Repository: guipsamora/pandas_exercises
Branch: master
Commit: daf0fd2c7c34
Files: 103
Total size: 79.8 MB

Directory structure:
gitextract_4ujte8zq/

├── .github/
│   └── FUNDING.yml
├── .gitignore
├── 01_Getting_&_Knowing_Your_Data/
│   ├── Chipotle/
│   │   ├── Exercise_with_Solutions.ipynb
│   │   ├── Exercises.ipynb
│   │   └── Solutions.ipynb
│   ├── Occupation/
│   │   ├── Exercise_with_Solution.ipynb
│   │   ├── Exercises.ipynb
│   │   └── Solutions.ipynb
│   └── World_Food_Facts/
│       ├── Exercises.ipynb
│       ├── Exercises_with_solutions.ipynb
│       └── Solutions.ipynb
├── 02_Filtering_&_Sorting/
│   ├── Chipotle/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions.ipynb
│   │   └── Solutions.ipynb
│   ├── Euro12/
│   │   ├── Euro_2012_stats_TEAM.csv
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_Solutions.ipynb
│   │   └── Solutions.ipynb
│   └── Fictional_Army/
│       ├── Exercise.ipynb
│       ├── Exercise_with_solutions.ipynb
│       └── Solutions.ipynb
├── 03_Grouping/
│   ├── Alcohol_Consumption/
│   │   ├── Exercise.ipynb
│   │   ├── Exercise_with_solutions.ipynb
│   │   └── Solutions.ipynb
│   ├── Occupation/
│   │   ├── Exercise.ipynb
│   │   ├── Exercises_with_solutions.ipynb
│   │   └── Solutions.ipynb
│   └── Regiment/
│       ├── Exercises.ipynb
│       ├── Exercises_solutions.ipynb
│       └── Solutions.ipynb
├── 04_Apply/
│   ├── Students_Alcohol_Consumption/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions.ipynb
│   │   ├── Solutions.ipynb
│   │   └── student-mat.csv
│   └── US_Crime_Rates/
│       ├── Exercises.ipynb
│       ├── Exercises_with_solutions.ipynb
│       ├── Solutions.ipynb
│       └── US_Crime_Rates_1960_2014.csv
├── 05_Merge/
│   ├── Auto_MPG/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions.ipynb
│   │   ├── Solutions.ipynb
│   │   ├── cars1.csv
│   │   └── cars2.csv
│   ├── Fictitous_Names/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions.ipynb
│   │   └── Solutions.ipynb
│   └── Housing_Market/
│       ├── Exercises.ipynb
│       ├── Exercises_with_solutions.ipynb
│       └── Solutions.ipynb
├── 06_Stats/
│   ├── US_Baby_Names/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions.ipynb
│   │   ├── Solutions.ipynb
│   │   └── US_Baby_Names_right.csv
│   └── Wind_Stats/
│       ├── Exercises.ipynb
│       ├── Exercises_with_solutions.ipynb
│       ├── Solutions.ipynb
│       ├── wind.data
│       └── wind.desc
├── 07_Visualization/
│   ├── Chipotle/
│   │   ├── Exercise_with_Solutions.ipynb
│   │   ├── Exercises.ipynb
│   │   └── Solutions.ipynb
│   ├── Online_Retail/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions_code.ipynb
│   │   ├── Online_Retail.csv
│   │   └── Solutions.ipynb
│   ├── Scores/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions_code.ipynb
│   │   └── Solutions.ipynb
│   ├── Tips/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_code_and_solutions.ipynb
│   │   ├── Solutions.ipynb
│   │   └── tips.csv
│   └── Titanic_Disaster/
│       ├── Exercises.ipynb
│       ├── Exercises_code_with_solutions.ipynb
│       ├── Solutions.ipynb
│       └── train.csv
├── 08_Creating_Series_and_DataFrames/
│   └── Pokemon/
│       ├── Exercises-with-solutions-and-code.ipynb
│       ├── Exercises.ipynb
│       └── Solutions.ipynb
├── 09_Time_Series/
│   ├── Apple_Stock/
│   │   ├── Exercises-with-solutions-code.ipynb
│   │   ├── Exercises.ipynb
│   │   ├── Solutions.ipynb
│   │   └── appl_1980_2014.csv
│   ├── Getting_Financial_Data/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_solutions.ipynb
│   │   ├── Exercises_with_solutions_and_code.ipynb
│   │   └── Solutions.ipynb
│   └── Investor_Flow_of_Funds_US/
│       ├── Exercises.ipynb
│       ├── Exercises_with_code_and_solutions.ipynb
│       └── Solutions.ipynb
├── 10_Deleting/
│   ├── Iris/
│   │   ├── Exercises.ipynb
│   │   ├── Exercises_with_solutions_and_code.ipynb
│   │   └── Solutions.ipynb
│   └── Wine/
│       ├── Exercises.ipynb
│       ├── Exercises_code_and_solutions.ipynb
│       └── Solutions.ipynb
├── 11_Indexing/
│   └── Exercises.ipynb
├── CODE_OF_CONDUCT.md
├── LICENSE
├── README.md
├── Template/
│   ├── Exercises.ipynb
│   └── Solutions.ipynb
└── requirements.txt

================================================
FILE CONTENTS
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================================================
FILE: .github/FUNDING.yml
================================================
# These are supported funding model platforms

github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
patreon: # Replace with a single Patreon username
open_collective: # Replace with a single Open Collective username
ko_fi: # Replace with a single Ko-fi username
tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
liberapay: # Replace with a single Liberapay username
issuehunt: # Replace with a single IssueHunt username
otechie: # Replace with a single Otechie username
custom: ['paypal.me/guisamora']


================================================
FILE: .gitignore
================================================
.ipynb_checkpoints
.Rproj
.Rproj.user
.python

================================================
FILE: 01_Getting_&_Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex2 - Getting and Knowing your Data\n",
    "\n",
    "Check out [Chipotle Exercises Video Tutorial](https://www.youtube.com/watch?v=lpuYZ5EUyS8&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=2) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
    "    \n",
    "chipo = pd.read_csv(url, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4622"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution 1\n",
    "\n",
    "chipo.shape[0]  # entries <= 4622 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4622 entries, 0 to 4621\n",
      "Data columns (total 5 columns):\n",
      "order_id              4622 non-null int64\n",
      "quantity              4622 non-null int64\n",
      "item_name             4622 non-null object\n",
      "choice_description    3376 non-null object\n",
      "item_price            4622 non-null object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 180.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Solution 2\n",
    "\n",
    "chipo.info() # entries <= 4622 observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'order_id', u'quantity', u'item_name', u'choice_description',\n",
       "       u'item_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4622, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Which was the most-ordered item? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>713926</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              order_id  quantity\n",
       "item_name                       \n",
       "Chicken Bowl    713926       761"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = chipo.groupby('item_name')\n",
    "c = c.sum()\n",
    "c = c.sort_values(['quantity'], ascending=False)\n",
    "c.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. For the most-ordered item, how many items were ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>713926</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              order_id  quantity\n",
       "item_name                       \n",
       "Chicken Bowl    713926       761"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = chipo.groupby('item_name')\n",
    "c = c.sum()\n",
    "c = c.sort_values(['quantity'], ascending=False)\n",
    "c.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. What was the most ordered item in the choice_description column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>choice_description</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[Diet Coke]</th>\n",
       "      <td>123455</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    order_id  quantity\n",
       "choice_description                    \n",
       "[Diet Coke]           123455       159"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = chipo.groupby('choice_description').sum()\n",
    "c = c.sort_values(['quantity'], ascending=False)\n",
    "c.head(1)\n",
    "# Diet Coke 159"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. How many items were orderd in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4972"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_items_orders = chipo.quantity.sum()\n",
    "total_items_orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Turn the item price into a float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.a. Check the item price type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.item_price.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.b. Create a lambda function and change the type of item price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dollarizer = lambda x: float(x[1:-1])\n",
    "chipo.item_price = chipo.item_price.apply(dollarizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.c. Check the item price type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.item_price.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. How much was the revenue for the period in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revenue was: $39237.02\n"
     ]
    }
   ],
   "source": [
    "revenue = (chipo['quantity']* chipo['item_price']).sum()\n",
    "\n",
    "print('Revenue was: $' + str(np.round(revenue,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. How many orders were made in the period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = chipo.order_id.value_counts().count()\n",
    "orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. What is the average revenue amount per order?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.394231188658654"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution 1\n",
    "\n",
    "chipo['revenue'] = chipo['quantity'] * chipo['item_price']\n",
    "order_grouped = chipo.groupby(by=['order_id']).sum()\n",
    "order_grouped.mean()['revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.394231188658654"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution 2\n",
    "\n",
    "chipo.groupby('order_id')['revenue'].sum().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. How many different items are sold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.item_name.value_counts().count()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 01_Getting_&_Knowing_Your_Data/Chipotle/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex2 - Getting and Knowing your Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Solution 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Solution 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Which was the most-ordered item? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. For the most-ordered item, how many items were ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. What was the most ordered item in the choice_description column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. How many items were orderd in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Turn the item price into a float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.a. Check the item price type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.b. Create a lambda function and change the type of item price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.c. Check the item price type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. How much was the revenue for the period in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. How many orders were made in the period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. What is the average revenue amount per order?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Solution 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Solution 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. How many different items are sold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
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    "name": "ipython",
    "version": 2
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 01_Getting_&_Knowing_Your_Data/Chipotle/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex2 - Getting and Knowing your Data\n",
    "\n",
    "Check out [Chipotle Exercises Video Tutorial](https://www.youtube.com/watch?v=lpuYZ5EUyS8&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=2) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4622"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4622 entries, 0 to 4621\n",
      "Data columns (total 5 columns):\n",
      "order_id              4622 non-null int64\n",
      "quantity              4622 non-null int64\n",
      "item_name             4622 non-null object\n",
      "choice_description    3376 non-null object\n",
      "item_price            4622 non-null object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 180.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Solution 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'order_id', u'quantity', u'item_name', u'choice_description',\n",
       "       u'item_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4622, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Which was the most-ordered item? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>713926</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              order_id  quantity\n",
       "item_name                       \n",
       "Chicken Bowl    713926       761"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. For the most-ordered item, how many items were ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>713926</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              order_id  quantity\n",
       "item_name                       \n",
       "Chicken Bowl    713926       761"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. What was the most ordered item in the choice_description column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>choice_description</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[Diet Coke]</th>\n",
       "      <td>123455</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    order_id  quantity\n",
       "choice_description                    \n",
       "[Diet Coke]           123455       159"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. How many items were orderd in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4972"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Turn the item price into a float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.a. Check the item price type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.b. Create a lambda function and change the type of item price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 13.c. Check the item price type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. How much was the revenue for the period in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revenue was: $39237.02\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. How many orders were made in the period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. What is the average revenue amount per order?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.394231188658654"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.394231188658654"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. How many different items are sold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
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================================================
FILE: 01_Getting_&_Knowing_Your_Data/Occupation/Exercise_with_Solution.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex3 - Getting and Knowing your Data\n",
    "\n",
    "Check out [Occupation Exercises Video Tutorial](https://www.youtube.com/watch?v=W8AB5s-L3Rw&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=4) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called users and use the 'user_id' as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "users = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n",
    "                      sep='|', index_col='user_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 25 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>executive</td>\n",
       "      <td>98101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>91344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>05201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>01002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>53</td>\n",
       "      <td>M</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>90703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>39</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>30329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>06405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>29206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>scientist</td>\n",
       "      <td>55106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>educator</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>10309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30</td>\n",
       "      <td>M</td>\n",
       "      <td>programmer</td>\n",
       "      <td>06355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>37212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>librarian</td>\n",
       "      <td>02138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>homemaker</td>\n",
       "      <td>95660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>30068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>40206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "      <td>artist</td>\n",
       "      <td>48197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>artist</td>\n",
       "      <td>94533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>55107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender     occupation zip_code\n",
       "user_id                                    \n",
       "1         24      M     technician    85711\n",
       "2         53      F          other    94043\n",
       "3         23      M         writer    32067\n",
       "4         24      M     technician    43537\n",
       "5         33      F          other    15213\n",
       "6         42      M      executive    98101\n",
       "7         57      M  administrator    91344\n",
       "8         36      M  administrator    05201\n",
       "9         29      M        student    01002\n",
       "10        53      M         lawyer    90703\n",
       "11        39      F          other    30329\n",
       "12        28      F          other    06405\n",
       "13        47      M       educator    29206\n",
       "14        45      M      scientist    55106\n",
       "15        49      F       educator    97301\n",
       "16        21      M  entertainment    10309\n",
       "17        30      M     programmer    06355\n",
       "18        35      F          other    37212\n",
       "19        40      M      librarian    02138\n",
       "20        42      F      homemaker    95660\n",
       "21        26      M         writer    30068\n",
       "22        25      M         writer    40206\n",
       "23        30      F         artist    48197\n",
       "24        21      F         artist    94533\n",
       "25        39      M       engineer    55107"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. See the last 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>22902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>doctor</td>\n",
       "      <td>66221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>other</td>\n",
       "      <td>32789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>48</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>98072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>technician</td>\n",
       "      <td>55038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>33319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>librarian</td>\n",
       "      <td>78209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>77841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender     occupation zip_code\n",
       "user_id                                    \n",
       "934       61      M       engineer    22902\n",
       "935       42      M         doctor    66221\n",
       "936       24      M          other    32789\n",
       "937       48      M       educator    98072\n",
       "938       38      F     technician    55038\n",
       "939       26      F        student    33319\n",
       "940       32      M  administrator    02215\n",
       "941       20      M        student    97229\n",
       "942       48      F      librarian    78209\n",
       "943       22      M        student    77841"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "943"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'occupation', 'zip_code'], dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "            ...\n",
       "            934, 935, 936, 937, 938, 939, 940, 941, 942, 943],\n",
       "           dtype='int64', name='user_id', length=943)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"the index\" (aka \"the labels\")\n",
    "users.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the data type of each column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            int64\n",
       "gender        object\n",
       "occupation    object\n",
       "zip_code      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Print only the occupation column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1         technician\n",
       "2              other\n",
       "3             writer\n",
       "4         technician\n",
       "5              other\n",
       "6          executive\n",
       "7      administrator\n",
       "8      administrator\n",
       "9            student\n",
       "10            lawyer\n",
       "11             other\n",
       "12             other\n",
       "13          educator\n",
       "14         scientist\n",
       "15          educator\n",
       "16     entertainment\n",
       "17        programmer\n",
       "18             other\n",
       "19         librarian\n",
       "20         homemaker\n",
       "21            writer\n",
       "22            writer\n",
       "23            artist\n",
       "24            artist\n",
       "25          engineer\n",
       "26          engineer\n",
       "27         librarian\n",
       "28            writer\n",
       "29        programmer\n",
       "30           student\n",
       "           ...      \n",
       "914            other\n",
       "915    entertainment\n",
       "916         engineer\n",
       "917          student\n",
       "918        scientist\n",
       "919            other\n",
       "920           artist\n",
       "921          student\n",
       "922    administrator\n",
       "923          student\n",
       "924            other\n",
       "925         salesman\n",
       "926    entertainment\n",
       "927       programmer\n",
       "928          student\n",
       "929        scientist\n",
       "930        scientist\n",
       "931         educator\n",
       "932         educator\n",
       "933          student\n",
       "934         engineer\n",
       "935           doctor\n",
       "936            other\n",
       "937         educator\n",
       "938       technician\n",
       "939          student\n",
       "940    administrator\n",
       "941          student\n",
       "942        librarian\n",
       "943          student\n",
       "Name: occupation, Length: 943, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.occupation\n",
    "\n",
    "#or\n",
    "\n",
    "users['occupation']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. How many different occupations are in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.occupation.nunique()\n",
    "#or by using value_counts() which returns the count of unique elements\n",
    "#users.occupation.value_counts().count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. What is the most frequent occupation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'student'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Because \"most\" is asked\n",
    "users.occupation.value_counts().head(1).index[0]\n",
    "\n",
    "#or\n",
    "#to have the top 5\n",
    "\n",
    "# users.occupation.value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Summarize the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>943.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.051962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.192740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age\n",
       "count  943.000000\n",
       "mean    34.051962\n",
       "std     12.192740\n",
       "min      7.000000\n",
       "25%     25.000000\n",
       "50%     31.000000\n",
       "75%     43.000000\n",
       "max     73.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.describe() #Notice: by default, only the numeric columns are returned. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Summarize all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>943.000000</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>670</td>\n",
       "      <td>196</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.051962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.192740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age gender occupation zip_code\n",
       "count   943.000000    943        943      943\n",
       "unique         NaN      2         21      795\n",
       "top            NaN      M    student    55414\n",
       "freq           NaN    670        196        9\n",
       "mean     34.051962    NaN        NaN      NaN\n",
       "std      12.192740    NaN        NaN      NaN\n",
       "min       7.000000    NaN        NaN      NaN\n",
       "25%      25.000000    NaN        NaN      NaN\n",
       "50%      31.000000    NaN        NaN      NaN\n",
       "75%      43.000000    NaN        NaN      NaN\n",
       "max      73.000000    NaN        NaN      NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.describe(include = \"all\") #Notice: By default, only the numeric columns are returned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. Summarize only the occupation column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         943\n",
       "unique         21\n",
       "top       student\n",
       "freq          196\n",
       "Name: occupation, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.occupation.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. What is the mean age of users?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(users.age.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 18. What is the age with least occurrence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    1\n",
       "10    1\n",
       "73    1\n",
       "66    1\n",
       "7     1\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.age.value_counts().tail() #7, 10, 11, 66 and 73 years -> only 1 occurrence"
   ]
  }
 ],
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================================================
FILE: 01_Getting_&_Knowing_Your_Data/Occupation/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex3 - Getting and Knowing your Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called users and use the 'user_id' as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 25 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. See the last 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the data type of each column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Print only the occupation column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. How many different occupations are in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. What is the most frequent occupation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Summarize the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Summarize all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. Summarize only the occupation column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. What is the mean age of users?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 18. What is the age with least occurrence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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================================================
FILE: 01_Getting_&_Knowing_Your_Data/Occupation/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex3 - Getting and Knowing your Data\n",
    "\n",
    "Check out [Occupation Exercises Video Tutorial](https://www.youtube.com/watch?v=W8AB5s-L3Rw&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=4) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called users and use the 'user_id' as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 25 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <td>01002</td>\n",
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       "      <td>53</td>\n",
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       "      <td>90703</td>\n",
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       "      <td>30329</td>\n",
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       "      <td>28</td>\n",
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       "      <td>06405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>29206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>scientist</td>\n",
       "      <td>55106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>educator</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>10309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30</td>\n",
       "      <td>M</td>\n",
       "      <td>programmer</td>\n",
       "      <td>06355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>37212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>librarian</td>\n",
       "      <td>02138</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>homemaker</td>\n",
       "      <td>95660</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
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       "      <td>writer</td>\n",
       "      <td>30068</td>\n",
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       "      <th>22</th>\n",
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       "      <td>writer</td>\n",
       "      <td>40206</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "      <td>artist</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
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       "      <td>artist</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>55107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender     occupation zip_code\n",
       "user_id                                    \n",
       "1         24      M     technician    85711\n",
       "2         53      F          other    94043\n",
       "3         23      M         writer    32067\n",
       "4         24      M     technician    43537\n",
       "5         33      F          other    15213\n",
       "6         42      M      executive    98101\n",
       "7         57      M  administrator    91344\n",
       "8         36      M  administrator    05201\n",
       "9         29      M        student    01002\n",
       "10        53      M         lawyer    90703\n",
       "11        39      F          other    30329\n",
       "12        28      F          other    06405\n",
       "13        47      M       educator    29206\n",
       "14        45      M      scientist    55106\n",
       "15        49      F       educator    97301\n",
       "16        21      M  entertainment    10309\n",
       "17        30      M     programmer    06355\n",
       "18        35      F          other    37212\n",
       "19        40      M      librarian    02138\n",
       "20        42      F      homemaker    95660\n",
       "21        26      M         writer    30068\n",
       "22        25      M         writer    40206\n",
       "23        30      F         artist    48197\n",
       "24        21      F         artist    94533\n",
       "25        39      M       engineer    55107"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. See the last 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "         age gender     occupation zip_code\n",
       "user_id                                    \n",
       "934       61      M       engineer    22902\n",
       "935       42      M         doctor    66221\n",
       "936       24      M          other    32789\n",
       "937       48      M       educator    98072\n",
       "938       38      F     technician    55038\n",
       "939       26      F        student    33319\n",
       "940       32      M  administrator    02215\n",
       "941       20      M        student    97229\n",
       "942       48      F      librarian    78209\n",
       "943       22      M        student    77841"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "943"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'occupation', 'zip_code'], dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "            ...\n",
       "            934, 935, 936, 937, 938, 939, 940, 941, 942, 943],\n",
       "           dtype='int64', name='user_id', length=943)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"the index\" (aka \"the labels\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the data type of each column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            int64\n",
       "gender        object\n",
       "occupation    object\n",
       "zip_code      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Print only the occupation column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1         technician\n",
       "2              other\n",
       "3             writer\n",
       "4         technician\n",
       "5              other\n",
       "6          executive\n",
       "7      administrator\n",
       "8      administrator\n",
       "9            student\n",
       "10            lawyer\n",
       "11             other\n",
       "12             other\n",
       "13          educator\n",
       "14         scientist\n",
       "15          educator\n",
       "16     entertainment\n",
       "17        programmer\n",
       "18             other\n",
       "19         librarian\n",
       "20         homemaker\n",
       "21            writer\n",
       "22            writer\n",
       "23            artist\n",
       "24            artist\n",
       "25          engineer\n",
       "26          engineer\n",
       "27         librarian\n",
       "28            writer\n",
       "29        programmer\n",
       "30           student\n",
       "           ...      \n",
       "914            other\n",
       "915    entertainment\n",
       "916         engineer\n",
       "917          student\n",
       "918        scientist\n",
       "919            other\n",
       "920           artist\n",
       "921          student\n",
       "922    administrator\n",
       "923          student\n",
       "924            other\n",
       "925         salesman\n",
       "926    entertainment\n",
       "927       programmer\n",
       "928          student\n",
       "929        scientist\n",
       "930        scientist\n",
       "931         educator\n",
       "932         educator\n",
       "933          student\n",
       "934         engineer\n",
       "935           doctor\n",
       "936            other\n",
       "937         educator\n",
       "938       technician\n",
       "939          student\n",
       "940    administrator\n",
       "941          student\n",
       "942        librarian\n",
       "943          student\n",
       "Name: occupation, Length: 943, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. How many different occupations are in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. What is the most frequent occupation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'student'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Summarize the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>943.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.051962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.192740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age\n",
       "count  943.000000\n",
       "mean    34.051962\n",
       "std     12.192740\n",
       "min      7.000000\n",
       "25%     25.000000\n",
       "50%     31.000000\n",
       "75%     43.000000\n",
       "max     73.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Summarize all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>943.000000</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>670</td>\n",
       "      <td>196</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.051962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.192740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age gender occupation zip_code\n",
       "count   943.000000    943        943      943\n",
       "unique         NaN      2         21      795\n",
       "top            NaN      M    student    55414\n",
       "freq           NaN    670        196        9\n",
       "mean     34.051962    NaN        NaN      NaN\n",
       "std      12.192740    NaN        NaN      NaN\n",
       "min       7.000000    NaN        NaN      NaN\n",
       "25%      25.000000    NaN        NaN      NaN\n",
       "50%      31.000000    NaN        NaN      NaN\n",
       "75%      43.000000    NaN        NaN      NaN\n",
       "max      73.000000    NaN        NaN      NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. Summarize only the occupation column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         943\n",
       "unique         21\n",
       "top       student\n",
       "freq          196\n",
       "Name: occupation, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. What is the mean age of users?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 18. What is the age with least occurrence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    1\n",
       "10    1\n",
       "73    1\n",
       "66    1\n",
       "7     1\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 01_Getting_&_Knowing_Your_Data/World_Food_Facts/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Download the dataset to your computer and unzip it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Use the tsv file and assign it to a dataframe called food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 5 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. What is the name of 105th column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. What is the type of the observations of the 105th column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. What is the product name of the 19th observation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
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   "name": "python2"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 01_Getting_&_Knowing_Your_Data/World_Food_Facts/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex1 - Getting and knowing your Data\n",
    "Check out [World Food Facts Exercises Video Tutorial](https://youtu.be/_jCSK4cMcVw) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Step 2. Download the dataset to your computer and unzip it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Use the tsv file and assign it to a dataframe called food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,3,5,19,20,24,25,26,27,28,36,37,38,39,48) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "food = pd.read_csv('~/Desktop/en.openfoodfacts.org.products.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 5 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_t</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_t</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>...</th>\n",
       "      <th>fruits-vegetables-nuts_100g</th>\n",
       "      <th>fruits-vegetables-nuts-estimate_100g</th>\n",
       "      <th>collagen-meat-protein-ratio_100g</th>\n",
       "      <th>cocoa_100g</th>\n",
       "      <th>chlorophyl_100g</th>\n",
       "      <th>carbon-footprint_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <th>nutrition-score-uk_100g</th>\n",
       "      <th>glycemic-index_100g</th>\n",
       "      <th>water-hardness_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>1474103866</td>\n",
       "      <td>2016-09-17T09:17:46Z</td>\n",
       "      <td>1474103893</td>\n",
       "      <td>2016-09-17T09:18:13Z</td>\n",
       "      <td>Farine de blé noir</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1kg</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4530</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Banana Chips Sweetened (Whole)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4559</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Peanuts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489055731</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>1489055731</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>Organic Salted Nut Mix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16094</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489055653</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>1489055653</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>Organic Polenta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code                                                url  \\\n",
       "0   3087  http://world-en.openfoodfacts.org/product/0000...   \n",
       "1   4530  http://world-en.openfoodfacts.org/product/0000...   \n",
       "2   4559  http://world-en.openfoodfacts.org/product/0000...   \n",
       "3  16087  http://world-en.openfoodfacts.org/product/0000...   \n",
       "4  16094  http://world-en.openfoodfacts.org/product/0000...   \n",
       "\n",
       "                      creator   created_t      created_datetime  \\\n",
       "0  openfoodfacts-contributors  1474103866  2016-09-17T09:17:46Z   \n",
       "1             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
       "2             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
       "3             usda-ndb-import  1489055731  2017-03-09T10:35:31Z   \n",
       "4             usda-ndb-import  1489055653  2017-03-09T10:34:13Z   \n",
       "\n",
       "  last_modified_t last_modified_datetime                    product_name  \\\n",
       "0      1474103893   2016-09-17T09:18:13Z              Farine de blé noir   \n",
       "1      1489069957   2017-03-09T14:32:37Z  Banana Chips Sweetened (Whole)   \n",
       "2      1489069957   2017-03-09T14:32:37Z                         Peanuts   \n",
       "3      1489055731   2017-03-09T10:35:31Z          Organic Salted Nut Mix   \n",
       "4      1489055653   2017-03-09T10:34:13Z                 Organic Polenta   \n",
       "\n",
       "  generic_name quantity         ...         fruits-vegetables-nuts_100g  \\\n",
       "0          NaN      1kg         ...                                 NaN   \n",
       "1          NaN      NaN         ...                                 NaN   \n",
       "2          NaN      NaN         ...                                 NaN   \n",
       "3          NaN      NaN         ...                                 NaN   \n",
       "4          NaN      NaN         ...                                 NaN   \n",
       "\n",
       "  fruits-vegetables-nuts-estimate_100g collagen-meat-protein-ratio_100g  \\\n",
       "0                                  NaN                              NaN   \n",
       "1                                  NaN                              NaN   \n",
       "2                                  NaN                              NaN   \n",
       "3                                  NaN                              NaN   \n",
       "4                                  NaN                              NaN   \n",
       "\n",
       "  cocoa_100g chlorophyl_100g carbon-footprint_100g nutrition-score-fr_100g  \\\n",
       "0        NaN             NaN                   NaN                     NaN   \n",
       "1        NaN             NaN                   NaN                    14.0   \n",
       "2        NaN             NaN                   NaN                     0.0   \n",
       "3        NaN             NaN                   NaN                    12.0   \n",
       "4        NaN             NaN                   NaN                     NaN   \n",
       "\n",
       "  nutrition-score-uk_100g glycemic-index_100g water-hardness_100g  \n",
       "0                     NaN                 NaN                 NaN  \n",
       "1                    14.0                 NaN                 NaN  \n",
       "2                     0.0                 NaN                 NaN  \n",
       "3                    12.0                 NaN                 NaN  \n",
       "4                     NaN                 NaN                 NaN  \n",
       "\n",
       "[5 rows x 163 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(356027, 163)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.shape #will give you both (observations/rows, columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356027"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.shape[0] #will give you only the observations/rows number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(356027, 163)\n",
      "163\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 356027 entries, 0 to 356026\n",
      "Columns: 163 entries, code to water-hardness_100g\n",
      "dtypes: float64(107), object(56)\n",
      "memory usage: 442.8+ MB\n"
     ]
    }
   ],
   "source": [
    "print(food.shape) #will give you both (observations/rows, columns)\n",
    "print(food.shape[1]) #will give you only the columns number\n",
    "\n",
    "#OR\n",
    "\n",
    "food.info() #Columns: 163 entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'code', u'url', u'creator', u'created_t', u'created_datetime',\n",
       "       u'last_modified_t', u'last_modified_datetime', u'product_name',\n",
       "       u'generic_name', u'quantity',\n",
       "       ...\n",
       "       u'fruits-vegetables-nuts_100g', u'fruits-vegetables-nuts-estimate_100g',\n",
       "       u'collagen-meat-protein-ratio_100g', u'cocoa_100g', u'chlorophyl_100g',\n",
       "       u'carbon-footprint_100g', u'nutrition-score-fr_100g',\n",
       "       u'nutrition-score-uk_100g', u'glycemic-index_100g',\n",
       "       u'water-hardness_100g'],\n",
       "      dtype='object', length=163)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. What is the name of 105th column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-glucose_100g'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.columns[104]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. What is the type of the observations of the 105th column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.dtypes['-glucose_100g']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=356027, step=1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. What is the product name of the 19th observation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lotus Organic Brown Jasmine Rice'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food.values[18][7]"
   ]
  }
 ],
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================================================
FILE: 01_Getting_&_Knowing_Your_Data/World_Food_Facts/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex1 - Getting and knowing your Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Step 2. Download the dataset to your computer and unzip it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Use the tsv file and assign it to a dataframe called food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (0,3,5,27,36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 5 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>url</th>\n",
       "      <th>creator</th>\n",
       "      <th>created_t</th>\n",
       "      <th>created_datetime</th>\n",
       "      <th>last_modified_t</th>\n",
       "      <th>last_modified_datetime</th>\n",
       "      <th>product_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>quantity</th>\n",
       "      <th>...</th>\n",
       "      <th>fruits-vegetables-nuts_100g</th>\n",
       "      <th>fruits-vegetables-nuts-estimate_100g</th>\n",
       "      <th>collagen-meat-protein-ratio_100g</th>\n",
       "      <th>cocoa_100g</th>\n",
       "      <th>chlorophyl_100g</th>\n",
       "      <th>carbon-footprint_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <th>nutrition-score-uk_100g</th>\n",
       "      <th>glycemic-index_100g</th>\n",
       "      <th>water-hardness_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>openfoodfacts-contributors</td>\n",
       "      <td>1474103866</td>\n",
       "      <td>2016-09-17T09:17:46Z</td>\n",
       "      <td>1474103893</td>\n",
       "      <td>2016-09-17T09:18:13Z</td>\n",
       "      <td>Farine de blé noir</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1kg</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4530</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Banana Chips Sweetened (Whole)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4559</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>1489069957</td>\n",
       "      <td>2017-03-09T14:32:37Z</td>\n",
       "      <td>Peanuts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16087</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489055731</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>1489055731</td>\n",
       "      <td>2017-03-09T10:35:31Z</td>\n",
       "      <td>Organic Salted Nut Mix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16094</td>\n",
       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>1489055653</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>1489055653</td>\n",
       "      <td>2017-03-09T10:34:13Z</td>\n",
       "      <td>Organic Polenta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code                                                url  \\\n",
       "0   3087  http://world-en.openfoodfacts.org/product/0000...   \n",
       "1   4530  http://world-en.openfoodfacts.org/product/0000...   \n",
       "2   4559  http://world-en.openfoodfacts.org/product/0000...   \n",
       "3  16087  http://world-en.openfoodfacts.org/product/0000...   \n",
       "4  16094  http://world-en.openfoodfacts.org/product/0000...   \n",
       "\n",
       "                      creator   created_t      created_datetime  \\\n",
       "0  openfoodfacts-contributors  1474103866  2016-09-17T09:17:46Z   \n",
       "1             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
       "2             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
       "3             usda-ndb-import  1489055731  2017-03-09T10:35:31Z   \n",
       "4             usda-ndb-import  1489055653  2017-03-09T10:34:13Z   \n",
       "\n",
       "  last_modified_t last_modified_datetime                    product_name  \\\n",
       "0      1474103893   2016-09-17T09:18:13Z              Farine de blé noir   \n",
       "1      1489069957   2017-03-09T14:32:37Z  Banana Chips Sweetened (Whole)   \n",
       "2      1489069957   2017-03-09T14:32:37Z                         Peanuts   \n",
       "3      1489055731   2017-03-09T10:35:31Z          Organic Salted Nut Mix   \n",
       "4      1489055653   2017-03-09T10:34:13Z                 Organic Polenta   \n",
       "\n",
       "  generic_name quantity         ...         fruits-vegetables-nuts_100g  \\\n",
       "0          NaN      1kg         ...                                 NaN   \n",
       "1          NaN      NaN         ...                                 NaN   \n",
       "2          NaN      NaN         ...                                 NaN   \n",
       "3          NaN      NaN         ...                                 NaN   \n",
       "4          NaN      NaN         ...                                 NaN   \n",
       "\n",
       "  fruits-vegetables-nuts-estimate_100g collagen-meat-protein-ratio_100g  \\\n",
       "0                                  NaN                              NaN   \n",
       "1                                  NaN                              NaN   \n",
       "2                                  NaN                              NaN   \n",
       "3                                  NaN                              NaN   \n",
       "4                                  NaN                              NaN   \n",
       "\n",
       "  cocoa_100g chlorophyl_100g carbon-footprint_100g nutrition-score-fr_100g  \\\n",
       "0        NaN             NaN                   NaN                     NaN   \n",
       "1        NaN             NaN                   NaN                    14.0   \n",
       "2        NaN             NaN                   NaN                     0.0   \n",
       "3        NaN             NaN                   NaN                    12.0   \n",
       "4        NaN             NaN                   NaN                     NaN   \n",
       "\n",
       "  nutrition-score-uk_100g glycemic-index_100g water-hardness_100g  \n",
       "0                     NaN                 NaN                 NaN  \n",
       "1                    14.0                 NaN                 NaN  \n",
       "2                     0.0                 NaN                 NaN  \n",
       "3                    12.0                 NaN                 NaN  \n",
       "4                     NaN                 NaN                 NaN  \n",
       "\n",
       "[5 rows x 163 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(356027, 163)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356027"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(356027, 163)\n",
      "163\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 356027 entries, 0 to 356026\n",
      "Columns: 163 entries, code to water-hardness_100g\n",
      "dtypes: float64(107), object(56)\n",
      "memory usage: 442.8+ MB\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['code', 'url', 'creator', 'created_t', 'created_datetime',\n",
       "       'last_modified_t', 'last_modified_datetime', 'product_name',\n",
       "       'generic_name', 'quantity',\n",
       "       ...\n",
       "       'fruits-vegetables-nuts_100g', 'fruits-vegetables-nuts-estimate_100g',\n",
       "       'collagen-meat-protein-ratio_100g', 'cocoa_100g', 'chlorophyl_100g',\n",
       "       'carbon-footprint_100g', 'nutrition-score-fr_100g',\n",
       "       'nutrition-score-uk_100g', 'glycemic-index_100g',\n",
       "       'water-hardness_100g'],\n",
       "      dtype='object', length=163)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. What is the name of 105th column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-glucose_100g'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. What is the type of the observations of the 105th column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. How is the dataset indexed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=356027, step=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. What is the product name of the 19th observation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Lotus Organic Brown Jasmine Rice'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 02_Filtering_&_Sorting/Chipotle/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex1 - Filtering and Sorting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. How many products cost more than $10.00?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the price of each item? \n",
    "###### print a data frame with only three columns item_name choice_description and product_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Sort by the name of the item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. What was the quantity of the most expensive item ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. How many times was a Veggie Salad Bowl ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How many times did someone order more than one Canned Soda?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 02_Filtering_&_Sorting/Chipotle/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex1 - Filtering and Sorting Data\n",
    "\n",
    "Check out [Chipotle Exercises Video Tutorial](https://youtu.be/ZZPiWZpdekA) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
    "\n",
    "chipo = pd.read_csv(url, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. How many products cost more than $10.00?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$2.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$2.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>496</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>672</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>790</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2135</th>\n",
       "      <td>859</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$2.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2544</th>\n",
       "      <td>1009</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>1132</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>1440</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$2.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3793</th>\n",
       "      <td>1518</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>1604</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>$1.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  quantity    item_name choice_description item_price\n",
       "162         73         2  Canned Soda        [Diet Coke]     $2.18 \n",
       "200         89         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "317        138         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "350        150         2  Canned Soda        [Diet Coke]     $2.18 \n",
       "370        160         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "779        321         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "1216       496         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "1662       672         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "1953       790         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "2135       859         2  Canned Soda        [Diet Coke]     $2.18 \n",
       "2544      1009         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "2850      1132         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "3592      1440         2  Canned Soda        [Diet Coke]     $2.18 \n",
       "3793      1518         1  Canned Soda        [Diet Coke]     $1.09 \n",
       "4008      1604         1  Canned Soda        [Diet Coke]     $1.09 "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the item price column is actullay the price of the product multiplied by the quantity\n",
    "chipo.loc[(chipo[\"choice_description\"] == '[Diet Coke]') & (chipo[\"item_name\"] == \"Canned Soda\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "      <th>product_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>16.98</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4617</th>\n",
       "      <td>1833</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n",
       "      <td>11.75</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4618</th>\n",
       "      <td>1833</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
       "      <td>11.75</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4619</th>\n",
       "      <td>1834</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
       "      <td>11.25</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4620</th>\n",
       "      <td>1834</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4621</th>\n",
       "      <td>1834</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4622 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  quantity                              item_name  \\\n",
       "0            1         1           Chips and Fresh Tomato Salsa   \n",
       "1            1         1                                   Izze   \n",
       "2            1         1                       Nantucket Nectar   \n",
       "3            1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4            2         2                           Chicken Bowl   \n",
       "...        ...       ...                                    ...   \n",
       "4617      1833         1                          Steak Burrito   \n",
       "4618      1833         1                          Steak Burrito   \n",
       "4619      1834         1                     Chicken Salad Bowl   \n",
       "4620      1834         1                     Chicken Salad Bowl   \n",
       "4621      1834         1                     Chicken Salad Bowl   \n",
       "\n",
       "                                     choice_description  item_price  \\\n",
       "0                                                   NaN        2.39   \n",
       "1                                          [Clementine]        3.39   \n",
       "2                                               [Apple]        3.39   \n",
       "3                                                   NaN        2.39   \n",
       "4     [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98   \n",
       "...                                                 ...         ...   \n",
       "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...       11.75   \n",
       "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...       11.75   \n",
       "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...       11.25   \n",
       "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...        8.75   \n",
       "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...        8.75   \n",
       "\n",
       "      product_price  \n",
       "0              2.39  \n",
       "1              3.39  \n",
       "2              3.39  \n",
       "3              2.39  \n",
       "4              8.49  \n",
       "...             ...  \n",
       "4617          11.75  \n",
       "4618          11.75  \n",
       "4619          11.25  \n",
       "4620           8.75  \n",
       "4621           8.75  \n",
       "\n",
       "[4622 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding a new column representing the price of each single product in float\n",
    "chipo[\"item_price\"] = chipo[\"item_price\"].str.replace(\"$\", \"\", regex=False).astype(float)\n",
    "chipo[\"product_price\"] = chipo[\"item_price\"] / chipo[\"quantity\"]\n",
    "chipo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "      <th>product_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>496</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>672</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>790</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2135</th>\n",
       "      <td>859</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2544</th>\n",
       "      <td>1009</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2850</th>\n",
       "      <td>1132</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>1440</td>\n",
       "      <td>2</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3793</th>\n",
       "      <td>1518</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>1604</td>\n",
       "      <td>1</td>\n",
       "      <td>Canned Soda</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  quantity    item_name choice_description  item_price  \\\n",
       "162         73         2  Canned Soda        [Diet Coke]        2.18   \n",
       "200         89         1  Canned Soda        [Diet Coke]        1.09   \n",
       "317        138         1  Canned Soda        [Diet Coke]        1.09   \n",
       "350        150         2  Canned Soda        [Diet Coke]        2.18   \n",
       "370        160         1  Canned Soda        [Diet Coke]        1.09   \n",
       "779        321         1  Canned Soda        [Diet Coke]        1.09   \n",
       "1216       496         1  Canned Soda        [Diet Coke]        1.09   \n",
       "1662       672         1  Canned Soda        [Diet Coke]        1.09   \n",
       "1953       790         1  Canned Soda        [Diet Coke]        1.09   \n",
       "2135       859         2  Canned Soda        [Diet Coke]        2.18   \n",
       "2544      1009         1  Canned Soda        [Diet Coke]        1.09   \n",
       "2850      1132         1  Canned Soda        [Diet Coke]        1.09   \n",
       "3592      1440         2  Canned Soda        [Diet Coke]        2.18   \n",
       "3793      1518         1  Canned Soda        [Diet Coke]        1.09   \n",
       "4008      1604         1  Canned Soda        [Diet Coke]        1.09   \n",
       "\n",
       "      product_price  \n",
       "162            1.09  \n",
       "200            1.09  \n",
       "317            1.09  \n",
       "350            1.09  \n",
       "370            1.09  \n",
       "779            1.09  \n",
       "1216           1.09  \n",
       "1662           1.09  \n",
       "1953           1.09  \n",
       "2135           1.09  \n",
       "2544           1.09  \n",
       "2850           1.09  \n",
       "3592           1.09  \n",
       "3793           1.09  \n",
       "4008           1.09  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking everything is correct\n",
    "chipo.loc[(chipo[\"choice_description\"] == '[Diet Coke]') & (chipo[\"item_name\"] == \"Canned Soda\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing duplicated products\n",
    "filtered_chipo=chipo.drop_duplicates(['item_name','choice_description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering products that costs more than $10\n",
    "filtered_chipo = filtered_chipo.loc[ filtered_chipo[\"product_price\"]>10.0 , [\"item_name\",\"choice_description\",\"product_price\"] ].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of products that cost more than $10.00 is 707\n"
     ]
    }
   ],
   "source": [
    "print(f\"the number of products that cost more than $10.00 is {filtered_chipo.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the price of each item? \n",
    "###### print a data frame with only three columns item_name choice_description and product_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>product_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicken Burrito</td>\n",
       "      <td>[[Tomatillo-Green Chili Salsa (Medium), Tomati...</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Barbacoa Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>Carnitas Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Sour Cream, ...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>Barbacoa Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Pinto Beans, Sour ...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Rice, Cheese, S...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>Veggie Burrito</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Rice, Fajita Ve...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>707 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           item_name                                 choice_description  \\\n",
       "0       Chicken Bowl  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...   \n",
       "1      Steak Burrito  [Tomatillo Red Chili Salsa, [Fajita Vegetables...   \n",
       "2       Chicken Bowl  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...   \n",
       "3    Chicken Burrito  [[Tomatillo-Green Chili Salsa (Medium), Tomati...   \n",
       "4      Barbacoa Bowl  [Roasted Chili Corn Salsa, [Fajita Vegetables,...   \n",
       "..               ...                                                ...   \n",
       "702    Carnitas Bowl  [Roasted Chili Corn Salsa, [Rice, Sour Cream, ...   \n",
       "703    Barbacoa Bowl  [Roasted Chili Corn Salsa, [Pinto Beans, Sour ...   \n",
       "704    Steak Burrito  [Tomatillo Green Chili Salsa, [Rice, Cheese, S...   \n",
       "705    Steak Burrito  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...   \n",
       "706   Veggie Burrito  [Tomatillo Green Chili Salsa, [Rice, Fajita Ve...   \n",
       "\n",
       "     product_price  \n",
       "0            10.98  \n",
       "1            11.75  \n",
       "2            11.25  \n",
       "3            10.98  \n",
       "4            11.75  \n",
       "..             ...  \n",
       "702          11.75  \n",
       "703          11.75  \n",
       "704          11.75  \n",
       "705          11.75  \n",
       "706          11.25  \n",
       "\n",
       "[707 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_chipo[[\"item_name\",\"choice_description\",\"product_price\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Sort by the name of the item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>1360</td>\n",
       "      <td>2</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>749</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>754</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2713</th>\n",
       "      <td>1076</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3422</th>\n",
       "      <td>1373</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>774</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>776</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>784</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3836</th>\n",
       "      <td>1537</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Sprite]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>798</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>481</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>1554</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>465</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3886</th>\n",
       "      <td>1558</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2108</th>\n",
       "      <td>849</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3010</th>\n",
       "      <td>1196</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4535</th>\n",
       "      <td>1803</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Lemonade]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4169</th>\n",
       "      <td>1664</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>1666</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4527</th>\n",
       "      <td>1800</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>1798</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3806</th>\n",
       "      <td>1525</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Sprite]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>949</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3132</th>\n",
       "      <td>1248</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3141</th>\n",
       "      <td>1253</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Lemonade]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Diet Coke]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>422</td>\n",
       "      <td>1</td>\n",
       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Sprite]</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>1192</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad</td>\n",
       "      <td>[Roasted Chili Corn Salsa (Medium), [Black Bea...</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3163</th>\n",
       "      <td>1263</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad</td>\n",
       "      <td>[[Fresh Tomato Salsa (Mild), Roasted Chili Cor...</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4084</th>\n",
       "      <td>1635</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad</td>\n",
       "      <td>[[Fresh Tomato Salsa (Mild), Roasted Chili Cor...</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>686</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad</td>\n",
       "      <td>[[Fresh Tomato Salsa (Mild), Roasted Chili Cor...</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2756</th>\n",
       "      <td>1094</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad</td>\n",
       "      <td>[[Tomatillo-Green Chili Salsa (Medium), Roaste...</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4201</th>\n",
       "      <td>1677</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Black...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>760</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>195</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3223</th>\n",
       "      <td>1289</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, Fajita Vegetables]</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>913</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4541</th>\n",
       "      <td>1805</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Fajita Vegetabl...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3293</th>\n",
       "      <td>1321</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Chees...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>394</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>869</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4261</th>\n",
       "      <td>1700</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4573</th>\n",
       "      <td>1818</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2683</th>\n",
       "      <td>1066</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>207</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Lettuce, Guacamole...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4109</th>\n",
       "      <td>1646</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Salad Bowl</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>304</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3889</th>\n",
       "      <td>1559</td>\n",
       "      <td>2</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Black Beans, Rice...</td>\n",
       "      <td>16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2384</th>\n",
       "      <td>948</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>322</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa, [Black Beans, Cheese, Sou...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>1132</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Roasted Chili Corn Salsa (Medium), [Black Bea...</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>688</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>567</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Pinto Beans, Rice...</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4622 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  quantity          item_name  \\\n",
       "3389      1360         2  6 Pack Soft Drink   \n",
       "341        148         1  6 Pack Soft Drink   \n",
       "1849       749         1  6 Pack Soft Drink   \n",
       "1860       754         1  6 Pack Soft Drink   \n",
       "2713      1076         1  6 Pack Soft Drink   \n",
       "3422      1373         1  6 Pack Soft Drink   \n",
       "553        230         1  6 Pack Soft Drink   \n",
       "1916       774         1  6 Pack Soft Drink   \n",
       "1922       776         1  6 Pack Soft Drink   \n",
       "1937       784         1  6 Pack Soft Drink   \n",
       "3836      1537         1  6 Pack Soft Drink   \n",
       "298        129         1  6 Pack Soft Drink   \n",
       "1976       798         1  6 Pack Soft Drink   \n",
       "1167       481         1  6 Pack Soft Drink   \n",
       "3875      1554         1  6 Pack Soft Drink   \n",
       "1124       465         1  6 Pack Soft Drink   \n",
       "3886      1558         1  6 Pack Soft Drink   \n",
       "2108       849         1  6 Pack Soft Drink   \n",
       "3010      1196         1  6 Pack Soft Drink   \n",
       "4535      1803         1  6 Pack Soft Drink   \n",
       "4169      1664         1  6 Pack Soft Drink   \n",
       "4174      1666         1  6 Pack Soft Drink   \n",
       "4527      1800         1  6 Pack Soft Drink   \n",
       "4522      1798         1  6 Pack Soft Drink   \n",
       "3806      1525         1  6 Pack Soft Drink   \n",
       "2389       949         1  6 Pack Soft Drink   \n",
       "3132      1248         1  6 Pack Soft Drink   \n",
       "3141      1253         1  6 Pack Soft Drink   \n",
       "639        264         1  6 Pack Soft Drink   \n",
       "1026       422         1  6 Pack Soft Drink   \n",
       "...        ...       ...                ...   \n",
       "2996      1192         1       Veggie Salad   \n",
       "3163      1263         1       Veggie Salad   \n",
       "4084      1635         1       Veggie Salad   \n",
       "1694       686         1       Veggie Salad   \n",
       "2756      1094         1       Veggie Salad   \n",
       "4201      1677         1  Veggie Salad Bowl   \n",
       "1884       760         1  Veggie Salad Bowl   \n",
       "455        195         1  Veggie Salad Bowl   \n",
       "3223      1289         1  Veggie Salad Bowl   \n",
       "2223       896         1  Veggie Salad Bowl   \n",
       "2269       913         1  Veggie Salad Bowl   \n",
       "4541      1805         1  Veggie Salad Bowl   \n",
       "3293      1321         1  Veggie Salad Bowl   \n",
       "186         83         1  Veggie Salad Bowl   \n",
       "960        394         1  Veggie Salad Bowl   \n",
       "1316       536         1  Veggie Salad Bowl   \n",
       "2156       869         1  Veggie Salad Bowl   \n",
       "4261      1700         1  Veggie Salad Bowl   \n",
       "295        128         1  Veggie Salad Bowl   \n",
       "4573      1818         1  Veggie Salad Bowl   \n",
       "2683      1066         1  Veggie Salad Bowl   \n",
       "496        207         1  Veggie Salad Bowl   \n",
       "4109      1646         1  Veggie Salad Bowl   \n",
       "738        304         1  Veggie Soft Tacos   \n",
       "3889      1559         2  Veggie Soft Tacos   \n",
       "2384       948         1  Veggie Soft Tacos   \n",
       "781        322         1  Veggie Soft Tacos   \n",
       "2851      1132         1  Veggie Soft Tacos   \n",
       "1699       688         1  Veggie Soft Tacos   \n",
       "1395       567         1  Veggie Soft Tacos   \n",
       "\n",
       "                                     choice_description  item_price  \n",
       "3389                                        [Diet Coke]       12.98  \n",
       "341                                         [Diet Coke]        6.49  \n",
       "1849                                             [Coke]        6.49  \n",
       "1860                                        [Diet Coke]        6.49  \n",
       "2713                                             [Coke]        6.49  \n",
       "3422                                             [Coke]        6.49  \n",
       "553                                         [Diet Coke]        6.49  \n",
       "1916                                        [Diet Coke]        6.49  \n",
       "1922                                             [Coke]        6.49  \n",
       "1937                                        [Diet Coke]        6.49  \n",
       "3836                                             [Coke]        6.49  \n",
       "298                                            [Sprite]        6.49  \n",
       "1976                                        [Diet Coke]        6.49  \n",
       "1167                                             [Coke]        6.49  \n",
       "3875                                        [Diet Coke]        6.49  \n",
       "1124                                             [Coke]        6.49  \n",
       "3886                                        [Diet Coke]        6.49  \n",
       "2108                                             [Coke]        6.49  \n",
       "3010                                        [Diet Coke]        6.49  \n",
       "4535                                         [Lemonade]        6.49  \n",
       "4169                                        [Diet Coke]        6.49  \n",
       "4174                                             [Coke]        6.49  \n",
       "4527                                        [Diet Coke]        6.49  \n",
       "4522                                        [Diet Coke]        6.49  \n",
       "3806                                           [Sprite]        6.49  \n",
       "2389                                             [Coke]        6.49  \n",
       "3132                                        [Diet Coke]        6.49  \n",
       "3141                                         [Lemonade]        6.49  \n",
       "639                                         [Diet Coke]        6.49  \n",
       "1026                                           [Sprite]        6.49  \n",
       "...                                                 ...         ...  \n",
       "2996  [Roasted Chili Corn Salsa (Medium), [Black Bea...        8.49  \n",
       "3163  [[Fresh Tomato Salsa (Mild), Roasted Chili Cor...        8.49  \n",
       "4084  [[Fresh Tomato Salsa (Mild), Roasted Chili Cor...        8.49  \n",
       "1694  [[Fresh Tomato Salsa (Mild), Roasted Chili Cor...        8.49  \n",
       "2756  [[Tomatillo-Green Chili Salsa (Medium), Roaste...        8.49  \n",
       "4201  [Fresh Tomato Salsa, [Fajita Vegetables, Black...       11.25  \n",
       "1884  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       11.25  \n",
       "455   [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       11.25  \n",
       "3223  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.25  \n",
       "2223      [Roasted Chili Corn Salsa, Fajita Vegetables]        8.75  \n",
       "2269  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...        8.75  \n",
       "4541  [Tomatillo Green Chili Salsa, [Fajita Vegetabl...        8.75  \n",
       "3293  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...        8.75  \n",
       "186   [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       11.25  \n",
       "960   [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...        8.75  \n",
       "1316  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...        8.75  \n",
       "2156  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.25  \n",
       "4261  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       11.25  \n",
       "295   [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...       11.25  \n",
       "4573  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...        8.75  \n",
       "2683  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75  \n",
       "496   [Fresh Tomato Salsa, [Rice, Lettuce, Guacamole...       11.25  \n",
       "4109  [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.25  \n",
       "738   [Tomatillo Red Chili Salsa, [Fajita Vegetables...       11.25  \n",
       "3889  [Fresh Tomato Salsa (Mild), [Black Beans, Rice...       16.98  \n",
       "2384  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75  \n",
       "781   [Fresh Tomato Salsa, [Black Beans, Cheese, Sou...        8.75  \n",
       "2851  [Roasted Chili Corn Salsa (Medium), [Black Bea...        8.49  \n",
       "1699  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       11.25  \n",
       "1395  [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice...        8.49  \n",
       "\n",
       "[4622 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.item_name.sort_values()\n",
    "\n",
    "# OR\n",
    "\n",
    "chipo.sort_values(by = \"item_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. What was the quantity of the most expensive item ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.loc[chipo[\"item_price\"].idxmax()][\"quantity\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. How many times was a Veggie Salad Bowl ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo[chipo[\"item_name\"]==\"Veggie Salad Bowl\"][\"quantity\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How many times did someone order more than one Canned Soda?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo[ ( chipo[\"item_name\"]==\"Canned Soda\" ) & ( chipo[\"quantity\"]>1 )].shape[0]"
   ]
  }
 ],
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================================================
FILE: 02_Filtering_&_Sorting/Chipotle/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex1 - Filtering and Sorting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. How many products cost more than $10.00?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of products that cost more than $10.00 is 707\n"
     ]
    }
   ],
   "source": [
    "# the item price column is actullay the price of the product multiplied by the quantity\n",
    "\n",
    "# adding a new column representing the price of each single product in float\n",
    "\n",
    "# removing duplicated products\n",
    "\n",
    "# filtering products that costs more than $10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the price of each item? \n",
    "###### print a data frame with only three columns item_name choice_description and product_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>product_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicken Burrito</td>\n",
       "      <td>[[Tomatillo-Green Chili Salsa (Medium), Tomati...</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Barbacoa Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>Carnitas Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Sour Cream, ...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>Barbacoa Bowl</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Pinto Beans, Sour ...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Rice, Cheese, S...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>Veggie Burrito</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Rice, Fajita Ve...</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>707 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           item_name                                 choice_description  \\\n",
       "0       Chicken Bowl  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...   \n",
       "1      Steak Burrito  [Tomatillo Red Chili Salsa, [Fajita Vegetables...   \n",
       "2       Chicken Bowl  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...   \n",
       "3    Chicken Burrito  [[Tomatillo-Green Chili Salsa (Medium), Tomati...   \n",
       "4      Barbacoa Bowl  [Roasted Chili Corn Salsa, [Fajita Vegetables,...   \n",
       "..               ...                                                ...   \n",
       "702    Carnitas Bowl  [Roasted Chili Corn Salsa, [Rice, Sour Cream, ...   \n",
       "703    Barbacoa Bowl  [Roasted Chili Corn Salsa, [Pinto Beans, Sour ...   \n",
       "704    Steak Burrito  [Tomatillo Green Chili Salsa, [Rice, Cheese, S...   \n",
       "705    Steak Burrito  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...   \n",
       "706   Veggie Burrito  [Tomatillo Green Chili Salsa, [Rice, Fajita Ve...   \n",
       "\n",
       "     product_price  \n",
       "0            10.98  \n",
       "1            11.75  \n",
       "2            11.25  \n",
       "3            10.98  \n",
       "4            11.75  \n",
       "..             ...  \n",
       "702          11.75  \n",
       "703          11.75  \n",
       "704          11.75  \n",
       "705          11.75  \n",
       "706          11.25  \n",
       "\n",
       "[707 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Sort by the name of the item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>6 Pack Soft Drink</td>\n",
       "      <td>[Coke]</td>\n",
       "      <td>6.49</td>\n",
       "      <td>6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2384</th>\n",
       "      <td>948</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>322</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa, [Black Beans, Cheese, Sou...</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>1132</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Roasted Chili Corn Salsa (Medium), [Black Bea...</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>688</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>11.25</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>567</td>\n",
       "      <td>1</td>\n",
       "      <td>Veggie Soft Tacos</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Pinto Beans, Rice...</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4622 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id  quantity          item_name  \\\n",
       "3389      1360         2  6 Pack Soft Drink   \n",
       "341        148         1  6 Pack Soft Drink   \n",
       "1849       749         1  6 Pack Soft Drink   \n",
       "1860       754         1  6 Pack Soft Drink   \n",
       "2713      1076         1  6 Pack Soft Drink   \n",
       "...        ...       ...                ...   \n",
       "2384       948         1  Veggie Soft Tacos   \n",
       "781        322         1  Veggie Soft Tacos   \n",
       "2851      1132         1  Veggie Soft Tacos   \n",
       "1699       688         1  Veggie Soft Tacos   \n",
       "1395       567         1  Veggie Soft Tacos   \n",
       "\n",
       "                                     choice_description  item_price  \\\n",
       "3389                                        [Diet Coke]       12.98   \n",
       "341                                         [Diet Coke]        6.49   \n",
       "1849                                             [Coke]        6.49   \n",
       "1860                                        [Diet Coke]        6.49   \n",
       "2713                                             [Coke]        6.49   \n",
       "...                                                 ...         ...   \n",
       "2384  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75   \n",
       "781   [Fresh Tomato Salsa, [Black Beans, Cheese, Sou...        8.75   \n",
       "2851  [Roasted Chili Corn Salsa (Medium), [Black Bea...        8.49   \n",
       "1699  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       11.25   \n",
       "1395  [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice...        8.49   \n",
       "\n",
       "      product_price  \n",
       "3389           6.49  \n",
       "341            6.49  \n",
       "1849           6.49  \n",
       "1860           6.49  \n",
       "2713           6.49  \n",
       "...             ...  \n",
       "2384           8.75  \n",
       "781            8.75  \n",
       "2851           8.49  \n",
       "1699          11.25  \n",
       "1395           8.49  \n",
       "\n",
       "[4622 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# OR\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. What was the quantity of the most expensive item ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. How many times was a Veggie Salad Bowl ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How many times did someone order more than one Canned Soda?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "base",
   "language": "python",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 02_Filtering_&_Sorting/Euro12/Euro_2012_stats_TEAM.csv
================================================
Team,Goals,Shots on target,Shots off target,Shooting Accuracy,% Goals-to-shots,Total shots (inc. Blocked),Hit Woodwork,Penalty goals,Penalties not scored,Headed goals,Passes,Passes completed,Passing Accuracy,Touches,Crosses,Dribbles,Corners Taken,Tackles,Clearances,Interceptions,Clearances off line,Clean Sheets,Blocks,Goals conceded,Saves made,Saves-to-shots ratio,Fouls Won,Fouls Conceded,Offsides,Yellow Cards,Red Cards,Subs on,Subs off,Players Used
Croatia,4,13,12,51.9%,16.0%,32,0,0,0,2,1076,828,76.9%,1706,60,42,14,49,83,56,,0,10,3,13,81.3%,41,62,2,9,0,9,9,16
Czech Republic,4,13,18,41.9%,12.9%,39,0,0,0,0,1565,1223,78.1%,2358,46,68,21,62,98,37,2,1,10,6,9,60.1%,53,73,8,7,0,11,11,19
Denmark,4,10,10,50.0%,20.0%,27,1,0,0,3,1298,1082,83.3%,1873,43,32,16,40,61,59,0,1,10,5,10,66.7%,25,38,8,4,0,7,7,15
England,5,11,18,50.0%,17.2%,40,0,0,0,3,1488,1200,80.6%,2440,58,60,16,86,106,72,1,2,29,3,22,88.1%,43,45,6,5,0,11,11,16
France,3,22,24,37.9%,6.5%,65,1,0,0,0,2066,1803,87.2%,2909,55,76,28,71,76,58,0,1,7,5,6,54.6%,36,51,5,6,0,11,11,19
Germany,10,32,32,47.8%,15.6%,80,2,1,0,2,2774,2427,87.4%,3761,101,60,35,91,73,69,0,1,11,6,10,62.6%,63,49,12,4,0,15,15,17
Greece,5,8,18,30.7%,19.2%,32,1,1,1,0,1187,911,76.7%,2016,52,53,10,65,123,87,0,1,23,7,13,65.1%,67,48,12,9,1,12,12,20
Italy,6,34,45,43.0%,7.5%,110,2,0,0,2,3016,2531,83.9%,4363,75,75,30,98,137,136,1,2,18,7,20,74.1%,101,89,16,16,0,18,18,19
Netherlands,2,12,36,25.0%,4.1%,60,2,0,0,0,1556,1381,88.7%,2163,50,49,22,34,41,41,0,0,9,5,12,70.6%,35,30,3,5,0,7,7,15
Poland,2,15,23,39.4%,5.2%,48,0,0,0,1,1059,852,80.4%,1724,55,39,14,67,87,62,0,0,8,3,6,66.7%,48,56,3,7,1,7,7,17
Portugal,6,22,42,34.3%,9.3%,82,6,0,0,2,1891,1461,77.2%,2958,91,64,41,78,92,86,0,2,11,4,10,71.5%,73,90,10,12,0,14,14,16
Republic of Ireland,1,7,12,36.8%,5.2%,28,0,0,0,1,851,606,71.2%,1433,43,18,8,45,78,43,1,0,23,9,17,65.4%,43,51,11,6,1,10,10,17
Russia,5,9,31,22.5%,12.5%,59,2,0,0,1,1602,1345,83.9%,2278,40,40,21,65,74,58,0,0,8,3,10,77.0%,34,43,4,6,0,7,7,16
Spain,12,42,33,55.9%,16.0%,100,0,1,0,2,4317,3820,88.4%,5585,69,106,44,122,102,79,0,5,8,1,15,93.8%,102,83,19,11,0,17,17,18
Sweden,5,17,19,47.2%,13.8%,39,3,0,0,1,1192,965,80.9%,1806,44,29,7,56,54,45,0,1,12,5,8,61.6%,35,51,7,7,0,9,9,18
Ukraine,2,7,26,21.2%,6.0%,38,0,0,0,2,1276,1043,81.7%,1894,33,26,18,65,97,29,0,0,4,4,13,76.5%,48,31,4,5,0,9,9,18

