiSOTJ259CCXUucuNW7WLIk8i/qL4kLo8WynFrehCxBBrmSWoIURG5lnQ/jcVlUJQgOxaaZCvydxYHYNYAQutPVIoGCKx5eD5qu1aTcTQQgfcB6G90rdFkSjOJNYXFqPQh1QsMhMYFzZqUmxtJ+JfolxsKGAX+c66pNSHT0nZT4+UR2PvD8JGfK+D72XPYjzC8qrNoSCyDNoFREkpMXaZnY8uTXB3FoOmMpkF7bsWCHH6xOqd24kc041za4CUgiVxSWV3Yi9dB4hPHQFCsZ1Ln3chQ+61/3mGwjG+3OlBQir4xuTnSVaSQCCKDUJMTJLnWZL3gdgv1bQ4+HdJIX9TlGBWiY6Mz83Jzruf6EHSAyzLNKGuqpjODpxhdOt3Oj0ldylBLCTmLnDOYdMjxRTmUkPHgLi5KypGY4xdwpS8KFA6PwVZFGHVXRoVKGQ0J5KgAAoMQwpEDCkISsyTQogwYYsmI2DKQe7n0NJBUPu8LoUg0GAQWYY8z0M0pZgb7EOtFr+/v1+SIYrnqGyYEf2+xNw4DxGw65vnpBWjOno+h0HUVYWpi+zk6AAcKYiohwEtPmHn8YpFFY87nXOYNAREDTTQQAk9kkhhLsduwISduK5pXIJMYuQqPBXjMSqXo0/XMdR4ZTzGxLXLuk7kc8PCUEgIwOkBhJDIM2Y9yGR08kEqCvCak137hRAo8jxBB+DXMKQBdo5HKjZrU4bciRNSSi4ZdBOlB70IY9+ToklURucpQWmW6BQ1IF350YEO5iCIeeJElP97kIIQoCoWmC2yHKPChZW7VHf2vWJa/GZsSK8HJvot37ulByVKPJJMAVhQROgZ1SYjSLaJUIwtU1haWcXWmvVuhFYhOObYygoKeRMAUCmdQE4YE3UHjClJIYIJzKcr5zqCVAkJ1uZigRcl7MQeeUUZT8rKxAf0iCJeb+HTjpEQHUJCcyjTfoWbuiu4eZLrKnggUVMs4IrGTpGhOfGb4kavErKj/8x/RLt39sLdqMiDvmgymWDixMfxZBLNsk2T5H5nfIt6+j336OHQID4MNNBACT2ySIHTfhSLzeuIBAq3aoyXlsJKoVSN3K3MqyvLIc/C0ngEpU3MAKxNsD4ICAhX8MUiiAjf67oO4cYcBTSRguhACkRRhACAUVGkCU474DMY3EeAwG3nIWqKCWF/XGkFWXGJixzcC1AwBWZ0mGqLEp3wPxEz0HlOQBo7IIoE47P+GwPU2gSkkBcFxg4dTpaXA1KYLC9DeAenRuj0/Yb29/N5bwumwGnRweuV+wQhd1l/l9hHoSsJSOfFOCpChN10cxOV1sidR6QhHROeMEZA0AzKE/I8mjGbTEGwyd8lPnjGEXUCWZyIDL6nNndqTTxeVLYLvqeGdu7pR4m5zr+HHb7UpMfdvLlcn0xk9DCFHawSXde3z/P9R7AK+TyYPpXm8niCsYuAbTIFHx1JUuyq5sNh0EEsfovQID4MNNBACb3tkEKLdqmpMYj5BCYrK8icyKDLMuQJyIsRxoUduuvXr2FtYxMrp07Y80xMfiqBUC9S8MpTjZW+aX0I4sMcsSKq+MJNw/smisAuBRxZMal/4WP2fwblOdLhFpcZy6EAIpY2roEUmP2/DxEsIhbsKD50+jkAxlsSoKyVyPV/PB5j4sLiOVIYTZaY8xKwSCl6f24fHdQKf5jixNuSKeyGD7QsEQxyj5eXkDtTVSW3IbQdrsnyMk4dsUFTd7duQJsYrCMEsXyJBKkdGFN1ItO3GQETGTCPEbA3bFoD/M4wAIY5Wsc3JeY4NJeYHC6zLARNQdUtMcFbUrRStgwdLOMRjBHwtmcMtpsLWh+a4s8CzMP32QCh8K6ua2gg5Kgcj8dYcoxgaXk5tEUmg/iw6ATcu4SxezvDYVkmBvFhoIEGSuhtiRS6aB7cajkwOZiZj0YYO0emcns7rDTQGqurNrdCduMWbly9ilOuEO04y0K2pFExRubEBhJR0Uf+eb3ooL2/9TJIj0WFZHxRCwi4+BChdM8tfcfcDdIzvZ+GL0NPzgVcCIqxHyydXJax+IoO/4XEhyGJcfA+4zspGn39TaYo1ayehI4OSizHti0+ow3GI++bsJQoF31byIhudlqNH6QO8qBFibclU+gcoN2MXMjRJ7HkJv90awshsbvWOP344wCAyVsXsbmxjsrdO9MqTCaldSgyIohCjUrPEFLlPiV/w3nJBvVsprqFLquC3Y7a+iYnDPIyEy1aH7rbkRU5tNaYjGKSldLltVS8OrXRIIoFZxJRIqmI3bBK6HTy+3u1GIQfZ1aUVps0hZ1/ewPEepVKQWkTzJBLSylTGE2sfiExo/Lh2Q01MP7OkP9+GzvbNIgPAw00UEJvG6SwH6clzpvtCuyyDmcZTp89CwAop1NshBVYY9khiCPLy7jlXaHhFi8XQTmdzULBkSyTwZHJMP+FLqIuBMA2WyJFA0VE350UJbQQRDJo7RFM9hBiQloAeZEH+L40HgeFXq0UcpblOWPxHjyjERcfOGpA0+egAzVorVJ00KjarZlYodkKX7E+GgBHXN4MKzKshLb0uSGSDM57EBAa+t/DBAAHqXR8ZJnCnl5+wZEzTOM+WbGw8uTjj6NyobbQGtLlWTh56iRuv/EWvn/hAgDgwx/9SPj4MiEwdXJ3PioCLOP5ELveJxUb2h8jdexPLCiMGXCNfbovzXUwpwPslFSsyJxJVWuNJef8s7m1jcI5cilVQylXbakoUgethokypj3jsRJRZFB1DeWyXHsmEBiBaTAFL74QEvEhFLRVCjLLcfLocQDAypEjifUhei/u0QS5wGXzecTup/g7Xqew1wHoG2rTOuaYQp5j7D7k46dOYbq1BQC4XdfIx5ZBPPbEE3j94iVsrK8DAEptMA55EwSmjnlM8hwZcy/mEXgLWcCpQ+XIGUOXQtGggxnY5zV9ENJrwEaBjRZbdbmfRJ7nYSJOxibUypBSQrkSe4oVtPWmThLch8HlkhQUkJqqq4BAWmigBx0oHZGGJgojW7PaGtrYDFsnH3sMQJqXs5iMI0Lo+lH6PqJD0TQuBi8Gk+RAAw10qPTIIIVD1ce2NPGWtJAQboTGy0s49bita7Nx717Qtq8eOYojK6vYdNB27e5djE9a78ZaqZDdaLssMXI5HuEsFORSji9Sp7BLZOhy8mG7O0WGsNuwukq8zcSKtPISgWVIt0d8/wHkeRGeM2XFemvpi91G+C+zDM1U6iErlZBxpdemUyxoIwWTiBJcZAhmSKWCWKcBnDhxAqccUigmE6w6pJAXeSMFXRMC7FF6N3M359A+tAV7nDT7LTD7XwP4z93jn4fN5nwWwBcAnIStGvV3jS0pt2s6NEbAUFlodjxMkwhYSmTA6jHri3D8scdClepiMsOpM49h+7ItinXz6lWcckzB2uPtj7o9K7HsAq3yhpsujIlyLKXcqUsf2cxH0HUsvBL3AownpT4MvW3+hCaLAHyEkGHdzvM8VNWazqZBf1JLCVm74LI6s4yhI0GqTW0Xn6Q7qnEHpmA69AjGBJGhyRR8abu8KHDi1CmcPOOYwniCybIzQ3boe8II9IkI98VJoV+UOGgd5p7FByI6B+AfAfiUMeZpWFf/nwHwTwD8U2PM+wHcAfC5g+joQAMNdH9ov+JDBmBCRBWAJQBXAPxVAH/HHf88gP8BwD/bzU3vu+sGxQW1yfS1U3oZQRCFPXr+vU8FS4TSGk889R5szdy2MZi5FWmSyaDcymS0RBR51kIK3vRGAuDhzhzypyAfqXKRKRHjSd2owbdT5BBkhu5289YE5iMI+EIrBAoxBcYY1E5kEKKKFa2yHNJZAOwxZp4UEsp7frLYD21MS7GYWB/Yu/l4DwNiNTpVsD6srB7F8ZOncOL0aQBWnJG5z0JF3fDMvV3/1mJ037/tPdB+KkRdIqL/GcCbALYBfAlWXLhrjPHOfxcBnFvofnvtyIK06P0b6oW4nwjKeeeJQuDJ970nnK9Jhg9xa3MzwGeijFWjFtiaOktEJpGxCUuNSQk2KRI20HTz7Xo/g5TZhP0dTIDrDhZps+thEN+N9cDqGuyeLM+hXX09VdfBKlHNZhBZFpiCzLIkslKr4JAR7kWI7+UZQiJa8GjIMBQmiAy1UoHZTFZWsXrsaHDbFlLOFRt66b77Nu9SUNijXLEf8eE4gM8AeA+AJwAsA/jru7h+KDA70EAPIe1HfPgPAfzAGHMDAIjo3wL4CQDHiChzaOE8gEtdF5tdFpjdC829KVPqEpgSKS6OvQuBJgItW2+4p97/Phw7fhzHTp0CAFy/fAnrG5v2VsxzsVIqpHArawVBsQiL0QYQPghIgJiiL0kt1kTyXeHGfSJGBwLYUWRotBM00BBNujw0bYYpuxqXsxnq2vkiVCXELAt+CzLPIBGTyMZAsegFmaZTMy1Fo39rjThMxnAvRh3S7B0/dRKj5eXw/MTa0IgjCWN5AKjgMNFwr41iD8aL/TCFNwH8GBEtwYoPPwXgGwD+CMBPw1ogfhaHVWC2QX3vvLNTkP14Oj2L5wyk/0hoaRknigJHnHx69PhxvP697wEAbt+6GRyWYEy0RFQVRqMiuvNyTpSYFyMYbgU2Nc2QrJGKD3Mm+z7EB85wmv1Li7D4KlIFVG3FByUl6tkMla/6LBmDMFmci4wRNNPdGwOmU2CZoUHht9GIXozKaEzG1jX9+MmTGE2WkkIv4Xfv/WBo7uZDT7vo757FB2NL0P8GgG/CmiMF7Mr/CwD+GyJ6FdYs+c/3+oyBBhro/tN+C8z+YwD/uLH7NQCf3s99d9WHxvZeGDg10EG4ZxpQ0HmtJoLJcsDVUDj2+ON4bMO6PN++cwfkIK6QMijGZlqjzDQKv1Bl0UmfepBCv7MRUijfRA9zEMC+xQek92uNFYV/bBiysWtQXVdWuefrdGYMKWRZeIekCC9DDT6JbBAZjAlKT4PY51qZID4YECYrLuhpZSUEPe1E/LVSvd3utHiHKTocND0yHo2cDBZAe3OoeY1pHCR2IDIIQh9jMIyrFOMRVlyB2mI0hp5uuZNMsEoYYzBTKhai1Tom82iZF7smaJMBpP1KYhySdzk88YEf85YYMv4fexvh4huUst6Nym1Xs1k0T2ZZjLHQGpEnpIVxmrEgnuHaICh7pKyrwBTy0RjHTp4EAIxYlmZ39/Qd+CpBnWc0rrufU94zv8N75hD7MNBAAyX0SCKFRdHBXs5roQaOnnmUY+/NCKunrPvs8ePHcOOKRQpSaxvzAJvcVEmCcSHGWqmwChpjgpsvCZ2s9C2l4w6uySaB/O1Q6V2JDz3Pi4cS+SGe7ndT3C+EhKorKOcboKoKFRMlvM+A1ob5fDAI55ya0mxV9t5lXcNXoSirGso9f1SMcMJZiMYuc3NniHjrLVLaLTY46PX8fmCSR5IpdNFhK4O5BNls85aBQeFyC5w4cRzXr9iYCKPq+IEbm2W4Vi6ZS54FTXrTi9FTMhHnxC7YJhMfEgmD39d0Xt/SQ7Tuyc/lHJP1NNntNjSixcbtC9YIIVFL6xFaZVlMq89ELq11ygS4jgHp7185L8pKmxBGkY9GKFy+TZll7tpu2SDVIyzyZe3RS2iXdL+ElEeWKdw3i1DDnwGwOgQyySefXuLk1eXVVZAvJ2dMQArWa6+Gc/CDFIQ8izoFHz3ZFNnbyVO6V/fW5EeqH7DMokNH0GrPQQpN5WLHM5E0Tet9ggu0rCEcg6jLWTAVWtDk7qsUjPNO9IrGxDeCOiY4RZfn8WSCfDRyz+tIxnoIH9T91DQcJA06hYEGGiihRwYp3C9kcBBA0F+/fPJUqDxU1iXMll0Nydi6kqULqhIESBFNZH35G+et7p3oIJUFWtaLnUOnk4c3ntfTrwaC6AqoamrOq3IWgqVUVUO4HAzGGHitig2IcuZFraIIAWeZ6HDk0lojc4FOti5oHo73f097/dJ6tA0HBBfuJ+p4qJnCo+A01s1EbM9HkzFOnrB5AK9srMfJpA00EWoHmQVRSMYyGo2gfak55vLb1hl051hsiwm8vYjI0HCUTqQSfqRbfEh3p31hj0i2lVIh6lRKCVHLcF4MdGJp1pS2yll3fdtaHA3WmfN/KEZFiIT0Wg3TOHs3tPDi8SCslvukQXwYaKCBEnrokMKjgA46qcOuKaQMRWMuvfUWEIrMEpRWYeWrVY3KBwsJgTxnsNjfssu60BU70UIHrFNN+L6D+MBepRdBdD4naZpmE6wjoT+zmc1kJVkYMwkRE7eaaEkwRocqVZaIjW1K/hzrURn7klqQDvarO0hQ0H+vw7N4PDRM4UExg8MY1vCxEbB03KZmy/MclXOFVlXpPlbPFBTqkIxFQYjaXU+sbFnXxO/WF3TJ/qno0JT1d3ChRjK1458O34T0tG4G0b42Pmd7eysmYymKwBSUjunetXMF9xW3tFbBksG/JEPxOUKIYN5s9BY2gVvfF7hbIeP+mCcPkwbxYaCBBkrooUEK95MOl48T8zMgLB2x4bqnTp/Gta0NAEA927Y5APwqRoTa1UeolQwxAkQqsUTMrfiUagT9KZ3teD2SbXY2u2963O7uUDImIKLdl6TVgRL8HqNVECWElNF5idWo1DCJY5NSKihtax4HbRAdxljBmSb14YTWvoaIeNg6xJ3vezg9eMcwhQP/4Zq/R88DvCPOyTNncPOyzTdTalsE1d9CaQ1iJc0y6WGxSDwdmxM/1RcsoB9oyfTJFGb34o9pMIPkgj6G07qixSD6mBIAVM6rKyvL6FrOqkn7nIzeMlHXUfwqlccTa+0AACAASURBVIJ2YhohVrFKcy96rUK7y3OFhPZlPfQImhwYDeLDQAMNlNDbGikcKJ9m+iPbXHQ1sOetnjqNLCQHpSQISgiKCrW6DoFCJATI+yyQaCxAfEXvbqcr9WKrdP9+0wAD6XP4Dboclnr70tEfXzSmritIt+qbRFyooYxJCsZ68aucldDO+UkJibJy96oqKIdAmr/YnpTcre+hfT8z76Q59KDxxduSKTzoQQ3EPoTJygqOPGbNkxs3b0CXMwj3+WhtQNRORS6EhHb7IdMKTZY8fO+Z8C3RYQ5jaIkG/fqBphiSzvHFmE+r3x3Pr6sqjR5l7zubzVB1pG+vVY3KMQKTZVifWUbwxutv4sTjtoL4UeyDuJyx0LrQCJ45lI/zYG88iA8DDTRQQm87pPAgUAIBSTboriWEAKwcsRmZhLT1Eo1b+nlYcK3qkPU4kxJKR0emGHrcuD+H7HxFBxpooQ3bQ5O3OgbxIMSH3n40lZvu36qOlao1qyYN2GzOM5eDoVYKFavvMHNiQjklbJQWNbzygx/gPe97LwDgxONnUbgSfv5pQYQ4NIcZt5o/AjrItwVTuG/ju48ftK4qTJaWAQBLR45i/e7tCI2BkK6cNDEorELlJC00yNdupI4kLx35EPrl+GYYc8eEZ4dajwrnN8WTxgVzGFSrzz399PoFO0bp9d4yMyvLoF+olMa2q9Z1e2uKjcru37xxG995/nkAwMmzZ3H6HK9RlJoVFuIL/BJqJONh90l7vDcdw2J0cNxmR6ZARP8CwN8CcN3VjAQRnQDwawCeAvA6gM8aY+6Q/cp/BcDfBLAF4O8ZY765717Ooa4h6PA4fiBE7JefbW+Hj/jutIR1zY2TwoQcgybxWfCBUppEMG+SIVe2rYP6dAqN7b7VuTlg/QxivrmxffoO+oQupmQQ/A9IyMAUdMOlWWsTFIrTssK9zW0AwKVbd7Hh9m/XBhdeex0A8LFLl3Dy7NngObkYpYyjRT1zshszuj0LX3N/aRGdwr9Eu/LTLwL4sjHmAwC+7LYB4G8A+ID7/+exyxqSAw000IOnHZGCMeZPiOipxu7PAPhJ1/48gD+GrffwGQD/ythl4GtEdIyIzhpjrhxUh5v0oGImeBamFixm5B1uppub+MH3XwMAvPXmmzieGQiXdowQV06uX1BKQ0kvVigIeE9HAzJo1HJk1OO8FA9H2Zz/sc0FkEG8eevcVpbhBZyfUqCQ6ip8lmYBguEVoUx0/vIVowCbl/Guq9B1/c49bNZu/LIcz7/yAwDAh178Lk6fO4/Hzj/h7hDTxSdjudPH1XHRDp9D4+aHIUrs/2Z71SmcYRP9KoAzrn0OwFvsPF9g9tCYwsNOlZNvb1y7jq996zsAgHJ9A8snjoLcsRFXerEPvK7r4NsghIguz158CJM/Vmd2N4nNjn1+u1+52AX/5xzvYRD9Oo3m89lFLT2E3a21CuKDUiowW8BOAz9mSmtsuEK+G9tTbDoF5HgiMHWixPPf+z4+9tGLOHnWmoizpAbEPDGh59gOkoXvY3uq7ixK7I32J0Dv2yRpkmSBi9NQYHaggR5O2itSuObFAiI6C+C6238JwLvYeQ+0wOzeqa2J7lg7O68wbEnQWuONH7wBAPjGt76DG+u2clS5voG7t27i6SctfG0iBQ/vlVZQLjxYahEsEYSm+BCtCWkqt34FXvJWvSt9z5s3z0+OzXdY8u+YPKjZx47nax0RlEcKStVo0rQssbltkUKldcjBUNYKwsVHvPzGZTz/wot47Nx5AMDp8+digit2L75itrFXPzToSJYVrugetYfLKrFXpvBbsMVjfxlpEdnfAvAPiegLAH4UwL3D1CccHNGcrbhvt7/VvTv38O3nrBnslbcuYuY+yjvbU0yvX8cnnrJmMVswlX12XizQJqQrz2QG8glDXMm0rn62cwO0Jx7Qb5Js6wRaDcYTdhiRA4isDJGRLKlKWVWoqgplaS0TVV2HY+tbU6xvWeuDZs9TSgdfhq1piT/9zot4+umPAABOnH0cRE5fg2jW4WPc/v0bv0AXr9jVR9MWJag5ZPuixbVvi5gk/y9YpeIpIroIWzvylwH8OhF9DsAbAD7rTv9dWHPkq7AmyZ/bTbcHGmigB0+LWB/+ds+hn+o41wD4B/vt1P2lnVFC6+gcFs6VXt/69nN4+ftW472xPUXpNOGl0lgrS7z05mUAwCfe926MnCXCICIFbUxQqCmtILR3ZHKFVz2i4HUVDaInTRMx7IQWwp/digwdCCMc69o/z3kqHjOGhUtrHVBTWVaYVVX0XCzrEDq9vjVF7Wt2AvBjUCsN6R2caoUbd9bxre9YFHfm/HmcdqKE9Q9zV5roRboXanu6LgLmDy1AYmF6W3g0WtqNxvWgDZnxh6xrhauXrwEAvvHNb2PbZSku6zrmZKwVRFHg3ro1nU1nJQpvnkwqRJng1FTXdUgDL2AjJuPUT6Fs135iR9p5FRrCQRezQDJvMXeMe2xyizksmURk8EyhrOuQZ6GsapRlFRyWZlWFja1puI+Kr5XMwmCxIMKs1vjz518CAPzwx57GkRO+pNw4iF+9DmL8HcAfgpZUEagx1+dP/UORHxamISBqoIEGSugRRgrzePjBIIFFmbSBQe1WrTdefwvPftP6I6xvb4fVTGkDFWzpLheAsybkmcTGtl3pxkWOImc5BNzqWtU1cp9FyGVhCkKDAQuWQmMB86IAJVgiXez5kt3lpNQeDePO3WlkugfRtNAJd97SLPYjiAxVjZIhhVkVkYLd31aoElEyMN4RCkQQWY51V5znK19/FsdO2gS753/oh5gFpzmw3dRS+7Lx35/7QU9QxSHTI8QU9jbRd/uj7HrsDaBqjUsXrZHlma9/E993PvbTWYmq9h+4xsyJEjb+XwfRYm1jE8ePHgEAbG5PoZQVJUZFHhx2SMeYCBLCfivEJ3x78reFim4RqyvXQud4zGMCOzCIuZmddaz+pLQOmZnLqmZMgTGBunZ6BW+ZEZj6cWK9JopAmAyFGp++OG3luvDc917H0x98HQCwevwEjj12GgAgQSGSFcaNa6/vUpcep0EdIkQ6El2XeFHm/okSg/gw0EADJfSQI4UHFdmwOGmjcffOXVx45VUAwKuvvY7NbWsnr5VmKcM0pi4LUO0ccVTIHKRh3Iq4PSvZfoVRYUWGcVGEjEJSSggSiULROJhrM0l3j5tfrVv5GFizq8o02mfPQQbte/Yd0yyWQQfLQJ0iAicyVFW6f1ZVKFyuhVrrKBowucTABLQgZYYsz905tmK1dNsbsxpf+urXAQBPPvkkVl2tDsolBBMlDNfu2tu03kxwRMaaASTsWZ6g+6Z4fAiZwsEygsMaw+iHr/HGxcv4zndeAABsTrfDh6y0QenFB61DngSlDZQxKJmHns8/OC5y3NvYsteMNdNDaExcKfVCZSCZmB8SCpO/P74afaIEh/Y7MYG2h2TXc2IzOGU5z/hgZVAKJRcZwuTnegPOFCoUMkPu4kIqNn5J/1ka/DzL0szOMMGaYwzh6o07AICvP/stHDlhmcLxx88EBygrOjSsOR3vrQGITpNwg/bkxHh/dAwPEVN4+FGBJ2MiU7h58zae+YtncXfD1XRQGkr5+gRRuaW0hs8CUCsNpQ02nU6BZxVaHY/CNXc3N4MZrVYqMAitNVZXlm0yV6QyrTFpcFTX5xnP38GM29It9H2QDf1ECNRi2ZKY0lQbA83eRymdKhTrDnTA2pkUyLO4ihsd070TRwrGQDg9ghAiFJuVeQHDmJSUGSo3FF/59ov46Ic/CABYPXECWeHRhb07MT1OH3mTqCATEuN4peP+5/LhmysHncJAAw2U0EOEFA6eDpaRRk87pQ1Kpx/46jPP4tKVa6hVXPVUCGiKq7HSMTdAWVU2W5C7hpuuVFVhZWLFhFqpYKqsahURiFsZvTiRZTJUP7Krp9fBpytV1zv5g1F0XiCgKexnMD2IBvEaY0zQG6gGMlDMMcm2ox6Fmx45UhAu81SRZ8gzGcQxrXVw8iJuejUmjouIY+SzWfk+51kWYye2S/zBn30NAHDqzBmc8mnbBLmx8eKDAfryWTjSJooShPTcpn5h96qGwxMl3pZMYb9jxKsTd+U+rKoaX33mWQDAhZe/h7Kqwgdq8yHYE+tahb4YRJFDSokSFJSQ3NSltUbhPtojS+MwkbamsyB3KxccNHMBQaMiD3kXMilDfgApRJwAaIdJJX4LQVEJtMUG1wqwXDcYAY/sjBNcacXa6X6tdINJqND2Y8mZAgCMnE4gzyQyGWs68BwUkSXCFehl+Sh8lClRoqgVQoQJLjKJV163gb0vvPBd/MhkAgBYWl2FLHI2Zg2lQA9r8DoJAZNEsCaixJ7lisMRJQbxYaCBBkrobYMU9scoeRBO1Kpzpx5jEFa6Z7/5HL7iIOb65laiBFRaswSjUQFmGDMXUkJImaAAwBaiFQC0c1IqMomjS+PQS58n4M76JpTWIYhqWpbInRKNI4VcirA/z2TQtgN2taQE/narJENCWWNCOjSPhvzqXjdWeo4AdM/+BEUkx+JY1ioW4Z2MiuDpmUtpF9fwo8U2fy/BisrazFUeKdgsVqF4rTFhnLTRqBxq+oOvfR1HXIHgJ9/9JMbLKxgtLdlxLrLoWMScx9J8FnFUlbG/rWCHdyMy7BgrcYCixNuCKexpHHgmZSZHc6201ZK7j7VWuHj5KgDgT//sa1hzeQDLqgaIgshgJwufSP6B8WsoihHMeAyU1vS4PSvDM0UmE5l87LTfXmcB2IjL22sbWBo7nYKUkE7LnkuJ3DOFLEPm92cZiiBWSAhBQXtvYbZnEIxZEBOlEr8K65EZTKyqOcH7GQHf36VTUMx0O8ozjJwvwSiP/c+lRKVUZ3Zn4X9EdDGF+L684Gxd1yjccwQEhLT776xt4otf+SoA4NP31vGuc2dx3LlDj5eXMXYp+2WRB50Moc0YPNn+OobVYA5m35LAwYkSg/gw0EADJfTIIoX9MMOmttxwy4LSITZ/bW0dN29Zp5brN2/ie6/abMw3bt4OsfzaePAYLQ4RaTBHImIaaClBWQ5T2u0tZ40ArHJQM6TgV7ojy5NEa24MQoYhQRRqGAiicF6eyeD1l+cyWXUFUWKx8EhBUEQQQojQrnUs914r266Zt2b3qt+jaHQiQrA4MI/GIs8xcenpxnkekNKoyJALr0wVKOsqVXT6rM9E8OpdbnFIxQoKykb/DXg/iSLP41iMl3Hluv39/79nv4UnL17E+bO2HuX5c2fxmIuRmCwvY7RsUUOWM9RAKWrgymbuV0rM+sTT+TVpB6+SeNY+RYlHiikclN5Am3Tiqlqhcpr8qzdu4dIlG9x07dp1XL95EwBw6+ZtbGxGuK+Z+EEw4UNI9AjsmdSoGi2khHIf7HZVhY9SijGMirK7YVB4ddlqwvNMJsfubW6hVtZ0mUnJ4DMlOgXPIEZ5bp1/OCPgDIJNHs4gYpZphYoXda1VKhokjKBfRPCMcJRnQRQaFRnGuXftzgMjK1h/tTPvcpHNixB1XTOLA2cKXHwQybZ/B3uMmCUng8ltv+7d28RLm1P84IrNlXH+4iWcd9mgzz3xOM659mRlBWOvdxgViSWEgODAZoyJYxutm4khY16M2Y46hnDS7mfNID4MNNBACT30SGFv6IBbDxDaHJb7lWFjYwtXrl7DxcsWHVy6dAWXXXtjcyucp7QOYdAV8z+AW1k5tGPAMPTFQ1b/fCEktFuRtisV/P2BqITSVusZrvFiwfLSGFkmcfPumj1P61AAZW1zq2UPByyC8Iq6winwZFDC9aMDvj+UsNPGiRC2z9VCikbupm0wyjMsj52YUOQBHYwKJjI4JyXff/8uZVUnSElrE36b7bIKrs1SyuhgRMSsDxSS3/rxi9aLCPmrug4BaRWAze2t4Bvxvc2LeP2KTWL+xFuXcP5xW/rk3BOP44knLGo4duwYxiu2qLAXK9L6oQFGQsRm/JYo/YatqMqoR6GJznMWn0kPNVNY7DXSfH99DkcGMXdiVdZ47XVbs+aVV7+PS5eu4No1+wNvT2eJrMyLjHgLgAHANdm8n0KIkCMwYRQUNeEaQFbkyIyFmZvM918pFXz0da2i2U1rGA/3kWEyGuHxk8cB2AkTobXG7bWN1lj484Do/ONNfHKefiG0BaRrZ1LanIlMfIiWiAYjYGbDUW6fPxkXlhEU9vmp7iDHiJlXw/NFmvGawH7POuZdmFVV+D2kkCGRDQlKfzPm8ASmb6jryNSN1mH8RqMCcmUV06kV02bTGQrHiF7bvoy3rtnaJWcuXsI5xiCePG89Ik+dPoXx0jJk3s4arU2UH8jERaElSszzNmXv1k0LMBBHey0w+z8B+I8BlAC+D+DnjDF33bFfAvA5AArAPzLGfHHh3mBBRtAwIUZ1XhMdxP2qVlhzk+XCK6/i+Rdtfr4337yIsqzChK8TOdgwWzbCB85X0MCHmenRTzC7esYVyCu2SABCFpiV1u9gZlSolFxWcXWqKUU3wWdAaAghg77gsRPHQrtguoNbaxthsvCJs7ltQlAREE2UgGUEQSHXo1/IpEQmRZDpK6USRMUnr0cn4yJjYeB5gg44g8hkFvpimVBEV0ExWdeo6hhZWdUqIIVNpTB1EUnjsYBxjJDYb2aMzYIVZGdvogQAHZWOoCwolA2sWXfkXMulzAKDQFUhc9Wt39y+hsvXbwEAXrt4CeffvAgAePe7zuM9Tz2J06dtLshiMglmZF6I2GaLghv/FDUIIZLKWAn574RS5rcX2muB2f8XwNPGmI8DeAXAL7lOfATAzwD4qLvmfyOfUH+ggQZ6JGhPBWaNMV9im18D8NOu/RkAXzDGzAD8gIheBfBpAF/d8Tk7d4ShAeaB6P7lHomaOcz4DMCXLl/DSxdeAQC8dOFl3LpzD4CNY2iig6DVbngkEvc4YR6AvO8EMIcZQgjzN3G/cRBRB9lV46bLofDY8SMwxnoxCuYU1YqHNohoRQqcPGo97/JMJhYHH1DFIb6Pm9h2QV1EVYIOuttI9vO4Cm2iFydMRAejvGFJaIgLXHfAxZeoe+GjG9Pde9QTwq3rOtSJLLWBcYisrGqMC2uxISHD76e0ZslT7Ooq/PpIKdLxIlfpxI2wuhMF1FCrGtsONUgiZA61lNObuH7TmjRfu3gJb1y8jPe82xZQe+rd78LpUx41jINoKBG/CxZz5b3sQvyGRbAdSMAwhzOKFo6F9A/+nRc+s5/+PoBfc+1zsEzCky8wuydqVk2K350JgSbGZzFy0HI6neHuPauAu33n/2/vWmI1Oa7yd6r7f9w7M56xZ8aJQ+I4DiZRYEEMAhYhLJBQiICA2AQhkQikCJFIRIBQIBs2LAABEgKBQEQkKAkCAcILkHgIwQIMJMaOkwB5Ova87Izv+390d1UdFvU61X//9/73zsy9f6I+0sztv7u6u7q6+tQ533ntYGvLv5QvfwXPfeV5AMBsXgux2mSuyc7PYFmHlu0T/RQiX1YUFohicZAAlfLDrwrc9h9v3eiYBDYH+hImQUqBVPJNcAzH9eHSfRdwftN9CPed28BXIxgpU6e77RCNeDCbx+AqpRJ2UIh7FgJoVESZOK6IoklwXtVR/JQfvjQvBnWhFL4VYWzC5G8PubXJf8SpCxpNk5jExL9PLQBF5yfiqChkpqqQ58JjP20GIdSMFOhFsMxQRoKzYWwKjIaeQWiNA+8/MhyUKLVnKpXG1tYenrvuan08eu06HnnYM4iHX40rVy4DAEYbGwipJBnJ5YD8ophFowrALFuYBCOxkSesDjTekUmSiD4IQAP46AnO7QvM9tTTGtKJJQUiejccAPm9nJb0Oy8wu1RNSNKB0QnY2t8/wM7uHra2dwAAW1vbcfvlrW3s7jg1Ye9gEs1JjdYJWDTWOzOF+4hnXPLsbZ7Lsq1cnZSKDkpENq1gVkMVhDIE+KgxJsaDjnWNuc/IdG5jnAN90oSoVFypoBJQZTnlC3jF5Qdwvw/o2Z/OIlBpW6HPVd1g6oOtGJyZKpN0kEsQ1I6diCJ7g5mXeqq6SpLCYBCBxqHP/xAlH+RSoRzgMK6y/HzjgcYg7c0ajXl4NiJQCPxSKr7z4ZggITqilBVLrqLk7IZxO4a7KwXmFP6cxVVINasoYmEfrTXmXgIblCXKpsDN624BvP3yLp675qSG1127gUcedhWqHrx6BQ9568Xm5mZmiSBAmKgFvM6LczI8YxjB42COJ2IKRPQ2AL8I4HuYeSoOPQHgY0T0WwBeBeAxAP+5yjXz2Hy3zwqsQDcm4gM3br6E6zfcgG5t72B7exfb205N2N/fjwlSjTHRPNho6aZr4357iFSVfewL/fUbPgBFlheTfgpR71YEHU1QFqASAz957Fyj8ecczCqc33CYwsZolNKJUcoHoIJHngj2Cd1RzLBhv3CTHg6Hmc+DZAphrABXkq0UKcy68AVn8wfkhCNhJdA6lHOrUc2reI9SPgulQZT/U9r0ezn2zwgTaOO9KgFgqjVM1J1VTOWuBkPAq2jT2RwjnxshULIciXwMLaYQ+2EtmIvUT1LCXCoYnHA5t8xQkfHWqEApWM0YXPfz9MXb23jumls7H3n41XjQYw2XLt6H1wST5gP3A9bG+QRum+IXVzX3LGFc765JsqvA7C8BGAH4Bz9wTzLzTzPzZ4jozwF8Fk6teC8zL7Gh9NRTT+tIJy0w+8eHtP9VAL96nE4wkCH+UTrQJpYbv3HzFp5/wXHTF65dxw0fxjybz9E0OvexF8lSZZqvtH9pOdQVO5xgYSkwxMOUVj0ZuqtEcZJCFSh8iO60Uii81PDVgynuP++cmowxGMbVVVw//m2JvW4DRbTEJEnBJYcVhVdbIG5wmBoOB53iu0wZByxKCmLdjaJqoVI+B6PTOwqrXL52SXkh7ZLOY3notsbMz425ZZgoKCgUPl6hGI1hODmMhfuroswfTiL2zszkj4vxhRvDZBgwnSCw81xNUkMAU8FO7Zl61apsNLQP/Boai+svuJiKl7Z28JqHnPpw9cplvODn+YNXr+KhVz6IC2MnRV68cD5JDcj9c+QARmHnlK0Pd06cSqppbWL+w+uCETx/7Tpu+WCUg8kkIs9hsshMx2GbfSp1144PVRWWds3/JaCbkXgdQ7aTklymgwqxXBXCMkEKxcBNkNvTfbxWFE4deNyhfX8WiiQDguFwNtmDXqOKlHItXUh8CJFUxB6AfC5RxhbSQcoaMpRvZ4X7txJNjNbgBcbcMbqMLB9DwIEabVA3BtNgcQBAXk0YjMYohu7DoXIYL0tIOSkGqp0ajTIGG/MzZMyRQGQzThaCm5S1iSkUCoVKKka4z2BQZnkrtDaYeCuFGVlY6x3WLOPLc+fwdOv2Fh64dBEAcP3WS/jy89fwwAPOi/XShfP4lsdeDwAYj0ZiLuSMLL2jU7I+9NRTT19/tBaSAjMwmTiuee3GLbzgpYNr12/glo9J2N+fZL4F0hHHiApBMvAptyq0OeXJXEAX+g5uqQ75HcL9lXD2ca68CvArEikVcwDMgYhYV3WDcx50VEgrk79xJxGp+KxudQvjklbDVJdQiv9C1gnROdInX97D/5dJDhKUQwhDTtJBls8ABC3VCfFA2VthCLUwz+cwaxrMgnSpCpReOlDDDZSjDf+cRUotwAzrn8taE1f22LcO65O0NiT4Px0LL1cDKCSI6+9TFJypEkWRHKiIVAwom83m8TmHwyGs9SqXmaCZu7mwt3mAre1d3LjlpOVBWeI5X7/0zd/8RjzyqofQTUFq+RpTH+ZVhX978hMAHHYQgpP29g+iSUkLtFmLqLzABLpyITpaJjZlAv+K1H2OvEObQSSVLqHSpU+5FhlGUcBw0nf3PWJ/X70RffrH4gNr3ze7P4nZS8IsIreZwSQi7gSDyJ5Ptok4gmyRPpjsbI8jkMkZgTRhSiZhtO5k3iwcrmSW56rRmDYalfegLwdDqJFjCuVwjMI7EhmW/E04IhkX62DEwpJZVvz9Myacj0xwKo3XDn0rsrnI0aksYA1SnZDjEYvq1jU2vJVkKJzqiAkwgJ7X/noKB94xbW97F9ceexQA8G1veiNGsWq5ZAa9+tBTTz2dkNZCUphMpnj6mWcBAHt7STpojIbWwnoQREkWbs6tcNLVpIR2m6OlhcVWSTKR4HvWjkWBckqWiKIoYI2JIPdoNEIzn/ntMW77ArWXmya6H59nXlylhMeVlBrS6pDs1OCWKiHGLXOBzcWOBNR1SAldEmkW8VcUsS8283MIsRTp2kaHVTupFCwfUaz02hjMrE3+GOUAha8JUQyGUALx7/J/IeVW5mBxMUaEqIvxU0jnB/krPkN73vl2jdbR4sDMQq1QUCpXJwLwPKQBdJCarI3Rl03TOBARbrwYjFqFyNYEWs6efz5m62pmFR77Ric1PPjApWVmiUNpLZhCVVW4veW8ECVeoLVIzMEpUCmbLGESHqUlAId8+6sxhkPP7sAVJOIrA6IKpVBVFTZ8kpGyKFB5RqAGQ+zPXMKUed3E0N2m0Rh5U5ovOyOvDvmgJCwRnAn6gnUQiQkjsQExlsL5pdUqY4Rt1CFTJULoMlEM9GmrDyHpCeDyGaTCLizukb+fw95WzkjiEwusJb9nMRhkJu38Rt13cjhEmo+SgspXFCKdHjMKzxjc+dL6BAx8XAiZZOo01mLf1yhtdINRVWHkzZjSpK2IcPPFW74vNsZhbIw2cMVbL45jhF8LpmAtY+7zCeSFVJNEYDn/BFaidsOuE6l9cMkkaF0mrjr+vy5cQarqDnQM+qXyOQ99w5Lix2OYYfxJ+/MKF+tknhx7G3XuF+BxlA7mR26tEx0IHyFnkkI+LISVPvZsx7JPliNjJKXi92WxyBTkdvBcNcbmfhIr8+0O2VGATQxnwg3A9Xg0RIjwJ0sxrX9+xVaNwbMDIQAAFWRJREFULdnnltQQWrlq4v5IAY8jBbN0LjUE35ZC5uoQWEddN85t2ksRo9EweogWhYrRmweTA9x80QGQly/fj0sXHVO4eF+qH3IU9ZhCTz31lNF6SArMMYtOKJ4atoP4G9UE9+MIaeH4etRJqW2SXMWRyXH2IkOpQ/UmAxvR832hPlS1jmM0GA5zTEGoD9KpCUqWW2WxgHu1gMUquoS6FmdpUVn1HNfe68NEsEZiCukMY2185xI7yq6c3Ar9n1wyyFUGZMfiH0oOX9PZHOc2NxaaydyNMQBJqDMsQBGSkqwY8pCv0rJFWZZCOqMU7CSkDmcVcttlUWRek2COEoG1FtpfTRHB+KpisAbKm7r3drexu7sFADh/bpnJcpHWgikADONTaNlMrF1FXuQVmMRdogzN61ZnlpkkcyKUZYHaR0PK5B1GW2z6ykPTve3ksyDSwA8DUwgTSc5+Th+JDFSCYKrum5DAYzuHxOJotp+DxP27ji+eL5g6FIoiYQyWOX5gTuFICIh0TTcRa1jeT88t03anqdr9CD4DihUmUxfXd25zMw80Ct6x3tSogmZB6dpOMxMj0MFkrWU0jY4l/cAqyunSZ0I+FTFnbvLG2pSqrkhRq0BKUe+wEY7n15Uzb08nk4U+LaNefeipp54yWgtJQXohLop8d3BdnMSmsNpZGdAoNrKz5eokVhbAoccBhHIZnN0KUhmDAK/NGdjzjkwX6wa1Xw1GxsSswOFGLKSAzMig5LqagMYMBQV3o6Nph3+EZUh863fnL873UABCc+kARJnUoP2YzRqNfe+4c1A3i3PkyEmTA43ytyx0M5vPMR6N8/MAgMmpE/KIDKISz7YA3MKPOVJpARScJD2RHi6FpQdJyfVL+eAqJXJaROuDkDSMqNYFtlDkzg+JcVehtWAKQJqwy8NkVmUVx9QlVuQcebP0K6IeLbUB7cs6UCHbHwJvjKVoklKqiC7PpihimrGq0bE2RN1obA6G+WNGRsDotCuQ/KQDphAOpfTpTO3xWz44mWVgaYsWM/BbLPM6ilZWbDeWIyOc1Q12fSKYnarBhCmmb5cqU6Y9LFhYWLQXxOnD0lqjVo75uKKzKa8mkMyl7jR5z+wO/ikJ0k9EtnDBWe59SqxBEgmzMSN4XnpcRhQVLkQCHunta6xN5fSO8VH06kNPPfWU0dpICitR19J4TxDGCPWs1Jl8dUg7lgKN3uae0rNx9H0HENWHcrSBaeUAsHnToAqpxbTB0BiUXroAydBf0S+xIqoFS0FLRYgyKy/Rf5ZT7rMg1Yz2yWl/VgvSuRPFNo1fjWtjUflVb9YYHIREs8zQyhXpBQAq2tNYim1d0kH4sagaKaUw9yqbIhXVOvbtQ/i5wDCz4KhlUkOUpsT5OvpDaBRlKswT5ZrWawGQpQgIYfVFUaAsU7CdFW2scP5bldaGKXSKwqu0PaT5sXxdjgk+HBdTcJKgZDYUX5iiVICEAPhUEdjcOIfaM4WDqsElkWOy0ToyhezzlrM1Q2g63JJl5wOSTpT3M37Hh78Uag/EYovoyGSsS3gTcl1YtlHlMsyo/cdSaYPKqw/TRmMasjlDgcoBlGcKqhxCFQPR/8Wn72JwXWxdJkyZTKe44Mu+uX2yOrlJH3/mGHXE5JWqZbyWRfBTRZnSymcnhHuLvCNl6fNDlCUGwc27LFAEz9esWldXeFc39epDTz31lNHaSAr3Qg84lgDQqTEcfYVVgUbmxZU6pJobDkuwThENpef0jWU0HliaaB1Dx+vGSQoD/3swGGSWgW6pK7nVdpKEvFMSgsXjSBJP9jhCUlhmfYirlndQy30Q3HZjGNqEbYtZIzI2+/0GwKAcRkmBqYCRK3g0ZLTfHef/d2mgjOhIZq3FxNv3L1w4D0UKNhZikAJZnoGchDhAyCdDdI3O3pewHlkb/TcKUVTXRc/JoDANY3zNUeHPUKgiqjyh5md4llVpjZjCMjoaO1g0A93h7RYYg7sLZ01WwBTijtA+fSBEiD7+oJRWfTQcxomjLWMwdp5229UMUx8fsjEeo25MLHBalmXM7Owme9JJcwpmr8XHjj0juNh9tEXh9lO2cHbKj7bPsJwS4RjmrABPY2ys8NQYEzGFxlpMfezHtNZo/AUtEepGQ6FINwyJEyxHpyxZsCbrIOfsQVovJCmVEqEcTKY4t7mRvDC9BWVxZChjAOmjpvyYGDOXGCf0LX34zBxzcIQANpnExwjHqnSbZIkoylIwuNW/kCPVByL6EBG9RESf7jj280TERHTF/yYi+h0i+gIRfYqIHl+5Jz311NNa0CqSwp8A+F0AH5E7ieg1AL4PwPNi9/fD1Xp4DMB3Avh9//ee0nFAyrt1wwVtowtolP1pgf2Nq3kOwJU13/Bp17TWMXRcjcc4712eJ3WFW77e5MUL59yKGmIh7DCtjkJFWKr4yOxMWSdzSwKJFG55njnOfwPd0gcRslJ1LLc5qkPzRmPuJYJ5o9F0AI0TrTELIfVsAEsofE20YjBIBTxUkhSUzLkgVtDwApepWYEUKSi/0tZ1A2bGRoxUTaHX0jXfWZaEP4pQF9ywp2N5tie/nS3TSWoolIIqi/hblgFsP0u0pJCKVo2Qu2EVOlGBWU+/DVcQ5m/EvncA+IivGPUkEV0iooeY+ebKPTrxR90tzndCBateaim+IO/lP8YjMAVrXWWoQFrrqDJMpzNh+mJUPiZiNBwCflIONjaxU7mcC3vTOYbDYYzb1yblHDz01Wd6LS0eaD0bpCoh8yowLTo5Zc/sz+HcJBZwA8sWjTGoRIHYWjhpBYelShscePPgflVH86RhgtEVSt+3kgEVUsBZjl58VikoK7c9s1Dt5xfvszX/wjviQkUMCHDh1jamtxMBUV0qAxAZQjS8Opt02hbtJFNJDlLub0iZzzK/iLR+ZO8kBVoFNWIVOmmFqHcAuM7Mz7TAnG8A8IL4HQrMrs4UvgaoC37M+EgAvayF9tFrRniahRUg/FaKYj6JDUp68LyuMatdUg2iArVH057f3kNRltELctDoqHsSUapM7HbEnqUJ2v4oJLU+Fgk2LIwAL54inl/mzpTb2lrUPk07kEsEtTYRX9ifzbHrk8/Mmga1Hz7DDlzU7GsoMDDw9ywKBeuX20xSYAYHZsEqFsntpsWVKYK/TajUjWgGZMWxuriUABYZhAQ/c0BWRozKDFWxlfeFiJJDUaAshaQizNBdUsM9TdxKRJsAfhlOdTgxEdF7ALwHADbPX7iTS/XUU093kU4iKbwewOsABCnh1QCeIqLvwAkLzF6++spjKg1thXClM47rn7QcIBC6dhtT0MZExFriA8YYWCMSqXGKtVdKxRWoKIpYiHU+n2MvrnQWtQ8I0jWgaCeuiK9URcxLSEqB/LJVFGmQ8oWCVlw5kjQg3PDTj85LkMghkEsKhkUxFy3Uh0bHvJyzusb2vktBtrU/iSZJwxzNjtoATCZJZFxFR6jRcATl45stqZhbwFpRxclLEEGdoCzvxHJy0oJrV9dNJpoHKSTHEVqSwRJMoa3KSRUjM122LROiKlXEa1i8p5ZVYlU6NlNg5mcBPChu9hyAb2fm20T0BID3EdGfwQGMu8fCE+6E7hXAuBRfSFta68QIjIaJ24lByAKpgHupAUdQqkBZenOd1mDvvlpVFSZeF1RsMZ96/VrXsE0TGYFSBTiWOiOMh2GyWmQewBIrXHGSsMgYQvnchXTZDYesCHRqJ8xphIrgcIT0e+LVp539Cbb2HFPYnc4wC6nZOJnV2PUmue4aA+PzBrDlWPVZKQtrU/qz6EHqsYaoWthULPYwXw5ZBo4AVL6S2XickqhKXulwgzRcWVWqQ3AEqfJJHIiBZAZlEVkKldYra0WbNFOP83msYpL8OIB/B/AGIrpGRD91SPO/BfAlAF8A8EcAfuYYfempp57WgE5aYFYef0RsM4D33nm3jkur8cETWSI6yDJHVcBY45NqulVDa5NLDUFlyFL/JLAIcAtDSDfO1mYqx8wn6iyJMPHFSQswdmdz3Nh2GbDHwwHGo7A65qtRACMhRN8AZh05agyk9O/tURNAozzFpr1SfahNLhmEfwBwMJtjx6sMO/sTbPksSJO6Rh0KtrSCfzn+59SxECIcslkBbkxVrAqV6j1yS32wykKFhKrEYgyF6B72UHAMSqt4VdXRVAnq9miU5kf5t73dHmaZOYtIrPwsJR+LunHP3TSNsPikdHLHERXW3KORD/m1fN/dJFn1OFUrMi2mUEefAWtTBt+cEaDLohl3ByTbWhP1w6IokvqBVLGIrcWksrB2H4BzZx17kXlQltnEC3bq84E5iLseyRwpKQbtcZamR2lP5/Dcfjt43TWiwldtHEPYn7iPf/dggt0D5068dTCJqsS8aZL7MlLuRpYyOYLFJT1zrC6tuj88JmdCjNXBmeI7s8pCRZVD5R+s0A0KpUAIxX8pRlZubIyFGzgtfPxdzCDDIeRxCKJwxdCVxAhlNGTdNJj5xcNam6WTW5X6gKieeuopo7WWFKSIKPYcKh6swg8Ps0QYUXfCGJPVMgzVi2Qba0wupjmPn4XrOicgv4KGNFwy85DfLgdl5O4EYM+voIVSUB41tNY5/4R2t7a3MfCg5Xg0jEi8UiqqKIPBAJsSdaRcVuCOLTlKnThrtogm6UiCW7F8vEnqwryuM+lg52CC3UkIEa8w9+Ci5ZRbIuuL9zFglSwuStRQ6AILc3kn/AqSgrDuM2CDTGZb4n4rKCzFJaTuzasqVnXK7t9WC9o+DMuAxoVnyPsAtCSFusZ05sayqioMvSXrwoXV6z6sNVNYme5Ah2C4jzwVIBHl6ayNDkbWmmw7vJDhcAAy7Wt26AkCrY+JuURShvBSx+UoMh+lFMajJl4glWADdD2PJr7JvMbNrW0AwKAo8NpXXHXthAlrUJYY+tTxQzmZ0d5cwi6lJrSg9+Y7M703RvVZzLxasHswxe7BBDvebXt3MsW+x06mTZPyLMhrIY2rKgrn6RksM0pFRqAyVUIUVmm5f4vveOE542aLuS8oXP5wWZTxmCUTcY1gBUl3bVHm5pz2dTOIhZPjJSW+EFLJA8B4PMY57yZ/DItkrz701FNPOX0dSAonExMkMNM0TUyKKgvZWmuzbWnnDkkzA6JtRDq17lVXhr06VYLbkgSC/0JSE4IoaoWzilEERQxdeUCJgT2P2N/Y2o6qTVU30bY/Gg4w8CtXUThnpy5fhaV4FHXJAumgdO0NF7Gccihoa6P1JKgOu8HKUFWYB6CWrZAOWuKzv4dSysU6CEkhSgQCHMxchkFHLJdBahO+AUjP3HVmUj9IFKslEAVwVfvkr8uvIR5u4Z5ZrERbtiF5xdRn1w/3Y2M8bkkrq9HXAVM4DrmX2DQ6qguJKXiToohRYGuXTqTkzaZAQKz/2GlaAABO6cjigY4vUGuN8ciJfNYasGcKxhiYkLFXkZy7aKpZDM7ZOTiIzEMbI4qdqjhBndefikEyWa+WzlyJOlB7VxLZM6AhFXOpG40DH8dwMJ3hYF7FIChtbVQZnPVisUNEySMxMLUQBOWiGbtUBoIU0WW/DltKjiFpAwDm1TzFoZRldIpyKmfKeXHcC3erD3Kcw7MhFpU9t3kuPutsXnXk5jyaevWhp556ymhNJYU79T6QKHNwHzaoRVn3tN2gbppM/E9SWRf6i4zjR1s2NeJg6r9I+59OjPbuRS7urBxOUinLQeT6TdNECaBQbmUM7jxsNEyQdCxj16cQk9mNjLUx7HY0GqEoSyglVjGklTqjDnSxcxwC+q/S6izVI20M5t4tuNIajTXQHlDV1mZlLaWalakFwa27KHyUYHLzlvfPJIWW81B6rFxSk35lq86+cMXxaIQDbz2pChXfkxv7ZAkqy/IQ/SxctHvOBVWoa+EviwE2fIauzY1xrE6+s7eHnb09AMDly+dXfCqAjuPUcK+IiL4KYALg9ln3RdAV9P05itatT31/DqfXMvPVoxqtBVMAACL6BDN/+1n3I1Dfn6Np3frU9+fuUI8p9NRTTxn1TKGnnnrKaJ2Ywh+edQda1PfnaFq3PvX9uQu0NphCTz31tB60TpJCTz31tAZ05kyBiN5GRP/nC8h84Iz68Boi+mci+iwRfYaIftbv/xUiuk5ET/t/bz/FPj1HRM/6+37C73uAiP6BiD7v/95/Sn15gxiDp4loj4jef9rjQx2FiZaNCTm6p4WJlvTnN4jof/09/5qILvn9jxDRTIzVH9zt/tw1Cskgz+IfgALAFwE8CmAI4BkAbzqDfjwE4HG/fQHA5wC8CcCvAPiFMxqb5wBcae37dQAf8NsfAPBrZ/TObgF47WmPD4C3AngcwKePGhMAbwfwd3D+P98F4D9OqT/fB6D0278m+vOIbLfO/85aUvgOAF9g5i8xcw3gz+AKypwqMfNNZn7Kb+8D+B+4ehXrRu8A8GG//WEAP3wGffheAF9k5q+c9o2Z+V8BbLV2LxuTWJiImZ8EcImIHrrX/WHmv2fm4B77JFxG868pOmumsKx4zJkRET0C4M0A/sPvep8XBT90WuK6Jwbw90T0SXI1MgDgFZyyY98C8IpT7E+gdwL4uPh9VuMTaNmYrMPc+kk4aSXQ64jov4noX4jou0+5LyvTWTOFtSIiOg/gLwG8n5n34Gphvh7At8JVufrNU+zOW5j5cbj6nO8lorfKg+xk0lM1HRHREMAPAfgLv+ssx2eBzmJMlhERfRCABvBRv+smgIeZ+c0Afg7Ax4jovrPq32F01kxh5eIx95qIaADHED7KzH8FAMz8IjMbdon0/whO3TkVYubr/u9LAP7a3/vFIAL7vy+dVn88fT+Ap5j5Rd+3MxsfQcvG5MzmFhG9G8APAPhxz6jAzBUzv+y3PwmHpX3TafTnuHTWTOG/ADxGRK/zq9A7ATxx2p0gF0L3xwD+h5l/S+yXOuiPAPh0+9x71J9zRHQhbMOBV5+GG5t3+WbvQl7c9zToxyBUh7ManxYtG5MnAPyEt0J8F06pMBERvQ2u8PIPMfNU7L9KRIXffhSuMvuX7nV/TkRnjXTCocSfg+OcHzyjPrwFTuz8FICn/b+3A/hTAM/6/U8AeOiU+vMonCXmGQCfCeMC4DKAfwLweQD/COCBUxyjcwBeBnBR7DvV8YFjSDcBNHAYwU8tGxM4q8Pv+Xn1LFwVs9PozxfgsIwwj/7At/1R/y6fBvAUgB88i7m+yr/eo7GnnnrK6KzVh5566mnNqGcKPfXUU0Y9U+ipp54y6plCTz31lFHPFHrqqaeMeqbQU089ZdQzhZ566imjnin01FNPGf0/jKtb7fUoHzMAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 40: 0.0011686340440064669\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 50: 0.0011685993522405624\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 60: 0.0011685638455674052\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 70: 0.0011685279896482825\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 80: 0.0011684943456202745\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 90: 0.00116845837328583\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 100: 0.0011684229830279946\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 110: 0.0011683866614475846\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 120: 0.001168351387605071\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 130: 0.0011683152988553047\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 140: 0.001168278744444251\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 150: 0.0011682428885251284\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 160: 0.001168206101283431\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 170: 0.0011681694304570556\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 180: 0.0011681318283081055\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\",\n      \"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"loss at step 190: 0.0011680949246510863\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feed_dict = {x : train_x, y : train_y, learning_rate: 0.00001}\\n\",\n    \"for step in range(200):\\n\",\n    \"    _ = sess.run(\\n\",\n    \"        [optimizer], feed_dict = feed_dict\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    if step % 10 == 0:\\n\",\n    \"        l, predictions = sess.run(\\n\",\n    \"            [loss, out], feed_dict = feed_dict\\n\",\n    \"        )\\n\",\n    \"        print('loss at step {0}: {1}'.format(step, l))\\n\",\n    \"        plt.imshow(predictions.reshape((img.shape[0], img.shape[1], 3)))\\n\",\n    \"        fname = \\\"image_seq/%04d.png\\\" % seq\\n\",\n    \"        plt.imsave(fname, predictions.reshape((img.shape[0], img.shape[1], 3)))\\n\",\n    \"        plt.show()\\n\",\n    \"        seq += 1\\n\",\n    \"        #plt.hist(predictions)\\n\",\n    \"        #plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/parallel_computing/1. Load Balancing.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:2ef1f469d0f8b11ef269f69c5c11d2204b7590f0e32571f15199f4c3077b9eac\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"The IPython architecture consists of four components:\\n\",\n      \"\\n\",\n      \"- The IPython engine\\n\",\n      \"- The IPython hub\\n\",\n      \"- The IPython schedulers\\n\",\n      \"- The controller client\\n\",\n      \"\\n\",\n      \"![](http://ipython.org/ipython-doc/stable/_images/wideView.png)\\n\",\n      \"\\n\",\n      \"Dependencies:\\n\",\n      \"\\n\",\n      \"- ZeroMQ - http://zeromq.org/\\n\",\n      \"![](http://zeromq.wdfiles.com/local--files/admin:css/logo.gif)\\n\",\n      \"\\n\",\n      \"To install on Ubuntu:\\n\",\n      \"\\n\",\n      \"```bash\\n\",\n      \"sudo apt-get install libzmq-dev\\n\",\n      \"sudo easy_install pyzmq\\n\",\n      \"```\\n\",\n      \"- pyzmq\\n\",\n      \"\\n\",\n      \"To install on Ubuntu:\\n\",\n      \"\\n\",\n      \"```bash\\n\",\n      \"sudo pip install pyzmq\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Parallel and Distributed Computing\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"**Parallel computing**: Multiple processors or cores to distribute the work on a sinlge machine.\\n\",\n      \"\\n\",\n      \"**Distributed computing**: Multiple processors or cores located on multiple machines to distribute the work on a several machines.\\n\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Load Balancing\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"This will be our first approach to parellel/distributed computing.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Start the engines\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"I'll start the engines on a profile named \\\"nbserver\\\".\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Import `Client`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from IPython.parallel import Client\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Create a `Client` and a `load_balanced_view`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"cluster = Client(profile=\\\"nbserver\\\")\\n\",\n      \"lb_view = cluster.load_balanced_view()\\n\",\n      \"\\n\",\n      \"print \\\"Profile: %s\\\" % cluster.profile\\n\",\n      \"print \\\"Engines: %s\\\" % len(lb_view)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Profile: nbserver\\n\",\n        \"Engines: 12\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"With no Load Balancing\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def f(x):\\n\",\n      \"    result = 1.0\\n\",\n      \"    for counter in range(100000):\\n\",\n      \"        result = (result * x * 0.5)\\n\",\n      \"        if result % 5 == 0:\\n\",\n      \"            result -=4\\n\",\n      \"    return result\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%timeit -r 1 -n 1\\n\",\n      \"result = []\\n\",\n      \"for i in range(1000):\\n\",\n      \"    result.append(f(i))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1 loops, best of 1: 23.8 s per loop\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"With Load Balancing\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Using load balanced view can help simplify the process of distributing code. There are two methods to implement this.\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"`view.map(f, *sequences, block=self.block, chunksize=1, ordered=True)`\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"def f(x):\\n\",\n      \"    result = 1.0\\n\",\n      \"    for counter in range(100000):\\n\",\n      \"        result = (result * x * 0.5)\\n\",\n      \"        if result % 5 == 0:\\n\",\n      \"            result -=4\\n\",\n      \"    return result\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"%%timeit -r 1 -n 1\\n\",\n      \"result = lb_view.map(f, range(1000), block=True)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"1 loops, best of 1: 5.13 s per loop\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"`@view.parallel()` function decorator\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Using `parallel` function decorator is by far the simplist way to implement parellel computing in IPython. It doesn't allow for much control but it is fast and works for most of the cases.\\n\",\n      \"\\n\",\n      \"```\\n\",\n      \"load_balanced_view.parallel(self, dist='b', block=None, **flags)\\n\",\n      \"\\n\",\n      \"Decorator for making a ParallelFunction\\n\",\n      \"```\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"@lb_view.parallel(block=True)\\n\",\n      \"def f(x):\\n\",\n      \"    result = 1.0\\n\",\n      \"    for counter in range(100000):\\n\",\n      \"        result = (result * x * 0.5)\\n\",\n      \"        if result % 5 == 0:\\n\",\n      \"            result -=4\\n\",\n      \"    return result\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 10\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"result = f.map(range(1000))\\n\",\n      \"print \\\"Results Count: %s\\\" % len(result)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Results Count: 1000\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 15\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Asynchronous Processing\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"@lb_view.parallel(block=False)\\n\",\n      \"def f(x):\\n\",\n      \"    result = 1.0\\n\",\n      \"    for counter in range(100000):\\n\",\n      \"        result = (result * x * 0.5)\\n\",\n      \"        if result % 5 == 0:\\n\",\n      \"            result -=4\\n\",\n      \"    return result\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 16\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"result = f.map(range(1000))\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 31\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 3,\n     \"metadata\": {},\n     \"source\": [\n      \"Retreving the results from AsyncMapResult \"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"result\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 34,\n       \"text\": [\n        \"<AsyncMapResult: finished>\"\n       ]\n      }\n     ],\n     \"prompt_number\": 34\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"print \\\"Results Count: %s\\\" % len(result.result)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"Results Count: 1000\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 33\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/quick_tips/2. Pickle Data and Model.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:8b8938eb7092509bfbaf1d7219101cdc7616a222c2bb7110275ca76fe0c218f7\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Saving your data and model is very handy to continue your work on in-memory objects over long time and multiple machines.\\n\",\n      \"\\n\",\n      \"**Video Tutorial**:\\n\",\n      \"\\n\",\n      \"http://youtu.be/SQDPVSVNq1k\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import Library\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"joblib library is available in external model inside scikit-learn library. This allows to store in memory objects as pickle files.\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"import pandas as pd\\n\",\n      \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n      \"from sklearn.externals import joblib\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Loading data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"url = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\\\"\\n\",\n      \"columns = [\\\"Sex\\\", \\\"Length\\\", \\\"Diameter\\\", \\\"Height\\\", \\\"Whole weight\\\", \\\"Shucked weight\\\",\\n\",\n      \"           \\\"Viscera weight\\\", \\\"Shell weight\\\", \\\"Rings\\\"]\\n\",\n      \"csv_data = pd.read_csv(url, names = columns)\\n\",\n      \"csv_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Sex</th>\\n\",\n        \"      <th>Length</th>\\n\",\n        \"      <th>Diameter</th>\\n\",\n        \"      <th>Height</th>\\n\",\n        \"      <th>Whole weight</th>\\n\",\n        \"      <th>Shucked weight</th>\\n\",\n        \"      <th>Viscera weight</th>\\n\",\n        \"      <th>Shell weight</th>\\n\",\n        \"      <th>Rings</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.455</td>\\n\",\n        \"      <td> 0.365</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.5140</td>\\n\",\n        \"      <td> 0.2245</td>\\n\",\n        \"      <td> 0.1010</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 0.265</td>\\n\",\n        \"      <td> 0.090</td>\\n\",\n        \"      <td> 0.2255</td>\\n\",\n        \"      <td> 0.0995</td>\\n\",\n        \"      <td> 0.0485</td>\\n\",\n        \"      <td> 0.070</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.530</td>\\n\",\n        \"      <td> 0.420</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.6770</td>\\n\",\n        \"      <td> 0.2565</td>\\n\",\n        \"      <td> 0.1415</td>\\n\",\n        \"      <td> 0.210</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.365</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.5160</td>\\n\",\n        \"      <td> 0.2155</td>\\n\",\n        \"      <td> 0.1140</td>\\n\",\n        \"      <td> 0.155</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.330</td>\\n\",\n        \"      <td> 0.255</td>\\n\",\n        \"      <td> 0.080</td>\\n\",\n        \"      <td> 0.2050</td>\\n\",\n        \"      <td> 0.0895</td>\\n\",\n        \"      <td> 0.0395</td>\\n\",\n        \"      <td> 0.055</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.300</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.3515</td>\\n\",\n        \"      <td> 0.1410</td>\\n\",\n        \"      <td> 0.0775</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.530</td>\\n\",\n        \"      <td> 0.415</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.7775</td>\\n\",\n        \"      <td> 0.2370</td>\\n\",\n        \"      <td> 0.1415</td>\\n\",\n        \"      <td> 0.330</td>\\n\",\n        \"      <td> 20</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.545</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.7680</td>\\n\",\n        \"      <td> 0.2940</td>\\n\",\n        \"      <td> 0.1495</td>\\n\",\n        \"      <td> 0.260</td>\\n\",\n        \"      <td> 16</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.475</td>\\n\",\n        \"      <td> 0.370</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.5095</td>\\n\",\n        \"      <td> 0.2165</td>\\n\",\n        \"      <td> 0.1125</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.8945</td>\\n\",\n        \"      <td> 0.3145</td>\\n\",\n        \"      <td> 0.1510</td>\\n\",\n        \"      <td> 0.320</td>\\n\",\n        \"      <td> 19</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.525</td>\\n\",\n        \"      <td> 0.380</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.6065</td>\\n\",\n        \"      <td> 0.1940</td>\\n\",\n        \"      <td> 0.1475</td>\\n\",\n        \"      <td> 0.210</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.430</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 0.110</td>\\n\",\n        \"      <td> 0.4060</td>\\n\",\n        \"      <td> 0.1675</td>\\n\",\n        \"      <td> 0.0810</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.490</td>\\n\",\n        \"      <td> 0.380</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.5415</td>\\n\",\n        \"      <td> 0.2175</td>\\n\",\n        \"      <td> 0.0950</td>\\n\",\n        \"      <td> 0.190</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.535</td>\\n\",\n        \"      <td> 0.405</td>\\n\",\n        \"      <td> 0.145</td>\\n\",\n        \"      <td> 0.6845</td>\\n\",\n        \"      <td> 0.2725</td>\\n\",\n        \"      <td> 0.1710</td>\\n\",\n        \"      <td> 0.205</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.4755</td>\\n\",\n        \"      <td> 0.1675</td>\\n\",\n        \"      <td> 0.0805</td>\\n\",\n        \"      <td> 0.185</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.500</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.130</td>\\n\",\n        \"      <td> 0.6645</td>\\n\",\n        \"      <td> 0.2580</td>\\n\",\n        \"      <td> 0.1330</td>\\n\",\n        \"      <td> 0.240</td>\\n\",\n        \"      <td> 12</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.280</td>\\n\",\n        \"      <td> 0.085</td>\\n\",\n        \"      <td> 0.2905</td>\\n\",\n        \"      <td> 0.0950</td>\\n\",\n        \"      <td> 0.0395</td>\\n\",\n        \"      <td> 0.115</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.340</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.4510</td>\\n\",\n        \"      <td> 0.1880</td>\\n\",\n        \"      <td> 0.0870</td>\\n\",\n        \"      <td> 0.130</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.365</td>\\n\",\n        \"      <td> 0.295</td>\\n\",\n        \"      <td> 0.080</td>\\n\",\n        \"      <td> 0.2555</td>\\n\",\n        \"      <td> 0.0970</td>\\n\",\n        \"      <td> 0.0430</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.320</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.3810</td>\\n\",\n        \"      <td> 0.1705</td>\\n\",\n        \"      <td> 0.0750</td>\\n\",\n        \"      <td> 0.115</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.280</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.2455</td>\\n\",\n        \"      <td> 0.0955</td>\\n\",\n        \"      <td> 0.0620</td>\\n\",\n        \"      <td> 0.075</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.380</td>\\n\",\n        \"      <td> 0.275</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.2255</td>\\n\",\n        \"      <td> 0.0800</td>\\n\",\n        \"      <td> 0.0490</td>\\n\",\n        \"      <td> 0.085</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.565</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.155</td>\\n\",\n        \"      <td> 0.9395</td>\\n\",\n        \"      <td> 0.4275</td>\\n\",\n        \"      <td> 0.2140</td>\\n\",\n        \"      <td> 0.270</td>\\n\",\n        \"      <td> 12</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.415</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.7635</td>\\n\",\n        \"      <td> 0.3180</td>\\n\",\n        \"      <td> 0.2100</td>\\n\",\n        \"      <td> 0.200</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.615</td>\\n\",\n        \"      <td> 0.480</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 1.1615</td>\\n\",\n        \"      <td> 0.5130</td>\\n\",\n        \"      <td> 0.3010</td>\\n\",\n        \"      <td> 0.305</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.560</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.9285</td>\\n\",\n        \"      <td> 0.3825</td>\\n\",\n        \"      <td> 0.1880</td>\\n\",\n        \"      <td> 0.300</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.580</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.185</td>\\n\",\n        \"      <td> 0.9955</td>\\n\",\n        \"      <td> 0.3945</td>\\n\",\n        \"      <td> 0.2720</td>\\n\",\n        \"      <td> 0.285</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.590</td>\\n\",\n        \"      <td> 0.445</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.9310</td>\\n\",\n        \"      <td> 0.3560</td>\\n\",\n        \"      <td> 0.2340</td>\\n\",\n        \"      <td> 0.280</td>\\n\",\n        \"      <td> 12</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.605</td>\\n\",\n        \"      <td> 0.475</td>\\n\",\n        \"      <td> 0.180</td>\\n\",\n        \"      <td> 0.9365</td>\\n\",\n        \"      <td> 0.3940</td>\\n\",\n        \"      <td> 0.2190</td>\\n\",\n        \"      <td> 0.295</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.575</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.8635</td>\\n\",\n        \"      <td> 0.3930</td>\\n\",\n        \"      <td> 0.2270</td>\\n\",\n        \"      <td> 0.200</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.580</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 0.9975</td>\\n\",\n        \"      <td> 0.3935</td>\\n\",\n        \"      <td> 0.2420</td>\\n\",\n        \"      <td> 0.330</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.680</td>\\n\",\n        \"      <td> 0.560</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 1.6390</td>\\n\",\n        \"      <td> 0.6055</td>\\n\",\n        \"      <td> 0.2805</td>\\n\",\n        \"      <td> 0.460</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.665</td>\\n\",\n        \"      <td> 0.525</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 1.3380</td>\\n\",\n        \"      <td> 0.5515</td>\\n\",\n        \"      <td> 0.3575</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 18</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.680</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.175</td>\\n\",\n        \"      <td> 1.7980</td>\\n\",\n        \"      <td> 0.8150</td>\\n\",\n        \"      <td> 0.3925</td>\\n\",\n        \"      <td> 0.455</td>\\n\",\n        \"      <td> 19</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.705</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.200</td>\\n\",\n        \"      <td> 1.7095</td>\\n\",\n        \"      <td> 0.6330</td>\\n\",\n        \"      <td> 0.4115</td>\\n\",\n        \"      <td> 0.490</td>\\n\",\n        \"      <td> 13</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.465</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.105</td>\\n\",\n        \"      <td> 0.4795</td>\\n\",\n        \"      <td> 0.2270</td>\\n\",\n        \"      <td> 0.1240</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.540</td>\\n\",\n        \"      <td> 0.475</td>\\n\",\n        \"      <td> 0.155</td>\\n\",\n        \"      <td> 1.2170</td>\\n\",\n        \"      <td> 0.5305</td>\\n\",\n        \"      <td> 0.3075</td>\\n\",\n        \"      <td> 0.340</td>\\n\",\n        \"      <td> 16</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.105</td>\\n\",\n        \"      <td> 0.5225</td>\\n\",\n        \"      <td> 0.2370</td>\\n\",\n        \"      <td> 0.1165</td>\\n\",\n        \"      <td> 0.145</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.575</td>\\n\",\n        \"      <td> 0.445</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.8830</td>\\n\",\n        \"      <td> 0.3810</td>\\n\",\n        \"      <td> 0.2035</td>\\n\",\n        \"      <td> 0.260</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.290</td>\\n\",\n        \"      <td> 0.090</td>\\n\",\n        \"      <td> 0.3275</td>\\n\",\n        \"      <td> 0.1340</td>\\n\",\n        \"      <td> 0.0860</td>\\n\",\n        \"      <td> 0.090</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.335</td>\\n\",\n        \"      <td> 0.105</td>\\n\",\n        \"      <td> 0.4250</td>\\n\",\n        \"      <td> 0.1865</td>\\n\",\n        \"      <td> 0.0910</td>\\n\",\n        \"      <td> 0.115</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.8515</td>\\n\",\n        \"      <td> 0.3620</td>\\n\",\n        \"      <td> 0.1960</td>\\n\",\n        \"      <td> 0.270</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.240</td>\\n\",\n        \"      <td> 0.175</td>\\n\",\n        \"      <td> 0.045</td>\\n\",\n        \"      <td> 0.0700</td>\\n\",\n        \"      <td> 0.0315</td>\\n\",\n        \"      <td> 0.0235</td>\\n\",\n        \"      <td> 0.020</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.205</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.055</td>\\n\",\n        \"      <td> 0.0420</td>\\n\",\n        \"      <td> 0.0255</td>\\n\",\n        \"      <td> 0.0150</td>\\n\",\n        \"      <td> 0.012</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.210</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.050</td>\\n\",\n        \"      <td> 0.0420</td>\\n\",\n        \"      <td> 0.0175</td>\\n\",\n        \"      <td> 0.0125</td>\\n\",\n        \"      <td> 0.015</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.390</td>\\n\",\n        \"      <td> 0.295</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.2030</td>\\n\",\n        \"      <td> 0.0875</td>\\n\",\n        \"      <td> 0.0450</td>\\n\",\n        \"      <td> 0.075</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.370</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.5795</td>\\n\",\n        \"      <td> 0.2930</td>\\n\",\n        \"      <td> 0.2270</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.460</td>\\n\",\n        \"      <td> 0.375</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.4605</td>\\n\",\n        \"      <td> 0.1775</td>\\n\",\n        \"      <td> 0.1100</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>48</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.325</td>\\n\",\n        \"      <td> 0.245</td>\\n\",\n        \"      <td> 0.070</td>\\n\",\n        \"      <td> 0.1610</td>\\n\",\n        \"      <td> 0.0755</td>\\n\",\n        \"      <td> 0.0255</td>\\n\",\n        \"      <td> 0.045</td>\\n\",\n        \"      <td>  6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.525</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.160</td>\\n\",\n        \"      <td> 0.8355</td>\\n\",\n        \"      <td> 0.3545</td>\\n\",\n        \"      <td> 0.2135</td>\\n\",\n        \"      <td> 0.245</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.520</td>\\n\",\n        \"      <td> 0.410</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.5950</td>\\n\",\n        \"      <td> 0.2385</td>\\n\",\n        \"      <td> 0.1110</td>\\n\",\n        \"      <td> 0.190</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>51</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.320</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.3030</td>\\n\",\n        \"      <td> 0.1335</td>\\n\",\n        \"      <td> 0.0600</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.485</td>\\n\",\n        \"      <td> 0.360</td>\\n\",\n        \"      <td> 0.130</td>\\n\",\n        \"      <td> 0.5415</td>\\n\",\n        \"      <td> 0.2595</td>\\n\",\n        \"      <td> 0.0960</td>\\n\",\n        \"      <td> 0.160</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.360</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.4775</td>\\n\",\n        \"      <td> 0.2105</td>\\n\",\n        \"      <td> 0.1055</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.405</td>\\n\",\n        \"      <td> 0.310</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.3850</td>\\n\",\n        \"      <td> 0.1730</td>\\n\",\n        \"      <td> 0.0915</td>\\n\",\n        \"      <td> 0.110</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>55</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.500</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.6615</td>\\n\",\n        \"      <td> 0.2565</td>\\n\",\n        \"      <td> 0.1755</td>\\n\",\n        \"      <td> 0.220</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.445</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.4425</td>\\n\",\n        \"      <td> 0.1920</td>\\n\",\n        \"      <td> 0.0955</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>57</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.385</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.5895</td>\\n\",\n        \"      <td> 0.2765</td>\\n\",\n        \"      <td> 0.1200</td>\\n\",\n        \"      <td> 0.170</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>58</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.245</td>\\n\",\n        \"      <td> 0.190</td>\\n\",\n        \"      <td> 0.060</td>\\n\",\n        \"      <td> 0.0860</td>\\n\",\n        \"      <td> 0.0420</td>\\n\",\n        \"      <td> 0.0140</td>\\n\",\n        \"      <td> 0.025</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>59</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.505</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.5830</td>\\n\",\n        \"      <td> 0.2460</td>\\n\",\n        \"      <td> 0.1300</td>\\n\",\n        \"      <td> 0.175</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>4177 rows \\u00d7 9 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 2,\n       \"text\": [\n        \"   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\\\\n\",\n        \"0    M   0.455     0.365   0.095        0.5140          0.2245   \\n\",\n        \"1    M   0.350     0.265   0.090        0.2255          0.0995   \\n\",\n        \"2    F   0.530     0.420   0.135        0.6770          0.2565   \\n\",\n        \"3    M   0.440     0.365   0.125        0.5160          0.2155   \\n\",\n        \"4    I   0.330     0.255   0.080        0.2050          0.0895   \\n\",\n        \"5    I   0.425     0.300   0.095        0.3515          0.1410   \\n\",\n        \"6    F   0.530     0.415   0.150        0.7775          0.2370   \\n\",\n        \"7    F   0.545     0.425   0.125        0.7680          0.2940   \\n\",\n        \"8    M   0.475     0.370   0.125        0.5095          0.2165   \\n\",\n        \"9    F   0.550     0.440   0.150        0.8945          0.3145   \\n\",\n        \"10   F   0.525     0.380   0.140        0.6065          0.1940   \\n\",\n        \"11   M   0.430     0.350   0.110        0.4060          0.1675   \\n\",\n        \"12   M   0.490     0.380   0.135        0.5415          0.2175   \\n\",\n        \"13   F   0.535     0.405   0.145        0.6845          0.2725   \\n\",\n        \"14   F   0.470     0.355   0.100        0.4755          0.1675   \\n\",\n        \"15   M   0.500     0.400   0.130        0.6645          0.2580   \\n\",\n        \"16   I   0.355     0.280   0.085        0.2905          0.0950   \\n\",\n        \"17   F   0.440     0.340   0.100        0.4510          0.1880   \\n\",\n        \"18   M   0.365     0.295   0.080        0.2555          0.0970   \\n\",\n        \"19   M   0.450     0.320   0.100        0.3810          0.1705   \\n\",\n        \"20   M   0.355     0.280   0.095        0.2455          0.0955   \\n\",\n        \"21   I   0.380     0.275   0.100        0.2255          0.0800   \\n\",\n        \"22   F   0.565     0.440   0.155        0.9395          0.4275   \\n\",\n        \"23   F   0.550     0.415   0.135        0.7635          0.3180   \\n\",\n        \"24   F   0.615     0.480   0.165        1.1615          0.5130   \\n\",\n        \"25   F   0.560     0.440   0.140        0.9285          0.3825   \\n\",\n        \"26   F   0.580     0.450   0.185        0.9955          0.3945   \\n\",\n        \"27   M   0.590     0.445   0.140        0.9310          0.3560   \\n\",\n        \"28   M   0.605     0.475   0.180        0.9365          0.3940   \\n\",\n        \"29   M   0.575     0.425   0.140        0.8635          0.3930   \\n\",\n        \"30   M   0.580     0.470   0.165        0.9975          0.3935   \\n\",\n        \"31   F   0.680     0.560   0.165        1.6390          0.6055   \\n\",\n        \"32   M   0.665     0.525   0.165        1.3380          0.5515   \\n\",\n        \"33   F   0.680     0.550   0.175        1.7980          0.8150   \\n\",\n        \"34   F   0.705     0.550   0.200        1.7095          0.6330   \\n\",\n        \"35   M   0.465     0.355   0.105        0.4795          0.2270   \\n\",\n        \"36   F   0.540     0.475   0.155        1.2170          0.5305   \\n\",\n        \"37   F   0.450     0.355   0.105        0.5225          0.2370   \\n\",\n        \"38   F   0.575     0.445   0.135        0.8830          0.3810   \\n\",\n        \"39   M   0.355     0.290   0.090        0.3275          0.1340   \\n\",\n        \"40   F   0.450     0.335   0.105        0.4250          0.1865   \\n\",\n        \"41   F   0.550     0.425   0.135        0.8515          0.3620   \\n\",\n        \"42   I   0.240     0.175   0.045        0.0700          0.0315   \\n\",\n        \"43   I   0.205     0.150   0.055        0.0420          0.0255   \\n\",\n        \"44   I   0.210     0.150   0.050        0.0420          0.0175   \\n\",\n        \"45   I   0.390     0.295   0.095        0.2030          0.0875   \\n\",\n        \"46   M   0.470     0.370   0.120        0.5795          0.2930   \\n\",\n        \"47   F   0.460     0.375   0.120        0.4605          0.1775   \\n\",\n        \"48   I   0.325     0.245   0.070        0.1610          0.0755   \\n\",\n        \"49   F   0.525     0.425   0.160        0.8355          0.3545   \\n\",\n        \"50   I   0.520     0.410   0.120        0.5950          0.2385   \\n\",\n        \"51   M   0.400     0.320   0.095        0.3030          0.1335   \\n\",\n        \"52   M   0.485     0.360   0.130        0.5415          0.2595   \\n\",\n        \"53   F   0.470     0.360   0.120        0.4775          0.2105   \\n\",\n        \"54   M   0.405     0.310   0.100        0.3850          0.1730   \\n\",\n        \"55   F   0.500     0.400   0.140        0.6615          0.2565   \\n\",\n        \"56   M   0.445     0.350   0.120        0.4425          0.1920   \\n\",\n        \"57   M   0.470     0.385   0.135        0.5895          0.2765   \\n\",\n        \"58   I   0.245     0.190   0.060        0.0860          0.0420   \\n\",\n        \"59   F   0.505     0.400   0.125        0.5830          0.2460   \\n\",\n        \"   ...     ...       ...     ...           ...             ...   \\n\",\n        \"\\n\",\n        \"    Viscera weight  Shell weight  Rings  \\n\",\n        \"0           0.1010         0.150     15  \\n\",\n        \"1           0.0485         0.070      7  \\n\",\n        \"2           0.1415         0.210      9  \\n\",\n        \"3           0.1140         0.155     10  \\n\",\n        \"4           0.0395         0.055      7  \\n\",\n        \"5           0.0775         0.120      8  \\n\",\n        \"6           0.1415         0.330     20  \\n\",\n        \"7           0.1495         0.260     16  \\n\",\n        \"8           0.1125         0.165      9  \\n\",\n        \"9           0.1510         0.320     19  \\n\",\n        \"10          0.1475         0.210     14  \\n\",\n        \"11          0.0810         0.135     10  \\n\",\n        \"12          0.0950         0.190     11  \\n\",\n        \"13          0.1710         0.205     10  \\n\",\n        \"14          0.0805         0.185     10  \\n\",\n        \"15          0.1330         0.240     12  \\n\",\n        \"16          0.0395         0.115      7  \\n\",\n        \"17          0.0870         0.130     10  \\n\",\n        \"18          0.0430         0.100      7  \\n\",\n        \"19          0.0750         0.115      9  \\n\",\n        \"20          0.0620         0.075     11  \\n\",\n        \"21          0.0490         0.085     10  \\n\",\n        \"22          0.2140         0.270     12  \\n\",\n        \"23          0.2100         0.200      9  \\n\",\n        \"24          0.3010         0.305     10  \\n\",\n        \"25          0.1880         0.300     11  \\n\",\n        \"26          0.2720         0.285     11  \\n\",\n        \"27          0.2340         0.280     12  \\n\",\n        \"28          0.2190         0.295     15  \\n\",\n        \"29          0.2270         0.200     11  \\n\",\n        \"30          0.2420         0.330     10  \\n\",\n        \"31          0.2805         0.460     15  \\n\",\n        \"32          0.3575         0.350     18  \\n\",\n        \"33          0.3925         0.455     19  \\n\",\n        \"34          0.4115         0.490     13  \\n\",\n        \"35          0.1240         0.125      8  \\n\",\n        \"36          0.3075         0.340     16  \\n\",\n        \"37          0.1165         0.145      8  \\n\",\n        \"38          0.2035         0.260     11  \\n\",\n        \"39          0.0860         0.090      9  \\n\",\n        \"40          0.0910         0.115      9  \\n\",\n        \"41          0.1960         0.270     14  \\n\",\n        \"42          0.0235         0.020      5  \\n\",\n        \"43          0.0150         0.012      5  \\n\",\n        \"44          0.0125         0.015      4  \\n\",\n        \"45          0.0450         0.075      7  \\n\",\n        \"46          0.2270         0.140      9  \\n\",\n        \"47          0.1100         0.150      7  \\n\",\n        \"48          0.0255         0.045      6  \\n\",\n        \"49          0.2135         0.245      9  \\n\",\n        \"50          0.1110         0.190      8  \\n\",\n        \"51          0.0600         0.100      7  \\n\",\n        \"52          0.0960         0.160     10  \\n\",\n        \"53          0.1055         0.150     10  \\n\",\n        \"54          0.0915         0.110      7  \\n\",\n        \"55          0.1755         0.220      8  \\n\",\n        \"56          0.0955         0.135      8  \\n\",\n        \"57          0.1200         0.170      8  \\n\",\n        \"58          0.0140         0.025      4  \\n\",\n        \"59          0.1300         0.175      7  \\n\",\n        \"               ...           ...    ...  \\n\",\n        \"\\n\",\n        \"[4177 rows x 9 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Training Model\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Preparing data\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"features = csv_data.drop(\\\"Sex\\\", 1)\\n\",\n      \"\\n\",\n      \"sex_dict = {\\\"M\\\":0, \\\"I\\\":1, \\\"F\\\":2}\\n\",\n      \"results = csv_data[\\\"Sex\\\"].map(sex_dict)\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Training a model\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"trained_model = KNeighborsClassifier(n_neighbors=5, weights='uniform')\\n\",\n      \"trained_model.fit(features, results)\\n\",\n      \"trained_model\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\\n\",\n        \"           n_neighbors=5, p=2, weights='uniform')\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Saving Data and Model on Disk\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"joblib.dump(csv_data, \\\"data.pkl\\\")\\n\",\n      \"joblib.dump(trained_model, \\\"model.pkl\\\");\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 5\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"ls\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"output_type\": \"stream\",\n       \"stream\": \"stdout\",\n       \"text\": [\n        \"2. Pickle Data and Model.ipynb  model.pkl         model.pkl_06.npy\\r\\n\",\n        \"Folder Management.ipynb         model.pkl_01.npy  model.pkl_07.npy\\r\\n\",\n        \"data.pkl                        model.pkl_02.npy  model.pkl_08.npy\\r\\n\",\n        \"data.pkl_01.npy                 model.pkl_03.npy  model.pkl_09.npy\\r\\n\",\n        \"data.pkl_02.npy                 model.pkl_04.npy\\r\\n\",\n        \"data.pkl_03.npy                 model.pkl_05.npy\\r\\n\"\n       ]\n      }\n     ],\n     \"prompt_number\": 6\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Loading Data and Model\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"loaded_data = joblib.load(\\\"data.pkl\\\")\\n\",\n      \"loaded_model = joblib.load(\\\"model.pkl\\\")\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 7\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"loaded_data\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"html\": [\n        \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n        \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n        \"  <thead>\\n\",\n        \"    <tr style=\\\"text-align: right;\\\">\\n\",\n        \"      <th></th>\\n\",\n        \"      <th>Sex</th>\\n\",\n        \"      <th>Length</th>\\n\",\n        \"      <th>Diameter</th>\\n\",\n        \"      <th>Height</th>\\n\",\n        \"      <th>Whole weight</th>\\n\",\n        \"      <th>Shucked weight</th>\\n\",\n        \"      <th>Viscera weight</th>\\n\",\n        \"      <th>Shell weight</th>\\n\",\n        \"      <th>Rings</th>\\n\",\n        \"    </tr>\\n\",\n        \"  </thead>\\n\",\n        \"  <tbody>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>0 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.455</td>\\n\",\n        \"      <td> 0.365</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.5140</td>\\n\",\n        \"      <td> 0.2245</td>\\n\",\n        \"      <td> 0.1010</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>1 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 0.265</td>\\n\",\n        \"      <td> 0.090</td>\\n\",\n        \"      <td> 0.2255</td>\\n\",\n        \"      <td> 0.0995</td>\\n\",\n        \"      <td> 0.0485</td>\\n\",\n        \"      <td> 0.070</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>2 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.530</td>\\n\",\n        \"      <td> 0.420</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.6770</td>\\n\",\n        \"      <td> 0.2565</td>\\n\",\n        \"      <td> 0.1415</td>\\n\",\n        \"      <td> 0.210</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>3 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.365</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.5160</td>\\n\",\n        \"      <td> 0.2155</td>\\n\",\n        \"      <td> 0.1140</td>\\n\",\n        \"      <td> 0.155</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>4 </th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.330</td>\\n\",\n        \"      <td> 0.255</td>\\n\",\n        \"      <td> 0.080</td>\\n\",\n        \"      <td> 0.2050</td>\\n\",\n        \"      <td> 0.0895</td>\\n\",\n        \"      <td> 0.0395</td>\\n\",\n        \"      <td> 0.055</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>5 </th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.300</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.3515</td>\\n\",\n        \"      <td> 0.1410</td>\\n\",\n        \"      <td> 0.0775</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>6 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.530</td>\\n\",\n        \"      <td> 0.415</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.7775</td>\\n\",\n        \"      <td> 0.2370</td>\\n\",\n        \"      <td> 0.1415</td>\\n\",\n        \"      <td> 0.330</td>\\n\",\n        \"      <td> 20</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>7 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.545</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.7680</td>\\n\",\n        \"      <td> 0.2940</td>\\n\",\n        \"      <td> 0.1495</td>\\n\",\n        \"      <td> 0.260</td>\\n\",\n        \"      <td> 16</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>8 </th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.475</td>\\n\",\n        \"      <td> 0.370</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.5095</td>\\n\",\n        \"      <td> 0.2165</td>\\n\",\n        \"      <td> 0.1125</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>9 </th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.8945</td>\\n\",\n        \"      <td> 0.3145</td>\\n\",\n        \"      <td> 0.1510</td>\\n\",\n        \"      <td> 0.320</td>\\n\",\n        \"      <td> 19</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>10</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.525</td>\\n\",\n        \"      <td> 0.380</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.6065</td>\\n\",\n        \"      <td> 0.1940</td>\\n\",\n        \"      <td> 0.1475</td>\\n\",\n        \"      <td> 0.210</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>11</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.430</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 0.110</td>\\n\",\n        \"      <td> 0.4060</td>\\n\",\n        \"      <td> 0.1675</td>\\n\",\n        \"      <td> 0.0810</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>12</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.490</td>\\n\",\n        \"      <td> 0.380</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.5415</td>\\n\",\n        \"      <td> 0.2175</td>\\n\",\n        \"      <td> 0.0950</td>\\n\",\n        \"      <td> 0.190</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>13</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.535</td>\\n\",\n        \"      <td> 0.405</td>\\n\",\n        \"      <td> 0.145</td>\\n\",\n        \"      <td> 0.6845</td>\\n\",\n        \"      <td> 0.2725</td>\\n\",\n        \"      <td> 0.1710</td>\\n\",\n        \"      <td> 0.205</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>14</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.4755</td>\\n\",\n        \"      <td> 0.1675</td>\\n\",\n        \"      <td> 0.0805</td>\\n\",\n        \"      <td> 0.185</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>15</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.500</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.130</td>\\n\",\n        \"      <td> 0.6645</td>\\n\",\n        \"      <td> 0.2580</td>\\n\",\n        \"      <td> 0.1330</td>\\n\",\n        \"      <td> 0.240</td>\\n\",\n        \"      <td> 12</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>16</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.280</td>\\n\",\n        \"      <td> 0.085</td>\\n\",\n        \"      <td> 0.2905</td>\\n\",\n        \"      <td> 0.0950</td>\\n\",\n        \"      <td> 0.0395</td>\\n\",\n        \"      <td> 0.115</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>17</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.340</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.4510</td>\\n\",\n        \"      <td> 0.1880</td>\\n\",\n        \"      <td> 0.0870</td>\\n\",\n        \"      <td> 0.130</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>18</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.365</td>\\n\",\n        \"      <td> 0.295</td>\\n\",\n        \"      <td> 0.080</td>\\n\",\n        \"      <td> 0.2555</td>\\n\",\n        \"      <td> 0.0970</td>\\n\",\n        \"      <td> 0.0430</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>19</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.320</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.3810</td>\\n\",\n        \"      <td> 0.1705</td>\\n\",\n        \"      <td> 0.0750</td>\\n\",\n        \"      <td> 0.115</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>20</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.280</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.2455</td>\\n\",\n        \"      <td> 0.0955</td>\\n\",\n        \"      <td> 0.0620</td>\\n\",\n        \"      <td> 0.075</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>21</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.380</td>\\n\",\n        \"      <td> 0.275</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.2255</td>\\n\",\n        \"      <td> 0.0800</td>\\n\",\n        \"      <td> 0.0490</td>\\n\",\n        \"      <td> 0.085</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>22</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.565</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.155</td>\\n\",\n        \"      <td> 0.9395</td>\\n\",\n        \"      <td> 0.4275</td>\\n\",\n        \"      <td> 0.2140</td>\\n\",\n        \"      <td> 0.270</td>\\n\",\n        \"      <td> 12</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>23</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.415</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.7635</td>\\n\",\n        \"      <td> 0.3180</td>\\n\",\n        \"      <td> 0.2100</td>\\n\",\n        \"      <td> 0.200</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>24</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.615</td>\\n\",\n        \"      <td> 0.480</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 1.1615</td>\\n\",\n        \"      <td> 0.5130</td>\\n\",\n        \"      <td> 0.3010</td>\\n\",\n        \"      <td> 0.305</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>25</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.560</td>\\n\",\n        \"      <td> 0.440</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.9285</td>\\n\",\n        \"      <td> 0.3825</td>\\n\",\n        \"      <td> 0.1880</td>\\n\",\n        \"      <td> 0.300</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>26</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.580</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.185</td>\\n\",\n        \"      <td> 0.9955</td>\\n\",\n        \"      <td> 0.3945</td>\\n\",\n        \"      <td> 0.2720</td>\\n\",\n        \"      <td> 0.285</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>27</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.590</td>\\n\",\n        \"      <td> 0.445</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.9310</td>\\n\",\n        \"      <td> 0.3560</td>\\n\",\n        \"      <td> 0.2340</td>\\n\",\n        \"      <td> 0.280</td>\\n\",\n        \"      <td> 12</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>28</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.605</td>\\n\",\n        \"      <td> 0.475</td>\\n\",\n        \"      <td> 0.180</td>\\n\",\n        \"      <td> 0.9365</td>\\n\",\n        \"      <td> 0.3940</td>\\n\",\n        \"      <td> 0.2190</td>\\n\",\n        \"      <td> 0.295</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>29</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.575</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.8635</td>\\n\",\n        \"      <td> 0.3930</td>\\n\",\n        \"      <td> 0.2270</td>\\n\",\n        \"      <td> 0.200</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>30</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.580</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 0.9975</td>\\n\",\n        \"      <td> 0.3935</td>\\n\",\n        \"      <td> 0.2420</td>\\n\",\n        \"      <td> 0.330</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>31</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.680</td>\\n\",\n        \"      <td> 0.560</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 1.6390</td>\\n\",\n        \"      <td> 0.6055</td>\\n\",\n        \"      <td> 0.2805</td>\\n\",\n        \"      <td> 0.460</td>\\n\",\n        \"      <td> 15</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>32</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.665</td>\\n\",\n        \"      <td> 0.525</td>\\n\",\n        \"      <td> 0.165</td>\\n\",\n        \"      <td> 1.3380</td>\\n\",\n        \"      <td> 0.5515</td>\\n\",\n        \"      <td> 0.3575</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 18</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>33</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.680</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.175</td>\\n\",\n        \"      <td> 1.7980</td>\\n\",\n        \"      <td> 0.8150</td>\\n\",\n        \"      <td> 0.3925</td>\\n\",\n        \"      <td> 0.455</td>\\n\",\n        \"      <td> 19</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>34</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.705</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.200</td>\\n\",\n        \"      <td> 1.7095</td>\\n\",\n        \"      <td> 0.6330</td>\\n\",\n        \"      <td> 0.4115</td>\\n\",\n        \"      <td> 0.490</td>\\n\",\n        \"      <td> 13</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>35</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.465</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.105</td>\\n\",\n        \"      <td> 0.4795</td>\\n\",\n        \"      <td> 0.2270</td>\\n\",\n        \"      <td> 0.1240</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>36</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.540</td>\\n\",\n        \"      <td> 0.475</td>\\n\",\n        \"      <td> 0.155</td>\\n\",\n        \"      <td> 1.2170</td>\\n\",\n        \"      <td> 0.5305</td>\\n\",\n        \"      <td> 0.3075</td>\\n\",\n        \"      <td> 0.340</td>\\n\",\n        \"      <td> 16</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>37</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.105</td>\\n\",\n        \"      <td> 0.5225</td>\\n\",\n        \"      <td> 0.2370</td>\\n\",\n        \"      <td> 0.1165</td>\\n\",\n        \"      <td> 0.145</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>38</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.575</td>\\n\",\n        \"      <td> 0.445</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.8830</td>\\n\",\n        \"      <td> 0.3810</td>\\n\",\n        \"      <td> 0.2035</td>\\n\",\n        \"      <td> 0.260</td>\\n\",\n        \"      <td> 11</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>39</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.355</td>\\n\",\n        \"      <td> 0.290</td>\\n\",\n        \"      <td> 0.090</td>\\n\",\n        \"      <td> 0.3275</td>\\n\",\n        \"      <td> 0.1340</td>\\n\",\n        \"      <td> 0.0860</td>\\n\",\n        \"      <td> 0.090</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>40</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.450</td>\\n\",\n        \"      <td> 0.335</td>\\n\",\n        \"      <td> 0.105</td>\\n\",\n        \"      <td> 0.4250</td>\\n\",\n        \"      <td> 0.1865</td>\\n\",\n        \"      <td> 0.0910</td>\\n\",\n        \"      <td> 0.115</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>41</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.550</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.8515</td>\\n\",\n        \"      <td> 0.3620</td>\\n\",\n        \"      <td> 0.1960</td>\\n\",\n        \"      <td> 0.270</td>\\n\",\n        \"      <td> 14</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>42</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.240</td>\\n\",\n        \"      <td> 0.175</td>\\n\",\n        \"      <td> 0.045</td>\\n\",\n        \"      <td> 0.0700</td>\\n\",\n        \"      <td> 0.0315</td>\\n\",\n        \"      <td> 0.0235</td>\\n\",\n        \"      <td> 0.020</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>43</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.205</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.055</td>\\n\",\n        \"      <td> 0.0420</td>\\n\",\n        \"      <td> 0.0255</td>\\n\",\n        \"      <td> 0.0150</td>\\n\",\n        \"      <td> 0.012</td>\\n\",\n        \"      <td>  5</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>44</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.210</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 0.050</td>\\n\",\n        \"      <td> 0.0420</td>\\n\",\n        \"      <td> 0.0175</td>\\n\",\n        \"      <td> 0.0125</td>\\n\",\n        \"      <td> 0.015</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>45</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.390</td>\\n\",\n        \"      <td> 0.295</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.2030</td>\\n\",\n        \"      <td> 0.0875</td>\\n\",\n        \"      <td> 0.0450</td>\\n\",\n        \"      <td> 0.075</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>46</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.370</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.5795</td>\\n\",\n        \"      <td> 0.2930</td>\\n\",\n        \"      <td> 0.2270</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>47</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.460</td>\\n\",\n        \"      <td> 0.375</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.4605</td>\\n\",\n        \"      <td> 0.1775</td>\\n\",\n        \"      <td> 0.1100</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>48</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.325</td>\\n\",\n        \"      <td> 0.245</td>\\n\",\n        \"      <td> 0.070</td>\\n\",\n        \"      <td> 0.1610</td>\\n\",\n        \"      <td> 0.0755</td>\\n\",\n        \"      <td> 0.0255</td>\\n\",\n        \"      <td> 0.045</td>\\n\",\n        \"      <td>  6</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>49</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.525</td>\\n\",\n        \"      <td> 0.425</td>\\n\",\n        \"      <td> 0.160</td>\\n\",\n        \"      <td> 0.8355</td>\\n\",\n        \"      <td> 0.3545</td>\\n\",\n        \"      <td> 0.2135</td>\\n\",\n        \"      <td> 0.245</td>\\n\",\n        \"      <td>  9</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>50</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.520</td>\\n\",\n        \"      <td> 0.410</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.5950</td>\\n\",\n        \"      <td> 0.2385</td>\\n\",\n        \"      <td> 0.1110</td>\\n\",\n        \"      <td> 0.190</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>51</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.320</td>\\n\",\n        \"      <td> 0.095</td>\\n\",\n        \"      <td> 0.3030</td>\\n\",\n        \"      <td> 0.1335</td>\\n\",\n        \"      <td> 0.0600</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>52</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.485</td>\\n\",\n        \"      <td> 0.360</td>\\n\",\n        \"      <td> 0.130</td>\\n\",\n        \"      <td> 0.5415</td>\\n\",\n        \"      <td> 0.2595</td>\\n\",\n        \"      <td> 0.0960</td>\\n\",\n        \"      <td> 0.160</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>53</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.360</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.4775</td>\\n\",\n        \"      <td> 0.2105</td>\\n\",\n        \"      <td> 0.1055</td>\\n\",\n        \"      <td> 0.150</td>\\n\",\n        \"      <td> 10</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>54</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.405</td>\\n\",\n        \"      <td> 0.310</td>\\n\",\n        \"      <td> 0.100</td>\\n\",\n        \"      <td> 0.3850</td>\\n\",\n        \"      <td> 0.1730</td>\\n\",\n        \"      <td> 0.0915</td>\\n\",\n        \"      <td> 0.110</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>55</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.500</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.140</td>\\n\",\n        \"      <td> 0.6615</td>\\n\",\n        \"      <td> 0.2565</td>\\n\",\n        \"      <td> 0.1755</td>\\n\",\n        \"      <td> 0.220</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>56</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.445</td>\\n\",\n        \"      <td> 0.350</td>\\n\",\n        \"      <td> 0.120</td>\\n\",\n        \"      <td> 0.4425</td>\\n\",\n        \"      <td> 0.1920</td>\\n\",\n        \"      <td> 0.0955</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>57</th>\\n\",\n        \"      <td> M</td>\\n\",\n        \"      <td> 0.470</td>\\n\",\n        \"      <td> 0.385</td>\\n\",\n        \"      <td> 0.135</td>\\n\",\n        \"      <td> 0.5895</td>\\n\",\n        \"      <td> 0.2765</td>\\n\",\n        \"      <td> 0.1200</td>\\n\",\n        \"      <td> 0.170</td>\\n\",\n        \"      <td>  8</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>58</th>\\n\",\n        \"      <td> I</td>\\n\",\n        \"      <td> 0.245</td>\\n\",\n        \"      <td> 0.190</td>\\n\",\n        \"      <td> 0.060</td>\\n\",\n        \"      <td> 0.0860</td>\\n\",\n        \"      <td> 0.0420</td>\\n\",\n        \"      <td> 0.0140</td>\\n\",\n        \"      <td> 0.025</td>\\n\",\n        \"      <td>  4</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th>59</th>\\n\",\n        \"      <td> F</td>\\n\",\n        \"      <td> 0.505</td>\\n\",\n        \"      <td> 0.400</td>\\n\",\n        \"      <td> 0.125</td>\\n\",\n        \"      <td> 0.5830</td>\\n\",\n        \"      <td> 0.2460</td>\\n\",\n        \"      <td> 0.1300</td>\\n\",\n        \"      <td> 0.175</td>\\n\",\n        \"      <td>  7</td>\\n\",\n        \"    </tr>\\n\",\n        \"    <tr>\\n\",\n        \"      <th></th>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"      <td>...</td>\\n\",\n        \"    </tr>\\n\",\n        \"  </tbody>\\n\",\n        \"</table>\\n\",\n        \"<p>4177 rows \\u00d7 9 columns</p>\\n\",\n        \"</div>\"\n       ],\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 8,\n       \"text\": [\n        \"   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\\\\n\",\n        \"0    M   0.455     0.365   0.095        0.5140          0.2245   \\n\",\n        \"1    M   0.350     0.265   0.090        0.2255          0.0995   \\n\",\n        \"2    F   0.530     0.420   0.135        0.6770          0.2565   \\n\",\n        \"3    M   0.440     0.365   0.125        0.5160          0.2155   \\n\",\n        \"4    I   0.330     0.255   0.080        0.2050          0.0895   \\n\",\n        \"5    I   0.425     0.300   0.095        0.3515          0.1410   \\n\",\n        \"6    F   0.530     0.415   0.150        0.7775          0.2370   \\n\",\n        \"7    F   0.545     0.425   0.125        0.7680          0.2940   \\n\",\n        \"8    M   0.475     0.370   0.125        0.5095          0.2165   \\n\",\n        \"9    F   0.550     0.440   0.150        0.8945          0.3145   \\n\",\n        \"10   F   0.525     0.380   0.140        0.6065          0.1940   \\n\",\n        \"11   M   0.430     0.350   0.110        0.4060          0.1675   \\n\",\n        \"12   M   0.490     0.380   0.135        0.5415          0.2175   \\n\",\n        \"13   F   0.535     0.405   0.145        0.6845          0.2725   \\n\",\n        \"14   F   0.470     0.355   0.100        0.4755          0.1675   \\n\",\n        \"15   M   0.500     0.400   0.130        0.6645          0.2580   \\n\",\n        \"16   I   0.355     0.280   0.085        0.2905          0.0950   \\n\",\n        \"17   F   0.440     0.340   0.100        0.4510          0.1880   \\n\",\n        \"18   M   0.365     0.295   0.080        0.2555          0.0970   \\n\",\n        \"19   M   0.450     0.320   0.100        0.3810          0.1705   \\n\",\n        \"20   M   0.355     0.280   0.095        0.2455          0.0955   \\n\",\n        \"21   I   0.380     0.275   0.100        0.2255          0.0800   \\n\",\n        \"22   F   0.565     0.440   0.155        0.9395          0.4275   \\n\",\n        \"23   F   0.550     0.415   0.135        0.7635          0.3180   \\n\",\n        \"24   F   0.615     0.480   0.165        1.1615          0.5130   \\n\",\n        \"25   F   0.560     0.440   0.140        0.9285          0.3825   \\n\",\n        \"26   F   0.580     0.450   0.185        0.9955          0.3945   \\n\",\n        \"27   M   0.590     0.445   0.140        0.9310          0.3560   \\n\",\n        \"28   M   0.605     0.475   0.180        0.9365          0.3940   \\n\",\n        \"29   M   0.575     0.425   0.140        0.8635          0.3930   \\n\",\n        \"30   M   0.580     0.470   0.165        0.9975          0.3935   \\n\",\n        \"31   F   0.680     0.560   0.165        1.6390          0.6055   \\n\",\n        \"32   M   0.665     0.525   0.165        1.3380          0.5515   \\n\",\n        \"33   F   0.680     0.550   0.175        1.7980          0.8150   \\n\",\n        \"34   F   0.705     0.550   0.200        1.7095          0.6330   \\n\",\n        \"35   M   0.465     0.355   0.105        0.4795          0.2270   \\n\",\n        \"36   F   0.540     0.475   0.155        1.2170          0.5305   \\n\",\n        \"37   F   0.450     0.355   0.105        0.5225          0.2370   \\n\",\n        \"38   F   0.575     0.445   0.135        0.8830          0.3810   \\n\",\n        \"39   M   0.355     0.290   0.090        0.3275          0.1340   \\n\",\n        \"40   F   0.450     0.335   0.105        0.4250          0.1865   \\n\",\n        \"41   F   0.550     0.425   0.135        0.8515          0.3620   \\n\",\n        \"42   I   0.240     0.175   0.045        0.0700          0.0315   \\n\",\n        \"43   I   0.205     0.150   0.055        0.0420          0.0255   \\n\",\n        \"44   I   0.210     0.150   0.050        0.0420          0.0175   \\n\",\n        \"45   I   0.390     0.295   0.095        0.2030          0.0875   \\n\",\n        \"46   M   0.470     0.370   0.120        0.5795          0.2930   \\n\",\n        \"47   F   0.460     0.375   0.120        0.4605          0.1775   \\n\",\n        \"48   I   0.325     0.245   0.070        0.1610          0.0755   \\n\",\n        \"49   F   0.525     0.425   0.160        0.8355          0.3545   \\n\",\n        \"50   I   0.520     0.410   0.120        0.5950          0.2385   \\n\",\n        \"51   M   0.400     0.320   0.095        0.3030          0.1335   \\n\",\n        \"52   M   0.485     0.360   0.130        0.5415          0.2595   \\n\",\n        \"53   F   0.470     0.360   0.120        0.4775          0.2105   \\n\",\n        \"54   M   0.405     0.310   0.100        0.3850          0.1730   \\n\",\n        \"55   F   0.500     0.400   0.140        0.6615          0.2565   \\n\",\n        \"56   M   0.445     0.350   0.120        0.4425          0.1920   \\n\",\n        \"57   M   0.470     0.385   0.135        0.5895          0.2765   \\n\",\n        \"58   I   0.245     0.190   0.060        0.0860          0.0420   \\n\",\n        \"59   F   0.505     0.400   0.125        0.5830          0.2460   \\n\",\n        \"   ...     ...       ...     ...           ...             ...   \\n\",\n        \"\\n\",\n        \"    Viscera weight  Shell weight  Rings  \\n\",\n        \"0           0.1010         0.150     15  \\n\",\n        \"1           0.0485         0.070      7  \\n\",\n        \"2           0.1415         0.210      9  \\n\",\n        \"3           0.1140         0.155     10  \\n\",\n        \"4           0.0395         0.055      7  \\n\",\n        \"5           0.0775         0.120      8  \\n\",\n        \"6           0.1415         0.330     20  \\n\",\n        \"7           0.1495         0.260     16  \\n\",\n        \"8           0.1125         0.165      9  \\n\",\n        \"9           0.1510         0.320     19  \\n\",\n        \"10          0.1475         0.210     14  \\n\",\n        \"11          0.0810         0.135     10  \\n\",\n        \"12          0.0950         0.190     11  \\n\",\n        \"13          0.1710         0.205     10  \\n\",\n        \"14          0.0805         0.185     10  \\n\",\n        \"15          0.1330         0.240     12  \\n\",\n        \"16          0.0395         0.115      7  \\n\",\n        \"17          0.0870         0.130     10  \\n\",\n        \"18          0.0430         0.100      7  \\n\",\n        \"19          0.0750         0.115      9  \\n\",\n        \"20          0.0620         0.075     11  \\n\",\n        \"21          0.0490         0.085     10  \\n\",\n        \"22          0.2140         0.270     12  \\n\",\n        \"23          0.2100         0.200      9  \\n\",\n        \"24          0.3010         0.305     10  \\n\",\n        \"25          0.1880         0.300     11  \\n\",\n        \"26          0.2720         0.285     11  \\n\",\n        \"27          0.2340         0.280     12  \\n\",\n        \"28          0.2190         0.295     15  \\n\",\n        \"29          0.2270         0.200     11  \\n\",\n        \"30          0.2420         0.330     10  \\n\",\n        \"31          0.2805         0.460     15  \\n\",\n        \"32          0.3575         0.350     18  \\n\",\n        \"33          0.3925         0.455     19  \\n\",\n        \"34          0.4115         0.490     13  \\n\",\n        \"35          0.1240         0.125      8  \\n\",\n        \"36          0.3075         0.340     16  \\n\",\n        \"37          0.1165         0.145      8  \\n\",\n        \"38          0.2035         0.260     11  \\n\",\n        \"39          0.0860         0.090      9  \\n\",\n        \"40          0.0910         0.115      9  \\n\",\n        \"41          0.1960         0.270     14  \\n\",\n        \"42          0.0235         0.020      5  \\n\",\n        \"43          0.0150         0.012      5  \\n\",\n        \"44          0.0125         0.015      4  \\n\",\n        \"45          0.0450         0.075      7  \\n\",\n        \"46          0.2270         0.140      9  \\n\",\n        \"47          0.1100         0.150      7  \\n\",\n        \"48          0.0255         0.045      6  \\n\",\n        \"49          0.2135         0.245      9  \\n\",\n        \"50          0.1110         0.190      8  \\n\",\n        \"51          0.0600         0.100      7  \\n\",\n        \"52          0.0960         0.160     10  \\n\",\n        \"53          0.1055         0.150     10  \\n\",\n        \"54          0.0915         0.110      7  \\n\",\n        \"55          0.1755         0.220      8  \\n\",\n        \"56          0.0955         0.135      8  \\n\",\n        \"57          0.1200         0.170      8  \\n\",\n        \"58          0.0140         0.025      4  \\n\",\n        \"59          0.1300         0.175      7  \\n\",\n        \"               ...           ...    ...  \\n\",\n        \"\\n\",\n        \"[4177 rows x 9 columns]\"\n       ]\n      }\n     ],\n     \"prompt_number\": 8\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"loaded_model\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 9,\n       \"text\": [\n        \"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\\n\",\n        \"           n_neighbors=5, p=2, weights='uniform')\"\n       ]\n      }\n     ],\n     \"prompt_number\": 9\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/quick_tips/3. Sentiment Analysis.ipynb",
    "content": "{\n \"metadata\": {\n  \"name\": \"\",\n  \"signature\": \"sha256:4d925c45189783ac33d4472859cf2e8441fb82698b89e81a3850fa5bea529982\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n  {\n   \"cells\": [\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Tutorial Brief\"\n     ]\n    },\n    {\n     \"cell_type\": \"markdown\",\n     \"metadata\": {},\n     \"source\": [\n      \"Using textblob library to perform basic sentement analysis.\\n\",\n      \"\\n\",\n      \"Text Blob Library Links:\\n\",\n      \"\\n\",\n      \"- Docs: http://textblob.readthedocs.org/en/dev/\\n\",\n      \"- PyPI: https://pypi.python.org/pypi/textblob\"\n     ]\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Import The Library\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"from textblob import TextBlob\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [],\n     \"prompt_number\": 1\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 1,\n     \"metadata\": {},\n     \"source\": [\n      \"Making a `TextBlob` Object\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sent = TextBlob(\\\"This tutorial is the best thing in the history of mankind\\\")\\n\",\n      \"sent\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 2,\n       \"text\": [\n        \"TextBlob(\\\"This tutorial is the best thing in the history of mankind\\\")\"\n       ]\n      }\n     ],\n     \"prompt_number\": 2\n    },\n    {\n     \"cell_type\": \"heading\",\n     \"level\": 2,\n     \"metadata\": {},\n     \"source\": [\n      \"Measuring sentiments\"\n     ]\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sent.sentiment\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 3,\n       \"text\": [\n        \"Sentiment(polarity=1.0, subjectivity=0.3)\"\n       ]\n      }\n     ],\n     \"prompt_number\": 3\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sent.polarity\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 4,\n       \"text\": [\n        \"1.0\"\n       ]\n      }\n     ],\n     \"prompt_number\": 4\n    },\n    {\n     \"cell_type\": \"code\",\n     \"collapsed\": false,\n     \"input\": [\n      \"sent.subjectivity\"\n     ],\n     \"language\": \"python\",\n     \"metadata\": {},\n     \"outputs\": [\n      {\n       \"metadata\": {},\n       \"output_type\": \"pyout\",\n       \"prompt_number\": 5,\n       \"text\": [\n        \"0.3\"\n       ]\n      }\n     ],\n     \"prompt_number\": 5\n    }\n   ],\n   \"metadata\": {}\n  }\n ]\n}\n"
  },
  {
    "path": "notebooks/quick_tips/4. XPath.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# XPath\\n\",\n    \"\\n\",\n    \"XPath is short for XML Path Language which is a query language for selecting nodes in an XML document. This is very useful in webscraping because all HTML documents are a form of XML documents.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import requests\\n\",\n    \"from lxml import html\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<html>\\n\",\n       \"  <body>\\n\",\n       \"    <h1>Favorite Python Librarires</h1>\\n\",\n       \"    <ul>\\n\",\n       \"      <li>Numpy</li>\\n\",\n       \"      <li>Pandas</li>\\n\",\n       \"      <li>requests</li>\\n\",\n       \"    </ul>\\n\",\n       \"</html>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%%HTML\\n\",\n    \"<html>\\n\",\n    \"  <body>\\n\",\n    \"    <h1>Favorite Python Librarires</h1>\\n\",\n    \"    <ul>\\n\",\n    \"      <li>Numpy</li>\\n\",\n    \"      <li>Pandas</li>\\n\",\n    \"      <li>requests</li>\\n\",\n    \"    </ul>\\n\",\n    \"  </body>\\n\",\n    \"</html>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Load HTML Code\\n\",\n    \"Now I'll read the code from cell number 2 and store it in `html_code`. Finally we will parse that into a lxml node object.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<html>\\n\",\n      \"  <body>\\n\",\n      \"    <h1>Favorite Python Librarires</h1>\\n\",\n      \"    <ul>\\n\",\n      \"      <li>Numpy</li>\\n\",\n      \"      <li>Pandas</li>\\n\",\n      \"      <li>requests</li>\\n\",\n      \"    </ul>\\n\",\n      \"</html>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"html_code = In[2]\\n\",\n    \"html_code = html_code[42:-2].replace(\\\"\\\\\\\\n\\\",\\\"\\\\n\\\")\\n\",\n    \"print(html_code)\\n\",\n    \"\\n\",\n    \"doc = html.fromstring(html_code)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Using xpath to find nodes in a document\\n\",\n    \"\\n\",\n    \"There many methods for fidning a node that you are interested in from a XML or HTML document. The first way is to write the whole path separated by forward slashes `/`\\n\",\n    \"\\n\",\n    \"## Reading `<h1>` tag\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<Element h1 at 0x7f447cafa458>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"title = doc.xpath(\\\"/html/body/h1\\\")[0]\\n\",\n    \"title\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To read the text inside that tag you can use the text variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Favorite Python Librarires'\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"title.text\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Another way is read the text is to use the `text()` function in xpath.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Favorite Python Librarires'\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"title = doc.xpath(\\\"/html/body/h1/text()\\\")[0]\\n\",\n    \"title\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Working with multiple items\\n\",\n    \"\\n\",\n    \"xpath always returns a list. If there are no matches, it will return an empty list. If there is one match it will return a list with one item.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<Element li at 0x7f447cafa9a8>,\\n\",\n       \" <Element li at 0x7f447cafaae8>,\\n\",\n       \" <Element li at 0x7f447cafab38>]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"item_list = doc.xpath(\\\"/html/body/ul/li\\\")\\n\",\n    \"item_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can use `text()` function with multiple items.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Numpy', 'Pandas', 'requests']\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"doc = html.fromstring(html_code)\\n\",\n    \"item_list = doc.xpath(\\\"/html/body/ul/li/text()\\\")\\n\",\n    \"item_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tag selector without full path\\n\",\n    \"\\n\",\n    \"you can select any node in your document that matches a node selector without using the full path with a double forward slash `//`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Numpy', 'Pandas', 'requests']\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"doc = html.fromstring(html_code)\\n\",\n    \"item_list = doc.xpath(\\\"//li/text()\\\")\\n\",\n    \"item_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Selecting one result\\n\",\n    \"\\n\",\n    \"You can select one result from a list using `[index]` after your tag selector. Make sure you use it on the tag selector and not a function selector.\\n\",\n    \"\\n\",\n    \"**Notice**: This is `index` starts from 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Numpy']\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"doc = html.fromstring(html_code)\\n\",\n    \"item_list = doc.xpath(\\\"/html/body/ul/li[1]/text()\\\")\\n\",\n    \"item_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<html>\\n\",\n       \"  <body>\\n\",\n       \"    <h1 class=\\\"text-muted\\\">Favorite Python Librarires</h1>\\n\",\n       \"    <ul class=\\\"nav nav-pills nav-stacked\\\">\\n\",\n       \"      <li role=\\\"presentation\\\"><a href=\\\"http://www.numpy.org/\\\">Numpy</a></li>\\n\",\n       \"      <li role=\\\"presentation\\\"><a href=\\\"http://pandas.pydata.org/\\\">Pandas</a></li>\\n\",\n       \"      <li role=\\\"presentation\\\"><a href=\\\"http://python-requests.org/\\\">requests</a></li>\\n\",\n       \"    </ul>\\n\",\n       \"    <h1 class=\\\"text-success\\\">Favorite JS Librarires</h1>\\n\",\n       \"    <ul class=\\\"nav nav-tabs\\\">\\n\",\n       \"      <li role=\\\"presentation\\\"><a href=\\\"http://getbootstrap.com/\\\">Bootstrap</a></li>\\n\",\n       \"      <li role=\\\"presentation\\\"><a href=\\\"https://jquery.com/\\\">jQuery</a></li>\\n\",\n       \"      <li role=\\\"presentation\\\"><a href=\\\"http://d3js.org/\\\">d3.js</a></li>\\n\",\n       \"    </ul>\\n\",\n       \"</html>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%%HTML\\n\",\n    \"<html>\\n\",\n    \"  <body>\\n\",\n    \"    <h1 class=\\\"text-muted\\\">Favorite Python Librarires</h1>\\n\",\n    \"    <ul class=\\\"nav nav-pills nav-stacked\\\">\\n\",\n    \"      <li role=\\\"presentation\\\"><a href=\\\"http://www.numpy.org/\\\">Numpy</a></li>\\n\",\n    \"      <li role=\\\"presentation\\\"><a href=\\\"http://pandas.pydata.org/\\\">Pandas</a></li>\\n\",\n    \"      <li role=\\\"presentation\\\"><a href=\\\"http://python-requests.org/\\\">requests</a></li>\\n\",\n    \"    </ul>\\n\",\n    \"    <h1 class=\\\"text-success\\\">Favorite JS Librarires</h1>\\n\",\n    \"    <ul class=\\\"nav nav-tabs\\\">\\n\",\n    \"      <li role=\\\"presentation\\\"><a href=\\\"http://getbootstrap.com/\\\">Bootstrap</a></li>\\n\",\n    \"      <li role=\\\"presentation\\\"><a href=\\\"https://jquery.com/\\\">jQuery</a></li>\\n\",\n    \"      <li role=\\\"presentation\\\"><a href=\\\"http://d3js.org/\\\">d3.js</a></li>\\n\",\n    \"    </ul>\\n\",\n    \"</html>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<html>\\n\",\n      \"  <body>\\n\",\n      \"    <h1 class=\\\"text-muted\\\">Favorite Python Librarires</h1>\\n\",\n      \"    <ul class=\\\"nav nav-pills nav-stacked\\\">\\n\",\n      \"      <li role=\\\"presentation\\\"><a href=\\\"http://www.numpy.org/\\\">Numpy</a></li>\\n\",\n      \"      <li role=\\\"presentation\\\"><a href=\\\"http://pandas.pydata.org/\\\">Pandas</a></li>\\n\",\n      \"      <li role=\\\"presentation\\\"><a href=\\\"http://python-requests.org/\\\">requests</a></li>\\n\",\n      \"    </ul>\\n\",\n      \"    <h1 class=\\\"text-success\\\">Favorite JS Librarires</h1>\\n\",\n      \"    <ul class=\\\"nav nav-tabs\\\">\\n\",\n      \"      <li role=\\\"presentation\\\"><a href=\\\"http://getbootstrap.com/\\\">Bootstrap</a></li>\\n\",\n      \"      <li role=\\\"presentation\\\"><a href=\\\"https://jquery.com/\\\">jQuery</a></li>\\n\",\n      \"      <li role=\\\"presentation\\\"><a href=\\\"http://d3js.org/\\\">d3.js</a></li>\\n\",\n      \"    </ul>\\n\",\n      \"</html>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"html_code = In[11]\\n\",\n    \"html_code = html_code[42:-2].replace(\\\"\\\\\\\\n\\\",\\\"\\\\n\\\")\\n\",\n    \"print(html_code)\\n\",\n    \"\\n\",\n    \"doc = html.fromstring(html_code)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Attributes selector\\n\",\n    \"\\n\",\n    \"In this example we have two `<h1>` tags with different css classes. We can select tags based on css classes as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'Favorite Python Librarires'\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"title = doc.xpath(\\\"/html/body/h1[@class='text-muted']/text()\\\")[0]\\n\",\n    \"title\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## `contains()` function\\n\",\n    \"\\n\",\n    \"I want to select all items in the first list. I could use the full class for selection or I could just use one of the classed only used in the first list with the `contains()` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Numpy', 'Pandas', 'requests']\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"item_list = doc.xpath(\\\"/html/body/ul[contains(@class,'nav-stacked')]/li/a/text()\\\")\\n\",\n    \"item_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Returning attributes\\n\",\n    \"\\n\",\n    \"What if we want to read the `href` attribute of the `<a>` tag to get the link. This is how you do that:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['http://www.numpy.org/',\\n\",\n       \" 'http://pandas.pydata.org/',\\n\",\n       \" 'http://python-requests.org/']\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"item_list = doc.xpath(\\\"/html/body/ul[contains(@class,'nav-stacked')]/li/a/@href\\\")\\n\",\n    \"item_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Real world example\\n\",\n    \"\\n\",\n    \"Read the list of languages with 1M+ articles on http://www.wikipedia.org/\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"response = requests.get(\\\"http://www.wikipedia.org\\\")\\n\",\n    \"doc = html.fromstring(response.content, parser=html.HTMLParser(encoding=\\\"utf-8\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['Deutsch',\\n\",\n       \" 'English',\\n\",\n       \" 'Español',\\n\",\n       \" 'Français',\\n\",\n       \" 'Italiano',\\n\",\n       \" 'Nederlands',\\n\",\n       \" 'Polski',\\n\",\n       \" 'Русский',\\n\",\n       \" 'Sinugboanong Binisaya',\\n\",\n       \" 'Svenska',\\n\",\n       \" 'Tiếng Việt',\\n\",\n       \" 'Winaray']\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"lang_list = doc.xpath(\\\"//div[@class='langlist langlist-large hlist'][1]/ul/li/a/text()\\\")\\n\",\n    \"lang_list\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/quick_tips/5. The largest Prime.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# The largest prime (so far)\\n\",\n    \"\\n\",\n    \"A new record for the largest prime has been found lately. Let explore this number and know more about it and how to deal with it using some Python, Numpy and finally using we will take a loog at Cython and GMP.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math\\n\",\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"%load_ext Cython\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This number of was found in 2016 using the Great Internet Mersenne Prime Search (GIMPS). This prime is one of the Mersenne Prime whice are of the defined by the following formula:\\n\",\n    \"\\n\",\n    \"$$2^p-1$$\\n\",\n    \"\\n\",\n    \"Where $p$ is a prime number. This is called a psudo-prime meaning not all Mersenne Prime are primes.\\n\",\n    \"\\n\",\n    \"$2^2-1=3$\\n\",\n    \"\\n\",\n    \"$2^3-1=7$\\n\",\n    \"\\n\",\n    \"$2^5-1=31$\\n\",\n    \"\\n\",\n    \"$2^7-1=127$   ...\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"2**2-1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"7\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"2**3-1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"31\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"2**5-1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"127\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"2**7-1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The smallest none-prime (composite) number is $2^{11}-1=2047$ which is a composite number.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2047\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"2**11-1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The largest prime number is that was found lately is:\\n\",\n    \"$$2^{74,207,281}-1$$\\n\",\n    \"\\n\",\n    \"The reason why I didn't put the actual value is that the number if very large (over 22 million digits).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"p = 74207281\\n\",\n    \"the_number = (2 ** p) - 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Lucas–Lehmer test\\n\",\n    \"\\n\",\n    \"A very easy way to test a Mersenne Prime is the Lucas-Lehmer test which uses the Lucas series.\\n\",\n    \"\\n\",\n    \"$4, 14, 37634 ... $\\n\",\n    \"\\n\",\n    \"The series starts with 4 then uses the last value in this formula:\\n\",\n    \"\\n\",\n    \"$S_i=S_{i-1}^2-2$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4\\n\",\n      \"1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"S = 4\\n\",\n    \"print(S)\\n\",\n    \"print(len(str(S)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"14\\n\",\n      \"2 digits\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"S = S ** 2 - 2\\n\",\n    \"print(S)\\n\",\n    \"print(len(str(S)), \\\"digits\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"194\\n\",\n      \"3 digits\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"S = S ** 2 - 2\\n\",\n    \"print(S)\\n\",\n    \"print(len(str(S)), \\\"digits\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"37634\\n\",\n      \"5 digits\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"S = S ** 2 - 2\\n\",\n    \"print(S)\\n\",\n    \"print(len(str(S)), \\\"digits\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1416317954\\n\",\n      \"10 digits\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"S = S ** 2 - 2\\n\",\n    \"print(S)\\n\",\n    \"print(len(str(S)), \\\"digits\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2005956546822746114\\n\",\n      \"19 digits\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"S = S ** 2 - 2\\n\",\n    \"print(S)\\n\",\n    \"print(len(str(S)), \\\"digits\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"22338618 digits\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# The largest prime (so far)\\n\",\n    \"the_number = 2 ** 74207281 - 1\\n\",\n    \"print(int(math.log10(the_number))+1, \\\"digits\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The test is if the Mersenne Prime number of $n^2-1$ is a divided by $S_{n-2}$ you should get a remaining of 0. The problem is this series goes very large very fast. So there is another way to do this test.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0 0:00:00.000423 \\n\",\n      \"1 0:00:00.000017 \\n\",\n      \"2 0:00:00.000012 \\n\",\n      \"3 0:00:00.000014 \\n\",\n      \"4 0:00:00.000014 \\n\",\n      \"5 0:00:00.000013 \\n\",\n      \"6 0:00:00.000014 \\n\",\n      \"7 0:00:00.000014 \\n\",\n      \"8 0:00:00.000015 \\n\",\n      \"9 0:00:00.000014 \\n\",\n      \"10 0:00:00.000036 \\n\",\n      \"11 0:00:00.000051 \\n\",\n      \"12 0:00:00.000127 \\n\",\n      \"13 0:00:00.000358 \\n\",\n      \"14 0:00:00.001040 \\n\",\n      \"15 0:00:00.002735 \\n\",\n      \"16 0:00:00.004968 \\n\",\n      \"17 0:00:00.014782 \\n\",\n      \"18 0:00:00.044420 \\n\",\n      \"19 0:00:00.132890 \\n\",\n      \"20 0:00:00.398497 \\n\",\n      \"21 0:00:01.198042 \\n\",\n      \"22 0:00:03.592832 \\n\",\n      \"23 0:00:10.796848 \\n\"\n     ]\n    },\n    {\n     \"ename\": \"KeyboardInterrupt\",\n     \"evalue\": \"\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[1;32m<ipython-input-20-4c550569e183>\\u001b[0m in \\u001b[0;36m<module>\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m      5\\u001b[0m \\u001b[0mtime_stamp\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mdatetime\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mnow\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      6\\u001b[0m \\u001b[1;32mfor\\u001b[0m \\u001b[0mi\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mrange\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mp\\u001b[0m\\u001b[1;33m-\\u001b[0m\\u001b[1;36m2\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 7\\u001b[1;33m     \\u001b[0mS\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[1;33m(\\u001b[0m\\u001b[0mS\\u001b[0m \\u001b[1;33m**\\u001b[0m \\u001b[1;36m2\\u001b[0m \\u001b[1;33m-\\u001b[0m \\u001b[1;36m2\\u001b[0m\\u001b[1;33m)\\u001b[0m \\u001b[1;33m%\\u001b[0m \\u001b[0mthe_number\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m      8\\u001b[0m     \\u001b[1;32mif\\u001b[0m \\u001b[0mi\\u001b[0m \\u001b[1;33m%\\u001b[0m \\u001b[1;36m1\\u001b[0m \\u001b[1;33m==\\u001b[0m \\u001b[1;36m0\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m      9\\u001b[0m         \\u001b[0mprint\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mi\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdatetime\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mnow\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m \\u001b[1;33m-\\u001b[0m \\u001b[0mtime_stamp\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;34m\\\"\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[1;31mKeyboardInterrupt\\u001b[0m: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"p = 74207281\\n\",\n    \"the_number = (2 ** p) - 1\\n\",\n    \"\\n\",\n    \"S = 4\\n\",\n    \"time_stamp = datetime.now()\\n\",\n    \"for i in range(p-2):\\n\",\n    \"    S = (S ** 2 - 2) % the_number\\n\",\n    \"    if i % 1 == 0:\\n\",\n    \"        print(i, datetime.now() - time_stamp,\\\"\\\")\\n\",\n    \"        time_stamp = datetime.now()\\n\",\n    \"if S == 0:\\n\",\n    \"    print(\\\"PRIME\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"30\\n\",\n      \"40\\n\",\n      \"50\\n\",\n      \"PRIME\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.4/dist-packages/IPython/utils/path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module\\n\",\n      \"  warn(\\\"get_ipython_cache_dir has moved to the IPython.paths module\\\")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%cython\\n\",\n    \"cdef unsigned long p = 61\\n\",\n    \"cdef unsigned long P = (2 ** p) - 1\\n\",\n    \"\\n\",\n    \"S = 4\\n\",\n    \"for i in range(p-2):\\n\",\n    \"    S = S ** 2 - 2\\n\",\n    \"    S = S % P\\n\",\n    \"    if i % 10 == 0:\\n\",\n    \"        print(i)\\n\",\n    \"if S == 0:\\n\",\n    \"    print(\\\"PRIME\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0\\n\",\n      \"1000\\n\",\n      \"2000\\n\",\n      \"3000\\n\",\n      \"4000\\n\",\n      \"5000\\n\",\n      \"6000\\n\",\n      \"7000\\n\",\n      \"8000\\n\",\n      \"9000\\n\",\n      \"10000\\n\",\n      \"11000\\n\",\n      \"12000\\n\",\n      \"13000\\n\",\n      \"14000\\n\",\n      \"15000\\n\",\n      \"16000\\n\",\n      \"17000\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%cython --link-args=-lgmp\\n\",\n    \"\\n\",\n    \"cdef extern from \\\"gmp.h\\\":\\n\",\n    \"    ctypedef struct mpz_t:\\n\",\n    \"        pass\\n\",\n    \"    \\n\",\n    \"    ctypedef unsigned long mp_bitcnt_t\\n\",\n    \"    \\n\",\n    \"    cdef void mpz_init(mpz_t)\\n\",\n    \"    cdef void mpz_init_set_ui(mpz_t, unsigned int)\\n\",\n    \"    \\n\",\n    \"    cdef void mpz_add(mpz_t, mpz_t, mpz_t)\\n\",\n    \"    cdef void mpz_add_ui(mpz_t, const mpz_t, unsigned long int)\\n\",\n    \"    \\n\",\n    \"    cdef void mpz_sub (mpz_t, const mpz_t, const mpz_t)\\n\",\n    \"    cdef void mpz_sub_ui (mpz_t, const mpz_t, unsigned long int)\\n\",\n    \"    cdef void mpz_ui_sub (mpz_t, unsigned long int, const mpz_t)\\n\",\n    \"    \\n\",\n    \"    cdef void mpz_mul (mpz_t, const mpz_t, const mpz_t)\\n\",\n    \"    cdef void mpz_mul_si (mpz_t, const mpz_t, long int)\\n\",\n    \"    cdef void mpz_mul_ui (mpz_t, const mpz_t, unsigned long int)\\n\",\n    \"    \\n\",\n    \"    cdef void mpz_mul_2exp (mpz_t, const mpz_t, mp_bitcnt_t)\\n\",\n    \"    \\n\",\n    \"    cdef void mpz_mod (mpz_t, const mpz_t, const mpz_t)\\n\",\n    \"    \\n\",\n    \"    cdef unsigned long int mpz_get_ui(const mpz_t)\\n\",\n    \"\\n\",\n    \"#cdef unsigned long p = 61\\n\",\n    \"cdef mp_bitcnt_t p = 74207281\\n\",\n    \"cdef mpz_t t # = 1\\n\",\n    \"cdef mpz_t a # = 1\\n\",\n    \"cdef mpz_t P # = (2 ** p) - 1\\n\",\n    \"cdef mpz_t S # = 4\\n\",\n    \"\\n\",\n    \"mpz_init(t)\\n\",\n    \"mpz_init_set_ui(t, 1)\\n\",\n    \"\\n\",\n    \"mpz_init(a)\\n\",\n    \"mpz_init_set_ui(a, 2)\\n\",\n    \"\\n\",\n    \"mpz_init(P)\\n\",\n    \"mpz_mul_2exp(P,t,p)\\n\",\n    \"mpz_sub_ui(P,P,1)\\n\",\n    \"\\n\",\n    \"mpz_init(S)\\n\",\n    \"mpz_init_set_ui(S, 4)\\n\",\n    \"\\n\",\n    \"for i in range(p-2):\\n\",\n    \"    #S = S ** 2 - 2\\n\",\n    \"    mpz_mul(S,S,S)\\n\",\n    \"    mpz_sub_ui(S,S,2)\\n\",\n    \"    \\n\",\n    \"    #S = S % P\\n\",\n    \"    mpz_mod(S,S,P)\\n\",\n    \"    \\n\",\n    \"    if i % 1000 == 0:\\n\",\n    \"        print(i)\\n\",\n    \"if mpz_get_ui(S) == 0:\\n\",\n    \"    print(\\\"PRIME\\\")\\n\",\n    \"else:\\n\",\n    \"    print(\\\"COMPOSITE\\\")\\n\",\n    \"\\n\",\n    \"#print(mpz_get_ui(P))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/tf/.ipynb_checkpoints/1. Machine Learning Basics-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"    <tr>\\n\",\n    \"        <td style=\\\"text-align:left;\\\"><div style=\\\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\\\">1. Machine Learning Basics</div><img src=\\\"images/roshan.png\\\" style=\\\"width:30%; display: inline; text-align: left; float:right;\\\"></td>\\n\",\n    \"        <td></td>\\n\",\n    \"    </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Why TensorFlow?\\n\",\n    \"\\n\",\n    \"TensorFlow is Google's framework for developing Machine Learning models and is has developed into the most popular machine learning library. Since 2011, Google Brain Team has been developing TensorFlow and was used internally until is was released as an open-source software in Nov, 2015. It comes packed with the wide range of tools from dataset management to a good selection of high-level algorithms and even ready made models. That's a lot to pack in one solution!\\n\",\n    \"\\n\",\n    \"The team that is working on developing TensorFlow are some of the most influencal researchers in machine learning. It also has a huge amount of contributors and commits for such a relativly new project on Github. You quickly realize that TensorFlow is getting more attention from people in the field than any other existing machine learning solution and will be the technology starndard for machine learning development.\\n\",\n    \"\\n\",\n    \"__OS Support__:\\n\",\n    \"\\n\",\n    \"- Linux\\n\",\n    \"- Windows\\n\",\n    \"- macOS\\n\",\n    \"- Android\\n\",\n    \"- iOS\\n\",\n    \"\\n\",\n    \"__Programming Languages__:\\n\",\n    \"\\n\",\n    \"- Python\\n\",\n    \"- C/C++\\n\",\n    \"- Java\\n\",\n    \"- Golang\\n\",\n    \"\\n\",\n    \"__Hardware__:\\n\",\n    \"\\n\",\n    \"- CPU\\n\",\n    \"- GPU\\n\",\n    \"- TPU\\n\",\n    \"- Distributed Systems\\n\",\n    \"\\n\",\n    \"There is also TensorFlow.js version which runs in browser on client-side with no need for a back-end. There is also TensorFlow Lite which converts machine learning models to a format that can run on mobile devices running iOS and Android.\\n\",\n    \"\\n\",\n    \"With in two years of its release, TensorFlow has been downloaded over 11 million times from almost every country on earth.\\n\",\n    \"\\n\",\n    \"![](images/tf_map.png)\\n\",\n    \"\\n\",\n    \"From Ragat Monga [@rajatmonga](https://twitter.com/rajatmonga) Slides in [TensorFlow Dev Summit 2018]() (2018-03-30)\\n\",\n    \"\\n\",\n    \"# What is Machine Learning?\\n\",\n    \"\\n\",\n    \"Before we dive any deeper in TensorFlow, I will go over some basic concepts of machine learning and deep learning.\\n\",\n    \"\\n\",\n    \"This tutorial is for developers who can develope in python but not well versed in machine learning or familiar with some other machine learning libraries but new to TensorFlow. It is also be useful for non-developers who are good at a specific knowledge domain like mathematics, statistics, medical field or, social studies and eager to experiment with machine learning in their fields.\\n\",\n    \"\\n\",\n    \"Machine Learning (ML) is a branch of Artificial Intelligence (AI). It uses data instead of code to produce logic. The goal is to perform tasks that we usually equate human level intelligence and in many cases exceeds human level of intelligence like what happened with Alpha Go and many other examples. You actually use machine learning all the time without realizing when you use Google, YouTube, Amazon and so many other services that reley on machine learning to give you a better experience.\\n\",\n    \"\\n\",\n    \"Machine learning is built on algorithms that use data to train models to perform a specific task.\\n\",\n    \"\\n\",\n    \"Meet `Tensy`, a robot eager to learn and solve human problems. [@TensyRobot](https://twitter.com/TensyRobot)\\n\",\n    \"\\n\",\n    \"![Algorithm](images/algorithm.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Learning Styles\\n\",\n    \"\\n\",\n    \"Models can learn in three styles depending the the training algorithms:\\n\",\n    \"\\n\",\n    \"## Supervised Learning (SL)\\n\",\n    \"\\n\",\n    \"Supervised learning is teaching a model to give an output based on some input. The the algorithm teachs the model by giving the it plenty of examples and their outputs. There are two types of problems that we can solve with supervised learning which are:\\n\",\n    \"\\n\",\n    \"### Classification\\n\",\n    \"\\n\",\n    \"Classification is used to teach a model to output a category or a class based on a data point. Like learning if a photo has a cat or a dog.\\n\",\n    \"\\n\",\n    \"I want `Tensy` to learn how to tell cats from dog. We can give `Tensy` photos of cats and until he learns to tell a cat from a dog. I can later test him on a new photos he has never seen before and see how accurate he is.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/CatsDogs.png)\\n\",\n    \"\\n\",\n    \"### Regression\\n\",\n    \"\\n\",\n    \"Regression is used to teach a model to output a continuous numerical value based on a data point. Like telling the age of a person based on their photo.\\n\",\n    \"\\n\",\n    \"This time we are teaching `Tensy` to tell the age of a person based on their photo. To do that we will give him a large amount of photos and tell him the age of each person. We can later test him by asking the age of people in new photos that he didn't see before.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/age.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Unsupervoised Learning (UL)\\n\",\n    \"\\n\",\n    \"Unsupervised Learning teaches a model to produce an output based on a data point without teaching the model the output. Like giving a model photos taken outdoor and photos taken indoor and ask then model to split them to two piles. There are many problems that can be solved using UL but the most common ones are:\\n\",\n    \"\\n\",\n    \"### Clustering\\n\",\n    \"\\n\",\n    \"Clustering is grouping similar data points into groups or clusters. different algorithms can find similarities in different ways and can end up clustering your data points into different groups. Some of these algorithms need you to tell them how many clusters do you want them to split your data into and some will try and figure that out of their own.\\n\",\n    \"\\n\",\n    \"This time we gave `Tensy` a group if photos and we asked him to split them into two groups. He is finding that all indoor photos are similar and all out door photos are similar.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/outdoorindoor.png)\\n\",\n    \"\\n\",\n    \"Other UL algorithms can solve some interesting problems using like:\\n\",\n    \"\\n\",\n    \"- Anomaly Detection\\n\",\n    \"- Generative adversarial networks (GANs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reinforcement Learning (RL)\\n\",\n    \"\\n\",\n    \"Reinforcement Learning  uses an algorithm to teach a model to produce a desired output without explicitly explaining how to perform it. This works by gaving the model an input and rewarding good outcome and punishing bad outcome. This might be similar to how you would teach a child or even train an animal to perform the desired actions.\\n\",\n    \"\\n\",\n    \"Let's teach `Tensy` to play some Atari games. The input in the screen feed from the game. After each frame from the input he will be able to choose one action (right, left, up, down and press action button). That action might lead to increased score if it is a good action or to no increase score if it is a neutral action or to loosing a life and lower score for a bad action.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/atari.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Machine Learning is More That!\\n\",\n    \"\\n\",\n    \"These major categories are not inclusive of every algorithms that you might use where some evolutionary algorithms, or recommendation systems do not fall under any of these categories. Some more complicated algorithms use multiple stages in a pipeline some with supervised and unsupervised so your end-to-end algorithm might also not fall under any specific category or sometimes will be called \\\"Semi-Supervised\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# The Math Behind It\\n\",\n    \"\\n\",\n    \"This section will explain some mathematical concepts at the end of each tutorial. These tutorials are going to be code intensive and that will help you develop your coding skills and get your model working. But what if it doesn't work?\\n\",\n    \"\\n\",\n    \"The math behind these algorithms use Linear Algebra, Tensor Calculus and Statistics. This section in each tutorial will explain the math behind what we discussed and will assume very little to no math background and will explain things from the ground up. This could help you fix your model or even improve your accuracy.\\n\",\n    \"\\n\",\n    \"Finally, It will serve as a bidge between machine learning concepts and centuries of mathimatical reasearch. Mathematics is the language of science and learning this language will open the door for you to explore some of the latest academic research papers in the machine learning due to the fact that they mostly look like this:\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/svm.png)\\n\",\n    \"A small section of \\\"[A Training Algorithm for Optimal Margin Classiers](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.3818&rep=rep1&type=pdf)\\\" By Boser, Guyon & Vapnik published 1992. AKA the original SVM paper\\n\",\n    \" \\n\",\n    \" So to kick of this part I'll start with a few simple concepts:\\n\",\n    \" \\n\",\n    \" ## The real numbers $\\\\mathbb{R}$\\n\",\n    \" \\n\",\n    \" The real numbers include positive, nagative numbers and zero. They can be of arbitrary precesion meaning they can have any number of decimal points or even numbers with infinite precision. The set real is denoted in mathimatics as $\\\\mathbb{R}$. Examples of real numbers include:\\n\",\n    \"\\n\",\n    \"- 5.0\\n\",\n    \"- -2.5\\n\",\n    \"- $\\\\frac{1}{3} = 0.3333\\\\overline3$\\n\",\n    \"- $\\\\pi = 3.14159 ...$ (Pi) this is small letter Pi\\n\",\n    \"- $e = 2.71828 ...$ (Euler's Number)\\n\",\n    \"\\n\",\n    \"## Sum $\\\\sum$\\n\",\n    \"\\n\",\n    \"To represent a finite sum of numbers in math, you can use the following:\\n\",\n    \"\\n\",\n    \"$$ 1 + 2 + 3 + 4 + 5 $$\\n\",\n    \"\\n\",\n    \"For longer sums like getting the sum of all numbers from 1 to 1000 can be harder to write in that way. We use sum represented by capital letter Sigma $\\\\Sigma$. To rewite the previous equation from 1 to 1000 using sum, you can do this:\\n\",\n    \"\\n\",\n    \"$$\\\\sum_{n=1}^{1000} n$$\\n\",\n    \"\\n\",\n    \"This can facilitate writing more complex functions like this:\\n\",\n    \"\\n\",\n    \"$$f(x) = x + 2x + 3x + 4x + 5x = \\\\sum_{n=1}^{5}nx$$\\n\",\n    \"\\n\",\n    \"With this notation we can even represent an infinite sum of numbers:\\n\",\n    \"\\n\",\n    \"$$\\\\frac{1}{1} + \\\\frac{1}{2} + \\\\frac{1}{4} + \\\\frac{1}{8} + \\\\frac{1}{16} ... = \\\\sum_{n=0}^{\\\\infty} \\\\frac{1}{2^n} = 2$$\\n\",\n    \"\\n\",\n    \"## Product $\\\\Pi$\\n\",\n    \"\\n\",\n    \"To represent a finite product of numbers in math, you can use the following:\\n\",\n    \"\\n\",\n    \"$$ 1 \\\\times 2 \\\\times 3 \\\\times 4 \\\\times 5 $$\\n\",\n    \"\\n\",\n    \"For longer products like getting the products of all numbers from 1 to 1000 can be harder to write in that way. We use products represented by capital letter Pi $\\\\Pi$. To rewrite the previous product from 1 to 5, we can write it this way:\\n\",\n    \"\\n\",\n    \"$$\\\\prod_{n=1}^{5} n$$\\n\",\n    \"\\n\",\n    \"> NOTE: It should also be noted that the previous product can also be written as $5!$ which is called 5 factorial.\\n\",\n    \"\\n\",\n    \"This can facilitate writing more complex products like:\\n\",\n    \"\\n\",\n    \"$$f(x) = x \\\\times 2x \\\\times 3x \\\\times 4x \\\\times 5x = \\\\prod_{n=1}^{5}nx$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\\n\",\n    \"\\n\",\n    \"Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/tf/.ipynb_checkpoints/2. Tensors-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"    <tr>\\n\",\n    \"        <td style=\\\"text-align:left;\\\"><div style=\\\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\\\">2. Tensors</div><img src=\\\"images/roshan.png\\\" style=\\\"width:30%; display: inline; text-align: left; float:right;\\\"></td>\\n\",\n    \"        <td></td>\\n\",\n    \"    </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Before we go into tensors and programming in TensorFlow, let's take a look at how does it work.\\n\",\n    \"\\n\",\n    \"# TensorFlow Basics\\n\",\n    \"\\n\",\n    \"TensorFlow has a few concepts that you should be familiar with. TensorFlow executes your code inside a execution engine that you communicate with using an API. In TensorFlow your data is called a `Tensor` that you can apply operations (`OP`) to. Your code is converted into a `Graph` that is executed in an execution engine called a `Session`. So your python code is just a representation of your graph that can be executed in a session.\\n\",\n    \"\\n\",\n    \"![](images/graph.png)\\n\",\n    \"\\n\",\n    \"You can see how your data (or tensors) flow from one operation to the next, hens the name TensorFlow.\\n\",\n    \"\\n\",\n    \"Since version 1.5 and as of version 1.8 there are two methods to execute code in TensorFlow:\\n\",\n    \"\\n\",\n    \"- Graph Execution\\n\",\n    \"- Eager Execution\\n\",\n    \"\\n\",\n    \"The main difference is in graph execution is a type of `declarative programming` and eager execution is a type of `imperative programming`. In plain English, the difference is graph execution defines your code as a graph and executes in a session. Your objects in python are not the actual objects inside the session, they are only a reference to them. Eager execution executes your code as you run it giving you a better control of your program while it is running so you are not stuck with a predefined graph.\\n\",\n    \"\\n\",\n    \"## So why do we even bother with graph execution?\\n\",\n    \"\\n\",\n    \"Performance is a big issue in machine learning and a small difference in execution time can save you in a long project weeks of your time and 1000s of hours of GPU time. Support is another issue for now where some features of TensorFlow do not work in Eager Execution like high level estimators.\\n\",\n    \"\\n\",\n    \"We will focus in the following tutorials only on graph execution and we will cover eager execution later in this series.\\n\",\n    \"\\n\",\n    \"# Tensors\\n\",\n    \"\\n\",\n    \"Tensors can be though of a scalar variable or an array of any dimension. Tensors are the main object to store pass data, sore variables and constants and all operations that take in data take it in tensor format and all operations that output data uses the tensor format for that too.\\n\",\n    \"\\n\",\n    \"## Tensor Shape and Rank\\n\",\n    \"\\n\",\n    \"Tensors have a rank and a shape so for scalar values, we use rank-0 tensors of a shape `()` which is an empty shape\\n\",\n    \"\\n\",\n    \"Assuming we need a variable or a constant number to use in our software, we can represent it as a tensor of rank-0.\\n\",\n    \"\\n\",\n    \"![](images/rank-0.png)\\n\",\n    \"\\n\",\n    \"A rank-1 tensor can be though of as an __vector__ or a one dimensional array. To create a rank-1 tensor with shape `(3)` this will create a tensor that can hold three values in a single dimensional array.\\n\",\n    \"\\n\",\n    \"![](images/rank-1.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"A rank-2 is a __matrix__ or a two dimensional array. This can be used to hold two dimensional data like a black and white image. The shape of the tensor can match the shape of the image so to hold a 256x256 pixel image in a tensor, you can create a rank-2 tensor of shape `(256,256)`.\\n\",\n    \"\\n\",\n    \"![](images/rank-2.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"A rank-3 tensor is a three dimensional array. To can be used to hold three dimensional data like a color image represented in (RGB). To create a tensor to hold an color image of size 256x256, you can create a rank-3 tensor of shape `(256,256,3)`.\\n\",\n    \"\\n\",\n    \"![](images/rank-3.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"TensorFlow allows tensors in higher dimensions but you will very rarely see tensors of a rank exceeding 5 of shape `(batch size, width, height, RGB, frames)` for representing a batch of video clips.\\n\",\n    \"\\n\",\n    \"## Importing Tensor Flow\\n\",\n    \"\\n\",\n    \"Let's import TensorFlow and start working with some tensors.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Python Version: 3.5.2\\n\",\n      \"TensorFlow Version: 1.8.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"print(\\\"Python Version:\\\",sys.version.split(\\\" \\\")[0])\\n\",\n    \"print(\\\"TensorFlow Version:\\\",tf.VERSION)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Graph Execution\\n\",\n    \"\\n\",\n    \"TensorFlow executes your code inside a C++ program and returns the results through the TensorFlow API. Since we are using Python, we will be using TensorFlow Python API which is the most documented and most used API of TensorFlow.\\n\",\n    \"\\n\",\n    \"Sice we are using graph execution, there are two ways to create a session:\\n\",\n    \"\\n\",\n    \"- Session\\n\",\n    \"- Interactive Session\\n\",\n    \"\\n\",\n    \"Sessions and interactive sessions, use your code to build a \\\"Graph\\\" which is a representation of your code inside TensorFlow's execution engine. The main difference between them is interactive session makes itself the default session so since we are using only one session for our code, we will use that.\\n\",\n    \"\\n\",\n    \"For now let's start an interactive session and start flowing some tensors!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sess = tf.InteractiveSession()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Generating new Tensors\\n\",\n    \"\\n\",\n    \"Since we have an interactive session, let's create a tensor. There are two common ways to create a tensor `tf.zeros()` and `tf.ones()`. Each one of them takes a python tuple or an array as the shape of the tensor.\\n\",\n    \"\\n\",\n    \"Let's start be creating a rank-0 tensor.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = tf.zeros(())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We create a tensor and assigned it to a local variable named `a`. When we check the value of `a` this is what we get.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'zeros:0' shape=() dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice there is no value. You need to call `eval()` method of the tensor to get the actual value. This method takes an optional parameter where you can pass your session. Since we are using interactive session, we have a default one so we don't need to pass a session.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.0\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You should know that `eval()` method returns a numpy.float32 (or what ever the type of the tensor is) if the rank of the tensor is 0 and numpy.ndarray if the tensor of rank 1 or higher. Numpy is a multi-dimensional array library for python that runs the operations in a C program and interfaces back with python to ensure fast array operations.\\n\",\n    \"\\n\",\n    \"We can also check the rank and shape of the tensor.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"the rank would be the number of dimensions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.shape.ndims\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice the name inside the TensorFlow execution engine is not `a`. It is `zeros:0` which is an auto generated name for the variable. The auto generated name is the name of the operation that generated the tensor and then an the index of the of the tensor in the output of the operation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'zeros:0'\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.name\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you created another variable using the name operation, it will be named `zeros_1:0`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'zeros_1:0' shape=() dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.zeros(())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's create a second tensor of shape (3) which is going to be a rank-1 tensor. This time we will name it `b` and store it in a local variable named `b`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'b:0' shape=(3,) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"b = tf.zeros((3), name=\\\"b\\\")\\n\",\n    \"b\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice the name of the variable now is `b:0` which is the name that we gave it and an auto incrementing index. We can also get the value in the same way using `eval()` method.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"numpy.ndarray\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(b.eval())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can also get the value of a tensor by executing the tensor using your interactive session.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0., 0., 0.], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sess.run(b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can also fill the tensor with any other value you want other than 0 and 1 using `fill()` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[5, 5],\\n\",\n       \"       [5, 5]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.fill((2,2), 5).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that the data type of this tensor is `int32` and not `float32` because you initialized the tensor with an integer `5` and not `5.0`.\\n\",\n    \"\\n\",\n    \"## Tensor Shape\\n\",\n    \"\\n\",\n    \"For multi dimensional tensors, the shape is passed as a tuple or an array. The way this array is arranged is from the outer most dimension to the inner dimensions. So for a tensor that should represents 10 items and each item has three numbers, the shape would be `(10,3)`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.zeros((10,3)).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For higher dimensions the same rules applies. Let say we have 2 items and each item has 3 parts and each on of these parts consists of 4 numbers the shape would be `(2,3,4)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.]],\\n\",\n       \"\\n\",\n       \"       [[0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.]]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.zeros((2,3,4)).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Note__: This is the opposite of the how matrix shape notation is written in mathematics. A matrix of shape $A_{(3,10)}$ can be represented in TensorFlow as `(10,3)`. The reason for that is in mathematics the shape is $(Columns,Rows)$ and TensorFlow uses `(Outer,Inner)` which translates in 2-D tensor as `(Rows,Columns)`.\\n\",\n    \"\\n\",\n    \"## Generating Tensors with Random Values\\n\",\n    \"\\n\",\n    \"In many cases, you want to generate a new tensor but we want to start with random values stored in the tensor. The way we do that is using one the random generator of TensorFlow.\\n\",\n    \"\\n\",\n    \"### Normal Distribution\\n\",\n    \"\\n\",\n    \"`tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'random_normal:0' shape=(1000,) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_normal((1000,))\\n\",\n    \"arr1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This function returns random values using normal distribution which is also known as Gaussian distribution or informally called a \\\"Bell Curve\\\". To better understand it, let's first look at a graph showing this distribution. Normal distribution of of mean $\\\\mu$ and standard deviation $\\\\sigma$ is denoted $N(\\\\mu, \\\\sigma)$ (More about that in \\\"The Math Behind It\\\").\\n\",\n    \"\\n\",\n    \"To do that we will import Matplotlib which is a very common charting library for Python.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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a4eLppF43L3G3zfmAvi7mnbjmZpX2SPAfYDLxrv39Zr0pV9WRVrWPxX9CnJ1l102RP6188UlWvXOam17J4T8ClE4yzLOMyJ3kz8DrgzFolX8Acwp/zajb2sR7qr5vn3gxcW1WfnXaeQ1FV30nyBeAsYFV9Af60PqM/mCRrR1bPBe6fVpbl6n7Zy3uA11fVD6adpzE+1mPCui82rwLuq6qPTzvPciSZ2Xd1W5KfYfHL+lXXFV51cwBJNgOnsnhFyCPARVW1qs/gkjwIPBt4rBu68wi4Uug84C+AGeA7wNaqes10Uy0tydnAn/Kjx3pcNuVIB5XkOuAMFp+quBu4tKqummqog0jyMuBfga+z+P8dwPu6u+xXpSQvBa5h8b+JnwJuqKoPTTfVT7LoJalxTt1IUuMseklqnEUvSY2z6CWpcRa9JDXOopekxln0ktQ4i16SGvd/0G8roSLIuTYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(arr1.eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice the bill shape of the curve where you get more values values around your mean a few an values as you move away from the mean. You can also change the mean.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEEFJREFUeJzt3X+MZWV9x/H3p6zaqlVAxu26C11St7bU1Eon1NZWrRgFMS5plUBMXYVkY8X6M8FFm/KHMYFqtdq0mq1LWRPCjyIWWmiFUAhtItQBkV8LskWQXYEdi6Ctae2Wb/+4h2a6zs7M3nPv3pmH9yuZ3HOe89xzvk9253OfOffcc1NVSJLa9ROTLkCSNF4GvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxqyZdAMARRxxR69evn3QZkrSi3HLLLd+tqqnF+i2LoF+/fj0zMzOTLkOSVpQkDy6ln6duJKlxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpccvik7HSYtZvuWqk+3vg3JNGuj9pOXNGL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhrn5ZXSiHgJqJYrZ/SS1Dhn9HpaGvXsW1rOFp3RJzk/yZ4kd86z7UNJKskR3XqSfDbJziS3Jzl2HEVLkpZuKaduLgBO2LcxyZHA64Fvz2k+EdjQ/WwGPte/RElSH4sGfVXdCDw2z6ZPA2cBNadtI/DFGrgJODTJmpFUKkkaylBvxibZCOyuqm/ss2kt8NCc9V1dmyRpQg74zdgkzwY+wuC0zdCSbGZweoejjjqqz64kSQsYZkb/c8DRwDeSPACsA25N8jPAbuDIOX3XdW0/pqq2VtV0VU1PTU0NUYYkaSkOeEZfVXcAL3xqvQv76ar6bpIrgfckuRj4NeCJqnp4VMVq5fDyRWn5WMrllRcBXwVekmRXkjMW6H41cD+wE/hL4N0jqVKSNLRFZ/RVddoi29fPWS7gzP5lSZJGxVsgSFLjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3FK+M/b8JHuS3Dmn7RNJ7klye5IvJzl0zrazk+xMcm+SN4yrcEnS0ixlRn8BcMI+bdcCL62qXwa+CZwNkOQY4FTgl7rn/EWSQ0ZWrSTpgC0a9FV1I/DYPm3XVNXebvUmYF23vBG4uKr+q6q+BewEjhthvZKkAzSKc/SnA3/fLa8FHpqzbVfXJkmakF5Bn+SjwF7gwiGeuznJTJKZ2dnZPmVIkhYwdNAneQfwJuBtVVVd827gyDnd1nVtP6aqtlbVdFVNT01NDVuGJGkRQwV9khOAs4A3V9UP52y6Ejg1ybOSHA1sAP6lf5mSpGGtWqxDkouA1wBHJNkFnMPgKptnAdcmAbipqt5VVXcluRS4m8EpnTOr6n/GVbwkaXGLBn1VnTZP87YF+n8c+HifoiRJo+MnYyWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhq36N0rJU3G+i1XjXR/D5x70kj3p5XDGb0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMWDfok5yfZk+TOOW2HJ7k2yX3d42Fde5J8NsnOJLcnOXacxUuSFreUGf0FwAn7tG0BrquqDcB13TrAicCG7mcz8LnRlClJGtaiQV9VNwKP7dO8EdjeLW8HTp7T/sUauAk4NMmaURUrSTpww56jX11VD3fLjwCru+W1wENz+u3q2iRJE9L7zdiqKqAO9HlJNieZSTIzOzvbtwxJ0n4MG/SPPnVKpnvc07XvBo6c029d1/ZjqmprVU1X1fTU1NSQZUiSFjNs0F8JbOqWNwFXzGl/e3f1zSuAJ+ac4pEkTcCiNzVLchHwGuCIJLuAc4BzgUuTnAE8CJzSdb8aeCOwE/gh8M4x1CxJOgCLBn1VnbafTcfP07eAM/sWJUkaHT8ZK0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcYt+laCeHtZvuWrSJUgak14z+iQfSHJXkjuTXJTkJ5McneTmJDuTXJLkmaMqVpJ04IYO+iRrgfcC01X1UuAQ4FTgPODTVfVi4HvAGaMoVJI0nL7n6FcBP5VkFfBs4GHgtcBl3fbtwMk9jyFJ6mHooK+q3cAngW8zCPgngFuAx6tqb9dtF7B2vucn2ZxkJsnM7OzssGVIkhbR59TNYcBG4GjgRcBzgBOW+vyq2lpV01U1PTU1NWwZkqRF9Dl18zrgW1U1W1X/DVwOvBI4tDuVA7AO2N2zRklSD32C/tvAK5I8O0mA44G7geuBt3R9NgFX9CtRktRHn3P0NzN40/VW4I5uX1uBDwMfTLITeAGwbQR1SpKG1OsDU1V1DnDOPs33A8f12a8kaXS8BYIkNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxvmdsdLTxDi+F/iBc08a+T41es7oJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqXK+gT3JoksuS3JNkR5JfT3J4kmuT3Nc9HjaqYiVJB67vjP4zwD9U1S8ALwN2AFuA66pqA3Bdty5JmpChgz7J84FXAdsAqupHVfU4sBHY3nXbDpzct0hJ0vD6zOiPBmaBv0ry9SRfSPIcYHVVPdz1eQRY3bdISdLw+gT9KuBY4HNV9XLgP9jnNE1VFVDzPTnJ5iQzSWZmZ2d7lCFJWkifoN8F7Kqqm7v1yxgE/6NJ1gB0j3vme3JVba2q6aqanpqa6lGGJGkhQwd9VT0CPJTkJV3T8cDdwJXApq5tE3BFrwolSb30vU3xHwAXJnkmcD/wTgYvHpcmOQN4EDil5zEkST30Cvqqug2YnmfT8X32q4WN477iktrlJ2MlqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxvUO+iSHJPl6kr/r1o9OcnOSnUku6b44XJI0IaOY0b8P2DFn/Tzg01X1YuB7wBkjOIYkaUi9gj7JOuAk4AvdeoDXApd1XbYDJ/c5hiSpn74z+j8FzgKe7NZfADxeVXu79V3A2p7HkCT1MHTQJ3kTsKeqbhny+ZuTzCSZmZ2dHbYMSdIi+szoXwm8OckDwMUMTtl8Bjg0yaquzzpg93xPrqqtVTVdVdNTU1M9ypAkLWTooK+qs6tqXVWtB04F/rGq3gZcD7yl67YJuKJ3lZKkoY3jOvoPAx9MspPBOfttYziGJGmJVi3eZXFVdQNwQ7d8P3DcKPYrSepvJEGvha3fctWkS5D0NOYtECSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjvLxS0tBGfenwA+eeNNL9acAZvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1Lihgz7JkUmuT3J3kruSvK9rPzzJtUnu6x4PG125kqQD1WdGvxf4UFUdA7wCODPJMcAW4Lqq2gBc161LkiZk6KCvqoer6tZu+QfADmAtsBHY3nXbDpzct0hJ0vBGco4+yXrg5cDNwOqqerjb9Aiwej/P2ZxkJsnM7OzsKMqQJM2jd9AneS7wJeD9VfX9uduqqoCa73lVtbWqpqtqempqqm8ZkqT96BX0SZ7BIOQvrKrLu+ZHk6zptq8B9vQrUZLUR5+rbgJsA3ZU1afmbLoS2NQtbwKuGL48SVJffb5K8JXA7wF3JLmta/sIcC5waZIzgAeBU/qVKEnqY+igr6p/BrKfzccPu19J0mj55eCSlg2/bHw8vAWCJDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapyXV85j1Jd4SdIkOaOXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjfPySknN8m6YA87oJalxK35G74ebJB0s48ibg/FXgjN6SWrc2II+yQlJ7k2yM8mWcR1HkrSwsQR9kkOAPwdOBI4BTktyzDiOJUla2Lhm9McBO6vq/qr6EXAxsHFMx5IkLWBcQb8WeGjO+q6uTZJ0kE3sqpskm4HN3eq/J7l3iU89AvjueKo66BzL8uRYlp9WxgH7jCXn9drXzy6l07iCfjdw5Jz1dV3b/6mqrcDWA91xkpmqmu5X3vLgWJYnx7L8tDIOmMxYxnXq5mvAhiRHJ3kmcCpw5ZiOJUlawFhm9FW1N8l7gK8AhwDnV9Vd4ziWJGlhYztHX1VXA1ePYdcHfLpnGXMsy5NjWX5aGQdMYCypqoN9TEnSQeQtECSpccs66JOcn2RPkjvntF2S5Lbu54Ekt02yxqXaz1h+JclN3Vhmkhw3yRqXaj9jeVmSrya5I8nfJnneJGtciiRHJrk+yd1J7kryvq798CTXJrmvezxs0rUuZoGxvLVbfzLJirhqZYGxfCLJPUluT/LlJIdOutbFLDCWj3XjuC3JNUleNNZCqmrZ/gCvAo4F7tzP9j8B/mjSdQ47FuAa4MRu+Y3ADZOus8dYvga8uls+HfjYpOtcwjjWAMd2yz8NfJPBLTv+GNjStW8Bzpt0rT3G8ovAS4AbgOlJ19lzLK8HVnXt563wf5fnzenzXuDz46xjWc/oq+pG4LH5tiUJcApw0UEtakj7GUsBT818nw9856AWNaT9jOXngRu75WuB3z2oRQ2hqh6uqlu75R8AOxh8gnsjsL3rth04eTIVLt3+xlJVO6pqqR9GXBYWGMs1VbW363YTg8/nLGsLjOX7c7o9h0EWjM1Kvh/9bwGPVtV9ky6kh/cDX0nySQan0X5jwvX0cReDgPwb4K38/w/MLXtJ1gMvB24GVlfVw92mR4DVEyprKPuMZUVbYCynA5cc7Hr62HcsST4OvB14AvjtcR57Wc/oF3EaK2Q2v4DfBz5QVUcCHwC2TbiePk4H3p3kFgZ/ov5owvUsWZLnAl8C3r/PTIsa/G29Yi5NW2gsK83+xpLko8Be4MJJ1Xag5htLVX20+92/EHjPOI+/IoM+ySrgd1hhr+jz2ARc3i3/NYO7fq5IVXVPVb2+qn6VwQvwv066pqVI8gwGv4AXVtVT/xaPJlnTbV8D7JlUfQdiP2NZkfY3liTvAN4EvK17EV72lvDvciFjPtW5IoMeeB1wT1XtmnQhPX0HeHW3/FpgxZ6GSvLC7vEngD8EPj/ZihbXvc+zDdhRVZ+as+lKBi/CdI9XHOzaDtQCY1lx9jeWJCcAZwFvrqofTqq+A7HAWDbM6bYRuGesdSznF8UkFwGvYXC3t0eBc6pqW5ILgJuqatmHyVPmGwtwL/AZBu+V/Cfw7qq6ZVI1LtV+xvJc4Myuy+XA2ct9xpXkN4F/Au4AnuyaP8LgHOqlwFHAg8ApVTXvRQHLxQJjeRbwZ8AU8DhwW1W9YSJFLtECY/ksg/H8W9d2U1W96+BXuHQLjOUMBldDPcng/9i7qmr3vDsZRR3L/HdRktTTSj11I0laIoNekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG/S/i2w9akWdgGQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_normal((1000,), mean=20.0)\\n\",\n    \"\\n\",\n    \"plt.hist(arr1.eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can also control how much of concentrated your random numbers will be around the mean by controlling the standard deviation. Higher standard deviation means less values around the mean and wider distribution.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_normal((1000,), stddev=2, name=\\\"arr1\\\")\\n\",\n    \"arr2 = tf.random_normal((1000,), stddev=1, name=\\\"arr2\\\")\\n\",\n    \"\\n\",\n    \"plt.hist([arr1.eval(), arr2.eval()], bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One more note on normal distribution, if you created a large tensor with millions or tens of millions of random values, some of these values will fall really far from the normal. With some machine learning algorithms this might create instability. You can avoid that by using `truncated_normal()` function instead of `random_normal()`. This will re-sample any values that falls more than 2 standard deviations from the mean.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(tf.truncated_normal((1000,)).eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Uniform Distribution\\n\",\n    \"\\n\",\n    \"The other common distribution will the uniform one. This will generate values with equal probability of falling anywhere between two numbers.\\n\",\n    \"\\n\",\n    \"`tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_uniform((1000,))\\n\",\n    \"plt.hist(arr1.eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Generating Tensors with Sequence Values\\n\",\n    \"\\n\",\n    \"You can generate tensors with sequence values using the following function:\\n\",\n    \"\\n\",\n    \"`tf.range(start, limit=None, delta=1, dtype=None, name='range')`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.range(5).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which is equivalent to:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.range(0, 5).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that the output of `range()` function will never reach the limit parameter.\\n\",\n    \"\\n\",\n    \"You can also control the delta which is the spacing between the tensor elements.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 2, 4], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.range(0, 5, 2).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reshaping Tensors\\n\",\n    \"\\n\",\n    \"You can reshape tensors using this function:\\n\",\n    \"\\n\",\n    \"`tf.reshape(tensor, shape, name=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 1],\\n\",\n       \"       [2, 3],\\n\",\n       \"       [4, 5]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.range(6)\\n\",\n    \"tf.reshape(a, (3,2)).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tensor Arithmetics\\n\",\n    \"\\n\",\n    \"You can use standard python arithmetics on tensors and get results in a new tensor.\\n\",\n    \"\\n\",\n    \"## Addition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[11., 11.],\\n\",\n       \"       [11., 11.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.ones((2,2))\\n\",\n    \"b = tf.fill((2,2), 10.0) # Notice we used 10.0 and not 10 to ensure the data type will be float32\\n\",\n    \"\\n\",\n    \"c = a + b\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Element-wise Operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[22., 22.],\\n\",\n       \"       [22., 22.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"d = c * 2.0\\n\",\n    \"d.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[25., 25.],\\n\",\n       \"       [25., 25.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"(d + 3).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix Operations\\n\",\n    \"\\n\",\n    \"TensorFlow support a variety of matrix operations.\\n\",\n    \"\\n\",\n    \"### Identity Matrix\\n\",\n    \"\\n\",\n    \"An identity matrix is a 2 dimensional matrix where all the values are zeros exception diagonally where it has values of 1. This is an example of an identity matrix of shape (3,3).\\n\",\n    \"\\n\",\n    \"$$\\\\begin{bmatrix}\\n\",\n    \"{1} & {0} & {0} \\\\\\\\\\n\",\n    \"{0} & {1} & {0} \\\\\\\\\\n\",\n    \"{0} & {0} & {1} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$$\\n\",\n    \"\\n\",\n    \"To do that in TensorFlow use the `tf.eye()` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 0., 0.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [0., 0., 1.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"i = tf.eye(3,3)\\n\",\n    \"i.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Transpose\\n\",\n    \"\\n\",\n    \"Transpose is another operation that is commonly used in matrix calculations. Transpose converts rows to columns. Assume you have a matrix $\\\\mathbf{A}$, a transpose operation over this matrix produces $\\\\mathbf{A^T}$ pronounced _transpose of $\\\\mathbf{A}$_.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3, 4],\\n\",\n       \"       [5, 6, 7, 8]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.range(1,9)\\n\",\n    \"i = tf.reshape(a, (2,4))\\n\",\n    \"i.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 5],\\n\",\n       \"       [2, 6],\\n\",\n       \"       [3, 7],\\n\",\n       \"       [4, 8]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"it = tf.matrix_transpose(i)\\n\",\n    \"it.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Matrix Multiplication\\n\",\n    \"\\n\",\n    \"One of the most common operations for matrices in deep learning in matrix multiplication. Matrix multiplication is not an element-wise operation. The exact math will be discussed in the last section of this tutorial \\\"The Math Behind It\\\". But for now to give you the basics you should know the following:\\n\",\n    \"\\n\",\n    \"Assume we have two matrices $\\\\mathbf{A}$ and $\\\\mathbf{B}$. The shape of $\\\\mathbf{A}$ is (m,n) and the shape of $\\\\mathbf{B}$ is (o,p) we can write these two matrices with their shape as $\\\\mathbf{A}_{(m,n)}$ and $\\\\mathbf{B}_{(o,p)}$. Multiplying these two matrices produces a matrix of the shape (m,p) __IF__ $n=o$ like this:\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{A}_{(m,n)} \\\\mathbf{B}_{(o,p)}=\\\\mathbf{C}_{(m,p)} \\\\leftarrow n=o$\\n\",\n    \"\\n\",\n    \"Notice the inner shape of these two matrices is the same and the output matrix has the shape of the outer shape of it these two matrices. If the inner shape of the matrices does not match the product doesn’t exist.\\n\",\n    \"\\n\",\n    \"We can use `tf.matmul()` function to do that.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"c has the shape of: (2, 4)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[3., 3., 3., 3.],\\n\",\n       \"       [3., 3., 3., 3.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.ones((2,3))\\n\",\n    \"b = tf.ones((3,4))\\n\",\n    \"\\n\",\n    \"c = tf.matmul(a ,b)\\n\",\n    \"\\n\",\n    \"print(\\\"c has the shape of:\\\", c.shape)\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# The Math Behind It\\n\",\n    \"\\n\",\n    \"## Standard Deviation $\\\\sigma$ or $s$\\n\",\n    \"\\n\",\n    \"Standard deviation is the measure of how elements in a set vary from the mean. So a sample with most data points close to the mean has low standard deviation and it gets higher as the data points start moving away from the mean. In statistics, standard deviation is denoted as the small letter sigma $\\\\sigma$.\\n\",\n    \"\\n\",\n    \"The formula to calculate standard deviation for a whole population is:\\n\",\n    \"\\n\",\n    \"$$\\\\sigma={\\\\sqrt {\\\\frac {\\\\sum_{i=1}^N(x_{i}-{\\\\overline {x}})^{2}}{N}}}$$\\n\",\n    \"\\n\",\n    \"Let's break it down and see how to calculate standard deviation.\\n\",\n    \"\\n\",\n    \"Assume we have exam grades of 10 students and the grades are so follow:\\n\",\n    \"\\n\",\n    \"| ID    | Grade  |\\n\",\n    \"| ----- |:------:|\\n\",\n    \"| 1     |  88    |\\n\",\n    \"| 2     |  94    |\\n\",\n    \"| 3     |  71    |\\n\",\n    \"| 4     |  97    |\\n\",\n    \"| 5     |  84    |\\n\",\n    \"| 6     |  82    |\\n\",\n    \"| 7     |  80    |\\n\",\n    \"| 8     |  98    |\\n\",\n    \"| 9     |  91    |\\n\",\n    \"| 10    |  93    |\\n\",\n    \"\\n\",\n    \"First thing we need to do is calculate the mean. The mean is denoted as $\\\\overline {x}$ (_pronouned \\\"x bar\\\"_). To calculate the mean (AKA average) get the sum all numbers and divide it by their count. It is also commonly denoted as a small letter mu $\\\\mu$. Assume you have $N$ values, this will be the formula to calculate the mean:\\n\",\n    \"\\n\",\n    \"$$\\\\overline {x} = \\\\frac{x_1 + x_2 + ... + x_N}{N} = \\\\frac{\\\\sum_{i=1}^N x_i}{N}$$\\n\",\n    \"\\n\",\n    \"So let's calculate that.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"87.8\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"g = [88, 94, 71, 97, 84, 82, 80, 98, 91, 93]\\n\",\n    \"\\n\",\n    \"total = sum(g)\\n\",\n    \"count = len(g)\\n\",\n    \"\\n\",\n    \"mean = total/count\\n\",\n    \"mean\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we know the mean, we can go back to the original equation and calculate the standard deviation. For that we will do it with loop comprehension (One of the greatest features of python).\\n\",\n    \"\\n\",\n    \"So looking at the equation one more:\\n\",\n    \"\\n\",\n    \"$$\\\\sigma={\\\\sqrt {\\\\frac {\\\\sum_{i=1}^N(x_{i}-{\\\\overline {x}})^{2}}{N}}}$$\\n\",\n    \"\\n\",\n    \"First we need to get each element in our grades $x_i$ and subtract it from the mean $\\\\overline {x}$ then square it and take the sum of that.\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"a = [(x-mean)**2 for x in g]\\n\",\n    \"b = sum(a)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Divide that by the number of elements $N$ then take the square root\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"variance = b / count\\n\",\n    \"σ = sqrt(variance)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"We can write the whole thing in one like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8.096912991998865\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from math import sqrt\\n\",\n    \"\\n\",\n    \"σ = sqrt(sum([(x-mean)**2 for x in g]) / count)\\n\",\n    \"σ\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that standard deviation is a build in function in NumPy, TensorFlow and many other languages and libraries.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8.096912991998865\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.std(g)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"t = tf.constant(g, dtype=tf.float64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8.096912991998865\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mean_t, var_t = tf.nn.moments(t, axes=0)\\n\",\n    \"sqrt(var_t.eval())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Variance $\\\\sigma^2$, $s^2$ or $Var(X)$\\n\",\n    \"\\n\",\n    \"It is just the square of the standard deviation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"65.56\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"variance = sum([(x-mean)**2 for x in g]) / count\\n\",\n    \"variance\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix Multiplication\\n\",\n    \"\\n\",\n    \"Arguably, the most common matrix operation you will perform in deep learning is multiplying matrices. Understanding this operation is a good start to understanding the math behind neural networks. This operation is also known as \\\"dot product\\\".\\n\",\n    \"\\n\",\n    \"Assume we have two matrices $\\\\mathbf{A}_{(2,3)}$ and $\\\\mathbf{B}_{(3,2)}$. The dot product of these two matrices $\\\\mathbf{A}_{(2,3)} . \\\\mathbf{B}_{(3,2)}$ is calculated as follows:\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{A}_{(2,3)} = \\\\begin{bmatrix}\\n\",\n    \"{1} & {0} \\\\\\\\\\n\",\n    \"{3} & {2} \\\\\\\\\\n\",\n    \"{1} & {4} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{B}_{(3,2)} = \\\\begin{bmatrix}\\n\",\n    \"{2} & {1} & {2} \\\\\\\\\\n\",\n    \"{1} & {2} & {3} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{C}_{(2,2)} = \\\\mathbf{A}_{(2,3)} . \\\\mathbf{B}_{(3,2)}$\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{C}_{(2,2)} = \\\\begin{bmatrix}\\n\",\n    \"{2\\\\times1 + 3\\\\times1 + 1\\\\times2} & {0\\\\times2 + 2\\\\times1 + 4\\\\times2} \\\\\\\\\\n\",\n    \"{1\\\\times1 + 3\\\\times2 + 1\\\\times3} & {0\\\\times1 + 2\\\\times2 + 4\\\\times3} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} = \\\\begin{bmatrix}\\n\",\n    \"{2 + 3 + 2} & {0 + 2 + 8} \\\\\\\\\\n\",\n    \"{1 + 6 + 3} & {0 + 4 + 12} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}= \\\\begin{bmatrix}\\n\",\n    \"{7} & {10} \\\\\\\\\\n\",\n    \"{10} & {16} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$\\n\",\n    \"\\n\",\n    \"This is an animation to shows how that is done step by step.\\n\",\n    \"\\n\",\n    \"![](images/Webp.net-gifmaker.gif)\\n\",\n    \"\\n\",\n    \"Now let's confirm it with TensorFlow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = [[1,0],\\n\",\n    \"     [3,2],\\n\",\n    \"     [1,4],\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"b = [[2,1,2],\\n\",\n    \"     [1,2,3],\\n\",\n    \"    ]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Remember from before, that mathematical shape of a matrix is opposite of TensorFlow shape of a tensor. So instead of rewriting our arrays, we will just use transpose you make rows into columns and columns into rows.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 7, 10],\\n\",\n       \"       [10, 16]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 44,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.constant(a)\\n\",\n    \"b = tf.constant(b)\\n\",\n    \"\\n\",\n    \"c = tf.matmul(tf.matrix_transpose(a), tf.matrix_transpose(b))\\n\",\n    \"\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Luckily there is also a easier way to to that.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 7, 10],\\n\",\n       \"       [10, 16]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 45,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"c = tf.matmul(a,b, transpose_a=True, transpose_b=True)\\n\",\n    \"\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\\n\",\n    \"\\n\",\n    \"Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/tf/1. Machine Learning Basics.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"    <tr>\\n\",\n    \"        <td style=\\\"text-align:left;\\\"><div style=\\\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\\\">1. Machine Learning Basics</div><img src=\\\"images/roshan.png\\\" style=\\\"width:30%; display: inline; text-align: left; float:right;\\\"></td>\\n\",\n    \"        <td></td>\\n\",\n    \"    </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Why TensorFlow?\\n\",\n    \"\\n\",\n    \"TensorFlow is Google's framework for developing Machine Learning models and is has emerged as the most popular machine learning library. Since 2011, Google Brain Team has been developing TensorFlow and used it internally. It was released as an open-source software in Nov, 2015. It comes packed with a wide range of tools from dataset management to a good selection of high-level algorithms and even ready made models and a web based dashboard. That's a lot to pack in one solution!\\n\",\n    \"\\n\",\n    \"The team that is working on developing TensorFlow are some of the most influencal researchers in machine learning. There is also a huge amount of contributors and commits for a relativly new project on Github. You quickly realize that TensorFlow is getting more attention from people in the field than any other existing machine learning solution and will be the technology starndard for machine learning development.\\n\",\n    \"\\n\",\n    \"__OS Support__:\\n\",\n    \"\\n\",\n    \"- Linux\\n\",\n    \"- Windows\\n\",\n    \"- macOS\\n\",\n    \"- Android\\n\",\n    \"- iOS\\n\",\n    \"\\n\",\n    \"__Programming Languages__:\\n\",\n    \"\\n\",\n    \"- Python\\n\",\n    \"- C/C++\\n\",\n    \"- Java\\n\",\n    \"- Golang\\n\",\n    \"\\n\",\n    \"__Hardware__:\\n\",\n    \"\\n\",\n    \"- CPU\\n\",\n    \"- GPU\\n\",\n    \"- TPU\\n\",\n    \"- Distributed Systems\\n\",\n    \"\\n\",\n    \"There is also TensorFlow.js version which runs in browser on client-side with no need for a back-end. There is also TensorFlow Lite which converts machine learning models to a format that can run on mobile devices running iOS and Android.\\n\",\n    \"\\n\",\n    \"With in two years of its release, TensorFlow has been downloaded over 11 million times from almost every country on earth.\\n\",\n    \"\\n\",\n    \"![](images/tf_map.png)\\n\",\n    \"\\n\",\n    \"From Ragat Monga [@rajatmonga](https://twitter.com/rajatmonga) Slides in [TensorFlow Dev Summit 2018]() (2018-03-30)\\n\",\n    \"\\n\",\n    \"# What is Machine Learning?\\n\",\n    \"\\n\",\n    \"Before we dive any deeper in TensorFlow, I will go over some basic concepts of machine learning.\\n\",\n    \"\\n\",\n    \"This tutorial is for developers who can develope in python but not well versed in machine learning or familiar with some other machine learning libraries but new to TensorFlow. It is also useful for non-developers working in other fields like statistics, medical field, marketing or social studies and eager to experiment with machine learning in their fields.\\n\",\n    \"\\n\",\n    \"Machine Learning (ML) is a branch of Artificial Intelligence (AI). It uses data instead of code to produce logic. The goal is to create software that can perform tasks that we usually equate with human level intelligence. In some cases it could exceed human level of intelligence like what happened with Alpha Go and some other examples. You actually use machine learning all the time without realizing when you use Google, YouTube, Amazon and so many other services that reley on machine learning to give you a better experience.\\n\",\n    \"\\n\",\n    \"Machine learning is built on algorithms that use data to train models to perform a specific task.\\n\",\n    \"\\n\",\n    \"Meet `Tensy`, a robot eager to learn and solve human problems. [@TensyRobot](https://twitter.com/TensyRobot)\\n\",\n    \"\\n\",\n    \"![Algorithm](images/algorithm.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Learning Styles\\n\",\n    \"\\n\",\n    \"Models can learn in three styles depending the the training algorithms:\\n\",\n    \"\\n\",\n    \"## Supervised Learning (SL)\\n\",\n    \"\\n\",\n    \"Supervised learning is teaching a model to give an output based on some input. The the algorithm teachs the model by giving the it plenty of examples and their outputs. There are two types of problems that we can solve with supervised learning which are:\\n\",\n    \"\\n\",\n    \"### Classification\\n\",\n    \"\\n\",\n    \"Classification is used to teach a model to output a category or a class based on a data point. Like learning if a photo has a cat or a dog.\\n\",\n    \"\\n\",\n    \"I want `Tensy` to learn how to tell cats from dog. We can give `Tensy` photos of cats and dogs until he learns to tell a cat from a dog. I can later test him on new photos he has never seen before and see how accurate he is.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/CatsDogs.png)\\n\",\n    \"\\n\",\n    \"### Regression\\n\",\n    \"\\n\",\n    \"Regression is used to teach a model to output a continuous numerical value based on a data point. Like telling the age of a person based on their photo.\\n\",\n    \"\\n\",\n    \"This time we are teaching `Tensy` to tell the age of a person based on their photo. To do that we will give him a large amount of photos and tell him the age of each person. We can later test him by asking the age of people in new photos that he didn't see before.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/age.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Unsupervoised Learning (UL)\\n\",\n    \"\\n\",\n    \"Unsupervised Learning teaches a model to produce an output based on a data point without teaching the model the output. Like giving a model photos taken outdoor and photos taken indoor and ask then model to split them to two piles. There are many problems that can be solved using UL but the most common ones are:\\n\",\n    \"\\n\",\n    \"### Clustering\\n\",\n    \"\\n\",\n    \"Clustering is grouping similar data points into groups or clusters. different algorithms can find similarities in different ways and can end up clustering your data points into different groups. Some of these algorithms need you to tell them how many clusters do you want them to split your data into and some will try and figure that out of their own.\\n\",\n    \"\\n\",\n    \"This time we gave `Tensy` a group if photos and we asked him to split them into two groups. He is finding that all indoor photos are similar and all outdoor photos are similar.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/outdoorindoor.png)\\n\",\n    \"\\n\",\n    \"Other UL algorithms can solve some interesting problems using like:\\n\",\n    \"\\n\",\n    \"- Anomaly Detection\\n\",\n    \"- Generative adversarial networks (GANs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reinforcement Learning (RL)\\n\",\n    \"\\n\",\n    \"Reinforcement Learning  uses an algorithm to teach a model to produce a desired output without explicitly explaining how to perform it. This works by giving the model an input and rewarding good outcome and punishing bad outcome. This is similar to we humans and animals learn.\\n\",\n    \"\\n\",\n    \"Let's teach `Tensy` to play some Atari games. The input is the screen feed from the game. After each frame from the input he will be able to choose one action (right, left, up, down and press action button). That action might lead to increased score if it is a good action or to no increase score if it is a neutral action. It could also lead to to loosing a life and lower score for a bad action.\\n\",\n    \"\\n\",\n    \"![CatsDogs!](images/atari.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Machine Learning is More That!\\n\",\n    \"\\n\",\n    \"These major categories are not inclusive of every algorithm that you might use. Some evolutionary algorithms, or recommendation systems do not fall under any of these categories. Some more complicated algorithms use multiple stages in a pipeline some with supervised and unsupervised so your end-to-end algorithm might also not fall under any specific category or sometimes will be called \\\"Semi-Supervised\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# The Math Behind It\\n\",\n    \"\\n\",\n    \"This section will explain some mathematical concepts at the end of each tutorial. These tutorials are going to be code intensive and that will help you develop your coding skills and get your model working. But what if it doesn't work?\\n\",\n    \"\\n\",\n    \"The math behind these algorithms use Linear Algebra, Tensor Calculus and Statistics. This section in each tutorial will explain the math behind what we discussed and will assume very little to no math background and will explain things from the ground up. This could help you fix your model or even improve your accuracy.\\n\",\n    \"\\n\",\n    \"Finally, It will serve as a bidge between machine learning concepts and centuries of mathimatical reasearch. Mathematics is the language of science and learning this language will open the door for you to explore some of the latest academic research papers in the machine learning due to the fact that they mostly look like this:\\n\",\n    \"\\n\",\n    \"![SVM Paper!](images/svm.png)\\n\",\n    \"\\n\",\n    \"A small section of \\\"[A Training Algorithm for Optimal Margin Classiers](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.3818&rep=rep1&type=pdf)\\\" published 1992. AKA the original SVM paper\\n\",\n    \" \\n\",\n    \"To kick of this section, We'll start with a few simple concepts:\\n\",\n    \" \\n\",\n    \"## The real numbers $\\\\mathbb{R}$\\n\",\n    \" \\n\",\n    \"The real numbers include positive, nagative numbers and zero. They can be of arbitrary precesion meaning they can have any number of decimal points or even numbers with infinite precision. The set real is denoted in mathimatics as $\\\\mathbb{R}$. Examples of real numbers include:\\n\",\n    \"\\n\",\n    \"- 5.0\\n\",\n    \"- -2.5\\n\",\n    \"- $\\\\frac{1}{3} = 0.3333\\\\overline3$\\n\",\n    \"- $\\\\pi = 3.14159 ...$ (Pi) this is small letter Pi\\n\",\n    \"- $e = 2.71828 ...$ (Euler's Number)\\n\",\n    \"\\n\",\n    \"## Sum $\\\\sum$\\n\",\n    \"\\n\",\n    \"To represent a finite sum of numbers in math, you can use the following:\\n\",\n    \"\\n\",\n    \"$$ 1 + 2 + 3 + 4 + 5 $$\\n\",\n    \"\\n\",\n    \"For longer sums like getting the sum of all numbers from 1 to 1000 can be harder to write in that way. We use sum denoted in mathematics as capital letter Sigma $\\\\Sigma$. To rewite the previous equation from 1 to 1000 using sum, you can do this:\\n\",\n    \"\\n\",\n    \"$$\\\\sum_{n=1}^{1000} n$$\\n\",\n    \"\\n\",\n    \"This can facilitate writing more complex functions like this:\\n\",\n    \"\\n\",\n    \"$$f(x) = x + 2x + 3x + 4x + 5x = \\\\sum_{n=1}^{5}nx$$\\n\",\n    \"\\n\",\n    \"With this notation we can even represent an infinite sum of numbers:\\n\",\n    \"\\n\",\n    \"$$\\\\frac{1}{1} + \\\\frac{1}{2} + \\\\frac{1}{4} + \\\\frac{1}{8} + \\\\frac{1}{16} ... = \\\\sum_{n=0}^{\\\\infty} \\\\frac{1}{2^n} = 2$$\\n\",\n    \"\\n\",\n    \"## Product $\\\\Pi$\\n\",\n    \"\\n\",\n    \"To represent a finite product of numbers in math, you can use the following:\\n\",\n    \"\\n\",\n    \"$$ 1 \\\\times 2 \\\\times 3 \\\\times 4 \\\\times 5 $$\\n\",\n    \"\\n\",\n    \"For longer products like getting the products of all numbers from 1 to 1000 can be harder to write in that way. We use product denoted in mathematics as capital letter Pi $\\\\Pi$. To rewrite the previous product from 1 to 1000, we can write it this way:\\n\",\n    \"\\n\",\n    \"$$\\\\prod_{n=1}^{1000} n$$\\n\",\n    \"\\n\",\n    \"> NOTE: It should also be noted that the previous product can also be written as $1000!$ which is called 1000 factorial.\\n\",\n    \"\\n\",\n    \"This can facilitate writing more complex products like:\\n\",\n    \"\\n\",\n    \"$$f(x) = x \\\\times 2x \\\\times 3x \\\\times 4x \\\\times 5x = \\\\prod_{n=1}^{5}nx$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\\n\",\n    \"\\n\",\n    \"Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/tf/2. Tensors.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"    <tr>\\n\",\n    \"        <td style=\\\"text-align:left;\\\"><div style=\\\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\\\">2. Tensors</div><img src=\\\"images/roshan.png\\\" style=\\\"width:30%; display: inline; text-align: left; float:right;\\\"></td>\\n\",\n    \"        <td></td>\\n\",\n    \"    </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Before we go into tensors and programming in TensorFlow, let's take a look at how does it work.\\n\",\n    \"\\n\",\n    \"# TensorFlow Basics\\n\",\n    \"\\n\",\n    \"TensorFlow has a few concepts that you should be familiar with. TensorFlow executes your code inside an execution engine that you communicate with using an API. In TensorFlow your data is called a `Tensor` that you can apply operations (`OPs`) to. Your code is converted into a `Graph` that is executed in an execution engine called a `Session`. So your python code is just a representation of your graph that can be executed in a session.\\n\",\n    \"\\n\",\n    \"![](images/graph.png)\\n\",\n    \"\\n\",\n    \"You can see how your data (or tensors) flow from one operation to the next, hence the name TensorFlow.\\n\",\n    \"\\n\",\n    \"Since version 1.5 and as of version 1.8 there are two methods to execute code in TensorFlow:\\n\",\n    \"\\n\",\n    \"- Graph Execution\\n\",\n    \"- Eager Execution\\n\",\n    \"\\n\",\n    \"The main difference is graph execution is a type of `declarative programming` and eager execution is a type of `imperative programming`. In plain English, the difference is graph execution defines your code as a graph and executes in a session. Your objects in python are not the actual objects inside the session, they are only a reference to them. Eager execution executes your code as you run it giving you a better control of your program while it is running so you are not stuck with a predefined graph.\\n\",\n    \"\\n\",\n    \"## So why do we even bother with graph execution?\\n\",\n    \"\\n\",\n    \"Performance is a big issue in machine learning and a small difference in execution time can save you in a long project weeks of your time and 1000s of hours of GPU time. Support is another issue for now where some features of TensorFlow do not work in Eager Execution like high level estimators.\\n\",\n    \"\\n\",\n    \"We will focus in the following tutorials only on graph execution and we will cover eager execution later in this series.\\n\",\n    \"\\n\",\n    \"# Tensors\\n\",\n    \"\\n\",\n    \"Tensors represent your data. It a scalar variable or an array of any dimension. Tensors are the main object to store and pass data between operations, the input and output of all operations are always tensors.\\n\",\n    \"\\n\",\n    \"## Tensor Shape and Rank\\n\",\n    \"\\n\",\n    \"Tensors have a rank and a shape so for scalar values, we use rank-0 tensors of a shape `()` which is an empty shape\\n\",\n    \"\\n\",\n    \"Assuming we need a variable or a constant number to use in our software, we can represent it as a tensor of rank-0.\\n\",\n    \"\\n\",\n    \"![](images/rank-0.png)\\n\",\n    \"\\n\",\n    \"A rank-1 tensor can be though of as an __vector__ or a one dimensional array. creating a rank-1 tensor with shape `(3)` will create a tensor that can hold three values in a one-dimensional array.\\n\",\n    \"\\n\",\n    \"![](images/rank-1.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"A rank-2 is a __matrix__ or a two dimensional array. This can be used to hold two dimensional data like a black and white image. The shape of the tensor can match the shape of the image so to hold a 256x256 pixel image in a tensor, you can create a rank-2 tensor of shape `(256,256)`.\\n\",\n    \"\\n\",\n    \"![](images/rank-2.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"A rank-3 tensor is a three dimensional array. It can be used to hold three dimensional data like a color image represented in (RGB). To create a tensor to hold an color image of size 256x256, you can create a rank-3 tensor of shape `(256,256,3)`.\\n\",\n    \"\\n\",\n    \"![](images/rank-3.png)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"TensorFlow allows tensors in higher dimensions but you will very rarely see tensors of a rank exceeding 5 `(batch size, width, height, RGB, frames)` for representing a batch of video clips.\\n\",\n    \"\\n\",\n    \"## Importing Tensor Flow\\n\",\n    \"\\n\",\n    \"Let's import TensorFlow and start working with some tensors.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Python Version: 3.5.2\\n\",\n      \"TensorFlow Version: 1.8.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"print(\\\"Python Version:\\\",sys.version.split(\\\" \\\")[0])\\n\",\n    \"print(\\\"TensorFlow Version:\\\",tf.VERSION)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Graph Execution\\n\",\n    \"\\n\",\n    \"TensorFlow executes your code inside a C++ program and returns the results through the TensorFlow API. Since we are using Python, we will be using TensorFlow Python API which is the most documented and most used API of TensorFlow.\\n\",\n    \"\\n\",\n    \"Since we are using graph execution, there are two ways to create a session:\\n\",\n    \"\\n\",\n    \"- Session\\n\",\n    \"- Interactive Session\\n\",\n    \"\\n\",\n    \"Sessions and interactive sessions, use your code to build a \\\"Graph\\\" which is a representation of your code inside TensorFlow's execution engine. The main difference between them is, an interactive session makes itself the default session. Since we are using only one session for our code, we will use that.\\n\",\n    \"\\n\",\n    \"For now let's start an interactive session and start flowing some tensors!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sess = tf.InteractiveSession()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Generating new Tensors\\n\",\n    \"\\n\",\n    \"Since we have an interactive session, let's create a tensor. There are two common ways to create a tensor `tf.zeros()` and `tf.ones()`. Each one of them takes a python tuple or an array as the shape of the tensor.\\n\",\n    \"\\n\",\n    \"Let's start be creating a rank-0 tensor.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = tf.zeros(())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We create a tensor and assigned it to a local variable named `a`. When we check the value of `a` this is what we get.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'zeros:0' shape=() dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice there is no value. You need to call `eval()` method of the tensor to get the actual value. This method takes an optional parameter where you can pass your session. Since we are using interactive session, we don't have ato pass anything.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.0\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You should know that `eval()` method returns a numpy.float32 (or what ever the type of the tensor is) if the rank of the tensor is 0 and numpy.ndarray if the tensor has rank 1 or higher.\\n\",\n    \"\\n\",\n    \" > __Numpy__: is a multi-dimensional array library for python that runs the operations in a C program and interfaces back with python to ensure fast array operations.\\n\",\n    \"\\n\",\n    \"We can also check the rank and shape of the tensor.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"the rank would be the number of dimensions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.shape.ndims\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice the name inside the TensorFlow execution engine is not `a`. It is `zeros:0` which is an auto generated name for the variable. The auto generated name is the name of the operation that generated the tensor and then an the index of the of the tensor in the output of the operation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'zeros:0'\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a.name\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you created another variable using the same operation, it will be named `zeros_1:0`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'zeros_1:0' shape=() dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.zeros(())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's create a second tensor of shape (3) which is going to be a rank-1 tensor. This time we will name it `b` and store it in a local variable named `b`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'b:0' shape=(3,) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"b = tf.zeros((3), name=\\\"b\\\")\\n\",\n    \"b\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice the name of the variable now is `b:0` which is the name that we gave it and the index of `0` of the return of the operation. We can also get the value in the same way using `eval()` method.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"numpy.ndarray\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(b.eval())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can also get the value of a tensor by executing the tensor using your session.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0., 0., 0.], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sess.run(b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can also fill the tensor with any other value you want other than 0 and 1 using `fill()` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[5, 5],\\n\",\n       \"       [5, 5]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.fill((2,2), 5).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that the data type of this tensor is `int32` and not `float32` because you initialized the tensor with an integer `5` and not `5.0`.\\n\",\n    \"\\n\",\n    \"## Tensor Shape\\n\",\n    \"\\n\",\n    \"For multi dimensional tensors, the shape is passed as a tuple or an array. The way this array is arranged is from the outer most dimension to the inner dimensions. So for a tensor that should represents 10 items and each item has three numbers, the shape would be `(10,3)`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.],\\n\",\n       \"       [0., 0., 0.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.zeros((10,3)).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"__Note__: This is the opposite of the how matrix shape notation is written in mathematics. A matrix of shape $A_{(3,10)}$ can be represented in TensorFlow as `(10,3)`. The reason for that is in mathematics the shape is $(Columns,Rows)$ and TensorFlow uses `(Outer,Inner)` which translates in 2-D tensor as `(Rows,Columns)`.\\n\",\n    \"\\n\",\n    \"For higher dimensions the same rules applies. Let say we have 2 items and each item has 3 parts and each part consists of 4 numbers the shape would be `(2,3,4)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[[0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.]],\\n\",\n       \"\\n\",\n       \"       [[0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.],\\n\",\n       \"        [0., 0., 0., 0.]]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.zeros((2,3,4)).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Generating Tensors with Random Values\\n\",\n    \"\\n\",\n    \"In many cases, you want to generate a new tensor but we want to start with random values stored in the tensor. The way we do that is using one the random generators of TensorFlow.\\n\",\n    \"\\n\",\n    \"### Normal Distribution\\n\",\n    \"\\n\",\n    \"`tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'random_normal:0' shape=(1000,) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_normal((1000,))\\n\",\n    \"arr1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This function returns random values using normal distribution which is also known as Gaussian distribution or informally called a \\\"Bell Curve\\\". To better understand it, let's first look at a graph showing this distribution. In Mathematics, a normal distribution of mean $\\\\mu$ and standard deviation $\\\\sigma$ is denoted $N(\\\\mu, \\\\sigma)$ (More about that in \\\"The Math Behind It\\\").\\n\",\n    \"\\n\",\n    \"To do that we will import Matplotlib which is a very common charting library for Python.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(arr1.eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice the bill shape of the curve where you get more values around your mean a fewer values as you move away from the mean.\\n\",\n    \"\\n\",\n    \"You can also change the mean.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_normal((1000,), mean=20.0)\\n\",\n    \"\\n\",\n    \"plt.hist(arr1.eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can also control how concentrated your random numbers will be around the mean by controlling the standard deviation. Higher standard deviation means less values around the mean and wider distribution.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEBFJREFUeJzt3X+sZGV9x/H3p4DYqF2kXOm6u/RSCxpsy49cEUPboPgD0biaWAJJcWtp1howmJC0C6Zxm5SEtirV2JCsQoWWikSxbJRaEWmNfwAuiPxarVuEspuFXRVXGlPM4rd/3LM61d2dufPjzr0P71dyM+c85zkz35Pd+5nnPnPOmVQVkqR2/dK0C5AkTZZBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrcodMuAOCoo46q2dnZaZchScvK3Xff/d2qmunXb0kE/ezsLFu2bJl2GZK0rCR5dJB+Tt1IUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjlsSVsdLYbFzRZ/uexalDWkIc0UtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcX2DPslzk9yV5BtJHkzyl137sUnuTLItyaeSPKdrP7xb39Ztn53sIUiSDmaQEf3TwGuq6kTgJOCsJKcBfw1cWVW/CTwJXND1vwB4smu/susnSZqSvkFf8/6nWz2s+yngNcCnu/Zrgbd2y2u7dbrtZybJ2CqWJC3IQHP0SQ5Jci+wC7gV+C/gB1W1t+uyHVjVLa8CHgPotu8BfnWcRUuSBjdQ0FfVM1V1ErAaOBV42agvnGR9ki1JtuzevXvUp5MkHcCCzrqpqh8AtwOvAo5Isu8bqlYDO7rlHcAagG77CuB7+3muTVU1V1VzMzMzQ5YvSepnkLNuZpIc0S3/MvA6YCvzgf/2rts64OZueXO3Trf9y1VV4yxakjS4Qb4zdiVwbZJDmH9juLGqPpfkIeCGJH8FfB24uut/NfCPSbYB3wfOnUDdkqQB9Q36qroPOHk/7Q8zP1//8+3/C/zBWKqTJI3MK2MlqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJalzfoE+yJsntSR5K8mCSi7v2jUl2JLm3+zm7Z59Lk2xL8q0kb5jkAUiSDu7QAfrsBS6pqnuSvAC4O8mt3bYrq+oDvZ2TnACcC7wceDHwpSTHV9Uz4yxckjSYviP6qtpZVfd0y08BW4FVB9llLXBDVT1dVd8BtgGnjqNYSdLCDTKi/6kks8DJwJ3A6cBFSd4BbGF+1P8k828Cd/Tstp39vDEkWQ+sBzjmmGOGKF1aJBtX9Nm+Z3HqkIY08IexSZ4PfAZ4b1X9ELgKeAlwErAT+OBCXriqNlXVXFXNzczMLGRXSdICDBT0SQ5jPuSvr6qbAKrqiap6pqp+AnyMn03P7ADW9Oy+umuTJE3BIGfdBLga2FpVH+ppX9nT7W3AA93yZuDcJIcnORY4DrhrfCVLkhZikDn604HzgfuT3Nu1XQacl+QkoIBHgHcBVNWDSW4EHmL+jJ0LPeNGkqanb9BX1VeB7GfTLQfZ53Lg8hHqkiSNiVfGSlLjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxvUN+iRrktye5KEkDya5uGs/MsmtSb7dPb6wa0+SjyTZluS+JKdM+iAkSQc2yIh+L3BJVZ0AnAZcmOQEYANwW1UdB9zWrQO8ETiu+1kPXDX2qiVJA+sb9FW1s6ru6ZafArYCq4C1wLVdt2uBt3bLa4Hrat4dwBFJVo69cknSQBY0R59kFjgZuBM4uqp2dpseB47ullcBj/Xstr1rkyRNwcBBn+T5wGeA91bVD3u3VVUBtZAXTrI+yZYkW3bv3r2QXSVJCzBQ0Cc5jPmQv76qbuqan9g3JdM97uradwBrenZf3bX9P1W1qarmqmpuZmZm2PolSX0MctZNgKuBrVX1oZ5Nm4F13fI64Oae9nd0Z9+cBuzpmeKRJC2yQwfoczpwPnB/knu7tsuAK4Abk1wAPAqc0227BTgb2Ab8CHjnWCuWJC1I36Cvqq8COcDmM/fTv4ALR6xLkjQmXhkrSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJatwgV8ZKk7FxxQB99ky+DqlxjuglqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP6Bn2Sa5LsSvJAT9vGJDuS3Nv9nN2z7dIk25J8K8kbJlW4JGkwg9yP/hPAR4Hrfq79yqr6QG9DkhOAc4GXAy8GvpTk+Kp6Zgy1SmM3u+Hzffs88txFKESaoL4j+qr6CvD9AZ9vLXBDVT1dVd8BtgGnjlCfJGlEo8zRX5Tkvm5q54Vd2yrgsZ4+27s2SdKUDBv0VwEvAU4CdgIfXOgTJFmfZEuSLbt37x6yDElSP0N9Z2xVPbFvOcnHgM91qzuANT1dV3dt+3uOTcAmgLm5uRqmDi1t/ea/h5n7nsRzSq0bakSfZGXP6tuAfWfkbAbOTXJ4kmOB44C7RitRkjSKviP6JJ8EzgCOSrIdeD9wRpKTgAIeAd4FUFUPJrkReAjYC1zoGTeSNF19g76qzttP89UH6X85cPkoRUmSxscrYyWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXFD3dRM0gg2ruizfc/i1KFnDUf0ktQ4g16SGmfQS1LjDHpJapwfxkpj5rdgaalxRC9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqXN+gT3JNkl1JHuhpOzLJrUm+3T2+sGtPko8k2ZbkviSnTLJ4SVJ/g1wZ+wngo8B1PW0bgNuq6ookG7r1PwfeCBzX/bwSuKp7lDSCvlfbXvGmRapEy1HfoK+qrySZ/bnmtcAZ3fK1wL8zH/RrgeuqqoA7khyRZGVV7RxXwZocw0Rq07Bz9Ef3hPfjwNHd8irgsZ5+27u2X5BkfZItSbbs3r17yDIkSf2M/GFsN3qvIfbbVFVzVTU3MzMzahmSpAMYNuifSLISoHvc1bXvANb09FvdtUmSpmTYoN8MrOuW1wE397S/ozv75jRgj/PzkjRdfT+MTfJJ5j94PSrJduD9wBXAjUkuAB4Fzum63wKcDWwDfgS8cwI1S5IWYJCzbs47wKYz99O3gAtHLUqSND5eGStJjTPoJalxBr0kNc4vB1+mvIpV0qAc0UtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DjPo9fgNq7os33P4tQhaUEc0UtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3Ei3QEjyCPAU8Aywt6rmkhwJfAqYBR4BzqmqJ0crU5I0rHGM6F9dVSdV1Vy3vgG4raqOA27r1iVJUzKJqZu1wLXd8rXAWyfwGpKkAY0a9AV8McndSdZ3bUdX1c5u+XHg6BFfQ5I0glFvU/y7VbUjyYuAW5N8s3djVVWS2t+O3RvDeoBjjjlmxDIkSQcy0oi+qnZ0j7uAzwKnAk8kWQnQPe46wL6bqmququZmZmZGKUOSdBBDB32S5yV5wb5l4PXAA8BmYF3XbR1w86hFSpKGN8rUzdHAZ5Pse55/rqovJPkacGOSC4BHgXNGL1OSNKyhg76qHgZO3E/794AzRylKkjQ+fmes9Cw0u+HzB93+yBVvWqRKtBi8BYIkNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnKdXLhJPZ5M0LY7oJalxBr0kNc6pG0lj4fTk0uWIXpIa54heasHGFX2271mcOrQkOaKXpMYZ9JLUOKdu9sMPlSS1xBG9JDXOoJekxhn0ktQ4g16SGmfQS1LjPOumVf0uoAEvopGeJQx6Sb9oCQwUPM15fCY2dZPkrCTfSrItyYZJvY4k6eAmMqJPcgjw98DrgO3A15JsrqqHJvF6kjSIZ+tfCZOaujkV2FZVDwMkuQFYC4w96Pv9w8Ey+cfzplSSJmRSQb8KeKxnfTvwygm9liRNxXIZaKaqxv+kyduBs6rqT7r184FXVtVFPX3WA+u71ZcC3wO+O/ZiFt9RtHEc0M6xtHIc0M6xtHIcMN1j+fWqmunXaVIj+h3Amp711V3bT1XVJmDTvvUkW6pqbkL1LJpWjgPaOZZWjgPaOZZWjgOWx7FM6qybrwHHJTk2yXOAc4HNE3otSdJBTGREX1V7k1wE/BtwCHBNVT04ideSJB3cxC6YqqpbgFsWsMum/l2WhVaOA9o5llaOA9o5llaOA5bBsUzkw1hJ0tLhTc0kqXFLLuiTvCfJN5M8mORvpl3PKJJckqSSHDXtWoaV5G+7f4/7knw2yRHTrmkhWrgVR5I1SW5P8lD3e3HxtGsaVZJDknw9yeemXcuwkhyR5NPd78fWJK+adk0HsqSCPsmrmb+C9sSqejnwgSmXNLQka4DXA/897VpGdCvwW1X1O8B/ApdOuZ6B9dyK443ACcB5SU6YblVD2QtcUlUnAKcBFy7T4+h1MbB12kWM6MPAF6rqZcCJLOHjWVJBD7wbuKKqngaoql1TrmcUVwJ/BizrD0Gq6otVtbdbvYP5ayKWi5/eiqOqfgzsuxXHslJVO6vqnm75KeYDZdV0qxpektXAm4CPT7uWYSVZAfw+cDVAVf24qn4w3aoObKkF/fHA7yW5M8l/JHnFtAsaRpK1wI6q+sa0axmzPwb+ddpFLMD+bsWxbAMSIMkscDJw53QrGcnfMT8I+sm0CxnBscBu4B+6KaiPJ3netIs6kEW/H32SLwG/tp9N72O+niOZ//P0FcCNSX6jluCpQX2O4zLmp22WhYMdS1Xd3PV5H/NTCNcvZm36mSTPBz4DvLeqfjjteoaR5M3Arqq6O8kZ065nBIcCpwDvqao7k3wY2AD8xXTL2r9FD/qqeu2BtiV5N3BTF+x3JfkJ8/eR2L1Y9Q3qQMeR5LeZf7f/RhKYn+q4J8mpVfX4IpY4sIP9mwAk+SPgzcCZS/FN9yD63opjuUhyGPMhf31V3TTtekZwOvCWJGcDzwV+Jck/VdUfTrmuhdoObK+qfX9ZfZr5oF+SltrUzb8ArwZIcjzwHJbZjY+q6v6qelFVzVbVLPP/IU5ZqiHfT5KzmP8z+y1V9aNp17NATdyKI/MjhquBrVX1oWnXM4qqurSqVne/G+cCX16GIU/3+/xYkpd2TWcygduwj8tS+yrBa4BrkjwA/BhYt8xGkC36KHA4cGv3F8odVfWn0y1pMA3diuN04Hzg/iT3dm2XdVefa3reA1zfDSIeBt455XoOyCtjJalxS23qRpI0Zga9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z+SCDSTP1qeDAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_normal((1000,), stddev=2, name=\\\"arr1\\\")\\n\",\n    \"arr2 = tf.random_normal((1000,), stddev=1, name=\\\"arr2\\\")\\n\",\n    \"\\n\",\n    \"plt.hist([arr1.eval(), arr2.eval()], bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"One more note on normal distribution, if you created a large tensor with millions or tens of millions of random values, some of these values will fall really far from the mean. With some machine learning algorithms this might create instability. You can avoid that by using `truncated_normal()` function instead of `random_normal()`. This will re-sample any values that falls more than 2 standard deviations from the mean.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.hist(tf.truncated_normal((1000,)).eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Uniform Distribution\\n\",\n    \"\\n\",\n    \"The other common distribution is the uniform one. This will generate values with equal probability of falling anywhere between two numbers.\\n\",\n    \"\\n\",\n    \"`tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"arr1 = tf.random_uniform((1000,))\\n\",\n    \"plt.hist(arr1.eval(), bins=15);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Generating Tensors with Sequence Values\\n\",\n    \"\\n\",\n    \"You can generate tensors with sequence values using the following function:\\n\",\n    \"\\n\",\n    \"`tf.range(start, limit=None, delta=1, dtype=None, name='range')`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.range(5).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which is equivalent to:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 1, 2, 3, 4], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.range(0, 5).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that the output of `range()` function will never reach the limit parameter.\\n\",\n    \"\\n\",\n    \"You can also control the delta which is the spacing between the tensor's elements.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([0, 2, 4], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.range(0, 5, 2).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Reshaping Tensors\\n\",\n    \"\\n\",\n    \"You can reshape tensors using this function:\\n\",\n    \"\\n\",\n    \"`tf.reshape(tensor, shape, name=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[0, 1],\\n\",\n       \"       [2, 3],\\n\",\n       \"       [4, 5]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.range(6)\\n\",\n    \"tf.reshape(a, (3,2)).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tensor Arithmetics\\n\",\n    \"\\n\",\n    \"You can use standard python arithmetics on tensors and get results in a new tensor.\\n\",\n    \"\\n\",\n    \"## Addition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[11., 11.],\\n\",\n       \"       [11., 11.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.ones((2,2))\\n\",\n    \"b = tf.fill((2,2), 10.0) # Notice we used 10.0 and not 10 to ensure the data type will be float32\\n\",\n    \"\\n\",\n    \"c = a + b\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Element-wise Operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[22., 22.],\\n\",\n       \"       [22., 22.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"d = c * 2.0\\n\",\n    \"d.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[25., 25.],\\n\",\n       \"       [25., 25.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"(d + 3).eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix Operations\\n\",\n    \"\\n\",\n    \"TensorFlow support a variety of matrix operations.\\n\",\n    \"\\n\",\n    \"### Identity Matrix\\n\",\n    \"\\n\",\n    \"An identity matrix is a matrix where all the values are zeros exception diagonally where it has values of 1. This is an example of an identity matrix of shape (3,3).\\n\",\n    \"\\n\",\n    \"$$\\\\begin{bmatrix}\\n\",\n    \"{1} & {0} & {0} \\\\\\\\\\n\",\n    \"{0} & {1} & {0} \\\\\\\\\\n\",\n    \"{0} & {0} & {1} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$$\\n\",\n    \"\\n\",\n    \"To do that in TensorFlow use the `tf.eye()` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1., 0., 0.],\\n\",\n       \"       [0., 1., 0.],\\n\",\n       \"       [0., 0., 1.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"i = tf.eye(3,3)\\n\",\n    \"i.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Transpose\\n\",\n    \"\\n\",\n    \"Transpose is another operation that is commonly used in matrix calculations. Transpose converts rows to columns. Assume you have a matrix $\\\\mathbf{A}$, a transpose operation over this matrix produces $\\\\mathbf{A^T}$ pronounced _$\\\\mathbf{A}$ transpose_.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 2, 3, 4],\\n\",\n       \"       [5, 6, 7, 8]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.range(1,9)\\n\",\n    \"i = tf.reshape(a, (2,4))\\n\",\n    \"i.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[1, 5],\\n\",\n       \"       [2, 6],\\n\",\n       \"       [3, 7],\\n\",\n       \"       [4, 8]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"it = tf.matrix_transpose(i)\\n\",\n    \"it.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Matrix Multiplication\\n\",\n    \"\\n\",\n    \"One of the most common operations for matrices in deep learning in matrix multiplication. Matrix multiplication is not an element-wise operation. The exact math will be discussed in the last section of this tutorial \\\"The Math Behind It\\\". But for now to give you the basics you should know the following:\\n\",\n    \"\\n\",\n    \"Assume we have two matrices $\\\\mathbf{A}$ and $\\\\mathbf{B}$. The shape of $\\\\mathbf{A}$ is (m,n) and the shape of $\\\\mathbf{B}$ is (o,p) we can write these two matrices with their shape as $\\\\mathbf{A}_{(m,n)}$ and $\\\\mathbf{B}_{(o,p)}$. Multiplying these two matrices produces a matrix of the shape (m,p) __IF__ $n=o$ like this:\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{A}_{(m,n)} . \\\\mathbf{B}_{(o,p)}=\\\\mathbf{C}_{(m,p)} \\\\leftarrow n=o$\\n\",\n    \"\\n\",\n    \"Notice the inner shape of these two matrices is the same and the output matrix has the shape of the outer shape of these two matrices. If the inner shape of the matrices does not match the product doesn’t exist.\\n\",\n    \"\\n\",\n    \"We can use `tf.matmul()` function to do that.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"c has the shape of: (2, 4)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[3., 3., 3., 3.],\\n\",\n       \"       [3., 3., 3., 3.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.ones((2,3))\\n\",\n    \"b = tf.ones((3,4))\\n\",\n    \"\\n\",\n    \"c = tf.matmul(a ,b)\\n\",\n    \"\\n\",\n    \"print(\\\"c has the shape of:\\\", c.shape)\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# The Math Behind It\\n\",\n    \"\\n\",\n    \"## Standard Deviation $\\\\sigma$ or $s$\\n\",\n    \"\\n\",\n    \"Standard deviation is the measure of how elements in a set vary from the mean. So a sample with most data points close to the mean has low standard deviation and it gets higher as the data points start moving away from the mean. In statistics, standard deviation is denoted as the small letter sigma $\\\\sigma$ or $s$.\\n\",\n    \"\\n\",\n    \"The formula to calculate standard deviation for a whole population is:\\n\",\n    \"\\n\",\n    \"$$\\\\sigma={\\\\sqrt {\\\\frac {\\\\sum_{i=1}^N(x_{i}-{\\\\overline {x}})^{2}}{N}}}$$\\n\",\n    \"\\n\",\n    \"Let's break it down and see how to calculate standard deviation.\\n\",\n    \"\\n\",\n    \"Assume we have exam grades of 10 students and the grades are so follow:\\n\",\n    \"\\n\",\n    \"| ID    | Grade  |\\n\",\n    \"| ----- |:------:|\\n\",\n    \"| 1     |  88    |\\n\",\n    \"| 2     |  94    |\\n\",\n    \"| 3     |  71    |\\n\",\n    \"| 4     |  97    |\\n\",\n    \"| 5     |  84    |\\n\",\n    \"| 6     |  82    |\\n\",\n    \"| 7     |  80    |\\n\",\n    \"| 8     |  98    |\\n\",\n    \"| 9     |  91    |\\n\",\n    \"| 10    |  93    |\\n\",\n    \"\\n\",\n    \"First thing we need to do is calculate the mean. The mean is denoted as $\\\\overline {x}$ (_pronouned \\\"x bar\\\"_). To calculate the mean (or average) get the sum all numbers and divide it by their count. It is also commonly denoted as a small letter mu $\\\\mu$. Assume you have $N$ values, this will be the formula to calculate the mean:\\n\",\n    \"\\n\",\n    \"$$\\\\overline {x} = \\\\frac{x_1 + x_2 + ... + x_N}{N} = \\\\frac{\\\\sum_{i=1}^N x_i}{N}$$\\n\",\n    \"\\n\",\n    \"So let's calculate that.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"87.8\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"g = [88, 94, 71, 97, 84, 82, 80, 98, 91, 93]\\n\",\n    \"\\n\",\n    \"total = sum(g)\\n\",\n    \"count = len(g)\\n\",\n    \"\\n\",\n    \"mean = total/count\\n\",\n    \"mean\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we know the mean, we can go back to the original equation and calculate the standard deviation.\\n\",\n    \"\\n\",\n    \"So looking at the equation one more:\\n\",\n    \"\\n\",\n    \"$$\\\\sigma={\\\\sqrt {\\\\frac {\\\\sum_{i=1}^N(x_{i}-{\\\\overline {x}})^{2}}{N}}}$$\\n\",\n    \"\\n\",\n    \"First we need to get each element in our grades $x_i$ and subtract it from the mean $\\\\overline {x}$ then square it and take the sum of that.\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"a = [(x-mean)**2 for x in g]\\n\",\n    \"b = sum(a)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Divide that by the number of elements $N$ then take the square root\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"variance = b / count\\n\",\n    \"σ = sqrt(variance)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"We can write the whole thing in one like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8.096912991998865\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from math import sqrt\\n\",\n    \"\\n\",\n    \"σ = sqrt(sum([(x-mean)**2 for x in g]) / count)\\n\",\n    \"σ\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that standard deviation is a build in function in NumPy, TensorFlow and many other languages and libraries.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8.096912991998865\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.std(g)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"t = tf.constant(g, dtype=tf.float64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8.096912991998865\"\n      ]\n     },\n     \"execution_count\": 38,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mean_t, var_t = tf.nn.moments(t, axes=0)\\n\",\n    \"sqrt(var_t.eval())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Variance $\\\\sigma^2$, $s^2$ or $Var(X)$\\n\",\n    \"\\n\",\n    \"It is just the square of the standard deviation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"65.56\"\n      ]\n     },\n     \"execution_count\": 39,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"variance = sum([(x-mean)**2 for x in g]) / count\\n\",\n    \"variance\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Matrix Multiplication\\n\",\n    \"\\n\",\n    \"Arguably, the most common matrix operation you will perform in deep learning is multiplying matrices. Understanding this operation is a good start to understanding the math behind neural networks. This operation is also known as \\\"dot product\\\".