================================================
FILE: 02_Filtering_&_Sorting/Euro12/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex2 - Filtering and Sorting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/02_Filtering_%26_Sorting/Euro12/Euro_2012_stats_TEAM.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called euro12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Select only the Goal column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. How many team participated in the Euro2012?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Sort the teams by Red Cards, then to Yellow Cards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Calculate the mean Yellow Cards given per Team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Filter teams that scored more than 6 goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Select the teams that start with G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Select the first 7 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Select all columns except the last 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Present only the Shooting Accuracy from England, Italy and Russia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 02_Filtering_&_Sorting/Euro12/Exercises_with_Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex2 - Filtering and Sorting Data\n",
    "Check out [Euro 12 Exercises Video Tutorial](https://youtu.be/iqk5d48Qisg) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/02_Filtering_%26_Sorting/Euro12/Euro_2012_stats_TEAM.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called euro12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>81.3%</td>\n",
       "      <td>41</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>60.1%</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>88.1%</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>54.6%</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>74.1%</td>\n",
       "      <td>101</td>\n",
       "      <td>89</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>70.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>71.5%</td>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>65.4%</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>77.0%</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>61.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>76.5%</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \\\n",
       "0              51.9%            16.0%                          32   \n",
       "1              41.9%            12.9%                          39   \n",
       "2              50.0%            20.0%                          27   \n",
       "3              50.0%            17.2%                          40   \n",
       "4              37.9%             6.5%                          65   \n",
       "5              47.8%            15.6%                          80   \n",
       "6              30.7%            19.2%                          32   \n",
       "7              43.0%             7.5%                         110   \n",
       "8              25.0%             4.1%                          60   \n",
       "9              39.4%             5.2%                          48   \n",
       "10             34.3%             9.3%                          82   \n",
       "11             36.8%             5.2%                          28   \n",
       "12             22.5%            12.5%                          59   \n",
       "13             55.9%            16.0%                         100   \n",
       "14             47.2%            13.8%                          39   \n",
       "15             21.2%             6.0%                          38   \n",
       "\n",
       "    Hit Woodwork  Penalty goals  Penalties not scored      ...       \\\n",
       "0              0              0                     0      ...        \n",
       "1              0              0                     0      ...        \n",
       "2              1              0                     0      ...        \n",
       "3              0              0                     0      ...        \n",
       "4              1              0                     0      ...        \n",
       "5              2              1                     0      ...        \n",
       "6              1              1                     1      ...        \n",
       "7              2              0                     0      ...        \n",
       "8              2              0                     0      ...        \n",
       "9              0              0                     0      ...        \n",
       "10             6              0                     0      ...        \n",
       "11             0              0                     0      ...        \n",
       "12             2              0                     0      ...        \n",
       "13             0              1                     0      ...        \n",
       "14             3              0                     0      ...        \n",
       "15             0              0                     0      ...        \n",
       "\n",
       "    Saves made  Saves-to-shots ratio  Fouls Won Fouls Conceded  Offsides  \\\n",
       "0           13                 81.3%         41             62         2   \n",
       "1            9                 60.1%         53             73         8   \n",
       "2           10                 66.7%         25             38         8   \n",
       "3           22                 88.1%         43             45         6   \n",
       "4            6                 54.6%         36             51         5   \n",
       "5           10                 62.6%         63             49        12   \n",
       "6           13                 65.1%         67             48        12   \n",
       "7           20                 74.1%        101             89        16   \n",
       "8           12                 70.6%         35             30         3   \n",
       "9            6                 66.7%         48             56         3   \n",
       "10          10                 71.5%         73             90        10   \n",
       "11          17                 65.4%         43             51        11   \n",
       "12          10                 77.0%         34             43         4   \n",
       "13          15                 93.8%        102             83        19   \n",
       "14           8                 61.6%         35             51         7   \n",
       "15          13                 76.5%         48             31         4   \n",
       "\n",
       "    Yellow Cards  Red Cards  Subs on  Subs off  Players Used  \n",
       "0              9          0        9         9            16  \n",
       "1              7          0       11        11            19  \n",
       "2              4          0        7         7            15  \n",
       "3              5          0       11        11            16  \n",
       "4              6          0       11        11            19  \n",
       "5              4          0       15        15            17  \n",
       "6              9          1       12        12            20  \n",
       "7             16          0       18        18            19  \n",
       "8              5          0        7         7            15  \n",
       "9              7          1        7         7            17  \n",
       "10            12          0       14        14            16  \n",
       "11             6          1       10        10            17  \n",
       "12             6          0        7         7            16  \n",
       "13            11          0       17        17            18  \n",
       "14             7          0        9         9            18  \n",
       "15             5          0        9         9            18  \n",
       "\n",
       "[16 rows x 35 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro12 = pd.read_csv('https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/02_Filtering_%26_Sorting/Euro12/Euro_2012_stats_TEAM.csv', sep=',')\n",
    "euro12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Select only the Goal column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4\n",
       "1      4\n",
       "2      4\n",
       "3      5\n",
       "4      3\n",
       "5     10\n",
       "6      5\n",
       "7      6\n",
       "8      2\n",
       "9      2\n",
       "10     6\n",
       "11     1\n",
       "12     5\n",
       "13    12\n",
       "14     5\n",
       "15     2\n",
       "Name: Goals, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro12.Goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. How many team participated in the Euro2012?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro12.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 35 columns):\n",
      "Team                          16 non-null object\n",
      "Goals                         16 non-null int64\n",
      "Shots on target               16 non-null int64\n",
      "Shots off target              16 non-null int64\n",
      "Shooting Accuracy             16 non-null object\n",
      "% Goals-to-shots              16 non-null object\n",
      "Total shots (inc. Blocked)    16 non-null int64\n",
      "Hit Woodwork                  16 non-null int64\n",
      "Penalty goals                 16 non-null int64\n",
      "Penalties not scored          16 non-null int64\n",
      "Headed goals                  16 non-null int64\n",
      "Passes                        16 non-null int64\n",
      "Passes completed              16 non-null int64\n",
      "Passing Accuracy              16 non-null object\n",
      "Touches                       16 non-null int64\n",
      "Crosses                       16 non-null int64\n",
      "Dribbles                      16 non-null int64\n",
      "Corners Taken                 16 non-null int64\n",
      "Tackles                       16 non-null int64\n",
      "Clearances                    16 non-null int64\n",
      "Interceptions                 16 non-null int64\n",
      "Clearances off line           15 non-null float64\n",
      "Clean Sheets                  16 non-null int64\n",
      "Blocks                        16 non-null int64\n",
      "Goals conceded                16 non-null int64\n",
      "Saves made                    16 non-null int64\n",
      "Saves-to-shots ratio          16 non-null object\n",
      "Fouls Won                     16 non-null int64\n",
      "Fouls Conceded                16 non-null int64\n",
      "Offsides                      16 non-null int64\n",
      "Yellow Cards                  16 non-null int64\n",
      "Red Cards                     16 non-null int64\n",
      "Subs on                       16 non-null int64\n",
      "Subs off                      16 non-null int64\n",
      "Players Used                  16 non-null int64\n",
      "dtypes: float64(1), int64(29), object(5)\n",
      "memory usage: 4.4+ KB\n"
     ]
    }
   ],
   "source": [
    "euro12.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Yellow Cards  Red Cards\n",
       "0               Croatia             9          0\n",
       "1        Czech Republic             7          0\n",
       "2               Denmark             4          0\n",
       "3               England             5          0\n",
       "4                France             6          0\n",
       "5               Germany             4          0\n",
       "6                Greece             9          1\n",
       "7                 Italy            16          0\n",
       "8           Netherlands             5          0\n",
       "9                Poland             7          1\n",
       "10             Portugal            12          0\n",
       "11  Republic of Ireland             6          1\n",
       "12               Russia             6          0\n",
       "13                Spain            11          0\n",
       "14               Sweden             7          0\n",
       "15              Ukraine             5          0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter only giving the column names\n",
    "\n",
    "discipline = euro12[['Team', 'Yellow Cards', 'Red Cards']]\n",
    "discipline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Sort the teams by Red Cards, then to Yellow Cards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Yellow Cards  Red Cards\n",
       "6                Greece             9          1\n",
       "9                Poland             7          1\n",
       "11  Republic of Ireland             6          1\n",
       "7                 Italy            16          0\n",
       "10             Portugal            12          0\n",
       "13                Spain            11          0\n",
       "0               Croatia             9          0\n",
       "1        Czech Republic             7          0\n",
       "14               Sweden             7          0\n",
       "4                France             6          0\n",
       "12               Russia             6          0\n",
       "3               England             5          0\n",
       "8           Netherlands             5          0\n",
       "15              Ukraine             5          0\n",
       "2               Denmark             4          0\n",
       "5               Germany             4          0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discipline.sort_values(['Red Cards', 'Yellow Cards'], ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Calculate the mean Yellow Cards given per Team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(discipline['Yellow Cards'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Filter teams that scored more than 6 goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Team  Goals  Shots on target  Shots off target Shooting Accuracy  \\\n",
       "5   Germany     10               32                32             47.8%   \n",
       "13    Spain     12               42                33             55.9%   \n",
       "\n",
       "   % Goals-to-shots  Total shots (inc. Blocked)  Hit Woodwork  Penalty goals  \\\n",
       "5             15.6%                          80             2              1   \n",
       "13            16.0%                         100             0              1   \n",
       "\n",
       "    Penalties not scored      ...       Saves made  Saves-to-shots ratio  \\\n",
       "5                      0      ...               10                 62.6%   \n",
       "13                     0      ...               15                 93.8%   \n",
       "\n",
       "    Fouls Won Fouls Conceded  Offsides  Yellow Cards  Red Cards  Subs on  \\\n",
       "5          63             49        12             4          0       15   \n",
       "13        102             83        19            11          0       17   \n",
       "\n",
       "    Subs off  Players Used  \n",
       "5         15            17  \n",
       "13        17            18  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro12[euro12.Goals > 6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Select the teams that start with G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team  Goals  Shots on target  Shots off target Shooting Accuracy  \\\n",
       "5  Germany     10               32                32             47.8%   \n",
       "6   Greece      5                8                18             30.7%   \n",
       "\n",
       "  % Goals-to-shots  Total shots (inc. Blocked)  Hit Woodwork  Penalty goals  \\\n",
       "5            15.6%                          80             2              1   \n",
       "6            19.2%                          32             1              1   \n",
       "\n",
       "   Penalties not scored      ...       Saves made  Saves-to-shots ratio  \\\n",
       "5                     0      ...               10                 62.6%   \n",
       "6                     1      ...               13                 65.1%   \n",
       "\n",
       "   Fouls Won Fouls Conceded  Offsides  Yellow Cards  Red Cards  Subs on  \\\n",
       "5         63             49        12             4          0       15   \n",
       "6         67             48        12             9          1       12   \n",
       "\n",
       "   Subs off  Players Used  \n",
       "5        15            17  \n",
       "6        12            20  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro12[euro12.Team.str.startswith('G')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Select the first 7 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \n",
       "0              51.9%            16.0%                          32  \n",
       "1              41.9%            12.9%                          39  \n",
       "2              50.0%            20.0%                          27  \n",
       "3              50.0%            17.2%                          40  \n",
       "4              37.9%             6.5%                          65  \n",
       "5              47.8%            15.6%                          80  \n",
       "6              30.7%            19.2%                          32  \n",
       "7              43.0%             7.5%                         110  \n",
       "8              25.0%             4.1%                          60  \n",
       "9              39.4%             5.2%                          48  \n",
       "10             34.3%             9.3%                          82  \n",
       "11             36.8%             5.2%                          28  \n",
       "12             22.5%            12.5%                          59  \n",
       "13             55.9%            16.0%                         100  \n",
       "14             47.2%            13.8%                          39  \n",
       "15             21.2%             6.0%                          38  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use .iloc to slices via the position of the passed integers\n",
    "# : means all, 0:7 means from 0 to 7\n",
    "\n",
    "euro12.iloc[: , 0:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Select all columns except the last 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Clean Sheets</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Goals conceded</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>81.3%</td>\n",
       "      <td>41</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>60.1%</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>88.1%</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>54.6%</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>74.1%</td>\n",
       "      <td>101</td>\n",
       "      <td>89</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>70.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>71.5%</td>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>65.4%</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>77.0%</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>61.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>76.5%</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \\\n",
       "0              51.9%            16.0%                          32   \n",
       "1              41.9%            12.9%                          39   \n",
       "2              50.0%            20.0%                          27   \n",
       "3              50.0%            17.2%                          40   \n",
       "4              37.9%             6.5%                          65   \n",
       "5              47.8%            15.6%                          80   \n",
       "6              30.7%            19.2%                          32   \n",
       "7              43.0%             7.5%                         110   \n",
       "8              25.0%             4.1%                          60   \n",
       "9              39.4%             5.2%                          48   \n",
       "10             34.3%             9.3%                          82   \n",
       "11             36.8%             5.2%                          28   \n",
       "12             22.5%            12.5%                          59   \n",
       "13             55.9%            16.0%                         100   \n",
       "14             47.2%            13.8%                          39   \n",
       "15             21.2%             6.0%                          38   \n",
       "\n",
       "    Hit Woodwork  Penalty goals  Penalties not scored    ...      \\\n",
       "0              0              0                     0    ...       \n",
       "1              0              0                     0    ...       \n",
       "2              1              0                     0    ...       \n",
       "3              0              0                     0    ...       \n",
       "4              1              0                     0    ...       \n",
       "5              2              1                     0    ...       \n",
       "6              1              1                     1    ...       \n",
       "7              2              0                     0    ...       \n",
       "8              2              0                     0    ...       \n",
       "9              0              0                     0    ...       \n",
       "10             6              0                     0    ...       \n",
       "11             0              0                     0    ...       \n",
       "12             2              0                     0    ...       \n",
       "13             0              1                     0    ...       \n",
       "14             3              0                     0    ...       \n",
       "15             0              0                     0    ...       \n",
       "\n",
       "    Clean Sheets  Blocks  Goals conceded Saves made  Saves-to-shots ratio  \\\n",
       "0              0      10               3         13                 81.3%   \n",
       "1              1      10               6          9                 60.1%   \n",
       "2              1      10               5         10                 66.7%   \n",
       "3              2      29               3         22                 88.1%   \n",
       "4              1       7               5          6                 54.6%   \n",
       "5              1      11               6         10                 62.6%   \n",
       "6              1      23               7         13                 65.1%   \n",
       "7              2      18               7         20                 74.1%   \n",
       "8              0       9               5         12                 70.6%   \n",
       "9              0       8               3          6                 66.7%   \n",
       "10             2      11               4         10                 71.5%   \n",
       "11             0      23               9         17                 65.4%   \n",
       "12             0       8               3         10                 77.0%   \n",
       "13             5       8               1         15                 93.8%   \n",
       "14             1      12               5          8                 61.6%   \n",
       "15             0       4               4         13                 76.5%   \n",
       "\n",
       "    Fouls Won  Fouls Conceded  Offsides  Yellow Cards  Red Cards  \n",
       "0          41              62         2             9          0  \n",
       "1          53              73         8             7          0  \n",
       "2          25              38         8             4          0  \n",
       "3          43              45         6             5          0  \n",
       "4          36              51         5             6          0  \n",
       "5          63              49        12             4          0  \n",
       "6          67              48        12             9          1  \n",
       "7         101              89        16            16          0  \n",
       "8          35              30         3             5          0  \n",
       "9          48              56         3             7          1  \n",
       "10         73              90        10            12          0  \n",
       "11         43              51        11             6          1  \n",
       "12         34              43         4             6          0  \n",
       "13        102              83        19            11          0  \n",
       "14         35              51         7             7          0  \n",
       "15         48              31         4             5          0  \n",
       "\n",
       "[16 rows x 32 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use negative to exclude the last 3 columns\n",
    "\n",
    "euro12.iloc[: , :-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Present only the Shooting Accuracy from England, Italy and Russia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>50.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>43.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>22.5%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Team Shooting Accuracy\n",
       "3   England             50.0%\n",
       "7     Italy             43.0%\n",
       "12   Russia             22.5%"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .loc is another way to slice, using the labels of the columns and indexes\n",
    "\n",
    "euro12.loc[euro12.Team.isin(['England', 'Italy', 'Russia']), ['Team','Shooting Accuracy']]"
   ]
  }
 ],
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================================================
FILE: 02_Filtering_&_Sorting/Euro12/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering and Sorting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/02_Filtering_%26_Sorting/Euro12/Euro_2012_stats_TEAM.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called euro12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>81.3%</td>\n",
       "      <td>41</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>60.1%</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>88.1%</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>54.6%</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>74.1%</td>\n",
       "      <td>101</td>\n",
       "      <td>89</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>70.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>71.5%</td>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>65.4%</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>77.0%</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>61.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>76.5%</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \\\n",
       "0              51.9%            16.0%                          32   \n",
       "1              41.9%            12.9%                          39   \n",
       "2              50.0%            20.0%                          27   \n",
       "3              50.0%            17.2%                          40   \n",
       "4              37.9%             6.5%                          65   \n",
       "5              47.8%            15.6%                          80   \n",
       "6              30.7%            19.2%                          32   \n",
       "7              43.0%             7.5%                         110   \n",
       "8              25.0%             4.1%                          60   \n",
       "9              39.4%             5.2%                          48   \n",
       "10             34.3%             9.3%                          82   \n",
       "11             36.8%             5.2%                          28   \n",
       "12             22.5%            12.5%                          59   \n",
       "13             55.9%            16.0%                         100   \n",
       "14             47.2%            13.8%                          39   \n",
       "15             21.2%             6.0%                          38   \n",
       "\n",
       "    Hit Woodwork  Penalty goals  Penalties not scored      ...       \\\n",
       "0              0              0                     0      ...        \n",
       "1              0              0                     0      ...        \n",
       "2              1              0                     0      ...        \n",
       "3              0              0                     0      ...        \n",
       "4              1              0                     0      ...        \n",
       "5              2              1                     0      ...        \n",
       "6              1              1                     1      ...        \n",
       "7              2              0                     0      ...        \n",
       "8              2              0                     0      ...        \n",
       "9              0              0                     0      ...        \n",
       "10             6              0                     0      ...        \n",
       "11             0              0                     0      ...        \n",
       "12             2              0                     0      ...        \n",
       "13             0              1                     0      ...        \n",
       "14             3              0                     0      ...        \n",
       "15             0              0                     0      ...        \n",
       "\n",
       "    Saves made  Saves-to-shots ratio  Fouls Won Fouls Conceded  Offsides  \\\n",
       "0           13                 81.3%         41             62         2   \n",
       "1            9                 60.1%         53             73         8   \n",
       "2           10                 66.7%         25             38         8   \n",
       "3           22                 88.1%         43             45         6   \n",
       "4            6                 54.6%         36             51         5   \n",
       "5           10                 62.6%         63             49        12   \n",
       "6           13                 65.1%         67             48        12   \n",
       "7           20                 74.1%        101             89        16   \n",
       "8           12                 70.6%         35             30         3   \n",
       "9            6                 66.7%         48             56         3   \n",
       "10          10                 71.5%         73             90        10   \n",
       "11          17                 65.4%         43             51        11   \n",
       "12          10                 77.0%         34             43         4   \n",
       "13          15                 93.8%        102             83        19   \n",
       "14           8                 61.6%         35             51         7   \n",
       "15          13                 76.5%         48             31         4   \n",
       "\n",
       "    Yellow Cards  Red Cards  Subs on  Subs off  Players Used  \n",
       "0              9          0        9         9            16  \n",
       "1              7          0       11        11            19  \n",
       "2              4          0        7         7            15  \n",
       "3              5          0       11        11            16  \n",
       "4              6          0       11        11            19  \n",
       "5              4          0       15        15            17  \n",
       "6              9          1       12        12            20  \n",
       "7             16          0       18        18            19  \n",
       "8              5          0        7         7            15  \n",
       "9              7          1        7         7            17  \n",
       "10            12          0       14        14            16  \n",
       "11             6          1       10        10            17  \n",
       "12             6          0        7         7            16  \n",
       "13            11          0       17        17            18  \n",
       "14             7          0        9         9            18  \n",
       "15             5          0        9         9            18  \n",
       "\n",
       "[16 rows x 35 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Select only the Goal column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4\n",
       "1      4\n",
       "2      4\n",
       "3      5\n",
       "4      3\n",
       "5     10\n",
       "6      5\n",
       "7      6\n",
       "8      2\n",
       "9      2\n",
       "10     6\n",
       "11     1\n",
       "12     5\n",
       "13    12\n",
       "14     5\n",
       "15     2\n",
       "Name: Goals, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. How many team participated in the Euro2012?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 35 columns):\n",
      "Team                          16 non-null object\n",
      "Goals                         16 non-null int64\n",
      "Shots on target               16 non-null int64\n",
      "Shots off target              16 non-null int64\n",
      "Shooting Accuracy             16 non-null object\n",
      "% Goals-to-shots              16 non-null object\n",
      "Total shots (inc. Blocked)    16 non-null int64\n",
      "Hit Woodwork                  16 non-null int64\n",
      "Penalty goals                 16 non-null int64\n",
      "Penalties not scored          16 non-null int64\n",
      "Headed goals                  16 non-null int64\n",
      "Passes                        16 non-null int64\n",
      "Passes completed              16 non-null int64\n",
      "Passing Accuracy              16 non-null object\n",
      "Touches                       16 non-null int64\n",
      "Crosses                       16 non-null int64\n",
      "Dribbles                      16 non-null int64\n",
      "Corners Taken                 16 non-null int64\n",
      "Tackles                       16 non-null int64\n",
      "Clearances                    16 non-null int64\n",
      "Interceptions                 16 non-null int64\n",
      "Clearances off line           15 non-null float64\n",
      "Clean Sheets                  16 non-null int64\n",
      "Blocks                        16 non-null int64\n",
      "Goals conceded                16 non-null int64\n",
      "Saves made                    16 non-null int64\n",
      "Saves-to-shots ratio          16 non-null object\n",
      "Fouls Won                     16 non-null int64\n",
      "Fouls Conceded                16 non-null int64\n",
      "Offsides                      16 non-null int64\n",
      "Yellow Cards                  16 non-null int64\n",
      "Red Cards                     16 non-null int64\n",
      "Subs on                       16 non-null int64\n",
      "Subs off                      16 non-null int64\n",
      "Players Used                  16 non-null int64\n",
      "dtypes: float64(1), int64(29), object(5)\n",
      "memory usage: 4.4+ KB\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Yellow Cards  Red Cards\n",
       "0               Croatia             9          0\n",
       "1        Czech Republic             7          0\n",
       "2               Denmark             4          0\n",
       "3               England             5          0\n",
       "4                France             6          0\n",
       "5               Germany             4          0\n",
       "6                Greece             9          1\n",
       "7                 Italy            16          0\n",
       "8           Netherlands             5          0\n",
       "9                Poland             7          1\n",
       "10             Portugal            12          0\n",
       "11  Republic of Ireland             6          1\n",
       "12               Russia             6          0\n",
       "13                Spain            11          0\n",
       "14               Sweden             7          0\n",
       "15              Ukraine             5          0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Sort the teams by Red Cards, then to Yellow Cards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Yellow Cards  Red Cards\n",
       "6                Greece             9          1\n",
       "9                Poland             7          1\n",
       "11  Republic of Ireland             6          1\n",
       "7                 Italy            16          0\n",
       "10             Portugal            12          0\n",
       "13                Spain            11          0\n",
       "0               Croatia             9          0\n",
       "1        Czech Republic             7          0\n",
       "14               Sweden             7          0\n",
       "4                France             6          0\n",
       "12               Russia             6          0\n",
       "3               England             5          0\n",
       "8           Netherlands             5          0\n",
       "15              Ukraine             5          0\n",
       "2               Denmark             4          0\n",
       "5               Germany             4          0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Calculate the mean Yellow Cards given per Team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Filter teams that scored more than 6 goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Team  Goals  Shots on target  Shots off target Shooting Accuracy  \\\n",
       "5   Germany     10               32                32             47.8%   \n",
       "13    Spain     12               42                33             55.9%   \n",
       "\n",
       "   % Goals-to-shots  Total shots (inc. Blocked)  Hit Woodwork  Penalty goals  \\\n",
       "5             15.6%                          80             2              1   \n",
       "13            16.0%                         100             0              1   \n",
       "\n",
       "    Penalties not scored      ...       Saves made  Saves-to-shots ratio  \\\n",
       "5                      0      ...               10                 62.6%   \n",
       "13                     0      ...               15                 93.8%   \n",
       "\n",
       "    Fouls Won Fouls Conceded  Offsides  Yellow Cards  Red Cards  Subs on  \\\n",
       "5          63             49        12             4          0       15   \n",
       "13        102             83        19            11          0       17   \n",
       "\n",
       "    Subs off  Players Used  \n",
       "5         15            17  \n",
       "13        17            18  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Select the teams that start with G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team  Goals  Shots on target  Shots off target Shooting Accuracy  \\\n",
       "5  Germany     10               32                32             47.8%   \n",
       "6   Greece      5                8                18             30.7%   \n",
       "\n",
       "  % Goals-to-shots  Total shots (inc. Blocked)  Hit Woodwork  Penalty goals  \\\n",
       "5            15.6%                          80             2              1   \n",
       "6            19.2%                          32             1              1   \n",
       "\n",
       "   Penalties not scored      ...       Saves made  Saves-to-shots ratio  \\\n",
       "5                     0      ...               10                 62.6%   \n",
       "6                     1      ...               13                 65.1%   \n",
       "\n",
       "   Fouls Won Fouls Conceded  Offsides  Yellow Cards  Red Cards  Subs on  \\\n",
       "5         63             49        12             4          0       15   \n",
       "6         67             48        12             9          1       12   \n",
       "\n",
       "   Subs off  Players Used  \n",
       "5        15            17  \n",
       "6        12            20  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Select the first 7 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \n",
       "0              51.9%            16.0%                          32  \n",
       "1              41.9%            12.9%                          39  \n",
       "2              50.0%            20.0%                          27  \n",
       "3              50.0%            17.2%                          40  \n",
       "4              37.9%             6.5%                          65  \n",
       "5              47.8%            15.6%                          80  \n",
       "6              30.7%            19.2%                          32  \n",
       "7              43.0%             7.5%                         110  \n",
       "8              25.0%             4.1%                          60  \n",
       "9              39.4%             5.2%                          48  \n",
       "10             34.3%             9.3%                          82  \n",
       "11             36.8%             5.2%                          28  \n",
       "12             22.5%            12.5%                          59  \n",
       "13             55.9%            16.0%                         100  \n",
       "14             47.2%            13.8%                          39  \n",
       "15             21.2%             6.0%                          38  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Select all columns except the last 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Clean Sheets</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Goals conceded</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>81.3%</td>\n",
       "      <td>41</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>60.1%</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>88.1%</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>54.6%</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>74.1%</td>\n",
       "      <td>101</td>\n",
       "      <td>89</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>70.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>71.5%</td>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>65.4%</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>77.0%</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>61.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>76.5%</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \\\n",
       "0              51.9%            16.0%                          32   \n",
       "1              41.9%            12.9%                          39   \n",
       "2              50.0%            20.0%                          27   \n",
       "3              50.0%            17.2%                          40   \n",
       "4              37.9%             6.5%                          65   \n",
       "5              47.8%            15.6%                          80   \n",
       "6              30.7%            19.2%                          32   \n",
       "7              43.0%             7.5%                         110   \n",
       "8              25.0%             4.1%                          60   \n",
       "9              39.4%             5.2%                          48   \n",
       "10             34.3%             9.3%                          82   \n",
       "11             36.8%             5.2%                          28   \n",
       "12             22.5%            12.5%                          59   \n",
       "13             55.9%            16.0%                         100   \n",
       "14             47.2%            13.8%                          39   \n",
       "15             21.2%             6.0%                          38   \n",
       "\n",
       "    Hit Woodwork  Penalty goals  Penalties not scored    ...      \\\n",
       "0              0              0                     0    ...       \n",
       "1              0              0                     0    ...       \n",
       "2              1              0                     0    ...       \n",
       "3              0              0                     0    ...       \n",
       "4              1              0                     0    ...       \n",
       "5              2              1                     0    ...       \n",
       "6              1              1                     1    ...       \n",
       "7              2              0                     0    ...       \n",
       "8              2              0                     0    ...       \n",
       "9              0              0                     0    ...       \n",
       "10             6              0                     0    ...       \n",
       "11             0              0                     0    ...       \n",
       "12             2              0                     0    ...       \n",
       "13             0              1                     0    ...       \n",
       "14             3              0                     0    ...       \n",
       "15             0              0                     0    ...       \n",
       "\n",
       "    Clean Sheets  Blocks  Goals conceded Saves made  Saves-to-shots ratio  \\\n",
       "0              0      10               3         13                 81.3%   \n",
       "1              1      10               6          9                 60.1%   \n",
       "2              1      10               5         10                 66.7%   \n",
       "3              2      29               3         22                 88.1%   \n",
       "4              1       7               5          6                 54.6%   \n",
       "5              1      11               6         10                 62.6%   \n",
       "6              1      23               7         13                 65.1%   \n",
       "7              2      18               7         20                 74.1%   \n",
       "8              0       9               5         12                 70.6%   \n",
       "9              0       8               3          6                 66.7%   \n",
       "10             2      11               4         10                 71.5%   \n",
       "11             0      23               9         17                 65.4%   \n",
       "12             0       8               3         10                 77.0%   \n",
       "13             5       8               1         15                 93.8%   \n",
       "14             1      12               5          8                 61.6%   \n",
       "15             0       4               4         13                 76.5%   \n",
       "\n",
       "    Fouls Won  Fouls Conceded  Offsides  Yellow Cards  Red Cards  \n",
       "0          41              62         2             9          0  \n",
       "1          53              73         8             7          0  \n",
       "2          25              38         8             4          0  \n",
       "3          43              45         6             5          0  \n",
       "4          36              51         5             6          0  \n",
       "5          63              49        12             4          0  \n",
       "6          67              48        12             9          1  \n",
       "7         101              89        16            16          0  \n",
       "8          35              30         3             5          0  \n",
       "9          48              56         3             7          1  \n",
       "10         73              90        10            12          0  \n",
       "11         43              51        11             6          1  \n",
       "12         34              43         4             6          0  \n",
       "13        102              83        19            11          0  \n",
       "14         35              51         7             7          0  \n",
       "15         48              31         4             5          0  \n",
       "\n",
       "[16 rows x 32 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Present only the Shooting Accuracy from England, Italy and Russia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>50.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>43.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>22.5%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Team Shooting Accuracy\n",
       "3   England             50.0%\n",
       "7     Italy             43.0%\n",
       "12   Russia             22.5%"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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================================================
FILE: 02_Filtering_&_Sorting/Fictional_Army/Exercise.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fictional Army - Filtering and Sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was inspired by this [page](http://chrisalbon.com/python/)\n",
    "\n",
    "Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. This is the data given as a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create an example dataframe about a fictional army\n",
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],\n",
    "            'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],\n",
    "            'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],\n",
    "            'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],\n",
    "            'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],\n",
    "            'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],\n",
    "            'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],\n",
    "            'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n",
    "            'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "            'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Create a dataframe and assign it to a variable called army. \n",
    "\n",
    "#### Don't forget to include the columns names in the order presented in the dictionary ('regiment', 'company', 'deaths'...) so that the column index order is consistent with the solutions. If omitted, pandas will order the columns alphabetically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Set the 'origin' colum as the index of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Print only the column veterans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print the columns 'veterans' and 'deaths'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Select the rows 3 to 7 and the columns 3 to 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Select every row after the fourth row and all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Select every row up to the 4th row and all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Select the 3rd column up to the 7th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Select rows where df.deaths is greater than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Select rows where df.deaths is greater than 500 or less than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Select all the regiments not named \"Dragoons\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. Select the rows called Texas and Arizona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. Select the third cell in the row named Arizona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 18. Select the third cell down in the column named deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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================================================
FILE: 02_Filtering_&_Sorting/Fictional_Army/Exercise_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fictional Army - Filtering and Sorting\n",
    "Check out [Fictional Army Exercises Video Tutorial](https://youtu.be/42LGuRea7DE) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was inspired by this [page](http://chrisalbon.com/python/)\n",
    "\n",
    "Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. This is the data given as a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an example dataframe about a fictional army\n",
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],\n",
    "            'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],\n",
    "            'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],\n",
    "            'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],\n",
    "            'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],\n",
    "            'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],\n",
    "            'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],\n",
    "            'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n",
    "            'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "            'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Create a dataframe and assign it to a variable called army. \n",
    "\n",
    "#### Don't forget to include the columns names in the order presented in the dictionary ('regiment', 'company', 'deaths'...) so that the column index order is consistent with the solutions. If omitted, pandas will order the columns alphabetically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
       "      <th>readiness</th>\n",
       "      <th>armored</th>\n",
       "      <th>deserters</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>957</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1592</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Maine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>234</td>\n",
       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Iowa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>849</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Oregon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>1005</td>\n",
       "      <td>435</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Louisana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Georgia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      regiment company  deaths  battles  size  veterans  readiness  armored  \\\n",
       "0   Nighthawks     1st     523        5  1045         1          1        1   \n",
       "1   Nighthawks     1st      52       42   957         5          2        0   \n",
       "2   Nighthawks     2nd      25        2  1099        62          3        1   \n",
       "3   Nighthawks     2nd     616        2  1400        26          3        1   \n",
       "4     Dragoons     1st      43        4  1592        73          2        0   \n",
       "5     Dragoons     1st     234        7  1006        37          1        1   \n",
       "6     Dragoons     2nd     523        8   987       949          2        0   \n",
       "7     Dragoons     2nd      62        3   849        48          3        1   \n",
       "8       Scouts     1st      62        4   973        48          2        0   \n",
       "9       Scouts     1st      73        7  1005       435          1        0   \n",
       "10      Scouts     2nd      37        8  1099        63          2        1   \n",
       "11      Scouts     2nd      35        9  1523       345          3        1   \n",
       "\n",
       "    deserters      origin  \n",
       "0           4     Arizona  \n",
       "1          24  California  \n",
       "2          31       Texas  \n",
       "3           2     Florida  \n",
       "4           3       Maine  \n",
       "5           4        Iowa  \n",
       "6          24      Alaska  \n",
       "7          31  Washington  \n",
       "8           2      Oregon  \n",
       "9           3     Wyoming  \n",
       "10          2    Louisana  \n",
       "11          3     Georgia  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army = pd.DataFrame(data=raw_data)\n",
    "army"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Set the 'origin' colum as the index of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "army.set_index('origin', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Print only the column veterans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "origin\n",
       "Arizona         1\n",
       "California      5\n",
       "Texas          62\n",
       "Florida        26\n",
       "Maine          73\n",
       "Iowa           37\n",
       "Alaska        949\n",
       "Washington     48\n",
       "Oregon         48\n",
       "Wyoming       435\n",
       "Louisana       63\n",
       "Georgia       345\n",
       "Name: veterans, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.veterans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print the columns 'veterans' and 'deaths'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>veterans</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>1</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>26</td>\n",
       "      <td>616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>73</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>37</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>949</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>48</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>48</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>435</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>63</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>345</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            veterans  deaths\n",
       "origin                      \n",
       "Arizona            1     523\n",
       "California         5      52\n",
       "Texas             62      25\n",
       "Florida           26     616\n",
       "Maine             73      43\n",
       "Iowa              37     234\n",
       "Alaska           949     523\n",
       "Washington        48      62\n",
       "Oregon            48      62\n",
       "Wyoming          435      73\n",
       "Louisana          63      37\n",
       "Georgia          345      35"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army[[\"veterans\", \"deaths\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['regiment', 'company', 'deaths', 'battles', 'size', 'veterans',\n",
       "       'readiness', 'armored', 'deserters'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deaths</th>\n",
       "      <th>size</th>\n",
       "      <th>deserters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>43</td>\n",
       "      <td>1592</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>523</td>\n",
       "      <td>987</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        deaths  size  deserters\n",
       "origin                         \n",
       "Maine       43  1592          3\n",
       "Alaska     523   987         24"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.loc[[\"Maine\", \"Alaska\"], [\"deaths\", \"size\", \"deserters\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Select the rows 3 to 7 and the columns 3 to 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1592</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>234</td>\n",
       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         deaths  battles  size  veterans\n",
       "origin                                  \n",
       "Texas        25        2  1099        62\n",
       "Florida     616        2  1400        26\n",
       "Maine        43        4  1592        73\n",
       "Iowa        234        7  1006        37\n",
       "Alaska      523        8   987       949"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.iloc[2:7, 2:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Select every row after the fourth row and all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
       "      <th>readiness</th>\n",
       "      <th>armored</th>\n",
       "      <th>deserters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1592</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>234</td>\n",
       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>849</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>1005</td>\n",
       "      <td>435</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                     \n",
       "Maine       Dragoons     1st      43        4  1592        73          2   \n",
       "Iowa        Dragoons     1st     234        7  1006        37          1   \n",
       "Alaska      Dragoons     2nd     523        8   987       949          2   \n",
       "Washington  Dragoons     2nd      62        3   849        48          3   \n",
       "Oregon        Scouts     1st      62        4   973        48          2   \n",
       "Wyoming       Scouts     1st      73        7  1005       435          1   \n",
       "Louisana      Scouts     2nd      37        8  1099        63          2   \n",
       "Georgia       Scouts     2nd      35        9  1523       345          3   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Maine             0          3  \n",
       "Iowa              1          4  \n",
       "Alaska            0         24  \n",
       "Washington        1         31  \n",
       "Oregon            0          2  \n",
       "Wyoming           0          3  \n",
       "Louisana          1          2  \n",
       "Georgia           1          3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.iloc[4:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Select every row up to the 4th row and all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
       "      <th>readiness</th>\n",
       "      <th>armored</th>\n",
       "      <th>deserters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>957</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                       \n",
       "Arizona     Nighthawks     1st     523        5  1045         1          1   \n",
       "California  Nighthawks     1st      52       42   957         5          2   \n",
       "Texas       Nighthawks     2nd      25        2  1099        62          3   \n",
       "Florida     Nighthawks     2nd     616        2  1400        26          3   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Arizona           1          4  \n",
       "California        0         24  \n",
       "Texas             1         31  \n",
       "Florida           1          2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.iloc[:4, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Select the 3rd column up to the 7th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
       "      <th>readiness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>957</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1592</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>234</td>\n",
       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>849</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>1005</td>\n",
       "      <td>435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            deaths  battles  size  veterans  readiness\n",
       "origin                                                \n",
       "Arizona        523        5  1045         1          1\n",
       "California      52       42   957         5          2\n",
       "Texas           25        2  1099        62          3\n",
       "Florida        616        2  1400        26          3\n",
       "Maine           43        4  1592        73          2\n",
       "Iowa           234        7  1006        37          1\n",
       "Alaska         523        8   987       949          2\n",
       "Washington      62        3   849        48          3\n",
       "Oregon          62        4   973        48          2\n",
       "Wyoming         73        7  1005       435          1\n",
       "Louisana        37        8  1099        63          2\n",
       "Georgia         35        9  1523       345          3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.iloc[:, 2:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Select rows where df.deaths is greater than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
       "      <th>readiness</th>\n",
       "      <th>armored</th>\n",
       "      <th>deserters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>957</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>234</td>\n",
       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>849</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>1005</td>\n",
       "      <td>435</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                       \n",
       "Arizona     Nighthawks     1st     523        5  1045         1          1   \n",
       "California  Nighthawks     1st      52       42   957         5          2   \n",
       "Florida     Nighthawks     2nd     616        2  1400        26          3   \n",
       "Iowa          Dragoons     1st     234        7  1006        37          1   \n",
       "Alaska        Dragoons     2nd     523        8   987       949          2   \n",
       "Washington    Dragoons     2nd      62        3   849        48          3   \n",
       "Oregon          Scouts     1st      62        4   973        48          2   \n",
       "Wyoming         Scouts     1st      73        7  1005       435          1   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Arizona           1          4  \n",
       "California        0         24  \n",
       "Florida           1          2  \n",
       "Iowa              1          4  \n",
       "Alaska            0         24  \n",
       "Washington        1         31  \n",
       "Oregon            0          2  \n",
       "Wyoming           0          3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army[army[\"deaths\"] > 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Select rows where df.deaths is greater than 500 or less than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1592</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                     \n",
       "Arizona   Nighthawks     1st     523        5  1045         1          1   \n",
       "Texas     Nighthawks     2nd      25        2  1099        62          3   \n",
       "Florida   Nighthawks     2nd     616        2  1400        26          3   \n",
       "Maine       Dragoons     1st      43        4  1592        73          2   \n",
       "Alaska      Dragoons     2nd     523        8   987       949          2   \n",
       "Louisana      Scouts     2nd      37        8  1099        63          2   \n",
       "Georgia       Scouts     2nd      35        9  1523       345          3   \n",
       "\n",
       "          armored  deserters  \n",
       "origin                        \n",
       "Arizona         1          4  \n",
       "Texas           1         31  \n",
       "Florida         1          2  \n",
       "Maine           0          3  \n",
       "Alaska          0         24  \n",
       "Louisana        1          2  \n",
       "Georgia         1          3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army[(army[\"deaths\"] > 500) | (army[\"deaths\"] < 50)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Select all the regiments not named \"Dragoons\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>957</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>1005</td>\n",
       "      <td>435</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                       \n",
       "Arizona     Nighthawks     1st     523        5  1045         1          1   \n",
       "California  Nighthawks     1st      52       42   957         5          2   \n",
       "Texas       Nighthawks     2nd      25        2  1099        62          3   \n",
       "Florida     Nighthawks     2nd     616        2  1400        26          3   \n",
       "Oregon          Scouts     1st      62        4   973        48          2   \n",
       "Wyoming         Scouts     1st      73        7  1005       435          1   \n",
       "Louisana        Scouts     2nd      37        8  1099        63          2   \n",
       "Georgia         Scouts     2nd      35        9  1523       345          3   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Arizona           1          4  \n",
       "California        0         24  \n",
       "Texas             1         31  \n",
       "Florida           1          2  \n",
       "Oregon            0          2  \n",
       "Wyoming           0          3  \n",
       "Louisana          1          2  \n",
       "Georgia           1          3  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army[army[\"regiment\"] != \"Dragoons\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. Select the rows called Texas and Arizona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                    \n",
       "Texas    Nighthawks     2nd      25        2  1099        62          3   \n",
       "Arizona  Nighthawks     1st     523        5  1045         1          1   \n",
       "\n",
       "         armored  deserters  \n",
       "origin                       \n",
       "Texas          1         31  \n",
       "Arizona        1          4  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.loc[[\"Texas\", \"Arizona\"], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. Select the third cell in the row named Arizona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "origin\n",
       "Arizona    523\n",
       "Name: deaths, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.loc[[\"Arizona\"]].iloc[:, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 18. Select the third cell down in the column named deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deaths    25\n",
       "Name: Texas, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "army.loc[:, [\"deaths\"]].iloc[2]"
   ]
  }
 ],
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================================================
FILE: 02_Filtering_&_Sorting/Fictional_Army/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fictional Army - Filtering and Sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was inspired by this [page](http://chrisalbon.com/python/)\n",
    "\n",
    "Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. This is the data given as a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an example dataframe about a fictional army\n",
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],\n",
    "            'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],\n",
    "            'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],\n",
    "            'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],\n",
    "            'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],\n",
    "            'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],\n",
    "            'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],\n",
    "            'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n",
    "            'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "            'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Create a dataframe and assign it to a variable called army. \n",
    "\n",
    "#### Don't forget to include the columns names in the order presented in the dictionary ('regiment', 'company', 'deaths'...) so that the column index order is consistent with the solutions. If omitted, pandas will order the columns alphabetically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
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         "name": "veterans",
         "rawType": "int64",
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         "name": "readiness",
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         "name": "armored",
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         "Scouts",
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         "Oregon"
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         "7",
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         "11",
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         "9",
         "1523",
         "345",
         "3",
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         "Georgia"
        ]
       ],
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
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       "      <td>Nighthawks</td>\n",
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       "      <td>4</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>52</td>\n",
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       "      <td>5</td>\n",
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       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1592</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Maine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dragoons</td>\n",
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       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Iowa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Alaska</td>\n",
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       "      <th>7</th>\n",
       "      <td>Dragoons</td>\n",
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       "      <td>2</td>\n",
       "      <td>Oregon</td>\n",
       "    </tr>\n",
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       "      <td>Wyoming</td>\n",
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       "      <th>10</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
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       "      <td>8</td>\n",
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       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Louisana</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Georgia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      regiment company  deaths  battles  size  veterans  readiness  armored  \\\n",
       "0   Nighthawks     1st     523        5  1045         1          1        1   \n",
       "1   Nighthawks     1st      52       42   957         5          2        0   \n",
       "2   Nighthawks     2nd      25        2  1099        62          3        1   \n",
       "3   Nighthawks     2nd     616        2  1400        26          3        1   \n",
       "4     Dragoons     1st      43        4  1592        73          2        0   \n",
       "5     Dragoons     1st     234        7  1006        37          1        1   \n",
       "6     Dragoons     2nd     523        8   987       949          2        0   \n",
       "7     Dragoons     2nd      62        3   849        48          3        1   \n",
       "8       Scouts     1st      62        4   973        48          2        0   \n",
       "9       Scouts     1st      73        7  1005       435          1        0   \n",
       "10      Scouts     2nd      37        8  1099        63          2        1   \n",
       "11      Scouts     2nd      35        9  1523       345          3        1   \n",
       "\n",
       "    deserters      origin  \n",
       "0           4     Arizona  \n",
       "1          24  California  \n",
       "2          31       Texas  \n",
       "3           2     Florida  \n",
       "4           3       Maine  \n",
       "5           4        Iowa  \n",
       "6          24      Alaska  \n",
       "7          31  Washington  \n",
       "8           2      Oregon  \n",
       "9           3     Wyoming  \n",
       "10          2    Louisana  \n",
       "11          3     Georgia  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Set the 'origin' colum as the index of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Print only the column veterans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "veterans",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "4a563480-d15b-4eb1-a5e7-424425cac7f7",
       "rows": [
        [
         "Arizona",
         "1"
        ],
        [
         "California",
         "5"
        ],
        [
         "Texas",
         "62"
        ],
        [
         "Florida",
         "26"
        ],
        [
         "Maine",
         "73"
        ],
        [
         "Iowa",
         "37"
        ],
        [
         "Alaska",
         "949"
        ],
        [
         "Washington",
         "48"
        ],
        [
         "Oregon",
         "48"
        ],
        [
         "Wyoming",
         "435"
        ],
        [
         "Louisana",
         "63"
        ],
        [
         "Georgia",
         "345"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 12
       }
      },
      "text/plain": [
       "origin\n",
       "Arizona         1\n",
       "California      5\n",
       "Texas          62\n",
       "Florida        26\n",
       "Maine          73\n",
       "Iowa           37\n",
       "Alaska        949\n",
       "Washington     48\n",
       "Oregon         48\n",
       "Wyoming       435\n",
       "Louisana       63\n",
       "Georgia       345\n",
       "Name: veterans, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print the columns 'veterans' and 'deaths'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "veterans",
         "rawType": "int64",
         "type": "integer"
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        {
         "name": "deaths",
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         "523"
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         "52"
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         "25"
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         "Florida",
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         "523"
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         "Washington",
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         "62"
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        [
         "Oregon",
         "48",
         "62"
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        [
         "Wyoming",
         "435",
         "73"
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         "37"
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        [
         "Georgia",
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         "35"
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       "      <th></th>\n",
       "      <th>veterans</th>\n",
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       "      <th>California</th>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>62</td>\n",
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       "      <td>26</td>\n",
       "      <td>616</td>\n",
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       "    <tr>\n",
       "      <th>Maine</th>\n",
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       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>37</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>949</td>\n",
       "      <td>523</td>\n",
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       "      <td>48</td>\n",
       "      <td>62</td>\n",
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       "      <td>35</td>\n",
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      "text/plain": [
       "            veterans  deaths\n",
       "origin                      \n",
       "Arizona            1     523\n",
       "California         5      52\n",
       "Texas             62      25\n",
       "Florida           26     616\n",
       "Maine             73      43\n",
       "Iowa              37     234\n",
       "Alaska           949     523\n",
       "Washington        48      62\n",
       "Oregon            48      62\n",
       "Wyoming          435      73\n",
       "Louisana          63      37\n",
       "Georgia          345      35"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the name of all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['regiment', 'company', 'deaths', 'battles', 'size', 'veterans',\n",
       "       'readiness', 'armored', 'deserters'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "deaths",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "size",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "deserters",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "58e9cbdb-a98a-4fe0-ab6f-2c368f5233a9",
       "rows": [
        [
         "Maine",
         "43",
         "1592",
         "3"
        ],
        [
         "Alaska",
         "523",
         "987",
         "24"
        ]
       ],
       "shape": {
        "columns": 3,
        "rows": 2
       }
      },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deaths</th>\n",
       "      <th>size</th>\n",
       "      <th>deserters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>43</td>\n",
       "      <td>1592</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>523</td>\n",
       "      <td>987</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        deaths  size  deserters\n",
       "origin                         \n",
       "Maine       43  1592          3\n",
       "Alaska     523   987         24"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Select the rows 3 to 7 and the columns 3 to 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "deaths",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "battles",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "size",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "veterans",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "834ddb56-c127-43e9-8b65-a54f91474282",
       "rows": [
        [
         "Texas",
         "25",
         "2",
         "1099",
         "62"
        ],
        [
         "Florida",
         "616",
         "2",
         "1400",
         "26"
        ],
        [
         "Maine",
         "43",
         "4",
         "1592",
         "73"
        ],
        [
         "Iowa",
         "234",
         "7",
         "1006",
         "37"
        ],
        [
         "Alaska",
         "523",
         "8",
         "987",
         "949"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 5
       }
      },
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       "      <th>Florida</th>\n",
       "      <td>616</td>\n",
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       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
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       "      <td>73</td>\n",
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       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>234</td>\n",
       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
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       "         deaths  battles  size  veterans\n",
       "origin                                  \n",
       "Texas        25        2  1099        62\n",
       "Florida     616        2  1400        26\n",
       "Maine        43        4  1592        73\n",
       "Iowa        234        7  1006        37\n",
       "Alaska      523        8   987       949"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Select every row after the fourth row and all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
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         "type": "string"
        },
        {
         "name": "deaths",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "battles",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "size",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "veterans",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "readiness",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "armored",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "deserters",
         "rawType": "int64",
         "type": "integer"
        }
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       "rows": [
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         "Maine",
         "Dragoons",
         "1st",
         "43",
         "4",
         "1592",
         "73",
         "2",
         "0",
         "3"
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         "2nd",
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         "987",
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         "24"
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         "62",
         "3",
         "849",
         "48",
         "3",
         "1",
         "31"
        ],
        [
         "Oregon",
         "Scouts",
         "1st",
         "62",
         "4",
         "973",
         "48",
         "2",
         "0",
         "2"
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        [
         "Wyoming",
         "Scouts",
         "1st",
         "73",
         "7",
         "1005",
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         "1",
         "0",
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         "Scouts",
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         "37",
         "8",
         "1099",
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         "2"
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         "1",
         "3"
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       "      <td>973</td>\n",
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       "      <td>2</td>\n",
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       "      <th>Wyoming</th>\n",
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       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>Scouts</td>\n",
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       "      <td>9</td>\n",
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       "      <td>3</td>\n",
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      ],
      "text/plain": [
       "            regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                     \n",
       "Maine       Dragoons     1st      43        4  1592        73          2   \n",
       "Iowa        Dragoons     1st     234        7  1006        37          1   \n",
       "Alaska      Dragoons     2nd     523        8   987       949          2   \n",
       "Washington  Dragoons     2nd      62        3   849        48          3   \n",
       "Oregon        Scouts     1st      62        4   973        48          2   \n",
       "Wyoming       Scouts     1st      73        7  1005       435          1   \n",
       "Louisana      Scouts     2nd      37        8  1099        63          2   \n",
       "Georgia       Scouts     2nd      35        9  1523       345          3   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Maine             0          3  \n",
       "Iowa              1          4  \n",
       "Alaska            0         24  \n",
       "Washington        1         31  \n",
       "Oregon            0          2  \n",
       "Wyoming           0          3  \n",
       "Louisana          1          2  \n",
       "Georgia           1          3  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Select every row up to the 4th row and all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "regiment",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "company",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "deaths",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "battles",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "size",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "veterans",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "readiness",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "armored",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "deserters",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "ee17663c-9127-4e43-b96d-842f7bffd1fa",
       "rows": [
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         "Nighthawks",
         "1st",
         "523",
         "5",
         "1045",
         "1",
         "1",
         "1",
         "4"
        ],
        [
         "California",
         "Nighthawks",
         "1st",
         "52",
         "42",
         "957",
         "5",
         "2",
         "0",
         "24"
        ],
        [
         "Texas",
         "Nighthawks",
         "2nd",
         "25",
         "2",
         "1099",
         "62",
         "3",
         "1",
         "31"
        ],
        [
         "Florida",
         "Nighthawks",
         "2nd",
         "616",
         "2",
         "1400",
         "26",
         "3",
         "1",
         "2"
        ]
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        "columns": 9,
        "rows": 4
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
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       "      <td>957</td>\n",
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       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Nighthawks</td>\n",
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       "      <td>2</td>\n",
       "      <td>1099</td>\n",
       "      <td>62</td>\n",
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       "      <td>1</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>Nighthawks</td>\n",
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       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "              regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                       \n",
       "Arizona     Nighthawks     1st     523        5  1045         1          1   \n",
       "California  Nighthawks     1st      52       42   957         5          2   \n",
       "Texas       Nighthawks     2nd      25        2  1099        62          3   \n",
       "Florida     Nighthawks     2nd     616        2  1400        26          3   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Arizona           1          4  \n",
       "California        0         24  \n",
       "Texas             1         31  \n",
       "Florida           1          2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Select the 3rd column up to the 7th column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "deaths",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "battles",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "size",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "veterans",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "readiness",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "71b80090-f2ab-40fd-ac46-53378114b362",
       "rows": [
        [
         "Arizona",
         "523",
         "5",
         "1045",
         "1",
         "1"
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        [
         "California",
         "52",
         "42",
         "957",
         "5",
         "2"
        ],
        [
         "Texas",
         "25",
         "2",
         "1099",
         "62",
         "3"
        ],
        [
         "Florida",
         "616",
         "2",
         "1400",
         "26",
         "3"
        ],
        [
         "Maine",
         "43",
         "4",
         "1592",
         "73",
         "2"
        ],
        [
         "Iowa",
         "234",
         "7",
         "1006",
         "37",
         "1"
        ],
        [
         "Alaska",
         "523",
         "8",
         "987",
         "949",
         "2"
        ],
        [
         "Washington",
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         "3",
         "849",
         "48",
         "3"
        ],
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         "Oregon",
         "62",
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         "973",
         "48",
         "2"
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         "Wyoming",
         "73",
         "7",
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         "435",
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        [
         "Louisana",
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         "8",
         "1099",
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        [
         "Georgia",
         "35",
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         "345",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>234</td>\n",
       "      <td>7</td>\n",
       "      <td>1006</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>849</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
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       "      <td>435</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            deaths  battles  size  veterans  readiness\n",
       "origin                                                \n",
       "Arizona        523        5  1045         1          1\n",
       "California      52       42   957         5          2\n",
       "Texas           25        2  1099        62          3\n",
       "Florida        616        2  1400        26          3\n",
       "Maine           43        4  1592        73          2\n",
       "Iowa           234        7  1006        37          1\n",
       "Alaska         523        8   987       949          2\n",
       "Washington      62        3   849        48          3\n",
       "Oregon          62        4   973        48          2\n",
       "Wyoming         73        7  1005       435          1\n",
       "Louisana        37        8  1099        63          2\n",
       "Georgia         35        9  1523       345          3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Select rows where df.deaths is greater than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "columns": [
        {
         "name": "origin",
         "rawType": "object",
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         "type": "integer"
        },
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         "rawType": "int64",
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         "type": "integer"
        },
        {
         "name": "deserters",
         "rawType": "int64",
         "type": "integer"
        }
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       "ref": "da499d8d-3fae-46b4-97a2-2d6c13a884c0",
       "rows": [
        [
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         "Nighthawks",
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         "523",
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         "1045",
         "1",
         "1",
         "1",
         "4"
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         "0",
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         "2"
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         "234",
         "7",
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         "1",
         "4"
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         "Dragoons",
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         "523",
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         "Dragoons",
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         "3",
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         "Scouts",
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         "4",
         "973",
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       "      <td>2</td>\n",
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       "      <th>Iowa</th>\n",
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       "      <td>7</td>\n",
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       "      <td>37</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <th>Alaska</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>849</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>1005</td>\n",
       "      <td>435</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                       \n",
       "Arizona     Nighthawks     1st     523        5  1045         1          1   \n",
       "California  Nighthawks     1st      52       42   957         5          2   \n",
       "Florida     Nighthawks     2nd     616        2  1400        26          3   \n",
       "Iowa          Dragoons     1st     234        7  1006        37          1   \n",
       "Alaska        Dragoons     2nd     523        8   987       949          2   \n",
       "Washington    Dragoons     2nd      62        3   849        48          3   \n",
       "Oregon          Scouts     1st      62        4   973        48          2   \n",
       "Wyoming         Scouts     1st      73        7  1005       435          1   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Arizona           1          4  \n",
       "California        0         24  \n",
       "Florida           1          2  \n",
       "Iowa              1          4  \n",
       "Alaska            0         24  \n",
       "Washington        1         31  \n",
       "Oregon            0          2  \n",
       "Wyoming           0          3  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Select rows where df.deaths is greater than 500 or less than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "columns": [
        {
         "name": "origin",
         "rawType": "object",
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         "5",
         "1045",
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         "1",
         "1",
         "4"
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         "3",
         "1",
         "31"
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         "2"
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         "43",
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         "1592",
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         "2",
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         "3"
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         "Dragoons",
         "2nd",
         "523",
         "8",
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         "2",
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        ],
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         "Louisana",
         "Scouts",
         "2nd",
         "37",
         "8",
         "1099",
         "63",
         "2",
         "1",
         "2"
        ],
        [
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         "Scouts",
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         "9",
         "1523",
         "345",
         "3",
         "1",
         "3"
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       "      <th>regiment</th>\n",
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       "      <td>Nighthawks</td>\n",
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       "      <td>616</td>\n",
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       "      <td>1400</td>\n",
       "      <td>26</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1592</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>523</td>\n",
       "      <td>8</td>\n",
       "      <td>987</td>\n",
       "      <td>949</td>\n",
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       "      <td>0</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                     \n",
       "Arizona   Nighthawks     1st     523        5  1045         1          1   \n",
       "Texas     Nighthawks     2nd      25        2  1099        62          3   \n",
       "Florida   Nighthawks     2nd     616        2  1400        26          3   \n",
       "Maine       Dragoons     1st      43        4  1592        73          2   \n",
       "Alaska      Dragoons     2nd     523        8   987       949          2   \n",
       "Louisana      Scouts     2nd      37        8  1099        63          2   \n",
       "Georgia       Scouts     2nd      35        9  1523       345          3   \n",
       "\n",
       "          armored  deserters  \n",
       "origin                        \n",
       "Arizona         1          4  \n",
       "Texas           1         31  \n",
       "Florida         1          2  \n",
       "Maine           0          3  \n",
       "Alaska          0         24  \n",
       "Louisana        1          2  \n",
       "Georgia         1          3  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Select all the regiments not named \"Dragoons\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        {
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         "3"
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       "      <td>24</td>\n",
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       "      <td>Nighthawks</td>\n",
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       "      <td>2</td>\n",
       "      <td>1099</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>1400</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>62</td>\n",
       "      <td>4</td>\n",
       "      <td>973</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>Wyoming</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>1005</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisana</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1099</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>1523</td>\n",
       "      <td>345</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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      ],
      "text/plain": [
       "              regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                       \n",
       "Arizona     Nighthawks     1st     523        5  1045         1          1   \n",
       "California  Nighthawks     1st      52       42   957         5          2   \n",
       "Texas       Nighthawks     2nd      25        2  1099        62          3   \n",
       "Florida     Nighthawks     2nd     616        2  1400        26          3   \n",
       "Oregon          Scouts     1st      62        4   973        48          2   \n",
       "Wyoming         Scouts     1st      73        7  1005       435          1   \n",
       "Louisana        Scouts     2nd      37        8  1099        63          2   \n",
       "Georgia         Scouts     2nd      35        9  1523       345          3   \n",
       "\n",
       "            armored  deserters  \n",
       "origin                          \n",
       "Arizona           1          4  \n",
       "California        0         24  \n",
       "Texas             1         31  \n",
       "Florida           1          2  \n",
       "Oregon            0          2  \n",
       "Wyoming           0          3  \n",
       "Louisana          1          2  \n",
       "Georgia           1          3  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. Select the rows called Texas and Arizona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "columns": [
        {
         "name": "origin",
         "rawType": "object",
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        }
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       "ref": "8abf448c-c193-46ee-996f-19223bfceb9d",
       "rows": [
        [
         "Texas",
         "Nighthawks",
         "2nd",
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         "3",
         "1",
         "31"
        ],
        [
         "Arizona",
         "Nighthawks",
         "1st",
         "523",
         "5",
         "1045",
         "1",
         "1",
         "1",
         "4"
        ]
       ],
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        "rows": 2
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>deaths</th>\n",
       "      <th>battles</th>\n",
       "      <th>size</th>\n",
       "      <th>veterans</th>\n",
       "      <th>readiness</th>\n",
       "      <th>armored</th>\n",
       "      <th>deserters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Texas</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
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       "      <td>2</td>\n",
       "      <td>1099</td>\n",
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       "      <td>31</td>\n",
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       "      <td>1st</td>\n",
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       "      <td>5</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
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      "text/plain": [
       "           regiment company  deaths  battles  size  veterans  readiness  \\\n",
       "origin                                                                    \n",
       "Texas    Nighthawks     2nd      25        2  1099        62          3   \n",
       "Arizona  Nighthawks     1st     523        5  1045         1          1   \n",
       "\n",
       "         armored  deserters  \n",
       "origin                       \n",
       "Texas          1         31  \n",
       "Arizona        1          4  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 17. Select the third cell in the row named Arizona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "origin",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "deaths",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "7612b5e1-f043-44b6-b7af-1156ebf9028f",
       "rows": [
        [
         "Arizona",
         "523"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 1
       }
      },
      "text/plain": [
       "origin\n",
       "Arizona    523\n",
       "Name: deaths, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 18. Select the third cell down in the column named deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Texas",
         "rawType": "int64",
         "type": "integer"
        }
       ],
       "ref": "d92bd101-0e14-4b3a-bffe-060f2863daee",
       "rows": [
        [
         "deaths",
         "25"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 1
       }
      },
      "text/plain": [
       "deaths    25\n",
       "Name: Texas, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "constructor",
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   "pygments_lexer": "ipython3",
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 },
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================================================
FILE: 03_Grouping/Alcohol_Consumption/Exercise.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex - GroupBy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "GroupBy can be summarized as Split-Apply-Combine.\n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "Check out this [Diagram](http://i.imgur.com/yjNkiwL.png)  \n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called drinks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Which continent drinks more beer on average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. For each continent print the statistics for wine consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print the mean alcohol consumption per continent for every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the median alcohol consumption per continent for every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Print the mean, min and max values for spirit consumption.\n",
    "#### This time output a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
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================================================
FILE: 03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex - GroupBy\n",
    "\n",
    "Check out [Alcohol Consumption Exercises Video Tutorial](https://youtu.be/az67CMdmS6s) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "GroupBy can be summarized as Split-Apply-Combine.\n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "Check out this [Diagram](http://i.imgur.com/yjNkiwL.png)  \n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called drinks.(Watch the values of Column continent NA (North America), and how Pandas interprets it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0        AS  \n",
       "1                           4.9        EU  \n",
       "2                           0.7        AF  \n",
       "3                          12.4        EU  \n",
       "4                           5.9        AF  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv',keep_default_na=False)\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Which continent drinks more beer on average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "AF     61.471698\n",
       "AS     37.045455\n",
       "EU    193.777778\n",
       "NA    145.434783\n",
       "OC     89.687500\n",
       "SA    175.083333\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. For each continent print the statistics for wine consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>53.0</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>38.846419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>44.0</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>21.667034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>45.0</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>97.421738</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>195.00</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>23.0</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>28.266378</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>16.0</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>64.555790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>23.25</td>\n",
       "      <td>212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>12.0</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>88.620189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>98.50</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean        std  min   25%    50%     75%    max\n",
       "continent                                                               \n",
       "AF          53.0   16.264151  38.846419  0.0   1.0    2.0   13.00  233.0\n",
       "AS          44.0    9.068182  21.667034  0.0   0.0    1.0    8.00  123.0\n",
       "EU          45.0  142.222222  97.421738  0.0  59.0  128.0  195.00  370.0\n",
       "NA          23.0   24.521739  28.266378  1.0   5.0   11.0   34.00  100.0\n",
       "OC          16.0   35.625000  64.555790  0.0   1.0    8.5   23.25  212.0\n",
       "SA          12.0   62.416667  88.620189  1.0   3.0   12.0   98.50  221.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').wine_servings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print the mean alcohol consumption per continent for every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>5.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                  \n",
       "AF             61.471698        16.339623      16.264151   \n",
       "AS             37.045455        60.840909       9.068182   \n",
       "EU            193.777778       132.555556     142.222222   \n",
       "NA            145.434783       165.739130      24.521739   \n",
       "OC             89.687500        58.437500      35.625000   \n",
       "SA            175.083333       114.750000      62.416667   \n",
       "\n",
       "           total_litres_of_pure_alcohol  \n",
       "continent                                \n",
       "AF                             3.007547  \n",
       "AS                             2.170455  \n",
       "EU                             8.617778  \n",
       "NA                             5.995652  \n",
       "OC                             3.381250  \n",
       "SA                             6.308333  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the median alcohol consumption per continent for every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>17.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>219.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>143.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>52.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>162.5</td>\n",
       "      <td>108.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                  \n",
       "AF                  32.0              3.0            2.0   \n",
       "AS                  17.5             16.0            1.0   \n",
       "EU                 219.0            122.0          128.0   \n",
       "NA                 143.0            137.0           11.0   \n",
       "OC                  52.5             37.0            8.5   \n",
       "SA                 162.5            108.5           12.0   \n",
       "\n",
       "           total_litres_of_pure_alcohol  \n",
       "continent                                \n",
       "AF                                 2.30  \n",
       "AS                                 1.20  \n",
       "EU                                10.00  \n",
       "NA                                 6.30  \n",
       "OC                                 1.75  \n",
       "SA                                 6.85  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').median(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Print the mean, min and max values for spirit consumption for each Continent.\n",
    "#### This time output a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>16.339623</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>60.840909</td>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>132.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>165.739130</td>\n",
       "      <td>68</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>58.437500</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>114.750000</td>\n",
       "      <td>25</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  min  max\n",
       "continent                      \n",
       "AF          16.339623    0  152\n",
       "AS          60.840909    0  326\n",
       "EU         132.555556    0  373\n",
       "NA         165.739130   68  438\n",
       "OC          58.437500    0  254\n",
       "SA         114.750000   25  302"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').spirit_servings.agg(['mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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================================================
FILE: 03_Grouping/Alcohol_Consumption/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "GroupBy can be summarized as Split-Apply-Combine.\n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "Check out this [Diagram](http://i.imgur.com/yjNkiwL.png)  \n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called drinks (Watch the values of the Column 'Continent' NA (North America), and how Pandas interprets it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0        AS  \n",
       "1                           4.9        EU  \n",
       "2                           0.7        AF  \n",
       "3                          12.4        EU  \n",
       "4                           5.9        AF  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Which continent drinks more beer on average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "AF     61.471698\n",
       "AS     37.045455\n",
       "EU    193.777778\n",
       "OC     89.687500\n",
       "SA    175.083333\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. For each continent print the statistics for wine consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent       \n",
       "AF         count     53.000000\n",
       "           mean      16.264151\n",
       "           std       38.846419\n",
       "           min        0.000000\n",
       "           25%        1.000000\n",
       "           50%        2.000000\n",
       "           75%       13.000000\n",
       "           max      233.000000\n",
       "AS         count     44.000000\n",
       "           mean       9.068182\n",
       "           std       21.667034\n",
       "           min        0.000000\n",
       "           25%        0.000000\n",
       "           50%        1.000000\n",
       "           75%        8.000000\n",
       "           max      123.000000\n",
       "EU         count     45.000000\n",
       "           mean     142.222222\n",
       "           std       97.421738\n",
       "           min        0.000000\n",
       "           25%       59.000000\n",
       "           50%      128.000000\n",
       "           75%      195.000000\n",
       "           max      370.000000\n",
       "OC         count     16.000000\n",
       "           mean      35.625000\n",
       "           std       64.555790\n",
       "           min        0.000000\n",
       "           25%        1.000000\n",
       "           50%        8.500000\n",
       "           75%       23.250000\n",
       "           max      212.000000\n",
       "SA         count     12.000000\n",
       "           mean      62.416667\n",
       "           std       88.620189\n",
       "           min        1.000000\n",
       "           25%        3.000000\n",
       "           50%       12.000000\n",
       "           75%       98.500000\n",
       "           max      221.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print the mean alcohol consumption per continent for every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                  \n",
       "AF             61.471698        16.339623      16.264151   \n",
       "AS             37.045455        60.840909       9.068182   \n",
       "EU            193.777778       132.555556     142.222222   \n",
       "OC             89.687500        58.437500      35.625000   \n",
       "SA            175.083333       114.750000      62.416667   \n",
       "\n",
       "           total_litres_of_pure_alcohol  \n",
       "continent                                \n",
       "AF                             3.007547  \n",
       "AS                             2.170455  \n",
       "EU                             8.617778  \n",
       "OC                             3.381250  \n",
       "SA                             6.308333  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the median alcohol consumption per continent for every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>17.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>219.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>52.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>162.5</td>\n",
       "      <td>108.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                  \n",
       "AF                  32.0              3.0            2.0   \n",
       "AS                  17.5             16.0            1.0   \n",
       "EU                 219.0            122.0          128.0   \n",
       "OC                  52.5             37.0            8.5   \n",
       "SA                 162.5            108.5           12.0   \n",
       "\n",
       "           total_litres_of_pure_alcohol  \n",
       "continent                                \n",
       "AF                                 2.30  \n",
       "AS                                 1.20  \n",
       "EU                                10.00  \n",
       "OC                                 1.75  \n",
       "SA                                 6.85  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Print the mean, min and max values for spirit consumption by Continent.\n",
    "#### This time output a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>16.339623</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>60.840909</td>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>132.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>58.437500</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>114.750000</td>\n",
       "      <td>25</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  min  max\n",
       "continent                      \n",
       "AF          16.339623    0  152\n",
       "AS          60.840909    0  326\n",
       "EU         132.555556    0  373\n",
       "OC          58.437500    0  254\n",
       "SA         114.750000   25  302"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 03_Grouping/Occupation/Exercise.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Occupation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Discover what is the mean age per occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. For each occupation, calculate the minimum and maximum ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. For each combination of occupation and gender, calculate the mean age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  For each occupation present the percentage of women and men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 03_Grouping/Occupation/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Occupation\n",
    "\n",
    "Check out [Occupation Exercises Video Tutorial](https://youtu.be/jL3EVCoYIJQ) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender  occupation zip_code\n",
       "user_id                                 \n",
       "1         24      M  technician    85711\n",
       "2         53      F       other    94043\n",
       "3         23      M      writer    32067\n",
       "4         24      M  technician    43537\n",
       "5         33      F       other    15213"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n",
    "                      sep='|', index_col='user_id')\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Discover what is the mean age per occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       "administrator    38.746835\n",
       "artist           31.392857\n",
       "doctor           43.571429\n",
       "educator         42.010526\n",
       "engineer         36.388060\n",
       "entertainment    29.222222\n",
       "executive        38.718750\n",
       "healthcare       41.562500\n",
       "homemaker        32.571429\n",
       "lawyer           36.750000\n",
       "librarian        40.000000\n",
       "marketing        37.615385\n",
       "none             26.555556\n",
       "other            34.523810\n",
       "programmer       33.121212\n",
       "retired          63.071429\n",
       "salesman         35.666667\n",
       "scientist        35.548387\n",
       "student          22.081633\n",
       "technician       33.148148\n",
       "writer           36.311111\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby('occupation').age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doctor           100.000000\n",
       "engineer          97.014925\n",
       "technician        96.296296\n",
       "retired           92.857143\n",
       "programmer        90.909091\n",
       "executive         90.625000\n",
       "scientist         90.322581\n",
       "entertainment     88.888889\n",
       "lawyer            83.333333\n",
       "salesman          75.000000\n",
       "educator          72.631579\n",
       "student           69.387755\n",
       "other             65.714286\n",
       "marketing         61.538462\n",
       "writer            57.777778\n",
       "none              55.555556\n",
       "administrator     54.430380\n",
       "artist            53.571429\n",
       "librarian         43.137255\n",
       "healthcare        31.250000\n",
       "homemaker         14.285714\n",
       "dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a function\n",
    "def gender_to_numeric(x):\n",
    "    if x == 'M':\n",
    "        return 1\n",
    "    if x == 'F':\n",
    "        return 0\n",
    "\n",
    "# apply the function to the gender column and create a new column\n",
    "users['gender_n'] = users['gender'].apply(gender_to_numeric)\n",
    "\n",
    "\n",
    "a = users.groupby('occupation').gender_n.sum() / users.occupation.value_counts() * 100 \n",
    "\n",
    "# sort to the most male \n",
    "a.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. For each occupation, calculate the minimum and maximum ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>administrator</th>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>19</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor</th>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educator</th>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineer</th>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>executive</th>\n",
       "      <td>22</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthcare</th>\n",
       "      <td>22</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homemaker</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lawyer</th>\n",
       "      <td>21</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>librarian</th>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>24</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>13</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>programmer</th>\n",
       "      <td>20</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>51</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salesman</th>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientist</th>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>21</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writer</th>\n",
       "      <td>18</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               min  max\n",
       "occupation             \n",
       "administrator   21   70\n",
       "artist          19   48\n",
       "doctor          28   64\n",
       "educator        23   63\n",
       "engineer        22   70\n",
       "entertainment   15   50\n",
       "executive       22   69\n",
       "healthcare      22   62\n",
       "homemaker       20   50\n",
       "lawyer          21   53\n",
       "librarian       23   69\n",
       "marketing       24   55\n",
       "none            11   55\n",
       "other           13   64\n",
       "programmer      20   63\n",
       "retired         51   73\n",
       "salesman        18   66\n",
       "scientist       23   55\n",
       "student          7   42\n",
       "technician      21   55\n",
       "writer          18   60"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby('occupation').age.agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. For each combination of occupation and gender, calculate the mean age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation     gender\n",
       "administrator  F         40.638889\n",
       "               M         37.162791\n",
       "artist         F         30.307692\n",
       "               M         32.333333\n",
       "doctor         M         43.571429\n",
       "educator       F         39.115385\n",
       "               M         43.101449\n",
       "engineer       F         29.500000\n",
       "               M         36.600000\n",
       "entertainment  F         31.000000\n",
       "               M         29.000000\n",
       "executive      F         44.000000\n",
       "               M         38.172414\n",
       "healthcare     F         39.818182\n",
       "               M         45.400000\n",
       "homemaker      F         34.166667\n",
       "               M         23.000000\n",
       "lawyer         F         39.500000\n",
       "               M         36.200000\n",
       "librarian      F         40.000000\n",
       "               M         40.000000\n",
       "marketing      F         37.200000\n",
       "               M         37.875000\n",
       "none           F         36.500000\n",
       "               M         18.600000\n",
       "other          F         35.472222\n",
       "               M         34.028986\n",
       "programmer     F         32.166667\n",
       "               M         33.216667\n",
       "retired        F         70.000000\n",
       "               M         62.538462\n",
       "salesman       F         27.000000\n",
       "               M         38.555556\n",
       "scientist      F         28.333333\n",
       "               M         36.321429\n",
       "student        F         20.750000\n",
       "               M         22.669118\n",
       "technician     F         38.000000\n",
       "               M         32.961538\n",
       "writer         F         37.631579\n",
       "               M         35.346154\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby(['occupation', 'gender']).age.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  For each occupation present the percentage of women and men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation     gender\n",
       "administrator  F          45.569620\n",
       "               M          54.430380\n",
       "artist         F          46.428571\n",
       "               M          53.571429\n",
       "doctor         M         100.000000\n",
       "educator       F          27.368421\n",
       "               M          72.631579\n",
       "engineer       F           2.985075\n",
       "               M          97.014925\n",
       "entertainment  F          11.111111\n",
       "               M          88.888889\n",
       "executive      F           9.375000\n",
       "               M          90.625000\n",
       "healthcare     F          68.750000\n",
       "               M          31.250000\n",
       "homemaker      F          85.714286\n",
       "               M          14.285714\n",
       "lawyer         F          16.666667\n",
       "               M          83.333333\n",
       "librarian      F          56.862745\n",
       "               M          43.137255\n",
       "marketing      F          38.461538\n",
       "               M          61.538462\n",
       "none           F          44.444444\n",
       "               M          55.555556\n",
       "other          F          34.285714\n",
       "               M          65.714286\n",
       "programmer     F           9.090909\n",
       "               M          90.909091\n",
       "retired        F           7.142857\n",
       "               M          92.857143\n",
       "salesman       F          25.000000\n",
       "               M          75.000000\n",
       "scientist      F           9.677419\n",
       "               M          90.322581\n",
       "student        F          30.612245\n",
       "               M          69.387755\n",
       "technician     F           3.703704\n",
       "               M          96.296296\n",
       "writer         F          42.222222\n",
       "               M          57.777778\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a data frame and apply count to gender\n",
    "gender_ocup = users.groupby(['occupation', 'gender']).agg({'gender': 'count'})\n",
    "\n",
    "# create a DataFrame and apply count for each occupation\n",
    "occup_count = users.groupby(['occupation']).agg('count')\n",
    "\n",
    "# divide the gender_ocup per the occup_count and multiply per 100\n",
    "occup_gender = gender_ocup.div(occup_count, level = \"occupation\") * 100\n",
    "\n",
    "# present all rows from the 'gender column'\n",
    "occup_gender.loc[: , 'gender']"
   ]
  }
 ],
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================================================
FILE: 03_Grouping/Occupation/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Occupation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender  occupation zip_code\n",
       "user_id                                 \n",
       "1         24      M  technician    85711\n",
       "2         53      F       other    94043\n",
       "3         23      M      writer    32067\n",
       "4         24      M  technician    43537\n",
       "5         33      F       other    15213"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Discover what is the mean age per occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       "administrator    38.746835\n",
       "artist           31.392857\n",
       "doctor           43.571429\n",
       "educator         42.010526\n",
       "engineer         36.388060\n",
       "entertainment    29.222222\n",
       "executive        38.718750\n",
       "healthcare       41.562500\n",
       "homemaker        32.571429\n",
       "lawyer           36.750000\n",
       "librarian        40.000000\n",
       "marketing        37.615385\n",
       "none             26.555556\n",
       "other            34.523810\n",
       "programmer       33.121212\n",
       "retired          63.071429\n",
       "salesman         35.666667\n",
       "scientist        35.548387\n",
       "student          22.081633\n",
       "technician       33.148148\n",
       "writer           36.311111\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doctor           100.000000\n",
       "engineer          97.014925\n",
       "technician        96.296296\n",
       "retired           92.857143\n",
       "programmer        90.909091\n",
       "executive         90.625000\n",
       "scientist         90.322581\n",
       "entertainment     88.888889\n",
       "lawyer            83.333333\n",
       "salesman          75.000000\n",
       "educator          72.631579\n",
       "student           69.387755\n",
       "other             65.714286\n",
       "marketing         61.538462\n",
       "writer            57.777778\n",
       "none              55.555556\n",
       "administrator     54.430380\n",
       "artist            53.571429\n",
       "librarian         43.137255\n",
       "healthcare        31.250000\n",
       "homemaker         14.285714\n",
       "dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. For each occupation, calculate the minimum and maximum ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>administrator</th>\n",
       "      <td>21</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artist</th>\n",
       "      <td>19</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor</th>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educator</th>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engineer</th>\n",
       "      <td>22</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>executive</th>\n",
       "      <td>22</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthcare</th>\n",
       "      <td>22</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homemaker</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lawyer</th>\n",
       "      <td>21</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>librarian</th>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>24</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>none</th>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>13</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>programmer</th>\n",
       "      <td>20</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>51</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salesman</th>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scientist</th>\n",
       "      <td>23</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>21</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writer</th>\n",
       "      <td>18</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               min  max\n",
       "occupation             \n",
       "administrator   21   70\n",
       "artist          19   48\n",
       "doctor          28   64\n",
       "educator        23   63\n",
       "engineer        22   70\n",
       "entertainment   15   50\n",
       "executive       22   69\n",
       "healthcare      22   62\n",
       "homemaker       20   50\n",
       "lawyer          21   53\n",
       "librarian       23   69\n",
       "marketing       24   55\n",
       "none            11   55\n",
       "other           13   64\n",
       "programmer      20   63\n",
       "retired         51   73\n",
       "salesman        18   66\n",
       "scientist       23   55\n",
       "student          7   42\n",
       "technician      21   55\n",
       "writer          18   60"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. For each combination of occupation and gender, calculate the mean age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation     gender\n",
       "administrator  F         40.638889\n",
       "               M         37.162791\n",
       "artist         F         30.307692\n",
       "               M         32.333333\n",
       "doctor         M         43.571429\n",
       "educator       F         39.115385\n",
       "               M         43.101449\n",
       "engineer       F         29.500000\n",
       "               M         36.600000\n",
       "entertainment  F         31.000000\n",
       "               M         29.000000\n",
       "executive      F         44.000000\n",
       "               M         38.172414\n",
       "healthcare     F         39.818182\n",
       "               M         45.400000\n",
       "homemaker      F         34.166667\n",
       "               M         23.000000\n",
       "lawyer         F         39.500000\n",
       "               M         36.200000\n",
       "librarian      F         40.000000\n",
       "               M         40.000000\n",
       "marketing      F         37.200000\n",
       "               M         37.875000\n",
       "none           F         36.500000\n",
       "               M         18.600000\n",
       "other          F         35.472222\n",
       "               M         34.028986\n",
       "programmer     F         32.166667\n",
       "               M         33.216667\n",
       "retired        F         70.000000\n",
       "               M         62.538462\n",
       "salesman       F         27.000000\n",
       "               M         38.555556\n",
       "scientist      F         28.333333\n",
       "               M         36.321429\n",
       "student        F         20.750000\n",
       "               M         22.669118\n",
       "technician     F         38.000000\n",
       "               M         32.961538\n",
       "writer         F         37.631579\n",
       "               M         35.346154\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  For each occupation present the percentage of women and men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation     gender\n",
       "administrator  F          45.569620\n",
       "               M          54.430380\n",
       "artist         F          46.428571\n",
       "               M          53.571429\n",
       "doctor         M         100.000000\n",
       "educator       F          27.368421\n",
       "               M          72.631579\n",
       "engineer       F           2.985075\n",
       "               M          97.014925\n",
       "entertainment  F          11.111111\n",
       "               M          88.888889\n",
       "executive      F           9.375000\n",
       "               M          90.625000\n",
       "healthcare     F          68.750000\n",
       "               M          31.250000\n",
       "homemaker      F          85.714286\n",
       "               M          14.285714\n",
       "lawyer         F          16.666667\n",
       "               M          83.333333\n",
       "librarian      F          56.862745\n",
       "               M          43.137255\n",
       "marketing      F          38.461538\n",
       "               M          61.538462\n",
       "none           F          44.444444\n",
       "               M          55.555556\n",
       "other          F          34.285714\n",
       "               M          65.714286\n",
       "programmer     F           9.090909\n",
       "               M          90.909091\n",
       "retired        F           7.142857\n",
       "               M          92.857143\n",
       "salesman       F          25.000000\n",
       "               M          75.000000\n",
       "scientist      F           9.677419\n",
       "               M          90.322581\n",
       "student        F          30.612245\n",
       "               M          69.387755\n",
       "technician     F           3.703704\n",
       "               M          96.296296\n",
       "writer         F          42.222222\n",
       "               M          57.777778\n",
       "Name: gender, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
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================================================
FILE: 03_Grouping/Regiment/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame with the following values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n",
    "        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n",
    "        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n",
    "        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called regiment.\n",
    "#### Don't forget to name each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. What is the mean preTestScore from the regiment Nighthawks?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Present general statistics by company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the mean of each company's preTestScore?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Present the mean preTestScores grouped by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Present the mean preTestScores grouped by regiment and company without heirarchical indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Group the entire dataframe by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the number of observations in each regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Iterate over a group and print the name and the whole data from the regiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 03_Grouping/Regiment/Exercises_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regiment\n",
    "\n",
    "Check out [Regiment Exercises Video Tutorial](https://youtu.be/MFZ3uakwAEk) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame with the following values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n",
    "        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n",
    "        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n",
    "        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called regiment.\n",
    "#### Don't forget to name each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>name</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>Miller</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ali</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Milner</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>Jacon</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ryaner</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Sone</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>Sloan</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>Piger</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Riani</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ali</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      regiment company      name  preTestScore  postTestScore\n",
       "0   Nighthawks     1st    Miller             4             25\n",
       "1   Nighthawks     1st  Jacobson            24             94\n",
       "2   Nighthawks     2nd       Ali            31             57\n",
       "3   Nighthawks     2nd    Milner             2             62\n",
       "4     Dragoons     1st     Cooze             3             70\n",
       "5     Dragoons     1st     Jacon             4             25\n",
       "6     Dragoons     2nd    Ryaner            24             94\n",
       "7     Dragoons     2nd      Sone            31             57\n",
       "8       Scouts     1st     Sloan             2             62\n",
       "9       Scouts     1st     Piger             3             70\n",
       "10      Scouts     2nd     Riani             2             62\n",
       "11      Scouts     2nd       Ali             3             70"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment = pd.DataFrame(raw_data, columns = raw_data.keys())\n",
    "regiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. What is the mean preTestScore from the regiment Nighthawks?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dragoons</th>\n",
       "      <td>15.50</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nighthawks</th>\n",
       "      <td>15.25</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scouts</th>\n",
       "      <td>2.50</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            preTestScore  postTestScore\n",
       "regiment                               \n",
       "Dragoons           15.50           61.5\n",
       "Nighthawks         15.25           59.5\n",
       "Scouts              2.50           66.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment[regiment['regiment'] == 'Nighthawks'].groupby('regiment').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Present general statistics by company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>postTestScore</th>\n",
       "      <th>preTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1st</th>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.666667</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.485754</td>\n",
       "      <td>8.524475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.250000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2nd</th>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.057027</td>\n",
       "      <td>14.652645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>58.250000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>29.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               postTestScore  preTestScore\n",
       "company                                   \n",
       "1st     count       6.000000      6.000000\n",
       "        mean       57.666667      6.666667\n",
       "        std        27.485754      8.524475\n",
       "        min        25.000000      2.000000\n",
       "        25%        34.250000      3.000000\n",
       "        50%        66.000000      3.500000\n",
       "        75%        70.000000      4.000000\n",
       "        max        94.000000     24.000000\n",
       "2nd     count       6.000000      6.000000\n",
       "        mean       67.000000     15.500000\n",
       "        std        14.057027     14.652645\n",
       "        min        57.000000      2.000000\n",
       "        25%        58.250000      2.250000\n",
       "        50%        62.000000     13.500000\n",
       "        75%        68.000000     29.250000\n",
       "        max        94.000000     31.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby('company').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the mean of each company's preTestScore?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company\n",
       "1st     6.666667\n",
       "2nd    15.500000\n",
       "Name: preTestScore, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby('company').preTestScore.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Present the mean preTestScores grouped by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regiment    company\n",
       "Dragoons    1st         3.5\n",
       "            2nd        27.5\n",
       "Nighthawks  1st        14.0\n",
       "            2nd        16.5\n",
       "Scouts      1st         2.5\n",
       "            2nd         2.5\n",
       "Name: preTestScore, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['regiment', 'company']).preTestScore.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Present the mean preTestScores grouped by regiment and company without heirarchical indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>company</th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dragoons</th>\n",
       "      <td>3.5</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nighthawks</th>\n",
       "      <td>14.0</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scouts</th>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "company      1st   2nd\n",
       "regiment              \n",
       "Dragoons     3.5  27.5\n",
       "Nighthawks  14.0  16.5\n",
       "Scouts       2.5   2.5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['regiment', 'company']).preTestScore.mean().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Group the entire dataframe by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Dragoons</th>\n",
       "      <th>1st</th>\n",
       "      <td>3.5</td>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>27.5</td>\n",
       "      <td>75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nighthawks</th>\n",
       "      <th>1st</th>\n",
       "      <td>14.0</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>16.5</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Scouts</th>\n",
       "      <th>1st</th>\n",
       "      <td>2.5</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>2.5</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    preTestScore  postTestScore\n",
       "regiment   company                             \n",
       "Dragoons   1st               3.5           47.5\n",
       "           2nd              27.5           75.5\n",
       "Nighthawks 1st              14.0           59.5\n",
       "           2nd              16.5           59.5\n",
       "Scouts     1st               2.5           66.0\n",
       "           2nd               2.5           66.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['regiment', 'company']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the number of observations in each regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company  regiment  \n",
       "1st      Dragoons      2\n",
       "         Nighthawks    2\n",
       "         Scouts        2\n",
       "2nd      Dragoons      2\n",
       "         Nighthawks    2\n",
       "         Scouts        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regiment.groupby(['company', 'regiment']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Iterate over a group and print the name and the whole data from the regiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dragoons\n",
      "   regiment company    name  preTestScore  postTestScore\n",
      "4  Dragoons     1st   Cooze             3             70\n",
      "5  Dragoons     1st   Jacon             4             25\n",
      "6  Dragoons     2nd  Ryaner            24             94\n",
      "7  Dragoons     2nd    Sone            31             57\n",
      "Nighthawks\n",
      "     regiment company      name  preTestScore  postTestScore\n",
      "0  Nighthawks     1st    Miller             4             25\n",
      "1  Nighthawks     1st  Jacobson            24             94\n",
      "2  Nighthawks     2nd       Ali            31             57\n",
      "3  Nighthawks     2nd    Milner             2             62\n",
      "Scouts\n",
      "   regiment company   name  preTestScore  postTestScore\n",
      "8    Scouts     1st  Sloan             2             62\n",
      "9    Scouts     1st  Piger             3             70\n",
      "10   Scouts     2nd  Riani             2             62\n",
      "11   Scouts     2nd    Ali             3             70\n"
     ]
    }
   ],
   "source": [
    "# Group the dataframe by regiment, and for each regiment,\n",
    "for name, group in regiment.groupby('regiment'):\n",
    "    # print the name of the regiment\n",
    "    print(name)\n",
    "    # print the data of that regiment\n",
    "    print(group)"
   ]
  }
 ],
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================================================
FILE: 03_Grouping/Regiment/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame with the following values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n",
    "        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n",
    "        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n",
    "        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called regiment.\n",
    "#### Don't forget to name each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th>name</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>Miller</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>1st</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ali</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nighthawks</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Milner</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>1st</td>\n",
       "      <td>Jacon</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ryaner</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dragoons</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Sone</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>Sloan</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>1st</td>\n",
       "      <td>Piger</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Riani</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Scouts</td>\n",
       "      <td>2nd</td>\n",
       "      <td>Ali</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      regiment company      name  preTestScore  postTestScore\n",
       "0   Nighthawks     1st    Miller             4             25\n",
       "1   Nighthawks     1st  Jacobson            24             94\n",
       "2   Nighthawks     2nd       Ali            31             57\n",
       "3   Nighthawks     2nd    Milner             2             62\n",
       "4     Dragoons     1st     Cooze             3             70\n",
       "5     Dragoons     1st     Jacon             4             25\n",
       "6     Dragoons     2nd    Ryaner            24             94\n",
       "7     Dragoons     2nd      Sone            31             57\n",
       "8       Scouts     1st     Sloan             2             62\n",
       "9       Scouts     1st     Piger             3             70\n",
       "10      Scouts     2nd     Riani             2             62\n",
       "11      Scouts     2nd       Ali             3             70"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. What is the mean preTestScore from the regiment Nighthawks?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dragoons</th>\n",
       "      <td>15.50</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nighthawks</th>\n",
       "      <td>15.25</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scouts</th>\n",
       "      <td>2.50</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            preTestScore  postTestScore\n",
       "regiment                               \n",
       "Dragoons           15.50           61.5\n",
       "Nighthawks         15.25           59.5\n",
       "Scouts              2.50           66.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Present general statistics by company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>postTestScore</th>\n",
       "      <th>preTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1st</th>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.666667</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.485754</td>\n",
       "      <td>8.524475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.250000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2nd</th>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.057027</td>\n",
       "      <td>14.652645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>58.250000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>29.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               postTestScore  preTestScore\n",
       "company                                   \n",
       "1st     count       6.000000      6.000000\n",
       "        mean       57.666667      6.666667\n",
       "        std        27.485754      8.524475\n",
       "        min        25.000000      2.000000\n",
       "        25%        34.250000      3.000000\n",
       "        50%        66.000000      3.500000\n",
       "        75%        70.000000      4.000000\n",
       "        max        94.000000     24.000000\n",
       "2nd     count       6.000000      6.000000\n",
       "        mean       67.000000     15.500000\n",
       "        std        14.057027     14.652645\n",
       "        min        57.000000      2.000000\n",
       "        25%        58.250000      2.250000\n",
       "        50%        62.000000     13.500000\n",
       "        75%        68.000000     29.250000\n",
       "        max        94.000000     31.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the mean of each company's preTestScore?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company\n",
       "1st     6.666667\n",
       "2nd    15.500000\n",
       "Name: preTestScore, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Present the mean preTestScores grouped by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regiment    company\n",
       "Dragoons    1st         3.5\n",
       "            2nd        27.5\n",
       "Nighthawks  1st        14.0\n",
       "            2nd        16.5\n",
       "Scouts      1st         2.5\n",
       "            2nd         2.5\n",
       "Name: preTestScore, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Present the mean preTestScores grouped by regiment and company without heirarchical indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>company</th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dragoons</th>\n",
       "      <td>3.5</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nighthawks</th>\n",
       "      <td>14.0</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scouts</th>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "company      1st   2nd\n",
       "regiment              \n",
       "Dragoons     3.5  27.5\n",
       "Nighthawks  14.0  16.5\n",
       "Scouts       2.5   2.5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Group the entire dataframe by regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regiment</th>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Dragoons</th>\n",
       "      <th>1st</th>\n",
       "      <td>3.5</td>\n",
       "      <td>47.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>27.5</td>\n",
       "      <td>75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nighthawks</th>\n",
       "      <th>1st</th>\n",
       "      <td>14.0</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>16.5</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Scouts</th>\n",
       "      <th>1st</th>\n",
       "      <td>2.5</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>2.5</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    preTestScore  postTestScore\n",
       "regiment   company                             \n",
       "Dragoons   1st               3.5           47.5\n",
       "           2nd              27.5           75.5\n",
       "Nighthawks 1st              14.0           59.5\n",
       "           2nd              16.5           59.5\n",
       "Scouts     1st               2.5           66.0\n",
       "           2nd               2.5           66.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the number of observations in each regiment and company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company  regiment  \n",
       "1st      Dragoons      2\n",
       "         Nighthawks    2\n",
       "         Scouts        2\n",
       "2nd      Dragoons      2\n",
       "         Nighthawks    2\n",
       "         Scouts        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Iterate over a group and print the name and the whole data from the regiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dragoons\n",
      "   regiment company    name  preTestScore  postTestScore\n",
      "4  Dragoons     1st   Cooze             3             70\n",
      "5  Dragoons     1st   Jacon             4             25\n",
      "6  Dragoons     2nd  Ryaner            24             94\n",
      "7  Dragoons     2nd    Sone            31             57\n",
      "Nighthawks\n",
      "     regiment company      name  preTestScore  postTestScore\n",
      "0  Nighthawks     1st    Miller             4             25\n",
      "1  Nighthawks     1st  Jacobson            24             94\n",
      "2  Nighthawks     2nd       Ali            31             57\n",
      "3  Nighthawks     2nd    Milner             2             62\n",
      "Scouts\n",
      "   regiment company   name  preTestScore  postTestScore\n",
      "8    Scouts     1st  Sloan             2             62\n",
      "9    Scouts     1st  Piger             3             70\n",
      "10   Scouts     2nd  Riani             2             62\n",
      "11   Scouts     2nd    Ali             3             70\n"
     ]
    }
   ],
   "source": []
  }
 ],
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   "pygments_lexer": "ipython2",
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 },
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================================================
FILE: 04_Apply/Students_Alcohol_Consumption/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Student Alcohol Consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will download a dataset from the UCI.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a lambda function that will capitalize strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Capitalize both Mjob and Fjob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the last elements of the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and capitalize Mjob and Fjob."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a function called majority that returns a boolean value to a new column called legal_drinker (Consider majority as older than 17 years old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Multiply every number of the dataset by 10. \n",
    "##### I know this makes no sense, don't forget it is just an exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python [default]",
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================================================
FILE: 04_Apply/Students_Alcohol_Consumption/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Student Alcohol Consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will download a dataset from the UCI.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv'\n",
    "df = pd.read_csv(csv_url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other   \n",
       "\n",
       "   reason guardian  \n",
       "0  course   mother  \n",
       "1  course   father  \n",
       "2   other   mother  \n",
       "3    home   mother  \n",
       "4    home   father  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stud_alcoh = df.loc[: , \"school\":\"guardian\"]\n",
    "stud_alcoh.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a lambda function that will capitalize strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "capitalizer = lambda x: x.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Capitalize both Mjob and Fjob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Teacher\n",
       "1         Other\n",
       "2         Other\n",
       "3      Services\n",
       "4         Other\n",
       "5         Other\n",
       "6         Other\n",
       "7       Teacher\n",
       "8         Other\n",
       "9         Other\n",
       "10       Health\n",
       "11        Other\n",
       "12     Services\n",
       "13        Other\n",
       "14        Other\n",
       "15        Other\n",
       "16     Services\n",
       "17        Other\n",
       "18     Services\n",
       "19        Other\n",
       "20        Other\n",
       "21       Health\n",
       "22        Other\n",
       "23        Other\n",
       "24       Health\n",
       "25     Services\n",
       "26        Other\n",
       "27     Services\n",
       "28        Other\n",
       "29      Teacher\n",
       "         ...   \n",
       "365       Other\n",
       "366    Services\n",
       "367    Services\n",
       "368    Services\n",
       "369     Teacher\n",
       "370    Services\n",
       "371    Services\n",
       "372     At_home\n",
       "373       Other\n",
       "374       Other\n",
       "375       Other\n",
       "376       Other\n",
       "377    Services\n",
       "378       Other\n",
       "379       Other\n",
       "380     Teacher\n",
       "381       Other\n",
       "382    Services\n",
       "383    Services\n",
       "384       Other\n",
       "385       Other\n",
       "386     At_home\n",
       "387       Other\n",
       "388    Services\n",
       "389       Other\n",
       "390    Services\n",
       "391    Services\n",
       "392       Other\n",
       "393       Other\n",
       "394     At_home\n",
       "Name: Fjob, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stud_alcoh['Mjob'].apply(capitalizer)\n",
    "stud_alcoh['Fjob'].apply(capitalizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the last elements of the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>at_home</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "390     MS   M   20       U     LE3       A     2     2  services  services   \n",
       "391     MS   M   17       U     LE3       T     3     1  services  services   \n",
       "392     MS   M   21       R     GT3       T     1     1     other     other   \n",
       "393     MS   M   18       R     LE3       T     3     2  services     other   \n",
       "394     MS   M   19       U     LE3       T     1     1     other   at_home   \n",
       "\n",
       "     reason guardian  \n",
       "390  course    other  \n",
       "391  course   mother  \n",
       "392  course    other  \n",
       "393  course   mother  \n",
       "394  course   father  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stud_alcoh.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and capitalize Mjob and Fjob."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Services</td>\n",
       "      <td>Services</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Services</td>\n",
       "      <td>Services</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Other</td>\n",
       "      <td>At_home</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "390     MS   M   20       U     LE3       A     2     2  Services  Services   \n",
       "391     MS   M   17       U     LE3       T     3     1  Services  Services   \n",
       "392     MS   M   21       R     GT3       T     1     1     Other     Other   \n",
       "393     MS   M   18       R     LE3       T     3     2  Services     Other   \n",
       "394     MS   M   19       U     LE3       T     1     1     Other   At_home   \n",
       "\n",
       "     reason guardian  \n",
       "390  course    other  \n",
       "391  course   mother  \n",
       "392  course    other  \n",
       "393  course   mother  \n",
       "394  course   father  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stud_alcoh['Mjob'] = stud_alcoh['Mjob'].apply(capitalizer)\n",
    "stud_alcoh['Fjob'] = stud_alcoh['Fjob'].apply(capitalizer)\n",
    "stud_alcoh.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a function called majority that returns a boolean value to a new column called legal_drinker (Consider majority as older than 17 years old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def majority(x):\n",
    "    if x > 17:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>legal_drinker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Health</td>\n",
       "      <td>Services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  At_home   Teacher   \n",
       "1     GP   F   17       U     GT3       T     1     1  At_home     Other   \n",
       "2     GP   F   15       U     LE3       T     1     1  At_home     Other   \n",
       "3     GP   F   15       U     GT3       T     4     2   Health  Services   \n",
       "4     GP   F   16       U     GT3       T     3     3    Other     Other   \n",
       "\n",
       "   reason guardian legal_drinker  \n",
       "0  course   mother          True  \n",
       "1  course   father         False  \n",
       "2   other   mother         False  \n",
       "3    home   mother         False  \n",
       "4    home   father         False  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stud_alcoh['legal_drinker'] = stud_alcoh['age'].apply(majority)\n",
    "stud_alcoh.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Multiply every number of the dataset by 10. \n",
    "##### I know this makes no sense, don't forget it is just an exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def times10(x):\n",
    "    if type(x) is int:\n",
    "        return 10 * x\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>legal_drinker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>GP</td>\n",
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       "      <td>40</td>\n",
       "      <td>At_home</td>\n",
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       "      <td>course</td>\n",
       "      <td>mother</td>\n",
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       "      <td>GT3</td>\n",
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       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>Health</td>\n",
       "      <td>Services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
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       "      <th>5</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>reputation</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>170</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "0     GP   F  180       U     GT3       A    40    40   At_home   Teacher   \n",
       "1     GP   F  170       U     GT3       T    10    10   At_home     Other   \n",
       "2     GP   F  150       U     LE3       T    10    10   At_home     Other   \n",
       "3     GP   F  150       U     GT3       T    40    20    Health  Services   \n",
       "4     GP   F  160       U     GT3       T    30    30     Other     Other   \n",
       "5     GP   M  160       U     LE3       T    40    30  Services     Other   \n",
       "6     GP   M  160       U     LE3       T    20    20     Other     Other   \n",
       "7     GP   F  170       U     GT3       A    40    40     Other   Teacher   \n",
       "8     GP   M  150       U     LE3       A    30    20  Services     Other   \n",
       "9     GP   M  150       U     GT3       T    30    40     Other     Other   \n",
       "\n",
       "       reason guardian legal_drinker  \n",
       "0      course   mother          True  \n",
       "1      course   father         False  \n",
       "2       other   mother         False  \n",
       "3        home   mother         False  \n",
       "4        home   father         False  \n",
       "5  reputation   mother         False  \n",
       "6        home   mother         False  \n",
       "7        home   mother         False  \n",
       "8        home   mother         False  \n",
       "9        home   mother         False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stud_alcoh.applymap(times10).head(10)"
   ]
  }
 ],
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================================================
FILE: 04_Apply/Students_Alcohol_Consumption/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Student Alcohol Consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will download a dataset from the UCI.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
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       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
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       "      <th>3</th>\n",
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       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
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       "      <td>health</td>\n",
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       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other   \n",
       "\n",
       "   reason guardian  \n",
       "0  course   mother  \n",
       "1  course   father  \n",
       "2   other   mother  \n",
       "3    home   mother  \n",
       "4    home   father  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a lambda function that will capitalize strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Capitalize both Mjob and Fjob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Teacher\n",
       "1         Other\n",
       "2         Other\n",
       "3      Services\n",
       "4         Other\n",
       "5         Other\n",
       "6         Other\n",
       "7       Teacher\n",
       "8         Other\n",
       "9         Other\n",
       "10       Health\n",
       "11        Other\n",
       "12     Services\n",
       "13        Other\n",
       "14        Other\n",
       "15        Other\n",
       "16     Services\n",
       "17        Other\n",
       "18     Services\n",
       "19        Other\n",
       "20        Other\n",
       "21       Health\n",
       "22        Other\n",
       "23        Other\n",
       "24       Health\n",
       "25     Services\n",
       "26        Other\n",
       "27     Services\n",
       "28        Other\n",
       "29      Teacher\n",
       "         ...   \n",
       "365       Other\n",
       "366    Services\n",
       "367    Services\n",
       "368    Services\n",
       "369     Teacher\n",
       "370    Services\n",
       "371    Services\n",
       "372     At_home\n",
       "373       Other\n",
       "374       Other\n",
       "375       Other\n",
       "376       Other\n",
       "377    Services\n",
       "378       Other\n",
       "379       Other\n",
       "380     Teacher\n",
       "381       Other\n",
       "382    Services\n",
       "383    Services\n",
       "384       Other\n",
       "385       Other\n",
       "386     At_home\n",
       "387       Other\n",
       "388    Services\n",
       "389       Other\n",
       "390    Services\n",
       "391    Services\n",
       "392       Other\n",
       "393       Other\n",
       "394     At_home\n",
       "Name: Fjob, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Print the last elements of the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>at_home</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "390     MS   M   20       U     LE3       A     2     2  services  services   \n",
       "391     MS   M   17       U     LE3       T     3     1  services  services   \n",
       "392     MS   M   21       R     GT3       T     1     1     other     other   \n",
       "393     MS   M   18       R     LE3       T     3     2  services     other   \n",
       "394     MS   M   19       U     LE3       T     1     1     other   at_home   \n",
       "\n",
       "     reason guardian  \n",
       "390  course    other  \n",
       "391  course   mother  \n",
       "392  course    other  \n",
       "393  course   mother  \n",
       "394  course   father  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and capitalize Mjob and Fjob."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Services</td>\n",
       "      <td>Services</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Services</td>\n",
       "      <td>Services</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Other</td>\n",
       "      <td>At_home</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "390     MS   M   20       U     LE3       A     2     2  Services  Services   \n",
       "391     MS   M   17       U     LE3       T     3     1  Services  Services   \n",
       "392     MS   M   21       R     GT3       T     1     1     Other     Other   \n",
       "393     MS   M   18       R     LE3       T     3     2  Services     Other   \n",
       "394     MS   M   19       U     LE3       T     1     1     Other   At_home   \n",
       "\n",
       "     reason guardian  \n",
       "390  course    other  \n",
       "391  course   mother  \n",
       "392  course    other  \n",
       "393  course   mother  \n",
       "394  course   father  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a function called majority that returns a boolean value to a new column called legal_drinker (Consider majority as older than 17 years old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>legal_drinker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Health</td>\n",
       "      <td>Services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  At_home   Teacher   \n",
       "1     GP   F   17       U     GT3       T     1     1  At_home     Other   \n",
       "2     GP   F   15       U     LE3       T     1     1  At_home     Other   \n",
       "3     GP   F   15       U     GT3       T     4     2   Health  Services   \n",
       "4     GP   F   16       U     GT3       T     3     3    Other     Other   \n",
       "\n",
       "   reason guardian legal_drinker  \n",
       "0  course   mother          True  \n",
       "1  course   father         False  \n",
       "2   other   mother         False  \n",
       "3    home   mother         False  \n",
       "4    home   father         False  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Multiply every number of the dataset by 10. \n",
    "##### I know this makes no sense, don't forget it is just an exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>legal_drinker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>180</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>170</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>Health</td>\n",
       "      <td>Services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>reputation</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>170</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "0     GP   F  180       U     GT3       A    40    40   At_home   Teacher   \n",
       "1     GP   F  170       U     GT3       T    10    10   At_home     Other   \n",
       "2     GP   F  150       U     LE3       T    10    10   At_home     Other   \n",
       "3     GP   F  150       U     GT3       T    40    20    Health  Services   \n",
       "4     GP   F  160       U     GT3       T    30    30     Other     Other   \n",
       "5     GP   M  160       U     LE3       T    40    30  Services     Other   \n",
       "6     GP   M  160       U     LE3       T    20    20     Other     Other   \n",
       "7     GP   F  170       U     GT3       A    40    40     Other   Teacher   \n",
       "8     GP   M  150       U     LE3       A    30    20  Services     Other   \n",
       "9     GP   M  150       U     GT3       T    30    40     Other     Other   \n",
       "\n",
       "       reason guardian legal_drinker  \n",
       "0      course   mother          True  \n",
       "1      course   father         False  \n",
       "2       other   mother         False  \n",
       "3        home   mother         False  \n",
       "4        home   father         False  \n",
       "5  reputation   mother         False  \n",
       "6        home   mother         False  \n",
       "7        home   mother         False  \n",
       "8        home   mother         False  \n",
       "9        home   mother         False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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================================================
FILE: 04_Apply/Students_Alcohol_Consumption/student-mat.csv
================================================
school,sex,age,address,famsize,Pstatus,Medu,Fedu,Mjob,Fjob,reason,guardian,traveltime,studytime,failures,schoolsup,famsup,paid,activities,nursery,higher,internet,romantic,famrel,freetime,goout,Dalc,Walc,health,absences,G1,G2,G3
GP,F,18,U,GT3,A,4,4,at_home,teacher,course,mother,2,2,0,yes,no,no,no,yes,yes,no,no,4,3,4,1,1,3,6,5,6,6
GP,F,17,U,GT3,T,1,1,at_home,other,course,father,1,2,0,no,yes,no,no,no,yes,yes,no,5,3,3,1,1,3,4,5,5,6
GP,F,15,U,LE3,T,1,1,at_home,other,other,mother,1,2,3,yes,no,yes,no,yes,yes,yes,no,4,3,2,2,3,3,10,7,8,10
GP,F,15,U,GT3,T,4,2,health,services,home,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,yes,3,2,2,1,1,5,2,15,14,15
GP,F,16,U,GT3,T,3,3,other,other,home,father,1,2,0,no,yes,yes,no,yes,yes,no,no,4,3,2,1,2,5,4,6,10,10
GP,M,16,U,LE3,T,4,3,services,other,reputation,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,5,4,2,1,2,5,10,15,15,15
GP,M,16,U,LE3,T,2,2,other,other,home,mother,1,2,0,no,no,no,no,yes,yes,yes,no,4,4,4,1,1,3,0,12,12,11
GP,F,17,U,GT3,A,4,4,other,teacher,home,mother,2,2,0,yes,yes,no,no,yes,yes,no,no,4,1,4,1,1,1,6,6,5,6
GP,M,15,U,LE3,A,3,2,services,other,home,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,2,2,1,1,1,0,16,18,19
GP,M,15,U,GT3,T,3,4,other,other,home,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,5,5,1,1,1,5,0,14,15,15
GP,F,15,U,GT3,T,4,4,teacher,health,reputation,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,3,3,3,1,2,2,0,10,8,9
GP,F,15,U,GT3,T,2,1,services,other,reputation,father,3,3,0,no,yes,no,yes,yes,yes,yes,no,5,2,2,1,1,4,4,10,12,12
GP,M,15,U,LE3,T,4,4,health,services,course,father,1,1,0,no,yes,yes,yes,yes,yes,yes,no,4,3,3,1,3,5,2,14,14,14
GP,M,15,U,GT3,T,4,3,teacher,other,course,mother,2,2,0,no,yes,yes,no,yes,yes,yes,no,5,4,3,1,2,3,2,10,10,11
GP,M,15,U,GT3,A,2,2,other,other,home,other,1,3,0,no,yes,no,no,yes,yes,yes,yes,4,5,2,1,1,3,0,14,16,16
GP,F,16,U,GT3,T,4,4,health,other,home,mother,1,1,0,no,yes,no,no,yes,yes,yes,no,4,4,4,1,2,2,4,14,14,14
GP,F,16,U,GT3,T,4,4,services,services,reputation,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,no,3,2,3,1,2,2,6,13,14,14
GP,F,16,U,GT3,T,3,3,other,other,reputation,mother,3,2,0,yes,yes,no,yes,yes,yes,no,no,5,3,2,1,1,4,4,8,10,10
GP,M,17,U,GT3,T,3,2,services,services,course,mother,1,1,3,no,yes,no,yes,yes,yes,yes,no,5,5,5,2,4,5,16,6,5,5
GP,M,16,U,LE3,T,4,3,health,other,home,father,1,1,0,no,no,yes,yes,yes,yes,yes,no,3,1,3,1,3,5,4,8,10,10
GP,M,15,U,GT3,T,4,3,teacher,other,reputation,mother,1,2,0,no,no,no,no,yes,yes,yes,no,4,4,1,1,1,1,0,13,14,15
GP,M,15,U,GT3,T,4,4,health,health,other,father,1,1,0,no,yes,yes,no,yes,yes,yes,no,5,4,2,1,1,5,0,12,15,15
GP,M,16,U,LE3,T,4,2,teacher,other,course,mother,1,2,0,no,no,no,yes,yes,yes,yes,no,4,5,1,1,3,5,2,15,15,16
GP,M,16,U,LE3,T,2,2,other,other,reputation,mother,2,2,0,no,yes,no,yes,yes,yes,yes,no,5,4,4,2,4,5,0,13,13,12
GP,F,15,R,GT3,T,2,4,services,health,course,mother,1,3,0,yes,yes,yes,yes,yes,yes,yes,no,4,3,2,1,1,5,2,10,9,8
GP,F,16,U,GT3,T,2,2,services,services,home,mother,1,1,2,no,yes,yes,no,no,yes,yes,no,1,2,2,1,3,5,14,6,9,8
GP,M,15,U,GT3,T,2,2,other,other,home,mother,1,1,0,no,yes,yes,no,yes,yes,yes,no,4,2,2,1,2,5,2,12,12,11
GP,M,15,U,GT3,T,4,2,health,services,other,mother,1,1,0,no,no,yes,no,yes,yes,yes,no,2,2,4,2,4,1,4,15,16,15
GP,M,16,U,LE3,A,3,4,services,other,home,mother,1,2,0,yes,yes,no,yes,yes,yes,yes,no,5,3,3,1,1,5,4,11,11,11
GP,M,16,U,GT3,T,4,4,teacher,teacher,home,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,yes,4,4,5,5,5,5,16,10,12,11
GP,M,15,U,GT3,T,4,4,health,services,home,mother,1,2,0,no,yes,yes,no,no,yes,yes,no,5,4,2,3,4,5,0,9,11,12
GP,M,15,U,GT3,T,4,4,services,services,reputation,mother,2,2,0,no,yes,no,yes,yes,yes,yes,no,4,3,1,1,1,5,0,17,16,17
GP,M,15,R,GT3,T,4,3,teacher,at_home,course,mother,1,2,0,no,yes,no,yes,yes,yes,yes,yes,4,5,2,1,1,5,0,17,16,16
GP,M,15,U,LE3,T,3,3,other,other,course,mother,1,2,0,no,no,no,yes,no,yes,yes,no,5,3,2,1,1,2,0,8,10,12
GP,M,16,U,GT3,T,3,2,other,other,home,mother,1,1,0,no,yes,yes,no,no,yes,yes,no,5,4,3,1,1,5,0,12,14,15
GP,F,15,U,GT3,T,2,3,other,other,other,father,2,1,0,no,yes,no,yes,yes,yes,no,no,3,5,1,1,1,5,0,8,7,6
GP,M,15,U,LE3,T,4,3,teacher,services,home,mother,1,3,0,no,yes,no,yes,yes,yes,yes,no,5,4,3,1,1,4,2,15,16,18
GP,M,16,R,GT3,A,4,4,other,teacher,reputation,mother,2,3,0,no,yes,no,yes,yes,yes,yes,yes,2,4,3,1,1,5,7,15,16,15
GP,F,15,R,GT3,T,3,4,services,health,course,mother,1,3,0,yes,yes,yes,yes,yes,yes,yes,no,4,3,2,1,1,5,2,12,12,11
GP,F,15,R,GT3,T,2,2,at_home,other,reputation,mother,1,1,0,yes,yes,yes,yes,yes,yes,no,no,4,3,1,1,1,2,8,14,13,13
GP,F,16,U,LE3,T,2,2,other,other,home,mother,2,2,1,no,yes,no,yes,no,yes,yes,yes,3,3,3,1,2,3,25,7,10,11
GP,M,15,U,LE3,T,4,4,teacher,other,home,other,1,1,0,no,yes,no,no,no,yes,yes,yes,5,4,3,2,4,5,8,12,12,12
GP,M,15,U,GT3,T,4,4,services,teacher,course,father,1,2,0,no,yes,no,yes,yes,yes,yes,no,4,3,3,1,1,5,2,19,18,18
GP,M,15,U,GT3,T,2,2,services,services,course,father,1,1,0,yes,yes,no,no,yes,yes,yes,no,5,4,1,1,1,1,0,8,8,11
GP,F,16,U,LE3,T,2,2,other,at_home,course,father,2,2,1,yes,no,no,yes,yes,yes,yes,no,4,3,3,2,2,5,14,10,10,9
GP,F,15,U,LE3,A,4,3,other,other,course,mother,1,2,0,yes,yes,yes,yes,yes,yes,yes,yes,5,2,2,1,1,5,8,8,8,6
GP,F,16,U,LE3,A,3,3,other,services,home,mother,1,2,0,no,yes,no,no,yes,yes,yes,no,2,3,5,1,4,3,12,11,12,11
GP,M,16,U,GT3,T,4,3,health,services,reputation,mother,1,4,0,no,no,no,yes,yes,yes,yes,no,4,2,2,1,1,2,4,19,19,20
GP,M,15,U,GT3,T,4,2,teacher,other,home,mother,1,2,0,no,yes,yes,no,yes,yes,no,no,4,3,3,2,2,5,2,15,15,14
GP,F,15,U,GT3,T,4,4,services,teacher,other,father,1,2,1,yes,yes,no,yes,no,yes,yes,no,4,4,4,1,1,3,2,7,7,7
GP,F,16,U,LE3,T,2,2,services,services,course,mother,3,2,0,no,yes,yes,no,yes,yes,yes,no,4,3,3,2,3,4,2,12,13,13
GP,F,15,U,LE3,T,4,2,health,other,other,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,3,3,1,1,5,2,11,13,13
GP,M,15,U,LE3,A,4,2,health,health,other,father,2,1,1,no,no,no,no,yes,yes,no,no,5,5,5,3,4,5,6,11,11,10
GP,F,15,U,GT3,T,4,4,services,services,course,mother,1,1,0,yes,yes,yes,no,yes,yes,yes,no,3,3,4,2,3,5,0,8,10,11
GP,F,15,U,LE3,A,3,3,other,other,other,mother,1,1,0,no,no,yes,no,yes,yes,yes,no,5,3,4,4,4,1,6,10,13,13
GP,F,16,U,GT3,A,2,1,other,other,other,mother,1,2,0,no,no,yes,yes,yes,yes,yes,yes,5,3,4,1,1,2,8,8,9,10
GP,F,15,U,GT3,A,4,3,services,services,reputation,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,4,3,2,1,1,1,0,14,15,15
GP,M,15,U,GT3,T,4,4,teacher,health,reputation,mother,1,2,0,no,yes,no,yes,yes,yes,no,no,3,2,2,1,1,5,4,14,15,15
GP,M,15,U,LE3,T,1,2,other,at_home,home,father,1,2,0,yes,yes,no,yes,yes,yes,yes,no,4,3,2,1,1,5,2,9,10,9
GP,F,16,U,GT3,T,4,2,services,other,course,mother,1,2,0,no,yes,no,no,yes,yes,yes,no,4,2,3,1,1,5,2,15,16,16
GP,F,16,R,GT3,T,4,4,health,teacher,other,mother,1,2,0,no,yes,no,yes,yes,yes,no,no,2,4,4,2,3,4,6,10,11,11
GP,F,16,U,GT3,T,1,1,services,services,course,father,4,1,0,yes,yes,no,yes,no,yes,yes,yes,5,5,5,5,5,5,6,10,8,11
GP,F,16,U,LE3,T,1,2,other,services,reputation,father,1,2,0,yes,no,no,yes,yes,yes,yes,no,4,4,3,1,1,1,4,8,10,9
GP,F,16,U,GT3,T,4,3,teacher,health,home,mother,1,3,0,yes,yes,yes,yes,yes,yes,yes,no,3,4,4,2,4,4,2,10,9,9
GP,F,15,U,LE3,T,4,3,services,services,reputation,father,1,2,0,yes,no,no,yes,yes,yes,yes,yes,4,4,4,2,4,2,0,10,10,10
GP,F,16,U,LE3,T,4,3,teacher,services,course,mother,3,2,0,no,yes,no,yes,yes,yes,yes,no,5,4,3,1,2,1,2,16,15,15
GP,M,15,U,GT3,A,4,4,other,services,reputation,mother,1,4,0,no,yes,no,yes,no,yes,yes,yes,1,3,3,5,5,3,4,13,13,12
GP,F,16,U,GT3,T,3,1,services,other,course,mother,1,4,0,yes,yes,yes,no,yes,yes,yes,no,4,3,3,1,2,5,4,7,7,6
GP,F,15,R,LE3,T,2,2,health,services,reputation,mother,2,2,0,yes,yes,yes,no,yes,yes,yes,no,4,1,3,1,3,4,2,8,9,8
GP,F,15,R,LE3,T,3,1,other,other,reputation,father,2,4,0,no,yes,no,no,no,yes,yes,no,4,4,2,2,3,3,12,16,16,16
GP,M,16,U,GT3,T,3,1,other,other,reputation,father,2,4,0,no,yes,yes,no,yes,yes,yes,no,4,3,2,1,1,5,0,13,15,15
GP,M,15,U,GT3,T,4,2,other,other,course,mother,1,4,0,no,no,no,no,yes,yes,yes,no,3,3,3,1,1,3,0,10,10,10
GP,F,15,R,GT3,T,1,1,other,other,reputation,mother,1,2,2,yes,yes,no,no,no,yes,yes,yes,3,3,4,2,4,5,2,8,6,5
GP,M,16,U,GT3,T,3,1,other,other,reputation,mother,1,1,0,no,no,no,yes,yes,yes,no,no,5,3,2,2,2,5,2,12,12,14
GP,F,16,U,GT3,T,3,3,other,services,home,mother,1,2,0,yes,yes,yes,yes,yes,yes,yes,no,4,3,3,2,4,5,54,11,12,11
GP,M,15,U,GT3,T,4,3,teacher,other,home,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,4,3,3,2,3,5,6,9,9,10
GP,M,15,U,GT3,T,4,0,teacher,other,course,mother,2,4,0,no,no,no,yes,yes,yes,yes,no,3,4,3,1,1,1,8,11,11,10
GP,F,16,U,GT3,T,2,2,other,other,reputation,mother,1,4,0,no,no,yes,no,yes,yes,yes,yes,5,2,3,1,3,3,0,11,11,11
GP,M,17,U,GT3,T,2,1,other,other,home,mother,2,1,3,yes,yes,no,yes,yes,no,yes,no,4,5,1,1,1,3,2,8,8,10
GP,F,16,U,GT3,T,3,4,at_home,other,course,mother,1,2,0,no,yes,no,no,yes,yes,yes,no,2,4,3,1,2,3,12,5,5,5
GP,M,15,U,GT3,T,2,3,other,services,course,father,1,1,0,yes,yes,yes,yes,no,yes,yes,yes,3,2,2,1,3,3,2,10,12,12
GP,M,15,U,GT3,T,2,3,other,other,home,mother,1,3,0,yes,no,yes,no,no,yes,yes,no,5,3,2,1,2,5,4,11,10,11
GP,F,15,U,LE3,T,3,2,services,other,reputation,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,4,4,1,1,5,10,7,6,6
GP,M,15,U,LE3,T,2,2,services,services,home,mother,2,2,0,no,no,yes,yes,yes,yes,yes,no,5,3,3,1,3,4,4,15,15,15
GP,F,15,U,GT3,T,1,1,other,other,home,father,1,2,0,no,yes,no,yes,no,yes,yes,no,4,3,2,2,3,4,2,9,10,10
GP,F,15,U,GT3,T,4,4,services,services,reputation,father,2,2,2,no,no,yes,no,yes,yes,yes,yes,4,4,4,2,3,5,6,7,9,8
GP,F,16,U,LE3,T,2,2,at_home,other,course,mother,1,2,0,no,yes,no,no,yes,yes,no,no,4,3,4,1,2,2,4,8,7,6
GP,F,15,U,GT3,T,4,2,other,other,reputation,mother,1,3,0,no,yes,no,yes,yes,yes,yes,no,5,3,3,1,3,1,4,13,14,14
GP,M,16,U,GT3,T,2,2,services,other,reputation,father,2,2,1,no,no,yes,yes,no,yes,yes,no,4,4,2,1,1,3,12,11,10,10
GP,M,16,U,LE3,A,4,4,teacher,health,reputation,mother,1,2,0,no,yes,no,no,yes,yes,no,no,4,1,3,3,5,5,18,8,6,7
GP,F,16,U,GT3,T,3,3,other,other,home,mother,1,3,0,no,yes,yes,no,yes,yes,yes,yes,4,3,3,1,3,4,0,7,7,8
GP,F,15,U,GT3,T,4,3,services,other,reputation,mother,1,1,0,no,no,yes,yes,yes,yes,yes,no,4,5,5,1,3,1,4,16,17,18
GP,F,16,U,LE3,T,3,1,other,other,home,father,1,2,0,yes,yes,no,no,yes,yes,no,no,3,3,3,2,3,2,4,7,6,6
GP,F,16,U,GT3,T,4,2,teacher,services,home,mother,2,2,0,no,yes,yes,yes,yes,yes,yes,no,5,3,3,1,1,1,0,11,10,10
GP,M,15,U,LE3,T,2,2,services,health,reputation,mother,1,4,0,no,yes,no,yes,yes,yes,yes,no,4,3,4,1,1,4,6,11,13,14
GP,F,15,R,GT3,T,1,1,at_home,other,home,mother,2,4,1,yes,yes,yes,yes,yes,yes,yes,no,3,1,2,1,1,1,2,7,10,10
GP,M,16,R,GT3,T,4,3,services,other,reputation,mother,2,1,0,yes,yes,no,yes,no,yes,yes,no,3,3,3,1,1,4,2,11,15,15
GP,F,16,U,GT3,T,2,1,other,other,course,mother,1,2,0,no,yes,yes,no,yes,yes,no,yes,4,3,5,1,1,5,2,8,9,10
GP,F,16,U,GT3,T,4,4,other,other,reputation,mother,1,1,0,no,no,no,yes,no,yes,yes,no,5,3,4,1,2,1,6,11,14,14
GP,F,16,U,GT3,T,4,3,other,at_home,course,mother,1,3,0,yes,yes,yes,no,yes,yes,yes,no,5,3,5,1,1,3,0,7,9,8
GP,M,16,U,GT3,T,4,4,services,services,other,mother,1,1,0,yes,yes,yes,yes,yes,yes,yes,no,4,5,5,5,5,4,14,7,7,5
GP,M,16,U,GT3,T,4,4,services,teacher,other,father,1,3,0,no,yes,no,yes,yes,yes,yes,yes,4,4,3,1,1,4,0,16,17,17
GP,M,15,U,GT3,T,4,4,services,other,course,mother,1,1,0,no,yes,no,yes,no,yes,yes,no,5,3,3,1,1,5,4,10,13,14
GP,F,15,U,GT3,T,3,2,services,other,home,mother,2,2,0,yes,yes,yes,no,yes,yes,yes,no,4,3,5,1,1,2,26,7,6,6
GP,M,15,U,GT3,A,3,4,services,other,course,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,5,4,4,1,1,1,0,16,18,18
GP,F,15,U,GT3,A,3,3,other,health,reputation,father,1,4,0,yes,no,no,no,yes,yes,no,no,4,3,3,1,1,4,10,10,11,11
GP,F,15,U,GT3,T,2,2,other,other,course,mother,1,4,0,yes,yes,yes,no,yes,yes,yes,no,5,1,2,1,1,3,8,7,8,8
GP,M,16,U,GT3,T,3,3,services,other,home,father,1,3,0,no,yes,no,yes,yes,yes,yes,no,5,3,3,1,1,5,2,16,18,18
GP,M,15,R,GT3,T,4,4,other,other,home,father,4,4,0,no,yes,yes,yes,yes,yes,yes,yes,1,3,5,3,5,1,6,10,13,13
GP,F,16,U,LE3,T,4,4,health,health,other,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,yes,5,4,5,1,1,4,4,14,15,16
GP,M,15,U,LE3,A,4,4,teacher,teacher,course,mother,1,1,0,no,no,no,yes,yes,yes,yes,no,5,5,3,1,1,4,6,18,19,19
GP,F,16,R,GT3,T,3,3,services,other,reputation,father,1,3,1,yes,yes,no,yes,yes,yes,yes,no,4,1,2,1,1,2,0,7,10,10
GP,F,16,U,GT3,T,2,2,at_home,other,home,mother,1,2,1,yes,no,no,yes,yes,yes,yes,no,3,1,2,1,1,5,6,10,13,13
GP,M,15,U,LE3,T,4,2,teacher,other,course,mother,1,1,0,no,no,no,no,yes,yes,yes,no,3,5,2,1,1,3,10,18,19,19
GP,M,15,R,GT3,T,2,1,health,services,reputation,mother,1,2,0,no,no,no,yes,yes,yes,yes,yes,5,4,2,1,1,5,8,9,9,9
GP,M,16,U,GT3,T,4,4,teacher,teacher,course,father,1,2,0,no,yes,no,yes,yes,yes,yes,no,5,4,4,1,2,5,2,15,15,16
GP,M,15,U,GT3,T,4,4,other,teacher,reputation,father,2,2,0,no,yes,no,yes,yes,yes,no,no,4,4,3,1,1,2,2,11,13,14
GP,M,16,U,GT3,T,3,3,other,services,home,father,2,1,0,no,no,no,yes,yes,yes,yes,no,5,4,2,1,1,5,0,13,14,13
GP,M,17,R,GT3,T,1,3,other,other,course,father,3,2,1,no,yes,no,yes,yes,yes,yes,no,5,2,4,1,4,5,20,9,7,8
GP,M,15,U,GT3,T,3,4,other,other,reputation,father,1,1,0,no,no,no,no,yes,yes,yes,no,3,4,3,1,2,4,6,14,13,13
GP,F,15,U,GT3,T,1,2,at_home,services,course,mother,1,2,0,no,no,no,no,no,yes,yes,no,3,2,3,1,2,1,2,16,15,15
GP,M,15,U,GT3,T,2,2,services,services,home,father,1,4,0,no,yes,yes,yes,yes,yes,yes,no,5,5,4,1,2,5,6,16,14,15
GP,F,16,U,LE3,T,2,4,other,health,course,father,2,2,0,no,yes,yes,yes,yes,yes,yes,yes,4,2,2,1,2,5,2,13,13,13
GP,M,16,U,GT3,T,4,4,health,other,course,mother,1,1,0,no,yes,no,yes,yes,yes,yes,no,3,4,4,1,4,5,18,14,11,13
GP,F,16,U,GT3,T,2,2,other,other,home,mother,1,2,0,no,no,yes,no,yes,yes,yes,yes,5,4,4,1,1,5,0,8,7,8
GP,M,15,U,GT3,T,3,4,services,services,home,father,1,1,0,yes,no,no,no,yes,yes,yes,no,5,5,5,3,2,5,0,13,13,12
GP,F,15,U,LE3,A,3,4,other,other,home,mother,1,2,0,yes,no,no,yes,yes,yes,yes,yes,5,3,2,1,1,1,0,7,10,11
GP,F,19,U,GT3,T,0,1,at_home,other,course,other,1,2,3,no,yes,no,no,no,no,no,no,3,4,2,1,1,5,2,7,8,9
GP,M,18,R,GT3,T,2,2,services,other,reputation,mother,1,1,2,no,yes,no,yes,yes,yes,yes,no,3,3,3,1,2,4,0,7,4,0
GP,M,16,R,GT3,T,4,4,teacher,teacher,course,mother,1,1,0,no,no,yes,yes,yes,yes,yes,no,3,5,5,2,5,4,8,18,18,18
GP,F,15,R,GT3,T,3,4,services,teacher,course,father,2,3,2,no,yes,no,no,yes,yes,yes,yes,4,2,2,2,2,5,0,12,0,0
GP,F,15,U,GT3,T,1,1,at_home,other,course,mother,3,1,0,no,yes,no,yes,no,yes,yes,yes,4,3,3,1,2,4,0,8,0,0
GP,F,17,U,LE3,T,2,2,other,other,course,father,1,1,0,no,yes,no,no,yes,yes,yes,yes,3,4,4,1,3,5,12,10,13,12
GP,F,16,U,GT3,A,3,4,services,other,course,father,1,1,0,no,no,no,no,yes,yes,yes,no,3,2,1,1,4,5,16,12,11,11
GP,M,15,R,GT3,T,3,4,at_home,teacher,course,mother,4,2,0,no,yes,no,no,yes,yes,no,yes,5,3,3,1,1,5,0,9,0,0
GP,F,15,U,GT3,T,4,4,services,at_home,course,mother,1,3,0,no,yes,no,yes,yes,yes,yes,yes,4,3,3,1,1,5,0,11,0,0
GP,M,17,R,GT3,T,3,4,at_home,other,course,mother,3,2,0,no,no,no,no,yes,yes,no,no,5,4,5,2,4,5,0,10,0,0
GP,F,16,U,GT3,A,3,3,other,other,course,other,2,1,2,no,yes,no,yes,no,yes,yes,yes,4,3,2,1,1,5,0,4,0,0
GP,M,16,U,LE3,T,1,1,services,other,course,mother,1,2,1,no,no,no,no,yes,yes,no,yes,4,4,4,1,3,5,0,14,12,12
GP,F,15,U,GT3,T,4,4,teacher,teacher,course,mother,2,1,0,no,no,no,yes,yes,yes,yes,no,4,3,2,1,1,5,0,16,16,15
GP,M,15,U,GT3,T,4,3,teacher,services,course,father,2,4,0,yes,yes,no,no,yes,yes,yes,no,2,2,2,1,1,3,0,7,9,0
GP,M,16,U,LE3,T,2,2,services,services,reputation,father,2,1,2,no,yes,no,yes,yes,yes,yes,no,2,3,3,2,2,2,8,9,9,9
GP,F,15,U,GT3,T,4,4,teacher,services,course,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,no,4,2,2,1,1,5,2,9,11,11
GP,F,16,U,LE3,T,1,1,at_home,at_home,course,mother,1,1,0,no,no,no,no,yes,yes,yes,no,3,4,4,3,3,1,2,14,14,13
GP,M,17,U,GT3,T,2,1,other,other,home,mother,1,1,3,no,yes,no,no,yes,yes,yes,no,5,4,5,1,2,5,0,5,0,0
GP,F,15,U,GT3,T,1,1,other,services,course,father,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,4,2,1,2,5,0,8,11,11
GP,F,15,U,GT3,T,3,2,health,services,home,father,1,2,3,no,yes,no,no,yes,yes,yes,no,3,3,2,1,1,3,0,6,7,0
GP,F,15,U,GT3,T,1,2,at_home,other,course,mother,1,2,0,no,yes,yes,no,no,yes,yes,no,4,3,2,1,1,5,2,10,11,11
GP,M,16,U,GT3,T,4,4,teacher,teacher,course,mother,1,1,0,no,yes,no,no,yes,no,yes,yes,3,3,2,2,1,5,0,7,6,0
GP,M,15,U,LE3,A,2,1,services,other,course,mother,4,1,3,no,no,no,no,yes,yes,yes,no,4,5,5,2,5,5,0,8,9,10
GP,M,18,U,LE3,T,1,1,other,other,course,mother,1,1,3,no,no,no,no,yes,no,yes,yes,2,3,5,2,5,4,0,6,5,0
GP,M,16,U,LE3,T,2,1,at_home,other,course,mother,1,1,1,no,no,no,yes,yes,yes,no,yes,4,4,4,3,5,5,6,12,13,14
GP,F,15,R,GT3,T,3,3,services,services,reputation,other,2,3,2,no,yes,yes,yes,yes,yes,yes,yes,4,2,1,2,3,3,8,10,10,10
GP,M,19,U,GT3,T,3,2,services,at_home,home,mother,1,1,3,no,yes,no,no,yes,no,yes,yes,4,5,4,1,1,4,0,5,0,0
GP,F,17,U,GT3,T,4,4,other,teacher,course,mother,1,1,0,yes,yes,no,no,yes,yes,no,yes,4,2,1,1,1,4,0,11,11,12
GP,M,15,R,GT3,T,2,3,at_home,services,course,mother,1,2,0,yes,no,yes,yes,yes,yes,no,no,4,4,4,1,1,1,2,11,8,8
GP,M,17,R,LE3,T,1,2,other,other,reputation,mother,1,1,0,no,no,no,no,yes,yes,no,no,2,2,2,3,3,5,8,16,12,13
GP,F,18,R,GT3,T,1,1,at_home,other,course,mother,3,1,3,no,yes,no,yes,no,yes,no,no,5,2,5,1,5,4,6,9,8,10
GP,M,16,R,GT3,T,2,2,at_home,other,course,mother,3,1,0,no,no,no,no,no,yes,no,no,4,2,2,1,2,3,2,17,15,15
GP,M,16,U,GT3,T,3,3,other,services,course,father,1,2,1,no,yes,yes,no,yes,yes,yes,yes,4,5,5,4,4,5,4,10,12,12
GP,M,17,R,LE3,T,2,1,at_home,other,course,mother,2,1,2,no,no,no,yes,yes,no,yes,yes,3,3,2,2,2,5,0,7,6,0
GP,M,15,R,GT3,T,3,2,other,other,course,mother,2,2,2,yes,yes,no,no,yes,yes,yes,yes,4,4,4,1,4,3,6,5,9,7
GP,M,16,U,LE3,T,1,2,other,other,course,mother,2,1,1,no,no,no,yes,yes,yes,no,no,4,4,4,2,4,5,0,7,0,0
GP,M,17,U,GT3,T,1,3,at_home,services,course,father,1,1,0,no,no,no,no,yes,no,yes,no,5,3,3,1,4,2,2,10,10,10
GP,M,17,R,LE3,T,1,1,other,services,course,mother,4,2,3,no,no,no,yes,yes,no,no,yes,5,3,5,1,5,5,0,5,8,7
GP,M,16,U,GT3,T,3,2,services,services,course,mother,2,1,1,no,yes,no,yes,no,no,no,no,4,5,2,1,1,2,16,12,11,12
GP,M,16,U,GT3,T,2,2,other,other,course,father,1,2,0,no,no,no,no,yes,no,yes,no,4,3,5,2,4,4,4,10,10,10
GP,F,16,U,GT3,T,4,2,health,services,home,father,1,2,0,no,no,yes,no,yes,yes,yes,yes,4,2,3,1,1,3,0,14,15,16
GP,F,16,U,GT3,T,2,2,other,other,home,mother,1,2,0,no,yes,yes,no,no,yes,yes,no,5,1,5,1,1,4,0,6,7,0
GP,F,16,U,GT3,T,4,4,health,health,reputation,mother,1,2,0,no,yes,yes,no,yes,yes,yes,yes,4,4,2,1,1,3,0,14,14,14
GP,M,16,U,GT3,T,3,4,other,other,course,father,3,1,2,no,yes,no,yes,no,yes,yes,no,3,4,5,2,4,2,0,6,5,0
GP,M,16,U,GT3,T,1,0,other,other,reputation,mother,2,2,0,no,yes,yes,yes,yes,yes,yes,yes,4,3,2,1,1,3,2,13,15,16
GP,M,17,U,LE3,T,4,4,teacher,other,reputation,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,4,4,4,1,3,5,0,13,11,10
GP,F,16,U,GT3,T,1,3,at_home,services,home,mother,1,2,3,no,no,no,yes,no,yes,yes,yes,4,3,5,1,1,3,0,8,7,0
GP,F,16,U,LE3,T,3,3,other,other,reputation,mother,2,2,0,no,yes,yes,yes,yes,yes,yes,no,4,4,5,1,1,4,4,10,11,9
GP,M,17,U,LE3,T,4,3,teacher,other,course,mother,2,2,0,no,no,yes,yes,yes,yes,yes,no,4,4,4,4,4,4,4,10,9,9
GP,F,16,U,GT3,T,2,2,services,other,reputation,mother,2,2,0,no,no,yes,yes,no,yes,yes,no,3,4,4,1,4,5,2,13,13,11
GP,M,17,U,GT3,T,3,3,other,other,reputation,father,1,2,0,no,no,no,yes,no,yes,yes,no,4,3,4,1,4,4,4,6,5,6
GP,M,16,R,GT3,T,4,2,teacher,services,other,mother,1,1,0,no,yes,no,yes,yes,yes,yes,yes,4,3,3,3,4,3,10,10,8,9
GP,M,17,U,GT3,T,4,3,other,other,course,mother,1,2,0,no,yes,no,yes,yes,yes,yes,yes,5,2,3,1,1,2,4,10,10,11
GP,M,16,U,GT3,T,4,3,teacher,other,home,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,3,4,3,2,3,3,10,9,8,8
GP,M,16,U,GT3,T,3,3,services,other,home,mother,1,2,0,no,no,yes,yes,yes,yes,yes,yes,4,2,3,1,2,3,2,12,13,12
GP,F,17,U,GT3,T,2,4,services,services,reputation,father,1,2,0,no,yes,no,yes,yes,yes,no,no,5,4,2,2,3,5,0,16,17,17
GP,F,17,U,LE3,T,3,3,other,other,reputation,mother,1,2,0,no,yes,no,yes,yes,yes,yes,yes,5,3,3,2,3,1,56,9,9,8
GP,F,16,U,GT3,T,3,2,other,other,reputation,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,1,2,2,1,2,1,14,12,13,12
GP,M,17,U,GT3,T,3,3,services,services,other,mother,1,2,0,no,yes,no,yes,yes,yes,yes,yes,4,3,4,2,3,4,12,12,12,11
GP,M,16,U,GT3,T,1,2,services,services,other,mother,1,1,0,no,yes,yes,yes,yes,yes,yes,yes,3,3,3,1,2,3,2,11,12,11
GP,M,16,U,LE3,T,2,1,other,other,course,mother,1,2,0,no,no,yes,yes,yes,yes,yes,yes,4,2,3,1,2,5,0,15,15,15
GP,F,17,U,GT3,A,3,3,health,other,reputation,mother,1,2,0,no,yes,no,no,no,yes,yes,yes,3,3,3,1,3,3,6,8,7,9
GP,M,17,R,GT3,T,1,2,at_home,other,home,mother,1,2,0,no,no,no,no,yes,yes,no,no,3,1,3,1,5,3,4,8,9,10
GP,F,16,U,GT3,T,2,3,services,services,course,mother,1,2,0,no,no,no,no,yes,yes,yes,no,4,3,3,1,1,2,10,11,12,13
GP,F,17,U,GT3,T,1,1,at_home,services,course,mother,1,2,0,no,no,no,yes,yes,yes,yes,no,5,3,3,1,1,3,0,8,8,9
GP,M,17,U,GT3,T,1,2,at_home,services,other,other,2,2,0,no,no,yes,yes,no,yes,yes,no,4,4,4,4,5,5,12,7,8,8
GP,M,16,R,GT3,T,3,3,services,services,reputation,mother,1,1,0,no,yes,no,yes,yes,yes,yes,no,4,3,2,3,4,5,8,8,9,10
GP,M,16,U,GT3,T,2,3,other,other,home,father,2,1,0,no,no,no,no,yes,yes,yes,no,5,3,3,1,1,3,0,13,14,14
GP,F,17,U,LE3,T,2,4,services,services,course,father,1,2,0,no,no,no,yes,yes,yes,yes,yes,4,3,2,1,1,5,0,14,15,15
GP,M,17,U,GT3,T,4,4,services,teacher,home,mother,1,1,0,no,no,no,no,yes,yes,yes,no,5,2,3,1,2,5,4,17,15,16
GP,M,16,R,LE3,T,3,3,teacher,other,home,father,3,1,0,no,yes,yes,yes,yes,yes,yes,no,3,3,4,3,5,3,8,9,9,10
GP,F,17,U,GT3,T,4,4,services,teacher,home,mother,2,1,1,no,yes,no,no,yes,yes,yes,no,4,2,4,2,3,2,24,18,18,18
GP,F,16,U,LE3,T,4,4,teacher,teacher,reputation,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,5,2,1,2,3,0,9,9,10
GP,F,16,U,GT3,T,4,3,health,other,home,mother,1,2,0,no,yes,no,yes,yes,yes,yes,no,4,3,5,1,5,2,2,16,16,16
GP,F,16,U,GT3,T,2,3,other,other,reputation,mother,1,2,0,yes,yes,yes,yes,yes,yes,no,no,4,4,3,1,3,4,6,8,10,10
GP,F,17,U,GT3,T,1,1,other,other,course,mother,1,2,0,no,yes,yes,no,no,yes,no,no,4,4,4,1,3,1,4,9,9,10
GP,F,17,R,GT3,T,2,2,other,other,reputation,mother,1,1,0,no,yes,no,no,yes,yes,yes,no,5,3,2,1,2,3,18,7,6,6
GP,F,16,R,GT3,T,2,2,services,services,reputation,mother,2,4,0,no,yes,yes,yes,no,yes,yes,no,5,3,5,1,1,5,6,10,10,11
GP,F,17,U,GT3,T,3,4,at_home,services,home,mother,1,3,1,no,yes,yes,no,yes,yes,yes,yes,4,4,3,3,4,5,28,10,9,9
GP,F,16,U,GT3,A,3,1,services,other,course,mother,1,2,3,no,yes,yes,no,yes,yes,yes,no,2,3,3,2,2,4,5,7,7,7
GP,F,16,U,GT3,T,4,3,teacher,other,other,mother,1,2,0,no,no,yes,yes,yes,yes,yes,yes,1,3,2,1,1,1,10,11,12,13
GP,F,16,U,GT3,T,1,1,at_home,other,home,mother,2,1,0,no,yes,yes,no,yes,yes,no,no,4,3,2,1,4,5,6,9,9,10
GP,F,17,R,GT3,T,4,3,teacher,other,reputation,mother,2,3,0,no,yes,yes,yes,yes,yes,yes,yes,4,4,2,1,1,4,6,7,7,7
GP,F,19,U,GT3,T,3,3,other,other,reputation,other,1,4,0,no,yes,yes,yes,yes,yes,yes,no,4,3,3,1,2,3,10,8,8,8
GP,M,17,U,LE3,T,4,4,services,other,home,mother,1,2,0,no,yes,yes,no,yes,yes,yes,yes,5,3,5,4,5,3,13,12,12,13
GP,F,16,U,GT3,A,2,2,other,other,reputation,mother,1,2,0,yes,yes,yes,no,yes,yes,yes,no,3,3,4,1,1,4,0,12,13,14
GP,M,18,U,GT3,T,2,2,services,other,home,mother,1,2,1,no,yes,yes,yes,yes,yes,yes,no,4,4,4,2,4,5,15,6,7,8
GP,F,17,R,LE3,T,4,4,services,other,other,mother,1,1,0,no,yes,yes,no,yes,yes,no,no,5,2,1,1,2,3,12,8,10,10
GP,F,17,U,LE3,T,3,2,other,other,reputation,mother,2,2,0,no,no,yes,no,yes,yes,yes,no,4,4,4,1,3,1,2,14,15,15
GP,F,17,U,GT3,T,4,3,other,other,reputation,mother,1,2,2,no,no,yes,no,yes,yes,yes,yes,3,4,5,2,4,1,22,6,6,4
GP,M,18,U,LE3,T,3,3,services,health,home,father,1,2,1,no,yes,yes,no,yes,yes,yes,no,3,2,4,2,4,4,13,6,6,8
GP,F,17,U,GT3,T,2,3,at_home,other,home,father,2,1,0,no,yes,yes,no,yes,yes,no,no,3,3,3,1,4,3,3,7,7,8
GP,F,17,U,GT3,T,2,2,at_home,at_home,course,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,no,4,3,3,1,1,4,4,9,10,10
GP,F,17,R,GT3,T,2,1,at_home,services,reputation,mother,2,2,0,no,yes,no,yes,yes,yes,yes,no,4,2,5,1,2,5,2,6,6,6
GP,F,17,U,GT3,T,1,1,at_home,other,reputation,mother,1,3,1,no,yes,no,yes,yes,yes,no,yes,4,3,4,1,1,5,0,6,5,0
GP,F,16,U,GT3,T,2,3,services,teacher,other,mother,1,2,0,yes,no,no,no,yes,yes,yes,no,2,3,1,1,1,3,2,16,16,17
GP,M,18,U,GT3,T,2,2,other,other,home,mother,2,2,0,no,yes,yes,no,yes,yes,yes,no,3,3,3,5,5,4,0,12,13,13
GP,F,16,U,GT3,T,4,4,teacher,services,home,mother,1,3,0,no,yes,no,yes,no,yes,yes,no,5,3,2,1,1,5,0,13,13,14
GP,F,18,R,GT3,T,3,1,other,other,reputation,mother,1,2,1,no,no,no,yes,yes,yes,yes,yes,5,3,3,1,1,4,16,9,8,7
GP,F,17,U,GT3,T,3,2,other,other,course,mother,1,2,0,no,no,no,yes,no,yes,yes,no,5,3,4,1,3,3,10,16,15,15
GP,M,17,U,LE3,T,2,3,services,services,reputation,father,1,2,0,no,yes,yes,no,no,yes,yes,no,5,3,3,1,3,3,2,12,11,12
GP,M,18,U,LE3,T,2,1,at_home,other,course,mother,4,2,0,yes,yes,yes,yes,yes,yes,yes,yes,4,3,2,4,5,3,14,10,8,9
GP,F,17,U,GT3,A,2,1,other,other,course,mother,2,3,0,no,no,no,yes,yes,yes,yes,yes,3,2,3,1,2,3,10,12,10,12
GP,F,17,U,LE3,T,4,3,health,other,reputation,father,1,2,0,no,no,no,yes,yes,yes,yes,yes,3,2,3,1,2,3,14,13,13,14
GP,M,17,R,GT3,T,2,2,other,other,course,father,2,2,0,no,yes,yes,yes,yes,yes,yes,no,4,5,2,1,1,1,4,11,11,11
GP,M,17,U,GT3,T,4,4,teacher,teacher,reputation,mother,1,2,0,yes,yes,no,yes,yes,yes,yes,yes,4,5,5,1,3,2,14,11,9,9
GP,M,16,U,GT3,T,4,4,health,other,reputation,father,1,2,0,no,yes,yes,yes,yes,yes,yes,no,4,2,4,2,4,1,2,14,13,13
GP,M,16,U,LE3,T,1,1,other,other,home,mother,2,2,0,no,yes,yes,no,yes,yes,yes,no,3,4,2,1,1,5,18,9,7,6
GP,M,16,U,GT3,T,3,2,at_home,other,reputation,mother,2,3,0,no,no,no,yes,yes,yes,yes,yes,5,3,3,1,3,2,10,11,9,10
GP,M,17,U,LE3,T,2,2,other,other,home,father,1,2,0,no,no,yes,yes,no,yes,yes,yes,4,4,2,5,5,4,4,14,13,13
GP,F,16,U,GT3,T,2,1,other,other,home,mother,1,1,0,no,no,no,no,yes,yes,yes,yes,4,5,2,1,1,5,20,13,12,12
GP,F,17,R,GT3,T,2,1,at_home,services,course,mother,3,2,0,no,no,no,yes,yes,yes,no,no,2,1,1,1,1,3,2,13,11,11
GP,M,18,U,GT3,T,2,2,other,services,reputation,father,1,2,1,no,no,no,no,yes,no,yes,no,5,5,4,3,5,2,0,7,7,0
GP,M,17,U,LE3,T,4,3,health,other,course,mother,2,2,0,no,no,no,yes,yes,yes,yes,yes,2,5,5,1,4,5,14,12,12,12
GP,M,17,R,LE3,A,4,4,teacher,other,course,mother,2,2,0,no,yes,yes,no,yes,yes,yes,no,3,3,3,2,3,4,2,10,11,12
GP,M,16,U,LE3,T,4,3,teacher,other,course,mother,1,1,0,no,no,no,yes,no,yes,yes,no,5,4,5,1,1,3,0,6,0,0
GP,M,16,U,GT3,T,4,4,services,services,course,mother,1,1,0,no,no,no,yes,yes,yes,yes,no,5,3,2,1,2,5,0,13,12,12
GP,F,18,U,GT3,T,2,1,other,other,course,other,2,3,0,no,yes,yes,no,no,yes,yes,yes,4,4,4,1,1,3,0,7,0,0
GP,M,16,U,GT3,T,2,1,other,other,course,mother,3,1,0,no,no,no,no,yes,yes,yes,no,4,3,3,1,1,4,6,18,18,18
GP,M,17,U,GT3,T,2,3,other,other,course,father,2,1,0,no,no,no,no,yes,yes,yes,no,5,2,2,1,1,2,4,12,12,13
GP,M,22,U,GT3,T,3,1,services,services,other,mother,1,1,3,no,no,no,no,no,no,yes,yes,5,4,5,5,5,1,16,6,8,8
GP,M,18,R,LE3,T,3,3,other,services,course,mother,1,2,1,no,yes,no,no,yes,yes,yes,yes,4,3,3,1,3,5,8,3,5,5
GP,M,16,U,GT3,T,0,2,other,other,other,mother,1,1,0,no,no,yes,no,no,yes,yes,no,4,3,2,2,4,5,0,13,15,15
GP,M,18,U,GT3,T,3,2,services,other,course,mother,2,1,1,no,no,no,no,yes,no,yes,no,4,4,5,2,4,5,0,6,8,8
GP,M,16,U,GT3,T,3,3,at_home,other,reputation,other,3,2,0,yes,yes,no,no,no,yes,yes,no,5,3,3,1,3,2,6,7,10,10
GP,M,18,U,GT3,T,2,1,services,services,other,mother,1,1,1,no,no,no,no,no,no,yes,no,3,2,5,2,5,5,4,6,9,8
GP,M,16,R,GT3,T,2,1,other,other,course,mother,2,1,0,no,no,no,yes,no,yes,no,no,3,3,2,1,3,3,0,8,9,8
GP,M,17,R,GT3,T,2,1,other,other,course,mother,1,1,0,no,no,no,no,no,yes,yes,no,4,4,2,2,4,5,0,8,12,12
GP,M,17,U,LE3,T,1,1,health,other,course,mother,2,1,1,no,yes,no,yes,yes,yes,yes,no,4,4,4,1,2,5,2,7,9,8
GP,F,17,U,LE3,T,4,2,teacher,services,reputation,mother,1,4,0,no,yes,yes,yes,yes,yes,yes,no,4,2,3,1,1,4,6,14,12,13
GP,M,19,U,LE3,A,4,3,services,at_home,reputation,mother,1,2,0,no,yes,no,no,yes,yes,yes,no,4,3,1,1,1,1,12,11,11,11
GP,M,18,U,GT3,T,2,1,other,other,home,mother,1,2,0,no,no,no,yes,yes,yes,yes,no,5,2,4,1,2,4,8,15,14,14
GP,F,17,U,LE3,T,2,2,services,services,course,father,1,4,0,no,no,yes,yes,yes,yes,yes,yes,3,4,1,1,1,2,0,10,9,0
GP,F,18,U,GT3,T,4,3,services,other,home,father,1,2,0,no,yes,yes,no,yes,yes,yes,yes,3,1,2,1,3,2,21,17,18,18
GP,M,18,U,GT3,T,4,3,teacher,other,course,mother,1,2,0,no,yes,yes,no,no,yes,yes,no,4,3,2,1,1,3,2,8,8,8
GP,M,18,R,GT3,T,3,2,other,other,course,mother,1,3,0,no,no,no,yes,no,yes,no,no,5,3,2,1,1,3,1,13,12,12
GP,F,17,U,GT3,T,3,3,other,other,home,mother,1,3,0,no,no,no,yes,no,yes,no,no,3,2,3,1,1,4,4,10,9,9
GP,F,18,U,GT3,T,2,2,at_home,services,home,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,yes,4,3,3,1,1,3,0,9,10,0
GP,M,18,R,LE3,A,3,4,other,other,reputation,mother,2,2,0,no,yes,yes,yes,yes,yes,yes,no,4,2,5,3,4,1,13,17,17,17
GP,M,17,U,GT3,T,3,1,services,other,other,mother,1,2,0,no,no,yes,yes,yes,yes,yes,yes,5,4,4,3,4,5,2,9,9,10
GP,F,18,R,GT3,T,4,4,teacher,other,reputation,mother,2,2,0,no,no,yes,yes,yes,yes,yes,no,4,3,4,2,2,4,8,12,10,11
GP,M,18,U,GT3,T,4,2,health,other,reputation,father,1,2,0,no,yes,yes,yes,yes,yes,yes,yes,5,4,5,1,3,5,10,10,9,10
GP,F,18,R,GT3,T,2,1,other,other,reputation,mother,2,2,0,no,yes,no,no,yes,no,yes,yes,4,3,5,1,2,3,0,6,0,0
GP,F,19,U,GT3,T,3,3,other,services,home,other,1,2,2,no,yes,yes,yes,yes,yes,yes,no,4,3,5,3,3,5,15,9,9,9
GP,F,18,U,GT3,T,2,3,other,services,reputation,father,1,4,0,no,yes,yes,yes,yes,yes,yes,yes,4,5,5,1,3,2,4,15,14,14
GP,F,18,U,LE3,T,1,1,other,other,home,mother,2,2,0,no,yes,yes,no,no,yes,no,no,4,4,3,1,1,3,2,11,11,11
GP,M,17,R,GT3,T,1,2,at_home,at_home,home,mother,1,2,0,no,yes,yes,yes,no,yes,no,yes,3,5,2,2,2,1,2,15,14,14
GP,F,17,U,GT3,T,2,4,at_home,health,reputation,mother,2,2,0,no,yes,yes,no,yes,yes,yes,yes,4,3,3,1,1,1,2,10,10,10
GP,F,17,U,LE3,T,2,2,services,other,course,mother,2,2,0,yes,yes,yes,no,yes,yes,yes,yes,4,4,4,2,3,5,6,12,12,12
GP,F,18,R,GT3,A,3,2,other,services,home,mother,2,2,0,no,no,no,no,no,no,yes,yes,4,1,1,1,1,5,75,10,9,9
GP,M,18,U,GT3,T,4,4,teacher,services,home,mother,2,1,0,no,no,yes,yes,yes,yes,yes,no,3,2,4,1,4,3,22,9,9,9
GP,F,18,U,GT3,T,4,4,health,health,reputation,father,1,2,1,yes,yes,no,yes,yes,yes,yes,yes,2,4,4,1,1,4,15,9,8,8
GP,M,18,U,LE3,T,4,3,teacher,services,course,mother,2,1,0,no,no,yes,yes,yes,yes,yes,no,4,2,3,1,2,1,8,10,11,10
GP,M,17,U,LE3,A,4,1,services,other,home,mother,2,1,0,no,no,yes,yes,yes,yes,yes,yes,4,5,4,2,4,5,30,8,8,8
GP,M,17,U,LE3,A,3,2,teacher,services,home,mother,1,1,1,no,no,no,no,yes,yes,yes,no,4,4,4,3,4,3,19,11,9,10
GP,F,18,R,LE3,T,1,1,at_home,other,reputation,mother,2,4,0,no,yes,yes,yes,yes,yes,no,no,5,2,2,1,1,3,1,12,12,12
GP,F,18,U,GT3,T,1,1,other,other,home,mother,2,2,0,yes,no,no,yes,yes,yes,yes,no,5,4,4,1,1,4,4,8,9,10
GP,F,17,U,GT3,T,2,2,other,other,course,mother,1,2,0,no,yes,no,no,no,yes,yes,no,5,4,5,1,2,5,4,10,9,11
GP,M,17,U,GT3,T,1,1,other,other,reputation,father,1,2,0,no,no,yes,no,no,yes,yes,no,4,3,3,1,2,4,2,12,10,11
GP,F,18,U,GT3,T,2,2,at_home,at_home,other,mother,1,3,0,no,yes,yes,no,yes,yes,yes,no,4,3,3,1,2,2,5,18,18,19
GP,F,17,U,GT3,T,1,1,services,teacher,reputation,mother,1,3,0,no,yes,yes,no,yes,yes,yes,no,4,3,3,1,1,3,6,13,12,12
GP,M,18,U,GT3,T,2,1,services,services,reputation,mother,1,3,0,no,no,yes,yes,yes,yes,yes,no,4,2,4,1,3,2,6,15,14,14
GP,M,18,U,LE3,A,4,4,teacher,teacher,reputation,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,no,5,4,3,1,1,2,9,15,13,15
GP,M,18,U,GT3,T,4,2,teacher,other,home,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,yes,4,3,2,1,4,5,11,12,11,11
GP,F,17,U,GT3,T,4,3,health,services,reputation,mother,1,3,0,no,yes,yes,no,yes,yes,yes,no,4,2,2,1,2,3,0,15,15,15
GP,F,18,U,LE3,T,2,1,services,at_home,reputation,mother,1,2,1,no,no,no,no,yes,yes,yes,yes,5,4,3,1,1,5,12,12,12,13
GP,F,17,R,LE3,T,3,1,services,other,reputation,mother,2,4,0,no,yes,yes,no,yes,yes,no,no,3,1,2,1,1,3,6,18,18,18
GP,M,18,R,LE3,T,3,2,services,other,reputation,mother,2,3,0,no,yes,yes,yes,yes,yes,yes,no,5,4,2,1,1,4,8,14,13,14
GP,M,17,U,GT3,T,3,3,health,other,home,mother,1,1,0,no,yes,yes,no,yes,yes,yes,no,4,4,3,1,3,5,4,14,12,11
GP,F,19,U,GT3,T,4,4,health,other,reputation,other,2,2,0,no,yes,yes,yes,yes,yes,yes,no,2,3,4,2,3,2,0,10,9,0
GP,F,18,U,LE3,T,4,3,other,other,home,other,2,2,0,no,yes,yes,no,yes,yes,yes,yes,4,4,5,1,2,2,10,10,8,8
GP,F,18,U,GT3,T,4,3,other,other,reputation,father,1,4,0,no,yes,yes,no,yes,yes,yes,no,4,3,3,1,1,3,0,14,13,14
GP,M,18,U,LE3,T,4,4,teacher,teacher,home,mother,1,1,0,no,yes,yes,no,yes,yes,yes,yes,1,4,2,2,2,1,5,16,15,16
GP,F,18,U,LE3,A,4,4,health,other,home,mother,1,2,0,no,yes,no,no,yes,yes,yes,yes,4,2,4,1,1,4,14,12,10,11
GP,M,17,U,LE3,T,4,4,other,teacher,home,father,2,1,0,no,no,yes,no,yes,yes,yes,no,4,1,1,2,2,5,0,11,11,10
GP,F,17,U,GT3,T,4,2,other,other,reputation,mother,2,3,0,no,yes,yes,no,yes,yes,yes,no,4,3,3,1,1,3,0,15,12,14
GP,F,17,U,GT3,T,3,2,health,health,reputation,father,1,4,0,no,yes,yes,yes,no,yes,yes,no,5,2,2,1,2,5,0,17,17,18
GP,M,19,U,GT3,T,3,3,other,other,home,other,1,2,1,no,yes,no,yes,yes,yes,yes,yes,4,4,4,1,1,3,20,15,14,13
GP,F,18,U,GT3,T,2,4,services,at_home,reputation,other,1,2,1,no,yes,yes,yes,yes,yes,yes,no,4,4,3,1,1,3,8,14,12,12
GP,M,20,U,GT3,A,3,2,services,other,course,other,1,1,0,no,no,no,yes,yes,yes,no,no,5,5,3,1,1,5,0,17,18,18
GP,M,19,U,GT3,T,4,4,teacher,services,reputation,other,2,1,1,no,yes,yes,no,yes,yes,yes,yes,4,3,4,1,1,4,38,8,9,8
GP,M,19,R,GT3,T,3,3,other,services,reputation,father,1,2,1,no,no,no,yes,yes,yes,no,yes,4,5,3,1,2,5,0,15,12,12
GP,F,19,U,LE3,T,1,1,at_home,other,reputation,other,1,2,1,yes,yes,no,yes,no,yes,yes,no,4,4,3,1,3,3,18,12,10,10
GP,F,19,U,LE3,T,1,2,services,services,home,other,1,2,1,no,no,no,yes,no,yes,no,yes,4,2,4,2,2,3,0,9,9,0
GP,F,19,U,GT3,T,2,1,at_home,other,other,other,3,2,0,no,yes,no,no,yes,no,yes,yes,3,4,1,1,1,2,20,14,12,13
GP,M,19,U,GT3,T,1,2,other,services,course,other,1,2,1,no,no,no,no,no,yes,yes,no,4,5,2,2,2,4,3,13,11,11
GP,F,19,U,LE3,T,3,2,services,other,reputation,other,2,2,1,no,yes,yes,no,no,yes,yes,yes,4,2,2,1,2,1,22,13,10,11
GP,F,19,U,GT3,T,1,1,at_home,health,home,other,1,3,2,no,no,no,no,no,yes,yes,yes,4,1,2,1,1,3,14,15,13,13
GP,F,19,R,GT3,T,2,3,other,other,reputation,other,1,3,1,no,no,no,no,yes,yes,yes,yes,4,1,2,1,1,3,40,13,11,11
GP,F,18,U,GT3,T,2,1,services,other,course,mother,2,2,0,no,yes,yes,yes,yes,yes,yes,no,5,3,3,1,2,1,0,8,8,0
GP,F,18,U,GT3,T,4,3,other,other,course,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,yes,4,3,4,1,1,5,9,9,10,9
GP,F,17,R,GT3,T,3,4,at_home,services,course,father,1,3,0,no,yes,yes,yes,no,yes,yes,no,4,3,4,2,5,5,0,11,11,10
GP,F,18,U,GT3,T,4,4,teacher,other,course,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,4,4,3,3,5,2,11,11,11
GP,F,17,U,GT3,A,4,3,services,services,course,mother,1,2,0,no,yes,yes,no,yes,yes,yes,yes,5,2,2,1,2,5,23,13,13,13
GP,F,17,U,GT3,T,2,2,other,other,course,mother,1,2,0,no,yes,no,no,yes,yes,no,yes,4,2,2,1,1,3,12,11,9,9
GP,F,17,R,LE3,T,2,2,services,services,course,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,no,3,3,2,2,2,3,3,11,11,11
GP,F,17,U,GT3,T,3,1,services,services,course,father,1,3,0,no,yes,no,no,no,yes,yes,no,3,4,3,2,3,5,1,12,14,15
GP,F,17,U,LE3,T,0,2,at_home,at_home,home,father,2,3,0,no,no,no,no,yes,yes,yes,no,3,3,3,2,3,2,0,16,15,15
GP,M,18,U,GT3,T,4,4,other,other,course,mother,1,3,0,no,no,no,yes,yes,yes,yes,no,4,3,3,2,2,3,3,9,12,11
GP,M,17,U,GT3,T,3,3,other,services,reputation,mother,1,1,0,no,no,no,yes,no,yes,yes,no,4,3,5,3,5,5,3,14,15,16
GP,M,17,R,GT3,T,2,2,services,other,course,mother,4,1,0,no,yes,no,no,yes,yes,yes,no,4,4,5,5,5,4,8,11,10,10
GP,F,17,U,GT3,T,4,4,teacher,services,course,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,no,5,4,4,1,3,4,7,10,9,9
GP,F,17,U,GT3,T,4,4,teacher,teacher,course,mother,2,3,0,no,yes,yes,no,no,yes,yes,yes,4,3,3,1,2,4,4,14,14,14
GP,M,18,U,LE3,T,2,2,other,other,course,mother,1,4,0,no,yes,no,yes,yes,yes,yes,no,4,5,5,2,4,5,2,9,8,8
GP,F,17,R,GT3,T,2,4,at_home,other,course,father,1,3,0,no,yes,no,no,yes,yes,yes,yes,4,4,3,1,1,5,7,12,14,14
GP,F,18,U,GT3,T,3,3,services,services,home,mother,1,2,0,no,no,no,yes,yes,yes,yes,no,5,3,4,1,1,4,0,7,0,0
GP,F,18,U,LE3,T,2,2,other,other,home,other,1,2,0,no,no,no,yes,no,yes,yes,yes,4,3,3,1,1,2,0,8,8,0
GP,F,18,R,GT3,T,2,2,at_home,other,course,mother,2,4,0,no,no,no,yes,yes,yes,no,no,4,4,4,1,1,4,0,10,9,0
GP,F,17,U,GT3,T,3,4,services,other,course,mother,1,3,0,no,no,no,no,yes,yes,yes,no,4,4,5,1,3,5,16,16,15,15
GP,F,19,R,GT3,A,3,1,services,at_home,home,other,1,3,1,no,no,yes,no,yes,yes,no,no,5,4,3,1,2,5,12,14,13,13
GP,F,17,U,GT3,T,3,2,other,other,home,mother,1,2,0,no,yes,yes,no,yes,yes,yes,yes,4,3,2,2,3,2,0,7,8,0
GP,F,18,U,LE3,T,3,3,services,services,home,mother,1,4,0,no,yes,no,no,yes,yes,yes,no,5,3,3,1,1,1,7,16,15,17
GP,F,17,R,GT3,A,3,2,other,other,home,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,3,3,2,3,2,4,9,10,10
GP,F,19,U,GT3,T,2,1,services,services,home,other,1,3,1,no,no,yes,yes,yes,yes,yes,yes,4,3,4,1,3,3,4,11,12,11
GP,M,18,U,GT3,T,4,4,teacher,services,home,father,1,2,1,no,yes,no,yes,yes,yes,yes,no,4,3,3,2,2,2,0,10,10,0
GP,M,18,U,LE3,T,3,4,services,other,home,mother,1,2,0,no,no,no,yes,yes,yes,yes,yes,4,3,3,1,3,5,11,16,15,15
GP,F,17,U,GT3,A,2,2,at_home,at_home,home,father,1,2,1,no,yes,no,no,yes,yes,yes,yes,3,3,1,1,2,4,0,9,8,0
GP,F,18,U,GT3,T,2,3,at_home,other,course,mother,1,3,0,no,yes,no,no,yes,yes,yes,no,4,3,3,1,2,3,4,11,10,10
GP,F,18,U,GT3,T,3,2,other,services,other,mother,1,3,0,no,no,no,no,yes,yes,yes,yes,5,4,3,2,3,1,7,13,13,14
GP,M,18,R,GT3,T,4,3,teacher,services,course,mother,1,3,0,no,no,no,no,yes,yes,yes,yes,5,3,2,1,2,4,9,16,15,16
GP,M,18,U,GT3,T,4,3,teacher,other,course,mother,1,3,0,no,yes,yes,no,yes,yes,yes,yes,5,4,5,2,3,5,0,10,10,9
GP,F,17,U,GT3,T,4,3,health,other,reputation,mother,1,3,0,no,yes,yes,yes,yes,yes,yes,yes,4,4,3,1,3,4,0,13,15,15
MS,M,18,R,GT3,T,3,2,other,other,course,mother,2,1,1,no,yes,no,no,no,yes,yes,no,2,5,5,5,5,5,10,11,13,13
MS,M,19,R,GT3,T,1,1,other,services,home,other,3,2,3,no,no,no,no,yes,yes,yes,no,5,4,4,3,3,2,8,8,7,8
MS,M,17,U,GT3,T,3,3,health,other,course,mother,2,2,0,no,yes,yes,no,yes,yes,yes,no,4,5,4,2,3,3,2,13,13,13
MS,M,18,U,LE3,T,1,3,at_home,services,course,mother,1,1,1,no,no,no,no,yes,no,yes,yes,4,3,3,2,3,3,7,8,7,8
MS,M,19,R,GT3,T,1,1,other,other,home,other,3,1,1,no,yes,no,no,yes,yes,yes,no,4,4,4,3,3,5,4,8,8,8
MS,M,17,R,GT3,T,4,3,services,other,home,mother,2,2,0,no,yes,yes,yes,no,yes,yes,yes,4,5,5,1,3,2,4,13,11,11
MS,F,18,U,GT3,T,3,3,services,services,course,father,1,2,0,no,yes,no,no,yes,yes,no,yes,5,3,4,1,1,5,0,10,9,9
MS,F,17,R,GT3,T,4,4,teacher,services,other,father,2,2,0,no,yes,yes,yes,yes,yes,yes,no,4,3,3,1,2,5,4,12,13,13
MS,F,17,U,LE3,A,3,2,services,other,reputation,mother,2,2,0,no,no,no,no,yes,yes,no,yes,1,2,3,1,2,5,2,12,12,11
MS,M,18,U,LE3,T,1,1,other,services,home,father,2,1,0,no,no,no,no,no,yes,yes,yes,3,3,2,1,2,3,4,10,10,10
MS,F,18,U,LE3,T,1,1,at_home,services,course,father,2,3,0,no,no,no,no,yes,yes,yes,no,5,3,2,1,1,4,0,18,16,16
MS,F,18,R,LE3,A,1,4,at_home,other,course,mother,3,2,0,no,no,no,no,yes,yes,no,yes,4,3,4,1,4,5,0,13,13,13
MS,M,18,R,LE3,T,1,1,at_home,other,other,mother,2,2,1,no,no,no,yes,no,no,no,no,4,4,3,2,3,5,2,13,12,12
MS,F,18,U,GT3,T,3,3,services,services,other,mother,2,2,0,no,yes,no,no,yes,yes,yes,yes,4,3,2,1,3,3,0,11,11,10
MS,F,17,U,LE3,T,4,4,at_home,at_home,course,mother,1,2,0,no,yes,yes,yes,yes,yes,yes,yes,2,3,4,1,1,1,0,16,15,15
MS,F,17,R,GT3,T,1,2,other,services,course,father,2,2,0,no,no,no,no,no,yes,no,no,3,2,2,1,2,3,0,12,11,12
MS,M,18,R,GT3,T,1,3,at_home,other,course,mother,2,2,0,no,yes,yes,no,yes,yes,no,no,3,3,4,2,4,3,4,10,10,10
MS,M,18,U,LE3,T,4,4,teacher,services,other,mother,2,3,0,no,no,yes,no,yes,yes,yes,yes,4,2,2,2,2,5,0,13,13,13
MS,F,17,R,GT3,T,1,1,other,services,reputation,mother,3,1,1,no,yes,yes,no,yes,yes,yes,yes,5,2,1,1,2,1,0,7,6,0
MS,F,18,U,GT3,T,2,3,at_home,services,course,father,2,1,0,no,yes,yes,no,yes,yes,yes,yes,5,2,3,1,2,4,0,11,10,10
MS,F,18,R,GT3,T,4,4,other,teacher,other,father,3,2,0,no,yes,yes,no,no,yes,yes,yes,3,2,2,4,2,5,10,14,12,11
MS,F,19,U,LE3,T,3,2,services,services,home,other,2,2,2,no,no,no,yes,yes,yes,no,yes,3,2,2,1,1,3,4,7,7,9
MS,M,18,R,LE3,T,1,2,at_home,services,other,father,3,1,0,no,yes,yes,yes,yes,no,yes,yes,4,3,3,2,3,3,3,14,12,12
MS,F,17,U,GT3,T,2,2,other,at_home,home,mother,1,3,0,no,no,no,yes,yes,yes,no,yes,3,4,3,1,1,3,8,13,11,11
MS,F,17,R,GT3,T,1,2,other,other,course,mother,1,1,0,no,no,no,yes,yes,yes,yes,no,3,5,5,1,3,1,14,6,5,5
MS,F,18,R,LE3,T,4,4,other,other,reputation,mother,2,3,0,no,no,no,no,yes,yes,yes,no,5,4,4,1,1,1,0,19,18,19
MS,F,18,R,GT3,T,1,1,other,other,home,mother,4,3,0,no,no,no,no,yes,yes,yes,no,4,3,2,1,2,4,2,8,8,10
MS,F,20,U,GT3,T,4,2,health,other,course,other,2,3,2,no,yes,yes,no,no,yes,yes,yes,5,4,3,1,1,3,4,15,14,15
MS,F,18,R,LE3,T,4,4,teacher,services,course,mother,1,2,0,no,no,yes,yes,yes,yes,yes,no,5,4,3,3,4,2,4,8,9,10
MS,F,18,U,GT3,T,3,3,other,other,home,mother,1,2,0,no,no,yes,no,yes,yes,yes,yes,4,1,3,1,2,1,0,15,15,15
MS,F,17,R,GT3,T,3,1,at_home,other,reputation,mother,1,2,0,no,yes,yes,yes,no,yes,yes,no,4,5,4,2,3,1,17,10,10,10
MS,M,18,U,GT3,T,4,4,teacher,teacher,home,father,1,2,0,no,no,yes,yes,no,yes,yes,no,3,2,4,1,4,2,4,15,14,14
MS,M,18,R,GT3,T,2,1,other,other,other,mother,2,1,0,no,no,no,yes,no,yes,yes,yes,4,4,3,1,3,5,5,7,6,7
MS,M,17,U,GT3,T,2,3,other,services,home,father,2,2,0,no,no,no,yes,yes,yes,yes,no,4,4,3,1,1,3,2,11,11,10
MS,M,19,R,GT3,T,1,1,other,services,other,mother,2,1,1,no,no,no,no,yes,yes,no,no,4,3,2,1,3,5,0,6,5,0
MS,M,18,R,GT3,T,4,2,other,other,home,father,2,1,1,no,no,yes,no,yes,yes,no,no,5,4,3,4,3,3,14,6,5,5
MS,F,18,R,GT3,T,2,2,at_home,other,other,mother,2,3,0,no,no,yes,no,yes,yes,no,no,5,3,3,1,3,4,2,10,9,10
MS,F,18,R,GT3,T,4,4,teacher,at_home,reputation,mother,3,1,0,no,yes,yes,yes,yes,yes,yes,yes,4,4,3,2,2,5,7,6,5,6
MS,F,19,R,GT3,T,2,3,services,other,course,mother,1,3,1,no,no,no,yes,no,yes,yes,no,5,4,2,1,2,5,0,7,5,0
MS,F,18,U,LE3,T,3,1,teacher,services,course,mother,1,2,0,no,yes,yes,no,yes,yes,yes,no,4,3,4,1,1,1,0,7,9,8
MS,F,18,U,GT3,T,1,1,other,other,course,mother,2,2,1,no,no,no,yes,yes,yes,no,no,1,1,1,1,1,5,0,6,5,0
MS,M,20,U,LE3,A,2,2,services,services,course,other,1,2,2,no,yes,yes,no,yes,yes,no,no,5,5,4,4,5,4,11,9,9,9
MS,M,17,U,LE3,T,3,1,services,services,course,mother,2,1,0,no,no,no,no,no,yes,yes,no,2,4,5,3,4,2,3,14,16,16
MS,M,21,R,GT3,T,1,1,other,other,course,other,1,1,3,no,no,no,no,no,yes,no,no,5,5,3,3,3,3,3,10,8,7
MS,M,18,R,LE3,T,3,2,services,other,course,mother,3,1,0,no,no,no,no,no,yes,yes,no,4,4,1,3,4,5,0,11,12,10
MS,M,19,U,LE3,T,1,1,other,at_home,course,father,1,1,0,no,no,no,no,yes,yes,yes,no,3,2,3,3,3,5,5,8,9,9


================================================
FILE: 04_Apply/US_Crime_Rates/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# United States - Crime Rates - 1960 - 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data \n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called crime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. What is the type of the columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Have you noticed that the type of Year is int64. But pandas has a different type to work with Time Series. Let's see it now.\n",
    "\n",
    "### Step 5. Convert the type of the column Year to datetime64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Set the Year column as the index of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Delete the Total column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Group the year by decades and sum the values\n",
    "\n",
    "#### Pay attention to the Population column number, summing this column is a mistake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. What is the most dangerous decade to live in the US?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
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}


================================================
FILE: 04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# United States - Crime Rates - 1960 - 2014\n",
    "\n",
    "Check out [Crime Rates Exercises Video Tutorial](https://youtu.be/46lmk1JvcWA) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data \n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,


   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called crime."
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>179323175</td>\n",
       "      <td>3384200</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>182992000</td>\n",
       "      <td>3488000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>185771000</td>\n",
       "      <td>3752200</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>188483000</td>\n",
       "      <td>4109500</td>\n",
       "      <td>316970</td>\n",
       "      <td>3792500</td>\n",
       "      <td>8640</td>\n",
       "      <td>17650</td>\n",
       "      <td>116470</td>\n",
       "      <td>174210</td>\n",
       "      <td>1086400</td>\n",
       "      <td>2297800</td>\n",
       "      <td>408300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>191141000</td>\n",
       "      <td>4564600</td>\n",
       "      <td>364220</td>\n",
       "      <td>4200400</td>\n",
       "      <td>9360</td>\n",
       "      <td>21420</td>\n",
       "      <td>130390</td>\n",
       "      <td>203050</td>\n",
       "      <td>1213200</td>\n",
       "      <td>2514400</td>\n",
       "      <td>472800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Population    Total  Violent  Property  Murder  Forcible_Rape  \\\n",
       "0  1960   179323175  3384200   288460   3095700    9110          17190   \n",
       "1  1961   182992000  3488000   289390   3198600    8740          17220   \n",
       "2  1962   185771000  3752200   301510   3450700    8530          17550   \n",
       "3  1963   188483000  4109500   316970   3792500    8640          17650   \n",
       "4  1964   191141000  4564600   364220   4200400    9360          21420   \n",
       "\n",
       "   Robbery  Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "0   107840              154320    912100        1855400         328200  \n",
       "1   106670              156760    949600        1913000         336000  \n",
       "2   110860              164570    994300        2089600         366800  \n",
       "3   116470              174210   1086400        2297800         408300  \n",
       "4   130390              203050   1213200        2514400         472800  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv\"\n",
    "crime = pd.read_csv(url)\n",
    "crime.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. What is the type of the columns?"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 3,


   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55 entries, 0 to 54\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype\n",
      "---  ------              --------------  -----\n",
      " 0   Year                55 non-null     int64\n",
      " 1   Population          55 non-null     int64\n",
      " 2   Total               55 non-null     int64\n",
      " 3   Violent             55 non-null     int64\n",
      " 4   Property            55 non-null     int64\n",
      " 5   Murder              55 non-null     int64\n",
      " 6   Forcible_Rape       55 non-null     int64\n",
      " 7   Robbery             55 non-null     int64\n",
      " 8   Aggravated_assault  55 non-null     int64\n",
      " 9   Burglary            55 non-null     int64\n",
      " 10  Larceny_Theft       55 non-null     int64\n",
      " 11  Vehicle_Theft       55 non-null     int64\n",
      "dtypes: int64(12)\n",
      "memory usage: 5.3 KB\n"
     ]
    }
   ],
   "source": [
    "crime.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Have you noticed that the type of Year is int64. But pandas has a different type to work with Time Series. Let's see it now.\n",
    "\n",
    "### Step 5. Convert the type of the column Year to datetime64"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55 entries, 0 to 54\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   Year                55 non-null     datetime64[ns]\n",
      " 1   Population          55 non-null     int64         \n",
      " 2   Total               55 non-null     int64         \n",
      " 3   Violent             55 non-null     int64         \n",
      " 4   Property            55 non-null     int64         \n",
      " 5   Murder              55 non-null     int64         \n",
      " 6   Forcible_Rape       55 non-null     int64         \n",
      " 7   Robbery             55 non-null     int64         \n",
      " 8   Aggravated_assault  55 non-null     int64         \n",
      " 9   Burglary            55 non-null     int64         \n",
      " 10  Larceny_Theft       55 non-null     int64         \n",
      " 11  Vehicle_Theft       55 non-null     int64         \n",
      "dtypes: datetime64[ns](1), int64(11)\n",
      "memory usage: 5.3 KB\n"
     ]
    }
   ],
   "source": [
    "# pd.to_datetime(crime)\n",
    "crime.Year = pd.to_datetime(crime.Year, format='%Y')\n",
    "crime.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Set the Year column as the index of the dataframe"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Total</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>179323175</td>\n",
       "      <td>3384200</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>182992000</td>\n",
       "      <td>3488000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>185771000</td>\n",
       "      <td>3752200</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>188483000</td>\n",
       "      <td>4109500</td>\n",
       "      <td>316970</td>\n",
       "      <td>3792500</td>\n",
       "      <td>8640</td>\n",
       "      <td>17650</td>\n",
       "      <td>116470</td>\n",
       "      <td>174210</td>\n",
       "      <td>1086400</td>\n",
       "      <td>2297800</td>\n",
       "      <td>408300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>191141000</td>\n",
       "      <td>4564600</td>\n",
       "      <td>364220</td>\n",
       "      <td>4200400</td>\n",
       "      <td>9360</td>\n",
       "      <td>21420</td>\n",
       "      <td>130390</td>\n",
       "      <td>203050</td>\n",
       "      <td>1213200</td>\n",
       "      <td>2514400</td>\n",
       "      <td>472800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population    Total  Violent  Property  Murder  Forcible_Rape  \\\n",
       "Year                                                                        \n",
       "1960-01-01   179323175  3384200   288460   3095700    9110          17190   \n",
       "1961-01-01   182992000  3488000   289390   3198600    8740          17220   \n",
       "1962-01-01   185771000  3752200   301510   3450700    8530          17550   \n",
       "1963-01-01   188483000  4109500   316970   3792500    8640          17650   \n",
       "1964-01-01   191141000  4564600   364220   4200400    9360          21420   \n",
       "\n",
       "            Robbery  Aggravated_assault  Burglary  Larceny_Theft  \\\n",
       "Year                                                               \n",
       "1960-01-01   107840              154320    912100        1855400   \n",
       "1961-01-01   106670              156760    949600        1913000   \n",
       "1962-01-01   110860              164570    994300        2089600   \n",
       "1963-01-01   116470              174210   1086400        2297800   \n",
       "1964-01-01   130390              203050   1213200        2514400   \n",
       "\n",
       "            Vehicle_Theft  \n",
       "Year                       \n",
       "1960-01-01         328200  \n",
       "1961-01-01         336000  \n",
       "1962-01-01         366800  \n",
       "1963-01-01         408300  \n",
       "1964-01-01         472800  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime = crime.set_index('Year', drop = True)\n",
    "crime.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Delete the Total column"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 6,

   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>179323175</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>182992000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>185771000</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>188483000</td>\n",
       "      <td>316970</td>\n",
       "      <td>3792500</td>\n",
       "      <td>8640</td>\n",
       "      <td>17650</td>\n",
       "      <td>116470</td>\n",
       "      <td>174210</td>\n",
       "      <td>1086400</td>\n",
       "      <td>2297800</td>\n",
       "      <td>408300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>191141000</td>\n",
       "      <td>364220</td>\n",
       "      <td>4200400</td>\n",
       "      <td>9360</td>\n",
       "      <td>21420</td>\n",
       "      <td>130390</td>\n",
       "      <td>203050</td>\n",
       "      <td>1213200</td>\n",
       "      <td>2514400</td>\n",
       "      <td>472800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population  Violent  Property  Murder  Forcible_Rape  Robbery  \\\n",
       "Year                                                                        \n",
       "1960-01-01   179323175   288460   3095700    9110          17190   107840   \n",
       "1961-01-01   182992000   289390   3198600    8740          17220   106670   \n",
       "1962-01-01   185771000   301510   3450700    8530          17550   110860   \n",
       "1963-01-01   188483000   316970   3792500    8640          17650   116470   \n",
       "1964-01-01   191141000   364220   4200400    9360          21420   130390   \n",
       "\n",
       "            Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "Year                                                                    \n",
       "1960-01-01              154320    912100        1855400         328200  \n",
       "1961-01-01              156760    949600        1913000         336000  \n",
       "1962-01-01              164570    994300        2089600         366800  \n",
       "1963-01-01              174210   1086400        2297800         408300  \n",
       "1964-01-01              203050   1213200        2514400         472800  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del crime['Total']\n",
    "crime.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Group the year by decades and sum the values\n",
    "\n",
    "#### Pay attention to the Population column number, summing this column is a mistake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Violent</th>\n",
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       "      <th>Murder</th>\n",
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       "      <th>Year</th>\n",
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       "      <th></th>\n",
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       "      <th>1960-01-01</th>\n",
       "      <td>201385000</td>\n",
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       "      <td>106180</td>\n",
       "      <td>236720</td>\n",
       "      <td>1633510</td>\n",
       "      <td>2158520</td>\n",
       "      <td>13321100</td>\n",
       "      <td>26547700</td>\n",
       "      <td>5292100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>220099000</td>\n",
       "      <td>9607930</td>\n",
       "      <td>91383800</td>\n",
       "      <td>192230</td>\n",
       "      <td>554570</td>\n",
       "      <td>4159020</td>\n",
       "      <td>4702120</td>\n",
       "      <td>28486000</td>\n",
       "      <td>53157800</td>\n",
       "      <td>9739900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>248239000</td>\n",
       "      <td>14074328</td>\n",
       "      <td>117048900</td>\n",
       "      <td>206439</td>\n",
       "      <td>865639</td>\n",
       "      <td>5383109</td>\n",
       "      <td>7619130</td>\n",
       "      <td>33073494</td>\n",
       "      <td>72040253</td>\n",
       "      <td>11935411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>272690813</td>\n",
       "      <td>17527048</td>\n",
       "      <td>119053499</td>\n",
       "      <td>211664</td>\n",
       "      <td>998827</td>\n",
       "      <td>5748930</td>\n",
       "      <td>10568963</td>\n",
       "      <td>26750015</td>\n",
       "      <td>77679366</td>\n",
       "      <td>14624418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>307006550</td>\n",
       "      <td>13968056</td>\n",
       "      <td>100944369</td>\n",
       "      <td>163068</td>\n",
       "      <td>922499</td>\n",
       "      <td>4230366</td>\n",
       "      <td>8652124</td>\n",
       "      <td>21565176</td>\n",
       "      <td>67970291</td>\n",
       "      <td>11412834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>318857056</td>\n",
       "      <td>6072017</td>\n",
       "      <td>44095950</td>\n",
       "      <td>72867</td>\n",
       "      <td>421059</td>\n",
       "      <td>1749809</td>\n",
       "      <td>3764142</td>\n",
       "      <td>10125170</td>\n",
       "      <td>30401698</td>\n",
       "      <td>3569080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population   Violent   Property  Murder  Forcible_Rape  Robbery  \\\n",
       "Year                                                                          \n",
       "1960-01-01   201385000   4134930   45160900  106180         236720  1633510   \n",
       "1970-01-01   220099000   9607930   91383800  192230         554570  4159020   \n",
       "1980-01-01   248239000  14074328  117048900  206439         865639  5383109   \n",
       "1990-01-01   272690813  17527048  119053499  211664         998827  5748930   \n",
       "2000-01-01   307006550  13968056  100944369  163068         922499  4230366   \n",
       "2010-01-01   318857056   6072017   44095950   72867         421059  1749809   \n",
       "\n",
       "            Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "Year                                                                    \n",
       "1960-01-01             2158520  13321100       26547700        5292100  \n",
       "1970-01-01             4702120  28486000       53157800        9739900  \n",
       "1980-01-01             7619130  33073494       72040253       11935411  \n",
       "1990-01-01            10568963  26750015       77679366       14624418  \n",
       "2000-01-01             8652124  21565176       67970291       11412834  \n",
       "2010-01-01             3764142  10125170       30401698        3569080  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To learn more about .resample (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html)\n",
    "# To learn more about Offset Aliases (http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)\n",
    "\n",
    "# Uses resample to sum each decade\n",
    "crimes = crime.resample('10AS').sum()\n",
    "\n",
    "# Uses resample to get the max value only for the \"Population\" column\n",
    "population = crime['Population'].resample('10AS').max()\n",
    "\n",
    "# Updating the \"Population\" column\n",
    "crimes['Population'] = population\n",
    "\n",
    "crimes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. What is the most dangerous decade to live in the US?"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 9,

   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population           2010-01-01\n",
       "Violent              1990-01-01\n",
       "Property             1990-01-01\n",
       "Murder               1990-01-01\n",
       "Forcible_Rape        1990-01-01\n",
       "Robbery              1990-01-01\n",
       "Aggravated_assault   1990-01-01\n",
       "Burglary             1980-01-01\n",
       "Larceny_Theft        1990-01-01\n",
       "Vehicle_Theft        1990-01-01\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apparently the 90s was a pretty dangerous time in the US\n",
    "crimes.idxmax(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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================================================
FILE: 04_Apply/US_Crime_Rates/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# United States - Crime Rates - 1960 - 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data \n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called crime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>179323175</td>\n",
       "      <td>3384200</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>182992000</td>\n",
       "      <td>3488000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>185771000</td>\n",
       "      <td>3752200</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>188483000</td>\n",
       "      <td>4109500</td>\n",
       "      <td>316970</td>\n",
       "      <td>3792500</td>\n",
       "      <td>8640</td>\n",
       "      <td>17650</td>\n",
       "      <td>116470</td>\n",
       "      <td>174210</td>\n",
       "      <td>1086400</td>\n",
       "      <td>2297800</td>\n",
       "      <td>408300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>191141000</td>\n",
       "      <td>4564600</td>\n",
       "      <td>364220</td>\n",
       "      <td>4200400</td>\n",
       "      <td>9360</td>\n",
       "      <td>21420</td>\n",
       "      <td>130390</td>\n",
       "      <td>203050</td>\n",
       "      <td>1213200</td>\n",
       "      <td>2514400</td>\n",
       "      <td>472800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Population    Total  Violent  Property  Murder  Forcible_Rape  \\\n",
       "0  1960   179323175  3384200   288460   3095700    9110          17190   \n",
       "1  1961   182992000  3488000   289390   3198600    8740          17220   \n",
       "2  1962   185771000  3752200   301510   3450700    8530          17550   \n",
       "3  1963   188483000  4109500   316970   3792500    8640          17650   \n",
       "4  1964   191141000  4564600   364220   4200400    9360          21420   \n",
       "\n",
       "   Robbery  Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "0   107840              154320    912100        1855400         328200  \n",
       "1   106670              156760    949600        1913000         336000  \n",
       "2   110860              164570    994300        2089600         366800  \n",
       "3   116470              174210   1086400        2297800         408300  \n",
       "4   130390              203050   1213200        2514400         472800  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. What is the type of the columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55 entries, 0 to 54\n",
      "Data columns (total 12 columns):\n",
      "Year                  55 non-null int64\n",
      "Population            55 non-null int64\n",
      "Total                 55 non-null int64\n",
      "Violent               55 non-null int64\n",
      "Property              55 non-null int64\n",
      "Murder                55 non-null int64\n",
      "Forcible_Rape         55 non-null int64\n",
      "Robbery               55 non-null int64\n",
      "Aggravated_assault    55 non-null int64\n",
      "Burglary              55 non-null int64\n",
      "Larceny_Theft         55 non-null int64\n",
      "Vehicle_Theft         55 non-null int64\n",
      "dtypes: int64(12)\n",
      "memory usage: 5.2 KB\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Have you noticed that the type of Year is int64. But pandas has a different type to work with Time Series. Let's see it now.\n",
    "\n",
    "### Step 5. Convert the type of the column Year to datetime64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55 entries, 0 to 54\n",
      "Data columns (total 12 columns):\n",
      "Year                  55 non-null datetime64[ns]\n",
      "Population            55 non-null int64\n",
      "Total                 55 non-null int64\n",
      "Violent               55 non-null int64\n",
      "Property              55 non-null int64\n",
      "Murder                55 non-null int64\n",
      "Forcible_Rape         55 non-null int64\n",
      "Robbery               55 non-null int64\n",
      "Aggravated_assault    55 non-null int64\n",
      "Burglary              55 non-null int64\n",
      "Larceny_Theft         55 non-null int64\n",
      "Vehicle_Theft         55 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(11)\n",
      "memory usage: 5.2 KB\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Set the Year column as the index of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Total</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>179323175</td>\n",
       "      <td>3384200</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>182992000</td>\n",
       "      <td>3488000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>185771000</td>\n",
       "      <td>3752200</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>188483000</td>\n",
       "      <td>4109500</td>\n",
       "      <td>316970</td>\n",
       "      <td>3792500</td>\n",
       "      <td>8640</td>\n",
       "      <td>17650</td>\n",
       "      <td>116470</td>\n",
       "      <td>174210</td>\n",
       "      <td>1086400</td>\n",
       "      <td>2297800</td>\n",
       "      <td>408300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>191141000</td>\n",
       "      <td>4564600</td>\n",
       "      <td>364220</td>\n",
       "      <td>4200400</td>\n",
       "      <td>9360</td>\n",
       "      <td>21420</td>\n",
       "      <td>130390</td>\n",
       "      <td>203050</td>\n",
       "      <td>1213200</td>\n",
       "      <td>2514400</td>\n",
       "      <td>472800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population    Total  Violent  Property  Murder  Forcible_Rape  \\\n",
       "Year                                                                        \n",
       "1960-01-01   179323175  3384200   288460   3095700    9110          17190   \n",
       "1961-01-01   182992000  3488000   289390   3198600    8740          17220   \n",
       "1962-01-01   185771000  3752200   301510   3450700    8530          17550   \n",
       "1963-01-01   188483000  4109500   316970   3792500    8640          17650   \n",
       "1964-01-01   191141000  4564600   364220   4200400    9360          21420   \n",
       "\n",
       "            Robbery  Aggravated_assault  Burglary  Larceny_Theft  \\\n",
       "Year                                                               \n",
       "1960-01-01   107840              154320    912100        1855400   \n",
       "1961-01-01   106670              156760    949600        1913000   \n",
       "1962-01-01   110860              164570    994300        2089600   \n",
       "1963-01-01   116470              174210   1086400        2297800   \n",
       "1964-01-01   130390              203050   1213200        2514400   \n",
       "\n",
       "            Vehicle_Theft  \n",
       "Year                       \n",
       "1960-01-01         328200  \n",
       "1961-01-01         336000  \n",
       "1962-01-01         366800  \n",
       "1963-01-01         408300  \n",
       "1964-01-01         472800  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Delete the Total column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>179323175</td>\n",
       "      <td>288460</td>\n",
       "      <td>3095700</td>\n",
       "      <td>9110</td>\n",
       "      <td>17190</td>\n",
       "      <td>107840</td>\n",
       "      <td>154320</td>\n",
       "      <td>912100</td>\n",
       "      <td>1855400</td>\n",
       "      <td>328200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>182992000</td>\n",
       "      <td>289390</td>\n",
       "      <td>3198600</td>\n",
       "      <td>8740</td>\n",
       "      <td>17220</td>\n",
       "      <td>106670</td>\n",
       "      <td>156760</td>\n",
       "      <td>949600</td>\n",
       "      <td>1913000</td>\n",
       "      <td>336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>185771000</td>\n",
       "      <td>301510</td>\n",
       "      <td>3450700</td>\n",
       "      <td>8530</td>\n",
       "      <td>17550</td>\n",
       "      <td>110860</td>\n",
       "      <td>164570</td>\n",
       "      <td>994300</td>\n",
       "      <td>2089600</td>\n",
       "      <td>366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>188483000</td>\n",
       "      <td>316970</td>\n",
       "      <td>3792500</td>\n",
       "      <td>8640</td>\n",
       "      <td>17650</td>\n",
       "      <td>116470</td>\n",
       "      <td>174210</td>\n",
       "      <td>1086400</td>\n",
       "      <td>2297800</td>\n",
       "      <td>408300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>191141000</td>\n",
       "      <td>364220</td>\n",
       "      <td>4200400</td>\n",
       "      <td>9360</td>\n",
       "      <td>21420</td>\n",
       "      <td>130390</td>\n",
       "      <td>203050</td>\n",
       "      <td>1213200</td>\n",
       "      <td>2514400</td>\n",
       "      <td>472800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Population  Violent  Property  Murder  Forcible_Rape  Robbery  \\\n",
       "Year                                                                        \n",
       "1960-01-01   179323175   288460   3095700    9110          17190   107840   \n",
       "1961-01-01   182992000   289390   3198600    8740          17220   106670   \n",
       "1962-01-01   185771000   301510   3450700    8530          17550   110860   \n",
       "1963-01-01   188483000   316970   3792500    8640          17650   116470   \n",
       "1964-01-01   191141000   364220   4200400    9360          21420   130390   \n",
       "\n",
       "            Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "Year                                                                    \n",
       "1960-01-01              154320    912100        1855400         328200  \n",
       "1961-01-01              156760    949600        1913000         336000  \n",
       "1962-01-01              164570    994300        2089600         366800  \n",
       "1963-01-01              174210   1086400        2297800         408300  \n",
       "1964-01-01              203050   1213200        2514400         472800  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Group the year by decades and sum the values\n",
    "\n",
    "#### Pay attention to the Population column number, summing this column is a mistake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Violent</th>\n",
       "      <th>Property</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Forcible_Rape</th>\n",
       "      <th>Robbery</th>\n",
       "      <th>Aggravated_assault</th>\n",
       "      <th>Burglary</th>\n",
       "      <th>Larceny_Theft</th>\n",
       "      <th>Vehicle_Theft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>201385000</td>\n",
       "      <td>4134930</td>\n",
       "      <td>45160900</td>\n",
       "      <td>106180</td>\n",
       "      <td>236720</td>\n",
       "      <td>1633510</td>\n",
       "      <td>2158520</td>\n",
       "      <td>13321100</td>\n",
       "      <td>26547700</td>\n",
       "      <td>5292100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>220099000</td>\n",
       "      <td>9607930</td>\n",
       "      <td>91383800</td>\n",
       "      <td>192230</td>\n",
       "      <td>554570</td>\n",
       "      <td>4159020</td>\n",
       "      <td>4702120</td>\n",
       "      <td>28486000</td>\n",
       "      <td>53157800</td>\n",
       "      <td>9739900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>248239000</td>\n",
       "      <td>14074328</td>\n",
       "      <td>117048900</td>\n",
       "      <td>206439</td>\n",
       "      <td>865639</td>\n",
       "      <td>5383109</td>\n",
       "      <td>7619130</td>\n",
       "      <td>33073494</td>\n",
       "      <td>72040253</td>\n",
       "      <td>11935411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>272690813</td>\n",
       "      <td>17527048</td>\n",
       "      <td>119053499</td>\n",
       "      <td>211664</td>\n",
       "      <td>998827</td>\n",
       "      <td>5748930</td>\n",
       "      <td>10568963</td>\n",
       "      <td>26750015</td>\n",
       "      <td>77679366</td>\n",
       "      <td>14624418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>307006550</td>\n",
       "      <td>13968056</td>\n",
       "      <td>100944369</td>\n",
       "      <td>163068</td>\n",
       "      <td>922499</td>\n",
       "      <td>4230366</td>\n",
       "      <td>8652124</td>\n",
       "      <td>21565176</td>\n",
       "      <td>67970291</td>\n",
       "      <td>11412834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>318857056</td>\n",
       "      <td>6072017</td>\n",
       "      <td>44095950</td>\n",
       "      <td>72867</td>\n",
       "      <td>421059</td>\n",
       "      <td>1749809</td>\n",
       "      <td>3764142</td>\n",
       "      <td>10125170</td>\n",
       "      <td>30401698</td>\n",
       "      <td>3569080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Population   Violent   Property  Murder  Forcible_Rape  Robbery  \\\n",
       "1960   201385000   4134930   45160900  106180         236720  1633510   \n",
       "1970   220099000   9607930   91383800  192230         554570  4159020   \n",
       "1980   248239000  14074328  117048900  206439         865639  5383109   \n",
       "1990   272690813  17527048  119053499  211664         998827  5748930   \n",
       "2000   307006550  13968056  100944369  163068         922499  4230366   \n",
       "2010   318857056   6072017   44095950   72867         421059  1749809   \n",
       "\n",
       "      Aggravated_assault  Burglary  Larceny_Theft  Vehicle_Theft  \n",
       "1960             2158520  13321100       26547700        5292100  \n",
       "1970             4702120  28486000       53157800        9739900  \n",
       "1980             7619130  33073494       72040253       11935411  \n",
       "1990            10568963  26750015       77679366       14624418  \n",
       "2000             8652124  21565176       67970291       11412834  \n",
       "2010             3764142  10125170       30401698        3569080  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. What is the most dangerous decade to live in the US?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population            2010\n",
       "Violent               1990\n",
       "Property              1990\n",
       "Murder                1990\n",
       "Forcible_Rape         1990\n",
       "Robbery               1990\n",
       "Aggravated_assault    1990\n",
       "Burglary              1980\n",
       "Larceny_Theft         1990\n",
       "Vehicle_Theft         1990\n",
       "dtype: int64"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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================================================
FILE: 04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv
================================================
Year,Population,Total,Violent,Property,Murder,Forcible_Rape,Robbery,Aggravated_assault,Burglary,Larceny_Theft,Vehicle_Theft
1960,179323175,3384200,288460,3095700,9110,17190,107840,154320,912100,1855400,328200
1961,182992000,3488000,289390,3198600,8740,17220,106670,156760,949600,1913000,336000
1962,185771000,3752200,301510,3450700,8530,17550,110860,164570,994300,2089600,366800
1963,188483000,4109500,316970,3792500,8640,17650,116470,174210,1086400,2297800,408300
1964,191141000,4564600,364220,4200400,9360,21420,130390,203050,1213200,2514400,472800
1965,193526000,4739400,387390,4352000,9960,23410,138690,215330,1282500,2572600,496900
1966,195576000,5223500,430180,4793300,11040,25820,157990,235330,1410100,2822000,561200
1967,197457000,5903400,499930,5403500,12240,27620,202910,257160,1632100,3111600,659800
1968,199399000,6720200,595010,6125200,13800,31670,262840,286700,1858900,3482700,783600
1969,201385000,7410900,661870,6749000,14760,37170,298850,311090,1981900,3888600,878500
1970,203235298,8098000,738820,7359200,16000,37990,349860,334970,2205000,4225800,928400
1971,206212000,8588200,816500,7771700,17780,42260,387700,368760,2399300,4424200,948200
1972,208230000,8248800,834900,7413900,18670,46850,376290,393090,2375500,4151200,887200
1973,209851000,8718100,875910,7842200,19640,51400,384220,420650,2565500,4347900,928800
1974,211392000,10253400,974720,9278700,20710,55400,442400,456210,3039200,5262500,977100
1975,213124000,11292400,1039710,10252700,20510,56090,470500,492620,3265300,5977700,1009600
1976,214659000,11349700,1004210,10345500,18780,57080,427810,500530,3108700,6270800,966000
1977,216332000,10984500,1029580,9955000,19120,63500,412610,534350,3071500,5905700,977700
1978,218059000,11209000,1085550,10123400,19560,67610,426930,571460,3128300,5991000,1004100
1979,220099000,12249500,1208030,11041500,21460,76390,480700,629480,3327700,6601000,1112800
1980,225349264,13408300,1344520,12063700,23040,82990,565840,672650,3795200,7136900,1131700
1981,229146000,13423800,1361820,12061900,22520,82500,592910,663900,3779700,7194400,1087800
1982,231534000,12974400,1322390,11652000,21010,78770,553130,669480,3447100,7142500,1062400
1983,233981000,12108600,1258090,10850500,19310,78920,506570,653290,3129900,6712800,1007900
1984,236158000,11881800,1273280,10608500,18690,84230,485010,685350,2984400,6591900,1032200
1985,238740000,12431400,1328800,11102600,18980,88670,497870,723250,3073300,6926400,1102900
1986,240132887,13211869,1489169,11722700,20613,91459,542775,834322,3241410,7257153,1224137
1987,242282918,13508700,1483999,12024700,20096,91110,517704,855088,3236184,7499900,1288674
1988,245807000,13923100,1566220,12356900,20680,92490,542970,910090,3218100,7705900,1432900
1989,248239000,14251400,1646040,12605400,21500,94500,578330,951710,3168200,7872400,1564800
1990,248709873,14475600,1820130,12655500,23440,102560,639270,1054860,3073900,7945700,1635900
1991,252177000,14872900,1911770,12961100,24700,106590,687730,1092740,3157200,8142200,1661700
1992,255082000,14438200,1932270,12505900,23760,109060,672480,1126970,2979900,7915200,1610800
1993,257908000,14144800,1926020,12218800,24530,106010,659870,1135610,2834800,7820900,1563100
1994,260341000,13989500,1857670,12131900,23330,102220,618950,1113180,2712800,7879800,1539300
1995,262755000,13862700,1798790,12063900,21610,97470,580510,1099210,2593800,7997700,1472400
1996,265228572,13493863,1688540,11805300,19650,96250,535590,1037050,2506400,7904700,1394200
1997,267637000,13194571,1634770,11558175,18208,96153,498534,1023201,2460526,7743760,1354189
1998,270296000,12475634,1531044,10944590,16914,93103,446625,974402,2329950,7373886,1240754
1999,272690813,11634378,1426044,10208334,15522,89411,409371,911740,2100739,6955520,1152075
2000,281421906,11608072,1425486,10182586,15586,90178,408016,911706,2050992,6971590,1160002
2001,285317559,11876669,1439480,10437480,16037,90863,423557,909023,2116531,7092267,1228391
2002,287973924,11878954,1423677,10455277,16229,95235,420806,891407,2151252,7057370,1246646
2003,290690788,11826538,1383676,10442862,16528,93883,414235,859030,2154834,7026802,1261226
2004,293656842,11679474,1360088,10319386,16148,95089,401470,847381,2144446,6937089,1237851
2005,296507061,11565499,1390745,10174754,16740,94347,417438,862220,2155448,6783447,1235859
2006,299398484,11401511,1418043,9983568,17030,92757,447403,860853,2183746,6607013,1192809
2007,301621157,11251828,1408337,9843481,16929,90427,445125,855856,2176140,6568572,1095769
2008,304374846,11160543,1392628,9767915,16442,90479,443574,842134,2228474,6588046,958629
2009,307006550,10762956,1325896,9337060,15399,89241,408742,812514,2203313,6338095,795652
2010,309330219,10363873,1251248,9112625,14772,85593,369089,781844,2168457,6204601,739565
2011,311587816,10258774,1206031,9052743,14661,84175,354772,752423,2185140,6151095,716508
2012,313873685,10219059,1217067,9001992,14866,85141,355051,762009,2109932,6168874,723186
2013,316497531,9850445,1199684,8650761,14319,82109,345095,726575,1931835,6018632,700294
2014,318857056,9475816,1197987,8277829,14249,84041,325802,741291,1729806,5858496,689527

================================================
FILE: 05_Merge/Auto_MPG/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MPG Cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### Step 3. Assign each to a variable called cars1 and cars2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Oops, it seems our first dataset has some unnamed blank columns, fix cars1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in each dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Join cars1 and cars2 into a single DataFrame called cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Oops, there is a column missing, called owners. Create a random number Series from 15,000 to 73,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Add the column owners to cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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================================================
FILE: 05_Merge/Auto_MPG/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MPG Cars\n",
    "\n",
    "Check out [Cars Exercises Video Tutorial](https://www.youtube.com/watch?v=avzLRBxoguU&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=3) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### Step 3. Assign each to a to a variable called cars1 and cars2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
      "0  18.0          8           307        130    3504          12.0     70   \n",
      "1  15.0          8           350        165    3693          11.5     70   \n",
      "2  18.0          8           318        150    3436          11.0     70   \n",
      "3  16.0          8           304        150    3433          12.0     70   \n",
      "4  17.0          8           302        140    3449          10.5     70   \n",
      "\n",
      "   origin                        car  Unnamed: 9  Unnamed: 10  Unnamed: 11  \\\n",
      "0       1  chevrolet chevelle malibu         NaN          NaN          NaN   \n",
      "1       1          buick skylark 320         NaN          NaN          NaN   \n",
      "2       1         plymouth satellite         NaN          NaN          NaN   \n",
      "3       1              amc rebel sst         NaN          NaN          NaN   \n",
      "4       1                ford torino         NaN          NaN          NaN   \n",
      "\n",
      "   Unnamed: 12  Unnamed: 13  \n",
      "0          NaN          NaN  \n",
      "1          NaN          NaN  \n",
      "2          NaN          NaN  \n",
      "3          NaN          NaN  \n",
      "4          NaN          NaN  \n",
      "    mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
      "0  33.0          4            91         53    1795          17.4     76   \n",
      "1  20.0          6           225        100    3651          17.7     76   \n",
      "2  18.0          6           250         78    3574          21.0     76   \n",
      "3  18.5          6           250        110    3645          16.2     76   \n",
      "4  17.5          6           258         95    3193          17.8     76   \n",
      "\n",
      "   origin                 car  \n",
      "0       3         honda civic  \n",
      "1       1      dodge aspen se  \n",
      "2       1   ford granada ghia  \n",
      "3       1  pontiac ventura sj  \n",
      "4       1       amc pacer d/l  \n"
     ]
    }
   ],
   "source": [
    "cars1 = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv\")\n",
    "cars2 = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv\")\n",
    "\n",
    "print(cars1.head())\n",
    "print(cars2.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Oops, it seems our first dataset has some unnamed blank columns, fix cars1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0  18.0          8           307        130    3504          12.0     70   \n",
       "1  15.0          8           350        165    3693          11.5     70   \n",
       "2  18.0          8           318        150    3436          11.0     70   \n",
       "3  16.0          8           304        150    3433          12.0     70   \n",
       "4  17.0          8           302        140    3449          10.5     70   \n",
       "\n",
       "   origin                        car  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars1 = cars1.loc[:, \"mpg\":\"car\"]\n",
    "cars1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in each dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(198, 9)\n",
      "(200, 9)\n"
     ]
    }
   ],
   "source": [
    "print(cars1.shape)\n",
    "print(cars2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Join cars1 and cars2 into a single DataFrame called cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>429</td>\n",
       "      <td>198</td>\n",
       "      <td>4341</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford galaxie 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>454</td>\n",
       "      <td>220</td>\n",
       "      <td>4354</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet impala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440</td>\n",
       "      <td>215</td>\n",
       "      <td>4312</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth fury iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455</td>\n",
       "      <td>225</td>\n",
       "      <td>4425</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac catalina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>390</td>\n",
       "      <td>190</td>\n",
       "      <td>3850</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc ambassador dpl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>383</td>\n",
       "      <td>170</td>\n",
       "      <td>3563</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge challenger se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>340</td>\n",
       "      <td>160</td>\n",
       "      <td>3609</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth 'cuda 340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400</td>\n",
       "      <td>150</td>\n",
       "      <td>3761</td>\n",
       "      <td>9.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet monte carlo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455</td>\n",
       "      <td>225</td>\n",
       "      <td>3086</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick estate wagon (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>113</td>\n",
       "      <td>95</td>\n",
       "      <td>2372</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corona mark ii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22.0</td>\n",
       "      <td>6</td>\n",
       "      <td>198</td>\n",
       "      <td>95</td>\n",
       "      <td>2833</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>97</td>\n",
       "      <td>2774</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc hornet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>85</td>\n",
       "      <td>2587</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>88</td>\n",
       "      <td>2130</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun pl510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>1835</td>\n",
       "      <td>20.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>volkswagen 1131 deluxe sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>87</td>\n",
       "      <td>2672</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>peugeot 504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>107</td>\n",
       "      <td>90</td>\n",
       "      <td>2430</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>104</td>\n",
       "      <td>95</td>\n",
       "      <td>2375</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>saab 99e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>121</td>\n",
       "      <td>113</td>\n",
       "      <td>2234</td>\n",
       "      <td>12.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>bmw 2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>90</td>\n",
       "      <td>2648</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc gremlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>360</td>\n",
       "      <td>215</td>\n",
       "      <td>4615</td>\n",
       "      <td>14.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford f250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>200</td>\n",
       "      <td>4376</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy c20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>210</td>\n",
       "      <td>4382</td>\n",
       "      <td>13.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge d200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>193</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>hi 1200d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>88</td>\n",
       "      <td>2130</td>\n",
       "      <td>14.5</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun pl510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>88</td>\n",
       "      <td>2640</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet cavalier wagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>34.0</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>88</td>\n",
       "      <td>2395</td>\n",
       "      <td>18.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet cavalier 2-door</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>85</td>\n",
       "      <td>2575</td>\n",
       "      <td>16.2</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac j2000 se hatchback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2525</td>\n",
       "      <td>16.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge aries se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>90</td>\n",
       "      <td>2735</td>\n",
       "      <td>18.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac phoenix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>92</td>\n",
       "      <td>2865</td>\n",
       "      <td>16.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford fairmont futura</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>?</td>\n",
       "      <td>3035</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>amc concord dl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>105</td>\n",
       "      <td>74</td>\n",
       "      <td>1980</td>\n",
       "      <td>15.3</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>volkswagen rabbit l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>37.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>68</td>\n",
       "      <td>2025</td>\n",
       "      <td>18.2</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>mazda glc custom l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>68</td>\n",
       "      <td>1970</td>\n",
       "      <td>17.6</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>mazda glc custom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>105</td>\n",
       "      <td>63</td>\n",
       "      <td>2125</td>\n",
       "      <td>14.7</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth horizon miser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>2125</td>\n",
       "      <td>17.3</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>mercury lynx l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>88</td>\n",
       "      <td>2160</td>\n",
       "      <td>14.5</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>nissan stanza xe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>2205</td>\n",
       "      <td>14.5</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda accord</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>34.0</td>\n",
       "      <td>4</td>\n",
       "      <td>108</td>\n",
       "      <td>70</td>\n",
       "      <td>2245</td>\n",
       "      <td>16.9</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corolla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>1965</td>\n",
       "      <td>15.0</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda civic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>1965</td>\n",
       "      <td>15.7</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda civic (auto)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>1995</td>\n",
       "      <td>16.2</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun 310 gx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>25.0</td>\n",
       "      <td>6</td>\n",
       "      <td>181</td>\n",
       "      <td>110</td>\n",
       "      <td>2945</td>\n",
       "      <td>16.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>buick century limited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>38.0</td>\n",
       "      <td>6</td>\n",
       "      <td>262</td>\n",
       "      <td>85</td>\n",
       "      <td>3015</td>\n",
       "      <td>17.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "      <td>92</td>\n",
       "      <td>2585</td>\n",
       "      <td>14.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chrysler lebaron medallion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>22.0</td>\n",
       "      <td>6</td>\n",
       "      <td>232</td>\n",
       "      <td>112</td>\n",
       "      <td>2835</td>\n",
       "      <td>14.7</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford granada l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>96</td>\n",
       "      <td>2665</td>\n",
       "      <td>13.9</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota celica gt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2370</td>\n",
       "      <td>13.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge charger 2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>90</td>\n",
       "      <td>2950</td>\n",
       "      <td>17.3</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet camaro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    18.0          8           307        130    3504          12.0     70   \n",
       "1    15.0          8           350        165    3693          11.5     70   \n",
       "2    18.0          8           318        150    3436          11.0     70   \n",
       "3    16.0          8           304        150    3433          12.0     70   \n",
       "4    17.0          8           302        140    3449          10.5     70   \n",
       "5    15.0          8           429        198    4341          10.0     70   \n",
       "6    14.0          8           454        220    4354           9.0     70   \n",
       "7    14.0          8           440        215    4312           8.5     70   \n",
       "8    14.0          8           455        225    4425          10.0     70   \n",
       "9    15.0          8           390        190    3850           8.5     70   \n",
       "10   15.0          8           383        170    3563          10.0     70   \n",
       "11   14.0          8           340        160    3609           8.0     70   \n",
       "12   15.0          8           400        150    3761           9.5     70   \n",
       "13   14.0          8           455        225    3086          10.0     70   \n",
       "14   24.0          4           113         95    2372          15.0     70   \n",
       "15   22.0          6           198         95    2833          15.5     70   \n",
       "16   18.0          6           199         97    2774          15.5     70   \n",
       "17   21.0          6           200         85    2587          16.0     70   \n",
       "18   27.0          4            97         88    2130          14.5     70   \n",
       "19   26.0          4            97         46    1835          20.5     70   \n",
       "20   25.0          4           110         87    2672          17.5     70   \n",
       "21   24.0          4           107         90    2430          14.5     70   \n",
       "22   25.0          4           104         95    2375          17.5     70   \n",
       "23   26.0          4           121        113    2234          12.5     70   \n",
       "24   21.0          6           199         90    2648          15.0     70   \n",
       "25   10.0          8           360        215    4615          14.0     70   \n",
       "26   10.0          8           307        200    4376          15.0     70   \n",
       "27   11.0          8           318        210    4382          13.5     70   \n",
       "28    9.0          8           304        193    4732          18.5     70   \n",
       "29   27.0          4            97         88    2130          14.5     71   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "170  27.0          4           112         88    2640          18.6     82   \n",
       "171  34.0          4           112         88    2395          18.0     82   \n",
       "172  31.0          4           112         85    2575          16.2     82   \n",
       "173  29.0          4           135         84    2525          16.0     82   \n",
       "174  27.0          4           151         90    2735          18.0     82   \n",
       "175  24.0          4           140         92    2865          16.4     82   \n",
       "176  23.0          4           151          ?    3035          20.5     82   \n",
       "177  36.0          4           105         74    1980          15.3     82   \n",
       "178  37.0          4            91         68    2025          18.2     82   \n",
       "179  31.0          4            91         68    1970          17.6     82   \n",
       "180  38.0          4           105         63    2125          14.7     82   \n",
       "181  36.0          4            98         70    2125          17.3     82   \n",
       "182  36.0          4           120         88    2160          14.5     82   \n",
       "183  36.0          4           107         75    2205          14.5     82   \n",
       "184  34.0          4           108         70    2245          16.9     82   \n",
       "185  38.0          4            91         67    1965          15.0     82   \n",
       "186  32.0          4            91         67    1965          15.7     82   \n",
       "187  38.0          4            91         67    1995          16.2     82   \n",
       "188  25.0          6           181        110    2945          16.4     82   \n",
       "189  38.0          6           262         85    3015          17.0     82   \n",
       "190  26.0          4           156         92    2585          14.5     82   \n",
       "191  22.0          6           232        112    2835          14.7     82   \n",
       "192  32.0          4           144         96    2665          13.9     82   \n",
       "193  36.0          4           135         84    2370          13.0     82   \n",
       "194  27.0          4           151         90    2950          17.3     82   \n",
       "195  27.0          4           140         86    2790          15.6     82   \n",
       "196  44.0          4            97         52    2130          24.6     82   \n",
       "197  32.0          4           135         84    2295          11.6     82   \n",
       "198  28.0          4           120         79    2625          18.6     82   \n",
       "199  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin                                car  \n",
       "0         1          chevrolet chevelle malibu  \n",
       "1         1                  buick skylark 320  \n",
       "2         1                 plymouth satellite  \n",
       "3         1                      amc rebel sst  \n",
       "4         1                        ford torino  \n",
       "5         1                   ford galaxie 500  \n",
       "6         1                   chevrolet impala  \n",
       "7         1                  plymouth fury iii  \n",
       "8         1                   pontiac catalina  \n",
       "9         1                 amc ambassador dpl  \n",
       "10        1                dodge challenger se  \n",
       "11        1                 plymouth 'cuda 340  \n",
       "12        1              chevrolet monte carlo  \n",
       "13        1            buick estate wagon (sw)  \n",
       "14        3              toyota corona mark ii  \n",
       "15        1                    plymouth duster  \n",
       "16        1                         amc hornet  \n",
       "17        1                      ford maverick  \n",
       "18        3                       datsun pl510  \n",
       "19        2       volkswagen 1131 deluxe sedan  \n",
       "20        2                        peugeot 504  \n",
       "21        2                        audi 100 ls  \n",
       "22        2                           saab 99e  \n",
       "23        2                           bmw 2002  \n",
       "24        1                        amc gremlin  \n",
       "25        1                          ford f250  \n",
       "26        1                          chevy c20  \n",
       "27        1                         dodge d200  \n",
       "28        1                           hi 1200d  \n",
       "29        3                       datsun pl510  \n",
       "..      ...                                ...  \n",
       "170       1           chevrolet cavalier wagon  \n",
       "171       1          chevrolet cavalier 2-door  \n",
       "172       1         pontiac j2000 se hatchback  \n",
       "173       1                     dodge aries se  \n",
       "174       1                    pontiac phoenix  \n",
       "175       1               ford fairmont futura  \n",
       "176       1                     amc concord dl  \n",
       "177       2                volkswagen rabbit l  \n",
       "178       3                 mazda glc custom l  \n",
       "179       3                   mazda glc custom  \n",
       "180       1             plymouth horizon miser  \n",
       "181       1                     mercury lynx l  \n",
       "182       3                   nissan stanza xe  \n",
       "183       3                       honda accord  \n",
       "184       3                     toyota corolla  \n",
       "185       3                        honda civic  \n",
       "186       3                 honda civic (auto)  \n",
       "187       3                      datsun 310 gx  \n",
       "188       1              buick century limited  \n",
       "189       1  oldsmobile cutlass ciera (diesel)  \n",
       "190       1         chrysler lebaron medallion  \n",
       "191       1                     ford granada l  \n",
       "192       3                   toyota celica gt  \n",
       "193       1                  dodge charger 2.2  \n",
       "194       1                   chevrolet camaro  \n",
       "195       1                    ford mustang gl  \n",
       "196       2                          vw pickup  \n",
       "197       1                      dodge rampage  \n",
       "198       1                        ford ranger  \n",
       "199       1                         chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = cars1.append(cars2)\n",
    "cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Oops, there is a column missing, called owners. Create a random number Series from 15,000 to 73,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29487, 25680, 65268, 31827, 69215, 72602, 52693, 58440, 16183,\n",
       "       45014, 32318, 72942, 62163, 35951, 57625, 59355, 36533, 67048,\n",
       "       58159, 69743, 25146, 22755, 44966, 46792, 56553, 65013, 55908,\n",
       "       69563, 22030, 59561, 15593, 52998, 54795, 16169, 24809, 35580,\n",
       "       46590, 38792, 43099, 37166, 21390, 56496, 68606, 21110, 56334,\n",
       "       45477, 51961, 27625, 51176, 30796, 61809, 65450, 67375, 23342,\n",
       "       27499, 50585, 57302, 56191, 60281, 32865, 58605, 66374, 15315,\n",
       "       31791, 28670, 38796, 69214, 41055, 32353, 31574, 65799, 42998,\n",
       "       72785, 18415, 31977, 29812, 65439, 21161, 60871, 67151, 22179,\n",
       "       32821, 55392, 34586, 67937, 31646, 66397, 35258, 63815, 71291,\n",
       "       51130, 27684, 49648, 52691, 50681, 68185, 32635, 51553, 28970,\n",
       "       19112, 26035, 67666, 55471, 51477, 62055, 53003, 41265, 18565,\n",
       "       48851, 48673, 45832, 67891, 57638, 29240, 41236, 16950, 31449,\n",
       "       50528, 22397, 15876, 26414, 16736, 23896, 46104, 17583, 65951,\n",
       "       38538, 31443, 19299, 46095, 31239, 19290, 38051, 68575, 61755,\n",
       "       22560, 34460, 35395, 34608, 56906, 44895, 48429, 20900, 49770,\n",
       "       50513, 59402, 26893, 37233, 19036, 20523, 18765, 46333, 42831,\n",
       "       53698, 25218, 63106, 16928, 34901, 43674, 65453, 54428, 68502,\n",
       "       19043, 20325, 45039, 29466, 49672, 67972, 30547, 22522, 69354,\n",
       "       40489, 72887, 15724, 51442, 65182, 64555, 42138, 72988, 20861,\n",
       "       67898, 20768, 36415, 47480, 16820, 48739, 62610, 43473, 23002,\n",
       "       43488, 62581, 37724, 63019, 44912, 35595, 59188, 51814, 65283,\n",
       "       53479, 27660, 38237, 22957, 47870, 15533, 41944, 51830, 56676,\n",
       "       57481, 48529, 72220, 66675, 50099, 30585, 25436, 49195, 26050,\n",
       "       24899, 37213, 25870, 67447, 23808, 71275, 67572, 18545, 43553,\n",
       "       54858, 23077, 33705, 31282, 26298, 23742, 36110, 51491, 18019,\n",
       "       60655, 27453, 35563, 63627, 35315, 56717, 59281, 55634, 18415,\n",
       "       59570, 47320, 20110, 18425, 19352, 18032, 31816, 28573, 66030,\n",
       "       54723, 21592, 37160, 59518, 35629, 47619, 52359, 34566, 64932,\n",
       "       24072, 39445, 31203, 63975, 62041, 70175, 51029, 32058, 19428,\n",
       "       65553, 50799, 48190, 68061, 68201, 53389, 15901, 44585, 54723,\n",
       "       30446, 63716, 57488, 67134, 22033, 53694, 40002, 24854, 59747,\n",
       "       59827, 53378, 53196, 68686, 20784, 28181, 33044, 41694, 39857,\n",
       "       57296, 69021, 17359, 29794, 22515, 55877, 22806, 50027, 56787,\n",
       "       50844, 17420, 65259, 19141, 40204, 19530, 30116, 34973, 15641,\n",
       "       53492, 59574, 59082, 64400, 70163, 43058, 69696, 67996, 26158,\n",
       "       32936, 45461, 47390, 32368, 15400, 40895, 16572, 31776, 62121,\n",
       "       56704, 39335, 27716, 52565, 50831, 45049, 25173, 25018, 18606,\n",
       "       71177, 66288, 46754, 68175, 35829, 24959, 54792, 19059, 29092,\n",
       "       58736, 62938, 44733, 17884, 33905, 33965, 24641, 52257, 28178,\n",
       "       29515, 37703, 56036, 51556, 23590, 61888, 70224, 53730, 41328,\n",
       "       16501, 30360, 54106, 29101, 35631, 56173, 30424, 46887, 23657,\n",
       "       17723, 71709, 45270, 30380, 27779, 33774, 36379, 47127, 63625,\n",
       "       16750, 65740, 53802, 40995, 37487, 42791, 21825, 69344, 63210,\n",
       "       15982, 20259])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nr_owners = np.random.randint(15000, high=73001, size=398, dtype='l')\n",
    "nr_owners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Add the column owners to cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "      <th>owners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "      <td>21825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "      <td>69344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "      <td>63210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "      <td>15982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "      <td>20259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "195  27.0          4           140         86    2790          15.6     82   \n",
       "196  44.0          4            97         52    2130          24.6     82   \n",
       "197  32.0          4           135         84    2295          11.6     82   \n",
       "198  28.0          4           120         79    2625          18.6     82   \n",
       "199  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin              car  owners  \n",
       "195       1  ford mustang gl   21825  \n",
       "196       2        vw pickup   69344  \n",
       "197       1    dodge rampage   63210  \n",
       "198       1      ford ranger   15982  \n",
       "199       1       chevy s-10   20259  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['owners'] = nr_owners\n",
    "cars.tail()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
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================================================
FILE: 05_Merge/Auto_MPG/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MPG Cars\n",
    "\n",
    "Check out [Cars Exercises Video Tutorial](https://www.youtube.com/watch?v=avzLRBxoguU&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=3) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### Step 3. Assign each to a to a variable called cars1 and cars2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
      "0  18.0          8           307        130    3504          12.0     70   \n",
      "1  15.0          8           350        165    3693          11.5     70   \n",
      "2  18.0          8           318        150    3436          11.0     70   \n",
      "3  16.0          8           304        150    3433          12.0     70   \n",
      "4  17.0          8           302        140    3449          10.5     70   \n",
      "\n",
      "   origin                        car  Unnamed: 9  Unnamed: 10  Unnamed: 11  \\\n",
      "0       1  chevrolet chevelle malibu         NaN          NaN          NaN   \n",
      "1       1          buick skylark 320         NaN          NaN          NaN   \n",
      "2       1         plymouth satellite         NaN          NaN          NaN   \n",
      "3       1              amc rebel sst         NaN          NaN          NaN   \n",
      "4       1                ford torino         NaN          NaN          NaN   \n",
      "\n",
      "   Unnamed: 12  Unnamed: 13  \n",
      "0          NaN          NaN  \n",
      "1          NaN          NaN  \n",
      "2          NaN          NaN  \n",
      "3          NaN          NaN  \n",
      "4          NaN          NaN  \n",
      "    mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
      "0  33.0          4            91         53    1795          17.4     76   \n",
      "1  20.0          6           225        100    3651          17.7     76   \n",
      "2  18.0          6           250         78    3574          21.0     76   \n",
      "3  18.5          6           250        110    3645          16.2     76   \n",
      "4  17.5          6           258         95    3193          17.8     76   \n",
      "\n",
      "   origin                 car  \n",
      "0       3         honda civic  \n",
      "1       1      dodge aspen se  \n",
      "2       1   ford granada ghia  \n",
      "3       1  pontiac ventura sj  \n",
      "4       1       amc pacer d/l  \n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Oops, it seems our first dataset has some unnamed blank columns, fix cars1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0  18.0          8           307        130    3504          12.0     70   \n",
       "1  15.0          8           350        165    3693          11.5     70   \n",
       "2  18.0          8           318        150    3436          11.0     70   \n",
       "3  16.0          8           304        150    3433          12.0     70   \n",
       "4  17.0          8           302        140    3449          10.5     70   \n",
       "\n",
       "   origin                        car  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. What is the number of observations in each dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(198, 9)\n",
      "(200, 9)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Join cars1 and cars2 into a single DataFrame called cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>429</td>\n",
       "      <td>198</td>\n",
       "      <td>4341</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford galaxie 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>454</td>\n",
       "      <td>220</td>\n",
       "      <td>4354</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet impala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440</td>\n",
       "      <td>215</td>\n",
       "      <td>4312</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth fury iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455</td>\n",
       "      <td>225</td>\n",
       "      <td>4425</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac catalina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>390</td>\n",
       "      <td>190</td>\n",
       "      <td>3850</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc ambassador dpl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>383</td>\n",
       "      <td>170</td>\n",
       "      <td>3563</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge challenger se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>340</td>\n",
       "      <td>160</td>\n",
       "      <td>3609</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth 'cuda 340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400</td>\n",
       "      <td>150</td>\n",
       "      <td>3761</td>\n",
       "      <td>9.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet monte carlo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>455</td>\n",
       "      <td>225</td>\n",
       "      <td>3086</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick estate wagon (sw)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>113</td>\n",
       "      <td>95</td>\n",
       "      <td>2372</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corona mark ii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22.0</td>\n",
       "      <td>6</td>\n",
       "      <td>198</td>\n",
       "      <td>95</td>\n",
       "      <td>2833</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>97</td>\n",
       "      <td>2774</td>\n",
       "      <td>15.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc hornet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>85</td>\n",
       "      <td>2587</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>88</td>\n",
       "      <td>2130</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun pl510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>46</td>\n",
       "      <td>1835</td>\n",
       "      <td>20.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>volkswagen 1131 deluxe sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>87</td>\n",
       "      <td>2672</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>peugeot 504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>107</td>\n",
       "      <td>90</td>\n",
       "      <td>2430</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>104</td>\n",
       "      <td>95</td>\n",
       "      <td>2375</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>saab 99e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>121</td>\n",
       "      <td>113</td>\n",
       "      <td>2234</td>\n",
       "      <td>12.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>bmw 2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>199</td>\n",
       "      <td>90</td>\n",
       "      <td>2648</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc gremlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>360</td>\n",
       "      <td>215</td>\n",
       "      <td>4615</td>\n",
       "      <td>14.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford f250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>200</td>\n",
       "      <td>4376</td>\n",
       "      <td>15.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy c20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318</td>\n",
       "      <td>210</td>\n",
       "      <td>4382</td>\n",
       "      <td>13.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge d200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304</td>\n",
       "      <td>193</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>hi 1200d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>88</td>\n",
       "      <td>2130</td>\n",
       "      <td>14.5</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun pl510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>88</td>\n",
       "      <td>2640</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet cavalier wagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>34.0</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>88</td>\n",
       "      <td>2395</td>\n",
       "      <td>18.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet cavalier 2-door</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>85</td>\n",
       "      <td>2575</td>\n",
       "      <td>16.2</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac j2000 se hatchback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2525</td>\n",
       "      <td>16.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge aries se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>90</td>\n",
       "      <td>2735</td>\n",
       "      <td>18.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>pontiac phoenix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>92</td>\n",
       "      <td>2865</td>\n",
       "      <td>16.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford fairmont futura</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>?</td>\n",
       "      <td>3035</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>amc concord dl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>105</td>\n",
       "      <td>74</td>\n",
       "      <td>1980</td>\n",
       "      <td>15.3</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>volkswagen rabbit l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>37.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>68</td>\n",
       "      <td>2025</td>\n",
       "      <td>18.2</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>mazda glc custom l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>68</td>\n",
       "      <td>1970</td>\n",
       "      <td>17.6</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>mazda glc custom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>105</td>\n",
       "      <td>63</td>\n",
       "      <td>2125</td>\n",
       "      <td>14.7</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth horizon miser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>70</td>\n",
       "      <td>2125</td>\n",
       "      <td>17.3</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>mercury lynx l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>88</td>\n",
       "      <td>2160</td>\n",
       "      <td>14.5</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>nissan stanza xe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>2205</td>\n",
       "      <td>14.5</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda accord</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>34.0</td>\n",
       "      <td>4</td>\n",
       "      <td>108</td>\n",
       "      <td>70</td>\n",
       "      <td>2245</td>\n",
       "      <td>16.9</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corolla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>1965</td>\n",
       "      <td>15.0</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda civic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>1965</td>\n",
       "      <td>15.7</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda civic (auto)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>1995</td>\n",
       "      <td>16.2</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun 310 gx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>25.0</td>\n",
       "      <td>6</td>\n",
       "      <td>181</td>\n",
       "      <td>110</td>\n",
       "      <td>2945</td>\n",
       "      <td>16.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>buick century limited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>38.0</td>\n",
       "      <td>6</td>\n",
       "      <td>262</td>\n",
       "      <td>85</td>\n",
       "      <td>3015</td>\n",
       "      <td>17.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "      <td>92</td>\n",
       "      <td>2585</td>\n",
       "      <td>14.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chrysler lebaron medallion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>22.0</td>\n",
       "      <td>6</td>\n",
       "      <td>232</td>\n",
       "      <td>112</td>\n",
       "      <td>2835</td>\n",
       "      <td>14.7</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford granada l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>96</td>\n",
       "      <td>2665</td>\n",
       "      <td>13.9</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota celica gt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2370</td>\n",
       "      <td>13.0</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge charger 2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>90</td>\n",
       "      <td>2950</td>\n",
       "      <td>17.3</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet camaro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "0    18.0          8           307        130    3504          12.0     70   \n",
       "1    15.0          8           350        165    3693          11.5     70   \n",
       "2    18.0          8           318        150    3436          11.0     70   \n",
       "3    16.0          8           304        150    3433          12.0     70   \n",
       "4    17.0          8           302        140    3449          10.5     70   \n",
       "5    15.0          8           429        198    4341          10.0     70   \n",
       "6    14.0          8           454        220    4354           9.0     70   \n",
       "7    14.0          8           440        215    4312           8.5     70   \n",
       "8    14.0          8           455        225    4425          10.0     70   \n",
       "9    15.0          8           390        190    3850           8.5     70   \n",
       "10   15.0          8           383        170    3563          10.0     70   \n",
       "11   14.0          8           340        160    3609           8.0     70   \n",
       "12   15.0          8           400        150    3761           9.5     70   \n",
       "13   14.0          8           455        225    3086          10.0     70   \n",
       "14   24.0          4           113         95    2372          15.0     70   \n",
       "15   22.0          6           198         95    2833          15.5     70   \n",
       "16   18.0          6           199         97    2774          15.5     70   \n",
       "17   21.0          6           200         85    2587          16.0     70   \n",
       "18   27.0          4            97         88    2130          14.5     70   \n",
       "19   26.0          4            97         46    1835          20.5     70   \n",
       "20   25.0          4           110         87    2672          17.5     70   \n",
       "21   24.0          4           107         90    2430          14.5     70   \n",
       "22   25.0          4           104         95    2375          17.5     70   \n",
       "23   26.0          4           121        113    2234          12.5     70   \n",
       "24   21.0          6           199         90    2648          15.0     70   \n",
       "25   10.0          8           360        215    4615          14.0     70   \n",
       "26   10.0          8           307        200    4376          15.0     70   \n",
       "27   11.0          8           318        210    4382          13.5     70   \n",
       "28    9.0          8           304        193    4732          18.5     70   \n",
       "29   27.0          4            97         88    2130          14.5     71   \n",
       "..    ...        ...           ...        ...     ...           ...    ...   \n",
       "170  27.0          4           112         88    2640          18.6     82   \n",
       "171  34.0          4           112         88    2395          18.0     82   \n",
       "172  31.0          4           112         85    2575          16.2     82   \n",
       "173  29.0          4           135         84    2525          16.0     82   \n",
       "174  27.0          4           151         90    2735          18.0     82   \n",
       "175  24.0          4           140         92    2865          16.4     82   \n",
       "176  23.0          4           151          ?    3035          20.5     82   \n",
       "177  36.0          4           105         74    1980          15.3     82   \n",
       "178  37.0          4            91         68    2025          18.2     82   \n",
       "179  31.0          4            91         68    1970          17.6     82   \n",
       "180  38.0          4           105         63    2125          14.7     82   \n",
       "181  36.0          4            98         70    2125          17.3     82   \n",
       "182  36.0          4           120         88    2160          14.5     82   \n",
       "183  36.0          4           107         75    2205          14.5     82   \n",
       "184  34.0          4           108         70    2245          16.9     82   \n",
       "185  38.0          4            91         67    1965          15.0     82   \n",
       "186  32.0          4            91         67    1965          15.7     82   \n",
       "187  38.0          4            91         67    1995          16.2     82   \n",
       "188  25.0          6           181        110    2945          16.4     82   \n",
       "189  38.0          6           262         85    3015          17.0     82   \n",
       "190  26.0          4           156         92    2585          14.5     82   \n",
       "191  22.0          6           232        112    2835          14.7     82   \n",
       "192  32.0          4           144         96    2665          13.9     82   \n",
       "193  36.0          4           135         84    2370          13.0     82   \n",
       "194  27.0          4           151         90    2950          17.3     82   \n",
       "195  27.0          4           140         86    2790          15.6     82   \n",
       "196  44.0          4            97         52    2130          24.6     82   \n",
       "197  32.0          4           135         84    2295          11.6     82   \n",
       "198  28.0          4           120         79    2625          18.6     82   \n",
       "199  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin                                car  \n",
       "0         1          chevrolet chevelle malibu  \n",
       "1         1                  buick skylark 320  \n",
       "2         1                 plymouth satellite  \n",
       "3         1                      amc rebel sst  \n",
       "4         1                        ford torino  \n",
       "5         1                   ford galaxie 500  \n",
       "6         1                   chevrolet impala  \n",
       "7         1                  plymouth fury iii  \n",
       "8         1                   pontiac catalina  \n",
       "9         1                 amc ambassador dpl  \n",
       "10        1                dodge challenger se  \n",
       "11        1                 plymouth 'cuda 340  \n",
       "12        1              chevrolet monte carlo  \n",
       "13        1            buick estate wagon (sw)  \n",
       "14        3              toyota corona mark ii  \n",
       "15        1                    plymouth duster  \n",
       "16        1                         amc hornet  \n",
       "17        1                      ford maverick  \n",
       "18        3                       datsun pl510  \n",
       "19        2       volkswagen 1131 deluxe sedan  \n",
       "20        2                        peugeot 504  \n",
       "21        2                        audi 100 ls  \n",
       "22        2                           saab 99e  \n",
       "23        2                           bmw 2002  \n",
       "24        1                        amc gremlin  \n",
       "25        1                          ford f250  \n",
       "26        1                          chevy c20  \n",
       "27        1                         dodge d200  \n",
       "28        1                           hi 1200d  \n",
       "29        3                       datsun pl510  \n",
       "..      ...                                ...  \n",
       "170       1           chevrolet cavalier wagon  \n",
       "171       1          chevrolet cavalier 2-door  \n",
       "172       1         pontiac j2000 se hatchback  \n",
       "173       1                     dodge aries se  \n",
       "174       1                    pontiac phoenix  \n",
       "175       1               ford fairmont futura  \n",
       "176       1                     amc concord dl  \n",
       "177       2                volkswagen rabbit l  \n",
       "178       3                 mazda glc custom l  \n",
       "179       3                   mazda glc custom  \n",
       "180       1             plymouth horizon miser  \n",
       "181       1                     mercury lynx l  \n",
       "182       3                   nissan stanza xe  \n",
       "183       3                       honda accord  \n",
       "184       3                     toyota corolla  \n",
       "185       3                        honda civic  \n",
       "186       3                 honda civic (auto)  \n",
       "187       3                      datsun 310 gx  \n",
       "188       1              buick century limited  \n",
       "189       1  oldsmobile cutlass ciera (diesel)  \n",
       "190       1         chrysler lebaron medallion  \n",
       "191       1                     ford granada l  \n",
       "192       3                   toyota celica gt  \n",
       "193       1                  dodge charger 2.2  \n",
       "194       1                   chevrolet camaro  \n",
       "195       1                    ford mustang gl  \n",
       "196       2                          vw pickup  \n",
       "197       1                      dodge rampage  \n",
       "198       1                        ford ranger  \n",
       "199       1                         chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Oops, there is a column missing, called owners. Create a random number Series from 15,000 to 73,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29487, 25680, 65268, 31827, 69215, 72602, 52693, 58440, 16183,\n",
       "       45014, 32318, 72942, 62163, 35951, 57625, 59355, 36533, 67048,\n",
       "       58159, 69743, 25146, 22755, 44966, 46792, 56553, 65013, 55908,\n",
       "       69563, 22030, 59561, 15593, 52998, 54795, 16169, 24809, 35580,\n",
       "       46590, 38792, 43099, 37166, 21390, 56496, 68606, 21110, 56334,\n",
       "       45477, 51961, 27625, 51176, 30796, 61809, 65450, 67375, 23342,\n",
       "       27499, 50585, 57302, 56191, 60281, 32865, 58605, 66374, 15315,\n",
       "       31791, 28670, 38796, 69214, 41055, 32353, 31574, 65799, 42998,\n",
       "       72785, 18415, 31977, 29812, 65439, 21161, 60871, 67151, 22179,\n",
       "       32821, 55392, 34586, 67937, 31646, 66397, 35258, 63815, 71291,\n",
       "       51130, 27684, 49648, 52691, 50681, 68185, 32635, 51553, 28970,\n",
       "       19112, 26035, 67666, 55471, 51477, 62055, 53003, 41265, 18565,\n",
       "       48851, 48673, 45832, 67891, 57638, 29240, 41236, 16950, 31449,\n",
       "       50528, 22397, 15876, 26414, 16736, 23896, 46104, 17583, 65951,\n",
       "       38538, 31443, 19299, 46095, 31239, 19290, 38051, 68575, 61755,\n",
       "       22560, 34460, 35395, 34608, 56906, 44895, 48429, 20900, 49770,\n",
       "       50513, 59402, 26893, 37233, 19036, 20523, 18765, 46333, 42831,\n",
       "       53698, 25218, 63106, 16928, 34901, 43674, 65453, 54428, 68502,\n",
       "       19043, 20325, 45039, 29466, 49672, 67972, 30547, 22522, 69354,\n",
       "       40489, 72887, 15724, 51442, 65182, 64555, 42138, 72988, 20861,\n",
       "       67898, 20768, 36415, 47480, 16820, 48739, 62610, 43473, 23002,\n",
       "       43488, 62581, 37724, 63019, 44912, 35595, 59188, 51814, 65283,\n",
       "       53479, 27660, 38237, 22957, 47870, 15533, 41944, 51830, 56676,\n",
       "       57481, 48529, 72220, 66675, 50099, 30585, 25436, 49195, 26050,\n",
       "       24899, 37213, 25870, 67447, 23808, 71275, 67572, 18545, 43553,\n",
       "       54858, 23077, 33705, 31282, 26298, 23742, 36110, 51491, 18019,\n",
       "       60655, 27453, 35563, 63627, 35315, 56717, 59281, 55634, 18415,\n",
       "       59570, 47320, 20110, 18425, 19352, 18032, 31816, 28573, 66030,\n",
       "       54723, 21592, 37160, 59518, 35629, 47619, 52359, 34566, 64932,\n",
       "       24072, 39445, 31203, 63975, 62041, 70175, 51029, 32058, 19428,\n",
       "       65553, 50799, 48190, 68061, 68201, 53389, 15901, 44585, 54723,\n",
       "       30446, 63716, 57488, 67134, 22033, 53694, 40002, 24854, 59747,\n",
       "       59827, 53378, 53196, 68686, 20784, 28181, 33044, 41694, 39857,\n",
       "       57296, 69021, 17359, 29794, 22515, 55877, 22806, 50027, 56787,\n",
       "       50844, 17420, 65259, 19141, 40204, 19530, 30116, 34973, 15641,\n",
       "       53492, 59574, 59082, 64400, 70163, 43058, 69696, 67996, 26158,\n",
       "       32936, 45461, 47390, 32368, 15400, 40895, 16572, 31776, 62121,\n",
       "       56704, 39335, 27716, 52565, 50831, 45049, 25173, 25018, 18606,\n",
       "       71177, 66288, 46754, 68175, 35829, 24959, 54792, 19059, 29092,\n",
       "       58736, 62938, 44733, 17884, 33905, 33965, 24641, 52257, 28178,\n",
       "       29515, 37703, 56036, 51556, 23590, 61888, 70224, 53730, 41328,\n",
       "       16501, 30360, 54106, 29101, 35631, 56173, 30424, 46887, 23657,\n",
       "       17723, 71709, 45270, 30380, 27779, 33774, 36379, 47127, 63625,\n",
       "       16750, 65740, 53802, 40995, 37487, 42791, 21825, 69344, 63210,\n",
       "       15982, 20259])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Add the column owners to cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model</th>\n",
       "      <th>origin</th>\n",
       "      <th>car</th>\n",
       "      <th>owners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "      <td>21825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "      <td>69344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "      <td>63210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "      <td>15982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "      <td>20259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  model  \\\n",
       "195  27.0          4           140         86    2790          15.6     82   \n",
       "196  44.0          4            97         52    2130          24.6     82   \n",
       "197  32.0          4           135         84    2295          11.6     82   \n",
       "198  28.0          4           120         79    2625          18.6     82   \n",
       "199  31.0          4           119         82    2720          19.4     82   \n",
       "\n",
       "     origin              car  owners  \n",
       "195       1  ford mustang gl   21825  \n",
       "196       2        vw pickup   69344  \n",
       "197       1    dodge rampage   63210  \n",
       "198       1      ford ranger   15982  \n",
       "199       1       chevy s-10   20259  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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================================================
FILE: 05_Merge/Auto_MPG/cars1.csv
================================================
mpg,cylinders,displacement,horsepower,weight,acceleration,model,origin,car,,,,,
18.0,8,307,130,3504,12.0,70,1,chevrolet chevelle malibu,,,,,
15.0,8,350,165,3693,11.5,70,1,buick skylark 320,,,,,
18.0,8,318,150,3436,11.0,70,1,plymouth satellite,,,,,
16.0,8,304,150,3433,12.0,70,1,amc rebel sst,,,,,
17.0,8,302,140,3449,10.5,70,1,ford torino,,,,,
15.0,8,429,198,4341,10.0,70,1,ford galaxie 500,,,,,
14.0,8,454,220,4354,9.0,70,1,chevrolet impala,,,,,
14.0,8,440,215,4312,8.5,70,1,plymouth fury iii,,,,,
14.0,8,455,225,4425,10.0,70,1,pontiac catalina,,,,,
15.0,8,390,190,3850,8.5,70,1,amc ambassador dpl,,,,,
15.0,8,383,170,3563,10.0,70,1,dodge challenger se,,,,,
14.0,8,340,160,3609,8.0,70,1,plymouth 'cuda 340,,,,,
15.0,8,400,150,3761,9.5,70,1,chevrolet monte carlo,,,,,
14.0,8,455,225,3086,10.0,70,1,buick estate wagon (sw),,,,,
24.0,4,113,95,2372,15.0,70,3,toyota corona mark ii,,,,,
22.0,6,198,95,2833,15.5,70,1,plymouth duster,,,,,
18.0,6,199,97,2774,15.5,70,1,amc hornet,,,,,
21.0,6,200,85,2587,16.0,70,1,ford maverick,,,,,
27.0,4,97,88,2130,14.5,70,3,datsun pl510,,,,,
26.0,4,97,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan,,,,,
25.0,4,110,87,2672,17.5,70,2,peugeot 504,,,,,
24.0,4,107,90,2430,14.5,70,2,audi 100 ls,,,,,
25.0,4,104,95,2375,17.5,70,2,saab 99e,,,,,
26.0,4,121,113,2234,12.5,70,2,bmw 2002,,,,,
21.0,6,199,90,2648,15.0,70,1,amc gremlin,,,,,
10.0,8,360,215,4615,14.0,70,1,ford f250,,,,,
10.0,8,307,200,4376,15.0,70,1,chevy c20,,,,,
11.0,8,318,210,4382,13.5,70,1,dodge d200,,,,,
9.0,8,304,193,4732,18.5,70,1,hi 1200d,,,,,
27.0,4,97,88,2130,14.5,71,3,datsun pl510,,,,,
28.0,4,140,90,2264,15.5,71,1,chevrolet vega 2300,,,,,
25.0,4,113,95,2228,14.0,71,3,toyota corona,,,,,
25.0,4,98,?,2046,19.0,71,1,ford pinto,,,,,
19.0,6,232,100,2634,13.0,71,1,amc gremlin,,,,,
16.0,6,225,105,3439,15.5,71,1,plymouth satellite custom,,,,,
17.0,6,250,100,3329,15.5,71,1,chevrolet chevelle malibu,,,,,
19.0,6,250,88,3302,15.5,71,1,ford torino 500,,,,,
18.0,6,232,100,3288,15.5,71,1,amc matador,,,,,
14.0,8,350,165,4209,12.0,71,1,chevrolet impala,,,,,
14.0,8,400,175,4464,11.5,71,1,pontiac catalina brougham,,,,,
14.0,8,351,153,4154,13.5,71,1,ford galaxie 500,,,,,
14.0,8,318,150,4096,13.0,71,1,plymouth fury iii,,,,,
12.0,8,383,180,4955,11.5,71,1,dodge monaco (sw),,,,,
13.0,8,400,170,4746,12.0,71,1,ford country squire (sw),,,,,
13.0,8,400,175,5140,12.0,71,1,pontiac safari (sw),,,,,
18.0,6,258,110,2962,13.5,71,1,amc hornet sportabout (sw),,,,,
22.0,4,140,72,2408,19.0,71,1,chevrolet vega (sw),,,,,
19.0,6,250,100,3282,15.0,71,1,pontiac firebird,,,,,
18.0,6,250,88,3139,14.5,71,1,ford mustang,,,,,
23.0,4,122,86,2220,14.0,71,1,mercury capri 2000,,,,,
28.0,4,116,90,2123,14.0,71,2,opel 1900,,,,,
30.0,4,79,70,2074,19.5,71,2,peugeot 304,,,,,
30.0,4,88,76,2065,14.5,71,2,fiat 124b,,,,,
31.0,4,71,65,1773,19.0,71,3,toyota corolla 1200,,,,,
35.0,4,72,69,1613,18.0,71,3,datsun 1200,,,,,
27.0,4,97,60,1834,19.0,71,2,volkswagen model 111,,,,,
26.0,4,91,70,1955,20.5,71,1,plymouth cricket,,,,,
24.0,4,113,95,2278,15.5,72,3,toyota corona hardtop,,,,,
25.0,4,98,80,2126,17.0,72,1,dodge colt hardtop,,,,,
23.0,4,97,54,2254,23.5,72,2,volkswagen type 3,,,,,
20.0,4,140,90,2408,19.5,72,1,chevrolet vega,,,,,
21.0,4,122,86,2226,16.5,72,1,ford pinto runabout,,,,,
13.0,8,350,165,4274,12.0,72,1,chevrolet impala,,,,,
14.0,8,400,175,4385,12.0,72,1,pontiac catalina,,,,,
15.0,8,318,150,4135,13.5,72,1,plymouth fury iii,,,,,
14.0,8,351,153,4129,13.0,72,1,ford galaxie 500,,,,,
17.0,8,304,150,3672,11.5,72,1,amc ambassador sst,,,,,
11.0,8,429,208,4633,11.0,72,1,mercury marquis,,,,,
13.0,8,350,155,4502,13.5,72,1,buick lesabre custom,,,,,
12.0,8,350,160,4456,13.5,72,1,oldsmobile delta 88 royale,,,,,
13.0,8,400,190,4422,12.5,72,1,chrysler newport royal,,,,,
19.0,3,70,97,2330,13.5,72,3,mazda rx2 coupe,,,,,
15.0,8,304,150,3892,12.5,72,1,amc matador (sw),,,,,
13.0,8,307,130,4098,14.0,72,1,chevrolet chevelle concours (sw),,,,,
13.0,8,302,140,4294,16.0,72,1,ford gran torino (sw),,,,,
14.0,8,318,150,4077,14.0,72,1,plymouth satellite custom (sw),,,,,
18.0,4,121,112,2933,14.5,72,2,volvo 145e (sw),,,,,
22.0,4,121,76,2511,18.0,72,2,volkswagen 411 (sw),,,,,
21.0,4,120,87,2979,19.5,72,2,peugeot 504 (sw),,,,,
26.0,4,96,69,2189,18.0,72,2,renault 12 (sw),,,,,
22.0,4,122,86,2395,16.0,72,1,ford pinto (sw),,,,,
28.0,4,97,92,2288,17.0,72,3,datsun 510 (sw),,,,,
23.0,4,120,97,2506,14.5,72,3,toyouta corona mark ii (sw),,,,,
28.0,4,98,80,2164,15.0,72,1,dodge colt (sw),,,,,
27.0,4,97,88,2100,16.5,72,3,toyota corolla 1600 (sw),,,,,
13.0,8,350,175,4100,13.0,73,1,buick century 350,,,,,
14.0,8,304,150,3672,11.5,73,1,amc matador,,,,,
13.0,8,350,145,3988,13.0,73,1,chevrolet malibu,,,,,
14.0,8,302,137,4042,14.5,73,1,ford gran torino,,,,,
15.0,8,318,150,3777,12.5,73,1,dodge coronet custom,,,,,
12.0,8,429,198,4952,11.5,73,1,mercury marquis brougham,,,,,
13.0,8,400,150,4464,12.0,73,1,chevrolet caprice classic,,,,,
13.0,8,351,158,4363,13.0,73,1,ford ltd,,,,,
14.0,8,318,150,4237,14.5,73,1,plymouth fury gran sedan,,,,,
13.0,8,440,215,4735,11.0,73,1,chrysler new yorker brougham,,,,,
12.0,8,455,225,4951,11.0,73,1,buick electra 225 custom,,,,,
13.0,8,360,175,3821,11.0,73,1,amc ambassador brougham,,,,,
18.0,6,225,105,3121,16.5,73,1,plymouth valiant,,,,,
16.0,6,250,100,3278,18.0,73,1,chevrolet nova custom,,,,,
18.0,6,232,100,2945,16.0,73,1,amc hornet,,,,,
18.0,6,250,88,3021,16.5,73,1,ford maverick,,,,,
23.0,6,198,95,2904,16.0,73,1,plymouth duster,,,,,
26.0,4,97,46,1950,21.0,73,2,volkswagen super beetle,,,,,
11.0,8,400,150,4997,14.0,73,1,chevrolet impala,,,,,
12.0,8,400,167,4906,12.5,73,1,ford country,,,,,
13.0,8,360,170,4654,13.0,73,1,plymouth custom suburb,,,,,
12.0,8,350,180,4499,12.5,73,1,oldsmobile vista cruiser,,,,,
18.0,6,232,100,2789,15.0,73,1,amc gremlin,,,,,
20.0,4,97,88,2279,19.0,73,3,toyota carina,,,,,
21.0,4,140,72,2401,19.5,73,1,chevrolet vega,,,,,
22.0,4,108,94,2379,16.5,73,3,datsun 610,,,,,
18.0,3,70,90,2124,13.5,73,3,maxda rx3,,,,,
19.0,4,122,85,2310,18.5,73,1,ford pinto,,,,,
21.0,6,155,107,2472,14.0,73,1,mercury capri v6,,,,,
26.0,4,98,90,2265,15.5,73,2,fiat 124 sport coupe,,,,,
15.0,8,350,145,4082,13.0,73,1,chevrolet monte carlo s,,,,,
16.0,8,400,230,4278,9.5,73,1,pontiac grand prix,,,,,
29.0,4,68,49,1867,19.5,73,2,fiat 128,,,,,
24.0,4,116,75,2158,15.5,73,2,opel manta,,,,,
20.0,4,114,91,2582,14.0,73,2,audi 100ls,,,,,
19.0,4,121,112,2868,15.5,73,2,volvo 144ea,,,,,
15.0,8,318,150,3399,11.0,73,1,dodge dart custom,,,,,
24.0,4,121,110,2660,14.0,73,2,saab 99le,,,,,
20.0,6,156,122,2807,13.5,73,3,toyota mark ii,,,,,
11.0,8,350,180,3664,11.0,73,1,oldsmobile omega,,,,,
20.0,6,198,95,3102,16.5,74,1,plymouth duster,,,,,
21.0,6,200,?,2875,17.0,74,1,ford maverick,,,,,
19.0,6,232,100,2901,16.0,74,1,amc hornet,,,,,
15.0,6,250,100,3336,17.0,74,1,chevrolet nova,,,,,
31.0,4,79,67,1950,19.0,74,3,datsun b210,,,,,
26.0,4,122,80,2451,16.5,74,1,ford pinto,,,,,
32.0,4,71,65,1836,21.0,74,3,toyota corolla 1200,,,,,
25.0,4,140,75,2542,17.0,74,1,chevrolet vega,,,,,
16.0,6,250,100,3781,17.0,74,1,chevrolet chevelle malibu classic,,,,,
16.0,6,258,110,3632,18.0,74,1,amc matador,,,,,
18.0,6,225,105,3613,16.5,74,1,plymouth satellite sebring,,,,,
16.0,8,302,140,4141,14.0,74,1,ford gran torino,,,,,
13.0,8,350,150,4699,14.5,74,1,buick century luxus (sw),,,,,
14.0,8,318,150,4457,13.5,74,1,dodge coronet custom (sw),,,,,
14.0,8,302,140,4638,16.0,74,1,ford gran torino (sw),,,,,
14.0,8,304,150,4257,15.5,74,1,amc matador (sw),,,,,
29.0,4,98,83,2219,16.5,74,2,audi fox,,,,,
26.0,4,79,67,1963,15.5,74,2,volkswagen dasher,,,,,
26.0,4,97,78,2300,14.5,74,2,opel manta,,,,,
31.0,4,76,52,1649,16.5,74,3,toyota corona,,,,,
32.0,4,83,61,2003,19.0,74,3,datsun 710,,,,,
28.0,4,90,75,2125,14.5,74,1,dodge colt,,,,,
24.0,4,90,75,2108,15.5,74,2,fiat 128,,,,,
26.0,4,116,75,2246,14.0,74,2,fiat 124 tc,,,,,
24.0,4,120,97,2489,15.0,74,3,honda civic,,,,,
26.0,4,108,93,2391,15.5,74,3,subaru,,,,,
31.0,4,79,67,2000,16.0,74,2,fiat x1.9,,,,,
19.0,6,225,95,3264,16.0,75,1,plymouth valiant custom,,,,,
18.0,6,250,105,3459,16.0,75,1,chevrolet nova,,,,,
15.0,6,250,72,3432,21.0,75,1,mercury monarch,,,,,
15.0,6,250,72,3158,19.5,75,1,ford maverick,,,,,
16.0,8,400,170,4668,11.5,75,1,pontiac catalina,,,,,
15.0,8,350,145,4440,14.0,75,1,chevrolet bel air,,,,,
16.0,8,318,150,4498,14.5,75,1,plymouth grand fury,,,,,
14.0,8,351,148,4657,13.5,75,1,ford ltd,,,,,
17.0,6,231,110,3907,21.0,75,1,buick century,,,,,
16.0,6,250,105,3897,18.5,75,1,chevroelt chevelle malibu,,,,,
15.0,6,258,110,3730,19.0,75,1,amc matador,,,,,
18.0,6,225,95,3785,19.0,75,1,plymouth fury,,,,,
21.0,6,231,110,3039,15.0,75,1,buick skyhawk,,,,,
20.0,8,262,110,3221,13.5,75,1,chevrolet monza 2+2,,,,,
13.0,8,302,129,3169,12.0,75,1,ford mustang ii,,,,,
29.0,4,97,75,2171,16.0,75,3,toyota corolla,,,,,
23.0,4,140,83,2639,17.0,75,1,ford pinto,,,,,
20.0,6,232,100,2914,16.0,75,1,amc gremlin,,,,,
23.0,4,140,78,2592,18.5,75,1,pontiac astro,,,,,
24.0,4,134,96,2702,13.5,75,3,toyota corona,,,,,
25.0,4,90,71,2223,16.5,75,2,volkswagen dasher,,,,,
24.0,4,119,97,2545,17.0,75,3,datsun 710,,,,,
18.0,6,171,97,2984,14.5,75,1,ford pinto,,,,,
29.0,4,90,70,1937,14.0,75,2,volkswagen rabbit,,,,,
19.0,6,232,90,3211,17.0,75,1,amc pacer,,,,,
23.0,4,115,95,2694,15.0,75,2,audi 100ls,,,,,
23.0,4,120,88,2957,17.0,75,2,peugeot 504,,,,,
22.0,4,121,98,2945,14.5,75,2,volvo 244dl,,,,,
25.0,4,121,115,2671,13.5,75,2,saab 99le,,,,,
33.0,4,91,53,1795,17.5,75,3,honda civic cvcc,,,,,
28.0,4,107,86,2464,15.5,76,2,fiat 131,,,,,
25.0,4,116,81,2220,16.9,76,2,opel 1900,,,,,
25.0,4,140,92,2572,14.9,76,1,capri ii,,,,,
26.0,4,98,79,2255,17.7,76,1,dodge colt,,,,,
27.0,4,101,83,2202,15.3,76,2,renault 12tl,,,,,
17.5,8,305,140,4215,13.0,76,1,chevrolet chevelle malibu classic,,,,,
16.0,8,318,150,4190,13.0,76,1,dodge coronet brougham,,,,,
15.5,8,304,120,3962,13.9,76,1,amc matador,,,,,
14.5,8,351,152,4215,12.8,76,1,ford gran torino,,,,,
22.0,6,225,100,3233,15.4,76,1,plymouth valiant,,,,,
22.0,6,250,105,3353,14.5,76,1,chevrolet nova,,,,,
24.0,6,200,81,3012,17.6,76,1,ford maverick,,,,,
22.5,6,232,90,3085,17.6,76,1,amc hornet,,,,,
29.0,4,85,52,2035,22.2,76,1,chevrolet chevette,,,,,
24.5,4,98,60,2164,22.1,76,1,chevrolet woody,,,,,
29.0,4,90,70,1937,14.2,76,2,vw rabbit,,,,,

================================================
FILE: 05_Merge/Auto_MPG/cars2.csv
================================================
mpg,cylinders,displacement,horsepower,weight,acceleration,model,origin,car
33.0,4,91,53,1795,17.4,76,3,honda civic
20.0,6,225,100,3651,17.7,76,1,dodge aspen se
18.0,6,250,78,3574,21.0,76,1,ford granada ghia
18.5,6,250,110,3645,16.2,76,1,pontiac ventura sj
17.5,6,258,95,3193,17.8,76,1,amc pacer d/l
29.5,4,97,71,1825,12.2,76,2,volkswagen rabbit
32.0,4,85,70,1990,17.0,76,3,datsun b-210
28.0,4,97,75,2155,16.4,76,3,toyota corolla
26.5,4,140,72,2565,13.6,76,1,ford pinto
20.0,4,130,102,3150,15.7,76,2,volvo 245
13.0,8,318,150,3940,13.2,76,1,plymouth volare premier v8
19.0,4,120,88,3270,21.9,76,2,peugeot 504
19.0,6,156,108,2930,15.5,76,3,toyota mark ii
16.5,6,168,120,3820,16.7,76,2,mercedes-benz 280s
16.5,8,350,180,4380,12.1,76,1,cadillac seville
13.0,8,350,145,4055,12.0,76,1,chevy c10
13.0,8,302,130,3870,15.0,76,1,ford f108
13.0,8,318,150,3755,14.0,76,1,dodge d100
31.5,4,98,68,2045,18.5,77,3,honda accord cvcc
30.0,4,111,80,2155,14.8,77,1,buick opel isuzu deluxe
36.0,4,79,58,1825,18.6,77,2,renault 5 gtl
25.5,4,122,96,2300,15.5,77,1,plymouth arrow gs
33.5,4,85,70,1945,16.8,77,3,datsun f-10 hatchback
17.5,8,305,145,3880,12.5,77,1,chevrolet caprice classic
17.0,8,260,110,4060,19.0,77,1,oldsmobile cutlass supreme
15.5,8,318,145,4140,13.7,77,1,dodge monaco brougham
15.0,8,302,130,4295,14.9,77,1,mercury cougar brougham
17.5,6,250,110,3520,16.4,77,1,chevrolet concours
20.5,6,231,105,3425,16.9,77,1,buick skylark
19.0,6,225,100,3630,17.7,77,1,plymouth volare custom
18.5,6,250,98,3525,19.0,77,1,ford granada
16.0,8,400,180,4220,11.1,77,1,pontiac grand prix lj
15.5,8,350,170,4165,11.4,77,1,chevrolet monte carlo landau
15.5,8,400,190,4325,12.2,77,1,chrysler cordoba
16.0,8,351,149,4335,14.5,77,1,ford thunderbird
29.0,4,97,78,1940,14.5,77,2,volkswagen rabbit custom
24.5,4,151,88,2740,16.0,77,1,pontiac sunbird coupe
26.0,4,97,75,2265,18.2,77,3,toyota corolla liftback
25.5,4,140,89,2755,15.8,77,1,ford mustang ii 2+2
30.5,4,98,63,2051,17.0,77,1,chevrolet chevette
33.5,4,98,83,2075,15.9,77,1,dodge colt m/m
30.0,4,97,67,1985,16.4,77,3,subaru dl
30.5,4,97,78,2190,14.1,77,2,volkswagen dasher
22.0,6,146,97,2815,14.5,77,3,datsun 810
21.5,4,121,110,2600,12.8,77,2,bmw 320i
21.5,3,80,110,2720,13.5,77,3,mazda rx-4
43.1,4,90,48,1985,21.5,78,2,volkswagen rabbit custom diesel
36.1,4,98,66,1800,14.4,78,1,ford fiesta
32.8,4,78,52,1985,19.4,78,3,mazda glc deluxe
39.4,4,85,70,2070,18.6,78,3,datsun b210 gx
36.1,4,91,60,1800,16.4,78,3,honda civic cvcc
19.9,8,260,110,3365,15.5,78,1,oldsmobile cutlass salon brougham
19.4,8,318,140,3735,13.2,78,1,dodge diplomat
20.2,8,302,139,3570,12.8,78,1,mercury monarch ghia
19.2,6,231,105,3535,19.2,78,1,pontiac phoenix lj
20.5,6,200,95,3155,18.2,78,1,chevrolet malibu
20.2,6,200,85,2965,15.8,78,1,ford fairmont (auto)
25.1,4,140,88,2720,15.4,78,1,ford fairmont (man)
20.5,6,225,100,3430,17.2,78,1,plymouth volare
19.4,6,232,90,3210,17.2,78,1,amc concord
20.6,6,231,105,3380,15.8,78,1,buick century special
20.8,6,200,85,3070,16.7,78,1,mercury zephyr
18.6,6,225,110,3620,18.7,78,1,dodge aspen
18.1,6,258,120,3410,15.1,78,1,amc concord d/l
19.2,8,305,145,3425,13.2,78,1,chevrolet monte carlo landau
17.7,6,231,165,3445,13.4,78,1,buick regal sport coupe (turbo)
18.1,8,302,139,3205,11.2,78,1,ford futura
17.5,8,318,140,4080,13.7,78,1,dodge magnum xe
30.0,4,98,68,2155,16.5,78,1,chevrolet chevette
27.5,4,134,95,2560,14.2,78,3,toyota corona
27.2,4,119,97,2300,14.7,78,3,datsun 510
30.9,4,105,75,2230,14.5,78,1,dodge omni
21.1,4,134,95,2515,14.8,78,3,toyota celica gt liftback
23.2,4,156,105,2745,16.7,78,1,plymouth sapporo
23.8,4,151,85,2855,17.6,78,1,oldsmobile starfire sx
23.9,4,119,97,2405,14.9,78,3,datsun 200-sx
20.3,5,131,103,2830,15.9,78,2,audi 5000
17.0,6,163,125,3140,13.6,78,2,volvo 264gl
21.6,4,121,115,2795,15.7,78,2,saab 99gle
16.2,6,163,133,3410,15.8,78,2,peugeot 604sl
31.5,4,89,71,1990,14.9,78,2,volkswagen scirocco
29.5,4,98,68,2135,16.6,78,3,honda accord lx
21.5,6,231,115,3245,15.4,79,1,pontiac lemans v6
19.8,6,200,85,2990,18.2,79,1,mercury zephyr 6
22.3,4,140,88,2890,17.3,79,1,ford fairmont 4
20.2,6,232,90,3265,18.2,79,1,amc concord dl 6
20.6,6,225,110,3360,16.6,79,1,dodge aspen 6
17.0,8,305,130,3840,15.4,79,1,chevrolet caprice classic
17.6,8,302,129,3725,13.4,79,1,ford ltd landau
16.5,8,351,138,3955,13.2,79,1,mercury grand marquis
18.2,8,318,135,3830,15.2,79,1,dodge st. regis
16.9,8,350,155,4360,14.9,79,1,buick estate wagon (sw)
15.5,8,351,142,4054,14.3,79,1,ford country squire (sw)
19.2,8,267,125,3605,15.0,79,1,chevrolet malibu classic (sw)
18.5,8,360,150,3940,13.0,79,1,chrysler lebaron town @ country (sw)
31.9,4,89,71,1925,14.0,79,2,vw rabbit custom
34.1,4,86,65,1975,15.2,79,3,maxda glc deluxe
35.7,4,98,80,1915,14.4,79,1,dodge colt hatchback custom
27.4,4,121,80,2670,15.0,79,1,amc spirit dl
25.4,5,183,77,3530,20.1,79,2,mercedes benz 300d
23.0,8,350,125,3900,17.4,79,1,cadillac eldorado
27.2,4,141,71,3190,24.8,79,2,peugeot 504
23.9,8,260,90,3420,22.2,79,1,oldsmobile cutlass salon brougham
34.2,4,105,70,2200,13.2,79,1,plymouth horizon
34.5,4,105,70,2150,14.9,79,1,plymouth horizon tc3
31.8,4,85,65,2020,19.2,79,3,datsun 210
37.3,4,91,69,2130,14.7,79,2,fiat strada custom
28.4,4,151,90,2670,16.0,79,1,buick skylark limited
28.8,6,173,115,2595,11.3,79,1,chevrolet citation
26.8,6,173,115,2700,12.9,79,1,oldsmobile omega brougham
33.5,4,151,90,2556,13.2,79,1,pontiac phoenix
41.5,4,98,76,2144,14.7,80,2,vw rabbit
38.1,4,89,60,1968,18.8,80,3,toyota corolla tercel
32.1,4,98,70,2120,15.5,80,1,chevrolet chevette
37.2,4,86,65,2019,16.4,80,3,datsun 310
28.0,4,151,90,2678,16.5,80,1,chevrolet citation
26.4,4,140,88,2870,18.1,80,1,ford fairmont
24.3,4,151,90,3003,20.1,80,1,amc concord
19.1,6,225,90,3381,18.7,80,1,dodge aspen
34.3,4,97,78,2188,15.8,80,2,audi 4000
29.8,4,134,90,2711,15.5,80,3,toyota corona liftback
31.3,4,120,75,2542,17.5,80,3,mazda 626
37.0,4,119,92,2434,15.0,80,3,datsun 510 hatchback
32.2,4,108,75,2265,15.2,80,3,toyota corolla
46.6,4,86,65,2110,17.9,80,3,mazda glc
27.9,4,156,105,2800,14.4,80,1,dodge colt
40.8,4,85,65,2110,19.2,80,3,datsun 210
44.3,4,90,48,2085,21.7,80,2,vw rabbit c (diesel)
43.4,4,90,48,2335,23.7,80,2,vw dasher (diesel)
36.4,5,121,67,2950,19.9,80,2,audi 5000s (diesel)
30.0,4,146,67,3250,21.8,80,2,mercedes-benz 240d
44.6,4,91,67,1850,13.8,80,3,honda civic 1500 gl
40.9,4,85,?,1835,17.3,80,2,renault lecar deluxe
33.8,4,97,67,2145,18.0,80,3,subaru dl
29.8,4,89,62,1845,15.3,80,2,vokswagen rabbit
32.7,6,168,132,2910,11.4,80,3,datsun 280-zx
23.7,3,70,100,2420,12.5,80,3,mazda rx-7 gs
35.0,4,122,88,2500,15.1,80,2,triumph tr7 coupe
23.6,4,140,?,2905,14.3,80,1,ford mustang cobra
32.4,4,107,72,2290,17.0,80,3,honda accord
27.2,4,135,84,2490,15.7,81,1,plymouth reliant
26.6,4,151,84,2635,16.4,81,1,buick skylark
25.8,4,156,92,2620,14.4,81,1,dodge aries wagon (sw)
23.5,6,173,110,2725,12.6,81,1,chevrolet citation
30.0,4,135,84,2385,12.9,81,1,plymouth reliant
39.1,4,79,58,1755,16.9,81,3,toyota starlet
39.0,4,86,64,1875,16.4,81,1,plymouth champ
35.1,4,81,60,1760,16.1,81,3,honda civic 1300
32.3,4,97,67,2065,17.8,81,3,subaru
37.0,4,85,65,1975,19.4,81,3,datsun 210 mpg
37.7,4,89,62,2050,17.3,81,3,toyota tercel
34.1,4,91,68,1985,16.0,81,3,mazda glc 4
34.7,4,105,63,2215,14.9,81,1,plymouth horizon 4
34.4,4,98,65,2045,16.2,81,1,ford escort 4w
29.9,4,98,65,2380,20.7,81,1,ford escort 2h
33.0,4,105,74,2190,14.2,81,2,volkswagen jetta
34.5,4,100,?,2320,15.8,81,2,renault 18i
33.7,4,107,75,2210,14.4,81,3,honda prelude
32.4,4,108,75,2350,16.8,81,3,toyota corolla
32.9,4,119,100,2615,14.8,81,3,datsun 200sx
31.6,4,120,74,2635,18.3,81,3,mazda 626
28.1,4,141,80,3230,20.4,81,2,peugeot 505s turbo diesel
30.7,6,145,76,3160,19.6,81,2,volvo diesel
25.4,6,168,116,2900,12.6,81,3,toyota cressida
24.2,6,146,120,2930,13.8,81,3,datsun 810 maxima
22.4,6,231,110,3415,15.8,81,1,buick century
26.6,8,350,105,3725,19.0,81,1,oldsmobile cutlass ls
20.2,6,200,88,3060,17.1,81,1,ford granada gl
17.6,6,225,85,3465,16.6,81,1,chrysler lebaron salon
28.0,4,112,88,2605,19.6,82,1,chevrolet cavalier
27.0,4,112,88,2640,18.6,82,1,chevrolet cavalier wagon
34.0,4,112,88,2395,18.0,82,1,chevrolet cavalier 2-door
31.0,4,112,85,2575,16.2,82,1,pontiac j2000 se hatchback
29.0,4,135,84,2525,16.0,82,1,dodge aries se
27.0,4,151,90,2735,18.0,82,1,pontiac phoenix
24.0,4,140,92,2865,16.4,82,1,ford fairmont futura
23.0,4,151,?,3035,20.5,82,1,amc concord dl
36.0,4,105,74,1980,15.3,82,2,volkswagen rabbit l
37.0,4,91,68,2025,18.2,82,3,mazda glc custom l
31.0,4,91,68,1970,17.6,82,3,mazda glc custom
38.0,4,105,63,2125,14.7,82,1,plymouth horizon miser
36.0,4,98,70,2125,17.3,82,1,mercury lynx l
36.0,4,120,88,2160,14.5,82,3,nissan stanza xe
36.0,4,107,75,2205,14.5,82,3,honda accord
34.0,4,108,70,2245,16.9,82,3,toyota corolla
38.0,4,91,67,1965,15.0,82,3,honda civic
32.0,4,91,67,1965,15.7,82,3,honda civic (auto)
38.0,4,91,67,1995,16.2,82,3,datsun 310 gx
25.0,6,181,110,2945,16.4,82,1,buick century limited
38.0,6,262,85,3015,17.0,82,1,oldsmobile cutlass ciera (diesel)
26.0,4,156,92,2585,14.5,82,1,chrysler lebaron medallion
22.0,6,232,112,2835,14.7,82,1,ford granada l
32.0,4,144,96,2665,13.9,82,3,toyota celica gt
36.0,4,135,84,2370,13.0,82,1,dodge charger 2.2
27.0,4,151,90,2950,17.3,82,1,chevrolet camaro
27.0,4,140,86,2790,15.6,82,1,ford mustang gl
44.0,4,97,52,2130,24.6,82,2,vw pickup
32.0,4,135,84,2295,11.6,82,1,dodge rampage
28.0,4,120,79,2625,18.6,82,1,ford ranger
31.0,4,119,82,2720,19.4,82,1,chevy s-10

================================================
FILE: 05_Merge/Fictitous_Names/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fictitious Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data again \n",
    "\n",
    "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n",
    "All the credits to this exercise belongs to him.  \n",
    "\n",
    "In order to understand about it go [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/).\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the 3 DataFrames based on the following raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign each to a variable called data1, data2, data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Join the two dataframes along rows and assign all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Join the two dataframes along columns and assing to all_data_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Merge all_data and data3 along the subject_id value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Merge all values in data1 and data2, with matching records from both sides where available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 05_Merge/Fictitous_Names/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fictitious Names\n",
    "\n",
    "Check out [Fictitious Names Exercises Video Tutorial](https://youtu.be/6DbgcHBiOqo) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data again \n",
    "\n",
    "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n",
    "All the credits to this exercise belongs to him.  \n",
    "\n",
    "In order to understand about it go to [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/).\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the 3 DataFrames based on the following raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign each to a variable called data1, data2, data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id  test_id\n",
       "0          1       51\n",
       "1          2       15\n",
       "2          3       15\n",
       "3          4       61\n",
       "4          5       16\n",
       "5          7       14\n",
       "6          8       15\n",
       "7          9        1\n",
       "8         10       61\n",
       "9         11       16"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.DataFrame(raw_data_1, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "data2 = pd.DataFrame(raw_data_2, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "data3 = pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])\n",
    "\n",
    "data3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Join the two dataframes along rows and assign all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches\n",
       "0          4      Billy    Bonder\n",
       "1          5      Brian     Black\n",
       "2          6       Bran   Balwner\n",
       "3          7      Bryce     Brice\n",
       "4          8      Betty    Btisan"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = pd.concat([data1, data2])\n",
    "all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Join the two dataframes along columns and assing to all_data_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name subject_id first_name last_name\n",
       "0          1       Alex  Anderson          4      Billy    Bonder\n",
       "1          2        Amy  Ackerman          5      Brian     Black\n",
       "2          3      Allen       Ali          6       Bran   Balwner\n",
       "3          4      Alice      Aoni          7      Bryce     Brice\n",
       "4          5     Ayoung   Atiches          8      Betty    Btisan"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data_col = pd.concat([data1, data2], axis = 1)\n",
    "all_data_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id  test_id\n",
       "0          1       51\n",
       "1          2       15\n",
       "2          3       15\n",
       "3          4       61\n",
       "4          5       16\n",
       "5          7       14\n",
       "6          8       15\n",
       "7          9        1\n",
       "8         10       61\n",
       "9         11       16"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Merge all_data and data3 along the subject_id value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name  test_id\n",
       "0          1       Alex  Anderson       51\n",
       "1          2        Amy  Ackerman       15\n",
       "2          3      Allen       Ali       15\n",
       "3          4      Alice      Aoni       61\n",
       "4          4      Billy    Bonder       61\n",
       "5          5     Ayoung   Atiches       16\n",
       "6          5      Brian     Black       16\n",
       "7          7      Bryce     Brice       14\n",
       "8          8      Betty    Btisan       15"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(all_data, data3, on='subject_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
       "      <th>first_name_y</th>\n",
       "      <th>last_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          4        Alice        Aoni        Billy      Bonder\n",
       "1          5       Ayoung     Atiches        Brian       Black"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(data1, data2, on='subject_id', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Merge all values in data1 and data2, with matching records from both sides where available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
       "      <th>first_name_y</th>\n",
       "      <th>last_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          1         Alex    Anderson          NaN         NaN\n",
       "1          2          Amy    Ackerman          NaN         NaN\n",
       "2          3        Allen         Ali          NaN         NaN\n",
       "3          4        Alice        Aoni        Billy      Bonder\n",
       "4          5       Ayoung     Atiches        Brian       Black\n",
       "5          6          NaN         NaN         Bran     Balwner\n",
       "6          7          NaN         NaN        Bryce       Brice\n",
       "7          8          NaN         NaN        Betty      Btisan"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(data1, data2, on='subject_id', how='outer')"
   ]
  }
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================================================
FILE: 05_Merge/Fictitous_Names/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fictitious Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data again \n",
    "\n",
    "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n",
    "All the credits to this exercise belongs to him.  \n",
    "\n",
    "In order to understand about it go to [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/).\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the 3 DataFrames based on the following raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign each to a variable called data1, data2, data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id  test_id\n",
       "0          1       51\n",
       "1          2       15\n",
       "2          3       15\n",
       "3          4       61\n",
       "4          5       16\n",
       "5          7       14\n",
       "6          8       15\n",
       "7          9        1\n",
       "8         10       61\n",
       "9         11       16"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Join the two dataframes along rows and assign all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches\n",
       "0          4      Billy    Bonder\n",
       "1          5      Brian     Black\n",
       "2          6       Bran   Balwner\n",
       "3          7      Bryce     Brice\n",
       "4          8      Betty    Btisan"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Join the two dataframes along columns and assing to all_data_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name subject_id first_name last_name\n",
       "0          1       Alex  Anderson          4      Billy    Bonder\n",
       "1          2        Amy  Ackerman          5      Brian     Black\n",
       "2          3      Allen       Ali          6       Bran   Balwner\n",
       "3          4      Alice      Aoni          7      Bryce     Brice\n",
       "4          5     Ayoung   Atiches          8      Betty    Btisan"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Print data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id  test_id\n",
       "0          1       51\n",
       "1          2       15\n",
       "2          3       15\n",
       "3          4       61\n",
       "4          5       16\n",
       "5          7       14\n",
       "6          8       15\n",
       "7          9        1\n",
       "8         10       61\n",
       "9         11       16"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Merge all_data and data3 along the subject_id value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name  test_id\n",
       "0          1       Alex  Anderson       51\n",
       "1          2        Amy  Ackerman       15\n",
       "2          3      Allen       Ali       15\n",
       "3          4      Alice      Aoni       61\n",
       "4          4      Billy    Bonder       61\n",
       "5          5     Ayoung   Atiches       16\n",
       "6          5      Brian     Black       16\n",
       "7          7      Bryce     Brice       14\n",
       "8          8      Betty    Btisan       15"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
       "      <th>first_name_y</th>\n",
       "      <th>last_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          4        Alice        Aoni        Billy      Bonder\n",
       "1          5       Ayoung     Atiches        Brian       Black"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Merge all values in data1 and data2, with matching records from both sides where available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name_x</th>\n",
       "      <th>last_name_x</th>\n",
       "      <th>first_name_y</th>\n",
       "      <th>last_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>Atiches</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name_x last_name_x first_name_y last_name_y\n",
       "0          1         Alex    Anderson          NaN         NaN\n",
       "1          2          Amy    Ackerman          NaN         NaN\n",
       "2          3        Allen         Ali          NaN         NaN\n",
       "3          4        Alice        Aoni        Billy      Bonder\n",
       "4          5       Ayoung     Atiches        Brian       Black\n",
       "5          6          NaN         NaN         Bran     Balwner\n",
       "6          7          NaN         NaN        Bryce       Brice\n",
       "7          8          NaN         NaN        Betty      Btisan"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 05_Merge/Housing_Market/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Housing Market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time we will create our own dataset with fictional numbers to describe a house market. As we are going to create random data don't try to reason of the numbers.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create 3 differents Series, each of length 100, as follows: \n",
    "1. The first a random number from 1 to 4 \n",
    "2. The second a random number from 1 to 3\n",
    "3. The third a random number from 10,000 to 30,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Let's create a DataFrame by joinning the Series by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Oops, it seems it is going only until index 99. Is it true?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Reindex the DataFrame so it goes from 0 to 299"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "pygments_lexer": "ipython2",
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  }
 },
 "nbformat": 4,
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}


================================================
FILE: 05_Merge/Housing_Market/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Housing Market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time we will create our own dataset with fictional numbers to describe a house market. As we are going to create random data don't try to reason of the numbers.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create 3 differents Series, each of length 100, as follows: \n",
    "1. The first a random number from 1 to 4 \n",
    "2. The second a random number from 1 to 3\n",
    "3. The third a random number from 10,000 to 30,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2\n",
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      "     ..\n",
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      "99    3\n",
      "dtype: int64 0     2\n",
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      "28    11446\n",
      "29    29281\n",
      "      ...  \n",
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      "98    25585\n",
      "99    26142\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s1 = pd.Series(np.random.randint(1, high=5, size=100, dtype='l'))\n",
    "s2 = pd.Series(np.random.randint(1, high=4, size=100, dtype='l'))\n",
    "s3 = pd.Series(np.random.randint(10000, high=30001, size=100, dtype='l'))\n",
    "\n",
    "print(s1, s2, s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Let's create a DataFrame by joinning the Series by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "   0  1      2\n",
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housemkt = pd.concat([s1, s2, s3], axis=1)\n",
    "housemkt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>bedrs</th>\n",
       "      <th>bathrs</th>\n",
       "      <th>price_sqr_meter</th>\n",
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       "   bedrs  bathrs  price_sqr_meter\n",
       "0      2       2            16957\n",
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       "3      2       3            14153\n",
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     },
     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "housemkt.rename(columns = {0: 'bedrs', 1: 'bathrs', 2: 'price_sqr_meter'}, inplace=True)\n",
    "housemkt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
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    {
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     "output_type": "stream",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>23084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>19052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>19922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>17088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>25468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>19834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>21288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>22410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>22348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>18812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>29522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>20838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>28695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>21684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>10866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>12337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>25158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>25585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>26142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 1 columns</p>\n",
       "</div>"
      ],
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       "        0\n",
       "0       2\n",
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       "24      3\n",
       "25      2\n",
       "26      3\n",
       "27      1\n",
       "28      3\n",
       "29      4\n",
       "..    ...\n",
       "70  23963\n",
       "71  26782\n",
       "72  11199\n",
       "73  23600\n",
       "74  26935\n",
       "75  27365\n",
       "76  23084\n",
       "77  19052\n",
       "78  19922\n",
       "79  17088\n",
       "80  25468\n",
       "81  10924\n",
       "82  10243\n",
       "83  19834\n",
       "84  21288\n",
       "85  22410\n",
       "86  22348\n",
       "87  18812\n",
       "88  29522\n",
       "89  20838\n",
       "90  28695\n",
       "91  23000\n",
       "92  21684\n",
       "93  26316\n",
       "94  10866\n",
       "95  12337\n",
       "96  13480\n",
       "97  25158\n",
       "98  25585\n",
       "99  26142\n",
       "\n",
       "[300 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join concat the values\n",
    "bigcolumn = pd.concat([s1, s2, s3], axis=0)\n",
    "\n",
    "# it is still a Series, so we need to transform it to a DataFrame\n",
    "bigcolumn = bigcolumn.to_frame()\n",
    "print(type(bigcolumn))\n",
    "\n",
    "bigcolumn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Oops, it seems it is going only until index 99. Is it true?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no the index are kept but the length of the DataFrame is 300\n",
    "len(bigcolumn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Reindex the DataFrame so it goes from 0 to 299"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>23084</td>\n",
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       "      <td>19052</td>\n",
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       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>19922</td>\n",
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       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>17088</td>\n",
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       "    <tr>\n",
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       "      <td>25468</td>\n",
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       "      <td>29522</td>\n",
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       "      <th>289</th>\n",
       "      <td>20838</td>\n",
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       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>28695</td>\n",
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       "      <td>23000</td>\n",
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       "      <td>21684</td>\n",
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       "<p>300 rows × 1 columns</p>\n",
       "</div>"
      ],
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       "         0\n",
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       "25       2\n",
       "26       3\n",
       "27       1\n",
       "28       3\n",
       "29       4\n",
       "..     ...\n",
       "270  23963\n",
       "271  26782\n",
       "272  11199\n",
       "273  23600\n",
       "274  26935\n",
       "275  27365\n",
       "276  23084\n",
       "277  19052\n",
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       "281  10924\n",
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       "284  21288\n",
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       "287  18812\n",
       "288  29522\n",
       "289  20838\n",
       "290  28695\n",
       "291  23000\n",
       "292  21684\n",
       "293  26316\n",
       "294  10866\n",
       "295  12337\n",
       "296  13480\n",
       "297  25158\n",
       "298  25585\n",
       "299  26142\n",
       "\n",
       "[300 rows x 1 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigcolumn.reset_index(drop=True, inplace=True)\n",
    "bigcolumn"
   ]
  }
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================================================
FILE: 05_Merge/Housing_Market/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Housing Market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time we will create our own dataset with fictional numbers to describe a house market. As we are going to create random data don't try to reason of the numbers.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create 3 differents Series, each of length 100, as follows: \n",
    "1. The first a random number from 1 to 4 \n",
    "2. The second a random number from 1 to 3\n",
    "3. The third a random number from 10,000 to 30,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2\n",
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      "29    4\n",
      "     ..\n",
      "70    4\n",
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      "99    3\n",
      "dtype: int64 0     2\n",
      "1     3\n",
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      "12    3\n",
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      "16    3\n",
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      "18    3\n",
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      "21    3\n",
      "22    1\n",
      "23    2\n",
      "24    3\n",
      "25    2\n",
      "26    2\n",
      "27    1\n",
      "28    3\n",
      "29    3\n",
      "     ..\n",
      "70    3\n",
      "71    2\n",
      "72    2\n",
      "73    2\n",
      "74    3\n",
      "75    2\n",
      "76    3\n",
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      "78    1\n",
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      "83    3\n",
      "84    1\n",
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      "86    1\n",
      "87    2\n",
      "88    3\n",
      "89    2\n",
      "90    2\n",
      "91    3\n",
      "92    2\n",
      "93    2\n",
      "94    2\n",
      "95    2\n",
      "96    2\n",
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      "dtype: int64 0     16957\n",
      "1     24571\n",
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      "3     14153\n",
      "4     23445\n",
      "5     21444\n",
      "6     16179\n",
      "7     22696\n",
      "8     18595\n",
      "9     27145\n",
      "10    14406\n",
      "11    15011\n",
      "12    17444\n",
      "13    26236\n",
      "14    23808\n",
      "15    21417\n",
      "16    15079\n",
      "17    13100\n",
      "18    21470\n",
      "19    17082\n",
      "20    21935\n",
      "21    26770\n",
      "22    10059\n",
      "23    11095\n",
      "24    25916\n",
      "25    17137\n",
      "26    22023\n",
      "27    21612\n",
      "28    11446\n",
      "29    29281\n",
      "      ...  \n",
      "70    23963\n",
      "71    26782\n",
      "72    11199\n",
      "73    23600\n",
      "74    26935\n",
      "75    27365\n",
      "76    23084\n",
      "77    19052\n",
      "78    19922\n",
      "79    17088\n",
      "80    25468\n",
      "81    10924\n",
      "82    10243\n",
      "83    19834\n",
      "84    21288\n",
      "85    22410\n",
      "86    22348\n",
      "87    18812\n",
      "88    29522\n",
      "89    20838\n",
      "90    28695\n",
      "91    23000\n",
      "92    21684\n",
      "93    26316\n",
      "94    10866\n",
      "95    12337\n",
      "96    13480\n",
      "97    25158\n",
      "98    25585\n",
      "99    26142\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Let's create a DataFrame by joinning the Series by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
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       "   0  1      2\n",
       "0  2  2  16957\n",
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       "3  2  3  14153\n",
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>bedrs</th>\n",
       "      <th>bathrs</th>\n",
       "      <th>price_sqr_meter</th>\n",
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      "text/plain": [
       "   bedrs  bathrs  price_sqr_meter\n",
       "0      2       2            16957\n",
       "1      2       3            24571\n",
       "2      4       2            28303\n",
       "3      2       3            14153\n",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
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       "      <th>70</th>\n",
       "      <td>23963</td>\n",
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       "      <th>71</th>\n",
       "      <td>26782</td>\n",
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       "      <th>72</th>\n",
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       "      <th>73</th>\n",
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       "      <th>77</th>\n",
       "      <td>19052</td>\n",
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       "      <th>78</th>\n",
       "      <td>19922</td>\n",
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       "      <th>79</th>\n",
       "      <td>17088</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>25468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>10924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>10243</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>19834</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>21288</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>22410</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>22348</td>\n",
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       "      <th>87</th>\n",
       "      <td>18812</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>29522</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>20838</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>28695</td>\n",
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       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23000</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>21684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>10866</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>12337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>25158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>25585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>26142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 1 columns</p>\n",
       "</div>"
      ],
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       "        0\n",
       "0       2\n",
       "1       2\n",
       "2       4\n",
       "3       2\n",
       "4       1\n",
       "5       1\n",
       "6       2\n",
       "7       3\n",
       "8       3\n",
       "9       2\n",
       "10      1\n",
       "11      2\n",
       "12      4\n",
       "13      1\n",
       "14      2\n",
       "15      3\n",
       "16      4\n",
       "17      4\n",
       "18      4\n",
       "19      3\n",
       "20      2\n",
       "21      1\n",
       "22      4\n",
       "23      1\n",
       "24      3\n",
       "25      2\n",
       "26      3\n",
       "27      1\n",
       "28      3\n",
       "29      4\n",
       "..    ...\n",
       "70  23963\n",
       "71  26782\n",
       "72  11199\n",
       "73  23600\n",
       "74  26935\n",
       "75  27365\n",
       "76  23084\n",
       "77  19052\n",
       "78  19922\n",
       "79  17088\n",
       "80  25468\n",
       "81  10924\n",
       "82  10243\n",
       "83  19834\n",
       "84  21288\n",
       "85  22410\n",
       "86  22348\n",
       "87  18812\n",
       "88  29522\n",
       "89  20838\n",
       "90  28695\n",
       "91  23000\n",
       "92  21684\n",
       "93  26316\n",
       "94  10866\n",
       "95  12337\n",
       "96  13480\n",
       "97  25158\n",
       "98  25585\n",
       "99  26142\n",
       "\n",
       "[300 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Oops, it seems it is going only until index 99. Is it true?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Reindex the DataFrame so it goes from 0 to 299"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>23963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>26782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>11199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>23600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>26935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>27365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>23084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>19052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>19922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>17088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>25468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>10924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>10243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>19834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>21288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>22410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>22348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>18812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>29522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>20838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>28695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>21684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>26316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>10866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>12337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>13480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>25158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>25585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>26142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0        2\n",
       "1        2\n",
       "2        4\n",
       "3        2\n",
       "4        1\n",
       "5        1\n",
       "6        2\n",
       "7        3\n",
       "8        3\n",
       "9        2\n",
       "10       1\n",
       "11       2\n",
       "12       4\n",
       "13       1\n",
       "14       2\n",
       "15       3\n",
       "16       4\n",
       "17       4\n",
       "18       4\n",
       "19       3\n",
       "20       2\n",
       "21       1\n",
       "22       4\n",
       "23       1\n",
       "24       3\n",
       "25       2\n",
       "26       3\n",
       "27       1\n",
       "28       3\n",
       "29       4\n",
       "..     ...\n",
       "270  23963\n",
       "271  26782\n",
       "272  11199\n",
       "273  23600\n",
       "274  26935\n",
       "275  27365\n",
       "276  23084\n",
       "277  19052\n",
       "278  19922\n",
       "279  17088\n",
       "280  25468\n",
       "281  10924\n",
       "282  10243\n",
       "283  19834\n",
       "284  21288\n",
       "285  22410\n",
       "286  22348\n",
       "287  18812\n",
       "288  29522\n",
       "289  20838\n",
       "290  28695\n",
       "291  23000\n",
       "292  21684\n",
       "293  26316\n",
       "294  10866\n",
       "295  12337\n",
       "296  13480\n",
       "297  25158\n",
       "298  25585\n",
       "299  26142\n",
       "\n",
       "[300 rows x 1 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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   "codemirror_mode": {
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 },
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================================================
FILE: 06_Stats/US_Baby_Names/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US - Baby Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "We are going to use a subset of [US Baby Names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle.  \n",
    "In the file it will be names from 2004 until 2014\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names/US_Baby_Names_right.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called baby_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Delete the column 'Unnamed: 0' and 'Id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What year has the highest number of baby names in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Is there more male or female names in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Group the dataset by name and assign to names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How many different names exist in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the name with most occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. How many different names have the least occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. What is the median name occurrence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. What is the standard deviation of names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Get a summary with the mean, min, max, std and quartiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US - Baby Names\n",
    "\n",
    "Check out [Baby Names Exercises Video Tutorial](https://youtu.be/Daf2QNAy-qA) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "We are going to use a subset of [US Baby Names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle.  \n",
    "In the file it will be names from 2004 until 2014\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names/US_Baby_Names_right.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called baby_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1016395 entries, 0 to 1016394\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count    Dtype \n",
      "---  ------      --------------    ----- \n",
      " 0   Unnamed: 0  1016395 non-null  int64 \n",
      " 1   Id          1016395 non-null  int64 \n",
      " 2   Name        1016395 non-null  object\n",
      " 3   Year        1016395 non-null  int64 \n",
      " 4   Gender      1016395 non-null  object\n",
      " 5   State       1016395 non-null  object\n",
      " 6   Count       1016395 non-null  int64 \n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 54.3+ MB\n"
     ]
    }
   ],
   "source": [
    "baby_names = pd.read_csv('https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names/US_Baby_Names_right.csv')\n",
    "baby_names.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11349</td>\n",
       "      <td>11350</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11350</td>\n",
       "      <td>11351</td>\n",
       "      <td>Madison</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11351</td>\n",
       "      <td>11352</td>\n",
       "      <td>Hannah</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11352</td>\n",
       "      <td>11353</td>\n",
       "      <td>Grace</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353</td>\n",
       "      <td>11354</td>\n",
       "      <td>Emily</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11354</td>\n",
       "      <td>11355</td>\n",
       "      <td>Abigail</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11355</td>\n",
       "      <td>11356</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11356</td>\n",
       "      <td>11357</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11357</td>\n",
       "      <td>11358</td>\n",
       "      <td>Alyssa</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11358</td>\n",
       "      <td>11359</td>\n",
       "      <td>Sophia</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Id      Name  Year Gender State  Count\n",
       "0       11349  11350      Emma  2004      F    AK     62\n",
       "1       11350  11351   Madison  2004      F    AK     48\n",
       "2       11351  11352    Hannah  2004      F    AK     46\n",
       "3       11352  11353     Grace  2004      F    AK     44\n",
       "4       11353  11354     Emily  2004      F    AK     41\n",
       "5       11354  11355   Abigail  2004      F    AK     37\n",
       "6       11355  11356    Olivia  2004      F    AK     33\n",
       "7       11356  11357  Isabella  2004      F    AK     30\n",
       "8       11357  11358    Alyssa  2004      F    AK     29\n",
       "9       11358  11359    Sophia  2004      F    AK     28"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Delete the column 'Unnamed: 0' and 'Id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emma</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Madison</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hannah</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grace</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emily</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Year Gender State  Count\n",
       "0     Emma  2004      F    AK     62\n",
       "1  Madison  2004      F    AK     48\n",
       "2   Hannah  2004      F    AK     46\n",
       "3    Grace  2004      F    AK     44\n",
       "4    Emily  2004      F    AK     41"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deletes Unnamed: 0\n",
    "del baby_names['Unnamed: 0']\n",
    "\n",
    "# deletes Unnamed: 0\n",
    "del baby_names['Id']\n",
    "\n",
    "baby_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What year has the highest number of baby names in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count    2007\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names.groupby(\"Year\").sum().idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Are there more male or female names in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    558846\n",
       "M    457549\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby_names['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Group the dataset by name and assign to names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17632, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jacob</th>\n",
       "      <td>242874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emma</th>\n",
       "      <td>214852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael</th>\n",
       "      <td>214405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethan</th>\n",
       "      <td>209277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isabella</th>\n",
       "      <td>204798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Count\n",
       "Name            \n",
       "Jacob     242874\n",
       "Emma      214852\n",
       "Michael   214405\n",
       "Ethan     209277\n",
       "Isabella  204798"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you don't want to sum the Year column, so you delete it\n",
    "del baby_names[\"Year\"]\n",
    "\n",
    "# group the data\n",
    "names = baby_names.groupby(\"Name\").sum()\n",
    "\n",
    "# print the first 5 observations\n",
    "names.head()\n",
    "\n",
    "# print the size of the dataset\n",
    "print(names.shape)\n",
    "\n",
    "# sort it from the biggest value to the smallest one\n",
    "names.sort_values(\"Count\", ascending = 0).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How many different names exist in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17632"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as we have already grouped by the name, all the names are unique already. \n",
    "# get the length of names\n",
    "len(names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the name with most occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jacob'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.Count.idxmax()\n",
    "\n",
    "# OR\n",
    "\n",
    "# names[names.Count == names.Count.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. How many different names have the least occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2578"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(names[names.Count == names.Count.min()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. What is the median name occurrence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aishani</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alara</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alysse</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ameir</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anely</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sriram</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinton</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vita</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yoni</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuleima</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count\n",
       "Name          \n",
       "Aishani     49\n",
       "Alara       49\n",
       "Alysse      49\n",
       "Ameir       49\n",
       "Anely       49\n",
       "...        ...\n",
       "Sriram      49\n",
       "Trinton     49\n",
       "Vita        49\n",
       "Yoni        49\n",
       "Zuleima     49\n",
       "\n",
       "[66 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.Count == names.Count.median()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. What is the standard deviation of names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11006.06946789057"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.Count.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Get a summary with the mean, min, max, std and quartiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17632.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2008.932169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11006.069468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>337.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>242874.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Count\n",
       "count   17632.000000\n",
       "mean     2008.932169\n",
       "std     11006.069468\n",
       "min         5.000000\n",
       "25%        11.000000\n",
       "50%        49.000000\n",
       "75%       337.000000\n",
       "max    242874.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.describe()"
   ]
  }
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================================================
FILE: 06_Stats/US_Baby_Names/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US - Baby Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "We are going to use a subset of [US Baby Names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle.  \n",
    "In the file it will be names from 2004 until 2014\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names/US_Baby_Names_right.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called baby_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1016395 entries, 0 to 1016394\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count    Dtype \n",
      "---  ------      --------------    ----- \n",
      " 0   Unnamed: 0  1016395 non-null  int64 \n",
      " 1   Id          1016395 non-null  int64 \n",
      " 2   Name        1016395 non-null  object\n",
      " 3   Year        1016395 non-null  int64 \n",
      " 4   Gender      1016395 non-null  object\n",
      " 5   State       1016395 non-null  object\n",
      " 6   Count       1016395 non-null  int64 \n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 54.3+ MB\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11349</td>\n",
       "      <td>11350</td>\n",
       "      <td>Emma</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11350</td>\n",
       "      <td>11351</td>\n",
       "      <td>Madison</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11351</td>\n",
       "      <td>11352</td>\n",
       "      <td>Hannah</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11352</td>\n",
       "      <td>11353</td>\n",
       "      <td>Grace</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11353</td>\n",
       "      <td>11354</td>\n",
       "      <td>Emily</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11354</td>\n",
       "      <td>11355</td>\n",
       "      <td>Abigail</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11355</td>\n",
       "      <td>11356</td>\n",
       "      <td>Olivia</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11356</td>\n",
       "      <td>11357</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11357</td>\n",
       "      <td>11358</td>\n",
       "      <td>Alyssa</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11358</td>\n",
       "      <td>11359</td>\n",
       "      <td>Sophia</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Id      Name  Year Gender State  Count\n",
       "0       11349  11350      Emma  2004      F    AK     62\n",
       "1       11350  11351   Madison  2004      F    AK     48\n",
       "2       11351  11352    Hannah  2004      F    AK     46\n",
       "3       11352  11353     Grace  2004      F    AK     44\n",
       "4       11353  11354     Emily  2004      F    AK     41\n",
       "5       11354  11355   Abigail  2004      F    AK     37\n",
       "6       11355  11356    Olivia  2004      F    AK     33\n",
       "7       11356  11357  Isabella  2004      F    AK     30\n",
       "8       11357  11358    Alyssa  2004      F    AK     29\n",
       "9       11358  11359    Sophia  2004      F    AK     28"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Delete the column 'Unnamed: 0' and 'Id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emma</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Madison</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hannah</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grace</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Emily</td>\n",
       "      <td>2004</td>\n",
       "      <td>F</td>\n",
       "      <td>AK</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Year Gender State  Count\n",
       "0     Emma  2004      F    AK     62\n",
       "1  Madison  2004      F    AK     48\n",
       "2   Hannah  2004      F    AK     46\n",
       "3    Grace  2004      F    AK     44\n",
       "4    Emily  2004      F    AK     41"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What year has the highest number of baby names in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count    2007\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Are there more male or female names in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    558846\n",
       "M    457549\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Group the dataset by name and assign to names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17632, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jacob</th>\n",
       "      <td>242874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emma</th>\n",
       "      <td>214852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michael</th>\n",
       "      <td>214405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethan</th>\n",
       "      <td>209277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isabella</th>\n",
       "      <td>204798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Count\n",
       "Name            \n",
       "Jacob     242874\n",
       "Emma      214852\n",
       "Michael   214405\n",
       "Ethan     209277\n",
       "Isabella  204798"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. How many different names exist in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17632"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. What is the name with most occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jacob'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. How many different names have the least occurrences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2578"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. What is the median name occurrence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aishani</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alara</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alysse</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ameir</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anely</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antonina</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aveline</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aziah</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baily</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caleah</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carlota</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cristine</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dahlila</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darvin</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deante</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deserae</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Devean</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elizah</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emmaly</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emmanuela</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Envy</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Esli</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fay</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gurshaan</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hareem</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iven</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaice</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaiyana</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamiracle</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jelissa</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kyndle</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kynsley</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leylanie</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maisha</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malillany</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mariann</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marquell</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maurilio</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mckynzie</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mehdi</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nabeel</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nalleli</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nassir</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nazier</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nishant</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rebecka</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reghan</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridwan</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riot</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rubin</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryatt</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sameera</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sanjuanita</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shalyn</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skylie</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sriram</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinton</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vita</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yoni</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zuleima</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count\n",
       "Name             \n",
       "Aishani        49\n",
       "Alara          49\n",
       "Alysse         49\n",
       "Ameir          49\n",
       "Anely          49\n",
       "Antonina       49\n",
       "Aveline        49\n",
       "Aziah          49\n",
       "Baily          49\n",
       "Caleah         49\n",
       "Carlota        49\n",
       "Cristine       49\n",
       "Dahlila        49\n",
       "Darvin         49\n",
       "Deante         49\n",
       "Deserae        49\n",
       "Devean         49\n",
       "Elizah         49\n",
       "Emmaly         49\n",
       "Emmanuela      49\n",
       "Envy           49\n",
       "Esli           49\n",
       "Fay            49\n",
       "Gurshaan       49\n",
       "Hareem         49\n",
       "Iven           49\n",
       "Jaice          49\n",
       "Jaiyana        49\n",
       "Jamiracle      49\n",
       "Jelissa        49\n",
       "...           ...\n",
       "Kyndle         49\n",
       "Kynsley        49\n",
       "Leylanie       49\n",
       "Maisha         49\n",
       "Malillany      49\n",
       "Mariann        49\n",
       "Marquell       49\n",
       "Maurilio       49\n",
       "Mckynzie       49\n",
       "Mehdi          49\n",
       "Nabeel         49\n",
       "Nalleli        49\n",
       "Nassir         49\n",
       "Nazier         49\n",
       "Nishant        49\n",
       "Rebecka        49\n",
       "Reghan         49\n",
       "Ridwan         49\n",
       "Riot           49\n",
       "Rubin          49\n",
       "Ryatt          49\n",
       "Sameera        49\n",
       "Sanjuanita     49\n",
       "Shalyn         49\n",
       "Skylie         49\n",
       "Sriram         49\n",
       "Trinton        49\n",
       "Vita           49\n",
       "Yoni           49\n",
       "Zuleima        49\n",
       "\n",
       "[66 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. What is the standard deviation of names?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11006.069467891111"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Get a summary with the mean, min, max, std and quartiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17632.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2008.932169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11006.069468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>337.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>242874.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Count\n",
       "count   17632.000000\n",
       "mean     2008.932169\n",
       "std     11006.069468\n",
       "min         5.000000\n",
       "25%        11.000000\n",
       "50%        49.000000\n",
       "75%       337.000000\n",
       "max    242874.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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================================================
FILE: 06_Stats/US_Baby_Names/US_Baby_Names_right.csv
================================================
[File too large to display: 34.1 MB]