\\n\",\n    \"\\n\",\n    \"Assume we have two matrices $\\\\mathbf{A}_{(2,3)}$ and $\\\\mathbf{B}_{(3,2)}$. The dot product of these two matrices $\\\\mathbf{A}_{(2,3)} . \\\\mathbf{B}_{(3,2)}$ is calculated as follows:\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{A}_{(2,3)} = \\\\begin{bmatrix}\\n\",\n    \"{1} & {0} \\\\\\\\\\n\",\n    \"{3} & {2} \\\\\\\\\\n\",\n    \"{1} & {4} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{B}_{(3,2)} = \\\\begin{bmatrix}\\n\",\n    \"{2} & {1} & {2} \\\\\\\\\\n\",\n    \"{1} & {2} & {3} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{C}_{(2,2)} = \\\\mathbf{A}_{(2,3)} . \\\\mathbf{B}_{(3,2)}$\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{C}_{(2,2)} = \\\\begin{bmatrix}\\n\",\n    \"{2\\\\times1 + 3\\\\times1 + 1\\\\times2} & {0\\\\times2 + 2\\\\times1 + 4\\\\times2} \\\\\\\\\\n\",\n    \"{1\\\\times1 + 3\\\\times2 + 1\\\\times3} & {0\\\\times1 + 2\\\\times2 + 4\\\\times3} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} = \\\\begin{bmatrix}\\n\",\n    \"{2 + 3 + 2} & {0 + 2 + 8} \\\\\\\\\\n\",\n    \"{1 + 6 + 3} & {0 + 4 + 12} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}= \\\\begin{bmatrix}\\n\",\n    \"{7} & {10} \\\\\\\\\\n\",\n    \"{10} & {16} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}$\\n\",\n    \"\\n\",\n    \"This is an animation to shows how that is done step by step.\\n\",\n    \"\\n\",\n    \"![](images/Webp.net-gifmaker.gif)\\n\",\n    \"\\n\",\n    \"Now let's confirm it with TensorFlow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = [[1,0],\\n\",\n    \"     [3,2],\\n\",\n    \"     [1,4],\\n\",\n    \"    ]\\n\",\n    \"\\n\",\n    \"b = [[2,1,2],\\n\",\n    \"     [1,2,3],\\n\",\n    \"    ]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Remember from before, that mathematical shape of a matrix is opposite of TensorFlow shape of a tensor. So instead of rewriting our arrays, we will just use transpose you make rows into columns and columns into rows.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 7, 10],\\n\",\n       \"       [10, 16]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"a = tf.constant(a)\\n\",\n    \"b = tf.constant(b)\\n\",\n    \"\\n\",\n    \"c = tf.matmul(tf.matrix_transpose(a), tf.matrix_transpose(b))\\n\",\n    \"\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Luckily there is also a easier way to to that.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 7, 10],\\n\",\n       \"       [10, 16]], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"c = tf.matmul(a,b, transpose_a=True, transpose_b=True)\\n\",\n    \"\\n\",\n    \"c.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\\n\",\n    \"\\n\",\n    \"Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/tf/3. Variables.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"    <tr>\\n\",\n    \"        <td style=\\\"text-align:left;\\\"><div style=\\\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\\\">3. Variables</div><img src=\\\"images/roshan.png\\\" style=\\\"width:30%; display: inline; text-align: left; float:right;\\\"></td>\\n\",\n    \"        <td></td>\\n\",\n    \"    </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So far all the tensors that we created were all constants where you can do operations that generate new tensors but you can never change the value of any tensor after creating it. To start doing \\\"stateful\\\" programming which keeps and updates values (or state) or tesnors you need to wrap your tensors in an instance of `ft.Varbiable()`.\\n\",\n    \"\\n\",\n    \"As usaul before we start, let's import TensorFlow and start an interactive session.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Python Version: 3.5.2\\n\",\n      \"TensorFlow Version: 1.7.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"print(\\\"Python Version:\\\",sys.version.split(\\\" \\\")[0])\\n\",\n    \"print(\\\"TensorFlow Version:\\\",tf.VERSION)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Creating Variables\\n\",\n    \"\\n\",\n    \"Let start a new interactive session and create some variables. To create a variables, your this function:\\n\",\n    \"\\n\",\n    \"`tf.Variable(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None, expected_shape=None, import_scope=None, constraint=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Variable 'a:0' shape=(2, 2) dtype=float32_ref>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sess = tf.InteractiveSession()\\n\",\n    \"\\n\",\n    \"a = tf.Variable(tf.ones((2,2)), name=\\\"a\\\")\\n\",\n    \"a\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first parameter that we passed to when creating an instanse of `tf.Variable()` is `initial_value`. This can accept a tensor that has values or a tensor initilizer method. We will discuss some of these later in this tutorial.\\n\",\n    \"\\n\",\n    \"You can also use `ft.get_variable()` function to create a variable.\\n\",\n    \"\\n\",\n    \"`tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, use_resource=None, custom_getter=None, constraint=None)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Variable 'b:0' shape=(2, 2) dtype=float32_ref>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"b = tf.get_variable(\\\"b\\\", [2,2])\\n\",\n    \"b\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This creates a variable named `b` with shape `(2,2)`.\\n\",\n    \"\\n\",\n    \"To initlize the value of your variable, you could use one of the many inilization method available in TensorFlow.\\n\",\n    \"\\n\",\n    \"`tf.zeros_initializer`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Variable 'c:0' shape=(2, 2) dtype=float32_ref>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"c = tf.get_variable(\\\"c\\\", [2,2], dtype=tf.float32, initializer=tf.zeros_initializer)\\n\",\n    \"c\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Similar to `tf.zeros_initializer` there is `tf.ones_initializer` which initializes your tensor with ones.\\n\",\n    \"\\n\",\n    \"There are also `tf.random_normal_initializer` and `random_uniform_initializer` that inialize your variables with a normal or uniform distribution. For truncated normal distribution, you can use `tf.truncated_normal_initializer` which will limit your normal distribution to 2 standard diviations from the mean.\\n\",\n    \"\\n\",\n    \"You can also initialize your variables with a constant.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Variable 'd:0' shape=(3,) dtype=int32_ref>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"d = tf.get_variable(\\\"d\\\", initializer=tf.constant([1,2,3]))\\n\",\n    \"d\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Initialize Variables\\n\",\n    \"\\n\",\n    \"Before you can use any of your variables, you should first run an operation that initializes them. To initialize all the variabes that that created already, you can use `tf.global_variables_initializer` to create the operation this you have to run that operation using your session.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"init_op = tf.global_variables_initializer()\\n\",\n    \"sess.run(init_op)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that you initlized your variables, you can start executing this and getting their values. To do that, you can just call `eval()` method of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"d.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Manually Initializing Variables\\n\",\n    \"\\n\",\n    \"Sometimes specially in an interactive environment, you would want to initialize some extra variables after you initialized all your variables using `tf.global_variables_initializer`. To do that, you can run one variable initializer which is an operation that can be retrieved for a single variable from the `initializer`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"e = tf.get_variable(\\\"e\\\", initializer=tf.constant([2,2,2]))\\n\",\n    \"sess.run(e.initializer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you try to redecalre your variable with the same name, you will get an error message because TensorFlow does't know if you want to resue the same variable or you want a new one.\\n\",\n    \"\\n\",\n    \"To avoid that clarify that you mean to reuse the same variable and you just to reinitize the variable using \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"Trying to share variable e/e, but specified dtype float32 and found dtype int32_ref.\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-19-6f62305b28d4>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m      1\\u001b[0m \\u001b[0;32mwith\\u001b[0m \\u001b[0mtf\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvariable_scope\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mreuse\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mtf\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mAUTO_REUSE\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mname_or_scope\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m\\\"e\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m     \\u001b[0me\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtf\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget_variable\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"e\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minitializer\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mtf\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mconstant\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36mget_variable\\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\\u001b[0m\\n\\u001b[1;32m   1295\\u001b[0m       \\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1296\\u001b[0m       \\u001b[0muse_resource\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0muse_resource\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1297\\u001b[0;31m       constraint=constraint)\\n\\u001b[0m\\u001b[1;32m   1298\\u001b[0m get_variable_or_local_docstring = (\\n\\u001b[1;32m   1299\\u001b[0m     \\\"\\\"\\\"%s\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36mget_variable\\u001b[0;34m(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\\u001b[0m\\n\\u001b[1;32m   1091\\u001b[0m           \\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1092\\u001b[0m           \\u001b[0muse_resource\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0muse_resource\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1093\\u001b[0;31m           constraint=constraint)\\n\\u001b[0m\\u001b[1;32m   1094\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1095\\u001b[0m   def _get_partitioned_variable(self,\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36mget_variable\\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\\u001b[0m\\n\\u001b[1;32m    437\\u001b[0m           \\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    438\\u001b[0m           \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0muse_resource\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0muse_resource\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 439\\u001b[0;31m           constraint=constraint)\\n\\u001b[0m\\u001b[1;32m    440\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    441\\u001b[0m   def _get_partitioned_variable(\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36m_true_getter\\u001b[0;34m(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint)\\u001b[0m\\n\\u001b[1;32m    406\\u001b[0m           \\u001b[0mtrainable\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mtrainable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcollections\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcollections\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    407\\u001b[0m           \\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 408\\u001b[0;31m           use_resource=use_resource, constraint=constraint)\\n\\u001b[0m\\u001b[1;32m    409\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    410\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0mcustom_getter\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36m_get_single_variable\\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint)\\u001b[0m\\n\\u001b[1;32m    756\\u001b[0m         raise ValueError(\\\"Trying to share variable %s, but specified dtype %s\\\"\\n\\u001b[1;32m    757\\u001b[0m                          \\\" and found dtype %s.\\\" % (name, dtype_str,\\n\\u001b[0;32m--> 758\\u001b[0;31m                                                    found_type_str))\\n\\u001b[0m\\u001b[1;32m    759\\u001b[0m       \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfound_var\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    760\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mValueError\\u001b[0m: Trying to share variable e/e, but specified dtype float32 and found dtype int32_ref.\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.variable_scope(reuse=tf.AUTO_REUSE, name_or_scope=\\\"e\\\"):\\n\",\n    \"    e = tf.get_variable(\\\"e\\\", initializer=tf.constant([2,2,2]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Variable 'e/e:0' shape=(3,) dtype=int32_ref>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"e\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Assigning Value to Variables\\n\",\n    \"\\n\",\n    \"So far variables are not much different that any constant tensor. Variables get interesting once you can change their values. To do that you can use `tf.assign()` function or the `assign()` method of a variable. These are operation and should be run using a your session for them to perform their assignment.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([2, 3, 4], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sess.run(d.assign([2,3,4]))\\n\",\n    \"d.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([4, 5, 6], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sess.run(tf.assign_add(d, [2,2,2]))\\n\",\n    \"d.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 2, 3], dtype=int32)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sess.run(tf.assign_sub(d, [3,3,3]))\\n\",\n    \"d.eval()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Variable Scrope\\n\",\n    \"\\n\",\n    \"You can group variables in a few way in TensorFlow and one these methods is Variable Scope\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Variable 'a:0' shape=(3,) dtype=float32_ref>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"with tf.name_scope(\\\"dense1\\\"):\\n\",\n    \"    a = tf.get_variable(\\\"a\\\", (3,))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Variable 'dense1_2/b:0' shape=(2, 2) dtype=float32_ref>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"with tf.name_scope(\\\"dense1\\\"):\\n\",\n    \"    b = tf.Variable(tf.ones((2,2)), name=\\\"b\\\")\\n\",\n    \"b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'dense1_3/add:0' shape=(2, 2) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"with tf.name_scope(\\\"dense1\\\"):\\n\",\n    \"    c = tf.Variable(tf.ones((2,2)), name=\\\"c\\\")\\n\",\n    \"    w = c+1\\n\",\n    \"w\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"ValueError\",\n     \"evalue\": \"Variable dense1/d already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\\n\\n  File \\\"<ipython-input-7-53e30bd6b3ba>\\\", line 2, in <module>\\n    d = tf.get_variable(\\\"d\\\", (3,))\\n  File \\\"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\\\", line 2963, in run_code\\n    exec(code_obj, self.user_global_ns, self.user_ns)\\n  File \\\"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\\\", line 2903, in run_ast_nodes\\n    if self.run_code(code, result):\\n\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mValueError\\u001b[0m                                Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-10-53e30bd6b3ba>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m      1\\u001b[0m \\u001b[0;32mwith\\u001b[0m \\u001b[0mtf\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvariable_scope\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"dense1\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m     \\u001b[0md\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtf\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget_variable\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"d\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      3\\u001b[0m     \\u001b[0me\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0md\\u001b[0m \\u001b[0;34m+\\u001b[0m \\u001b[0mc\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36mget_variable\\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\\u001b[0m\\n\\u001b[1;32m   1295\\u001b[0m       \\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1296\\u001b[0m       \\u001b[0muse_resource\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0muse_resource\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1297\\u001b[0;31m       constraint=constraint)\\n\\u001b[0m\\u001b[1;32m   1298\\u001b[0m get_variable_or_local_docstring = (\\n\\u001b[1;32m   1299\\u001b[0m     \\\"\\\"\\\"%s\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36mget_variable\\u001b[0;34m(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\\u001b[0m\\n\\u001b[1;32m   1091\\u001b[0m           \\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1092\\u001b[0m           \\u001b[0muse_resource\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0muse_resource\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcustom_getter\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1093\\u001b[0;31m           constraint=constraint)\\n\\u001b[0m\\u001b[1;32m   1094\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m   1095\\u001b[0m   def _get_partitioned_variable(self,\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36mget_variable\\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\\u001b[0m\\n\\u001b[1;32m    437\\u001b[0m           \\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpartitioner\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    438\\u001b[0m           \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0muse_resource\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0muse_resource\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 439\\u001b[0;31m           constraint=constraint)\\n\\u001b[0m\\u001b[1;32m    440\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    441\\u001b[0m   def _get_partitioned_variable(\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36m_true_getter\\u001b[0;34m(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint)\\u001b[0m\\n\\u001b[1;32m    406\\u001b[0m           \\u001b[0mtrainable\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mtrainable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcollections\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcollections\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    407\\u001b[0m           \\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcaching_device\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mvalidate_shape\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 408\\u001b[0;31m           use_resource=use_resource, constraint=constraint)\\n\\u001b[0m\\u001b[1;32m    409\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    410\\u001b[0m     \\u001b[0;32mif\\u001b[0m \\u001b[0mcustom_getter\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\\u001b[0m in \\u001b[0;36m_get_single_variable\\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint)\\u001b[0m\\n\\u001b[1;32m    745\\u001b[0m                          \\u001b[0;34m\\\"reuse=tf.AUTO_REUSE in VarScope? \\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    746\\u001b[0m                          \\\"Originally defined at:\\\\n\\\\n%s\\\" % (\\n\\u001b[0;32m--> 747\\u001b[0;31m                              name, \\\"\\\".join(traceback.format_list(tb))))\\n\\u001b[0m\\u001b[1;32m    748\\u001b[0m       \\u001b[0mfound_var\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_vars\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m    749\\u001b[0m       \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mshape\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mis_compatible_with\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfound_var\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget_shape\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n      \"\\u001b[0;31mValueError\\u001b[0m: Variable dense1/d already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\\n\\n  File \\\"<ipython-input-7-53e30bd6b3ba>\\\", line 2, in <module>\\n    d = tf.get_variable(\\\"d\\\", (3,))\\n  File \\\"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\\\", line 2963, in run_code\\n    exec(code_obj, self.user_global_ns, self.user_ns)\\n  File \\\"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\\\", line 2903, in run_ast_nodes\\n    if self.run_code(code, result):\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with tf.variable_scope(\\\"dense1\\\"):\\n\",\n    \"    d = tf.get_variable(\\\"d\\\", (3,))\\n\",\n    \"    e = d + c\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tf.Tensor 'dense1_10/add:0' shape=(3,) dtype=float32>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"with tf.variable_scope(\\\"dense1\\\", ):\\n\",\n    \"    f = tf.get_variable(\\\"f\\\", (3,))\\n\",\n    \"    g = tf.get_variable(\\\"g\\\", (3,))\\n\",\n    \"    h = f + g\\n\",\n    \"h\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# The Match Behind It\\n\",\n    \"\\n\",\n    \"Since we are talking about variables we cannot escape the fact that we will use them in the next tutorial for creating a predective model. This means we will have to get a head start start with some basic concepts about calculus. Calculus is branch of math that studies change. It can study the change of a variable as it relates to another variable. So basically it studies the realtionship between two or more variables. There are two main studies in calculus:\\n\",\n    \"\\n\",\n    \"- Diffrentiation\\n\",\n    \"- Integration\\n\",\n    \"\\n\",\n    \"## Diffrentiation\\n\",\n    \"\\n\",\n    \"We will focus for now on diffrentiation because the we will use that to train neural network with an algorithm called \\\"Back Probagation\\\". Diffrentiation is the study of the rate of change or the slope.\\n\",\n    \"\\n\",\n    \"We will use numpy and matplotlib for illustration so let's import them\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Slope\\n\",\n    \"\\n\",\n    \"Slope is the mesure of change of a variable as another variables changes. In here we will see the change in $y$ as $x$ changes. The mathematical way of saying that is:\\n\",\n    \"\\n\",\n    \"$$\\\\frac{dy}{dx} = \\\\frac{\\\\Delta y}{\\\\Delta x}$$\\n\",\n    \"\\n\",\n    \"$\\\\Delta$ is the capital letter delta and it means the change of. So the change of $y$ as $x$ changes.\\n\",\n    \"\\n\",\n    \"- Slope is __positive__ if numbers are __increasing__.\\n\",\n    \"- Slope is __nagative__ if numbers are __decreasing__.\\n\",\n    \"- Slope is __zero__ if numbers are __not changing__.\\n\",\n    \"\\n\",\n    \"Let's look at three lines to show how slope works.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 71,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(5)\\n\",\n    \"fig = plt.figure(figsize=(12,3))\\n\",\n    \"axarr = fig.subplots(1,3)\\n\",\n    \"\\n\",\n    \"axarr[0].plot(x, x*2)\\n\",\n    \"axarr[0].set_title(\\\"Positive Slope\\\")\\n\",\n    \"axarr[1].plot(x, x*-2)\\n\",\n    \"axarr[1].set_title(\\\"Negative Slope\\\")\\n\",\n    \"axarr[2].plot(x, x*0 + 2)\\n\",\n    \"axarr[2].set_title(\\\"Zero Slope\\\")\\n\",\n    \"plt.show();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Linear Diffrentiation\\n\",\n    \"\\n\",\n    \"Consider this situation where you are driving at a constant speed of 10 $km/h$.\\n\",\n    \"\\n\",\n    \"We can represent this in a mathematical way with a simple function that looks like this:\\n\",\n    \"\\n\",\n    \"$Distance = 10 \\\\times Time$\\n\",\n    \"\\n\",\n    \"Or to make it more abstract we can call $Distance$ $y$ because it will be represented on the Y axis and call $Time$ $x$ because it will be represented on the X axis. So our function for this relationship will look like this:\\n\",\n    \"\\n\",\n    \"$y = 10x$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"speed = 10\\n\",\n    \"time = np.arange(0,9)\\n\",\n    \"distance = speed * time\\n\",\n    \"\\n\",\n    \"plt.xlabel(\\\"Time $Hours$\\\")\\n\",\n    \"plt.ylabel(\\\"Distance $km$\\\")\\n\",\n    \"plt.grid()\\n\",\n    \"plt.plot(time, distance);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"After 1 hour you have travelled 10 $km$ and after 4 hours would have travelled 40 $km$. This realtionship is called linear because it can be represented by a straight line. With linear relationships we can measure the slope using any two points $(x_1,y_1)$ and $(x_2,y_2)$. If we get these two points are 1 hour and 4 hours we get these two points $(1,10)$ and $(4,40)$. To measure the slope (represented by the letter $m$) now we use this function:\\n\",\n    \"\\n\",\n    \"$$m = \\\\frac{y_2 - y_1}{x_2 - x_1}$$\\n\",\n    \"\\n\",\n    \"If we substitute our points we can measure the slope.\\n\",\n    \"\\n\",\n    \"$$m = \\\\frac{40 - 10}{4 - 1} = \\\\frac{30}{3} = 10$$\\n\",\n    \"\\n\",\n    \"There is nothing interesting about this finding! We already knew the speed was 10 $km/h$. This is a linear function and most of calculus is more interested in non-linear functions. So let's see how does that work.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Polynomial Diffrentiation\\n\",\n    \"\\n\",\n    \"Consider a situation where you are not travelling at constant speed but perhapse your speed in increasing over time. So in the begenning you are starting at a low speed and over time your speed increases. We call this acceleration and it is measured in $m/s^2$ but for a unit that we can relate to I'll use $km/h^2$. Let's illustrate this and it would make more sense.\\n\",\n    \"\\n\",\n    \"We can represent this in a mathematical way with a simple function that looks like this:\\n\",\n    \"\\n\",\n    \"$Distance = Acceleration * Time^2$\\n\",\n    \"\\n\",\n    \"Again to abstract this function we can write it like this:\\n\",\n    \"\\n\",\n    \"$y = ax^2$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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/uNkREjthnO8qZ+LaGMI0nUT/37u5lwOLg9WytJxKRRitr7U4mLiiiT+c2GsI0juh8TkTiwidf5HLN0/Po0Nz4x3WjNIRpHFG5FpHQzVy0iTue/5QeHVtx0/HldG7TPOxIUkm1zygs4goz+0Uw3cPM9GCciByRp2av4bbpCxiS2o4ZN46hY0s1dMSbaP5G/gaMAS4NpvcAf631RCLSKFRUOP/3xjLun7mUbw5I4dnr1dwUr6Jpehrl7sPNbAGAu+dpmFIRqYmSsgp+OuNT/rlwE1eO7sl95w3UcxJxLJpCURr07eTwZRceFTFJJSIN1p6iUm58NpuPVuXykzP7cfPYY9QtR5yLplA8CrwCHGVmDxLpCvx/Y5JKRBqkbbuLGP/0PFZs3cMjFw3VeBL1RDTdjD9nZtlExo0wIl14LItZMhFpUL7YXsBVk+eSV1jC5PEjGNvvqLAjSTVFc9fTFGCLu//V3f8CbDGzp2IXTUQaiux1eXzvsY8pLisnfcJoFYl6JpqmpyHuvmv/RHAxe1gMMolIA/L20q3cOm0+Xdq1YOq1I+nZsXXYkSRK0dwe28TMOuyfMLNk9MCeiFRhWuZ6fvCPLPp1acNLN52kIlFPRXOg/z3wiZntH/r0IuDB2o8kIvWdu/PHd1by6LsrGduvM3+9bDit1W9TvRXNxeypZpYFnBbMusDdl8YmlojUV2XlFfz8lSU8n7WBi0ek8uB3B9OsqZ62rs+iKvFBYVBxEJGDKiwp49ZpC3jv823cdtqx3HHGcXpGogGodqEws+bA94gMXPTleu5+f+3HEpH6JregmGunZLE4ZxcPfGcQV4zuGXYkqSXRnFG8CuQD2UBxbOKISH20PreQ8U/PZdOufTx+RRrfHNgl7EhSi6IpFKnuflZt7tzM2gOTgEFEuga5FlgOPE/kzGUtcLG759XmfkWk9izOyeeaZ+ZSVuFMu2EUaT2Tw44ktSyaK0wfm9ngWt7/ROBf7t6fyNCqy4C7gXfdvS/wbjAtInHogxXbueTJT2ie0JQZN45RkWigojmj+BpwtZmtIdL0ZIC7+5Ca7NjM2gGnAlcT2VAJUGJm5wNjg8WmAO8Dd9VkHyISOy9l53DXS4vom9KGZ645kZS2LcKOJDFi7l69Bc0OemXK3dfVaMdmJwBPErmLaiiRax+3AxvdvX2wjAF5+6cPWH8CMAEgJSUlLT09vSYxKCgoICkpqUbrxlK85oL4zaZc0alpLnfn9TWlzFhRyvHJTbhtWAtaNau9O5sa2ucVa0eSa9y4cdnuPuKwC7p7tV9AB2AkkTOBU4FTo1n/gG2NAMqIjHMBkWaoXwO7Dlgu73DbSktL85rKyMio8bqxFK+53OM3m3JFpya5ysor/Bf/XOw975rpt02b70WlZXGRqy40xFxAllfjeB3N7bHXE/nGnwosBEYDn/CfB/CilQPkuHtmMD2DyPWIrWbW1d03m1lXYFsNty8itaiotJwfPb+QN5ds4YZTenPP2cfTRIMNNQrRXMy+HTgRWOfu44BhwK6qVzk0d98CbDCzfsGs04k0Q70GjA/mjSdyW66IhCi/sJSrJs/lzSVbuPec4/n5OQNUJBqRaC5mF7l7kZlhZs3d/fNKB/maug14LhhSdTVwDZHi9YKZXQesAy4+wn2IyBHYtGsf45+ay7rcQh69dBjnDT067EhSx6IpFDnBcw//BN42szwiB/Iac/eFRK5VHOj0I9muiNSOz7fs5uqn5rG3uIxnrj2Rk47pFHYkCUE0nQJ+N3h7n5llAO2AN2OSSkRCN2d1LjdMzaJls6a8cOMYju/aNuxIEpJoRrh7eP97d/+3u78GPBCTVCISqtcXbeaqyXNJaduCl28+SUWikYvmYvYZB5l3dm0FEZH48PRHa7h1+nyGpLZjxo1jSO3QKuxIErLDNj2Z2U3AzcAxZrZo/2ygDfBRDLOJSB2qqHAe/tfnPPHBar45IIVHLx1Gi2ZNw44lcaA61yimEbkW8Rv+u9+lPe6+MyapRKROlZRV8NMZn/LPhZu4YnQPfnXeIJrq9lcJHLZQuHs+kG9mLwM73X2Pmd0LDDezX7v7gpinFJGY2VNUyk3Pzmf2qh385Mx+3Dz2GA02JP8lmmsU/xsUia8B3wAmA4/HJpaI1IVtu4v4/hNz+GR1Lr+7cAi3jDtWRUK+IppCUR78PAd40t1fBxJrP5KI1IXNBRVc8NjHrM3dy6TxI7hoRPewI0mciuaBu41m9gSRu58eDoZG1YjpIvXQ/PV5PJi5j+aJiUy/YTRDu3+lg2aRL0VzoL8YmAWc6e67gGTgJzFJJSIx4e5Mn7ueS5+cQ6tmxks3naQiIYcVzZPZhcDLlaY3A5tjEUpEal/+vlJ+9vJiXl+8ma8d24mLuxfSq1PrsGNJPXDYMwozmx383GNmu4Of+1+7Yx9RRI5U9ro8vjXxQ2Z9toW7zurP1GtH0ra5LlpL9VTn9tivBT/bmNlRwXuNESFSD5RXOI//+wv+8PYKurZrwQs3jmF4jw5hx5J6plpNT2Z2H3AL0DSYLgf+7O73xy6aiByJbbuL+NELC/loVS7nDunK/10wmLYtmoUdS+qh6nThcQdwMjDS3dcE8/oAj5nZj9z9jzHOKCJRyli+jTtf+JS9JWU8/L3BXDyiu56PkBqrzhnFlcAZ7r5j/wx3X21mVwBvASoUInGipKyC3/7rcybNXkP/Lm14/rLRHHtUm7BjST1XnULRrHKR2M/dt5uZzmNF4sTaHXu5bfoCFm/M56oxPfnZt45Xp35SK6pTKEpq+DsRqSOvLMjh3leWkNC0CU9cmcaZA7uEHUkakOoUiqGHuA3WgBa1nEdEorC3uIz/fXUJL8/fyMheyfzpkhM4un3LsGNJA1Od22N17ioSh5ZszOe26QtYl7uXH57elx+ediwJTdWrjtS+aPp6EpE44O489dFaHnpzGR1bN2faDaMZ3adj2LGkAQu9UJhZUyAL2Oju55pZbyAd6AhkA1e6u66FiAC5BcX8ZMYi3vt8G984/ih+d+FQOrRWJ84SW/Fwnno7sKzS9MPAH939WCAPuC6UVCJx5uMvdnD2xA+ZvXIH9317AH+/aoSKhNSJUAuFmaUSGd9iUjBtwGnAjGCRKcB3wkknEh/Kyit4ZNZyLp+USVKLBF655SSuPrm3HqCTOhN209OfgJ8C+58I6gjscveyYDoH6BZGMJF4kJNXyO3pC8lel8dFaan86vyBtEoM+7+tNDbm7uHs2Oxc4FvufrOZjQXuBK4G5gTNTphZd+BNdx90kPUnABMAUlJS0tLT02uUo6CggKSkpBqtG0vxmgviN1tDyzVvSxlPLymmwuHqgc0ZfXTtFoiG9nnFWkPMNW7cuGx3H3HYBd09lBfwGyJnDGuBLUAh8BywA0gIlhkDzDrcttLS0rymMjIyarxuLMVrLvf4zdZQcu0rKfN7Xl7kPe+a6ef9+UNfu6MgLnLVFeWKzpHkArK8Gsfr0K5RuPs97p7q7r2AS4D33P1yIAO4MFhsPPBqSBFF6tyKrXs47y+zmZa5nh+c2ocXbzyJnh01uJCEKx4bO+8C0s3sAWABMDnkPCIx5+5Mm7ue+//fUtq0SGDKtSP5+nGdw44lAsRJoXD394H3g/ergZFh5hGpS/n7Srnn5UW8sXgLp/TtxO8vHspRbdQ7jsSPuCgUIo1V9rqd/HD6QrbuLuLus/sz4ZQ+NGmi214lvqhQiITgwCFKX7xxDMM0RKnEKRUKkTq2dXcRP3p+IR9/oSFKpX5QoRCpQ+99vpU7X1xEoYYolXpEhUKkDhSXlfPbfy1ncjBE6V80RKnUIyoUIjG2ZW8F33vsY5Zs3K0hSqVeUqEQiRF355UFG7nv4300TyzTEKVSb6lQiMTAZ5vyeWDmMj5ZnctxHZrwzA9O0RClUm+pUIjUom17ivjDWyt4PmsD7Vs249fnD+TofWtUJKReU6EQqQVFpeVMnr2Gv2WsoqS8gutO7s1tp/elXctmvP/+2rDjiRwRFQqRI+DuvLF4C795cxk5efv45oAU7vnW8fTupI78pOFQoRCpoUU5u/j1zKXMW5tH/y5tmHb9KE46tlPYsURqnQqFSJS25Bfx21mf8/L8jXRKSuShCwZz0YjuNFUfTdJAqVCIVNO+knKe/GA1j//7C8ornJvGHsPNY4+hjbrfkAZOhULkMCoqnNc+3cTD//qczflFnDO4K3ef3Z/uya3CjiZSJ1QoRKqQvS6P+2cu5dMNuxjcrR0TLxnGyN7JYccSqVMqFCIHkZNXyMP/Ws7/+3QTKW2b88hFQ7lgWDeNFSGNkgqFSCV7i8t47P0v+PuHqwH44el9ufHrfWiVqP8q0njpX78IkesQM+bn8LtZy9m+p5jzTzian57Vn256olpEhUIkc3Uuv359KUs27mZYj/Y8cWUawzXanMiXVCik0VqfW8hv3lzGm0u2cHS7Fky85ATOG3q0BhISOYAKhTQ6u4tK+et7q3j6o7UkNDV+fMZxXH9KH1omaowIkYMJrVCYWXdgKpACOPCku080s2TgeaAXsBa42N3zwsopDUd5hZM+bz1/eGsFuXtLuDAtlZ+c2Y+Uti3CjiYS18I8oygDfuzu882sDZBtZm8DVwPvuvtDZnY3cDdwV4g5pQGYvXIHD7y+lM+37GFkr2SeuWYAg1PbhR1LpF4IrVC4+2Zgc/B+j5ktA7oB5wNjg8WmAO+jQiE1tHp7Af/3xjLeWbaN7skteezy4Zw1qIuuQ4hEIS6uUZhZL2AYkAmkBEUEYAuRpimRqOQXljLx3ZVM/WQtLZo15e6z+3P1Sb00VrVIDZi7hxvALAn4N/Cgu79sZrvcvX2l3+e5+1fuVTSzCcAEgJSUlLT09PQa7b+goICkpKSahY+heM0F8ZutoKCAFq1a8/6GMl5ZVUJhKXw9NYHv9k2kXfPwziDi+fNSrupriLnGjRuX7e4jDrugu4f2ApoBs4A7Ks1bDnQN3ncFlh9uO2lpaV5TGRkZNV43luI1l3v8Zpv4wtt+2iMZ3vOumX7pk5/4Zxvzw47k7vH7eSlXdBpiLiDLq3GsDvOuJwMmA8vc/Q+VfvUaMB54KPj5agjxpJ6oqHA+/iKXJz74gg9XFtO7UwJ/v2oE3zj+KF2HEKklYV6jOBm4ElhsZguDeT8jUiBeMLPrgHXAxSHlkzi2c28JM7I3MC1zPWtzC+nQqhmX9k/kV1ecSmJCk7DjiTQoYd71NBs41Fe+0+syi9QP7s68tXlMy1zHG4u3UFJewcheyfzojOM4c2AX5nz0oYqESAzExV1PIlXJ31fKK/NzeC5zPSu3FdCmRQKXjerBZaN6cFxKm7DjiTR4KhQSl9ydRTn5PJe5jtc+3URRaQVDU9vx2+8N4dyhXdXtt0gd0v82iSt7i8t47dNNPJe5jiUbd9MqsSnfHdaNy0b21JPUIiFRoZC4sGzzbqZlrueVBRspKC6jf5c2/Pr8gZw/rBttWzQLO55Io6ZCIaEpKi3njcWbeS5zPdnr8khMaMK5Q7py+ageDO/RQbe3isQJFQqpc19sL2Ba5npmZOeQv6+UPp1ac+85x/O94al0aJ0YdjwROYAKhdSJkrIK3lq6hefmrOeT1bkkNDHOHNSFy0f1YEyfjjp7EIljKhQSUxt2FjJ97npeyNrAjoISUju05Cdn9uOiEakc1UbjQIjUByoUUuvKyivIWL6d5zLX8e8V2zHgtP4pXD66B6f27UzTJjp7EKlPVCik1mzJL+L5eRtIn7eezflFpLRtzm2n9eWSE7tzdPuWYccTkRpSoZAjUlHhzF61g+cy1/HOsm2UVzinHteZ+84byOn9jyKhqbrUEKnvVCikRnYUFPNiVg7T565n/c5COrZO5IZT+nDpyO707Ng67HgiUotUKKTaysormLt2J48tLGL+2+9SWu6M6p3MnWf248yBKTRP0OhxIg2RCoUcUnFZOYty8slcnUvmmp1kr8ujsKScVglwxeheXD6qB8cepU75RBo6FQr5UmFJGQvW7yJzzU4yV+eyYMMuSsoqAOjfpQ0XpqUyqndHErZ/zpmnDww5rYjUFRWKRmx3USnZa/PIXLOTuWtyWZSTT1mF08RgULd2XDW6JyNONYUmAAAKIklEQVR7JzOydzLtW/3nien3318eYmoRqWsqFI1I3t4S5q7dydw1O8lck8vSTbupcGjW1BiS2p4Jp/ZhZO9k0np2oI064hORgApFA7Ztd1FwthB5Ld+6B4DmCU0Y3qMDt53Wl1F9khnWvQMtE3UhWkQOToWiAcnJK4ycLazeydy1O1mzYy8ArRObMqJXMuedcDSjeiczOLWd7lASkWpToain3J01O/Z+ebaQuWYnG3ftA6Bdy2ac2CuZy0f1YGTvZAZ0basH30SkxlQo6omKCmfltgIy1+R+2Zy0fU8xAJ2SEhnVuyMTTu3DqD7JHHdUG5qoPyURqSUqFHGqwp3FOflfFoZ5a3eyq7AUgK7tWnDyMR0Z1acjI3sn06dTa3XTLSIxE7eFwszOAiYCTYFJ7v5QyJGO2L6ScnYWlrCzoITcvcXs3FvyX6/c4Gfe3hI25hVSPGs2AL06tuKbA1IY2bsjo3onk9qhpQqDiNSZuCwUZtYU+CtwBpADzDOz19x9abjJ/sPd2V1UFhzki8ktKCGvMDjYFwQH/8KgAATT+0rLD7qthCZGcuvEL18Djm7LMa2L+fZJgxnZK5ku7TRug4iEJy4LBTASWOXuqwHMLB04H4hZoSgrryCvsPSAb/nF5Abf8HMP+Oaft7eEsgo/6LZaNmtKcutEOiZFDvzHHpVEcqtEkpMS6dg6keTWzUlu3Sz4mUjbFglfOUN4//33GTv06Fj9cUVEqi1eC0U3YEOl6RxgVG3vJGP5Nu75sJCiD94if18pfvDjPu1aNgsO8Il0T27FCd3bf/ntv2NSIh1aJdKxdXOSkxJJbpWoZxJEpEExP9TRMURmdiFwlrtfH0xfCYxy91srLTMBmACQkpKSlp6eHvV+vthVzuur9tGhVTPaJBpJzYy2iUabSq/WzSJNQ3WtoKCApKSkOt9vdcRrNuWKjnJFpyHmGjduXLa7jzjsgu4edy9gDDCr0vQ9wD2HWj4tLc1rKiMjo8brxlK85nKP32zKFR3lik5DzAVkeTWOyfH6FNY8oK+Z9TazROAS4LWQM4mINEpxeY3C3cvM7FZgFpHbY59y989CjiUi0ijFZaEAcPc3gDfCziEi0tjFa9OTiIjECRUKERGpkgqFiIhUSYVCRESqpEIhIiJVissns6NlZtuBdTVcvROwoxbj1JZ4zQXxm025oqNc0WmIuXq6e+fDLdQgCsWRMLMsr84j7HUsXnNB/GZTrugoV3Qacy41PYmISJVUKEREpEoqFPBk2AEOIV5zQfxmU67oKFd0Gm2uRn+NQkREqqYzChERqVKjLhRmdpaZLTezVWZ2d9h5AMzsKTPbZmZLws5SmZl1N7MMM1tqZp+Z2e1hZwIwsxZmNtfMPg1y/SrsTJWZWVMzW2BmM8POsp+ZrTWzxWa20Myyws6zn5m1N7MZZva5mS0zszFxkKlf8Dntf+02s/8JOxeAmf0o+De/xMymm1mLmO2rsTY9mVlTYAVwBpGhVucBl7p7zMblrmauU4ECYKq7DwozS2Vm1hXo6u7zzawNkA18Jw4+LwNau3uBmTUDZgO3u/ucMHPtZ2Z3ACOAtu5+bth5IFIogBHuHlfPBJjZFOBDd58UjEPTyt13hZ1rv+CYsZHIaJs1fW6rtrJ0I/JvfYC77zOzF4A33P2ZWOyvMZ9RjARWuftqdy8B0oHzQ86Eu38A7Aw7x4HcfbO7zw/e7wGWERnbPFTBQF0FwWSz4BUX337MLBU4B5gUdpZ4Z2btgFOByQDuXhJPRSJwOvBF2EWikgSgpZklAK2ATbHaUWMuFN2ADZWmc4iDA199YGa9gGFAZrhJIoLmnYXANuBtd4+LXMCfgJ8CFWEHOYADb5lZdjD2fDzoDWwHng6a6iaZWeuwQx3gEmB62CEA3H0j8AiwHtgM5Lv7W7HaX2MuFFIDZpYEvAT8j7vvDjsPgLuXu/sJQCow0sxCb7Izs3OBbe6eHXaWg/iauw8HzgZuCZo7w5YADAcec/dhwF4gLq4bAgRNYecBL4adBcDMOhBpAekNHA20NrMrYrW/xlwoNgLdK02nBvPkEIJrAC8Bz7n7y2HnOVDQVJEBnBV2FuBk4LzgekA6cJqZPRtupIjg2yjuvg14hUgzbNhygJxKZ4MziBSOeHE2MN/dt4YdJPANYI27b3f3UuBl4KRY7awxF4p5QF8z6x18W7gEeC3kTHEruGg8GVjm7n8IO89+ZtbZzNoH71sSuTnh83BTgbvf4+6p7t6LyL+t99w9Zt/4qsvMWgc3IxA07XwTCP0OO3ffAmwws37BrNOBUG+UOMClxEmzU2A9MNrMWgX/N08nct0wJuJ2zOxYc/cyM7sVmAU0BZ5y989CjoWZTQfGAp3MLAf4pbtPDjcVEPmGfCWwOLgeAPCzYGzzMHUFpgR3pDQBXnD3uLkVNQ6lAK9Eji0kANPc/V/hRvrSbcBzwRe31cA1IecBviyoZwA/CDvLfu6eaWYzgPlAGbCAGD6h3WhvjxURkeppzE1PIiJSDSoUIiJSJRUKERGpkgqFiIhUSYVCRESqpEIhIiJVUqEQEZEqqVBIo2FmHSuNK7DFzDZWmk40s49jtN8fmNljB8xbYmbHx2J/IrWt0T6ZLY2Pu+cCJwCY2X1Agbs/UmmRWPWVM5jIE7QE+24B9CIyHsoRMbOm7l5+pNsRqYrOKEQCZlZgZr2CEdaeMbMVZvacmX3DzD4ys5VmNrLS8lcEo+stNLMngm5EDmYIlQoFkcKxwt3Lzay/mb0XbOMdM+sUbPsTM+sdvO9mZl/2QmtmLwb7mwPcY2bjgy7DF5nZ7Fr/YKTRU6EQ+apjgd8D/YPXZcDXgDuBnwEEzUbfB04OujgvBy4/xPYGAi8HQ5CuBd4EFplZcyK98d4RbONt4Edm1gToCawN1h8CLKq0vcHAVncfDUwE7gLGuPsQIC5G0ZOGRU1PIl+1xt0XA5jZZ8C77u5mtphIkxFEeutMA+YFHey1JDJw0n8xs+7AdnfvX2neX4A1wHeA2e6+v5PFpUTGPDgmyLC/I7YhwP48LYBk4P7gd+XBvn9vZlPcPW7GwJaGQ2cUIl9VXOl9RaXpCv7z5cqAKe5+QvDq5+73HWRbg4EDeyUeQOQMYQBBAai07NLgZ+X5IypNDwQy3b0MwN0LgUHAR8CTZnZzdf+QItWlQiFSM+8CF5rZUQBmlmxmPQ+y3BC+Oq7CQCIH/o1EigVm1odIN+5TiZwx7ArmH09k3O39TU+DK73HzPq6+153TwdmAi1q5U8nUomankRqwN2Xmtm9RMaebgKUArcA6w5YdDCRAzgQKShEuvffYmb/AL4VNGntA65191wzmwXcGjRbLQdyK42sNhiYW2n7PzezMUSGDv0MuKHW/7DS6Gk8ChERqZKankREpEoqFCIiUiUVChERqZIKhYiIVEmFQkREqqRCISIiVVKhEBGRKqlQiIhIlf4/ZKjzuo+qtrIAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"acceleration = 2\\n\",\n    \"time = np.arange(0,9)\\n\",\n    \"distance = acceleration * time**2\\n\",\n    \"\\n\",\n    \"plt.xlabel(\\\"Time $Hours$\\\")\\n\",\n    \"plt.ylabel(\\\"Distance $km$\\\")\\n\",\n    \"plt.grid()\\n\",\n    \"plt.plot(time, distance);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, let's look at the distance over time.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([  0,   2,   8,  18,  32,  50,  72,  98, 128])\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"distance\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can notice that we travelled 2 $km$ in the first hour and 8 $km$ after two hours making the distance we travelled for the second hour 6 $km$. This means our speed is changing over time. To measure the slope for this, we will need to specify at what time do we want the speed because it is changing over time.\\n\",\n    \"\\n\",\n    \"To get the slope we derive another function that measure the slope of this function. This function is called __derevative__ and has the notation $f'$ or $\\\\frac{dy}{dx}$\\n\",\n    \"\\n\",\n    \"There is no standard mthematical way to come up with the the derevative function. It depends of the function type. Let's look a polipomial function and see how to get the derevative of that function:\\n\",\n    \"\\n\",\n    \"$f(x) = ax^n$\\n\",\n    \"\\n\",\n    \"to detive a polinomial function we use this rule:\\n\",\n    \"\\n\",\n    \"$f'(x)=n \\\\times ax^{n-1}$\\n\",\n    \"\\n\",\n    \"to apply this to our function from before:\\n\",\n    \"\\n\",\n    \"$y = 2x^2$\\n\",\n    \"\\n\",\n    \"$y' = 2 \\\\times 2x^{2-1} = 4x$\\n\",\n    \"\\n\",\n    \"to to get the speed at any point in time, we can use the derevative function. So the speed after 3 hours is:\\n\",\n    \"\\n\",\n    \"$y' = 4 \\\\times 3 = 12$\\n\",\n    \"\\n\",\n    \"Now let's visualize both of these functions to see how does they ralate to each other.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 73,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"acceleration = 2\\n\",\n    \"time = np.arange(0,9)\\n\",\n    \"distance = acceleration * time**2\\n\",\n    \"speed = 4 * time\\n\",\n    \"\\n\",\n    \"plt.xlabel(\\\"Time $Hours$\\\")\\n\",\n    \"plt.ylabel(\\\"Distance $km$\\\")\\n\",\n    \"plt.grid()\\n\",\n    \"l1 = plt.plot(time, distance, label=\\\"Distance\\\")\\n\",\n    \"plt.twinx()\\n\",\n    \"plt.ylabel(\\\"Speed $km/h$\\\")\\n\",\n    \"l2 = plt.plot(time, speed, \\\"r\\\", label=\\\"Speed\\\")\\n\",\n    \"plt.legend(l1+l2, (\\\"Distance\\\", \\\"Speed\\\"));\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since you know the rule now, let's try to apply it to out first example\\n\",\n    \"\\n\",\n    \"$y = 10x = 10x^1$\\n\",\n    \"\\n\",\n    \"$y' = 1 \\\\times 10x^{1-1} = 10x^0 = 10 \\\\times 1 = 10$\\n\",\n    \"\\n\",\n    \"Anything to the power 0 is equal to 1. It might be weird, but that's just math!\\n\",\n    \"\\n\",\n    \"### Complex Ploynominal Functions\\n\",\n    \"\\n\",\n    \"The same rule applies for any polynominal function. Let's try some examples:\\n\",\n    \"\\n\",\n    \"$f(x) = 2x^2 + 4x + 10$\\n\",\n    \"\\n\",\n    \"$f'(x) = 2 \\\\times 2x^{2-1} + 1 \\\\times 4x{1-1} + 0 = 4x + 4$\\n\",\n    \"\\n\",\n    \"Notice that we can apply the rule to each part of the function seperatly. Any part of the functiona that's not muliplied by x will give you a derevative of 0.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Trigonometry\\n\",\n    \"\\n\",\n    \"Derevatives of trigonometric functions can be derived to some level straight forward out of a lookup table. This is an incomlete table of functions and their derevatives:\\n\",\n    \"\\n\",\n    \"| Function $f(x)$ | Deravative  $f'(x)$|\\n\",\n    \"| --------------- | ------------------ |\\n\",\n    \"| $sin(x)$        | $cos(x)$           |\\n\",\n    \"| $cos(x)$        | $-sin(x)$          |\\n\",\n    \"| $tan(x)$        | $sec^2(x)$         |\\n\",\n    \"| $cot(x)$        | $-csc^2(x)$        |\\n\",\n    \"| $sec(x)$        | $sec(x)tan(x)$     |\\n\",\n    \"| $csc(x)$        | $-csc(x)cost(x)$   |\\n\",\n    \"\\n\",\n    \"You don't have to memorize this. This is available everywhere!\\n\",\n    \"\\n\",\n    \"Just for fun let's visualize one of them as see if it makes sense.\\n\",\n    \"\\n\",\n    \"$f(x) = sin(x)$\\n\",\n    \"\\n\",\n    \"$f'(x) = cos(x)$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(0,10, 0.1)\\n\",\n    \"sin_x = np.sin(x)\\n\",\n    \"cos_x = np.cos(x)\\n\",\n    \"\\n\",\n    \"plt.xlabel(\\\"x\\\")\\n\",\n    \"plt.ylabel(\\\"y\\\")\\n\",\n    \"plt.grid()\\n\",\n    \"plt.plot(x, sin_x, label=\\\"sin(x)\\\")\\n\",\n    \"plt.plot(x, cos_x, \\\"r\\\", label=\\\"cos(x)\\\")\\n\",\n    \"plt.legend();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Next\\n\",\n    \"\\n\",\n    \"Did I get existed about calculus yet? well you will love the next part where we get into some more interesting derevativesa dn a more intuative understanding of why do we need that whole derevative in the first place.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\\n\",\n    \"\\n\",\n    \"Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/vlogs/4th dem debate.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Analyzing the Democratic Debate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import requests\\n\",\n    \"from lxml import html\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n    \"from sklearn.naive_bayes import MultinomialNB\\n\",\n    \"from pprint import pprint\\n\",\n    \"import re\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"url = \\\"https://www.washingtonpost.com/news/the-fix/wp/2016/01/17/the-4th-democratic-debate-transcript-annotated-who-said-what-and-what-it-meant/\\\"\\n\",\n    \"response = requests.get(url)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"doc = html.fromstring(response.text)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"para_list = doc.xpath(\\\"//article/p/text()\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"para_list = para_list[2:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[\\\"HOLT: We'll begin with 45 second opening statements from each candidate, \\\"\\n\",\n      \" 'starting with Secretary Clinton. ',\\n\",\n      \" 'CLINTON: Well, good evening. And I want to thank the Congressional Black '\\n\",\n      \" 'Caucus Institute and the people of Charleston for hosting us here on the '\\n\",\n      \" 'eve of Martin Luther King Day tomorrow. ']\\n\",\n      \"['HOLT: All right. Well thank you and thanks to all of you for being here tonight shedding light on some of the differences as Americans get ready to vote. ', \\\"I also want to thank the Congressional Black Caucus Institute and certainly my friend and colleague, Andrea Mitchell. This has been great. It's been a great spirited conversation and American people appreciate it. \\\"]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pprint(para_list[:2], compact=True)\\n\",\n    \"print(para_list[-2:])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>raw</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>HOLT: We'll begin with 45 second opening state...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>CLINTON: Well, good evening. And I want to tha...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>SANDERS: Thank you. As we honor the extraordin...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>SANDERS: And then, to make a bad situation wor...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>HOLT: Senator, thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>And Governor O'Malley, your opening statement,...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>HOLT: All right. And Governor, thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>HOLT: All right, to our first question, now. T...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>President Obama came to office determined to s...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>Senator Sanders.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>SANDERS: Well, that's what our campaign is abo...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton, same question, my fir...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>592</th>\\n\",\n       \"      <td>HOLT: Welcome back everybody. Finally, before ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>593</th>\\n\",\n       \"      <td>And, we'll start with Governor O'Malley.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>594</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>595</th>\\n\",\n       \"      <td>HOLT: Didn't see that coming, did you?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>596</th>\\n\",\n       \"      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>597</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>598</th>\\n\",\n       \"      <td>MITCHELL: ...too long (ph).</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>599</th>\\n\",\n       \"      <td>O'MALLEY: I believe there are many issues. I h...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>600</th>\\n\",\n       \"      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>601</th>\\n\",\n       \"      <td>O'MALLEY: There are so many issues that we hav...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>602</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>603</th>\\n\",\n       \"      <td>O'MALLEY: We haven't discussed the fact that i...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>604</th>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>605</th>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>606</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>607</th>\\n\",\n       \"      <td>O'MALLEY: Thanks a lot.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>608</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>609</th>\\n\",\n       \"      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>610</th>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>611</th>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>612</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>613</th>\\n\",\n       \"      <td>CLINTON: I want to be a president who takes ca...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>614</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>615</th>\\n\",\n       \"      <td>HOLT: Thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>616</th>\\n\",\n       \"      <td>Senator Sanders?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>617</th>\\n\",\n       \"      <td>SANDERS: Well, Secretary Clinton was right and...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>618</th>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>619</th>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>620</th>\\n\",\n       \"      <td>HOLT: All right. Well thank you and thanks to ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>621</th>\\n\",\n       \"      <td>I also want to thank the Congressional Black C...</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>622 rows × 1 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                   raw\\n\",\n       \"0    HOLT: We'll begin with 45 second opening state...\\n\",\n       \"1    CLINTON: Well, good evening. And I want to tha...\\n\",\n       \"2    You know, I remember well when my youth minist...\\n\",\n       \"3    And that is our fight still. We have to get th...\\n\",\n       \"4    I understand that this is the hardest job in t...\\n\",\n       \"5                                           (APPLAUSE)\\n\",\n       \"6    HOLT: Thank you. Senator Sanders, your opening...\\n\",\n       \"7    SANDERS: Thank you. As we honor the extraordin...\\n\",\n       \"8    SANDERS: And then, to make a bad situation wor...\\n\",\n       \"9    This campaign is about a political revolution ...\\n\",\n       \"10                           HOLT: Senator, thank you.\\n\",\n       \"11                                          (APPLAUSE)\\n\",\n       \"12   And Governor O'Malley, your opening statement,...\\n\",\n       \"13   O'MALLEY: Thank you. My name is Martin O'Malle...\\n\",\n       \"14   And I want to thank the people of South Caroli...\\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...\\n\",\n       \"16   Eight years ago, you brought forward a new lea...\\n\",\n       \"17   But in order to make good on the promise of eq...\\n\",\n       \"18   We need new leadership. We need to come togeth...\\n\",\n       \"19   That's why I'm running for president. I need y...\\n\",\n       \"20                                         Thank you. \\n\",\n       \"21           HOLT: All right. And Governor, thank you.\\n\",\n       \"22                                          (APPLAUSE)\\n\",\n       \"23   HOLT: All right, to our first question, now. T...\\n\",\n       \"24   President Obama came to office determined to s...\\n\",\n       \"25                                   Senator Sanders. \\n\",\n       \"26   SANDERS: Well, that's what our campaign is abo...\\n\",\n       \"27   So, what my first days are about is bringing A...\\n\",\n       \"28                                          (APPLAUSE)\\n\",\n       \"29   HOLT: Secretary Clinton, same question, my fir...\\n\",\n       \"..                                                 ...\\n\",\n       \"592  HOLT: Welcome back everybody. Finally, before ...\\n\",\n       \"593           And, we'll start with Governor O'Malley.\\n\",\n       \"594                                         (LAUGHTER)\\n\",\n       \"595             HOLT: Didn't see that coming, did you?\\n\",\n       \"596  O'MALLEY: Yes, but we're going to have to get ...\\n\",\n       \"597                                         (LAUGHTER)\\n\",\n       \"598                        MITCHELL: ...too long (ph).\\n\",\n       \"599  O'MALLEY: I believe there are many issues. I h...\\n\",\n       \"600           HOLT: Sixty seconds, we'd appreciate it.\\n\",\n       \"601  O'MALLEY: There are so many issues that we hav...\\n\",\n       \"602                                         (APPLAUSE)\\n\",\n       \"603  O'MALLEY: We haven't discussed the fact that i...\\n\",\n       \"604  I guess the bottom line is this, look we are a...\\n\",\n       \"605  We're on the threshold of a new era of America...\\n\",\n       \"606                            HOLT: And that's time. \\n\",\n       \"607                           O'MALLEY: Thanks a lot. \\n\",\n       \"608                          HOLT: Secretary Clinton? \\n\",\n       \"609  CLINTON: Well Lester, I spent a lot of time la...\\n\",\n       \"610  He had request for help and he had basically s...\\n\",\n       \"611  So I sent my top campaign aide down there to t...\\n\",\n       \"612                            HOLT: And that's time. \\n\",\n       \"613  CLINTON: I want to be a president who takes ca...\\n\",\n       \"614                                         (APPLAUSE)\\n\",\n       \"615                                  HOLT: Thank you. \\n\",\n       \"616                                  Senator Sanders? \\n\",\n       \"617  SANDERS: Well, Secretary Clinton was right and...\\n\",\n       \"618  Now, we are a great nation -- and we've heard ...\\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...\\n\",\n       \"620  HOLT: All right. Well thank you and thanks to ...\\n\",\n       \"621  I also want to thank the Congressional Black C...\\n\",\n       \"\\n\",\n       \"[622 rows x 1 columns]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataset = pd.DataFrame(para_list, columns=[\\\"raw\\\"])\\n\",\n    \"dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_name(x):\\n\",\n    \"    r = re.findall(r\\\"^([A-Z']*):\\\", x)\\n\",\n    \"    if r:\\n\",\n    \"        return r[0]\\n\",\n    \"    else:\\n\",\n    \"        return np.NaN\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>raw</th>\\n\",\n       \"      <th>speaker</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>HOLT: We'll begin with 45 second opening state...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>CLINTON: Well, good evening. And I want to tha...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>SANDERS: Thank you. As we honor the extraordin...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>SANDERS: And then, to make a bad situation wor...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>HOLT: Senator, thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>And Governor O'Malley, your opening statement,...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>HOLT: All right. And Governor, thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>HOLT: All right, to our first question, now. T...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>President Obama came to office determined to s...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>Senator Sanders.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>SANDERS: Well, that's what our campaign is abo...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton, same question, my fir...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>592</th>\\n\",\n       \"      <td>HOLT: Welcome back everybody. Finally, before ...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>593</th>\\n\",\n       \"      <td>And, we'll start with Governor O'Malley.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>594</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>595</th>\\n\",\n       \"      <td>HOLT: Didn't see that coming, did you?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>596</th>\\n\",\n       \"      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>597</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>598</th>\\n\",\n       \"      <td>MITCHELL: ...too long (ph).</td>\\n\",\n       \"      <td>MITCHELL</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>599</th>\\n\",\n       \"      <td>O'MALLEY: I believe there are many issues. I h...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>600</th>\\n\",\n       \"      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>601</th>\\n\",\n       \"      <td>O'MALLEY: There are so many issues that we hav...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>602</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>603</th>\\n\",\n       \"      <td>O'MALLEY: We haven't discussed the fact that i...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>604</th>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>605</th>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>606</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>607</th>\\n\",\n       \"      <td>O'MALLEY: Thanks a lot.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>608</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>609</th>\\n\",\n       \"      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>610</th>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>611</th>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>612</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>613</th>\\n\",\n       \"      <td>CLINTON: I want to be a president who takes ca...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>614</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>615</th>\\n\",\n       \"      <td>HOLT: Thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>616</th>\\n\",\n       \"      <td>Senator Sanders?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>617</th>\\n\",\n       \"      <td>SANDERS: Well, Secretary Clinton was right and...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>618</th>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>619</th>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>620</th>\\n\",\n       \"      <td>HOLT: All right. Well thank you and thanks to ...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>621</th>\\n\",\n       \"      <td>I also want to thank the Congressional Black C...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>622 rows × 2 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                   raw   speaker\\n\",\n       \"0    HOLT: We'll begin with 45 second opening state...      HOLT\\n\",\n       \"1    CLINTON: Well, good evening. And I want to tha...   CLINTON\\n\",\n       \"2    You know, I remember well when my youth minist...   CLINTON\\n\",\n       \"3    And that is our fight still. We have to get th...   CLINTON\\n\",\n       \"4    I understand that this is the hardest job in t...   CLINTON\\n\",\n       \"5                                           (APPLAUSE)   CLINTON\\n\",\n       \"6    HOLT: Thank you. Senator Sanders, your opening...      HOLT\\n\",\n       \"7    SANDERS: Thank you. As we honor the extraordin...   SANDERS\\n\",\n       \"8    SANDERS: And then, to make a bad situation wor...   SANDERS\\n\",\n       \"9    This campaign is about a political revolution ...   SANDERS\\n\",\n       \"10                           HOLT: Senator, thank you.      HOLT\\n\",\n       \"11                                          (APPLAUSE)      HOLT\\n\",\n       \"12   And Governor O'Malley, your opening statement,...      HOLT\\n\",\n       \"13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY\\n\",\n       \"14   And I want to thank the people of South Caroli...  O'MALLEY\\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY\\n\",\n       \"16   Eight years ago, you brought forward a new lea...  O'MALLEY\\n\",\n       \"17   But in order to make good on the promise of eq...  O'MALLEY\\n\",\n       \"18   We need new leadership. We need to come togeth...  O'MALLEY\\n\",\n       \"19   That's why I'm running for president. I need y...  O'MALLEY\\n\",\n       \"20                                         Thank you.   O'MALLEY\\n\",\n       \"21           HOLT: All right. And Governor, thank you.      HOLT\\n\",\n       \"22                                          (APPLAUSE)      HOLT\\n\",\n       \"23   HOLT: All right, to our first question, now. T...      HOLT\\n\",\n       \"24   President Obama came to office determined to s...      HOLT\\n\",\n       \"25                                   Senator Sanders.       HOLT\\n\",\n       \"26   SANDERS: Well, that's what our campaign is abo...   SANDERS\\n\",\n       \"27   So, what my first days are about is bringing A...   SANDERS\\n\",\n       \"28                                          (APPLAUSE)   SANDERS\\n\",\n       \"29   HOLT: Secretary Clinton, same question, my fir...      HOLT\\n\",\n       \"..                                                 ...       ...\\n\",\n       \"592  HOLT: Welcome back everybody. Finally, before ...      HOLT\\n\",\n       \"593           And, we'll start with Governor O'Malley.      HOLT\\n\",\n       \"594                                         (LAUGHTER)      HOLT\\n\",\n       \"595             HOLT: Didn't see that coming, did you?      HOLT\\n\",\n       \"596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY\\n\",\n       \"597                                         (LAUGHTER)  O'MALLEY\\n\",\n       \"598                        MITCHELL: ...too long (ph).  MITCHELL\\n\",\n       \"599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY\\n\",\n       \"600           HOLT: Sixty seconds, we'd appreciate it.      HOLT\\n\",\n       \"601  O'MALLEY: There are so many issues that we hav...  O'MALLEY\\n\",\n       \"602                                         (APPLAUSE)  O'MALLEY\\n\",\n       \"603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY\\n\",\n       \"604  I guess the bottom line is this, look we are a...  O'MALLEY\\n\",\n       \"605  We're on the threshold of a new era of America...  O'MALLEY\\n\",\n       \"606                            HOLT: And that's time.       HOLT\\n\",\n       \"607                           O'MALLEY: Thanks a lot.   O'MALLEY\\n\",\n       \"608                          HOLT: Secretary Clinton?       HOLT\\n\",\n       \"609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON\\n\",\n       \"610  He had request for help and he had basically s...   CLINTON\\n\",\n       \"611  So I sent my top campaign aide down there to t...   CLINTON\\n\",\n       \"612                            HOLT: And that's time.       HOLT\\n\",\n       \"613  CLINTON: I want to be a president who takes ca...   CLINTON\\n\",\n       \"614                                         (APPLAUSE)   CLINTON\\n\",\n       \"615                                  HOLT: Thank you.       HOLT\\n\",\n       \"616                                  Senator Sanders?       HOLT\\n\",\n       \"617  SANDERS: Well, Secretary Clinton was right and...   SANDERS\\n\",\n       \"618  Now, we are a great nation -- and we've heard ...   SANDERS\\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...   SANDERS\\n\",\n       \"620  HOLT: All right. Well thank you and thanks to ...      HOLT\\n\",\n       \"621  I also want to thank the Congressional Black C...      HOLT\\n\",\n       \"\\n\",\n       \"[622 rows x 2 columns]\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataset[\\\"speaker\\\"] = dataset.raw.apply(get_name).fillna(method='ffill')\\n\",\n    \"dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SANDERS     168\\n\",\n       \"HOLT        152\\n\",\n       \"CLINTON     131\\n\",\n       \"O'MALLEY    113\\n\",\n       \"MITCHELL     43\\n\",\n       \"TODD          7\\n\",\n       \"BROWNLEE      4\\n\",\n       \"FRANTA        2\\n\",\n       \"MILLER        2\\n\",\n       \"Name: speaker, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataset.speaker.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>raw</th>\\n\",\n       \"      <th>speaker</th>\\n\",\n       \"      <th>speach</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>HOLT: We'll begin with 45 second opening state...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>We'll begin with 45 second opening statements ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>CLINTON: Well, good evening. And I want to tha...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>Well, good evening. And I want to thank the Co...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Thank you. Senator Sanders, your opening state...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>SANDERS: Thank you. As we honor the extraordin...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Thank you. As we honor the extraordinary life ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>SANDERS: And then, to make a bad situation wor...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>And then, to make a bad situation worse, we ha...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>HOLT: Senator, thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Senator, thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>And Governor O'Malley, your opening statement,...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And Governor O'Malley, your opening statement,...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thank you. My name is Martin O'Malley, I was b...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>HOLT: All right. And Governor, thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>All right. And Governor, thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>HOLT: All right, to our first question, now. T...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>All right, to our first question, now. The fir...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>President Obama came to office determined to s...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>President Obama came to office determined to s...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>Senator Sanders.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Senator Sanders.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>SANDERS: Well, that's what our campaign is abo...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Well, that's what our campaign is about. It is...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton, same question, my fir...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Secretary Clinton, same question, my first 100...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>592</th>\\n\",\n       \"      <td>HOLT: Welcome back everybody. Finally, before ...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Welcome back everybody. Finally, before we go ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>593</th>\\n\",\n       \"      <td>And, we'll start with Governor O'Malley.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And, we'll start with Governor O'Malley.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>594</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>595</th>\\n\",\n       \"      <td>HOLT: Didn't see that coming, did you?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Didn't see that coming, did you?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>596</th>\\n\",\n       \"      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Yes, but we're going to have to get 20 minutes...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>597</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>598</th>\\n\",\n       \"      <td>MITCHELL: ...too long (ph).</td>\\n\",\n       \"      <td>MITCHELL</td>\\n\",\n       \"      <td>...too long (ph).</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>599</th>\\n\",\n       \"      <td>O'MALLEY: I believe there are many issues. I h...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>I believe there are many issues. I have 60 sec...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>600</th>\\n\",\n       \"      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Sixty seconds, we'd appreciate it.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>601</th>\\n\",\n       \"      <td>O'MALLEY: There are so many issues that we hav...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>There are so many issues that we haven't been ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>602</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>603</th>\\n\",\n       \"      <td>O'MALLEY: We haven't discussed the fact that i...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We haven't discussed the fact that in our hemi...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>604</th>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>605</th>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>606</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And that's time.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>607</th>\\n\",\n       \"      <td>O'MALLEY: Thanks a lot.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thanks a lot.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>608</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Secretary Clinton?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>609</th>\\n\",\n       \"      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>Well Lester, I spent a lot of time last week b...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>610</th>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>611</th>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>612</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And that's time.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>613</th>\\n\",\n       \"      <td>CLINTON: I want to be a president who takes ca...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I want to be a president who takes care of the...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>614</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>615</th>\\n\",\n       \"      <td>HOLT: Thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>616</th>\\n\",\n       \"      <td>Senator Sanders?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Senator Sanders?</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>617</th>\\n\",\n       \"      <td>SANDERS: Well, Secretary Clinton was right and...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Well, Secretary Clinton was right and what I d...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>618</th>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>619</th>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>620</th>\\n\",\n       \"      <td>HOLT: All right. Well thank you and thanks to ...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>All right. Well thank you and thanks to all of...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>621</th>\\n\",\n       \"      <td>I also want to thank the Congressional Black C...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>I also want to thank the Congressional Black C...</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>622 rows × 3 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                   raw   speaker  \\\\\\n\",\n       \"0    HOLT: We'll begin with 45 second opening state...      