================================================
FILE: 06_Stats/Wind_Stats/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wind Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The data have been modified to contain some missing values, identified by NaN.  \n",
    "Using pandas should make this exercise\n",
    "easier, in particular for the bonus question.\n",
    "\n",
    "You should be able to perform all of these operations without using\n",
    "a for loop or other looping construct.\n",
    "\n",
    "\n",
    "1. The data in 'wind.data' has the following format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nYr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\\n61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\\n61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\\n'"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Yr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\n",
    "61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\n",
    "61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\n",
    "61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   The first three columns are year, month and day.  The\n",
    "   remaining 12 columns are average windspeeds in knots at 12\n",
    "   locations in Ireland on that day.   \n",
    "\n",
    "   More information about the dataset go [here](wind.desc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/Wind_Stats/wind.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Compute how many values are missing for each location over the entire record.  \n",
    "#### They should be ignored in all calculations below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Compute how many non-missing values there are in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.\n",
    "#### A single number for the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days \n",
    "\n",
    "#### A different set of numbers for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.\n",
    "\n",
    "#### A different set of numbers for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Find the average windspeed in January for each location.  \n",
    "#### Treat January 1961 and January 1962 both as January."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Downsample the record to a yearly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Downsample the record to a monthly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Downsample the record to a weekly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
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================================================
FILE: 06_Stats/Wind_Stats/Exercises_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wind Statistics\n",
    "\n",
    "Check out [Wind Statistics Exercises Video Tutorial](https://youtu.be/2x3WsWiNV18) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The data have been modified to contain some missing values, identified by NaN.  \n",
    "Using pandas should make this exercise\n",
    "easier, in particular for the bonus question.\n",
    "\n",
    "You should be able to perform all of these operations without using\n",
    "a for loop or other looping construct.\n",
    "\n",
    "\n",
    "1. The data in 'wind.data' has the following format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nYr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\\n61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\\n61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Yr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\n",
    "61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\n",
    "61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\n",
    "61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   The first three columns are year, month and day.  The\n",
    "   remaining 12 columns are average windspeeds in knots at 12\n",
    "   locations in Ireland on that day.   \n",
    "\n",
    "   More information about the dataset go [here](wind.desc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/06_Stats/Wind_Stats/wind.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2061-01-01</td>\n",
       "      <td>15.04</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.17</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.87</td>\n",
       "      <td>13.67</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.83</td>\n",
       "      <td>12.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2061-01-02</td>\n",
       "      <td>14.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.83</td>\n",
       "      <td>6.50</td>\n",
       "      <td>12.62</td>\n",
       "      <td>7.67</td>\n",
       "      <td>11.50</td>\n",
       "      <td>10.04</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.67</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2061-01-03</td>\n",
       "      <td>18.50</td>\n",
       "      <td>16.88</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.13</td>\n",
       "      <td>11.17</td>\n",
       "      <td>6.17</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.67</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2061-01-04</td>\n",
       "      <td>10.58</td>\n",
       "      <td>6.63</td>\n",
       "      <td>11.75</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.54</td>\n",
       "      <td>2.88</td>\n",
       "      <td>8.63</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.46</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2061-01-05</td>\n",
       "      <td>13.33</td>\n",
       "      <td>13.25</td>\n",
       "      <td>11.42</td>\n",
       "      <td>6.17</td>\n",
       "      <td>10.71</td>\n",
       "      <td>8.21</td>\n",
       "      <td>11.92</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.92</td>\n",
       "      <td>10.34</td>\n",
       "      <td>12.92</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Yr_Mo_Dy    RPT    VAL    ROS    KIL    SHA   BIR    DUB    CLA    MUL  \\\n",
       "0 2061-01-01  15.04  14.96  13.17   9.29    NaN  9.87  13.67  10.25  10.83   \n",
       "1 2061-01-02  14.71    NaN  10.83   6.50  12.62  7.67  11.50  10.04   9.79   \n",
       "2 2061-01-03  18.50  16.88  12.33  10.13  11.17  6.17  11.25    NaN   8.50   \n",
       "3 2061-01-04  10.58   6.63  11.75   4.58   4.54  2.88   8.63   1.79   5.83   \n",
       "4 2061-01-05  13.33  13.25  11.42   6.17  10.71  8.21  11.92   6.54  10.92   \n",
       "\n",
       "     CLO    BEL    MAL  \n",
       "0  12.58  18.50  15.04  \n",
       "1   9.67  17.54  13.83  \n",
       "2   7.67  12.75  12.71  \n",
       "3   5.88   5.46  10.88  \n",
       "4  10.34  12.92  11.83  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parse_dates gets 0, 1, 2 columns and parses them as the index\n",
    "data_url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/Wind_Stats/wind.data'\n",
    "data = pd.read_csv(data_url, sep = \"\\s+\", parse_dates = [[0,1,2]]) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>15.04</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.17</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.87</td>\n",
       "      <td>13.67</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.83</td>\n",
       "      <td>12.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1961-01-02</td>\n",
       "      <td>14.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.83</td>\n",
       "      <td>6.50</td>\n",
       "      <td>12.62</td>\n",
       "      <td>7.67</td>\n",
       "      <td>11.50</td>\n",
       "      <td>10.04</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.67</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1961-01-03</td>\n",
       "      <td>18.50</td>\n",
       "      <td>16.88</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.13</td>\n",
       "      <td>11.17</td>\n",
       "      <td>6.17</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.67</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1961-01-04</td>\n",
       "      <td>10.58</td>\n",
       "      <td>6.63</td>\n",
       "      <td>11.75</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.54</td>\n",
       "      <td>2.88</td>\n",
       "      <td>8.63</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.46</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1961-01-05</td>\n",
       "      <td>13.33</td>\n",
       "      <td>13.25</td>\n",
       "      <td>11.42</td>\n",
       "      <td>6.17</td>\n",
       "      <td>10.71</td>\n",
       "      <td>8.21</td>\n",
       "      <td>11.92</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.92</td>\n",
       "      <td>10.34</td>\n",
       "      <td>12.92</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr_Mo_Dy    RPT    VAL    ROS    KIL    SHA   BIR    DUB    CLA    MUL  \\\n",
       "0  1961-01-01  15.04  14.96  13.17   9.29    NaN  9.87  13.67  10.25  10.83   \n",
       "1  1961-01-02  14.71    NaN  10.83   6.50  12.62  7.67  11.50  10.04   9.79   \n",
       "2  1961-01-03  18.50  16.88  12.33  10.13  11.17  6.17  11.25    NaN   8.50   \n",
       "3  1961-01-04  10.58   6.63  11.75   4.58   4.54  2.88   8.63   1.79   5.83   \n",
       "4  1961-01-05  13.33  13.25  11.42   6.17  10.71  8.21  11.92   6.54  10.92   \n",
       "\n",
       "     CLO    BEL    MAL  \n",
       "0  12.58  18.50  15.04  \n",
       "1   9.67  17.54  13.83  \n",
       "2   7.67  12.75  12.71  \n",
       "3   5.88   5.46  10.88  \n",
       "4  10.34  12.92  11.83  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The problem is that the dates are 2061 and so on...\n",
    "\n",
    "# function that uses datetime\n",
    "def fix_century(x):\n",
    "  year = x.year - 100 if x.year > 1989 else x.year\n",
    "  return datetime.date(year, x.month, x.day)\n",
    "\n",
    "# apply the function fix_century on the column and replace the values to the right ones\n",
    "data['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)\n",
    "\n",
    "# data.info()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>15.04</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.17</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.87</td>\n",
       "      <td>13.67</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.83</td>\n",
       "      <td>12.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1961-01-02</td>\n",
       "      <td>14.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.83</td>\n",
       "      <td>6.50</td>\n",
       "      <td>12.62</td>\n",
       "      <td>7.67</td>\n",
       "      <td>11.50</td>\n",
       "      <td>10.04</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.67</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1961-01-03</td>\n",
       "      <td>18.50</td>\n",
       "      <td>16.88</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.13</td>\n",
       "      <td>11.17</td>\n",
       "      <td>6.17</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.67</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1961-01-04</td>\n",
       "      <td>10.58</td>\n",
       "      <td>6.63</td>\n",
       "      <td>11.75</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.54</td>\n",
       "      <td>2.88</td>\n",
       "      <td>8.63</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.46</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1961-01-05</td>\n",
       "      <td>13.33</td>\n",
       "      <td>13.25</td>\n",
       "      <td>11.42</td>\n",
       "      <td>6.17</td>\n",
       "      <td>10.71</td>\n",
       "      <td>8.21</td>\n",
       "      <td>11.92</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.92</td>\n",
       "      <td>10.34</td>\n",
       "      <td>12.92</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RPT    VAL    ROS    KIL    SHA   BIR    DUB    CLA    MUL  \\\n",
       "Yr_Mo_Dy                                                                   \n",
       "1961-01-01  15.04  14.96  13.17   9.29    NaN  9.87  13.67  10.25  10.83   \n",
       "1961-01-02  14.71    NaN  10.83   6.50  12.62  7.67  11.50  10.04   9.79   \n",
       "1961-01-03  18.50  16.88  12.33  10.13  11.17  6.17  11.25    NaN   8.50   \n",
       "1961-01-04  10.58   6.63  11.75   4.58   4.54  2.88   8.63   1.79   5.83   \n",
       "1961-01-05  13.33  13.25  11.42   6.17  10.71  8.21  11.92   6.54  10.92   \n",
       "\n",
       "              CLO    BEL    MAL  \n",
       "Yr_Mo_Dy                         \n",
       "1961-01-01  12.58  18.50  15.04  \n",
       "1961-01-02   9.67  17.54  13.83  \n",
       "1961-01-03   7.67  12.75  12.71  \n",
       "1961-01-04   5.88   5.46  10.88  \n",
       "1961-01-05  10.34  12.92  11.83  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform Yr_Mo_Dy it to date type datetime64\n",
    "data[\"Yr_Mo_Dy\"] = pd.to_datetime(data[\"Yr_Mo_Dy\"])\n",
    "\n",
    "# set 'Yr_Mo_Dy' as the index\n",
    "data = data.set_index('Yr_Mo_Dy')\n",
    "\n",
    "data.head()\n",
    "# data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Compute how many values are missing for each location over the entire record.  \n",
    "#### They should be ignored in all calculations below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPT    6\n",
       "VAL    3\n",
       "ROS    2\n",
       "KIL    5\n",
       "SHA    2\n",
       "BIR    0\n",
       "DUB    3\n",
       "CLA    2\n",
       "MUL    3\n",
       "CLO    1\n",
       "BEL    0\n",
       "MAL    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Number of non-missing values for each location: \"\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Compute how many non-missing values there are in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPT    6568\n",
       "VAL    6571\n",
       "ROS    6572\n",
       "KIL    6569\n",
       "SHA    6572\n",
       "BIR    6574\n",
       "DUB    6571\n",
       "CLA    6572\n",
       "MUL    6571\n",
       "CLO    6573\n",
       "BEL    6574\n",
       "MAL    6570\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of columns minus the number of missing values for each location\n",
    "data.shape[0] - data.isnull().sum()\n",
    "\n",
    "#or\n",
    "\n",
    "data.notnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.\n",
    "#### A single number for the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.227883764282167"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sum().sum() / data.notna().sum().sum()\n",
    "\n",
    "# data.mean().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days \n",
    "\n",
    "#### A different set of numbers for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6568.000000</td>\n",
       "      <td>6571.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6569.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6574.000000</td>\n",
       "      <td>6571.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6571.000000</td>\n",
       "      <td>6573.000000</td>\n",
       "      <td>6574.000000</td>\n",
       "      <td>6570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.362987</td>\n",
       "      <td>10.644314</td>\n",
       "      <td>11.660526</td>\n",
       "      <td>6.306468</td>\n",
       "      <td>10.455834</td>\n",
       "      <td>7.092254</td>\n",
       "      <td>9.797343</td>\n",
       "      <td>8.495053</td>\n",
       "      <td>8.493590</td>\n",
       "      <td>8.707332</td>\n",
       "      <td>13.121007</td>\n",
       "      <td>15.599079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.618413</td>\n",
       "      <td>5.267356</td>\n",
       "      <td>5.008450</td>\n",
       "      <td>3.605811</td>\n",
       "      <td>4.936125</td>\n",
       "      <td>3.968683</td>\n",
       "      <td>4.977555</td>\n",
       "      <td>4.499449</td>\n",
       "      <td>4.166872</td>\n",
       "      <td>4.503954</td>\n",
       "      <td>5.835037</td>\n",
       "      <td>6.699794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.710000</td>\n",
       "      <td>10.170000</td>\n",
       "      <td>10.920000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>9.960000</td>\n",
       "      <td>6.830000</td>\n",
       "      <td>9.210000</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>8.290000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.800000</td>\n",
       "      <td>33.370000</td>\n",
       "      <td>33.840000</td>\n",
       "      <td>28.460000</td>\n",
       "      <td>37.540000</td>\n",
       "      <td>26.160000</td>\n",
       "      <td>30.370000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>25.880000</td>\n",
       "      <td>28.210000</td>\n",
       "      <td>42.380000</td>\n",
       "      <td>42.540000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RPT          VAL          ROS          KIL          SHA  \\\n",
       "count  6568.000000  6571.000000  6572.000000  6569.000000  6572.000000   \n",
       "mean     12.362987    10.644314    11.660526     6.306468    10.455834   \n",
       "std       5.618413     5.267356     5.008450     3.605811     4.936125   \n",
       "min       0.670000     0.210000     1.500000     0.000000     0.130000   \n",
       "50%      11.710000    10.170000    10.920000     5.750000     9.960000   \n",
       "max      35.800000    33.370000    33.840000    28.460000    37.540000   \n",
       "\n",
       "               BIR          DUB          CLA          MUL          CLO  \\\n",
       "count  6574.000000  6571.000000  6572.000000  6571.000000  6573.000000   \n",
       "mean      7.092254     9.797343     8.495053     8.493590     8.707332   \n",
       "std       3.968683     4.977555     4.499449     4.166872     4.503954   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.040000   \n",
       "50%       6.830000     9.210000     8.080000     8.170000     8.290000   \n",
       "max      26.160000    30.370000    31.080000    25.880000    28.210000   \n",
       "\n",
       "               BEL          MAL  \n",
       "count  6574.000000  6570.000000  \n",
       "mean     13.121007    15.599079  \n",
       "std       5.835037     6.699794  \n",
       "min       0.130000     0.670000  \n",
       "50%      12.500000    15.000000  \n",
       "max      42.380000    42.540000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(percentiles=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.\n",
    "\n",
    "#### A different set of numbers for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>9.29</td>\n",
       "      <td>18.50</td>\n",
       "      <td>13.018182</td>\n",
       "      <td>2.808875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-02</th>\n",
       "      <td>6.50</td>\n",
       "      <td>17.54</td>\n",
       "      <td>11.336364</td>\n",
       "      <td>3.188994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-03</th>\n",
       "      <td>6.17</td>\n",
       "      <td>18.50</td>\n",
       "      <td>11.641818</td>\n",
       "      <td>3.681912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-04</th>\n",
       "      <td>1.79</td>\n",
       "      <td>11.75</td>\n",
       "      <td>6.619167</td>\n",
       "      <td>3.198126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-05</th>\n",
       "      <td>6.17</td>\n",
       "      <td>13.33</td>\n",
       "      <td>10.630000</td>\n",
       "      <td>2.445356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             min    max       mean       std\n",
       "Yr_Mo_Dy                                    \n",
       "1961-01-01  9.29  18.50  13.018182  2.808875\n",
       "1961-01-02  6.50  17.54  11.336364  3.188994\n",
       "1961-01-03  6.17  18.50  11.641818  3.681912\n",
       "1961-01-04  1.79  11.75   6.619167  3.198126\n",
       "1961-01-05  6.17  13.33  10.630000  2.445356"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the dataframe\n",
    "day_stats = pd.DataFrame()\n",
    "\n",
    "# this time we determine axis equals to one so it gets each row.\n",
    "day_stats['min'] = data.min(axis = 1) # min\n",
    "day_stats['max'] = data.max(axis = 1) # max \n",
    "day_stats['mean'] = data.mean(axis = 1) # mean\n",
    "day_stats['std'] = data.std(axis = 1) # standard deviations\n",
    "\n",
    "day_stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Find the average windspeed in January for each location.  \n",
    "#### Treat January 1961 and January 1962 both as January."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPT    14.847325\n",
       "VAL    12.914560\n",
       "ROS    13.299624\n",
       "KIL     7.199498\n",
       "SHA    11.667734\n",
       "BIR     8.054839\n",
       "DUB    11.819355\n",
       "CLA     9.512047\n",
       "MUL     9.543208\n",
       "CLO    10.053566\n",
       "BEL    14.550520\n",
       "MAL    18.028763\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.index.month == 1].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Downsample the record to a yearly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>12.299583</td>\n",
       "      <td>10.351796</td>\n",
       "      <td>11.362369</td>\n",
       "      <td>6.958227</td>\n",
       "      <td>10.881763</td>\n",
       "      <td>7.729726</td>\n",
       "      <td>9.733923</td>\n",
       "      <td>8.858788</td>\n",
       "      <td>8.647652</td>\n",
       "      <td>9.835577</td>\n",
       "      <td>13.502795</td>\n",
       "      <td>13.680773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>12.246923</td>\n",
       "      <td>10.110438</td>\n",
       "      <td>11.732712</td>\n",
       "      <td>6.960440</td>\n",
       "      <td>10.657918</td>\n",
       "      <td>7.393068</td>\n",
       "      <td>11.020712</td>\n",
       "      <td>8.793753</td>\n",
       "      <td>8.316822</td>\n",
       "      <td>9.676247</td>\n",
       "      <td>12.930685</td>\n",
       "      <td>14.323956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>12.813452</td>\n",
       "      <td>10.836986</td>\n",
       "      <td>12.541151</td>\n",
       "      <td>7.330055</td>\n",
       "      <td>11.724110</td>\n",
       "      <td>8.434712</td>\n",
       "      <td>11.075699</td>\n",
       "      <td>10.336548</td>\n",
       "      <td>8.903589</td>\n",
       "      <td>10.224438</td>\n",
       "      <td>13.638877</td>\n",
       "      <td>14.999014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>12.363661</td>\n",
       "      <td>10.920164</td>\n",
       "      <td>12.104372</td>\n",
       "      <td>6.787787</td>\n",
       "      <td>11.454481</td>\n",
       "      <td>7.570874</td>\n",
       "      <td>10.259153</td>\n",
       "      <td>9.467350</td>\n",
       "      <td>7.789016</td>\n",
       "      <td>10.207951</td>\n",
       "      <td>13.740546</td>\n",
       "      <td>14.910301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>12.451370</td>\n",
       "      <td>11.075534</td>\n",
       "      <td>11.848767</td>\n",
       "      <td>6.858466</td>\n",
       "      <td>11.024795</td>\n",
       "      <td>7.478110</td>\n",
       "      <td>10.618712</td>\n",
       "      <td>8.879918</td>\n",
       "      <td>7.907425</td>\n",
       "      <td>9.918082</td>\n",
       "      <td>12.964247</td>\n",
       "      <td>15.591644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>13.461973</td>\n",
       "      <td>11.557205</td>\n",
       "      <td>12.020630</td>\n",
       "      <td>7.345726</td>\n",
       "      <td>11.805041</td>\n",
       "      <td>7.793671</td>\n",
       "      <td>10.579808</td>\n",
       "      <td>8.835096</td>\n",
       "      <td>8.514438</td>\n",
       "      <td>9.768959</td>\n",
       "      <td>14.265836</td>\n",
       "      <td>16.307260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>12.737151</td>\n",
       "      <td>10.990986</td>\n",
       "      <td>11.739397</td>\n",
       "      <td>7.143425</td>\n",
       "      <td>11.630740</td>\n",
       "      <td>7.368164</td>\n",
       "      <td>10.652027</td>\n",
       "      <td>9.325616</td>\n",
       "      <td>8.645014</td>\n",
       "      <td>9.547425</td>\n",
       "      <td>14.774548</td>\n",
       "      <td>17.135945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>11.835628</td>\n",
       "      <td>10.468197</td>\n",
       "      <td>11.409754</td>\n",
       "      <td>6.477678</td>\n",
       "      <td>10.760765</td>\n",
       "      <td>6.067322</td>\n",
       "      <td>8.859180</td>\n",
       "      <td>8.255519</td>\n",
       "      <td>7.224945</td>\n",
       "      <td>7.832978</td>\n",
       "      <td>12.808634</td>\n",
       "      <td>15.017486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>11.166356</td>\n",
       "      <td>9.723699</td>\n",
       "      <td>10.902000</td>\n",
       "      <td>5.767973</td>\n",
       "      <td>9.873918</td>\n",
       "      <td>6.189973</td>\n",
       "      <td>8.564493</td>\n",
       "      <td>7.711397</td>\n",
       "      <td>7.924521</td>\n",
       "      <td>7.754384</td>\n",
       "      <td>12.621233</td>\n",
       "      <td>15.762904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>12.600329</td>\n",
       "      <td>10.726932</td>\n",
       "      <td>11.730247</td>\n",
       "      <td>6.217178</td>\n",
       "      <td>10.567370</td>\n",
       "      <td>7.609452</td>\n",
       "      <td>9.609890</td>\n",
       "      <td>8.334630</td>\n",
       "      <td>9.297616</td>\n",
       "      <td>8.289808</td>\n",
       "      <td>13.183644</td>\n",
       "      <td>16.456027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>11.273123</td>\n",
       "      <td>9.095178</td>\n",
       "      <td>11.088329</td>\n",
       "      <td>5.241507</td>\n",
       "      <td>9.440329</td>\n",
       "      <td>6.097151</td>\n",
       "      <td>8.385890</td>\n",
       "      <td>6.757315</td>\n",
       "      <td>7.915370</td>\n",
       "      <td>7.229753</td>\n",
       "      <td>12.208932</td>\n",
       "      <td>15.025233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>12.463962</td>\n",
       "      <td>10.561311</td>\n",
       "      <td>12.058333</td>\n",
       "      <td>5.929699</td>\n",
       "      <td>9.430410</td>\n",
       "      <td>6.358825</td>\n",
       "      <td>9.704508</td>\n",
       "      <td>7.680792</td>\n",
       "      <td>8.357295</td>\n",
       "      <td>7.515273</td>\n",
       "      <td>12.727377</td>\n",
       "      <td>15.028716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>11.828466</td>\n",
       "      <td>10.680493</td>\n",
       "      <td>10.680493</td>\n",
       "      <td>5.547863</td>\n",
       "      <td>9.640877</td>\n",
       "      <td>6.548740</td>\n",
       "      <td>8.482110</td>\n",
       "      <td>7.614274</td>\n",
       "      <td>8.245534</td>\n",
       "      <td>7.812411</td>\n",
       "      <td>12.169699</td>\n",
       "      <td>15.441096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>13.643096</td>\n",
       "      <td>11.811781</td>\n",
       "      <td>12.336356</td>\n",
       "      <td>6.427041</td>\n",
       "      <td>11.110986</td>\n",
       "      <td>6.809781</td>\n",
       "      <td>10.084603</td>\n",
       "      <td>9.896986</td>\n",
       "      <td>9.331753</td>\n",
       "      <td>8.736356</td>\n",
       "      <td>13.252959</td>\n",
       "      <td>16.947671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>12.008575</td>\n",
       "      <td>10.293836</td>\n",
       "      <td>11.564712</td>\n",
       "      <td>5.269096</td>\n",
       "      <td>9.190082</td>\n",
       "      <td>5.668521</td>\n",
       "      <td>8.562603</td>\n",
       "      <td>7.843836</td>\n",
       "      <td>8.797945</td>\n",
       "      <td>7.382822</td>\n",
       "      <td>12.631671</td>\n",
       "      <td>15.307863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>11.737842</td>\n",
       "      <td>10.203115</td>\n",
       "      <td>10.761230</td>\n",
       "      <td>5.109426</td>\n",
       "      <td>8.846339</td>\n",
       "      <td>6.311038</td>\n",
       "      <td>9.149126</td>\n",
       "      <td>7.146202</td>\n",
       "      <td>8.883716</td>\n",
       "      <td>7.883087</td>\n",
       "      <td>12.332377</td>\n",
       "      <td>15.471448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>13.099616</td>\n",
       "      <td>11.144493</td>\n",
       "      <td>12.627836</td>\n",
       "      <td>6.073945</td>\n",
       "      <td>10.003836</td>\n",
       "      <td>8.586438</td>\n",
       "      <td>11.523205</td>\n",
       "      <td>8.378384</td>\n",
       "      <td>9.098192</td>\n",
       "      <td>8.821616</td>\n",
       "      <td>13.459068</td>\n",
       "      <td>16.590849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>12.504356</td>\n",
       "      <td>11.044274</td>\n",
       "      <td>11.380000</td>\n",
       "      <td>6.082356</td>\n",
       "      <td>10.167233</td>\n",
       "      <td>7.650658</td>\n",
       "      <td>9.489342</td>\n",
       "      <td>8.800466</td>\n",
       "      <td>9.089753</td>\n",
       "      <td>8.301699</td>\n",
       "      <td>12.967397</td>\n",
       "      <td>16.771370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                RPT        VAL        ROS       KIL        SHA       BIR  \\\n",
       "Yr_Mo_Dy                                                                   \n",
       "1961      12.299583  10.351796  11.362369  6.958227  10.881763  7.729726   \n",
       "1962      12.246923  10.110438  11.732712  6.960440  10.657918  7.393068   \n",
       "1963      12.813452  10.836986  12.541151  7.330055  11.724110  8.434712   \n",
       "1964      12.363661  10.920164  12.104372  6.787787  11.454481  7.570874   \n",
       "1965      12.451370  11.075534  11.848767  6.858466  11.024795  7.478110   \n",
       "1966      13.461973  11.557205  12.020630  7.345726  11.805041  7.793671   \n",
       "1967      12.737151  10.990986  11.739397  7.143425  11.630740  7.368164   \n",
       "1968      11.835628  10.468197  11.409754  6.477678  10.760765  6.067322   \n",
       "1969      11.166356   9.723699  10.902000  5.767973   9.873918  6.189973   \n",
       "1970      12.600329  10.726932  11.730247  6.217178  10.567370  7.609452   \n",
       "1971      11.273123   9.095178  11.088329  5.241507   9.440329  6.097151   \n",
       "1972      12.463962  10.561311  12.058333  5.929699   9.430410  6.358825   \n",
       "1973      11.828466  10.680493  10.680493  5.547863   9.640877  6.548740   \n",
       "1974      13.643096  11.811781  12.336356  6.427041  11.110986  6.809781   \n",
       "1975      12.008575  10.293836  11.564712  5.269096   9.190082  5.668521   \n",
       "1976      11.737842  10.203115  10.761230  5.109426   8.846339  6.311038   \n",
       "1977      13.099616  11.144493  12.627836  6.073945  10.003836  8.586438   \n",
       "1978      12.504356  11.044274  11.380000  6.082356  10.167233  7.650658   \n",
       "\n",
       "                DUB        CLA       MUL        CLO        BEL        MAL  \n",
       "Yr_Mo_Dy                                                                   \n",
       "1961       9.733923   8.858788  8.647652   9.835577  13.502795  13.680773  \n",
       "1962      11.020712   8.793753  8.316822   9.676247  12.930685  14.323956  \n",
       "1963      11.075699  10.336548  8.903589  10.224438  13.638877  14.999014  \n",
       "1964      10.259153   9.467350  7.789016  10.207951  13.740546  14.910301  \n",
       "1965      10.618712   8.879918  7.907425   9.918082  12.964247  15.591644  \n",
       "1966      10.579808   8.835096  8.514438   9.768959  14.265836  16.307260  \n",
       "1967      10.652027   9.325616  8.645014   9.547425  14.774548  17.135945  \n",
       "1968       8.859180   8.255519  7.224945   7.832978  12.808634  15.017486  \n",
       "1969       8.564493   7.711397  7.924521   7.754384  12.621233  15.762904  \n",
       "1970       9.609890   8.334630  9.297616   8.289808  13.183644  16.456027  \n",
       "1971       8.385890   6.757315  7.915370   7.229753  12.208932  15.025233  \n",
       "1972       9.704508   7.680792  8.357295   7.515273  12.727377  15.028716  \n",
       "1973       8.482110   7.614274  8.245534   7.812411  12.169699  15.441096  \n",
       "1974      10.084603   9.896986  9.331753   8.736356  13.252959  16.947671  \n",
       "1975       8.562603   7.843836  8.797945   7.382822  12.631671  15.307863  \n",
       "1976       9.149126   7.146202  8.883716   7.883087  12.332377  15.471448  \n",
       "1977      11.523205   8.378384  9.098192   8.821616  13.459068  16.590849  \n",
       "1978       9.489342   8.800466  9.089753   8.301699  12.967397  16.771370  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(data.index.to_period('A')).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Downsample the record to a monthly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01</th>\n",
       "      <td>14.841333</td>\n",
       "      <td>11.988333</td>\n",
       "      <td>13.431613</td>\n",
       "      <td>7.736774</td>\n",
       "      <td>11.072759</td>\n",
       "      <td>8.588065</td>\n",
       "      <td>11.184839</td>\n",
       "      <td>9.245333</td>\n",
       "      <td>9.085806</td>\n",
       "      <td>10.107419</td>\n",
       "      <td>13.880968</td>\n",
       "      <td>14.703226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02</th>\n",
       "      <td>16.269286</td>\n",
       "      <td>14.975357</td>\n",
       "      <td>14.441481</td>\n",
       "      <td>9.230741</td>\n",
       "      <td>13.852143</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>11.890714</td>\n",
       "      <td>11.846071</td>\n",
       "      <td>11.821429</td>\n",
       "      <td>12.714286</td>\n",
       "      <td>18.583214</td>\n",
       "      <td>15.411786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03</th>\n",
       "      <td>10.890000</td>\n",
       "      <td>11.296452</td>\n",
       "      <td>10.752903</td>\n",
       "      <td>7.284000</td>\n",
       "      <td>10.509355</td>\n",
       "      <td>8.866774</td>\n",
       "      <td>9.644194</td>\n",
       "      <td>9.829677</td>\n",
       "      <td>10.294138</td>\n",
       "      <td>11.251935</td>\n",
       "      <td>16.410968</td>\n",
       "      <td>15.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04</th>\n",
       "      <td>10.722667</td>\n",
       "      <td>9.427667</td>\n",
       "      <td>9.998000</td>\n",
       "      <td>5.830667</td>\n",
       "      <td>8.435000</td>\n",
       "      <td>6.495000</td>\n",
       "      <td>6.925333</td>\n",
       "      <td>7.094667</td>\n",
       "      <td>7.342333</td>\n",
       "      <td>7.237000</td>\n",
       "      <td>11.147333</td>\n",
       "      <td>10.278333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05</th>\n",
       "      <td>9.860968</td>\n",
       "      <td>8.850000</td>\n",
       "      <td>10.818065</td>\n",
       "      <td>5.905333</td>\n",
       "      <td>9.490323</td>\n",
       "      <td>6.574839</td>\n",
       "      <td>7.604000</td>\n",
       "      <td>8.177097</td>\n",
       "      <td>8.039355</td>\n",
       "      <td>8.499355</td>\n",
       "      <td>11.900323</td>\n",
       "      <td>12.011613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06</th>\n",
       "      <td>9.904138</td>\n",
       "      <td>8.520333</td>\n",
       "      <td>8.867000</td>\n",
       "      <td>6.083000</td>\n",
       "      <td>10.824000</td>\n",
       "      <td>6.707333</td>\n",
       "      <td>9.095667</td>\n",
       "      <td>8.849333</td>\n",
       "      <td>9.086667</td>\n",
       "      <td>9.940333</td>\n",
       "      <td>13.995000</td>\n",
       "      <td>14.553793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07</th>\n",
       "      <td>10.614194</td>\n",
       "      <td>8.221613</td>\n",
       "      <td>9.110323</td>\n",
       "      <td>6.340968</td>\n",
       "      <td>10.532581</td>\n",
       "      <td>6.198387</td>\n",
       "      <td>8.353333</td>\n",
       "      <td>8.284194</td>\n",
       "      <td>8.077097</td>\n",
       "      <td>8.891613</td>\n",
       "      <td>11.092581</td>\n",
       "      <td>12.312903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-08</th>\n",
       "      <td>12.035000</td>\n",
       "      <td>10.133871</td>\n",
       "      <td>10.335806</td>\n",
       "      <td>6.845806</td>\n",
       "      <td>12.715161</td>\n",
       "      <td>8.441935</td>\n",
       "      <td>10.093871</td>\n",
       "      <td>10.460968</td>\n",
       "      <td>9.111613</td>\n",
       "      <td>10.544667</td>\n",
       "      <td>14.410000</td>\n",
       "      <td>14.345333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-09</th>\n",
       "      <td>12.531000</td>\n",
       "      <td>9.656897</td>\n",
       "      <td>10.776897</td>\n",
       "      <td>7.155517</td>\n",
       "      <td>11.003333</td>\n",
       "      <td>7.234000</td>\n",
       "      <td>8.206000</td>\n",
       "      <td>8.936552</td>\n",
       "      <td>7.728333</td>\n",
       "      <td>9.931333</td>\n",
       "      <td>13.718333</td>\n",
       "      <td>12.921667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-10</th>\n",
       "      <td>14.289667</td>\n",
       "      <td>10.915806</td>\n",
       "      <td>12.236452</td>\n",
       "      <td>8.154839</td>\n",
       "      <td>11.865484</td>\n",
       "      <td>8.333871</td>\n",
       "      <td>11.194194</td>\n",
       "      <td>9.271935</td>\n",
       "      <td>8.942667</td>\n",
       "      <td>11.455806</td>\n",
       "      <td>14.229355</td>\n",
       "      <td>16.793226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-11</th>\n",
       "      <td>10.896333</td>\n",
       "      <td>8.592667</td>\n",
       "      <td>11.850333</td>\n",
       "      <td>6.045667</td>\n",
       "      <td>9.123667</td>\n",
       "      <td>6.250667</td>\n",
       "      <td>10.869655</td>\n",
       "      <td>6.313667</td>\n",
       "      <td>6.575000</td>\n",
       "      <td>8.383667</td>\n",
       "      <td>10.776667</td>\n",
       "      <td>12.146000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-12</th>\n",
       "      <td>14.973548</td>\n",
       "      <td>11.903871</td>\n",
       "      <td>13.980323</td>\n",
       "      <td>7.073871</td>\n",
       "      <td>11.323548</td>\n",
       "      <td>8.302258</td>\n",
       "      <td>11.753548</td>\n",
       "      <td>8.163226</td>\n",
       "      <td>7.965806</td>\n",
       "      <td>9.246774</td>\n",
       "      <td>12.239355</td>\n",
       "      <td>13.098710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01</th>\n",
       "      <td>14.783871</td>\n",
       "      <td>13.160323</td>\n",
       "      <td>12.591935</td>\n",
       "      <td>7.538065</td>\n",
       "      <td>11.779677</td>\n",
       "      <td>8.720000</td>\n",
       "      <td>14.211935</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>9.670000</td>\n",
       "      <td>11.498710</td>\n",
       "      <td>16.369355</td>\n",
       "      <td>15.661613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02</th>\n",
       "      <td>15.844643</td>\n",
       "      <td>12.041429</td>\n",
       "      <td>15.178929</td>\n",
       "      <td>9.262963</td>\n",
       "      <td>13.821429</td>\n",
       "      <td>9.726786</td>\n",
       "      <td>16.916429</td>\n",
       "      <td>11.285357</td>\n",
       "      <td>12.021071</td>\n",
       "      <td>12.126429</td>\n",
       "      <td>16.705357</td>\n",
       "      <td>18.426786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03</th>\n",
       "      <td>11.634333</td>\n",
       "      <td>8.602258</td>\n",
       "      <td>12.110645</td>\n",
       "      <td>6.403226</td>\n",
       "      <td>10.352258</td>\n",
       "      <td>6.732258</td>\n",
       "      <td>10.223226</td>\n",
       "      <td>7.641935</td>\n",
       "      <td>7.092258</td>\n",
       "      <td>8.052581</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>11.509000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04</th>\n",
       "      <td>12.160667</td>\n",
       "      <td>9.676667</td>\n",
       "      <td>12.088333</td>\n",
       "      <td>7.163000</td>\n",
       "      <td>10.544000</td>\n",
       "      <td>7.558000</td>\n",
       "      <td>11.480000</td>\n",
       "      <td>8.722000</td>\n",
       "      <td>8.703667</td>\n",
       "      <td>9.311667</td>\n",
       "      <td>12.234333</td>\n",
       "      <td>11.780667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05</th>\n",
       "      <td>12.745806</td>\n",
       "      <td>10.865484</td>\n",
       "      <td>11.874839</td>\n",
       "      <td>7.471935</td>\n",
       "      <td>11.285806</td>\n",
       "      <td>7.209032</td>\n",
       "      <td>10.105806</td>\n",
       "      <td>9.084516</td>\n",
       "      <td>7.868065</td>\n",
       "      <td>9.293226</td>\n",
       "      <td>12.130000</td>\n",
       "      <td>12.922581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-06</th>\n",
       "      <td>10.305667</td>\n",
       "      <td>9.677000</td>\n",
       "      <td>9.996333</td>\n",
       "      <td>6.846667</td>\n",
       "      <td>10.711333</td>\n",
       "      <td>7.441333</td>\n",
       "      <td>10.548667</td>\n",
       "      <td>10.306667</td>\n",
       "      <td>9.196000</td>\n",
       "      <td>10.520333</td>\n",
       "      <td>13.757000</td>\n",
       "      <td>15.218333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-07</th>\n",
       "      <td>9.981935</td>\n",
       "      <td>8.370645</td>\n",
       "      <td>9.753548</td>\n",
       "      <td>6.093226</td>\n",
       "      <td>9.112903</td>\n",
       "      <td>5.877097</td>\n",
       "      <td>7.781613</td>\n",
       "      <td>8.123226</td>\n",
       "      <td>6.829677</td>\n",
       "      <td>8.613226</td>\n",
       "      <td>10.783871</td>\n",
       "      <td>11.326129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-08</th>\n",
       "      <td>10.964194</td>\n",
       "      <td>9.694194</td>\n",
       "      <td>10.184516</td>\n",
       "      <td>6.701290</td>\n",
       "      <td>10.465161</td>\n",
       "      <td>7.009032</td>\n",
       "      <td>11.136774</td>\n",
       "      <td>9.097419</td>\n",
       "      <td>8.645484</td>\n",
       "      <td>9.511613</td>\n",
       "      <td>13.119032</td>\n",
       "      <td>15.420968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-09</th>\n",
       "      <td>11.176333</td>\n",
       "      <td>9.507000</td>\n",
       "      <td>11.640000</td>\n",
       "      <td>6.164333</td>\n",
       "      <td>9.722333</td>\n",
       "      <td>6.214000</td>\n",
       "      <td>8.488000</td>\n",
       "      <td>7.020333</td>\n",
       "      <td>6.372667</td>\n",
       "      <td>8.286000</td>\n",
       "      <td>11.483667</td>\n",
       "      <td>12.313333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-10</th>\n",
       "      <td>9.699355</td>\n",
       "      <td>8.063548</td>\n",
       "      <td>9.357097</td>\n",
       "      <td>4.818065</td>\n",
       "      <td>8.432258</td>\n",
       "      <td>5.730000</td>\n",
       "      <td>8.448065</td>\n",
       "      <td>7.626774</td>\n",
       "      <td>6.630645</td>\n",
       "      <td>9.091290</td>\n",
       "      <td>13.286774</td>\n",
       "      <td>14.090323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-11</th>\n",
       "      <td>11.071333</td>\n",
       "      <td>7.984000</td>\n",
       "      <td>12.035667</td>\n",
       "      <td>5.740000</td>\n",
       "      <td>8.135667</td>\n",
       "      <td>6.338333</td>\n",
       "      <td>9.615333</td>\n",
       "      <td>5.943000</td>\n",
       "      <td>6.362333</td>\n",
       "      <td>8.084333</td>\n",
       "      <td>9.786667</td>\n",
       "      <td>13.298333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-12</th>\n",
       "      <td>16.785484</td>\n",
       "      <td>13.753548</td>\n",
       "      <td>14.276452</td>\n",
       "      <td>9.557419</td>\n",
       "      <td>13.724839</td>\n",
       "      <td>10.321613</td>\n",
       "      <td>13.735806</td>\n",
       "      <td>11.212258</td>\n",
       "      <td>10.683548</td>\n",
       "      <td>11.881935</td>\n",
       "      <td>16.043548</td>\n",
       "      <td>20.074516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01</th>\n",
       "      <td>14.868387</td>\n",
       "      <td>11.112903</td>\n",
       "      <td>15.121613</td>\n",
       "      <td>6.635806</td>\n",
       "      <td>11.080645</td>\n",
       "      <td>7.835484</td>\n",
       "      <td>12.797419</td>\n",
       "      <td>9.844839</td>\n",
       "      <td>7.841613</td>\n",
       "      <td>9.390000</td>\n",
       "      <td>11.428710</td>\n",
       "      <td>18.822258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-02</th>\n",
       "      <td>14.418929</td>\n",
       "      <td>11.876429</td>\n",
       "      <td>15.697500</td>\n",
       "      <td>8.611786</td>\n",
       "      <td>12.887857</td>\n",
       "      <td>9.600357</td>\n",
       "      <td>12.729286</td>\n",
       "      <td>10.823214</td>\n",
       "      <td>8.981786</td>\n",
       "      <td>10.355714</td>\n",
       "      <td>13.266429</td>\n",
       "      <td>17.120714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-03</th>\n",
       "      <td>14.853871</td>\n",
       "      <td>12.271290</td>\n",
       "      <td>14.295806</td>\n",
       "      <td>9.268387</td>\n",
       "      <td>13.112903</td>\n",
       "      <td>10.088065</td>\n",
       "      <td>12.168387</td>\n",
       "      <td>11.340968</td>\n",
       "      <td>9.690968</td>\n",
       "      <td>11.515484</td>\n",
       "      <td>13.982903</td>\n",
       "      <td>14.132581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-04</th>\n",
       "      <td>11.616000</td>\n",
       "      <td>10.138000</td>\n",
       "      <td>13.233667</td>\n",
       "      <td>7.990333</td>\n",
       "      <td>11.515333</td>\n",
       "      <td>9.727000</td>\n",
       "      <td>11.979000</td>\n",
       "      <td>11.353000</td>\n",
       "      <td>10.341667</td>\n",
       "      <td>11.900333</td>\n",
       "      <td>13.875667</td>\n",
       "      <td>16.333667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-05</th>\n",
       "      <td>12.879677</td>\n",
       "      <td>11.010645</td>\n",
       "      <td>12.881290</td>\n",
       "      <td>8.411613</td>\n",
       "      <td>12.981613</td>\n",
       "      <td>9.739677</td>\n",
       "      <td>12.280968</td>\n",
       "      <td>10.964194</td>\n",
       "      <td>10.745161</td>\n",
       "      <td>11.394839</td>\n",
       "      <td>14.777097</td>\n",
       "      <td>14.975161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-06</th>\n",
       "      <td>10.623333</td>\n",
       "      <td>8.434667</td>\n",
       "      <td>11.685000</td>\n",
       "      <td>6.420333</td>\n",
       "      <td>10.142667</td>\n",
       "      <td>7.219333</td>\n",
       "      <td>9.267333</td>\n",
       "      <td>9.589333</td>\n",
       "      <td>8.583667</td>\n",
       "      <td>9.585333</td>\n",
       "      <td>12.098000</td>\n",
       "      <td>11.358667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07</th>\n",
       "      <td>9.687742</td>\n",
       "      <td>7.980968</td>\n",
       "      <td>8.267742</td>\n",
       "      <td>4.631613</td>\n",
       "      <td>7.576774</td>\n",
       "      <td>4.927419</td>\n",
       "      <td>6.994839</td>\n",
       "      <td>5.135806</td>\n",
       "      <td>7.941290</td>\n",
       "      <td>6.491290</td>\n",
       "      <td>10.264194</td>\n",
       "      <td>11.912258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08</th>\n",
       "      <td>7.640645</td>\n",
       "      <td>5.366129</td>\n",
       "      <td>9.000645</td>\n",
       "      <td>3.142258</td>\n",
       "      <td>4.695484</td>\n",
       "      <td>3.847742</td>\n",
       "      <td>5.437097</td>\n",
       "      <td>3.362581</td>\n",
       "      <td>5.946452</td>\n",
       "      <td>4.496452</td>\n",
       "      <td>7.079677</td>\n",
       "      <td>9.438387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09</th>\n",
       "      <td>11.703667</td>\n",
       "      <td>10.515333</td>\n",
       "      <td>10.466333</td>\n",
       "      <td>5.313333</td>\n",
       "      <td>8.761333</td>\n",
       "      <td>7.062333</td>\n",
       "      <td>8.617667</td>\n",
       "      <td>6.415333</td>\n",
       "      <td>8.953333</td>\n",
       "      <td>7.263333</td>\n",
       "      <td>11.587000</td>\n",
       "      <td>17.634000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-10</th>\n",
       "      <td>12.427097</td>\n",
       "      <td>9.572258</td>\n",
       "      <td>10.640000</td>\n",
       "      <td>4.885484</td>\n",
       "      <td>9.393548</td>\n",
       "      <td>6.906452</td>\n",
       "      <td>6.380323</td>\n",
       "      <td>6.933226</td>\n",
       "      <td>7.552258</td>\n",
       "      <td>7.449032</td>\n",
       "      <td>11.837742</td>\n",
       "      <td>15.078065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-11</th>\n",
       "      <td>10.962667</td>\n",
       "      <td>9.443667</td>\n",
       "      <td>9.202000</td>\n",
       "      <td>3.696000</td>\n",
       "      <td>7.459333</td>\n",
       "      <td>7.026333</td>\n",
       "      <td>9.058333</td>\n",
       "      <td>5.791000</td>\n",
       "      <td>6.577000</td>\n",
       "      <td>7.512333</td>\n",
       "      <td>12.568333</td>\n",
       "      <td>15.685333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-12</th>\n",
       "      <td>11.962258</td>\n",
       "      <td>10.086774</td>\n",
       "      <td>10.474516</td>\n",
       "      <td>3.383871</td>\n",
       "      <td>7.645484</td>\n",
       "      <td>6.148387</td>\n",
       "      <td>8.034516</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.952258</td>\n",
       "      <td>6.147742</td>\n",
       "      <td>7.814839</td>\n",
       "      <td>14.346774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01</th>\n",
       "      <td>13.404516</td>\n",
       "      <td>10.377742</td>\n",
       "      <td>12.764839</td>\n",
       "      <td>5.884516</td>\n",
       "      <td>9.159677</td>\n",
       "      <td>8.005161</td>\n",
       "      <td>10.107419</td>\n",
       "      <td>7.211613</td>\n",
       "      <td>8.280000</td>\n",
       "      <td>9.328387</td>\n",
       "      <td>12.131935</td>\n",
       "      <td>18.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-02</th>\n",
       "      <td>12.336786</td>\n",
       "      <td>11.898929</td>\n",
       "      <td>12.016786</td>\n",
       "      <td>5.317500</td>\n",
       "      <td>10.134643</td>\n",
       "      <td>9.423929</td>\n",
       "      <td>10.949643</td>\n",
       "      <td>7.965357</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>8.711429</td>\n",
       "      <td>11.435357</td>\n",
       "      <td>17.561429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-03</th>\n",
       "      <td>16.750000</td>\n",
       "      <td>14.499677</td>\n",
       "      <td>16.118387</td>\n",
       "      <td>8.414516</td>\n",
       "      <td>13.293871</td>\n",
       "      <td>11.562258</td>\n",
       "      <td>14.283226</td>\n",
       "      <td>11.361613</td>\n",
       "      <td>12.102581</td>\n",
       "      <td>11.906452</td>\n",
       "      <td>15.863226</td>\n",
       "      <td>19.133548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-04</th>\n",
       "      <td>14.955333</td>\n",
       "      <td>12.293000</td>\n",
       "      <td>12.689667</td>\n",
       "      <td>7.422333</td>\n",
       "      <td>11.740000</td>\n",
       "      <td>10.137000</td>\n",
       "      <td>13.887667</td>\n",
       "      <td>9.574000</td>\n",
       "      <td>10.342333</td>\n",
       "      <td>11.419667</td>\n",
       "      <td>15.593667</td>\n",
       "      <td>18.274667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-05</th>\n",
       "      <td>9.441290</td>\n",
       "      <td>7.173871</td>\n",
       "      <td>12.455806</td>\n",
       "      <td>4.507742</td>\n",
       "      <td>6.198387</td>\n",
       "      <td>6.689677</td>\n",
       "      <td>9.226452</td>\n",
       "      <td>5.638387</td>\n",
       "      <td>6.699355</td>\n",
       "      <td>6.045484</td>\n",
       "      <td>10.213548</td>\n",
       "      <td>11.936129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-06</th>\n",
       "      <td>11.040000</td>\n",
       "      <td>8.353000</td>\n",
       "      <td>12.228000</td>\n",
       "      <td>4.864000</td>\n",
       "      <td>8.790333</td>\n",
       "      <td>7.209667</td>\n",
       "      <td>8.799667</td>\n",
       "      <td>5.931000</td>\n",
       "      <td>7.065333</td>\n",
       "      <td>6.583333</td>\n",
       "      <td>11.321333</td>\n",
       "      <td>11.175333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-07</th>\n",
       "      <td>10.881935</td>\n",
       "      <td>8.663548</td>\n",
       "      <td>10.816452</td>\n",
       "      <td>5.419677</td>\n",
       "      <td>9.014839</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>9.961935</td>\n",
       "      <td>6.526129</td>\n",
       "      <td>7.980968</td>\n",
       "      <td>7.620000</td>\n",
       "      <td>12.924194</td>\n",
       "      <td>12.186774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-08</th>\n",
       "      <td>9.233548</td>\n",
       "      <td>7.727742</td>\n",
       "      <td>10.679032</td>\n",
       "      <td>4.453871</td>\n",
       "      <td>6.620645</td>\n",
       "      <td>5.961290</td>\n",
       "      <td>8.943548</td>\n",
       "      <td>4.543226</td>\n",
       "      <td>6.384839</td>\n",
       "      <td>5.694839</td>\n",
       "      <td>9.825161</td>\n",
       "      <td>11.659355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09</th>\n",
       "      <td>12.472333</td>\n",
       "      <td>10.742667</td>\n",
       "      <td>11.849333</td>\n",
       "      <td>5.638667</td>\n",
       "      <td>10.077333</td>\n",
       "      <td>8.242667</td>\n",
       "      <td>11.939333</td>\n",
       "      <td>7.923000</td>\n",
       "      <td>8.828000</td>\n",
       "      <td>8.506333</td>\n",
       "      <td>14.051000</td>\n",
       "      <td>17.030333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-10</th>\n",
       "      <td>15.004516</td>\n",
       "      <td>13.960000</td>\n",
       "      <td>12.819677</td>\n",
       "      <td>6.754194</td>\n",
       "      <td>11.779032</td>\n",
       "      <td>9.671613</td>\n",
       "      <td>12.924839</td>\n",
       "      <td>11.875161</td>\n",
       "      <td>11.481290</td>\n",
       "      <td>10.340323</td>\n",
       "      <td>17.640968</td>\n",
       "      <td>19.842903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-11</th>\n",
       "      <td>16.946667</td>\n",
       "      <td>15.444667</td>\n",
       "      <td>13.561333</td>\n",
       "      <td>7.584000</td>\n",
       "      <td>12.088667</td>\n",
       "      <td>9.161333</td>\n",
       "      <td>14.051000</td>\n",
       "      <td>11.286000</td>\n",
       "      <td>10.318667</td>\n",
       "      <td>10.327000</td>\n",
       "      <td>17.215333</td>\n",
       "      <td>22.333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-12</th>\n",
       "      <td>14.751935</td>\n",
       "      <td>12.744839</td>\n",
       "      <td>13.469677</td>\n",
       "      <td>6.592258</td>\n",
       "      <td>11.247742</td>\n",
       "      <td>9.466774</td>\n",
       "      <td>13.231613</td>\n",
       "      <td>10.703871</td>\n",
       "      <td>10.401613</td>\n",
       "      <td>9.415484</td>\n",
       "      <td>13.237419</td>\n",
       "      <td>19.299677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01</th>\n",
       "      <td>14.291935</td>\n",
       "      <td>11.872258</td>\n",
       "      <td>12.014194</td>\n",
       "      <td>6.463226</td>\n",
       "      <td>11.402903</td>\n",
       "      <td>7.517097</td>\n",
       "      <td>12.207097</td>\n",
       "      <td>10.206452</td>\n",
       "      <td>9.549032</td>\n",
       "      <td>9.247419</td>\n",
       "      <td>15.101613</td>\n",
       "      <td>20.715806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-02</th>\n",
       "      <td>14.143571</td>\n",
       "      <td>12.153214</td>\n",
       "      <td>13.803214</td>\n",
       "      <td>6.828929</td>\n",
       "      <td>11.196786</td>\n",
       "      <td>7.858929</td>\n",
       "      <td>11.903214</td>\n",
       "      <td>11.068929</td>\n",
       "      <td>10.052143</td>\n",
       "      <td>8.093929</td>\n",
       "      <td>10.353929</td>\n",
       "      <td>17.298571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-03</th>\n",
       "      <td>14.717097</td>\n",
       "      <td>14.601935</td>\n",
       "      <td>13.334194</td>\n",
       "      <td>8.231290</td>\n",
       "      <td>12.783226</td>\n",
       "      <td>9.488710</td>\n",
       "      <td>12.129355</td>\n",
       "      <td>11.665161</td>\n",
       "      <td>11.656452</td>\n",
       "      <td>9.657097</td>\n",
       "      <td>14.234194</td>\n",
       "      <td>18.611290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-04</th>\n",
       "      <td>11.805000</td>\n",
       "      <td>11.255667</td>\n",
       "      <td>12.516333</td>\n",
       "      <td>5.920333</td>\n",
       "      <td>10.218000</td>\n",
       "      <td>7.301667</td>\n",
       "      <td>8.586333</td>\n",
       "      <td>8.306667</td>\n",
       "      <td>8.537000</td>\n",
       "      <td>6.999000</td>\n",
       "      <td>11.190667</td>\n",
       "      <td>14.152000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-05</th>\n",
       "      <td>8.270645</td>\n",
       "      <td>7.226774</td>\n",
       "      <td>6.901613</td>\n",
       "      <td>3.740645</td>\n",
       "      <td>6.973871</td>\n",
       "      <td>4.449677</td>\n",
       "      <td>5.420968</td>\n",
       "      <td>6.130645</td>\n",
       "      <td>5.742581</td>\n",
       "      <td>5.926452</td>\n",
       "      <td>9.263548</td>\n",
       "      <td>10.756452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06</th>\n",
       "      <td>11.386667</td>\n",
       "      <td>9.474333</td>\n",
       "      <td>10.253333</td>\n",
       "      <td>6.053000</td>\n",
       "      <td>10.395333</td>\n",
       "      <td>7.490333</td>\n",
       "      <td>7.928000</td>\n",
       "      <td>7.802000</td>\n",
       "      <td>8.220333</td>\n",
       "      <td>7.550000</td>\n",
       "      <td>11.501000</td>\n",
       "      <td>15.078667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07</th>\n",
       "      <td>12.820000</td>\n",
       "      <td>9.750968</td>\n",
       "      <td>9.910323</td>\n",
       "      <td>6.483871</td>\n",
       "      <td>10.055161</td>\n",
       "      <td>7.820645</td>\n",
       "      <td>7.831935</td>\n",
       "      <td>8.459355</td>\n",
       "      <td>8.523871</td>\n",
       "      <td>7.732903</td>\n",
       "      <td>12.648710</td>\n",
       "      <td>14.077419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08</th>\n",
       "      <td>9.645161</td>\n",
       "      <td>8.259355</td>\n",
       "      <td>9.032258</td>\n",
       "      <td>4.502903</td>\n",
       "      <td>7.368065</td>\n",
       "      <td>5.935161</td>\n",
       "      <td>5.650323</td>\n",
       "      <td>5.417742</td>\n",
       "      <td>7.241290</td>\n",
       "      <td>5.536774</td>\n",
       "      <td>10.466774</td>\n",
       "      <td>12.054194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09</th>\n",
       "      <td>10.913667</td>\n",
       "      <td>10.895000</td>\n",
       "      <td>10.635000</td>\n",
       "      <td>5.725000</td>\n",
       "      <td>10.372000</td>\n",
       "      <td>9.278333</td>\n",
       "      <td>10.790333</td>\n",
       "      <td>9.583000</td>\n",
       "      <td>10.069333</td>\n",
       "      <td>8.939000</td>\n",
       "      <td>15.680333</td>\n",
       "      <td>19.391333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10</th>\n",
       "      <td>9.897742</td>\n",
       "      <td>8.670968</td>\n",
       "      <td>9.295806</td>\n",
       "      <td>4.721290</td>\n",
       "      <td>8.525161</td>\n",
       "      <td>6.774194</td>\n",
       "      <td>8.115484</td>\n",
       "      <td>7.337742</td>\n",
       "      <td>8.297742</td>\n",
       "      <td>8.243871</td>\n",
       "      <td>13.776774</td>\n",
       "      <td>17.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11</th>\n",
       "      <td>16.151667</td>\n",
       "      <td>14.802667</td>\n",
       "      <td>13.508000</td>\n",
       "      <td>7.317333</td>\n",
       "      <td>11.475000</td>\n",
       "      <td>8.743000</td>\n",
       "      <td>11.492333</td>\n",
       "      <td>9.657333</td>\n",
       "      <td>10.701333</td>\n",
       "      <td>10.676000</td>\n",
       "      <td>17.404667</td>\n",
       "      <td>20.723000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12</th>\n",
       "      <td>16.175484</td>\n",
       "      <td>13.748065</td>\n",
       "      <td>15.635161</td>\n",
       "      <td>7.094839</td>\n",
       "      <td>11.398710</td>\n",
       "      <td>9.241613</td>\n",
       "      <td>12.077419</td>\n",
       "      <td>10.194839</td>\n",
       "      <td>10.616774</td>\n",
       "      <td>11.028710</td>\n",
       "      <td>13.859677</td>\n",
       "      <td>21.371613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                RPT        VAL        ROS       KIL        SHA        BIR  \\\n",
       "Yr_Mo_Dy                                                                    \n",
       "1961-01   14.841333  11.988333  13.431613  7.736774  11.072759   8.588065   \n",
       "1961-02   16.269286  14.975357  14.441481  9.230741  13.852143  10.937500   \n",
       "1961-03   10.890000  11.296452  10.752903  7.284000  10.509355   8.866774   \n",
       "1961-04   10.722667   9.427667   9.998000  5.830667   8.435000   6.495000   \n",
       "1961-05    9.860968   8.850000  10.818065  5.905333   9.490323   6.574839   \n",
       "1961-06    9.904138   8.520333   8.867000  6.083000  10.824000   6.707333   \n",
       "1961-07   10.614194   8.221613   9.110323  6.340968  10.532581   6.198387   \n",
       "1961-08   12.035000  10.133871  10.335806  6.845806  12.715161   8.441935   \n",
       "1961-09   12.531000   9.656897  10.776897  7.155517  11.003333   7.234000   \n",
       "1961-10   14.289667  10.915806  12.236452  8.154839  11.865484   8.333871   \n",
       "1961-11   10.896333   8.592667  11.850333  6.045667   9.123667   6.250667   \n",
       "1961-12   14.973548  11.903871  13.980323  7.073871  11.323548   8.302258   \n",
       "1962-01   14.783871  13.160323  12.591935  7.538065  11.779677   8.720000   \n",
       "1962-02   15.844643  12.041429  15.178929  9.262963  13.821429   9.726786   \n",
       "1962-03   11.634333   8.602258  12.110645  6.403226  10.352258   6.732258   \n",
       "1962-04   12.160667   9.676667  12.088333  7.163000  10.544000   7.558000   \n",
       "1962-05   12.745806  10.865484  11.874839  7.471935  11.285806   7.209032   \n",
       "1962-06   10.305667   9.677000   9.996333  6.846667  10.711333   7.441333   \n",
       "1962-07    9.981935   8.370645   9.753548  6.093226   9.112903   5.877097   \n",
       "1962-08   10.964194   9.694194  10.184516  6.701290  10.465161   7.009032   \n",
       "1962-09   11.176333   9.507000  11.640000  6.164333   9.722333   6.214000   \n",
       "1962-10    9.699355   8.063548   9.357097  4.818065   8.432258   5.730000   \n",
       "1962-11   11.071333   7.984000  12.035667  5.740000   8.135667   6.338333   \n",
       "1962-12   16.785484  13.753548  14.276452  9.557419  13.724839  10.321613   \n",
       "1963-01   14.868387  11.112903  15.121613  6.635806  11.080645   7.835484   \n",
       "1963-02   14.418929  11.876429  15.697500  8.611786  12.887857   9.600357   \n",
       "1963-03   14.853871  12.271290  14.295806  9.268387  13.112903  10.088065   \n",
       "1963-04   11.616000  10.138000  13.233667  7.990333  11.515333   9.727000   \n",
       "1963-05   12.879677  11.010645  12.881290  8.411613  12.981613   9.739677   \n",
       "1963-06   10.623333   8.434667  11.685000  6.420333  10.142667   7.219333   \n",
       "...             ...        ...        ...       ...        ...        ...   \n",
       "1976-07    9.687742   7.980968   8.267742  4.631613   7.576774   4.927419   \n",
       "1976-08    7.640645   5.366129   9.000645  3.142258   4.695484   3.847742   \n",
       "1976-09   11.703667  10.515333  10.466333  5.313333   8.761333   7.062333   \n",
       "1976-10   12.427097   9.572258  10.640000  4.885484   9.393548   6.906452   \n",
       "1976-11   10.962667   9.443667   9.202000  3.696000   7.459333   7.026333   \n",
       "1976-12   11.962258  10.086774  10.474516  3.383871   7.645484   6.148387   \n",
       "1977-01   13.404516  10.377742  12.764839  5.884516   9.159677   8.005161   \n",
       "1977-02   12.336786  11.898929  12.016786  5.317500  10.134643   9.423929   \n",
       "1977-03   16.750000  14.499677  16.118387  8.414516  13.293871  11.562258   \n",
       "1977-04   14.955333  12.293000  12.689667  7.422333  11.740000  10.137000   \n",
       "1977-05    9.441290   7.173871  12.455806  4.507742   6.198387   6.689677   \n",
       "1977-06   11.040000   8.353000  12.228000  4.864000   8.790333   7.209667   \n",
       "1977-07   10.881935   8.663548  10.816452  5.419677   9.014839   7.600000   \n",
       "1977-08    9.233548   7.727742  10.679032  4.453871   6.620645   5.961290   \n",
       "1977-09   12.472333  10.742667  11.849333  5.638667  10.077333   8.242667   \n",
       "1977-10   15.004516  13.960000  12.819677  6.754194  11.779032   9.671613   \n",
       "1977-11   16.946667  15.444667  13.561333  7.584000  12.088667   9.161333   \n",
       "1977-12   14.751935  12.744839  13.469677  6.592258  11.247742   9.466774   \n",
       "1978-01   14.291935  11.872258  12.014194  6.463226  11.402903   7.517097   \n",
       "1978-02   14.143571  12.153214  13.803214  6.828929  11.196786   7.858929   \n",
       "1978-03   14.717097  14.601935  13.334194  8.231290  12.783226   9.488710   \n",
       "1978-04   11.805000  11.255667  12.516333  5.920333  10.218000   7.301667   \n",
       "1978-05    8.270645   7.226774   6.901613  3.740645   6.973871   4.449677   \n",
       "1978-06   11.386667   9.474333  10.253333  6.053000  10.395333   7.490333   \n",
       "1978-07   12.820000   9.750968   9.910323  6.483871  10.055161   7.820645   \n",
       "1978-08    9.645161   8.259355   9.032258  4.502903   7.368065   5.935161   \n",
       "1978-09   10.913667  10.895000  10.635000  5.725000  10.372000   9.278333   \n",
       "1978-10    9.897742   8.670968   9.295806  4.721290   8.525161   6.774194   \n",
       "1978-11   16.151667  14.802667  13.508000  7.317333  11.475000   8.743000   \n",
       "1978-12   16.175484  13.748065  15.635161  7.094839  11.398710   9.241613   \n",
       "\n",
       "                DUB        CLA        MUL        CLO        BEL        MAL  \n",
       "Yr_Mo_Dy                                                                    \n",
       "1961-01   11.184839   9.245333   9.085806  10.107419  13.880968  14.703226  \n",
       "1961-02   11.890714  11.846071  11.821429  12.714286  18.583214  15.411786  \n",
       "1961-03    9.644194   9.829677  10.294138  11.251935  16.410968  15.720000  \n",
       "1961-04    6.925333   7.094667   7.342333   7.237000  11.147333  10.278333  \n",
       "1961-05    7.604000   8.177097   8.039355   8.499355  11.900323  12.011613  \n",
       "1961-06    9.095667   8.849333   9.086667   9.940333  13.995000  14.553793  \n",
       "1961-07    8.353333   8.284194   8.077097   8.891613  11.092581  12.312903  \n",
       "1961-08   10.093871  10.460968   9.111613  10.544667  14.410000  14.345333  \n",
       "1961-09    8.206000   8.936552   7.728333   9.931333  13.718333  12.921667  \n",
       "1961-10   11.194194   9.271935   8.942667  11.455806  14.229355  16.793226  \n",
       "1961-11   10.869655   6.313667   6.575000   8.383667  10.776667  12.146000  \n",
       "1961-12   11.753548   8.163226   7.965806   9.246774  12.239355  13.098710  \n",
       "1962-01   14.211935   9.600000   9.670000  11.498710  16.369355  15.661613  \n",
       "1962-02   16.916429  11.285357  12.021071  12.126429  16.705357  18.426786  \n",
       "1962-03   10.223226   7.641935   7.092258   8.052581   9.690000  11.509000  \n",
       "1962-04   11.480000   8.722000   8.703667   9.311667  12.234333  11.780667  \n",
       "1962-05   10.105806   9.084516   7.868065   9.293226  12.130000  12.922581  \n",
       "1962-06   10.548667  10.306667   9.196000  10.520333  13.757000  15.218333  \n",
       "1962-07    7.781613   8.123226   6.829677   8.613226  10.783871  11.326129  \n",
       "1962-08   11.136774   9.097419   8.645484   9.511613  13.119032  15.420968  \n",
       "1962-09    8.488000   7.020333   6.372667   8.286000  11.483667  12.313333  \n",
       "1962-10    8.448065   7.626774   6.630645   9.091290  13.286774  14.090323  \n",
       "1962-11    9.615333   5.943000   6.362333   8.084333   9.786667  13.298333  \n",
       "1962-12   13.735806  11.212258  10.683548  11.881935  16.043548  20.074516  \n",
       "1963-01   12.797419   9.844839   7.841613   9.390000  11.428710  18.822258  \n",
       "1963-02   12.729286  10.823214   8.981786  10.355714  13.266429  17.120714  \n",
       "1963-03   12.168387  11.340968   9.690968  11.515484  13.982903  14.132581  \n",
       "1963-04   11.979000  11.353000  10.341667  11.900333  13.875667  16.333667  \n",
       "1963-05   12.280968  10.964194  10.745161  11.394839  14.777097  14.975161  \n",
       "1963-06    9.267333   9.589333   8.583667   9.585333  12.098000  11.358667  \n",
       "...             ...        ...        ...        ...        ...        ...  \n",
       "1976-07    6.994839   5.135806   7.941290   6.491290  10.264194  11.912258  \n",
       "1976-08    5.437097   3.362581   5.946452   4.496452   7.079677   9.438387  \n",
       "1976-09    8.617667   6.415333   8.953333   7.263333  11.587000  17.634000  \n",
       "1976-10    6.380323   6.933226   7.552258   7.449032  11.837742  15.078065  \n",
       "1976-11    9.058333   5.791000   6.577000   7.512333  12.568333  15.685333  \n",
       "1976-12    8.034516   4.500000   5.952258   6.147742   7.814839  14.346774  \n",
       "1977-01   10.107419   7.211613   8.280000   9.328387  12.131935  18.830000  \n",
       "1977-02   10.949643   7.965357   9.320000   8.711429  11.435357  17.561429  \n",
       "1977-03   14.283226  11.361613  12.102581  11.906452  15.863226  19.133548  \n",
       "1977-04   13.887667   9.574000  10.342333  11.419667  15.593667  18.274667  \n",
       "1977-05    9.226452   5.638387   6.699355   6.045484  10.213548  11.936129  \n",
       "1977-06    8.799667   5.931000   7.065333   6.583333  11.321333  11.175333  \n",
       "1977-07    9.961935   6.526129   7.980968   7.620000  12.924194  12.186774  \n",
       "1977-08    8.943548   4.543226   6.384839   5.694839   9.825161  11.659355  \n",
       "1977-09   11.939333   7.923000   8.828000   8.506333  14.051000  17.030333  \n",
       "1977-10   12.924839  11.875161  11.481290  10.340323  17.640968  19.842903  \n",
       "1977-11   14.051000  11.286000  10.318667  10.327000  17.215333  22.333000  \n",
       "1977-12   13.231613  10.703871  10.401613   9.415484  13.237419  19.299677  \n",
       "1978-01   12.207097  10.206452   9.549032   9.247419  15.101613  20.715806  \n",
       "1978-02   11.903214  11.068929  10.052143   8.093929  10.353929  17.298571  \n",
       "1978-03   12.129355  11.665161  11.656452   9.657097  14.234194  18.611290  \n",
       "1978-04    8.586333   8.306667   8.537000   6.999000  11.190667  14.152000  \n",
       "1978-05    5.420968   6.130645   5.742581   5.926452   9.263548  10.756452  \n",
       "1978-06    7.928000   7.802000   8.220333   7.550000  11.501000  15.078667  \n",
       "1978-07    7.831935   8.459355   8.523871   7.732903  12.648710  14.077419  \n",
       "1978-08    5.650323   5.417742   7.241290   5.536774  10.466774  12.054194  \n",
       "1978-09   10.790333   9.583000  10.069333   8.939000  15.680333  19.391333  \n",
       "1978-10    8.115484   7.337742   8.297742   8.243871  13.776774  17.150000  \n",
       "1978-11   11.492333   9.657333  10.701333  10.676000  17.404667  20.723000  \n",
       "1978-12   12.077419  10.194839  10.616774  11.028710  13.859677  21.371613  \n",
       "\n",
       "[216 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(data.index.to_period('M')).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Downsample the record to a weekly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-12-26/1961-01-01</th>\n",
       "      <td>15.040000</td>\n",
       "      <td>14.960000</td>\n",
       "      <td>13.170000</td>\n",
       "      <td>9.290000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.870000</td>\n",
       "      <td>13.670000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>12.580000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>15.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-02/1961-01-08</th>\n",
       "      <td>13.541429</td>\n",
       "      <td>11.486667</td>\n",
       "      <td>10.487143</td>\n",
       "      <td>6.417143</td>\n",
       "      <td>9.474286</td>\n",
       "      <td>6.435714</td>\n",
       "      <td>11.061429</td>\n",
       "      <td>6.616667</td>\n",
       "      <td>8.434286</td>\n",
       "      <td>8.497143</td>\n",
       "      <td>12.481429</td>\n",
       "      <td>13.238571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-09/1961-01-15</th>\n",
       "      <td>12.468571</td>\n",
       "      <td>8.967143</td>\n",
       "      <td>11.958571</td>\n",
       "      <td>4.630000</td>\n",
       "      <td>7.351429</td>\n",
       "      <td>5.072857</td>\n",
       "      <td>7.535714</td>\n",
       "      <td>6.820000</td>\n",
       "      <td>5.712857</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>11.125714</td>\n",
       "      <td>11.024286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-16/1961-01-22</th>\n",
       "      <td>13.204286</td>\n",
       "      <td>9.862857</td>\n",
       "      <td>12.982857</td>\n",
       "      <td>6.328571</td>\n",
       "      <td>8.966667</td>\n",
       "      <td>7.417143</td>\n",
       "      <td>9.257143</td>\n",
       "      <td>7.875714</td>\n",
       "      <td>7.145714</td>\n",
       "      <td>8.124286</td>\n",
       "      <td>9.821429</td>\n",
       "      <td>11.434286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-23/1961-01-29</th>\n",
       "      <td>19.880000</td>\n",
       "      <td>16.141429</td>\n",
       "      <td>18.225714</td>\n",
       "      <td>12.720000</td>\n",
       "      <td>17.432857</td>\n",
       "      <td>14.828571</td>\n",
       "      <td>15.528571</td>\n",
       "      <td>15.160000</td>\n",
       "      <td>14.480000</td>\n",
       "      <td>15.640000</td>\n",
       "      <td>20.930000</td>\n",
       "      <td>22.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-30/1961-02-05</th>\n",
       "      <td>16.827143</td>\n",
       "      <td>15.460000</td>\n",
       "      <td>12.618571</td>\n",
       "      <td>8.247143</td>\n",
       "      <td>13.361429</td>\n",
       "      <td>9.107143</td>\n",
       "      <td>12.204286</td>\n",
       "      <td>8.548571</td>\n",
       "      <td>9.821429</td>\n",
       "      <td>9.460000</td>\n",
       "      <td>14.012857</td>\n",
       "      <td>11.935714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-06/1961-02-12</th>\n",
       "      <td>19.684286</td>\n",
       "      <td>16.417143</td>\n",
       "      <td>17.304286</td>\n",
       "      <td>10.774286</td>\n",
       "      <td>14.718571</td>\n",
       "      <td>12.522857</td>\n",
       "      <td>14.934286</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>14.064286</td>\n",
       "      <td>14.440000</td>\n",
       "      <td>21.832857</td>\n",
       "      <td>19.155714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-13/1961-02-19</th>\n",
       "      <td>15.130000</td>\n",
       "      <td>15.091429</td>\n",
       "      <td>13.797143</td>\n",
       "      <td>10.083333</td>\n",
       "      <td>13.410000</td>\n",
       "      <td>11.868571</td>\n",
       "      <td>9.542857</td>\n",
       "      <td>12.128571</td>\n",
       "      <td>12.375714</td>\n",
       "      <td>13.542857</td>\n",
       "      <td>21.167143</td>\n",
       "      <td>16.584286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-20/1961-02-26</th>\n",
       "      <td>15.221429</td>\n",
       "      <td>13.625714</td>\n",
       "      <td>14.334286</td>\n",
       "      <td>8.524286</td>\n",
       "      <td>13.655714</td>\n",
       "      <td>10.114286</td>\n",
       "      <td>11.150000</td>\n",
       "      <td>10.875714</td>\n",
       "      <td>10.392857</td>\n",
       "      <td>12.730000</td>\n",
       "      <td>16.304286</td>\n",
       "      <td>14.322857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-27/1961-03-05</th>\n",
       "      <td>12.101429</td>\n",
       "      <td>12.951429</td>\n",
       "      <td>11.063333</td>\n",
       "      <td>7.834286</td>\n",
       "      <td>12.101429</td>\n",
       "      <td>9.238571</td>\n",
       "      <td>10.232857</td>\n",
       "      <td>11.130000</td>\n",
       "      <td>10.383333</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>17.842857</td>\n",
       "      <td>13.951667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-06/1961-03-12</th>\n",
       "      <td>9.376667</td>\n",
       "      <td>11.578571</td>\n",
       "      <td>10.845714</td>\n",
       "      <td>7.137143</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>9.488571</td>\n",
       "      <td>6.881429</td>\n",
       "      <td>9.637143</td>\n",
       "      <td>9.885714</td>\n",
       "      <td>10.458571</td>\n",
       "      <td>16.701429</td>\n",
       "      <td>14.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-13/1961-03-19</th>\n",
       "      <td>11.911429</td>\n",
       "      <td>13.501429</td>\n",
       "      <td>11.607143</td>\n",
       "      <td>7.084286</td>\n",
       "      <td>10.751429</td>\n",
       "      <td>8.652857</td>\n",
       "      <td>10.041429</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>10.101429</td>\n",
       "      <td>11.627143</td>\n",
       "      <td>19.350000</td>\n",
       "      <td>16.227143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-20/1961-03-26</th>\n",
       "      <td>9.567143</td>\n",
       "      <td>8.387143</td>\n",
       "      <td>9.695714</td>\n",
       "      <td>6.648571</td>\n",
       "      <td>8.964286</td>\n",
       "      <td>7.982857</td>\n",
       "      <td>10.774286</td>\n",
       "      <td>8.977143</td>\n",
       "      <td>10.904286</td>\n",
       "      <td>11.481429</td>\n",
       "      <td>14.037143</td>\n",
       "      <td>18.134286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-27/1961-04-02</th>\n",
       "      <td>10.757143</td>\n",
       "      <td>8.852857</td>\n",
       "      <td>9.501429</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>9.975714</td>\n",
       "      <td>9.165714</td>\n",
       "      <td>11.125714</td>\n",
       "      <td>9.061429</td>\n",
       "      <td>10.478333</td>\n",
       "      <td>9.631429</td>\n",
       "      <td>13.471429</td>\n",
       "      <td>13.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-03/1961-04-09</th>\n",
       "      <td>11.964286</td>\n",
       "      <td>10.654286</td>\n",
       "      <td>13.607143</td>\n",
       "      <td>5.958571</td>\n",
       "      <td>9.494286</td>\n",
       "      <td>7.637143</td>\n",
       "      <td>7.107143</td>\n",
       "      <td>8.041429</td>\n",
       "      <td>8.161429</td>\n",
       "      <td>7.238571</td>\n",
       "      <td>11.712857</td>\n",
       "      <td>11.371429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-10/1961-04-16</th>\n",
       "      <td>8.965714</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.787143</td>\n",
       "      <td>4.971429</td>\n",
       "      <td>6.405714</td>\n",
       "      <td>4.947143</td>\n",
       "      <td>5.005714</td>\n",
       "      <td>4.994286</td>\n",
       "      <td>5.718571</td>\n",
       "      <td>6.178571</td>\n",
       "      <td>9.482857</td>\n",
       "      <td>8.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-17/1961-04-23</th>\n",
       "      <td>12.621429</td>\n",
       "      <td>10.438571</td>\n",
       "      <td>10.255714</td>\n",
       "      <td>7.768571</td>\n",
       "      <td>10.357143</td>\n",
       "      <td>7.798571</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.111429</td>\n",
       "      <td>8.767143</td>\n",
       "      <td>9.551429</td>\n",
       "      <td>13.620000</td>\n",
       "      <td>12.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-24/1961-04-30</th>\n",
       "      <td>10.117143</td>\n",
       "      <td>9.798571</td>\n",
       "      <td>8.281429</td>\n",
       "      <td>4.801429</td>\n",
       "      <td>7.892857</td>\n",
       "      <td>5.197143</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>6.377143</td>\n",
       "      <td>6.242857</td>\n",
       "      <td>6.124286</td>\n",
       "      <td>9.720000</td>\n",
       "      <td>8.637143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-01/1961-05-07</th>\n",
       "      <td>15.367143</td>\n",
       "      <td>13.970000</td>\n",
       "      <td>13.834286</td>\n",
       "      <td>9.952857</td>\n",
       "      <td>14.917143</td>\n",
       "      <td>10.864286</td>\n",
       "      <td>11.435714</td>\n",
       "      <td>12.244286</td>\n",
       "      <td>11.677143</td>\n",
       "      <td>11.585714</td>\n",
       "      <td>17.548571</td>\n",
       "      <td>14.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-08/1961-05-14</th>\n",
       "      <td>7.772857</td>\n",
       "      <td>8.712857</td>\n",
       "      <td>8.172857</td>\n",
       "      <td>5.295714</td>\n",
       "      <td>9.150000</td>\n",
       "      <td>6.391429</td>\n",
       "      <td>8.013333</td>\n",
       "      <td>7.052857</td>\n",
       "      <td>7.528571</td>\n",
       "      <td>7.822857</td>\n",
       "      <td>10.421429</td>\n",
       "      <td>10.382857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-15/1961-05-21</th>\n",
       "      <td>8.225714</td>\n",
       "      <td>5.631667</td>\n",
       "      <td>12.042857</td>\n",
       "      <td>4.258571</td>\n",
       "      <td>7.597143</td>\n",
       "      <td>5.022857</td>\n",
       "      <td>5.695714</td>\n",
       "      <td>6.970000</td>\n",
       "      <td>6.847143</td>\n",
       "      <td>7.114286</td>\n",
       "      <td>9.624286</td>\n",
       "      <td>10.612857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-22/1961-05-28</th>\n",
       "      <td>8.155714</td>\n",
       "      <td>7.388571</td>\n",
       "      <td>8.512857</td>\n",
       "      <td>3.748333</td>\n",
       "      <td>6.941429</td>\n",
       "      <td>4.112857</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>6.272857</td>\n",
       "      <td>6.108571</td>\n",
       "      <td>7.535714</td>\n",
       "      <td>10.518571</td>\n",
       "      <td>11.697143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-29/1961-06-04</th>\n",
       "      <td>10.321429</td>\n",
       "      <td>7.407143</td>\n",
       "      <td>10.065714</td>\n",
       "      <td>6.310000</td>\n",
       "      <td>9.754286</td>\n",
       "      <td>6.451429</td>\n",
       "      <td>8.344286</td>\n",
       "      <td>8.635714</td>\n",
       "      <td>8.714286</td>\n",
       "      <td>9.035714</td>\n",
       "      <td>12.298571</td>\n",
       "      <td>13.597143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-05/1961-06-11</th>\n",
       "      <td>10.917143</td>\n",
       "      <td>8.992857</td>\n",
       "      <td>8.095714</td>\n",
       "      <td>5.214286</td>\n",
       "      <td>10.030000</td>\n",
       "      <td>5.460000</td>\n",
       "      <td>7.084286</td>\n",
       "      <td>6.884286</td>\n",
       "      <td>8.034286</td>\n",
       "      <td>8.397143</td>\n",
       "      <td>10.148571</td>\n",
       "      <td>12.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-12/1961-06-18</th>\n",
       "      <td>10.571429</td>\n",
       "      <td>9.565714</td>\n",
       "      <td>10.875714</td>\n",
       "      <td>6.520000</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>6.947143</td>\n",
       "      <td>9.278571</td>\n",
       "      <td>9.102857</td>\n",
       "      <td>8.992857</td>\n",
       "      <td>9.594286</td>\n",
       "      <td>15.351429</td>\n",
       "      <td>15.025714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-19/1961-06-25</th>\n",
       "      <td>7.345714</td>\n",
       "      <td>6.108571</td>\n",
       "      <td>8.084286</td>\n",
       "      <td>5.478571</td>\n",
       "      <td>11.477143</td>\n",
       "      <td>7.492857</td>\n",
       "      <td>11.868571</td>\n",
       "      <td>9.447143</td>\n",
       "      <td>10.458571</td>\n",
       "      <td>11.257143</td>\n",
       "      <td>14.370000</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-26/1961-07-02</th>\n",
       "      <td>10.236667</td>\n",
       "      <td>9.482857</td>\n",
       "      <td>8.648571</td>\n",
       "      <td>6.772857</td>\n",
       "      <td>10.975714</td>\n",
       "      <td>6.507143</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>9.237143</td>\n",
       "      <td>7.904286</td>\n",
       "      <td>10.268571</td>\n",
       "      <td>14.535714</td>\n",
       "      <td>12.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07-03/1961-07-09</th>\n",
       "      <td>11.715714</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>7.544286</td>\n",
       "      <td>12.494286</td>\n",
       "      <td>7.982857</td>\n",
       "      <td>11.888333</td>\n",
       "      <td>9.308571</td>\n",
       "      <td>10.732857</td>\n",
       "      <td>10.547143</td>\n",
       "      <td>12.220000</td>\n",
       "      <td>15.987143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07-10/1961-07-16</th>\n",
       "      <td>16.680000</td>\n",
       "      <td>13.518571</td>\n",
       "      <td>11.171429</td>\n",
       "      <td>9.277143</td>\n",
       "      <td>14.524286</td>\n",
       "      <td>8.412857</td>\n",
       "      <td>10.171429</td>\n",
       "      <td>10.507143</td>\n",
       "      <td>9.530000</td>\n",
       "      <td>10.157143</td>\n",
       "      <td>13.520000</td>\n",
       "      <td>12.524286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07-17/1961-07-23</th>\n",
       "      <td>4.202857</td>\n",
       "      <td>4.255714</td>\n",
       "      <td>6.738571</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>6.112857</td>\n",
       "      <td>2.715714</td>\n",
       "      <td>3.964286</td>\n",
       "      <td>5.642857</td>\n",
       "      <td>5.297143</td>\n",
       "      <td>6.041429</td>\n",
       "      <td>7.524286</td>\n",
       "      <td>8.415714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-05/1978-06-11</th>\n",
       "      <td>12.022857</td>\n",
       "      <td>9.154286</td>\n",
       "      <td>9.488571</td>\n",
       "      <td>5.971429</td>\n",
       "      <td>10.637143</td>\n",
       "      <td>8.030000</td>\n",
       "      <td>8.678571</td>\n",
       "      <td>8.227143</td>\n",
       "      <td>9.172857</td>\n",
       "      <td>9.642857</td>\n",
       "      <td>11.632857</td>\n",
       "      <td>17.778571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-12/1978-06-18</th>\n",
       "      <td>9.410000</td>\n",
       "      <td>8.770000</td>\n",
       "      <td>14.135714</td>\n",
       "      <td>6.457143</td>\n",
       "      <td>8.564286</td>\n",
       "      <td>6.898571</td>\n",
       "      <td>7.297143</td>\n",
       "      <td>7.464286</td>\n",
       "      <td>7.054286</td>\n",
       "      <td>6.225714</td>\n",
       "      <td>11.398571</td>\n",
       "      <td>12.957143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-19/1978-06-25</th>\n",
       "      <td>12.707143</td>\n",
       "      <td>10.244286</td>\n",
       "      <td>8.912857</td>\n",
       "      <td>5.878571</td>\n",
       "      <td>10.372857</td>\n",
       "      <td>6.852857</td>\n",
       "      <td>7.648571</td>\n",
       "      <td>7.875714</td>\n",
       "      <td>7.865714</td>\n",
       "      <td>7.084286</td>\n",
       "      <td>13.030000</td>\n",
       "      <td>16.678571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-26/1978-07-02</th>\n",
       "      <td>12.208571</td>\n",
       "      <td>9.640000</td>\n",
       "      <td>10.482857</td>\n",
       "      <td>7.011429</td>\n",
       "      <td>12.772857</td>\n",
       "      <td>9.005714</td>\n",
       "      <td>11.055714</td>\n",
       "      <td>8.917143</td>\n",
       "      <td>9.994286</td>\n",
       "      <td>7.498571</td>\n",
       "      <td>12.268571</td>\n",
       "      <td>15.287143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-03/1978-07-09</th>\n",
       "      <td>18.052857</td>\n",
       "      <td>12.630000</td>\n",
       "      <td>11.984286</td>\n",
       "      <td>9.220000</td>\n",
       "      <td>13.414286</td>\n",
       "      <td>10.762857</td>\n",
       "      <td>11.368571</td>\n",
       "      <td>11.218571</td>\n",
       "      <td>11.272857</td>\n",
       "      <td>11.082857</td>\n",
       "      <td>14.754286</td>\n",
       "      <td>18.215714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-10/1978-07-16</th>\n",
       "      <td>5.882857</td>\n",
       "      <td>3.244286</td>\n",
       "      <td>5.358571</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>4.618571</td>\n",
       "      <td>2.631429</td>\n",
       "      <td>2.494286</td>\n",
       "      <td>3.540000</td>\n",
       "      <td>3.397143</td>\n",
       "      <td>3.214286</td>\n",
       "      <td>7.198571</td>\n",
       "      <td>7.578571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-17/1978-07-23</th>\n",
       "      <td>13.654286</td>\n",
       "      <td>10.007143</td>\n",
       "      <td>9.915714</td>\n",
       "      <td>6.577143</td>\n",
       "      <td>10.757143</td>\n",
       "      <td>8.282857</td>\n",
       "      <td>8.147143</td>\n",
       "      <td>9.301429</td>\n",
       "      <td>8.952857</td>\n",
       "      <td>8.402857</td>\n",
       "      <td>13.847143</td>\n",
       "      <td>14.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-24/1978-07-30</th>\n",
       "      <td>12.172857</td>\n",
       "      <td>11.854286</td>\n",
       "      <td>11.094286</td>\n",
       "      <td>6.631429</td>\n",
       "      <td>9.918571</td>\n",
       "      <td>8.707143</td>\n",
       "      <td>7.458571</td>\n",
       "      <td>9.117143</td>\n",
       "      <td>9.304286</td>\n",
       "      <td>8.148571</td>\n",
       "      <td>15.192857</td>\n",
       "      <td>14.584286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-31/1978-08-06</th>\n",
       "      <td>12.475714</td>\n",
       "      <td>9.488571</td>\n",
       "      <td>10.584286</td>\n",
       "      <td>5.457143</td>\n",
       "      <td>8.724286</td>\n",
       "      <td>5.855714</td>\n",
       "      <td>7.065714</td>\n",
       "      <td>5.410000</td>\n",
       "      <td>6.631429</td>\n",
       "      <td>4.962857</td>\n",
       "      <td>9.084286</td>\n",
       "      <td>11.405714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-07/1978-08-13</th>\n",
       "      <td>10.114286</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>7.635714</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>8.101429</td>\n",
       "      <td>6.702857</td>\n",
       "      <td>5.452857</td>\n",
       "      <td>5.964286</td>\n",
       "      <td>7.518571</td>\n",
       "      <td>5.661429</td>\n",
       "      <td>10.691429</td>\n",
       "      <td>11.927143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-14/1978-08-20</th>\n",
       "      <td>11.100000</td>\n",
       "      <td>11.237143</td>\n",
       "      <td>10.505714</td>\n",
       "      <td>5.697143</td>\n",
       "      <td>9.910000</td>\n",
       "      <td>8.034286</td>\n",
       "      <td>7.267143</td>\n",
       "      <td>8.517143</td>\n",
       "      <td>9.815714</td>\n",
       "      <td>7.941429</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14.405714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-21/1978-08-27</th>\n",
       "      <td>6.208571</td>\n",
       "      <td>5.060000</td>\n",
       "      <td>8.565714</td>\n",
       "      <td>3.121429</td>\n",
       "      <td>4.638571</td>\n",
       "      <td>4.077143</td>\n",
       "      <td>3.291429</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>5.877143</td>\n",
       "      <td>4.447143</td>\n",
       "      <td>8.131429</td>\n",
       "      <td>10.661429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-28/1978-09-03</th>\n",
       "      <td>8.232857</td>\n",
       "      <td>4.888571</td>\n",
       "      <td>7.767143</td>\n",
       "      <td>3.588571</td>\n",
       "      <td>3.892857</td>\n",
       "      <td>5.090000</td>\n",
       "      <td>6.184286</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.202857</td>\n",
       "      <td>4.745714</td>\n",
       "      <td>8.105714</td>\n",
       "      <td>13.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-04/1978-09-10</th>\n",
       "      <td>11.487143</td>\n",
       "      <td>12.742857</td>\n",
       "      <td>11.124286</td>\n",
       "      <td>5.702857</td>\n",
       "      <td>10.721429</td>\n",
       "      <td>10.927143</td>\n",
       "      <td>9.157143</td>\n",
       "      <td>9.458571</td>\n",
       "      <td>10.588571</td>\n",
       "      <td>8.274286</td>\n",
       "      <td>14.560000</td>\n",
       "      <td>16.752857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-11/1978-09-17</th>\n",
       "      <td>12.067143</td>\n",
       "      <td>10.648571</td>\n",
       "      <td>11.610000</td>\n",
       "      <td>6.864286</td>\n",
       "      <td>12.252857</td>\n",
       "      <td>11.868571</td>\n",
       "      <td>13.017143</td>\n",
       "      <td>12.447143</td>\n",
       "      <td>11.908571</td>\n",
       "      <td>10.957143</td>\n",
       "      <td>18.422857</td>\n",
       "      <td>23.441429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-18/1978-09-24</th>\n",
       "      <td>5.845714</td>\n",
       "      <td>8.317143</td>\n",
       "      <td>9.305714</td>\n",
       "      <td>2.554286</td>\n",
       "      <td>5.625714</td>\n",
       "      <td>5.171429</td>\n",
       "      <td>7.047143</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>6.870000</td>\n",
       "      <td>6.291429</td>\n",
       "      <td>13.447143</td>\n",
       "      <td>15.324286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-25/1978-10-01</th>\n",
       "      <td>16.252857</td>\n",
       "      <td>14.131429</td>\n",
       "      <td>12.098571</td>\n",
       "      <td>8.832857</td>\n",
       "      <td>15.810000</td>\n",
       "      <td>10.338571</td>\n",
       "      <td>15.124286</td>\n",
       "      <td>12.378571</td>\n",
       "      <td>12.275714</td>\n",
       "      <td>11.970000</td>\n",
       "      <td>19.160000</td>\n",
       "      <td>24.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-02/1978-10-08</th>\n",
       "      <td>12.865714</td>\n",
       "      <td>13.302857</td>\n",
       "      <td>11.671429</td>\n",
       "      <td>6.531429</td>\n",
       "      <td>11.731429</td>\n",
       "      <td>8.881429</td>\n",
       "      <td>9.707143</td>\n",
       "      <td>9.708571</td>\n",
       "      <td>11.391429</td>\n",
       "      <td>10.161429</td>\n",
       "      <td>16.498571</td>\n",
       "      <td>20.618571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-09/1978-10-15</th>\n",
       "      <td>9.611429</td>\n",
       "      <td>6.327143</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>4.167143</td>\n",
       "      <td>6.821429</td>\n",
       "      <td>6.237143</td>\n",
       "      <td>5.577143</td>\n",
       "      <td>6.348571</td>\n",
       "      <td>7.232857</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>12.197143</td>\n",
       "      <td>14.177143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-16/1978-10-22</th>\n",
       "      <td>9.804286</td>\n",
       "      <td>7.817143</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>5.314286</td>\n",
       "      <td>9.124286</td>\n",
       "      <td>6.862857</td>\n",
       "      <td>9.391429</td>\n",
       "      <td>7.428571</td>\n",
       "      <td>7.765714</td>\n",
       "      <td>8.048571</td>\n",
       "      <td>12.708571</td>\n",
       "      <td>17.868571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-23/1978-10-29</th>\n",
       "      <td>7.504286</td>\n",
       "      <td>7.702857</td>\n",
       "      <td>8.102857</td>\n",
       "      <td>3.204286</td>\n",
       "      <td>7.464286</td>\n",
       "      <td>5.905714</td>\n",
       "      <td>8.727143</td>\n",
       "      <td>6.652857</td>\n",
       "      <td>7.605714</td>\n",
       "      <td>8.120000</td>\n",
       "      <td>14.487143</td>\n",
       "      <td>16.915714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-30/1978-11-05</th>\n",
       "      <td>13.060000</td>\n",
       "      <td>13.465714</td>\n",
       "      <td>12.137143</td>\n",
       "      <td>6.682857</td>\n",
       "      <td>9.891429</td>\n",
       "      <td>8.314286</td>\n",
       "      <td>9.775714</td>\n",
       "      <td>9.638571</td>\n",
       "      <td>10.185714</td>\n",
       "      <td>10.422857</td>\n",
       "      <td>18.451429</td>\n",
       "      <td>18.721429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-06/1978-11-12</th>\n",
       "      <td>14.857143</td>\n",
       "      <td>15.237143</td>\n",
       "      <td>12.007143</td>\n",
       "      <td>7.684286</td>\n",
       "      <td>12.460000</td>\n",
       "      <td>9.352857</td>\n",
       "      <td>10.224286</td>\n",
       "      <td>10.554286</td>\n",
       "      <td>11.168571</td>\n",
       "      <td>12.232857</td>\n",
       "      <td>19.307143</td>\n",
       "      <td>22.522857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-13/1978-11-19</th>\n",
       "      <td>20.590000</td>\n",
       "      <td>18.998571</td>\n",
       "      <td>17.272857</td>\n",
       "      <td>10.417143</td>\n",
       "      <td>14.220000</td>\n",
       "      <td>11.208571</td>\n",
       "      <td>16.081429</td>\n",
       "      <td>12.915714</td>\n",
       "      <td>13.297143</td>\n",
       "      <td>13.242857</td>\n",
       "      <td>20.357143</td>\n",
       "      <td>23.905714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-20/1978-11-26</th>\n",
       "      <td>16.498571</td>\n",
       "      <td>13.971429</td>\n",
       "      <td>13.544286</td>\n",
       "      <td>6.361429</td>\n",
       "      <td>10.438571</td>\n",
       "      <td>7.404286</td>\n",
       "      <td>12.797143</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>9.998571</td>\n",
       "      <td>8.915714</td>\n",
       "      <td>15.207143</td>\n",
       "      <td>19.491429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-27/1978-12-03</th>\n",
       "      <td>14.934286</td>\n",
       "      <td>11.232857</td>\n",
       "      <td>13.941429</td>\n",
       "      <td>5.565714</td>\n",
       "      <td>10.215714</td>\n",
       "      <td>8.618571</td>\n",
       "      <td>9.642857</td>\n",
       "      <td>7.685714</td>\n",
       "      <td>9.011429</td>\n",
       "      <td>9.547143</td>\n",
       "      <td>11.835714</td>\n",
       "      <td>18.728571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-04/1978-12-10</th>\n",
       "      <td>20.740000</td>\n",
       "      <td>19.190000</td>\n",
       "      <td>17.034286</td>\n",
       "      <td>9.777143</td>\n",
       "      <td>15.287143</td>\n",
       "      <td>12.774286</td>\n",
       "      <td>14.437143</td>\n",
       "      <td>12.488571</td>\n",
       "      <td>13.870000</td>\n",
       "      <td>14.082857</td>\n",
       "      <td>18.517143</td>\n",
       "      <td>23.061429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-11/1978-12-17</th>\n",
       "      <td>16.758571</td>\n",
       "      <td>14.692857</td>\n",
       "      <td>14.987143</td>\n",
       "      <td>6.917143</td>\n",
       "      <td>11.397143</td>\n",
       "      <td>7.272857</td>\n",
       "      <td>10.208571</td>\n",
       "      <td>7.967143</td>\n",
       "      <td>9.168571</td>\n",
       "      <td>8.565714</td>\n",
       "      <td>11.102857</td>\n",
       "      <td>15.562857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-18/1978-12-24</th>\n",
       "      <td>11.155714</td>\n",
       "      <td>8.008571</td>\n",
       "      <td>13.172857</td>\n",
       "      <td>4.004286</td>\n",
       "      <td>7.825714</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>7.798571</td>\n",
       "      <td>8.667143</td>\n",
       "      <td>7.151429</td>\n",
       "      <td>8.072857</td>\n",
       "      <td>11.845714</td>\n",
       "      <td>18.977143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-25/1978-12-31</th>\n",
       "      <td>14.951429</td>\n",
       "      <td>11.801429</td>\n",
       "      <td>16.035714</td>\n",
       "      <td>6.507143</td>\n",
       "      <td>9.660000</td>\n",
       "      <td>8.620000</td>\n",
       "      <td>13.708571</td>\n",
       "      <td>10.477143</td>\n",
       "      <td>10.868571</td>\n",
       "      <td>11.471429</td>\n",
       "      <td>12.947143</td>\n",
       "      <td>26.844286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>940 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             RPT        VAL        ROS        KIL        SHA  \\\n",
       "Yr_Mo_Dy                                                                       \n",
       "1960-12-26/1961-01-01  15.040000  14.960000  13.170000   9.290000        NaN   \n",
       "1961-01-02/1961-01-08  13.541429  11.486667  10.487143   6.417143   9.474286   \n",
       "1961-01-09/1961-01-15  12.468571   8.967143  11.958571   4.630000   7.351429   \n",
       "1961-01-16/1961-01-22  13.204286   9.862857  12.982857   6.328571   8.966667   \n",
       "1961-01-23/1961-01-29  19.880000  16.141429  18.225714  12.720000  17.432857   \n",
       "1961-01-30/1961-02-05  16.827143  15.460000  12.618571   8.247143  13.361429   \n",
       "1961-02-06/1961-02-12  19.684286  16.417143  17.304286  10.774286  14.718571   \n",
       "1961-02-13/1961-02-19  15.130000  15.091429  13.797143  10.083333  13.410000   \n",
       "1961-02-20/1961-02-26  15.221429  13.625714  14.334286   8.524286  13.655714   \n",
       "1961-02-27/1961-03-05  12.101429  12.951429  11.063333   7.834286  12.101429   \n",
       "1961-03-06/1961-03-12   9.376667  11.578571  10.845714   7.137143  10.940000   \n",
       "1961-03-13/1961-03-19  11.911429  13.501429  11.607143   7.084286  10.751429   \n",
       "1961-03-20/1961-03-26   9.567143   8.387143   9.695714   6.648571   8.964286   \n",
       "1961-03-27/1961-04-02  10.757143   8.852857   9.501429   7.300000   9.975714   \n",
       "1961-04-03/1961-04-09  11.964286  10.654286  13.607143   5.958571   9.494286   \n",
       "1961-04-10/1961-04-16   8.965714   8.000000   8.787143   4.971429   6.405714   \n",
       "1961-04-17/1961-04-23  12.621429  10.438571  10.255714   7.768571  10.357143   \n",
       "1961-04-24/1961-04-30  10.117143   9.798571   8.281429   4.801429   7.892857   \n",
       "1961-05-01/1961-05-07  15.367143  13.970000  13.834286   9.952857  14.917143   \n",
       "1961-05-08/1961-05-14   7.772857   8.712857   8.172857   5.295714   9.150000   \n",
       "1961-05-15/1961-05-21   8.225714   5.631667  12.042857   4.258571   7.597143   \n",
       "1961-05-22/1961-05-28   8.155714   7.388571   8.512857   3.748333   6.941429   \n",
       "1961-05-29/1961-06-04  10.321429   7.407143  10.065714   6.310000   9.754286   \n",
       "1961-06-05/1961-06-11  10.917143   8.992857   8.095714   5.214286  10.030000   \n",
       "1961-06-12/1961-06-18  10.571429   9.565714  10.875714   6.520000  10.260000   \n",
       "1961-06-19/1961-06-25   7.345714   6.108571   8.084286   5.478571  11.477143   \n",
       "1961-06-26/1961-07-02  10.236667   9.482857   8.648571   6.772857  10.975714   \n",
       "1961-07-03/1961-07-09  11.715714   7.220000   9.320000   7.544286  12.494286   \n",
       "1961-07-10/1961-07-16  16.680000  13.518571  11.171429   9.277143  14.524286   \n",
       "1961-07-17/1961-07-23   4.202857   4.255714   6.738571   3.300000   6.112857   \n",
       "...                          ...        ...        ...        ...        ...   \n",
       "1978-06-05/1978-06-11  12.022857   9.154286   9.488571   5.971429  10.637143   \n",
       "1978-06-12/1978-06-18   9.410000   8.770000  14.135714   6.457143   8.564286   \n",
       "1978-06-19/1978-06-25  12.707143  10.244286   8.912857   5.878571  10.372857   \n",
       "1978-06-26/1978-07-02  12.208571   9.640000  10.482857   7.011429  12.772857   \n",
       "1978-07-03/1978-07-09  18.052857  12.630000  11.984286   9.220000  13.414286   \n",
       "1978-07-10/1978-07-16   5.882857   3.244286   5.358571   2.250000   4.618571   \n",
       "1978-07-17/1978-07-23  13.654286  10.007143   9.915714   6.577143  10.757143   \n",
       "1978-07-24/1978-07-30  12.172857  11.854286  11.094286   6.631429   9.918571   \n",
       "1978-07-31/1978-08-06  12.475714   9.488571  10.584286   5.457143   8.724286   \n",
       "1978-08-07/1978-08-13  10.114286   9.600000   7.635714   4.790000   8.101429   \n",
       "1978-08-14/1978-08-20  11.100000  11.237143  10.505714   5.697143   9.910000   \n",
       "1978-08-21/1978-08-27   6.208571   5.060000   8.565714   3.121429   4.638571   \n",
       "1978-08-28/1978-09-03   8.232857   4.888571   7.767143   3.588571   3.892857   \n",
       "1978-09-04/1978-09-10  11.487143  12.742857  11.124286   5.702857  10.721429   \n",
       "1978-09-11/1978-09-17  12.067143  10.648571  11.610000   6.864286  12.252857   \n",
       "1978-09-18/1978-09-24   5.845714   8.317143   9.305714   2.554286   5.625714   \n",
       "1978-09-25/1978-10-01  16.252857  14.131429  12.098571   8.832857  15.810000   \n",
       "1978-10-02/1978-10-08  12.865714  13.302857  11.671429   6.531429  11.731429   \n",
       "1978-10-09/1978-10-15   9.611429   6.327143   9.250000   4.167143   6.821429   \n",
       "1978-10-16/1978-10-22   9.804286   7.817143   7.642857   5.314286   9.124286   \n",
       "1978-10-23/1978-10-29   7.504286   7.702857   8.102857   3.204286   7.464286   \n",
       "1978-10-30/1978-11-05  13.060000  13.465714  12.137143   6.682857   9.891429   \n",
       "1978-11-06/1978-11-12  14.857143  15.237143  12.007143   7.684286  12.460000   \n",
       "1978-11-13/1978-11-19  20.590000  18.998571  17.272857  10.417143  14.220000   \n",
       "1978-11-20/1978-11-26  16.498571  13.971429  13.544286   6.361429  10.438571   \n",
       "1978-11-27/1978-12-03  14.934286  11.232857  13.941429   5.565714  10.215714   \n",
       "1978-12-04/1978-12-10  20.740000  19.190000  17.034286   9.777143  15.287143   \n",
       "1978-12-11/1978-12-17  16.758571  14.692857  14.987143   6.917143  11.397143   \n",
       "1978-12-18/1978-12-24  11.155714   8.008571  13.172857   4.004286   7.825714   \n",
       "1978-12-25/1978-12-31  14.951429  11.801429  16.035714   6.507143   9.660000   \n",
       "\n",
       "                             BIR        DUB        CLA        MUL        CLO  \\\n",
       "Yr_Mo_Dy                                                                       \n",
       "1960-12-26/1961-01-01   9.870000  13.670000  10.250000  10.830000  12.580000   \n",
       "1961-01-02/1961-01-08   6.435714  11.061429   6.616667   8.434286   8.497143   \n",
       "1961-01-09/1961-01-15   5.072857   7.535714   6.820000   5.712857   7.571429   \n",
       "1961-01-16/1961-01-22   7.417143   9.257143   7.875714   7.145714   8.124286   \n",
       "1961-01-23/1961-01-29  14.828571  15.528571  15.160000  14.480000  15.640000   \n",
       "1961-01-30/1961-02-05   9.107143  12.204286   8.548571   9.821429   9.460000   \n",
       "1961-02-06/1961-02-12  12.522857  14.934286  14.850000  14.064286  14.440000   \n",
       "1961-02-13/1961-02-19  11.868571   9.542857  12.128571  12.375714  13.542857   \n",
       "1961-02-20/1961-02-26  10.114286  11.150000  10.875714  10.392857  12.730000   \n",
       "1961-02-27/1961-03-05   9.238571  10.232857  11.130000  10.383333  12.370000   \n",
       "1961-03-06/1961-03-12   9.488571   6.881429   9.637143   9.885714  10.458571   \n",
       "1961-03-13/1961-03-19   8.652857  10.041429  10.220000  10.101429  11.627143   \n",
       "1961-03-20/1961-03-26   7.982857  10.774286   8.977143  10.904286  11.481429   \n",
       "1961-03-27/1961-04-02   9.165714  11.125714   9.061429  10.478333   9.631429   \n",
       "1961-04-03/1961-04-09   7.637143   7.107143   8.041429   8.161429   7.238571   \n",
       "1961-04-10/1961-04-16   4.947143   5.005714   4.994286   5.718571   6.178571   \n",
       "1961-04-17/1961-04-23   7.798571   9.000000   9.111429   8.767143   9.551429   \n",
       "1961-04-24/1961-04-30   5.197143   6.150000   6.377143   6.242857   6.124286   \n",
       "1961-05-01/1961-05-07  10.864286  11.435714  12.244286  11.677143  11.585714   \n",
       "1961-05-08/1961-05-14   6.391429   8.013333   7.052857   7.528571   7.822857   \n",
       "1961-05-15/1961-05-21   5.022857   5.695714   6.970000   6.847143   7.114286   \n",
       "1961-05-22/1961-05-28   4.112857   5.142857   6.272857   6.108571   7.535714   \n",
       "1961-05-29/1961-06-04   6.451429   8.344286   8.635714   8.714286   9.035714   \n",
       "1961-06-05/1961-06-11   5.460000   7.084286   6.884286   8.034286   8.397143   \n",
       "1961-06-12/1961-06-18   6.947143   9.278571   9.102857   8.992857   9.594286   \n",
       "1961-06-19/1961-06-25   7.492857  11.868571   9.447143  10.458571  11.257143   \n",
       "1961-06-26/1961-07-02   6.507143   7.642857   9.237143   7.904286  10.268571   \n",
       "1961-07-03/1961-07-09   7.982857  11.888333   9.308571  10.732857  10.547143   \n",
       "1961-07-10/1961-07-16   8.412857  10.171429  10.507143   9.530000  10.157143   \n",
       "1961-07-17/1961-07-23   2.715714   3.964286   5.642857   5.297143   6.041429   \n",
       "...                          ...        ...        ...        ...        ...   \n",
       "1978-06-05/1978-06-11   8.030000   8.678571   8.227143   9.172857   9.642857   \n",
       "1978-06-12/1978-06-18   6.898571   7.297143   7.464286   7.054286   6.225714   \n",
       "1978-06-19/1978-06-25   6.852857   7.648571   7.875714   7.865714   7.084286   \n",
       "1978-06-26/1978-07-02   9.005714  11.055714   8.917143   9.994286   7.498571   \n",
       "1978-07-03/1978-07-09  10.762857  11.368571  11.218571  11.272857  11.082857   \n",
       "1978-07-10/1978-07-16   2.631429   2.494286   3.540000   3.397143   3.214286   \n",
       "1978-07-17/1978-07-23   8.282857   8.147143   9.301429   8.952857   8.402857   \n",
       "1978-07-24/1978-07-30   8.707143   7.458571   9.117143   9.304286   8.148571   \n",
       "1978-07-31/1978-08-06   5.855714   7.065714   5.410000   6.631429   4.962857   \n",
       "1978-08-07/1978-08-13   6.702857   5.452857   5.964286   7.518571   5.661429   \n",
       "1978-08-14/1978-08-20   8.034286   7.267143   8.517143   9.815714   7.941429   \n",
       "1978-08-21/1978-08-27   4.077143   3.291429   3.500000   5.877143   4.447143   \n",
       "1978-08-28/1978-09-03   5.090000   6.184286   3.000000   6.202857   4.745714   \n",
       "1978-09-04/1978-09-10  10.927143   9.157143   9.458571  10.588571   8.274286   \n",
       "1978-09-11/1978-09-17  11.868571  13.017143  12.447143  11.908571  10.957143   \n",
       "1978-09-18/1978-09-24   5.171429   7.047143   6.750000   6.870000   6.291429   \n",
       "1978-09-25/1978-10-01  10.338571  15.124286  12.378571  12.275714  11.970000   \n",
       "1978-10-02/1978-10-08   8.881429   9.707143   9.708571  11.391429  10.161429   \n",
       "1978-10-09/1978-10-15   6.237143   5.577143   6.348571   7.232857   7.285714   \n",
       "1978-10-16/1978-10-22   6.862857   9.391429   7.428571   7.765714   8.048571   \n",
       "1978-10-23/1978-10-29   5.905714   8.727143   6.652857   7.605714   8.120000   \n",
       "1978-10-30/1978-11-05   8.314286   9.775714   9.638571  10.185714  10.422857   \n",
       "1978-11-06/1978-11-12   9.352857  10.224286  10.554286  11.168571  12.232857   \n",
       "1978-11-13/1978-11-19  11.208571  16.081429  12.915714  13.297143  13.242857   \n",
       "1978-11-20/1978-11-26   7.404286  12.797143   7.571429   9.998571   8.915714   \n",
       "1978-11-27/1978-12-03   8.618571   9.642857   7.685714   9.011429   9.547143   \n",
       "1978-12-04/1978-12-10  12.774286  14.437143  12.488571  13.870000  14.082857   \n",
       "1978-12-11/1978-12-17   7.272857  10.208571   7.967143   9.168571   8.565714   \n",
       "1978-12-18/1978-12-24   6.290000   7.798571   8.667143   7.151429   8.072857   \n",
       "1978-12-25/1978-12-31   8.620000  13.708571  10.477143  10.868571  11.471429   \n",
       "\n",
       "                             BEL        MAL  \n",
       "Yr_Mo_Dy                                     \n",
       "1960-12-26/1961-01-01  18.500000  15.040000  \n",
       "1961-01-02/1961-01-08  12.481429  13.238571  \n",
       "1961-01-09/1961-01-15  11.125714  11.024286  \n",
       "1961-01-16/1961-01-22   9.821429  11.434286  \n",
       "1961-01-23/1961-01-29  20.930000  22.530000  \n",
       "1961-01-30/1961-02-05  14.012857  11.935714  \n",
       "1961-02-06/1961-02-12  21.832857  19.155714  \n",
       "1961-02-13/1961-02-19  21.167143  16.584286  \n",
       "1961-02-20/1961-02-26  16.304286  14.322857  \n",
       "1961-02-27/1961-03-05  17.842857  13.951667  \n",
       "1961-03-06/1961-03-12  16.701429  14.420000  \n",
       "1961-03-13/1961-03-19  19.350000  16.227143  \n",
       "1961-03-20/1961-03-26  14.037143  18.134286  \n",
       "1961-03-27/1961-04-02  13.471429  13.900000  \n",
       "1961-04-03/1961-04-09  11.712857  11.371429  \n",
       "1961-04-10/1961-04-16   9.482857   8.690000  \n",
       "1961-04-17/1961-04-23  13.620000  12.470000  \n",
       "1961-04-24/1961-04-30   9.720000   8.637143  \n",
       "1961-05-01/1961-05-07  17.548571  14.571429  \n",
       "1961-05-08/1961-05-14  10.421429  10.382857  \n",
       "1961-05-15/1961-05-21   9.624286  10.612857  \n",
       "1961-05-22/1961-05-28  10.518571  11.697143  \n",
       "1961-05-29/1961-06-04  12.298571  13.597143  \n",
       "1961-06-05/1961-06-11  10.148571  12.250000  \n",
       "1961-06-12/1961-06-18  15.351429  15.025714  \n",
       "1961-06-19/1961-06-25  14.370000  17.410000  \n",
       "1961-06-26/1961-07-02  14.535714  12.133333  \n",
       "1961-07-03/1961-07-09  12.220000  15.987143  \n",
       "1961-07-10/1961-07-16  13.520000  12.524286  \n",
       "1961-07-17/1961-07-23   7.524286   8.415714  \n",
       "...                          ...        ...  \n",
       "1978-06-05/1978-06-11  11.632857  17.778571  \n",
       "1978-06-12/1978-06-18  11.398571  12.957143  \n",
       "1978-06-19/1978-06-25  13.030000  16.678571  \n",
       "1978-06-26/1978-07-02  12.268571  15.287143  \n",
       "1978-07-03/1978-07-09  14.754286  18.215714  \n",
       "1978-07-10/1978-07-16   7.198571   7.578571  \n",
       "1978-07-17/1978-07-23  13.847143  14.785714  \n",
       "1978-07-24/1978-07-30  15.192857  14.584286  \n",
       "1978-07-31/1978-08-06   9.084286  11.405714  \n",
       "1978-08-07/1978-08-13  10.691429  11.927143  \n",
       "1978-08-14/1978-08-20  15.000000  14.405714  \n",
       "1978-08-21/1978-08-27   8.131429  10.661429  \n",
       "1978-08-28/1978-09-03   8.105714  13.150000  \n",
       "1978-09-04/1978-09-10  14.560000  16.752857  \n",
       "1978-09-11/1978-09-17  18.422857  23.441429  \n",
       "1978-09-18/1978-09-24  13.447143  15.324286  \n",
       "1978-09-25/1978-10-01  19.160000  24.158571  \n",
       "1978-10-02/1978-10-08  16.498571  20.618571  \n",
       "1978-10-09/1978-10-15  12.197143  14.177143  \n",
       "1978-10-16/1978-10-22  12.708571  17.868571  \n",
       "1978-10-23/1978-10-29  14.487143  16.915714  \n",
       "1978-10-30/1978-11-05  18.451429  18.721429  \n",
       "1978-11-06/1978-11-12  19.307143  22.522857  \n",
       "1978-11-13/1978-11-19  20.357143  23.905714  \n",
       "1978-11-20/1978-11-26  15.207143  19.491429  \n",
       "1978-11-27/1978-12-03  11.835714  18.728571  \n",
       "1978-12-04/1978-12-10  18.517143  23.061429  \n",
       "1978-12-11/1978-12-17  11.102857  15.562857  \n",
       "1978-12-18/1978-12-24  11.845714  18.977143  \n",
       "1978-12-25/1978-12-31  12.947143  26.844286  \n",
       "\n",
       "[940 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(data.index.to_period('W')).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">RPT</th>\n",
       "      <th colspan=\"4\" halign=\"left\">VAL</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ROS</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CLO</th>\n",
       "      <th colspan=\"4\" halign=\"left\">BEL</th>\n",
       "      <th colspan=\"4\" halign=\"left\">MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-08</th>\n",
       "      <td>10.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>13.541429</td>\n",
       "      <td>2.631321</td>\n",
       "      <td>6.63</td>\n",
       "      <td>16.88</td>\n",
       "      <td>11.486667</td>\n",
       "      <td>3.949525</td>\n",
       "      <td>7.62</td>\n",
       "      <td>12.33</td>\n",
       "      <td>...</td>\n",
       "      <td>8.497143</td>\n",
       "      <td>1.704941</td>\n",
       "      <td>5.46</td>\n",
       "      <td>17.54</td>\n",
       "      <td>12.481429</td>\n",
       "      <td>4.349139</td>\n",
       "      <td>10.88</td>\n",
       "      <td>16.46</td>\n",
       "      <td>13.238571</td>\n",
       "      <td>1.773062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-15</th>\n",
       "      <td>9.04</td>\n",
       "      <td>19.75</td>\n",
       "      <td>12.468571</td>\n",
       "      <td>3.555392</td>\n",
       "      <td>3.54</td>\n",
       "      <td>12.08</td>\n",
       "      <td>8.967143</td>\n",
       "      <td>3.148945</td>\n",
       "      <td>7.08</td>\n",
       "      <td>19.50</td>\n",
       "      <td>...</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>4.084293</td>\n",
       "      <td>5.25</td>\n",
       "      <td>20.71</td>\n",
       "      <td>11.125714</td>\n",
       "      <td>5.552215</td>\n",
       "      <td>5.17</td>\n",
       "      <td>16.92</td>\n",
       "      <td>11.024286</td>\n",
       "      <td>4.692355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-22</th>\n",
       "      <td>4.92</td>\n",
       "      <td>19.83</td>\n",
       "      <td>13.204286</td>\n",
       "      <td>5.337402</td>\n",
       "      <td>3.42</td>\n",
       "      <td>14.37</td>\n",
       "      <td>9.862857</td>\n",
       "      <td>3.837785</td>\n",
       "      <td>7.29</td>\n",
       "      <td>20.79</td>\n",
       "      <td>...</td>\n",
       "      <td>8.124286</td>\n",
       "      <td>4.783952</td>\n",
       "      <td>6.50</td>\n",
       "      <td>15.92</td>\n",
       "      <td>9.821429</td>\n",
       "      <td>3.626584</td>\n",
       "      <td>6.79</td>\n",
       "      <td>17.96</td>\n",
       "      <td>11.434286</td>\n",
       "      <td>4.237239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-29</th>\n",
       "      <td>13.62</td>\n",
       "      <td>25.04</td>\n",
       "      <td>19.880000</td>\n",
       "      <td>4.619061</td>\n",
       "      <td>9.96</td>\n",
       "      <td>23.91</td>\n",
       "      <td>16.141429</td>\n",
       "      <td>5.170224</td>\n",
       "      <td>12.67</td>\n",
       "      <td>25.84</td>\n",
       "      <td>...</td>\n",
       "      <td>15.640000</td>\n",
       "      <td>3.713368</td>\n",
       "      <td>14.04</td>\n",
       "      <td>27.71</td>\n",
       "      <td>20.930000</td>\n",
       "      <td>5.210726</td>\n",
       "      <td>17.50</td>\n",
       "      <td>27.63</td>\n",
       "      <td>22.530000</td>\n",
       "      <td>3.874721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-05</th>\n",
       "      <td>10.58</td>\n",
       "      <td>24.21</td>\n",
       "      <td>16.827143</td>\n",
       "      <td>5.251408</td>\n",
       "      <td>9.46</td>\n",
       "      <td>24.21</td>\n",
       "      <td>15.460000</td>\n",
       "      <td>5.187395</td>\n",
       "      <td>9.04</td>\n",
       "      <td>19.70</td>\n",
       "      <td>...</td>\n",
       "      <td>9.460000</td>\n",
       "      <td>2.839501</td>\n",
       "      <td>9.17</td>\n",
       "      <td>19.33</td>\n",
       "      <td>14.012857</td>\n",
       "      <td>4.210858</td>\n",
       "      <td>7.17</td>\n",
       "      <td>19.25</td>\n",
       "      <td>11.935714</td>\n",
       "      <td>4.336104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-12</th>\n",
       "      <td>16.00</td>\n",
       "      <td>24.54</td>\n",
       "      <td>19.684286</td>\n",
       "      <td>3.587677</td>\n",
       "      <td>11.54</td>\n",
       "      <td>21.42</td>\n",
       "      <td>16.417143</td>\n",
       "      <td>3.608373</td>\n",
       "      <td>13.67</td>\n",
       "      <td>21.34</td>\n",
       "      <td>...</td>\n",
       "      <td>14.440000</td>\n",
       "      <td>1.746749</td>\n",
       "      <td>15.21</td>\n",
       "      <td>26.38</td>\n",
       "      <td>21.832857</td>\n",
       "      <td>4.063753</td>\n",
       "      <td>17.04</td>\n",
       "      <td>21.84</td>\n",
       "      <td>19.155714</td>\n",
       "      <td>1.828705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-19</th>\n",
       "      <td>6.04</td>\n",
       "      <td>22.50</td>\n",
       "      <td>15.130000</td>\n",
       "      <td>5.064609</td>\n",
       "      <td>11.63</td>\n",
       "      <td>20.17</td>\n",
       "      <td>15.091429</td>\n",
       "      <td>3.575012</td>\n",
       "      <td>6.13</td>\n",
       "      <td>19.41</td>\n",
       "      <td>...</td>\n",
       "      <td>13.542857</td>\n",
       "      <td>2.531361</td>\n",
       "      <td>14.09</td>\n",
       "      <td>29.63</td>\n",
       "      <td>21.167143</td>\n",
       "      <td>5.910938</td>\n",
       "      <td>10.96</td>\n",
       "      <td>22.58</td>\n",
       "      <td>16.584286</td>\n",
       "      <td>4.685377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-26</th>\n",
       "      <td>7.79</td>\n",
       "      <td>25.80</td>\n",
       "      <td>15.221429</td>\n",
       "      <td>7.020716</td>\n",
       "      <td>7.08</td>\n",
       "      <td>21.50</td>\n",
       "      <td>13.625714</td>\n",
       "      <td>5.147348</td>\n",
       "      <td>6.08</td>\n",
       "      <td>22.42</td>\n",
       "      <td>...</td>\n",
       "      <td>12.730000</td>\n",
       "      <td>4.920064</td>\n",
       "      <td>9.59</td>\n",
       "      <td>23.21</td>\n",
       "      <td>16.304286</td>\n",
       "      <td>5.091162</td>\n",
       "      <td>6.67</td>\n",
       "      <td>23.87</td>\n",
       "      <td>14.322857</td>\n",
       "      <td>6.182283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-05</th>\n",
       "      <td>10.96</td>\n",
       "      <td>13.33</td>\n",
       "      <td>12.101429</td>\n",
       "      <td>0.997721</td>\n",
       "      <td>8.83</td>\n",
       "      <td>17.00</td>\n",
       "      <td>12.951429</td>\n",
       "      <td>2.851955</td>\n",
       "      <td>8.17</td>\n",
       "      <td>13.67</td>\n",
       "      <td>...</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>1.593685</td>\n",
       "      <td>11.58</td>\n",
       "      <td>23.45</td>\n",
       "      <td>17.842857</td>\n",
       "      <td>4.332331</td>\n",
       "      <td>8.83</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.951667</td>\n",
       "      <td>3.021387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-12</th>\n",
       "      <td>4.88</td>\n",
       "      <td>14.79</td>\n",
       "      <td>9.376667</td>\n",
       "      <td>3.732263</td>\n",
       "      <td>8.08</td>\n",
       "      <td>16.96</td>\n",
       "      <td>11.578571</td>\n",
       "      <td>3.230167</td>\n",
       "      <td>7.54</td>\n",
       "      <td>16.38</td>\n",
       "      <td>...</td>\n",
       "      <td>10.458571</td>\n",
       "      <td>3.655113</td>\n",
       "      <td>10.21</td>\n",
       "      <td>22.71</td>\n",
       "      <td>16.701429</td>\n",
       "      <td>4.358759</td>\n",
       "      <td>5.54</td>\n",
       "      <td>22.54</td>\n",
       "      <td>14.420000</td>\n",
       "      <td>5.769890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              RPT                                VAL                    \\\n",
       "              min    max       mean       std    min    max       mean   \n",
       "Yr_Mo_Dy                                                                 \n",
       "1961-01-08  10.58  18.50  13.541429  2.631321   6.63  16.88  11.486667   \n",
       "1961-01-15   9.04  19.75  12.468571  3.555392   3.54  12.08   8.967143   \n",
       "1961-01-22   4.92  19.83  13.204286  5.337402   3.42  14.37   9.862857   \n",
       "1961-01-29  13.62  25.04  19.880000  4.619061   9.96  23.91  16.141429   \n",
       "1961-02-05  10.58  24.21  16.827143  5.251408   9.46  24.21  15.460000   \n",
       "1961-02-12  16.00  24.54  19.684286  3.587677  11.54  21.42  16.417143   \n",
       "1961-02-19   6.04  22.50  15.130000  5.064609  11.63  20.17  15.091429   \n",
       "1961-02-26   7.79  25.80  15.221429  7.020716   7.08  21.50  13.625714   \n",
       "1961-03-05  10.96  13.33  12.101429  0.997721   8.83  17.00  12.951429   \n",
       "1961-03-12   4.88  14.79   9.376667  3.732263   8.08  16.96  11.578571   \n",
       "\n",
       "                        ROS         ...        CLO              BEL         \\\n",
       "                 std    min    max  ...       mean       std    min    max   \n",
       "Yr_Mo_Dy                            ...                                      \n",
       "1961-01-08  3.949525   7.62  12.33  ...   8.497143  1.704941   5.46  17.54   \n",
       "1961-01-15  3.148945   7.08  19.50  ...   7.571429  4.084293   5.25  20.71   \n",
       "1961-01-22  3.837785   7.29  20.79  ...   8.124286  4.783952   6.50  15.92   \n",
       "1961-01-29  5.170224  12.67  25.84  ...  15.640000  3.713368  14.04  27.71   \n",
       "1961-02-05  5.187395   9.04  19.70  ...   9.460000  2.839501   9.17  19.33   \n",
       "1961-02-12  3.608373  13.67  21.34  ...  14.440000  1.746749  15.21  26.38   \n",
       "1961-02-19  3.575012   6.13  19.41  ...  13.542857  2.531361  14.09  29.63   \n",
       "1961-02-26  5.147348   6.08  22.42  ...  12.730000  4.920064   9.59  23.21   \n",
       "1961-03-05  2.851955   8.17  13.67  ...  12.370000  1.593685  11.58  23.45   \n",
       "1961-03-12  3.230167   7.54  16.38  ...  10.458571  3.655113  10.21  22.71   \n",
       "\n",
       "                                   MAL                              \n",
       "                 mean       std    min    max       mean       std  \n",
       "Yr_Mo_Dy                                                            \n",
       "1961-01-08  12.481429  4.349139  10.88  16.46  13.238571  1.773062  \n",
       "1961-01-15  11.125714  5.552215   5.17  16.92  11.024286  4.692355  \n",
       "1961-01-22   9.821429  3.626584   6.79  17.96  11.434286  4.237239  \n",
       "1961-01-29  20.930000  5.210726  17.50  27.63  22.530000  3.874721  \n",
       "1961-02-05  14.012857  4.210858   7.17  19.25  11.935714  4.336104  \n",
       "1961-02-12  21.832857  4.063753  17.04  21.84  19.155714  1.828705  \n",
       "1961-02-19  21.167143  5.910938  10.96  22.58  16.584286  4.685377  \n",
       "1961-02-26  16.304286  5.091162   6.67  23.87  14.322857  6.182283  \n",
       "1961-03-05  17.842857  4.332331   8.83  17.54  13.951667  3.021387  \n",
       "1961-03-12  16.701429  4.358759   5.54  22.54  14.420000  5.769890  \n",
       "\n",
       "[10 rows x 48 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resample data to 'W' week and use the functions\n",
    "weekly = data.resample('W').agg(['min','max','mean','std'])\n",
    "\n",
    "# slice it for the first 52 weeks and locations\n",
    "weekly.loc[weekly.index[1:53], \"RPT\":\"MAL\"] .head(10)"
   ]
  }
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================================================
FILE: 06_Stats/Wind_Stats/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wind Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The data have been modified to contain some missing values, identified by NaN.  \n",
    "Using pandas should make this exercise\n",
    "easier, in particular for the bonus question.\n",
    "\n",
    "You should be able to perform all of these operations without using\n",
    "a for loop or other looping construct.\n",
    "\n",
    "\n",
    "1. The data in 'wind.data' has the following format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nYr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\\n61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\\n61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Yr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\n",
    "61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\n",
    "61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\n",
    "61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   The first three columns are year, month and day.  The\n",
    "   remaining 12 columns are average windspeeds in knots at 12\n",
    "   locations in Ireland on that day.   \n",
    "\n",
    "   More information about the dataset go [here](wind.desc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/06_Stats/Wind_Stats/wind.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2061-01-01</td>\n",
       "      <td>15.04</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.17</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.87</td>\n",
       "      <td>13.67</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.83</td>\n",
       "      <td>12.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2061-01-02</td>\n",
       "      <td>14.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.83</td>\n",
       "      <td>6.50</td>\n",
       "      <td>12.62</td>\n",
       "      <td>7.67</td>\n",
       "      <td>11.50</td>\n",
       "      <td>10.04</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.67</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2061-01-03</td>\n",
       "      <td>18.50</td>\n",
       "      <td>16.88</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.13</td>\n",
       "      <td>11.17</td>\n",
       "      <td>6.17</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.67</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2061-01-04</td>\n",
       "      <td>10.58</td>\n",
       "      <td>6.63</td>\n",
       "      <td>11.75</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.54</td>\n",
       "      <td>2.88</td>\n",
       "      <td>8.63</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.46</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2061-01-05</td>\n",
       "      <td>13.33</td>\n",
       "      <td>13.25</td>\n",
       "      <td>11.42</td>\n",
       "      <td>6.17</td>\n",
       "      <td>10.71</td>\n",
       "      <td>8.21</td>\n",
       "      <td>11.92</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.92</td>\n",
       "      <td>10.34</td>\n",
       "      <td>12.92</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Yr_Mo_Dy    RPT    VAL    ROS    KIL    SHA   BIR    DUB    CLA    MUL  \\\n",
       "0 2061-01-01  15.04  14.96  13.17   9.29    NaN  9.87  13.67  10.25  10.83   \n",
       "1 2061-01-02  14.71    NaN  10.83   6.50  12.62  7.67  11.50  10.04   9.79   \n",
       "2 2061-01-03  18.50  16.88  12.33  10.13  11.17  6.17  11.25    NaN   8.50   \n",
       "3 2061-01-04  10.58   6.63  11.75   4.58   4.54  2.88   8.63   1.79   5.83   \n",
       "4 2061-01-05  13.33  13.25  11.42   6.17  10.71  8.21  11.92   6.54  10.92   \n",
       "\n",
       "     CLO    BEL    MAL  \n",
       "0  12.58  18.50  15.04  \n",
       "1   9.67  17.54  13.83  \n",
       "2   7.67  12.75  12.71  \n",
       "3   5.88   5.46  10.88  \n",
       "4  10.34  12.92  11.83  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>15.04</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.17</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.87</td>\n",
       "      <td>13.67</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.83</td>\n",
       "      <td>12.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-01-02</td>\n",
       "      <td>14.71</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>12.62</td>\n",
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       "      <td>11.50</td>\n",
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       "      <td>9.79</td>\n",
       "      <td>9.67</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-01-03</td>\n",
       "      <td>18.50</td>\n",
       "      <td>16.88</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.13</td>\n",
       "      <td>11.17</td>\n",
       "      <td>6.17</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.67</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961-01-04</td>\n",
       "      <td>10.58</td>\n",
       "      <td>6.63</td>\n",
       "      <td>11.75</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.54</td>\n",
       "      <td>2.88</td>\n",
       "      <td>8.63</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.46</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-01-05</td>\n",
       "      <td>13.33</td>\n",
       "      <td>13.25</td>\n",
       "      <td>11.42</td>\n",
       "      <td>6.17</td>\n",
       "      <td>10.71</td>\n",
       "      <td>8.21</td>\n",
       "      <td>11.92</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.92</td>\n",
       "      <td>10.34</td>\n",
       "      <td>12.92</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr_Mo_Dy    RPT    VAL    ROS    KIL    SHA   BIR    DUB    CLA    MUL  \\\n",
       "0  1961-01-01  15.04  14.96  13.17   9.29    NaN  9.87  13.67  10.25  10.83   \n",
       "1  1961-01-02  14.71    NaN  10.83   6.50  12.62  7.67  11.50  10.04   9.79   \n",
       "2  1961-01-03  18.50  16.88  12.33  10.13  11.17  6.17  11.25    NaN   8.50   \n",
       "3  1961-01-04  10.58   6.63  11.75   4.58   4.54  2.88   8.63   1.79   5.83   \n",
       "4  1961-01-05  13.33  13.25  11.42   6.17  10.71  8.21  11.92   6.54  10.92   \n",
       "\n",
       "     CLO    BEL    MAL  \n",
       "0  12.58  18.50  15.04  \n",
       "1   9.67  17.54  13.83  \n",
       "2   7.67  12.75  12.71  \n",
       "3   5.88   5.46  10.88  \n",
       "4  10.34  12.92  11.83  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>15.04</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.17</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.87</td>\n",
       "      <td>13.67</td>\n",
       "      <td>10.25</td>\n",
       "      <td>10.83</td>\n",
       "      <td>12.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-02</th>\n",
       "      <td>14.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.83</td>\n",
       "      <td>6.50</td>\n",
       "      <td>12.62</td>\n",
       "      <td>7.67</td>\n",
       "      <td>11.50</td>\n",
       "      <td>10.04</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.67</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-03</th>\n",
       "      <td>18.50</td>\n",
       "      <td>16.88</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.13</td>\n",
       "      <td>11.17</td>\n",
       "      <td>6.17</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.67</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-04</th>\n",
       "      <td>10.58</td>\n",
       "      <td>6.63</td>\n",
       "      <td>11.75</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.54</td>\n",
       "      <td>2.88</td>\n",
       "      <td>8.63</td>\n",
       "      <td>1.79</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.46</td>\n",
       "      <td>10.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-05</th>\n",
       "      <td>13.33</td>\n",
       "      <td>13.25</td>\n",
       "      <td>11.42</td>\n",
       "      <td>6.17</td>\n",
       "      <td>10.71</td>\n",
       "      <td>8.21</td>\n",
       "      <td>11.92</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.92</td>\n",
       "      <td>10.34</td>\n",
       "      <td>12.92</td>\n",
       "      <td>11.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RPT    VAL    ROS    KIL    SHA   BIR    DUB    CLA    MUL  \\\n",
       "Yr_Mo_Dy                                                                   \n",
       "1961-01-01  15.04  14.96  13.17   9.29    NaN  9.87  13.67  10.25  10.83   \n",
       "1961-01-02  14.71    NaN  10.83   6.50  12.62  7.67  11.50  10.04   9.79   \n",
       "1961-01-03  18.50  16.88  12.33  10.13  11.17  6.17  11.25    NaN   8.50   \n",
       "1961-01-04  10.58   6.63  11.75   4.58   4.54  2.88   8.63   1.79   5.83   \n",
       "1961-01-05  13.33  13.25  11.42   6.17  10.71  8.21  11.92   6.54  10.92   \n",
       "\n",
       "              CLO    BEL    MAL  \n",
       "Yr_Mo_Dy                         \n",
       "1961-01-01  12.58  18.50  15.04  \n",
       "1961-01-02   9.67  17.54  13.83  \n",
       "1961-01-03   7.67  12.75  12.71  \n",
       "1961-01-04   5.88   5.46  10.88  \n",
       "1961-01-05  10.34  12.92  11.83  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Compute how many values are missing for each location over the entire record.  \n",
    "#### They should be ignored in all calculations below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPT    6\n",
       "VAL    3\n",
       "ROS    2\n",
       "KIL    5\n",
       "SHA    2\n",
       "BIR    0\n",
       "DUB    3\n",
       "CLA    2\n",
       "MUL    3\n",
       "CLO    1\n",
       "BEL    0\n",
       "MAL    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Compute how many non-missing values there are in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPT    6568\n",
       "VAL    6571\n",
       "ROS    6572\n",
       "KIL    6569\n",
       "SHA    6572\n",
       "BIR    6574\n",
       "DUB    6571\n",
       "CLA    6572\n",
       "MUL    6571\n",
       "CLO    6573\n",
       "BEL    6574\n",
       "MAL    6570\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.\n",
    "#### A single number for the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.227883764282167"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days \n",
    "\n",
    "#### A different set of numbers for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py:4291: RuntimeWarning: Invalid value encountered in percentile\n",
      "  interpolation=interpolation)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6568.000000</td>\n",
       "      <td>6571.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6569.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6574.000000</td>\n",
       "      <td>6571.000000</td>\n",
       "      <td>6572.000000</td>\n",
       "      <td>6571.000000</td>\n",
       "      <td>6573.000000</td>\n",
       "      <td>6574.000000</td>\n",
       "      <td>6570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.362987</td>\n",
       "      <td>10.644314</td>\n",
       "      <td>11.660526</td>\n",
       "      <td>6.306468</td>\n",
       "      <td>10.455834</td>\n",
       "      <td>7.092254</td>\n",
       "      <td>9.797343</td>\n",
       "      <td>8.495053</td>\n",
       "      <td>8.493590</td>\n",
       "      <td>8.707332</td>\n",
       "      <td>13.121007</td>\n",
       "      <td>15.599079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.618413</td>\n",
       "      <td>5.267356</td>\n",
       "      <td>5.008450</td>\n",
       "      <td>3.605811</td>\n",
       "      <td>4.936125</td>\n",
       "      <td>3.968683</td>\n",
       "      <td>4.977555</td>\n",
       "      <td>4.499449</td>\n",
       "      <td>4.166872</td>\n",
       "      <td>4.503954</td>\n",
       "      <td>5.835037</td>\n",
       "      <td>6.699794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.830000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.800000</td>\n",
       "      <td>33.370000</td>\n",
       "      <td>33.840000</td>\n",
       "      <td>28.460000</td>\n",
       "      <td>37.540000</td>\n",
       "      <td>26.160000</td>\n",
       "      <td>30.370000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>25.880000</td>\n",
       "      <td>28.210000</td>\n",
       "      <td>42.380000</td>\n",
       "      <td>42.540000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RPT          VAL          ROS          KIL          SHA  \\\n",
       "count  6568.000000  6571.000000  6572.000000  6569.000000  6572.000000   \n",
       "mean     12.362987    10.644314    11.660526     6.306468    10.455834   \n",
       "std       5.618413     5.267356     5.008450     3.605811     4.936125   \n",
       "min       0.670000     0.210000     1.500000     0.000000     0.130000   \n",
       "50%            NaN          NaN          NaN          NaN          NaN   \n",
       "max      35.800000    33.370000    33.840000    28.460000    37.540000   \n",
       "\n",
       "               BIR          DUB          CLA          MUL          CLO  \\\n",
       "count  6574.000000  6571.000000  6572.000000  6571.000000  6573.000000   \n",
       "mean      7.092254     9.797343     8.495053     8.493590     8.707332   \n",
       "std       3.968683     4.977555     4.499449     4.166872     4.503954   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.040000   \n",
       "50%       6.830000          NaN          NaN          NaN          NaN   \n",
       "max      26.160000    30.370000    31.080000    25.880000    28.210000   \n",
       "\n",
       "               BEL          MAL  \n",
       "count  6574.000000  6570.000000  \n",
       "mean     13.121007    15.599079  \n",
       "std       5.835037     6.699794  \n",
       "min       0.130000     0.670000  \n",
       "50%      12.500000          NaN  \n",
       "max      42.380000    42.540000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.\n",
    "\n",
    "#### A different set of numbers for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>9.29</td>\n",
       "      <td>18.50</td>\n",
       "      <td>13.018182</td>\n",
       "      <td>2.808875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-02</th>\n",
       "      <td>6.50</td>\n",
       "      <td>17.54</td>\n",
       "      <td>11.336364</td>\n",
       "      <td>3.188994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-03</th>\n",
       "      <td>6.17</td>\n",
       "      <td>18.50</td>\n",
       "      <td>11.641818</td>\n",
       "      <td>3.681912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-04</th>\n",
       "      <td>1.79</td>\n",
       "      <td>11.75</td>\n",
       "      <td>6.619167</td>\n",
       "      <td>3.198126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-05</th>\n",
       "      <td>6.17</td>\n",
       "      <td>13.33</td>\n",
       "      <td>10.630000</td>\n",
       "      <td>2.445356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             min    max       mean       std\n",
       "Yr_Mo_Dy                                    \n",
       "1961-01-01  9.29  18.50  13.018182  2.808875\n",
       "1961-01-02  6.50  17.54  11.336364  3.188994\n",
       "1961-01-03  6.17  18.50  11.641818  3.681912\n",
       "1961-01-04  1.79  11.75   6.619167  3.198126\n",
       "1961-01-05  6.17  13.33  10.630000  2.445356"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Find the average windspeed in January for each location.  \n",
    "#### Treat January 1961 and January 1962 both as January."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RPT    14.847325\n",
       "VAL    12.914560\n",
       "ROS    13.299624\n",
       "KIL     7.199498\n",
       "SHA    11.667734\n",
       "BIR     8.054839\n",
       "DUB    11.819355\n",
       "CLA     9.512047\n",
       "MUL     9.543208\n",
       "CLO    10.053566\n",
       "BEL    14.550520\n",
       "MAL    18.028763\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Downsample the record to a yearly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>12.299583</td>\n",
       "      <td>10.351796</td>\n",
       "      <td>11.362369</td>\n",
       "      <td>6.958227</td>\n",
       "      <td>10.881763</td>\n",
       "      <td>7.729726</td>\n",
       "      <td>9.733923</td>\n",
       "      <td>8.858788</td>\n",
       "      <td>8.647652</td>\n",
       "      <td>9.835577</td>\n",
       "      <td>13.502795</td>\n",
       "      <td>13.680773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>12.246923</td>\n",
       "      <td>10.110438</td>\n",
       "      <td>11.732712</td>\n",
       "      <td>6.960440</td>\n",
       "      <td>10.657918</td>\n",
       "      <td>7.393068</td>\n",
       "      <td>11.020712</td>\n",
       "      <td>8.793753</td>\n",
       "      <td>8.316822</td>\n",
       "      <td>9.676247</td>\n",
       "      <td>12.930685</td>\n",
       "      <td>14.323956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>12.813452</td>\n",
       "      <td>10.836986</td>\n",
       "      <td>12.541151</td>\n",
       "      <td>7.330055</td>\n",
       "      <td>11.724110</td>\n",
       "      <td>8.434712</td>\n",
       "      <td>11.075699</td>\n",
       "      <td>10.336548</td>\n",
       "      <td>8.903589</td>\n",
       "      <td>10.224438</td>\n",
       "      <td>13.638877</td>\n",
       "      <td>14.999014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>12.363661</td>\n",
       "      <td>10.920164</td>\n",
       "      <td>12.104372</td>\n",
       "      <td>6.787787</td>\n",
       "      <td>11.454481</td>\n",
       "      <td>7.570874</td>\n",
       "      <td>10.259153</td>\n",
       "      <td>9.467350</td>\n",
       "      <td>7.789016</td>\n",
       "      <td>10.207951</td>\n",
       "      <td>13.740546</td>\n",
       "      <td>14.910301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>12.451370</td>\n",
       "      <td>11.075534</td>\n",
       "      <td>11.848767</td>\n",
       "      <td>6.858466</td>\n",
       "      <td>11.024795</td>\n",
       "      <td>7.478110</td>\n",
       "      <td>10.618712</td>\n",
       "      <td>8.879918</td>\n",
       "      <td>7.907425</td>\n",
       "      <td>9.918082</td>\n",
       "      <td>12.964247</td>\n",
       "      <td>15.591644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>13.461973</td>\n",
       "      <td>11.557205</td>\n",
       "      <td>12.020630</td>\n",
       "      <td>7.345726</td>\n",
       "      <td>11.805041</td>\n",
       "      <td>7.793671</td>\n",
       "      <td>10.579808</td>\n",
       "      <td>8.835096</td>\n",
       "      <td>8.514438</td>\n",
       "      <td>9.768959</td>\n",
       "      <td>14.265836</td>\n",
       "      <td>16.307260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>12.737151</td>\n",
       "      <td>10.990986</td>\n",
       "      <td>11.739397</td>\n",
       "      <td>7.143425</td>\n",
       "      <td>11.630740</td>\n",
       "      <td>7.368164</td>\n",
       "      <td>10.652027</td>\n",
       "      <td>9.325616</td>\n",
       "      <td>8.645014</td>\n",
       "      <td>9.547425</td>\n",
       "      <td>14.774548</td>\n",
       "      <td>17.135945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>11.835628</td>\n",
       "      <td>10.468197</td>\n",
       "      <td>11.409754</td>\n",
       "      <td>6.477678</td>\n",
       "      <td>10.760765</td>\n",
       "      <td>6.067322</td>\n",
       "      <td>8.859180</td>\n",
       "      <td>8.255519</td>\n",
       "      <td>7.224945</td>\n",
       "      <td>7.832978</td>\n",
       "      <td>12.808634</td>\n",
       "      <td>15.017486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>11.166356</td>\n",
       "      <td>9.723699</td>\n",
       "      <td>10.902000</td>\n",
       "      <td>5.767973</td>\n",
       "      <td>9.873918</td>\n",
       "      <td>6.189973</td>\n",
       "      <td>8.564493</td>\n",
       "      <td>7.711397</td>\n",
       "      <td>7.924521</td>\n",
       "      <td>7.754384</td>\n",
       "      <td>12.621233</td>\n",
       "      <td>15.762904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>12.600329</td>\n",
       "      <td>10.726932</td>\n",
       "      <td>11.730247</td>\n",
       "      <td>6.217178</td>\n",
       "      <td>10.567370</td>\n",
       "      <td>7.609452</td>\n",
       "      <td>9.609890</td>\n",
       "      <td>8.334630</td>\n",
       "      <td>9.297616</td>\n",
       "      <td>8.289808</td>\n",
       "      <td>13.183644</td>\n",
       "      <td>16.456027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>11.273123</td>\n",
       "      <td>9.095178</td>\n",
       "      <td>11.088329</td>\n",
       "      <td>5.241507</td>\n",
       "      <td>9.440329</td>\n",
       "      <td>6.097151</td>\n",
       "      <td>8.385890</td>\n",
       "      <td>6.757315</td>\n",
       "      <td>7.915370</td>\n",
       "      <td>7.229753</td>\n",
       "      <td>12.208932</td>\n",
       "      <td>15.025233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>12.463962</td>\n",
       "      <td>10.561311</td>\n",
       "      <td>12.058333</td>\n",
       "      <td>5.929699</td>\n",
       "      <td>9.430410</td>\n",
       "      <td>6.358825</td>\n",
       "      <td>9.704508</td>\n",
       "      <td>7.680792</td>\n",
       "      <td>8.357295</td>\n",
       "      <td>7.515273</td>\n",
       "      <td>12.727377</td>\n",
       "      <td>15.028716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>11.828466</td>\n",
       "      <td>10.680493</td>\n",
       "      <td>10.680493</td>\n",
       "      <td>5.547863</td>\n",
       "      <td>9.640877</td>\n",
       "      <td>6.548740</td>\n",
       "      <td>8.482110</td>\n",
       "      <td>7.614274</td>\n",
       "      <td>8.245534</td>\n",
       "      <td>7.812411</td>\n",
       "      <td>12.169699</td>\n",
       "      <td>15.441096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>13.643096</td>\n",
       "      <td>11.811781</td>\n",
       "      <td>12.336356</td>\n",
       "      <td>6.427041</td>\n",
       "      <td>11.110986</td>\n",
       "      <td>6.809781</td>\n",
       "      <td>10.084603</td>\n",
       "      <td>9.896986</td>\n",
       "      <td>9.331753</td>\n",
       "      <td>8.736356</td>\n",
       "      <td>13.252959</td>\n",
       "      <td>16.947671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>12.008575</td>\n",
       "      <td>10.293836</td>\n",
       "      <td>11.564712</td>\n",
       "      <td>5.269096</td>\n",
       "      <td>9.190082</td>\n",
       "      <td>5.668521</td>\n",
       "      <td>8.562603</td>\n",
       "      <td>7.843836</td>\n",
       "      <td>8.797945</td>\n",
       "      <td>7.382822</td>\n",
       "      <td>12.631671</td>\n",
       "      <td>15.307863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>11.737842</td>\n",
       "      <td>10.203115</td>\n",
       "      <td>10.761230</td>\n",
       "      <td>5.109426</td>\n",
       "      <td>8.846339</td>\n",
       "      <td>6.311038</td>\n",
       "      <td>9.149126</td>\n",
       "      <td>7.146202</td>\n",
       "      <td>8.883716</td>\n",
       "      <td>7.883087</td>\n",
       "      <td>12.332377</td>\n",
       "      <td>15.471448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>13.099616</td>\n",
       "      <td>11.144493</td>\n",
       "      <td>12.627836</td>\n",
       "      <td>6.073945</td>\n",
       "      <td>10.003836</td>\n",
       "      <td>8.586438</td>\n",
       "      <td>11.523205</td>\n",
       "      <td>8.378384</td>\n",
       "      <td>9.098192</td>\n",
       "      <td>8.821616</td>\n",
       "      <td>13.459068</td>\n",
       "      <td>16.590849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>12.504356</td>\n",
       "      <td>11.044274</td>\n",
       "      <td>11.380000</td>\n",
       "      <td>6.082356</td>\n",
       "      <td>10.167233</td>\n",
       "      <td>7.650658</td>\n",
       "      <td>9.489342</td>\n",
       "      <td>8.800466</td>\n",
       "      <td>9.089753</td>\n",
       "      <td>8.301699</td>\n",
       "      <td>12.967397</td>\n",
       "      <td>16.771370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                RPT        VAL        ROS       KIL        SHA       BIR  \\\n",
       "Yr_Mo_Dy                                                                   \n",
       "1961      12.299583  10.351796  11.362369  6.958227  10.881763  7.729726   \n",
       "1962      12.246923  10.110438  11.732712  6.960440  10.657918  7.393068   \n",
       "1963      12.813452  10.836986  12.541151  7.330055  11.724110  8.434712   \n",
       "1964      12.363661  10.920164  12.104372  6.787787  11.454481  7.570874   \n",
       "1965      12.451370  11.075534  11.848767  6.858466  11.024795  7.478110   \n",
       "1966      13.461973  11.557205  12.020630  7.345726  11.805041  7.793671   \n",
       "1967      12.737151  10.990986  11.739397  7.143425  11.630740  7.368164   \n",
       "1968      11.835628  10.468197  11.409754  6.477678  10.760765  6.067322   \n",
       "1969      11.166356   9.723699  10.902000  5.767973   9.873918  6.189973   \n",
       "1970      12.600329  10.726932  11.730247  6.217178  10.567370  7.609452   \n",
       "1971      11.273123   9.095178  11.088329  5.241507   9.440329  6.097151   \n",
       "1972      12.463962  10.561311  12.058333  5.929699   9.430410  6.358825   \n",
       "1973      11.828466  10.680493  10.680493  5.547863   9.640877  6.548740   \n",
       "1974      13.643096  11.811781  12.336356  6.427041  11.110986  6.809781   \n",
       "1975      12.008575  10.293836  11.564712  5.269096   9.190082  5.668521   \n",
       "1976      11.737842  10.203115  10.761230  5.109426   8.846339  6.311038   \n",
       "1977      13.099616  11.144493  12.627836  6.073945  10.003836  8.586438   \n",
       "1978      12.504356  11.044274  11.380000  6.082356  10.167233  7.650658   \n",
       "\n",
       "                DUB        CLA       MUL        CLO        BEL        MAL  \n",
       "Yr_Mo_Dy                                                                   \n",
       "1961       9.733923   8.858788  8.647652   9.835577  13.502795  13.680773  \n",
       "1962      11.020712   8.793753  8.316822   9.676247  12.930685  14.323956  \n",
       "1963      11.075699  10.336548  8.903589  10.224438  13.638877  14.999014  \n",
       "1964      10.259153   9.467350  7.789016  10.207951  13.740546  14.910301  \n",
       "1965      10.618712   8.879918  7.907425   9.918082  12.964247  15.591644  \n",
       "1966      10.579808   8.835096  8.514438   9.768959  14.265836  16.307260  \n",
       "1967      10.652027   9.325616  8.645014   9.547425  14.774548  17.135945  \n",
       "1968       8.859180   8.255519  7.224945   7.832978  12.808634  15.017486  \n",
       "1969       8.564493   7.711397  7.924521   7.754384  12.621233  15.762904  \n",
       "1970       9.609890   8.334630  9.297616   8.289808  13.183644  16.456027  \n",
       "1971       8.385890   6.757315  7.915370   7.229753  12.208932  15.025233  \n",
       "1972       9.704508   7.680792  8.357295   7.515273  12.727377  15.028716  \n",
       "1973       8.482110   7.614274  8.245534   7.812411  12.169699  15.441096  \n",
       "1974      10.084603   9.896986  9.331753   8.736356  13.252959  16.947671  \n",
       "1975       8.562603   7.843836  8.797945   7.382822  12.631671  15.307863  \n",
       "1976       9.149126   7.146202  8.883716   7.883087  12.332377  15.471448  \n",
       "1977      11.523205   8.378384  9.098192   8.821616  13.459068  16.590849  \n",
       "1978       9.489342   8.800466  9.089753   8.301699  12.967397  16.771370  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Downsample the record to a monthly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01</th>\n",
       "      <td>14.841333</td>\n",
       "      <td>11.988333</td>\n",
       "      <td>13.431613</td>\n",
       "      <td>7.736774</td>\n",
       "      <td>11.072759</td>\n",
       "      <td>8.588065</td>\n",
       "      <td>11.184839</td>\n",
       "      <td>9.245333</td>\n",
       "      <td>9.085806</td>\n",
       "      <td>10.107419</td>\n",
       "      <td>13.880968</td>\n",
       "      <td>14.703226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02</th>\n",
       "      <td>16.269286</td>\n",
       "      <td>14.975357</td>\n",
       "      <td>14.441481</td>\n",
       "      <td>9.230741</td>\n",
       "      <td>13.852143</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>11.890714</td>\n",
       "      <td>11.846071</td>\n",
       "      <td>11.821429</td>\n",
       "      <td>12.714286</td>\n",
       "      <td>18.583214</td>\n",
       "      <td>15.411786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03</th>\n",
       "      <td>10.890000</td>\n",
       "      <td>11.296452</td>\n",
       "      <td>10.752903</td>\n",
       "      <td>7.284000</td>\n",
       "      <td>10.509355</td>\n",
       "      <td>8.866774</td>\n",
       "      <td>9.644194</td>\n",
       "      <td>9.829677</td>\n",
       "      <td>10.294138</td>\n",
       "      <td>11.251935</td>\n",
       "      <td>16.410968</td>\n",
       "      <td>15.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04</th>\n",
       "      <td>10.722667</td>\n",
       "      <td>9.427667</td>\n",
       "      <td>9.998000</td>\n",
       "      <td>5.830667</td>\n",
       "      <td>8.435000</td>\n",
       "      <td>6.495000</td>\n",
       "      <td>6.925333</td>\n",
       "      <td>7.094667</td>\n",
       "      <td>7.342333</td>\n",
       "      <td>7.237000</td>\n",
       "      <td>11.147333</td>\n",
       "      <td>10.278333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05</th>\n",
       "      <td>9.860968</td>\n",
       "      <td>8.850000</td>\n",
       "      <td>10.818065</td>\n",
       "      <td>5.905333</td>\n",
       "      <td>9.490323</td>\n",
       "      <td>6.574839</td>\n",
       "      <td>7.604000</td>\n",
       "      <td>8.177097</td>\n",
       "      <td>8.039355</td>\n",
       "      <td>8.499355</td>\n",
       "      <td>11.900323</td>\n",
       "      <td>12.011613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06</th>\n",
       "      <td>9.904138</td>\n",
       "      <td>8.520333</td>\n",
       "      <td>8.867000</td>\n",
       "      <td>6.083000</td>\n",
       "      <td>10.824000</td>\n",
       "      <td>6.707333</td>\n",
       "      <td>9.095667</td>\n",
       "      <td>8.849333</td>\n",
       "      <td>9.086667</td>\n",
       "      <td>9.940333</td>\n",
       "      <td>13.995000</td>\n",
       "      <td>14.553793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07</th>\n",
       "      <td>10.614194</td>\n",
       "      <td>8.221613</td>\n",
       "      <td>9.110323</td>\n",
       "      <td>6.340968</td>\n",
       "      <td>10.532581</td>\n",
       "      <td>6.198387</td>\n",
       "      <td>8.353333</td>\n",
       "      <td>8.284194</td>\n",
       "      <td>8.077097</td>\n",
       "      <td>8.891613</td>\n",
       "      <td>11.092581</td>\n",
       "      <td>12.312903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-08</th>\n",
       "      <td>12.035000</td>\n",
       "      <td>10.133871</td>\n",
       "      <td>10.335806</td>\n",
       "      <td>6.845806</td>\n",
       "      <td>12.715161</td>\n",
       "      <td>8.441935</td>\n",
       "      <td>10.093871</td>\n",
       "      <td>10.460968</td>\n",
       "      <td>9.111613</td>\n",
       "      <td>10.544667</td>\n",
       "      <td>14.410000</td>\n",
       "      <td>14.345333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-09</th>\n",
       "      <td>12.531000</td>\n",
       "      <td>9.656897</td>\n",
       "      <td>10.776897</td>\n",
       "      <td>7.155517</td>\n",
       "      <td>11.003333</td>\n",
       "      <td>7.234000</td>\n",
       "      <td>8.206000</td>\n",
       "      <td>8.936552</td>\n",
       "      <td>7.728333</td>\n",
       "      <td>9.931333</td>\n",
       "      <td>13.718333</td>\n",
       "      <td>12.921667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-10</th>\n",
       "      <td>14.289667</td>\n",
       "      <td>10.915806</td>\n",
       "      <td>12.236452</td>\n",
       "      <td>8.154839</td>\n",
       "      <td>11.865484</td>\n",
       "      <td>8.333871</td>\n",
       "      <td>11.194194</td>\n",
       "      <td>9.271935</td>\n",
       "      <td>8.942667</td>\n",
       "      <td>11.455806</td>\n",
       "      <td>14.229355</td>\n",
       "      <td>16.793226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-11</th>\n",
       "      <td>10.896333</td>\n",
       "      <td>8.592667</td>\n",
       "      <td>11.850333</td>\n",
       "      <td>6.045667</td>\n",
       "      <td>9.123667</td>\n",
       "      <td>6.250667</td>\n",
       "      <td>10.869655</td>\n",
       "      <td>6.313667</td>\n",
       "      <td>6.575000</td>\n",
       "      <td>8.383667</td>\n",
       "      <td>10.776667</td>\n",
       "      <td>12.146000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-12</th>\n",
       "      <td>14.973548</td>\n",
       "      <td>11.903871</td>\n",
       "      <td>13.980323</td>\n",
       "      <td>7.073871</td>\n",
       "      <td>11.323548</td>\n",
       "      <td>8.302258</td>\n",
       "      <td>11.753548</td>\n",
       "      <td>8.163226</td>\n",
       "      <td>7.965806</td>\n",
       "      <td>9.246774</td>\n",
       "      <td>12.239355</td>\n",
       "      <td>13.098710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01</th>\n",
       "      <td>14.783871</td>\n",
       "      <td>13.160323</td>\n",
       "      <td>12.591935</td>\n",
       "      <td>7.538065</td>\n",
       "      <td>11.779677</td>\n",
       "      <td>8.720000</td>\n",
       "      <td>14.211935</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>9.670000</td>\n",
       "      <td>11.498710</td>\n",
       "      <td>16.369355</td>\n",
       "      <td>15.661613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02</th>\n",
       "      <td>15.844643</td>\n",
       "      <td>12.041429</td>\n",
       "      <td>15.178929</td>\n",
       "      <td>9.262963</td>\n",
       "      <td>13.821429</td>\n",
       "      <td>9.726786</td>\n",
       "      <td>16.916429</td>\n",
       "      <td>11.285357</td>\n",
       "      <td>12.021071</td>\n",
       "      <td>12.126429</td>\n",
       "      <td>16.705357</td>\n",
       "      <td>18.426786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03</th>\n",
       "      <td>11.634333</td>\n",
       "      <td>8.602258</td>\n",
       "      <td>12.110645</td>\n",
       "      <td>6.403226</td>\n",
       "      <td>10.352258</td>\n",
       "      <td>6.732258</td>\n",
       "      <td>10.223226</td>\n",
       "      <td>7.641935</td>\n",
       "      <td>7.092258</td>\n",
       "      <td>8.052581</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>11.509000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04</th>\n",
       "      <td>12.160667</td>\n",
       "      <td>9.676667</td>\n",
       "      <td>12.088333</td>\n",
       "      <td>7.163000</td>\n",
       "      <td>10.544000</td>\n",
       "      <td>7.558000</td>\n",
       "      <td>11.480000</td>\n",
       "      <td>8.722000</td>\n",
       "      <td>8.703667</td>\n",
       "      <td>9.311667</td>\n",
       "      <td>12.234333</td>\n",
       "      <td>11.780667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05</th>\n",
       "      <td>12.745806</td>\n",
       "      <td>10.865484</td>\n",
       "      <td>11.874839</td>\n",
       "      <td>7.471935</td>\n",
       "      <td>11.285806</td>\n",
       "      <td>7.209032</td>\n",
       "      <td>10.105806</td>\n",
       "      <td>9.084516</td>\n",
       "      <td>7.868065</td>\n",
       "      <td>9.293226</td>\n",
       "      <td>12.130000</td>\n",
       "      <td>12.922581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-06</th>\n",
       "      <td>10.305667</td>\n",
       "      <td>9.677000</td>\n",
       "      <td>9.996333</td>\n",
       "      <td>6.846667</td>\n",
       "      <td>10.711333</td>\n",
       "      <td>7.441333</td>\n",
       "      <td>10.548667</td>\n",
       "      <td>10.306667</td>\n",
       "      <td>9.196000</td>\n",
       "      <td>10.520333</td>\n",
       "      <td>13.757000</td>\n",
       "      <td>15.218333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-07</th>\n",
       "      <td>9.981935</td>\n",
       "      <td>8.370645</td>\n",
       "      <td>9.753548</td>\n",
       "      <td>6.093226</td>\n",
       "      <td>9.112903</td>\n",
       "      <td>5.877097</td>\n",
       "      <td>7.781613</td>\n",
       "      <td>8.123226</td>\n",
       "      <td>6.829677</td>\n",
       "      <td>8.613226</td>\n",
       "      <td>10.783871</td>\n",
       "      <td>11.326129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-08</th>\n",
       "      <td>10.964194</td>\n",
       "      <td>9.694194</td>\n",
       "      <td>10.184516</td>\n",
       "      <td>6.701290</td>\n",
       "      <td>10.465161</td>\n",
       "      <td>7.009032</td>\n",
       "      <td>11.136774</td>\n",
       "      <td>9.097419</td>\n",
       "      <td>8.645484</td>\n",
       "      <td>9.511613</td>\n",
       "      <td>13.119032</td>\n",
       "      <td>15.420968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-09</th>\n",
       "      <td>11.176333</td>\n",
       "      <td>9.507000</td>\n",
       "      <td>11.640000</td>\n",
       "      <td>6.164333</td>\n",
       "      <td>9.722333</td>\n",
       "      <td>6.214000</td>\n",
       "      <td>8.488000</td>\n",
       "      <td>7.020333</td>\n",
       "      <td>6.372667</td>\n",
       "      <td>8.286000</td>\n",
       "      <td>11.483667</td>\n",
       "      <td>12.313333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-10</th>\n",
       "      <td>9.699355</td>\n",
       "      <td>8.063548</td>\n",
       "      <td>9.357097</td>\n",
       "      <td>4.818065</td>\n",
       "      <td>8.432258</td>\n",
       "      <td>5.730000</td>\n",
       "      <td>8.448065</td>\n",
       "      <td>7.626774</td>\n",
       "      <td>6.630645</td>\n",
       "      <td>9.091290</td>\n",
       "      <td>13.286774</td>\n",
       "      <td>14.090323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-11</th>\n",
       "      <td>11.071333</td>\n",
       "      <td>7.984000</td>\n",
       "      <td>12.035667</td>\n",
       "      <td>5.740000</td>\n",
       "      <td>8.135667</td>\n",
       "      <td>6.338333</td>\n",
       "      <td>9.615333</td>\n",
       "      <td>5.943000</td>\n",
       "      <td>6.362333</td>\n",
       "      <td>8.084333</td>\n",
       "      <td>9.786667</td>\n",
       "      <td>13.298333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-12</th>\n",
       "      <td>16.785484</td>\n",
       "      <td>13.753548</td>\n",
       "      <td>14.276452</td>\n",
       "      <td>9.557419</td>\n",
       "      <td>13.724839</td>\n",
       "      <td>10.321613</td>\n",
       "      <td>13.735806</td>\n",
       "      <td>11.212258</td>\n",
       "      <td>10.683548</td>\n",
       "      <td>11.881935</td>\n",
       "      <td>16.043548</td>\n",
       "      <td>20.074516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01</th>\n",
       "      <td>14.868387</td>\n",
       "      <td>11.112903</td>\n",
       "      <td>15.121613</td>\n",
       "      <td>6.635806</td>\n",
       "      <td>11.080645</td>\n",
       "      <td>7.835484</td>\n",
       "      <td>12.797419</td>\n",
       "      <td>9.844839</td>\n",
       "      <td>7.841613</td>\n",
       "      <td>9.390000</td>\n",
       "      <td>11.428710</td>\n",
       "      <td>18.822258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-02</th>\n",
       "      <td>14.418929</td>\n",
       "      <td>11.876429</td>\n",
       "      <td>15.697500</td>\n",
       "      <td>8.611786</td>\n",
       "      <td>12.887857</td>\n",
       "      <td>9.600357</td>\n",
       "      <td>12.729286</td>\n",
       "      <td>10.823214</td>\n",
       "      <td>8.981786</td>\n",
       "      <td>10.355714</td>\n",
       "      <td>13.266429</td>\n",
       "      <td>17.120714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-03</th>\n",
       "      <td>14.853871</td>\n",
       "      <td>12.271290</td>\n",
       "      <td>14.295806</td>\n",
       "      <td>9.268387</td>\n",
       "      <td>13.112903</td>\n",
       "      <td>10.088065</td>\n",
       "      <td>12.168387</td>\n",
       "      <td>11.340968</td>\n",
       "      <td>9.690968</td>\n",
       "      <td>11.515484</td>\n",
       "      <td>13.982903</td>\n",
       "      <td>14.132581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-04</th>\n",
       "      <td>11.616000</td>\n",
       "      <td>10.138000</td>\n",
       "      <td>13.233667</td>\n",
       "      <td>7.990333</td>\n",
       "      <td>11.515333</td>\n",
       "      <td>9.727000</td>\n",
       "      <td>11.979000</td>\n",
       "      <td>11.353000</td>\n",
       "      <td>10.341667</td>\n",
       "      <td>11.900333</td>\n",
       "      <td>13.875667</td>\n",
       "      <td>16.333667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-05</th>\n",
       "      <td>12.879677</td>\n",
       "      <td>11.010645</td>\n",
       "      <td>12.881290</td>\n",
       "      <td>8.411613</td>\n",
       "      <td>12.981613</td>\n",
       "      <td>9.739677</td>\n",
       "      <td>12.280968</td>\n",
       "      <td>10.964194</td>\n",
       "      <td>10.745161</td>\n",
       "      <td>11.394839</td>\n",
       "      <td>14.777097</td>\n",
       "      <td>14.975161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-06</th>\n",
       "      <td>10.623333</td>\n",
       "      <td>8.434667</td>\n",
       "      <td>11.685000</td>\n",
       "      <td>6.420333</td>\n",
       "      <td>10.142667</td>\n",
       "      <td>7.219333</td>\n",
       "      <td>9.267333</td>\n",
       "      <td>9.589333</td>\n",
       "      <td>8.583667</td>\n",
       "      <td>9.585333</td>\n",
       "      <td>12.098000</td>\n",
       "      <td>11.358667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07</th>\n",
       "      <td>9.687742</td>\n",
       "      <td>7.980968</td>\n",
       "      <td>8.267742</td>\n",
       "      <td>4.631613</td>\n",
       "      <td>7.576774</td>\n",
       "      <td>4.927419</td>\n",
       "      <td>6.994839</td>\n",
       "      <td>5.135806</td>\n",
       "      <td>7.941290</td>\n",
       "      <td>6.491290</td>\n",
       "      <td>10.264194</td>\n",
       "      <td>11.912258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08</th>\n",
       "      <td>7.640645</td>\n",
       "      <td>5.366129</td>\n",
       "      <td>9.000645</td>\n",
       "      <td>3.142258</td>\n",
       "      <td>4.695484</td>\n",
       "      <td>3.847742</td>\n",
       "      <td>5.437097</td>\n",
       "      <td>3.362581</td>\n",
       "      <td>5.946452</td>\n",
       "      <td>4.496452</td>\n",
       "      <td>7.079677</td>\n",
       "      <td>9.438387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09</th>\n",
       "      <td>11.703667</td>\n",
       "      <td>10.515333</td>\n",
       "      <td>10.466333</td>\n",
       "      <td>5.313333</td>\n",
       "      <td>8.761333</td>\n",
       "      <td>7.062333</td>\n",
       "      <td>8.617667</td>\n",
       "      <td>6.415333</td>\n",
       "      <td>8.953333</td>\n",
       "      <td>7.263333</td>\n",
       "      <td>11.587000</td>\n",
       "      <td>17.634000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-10</th>\n",
       "      <td>12.427097</td>\n",
       "      <td>9.572258</td>\n",
       "      <td>10.640000</td>\n",
       "      <td>4.885484</td>\n",
       "      <td>9.393548</td>\n",
       "      <td>6.906452</td>\n",
       "      <td>6.380323</td>\n",
       "      <td>6.933226</td>\n",
       "      <td>7.552258</td>\n",
       "      <td>7.449032</td>\n",
       "      <td>11.837742</td>\n",
       "      <td>15.078065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-11</th>\n",
       "      <td>10.962667</td>\n",
       "      <td>9.443667</td>\n",
       "      <td>9.202000</td>\n",
       "      <td>3.696000</td>\n",
       "      <td>7.459333</td>\n",
       "      <td>7.026333</td>\n",
       "      <td>9.058333</td>\n",
       "      <td>5.791000</td>\n",
       "      <td>6.577000</td>\n",
       "      <td>7.512333</td>\n",
       "      <td>12.568333</td>\n",
       "      <td>15.685333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-12</th>\n",
       "      <td>11.962258</td>\n",
       "      <td>10.086774</td>\n",
       "      <td>10.474516</td>\n",
       "      <td>3.383871</td>\n",
       "      <td>7.645484</td>\n",
       "      <td>6.148387</td>\n",
       "      <td>8.034516</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.952258</td>\n",
       "      <td>6.147742</td>\n",
       "      <td>7.814839</td>\n",
       "      <td>14.346774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01</th>\n",
       "      <td>13.404516</td>\n",
       "      <td>10.377742</td>\n",
       "      <td>12.764839</td>\n",
       "      <td>5.884516</td>\n",
       "      <td>9.159677</td>\n",
       "      <td>8.005161</td>\n",
       "      <td>10.107419</td>\n",
       "      <td>7.211613</td>\n",
       "      <td>8.280000</td>\n",
       "      <td>9.328387</td>\n",
       "      <td>12.131935</td>\n",
       "      <td>18.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-02</th>\n",
       "      <td>12.336786</td>\n",
       "      <td>11.898929</td>\n",
       "      <td>12.016786</td>\n",
       "      <td>5.317500</td>\n",
       "      <td>10.134643</td>\n",
       "      <td>9.423929</td>\n",
       "      <td>10.949643</td>\n",
       "      <td>7.965357</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>8.711429</td>\n",
       "      <td>11.435357</td>\n",
       "      <td>17.561429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-03</th>\n",
       "      <td>16.750000</td>\n",
       "      <td>14.499677</td>\n",
       "      <td>16.118387</td>\n",
       "      <td>8.414516</td>\n",
       "      <td>13.293871</td>\n",
       "      <td>11.562258</td>\n",
       "      <td>14.283226</td>\n",
       "      <td>11.361613</td>\n",
       "      <td>12.102581</td>\n",
       "      <td>11.906452</td>\n",
       "      <td>15.863226</td>\n",
       "      <td>19.133548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-04</th>\n",
       "      <td>14.955333</td>\n",
       "      <td>12.293000</td>\n",
       "      <td>12.689667</td>\n",
       "      <td>7.422333</td>\n",
       "      <td>11.740000</td>\n",
       "      <td>10.137000</td>\n",
       "      <td>13.887667</td>\n",
       "      <td>9.574000</td>\n",
       "      <td>10.342333</td>\n",
       "      <td>11.419667</td>\n",
       "      <td>15.593667</td>\n",
       "      <td>18.274667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-05</th>\n",
       "      <td>9.441290</td>\n",
       "      <td>7.173871</td>\n",
       "      <td>12.455806</td>\n",
       "      <td>4.507742</td>\n",
       "      <td>6.198387</td>\n",
       "      <td>6.689677</td>\n",
       "      <td>9.226452</td>\n",
       "      <td>5.638387</td>\n",
       "      <td>6.699355</td>\n",
       "      <td>6.045484</td>\n",
       "      <td>10.213548</td>\n",
       "      <td>11.936129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-06</th>\n",
       "      <td>11.040000</td>\n",
       "      <td>8.353000</td>\n",
       "      <td>12.228000</td>\n",
       "      <td>4.864000</td>\n",
       "      <td>8.790333</td>\n",
       "      <td>7.209667</td>\n",
       "      <td>8.799667</td>\n",
       "      <td>5.931000</td>\n",
       "      <td>7.065333</td>\n",
       "      <td>6.583333</td>\n",
       "      <td>11.321333</td>\n",
       "      <td>11.175333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-07</th>\n",
       "      <td>10.881935</td>\n",
       "      <td>8.663548</td>\n",
       "      <td>10.816452</td>\n",
       "      <td>5.419677</td>\n",
       "      <td>9.014839</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>9.961935</td>\n",
       "      <td>6.526129</td>\n",
       "      <td>7.980968</td>\n",
       "      <td>7.620000</td>\n",
       "      <td>12.924194</td>\n",
       "      <td>12.186774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-08</th>\n",
       "      <td>9.233548</td>\n",
       "      <td>7.727742</td>\n",
       "      <td>10.679032</td>\n",
       "      <td>4.453871</td>\n",
       "      <td>6.620645</td>\n",
       "      <td>5.961290</td>\n",
       "      <td>8.943548</td>\n",
       "      <td>4.543226</td>\n",
       "      <td>6.384839</td>\n",
       "      <td>5.694839</td>\n",
       "      <td>9.825161</td>\n",
       "      <td>11.659355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-09</th>\n",
       "      <td>12.472333</td>\n",
       "      <td>10.742667</td>\n",
       "      <td>11.849333</td>\n",
       "      <td>5.638667</td>\n",
       "      <td>10.077333</td>\n",
       "      <td>8.242667</td>\n",
       "      <td>11.939333</td>\n",
       "      <td>7.923000</td>\n",
       "      <td>8.828000</td>\n",
       "      <td>8.506333</td>\n",
       "      <td>14.051000</td>\n",
       "      <td>17.030333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-10</th>\n",
       "      <td>15.004516</td>\n",
       "      <td>13.960000</td>\n",
       "      <td>12.819677</td>\n",
       "      <td>6.754194</td>\n",
       "      <td>11.779032</td>\n",
       "      <td>9.671613</td>\n",
       "      <td>12.924839</td>\n",
       "      <td>11.875161</td>\n",
       "      <td>11.481290</td>\n",
       "      <td>10.340323</td>\n",
       "      <td>17.640968</td>\n",
       "      <td>19.842903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-11</th>\n",
       "      <td>16.946667</td>\n",
       "      <td>15.444667</td>\n",
       "      <td>13.561333</td>\n",
       "      <td>7.584000</td>\n",
       "      <td>12.088667</td>\n",
       "      <td>9.161333</td>\n",
       "      <td>14.051000</td>\n",
       "      <td>11.286000</td>\n",
       "      <td>10.318667</td>\n",
       "      <td>10.327000</td>\n",
       "      <td>17.215333</td>\n",
       "      <td>22.333000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-12</th>\n",
       "      <td>14.751935</td>\n",
       "      <td>12.744839</td>\n",
       "      <td>13.469677</td>\n",
       "      <td>6.592258</td>\n",
       "      <td>11.247742</td>\n",
       "      <td>9.466774</td>\n",
       "      <td>13.231613</td>\n",
       "      <td>10.703871</td>\n",
       "      <td>10.401613</td>\n",
       "      <td>9.415484</td>\n",
       "      <td>13.237419</td>\n",
       "      <td>19.299677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01</th>\n",
       "      <td>14.291935</td>\n",
       "      <td>11.872258</td>\n",
       "      <td>12.014194</td>\n",
       "      <td>6.463226</td>\n",
       "      <td>11.402903</td>\n",
       "      <td>7.517097</td>\n",
       "      <td>12.207097</td>\n",
       "      <td>10.206452</td>\n",
       "      <td>9.549032</td>\n",
       "      <td>9.247419</td>\n",
       "      <td>15.101613</td>\n",
       "      <td>20.715806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-02</th>\n",
       "      <td>14.143571</td>\n",
       "      <td>12.153214</td>\n",
       "      <td>13.803214</td>\n",
       "      <td>6.828929</td>\n",
       "      <td>11.196786</td>\n",
       "      <td>7.858929</td>\n",
       "      <td>11.903214</td>\n",
       "      <td>11.068929</td>\n",
       "      <td>10.052143</td>\n",
       "      <td>8.093929</td>\n",
       "      <td>10.353929</td>\n",
       "      <td>17.298571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-03</th>\n",
       "      <td>14.717097</td>\n",
       "      <td>14.601935</td>\n",
       "      <td>13.334194</td>\n",
       "      <td>8.231290</td>\n",
       "      <td>12.783226</td>\n",
       "      <td>9.488710</td>\n",
       "      <td>12.129355</td>\n",
       "      <td>11.665161</td>\n",
       "      <td>11.656452</td>\n",
       "      <td>9.657097</td>\n",
       "      <td>14.234194</td>\n",
       "      <td>18.611290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-04</th>\n",
       "      <td>11.805000</td>\n",
       "      <td>11.255667</td>\n",
       "      <td>12.516333</td>\n",
       "      <td>5.920333</td>\n",
       "      <td>10.218000</td>\n",
       "      <td>7.301667</td>\n",
       "      <td>8.586333</td>\n",
       "      <td>8.306667</td>\n",
       "      <td>8.537000</td>\n",
       "      <td>6.999000</td>\n",
       "      <td>11.190667</td>\n",
       "      <td>14.152000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-05</th>\n",
       "      <td>8.270645</td>\n",
       "      <td>7.226774</td>\n",
       "      <td>6.901613</td>\n",
       "      <td>3.740645</td>\n",
       "      <td>6.973871</td>\n",
       "      <td>4.449677</td>\n",
       "      <td>5.420968</td>\n",
       "      <td>6.130645</td>\n",
       "      <td>5.742581</td>\n",
       "      <td>5.926452</td>\n",
       "      <td>9.263548</td>\n",
       "      <td>10.756452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06</th>\n",
       "      <td>11.386667</td>\n",
       "      <td>9.474333</td>\n",
       "      <td>10.253333</td>\n",
       "      <td>6.053000</td>\n",
       "      <td>10.395333</td>\n",
       "      <td>7.490333</td>\n",
       "      <td>7.928000</td>\n",
       "      <td>7.802000</td>\n",
       "      <td>8.220333</td>\n",
       "      <td>7.550000</td>\n",
       "      <td>11.501000</td>\n",
       "      <td>15.078667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07</th>\n",
       "      <td>12.820000</td>\n",
       "      <td>9.750968</td>\n",
       "      <td>9.910323</td>\n",
       "      <td>6.483871</td>\n",
       "      <td>10.055161</td>\n",
       "      <td>7.820645</td>\n",
       "      <td>7.831935</td>\n",
       "      <td>8.459355</td>\n",
       "      <td>8.523871</td>\n",
       "      <td>7.732903</td>\n",
       "      <td>12.648710</td>\n",
       "      <td>14.077419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08</th>\n",
       "      <td>9.645161</td>\n",
       "      <td>8.259355</td>\n",
       "      <td>9.032258</td>\n",
       "      <td>4.502903</td>\n",
       "      <td>7.368065</td>\n",
       "      <td>5.935161</td>\n",
       "      <td>5.650323</td>\n",
       "      <td>5.417742</td>\n",
       "      <td>7.241290</td>\n",
       "      <td>5.536774</td>\n",
       "      <td>10.466774</td>\n",
       "      <td>12.054194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09</th>\n",
       "      <td>10.913667</td>\n",
       "      <td>10.895000</td>\n",
       "      <td>10.635000</td>\n",
       "      <td>5.725000</td>\n",
       "      <td>10.372000</td>\n",
       "      <td>9.278333</td>\n",
       "      <td>10.790333</td>\n",
       "      <td>9.583000</td>\n",
       "      <td>10.069333</td>\n",
       "      <td>8.939000</td>\n",
       "      <td>15.680333</td>\n",
       "      <td>19.391333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10</th>\n",
       "      <td>9.897742</td>\n",
       "      <td>8.670968</td>\n",
       "      <td>9.295806</td>\n",
       "      <td>4.721290</td>\n",
       "      <td>8.525161</td>\n",
       "      <td>6.774194</td>\n",
       "      <td>8.115484</td>\n",
       "      <td>7.337742</td>\n",
       "      <td>8.297742</td>\n",
       "      <td>8.243871</td>\n",
       "      <td>13.776774</td>\n",
       "      <td>17.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11</th>\n",
       "      <td>16.151667</td>\n",
       "      <td>14.802667</td>\n",
       "      <td>13.508000</td>\n",
       "      <td>7.317333</td>\n",
       "      <td>11.475000</td>\n",
       "      <td>8.743000</td>\n",
       "      <td>11.492333</td>\n",
       "      <td>9.657333</td>\n",
       "      <td>10.701333</td>\n",
       "      <td>10.676000</td>\n",
       "      <td>17.404667</td>\n",
       "      <td>20.723000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12</th>\n",
       "      <td>16.175484</td>\n",
       "      <td>13.748065</td>\n",
       "      <td>15.635161</td>\n",
       "      <td>7.094839</td>\n",
       "      <td>11.398710</td>\n",
       "      <td>9.241613</td>\n",
       "      <td>12.077419</td>\n",
       "      <td>10.194839</td>\n",
       "      <td>10.616774</td>\n",
       "      <td>11.028710</td>\n",
       "      <td>13.859677</td>\n",
       "      <td>21.371613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                RPT        VAL        ROS       KIL        SHA        BIR  \\\n",
       "Yr_Mo_Dy                                                                    \n",
       "1961-01   14.841333  11.988333  13.431613  7.736774  11.072759   8.588065   \n",
       "1961-02   16.269286  14.975357  14.441481  9.230741  13.852143  10.937500   \n",
       "1961-03   10.890000  11.296452  10.752903  7.284000  10.509355   8.866774   \n",
       "1961-04   10.722667   9.427667   9.998000  5.830667   8.435000   6.495000   \n",
       "1961-05    9.860968   8.850000  10.818065  5.905333   9.490323   6.574839   \n",
       "1961-06    9.904138   8.520333   8.867000  6.083000  10.824000   6.707333   \n",
       "1961-07   10.614194   8.221613   9.110323  6.340968  10.532581   6.198387   \n",
       "1961-08   12.035000  10.133871  10.335806  6.845806  12.715161   8.441935   \n",
       "1961-09   12.531000   9.656897  10.776897  7.155517  11.003333   7.234000   \n",
       "1961-10   14.289667  10.915806  12.236452  8.154839  11.865484   8.333871   \n",
       "1961-11   10.896333   8.592667  11.850333  6.045667   9.123667   6.250667   \n",
       "1961-12   14.973548  11.903871  13.980323  7.073871  11.323548   8.302258   \n",
       "1962-01   14.783871  13.160323  12.591935  7.538065  11.779677   8.720000   \n",
       "1962-02   15.844643  12.041429  15.178929  9.262963  13.821429   9.726786   \n",
       "1962-03   11.634333   8.602258  12.110645  6.403226  10.352258   6.732258   \n",
       "1962-04   12.160667   9.676667  12.088333  7.163000  10.544000   7.558000   \n",
       "1962-05   12.745806  10.865484  11.874839  7.471935  11.285806   7.209032   \n",
       "1962-06   10.305667   9.677000   9.996333  6.846667  10.711333   7.441333   \n",
       "1962-07    9.981935   8.370645   9.753548  6.093226   9.112903   5.877097   \n",
       "1962-08   10.964194   9.694194  10.184516  6.701290  10.465161   7.009032   \n",
       "1962-09   11.176333   9.507000  11.640000  6.164333   9.722333   6.214000   \n",
       "1962-10    9.699355   8.063548   9.357097  4.818065   8.432258   5.730000   \n",
       "1962-11   11.071333   7.984000  12.035667  5.740000   8.135667   6.338333   \n",
       "1962-12   16.785484  13.753548  14.276452  9.557419  13.724839  10.321613   \n",
       "1963-01   14.868387  11.112903  15.121613  6.635806  11.080645   7.835484   \n",
       "1963-02   14.418929  11.876429  15.697500  8.611786  12.887857   9.600357   \n",
       "1963-03   14.853871  12.271290  14.295806  9.268387  13.112903  10.088065   \n",
       "1963-04   11.616000  10.138000  13.233667  7.990333  11.515333   9.727000   \n",
       "1963-05   12.879677  11.010645  12.881290  8.411613  12.981613   9.739677   \n",
       "1963-06   10.623333   8.434667  11.685000  6.420333  10.142667   7.219333   \n",
       "...             ...        ...        ...       ...        ...        ...   \n",
       "1976-07    9.687742   7.980968   8.267742  4.631613   7.576774   4.927419   \n",
       "1976-08    7.640645   5.366129   9.000645  3.142258   4.695484   3.847742   \n",
       "1976-09   11.703667  10.515333  10.466333  5.313333   8.761333   7.062333   \n",
       "1976-10   12.427097   9.572258  10.640000  4.885484   9.393548   6.906452   \n",
       "1976-11   10.962667   9.443667   9.202000  3.696000   7.459333   7.026333   \n",
       "1976-12   11.962258  10.086774  10.474516  3.383871   7.645484   6.148387   \n",
       "1977-01   13.404516  10.377742  12.764839  5.884516   9.159677   8.005161   \n",
       "1977-02   12.336786  11.898929  12.016786  5.317500  10.134643   9.423929   \n",
       "1977-03   16.750000  14.499677  16.118387  8.414516  13.293871  11.562258   \n",
       "1977-04   14.955333  12.293000  12.689667  7.422333  11.740000  10.137000   \n",
       "1977-05    9.441290   7.173871  12.455806  4.507742   6.198387   6.689677   \n",
       "1977-06   11.040000   8.353000  12.228000  4.864000   8.790333   7.209667   \n",
       "1977-07   10.881935   8.663548  10.816452  5.419677   9.014839   7.600000   \n",
       "1977-08    9.233548   7.727742  10.679032  4.453871   6.620645   5.961290   \n",
       "1977-09   12.472333  10.742667  11.849333  5.638667  10.077333   8.242667   \n",
       "1977-10   15.004516  13.960000  12.819677  6.754194  11.779032   9.671613   \n",
       "1977-11   16.946667  15.444667  13.561333  7.584000  12.088667   9.161333   \n",
       "1977-12   14.751935  12.744839  13.469677  6.592258  11.247742   9.466774   \n",
       "1978-01   14.291935  11.872258  12.014194  6.463226  11.402903   7.517097   \n",
       "1978-02   14.143571  12.153214  13.803214  6.828929  11.196786   7.858929   \n",
       "1978-03   14.717097  14.601935  13.334194  8.231290  12.783226   9.488710   \n",
       "1978-04   11.805000  11.255667  12.516333  5.920333  10.218000   7.301667   \n",
       "1978-05    8.270645   7.226774   6.901613  3.740645   6.973871   4.449677   \n",
       "1978-06   11.386667   9.474333  10.253333  6.053000  10.395333   7.490333   \n",
       "1978-07   12.820000   9.750968   9.910323  6.483871  10.055161   7.820645   \n",
       "1978-08    9.645161   8.259355   9.032258  4.502903   7.368065   5.935161   \n",
       "1978-09   10.913667  10.895000  10.635000  5.725000  10.372000   9.278333   \n",
       "1978-10    9.897742   8.670968   9.295806  4.721290   8.525161   6.774194   \n",
       "1978-11   16.151667  14.802667  13.508000  7.317333  11.475000   8.743000   \n",
       "1978-12   16.175484  13.748065  15.635161  7.094839  11.398710   9.241613   \n",
       "\n",
       "                DUB        CLA        MUL        CLO        BEL        MAL  \n",
       "Yr_Mo_Dy                                                                    \n",
       "1961-01   11.184839   9.245333   9.085806  10.107419  13.880968  14.703226  \n",
       "1961-02   11.890714  11.846071  11.821429  12.714286  18.583214  15.411786  \n",
       "1961-03    9.644194   9.829677  10.294138  11.251935  16.410968  15.720000  \n",
       "1961-04    6.925333   7.094667   7.342333   7.237000  11.147333  10.278333  \n",
       "1961-05    7.604000   8.177097   8.039355   8.499355  11.900323  12.011613  \n",
       "1961-06    9.095667   8.849333   9.086667   9.940333  13.995000  14.553793  \n",
       "1961-07    8.353333   8.284194   8.077097   8.891613  11.092581  12.312903  \n",
       "1961-08   10.093871  10.460968   9.111613  10.544667  14.410000  14.345333  \n",
       "1961-09    8.206000   8.936552   7.728333   9.931333  13.718333  12.921667  \n",
       "1961-10   11.194194   9.271935   8.942667  11.455806  14.229355  16.793226  \n",
       "1961-11   10.869655   6.313667   6.575000   8.383667  10.776667  12.146000  \n",
       "1961-12   11.753548   8.163226   7.965806   9.246774  12.239355  13.098710  \n",
       "1962-01   14.211935   9.600000   9.670000  11.498710  16.369355  15.661613  \n",
       "1962-02   16.916429  11.285357  12.021071  12.126429  16.705357  18.426786  \n",
       "1962-03   10.223226   7.641935   7.092258   8.052581   9.690000  11.509000  \n",
       "1962-04   11.480000   8.722000   8.703667   9.311667  12.234333  11.780667  \n",
       "1962-05   10.105806   9.084516   7.868065   9.293226  12.130000  12.922581  \n",
       "1962-06   10.548667  10.306667   9.196000  10.520333  13.757000  15.218333  \n",
       "1962-07    7.781613   8.123226   6.829677   8.613226  10.783871  11.326129  \n",
       "1962-08   11.136774   9.097419   8.645484   9.511613  13.119032  15.420968  \n",
       "1962-09    8.488000   7.020333   6.372667   8.286000  11.483667  12.313333  \n",
       "1962-10    8.448065   7.626774   6.630645   9.091290  13.286774  14.090323  \n",
       "1962-11    9.615333   5.943000   6.362333   8.084333   9.786667  13.298333  \n",
       "1962-12   13.735806  11.212258  10.683548  11.881935  16.043548  20.074516  \n",
       "1963-01   12.797419   9.844839   7.841613   9.390000  11.428710  18.822258  \n",
       "1963-02   12.729286  10.823214   8.981786  10.355714  13.266429  17.120714  \n",
       "1963-03   12.168387  11.340968   9.690968  11.515484  13.982903  14.132581  \n",
       "1963-04   11.979000  11.353000  10.341667  11.900333  13.875667  16.333667  \n",
       "1963-05   12.280968  10.964194  10.745161  11.394839  14.777097  14.975161  \n",
       "1963-06    9.267333   9.589333   8.583667   9.585333  12.098000  11.358667  \n",
       "...             ...        ...        ...        ...        ...        ...  \n",
       "1976-07    6.994839   5.135806   7.941290   6.491290  10.264194  11.912258  \n",
       "1976-08    5.437097   3.362581   5.946452   4.496452   7.079677   9.438387  \n",
       "1976-09    8.617667   6.415333   8.953333   7.263333  11.587000  17.634000  \n",
       "1976-10    6.380323   6.933226   7.552258   7.449032  11.837742  15.078065  \n",
       "1976-11    9.058333   5.791000   6.577000   7.512333  12.568333  15.685333  \n",
       "1976-12    8.034516   4.500000   5.952258   6.147742   7.814839  14.346774  \n",
       "1977-01   10.107419   7.211613   8.280000   9.328387  12.131935  18.830000  \n",
       "1977-02   10.949643   7.965357   9.320000   8.711429  11.435357  17.561429  \n",
       "1977-03   14.283226  11.361613  12.102581  11.906452  15.863226  19.133548  \n",
       "1977-04   13.887667   9.574000  10.342333  11.419667  15.593667  18.274667  \n",
       "1977-05    9.226452   5.638387   6.699355   6.045484  10.213548  11.936129  \n",
       "1977-06    8.799667   5.931000   7.065333   6.583333  11.321333  11.175333  \n",
       "1977-07    9.961935   6.526129   7.980968   7.620000  12.924194  12.186774  \n",
       "1977-08    8.943548   4.543226   6.384839   5.694839   9.825161  11.659355  \n",
       "1977-09   11.939333   7.923000   8.828000   8.506333  14.051000  17.030333  \n",
       "1977-10   12.924839  11.875161  11.481290  10.340323  17.640968  19.842903  \n",
       "1977-11   14.051000  11.286000  10.318667  10.327000  17.215333  22.333000  \n",
       "1977-12   13.231613  10.703871  10.401613   9.415484  13.237419  19.299677  \n",
       "1978-01   12.207097  10.206452   9.549032   9.247419  15.101613  20.715806  \n",
       "1978-02   11.903214  11.068929  10.052143   8.093929  10.353929  17.298571  \n",
       "1978-03   12.129355  11.665161  11.656452   9.657097  14.234194  18.611290  \n",
       "1978-04    8.586333   8.306667   8.537000   6.999000  11.190667  14.152000  \n",
       "1978-05    5.420968   6.130645   5.742581   5.926452   9.263548  10.756452  \n",
       "1978-06    7.928000   7.802000   8.220333   7.550000  11.501000  15.078667  \n",
       "1978-07    7.831935   8.459355   8.523871   7.732903  12.648710  14.077419  \n",
       "1978-08    5.650323   5.417742   7.241290   5.536774  10.466774  12.054194  \n",
       "1978-09   10.790333   9.583000  10.069333   8.939000  15.680333  19.391333  \n",
       "1978-10    8.115484   7.337742   8.297742   8.243871  13.776774  17.150000  \n",
       "1978-11   11.492333   9.657333  10.701333  10.676000  17.404667  20.723000  \n",
       "1978-12   12.077419  10.194839  10.616774  11.028710  13.859677  21.371613  \n",
       "\n",
       "[216 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Downsample the record to a weekly frequency for each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPT</th>\n",
       "      <th>VAL</th>\n",
       "      <th>ROS</th>\n",
       "      <th>KIL</th>\n",
       "      <th>SHA</th>\n",
       "      <th>BIR</th>\n",
       "      <th>DUB</th>\n",
       "      <th>CLA</th>\n",
       "      <th>MUL</th>\n",
       "      <th>CLO</th>\n",
       "      <th>BEL</th>\n",
       "      <th>MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-12-26/1961-01-01</th>\n",
       "      <td>15.040000</td>\n",
       "      <td>14.960000</td>\n",
       "      <td>13.170000</td>\n",
       "      <td>9.290000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.870000</td>\n",
       "      <td>13.670000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>10.830000</td>\n",
       "      <td>12.580000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>15.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-02/1961-01-08</th>\n",
       "      <td>13.541429</td>\n",
       "      <td>11.486667</td>\n",
       "      <td>10.487143</td>\n",
       "      <td>6.417143</td>\n",
       "      <td>9.474286</td>\n",
       "      <td>6.435714</td>\n",
       "      <td>11.061429</td>\n",
       "      <td>6.616667</td>\n",
       "      <td>8.434286</td>\n",
       "      <td>8.497143</td>\n",
       "      <td>12.481429</td>\n",
       "      <td>13.238571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-09/1961-01-15</th>\n",
       "      <td>12.468571</td>\n",
       "      <td>8.967143</td>\n",
       "      <td>11.958571</td>\n",
       "      <td>4.630000</td>\n",
       "      <td>7.351429</td>\n",
       "      <td>5.072857</td>\n",
       "      <td>7.535714</td>\n",
       "      <td>6.820000</td>\n",
       "      <td>5.712857</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>11.125714</td>\n",
       "      <td>11.024286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-16/1961-01-22</th>\n",
       "      <td>13.204286</td>\n",
       "      <td>9.862857</td>\n",
       "      <td>12.982857</td>\n",
       "      <td>6.328571</td>\n",
       "      <td>8.966667</td>\n",
       "      <td>7.417143</td>\n",
       "      <td>9.257143</td>\n",
       "      <td>7.875714</td>\n",
       "      <td>7.145714</td>\n",
       "      <td>8.124286</td>\n",
       "      <td>9.821429</td>\n",
       "      <td>11.434286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-23/1961-01-29</th>\n",
       "      <td>19.880000</td>\n",
       "      <td>16.141429</td>\n",
       "      <td>18.225714</td>\n",
       "      <td>12.720000</td>\n",
       "      <td>17.432857</td>\n",
       "      <td>14.828571</td>\n",
       "      <td>15.528571</td>\n",
       "      <td>15.160000</td>\n",
       "      <td>14.480000</td>\n",
       "      <td>15.640000</td>\n",
       "      <td>20.930000</td>\n",
       "      <td>22.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-30/1961-02-05</th>\n",
       "      <td>16.827143</td>\n",
       "      <td>15.460000</td>\n",
       "      <td>12.618571</td>\n",
       "      <td>8.247143</td>\n",
       "      <td>13.361429</td>\n",
       "      <td>9.107143</td>\n",
       "      <td>12.204286</td>\n",
       "      <td>8.548571</td>\n",
       "      <td>9.821429</td>\n",
       "      <td>9.460000</td>\n",
       "      <td>14.012857</td>\n",
       "      <td>11.935714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-06/1961-02-12</th>\n",
       "      <td>19.684286</td>\n",
       "      <td>16.417143</td>\n",
       "      <td>17.304286</td>\n",
       "      <td>10.774286</td>\n",
       "      <td>14.718571</td>\n",
       "      <td>12.522857</td>\n",
       "      <td>14.934286</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>14.064286</td>\n",
       "      <td>14.440000</td>\n",
       "      <td>21.832857</td>\n",
       "      <td>19.155714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-13/1961-02-19</th>\n",
       "      <td>15.130000</td>\n",
       "      <td>15.091429</td>\n",
       "      <td>13.797143</td>\n",
       "      <td>10.083333</td>\n",
       "      <td>13.410000</td>\n",
       "      <td>11.868571</td>\n",
       "      <td>9.542857</td>\n",
       "      <td>12.128571</td>\n",
       "      <td>12.375714</td>\n",
       "      <td>13.542857</td>\n",
       "      <td>21.167143</td>\n",
       "      <td>16.584286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-20/1961-02-26</th>\n",
       "      <td>15.221429</td>\n",
       "      <td>13.625714</td>\n",
       "      <td>14.334286</td>\n",
       "      <td>8.524286</td>\n",
       "      <td>13.655714</td>\n",
       "      <td>10.114286</td>\n",
       "      <td>11.150000</td>\n",
       "      <td>10.875714</td>\n",
       "      <td>10.392857</td>\n",
       "      <td>12.730000</td>\n",
       "      <td>16.304286</td>\n",
       "      <td>14.322857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-27/1961-03-05</th>\n",
       "      <td>12.101429</td>\n",
       "      <td>12.951429</td>\n",
       "      <td>11.063333</td>\n",
       "      <td>7.834286</td>\n",
       "      <td>12.101429</td>\n",
       "      <td>9.238571</td>\n",
       "      <td>10.232857</td>\n",
       "      <td>11.130000</td>\n",
       "      <td>10.383333</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>17.842857</td>\n",
       "      <td>13.951667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-06/1961-03-12</th>\n",
       "      <td>9.376667</td>\n",
       "      <td>11.578571</td>\n",
       "      <td>10.845714</td>\n",
       "      <td>7.137143</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>9.488571</td>\n",
       "      <td>6.881429</td>\n",
       "      <td>9.637143</td>\n",
       "      <td>9.885714</td>\n",
       "      <td>10.458571</td>\n",
       "      <td>16.701429</td>\n",
       "      <td>14.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-13/1961-03-19</th>\n",
       "      <td>11.911429</td>\n",
       "      <td>13.501429</td>\n",
       "      <td>11.607143</td>\n",
       "      <td>7.084286</td>\n",
       "      <td>10.751429</td>\n",
       "      <td>8.652857</td>\n",
       "      <td>10.041429</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>10.101429</td>\n",
       "      <td>11.627143</td>\n",
       "      <td>19.350000</td>\n",
       "      <td>16.227143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-20/1961-03-26</th>\n",
       "      <td>9.567143</td>\n",
       "      <td>8.387143</td>\n",
       "      <td>9.695714</td>\n",
       "      <td>6.648571</td>\n",
       "      <td>8.964286</td>\n",
       "      <td>7.982857</td>\n",
       "      <td>10.774286</td>\n",
       "      <td>8.977143</td>\n",
       "      <td>10.904286</td>\n",
       "      <td>11.481429</td>\n",
       "      <td>14.037143</td>\n",
       "      <td>18.134286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-27/1961-04-02</th>\n",
       "      <td>10.757143</td>\n",
       "      <td>8.852857</td>\n",
       "      <td>9.501429</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>9.975714</td>\n",
       "      <td>9.165714</td>\n",
       "      <td>11.125714</td>\n",
       "      <td>9.061429</td>\n",
       "      <td>10.478333</td>\n",
       "      <td>9.631429</td>\n",
       "      <td>13.471429</td>\n",
       "      <td>13.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-03/1961-04-09</th>\n",
       "      <td>11.964286</td>\n",
       "      <td>10.654286</td>\n",
       "      <td>13.607143</td>\n",
       "      <td>5.958571</td>\n",
       "      <td>9.494286</td>\n",
       "      <td>7.637143</td>\n",
       "      <td>7.107143</td>\n",
       "      <td>8.041429</td>\n",
       "      <td>8.161429</td>\n",
       "      <td>7.238571</td>\n",
       "      <td>11.712857</td>\n",
       "      <td>11.371429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-10/1961-04-16</th>\n",
       "      <td>8.965714</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.787143</td>\n",
       "      <td>4.971429</td>\n",
       "      <td>6.405714</td>\n",
       "      <td>4.947143</td>\n",
       "      <td>5.005714</td>\n",
       "      <td>4.994286</td>\n",
       "      <td>5.718571</td>\n",
       "      <td>6.178571</td>\n",
       "      <td>9.482857</td>\n",
       "      <td>8.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-17/1961-04-23</th>\n",
       "      <td>12.621429</td>\n",
       "      <td>10.438571</td>\n",
       "      <td>10.255714</td>\n",
       "      <td>7.768571</td>\n",
       "      <td>10.357143</td>\n",
       "      <td>7.798571</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.111429</td>\n",
       "      <td>8.767143</td>\n",
       "      <td>9.551429</td>\n",
       "      <td>13.620000</td>\n",
       "      <td>12.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-04-24/1961-04-30</th>\n",
       "      <td>10.117143</td>\n",
       "      <td>9.798571</td>\n",
       "      <td>8.281429</td>\n",
       "      <td>4.801429</td>\n",
       "      <td>7.892857</td>\n",
       "      <td>5.197143</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>6.377143</td>\n",
       "      <td>6.242857</td>\n",
       "      <td>6.124286</td>\n",
       "      <td>9.720000</td>\n",
       "      <td>8.637143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-01/1961-05-07</th>\n",
       "      <td>15.367143</td>\n",
       "      <td>13.970000</td>\n",
       "      <td>13.834286</td>\n",
       "      <td>9.952857</td>\n",
       "      <td>14.917143</td>\n",
       "      <td>10.864286</td>\n",
       "      <td>11.435714</td>\n",
       "      <td>12.244286</td>\n",
       "      <td>11.677143</td>\n",
       "      <td>11.585714</td>\n",
       "      <td>17.548571</td>\n",
       "      <td>14.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-08/1961-05-14</th>\n",
       "      <td>7.772857</td>\n",
       "      <td>8.712857</td>\n",
       "      <td>8.172857</td>\n",
       "      <td>5.295714</td>\n",
       "      <td>9.150000</td>\n",
       "      <td>6.391429</td>\n",
       "      <td>8.013333</td>\n",
       "      <td>7.052857</td>\n",
       "      <td>7.528571</td>\n",
       "      <td>7.822857</td>\n",
       "      <td>10.421429</td>\n",
       "      <td>10.382857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-15/1961-05-21</th>\n",
       "      <td>8.225714</td>\n",
       "      <td>5.631667</td>\n",
       "      <td>12.042857</td>\n",
       "      <td>4.258571</td>\n",
       "      <td>7.597143</td>\n",
       "      <td>5.022857</td>\n",
       "      <td>5.695714</td>\n",
       "      <td>6.970000</td>\n",
       "      <td>6.847143</td>\n",
       "      <td>7.114286</td>\n",
       "      <td>9.624286</td>\n",
       "      <td>10.612857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-22/1961-05-28</th>\n",
       "      <td>8.155714</td>\n",
       "      <td>7.388571</td>\n",
       "      <td>8.512857</td>\n",
       "      <td>3.748333</td>\n",
       "      <td>6.941429</td>\n",
       "      <td>4.112857</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>6.272857</td>\n",
       "      <td>6.108571</td>\n",
       "      <td>7.535714</td>\n",
       "      <td>10.518571</td>\n",
       "      <td>11.697143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-05-29/1961-06-04</th>\n",
       "      <td>10.321429</td>\n",
       "      <td>7.407143</td>\n",
       "      <td>10.065714</td>\n",
       "      <td>6.310000</td>\n",
       "      <td>9.754286</td>\n",
       "      <td>6.451429</td>\n",
       "      <td>8.344286</td>\n",
       "      <td>8.635714</td>\n",
       "      <td>8.714286</td>\n",
       "      <td>9.035714</td>\n",
       "      <td>12.298571</td>\n",
       "      <td>13.597143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-05/1961-06-11</th>\n",
       "      <td>10.917143</td>\n",
       "      <td>8.992857</td>\n",
       "      <td>8.095714</td>\n",
       "      <td>5.214286</td>\n",
       "      <td>10.030000</td>\n",
       "      <td>5.460000</td>\n",
       "      <td>7.084286</td>\n",
       "      <td>6.884286</td>\n",
       "      <td>8.034286</td>\n",
       "      <td>8.397143</td>\n",
       "      <td>10.148571</td>\n",
       "      <td>12.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-12/1961-06-18</th>\n",
       "      <td>10.571429</td>\n",
       "      <td>9.565714</td>\n",
       "      <td>10.875714</td>\n",
       "      <td>6.520000</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>6.947143</td>\n",
       "      <td>9.278571</td>\n",
       "      <td>9.102857</td>\n",
       "      <td>8.992857</td>\n",
       "      <td>9.594286</td>\n",
       "      <td>15.351429</td>\n",
       "      <td>15.025714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-19/1961-06-25</th>\n",
       "      <td>7.345714</td>\n",
       "      <td>6.108571</td>\n",
       "      <td>8.084286</td>\n",
       "      <td>5.478571</td>\n",
       "      <td>11.477143</td>\n",
       "      <td>7.492857</td>\n",
       "      <td>11.868571</td>\n",
       "      <td>9.447143</td>\n",
       "      <td>10.458571</td>\n",
       "      <td>11.257143</td>\n",
       "      <td>14.370000</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-06-26/1961-07-02</th>\n",
       "      <td>10.236667</td>\n",
       "      <td>9.482857</td>\n",
       "      <td>8.648571</td>\n",
       "      <td>6.772857</td>\n",
       "      <td>10.975714</td>\n",
       "      <td>6.507143</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>9.237143</td>\n",
       "      <td>7.904286</td>\n",
       "      <td>10.268571</td>\n",
       "      <td>14.535714</td>\n",
       "      <td>12.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07-03/1961-07-09</th>\n",
       "      <td>11.715714</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>7.544286</td>\n",
       "      <td>12.494286</td>\n",
       "      <td>7.982857</td>\n",
       "      <td>11.888333</td>\n",
       "      <td>9.308571</td>\n",
       "      <td>10.732857</td>\n",
       "      <td>10.547143</td>\n",
       "      <td>12.220000</td>\n",
       "      <td>15.987143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07-10/1961-07-16</th>\n",
       "      <td>16.680000</td>\n",
       "      <td>13.518571</td>\n",
       "      <td>11.171429</td>\n",
       "      <td>9.277143</td>\n",
       "      <td>14.524286</td>\n",
       "      <td>8.412857</td>\n",
       "      <td>10.171429</td>\n",
       "      <td>10.507143</td>\n",
       "      <td>9.530000</td>\n",
       "      <td>10.157143</td>\n",
       "      <td>13.520000</td>\n",
       "      <td>12.524286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-07-17/1961-07-23</th>\n",
       "      <td>4.202857</td>\n",
       "      <td>4.255714</td>\n",
       "      <td>6.738571</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>6.112857</td>\n",
       "      <td>2.715714</td>\n",
       "      <td>3.964286</td>\n",
       "      <td>5.642857</td>\n",
       "      <td>5.297143</td>\n",
       "      <td>6.041429</td>\n",
       "      <td>7.524286</td>\n",
       "      <td>8.415714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-05/1978-06-11</th>\n",
       "      <td>12.022857</td>\n",
       "      <td>9.154286</td>\n",
       "      <td>9.488571</td>\n",
       "      <td>5.971429</td>\n",
       "      <td>10.637143</td>\n",
       "      <td>8.030000</td>\n",
       "      <td>8.678571</td>\n",
       "      <td>8.227143</td>\n",
       "      <td>9.172857</td>\n",
       "      <td>9.642857</td>\n",
       "      <td>11.632857</td>\n",
       "      <td>17.778571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-12/1978-06-18</th>\n",
       "      <td>9.410000</td>\n",
       "      <td>8.770000</td>\n",
       "      <td>14.135714</td>\n",
       "      <td>6.457143</td>\n",
       "      <td>8.564286</td>\n",
       "      <td>6.898571</td>\n",
       "      <td>7.297143</td>\n",
       "      <td>7.464286</td>\n",
       "      <td>7.054286</td>\n",
       "      <td>6.225714</td>\n",
       "      <td>11.398571</td>\n",
       "      <td>12.957143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-19/1978-06-25</th>\n",
       "      <td>12.707143</td>\n",
       "      <td>10.244286</td>\n",
       "      <td>8.912857</td>\n",
       "      <td>5.878571</td>\n",
       "      <td>10.372857</td>\n",
       "      <td>6.852857</td>\n",
       "      <td>7.648571</td>\n",
       "      <td>7.875714</td>\n",
       "      <td>7.865714</td>\n",
       "      <td>7.084286</td>\n",
       "      <td>13.030000</td>\n",
       "      <td>16.678571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-06-26/1978-07-02</th>\n",
       "      <td>12.208571</td>\n",
       "      <td>9.640000</td>\n",
       "      <td>10.482857</td>\n",
       "      <td>7.011429</td>\n",
       "      <td>12.772857</td>\n",
       "      <td>9.005714</td>\n",
       "      <td>11.055714</td>\n",
       "      <td>8.917143</td>\n",
       "      <td>9.994286</td>\n",
       "      <td>7.498571</td>\n",
       "      <td>12.268571</td>\n",
       "      <td>15.287143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-03/1978-07-09</th>\n",
       "      <td>18.052857</td>\n",
       "      <td>12.630000</td>\n",
       "      <td>11.984286</td>\n",
       "      <td>9.220000</td>\n",
       "      <td>13.414286</td>\n",
       "      <td>10.762857</td>\n",
       "      <td>11.368571</td>\n",
       "      <td>11.218571</td>\n",
       "      <td>11.272857</td>\n",
       "      <td>11.082857</td>\n",
       "      <td>14.754286</td>\n",
       "      <td>18.215714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-10/1978-07-16</th>\n",
       "      <td>5.882857</td>\n",
       "      <td>3.244286</td>\n",
       "      <td>5.358571</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>4.618571</td>\n",
       "      <td>2.631429</td>\n",
       "      <td>2.494286</td>\n",
       "      <td>3.540000</td>\n",
       "      <td>3.397143</td>\n",
       "      <td>3.214286</td>\n",
       "      <td>7.198571</td>\n",
       "      <td>7.578571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-17/1978-07-23</th>\n",
       "      <td>13.654286</td>\n",
       "      <td>10.007143</td>\n",
       "      <td>9.915714</td>\n",
       "      <td>6.577143</td>\n",
       "      <td>10.757143</td>\n",
       "      <td>8.282857</td>\n",
       "      <td>8.147143</td>\n",
       "      <td>9.301429</td>\n",
       "      <td>8.952857</td>\n",
       "      <td>8.402857</td>\n",
       "      <td>13.847143</td>\n",
       "      <td>14.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-24/1978-07-30</th>\n",
       "      <td>12.172857</td>\n",
       "      <td>11.854286</td>\n",
       "      <td>11.094286</td>\n",
       "      <td>6.631429</td>\n",
       "      <td>9.918571</td>\n",
       "      <td>8.707143</td>\n",
       "      <td>7.458571</td>\n",
       "      <td>9.117143</td>\n",
       "      <td>9.304286</td>\n",
       "      <td>8.148571</td>\n",
       "      <td>15.192857</td>\n",
       "      <td>14.584286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-07-31/1978-08-06</th>\n",
       "      <td>12.475714</td>\n",
       "      <td>9.488571</td>\n",
       "      <td>10.584286</td>\n",
       "      <td>5.457143</td>\n",
       "      <td>8.724286</td>\n",
       "      <td>5.855714</td>\n",
       "      <td>7.065714</td>\n",
       "      <td>5.410000</td>\n",
       "      <td>6.631429</td>\n",
       "      <td>4.962857</td>\n",
       "      <td>9.084286</td>\n",
       "      <td>11.405714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-07/1978-08-13</th>\n",
       "      <td>10.114286</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>7.635714</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>8.101429</td>\n",
       "      <td>6.702857</td>\n",
       "      <td>5.452857</td>\n",
       "      <td>5.964286</td>\n",
       "      <td>7.518571</td>\n",
       "      <td>5.661429</td>\n",
       "      <td>10.691429</td>\n",
       "      <td>11.927143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-14/1978-08-20</th>\n",
       "      <td>11.100000</td>\n",
       "      <td>11.237143</td>\n",
       "      <td>10.505714</td>\n",
       "      <td>5.697143</td>\n",
       "      <td>9.910000</td>\n",
       "      <td>8.034286</td>\n",
       "      <td>7.267143</td>\n",
       "      <td>8.517143</td>\n",
       "      <td>9.815714</td>\n",
       "      <td>7.941429</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14.405714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-21/1978-08-27</th>\n",
       "      <td>6.208571</td>\n",
       "      <td>5.060000</td>\n",
       "      <td>8.565714</td>\n",
       "      <td>3.121429</td>\n",
       "      <td>4.638571</td>\n",
       "      <td>4.077143</td>\n",
       "      <td>3.291429</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>5.877143</td>\n",
       "      <td>4.447143</td>\n",
       "      <td>8.131429</td>\n",
       "      <td>10.661429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-08-28/1978-09-03</th>\n",
       "      <td>8.232857</td>\n",
       "      <td>4.888571</td>\n",
       "      <td>7.767143</td>\n",
       "      <td>3.588571</td>\n",
       "      <td>3.892857</td>\n",
       "      <td>5.090000</td>\n",
       "      <td>6.184286</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.202857</td>\n",
       "      <td>4.745714</td>\n",
       "      <td>8.105714</td>\n",
       "      <td>13.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-04/1978-09-10</th>\n",
       "      <td>11.487143</td>\n",
       "      <td>12.742857</td>\n",
       "      <td>11.124286</td>\n",
       "      <td>5.702857</td>\n",
       "      <td>10.721429</td>\n",
       "      <td>10.927143</td>\n",
       "      <td>9.157143</td>\n",
       "      <td>9.458571</td>\n",
       "      <td>10.588571</td>\n",
       "      <td>8.274286</td>\n",
       "      <td>14.560000</td>\n",
       "      <td>16.752857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-11/1978-09-17</th>\n",
       "      <td>12.067143</td>\n",
       "      <td>10.648571</td>\n",
       "      <td>11.610000</td>\n",
       "      <td>6.864286</td>\n",
       "      <td>12.252857</td>\n",
       "      <td>11.868571</td>\n",
       "      <td>13.017143</td>\n",
       "      <td>12.447143</td>\n",
       "      <td>11.908571</td>\n",
       "      <td>10.957143</td>\n",
       "      <td>18.422857</td>\n",
       "      <td>23.441429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-18/1978-09-24</th>\n",
       "      <td>5.845714</td>\n",
       "      <td>8.317143</td>\n",
       "      <td>9.305714</td>\n",
       "      <td>2.554286</td>\n",
       "      <td>5.625714</td>\n",
       "      <td>5.171429</td>\n",
       "      <td>7.047143</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>6.870000</td>\n",
       "      <td>6.291429</td>\n",
       "      <td>13.447143</td>\n",
       "      <td>15.324286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-09-25/1978-10-01</th>\n",
       "      <td>16.252857</td>\n",
       "      <td>14.131429</td>\n",
       "      <td>12.098571</td>\n",
       "      <td>8.832857</td>\n",
       "      <td>15.810000</td>\n",
       "      <td>10.338571</td>\n",
       "      <td>15.124286</td>\n",
       "      <td>12.378571</td>\n",
       "      <td>12.275714</td>\n",
       "      <td>11.970000</td>\n",
       "      <td>19.160000</td>\n",
       "      <td>24.158571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-02/1978-10-08</th>\n",
       "      <td>12.865714</td>\n",
       "      <td>13.302857</td>\n",
       "      <td>11.671429</td>\n",
       "      <td>6.531429</td>\n",
       "      <td>11.731429</td>\n",
       "      <td>8.881429</td>\n",
       "      <td>9.707143</td>\n",
       "      <td>9.708571</td>\n",
       "      <td>11.391429</td>\n",
       "      <td>10.161429</td>\n",
       "      <td>16.498571</td>\n",
       "      <td>20.618571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-09/1978-10-15</th>\n",
       "      <td>9.611429</td>\n",
       "      <td>6.327143</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>4.167143</td>\n",
       "      <td>6.821429</td>\n",
       "      <td>6.237143</td>\n",
       "      <td>5.577143</td>\n",
       "      <td>6.348571</td>\n",
       "      <td>7.232857</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>12.197143</td>\n",
       "      <td>14.177143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-16/1978-10-22</th>\n",
       "      <td>9.804286</td>\n",
       "      <td>7.817143</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>5.314286</td>\n",
       "      <td>9.124286</td>\n",
       "      <td>6.862857</td>\n",
       "      <td>9.391429</td>\n",
       "      <td>7.428571</td>\n",
       "      <td>7.765714</td>\n",
       "      <td>8.048571</td>\n",
       "      <td>12.708571</td>\n",
       "      <td>17.868571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-23/1978-10-29</th>\n",
       "      <td>7.504286</td>\n",
       "      <td>7.702857</td>\n",
       "      <td>8.102857</td>\n",
       "      <td>3.204286</td>\n",
       "      <td>7.464286</td>\n",
       "      <td>5.905714</td>\n",
       "      <td>8.727143</td>\n",
       "      <td>6.652857</td>\n",
       "      <td>7.605714</td>\n",
       "      <td>8.120000</td>\n",
       "      <td>14.487143</td>\n",
       "      <td>16.915714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-10-30/1978-11-05</th>\n",
       "      <td>13.060000</td>\n",
       "      <td>13.465714</td>\n",
       "      <td>12.137143</td>\n",
       "      <td>6.682857</td>\n",
       "      <td>9.891429</td>\n",
       "      <td>8.314286</td>\n",
       "      <td>9.775714</td>\n",
       "      <td>9.638571</td>\n",
       "      <td>10.185714</td>\n",
       "      <td>10.422857</td>\n",
       "      <td>18.451429</td>\n",
       "      <td>18.721429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-06/1978-11-12</th>\n",
       "      <td>14.857143</td>\n",
       "      <td>15.237143</td>\n",
       "      <td>12.007143</td>\n",
       "      <td>7.684286</td>\n",
       "      <td>12.460000</td>\n",
       "      <td>9.352857</td>\n",
       "      <td>10.224286</td>\n",
       "      <td>10.554286</td>\n",
       "      <td>11.168571</td>\n",
       "      <td>12.232857</td>\n",
       "      <td>19.307143</td>\n",
       "      <td>22.522857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-13/1978-11-19</th>\n",
       "      <td>20.590000</td>\n",
       "      <td>18.998571</td>\n",
       "      <td>17.272857</td>\n",
       "      <td>10.417143</td>\n",
       "      <td>14.220000</td>\n",
       "      <td>11.208571</td>\n",
       "      <td>16.081429</td>\n",
       "      <td>12.915714</td>\n",
       "      <td>13.297143</td>\n",
       "      <td>13.242857</td>\n",
       "      <td>20.357143</td>\n",
       "      <td>23.905714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-20/1978-11-26</th>\n",
       "      <td>16.498571</td>\n",
       "      <td>13.971429</td>\n",
       "      <td>13.544286</td>\n",
       "      <td>6.361429</td>\n",
       "      <td>10.438571</td>\n",
       "      <td>7.404286</td>\n",
       "      <td>12.797143</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>9.998571</td>\n",
       "      <td>8.915714</td>\n",
       "      <td>15.207143</td>\n",
       "      <td>19.491429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-11-27/1978-12-03</th>\n",
       "      <td>14.934286</td>\n",
       "      <td>11.232857</td>\n",
       "      <td>13.941429</td>\n",
       "      <td>5.565714</td>\n",
       "      <td>10.215714</td>\n",
       "      <td>8.618571</td>\n",
       "      <td>9.642857</td>\n",
       "      <td>7.685714</td>\n",
       "      <td>9.011429</td>\n",
       "      <td>9.547143</td>\n",
       "      <td>11.835714</td>\n",
       "      <td>18.728571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-04/1978-12-10</th>\n",
       "      <td>20.740000</td>\n",
       "      <td>19.190000</td>\n",
       "      <td>17.034286</td>\n",
       "      <td>9.777143</td>\n",
       "      <td>15.287143</td>\n",
       "      <td>12.774286</td>\n",
       "      <td>14.437143</td>\n",
       "      <td>12.488571</td>\n",
       "      <td>13.870000</td>\n",
       "      <td>14.082857</td>\n",
       "      <td>18.517143</td>\n",
       "      <td>23.061429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-11/1978-12-17</th>\n",
       "      <td>16.758571</td>\n",
       "      <td>14.692857</td>\n",
       "      <td>14.987143</td>\n",
       "      <td>6.917143</td>\n",
       "      <td>11.397143</td>\n",
       "      <td>7.272857</td>\n",
       "      <td>10.208571</td>\n",
       "      <td>7.967143</td>\n",
       "      <td>9.168571</td>\n",
       "      <td>8.565714</td>\n",
       "      <td>11.102857</td>\n",
       "      <td>15.562857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-18/1978-12-24</th>\n",
       "      <td>11.155714</td>\n",
       "      <td>8.008571</td>\n",
       "      <td>13.172857</td>\n",
       "      <td>4.004286</td>\n",
       "      <td>7.825714</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>7.798571</td>\n",
       "      <td>8.667143</td>\n",
       "      <td>7.151429</td>\n",
       "      <td>8.072857</td>\n",
       "      <td>11.845714</td>\n",
       "      <td>18.977143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-12-25/1978-12-31</th>\n",
       "      <td>14.951429</td>\n",
       "      <td>11.801429</td>\n",
       "      <td>16.035714</td>\n",
       "      <td>6.507143</td>\n",
       "      <td>9.660000</td>\n",
       "      <td>8.620000</td>\n",
       "      <td>13.708571</td>\n",
       "      <td>10.477143</td>\n",
       "      <td>10.868571</td>\n",
       "      <td>11.471429</td>\n",
       "      <td>12.947143</td>\n",
       "      <td>26.844286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>940 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             RPT        VAL        ROS        KIL        SHA  \\\n",
       "Yr_Mo_Dy                                                                       \n",
       "1960-12-26/1961-01-01  15.040000  14.960000  13.170000   9.290000        NaN   \n",
       "1961-01-02/1961-01-08  13.541429  11.486667  10.487143   6.417143   9.474286   \n",
       "1961-01-09/1961-01-15  12.468571   8.967143  11.958571   4.630000   7.351429   \n",
       "1961-01-16/1961-01-22  13.204286   9.862857  12.982857   6.328571   8.966667   \n",
       "1961-01-23/1961-01-29  19.880000  16.141429  18.225714  12.720000  17.432857   \n",
       "1961-01-30/1961-02-05  16.827143  15.460000  12.618571   8.247143  13.361429   \n",
       "1961-02-06/1961-02-12  19.684286  16.417143  17.304286  10.774286  14.718571   \n",
       "1961-02-13/1961-02-19  15.130000  15.091429  13.797143  10.083333  13.410000   \n",
       "1961-02-20/1961-02-26  15.221429  13.625714  14.334286   8.524286  13.655714   \n",
       "1961-02-27/1961-03-05  12.101429  12.951429  11.063333   7.834286  12.101429   \n",
       "1961-03-06/1961-03-12   9.376667  11.578571  10.845714   7.137143  10.940000   \n",
       "1961-03-13/1961-03-19  11.911429  13.501429  11.607143   7.084286  10.751429   \n",
       "1961-03-20/1961-03-26   9.567143   8.387143   9.695714   6.648571   8.964286   \n",
       "1961-03-27/1961-04-02  10.757143   8.852857   9.501429   7.300000   9.975714   \n",
       "1961-04-03/1961-04-09  11.964286  10.654286  13.607143   5.958571   9.494286   \n",
       "1961-04-10/1961-04-16   8.965714   8.000000   8.787143   4.971429   6.405714   \n",
       "1961-04-17/1961-04-23  12.621429  10.438571  10.255714   7.768571  10.357143   \n",
       "1961-04-24/1961-04-30  10.117143   9.798571   8.281429   4.801429   7.892857   \n",
       "1961-05-01/1961-05-07  15.367143  13.970000  13.834286   9.952857  14.917143   \n",
       "1961-05-08/1961-05-14   7.772857   8.712857   8.172857   5.295714   9.150000   \n",
       "1961-05-15/1961-05-21   8.225714   5.631667  12.042857   4.258571   7.597143   \n",
       "1961-05-22/1961-05-28   8.155714   7.388571   8.512857   3.748333   6.941429   \n",
       "1961-05-29/1961-06-04  10.321429   7.407143  10.065714   6.310000   9.754286   \n",
       "1961-06-05/1961-06-11  10.917143   8.992857   8.095714   5.214286  10.030000   \n",
       "1961-06-12/1961-06-18  10.571429   9.565714  10.875714   6.520000  10.260000   \n",
       "1961-06-19/1961-06-25   7.345714   6.108571   8.084286   5.478571  11.477143   \n",
       "1961-06-26/1961-07-02  10.236667   9.482857   8.648571   6.772857  10.975714   \n",
       "1961-07-03/1961-07-09  11.715714   7.220000   9.320000   7.544286  12.494286   \n",
       "1961-07-10/1961-07-16  16.680000  13.518571  11.171429   9.277143  14.524286   \n",
       "1961-07-17/1961-07-23   4.202857   4.255714   6.738571   3.300000   6.112857   \n",
       "...                          ...        ...        ...        ...        ...   \n",
       "1978-06-05/1978-06-11  12.022857   9.154286   9.488571   5.971429  10.637143   \n",
       "1978-06-12/1978-06-18   9.410000   8.770000  14.135714   6.457143   8.564286   \n",
       "1978-06-19/1978-06-25  12.707143  10.244286   8.912857   5.878571  10.372857   \n",
       "1978-06-26/1978-07-02  12.208571   9.640000  10.482857   7.011429  12.772857   \n",
       "1978-07-03/1978-07-09  18.052857  12.630000  11.984286   9.220000  13.414286   \n",
       "1978-07-10/1978-07-16   5.882857   3.244286   5.358571   2.250000   4.618571   \n",
       "1978-07-17/1978-07-23  13.654286  10.007143   9.915714   6.577143  10.757143   \n",
       "1978-07-24/1978-07-30  12.172857  11.854286  11.094286   6.631429   9.918571   \n",
       "1978-07-31/1978-08-06  12.475714   9.488571  10.584286   5.457143   8.724286   \n",
       "1978-08-07/1978-08-13  10.114286   9.600000   7.635714   4.790000   8.101429   \n",
       "1978-08-14/1978-08-20  11.100000  11.237143  10.505714   5.697143   9.910000   \n",
       "1978-08-21/1978-08-27   6.208571   5.060000   8.565714   3.121429   4.638571   \n",
       "1978-08-28/1978-09-03   8.232857   4.888571   7.767143   3.588571   3.892857   \n",
       "1978-09-04/1978-09-10  11.487143  12.742857  11.124286   5.702857  10.721429   \n",
       "1978-09-11/1978-09-17  12.067143  10.648571  11.610000   6.864286  12.252857   \n",
       "1978-09-18/1978-09-24   5.845714   8.317143   9.305714   2.554286   5.625714   \n",
       "1978-09-25/1978-10-01  16.252857  14.131429  12.098571   8.832857  15.810000   \n",
       "1978-10-02/1978-10-08  12.865714  13.302857  11.671429   6.531429  11.731429   \n",
       "1978-10-09/1978-10-15   9.611429   6.327143   9.250000   4.167143   6.821429   \n",
       "1978-10-16/1978-10-22   9.804286   7.817143   7.642857   5.314286   9.124286   \n",
       "1978-10-23/1978-10-29   7.504286   7.702857   8.102857   3.204286   7.464286   \n",
       "1978-10-30/1978-11-05  13.060000  13.465714  12.137143   6.682857   9.891429   \n",
       "1978-11-06/1978-11-12  14.857143  15.237143  12.007143   7.684286  12.460000   \n",
       "1978-11-13/1978-11-19  20.590000  18.998571  17.272857  10.417143  14.220000   \n",
       "1978-11-20/1978-11-26  16.498571  13.971429  13.544286   6.361429  10.438571   \n",
       "1978-11-27/1978-12-03  14.934286  11.232857  13.941429   5.565714  10.215714   \n",
       "1978-12-04/1978-12-10  20.740000  19.190000  17.034286   9.777143  15.287143   \n",
       "1978-12-11/1978-12-17  16.758571  14.692857  14.987143   6.917143  11.397143   \n",
       "1978-12-18/1978-12-24  11.155714   8.008571  13.172857   4.004286   7.825714   \n",
       "1978-12-25/1978-12-31  14.951429  11.801429  16.035714   6.507143   9.660000   \n",
       "\n",
       "                             BIR        DUB        CLA        MUL        CLO  \\\n",
       "Yr_Mo_Dy                                                                       \n",
       "1960-12-26/1961-01-01   9.870000  13.670000  10.250000  10.830000  12.580000   \n",
       "1961-01-02/1961-01-08   6.435714  11.061429   6.616667   8.434286   8.497143   \n",
       "1961-01-09/1961-01-15   5.072857   7.535714   6.820000   5.712857   7.571429   \n",
       "1961-01-16/1961-01-22   7.417143   9.257143   7.875714   7.145714   8.124286   \n",
       "1961-01-23/1961-01-29  14.828571  15.528571  15.160000  14.480000  15.640000   \n",
       "1961-01-30/1961-02-05   9.107143  12.204286   8.548571   9.821429   9.460000   \n",
       "1961-02-06/1961-02-12  12.522857  14.934286  14.850000  14.064286  14.440000   \n",
       "1961-02-13/1961-02-19  11.868571   9.542857  12.128571  12.375714  13.542857   \n",
       "1961-02-20/1961-02-26  10.114286  11.150000  10.875714  10.392857  12.730000   \n",
       "1961-02-27/1961-03-05   9.238571  10.232857  11.130000  10.383333  12.370000   \n",
       "1961-03-06/1961-03-12   9.488571   6.881429   9.637143   9.885714  10.458571   \n",
       "1961-03-13/1961-03-19   8.652857  10.041429  10.220000  10.101429  11.627143   \n",
       "1961-03-20/1961-03-26   7.982857  10.774286   8.977143  10.904286  11.481429   \n",
       "1961-03-27/1961-04-02   9.165714  11.125714   9.061429  10.478333   9.631429   \n",
       "1961-04-03/1961-04-09   7.637143   7.107143   8.041429   8.161429   7.238571   \n",
       "1961-04-10/1961-04-16   4.947143   5.005714   4.994286   5.718571   6.178571   \n",
       "1961-04-17/1961-04-23   7.798571   9.000000   9.111429   8.767143   9.551429   \n",
       "1961-04-24/1961-04-30   5.197143   6.150000   6.377143   6.242857   6.124286   \n",
       "1961-05-01/1961-05-07  10.864286  11.435714  12.244286  11.677143  11.585714   \n",
       "1961-05-08/1961-05-14   6.391429   8.013333   7.052857   7.528571   7.822857   \n",
       "1961-05-15/1961-05-21   5.022857   5.695714   6.970000   6.847143   7.114286   \n",
       "1961-05-22/1961-05-28   4.112857   5.142857   6.272857   6.108571   7.535714   \n",
       "1961-05-29/1961-06-04   6.451429   8.344286   8.635714   8.714286   9.035714   \n",
       "1961-06-05/1961-06-11   5.460000   7.084286   6.884286   8.034286   8.397143   \n",
       "1961-06-12/1961-06-18   6.947143   9.278571   9.102857   8.992857   9.594286   \n",
       "1961-06-19/1961-06-25   7.492857  11.868571   9.447143  10.458571  11.257143   \n",
       "1961-06-26/1961-07-02   6.507143   7.642857   9.237143   7.904286  10.268571   \n",
       "1961-07-03/1961-07-09   7.982857  11.888333   9.308571  10.732857  10.547143   \n",
       "1961-07-10/1961-07-16   8.412857  10.171429  10.507143   9.530000  10.157143   \n",
       "1961-07-17/1961-07-23   2.715714   3.964286   5.642857   5.297143   6.041429   \n",
       "...                          ...        ...        ...        ...        ...   \n",
       "1978-06-05/1978-06-11   8.030000   8.678571   8.227143   9.172857   9.642857   \n",
       "1978-06-12/1978-06-18   6.898571   7.297143   7.464286   7.054286   6.225714   \n",
       "1978-06-19/1978-06-25   6.852857   7.648571   7.875714   7.865714   7.084286   \n",
       "1978-06-26/1978-07-02   9.005714  11.055714   8.917143   9.994286   7.498571   \n",
       "1978-07-03/1978-07-09  10.762857  11.368571  11.218571  11.272857  11.082857   \n",
       "1978-07-10/1978-07-16   2.631429   2.494286   3.540000   3.397143   3.214286   \n",
       "1978-07-17/1978-07-23   8.282857   8.147143   9.301429   8.952857   8.402857   \n",
       "1978-07-24/1978-07-30   8.707143   7.458571   9.117143   9.304286   8.148571   \n",
       "1978-07-31/1978-08-06   5.855714   7.065714   5.410000   6.631429   4.962857   \n",
       "1978-08-07/1978-08-13   6.702857   5.452857   5.964286   7.518571   5.661429   \n",
       "1978-08-14/1978-08-20   8.034286   7.267143   8.517143   9.815714   7.941429   \n",
       "1978-08-21/1978-08-27   4.077143   3.291429   3.500000   5.877143   4.447143   \n",
       "1978-08-28/1978-09-03   5.090000   6.184286   3.000000   6.202857   4.745714   \n",
       "1978-09-04/1978-09-10  10.927143   9.157143   9.458571  10.588571   8.274286   \n",
       "1978-09-11/1978-09-17  11.868571  13.017143  12.447143  11.908571  10.957143   \n",
       "1978-09-18/1978-09-24   5.171429   7.047143   6.750000   6.870000   6.291429   \n",
       "1978-09-25/1978-10-01  10.338571  15.124286  12.378571  12.275714  11.970000   \n",
       "1978-10-02/1978-10-08   8.881429   9.707143   9.708571  11.391429  10.161429   \n",
       "1978-10-09/1978-10-15   6.237143   5.577143   6.348571   7.232857   7.285714   \n",
       "1978-10-16/1978-10-22   6.862857   9.391429   7.428571   7.765714   8.048571   \n",
       "1978-10-23/1978-10-29   5.905714   8.727143   6.652857   7.605714   8.120000   \n",
       "1978-10-30/1978-11-05   8.314286   9.775714   9.638571  10.185714  10.422857   \n",
       "1978-11-06/1978-11-12   9.352857  10.224286  10.554286  11.168571  12.232857   \n",
       "1978-11-13/1978-11-19  11.208571  16.081429  12.915714  13.297143  13.242857   \n",
       "1978-11-20/1978-11-26   7.404286  12.797143   7.571429   9.998571   8.915714   \n",
       "1978-11-27/1978-12-03   8.618571   9.642857   7.685714   9.011429   9.547143   \n",
       "1978-12-04/1978-12-10  12.774286  14.437143  12.488571  13.870000  14.082857   \n",
       "1978-12-11/1978-12-17   7.272857  10.208571   7.967143   9.168571   8.565714   \n",
       "1978-12-18/1978-12-24   6.290000   7.798571   8.667143   7.151429   8.072857   \n",
       "1978-12-25/1978-12-31   8.620000  13.708571  10.477143  10.868571  11.471429   \n",
       "\n",
       "                             BEL        MAL  \n",
       "Yr_Mo_Dy                                     \n",
       "1960-12-26/1961-01-01  18.500000  15.040000  \n",
       "1961-01-02/1961-01-08  12.481429  13.238571  \n",
       "1961-01-09/1961-01-15  11.125714  11.024286  \n",
       "1961-01-16/1961-01-22   9.821429  11.434286  \n",
       "1961-01-23/1961-01-29  20.930000  22.530000  \n",
       "1961-01-30/1961-02-05  14.012857  11.935714  \n",
       "1961-02-06/1961-02-12  21.832857  19.155714  \n",
       "1961-02-13/1961-02-19  21.167143  16.584286  \n",
       "1961-02-20/1961-02-26  16.304286  14.322857  \n",
       "1961-02-27/1961-03-05  17.842857  13.951667  \n",
       "1961-03-06/1961-03-12  16.701429  14.420000  \n",
       "1961-03-13/1961-03-19  19.350000  16.227143  \n",
       "1961-03-20/1961-03-26  14.037143  18.134286  \n",
       "1961-03-27/1961-04-02  13.471429  13.900000  \n",
       "1961-04-03/1961-04-09  11.712857  11.371429  \n",
       "1961-04-10/1961-04-16   9.482857   8.690000  \n",
       "1961-04-17/1961-04-23  13.620000  12.470000  \n",
       "1961-04-24/1961-04-30   9.720000   8.637143  \n",
       "1961-05-01/1961-05-07  17.548571  14.571429  \n",
       "1961-05-08/1961-05-14  10.421429  10.382857  \n",
       "1961-05-15/1961-05-21   9.624286  10.612857  \n",
       "1961-05-22/1961-05-28  10.518571  11.697143  \n",
       "1961-05-29/1961-06-04  12.298571  13.597143  \n",
       "1961-06-05/1961-06-11  10.148571  12.250000  \n",
       "1961-06-12/1961-06-18  15.351429  15.025714  \n",
       "1961-06-19/1961-06-25  14.370000  17.410000  \n",
       "1961-06-26/1961-07-02  14.535714  12.133333  \n",
       "1961-07-03/1961-07-09  12.220000  15.987143  \n",
       "1961-07-10/1961-07-16  13.520000  12.524286  \n",
       "1961-07-17/1961-07-23   7.524286   8.415714  \n",
       "...                          ...        ...  \n",
       "1978-06-05/1978-06-11  11.632857  17.778571  \n",
       "1978-06-12/1978-06-18  11.398571  12.957143  \n",
       "1978-06-19/1978-06-25  13.030000  16.678571  \n",
       "1978-06-26/1978-07-02  12.268571  15.287143  \n",
       "1978-07-03/1978-07-09  14.754286  18.215714  \n",
       "1978-07-10/1978-07-16   7.198571   7.578571  \n",
       "1978-07-17/1978-07-23  13.847143  14.785714  \n",
       "1978-07-24/1978-07-30  15.192857  14.584286  \n",
       "1978-07-31/1978-08-06   9.084286  11.405714  \n",
       "1978-08-07/1978-08-13  10.691429  11.927143  \n",
       "1978-08-14/1978-08-20  15.000000  14.405714  \n",
       "1978-08-21/1978-08-27   8.131429  10.661429  \n",
       "1978-08-28/1978-09-03   8.105714  13.150000  \n",
       "1978-09-04/1978-09-10  14.560000  16.752857  \n",
       "1978-09-11/1978-09-17  18.422857  23.441429  \n",
       "1978-09-18/1978-09-24  13.447143  15.324286  \n",
       "1978-09-25/1978-10-01  19.160000  24.158571  \n",
       "1978-10-02/1978-10-08  16.498571  20.618571  \n",
       "1978-10-09/1978-10-15  12.197143  14.177143  \n",
       "1978-10-16/1978-10-22  12.708571  17.868571  \n",
       "1978-10-23/1978-10-29  14.487143  16.915714  \n",
       "1978-10-30/1978-11-05  18.451429  18.721429  \n",
       "1978-11-06/1978-11-12  19.307143  22.522857  \n",
       "1978-11-13/1978-11-19  20.357143  23.905714  \n",
       "1978-11-20/1978-11-26  15.207143  19.491429  \n",
       "1978-11-27/1978-12-03  11.835714  18.728571  \n",
       "1978-12-04/1978-12-10  18.517143  23.061429  \n",
       "1978-12-11/1978-12-17  11.102857  15.562857  \n",
       "1978-12-18/1978-12-24  11.845714  18.977143  \n",
       "1978-12-25/1978-12-31  12.947143  26.844286  \n",
       "\n",
       "[940 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">RPT</th>\n",
       "      <th colspan=\"4\" halign=\"left\">VAL</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ROS</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CLO</th>\n",
       "      <th colspan=\"4\" halign=\"left\">BEL</th>\n",
       "      <th colspan=\"4\" halign=\"left\">MAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yr_Mo_Dy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-08</th>\n",
       "      <td>10.58</td>\n",
       "      <td>18.50</td>\n",
       "      <td>13.541429</td>\n",
       "      <td>2.631321</td>\n",
       "      <td>6.63</td>\n",
       "      <td>16.88</td>\n",
       "      <td>11.486667</td>\n",
       "      <td>3.949525</td>\n",
       "      <td>7.62</td>\n",
       "      <td>12.33</td>\n",
       "      <td>...</td>\n",
       "      <td>8.497143</td>\n",
       "      <td>1.704941</td>\n",
       "      <td>5.46</td>\n",
       "      <td>17.54</td>\n",
       "      <td>12.481429</td>\n",
       "      <td>4.349139</td>\n",
       "      <td>10.88</td>\n",
       "      <td>16.46</td>\n",
       "      <td>13.238571</td>\n",
       "      <td>1.773062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-15</th>\n",
       "      <td>9.04</td>\n",
       "      <td>19.75</td>\n",
       "      <td>12.468571</td>\n",
       "      <td>3.555392</td>\n",
       "      <td>3.54</td>\n",
       "      <td>12.08</td>\n",
       "      <td>8.967143</td>\n",
       "      <td>3.148945</td>\n",
       "      <td>7.08</td>\n",
       "      <td>19.50</td>\n",
       "      <td>...</td>\n",
       "      <td>7.571429</td>\n",
       "      <td>4.084293</td>\n",
       "      <td>5.25</td>\n",
       "      <td>20.71</td>\n",
       "      <td>11.125714</td>\n",
       "      <td>5.552215</td>\n",
       "      <td>5.17</td>\n",
       "      <td>16.92</td>\n",
       "      <td>11.024286</td>\n",
       "      <td>4.692355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-22</th>\n",
       "      <td>4.92</td>\n",
       "      <td>19.83</td>\n",
       "      <td>13.204286</td>\n",
       "      <td>5.337402</td>\n",
       "      <td>3.42</td>\n",
       "      <td>14.37</td>\n",
       "      <td>9.862857</td>\n",
       "      <td>3.837785</td>\n",
       "      <td>7.29</td>\n",
       "      <td>20.79</td>\n",
       "      <td>...</td>\n",
       "      <td>8.124286</td>\n",
       "      <td>4.783952</td>\n",
       "      <td>6.50</td>\n",
       "      <td>15.92</td>\n",
       "      <td>9.821429</td>\n",
       "      <td>3.626584</td>\n",
       "      <td>6.79</td>\n",
       "      <td>17.96</td>\n",
       "      <td>11.434286</td>\n",
       "      <td>4.237239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-29</th>\n",
       "      <td>13.62</td>\n",
       "      <td>25.04</td>\n",
       "      <td>19.880000</td>\n",
       "      <td>4.619061</td>\n",
       "      <td>9.96</td>\n",
       "      <td>23.91</td>\n",
       "      <td>16.141429</td>\n",
       "      <td>5.170224</td>\n",
       "      <td>12.67</td>\n",
       "      <td>25.84</td>\n",
       "      <td>...</td>\n",
       "      <td>15.640000</td>\n",
       "      <td>3.713368</td>\n",
       "      <td>14.04</td>\n",
       "      <td>27.71</td>\n",
       "      <td>20.930000</td>\n",
       "      <td>5.210726</td>\n",
       "      <td>17.50</td>\n",
       "      <td>27.63</td>\n",
       "      <td>22.530000</td>\n",
       "      <td>3.874721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-05</th>\n",
       "      <td>10.58</td>\n",
       "      <td>24.21</td>\n",
       "      <td>16.827143</td>\n",
       "      <td>5.251408</td>\n",
       "      <td>9.46</td>\n",
       "      <td>24.21</td>\n",
       "      <td>15.460000</td>\n",
       "      <td>5.187395</td>\n",
       "      <td>9.04</td>\n",
       "      <td>19.70</td>\n",
       "      <td>...</td>\n",
       "      <td>9.460000</td>\n",
       "      <td>2.839501</td>\n",
       "      <td>9.17</td>\n",
       "      <td>19.33</td>\n",
       "      <td>14.012857</td>\n",
       "      <td>4.210858</td>\n",
       "      <td>7.17</td>\n",
       "      <td>19.25</td>\n",
       "      <td>11.935714</td>\n",
       "      <td>4.336104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-12</th>\n",
       "      <td>16.00</td>\n",
       "      <td>24.54</td>\n",
       "      <td>19.684286</td>\n",
       "      <td>3.587677</td>\n",
       "      <td>11.54</td>\n",
       "      <td>21.42</td>\n",
       "      <td>16.417143</td>\n",
       "      <td>3.608373</td>\n",
       "      <td>13.67</td>\n",
       "      <td>21.34</td>\n",
       "      <td>...</td>\n",
       "      <td>14.440000</td>\n",
       "      <td>1.746749</td>\n",
       "      <td>15.21</td>\n",
       "      <td>26.38</td>\n",
       "      <td>21.832857</td>\n",
       "      <td>4.063753</td>\n",
       "      <td>17.04</td>\n",
       "      <td>21.84</td>\n",
       "      <td>19.155714</td>\n",
       "      <td>1.828705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-19</th>\n",
       "      <td>6.04</td>\n",
       "      <td>22.50</td>\n",
       "      <td>15.130000</td>\n",
       "      <td>5.064609</td>\n",
       "      <td>11.63</td>\n",
       "      <td>20.17</td>\n",
       "      <td>15.091429</td>\n",
       "      <td>3.575012</td>\n",
       "      <td>6.13</td>\n",
       "      <td>19.41</td>\n",
       "      <td>...</td>\n",
       "      <td>13.542857</td>\n",
       "      <td>2.531361</td>\n",
       "      <td>14.09</td>\n",
       "      <td>29.63</td>\n",
       "      <td>21.167143</td>\n",
       "      <td>5.910938</td>\n",
       "      <td>10.96</td>\n",
       "      <td>22.58</td>\n",
       "      <td>16.584286</td>\n",
       "      <td>4.685377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-02-26</th>\n",
       "      <td>7.79</td>\n",
       "      <td>25.80</td>\n",
       "      <td>15.221429</td>\n",
       "      <td>7.020716</td>\n",
       "      <td>7.08</td>\n",
       "      <td>21.50</td>\n",
       "      <td>13.625714</td>\n",
       "      <td>5.147348</td>\n",
       "      <td>6.08</td>\n",
       "      <td>22.42</td>\n",
       "      <td>...</td>\n",
       "      <td>12.730000</td>\n",
       "      <td>4.920064</td>\n",
       "      <td>9.59</td>\n",
       "      <td>23.21</td>\n",
       "      <td>16.304286</td>\n",
       "      <td>5.091162</td>\n",
       "      <td>6.67</td>\n",
       "      <td>23.87</td>\n",
       "      <td>14.322857</td>\n",
       "      <td>6.182283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-05</th>\n",
       "      <td>10.96</td>\n",
       "      <td>13.33</td>\n",
       "      <td>12.101429</td>\n",
       "      <td>0.997721</td>\n",
       "      <td>8.83</td>\n",
       "      <td>17.00</td>\n",
       "      <td>12.951429</td>\n",
       "      <td>2.851955</td>\n",
       "      <td>8.17</td>\n",
       "      <td>13.67</td>\n",
       "      <td>...</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>1.593685</td>\n",
       "      <td>11.58</td>\n",
       "      <td>23.45</td>\n",
       "      <td>17.842857</td>\n",
       "      <td>4.332331</td>\n",
       "      <td>8.83</td>\n",
       "      <td>17.54</td>\n",
       "      <td>13.951667</td>\n",
       "      <td>3.021387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-03-12</th>\n",
       "      <td>4.88</td>\n",
       "      <td>14.79</td>\n",
       "      <td>9.376667</td>\n",
       "      <td>3.732263</td>\n",
       "      <td>8.08</td>\n",
       "      <td>16.96</td>\n",
       "      <td>11.578571</td>\n",
       "      <td>3.230167</td>\n",
       "      <td>7.54</td>\n",
       "      <td>16.38</td>\n",
       "      <td>...</td>\n",
       "      <td>10.458571</td>\n",
       "      <td>3.655113</td>\n",
       "      <td>10.21</td>\n",
       "      <td>22.71</td>\n",
       "      <td>16.701429</td>\n",
       "      <td>4.358759</td>\n",
       "      <td>5.54</td>\n",
       "      <td>22.54</td>\n",
       "      <td>14.420000</td>\n",
       "      <td>5.769890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              RPT                                VAL                    \\\n",
       "              min    max       mean       std    min    max       mean   \n",
       "Yr_Mo_Dy                                                                 \n",
       "1961-01-08  10.58  18.50  13.541429  2.631321   6.63  16.88  11.486667   \n",
       "1961-01-15   9.04  19.75  12.468571  3.555392   3.54  12.08   8.967143   \n",
       "1961-01-22   4.92  19.83  13.204286  5.337402   3.42  14.37   9.862857   \n",
       "1961-01-29  13.62  25.04  19.880000  4.619061   9.96  23.91  16.141429   \n",
       "1961-02-05  10.58  24.21  16.827143  5.251408   9.46  24.21  15.460000   \n",
       "1961-02-12  16.00  24.54  19.684286  3.587677  11.54  21.42  16.417143   \n",
       "1961-02-19   6.04  22.50  15.130000  5.064609  11.63  20.17  15.091429   \n",
       "1961-02-26   7.79  25.80  15.221429  7.020716   7.08  21.50  13.625714   \n",
       "1961-03-05  10.96  13.33  12.101429  0.997721   8.83  17.00  12.951429   \n",
       "1961-03-12   4.88  14.79   9.376667  3.732263   8.08  16.96  11.578571   \n",
       "\n",
       "                        ROS           ...           CLO              BEL  \\\n",
       "                 std    min    max    ...          mean       std    min   \n",
       "Yr_Mo_Dy                              ...                                  \n",
       "1961-01-08  3.949525   7.62  12.33    ...      8.497143  1.704941   5.46   \n",
       "1961-01-15  3.148945   7.08  19.50    ...      7.571429  4.084293   5.25   \n",
       "1961-01-22  3.837785   7.29  20.79    ...      8.124286  4.783952   6.50   \n",
       "1961-01-29  5.170224  12.67  25.84    ...     15.640000  3.713368  14.04   \n",
       "1961-02-05  5.187395   9.04  19.70    ...      9.460000  2.839501   9.17   \n",
       "1961-02-12  3.608373  13.67  21.34    ...     14.440000  1.746749  15.21   \n",
       "1961-02-19  3.575012   6.13  19.41    ...     13.542857  2.531361  14.09   \n",
       "1961-02-26  5.147348   6.08  22.42    ...     12.730000  4.920064   9.59   \n",
       "1961-03-05  2.851955   8.17  13.67    ...     12.370000  1.593685  11.58   \n",
       "1961-03-12  3.230167   7.54  16.38    ...     10.458571  3.655113  10.21   \n",
       "\n",
       "                                          MAL                              \n",
       "              max       mean       std    min    max       mean       std  \n",
       "Yr_Mo_Dy                                                                   \n",
       "1961-01-08  17.54  12.481429  4.349139  10.88  16.46  13.238571  1.773062  \n",
       "1961-01-15  20.71  11.125714  5.552215   5.17  16.92  11.024286  4.692355  \n",
       "1961-01-22  15.92   9.821429  3.626584   6.79  17.96  11.434286  4.237239  \n",
       "1961-01-29  27.71  20.930000  5.210726  17.50  27.63  22.530000  3.874721  \n",
       "1961-02-05  19.33  14.012857  4.210858   7.17  19.25  11.935714  4.336104  \n",
       "1961-02-12  26.38  21.832857  4.063753  17.04  21.84  19.155714  1.828705  \n",
       "1961-02-19  29.63  21.167143  5.910938  10.96  22.58  16.584286  4.685377  \n",
       "1961-02-26  23.21  16.304286  5.091162   6.67  23.87  14.322857  6.182283  \n",
       "1961-03-05  23.45  17.842857  4.332331   8.83  17.54  13.951667  3.021387  \n",
       "1961-03-12  22.71  16.701429  4.358759   5.54  22.54  14.420000  5.769890  \n",
       "\n",
       "[10 rows x 48 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": []
  }
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================================================
FILE: 06_Stats/Wind_Stats/wind.data
================================================

Yr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL
61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04
61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83
61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71
61  1  4 10.58  6.63 11.75  4.58  4.54  2.88  8.63  1.79  5.83  5.88  5.46 10.88
61  1  5 13.33 13.25 11.42  6.17 10.71  8.21 11.92  6.54 10.92 10.34 12.92 11.83
61  1  6 13.21  8.12  9.96  6.67  5.37  4.50 10.67  4.42  7.17  7.50  8.12 13.17
61  1  7 13.50 14.29  9.50  4.96 12.29  8.33  9.17  9.29  7.58  7.96 13.96 13.79
61  1  8 10.96  9.75  7.62  5.91  9.62  7.29 14.29  7.62  9.25 10.46 16.62 16.46
61  1  9 12.58 10.83 10.00  4.75 10.37  6.79  8.04 10.13  7.79  9.08 13.04 15.37
61  1 10 13.37 11.12 19.50  8.33  9.71  6.54 11.42  7.79  8.54  9.00  8.58 11.83
61  1 11 10.58  9.87  8.42  2.79  8.71  7.25  7.54  8.33  5.71  8.67 20.71 16.92
61  1 12 19.75 12.08 18.50 10.54 10.29  9.46 15.54 11.50 10.37 14.58 15.59 14.09
61  1 13  9.92  3.54  8.46  2.96  2.29  0.96  4.63  0.58  2.33  3.37  5.25  7.04
61  1 14  9.04  5.66  7.08  0.67  2.71  1.38  3.08  2.58  0.50  2.67  7.17  5.17
61  1 15 12.04  9.67 11.75  2.37  7.38  3.13  2.50  6.83  4.75  5.63  7.54  6.75
61  1 16 16.42 11.25 15.67  4.71 11.34  6.92  9.25  8.79  8.21  7.33 13.04  9.04
61  1 17 17.75 14.37 17.33 10.13 13.96 13.37 13.42 11.04  8.71 11.38 15.92 16.08
61  1 18 19.83 12.04 20.79 18.54   NaN 10.29 17.83 11.38 14.67 16.71  8.79 17.96
61  1 19  4.92  3.42  7.29  1.04  3.67  3.17  3.71  2.79  1.92  2.71  6.87  7.83
61  1 20  9.59 11.83  7.96  1.58  7.92  5.00  3.17  4.92  3.13  3.37  6.50  6.79
61  1 21 14.33 10.25 11.92  6.13 10.04  7.67  8.04  9.17  7.04  7.87  6.75 12.42
61  1 22  9.59  5.88  9.92  2.17  6.87  5.50  9.38  7.04  6.34  7.50 10.88  9.92
61  1 23 16.54  9.96 18.54 10.46 13.50 12.67 13.70 13.75 10.75 13.17 14.79 20.58
61  1 24 25.04 14.83 25.84 15.67 21.46 18.58 20.38 19.38 15.37 15.12 23.09 25.25
61  1 25 13.62 11.17 12.67  6.04 10.00  9.42  9.25  8.71  7.12 12.04 14.04 17.50
61  1 26 24.37 18.79 17.50 14.25 18.91 15.67 14.33 15.16 16.08 19.08 20.50 25.25
61  1 27 22.04 20.79 17.41 16.21 19.04 16.13 16.79 18.29 18.66 19.08 26.08 27.63
61  1 28 17.67 13.54 13.33  8.87 15.04 11.63 12.25 10.58 11.92 11.04 20.30 18.12
61  1 29   NaN 23.91 22.29 17.54 24.08 19.70 22.00 20.25 21.46 19.95 27.71 23.38
61  1 30 12.21 11.42 10.92  7.92 13.08  9.62 14.50 10.21  9.92 11.96 18.88 19.25
61  1 31 24.21 19.55 16.71 11.96 14.42 10.46 14.88  8.21 10.50  9.96 12.42 13.92
61  2  1 14.25 15.12  9.04  5.88 12.08  7.17 10.17  3.63  6.50  5.50  9.17  8.00
61  2  2 20.17 24.21 10.00  6.54 17.37  7.17 10.88  6.08  9.08  7.12 12.08  7.17
61  2  3 14.37 11.71 11.04  5.09  9.87  5.83  8.50  5.13  6.34  7.38  9.71  8.83
61  2  4 10.58  9.46 10.92  8.71 12.46 11.46 12.54 11.04 13.04 11.17 16.50 11.71
61  2  5 22.00 16.75 19.70 11.63 14.25 12.04 13.96 15.54 13.37 13.13 19.33 14.67
61  2  6 24.50 20.75 20.62 15.37 25.33 17.62 19.17 18.79 18.96 14.46 26.38 21.84
61  2  7 18.05 14.37 14.88  9.75 10.96 11.17 14.21  9.71 13.04 12.25 15.21 17.58
61  2  8 24.54 21.42 21.34 11.63 17.04 14.21 16.50 17.33 14.67 17.79 23.79 19.58
61  2  9 17.41 15.92 13.67 10.71 14.00 12.92 14.37 15.71 14.46 14.67 23.04 19.21
61  2 10 20.50 11.54 19.67 10.04  8.12  8.08 11.08 12.54 11.12 12.96 17.83 17.75
61  2 11 16.79 14.00 13.70  9.54 15.83 13.29 17.50 15.50 15.83 14.33 21.17 21.09
61  2 12 16.00 16.92 17.25  8.38 11.75 10.37 11.71 14.37 10.37 14.62 25.41 17.04
61  2 13 22.50 19.70 19.41 15.34 16.13 14.62 16.08 14.12 13.96 16.58 28.62 19.67
61  2 14 17.08 11.63 17.25 12.12 13.75 13.46 15.46 12.29 14.88 15.67 14.09 11.42
61  2 15  6.04 12.08  6.13  4.21  9.87  6.92  5.17  7.41  8.17  9.21 17.12 10.96
61  2 16 16.79 20.17 12.67 13.25 15.29 15.12  3.42 14.29 14.79 13.79 21.92 22.58
61  2 17 15.25 14.09 14.75  9.54 14.62 12.92  6.17 12.58 13.25 14.54 18.21 17.67
61  2 18 12.08 12.38 12.67  6.04 10.71  9.08 10.88 12.38 10.00 11.34 18.58 13.25
61  2 19 16.17 15.59 13.70   NaN 13.50 10.96  9.62 11.83 11.58 13.67 29.63 20.54
61  2 20 11.04 11.08 10.63  8.33 10.92  8.29  7.25  7.75  8.83 11.08 15.09 12.17
61  2 21  7.79  8.63  6.08  2.21  7.41  4.33  3.75  7.04  4.50  7.41 12.12  6.67
61  2 22 12.29 13.21 13.54  6.46 11.58  9.21  8.25 10.29  9.21 13.08 15.25 13.21
61  2 23 22.08 17.46 18.88 12.42 20.50 16.13 16.38 16.92 13.21 18.00 23.21 21.21
61  2 24  8.71  7.08 11.38  4.83  5.96  4.58  8.17  4.67  6.21  7.00  9.59  9.21
61  2 25 18.84 16.42 17.41 10.21 16.13 11.38 13.08 12.21 12.33 12.50 16.04 13.92
61  2 26 25.80 21.50 22.42 15.21 23.09 16.88 21.17 17.25 18.46 20.04 22.83 23.87
61  2 27 11.00 13.37 11.17  6.87 13.21  8.75 12.75 10.83 10.88 12.67 20.00 17.54
61  2 28 12.92 12.75   NaN  8.92 16.13 12.29 14.75 14.46 13.96 14.04 18.41 13.17
61  3  1 12.67 13.13 11.79  6.42  9.79  8.54 10.25 13.29   NaN 12.21 20.62   NaN
61  3  2 12.58 10.04 11.17  7.12 10.29  9.21 10.88 10.50 10.58 12.96 18.38 13.79
61  3  3 13.33 15.54 13.67  9.59 11.58  8.71 11.29 12.83  8.92 14.09 23.45 16.38
61  3  4 10.96  8.83  8.17  5.88  8.25  6.00  6.21  5.66  6.25  9.79 12.46  8.83
61  3  5 11.25 17.00 10.41 10.04 15.46 11.17  5.50 10.34 11.71 10.83 11.58 14.00
61  3  6  4.88  8.08  7.54  3.13  5.41  4.08  2.83  5.29  5.21  5.09 10.21  5.54
61  3  7  6.46 10.50  8.00  5.41 10.58  6.58  3.63  6.67  7.08  7.04 16.08  8.75
61  3  8  7.54  8.33  9.29  8.12 11.00 10.58  1.71  8.50  8.71  9.71 17.58 15.37
61  3  9 11.96 13.13 10.17  9.46 14.17 12.67  3.75  9.71 12.46 11.54 17.54 18.16
61  3 10 10.63 10.17  9.04  5.13 10.50  7.92  8.79  7.92  8.87 10.50 12.29 13.50
61  3 11 14.79 16.96 15.50  8.46 10.00 10.92 10.34 15.67 10.58 13.54 22.71 17.08
61  3 12   NaN 13.88 16.38 10.25 14.92 13.67 17.12 13.70 16.29 15.79 20.50 22.54
61  3 13 11.21 11.34 12.33  5.63 11.17 10.58 13.96 10.50 13.88 11.50 19.70 18.88
61  3 14 12.12 15.29 12.75  5.17  9.38  7.04  8.12 11.12  8.75  9.29 20.04 15.71
61  3 15 11.12 15.54 13.96  8.42 10.88  8.83 11.54 13.54  8.46 13.92 22.13 14.12
61  3 16  4.92 12.92  5.25  4.29  8.96  7.04  2.75  5.13  6.00  7.29 20.12 11.42
61  3 17 15.71 14.71 11.21  8.87 14.37 10.75 13.00 14.29 13.08 15.34 22.79 19.17
61  3 18 16.88 15.25 12.29 11.04 13.67 10.54 13.17 10.46 13.50 14.67 19.38 22.95
61  3 19 11.42  9.46 13.46  6.17  6.83  5.79  7.75  6.50  7.04  9.38 11.29 11.34
61  3 20 13.75 10.41 12.00 10.75 11.46 10.29 15.41 10.04 13.67 13.83 14.29 21.09
61  3 21  9.54 10.58 15.63  7.29  7.54  7.71  8.17  6.58  8.58 10.50  9.38 14.88
61  3 22  6.34  4.42  5.71  4.42  5.13  4.29  8.00  4.92  8.00  9.04  8.25 13.13
61  3 23  6.38  2.58  4.79  5.13  6.46  7.21 10.88  7.38  9.79  8.33 15.21 18.34
61  3 24  6.29  7.46  6.75  4.54  8.25  6.25  8.46 10.34 10.00 10.29 14.42 15.46
61  3 25  9.67 11.63 10.41  5.58 10.58  9.92 10.75 13.04 11.92 14.50 21.34 21.54
61  3 26 15.00 11.63 12.58  8.83 13.33 10.21 13.75 10.54 14.37 13.88 15.37 22.50
61  3 27  5.88  3.50  5.17  2.75  2.83  3.50  6.75  1.46  6.50  7.12  7.21 15.34
61  3 28  8.46 10.63 12.04   NaN  7.83  6.79 10.88 10.92  9.54 10.63 18.16 19.58
61  3 29 18.25 16.29 14.96 12.00 18.34 14.33 16.62 13.37 17.54 14.21 18.63 16.83
61  3 30 17.75 12.92 11.79 10.13 16.08 14.21 14.79 14.92   NaN 13.46 15.67 13.17
61  3 31  8.96  8.04  9.13  8.50 10.75  9.54 11.92  9.59 11.25  8.54 11.96 12.21
61  4  1  8.38  6.34  8.33  6.75  9.33  9.54 11.67  8.21 11.21  6.46 11.96  7.17
61  4  2  7.62  4.25  5.09  3.67  4.67  6.25  5.25  4.96  6.83  7.00 10.71 13.00
61  4  3 15.29  8.63 15.67  4.12 10.25  9.04  5.50  8.54  7.58  4.96 11.08  9.38
61  4  4 13.92 13.67 13.88  6.75  9.79  6.38  6.00  8.54  7.67  8.08 14.50 11.92
61  4  5 18.12 14.62 18.29 10.63 13.33 10.83  9.25  8.21 10.34  8.50 17.16 13.13
61  4  6  4.50  8.21  9.29  3.21  5.96  4.33  5.00  7.33  6.13  6.54 10.25 11.50
61  4  7  8.21  7.04 13.75  3.75  6.00  5.00  5.33  4.00  5.66  3.67  7.62  7.21
61  4  8 10.34 11.29 11.29  4.42  9.38  7.67  8.71 10.08  9.21  8.42 11.25 11.12
61  4  9 13.37 11.12 13.08  8.83 11.75 10.21  9.96  9.59 10.54 10.50 10.13 15.34
61  4 10  5.75  4.83  8.25  3.00  5.88  4.25  5.21  3.58  6.00  4.58  7.62 11.25
61  4 11 10.50 12.25 12.58  6.96  8.71  8.58  5.66  9.92  9.00  9.67 16.17 12.87
61  4 12 10.63 10.04 11.38  5.13  5.71  5.13  6.54  4.75  4.50  5.17  8.50  6.38
61  4 13 15.50  9.29 15.79  9.71  7.29  7.17  8.00  4.75  7.87  7.58  7.17  5.66
61  4 14 10.50  6.17  4.50  5.00  8.67  5.00  4.29  4.50  5.71  7.79  8.50  7.71
61  4 15  5.17  6.42  3.92  2.67  3.37  1.54  2.46  2.29  3.58  4.00  5.75  6.79
61  4 16  4.71  7.00  5.09  2.33  5.21  2.96  2.88  5.17  3.37  4.46 12.67 10.17
61  4 17  4.00  3.71  3.33  1.58  2.21  0.83  4.46  2.54  3.58  3.83 10.04  7.25
61  4 18 10.83 12.42  6.04  5.50 11.92  7.08  5.66  9.96  7.46  9.46 12.92 11.58
61  4 19 16.71 13.96 14.67 11.42 14.37 11.63 12.96 13.25 13.29 14.37 19.21 20.46
61  4 20 21.09 15.41 17.00 11.63 13.25 11.17 12.25 10.54 11.29 11.58 18.05 20.08
61  4 21 15.34 14.04 15.87  9.79 11.71  8.79  9.50 12.25  9.00  9.87 17.54 12.29
61  4 22  8.17  5.66  6.92  4.79  7.87  5.09  7.46  4.83  4.88  7.12  6.75  4.96
61  4 23 12.21  7.87  7.96  9.67 11.17 10.00 10.71 10.41 11.87 10.63 10.83 10.67
61  4 24  9.54  8.04  9.13  4.79  7.54  5.75  5.25  7.08  6.00  7.62  9.87  8.46
61  4 25 14.04 11.96 14.96  7.87 10.75  8.83 13.75 11.96 11.08 11.46 12.17 17.50
61  4 26 16.29 14.46  6.92  5.88 12.62  7.46  6.00  6.34  7.54  6.00  8.33 12.12
61  4 27  4.08  7.04  7.92  3.83  3.88  3.13  6.25  3.17  5.41  4.71  7.29  7.75
61  4 28  5.58  6.50  2.54  1.87  4.21  1.54  2.67  1.38  2.54  2.71  5.13  3.21
61  4 29  9.62  9.59  6.96  3.83  6.83  3.88  4.04  6.46  4.17  4.12 13.04  2.67
61  4 30 11.67 11.00  9.54  5.54  9.42  5.79  5.09  8.25  6.96  6.25 12.21  8.75
61  5  1 15.87 13.88 15.37  9.79 13.46 10.17  9.96 14.04  9.75  9.92 18.63 11.12
61  5  2 19.04 14.83 14.92  8.67 15.63 11.63 10.46  9.75 11.21 10.71 12.96 12.67
61  5  3 11.00 10.92 12.87  8.67 10.58  8.42  9.50 10.04  9.62  9.42 15.46 12.25
61  5  4  9.87 10.29  8.42  7.50  9.42  7.33  5.96  6.25  7.79  8.92  4.79  3.83
61  5  5 10.63 10.37  9.17  6.46  9.83  8.00  9.13  9.87 10.04  8.63 16.29 12.42
61  5  6 23.00 19.79 21.21 14.12 22.34 15.04 16.83 17.71 16.29 17.54 28.08 23.13
61  5  7 18.16 17.71 14.88 14.46 23.16 15.46 18.21 18.05 17.04 15.96 26.63 26.58
61  5  8 12.79 10.08 12.33 10.75 15.71 14.04 18.58 15.79 17.33 17.92 18.66 26.30
61  5  9  6.04  3.96  7.00  3.83  4.17  4.00  7.25  3.37  7.08  9.21  6.54 11.38
61  5 10  4.79  6.21  4.63  1.04  3.42  1.17  3.33  3.79  2.46  1.83  7.58  3.88
61  5 11  3.54  7.54  5.33  2.08  5.25  1.58  3.67  3.71  2.62  3.04  9.25  5.96
61  5 12  7.00 15.12  9.17  4.54 11.21  5.91   NaN  4.04  5.25  4.38 10.63  9.50
61  5 13 11.00 11.54 11.71  8.83 12.83  9.92  5.21  8.71  7.92  8.63  9.17 12.33
61  5 14  9.25  6.54  7.04  6.00 11.46  8.12 10.04  9.96 10.04  9.75 11.12  3.33
61  5 15  9.62  3.92  9.75  3.88  5.63  3.46  5.91  5.88  6.38  6.25  6.63  8.42
61  5 16 15.04 10.17 15.25  5.17 12.92  6.79  8.38 11.12  9.46  9.17 12.00 13.70
61  5 17 10.00  6.54 15.63  4.88  8.21  6.75  5.83  7.38  8.00  7.96  9.46  8.21
61  5 18  4.88  3.58 15.96  3.71  6.34  3.83  5.04  3.79  5.00  4.17  7.29  5.91
61  5 19  5.00  5.29  5.91  3.63  7.25  4.17  6.83  6.79  7.17  9.29 10.83 14.96
61  5 20  6.21   NaN 10.13  4.83  7.33  5.66  4.71  7.58  7.00  8.67 10.41 12.75
61  5 21  6.83  4.29 11.67  3.71  5.50  4.50  3.17  6.25  4.92  4.29 10.75 10.34
61  5 22  6.25  3.67  5.33  2.50  4.17  3.79  2.42  4.21  4.63  5.37  8.21 10.92
61  5 23  4.96  3.96  3.58  2.42  3.75  2.25  3.13  2.83  4.50  5.54  6.13  8.67
61  5 24  5.54  5.96  3.71  3.37  3.92  1.63  4.00  3.37  4.92  6.50  8.83 12.29
61  5 25  7.67 11.17 13.13  5.50  9.13  6.04  6.79  7.96  7.33 10.46 13.88 17.04
61  5 26 11.79 12.50 20.96   NaN 10.21  6.50  9.33 12.08  9.59 11.46 14.33 16.58
61  5 27 10.92  6.79  6.46  5.41 10.00  5.25  4.92  7.04  7.29  8.25 10.46  8.00
61  5 28  9.96  7.67  6.42  3.29  7.41  3.33  5.41  6.42  4.50  5.17 11.79  8.38
61  5 29 12.04  6.67 15.96  9.87  8.04  8.46 11.71  9.21 11.17 12.04 11.54 17.96
61  5 30 10.00  4.75  9.21  3.42  7.67  5.25  5.83  7.21  6.34  5.91  8.71 12.92
61  5 31  7.00  9.79 12.25  4.83  8.25  5.37  6.58  9.29  6.58  7.12 11.87 10.63
61  6  1 15.92  9.59 12.04  8.79 11.54  6.04  9.75  8.29  9.33 10.34 10.67 12.12
61  6  2 11.29  6.34  6.92  6.71 12.12  7.79  7.92  8.71  9.83  9.17 12.42 15.71
61  6  3  7.50  8.29  6.83  5.88 12.87  7.08 10.41  7.87 10.46 10.34 13.88 14.42
61  6  4  8.50  6.42  7.25  4.67  7.79  5.17  6.21  9.87  7.29  8.33 17.00 11.42
61  6  5 11.58 11.54  7.50  5.66  9.96  6.04  3.42  9.17  8.08 10.92 16.17 12.29
61  6  6  8.33  8.54  6.13  3.21  8.58  5.17  4.21  7.96  6.92  6.71 11.42  8.75
61  6  7 11.92  9.42  8.96  6.34 13.13  8.63  8.17  9.71  9.59  9.87 13.25 16.38
61  6  8 12.17  8.87  8.17  6.92 11.92  7.46  9.54  9.59 10.58 10.37 11.21 19.83
61  6  9  9.71  7.79  8.29  4.29  7.75  4.38  8.29  4.38  8.08  8.83  7.79 14.21
61  6 10 14.42  9.96 11.58  6.79 11.54  4.67  9.71  4.25  7.58  6.87  5.83  8.33
61  6 11  8.29  6.83  6.04  3.29  7.33  1.87  6.25  3.13  5.41  5.21  5.37  5.96
61  6 12  9.25  7.08 13.46  5.17  5.66  3.63  9.17  2.88  6.50  6.83  7.29 11.34
61  6 13  6.13  4.12  6.50  2.92  3.50  1.58  4.67  3.63  4.21  3.08  7.71  6.13
61  6 14  8.33 12.42  8.71  6.79 11.87  7.12  5.66  8.63  8.08 10.25 20.21 11.83
61  6 15  9.96  8.17 10.54  5.88 10.25  6.92  8.25  9.29  9.50  9.25 14.58 13.96
61  6 16 13.88 14.54 13.75  8.29 13.29 10.00 10.54 16.54 11.54 14.71 25.25 20.88
61  6 17 14.33 11.34 15.50  9.42 12.33  9.04 12.96  9.50 11.00 10.50 14.67 16.33
61  6 18 12.12  9.29  7.67  7.17 14.92 10.34 13.70 13.25 12.12 12.54 17.75 24.71
61  6 19  8.92  6.38  7.67  6.42 11.34  7.75 10.37  9.38 10.29 10.17 12.92 16.88
61  6 20  4.00  3.25  5.13  2.88  8.58  4.46  7.92  6.34  6.50  7.83 11.00 14.25
61  6 21  8.54  9.50 10.37  6.63 11.50  7.33 10.54  9.83  9.54 10.17 14.71 13.75
61  6 22  8.83  6.25  6.38  6.42 11.96  8.58 13.29  9.29 11.54 11.00 13.25 18.91
61  6 23  5.71  4.21  7.79  5.29 11.58  7.33 14.21  7.38 11.46 11.63 14.96 21.50
61  6 24  6.42  4.92  9.33  4.75 12.17  8.33 14.88 11.87 12.92 15.00 19.08 20.75
61  6 25  9.00  8.25  9.92  5.96 13.21  8.67 11.87 12.04 10.96 13.00 14.67 15.83
61  6 26 13.13 11.42  7.62  7.08 12.42  5.96  8.38  7.75  9.67 10.04 10.83 11.63
61  6 27  9.00  6.34  8.00  5.71  8.04  6.96 10.54  8.96 10.41 10.37 13.29 16.79
61  6 28  7.75  9.71  9.21  5.83 11.12  6.25  8.79 12.83  7.38 11.58 19.92 16.38
61  6 29   NaN 10.46  7.96  6.79 12.62  7.08  8.33  9.46  7.08 10.92 20.88 10.79
61  6 30 12.29 14.37 10.79 10.54 13.83  9.59  4.92 13.70  8.75 12.38 21.87   NaN
61  7  1  7.21  6.83  7.71  4.42  8.46  4.79  6.71  6.00  5.79  7.96  6.96  8.71
61  7  2 12.04  7.25  9.25  7.04 10.34  4.92  5.83  5.96  6.25  8.63  8.00  8.50
61  7  3 15.34 10.58 12.17 10.08 18.58 11.38 15.75 14.33 15.46 16.79 20.41 21.29
61  7  4 17.50 10.75 14.92 12.00 12.62  9.62 14.92 11.08 13.13 13.50 12.46 19.67
61  7  5 11.50  4.96  7.62  5.83  8.92  6.17 10.08  5.58  8.08  8.75  7.08 12.42
61  7  6  8.00  3.75  7.62  3.54  8.00  4.58  7.08  6.87  6.25  6.21 10.21 12.08
61  7  7 11.17  7.21  7.41  8.12 14.29  9.75   NaN  9.96 13.04 11.54 15.34 17.41
61  7  8 11.21  7.58  7.83  7.87 14.42  8.38 14.04 10.96 11.63  9.33 11.83 16.04
61  7  9  7.29  5.71  7.67  5.37 10.63  6.00  9.46  6.38  7.54  7.71  8.21 13.00
61  7 10  8.63  7.87  9.33  5.58  9.46  3.46  2.67  2.96  2.92  4.25  5.04  5.96
61  7 11 11.08 11.42 10.37  6.87  9.46  5.63  7.41  8.17  6.50  8.38 10.21 13.29
61  7 12 19.46 16.88 12.29 10.08 15.04  8.83  9.08 11.75 10.50 10.71 17.21  9.92
61  7 13 16.75 13.79 12.92 12.33 19.95 13.17 16.83 16.17 15.37 14.92 20.25 21.96
61  7 14 22.50 19.29 14.29 12.42 20.88 10.17 11.83 12.33 10.63 10.88 15.63 14.00
61  7 15 16.92 14.50  8.00  7.62 12.21  7.92 10.25 10.96  8.92 10.17 14.09  9.92
61  7 16 21.42 10.88 11.00 10.04 14.67  9.71 13.13 11.21 11.87 11.79 12.21 12.62
61  7 17  4.25  3.08  5.46  1.46  5.04  1.96  7.08  5.21  5.66  7.62  6.17  9.13
61  7 18  4.38  6.79  5.29  4.63  7.33  3.88  4.96  7.79  6.50  7.75  9.79 10.83
61  7 19  5.17  3.42  6.63  2.96  6.75  2.92  3.50  7.29  5.58  7.00 10.13 10.92
61  7 20  5.88  2.92  4.08  2.42  6.21  2.71  3.04  5.46  5.63  5.37  7.29  8.33
61  7 21  3.33  4.58  6.96  4.67  5.75  2.79  2.21  5.13  4.25  5.09  7.08  8.25
61  7 22  3.37  5.29  6.08  3.67  5.54  2.58  4.75  5.25  5.46  5.54  8.00  6.04
61  7 23  3.04  3.71 12.67  3.29  6.17  2.17  2.21  3.37  4.00  3.92  4.21  5.41
61  7 24  7.00  4.63  6.42  2.83  4.96  2.92  3.58  4.17  2.21  4.92  9.25  6.13
61  7 25 15.92 13.79 15.67  8.63 14.33  9.33 12.29 14.09 11.63 15.37 22.17 19.50
61  7 26 11.46  7.50 10.46  9.92 17.50 13.50 18.79 15.16 15.59 15.75 19.38 25.37
61  7 27 10.13  8.83  6.79  3.75  9.50  4.54  7.87  6.46  6.83  6.63  7.87 13.00
61  7 28 16.08  9.13 10.29  7.33 10.54  7.33  9.67  8.67  9.33  8.87  9.38 13.75
61  7 29  7.21  4.83  6.67  3.42  5.96  3.67  5.79  6.67  5.09  6.54 11.17  9.25
61  7 30  6.13 10.41  9.17  4.21  7.75  4.58  6.04  9.17  5.58  8.21 10.67  9.33
61  7 31  7.67  6.71  9.38  4.17  5.25  2.79  3.75  2.25  3.17  5.54  6.17  9.67
61  8  1  9.59  5.09  5.54  4.63  8.29  5.25  4.21  5.25  5.37  5.41  8.38  9.08
61  8  2 10.00  5.58  6.71  6.00  8.96  6.67  8.87  7.12  7.79  8.75  8.71 11.87
61  8  3 16.08 15.79 15.59  8.96 16.92 10.21 11.92 13.04 11.12 12.83 18.54 17.54
61  8  4 11.00  9.21 10.58  7.54 13.17  9.62 13.54 11.79 11.67 10.21 16.04 20.25
61  8  5 13.37 12.46 12.79  6.54 15.50  9.00 10.54 11.29 10.50 11.92 15.41 17.12
61  8  6  8.38  6.71  8.42  5.83 10.04  6.75  7.87  7.12  6.46  8.00  7.92 10.79
61  8  7  2.88  4.42  6.34  3.04  6.58  3.96  3.88  5.04  3.17  5.71  9.83  5.88
61  8  8 14.21  9.87 11.63  6.42 12.38  6.96 10.63  7.12  7.38  7.79 10.08  6.83
61  8  9 12.96 10.00 11.29  6.46 14.58  8.96 11.25 10.37  8.87  9.33 12.50 10.46
61  8 10  6.63  6.71  6.00  2.50  7.58  3.58  4.67  8.33  2.83  5.41  9.96 10.96
61  8 11   NaN  6.75  8.29  5.00  8.58  4.83  6.83  6.25  5.79  5.25  5.88  8.38
61  8 12 10.71  8.71  8.92  4.38  8.29  5.83  6.83  8.25  5.88   NaN 14.29 13.21
61  8 13 12.96  9.13  8.63  7.75 12.75  8.00 10.92 10.79 10.08  9.92 14.00 15.16
61  8 14 16.42 10.88  9.87  8.79 16.21 11.46 15.09 13.37 12.87 12.92 15.37   NaN
61  8 15 15.34  8.75 10.54  6.83 12.96  7.54  9.42  8.92  8.79  9.13  9.59 13.04
61  8 16 13.13  7.25  9.17  8.21 13.29  9.04 12.96 10.54 10.54 12.00 11.00 16.08
61  8 17 13.62  8.17  9.71  7.00 14.25  8.83 10.21 10.50 10.79 11.25 15.50 14.62
61  8 18 18.91 12.87 11.46 11.79 20.38 14.25 19.04 17.71 16.13 17.83 21.92 24.30
61  8 19 18.08 12.42 12.67 10.58 15.87 11.54 13.21 11.04 12.46 11.79 13.79 15.59
61  8 20 13.75 13.62 14.04  7.00 13.88 10.25 12.58  9.92 10.88 10.79 17.29 16.13
61  8 21 18.16 14.58 14.62 11.67 21.04 15.34 19.00 17.79 17.50 15.46 20.21 22.29
61  8 22 13.33  8.58  9.50  7.67 13.83  9.50 12.08  9.83 10.75  9.96 12.87 15.37
61  8 23  9.62  8.92 10.46  3.96  9.46  6.79  8.17  7.96  7.08  8.00 10.04 12.62
61  8 24  7.67  6.87  9.67  5.46  7.46  4.25  5.66  4.50  3.96  7.46  5.75  8.29
61  8 25 14.33 13.59 17.83  8.21 12.17  9.67 10.92 14.00  9.59 15.21 19.55 16.17
61  8 26 12.38 13.88 14.09  6.71 15.50 10.29  9.79 14.50 10.13 15.50 21.96 20.04
61  8 27 11.25 13.59 13.21  6.08 13.29 10.13 12.12 16.83  9.71 15.96 23.38 20.62
61  8 28 13.37 22.00 12.75 12.42 17.62 13.08  9.08 14.58 13.50 12.92 20.75 16.79
61  8 29 14.46 16.04 12.75  9.17 19.21 12.12 14.12 16.54 12.00 16.13 24.71 22.54
61  8 30  3.63  4.67  4.88  2.37  5.63  3.17  4.58  7.58  4.58  6.96 14.33 10.34
61  8 31  4.83  7.04  2.46  3.25  8.50  4.83  2.92  6.42  4.29  6.54 17.16  8.00
61  9  1  5.58  1.13  4.96  3.04  4.25  2.25  4.63  2.71  3.67  6.00  4.79  5.41
61  9  2  7.25  3.58  2.42  3.50  6.96  3.13  3.83  6.58  3.92  5.96  7.12  6.96
61  9  3 11.63  7.29  7.00  5.75  5.58  5.37  5.88  5.88  5.25  7.96  6.79  7.12
61  9  4  6.79  3.04  4.79  3.17  4.75  2.17  1.25  2.21  2.96  1.75  5.41  3.37
61  9  5 12.67 10.00  6.38  5.83 10.41  5.83  9.38  8.54  9.04  9.96 13.62 15.04
61  9  6 17.62 12.54 11.58 10.17 14.54  9.13 14.09 10.79 12.71 11.71 15.83 20.25
61  9  7  9.21  3.92  7.58  5.83  7.54  5.96  7.87  4.88  7.04  7.41  5.66 10.83
61  9  8  5.75  9.71  3.63  2.75  7.67  2.67  3.17  4.42  2.50  4.21  8.25  5.79
61  9  9 16.75 13.59 14.88 10.50 11.42  9.83  6.92  9.50  9.71 10.96 13.92 15.00
61  9 10  5.00  8.79  8.46  4.17  8.29  4.21  4.83  6.38  4.88  6.96 12.21  6.96
61  9 11  9.87  8.42  7.29  5.46 12.12  7.92  8.63  8.75  8.75  9.67 13.92 14.67
61  9 12 20.75 18.66 16.66 10.75 15.75 10.96 11.08 14.50 10.34 12.92 23.91 16.71
61  9 13 16.71 13.96 15.21  9.67 14.62 10.25 11.75 13.42  9.71 13.00 21.37 15.71
61  9 14 18.00 16.88 15.54  9.71 15.21 11.12 12.75 13.79 12.21 13.54 23.91 18.25
61  9 15 20.71 13.96 20.00 12.46 13.70 11.08 13.00 12.92 11.58 14.83 18.63 17.71
61  9 16 28.75 22.08 26.50 21.09 28.50 20.67 20.79 21.37 21.34 25.21 23.45 33.09
61  9 17  5.33  7.12  8.12  3.96  8.29  5.58  7.87  7.17  5.88 10.71 13.96 16.75
61  9 18  9.75  4.21   NaN   NaN  5.79  4.46  4.17   NaN  4.33  6.21  7.21  9.21
61  9 19  8.50  8.25 12.17  7.17 11.54  7.54  8.17  7.96  7.33 10.71  9.08 11.04
61  9 20  7.08  2.92  5.33  2.62  3.88  1.63  4.79  1.46  2.83  2.83  4.54  5.25
61  9 21  6.92  7.75  7.79  4.12  7.75  4.25  6.46  7.50  4.38  6.63 13.17  7.12
61  9 22  9.00 13.62  6.46  5.41 12.92  7.75  4.12 11.63  6.08  7.67 20.96 14.62
61  9 23 10.25  7.29  8.17  5.83  8.12  6.46  6.08  7.33  6.08  7.50 10.46 12.04
61  9 24  8.00  6.04  8.04  3.96  7.50  3.21  5.91  5.41  4.21  6.04 11.71  9.42
61  9 25 10.34 12.92  8.87  5.79 11.58  8.00  8.92 12.50  8.04 13.00 20.17 16.21
61  9 26 18.46 13.13 15.87 10.17 13.46 10.37 12.46 11.38 10.04 13.50 17.75 15.83
61  9 27 11.34  6.96  9.38  6.00 11.25  6.34  8.08  6.25  7.00 10.13 14.29 14.29
61  9 28 15.59  8.67 14.71  7.87 11.29  7.75  9.13  6.21  8.71  9.50  7.79 10.13
61  9 29 19.12 13.62 17.16  9.59 14.67  9.13  9.21 12.38  9.25 14.00 20.30 15.71
61  9 30 23.21   NaN 17.58 11.17 20.75 12.00 10.96 15.34 12.08 17.46 21.37 17.16
61 10  1 14.25 12.87  7.87  8.00 13.00  7.75  5.83  9.00  7.08  5.29 11.79  4.04
61 10  2 14.09  8.67 13.54  8.54  9.62  9.42  8.71  9.00 10.58 12.12  7.58 19.62
61 10  3  4.29  4.00  4.96  2.13  3.75  1.79  4.04  2.75  2.42  3.88  4.96  4.83
61 10  4  3.13  4.42  6.13  1.46  3.79  1.04  3.50  1.87  1.42  2.42  4.92  6.50
61 10  5  9.75  3.63  8.71  2.83  5.91  3.04  5.63  3.96  4.00  6.17  4.79  8.33
61 10  6 16.04 16.96 17.83  9.79  9.75  9.71 12.96  9.50  8.67 11.96  9.96 14.42
61 10  7 16.08 11.75  9.71  5.46 11.38  5.41  6.87  6.96  3.25  4.88 11.63  9.92
61 10  8 15.37 11.87 10.29  6.63 13.59  8.46 10.17  9.50  9.21 12.21 17.83 16.25
61 10  9 17.12 12.71 20.96  9.38 10.58  8.25  9.04  9.17  7.46 10.21  8.54 12.83
61 10 10 14.46 13.25 11.46  9.42 13.17 11.04 11.75 12.38 11.12 15.21 20.46 19.41
61 10 11 11.96 10.13 10.13  7.79 13.54  9.13 12.71  8.79 10.00 12.00 18.91 21.04
61 10 12   NaN  8.71  4.67  3.67  9.08  6.54  5.58  6.38  4.00  5.37 12.46 10.75
61 10 13 10.25 10.34  7.92  6.58 11.29  8.67  9.92  5.96  6.46  7.38 12.50 11.96
61 10 14  3.71  6.13  4.38  4.63  7.29  4.92  2.96  4.88  3.13  5.71 13.00 12.87
61 10 15  7.79  6.87  4.21  4.58  9.46  7.21  9.00  7.75  8.42 10.58 15.29 17.96
61 10 16 12.58 12.67  9.92  9.21 15.63  9.42 14.58 12.42 12.58 12.92 19.58 24.62
61 10 17 28.62 19.46 19.67 19.38 22.46 16.42 22.17 20.17 18.00 23.13 24.71 32.63
61 10 18 28.33 18.29 22.63 17.83 18.08 11.96 21.84 17.33 16.17 26.38 24.21 33.45
61 10 19 17.12 12.67 16.50  9.59 10.25  8.29 13.21 10.63 12.00 13.13 13.96 23.00
61 10 20 12.67  3.75 10.37  9.42  7.75  7.21 12.08  6.87  9.83 13.00 11.71 21.67
61 10 21 10.46  7.83 10.71  6.83  9.54  6.83  7.50  6.08  7.38 11.08 10.88 13.46
61 10 22 25.04 18.88 21.00 16.08 22.25 14.92 17.58 14.42 15.87 15.59 17.29 16.66
61 10 23 19.25 15.41 15.59 10.79 17.67 12.17 16.21 13.25 14.12 17.75 22.29 25.00
61 10 24 26.42 21.25 23.09 16.54 24.41 15.46 17.96 18.46   NaN 22.13 27.29 30.88
61 10 25 21.34 17.00 22.88 13.62 18.21 13.21 18.34 12.12 15.37 17.46 17.12 25.12
61 10 26 16.29 11.29 17.41  6.58 12.21  8.92  9.79 10.13  8.54 12.12 13.08 12.75
61 10 27 18.41 12.08 13.00  7.41 12.00  7.79  7.79  8.83  8.54  8.71 12.00 11.96
61 10 28  6.50  3.29  4.79  2.21  4.58  2.62  9.75  3.71  5.79  6.67  8.50 13.29
61 10 29  4.33  2.75  5.50  1.71  4.46  2.33  9.42  2.62  4.25  5.83  8.87  9.83
61 10 30  9.42  8.71 10.50  6.38 11.63  8.71 14.88 10.58 11.08 11.92 17.25 20.62
61 10 31 13.62 10.75 13.00  8.33 11.50  9.71 15.25 11.96 11.54 11.92 17.75 14.92
61 11  1 13.21 13.13 14.33  8.54 12.17 10.21 13.08 12.17 10.92 13.54 20.17 20.04
61 11  2 15.79 13.46 10.13 11.79 17.25 12.21 15.83 14.00 15.09 15.79 19.79 23.58
61 11  3  9.75  4.83 10.25  6.63  7.29  6.92 11.21  5.75  7.83  8.92 10.96 23.13
61 11  4  5.88  3.96  6.54  3.79  6.00  4.33  8.50  4.21  5.71  7.67 10.29 13.37
61 11  5 13.33 12.29 11.04  7.33 10.04  8.58 10.79 11.12  9.04 13.29 18.05 18.71
61 11  6  7.96  5.83  4.83  3.04  7.92  4.71  8.21  6.63  6.08  8.12 12.58 13.50
61 11  7 11.83  9.04 12.58  5.13 10.00  6.42  9.29  6.58  7.38 10.88 11.12 13.46
61 11  8  5.46  5.50  5.29  2.71  7.33  4.33  6.58  2.71  5.33  5.50  7.67  9.71
61 11  9  7.71  6.21  3.83  1.87  6.21  3.33  8.21  1.42  3.37  3.71  6.34  6.63
61 11 10  9.13  6.63  6.92  2.50  3.37  2.04  7.71  2.00  1.33  2.62  6.13  5.71
61 11 11 16.08 10.75 20.41  8.79  8.42  5.17 12.12  5.00  5.71  5.96  9.62 11.46
61 11 12 14.83  9.87 18.05  8.58  8.12  3.54 13.04  5.46  7.12  8.12 11.00 15.54
61 11 13 15.00 13.00 32.71 13.33  9.59 10.13 21.87 10.04 10.37 14.04  9.25 13.25
61 11 14 10.67 10.67 26.63  9.71  8.08  8.42 15.71  4.92  7.50  9.13  5.50  2.75
61 11 15  8.58  5.50 15.71  5.17  8.17  3.29  6.00  2.83  4.38  4.21  3.83  3.58
61 11 16  7.50  4.21  9.00  2.42  5.04  1.83  2.29  1.04  0.75  2.00  0.71  7.08
61 11 17  8.71  5.33  6.46  1.50  5.66  1.25  3.13  0.63  0.92  1.25  2.71  2.00
61 11 18 13.67  9.33 14.29  5.41 10.25  7.62  9.29  7.75  4.71  5.71  9.59  5.37
61 11 19 11.46  7.62 14.12  4.25 10.13  5.91  9.33  5.17  2.88  5.88  9.54  6.13
61 11 20 10.83  3.29 14.46  4.50  7.96  4.71 13.04  7.67  6.50  7.87  7.71  8.71
61 11 21 14.29  4.96 10.46  4.21  6.00  4.54  7.00  4.96  4.88  8.33  6.50  8.71
61 11 22  6.46  4.33  8.96  4.83  7.62  5.50  9.17  3.42  4.67  7.38  7.08 10.34
61 11 23  9.00  9.83  6.34  6.25 10.13  7.41 15.41  9.08  9.33 12.42 20.46 22.58
61 11 24 11.50 11.79 10.96  7.96 12.75  9.83 16.17 10.00 10.04 14.12 17.37 18.58
61 11 25  5.75  7.12  5.04  5.00  9.04  6.25 11.83  6.38  6.79 10.04 12.71 15.96
61 11 26  6.25 10.04  4.42  2.04  8.83  6.29   NaN  6.96  3.71  6.92 10.58  4.25
61 11 27  7.92  4.67 10.58  4.71  4.17  2.29  5.83  1.92  2.25  3.63  5.50  5.88
61 11 28  8.92 10.21  8.25  5.21 10.96  8.63 12.79  9.46  8.83 11.17 18.96 19.46
61 11 29 15.67 15.67 13.00  9.71 15.29 10.79 15.29 11.50 11.25 13.00 19.04 21.29
61 11 30 23.75 18.71 19.92 14.46 19.92 11.04 16.50  8.63 12.58 10.29 12.54 13.62
61 12  1  9.67  7.75  8.00  3.96  6.00  2.75  7.25  2.50  5.58  5.58  7.79 11.17
61 12  2  8.58  5.96  8.83  4.17  8.33  5.79 12.21  3.46  5.66  7.71  8.17 17.29
61 12  3 13.75 10.13 11.79  3.29  7.83  6.79  7.67  6.25  5.46  4.00  8.25 14.37
61 12  4 22.83 17.00 19.79 10.37 13.54 12.75 13.79 15.29  9.83 14.50 21.79 19.21
61 12  5 17.67 14.09 14.92 12.92 13.33 11.83 17.83 11.92 15.12 15.41 19.21 29.33
61 12  6 11.92  9.21 10.92  6.50  7.79  5.50 12.62  3.13  8.29  7.87 10.71 13.67
61 12  7 17.96 15.83 12.17  4.50 13.59  8.04  8.87  8.04  5.09  5.75 12.21  7.58
61 12  8 16.62 12.08 20.54  9.21 12.96 14.54 19.83 10.00 11.92 15.50 16.33 19.41
61 12  9 10.83 10.96  9.71  3.83  9.21  7.12 10.04  6.96  7.08  9.67 11.08  9.08
61 12 10 23.71 21.37 20.17 13.04 18.08 13.17 18.84 15.46 12.46 15.46 20.50 15.41
61 12 11 21.34 16.38 19.00 13.62 20.00 14.83 25.62 14.50 17.25 17.41 22.83 18.34
61 12 12 15.46 14.88 14.67  6.63 15.46 12.96 16.83 13.46 12.38 13.54 18.34 16.13
61 12 13 16.25 11.96 16.04  9.83 14.62 11.34 16.66 11.46 11.79 13.59 16.79 18.16
61 12 14 11.34 17.62  9.71  6.92 14.92 10.63  9.38  9.08  7.75  7.04 14.33 15.09
61 12 15 18.05 15.71 16.66 12.92 16.75 16.42 10.71 12.50 13.59 13.62 20.62 21.12
61 12 16  9.29  8.63  9.04  6.50  9.50  7.04  6.92  5.46  7.12  7.54  7.38  6.50
61 12 17 13.17  9.13 10.92  4.46  8.83  8.67  9.54  6.54  4.71  4.67  5.50  7.17
61 12 18 12.17 11.63  8.08  1.79  8.71  6.63  4.79  3.88  3.79  3.88  5.83  5.91
61 12 19 12.38 10.88  8.96  0.58  7.33  2.83  3.54  5.04  1.71  5.75  6.67  5.66
61 12 20 11.46  9.79  8.63  2.54  7.50  3.37  3.63  3.46  1.25  5.41  5.29  3.92
61 12 21 13.13 10.00  8.83  3.21  9.96  3.96  4.33  3.71  2.75  2.46  6.50  2.62
61 12 22 16.21 11.87 15.00  7.62 14.12  9.75 13.59 10.04  8.25  9.17 12.83 12.38
61 12 23 24.41 16.13 22.13 15.59 20.38 13.59 18.12 14.33 12.54 15.34 17.67 16.62
61 12 24 20.54 13.50 18.25  9.17 15.50  9.46  8.50  8.63  8.33 11.87 14.92 10.04
61 12 25 19.58 10.29 18.34  6.50 11.34  8.12 11.92 11.92 10.37  9.54 13.21 13.62
61 12 26 20.54 16.04 20.91  8.33 11.21  7.08 17.12 12.75  9.04 10.83 13.21 11.34
61 12 27  9.25  5.21  9.04  4.08  5.91  1.83  2.92  1.87  1.83  1.96  3.75  6.63
61 12 28  5.04  3.08  2.13  0.42  3.67  2.42  8.96  4.17  3.33  7.41  9.96 15.21
61 12 29 14.33 13.83 23.21 11.25  9.83  7.25 15.00  8.42  8.25 10.79 10.54 14.62
61 12 30 16.83 10.25 29.33 11.79  9.17  7.41 17.29  5.75  9.38  9.59  9.17 13.79
61 12 31  9.87  7.83  7.67  3.75  5.66  3.50 10.04  3.08  5.04  3.79  8.04 14.67
62  1  1  9.29  3.42 11.54  3.50  2.21  1.96 10.41  2.79  3.54  5.17  4.38  7.92
62  1  2  6.08  3.13  5.09  0.87  0.42  0.33  8.46  0.00  0.54  4.54  1.96  7.71
62  1  3  7.75  4.46  6.04  3.17  3.58  0.42  4.58  0.00  3.08  0.92  0.58  6.34
62  1  4  3.17  4.92  4.38  1.04  3.50  1.79  5.50  3.75  1.63  3.17 12.38 11.71
62  1  5 11.67 13.04 11.38  4.79 11.42  8.96 15.29 10.75  9.13 11.87 17.04 14.67
62  1  6 15.04 12.29 12.21  4.71  9.00  6.13  8.46  6.92  5.46  5.91 12.62 10.92
62  1  7 12.62 11.04 11.34  3.54 11.75  8.04 14.29  8.42  8.63 13.75 18.88 16.71
62  1  8 21.09 16.62 20.08 12.92 17.41 11.71 17.71 13.50 12.17 16.38 26.12 19.50
62  1  9 12.29 12.04 10.63  6.50 11.54  8.96 15.29  8.87 10.29 14.50 18.63 21.75
62  1 10 21.62 20.88 16.46 10.04 15.71 10.96 14.29 12.17  9.00 12.33 16.75 16.79
62  1 11 29.71 25.46 20.67 17.75 23.09 17.00 25.96 16.21 17.50 17.62 21.92 20.04
62  1 12 20.91 20.08 16.79 16.62 22.29 14.83 23.54 15.04 19.79 16.83 21.04 18.66
62  1 13 15.71 14.83  9.92  8.87 13.54  9.67 19.70 10.63 13.21 12.96 19.46 23.33
62  1 14 10.34  9.42  8.00  5.00  8.58  6.25 12.42  6.79  7.17  8.25 11.25 12.50
62  1 15 31.13 22.34 26.25 18.25 20.54 17.25 20.38 16.83 16.96 21.67 25.88 23.13
62  1 16 27.79 26.54 19.83 12.54 20.46 16.25 19.75 16.71 14.29 20.08 29.42 26.50
62  1 17 20.54 14.79 19.46 12.83 18.41 13.88 22.79 12.75 15.63 19.38 18.66 22.79
62  1 18 13.33  9.96 10.63  4.12  8.75  8.00 12.46  7.67  7.41 10.00 13.67 13.25
62  1 19 10.46  9.54  8.46  5.00 10.83  9.42 16.08 10.04 10.54 13.00 19.00 19.92
62  1 20 11.46 10.17  9.75  3.00  9.46  6.75 10.29  8.04  6.67 12.04 13.92 14.42
62  1 21 19.29 18.79 17.41 11.63 15.67 12.12 17.29 15.34 14.09 17.92 26.00 21.92
62  1 22 20.54 21.42 14.33 14.29 22.25 12.92 23.25 16.92 16.88 15.67 26.67 27.00
62  1 23 14.58 15.75 12.50  5.04 10.83  8.38 12.71  6.87  6.96  8.63 12.54 11.83
62  1 24 24.13 19.55 18.66 12.62 16.17 12.96 17.25 15.29 12.96 13.75 19.75 12.46
62  1 25 12.92 13.29 12.96  7.75 13.04 10.92 18.29  9.59 13.04 11.54 16.38 10.58
62  1 26  5.96  4.08  4.92  2.13  4.54  3.08  7.41  1.92  4.04  4.08  4.29  7.08
62  1 27  4.46  7.96  8.12  1.83  5.46  2.71  5.17  3.83  5.33  4.25  5.13  3.25
62  1 28  0.67  4.50  2.96  0.87  1.38  1.21  3.63  1.54  1.71  1.42  8.67  6.79
62  1 29  7.67  7.58  7.96  5.13  5.88  5.83  2.17  6.17  6.38  5.71 16.96 13.62
62  1 30 14.12 12.83 12.87  5.25 10.96  9.38 13.33 15.25  9.87 15.54 24.37 20.17
62  1 31 21.96 17.25 18.75 12.08 16.50 12.25 22.42 17.00 15.87 17.58 23.13 22.25
62  2  1 19.12 13.96 12.21 10.58 15.71 10.63 15.71 11.08 13.17 12.62 17.67 22.71
62  2  2  7.67 10.29  8.83  5.09 10.00  7.17 13.17  6.29  8.58  8.46 11.71 11.96
62  2  3 14.92 11.54 11.67  6.71 13.50 10.04 15.96 11.46 12.42 12.12 18.58 20.71
62  2  4 20.30 16.96 17.83 11.08 19.46 13.25 18.88 11.29 15.34 13.83 17.08 17.21
62  2  5 13.83 12.75 12.71  7.62 12.12 10.13 15.75 11.83 12.17 12.38 19.29 19.95
62  2  6 21.84 18.29 20.25 12.12 18.79 12.29 18.75 18.41 14.21 17.75 28.12 23.16
62  2  7 30.13 25.17 21.17 16.62 22.58 16.29 25.00 20.00 20.71 20.67 27.04 25.29
62  2  8 10.92  7.25 11.42  5.79  7.75  5.58  8.21  5.58  6.83  7.54 10.13 11.21
62  2  9  9.21  7.38  7.96  4.04 10.50  6.46 12.08  7.71  8.17  8.54 14.29 14.46
62  2 10 14.42 11.21 10.08  7.12 13.08  8.21 14.96 10.17 11.54 12.38 16.62 20.88
62  2 11 18.38 14.42 15.59 12.79 21.00 14.67 24.41 17.33 19.04 18.38 28.04 29.08
62  2 12 29.17 22.63 21.34 22.88 30.00 20.58 29.54 21.92 25.29 22.37 25.84 28.91
62  2 13 26.30 23.38 17.29 12.62 17.21 10.08 15.79 11.87 10.29 12.92 19.50 21.71
62  2 14 12.96  7.50 12.96  6.96  8.50  5.54 11.71  3.33  7.46  7.62 10.25 13.62
62  2 15 13.21 10.67 12.08 10.37 18.00 13.67 23.45 17.83 16.50 16.42 21.92 25.75
62  2 16 26.04 16.83 15.46 18.16 23.42 19.29 28.84 20.75 24.79 25.00 25.33 33.50
62  2 17 12.87  4.75  9.00  5.13 10.04  6.42 14.12  8.21  8.96  8.67 10.41 14.25
62  2 18  5.79  5.83  6.46  3.50  6.63  5.63  8.29  5.63  5.79  7.96 13.08 12.29
62  2 19 10.54  9.50  8.79  3.21  7.96  4.17  7.92  5.58  4.83  6.17 14.17  8.71
62  2 20  5.66  6.67  4.29  1.46  7.21  3.29  1.46  2.04  1.63  2.08  6.25  3.04
62  2 21  7.41  5.91  9.96   NaN  3.08  4.00  7.04  3.71  5.29  6.17  7.46  8.00
62  2 22 15.75  6.58 17.50  6.29  9.62  7.96 15.29  8.25 10.92  9.21 10.13 10.34
62  2 23 17.50  7.62 19.58  6.96 12.12  8.67 20.21 10.25 13.83  9.54 15.29 10.75
62  2 24 13.92  8.58 21.37  8.79 11.75  9.29 18.12 10.13 11.00  9.33 11.46 12.50
62  2 25 16.00 13.37 25.92 12.33 15.34 11.42 22.63 13.13 12.46 12.42 15.75 25.08
62  2 26 17.88 15.50 27.16 12.38 14.88 10.58 25.62 16.13 13.96 15.37 19.79 27.88
62  2 27 18.91 13.33 26.92 13.00 15.63 11.00 26.87 17.04 14.33 14.29 21.59 27.25
62  2 28 13.00  9.29 19.21  6.50 11.12  6.04 13.88  9.04  7.08  9.33 10.96 15.75
62  3  1  8.21  4.83  9.00  4.83  6.00  2.21  7.96  1.87  4.08  3.92  4.08  5.41
62  3  2  8.08  7.29  6.54  3.08  8.71  4.63  9.59  6.17  4.29  5.63 11.42 14.96
62  3  3 11.50  6.00 12.50  4.63  7.67  4.21  6.71  8.12  6.13  5.91  7.58 12.96
62  3  4   NaN  5.33 13.50  6.13  6.46  2.71  5.33  3.79  4.46  4.04  2.71  7.08
62  3  5  6.63  3.96  5.88  2.04  3.29  3.08  5.79  1.63  1.67  2.79  2.67  2.46
62  3  6 15.09 14.37  7.92  5.04 12.62  6.92  8.00  8.42  3.92  5.13 10.75  5.29
62  3  7 35.80 31.63 30.37 20.79 32.42 24.25 29.58 23.29 19.08 20.75 21.37 18.16
62  3  8 13.29 11.67 21.29  9.38 14.75 13.08 18.00 12.75  8.75 10.92 13.46 14.71
62  3  9  8.54  7.17 11.08  3.83  7.96  4.96  9.00  6.58  4.08  4.42  5.83  6.63
62  3 10 15.50  7.17  5.33  4.38 11.00  2.17  3.83  2.04  3.04  6.13  2.21 10.96
62  3 11  5.46  2.88  6.54  3.58  6.00  5.71 10.92  9.67  9.83  7.75  7.87 11.21
62  3 12  6.54  4.42 14.62  4.75  8.00  5.29  7.71  6.58  6.38  6.17  8.67  9.29
62  3 13  6.71  4.21  7.41  5.04  6.00  2.88  6.83  5.46  4.67  5.29  4.29  9.62
62  3 14  5.50  9.92  3.88  2.08  8.71  2.17  4.00  3.50  1.71  2.04  6.67  4.67
62  3 15 15.79 10.83 21.37 10.13 15.75 14.67 16.54 12.87 12.38 15.34 17.92 21.09
62  3 16  7.00  4.46 18.38  6.38 10.00  7.41 12.58  5.83  8.42 12.46  8.71   NaN
62  3 17 12.71 10.67 11.67  5.75 11.71  8.21  7.71  7.75  6.58  7.67  7.79 11.50
62  3 18 14.71 11.08 11.83  4.50 11.00  5.33  5.79  7.67  6.04  5.96  7.83  6.75
62  3 19 10.92  7.87 13.70  5.41  8.29  5.54  9.04  6.54  5.66  6.04  9.21  5.21
62  3 20  9.21  7.38 14.12  2.29  5.91  1.92  7.67  5.96  5.66  6.34 10.83 12.42
62  3 21 10.13  5.66 16.08  4.92  5.83  3.71  6.71  3.00  3.83  5.63  3.63  6.67
62  3 22  4.33  2.25  5.66  2.88  5.29  2.46  5.96  4.04  3.83  5.75  5.50 10.46
62  3 23  9.42  6.50 16.42  7.04  7.83  5.88  9.42  5.41  6.25  6.00  9.92  9.79
62  3 24  6.83  3.21  8.25  3.71  5.96  1.00  3.50  3.63  1.46  3.13  6.17  8.96
62  3 25 17.67 14.62 14.88 10.58 17.92 13.37 19.41 17.83 16.25 18.46 22.46 21.29
62  3 26 12.92  8.21  8.92 11.12 12.50  9.67 14.88  9.54 13.50 11.79 11.92 13.17
62  3 27  6.92  3.83  5.91  5.88  6.58  4.96  9.92  5.75  6.29 10.54  9.04 13.13
62  3 28 14.83 14.12 16.42  9.00 14.33 12.17 13.54 11.46  9.79 11.00 16.21 16.50
62  3 29 16.66 13.17 14.71  8.71 12.17  8.71 10.83  9.79  8.63 10.17 14.88 18.63
62  3 30 13.92  6.50  9.83  8.87 12.38  8.46 13.75  9.13 10.37 11.08 11.00 18.50
62  3 31 18.21 15.46 11.42 11.75 17.88 10.96 16.42 10.83 12.83 11.38 17.79 17.79
62  4  1 14.33 12.25 11.87 10.37 14.92 11.00 19.79 11.67 14.09 15.46 16.62 23.58
62  4  2 26.75 20.25 24.00 13.59 18.25 12.79 20.83 14.46 14.12 16.25 18.75 16.08
62  4  3 17.71 10.46  7.12  8.25 12.62  7.75 10.67  9.50  8.71 10.34 13.17 12.08
62  4  4 29.04 23.75 15.63 15.71 26.04 16.75 17.83 20.54 16.08 14.62 23.91 13.83
62  4  5 28.33 16.38 18.08 17.88 21.21 15.16 22.42 18.05 20.83 18.34 21.42 18.16
62  4  6 14.37 12.87 12.29 10.29 14.79  9.79 17.08 10.79 13.21 11.92 16.96 20.12
62  4  7 19.29 16.29 17.67 11.96 19.29 12.79 22.50 12.75 16.83 14.33 16.88 17.41
62  4  8 32.58 23.21 17.88 16.92 24.54 14.17 18.25 15.71 15.41 13.04 22.46 14.67
62  4  9 17.21 11.63 10.79 10.37 16.17 10.04 16.21 12.50 12.92 12.87 14.46 15.25
62  4 10 10.13  6.67  7.58  4.96 10.34  5.41 12.04  8.67  9.04  9.00 10.37 14.42
62  4 11  8.79  5.58  9.67  7.41 11.21  9.42 14.12  8.75 10.34 12.96 10.17 15.71
62  4 12  4.58  5.58  5.88  1.58  3.96  2.50  3.79  3.96  2.92  4.42  6.21  7.21
62  4 13  8.92  5.58 13.79  3.75  7.33  4.21  6.58  6.25  5.66  4.71  7.29 13.08
62  4 14 11.67 10.37 13.33  4.54  7.96  6.04 11.42  9.21  9.21  9.21  9.59 15.79
62  4 15 13.04  7.83 23.13  7.71  9.67  8.75 13.83  8.25 10.08  9.50  8.63 14.09
62  4 16 10.17  8.92 27.04 10.88  9.75  8.92 15.34  8.38  8.75 12.42 10.17 13.33
62  4 17 10.41  5.88 20.12  9.46  7.54  8.29 14.83  8.63  7.21 12.87 11.12 11.63
62  4 18 10.13  8.17 12.79  6.58  8.92  7.33 11.71  8.63  8.17 11.92 11.17 12.12
62  4 19  5.58  4.38  6.79  3.63  4.92  3.58  6.21  2.71  2.42  5.21  7.29  7.50
62  4 20  8.58  8.83  8.79  4.21  8.92  5.71  6.17  8.21  5.58  7.41 14.37 10.25
62  4 21  5.75  5.09  7.54  2.25  5.13  3.33  3.58  2.62  2.75  2.92  4.63  7.46
62  4 22  9.96 14.83  9.59  7.38 12.46  7.92  8.58  8.79  7.46  7.71 11.63  7.08
62  4 23  7.50  7.33  7.62  5.54  9.59  7.38  8.96  7.54  5.91  7.96 10.25 12.58
62  4 24  5.71  5.46  5.29  2.04  4.08  2.33  5.79  4.92  3.46  4.50 11.75  5.63
62  4 25  4.04  8.29  4.08  2.04  4.54  3.79  5.83  9.17  4.00  6.25 19.00 11.54
62  4 26  3.58  2.75  4.63  1.13  2.96  2.42  7.67  3.04  3.58  6.87  7.71  7.79
62  4 27  6.17  1.42 12.25  3.25  3.46  5.63  6.42  2.79  5.37  3.37  8.50  4.33
62  4 28  4.75  2.92 10.08  2.71  3.21  3.29  5.17  3.00  4.50  3.17  9.17  2.21
62  4 29  9.17  7.62  8.54  3.79  6.46  4.79  5.37  6.54  6.75  5.13  6.46  5.91
62  4 30  6.58  9.71  8.79  4.71  6.08  5.46  5.41  5.63  5.75  4.67  6.92  2.58
62  5  1  9.62  9.54  3.58  3.33  8.75  3.75  2.25  2.58  1.67  2.37  7.29  3.25
62  5  2  9.83 11.17  5.46  4.33  9.75  4.42  4.00  5.09  3.54  4.79  6.79  5.29
62  5  3  9.50  8.33  6.46  4.83  7.38  3.71  3.50  3.13  3.67  4.08  3.63  3.33
62  5  4  5.54  8.21  8.50  3.46  5.17  1.75  5.83  0.54  2.62  3.42  5.71  3.37
62  5  5 11.67 11.79 12.58  7.12 10.96  8.08  8.79 11.17  6.04  7.62 14.54 12.42
62  5  6 14.25  8.38 14.96  6.04  6.63  4.75  4.88  7.41  5.13  7.29 15.09  5.63
62  5  7 16.88 15.67 16.50 11.38 13.67 10.67 14.09 12.71 10.29 10.41 18.21 11.50
62  5  8 12.75 11.04 13.08  8.17  9.96  6.71 11.34  9.13  6.29  8.87 14.83  8.17
62  5  9  8.63  8.25  9.59  1.87  7.87  3.71  7.04  6.29  3.54  4.04 11.38  6.29
62  5 10 14.92 11.79  9.21  4.08 10.92  5.88  5.96  4.58  3.42  4.04  5.71  8.00
62  5 11 14.96 14.25 12.67  8.75 11.79  7.67 11.04 11.54  8.42  9.83 12.08 13.75
62  5 12 16.25 12.62 15.41 10.79 12.50  9.25 10.46 12.08 10.92 13.00 12.29 14.54
62  5 13 12.42 10.21  6.67  6.83  9.50  6.46  8.75  7.33  7.83 10.00  9.38  9.83
62  5 14 17.58  9.46 11.04 10.00 13.25  9.21 13.79 11.08 10.17 10.46 10.63 14.79
62  5 15 13.42  9.75 11.63  7.00 12.87  7.50  9.83 10.25  8.50  9.25 13.92 15.34
62  5 16 20.17 16.42 16.08 14.62 23.50 16.00 23.25 19.95 20.00 20.04 23.42 32.17
62  5 17 14.00 14.00 11.50 11.25 17.33 12.17 18.05 13.13 13.59 12.87 17.88 25.25
62  5 18 14.75 12.17 10.96  7.29 10.79  6.54 11.21  8.08  5.96  8.63 11.54 10.37
62  5 19 21.04 11.04 15.87 12.29 16.66 10.75 17.12 12.46 14.29 14.09 14.00 17.00
62  5 20 17.21 13.67 15.50  7.96 13.29  9.33 12.42  8.58  9.96 10.34 12.50 14.09
62  5 21 18.63 13.17 11.08  7.79 15.41  9.79 11.63 10.29  9.38  8.75 13.25 13.67
62  5 22 16.96 15.04 12.42 11.67 18.66 13.13 16.00 13.59 13.50 15.83 18.91 16.38
62  5 23 12.87 10.00 11.25  8.83 14.50 10.17 16.42 12.33 12.00 12.67 14.62 18.79
62  5 24 10.13  9.33  7.21  7.08  9.29  6.21 11.67 10.21  8.75 12.83 11.04 18.41
62  5 25  9.59 10.79 17.67  6.83  9.79  6.34  8.00 10.25  7.38 11.08 12.92 18.46
62  5 26  5.91  8.79 17.04  5.83  8.08  4.42  7.46  8.50  4.67  6.92 14.42 13.75
62  5 27  6.13  7.62 19.87  7.50  8.04  5.91  9.92  7.92  6.50  8.12 12.50 10.46
62  5 28 10.58  8.75 10.17  7.96  8.00  4.00  6.83  7.38  6.21  7.46  9.42 11.54
62  5 29 11.34  9.25  7.62  5.63 10.17  5.79  6.75  9.50  6.34  9.83 10.96 13.88
62  5 30  6.71  7.87  6.42  4.54  7.46  4.08  7.17  6.42  6.54  9.33  8.00 16.79
62  5 31 10.88  8.46 20.12  6.58  7.92  5.33  7.83  8.12  6.79  9.83  9.17 14.09
62  6  1  5.88  6.29  8.67  5.21  5.00  4.25  5.91  5.41  4.79  9.25  5.25 10.71
62  6  2  4.96  4.63  5.75  2.58  3.92  1.08  3.17  2.79  2.42  1.29  4.12  5.66
62  6  3  5.58 10.37  4.96  2.29  7.17  3.08  3.00  5.41  3.46  4.17  7.67  9.46
62  6  4  8.79 11.67  4.04  5.46 10.92  6.00  3.33  8.92  4.50  7.21  7.04  8.71
62  6  5 10.00 12.12  7.67  6.71 12.75  6.79  8.21 11.67  8.00 10.08 13.17 14.79
62  6  6  7.67 10.83  4.50  5.37 10.63  6.79  6.08 10.29  5.96  7.54 15.34  9.79
62  6  7  5.17  7.00  4.00  4.83  6.75  5.71  3.83  5.21  3.58  4.58 12.25  5.50
62  6  8  2.54  4.29  5.71  2.92  4.67  3.46  5.41  5.83  4.63  5.50  8.79  4.29
62  6  9  6.87  5.50  6.54  3.79  4.50  4.04  8.29  4.29  5.37  6.00  9.04 13.04
62  6 10  8.87  5.25  9.08  4.58  4.54  5.29  6.50  5.04  5.33  8.79  6.96 11.34
62  6 11  8.08  7.87  9.13  4.42  8.83  4.79  7.96  9.59  6.17  8.17 12.96  8.63
62  6 12 10.54  5.37 12.92  4.21  7.46  5.83  9.08  7.17  6.79  8.08  8.58 11.92
62  6 13  9.46 13.96 11.71  7.29 10.50  7.96  9.62 14.04  7.92 11.63 23.29 16.75
62  6 14 14.71 13.21 16.17  8.25 17.33 11.38 14.00 16.62 13.67 17.50 24.41 26.71
62  6 15  1.00  5.09  6.46  1.21  5.25  4.04  2.79  8.08  6.21  7.12 13.50 11.54
62  6 16  5.75  5.50  8.08  3.67  7.71  4.42  8.42  7.58  7.38  8.00 13.17 15.37
62  6 17 13.83 15.59 10.21  8.63 14.54  9.59 10.37 10.67 10.63  9.29 14.04 13.83
62  6 18 22.13 21.04 22.54 16.50 23.50 14.12 21.50 21.92 18.96 21.79 29.79 26.58
62  6 19 18.38 15.12 18.91 15.09 23.04 16.38 23.42 18.84 20.25 18.91 25.62 28.38
62  6 20 11.42  9.71 12.75  6.25  8.83  6.25 12.54 10.21  9.42 10.71 14.33 19.70
62  6 21 17.58 16.42 19.83 11.67 15.34 10.96 16.04 15.71 12.58 15.87 18.79 19.55
62  6 22 13.33 10.13 12.96  8.67 15.79 10.13 13.67 11.38 13.29 12.92 18.75 23.71
62  6 23 11.71 16.21 13.33 10.04 15.75 11.79 16.66 15.04 13.54 14.50 24.46 23.04
62  6 24 19.67 13.67 14.09 12.21 17.71 14.88 25.29 19.00 19.62 20.41 20.30 27.46
62  6 25 11.08  7.92 10.50  8.71 15.12 11.21 18.79 13.25 15.12 13.21 16.83 23.54
62  6 26 15.34 10.63 10.92  8.75 10.96  9.38 14.33 11.08 11.08 13.79 11.38 18.88
62  6 27  9.00  4.75  7.92  6.08  7.83  6.13 11.92  8.50  8.96 10.75  9.96 16.54
62  6 28 12.54  6.50  9.25  8.21 11.71  8.71 12.79 10.50 11.87 10.67 11.00  7.33
62  6 29 10.79  8.54  6.96  6.67  7.75  5.09  6.42  7.83  7.75  9.17  6.42 11.38
62  6 30  6.50  5.13  4.33  5.13  5.54  3.71  7.12  7.33  6.63  8.71  5.50 12.42
62  7  1  8.67  4.17  6.92  6.71  8.17  5.66 11.17  9.38  8.75 11.12 10.25 17.08
62  7  2 14.67  6.67  9.54 10.21 13.67 10.58 17.50 13.00 12.38 15.09 12.42 18.63
62  7  3 15.83 10.29 10.00  9.59 10.34  7.21 12.87  8.58 10.58 13.25  8.21 16.71
62  7  4 15.96 10.25  9.21  7.29 11.71  7.62 10.08 10.71  8.71 12.46 13.59 16.25
62  7  5 11.21  8.58  7.25  6.75  7.41  6.63  7.79  8.96  8.17 12.38 10.50 14.79
62  7  6  8.04  4.29  6.13  3.17  4.12  2.58  5.46  2.25  4.71  5.17  5.96  5.71
62  7  7  6.38  8.92  6.96  4.00  5.29  3.54  3.13  4.21  2.17  4.38 10.71  5.09
62  7  8  8.96 11.38  7.25  5.46 10.58  5.66  4.67  8.96  6.42  6.83 10.21  9.08
62  7  9 12.58  8.46  6.75  6.00 10.25  5.04  4.63  9.67  9.33  9.79 11.58 15.12
62  7 10  6.96  5.46  8.92  5.17  8.58  5.37  7.50 10.63  8.58 10.37  9.50 16.96
62  7 11  8.63  7.75  3.42  2.75  6.13  2.88  5.71  5.83  4.42  7.04 10.58 11.83
62  7 12  7.58  5.37  7.33  3.92  2.88  2.04  4.75  1.04  1.71  4.50  4.79  6.21
62  7 13 10.54 11.75  7.92  6.04 11.04  6.50  5.54  8.83  5.54  6.13  8.96  6.71
62  7 14  5.88  6.42  9.59  4.25  7.25  5.71  8.00  9.71  9.33  8.46 11.12 12.38
62  7 15  4.17  4.08 16.54  3.75  3.25  3.79  5.79  3.58  4.50  5.04  7.79  4.88
62  7 16  6.29  4.79  6.00  2.21  6.17  1.75  1.87  0.96  0.37  3.13  5.41  2.29
62  7 17 12.92 10.54  5.46  6.25 11.29  6.63  4.54  8.83  5.66  9.04  9.33 11.42
62  7 18 18.88 16.62 15.67 11.00 16.25 12.96 12.33 14.92 11.96 14.92 19.38 20.83
62  7 19 16.04 15.41 16.21 10.21 18.54 12.08 13.04 15.96 12.87 17.33 23.13 20.08
62  7 20  7.92 11.00 10.29  8.33 10.00  6.83  9.17 13.67  6.42 12.33 20.38 17.75
62  7 21 16.33 16.04 13.33 11.08 17.67 11.58 12.96 11.75 11.34  9.59 15.54  5.91
62  7 22 12.42  9.54 10.75  9.96 15.34  9.17 11.29  8.54 10.08  9.25  9.21 12.21
62  7 23  3.08  6.17  6.38  1.96  4.42  2.00  5.88  2.96  2.58  4.92  7.54  6.54
62  7 24 10.04  6.34  8.67  2.62  7.08  2.46  4.38  3.17  2.88  5.58  4.67  8.17
62  7 25  9.38  7.58 21.46  6.87  7.12  5.13  6.34  8.08  6.13  6.87 11.79 10.92
62  7 26 10.79 10.75 25.12 10.13  9.00  6.63 10.00  8.92  7.29  9.54 12.04  6.34
62  7 27  4.75  2.67  5.75  2.50  2.62  0.25  3.67  2.96  0.46  2.04  4.67  4.63
62  7 28  4.50  4.08  5.25  2.33  3.42  2.29  2.88  6.34  3.13  4.83  7.71  5.50
62  7 29  8.83  8.12  8.96  4.71  7.83  4.58  6.58  7.46  5.54  5.21  9.79  6.00
62  7 30 14.17  9.00 11.25  8.21 14.12  9.71 12.17 12.04 10.04 11.50 14.58 18.63
62  7 31  7.04  7.00  8.08  5.46 10.96  7.33  9.54  9.92  9.67  8.92 12.96 16.46
62  8  1  4.58  5.37  6.04  2.29  7.87  3.71  4.46  2.58  4.00  4.79  7.21  7.46
62  8  2  7.46  8.75  6.00  4.71 10.29  6.75  8.38  7.83  6.79  7.38 10.83  9.21
62  8  3 16.04 11.75 11.54  8.79 10.75  6.71  8.42  9.42  7.46  8.63 11.79 13.62
62  8  4  9.83  7.58  9.17  5.75 10.29  7.00 14.25 11.08  9.54 11.25 15.09 20.30
62  8  5  5.88  4.50  7.83  4.25  8.92  6.54 10.75  7.71  7.87  9.59  9.92 19.46
62  8  6 11.46  8.83  6.75  4.08  6.42  3.63  5.63  7.00  3.63  7.12 11.08 13.75
62  8  7 14.50  9.29 10.13  7.92 10.67  7.08 10.50  7.25  8.79  9.83 11.12 12.83
62  8  8  8.87  6.96  9.25  3.42  5.75  4.08  7.12  4.71  5.41  4.88  9.29 10.75
62  8  9 14.67 11.71 14.96 10.34 14.62 10.00 12.25 12.04 10.75 13.79 19.04 21.79
62  8 10 13.25 13.50 13.88  9.29 12.54  9.75 14.29 13.88 11.54 13.21 18.79 17.08
62  8 11 16.92 15.16 16.46 13.00 18.12 12.96 18.50 17.25 17.92 17.37 24.08 29.95
62  8 12  2.88  4.92  4.96  2.83  5.91  3.50  9.38  5.88  5.75  8.63  7.92 13.67
62  8 13 10.04  5.25 12.04  4.29  5.75  2.29  5.88  4.58  6.08  3.08  5.50  6.92
62  8 14 12.00 10.25 11.83  4.63 10.29  4.25  8.67  9.29  9.00  9.92 10.67 12.92
62  8 15 10.79 10.46  8.08  7.41 12.17  6.83 10.41  8.46  8.58  6.50 11.46 13.21
62  8 16 11.17  8.25 10.21  6.04 10.96  5.88 11.38  6.79  7.54  7.50 11.50 12.87
62  8 17  8.21  6.58  8.71  4.71 10.58  6.83 11.25  7.83  6.75  7.08  8.83 10.92
62  8 18  6.87 10.79  9.21  5.00  7.17  5.63  7.54  5.58  5.17  5.91 12.25  7.29
62  8 19 14.17 13.62 13.59  9.83 10.79  8.54 10.50 11.67  9.33 11.08 18.63 13.70
62  8 20 14.29 12.62 12.54  7.38 14.12  7.96 11.25 11.54  8.58  8.75 14.88 10.92
62  8 21 14.67 12.38 13.37  9.54 15.46 10.34 14.21 14.50 12.42 11.83 17.75 20.75
62  8 22 11.25 10.96 11.17  8.87 12.92 10.17 15.09 12.33 11.87 12.46 16.13 23.63
62  8 23 18.84 14.79 17.33  9.25 14.25 10.00 17.04 14.04 11.75 14.25 20.67 21.67
62  8 24 15.12 14.29 11.50 11.71 17.25 12.96 21.09 15.75 15.96 14.54 19.79 26.87
62  8 25 13.25 11.54 10.88  8.33 12.17  8.63 15.50 11.04 10.67 11.12 16.50 21.75
62  8 26 24.67 20.12 19.83 17.75 20.88 15.96 22.13 18.08 19.41 19.75 23.63 19.46
62  8 27 10.00  8.67  9.83  6.54  9.92  7.25 14.29  7.41  9.83 10.50 10.63 17.25
62  8 28  6.04  7.04  4.92  2.08  4.71  3.17  5.54  2.29  3.04  4.58  5.63 11.21
62  8 29  4.83  3.88  5.29  1.87  1.92  1.92  7.75  4.50  3.75  7.29 10.37 15.34
62  8 30  0.96  2.33  4.17  2.21  4.38  3.83  6.42  5.04  5.00  6.46  9.42 13.96
62  8 31  6.38  8.38  4.25  3.63  6.58  3.13  5.37  4.67  3.83  5.79  6.29  7.54
62  9  1 10.00 12.08 10.96  9.25  9.29  7.62  7.41  8.75  7.67  9.62 14.58 11.92
62  9  2  3.83  7.67 10.37  5.13  4.46  1.21  5.00  5.88  1.25  4.29 12.75  6.17
62  9  3 11.08 11.50 11.38  6.29  7.87  6.67  6.17  4.92  4.92  6.46 12.33  5.04
62  9  4 13.25  9.71 12.33  5.66 11.42  6.50  6.46  6.08  5.37  6.54  7.29  4.46
62  9  5  5.41  5.25  7.96  3.58  4.92  3.58  7.50  2.25  3.33  5.13  5.25  9.21
62  9  6 10.92  8.92 10.08  4.75  8.29  2.21  5.25  8.04  2.88  7.17 14.54 12.08
62  9  7 12.83 11.00 10.79  8.46 13.13 10.13 11.63 10.79 11.38 11.25 13.25 21.00
62  9  8 10.46 11.63  9.67  6.46 10.34  6.29  8.04  6.46  5.96  7.41  9.50 11.42
62  9  9 16.83 15.87 20.12 13.13 17.21 13.13 18.79 14.50 13.79 16.54 17.33 15.71
62  9 10  9.59 10.63 11.87  4.88  8.63  7.00  9.59  3.13  4.46  4.63  8.42  8.38
62  9 11 18.96 10.92 15.59  6.29  9.87  6.00  8.38  8.04  5.25  7.58 15.63 13.75
62  9 12 12.67  9.67  9.71  5.79 10.75  5.58  7.54  8.17  5.71  8.67 12.67 15.67
62  9 13  7.96  8.42  6.29  4.21  9.46  4.96  6.13  4.83  3.17  5.29  6.92  6.04
62  9 14 11.25 10.34 12.04  6.34 12.21  6.50  7.25  9.71  7.46 11.50 16.21 16.29
62  9 15 10.29  8.08  7.17  5.21 10.25  4.79  7.92  6.29  7.46  7.33 11.63 13.79
62  9 16 13.46 10.63 10.04  7.92 12.67  9.04 11.75 10.54 11.08  9.71 14.79 18.05
62  9 17 11.34  8.75 10.79  6.54  9.17  7.17 10.58  6.38  7.79 11.34 11.96 17.75
62  9 18  6.83  5.46  9.38  4.04  4.25  2.46  6.79  2.13  4.63  7.29  5.04 11.25
62  9 19  6.46  4.71 12.42  1.67  4.42  0.83  2.88  1.04  2.13  1.75  3.17  3.00
62  9 20  9.08  6.96 16.17  4.21  7.33  3.67  4.63  1.42  2.04  3.37  2.79  0.67
62  9 21  6.29  3.08 10.54  2.17  4.96  2.50  2.29  1.42  0.87  1.25  4.33  7.54
62  9 22  4.88  5.25  6.29  2.21  5.88  4.33  6.58  6.08  4.29  7.50 14.29 16.21
62  9 23  9.25 10.67  9.62  3.75 10.54  6.38  8.33 11.67  6.58 12.54 19.67 16.96
62  9 24  8.54  9.13  8.50  4.12  9.00  4.79  4.88  4.50  4.29  5.21  6.25  8.50
62  9 25 12.62  9.33 12.62  6.54  8.67  6.08 10.83  6.87  5.58  7.58 12.12 12.04
62  9 26 12.00  6.63 12.54  6.75  8.17  4.50 12.00  4.67  8.71  9.00  5.33 17.00
62  9 27  9.59 10.21 12.17  5.91 10.71  7.38  8.42  6.17  6.13  7.96  6.42 13.75
62  9 28 11.42  9.54 10.46  7.29 13.75  8.87 11.29  9.00  9.25 10.71 13.83 12.71
62  9 29 21.37 16.75 18.38 11.50 17.67 12.50 13.79 14.00 13.13 14.58 19.92 20.04
62  9 30 26.83 16.42 22.95 14.88 16.38 13.75 16.54 16.88 14.62 19.38 26.30 23.00
62 10  1 14.58  7.83 19.21 10.08 11.54  8.38 13.29 10.63  8.21 12.92 18.05 18.12
62 10  2  8.79  5.58  8.67  3.21  9.42  6.00  7.12  7.83  6.58  9.33 15.79 13.83
62 10  3  9.96 12.29  8.25  3.63 10.34  7.87  7.33 10.46  6.42 10.34 20.08 16.58
62 10  4 21.50 16.75 18.88 12.54 17.41 13.92 11.54 15.50 13.79 16.00 25.84 24.04
62 10  5  5.83 10.37  7.79  4.58  8.42  6.34  8.29 10.92  6.21  9.33 16.88 13.46
62 10  6  9.59 11.08  8.25  4.88 10.08  6.87  6.25  9.54  5.46  8.96 17.46 12.38
62 10  7 12.12 14.25  8.79  8.50 14.00  9.25  8.04  9.79  8.46 10.88 15.87 13.88
62 10  8  7.71  7.54  9.00  6.29  8.92  6.00  8.46  6.13  6.13  7.79  7.08  9.17
62 10  9  5.63  0.87  6.38  1.38  5.46  2.37  4.88  3.00  2.67  4.75  2.58  6.58
62 10 10  5.91  1.58  7.04  1.71  2.83  1.67  4.83  2.29  4.54  6.04  2.54 10.71
62 10 11 14.96  9.17  8.00  2.04  7.12  2.75  7.79  7.38  8.42  7.62  7.83  8.33
62 10 12 11.67  6.08 11.67  3.13  6.29  2.13  4.96  4.12  4.08  5.96  5.00  6.38
62 10 13  5.13  1.42 10.79  2.79  2.13  0.04  2.50  0.25  0.87  2.71  2.67  7.75
62 10 14  3.04  1.63  3.37  1.00  3.00  0.37  4.88  0.46  1.04  5.04  6.08  9.25
62 10 15  3.42  2.54  2.50  0.25  1.87  0.79  3.92  0.42  2.00  5.17  6.38 11.67
62 10 16  3.08  2.75  1.96  0.67  1.96  0.92  4.38  3.88  0.50  3.83 11.71  8.50
62 10 17  2.04  3.79  3.67  1.21  3.17  2.37  6.29  6.87  3.67  8.33 15.50 14.83
62 10 18  4.88  2.42  5.25  3.29  5.75  5.83 11.63  7.83  7.21  8.29 14.83 18.12
62 10 19  6.38  1.71  7.00  3.04  5.21  2.88  5.58  2.37  4.38  6.13  4.96  8.42
62 10 20  8.38  5.63  9.46  2.46  4.42  2.71  3.79  3.88  4.46  4.96  6.38  5.04
62 10 21 12.00  7.62 11.58  3.13  8.08  5.09  6.34  7.71  4.12  7.83  9.13  6.92
62 10 22  4.96  4.67  8.33  1.67  4.17  2.13  4.92  0.79  2.21  6.13  7.12  7.17
62 10 23  1.50  3.75  3.50  0.67  3.29  3.54  6.54  7.83  4.46  8.00 12.79 11.34
62 10 24  8.96 11.83 10.46  4.63  9.59  7.50  8.50 11.75  6.79 12.50 20.46 16.75
62 10 25 13.13 12.87 11.21  7.92 12.04  8.96 11.79 11.38  9.79 14.17 19.21 21.71
62 10 26 14.04  8.54 18.79  8.29  8.38  7.62 11.04  6.25  7.50  9.83 12.25 15.79
62 10 27 15.16 15.41 12.92  8.25 17.00 11.12 15.63 15.67 14.04 15.67 24.83 25.75
62 10 28 19.95 14.17 14.09 12.62 14.09 10.75 15.92 13.54 14.37 16.04 19.17 28.12
62 10 29 12.92 10.79 13.13  5.79 10.96  9.21 11.96  8.71  8.46 10.83 15.79 18.00
62 10 30 18.25 22.79 11.75 10.67 20.79 11.67 20.75 17.29 16.46 14.58 29.42 29.17
62 10 31 15.21 12.25  8.38  9.04 13.67 10.58 12.75 11.96 12.25 11.87 18.21 19.04
62 11  1 16.88 13.25 16.00  8.96 13.46 11.46 10.46 10.17 10.37 13.21 14.83 15.16
62 11  2 15.96  8.46 13.70  6.13  7.71  7.67  6.04  5.83  4.75  5.83  6.46  9.04
62 11  3  5.21  3.75  6.17  3.37  8.08  3.88  6.83  1.08  2.79  3.63  5.13  7.00
62 11  4 13.75  7.29 15.67  7.41 11.54  8.67 10.92  8.29  9.96 12.96 11.58 17.62
62 11  5 16.08  6.50 18.91  7.96  7.71  9.62 19.75  6.00 11.79 16.54  9.62 23.87
62 11  6 12.17  8.08 12.46  6.17  7.92  6.79  8.29  8.71  6.75  8.25  9.67 11.71
62 11  7  2.96  4.33  5.04  2.50  4.71  2.88  3.50  2.79  2.37  3.46  3.13  4.92
62 11  8  4.12  3.25 11.08  3.42  5.00  3.21  4.42  2.04  2.54  3.04  2.96  3.00
62 11  9 12.42 13.62 23.63  8.50  9.87  9.04 12.58  9.29  8.25 11.75 14.12 15.96
62 11 10 11.25  6.79 22.71  9.50 10.67 10.71 16.25 11.42 11.92 11.87 10.34 22.08
62 11 11 12.04  6.42 16.58  5.58  9.00  5.46 14.75  9.13  7.71  8.87  8.58 11.46
62 11 12  7.29  5.29 10.58  3.33  4.25  2.62  5.50  2.75  2.67  5.17  4.00  4.50
62 11 13  7.79  1.75  7.83  2.83  4.04  2.08  7.29  1.67  3.17  5.21  8.63 11.21
62 11 14 13.62 11.29 11.04  7.96 13.79 10.25 13.70 11.54 11.29 13.62 17.46 25.62
62 11 15 10.46  8.54  9.13  6.54  6.92  5.29  9.42  4.54  4.83  6.92 12.29 12.87
62 11 16 14.83 13.75 10.75  5.25 11.12  8.50 10.41  8.12  8.79 10.29 15.67 15.54
62 11 17 24.96 21.59 19.83 16.04 16.66 13.08 18.00 15.37 14.79 19.12 21.00 31.34
62 11 18 23.63 15.09 24.62 15.83 13.96 10.17 16.46 10.50 11.34 14.62 22.29 30.34
62 11 19 10.41  4.46 13.83  6.54  6.46  6.38 10.37  1.96  6.87  6.75  9.38 14.09
62 11 20 10.79 12.71 13.67  6.25  8.46  9.13 10.54  9.13  8.63  8.00  9.83 10.46
62 11 21  7.67  5.00 13.21  3.88  3.25  4.08  8.42  1.00  4.58  4.96  2.50  6.54
62 11 22  5.13  5.13  5.88  1.87  6.75  6.38  8.38  4.08  2.96  4.92  9.33  8.79
62 11 23 11.29 12.08 11.63  6.00 11.83 10.34 12.96 12.83 10.13 12.08 15.21 11.83
62 11 24 20.50 15.21 15.04 11.79 15.50 11.67 11.17 11.96  9.96 11.75 16.58 16.79
62 11 25  5.88  5.13  5.63  2.50  5.33  3.50  4.00  2.54  2.17  3.04  4.63  6.00
62 11 26  4.00  2.21  3.21  0.21  2.33  1.00  5.96  0.00  0.83  0.92  2.75  8.42
62 11 27  4.88  2.67  7.83  3.04  2.37  0.46  5.09  0.00  1.75  1.87  2.46  8.33
62 11 28  6.17  1.50  6.04  1.63  2.46  0.33  4.88  1.00  1.58  3.04  6.34 12.75
62 11 29  7.17  4.38  3.58  0.21  3.54  0.21  7.41  0.17  2.96  7.21  9.04 13.13
62 11 30 12.83 10.00  5.79  1.00  9.38  5.29  4.71  4.38  2.37  3.63  7.79  8.58
62 12  1 18.38 15.41 11.75  6.79 12.21  8.04  8.42 10.83  5.66  9.08 11.50 11.50
62 12  2 17.41 18.38 14.71 10.41 16.21 10.17 12.96  9.62 10.00 10.34 14.96 18.21
62 12  3 17.33 16.13 13.88  8.87 14.04 12.38 10.13  9.75  9.71 10.79 13.96 17.50
62 12  4 13.62 13.33 10.75  6.46 13.62 10.37  4.79 10.75  6.87  9.04 10.46 13.62
62 12  5 15.46 16.42 13.00  7.71 10.83  9.96  8.96  7.00  7.83  8.29 18.41 18.58
62 12  6 13.17 11.67 11.25  7.46  9.92  7.87  4.17  8.38  6.13  8.58 17.21 16.17
62 12  7 25.46 17.50 22.34 16.21 15.34 12.75 13.13 14.67  9.96 13.96 23.21 24.50
62 12  8 23.29 18.00 21.59 17.12 21.12 15.67 23.67 13.92 17.46 16.71 21.62 23.42
62 12  9 23.33 18.88 10.75 16.71 20.12 14.42 20.25 14.79 19.00 17.21 21.04 28.54
62 12 10 13.13 11.21  8.12  7.58 14.96  9.71 12.67  9.96 10.83 10.75 15.04 20.79
62 12 11 17.92 15.87 11.63  9.13 17.37 12.21 16.42 12.46 12.29 12.17 19.92 19.46
62 12 12 30.91 24.79 21.87 16.75 19.92 12.12 19.67 18.88 15.96 23.21 28.33 37.12
62 12 13 17.16 11.12 15.09  8.42 10.63  8.46 14.00  9.50 10.41 13.70 14.75 23.00
62 12 14 13.96 13.37 11.08 13.46 20.30 16.33 20.00 18.12 17.33 16.79 21.12 24.04
62 12 15 29.38 18.46 17.58 24.33 29.46 22.83 28.91 26.67 25.80 28.21 29.25 32.79
62 12 16 17.83 12.62 13.25 12.42 13.13 11.58 16.54  9.67 12.42 16.54 15.09 25.58
62 12 17 13.37 11.96  6.46  4.42 12.17  7.83  9.59  8.42  7.08  8.04 14.29 13.92
62 12 18 16.33 15.37 10.21 10.71 15.34 10.79 15.92 14.42 13.17 12.54 22.08 23.45
62 12 19 11.25  9.75  8.25  4.79 11.04  8.21  9.87  7.67  8.79 10.58 14.17 16.54
62 12 20 14.58 14.46 12.21  8.96 16.00 11.96 16.08  9.75 12.12 13.62 15.96 20.12
62 12 21  5.75  5.17  6.38  2.29  7.54  5.17 10.41  3.50  6.13  6.25  9.87 11.46
62 12 22 17.83 17.50  6.71  3.58 11.04  9.13  5.71 10.92  7.33  9.46 15.75 13.25
62 12 23 13.25 11.29 12.21  6.54 11.12  8.96  9.33  9.46  8.12 11.04 15.41 18.12
62 12 24  9.13  6.08  9.79  2.00  8.42  4.38  4.92  4.29  3.13  4.17  7.75 11.83
62 12 25  8.00  2.58  8.75  1.79  1.00  0.75  7.00  0.75  2.33  3.67  6.96 12.46
62 12 26 12.87  5.66 10.08  5.00  8.38  6.50 12.38  6.50  7.25  9.67 11.08 17.29
62 12 27 13.62 12.96 19.83  8.42  5.66  4.92 11.92  7.21  6.58  5.88  7.67 16.79
62 12 28 12.67 10.71 17.75  6.83  7.75  4.42  7.79  4.92  5.71  2.96  3.83  7.87
62 12 29 19.29 13.13 22.79  7.17 11.00  6.54 13.62  8.00  7.21  9.25  9.33 16.88
62 12 30 22.00 19.70 33.84 19.83 20.08 18.46 28.79 21.59 18.75 18.05 21.87 29.88
62 12 31 22.67 16.88 28.67 14.12 19.75 17.08 27.79 25.21 19.83 17.79 25.46 37.63
63  1  1 15.59 13.62 19.79  8.38 12.25 10.00 23.45 15.71 13.59 14.37 17.58 34.13
63  1  2 13.00 13.92 21.50  6.63 10.67  8.54 20.75 11.46 10.75 14.09 12.83 30.75
63  1  3 14.09  8.25 20.12  9.00  9.96  9.67 13.83 10.17  9.00 13.70 11.67 27.21
63  1  4 10.29  1.54  6.67  5.21  6.08  5.13  8.54  8.21  5.91 11.17 11.54 25.54
63  1  5  7.50  6.04  9.87  3.37  5.96  6.29 13.08 12.46  7.04 11.63 14.96 29.04
63  1  6 19.70 13.75 19.33 10.46 16.96 12.12 20.91 15.67 13.17 12.71 19.08 24.54
63  1  7 18.75 18.12 18.29  8.92 16.17 12.46 16.75 11.71  9.87 11.92 14.21 19.58
63  1  8 20.67 18.79 17.67  7.33 20.41 13.50 16.83 12.83  9.67 12.83 15.34 17.41
63  1  9 26.30 20.04 25.66 14.96 24.00 16.04 21.67 19.95 16.00 14.58 18.54 25.88
63  1 10 25.58 19.75 24.00 10.41 20.25 12.00 19.38 16.71 12.25 12.33 17.37 26.58
63  1 11 18.84 10.58 16.79  4.25 10.75  6.42  9.79  8.79  5.41  7.46  8.17 10.17
63  1 12 10.04 10.34  8.54  2.08  4.54  1.25  3.96  2.25  2.29  1.58  2.62  4.54
63  1 13 10.00  9.00  7.25  1.87  2.50  0.75  5.21  2.79  2.37  2.50  3.00  9.46
63  1 14 11.92  6.83 10.88  4.75  4.33  2.25 10.46  6.54  5.04  6.00  6.63 12.04
63  1 15  8.50  3.17  5.41  3.83  7.50  4.63  9.17  5.66  5.88  6.83  9.46 12.58
63  1 16 11.54  9.71 11.38  5.41  8.21  8.21 12.58 10.58  8.83 11.04 12.54 21.25
63  1 17 20.33 12.67 22.42 10.71 14.21 10.63 15.37 12.75  8.92 10.63 11.46 10.71
63  1 18 28.21 16.79 27.21 11.38 16.29 13.83 21.50 13.96 13.13 13.67 17.21 25.80
63  1 19 22.21 15.12 32.25 12.04 19.62 13.42 23.79 17.46 15.50 15.59 14.17 27.75
63  1 20 19.00 14.25 20.91 11.00 18.50 15.63 19.62 18.88 15.87 14.50 23.04 32.83
63  1 21 19.25 16.08 17.21  9.17 15.59  9.42 13.83 11.71 11.08 14.25 12.96 23.04
63  1 22 17.16 12.04 14.33  6.13 13.62 10.50 11.92  8.75  4.58  7.87 10.79 13.79
63  1 23 14.04 11.42 11.50  5.41 11.29  7.71  2.17  7.08  5.66  5.04  7.21 14.67
63  1 24  7.04  6.17  8.46  1.79  6.13  3.96  3.04  2.62  2.13  0.46  8.79 12.21
63  1 25  6.00  2.83  2.75  1.42  4.17  1.71  8.92  2.33  2.04  4.54  4.75 11.08
63  1 26  7.67  3.75  7.17  1.38  0.92  0.25  8.38  2.08  2.29  4.79  0.13  8.29
63  1 27 10.54  9.83  6.04  0.67  7.12  2.29  1.63  5.50  1.33  1.92  5.37  5.71
63  1 28  7.50  6.34  3.37  0.92  5.25  1.08  1.33  1.71  0.29  1.38  1.96  5.37
63  1 29  5.41  3.25  4.83  1.50  5.17  2.88  7.41  4.12  3.08  4.71 10.17 15.41
63  1 30 21.42 21.84 25.21 15.46 17.29 13.54 19.62 17.00 14.04 17.83 21.87 27.50
63  1 31 12.83  8.67 21.96  9.87  7.79  6.79 11.83  7.75  6.08  9.17  8.87 18.63
63  2  1 15.41  7.62 24.67 11.42  9.21  8.17 14.04  7.54  7.54 10.08 10.17 17.67
63  2  2 15.04 14.37 26.34 10.71 12.71  8.33 15.16 11.08  9.42  9.46  9.25 15.25
63  2  3 11.12  4.00  9.87  4.92  5.91  4.00  8.67  4.42  5.00  6.87  5.79 13.79
63  2  4 12.17 15.46  7.83  4.21 15.67 10.54  9.13 12.83  6.92  8.08 15.92 12.83
63  2  5 12.62 11.46 28.75 13.13 12.58 12.83 22.00 15.29 15.41 17.41 16.79 29.17
63  2  6 17.79  9.42 22.67 11.12 14.00 14.79 20.25 15.16 14.79 20.04 15.83 31.00
63  2  7 11.87  7.75 10.54  7.21  7.92  7.83 11.83  5.54  6.58  7.50  8.71 18.34
63  2  8 11.87 12.17 13.13  8.54 11.71  9.71 10.37  8.63  6.34  5.66 12.25 11.75
63  2  9 14.83  9.75 23.33 12.33 13.25 13.67 16.88 14.58 12.67 13.21 13.75 15.37
63  2 10 19.33 12.71 19.67 10.00 14.33 11.63 15.67 14.29 13.17 12.71 12.33 20.71
63  2 11  7.71  4.88 11.63  3.63  7.12  4.17  6.79  5.71  4.79  5.29  5.00 11.92
63  2 12  7.17  2.96  5.79  4.33  4.08  0.13  5.21  1.63  2.42  1.79  7.29  5.09
63  2 13 18.91 20.12 16.54 10.75 18.00 13.70 12.83 14.79 12.96 16.42 19.58 23.54
63  2 14 17.41 14.58 19.04 14.50 16.17 12.71 19.17 13.70 14.17 19.33 16.25 29.88
63  2 15 10.04 10.63 15.92  6.87 12.33  9.21 15.50 13.79 10.63 15.79 14.83 24.25
63  2 16 12.38  7.71 13.88  7.71 14.21  7.58 13.54 14.29 11.63 12.38 15.50 23.09
63  2 17 12.46 14.67 12.46  5.33 11.12  6.96  7.83  8.33  6.63  7.33 11.63 13.70
63  2 18 21.12 17.54 17.08  8.75 17.67 10.67 10.34 11.58  7.92  8.00 13.92  9.50
63  2 19 16.83  9.79 18.34  9.87 13.42  8.67 14.54 10.04  8.71  9.17  9.75  8.12
63  2 20 11.75  1.92 14.71  7.54  8.33  4.33  9.92  7.21  7.33  6.04  6.87  8.17
63  2 21  5.58  2.92  4.67  3.58  4.58  2.17  8.50  1.50  1.42  3.17  5.46  4.54
63  2 22  4.08  5.58  2.71  1.38  7.33  3.17  5.50  4.42  2.29  4.04  7.33 11.21
63  2 23 18.58 16.66 14.46  7.04 13.79 10.83  9.33 11.50  6.42  7.67 10.92 13.59
63  2 24 17.67 20.17 14.37 11.42 17.37 13.67 13.75 13.04  9.42 10.37 19.75 16.79
63  2 25 19.75 18.88 15.96 13.13 21.17 13.33 11.46 14.79 12.92 12.50 21.59 23.58
63  2 26 22.83 22.21 20.67 14.17 22.63 16.88 19.25 19.70 15.04 15.12 27.42 30.37
63  2 27 17.62 16.66 15.50  7.87 15.71 13.00 12.50 12.42  8.58 10.83 16.96 14.54
63  2 28 19.79 19.95 19.00  9.67 18.54 16.13 16.46 15.25 10.37 13.70 20.62 21.62
63  3  1 16.75 19.67 17.67  8.87 19.08 15.37 16.21 14.29 11.29  9.21 19.92 19.79
63  3  2 16.79 15.54 19.83 10.54 15.96 13.70 15.83 14.50 10.54 12.00 18.41 19.25
63  3  3 12.83 15.83 14.50  7.62 13.70 11.54 14.46 12.25  9.17  9.92 15.34 17.37
63  3  4 20.08 18.96 18.34 14.58 18.79 15.67 14.42 17.67 14.54 17.41 21.54 21.59
63  3  5 27.46 22.67 25.08 20.00 21.46 19.95 20.21 23.13 17.21 25.37 32.63 30.09
63  3  6 16.96 15.21 18.46 12.83 13.92 10.75  9.96 12.33  8.63 12.96 11.54 12.96
63  3  7 20.58 18.54 19.83 15.29 18.08 13.92 17.88 16.62 15.59 18.84 17.46 17.33
63  3  8 24.33 20.79 23.50 18.12 22.04 19.12 17.50 18.29 16.21 21.34 19.21 22.17
63  3  9 18.75 11.54 17.50 11.38 14.00  9.71 13.33 12.38  8.75 13.67 11.75 18.38
63  3 10 13.13  9.59 13.21  9.42 13.70 11.46 17.33 11.92 14.29 11.67 12.71 11.25
63  3 11  8.38  3.37 11.79  4.88  5.54  2.92  5.66  2.62  2.88  3.50  3.67  5.37
63  3 12  5.21  5.29  5.75  0.87  3.29  1.87  6.67  1.67  2.04  3.04  3.17  4.54
63  3 13 20.79 18.00 16.29 11.12 15.63 12.29 11.46 13.50 10.75 12.29 15.37 17.92
63  3 14 25.84 17.25 24.71 20.50 16.33 14.54 13.92 16.25 15.87 18.91 19.58 15.46
63  3 15 15.09  9.92 17.79 11.92 14.58 11.83 14.25 10.83 11.79 14.54 13.92 14.09
63  3 16 18.25 13.79 18.25 10.67 16.75 12.38 16.62 13.37 14.42 13.83 13.83 15.75
63  3 17 16.96 10.79 16.54 10.37 12.50 10.37 13.17 11.34 10.21 13.42 12.21 18.79
63  3 18 14.25  8.46  9.25  9.38 12.33 10.21 12.42 10.41 10.67 12.08 10.50 16.25
63  3 19 11.00 11.96  9.04  4.75 11.87  6.92  9.54  9.04  7.21  7.21 10.21  7.41
63  3 20 11.29  8.63 11.21  6.34  9.54  5.46  7.54  7.58  5.79  8.21  6.21  4.83
63  3 21  4.38  5.66  5.71  1.04  5.83  1.29  4.33  2.13  1.25  3.17  5.17  5.13
63  3 22  7.54  2.62 13.42  3.50  5.66  3.67  4.17  3.04  2.50  2.96  5.71  3.21
63  3 23  6.34  8.04  4.88  1.63  6.42  3.88  4.79  7.75  3.04  6.67 14.96 10.25
63  3 24 26.25 22.37 20.79 14.33 18.50 16.66 19.75 19.95 14.17 18.84 27.16 23.04
63  3 25 15.54 12.38 14.00 11.96 17.25 14.37 19.55 15.09 16.88 16.17 16.92 15.63
63  3 26 10.46  7.62  8.25  4.25  8.29  5.88  7.62 10.17  6.58  8.08 12.62 13.92
63  3 27 12.83 12.21 12.33  8.08 12.54  9.54 13.92 10.79 11.12 12.58 15.50 17.29
63  3 28 11.50  8.38 11.58  5.41 11.34  8.04  9.83  8.50  7.46 10.17 11.83 14.17
63  3 29  7.54  8.87  5.33  5.00 10.96  6.13  8.67  8.12  5.41  4.83  9.71  3.08
63  3 30 15.96  6.63 10.34  7.54 11.75  6.25  9.38  6.42  7.29  5.13  7.21  3.88
63  3 31  7.41  9.83  8.00  5.13  8.87  7.04  6.83  9.62  6.87  8.96 17.50 17.92
63  4  1 10.54  9.59 12.46  7.33  9.46  9.59 11.79 11.87  9.79 10.71 13.37 18.21
63  4  2  3.88  3.13  5.04  3.75  5.46  4.50  8.96  6.50  7.41  6.54  7.08 11.79
63  4  3  7.54  4.21  5.63  6.58  6.17  4.17  8.71  7.62  7.54  9.08  4.33 10.50
63  4  4 12.71  9.04 16.83  8.83 10.88  9.00 13.00 10.54  9.79 13.67 13.33 20.71
63  4  5 13.08 12.46 29.04 11.83 12.12 11.25 20.04 14.75 12.04 17.71 19.29 23.21
63  4  6 13.50 11.42 25.25 12.83 15.92 16.00 20.50 19.95 17.62 18.25 21.75 32.96
63  4  7 16.25 13.79 22.75 11.92 16.38 16.38 18.50 18.16 17.04 15.21 16.92 26.25
63  4  8  9.83  9.46 12.25  5.96 11.08  7.92 10.41 12.50 11.04 12.42 10.50 20.38
63  4  9 16.46 11.79 15.21  7.29  9.96 12.21 11.67 13.70 12.33 12.50 12.92 20.91
63  4 10  4.75  4.42 13.59  5.25  7.83  6.79  7.25  8.00  6.34  8.54 11.38  8.83
63  4 11 16.21 13.37  9.29  8.46 13.75 10.29  9.54 12.83 11.25  9.29 16.83 16.46
63  4 12 11.12 10.13 11.79  8.38 13.13 11.00 16.00 12.08 12.71 16.00 19.62 24.79
63  4 13 15.09 11.29 16.38  8.75 13.88 13.54 15.21 12.67 12.38 15.46 15.87 21.79
63  4 14 19.12 15.54 18.96 11.04 15.16 13.33 16.46 11.29 11.54 13.79 13.70 14.75
63  4 15  6.79  7.25  9.04  5.46  9.25  5.50  8.87  5.29  6.34  5.63  7.58 10.46
63  4 16 15.54 15.71 14.62  8.92 14.25 10.63  9.67 13.83 10.29 13.70 20.46 16.38
63  4 17 11.04 10.83  9.79  6.83 10.96  7.96  8.00 12.71  8.21 12.08 18.91 13.50
63  4 18  5.66  5.21  9.42  4.67  9.29  7.75  9.92  9.13  8.79  8.29 11.29 11.29
63  4 19  9.08 10.75  8.79  6.67 12.42  8.87  9.50  8.42  7.17  8.92  9.08 10.21
63  4 20 25.50 20.67 24.87 17.04 22.00 17.71 21.54 19.29 17.00 19.67 17.92 22.63
63  4 21 27.08 18.88 23.75 16.54 18.66 15.16 19.58 17.75 16.42 21.84 20.30 24.62
63  4 22 14.09 12.42 15.12 10.29 14.54 12.21 13.42 13.13 12.42 15.67 16.83 13.70
63  4 23  9.87 11.67  7.25  5.63 11.87  8.42  6.00  8.71  8.25 10.00  9.67 10.63
63  4 24  8.54  3.54  5.04  5.79  9.00  7.25  6.67  8.83  7.46  8.87 10.00  9.67
63  4 25  4.25  6.00  4.92  2.75  4.00  2.00  4.83  2.04  2.83  3.83  8.58  6.92
63  4 26  5.09  8.87  8.38  5.88  6.79  6.96  5.71  8.29  5.83  6.21 15.09  8.83
63  4 27  5.37  6.29 10.13  4.00  6.67  7.50 12.12 10.67  9.13 10.46 15.21 15.63
63  4 28  5.88  6.50 10.21  5.54  8.96  8.04 11.42  7.54 10.46  8.92 12.00 12.71
63  4 29 11.79  9.08  8.75  7.83 13.08  9.13 11.75 10.92 10.08 11.67 11.96 15.21
63  4 30 12.83 10.83 12.46  7.67 12.54 10.75 12.33 11.58 10.75 12.08 14.50 16.08
63  5  1 18.79 14.17 13.59 11.63 14.17 11.96 14.46 12.46 12.87 13.96 15.29 21.62
63  5  2 18.00 13.75 11.83 12.00 16.50 12.54 15.37 13.96 12.75 12.87 15.83 20.58
63  5  3 16.13  9.59 10.29 10.00 13.46  9.59 12.96 10.17 11.12 12.54 14.37 13.04
63  5  4 13.88 12.21 12.58  8.38 14.42 11.63 13.37 13.88 12.46 12.71 17.96 18.21
63  5  5 12.42 12.25  9.71 10.37 14.75 12.00 14.96 12.50 12.67 12.67 16.38 18.38
63  5  6 12.38 11.58 12.29  8.25 12.58  9.46 13.67 11.67 10.13 12.62 17.71 15.54
63  5  7 19.83 16.96 22.34 15.16 17.41 15.34 18.75 16.66 15.37 19.17 22.83 21.00
63  5  8 14.00 12.87 15.37  7.87 15.04 10.83 11.67 14.29 11.83 14.46 18.54 19.55
63  5  9 19.79 19.29 16.33 12.58 17.92 13.13 13.88 12.79 12.92 12.62 15.16 14.96
63  5 10 30.91 18.21 21.12 19.62 26.20 18.38 23.09 17.37 20.08 17.37 20.25 22.21
63  5 11 11.17 10.34 13.37  8.38 11.08  9.59 12.25 10.83 10.75 11.67 16.66 15.79
63  5 12 23.75 22.17 23.09 13.42 21.92 16.00 15.59 23.00 15.34 20.88 32.91 24.54
63  5 13 20.62 18.91 17.04 12.75 24.67 18.34 20.33 21.54 17.62 20.38 26.42 28.01
63  5 14 13.29 10.75 10.25  7.04 12.62  8.58 13.42 11.87 11.46 12.04 15.75 18.29
63  5 15 10.21  7.50  8.17  6.34 10.46  8.08 12.83  8.63 10.88 10.88 12.87 15.59
63  5 16  8.08  9.13  9.59  6.58 11.00  8.83 14.42 10.08 10.67 12.29 13.70 13.62
63  5 17  9.59  8.21  9.92  7.08 13.13 10.00 13.96 11.08 13.00 12.58 16.04 20.54
63  5 18 17.79 12.96 13.00 11.42 17.75 14.33 19.33 16.79 17.04 18.12 18.63 24.21
63  5 19 10.50  9.33  9.71  6.50 11.17  8.29 11.38  8.71  9.42  8.83 11.79 12.54
63  5 20 16.00 13.50 15.41  9.21 16.88 12.33 14.50 10.96 11.75 10.04 13.33 12.92
63  5 21 12.54 12.83 11.04  5.29 13.37  7.83  7.21  9.79  8.63 10.46 13.46 15.92
63  5 22  8.87  6.83  9.87  5.17  7.58  6.00  7.62  5.17  6.87  6.04  8.71  8.17
63  5 23  5.25 10.46  5.13  4.12  9.59  6.29  7.00  6.54  6.38  5.79 13.04  8.04
63  5 24  9.59  9.21  6.17  3.88 10.54  5.29  4.96  6.29  5.33  5.04  7.54  8.96
63  5 25  8.38  8.12 12.33  7.29  8.63  8.04  8.38  8.04  6.71  7.58 14.54  7.29
63  5 26 11.63  8.58  8.12  6.71  9.71  7.38  9.29  9.62  9.17 11.04 12.38 12.75
63  5 27  8.21  2.92 10.04  3.71  3.42  1.87  5.13  1.71  3.17  2.67  5.83  4.92
63  5 28  3.63  5.91 13.83  4.63  7.75  3.71  5.63  5.17  3.67  4.21  5.04  1.75
63  5 29  7.12  7.62 21.50  7.83 10.08  7.41 10.92  7.12  8.75  7.25 12.21  4.75
63  5 30  4.88  2.96 17.25  4.67  5.96  6.71  8.25  7.62  7.83  7.54  7.96 11.12
63  5 31  2.04  2.21  9.04  2.88  2.67  2.17  6.13  3.58  6.46  6.92  4.96  9.42
63  6  1 13.37  6.87 12.00  8.50 10.04  9.42 10.92 12.96 11.79 11.04 10.92 13.67
63  6  2 17.62  8.79 20.62  8.63 11.38 10.75 14.46 13.70 13.70 11.79 12.83 16.46
63  6  3 19.04  9.17 24.13 11.54 12.12 13.04 17.67 14.50 14.00 11.63 14.09 11.75
63  6  4  8.96  7.17 21.92  7.04  8.79  8.79  9.79  9.08  8.58  7.79 11.21  4.96
63  6  5  3.37  4.75 16.83  2.21  3.54  3.88  6.00  7.00  6.21  7.08 12.08  7.00
63  6  6  4.04  3.08  4.00  3.71  4.54  2.54  6.29  6.08  7.00  9.21  6.79 13.46
63  6  7  6.34  8.04  4.63  4.50  6.96  2.92  3.58  4.25  3.54  4.67  5.71 10.96
63  6  8  7.17  7.83  3.67  2.58  7.62  4.17  4.25  5.91  4.83  4.83  5.17  3.33
63  6  9  6.75  3.37 13.25  3.58  4.25  4.38  8.38  7.96  8.42  7.50  6.96  5.96
63  6 10  7.58  4.58  8.87  3.58  6.54  4.08  5.75  7.58  7.79  7.21  7.87  6.67
63  6 11  4.12  3.75  7.87  1.21  5.09  2.92  2.92  4.04  4.25  5.09  6.83  4.63
63  6 12  6.08  4.92  4.42  2.25  5.50  2.21  4.25  4.08  4.38  4.04  8.63  5.63
63  6 13 10.00  3.13  4.17  5.54  5.91  3.67  4.92  3.96  4.67  8.38  5.71  5.41
63  6 14  7.62  3.21  3.75  2.62  5.13  2.42  4.83  2.79  4.54  2.88  5.79  2.79
63  6 15  8.92  8.08  8.04  4.50  9.13  6.58  7.17 11.46  7.67 10.54 14.17 11.87
63  6 16  5.46  6.63  7.33  4.92  7.50  5.58  8.29  6.71  6.75  6.50 10.25 13.21
63  6 17 10.25 10.58 11.83  5.83  8.75  4.92  5.88  8.87  4.46  7.71 13.00  8.25
63  6 18 20.75 14.46 15.34 12.96 20.83 15.29 17.83 17.54 17.50 16.92 21.17 18.54
63  6 19  7.04  7.67  7.33  5.04  8.54  5.17  8.92  7.50  6.50  8.75 11.12 12.79
63  6 20 11.38 11.67 13.00  7.62 13.17  9.50  8.67 12.46 10.21 13.25 17.67 13.25
63  6 21 14.54 10.00 15.34  6.50 11.71  8.79 10.34 10.41 10.96 10.96 13.70 16.08
63  6 22 10.00  9.62  9.17  8.92 13.79  9.92 11.83 10.67 10.25  9.38 14.17 12.71
63  6 23 15.59 14.00 14.83  7.96 15.04 10.75 11.87 13.00  9.83 12.71 17.96 16.25
63  6 24 14.04 11.54 16.38  8.00 16.96 11.67 12.04 10.13 10.79 11.92 13.46 11.54
63  6 25  9.83  9.25  8.96  6.42 14.50  8.63  8.17 10.79  8.17  8.54 13.37 11.92
63  6 26 13.17 10.71 10.83  9.17 14.83  9.59 12.71 11.63 10.67 11.54 14.09 12.71
63  6 27 13.70 12.17 11.79  8.83 13.42  9.75 13.04 13.25 10.92 14.58 14.88 24.25
63  6 28 16.25 13.13 16.04  9.83 15.29  9.00 12.17 14.25 10.25 13.50 18.88 20.67
63  6 29 15.09 14.04 15.21 10.54 14.58  9.00 13.50 15.04 11.50 15.54 20.96 18.75
63  6 30 10.63 10.83 19.00  8.08  8.83  7.25 11.58 10.08  7.38 12.08 13.50  5.29
63  7  1  3.83  5.50  8.50  4.12  6.79  4.04  8.54  8.25  8.04  9.00 10.75 10.83
63  7  2  5.21  1.13  5.96  4.33  5.88  4.12  5.09  8.46  8.00  8.71  9.79 16.83
63  7  3  2.00  2.17  8.25  2.58  4.83  3.13  6.46  5.96  6.34  6.00  6.00  9.00
63  7  4  2.37  2.42  7.79  2.62  3.04  3.17  8.50  6.29  5.17  8.29  6.54 13.79
63  7  5  2.67  2.08  7.46  1.92  3.04  2.88  5.17  5.41  4.67  6.04  7.33  7.29
63  7  6  4.04  5.41  2.83  2.17  4.50  2.79  2.67  6.75  3.33  6.34 10.71  5.17
63  7  7  7.00  7.83  5.83  4.88  5.17  4.50  6.46  7.75  5.54  7.12  9.46 11.21
63  7  8  7.96  7.41  7.67  5.29  8.63  7.00 10.92 10.71  9.67  9.25 11.42 15.83
63  7  9  8.38  8.46  9.50  8.21 12.71  9.87 13.96 11.96 10.17 10.00 12.67 13.21
63  7 10  9.04  7.83  6.21  5.04  8.96  4.88  8.17  7.08  6.79  7.71  8.00  9.17
63  7 11  6.67  5.00  7.12  3.13  7.87  2.58  4.46  4.12  2.79  3.21  3.71  5.79
63  7 12  3.08  5.96  4.25  2.79  5.46  3.37  4.79  5.75  4.75  6.58  7.92  6.46
63  7 13 10.37  7.96  7.54  5.17  7.21  3.83  5.54  6.42  4.00  4.71  9.04  5.83
63  7 14 19.46 17.21 19.33 13.50 15.63 12.62 14.71 19.29 12.33 15.83 23.42 18.50
63  7 15 12.62 10.79 15.87  7.83 11.96  9.08 12.04 10.83  8.75 11.17 14.29 10.58
63  7 16  8.87  9.17  7.21  6.04 10.29  7.00 10.29  9.50  8.75  8.67 12.50 13.29
63  7 17 11.75 10.63 10.92  6.25 10.58  7.58  9.75 10.50  7.71 11.92 12.50 15.04
63  7 18  8.00  6.46  9.04  4.83  7.21  4.88  6.50  8.50  5.21  5.79 12.96  9.08
63  7 19  7.04  5.37  9.42  5.09 11.12  6.54 10.04  7.92  8.54 10.25  9.96 14.54
63  7 20  3.96  5.54  5.75  3.88  8.71  4.58  6.50  6.17  4.17  6.13  9.75  9.33
63  7 21  5.13  8.46  5.17  6.75  8.54  6.13  7.29 11.46  6.54  9.79 16.79 13.33
63  7 22  3.88  2.08  3.67  1.67  4.08  1.50  6.50  3.46  3.88  4.83  4.75  5.88
63  7 23 12.29 10.67 12.79  8.83  9.96  7.12  7.87  7.62  7.58  9.46  8.46  7.67
63  7 24 14.79 11.83 10.63  8.67 13.88  9.33 11.17 12.21  9.62 11.79 12.87 15.83
63  7 25 15.12 10.75  8.96  7.71 11.71  8.42 11.00 10.75 10.37 11.00 13.88 15.54
63  7 26  4.75  6.13  4.00  3.00  7.29  2.92  6.50  5.91  4.58  5.63 11.58  7.62
63  7 27  5.75 10.75  6.58  6.08 10.79  7.96  5.50 10.21  7.21 10.92 19.67 10.08
63  7 28  5.75  8.38  2.83  4.12  9.96  5.00  5.91  7.83  5.66  7.67 12.38  7.58
63  7 29  7.75 12.33  5.54  6.75 11.75  8.29  8.21 11.34  7.75 10.79 19.70 12.96
63  7 30  5.09 12.83  3.08  5.91  9.17  6.83  4.33  9.87  6.54  7.54 16.88  9.79
63  7 31  5.00  8.79  3.50  3.92  9.75  3.42  4.33  9.75  4.46  4.88  7.87  9.00
63  8  1 10.21  7.83 13.70  5.37 11.67  5.00  5.41 11.25  5.66  5.37 13.37  3.67
63  8  2  7.58  7.46 18.25  6.08  9.62  5.00  4.08  8.17  5.41  5.66 11.42  6.96
63  8  3  5.75  3.29  8.96  3.29  6.38  2.29  2.62  1.79  2.21  3.04  3.79  2.25
63  8  4  7.00  6.50  4.79  2.37  5.91  1.87  3.13  3.50  3.67  4.29  8.71  3.71
63  8  5 18.08 12.21 11.08  8.54 11.12  8.58 10.50  9.75  9.92 12.12 10.34 17.75
63  8  6 11.12  7.50  8.17  5.33  9.62  7.41  7.00 10.71  8.63  8.58 14.12 14.00
63  8  7 13.13  9.71 11.92  8.00 14.46 11.38 12.58 13.67 12.25 12.25 16.08 19.79
63  8  8 14.46  7.75 10.83  8.58 13.62  8.96 13.50 10.29 12.25 12.79 12.92 23.29
63  8  9  9.71 10.41  8.96  5.54 10.67  6.21  7.96  5.96  7.41  7.29 10.29 11.21
63  8 10 12.92  8.92 10.00  9.38 14.00 11.38 14.09 12.00 12.71 11.50 11.58 10.21
63  8 11 14.00 10.79  7.29  7.54 13.25  8.87 11.25 10.58 11.34  9.25 12.12 12.50
63  8 12 12.29  9.29  5.66  6.29  9.29  7.21  8.38  9.25  8.46 10.00 12.17 13.70
63  8 13  7.96  7.08  6.67  3.67  7.12  4.12  6.58  6.63  5.91  8.96  8.83 10.75
63  8 14  8.00  7.00  5.04  3.08  8.21  5.33  6.67  7.41  6.58  7.33 10.71 10.25
63  8 15  9.67  8.50  9.08  4.12  9.71  5.37  9.21  7.21  7.46  7.29 10.13 10.29
63  8 16 22.37 16.38 19.75 12.33 14.83  8.79 17.67 12.96 12.08 13.50 16.38 17.62
63  8 17 24.33 15.12 18.75 14.50 15.12 11.12 15.67 11.04 13.92 16.66 12.08 16.08
63  8 18  8.00  4.63  8.42  4.25  7.25  4.17  6.92  4.88  6.54  5.96  7.54  5.33
63  8 19  7.08  6.46  6.58  5.04 10.37  7.25  7.79  8.92  6.92  8.83 10.13 10.25
63  8 20 15.63 12.75  7.00  8.21 13.88  9.04 11.08 10.34 10.21 11.63 11.29 15.29
63  8 21  9.38 10.67 10.58  7.04  9.59  7.38  9.46  8.21  9.79  8.38 10.37 12.00
63  8 22 12.08 10.75 10.41  4.50 11.96  7.58  7.62  9.71  8.08  9.59 15.46  9.67
63  8 23 18.21 15.54 16.33  8.54 17.92 12.54 13.13 12.58 12.17 14.88 17.16 16.71
63  8 24 16.21 12.75 12.79  8.83 14.83 10.96 13.59 11.08 12.29  9.92 12.50 13.21
63  8 25 15.41 14.09 15.83  7.71 14.67  9.59  9.00 15.71 10.41 14.37 22.88 13.21
63  8 26 21.50 20.62 18.54 13.46 23.63 15.87 19.62 19.08 17.54 17.25 23.16 21.54
63  8 27 16.71 11.54 10.88 10.37 15.92 12.25 14.62 14.25 13.79  9.92 16.42 13.17
63  8 28 11.54  9.46  8.83  5.37 10.37  6.75  7.41  8.33  6.58  7.12 12.83  8.50
63  8 29 10.71  7.96 10.13  7.04 10.25  8.46  8.33 11.34  8.75 11.29 16.00 14.42
63  8 30 17.33 19.00 17.04  9.17 20.08 12.75 11.12 14.79 12.29 15.50 19.25 15.21
63  8 31 13.21 20.00 17.12  7.83 13.04  8.54  9.59 12.50  8.79 10.96 17.29 17.46
63  9  1 14.37 12.17 12.42  7.41 10.96  7.41 10.34 11.83  9.13 12.83 16.08 18.00
63  9  2 19.92 16.79 10.08  9.21 12.17  7.96  8.96 11.54  9.42 11.87 14.09 16.08
63  9  3 11.87  7.96  9.92  6.04  9.92  6.71 11.25  8.71  8.63  8.87  8.25 13.59
63  9  4 10.63 10.71  7.12  4.83  8.42  4.42  5.96  6.08  4.58  6.83  7.08 13.50
63  9  5 15.00 10.88  8.42  5.37 11.87  7.00  9.25  9.87  8.29  9.13 11.92 14.67
63  9  6 10.58 10.25  7.50  6.00 10.54  7.04 10.34  8.46  7.54  8.04 12.25 13.88
63  9  7 15.96 13.92 14.75  7.54 13.75 10.88 14.67 12.71 11.38 12.96 12.12 10.67
63  9  8 15.63 13.88 12.83  7.92 16.42 10.00 13.13 10.88 10.71 11.71 19.17 17.29
63  9  9 11.42  9.96 11.00  6.63 10.75  7.71 14.04 10.00 10.21 10.75 13.29 17.37
63  9 10  7.41  7.67  5.54  1.58  4.83  0.67  3.71  2.67  2.37  1.83  3.50  7.38
63  9 11  6.79  3.04  5.46  2.96  5.71  1.71  2.54  1.75  3.67  3.04  5.17  6.25
63  9 12  5.21  6.50  5.79  2.54  4.00  2.42  4.29  7.38  2.71  4.54 14.79  8.54
63  9 13  6.50  6.54  8.04  3.67  7.79  6.50  9.79 10.54  8.63  9.96 16.25 19.55
63  9 14  5.58  2.75  4.92  2.21  6.63  4.92  9.33  7.17  6.34  7.96 15.59 19.67
63  9 15  3.83  7.21  6.71  2.25  4.38  4.46  5.91 12.67  6.08 10.54 20.00 14.92
63  9 16  2.33  8.25  4.04  2.00  5.66  4.08  4.75  8.25  3.83  5.88 17.04 10.08
63  9 17  4.67  7.96  5.46  2.33  5.79  2.50  7.83  8.25  4.54  7.29 13.62 11.38
63  9 18  6.96  7.17 16.54  3.63  8.42  4.88  5.41  6.54  5.29  4.75  7.25 12.08
63  9 19  8.87  6.63  6.63  3.21  9.87  4.75  6.75  5.63  5.21  5.58  5.63 10.71
63  9 20  7.08  7.79  5.13  4.17  9.46  4.42  5.50  5.21  3.96  5.91  7.25  7.83
63  9 21  3.63  6.71  4.42  1.00  7.54  2.21  2.54  5.83  1.42  4.33 10.25  9.21
63  9 22  1.29  8.21  3.96  1.54  6.42  2.04  3.21  4.79  3.33  5.21 15.59 10.50
63  9 23  8.54 14.96  5.25  5.04 12.00  6.83  5.09 11.21  6.87 10.75 23.75 17.16
63  9 24 18.84 15.37 13.25 10.34 16.83 11.04 14.67 12.00 11.58 13.88 16.58 20.67
63  9 25 18.25 15.87 14.42  8.46 13.88  9.87 13.37 11.38 10.08 12.46 19.33 19.33
63  9 26 19.58 17.92 16.66 13.75 24.13 17.96 23.04 22.58 18.88 16.96 26.12 33.42
63  9 27 11.92 10.25  9.17  7.12 13.46 10.83 15.00 11.38 12.38 13.21 17.12 24.41
63  9 28  9.67 10.08 10.54  4.21  6.75  6.54 10.17  9.54  6.58  8.63 13.96 14.00
63  9 29 17.50 10.50 13.70 10.46 14.71 11.87 17.88 14.37 14.58 14.58 16.88 23.96
63  9 30 11.38  7.92  7.67  7.38 12.21  8.00 12.50  8.42 10.54  9.38 11.63 18.29
63 10  1 16.00 14.71 10.96 10.37 16.46 11.21 15.71 14.62 13.46 11.67 19.55 21.67
63 10  2 15.16 12.42 11.25  8.42 13.50  7.79 10.54 10.00  9.00 10.21 15.54 14.92
63 10  3 16.46 14.33 13.08  9.13 12.83 10.41 14.21 10.17 11.29 10.58 14.62 19.21
63 10  4 14.17 12.08  9.21  8.50 14.21  9.04 14.25 12.62 12.00 10.75 17.33 19.55
63 10  5 12.17  8.42  9.29  6.92 13.13  3.88  9.04  5.75  7.25  8.38  7.83 17.04
63 10  6 12.75 11.87  8.63  6.54 14.04  9.42  8.71 10.21  8.83  5.66 14.12 11.29
63 10  7 13.08 10.88  9.46  5.41 10.41  7.71 11.17 11.79  9.08  8.50 16.46  9.62
63 10  8 17.29 13.13 13.42  9.96 14.50 11.21 14.33 15.96 12.25 13.59 20.41 22.37
63 10  9 10.08  9.42 10.29  6.08 17.33 11.83 15.25 14.92 12.71 15.54 23.38 28.50
63 10 10  7.62  5.79  8.21  5.88 11.96  9.00 14.42 11.34 11.17 11.21 16.38 20.62
63 10 11 10.29 12.83 12.50  4.83  8.12  6.34  9.83 13.42  7.25 13.04 18.66 22.08
63 10 12 11.04 10.58 13.00  3.83 10.58  7.38  9.42  9.21  7.25  9.08 17.83 15.54
63 10 13 13.88  7.92 10.54  7.41  9.79  8.42 14.21  7.08 11.25 11.50 13.37 22.92
63 10 14  9.79 10.50  9.75  3.79  8.25  7.67  3.83  6.58  6.34  5.54 13.50 10.50
63 10 15 15.00 15.37 15.04  8.54 13.00 12.00 11.67 13.46 10.83 13.54 21.25 19.79
63 10 16 12.17  9.08  8.25  5.46 11.54  7.50  7.46  8.79  6.25  8.29 16.00 13.46
63 10 17 13.08 12.46 11.08  5.50 12.38  8.67 10.37 11.38  8.46 10.75 19.62 16.75
63 10 18 16.29 15.34 15.09  9.17 12.33 10.67 12.96 13.92  8.92 13.67 20.96 16.17
63 10 19 22.00 17.54 19.55 14.29 18.71 15.34 15.09 16.38 13.83 16.79 23.54 22.13
63 10 20 19.21 18.05 14.58  9.13 15.71 12.71 13.42 15.83 10.75 15.67 26.38 19.29
63 10 21 19.50 11.12 18.63  9.92 12.46 10.71 13.88 10.41 10.17 12.21 15.09 17.29
63 10 22 12.92 12.29 10.63  6.50 10.37  9.00  9.79  7.21  5.58  7.41 11.71 13.33
63 10 23 16.17 14.33 14.71  9.50 15.25 11.21 12.92 12.29 11.83 14.09 20.91 18.05
63 10 24  6.25  9.83  6.71  2.96  8.71  5.25  4.83  7.75  4.33 10.08 15.50 13.50
63 10 25 10.00 11.17  5.63  4.75 12.12  7.12  6.04  7.12  5.33  5.66 10.46 10.54
63 10 26 16.46 17.96 12.79  8.54 16.00  9.87 11.21 11.29  8.25  9.79 14.12 10.41
63 10 27 20.41 18.79 15.29 12.12 21.54 13.33 15.37 15.59 10.79 11.17 19.41 11.42
63 10 28 16.42 13.29 15.87 10.34 12.08  8.96 12.04 11.38 11.83 12.96 14.17 19.87
63 10 29 12.46  9.79 14.96 10.08  9.92  7.08  8.21  8.71  8.33 11.38 11.92 12.33
63 10 30 13.88  3.71 18.66 11.04 12.42 11.00 15.92 10.04 11.04 14.37 10.58 17.00
63 10 31  6.75  7.79  8.29  3.37  8.33  4.38  5.25  8.04  5.75  8.00  9.87 15.83
63 11  1 10.34  7.87 15.21  6.04  8.21  6.25  8.25  6.87  6.54  6.79  7.17 11.12
63 11  2  7.08  4.88  6.46  2.83  5.17  1.67  6.00  4.83  4.54  5.00  7.58  8.79
63 11  3 12.87 10.08 11.25  4.25  8.12  5.66  7.33  9.96  6.50  8.58 11.50 15.54
63 11  4 13.50 11.04 19.29  8.83 10.54  8.87 16.13 11.71  9.92 11.34 12.38 20.25
63 11  5  9.29  7.50 10.67  5.04  9.92  7.08 11.12 10.83  7.29 10.58 12.83 16.17
63 11  6  4.00  1.92  4.83  0.96  4.17  0.37  3.00  2.58  0.87  0.87  7.25  2.96
63 11  7 11.79  7.67  7.75  4.92  9.17  4.38  7.62  4.50  4.50  4.12  6.25  5.79
63 11  8 11.67 12.08 10.50  6.50 14.09  9.75 13.00  7.62  8.92  9.59 12.25 12.42
63 11  9  7.41  9.96  7.54  3.88  8.79  5.66  6.58  5.54  4.67  3.92  7.83  8.63
63 11 10 32.96 22.25 26.67 20.46 23.75 16.83 19.83 21.29 16.71 17.25 24.13 33.87
63 11 11 26.92 22.04 22.67 19.50 27.04 19.12 18.29 15.67 16.21 18.63 14.17 15.59
63 11 12 16.42 15.71 14.75 14.96 21.17 19.00 25.92 20.33 22.08 20.04 28.62 24.30
63 11 13 10.29  9.42  6.67  3.67 11.12  7.79 14.42  7.79  9.21 11.29 16.96 22.08
63 11 14 13.62 13.08 13.67  4.42 14.37  7.62 12.08  6.54  7.46  9.13 18.00 14.46
63 11 15  8.50 11.04  4.12  1.04 10.00  5.13  2.92  7.21  3.08  4.42 10.13  4.79
63 11 16 16.50 14.67  8.58  6.67 12.79  5.66  6.29  6.08  2.54  3.37 14.58 11.00
63 11 17 21.67 19.75 16.96  8.17 19.17 13.50 13.75 15.16 11.04 11.71 18.21 19.17
63 11 18 25.12 21.04 20.71 14.50 21.21 15.71 22.50 14.37 16.46 15.34 15.34  8.92
63 11 19  9.29  6.54  8.63  5.17  9.00  5.75 10.08  7.08  6.08  7.33 16.79 18.91
63 11 20 10.75  8.71 10.96  3.58  8.38  3.46  7.17  5.96  5.71  7.21 13.96 15.21
63 11 21 26.38 24.67 22.67 17.41 26.42 18.91 24.71 20.38 20.50 21.34 30.00 31.05
63 11 22  8.71 10.92  6.58  4.79  8.17  6.04 10.21  7.58  7.00  8.75 12.96 18.88
63 11 23 22.63 19.38 19.29 10.92 13.96 12.79 13.17 12.79  9.67 13.04 13.88 18.50
63 11 24 26.50 21.29 21.92 16.46 20.08 15.21 14.62 17.21 15.41 17.33 20.33 18.75
63 11 25 26.58 26.54 19.46 14.75 23.09 15.54 18.41 18.54 17.62 17.04 24.13 17.92
63 11 26 22.83 13.96 14.00 12.12 13.37  9.83 14.42 10.83 13.96 12.62 14.21 17.92
63 11 27  8.79 10.88  9.08  4.96  9.92  7.17  5.91 10.04  7.00  9.13 18.91 15.83
63 11 28 15.83 11.12 13.54  8.21  8.54  6.34 10.41  5.96  9.50  8.17 10.54 14.37
63 11 29  3.08  6.17  9.71  1.33  5.88  2.21  6.46  3.92  1.58  2.29 10.21  7.08
63 11 30 19.17 17.46 11.08  3.71 14.88  7.50  6.42 11.87  5.79  6.13 16.88 12.50
63 12  1 24.79 16.46 18.75 11.17 21.71 12.83 13.79 18.25 12.08 13.79 19.79 15.37
63 12  2 15.67 12.21 17.96  9.75 11.17 10.88 16.00 15.12 14.12 12.75 16.88 26.16
63 12  3 22.79 18.50 27.67 16.17 19.41 19.87 21.29 20.75 18.88 17.33 21.29 31.75
63 12  4 13.96 10.83 15.50  7.75 10.29 10.17 16.08 12.79 13.17 12.12 12.71 24.33
63 12  5 10.04  7.12 13.62  3.96  7.04  3.79 10.71  7.33  6.13  7.58  7.71  5.09
63 12  6 11.17  7.46 12.62  4.00  7.83  4.12 13.21  6.96  4.75  6.67  8.29 12.92
63 12  7 10.13  8.63 11.42  3.29  7.08  3.96  8.92  4.67  3.04  5.50  7.33 10.08
63 12  8  7.29  1.08  7.38  0.42  6.54  1.46  1.83  1.29  1.13  1.29  6.29  6.67
63 12  9  6.83  4.88  6.25  0.46  6.13  1.75  3.46  2.04  0.83  1.25  7.50  7.38
63 12 10 20.12 20.33 18.66 10.63 18.05 13.17 12.17 15.29  9.25 13.29 18.75 14.12
63 12 11 18.34  3.37 23.13 12.38 18.29 16.08 17.54 18.84 12.50 15.00 18.41 20.67
63 12 12 16.42 11.71 17.50 10.00 15.71 11.00 16.96 15.75 11.71 12.92 15.54 20.79
63 12 13 17.46 12.54 17.96  6.04 12.00  6.87 13.04 10.88  7.83  8.79 11.38 14.29
63 12 14 12.58  8.42 16.50  7.00  8.87  4.04  9.08  7.25  5.83  4.67  6.87 10.00
63 12 15  9.42 10.21 13.13  5.00  7.67  3.04  6.50  4.88  3.83  3.92  3.83 10.75
63 12 16  9.42  9.83 10.37  3.25  6.00  4.88  4.92  4.50  2.29  3.79  6.00  4.25
63 12 17  9.59  9.71  9.13  3.42  6.04  4.75  6.42  5.54  3.21  3.25  6.38  9.42
63 12 18 11.67 10.46 15.12  5.54  7.29  3.75  7.50  5.17  4.79  4.96  7.25 14.54
63 12 19 12.58 10.37 19.29  8.08 10.08  3.92 10.58  5.66  5.33  4.21  5.37 15.12
63 12 20 11.83  5.75 11.50  3.21  4.71  0.79  8.38  5.21  3.00  1.92  4.17 11.12
63 12 21  9.96  2.13  8.29  2.46  4.42  2.37 10.88  1.92  3.96  6.67  5.63 11.71
63 12 22  9.50 12.08  6.42  0.50 10.54  6.29  6.63  9.79  2.92  8.38 19.38 13.25
63 12 23 27.37 22.71 18.96 10.58 20.25 14.46 12.08 19.46 12.42 20.58 31.25 30.50
63 12 24 32.50 26.54 26.25 17.00 24.71 18.71 18.08 21.21 18.58 21.37 29.38 29.63
63 12 25  7.96  6.92 10.63  5.13  9.92  8.29 12.21  9.17  8.79 14.17 19.04 24.67
63 12 26  4.25  4.79  5.79  1.13  7.12  5.58  8.29  6.13  5.63  6.87 13.17 15.25
63 12 27 16.79 17.29 15.09  9.29 14.25 12.17 10.54 15.75  9.42 15.50 26.63 21.21
63 12 28 19.21 19.38 16.25 14.29 17.88 14.79 12.87 16.96 12.71 16.88 29.71 24.83
63 12 29 17.96 16.79 14.96 11.21 15.79 13.37 14.29 13.37 13.37 15.16 22.00 20.91
63 12 30 22.21 19.08 20.46 12.29 16.58 14.17 17.29 17.12 12.96 18.54 26.08 21.59
63 12 31 13.88 14.42 12.12  9.25 14.33 10.67 18.29 11.96 12.04 15.37 16.79 14.09
64  1  1 25.80 22.13 18.21 13.25 21.29 14.79 14.12 19.58 13.25 16.75 28.96 21.00
64  1  2 33.34 28.50 24.37 28.46 23.50 20.33 22.63 25.92 17.71 24.17 36.95 28.96
64  1  3 22.63  9.92 19.67 18.12 11.92 12.12 16.13  9.67 12.58 15.29 14.50 14.71
64  1  4  8.67  4.79 14.50  7.75  7.54  2.58  6.50  2.42  4.42  5.13  4.58  4.83
64  1  5  5.50  6.75  3.79  3.58  8.83  3.50  3.67  6.71  5.79  5.63  8.63  9.71
64  1  6  6.87 10.75  7.00  4.21  9.25  5.29  2.54  6.21  2.46  5.79 17.08 10.37
64  1  7 12.29 11.92  7.92  4.17 10.63  4.46  2.92  9.54  3.67  8.12 21.54 10.00
64  1  8 15.09 12.54  7.71  6.29  9.13  4.29  4.08 11.87  5.25  9.46 22.75 17.37
64  1  9 10.67  9.08  7.96  4.38  7.00  2.75  2.67  5.88  2.25  2.83 14.04 10.34
64  1 10 11.25  9.92  8.71  3.00  9.17  2.67  2.71  7.79  3.46  3.00  9.08  3.63
64  1 11 10.04 10.41 11.96  2.67  9.92  2.92  4.42  4.67  3.29  4.12  5.96  4.71
64  1 12  8.21  5.88 17.08  6.79  9.17  2.25  7.54  5.91  6.04  5.66  5.21 11.96
64  1 13 14.25 11.71 18.50  7.41 12.29  6.25 15.04 12.21  8.75 14.33 14.21 24.96
64  1 14 15.96 18.91 12.46  3.00 12.67  6.42  8.71 11.29  3.67  9.25 14.50 20.12
64  1 15 19.04 21.62 15.34  7.67 15.21 10.25 11.12 14.09  9.00 15.00 15.41 16.54
64  1 16 19.46 20.58 15.79  9.38 15.41 10.50 11.34 13.25  7.87 12.17 14.67 18.58
64  1 17 16.25 17.75 15.59  9.13 15.83 10.29 12.25 10.54  6.54  8.58 16.38  9.67
64  1 18 16.42 16.96 18.63 10.71 15.46 12.54 15.29 13.59 11.29 12.62 20.12 19.50
64  1 19  2.92  9.62  7.83  1.00  6.29  2.75  2.62  6.71  1.38  5.79 16.66  8.79
64  1 20  2.08  5.71  4.54  2.21  5.88  3.08  7.58  6.17  4.42  8.29 12.33 13.13
64  1 21  1.87  4.75  4.29  0.50  5.04  0.71  1.92  0.96  0.46  0.46  7.21  5.63
64  1 22 10.37  9.96  8.46  2.67  8.17  3.13  3.00  3.25  2.37  4.71  7.62  6.87
64  1 23  7.83  3.13  9.08  4.63  7.17  5.71  9.92  5.71  5.33  8.67  9.75 15.00
64  1 24  8.29  4.21  9.71  4.33  4.04  1.00  6.34  0.75  2.00  5.04  3.88  8.17
64  1 25  3.63  3.04  6.00  0.67  4.12  1.21  9.08  5.00  2.75  7.08  9.50 11.71
64  1 26  3.08  5.25  5.13  0.75  5.79  2.29  7.12  5.91  2.42  6.13 12.00 10.71
64  1 27 15.46 13.13 14.33  6.58 12.79  9.33 11.08 13.21  8.12 12.29 21.29 15.46
64  1 28 16.04 11.12 10.63 10.46 13.17  8.67 15.87 11.54 12.21 13.75 16.66 17.96
64  1 29 12.62 12.21 12.17  8.58 16.83 11.63 17.25 13.37 13.59 15.83 20.25 17.46
64  1 30 18.16 16.66 11.87 11.67 18.16 12.67 19.00 15.25 14.33 14.54 21.46 25.12
64  1 31 18.41 17.46 14.75 11.54 21.75 16.46 22.92 21.34 18.71 19.50 23.91 25.54
64  2  1 13.25 13.83 12.00  8.79 13.42 12.50 17.67 14.50 11.96 15.87 15.75 18.34
64  2  2 16.92 18.25 17.25  9.21 15.34 13.21 15.34 19.12 11.25 17.46 26.92 22.00
64  2  3 16.08 13.50 13.83 10.41 15.46 11.67 16.79 12.50 12.96 16.33 20.58 24.41
64  2  4  9.21  7.83  8.58  6.63 11.34  9.17 15.50 11.79 10.21 17.04 16.62 26.04
64  2  5  8.08  6.21 11.21  3.25  6.83  3.25  7.25  3.17  3.08  3.83  6.29  9.04
64  2  6  9.04  5.25  4.08  0.33  6.54  0.58  5.63  2.67  0.96  4.17  4.92 10.46
64  2  7  7.50  7.38  4.17  0.71  6.75  1.87  5.88  2.50  0.50  3.96  7.33  7.71
64  2  8  4.33  6.79  6.29  1.00  3.88  0.75  7.21  1.50  2.96  4.92  7.17 10.50
64  2  9  3.67  2.96  3.67  1.08  4.29  1.33  7.54  4.17  1.08  5.25 11.29 10.58
64  2 10  4.88  3.79  5.50  0.42  4.63  1.87 10.83  4.75  3.21  5.88  8.92 12.12
64  2 11  7.08  9.92  7.29  1.67  7.00  4.21  8.50  6.75  3.08  6.75 11.92  9.83
64  2 12 20.96 15.63 14.09  9.00 15.63 11.12  9.67 14.42  8.92 10.04 17.46 16.08
64  2 13 26.20 16.08 24.54 14.50 22.13 17.33 20.30 18.58 13.17 17.88 19.25 19.21
64  2 14 14.17 10.34 16.42  9.08 15.12 11.00 17.04 12.87 10.41 14.96 13.67 23.54
64  2 15 15.50 12.71 15.34  9.75 14.12 11.38 13.88 11.92  8.87 13.70 13.92 16.46
64  2 16 12.54  9.17 18.54  7.54 10.54  7.50 12.96  9.54  6.42 10.92  8.71 21.37
64  2 17 11.46  8.04 20.88  8.71 10.34  7.50 12.83  9.13  8.96 10.34 11.29 22.83
64  2 18 18.21 16.00 22.79 12.75 18.16 11.87 24.00 20.67 12.71 18.34 22.46 30.91
64  2 19 16.04 12.17 16.58  8.67 15.87  9.29 16.38 14.25  8.96 10.83 14.17 17.37
64  2 20 17.58 16.50 13.70  8.12 16.33 11.63  9.67 13.67  6.96 11.34 12.92  9.87
64  2 21 25.25 21.84 22.79 12.21 21.59 16.54 18.29 18.12  9.38 16.33 18.38 13.96