HOLT   \\n\",\n       \"1    CLINTON: Well, good evening. And I want to tha...   CLINTON   \\n\",\n       \"2    You know, I remember well when my youth minist...   CLINTON   \\n\",\n       \"3    And that is our fight still. We have to get th...   CLINTON   \\n\",\n       \"4    I understand that this is the hardest job in t...   CLINTON   \\n\",\n       \"5                                           (APPLAUSE)   CLINTON   \\n\",\n       \"6    HOLT: Thank you. Senator Sanders, your opening...      HOLT   \\n\",\n       \"7    SANDERS: Thank you. As we honor the extraordin...   SANDERS   \\n\",\n       \"8    SANDERS: And then, to make a bad situation wor...   SANDERS   \\n\",\n       \"9    This campaign is about a political revolution ...   SANDERS   \\n\",\n       \"10                           HOLT: Senator, thank you.      HOLT   \\n\",\n       \"11                                          (APPLAUSE)      HOLT   \\n\",\n       \"12   And Governor O'Malley, your opening statement,...      HOLT   \\n\",\n       \"13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY   \\n\",\n       \"14   And I want to thank the people of South Caroli...  O'MALLEY   \\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY   \\n\",\n       \"16   Eight years ago, you brought forward a new lea...  O'MALLEY   \\n\",\n       \"17   But in order to make good on the promise of eq...  O'MALLEY   \\n\",\n       \"18   We need new leadership. We need to come togeth...  O'MALLEY   \\n\",\n       \"19   That's why I'm running for president. I need y...  O'MALLEY   \\n\",\n       \"20                                         Thank you.   O'MALLEY   \\n\",\n       \"21           HOLT: All right. And Governor, thank you.      HOLT   \\n\",\n       \"22                                          (APPLAUSE)      HOLT   \\n\",\n       \"23   HOLT: All right, to our first question, now. T...      HOLT   \\n\",\n       \"24   President Obama came to office determined to s...      HOLT   \\n\",\n       \"25                                   Senator Sanders.       HOLT   \\n\",\n       \"26   SANDERS: Well, that's what our campaign is abo...   SANDERS   \\n\",\n       \"27   So, what my first days are about is bringing A...   SANDERS   \\n\",\n       \"28                                          (APPLAUSE)   SANDERS   \\n\",\n       \"29   HOLT: Secretary Clinton, same question, my fir...      HOLT   \\n\",\n       \"..                                                 ...       ...   \\n\",\n       \"592  HOLT: Welcome back everybody. Finally, before ...      HOLT   \\n\",\n       \"593           And, we'll start with Governor O'Malley.      HOLT   \\n\",\n       \"594                                         (LAUGHTER)      HOLT   \\n\",\n       \"595             HOLT: Didn't see that coming, did you?      HOLT   \\n\",\n       \"596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY   \\n\",\n       \"597                                         (LAUGHTER)  O'MALLEY   \\n\",\n       \"598                        MITCHELL: ...too long (ph).  MITCHELL   \\n\",\n       \"599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY   \\n\",\n       \"600           HOLT: Sixty seconds, we'd appreciate it.      HOLT   \\n\",\n       \"601  O'MALLEY: There are so many issues that we hav...  O'MALLEY   \\n\",\n       \"602                                         (APPLAUSE)  O'MALLEY   \\n\",\n       \"603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY   \\n\",\n       \"604  I guess the bottom line is this, look we are a...  O'MALLEY   \\n\",\n       \"605  We're on the threshold of a new era of America...  O'MALLEY   \\n\",\n       \"606                            HOLT: And that's time.       HOLT   \\n\",\n       \"607                           O'MALLEY: Thanks a lot.   O'MALLEY   \\n\",\n       \"608                          HOLT: Secretary Clinton?       HOLT   \\n\",\n       \"609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON   \\n\",\n       \"610  He had request for help and he had basically s...   CLINTON   \\n\",\n       \"611  So I sent my top campaign aide down there to t...   CLINTON   \\n\",\n       \"612                            HOLT: And that's time.       HOLT   \\n\",\n       \"613  CLINTON: I want to be a president who takes ca...   CLINTON   \\n\",\n       \"614                                         (APPLAUSE)   CLINTON   \\n\",\n       \"615                                  HOLT: Thank you.       HOLT   \\n\",\n       \"616                                  Senator Sanders?       HOLT   \\n\",\n       \"617  SANDERS: Well, Secretary Clinton was right and...   SANDERS   \\n\",\n       \"618  Now, we are a great nation -- and we've heard ...   SANDERS   \\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...   SANDERS   \\n\",\n       \"620  HOLT: All right. Well thank you and thanks to ...      HOLT   \\n\",\n       \"621  I also want to thank the Congressional Black C...      HOLT   \\n\",\n       \"\\n\",\n       \"                                                speach  \\n\",\n       \"0    We'll begin with 45 second opening statements ...  \\n\",\n       \"1    Well, good evening. And I want to thank the Co...  \\n\",\n       \"2    You know, I remember well when my youth minist...  \\n\",\n       \"3    And that is our fight still. We have to get th...  \\n\",\n       \"4    I understand that this is the hardest job in t...  \\n\",\n       \"5                                           (APPLAUSE)  \\n\",\n       \"6    Thank you. Senator Sanders, your opening state...  \\n\",\n       \"7    Thank you. As we honor the extraordinary life ...  \\n\",\n       \"8    And then, to make a bad situation worse, we ha...  \\n\",\n       \"9    This campaign is about a political revolution ...  \\n\",\n       \"10                                 Senator, thank you.  \\n\",\n       \"11                                          (APPLAUSE)  \\n\",\n       \"12   And Governor O'Malley, your opening statement,...  \\n\",\n       \"13   Thank you. My name is Martin O'Malley, I was b...  \\n\",\n       \"14   And I want to thank the people of South Caroli...  \\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...  \\n\",\n       \"16   Eight years ago, you brought forward a new lea...  \\n\",\n       \"17   But in order to make good on the promise of eq...  \\n\",\n       \"18   We need new leadership. We need to come togeth...  \\n\",\n       \"19   That's why I'm running for president. I need y...  \\n\",\n       \"20                                         Thank you.   \\n\",\n       \"21                 All right. And Governor, thank you.  \\n\",\n       \"22                                          (APPLAUSE)  \\n\",\n       \"23   All right, to our first question, now. The fir...  \\n\",\n       \"24   President Obama came to office determined to s...  \\n\",\n       \"25                                   Senator Sanders.   \\n\",\n       \"26   Well, that's what our campaign is about. It is...  \\n\",\n       \"27   So, what my first days are about is bringing A...  \\n\",\n       \"28                                          (APPLAUSE)  \\n\",\n       \"29   Secretary Clinton, same question, my first 100...  \\n\",\n       \"..                                                 ...  \\n\",\n       \"592  Welcome back everybody. Finally, before we go ...  \\n\",\n       \"593           And, we'll start with Governor O'Malley.  \\n\",\n       \"594                                         (LAUGHTER)  \\n\",\n       \"595                   Didn't see that coming, did you?  \\n\",\n       \"596  Yes, but we're going to have to get 20 minutes...  \\n\",\n       \"597                                         (LAUGHTER)  \\n\",\n       \"598                                  ...too long (ph).  \\n\",\n       \"599  I believe there are many issues. I have 60 sec...  \\n\",\n       \"600                 Sixty seconds, we'd appreciate it.  \\n\",\n       \"601  There are so many issues that we haven't been ...  \\n\",\n       \"602                                         (APPLAUSE)  \\n\",\n       \"603  We haven't discussed the fact that in our hemi...  \\n\",\n       \"604  I guess the bottom line is this, look we are a...  \\n\",\n       \"605  We're on the threshold of a new era of America...  \\n\",\n       \"606                                  And that's time.   \\n\",\n       \"607                                     Thanks a lot.   \\n\",\n       \"608                                Secretary Clinton?   \\n\",\n       \"609  Well Lester, I spent a lot of time last week b...  \\n\",\n       \"610  He had request for help and he had basically s...  \\n\",\n       \"611  So I sent my top campaign aide down there to t...  \\n\",\n       \"612                                  And that's time.   \\n\",\n       \"613  I want to be a president who takes care of the...  \\n\",\n       \"614                                         (APPLAUSE)  \\n\",\n       \"615                                        Thank you.   \\n\",\n       \"616                                  Senator Sanders?   \\n\",\n       \"617  Well, Secretary Clinton was right and what I d...  \\n\",\n       \"618  Now, we are a great nation -- and we've heard ...  \\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...  \\n\",\n       \"620  All right. Well thank you and thanks to all of...  \\n\",\n       \"621  I also want to thank the Congressional Black C...  \\n\",\n       \"\\n\",\n       \"[622 rows x 3 columns]\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"get_speach = lambda x: re.sub(\\\"^[A-Z']*:\\\\s\\\", \\\"\\\", x)\\n\",\n    \"dataset[\\\"speach\\\"] = dataset.raw.apply(get_speach)\\n\",\n    \"dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"34\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"applause_ds = dataset[dataset.speach == \\\"(APPLAUSE)\\\"]\\n\",\n    \"len(applause_ds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"SANDERS     12\\n\",\n       \"CLINTON     12\\n\",\n       \"O'MALLEY     7\\n\",\n       \"HOLT         3\\n\",\n       \"Name: speaker, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"applause_ds.speaker.value_counts()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"HOLT         3\\n\",\n       \"O'MALLEY     7\\n\",\n       \"SANDERS     12\\n\",\n       \"CLINTON     12\\n\",\n       \"Name: speaker, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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HuCMSZan+6+yTslC4B/Bfbp3V8DuAB4T/cASoAXAG+eYIgvMAjsU5Ns\\nyeABne/1xn8ucHtVfbe7j1nda5OJ6ll04EzOQpLWHyMjI4yMjNy/vnjx4jXqP9WMDuClwMeTHMXg\\n4ZXzGMyGxhv7QKeqvjzB/oMYPMHZdxbwWR4IuquTjM3G/g24GliY5IDeMf6sW+7fo1teVQvH1TKZ\\n9zN4YOTMbrb5/ao6oKruSvJu4Iqu3eLuoRSSLAa+VVXnVtUFSV6Y5Drgd8Abq+qurl0YzOT+shvj\\nE8AZDO7l/f1qapIkzZFUrS4TNJeSVJ0x7Cqkh4CD/RzS9CWhqqZ9Vc9vRpEkNc2gkyQ1zaCTJDXN\\noJMkNc2gkyQ1zaCTJDXNoJMkNc2gkyQ1zaCTJDXNoJMkNc2gkyQ1zaCTJDXNoJMkNc2gkyQ1zaCT\\nJDXNoJMkNc2gkyQ1zaCTJDUtVf4J+2FJUr7/krRmklBVmW57Z3SSpKYZdJKkphl0kqSmGXSSpKYZ\\ndJKkphl0kqSmGXSSpKYZdJKkphl0kqSmGXSSpKbNG3YB670l0/4WG03lYL9OTdLvc0YnSWqaQSdJ\\nappBJ0lqmkEnSWqaQSdJappBJ0lqmkEnSWqaQSdJappBJ0lqmkEnSWqaQSdJappBJ0lqmkEnSWqa\\nQSdJappBJ0lqmkEnSWqaQSdJatpDOuiS3DNufWGSj/XWD0tyffe6PMmzevtGkzy9Wz4hybIk1yX5\\ndbe8LMlfjBt/+yRf7/YtT/Lnk9T110mu6dp8KckfdtsP77afl2TDbtueSY6dvXdFkrQmHtJBB9Rk\\n60leDBwGPKuqdgT+DliSZKvxbavqdVW1AHgR8N2qWtC9zh43/juA07u2BwEfH19Qko2ADwLPrapd\\ngKuB13e7D66qpwKXAvskSTfm0TM4d0nSLHioB9146S2/GXhjVf0MoKqWAacBr5tm/4n8ENisW94c\\nuH2CNvcBdwGbdkG2Wa9dkjwS2ARYCRwCnF9Vd09xXEnSHJk37AKmsHGSZb31LYAvdss7AVeOa/8t\\n4NC1ON6/AJclORx4NLD3+AZVtSrJEcC1wD3AjcD/6HYfD1zW7ftmV+sL16IeSdJaeqgH3W+6y4gA\\nJDkU2G017aeasU3lWOCTVfXhJHsApwM7P+gAyWOAjwK7VNXN3T3DtwL/XFWnd31I8k7gOGC/JK8C\\nbgXeUFUPuhy76KwHlkd2hJGd1vIMJKkxo6OjjI6Ozrj/Qz3oxusH2QoGobe0t+0ZDGZTM/VM4F0A\\nVfV/kzwqyZZV9ZNemx2Bm6vq5m798wwuoz5QZLIN8KdVdXSSUWAv4CgGM8SL+20XHbgW1UrSemBk\\nZISRkZH71xcvXrxG/R9u9+j63g+8L8kWAEl2ZXDZsv8AyZrO8G4Ant+NtyPwqHEhB/A94E+SbNmt\\nv4BB6Pa9m0GwAWzc1VHdsiRpHXqoz+gmeuqyAKrq3CSPBy5NUsAvgFdW1R299uclWdktXwr80wRj\\n9r0JOCnJkV27++/3JVnWPan54yRvA5YmWQXcAizstdsVWFVVV3WbljB4MvMHwHunfeaSpFmRcbeM\\ntA4lqTpj2FU05GB/lqX1QRKqatpX7B7Oly4lSZqSQSdJappBJ0lqmkEnSWqaQSdJappBJ0lqmkEn\\nSWqaQSdJappBJ0lqmkEnSWqaQSdJappBJ0lqmkEnSWqaQSdJappBpzkzOv7P0TZmdHR02CXMmZbP\\nDTy/9Y1Bpzkzev2wK5hbLX+YtHxu4Pmtbww6SVLTDDpJUtNSVcOuYb2VxDdfkmagqjLdtgadJKlp\\nXrqUJDXNoJMkNc2gG4Ik+ya5Icl3krx52PXMpiTzkyxNcl2Sa5P8w7BrmgtJNkiyLMm5w65ltiXZ\\nPMmZSa5PsiLJHsOuaTYleWv383lNkiVJHjnsmtZGkpOT3JHkmt62LZJclOTGJBcm2XyYNc7UJOf2\\nge5nc3mSs5NsNtU4Bt06lmQD4HhgX2An4BVJdhxuVbNqJXBkVe0M7AG8rrHzG3MEsAJo8Sb3ccD5\\nVbUj8DSgmX8RmWQ74DXA06vqqcAGwEHDrGkWnMLg86TvLcBFVbUD8JVu/eFoonO7ENi5qnYBbgTe\\nOtUgBt26tztwU1XdUlUrgc8CLxlyTbOmqn5UVVd1y/cw+JDcZrhVza4k2wIvAj4JTPvJr4eD7rfj\\nZ1fVyQBVdV9V/XzIZc2mXzD4ZWyTJPOATYDbh1vS2qmqrwN3jdu8P3Bat3wacMA6LWqWTHRuVXVR\\nVa3qVi8Htp1qHINu3Xs8cGtv/bZuW3O6354XMPhhbMmHgTcBq6Zq+DD0BODHSU5J8u0kJybZZNhF\\nzZaq+hnwIeAHwH8Ad1fVxcOtak5sVVV3dMt3AFsNs5g59Grg/KkaGXTrXouXun5Pkk2BM4Ejupld\\nE5K8GLizqpbR2GyuMw94OvDxqno68Csevpe9fk+SJwL/CGzH4ErDpkleOdSi5lgN/g1Zc587Sd4O\\n3FtVS6Zqa9Cte7cD83vr8xnM6pqRZEPgLOD0qvrCsOuZZc8E9k9yM/AZ4HlJPjXkmmbTbcBtVXVF\\nt34mg+BrxW7ApVX106q6DzibwX/T1tyR5HEASbYG7hxyPbMqyUIGtw+m9UuKQbfufQt4UpLtkmwE\\nvBw4Z8g1zZokAU4CVlTVR4Zdz2yrqrdV1fyqegKDhxguqaq/GnZds6WqfgTcmmSHbtPzgeuGWNJs\\nuwHYI8nG3c/q8xk8VNSac4BDu+VDgWZ+4UyyL4NbBy+pqt9Op49Bt451v0W+HriAwf9gn6uqZp5q\\nA54FHALs1T1+v6z7wWxVc5eEgMOBM5IsZ/DU5XuGXM+sqarlwKcY/MJ5dbf5E8OraO0l+QxwKfDk\\nJLcm+WvgvcALktwIPK9bf9iZ4NxeDXwM2BS4qPt8+fiU4/gVYJKkljmjkyQ1zaCTJDXNoJMkNc2g\\nkyQ1zaCTJDXNoJMkNc2gkyQ1zaCTJDXt/wO7Y4RZJ8QDcwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7feabc0afe48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"applause_counts = applause_ds.speaker.value_counts().sort_values()\\n\",\n    \"\\n\",\n    \"bottom = [index for index, item in enumerate(applause_counts.index)]\\n\",\n    \"plt.barh(bottom, width=applause_counts, color=\\\"orange\\\", linewidth=0)\\n\",\n    \"\\n\",\n    \"y_labels = [\\\"%s %.1f%%\\\" % (item, 100.0*applause_counts[item]/len(applause_ds)) for index,item in enumerate(applause_counts.index)]\\n\",\n    \"plt.yticks(np.array(bottom)+0.4, y_labels)\\n\",\n    \"\\n\",\n    \"applause_counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"word_count = lambda x: len(re.findall(\\\"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\\\\'\\\\w\\\\-]+\\\",x))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>raw</th>\\n\",\n       \"      <th>speaker</th>\\n\",\n       \"      <th>speach</th>\\n\",\n       \"      <th>word_count</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>HOLT: We'll begin with 45 second opening state...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>We'll begin with 45 second opening statements ...</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>CLINTON: Well, good evening. And I want to tha...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>Well, good evening. And I want to thank the Co...</td>\\n\",\n       \"      <td>31</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"      <td>67</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"      <td>42</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Thank you. Senator Sanders, your opening state...</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>SANDERS: Thank you. As we honor the extraordin...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Thank you. As we honor the extraordinary life ...</td>\\n\",\n       \"      <td>88</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>SANDERS: And then, to make a bad situation wor...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>And then, to make a bad situation worse, we ha...</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"      <td>18</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>HOLT: Senator, thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Senator, thank you.</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>And Governor O'Malley, your opening statement,...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And Governor O'Malley, your opening statement,...</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thank you. My name is Martin O'Malley, I was b...</td>\\n\",\n       \"      <td>21</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"      <td>38</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"      <td>37</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"      <td>34</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"      <td>47</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"      <td>23</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"      <td>26</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>HOLT: All right. And Governor, thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>All right. And Governor, thank you.</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>HOLT: All right, to our first question, now. T...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>All right, to our first question, now. The fir...</td>\\n\",\n       \"      <td>18</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>President Obama came to office determined to s...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>President Obama came to office determined to s...</td>\\n\",\n       \"      <td>52</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>Senator Sanders.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Senator Sanders.</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>SANDERS: Well, that's what our campaign is abo...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Well, that's what our campaign is about. It is...</td>\\n\",\n       \"      <td>68</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"      <td>61</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton, same question, my fir...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Secretary Clinton, same question, my first 100...</td>\\n\",\n       \"      <td>16</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>592</th>\\n\",\n       \"      <td>HOLT: Welcome back everybody. Finally, before ...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Welcome back everybody. Finally, before we go ...</td>\\n\",\n       \"      <td>48</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>593</th>\\n\",\n       \"      <td>And, we'll start with Governor O'Malley.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And, we'll start with Governor O'Malley.</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>594</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>595</th>\\n\",\n       \"      <td>HOLT: Didn't see that coming, did you?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Didn't see that coming, did you?</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>596</th>\\n\",\n       \"      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Yes, but we're going to have to get 20 minutes...</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>597</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>598</th>\\n\",\n       \"      <td>MITCHELL: ...too long (ph).</td>\\n\",\n       \"      <td>MITCHELL</td>\\n\",\n       \"      <td>...too long (ph).</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>599</th>\\n\",\n       \"      <td>O'MALLEY: I believe there are many issues. I h...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>I believe there are many issues. I have 60 sec...</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>600</th>\\n\",\n       \"      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Sixty seconds, we'd appreciate it.</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>601</th>\\n\",\n       \"      <td>O'MALLEY: There are so many issues that we hav...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>There are so many issues that we haven't been ...</td>\\n\",\n       \"      <td>61</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>602</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>603</th>\\n\",\n       \"      <td>O'MALLEY: We haven't discussed the fact that i...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We haven't discussed the fact that in our hemi...</td>\\n\",\n       \"      <td>27</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>604</th>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"      <td>80</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>605</th>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"      <td>33</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>606</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And that's time.</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>607</th>\\n\",\n       \"      <td>O'MALLEY: Thanks a lot.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thanks a lot.</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>608</th>\\n\",\n       \"      <td>HOLT: Secretary Clinton?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Secretary Clinton?</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>609</th>\\n\",\n       \"      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>Well Lester, I spent a lot of time last week b...</td>\\n\",\n       \"      <td>72</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>610</th>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"      <td>38</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>611</th>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"      <td>59</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>612</th>\\n\",\n       \"      <td>HOLT: And that's time.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>And that's time.</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>613</th>\\n\",\n       \"      <td>CLINTON: I want to be a president who takes ca...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I want to be a president who takes care of the...</td>\\n\",\n       \"      <td>25</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>614</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>615</th>\\n\",\n       \"      <td>HOLT: Thank you.</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>616</th>\\n\",\n       \"      <td>Senator Sanders?</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>Senator Sanders?</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>617</th>\\n\",\n       \"      <td>SANDERS: Well, Secretary Clinton was right and...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Well, Secretary Clinton was right and what I d...</td>\\n\",\n       \"      <td>32</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>618</th>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"      <td>58</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>619</th>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"      <td>73</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>620</th>\\n\",\n       \"      <td>HOLT: All right. Well thank you and thanks to ...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>All right. Well thank you and thanks to all of...</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>621</th>\\n\",\n       \"      <td>I also want to thank the Congressional Black C...</td>\\n\",\n       \"      <td>HOLT</td>\\n\",\n       \"      <td>I also want to thank the Congressional Black C...</td>\\n\",\n       \"      <td>33</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>622 rows × 4 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                   raw   speaker  \\\\\\n\",\n       \"0    HOLT: We'll begin with 45 second opening state...      HOLT   \\n\",\n       \"1    CLINTON: Well, good evening. And I want to tha...   CLINTON   \\n\",\n       \"2    You know, I remember well when my youth minist...   CLINTON   \\n\",\n       \"3    And that is our fight still. We have to get th...   CLINTON   \\n\",\n       \"4    I understand that this is the hardest job in t...   CLINTON   \\n\",\n       \"5                                           (APPLAUSE)   CLINTON   \\n\",\n       \"6    HOLT: Thank you. Senator Sanders, your opening...      HOLT   \\n\",\n       \"7    SANDERS: Thank you. As we honor the extraordin...   SANDERS   \\n\",\n       \"8    SANDERS: And then, to make a bad situation wor...   SANDERS   \\n\",\n       \"9    This campaign is about a political revolution ...   SANDERS   \\n\",\n       \"10                           HOLT: Senator, thank you.      HOLT   \\n\",\n       \"11                                          (APPLAUSE)      HOLT   \\n\",\n       \"12   And Governor O'Malley, your opening statement,...      HOLT   \\n\",\n       \"13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY   \\n\",\n       \"14   And I want to thank the people of South Caroli...  O'MALLEY   \\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY   \\n\",\n       \"16   Eight years ago, you brought forward a new lea...  O'MALLEY   \\n\",\n       \"17   But in order to make good on the promise of eq...  O'MALLEY   \\n\",\n       \"18   We need new leadership. We need to come togeth...  O'MALLEY   \\n\",\n       \"19   That's why I'm running for president. I need y...  O'MALLEY   \\n\",\n       \"20                                         Thank you.   O'MALLEY   \\n\",\n       \"21           HOLT: All right. And Governor, thank you.      HOLT   \\n\",\n       \"22                                          (APPLAUSE)      HOLT   \\n\",\n       \"23   HOLT: All right, to our first question, now. T...      HOLT   \\n\",\n       \"24   President Obama came to office determined to s...      HOLT   \\n\",\n       \"25                                   Senator Sanders.       HOLT   \\n\",\n       \"26   SANDERS: Well, that's what our campaign is abo...   SANDERS   \\n\",\n       \"27   So, what my first days are about is bringing A...   SANDERS   \\n\",\n       \"28                                          (APPLAUSE)   SANDERS   \\n\",\n       \"29   HOLT: Secretary Clinton, same question, my fir...      HOLT   \\n\",\n       \"..                                                 ...       ...   \\n\",\n       \"592  HOLT: Welcome back everybody. Finally, before ...      HOLT   \\n\",\n       \"593           And, we'll start with Governor O'Malley.      HOLT   \\n\",\n       \"594                                         (LAUGHTER)      HOLT   \\n\",\n       \"595             HOLT: Didn't see that coming, did you?      HOLT   \\n\",\n       \"596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY   \\n\",\n       \"597                                         (LAUGHTER)  O'MALLEY   \\n\",\n       \"598                        MITCHELL: ...too long (ph).  MITCHELL   \\n\",\n       \"599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY   \\n\",\n       \"600           HOLT: Sixty seconds, we'd appreciate it.      HOLT   \\n\",\n       \"601  O'MALLEY: There are so many issues that we hav...  O'MALLEY   \\n\",\n       \"602                                         (APPLAUSE)  O'MALLEY   \\n\",\n       \"603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY   \\n\",\n       \"604  I guess the bottom line is this, look we are a...  O'MALLEY   \\n\",\n       \"605  We're on the threshold of a new era of America...  O'MALLEY   \\n\",\n       \"606                            HOLT: And that's time.       HOLT   \\n\",\n       \"607                           O'MALLEY: Thanks a lot.   O'MALLEY   \\n\",\n       \"608                          HOLT: Secretary Clinton?       HOLT   \\n\",\n       \"609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON   \\n\",\n       \"610  He had request for help and he had basically s...   CLINTON   \\n\",\n       \"611  So I sent my top campaign aide down there to t...   CLINTON   \\n\",\n       \"612                            HOLT: And that's time.       HOLT   \\n\",\n       \"613  CLINTON: I want to be a president who takes ca...   CLINTON   \\n\",\n       \"614                                         (APPLAUSE)   CLINTON   \\n\",\n       \"615                                  HOLT: Thank you.       HOLT   \\n\",\n       \"616                                  Senator Sanders?       HOLT   \\n\",\n       \"617  SANDERS: Well, Secretary Clinton was right and...   SANDERS   \\n\",\n       \"618  Now, we are a great nation -- and we've heard ...   SANDERS   \\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...   SANDERS   \\n\",\n       \"620  HOLT: All right. Well thank you and thanks to ...      HOLT   \\n\",\n       \"621  I also want to thank the Congressional Black C...      HOLT   \\n\",\n       \"\\n\",\n       \"                                                speach  word_count  \\n\",\n       \"0    We'll begin with 45 second opening statements ...          14  \\n\",\n       \"1    Well, good evening. And I want to thank the Co...          31  \\n\",\n       \"2    You know, I remember well when my youth minist...          67  \\n\",\n       \"3    And that is our fight still. We have to get th...          50  \\n\",\n       \"4    I understand that this is the hardest job in t...          42  \\n\",\n       \"5                                           (APPLAUSE)           1  \\n\",\n       \"6    Thank you. Senator Sanders, your opening state...           8  \\n\",\n       \"7    Thank you. As we honor the extraordinary life ...          88  \\n\",\n       \"8    And then, to make a bad situation worse, we ha...          28  \\n\",\n       \"9    This campaign is about a political revolution ...          18  \\n\",\n       \"10                                 Senator, thank you.           3  \\n\",\n       \"11                                          (APPLAUSE)           1  \\n\",\n       \"12   And Governor O'Malley, your opening statement,...           7  \\n\",\n       \"13   Thank you. My name is Martin O'Malley, I was b...          21  \\n\",\n       \"14   And I want to thank the people of South Caroli...          38  \\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...          37  \\n\",\n       \"16   Eight years ago, you brought forward a new lea...          34  \\n\",\n       \"17   But in order to make good on the promise of eq...          47  \\n\",\n       \"18   We need new leadership. We need to come togeth...          23  \\n\",\n       \"19   That's why I'm running for president. I need y...          26  \\n\",\n       \"20                                         Thank you.            2  \\n\",\n       \"21                 All right. And Governor, thank you.           6  \\n\",\n       \"22                                          (APPLAUSE)           1  \\n\",\n       \"23   All right, to our first question, now. The fir...          18  \\n\",\n       \"24   President Obama came to office determined to s...          52  \\n\",\n       \"25                                   Senator Sanders.            2  \\n\",\n       \"26   Well, that's what our campaign is about. It is...          68  \\n\",\n       \"27   So, what my first days are about is bringing A...          61  \\n\",\n       \"28                                          (APPLAUSE)           1  \\n\",\n       \"29   Secretary Clinton, same question, my first 100...          16  \\n\",\n       \"..                                                 ...         ...  \\n\",\n       \"592  Welcome back everybody. Finally, before we go ...          48  \\n\",\n       \"593           And, we'll start with Governor O'Malley.           6  \\n\",\n       \"594                                         (LAUGHTER)           1  \\n\",\n       \"595                   Didn't see that coming, did you?           6  \\n\",\n       \"596  Yes, but we're going to have to get 20 minutes...          14  \\n\",\n       \"597                                         (LAUGHTER)           1  \\n\",\n       \"598                                  ...too long (ph).           3  \\n\",\n       \"599  I believe there are many issues. I have 60 sec...          12  \\n\",\n       \"600                 Sixty seconds, we'd appreciate it.           5  \\n\",\n       \"601  There are so many issues that we haven't been ...          61  \\n\",\n       \"602                                         (APPLAUSE)           1  \\n\",\n       \"603  We haven't discussed the fact that in our hemi...          27  \\n\",\n       \"604  I guess the bottom line is this, look we are a...          80  \\n\",\n       \"605  We're on the threshold of a new era of America...          33  \\n\",\n       \"606                                  And that's time.            3  \\n\",\n       \"607                                     Thanks a lot.            3  \\n\",\n       \"608                                Secretary Clinton?            2  \\n\",\n       \"609  Well Lester, I spent a lot of time last week b...          72  \\n\",\n       \"610  He had request for help and he had basically s...          38  \\n\",\n       \"611  So I sent my top campaign aide down there to t...          59  \\n\",\n       \"612                                  And that's time.            3  \\n\",\n       \"613  I want to be a president who takes care of the...          25  \\n\",\n       \"614                                         (APPLAUSE)           1  \\n\",\n       \"615                                        Thank you.            2  \\n\",\n       \"616                                  Senator Sanders?            2  \\n\",\n       \"617  Well, Secretary Clinton was right and what I d...          32  \\n\",\n       \"618  Now, we are a great nation -- and we've heard ...          58  \\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...          73  \\n\",\n       \"620  All right. Well thank you and thanks to all of...          28  \\n\",\n       \"621  I also want to thank the Congressional Black C...          33  \\n\",\n       \"\\n\",\n       \"[622 rows x 4 columns]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dataset[\\\"word_count\\\"] = dataset.speach.apply(word_count)\\n\",\n    \"dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"words_ds = dataset[dataset.speaker.isin([\\\"CLINTON\\\",\\\"SANDERS\\\",\\\"O'MALLEY\\\"])]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"speaker\\n\",\n       \"O'MALLEY    25.115044\\n\",\n       \"SANDERS     27.351190\\n\",\n       \"CLINTON     32.656489\\n\",\n       \"Name: word_count, dtype: float64\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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YWquqmqrgYW0wWqAIclOQ/4OrBJ71P6lVU1USPA3kmWAOcCj6W7mD8a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\\nJNowfgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7feab583bac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"words_counts = words_ds.pivot_table(values=\\\"word_count\\\", index=\\\"speaker\\\", columns=None, aggfunc='mean',).sort_values()\\n\",\n    \"\\n\",\n    \"bottom = [index for index, item in enumerate(words_counts.index)]\\n\",\n    \"plt.barh(bottom, width=words_counts, color=\\\"orange\\\", linewidth=0)\\n\",\n    \"\\n\",\n    \"y_labels = [\\\"%s %.1f words/paragraph\\\" % (item, words_counts[item]) for index,item in enumerate(words_counts.index)]\\n\",\n    \"plt.yticks(np.array(bottom)+0.4, y_labels)\\n\",\n    \"\\n\",\n    \"words_counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"speaker\\n\",\n       \"O'MALLEY    2838\\n\",\n       \"CLINTON     4278\\n\",\n       \"SANDERS     4595\\n\",\n       \"Name: word_count, dtype: int64\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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8kbgJ8B17Hi2e2j2jPVGYfN1Uib4d2yupNV1XfbUvIxSbZgmOH9TVV9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\\nKgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7feab57e6a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"words_counts = words_ds.pivot_table(values=\\\"word_count\\\", index=\\\"speaker\\\", columns=None, aggfunc='sum',).sort_values()\\n\",\n    \"\\n\",\n    \"bottom = [index for index, item in enumerate(words_counts.index)]\\n\",\n    \"plt.barh(bottom, width=words_counts, color=\\\"orange\\\", linewidth=0)\\n\",\n    \"\\n\",\n    \"y_labels = [\\\"%s %d (%.1f%%)\\\" % (item, words_counts[item], 100.0*words_counts[item]/np.sum(words_counts)) for index,item in enumerate(words_counts.index)]\\n\",\n    \"plt.yticks(np.array(bottom)+0.4, y_labels)\\n\",\n    \"\\n\",\n    \"words_counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'CLINTON': 0, \\\"O'MALLEY\\\": 2, 'SANDERS': 1}\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"speaker_dict = {value:index for index,value in enumerate(words_ds.speaker.unique())}\\n\",\n    \"speaker_dict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"-c:1: SettingWithCopyWarning: \\n\",\n      \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n      \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n      \"\\n\",\n      \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>raw</th>\\n\",\n       \"      <th>speaker</th>\\n\",\n       \"      <th>speach</th>\\n\",\n       \"      <th>word_count</th>\\n\",\n       \"      <th>speaker_no</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>CLINTON: Well, good evening. And I want to tha...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>Well, good evening. And I want to thank the Co...</td>\\n\",\n       \"      <td>31</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>You know, I remember well when my youth minist...</td>\\n\",\n       \"      <td>67</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>And that is our fight still. We have to get th...</td>\\n\",\n       \"      <td>50</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I understand that this is the hardest job in t...</td>\\n\",\n       \"      <td>42</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>SANDERS: Thank you. As we honor the extraordin...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Thank you. As we honor the extraordinary life ...</td>\\n\",\n       \"      <td>88</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>SANDERS: And then, to make a bad situation wor...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>And then, to make a bad situation worse, we ha...</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>This campaign is about a political revolution ...</td>\\n\",\n       \"      <td>18</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thank you. My name is Martin O'Malley, I was b...</td>\\n\",\n       \"      <td>21</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>And I want to thank the people of South Caroli...</td>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\\n\",\n       \"      <td>37</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Eight years ago, you brought forward a new lea...</td>\\n\",\n       \"      <td>34</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>But in order to make good on the promise of eq...</td>\\n\",\n       \"      <td>47</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We need new leadership. We need to come togeth...</td>\\n\",\n       \"      <td>23</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>That's why I'm running for president. I need y...</td>\\n\",\n       \"      <td>26</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thank you.</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>SANDERS: Well, that's what our campaign is abo...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Well, that's what our campaign is about. It is...</td>\\n\",\n       \"      <td>68</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>So, what my first days are about is bringing A...</td>\\n\",\n       \"      <td>61</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>30</th>\\n\",\n       \"      <td>CLINTON: I would work quickly to present to th...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I would work quickly to present to the Congres...</td>\\n\",\n       \"      <td>35</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>31</th>\\n\",\n       \"      <td>I would also...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I would also...</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>32</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>33</th>\\n\",\n       \"      <td>I would also be presenting my plans to build o...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I would also be presenting my plans to build o...</td>\\n\",\n       \"      <td>67</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>34</th>\\n\",\n       \"      <td>And third, I would be working, in every way th...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>And third, I would be working, in every way th...</td>\\n\",\n       \"      <td>78</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>35</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>37</th>\\n\",\n       \"      <td>O'MALLEY: Thank you. First of all, I would lay...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thank you. First of all, I would lay out an ag...</td>\\n\",\n       \"      <td>77</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>38</th>\\n\",\n       \"      <td>Secondly, I believe the greatest business oppo...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Secondly, I believe the greatest business oppo...</td>\\n\",\n       \"      <td>48</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>41</th>\\n\",\n       \"      <td>O'MALLEY: Finally -- I'm sorry, that was secon...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Finally -- I'm sorry, that was second, Lester.</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42</th>\\n\",\n       \"      <td>O'MALLEY: And third and finally, we need a new...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>And third and finally, we need a new agenda fo...</td>\\n\",\n       \"      <td>77</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>573</th>\\n\",\n       \"      <td>We just have to do more of it, and we have to ...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>We just have to do more of it, and we have to ...</td>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>576</th>\\n\",\n       \"      <td>SANDERS: Great ideas, Governor O'Malley, Secre...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Great ideas, Governor O'Malley, Secretary Clin...</td>\\n\",\n       \"      <td>27</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>577</th>\\n\",\n       \"      <td>So here's a promise that I make -- and I menti...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>So here's a promise that I make -- and I menti...</td>\\n\",\n       \"      <td>43</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>578</th>\\n\",\n       \"      <td>Here's a promise. If elected president, Goldma...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Here's a promise. If elected president, Goldma...</td>\\n\",\n       \"      <td>24</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>579</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>581</th>\\n\",\n       \"      <td>SANDERS: I was asked a question. You know, one...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>I was asked a question. You know, one of the t...</td>\\n\",\n       \"      <td>58</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>582</th>\\n\",\n       \"      <td>I have avoided doing that. Trying to run an is...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>I have avoided doing that. Trying to run an is...</td>\\n\",\n       \"      <td>11</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>583</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>584</th>\\n\",\n       \"      <td>SANDERS: I was asked a question.</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>I was asked a question.</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>586</th>\\n\",\n       \"      <td>SANDERS: Well -- then if I don't answer it, th...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Well -- then if I don't answer it, then there'...</td>\\n\",\n       \"      <td>17</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>587</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>588</th>\\n\",\n       \"      <td>And I mean this seriously. You know that. We'v...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>And I mean this seriously. You know that. We'v...</td>\\n\",\n       \"      <td>51</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>589</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>596</th>\\n\",\n       \"      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Yes, but we're going to have to get 20 minutes...</td>\\n\",\n       \"      <td>14</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>597</th>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>(LAUGHTER)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>599</th>\\n\",\n       \"      <td>O'MALLEY: I believe there are many issues. I h...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>I believe there are many issues. I have 60 sec...</td>\\n\",\n       \"      <td>12</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>601</th>\\n\",\n       \"      <td>O'MALLEY: There are so many issues that we hav...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>There are so many issues that we haven't been ...</td>\\n\",\n       \"      <td>61</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>602</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>603</th>\\n\",\n       \"      <td>O'MALLEY: We haven't discussed the fact that i...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We haven't discussed the fact that in our hemi...</td>\\n\",\n       \"      <td>27</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>604</th>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>I guess the bottom line is this, look we are a...</td>\\n\",\n       \"      <td>80</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>605</th>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>We're on the threshold of a new era of America...</td>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>607</th>\\n\",\n       \"      <td>O'MALLEY: Thanks a lot.</td>\\n\",\n       \"      <td>O'MALLEY</td>\\n\",\n       \"      <td>Thanks a lot.</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>609</th>\\n\",\n       \"      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>Well Lester, I spent a lot of time last week b...</td>\\n\",\n       \"      <td>72</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>610</th>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>He had request for help and he had basically s...</td>\\n\",\n       \"      <td>38</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>611</th>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>So I sent my top campaign aide down there to t...</td>\\n\",\n       \"      <td>59</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>613</th>\\n\",\n       \"      <td>CLINTON: I want to be a president who takes ca...</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>I want to be a president who takes care of the...</td>\\n\",\n       \"      <td>25</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>614</th>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>CLINTON</td>\\n\",\n       \"      <td>(APPLAUSE)</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>617</th>\\n\",\n       \"      <td>SANDERS: Well, Secretary Clinton was right and...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Well, Secretary Clinton was right and what I d...</td>\\n\",\n       \"      <td>32</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>618</th>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>Now, we are a great nation -- and we've heard ...</td>\\n\",\n       \"      <td>58</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>619</th>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"      <td>SANDERS</td>\\n\",\n       \"      <td>We've got to get rid of Super PACs, we've got ...</td>\\n\",\n       \"      <td>73</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>412 rows × 5 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                   raw   speaker  \\\\\\n\",\n       \"1    CLINTON: Well, good evening. And I want to tha...   CLINTON   \\n\",\n       \"2    You know, I remember well when my youth minist...   CLINTON   \\n\",\n       \"3    And that is our fight still. We have to get th...   CLINTON   \\n\",\n       \"4    I understand that this is the hardest job in t...   CLINTON   \\n\",\n       \"5                                           (APPLAUSE)   CLINTON   \\n\",\n       \"7    SANDERS: Thank you. As we honor the extraordin...   SANDERS   \\n\",\n       \"8    SANDERS: And then, to make a bad situation wor...   SANDERS   \\n\",\n       \"9    This campaign is about a political revolution ...   SANDERS   \\n\",\n       \"13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY   \\n\",\n       \"14   And I want to thank the people of South Caroli...  O'MALLEY   \\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY   \\n\",\n       \"16   Eight years ago, you brought forward a new lea...  O'MALLEY   \\n\",\n       \"17   But in order to make good on the promise of eq...  O'MALLEY   \\n\",\n       \"18   We need new leadership. We need to come togeth...  O'MALLEY   \\n\",\n       \"19   That's why I'm running for president. I need y...  O'MALLEY   \\n\",\n       \"20                                         Thank you.   O'MALLEY   \\n\",\n       \"26   SANDERS: Well, that's what our campaign is abo...   SANDERS   \\n\",\n       \"27   So, what my first days are about is bringing A...   SANDERS   \\n\",\n       \"28                                          (APPLAUSE)   SANDERS   \\n\",\n       \"30   CLINTON: I would work quickly to present to th...   CLINTON   \\n\",\n       \"31                                     I would also...   CLINTON   \\n\",\n       \"32                                          (APPLAUSE)   CLINTON   \\n\",\n       \"33   I would also be presenting my plans to build o...   CLINTON   \\n\",\n       \"34   And third, I would be working, in every way th...   CLINTON   \\n\",\n       \"35                                          (APPLAUSE)   CLINTON   \\n\",\n       \"37   O'MALLEY: Thank you. First of all, I would lay...  O'MALLEY   \\n\",\n       \"38   Secondly, I believe the greatest business oppo...  O'MALLEY   \\n\",\n       \"39                                          (APPLAUSE)  O'MALLEY   \\n\",\n       \"41   O'MALLEY: Finally -- I'm sorry, that was secon...  O'MALLEY   \\n\",\n       \"42   O'MALLEY: And third and finally, we need a new...  O'MALLEY   \\n\",\n       \"..                                                 ...       ...   \\n\",\n       \"573  We just have to do more of it, and we have to ...   CLINTON   \\n\",\n       \"576  SANDERS: Great ideas, Governor O'Malley, Secre...   SANDERS   \\n\",\n       \"577  So here's a promise that I make -- and I menti...   SANDERS   \\n\",\n       \"578  Here's a promise. If elected president, Goldma...   SANDERS   \\n\",\n       \"579                                        (APPLAUSE)    SANDERS   \\n\",\n       \"581  SANDERS: I was asked a question. You know, one...   SANDERS   \\n\",\n       \"582  I have avoided doing that. Trying to run an is...   SANDERS   \\n\",\n       \"583                                         (APPLAUSE)   SANDERS   \\n\",\n       \"584                  SANDERS: I was asked a question.    SANDERS   \\n\",\n       \"586  SANDERS: Well -- then if I don't answer it, th...   SANDERS   \\n\",\n       \"587                                        (LAUGHTER)    SANDERS   \\n\",\n       \"588  And I mean this seriously. You know that. We'v...   SANDERS   \\n\",\n       \"589                                        (APPLAUSE)    SANDERS   \\n\",\n       \"596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY   \\n\",\n       \"597                                         (LAUGHTER)  O'MALLEY   \\n\",\n       \"599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY   \\n\",\n       \"601  O'MALLEY: There are so many issues that we hav...  O'MALLEY   \\n\",\n       \"602                                         (APPLAUSE)  O'MALLEY   \\n\",\n       \"603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY   \\n\",\n       \"604  I guess the bottom line is this, look we are a...  O'MALLEY   \\n\",\n       \"605  We're on the threshold of a new era of America...  O'MALLEY   \\n\",\n       \"607                           O'MALLEY: Thanks a lot.   O'MALLEY   \\n\",\n       \"609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON   \\n\",\n       \"610  He had request for help and he had basically s...   CLINTON   \\n\",\n       \"611  So I sent my top campaign aide down there to t...   CLINTON   \\n\",\n       \"613  CLINTON: I want to be a president who takes ca...   CLINTON   \\n\",\n       \"614                                         (APPLAUSE)   CLINTON   \\n\",\n       \"617  SANDERS: Well, Secretary Clinton was right and...   SANDERS   \\n\",\n       \"618  Now, we are a great nation -- and we've heard ...   SANDERS   \\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...   SANDERS   \\n\",\n       \"\\n\",\n       \"                                                speach  word_count  speaker_no  \\n\",\n       \"1    Well, good evening. And I want to thank the Co...          31           0  \\n\",\n       \"2    You know, I remember well when my youth minist...          67           0  \\n\",\n       \"3    And that is our fight still. We have to get th...          50           0  \\n\",\n       \"4    I understand that this is the hardest job in t...          42           0  \\n\",\n       \"5                                           (APPLAUSE)           1           0  \\n\",\n       \"7    Thank you. As we honor the extraordinary life ...          88           1  \\n\",\n       \"8    And then, to make a bad situation worse, we ha...          28           1  \\n\",\n       \"9    This campaign is about a political revolution ...          18           1  \\n\",\n       \"13   Thank you. My name is Martin O'Malley, I was b...          21           2  \\n\",\n       \"14   And I want to thank the people of South Caroli...          38           2  \\n\",\n       \"15   You taught us, in fact, in keeping with Dr. Ki...          37           2  \\n\",\n       \"16   Eight years ago, you brought forward a new lea...          34           2  \\n\",\n       \"17   But in order to make good on the promise of eq...          47           2  \\n\",\n       \"18   We need new leadership. We need to come togeth...          23           2  \\n\",\n       \"19   That's why I'm running for president. I need y...          26           2  \\n\",\n       \"20                                         Thank you.            2           2  \\n\",\n       \"26   Well, that's what our campaign is about. It is...          68           1  \\n\",\n       \"27   So, what my first days are about is bringing A...          61           1  \\n\",\n       \"28                                          (APPLAUSE)           1           1  \\n\",\n       \"30   I would work quickly to present to the Congres...          35           0  \\n\",\n       \"31                                     I would also...           3           0  \\n\",\n       \"32                                          (APPLAUSE)           1           0  \\n\",\n       \"33   I would also be presenting my plans to build o...          67           0  \\n\",\n       \"34   And third, I would be working, in every way th...          78           0  \\n\",\n       \"35                                          (APPLAUSE)           1           0  \\n\",\n       \"37   Thank you. First of all, I would lay out an ag...          77           2  \\n\",\n       \"38   Secondly, I believe the greatest business oppo...          48           2  \\n\",\n       \"39                                          (APPLAUSE)           1           2  \\n\",\n       \"41      Finally -- I'm sorry, that was second, Lester.           8           2  \\n\",\n       \"42   And third and finally, we need a new agenda fo...          77           2  \\n\",\n       \"..                                                 ...         ...         ...  \\n\",\n       \"573  We just have to do more of it, and we have to ...          32           0  \\n\",\n       \"576  Great ideas, Governor O'Malley, Secretary Clin...          27           1  \\n\",\n       \"577  So here's a promise that I make -- and I menti...          43           1  \\n\",\n       \"578  Here's a promise. If elected president, Goldma...          24           1  \\n\",\n       \"579                                        (APPLAUSE)            1           1  \\n\",\n       \"581  I was asked a question. You know, one of the t...          58           1  \\n\",\n       \"582  I have avoided doing that. Trying to run an is...          11           1  \\n\",\n       \"583                                         (APPLAUSE)           1           1  \\n\",\n       \"584                           I was asked a question.            5           1  \\n\",\n       \"586  Well -- then if I don't answer it, then there'...          17           1  \\n\",\n       \"587                                        (LAUGHTER)            1           1  \\n\",\n       \"588  And I mean this seriously. You know that. We'v...          51           1  \\n\",\n       \"589                                        (APPLAUSE)            1           1  \\n\",\n       \"596  Yes, but we're going to have to get 20 minutes...          14           2  \\n\",\n       \"597                                         (LAUGHTER)           1           2  \\n\",\n       \"599  I believe there are many issues. I have 60 sec...          12           2  \\n\",\n       \"601  There are so many issues that we haven't been ...          61           2  \\n\",\n       \"602                                         (APPLAUSE)           1           2  \\n\",\n       \"603  We haven't discussed the fact that in our hemi...          27           2  \\n\",\n       \"604  I guess the bottom line is this, look we are a...          80           2  \\n\",\n       \"605  We're on the threshold of a new era of America...          33           2  \\n\",\n       \"607                                     Thanks a lot.            3           2  \\n\",\n       \"609  Well Lester, I spent a lot of time last week b...          72           0  \\n\",\n       \"610  He had request for help and he had basically s...          38           0  \\n\",\n       \"611  So I sent my top campaign aide down there to t...          59           0  \\n\",\n       \"613  I want to be a president who takes care of the...          25           0  \\n\",\n       \"614                                         (APPLAUSE)           1           0  \\n\",\n       \"617  Well, Secretary Clinton was right and what I d...          32           1  \\n\",\n       \"618  Now, we are a great nation -- and we've heard ...          58           1  \\n\",\n       \"619  We've got to get rid of Super PACs, we've got ...          73           1  \\n\",\n       \"\\n\",\n       \"[412 rows x 5 columns]\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"words_ds[\\\"speaker_no\\\"] = words_ds.speaker.map(speaker_dict)\\n\",\n    \"words_ds\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  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\"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1938</th>\\n\",\n       \"      <td>leaders</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1939</th>\\n\",\n       \"      <td>boldly</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1940</th>\\n\",\n       \"      <td>lay</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1941</th>\\n\",\n       \"      <td>book</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1942</th>\\n\",\n       \"      <td>border</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1943</th>\\n\",\n       \"      <td>larger</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1944</th>\\n\",\n       \"      <td>land</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1945</th>\\n\",\n       \"      <td>lake</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1946</th>\\n\",\n       \"      <td>laid</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1947</th>\\n\",\n       \"      <td>lady</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1948</th>\\n\",\n       \"      <td>legally</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1949</th>\\n\",\n       \"      <td>less</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1950</th>\\n\",\n       \"      <td>lost</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1951</th>\\n\",\n       \"      <td>lesson</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1952</th>\\n\",\n       \"      <td>loop</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1953</th>\\n\",\n       \"      <td>biosurveillance</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1954</th>\\n\",\n       \"      <td>living</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1955</th>\\n\",\n       \"      <td>blame</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1956</th>\\n\",\n       \"      <td>listened</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1957</th>\\n\",\n       \"      <td>listen</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1958</th>\\n\",\n       \"      <td>lindsey</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1959</th>\\n\",\n       \"      <td>limit</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1960</th>\\n\",\n       \"      <td>block</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1961</th>\\n\",\n       \"      <td>lifting</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1962</th>\\n\",\n       \"      <td>liable</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1963</th>\\n\",\n       \"      <td>liability</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1964</th>\\n\",\n       \"      <td>level</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1965</th>\\n\",\n       \"      <td>lethal</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1966</th>\\n\",\n       \"      <td>bloodiest</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1967</th>\\n\",\n       \"      <td>zero</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1968 rows × 2 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                 word  count\\n\",\n       \"0                 the    546\\n\",\n       \"1                  to    414\\n\",\n       \"2                 and    362\\n\",\n       \"3                  of    293\\n\",\n       \"4                  we    280\\n\",\n       \"5                that    274\\n\",\n       \"6                  in    223\\n\",\n       \"7                  is    157\\n\",\n       \"8                have    144\\n\",\n       \"9                  it    126\\n\",\n       \"10                 on    117\\n\",\n       \"11                you    104\\n\",\n       \"12               what    102\\n\",\n       \"13                for    102\\n\",\n       \"14             people     86\\n\",\n       \"15                our     85\\n\",\n       \"16               with     82\\n\",\n       \"17               this     78\\n\",\n       \"18                 do     71\\n\",\n       \"19                not     69\\n\",\n       \"20                but     62\\n\",\n       \"21                 as     61\\n\",\n       \"22                are     58\\n\",\n       \"23                 be     55\\n\",\n       \"24                can     51\\n\",\n       \"25           applause     49\\n\",\n       \"26               need     49\\n\",\n       \"27                was     48\\n\",\n       \"28                 so     48\\n\",\n       \"29                all     47\\n\",\n       \"...               ...    ...