64  2 22 16.66 17.25 24.25 14.04 18.16 16.83 19.25 17.04 13.13 18.96 16.62 22.63
64  2 23 11.54 11.50 11.58  6.92  9.96  6.58  8.92  7.96  5.21  9.50 11.42 11.96
64  2 24 24.87 19.87 19.00 17.25 20.46 16.29 15.04 18.08 13.88 18.41 18.05 15.54
64  2 25 15.83 14.12 14.88 11.42 14.04 10.41 11.08 12.58 11.29 15.29 17.50 18.66
64  2 26 23.21 25.66 20.21 14.83 22.54 16.17 16.25 17.37 13.13 17.50 19.55 18.16
64  2 27 21.34 18.08 17.79 13.70 17.67 12.42 13.79 16.25 11.75 17.96 20.25 19.12
64  2 28  9.08  9.29  9.54  5.17 11.12  6.50  8.46 10.50  6.00 12.71 18.84 14.96
64  2 29  8.83 12.17  8.04  5.13 12.50  6.92  5.88 11.38  5.58 11.38 19.95 15.21
64  3  1 13.67 14.92 11.00  6.46 15.37 10.21  8.87 12.25  5.13  9.29 13.46 12.25
64  3  2 10.63  9.92  7.87  3.08 10.00  2.50  6.04  6.46  3.37  6.29  7.21  6.25
64  3  3  7.46  8.21  7.46  2.37  9.54  1.21  4.83  3.67  2.46  3.29  5.09  5.66
64  3  4  8.12  5.09 10.29  3.04  8.79  3.33  7.83  5.04  3.25  5.96  7.08 10.00
64  3  5  7.62  7.38 14.79  4.46  7.33  2.92  7.67  4.00  3.25  5.88  5.37  5.33
64  3  6  8.71  6.87 13.13  3.88  8.33  2.50  5.88  5.79  3.50  6.29  5.46  8.17
64  3  7 12.83  9.79 20.83  7.21  9.75  5.33 10.08  5.21  6.08  6.42  4.38  5.25
64  3  8 11.58  9.25 22.46  6.87  9.38  3.67  9.50  8.08  5.83  6.79  7.41  8.75
64  3  9  9.75  6.25 19.04  6.34  8.83  5.79 10.29  6.96  5.33  7.96 10.71 12.38
64  3 10 16.66  9.54 16.00  6.54 12.58  8.38 10.67 10.25  7.83 10.08 12.42 17.25
64  3 11 23.63 20.33 19.21 12.67 23.54 15.92 15.16 16.17 12.29 14.50 16.58 16.96
64  3 12 17.71 15.16 20.50 10.21 21.34 15.50 15.37 18.79 14.42 18.29 19.55 25.21
64  3 13 11.46 13.37 13.33  8.79 15.29  9.75 12.87  9.50  8.29 13.37 13.13 17.79
64  3 14 18.88 18.58 15.75 10.21 18.12 13.50 13.21 17.62 10.50 17.46 23.38 22.34
64  3 15  8.83 11.25  7.17  3.54 11.21  6.25  6.54  9.04  4.54 11.79 16.83 19.83
64  3 16 21.25 20.12 19.95 11.50 23.16 12.33 17.41 16.75 11.67 16.00 18.00 22.21
64  3 17 16.58 15.92 29.08 14.54 18.08 18.54 23.13 19.04 15.83 23.13 20.50 32.25
64  3 18 16.88 13.83 25.80 14.12 19.41 19.12 22.54 18.91 13.42 18.63 21.34 32.88
64  3 19 17.29 14.62 19.87 11.17 16.83 11.34 15.37 15.92 11.34 16.33 18.79 30.88
64  3 20 10.17 10.25 12.46  7.08  9.17  5.63 11.34  6.75  6.50 11.38  7.67 13.96
64  3 21 10.21  8.33  7.50  3.08  6.63  3.71  8.71  4.38  4.42  8.42  4.63 11.96
64  3 22  5.21  4.29  4.71  0.71  4.33  0.13  6.08  2.67  4.58  5.83  5.88 10.04
64  3 23 10.34 10.37 13.17  6.71 10.25  9.21 11.00  8.21  8.46 10.29 12.38 14.29
64  3 24 13.79 13.37 10.63  7.29 12.17  6.63  7.58  7.92  6.75  9.46 13.33 14.92
64  3 25 17.16 11.75 16.92 11.92 14.75  8.83 15.37  9.46 12.71 15.67 12.04 20.38
64  3 26  7.08  9.42  7.21  3.42  8.71  4.25  6.00  7.54  3.25  6.71 16.71  9.54
64  3 27 15.00 14.71 12.42  8.04 14.17  9.17  9.67 12.33  9.08 13.67 16.79 17.79
64  3 28  9.50  8.33 10.75  5.37 12.71  8.21  8.92  9.71  6.29 10.29  7.92 15.12
64  3 29  7.87  6.08  7.79  2.04 10.88  4.04  7.71  8.38  5.96  8.83  9.17 10.25
64  3 30 15.04 15.25  8.25  5.21 16.50 10.08  7.71 12.83  7.08  9.62 16.21 13.13
64  3 31 13.96 11.83 13.04  6.08 14.29  7.29 10.58 12.33  6.75  8.92 13.54 14.67
64  4  1 11.38 11.21 23.00  8.25 12.71  7.71 12.04 11.34  7.04 10.34 12.21 12.96
64  4  2 13.75  9.83 24.79  8.75  9.62  5.46 10.63  7.96  6.08  7.50  7.75 11.34
64  4  3 13.17 12.38 24.75  9.92 10.34  8.29 11.83  9.62  7.71 11.92 13.42 15.75
64  4  4 14.12 12.92 24.17  9.87 10.50  7.62 12.75  9.87  6.42  9.25 10.21 11.96
64  4  5  9.62  6.58 10.58  7.25  9.04  3.17  6.58  6.75  4.71  6.87  6.92 10.37
64  4  6 10.83  9.21  7.21  5.50 10.41  4.83  7.33  8.42  4.88  7.75 10.63  9.42
64  4  7  6.71  4.08  7.17  3.17  6.75  2.71  7.29  6.58  4.00  7.17 11.58 11.54
64  4  8 11.83 13.04 12.17  6.34 10.37  8.33 11.63 14.17  6.54 11.83 20.30 18.08
64  4  9 18.75 12.92 16.83 13.04 19.21 13.59 18.05 15.29 13.25 16.88 19.17 19.92
64  4 10  8.46  6.83  8.29  3.37  6.79  5.79  8.54  8.46  5.66  8.67 14.42 11.58
64  4 11 10.08  7.83 11.38  4.50 10.13  7.96 10.04 12.42  7.29 11.21 14.21 14.00
64  4 12 14.37  9.83 12.33  7.96 16.50 11.83 14.37 13.25 11.75 13.42 14.58 17.54
64  4 13 14.88 15.79 13.13 11.12 18.71 10.25 15.96 14.00 11.46 15.04 20.50 19.75
64  4 14 16.88 15.75 15.09 10.08 18.71 12.83 19.04 16.33 14.79 16.54 21.96 21.71
64  4 15 17.12 12.71 17.58  9.71 12.62  7.92 10.71 11.08  6.75 12.08 17.25 14.46
64  4 16 10.37  7.12 10.67  5.88  9.92  6.58  9.17  7.50  4.96  8.42 11.75 10.13
64  4 17 15.00 11.58 14.25  9.87 14.09 10.79 12.79 11.75  8.79 11.25 13.25 11.00
64  4 18 10.88  7.41 15.87  9.75 11.42 12.87 12.87 12.29 11.00 13.54 11.58 16.88
64  4 19  7.71  4.54  4.83  2.33  6.58  2.42  4.75  3.88  1.38  5.83  7.87 11.58
64  4 20  6.63  5.29 10.00  5.29  6.71  5.96  9.25  4.08  5.50  8.75  6.29 12.67
64  4 21  7.71  6.50  7.21  3.88  8.38  3.13  5.41  5.63  3.25  6.63  9.00 15.63
64  4 22  9.21  9.42 10.29  4.25  8.71  4.38  9.04  6.08  4.04  6.25  7.67  5.79
64  4 23 11.92  8.00  7.54  7.96 12.21  7.41  9.79  7.17  6.34  7.29  9.17  6.54
64  4 24  5.71  8.58  6.42  4.50  9.54  5.46  7.83  7.17  4.08  7.50  9.08 12.58
64  4 25 12.87 15.71 13.83  9.96 14.71 12.42 12.29 15.21  8.75 14.75 20.21 18.54
64  4 26  9.13 11.38  9.75  8.04 11.54  8.08  7.21 10.21  6.50 11.25 10.63  8.04
64  4 27 16.50 15.25 15.83 10.25 15.09 12.71 11.42 14.37  9.96 14.46 16.75 17.16
64  4 28 15.59 11.63 12.25  7.29 14.62  9.33 11.63  8.08  8.79 11.46 14.25 17.83
64  4 29 14.37 14.96 11.12  9.71 17.04 12.00 15.09 14.62 11.87 14.67 17.67 19.92
64  4 30 14.50 12.96 12.29  9.59 16.13 11.58 14.50 12.33 10.71 13.21 17.29 20.96
64  5  1 10.96 10.71 10.50  6.67 10.41  6.54 11.08  9.87  6.92 11.34 13.46 18.38
64  5  2 17.54 17.62 16.46  9.75 13.92 10.54  9.79 14.67  8.38 13.79 21.17 13.13
64  5  3 22.17 21.84 20.54 12.67 25.66 17.04 20.08 21.12 15.67 20.25 28.33 25.66
64  5  4 18.29 15.67 15.67 11.00 20.91 14.75 19.41 14.09 13.08 13.46 21.79 22.95
64  5  5 17.75 15.34 14.83  9.33 17.96 12.46 15.54 17.88 11.25 17.62 24.58 23.16
64  5  6  9.92 12.46 11.25  7.83 12.42  8.38  9.75 11.83  7.12 14.00 17.62 15.75
64  5  7 19.50 15.25 20.04 13.33 17.25 13.70 16.66 12.12 12.50 15.75 14.96 11.96
64  5  8 22.37 21.09 20.88 13.04 21.17 15.00 19.70 18.12 13.59 18.12 23.09 22.37
64  5  9 23.29 20.96 21.34 13.42 19.67 15.59 15.96 18.21 12.21 17.41 29.00 22.46
64  5 10 17.25 17.33 17.67 10.75 17.21 12.54 15.92 15.46 11.79 14.58 20.88 19.29
64  5 11 18.91 16.92 20.46 10.34 14.00 12.33 15.04 15.34 10.21 16.13 20.62 12.58
64  5 12 17.88 14.83 17.54 11.25 17.67 13.29 14.37 16.33 13.13 16.33 24.54 26.30
64  5 13  7.83  6.54  8.00  6.29 11.96  8.92 11.54 10.92  8.38 10.88 16.54 20.41
64  5 14  8.12  4.46  9.33  4.33  8.33  5.04  9.92  6.46  6.21  9.59  9.25 15.21
64  5 15  5.33  9.46  7.29  5.37  9.71  5.71  5.25  6.54  4.42  7.21  9.38  7.50
64  5 16  6.54 12.08  8.46  6.58 11.63  8.83  5.66  9.21  5.96  9.62 15.34 10.54
64  5 17 12.12 11.83  7.54  6.54 11.67  8.50  7.08  9.29  6.04  8.54  7.54  9.75
64  5 18 13.50 12.04 14.71  8.79 15.00  9.17 13.46 10.34 10.29 10.34 13.21 15.50
64  5 19  8.96  9.71  9.33  4.96 11.04  7.46  7.33  8.79  6.46  7.17 13.46 14.58
64  5 20 10.96 12.46 10.83  5.33  9.46  6.00  7.54 10.08  5.75  8.50 14.88  9.33
64  5 21 14.75 13.75 14.58  6.96 13.08  8.87  6.96 12.50  7.79 12.42 20.33 12.29
64  5 22 12.71 11.75 12.83  8.58 11.21  8.92 10.71 10.79  9.13 11.04 12.54 13.37
64  5 23 10.67 10.92 17.75  8.17 10.29  7.50 10.92 10.63  8.12  9.67 12.79  4.46
64  5 24  9.00  9.71 23.29  9.54 11.54  6.75 11.08 11.63  7.08  6.96 15.96  5.83
64  5 25 10.21  6.08 24.00  9.25  8.42  5.54 11.29  8.46  6.63  7.00 13.88  4.71
64  5 26  4.88  3.33 15.21  4.29  3.83  5.83  6.13  6.17  6.87  7.58  7.46  7.75
64  5 27 12.17 16.42  6.67  5.79 13.83  6.46  8.87 10.34  8.75 10.54 11.92 17.58
64  5 28 10.88 12.75 14.58 10.75 12.96 12.62 10.54 12.92 10.96 12.62 11.92 14.83
64  5 29 10.83 12.54  6.96  5.75 12.58  6.08  8.17  7.75  6.42  8.12  8.38  8.29
64  5 30  9.33  6.92  9.71  2.71  7.41  3.13  3.08  3.71  4.38  5.00  7.92  7.79
64  5 31 12.29  8.75 19.33  7.92 12.62 11.00 13.54 13.37 12.17 13.70 16.21 18.12
64  6  1 18.50 16.08 26.12 10.46 16.33 13.29 17.37 15.96 13.46 13.42 19.29 19.87
64  6  2 10.75 11.67 22.29  7.38 10.54  6.79 10.67 11.96  8.87 11.58 15.75 16.79
64  6  3 12.04 10.08  9.79  5.63 10.34  5.58  7.67  9.04  7.75  7.87 11.00 15.46
64  6  4 19.04 16.71 15.75 13.37 17.62 11.38 15.50 12.62 13.00 15.25 13.00 19.12
64  6  5 16.25 14.42 14.88 10.21 12.67  9.33 11.12 13.25  8.38 11.29 20.50 12.83
64  6  6 11.00  5.91 13.33  5.00  8.29  4.12  8.25  6.00  5.58  7.00  8.75 11.12
64  6  7 11.54  8.58  8.58  6.54 11.42  6.38 10.04  8.54  8.08 10.83 10.67 13.75
64  6  8 12.46 12.83 10.34  7.92 12.46  8.75 10.71  9.54  9.13 10.71 14.04 11.29
64  6  9 15.09 14.29 15.00 10.25 11.50  9.96  8.58 10.75 10.21 12.50 16.92 13.50
64  6 10 20.41 17.25 15.92  9.33 16.21 10.21  9.62 13.79 10.50 14.29 22.75 14.67
64  6 11 16.08 14.58 15.96  9.38 12.42  8.54 11.21 12.38 10.00 15.04 15.79 12.75
64  6 12 10.54  8.75  9.71  6.21  9.71  5.41  6.75  8.63  6.67  7.83  9.29  8.87
64  6 13 17.37 16.58 13.50  8.08 14.09  7.12  8.63 12.04  8.63 10.58 15.67 14.46
64  6 14 15.21 13.92 12.54  8.92 17.88  9.92 13.25 13.21 11.58 12.96 19.08 20.04
64  6 15 11.00 10.21  9.25  7.92 14.17  9.87 15.37 12.17 12.33 14.46 17.21 23.58
64  6 16 10.63  8.42 11.17  6.54 13.79  8.29 13.21 10.21 10.46 11.25 15.34 15.21
64  6 17  7.92  9.33  6.17  3.33  7.92  2.58  6.17  6.29  4.17  4.71  8.38  8.29
64  6 18  8.63 10.34 10.00  4.92  9.50  3.63  5.91  8.71  5.21  8.25 11.50 10.88
64  6 19 14.12 10.54 14.33  9.59 11.34  7.54 13.25 10.88 11.21 14.09 13.17 23.54
64  6 20 11.50  6.04 11.58  6.63 10.13  6.08 10.25  7.38  9.17 11.63  8.75 15.96
64  6 21 12.29  7.21  7.87  6.83 10.58  6.63  8.96  7.58  8.12  9.79  9.38 12.04
64  6 22  8.08  7.33  5.50  5.66 11.12  5.29  3.67  6.17  4.79  4.46  8.87  6.25
64  6 23  8.00  4.88  5.71  6.00  7.67  3.96  4.63  4.12  5.54  6.42  6.96 10.79
64  6 24  3.54  3.17  5.46  2.79  4.71  2.92  4.29  3.58  4.29  5.25  9.96 12.54
64  6 25  3.88  2.37  1.83  2.71  3.37  0.67  3.92  2.83  1.79  3.13  9.54  4.71
64  6 26  6.21  7.50  6.17  3.29  8.58  5.41  8.00 12.08  7.92 11.50 19.62 15.46
64  6 27  8.54  9.75  7.17  5.91 12.50  7.21 10.41  7.00  9.08  7.46  9.29 13.08
64  6 28  7.38  7.62  5.83  5.25 10.75  4.92  5.21  4.67  5.75  8.08  7.33 14.71
64  6 29 10.29  6.42  8.12  5.21  9.38  5.37  8.92  6.63  8.29 10.63 12.42 16.21
64  6 30 13.08  7.50  7.12  6.63 13.17  8.21 14.88 11.38 11.04 13.17 12.00 16.66
64  7  1 10.21  7.75  6.96  5.21  9.62  3.58  7.92  7.21  6.54  9.33  9.92 14.37
64  7  2 12.00  9.67  6.00  6.17 10.34  6.38  9.87 10.17  9.00 10.34 13.00 16.50
64  7  3  8.83  8.54  5.00  6.04 10.17  5.54  6.58  7.96  7.17  8.83 10.08 14.62
64  7  4  8.21 10.08  7.62  6.71  8.04  4.17  5.21  7.00  7.33  8.29  9.46 13.46
64  7  5 12.42 10.46  5.88  6.96 10.08  7.21  8.42  8.75  9.38  9.67 13.33 14.17
64  7  6  9.50  7.62  6.87  5.25  9.17  5.91  6.42  7.41  6.63  7.75 10.75 14.67
64  7  7 20.30 18.38 17.79 11.46 19.70 13.29 19.41 17.83 14.88 17.83 23.83 28.46
64  7  8 17.00 13.67 12.38 10.04 16.96 11.54 16.21 13.00 13.54 14.79 18.05 27.25
64  7  9 14.58 12.83 10.54  8.29 15.79 10.50 14.50 12.21 13.21 14.46 15.92 25.62
64  7 10 14.12 12.71 11.63  7.58 14.37  9.25 12.25 10.04 10.41 11.42 16.42 16.29
64  7 11 13.67 12.67 15.29  7.50 12.96  8.71 13.42 10.58 11.34 10.21 13.46 19.79
64  7 12  8.25  5.50  7.79  3.63  7.08  4.92  6.54  6.08  6.38  5.37  8.71 12.12
64  7 13 14.62 15.67 12.62 10.96 16.08 10.54  9.54 12.25 11.34 12.62 16.33 17.79
64  7 14 15.09 15.21 18.05 10.58 14.67 11.34 13.54 14.79 11.50 15.79 23.58 17.41
64  7 15 11.42 12.38 13.37  7.12 12.83  8.63 11.58 14.42  8.50 13.67 22.37 17.75
64  7 16  6.79  9.46  7.00  5.46  9.96  6.08  4.29  6.21  4.75  6.42 13.88  7.96
64  7 17  4.08  3.79  3.37  3.92  5.71  3.37  4.08  4.58  3.67  4.33  6.38  4.50
64  7 18 11.54  9.50  4.12  4.08 10.21  4.92  4.63  9.87  5.29  5.33 10.79  8.50
64  7 19 11.92  7.25  8.67  5.41  6.58  4.67  7.79  5.04  6.38  6.54  6.67 11.34
64  7 20  6.46  7.96  6.17  3.04  5.91  2.96  2.50  3.46  3.58  3.67  8.87  6.83
64  7 21  8.00  7.62  3.67  3.83  8.33  4.04  1.96  5.04  3.08  4.54  6.29  7.96
64  7 22  6.42  3.21  2.50  3.79  4.42  2.83  4.63  3.71  5.21  5.66  6.46  9.04
64  7 23  7.25  5.09  3.83  4.17  7.41  4.83  6.83  6.42  6.25  7.58 10.83 12.33
64  7 24  9.13 11.29 11.21  5.58 10.92  9.25  9.96 14.33  9.38 12.46 16.21 14.92
64  7 25  5.88  7.00  7.75  4.46  7.17  5.79  8.17  7.38  7.04  7.29  9.54 13.83
64  7 26  7.46  4.21  8.58  4.63  4.25  2.92  3.96  5.17  5.09  6.21 14.12 15.37
64  7 27 10.37  9.75 11.63  7.33 16.92 11.54 15.09 13.00 12.75 12.42 19.75 24.50
64  7 28 10.71  9.33  7.67  7.21 13.83 12.00 11.96 10.17  9.67  8.50 12.04 17.16
64  7 29  8.21  5.91  9.38  3.13  9.59  8.33  6.71  7.79  6.83  6.83 12.67 12.12
64  7 30  6.92  6.21 10.50  4.92 10.63  7.08 10.13  9.67  8.46  9.75 13.75 16.58
64  7 31 11.83  8.75 10.21  8.92 17.54 11.79 13.79 12.29 13.17 13.37 16.75 20.25
64  8  1 16.88 10.34 13.04 11.54 17.75 13.50 16.54 15.04 14.21 14.42 15.41 17.04
64  8  2 13.08  7.00 10.92  7.58 11.96  9.25 14.04 12.50 10.50 13.08 12.96 17.79
64  8  3  7.92  7.08  5.96  4.21  8.50  5.50  8.71  7.46  6.71  8.75  9.25 16.66
64  8  4  5.79  2.92  5.33  2.21  4.21  2.29  4.42  5.41  2.33  6.00 10.92 10.34
64  8  5  8.04  7.08 11.04  4.75  8.33  5.91  7.67  8.50  7.12 10.79 14.50 14.46
64  8  6 12.96  9.42  9.42  5.04 12.42  7.25  6.83  8.87  8.04  8.54 13.70 14.33
64  8  7 16.17 14.71  8.46  7.67 15.96  9.62  9.21 10.21  8.33  9.50 14.67  9.50
64  8  8 11.25  6.13  8.04  6.63  9.59  5.88  6.96  3.46  6.87  7.58  7.17 10.17
64  8  9  7.00  4.96  6.83  3.46  4.42  4.63  4.88  3.75  3.50  6.00  6.63  9.96
64  8 10  3.33  5.09  3.67  2.46  5.79  3.50  3.08  2.33  3.08  2.29  3.83  6.08
64  8 11  6.00  2.58  7.00  1.79  7.21  1.29  3.63  2.21  4.42  4.67  4.63  7.33
64  8 12  7.54  2.25 11.38  1.79  5.71  3.67  2.46  4.67  4.21  4.75  8.58  4.88
64  8 13  4.92  3.13 16.71  4.04  4.75  5.71  5.09  4.58  4.04  5.21 10.34  5.54
64  8 14  5.25  2.00 11.46  2.71  3.08  2.75  3.04  1.63  2.58  3.79  7.79  4.29
64  8 15  4.21  6.13  4.67  2.79  5.54  2.58  2.79  2.37  2.25  4.83  5.33  5.63
64  8 16 15.59 10.21 11.25  7.71 13.08 10.13  7.08  8.50  7.50  8.71 10.17  9.62
64  8 17 12.54  7.79 15.79  7.33  9.67  7.58 13.42  7.50  7.92 11.29 11.50 16.38
64  8 18 21.92 19.12 14.17 11.25 18.50 10.37 15.09 15.79 13.13 16.50 24.83 30.46
64  8 19 12.54  7.71 11.46  7.83 10.79  6.67 10.67  7.92  9.25 12.21 10.34 15.83
64  8 20  4.63  4.17  4.58  1.67  5.00  1.96  5.71  4.08  3.54  5.46  5.41  6.58
64  8 21  6.87  4.96  6.58  2.88  6.17  2.46  4.25  5.58  3.25  5.21 12.04 11.42
64  8 22  9.33 10.08 10.67  3.96 11.67  8.04  9.42 10.50  8.50 11.67 18.34 18.00
64  8 23 19.55 17.54 18.88  9.71 14.75 12.25 13.62 15.12 10.41 15.59 21.92 20.46
64  8 24 15.83 14.25 17.04 10.00 15.87 12.25 15.34 14.12 11.50 15.46 19.29 22.29
64  8 25 15.09 16.38 15.37 10.88 14.00 12.42 13.00 13.46 11.71 14.92 20.62 16.62
64  8 26 11.75  9.50 12.38  6.42  9.71  6.42  8.04  3.21  6.87  9.59  8.79  9.83
64  8 27  8.67  8.29 10.50  4.96  7.46  5.00  7.12  4.04  4.67  5.66  7.04  5.46
64  8 28 12.25 10.71  9.08  6.00 11.46  6.87 11.34  6.87  7.33  8.38  9.54  9.50
64  8 29  7.12  6.71  7.83  5.13  4.29  4.58  9.25  4.88  7.04  9.54  7.04 16.13
64  8 30  7.41  5.00  6.92  2.08  4.54  1.71  4.38  2.46  1.58  2.83  3.50  6.58
64  8 31 14.12 14.09 11.54  5.21 12.87  8.54  7.21  8.96  6.96  8.75  9.17 10.34
64  9  1 17.71 16.00 12.71  8.21 15.37 11.17 11.29 11.17  9.00 11.46 15.59 14.42
64  9  2  8.75 10.67  9.96  5.54 10.88  8.00 10.00  8.46  6.67  7.96 10.54 12.71
64  9  3  5.83  2.25  3.46  1.87  6.00  1.79  2.04  2.58  2.00  2.08  5.96  3.67
64  9  4  6.71  3.21  4.29  2.42  5.25  1.17  2.46  2.17  2.37  4.08  4.50  4.71
64  9  5 11.63  8.04  8.71  5.04 11.12  6.79  5.63  8.87  6.71  6.75 11.17 10.34
64  9  6 10.58  4.63  5.83  6.04 10.29  6.75  8.58  7.54  7.87  9.42 11.08 14.54
64  9  7 10.88  9.04  9.62  6.50 13.00  9.67 10.37 12.33 10.34 11.17 15.71 18.12
64  9  8  9.42  7.67 11.25  5.75 10.88  8.04 12.33 10.21  9.96  9.38 16.42 17.58
64  9  9  8.46 10.29 10.88  6.04 10.00  8.42 13.29 12.79  9.00 10.21 16.50 16.42
64  9 10 14.09 13.96 14.50  8.71 11.58 10.04 14.58 10.50 10.37 11.92 17.33 15.37
64  9 11 10.21  5.66  9.42  6.87  8.96  7.50 10.75  9.33  9.54 12.12 12.67 18.50
64  9 12  9.42  9.33  7.38  3.67  8.83  4.29  4.50  6.87  4.88  6.08  8.54  9.79
64  9 13 12.75 11.75 13.17  8.29  9.62  6.96  9.17  9.33  7.50 10.46 16.66 14.21
64  9 14 17.67 13.46 14.04  8.29 15.12  9.96  9.42 11.21 11.71 13.54 17.96 18.63
64  9 15 16.04 14.12 16.04  8.71 15.96 10.92 10.13 11.46 10.58 13.79 19.50 17.41
64  9 16 16.79 12.79 12.25  7.41 13.37  9.29  8.08  7.96  8.83  9.50 12.87 15.87
64  9 17 12.00 10.41  7.96  6.42 12.62  7.75  9.29  7.21  8.38  9.67 12.42 17.58
64  9 18  9.29  9.96  9.62  5.66 11.67  7.75 10.75  7.46  7.62  7.87 14.75 15.87
64  9 19 11.12 12.50  9.21  7.17 13.92  9.71 15.12 10.34 10.71 11.46 17.04 22.42
64  9 20 11.38  6.21  9.87  5.00  8.75  6.21 10.17  5.37  8.87  7.38 11.17 12.67
64  9 21 14.54 15.04 11.42  6.79 11.87  8.87  8.33 10.67  7.54  8.67 18.41 14.12
64  9 22 18.12 18.38 17.54 10.00 16.04 12.54 14.21 16.42 11.34 18.54 18.50 14.58
64  9 23  9.62  9.83 10.88  7.54 12.25  7.38  6.83  9.08  9.00 12.04 10.46 11.87
64  9 24 22.67 15.75 20.12 12.67 18.50 13.21 15.04 14.88 13.04 14.62 15.34 15.09
64  9 25 14.96 10.92 15.29  7.96 12.92  7.58 10.58 14.21  9.33 14.33 25.00 21.29
64  9 26 10.58  6.46 10.92  5.88 11.17  7.33  9.08  8.54  8.33  9.79 12.50 14.92
64  9 27  4.08  2.88  6.46  0.87  5.00  2.21  6.25  2.21  3.83  4.50  8.83 15.59
64  9 28  3.21  7.58  7.00  3.33  7.58  5.13  6.87  9.04  4.67  8.12 15.83 15.29
64  9 29  4.04  3.13  5.37  2.08  4.79  2.29  5.88  4.63  4.67  7.71  6.75  9.62
64  9 30  9.21  1.75 15.29  1.58  3.67  0.92  3.67  2.25  3.63  3.08  4.08  3.88
64 10  1  5.83  2.58 13.13  1.79  2.58  1.29  3.08  3.58  2.13  1.96  4.42  3.58
64 10  2  8.67  4.21 10.96  2.75  5.17  3.08  6.96  5.54  6.21  8.12  6.13  8.12
64 10  3 10.75 10.54  8.58  3.58 10.67  6.46  7.67  7.12  5.63  8.67 10.04 15.87
64 10  4 12.21 15.92 12.08  6.83 15.83  8.58  6.87 12.79  7.79 10.17 15.50 14.50
64 10  5 16.88 10.96 13.29  8.54 12.12  9.08 10.41  8.96  9.42 11.75 10.08 13.21
64 10  6 22.92 20.71 18.71 10.92 18.08 14.17 16.46 14.21 12.33 15.21 16.25 14.17
64 10  7 19.04 18.79 14.88 10.88 19.67 10.96 18.71 12.75 13.92 13.04 20.46 21.67
64 10  8 17.71 16.83 10.41  8.67 15.50 10.17 14.83 10.79 12.46 10.41 20.04 22.34
64 10  9 15.09 15.75  9.21  6.71 14.29  6.96  7.12  5.04  7.71  7.21  8.21  7.87
64 10 10 13.59  9.62 12.12  8.50  8.79  7.62 15.37  3.63 10.83 13.54  9.08 10.92
64 10 11  8.71  7.08 11.42  5.50  4.96  3.63  8.71  3.96  5.96  8.50  7.25 13.17
64 10 12  5.75  5.37  7.83  3.54  4.88  3.04  6.75  3.21  3.13  4.63  7.83  8.46
64 10 13  6.13  2.75  6.67  2.75  5.58  4.79  9.08  4.88  4.67  8.38 10.63 12.08
64 10 14 14.50 12.08  9.71  6.42 10.21  7.54  9.13  7.33  7.12  8.79 16.46 13.42
64 10 15 21.71 16.50 14.50 14.09 18.08 12.58 12.04 12.62 12.67 10.08 21.50 14.50
64 10 16 10.21  9.59  9.62  7.04  8.33  6.96 10.13  6.71  8.25  8.58 10.13 16.79
64 10 17  6.83  7.08  4.58  3.04  9.04  5.29  5.58  5.25  3.04  5.54 10.04  9.62
64 10 18  7.67 11.38  9.83  3.21  6.25  5.96  4.17  9.54  4.21  9.00 12.21 11.54
64 10 19 12.29 12.67 10.46  7.04 10.75  6.13  5.17  8.12  6.13  9.87 19.04 13.21
64 10 20  7.54  8.38  7.79  1.96  4.00  3.42  5.75  5.33  3.67  7.92 11.00 10.00
64 10 21  5.09  5.17  9.79  3.71  5.71  3.04  5.25  5.58  3.96  4.29  9.29 10.79
64 10 22 14.00 11.96  8.17  6.75 13.25  9.00 12.00  9.50 11.21 12.29 20.58 24.71
64 10 23 25.17 21.67 15.54 12.71 18.50 12.29 14.71 14.21 13.29 14.88 22.34 28.04
64 10 24  9.67  8.54  8.75  4.08  9.13  6.50 10.37  6.25  8.00  9.08 11.54 16.96
64 10 25  6.63  7.25  5.54  3.17  7.92  5.33  6.25  8.17  5.96  8.21 13.50 12.08
64 10 26 13.67 11.08 11.75  5.09  7.67  6.63  6.25  9.96  5.83 11.12 15.37 14.37
64 10 27 14.12  9.87  8.17  6.92 11.04  7.67  2.54 10.50  6.63 10.58 12.87 17.04
64 10 28  8.42 11.54  4.96  2.54  9.71  5.25  2.46  8.67  4.50  7.87 11.96 12.87
64 10 29  4.88  8.79  2.37  1.46  7.67  2.54  1.92  4.75  2.75  5.50 10.75 11.58
64 10 30  6.58 10.04  7.04  1.54  8.38  3.33  1.75  6.04  2.62  4.04  7.54  9.79
64 10 31 15.29 10.41 13.04  4.75  9.96  6.50  6.21  6.54  4.50  5.00  5.66 10.71
64 11  1 12.87 11.92 10.50  3.37 11.12  3.63  2.46  4.63  3.54  3.63  7.50  7.83
64 11  2  8.58  5.00 13.70  3.46  5.63  2.62  6.17  3.25  3.42  3.00  7.29 13.50
64 11  3 10.21  6.58 21.59  6.50  6.29  4.71  8.54  5.54  5.96  5.00  6.17 12.25
64 11  4 11.42 11.92 19.87  6.38  9.62  5.46  9.67  7.29  6.25  6.17  4.88 10.29
64 11  5  9.46  6.71 18.46  6.54  9.42  3.04  8.00  5.88  4.88  3.88  1.50  3.63
64 11  6  8.54  4.21 14.54  4.83  6.83  1.58  5.88  3.37  2.96  2.42  2.67  5.96
64 11  7 10.75  8.54 12.33  2.96  6.63  3.75  9.21  6.63  3.25  5.79  7.38  8.71
64 11  8 10.25 10.58 10.08  5.41  9.00  6.50  7.71  6.34  4.83  5.75  8.75  8.00
64 11  9  5.50  6.17  6.13  1.25  7.96  1.08  3.46  4.04  2.13  4.71  8.33 11.67
64 11 10  7.33 10.96  6.96  1.58  8.12  4.00  2.96  4.25  3.67  4.96  9.50 10.29
64 11 11 13.83 14.79 13.08  6.08 12.46  9.42  8.96 12.71  7.87 11.67 16.21 17.29
64 11 12 12.21 12.46  9.42  6.58 13.92  8.54 14.04  9.25  8.42 11.38 18.38 20.04
64 11 13 20.54 19.92 18.12 10.34 15.87 12.04 16.13 15.21 11.71 15.92 24.04 22.83
64 11 14 22.00 20.58 18.21 13.59 22.92 16.54 24.08 19.38 17.04 20.96 30.88 32.46
64 11 15 19.62 19.95 16.08 11.75 17.50 13.50 17.88 13.83 13.96 13.37 23.63 23.87
64 11 16 13.04 11.75 11.71 11.34 14.33 10.41 17.33  9.50 13.46 12.79 15.16 22.21
64 11 17 15.50 16.54 11.75  5.17 11.54  7.67  9.62  8.17  6.58  6.50 10.50  9.08
64 11 18 15.09 16.66 15.50  7.04 11.42 10.54 12.42 15.09  9.00 14.46 21.46 16.33
64 11 19  7.83  8.42  8.67  4.00  7.12  5.66 11.29  8.67  6.13  9.79 16.38 15.12
64 11 20  5.66  8.96 10.21  3.13  4.83  2.75  9.25 13.17  4.79 10.21 16.71 14.96
64 11 21  3.25  9.21  6.13  0.75  5.33  2.50  6.46  7.29  3.54  7.33 14.96 12.96
64 11 22 14.67 11.71 12.54  5.46  7.41  6.50 11.63 10.50  6.00  9.46 14.58 15.12
64 11 23 10.13  9.25  8.87  5.46 10.54  8.54 13.54 11.12 10.34 12.79 19.00 18.88
64 11 24 14.79 12.71 15.54  9.83 16.58 13.46 18.16 12.83 15.00 16.25 20.21 22.21
64 11 25  9.87  7.79  8.79  4.50  7.62  5.13  8.50  6.17  5.79  7.67 10.46 15.12
64 11 26 18.12 14.67 15.04  7.21 12.29 10.58 13.21 13.29 10.54 14.79 21.29 25.33
64 11 27 11.21 12.87  9.42  6.34 13.59  8.08 16.08  9.71 10.75 12.42 20.21 22.75
64 11 28 18.63 15.83 11.34 10.04 11.67  8.71 15.41 12.08 12.42 12.67 22.83 27.84
64 11 29 24.54 17.54 19.70 11.04 15.37  7.38 14.71 12.00 10.13 13.92 18.34 22.63
64 11 30 13.42 14.96  9.33  4.96 10.50  7.41 11.12  7.75  8.92  8.12 16.08 17.79
64 12  1 23.87 18.46 14.33  9.59 12.87  8.87 12.42 11.63  9.25 11.42 18.54 19.58
64 12  2 10.29  8.46  9.38  5.29 12.21  7.50 15.04  9.04 10.63 12.46 14.58 23.33
64 12  3 19.50 13.33 13.70 11.42 14.83 11.58 15.79 11.58 13.04 14.09 16.75 28.62
64 12  4  9.04  5.50 10.29  4.75  4.79  4.29  9.54  3.17  5.46  7.08  8.25 17.50
64 12  5  9.25 10.67  9.54  5.29 12.25  8.96 16.42  9.83 10.37 10.63 17.25 16.04
64 12  6 21.75 16.96 20.12 10.83 16.75 13.17 19.38 14.33 13.08 16.92 21.25 23.58
64 12  7 25.84 14.54 23.96 14.71 13.62 11.25 16.75  9.87 11.50  9.92 12.62 15.63
64 12  8 25.54 22.37 23.71 12.67 17.62 15.50 20.88 17.21 13.88 20.00 27.33 28.46
64 12  9 12.96 12.42 12.58  5.29 11.92  8.38 13.54  7.17  9.08  8.92 14.67 16.83
64 12 10  4.79  7.67  6.13  1.63  7.71  5.66  7.17  4.96  5.50  7.58 11.54 15.96
64 12 11 17.58 17.71 17.04  8.71 15.75 12.87 14.00 15.00 11.08 14.21 25.37 22.95
64 12 12 28.88 16.42 25.29  7.83 10.96  7.29 12.75  7.46  7.46 10.96 13.29 18.96
64 12 13 17.54 14.88 13.79  9.67 15.54 10.41 15.71 10.25 12.12 11.79 18.38 19.00
64 12 14  4.75  3.17  5.88  0.54  4.71  3.92  8.87  2.17  4.54  7.62 10.21 13.62
64 12 15 16.00 12.08 11.34  5.58 11.71 10.54  7.83 12.54  8.38 13.33 18.00 19.08
64 12 16 15.09 15.92  8.96  6.50 11.25  8.08  9.87 12.67  9.29 13.79 24.41 20.00
64 12 17 18.34 13.04 16.17  9.50 11.34  7.08  8.21  8.33  7.75  9.92 13.13 17.12
64 12 18  6.29  2.25  4.63  1.29  3.83  0.04  3.21  0.54  0.75  1.17  5.46  5.66
64 12 19  7.17  4.54  7.41  2.17  3.08  0.96  6.29  2.88  2.08  4.50  9.79 12.67
64 12 20 15.87 11.87 26.20  8.46  6.58  3.63 10.21  7.08  5.83  9.96 13.62 17.50
64 12 21 13.29 13.21 27.50  8.12 12.25  5.79 14.37  7.75  8.00 10.88 12.38 17.37
64 12 22 14.67  9.62 20.71  6.63 11.00  4.63 15.04  9.79  7.54  9.83  8.08 10.17
64 12 23 11.17  7.87 12.04  5.88  7.79  4.00 10.34  4.46  5.50  8.75  3.04  5.25
64 12 24 15.50 10.50 13.75  7.67  7.12  3.58 10.17  7.87  6.87  9.17 16.33 21.75
64 12 25 13.08  7.58 14.12  6.71  6.58  4.96 10.79  4.38  7.00  7.83 12.00 17.75
64 12 26  8.87 11.12  5.96  2.75  8.17  5.79  8.71  5.25  5.63  8.83 11.92 12.50
64 12 27 21.67 16.17 20.88  8.71  8.87  5.79 10.37  7.17  6.08  8.12 11.96 12.54
64 12 28  7.17  6.08  7.67  1.54  7.00  4.50  6.96  5.79  4.25  8.71 10.88 17.33
64 12 29 17.71 18.29 15.63  7.92 17.33 12.71 17.88 11.17 12.08 15.29 22.75 22.08
64 12 30 23.38 24.17 18.75 11.50 22.75 15.96 20.30 14.29 14.46 17.33 30.46 26.83
64 12 31 16.33 19.25 13.37 10.08 17.04 12.54 19.83 13.79 12.67 15.04 21.37 23.58
65  1  1  9.54 11.92  9.00  4.38  6.08  5.21 10.25  6.08  5.71  8.63 12.04 17.41
65  1  2 11.75  9.83 10.13  4.71  7.62  4.29  7.79  5.13  5.25  5.91 12.12 14.96
65  1  3 13.70  7.04 15.16  6.42  5.91  0.83  8.63  4.83  4.38  6.92  7.92 13.70
65  1  4  4.04  4.21  6.46  1.21  2.75  2.08  7.71  3.13  2.04  6.00 12.08 15.67
65  1  5  8.63 12.67  6.34  0.46  9.42  5.96  7.62  6.79  5.13  6.54 17.62 17.04
65  1  6 16.79 16.62 14.88  5.33 11.83  9.13 14.79 13.33  7.41 14.42 21.92 19.58
65  1  7 14.50 15.37 14.54  6.17 12.17  9.08 13.17 10.58  9.38 14.37 16.38 16.88
65  1  8 13.04 15.29  8.79  7.17 13.62  9.04 13.21 11.50  9.75 12.87 20.21 22.67
65  1  9 22.00 21.67 16.50 14.58 22.34 16.96 21.84 21.84 18.88 12.54 18.84 22.17
65  1 10 28.88 23.38 24.92 15.59 15.96 16.13 19.62 17.37 13.00 20.25 21.71 19.67
65  1 11 22.00 18.38 20.25 11.54 17.29 14.50 18.12 15.25 13.46 20.79 22.83 23.71
65  1 12 15.29 18.88 12.79 10.34 17.62 12.75 17.21 13.75 12.50 15.83 23.87 23.04
65  1 13 28.12 26.46 20.25 16.88 26.25 19.00 23.45 21.96 20.91 22.71 28.21 25.75
65  1 14 16.92 22.67 15.41 11.50 21.46 12.62 20.12 12.38 14.29 16.66 24.71 28.58
65  1 15 16.83 19.33 11.79  8.79 14.46 10.75 17.08 12.38 11.46 13.33 20.08 21.34
65  1 16 25.75 27.25 19.55 13.88 25.66 16.29 18.84 17.21 17.54 15.25 27.58 22.88
65  1 17 32.17 33.04 21.71 22.63 37.54 26.16 28.50 30.63 25.88 20.96 39.04 27.84
65  1 18 17.08 15.50 11.75 11.00 14.37 10.29 16.96 11.17 13.37 10.92 18.34 14.09
65  1 19 12.71 11.46 11.54  6.71 10.63  7.75 10.63  8.67  7.50  8.79 12.00  9.87
65  1 20 16.54 15.59 19.87  9.00 12.42 11.79 20.12 13.70 12.29 14.33 15.50 20.83
65  1 21 16.66 15.59 13.92  6.29 13.04  9.54 10.63  9.38  7.58 10.63 17.41 19.33
65  1 22 12.38 12.79  9.00  5.75 11.67  8.08 11.34  8.38  8.17  8.12 15.50 16.00
65  1 23 16.79 12.83 13.29  7.38 14.37  9.59  9.92 10.21  8.79 11.67 12.75 16.42
65  1 24  3.71  1.75  7.21  1.00  3.54  0.79  5.58  0.54  1.04  3.08  3.33  7.83
65  1 25  7.00  3.21 17.08  3.50  4.21  4.42  5.50  5.21  4.38  5.37  7.17  8.12
65  1 26 10.08  8.50 15.96  5.71  7.58  5.04  8.87  7.79  6.00  8.42  7.17 16.92
65  1 27 10.83 11.63 14.17  6.17  8.38  4.75  9.33 11.46  7.33 12.33 12.17 21.42
65  1 28 13.37 15.63 19.00  7.67 10.75  4.63 10.29  8.58  7.17  7.96  5.75 11.79
65  1 29 15.59 17.33 23.25  8.21 14.17  6.25 13.29  9.08  8.12  8.87  7.08  7.79
65  1 30 18.88 21.25 27.12  8.92 13.83  8.17 15.09  9.96  9.13  8.08  9.79 16.96
65  1 31 16.42 14.88 21.87  7.12 14.29  4.92 13.88 10.79  9.04 10.34 10.96 14.29
65  2  1 15.83  9.71 15.29  5.00  9.29  6.96  8.63  7.96  6.79  5.17  9.50  6.34
65  2  2 16.88 15.04 12.92  6.87 11.50  9.83  8.83  7.58  4.88  8.12  7.92  4.83
65  2  3  8.67  3.17 12.38  2.46  6.21  2.71  4.21  4.50  3.88  5.83  5.13  7.12
65  2  4  6.63  2.75 12.00  4.00  3.96  3.08  3.63  3.25  2.42  3.88  7.46 13.42
65  2  5  7.92  4.88  7.71  2.08  2.50  0.29  5.66  1.46  2.17  2.75  5.21 12.08
65  2  6  8.75  2.21  9.87  4.25  3.96  0.08  4.79  1.67  2.88  4.21  2.04  9.54
65  2  7  9.21  7.21 13.67  7.29  7.17  3.33  9.38  6.83  6.08  8.38  7.92 14.92
65  2  8  6.92  6.46 13.92  4.25  4.42  1.46  4.79  4.50  3.21  4.54  5.04 12.21
65  2  9  5.79  4.08  5.79  3.75  4.75  0.92  6.00  2.33  4.42  5.58  4.17 12.42
65  2 10  8.75  2.21  6.04  3.46  7.67  2.21  7.29  5.09  5.33  6.04  5.33 11.25
65  2 11  7.62  5.96  6.87  4.71  9.62  6.87 12.87  8.75  8.92 11.34 15.21 17.29
65  2 12 14.71 14.46 14.33 10.29 17.46 13.21 21.04 14.21 16.42 16.33 20.21 28.04
65  2 13 18.75 14.42 17.71 12.75 14.67 11.75 18.63 13.37 14.54 19.04 19.25 32.63
65  2 14  6.71  2.50 11.79  4.29  4.17  2.79  9.29  2.37  5.83  8.92  3.50 18.75
65  2 15  2.92  2.79  8.38  3.63  1.92  1.58  8.33  1.17  5.17  6.08  4.42 10.71
65  2 16  3.79  3.58  6.75  2.92  1.25  0.00  5.88  0.17  3.46  3.54  4.75 10.00
65  2 17  7.92  6.83 12.96  5.66  5.79  2.42  9.42  4.75  5.29  6.17  4.33  7.96
65  2 18  6.75  5.04  7.29  3.21  5.04  1.87  4.25  3.88  2.79  4.21  6.17  5.13
65  2 19  9.50  6.87 19.33  8.33  8.04  7.83 13.46  8.79  8.29 11.92 10.58 16.25
65  2 20 14.37 12.79 17.79  7.50 13.04  8.21 15.92 13.67 10.29 11.83 13.21 20.67
65  2 21 12.38 10.88 17.58  7.00 10.17  5.88 11.83  8.83  7.58  9.59  7.71 12.79
65  2 22  9.13  7.58 14.62  6.54  7.58  3.96 10.00  6.04  5.00  4.21  7.67 10.75
65  2 23  6.87  4.88  8.79  3.71  5.50  2.29  7.41  4.88  3.71  7.54  8.54 14.21
65  2 24  9.04  9.50  9.59  4.63  8.58  5.46 12.42  9.50  7.46  9.00 11.54 16.13
65  2 25 11.17  8.71 10.83  4.12  7.00  1.63  4.79  2.79  3.29  1.87  4.21  9.75
65  2 26 10.71  5.54 10.21  6.71  7.83  3.46 10.83  5.58  5.79  8.63  7.54 16.29
65  2 27  4.04  2.00  6.08  1.96  3.79  1.17  8.46  2.25  3.50  3.58  3.67  8.92
65  2 28  8.17  6.38  7.21  5.33  6.17  3.29 11.00  5.66  5.66  7.38  8.46 18.46
65  3  1 15.41 18.50 18.75  7.54 14.50  9.42 17.33 14.29 10.83 15.04 18.66 24.17
65  3  2  8.25  8.50 10.54  3.88  7.83  4.50  8.79  7.75  4.63  7.75 16.79 19.41
65  3  3 17.67 12.92 15.50  8.08 14.79  8.42 14.83 10.13  9.54 14.17 13.08 23.87
65  3  4 16.38 18.63 28.42 11.54 15.67 13.33 22.46 16.66 13.75 15.96 16.21 25.88
65  3  5 12.21  7.50 13.42  6.71  4.96  4.29 11.54  4.25  4.17  8.63  6.34 12.58
65  3  6  4.58  4.46  3.88  1.83  4.50  3.13  8.46  3.25  2.42  4.92  7.17  6.58
65  3  7  3.92 10.29  8.04  3.13  7.71  5.41 10.63  7.92  6.17  8.63 13.50 16.17
65  3  8 13.00 14.75  7.75  4.58 10.21  6.79  8.46  7.25  5.54  8.83 11.29 10.25
65  3  9 17.00 20.75 14.88 10.58 18.38 11.34  9.33 12.79  9.54 13.29 13.75 14.62
65  3 10 20.58 24.50 20.83 12.08 22.58 16.46 18.34 13.25 12.92 12.38 19.83 19.00
65  3 11 18.96 14.83 19.12  9.83 17.67 13.75 12.42 12.38 10.50 11.34 17.71 17.41
65  3 12 14.46 13.46 15.50  9.38 14.88 12.17 13.17 12.75 11.71 11.87 12.54 20.75
65  3 13  6.21  5.50 10.79  4.38  4.29  2.96  8.58  4.12  4.92  8.04  8.21  9.79
65  3 14  9.92 12.25 11.54  7.50 10.83  8.79  9.21 10.63  6.13 11.08 15.46 12.96
65  3 15 18.58 20.71 16.62 10.37 19.70 13.54 15.92 14.62 13.13 15.83 22.13 20.91
65  3 16 18.84 16.38 16.58 11.12 20.79 14.75 20.30 15.34 15.83 19.04 23.71 28.12
65  3 17 13.25 10.88 12.96  4.21  9.04  3.63  6.63  5.00  4.12  6.75  7.67 14.50
65  3 18  9.62  4.21  7.12  5.54 11.63  7.67 12.96  8.29  9.67 10.13 11.29 12.42
65  3 19  7.50  7.58  6.79  3.54  7.00  2.79  7.12  3.75  4.96  7.92  6.46  9.54
65  3 20  9.59 10.96 11.75  6.71 13.75  6.00 12.58 15.63 10.58 13.21 19.12 28.42
65  3 21  6.17  8.79  7.38  2.67  6.42  3.13 12.08 10.83  7.50 12.50 14.12 29.58
65  3 22 11.58  9.29 12.58  5.09 10.41  6.54  8.38  9.75  4.29 11.75 14.46 25.41
65  3 23  6.38  7.87  9.46  4.12  7.00  5.88 10.50  7.29  7.83 11.29 12.12 23.00
65  3 24 13.37 15.00 11.17  8.33 16.17  9.46 14.04 13.67 10.88 12.58 16.08 14.04
65  3 25 21.04 19.46 15.29 11.00 20.00 12.54 17.12 12.62 13.04 14.09 14.79 14.75
65  3 26 14.75 17.00 13.75  7.75 16.62 10.37 14.04 12.50  9.96 11.67 16.46 12.96
65  3 27 15.83 19.21 17.62 11.46 14.71 12.62 16.66 15.21 12.25 16.42 25.17 18.66
65  3 28  7.08 17.54 10.75  6.34 11.29  9.13  7.41  9.92  7.92  9.38 20.17 17.04
65  3 29 12.08 14.58  3.75  6.00 10.13  9.13  5.71  7.92  6.54  7.12 13.29 14.21
65  3 30 20.71 17.37 11.25  7.87 14.88 12.38 10.83 12.25  9.79 14.50 14.12 16.38
65  3 31 18.38 21.71 11.08  9.54 20.54 14.21 10.67 15.75 10.34 13.70 17.33 14.09
65  4  1  4.50  6.67  7.04  4.79  7.33  4.96  8.67  5.58  5.25  7.17 13.08  8.71
65  4  2  4.83  4.88  3.71  2.37  3.08  1.79  8.12  6.75  3.88  4.54 13.21  6.79
65  4  3  8.46 15.09  8.46  6.00 13.42  8.67  8.71 10.92  5.83 11.71 17.79 13.75
65  4  4 14.25 14.21 13.04  8.04 12.96  8.29 11.34 10.88  6.75 11.34 15.83 12.25
65  4  5  9.62 10.25  6.46  3.29  7.21  2.62  4.88  2.75  2.88  4.75  4.08  4.75
65  4  6  7.12  7.46  8.83  4.04  9.08  5.88  9.17  7.83  5.17  6.87 12.75 12.33
65  4  7  9.04  6.34  8.63  3.50  7.62  5.21  8.08  7.96  6.67  8.63 12.08 11.12
65  4  8  5.54  5.29  7.54  4.83  8.92  6.71  9.92  7.29  6.58  7.58  9.04 12.96
65  4  9 15.09 17.46 14.54  7.21 14.25 11.00 12.17 12.87 10.08 12.62 19.04 17.92
65  4 10 16.58 15.41 13.59 11.25 17.83 12.38 18.00 14.71 13.33 13.04 18.05 21.17
65  4 11 16.42 18.21 13.92 10.67 21.71 13.50 19.55 13.96 12.96 15.75 21.34 23.75
65  4 12 18.91 14.88 14.12 13.83 20.21 13.37 18.66 14.42 16.13 16.62 18.08 20.38
65  4 13 10.37  7.50 10.67  5.71 10.50  7.25 10.37  7.67  8.33  8.50 12.38 11.92
65  4 14 10.34 10.96 10.96  7.54 15.83 10.08 12.46 10.75 10.79 11.12 15.71 16.66
65  4 15 12.79  9.42 10.83  9.25 17.16 13.54 15.34 13.59 13.79 11.92 13.62 17.21
65  4 16 15.96 10.54 11.79  8.87 15.09 11.71 16.21 13.59 13.04 13.88 14.96 18.34
65  4 17 23.54 19.38 20.58 18.34 25.37 19.55 27.08 21.25 23.58 20.91 24.08 26.46
65  4 18 23.42 16.04 11.83 14.58 21.12 15.37 21.87 17.33 19.50 20.33 20.62 29.46
65  4 19 23.54 18.21 19.70 14.92 17.16 13.17 20.38 14.12 13.92 20.41 16.88 28.67
65  4 20 11.54  4.92 16.42  5.71  6.83  4.42  8.04  3.17  4.63  8.08  4.83 10.54
65  4 21 10.75  9.62  5.63  4.79 10.92  7.12  7.50  7.58  5.79  8.21 12.25 13.62
65  4 22  8.25 10.75  7.54  4.54  8.83  6.50  4.79  7.62  4.38  5.71  9.83 13.25
65  4 23 10.34  7.92  9.13  5.46  9.17  5.58  8.67  6.83  7.00  7.62 10.92 12.96
65  4 24 14.71 13.13 10.46  7.21 14.62  7.50  8.08  5.25  5.37  8.08 10.67 11.54
65  4 25 18.50 13.08 11.00  9.04 17.54 11.50 10.88 13.67 10.50  9.59 16.00 15.37
65  4 26 26.63 18.08 18.46 16.25 23.25 16.21 21.50 17.08 17.83 19.62 23.50 25.80
65  4 27 17.46 16.04 12.67 10.13 13.96  9.59 12.21 12.08 11.00 13.62 14.12 22.04
65  4 28  6.83  7.41  8.33  4.79  6.46  4.83  8.33  4.92  5.79  7.58  8.38 18.21
65  4 29 13.17 12.42  6.92  5.25 11.96  6.92  6.92  9.59  6.96  8.79 10.54 18.46
65  4 30 12.50 10.67  9.79  7.33 14.09 11.29 10.25 12.08  6.83 10.08 14.67 15.00
65  5  1  7.71  4.25 10.17  5.71  9.75  7.87 12.12  8.79  8.00 11.87 10.67 15.79
65  5  2  9.96 11.00 13.08  7.50 10.67  8.50 14.00  9.13  8.75 13.00 12.50 17.29
65  5  3  9.54 11.17 11.38  4.92  9.79  7.17  7.46  5.13  4.21  6.42  8.67 10.13
65  5  4 14.29 12.67 10.25  6.92 14.09 10.21 11.00  9.71  9.25  9.29 12.04 10.79
65  5  5 11.42  9.25 10.08  8.42 12.92 11.00 15.25 10.29 12.08 14.42 12.87 21.29
65  5  6 10.92 12.04 12.71  6.71 11.17  8.29  7.08  8.25  5.75  7.92 12.46 10.63
65  5  7 14.96 17.41 15.04  7.75 14.54 11.17 11.58 12.67 10.21 12.25 19.79 14.46
65  5  8 18.63 16.58 20.50 12.58 18.91 15.00 17.46 15.83 15.87 18.16 24.46 27.12
65  5  9 10.79  8.92 11.87 10.08 13.29 11.08 15.25 11.71 12.38 15.41 13.59 20.54
65  5 10  6.79 11.79  6.96  5.88  9.96  6.75  4.79  5.58  5.00  7.71 10.41 10.46
65  5 11  7.58  8.25  6.83  4.83  7.96  5.46  3.79  5.00  3.29  8.12 10.21  6.87
65  5 12 12.46 21.54 11.12  8.71 13.70 11.17  8.46 11.12  8.58 10.54 12.62 14.75
65  5 13  8.17 13.92 11.34  8.33 10.79  8.67  7.41  9.79  7.21  9.79 12.25 14.46
65  5 14  6.58  7.83  6.71  2.62  4.79  1.96  3.37  3.54  2.00  3.50  9.38  6.29
65  5 15 10.41 11.08 10.67  5.79  7.62  5.09  3.25  5.13  2.46  5.09  9.83  8.75
65  5 16  9.38  6.42 11.71  5.50  8.54  5.25  5.88  7.38  4.42  6.34 11.46 12.92
65  5 17 12.04 13.92  8.29  5.83 12.21  7.71  6.83 10.63  6.75  8.42 19.00 19.38
65  5 18 16.00  9.96 13.42  8.71  9.92  6.58  9.87  7.04  7.41 12.17 10.21 14.37
65  5 19  6.83  5.25  8.42  2.58  7.12  3.33  4.08  4.79  3.04  4.58 10.00  7.58
65  5 20 10.58 13.67  9.67  8.17 13.04 10.04  7.17  9.75  6.75  9.96 20.41 12.38
65  5 21 19.87 16.13 18.79 12.79 17.33 11.96 12.62 13.88 12.54 16.62 21.12 21.50
65  5 22 15.12 12.08 12.58  9.92 13.37 11.29 10.21  9.04  9.38 11.87 12.46 14.04
65  5 23  6.83  4.42 10.50  5.17  4.96  6.79 12.83  8.08  6.25 10.54  9.21 15.34
65  5 24 10.71 10.08 10.34  5.00 12.00  6.25  8.96  3.54  3.96  6.75  5.17 12.75
65  5 25  9.13  6.87 10.37  4.38  8.58  4.29  7.87  5.09  4.04  6.34  9.29  4.54
65  5 26 10.37  7.83  6.25  4.29  9.83  5.54  9.54  4.17  4.71  8.25  7.21 11.67
65  5 27 16.17 11.96 11.46  8.08  9.38  7.92 12.38  8.54  7.58 13.59 10.50 13.79
65  5 28  7.50  9.17 19.12  6.71  8.38  5.63  9.13  9.25  4.25  7.50 16.04 15.12
65  5 29  8.21  6.67 20.79  5.79  7.58  5.37  8.50  8.12  4.17  8.33 14.37 11.00
65  5 30 12.25  9.92  8.75  6.67  9.50  7.17  9.17 10.29  6.79 11.54 15.12 17.67
65  5 31 14.00 10.92 16.08  6.96 12.62  9.42  7.33 10.79  7.79 12.58 13.42 14.04
65  6  1  5.66  4.17 16.08  4.00  4.38  3.83  4.17  2.96  3.00  3.42  6.79  5.04
65  6  2  6.38  2.75  6.00  1.67  3.42  1.71  1.83  3.17  1.00  3.50  3.42  7.17
65  6  3  8.46  6.21  5.91  2.17  4.75  3.71  3.79  5.58  2.79  4.38 12.79  5.50
65  6  4 10.58  9.46 11.67  4.08  7.75  5.29  6.50  9.33  4.12  8.46 16.38 13.83
65  6  5  9.75  8.08  9.25  3.88  6.71  3.29  3.46  6.58  3.75  6.21 10.96  9.59
65  6  6  4.75  3.54  6.50  2.75  1.83  1.67  4.58  2.13  1.79  2.42  7.17  6.83
65  6  7  5.00  3.13  4.46  2.04  3.17  1.50  2.50  4.33  2.46  3.83 10.46  3.37
65  6  8  8.54  7.83  7.12  5.09  6.96  3.54  4.42  5.71  5.41  5.46  6.50  5.29
65  6  9  7.25  4.29  7.83  3.21  4.54  1.96  3.54  2.29  4.46  4.12  7.33  6.42
65  6 10  9.87 14.54  6.50  5.37 12.25  7.41  4.42  8.04  5.13  7.71  9.62  4.58
65  6 11 11.71  9.83 11.50  7.08  7.92  5.83  6.29  6.54  7.04  7.92  8.92 10.00
65  6 12 10.92  9.71  9.54  4.75 10.34  7.67  5.46  6.67  6.54  6.38 10.46  8.33
65  6 13  5.21 10.71  6.83  2.83  3.46  3.75  8.50  7.92  3.71  4.63 14.83  8.33
65  6 14 12.38 10.41 11.92  6.87 10.17  8.83  7.17  7.54  7.50  7.33 15.50 10.63
65  6 15 18.63 15.79 18.58 12.12 18.79 13.42 15.67 12.21 13.54 15.54  9.54 13.59
65  6 16 11.12  8.79  9.13  5.88 13.17  8.00 11.17  9.08  7.58  9.87 12.42 20.75
65  6 17 16.66 17.37 15.25  9.96 14.67 11.04 13.42 12.00 10.71 11.71 16.71 14.83
65  6 18 21.59 19.33 19.62 13.08 23.16 13.96 24.21 14.00 17.54 16.04 18.96 18.50
65  6 19 15.79 14.09 12.96  7.50 14.54 10.08 13.83 11.96  9.96 10.67 18.46 17.83
65  6 20  9.50 12.29 14.29  7.54 10.34  8.71 13.46 13.83  9.62 15.25 17.41 15.71
65  6 21  7.08  9.46 10.67  5.33  5.96  3.29  5.17  4.29  3.63  5.41  9.00  9.25
65  6 22 16.79 14.17 14.50  9.25 12.92  8.67 10.92 10.25  9.25 10.13 17.21  9.46
65  6 23 22.42 19.41 20.75 14.54 24.33 17.12 21.04 16.33 18.25 20.04 24.58 28.08
65  6 24 18.84 17.41 19.41 10.34 17.21 11.87 16.92 12.92 11.38 13.59 14.50 15.50
65  6 25 17.58 15.29 15.54 11.42 17.71 13.08 15.12 13.62 13.17 14.33 19.55 21.09
65  6 26 13.79 11.96 11.71  9.62 16.71 11.00 17.00 13.42 12.96 14.21 16.29 23.63
65  6 27  8.12  6.08  5.79  4.21  8.50  6.08  8.83  6.79  7.29  8.58 13.00 16.66
65  6 28  5.79  3.25  6.42  3.13  8.96  6.42 11.63  8.75  7.75  9.83 12.38 17.04
65  6 29  5.63  6.38  9.38  4.17  6.17  3.29  4.12  4.08  3.79  5.88  6.00 13.17
65  6 30 11.96  9.00 10.25  8.50  8.25  4.79  8.50  7.62  7.12  9.62  8.42 13.79
65  7  1  9.59 10.08  9.59  5.96  6.96  4.96  8.54  6.92  6.96  9.42  8.42 15.54
65  7  2 14.71 11.04  8.75  7.38  9.25  6.29 10.17  7.71  8.63 11.54 10.37 16.21
65  7  3  9.33  9.67 14.37  8.08  7.41  5.54  9.38  8.67  6.96 10.34 11.00 16.33
65  7  4  9.67  8.58  7.58  6.83  8.83  4.08  7.79  5.13  7.33  9.38  7.46 14.42
65  7  5  8.42  5.63  8.33  4.71  8.58  4.79  9.92  3.96  7.29  7.75  7.62 13.17
65  7  6  5.13  6.92  5.88  2.21  3.63  2.33  4.88  2.46  5.37  4.92  4.83 10.92
65  7  7  9.46  9.87  5.09  6.00  7.75  4.71  7.75  7.29  6.50 10.58 10.34 15.83
65  7  8 11.58  7.54  9.17  6.92  9.79  5.83  9.42  6.08  7.83  9.75  9.50 13.21
65  7  9  7.62  6.00  8.67  5.13  9.38  6.58  7.04  6.58  7.29  7.46  7.87  9.25
65  7 10 12.38 11.63 10.41  5.66 10.79  7.21  8.04 10.13  7.08  7.08 17.37 12.38
65  7 11 12.87  8.58 13.37  6.38 10.50  7.25 10.08  9.29  8.71  9.59 14.04 13.83
65  7 12  7.41  6.63  9.87  2.83  6.46  1.96  5.71  5.83  4.83  5.00  7.54 11.25
65  7 13  9.42  5.88 10.21  3.88  5.83  3.92  4.50  7.67  5.33  7.25 11.29 16.04
65  7 14  2.96  8.17  5.79  5.21  7.92  5.09  5.96  7.71  5.46  9.25 13.96 14.96
65  7 15  5.21  2.62 16.92  3.33  5.88  5.54  8.04  6.79  6.83  6.21  5.04 13.42
65  7 16  5.29  6.79  6.96  2.33  5.13  1.29  5.41  4.12  4.21  6.21  8.17 12.46
65  7 17  6.00 10.67  3.50  3.83  7.54  3.04  4.88  4.33  3.71  6.34 13.21  9.29
65  7 18 13.59 10.13 12.42  8.29 11.17  7.25 10.46  8.87  9.59 11.38 12.54 16.42
65  7 19  4.54  3.71 12.00  5.83  4.33  7.33  8.83  7.00  7.58  8.71  7.33 15.34
65  7 20  7.38  5.71  6.83  4.50  7.87  2.62  4.79  4.00  3.37  5.21  5.09  6.87
65  7 21 10.92  8.08  6.21  4.63 10.04  3.92  7.21  6.79  5.71  6.42  9.17  8.83
65  7 22  5.33  2.79  4.67  2.00  2.79  0.83  4.79  2.37  2.17  2.75  6.00  8.29
65  7 23  8.00  4.83  8.87  3.79  5.54  3.33  6.96  7.21  4.42  4.96 10.79 12.21
65  7 24  7.54  7.21  7.38  5.00  8.58  5.29 10.96  6.63  7.21  7.75  9.71  9.67
65  7 25 13.79 11.21  9.04  7.21  9.92  7.29  7.79  9.83  8.50  9.13 12.33 16.54
65  7 26 10.58  7.50  8.67  6.75  9.96  6.63 10.67  7.50  7.29  9.62 10.13 15.92
65  7 27 15.37 13.17 11.12  7.17 12.12  8.71 12.96 10.04  8.87  9.87 12.50 10.00
65  7 28 16.21 14.17 14.12  8.50 17.08  9.46 14.09 13.42 11.34 12.79 16.00 16.88
65  7 29 15.67 10.92 13.46 12.00 19.08 12.75 18.71 14.29 15.04 14.00 16.71 18.58
65  7 30  8.92  8.92 10.37  4.88  6.46  3.75  5.33  7.29  5.75  7.21 10.29 15.16
65  7 31 11.08  7.04  9.67  6.54 11.58  9.13 13.29 11.92  9.50 12.00 13.96 18.79
65  8  1 11.87 10.67 10.67  5.09 10.54  5.66  9.92  7.00  5.79  7.00  9.59 11.96
65  8  2 11.29  7.92 11.75  4.58  8.38  5.17  7.71  5.75  4.58  6.92 11.46 14.17
65  8  3  7.46  5.58  5.83  3.13  3.33  1.50  5.63  1.92  2.17  4.08  9.87  7.41
65  8  4 18.71 16.83 16.25 12.04 14.67  9.54 11.92 12.08  9.46 11.46 17.46 18.63
65  8  5 13.67 10.79 15.25  8.00 17.96 14.92 17.54 17.00 15.71 17.96 21.34 24.83
65  8  6  9.46  9.33  9.75  6.83 13.96  8.79 12.33 10.04  9.75 11.75 14.46 18.63
65  8  7  7.62  4.08  6.71  4.29  9.38  4.79  8.63  6.38  7.96  9.75  8.54 14.83
65  8  8  5.00  3.37  7.21  3.04  5.46  2.58  3.58  2.08  2.17  2.25  5.96  6.96
65  8  9 10.75  9.71  8.12  5.58 11.21  7.12  6.54  7.96  4.71  8.63  8.50 12.87
65  8 10 11.42 10.83 10.88  9.04 13.54  9.96  9.75 11.87  9.42 11.58 14.21 17.79
65  8 11 10.83 15.00 11.63 10.54 13.29  9.08 11.08 11.08  9.79 12.38 16.92 17.88
65  8 12 10.58 10.46 10.92  7.62  9.79 10.50 11.17  8.71 10.25 12.42  9.59 13.21
65  8 13  5.58  3.83  5.83  4.54  2.46  4.88  6.50  3.21  5.54  8.38  3.29 14.46
65  8 14 10.41  7.33  9.50  5.46  8.46  5.41  7.12  5.63  5.58  7.50  8.63 11.54
65  8 15  7.41  5.04  6.08  3.75  7.08  3.83  7.21  4.54  4.83  6.38  7.75  8.17
65  8 16  7.87  4.08  6.92  2.04  3.17  2.00  4.21  2.50  2.33  3.00  8.50  5.21
65  8 17  7.75 11.17  8.83  4.58  9.21  6.46  7.87 10.34  5.50 10.17 17.41 13.46
65  8 18 12.79 11.00 12.58  6.79 12.75  9.50 12.67 11.79 10.00 12.38 16.54 17.16
65  8 19  8.87  8.25  7.54  4.08 11.04  7.67 10.96  9.04  7.62  9.29 16.17 17.12
65  8 20 17.41 15.21 13.42  8.21 15.12 10.29 14.17 12.75 10.37 13.96 19.29 19.87
65  8 21 20.58 15.75 12.67  9.25 18.75 10.21 14.88 12.38 11.12 12.79 17.00 19.79
65  8 22 26.20 23.42 16.71 15.67 20.17 13.59 16.83 16.00 14.04 16.71 22.50 26.67
65  8 23 10.71  6.58  9.92  5.50  8.96  6.13 11.87  5.66  6.87  9.87 10.37 13.42
65  8 24 16.96 15.21 12.12  7.71 16.96 10.75 16.42 12.38 12.67 13.50 17.25 20.00
65  8 25 12.67 11.63  9.25  8.12 16.13 11.63 15.71 12.54 12.00 11.00 16.38 22.50
65  8 26  8.33  8.21  8.21  3.54  5.58  4.29  7.79  4.58  4.04  5.37  7.62 12.92
65  8 27  8.54  9.67 12.67  6.29 10.29  7.21  8.58  7.92  6.08  8.42 14.71 14.33
65  8 28 12.29 10.63 10.75  8.04 16.25 10.83 16.92 12.87 12.46 12.00 18.05 21.29
65  8 29 11.92  8.00  9.46  8.42 13.70 10.92 15.25 14.33 10.75 11.75 15.29 22.17
65  8 30  8.67  5.96  8.33  5.17 10.25  6.87 11.87  8.87  8.08  9.33 13.25 20.67
65  8 31 14.96 10.58 11.46  8.29 10.46  8.79  9.87  7.67  8.04 10.17 10.54 16.54
65  9  1  9.54  2.92 12.96  5.79  2.62  2.92  7.75  2.21  4.67  5.83  5.75  8.25
65  9  2  3.37  1.38  7.92  2.04  3.92  0.71  3.29  2.33  0.87  1.17  7.25  4.42
65  9  3 13.08 11.92 12.00  8.12 10.96  6.29 10.63  8.29  7.12  8.50 11.12 15.96
65  9  4 21.00 18.54 14.09 11.87 17.29 10.37 13.54 14.25 11.50 14.83 22.17 28.84
65  9  5 20.62 11.58 13.13 10.63 13.54  8.58 11.29  9.67 10.04 12.42 12.00 19.38
65  9  6  5.75  5.04  2.79  1.25  5.33  0.92  3.96  2.62  1.00  3.21  6.67  8.63
65  9  7  6.96  4.58  5.00  2.88  3.54  2.29  5.33  4.46  3.50  4.12  7.67  9.50
65  9  8 12.25  9.71  9.38  5.83 10.17  5.37  7.29  7.33  5.96  8.58 13.62 20.83
65  9  9 15.54  9.62  9.87 10.00 13.04 10.04 16.33 11.54 13.29 13.59 16.50 27.04
65  9 10 18.00 12.38 10.21 10.17 16.13 10.04 14.21 12.92 12.38 13.46 17.25 22.95
65  9 11 14.42  7.38 12.08  7.41  9.75  6.75 10.92  7.41  8.96 10.29  8.79 16.50
65  9 12  6.63  7.50  8.75  3.42  7.50  4.29  3.42  4.88  3.50  3.96  8.17  6.29
65  9 13  2.79  5.63  6.75  2.83  3.71  1.96  2.71  2.00  1.42  1.42  5.25  8.21
65  9 14 10.04 13.17 12.00  6.83 11.21  7.58  4.25  9.59  7.29 11.42 13.00 14.67
65  9 15  7.83  5.29 11.96  4.17  6.04  4.38  6.42  7.92  5.09  8.96 12.12 11.50
65  9 16  9.54  8.12  9.83  4.04 11.58  6.58  9.54  8.71  7.41 11.21 14.96 18.05
65  9 17 24.87 19.25 19.08 12.21 19.50 12.92 12.71 13.25 12.42 13.29 13.79 15.87
65  9 18 14.25  9.17 10.75  8.50 12.04  9.13 13.04  8.67  9.92 11.75 11.54 18.21
65  9 19 13.96 15.83 11.46  7.21 12.71  9.38  9.25 12.00  7.50 11.00 24.71 16.62
65  9 20 13.37 14.12 12.79  7.75  9.96  8.71  9.54 12.96  6.58 14.29 21.71 19.75
65  9 21 12.83 17.00 14.54  8.58 12.71 10.92 12.21 14.37  9.59 17.12 19.87 19.70
65  9 22 10.13  7.92 12.12  4.67  8.79  6.54  8.25  7.50  5.91  8.46 13.50  9.04
65  9 23  9.54  4.67 10.92  4.58  6.50  3.63  4.21  4.75  3.67  7.38  9.59 11.25
65  9 24  6.17  4.33  9.38  4.67  6.29  3.63  5.96  4.33  3.46  5.79  7.67 11.38
65  9 25  9.54  7.29  5.75  4.50  8.67  5.41  4.29  9.38  3.58  5.63 12.62 12.87
65  9 26  8.83 10.34  3.58  2.58  8.12  4.17  3.42  5.63  2.83  4.50 12.17  8.71
65  9 27 12.42 11.87  7.96  5.17 11.34  6.79  5.37  7.96  6.04  8.63 11.34 14.00
65  9 28 10.83 10.88  5.66  2.71  9.71  3.54  2.50  6.87  2.83  6.17  9.92 11.08
65  9 29 13.29  7.83 10.50  9.13 10.67  6.63 11.63 12.29  9.46 13.75 15.09 25.17
65  9 30 16.79 12.38 22.04 11.17 10.96  7.75 15.63  9.21  7.75 10.00 13.96 13.46
65 10  1 10.08  4.29  9.54  6.50  6.71  3.25  8.00  4.38  4.63  5.71  8.38  9.00
65 10  2  9.59  9.13  8.67  3.92  7.92  5.91  4.67  5.09  5.41  7.92  8.21 11.04
65 10  3 17.16 13.83 14.58  8.75 14.46 10.00  9.79 11.04  7.25 10.83 13.08 12.25
65 10  4  7.25  4.63  8.12  5.17  5.88  3.58  6.79  4.58  3.46  7.17  6.79 10.63
65 10  5  7.17  0.75  4.75  1.79  2.25  0.87  2.13  0.13  1.38  1.71  2.42  2.04
65 10  6  5.96  4.96  2.54  2.21  3.67  1.83  0.42  2.17  1.42  1.87  2.79  6.25
65 10  7  8.92  4.67  8.00  3.17  2.13  2.33  4.96  3.75  3.71  5.04  4.71 18.96
65 10  8 16.50  8.96 14.62  6.04  7.62  8.00 10.71  9.96 11.17  9.33 11.04 14.92
65 10  9 13.50  7.41 15.12  5.58  8.50  7.38 11.79  8.71 11.71  9.17 12.08 11.54
65 10 10 13.96  7.83 14.67  4.96  6.75  5.63  9.04  7.17  8.04  7.00  9.92  8.71
65 10 11  9.62  5.58  7.58  1.50  2.83  1.08  4.00  3.58  4.38  6.87  4.42 10.79
65 10 12  7.79  5.91  5.58  2.29  4.88  1.83  3.00  2.29  1.71  2.21  4.92  8.42
65 10 13  7.33  7.96  6.75  3.58  6.13  5.09  3.29  3.92  3.21  7.33 14.37 13.25
65 10 14 10.58 10.29  6.13  2.88  8.38  5.29  6.75  6.46  5.54  6.54 12.92 14.50
65 10 15 12.67  8.08  8.21  5.00  9.50  5.50 10.08  7.87  7.62  9.17 12.46 18.96
65 10 16  4.54  6.92  5.66  1.46  4.25  3.71  5.29  6.96  2.46  7.17 15.12 13.54
65 10 17  3.67  7.12  3.58  0.75  3.42  3.37  4.96  4.46  4.17  6.42  6.71  9.92
65 10 18  8.38  5.46  9.29  2.83  3.83  1.46  5.33  2.83  4.42  4.88  5.04  7.04
65 10 19 14.33 11.71 13.29  5.75  9.87  7.58  9.21  6.83  5.91  8.58 12.17 14.29
65 10 20 16.88 15.41 15.96  8.12 15.25 12.38 10.46  9.25  5.58  7.67 14.79 16.75
65 10 21 17.25 14.88 16.04 10.34 16.38 12.33 11.87 12.75  9.59 11.38 14.54 18.54
65 10 22 10.25 12.25 12.04  7.87 12.33  8.83  8.96 10.00  8.87  9.29 12.50 15.75
65 10 23 12.04 12.33 10.34  6.00 10.41  7.83  7.87  7.21  4.71  8.29 11.17 13.04
65 10 24 14.54 15.25 10.08  5.41 13.13  9.87  6.83  9.29  5.25  7.58 11.63 12.08
65 10 25 11.29 10.21  9.62  6.50  9.46  8.42  7.17  6.75  5.37  6.96 10.29 12.96
65 10 26 10.21 11.42 11.34  6.38 10.08  8.42  6.08  9.79  7.33 12.58 18.84 17.33
65 10 27 22.50 19.83 21.25 12.46 16.13 13.33 17.00 18.54 12.46 19.55 25.17 20.67
65 10 28 18.05 19.21 13.96 12.33 19.21 13.62 20.00 16.92 15.83 18.21 27.37 33.12
65 10 29 18.25 18.54 14.79  9.50 15.54 11.58 15.12 15.59 11.00 16.17 23.21 24.50
65 10 30 19.29 16.71 17.08 13.17 19.79 13.59 21.62 14.04 15.09 16.17 23.33 28.25
65 10 31 25.46 22.00 20.54 12.71 19.95 13.25 19.21 16.92 14.50 17.08 23.16 23.38
65 11  1 23.42 22.17 18.91 17.00 27.58 20.30 29.17 22.67 24.46 27.29 30.96 37.59
65 11  2 11.71 11.71 12.67  6.96  9.42  6.54 12.04  5.21  8.33  9.13 12.71 21.79
65 11  3 15.54 14.67 14.25  8.08  8.87  7.41 11.42  7.33  9.13 10.83 17.79 24.54
65 11  4  8.17  5.79 11.42  3.17  2.92  1.29  6.04  2.00  2.54  3.50  8.33  9.96
65 11  5  7.67  6.92  8.83  4.54  6.42  2.67  4.83  0.71  2.37  2.04  4.50  6.92
65 11  6 21.12 12.29 22.42 10.41 13.08 10.54 16.00 12.38 12.58 12.17 12.71 14.37
65 11  7 18.63 15.34 13.59  8.67 15.96 10.54 11.58 14.37 11.38 13.00 16.71 25.96
65 11  8 16.13  8.54 15.12 10.29 12.62  8.12  9.59  8.08  8.75 12.87 12.83 14.92
65 11  9 13.79  8.08 10.88  8.04  7.92  5.75  9.38  4.63  7.17  9.17  5.79 11.75
65 11 10  9.83 11.08 11.34  4.29 10.08  5.33  6.96  6.38  5.46  6.54 10.25  8.21
65 11 11 17.50 15.34 17.41  8.71 14.92  9.38 14.83 12.12  9.46 11.58 11.00 19.67
65 11 12  8.58  6.17 11.04  2.71  6.04  3.37  6.75  6.29  4.29  7.41  6.96 13.33
65 11 13 15.29  7.58 13.33  4.29 10.71  6.00  8.50  8.87  6.08  9.04 11.71 16.38
65 11 14 22.54 13.83 19.70  8.00 17.00 12.00 11.63 14.50  9.29  9.29 15.37 13.59
65 11 15 23.54 22.00 17.92  7.71 17.41  9.62 11.34 11.67  8.83  7.87 14.42 12.46
65 11 16 27.21 16.79 33.25 14.92 23.42 18.58 27.58 23.50 19.75 19.70 22.83 33.50
65 11 17 13.54 12.42 18.84 10.54 12.50 11.17 25.33 17.92 15.92 18.75 20.91 41.25
65 11 18  4.04  8.75  1.83  1.71  9.33  4.08 15.92 12.96 11.50 14.75 18.54 35.50
65 11 19 10.00 10.08  9.87  6.25 11.17  8.00  9.75 11.00  8.71 11.63 11.96 25.88
65 11 20 10.29  9.83 18.96  7.29  9.54  7.71 15.75 10.63  8.67 12.00 10.54 27.92
65 11 21 13.92  9.62 19.75  9.08  9.79  6.13 10.75  8.54  6.87 10.67  9.13 18.25
65 11 22 12.04  3.29 14.42  6.17  4.08  3.04 10.67  4.58  6.67  9.08  8.42 15.79
65 11 23 14.83 13.50 13.04 10.25 14.46 11.46 20.00 11.92 13.25 16.75 18.96 29.38
65 11 24  8.87  7.50  6.96  1.75  6.25  3.96  7.46  2.50  2.62  4.88  6.38 13.59
65 11 25 20.00 14.00 17.79 10.17 12.08  8.58 13.75  9.42  8.17 12.00 14.12 18.25
65 11 26 11.50 11.50  9.54  3.63 10.25  6.83  9.33  6.00  5.25  6.87 11.75 14.29
65 11 27 19.87 18.25 13.79 11.08 13.54  9.25 16.33 10.41 10.50 12.12 15.09 24.71
65 11 28  9.38 10.54  7.33  3.33  9.75  4.79  9.50  2.54  5.17  6.29  6.25 12.75
65 11 29 28.42 24.08 28.46 19.08 18.66 15.00 20.33 14.75 14.42 21.29 23.87 32.05
65 11 30 14.50 13.33 18.38 12.46 10.50  9.67 19.38  9.50 12.75 15.00 16.33 26.92
65 12  1 10.29 10.46 10.08  3.83 10.50  8.21  9.96  5.29  6.25  7.12 10.54 12.46
65 12  2 18.50 23.13 13.79 12.67 21.84 13.37 19.25 15.71 12.58 13.88 24.54 20.04
65 12  3 24.87 23.33 15.37 13.21 19.25 11.50 15.96 12.38 13.42 11.42 17.12 14.25
65 12  4 15.63 12.67 10.54  6.58 12.04  7.17  6.46  6.04  5.79  6.46 13.79 12.33
65 12  5 14.96 17.37 10.63 10.37 17.37 10.00 15.63 11.83 10.58 13.08 20.08 22.25
65 12  6 21.62 21.29 12.87 14.96 17.58 12.83 18.63 16.62 16.25 15.25 26.08 25.66
65 12  7 14.71  9.67 11.54  8.63  7.25  6.75 11.00  7.62  7.54  8.96 13.92 17.21
65 12  8 16.79 16.00 15.00  7.12 15.04 10.54 13.46 11.54 10.67 13.42 16.38 16.46
65 12  9 26.46 23.21 21.00 17.37 27.29 18.75 24.00 21.75 20.38 20.21 26.38 24.17
65 12 10 21.87 21.34 11.38 11.21 16.17  8.63 14.29  8.71 10.71  9.00 12.04 17.58
65 12 11 10.96 10.75  7.92  6.08 10.63  7.08 11.54  6.25  9.42 11.00 11.04 15.29
65 12 12 18.05 16.13 11.79  6.17 13.04  8.87  8.92  9.96  7.96  7.54  9.96  7.54
65 12 13 10.79 10.75 10.29  6.75 12.17  8.79 14.29 11.12  9.83 11.46 15.96 15.00
65 12 14 24.41 21.00 19.00 12.75 18.91 15.16 11.63 14.62 12.58 17.37 18.25 19.29
65 12 15 14.21 11.50 15.21  7.96  9.62  8.71  8.54 10.17  8.54 13.62 20.08 14.58
65 12 16 15.67 14.92 11.67  5.83 14.00  9.04  8.21  9.75  7.38 10.71 15.50 13.88
65 12 17 26.30 20.75 23.09 11.67 16.42 11.34 17.92 14.50 11.29 16.96 18.21 19.58
65 12 18  5.29  2.83  5.50  1.83  6.04  4.75  7.50  5.63  5.00  8.08 13.04 14.37
65 12 19  4.96  4.12  6.71  3.63  7.12  4.79 11.71  4.50  6.96  9.92 14.29 21.34
65 12 20  8.46  2.46 10.63  2.83  2.25  1.75  8.96  1.92  3.33  6.34  7.83 10.29
65 12 21  8.92  9.13  7.79  1.29  8.63  5.46  9.42  5.88  5.00  7.33 10.67 13.04
65 12 22 17.12 13.00 16.58  7.12 13.88  9.13  9.38  8.79  7.58  8.67  9.13 10.58
65 12 23 14.88 15.92  9.59  7.87 12.87  8.08 11.04  7.04  7.29  8.04 11.08  9.21
65 12 24  8.50 11.04 13.42  4.75  8.08  5.09  7.12  7.04  5.21  7.50  7.92 13.04
65 12 25 11.00  5.91 11.96  5.79  6.87  3.58  7.38  3.33  5.17  7.25  8.63 15.37
65 12 26  7.96  6.04 11.17  2.71  5.09  1.13  5.79  1.17  1.29  4.25  4.46 11.21
65 12 27 12.58  6.71 17.67  5.13  8.08  3.37  6.38  2.88  3.54  2.50  5.13 11.25
65 12 28 10.08  9.71  8.25  2.58  9.96  5.54  6.58  5.58  2.96  4.67 10.21  8.42
65 12 29 20.12 17.21 18.66 12.46 16.00 12.58 12.50 11.96 11.04 15.09 19.46 19.08
65 12 30 14.42 20.67 11.63 10.29 18.08 10.96 17.41 14.04 12.83 14.33 22.63 19.17
65 12 31 13.62 13.88 12.29  6.08 12.33  7.41  9.59 10.21  7.46 12.17 15.71 16.75
66  1  1 22.04 21.50 17.08 12.75 22.17 15.59 21.79 18.12 16.66 17.83 28.33 23.79
66  1  2 18.71 19.50 12.67 10.08 16.29  9.79 17.46 10.83 13.88 12.38 15.63 16.46
66  1  3  6.83  9.04  6.00  2.33  7.12  4.67  7.83  3.58  3.46  3.79  7.92  7.71
66  1  4 16.54 16.75 16.42  9.62 13.96 13.70 15.46 13.79 11.04 14.58 18.66 24.00
66  1  5 20.50 16.88 18.79 13.17 14.17 12.38 12.83 14.29 11.63 15.92 23.91 20.08
66  1  6 19.70 16.29 17.50 13.17 13.13 11.87 10.96 14.25 10.00 16.13 22.13 20.88
66  1  7 18.88 16.50 13.50 11.71 14.83 10.37 10.50 13.04 10.34 15.29 21.04 21.17
66  1  8 13.42 10.17 16.21 12.04 10.71  9.08 12.50  8.96 11.42 14.96 16.79 20.25
66  1  9 13.42 17.46 16.46 10.58 13.70 10.83 13.37 12.12  9.50 13.25 15.21 16.66
66  1 10 17.62  3.33 26.67 14.54 18.21 17.04 19.21 20.75 15.50 18.25 21.37 29.04
66  1 11 18.34 15.29 21.67 10.21 17.25 12.54 19.55 14.96 12.17 14.88 19.67 21.92
66  1 12 19.21 18.96 17.25  8.17 15.37  9.21 16.46  9.87  9.04 10.37 12.79 15.16
66  1 13 21.00 17.54 16.79  8.00 20.21  9.83 12.29  9.71  7.54  8.54 12.29 10.29
66  1 14 19.08 13.96 17.96  5.54 13.08  6.67 10.83  6.75  6.00  6.71  8.75  7.38
66  1 15 11.92  8.87 15.21  5.91  7.08  2.21  6.29  3.08  4.08  4.38  4.42  7.29
66  1 16  9.00  5.58  9.25  4.79  4.50  2.21  4.71  3.25  3.50  4.04  3.63 10.75
66  1 17 11.21  9.59  8.75  1.92  6.21  2.17  2.79  4.12  1.71  3.00  7.71  4.63
66  1 18 20.41 15.50 15.46  7.75 14.50  8.46  8.50  9.08  4.67  5.25 11.08  8.42
66  1 19 22.42 17.08 20.91 10.41 18.88 12.08 14.33 13.83 10.13  8.75 14.96 15.63
66  1 20  9.71  3.96 16.58  4.46  5.50  5.33 12.33  5.96  9.13  9.13  7.83 15.75
66  1 21  9.25  7.29  9.42  2.62  5.09  3.54  4.75  4.17  2.88  3.46  7.58  6.96
66  1 22 12.42  6.21 14.58  4.50  8.00  5.75  8.54  8.33  5.50  7.75  9.00 14.83
66  1 23  6.96  2.79  4.96  0.67  3.88  0.25  1.13  2.29  1.54  4.04  5.13  4.88
66  1 24  7.96  7.67  7.71  5.33  9.00  7.17  8.08 11.04  8.83  8.21 10.79  7.54
66  1 25 24.08 22.58 22.67 15.83 24.00 18.12 20.46 21.92 14.92 18.12 20.91 28.54
66  1 26 18.84 15.71 15.96 11.58 16.46 11.96 19.08 10.79  9.46 14.88 10.96 22.83
66  1 27 12.87 14.29 11.92  9.42 13.00 10.58 12.25 10.08  8.12  6.21 12.08  9.50
66  1 28 23.58 23.58 20.08 15.92 19.17 14.71 17.46 16.29 12.75 18.12 21.87 21.34
66  1 29 23.67 19.04 22.13 15.46 15.16 13.42 16.79 15.79 12.79 16.08 20.67 19.00
66  1 30 11.79 11.08 14.50  8.38 13.92  9.79 14.33  9.04 10.37 15.59 15.37 21.96
66  1 31  9.50  7.79  4.17  1.58  7.46  2.75  3.79  4.67  2.71  3.04  7.83  7.54
66  2  1 17.21 22.83 14.17  9.38 19.17 12.21 13.75 15.04 10.75 11.08 18.00 16.71
66  2  2 16.42 14.83 15.46 10.58 15.21 11.34 13.54 12.62 10.54 15.71 21.04 19.55
66  2  3 21.21 18.88 17.29 13.92 16.46 12.50 10.96 14.29 11.08 14.42 20.33 18.16
66  2  4 20.04 15.29 17.21 10.50 14.33  9.59 10.96 10.75 10.25 12.50 18.12 16.54
66  2  5 23.45 18.34 20.75 12.79 16.66 13.00 19.33 15.21 13.25 16.66 22.00 20.33
66  2  6 11.42  9.42 10.21  3.83  9.21  6.00 10.63  8.92  6.96 10.79 14.50 15.37
66  2  7 14.04 12.83 12.04  5.63 11.96  7.00  8.04  8.17  6.13  8.63 12.29 16.33
66  2  8 17.16 12.00 15.00  6.83 10.34  3.33  4.58  2.54  2.96 10.13  7.12 33.04
66  2  9  3.37  3.17  5.09  2.54  6.25  4.00  6.13  5.75  6.13  8.50  8.79 21.96
66  2 10  9.17  5.21 11.63  3.96  8.63  7.67 13.29 10.29  9.71  9.50 13.25 16.88
66  2 11 12.58 11.79 11.50  5.09 15.71  8.67 11.42 14.25  9.96 10.25 17.75 16.04
66  2 12 11.25  6.25 14.50  6.13 10.79  8.58 12.67 10.67  9.25 14.37 15.59 22.29
66  2 13 14.58 16.21 11.00  4.71 14.50  6.79 11.63  9.67  7.00 10.54 14.37 17.62
66  2 14 21.67 20.41 16.88 10.67 22.29 13.37 12.42 17.29 11.00 13.00 20.96 18.41
66  2 15 19.08 15.75 23.16 12.12 22.34 15.12 16.46 19.21 13.08 16.13 21.79 25.04
66  2 16 19.08 21.62 16.75 10.46 21.37 12.29 10.96 17.04 10.92 15.41 19.25 21.62
66  2 17 21.96 24.21 17.62 11.29 24.00 14.25 13.70 18.25 11.87 15.63 22.25 22.04
66  2 18 15.87 15.29 21.46  9.96 18.79 13.50 13.59 17.83 11.12 13.70 19.29 21.79
66  2 19 15.12 13.62 13.54  8.17 12.75  7.96 10.04  9.83  7.25  9.13 13.54 18.58
66  2 20 15.21 13.17 15.04  9.79 12.33  8.67 11.92  9.83  7.87 11.75 12.46 21.79
66  2 21 10.08  7.58 12.42  5.79  9.38  5.71  8.71  7.87  5.79  8.96  8.12 13.62
66  2 22 21.17 19.46 12.96 11.29 19.83  8.63 13.59  7.38  9.46  8.75 12.96 12.87
66  2 23 12.33 11.67 11.67  9.33 15.25 10.17 17.79 11.50 12.67 15.71 18.71 18.25
66  2 24 20.08 16.58 17.12 11.12 15.25 10.50 12.00 11.50  9.13 14.42 16.75 16.54
66  2 25 32.38 29.33 25.00 21.59 25.41 19.50 21.84 24.37 16.66 24.25 35.08 29.46
66  2 26 27.63 19.29 24.21 15.37 19.46 16.04 22.95 18.66 16.04 22.50 24.67 26.71
66  2 27 21.34 17.79 19.92 14.17 21.09 14.88 23.63 16.96 17.96 21.87 24.25 27.96
66  2 28 14.21 11.71 11.92  9.67 14.12  9.67 14.79 10.37 13.67 12.71 14.92 17.75
66  3  1 23.13 21.46 17.62  9.21 16.38 12.08  9.38 14.09  9.83 14.88 22.13 18.12
66  3  2 18.05 14.42 16.58 10.79 15.79 10.67 13.92 13.62  9.75 14.54 21.62 19.08
66  3  3 11.25 10.08 10.75  5.96 12.62  8.71 10.46 10.17  8.33 12.96 17.08 18.46
66  3  4 11.87  6.71  7.92  4.96 10.79  6.21  9.71  7.00  8.63 10.25 11.54 15.67
66  3  5  9.00 11.67 10.34  3.96 11.21  6.71 10.29 13.42  7.92 14.29 21.59 20.50
66  3  6 18.34 17.96 15.96  9.04 13.13 12.42 12.17 17.46 10.46 16.04 25.46 20.62
66  3  7 18.54 13.37 17.79  9.96 14.09 12.08 16.04 11.34 10.29 14.29 19.29 16.96
66  3  8  7.17  3.46  6.21  4.71  7.75  5.00  9.04  5.91  6.42  8.83  9.71 14.42
66  3  9 11.63  8.54  8.63  5.88 14.83  8.87 16.13  8.79 10.41 11.92 15.29 17.58
66  3 10 18.71 16.00 14.54 11.79 20.88 14.00 22.29 17.79 18.29 19.46 23.04 26.38
66  3 11 23.71 16.42 15.63 14.88 21.34 13.67 20.62 16.79 15.87 17.29 24.08 31.17
66  3 12 13.46  8.38 10.58  9.92 11.38  7.87 11.25  7.00  9.00 11.92 11.79 15.67
66  3 13 10.83  6.21  7.50  7.00 10.54  5.41 11.21  4.92  7.62  8.12  8.08 10.75
66  3 14  6.13  1.58  5.79  3.63  5.79  2.58  8.63  3.04  5.17  6.79  5.50 11.54
66  3 15  1.79  2.04  4.17  2.00  3.79  1.50  8.21  0.67  3.63  4.50  7.17 11.79
66  3 16  3.21 10.21  4.96  3.42  9.42  5.33  6.87  7.33  4.00  8.21 17.08 12.83
66  3 17 11.38  8.54  9.50  6.17 10.83  8.87 14.83 10.88  9.83 12.92 14.62 19.12
66  3 18  4.33  4.33  3.46  2.54  3.25  2.17  7.21  3.04  1.87  4.38 10.96  7.12
66  3 19  4.04  9.33  5.88  3.21  6.13  4.42  5.25  5.71  3.42  3.58 17.12 11.96
66  3 20  5.75  7.33  6.08  2.67  9.08  6.29  8.87  8.33  7.50 10.34 16.71 18.16
66  3 21  7.54  9.04  5.21  3.50 10.83  6.83  9.96  6.17  7.38  9.71 11.58 14.67
66  3 22  9.67  4.58  5.63  7.50 11.17  9.67 13.17  9.54 10.34 11.79 15.92 21.96
66  3 23 19.46 13.17 13.42 14.09 19.87 12.71 20.54 13.59 16.50 16.75 21.59 27.84
66  3 24 25.08 19.12 19.21 17.41 21.12 16.54 20.83 16.13 16.79 19.79 25.75 27.12
66  3 25 14.04 11.29 13.37  8.71 10.17  8.79 10.92  8.04  8.38  9.50 15.63 16.33
66  3 26 20.83 19.87 15.75 14.96 24.37 17.88 21.29 17.79 17.75 18.08 24.87 25.75
66  3 27 32.13 23.29 21.87 24.17 30.09 23.00 28.62 23.79 24.87 24.58 31.71 34.00
66  3 28 15.09 11.79 12.50 10.67 13.88  9.79 14.54  8.83 11.04 12.96 15.00 18.84
66  3 29 10.17  6.54  7.96  7.08 10.21  7.54 10.92  5.79  8.42  9.67 11.42 16.66
66  3 30  8.71  6.42  9.17  7.38 11.42  8.46 13.59  6.75  9.42  9.62 13.00 16.17
66  3 31  9.87  7.58 10.34  6.50 10.04  8.12 12.08  6.79  9.87  9.96 13.17 14.50
66  4  1 19.08 12.67 18.88 10.63 13.62 13.04 14.42 12.79 10.92  9.62 17.58 18.08
66  4  2 12.83 11.12 23.50  9.04  9.21  6.38 11.25  3.50  5.29  8.75  7.71  9.00
66  4  3 15.63 15.63 11.29  6.21 14.75  7.87 10.58  9.29  8.75  6.96 12.79 11.25
66  4  4 28.29 27.58 25.62 14.79 27.29 17.88 21.25 20.04 16.88 14.54 21.71 24.71
66  4  5 13.92 12.42 20.00 10.88 16.21 13.42 17.37 15.63 12.54 16.08 17.41 32.00
66  4  6  5.04  4.67  8.33  2.54  4.96  2.42  4.88  3.50  3.79  6.50  5.29 15.79
66  4  7 11.25  7.87  8.04  4.67  9.08  5.17  9.33 10.46  9.87  9.08 12.12 16.33
66  4  8 15.37 15.71 10.92  7.58 16.66 10.88 11.42 11.92 11.12 10.54 17.62 24.87
66  4  9 11.38  6.79 14.42  7.87 10.50  9.21 18.63  9.33 12.50 13.17 12.08 32.55
66  4 10  9.79 11.42  9.54  6.46 11.00  6.25 10.41  8.63  7.21  9.33 11.21 24.96
66  4 11 12.21  9.96 10.21  7.25 15.09  9.83 14.21 15.25 10.71 11.25 16.88 23.87
66  4 12 14.96 12.38 14.71  9.71 15.96 12.75 17.71 16.66 13.62 10.92 17.08 29.92
66  4 13 23.13 22.17 20.30 13.62 20.88 13.59 24.71 21.71 17.50 15.25 23.16 28.25
66  4 14 28.25 26.96 23.13 14.42 22.54 15.46 21.79 22.37 16.17 18.12 25.84 33.55
66  4 15 16.33 16.00 16.54 12.08 16.04 11.00 20.62 20.25 15.12 14.50 25.37 33.95
66  4 16  7.29  9.29  8.79  3.46  6.96  6.67 10.41 13.54 12.12 11.38 19.00 29.33
66  4 17 15.71 14.29 12.58  5.54 11.58  9.50 10.71 13.67 11.00 10.00 16.92 21.04
66  4 18 20.46 14.62 15.29 10.13 13.00  9.50 16.66 14.58 12.25 11.34 16.88 26.12
66  4 19 18.41 14.46 10.50  8.83 12.08  9.21 11.71 10.75  9.67  9.50 15.12 15.09
66  4 20 12.87 11.38 11.71  9.00 11.75  8.29 11.96  7.62  9.17 10.34 12.08 14.29
66  4 21 14.33 14.75 10.88  7.29 11.00  9.04  7.87  7.00  7.41  8.75 12.33 15.96
66  4 22 22.08 21.62 18.41 12.42 15.63 12.75 15.29 16.25 11.63 16.00 23.96 16.38
66  4 23 19.33 16.50 18.58 12.54 14.29 10.54 12.96 10.75  9.08 12.58 13.21 12.54
66  4 24 13.62 11.21 10.41  7.41 13.79  7.75 11.08  8.42  9.00  9.42 11.83  9.62
66  4 25 10.46 12.46 10.00  5.91  9.17  7.17  9.21 10.29  6.21  9.87 15.00 12.79
66  4 26 23.33 21.84 18.88 16.08 20.50 15.71 13.75 15.09 13.33 17.33 17.50 18.46
66  4 27 21.71 20.67 16.62 16.21 23.67 18.21 20.88 19.33 17.75 18.96 23.00 26.92
66  4 28 12.67 15.63 11.75  7.33 12.33 10.50 13.54 14.50  9.50 13.37 23.54 19.29
66  4 29 14.17 16.54 15.37 11.17 14.33 12.42 13.29 13.17  8.33 15.09 26.12 21.54
66  4 30 10.34 14.25  7.21  8.54 13.70  8.87  4.75 10.50  8.21 10.58 17.88 16.17
66  5  1  4.04  5.50  4.75  3.79  6.25  3.75  4.54  5.13  3.29  6.38 11.38  7.87
66  5  2  7.12  5.91  7.25  2.08  5.71  1.50  1.96  3.46  1.75  4.00  9.62  5.00
66  5  3 13.25 10.71 11.08  7.12  9.50  6.29  7.38  5.54  5.25  6.17  8.67  6.04
66  5  4 18.34 14.62 14.79 11.42 18.38 12.92 15.34 15.09 13.54 14.54 19.58 21.84
66  5  5 20.50 18.91 15.09 10.25 18.84 10.21 11.34 10.00  8.75  8.42 15.29 13.04
66  5  6 27.25 19.70 19.12 15.75 20.79 13.75 16.00 14.42 12.92 15.92 19.70 22.54
66  5  7 15.71 12.08 14.21  8.71 10.75  7.58  9.38  7.58  8.67 10.41 14.37 13.70
66  5  8 16.88 13.67 10.17  5.91  9.33  5.04  5.96  5.54  4.46  6.13  9.46  8.54
66  5  9 14.54  9.79 12.08  9.59  9.13  9.46 12.62  6.58 10.88 13.88 11.79 20.30
66  5 10 16.29 15.09 14.67 11.42 13.21 11.87 10.50 13.25 11.75 14.42 23.16 21.29
66  5 11 10.13  7.87 10.37  5.04  5.88  4.75  6.34  5.17  5.37  8.46 10.08  9.08
66  5 12  7.29  6.46  5.63  2.50  6.67  3.46  5.79  1.96  4.04  4.83  7.62  6.34
66  5 13 15.71 16.83 16.08  8.54 16.46  9.46 11.54 14.33  9.87 14.67 24.71 18.21
66  5 14 13.59 13.46 12.17  7.46 15.21  9.79 11.79 11.08  9.71 12.21 18.84 18.29
66  5 15  6.21  9.29  5.09  2.92  6.96  4.29  6.25  8.21  4.42  8.12 17.33 13.29
66  5 16 10.37 14.25 10.29  6.42 12.00  8.33  6.75  9.62  7.08 10.21 21.12 16.71
66  5 17  8.96  7.71  9.08  6.54 11.38  8.25  9.87  7.50  8.83  8.58 13.75 15.87
66  5 18 10.00 11.54 10.41  5.21 12.83  8.42  9.17  8.71  7.79  6.92 16.46 15.83
66  5 19 17.46 14.09 13.42  9.54 15.04 11.38 12.00 10.17 11.25 12.75 17.58 18.63
66  5 20  8.71 11.79 10.96  6.67  9.13  7.29  9.25  6.46  7.33  8.29 13.83 16.71
66  5 21 18.08 19.38 18.16 12.04 14.50 12.62 15.96 13.75 10.71 12.67 20.75 12.00
66  5 22 23.13 19.12 18.08 17.16 22.04 17.50 24.41 19.08 20.25 21.37 25.88 27.04
66  5 23 14.50 13.33 11.29  9.13 12.75 11.29 15.04 10.79 12.71 13.21 15.87 20.12
66  5 24 14.92 14.58 12.71  7.08 12.12  9.96 10.75  8.79  9.92 10.29 16.58 10.17
66  5 25 17.00 15.37 13.37 10.92 18.25 13.00 14.96 13.67 13.33 12.71 18.96 18.00
66  5 26 13.13  9.17  7.54  8.08  8.67  7.87 11.38  7.41  9.38 11.58 11.54 12.08
66  5 27 11.67  5.09 10.67  4.58  4.83  4.63  8.17  5.25  7.25  6.71  7.00 12.00
66  5 28 13.46 13.92 12.08  4.67  8.54  7.54  6.54  6.71  7.87  7.29  9.33 15.09
66  5 29 15.12 14.46 12.42  5.46  9.38  8.33  5.66  7.67  7.25  8.46  9.08  8.92
66  5 30 10.92  9.21  7.96  3.37  6.29  4.92  2.62  3.92  4.75  4.79  5.37  4.25
66  5 31  5.33  3.50  4.12  1.38  2.46  1.58  0.83  2.54  1.79  1.92  5.29  2.54
66  6  1  7.67  6.25  3.79  4.71  6.00  3.88  4.54  5.37  6.00  8.00  7.25 12.83
66  6  2  5.66  2.67  7.21  3.04  1.87  3.17  4.79  2.96  5.09  5.71  8.96 14.25
66  6  3  6.67 10.58 10.83  5.33  4.71  6.63  8.58  8.21  5.75  8.58 13.83 12.62
66  6  4 21.21 18.29 19.79 11.08 17.21 12.67 16.54 15.75 13.08 16.88 22.21 16.71
66  6  5 10.63  9.79  9.17  7.58  9.96  8.58 10.41  6.96  8.75  9.71 11.12 15.41
66  6  6 12.58 10.04 11.58  5.04  6.96  6.00  5.33  2.37  4.88  4.29  6.67  8.25
66  6  7  7.04  5.13  8.17  2.42  3.25  1.33  2.50  1.54  2.04  4.00  6.92  4.08
66  6  8  4.79 10.08  7.00  4.46  5.58  5.58  5.04  4.00  5.71  7.71 13.50  9.59
66  6  9 15.83 18.12 14.62  8.17 11.67  5.88  7.96 10.29  6.54  7.83 16.29  8.25
66  6 10 20.67 16.08 20.46 11.17 11.50  8.29 12.08 11.00  8.87  9.54 14.54 10.92
66  6 11 10.92  6.54 16.33  3.96  4.46  7.29  7.04  4.83  7.00  8.50 10.41  6.58
66  6 12  6.79  1.83  8.75  3.88  0.50  1.38  2.50  1.33  4.04  5.58  5.00 11.71
66  6 13  8.46  4.71 10.41  3.58  2.58  3.29  2.29  0.75  2.58  2.37  4.92 10.29
66  6 14 12.29 10.92 10.46  7.33  7.92  6.21  5.17  4.38  6.38  7.87 10.25  9.04
66  6 15 16.96 17.25 11.87 11.17 13.75  9.33  8.67 10.25 10.04 12.71 12.92 15.37
66  6 16 16.17 13.54 12.38 10.25 14.21 10.67 10.34 10.41 10.92 12.96 12.79 18.21
66  6 17 12.79 10.71 11.58  7.29  6.83  7.04  8.63  8.54  7.38 10.34 16.00 14.12
66  6 18  9.17  8.75 11.42  4.83  8.00  6.50  5.58  7.08  6.46  9.00 13.29  8.04
66  6 19  8.63  8.04  8.87  5.04  6.50  5.66  6.34  3.88  4.75  7.41  8.42  7.17
66  6 20  7.50  9.42  7.17  2.58  5.66  3.46  4.21  2.37  3.79  5.25  8.21  8.25
66  6 21  6.50  6.38  9.33  2.46  4.63  2.46  4.71  2.00  4.29  3.25  9.08  4.50
66  6 22  7.75  5.75  5.79  3.58  3.46  3.42  3.63  1.58  3.46  2.92  5.66  6.50
66  6 23  6.71  6.83 10.17  5.09  9.00  5.88  8.92  4.38  6.83  6.54  7.54  8.12
66  6 24 15.12 11.67 11.34  9.38 13.83 11.00 14.42 10.21 12.08 11.87 13.25 15.67
66  6 25 10.83  9.67  9.87  5.37 11.50  8.50  9.71  7.87  9.42  9.59 13.75 12.38
66  6 26 12.33 13.67 13.37  7.41 14.79  9.75 10.75  8.12  9.25 11.08 16.88 13.50
66  6 27 20.50 14.25 15.21 10.63 16.92 11.87 17.00 11.25 13.59 15.67 16.66 21.62
66  6 28  9.21  5.41  9.33  6.08  8.29  6.46 11.12  4.04  9.42 11.29  9.17 13.50
66  6 29  7.75  4.00  7.41  2.58  6.21  2.67  4.46  2.21  4.58  3.25 11.29  6.96
66  6 30  5.83  5.04  6.21  4.04  8.25  5.37  6.42  4.54  6.63  6.54 11.87 14.67
66  7  1  9.42  5.91  7.41  3.54  7.12  4.17  3.71  5.29  5.33  6.87 13.33 15.87
66  7  2  9.25  7.25  8.12  4.12  6.83  4.29  4.83  3.29  6.42  7.21  9.00 13.33
66  7  3  8.21  3.71  6.00  3.54  3.17  0.58  2.88  1.00  2.04  2.83  8.63  6.25
66  7  4  2.58  4.08  6.25  1.42  4.12  1.08  1.46  0.96  3.37  3.00  7.67 10.63
66  7  5 14.83 11.21  4.42  5.00 11.75  5.00  1.63  4.25  5.91  5.75 10.96 11.92
66  7  6 10.08 10.92  9.46  6.54  7.71  5.66  6.96  4.08  7.33  9.17  9.33 12.00
66  7  7  8.33  4.21  8.21  4.75  8.33  5.13  6.79  4.29  7.21  6.83  8.38 12.92
66  7  8  7.92  8.50  7.79  3.79  8.00  4.63  5.83  5.63  6.50  7.33 11.50 12.58
66  7  9  6.71  6.92  6.96  5.09 10.21  6.08  7.67  6.29  7.21  7.12 14.50 15.75
66  7 10 15.54 13.70 11.04  7.62 13.42  9.87  7.50 11.38  8.67 11.08 16.00 15.37
66  7 11 11.42  7.50 12.04  6.54  8.79  5.83  7.58  6.58  7.92  6.54 12.04 13.29
66  7 12 10.25 10.17 10.34  8.87 16.04 10.29 13.79 10.63 12.29 10.83 17.21 20.58
66  7 13 18.91 15.00 12.21 10.88 17.83 11.63 14.83 12.58 13.75 14.58 18.25 21.34
66  7 14 14.25  9.46 10.54  8.46 12.62  8.54 10.08  8.08 10.13  9.92 13.79 15.54
66  7 15 18.75 13.29 11.34  9.54 15.79 10.46 10.25 10.92 10.37 10.96 15.37 12.83
66  7 16 18.08 12.83  8.96  8.83 13.17  9.04  6.17  8.08  8.33  7.75 12.42 10.83
66  7 17 12.71 10.08  9.04  6.29  7.50  4.75  4.75  6.38  6.29  7.38  9.62 10.67
66  7 18  6.29  4.21  7.29  2.21  5.04  1.00  1.38  1.67  2.08  1.83  5.33  2.96
66  7 19 10.17  7.33 13.88  5.54  9.54  3.83  5.46  5.75  5.50  5.88  6.92  5.63
66  7 20 13.50  9.46 23.00  9.71 11.83  6.58  9.38  9.13  8.67  8.58 13.59 12.29
66  7 21 10.96  8.17 17.33  8.08  8.38  6.38  7.17  8.75  7.41  8.96 13.25  9.00
66  7 22 12.17  6.54  7.21  7.04 10.92  6.42  7.79  7.71  7.92  7.75 11.38 17.16
66  7 23  9.50  9.79 10.37  7.96 16.42  9.59 13.21  8.54 12.21 10.46 14.71 18.71
66  7 24 22.34 14.12 14.25 10.46 18.66 10.04 12.46  9.79 11.50 11.92 14.83 16.46
66  7 25 12.17 10.50  9.83  6.96 13.29  7.62  9.42  8.46  9.46  8.33 14.21 12.00
66  7 26 16.92 14.50 11.17  8.79 15.41  7.83 10.04 12.00 10.08 11.00 16.25 18.29
66  7 27  9.29  6.96  8.63  4.88  7.50  4.17  5.46  6.34  6.42  6.00 10.54 14.04
66  7 28 14.00 10.79 10.13  7.79 16.04 10.25 11.21 12.75 10.92 11.71 14.33 17.58
66  7 29 17.92 12.29  9.13  9.46 15.67 10.71 10.71 11.46 13.25 10.88 15.67 16.71
66  7 30 18.66 12.04  9.96 11.38 16.66 10.37 13.17  9.83 11.96 11.79 12.79 21.75
66  7 31  8.54  7.25  5.88  3.13  6.25  1.75  3.33  1.42  3.63  2.21  4.33  6.87
66  8  1  6.54  7.29  8.33  3.83  4.79  2.17  5.41  1.67  4.88  5.96  5.09 10.37
66  8  2 15.46 11.50 13.29  6.04 10.79  6.00  5.41  6.42  5.66  4.96 10.25 10.08
66  8  3 10.00  8.42  9.46  6.46  8.38  4.29  7.08  7.04  7.25  7.67  7.96  9.46
66  8  4 14.46  7.50  9.04  7.96 11.79  7.12 10.63  9.21 10.63  9.04 11.54 14.17
66  8  5  8.50  6.50  9.13  5.96  9.46  6.92  9.50  8.33  8.00  7.29  9.54 12.12
66  8  6  6.13  6.00  6.96  1.71  5.63  1.79  3.17  1.83  2.17  2.71  5.09  5.91
66  8  7 10.29  6.50  6.54  5.71  7.96  3.92  5.83  4.17  5.71  2.54  9.33  4.17
66  8  8  9.96 10.71  9.54  3.88 12.29  6.83  8.25  8.58  8.29  7.54 13.21 10.34
66  8  9 20.08 17.46 16.38  9.79 18.79 12.92 12.79 14.88 12.29 13.13 18.12 20.00
66  8 10 14.42 10.25 15.12  7.92 15.29  9.33 17.46 11.42 13.46 13.21 17.67 27.54
66  8 11 13.21  9.75 12.25  7.41 10.13  6.34  8.58  4.79  5.46  3.54  7.75  9.46
66  8 12  6.46  3.54  4.42  3.46  6.75  2.42  6.54  3.75  3.83  2.67  6.63  7.08
66  8 13 12.87  5.33  9.96  6.79  8.58  3.04  9.67  5.29  6.21  5.09 11.42 10.83
66  8 14 12.25  8.17  6.04  2.96 10.92  2.46  2.88  5.66  3.29  1.92  8.63  8.79
66  8 15  6.75  6.29  7.50  2.79  4.21  1.87  4.17  3.50  2.96  2.08 11.21  5.09
66  8 16  9.04 14.96 12.42  5.25  9.92  6.96  8.87 11.83  7.25  9.29 21.46 19.50
66  8 17  8.38  9.92 10.79  4.71  6.34  4.29  7.00  3.67  5.21  4.00  9.08  7.83
66  8 18  4.75  4.08  3.58  2.42  3.21  0.37  3.08  1.58  0.96  1.29  9.96  8.29
66  8 19  4.08  7.41  5.50  4.04  4.54  3.29  4.63  4.33  5.00  6.08  8.00  9.25
66  8 20  7.29  8.00  8.04  3.21  8.25  3.88  3.46  5.88  4.38  6.46 11.83 10.83
66  8 21  9.87 10.08 17.37  6.58  9.25  5.91  8.46  8.79  6.46  8.63 14.58 11.21
66  8 22  9.83  6.46 18.41  4.92  6.96  4.38  6.58  3.79  5.75  6.71 10.67  7.29
66  8 23  4.96  4.25  8.71  1.87  4.75  0.71  3.21  2.00  1.67  3.67  4.08  5.21
66  8 24  4.00 10.46  3.58  2.58  7.67  3.13  5.09  1.79  2.08  4.38  8.54  9.38
66  8 25 10.58 12.96  7.33  4.12 10.21  3.79  6.38  7.29  7.21  8.00 12.92 12.21
66  8 26 13.29 12.17 11.00  5.96 14.00  6.54  9.17 10.17  8.83 10.17 12.75 17.46
66  8 27 16.13 15.09 11.12  7.33 15.34  9.00  8.17 11.50  8.75  9.33 13.67 18.00
66  8 28 15.46 12.12 11.08  6.79 13.96  8.96  8.96  9.62  9.46 10.34 12.00 17.92
66  8 29 11.42  7.83 11.71  8.29 11.96  9.04 10.25 12.25  9.38 10.13 14.04 18.66
66  8 30 16.92 12.46 11.25  7.75 11.83  5.50  6.58  8.33  5.79  7.00 12.25 10.04
66  8 31 14.33  8.25  8.63  7.54  9.67  5.25  6.13  5.29  6.04  6.00  8.79 10.25
66  9  1 17.41 16.54 15.04  7.83 11.83  7.62  7.62 10.96  6.67  7.00 13.54 11.12
66  9  2 14.71 11.83 12.96 10.88 16.66  9.96 12.50 10.08 10.88  8.54 11.75 12.29
66  9  3 19.00 18.84 15.12 10.46 15.96 11.38 14.37 12.21 10.25 14.21 19.41 18.91
66  9  4 16.46 13.42 15.79 10.29 17.00 11.08 14.67 12.21 11.87 13.50 18.66 24.08
66  9  5 17.04 16.66 16.38  9.75 16.54 11.63 15.67 15.46 12.83 15.50 24.17 24.83
66  9  6 10.04  8.96  9.71  7.71 15.75 10.34 16.38 11.63 14.00 13.17 20.25 30.34
66  9  7  5.63  4.42  2.29  1.96  6.42  3.71  6.75  4.17  4.92  6.75  9.59 16.96
66  9  8  6.17  6.71  6.75  3.17  4.83  1.29  3.83  0.92  2.54  4.08  7.62  9.42
66  9  9 10.96 12.62 10.04  6.63 12.25  8.58  7.96 10.71  7.58 10.29 17.83 14.42
66  9 10 16.88 12.04 14.88  7.04 11.17  8.25 11.96  9.79  7.79 12.21 15.41 11.54
66  9 11 10.34  5.41 12.58  5.04  6.00  3.54  3.96  6.21  3.54  4.17 15.16 11.63
66  9 12 15.63 17.04 14.62  8.96 18.29 11.00 13.04 11.34 11.83 12.17 17.79 16.96
66  9 13 16.50 13.13  9.83  9.00 16.46 10.50 15.41 11.71 11.75 11.25 16.42 19.12
66  9 14 18.16 16.58 17.50 10.04 20.62 12.33 18.21 16.08 14.04 15.96 22.92 26.92
66  9 15 17.96 14.96 13.13 11.17 13.88  9.75 16.29 11.67 11.17 15.04 16.25 24.96
66  9 16  7.75  7.08  6.38  2.62  5.83  4.46  6.13  8.58  5.00  8.38 15.83 16.42
66  9 17  6.13  7.38  7.08  3.33  8.04  4.92  2.04  5.71  3.71  6.46 10.83 10.34
66  9 18  8.63  6.21  8.42  1.79  4.67  1.50  1.71  0.63  1.29  1.75  4.92  4.58
66  9 19  8.00  5.25  5.09  1.63  5.04  1.83  1.21  1.67  0.75  2.25  6.38  8.58
66  9 20  5.09  3.00  4.00  0.46  3.46  0.58  1.71  0.37  0.79  2.42  6.25  9.04
66  9 21  5.71  1.83 11.17  0.75  3.37  1.21  1.83  1.17  2.08  3.08  5.25  6.25
66  9 22 13.00  4.96 13.00  1.50  4.54  1.92  2.71  5.54  6.04  4.38  9.62  4.83
66  9 23  8.54  9.92  5.91  1.42  7.50  2.33  3.63  3.71  4.50  5.25  7.79  6.34
66  9 24  6.50  9.54  4.79  2.54  8.12  1.92  2.33  2.92  2.50  1.92  4.50  7.08
66  9 25 10.96  9.83  5.71  1.83  7.87  2.96  3.88  4.71  4.29  5.13  6.67  7.83
66  9 26 10.00  9.04  8.04  1.75  8.79  3.17  1.87  5.04  4.00  3.17  6.29  6.46
66  9 27 12.04  8.38  7.96  2.58  8.75  2.37  4.79  3.75  3.42  4.38  6.17  7.67
66  9 28  7.83  1.38  8.12  1.17  7.62  2.83  3.88  0.67  1.13  2.92  5.54  7.04
66  9 29  3.37  3.29  4.38  0.96  5.91  2.25  0.42  1.87  1.50  3.67  7.87  7.50
66  9 30  5.13  4.92  2.25  2.17  6.08  2.04  2.33  3.00  2.08  5.50  8.83  9.38
66 10  1  8.00  3.63  9.17  4.04  7.29  2.33  4.25  0.42  2.17  2.00  5.04  6.71
66 10  2  6.75  6.83  6.13  2.25  7.46  1.75  4.33  2.21  1.54  3.83  6.67  7.62
66 10  3 16.96 24.17 12.33 12.46 23.38 13.13 21.29 15.79 14.62 17.41 25.25 26.34
66 10  4 20.17 17.33 29.54 12.87 14.37 11.00 17.79 11.79 11.38 13.67 18.50 22.58
66 10  5 11.79  9.25 11.12  4.12  9.33  4.83  7.58  7.58  6.04 10.00 12.96 16.25
66 10  6 13.33  9.13 13.96  6.67  9.04  6.38  9.71  5.25  6.83  8.25 10.00 11.29
66 10  7  8.92  7.04  7.87  1.79  7.50  3.04  2.00  2.21  2.71  2.75  6.58  7.83
66 10  8  9.00  5.66  7.62  3.83  6.17  2.13  4.42  2.04  3.29  3.58  6.67  9.04
66 10  9 11.25 10.46  8.50  3.71 10.71  5.83  3.79  5.71  4.21  6.42 12.08 11.71
66 10 10 12.87  8.96 10.04  3.96  9.13  4.25  2.83  3.83  3.71  6.04 13.08 11.92
66 10 11  7.71 10.79  8.83  4.25 10.54  6.21  7.12  7.08  5.50  7.87 14.12 15.12
66 10 12 22.54 18.91 15.59 11.63 18.96 12.42 13.17 13.17 11.58 13.92 17.46 22.75
66 10 13 13.67  9.50  9.54  5.04  9.75  5.50  4.75  6.75  5.09  5.00 10.25 12.92
66 10 14 12.54  8.29 12.17  4.67  8.46  4.50  2.83  3.08  4.00  3.04  5.29  4.08
66 10 15 11.79  6.38 15.04  7.17  8.04  2.58  5.79  5.17  4.96  6.21  8.79 12.83
66 10 16 11.67  9.50  7.87  3.67 11.08  5.75  5.63  5.46  4.42  5.50  8.75 10.25
66 10 17 17.67 12.29 16.42  7.08 14.92  9.33 11.96 10.96  8.29  9.96 15.00 14.37
66 10 18 11.83  7.92 13.37  6.17  7.96  3.71 10.13  3.88  7.25 10.83  9.04 25.04
66 10 19 13.79  9.83  8.75  8.00  8.38  6.17  5.88  5.21  5.50  8.17  9.42 15.92
66 10 20  5.41  6.63  4.21  1.25  6.42  2.75  8.50  3.33  3.88  5.79  7.79 12.96
66 10 21  8.75  5.88  6.50  3.79  7.75  5.58  9.67  5.33  7.29  7.29 12.96 19.38
66 10 22  4.75  3.00  3.96  0.71  4.88  2.62  5.91  0.67  1.96  6.04  8.12 13.79
66 10 23  9.04  6.58  7.58  3.58  5.79  2.79  8.12  2.83  5.58  7.79 10.25 19.79
66 10 24 12.67 11.50 18.63  7.04  6.04  1.71  7.62  4.75  4.38  6.96 13.37 16.25
66 10 25 12.29  6.87 14.33  5.83  7.08  1.96  6.00  2.46  3.92  3.75  8.83 13.75
66 10 26 14.54 13.59 11.21  6.29  9.59  5.54 11.08  7.71  9.46 10.13 18.54 26.12
66 10 27 19.75 17.16 22.54 12.62 12.87  9.29 14.79  9.67  9.79 14.21 19.62 25.96
66 10 28 12.38  9.21 18.54  8.17  6.58  2.33  7.75  2.58  4.29  5.09  6.71 13.08
66 10 29  9.08  2.96 10.46  4.75  5.63  1.08  5.88  1.08  4.00  5.46  3.83 10.34
66 10 30  6.71  3.79  7.46  1.67  4.50  1.00  5.46  1.50  2.62  3.79  8.42 13.25
66 10 31  9.71  8.38  8.38  4.42  9.67  6.50  9.92  7.79  7.38  8.63 15.41 23.21
66 11  1 22.71 21.25 25.75 15.34 18.00 11.92 18.58 15.34 12.75 16.13 27.04 33.12
66 11  2 16.38 13.75 27.37 10.96 11.38  7.38 13.79  7.79  6.63  8.79 10.37 13.42
66 11  3 10.67  4.83  7.25  2.42  6.63  3.17  5.17  5.46  3.58  5.58  7.96 16.13
66 11  4  7.75 11.17 10.75  1.96  9.17  5.04  5.63  6.04  4.75  8.54  8.33 21.50
66 11  5 13.08 17.67 18.12  9.67 14.88 10.79 17.67 16.29 11.54 14.33 25.08 33.79
66 11  6 21.09 19.41 26.83 14.00 13.42 13.46 19.87 14.67 10.79 15.75 25.25 30.84
66 11  7 13.70 10.00 10.37  5.79  7.71  4.96  6.17  5.71  4.46  8.25 11.75 12.12
66 11  8  7.83  4.21  5.91  2.50  6.50  3.29  7.38  2.79  4.29  5.58 10.58 14.79
66 11  9  9.00  7.67  6.96  3.17  7.79  4.33  8.71  3.71  7.83  7.38 12.04 21.42
66 11 10  4.29  5.09  4.42  0.63  5.54  3.54  6.75  1.54  3.33  4.71  6.83 13.59
66 11 11 13.50 11.46 10.75  3.75  9.79  7.62  7.96 11.17  6.63 10.96 18.91 19.95
66 11 12 14.58  8.21 12.12  5.17  6.67  6.96 11.17  8.38  6.87 10.17  8.54 10.79
66 11 13 11.50  8.58 11.96  5.63  7.92  7.50 11.42  7.83  7.33 11.67 16.33 20.12
66 11 14 12.08 13.08  7.96  6.13 13.88  8.38 12.12  8.71  9.92 10.63 17.62 22.54
66 11 15 25.62 21.67 17.46 18.12 27.58 18.12 23.09 21.29 20.33 22.88 31.96 36.71
66 11 16 28.33 24.41 27.04 19.04 23.00 18.12 21.87 19.21 20.46 23.83 27.12 40.41
66 11 17 22.92 16.83 20.75 12.17 13.04  9.71 13.92  9.59 12.46 15.37 18.75 30.29
66 11 18 12.96  8.75 16.62  8.08  6.50  0.79  8.42  2.92  5.25  6.38  7.17 11.58
66 11 19  7.46  4.33  9.54  2.29  4.46  0.17  2.92  1.21  2.79  4.08  6.54 10.00
66 11 20 10.21 12.75  9.08  3.67  7.62  2.42  3.54  4.92  3.04  4.46  9.50 11.12
66 11 21 12.00  7.96 12.50  4.46  6.46  1.87  6.71  5.41  5.83  6.87 14.25 21.04
66 11 22 11.58  3.92 13.46  5.88  6.08  0.67  8.17  1.13  4.21  3.13  5.83  9.59
66 11 23  4.79  6.92  5.83  0.25  7.83  0.00  5.33  0.96  0.71  1.04 12.00 10.79
66 11 24  8.75  6.54  7.33  2.67  6.96  1.46  7.96  3.33  5.13  6.08 12.04 14.50
66 11 25 10.37 10.41  7.17  5.41 10.46  7.25 12.12  8.00  8.25  8.92 14.17 17.33
66 11 26  8.83  9.59  8.71  6.25 11.17  8.67 14.79  9.42 11.46 10.75 16.42 20.04
66 11 27 17.33 15.92 12.46  8.42 14.12 10.50 16.75 11.42 12.17 14.09 20.38 26.71
66 11 28 17.25 13.29 11.96 10.54 11.12  9.25 15.37  8.04 13.08 13.33 19.92 31.71
66 11 29 16.58 14.12 14.09  7.92 14.33 11.08 19.38 11.04 13.13 14.21 22.04 22.71
66 11 30 21.29 19.58 13.75 14.54 18.71 13.13 21.75 13.25 15.50 14.88 22.29 29.50
66 12  1 34.37 33.37 24.04 23.54 33.63 26.04 30.37 31.08 25.62 24.41 42.38 31.08
66 12  2 28.21 27.37 22.54 20.62 22.08 16.13 28.16 17.29 22.50 23.16 25.88 42.54
66 12  3 14.29 12.25 12.08  8.75 11.29  7.29 14.50  6.17 10.08  7.75 14.29 24.54
66 12  4  8.00  5.58  6.96  1.38  8.63  4.92 11.75  4.50  6.21  6.34 15.46 17.41
66 12  5 10.37 11.38 11.96  5.66 13.04  9.04 14.96 11.58  9.62 12.50 18.91 19.92
66 12  6 11.29  9.54  5.91  5.37 10.83  5.75 12.21  5.66  7.87  7.29 15.71 20.54
66 12  7 10.46 11.25 10.50  5.33 12.29  9.79 15.09 11.79  9.87 11.75 20.21 20.58
66 12  8 22.54 21.42 14.12 13.42 21.84 16.38 25.12 17.08 19.62 18.25 28.16 31.49
66 12  9 21.25 21.00 14.62 17.37 23.91 17.33 23.75 21.00 19.33 11.92 25.33 23.67
66 12 10 20.75 17.50 13.62 13.59 18.46 12.12 14.58 11.83 13.25  9.13 16.71 19.75
66 12 11 11.67 15.12 10.13  5.50 13.96  9.50 11.21  9.17  8.38  8.96 16.13 15.25
66 12 12 26.75 22.21 16.42 17.62 24.79 17.75 20.04 19.70 18.63 10.79 26.08 11.46
66 12 13 15.79 12.50 15.12 11.08 11.83  9.13 13.33  8.29 11.79  5.54 15.67 10.75
66 12 14  7.08 11.46  6.63  1.04 10.08  5.46  9.87  5.63  4.83  4.67 13.33 12.92
66 12 15 20.83 16.88 14.62  9.92 14.58 10.63 11.29 12.58 11.17 12.92 19.55 21.34
66 12 16 10.79 10.75  6.79  4.00 10.67  7.33 14.25  7.96  8.29 10.08 17.88 19.87
66 12 17 18.29 18.88 19.50 11.87 20.67 17.75 26.00 19.70 19.83 21.75 29.17 34.92
66 12 18 14.83 11.67 12.71  9.83 13.04 10.08 18.50  9.29 12.75 13.00 15.92 25.12
66 12 19 15.59 16.38 15.29 12.04 22.58 15.59 23.04 13.70 19.00 13.50 23.25 21.87
66 12 20 14.29 11.38 13.29  8.87  9.46  7.96 12.58  7.12  8.96  8.71 12.92 22.50
66 12 21 16.38 11.25  8.17  7.21 15.00  8.29 17.16 12.25 12.83 11.08 17.54 21.62
66 12 22 18.66 14.79 10.67 11.83 17.96 12.42 17.37 15.92 14.46 12.75 17.71 20.21
66 12 23 15.75 16.71 10.50 10.41 18.00 11.83 16.88 12.54 14.09 14.17 15.50 26.96
66 12 24 15.92 13.54 11.67 10.17 15.75  9.67 10.67  8.87 10.92  7.38 11.63 18.08
66 12 25 10.67  7.21  8.42  4.21 10.79  5.63  4.96  6.13  5.17  6.21 14.33 15.16
66 12 26 19.92 16.00 17.25 13.54 15.59 11.58 10.41 13.08 12.83 16.13 21.09 25.08
66 12 27 11.17 10.88  7.54  4.38 10.50  6.63  9.67  5.17  6.67  6.87 13.46 12.33
66 12 28 15.25 12.71 11.79  4.58 12.00  8.54  9.62  7.50  8.38 11.63 12.75 17.21
66 12 29 19.79 17.62 16.33 10.79 18.50 10.75 16.50  7.83 11.12  7.75 10.79 13.25
66 12 30  5.25  4.25  8.21  1.25  6.42  3.37  5.25  3.67  4.29  3.79 11.67 16.71
66 12 31 13.00 11.46 10.13  6.34 11.87  7.50 13.50  8.46 11.00 10.04 17.29 22.46
67  1  1  6.46  4.46  6.50  3.21  6.67  3.79 11.38  3.83  7.71  9.08 10.67 20.91
67  1  2  8.29  6.34  8.54  1.75  3.33  0.33  8.29  0.58  3.92  5.04  2.75 17.46
67  1  3  5.09  4.29  8.71  0.46  3.21  0.50  5.66  0.25  2.50  5.09  2.54 15.96
67  1  4 10.75 13.29  8.21  1.33  8.63  0.79  5.63  0.71  1.87  2.88  3.37 11.92
67  1  5 10.92  4.33 12.67  5.41  6.25  2.62  6.87  1.67  4.88  6.50  4.54 11.67
67  1  6 11.08  8.67  7.33  3.63  8.92  6.25 10.63  4.42  7.75  8.00  9.92 12.92
67  1  7 12.12 13.54 12.87  6.13  9.87  6.13  8.63  7.71  7.75  8.25 15.79 17.75
67  1  8  9.79  6.42 10.25  4.29  6.96  2.54  6.34  3.92  3.96  3.37  9.00 12.04
67  1  9  8.33  3.33  7.21  2.17  4.17  1.42  7.33  0.37  2.75  2.75  4.04  7.96
67  1 10 11.34  5.09  7.29  3.33  6.00  4.96 11.00  7.29  7.87  7.79 12.33 14.58
67  1 11 17.33  9.38  9.62  8.46  6.34  5.63  9.59  6.83  7.71 11.79 10.21 15.54
67  1 12  8.38  4.17  5.83  5.66  7.41  4.29  9.54  4.58  7.96  7.41  8.96 15.71
67  1 13  4.58  3.79  5.17  2.21  5.41  2.88  9.96  1.25  5.41  6.00 10.21 16.21
67  1 14 10.13 10.13  5.13  3.21  8.17  4.21  6.21  6.63  3.96  7.46 14.62 11.75
67  1 15 18.84 16.58 11.63 10.75 14.62  9.00  3.92 11.50  8.63 11.67 22.63 17.16
67  1 16 21.29 18.41 15.83 14.04 16.29 10.41  7.75 12.67  9.38 14.67 17.96 20.88
67  1 17 18.29 13.04 17.25 11.12 12.21 10.37 14.79 11.92 12.00 15.50 13.62 20.71
67  1 18 13.79 11.50 12.08  6.42 12.33  8.96  9.00  7.67  7.08  7.54 11.54 19.87
67  1 19 16.33 12.50 18.29 10.34 11.79  8.17 13.67  7.96  8.46 11.63 12.71 17.54
67  1 20 10.96  8.75 14.42  7.21  9.92  6.46 10.21  7.17  6.63 10.04 10.37 15.79
67  1 21 12.46 12.00  9.08  6.67 13.92  7.62 13.54  6.63  8.92 10.00 18.08 17.41
67  1 22 18.38 13.92 14.04  8.12 14.33 10.08 13.29 10.21  9.33 11.25 15.12 17.54
67  1 23 22.88 19.79 15.25 10.25 17.83 10.21 10.08 11.92  9.54 13.00 15.67 23.67
67  1 24 16.92 13.59 10.54  6.13 11.29  5.66  6.71  5.41  4.67  9.33  9.42 14.67
67  1 25 15.54 13.75 14.58  9.29 13.46  8.92 11.79 10.00  9.67 12.25 17.25 17.16
67  1 26  7.08 10.13  8.00  3.29  9.59  7.08  7.75  6.50  5.71  8.04 12.67 14.29
67  1 27 24.30 23.75 19.38 16.25 20.25 13.83 12.38 17.62 13.29 19.58 28.75 27.21
67  1 28 20.33 19.70 14.29 10.88 15.41  8.83  9.25 11.79  8.38 15.16 22.58 19.29
67  1 29 26.54 23.79 20.71 17.92 21.59 15.04 13.13 18.38 15.54 20.54 25.92 22.95
67  1 30 15.92 15.25 14.17  8.67 14.96 11.25 12.92 14.58 11.17 17.46 25.37 21.34
67  1 31 11.38 11.92 12.79  6.17 14.71 10.41 16.17  9.75 12.04 16.17 18.58 21.00
67  2  1 13.59 13.54 12.58  7.62 12.17  9.50 14.67 10.96 11.54 13.88 17.50 16.54
67  2  2 16.38 17.08 17.21 10.37 18.84 14.12 16.88 16.21 14.29 18.91 24.04 23.54
67  2  3 12.54 11.42 11.21  9.33 13.67 11.50 20.12 14.25 14.54 15.04 21.17 26.79
67  2  4  6.38  4.42  5.96  3.33  8.54  5.00 10.00  5.37  6.71  8.46 11.58 16.04
67  2  5  5.58  7.00  6.54  2.71  8.58  6.13  9.96  7.67  6.75 11.12 16.88 21.71
67  2  6  8.08 10.08  7.12  4.33  8.92  5.58 10.41  5.46  6.46  9.21 14.50 17.21
67  2  7  7.12  8.50 16.71  5.71  9.17  4.75  7.96  5.17  4.75  8.58  9.46 12.62
67  2  8  8.29  5.09 10.13  3.29  4.46  0.13  6.25  0.67  3.21  5.83  2.04 10.25
67  2  9  2.79  4.12  4.79  1.71  6.79  1.79  5.66  3.79  4.12  6.67 10.79 14.29
67  2 10  7.92 10.88  4.21  2.83  8.96  4.50  4.50  6.63  4.33  6.87 17.08 13.17
67  2 11 17.71 15.75 15.75  8.67 13.29  6.71  9.38 10.17  8.50 12.00 14.54 14.42
67  2 12 15.96 12.33 16.58  7.08 13.88  8.04 11.00  8.83  7.21  8.83 10.71 12.46
67  2 13 15.54 14.42 15.54  7.62 13.62  9.08 12.54 10.21  8.29 11.92 15.54 14.04
67  2 14 18.46 17.58 16.96  8.08 20.58 11.87 14.62 11.79  9.38 13.21 17.54 23.04
67  2 15 26.08 21.54 27.00 16.71 21.46 20.25 26.54 20.46 16.83 22.42 23.75 34.62
67  2 16 10.13 11.17 10.08  4.12 10.83  5.46  7.17  9.25  6.21 11.92 16.25 19.04
67  2 17  3.13  7.12  4.67  1.58  7.38  2.88  4.54  4.38  2.50  3.25 11.12  8.08
67  2 18 18.79 17.46 16.92  8.67 15.67 10.13 10.50 10.04  9.79 13.92 16.17 20.00
67  2 19 23.63 24.21 18.75 13.50 21.29 14.25 19.08 14.17 13.92 17.67 24.21 24.58
67  2 20 13.92 12.12 13.00  9.29 15.21  9.59 14.83 13.50 11.83 14.62 24.04 30.13
67  2 21 15.46 17.04 13.96 13.62 19.08 12.12 16.17 13.25 13.50 16.96 24.33 28.67
67  2 22 18.58 11.83 16.04  6.50 13.92  9.25 11.34  6.71  9.33 10.79  9.50 11.29
67  2 23 18.16 13.96 13.13 10.71 14.17  9.46 16.33 11.04 11.21 14.79 19.00 23.63
67  2 24 24.41 19.17 20.96 14.79 18.63 13.54 15.09 14.67 13.83 18.84 21.71 26.00
67  2 25 18.25 13.33 17.96 12.29 14.17 11.87 12.04 12.62 11.96 17.29 14.71 19.33
67  2 26 11.25 12.96  8.71  7.33 13.88  8.46 13.75  9.87  9.87 11.38 14.75 16.54
67  2 27 28.91 25.17 23.91 18.21 24.83 18.29 22.08 19.62 18.29 22.75 31.13 31.66
67  2 28 19.25 22.54 14.71 16.17 24.37 16.75 23.04 17.16 18.16 18.46 27.29 29.79
67  3  1 14.67 17.16 13.29 12.33 19.08 12.92 20.88 16.04 16.62 16.38 24.41 28.16
67  3  2 13.42 12.87 11.00 10.29 16.50 15.21 21.92 15.37 16.96 16.79 23.29 30.63
67  3  3 13.59 13.21 13.96  8.42 11.17 10.67 18.71 14.29 12.17 14.25 21.34 24.30
67  3  4 15.92 13.21 14.96 11.38 10.79 11.79 14.71 14.88 11.79 16.17 21.04 21.12
67  3  5 19.55 19.58 15.79 12.75 17.33 13.08 15.46 21.62 12.75 18.21 29.38 26.54
67  3  6 20.41 18.58 19.38 13.29 16.83 14.79 19.17 15.96 16.00 18.05 26.46 28.42
67  3  7 24.92 16.54 21.87 17.75 17.75 14.37 16.75 15.79 15.87 20.62 24.92 28.04
67  3  8  9.00  8.58 11.12  4.67  8.63  6.17  9.25  7.33  8.42  9.87  8.33 16.42
67  3  9 19.12 12.46 19.00  9.25 11.25  6.67 13.50  5.17  8.96  8.42  8.33 18.66
67  3 10 19.58 23.25 17.46 13.00 23.09 15.25 22.83 16.25 15.37 17.92 28.21 27.04
67  3 11 21.09 24.62 15.16 16.75 24.67 16.75 26.54 20.38 20.50 20.50 28.84 37.59
67  3 12  5.88  9.08  5.88  2.42  9.00  5.00 11.92  6.96  7.04  9.08 16.33 17.00
67  3 13 17.58 16.33 11.83 10.67 17.21  9.92 16.71 12.71 13.54 12.33 22.21 22.83
67  3 14 22.34 20.00 20.08 14.12 21.09 16.00 22.29 21.54 17.50 21.00 30.75 31.96
67  3 15 15.09 11.12 10.88  9.87 13.79 10.58 17.92 12.42 15.09 13.46 19.67 25.84
67  3 16 11.42 10.13 10.08  6.21 10.71  8.17 14.71 13.17  9.62 12.83 20.50 21.12
67  3 17 19.79 12.87 17.33 12.92 19.25 16.42 25.25 19.33 21.12 20.46 23.58 31.88
67  3 18 21.21  8.75 10.54 11.63 13.42 11.58 17.08 12.67 13.29 11.71 15.71 20.91
67  3 19 17.83  7.83 13.00 11.34 12.33  9.33 19.95 11.71 14.96 16.13 16.21 22.42
67  3 20  8.50  8.25  9.42  6.46 13.37  9.87 17.21 10.46 14.25 12.50 19.00 24.17
67  3 21  9.71  8.04  9.50  6.13 10.21 10.25 20.38 12.25 14.83 13.04 19.41 23.13
67  3 22 13.83 10.92  9.46  7.83 13.59 11.83 17.04 12.46 14.37 12.00 16.62 19.12
67  3 23 12.46  9.59  8.17  7.92 12.38  8.83 15.04  9.83 10.96 11.92 17.37 21.04
67  3 24 17.83 16.29 16.46 10.96 14.09 10.96 16.29 14.29 12.33 15.09 22.25 23.67
67  3 25 20.79 15.37 17.75 12.71 15.16 12.67 17.12 14.25 13.59 14.92 20.50 23.91
67  3 26 18.29 15.46 12.62 11.38 18.38 11.75 20.38 12.92 14.54 13.83 24.17 27.79
67  3 27 16.08 15.34 11.17 10.75 17.46 12.08 19.62 11.04 15.16 13.70 19.46 22.13
67  3 28 15.16 10.13 11.17 12.29 14.00 11.00 18.16  9.50 13.75 11.38 17.71 27.84
67  3 29 12.79  5.29  9.08  8.08  9.67  5.96 15.09  6.79 11.42 10.34 12.79 22.58
67  3 30 12.38  5.04  7.75  7.21  7.79  4.67 11.50  3.71  8.12  7.21 10.79 12.75
67  3 31  9.79  7.29  8.25  4.50  5.75  1.38  6.96  2.13  3.79  5.88  5.83  9.29
67  4  1 22.25 17.92 16.54 11.42 16.96 12.71 11.87 11.87 12.75 14.50 21.04 25.88
67  4  2 13.08 12.46  9.59  9.71 14.96  9.96 15.09 11.25 12.46  9.87 16.42 18.41
67  4  3 12.62 11.21  9.04  9.62 14.37  9.87 13.54 10.34 11.21 10.92 15.46 17.96
67  4  4 10.08  9.83  7.87  7.92 12.62  9.00 15.83 10.58 12.62 13.59 17.92 23.50
67  4  5 20.50 12.54 13.62 13.67 15.83 11.46 19.67 13.08 15.92 18.25 16.79 27.71
67  4  6 20.12 15.54 22.00 14.04 14.54 11.92 18.54 13.83 13.92 16.38 21.04 31.13
67  4  7 14.37 14.42 25.54 11.42 11.42  8.21 15.92 10.63  9.71 13.88 21.92 26.08
67  4  8 13.50 11.67 23.79 11.46 11.87  6.38 15.16  9.13  9.17 10.88 11.42 16.29
67  4  9 13.79 13.88 26.34 11.58 13.75 10.58 18.05 11.63 11.50 14.42 18.00 14.92
67  4 10 14.42 12.67 30.54 12.17 10.25  9.04 17.12  9.21 10.13 11.46 15.00 12.38
67  4 11 11.67 13.96 19.67  9.67 10.58  7.25 11.83  7.92  7.50 10.54 12.33 15.67
67  4 12  7.71  7.50 15.21  6.13  8.08  2.42  7.79  4.96  4.38  6.17 10.92 11.96
67  4 13  6.29  5.54 15.79  6.42  8.42  2.62  8.71  3.37  3.83  4.21  5.46  8.63
67  4 14  6.83  5.71 14.83  4.33  7.21  2.08  4.54  1.25  2.29  1.75  4.88  6.08
67  4 15 10.37  5.91  5.21  4.54  8.46  2.75  9.38  4.33  6.13  6.38  8.87 12.04
67  4 16  8.71  6.67  4.71  4.21  8.79  3.25  6.29  4.00  6.08  9.25  7.33 15.16
67  4 17 11.12  7.58  6.38  6.04  8.87  4.42 12.79  4.63  8.46 10.41  8.75 17.75
67  4 18  9.08  6.71 10.54  5.37  5.46  4.29  7.08  3.96  4.92  7.04  8.71 11.42
67  4 19 12.58 10.88 14.17  9.67 14.83 11.17 17.71 12.83 14.58 14.71 20.50 26.34
67  4 20 19.92 16.08 12.17 13.54 20.67 14.12 18.12 15.41 16.38 15.54 22.58 22.50
67  4 21 18.05 12.25 14.29 10.13 13.92  8.75 12.33  7.71 11.50 12.00 12.96 19.83
67  4 22 11.54 11.29  9.59  6.87 12.54  8.38 10.88  9.59  9.08 10.83 15.00 15.41
67  4 23  9.83  6.04  6.17  4.38  6.13  3.17  7.50  4.46  4.29  4.83 10.75 11.21
67  4 24 14.09 13.00 11.38  8.54 12.00 10.37 10.71 10.83 10.50 11.63 17.04 16.42
67  4 25  8.71  6.17  6.71  4.75  6.54  3.88  6.83  2.17  5.88  4.92 10.21  7.83
67  4 26  8.54 10.08  7.87  4.67  9.62  6.42  9.38  9.54  7.87  9.08 15.83 15.25
67  4 27  8.25  6.83 13.59  6.54  6.96  4.29  7.38  5.96  5.13  6.13  5.88  8.54
67  4 28  5.66  5.29  8.87  6.92  7.87  5.37  6.46  5.75  5.71  5.71  5.04 11.54
67  4 29  7.58  6.38  9.25  7.96  8.67  3.79  5.58  2.88  6.75  6.17  6.50 12.58
67  4 30 18.88 12.46  9.46  8.92 14.54  8.75 13.13 11.08 12.04 11.46 17.12 22.00
67  5  1 20.46 15.71 13.92 11.42 16.75 12.00 13.83 13.08 12.46 12.08 19.50 22.13
67  5  2 16.58  9.54 10.00  7.17 11.67  7.54  8.75  7.00  6.71  8.12 11.21 13.96
67  5  3 16.88 14.42 15.75 10.00 16.21 12.38 16.46 12.38 11.34 13.62 18.38 25.12
67  5  4 13.21 11.46 19.00 11.08 12.54 10.88 17.58 10.58 12.54 14.29 12.33 27.33
67  5  5 13.21 15.25 16.04 12.00 15.29 11.46 15.29 12.71 14.17 16.50 18.63 26.79
67  5  6 14.42 12.50 10.04  8.75 12.25  8.17  9.21  9.04  6.71  7.87 12.33 16.79
67  5  7 17.96 15.87 14.12  9.21 12.04  8.29  9.29  7.92  9.67  8.08 10.54 15.25
67  5  8 15.12 11.67 15.96  8.46 12.62  8.87 10.71  6.25  8.21  5.83  6.96  7.62
67  5  9  5.09  4.75  7.17  4.42  4.75  3.63  6.46  2.88  4.38  4.38  6.38  8.50
67  5 10  4.17  4.38  8.12  2.17  2.83  0.13  2.29  1.38  1.13  2.37  7.00  4.79
67  5 11 10.00 13.59 22.83  8.12 10.96  6.58  9.50 10.08  6.71  6.58 12.83 11.29
67  5 12 14.67 16.79 22.42 10.25 11.42  7.75 12.71 10.13  9.00  8.08 16.38 15.46
67  5 13 10.79 14.04 18.05  6.42 11.46  9.08 12.67  9.38  8.25  6.63 17.04 11.00
67  5 14  9.17 10.46 17.54  6.38  7.92  6.25  9.83  6.08  6.25  6.83 11.87 15.04
67  5 15 11.87 11.42 23.25  9.67  9.00  8.25 15.09 11.63  8.79  8.83 18.05 17.71
67  5 16 11.46 11.34 11.87  5.88  9.79  3.71  7.79 10.00  5.13  6.04 15.34 12.46
67  5 17 11.63 10.25 11.50  5.37 11.71  6.17  8.50  9.38  6.92  8.79 16.50 20.46
67  5 18 11.08 10.13 10.63  5.58  7.87  5.21  9.50  7.08  5.37  6.04 15.54 13.46
67  5 19 22.75 18.91 15.92 11.25 18.54 12.08 15.09 11.96 12.46 11.38 18.08 17.88
67  5 20 22.08 18.38 19.46 11.67 21.87 13.67 18.58 14.83 16.54 17.41 21.75 25.25
67  5 21 24.62 19.21 19.92 13.62 17.58 11.75 14.62 13.08 11.92 15.96 19.70 19.50
67  5 22 24.83 20.96 21.54 15.41 21.17 13.62 16.83 11.29 14.96 15.46 15.87 16.62
67  5 23 17.58 16.29 14.12  8.83 17.16  9.04 11.34  9.71  9.25 10.63 13.37 13.33
67  5 24 15.63 12.54 15.16  9.92 17.00 10.96 12.79 11.21 10.67  9.96 10.08 10.79
67  5 25  6.08  8.08  8.08  5.41 12.46  7.00  8.83 11.00  7.04  7.92 13.00 14.33
67  5 26 12.50 12.04 12.71  6.29 13.62  6.67  9.00 14.54  6.71 12.75 19.41 13.92
67  5 27  5.63  5.96  4.29  2.08  7.38  3.58  3.63  8.63  3.21  6.67 12.75 11.96
67  5 28 10.17  6.08  7.83  3.88  5.96  2.42  4.25  5.41  2.62  2.75  5.83  6.00
67  5 29  5.96  8.21  8.79  4.75  6.38  2.88  6.29  8.12  3.67  5.58  8.92  6.83
67  5 30  3.96  4.42  4.08  2.08  5.71  1.21  2.83  6.50  2.46  5.29  6.17  6.34
67  5 31  7.12  8.58  5.91  2.62  6.34  1.87  4.79  5.41  1.92  3.08 10.17  7.54
67  6  1 10.88  9.50  8.92  5.79 10.92  5.58  3.88  9.21  6.04  6.13 11.08  6.75
67  6  2  5.50  6.42  8.04  4.67  5.66  4.00  5.09 11.34  5.17  8.29 14.42 11.63
67  6  3  7.92  9.17 13.08  4.33  7.96  6.25 10.00 15.00  9.38 12.38 18.63 16.50
67  6  4  3.13  5.63  7.25  4.42  8.87  6.17  8.75  9.79  7.87  8.92 10.41 15.79
67  6  5  8.38 12.58 11.17  7.00 13.75  8.71 10.88 11.75 10.13  8.42 17.79 17.16
67  6  6 14.96 11.42 15.92  9.42 17.37 11.34 16.38 13.29 12.87 13.25 17.54 22.67
67  6  7 14.25  9.54  8.04  6.71 13.75  6.34  8.67 10.25  9.08 10.92 12.71 15.63
67  6  8  7.17  6.00  7.04  4.21  7.29  3.54  6.13  6.54  4.92  6.29  6.96 10.71
67  6  9  7.79  4.25  6.54  2.83  6.00  3.21  8.17  3.79  5.09  3.92  9.21 12.79
67  6 10  4.08  2.67  3.88  2.00  5.58  0.00  3.92  1.67  1.54  3.83  4.04  3.83
67  6 11  4.21  3.63  4.58  2.08  5.00  0.92  5.00  1.58  3.29  3.29  5.21  7.08
67  6 12  5.29  5.09  5.88  2.96  6.50  3.08  4.96  6.25  4.29  5.37  5.37 11.92
67  6 13  4.75  8.25  2.37  1.50  7.29  3.75  2.75  5.46  3.17  3.25  6.42  6.50
67  6 14  6.13  3.75  4.96  2.29  4.29  2.04  4.67  2.92  1.71  1.46  7.87  5.21
67  6 15  6.13  3.00  6.29  2.71  4.63  1.75  1.79  5.09  2.71  2.37  9.17  4.67
67  6 16  6.25  4.54  9.29  3.29  4.12  1.50  2.46  4.42  2.96  4.42  5.88  6.34
67  6 17  5.41  5.96  7.29  2.42  6.50  2.13  3.04  5.17  1.79  3.04  6.00  5.54
67  6 18 11.50  9.21  5.00  4.42  8.79  3.79  2.79  6.79  4.25  5.71  8.67  8.17
67  6 19  9.67 10.04  8.96  4.38 12.08  6.50  8.63 10.13  7.87  8.29 16.00 13.59
67  6 20 18.34 12.25 13.21 11.50 18.46 13.59 15.16 16.25 13.21 13.50 18.46 23.87
67  6 21 13.04 12.62 13.25  8.67 14.67  8.67 11.34 10.63  9.46 10.41 14.67 17.50
67  6 22 12.62  9.38 11.46  6.38  9.33  4.00  8.33  6.25  5.83  7.38 11.21 15.09
67  6 23 11.46  7.38 11.71  3.75  8.38  2.83  6.08  6.08  4.63  4.83  8.17 11.63
67  6 24 14.37 17.75 10.50  7.92 16.83  9.04  7.92 12.50  9.00  8.58 10.00 14.83
67  6 25 13.04  6.54 14.46  6.79 13.08  8.58  6.21  9.59  5.66  6.71 13.62  8.96
67  6 26 14.83  6.92 14.96  5.83 11.63  3.46  3.54  9.04  4.33  3.88  8.87  8.83
67  6 27  9.46  5.50  6.92  3.58  9.00  3.29  5.37  7.54  5.37  5.63 12.33  9.79
67  6 28 14.71 11.34 16.13  8.58 13.83 11.17 14.42 15.34 12.79 14.12 21.79 25.17
67  6 29 10.17  6.21  8.08  7.12 13.00  9.17 12.17 12.12  9.38 10.46 15.41 23.16
67  6 30  4.42  4.54  5.25  1.92  5.37  2.33  6.50  6.21  3.42  4.00 11.79 11.42
67  7  1  8.79  9.46  9.96  4.71 10.75  7.04  6.00  8.79  6.79  7.46 15.92 14.67
67  7  2 11.75 11.29  9.62  6.75 14.46  8.58 11.58 11.21 10.50  9.87 18.63 19.08
67  7  3 12.62 10.34  9.62  9.46 16.04 10.88 13.29 13.08 12.71 10.92 15.92 22.25
67  7  4  6.79  4.96  8.12  2.71  7.92  3.92  4.96  4.38  4.96  4.25  9.13 13.33
67  7  5  5.96  8.04  8.33  5.37  7.83  5.00  5.83 10.46  5.09  7.12 17.96 10.79
67  7  6 10.71 12.87 11.67  6.96 10.50  8.08  8.46 11.34  7.79  8.92 15.59 15.67
67  7  7 15.75 11.92 11.79  8.33 12.38  9.21  9.25 11.38 10.13 10.37 12.33 14.50
67  7  8 16.13 12.71 11.04  9.29 16.17 10.54 12.58 14.09 11.21 10.92 16.33 22.75
67  7  9 11.38 10.79 10.88  5.41 13.00  7.38 10.00 14.75  9.13 10.17 22.00 19.75
67  7 10  9.08  8.25 11.87  3.54 10.88  7.21  8.25 12.54  9.75 10.08 15.96 17.37
67  7 11  4.79  3.75  3.96  1.87  6.21  2.21  4.25  3.00  3.04  1.08  5.58  9.83
67  7 12  3.13  3.29  8.12  2.71  5.17  1.46  3.46  4.88  5.79  6.63  5.58  8.42
67  7 13  5.33  5.75  5.33  2.17  6.96  2.00  5.50  7.17  5.37 10.00  8.71 21.04
67  7 14 12.29 10.37 10.58  4.42 11.12  3.79  5.00  5.37  4.00  6.17  5.17 24.46
67  7 15  8.83 12.96  8.29  4.67 10.58  4.42  5.50  7.87  4.71  7.87 12.00 13.96
67  7 16 16.38 17.79 15.12 11.42 16.42 10.29  7.04 14.33 11.79 15.92 20.75 18.00
67  7 17 10.29 13.83  9.17  7.25 12.83  8.08  4.71 14.33  8.54 13.25 20.00 18.38
67  7 18 11.50  9.13 12.62  7.38 10.34  6.87  7.12  7.79  6.79 10.63 12.04  8.83
67  7 19  7.17  5.88 10.17  3.96 10.37  6.63  6.63  7.75  7.33  9.71 12.33 16.46
67  7 20  5.41  4.29  6.96  2.79  7.38  4.04  6.25  4.42  5.04  5.46  8.00 12.75
67  7 21  4.63  4.50  4.46  1.96  4.29  0.25  4.04  1.25  2.37  3.08  4.08  7.00
67  7 22  6.00  7.54 15.04  2.67  5.71  2.67  3.21  5.25  3.33  4.46  7.21  4.83
67  7 23 12.17  9.92  9.13  3.67 10.29  5.04  3.54 11.12  5.09  7.71 14.17 12.12
67  7 24 13.04 10.37 13.37  6.00 12.96  8.79 11.29 10.29  8.33 11.34 12.87 19.55
67  7 25 11.34 11.17 10.63  4.71 11.17  6.42  7.12 10.54  7.33  9.25 16.25 16.25
67  7 26 12.46 11.42 14.00  7.96 13.33  9.54 10.71 15.41 10.96 14.54 18.05 19.62
67  7 27  5.04  5.33  5.66  3.83  8.87  5.25  6.25  6.42  5.66  5.88 11.63 14.58
67  7 28  9.54  8.58  7.83  4.38 10.00  5.66  7.58  8.08  7.67  8.12 13.83 15.71
67  7 29 14.04 12.83 14.88  6.34  9.50  7.46  8.08 11.50  7.79  9.29 17.54 14.50
67  7 30 13.25  9.38 12.62  6.04  9.96  7.83  8.12 12.21  8.42 11.75 18.96 15.71
67  7 31  7.04  4.46  6.58  4.33  5.88  4.83  3.83  8.04  6.00  7.54 15.83 14.46
67  8  1 10.34  6.92  8.54  4.63  9.42  5.33  7.62  8.54  7.21  6.08 17.75 13.54
67  8  2  4.63 10.29  5.37  4.12 10.67  7.12  5.50  8.50  7.12  6.87 15.16 16.54
67  8  3 14.71 12.54  7.08  6.13 12.54  7.87 11.46 10.67 10.58 10.92 17.37 22.50
67  8  4 13.13 10.21  8.04  7.71 10.37  7.67 11.71  9.29 10.50 11.38 12.50 19.08
67  8  5  7.58 10.58  9.87  4.79 10.29  7.38  6.71  7.58  6.75  8.42 13.04 13.67
67  8  6 18.34 15.04 12.46  9.59 12.87  9.25  6.42 10.63  8.21 10.96 10.29  9.59
67  8  7 16.25  9.54 12.96  9.29 15.75 14.71 11.63 14.25 11.58 10.37 12.96 17.71
67  8  8 10.63  6.50 11.04  7.46 11.63  9.71  7.38 12.50 10.17  8.71 15.71 21.84
67  8  9 10.25  9.00  8.38  5.54  9.67  6.42  4.50  8.79  7.54  5.79 12.04 12.75
67  8 10  8.29  5.41  7.71  4.33  9.50  4.83  4.88  2.46  3.71  2.21  5.58  6.71
67  8 11 15.25 10.41  7.75  8.71 15.16  8.96 11.50 11.00 10.17  8.58 13.75 16.92
67  8 12 18.71 11.67 10.46 10.41 15.87 11.08 15.71 12.54 13.29 16.13 15.00 24.96
67  8 13 10.79 10.04  9.29  5.96  9.46  6.50  9.21  6.67  5.96  6.29  7.54 10.34
67  8 14 15.29 10.54 11.12  9.42 16.46 10.50 15.50 13.33 11.87 11.25 13.67 16.21
67  8 15  9.87  8.42  9.46  5.71 11.38  6.96 11.54  9.50  5.88  8.46 13.17 10.17
67  8 16 14.42 11.79 12.75  7.96 17.29  9.08 10.96  8.75  9.17  8.38 10.67 11.04
67  8 17 13.88 10.71 12.29  6.79 11.96  6.08  8.75  7.04  5.96  6.50 10.25  7.21
67  8 18 13.88  9.83 13.96  8.29 12.25  7.83  9.83  8.67  8.04  5.37 12.71 14.37
67  8 19  4.42  5.96 13.62  3.92  6.34  2.83  4.33  6.71  4.21  3.92  6.58  8.17
67  8 20  8.67  3.08  9.46  2.42  6.08  2.46  3.37  2.50  1.92  1.13  4.38  6.75
67  8 21  8.12  8.67  4.96  2.96 10.88  4.88  5.17  6.29  4.54  5.50  5.54  8.67
67  8 22  3.83  6.79  5.46  2.37  6.29  3.17  3.54  3.75  2.71  3.29  7.75  8.04
67  8 23  4.33  5.21  4.25  1.42  4.04  1.33  3.92  4.75  1.87  1.25 10.08  4.12
67  8 24  8.54 11.34  7.08  4.71  9.13  4.33  2.83  7.67  3.21  3.54 10.08  6.38
67  8 25 11.21  9.87 10.13  8.08 10.37  8.08  3.79  9.71  8.00  8.79 15.75 12.29
67  8 26  8.00  6.71  7.50  2.04  7.41  2.62  4.54  5.79  4.25  5.75  7.08  9.54
67  8 27  4.17  2.50  6.00  2.13  4.12  1.13  4.71  3.13  3.92  3.13  4.54  9.04
67  8 28  8.33  7.04  5.33  2.29  8.04  4.17  5.71  9.00  4.58  4.79 14.42 12.33
67  8 29 10.83  7.33  7.17  5.00 11.46  7.17 10.54  8.29  8.46  8.79 14.37 16.54
67  8 30 10.37  8.75  9.25  5.88 14.79  8.21 12.96 10.92 10.37 10.25 16.92 22.04
67  8 31  6.21  6.17  9.46  3.50  7.25  4.25  3.96  4.04  3.63  4.42  6.71 12.46
67  9  1 10.71  9.33  9.92  6.29 11.00  7.50  8.87  9.04  7.79  6.71 12.92 15.59
67  9  2 14.04 12.92 12.17  9.17 17.21 12.12 15.00 14.62 11.87 10.83 19.62 25.58
67  9  3 23.38 23.54 17.96 14.67 25.04 16.46 19.67 17.00 16.62 14.79 26.46 24.04
67  9  4 22.58 19.92 16.46 12.08 16.62 12.38 14.54 13.70 12.38 13.21 19.25 18.38
67  9  5 27.63 22.79 20.04 16.46 23.71 17.33 22.34 18.16 19.12 18.71 23.79 25.54
67  9  6 12.79 11.34  8.12  7.71 11.17  8.54 12.75  9.25 11.12  8.96 16.17 18.54
67  9  7  5.66  4.63  4.54  2.75  5.09  2.96  5.41  3.71  4.67  3.58  8.00  6.67
67  9  8  3.92  5.21  4.71  1.58  1.29  0.54  4.54  3.42  2.29  1.87 11.04  8.75
67  9  9  4.67 11.75  7.00  2.54  3.58  3.79  5.79  6.21  3.33  4.17 16.13  9.46
67  9 10  8.75  3.00  7.29  3.50  6.21  4.50  3.50  6.17  5.29  5.79  6.17 10.96
67  9 11 20.30 10.75 10.50  8.79 13.17  8.71  9.46 11.50 10.54 10.29 13.50 18.41
67  9 12 15.00 10.21 18.46  8.12 10.34  6.67  8.58 10.04  6.46  7.46 10.88 10.41
67  9 13  4.25  2.17  9.87  1.87  2.21  0.96  3.67  1.50  2.33  1.54  2.54  3.54
67  9 14  3.71  5.17  7.33  1.29  3.37  1.13  1.83  2.54  2.54  3.17  6.71  8.58
67  9 15  4.63  7.04  4.88  0.58  2.58  0.21  2.96  2.58  0.87  1.46 13.08  4.83
67  9 16  7.71  4.12  6.96  3.00  8.12  6.29  4.25  4.88  3.71  4.08  6.29 10.37
67  9 17 16.38  7.67 10.37  6.75  9.13  6.21  9.62  5.66  8.50  7.33 10.04 11.87
67  9 18 16.17 13.88 13.46  8.12 12.42  9.17 10.08 10.37  9.59 10.34 18.71 15.00
67  9 19 13.04 14.62 10.00  5.79 15.21  7.83 11.46  9.21  8.29  7.17 17.25 13.08
67  9 20  9.08  8.33  8.08  5.91  9.04  5.17  9.33  6.75  8.21  6.25 10.34  7.67
67  9 21 11.67  4.00  8.75  4.25  5.66  2.37  7.83  6.54  4.38  5.37  8.87 12.67
67  9 22  7.17  7.96  6.25  3.04  8.00  3.46  4.92  3.88  1.92  1.71  5.46  6.38
67  9 23 14.79 12.67 13.25  6.92 13.21  8.50 10.08 11.58  7.46  7.25 13.29 14.09
67  9 24 18.66 16.54 14.71  9.71 14.54 10.92 11.00 15.04 10.79 12.42 20.33 18.84
67  9 25 16.92 13.67 15.21  9.75 11.58  9.46 11.75 10.83 10.29 12.58 15.21 16.08
67  9 26 21.04 15.92 14.88  9.87 15.25 10.21  9.54 14.62 10.25  9.79 20.71 17.21
67  9 27 18.63 15.54 15.67 11.04 12.38  9.46  9.33 12.71  9.92 11.63 16.54 15.29
67  9 28 18.12 18.41 15.79 10.17 14.12  9.75 11.21 12.96  8.71 10.21 19.04 19.55
67  9 29 16.54 15.00 13.75  7.62 12.17  9.00  7.21 12.62  8.38 10.75 21.59 16.62
67  9 30 16.54 16.17 14.83  9.33 18.16 11.25 17.83 14.62 14.29 12.67 23.67 25.54
67 10  1 20.08 18.05 18.63  9.38 14.37 11.71 15.34 14.37 11.75 15.92 22.79 20.83
67 10  2 15.21 15.21 11.71  7.96 16.33  9.54 15.92 12.08 12.00 13.70 22.29 24.58
67 10  3 19.79 20.54 17.21 11.58 18.38 12.46 18.00 16.17 14.00 14.12 24.30 22.25
67 10  4 20.91 13.96 15.21 11.54 16.04 11.04 15.09 14.25 14.00 13.83 18.21 23.33
67 10  5  8.54 11.67 10.37  4.67  9.38  6.71  6.83  7.75  7.38  8.04 12.71 12.46
67 10  6 16.62 19.62 16.13  7.50 12.50 10.13 13.46 16.46 11.00 14.09 24.08 20.04
67 10  7 16.75 18.84 16.04  8.96 17.50 10.79 15.50 12.79 12.29 13.59 22.17 22.83
67 10  8 14.71 14.75 15.71  5.54  9.92  7.29 11.38  8.96  7.00  9.67 15.25 14.75
67 10  9 18.58 16.13 18.66  9.59 13.25  9.87 14.58 13.04 11.46 13.17 18.05 18.50
67 10 10  7.71  8.58  7.62  2.71  7.41  2.75  4.42  5.50  2.92  5.37 14.00 11.54
67 10 11 10.21 12.33 10.21  4.38 14.37  7.71 10.00 11.12  8.63 11.83 21.92 19.70
67 10 12 16.13 12.25 12.21  5.29 13.33  7.87  8.38  9.83  7.54 11.87 16.17 19.25
67 10 13 20.62 19.92 18.41 10.75 18.54 12.50 15.67 14.17 12.79 13.96 24.83 23.38
67 10 14 24.50 21.62 22.08 12.12 22.04 14.17 19.33 15.87 15.21 16.79 26.42 26.30
67 10 15 13.70 13.17 12.25  6.08 12.79  6.92 13.25  8.67  8.42  9.62 14.79 17.67
67 10 16 10.54 15.29 10.13  4.71 12.87  7.08  9.42  6.83  6.71  6.08  6.96 13.79
67 10 17 19.75 13.50 13.96 11.25 15.46 11.58 16.21 12.42 14.75 11.96 19.67 27.08
67 10 18 18.00 17.00 13.46  7.38 14.58  9.00 10.04  9.13  7.87  8.04 15.41 16.58
67 10 19 22.13 20.75 22.42 14.54 20.62 14.33 14.29 20.79 15.21 18.34 31.42 25.70
67 10 20 16.50 11.96 15.16  8.79 15.46 10.83 14.42 14.33 11.25 14.67 24.33 25.66
67 10 21  4.38  2.71  3.79  1.67  7.21  3.13  7.38  4.67  4.04  5.09  8.87 14.58
67 10 22  7.67  6.50  6.63  2.92  7.21  3.96  7.25  7.71  4.21  7.67 14.54 14.21
67 10 23 13.25  9.33 11.42  6.13 12.83  8.79 11.71  8.92  9.13 10.46 20.58 21.75
67 10 24 13.70 13.29 10.41  6.87 11.50  8.71 10.34 11.25  8.83 10.25 21.54 19.50
67 10 25 29.08 26.71 24.87 18.25 25.92 19.08 21.29 26.08 17.08 23.75 38.96 34.54
67 10 26 19.87 16.25 19.38 12.33 19.92 12.87 22.00 14.46 15.96 16.08 23.96 28.88
67 10 27 18.00 18.25 12.83  7.04 14.67  7.12  7.75  7.83  7.79  8.08 11.46 13.88
67 10 28 16.88 15.71 10.96  8.79 11.08  6.42 11.79  7.54  8.25  8.75 15.87 21.79
67 10 29 14.21 11.34  8.67  7.67 11.96  7.58 10.04  8.21  9.21  6.96 14.75 19.79
67 10 30 16.25 15.25  9.75  4.25 14.09  7.92  8.29  9.87  7.54  8.08 15.16 13.37
67 10 31 21.59 14.21 18.71 13.50 16.62 11.04 14.46  9.08 12.42 11.54 15.29 18.63
67 11  1 23.42 22.79 15.34  8.79 17.00 12.33 11.29 10.96 10.17  8.87 15.46 26.16
67 11  2 30.09 21.09 19.67 18.63 21.21 14.79 19.12 16.66 16.79 19.58 25.33 35.41
67 11  3  5.58  4.17  5.75  5.09  5.96  4.00 10.67  3.13  6.04  7.71  8.21 20.91
67 11  4 11.96  8.67 14.58  4.79  5.79  1.58  7.17  3.88  3.75  3.29  9.50 13.08
67 11  5 20.21 12.50 16.92 10.50 12.50  7.12 12.79  9.33 11.38 10.71 18.34 25.50
67 11  6 13.33  8.17 10.41  7.79 10.37  6.50 10.34  7.25  8.25  5.50 12.25 19.87
67 11  7 12.21 14.17 16.54  8.29 10.92  5.33  5.91  7.87  4.21  1.96 20.91 15.25
67 11  8  6.58  5.21  9.00  3.67  4.67  1.71  4.46  5.46  3.33  3.46 11.75 12.62
67 11  9  8.38  7.38  8.42  4.83  8.42  5.79 10.83  8.17  8.71  8.54 13.67 20.00
67 11 10  8.75 10.58 10.92  5.96 11.63  9.04 12.54 10.92  9.00 11.34 20.41 21.87
67 11 11 13.96 11.79 12.25  8.42 14.71 10.83 17.62 14.96 14.09 14.17 20.88 28.04
67 11 12  7.54 10.08  8.67  5.17  7.96  5.54  5.50  7.71  5.75  7.87 15.63 16.50
67 11 13 13.88 13.83 15.71 10.37 13.50 10.54 12.38 12.38  9.67 14.04 21.09 21.50
67 11 14 10.63 13.92  9.46  6.29 12.75  7.08 14.92 10.79  9.50 12.08 22.37 28.21
67 11 15 18.84 13.08 12.96 10.21 12.50  8.63 14.58 14.42 12.33 12.46 21.79 32.50
67 11 16 10.34  3.96 13.13  4.08  5.75  2.42  6.54  3.54  4.29  6.71  9.08 15.83
67 11 17  5.37  2.71  8.46  0.79  4.38  0.83  2.17  3.96  1.42  2.88 11.92 14.96
67 11 18 10.41  2.25 14.75  3.17  3.71  0.83  3.04  4.63  5.33  4.08  7.12  7.71
67 11 19  9.08  2.96  6.58  1.29  4.25  1.67  2.71  3.58  3.75  3.83  3.75  7.79
67 11 20  2.92  1.63  6.34  1.50  5.83  1.21  0.58  2.67  1.08  1.29  5.91  8.33
67 11 21  8.00  4.42 15.50  4.88  5.91  2.88  3.83  4.54  4.71  5.25  5.66  5.58
67 11 22  9.21  7.00  9.83  2.88  7.83  3.37  4.25  7.12  5.13  4.00  7.00  8.71
67 11 23  2.75  3.25  5.41  0.75  6.04  1.29  0.54  3.21  0.29  1.21  9.59  9.21
67 11 24  8.96 11.50  6.58  4.54  9.54  4.75  3.42  8.25  6.17  7.96 15.34 18.29
67 11 25 13.37 10.71 11.00  6.42  8.96  4.42  8.87  4.25  8.42  5.75 13.67 17.88
67 11 26  9.62  8.54  8.38  4.42  7.25  3.71  9.29  5.21  7.62  6.34 12.87 15.41
67 11 27  9.42 12.17  9.79  6.63 14.79  8.17 11.08  9.83 12.33  9.71 16.38 17.08
67 11 28 17.62 20.25 13.70 11.58 21.25 12.62 17.88 14.79 16.29 12.08 22.29 22.71
67 11 29 11.83 14.04 10.96  7.46 14.29  7.04 13.88  9.13  9.54  8.46 17.16 17.29
67 11 30 10.37  5.63  6.29  5.66 10.58  5.21 11.29  9.79  8.50  5.75 12.83 17.83
67 12  1  5.21  1.46  6.46  1.83  3.83  0.87  5.88  1.87  2.33  2.75  6.79  7.92
67 12  2  8.67  8.63  5.91  3.67  7.58  5.00  4.83  8.58  5.17  7.12 16.13 16.17
67 12  3  5.25  2.33  5.58  3.13  6.71  3.33 10.17  3.92  7.50  6.34 12.54 22.54
67 12  4  4.12  7.00  7.04  3.29 10.00  7.04 16.62  8.38 11.42 12.67 20.04 27.12
67 12  5 12.08 11.54 10.79 10.54 17.62 13.08 19.75 13.17 16.50 11.00 18.66 16.00
67 12  6 18.25 14.88 13.67 10.79 14.09  7.21 12.25 10.41 10.25  7.29 18.46 26.50
67 12  7 11.63  8.12  8.04  8.00  8.33  6.04 12.62  7.92  9.92  5.37 17.29 25.25
67 12  8 21.25 15.12 18.63 12.29 13.50  9.17 15.79 11.42 13.00  7.58 21.75 25.46
67 12  9 17.83 13.75 19.04  8.42  9.25  5.04 12.29  9.54  8.92  5.83 19.75 23.45
67 12 10 10.17  4.67 14.46  4.42  5.46  0.50  8.83  3.13  4.58  3.21 10.96 15.75
67 12 11 12.62  7.38  8.83  4.38  8.71  4.75 12.83  6.92  9.33  7.75 13.70 19.87
67 12 12 12.58  5.33  9.33  6.04  6.34  2.75 10.29  4.67  7.38  4.83  8.79 13.96
67 12 13  3.13  0.75  5.29  2.25  4.71  0.75  6.67  1.83  3.04  3.33  9.54 12.42
67 12 14  3.29  6.67  2.83  2.29  7.17  4.54  9.21  9.13  6.71  8.46 17.92 18.71
67 12 15  7.50  6.63  9.54  5.63  9.62  8.08 16.21  9.50 11.71 11.04 15.67 21.75
67 12 16  6.63  1.92  9.08  3.13  5.46  0.75  6.13  2.33  1.87  2.13  5.79 14.21
67 12 17  3.71  6.67  3.63  0.42  8.54  1.29  1.63  3.04  0.63  0.71  5.50  9.67
67 12 18  8.33  8.92  7.79  2.08  9.21  5.50  8.04  6.34  4.96  4.00  6.54 10.41
67 12 19  8.17  4.08 17.46  2.96  8.38  6.34 10.34  8.00  7.41  5.54 10.46 14.00
67 12 20 17.92 16.62 17.58 11.04 16.46 11.58 10.58 11.21 11.54 10.58 17.08 22.75
67 12 21 16.54 15.41 16.25  8.67 13.54  8.54 14.88 14.92 10.08 10.71 23.13 20.62
67 12 22 20.21 20.00 17.75 10.79 17.00 11.87 18.41 16.54 12.21 14.67 25.37 20.75
67 12 23 18.12 16.54 16.58 11.34 18.96 11.42 18.88 13.00 16.38 14.12 20.79 22.00
67 12 24 19.25 16.96 11.46 10.83 17.83  8.75 14.17 12.04 12.17 10.46 17.33 16.66
67 12 25 26.50 18.96 18.96 16.00 21.21 13.70 19.25 17.96 18.79 17.79 23.13 33.45
67 12 26  9.71  4.92  8.54  6.00  8.46  4.67 10.08  5.46  7.75  7.83  9.59 15.79
67 12 27 12.67  9.17  9.50  8.92 14.33  8.63 16.13 11.87 12.87 12.96 17.37 21.87
67 12 28 19.25 14.29 13.17  9.46 15.16  7.87 16.25 12.71 15.09 15.34 22.88 33.84
67 12 29 18.29 12.58 15.96  8.92 10.71  5.17 10.63  8.67 10.08 10.13 15.29 22.71
67 12 30 13.62 13.83  9.92  8.00 12.67  7.12 14.58 11.29 13.33  9.62 17.79 24.30
67 12 31 16.88 13.75 11.34  9.08 13.54  7.71 11.75 11.83 11.83 11.75 17.25 22.63
68  1  1 30.04 17.88 16.25 16.25 21.79 12.54 18.16 16.62 18.75 17.62 22.25 27.29
68  1  2 12.92 12.75 13.21 10.54 13.83  8.63 12.67 10.21 11.04  9.59 15.25 22.04
68  1  3 18.91 13.37 13.46 12.50 14.83 10.58 17.00 11.17 14.88 13.00 16.29 27.42
68  1  4  9.25 10.17  7.87  3.58 11.96  7.25  7.71  7.00  5.96  5.71 13.08 13.42
68  1  5 15.79 15.96 11.29  5.79 11.67  4.58  9.75  5.83  7.79  6.71  9.21 13.42
68  1  6 12.92 12.50 13.42  7.25 11.92  5.13  9.87  5.54  5.66  5.46  6.50 13.92
68  1  7  8.67  7.79  9.54  3.96  8.08  2.33  7.12  3.88  3.75  2.58  5.96 12.92
68  1  8 16.54 15.87 18.29  9.33 14.96 11.50 18.00  9.17 11.29 12.46 14.46 28.08
68  1  9 17.67 16.62 20.17 10.13 14.71  7.92 14.88 10.37 10.96  8.54 11.42 23.58
68  1 10  5.29  6.17  7.17  1.58  4.63  2.62 10.96  2.21  4.67  5.46  7.25 16.88
68  1 11  5.04  4.71  5.37  0.75  7.50  1.79  4.42  4.38  0.71  1.96  6.29 10.92
68  1 12 12.12  9.96 13.59  6.58 11.79  9.62 10.00  8.08  9.33 10.00 10.88 21.54
68  1 13 15.92 13.67 16.92  6.96  9.87  6.29 10.00  8.46  7.17  9.25 14.67 19.58
68  1 14 30.29 22.21 24.00 14.50 22.54 14.00 22.04 20.58 15.46 18.66 24.96 23.16
68  1 15 15.41 15.63 12.79 11.58 16.42 10.71 19.41 13.83 15.50 14.25 19.70 29.04
68  1 16 24.92 22.25 21.00 14.37 17.29 12.50 15.54 14.37 12.04 10.58 16.66 14.96
68  1 17 15.54 15.50 11.50 12.21 17.37 10.63 20.12 13.17 15.59 13.33 17.71 23.13
68  1 18 14.54 14.71 13.75  8.75 17.96 10.21 16.42 12.50 14.17 11.04 20.79 20.08
68  1 19  9.79 14.09 10.25  4.88 10.75  6.42  8.08 11.79  7.25  9.42 23.04 17.96
68  1 20  7.58  8.63  7.21  1.21  6.25  2.29  3.46  5.21  1.00  2.58 12.17 13.37
68  1 21  4.96  5.25  3.00  0.08  4.63  0.04  0.46  0.58  0.46  1.33  1.87  4.79
68  1 22  7.21  4.67  3.08  0.92  4.21  0.17  0.54  1.33  0.67  2.04  6.58  6.17
68  1 23  8.00  7.21  6.42  3.92  4.75  2.62  8.75  4.58  5.83  6.21 11.96 21.04
68  1 24 17.41  9.83  7.83  9.08 10.08  6.00 13.04  9.17 10.41 11.00 13.70 23.50
68  1 25 25.12 12.04 14.75 12.83 14.88  7.46 19.79 14.75 16.71 12.87 17.33 22.58
68  1 26 14.17  9.96  9.00  8.08 11.12  7.79 14.50 10.17 11.25 10.21 16.38 20.75
68  1 27 13.83 11.34 11.12  6.34 12.38  6.38 12.29  6.71 10.41  9.50 15.34 20.00
68  1 28  7.79  7.21  8.33  4.00  6.63  2.92  8.75  4.25  5.75  6.38 13.50 17.62
68  1 29 15.75 16.83 13.79  7.87 13.00  9.96 17.62 13.59 12.08 13.25 23.09 25.84
68  1 30 22.67 21.92 18.75 14.29 14.88  9.67 16.92 11.83 12.42 13.92 15.09 11.71
68  1 31 20.67 21.00 18.00 13.21 17.46 10.58 16.00 14.67 12.92 12.00 17.25 25.25
68  2  1 16.08 17.21 10.17  8.29 14.88  8.12 15.59  9.83 12.21  9.75 18.75 24.83
68  2  2 14.62 16.29  8.92  8.04 12.38  6.34 13.46  8.25 12.21  6.79 16.04 15.59
68  2  3 13.33 12.21  9.59  6.13  8.33  4.42  9.29  6.00  7.25  5.54 13.46 15.25
68  2  4 21.75 22.08 15.67 11.67 17.46 11.54 15.04 14.33 14.25 15.29 22.54 26.71
68  2  5 10.21 10.41  5.79  3.00 11.50  4.21  6.67  6.50  3.67  5.00 10.21 13.37
68  2  6 11.21  6.71  7.79  4.67  6.71  2.37  8.71  5.29  5.83  4.79  6.92 16.08
68  2  7  8.42  5.41  4.04  0.96  4.29  0.46  5.63  4.04  1.58  2.37  7.71  9.83
68  2  8  6.50  3.42  5.83  3.00  3.50  0.46  1.75  2.37  0.71  1.38  7.38 17.04
68  2  9  6.29  3.04  9.75  1.79  5.00  1.46  3.88  3.71  2.50  2.79  8.63 15.59
68  2 10 17.46 19.62 17.00  9.08 17.16 10.63 10.04 11.50  9.33  7.12 13.70 16.62
68  2 11 24.25 30.96 21.25 12.50 22.67 13.88 14.71 16.33 14.58 11.75 16.38 23.04
68  2 12  8.50  7.75 10.54  2.54  8.46  5.41  6.42  4.17  2.46  3.17  7.25 13.29
68  2 13 17.46 11.79 12.50  7.00 11.00  6.38  9.92 10.21  8.46  6.79 12.79 23.75
68  2 14 17.08 14.71 15.41  8.21 12.92  8.17 14.71 13.08 10.25  4.83 13.13 13.46
68  2 15  7.87  5.75 11.58  4.50  7.04  2.08  5.71  3.42  1.96  0.83  3.00  9.33
68  2 16  3.92  2.67  4.79  0.79  2.96  1.04  6.17  1.87  2.92  1.13  2.67 10.37
68  2 17  4.25  7.21  3.50  0.92  3.25  0.67  7.58  3.25  1.92  4.83  5.83  9.42
68  2 18 10.34  8.33  7.17  2.13  6.08  1.04  3.63  2.62  0.83  3.50  5.58  6.25
68  2 19 11.96  7.08 13.88  2.83  7.62  1.79  3.00  4.04  1.63  4.92  3.92  5.46
68  2 20  9.62  6.00  9.38  2.83  5.00  0.29  2.37  2.25  1.08  5.41  4.25 16.79
68  2 21 12.92  9.83 15.46  5.96 10.25  6.96 12.12  9.62  8.71 11.46 11.17 16.13
68  2 22 15.34 14.50 27.33 10.75 14.79  6.67 14.92  9.83  9.71  9.83 12.25 14.21
68  2 23 11.71  9.33 21.92  6.83  8.12  3.08  8.46  4.21  3.71  4.29  2.17  8.08
68  2 24  8.42  5.88  8.75  3.25  7.21  2.04  8.29  5.54  3.42  8.96 10.17 13.88
68  2 25 14.17  9.71 16.38  5.00  8.17  3.79  9.87  5.66  6.63  6.00  8.21  7.41
68  2 26 11.08  6.04 14.09  3.71  6.29  1.17  4.83  4.83  5.00  7.04  7.17  8.00
68  2 27 10.67 12.17  7.96  4.08  9.21  4.33  6.21  7.21  4.21  7.38 11.92 12.87
68  2 28 21.04 20.25 15.41 10.83 18.66 11.92 11.83 11.67 11.79 13.42 19.55 24.25
68  2 29 10.08  6.92  8.67  4.12  9.62  3.71  5.54  5.66  5.21  6.87  7.21 12.67
68  3  1  8.67  6.21  9.59  3.88  4.67  0.58  4.63  3.17  1.17  3.42  5.29  4.46
68  3  2 10.54  8.29  6.50  4.71  6.25  2.88  7.08  4.17  5.04  6.96  8.54 10.46
68  3  3  8.75  5.66  7.12  3.54  6.04  1.54  7.54  4.71  4.04  7.87  7.21 14.00
68  3  4  9.04  4.33  6.00  3.96  7.41  5.09 10.75  5.09  7.41  9.54 12.29 16.21
68  3  5 16.54  9.25 10.04 10.37 13.33  9.00 16.92 11.46 14.25 17.25 16.66 22.75
68  3  6 20.38 15.25 19.12 14.21 14.04  9.38 16.92 13.70 11.00 15.04 14.25 26.00
68  3  7 14.00 12.54 26.12  8.04  8.96  3.71 10.75  7.38  4.33  7.71 13.33 13.96
68  3  8 10.00  7.12  9.29  6.13  6.25  3.71  5.79  5.66  4.46  9.67  8.58 12.54
68  3  9  8.63  7.54  5.54  6.00  6.87  4.25  8.12  5.91  5.88  9.96  8.42 12.04
68  3 10 16.46  7.92 10.46  9.17  9.62  8.71 11.08  9.00 11.71 14.21 11.17 16.08
68  3 11 14.25  7.29  9.25  8.67  7.96  6.71 11.34  6.75  9.54 12.62  7.46 14.67
68  3 12  5.96  3.29  4.29  4.21  5.83  3.83  6.25  4.92  4.54  8.12 10.75 14.00
68  3 13 15.09  9.67 11.75  9.42 14.04 10.08 15.71 12.58 12.83 15.00 16.17 19.87
68  3 14 14.96 13.54 10.71  9.67 15.67  9.46 13.04 12.71 10.58 14.21 18.71 19.70
68  3 15 19.70 17.04 13.17 11.04 18.63 10.08 14.50 14.33 13.25 13.13 20.25 20.12
68  3 16 18.79 18.71 15.37 11.75 21.92 14.83 19.95 20.17 15.79 18.96 26.71 29.71
68  3 17 25.25 28.46 17.21 19.50 29.54 19.75 24.50 28.21 22.67 21.79 34.92 40.37
68  3 18 17.33 18.46 12.38 12.83 19.29 11.75 21.09 14.54 17.46 17.71 23.63 26.83
68  3 19 23.83 19.79 20.50 10.54 18.91 12.62 18.66 12.67 13.59 15.16 23.42 23.63
68  3 20 15.34 11.58 12.54  8.29 11.67  8.29 11.25  9.13  8.75 11.17 11.17 12.50
68  3 21  9.92 10.75  5.33  4.96 11.92  5.83  7.67  7.00  4.50  6.83 10.58 10.41
68  3 22 17.25 17.16 12.25  8.17 16.08 10.71 13.29 12.00  9.83 11.08 20.00 17.58
68  3 23 22.17  9.62 24.17 12.58 10.46  7.41 13.62  8.21  6.87  8.17 12.25 13.67
68  3 24  8.33  6.67  5.09  4.58  9.87  6.00  4.00 10.50  4.33  8.87 17.54 14.83
68  3 25 13.83 11.79 12.17  8.46 17.00 11.75 13.67 14.62 12.29 16.62 21.67 22.79
68  3 26 18.96 19.67 16.92 12.29 18.58 15.34 20.50 19.04 14.00 18.00 28.21 29.50
68  3 27 17.37 20.67 17.71  9.92 16.58 12.33 19.79 21.62 12.87 23.29 32.21 28.62
68  3 28  7.12  8.08  6.42  4.88 10.08  7.87  6.04  7.33  6.00 10.21 17.25  7.67
68  3 29 15.59 13.04  7.58  6.00 10.46  5.37  6.38  8.04  5.66  8.50 12.33 12.08
68  3 30  9.83  6.67  7.04  5.58 10.67  6.63  8.50  8.17  7.46  9.00 13.04 15.41
68  3 31 14.12 13.04 11.71  9.54 17.67 11.58 16.71 13.21 14.54 13.17 20.17 13.13
68  4  1 19.70 17.04 16.54 13.21 19.12 13.59 21.12 16.25 15.79 17.54 21.29 19.55
68  4  2 23.83 19.58 18.58 15.04 18.00 11.92 17.00 14.12 14.58 16.38 25.50 32.50
68  4  3 17.41  9.54 10.29  9.38 12.00  7.17 13.29  8.54  9.75 12.00 15.16 19.41
68  4  4  7.25  7.58  7.38  4.71  6.04  3.25  6.58  8.25  4.21  9.08 10.88 17.58
68  4  5 13.79  7.38  8.75  6.63  9.13  6.42 10.08  9.21  7.21  8.38 10.41 17.88
68  4  6  6.46  4.71  6.50  3.71  7.29  3.92  9.33  6.42  5.21  8.21  9.00 15.75
68  4  7  7.17  4.38 14.21  4.17  6.92  3.71  5.71  2.92  2.46  3.75  4.12  8.54
68  4  8  4.42  7.46  4.12  1.25  6.42  2.83  4.63  5.58  2.37  3.92 11.71 11.21
68  4  9  5.13  7.54  1.75  2.17  7.12  1.46  4.96  3.42  1.46  2.08  7.71  5.37
68  4 10  7.54  8.79  5.75  3.17  6.92  3.17  4.83  4.79  3.00  6.75  8.92 10.37
68  4 11  9.83  9.17  9.33  3.67  8.67  4.71  5.75  7.12  4.42  5.71  8.08 12.04
68  4 12 13.29 14.04 10.63  7.33 14.88  8.50  8.50 10.25  6.79  9.83 14.58 12.92
68  4 13 10.67 12.46 11.34  4.63  9.54  4.63  7.38  9.17  6.79  7.46 11.25 11.63
68  4 14 15.25 10.96 16.17  7.79 11.04  8.96 11.75 10.67  9.21  8.21 11.50 14.83
68  4 15 16.08 14.25 10.63  8.75 14.37  9.71 11.17 12.96  9.96 10.29 13.67 18.38
68  4 16 16.29 14.46 11.96  9.42 14.54 10.41 11.00 13.37  9.29 10.54 14.46 27.12
68  4 17 20.91 17.83 18.66 15.75 20.30 17.16 14.29 17.58 14.17 14.79 18.91 24.67
68  4 18 18.34 19.33 13.96 13.67 20.67 13.67 11.46 16.46 13.04 14.09 18.88 20.83
68  4 19 15.04 14.29 14.92 10.37 13.83 10.54 10.58 10.63  9.29 11.12 13.62 14.54
68  4 20 11.21 12.29  9.59  8.08 11.50  9.04  8.38  9.17  7.29  7.96 13.42  8.83
68  4 21  5.83  8.42  8.92  3.83  8.38  2.79  3.17  5.41  1.50  1.92  3.46  7.54
68  4 22 16.92 13.62 13.62 12.17 16.08 12.83  7.96 11.87 10.54 12.21 15.54 15.09
68  4 23  9.42  6.58 11.46  7.12 12.04  7.38  7.58  9.08  5.75  8.71  9.00 11.71
68  4 24  5.96 11.25  6.34  4.12  8.46  4.04  6.17  5.79  2.75  4.96 13.42  8.08
68  4 25 13.54 18.34 10.04 10.50 17.08 12.67  5.66 12.87 10.41 11.46 16.92 15.92
68  4 26 17.83 14.71 16.83 12.83 19.00 13.88 12.62 11.67 13.83 14.17 18.54 23.33
68  4 27 10.58  6.75  9.75  7.87 10.75  8.79  8.17  6.87  8.63 11.08  7.46 18.96
68  4 28  9.79  5.79  9.38  5.79 10.13  6.38  9.29  6.00  5.17  7.29  7.00 12.71
68  4 29  8.21  3.79  5.63  2.75  8.83  2.42  4.17  5.25  2.58  4.38  8.04 13.79
68  4 30 12.83  8.04  7.75  6.42 10.04  4.75  6.21  8.75  3.83  7.21 11.92  9.08
68  5  1  6.96  6.00  6.25  3.88  9.00  3.63  3.88  5.66  0.67  2.46  8.87  5.50
68  5  2  9.79  6.34  6.17  2.29  8.21  1.46  3.17  5.79  1.17  3.13 13.29 12.62
68  5  3 10.83  8.21  8.71  5.88  9.75  5.25  6.96  5.13  6.46  6.38  8.21 10.08
68  5  4 10.13 13.33  6.21  3.83  9.62  2.00  3.63  8.17  2.67  4.29 17.96 11.58
68  5  5 15.46 17.67  8.00  6.50 14.50  6.42  6.92 13.37  6.13  7.62 26.12 28.75
68  5  6 25.80 17.67 19.46 14.29 18.29 13.13 15.96 13.46 14.00 16.08 19.67 26.34
68  5  7 14.09 10.88 11.21  6.54  9.75  4.29  7.33  5.37  4.96  3.63 11.42  9.08
68  5  8 16.46 14.88 24.08 10.96 11.96  8.54 13.50 10.83  8.12  7.17 15.87 14.12
68  5  9 12.83 13.79 13.33  7.41 15.00  7.79  9.96 10.50  7.38 10.41 18.91 18.29
68  5 10 17.41 17.25 13.96  9.96 19.67 11.42 16.25 13.96 13.54 10.34 17.16 14.67
68  5 11 20.08 13.25 12.21 12.04 15.04 10.41 12.00 13.59 11.17 12.96 18.75 19.08
68  5 12 16.50 12.58 14.75  7.04 10.88  7.46 11.83  9.25  9.17  8.50 13.08 18.38
68  5 13  5.75  4.54  8.21  2.88  5.58  2.08  5.58  4.00  3.46  5.79  8.75 14.42
68  5 14  8.71  9.21 11.08  4.21 10.21  5.79  6.92  9.38  5.96  4.83 14.58 10.54
68  5 15 13.00  8.42 11.08  9.62 14.00 10.63 16.58 13.33 12.83 11.29 14.71 19.62
68  5 16 11.63  6.96  8.04  7.87 10.00  5.21  8.79  6.50  6.38  8.08  6.34 11.87
68  5 17  9.83  6.00  7.41  4.29  9.17  4.00  4.46  6.54  3.67  4.50 10.54 10.71
68  5 18  7.17  8.71 16.00  5.17  6.75  2.33  5.37  6.17  3.29  2.46  9.00  8.33
68  5 19  6.04  3.92  7.04  1.79  6.13  2.00  3.33  6.04  1.75  5.41  9.96 11.54
68  5 20 10.34  6.21  6.17  5.88  7.87  4.04  5.29  6.75  4.75  5.88  9.75 12.96
68  5 21  5.58  4.25  5.75  2.54  4.71  0.54  3.92  2.54  2.00  2.75  6.46  7.17
68  5 22  4.12  5.50  8.83  1.79  5.91  2.58  4.12  6.00  4.25  4.33  5.13 10.79
68  5 23 11.92 14.46  9.08  5.21 14.42  6.71  5.04  8.87  4.92  7.41 11.58 15.46
68  5 24 15.16 13.37 15.67  8.71 17.04 12.92 10.08 13.83 10.21 12.46 16.29 19.62
68  5 25  6.58  4.33  7.41  3.58  5.91  1.08  3.46  5.04  1.92  4.12  6.79 17.37
68  5 26  8.63  7.50  7.83  4.38  5.83  2.79  3.67  3.00  1.50  2.75  8.25 15.29
68  5 27  5.33  3.63  7.96  2.54  5.46  1.38  3.29  4.04  1.21  1.67  4.63  6.58
68  5 28  5.17  5.96  4.50  3.42  5.83  1.63  3.00  4.04  0.96  2.21 11.21  2.67
68  5 29  4.38  9.83  5.13  3.17  8.54  1.75  4.25  3.83  1.13  3.00 10.58  5.04
68  5 30  8.50 14.79  5.96  7.71 13.79  8.42  4.63 10.17  6.67 10.13 16.88 15.12
68  5 31  8.54 10.88  5.33  8.50  7.50  7.79  3.04  9.04  6.92  9.71 18.16 13.13
68  6  1  8.33 10.17  4.33  5.75 10.04  4.46  4.96  8.46  3.88  5.41 13.67 11.50
68  6  2 15.54  7.79  7.29  6.46  9.62  3.75  4.54  6.29  3.71  4.29  4.58  4.08
68  6  3 10.96  9.83  8.08  5.04  8.46  3.58  3.29  6.29  3.83  3.13 14.46  5.00
68  6  4  9.96 10.25  7.71  5.88 12.67  6.54  7.83  9.33  7.33  7.08 17.04 15.67
68  6  5  6.58  8.92  6.21  4.38 11.00  6.13  6.13  7.50  6.87  6.63 17.67 17.62
68  6  6 10.00 10.79  8.00  7.50 13.29  7.92 11.08 10.00  9.50  7.25 16.17 19.00
68  6  7  6.38 10.29  8.17  5.96  8.71  4.46  6.50  4.63  3.50  2.46  9.50 11.29
68  6  8  4.71  6.63  4.17  3.67  8.67  4.04  3.54  6.08  3.13  4.38  9.83  6.00
68  6  9  3.92  3.17  3.33  1.38  5.91  0.75  2.96  3.29  0.67  0.87  7.12  6.50
68  6 10  3.79  6.38  1.87  3.37  5.50  3.08  1.87  3.46  2.33  2.04  3.08  7.71
68  6 11  4.54  6.63  5.04  3.71  3.08  1.92  4.67  2.71  4.00  5.00  4.58  5.88
68  6 12  3.88  3.63  3.17  2.42  5.58  1.96  3.67  3.58  1.96  3.50  7.87  7.33
68  6 13  4.25  2.17  9.75  2.58  5.96  2.50  3.33  2.88  3.25  2.37  5.88  3.63
68  6 14  5.79  4.83 16.88  6.50  7.04  2.04  5.33  6.63  2.46  2.13 11.58  8.25
68  6 15  6.34  2.37 11.12  5.91  7.00  2.00  3.42  3.63  2.54  2.13 12.08  7.00
68  6 16  4.92  6.96  5.33  3.79  5.33  2.96  3.13  5.00  1.75  3.54 10.71  7.79
68  6 17  6.75  8.54  8.38  3.29  6.34  2.17  5.13  7.54  1.83  4.50 16.29  4.83
68  6 18  6.25  6.17  8.12  4.29 10.75  3.79  5.96  5.46  4.46  3.75 12.12 11.46
68  6 19  9.75  9.13  8.33  5.04 12.38  4.38  5.41  6.87  4.46  3.88 12.00  8.46
68  6 20 10.88 12.62  9.04  7.17 14.17  7.25  9.38  9.21  7.54  7.00 13.50 13.59
68  6 21 10.29 10.08 12.21  8.46 13.13  6.17 10.41  7.54  7.58  5.29 11.42 14.75
68  6 22  7.92  9.50  7.71  6.83 11.25  5.46  6.00  7.08  4.46  4.12 14.33 10.96
68  6 23 17.00 12.62 11.54 11.79 17.54 11.38 13.75 12.04 11.96 10.29 17.83 17.04
68  6 24  9.00  9.96  8.83  5.88  9.50  5.79  8.00  7.04  5.33  6.75 10.17 14.25
68  6 25 18.75 16.21 18.08 11.58 19.41 10.25 11.75 12.08 11.87 10.63 17.54 16.75
68  6 26 10.37  8.00 10.50  7.41  8.46  4.29  7.12  6.21  4.50  5.13 10.04 15.50
68  6 27  8.75  5.75  7.83  5.13  7.54  2.54  5.37  5.88  2.58  5.66  8.33  7.46
68  6 28 10.63  8.08 11.04  4.50  9.08  3.54  8.21  9.00  7.41  5.46 13.00 11.17
68  6 29 12.00 16.25 12.96  9.75 16.08  9.75  9.13 11.67  9.79  9.59 18.96 15.79
68  6 30 13.54 15.09 13.62 10.29 16.04 11.67 12.00 12.79 11.71 12.83 24.71 19.04
68  7  1  7.17  5.29  8.29  3.04  8.42  3.42  2.71  6.42  3.29  4.92 14.75  8.29
68  7  2 18.54 11.34 13.62 10.46 16.13  9.75 12.38 10.50 12.08 13.25 14.62 20.17
68  7  3 11.00  9.08 10.92  7.92 15.50 10.46 15.67 10.96 14.25 11.96 16.42 20.75
68  7  4  6.58  3.50  8.46  4.04 10.00  4.67  7.96  6.58  7.41  6.25  9.42 17.92
68  7  5  2.71  2.75  3.92  1.92  4.46  0.50  3.13  1.63  1.29  1.63  4.75  9.62
68  7  6  3.33  1.17  3.54  1.29  3.88  1.04  2.42  3.29  2.92  1.71  6.92  6.34
68  7  7  7.58  8.50 11.08  2.46  8.92  4.21  4.12  7.62  3.08  2.25 13.70 10.83
68  7  8  4.75  6.54  8.75  3.42  7.00  1.63  3.79  4.04  3.25  2.33  6.13  6.87
68  7  9  4.92  3.29  3.92  1.87  3.96  0.29  3.46  3.71  1.29  0.87 10.67  7.75
68  7 10  5.83  8.25 17.58  7.04  9.29  5.63 10.17  8.46  6.75  5.75 13.92 11.34
68  7 11 13.13  8.29 10.00  7.08 10.29  5.50  6.67  5.75  6.96  5.96  9.46  9.17
68  7 12  3.75  5.41  4.25  2.13  4.83  1.38  5.25  3.13  1.67  2.50  7.83  7.08
68  7 13 11.79 10.79  9.67  6.83 11.96  6.34  8.08  7.21  7.62  4.63 13.59 11.67
68  7 14 12.58 15.09 12.08  9.33 15.09  8.96 12.12 12.62 10.41 11.50 16.58 13.79
68  7 15 13.17  8.04  8.96  9.50 11.08  8.00 11.08  8.21  9.71  9.29 10.37 10.88
68  7 16 14.00  9.54  8.46  7.17 10.79  6.87 10.04  8.38  8.21  9.87 13.67 18.05
68  7 17  8.87  4.25  7.96  4.12  4.96  1.67  4.58  4.75  5.00  4.42  7.62 11.96
68  7 18  7.67  4.17  5.17  2.67  3.46  0.96  2.46  2.29  2.13  0.67  8.17  6.50
68  7 19  5.54  4.25  4.08  3.50  6.54  3.67  5.37  5.83  4.50  3.75  9.67 11.00
68  7 20  5.25  2.88  3.71  2.79  5.88  1.87  4.46  2.58  3.13  2.71  8.04 12.87
68  7 21  4.92  2.13  5.71  2.29  3.63  0.25  2.83  2.00  1.17  0.37  4.71  6.71
68  7 22  7.46  4.88  7.12  5.41  8.21  4.54  8.87  5.58  7.38  5.54 10.13 14.29
68  7 23  8.83  6.87 13.59  6.71  8.83  5.25  7.04  7.46  6.21  8.58 11.92 13.42
68  7 24  5.66  3.37  6.63  4.38  4.92  2.00  2.42  1.92  1.79  3.75  6.34  8.50
68  7 25  3.46  2.42  3.42  2.54  4.00  1.54  2.25  1.75  1.04  1.33  6.96  5.04
68  7 26  3.71  2.46  5.13  2.92  5.75  1.00  4.54  3.17  1.87  2.54  4.38  6.75
68  7 27  2.92  3.13  2.54  1.87  4.83  1.00  2.13  3.08  2.21  1.50  4.08  3.50
68  7 28  5.09  2.08 15.16  3.21  4.38  1.96  3.04  3.42  2.37  2.29  5.79  6.67
68  7 29  5.58  3.75 10.34  2.92  3.92  2.21  5.96  4.00  5.58  3.08  3.46  9.13
68  7 30  2.79  5.63  8.08  1.63  5.71  0.75  2.96  1.13  2.08  1.04  8.08  3.92
68  7 31  4.58  7.58  9.71  3.50  6.63  0.92  2.37  4.67  1.54  0.67 10.96  4.00
68  8  1  4.33  2.00 10.88  3.92  6.08  1.42  3.63  1.92  2.88  1.38  7.50  3.37
68  8  2  3.88  2.37  6.04  2.46  3.75  1.38  2.58  2.21  1.75  1.58  9.13  5.54
68  8  3  3.50  1.71  5.83  2.50  3.25  1.04  2.42  2.25  3.37  1.67  5.13  5.58
68  8  4  3.63  0.71 11.38  2.62  3.67  1.96  2.71  1.79  2.88  1.58  4.42  7.29
68  8  5  4.79  2.88 14.17  4.75  4.21  3.42  6.13  2.92  5.29  2.42  8.00  8.12
68  8  6  6.42  4.92 18.54  5.33  6.75  4.75  7.04  2.33  5.13  3.21  8.63  4.29
68  8  7  9.50  4.25 20.83  7.54  8.54  4.33  7.87  5.50  5.29  3.58 10.00  5.63
68  8  8  8.25  5.63 20.67  7.62  7.00  2.54  8.83  6.04  4.46  3.25 13.67  5.46
68  8  9  6.00  4.75 21.62  7.12  7.46  3.00  7.38  3.79  4.96  2.54 10.67  3.04
68  8 10  5.91  2.83  9.71  4.33  5.41  1.67  2.83  4.12  2.92  1.54  6.75  5.83
68  8 11  7.54  4.46  6.50  2.21  3.08  1.75  5.29  1.96  2.42  1.17  6.21  5.54
68  8 12 14.50 10.34 11.87  6.42 12.58  6.75  5.96  7.41  5.66  4.42 10.21 11.04
68  8 13 17.12 16.04 11.46 11.92 19.38 13.33 13.33 16.29 14.33  8.63 19.17 12.12
68  8 14 18.66 11.42 13.62 11.21 16.83 10.46 10.37 10.83 11.12  8.83 10.58 12.33
68  8 15 13.75  9.79 10.13  6.13 13.42  5.46  6.46  6.83  7.04  4.50  7.83 10.88
68  8 16  6.50  6.42  6.21  3.21  6.96  2.33  4.88  3.67  2.62  1.92  6.13  6.71
68  8 17 11.25  9.83  8.67  6.67 10.58  5.91  7.41  8.21  6.71  4.38 10.92 13.79
68  8 18  9.33  6.04  8.00  3.96  8.42  4.42  9.33  6.34  7.00  6.21  9.50 12.79
68  8 19 18.34 18.84 16.79  9.21 14.29  9.33 10.46 12.25  9.33  8.42 18.46 16.25
68  8 20 13.08 13.13 12.87  6.71 12.79  7.92 10.00 13.67  9.25 10.46 19.38 18.58
68  8 21 11.29 16.50 12.08  8.83 13.88  9.25  6.38 12.17  8.96 11.58 23.38 18.29
68  8 22 12.54  8.00 11.08 10.29  9.54  7.87  6.08 10.08  9.00  9.67 16.21 15.00
68  8 23 10.71  5.25  8.17  5.37  6.96  2.25  4.46  4.33  3.21  4.21  5.41  7.41
68  8 24  9.04  7.04 18.75  7.17  7.46  4.12  8.46  6.08  6.38  4.75  9.13  7.38
68  8 25  9.62  1.83 16.92  4.96  6.54  5.83  7.25  6.75  6.34  3.46  7.46  8.92
68  8 26  7.46  4.17 19.08  4.79  7.96  5.21  7.46  6.50  6.71  4.96  7.71 11.25
68  8 27  4.46  3.96 19.92  6.38  6.96  4.25  6.25  4.21  4.67  4.17  8.75  6.21
68  8 28  6.00  4.83 15.75  7.41 10.34  2.62  5.13  7.46  4.08  2.96 14.04 10.96
68  8 29  8.21  5.00 19.33  6.04  9.00  2.37  6.00  5.21  2.96  2.42 10.25 10.37
68  8 30 10.17  9.00  9.29  5.83 11.54  6.42  8.54  8.08  7.38  8.33 16.13 17.88
68  8 31 10.41  9.04  9.17  5.41 12.96  6.54  8.42  8.12  8.25  8.54 13.83 16.54
68  9  1  5.41  3.46  7.46  3.08  6.42  2.17  4.00  3.96  2.04  3.04  7.41  7.67
68  9  2  5.17  3.46  5.29  3.25  5.58  2.29  6.42  2.46  4.00  4.63  5.50  6.71
68  9  3 12.67  4.25  4.50  4.00  9.71  3.83  7.38  7.38  7.33  7.54 10.96 16.21
68  9  4  8.92 10.34  6.79  3.88  7.96  3.04  5.50  4.83  4.71  4.08  8.38  8.79
68  9  5 15.04 12.21 13.42  8.83 13.33  6.71 10.41 10.67  7.83 10.04 16.54 17.04
68  9  6  3.25  7.08  3.42  1.54  6.29  2.50  5.75  5.75  3.08  3.33 11.71  8.75
68  9  7 13.92 14.96  8.42  5.96 14.92  7.62  7.38 11.17  7.79  9.71 15.67 16.79
68  9  8  8.50  5.25 11.96  8.08  6.87  6.58 10.13  5.88  8.58  8.00 10.17 15.83
68  9  9 15.00 16.71 15.54 12.54 18.38 12.92 13.42 13.59 15.04 15.09 18.00 23.79
68  9 10 11.38 13.83  8.67  6.79 12.67  6.46  4.92 10.63  7.50  9.59 18.41 16.29
68  9 11  5.21  7.54  6.83  1.67  6.17  2.33  3.46  4.63  2.37  2.62 11.12  9.54
68  9 12  3.42  2.58  5.46  1.38  6.17  2.04  2.46  1.92  0.46  0.58  5.21  6.42
68  9 13  3.83  4.96  4.21  1.58  4.21  1.04  1.42  2.83  1.21  1.96  4.67 10.17
68  9 14 13.17 11.25 22.71  9.62 12.79  8.29 11.04  9.83  8.67  8.96 14.00 14.54
68  9 15 14.62 12.29 31.63 10.21 10.29  6.34 14.96 10.08  9.13  9.59 13.70 17.25
68  9 16 14.79  8.83 22.54  8.83  8.54  3.88 10.79  6.50  7.54  7.12 10.00  6.42
68  9 17  6.34  3.08 14.92  3.58  2.96  1.46  4.17  2.21  3.92  2.21  4.42  6.79
68  9 18 13.29 15.00  9.83  5.09 12.54  6.63  5.13  9.67  5.75  7.04 13.04 12.54
68  9 19 14.46  7.62 16.50  8.87 11.46 10.21 13.29  9.25  8.96 10.34 11.67 17.96
68  9 20 22.88 19.67 17.25 13.42 17.79 10.83 18.75 12.54 13.04  9.29 13.96 16.04
68  9 21 12.38  8.42  9.42  7.29 12.83  6.54 13.59  8.38  9.79  5.91  9.96  9.79
68  9 22 15.21 15.16 11.04  5.46 12.46  6.79  8.29  8.21  7.04  6.29 13.96  7.96
68  9 23 16.08 21.25 12.04  8.75 19.17  9.50 14.37 11.67 11.21  8.83 18.05 10.63
68  9 24 11.67  9.83  6.34  6.08 12.08  4.71  8.96  7.92  7.79  4.17 11.63  9.46
68  9 25 14.04 11.96 12.46  5.71 11.87  7.79  8.00  9.08  7.58  8.12 12.92 13.88
68  9 26 10.00 11.21  9.38  6.17 11.54  7.21  9.67  8.42  7.71  7.08 13.75 15.25
68  9 27 25.41 23.45 20.54 14.00 21.21 14.54 16.92 17.12 14.58 17.50 27.58 24.50
68  9 28 18.05 19.38 17.58 11.50 18.84 10.92 17.33 14.58 15.41 13.37 21.29 21.46
68  9 29 15.46 14.96 11.67  9.67 16.96  9.29 13.54 12.50 11.00 11.08 17.58 18.63
68  9 30 14.54 14.83 12.42  9.75 19.55 10.46 12.83 11.46 12.38 10.41 15.75 20.41
68 10  1 14.25 16.79 16.00  8.38 17.79 10.67 17.33 16.21 15.12 14.12 23.83 20.46
68 10  2 14.83 14.75 15.75  7.25 10.79  7.62 14.00 10.71 10.79  9.00 13.29 15.63
68 10  3 10.71 11.34 12.83  5.04  7.08  3.50  5.46  9.46  5.71  6.34 10.21  5.21
68 10  4 13.33 15.34  9.79  7.87 12.62  6.79  4.79  9.87  8.17  8.38 11.08 13.29
68 10  5  6.25  8.96  8.38  3.83  6.67  3.42  2.04  4.75  4.83  3.75  9.79  7.67
68 10  6  2.37  5.46  4.54  0.92  2.08  0.33  4.46  0.33  0.92  1.87  3.13  7.87
68 10  7  4.00  3.46  2.37  0.71  3.96  0.63  1.75  1.54  0.71  1.33  4.08 10.34
68 10  8  7.92  5.83 12.46  2.67  4.00  1.13  3.21  5.50  2.08  2.54  9.33  7.17
68 10  9 17.29 15.09 15.00  8.67 12.50  7.87  8.17 11.08  9.38 12.04 17.37 19.12
68 10 10 18.46 15.96 16.42  9.79 14.75  9.42 12.58 11.83 10.88 11.04 19.92 19.04
68 10 11 24.83 22.83 21.17 14.00 21.42 13.67 14.42 20.91 15.21 19.17 33.21 27.58
68 10 12 24.92 22.67 20.17 14.62 27.04 17.50 20.41 20.96 19.55 20.12 34.54 34.46
68 10 13 15.16 11.79 12.08  7.92 16.29  9.17 16.46 11.54 11.87 10.75 19.87 27.67
68 10 14  5.50  8.00  5.54  3.96  9.96  5.25  9.71  6.71  6.63  5.25 12.58 17.08
68 10 15 13.70 15.09 10.54  6.13 15.37  7.21 11.08 11.42 10.34 10.21 21.75 23.16
68 10 16 11.67  9.46  8.75  6.50 12.92  7.12 12.08 12.46 10.71  9.96 17.62 27.16
68 10 17  9.62 11.08  7.46  3.67  9.92  5.54  7.62  6.96  6.38  3.13  8.96 12.79
68 10 18 15.63 17.50 16.08  9.54 17.88 12.83 15.59 15.87 11.96 14.54 21.50 26.96
68 10 19 19.41 16.54 20.46 14.17 16.46 10.46 13.88 11.75 14.04 15.37 21.09 21.17
68 10 20  9.87 11.04  9.46  4.75  9.13  6.87  7.50 12.04  7.46 10.96 23.16 18.84
68 10 21  6.25  5.83  2.17  2.00  6.08  3.00  3.04  5.00  2.83  2.79  6.08  9.13
68 10 22  5.66 10.04  3.58  3.58  8.67  2.79  1.21  4.75  3.25  2.54  8.92  9.46
68 10 23 14.37 19.50  9.92  7.67 14.67  9.79  8.96  9.17  7.54  4.88 13.17 11.00
68 10 24 11.71  7.96 13.46  4.38  7.83  3.71  6.04  7.41  6.25  3.83 10.25  9.67
68 10 25  5.04  1.58  5.29  1.04  4.50  0.37  1.13  2.21  2.00  1.71  5.83  7.71
68 10 26 12.21 10.21  8.58  6.17  9.29  5.54  3.54  7.12  5.63  5.29  9.00 12.46
68 10 27 11.83  8.87 11.87  5.88 10.54  5.37  4.63  6.46  6.67  5.63  8.92 12.17
68 10 28 18.29 12.87 15.00  7.96 13.37  8.79  9.29 11.87 11.34 14.04 16.38 16.54
68 10 29 16.33 13.62 12.58  6.96 12.21  6.79  9.04 10.00  9.38 10.13 16.25 16.13
68 10 30 15.63  9.96 11.96  9.00 11.42  7.96  6.42  7.08  9.75 11.21  8.38 15.34
68 10 31 11.12  5.00  8.63  4.42  5.63  2.08  3.25 10.00  3.79  7.58 21.54 25.75
68 11  1 13.70 16.50 11.29  5.50 13.33  5.29  8.92 23.58 11.46 21.75 35.30 37.59
68 11  2 17.75 21.21 26.34 12.96 19.25 12.79 20.33 16.33 14.58 16.13 24.30 27.92
68 11  3 11.42  6.63 18.21  6.50  6.04  1.00  9.59  4.54  4.83  4.63  8.08 15.12
68 11  4 15.83 18.96 12.04  4.67 12.62  7.00  6.38 10.29  4.92  7.75 14.54 12.50
68 11  5 21.34 21.54 19.55 11.00 17.83 12.83 15.12 12.96 10.79 14.62 18.71 21.17
68 11  6 23.29 21.04 21.37  7.92 21.25 10.92 16.75 14.29 10.96 12.04 16.54 23.87
68 11  7 12.75 15.83 15.92  6.00 14.00  7.54 17.67 13.13 10.58 11.17 16.00 31.25
68 11  8 10.92  9.87 12.12  3.88 10.29  5.88  9.71  9.92  7.58  7.83 12.87 17.75
68 11  9 19.50 18.50 14.62  7.62 17.71 14.88  9.87 15.63 10.63 11.00 18.88 21.25
68 11 10 17.67 12.87 17.79 11.87 14.33 13.37 17.96 11.46 14.50 18.41 13.75 29.58
68 11 11 16.71 17.08 13.13  7.41 12.67  8.87  7.38 12.00  8.75 10.92 15.96 18.08
68 11 12 23.79 26.25 23.79 15.92 26.08 16.00 17.75 19.55 18.00 19.29 24.30 30.25
68 11 13 11.46  7.58 19.00  8.46 11.50 13.13 16.33 11.63 10.25 11.50 13.33 22.79
68 11 14 21.17 21.34 20.21 10.34 20.79 12.46 14.12 14.37 12.58 12.33 15.83 23.33
68 11 15 22.37 22.34 18.46  9.17 20.25 12.67 12.58 14.50 12.25 15.29 15.75 23.04
68 11 16 11.75 10.75  8.92  2.33 12.83  4.42  4.04  7.00  3.33  5.83 10.58 19.00
68 11 17  6.25 10.41  5.25  1.63  9.71  2.79  3.83  7.38  1.96  3.67  8.54 10.96
68 11 18  8.38 12.38  3.75  1.25 10.71  3.04  1.54  7.92  2.88  2.62  9.25 10.21
68 11 19 16.46 16.08 13.75  8.12 15.87 12.71  9.08 13.79 10.46 10.54 16.17 17.88
68 11 20  9.75 12.08 12.96  5.54  7.21  4.25 11.96  6.04  5.46  7.62  9.04 15.63
68 11 21 19.58  8.75 18.66 12.21 11.83 11.21 10.88  7.29 14.00 15.16 17.67 21.34
68 11 22 12.83 11.54 13.50  5.75  9.29  6.54 10.08  8.00  8.79 10.37 15.37 14.71
68 11 23 13.29 13.62 12.67  5.71 12.25  5.66 12.38  8.38 10.17 11.50 16.92 20.91
68 11 24 13.29 15.87 10.25  5.91 12.54  6.13 11.83  9.62  8.79 11.67 19.38 21.17
68 11 25 19.29 19.95 18.29 12.50 18.71 10.88 14.09 17.12 13.13 18.25 28.46 28.21
68 11 26  4.12  3.96  7.25  0.96  6.87  2.37  4.04  6.13  3.96  5.33 13.13 18.63
68 11 27  8.42  9.83  8.25  2.96  7.92  3.00  5.50  7.75  6.25  7.04 11.58 12.54
68 11 28 12.17  8.04  8.58  2.42  4.58  3.92  5.09  7.96  5.63  6.83 12.50 10.34
68 11 29 12.33  7.17 15.41  4.83  6.79  6.21 10.92 10.25 10.54  8.50 12.33 17.37
68 11 30 10.54  9.75 11.34  3.50  6.34  2.50  8.67  7.62  7.25  7.46  9.50 15.00
68 12  1 15.29 14.58 14.00  6.29 12.62  7.96 12.42 10.71  9.67 10.67 12.96 22.95
68 12  2 12.00  9.96 14.25  8.75  9.67  7.67 10.04  9.04 10.92 12.00  9.87 20.88
68 12  3 14.71 10.29 16.54 10.54 10.67  7.00 11.63 10.50 10.00 13.88 14.79 19.50
68 12  4  1.21  9.59  5.25  0.46  6.79  1.29  1.63  4.50  1.79  3.42 12.33 10.63
68 12  5 10.63 13.54 16.33  7.29  8.46  5.29  8.29  6.17  7.50  7.29  8.83 10.79
68 12  6 17.08 16.25 15.16  6.63 13.83  8.96  8.96  9.46  6.04  8.71 10.13 16.21
68 12  7  9.25  7.50 12.75  2.33  6.75  1.04  6.46  2.75  3.04  5.41  7.29 11.38
68 12  8  3.92  4.46 10.34  4.12  4.79  2.96  5.83  3.08  4.58  6.21  9.29 14.58
68 12  9  7.96 12.12 13.04  4.63  9.50  6.50 10.63  9.29  9.62 13.25 13.33 22.63
68 12 10  5.91  6.71  9.83  2.71  7.21  2.75  6.71  6.17  5.91  5.91  7.83 12.33
68 12 11  1.96  5.83  6.29  0.75  4.17  1.87  2.29  2.42  0.75  1.33  9.08  9.96
68 12 12 15.59 13.96 14.75  9.67 12.12 10.25  7.50 11.08 11.96 14.09 18.16 26.58
68 12 13 21.46 11.63 19.50  6.96  5.91  2.62 12.04  2.71  7.04  9.13  6.13  6.79
68 12 14 13.37 13.04  6.50  3.13  9.29  3.75  1.96  7.58  4.25  5.50 11.42 17.25
68 12 15 11.54 12.21  9.59  5.83 10.83  6.34 10.29  5.54  7.83 12.46 12.42 20.38
68 12 16 14.50  9.87  9.54  3.75 11.42  5.88  4.50  7.17  6.38  5.41 11.75  8.83
68 12 17 19.75 17.12 13.62 10.79 15.63 10.92 20.88 15.16 15.37 18.12 18.29 26.20
68 12 18 22.08 16.29 27.54 13.37 13.59  9.92 17.37  9.62 12.42 14.79 12.58 24.67
68 12 19 15.96  9.42 13.25  5.46  9.33  5.75  6.87  8.29  7.46  9.79 15.54 20.17
68 12 20 15.75  8.87 13.08  5.54  6.92  3.88  7.50  2.04  6.04  5.66  9.29 16.71
68 12 21 17.12 17.37 13.83  8.63 12.96  9.21  9.42 10.67  9.54 10.71 14.09 17.54
68 12 22 28.71 29.38 22.67 17.46 28.04 16.21 24.62 21.09 20.54 22.34 30.84 30.71
68 12 23  8.17  7.71  6.17  3.21  7.00  2.88  9.25  4.38  6.87  7.00  9.62 20.33
68 12 24 13.59 18.00  9.83  3.42 13.46  5.91  9.42  8.79  8.08  7.29 13.00 19.17
68 12 25 10.88 12.54 11.29  5.75 10.00  4.08  9.59  8.04  7.67  8.87 10.21 20.21
68 12 26  9.54 10.00 15.21  5.33  4.63  0.46  7.83  3.29  5.00  4.67  6.83 22.75
68 12 27 12.21  3.96 15.71  5.83  6.67  3.42  9.87  5.46  7.21  8.29 12.12 25.00
68 12 28 16.96  7.75 18.00  7.79  9.17  5.88 10.46  4.25  9.25  9.08 18.58 30.16
68 12 29 16.62  5.96 16.88  5.00  8.83  5.41 10.83  5.54 10.88 10.00 13.08 26.25
68 12 30 12.96  7.08 12.25  4.25  5.63  2.25  7.58  4.12  7.54  8.67  8.17 19.04
68 12 31  9.13  2.13  7.38  2.50  4.04  0.50  6.83  2.54  3.54  5.50  5.71 12.42
69  1  1  6.13  1.63  5.41  1.08  2.54  1.00  8.50  2.42  4.58  6.34  9.17 16.71
69  1  2  2.62  0.54  6.71  2.88  5.63  1.13  7.75  1.87  5.83  5.41  8.17 13.29
69  1  3  4.17  2.37  6.29  1.67  4.00  2.37  7.96  5.41  5.04  7.25 11.96 16.17
69  1  4  6.00  6.87  7.67  4.63  9.50  5.04 11.25  6.87  8.58 11.17 16.08 17.25
69  1  5 10.50  8.38  7.71  3.50  5.41  2.42  7.67  5.37  4.17  5.63 12.17 17.12
69  1  6 14.25 11.12 19.50  7.17 11.08  5.04 10.63  6.92  7.50  8.92  6.08 12.50
69  1  7 22.34 18.16 18.21  8.83 15.00  7.25 13.46 13.21 10.96 13.21 17.33 26.54
69  1  8 11.79 13.46  8.46  5.21 10.71  5.50  8.38  6.13  7.87  8.29 12.62 19.95
69  1  9 22.25 16.00 20.91 11.29 18.21 13.04 14.92 14.54 13.67 15.75 18.84 26.75
69  1 10  9.33  3.29 15.21  6.00  5.88  5.25 12.04  3.96  7.67  9.21  6.04 19.21
69  1 11 13.29 18.50  9.33  3.71 12.62  5.09  4.42  8.38  5.91  5.66 11.00 10.04
69  1 12  9.71  7.12 14.33  5.75 10.08  6.04 10.29 11.08 10.17  9.83 13.00 27.71
69  1 13  4.50  1.50  4.63  2.13  3.54  0.42  3.00  0.46  2.54  4.25  2.62 11.46
69  1 14 11.63 20.38  5.58  3.92  9.38  3.67  6.00  5.25  4.88  4.83 10.58  7.62
69  1 15 18.91 18.88 12.33  8.79 13.33  9.08  4.12 14.33  6.04  4.75 19.46  7.75
69  1 16 16.33 13.29 14.04 11.54 15.09  9.96 17.29 12.42 17.08 15.54 18.46 24.25
69  1 17 25.08 25.17 17.79 10.50 16.96 11.04 14.04 11.83 10.71 13.08 14.37 22.83
69  1 18 19.17 22.58 14.33 14.46 21.12 11.00 17.12 12.75 15.96  9.71 19.38  9.96
69  1 19  9.59 13.37  6.75  3.08  8.71  2.58  8.42  4.67  6.34  6.54 10.50 15.87
69  1 20 18.12 16.21 19.21  8.46 11.96  9.87 12.54 11.92  9.38 11.12 16.62 18.63
69  1 21 10.25  5.13 11.79  5.04  3.54  3.17  5.04  6.42  2.62  6.92 12.54 12.00
69  1 22  4.00  6.63  4.79  1.71  2.83  0.33  3.42  1.33  1.25  2.37  3.46  7.46
69  1 23 11.67 17.08 10.83  6.71 13.25  7.38  3.42 10.67  8.12  8.25 17.41 15.71
69  1 24 17.41 17.16 12.96  9.17 11.46  8.42  8.92 12.62  8.63 13.79 22.04 18.25
69  1 25 19.08 20.25 14.83  9.75 15.04 11.25 14.09 15.67 12.29 17.71 21.34 19.62
69  1 26 20.54 17.41 16.71 13.21 16.17  9.38 12.33 11.58 11.75 14.88 14.92 19.12
69  1 27 18.29 13.59 15.34 11.12 11.87  7.58 12.46  9.13  9.17 14.04 10.13 14.00
69  1 28  5.21  3.96  3.83  2.58  4.33  0.33  5.13  2.00  3.92  4.12  4.83  9.13
69  1 29  9.75  6.54  6.00  6.17  8.87  3.25  9.92  6.63  8.92  8.83 12.58 13.62
69  1 30 15.16 17.29 12.79 10.58 18.16 11.67 19.55 17.29 16.79 18.58 26.30 31.54
69  1 31 10.50 11.67  9.25  7.33 11.00  4.17 12.29  6.50 11.38  9.00 15.71 23.63
69  2  1 18.58 19.79 12.75 10.54 18.05  7.62 15.29 10.21 13.17 12.00 21.84 24.41
69  2  2 17.08 10.58 14.96  8.46  8.79  4.67 11.50  7.00  9.71  8.83 18.91 25.66
69  2  3 10.92  9.83 11.21  3.25  7.54  1.38  8.92  5.46  3.83  4.42 14.79 13.21
69  2  4  5.17  2.54  6.63  2.50  5.17  1.13  6.75  4.83  5.04  5.50 11.04 13.08
69  2  5  7.50 10.58  5.25  4.58 11.42  6.25 10.79 11.71  8.46 13.62 22.29 23.63
69  2  6 21.50 18.38 15.50 13.42 20.21  9.13 17.50 12.67 16.25  9.83 22.75 17.50
69  2  7 20.71 19.00 19.92 13.83 14.04  8.50 16.08 12.25 12.38 12.46 24.62 27.00
69  2  8 11.46  3.42 11.58  8.38  6.75  4.50 11.71  5.00  9.59  6.42 14.12 21.25
69  2  9 11.63  1.96 12.12  6.42  5.79  2.50  9.67  3.42  6.71  7.67  9.33 20.12
69  2 10 12.75  6.17  6.79  2.67 11.58  1.54  8.67  5.91  5.41  4.42 12.54 12.83
69  2 11 24.75 15.34 14.71 16.25 21.59 11.46 17.58 18.63 18.21 19.33 24.25 25.17
69  2 12 18.54 14.00 17.33  8.17 13.04  4.58 12.17  8.96  8.83  8.29 14.71 17.83
69  2 13 13.08  5.00 15.83  6.50  8.29  2.25  8.08  5.46  7.29  5.75  7.04 15.41
69  2 14 15.71 10.58 12.92  6.46 10.54  4.75  7.08  5.66  8.04 10.25 18.75 20.30
69  2 15 12.71  8.04 14.17  6.34  6.42  0.87  6.34  4.50  3.63  2.92  6.25 12.92
69  2 16 13.59  8.38 12.87  3.46  9.54  1.21  4.58  2.79  2.75  4.08  5.88 10.37
69  2 17  8.04  4.25  6.83  1.33  3.17  0.04  4.08  0.25  0.96  1.96  6.67 12.96
69  2 18 20.08 14.92 19.41  7.33 12.58  6.21 10.88  8.75  8.42  8.17 10.21 15.75
69  2 19 27.79 23.58 29.83 17.00 24.25 17.92 27.71 21.92 22.04 17.71 23.67 38.20
69  2 20 14.37 14.04 32.71 13.62 18.58 13.21 21.67 17.75 16.13 17.88 22.79 33.63
69  2 21 14.21 15.12 22.92  8.63 18.38 10.13 14.29 15.83 14.04 15.41 20.21 26.25
69  2 22 14.92 12.75 19.38 10.92 12.04 11.54 17.21 16.38 15.12 14.67 21.67 34.05
69  2 23 11.12 13.67  9.87  6.34 11.58  8.00  8.25 10.41  8.96 10.50 12.25 18.58
69  2 24  5.50  4.12  5.29  1.87  6.58  2.42  5.91  8.33  6.50  8.08 11.12 18.38
69  2 25  0.67  3.58  4.17  0.67  4.88  0.21  1.08  2.79  1.54  5.21  2.54 13.21
69  2 26  4.50  4.21 11.71  3.25  6.63  3.25  4.92  3.92  5.50  4.21  4.75  8.00
69  2 27 10.75  7.29 15.75  5.79  8.00  3.50  6.38  6.38  5.71  5.09  7.38 11.58
69  2 28 10.50 10.83 16.33  7.17 11.38  4.75 12.46  7.75  7.62  9.59  9.00 22.75
69  3  1  9.00  6.75 13.33  4.42  8.92  4.04  8.67  6.50  7.17  5.66  6.34  9.25
69  3  2  9.08  7.46 17.62  6.13  7.12  2.92  7.62  4.54  5.29  5.75  4.21  4.75
69  3  3 10.79  6.83 20.54  7.29  6.34  2.54  6.83  2.50  4.08  3.17  4.92  5.88
69  3  4 12.58  9.08 22.25  7.62 10.67  6.08 11.21  8.54  8.83  6.92  7.71  7.50
69  3  5 16.46  7.62 15.29  4.75  8.12  6.17  7.17  7.58  9.92  6.67  9.29 10.17
69  3  6  7.96  3.04  7.87  2.21  5.13  0.71  2.62  3.46  3.92  3.54  4.83  4.17
69  3  7  2.92  3.04  4.46  1.67  4.21  0.25  1.08  1.87  0.75  2.46  3.92  7.46
69  3  8  2.29  7.21  6.17  1.54  5.04  1.42  7.96  4.71  4.25  5.91  9.79 17.71
69  3  9  9.83 10.41  7.00  4.75 10.34  3.83  9.00 10.34  9.50  8.58 13.33 17.12
69  3 10 10.13  9.08 12.50  4.92 10.34  3.71  3.92 10.13  7.41  8.58 12.87 20.83
69  3 11 16.21 11.87 12.54  5.91 12.21  6.38  8.50 13.00 10.75  9.71 15.34 21.67
69  3 12  9.29  8.92 12.04  7.83 11.21  6.04 11.58 14.79 12.00 13.46 17.04 29.17
69  3 13  6.00  2.92 14.71  6.92 10.04  6.96 15.34 11.63 11.00 12.17 14.79 33.66
69  3 14  7.12  3.96  4.00  2.29  8.08  1.71  5.33  6.75  5.13  5.33 11.00 21.46
69  3 15 13.08 12.92  9.92  5.04  9.00  4.96  5.75  7.83  7.38  6.04  8.08 16.33
69  3 16 21.12 17.62 18.66 11.25 14.88  9.21 16.17 18.75 16.29 15.71 18.05 30.71
69  3 17 21.46 15.37 18.58 12.00 14.29 11.54 15.00 18.46 17.79 13.96 17.58 31.58
69  3 18  9.38  4.50 15.12 11.12 11.67 10.88 14.67 20.00 16.33 14.62 17.00 37.99
69  3 19  2.92  7.83  5.79  4.42  6.79  3.00  7.33  8.00  7.00  6.50 10.34 19.95
69  3 20  6.87 11.04  7.71  4.96  8.96  5.50  4.08  6.38  5.58  7.79 10.54 18.84
69  3 21 16.00 16.38 12.71  7.62 14.92  9.96  9.50 12.50  8.42 10.50 12.75 18.16
69  3 22 16.25 14.88 17.79  9.00 14.00  9.25 14.92 12.00 12.71 12.08 14.54 19.70
69  3 23  7.62  5.54 12.83  4.92  6.13  2.62  9.75  4.96  7.38  4.00  7.41  8.33
69  3 24  3.58  2.83  8.38  5.04  4.58  1.75  7.04  2.75  3.21  4.79  4.92 12.83
69  3 25  7.41  4.38 15.29  5.46  5.63  1.75  6.50  3.83  3.33  2.75  7.12  4.96
69  3 26  6.21  3.00 14.46  4.88  4.88  2.33  5.37  2.67  4.08  2.92  4.29  7.83
69  3 27  8.17  4.63 13.25  4.25  4.12  1.04  5.33  1.96  3.21  2.04  6.17  6.63
69  3 28  6.34  4.88  5.50  2.67  7.79  2.42 10.54  6.13  6.79  6.79 10.25 13.59
69  3 29 11.08 10.58  7.67  7.25 14.75  5.96 13.17  9.79 12.08  9.13 13.59 19.25
69  3 30 12.87 13.50 10.54  9.92 15.83  8.54 14.54 11.83 13.54 11.63 12.38 14.92
69  3 31 22.34 16.46 16.08 11.83 22.17  9.75 18.50  9.42 14.54 12.67 11.83 16.62
69  4  1 12.79  9.79 11.34  7.08  9.08  4.33  9.42  7.46  8.04 10.75  8.67 16.54
69  4  2  5.75  3.46  6.42  3.42  4.50  0.29  5.46  3.29  2.62  2.21  8.67 10.21
69  4  3  4.17  3.21  3.04  1.63  4.33  0.29  4.96  3.46  1.17  3.17 12.29 11.00
69  4  4  5.75  2.88 12.29  3.21  4.63  1.17  3.42  1.50  1.58  1.54  5.63  4.63
69  4  5 11.21  3.79 16.92  5.91  7.29  4.88  7.87  5.33  7.83  5.25  7.50  7.83
69  4  6 18.66  5.37 18.38  6.21  9.42  7.41 13.75 10.67 13.62  8.92 11.29 14.29
69  4  7 12.62  1.63  8.17  3.71  6.79  2.58  6.38  5.33  8.17  6.54  6.71 13.08
69  4  8  7.46 11.83  6.38  6.96 11.63  7.46  5.04 10.17  9.13 10.83 22.08 18.54
69  4  9 16.29  9.25 16.13  9.71  8.33  5.96 14.37  7.83  8.58  9.59 11.12 13.75
69  4 10  6.50  5.83  8.38  4.50  6.75  4.08  9.59  7.75  8.87  7.71 10.34 13.00
69  4 11 22.88 20.62 18.12 12.12 22.88 12.25 17.62 15.12 17.83 11.71 22.50 21.50
69  4 12 24.75 20.75 16.50 15.00 22.13 12.08 19.04 14.83 17.37 14.29 20.33 27.67
69  4 13 11.63  6.46  7.96  6.54 11.08  5.00  9.96  7.92 10.04  8.54  9.62 10.17
69  4 14 13.62 11.17  9.04 11.25 19.79 10.17 14.50 14.88 15.12 11.46 15.37 17.41
69  4 15 18.16 12.38 11.54 11.83 17.00  9.75 17.16 11.75 16.50 12.08 14.04 17.96
69  4 16  7.41  6.83  5.83  4.88  6.71  3.13  9.87  3.79  9.13  8.96  7.25 13.83
69  4 17  8.04  5.83  9.75  3.08  6.34  2.25  3.71  3.37  4.50  2.88  5.50  9.38
69  4 18  7.83 10.46 11.21  5.63  9.38  4.67  8.67  5.33  7.12  6.29  6.46 13.88
69  4 19 16.33 14.21  8.71  5.88 14.33  6.25  6.92  8.75  8.54 10.50 11.63 15.92
69  4 20 15.87 15.59 11.38  6.58 14.29  7.17  7.41  9.79  8.29  8.38 11.92 19.00
69  4 21 13.33 12.04 15.34  7.71 13.00  7.71 12.83 13.13 10.08 13.17 19.62 32.05
69  4 22 21.00 13.79 14.75 12.42 17.54 10.13 12.71 12.21 15.29 13.08 17.04 17.25
69  4 23 10.04  8.21 11.50  7.54  8.96  5.04  8.67  7.83  9.00  8.75 12.71 10.37
69  4 24 20.08 16.71 16.50  9.50 14.96  7.96  9.25  8.96  8.50  5.75 10.25  6.83
69  4 25 14.62 13.29 15.75  7.58 15.96  8.12 14.29 11.54 13.62 13.29 15.21 11.42
69  4 26  9.79 12.92  7.17  4.75 11.71  3.54  4.17  5.88  6.71  6.13 10.83 10.25
69  4 27 13.83 13.83  9.42  8.12 15.50  6.38 10.25 10.34 13.17 10.25 15.92 15.16
69  4 28 16.75 13.62  8.00  7.58 13.70  6.25  6.17  8.25  7.75  7.75 12.50  7.38
69  4 29  6.00  4.25  4.63  3.96  5.63  0.71  4.71  4.46  4.83  6.71  9.04 13.75
69  4 30  4.58  3.08  4.29  3.83  5.50  0.33  3.33  3.33  3.04  4.63  5.13  9.67
69  5  1  5.41  2.96  6.21  2.29  3.46  0.63  4.08  1.33  3.29  4.54  6.13  9.54
69  5  2 13.08  3.75 16.75  5.88  6.04  4.88 11.38  7.96  9.54  9.25  7.92 20.62
69  5  3  8.08  4.67  5.58  3.79  6.50  2.00  5.71  3.29  3.46  6.50  5.33 27.88
69  5  4  5.25  6.42  6.25  2.58  3.71  0.33  4.08  1.33  2.88  2.71  3.29 13.33
69  5  5  6.46  8.08  2.29  1.67  6.79  0.29  2.83  1.96  2.00  5.04  4.88  4.46
69  5  6  5.13  4.46  7.46  1.71  5.29  0.96  2.04  1.83  1.83  2.29  5.09 16.04
69  5  7  5.83  5.79  9.71  2.71  7.29  1.33  4.71  4.50  2.13  4.08 10.00  6.79
69  5  8  4.08  5.96  8.33  4.08  9.13  2.54  6.87  3.96  5.41  4.42  9.87  4.08
69  5  9 13.13 10.96 11.34  6.79 12.46  6.63  9.42  6.46  9.87  5.83 10.41 10.00
69  5 10 14.29 13.88 14.96  7.87 13.33  8.46  8.58 11.83  9.79  9.29 15.92 13.88
69  5 11  8.04  9.83 12.12  3.67  8.21  3.92  2.29  8.00  3.96  4.50 14.88  8.83
69  5 12  8.42 10.88  6.83  5.00  9.00  5.25  5.66  6.58  5.29  5.46  7.75 10.34
69  5 13  6.34  5.71  8.87  4.71  6.58  3.96  5.46  5.41  4.21  7.17 11.12 11.29
69  5 14  9.04 11.54 11.67  6.92  9.79  5.04  7.54  8.54  5.75  6.42 16.08  7.62
69  5 15  7.00  7.92  7.71  3.88  7.25  3.75  4.21  6.08  2.58  3.63  9.13  5.71
69  5 16 12.67  7.67  9.38  7.67 11.38  7.96 10.29  7.50  9.75  7.04  9.75  7.08
69  5 17 12.67 12.75  9.75  6.75 11.58  6.87  6.71  6.17  6.25  8.71 12.08 15.04
69  5 18 15.09 11.46 11.12  8.58 11.83  9.17 12.46  8.46 10.08 11.96 13.00 19.58
69  5 19  5.00  5.13  6.21  4.17  6.38  3.17  3.92  1.79  4.42  5.41  6.04  7.75
69  5 20  7.87  6.63  7.67  3.50  9.83  5.71  1.96  4.46  2.92  2.33  7.83  8.58
69  5 21  3.54  5.21  5.50  2.75  4.46  1.38  4.17  2.04  1.63  2.13  5.09  5.41
69  5 22 12.75  8.54  8.58  2.67  8.58  4.71  3.25  4.12  3.63  4.21  5.96  8.42
69  5 23 15.79 10.96 15.96  7.75 12.50 12.62  7.96 11.38 11.12 10.34 11.83 17.41
69  5 24 11.17  7.54  9.17  8.08 11.34 10.25  9.96  7.41 11.58 13.29 12.75 21.79
69  5 25 11.38  7.62 10.13  6.83  8.96  9.42  7.12  6.38  7.50  8.00  8.46 13.42
69  5 26  8.92 10.13  8.12  4.29  6.00  4.08  5.17  2.88  2.42  3.79  6.38 12.00
69  5 27 13.70 12.96  8.79  6.50 13.17 10.46  7.62  8.21  8.38  8.75 10.92 13.54
69  5 28 11.83  8.75  9.54  5.83 10.67  7.71  6.79  4.88  6.96  3.96  9.04 12.96
69  5 29  5.83  5.13 11.92  6.21  8.12  8.87  8.29  9.38  9.33  6.75 11.67 10.79
69  5 30 13.62  7.58 12.71  7.58  9.17  8.92  8.42 10.17  9.00 10.58 13.62 11.92
69  5 31 10.58  5.33  7.62  6.54  8.38  5.88  6.63  4.04  6.17  7.46  7.08  9.54
69  6  1  4.46  5.13  6.54  2.50  5.17  2.62  4.71  2.17  4.25  4.33  6.13  8.79
69  6  2 12.83 12.71 11.92  6.34 12.29 10.00  9.62  9.25  9.42  7.46 14.83 11.63
69  6  3 14.17  7.46 10.63  7.21 11.63  9.59  8.58  8.38  8.25  7.58 11.92  9.54
69  6  4  6.58  4.67 12.00  2.62  4.25  3.71  3.42  1.71  4.17  3.13  7.12 10.34
69  6  5  4.96  4.38  7.58  2.62  4.83  6.25  2.96  3.75  3.37  3.50  9.17  9.79
69  6  6  2.50  4.21  3.67  0.71  2.25  2.67  0.21  4.08  0.50  1.83 13.54  8.21
69  6  7  2.17  3.79  2.75  2.42  3.67  3.33  4.42  3.50  1.17  2.17  9.62  4.46
69  6  8  6.46  3.46  8.75  3.25  2.71  3.71  2.88  2.62  4.38  3.33  5.29  2.21
69  6  9 11.00  3.83 15.71  4.04  5.09  6.46  3.75  3.79  6.34  4.29  9.21  5.71
69  6 10 12.58  5.33 16.58  5.58  7.29  9.04  6.50  4.75  6.96  3.04  7.58  6.67
69  6 11  5.04  4.25 11.21  3.88  5.25  5.41  3.29  1.29  4.63  2.04  3.83  3.96
69  6 12  4.42  2.88  4.12  2.54  4.42  3.63  2.92  2.62  2.79  2.92  6.46  5.33
69  6 13  9.59  6.96  7.71  3.83  6.00  4.04  3.79  3.92  2.92  2.17  5.79  5.09
69  6 14  7.92  8.12  8.42  4.00  7.71  6.50  4.63  3.54  7.41  5.96  5.71 14.04
69  6 15 15.34 15.71 11.92  8.96 12.46 12.54 10.46  8.04 10.54 11.75 11.96 20.67
69  6 16 21.96 16.83 10.88  9.33 16.50 13.42 10.37 10.46  9.75  9.92 12.87 14.21
69  6 17 15.79 16.17 13.37 10.37 15.79 14.09 11.29 11.00 11.58 13.46 11.42 18.05
69  6 18 11.21 14.79  8.87  7.12 14.29 11.96  8.29  8.46  9.79  7.41 16.25 11.96
69  6 19 14.37 14.96 12.33  8.29 12.42 12.00 13.42  8.83 11.17  9.46 12.46 11.63
69  6 20 18.34 14.83 12.00  9.25 15.75 13.92 11.04 10.08 12.08  8.50 16.17 13.21
69  6 21 11.96 11.54 13.37  7.17 12.25 13.13  9.38  6.50  9.42  8.92  8.29 16.83
69  6 22  9.17  8.50 10.67  5.00  7.67  7.33  4.17  5.66  5.41  5.79 10.25 13.04
69  6 23  3.46  4.38  5.37  2.71  4.71  3.42  3.63  3.54  1.71  2.92  9.38  9.59
69  6 24  6.29  6.29  7.17  3.08  6.71  7.00  7.00  4.67  5.33  4.38 10.34  9.87
69  6 25  9.29  8.08 10.08  4.38  6.71  6.00  5.71  4.21  5.17  3.46  8.17 12.71
69  6 26 12.62 13.79 13.04  8.63 13.33 11.29  9.83 11.67 11.75 11.38 19.58 21.54
69  6 27  9.67  7.54  7.75  6.75 10.63  7.92  9.54  9.33  8.92  9.92 15.09 18.21
69  6 28  4.96  5.50  7.00  3.67  5.50  3.63  5.37  6.17  4.21  4.00 14.33 12.71
69  6 29  6.04  7.12  8.29  4.00  5.58  4.46  5.96 10.29  5.83  8.38 19.29 14.67
69  6 30  5.66  8.50  7.83  2.71  7.46  5.46  6.25  9.75  8.71  7.87 15.46 10.92
69  7  1  7.67  5.21 12.33  4.54  9.08  6.71  7.92  9.25  9.87  7.79 16.54 14.29
69  7  2  5.54  6.92  4.63  4.58  7.38  4.46  6.00  3.75  6.96  6.79  9.46 13.04
69  7  3  6.79  4.42  7.38  3.46  5.00  2.83  4.21  4.96  3.92  5.54 14.83 15.46
69  7  4 10.13  9.83 10.41  5.37 12.38  9.96 12.96 11.79 12.42 12.87 19.08 26.08
69  7  5  8.08  7.46  5.88  5.75 10.17  7.54  9.17  8.42  9.96  8.71 16.25 20.08
69  7  6 13.25  9.75  6.29  5.88 12.17  6.50  7.08  8.08  9.50  8.58 14.46 17.00
69  7  7 17.88 10.25 12.67  9.00 14.42  9.87 12.21  7.75 12.04 10.71 14.17 18.34
69  7  8 12.62  6.42  8.33  6.83 10.17  7.50  8.17  6.29  8.96  9.75 10.37 16.79
69  7  9  7.46  4.83  5.17  4.54  8.67  5.04  7.62  4.83  7.79  6.08 10.17 11.38
69  7 10  7.92  4.29  7.12  5.63 10.67  8.38 12.38  6.13 10.67  8.38 14.29 20.50
69  7 11  7.38  5.71  6.96  6.83 11.71  8.54 13.92  8.79 12.62  8.46 16.88 24.54
69  7 12  6.71  3.46  6.83  4.46  7.75  6.04  8.92  4.83  9.67  7.00 14.67 17.75
69  7 13  4.04  5.33  3.58  2.92  4.33  2.13  6.67  2.37  4.29  3.67  8.75 11.12
69  7 14  3.88  5.83  3.96  4.50  6.87  7.12  2.42  4.08  5.33  4.63 10.92  8.21
69  7 15  8.63  6.92  3.13  4.25  7.67  5.04  3.71  3.63  4.08  3.75  5.88  7.62
69  7 16 11.42 10.34  8.12  6.38 12.12  7.50  9.00  8.50  9.13  8.33 13.04 15.75
69  7 17 10.37  9.59  9.00  4.75  7.67  4.83  7.87  5.88  8.17  5.71 12.00 18.91
69  7 18 18.84 18.54 16.29  8.50 14.75 11.42 12.25 13.88 11.87 13.37 24.41 20.04
69  7 19 12.33 12.50 14.50  7.83 14.21 10.37 16.25 10.21 15.21 12.21 17.46 21.62
69  7 20  9.13  9.92 10.79  4.83  8.04  6.92  9.59 10.96 10.41  9.00 17.79 17.16
69  7 21 10.63 12.62 13.21  5.91  8.42  8.50  8.00 11.67  9.13 10.34 19.62 17.58
69  7 22 12.83 10.21 13.17  6.87  7.21  8.04 10.71  7.50  8.46  9.46 13.96 14.62
69  7 23  4.50  5.66  6.46  2.37  6.75  4.33  3.50  5.50  5.37  6.63 10.41 12.58
69  7 24  5.37  6.25  7.21  2.62  2.71  1.71  2.71  2.50  2.21  1.83  8.42  6.46
69  7 25 11.46 15.67 10.63  8.17 13.50 10.67  7.96 10.37  9.71 11.71 22.37 19.83
69  7 26  7.17  5.25  8.71  3.37  4.33  2.08  1.50  2.54  2.62  1.96  9.92  5.58
69  7 27  6.00 10.46  8.00  4.25  8.21  5.50  4.71  5.88  4.54  7.21 15.25 14.88
69  7 28 11.87  8.83  6.54  4.04  9.42  4.79  3.42  2.71  6.17  4.63  6.58  5.66
69  7 29  9.96 10.50  9.21  5.04 10.71  5.46  8.42  5.04  7.58  4.50 13.13 13.17
69  7 30 14.12 17.79  9.54  7.33 13.79 10.04  7.21 11.29  9.08 10.83 21.67 17.67
69  7 31 11.08 14.54  7.92  7.08 12.79  9.25  7.04  9.38  9.13 10.83 12.83 13.70
69  8  1 10.13  6.50  4.29  3.33 10.04  5.54  2.50  4.71  3.50  3.92  6.50 10.96
69  8  2 10.54  6.63  9.00  3.96  6.34  4.92  6.63  3.25  6.96  5.91  8.83 10.25
69  8  3 14.37 14.83 11.50  5.29 12.33  8.04  8.00  6.96  8.42  8.17 13.70 10.46
69  8  4 14.37 16.21 13.54  9.59 14.88 11.04  9.59 10.25 10.88 13.62 22.00 16.83
69  8  5 12.29 11.25 11.58  6.25 12.50  9.08  7.12  9.50 10.21 11.00 18.25 18.66
69  8  6 10.04 17.21  6.54  5.66 11.71  7.33  5.63  7.33  6.92  7.54 13.37 11.87
69  8  7 12.17 17.62 11.38  8.25 16.25  9.79  8.21  8.54 10.92 11.25 16.88 19.50
69  8  8  8.33  9.92  8.17  5.71  8.42  5.79  5.58  7.54  6.13  4.92 13.33 14.17
69  8  9  4.54  8.96  5.17  2.37  3.08  1.29  1.54  3.46  2.58  1.42 10.46  3.63
69  8 10  9.46 13.42  7.67  5.33 10.00  5.58  5.50  5.54  5.50  4.00  9.67 10.00
69  8 11 10.67  9.08  7.96  5.37 12.25  7.67  6.17 10.92  9.25 10.83 22.13 21.71
69  8 12 14.92  4.75 15.59  6.71  8.92  5.79  7.21  3.79  7.46  5.96  7.58 11.67
69  8 13  8.67  2.79  6.58  3.00  2.50  3.33  2.79  1.50  3.88  1.21  1.58  4.88
69  8 14  3.67  7.17  4.50  1.00  3.29  0.63  2.42  1.00  1.63  1.13  3.71  8.58
69  8 15  4.54  3.67  2.92  0.83  2.04  1.54  0.71  3.75  1.29  1.21  5.46  2.50
69  8 16  8.04  3.88  5.00  3.21  3.46  3.13  3.50  1.58  3.92  4.25  5.88  7.79
69  8 17  9.04 10.13  8.00  3.83  5.17  5.00  3.96  5.88  4.33  5.75 17.71 12.46
69  8 18  8.38  8.46  9.21  3.71  7.92  5.09  4.92  7.46  6.92  8.21 16.08 12.46
69  8 19 12.87 11.42  9.75  5.79 14.25  9.25 10.08  7.96 11.00 10.08 19.75 18.46
69  8 20 16.38 14.92 11.29 10.04 17.50 10.71 13.59 11.96 13.33 13.25 18.54 19.62
69  8 21 16.42  9.79  9.46  9.04 11.00  8.54 10.21  6.38 10.41 12.17 11.42 19.46
69  8 22 15.54 10.75 10.75  8.33 11.96  8.12 10.79  6.92 11.08 10.96 14.50 22.37
69  8 23 16.79  9.96 11.17  8.92 10.41  7.33  9.54  5.29  8.71 10.21  9.17 16.92
69  8 24  8.96  9.29  8.46  4.42  9.59  6.42  7.87  6.13  7.25  7.71 13.70 14.33
69  8 25 20.08 10.50 12.21  6.87 12.58  8.58 12.17  8.75 11.71 11.12 14.62 20.62
69  8 26 19.58 12.83 13.08  9.83 16.08 10.46 15.79 12.04 14.71 14.00 15.00 19.55
69  8 27 18.63  9.33 11.00  8.46 13.08  8.67 13.00  8.46 12.21 13.88 11.79 20.75
69  8 28 18.21  8.96 11.00  7.62 12.21  8.25  8.92  6.92  9.42  8.29 11.96 13.00
69  8 29  7.96  7.17  9.04  5.09  6.96  4.04  5.91  4.54  4.83  6.34  5.29 10.29
69  8 30  8.83  4.88  5.66  3.17  6.29  2.96  5.21  4.38  4.50  7.33  4.54  8.42
69  8 31  4.17  7.08 13.50  4.54  6.67  4.71  4.12  4.67  4.75  6.17  7.12 13.00
69  9  1  5.79  8.08 15.21  4.04  5.91  3.71  6.34  4.21  4.38  5.13  8.92 11.38
69  9  2  3.37  2.88 11.79  3.83  5.54  2.79  2.08  2.54  2.75  1.92  5.79  6.58
69  9  3  8.04  4.25 14.09  4.33  6.13  2.50  3.83  2.29  3.46  3.71  2.96  9.25
69  9  4  4.71  4.21  4.92  3.54  5.17  1.25  3.50  1.50  2.62  3.50  6.42 10.83
69  9  5  5.96  3.33  6.08  3.00  6.63  1.87  4.92  2.17  3.00  4.42  5.63 11.04
69  9  6  3.17  1.00 10.58  1.83  4.33  1.08  2.17  0.67  0.83  0.13  7.12  6.00
69  9  7  1.50  1.25  5.63  1.08  3.21  1.33  2.42  3.33  1.33  1.96 11.79  7.25
69  9  8  5.96  7.50  5.83  2.13  5.09  2.92  4.00  7.08  3.13  5.83 12.38 12.38
69  9  9 16.50 15.63 12.67  8.63 14.83 10.04 10.04  9.29 10.41 13.00 18.12 20.71
69  9 10 12.17  4.96 11.58  5.21  6.75  2.62  5.25  3.33  3.04  3.46  6.54 11.17
69  9 11 11.87  5.63  6.87  3.00  4.50  3.46  6.83  2.88  4.54  4.04  4.79 11.75
69  9 12  7.58  7.87  4.54  2.04  6.71  3.25  1.92  6.34  3.17  3.25 13.21  9.62
69  9 13  5.25  3.04 12.29  2.25  3.58  1.25  3.25  2.37  2.42  1.96  4.96 13.70
69  9 14  4.25  3.33 14.83  3.50  6.25  5.29  9.21  8.25  6.71  5.25  7.41 13.29
69  9 15  5.58  3.63 14.12  3.08  6.00  4.71  6.92  7.29  7.33  4.38  7.38 12.00
69  9 16 10.71  9.21 10.54  4.29 11.83  6.29  7.71 10.29  8.67  6.04 11.25 18.05
69  9 17  3.83  0.87 10.00  2.75  5.09  3.37  3.75  4.71  4.54  4.33  5.00  7.41
69  9 18  7.50  5.21 13.25  4.08  6.46  4.17  3.79  5.91  4.96  3.29  6.34 10.17
69  9 19  9.04  5.13 13.75  4.00  5.96  1.58  3.54  3.29  1.17  0.67  8.21  7.00
69  9 20  8.83  9.13 10.04  4.25 11.34  7.29 10.50 10.04  9.50  8.04 13.00 15.83
69  9 21 18.54 16.62 17.54  9.50 17.67 10.67 16.17 15.75 12.83 13.54 22.04 22.88
69  9 22  7.87  6.29  8.25  3.25  9.42  4.71  7.38  7.29  6.21  6.87 13.88 20.75
69  9 23 13.08 15.37 13.79  6.42 13.21 10.71 10.37 14.88  9.50 11.00 23.79 21.84
69  9 24 12.58 11.54 11.46  5.66 11.54  8.58 10.13 10.34  8.92 11.21 15.41 17.88
69  9 25 10.41 10.58  9.54  4.42 13.88  8.17  9.96 11.83 10.00 10.13 22.13 23.58
69  9 26 15.83 15.75 14.46  9.00 20.30 11.08 17.12 11.71 14.88 14.04 19.95 23.00
69  9 27  7.41  6.42  6.83  2.04  7.08  4.00  6.46  5.75  6.17  5.09 14.29 17.33
69  9 28 16.17 15.34 14.96  8.08 15.63 10.96 16.92 16.38 13.59 15.75 24.75 33.63
69  9 29  4.00  2.37  8.21  1.29  5.17  1.79  5.75  4.29  4.08  4.83 11.08 18.46
69  9 30  8.83  7.96  7.29  4.33 11.63  6.58 11.38  9.17  8.54  9.33 15.41 24.41
69 10  1  6.75  5.13  6.21  1.54  7.54  4.33  7.33  6.87  4.96  7.21 13.46 14.00
69 10  2 15.09 15.16 13.42  7.00 15.34 10.79 16.42 11.50 13.42 11.25 21.42 23.00
69 10  3 10.58  9.59 11.08  4.46 10.04  6.87  7.38 11.54  7.46  9.04 18.34 15.71
69 10  4 12.54 11.25 11.71  7.96 11.58  6.96  8.08  9.75  8.17  7.41 16.00 13.25
69 10  5  8.79  7.96 11.08  6.50 10.29  5.25  7.25  8.46  7.87  7.62 12.38 14.42
69 10  6 11.75 14.09 10.92  7.79 13.88  9.13  8.04 11.29  9.67  9.83 21.29 17.71
69 10  7 10.67 11.04 14.12  5.88 12.67  9.08 10.79 12.54 10.29 12.75 21.75 23.67
69 10  8 20.08 21.29 18.50 12.12 18.54 11.83 14.00 15.29 10.92 13.25 19.50 18.16
69 10  9  9.75 11.12  7.41  5.04  9.71  5.88  4.25  7.46  6.75  7.67  6.79  6.29
69 10 10  4.67  3.17  5.96  1.25  7.08  1.58  1.17  4.21  4.17  1.75  4.71  5.88
69 10 11 12.87 14.29  9.25  3.04  8.04  5.13  3.58  7.75  5.71  5.21  9.13  8.67
69 10 12 18.79 21.59 16.17  9.62 16.21 11.12 10.63 13.96 10.92 11.54 16.04 20.00
69 10 13 14.25  8.50 17.79  9.83 10.50  8.12 11.92  8.12 10.75 12.46 12.08 17.12
69 10 14 15.63 16.04 13.46  7.46 13.33  9.00 11.50 13.00 10.37 11.71 22.46 21.21
69 10 15 24.46 23.58 20.62 14.75 19.58 16.42 16.13 19.08 15.63 20.38 31.88 29.38
69 10 16 19.46 18.79 18.91 13.46 17.00 14.54  9.87 16.29 13.37 16.62 25.58 24.67
69 10 17 12.38 16.29 15.46 10.67 17.04  9.59 14.37 13.50 14.17 10.79 21.87 25.25
69 10 18  9.96  4.54  9.38  3.33 11.17  6.34  4.33  7.58  4.71  3.58  4.04 11.29
69 10 19  5.37  1.21  7.00  0.87  6.08  0.29  1.04  2.58  2.00  1.67  9.59  6.08
69 10 20  2.42  3.08  2.83  0.29  7.50  2.25  1.38  2.25  2.58  1.71  4.83  6.17
69 10 21  4.79  1.67  2.17  1.46  6.83  2.83  1.50  1.83  1.58  1.58  2.96  7.62
69 10 22  9.96  8.29  7.46  3.88  8.25  5.58  3.33  6.25  6.34  5.91 10.75 12.54
69 10 23 13.29 18.00 13.08  6.63 17.46 10.71 13.88 16.42 12.42 12.21 28.62 27.37
69 10 24 10.34 12.96  9.33  6.29 15.37  8.21 13.42 12.38 12.42 10.71 20.41 26.34
69 10 25  7.83  8.54  8.17  6.04 14.37  9.50 12.54 13.13 13.13  8.46 17.16 22.75
69 10 26  2.62  2.88  6.67  4.08  9.00  5.09  9.29  8.63  9.25  6.04 12.17 15.46
69 10 27  5.58  6.92  5.50  2.37  6.29  2.79  4.04  5.91  4.46  5.50 10.79 12.92
69 10 28  9.04  9.17  9.62  5.09 11.00  6.13  8.25  9.67  7.92  9.96 16.54 20.58
69 10 29  8.83  6.38 11.17  3.13  6.04  3.63  7.46  6.08  5.29  7.25 11.42 22.46
69 10 30  7.04  3.88  6.75  4.58 10.46  4.58 11.58 10.46  9.59  8.50 16.21 25.17
69 10 31  4.29  6.75  5.00  3.50  9.71  5.79  8.25  7.33  7.08 10.67 15.63 21.12
69 11  1 10.63  9.21 11.83  4.75 12.50  9.04 13.37 11.63 12.33 13.62 20.25 20.83
69 11  2 24.04 24.87 21.37 13.54 22.08 15.21 19.41 17.58 18.41 17.75 23.42 28.25
69 11  3 22.63 12.38 17.62  5.25  6.42  3.58  4.75  5.71  5.96  5.91  8.63 13.88
69 11  4 13.13 10.37 10.63  4.54  8.46  5.54 10.00  6.83  8.67  6.96 12.58 20.96
69 11  5  5.50  6.17  6.38  2.00  7.46  7.00  9.67  6.04  8.33  7.62 14.42 19.12
69 11  6 14.42 10.13 11.50  2.21  7.12  4.46  2.88  3.67  3.42  3.88  6.54 14.17
69 11  7 13.59 16.29 12.33  6.67 14.25  8.04 11.29  9.96  9.71  9.08 20.88 23.29
69 11  8 18.25 21.96 12.25 10.63 19.41 11.21 18.16 16.25 16.33 14.00 23.87 29.46
69 11  9 21.75 27.33 11.71  9.00 18.00  9.83 10.71 10.88 10.79  8.50 17.46 18.29
69 11 10  8.38 14.58  8.75  6.04 14.25  9.21 12.87 10.58  9.00 10.37 21.25 22.42
69 11 11  9.08  6.08 10.67  3.96  5.66  2.83  5.79  2.21  3.42  2.46  5.83  9.75
69 11 12  9.13  6.92  5.88  2.58  8.25  3.63  7.04  3.54  2.75  3.54  9.00 14.88
69 11 13  4.79  3.75  2.21  1.17  5.09  2.71  5.41  3.13  2.42  5.46  7.54 17.58
69 11 14  9.54  3.29 11.38  4.04  4.58  3.25  4.29  0.96  2.37  0.33  6.34  7.33
69 11 15 12.38 10.71 10.50  6.17  7.79  5.79  9.79  6.17  8.17  5.54 11.00 15.41
69 11 16 19.87 20.71 20.83  9.13 12.12  9.67 11.96 12.33 10.92  9.92 21.00 22.92
69 11 17 11.29  9.38 13.67  5.63  6.38  6.46 10.25  6.42  8.92  7.46 11.50 15.34
69 11 18  9.25  7.83 10.41  7.50 10.29  9.50 16.25 13.54 12.33 10.21 17.04 22.88
69 11 19 11.79 15.00 12.38  9.04 14.25 12.08 19.25 16.62 16.38 14.96 22.54 30.04
69 11 20 10.58 14.67  8.50  4.21 11.75  7.17 11.96  8.54  9.17  8.96 13.67 21.42
69 11 21  3.46  2.75  5.75  2.17  5.75  4.00  2.96  9.42  5.33  7.41 14.17 20.83
69 11 22  9.13 12.00  9.08  2.33  8.12  4.00  2.58  7.08  4.12  4.75 20.54 34.08
69 11 23 20.04 19.46 28.21  9.04 16.96 12.04 14.96 13.67 12.62  9.79 17.67 18.75
69 11 24 12.17 10.58 18.08  6.21  6.63  3.71  8.92  6.58  6.38  6.42 13.04 20.50
69 11 25 13.33  5.83 17.04  4.67  6.00  3.21  9.50  5.17  6.87  8.12 10.67 18.88
69 11 26  6.54  3.54  9.04  1.71  4.63  3.29  8.21  4.29  4.75  3.75  8.75 14.46
69 11 27 12.79 11.63  8.87  6.46 11.25  7.92 13.17  9.42 13.08  9.08 17.41 24.79
69 11 28 15.21 10.41 13.70  8.08  8.21  7.67 12.38 10.13  8.92  9.13 18.96 29.33
69 11 29 13.92  8.46 17.00  4.88  5.91  4.75  9.50  4.50  6.58  6.21 14.29 19.46
69 11 30  7.25  6.63  6.67  1.38  5.29  4.50  7.71  6.29  5.29  4.75 12.50 13.54
69 12  1  9.62  7.87  7.04  4.42  8.67  5.83  8.96  6.58  8.92  6.71 13.42 20.17
69 12  2 13.13 15.04  9.96  8.96 16.54 10.71 12.87 14.67 13.67 10.79 17.25 18.16
69 12  3 19.58 16.96 12.67  9.83 12.67  8.21 12.21  9.42 12.79  8.92 13.92 21.59
69 12  4 12.25 13.13 14.17  4.75  6.67  4.04  7.21  4.71  6.67  5.50 11.46 16.17
69 12  5  9.46 12.92  8.33  3.37  9.29  5.75  8.00  8.25  6.67  5.66 11.79 18.79
69 12  6 17.46 14.62 13.17  8.00 10.25  8.42 11.04  8.92 10.13  7.41 14.09 19.04
69 12  7 23.04 12.12 13.67  8.58 14.79  9.87 15.54 14.79 16.58 12.33 17.96 21.42
69 12  8 11.58  7.25  9.83  6.08  6.67  4.71  7.25  7.62  6.58  6.29 10.96 14.83
69 12  9  5.66  5.75  4.63  1.75  6.63  4.92  5.50  6.96  5.00  5.71 15.12 14.92
69 12 10 13.13  9.59 10.04  3.46  8.29  7.54  7.50  6.38  8.42  9.54 12.33 15.37
69 12 11  6.71  3.37  8.33  0.25  4.29  3.33  5.66  1.79  1.54  1.96  7.83  8.75
69 12 12  3.46  6.21  4.38  0.21  6.71  5.00  2.88  4.75  1.38  0.50 12.87 13.00
69 12 13 22.67 16.88 16.25  5.21 13.08 10.54 11.34 11.83  9.87 13.21 19.50 22.29
69 12 14 18.29 22.54 14.96  9.67 17.00 10.79 14.92 12.87 13.13 12.17 18.66 19.17
69 12 15 17.16 20.12 12.25  7.71 12.79  7.08 12.92  6.79 11.96  5.75 15.87 15.16
69 12 16 13.00  9.29 10.46  3.17  9.75  8.75  7.12  7.17  6.38  5.25 10.08 15.54
69 12 17  6.17  4.46 21.37  6.00  3.29  7.04 17.67  7.58  8.87 10.50 12.75 26.83
69 12 18 11.71  8.42 12.21  4.58  8.46  7.38 10.13  9.54 10.21 10.41  9.54 19.21
69 12 19  7.62 10.67 13.79  5.21  9.79  9.04 11.79  9.08 10.00  8.63 15.79 22.21
69 12 20 10.50 13.92 10.00  3.58  9.38  9.38  8.75  9.25  7.38  5.83 13.62 17.21
69 12 21 24.41 20.41 19.70 11.92 15.50 13.62 14.46 13.96 14.37 14.17 20.50 19.46
69 12 22 16.38 13.88 12.17  9.29 13.50  8.38 13.33 10.08 13.25  8.79 16.38 21.21
69 12 23 12.83 13.37  9.79  5.41 11.00  9.25 10.50 11.63 11.00  9.50 16.62 15.00
69 12 24  7.67  5.75  5.50  2.04  4.71  4.25  4.96  4.96  3.25  2.83  6.79 11.34
69 12 25 12.83 12.58  8.46  4.17  5.41  4.67  6.21  5.41  5.17  3.92 13.88 10.96
69 12 26 12.71  6.67 10.75  3.21  2.71  1.25  5.25  2.37  3.25  2.79  5.13  9.50
69 12 27  6.71  6.21  5.29  0.42  5.96  4.17  3.04  5.25  2.17  3.75 12.46 13.21
69 12 28 18.34 18.00 15.75  6.08 13.29 11.34  9.71 11.87  9.21  8.75 15.87 18.12
69 12 29 18.66 15.96 19.38  6.38 13.54 10.08 14.29 11.46 10.58  9.59 13.75 25.04
69 12 30 16.25 13.25 23.42  8.04 10.04  8.17 16.79 11.42 11.92 11.42 11.50 27.84
69 12 31 14.42 13.83 27.71  7.08 12.08 10.00 14.58 11.00 12.54  7.12 11.17 17.41
70  1  1  9.59  2.96 11.79  3.42  6.13  4.08  9.00  4.46  7.29  3.50  7.33 13.00
70  1  2  9.00  4.29  6.75  4.58  5.79  6.58  8.58  6.21  9.59  6.04 10.17 16.96
70  1  3  8.08  7.46 11.71  3.58  4.50  3.63  5.13  4.75  3.13  2.50 12.54 18.25
70  1  4 11.21  7.38 11.58  2.13  3.29  1.13  8.92  4.92  5.54  5.71  7.58 15.54
70  1  5 10.46  5.29 10.83  3.63  4.75  5.29 12.33  5.83  6.79  6.21 11.83 20.21
70  1  6  7.92  3.50  8.12  3.79  2.50  4.29 10.34  4.00  6.63  6.08  7.04 16.66
70  1  7 18.91 17.50 14.75  4.63 12.83  9.00  8.58  9.75  6.92  4.29 10.79 12.17
70  1  8 22.92 20.75 24.08 10.34 17.96 17.29 19.08 15.54 14.17 11.46 17.12 25.62
70  1  9 16.17  8.87 18.25  7.41  8.92  9.08 13.54 10.37 10.67  7.50  9.13 19.21
70  1 10  7.96  5.41  8.25  1.79  3.92  3.50  4.63  2.29  4.50  2.54  1.71  8.50
70  1 11  5.00  3.71  6.34  3.00  4.00  3.21  5.50  4.83  7.67  6.13  9.21  9.62
70  1 12  7.96  7.50  6.08  2.79  4.46  4.75  6.13  3.42  4.63  2.62  6.63  8.04
70  1 13 21.67 18.79 18.41  9.21 15.67 11.54 11.29 14.09 11.38  9.13 13.42 15.96
70  1 14  8.46 11.58  9.38  4.33 10.00  8.12  9.62 11.34  7.71  7.46 14.33 19.92
70  1 15  6.71  5.00  6.38  3.00  3.75  3.37  6.00  1.71  3.54  2.37  5.58  5.96
70  1 16  7.29 10.29  6.17  0.75  6.83  2.54  3.83  4.88  3.75  3.42  7.08 15.75
70  1 17 20.88 18.88 21.42 10.34 17.33 16.21 16.54 16.29 13.25 14.33 18.88 32.00
70  1 18 14.42 15.67 15.16  7.87 12.00 10.04 12.87 10.92 12.58 14.37 15.37 21.79
70  1 19 19.08 22.37 20.91 13.17 18.34 14.96 16.79 15.16 15.37 15.63 23.67 21.87
70  1 20 22.63 23.67 14.71 11.75 18.16 14.25  9.96 17.71 15.71 17.92 28.12 25.37
70  1 21 22.08 17.58 17.92 11.54 12.62 12.67 13.33 14.42 13.33 16.75 23.63 22.67
70  1 22 15.25 15.41 13.13  7.25 13.25 11.38  8.54 10.63  8.25  5.91 12.87 14.75
70  1 23 17.16 13.08 18.46  7.71  8.21  6.79 11.29 10.08  8.79  8.58 10.83 22.75
70  1 24 13.88 14.33 13.37  5.54 10.21  9.71 11.08 11.04  9.17 10.63 15.29 21.67
70  1 25  5.00  4.29  7.58  1.13  4.04  3.42  2.25  4.67  2.62  2.08 10.21 12.33
70  1 26  4.79  2.17  4.42  0.75  3.21  2.46  7.04  2.21  2.71  2.67  5.91 10.83
70  1 27 10.54 19.08  7.83  3.17 10.00  7.00  5.17  8.79  5.21  2.88 12.42  9.96
70  1 28 17.50 17.67 17.83  8.58 17.37 14.62 13.75 14.88 11.42 12.50 18.79 21.25
70  1 29 12.96 11.08 18.12  6.83  9.83  9.46 10.83 10.63 10.17  8.58 10.58 19.00
70  1 30  9.79  6.63 14.96  3.17  5.54  3.21  5.66  2.79  2.67  2.17  4.50  8.54
70  1 31 22.46 21.50 20.91 12.96 19.33 15.67 17.54 15.25 15.46 14.46 21.96 26.83
70  2  1 22.08 21.79 17.83 13.46 19.75 15.41 18.25 17.41 17.12 17.83 24.08 25.08
70  2  2 19.33 18.12  9.83  8.75 14.92  9.38 13.96 11.87 13.08 13.62 22.83 28.96
70  2  3 10.92 11.54 10.46  5.46 10.63  8.71 14.29 10.63 11.63 11.38 19.08 26.83
70  2  4  9.79 12.46 10.21  4.54  8.54  4.96  5.63  4.79  3.67  1.17  8.00  5.29
70  2  5  8.38  8.33 14.12  4.63  3.67  2.75  5.33  4.25  4.25  2.17  6.54 13.37
70  2  6  7.25  7.29  7.54  3.08  5.96  5.29  7.17  5.75  5.75  6.42 12.50 16.13
70  2  7 18.21 22.25 12.87 11.46 20.08 10.71 17.54 13.46 15.37 12.12 23.25 23.25
70  2  8 16.92 20.54 10.34  8.33 17.46  8.83 15.50 12.62 13.37 10.41 22.04 20.75
70  2  9 19.87 26.75 13.13  9.42 20.41 10.34 17.00 13.08 15.75 12.46 23.71 19.70
70  2 10 10.88  7.79  8.33  4.96  8.42  6.04 10.83  5.83  7.92  4.92  9.92 10.79
70  2 11 10.79  6.83  7.08  1.96  6.00  2.67  6.50  3.46  3.71  3.04  4.29  9.25
70  2 12 17.46 10.29 21.71  5.50  8.79  6.17 10.13  6.38  7.54  7.17  6.58 17.83
70  2 13 17.79 15.83 21.25  7.17 11.83  8.21  8.75 11.04 10.04  7.25 14.71 21.59
70  2 14 12.54  7.79 11.79  4.04  7.25  4.75  5.91  3.67  5.13  4.38  8.71 14.12
70  2 15 11.63 11.29  9.33  3.58  4.29  2.71  5.41  2.67  4.67  4.54 10.08 14.71
70  2 16  7.46  9.71  7.12  1.54  8.63  6.96  7.67  4.63  6.87  4.21  9.33 15.92
70  2 17 20.91 15.29 14.00 10.54 13.83  9.46 15.79 10.71 15.87 10.58 19.08 25.41
70  2 18 12.25 10.96  9.62  5.09  9.96  8.38 13.50 10.04  9.62  9.08 15.00 20.96
70  2 19 19.38 19.83 15.59 10.00 18.21 12.92 16.54 12.58 14.33 12.00 15.04 16.13
70  2 20 18.29 17.67 13.83  9.67 16.17 11.29 17.04 12.83 14.67 10.04 18.25 22.88
70  2 21 21.84 20.83 18.96 13.75 24.96 16.33 21.96 16.58 20.41 13.13 22.63 18.12
70  2 22 16.92 16.71 12.58  9.17 19.75 13.29 16.88 16.00 15.21 16.21 24.83 28.29
70  2 23 10.13  9.42  9.38  5.54 10.58  8.87 15.29 10.71 13.08 11.75 17.54 27.25
70  2 24 17.16 15.87  9.17  7.62 14.67  8.29 15.29 10.75 12.33  9.79 19.25 17.54
70  2 25 23.29 14.96 13.25 10.58 13.25  9.17 10.88  9.92 11.58  8.54 17.88 13.70
70  2 26 11.34  6.38 10.54  3.42  4.54  2.50  4.33  3.96  4.42  2.17  8.46  8.46
70  2 27 12.21 10.83 17.88  5.00  5.91  4.92  7.67  8.00  7.12  6.08  9.00 10.41
70  2 28  9.96  8.38 16.29  4.79  5.41  3.42  6.42  4.63  5.00  3.17  4.58 11.75
70  3  1 13.13  6.04  8.96  4.63 10.58  7.67 14.62 10.50 11.83 10.17 14.46 22.21
70  3  2 15.34 15.12 13.79  9.62 10.71  8.33 10.63 11.42  9.59 10.75 19.50 26.16
70  3  3 15.29 10.34 13.62  5.63 10.13  7.12 10.25  8.21  8.58  6.87 15.54 17.12
70  3  4 23.04 14.67 16.79 12.21 13.37 10.13 13.62 11.21 12.62 10.41 17.16 20.38
70  3  5  7.87  5.29  9.13  2.21  6.79  2.92  8.83  3.79  6.34  7.17  8.33 13.79
70  3  6 11.96  6.83  7.54  3.50  7.87  4.83  8.50  5.21  7.21  5.04 10.54 13.88
70  3  7 20.38 10.79  9.71  7.54 10.29  8.29 10.29  8.38 11.29  6.46 17.25 20.50
70  3  8 22.79 13.67 14.29  9.92 12.71 10.34 11.21 12.04 12.71 12.08 20.21 26.46
70  3  9 12.08  8.63  6.00  4.21  8.29  5.37  7.25  6.00  6.38  7.87 10.71 20.12
70  3 10 10.54  6.25  8.04  4.58  8.00  5.96 10.08  8.00  8.71  5.54 12.54 14.71
70  3 11 19.12 17.96 10.71  9.08 17.79 11.17 16.88 14.37 16.33 14.96 21.12 27.67
70  3 12 24.17 17.21 25.41 13.50 14.79 12.54 14.37 15.09 13.92 16.54 21.42 23.58
70  3 13 16.50 13.67 31.83  9.96 12.42  9.83 16.75 13.21 11.83 10.96 14.04 15.75
70  3 14 11.08  7.25 17.29  6.71  7.71  3.08  7.29  5.13  5.46  4.42  4.21  9.96
70  3 15 12.54  8.42  8.92  5.46  7.21  5.41  8.33  6.34  8.04  7.29  9.87 12.75
70  3 16 10.41  7.67  8.46  6.58 13.21  8.67 14.92 10.71 13.70 10.54 18.50 23.58
70  3 17 17.25 16.66 13.08 10.79 19.38 14.62 22.04 18.08 20.71 15.54 20.67 24.37
70  3 18 22.95 16.96 14.71 13.88 19.04 12.58 19.70 17.12 17.83 17.79 22.34 27.16
70  3 19 12.17  8.92 11.71  5.91  9.54  8.04 12.25 10.34 11.29  7.71 15.75 17.71
70  3 20 14.12 13.08 12.58  7.58 12.87 11.21 15.29 12.38 13.92 10.71 16.33 18.41
70  3 21  7.58  6.63  5.50  2.71  8.29  6.08  7.38  7.38  5.88  5.96 11.67 12.50
70  3 22 10.21  8.87  8.67  2.62  7.50  5.58  5.25  5.37  4.08  3.25  9.83  7.17
70  3 23 13.62 17.08 21.17  8.29 11.46  8.71 11.34 10.54  8.92  8.75 12.67 10.08
70  3 24 11.17 10.92 22.58  6.34  9.46  5.25  8.17  6.50  6.79  5.46  5.41  8.04
70  3 25  6.38  3.37  6.46  1.87  5.13  3.46  4.25  5.66  4.75  5.00 12.29 16.08
70  3 26 14.83 11.58 12.00  8.00 11.63  8.33 11.58  9.92 12.79 11.21 15.46 23.67
70  3 27 13.70  9.83 12.58  7.87  8.96  8.33 11.92  7.71 10.58 10.71 13.33 24.79
70  3 28 17.71  9.75 13.04 10.08 12.92  8.75 13.46  9.87 12.17 10.79 14.17 19.79
70  3 29 14.96 13.67 13.29  8.87 15.67 10.17 16.38 14.50 15.37 11.54 19.38 22.42
70  3 30 18.88 18.79 13.54  9.71 16.92 11.58 19.00 14.29 15.59 12.17 18.75 17.00
70  3 31 21.42 20.46 17.21 12.46 15.37 11.17 15.16 13.29 13.83 17.37 22.83 34.08
70  4  1 18.96 12.17 19.25 10.21 10.88  8.87 13.08  8.29  9.83 11.79 15.29 22.29
70  4  2 12.42  7.00 10.92  6.96  8.96  5.21 10.83  5.91 10.46  8.75  9.67 16.17
70  4  3 17.79 10.46  9.83  8.33 11.63  8.83 12.04  8.29 12.29  9.71 11.29 17.33
70  4  4 17.46  9.79 12.00  8.87 10.46  6.67 11.34  6.75 10.21  9.71  9.17 13.92
70  4  5 18.00 14.04 12.79 10.17 15.63 10.46 14.25 12.21 14.42 11.83 16.38 19.58
70  4  6 14.88 14.54 23.75  8.75  9.87  9.21 14.42 11.42  9.50 11.21 15.25 23.67
70  4  7 11.92  9.29 11.17  4.50  7.17  6.17  8.00  8.54  7.92 10.04 12.50 21.42
70  4  8 12.83  9.33 12.54  6.21  7.46  6.50  7.46  8.12  6.38 10.17 15.09 25.12
70  4  9 13.21  8.08  8.79  7.17  7.50  8.38  8.92  8.42  8.46 10.50 10.29 23.67
70  4 10  6.71  3.92  6.75  2.92  4.21  4.17  6.25  5.09  5.71  3.04  6.71 11.63
70  4 11  8.12  5.33  6.87  1.96  4.17  3.96  3.96  4.50  3.21  2.13  7.41 10.58
70  4 12 19.79  9.29 17.33  9.79  9.38  8.83 14.58  8.79 10.46 10.96 10.75 17.41
70  4 13  7.92  6.17  9.25  4.25  6.25  4.50  4.96  4.79  4.38  4.42  7.62  8.75
70  4 14  9.71 12.87 11.08  5.58  8.67  8.29  5.66  9.59  6.25 10.29 17.46 17.54
70  4 15 17.29 17.54 15.50  7.79  9.21  9.46  9.71 10.50  8.75 12.33 18.21 16.29
70  4 16 15.71 17.00 16.66  7.46 10.54 11.17 13.17 11.00  9.67 12.75 15.50 12.58
70  4 17 11.50 12.96 14.42  7.50 12.87 11.12 11.75 12.96 11.08 12.67 21.04 16.66
70  4 18 11.46  8.46  9.38  5.17 10.92  8.58 11.00  8.87 10.08 12.12 13.00 14.04
70  4 19 12.25 12.58  8.79  7.33 12.62  9.21 11.08  8.87  9.59  7.50 14.04 16.00
70  4 20 15.75 16.25 12.87  8.33 15.00 10.41 15.41  8.63 11.38 10.75 14.42 16.96
70  4 21 20.50 20.91 17.67 10.17 20.83 14.50 14.58  9.96 14.75  9.13 13.42 11.92
70  4 22 26.83 23.16 23.00 12.21 18.25 14.71 19.55 18.66 15.59 18.00 25.29 22.29
70  4 23 16.00  9.75 12.71  8.67 14.75 11.54 16.21 13.13 14.54 16.08 15.75 21.17
70  4 24 11.71 13.29  8.83  4.08  9.79  6.38  6.92  7.58  8.00  6.21  8.17 11.67
70  4 25 26.71 16.83 21.50 13.21 17.75 13.83 19.41 15.96 16.42 14.71 18.38 24.08
70  4 26 19.12 13.00 13.88  8.96 12.08 10.13 12.12  9.04 11.42 13.83 11.96 19.79
70  4 27 12.12 11.58  7.92  5.54 10.34  6.38  7.33  6.58  8.00  7.67  9.38 11.38
70  4 28 12.75 10.17  8.63  7.41 13.59  9.50 13.83 11.25 14.04 10.92 12.75 18.46
70  4 29 13.75  9.21 10.88  6.63  8.79  7.38  9.04  5.41  8.21  9.75  6.21 13.08
70  4 30  8.83 10.34  6.38  3.37  8.58  6.87  8.29  8.38  9.13  6.38 14.71 11.54
70  5  1 14.17 17.96 15.12  7.08 14.17 11.83 12.75 16.92 11.71 15.41 25.75 19.58
70  5  2 16.75 18.91 16.00 10.71 17.67 13.62 16.66 16.88 14.00 15.09 27.50 17.58
70  5  3 13.67 14.83 15.37  9.62 17.21 12.71  9.71 11.21 13.70 15.46 17.67 20.04
70  5  4 13.50 11.42  9.71  7.58 13.88  9.17  9.25  9.87  9.38 12.42 14.33 17.88
70  5  5 12.87 11.34  9.59  8.21  9.79  9.59  6.79  7.33 11.17 12.12 11.75 16.00
70  5  6 11.71  7.08  5.58  5.71  9.79  6.71  3.37  5.46  7.67  5.54  9.46 11.79
70  5  7 15.34 10.41  8.67  5.75  9.54  5.09  5.91  7.67  6.21  5.75  8.54 12.38
70  5  8  7.87  8.58  7.96  3.79  8.46  6.46  7.92  6.42  8.83  9.29  5.58 17.08
70  5  9  7.96  7.12 17.12  5.88  9.42  9.71 12.50 10.08 11.12 10.83  9.33 15.75
70  5 10 11.71  7.96 17.00  6.79  8.79 10.58 10.21 10.67 10.75  9.54 12.79 17.41
70  5 11  6.08  6.96  3.54  2.42  4.38  5.25  5.75  4.58  4.79  6.87  8.58 16.71
70  5 12  6.25  4.08 15.63  4.12  4.25  7.04  7.25  5.75  7.33  5.79 10.25  9.17
70  5 13  5.37  3.67  6.58  2.92  3.21  3.21  2.54  6.21  2.58  1.75 12.12  6.21
70  5 14  3.33  5.63  5.17  1.21  4.25  1.71  3.25  6.42  4.54  4.25  9.71  4.12
70  5 15  8.79  9.59  8.63  4.08 11.12  5.09  3.83  7.92  5.41  3.71  7.38  3.58
70  5 16 10.92  6.67  6.96  4.88  6.67  6.08  7.17  4.75  5.91  6.08  8.50 12.29
70  5 17  3.83  2.42  6.21  0.92  5.50  3.67  2.79  4.46  4.08  3.13 12.38  9.25
70  5 18  5.71  4.33  8.54  2.83  6.42  5.33  8.54  8.96  8.04  7.04 16.88 15.75
70  5 19 13.33  8.92  8.33  7.12 13.42  9.54 12.04 10.92 12.67 11.83 14.75 20.96
70  5 20 11.63 12.42 11.00  8.12 17.25 12.21 15.83 12.04 16.42 12.25 19.50 26.25
70  5 21 21.84 13.42 13.46 10.92 18.96 13.25 17.37 12.75 16.92 16.29 16.25 25.50
70  5 22  9.46  5.71  8.75  4.83  8.79  6.79  8.87  6.29  9.04  7.08 10.46 14.75
70  5 23  7.92 13.92  8.04  4.88 11.54  6.87  8.75  7.12  6.08  8.21 20.33 13.70
70  5 24 13.46 13.59 12.87  6.42 14.25  8.42 10.08 10.79 10.63 11.04 20.96 19.25
70  5 25  9.92  9.21  8.58  7.54 13.62 10.29 14.17 11.29 13.04 10.96 17.00 20.41
70  5 26  6.92  6.92  6.63  5.25 10.54  7.71 13.37  8.12 11.17  6.96 14.17 19.58
70  5 27  6.34  8.25  7.58  3.46  6.17  4.75  6.46 10.88  5.58  5.09 17.67  9.42
70  5 28 10.67  7.92  6.83  4.50 10.00  5.79  5.33  6.08  7.17  6.17 10.79 11.96
70  5 29 10.00  9.25  8.25  4.63 11.04  7.12  6.04  8.92  7.62  8.54 14.83 16.21
70  5 30 10.50  7.62  7.96  5.66 11.79  9.21 10.58 10.83 11.04  8.04 13.92 17.08
70  5 31  7.92  7.67  9.67  7.00 12.54  9.46 13.37  9.50 12.21  9.79 14.54 17.75
70  6  1  6.34  5.88  8.54  4.38  8.79  4.75  4.12  6.08  5.96  3.71 11.38  9.83
70  6  2  5.37  7.12  5.50  2.75  6.71  4.58  6.50  7.54  4.92  6.08 10.96 12.96
70  6  3 11.92 18.58  8.00  6.96 13.83  8.17  6.75  9.71  8.71  9.13 14.12 15.29