\\n\",\n       \"1938          leaders      1\\n\",\n       \"1939           boldly      1\\n\",\n       \"1940              lay      1\\n\",\n       \"1941             book      1\\n\",\n       \"1942           border      1\\n\",\n       \"1943           larger      1\\n\",\n       \"1944             land      1\\n\",\n       \"1945             lake      1\\n\",\n       \"1946             laid      1\\n\",\n       \"1947             lady      1\\n\",\n       \"1948          legally      1\\n\",\n       \"1949             less      1\\n\",\n       \"1950             lost      1\\n\",\n       \"1951           lesson      1\\n\",\n       \"1952             loop      1\\n\",\n       \"1953  biosurveillance      1\\n\",\n       \"1954           living      1\\n\",\n       \"1955            blame      1\\n\",\n       \"1956         listened      1\\n\",\n       \"1957           listen      1\\n\",\n       \"1958          lindsey      1\\n\",\n       \"1959            limit      1\\n\",\n       \"1960            block      1\\n\",\n       \"1961          lifting      1\\n\",\n       \"1962           liable      1\\n\",\n       \"1963        liability      1\\n\",\n       \"1964            level      1\\n\",\n       \"1965           lethal      1\\n\",\n       \"1966        bloodiest      1\\n\",\n       \"1967             zero      1\\n\",\n       \"\\n\",\n       \"[1968 rows x 2 columns]\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cv = CountVectorizer()\\n\",\n    \"count_matrix = cv.fit_transform(words_ds.speach)\\n\",\n    \"count_matrix = count_matrix.toarray()\\n\",\n    \"\\n\",\n    \"word_count = pd.DataFrame(cv.get_feature_names(), columns=[\\\"word\\\"])\\n\",\n    \"word_count[\\\"count\\\"] = count_matrix.sum(axis=0)\\n\",\n    \"word_count = word_count.sort_values(by=\\\"count\\\", ascending=False).reset_index(drop=True)\\n\",\n    \"word_count[:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Vocab</th>\\n\",\n       \"      <th>Vocab_index</th>\\n\",\n       \"      <th>proba</th>\\n\",\n       \"      <th>anti_proba</th>\\n\",\n       \"      <th>difference</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1064</th>\\n\",\n       \"      <td>major</td>\\n\",\n       \"      <td>1064</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.454097</td>\\n\",\n       \"      <td>-2.626476</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>759</th>\\n\",\n       \"      <td>goldman</td>\\n\",\n       \"      <td>759</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.677241</td>\\n\",\n       \"      <td>-2.403332</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1507</th>\\n\",\n       \"      <td>sachs</td>\\n\",\n       \"      <td>1507</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.677241</td>\\n\",\n       \"      <td>-2.403332</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>423</th>\\n\",\n       \"      <td>countries</td>\\n\",\n       \"      <td>423</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.810772</td>\\n\",\n       \"      <td>-2.269801</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>406</th>\\n\",\n       \"      <td>contributions</td>\\n\",\n       \"      <td>406</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.810772</td>\\n\",\n       \"      <td>-2.269801</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>418</th>\\n\",\n       \"      <td>corrupt</td>\\n\",\n       \"      <td>418</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.810772</td>\\n\",\n       \"      <td>-2.269801</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1627</th>\\n\",\n       \"      <td>spending</td>\\n\",\n       \"      <td>1627</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.964923</td>\\n\",\n       \"      <td>-2.115650</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>381</th>\\n\",\n       \"      <td>companies</td>\\n\",\n       \"      <td>381</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.964923</td>\\n\",\n       \"      <td>-2.115650</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1411</th>\\n\",\n       \"      <td>real</td>\\n\",\n       \"      <td>1411</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.964923</td>\\n\",\n       \"      <td>-2.115650</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1852</th>\\n\",\n       \"      <td>vermont</td>\\n\",\n       \"      <td>1852</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-6.964923</td>\\n\",\n       \"      <td>-2.115650</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>407</th>\\n\",\n       \"      <td>contributors</td>\\n\",\n       \"      <td>407</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1480</th>\\n\",\n       \"      <td>revolution</td>\\n\",\n       \"      <td>1480</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1070</th>\\n\",\n       \"      <td>man</td>\\n\",\n       \"      <td>1070</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1481</th>\\n\",\n       \"      <td>rhetoric</td>\\n\",\n       \"      <td>1481</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1776</th>\\n\",\n       \"      <td>transform</td>\\n\",\n       \"      <td>1776</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1672</th>\\n\",\n       \"      <td>super</td>\\n\",\n       \"      <td>1672</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1484</th>\\n\",\n       \"      <td>rid</td>\\n\",\n       \"      <td>1484</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1782</th>\\n\",\n       \"      <td>treasury</td>\\n\",\n       \"      <td>1782</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1576</th>\\n\",\n       \"      <td>she</td>\\n\",\n       \"      <td>1576</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.147245</td>\\n\",\n       \"      <td>-1.933329</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>447</th>\\n\",\n       \"      <td>crumbling</td>\\n\",\n       \"      <td>447</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>997</th>\\n\",\n       \"      <td>latino</td>\\n\",\n       \"      <td>997</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>795</th>\\n\",\n       \"      <td>hampshire</td>\\n\",\n       \"      <td>795</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1712</th>\\n\",\n       \"      <td>terms</td>\\n\",\n       \"      <td>1712</td>\\n\",\n       \"      <td>-8.387426</td>\\n\",\n       \"      <td>-6.677241</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1675</th>\\n\",\n       \"      <td>supported</td>\\n\",\n       \"      <td>1675</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1346</th>\\n\",\n       \"      <td>private</td>\\n\",\n       \"      <td>1346</td>\\n\",\n       \"      <td>-8.387426</td>\\n\",\n       \"      <td>-6.677241</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1343</th>\\n\",\n       \"      <td>priority</td>\\n\",\n       \"      <td>1343</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>995</th>\\n\",\n       \"      <td>largest</td>\\n\",\n       \"      <td>995</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>151</th>\\n\",\n       \"      <td>area</td>\\n\",\n       \"      <td>151</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>133</th>\\n\",\n       \"      <td>anti</td>\\n\",\n       \"      <td>133</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1238</th>\\n\",\n       \"      <td>pacs</td>\\n\",\n       \"      <td>1238</td>\\n\",\n       \"      <td>-9.080573</td>\\n\",\n       \"      <td>-7.370388</td>\\n\",\n       \"      <td>-1.710185</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>128</th>\\n\",\n       \"      <td>andrea</td>\\n\",\n       \"      <td>128</td>\\n\",\n       \"      <td>-6.307985</td>\\n\",\n       \"      <td>-7.658070</td>\\n\",\n       \"      <td>1.350086</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1719</th>\\n\",\n       \"      <td>thank</td>\\n\",\n       \"      <td>1719</td>\\n\",\n       \"      <td>-6.682678</td>\\n\",\n       \"      <td>-8.063535</td>\\n\",\n       \"      <td>1.380857</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>718</th>\\n\",\n       \"      <td>frank</td>\\n\",\n       \"      <td>718</td>\\n\",\n       \"      <td>-6.682678</td>\\n\",\n       \"      <td>-8.063535</td>\\n\",\n       \"      <td>1.380857</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>23</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>481</th>\\n\",\n       \"      <td>defend</td>\\n\",\n       \"      <td>481</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1084</th>\\n\",\n       \"      <td>matter</td>\\n\",\n       \"      <td>1084</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1799</th>\\n\",\n       \"      <td>try</td>\\n\",\n       \"      <td>1799</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>809</th>\\n\",\n       \"      <td>haven</td>\\n\",\n       \"      <td>809</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>918</th>\\n\",\n       \"      <td>intelligence</td>\\n\",\n       \"      <td>918</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>86</th>\\n\",\n       \"      <td>age</td>\\n\",\n       \"      <td>86</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>621</th>\\n\",\n       \"      <td>equal</td>\\n\",\n       \"      <td>621</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1874</th>\\n\",\n       \"      <td>visiting</td>\\n\",\n       \"      <td>1874</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>156</th>\\n\",\n       \"      <td>around</td>\\n\",\n       \"      <td>156</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>562</th>\\n\",\n       \"      <td>door</td>\\n\",\n       \"      <td>562</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>707</th>\\n\",\n       \"      <td>forces</td>\\n\",\n       \"      <td>707</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1842</th>\\n\",\n       \"      <td>use</td>\\n\",\n       \"      <td>1842</td>\\n\",\n       \"      <td>-7.288814</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.467869</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1957</th>\\n\",\n       \"      <td>year</td>\\n\",\n       \"      <td>1957</td>\\n\",\n       \"      <td>-6.515624</td>\\n\",\n       \"      <td>-8.063535</td>\\n\",\n       \"      <td>1.547911</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>972</th>\\n\",\n       \"      <td>keep</td>\\n\",\n       \"      <td>972</td>\\n\",\n       \"      <td>-7.134663</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.622019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>273</th>\\n\",\n       \"      <td>build</td>\\n\",\n       \"      <td>273</td>\\n\",\n       \"      <td>-7.134663</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.622019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1634</th>\\n\",\n       \"      <td>stage</td>\\n\",\n       \"      <td>1634</td>\\n\",\n       \"      <td>-7.001132</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.755551</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>59</th>\\n\",\n       \"      <td>actually</td>\\n\",\n       \"      <td>59</td>\\n\",\n       \"      <td>-6.307985</td>\\n\",\n       \"      <td>-8.063535</td>\\n\",\n       \"      <td>1.755551</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>169</th>\\n\",\n       \"      <td>attacks</td>\\n\",\n       \"      <td>169</td>\\n\",\n       \"      <td>-7.001132</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.755551</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>420</th>\\n\",\n       \"      <td>costs</td>\\n\",\n       \"      <td>420</td>\\n\",\n       \"      <td>-7.001132</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.755551</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>791</th>\\n\",\n       \"      <td>had</td>\\n\",\n       \"      <td>791</td>\\n\",\n       \"      <td>-6.307985</td>\\n\",\n       \"      <td>-8.063535</td>\\n\",\n       \"      <td>1.755551</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>365</th>\\n\",\n       \"      <td>come</td>\\n\",\n       \"      <td>365</td>\\n\",\n       \"      <td>-7.001132</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.755551</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1589</th>\\n\",\n       \"      <td>since</td>\\n\",\n       \"      <td>1589</td>\\n\",\n       \"      <td>-6.777988</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.978694</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>553</th>\\n\",\n       \"      <td>dodd</td>\\n\",\n       \"      <td>553</td>\\n\",\n       \"      <td>-6.777988</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.978694</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>84</th>\\n\",\n       \"      <td>again</td>\\n\",\n       \"      <td>84</td>\\n\",\n       \"      <td>-6.777988</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>1.978694</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1281</th>\\n\",\n       \"      <td>plan</td>\\n\",\n       \"      <td>1281</td>\\n\",\n       \"      <td>-6.682678</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>2.074004</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1021</th>\\n\",\n       \"      <td>lester</td>\\n\",\n       \"      <td>1021</td>\\n\",\n       \"      <td>-6.682678</td>\\n\",\n       \"      <td>-8.756682</td>\\n\",\n       \"      <td>2.074004</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1968 rows × 5 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              Vocab  Vocab_index     proba  anti_proba  difference\\n\",\n       \"1064          major         1064 -9.080573   -6.454097   -2.626476\\n\",\n       \"759         goldman          759 -9.080573   -6.677241   -2.403332\\n\",\n       \"1507          sachs         1507 -9.080573   -6.677241   -2.403332\\n\",\n       \"423       countries          423 -9.080573   -6.810772   -2.269801\\n\",\n       \"406   contributions          406 -9.080573   -6.810772   -2.269801\\n\",\n       \"418         corrupt          418 -9.080573   -6.810772   -2.269801\\n\",\n       \"1627       spending         1627 -9.080573   -6.964923   -2.115650\\n\",\n       \"381       companies          381 -9.080573   -6.964923   -2.115650\\n\",\n       \"1411           real         1411 -9.080573   -6.964923   -2.115650\\n\",\n       \"1852        vermont         1852 -9.080573   -6.964923   -2.115650\\n\",\n       \"407    contributors          407 -9.080573   -7.147245   -1.933329\\n\",\n       \"1480     revolution         1480 -9.080573   -7.147245   -1.933329\\n\",\n       \"1070            man         1070 -9.080573   -7.147245   -1.933329\\n\",\n       \"1481       rhetoric         1481 -9.080573   -7.147245   -1.933329\\n\",\n       \"1776      transform         1776 -9.080573   -7.147245   -1.933329\\n\",\n       \"1672          super         1672 -9.080573   -7.147245   -1.933329\\n\",\n       \"1484            rid         1484 -9.080573   -7.147245   -1.933329\\n\",\n       \"1782       treasury         1782 -9.080573   -7.147245   -1.933329\\n\",\n       \"1576            she         1576 -9.080573   -7.147245   -1.933329\\n\",\n       \"447       crumbling          447 -9.080573   -7.370388   -1.710185\\n\",\n       \"997          latino          997 -9.080573   -7.370388   -1.710185\\n\",\n       \"795       hampshire          795 -9.080573   -7.370388   -1.710185\\n\",\n       \"1712          terms         1712 -8.387426   -6.677241   -1.710185\\n\",\n       \"1675      supported         1675 -9.080573   -7.370388   -1.710185\\n\",\n       \"1346        private         1346 -8.387426   -6.677241   -1.710185\\n\",\n       \"1343       priority         1343 -9.080573   -7.370388   -1.710185\\n\",\n       \"995         largest          995 -9.080573   -7.370388   -1.710185\\n\",\n       \"151            area          151 -9.080573   -7.370388   -1.710185\\n\",\n       \"133            anti          133 -9.080573   -7.370388   -1.710185\\n\",\n       \"1238           pacs         1238 -9.080573   -7.370388   -1.710185\\n\",\n       \"...             ...          ...       ...         ...         ...\\n\",\n       \"128          andrea          128 -6.307985   -7.658070    1.350086\\n\",\n       \"1719          thank         1719 -6.682678   -8.063535    1.380857\\n\",\n       \"718           frank          718 -6.682678   -8.063535    1.380857\\n\",\n       \"23               30           23 -7.288814   -8.756682    1.467869\\n\",\n       \"481          defend          481 -7.288814   -8.756682    1.467869\\n\",\n       \"1084         matter         1084 -7.288814   -8.756682    1.467869\\n\",\n       \"1799            try         1799 -7.288814   -8.756682    1.467869\\n\",\n       \"809           haven          809 -7.288814   -8.756682    1.467869\\n\",\n       \"918    intelligence          918 -7.288814   -8.756682    1.467869\\n\",\n       \"86              age           86 -7.288814   -8.756682    1.467869\\n\",\n       \"621           equal          621 -7.288814   -8.756682    1.467869\\n\",\n       \"1874       visiting         1874 -7.288814   -8.756682    1.467869\\n\",\n       \"156          around          156 -7.288814   -8.756682    1.467869\\n\",\n       \"562            door          562 -7.288814   -8.756682    1.467869\\n\",\n       \"707          forces          707 -7.288814   -8.756682    1.467869\\n\",\n       \"1842            use         1842 -7.288814   -8.756682    1.467869\\n\",\n       \"1957           year         1957 -6.515624   -8.063535    1.547911\\n\",\n       \"972            keep          972 -7.134663   -8.756682    1.622019\\n\",\n       \"273           build          273 -7.134663   -8.756682    1.622019\\n\",\n       \"1634          stage         1634 -7.001132   -8.756682    1.755551\\n\",\n       \"59         actually           59 -6.307985   -8.063535    1.755551\\n\",\n       \"169         attacks          169 -7.001132   -8.756682    1.755551\\n\",\n       \"420           costs          420 -7.001132   -8.756682    1.755551\\n\",\n       \"791             had          791 -6.307985   -8.063535    1.755551\\n\",\n       \"365            come          365 -7.001132   -8.756682    1.755551\\n\",\n       \"1589          since         1589 -6.777988   -8.756682    1.978694\\n\",\n       \"553            dodd          553 -6.777988   -8.756682    1.978694\\n\",\n       \"84            again           84 -6.777988   -8.756682    1.978694\\n\",\n       \"1281           plan         1281 -6.682678   -8.756682    2.074004\\n\",\n       \"1021         lester         1021 -6.682678   -8.756682    2.074004\\n\",\n       \"\\n\",\n       \"[1968 rows x 5 columns]\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cl = MultinomialNB()\\n\",\n    \"cl.fit(count_matrix, words_ds.speaker==\\\"SANDERS\\\")\\n\",\n    \"\\n\",\n    \"df_vocab = pd.DataFrame(list(cv.vocabulary_.keys()), columns=[\\\"Vocab\\\"])\\n\",\n    \"df_vocab[\\\"Vocab_index\\\"] = cv.vocabulary_.values()\\n\",\n    \"df_vocab = df_vocab.sort_values(\\\"Vocab_index\\\").reset_index(drop=True)\\n\",\n    \"df_vocab[\\\"proba\\\"] = cl.feature_log_prob_[0]\\n\",\n    \"df_vocab[\\\"anti_proba\\\"] = cl.feature_log_prob_[1]\\n\",\n    \"df_vocab[\\\"difference\\\"] = cl.feature_log_prob_[0] - cl.feature_log_prob_[1]\\n\",\n    \"df_vocab.sort_values(\\\"difference\\\", ascending=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Vocab</th>\\n\",\n       \"      <th>Vocab_index</th>\\n\",\n       \"      <th>proba</th>\\n\",\n       \"      <th>anti_proba</th>\\n\",\n       \"      <th>difference</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>553</th>\\n\",\n       \"      <td>dodd</td>\\n\",\n       \"      <td>553</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-6.410175</td>\\n\",\n       \"      <td>-2.701008</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1799</th>\\n\",\n       \"      <td>try</td>\\n\",\n       \"      <td>1799</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-6.921001</td>\\n\",\n       \"      <td>-2.190182</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>718</th>\\n\",\n       \"      <td>frank</td>\\n\",\n       \"      <td>718</td>\\n\",\n       \"      <td>-8.418036</td>\\n\",\n       \"      <td>-6.314865</td>\\n\",\n       \"      <td>-2.103171</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1925</th>\\n\",\n       \"      <td>white</td>\\n\",\n       \"      <td>1925</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.103322</td>\\n\",\n       \"      <td>-2.007861</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1287</th>\\n\",\n       \"      <td>pleased</td>\\n\",\n       \"      <td>1287</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.103322</td>\\n\",\n       \"      <td>-2.007861</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1608</th>\\n\",\n       \"      <td>someone</td>\\n\",\n       \"      <td>1608</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.103322</td>\\n\",\n       \"      <td>-2.007861</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1628</th>\\n\",\n       \"      <td>spent</td>\\n\",\n       \"      <td>1628</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.103322</td>\\n\",\n       \"      <td>-2.007861</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>625</th>\\n\",\n       \"      <td>especially</td>\\n\",\n       \"      <td>625</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.103322</td>\\n\",\n       \"      <td>-2.007861</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>927</th>\\n\",\n       \"      <td>introduced</td>\\n\",\n       \"      <td>927</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.103322</td>\\n\",\n       \"      <td>-2.007861</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1487</th>\\n\",\n       \"      <td>rights</td>\\n\",\n       \"      <td>1487</td>\\n\",\n       \"      <td>-8.418036</td>\\n\",\n       \"      <td>-6.515535</td>\\n\",\n       \"      <td>-1.902500</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1516</th>\\n\",\n       \"      <td>sanctions</td>\\n\",\n       \"      <td>1516</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1349</th>\\n\",\n       \"      <td>problem</td>\\n\",\n       \"      <td>1349</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>895</th>\\n\",\n       \"      <td>incomes</td>\\n\",\n       \"      <td>895</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>650</th>\\n\",\n       \"      <td>experience</td>\\n\",\n       \"      <td>650</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>370</th>\\n\",\n       \"      <td>comments</td>\\n\",\n       \"      <td>370</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1912</th>\\n\",\n       \"      <td>week</td>\\n\",\n       \"      <td>1912</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>679</th>\\n\",\n       \"      <td>fighters</td>\\n\",\n       \"      <td>679</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>147</th>\\n\",\n       \"      <td>approach</td>\\n\",\n       \"      <td>147</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1544</th>\\n\",\n       \"      <td>sector</td>\\n\",\n       \"      <td>1544</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1563</th>\\n\",\n       \"      <td>serious</td>\\n\",\n       \"      <td>1563</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>2011</td>\\n\",\n       \"      <td>16</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>-1.784717</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>791</th>\\n\",\n       \"      <td>had</td>\\n\",\n       \"      <td>791</td>\\n\",\n       \"      <td>-7.724888</td>\\n\",\n       \"      <td>-6.073703</td>\\n\",\n       \"      <td>-1.651186</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>420</th>\\n\",\n       \"      <td>costs</td>\\n\",\n       \"      <td>420</td>\\n\",\n       \"      <td>-8.418036</td>\\n\",\n       \"      <td>-6.766850</td>\\n\",\n       \"      <td>-1.651186</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1877</th>\\n\",\n       \"      <td>voted</td>\\n\",\n       \"      <td>1877</td>\\n\",\n       \"      <td>-8.012571</td>\\n\",\n       \"      <td>-6.515535</td>\\n\",\n       \"      <td>-1.497035</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1639</th>\\n\",\n       \"      <td>standing</td>\\n\",\n       \"      <td>1639</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.614148</td>\\n\",\n       \"      <td>-1.497035</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1786</th>\\n\",\n       \"      <td>treaty</td>\\n\",\n       \"      <td>1786</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.614148</td>\\n\",\n       \"      <td>-1.497035</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>55</th>\\n\",\n       \"      <td>acted</td>\\n\",\n       \"      <td>55</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.614148</td>\\n\",\n       \"      <td>-1.497035</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>699</th>\\n\",\n       \"      <td>flint</td>\\n\",\n       \"      <td>699</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.614148</td>\\n\",\n       \"      <td>-1.497035</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1878</th>\\n\",\n       \"      <td>votes</td>\\n\",\n       \"      <td>1878</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.614148</td>\\n\",\n       \"      <td>-1.497035</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1041</th>\\n\",\n       \"      <td>lone</td>\\n\",\n       \"      <td>1041</td>\\n\",\n       \"      <td>-9.111183</td>\\n\",\n       \"      <td>-7.614148</td>\\n\",\n       \"      <td>-1.497035</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1274</th>\\n\",\n       \"      <td>ph</td>\\n\",\n       \"      <td>1274</td>\\n\",\n       \"      <td>-7.319423</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.393337</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1371</th>\\n\",\n       \"      <td>provide</td>\\n\",\n       \"      <td>1371</td>\\n\",\n       \"      <td>-7.319423</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.393337</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1852</th>\\n\",\n       \"      <td>vermont</td>\\n\",\n       \"      <td>1852</td>\\n\",\n       \"      <td>-7.319423</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.393337</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>15</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>-7.319423</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.393337</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>809</th>\\n\",\n       \"      <td>haven</td>\\n\",\n       \"      <td>809</td>\\n\",\n       \"      <td>-7.319423</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.393337</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>30</td>\\n\",\n       \"      <td>23</td>\\n\",\n       \"      <td>-7.319423</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.393337</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1935</th>\\n\",\n       \"      <td>without</td>\\n\",\n       \"      <td>1935</td>\\n\",\n       \"      <td>-7.319423</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.393337</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>711</th>\\n\",\n       \"      <td>forward</td>\\n\",\n       \"      <td>711</td>\\n\",\n       \"      <td>-6.546233</td>\\n\",\n       \"      <td>-8.019613</td>\\n\",\n       \"      <td>1.473379</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1540</th>\\n\",\n       \"      <td>seconds</td>\\n\",\n       \"      <td>1540</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>632</th>\\n\",\n       \"      <td>ever</td>\\n\",\n       \"      <td>632</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>729</th>\\n\",\n       \"      <td>front</td>\\n\",\n       \"      <td>729</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>271</th>\\n\",\n       \"      <td>budget</td>\\n\",\n       \"      <td>271</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1792</th>\\n\",\n       \"      <td>true</td>\\n\",\n       \"      <td>1792</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>418</th>\\n\",\n       \"      <td>corrupt</td>\\n\",\n       \"      <td>418</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1271</th>\\n\",\n       \"      <td>person</td>\\n\",\n       \"      <td>1271</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1267</th>\\n\",\n       \"      <td>percent</td>\\n\",\n       \"      <td>1267</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>406</th>\\n\",\n       \"      <td>contributions</td>\\n\",\n       \"      <td>406</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>561</th>\\n\",\n       \"      <td>done</td>\\n\",\n       \"      <td>561</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>952</th>\\n\",\n       \"      <td>issues</td>\\n\",\n       \"      <td>952</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>423</th>\\n\",\n       \"      <td>countries</td>\\n\",\n       \"      <td>423</td>\\n\",\n       \"      <td>-7.165273</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.547487</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1537</th>\\n\",\n       \"      <td>second</td>\\n\",\n       \"      <td>1537</td>\\n\",\n       \"      <td>-7.031741</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.681019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1542</th>\\n\",\n       \"      <td>secretary</td>\\n\",\n       \"      <td>1542</td>\\n\",\n       \"      <td>-5.645447</td>\\n\",\n       \"      <td>-7.326466</td>\\n\",\n       \"      <td>1.681019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1507</th>\\n\",\n       \"      <td>sachs</td>\\n\",\n       \"      <td>1507</td>\\n\",\n       \"      <td>-7.031741</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.681019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>759</th>\\n\",\n       \"      <td>goldman</td>\\n\",\n       \"      <td>759</td>\\n\",\n       \"      <td>-7.031741</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.681019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>128</th>\\n\",\n       \"      <td>andrea</td>\\n\",\n       \"      <td>128</td>\\n\",\n       \"      <td>-6.277969</td>\\n\",\n       \"      <td>-8.019613</td>\\n\",\n       \"      <td>1.741643</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1731</th>\\n\",\n       \"      <td>things</td>\\n\",\n       \"      <td>1731</td>\\n\",\n       \"      <td>-6.220811</td>\\n\",\n       \"      <td>-8.019613</td>\\n\",\n       \"      <td>1.798802</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1712</th>\\n\",\n       \"      <td>terms</td>\\n\",\n       \"      <td>1712</td>\\n\",\n       \"      <td>-6.913958</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.798802</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1064</th>\\n\",\n       \"      <td>major</td>\\n\",\n       \"      <td>1064</td>\\n\",\n       \"      <td>-6.808598</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>1.904162</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1728</th>\\n\",\n       \"      <td>these</td>\\n\",\n       \"      <td>1728</td>\\n\",\n       \"      <td>-6.472125</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>2.240635</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>350</th>\\n\",\n       \"      <td>clinton</td>\\n\",\n       \"      <td>350</td>\\n\",\n       \"      <td>-6.066660</td>\\n\",\n       \"      <td>-8.712760</td>\\n\",\n       \"      <td>2.646100</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1968 rows × 5 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              Vocab  Vocab_index     proba  anti_proba  difference\\n\",\n       \"553            dodd          553 -9.111183   -6.410175   -2.701008\\n\",\n       \"1799            try         1799 -9.111183   -6.921001   -2.190182\\n\",\n       \"718           frank          718 -8.418036   -6.314865   -2.103171\\n\",\n       \"1925          white         1925 -9.111183   -7.103322   -2.007861\\n\",\n       \"1287        pleased         1287 -9.111183   -7.103322   -2.007861\\n\",\n       \"1608        someone         1608 -9.111183   -7.103322   -2.007861\\n\",\n       \"1628          spent         1628 -9.111183   -7.103322   -2.007861\\n\",\n       \"625      especially          625 -9.111183   -7.103322   -2.007861\\n\",\n       \"927      introduced          927 -9.111183   -7.103322   -2.007861\\n\",\n       \"1487         rights         1487 -8.418036   -6.515535   -1.902500\\n\",\n       \"1516      sanctions         1516 -9.111183   -7.326466   -1.784717\\n\",\n       \"1349        problem         1349 -9.111183   -7.326466   -1.784717\\n\",\n       \"895         incomes          895 -9.111183   -7.326466   -1.784717\\n\",\n       \"650      experience          650 -9.111183   -7.326466   -1.784717\\n\",\n       \"370        comments          370 -9.111183   -7.326466   -1.784717\\n\",\n       \"1912           week         1912 -9.111183   -7.326466   -1.784717\\n\",\n       \"679        fighters          679 -9.111183   -7.326466   -1.784717\\n\",\n       \"147        approach          147 -9.111183   -7.326466   -1.784717\\n\",\n       \"1544         sector         1544 -9.111183   -7.326466   -1.784717\\n\",\n       \"1563        serious         1563 -9.111183   -7.326466   -1.784717\\n\",\n       \"16             2011           16 -9.111183   -7.326466   -1.784717\\n\",\n       \"791             had          791 -7.724888   -6.073703   -1.651186\\n\",\n       \"420           costs          420 -8.418036   -6.766850   -1.651186\\n\",\n       \"1877          voted         1877 -8.012571   -6.515535   -1.497035\\n\",\n       \"1639       standing         1639 -9.111183   -7.614148   -1.497035\\n\",\n       \"1786         treaty         1786 -9.111183   -7.614148   -1.497035\\n\",\n       \"55            acted           55 -9.111183   -7.614148   -1.497035\\n\",\n       \"699           flint          699 -9.111183   -7.614148   -1.497035\\n\",\n       \"1878          votes         1878 -9.111183   -7.614148   -1.497035\\n\",\n       \"1041           lone         1041 -9.111183   -7.614148   -1.497035\\n\",\n       \"...             ...          ...       ...         ...         ...\\n\",\n       \"1274             ph         1274 -7.319423   -8.712760    1.393337\\n\",\n       \"1371        provide         1371 -7.319423   -8.712760    1.393337\\n\",\n       \"1852        vermont         1852 -7.319423   -8.712760    1.393337\\n\",\n       \"7                15            7 -7.319423   -8.712760    1.393337\\n\",\n       \"809           haven          809 -7.319423   -8.712760    1.393337\\n\",\n       \"23               30           23 -7.319423   -8.712760    1.393337\\n\",\n       \"1935        without         1935 -7.319423   -8.712760    1.393337\\n\",\n       \"711         forward          711 -6.546233   -8.019613    1.473379\\n\",\n       \"1540        seconds         1540 -7.165273   -8.712760    1.547487\\n\",\n       \"632            ever          632 -7.165273   -8.712760    1.547487\\n\",\n       \"729           front          729 -7.165273   -8.712760    1.547487\\n\",\n       \"271          budget          271 -7.165273   -8.712760    1.547487\\n\",\n       \"1792           true         1792 -7.165273   -8.712760    1.547487\\n\",\n       \"418         corrupt          418 -7.165273   -8.712760    1.547487\\n\",\n       \"1271         person         1271 -7.165273   -8.712760    1.547487\\n\",\n       \"1267        percent         1267 -7.165273   -8.712760    1.547487\\n\",\n       \"406   contributions          406 -7.165273   -8.712760    1.547487\\n\",\n       \"561            done          561 -7.165273   -8.712760    1.547487\\n\",\n       \"952          issues          952 -7.165273   -8.712760    1.547487\\n\",\n       \"423       countries          423 -7.165273   -8.712760    1.547487\\n\",\n       \"1537         second         1537 -7.031741   -8.712760    1.681019\\n\",\n       \"1542      secretary         1542 -5.645447   -7.326466    1.681019\\n\",\n       \"1507          sachs         1507 -7.031741   -8.712760    1.681019\\n\",\n       \"759         goldman          759 -7.031741   -8.712760    1.681019\\n\",\n       \"128          andrea          128 -6.277969   -8.019613    1.741643\\n\",\n       \"1731         things         1731 -6.220811   -8.019613    1.798802\\n\",\n       \"1712          terms         1712 -6.913958   -8.712760    1.798802\\n\",\n       \"1064          major         1064 -6.808598   -8.712760    1.904162\\n\",\n       \"1728          these         1728 -6.472125   -8.712760    2.240635\\n\",\n       \"350         clinton          350 -6.066660   -8.712760    2.646100\\n\",\n       \"\\n\",\n       \"[1968 rows x 5 columns]\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cl = MultinomialNB()\\n\",\n    \"cl.fit(count_matrix, words_ds.speaker==\\\"CLINTON\\\")\\n\",\n    \"\\n\",\n    \"df_vocab = pd.DataFrame(list(cv.vocabulary_.keys()), columns=[\\\"Vocab\\\"])\\n\",\n    \"df_vocab[\\\"Vocab_index\\\"] = cv.vocabulary_.values()\\n\",\n    \"df_vocab = df_vocab.sort_values(\\\"Vocab_index\\\").reset_index(drop=True)\\n\",\n    \"df_vocab[\\\"proba\\\"] = cl.feature_log_prob_[0]\\n\",\n    \"df_vocab[\\\"anti_proba\\\"] = cl.feature_log_prob_[1]\\n\",\n    \"df_vocab[\\\"difference\\\"] = cl.feature_log_prob_[0] - cl.feature_log_prob_[1]\\n\",\n    \"df_vocab.sort_values(\\\"difference\\\", ascending=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Vocab</th>\\n\",\n       \"      <th>Vocab_index</th>\\n\",\n       \"      <th>proba</th>\\n\",\n       \"      <th>anti_proba</th>\\n\",\n       \"      <th>difference</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>983</th>\\n\",\n       \"      <td>knew</td>\\n\",\n       \"      <td>983</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1129</th>\\n\",\n       \"      <td>momentum</td>\\n\",\n       \"      <td>1129</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1127</th>\\n\",\n       \"      <td>mom</td>\\n\",\n       \"      <td>1127</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1124</th>\\n\",\n       \"      <td>mixed</td>\\n\",\n       \"      <td>1124</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1122</th>\\n\",\n       \"      <td>mission</td>\\n\",\n       \"      <td>1122</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1121</th>\\n\",\n       \"      <td>missing</td>\\n\",\n       \"      <td>1121</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1120</th>\\n\",\n       \"      <td>minutes</td>\\n\",\n       \"      <td>1120</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1119</th>\\n\",\n       \"      <td>minus</td>\\n\",\n       \"      <td>1119</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1131</th>\\n\",\n       \"      <td>months</td>\\n\",\n       \"      <td>1131</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1116</th>\\n\",\n       \"      <td>mindful</td>\\n\",\n       \"      <td>1116</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1111</th>\\n\",\n       \"      <td>militarize</td>\\n\",\n       \"      <td>1111</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1109</th>\\n\",\n       \"      <td>midst</td>\\n\",\n       \"      <td>1109</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1107</th>\\n\",\n       \"      <td>michigan</td>\\n\",\n       \"      <td>1107</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1105</th>\\n\",\n       \"      <td>message</td>\\n\",\n       \"      <td>1105</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1103</th>\\n\",\n       \"      <td>mention</td>\\n\",\n       \"      <td>1103</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1102</th>\\n\",\n       \"      <td>mentally</td>\\n\",\n       \"      <td>1102</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1099</th>\\n\",\n       \"      <td>memphis</td>\\n\",\n       \"      <td>1099</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1114</th>\\n\",\n       \"      <td>millionaires</td>\\n\",\n       \"      <td>1114</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1132</th>\\n\",\n       \"      <td>moon</td>\\n\",\n       \"      <td>1132</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1133</th>\\n\",\n       \"      <td>moral</td>\\n\",\n       \"      <td>1133</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1140</th>\\n\",\n       \"      <td>moving</td>\\n\",\n       \"      <td>1140</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1173</th>\\n\",\n       \"      <td>nothing</td>\\n\",\n       \"      <td>1173</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1171</th>\\n\",\n       \"      <td>normalize</td>\\n\",\n       \"      <td>1171</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1170</th>\\n\",\n       \"      <td>normalization</td>\\n\",\n       \"      <td>1170</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1169</th>\\n\",\n       \"      <td>nonsense</td>\\n\",\n       \"      <td>1169</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1165</th>\\n\",\n       \"      <td>nightmare</td>\\n\",\n       \"      <td>1165</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1164</th>\\n\",\n       \"      <td>nickel</td>\\n\",\n       \"      <td>1164</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1160</th>\\n\",\n       \"      <td>neither</td>\\n\",\n       \"      <td>1160</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1159</th>\\n\",\n       \"      <td>neighbors</td>\\n\",\n       \"      <td>1159</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1158</th>\\n\",\n       \"      <td>negotiating</td>\\n\",\n       \"      <td>1158</td>\\n\",\n       \"      <td>-8.792398</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>-1.207625</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>103</th>\\n\",\n       \"      <td>all</td>\\n\",\n       \"      <td>103</td>\\n\",\n       \"      <td>-5.614344</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>1.970429</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1602</th>\\n\",\n       \"      <td>so</td>\\n\",\n       \"      <td>1602</td>\\n\",\n       \"      <td>-5.593725</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>1.991048</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1897</th>\\n\",\n       \"      <td>was</td>\\n\",\n       \"      <td>1897</td>\\n\",\n       \"      <td>-5.593725</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>1.991048</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>144</th>\\n\",\n       \"      <td>applause</td>\\n\",\n       \"      <td>144</td>\\n\",\n       \"      <td>-5.573522</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.011251</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1154</th>\\n\",\n       \"      <td>need</td>\\n\",\n       \"      <td>1154</td>\\n\",\n       \"      <td>-5.573522</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.011251</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>293</th>\\n\",\n       \"      <td>can</td>\\n\",\n       \"      <td>293</td>\\n\",\n       \"      <td>-5.534301</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.050472</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>202</th>\\n\",\n       \"      <td>be</td>\\n\",\n       \"      <td>202</td>\\n\",\n       \"      <td>-5.460193</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.124580</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>150</th>\\n\",\n       \"      <td>are</td>\\n\",\n       \"      <td>150</td>\\n\",\n       \"      <td>-5.408007</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.176766</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>158</th>\\n\",\n       \"      <td>as</td>\\n\",\n       \"      <td>158</td>\\n\",\n       \"      <td>-5.358411</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.226363</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>284</th>\\n\",\n       \"      <td>but</td>\\n\",\n       \"      <td>284</td>\\n\",\n       \"      <td>-5.342410</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.242363</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1172</th>\\n\",\n       \"      <td>not</td>\\n\",\n       \"      <td>1172</td>\\n\",\n       \"      <td>-5.237050</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.347723</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>551</th>\\n\",\n       \"      <td>do</td>\\n\",\n       \"      <td>551</td>\\n\",\n       \"      <td>-5.208879</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.375894</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1737</th>\\n\",\n       \"      <td>this</td>\\n\",\n       \"      <td>1737</td>\\n\",\n       \"      <td>-5.116097</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.468676</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1933</th>\\n\",\n       \"      <td>with</td>\\n\",\n       \"      <td>1933</td>\\n\",\n       \"      <td>-5.066704</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.518069</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1223</th>\\n\",\n       \"      <td>our</td>\\n\",\n       \"      <td>1223</td>\\n\",\n       \"      <td>-5.031198</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.553575</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1265</th>\\n\",\n       \"      <td>people</td>\\n\",\n       \"      <td>1265</td>\\n\",\n       \"      <td>-5.019637</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.565136</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>705</th>\\n\",\n       \"      <td>for</td>\\n\",\n       \"      <td>705</td>\\n\",\n       \"      <td>-4.850816</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.733957</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1917</th>\\n\",\n       \"      <td>what</td>\\n\",\n       \"      <td>1917</td>\\n\",\n       \"      <td>-4.850816</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.733957</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1961</th>\\n\",\n       \"      <td>you</td>\\n\",\n       \"      <td>1961</td>\\n\",\n       \"      <td>-4.831585</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.753189</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1198</th>\\n\",\n       \"      <td>on</td>\\n\",\n       \"      <td>1198</td>\\n\",\n       \"      <td>-4.714860</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.869913</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>953</th>\\n\",\n       \"      <td>it</td>\\n\",\n       \"      <td>953</td>\\n\",\n       \"      <td>-4.641358</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>2.943415</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>808</th>\\n\",\n       \"      <td>have</td>\\n\",\n       \"      <td>808</td>\\n\",\n       \"      <td>-4.508811</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>3.075962</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>943</th>\\n\",\n       \"      <td>is</td>\\n\",\n       \"      <td>943</td>\\n\",\n       \"      <td>-4.422950</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>3.161823</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>886</th>\\n\",\n       \"      <td>in</td>\\n\",\n       \"      <td>886</td>\\n\",\n       \"      <td>-4.073899</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>3.510874</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1721</th>\\n\",\n       \"      <td>that</td>\\n\",\n       \"      <td>1721</td>\\n\",\n       \"      <td>-3.868774</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>3.715999</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1903</th>\\n\",\n       \"      <td>we</td>\\n\",\n       \"      <td>1903</td>\\n\",\n       \"      <td>-3.847190</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>3.737583</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1186</th>\\n\",\n       \"      <td>of</td>\\n\",\n       \"      <td>1186</td>\\n\",\n       \"      <td>-3.801965</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>3.782808</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>127</th>\\n\",\n       \"      <td>and</td>\\n\",\n       \"      <td>127</td>\\n\",\n       \"      <td>-3.591142</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>3.993631</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1751</th>\\n\",\n       \"      <td>to</td>\\n\",\n       \"      <td>1751</td>\\n\",\n       \"      <td>-3.457266</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>4.127507</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1722</th>\\n\",\n       \"      <td>the</td>\\n\",\n       \"      <td>1722</td>\\n\",\n       \"      <td>-3.181096</td>\\n\",\n       \"      <td>-7.584773</td>\\n\",\n       \"      <td>4.403677</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>1968 rows × 5 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"              Vocab  Vocab_index     proba  anti_proba  difference\\n\",\n       \"983            knew          983 -8.792398   -7.584773   -1.207625\\n\",\n       \"1129       momentum         1129 -8.792398   -7.584773   -1.207625\\n\",\n       \"1127            mom         1127 -8.792398   -7.584773   -1.207625\\n\",\n       \"1124          mixed         1124 -8.792398   -7.584773   -1.207625\\n\",\n       \"1122        mission         1122 -8.792398   -7.584773   -1.207625\\n\",\n       \"1121        missing         1121 -8.792398   -7.584773   -1.207625\\n\",\n       \"1120        minutes         1120 -8.792398   -7.584773   -1.207625\\n\",\n       \"1119          minus         1119 -8.792398   -7.584773   -1.207625\\n\",\n       \"1131         months         1131 -8.792398   -7.584773   -1.207625\\n\",\n       \"1116        mindful         1116 -8.792398   -7.584773   -1.207625\\n\",\n       \"1111     militarize         1111 -8.792398   -7.584773   -1.207625\\n\",\n       \"1109          midst         1109 -8.792398   -7.584773   -1.207625\\n\",\n       \"1107       michigan         1107 -8.792398   -7.584773   -1.207625\\n\",\n       \"1105        message         1105 -8.792398   -7.584773   -1.207625\\n\",\n       \"1103        mention         1103 -8.792398   -7.584773   -1.207625\\n\",\n       \"1102       mentally         1102 -8.792398   -7.584773   -1.207625\\n\",\n       \"1099        memphis         1099 -8.792398   -7.584773   -1.207625\\n\",\n       \"1114   millionaires         1114 -8.792398   -7.584773   -1.207625\\n\",\n       \"1132           moon         1132 -8.792398   -7.584773   -1.207625\\n\",\n       \"1133          moral         1133 -8.792398   -7.584773   -1.207625\\n\",\n       \"1140         moving         1140 -8.792398   -7.584773   -1.207625\\n\",\n       \"1173        nothing         1173 -8.792398   -7.584773   -1.207625\\n\",\n       \"1171      normalize         1171 -8.792398   -7.584773   -1.207625\\n\",\n       \"1170  normalization         1170 -8.792398   -7.584773   -1.207625\\n\",\n       \"1169       nonsense         1169 -8.792398   -7.584773   -1.207625\\n\",\n       \"1165      nightmare         1165 -8.792398   -7.584773   -1.207625\\n\",\n       \"1164         nickel         1164 -8.792398   -7.584773   -1.207625\\n\",\n       \"1160        neither         1160 -8.792398   -7.584773   -1.207625\\n\",\n       \"1159      neighbors         1159 -8.792398   -7.584773   -1.207625\\n\",\n       \"1158    negotiating         1158 -8.792398   -7.584773   -1.207625\\n\",\n       \"...             ...          ...       ...         ...         ...\\n\",\n       \"103             all          103 -5.614344   -7.584773    1.970429\\n\",\n       \"1602             so         1602 -5.593725   -7.584773    1.991048\\n\",\n       \"1897            was         1897 -5.593725   -7.584773    1.991048\\n\",\n       \"144        applause          144 -5.573522   -7.584773    2.011251\\n\",\n       \"1154           need         1154 -5.573522   -7.584773    2.011251\\n\",\n       \"293             can          293 -5.534301   -7.584773    2.050472\\n\",\n       \"202              be          202 -5.460193   -7.584773    2.124580\\n\",\n       \"150             are          150 -5.408007   -7.584773    2.176766\\n\",\n       \"158              as          158 -5.358411   -7.584773    2.226363\\n\",\n       \"284             but          284 -5.342410   -7.584773    2.242363\\n\",\n       \"1172            not         1172 -5.237050   -7.584773    2.347723\\n\",\n       \"551              do          551 -5.208879   -7.584773    2.375894\\n\",\n       \"1737           this         1737 -5.116097   -7.584773    2.468676\\n\",\n       \"1933           with         1933 -5.066704   -7.584773    2.518069\\n\",\n       \"1223            our         1223 -5.031198   -7.584773    2.553575\\n\",\n       \"1265         people         1265 -5.019637   -7.584773    2.565136\\n\",\n       \"705             for          705 -4.850816   -7.584773    2.733957\\n\",\n       \"1917           what         1917 -4.850816   -7.584773    2.733957\\n\",\n       \"1961            you         1961 -4.831585   -7.584773    2.753189\\n\",\n       \"1198             on         1198 -4.714860   -7.584773    2.869913\\n\",\n       \"953              it          953 -4.641358   -7.584773    2.943415\\n\",\n       \"808            have          808 -4.508811   -7.584773    3.075962\\n\",\n       \"943              is          943 -4.422950   -7.584773    3.161823\\n\",\n       \"886              in          886 -4.073899   -7.584773    3.510874\\n\",\n       \"1721           that         1721 -3.868774   -7.584773    3.715999\\n\",\n       \"1903             we         1903 -3.847190   -7.584773    3.737583\\n\",\n       \"1186             of         1186 -3.801965   -7.584773    3.782808\\n\",\n       \"127             and          127 -3.591142   -7.584773    3.993631\\n\",\n       \"1751             to         1751 -3.457266   -7.584773    4.127507\\n\",\n       \"1722            the         1722 -3.181096   -7.584773    4.403677\\n\",\n       \"\\n\",\n       \"[1968 rows x 5 columns]\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cl = MultinomialNB()\\n\",\n    \"cl.fit(count_matrix, words_ds.speaker==\\\"O'MALLY\\\")\\n\",\n    \"\\n\",\n    \"df_vocab = pd.DataFrame(list(cv.vocabulary_.keys()), columns=[\\\"Vocab\\\"])\\n\",\n    \"df_vocab[\\\"Vocab_index\\\"] = cv.vocabulary_.values()\\n\",\n    \"df_vocab = df_vocab.sort_values(\\\"Vocab_index\\\").reset_index(drop=True)\\n\",\n    \"df_vocab[\\\"proba\\\"] = cl.feature_log_prob_[0]\\n\",\n    \"df_vocab[\\\"anti_proba\\\"] = cl.feature_log_prob_[1]\\n\",\n    \"df_vocab[\\\"difference\\\"] = cl.feature_log_prob_[0] - cl.feature_log_prob_[1]\\n\",\n    \"df_vocab.sort_values(\\\"difference\\\", ascending=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.4.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "notebooks/youtube.csv",
    "content": "Date,Region,Channel,Display name,Views,Unique users,Unique users (7 days),Unique users (30 days),Popularity,Comments,Favorites,Rating 1,Rating 2,Rating 3,Rating 4,Rating 5,Subscriptions,Unsubscriptions\n2015-03-23,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-03-23,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,5,17,0,0,0,0,0,0,0,1,0,0\n2015-03-23,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,15,0,0,0,0,0,0,0,0,0,0\n2015-03-23,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,11,51,0,0,0,0,0,0,0,0,0,0\n2015-03-23,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-23,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,18,0,0,0,0,0,0,0,0,0,0\n2015-03-23,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-23,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-23,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,3,0,0,0,0,0,0,0,1,0,0\n2015-03-23,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,25,67,0,0,0,0,0,0,0,0,0,0\n2015-03-23,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,8,4,26,83,0,0,0,0,0,0,0,0,0,0\n2015-03-23,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,20,0,0,0,0,0,0,0,0,0,0\n2015-03-23,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,13,0,0,0,0,0,0,0,0,0,0\n2015-03-23,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-23,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,28,0,0,0,0,0,0,0,0,0,0\n2015-03-23,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-03-23,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,54,175,0,0,0,0,0,0,0,0,0,0\n2015-03-23,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,5,27,0,0,0,0,0,0,0,0,0,0\n2015-03-23,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-23,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,3,0,0,0,0,0,0,0,0,0,0\n2015-03-23,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-23,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,19,53,0,0,0,0,0,0,0,0,0,0\n2015-03-23,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,11,0,0,0,0,0,0,0,0,0,0\n2015-03-23,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,17,58,0,0,0,0,0,0,0,0,0,0\n2015-03-23,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,32,159,0,0,0,0,0,0,0,0,0,0\n2015-03-23,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-23,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,2,15,0,0,2,0,0,0,0,0,0,0\n2015-03-23,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,19,0,0,0,0,0,0,0,0,0,0\n2015-03-23,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-23,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-03-23,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,18,0,0,0,0,0,0,0,0,0,0\n2015-03-23,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,33,0,0,0,0,0,0,0,0,0,0\n2015-03-23,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,31,0,0,0,0,0,0,0,0,0,0\n2015-03-23,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,14,10,71,221,0,0,0,0,0,0,0,0,0,0\n2015-03-23,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-23,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,9,34,0,0,0,0,0,0,0,0,0,0\n2015-03-23,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,45,0,0,0,0,0,0,0,0,0,0\n2015-03-23,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-23,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,45,4,19,45,0,0,0,0,0,0,0,0,1,0\n2015-03-23,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-23,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,7,0,0,0,0,0,0,0,0,0,0\n2015-03-23,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,14,39,0,0,0,0,0,0,0,0,0,0\n2015-03-23,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,22,0,0,0,0,0,0,0,0,0,0\n2015-03-23,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-03-23,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,11,3,17,51,0,0,0,0,0,0,0,0,0,0\n2015-03-23,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,23,0,0,0,0,0,0,0,0,0,0\n2015-03-23,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-23,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-03-23,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-23,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,10,0,0,0,0,0,0,0,0,0,0\n2015-03-23,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,4,0,0,0,0,0,0,0,0,1,0\n2015-03-23,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,37,0,0,0,0,0,0,0,0,0,0\n2015-03-23,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-23,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,19,0,0,0,0,0,0,0,0,0,0\n2015-03-23,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-03-23,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-23,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,43,0,0,0,0,0,0,0,0,0,0\n2015-03-23,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,6,17,0,0,0,0,0,0,0,0,0,0\n2015-03-23,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,30,0,0,0,0,0,0,0,0,0,0\n2015-03-23,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,24,0,0,0,0,0,0,0,0,0,0\n2015-03-23,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0\n2015-03-23,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-23,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0\n2015-03-23,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-03-23,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,21,5,14,47,0,0,0,0,0,0,0,0,0,0\n2015-03-23,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,31,0,0,0,0,0,0,0,0,0,0\n2015-03-23,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0\n2015-03-23,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,US,98x5I1LVPhtnUHDyujq7zg,Roshan,89,39,244,843,0,1,0,0,0,0,0,2,2,0\n2015-03-23,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-23,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-23,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-23,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,27,0,0,0,0,0,0,0,0,0,0\n2015-03-23,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,13,0,0,0,0,0,0,0,0,0,0\n2015-03-24,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-03-24,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-03-24,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,15,0,0,0,0,0,0,0,0,0,0\n2015-03-24,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,12,52,0,0,0,0,0,0,0,0,0,0\n2015-03-24,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-24,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,3,4,19,0,0,0,0,0,0,0,0,0,0\n2015-03-24,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-24,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-24,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-24,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,21,66,0,0,0,0,0,0,0,1,0,0\n2015-03-24,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,25,84,0,0,0,0,0,0,0,0,0,0\n2015-03-24,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,19,0,0,0,0,0,0,0,0,0,0\n2015-03-24,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-03-24,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-24,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,29,0,0,0,0,0,0,0,0,0,0\n2015-03-24,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,13,0,0,0,0,0,0,0,0,0,0\n2015-03-24,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,33,13,59,174,0,0,0,0,0,0,0,0,0,0\n2015-03-24,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,6,29,0,0,0,0,0,0,0,0,0,0\n2015-03-24,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-24,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0\n2015-03-24,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-24,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-24,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,22,6,20,54,0,0,0,0,0,0,0,0,0,0\n2015-03-24,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0\n2015-03-24,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,20,60,0,0,0,0,0,0,0,0,0,0\n2015-03-24,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,31,157,0,0,0,0,0,0,0,0,0,0\n2015-03-24,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-03-24,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-03-24,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,18,0,0,0,0,0,0,0,0,0,0\n2015-03-24,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-24,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0\n2015-03-24,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-24,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,11,2,11,31,0,0,0,0,0,0,0,0,0,0\n2015-03-24,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,29,0,0,0,0,0,0,0,0,0,0\n2015-03-24,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,15,9,71,224,0,0,0,0,0,0,0,0,1,0\n2015-03-24,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-24,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-24,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,32,0,0,0,0,0,0,0,0,0,0\n2015-03-24,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,12,4,12,49,0,0,0,0,0,0,0,0,0,0\n2015-03-24,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-24,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,47,4,16,46,0,0,0,0,0,0,0,0,1,0\n2015-03-24,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-24,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-24,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-24,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-24,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,38,0,0,0,0,0,0,0,0,0,0\n2015-03-24,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,21,0,0,0,0,0,0,0,0,0,0\n2015-03-24,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-24,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,12,4,20,51,0,0,0,0,0,0,0,0,0,0\n2015-03-24,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,22,0,0,0,0,0,0,0,0,1,0\n2015-03-24,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-24,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-24,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-03-24,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,10,0,0,0,0,0,0,0,0,0,0\n2015-03-24,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-24,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,34,0,0,0,0,0,0,0,0,0,0\n2015-03-24,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-24,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,19,0,0,0,0,0,0,0,0,0,0\n2015-03-24,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-03-24,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-24,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,12,44,0,0,0,0,0,0,0,0,0,0\n2015-03-24,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-24,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,7,33,0,0,0,0,0,0,0,0,0,0\n2015-03-24,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,25,0,0,0,0,0,0,0,0,0,0\n2015-03-24,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0\n2015-03-24,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-24,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,4,9,0,0,0,0,0,0,0,0,0,0\n2015-03-24,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-24,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,16,2,14,48,0,0,0,0,0,0,0,0,0,0\n2015-03-24,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,14,32,0,0,0,0,0,0,0,0,0,0\n2015-03-24,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-03-24,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,US,98x5I1LVPhtnUHDyujq7zg,Roshan,65,37,247,843,0,0,0,0,0,0,0,0,1,0\n2015-03-24,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-24,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-03-24,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-24,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,10,27,0,0,0,0,0,0,0,0,0,0\n2015-03-24,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,14,0,0,0,0,0,0,0,0,0,0\n2015-03-25,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-03-25,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,12,5,9,21,0,0,0,0,0,0,0,0,0,0\n2015-03-25,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,16,0,0,0,0,0,0,0,0,0,0\n2015-03-25,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,15,54,0,0,0,0,0,0,0,0,0,0\n2015-03-25,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,8,0,0,0,0,0,0,0,0,0,0\n2015-03-25,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,19,0,0,0,0,0,0,0,0,0,0\n2015-03-25,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-25,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-25,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,20,68,0,0,0,0,0,0,0,0,0,0\n2015-03-25,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,25,83,0,0,0,0,0,0,0,0,1,0\n2015-03-25,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,10,21,0,0,0,0,0,0,0,0,0,0\n2015-03-25,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0\n2015-03-25,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-25,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,27,0,0,0,0,0,0,0,0,0,0\n2015-03-25,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0\n2015-03-25,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,6,57,172,0,0,0,0,0,0,0,0,0,0\n2015-03-25,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,29,0,0,0,0,0,0,0,0,0,0\n2015-03-25,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0\n2015-03-25,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-03-25,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-25,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,24,57,0,0,0,0,0,0,0,0,0,0\n2015-03-25,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,2,11,0,0,0,0,0,0,0,0,0,0\n2015-03-25,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,19,60,0,0,0,0,0,0,0,0,0,0\n2015-03-25,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,28,15,38,163,0,0,0,0,0,0,0,0,2,0\n2015-03-25,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-03-25,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,5,16,0,0,0,0,0,0,0,0,0,0\n2015-03-25,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,20,0,0,0,0,0,0,0,0,1,0\n2015-03-25,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0\n2015-03-25,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,9,0,0,0,0,0,0,0,0,0,0\n2015-03-25,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,18,0,0,0,0,0,0,0,0,0,0\n2015-03-25,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,12,30,0,0,0,0,0,0,0,0,0,0\n2015-03-25,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,8,29,0,0,0,0,0,0,0,0,0,0\n2015-03-25,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,20,14,71,227,0,0,0,0,0,0,0,0,0,0\n2015-03-25,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-25,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-25,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,32,0,0,0,0,0,0,0,0,0,0\n2015-03-25,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,12,48,0,0,0,0,0,0,0,0,0,0\n2015-03-25,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-25,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,15,48,0,0,0,0,0,0,0,0,1,0\n2015-03-25,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-25,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,20,4,12,42,0,1,0,0,0,0,0,1,1,0\n2015-03-25,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,21,0,0,0,0,0,0,0,0,0,0\n2015-03-25,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-25,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,21,52,0,0,0,0,0,0,0,0,0,0\n2015-03-25,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,22,0,0,0,0,0,0,0,0,0,0\n2015-03-25,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-25,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0\n2015-03-25,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,11,0,0,0,0,0,0,0,0,0,0\n2015-03-25,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,10,0,0,0,0,0,0,0,0,0,0\n2015-03-25,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-25,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,33,0,0,0,0,0,0,0,0,0,0\n2015-03-25,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-25,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,19,0,0,0,0,0,0,0,0,0,0\n2015-03-25,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-03-25,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-25,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,15,45,0,0,0,0,0,0,0,0,0,0\n2015-03-25,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-25,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,10,36,0,0,0,0,0,0,0,0,1,0\n2015-03-25,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,25,0,0,0,0,0,0,0,0,0,0\n2015-03-25,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,14,0,0,0,0,0,0,0,0,0,0\n2015-03-25,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-25,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,9,0,0,0,0,0,0,0,0,0,0\n2015-03-25,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-25,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,15,48,0,0,0,0,0,0,0,0,0,0\n2015-03-25,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,32,0,0,0,0,0,0,0,0,0,0\n2015-03-25,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,13,0,0,0,0,0,0,0,0,0,0\n2015-03-25,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,US,98x5I1LVPhtnUHDyujq7zg,Roshan,114,41,241,840,0,0,0,0,0,0,0,1,1,0\n2015-03-25,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-25,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-25,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-25,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,13,30,0,0,0,0,0,0,0,0,0,0\n2015-03-25,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,7,14,0,0,0,0,0,0,0,0,0,0\n2015-03-26,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-26,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,11,23,0,0,0,0,0,0,0,0,0,0\n2015-03-26,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0\n2015-03-26,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,18,55,0,0,0,0,0,0,0,0,1,0\n2015-03-26,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0\n2015-03-26,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,19,0,0,0,0,0,0,0,0,0,0\n2015-03-26,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-26,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-26,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,21,65,0,0,0,0,0,0,0,0,1,0\n2015-03-26,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,11,5,27,84,0,0,0,0,0,0,0,0,0,0\n2015-03-26,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,22,0,0,0,0,0,0,0,0,0,0\n2015-03-26,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0\n2015-03-26,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-26,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0\n2015-03-26,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-03-26,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,26,12,59,176,0,0,0,0,0,0,0,0,0,0\n2015-03-26,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,29,0,0,0,0,0,0,0,0,0,0\n2015-03-26,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-26,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-26,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-26,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,21,57,0,0,0,0,0,0,0,0,0,0\n2015-03-26,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,2,11,0,0,0,0,0,0,0,0,0,0\n2015-03-26,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,20,62,0,0,0,0,0,0,0,0,0,0\n2015-03-26,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,17,6,41,164,0,0,0,0,0,0,0,0,1,0\n2015-03-26,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,6,0,0,0,0,0,0,0,0,0,0\n2015-03-26,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,16,3,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-26,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0\n2015-03-26,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0\n2015-03-26,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-03-26,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,8,19,0,0,0,0,0,0,0,0,0,0\n2015-03-26,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,29,0,0,0,0,0,0,0,0,0,0\n2015-03-26,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,28,0,0,0,0,0,0,0,0,0,0\n2015-03-26,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,22,13,69,230,0,0,0,0,0,0,0,0,0,0\n2015-03-26,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-26,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-26,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,31,0,0,0,0,0,0,0,0,0,0\n2015-03-26,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,11,5,14,51,0,0,0,0,0,0,0,0,0,0\n2015-03-26,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-26,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,15,50,0,0,0,0,0,0,0,0,0,0\n2015-03-26,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-26,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,8,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,10,2,11,42,0,0,0,0,0,0,0,0,0,0\n2015-03-26,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,22,0,0,0,0,0,0,0,0,0,0\n2015-03-26,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-26,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,20,51,0,0,0,0,0,0,0,0,0,0\n2015-03-26,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,23,0,0,0,0,0,0,0,0,0,0\n2015-03-26,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-26,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-03-26,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-03-26,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-26,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-03-26,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,8,31,0,0,0,0,0,0,0,0,0,0\n2015-03-26,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-26,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,19,0,0,0,0,0,0,0,0,0,0\n2015-03-26,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-03-26,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-26,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,44,0,0,0,0,0,0,0,0,0,0\n2015-03-26,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,16,0,0,0,0,0,0,0,0,0,0\n2015-03-26,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,9,35,0,0,0,0,0,0,0,0,0,0\n2015-03-26,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,26,0,0,0,0,0,0,0,0,0,0\n2015-03-26,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,15,0,0,0,0,0,0,0,0,0,0\n2015-03-26,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-26,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,9,0,0,0,0,0,0,0,0,0,0\n2015-03-26,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-26,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,13,47,0,0,0,0,0,0,0,0,1,0\n2015-03-26,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,13,35,0,0,0,0,0,0,0,0,0,0\n2015-03-26,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,12,0,0,0,0,0,0,0,0,0,0\n2015-03-26,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,US,98x5I1LVPhtnUHDyujq7zg,Roshan,50,32,244,851,0,0,0,0,0,0,0,0,0,0\n2015-03-26,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-26,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-26,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-03-26,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,31,0,0,0,0,0,0,0,0,0,0\n2015-03-26,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,14,0,0,0,0,0,0,0,0,0,0\n2015-03-27,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-27,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,23,0,0,0,0,0,0,0,0,0,0\n2015-03-27,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,17,0,0,0,0,0,0,0,0,0,0\n2015-03-27,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,20,52,0,0,0,0,0,0,0,0,0,0\n2015-03-27,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-27,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-27,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-27,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,21,66,0,0,0,0,0,0,0,0,0,0\n2015-03-27,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,9,4,24,85,0,0,0,0,0,0,0,0,0,0\n2015-03-27,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,22,0,0,0,0,0,0,0,0,0,0\n2015-03-27,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0\n2015-03-27,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-27,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0\n2015-03-27,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-03-27,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,9,58,180,0,0,0,0,0,0,0,0,1,0\n2015-03-27,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,29,0,0,0,0,0,0,0,0,0,0\n2015-03-27,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-03-27,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-27,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-27,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,21,53,0,0,0,0,0,0,0,0,0,0\n2015-03-27,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-03-27,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,19,8,25,69,0,0,0,0,0,0,0,0,1,0\n2015-03-27,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,38,164,0,0,0,0,0,0,0,0,0,0\n2015-03-27,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-27,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-27,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,20,0,0,0,0,0,0,0,0,0,0\n2015-03-27,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0\n2015-03-27,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-03-27,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,19,0,0,0,0,0,0,0,0,0,0\n2015-03-27,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,28,0,0,0,0,0,0,0,0,0,0\n2015-03-27,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,26,0,0,0,0,0,0,0,0,0,0\n2015-03-27,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,15,9,70,230,0,0,0,0,0,0,0,0,0,0\n2015-03-27,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-27,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-27,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,7,32,0,0,0,0,0,0,0,0,0,0\n2015-03-27,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,18,55,0,0,0,0,0,0,0,0,0,0\n2015-03-27,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-27,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,50,0,0,0,0,0,0,0,0,0,0\n2015-03-27,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-27,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-27,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,9,41,0,0,0,0,0,0,0,0,0,0\n2015-03-27,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,22,0,0,0,0,0,0,0,0,0,0\n2015-03-27,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-27,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,51,0,0,0,0,0,0,0,0,0,0\n2015-03-27,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,23,0,0,0,0,0,0,0,0,0,0\n2015-03-27,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-27,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-03-27,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,12,0,0,0,0,0,0,0,0,0,0\n2015-03-27,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-27,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-27,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,31,0,0,0,0,0,0,0,0,0,0\n2015-03-27,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-27,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,20,0,0,0,0,0,0,0,0,0,0\n2015-03-27,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,12,0,0,0,0,0,0,0,0,0,0\n2015-03-27,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-27,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,44,0,0,0,0,0,0,0,0,0,0\n2015-03-27,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,17,0,0,0,0,0,0,0,0,0,0\n2015-03-27,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,10,34,0,0,0,0,0,0,0,0,0,0\n2015-03-27,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,27,0,0,0,0,0,0,0,0,0,0\n2015-03-27,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,15,0,0,0,0,0,0,0,0,0,0\n2015-03-27,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-03-27,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0\n2015-03-27,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-27,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,12,46,0,0,0,0,0,0,0,0,0,0\n2015-03-27,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,12,38,0,0,0,0,0,0,0,0,0,0\n2015-03-27,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,12,0,0,0,0,0,0,0,0,0,0\n2015-03-27,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,US,98x5I1LVPhtnUHDyujq7zg,Roshan,69,28,231,847,0,0,0,0,0,0,0,0,2,0\n2015-03-27,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-27,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-27,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-03-27,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,32,0,0,0,0,0,0,0,0,0,0\n2015-03-27,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,13,0,0,0,0,0,0,0,0,0,0\n2015-03-28,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-28,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,11,24,0,0,0,0,0,0,0,0,0,0\n2015-03-28,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0\n2015-03-28,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,48,0,0,0,0,0,0,0,0,0,0\n2015-03-28,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-28,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-28,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-28,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,20,66,0,0,0,0,0,0,0,0,0,0\n2015-03-28,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,22,85,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,22,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,13,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,23,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0\n2015-03-28,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-03-28,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,54,182,0,0,0,0,0,0,0,0,0,0\n2015-03-28,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,29,0,0,0,0,0,0,0,0,0,0\n2015-03-28,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0\n2015-03-28,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-28,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-03-28,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,51,0,0,0,0,0,0,0,0,0,0\n2015-03-28,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-03-28,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,29,7,29,74,0,0,0,0,0,0,0,0,1,0\n2015-03-28,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,16,10,41,167,0,0,0,0,0,0,0,0,0,0\n2015-03-28,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-28,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,17,0,0,0,0,0,0,0,0,0,0\n2015-03-28,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,19,0,0,0,0,0,0,0,0,0,0\n2015-03-28,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-28,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-03-28,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,20,0,0,0,0,0,0,0,0,0,0\n2015-03-28,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,8,28,0,0,0,0,0,0,0,0,0,0\n2015-03-28,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,26,0,0,0,0,0,0,0,0,0,0\n2015-03-28,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,19,13,72,234,0,0,0,0,0,0,0,0,0,0\n2015-03-28,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-28,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-28,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,8,34,0,0,0,0,0,0,0,0,0,0\n2015-03-28,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,0,0,0,1,0,0,0,0,0,1,0\n2015-03-28,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,17,56,0,0,0,0,0,0,0,0,0,0\n2015-03-28,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-28,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,14,51,0,0,0,0,0,0,0,0,0,0\n2015-03-28,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-28,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-28,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,9,40,0,0,0,0,0,0,0,0,0,0\n2015-03-28,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,20,0,0,0,0,0,0,0,0,0,0\n2015-03-28,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-28,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,16,51,0,0,0,0,0,0,0,0,0,0\n2015-03-28,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,23,0,0,0,0,0,0,0,0,0,0\n2015-03-28,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-28,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-28,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-03-28,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0\n2015-03-28,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-28,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,31,0,0,0,0,0,0,0,0,0,0\n2015-03-28,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-28,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,20,0,0,0,0,0,0,0,0,0,0\n2015-03-28,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0\n2015-03-28,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-28,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,42,0,0,0,0,0,0,0,0,0,0\n2015-03-28,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,17,0,0,0,0,0,0,0,0,0,0\n2015-03-28,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,13,4,13,37,0,0,0,0,0,0,0,0,0,0\n2015-03-28,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,28,0,0,0,0,0,0,0,0,0,0\n2015-03-28,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0\n2015-03-28,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-03-28,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,3,9,0,0,0,0,0,0,0,0,0,0\n2015-03-28,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-03-28,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,13,47,0,0,0,0,0,0,0,0,0,0\n2015-03-28,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,12,40,0,0,0,0,0,0,0,0,0,0\n2015-03-28,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,13,0,0,0,0,0,0,0,0,0,0\n2015-03-28,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,US,98x5I1LVPhtnUHDyujq7zg,Roshan,87,39,234,864,0,0,0,0,0,0,0,0,0,0\n2015-03-28,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-28,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-28,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,12,0,0,0,0,0,0,0,0,0,0\n2015-03-28,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,10,31,0,0,0,0,0,0,0,0,0,0\n2015-03-28,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,12,0,0,0,0,0,0,0,0,0,0\n2015-03-29,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,2,6,0,0,0,0,0,0,0,0,0,0\n2015-03-29,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,13,27,0,0,0,0,0,0,0,0,0,0\n2015-03-29,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0\n2015-03-29,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,16,47,0,0,0,0,0,0,0,0,0,0\n2015-03-29,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,7,0,0,0,0,0,0,0,0,0,0\n2015-03-29,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-03-29,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-29,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,66,0,0,0,0,0,0,0,0,0,0\n2015-03-29,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,20,86,0,0,0,0,0,0,0,0,0,0\n2015-03-29,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,24,0,0,0,0,0,0,0,0,0,0\n2015-03-29,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,12,0,0,0,0,0,0,0,0,0,0\n2015-03-29,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-29,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,24,0,0,0,0,0,0,0,0,1,0\n2015-03-29,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-03-29,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,29,9,55,190,0,0,0,0,0,0,0,0,0,0\n2015-03-29,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,11,30,0,0,0,0,0,0,0,0,0,0\n2015-03-29,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0\n2015-03-29,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-29,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-03-29,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,15,54,0,0,0,0,0,0,0,0,0,0\n2015-03-29,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-03-29,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,32,76,0,0,0,0,0,0,0,0,0,0\n2015-03-29,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,21,8,41,169,0,0,0,0,0,0,0,0,1,0\n2015-03-29,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-03-29,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,18,0,0,0,0,0,0,0,0,0,0\n2015-03-29,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,20,0,0,0,0,0,0,0,0,1,0\n2015-03-29,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-29,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-03-29,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0\n2015-03-29,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,7,28,0,0,0,0,0,0,0,1,0,0\n2015-03-29,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,6,28,0,0,0,0,0,0,0,0,0,0\n2015-03-29,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,26,6,68,231,0,0,0,0,0,0,0,0,0,0\n2015-03-29,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-03-29,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-03-29,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,12,1,8,34,0,0,1,0,0,0,0,0,0,0\n2015-03-29,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,18,57,0,0,0,0,0,0,0,0,0,0\n2015-03-29,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-29,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,30,6,19,55,0,0,0,0,0,0,0,0,0,0\n2015-03-29,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-29,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,4,5,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,7,40,0,0,0,0,0,0,0,0,0,0\n2015-03-29,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,18,0,0,0,0,0,0,0,0,0,0\n2015-03-29,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-29,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,16,53,0,0,0,0,0,0,0,0,0,0\n2015-03-29,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,23,0,0,0,0,0,0,0,0,0,0\n2015-03-29,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-29,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-29,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-03-29,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-29,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-03-29,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,7,32,0,0,0,0,0,0,0,0,0,0\n2015-03-29,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-29,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,0,0\n2015-03-29,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-03-29,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-29,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,13,44,0,0,0,0,0,0,0,0,0,0\n2015-03-29,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0\n2015-03-29,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,37,0,0,0,0,0,0,0,0,0,0\n2015-03-29,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,26,0,0,0,0,0,0,0,0,0,0\n2015-03-29,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,16,0,0,0,0,0,0,0,0,0,0\n2015-03-29,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-29,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-29,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-03-29,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,13,48,0,0,0,0,0,0,0,0,0,0\n2015-03-29,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,41,0,0,0,0,0,0,0,0,0,1\n2015-03-29,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0\n2015-03-29,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,US,98x5I1LVPhtnUHDyujq7zg,Roshan,77,35,233,875,0,0,0,0,0,0,0,1,3,0\n2015-03-29,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-29,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-29,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-03-29,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,32,0,0,0,0,0,0,0,0,0,0\n2015-03-29,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,12,0,0,0,0,0,0,0,0,0,0\n2015-03-30,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,6,0,0,0,0,0,0,0,0,0,0\n2015-03-30,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,0,12,26,0,0,0,0,0,0,0,0,1,0\n2015-03-30,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,18,0,0,0,0,0,0,0,0,1,0\n2015-03-30,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,18,49,0,0,0,0,0,0,0,0,0,0\n2015-03-30,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-03-30,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,8,21,0,0,0,0,0,0,0,0,0,0\n2015-03-30,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-30,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,18,70,0,0,0,0,0,0,0,0,0,0\n2015-03-30,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,19,3,19,87,0,0,0,0,0,0,0,0,0,0\n2015-03-30,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,24,0,0,0,0,0,0,0,0,0,0\n2015-03-30,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,13,0,0,0,0,0,0,0,0,0,0\n2015-03-30,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-30,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0\n2015-03-30,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-03-30,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,15,10,60,195,0,0,0,0,0,0,0,0,0,0\n2015-03-30,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,29,0,0,0,0,0,0,0,0,0,0\n2015-03-30,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,11,0,0,0,0,0,0,0,0,0,0\n2015-03-30,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-03-30,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-03-30,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,53,0,0,0,0,0,0,0,0,0,0\n2015-03-30,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,13,0,0,0,0,0,0,0,0,0,0\n2015-03-30,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,32,77,0,0,0,0,0,0,0,0,0,0\n2015-03-30,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,21,14,50,177,0,0,0,0,0,0,0,0,1,0\n2015-03-30,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-03-30,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,18,0,0,0,0,0,0,0,0,0,0\n2015-03-30,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-03-30,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-03-30,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-30,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,21,0,0,0,0,0,0,0,0,0,0\n2015-03-30,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,11,33,0,0,0,0,0,0,0,0,1,0\n2015-03-30,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,28,0,0,0,0,0,0,0,0,0,0\n2015-03-30,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,18,12,71,239,0,0,0,0,0,0,0,0,0,0\n2015-03-30,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-30,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-30,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,9,0,7,33,0,0,0,0,0,0,0,0,0,0\n2015-03-30,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,55,0,0,0,0,0,0,0,0,0,0\n2015-03-30,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-30,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,56,0,0,0,0,0,0,0,0,0,0\n2015-03-30,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-30,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,4,4,0,0,0,0,0,0,0,0,0,0\n2015-03-30,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,5,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,9,42,0,0,0,0,0,0,0,0,1,0\n2015-03-30,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,16,0,0,0,0,0,0,0,0,0,0\n2015-03-30,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-30,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,13,53,0,0,0,0,0,0,0,0,0,0\n2015-03-30,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,13,2,6,24,0,0,0,0,0,0,0,0,0,0\n2015-03-30,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-30,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-30,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0\n2015-03-30,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-03-30,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-30,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,31,0,0,0,0,0,0,0,0,0,0\n2015-03-30,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-30,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0\n2015-03-30,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,14,0,0,0,0,0,0,0,0,0,0\n2015-03-30,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-30,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,12,43,0,0,0,0,0,0,0,0,0,0\n2015-03-30,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,11,1,5,17,0,0,0,0,0,0,0,0,0,0\n2015-03-30,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,37,0,0,0,0,0,0,0,0,0,0\n2015-03-30,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,27,0,0,0,0,0,0,0,0,0,0\n2015-03-30,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0\n2015-03-30,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-30,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-03-30,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-30,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,9,46,0,0,0,0,0,0,0,0,0,0\n2015-03-30,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,15,43,0,0,1,0,0,0,0,0,0,0\n2015-03-30,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0\n2015-03-30,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,US,98x5I1LVPhtnUHDyujq7zg,Roshan,46,26,219,880,0,0,1,0,0,0,0,0,0,0\n2015-03-30,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-30,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-30,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,13,0,0,0,0,0,0,0,0,0,0\n2015-03-30,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,33,0,0,0,0,0,0,0,0,0,0\n2015-03-30,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,5,14,0,0,0,0,0,0,0,0,0,0\n2015-03-31,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-03-31,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,13,27,0,0,0,0,0,0,0,0,0,0\n2015-03-31,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0\n2015-03-31,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,17,47,0,0,0,0,0,0,0,0,0,0\n2015-03-31,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-03-31,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,10,25,0,0,0,0,0,0,0,0,0,0\n2015-03-31,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-31,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,20,72,0,0,0,0,0,0,0,0,0,0\n2015-03-31,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,22,91,0,0,0,0,0,0,0,0,0,0\n2015-03-31,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,6,24,0,0,0,0,0,0,0,0,0,0\n2015-03-31,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-03-31,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-31,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,14,4,7,24,0,0,0,0,0,0,0,0,0,0\n2015-03-31,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-03-31,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,13,7,55,198,0,0,0,0,0,0,0,0,0,0\n2015-03-31,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0\n2015-03-31,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0\n2015-03-31,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-31,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-03-31,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,53,0,0,0,0,0,0,0,0,0,0\n2015-03-31,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,6,14,0,0,0,0,0,0,0,0,0,0\n2015-03-31,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,30,78,0,0,0,0,0,0,0,0,0,0\n2015-03-31,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,16,7,52,175,0,0,0,0,0,0,0,0,2,0\n2015-03-31,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-03-31,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,18,2,9,19,0,0,0,0,0,0,0,0,1,0\n2015-03-31,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-03-31,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-03-31,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-31,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,23,0,0,0,0,0,0,0,0,0,0\n2015-03-31,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,33,0,0,0,0,0,0,0,0,0,0\n2015-03-31,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,27,0,0,0,0,0,0,0,0,0,0\n2015-03-31,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,32,16,75,244,0,0,0,0,0,0,0,0,0,0\n2015-03-31,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-31,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-03-31,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,10,35,0,0,0,0,0,0,0,0,0,0\n2015-03-31,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,15,56,0,0,0,0,0,0,0,0,0,0\n2015-03-31,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-03-31,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,17,58,0,0,0,0,0,0,0,0,1,0\n2015-03-31,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0\n2015-03-31,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-03-31,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,5,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,7,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,43,0,0,0,0,0,0,0,0,0,0\n2015-03-31,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,4,17,0,0,0,0,0,0,0,0,0,0\n2015-03-31,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-31,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,11,53,0,0,0,0,0,0,0,0,0,0\n2015-03-31,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,7,26,0,0,0,0,0,0,0,0,0,0\n2015-03-31,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-03-31,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-03-31,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,11,0,0,0,0,0,0,0,0,0,0\n2015-03-31,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-03-31,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-03-31,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,31,0,0,0,0,0,0,0,0,0,0\n2015-03-31,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,22,0,0,0,0,0,0,0,0,0,0\n2015-03-31,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-03-31,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,3,6,0,0,0,0,0,0,0,0,0,0\n2015-03-31,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,12,45,0,0,0,0,0,0,0,0,0,0\n2015-03-31,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0\n2015-03-31,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,10,36,0,0,0,0,0,0,0,0,0,0\n2015-03-31,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,27,0,0,0,0,0,0,0,0,0,0\n2015-03-31,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,16,0,0,0,0,0,0,0,0,0,0\n2015-03-31,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-03-31,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0\n2015-03-31,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-03-31,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,9,43,0,0,0,0,0,0,0,0,0,0\n2015-03-31,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,46,0,0,0,0,0,0,0,0,0,0\n2015-03-31,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-03-31,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,US,98x5I1LVPhtnUHDyujq7zg,Roshan,66,33,213,887,0,1,0,0,0,0,0,1,1,0\n2015-03-31,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-03-31,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-03-31,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-03-31,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,31,0,0,0,0,0,0,0,0,0,0\n2015-03-31,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,14,0,0,0,0,0,0,0,0,0,0\n2015-04-01,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-01,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,8,26,0,0,0,0,0,0,0,0,0,0\n2015-04-01,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-01,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,44,0,0,0,0,0,0,0,0,0,0\n2015-04-01,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-01,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,26,0,0,0,0,0,0,0,0,0,0\n2015-04-01,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-01,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-01,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,8,6,22,74,0,0,0,0,0,0,0,0,0,0\n2015-04-01,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-01,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,4,25,91,0,0,0,0,0,0,0,0,0,0\n2015-04-01,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,7,26,0,0,0,0,0,0,0,0,0,0\n2015-04-01,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-01,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-01,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,8,27,0,0,0,0,0,0,0,0,0,0\n2015-04-01,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-04-01,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,17,5,55,196,0,0,0,0,0,0,0,0,0,0\n2015-04-01,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,26,0,0,0,0,0,0,0,0,0,0\n2015-04-01,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0\n2015-04-01,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-01,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-01,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-01,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,8,5,12,57,0,0,0,0,0,0,0,0,0,0\n2015-04-01,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,14,0,0,0,0,0,0,0,0,0,0\n2015-04-01,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,20,9,36,83,0,0,0,0,0,0,0,0,0,0\n2015-04-01,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,23,9,46,181,0,0,0,0,0,0,0,0,1,0\n2015-04-01,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,1,2,0,0,0,0,0,0,0,0,1,0\n2015-04-01,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-01,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-04-01,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-01,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-04-01,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-01,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,23,0,0,0,0,0,0,0,0,0,0\n2015-04-01,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,9,33,0,0,0,0,0,0,0,0,0,0\n2015-04-01,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,8,28,0,0,0,0,0,0,0,0,0,0\n2015-04-01,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,25,7,71,245,0,0,0,0,0,0,0,0,0,0\n2015-04-01,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-01,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-01,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,13,38,0,0,0,0,0,0,0,0,0,0\n2015-04-01,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-04-01,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,14,3,17,57,0,0,0,0,0,0,0,0,0,0\n2015-04-01,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-01,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,56,0,0,0,0,0,0,0,0,0,0\n2015-04-01,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0\n2015-04-01,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-01,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,45,0,0,0,0,0,0,0,0,0,0\n2015-04-01,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-01,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-01,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,53,0,0,0,0,0,0,0,0,0,0\n2015-04-01,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,25,0,0,0,0,0,0,0,0,0,0\n2015-04-01,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-01,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-04-01,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-01,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-04-01,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-01,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,7,30,0,0,0,0,0,0,0,0,0,0\n2015-04-01,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-01,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,20,0,0,0,0,0,0,0,0,0,0\n2015-04-01,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-01,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,7,0,0,0,0,0,0,0,0,0,0\n2015-04-01,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,45,0,0,0,0,0,0,0,0,0,0\n2015-04-01,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-01,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,8,36,0,0,0,0,0,0,0,0,0,0\n2015-04-01,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,25,0,0,0,0,0,0,0,0,0,0\n2015-04-01,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-01,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-01,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-01,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-01,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,8,42,0,0,0,0,0,0,0,0,0,0\n2015-04-01,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,16,46,0,0,0,0,0,0,0,0,0,0\n2015-04-01,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-01,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,US,98x5I1LVPhtnUHDyujq7zg,Roshan,53,29,197,890,0,0,0,0,0,0,0,0,3,0\n2015-04-01,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-01,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-01,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,12,0,0,0,0,0,0,0,0,0,0\n2015-04-01,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,8,30,0,0,0,0,0,0,0,0,0,0\n2015-04-01,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,5,15,0,0,0,0,0,0,0,0,0,0\n2015-04-02,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-02,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,26,0,0,0,0,0,0,0,0,0,0\n2015-04-02,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-02,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,12,3,12,46,0,0,0,0,0,0,0,0,0,0\n2015-04-02,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-02,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,11,27,0,0,0,0,0,0,0,0,0,0\n2015-04-02,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-02,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,21,73,0,0,0,0,0,0,0,0,0,0\n2015-04-02,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,23,92,0,0,0,0,0,0,0,0,0,0\n2015-04-02,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,25,0,0,0,0,0,0,0,0,0,0\n2015-04-02,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-02,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-02,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,9,28,0,0,0,0,0,0,0,0,0,0\n2015-04-02,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-04-02,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,18,10,52,199,0,0,0,0,0,0,0,0,0,0\n2015-04-02,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,25,0,0,0,0,0,0,0,0,0,0\n2015-04-02,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-02,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-02,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,13,58,0,0,0,0,0,0,0,0,0,0\n2015-04-02,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,14,0,0,0,0,0,0,0,0,0,0\n2015-04-02,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,35,85,0,0,0,0,0,0,0,0,0,0\n2015-04-02,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,14,4,44,176,0,0,0,0,0,0,0,0,1,0\n2015-04-02,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0\n2015-04-02,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,18,0,0,0,0,0,0,0,0,0,0\n2015-04-02,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-02,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-02,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,24,0,0,0,0,0,0,0,0,0,0\n2015-04-02,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,9,33,0,0,0,0,0,0,0,0,0,0\n2015-04-02,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,26,0,0,0,0,0,0,0,0,0,0\n2015-04-02,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,19,12,68,248,0,0,0,0,0,0,0,0,0,0\n2015-04-02,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,15,40,0,0,0,0,0,0,0,0,0,0\n2015-04-02,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,6,6,20,62,0,0,0,0,0,0,0,0,0,0\n2015-04-02,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,14,56,0,0,0,0,0,0,0,0,0,0\n2015-04-02,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,LA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0\n2015-04-02,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,3,9,0,0,0,0,0,0,0,0,0,0\n2015-04-02,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,45,0,0,0,0,0,0,0,0,0,0\n2015-04-02,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-02,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-02,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,10,53,0,0,0,0,0,0,0,0,0,0\n2015-04-02,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,23,0,0,0,0,0,0,0,0,0,0\n2015-04-02,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-02,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,8,0,0,0,0,0,0,0,0,0,0\n2015-04-02,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,6,14,0,0,0,0,0,0,0,0,0,0\n2015-04-02,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-04-02,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-02,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,29,0,0,0,0,0,0,0,0,0,0\n2015-04-02,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,21,0,0,0,0,0,0,0,0,0,0\n2015-04-02,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-02,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,1\n2015-04-02,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,46,0,0,0,0,0,0,0,0,0,0\n2015-04-02,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-02,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,36,0,0,0,0,0,0,0,0,0,0\n2015-04-02,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,4,24,0,0,0,0,0,0,0,0,0,0\n2015-04-02,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-02,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-02,SY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0\n2015-04-02,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-02,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-02,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,43,0,0,0,0,0,0,0,0,0,0\n2015-04-02,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,44,0,0,0,0,0,0,0,0,0,0\n2015-04-02,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-02,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-02,US,98x5I1LVPhtnUHDyujq7zg,Roshan,46,29,193,885,0,0,0,0,0,0,0,1,2,1\n2015-04-02,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-02,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-02,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,6,14,0,0,0,0,0,0,0,0,0,0\n2015-04-02,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,31,0,0,0,0,0,0,0,0,0,0\n2015-04-02,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,15,0,0,0,0,0,0,0,0,0,0\n2015-04-03,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,7,0,0,0,0,0,0,0,0,0,0\n2015-04-03,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,7,27,0,0,0,0,0,0,0,0,0,0\n2015-04-03,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,19,0,0,0,0,0,0,0,0,0,0\n2015-04-03,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,6,6,15,50,0,0,0,0,0,0,0,0,0,0\n2015-04-03,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-03,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,28,0,0,0,0,0,0,0,0,0,0\n2015-04-03,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-03,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,20,8,27,79,0,0,0,0,0,0,0,0,0,0\n2015-04-03,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-03,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,5,5,25,94,0,0,0,0,0,0,0,0,0,0\n2015-04-03,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,26,0,0,0,0,0,0,0,0,0,0\n2015-04-03,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,6,16,0,0,0,0,0,0,0,0,0,0\n2015-04-03,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-03,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,30,0,0,0,0,0,0,0,0,0,0\n2015-04-03,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-04-03,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,21,10,53,205,0,0,0,0,0,0,0,0,0,0\n2015-04-03,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,24,0,0,0,0,0,0,0,0,0,0\n2015-04-03,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-03,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-03,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,16,5,17,61,0,0,0,0,0,0,0,0,0,0\n2015-04-03,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,5,15,0,0,0,0,0,0,0,0,0,0\n2015-04-03,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,17,9,37,91,0,0,0,0,0,0,0,0,0,0\n2015-04-03,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,9,9,53,180,0,0,0,0,0,0,0,0,0,0\n2015-04-03,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-03,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,19,0,0,0,0,0,0,0,0,0,0\n2015-04-03,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,19,0,0,0,0,0,0,0,0,0,0\n2015-04-03,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-03,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-03,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,24,0,0,0,0,0,0,0,0,0,0\n2015-04-03,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,10,33,0,0,0,0,0,0,0,0,0,0\n2015-04-03,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,10,27,0,0,0,0,0,0,0,0,0,0\n2015-04-03,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,36,10,70,247,0,0,0,0,0,0,0,0,0,0\n2015-04-03,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,14,39,0,0,0,0,0,0,0,0,0,0\n2015-04-03,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-03,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,20,66,0,0,0,0,0,0,0,0,0,0\n2015-04-03,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,15,56,0,0,0,0,0,0,0,0,1,0\n2015-04-03,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-04-03,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,9,45,0,0,0,0,0,0,0,0,0,0\n2015-04-03,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-03,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-03,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,11,54,0,0,0,0,0,0,0,0,0,0\n2015-04-03,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,6,24,0,0,0,0,0,0,0,0,0,0\n2015-04-03,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-03,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-04-03,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,6,15,0,0,0,0,0,0,0,0,0,0\n2015-04-03,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-04-03,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-03,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,29,0,0,0,0,0,0,0,0,0,0\n2015-04-03,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-03,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-03,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,22,0,0,0,0,0,0,0,0,0,0\n2015-04-03,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-03,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-03,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,45,0,0,0,0,0,0,0,0,0,0\n2015-04-03,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-03,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,38,0,0,0,0,0,0,0,0,0,0\n2015-04-03,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,6,25,0,0,0,0,0,0,0,0,0,0\n2015-04-03,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-03,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-03,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-03,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-03,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,10,45,0,0,0,0,0,0,0,0,0,0\n2015-04-03,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,44,0,0,0,0,0,0,0,0,0,0\n2015-04-03,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-03,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-03,US,98x5I1LVPhtnUHDyujq7zg,Roshan,87,54,221,907,0,0,0,0,0,0,0,0,3,0\n2015-04-03,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-03,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-03,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,14,0,0,0,0,0,0,0,0,0,0\n2015-04-03,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,32,0,0,0,0,0,0,0,0,0,0\n2015-04-03,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,16,0,0,0,0,0,0,0,0,0,0\n2015-04-04,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-04,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,28,0,0,0,0,0,0,0,0,0,0\n2015-04-04,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,19,0,0,0,0,0,0,0,0,0,0\n2015-04-04,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,50,0,0,0,0,0,0,0,0,0,0\n2015-04-04,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-04,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,28,0,0,0,0,0,0,0,0,0,0\n2015-04-04,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-04,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,29,81,0,0,0,0,0,0,0,0,0,0\n2015-04-04,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-04,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,14,7,29,100,0,0,0,0,0,0,0,0,0,0\n2015-04-04,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,11,29,0,0,0,0,0,0,0,0,0,0\n2015-04-04,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,16,0,0,0,0,0,0,0,0,0,0\n2015-04-04,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-04,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,13,32,0,0,0,0,0,0,0,0,0,0\n2015-04-04,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0\n2015-04-04,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,13,0,0,0,0,0,0,0,0,0,0\n2015-04-04,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,12,3,49,201,0,0,0,0,0,0,0,3,2,0\n2015-04-04,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0\n2015-04-04,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-04,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-04,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-04,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-04,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,19,62,0,0,0,0,0,0,0,0,0,0\n2015-04-04,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,5,15,0,0,0,0,0,0,0,0,0,0\n2015-04-04,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,6,35,95,0,0,0,0,0,0,0,0,0,0\n2015-04-04,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,52,181,0,0,0,0,0,0,0,0,0,0\n2015-04-04,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-04,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-04,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,19,0,0,0,0,0,0,0,0,0,0\n2015-04-04,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,5,19,0,0,0,0,0,0,0,0,0,0\n2015-04-04,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-04,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-04,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,24,0,0,0,0,0,0,0,0,0,0\n2015-04-04,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,33,0,0,0,0,0,0,0,0,0,0\n2015-04-04,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,27,0,0,0,0,0,0,0,0,0,0\n2015-04-04,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,33,14,72,258,0,0,0,0,0,0,0,0,0,0\n2015-04-04,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-04,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-04,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,14,39,0,0,0,0,0,0,0,0,0,0\n2015-04-04,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-04,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,18,65,0,0,0,0,0,0,0,0,0,0\n2015-04-04,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-04,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,15,58,0,0,0,0,0,0,0,0,0,0\n2015-04-04,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-04,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-04,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,46,0,0,0,0,0,0,0,0,0,0\n2015-04-04,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-04,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-04,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,13,54,0,0,0,0,0,0,0,1,1,0\n2015-04-04,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,7,24,0,0,0,0,0,0,0,0,0,0\n2015-04-04,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-04,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0\n2015-04-04,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,16,0,0,0,0,0,0,0,0,0,0\n2015-04-04,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-04-04,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-04,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,7,30,0,0,0,0,0,0,0,0,0,0\n2015-04-04,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-04,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-04,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,23,0,0,0,0,0,0,0,0,0,0\n2015-04-04,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-04,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-04,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,45,0,0,0,0,0,0,0,0,0,0\n2015-04-04,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0\n2015-04-04,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,7,38,0,0,0,0,0,0,0,0,0,0\n2015-04-04,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,6,25,0,0,0,0,0,0,0,0,0,0\n2015-04-04,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,14,0,0,0,0,0,0,0,0,0,0\n2015-04-04,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-04,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-04,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-04,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,43,0,0,0,0,0,0,0,0,0,0\n2015-04-04,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,42,0,0,0,0,0,0,0,0,0,0\n2015-04-04,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-04,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-04,US,98x5I1LVPhtnUHDyujq7zg,Roshan,68,34,218,916,0,0,0,0,0,0,0,0,1,1\n2015-04-04,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-04,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-04,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,20,2,6,16,0,0,0,0,0,0,0,0,0,0\n2015-04-04,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,32,0,0,0,0,0,0,0,0,0,0\n2015-04-04,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,8,18,0,0,0,0,0,0,0,0,0,0\n2015-04-05,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-05,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,28,0,0,0,0,0,0,0,0,0,0\n2015-04-05,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,18,0,0,0,0,0,0,0,0,0,0\n2015-04-05,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,16,51,0,0,0,0,0,0,0,0,0,0\n2015-04-05,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,9,0,0,0,0,0,0,0,0,0,0\n2015-04-05,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,28,0,0,0,0,0,0,0,0,0,0\n2015-04-05,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-05,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,29,81,0,0,0,0,0,0,0,0,0,0\n2015-04-05,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-05,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,29,98,0,0,0,0,0,0,0,0,0,0\n2015-04-05,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,29,0,0,0,0,0,0,0,0,0,0\n2015-04-05,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,17,0,0,0,0,0,0,0,0,0,0\n2015-04-05,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-05,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,30,0,0,0,0,0,0,0,0,0,0\n2015-04-05,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-05,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0\n2015-04-05,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,6,47,204,0,0,0,0,0,0,0,0,0,0\n2015-04-05,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,22,0,0,0,0,0,0,0,0,0,0\n2015-04-05,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-05,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-05,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-05,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-05,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,17,61,0,0,0,0,0,0,0,0,0,0\n2015-04-05,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,17,0,0,0,0,0,0,0,0,1,0\n2015-04-05,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,31,95,0,0,0,0,0,0,0,0,0,0\n2015-04-05,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,8,5,50,180,0,0,0,0,0,0,0,0,0,0\n2015-04-05,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-05,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-05,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,19,0,0,0,0,0,0,0,0,0,0\n2015-04-05,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,6,22,0,0,0,0,0,0,0,1,0,0\n2015-04-05,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-05,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-05,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,24,0,0,0,0,0,0,0,0,0,0\n2015-04-05,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,32,0,0,0,0,0,0,0,0,0,0\n2015-04-05,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,8,28,0,0,0,0,0,0,0,0,0,0\n2015-04-05,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,21,12,76,264,0,0,0,0,0,0,0,1,2,0\n2015-04-05,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-05,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,7,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-05,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,15,41,0,0,0,0,0,0,0,0,0,0\n2015-04-05,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-05,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,21,67,0,0,0,0,0,0,0,0,0,0\n2015-04-05,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-05,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,18,6,14,58,0,0,0,0,0,0,0,0,0,0\n2015-04-05,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-05,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-05,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,45,0,0,0,0,0,0,0,0,0,0\n2015-04-05,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-05,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0\n2015-04-05,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,11,53,0,0,0,0,0,0,0,0,0,0\n2015-04-05,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,22,0,0,0,0,0,0,0,0,0,0\n2015-04-05,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-05,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0\n2015-04-05,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,16,0,0,0,0,0,0,0,0,0,0\n2015-04-05,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-04-05,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-05,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,30,0,0,0,0,0,0,0,0,0,0\n2015-04-05,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-05,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-05,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,24,0,0,0,0,0,0,0,0,0,0\n2015-04-05,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0\n2015-04-05,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-05,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,45,0,0,0,0,0,0,0,0,0,0\n2015-04-05,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,1,0\n2015-04-05,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,8,38,0,0,0,0,0,0,0,0,0,0\n2015-04-05,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,24,0,0,0,0,0,0,0,0,0,0\n2015-04-05,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-05,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-05,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-05,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-05,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,43,0,0,0,0,0,0,0,0,0,0\n2015-04-05,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,41,0,0,0,0,0,0,0,0,0,0\n2015-04-05,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-05,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-05,US,98x5I1LVPhtnUHDyujq7zg,Roshan,87,31,216,920,0,0,0,0,0,0,0,0,0,0\n2015-04-05,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-05,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-05,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,16,0,0,0,0,0,0,0,0,0,0\n2015-04-05,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,31,0,0,0,0,0,0,0,0,0,0\n2015-04-05,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,18,0,0,0,0,0,0,0,0,0,0\n2015-04-06,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-06,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,28,0,0,0,0,0,0,0,0,0,0\n2015-04-06,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0\n2015-04-06,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,15,52,0,0,0,0,0,0,0,0,0,0\n2015-04-06,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,9,0,0,0,0,0,0,0,0,0,0\n2015-04-06,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,27,0,0,0,0,0,0,0,0,0,0\n2015-04-06,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-06,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,25,83,0,0,0,0,0,0,0,0,0,0\n2015-04-06,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-06,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,21,5,31,102,0,0,0,0,0,0,0,0,0,0\n2015-04-06,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,9,29,0,0,0,0,0,0,0,0,0,0\n2015-04-06,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,17,0,0,0,0,0,0,0,0,0,0\n2015-04-06,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-06,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,29,0,0,0,0,0,0,0,0,0,0\n2015-04-06,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-06,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-06,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,9,45,203,0,0,0,0,0,0,0,0,0,0\n2015-04-06,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,23,0,0,0,0,0,0,0,0,0,0\n2015-04-06,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,12,0,0,0,0,0,0,0,0,0,0\n2015-04-06,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-06,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,62,0,0,0,0,0,0,0,0,0,0\n2015-04-06,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-06,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,3,30,98,0,0,0,0,0,0,0,0,0,0\n2015-04-06,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,32,14,48,189,0,0,0,0,0,0,0,1,0,0\n2015-04-06,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-06,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0\n2015-04-06,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,23,0,0,0,0,0,0,0,0,0,0\n2015-04-06,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-06,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-06,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,24,0,0,0,0,0,0,0,0,0,0\n2015-04-06,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,23,6,11,38,0,0,0,0,0,0,0,0,0,0\n2015-04-06,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,28,0,0,0,0,0,0,0,0,0,0\n2015-04-06,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,18,9,74,266,0,0,1,0,0,0,0,0,1,0\n2015-04-06,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-06,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,16,42,0,0,0,0,0,0,0,0,0,0\n2015-04-06,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-06,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,24,70,0,0,0,0,0,0,0,0,0,0\n2015-04-06,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,57,0,0,0,0,0,0,0,0,0,0\n2015-04-06,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-06,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,45,0,0,0,0,0,0,0,0,0,0\n2015-04-06,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-06,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0\n2015-04-06,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,12,54,0,0,0,0,0,0,0,0,0,0\n2015-04-06,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,22,0,0,0,0,0,0,0,0,0,0\n2015-04-06,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-06,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-06,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,16,0,0,0,0,0,0,0,0,0,0\n2015-04-06,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-04-06,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-06,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,30,0,0,0,0,0,0,0,0,0,0\n2015-04-06,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-06,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,24,0,0,0,0,0,0,0,0,0,0\n2015-04-06,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0\n2015-04-06,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,7,0,0,0,0,0,0,0,0,0,0\n2015-04-06,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,11,6,13,46,0,0,0,0,0,0,0,0,0,0\n2015-04-06,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,18,0,0,0,0,0,0,0,0,0,0\n2015-04-06,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,35,0,0,0,0,0,0,0,0,0,0\n2015-04-06,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0\n2015-04-06,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-06,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-06,SV,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0\n2015-04-06,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-06,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-06,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,44,0,0,0,0,0,0,0,0,0,0\n2015-04-06,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,41,0,0,0,0,0,0,0,0,0,0\n2015-04-06,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,14,0,0,0,0,0,0,0,1,1,0\n2015-04-06,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-06,US,98x5I1LVPhtnUHDyujq7zg,Roshan,84,46,239,946,0,0,0,0,0,0,0,0,3,0\n2015-04-06,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-06,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-06,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0\n2015-04-06,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,4,32,0,0,0,0,0,0,0,0,0,0\n2015-04-06,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-04-07,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-07,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,28,0,0,0,0,0,0,0,0,0,0\n2015-04-07,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-07,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,14,52,0,0,0,0,0,0,0,0,0,0\n2015-04-07,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,11,0,0,0,0,0,0,0,0,0,0\n2015-04-07,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,27,0,0,0,0,0,0,0,0,0,0\n2015-04-07,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-07,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,24,85,0,0,0,0,0,0,0,0,0,0\n2015-04-07,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-07,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,30,107,0,0,0,0,0,0,0,0,1,0\n2015-04-07,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,28,0,0,0,0,0,0,0,0,0,0\n2015-04-07,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,17,0,0,0,0,0,0,0,0,0,0\n2015-04-07,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-07,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,30,0,0,0,0,0,0,0,0,0,0\n2015-04-07,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-07,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-07,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,26,6,44,207,0,0,0,0,0,0,0,0,1,0\n2015-04-07,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,25,0,0,0,0,0,0,0,0,0,0\n2015-04-07,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,13,0,0,0,0,0,0,0,0,1,0\n2015-04-07,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-07,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-07,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-07,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,18,64,0,0,0,0,0,0,0,0,0,0\n2015-04-07,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,1,0\n2015-04-07,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,32,102,0,0,0,0,0,0,0,0,0,0\n2015-04-07,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,21,13,53,192,0,0,0,0,0,0,0,0,1,0\n2015-04-07,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-07,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-07,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0\n2015-04-07,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,8,22,0,0,0,0,0,0,0,0,0,0\n2015-04-07,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-07,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-07,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,24,0,0,0,0,0,0,0,0,0,0\n2015-04-07,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,12,39,0,0,0,0,0,0,0,0,0,0\n2015-04-07,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,7,28,0,0,0,0,0,0,0,0,0,0\n2015-04-07,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,64,267,0,0,0,0,0,0,0,0,0,0\n2015-04-07,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-07,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-07,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,40,0,0,0,0,0,0,0,0,0,0\n2015-04-07,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-07,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,21,69,0,0,0,0,0,0,0,0,0,0\n2015-04-07,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-07,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,13,58,0,0,0,0,0,0,0,0,0,0\n2015-04-07,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0\n2015-04-07,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-07,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,44,0,0,0,0,0,0,0,0,0,0\n2015-04-07,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0\n2015-04-07,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-07,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,54,0,0,0,0,0,0,0,0,0,0\n2015-04-07,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,8,23,0,0,0,0,0,0,0,0,0,0\n2015-04-07,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-07,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,17,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,29,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,26,0,0,0,0,0,0,0,0,0,0\n2015-04-07,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0\n2015-04-07,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-07,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,46,0,0,0,0,0,0,0,0,0,0\n2015-04-07,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-07,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,33,0,0,0,0,0,0,0,0,0,0\n2015-04-07,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,7,25,0,0,0,0,0,0,0,0,0,0\n2015-04-07,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-07,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-07,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,12,0,0,0,0,0,0,0,0,0,0\n2015-04-07,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-07,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,8,44,0,0,0,0,0,0,0,0,0,0\n2015-04-07,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,40,0,0,0,0,0,0,0,0,0,0\n2015-04-07,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,5,15,0,0,0,0,0,0,0,0,0,0\n2015-04-07,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-07,US,98x5I1LVPhtnUHDyujq7zg,Roshan,73,44,250,966,0,0,0,0,0,0,0,0,1,0\n2015-04-07,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-07,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-07,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,6,16,0,0,0,0,0,0,0,0,0,0\n2015-04-07,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,7,34,0,0,0,0,0,0,0,0,0,0\n2015-04-07,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,19,0,0,0,0,0,0,0,0,0,0\n2015-04-08,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-08,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,28,0,0,0,0,0,0,0,0,0,0\n2015-04-08,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,10,2,4,18,0,0,0,0,0,0,0,0,2,1\n2015-04-08,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,51,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,27,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,19,84,0,0,0,0,0,0,0,0,0,0\n2015-04-08,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-08,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,30,105,0,0,0,0,0,0,0,0,0,0\n2015-04-08,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,29,0,0,0,0,0,0,0,0,0,0\n2015-04-08,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,18,0,0,0,0,0,0,0,0,0,0\n2015-04-08,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-08,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,31,0,0,0,0,0,0,0,0,0,0\n2015-04-08,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-08,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-08,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,45,205,0,0,0,0,0,0,0,0,0,0\n2015-04-08,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,25,0,0,0,0,0,0,0,0,0,0\n2015-04-08,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,12,0,0,0,0,0,0,0,0,0,0\n2015-04-08,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-08,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-08,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-08,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,63,0,0,0,0,0,0,0,0,0,0\n2015-04-08,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-08,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,26,98,0,0,0,0,0,0,0,0,0,0\n2015-04-08,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,13,7,50,190,0,0,0,0,0,0,0,0,0,0\n2015-04-08,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-08,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-08,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-08,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,21,0,0,0,0,0,0,0,0,0,0\n2015-04-08,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-08,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-08,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,24,0,0,0,0,0,0,0,0,0,0\n2015-04-08,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,11,40,0,0,0,0,0,0,0,0,0,0\n2015-04-08,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,7,29,0,0,0,0,0,0,0,0,0,0\n2015-04-08,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,17,5,63,267,0,0,0,0,0,0,0,0,0,0\n2015-04-08,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-08,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-08,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,40,0,0,0,0,0,0,0,0,0,0\n2015-04-08,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-08,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,21,68,0,0,0,0,0,0,0,0,0,0\n2015-04-08,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-08,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,16,59,0,0,0,0,0,0,0,0,1,0\n2015-04-08,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-08,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-08,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,7,46,0,0,0,0,0,0,0,0,0,0\n2015-04-08,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-08,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-08,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,11,53,0,0,0,0,0,0,0,0,0,0\n2015-04-08,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,23,0,0,0,0,0,0,0,0,0,0\n2015-04-08,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-08,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,15,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,3,28,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,24,0,0,0,0,0,0,0,0,0,0\n2015-04-08,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-08,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-04-08,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,12,46,0,0,0,0,0,0,0,0,0,0\n2015-04-08,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-08,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,6,34,0,0,0,0,0,0,0,0,0,0\n2015-04-08,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,23,0,0,0,0,0,0,0,0,0,0\n2015-04-08,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,14,0,0,0,0,0,0,0,0,0,0\n2015-04-08,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-08,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-04-08,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-08,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,44,0,0,0,0,0,0,0,0,0,0\n2015-04-08,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,4,42,0,0,0,0,0,0,0,0,0,0\n2015-04-08,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-08,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-08,US,98x5I1LVPhtnUHDyujq7zg,Roshan,107,48,268,984,0,0,0,0,0,0,0,0,1,0\n2015-04-08,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-08,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-08,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,15,0,0,0,0,0,0,0,0,0,0\n2015-04-08,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,35,0,0,0,0,0,0,0,0,0,0\n2015-04-08,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,7,20,0,0,0,0,0,0,0,0,0,0\n2015-04-09,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,3,9,0,0,0,0,0,0,0,0,0,0\n2015-04-09,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,27,0,0,0,0,0,0,0,0,0,0\n2015-04-09,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-09,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,5,18,56,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,12,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,0,3,27,0,0,0,0,0,0,0,1,1,0\n2015-04-09,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,17,80,0,0,0,0,0,0,0,0,0,0\n2015-04-09,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,30,102,0,0,0,0,0,0,0,0,0,0\n2015-04-09,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,10,32,0,0,0,0,0,0,0,0,0,0\n2015-04-09,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0\n2015-04-09,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,21,1,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,32,0,0,0,0,0,0,0,0,0,0\n2015-04-09,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-09,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-09,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,21,10,46,210,0,0,0,0,0,0,0,0,0,0\n2015-04-09,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,7,23,0,0,0,0,0,0,0,0,1,0\n2015-04-09,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-09,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,62,0,0,0,0,0,0,0,0,0,0\n2015-04-09,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,16,0,0,0,0,0,0,0,0,0,0\n2015-04-09,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,25,7,30,103,0,0,0,0,0,0,0,0,1,0\n2015-04-09,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,13,6,49,186,0,0,0,0,0,0,0,0,0,0\n2015-04-09,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-09,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-09,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,22,0,0,0,0,0,0,0,0,0,0\n2015-04-09,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,11,0,0,0,0,0,0,0,0,0,0\n2015-04-09,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,23,0,0,0,0,0,0,0,0,0,0\n2015-04-09,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,12,40,0,0,0,0,0,0,0,0,0,0\n2015-04-09,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,28,0,0,0,0,0,0,0,0,0,0\n2015-04-09,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,9,9,61,266,0,0,0,0,0,0,0,0,0,0\n2015-04-09,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-09,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,40,0,0,0,0,0,0,0,0,0,0\n2015-04-09,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,62,0,0,0,0,0,0,0,0,0,0\n2015-04-09,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-09,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,11,6,20,61,0,0,0,0,0,0,0,0,0,0\n2015-04-09,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-09,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-09,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,44,0,0,0,0,0,0,0,0,0,0\n2015-04-09,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-09,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0\n2015-04-09,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,16,8,17,59,0,0,0,0,0,0,0,0,0,0\n2015-04-09,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,22,0,0,0,0,0,0,0,0,0,0\n2015-04-09,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,3,26,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0\n2015-04-09,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-09,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-09,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,45,0,0,0,0,0,0,0,0,0,0\n2015-04-09,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-09,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,33,0,0,0,0,0,0,0,0,0,0\n2015-04-09,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,23,0,0,0,0,0,0,0,0,1,0\n2015-04-09,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,15,0,0,0,0,0,0,0,0,0,0\n2015-04-09,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-09,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-09,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-09,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,44,0,0,0,0,0,0,0,0,0,0\n2015-04-09,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,40,0,0,0,0,0,0,0,0,0,0\n2015-04-09,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0\n2015-04-09,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-09,US,98x5I1LVPhtnUHDyujq7zg,Roshan,56,34,274,981,0,0,0,0,0,0,0,0,3,0\n2015-04-09,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-09,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-09,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-09,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,36,0,0,0,0,0,0,0,0,0,0\n2015-04-09,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0\n2015-04-10,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-04-10,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,26,0,0,0,0,0,0,0,0,0,0\n2015-04-10,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,6,20,0,0,0,0,0,0,0,0,0,0\n2015-04-10,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,55,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,12,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,24,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,81,0,0,0,0,0,0,0,0,0,0\n2015-04-10,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,3,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,28,101,0,0,0,0,0,0,0,0,0,0\n2015-04-10,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,31,0,0,0,0,0,0,0,0,0,0\n2015-04-10,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0\n2015-04-10,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-10,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,30,0,0,0,0,0,0,0,0,0,0\n2015-04-10,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-10,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,3,12,0,0,0,0,0,0,0,4,1,0\n2015-04-10,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,41,204,0,0,0,0,0,0,0,0,0,0\n2015-04-10,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,24,0,0,0,0,0,0,0,0,0,0\n2015-04-10,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-10,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-10,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-10,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-10,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,59,0,0,0,0,0,0,0,0,0,0\n2015-04-10,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,16,0,0,0,0,0,0,0,0,0,0\n2015-04-10,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,12,5,26,103,0,0,0,0,0,0,0,0,0,0\n2015-04-10,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,8,5,47,176,0,0,0,0,0,0,0,0,0,0\n2015-04-10,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-10,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,16,0,0,0,0,0,0,0,0,0,0\n2015-04-10,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,20,0,0,0,0,0,0,0,0,0,0\n2015-04-10,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,10,0,0,0,0,0,0,0,0,0,0\n2015-04-10,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,23,0,0,0,0,0,0,0,0,0,0\n2015-04-10,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,12,39,0,0,0,0,0,0,0,0,0,0\n2015-04-10,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,28,0,0,0,0,0,0,0,0,0,0\n2015-04-10,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,58,265,0,0,0,0,0,0,0,0,0,0\n2015-04-10,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,9,40,0,0,0,0,0,0,0,0,0,0\n2015-04-10,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-10,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,11,58,0,0,0,0,0,0,0,0,0,0\n2015-04-10,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-10,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,4,21,63,0,0,0,0,0,0,0,0,0,0\n2015-04-10,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-10,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-10,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,44,0,0,0,0,0,0,0,0,0,0\n2015-04-10,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-04-10,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-04-10,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,14,2,16,58,0,0,0,0,0,0,0,0,1,0\n2015-04-10,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,23,0,0,0,0,0,0,0,0,0,0\n2015-04-10,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-10,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-04-10,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0\n2015-04-10,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-10,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-10,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,4,26,0,0,0,0,0,0,0,1,0,0\n2015-04-10,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-10,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,21,0,0,0,0,0,0,0,0,0,0\n2015-04-10,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-10,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-10,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,9,43,0,0,0,0,0,0,0,0,0,0\n2015-04-10,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-10,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,32,0,0,0,0,0,0,0,0,0,0\n2015-04-10,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,5,24,0,0,0,0,0,0,0,0,0,0\n2015-04-10,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0\n2015-04-10,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-10,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-10,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-10,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,43,0,0,0,0,0,0,0,0,0,0\n2015-04-10,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,39,0,0,0,0,0,0,0,0,0,0\n2015-04-10,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-10,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-10,US,98x5I1LVPhtnUHDyujq7zg,Roshan,67,39,258,960,0,2,0,0,0,0,0,1,1,0\n2015-04-10,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-10,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-10,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-10,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,17,4,11,38,0,1,0,0,0,0,0,0,2,0\n2015-04-10,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,21,0,0,0,0,0,0,0,0,0,0\n2015-04-11,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-11,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,26,0,0,0,0,0,0,0,0,0,0\n2015-04-11,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,19,0,0,0,0,0,0,0,0,0,0\n2015-04-11,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,11,54,0,0,0,0,0,0,0,0,0,0\n2015-04-11,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,13,0,0,0,0,0,0,0,0,0,0\n2015-04-11,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,24,0,0,0,0,0,0,0,0,0,0\n2015-04-11,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-11,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,11,83,0,0,0,0,0,0,0,1,0,0\n2015-04-11,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0\n2015-04-11,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,27,104,0,0,0,0,0,0,0,0,0,0\n2015-04-11,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,8,30,0,0,0,0,0,0,0,0,0,0\n2015-04-11,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-11,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-11,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,30,0,0,0,0,0,0,0,0,0,0\n2015-04-11,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-11,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,13,0,0,0,0,0,0,0,1,0,0\n2015-04-11,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,6,45,204,0,0,0,0,0,0,0,0,0,0\n2015-04-11,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,10,26,0,0,0,0,0,0,0,0,0,0\n2015-04-11,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-11,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-11,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-11,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-11,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,7,56,0,0,0,0,0,0,0,0,0,0\n2015-04-11,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-11,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,15,7,25,106,0,0,0,0,0,0,0,0,0,0\n2015-04-11,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,47,172,0,0,0,0,0,0,0,0,1,0\n2015-04-11,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-11,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-11,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,16,0,0,0,0,0,0,0,0,0,0\n2015-04-11,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,19,0,0,0,0,0,0,0,0,0,0\n2015-04-11,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-11,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,10,0,0,0,0,0,0,0,0,0,0\n2015-04-11,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,21,0,0,0,0,0,0,0,0,0,0\n2015-04-11,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,14,40,0,0,0,0,0,0,0,0,0,0\n2015-04-11,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,27,0,0,0,0,0,0,0,0,0,0\n2015-04-11,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,15,10,55,266,0,0,0,0,0,0,0,0,0,0\n2015-04-11,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-11,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-11,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,39,0,0,0,0,0,0,0,0,0,0\n2015-04-11,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,11,57,0,0,0,0,0,0,0,1,0,0\n2015-04-11,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-11,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,22,62,0,0,0,0,0,0,0,0,0,0\n2015-04-11,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-11,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-11,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,43,0,0,0,0,0,0,0,0,0,0\n2015-04-11,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-11,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-04-11,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,14,59,0,0,0,0,0,0,0,0,0,0\n2015-04-11,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,22,0,0,0,0,0,0,0,0,0,0\n2015-04-11,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-11,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,27,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,22,0,0,0,0,0,0,0,0,0,0\n2015-04-11,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,14,0,0,0,0,0,0,0,0,0,0\n2015-04-11,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-11,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,39,0,0,0,0,0,0,0,0,0,0\n2015-04-11,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,18,0,0,0,0,0,0,0,0,0,0\n2015-04-11,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,28,0,0,0,0,0,0,0,0,0,0\n2015-04-11,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,4,24,0,0,0,0,0,0,0,0,0,0\n2015-04-11,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-04-11,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-11,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0\n2015-04-11,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-11,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,40,0,0,0,0,0,0,0,0,0,0\n2015-04-11,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,38,0,0,0,0,0,0,0,0,0,0\n2015-04-11,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-11,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-04-11,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-11,US,98x5I1LVPhtnUHDyujq7zg,Roshan,52,29,255,957,0,0,0,0,0,0,0,0,1,0\n2015-04-11,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-11,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0\n2015-04-11,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,11,37,0,0,0,0,0,0,0,0,0,0\n2015-04-11,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,0,0\n2015-04-12,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-12,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,26,0,0,0,0,0,0,0,0,0,0\n2015-04-12,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,5,18,0,0,0,0,0,0,0,0,0,0\n2015-04-12,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,10,54,0,0,0,0,0,0,0,0,0,0\n2015-04-12,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0\n2015-04-12,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,7,27,0,0,0,0,0,0,0,0,0,0\n2015-04-12,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-12,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,82,0,0,0,0,0,0,0,0,0,0\n2015-04-12,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,4,0,0,0,0,0,0,0,0,0,0\n2015-04-12,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,28,104,0,0,0,0,0,0,0,0,1,0\n2015-04-12,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,8,29,0,0,0,0,0,0,0,0,0,0\n2015-04-12,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0\n2015-04-12,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-12,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,29,0,0,0,0,0,0,0,0,0,0\n2015-04-12,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-12,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,3,13,0,0,0,0,0,0,0,1,0,0\n2015-04-12,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,8,46,207,0,0,0,0,0,0,0,0,0,0\n2015-04-12,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,18,2,11,26,0,0,0,0,0,0,0,0,0,0\n2015-04-12,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-12,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-12,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-12,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-12,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,8,58,0,0,0,0,0,0,0,0,0,0\n2015-04-12,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0\n2015-04-12,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,15,7,30,110,0,0,0,0,0,0,0,0,0,0\n2015-04-12,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,20,8,49,175,0,0,0,0,0,0,0,1,0,0\n2015-04-12,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-12,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-12,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,15,0,0,0,0,0,0,0,0,0,0\n2015-04-12,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,19,0,0,0,0,0,0,0,0,0,0\n2015-04-12,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-12,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0\n2015-04-12,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,22,0,0,0,0,0,0,0,0,0,0\n2015-04-12,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,39,0,0,0,0,0,0,0,0,0,0\n2015-04-12,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,28,0,0,0,0,0,0,0,0,0,0\n2015-04-12,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,50,265,0,0,0,0,0,0,0,0,0,0\n2015-04-12,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-12,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-12,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,36,0,0,0,0,0,0,0,0,0,0\n2015-04-12,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,10,58,0,0,0,0,0,0,0,0,0,0\n2015-04-12,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-12,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,17,62,0,0,0,0,0,0,0,0,0,0\n2015-04-12,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-12,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-12,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-12,LV,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,6,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,41,0,0,0,0,0,0,0,0,0,0\n2015-04-12,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-12,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0\n2015-04-12,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,15,59,0,0,0,0,0,0,0,0,0,0\n2015-04-12,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,22,0,0,0,0,0,0,0,0,0,0\n2015-04-12,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-12,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,7,27,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,23,0,0,0,0,0,0,0,0,0,0\n2015-04-12,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,5,18,0,0,0,0,0,0,0,0,0,0\n2015-04-12,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-12,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,9,38,0,0,0,0,0,0,0,0,0,0\n2015-04-12,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,13,2,5,19,0,0,0,0,0,0,0,0,0,0\n2015-04-12,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,7,29,0,0,0,0,0,0,0,0,0,0\n2015-04-12,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0\n2015-04-12,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0\n2015-04-12,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-12,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,2,9,0,0,0,0,0,0,0,0,0,0\n2015-04-12,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-12,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,6,42,0,0,0,0,0,0,0,0,0,0\n2015-04-12,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,37,0,0,0,0,0,0,0,0,0,0\n2015-04-12,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-12,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,13,0,0,0,0,0,0,0,0,0,0\n2015-04-12,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,US,98x5I1LVPhtnUHDyujq7zg,Roshan,64,36,258,964,0,0,0,0,0,0,0,0,0,0\n2015-04-12,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-12,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-12,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,12,37,0,0,0,0,0,0,0,0,0,0\n2015-04-12,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,4,21,0,0,0,0,0,0,0,0,0,0\n2015-04-13,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-13,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,26,0,0,0,0,0,0,0,0,0,0\n2015-04-13,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,18,0,0,0,0,0,0,0,0,0,0\n2015-04-13,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,10,56,0,0,0,0,0,0,0,0,1,0\n2015-04-13,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,12,0,0,0,0,0,0,0,0,0,0\n2015-04-13,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,8,27,0,0,0,0,0,0,0,0,0,0\n2015-04-13,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-13,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,11,81,0,0,0,0,0,0,0,0,1,0\n2015-04-13,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,4,0,0,0,0,0,0,0,0,0,0\n2015-04-13,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,27,107,0,0,0,0,0,0,0,0,0,0\n2015-04-13,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,19,6,14,35,0,0,0,0,0,0,0,0,0,0\n2015-04-13,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-13,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,2,3,0,0,0,0,0,0,0,0,1,0\n2015-04-13,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,28,0,0,0,0,0,0,0,0,0,0\n2015-04-13,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-13,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-04-13,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,17,8,46,206,0,0,0,0,0,0,0,0,0,0\n2015-04-13,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,27,0,0,0,0,0,0,0,0,0,0\n2015-04-13,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-13,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-13,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-13,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-13,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,11,1,8,59,0,0,0,0,0,0,0,0,0,0\n2015-04-13,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-13,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,32,110,0,0,0,0,0,0,0,0,0,0\n2015-04-13,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,9,7,44,176,0,0,0,0,0,0,0,0,0,0\n2015-04-13,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-13,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,15,0,0,0,0,0,0,0,0,0,0\n2015-04-13,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,6,21,0,0,0,0,0,0,0,0,0,0\n2015-04-13,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-13,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-04-13,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,22,0,0,0,0,0,0,0,0,0,0\n2015-04-13,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,40,0,0,0,0,0,0,0,0,0,0\n2015-04-13,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,28,0,0,0,0,0,0,0,0,0,0\n2015-04-13,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,33,18,59,274,0,0,0,0,0,0,0,0,1,0\n2015-04-13,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-13,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-13,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,36,0,0,0,0,0,0,0,0,0,0\n2015-04-13,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,8,59,0,0,0,0,0,0,0,0,0,0\n2015-04-13,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-13,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,18,64,0,0,0,0,0,0,0,0,0,0\n2015-04-13,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,3,11,0,0,0,0,0,0,0,1,0,0\n2015-04-13,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-13,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,5,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,4,40,0,0,0,0,0,0,0,0,0,0\n2015-04-13,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-13,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-13,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,58,0,0,0,0,0,0,0,0,0,0\n2015-04-13,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0\n2015-04-13,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-13,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,7,27,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0\n2015-04-13,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,17,0,0,0,0,0,0,0,0,0,0\n2015-04-13,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-13,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,22,4,8,40,0,0,0,0,0,0,0,0,0,0\n2015-04-13,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,18,0,0,0,0,0,0,0,0,0,0\n2015-04-13,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,28,0,0,0,0,0,0,0,0,0,0\n2015-04-13,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,23,0,0,0,0,0,0,0,0,0,0\n2015-04-13,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,12,0,0,0,0,0,0,0,0,0,0\n2015-04-13,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-13,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-13,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-13,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,43,0,0,0,0,0,0,0,0,0,0\n2015-04-13,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,5,38,0,0,0,0,0,0,0,0,0,0\n2015-04-13,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-13,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-13,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,US,98x5I1LVPhtnUHDyujq7zg,Roshan,46,31,248,971,0,1,0,0,0,0,0,0,1,0\n2015-04-13,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-13,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-13,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,35,0,0,0,0,0,0,0,0,0,0\n2015-04-13,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0\n2015-04-14,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-14,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,11,3,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,26,0,0,0,0,0,0,0,0,0,0\n2015-04-14,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,18,0,0,0,0,0,0,0,0,0,0\n2015-04-14,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,57,0,0,0,0,0,0,0,0,0,0\n2015-04-14,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0\n2015-04-14,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,28,0,0,0,0,0,0,0,0,0,0\n2015-04-14,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-14,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,13,84,0,0,0,0,0,0,0,2,0,0\n2015-04-14,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,22,105,0,0,0,0,0,0,0,0,0,0\n2015-04-14,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,11,6,19,41,0,0,0,0,0,0,0,0,0,0\n2015-04-14,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0\n2015-04-14,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,29,0,0,0,0,0,0,0,0,0,0\n2015-04-14,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-14,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,11,0,0,0,0,0,0,0,0,0,0\n2015-04-14,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,47,206,0,0,0,0,0,0,0,0,0,0\n2015-04-14,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,27,0,0,0,0,0,0,0,0,0,0\n2015-04-14,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-14,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-14,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-14,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-14,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,11,2,7,60,0,0,0,0,0,0,0,0,1,0\n2015-04-14,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,14,0,0,0,0,0,0,0,0,0,0\n2015-04-14,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,34,114,0,0,0,0,0,0,0,0,0,0\n2015-04-14,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,15,6,36,176,0,0,0,0,0,0,0,0,0,0\n2015-04-14,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,15,0,0,0,0,0,0,0,0,0,0\n2015-04-14,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,23,0,0,0,0,0,0,0,0,0,0\n2015-04-14,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-14,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-14,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,22,0,0,0,0,0,0,0,0,0,0\n2015-04-14,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,38,0,0,0,0,0,0,0,0,0,0\n2015-04-14,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,26,0,0,0,0,0,0,0,0,0,0\n2015-04-14,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,10,8,62,276,0,0,0,0,0,0,0,0,1,0\n2015-04-14,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-14,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,36,0,0,0,0,0,0,0,0,0,0\n2015-04-14,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,61,0,0,0,0,0,0,0,0,0,0\n2015-04-14,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,18,64,0,0,0,0,0,0,0,0,0,0\n2015-04-14,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,1,0\n2015-04-14,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-14,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,35,0,0,0,0,0,0,0,0,0,0\n2015-04-14,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-14,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-14,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,15,59,0,0,0,0,0,0,0,0,0,0\n2015-04-14,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0\n2015-04-14,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-14,OM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,25,0,0,0,0,0,0,0,0,0,0\n2015-04-14,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0\n2015-04-14,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-14,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,11,43,0,0,0,0,0,0,0,0,0,0\n2015-04-14,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,18,0,0,0,0,0,0,0,0,0,0\n2015-04-14,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,9,29,0,0,0,0,0,0,0,0,0,0\n2015-04-14,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,5,21,0,0,0,0,0,0,0,0,0,0\n2015-04-14,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-14,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-14,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0\n2015-04-14,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-14,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,9,42,0,0,0,0,0,0,0,0,0,0\n2015-04-14,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,38,0,0,0,0,0,0,0,0,0,0\n2015-04-14,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-14,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-14,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-14,US,98x5I1LVPhtnUHDyujq7zg,Roshan,101,49,249,981,0,0,0,0,0,0,0,1,0,0\n2015-04-14,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-14,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-14,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,9,35,0,0,0,0,0,0,0,0,0,0\n2015-04-14,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,23,0,0,0,0,0,0,0,0,0,0\n2015-04-15,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-15,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-15,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,24,0,0,0,0,0,0,0,0,0,0\n2015-04-15,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,17,0,0,0,0,0,0,0,0,0,0\n2015-04-15,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,55,0,0,0,0,0,0,0,0,0,0\n2015-04-15,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-15,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0\n2015-04-15,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,28,0,0,0,0,0,0,0,0,0,0\n2015-04-15,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-15,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,83,0,0,0,0,0,0,0,0,0,0\n2015-04-15,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-15,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,21,105,0,0,0,0,0,0,0,0,0,0\n2015-04-15,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,19,42,0,0,0,0,0,0,0,0,0,0\n2015-04-15,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,16,0,0,0,0,0,0,0,0,0,0\n2015-04-15,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0\n2015-04-15,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,28,0,0,0,0,0,0,0,0,0,0\n2015-04-15,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-15,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-15,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,9,49,209,0,0,0,0,0,0,0,0,0,0\n2015-04-15,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,28,0,0,0,0,0,0,0,0,0,0\n2015-04-15,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-15,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-15,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-15,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-15,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-15,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,8,59,0,0,0,0,0,0,0,0,0,0\n2015-04-15,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,5,14,0,0,0,0,0,0,0,0,0,0\n2015-04-15,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,8,4,35,114,0,0,0,0,0,0,0,0,1,0\n2015-04-15,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,18,8,39,173,0,0,0,0,0,0,0,0,0,0\n2015-04-15,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-15,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-15,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,13,0,0,0,0,0,0,0,0,0,0\n2015-04-15,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,7,24,0,0,0,0,0,0,0,0,0,0\n2015-04-15,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-15,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-15,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,23,0,0,0,0,0,0,0,0,0,0\n2015-04-15,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,35,0,0,0,0,0,0,0,0,0,0\n2015-04-15,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,27,0,0,0,0,0,0,0,0,0,0\n2015-04-15,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,14,10,67,280,0,0,0,0,0,0,0,0,0,0\n2015-04-15,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-15,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-15,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,38,0,0,0,0,0,0,0,0,0,0\n2015-04-15,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-15,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,61,0,0,0,0,0,0,0,0,0,0\n2015-04-15,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-15,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,17,63,0,0,0,0,0,0,0,0,0,0\n2015-04-15,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-15,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-15,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-15,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0\n2015-04-15,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-15,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-15,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-15,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,33,0,0,0,0,0,0,0,0,0,0\n2015-04-15,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,13,0,0,0,0,0,0,0,0,0,0\n2015-04-15,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-15,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,17,58,0,0,0,0,0,0,0,0,0,0\n2015-04-15,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,4,19,0,0,0,0,0,0,0,0,0,0\n2015-04-15,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-15,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-15,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-15,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,8,0,0,0,0,0,0,0,0,0,0\n2015-04-15,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-15,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,9,27,0,0,0,0,0,0,0,0,1,0\n2015-04-15,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-15,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,22,0,0,0,0,0,0,0,0,0,0\n2015-04-15,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,13,0,0,0,0,0,0,0,0,0,0\n2015-04-15,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-15,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,14,2,12,44,0,0,0,0,0,0,0,0,0,0\n2015-04-15,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0\n2015-04-15,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,0,8,29,0,0,0,0,0,0,0,0,0,0\n2015-04-15,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,20,0,0,0,0,0,0,0,0,0,0\n2015-04-15,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0\n2015-04-15,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-15,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-15,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-15,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,38,0,0,0,0,0,0,0,0,0,0\n2015-04-15,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,6,38,0,0,0,0,0,0,0,0,0,0\n2015-04-15,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-15,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-15,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-15,US,98x5I1LVPhtnUHDyujq7zg,Roshan,68,37,235,979,0,0,0,0,0,0,0,1,1,0\n2015-04-15,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-15,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-15,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,35,0,0,0,0,0,0,0,0,0,0\n2015-04-15,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,3,22,0,0,0,0,0,0,0,0,0,0\n2015-04-16,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-16,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,23,0,0,0,0,0,0,0,0,0,0\n2015-04-16,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,17,0,0,0,0,0,0,0,0,0,0\n2015-04-16,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,55,0,0,0,0,0,0,0,0,0,0\n2015-04-16,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-16,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,27,0,0,0,0,0,0,0,0,0,0\n2015-04-16,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-16,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,18,82,0,0,0,0,0,0,0,0,0,0\n2015-04-16,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-16,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,14,10,29,111,0,0,0,0,0,0,0,0,0,0\n2015-04-16,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,42,0,0,0,0,0,0,0,0,0,0\n2015-04-16,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,15,0,0,0,0,0,0,0,0,0,0\n2015-04-16,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-16,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,28,0,0,0,0,0,0,0,0,0,0\n2015-04-16,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-16,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-16,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,6,44,209,0,0,0,0,0,0,0,0,0,0\n2015-04-16,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,27,0,0,0,0,0,0,0,0,0,0\n2015-04-16,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-16,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,11,0,0,0,0,0,0,0,0,0,0\n2015-04-16,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-16,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-16,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,10,58,0,0,0,0,0,0,0,0,0,0\n2015-04-16,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,15,0,0,0,0,0,0,0,0,0,0\n2015-04-16,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,17,5,33,115,0,0,0,0,0,0,0,0,0,0\n2015-04-16,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,38,172,0,0,0,0,0,0,0,0,1,1\n2015-04-16,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,14,0,0,0,0,0,0,0,0,0,0\n2015-04-16,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,24,0,0,0,0,0,0,0,0,0,0\n2015-04-16,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,8,0,0,0,0,0,0,0,0,0,0\n2015-04-16,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-16,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,23,0,0,0,0,0,0,0,0,0,0\n2015-04-16,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,7,35,0,0,0,0,0,0,0,0,0,0\n2015-04-16,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,4,27,0,0,0,0,0,0,0,0,0,0\n2015-04-16,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,13,8,66,278,0,0,0,0,0,0,0,0,0,0\n2015-04-16,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,6,0,0,0,0,0,0,0,0,1,0\n2015-04-16,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-16,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,7,37,0,0,0,0,0,0,0,0,0,0\n2015-04-16,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-16,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,59,0,0,0,0,0,0,0,0,0,0\n2015-04-16,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-16,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,14,60,0,0,0,0,0,0,0,0,0,0\n2015-04-16,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-16,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-16,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-16,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-16,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-16,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-16,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-16,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,32,0,0,0,0,0,0,0,0,0,0\n2015-04-16,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-16,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,15,2,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-16,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,47,2,11,59,0,0,0,0,0,0,0,0,0,0\n2015-04-16,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,4,18,0,0,0,0,0,0,0,0,0,0\n2015-04-16,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,7,0,0,0,0,0,0,0,0,0,0\n2015-04-16,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,15,4,7,20,0,0,0,0,0,0,0,0,1,0\n2015-04-16,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,3,9,0,0,0,0,0,0,0,0,0,0\n2015-04-16,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,12,30,0,0,0,0,0,0,0,0,0,0\n2015-04-16,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-16,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,21,0,0,0,0,0,0,0,0,0,0\n2015-04-16,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,12,0,0,0,0,0,0,0,0,0,0\n2015-04-16,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-16,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,44,0,0,0,0,0,0,0,0,0,0\n2015-04-16,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,4,16,0,0,0,0,0,0,0,0,0,0\n2015-04-16,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,9,30,0,0,0,0,0,0,0,0,0,0\n2015-04-16,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,0,0\n2015-04-16,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0\n2015-04-16,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-16,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,10,0,0,0,0,0,0,0,0,1,0\n2015-04-16,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-16,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,37,0,0,0,0,0,0,0,0,0,0\n2015-04-16,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,12,2,8,39,0,0,0,0,0,0,0,0,1,0\n2015-04-16,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-16,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,US,98x5I1LVPhtnUHDyujq7zg,Roshan,73,45,241,985,0,0,0,0,0,0,0,2,2,0\n2015-04-16,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-16,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-16,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-16,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,35,0,0,0,0,0,0,0,0,0,0\n2015-04-16,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,22,0,0,0,0,0,0,0,0,0,0\n2015-04-17,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-17,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,25,0,0,0,0,0,0,0,0,0,0\n2015-04-17,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-17,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,9,55,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,10,30,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,16,77,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BW,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,30,111,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,10,3,17,43,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,28,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-17,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,8,48,209,0,0,0,0,0,0,0,0,0,0\n2015-04-17,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,28,0,0,0,0,0,0,0,0,0,0\n2015-04-17,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,11,0,0,0,0,0,0,0,0,0,0\n2015-04-17,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-17,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-17,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,59,0,0,0,0,0,0,0,0,0,0\n2015-04-17,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-17,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,6,34,116,0,0,0,0,0,0,0,0,0,0\n2015-04-17,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,38,168,0,0,0,0,0,0,0,0,0,0\n2015-04-17,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,14,0,0,0,0,0,0,0,0,0,0\n2015-04-17,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,6,24,0,0,0,0,0,0,0,0,0,0\n2015-04-17,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,8,0,0,0,0,0,0,0,0,0,0\n2015-04-17,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-17,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0\n2015-04-17,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,35,0,0,0,0,0,0,0,0,0,0\n2015-04-17,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,27,0,0,0,0,0,0,0,0,0,0\n2015-04-17,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,17,8,68,276,0,0,0,0,0,0,0,0,1,0\n2015-04-17,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-17,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-17,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,11,3,7,38,0,0,0,0,0,0,0,0,1,0\n2015-04-17,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-17,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,8,4,14,61,0,0,0,0,0,0,0,0,0,0\n2015-04-17,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-17,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,4,15,60,0,0,0,0,0,0,0,0,0,0\n2015-04-17,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,4,10,0,0,0,0,0,0,0,0,0,0\n2015-04-17,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,7,0,0,0,0,0,0,0,0,0,0\n2015-04-17,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-17,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-17,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,30,0,0,0,0,0,0,0,0,0,0\n2015-04-17,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-17,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-17,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,15,63,0,0,0,0,0,0,0,0,0,0\n2015-04-17,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0\n2015-04-17,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-17,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,20,0,0,0,0,0,0,0,0,0,0\n2015-04-17,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-17,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,15,33,0,0,0,0,0,0,0,0,1,0\n2015-04-17,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-17,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0\n2015-04-17,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,11,0,0,0,0,0,0,0,0,0,0\n2015-04-17,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-17,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,12,46,0,0,0,0,0,0,0,0,0,0\n2015-04-17,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,17,0,0,0,0,0,0,0,0,0,0\n2015-04-17,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,8,31,0,0,0,0,0,0,0,0,0,0\n2015-04-17,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,1,0\n2015-04-17,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0\n2015-04-17,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-17,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-17,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-17,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,37,0,0,0,0,0,0,0,0,0,0\n2015-04-17,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,10,37,0,0,0,0,0,0,0,0,0,0\n2015-04-17,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-17,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,US,98x5I1LVPhtnUHDyujq7zg,Roshan,83,43,245,980,0,0,0,0,0,0,0,0,2,0\n2015-04-17,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-17,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-17,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-17,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,8,39,0,0,0,0,0,0,0,0,0,0\n2015-04-17,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,21,0,0,0,0,0,0,0,0,0,0\n2015-04-18,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-18,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-18,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,25,0,0,0,0,0,0,0,0,0,0\n2015-04-18,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-18,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,57,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,31,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,76,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,27,112,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,19,44,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,28,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-18,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,9,0,0,0,0,0,0,0,0,0,0\n2015-04-18,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,7,49,206,0,0,0,0,0,0,0,0,0,0\n2015-04-18,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,27,0,0,0,0,0,0,0,0,0,0\n2015-04-18,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0\n2015-04-18,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-04-18,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-18,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-18,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,12,58,0,0,0,0,0,0,0,0,0,0\n2015-04-18,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-18,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,29,116,0,0,0,0,0,0,0,0,0,0\n2015-04-18,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,12,5,38,170,0,1,0,0,0,0,0,2,1,0\n2015-04-18,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0\n2015-04-18,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-18,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,23,0,0,0,0,0,0,0,0,0,0\n2015-04-18,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-18,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-18,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,23,0,0,0,0,0,0,0,0,0,0\n2015-04-18,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,8,36,0,0,0,0,0,0,0,0,0,0\n2015-04-18,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,28,0,0,0,0,0,0,0,0,0,0\n2015-04-18,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,17,11,69,275,0,0,0,0,0,0,0,0,0,0\n2015-04-18,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-18,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-18,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,38,0,0,0,0,0,0,0,0,0,0\n2015-04-18,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-18,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,16,62,0,0,0,0,0,0,0,0,0,0\n2015-04-18,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-18,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,59,0,0,0,0,0,0,0,0,0,0\n2015-04-18,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,4,9,0,1,0,0,0,0,0,0,0,0\n2015-04-18,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0\n2015-04-18,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-18,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-18,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,28,0,0,0,0,0,0,0,0,0,0\n2015-04-18,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0\n2015-04-18,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-18,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,16,63,0,0,0,0,0,0,0,0,0,0\n2015-04-18,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0\n2015-04-18,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-18,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,20,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,35,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,19,0,0,0,0,0,0,0,0,0,0\n2015-04-18,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0\n2015-04-18,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-18,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,45,0,0,0,0,0,0,0,0,0,0\n2015-04-18,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-18,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,31,0,0,0,0,0,0,0,0,0,0\n2015-04-18,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,20,0,0,0,0,0,0,0,0,0,0\n2015-04-18,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-18,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-18,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0\n2015-04-18,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-18,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,35,0,0,0,0,0,0,0,0,0,0\n2015-04-18,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,36,0,0,0,0,0,0,0,0,0,0\n2015-04-18,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0\n2015-04-18,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-18,US,98x5I1LVPhtnUHDyujq7zg,Roshan,65,36,252,979,0,0,0,0,0,0,0,0,2,0\n2015-04-18,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-18,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-18,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-18,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,36,0,0,0,0,0,0,0,0,0,0\n2015-04-18,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,20,0,0,0,0,0,0,0,0,0,0\n2015-04-19,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-19,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0\n2015-04-19,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,24,0,0,0,0,0,0,0,0,0,0\n2015-04-19,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0\n2015-04-19,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,13,60,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,31,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,14,76,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,26,108,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,20,44,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,16,1,2,3,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,28,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-19,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0\n2015-04-19,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,13,4,43,198,0,0,0,0,0,0,0,0,0,0\n2015-04-19,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,27,0,0,0,0,0,0,0,0,0,0\n2015-04-19,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-19,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0\n2015-04-19,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0\n2015-04-19,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-19,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-19,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,11,58,0,0,0,0,0,0,0,0,0,0\n2015-04-19,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-19,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,29,119,0,0,0,0,0,0,0,0,0,0\n2015-04-19,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,12,7,36,168,0,0,0,0,0,0,0,0,0,0\n2015-04-19,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-19,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0\n2015-04-19,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,22,0,0,0,0,0,0,0,0,0,0\n2015-04-19,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,8,0,0,0,0,0,0,0,0,0,0\n2015-04-19,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0\n2015-04-19,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0\n2015-04-19,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,35,0,0,0,0,0,0,0,0,0,0\n2015-04-19,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,29,0,0,0,0,0,0,0,0,0,0\n2015-04-19,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,9,4,65,271,0,0,0,0,0,0,0,0,0,0\n2015-04-19,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-19,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,8,38,0,0,0,0,0,0,0,0,0,0\n2015-04-19,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-19,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,16,65,0,0,0,0,0,0,0,0,0,0\n2015-04-19,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,15,58,0,0,0,0,0,0,0,0,0,0\n2015-04-19,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-19,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,9,0,0,0,0,0,0,0,0,0,0\n2015-04-19,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0\n2015-04-19,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0\n2015-04-19,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0\n2015-04-19,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-19,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,25,0,0,0,0,0,0,0,0,0,0\n2015-04-19,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,4,12,0,0,0,0,0,0,0,0,0,0\n2015-04-19,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-19,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,61,0,0,0,0,0,0,0,0,0,0\n2015-04-19,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,3,18,0,0,0,0,0,0,0,0,0,0\n2015-04-19,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0\n2015-04-19,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,21,0,0,0,0,0,0,0,0,1,0\n2015-04-19,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0\n2015-04-19,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,33,0,0,0,0,0,0,0,0,0,0\n2015-04-19,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0\n2015-04-19,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,19,0,0,0,0,0,0,0,0,0,0\n2015-04-19,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0\n2015-04-19,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,11,0,0,0,0,0,0,0,0,0,0\n2015-04-19,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0\n2015-04-19,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,44,0,0,0,0,0,0,0,0,0,0\n2015-04-19,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,4,17,0,0,0,0,0,0,0,0,0,0\n2015-04-19,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,33,0,0,0,0,0,0,0,0,0,0\n2015-04-19,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,21,0,0,0,0,0,0,0,0,0,0\n2015-04-19,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,4,14,0,0,0,0,0,0,0,0,0,0\n2015-04-19,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0\n2015-04-19,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0\n2015-04-19,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0\n2015-04-19,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,35,0,0,0,0,0,0,0,0,0,0\n2015-04-19,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,13,34,0,0,0,0,0,0,0,0,0,0\n2015-04-19,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,5,16,0,0,0,0,0,0,0,0,1,0\n2015-04-19,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0\n2015-04-19,US,98x5I1LVPhtnUHDyujq7zg,Roshan,102,40,255,977,0,0,0,0,0,0,0,1,5,0\n2015-04-19,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0\n2015-04-19,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0\n2015-04-19,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0\n2015-04-19,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,37,0,0,0,0,0,0,0,0,0,0\n2015-04-19,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,20,0,0,0,0,0,0,0,0,0,0\n"
  }
]