70  6  4 17.75 13.54 12.92  6.67 12.33  8.79 11.08 10.29 10.04  9.38  9.54 16.50
70  6  5 14.29  9.38 14.17  6.34  9.71  8.00  8.33  7.75  9.50  7.29  8.17 13.92
70  6  6 10.21  6.38 19.17  4.46  5.25  6.83  5.91  5.71  7.67  5.58  8.46  9.29
70  6  7  4.96  3.83 15.21  3.71  4.71  4.54  5.04  3.67  6.21  4.50  7.29  6.63
70  6  8  5.46  2.42  4.71  3.37  3.88  2.17  3.42  4.00  6.58  5.63  4.83 11.08
70  6  9  2.21  2.54  5.33  1.96  3.17  0.71  1.46  3.29  2.00  2.13  5.33  8.54
70  6 10  6.54  2.46  5.96  1.58  6.13  1.33  1.63  2.33  1.50  1.79  4.04  3.79
70  6 11  6.92  2.58  7.54  1.71  4.54  1.63  2.42  1.63  2.37  0.71  3.21  5.17
70  6 12  4.12  1.71  5.91  2.58  3.04  2.37  2.25  4.04  3.88  2.13  7.58  3.79
70  6 13  2.71  3.08  6.79  1.50  2.46  2.75  3.13  2.92  3.37  1.96  7.62  8.75
70  6 14  6.54  2.96 16.58  5.63  5.66  6.50  5.21  4.12  6.42  2.50 12.33  3.13
70  6 15  6.58  5.83  6.71  2.46  4.79  3.46  1.75  5.88  3.00  3.63  9.25  7.75
70  6 16  7.46  5.41 18.46  6.75  9.29  6.42  9.25  6.42  8.08  4.50 11.67 10.17
70  6 17 11.50  5.58 11.75  4.38 10.71  5.66 11.96  8.54 10.79  8.21 11.12 16.38
70  6 18  6.87  3.71  4.75  1.83  6.87  4.29  2.00  4.96  1.96  2.46  8.21  8.58
70  6 19 11.08  6.71  7.29  1.92  3.37  1.50  2.42  3.96  1.92  0.79  7.58  3.08
70  6 20 15.71 13.21 13.25  6.25 13.29  8.12  7.21 10.08  8.08  8.25 18.66 10.58
70  6 21 22.71 13.04 17.41 14.83 17.71 14.17 15.41 12.75 16.75 17.83 13.29 27.33
70  6 22 14.71 11.46 12.12  5.96 11.67  7.08  8.92  8.67  6.42  7.38 13.33  8.29
70  6 23 13.04 11.87 12.83  5.41 12.21  8.29  8.33  9.54  8.79  9.21 14.09 14.04
70  6 24 15.12 14.71 15.00  9.00 18.75 11.38 14.50 11.63 13.67 11.21 19.83 19.70
70  6 25 10.50  9.17 10.96  6.00 10.46  7.54  9.29  9.04 10.58  9.92 16.00 20.54
70  6 26  9.79  9.50 10.88  5.88 11.08  7.38  7.83  7.33  8.46  7.67 10.21 11.54
70  6 27  6.38  7.08  8.67  3.75 11.04  6.79  4.79  7.29  6.63  7.83 10.21  9.79
70  6 28 16.29 12.75 11.54  8.29 13.83  9.79 13.67  9.87 12.87 11.04 16.92 19.62
70  6 29 16.04 12.04 12.71 10.25 14.92 11.79 15.92 10.00 13.79 14.50 15.46 22.21
70  6 30 12.17 10.92 10.13  6.71 12.96  8.33  7.87 10.46 10.75  9.08 16.71 16.25
70  7  1 20.79 17.12 12.50 11.67 20.30 13.54 19.95 14.92 17.41 15.37 23.00 29.63
70  7  2 20.96 13.17 13.25 10.63 18.54 13.13 18.29 10.63 16.42 15.21 16.17 24.33
70  7  3  8.92  4.12  9.17  4.75 10.00  6.46  9.42  5.88  9.71  7.62 12.08 14.04
70  7  4 10.92  9.13  8.12  6.87 12.21  9.25 12.12  7.96 10.29  7.33 11.54 16.79
70  7  5 10.71  8.83 10.92  5.13  8.83  8.12  9.00  9.13  8.87  9.04 19.12 19.08
70  7  6  7.79  7.29  9.62  4.21  7.29  6.46  7.08  9.71  7.83  8.00 13.59 11.00
70  7  7  4.50  5.50  4.67  2.67  2.25  3.25  3.21  6.46  3.46  5.00 14.50  8.17
70  7  8 12.62 11.17 10.17  7.29 11.17  7.79  6.34  8.25  8.75  7.87 16.79 13.70
70  7  9 13.92 10.17 11.42  7.92 13.42 10.41 12.62 10.96 12.46 11.46 14.83 19.87
70  7 10 11.83  9.17 10.79  6.34 12.75  9.62 10.79  9.13 12.25  8.29 16.17 20.79
70  7 11 13.50 11.38 12.25  7.38 16.17 11.12 15.87 10.63 13.25 13.50 20.17 25.29
70  7 12 13.42 10.08 13.70  7.96 16.46 12.42 17.50 13.96 16.42 13.29 21.34 23.09
70  7 13 12.38  9.62 10.88  6.21 14.96 11.21 14.58  9.83 13.37  9.92 16.25 21.84
70  7 14 21.54 15.29 14.29  9.38 14.79  9.87 12.87 10.50 14.09 11.34 15.96 23.83
70  7 15 17.12  9.96 13.88  9.42 11.54  8.96 12.46  7.62 12.25 12.00 11.75 20.21
70  7 16 10.63  3.71  6.34  3.63  8.38  5.17  7.41  4.88  8.33  5.83 10.34 13.59
70  7 17  7.62  4.42  7.71  3.92  8.25  5.96  8.71  4.63  9.08  4.75 13.59 16.08
70  7 18  8.96  8.79  8.38  5.66 12.46  7.75 11.04  6.04 10.21  6.92 12.67 18.91
70  7 19 14.67  8.25  9.46  6.67  9.75  6.63  8.21  6.29 10.17  8.25 11.17 14.79
70  7 20 14.33  8.33 11.54  7.21  9.13  5.88  9.33  6.96 10.54  8.71 12.62 20.38
70  7 21  6.54  4.12  8.83  4.83  8.42  6.46  9.17  7.04  9.54  7.71 10.37 14.71
70  7 22  7.38  3.04  8.08  4.04  7.17  5.63  5.79  4.38  6.25  5.54  9.75 11.71
70  7 23 13.37  6.50 15.87  6.50  6.17  4.38  4.58  2.62  4.54  2.08  4.92  5.75
70  7 24 18.79 11.92 14.71  8.87 16.88 11.42 10.79  8.67 10.58  8.42 13.42 19.12
70  7 25 10.46  9.13 10.37  6.54 10.54  7.50 10.34  5.75  9.46  8.12  9.13 12.92
70  7 26 11.08  9.21  9.50  4.63 11.75  8.00  8.08  6.67 10.34  4.92 12.87  8.96
70  7 27 10.46 10.67 12.46  5.37 11.29  7.17  7.79  4.79  8.21  2.58  7.46  5.37
70  7 28 10.21  9.96  8.58  4.67 10.96  7.87  7.41  6.75  9.00  7.38 10.00 10.63
70  7 29 11.12 10.04  8.21  4.29 11.58  6.87  6.96  7.92  8.96  7.96 15.54 16.92
70  7 30 13.67 15.96 13.96  6.67 13.88 10.67 12.17 13.67 12.21 13.04 20.58 20.12
70  7 31  7.67  6.96 11.34  3.83  6.63  5.33  4.67  5.21  7.25  5.54 10.04  5.79
70  8  1  3.92  8.92  7.50  3.88  4.42  3.88  2.58  5.09  3.37  3.75 13.96 12.38
70  8  2  4.96  9.83  4.50  2.46  5.50  4.92  2.62  3.54  4.00  5.25 11.67  6.54
70  8  3  7.17 13.59  6.79  3.37  8.17  3.88  4.50  3.33  4.92  5.09  5.04  8.87
70  8  4  2.67  7.92  9.42  1.75  3.96  1.54  2.25  2.04  3.79  3.50  6.00  9.25
70  8  5 10.17  4.29 12.17  2.83  5.17  5.66  5.37  4.54  8.42  3.50 11.29  6.75
70  8  6  4.63  3.17  7.79  1.79  3.33  3.46  3.04  2.54  5.54  4.83 10.92  4.96
70  8  7  5.63  2.79  4.33  1.67  6.04  2.37  1.29  2.29  0.58  2.17  9.92  4.42
70  8  8 13.50  6.75  4.21  3.96  6.67  5.79  4.46  4.33  7.38  5.46  7.50 11.04
70  8  9 11.17  8.38  9.54  7.17 14.62  9.59 13.13  8.58 13.29  9.46 15.71 18.29
70  8 10 10.08  9.08  8.92  6.34 11.25  7.46  8.29  7.04  9.17  7.62  6.58 11.00
70  8 11  4.08  3.83  5.66  3.21  3.13  2.79  3.17  4.04  3.92  3.67  7.62  6.71
70  8 12  9.46 11.21 11.00  6.34  9.00  7.67  4.96 10.04  9.29  9.50 18.21 15.54
70  8 13 13.04  8.67 10.79  6.58 10.96  7.12  7.50  7.25  8.58  6.50 12.00 14.17
70  8 14 10.75 10.34  9.25  3.42 11.38  7.29  7.96  6.04  8.46  5.91 14.96 17.54
70  8 15 18.46 16.17 16.38  8.46 15.54 10.13 10.37  9.50  9.87 10.83 11.71 15.67
70  8 16 26.38 14.96 18.50 17.33 21.59 18.12 20.75 14.96 22.71 23.71 19.87 34.33
70  8 17  7.50  5.71  8.21  6.25  7.46  7.33 10.41  5.75 10.17 12.17  9.04 21.29
70  8 18  4.96  4.46  9.50  2.50  4.38  1.33  1.21  1.67  1.71  0.67  5.33  8.87
70  8 19 11.00 10.25 16.38  5.50  8.71  4.96  5.88  6.29  6.67  3.37 13.04 14.50
70  8 20 19.50 13.54 17.29 10.34 13.37 10.00 13.50 11.71 13.25 15.54 15.79 25.58
70  8 21 24.30 13.83 14.54 10.34 12.46 10.79 10.88 10.34 12.42 12.38 15.83 15.63
70  8 22 14.71  7.17 12.12  5.33  8.08  4.92  6.34  4.42  5.58  3.67  4.38  4.67
70  8 23  5.04  3.46  5.96  1.79  2.54  0.63  1.42  0.50  2.50  0.13  2.83  3.37
70  8 24  2.75  5.96  5.88  1.00  2.46  1.13  1.75  2.21  2.75  2.50  2.96  4.38
70  8 25  5.83  6.63  3.79  1.75  5.25  3.17  1.21  1.33  2.21  2.62  4.29  6.29
70  8 26  4.88  3.71  8.71  0.87  3.50  1.21  0.37  0.71  0.54  0.04  3.25  4.12
70  8 27  4.08  2.25  7.58  1.21  2.54  2.13  1.25  1.33  2.83  2.67  2.67  5.04
70  8 28  2.54  2.54  4.25  1.25  2.25  1.83  1.00  0.79  0.96  1.33  5.58  5.50
70  8 29  5.09  7.92  6.04  2.46  3.54  2.50  4.67  3.21  1.63  2.71  9.25  5.96
70  8 30 13.62 16.46 15.96  7.38 12.54  9.29  9.42 12.04  9.87 12.12 21.25 18.00
70  8 31 14.54 16.29 11.08  9.21 18.71 12.58 14.04 14.17 15.96 13.75 22.34 27.33
70  9  1 12.96 13.79 12.92  7.58 12.75  9.04 11.38  9.59 12.17 10.46 16.17 21.75
70  9  2 16.17 12.67 16.88  8.96 14.58 10.92 11.29  9.92 13.70 12.71 14.04 18.29
70  9  3 12.54 11.79  9.62  5.25 10.96  7.12  7.17  6.96  9.67  8.12 12.87 16.46
70  9  4  6.96  8.63  9.92  6.08 13.79  9.08 12.92 11.25 12.21  8.25 15.16 20.75
70  9  5  3.37  6.67  7.62  4.83  9.46  7.33 10.34  7.62  9.87  8.58 12.50 20.21
70  9  6  6.92 12.38  8.08  4.04  8.92  6.17  5.17  7.38  6.25  2.25 13.21  8.08
70  9  7 10.50 14.50  9.96  7.29 12.00  8.04  5.71  8.83  9.96 10.25 13.88 19.50
70  9  8 19.46 20.17 15.37  9.67 16.71 10.50 11.63 10.67 12.71 11.58 17.37 15.46
70  9  9 31.42 28.50 28.21 20.33 30.00 20.67 22.37 18.29 21.46 22.42 25.75 27.75
70  9 10 19.00 18.58 16.13 10.41 18.96 11.79 15.71 11.87 14.50 11.58 17.37 17.50
70  9 11  9.79  9.38  8.29  4.17  8.00  5.17  8.54  4.79  7.58  5.41  9.00 11.29
70  9 12 10.67  8.50  8.58  4.38  9.00  4.88  5.54  4.29  6.13  5.29  9.38 13.70
70  9 13 13.25 10.54 10.79  6.08 12.71  6.96  5.58  4.83  7.50  6.83 11.71 14.46
70  9 14  4.17  3.37  5.96  1.08  6.08  3.21  5.04  3.50  4.83  2.54 10.04 11.34
70  9 15 11.50  8.54  5.88  4.00  9.04  4.04  5.96  3.63  6.58  3.46  9.54 13.37
70  9 16 15.87 18.58 13.88  7.87 12.75  8.79  9.75 10.08  9.21 10.08 19.21 17.41
70  9 17 21.04 18.58 19.95 14.83 13.59 13.13 19.08 13.67 13.33 14.67 15.46 18.50
70  9 18  3.83  3.92  8.50  1.42  4.08  2.33  0.54  1.17  2.50  2.25  7.58 10.29
70  9 19  8.67  9.92  8.33  2.92  7.50  3.92  4.00  5.88  5.41  4.12  9.75 10.83
70  9 20  6.00  8.46  8.17  3.04  3.58  3.33  4.08  7.87  5.13  7.04 14.00 14.04
70  9 21  7.50  4.75  5.09  2.58  8.00  4.46  3.17  3.04  4.71  5.37  6.21 10.58
70  9 22 11.04 12.42  9.38  5.50  9.87  9.33  6.54  5.50  7.54  5.04 10.71 11.79
70  9 23  9.21  8.17  6.79  5.63  8.21  7.00  4.54  4.25  7.00  5.29  7.75 11.17
70  9 24  6.50  7.58  9.25  4.88  7.75  3.58  1.17  2.83  4.88  2.58  9.87  7.17
70  9 25 14.58 17.29 11.79  7.25 13.17  9.33  4.50 10.83 11.67 11.46 18.79 15.92
70  9 26 16.42 17.96 14.96 10.50 16.50 11.34 10.75 13.59 13.75 15.25 25.04 20.91
70  9 27  5.75  8.00  4.42  1.92  5.75  3.33  3.13  4.42  4.25  4.92 12.96 12.87
70  9 28 11.71 11.29 12.04  7.04 10.67  7.79  4.67  7.25 10.21  9.79 13.75 15.50
70  9 29 16.00 14.79 12.92  7.96 14.29  9.92 13.17 11.50 13.04 12.71 22.08 23.54
70  9 30 15.00 12.75 11.38  7.79 15.79  8.29 12.08 12.33 13.04 12.62 18.71 25.54
70 10  1 14.75 15.37 12.21  9.38 18.05 11.34 17.29 14.04 17.37 12.33 21.17 28.50
70 10  2 25.29 17.54 15.09 15.25 22.13 14.88 20.62 17.79 20.33 19.12 25.41 32.96
70 10  3 14.29 10.46 11.42  8.54 14.79  8.75 11.75 10.96 13.25  9.17 16.21 20.79
70 10  4 16.42 14.21 14.92  8.25 15.83 11.42 18.75 15.00 16.33 13.88 22.21 26.25
70 10  5 16.54 14.21 13.62  7.96 14.50  8.92 14.88 12.12 12.38 13.54 23.45 30.13
70 10  6 10.79 10.79  8.08  4.71  9.87  6.08 11.63  9.67 10.67  9.38 15.09 22.88
70 10  7 12.67 11.00  7.17  4.33  8.71  6.04  8.25  8.63  8.96  7.67 16.71 24.33
70 10  8  9.00  5.71  9.79  3.00  4.29  2.46  4.42  3.29  4.54  5.25  9.08 14.92
70 10  9  5.91  6.42  5.37  1.58  6.63  5.09  4.17  8.54  5.83  6.87 16.33 19.33
70 10 10  4.38  2.37  5.33  1.25  3.08  3.54  3.25  5.75  4.46  4.42 10.63  8.71
70 10 11 11.38  9.29  8.92  3.13  7.54  5.66  3.04  6.34  5.41  3.08  7.50 10.00
70 10 12 13.33 18.91 10.00  7.41 12.00  8.42  6.13  8.92  9.38  7.29  8.71 11.71
70 10 13 11.54 13.67  9.87  6.79 10.88  9.04  8.12  9.83  8.38  5.50 13.96 12.21
70 10 14  9.54  9.79  7.12  5.71 11.17  9.67  6.08  7.87  8.83  5.63 10.21 10.17
70 10 15  9.13  7.87  6.34  4.17  9.13  6.92  6.13  4.50  6.21  3.63  6.46  9.42
70 10 16  7.50  9.25  6.79  2.96  7.38  5.21  4.12  5.71  3.63  4.00  7.79 10.96
70 10 17  3.63  4.12  4.21  2.04  5.41  4.67  5.88  7.33  5.75  6.04 13.75 19.67
70 10 18  9.79 11.29  8.29  7.96 16.29 11.54 19.62 14.29 18.91 14.92 23.71 32.71
70 10 19 22.67 17.71 17.41 11.63 18.66 12.96 18.58 15.29 17.12 15.41 23.42 32.58
70 10 20 24.92 17.92 22.58 13.13 16.88 10.67 16.33 15.29 15.83 13.75 24.75 35.92
70 10 21 15.16 10.13 17.25  7.58  9.08  6.21 10.96  8.04  9.13  8.54 13.21 21.59
70 10 22  9.00  4.67  7.58  3.25  3.58  4.50  6.87  4.12  5.25  4.00  9.29 12.25
70 10 23 10.50 11.08  9.92  3.83 10.96  9.00 12.25 12.54 11.71 11.21 17.50 19.79
70 10 24 14.67 13.33 12.54  6.75 13.17 11.17 15.87 13.25 13.21 11.71 13.79 14.42
70 10 25 17.62 12.92 14.96  8.58 14.33  9.96 12.21  9.25  9.29  7.96 11.17 15.96
70 10 26  5.54  6.13  6.17  4.46  7.58  5.79  8.54  6.71  7.17  8.67 12.50 20.58
70 10 27 14.46 14.96 12.67  5.63 13.04  9.25 10.17  9.17  9.67  7.62 12.75 14.42
70 10 28 13.70 13.13 12.58  7.04 12.54  8.00  9.33  8.83  9.04  3.96 15.09 12.21
70 10 29 17.33 16.75 16.50  6.96 13.62 10.92 13.17 13.25 12.54 12.21 20.75 21.21
70 10 30 20.30 18.75 18.66 10.96 12.08  9.87 12.46  8.71  9.92  8.71 13.54 21.84
70 10 31 22.25 20.30 19.38 12.42 20.38 14.00 16.25 18.50 16.88 15.16 26.00 25.08
70 11  1 10.58 10.29  9.00  4.08  9.21  7.54 11.46  9.71  9.54 10.37 15.92 24.67
70 11  2 17.50 17.33 14.67  7.04 15.00 11.42 13.33 13.92 12.92 13.50 21.84 21.54
70 11  3 19.58 19.95 15.75 16.83 23.09 18.05 23.75 19.79 22.54 15.79 26.67 26.87
70 11  4 11.38 10.08  9.17  4.96 10.79  8.71 12.92 11.79 12.25 11.17 19.79 24.33
70 11  5 10.96  8.83  8.12  6.08 11.42  9.25 13.50 10.13 13.29 11.92 16.33 26.92
70 11  6 13.33 15.00  9.04  4.63 11.21  8.00  7.33  8.00  7.96  6.87  9.21 14.00
70 11  7 18.08 11.17 21.87  8.12  8.08  7.00  9.71  6.29  7.58  6.38  8.92 11.08
70 11  8 12.67 11.38 10.75  6.04 13.21 10.83 14.42 14.46 16.04 14.75 20.25 29.25
70 11  9 14.83  9.62  9.59  6.63 12.25  9.08 15.75 10.34 14.50 13.17 15.83 24.30
70 11 10 13.04 14.92 11.00  6.71 15.87 10.08 10.96  8.75 11.79  8.79 16.13 15.96
70 11 11 12.58 15.29 10.79  7.08 12.08  8.75 11.92  8.71 10.41  9.92 18.71 22.00
70 11 12 12.71 15.54  9.29  5.88 11.75  7.92 15.09  9.75 10.37 10.17 17.46 23.00
70 11 13  7.67 11.17  6.75  1.33  9.46  5.41  8.75  5.25  7.33  6.67 11.75 17.75
70 11 14 18.00 13.04 15.54  7.33  7.62  6.21  8.25  6.42  8.79  5.50 11.08 18.91
70 11 15 12.75  9.71 10.34  4.25  7.38  6.29  6.04  6.46  6.54  7.08 12.54 14.96
70 11 16 12.92 11.67 12.33  6.83 11.25  8.58 10.75  8.17 10.50  7.92 15.29 20.91
70 11 17 13.08  7.41 12.92  3.54  6.04  4.17  4.92  3.37  3.13  2.88  9.59 13.59
70 11 18 17.62 14.67 17.25  7.41 11.63  8.33  8.96  5.50  8.92  5.33  7.46  8.29
70 11 19 13.59  9.21 10.04  5.83  9.04  6.96  9.50  5.66  9.21  5.96 11.92 17.00
70 11 20 12.54  6.83 13.17  7.25  8.33  6.67  7.92  6.79  9.33  6.17 11.42 14.04
70 11 21  9.50  9.79  7.12  3.17  9.08  6.25 10.21  5.37  9.92  7.75 11.08 20.12
70 11 22 10.21 12.08  9.46  2.00  9.50  7.75  5.79  6.17  6.87  6.79 10.75 17.54
70 11 23 25.12 14.00 23.58 12.96 10.54 11.08 12.00 11.08 13.25 10.29 13.92 16.25
70 11 24  9.83  5.13 14.83  7.38  5.66  6.00  6.79  3.63  8.29  6.96  7.67 11.87
70 11 25 21.79 23.00 16.96 10.71 17.67 11.12 11.21 10.75 11.04  6.34 14.04 10.92
70 11 26 16.50 20.12 12.17  7.75 13.96 10.25  7.21 10.67  9.38  7.12 13.25 12.17
70 11 27 14.21 12.00 14.17  7.92 12.58  8.50  9.54  7.38  8.83  6.29  8.71 17.71
70 11 28 15.83 10.13 15.09  9.42 10.92  9.33  9.96  6.00 11.29  9.59  7.17 13.88
70 11 29  4.00  2.67  5.09  0.67  3.46  1.17  2.21  2.54  1.92  1.79  4.46 10.50
70 11 30  9.25  7.92  7.71  5.29  7.75  4.38  7.83  4.12  6.58  6.13  8.96 15.25
70 12  1  9.71  8.00  7.04  4.83  8.96  6.46  9.33  4.50  8.75  7.17 12.54 17.41
70 12  2 15.54 15.75 13.59  6.92 15.25 10.46 14.75 12.08 12.58 12.25 18.75 18.84
70 12  3 11.21 11.92  8.46  3.21 10.34  6.34  6.96  7.00  8.63  5.75 14.12 16.38
70 12  4 12.67 14.58 10.46  4.12 15.75 10.46 12.50 11.29 12.54 13.29 22.83 26.16
70 12  5 13.96 12.58 12.12  6.08 14.12  9.67 13.75 12.92 12.87 13.08 20.17 25.96
70 12  6 21.62 18.21 15.96 12.42 19.95 12.87 17.33 14.79 16.88 15.16 22.50 28.75
70 12  7 12.21  5.83 13.79  7.00  6.71  4.50  7.54  3.00  5.37  6.58  5.58 14.79
70 12  8  6.38  1.25  9.25  1.29  1.58  0.46  3.46  0.04  0.37  0.63  3.67  9.17
70 12  9  5.66  1.42 13.00  0.54  0.13  0.00  1.79  0.92  2.58  0.13  4.38  8.17
70 12 10 14.00  6.75 13.88  2.50  7.71  3.63  6.58  5.37  6.92  4.12  7.25  9.71
70 12 11 16.71 13.75 15.25  7.79 14.62 10.67  9.21  9.50  7.83  6.34  8.75 13.67
70 12 12 17.88 12.08 13.17  9.59 15.92 10.79  8.17 11.92 11.34  8.50 15.16 18.79
70 12 13  6.00  6.63  9.62  3.46  6.25  2.92  6.79  2.62  4.92  1.96  6.71 14.37
70 12 14 16.46 17.25  7.87  3.88 14.17  8.71  2.42  8.46  5.58  7.50 12.67 16.66
70 12 15  6.04  7.25  9.21  3.58  8.08  5.63  5.04  6.25  6.46  7.83  9.42 16.79
70 12 16 18.21 16.21 15.67  9.08 15.79 11.58 10.96 11.96 11.79 12.50 21.17 23.96
70 12 17  7.21  8.25  7.17  3.00  9.79  6.42  7.62  8.17  8.17  9.42 17.41 21.09
70 12 18 16.58 17.41 14.46  8.29 12.54 11.12  9.83 13.17 11.17 14.17 15.29 20.17
70 12 19 16.08 12.79 15.12  7.83 12.54 10.46 12.75  9.13 10.83 11.67 12.42 14.96
70 12 20  9.33  4.75 10.25  2.42  3.88  1.67  6.13  1.75  4.54  4.50  7.58 19.62
70 12 21  7.79  3.21 14.92  3.63  4.79  2.75  5.83  1.67  4.67  2.42  3.46 13.25
70 12 22 10.41  4.12 11.42  2.33  6.17  0.75  5.96  3.13  4.54  6.08  6.13 13.37
70 12 23 15.00  8.87 19.21  6.58 10.88  6.75 10.13  6.54 10.21 11.38 14.25 25.29
70 12 24 11.75  9.79 14.25  5.79 10.54  4.25  9.42  5.29  7.54  7.58  6.96 16.13
70 12 25 10.41  8.50 19.50  5.41 10.04  2.92  8.42  6.08  7.46  5.63  3.04 16.17
70 12 26 16.83 10.00 21.62  7.83 10.96  4.04 13.33  8.25  9.38  7.38  9.13 18.71
70 12 27 15.25  9.62 22.54  7.41 11.54  7.04 11.58  7.87  9.04  7.62  5.88 17.83
70 12 28 12.46  6.54 13.50  6.29  7.00  2.58  7.21  4.33  6.87  5.00  3.00 15.00
70 12 29 16.75 14.17 28.29  9.54 14.00  9.71 17.88 10.34 12.83 11.29 13.37 23.13
70 12 30 17.96 11.58 27.29 10.96  8.67  6.08 13.79  5.33  8.33  8.58  9.96 18.25
70 12 31  8.38  0.37  9.59  2.62  1.75  0.08  4.83  2.13  2.54  1.17  3.67  7.21
71  1  1  3.71  0.79  4.71  0.17  1.42  1.04  4.63  0.75  1.54  1.08  4.21  9.54
71  1  2  4.04  0.75  6.96  1.21  1.67  0.54  5.83  0.00  3.00  2.58  3.00 10.46
71  1  3  7.41  5.41  7.17  0.63  1.79  0.21  2.50  0.33  0.71  0.13  4.83  7.12
71  1  4 17.50 18.79 14.88  3.46 13.13  7.25  7.12  8.17  6.67  5.21 11.92 10.29
71  1  5 15.21 13.50 21.92  8.08 15.00 12.79 17.96 12.33 13.88 10.21 16.58 22.75
71  1  6 18.96 21.29 17.21  9.67 16.46 14.00 11.63 14.75 13.62 17.08 23.71 23.29
71  1  7 22.83 20.96 20.38 12.71 16.04 14.21 19.21 19.21 14.46 19.83 25.75 25.50
71  1  8 24.92 22.88 19.46 15.34 17.00 14.33 14.62 15.87 15.16 17.50 30.04 26.46
71  1  9 28.21 25.75 23.58 15.00 22.00 19.67 20.30 20.88 19.79 21.04 38.20 32.91
71  1 10 17.04 22.83 14.54  7.50 22.00 16.17 17.96 14.88 15.00 12.54 26.20 19.08
71  1 11 16.62 19.04 16.75  7.08 16.66 13.33 11.04 12.50 11.17 10.37 17.08 21.37
71  1 12  6.63  6.34  4.38  1.08  5.17  2.13  3.88  4.33  4.08  4.92 12.17 10.75
71  1 13 14.88 14.46 11.29  4.38 10.41  7.87  6.87  8.04  7.92  5.37 11.04  9.13
71  1 14 11.04 10.88  9.50  2.92  8.75  3.96  4.17  6.25  5.66  3.58  7.71 10.79
71  1 15  7.75  7.83  5.17  0.17  6.71  2.46  2.00  3.63  1.96  1.67  6.21  8.04
71  1 16 11.12 11.25  8.04  4.08  9.46  6.92  4.33  6.50  7.12  7.21 11.34 14.25
71  1 17 18.25 11.29 17.16 11.34 12.21 10.54 12.42  8.42 10.83 13.33 13.88 17.25
71  1 18 24.87 20.17 17.88 13.54 18.54 14.29 14.46 14.83 15.75 14.42 17.21 19.55
71  1 19 13.88 11.96 11.50  6.38 13.04  8.33 15.96  7.92 10.54 10.92 15.96 23.00
71  1 20 18.08 16.38 13.92  6.54 14.00 10.29 12.62 11.79 10.25  8.83 15.54 19.55
71  1 21 19.75 14.88 15.04  5.13  9.00  4.08  5.58  3.88  5.66  3.75  7.58 13.54
71  1 22 16.88 12.83 12.46  7.04 11.92  7.96 10.00  8.87 10.50  8.54 15.09 14.50
71  1 23 12.92 14.09 10.71  5.96 14.62  9.42 13.00 12.75 11.25 13.83 22.13 22.04
71  1 24 22.29 18.54 18.66 11.12 18.29 13.42 21.17 14.54 16.29 17.67 20.08 25.04
71  1 25 18.84 17.25 13.46  7.17 14.29  9.17 11.71  8.21 10.29 10.67 11.87 15.54
71  1 26 10.50  7.67  9.25  3.50  5.91  2.92  6.13  0.67  5.63  3.13  2.75  9.29
71  1 27 13.62 12.83  7.79  4.25  9.08  2.54  5.83  0.42  4.58  1.63  4.33  7.17
71  1 28 12.29 10.67  6.79  3.42  8.04  0.50  8.38  1.67  3.29  2.88  9.13  5.83
71  1 29  7.04  6.92  6.92  3.25  4.25  1.42  7.08  2.96  6.13  4.33  9.08 13.21
71  1 30 11.67  2.83 12.71  4.21  4.17  1.29  7.04  2.75  4.42  2.79 13.17 13.92
71  1 31 19.29 14.29 25.29 10.96 10.58  7.50 13.79 10.75 11.21 11.63 18.38 26.00
71  2  1 12.58  6.63 20.83  7.08  7.12  1.63  8.21  2.58  4.96  3.08  4.71 12.87
71  2  2  6.29  2.75  7.12  1.42  7.08  4.00 11.67  6.92  8.33  8.21 14.92 21.34
71  2  3 11.67  1.83  7.04  4.25  6.17  2.17 10.92  4.67  8.29  7.87  7.62 15.29
71  2  4  7.04  1.21 10.21  3.04  5.41  0.83  3.50  2.00  4.08  2.29  3.79  6.46
71  2  5 10.75  6.96  9.13  3.92  6.87  2.83  4.96  4.46  3.46  3.21  4.21  6.71
71  2  6  3.58  1.17  5.25  0.37  2.83  0.42  2.83  0.83  0.83  1.96  6.38 10.08
71  2  7  2.71  2.75  3.88  0.83  1.25  0.29  7.08  1.63  1.75  2.71  6.17 11.34
71  2  8  9.79  9.04  8.29  2.92  9.13  5.50  6.79  7.62  6.79  9.54 15.50 17.46
71  2  9 18.66 15.87 14.58  9.33 13.75 10.21  8.67 12.67 12.58 13.96 22.37 19.62
71  2 10 11.25  1.38 12.75  6.34  4.21  4.04  5.71  3.71  6.75  7.96  6.50 12.71
71  2 11  9.25  8.08 10.37  2.79  6.46  4.92  5.00  8.87  6.87  8.79 17.12 18.54
71  2 12 25.33 22.46 22.67 15.79 24.67 18.34 21.71 18.25 20.71 19.21 27.16 29.29
71  2 13 18.84 22.46 14.33 12.67 22.79 13.46 19.62 17.04 19.70 15.34 26.79 29.50
71  2 14 19.17 18.08 16.25  9.83 16.71 11.58 18.50 13.37 14.50 16.33 21.62 25.37
71  2 15 12.00 11.29  7.83  4.38  9.96  4.67  9.54  5.66  7.87  8.38  9.54 22.71
71  2 16 21.79 13.00 14.42  8.25 13.21  8.79 11.96 11.00 12.50 10.92 15.46 16.38
71  2 17 16.04 17.58 12.67  7.87 17.37 10.79 13.21 13.54 15.34 10.79 18.29 13.33
71  2 18 15.75 10.29 12.08  8.00 15.00  7.29 11.71  7.33 12.00  7.54  9.13 12.67
71  2 19  9.21 14.83  8.67  3.21 10.58  6.38  5.91  7.87  7.79  5.96 14.71 14.37
71  2 20 17.25 15.67 15.16  9.29 18.05 12.17 17.04 14.25 14.58 15.87 22.92 23.25
71  2 21  9.75  5.83  9.54  5.41  8.87  5.13 12.08  5.04 11.08  9.50 10.34 18.05
71  2 22  9.96 12.21  6.42  3.75  8.96  5.46  6.25  5.79  6.13  6.79 16.08 14.29
71  2 23 11.96 12.04  9.62  3.29  8.83  4.12  6.58  6.83  6.54  8.17 16.46 18.54
71  2 24  8.33 10.17  5.75  2.33  6.96  1.67  3.17  2.33  2.75  1.58  6.96  7.21
71  2 25  7.92  9.62  6.08  3.33  5.46  2.17  2.33  3.42  2.50  2.25  8.54  8.25
71  2 26  5.54  8.58  5.37  1.71  4.50  1.00  2.54  3.92  2.58  2.13 10.50 11.21
71  2 27  9.42  8.42  7.33  3.88  7.08  3.88  2.62  5.37  6.50  4.88 11.83 16.08
71  2 28  6.46  3.00  5.09  1.50  1.67  0.04  3.04  1.13  3.88  4.12  5.13 11.25
71  3  1 13.50 11.83 14.21  4.83  7.79  4.54  6.71  8.08  8.75 10.54 15.37 20.25
71  3  2 10.37  4.00 11.75  4.58  5.29  2.79  7.87  3.71  7.54  7.38  5.29 11.63
71  3  3  5.04  6.75  8.33  2.67  4.92  1.42  6.38  3.04  4.08  4.58  9.38 13.00
71  3  4 10.37 10.34  6.21  4.33 10.04  5.96  8.25  6.50  7.21  7.67 12.00 18.34
71  3  5  7.41  4.29  8.04  2.13  4.46  1.46  8.58  3.42  5.96  5.88  9.25 13.88
71  3  6  8.87  6.63 16.00  5.33  7.12  2.75  6.87  3.54  5.71  2.13  5.75  7.00
71  3  7  5.33  3.50  6.34  1.50  3.46  0.33  3.42  2.54  3.75  1.75  3.29 10.67
71  3  8  6.29  4.50 13.83  3.71  4.00  1.17  2.25  2.33  3.79  2.17  4.67 10.54
71  3  9  7.75  4.38  7.00  3.96  4.08  1.87  6.71  3.17  7.33  6.34  5.50 13.37
71  3 10  8.83  1.50  5.58  5.58  5.33  2.67  7.00  4.71  7.58  6.46  7.46 13.46
71  3 11  7.00  4.88  6.96  3.79  8.67  4.50  8.92  7.00 10.04  8.38 14.33 17.96
71  3 12 11.67 11.83 10.58  6.29 11.67  9.08 13.29 10.41 12.12 12.17 17.50 22.17
71  3 13 10.83  1.71 11.12  1.42  6.08  2.58  4.71  3.67  5.33  4.88 11.34 14.75
71  3 14  8.50  3.17  5.21  2.37  4.08  1.67  3.25  2.29  1.42  1.00  7.08  9.13
71  3 15  9.92  6.25  7.29  2.21  9.42  3.37  4.33  2.13  1.79  0.71  7.17  5.04
71  3 16  8.08  9.54  7.50  3.58  9.50  5.91  8.21  4.29  6.96  4.25  7.62  8.12
71  3 17 16.17 14.79 11.96  7.79 14.00  9.13 11.21  9.33  8.87  5.83 10.37 10.79
71  3 18 19.00 17.83 20.67 14.12 18.79 18.00 23.25 21.04 21.00 20.30 28.08 33.37
71  3 19 33.04 25.41 26.75 20.46 23.50 18.46 24.25 19.21 19.79 23.63 26.58 35.00
71  3 20 27.50 15.83 24.17 15.79 17.58 13.42 19.08 13.59 16.17 20.38 17.71 32.30
71  3 21 12.62  7.00 20.88  8.38  7.67  5.17 12.12  7.12  8.29  7.75  9.71 16.08
71  3 22  5.63  7.83 14.09  3.50  4.88  1.58  5.96  5.83  4.67  5.33 14.04 14.54
71  3 23 10.63  8.83 11.25  6.13 11.79  8.71 12.21 10.34 12.75 10.96 15.00 19.79
71  3 24 16.62 15.09 15.12  8.71 17.16 11.46 15.92 11.83 15.25 11.21 18.29 22.75
71  3 25 14.50 13.25 11.12  9.67 13.70  9.04 14.83 10.25 15.46 11.12 15.87 21.71
71  3 26 11.17  8.92  7.87  6.13  9.54  5.29 10.92  4.71 11.50  8.00  7.67 20.46
71  3 27  3.96  5.83  7.46  2.04  6.75  3.75  5.79  5.54  5.46  5.37 13.70 13.96
71  3 28 14.17 12.25 13.17  7.46 12.62 10.25  9.96 10.54 11.50 13.88 17.25 20.30
71  3 29  3.67  1.54  6.58  2.96  5.33  1.13  3.21  2.62  4.38  3.83  6.63  6.13
71  3 30  3.92  4.08  5.75  1.08  5.29  1.67  2.04  3.71  4.92  3.50  6.87  5.21
71  3 31  3.83  1.96  5.29  2.00  3.25  0.96  3.42  1.21  3.96  3.13  6.04  4.33
71  4  1 15.96  9.42 11.79  8.54 14.37 11.54  9.29 10.63 10.92 10.63 16.79 19.00
71  4  2 17.29 16.13 20.79  9.67 15.34 13.37 14.79 15.63 14.79 10.21 17.33 20.79
71  4  3 12.92 13.17 24.04  7.62 11.79  8.08 12.96  8.04 10.75  7.50 11.96 15.12
71  4  4  9.38  7.08 19.17  6.38  8.96  4.75  8.25  3.54  7.12  4.33  5.17 12.25
71  4  5  7.29  2.67  5.46  3.13  4.33  0.50  1.46  1.29  3.92  2.46  2.88 11.92
71  4  6  4.17  4.54  6.04  1.79  3.21  0.79  1.42  1.83  1.63  3.17  5.63  8.12
71  4  7  7.46  5.83 13.17  4.38  7.12  2.25  2.92  4.50  3.75  2.92  5.96  7.12
71  4  8  8.42  5.37 18.25  7.25  7.00  3.92  6.63  2.96  6.04  2.54  4.38  3.71
71  4  9  9.75  6.42 19.70  6.50  7.92  4.58  5.17  4.25  6.83  3.63  6.58  4.58
71  4 10  8.17  3.79 11.42  3.29  5.37  3.50  4.33  2.25  6.08  2.42  6.13  3.88
71  4 11  6.25  1.87  9.17  1.92  2.58  0.96  1.42  1.25  2.75  1.50  5.13  3.75
71  4 12 14.04  9.79 10.79  4.38  7.00  5.33  5.79  6.96  8.21  5.58  9.46  7.62
71  4 13 10.79  4.12  9.62  4.25  8.25  4.71  5.63  5.96  6.46  4.54  4.46 13.79
71  4 14  7.41  1.79  7.96  1.42  2.46  2.04  2.13  2.08  4.71  1.38  6.67  4.50
71  4 15  6.38  1.96  8.42  2.33  6.71  3.29  2.29  3.83  3.83  3.17  7.96  8.46
71  4 16 14.79  9.21 12.12  9.17 14.33  9.71 12.00 10.08 12.75 11.87 13.88 21.84
71  4 17 11.42  7.33 11.46  5.91 13.75  9.29 13.75 10.29 12.21 11.63 17.67 22.13
71  4 18 11.04  9.00 12.71  6.63 13.54 10.25 15.83 10.08 15.00 11.96 16.04 23.83
71  4 19  5.00  4.71  8.67  3.63  7.58  5.46  6.38  5.33  9.17  8.17  8.83 13.67
71  4 20  9.25 11.42  5.17  3.25  7.29  4.29  3.88  4.38  6.00  4.38  7.46  9.96
71  4 21 10.13  4.71  6.42  4.21  6.46  7.58  7.50  4.42  8.17  8.79 12.21 15.67
71  4 22 13.92  9.75  6.71  4.50  7.46  3.92  1.50  6.21  6.75  5.33  6.67 10.88
71  4 23 18.34  9.00 15.87  9.46  9.46  7.83 11.25  6.34 14.21 12.92  5.66 15.12
71  4 24 12.25  8.33 10.54  8.17  9.92  8.08 12.62  6.75  9.17 11.21  7.67 18.66
71  4 25 12.08 13.33 11.79  5.75 15.29  7.67  8.46 12.12 10.25  8.54 14.75 18.41
71  4 26  9.75  6.79 16.46  5.96  9.17  7.38 11.21 10.37  9.21  7.79 13.88 14.04
71  4 27  5.46  6.87  8.17  2.92  5.41  2.50  3.83  6.58  5.83  6.54 10.88 15.92
71  4 28  5.79  1.46  5.83  0.87  3.63  1.21  2.37  2.29  4.08  2.13  3.83  8.71
71  4 29  6.42  3.79  3.71  1.63  4.08  2.33  2.75  2.33  1.75  0.67  7.79  7.46
71  4 30  5.21  5.79  4.21  1.83  5.17  2.46  2.83  3.13  3.04  3.63  6.71  8.12
71  5  1  3.46  1.92  6.83  0.83  1.83  0.37  1.42  1.96  1.54  0.50  4.17  4.00
71  5  2  6.00  7.54  6.00  2.25  3.46  3.58  3.50  6.25  3.71  4.21 15.67  7.54
71  5  3 12.92 15.21  7.92  5.50 12.21  8.17  4.33  9.33  8.63  8.71 16.54 13.08
71  5  4 12.33 13.42  7.08  5.21 11.58  7.46  5.41  6.71  8.96 10.00 11.67 10.50
71  5  5 16.21 19.46 12.38  7.83 16.50 13.13  7.21 12.38 11.96 10.46 14.33 17.83
71  5  6 14.12 16.50 10.88  5.25 11.75  6.92  6.54  8.96  9.17  7.58 11.83 17.04
71  5  7 17.54 15.96 13.59 10.29 14.71 10.71  8.63 10.67 12.29 11.17 10.75 17.25
71  5  8 15.41 12.67 13.08  9.13 11.34  9.71  7.87 10.00 11.29 12.46 16.58 12.67
71  5  9 16.42 15.46 14.54  9.17 12.00 10.83 11.46 12.38 12.75 13.92 20.71 18.79
71  5 10 12.92 13.04 14.21  7.75 11.58  8.83 10.04 15.25 10.88 12.75 24.37 18.34
71  5 11  5.41  6.04  4.12  1.92  4.38  2.83  1.63  7.41  4.33  6.67 14.71 13.67
71  5 12 10.08  1.83 14.50  3.33  3.67  3.33  5.17  2.37  4.96  2.29  6.71  6.75
71  5 13 17.16  6.25 13.75  4.63  5.41  6.08  7.17  8.21 10.96  7.25  8.00 14.21
71  5 14  7.87  3.29  5.33  3.58  3.37  2.62  4.42  3.37  6.00  5.09  7.21 10.75
71  5 15 20.91 12.00 13.75  9.04 11.96  8.63 11.12  9.25 12.33 11.87 16.54 21.34
71  5 16 15.92 11.00  7.21  5.71 13.33  5.75  9.46 10.34  9.67  6.38 17.33 21.84
71  5 17 12.04  8.96  8.67  5.66  9.92  5.79  7.38  7.46  9.92  9.13 13.21 13.04
71  5 18  8.87  4.58  6.71  3.92  6.21  3.04  4.25  5.17  7.71  6.04  8.00 13.50
71  5 19  8.50  3.08  5.71  2.42  3.25  1.38  1.54  2.88  3.00  2.33  6.67 11.96
71  5 20  7.96  3.46  8.33  2.54  3.21  1.00  1.21  2.71  3.04  4.33  5.54  9.46
71  5 21  6.92  6.13  8.04  2.46  4.96  3.37  3.46  8.00  4.33  4.58 13.54  9.08
71  5 22 11.83  7.33  7.62  4.71  8.25  4.21  3.00  6.08  6.25  6.38  7.75 11.00
71  5 23 16.71 11.54 10.63  6.75 11.12  5.17  4.67 10.13  8.04  9.96 15.54 15.79
71  5 24  8.08  7.54  7.33  2.33  4.29  2.58  2.54  5.00  5.46  4.88 12.67 16.71
71  5 25  6.04  3.25 15.54  3.46  3.13  3.96  7.04  2.54  5.71  2.75  5.63  9.83
71  5 26  8.29  7.29  8.75  4.79  9.71  5.58  7.46  6.96  8.04  7.83 12.96 16.21
71  5 27 17.92 11.38  9.75  6.29 13.88  7.17 10.63 10.34 10.58  9.62 17.37 18.08
71  5 28  8.25  9.67  9.25  3.92 10.67  5.33  8.50  6.58  6.83  6.83  9.29  9.29
71  5 29  7.17  6.67  9.17  4.83  6.54  4.96  6.17  6.00  6.25  7.41 11.04 13.79
71  5 30  8.87  8.46 10.13  4.17 10.04  4.63  4.88  7.58  5.50  5.75 10.71  7.50
71  5 31  5.09  4.46  5.71  1.87  3.75  1.00  4.04  1.87  2.25  1.67  2.67  5.50
71  6  1  7.17 12.25  6.04  3.54  8.21  4.25  6.42  5.46  4.75  5.63  6.71  8.92
71  6  2 13.46 10.58 12.29  4.75  7.04  5.29  6.13  7.58  8.33  6.25  9.21 11.67
71  6  3 13.54  9.04 25.50  6.04  9.75 10.00  8.63  9.67 12.04  9.00 10.92 10.46
71  6  4 12.29  2.92 23.71  5.25  7.87  8.96  9.96  7.58 10.54  7.21  9.75 10.41
71  6  5  7.17  4.38 20.83  5.58  8.29  5.58  7.75  7.67  6.46  5.09 11.75 10.41
71  6  6  3.96  3.25 15.12  3.83  4.67  2.67  4.71  5.33  3.29  2.08 12.04  9.25
71  6  7  4.96  3.92 12.08  3.29  5.04  1.83  4.00  4.75  3.25  2.62  9.04  8.54
71  6  8  9.08  8.42  7.08  3.42 10.04  4.88  4.38 10.13  6.17  5.41 16.83 14.67
71  6  9 13.21 11.38 24.08  8.96 10.50  7.38 10.13  7.92  7.67  6.25 15.34 12.04
71  6 10  9.38 13.37 22.75  7.08 10.83  5.88  8.29  8.96  7.12  6.29 14.04 14.33
71  6 11 16.33 10.92 12.29  7.33 11.00  5.13  9.54  8.46  9.25  7.46 12.83 16.58
71  6 12 11.04  8.17  8.29  4.79  9.71  3.67  4.50  4.88  6.21  3.50  7.12 21.12
71  6 13 10.88  8.63  9.83  3.33  7.71  3.17  3.88  5.75  4.00  4.63 11.34 15.96
71  6 14 15.25 12.71 25.62  8.58 11.12  5.79  9.67 11.87  8.08  8.00 17.21 19.75
71  6 15 15.00  9.59 11.34  6.46 12.50  6.58  8.04  7.29  9.96  6.75 11.08 14.88
71  6 16 12.42  7.96  9.42  6.38 12.54  6.92  9.75  6.96 10.46  8.25 10.54 14.88
71  6 17  7.33  6.38  8.04  4.12  7.25  4.00  6.21  3.42  5.50  6.42  6.71 11.29
71  6 18 19.12 17.21 12.50  7.00 16.17  8.63  7.96  9.29 10.08  4.04 13.92 12.42
71  6 19 14.58 10.04 11.58  6.96 12.33  5.96  7.41  7.12  7.71  3.37  7.46 10.34
71  6 20 16.42  9.75 11.63  9.04 14.04  8.71 11.25  8.71 11.63  8.87 12.33 13.70
71  6 21 13.54 11.29 11.75  7.21 13.21  8.58  9.83 11.29 12.12 11.71 16.08 21.62
71  6 22  7.38  4.50  6.75  2.79  5.96  2.54  6.04  1.92  5.29  5.29  4.42 10.17
71  6 23  4.79  6.71  4.63  3.08  2.79  2.08  3.83  3.88  2.88  5.13  7.33  3.88
71  6 24  8.71 10.00 12.00  3.83  9.96  5.04  5.79  9.21  7.67  7.62 14.92 10.13
71  6 25 14.29 12.87 13.21  7.33 11.38  6.21  6.71  8.58  8.92  8.33 12.50 10.71
71  6 26 14.46 12.21 11.83  8.79 16.21 10.25 12.92 11.92 13.33 11.42 16.13 14.50
71  6 27 10.75  9.08 11.29  6.21 13.00  7.00 10.21  8.67 10.88  8.50 15.09 16.66
71  6 28  8.54  7.75  9.54  4.96  8.38  4.54  6.75  4.71  8.00  5.63  7.17 13.33
71  6 29  3.96  7.29  8.38  3.67  5.25  3.25  3.79  5.63  6.46  1.46  6.21  4.54
71  6 30  6.87  7.04  7.33  4.42  9.50  5.04  3.71  6.04  7.21  4.54 14.04  9.13
71  7  1  5.46  8.12  5.33  2.67  6.42  2.96  7.29  8.67  6.21  5.96 16.75  9.33
71  7  2  5.75 10.34  6.42  4.42 10.79  4.58  3.37  6.17  5.37  7.04 15.12 10.67
71  7  3  4.79  6.34  6.29  4.42  6.42  3.33  5.17  4.12  5.13  3.04  4.33  7.50
71  7  4  4.54  4.25  6.25  2.37  5.17  2.67  2.37  4.75  4.63  3.79  7.75  6.92
71  7  5  8.04  4.29  6.29  2.58  4.25  1.00  3.88  2.54  3.54  1.87  7.25  9.08
71  7  6  4.88  2.25  6.38  1.63  2.96  0.71  3.63  1.79  2.42  0.96  6.87  4.88
71  7  7  6.96  5.91  4.71  4.58  9.00  4.63  5.09  4.29  5.71  5.91  4.71  6.79
71  7  8  7.96  9.13  7.62  4.46  7.92  4.17  6.54  6.00  5.79  4.21 10.25 12.08
71  7  9  9.75  7.33 18.50  5.13  7.87  3.00  6.21  2.33  4.38  3.46  4.04  6.50
71  7 10  5.71  2.75  6.96  1.83  5.41  1.75  3.83  4.50  4.00  4.04 11.29 13.46
71  7 11  9.17  6.17  5.13  3.96  8.38  3.79  8.29  5.63  6.79  6.67  8.87 15.16
71  7 12  6.21 11.42 22.04  7.29  9.92  6.04  9.46  7.21  7.87  7.50 10.13 12.62
71  7 13  7.00  7.67 15.16  6.54  8.17  3.67  4.83  3.83  5.91  4.96  6.54 11.29
71  7 14  9.29  6.75  6.13  5.71  6.87  3.46  8.04  4.50  8.17  7.33  6.25 16.17
71  7 15 13.54  8.04 10.34  6.92 10.63  6.50 12.67  9.21 11.83 10.75 10.29 21.67
71  7 16  7.75  7.71 13.04  6.42  8.83  4.83  8.50  6.17  7.46  7.58  7.33 15.34
71  7 17  6.34  5.83  9.79  3.67  5.71  0.83  6.50  2.62  3.71  2.00  3.25  7.71
71  7 18  6.83  2.75  6.83  1.96  6.46  2.33  4.79  2.79  2.58  1.87  4.83  7.38
71  7 19  9.79  3.04  7.41  2.67  7.21  2.71  7.38  4.04  5.66  4.50  5.79  8.08
71  7 20  6.25  5.04  6.34  1.17  3.92  1.21  4.58  1.87  2.17  0.92  5.29  7.67
71  7 21 10.37 10.92  7.67  3.63  9.83  3.08  4.50  2.08  2.71  1.33  5.83  5.29
71  7 22  9.21  7.50  9.17  3.96  9.21  5.71  7.25  6.58  7.25  4.29  8.54 20.54
71  7 23 11.12  7.41  9.50  5.13 11.58  7.04  7.17  7.87  7.00  6.58  7.62 24.37
71  7 24 11.58  5.66 11.00  4.04  9.29  3.96  7.50  3.46  7.17  4.38  9.38 14.92
71  7 25  8.63  5.88  9.59  6.00  7.29  4.50  8.71  1.83  5.88  3.92  9.42  5.63
71  7 26  5.63  4.33  9.00  1.38  5.37  1.46  3.00  3.67  2.58  0.92  9.83  4.12
71  7 27  8.42  5.25  5.66  2.79  5.04  1.79  2.92  1.75  4.42  1.21  6.13  2.88
71  7 28  7.50 12.33  6.75  4.50  9.62  4.54  4.67  6.58  5.63  5.88 16.96 10.00
71  7 29 11.29 13.50  7.79  7.08 12.92  7.67  3.63  8.71  8.08  8.25 18.25 10.92
71  7 30  7.38  8.87  6.71  5.00  9.92  3.17  5.58  5.88  6.04  5.00  8.08  9.71
71  7 31 12.38  7.58 10.54  4.67  9.67  5.50  7.96  6.00  7.12  3.17  7.67  9.17
71  8  1  7.71  6.00  9.54  2.75  8.75  5.09  6.25  2.21  5.71  3.88  5.25  9.33
71  8  2  7.25  6.71  8.46  5.58  8.87  5.37  7.67  3.25  5.75  3.63  6.71  6.29
71  8  3 11.87  5.54 11.34  5.66  9.46  7.67  9.59  7.62  7.58  6.54  8.21 13.79
71  8  4  9.92  7.75 11.92  4.71  7.08  3.63  6.92  4.08  5.63  2.75  4.83 13.46
71  8  5  5.21  5.29  8.79  2.42  5.91  4.25  5.37  4.58  5.75  3.83 10.17 10.25
71  8  6 11.92  6.71  7.04  5.33  9.62  5.63  7.67  7.58  8.96  7.58 12.25 20.21
71  8  7  8.33  6.21  9.46  3.67  8.25  4.12  7.67  4.79  6.13  4.21  9.59  9.50
71  8  8  9.25  7.21  9.92  4.71 13.50  7.12 12.25  7.17 10.25  6.46 12.58 14.50
71  8  9  7.41  5.41  6.50  3.71  7.54  3.46  8.17  4.88  7.00  5.17 10.21 14.50
71  8 10 15.83  8.33 12.58  7.83 10.88  4.00  7.58  4.12  7.58  4.92  8.21  8.08
71  8 11  8.46  6.92  7.92  3.37  7.71  2.46  6.17  3.33  5.66  1.67  6.87  8.54
71  8 12 11.08  7.00  9.04  3.37  9.59  3.92  6.29  7.21  7.29  5.21  7.33  8.83
71  8 13  6.92  3.71  6.58  3.50  7.08  3.54  5.79  5.41  6.25  6.00 10.83 18.46
71  8 14 12.04  9.08 15.59  6.79 10.92  8.08 13.08 11.38 11.08 12.92 19.41 23.38
71  8 15  6.38  6.08  8.92  4.12  7.38  1.63  5.63  4.88  6.50  8.21  9.29 13.70
71  8 16  5.29  2.92  6.13  1.46  4.38  0.46  4.50  1.33  2.00  1.42  3.96  7.50
71  8 17 11.29  3.17 17.37  3.21  6.21  3.42  4.21  3.88  3.79  1.67  6.75  5.41
71  8 18  9.92  7.29 19.92  5.71  7.41  6.21  9.42  4.33  7.46  3.79  9.13  5.13
71  8 19  7.33  5.04 16.96  4.75  7.79  3.21  5.96  2.92  4.83  1.50  5.29  2.17
71  8 20 13.50  2.46  6.67  5.50  7.17  3.83  7.08  2.67  4.58  3.88  5.88  5.33
71  8 21  5.17  3.71  5.00  2.92  2.50  1.17  4.71  2.04  1.38  0.79 10.79  4.71
71  8 22  9.67 12.79  9.46  3.83  7.75  3.25  5.41  7.71  4.46  3.58 13.88  6.75
71  8 23  9.87  7.25  8.67  3.54  9.21  3.75  3.50  7.38  6.79  5.33 11.58  9.92
71  8 24  4.21  0.83  7.17  1.79  3.46  0.21  1.46  0.08  1.92  0.13  1.63  3.92
71  8 25  3.79  3.92  6.29  3.00  5.04  3.04  2.79  5.25  4.54  5.88 12.79 11.42
71  8 26  9.42  9.62  8.00  4.12 11.46  6.54  6.46  8.83  8.12  8.46 13.00 15.67
71  8 27 14.21 10.46 11.96  6.17 13.62  8.42 11.29  8.21 11.04  9.83 16.33 17.41
71  8 28 18.29 13.92 15.75  6.96 12.79  7.08 10.79  5.71  9.54  5.83 11.71 16.08
71  8 29 14.62 10.96 11.63  7.67 14.46  8.79 11.04  6.21 10.67  6.00 13.17 13.75
71  8 30 12.00 10.50  9.13  5.41 15.16  8.04 11.58  8.12 11.08  8.25 13.75 17.33
71  8 31 15.12 13.96 13.33  7.21 17.37 11.34 14.67 12.12 13.54 13.25 21.34 21.21
71  9  1 11.08  8.54 10.08  7.17 13.70 11.50 13.25 10.29 12.75 12.83 17.33 24.87
71  9  2 12.75  9.54 13.88  5.58 11.75  8.92 12.04  9.33  9.92  8.50 14.79 17.50
71  9  3  6.38  1.46  8.67  3.04  1.83  2.25  2.83  1.38  1.25  2.83  9.62 14.62
71  9  4  2.50  3.79  3.63  1.71  2.29  0.92  4.63  1.33  3.13  3.54  6.46 13.46
71  9  5 10.08  6.71  5.09  3.37 10.17  6.21  5.04  4.12  3.92  3.21  6.63  9.42
71  9  6 11.67 11.21  6.34  5.25 10.29  8.92  6.83  4.92  6.29  5.33  7.17 12.46
71  9  7 10.08  8.29  7.00  4.79  9.42  6.87  6.71  5.25  6.83  6.00  6.83 14.71
71  9  8 11.04  8.04  8.67  4.50 10.63  6.58  5.46  6.42  7.58  7.71  6.67 15.12
71  9  9 12.67  7.62 10.46  5.41 11.00 11.12  6.08  9.42 10.67 11.34 11.25 19.08
71  9 10  9.79 10.25  8.54  4.88 12.17  9.71  8.96  9.67 10.13 11.83 13.37 19.70
71  9 11 10.46  6.50 10.17  5.17 10.54  8.21  8.21  6.71  8.92  8.17  9.62 15.92
71  9 12  5.33  1.83  4.54  0.87  3.71  0.92  1.67  0.83  0.33  2.04  1.63  4.21
71  9 13  1.79  7.12  4.92  1.13  5.29  2.79  2.54  1.50  2.50  1.25 10.13  5.13
71  9 14  3.21  5.63  3.29  0.87  3.17  2.00  4.25  1.67  1.58  0.79 10.50  4.42
71  9 15  3.46  3.79  3.88  0.96  1.46  1.46  3.92  0.67  0.92  0.29  6.17  3.00
71  9 16  5.54  7.75  6.00  2.33  8.46  5.46  3.67  2.71  3.00  2.50 12.12  7.87
71  9 17 12.25  9.00  8.17  5.21 11.08  7.29  7.33  4.54  5.75  5.63 13.21 11.04
71  9 18  3.46  2.75  2.96  1.54  4.54  1.92  1.58  1.83  0.83  1.13  4.21 10.46
71  9 19  8.50  2.37  5.09  2.25  6.00  4.04  6.96  3.33  4.38  3.13  7.41 13.67
71  9 20  6.42  2.42  3.42  2.08  6.13  3.50  5.13  2.92  6.21  3.92  8.92 14.71
71  9 21  2.46  1.87  2.37  1.04  3.42  2.33  2.83  2.96  3.46  2.83  7.67 13.21
71  9 22  4.79  1.50 12.38  1.33  2.67  1.08  2.00  1.08  0.67  0.37  4.29  2.62
71  9 23  4.92  5.04  4.46  1.04  3.92  2.04  6.00  4.21  4.63  4.58 10.75 14.67
71  9 24 10.75  2.17 18.41  2.79  7.29  6.92  6.17  4.83  6.38  2.71  8.46 10.00
71  9 25  9.83  6.83  8.00  4.21  9.33  4.88  6.54  5.00  5.71  4.67 10.13 13.67
71  9 26 16.29 17.16 11.54  7.79 16.66 10.13  8.71  8.38  8.42  7.21 12.54 14.88
71  9 27 10.54  3.58  9.00  4.92  8.21  5.04  8.38  3.04  6.08  3.75  4.42  9.96
71  9 28  7.12  6.50  6.50  2.13  5.46  4.92  4.17  4.58  3.71  3.58 12.67 12.54
71  9 29 15.50 13.08 14.46  6.38  9.71  8.12 10.79  4.54  8.50  6.29  8.25 10.71
71  9 30 10.29 13.04  9.21  4.38 10.00  7.83  7.41  5.66  7.12  4.50 13.75 15.71
71 10  1 11.08 11.54 11.38  8.17  9.38  7.29  5.71  7.46  7.96  8.54 14.58 15.63
71 10  2 10.04  6.96  5.50  3.58  8.46  5.75  4.79  3.79  4.25  3.08  7.17  8.79
71 10  3 10.00  3.13  6.83  2.67  6.00  2.67  2.92  1.04  3.08  2.71  2.21  5.71
71 10  4  6.08 10.67  4.63  2.67  7.62  5.54  5.17  3.71  5.50  5.33  8.63 11.87
71 10  5 13.88 13.42 12.46  7.75 13.42 12.08  7.04  8.33 10.50 10.25 14.17 20.41
71 10  6 12.75 10.83 10.92  7.00 11.63 10.17 10.79 10.58 10.71 12.87 22.17 20.67
71 10  7  7.46  6.92  8.75  3.58  8.00  6.50  8.67 10.08  8.58 11.25 15.00 10.25
71 10  8  4.29  6.29  6.00  3.21  8.87  6.54  9.75  7.38  7.87  8.75  7.41 10.96
71 10  9  5.13  6.00  6.08  2.75  9.59  5.91  9.92  5.96  8.17  7.38 14.42 18.79
71 10 10 17.46 16.62 15.79  8.83 16.75 13.21 16.92 13.75 14.46 15.34 20.25 24.75
71 10 11  9.46  6.25  9.67  4.17 10.08  6.87  8.33  4.83  8.25  5.96 14.96 19.70
71 10 12  8.25  7.67 14.71  3.79  6.96  4.50  7.33  5.09  6.58  5.88 10.96 19.95
71 10 13 13.59 11.87 20.67  4.58  9.21  6.87 12.46  6.00  8.29  7.00  5.66 12.08
71 10 14 13.29 10.08 12.04  4.79  9.92  6.58  6.38  7.71  7.71  9.25 15.87 16.71
71 10 15 19.41 12.21 18.46  8.42 14.33 10.88 11.92  9.71 11.75 13.79 20.58 23.16
71 10 16 10.08  9.46 10.54  3.25  8.79  6.42  6.42  5.50  7.17  7.58 12.54 14.50
71 10 17 22.42 19.92 18.16 11.21 19.41 14.79 17.00 12.96 15.63 14.25 21.34 22.75
71 10 18 22.46 19.21 19.00  8.54 18.21 12.29 16.62 12.21 12.87 12.58 23.29 24.87
71 10 19 17.04 17.83 14.88  9.87 17.96 11.79 17.08 10.67 13.33 13.37 21.87 26.08
71 10 20 14.50 11.38 10.75  4.83 12.33  8.54 14.21  4.88  9.54 10.17 15.67 25.17
71 10 21 26.20 20.54 21.67 12.00 17.54 14.92 16.79 18.16 14.79 19.12 32.63 28.67
71 10 22 13.25 14.42 17.71  9.08 10.96  9.59 14.46 12.25 13.88 14.92 26.25 21.59
71 10 23 12.38 12.96 11.29  6.42  8.58  7.92  6.71  6.21  7.12  8.29 15.21 17.67
71 10 24 13.00  6.21 12.38  4.88  8.29  7.00  8.04  4.04  7.25  6.34  6.17 11.96
71 10 25 10.21 10.00 10.63  2.08  8.33  5.79  7.12  4.63  6.63  3.88  8.75 10.13
71 10 26 12.21 11.25 12.92  5.04 11.96  9.04 10.46  8.25  7.62  7.50 12.96 16.92
71 10 27 14.37 13.00 13.70  4.58 13.25  8.75 10.67  8.00  7.92  8.58 16.08  9.92
71 10 28 16.29 13.33 18.21  8.29 13.92 13.00 13.75 10.79 11.12 11.63 16.08 19.70
71 10 29  9.75  8.17  7.38  3.75 10.92  7.71  7.92  8.38  8.08 10.50 20.71 20.83
71 10 30 15.75  8.29 14.09  6.42 10.96  9.54 10.71 10.29  9.96 11.54 21.37 24.87
71 10 31  8.63  7.87  9.29  2.92  8.83  7.00  6.21  8.21  6.21  9.08 19.17 19.55
71 11  1 14.21 10.92 13.50  6.08  9.75 10.67  9.79 10.08  9.13 12.33 20.62 21.59
71 11  2 17.00 12.21 18.08 10.46 10.37 10.25 14.96 10.83 10.96 12.46 18.00 25.12
71 11  3 14.04 12.38 14.71  6.34  9.92  8.08  9.00 11.54  7.83 10.37 22.42 21.29
71 11  4 18.71 15.16 17.08  9.17 14.37 12.42 15.12 14.25 12.29 16.13 10.71 12.38
71 11  5 16.04  9.92 12.42  6.67 10.21  7.75 11.96  5.33 12.38  7.46 13.29 20.96
71 11  6 15.83 12.17 12.46  6.75 11.29  7.46  9.54  7.25  9.83 10.25 15.54 22.29
71 11  7 14.33 12.83 12.17  5.96 14.79 10.79 13.92 10.17 13.59 11.50 19.41 27.79
71 11  8 23.96 18.25 16.71 10.83 15.16 10.08 15.12 11.25 15.50 15.12 24.30 38.04
71 11  9 17.62 11.87 20.00  6.71  9.17  6.58 10.21  4.71  7.62  7.00 14.09 24.13
71 11 10  7.75  2.92 10.50  1.42  4.92  1.87  6.21  1.63  4.67  3.25  8.33 14.42
71 11 11  4.25  4.42  7.17  0.92  4.79  0.96  2.88  0.87  1.79  1.29  5.71 10.88
71 11 12  5.37  4.88  7.00  3.33  8.92  5.33  9.08  5.13  8.12  6.50 12.25 16.08
71 11 13 13.88 12.25 11.50  7.12 10.37  8.54 10.00  6.71  9.87  9.96 15.96 25.96
71 11 14  9.29  5.46  9.33  3.17  6.83  3.54  7.58  4.21  6.21  6.42  9.38 18.29
71 11 15  7.54  8.17  6.46  4.58 11.29  7.75 14.37  7.00 12.46 10.67 18.54 24.08
71 11 16 11.21  9.96 10.41  8.04 12.54  8.75 14.37  7.08 13.37  9.87 12.29 27.16
71 11 17 11.34 12.17  9.33  7.17 14.17  8.67 12.17  7.92 11.17  8.71 12.71 16.83
71 11 18 16.92 11.17 15.96  9.87 14.00  9.62 11.63  7.21  9.59  8.83 13.42 24.83
71 11 19 12.75  6.13 15.59  6.13  7.04  5.09 11.34  3.42  7.96  7.87 10.63 22.17
71 11 20 18.75 18.91 15.34  9.87 19.67 12.96 13.13 12.21 13.33  9.04 20.67 22.71
71 11 21 23.58 20.62 21.50 15.63 18.84 14.50 20.33 12.96 16.54 16.92 24.58 35.92
71 11 22 18.21 16.17 16.83  9.71 12.67  9.59 12.38  8.50 11.54  8.00 18.96 24.46
71 11 23 20.54 19.04 16.62  9.96 15.09  9.59 12.50 10.41 12.29 10.54 17.37 20.96
71 11 24  6.00  1.21  8.63  2.21  3.04  1.29  7.08  1.33  4.04  2.33  5.63 12.58
71 11 25  5.91  4.17  6.87  2.71  8.42  5.79 12.42  3.54  8.83  6.25 10.46 14.37
71 11 26  7.92  6.58  5.50  3.71  9.54  5.96  9.21  6.96  9.38  7.71 16.33 17.12
71 11 27 11.63 10.13  8.83  4.50  9.38  7.46  9.62  5.71  8.92  8.33 13.46 17.33
71 11 28  7.50  8.67  5.83  1.29  7.17  4.67  9.04  2.17  6.46  5.13 10.75 14.21
71 11 29 11.67 10.79 12.04  3.21 11.79  8.00  7.83  7.62  7.67 10.00 19.75 21.37
71 11 30 16.29 16.08  8.33  4.00 11.34  5.79  9.08  5.83  8.08  4.21 18.79 15.29
71 12  1 13.04 10.37  9.00  3.42  8.25  5.50  8.54  3.00  7.04  4.54 10.71 14.12
71 12  2 12.50 10.29 12.96  3.46  6.17  1.96  5.13  1.46  3.58  2.17  6.04  9.87
71 12  3  3.33  7.92  6.34  0.37  5.58  4.67  7.41  5.41  5.00  4.92 14.33 18.12
71 12  4  4.08  6.67  7.12  2.08  5.09  5.25  9.67  4.54  6.58  7.00 13.04 16.38
71 12  5 11.50  6.79  6.08  2.46  8.58  4.08  4.96  7.04  4.33  7.62 16.75 16.71
71 12  6  5.29  4.04  4.46  1.04  4.67  2.79  5.96  3.75  3.75  5.75 10.63 18.00
71 12  7  5.46  0.42  5.09  0.50  1.63  0.46  8.33  0.04  2.17  4.08  7.54 17.79
71 12  8  4.67  0.67  5.75  0.33  2.13  1.71  8.42  1.21  4.17  4.17 10.83 16.38
71 12  9  6.96  3.75  6.17  4.67  7.58  5.41 11.12  7.08  9.71  9.71 13.88 22.95
71 12 10  7.96  6.21  7.04  5.25  8.87  6.75 11.58  7.12 11.54  9.08 15.87 20.75
71 12 11  9.13  7.71  6.67  3.37  8.17  7.17 12.29  5.63  8.79 10.75 15.29 21.00
71 12 12 16.33 12.75 13.67  6.13 10.25  9.42 10.88 10.34  9.29 12.25 21.75 20.79
71 12 13 15.79 10.83 13.92  6.29 10.83  8.38 12.08  6.58  9.75 10.67 15.12 19.38
71 12 14 20.38 18.58 19.12 11.25 17.58 14.00 17.04 15.00 14.62 18.54 26.34 28.08
71 12 15 19.00 15.83 16.29  8.17 13.50 12.29 15.37 15.63 13.33 15.96 26.67 29.04
71 12 16 17.00 16.96 14.46  7.12 11.83 11.17 12.29 10.75 10.79 12.75 20.96 21.59
71 12 17 18.12 16.21 14.54  8.75 12.62  9.96  9.17 11.75 11.54 14.96 26.50 24.83
71 12 18 26.50 20.25 20.00 15.09 17.58 14.21 14.62 14.21 16.29 17.29 28.04 26.42
71 12 19 25.00 23.54 19.70 14.58 23.91 16.71 19.33 16.96 17.08 17.46 29.20 31.42
71 12 20 21.84 21.17 20.08 10.63 20.38 16.46 21.92 17.54 18.71 18.50 28.16 29.95
71 12 21 10.46  8.08 10.34  6.50 11.42  8.42 15.41 11.08 13.92 13.25 23.13 30.13
71 12 22  7.58  5.09 10.83  3.04  4.21  1.38  3.96  0.54  1.87  1.58  7.46 14.88
71 12 23  8.12  8.08 10.13  3.00  9.33  7.41 13.13  8.46  8.87  9.38 22.88 24.58
71 12 24 18.46 15.25 16.38  7.83 12.29 11.96 16.42 14.92 13.62 15.67 27.84 26.04
71 12 25 22.08 15.96 15.04 10.08 14.96 12.46 11.79 11.67 12.00 14.75 15.79 19.46
71 12 26 15.12 12.75 17.04  7.79 11.17  8.21 11.04  9.25  8.83  7.08 12.04 13.88
71 12 27  8.75  6.13  9.04  3.13  7.00  7.41 13.13 12.96 11.12 10.88 19.25 27.00
71 12 28 13.25  9.67 19.12  7.50 11.34  7.33 13.25  9.21  9.46  7.75  9.04 15.46
71 12 29 12.42 10.00 24.25  7.46 10.25  6.00 14.58  6.67  9.21  8.63  5.66 17.46
71 12 30 13.04  6.34 27.67  8.42 11.54  4.12 16.21  6.71 10.46  7.29  6.67 16.46
71 12 31 14.88 10.50 26.08  8.46 13.50 10.04 21.04 10.25 13.54 11.34 12.12 27.33
72  1  1  9.29  3.63 14.54  4.25  6.75  4.42 13.00  5.33 10.04  8.54  8.71 19.17
72  1  2  8.96  1.46 12.00  4.50  3.21  0.83  6.04  0.67  3.79  0.79  1.75  7.83
72  1  3  6.83  0.67  9.25  2.29  2.46  0.17  3.88  0.21  2.88  1.25  2.21  5.88
72  1  4  8.46  0.87  5.41  2.04  4.79  0.29  2.62  0.92  1.63  1.96  4.38  6.58
72  1  5 10.88  9.17  5.88  0.87  2.04  0.13  2.75  1.38  1.08  1.13  4.83  7.29
72  1  6 17.00 13.92 13.54  4.46 12.29 10.25  7.62  7.75  7.87  6.17  9.79 13.54
72  1  7 13.92 12.00 19.12  8.00 12.50 12.08 16.46 11.63 11.12 13.42 14.67 25.04
72  1  8 14.46 10.29 16.17  8.79 12.58 11.29 17.00  9.21 13.29 15.59 11.34 28.33
72  1  9 11.00  7.41  8.50  3.04  7.54  4.29  5.25  3.37  5.79  4.21  5.00 11.08
72  1 10 20.88 18.34 18.12 10.17 14.04 10.08 14.04  9.29 12.17 13.62 17.83 21.29
72  1 11 13.29 12.17 13.29  4.96  8.83  6.67 11.34  6.08  8.17  9.38 16.04 16.13
72  1 12 19.83 14.42 18.16  7.58 13.42 10.25 11.87  9.38 11.38 13.59 18.08 23.09
72  1 13  8.63  4.58  9.29  2.17  4.08  1.87  4.12  0.75  3.46  2.71  4.50  8.92
72  1 14 23.96 20.62 19.50  9.17 18.05 12.50 16.08 10.54 13.08 13.21 15.04 19.12
72  1 15  9.17 10.71 15.16  6.34 10.37  7.54 15.34  7.58 10.21 10.46 11.00 21.96
72  1 16 23.67 20.62 20.17  8.79 16.83 12.75 16.58 12.50 12.33 12.71 15.16 21.04
72  1 17 14.50 18.71 10.25  5.91 18.41 12.75 12.38 12.87 13.25 11.25 13.96 21.62
72  1 18 20.25 15.96 15.75  7.79 14.17 10.37 12.33  9.25 12.17 12.50 19.50 22.83
72  1 19 10.34 10.88  6.96  4.46 10.04  5.46 11.25  3.96  9.00  7.33 12.21 21.25
72  1 20  7.54 10.37  6.38  3.67  8.87  5.83 10.41  4.29  7.92  5.96 12.67 19.70
72  1 21  7.21  7.29  7.12  1.46  7.71  4.75 10.41  3.54  6.75  6.13 10.83 16.46
72  1 22 12.79 13.42 11.42  4.92 10.00  8.75 13.70  8.08 10.71 10.54 12.58 14.75
72  1 23 20.41 17.00 18.46  8.92 13.92 11.29 13.00 12.08 12.46 14.50 22.29 23.00
72  1 24 13.00 16.13 10.63  5.50 12.04  6.34 14.00  5.13  9.42  9.33 15.09 20.04
72  1 25 13.17 14.46 10.79  4.46 11.67  7.71  9.13  6.87  9.04  8.04 14.67 17.37
72  1 26 22.71 19.12 15.71 10.21 17.62  9.87 12.87  8.75 12.71  8.71 17.54 19.50
72  1 27 26.42 14.83 22.13 15.21 15.00 12.29 18.05 10.71 16.62 19.55 19.25 33.25
72  1 28 11.29  7.25 23.29  7.00  8.42  3.13 14.04  3.37  8.38  6.79  7.25 10.83
72  1 29 12.29  8.58 19.29  6.38  8.83  5.00 14.92  4.63  9.17  6.58  8.63 14.21
72  1 30 11.25 10.13 10.34  2.37  8.08  2.58  6.92  4.29  5.00  4.38 10.08 12.29
72  1 31 22.50 22.29 19.08  8.00 20.96 14.50 12.50 14.33 11.50 10.25 20.41 22.17
72  2  1 12.87 11.00 15.83  6.79  9.04  9.04 17.29  6.83 10.41 13.50 11.63 28.33
72  2  2 33.84 26.38 28.16 18.41 24.25 22.50 25.04 20.79 19.92 20.96 24.21 33.63
72  2  3 19.55 17.46 11.34  7.17 15.29 10.71  9.08 13.54 11.12 14.42 23.75 22.46
72  2  4 13.04  9.67 12.79  3.58  8.08  6.25  6.04  4.54  6.50  4.96 10.17 12.04
72  2  5  6.13  6.96 10.04  4.04  8.25  4.63 10.46  5.96  7.79  6.87  9.75 25.00
72  2  6  2.79  2.29  3.21  0.96  3.21  0.00  1.92  0.21  0.87  0.50  2.08  6.71
72  2  7  5.09  5.66  7.08  0.50  5.13  2.50  3.33  0.46  2.37  0.67  5.09  9.71
72  2  8 14.21 13.75 12.08  5.63 12.83  8.08  8.38  6.87  8.17  6.83 12.38 12.38
72  2  9 10.71  8.75  9.13  3.50  9.87  6.79  7.83  7.50  7.92 11.34 17.88 18.34
72  2 10 17.12 11.25 13.17  7.17 12.92  9.59 10.21  6.29 10.88 10.46 16.71 17.88
72  2 11 18.96 14.12 15.00  7.41 12.92  8.83 11.46  6.92  9.83 10.46 12.67 16.79
72  2 12 16.33 14.21 10.71  7.54 15.00  7.83 13.67  7.58 10.21  8.96 15.29 19.04
72  2 13 15.12 10.08  8.92  5.96 11.54  6.96 12.58  8.75 11.63  8.83 17.79 20.58
72  2 14 12.46 10.79  9.17  4.21 10.92  6.87  9.21  5.54  7.54  6.63 10.13 14.42
72  2 15 20.21 15.34 16.92  9.54 11.83  8.00 13.96  8.54 10.37 11.50 16.79 22.67
72  2 16 24.37 23.79 16.42 11.54 20.62 13.08 17.12 18.91 14.42 20.91 32.08 37.04
72  2 17 17.08 13.04 18.29 10.46 12.79  9.38 14.17 10.13 10.75 13.00 15.29 18.34
72  2 18  9.17  5.00 22.00  6.63  7.58  4.58 12.96  3.08  7.25  6.08  5.00 10.58
72  2 19  6.58  5.25  7.38  1.50  7.00  0.58  4.33  3.92  3.83  4.12  9.13 11.79
72  2 20 10.17  9.83  6.21  2.50 10.37  6.25  4.12  6.58  4.96  4.83 12.04 11.71
72  2 21  6.17  1.38  7.62  2.67  3.79  0.75  4.58  1.00  2.37  0.96  2.33  5.09
72  2 22  9.29  3.21 16.29  5.58  5.41  1.25  7.50  0.58  4.25  2.04  3.13  5.54
72  2 23 15.29  8.67 17.54  5.83  8.87  7.87 14.12  8.21 11.34  7.33  8.63 13.59
72  2 24 17.16 12.17 17.12  8.58 11.58  9.00 14.42 10.46 12.71 10.79 12.87 16.04
72  2 25  7.79  6.87 10.54  4.88  9.04  6.63  7.50  6.29  8.25  6.96  9.46 17.88
72  2 26 11.17 11.25 10.58  5.46  9.96  6.92  8.29  7.17  7.29  8.33 11.42 13.70
72  2 27 25.62 14.12 16.92 12.29 16.17 12.58 11.83 10.54 13.62 15.54 16.66 22.83
72  2 28 12.92  6.21 12.92  6.29  7.71  3.04  8.04  3.92  5.66  6.29 13.42 13.37
72  2 29 16.13 15.12 12.46  7.46 14.67  9.62  8.58 10.92 10.37 12.71 22.83 23.00
72  3  1  6.21  7.50  6.67  1.96  7.46  3.13  7.38  1.67  4.21  2.37  7.83 11.25
72  3  2 20.17 13.62 16.92  7.38 12.62  8.12 10.88  6.96  9.87 10.92 13.88 18.50
72  3  3 26.30 22.29 19.04 14.17 21.96 15.71 18.12 15.79 17.96 19.04 23.33 27.54
72  3  4 12.79 10.67 12.25  7.46 14.58  9.04 11.42  5.88 10.96 12.33 13.50 21.62
72  3  5 16.50 12.71 10.63  6.25 11.54  6.79  8.87  6.75  8.04  8.42 15.09 16.58
72  3  6 23.38 13.83 23.16 12.92 18.58 15.16 18.41 13.88 14.46 14.00 16.92 28.79
72  3  7 24.71 19.33 29.95 12.75 16.00 11.50 15.83 11.08 11.75 11.08 17.79 21.50
72  3  8 10.58  6.38 15.71  4.92  5.75  1.79  7.87  1.08  4.08  4.08  5.17  9.21
72  3  9  6.29  9.33  6.58  2.29  7.92  3.08  4.83  2.75  4.67  6.04  9.79 14.12
72  3 10 10.25  6.50 18.29  6.63  6.58  5.50 11.17  4.75  9.13  7.67  7.38 14.79
72  3 11 15.21 12.54 32.75 11.12 12.83 11.46 18.96  6.71 12.33 10.63 10.46 15.59
72  3 12 17.46  8.83 23.25  9.33 11.58 10.58 16.50  8.46 12.29  8.29  9.13 12.96
72  3 13 15.83 11.08 15.67  7.04 11.46  8.29 11.46  6.21 10.37  8.12 11.21 15.00
72  3 14  9.79  8.58  9.87  6.17  9.71  8.79  7.75  6.00  8.50  8.50 11.42 13.25
72  3 15  1.46  6.67  2.92  1.08  4.50  1.67  3.29  2.17  1.87  2.00  9.59  6.54
72  3 16  6.17  6.96  6.58  2.37  7.41  1.58  2.29  4.88  3.25  2.96  3.83  9.46
72  3 17  9.04 11.08  8.67  5.00 11.46  6.87  6.50  8.12  7.83  5.66  8.58 13.25
72  3 18  7.96  2.88  8.92  7.71  6.21  5.41  6.79  4.33  7.21  7.29  8.79  9.67
72  3 19  9.87  2.00  4.92  3.33  4.75  2.67  3.71  1.54  5.09  3.08  2.79  8.08
72  3 20  5.88  3.92 10.58  3.58  4.33  0.54  4.12  1.83  3.00  1.87  2.96  9.08
72  3 21  5.71  4.63 11.08  3.79  4.75  0.71  3.63  2.79  3.21  3.71  8.17 14.29
72  3 22  5.83  4.21  4.79  2.79  3.29  1.00  6.87  4.17  4.50  5.17  9.62 15.46
72  3 23  5.58  4.21  8.63  3.21  4.79  1.17  3.88  2.04  3.58  2.96  6.79  5.88
72  3 24  8.25 10.41  9.46  6.38 11.29  7.17  6.04  6.71  8.58  8.17  8.63 13.04
72  3 25 13.79 14.62 11.38  7.12 12.62  8.79  9.46  7.67  8.17  6.13 14.04 18.46
72  3 26 16.42 17.00 11.79 10.29 18.88 11.17 15.63 15.04 14.33 11.46 23.04 21.34
72  3 27 27.12 24.67 17.29 17.16 28.16 17.00 22.92 21.75 22.50 17.96 28.88 27.37
72  3 28 19.55 14.92 14.12 13.13 19.87 11.83 16.08 13.62 17.92 12.04 17.67 20.38
72  3 29 11.54 10.04 11.00  4.92  9.46  6.34  9.54  7.17  9.00  7.12 11.71 15.34
72  3 30 18.05 13.21 14.88  7.04  9.38  5.54  9.33  7.04  8.00  6.87  6.92 14.17
72  3 31 12.21 11.04 12.08  6.13 11.17  7.04  8.54  8.96  9.17  6.96 12.29 11.25
72  4  1 14.83 14.12 15.63  8.50 15.83 11.87 17.04 15.25 14.46 11.83 18.66 17.83
72  4  2 19.95 17.21 17.08 11.34 17.04 12.08 17.33 15.25 14.67 13.46 19.29 20.30
72  4  3 14.88 13.88 13.54  8.17 15.41 10.50 14.42 14.21 13.62 12.75 23.67 23.71
72  4  4 23.13 19.55 16.58 12.00 18.91 12.67 14.21 14.79 13.88 11.63 17.29 14.25
72  4  5 18.12 17.29 17.16 11.54 20.12 13.46 19.29 14.62 17.46 14.83 22.54 23.91
72  4  6 18.91 16.21 14.21  8.33 13.25  7.67 12.62  9.33  9.83  8.17 15.00 16.88
72  4  7 18.34 15.92 15.54  8.96 15.54 10.04 11.67 11.83 12.38  7.79 14.37  8.12
72  4  8 15.92 12.42 11.29 10.29 13.67  8.83 14.88  9.00 12.79 10.83 11.12 21.00
72  4  9 18.05 13.29 13.67  8.50 12.50  9.59 11.54 11.50 11.17 10.83 17.88 19.04
72  4 10 21.54 22.29 15.12 14.33 26.58 15.59 18.16 21.04 17.96 12.83 25.50 20.30
72  4 11 29.79 19.33 20.08 16.29 21.59 13.59 18.12 16.66 16.92 18.66 17.71 26.54
72  4 12  8.42  5.91  9.71  4.58  6.25  2.17  9.17  4.83  7.12  4.00  7.58 10.63
72  4 13  6.75  6.67  6.04  2.33  4.79  0.79  5.46  2.46  2.67  1.96  6.25  7.50
72  4 14 19.29  9.29 13.46  9.17 13.46  8.67 13.92 11.71 13.42 12.38 15.83 21.42
72  4 15  7.83  5.88  8.46  4.92  8.50  3.96  9.50  4.88  8.21  6.08 10.34 13.13
72  4 16  5.79  5.17  4.08  3.37  7.38  3.42  6.25  4.79  5.63  3.79  9.29 13.62
72  4 17  6.29  2.58  4.83  3.25  4.96  1.79  5.46  3.21  5.17  4.50  5.96 13.04
72  4 18  9.21  2.17  5.37  3.50  4.50  1.75  7.75  3.29  5.33  6.83  4.79 10.29
72  4 19  7.04  3.17 13.29  3.29  4.71  3.67  6.34  2.62  4.54  2.21  7.54  7.58
72  4 20  8.38  3.54 14.04  3.88  5.54  3.54  6.29  3.42  5.66  3.92  4.63 10.67
72  4 21  8.79  6.58 10.34  4.71  5.37  2.62  6.71  5.54  5.71  6.63  6.54 10.08
72  4 22 13.42 11.29 23.16  8.58 11.58  7.25 12.42  9.46  9.25  9.25 13.59 10.13
72  4 23  7.96  7.67 22.58  6.79  7.25  5.41  8.71  7.33  6.79  4.04  9.67  7.46
72  4 24 10.13  6.75 22.08  6.00  8.42  6.79 11.34  5.58  7.92  5.33  9.83  6.79
72  4 25  7.00  4.67  9.29  2.58  4.21  1.83  5.21  3.96  4.42  2.17  5.50  6.38
72  4 26 12.17  7.58  8.54  4.54  6.71  3.71  8.00  5.66  6.38  7.87  7.67 13.13
72  4 27 13.04  8.63 10.21  5.91  6.29  3.92  9.17  5.88  8.12  8.92  9.21 13.59
72  4 28 14.79 10.92 12.92  6.75 12.33  8.29 13.00 11.96 11.38 12.46 19.08 21.25
72  4 29 16.88 16.29 13.46  8.29 14.09 10.04 15.04 13.17 13.83 12.54 16.66 22.79
72  4 30 13.62 12.87 15.37  6.29 10.83  5.58 11.50  8.58  8.58  8.58 16.04 13.00
72  5  1 12.62 12.92 11.42  4.88 11.00  3.46  6.67  4.04  5.13  4.96 11.92  7.79
72  5  2 13.79 11.50  5.63  3.88  9.33  3.88  4.50  6.58  4.46  5.09 11.83 10.88
72  5  3 18.88 12.17 11.42  8.21  9.79  5.79  9.62  9.00  8.63  8.12 12.87 11.71
72  5  4 17.41 11.71 20.30  8.92  9.59  4.88  9.83  9.54  8.00 10.41 12.04  8.38
72  5  5  8.29  6.29 20.25  6.34  5.17  3.92  9.71  2.54  6.58  7.12  6.21 13.04
72  5  6  9.50  5.54  8.08  2.29  6.29  1.00  3.17  3.29  1.75  1.29  7.87  8.21
72  5  7 13.50  7.79 12.33  6.00  8.29  4.96  8.17  7.04  7.25  6.87  9.13  9.62
72  5  8 22.13 16.00 19.08 10.17 15.41 10.67 15.09 13.08 11.87 15.29 17.33 21.59
72  5  9 21.71 14.04 17.79  7.87 15.04  8.83 10.00 10.25 11.25 10.21 16.83 15.96
72  5 10 13.62 11.50  8.79  5.41 12.12  7.58  8.67  9.08  7.96  5.41 14.54 11.17
72  5 11 19.75 14.50 12.67  6.21 12.17  7.08  7.67  8.12  8.08  8.87 14.00 16.38
72  5 12 13.96 10.29 12.67  6.87 10.96  5.58  8.87 10.08  8.83  8.54 16.21 19.75
72  5 13 13.33 11.67 25.62  8.96  9.96  6.75 10.88 11.79  8.58 12.25 17.71 18.34
72  5 14 14.12 14.04 22.17  8.83 10.58  9.04 10.41 12.12  9.62 12.87 17.41 21.87
72  5 15 15.87 11.58 15.92  8.96  9.67  6.38 11.67  8.96  9.67 12.33 11.92 17.04
72  5 16  8.58  4.33  7.21  3.04  5.29  2.00  4.21  3.79  3.83  2.75 11.71  9.13
72  5 17  6.00  7.00  6.92  2.04  5.58  0.96  4.71  6.58  1.87  2.67  6.71  8.12
72  5 18 10.79  9.29 10.37  5.63  8.79  3.71  3.21  3.92  4.00  2.71  6.83 11.54
72  5 19  9.79  9.83 13.33  8.42 10.63  8.63  8.87  8.75  9.04 10.37 11.38 14.00
72  5 20  7.12  7.50 11.25  3.96  8.79  2.67 10.04  4.58  6.79  8.54  8.17 15.25
72  5 21 15.09 11.67 13.37  5.58 11.29  6.17  9.50  9.75  7.38  9.92 15.00 18.16
72  5 22 20.00 16.29 12.29  9.46 14.83  9.87  8.08 11.25 10.34 10.71 19.33 14.67
72  5 23 20.17 13.79 18.12 12.04 15.63 10.13 12.25 11.87 12.54 14.96 17.96 24.54
72  5 24 22.67 21.46 18.08 10.04 20.54 12.12 13.33 17.88 15.21 15.92 27.16 22.95
72  5 25 25.12 21.17 21.17 13.79 23.09 15.54 19.75 17.00 16.96 16.17 28.12 24.25
72  5 26 28.79 26.00 23.63 16.83 33.04 17.88 24.50 20.41 21.62 17.79 27.71 29.50
72  5 27 23.42 14.42 16.92 13.25 17.96 12.75 18.00 14.17 16.46 15.41 17.33 21.79
72  5 28 17.88 15.37 15.50  9.00 19.29 10.37 15.00 15.67 13.59 13.29 24.58 21.46
72  5 29 21.84 19.55 17.83 12.46 23.58 13.13 20.08 17.92 17.33 17.50 26.38 28.04
72  5 30 19.08 15.59 13.37 12.25 20.04 12.83 19.55 16.33 16.62 16.42 20.04 23.25
72  5 31 10.04  6.92  8.46  5.63  9.92  5.83  7.96  7.21  9.33  7.83 10.13 12.25
72  6  1  5.58  5.25  5.91  2.17  4.92  1.58  3.25  4.12  4.08  4.38 11.58 11.87
72  6  2 16.13 15.21 13.92  6.83 13.75  9.08 10.63 13.25 11.25 12.54 21.21 22.13
72  6  3 13.79 10.21 11.83  5.71 11.08  8.25 11.38  8.96  8.83 10.96 19.00 17.92
72  6  4  6.29  7.54  4.75  2.62  6.42  4.54  4.58  7.67  4.71  7.71 16.04 11.96
72  6  5  9.96  6.38 11.38  4.88  3.83  5.00  5.96  5.04  4.21  4.08  9.17  7.50
72  6  6 10.21  7.08 10.83  5.79  6.67  3.46  6.92  4.12  4.29  3.92  7.41  8.67
72  6  7 15.71 10.21  9.25  7.29  9.29  6.92  9.04  7.58  8.96  9.67 10.58 11.96
72  6  8  9.29 11.00  8.29  5.09  9.75  5.83  9.04  5.66  6.79  5.33 12.12 11.58
72  6  9 14.96 15.50  6.58  5.66 11.04  7.58  7.50 11.04  7.96  8.87 16.71 15.09
72  6 10 16.79 14.04 11.08  8.71 12.04  7.25  9.00 10.92 10.50 14.37 15.09 20.54
72  6 11  9.50  9.79  8.25  3.08  5.54  1.87  6.00  3.21  2.83  5.66  7.21  9.96
72  6 12 10.04 13.92  3.63  2.37  8.21  5.46  4.71  7.83  5.21  6.83 10.83 11.08
72  6 13 12.21 11.96 17.25  7.83  8.25  6.42  9.08  9.33  6.54  7.00 12.21  9.71
72  6 14  8.00  6.75  9.25  5.37  6.42  3.04  3.08  5.46  5.37  4.88  7.17  9.71
72  6 15 10.71  8.75  5.46  5.09  7.71  4.33  6.92  6.38  5.50  6.25  8.42 10.41
72  6 16  7.38  5.37  6.17  3.29  6.54  4.17  3.54  6.17  4.88  4.00 12.17 12.79
72  6 17 20.75 18.29 18.46 10.13 16.50 13.13 15.04 16.38 12.87 15.37 23.42 22.54
72  6 18 16.66 12.38 12.08  9.71 15.12 10.41 13.00 12.87 11.58 11.25 17.25 20.00
72  6 19 14.67 12.92  8.75  7.00 12.79  9.33 11.63  9.59  9.29  9.29 14.79 14.88
72  6 20 19.00 15.71 14.25  7.17 15.29 10.71 10.83 13.67 10.54 12.08 22.46 20.12
72  6 21 15.59 14.04 15.59 10.21 16.75 12.83 18.38 14.88 15.71 14.21 21.25 25.17
72  6 22 15.75 12.75 11.21 10.50 15.83 12.04 15.29 15.21 15.54 15.04 18.63 25.12
72  6 23 10.37  8.96  9.38  6.04 12.67  8.25 12.58  6.17 11.00  8.67 14.92 16.50
72  6 24 11.58  9.71 12.42  5.54 11.50  7.71 11.17  9.42  9.92 10.13 16.79 14.50
72  6 25 11.58  9.59 13.33  5.21 10.63  7.75  8.21 11.50  8.21 10.83 19.55 17.21
72  6 26  5.63  6.87  9.75  5.58  7.00  4.67  5.91  7.29  5.37  6.50 14.12 10.37
72  6 27  9.71 10.04  7.04  6.25  8.87  6.63  8.71  5.79  6.42  5.09  9.79  9.00
72  6 28  4.63  7.83  9.54  4.96  9.17  6.63  7.87  6.79  7.92  6.38 12.67 12.58
72  6 29  6.92  9.21  7.04  3.46  5.25  3.33  5.09  5.09  4.67  4.75 14.83  9.87
72  6 30 14.83 13.29 13.21  7.12 12.83  9.38  9.79 11.96 10.96 12.00 21.84 17.79
72  7  1 11.83 11.87  8.08  6.04 13.04  9.29 12.71  9.71 10.63  8.04 17.29 17.12
72  7  2  9.00 10.08  8.63  5.21  9.42  7.04  9.04  5.88  8.54  7.21  9.17 12.21
72  7  3 14.50 13.92 13.25  6.04 13.92  6.42  8.75  4.29  7.83  3.79  6.87  8.75
72  7  4 14.58 10.04 16.58  5.17 10.34  6.00  7.62  4.92  7.92  4.33 11.71  7.50
72  7  5  7.58  9.04  8.46  5.04  9.71  6.13  8.42  6.46  8.96  5.75 13.37 16.66
72  7  6  9.17 11.50  8.29  2.83 10.13  5.33  5.00  8.08  5.91  5.17 12.96 10.67
72  7  7 12.46  8.71 12.25  5.91 11.63  7.54 10.92  6.04 10.83  7.83 11.67 15.46
72  7  8  6.79  7.17  5.71  2.33  7.50  3.88  4.17  5.04  6.38  4.00 12.17 12.42
72  7  9 11.79 12.96  9.08  5.58 12.38  8.25  9.92  8.92 10.21  7.25 15.71 15.79
72  7 10 13.92 14.46 12.75  7.87 18.05 10.92 15.25 11.17 13.46 11.21 18.58 22.37
72  7 11  9.50  8.42  9.08  3.58  7.75  4.63  8.04  5.71  7.21  4.63 11.58 13.67
72  7 12 10.04 10.08 11.87  6.08  9.87  6.50  7.29  9.08  8.50  8.17 14.46 16.33
72  7 13  3.25  7.12  6.46  3.08  4.38  3.00  3.79  4.67  4.12  3.96  7.71  8.71
72  7 14  2.75  3.96  4.50  1.21  3.17  1.00  2.50  2.92  2.88  0.67  9.08  4.08
72  7 15  4.75  1.96  9.42  1.54  2.33  2.67  3.54  1.33  4.75  1.38  4.50  4.88
72  7 16  6.25  3.92 15.00  4.04  3.79  4.21  4.33  2.42  6.34  2.62  7.54  4.21
72  7 17 11.54  5.37 19.04  5.46  6.63  6.29  6.58  5.58  7.92  5.13  9.33  2.92
72  7 18 12.67  4.71 17.04  4.50  6.38  8.00  9.96  5.66  8.58  3.54 10.29  4.75
72  7 19  3.37  0.96  9.92  2.79  3.63  2.42  4.08  2.13  4.79  1.83  9.71  2.50
72  7 20  2.13  3.08 10.25  2.33  5.46  1.63  3.67  5.21  3.79  1.71 12.00  6.17
72  7 21  3.00  1.42 10.46  2.50  4.83  2.88  3.08  2.67  4.29  2.58 12.87  7.50
72  7 22  4.38  2.50  7.87  3.79  4.08  1.17  3.21  0.67  2.62  0.79  5.21  8.67
72  7 23  4.42  3.92  5.00  2.37  5.75  1.75  4.29  2.13  4.00  1.46  3.33  4.08
72  7 24  6.92  3.96  5.09  2.46  6.38  1.38  5.46  5.00  4.42  4.08 11.83  8.42
72  7 25  4.17  3.63  3.88  3.58  4.63  0.54  4.21  2.67  3.63  1.50 12.87  9.04
72  7 26  4.54  2.58  9.87  2.25  3.46  1.21  4.04  1.33  4.38  1.29  6.50  4.96
72  7 27  4.71  3.54  5.79  1.29  4.17  0.79  2.33  2.21  1.92  0.96  5.25  6.04
72  7 28  7.96  5.50  5.50  3.46  5.75  2.62  3.75  3.54  6.00  1.87  8.71  4.21
72  7 29  7.62  6.29  7.50  4.17  4.33  3.21  6.17  1.71  5.75  3.25  7.79  7.04
72  7 30 10.92 13.54  8.33  4.54 10.71  5.25  5.09  7.41  7.08  4.71 14.83 10.34
72  7 31 22.42 19.41  9.00  8.00 15.83  7.29  8.33 10.67 10.13  6.29 17.25 12.79
72  8  1 21.87 15.16 17.37 11.46 13.79 10.75 12.42 10.37 13.00 10.92 15.41 19.29
72  8  2 14.88 10.46  8.25  7.25 10.08  5.13 11.50  8.33 10.71 10.17 13.08 18.12
72  8  3 12.29  8.63 11.29  4.04  5.33  2.46  8.63  5.37  6.34  4.71 12.42 10.46
72  8  4 13.62 11.08 10.75  8.12 13.62 10.21 17.54 11.54 13.17 10.79 13.08 18.05
72  8  5 11.46 12.46 11.42  6.34  9.21  6.63 11.21  8.08  9.08  7.00 14.62 13.83
72  8  6 14.04 13.75 13.25  7.33 10.13  6.25  9.83  8.92  6.21  7.38 17.08 13.62
72  8  7 13.88 10.71 14.12  7.08 10.13  8.21 12.62  8.92 10.17  8.04 11.50 12.50
72  8  8 14.79 12.58 11.00  8.25 13.21  8.04 14.12  9.71 11.96 10.54 12.67 15.16
72  8  9  6.92  7.75  6.92  4.67  8.12  6.08  9.87  6.83  8.71  6.38 11.83 16.50
72  8 10  5.75  7.12  6.67  2.58  7.41  4.79  7.17  5.54  6.46  4.42  7.96 10.50
72  8 11 11.46 14.17  7.87  5.66  8.67  6.38  6.92  9.21  8.08  6.75 21.37 15.75
72  8 12 15.96  9.17 10.41  6.92  8.08  9.59  9.92  7.41 10.58 12.12 10.71 18.75
72  8 13  6.04  4.79  4.50  1.96  1.87  0.87  3.92  1.17  2.50  1.71  5.00  5.91
72  8 14  6.17  5.91  5.63  3.04  3.54  2.17  7.04  4.42  4.88  4.08  9.33 10.50
72  8 15  7.83  5.63  7.92  3.00  4.58  1.67  6.67  2.25  4.25  2.54  7.21 10.54
72  8 16  8.29  7.46 10.29  5.04  8.75  7.29 11.54 10.88 10.41  8.63 18.91 20.71
72  8 17 17.37 10.25 12.71  9.21 11.83  9.13 15.12 11.25 13.08 10.63 13.25 19.75
72  8 18  9.96  5.04  8.08  3.42  5.37  4.25  8.87  3.50  7.75  6.38  8.38 14.75
72  8 19  4.92  2.13  6.29  1.75  5.54  3.92  8.58  3.79  6.79  4.00 12.79 15.37
72  8 20 10.34  6.87  7.17  4.08  8.00  5.41  8.25  5.21  8.12  5.41  6.29 11.17
72  8 21  5.83  3.75  7.79  1.63  2.04  0.75  4.29  1.13  2.62  2.67  3.17  8.71
72  8 22  3.75  2.21  4.67  1.42  1.54  0.92  2.62  1.42  2.79  1.42  3.50  6.75
72  8 23  7.21  2.96  5.50  2.88  2.92  3.00  7.92  4.04  5.33  4.71  7.29 14.58
72  8 24  7.25  1.96  9.62  3.42  1.63  1.79  5.00  2.54  4.88  4.12  4.21  9.67
72  8 25  7.62  2.33 17.83  3.29  3.63  5.09  5.88  3.17  6.34  3.29  8.17  7.17
72  8 26  8.04  1.42  9.54  1.25  2.17  1.38  6.71  2.54  6.00  3.54  5.79 10.58
72  8 27 10.34  2.54 10.41  3.33  5.50  2.71  6.13  4.25  6.08  3.13  4.38  9.87
72  8 28  8.17  5.09 11.42  2.25  4.00  2.04  6.83  2.92  6.13  3.92  4.42 10.54
72  8 29  6.08  1.63  8.12  2.13  3.92  1.50  6.92  2.62  6.21  5.37  3.21 11.08
72  8 30  5.37  4.08  9.62  2.67  3.13  0.08  4.88  2.13  2.83  1.25  3.79  7.75
72  8 31  7.00  2.46 16.04  2.29  3.46  3.04  5.33  2.17  4.92  2.13  6.54  5.66
72  9  1  8.83  1.92 12.67  2.92  2.50  1.29  3.67  1.79  4.46  1.67  3.75  3.83
72  9  2  6.08  4.67 11.17  3.13  2.58  2.08  4.75  2.58  4.00  2.71  6.63  6.83
72  9  3  8.71  6.08 19.50  5.66  7.38  4.46  6.38  6.58  6.71  5.00 11.46 11.38
72  9  4  4.58  5.83 18.46  5.71  6.29  4.29  5.91  6.83  5.21  3.17 13.17 12.08
72  9  5  3.96  2.08  5.09  1.13  2.08  0.67  3.96  0.54  1.96  0.29  5.09  4.04
72  9  6  8.54  4.88  8.67  3.00  4.79  2.67  5.96  4.33  4.67  4.63 11.29 11.67
72  9  7 10.46  7.87  7.83  4.46  7.67  5.09 10.00  4.63  8.96  5.25  9.50 14.42
72  9  8 10.54 10.71 19.62  3.42  5.13  2.58  6.17  2.54  3.58  2.58  6.54  6.92
72  9  9 14.21  8.79 14.58  6.54  8.54  4.67 10.67  4.71  9.46  7.38 10.83 14.92
72  9 10 10.46  7.38  8.04  4.21  7.62  6.21 11.58  5.37  9.21  7.54 12.17 18.38
72  9 11  9.83  9.29  8.25  4.83  8.25  6.58 12.38  6.13  9.92  7.04 12.58 14.25
72  9 12  9.96  6.79  6.92  5.00  6.17  4.08 10.67  5.37  9.17  6.17 10.63 16.08
72  9 13 13.33 10.17  9.59  6.00  9.08  5.54 10.13  6.75  8.83  7.04 12.67 16.04
72  9 14  6.54  4.67 13.62  3.58  3.42  0.87  5.00  0.96  3.37  1.08  5.66  2.83
72  9 15  5.33  2.67  5.37  1.54  1.92  1.42  4.54  1.96  2.33  0.79  3.79  4.38
72  9 16  6.96  3.33  7.00  2.75  3.25  2.00  7.58  2.88  6.00  1.63  6.58  5.63
72  9 17  7.21  5.63  9.13  2.75  4.42  1.00  3.88  5.17  2.37  0.71  5.41  5.29
72  9 18  7.75  4.46  8.00  2.71  2.17  0.96  4.50  2.46  2.21  1.33  3.58  8.00
72  9 19  6.79  4.42 12.42  3.08  1.87  1.17  5.83  2.83  2.79  1.38  2.83  5.25
72  9 20  5.25  6.58  7.41  2.37  4.17  3.88  6.17  9.59  4.71  6.87 14.42 15.12
72  9 21  6.63  7.92  8.87  3.21  3.46  2.33  7.08  5.75  4.75  5.33  9.29 13.54
72  9 22  6.63  7.17  6.17  1.04  3.54  2.21  4.38  5.17  3.25  0.75  8.50  7.67
72  9 23 16.00 12.96 11.87  3.46  5.41  5.50  7.87  8.42  8.00  3.96  9.33 11.25
72  9 24 12.75 12.62 13.33  4.46  8.08  4.88 10.29  8.79  8.04  4.42  7.38 12.75
72  9 25 11.42 11.08 10.75  3.37  5.88  3.88  6.63  6.21  5.21  4.71  8.54 12.50
72  9 26  8.79  9.79  6.25  1.50  3.08  1.71  3.54  3.71  2.00  2.29  6.17  6.00
72  9 27  4.63  5.63  3.42  1.00  2.25  1.17  3.50  2.17  1.71  0.46  4.71  5.83
72  9 28  6.29  9.13  5.58  1.79  5.71  3.63  2.54  6.00  3.54  4.38 13.08 10.88
72  9 29 15.92 14.88 11.71  7.50 10.54  9.25  8.33 11.92  9.13 10.13 24.54 19.00
72  9 30 23.04 16.62 15.63 11.63 12.42 11.34 11.50 15.09 13.29 14.46 21.25 20.04
72 10  1 14.17 12.62 11.17  6.38 11.29  8.25 11.42 10.37  9.21  9.25 15.04 20.38
72 10  2  9.59  6.50 10.25  3.96  2.46  2.29  9.13  7.00  7.71  4.63  7.67 13.54
72 10  3 10.41  3.54  5.75  1.25  1.42  0.33  3.50  2.75  4.08  1.08  3.17  7.46
72 10  4 10.54  5.17  8.71  1.29  1.79  0.46  4.04  3.33  4.17  1.67  3.46  3.75
72 10  5 18.88 16.66 11.92  5.21  9.42  5.41  7.04 10.00  9.50  7.04 13.00 11.08
72 10  6 15.12 12.79  7.62  3.46  8.12  4.54  5.75  7.46  6.63  5.83  9.50 12.00
72 10  7 10.88 12.79 10.58  5.66  9.38  6.21  5.50  7.46  6.92  5.33 11.08 12.96
72 10  8 16.71 14.17 15.92 10.13 11.38 10.25 10.08 11.79 12.54 14.09 14.96 17.75
72 10  9 10.41 10.88 11.54  5.46  6.17  4.29  7.71  6.00  7.00  6.79 10.17 11.67
72 10 10 13.75 13.33 10.83  5.46  7.92  4.38  9.38 10.17  7.75  7.33 18.71 17.88
72 10 11  8.71  9.08 12.62  4.96  6.63  5.09 11.25  7.67  7.79  5.58 13.50  9.33
72 10 12  8.67  6.38 11.54  2.37  3.88  2.17  5.75  4.67  3.29  2.08  8.38 12.83
72 10 13  7.17  5.83 13.17  3.71  2.79  3.33  5.91  3.13  4.46  1.79  3.88  3.33
72 10 14  9.00  6.83 16.17  5.50  5.58  4.83  8.87  6.54  6.75  4.17  8.92  6.75
72 10 15 12.71  7.87 13.46  5.04  6.38  4.83 10.29  8.54  7.50  5.13  8.67  6.13
72 10 16 11.58  6.79 12.38  4.00  5.13  3.04  6.75  5.54  4.63  2.88  6.13  9.83
72 10 17  8.79  5.91 14.83  4.92  7.29  3.58 10.50  7.38  7.04  3.04  6.25  8.33
72 10 18 11.87  6.75 15.46  5.83  6.83  3.71 11.34  7.41  7.41  1.87  5.09  7.38
72 10 19  8.63  7.96 13.62  4.92  5.63  1.54  5.83  6.29  5.13  3.63  9.13 14.09
72 10 20  9.17  7.50 16.25  5.50  5.21  2.96  9.08  6.58  6.75  6.96 11.67 17.79
72 10 21  8.46  5.13 10.96  4.67  5.75  4.71 10.88  8.04  8.75  5.79 12.17 20.41
72 10 22 11.46 12.58 10.67  8.04 11.25  9.92 20.62 15.63 15.12 14.25 20.25 28.21
72 10 23 10.00 12.83  8.79  6.63  9.83  7.08 14.00 10.54 10.67  9.79 13.83 23.16
72 10 24  4.83  7.96  6.46  2.75  5.79  4.17  9.33  5.58  6.71  5.50 11.71 15.41
72 10 25 11.83 11.34  8.87  4.21  9.21  5.83  9.17 10.25  8.71  9.25 19.38 20.04
72 10 26 17.50 15.34 17.00 10.13 13.46 10.96 12.38 13.37 13.33 14.33 16.46 23.04
72 10 27 11.92 14.09 13.00  5.09  7.38  4.08  8.33  8.33  5.33  5.04 15.83 12.25
72 10 28 17.04 16.83 14.12  8.04 11.58  9.71 11.96 12.46 11.67 12.54 22.37 20.54
72 10 29 22.00 17.41 21.42 12.29 15.00 12.62 12.87 14.58 14.12 16.46 25.17 25.75
72 10 30  3.67  5.21  4.42  1.42  4.42  3.75  6.42  4.75  5.09  4.71 11.42 18.58
72 10 31 10.71  6.25 11.04  3.88  4.38  1.13  4.58  3.37  3.75  1.71 12.54 13.13
72 11  1  4.79  8.38  8.25  2.33  5.63  7.17  9.25 11.21  8.63  9.59 17.75 18.91
72 11  2  7.54  7.33 11.46  2.13  4.08  5.63  8.12 10.41  7.75  9.13 18.41 19.55
72 11  3  9.42  8.38  9.87  3.33  5.04  4.12  8.50  6.38  6.46  4.63  7.33  9.71
72 11  4 12.25 11.67 12.96  4.17  4.92  3.25  3.79  5.63  5.41  3.17  7.67  6.71
72 11  5  4.92  6.42  6.38  2.37  3.17  2.67  3.88  5.96  3.92  3.50  9.38 10.71
72 11  6  7.12 10.13 12.92  3.00  4.88  6.08  8.46 10.75  9.25  8.42 14.79 17.62
72 11  7  9.79  8.46 10.79  3.13  3.96  2.25  6.50  3.46  5.79  3.37 10.83 15.75
72 11  8  7.38  7.79  5.71  1.42  5.83  4.38  7.67  7.41  6.21  6.50 15.54 20.96
72 11  9 26.83 23.00 21.84 13.29 15.92 13.50 18.29 16.75 15.71 16.33 26.75 33.21
72 11 10 15.67 19.08 10.63  8.71 12.46  7.12 15.54 13.13 12.12 11.04 23.25 28.96
72 11 11 17.83 18.25 11.38  9.42 11.79  8.33 15.83 13.59 14.54 11.12 22.17 27.25
72 11 12 21.50 20.12 15.96  9.04 12.71  7.41 12.33 10.63 10.88  8.79 13.62 14.46
72 11 13  8.46  8.63  8.08  3.92  5.46  4.25 10.17  5.50  8.33  5.46  8.12 16.04
72 11 14  8.50  6.54  8.08  2.58  3.71  2.04  7.67  2.37  5.41  3.58  7.29 12.92
72 11 15  7.29  7.29  5.71  1.04  3.71  3.08  8.63  4.25  5.88  4.38 10.04 18.96
72 11 16  7.92 11.04 14.29  3.63  4.12  1.58  6.71  4.29  3.46  1.29  6.87 13.75
72 11 17 12.25 10.41 13.62  3.37  4.38  2.08  9.25  4.92  5.00  4.04 11.08 14.50
72 11 18 17.83 14.21 15.75  6.21 10.88  7.83 10.88  8.33  7.75  8.75 11.38 18.00
72 11 19 18.63 14.79 15.04  6.21  6.96  5.33  8.29  6.54  5.96  3.83  7.33 13.17
72 11 20 19.62 26.46 14.37 11.87 17.58  9.42 14.50 10.37 13.00  6.34 12.25 11.54
72 11 21 14.96 16.88 13.50  9.33 11.50  8.54 18.91 12.33 14.46  9.87 16.71 13.92
72 11 22  8.71  8.83  7.87  3.88  6.00  3.79  9.96  5.79  7.83  5.66 12.42 22.63
72 11 23 10.21  6.13 11.50  3.75  3.75  2.33 10.83  4.54  8.58  8.29 10.00 22.88
72 11 24  7.29  3.46  9.50  3.08  2.25  0.29  6.71  1.75  2.83  1.67  4.29  8.46
72 11 25  5.37  2.62  8.12  2.62  6.29  3.42 11.75  6.67  8.33  5.41 11.00 18.71
72 11 26  7.50 10.17  5.96  1.67  5.29  3.83  7.87  7.00  3.58  4.92 14.83 16.46
72 11 27 23.38 22.46 19.75 12.96 13.92 13.96 15.96 18.58 14.17 17.67 27.58 25.84
72 11 28 13.08 17.00  9.62  6.21 11.50  8.79 15.00 10.21 11.17 11.83 18.79 25.46
72 11 29 18.21 17.58 17.83  9.96 11.71 11.34 16.96 12.04 12.33 11.67 18.88 21.62
72 11 30 13.17 17.04 10.34  6.29 11.21  9.04 14.62 10.46  9.75  9.29 16.92 18.00
72 12  1 20.58 17.92 18.46  9.62 16.75 13.21 18.29 12.75 13.83 13.83 22.42 26.20
72 12  2  6.00 10.21  4.75  0.87  7.17  4.92  5.37  5.58  3.71  1.71  8.08  9.59
72 12  3 16.71 16.88 12.29  7.46 10.13  9.46 13.25 10.67  8.67  8.00 14.00 17.08
72 12  4 20.21 22.79 16.83 11.00 12.00 10.79 19.38 10.71 13.04 11.54 14.25 20.54
72 12  5 25.58 20.46 22.08 10.37 18.46 15.25 21.71 17.92 16.79 16.29 27.50 29.83
72 12  6 16.88 17.67 11.58  5.54 12.08  9.59 12.87  8.83  7.75  8.50 12.87 18.63
72 12  7 11.38 14.46 11.46  6.83 11.75  9.83 15.29  8.87 10.17  8.21 12.38 12.54
72 12  8 11.08 10.08  7.21  2.08  4.67  3.67  6.87  3.67  3.21  2.13  4.75  6.92
72 12  9 11.75 12.67 10.04  3.71  8.67  8.08 13.37 10.00  8.50  8.46 17.25 20.08
72 12 10 15.46 18.50 12.67  6.42 13.88 11.00 17.50  9.33 11.79 11.50 19.00 22.71
72 12 11 24.13 22.04 20.62 13.08 17.37 14.88 15.92 16.13 13.29 16.38 29.08 26.54
72 12 12 20.25 18.63 17.33 10.17 16.42 14.62 16.92 16.46 14.04 16.17 27.21 26.25
72 12 13 14.88 14.17 16.29  7.62  8.75  9.50 11.46 11.29  9.38 11.54 22.92 25.62
72 12 14 25.96 23.45 20.04 18.46 19.04 15.71 19.08 19.12 15.12 14.92 24.25 20.79
72 12 15 17.50 23.42 16.66 11.38 15.71 13.46 14.54 14.37 12.75 11.12 19.46 19.87
72 12 16 16.25 16.13 15.21  9.96 12.50 10.88 13.88 12.08 11.38 10.92 15.29 20.17
72 12 17 20.71 25.17 17.12  9.79 18.41 13.70 17.71 16.62 12.83 11.42 20.38 25.66
72 12 18  9.87 16.04 14.33  8.25 13.75 11.58 15.71 10.63  9.38  6.17 13.79 25.50
72 12 19  9.59 13.37  8.96  6.46  7.33  7.29  5.29  9.87  8.87 10.46 25.46 19.83
72 12 20  8.46 12.08  8.42  5.17  7.46  7.17  2.13  9.59  6.54  5.37 20.54 18.16
72 12 21  1.79  2.21  3.96  0.37  1.71  0.92  2.08  2.25  0.71  0.92  6.17  9.59
72 12 22  5.91  9.00  4.75  2.17  6.08  3.79  2.62  5.46  4.54  4.17 14.88 15.29
72 12 23 13.00 10.71 10.29  3.04  6.87  4.88  8.96  5.58  8.17  6.87 13.88 18.08
72 12 24 15.00 16.62 13.96  5.17 11.38  9.29  9.71  9.96  9.00  7.41 14.42 14.88
72 12 25 26.71 23.54 23.33 16.50 23.63 18.25 21.46 21.37 19.75 21.29 27.46 31.25
72 12 26 14.29 12.33 11.96  5.41  8.92  6.54 11.08  9.13  9.59  9.17 12.62 15.79
72 12 27 19.04 11.58 21.42 10.92 12.29 12.87 18.41 13.00 12.42  9.79 12.42 14.50
72 12 28 30.42 20.30 17.08 14.12 14.17 11.96 14.33 12.42 13.70 11.54 15.87 16.04
72 12 29  8.04  7.83  9.25  3.37  4.79  3.58  5.88  5.33  5.50  4.58 17.21 14.67
72 12 30 13.62 14.67 11.83  7.54 11.04  8.54  7.83 12.00 11.29 12.12 21.04 25.00
72 12 31 13.83 14.46 15.87  9.75  8.71 11.00 10.67 11.54 11.50 10.75 18.00 17.50
73  1  1 16.50 15.92 14.62  7.41  8.29 11.21 13.54  7.79 10.46 10.79 13.37  9.71
73  1  2 15.75 12.12 15.04  8.00  8.79 10.41 13.25  9.67  9.04 10.17 12.29 14.04
73  1  3  7.38  9.33  9.17  2.21  3.13  2.54  5.33  1.63  4.46  0.71  2.83  5.63
73  1  4  2.92  4.58  4.63  1.00  2.75  0.96  4.50  0.46  3.08  0.79  4.12  4.17
73  1  5  6.42  6.79  6.25  1.33  3.29  1.00  2.71  1.79  3.13  0.58  3.67  4.83
73  1  6 10.58  9.04  7.54  2.25  5.96  3.00  5.79  4.50  5.54  3.08  5.37  6.04
73  1  7 12.08 10.88 10.13  4.25  6.13  4.96  5.63  6.34  5.25  4.08  6.46  7.87
73  1  8  7.75  8.12  3.54  0.63  5.21  2.88  2.67  1.25  2.58  0.33  5.00  6.79
73  1  9 12.17 12.08  6.21  1.50  8.38  5.41  3.13  6.42  3.08  1.21  8.63  7.79
73  1 10 14.46 14.37 11.58  3.58  9.08  6.42  8.29  7.54  6.21  5.37 10.71 10.29
73  1 11 15.79 14.75 15.71  7.38  8.58 10.29 11.58 10.34  8.42  8.42 12.79 13.04
73  1 12 22.37 16.92 22.04 13.54 15.16 14.37 17.41 13.79 15.37 16.66 16.66 23.63
73  1 13 12.71  2.67 13.42  5.46  7.33  6.63 10.00  4.25  8.50  7.62  4.83 14.79
73  1 14 22.00 21.71 18.05 11.08 17.16 13.17 12.96 10.79 11.87 11.46 16.08 17.12
73  1 15 14.33 19.62 11.71 10.88 17.33 14.00 22.04 16.54 15.83 18.96 25.37 30.37
73  1 16  7.38  4.17  4.42  1.75  4.63  3.33  7.92  2.21  4.42  3.75  7.00 14.09
73  1 17  6.42  3.08  8.38  1.54  2.83  0.13  4.50  0.79  0.67  0.04  7.12  6.50
73  1 18 23.67 22.37 15.87  7.67 18.58 13.50 12.17 15.09 11.87 11.58 22.67 23.45
73  1 19 17.21  8.42 25.08 13.75 10.08 13.17 25.12  7.75 19.12 20.83  8.63 32.66
73  1 20 11.17 12.00  7.50  4.96  9.59  5.21 11.12  7.46  8.33  6.83 14.25 17.54
73  1 21 11.58  8.17  7.62  4.63  7.83  5.71 14.71  5.50 10.13  7.71 11.38 24.25
73  1 22 10.96 12.58 11.71  2.75  9.00  7.25 11.29  7.71  7.54  5.75 13.42 16.33
73  1 23 10.34 12.25  8.92  3.33  9.42  7.96 12.87 11.25  9.33  8.08 17.29 14.50
73  1 24 13.37 16.96 10.67  4.29 10.04  7.54  9.54 12.50  9.17 10.21 21.00 19.55
73  1 25 15.54 14.17 12.75  7.67 12.00 11.38 12.83 10.83 12.50 10.63 16.25 16.75
73  1 26 15.37 16.75 13.13  7.33 14.79 11.21 16.71 12.12 13.33 11.08 17.88 21.29
73  1 27 15.50 16.42  6.04  5.04 13.29  5.58  6.83  8.38  8.00  3.00  9.46 10.75
73  1 28 16.08  7.67  7.96  6.75  9.71  7.75 13.08 10.41 11.58  7.62 13.04 15.00
73  1 29 10.21 11.42  8.04  4.21  7.25  6.92  9.59 10.17  9.38 10.54 15.34 16.79
73  1 30 15.25 18.50 10.75  7.79 12.08  8.71 14.71 10.63 11.54 10.50 18.50 18.66
73  1 31 15.25 14.46  8.21  5.13 11.46  7.41 11.08  8.38  8.46  7.12 12.96 11.00
73  2  1 14.79 12.33  6.67  6.67 10.37  6.58  8.00  6.34  8.75  6.08  9.92  9.38
73  2  2  4.00  3.54  4.29  1.63  2.62  0.37  4.25  2.58  3.92  2.96  6.21  6.79
73  2  3 12.71 12.17  7.96  4.79  8.17  7.08  6.34  7.87  6.79  8.21 17.96 13.37
73  2  4 17.88 13.08 12.75  6.21 10.37  7.33  9.67  9.33  8.50 10.54 14.88 17.92
73  2  5 10.54 10.54  8.46  5.00 10.08  7.38 13.42  8.38 11.12  9.50 17.04 20.62
73  2  6 12.83 12.92 11.67  8.33 16.29 12.21 20.08 11.63 17.50 14.96 19.58 27.08
73  2  7 17.21 13.00 15.92  7.92 12.79  9.13 12.71  7.71 11.46 11.17 16.42 22.46
73  2  8 13.70 10.37  9.54  5.21  9.75  5.79  8.79  6.50  8.92  7.41 11.08 15.29
73  2  9 17.92 16.38 12.29 10.13 11.17  9.04 14.09  9.96 13.13  9.25 18.25 20.83
73  2 10 18.46 17.67 13.67 10.58 15.75 10.29 14.71 12.83 16.00 14.09 18.46 27.71
73  2 11 16.96 18.75 12.29 11.04 19.12 13.00 18.50 14.92 16.79 14.46 23.09 27.04
73  2 12 28.01 28.16 17.58 16.08 28.62 18.58 24.21 22.67 23.63 19.67 30.63 35.75
73  2 13 21.25 22.50 13.17 10.50 20.21 11.71 16.88 15.79 16.54 15.21 26.16 28.42
73  2 14 18.46 19.00 10.13  8.25 12.04  7.96 10.88  9.62 10.41  5.04 20.21 13.59
73  2 15 19.67 21.29 12.92  8.42 15.00  8.67  7.92  9.71  9.96  5.41 18.54 11.38
73  2 16 11.12  9.50  9.46  2.88  6.96  2.50  3.88  5.25  5.04  1.63 10.71 13.00
73  2 17 13.67  5.71  9.21  2.88 11.17  3.50  2.79  4.75  4.54  2.33  4.88  9.59
73  2 18  8.21  6.00  5.04  2.88  7.75  4.38  9.08  6.29  7.08  5.09 12.33 17.04
73  2 19 10.58  8.33  7.79  5.66  9.67  7.21 12.00  8.87 10.88  5.96 11.17 17.58
73  2 20  8.79  8.38  6.79  5.13  9.83  7.29 11.58  7.83 12.17  9.17 14.12 19.75
73  2 21 14.54 13.50 10.00  7.41 13.17  8.46 13.13  9.21 12.79 12.12 13.37 19.41
73  2 22 19.50 16.54 11.04 11.75 17.41 10.46 17.83 14.42 18.29 13.88 21.59 31.46
73  2 23 19.58 15.92 11.29 13.21 17.62 13.04 16.38 15.29 18.12 16.96 20.08 33.09
73  2 24 11.08  9.00 10.25  4.29  7.67  4.54  9.13  4.50  7.33  9.13  7.96 20.83
73  2 25  8.38 14.92  8.17  1.87  9.59  6.04  5.46  5.58  6.29  4.58 11.21 10.21
73  2 26 11.42  8.21  6.79  0.63  8.46  0.33  3.17  0.96  1.75  1.79  3.33  8.67
73  2 27 13.04 10.25 11.83  5.50  9.83  7.58  6.96  6.71  8.79  7.92 10.50 17.96
73  2 28  9.21  9.67  6.58  3.00  6.71  4.42  4.58  5.13  7.04  5.33  8.67 11.12
73  3  1 17.83 15.16 14.46  8.17 12.75 10.79 13.54 11.12 12.04 12.50 16.21 20.04
73  3  2 18.25 19.08 15.16  9.13 15.25 11.38 14.83 17.79 13.00 15.59 22.50 24.37
73  3  3 17.12  8.87 15.59  4.00  5.46  4.54  7.46  6.46  7.21  6.38 11.34 14.12
73  3  4 13.08  8.75 13.25  6.50  8.08  6.54  8.71  6.67  8.79  8.42 12.04 17.96
73  3  5  8.42 10.54  6.00  4.17  9.96  5.17  7.71  6.63  8.63  8.12 14.46 18.12
73  3  6 17.16 11.87 13.62  8.71 12.87  8.92 13.13  9.54 12.87 11.46 13.00 19.87
73  3  7  5.58  9.13  6.71  1.21  4.33  3.46  6.58  6.25  5.29  5.29 10.46 13.54
73  3  8 14.29 18.16  8.79  4.67 10.96  6.63  5.21  8.83  7.41  7.87 12.75 14.83
73  3  9 12.62 15.67  7.87  5.04 10.58  8.67  6.46  7.25  5.75  4.33  8.42 14.04
73  3 10 12.83 15.71  6.87  3.17 10.21  4.08  3.79  4.96  4.17  2.42  4.42  7.58
73  3 11 11.17 11.58  7.75  1.92  7.17  2.67  3.04  6.38  5.04  3.04  3.79  6.92
73  3 12  6.42  8.42 11.83  2.21  3.96  2.04  6.58  3.75  6.42  4.33  3.50  9.17
73  3 13  8.50  7.04  9.79  1.79  4.71  1.87  3.50  3.54  3.92  3.00  2.25  5.00
73  3 14  6.83  7.17 10.46  2.37  3.21  1.96  4.54  3.25  5.00  2.54  3.21  6.29
73  3 15  6.54  5.54  9.54  1.33  4.58  0.37  2.92  1.42  1.83  0.92  3.00  6.54
73  3 16  5.21  5.63  8.58  3.58  4.25  1.33  3.17  0.75  3.88  3.46  3.00 12.54
73  3 17  7.04  4.00  7.29  3.08  3.54  0.75  4.92  2.67  4.79  4.17  3.71 10.46
73  3 18  4.42  4.79  8.12  3.46  4.83  1.54  6.96  5.29  6.96  7.25  6.96 12.67
73  3 19  5.88  3.13  8.33  3.17  4.75  2.29  3.67  2.50  4.67  0.37  4.12  4.75
73  3 20  4.17  7.67  4.00  2.67  5.21  2.42  3.83  2.79  3.33  2.71  6.67  5.17
73  3 21  7.50 11.96  6.96  5.21  9.08  6.71  7.12  7.50  7.17  6.42 18.75 15.59
73  3 22 11.25 12.04 11.12  3.96  9.21  7.12  9.00 13.67  7.21 10.83 21.25 20.08
73  3 23 16.75 18.25 13.04 11.04 16.29 12.54  8.38 14.04 12.62 14.17 22.63 21.37
73  3 24 22.58 20.91 18.58 12.50 17.50 14.12 16.38 15.59 16.04 16.83 24.75 25.37
73  3 25 18.58 14.37 12.38  9.50 15.34  9.50 13.75 12.50 13.42 11.54 16.79 20.67
73  3 26 11.00 14.92  8.75  5.29 11.17  6.29  8.21  7.75  6.38  6.21 13.37 13.96
73  3 27 16.29 17.08 15.41 11.34 16.46 10.63 11.25 12.46 12.79 14.83 17.79 24.50
73  3 28 12.04  9.46 11.87  5.83  9.04  5.83  9.79  7.83  7.87  7.04 12.08 18.00
73  3 29 14.29 16.00 13.88  7.58 11.67 10.67 12.17 16.08 11.83 13.59 23.00 26.75
73  3 30 18.50 13.96 14.54 10.21 15.25 10.79 14.12 11.21 13.59 12.12 18.41 22.37
73  3 31 14.92 16.04 13.37 10.00 16.66 12.87 18.88 14.04 17.75 15.79 23.87 27.75
73  4  1 17.41 17.08 13.59  8.04 12.00 10.04 12.42 10.88 11.63 10.83 14.58 18.34
73  4  2 28.71 20.46 22.34 15.63 20.50 13.29 17.41 13.25 15.37 14.12 18.29 21.71
73  4  3 14.62 13.83 12.58  7.46 10.46  9.46 12.21 10.50 10.08 10.54 18.58 19.95
73  4  4 19.62 18.84 20.41 11.67 18.34 14.79 21.54 15.67 18.91 16.83 22.42 28.16
73  4  5 12.17 14.21 10.79  8.54 13.67 11.71 17.00 17.62 16.66 15.29 22.25 28.21
73  4  6 20.21 15.92 14.50 11.12 17.67 13.88 18.08 14.54 17.96 16.92 21.17 28.16
73  4  7 16.71 10.92 14.83  7.79  8.87  8.92 11.46  7.83 10.08 10.54 14.21 22.00
73  4  8 19.00 14.58 18.54  9.46  9.29  9.67 12.04  9.33 10.21 11.12 14.88 21.25
73  4  9 11.46  7.08 10.34  6.21  7.41  3.46  8.00  4.38  5.66  3.63  8.63  7.96
73  4 10 13.75 13.83  8.12  5.54  9.59  6.87  9.00  8.63  9.21  8.63 13.17 15.96
73  4 11 19.21 15.37 11.75  9.87 10.58  9.83 10.04 11.42 10.63 11.75 13.96 15.75
73  4 12 19.75 14.09 13.37 10.83 13.83 10.83 10.75 11.21 13.79 12.62 13.54 14.96
73  4 13  8.63  5.75  7.04  4.79  5.46  5.04  8.08  4.25  7.21  7.38  6.58 12.75
73  4 14 10.88  7.38  6.87  5.46  6.58  4.58  8.54  4.42  8.42  7.00  7.25 12.04
73  4 15  7.79  6.63  7.29  4.63  6.63  4.58  6.63  5.96  8.08  6.38  8.12 10.92
73  4 16 10.71  8.50  8.87  6.34  7.04  6.00  8.46  6.96  8.42  8.63  7.96 13.08
73  4 17  9.08  8.92  8.42  5.00  5.29  4.08  8.71  5.04  6.38  7.62  7.92 14.09
73  4 18 13.21 10.46 12.92  8.71  9.67  9.79 12.96  8.79  9.54 12.08 11.00 17.75
73  4 19 11.17 12.58 15.21  7.41 11.04  8.71  9.08  9.83 11.54 13.83 12.42 17.29
73  4 20 15.16 12.38 12.54  8.38 10.63  9.46 12.96  9.46 11.29 14.25 13.67 21.21
73  4 21 16.33 13.46 18.41  8.83 13.21 10.41 11.71 11.17 10.50 12.79 14.17 20.41
73  4 22 13.79 10.96 25.88 10.21  9.87  8.25 14.37  9.25 10.46 12.21 15.59 17.92
73  4 23 14.67 15.41 29.79 11.25 13.46 12.29 20.17 14.33 14.21 17.75 21.21 22.54
73  4 24 16.00 14.00 20.62  8.83 13.50 12.17 15.54 12.42 13.04 12.29 14.88 16.42
73  4 25 10.54  9.25 10.58  3.08  6.87  6.83  5.04  6.34  7.41  4.38 10.41  6.42
73  4 26  4.50  3.58  5.63  2.13  4.33  1.96  3.79  3.29  3.25  2.58  4.58 10.13
73  4 27  4.25  4.96  5.91  2.42  4.50  1.83  1.75  3.75  2.83  3.29  6.54  8.42
73  4 28  8.25 10.79 10.41  4.17  6.08  3.58  5.58  5.83  5.46  4.96  9.83 13.92
73  4 29 14.75 12.96 13.13  5.71 10.63  6.00  6.63  8.33  6.92  4.42  9.59  8.38
73  4 30 12.50 13.50  8.63  6.25 10.08  8.08  8.12  8.38  8.12  6.87 16.62 17.75
73  5  1 11.92  6.38  8.46  4.04  8.00  5.71 10.58  4.88  8.79  6.42  8.17 15.37
73  5  2 12.58 11.04 10.17  4.38  7.29  5.66  7.62  5.54  6.58  3.71  8.83  9.67
73  5  3 16.29 14.09 14.79  9.59 13.21 11.34 11.75 15.09 13.25 12.00 17.33 24.08
73  5  4 14.71  7.17 18.25  8.12 10.83 10.17 12.17  7.29  9.71  9.38  9.38 16.66
73  5  5 12.00 13.59 11.63  6.25 11.08  8.54 10.92  8.29 10.21  8.50 12.08 12.87
73  5  6 13.04 10.67 12.08  6.38 11.71  9.29  9.00  9.54 12.25 10.37 15.46 12.75
73  5  7 22.46 17.08 13.50 11.50 14.83 11.92 12.38 12.17 13.92 13.79 16.17 21.87
73  5  8 15.75 12.54  8.75  8.38 14.37 10.37 12.87 12.08 12.21  9.79 14.71 17.58
73  5  9 16.00 15.00 14.37  8.42 16.38 10.04 14.50 11.92 14.17 11.92 17.79 20.17
73  5 10 21.50 16.38 13.59 12.21 19.29 12.96 17.16 13.54 16.92 15.83 19.04 26.83
73  5 11 11.00 11.00  8.29  7.25 11.63  8.63 11.12 10.29 11.87  9.59 14.09 17.88
73  5 12 16.58 15.75 14.67  8.46 15.12 11.71 14.00 14.42 12.96 13.92 20.54 21.79
73  5 13 13.17 11.12 12.33  5.91  9.38  6.96  7.96  7.00  8.21  9.04  8.96 14.96
73  5 14  5.17  9.54  9.38  3.92  5.58  4.50  3.29  4.17  5.88  6.29  5.66 11.12
73  5 15  9.67  6.58 11.12  3.00  5.33  3.46  4.08  4.04  6.29  2.92  4.67  9.92
73  5 16 19.04 20.54 12.38  7.92 13.96 11.12  9.13 12.21 11.42 11.25 12.71 18.12
73  5 17 21.79 17.88 17.83 11.58 17.37 14.50 14.29 18.50 15.63 16.92 16.79 22.42
73  5 18 15.50 14.46 10.46  6.83 13.33 10.41  9.29 13.08 10.67 10.13 12.96 20.88
73  5 19  9.75  7.08 17.92  6.79  9.54 10.79 13.04 11.17 13.37 11.12 12.04 20.54
73  5 20 10.46 11.34 14.58  7.33 10.75 12.87 12.92 14.37 13.04 13.37 17.67 26.08
73  5 21 13.33  8.79  9.54  6.17 10.25  9.87  6.54 10.29  9.08 10.17 11.83 19.75
73  5 22  9.38  7.83  8.58  6.17  9.46  6.96  8.54  6.04  8.25  7.38  6.17 14.00
73  5 23  8.00  4.92  5.54  2.75  7.41  3.58  4.79  2.13  5.75  5.79  3.21  8.21
73  5 24  6.58  9.96  8.83  3.33  6.13  4.04  5.46  4.21  5.21  3.58  7.50  5.50
73  5 25 11.58 13.50  9.62  6.58 12.33  9.00  4.75  7.83  7.87  6.87 10.71 12.04
73  5 26  6.04  7.79  8.08  4.79  7.33  5.88  5.09  6.58  6.67  5.54  9.25 12.92
73  5 27  9.04  3.54  5.88  1.96  5.04  4.04  2.25  4.12  5.71  4.75  7.87  8.21
73  5 28  3.04  3.83  5.63  2.50  5.83  2.33  4.83  2.33  6.13  4.17  4.21 11.42
73  5 29 11.71  9.96  9.96  3.96  8.50  5.00  2.96  5.75  6.17  3.58  9.08  7.08
73  5 30 11.04 12.08  9.38  6.25 11.12  7.41  9.21  9.17  9.71  8.00 14.37 13.08
73  5 31 18.25 14.29 11.25  7.92 12.46  7.17  8.00  7.71  9.29  9.42 11.92 16.71
73  6  1 14.92 11.34 10.54  5.25 11.21  7.79  8.12  9.54  8.71  9.67 16.46 17.21
73  6  2 14.92 13.79 14.50  6.50 13.92  8.46  8.58  8.17 10.25  7.25 11.87  8.75
73  6  3  9.17  7.41 10.21  5.91 10.34  7.17  6.87  7.79  8.38  7.38 10.13 12.83
73  6  4  3.37  4.08  5.66  2.13  4.08  1.08  3.17  2.04  1.54  1.13  5.29  7.62
73  6  5  3.79  5.21  5.21  1.46  5.63  4.17  3.71  4.63  4.38  2.50 13.37 13.25
73  6  6  5.09  3.13  7.62  3.00  4.21  1.63  3.21  1.79  2.79  0.96  8.92 10.75
73  6  7  4.50  4.25  5.58  2.37  5.66  1.96  1.04  3.21  2.04  1.75  5.25  5.04
73  6  8 11.42  6.79  4.50  3.96  9.42  6.13  6.63  5.79  7.17  4.00 11.12 13.62
73  6  9 12.00 10.54  7.75  8.08 14.88 10.46 14.79 11.75 12.04 10.75 15.25 20.33
73  6 10 12.71  8.42  7.87  6.87 11.79  8.79 10.00  8.46  8.96  8.25 10.88 12.38
73  6 11  9.96  7.96  9.92  4.46 10.21  7.25  9.67  9.33  9.92  7.83 17.37 18.63
73  6 12 16.17 16.75 16.29  9.87 14.83 12.33 13.75 16.33 13.83 15.59 23.13 27.16
73  6 13  9.29  8.17  8.79  6.38 10.34  7.96 11.04  8.92 10.17 11.54 13.17 21.50
73  6 14  7.12 13.25  9.00  5.54  7.96  6.29  5.37  7.41  5.46  5.75 18.05 12.29
73  6 15 10.37 12.58 11.54  7.46 11.17  8.75  8.17  8.58 10.00 10.41 12.83 15.46
73  6 16 12.54 14.96 13.17  7.17 11.92  9.71  9.04 11.71  9.29 11.71 20.30 17.92
73  6 17 11.96 10.25 11.21  5.75 11.25  9.17 11.21  9.83 10.37  8.71 15.21 13.46
73  6 18 10.96 12.71 11.92  5.88  9.92  8.38  7.79 13.08  8.38  9.29 20.25 17.29
73  6 19 16.38 12.62 11.04  7.92 15.75  9.08 10.00 10.13 11.04 11.08 16.62 17.12
73  6 20 14.50  6.54 13.67  7.62 10.17  7.08 10.88  4.17  9.67  8.67  8.83 12.04
73  6 21  6.38  6.96  6.54  1.67  4.75  2.54  3.04  5.71  3.21  2.54 14.67  4.63
73  6 22  6.92  4.46  5.71  3.00  7.00  4.42  2.75  4.54  5.75  4.92 12.87  7.92
73  6 23 10.29  5.04  7.92  4.58  5.71  3.04  4.50  3.04  4.29  3.37  6.50  8.83
73  6 24  6.54  3.21  4.46  1.87  4.79  2.08  2.71  1.46  1.96  2.67  7.21  5.09
73  6 25  4.92  6.58  5.13  1.92  5.66  2.46  2.54  3.79  4.12  4.71  9.25  9.21
73  6 26  3.75  4.67 13.13  3.13  5.54  2.46  3.63  1.42  3.71  2.13  4.21  5.41
73  6 27  4.50  6.29 12.00  2.83  5.58  3.37  2.67  3.00  4.46  3.63  7.17  9.21
73  6 28 14.21  9.87 10.25  3.75  8.29  5.41  5.96  9.75  7.38  7.71 17.41 13.17
73  6 29 18.05 16.50 16.21  8.46 12.92 11.58 13.21 13.88 12.00 13.21 19.21 17.79
73  6 30 19.46 12.42 16.66 13.04 11.25 12.08 12.79  9.83 13.17 15.21 18.05 17.33
73  7  1 14.71  8.17 13.62  9.21  8.50  7.67 10.79  3.63  9.54  8.92  8.67  8.04
73  7  2  8.00 12.17  7.75  5.37  9.83  7.33  6.00  7.79  6.92  6.08 15.50 10.00
73  7  3 11.21  8.75 11.50  8.08  6.00  6.13  9.38  7.50  6.58  7.54 13.75 16.83
73  7  4  4.50  9.83  6.08  3.75  3.58  2.75  3.58  5.21  3.33  4.79 11.67  9.21
73  7  5  6.96 10.04  5.29  4.67  8.96  8.00  5.91  9.29  7.21  9.08 18.84 16.29
73  7  6 16.21 11.21  7.25  6.42 10.04  6.54  6.42  4.04  7.83  8.58  9.33 12.54
73  7  7  6.54  4.71  8.25  4.25  5.75  3.83  4.33  2.92  5.58  5.09  6.83  9.75
73  7  8  4.88  9.13  5.13  3.58  4.21  3.71  4.08  3.96  3.29  3.46 14.50  7.38
73  7  9  8.71  8.46  9.83  3.37  7.87  5.41  5.50  5.83  5.91  6.71 13.13 12.38
73  7 10  5.91  4.96  6.79  4.50  9.50  6.71 10.00  4.63  9.25  5.83 13.00 13.88
73  7 11 13.29  8.04  9.50  5.83 10.58  7.62 10.92  7.50 11.04  9.17 11.00 13.17
73  7 12  5.96  5.91  6.75  2.88  8.25  4.58  5.54  6.25  6.34  4.63 12.92 11.08
73  7 13  6.54  7.41  8.04  3.13  8.12  5.41  5.63  5.00  6.42  5.09  7.92 10.04
73  7 14  4.92  6.63  5.04  2.62  6.08  2.62  5.09  3.25  3.46  2.08  6.17  9.50
73  7 15  8.12  5.75  8.83  3.96  7.67  7.17 11.96  9.33  9.75 11.21 14.21 15.25
73  7 16 10.37 14.37 16.04 11.04 14.92 12.79 17.92 13.88 12.58 15.67 18.50 18.54
73  7 17 15.34 13.21 10.92  7.83 11.92  7.71  7.54 10.25  9.38 10.79 15.12 14.92
73  7 18 12.08  9.08 10.75  4.67  9.96  6.42  6.58  6.96  7.04  5.75 11.38 10.29
73  7 19 13.62 11.67  9.75  8.63 14.83  9.50 12.12 10.41 11.71  9.13 15.25 17.29
73  7 20 11.63  9.50  8.75  5.79 12.21  8.71 10.50  7.71  9.62 10.34 15.59 13.17
73  7 21 16.17 10.17  9.21  6.50 10.79  7.29  8.38  8.87  9.00  9.17 14.37 18.79
73  7 22  7.17  3.33  9.83  4.33  7.54  5.71  8.33  5.71  8.42  6.63 10.17 13.67
73  7 23  5.29  2.83  4.67  3.67  6.00  4.00  7.00  4.38  7.12  5.29  8.17  6.46
73  7 24  1.25  6.00  3.42  0.96  2.33  0.13  1.42  1.13  2.67  1.29  4.33  3.88
73  7 25  9.50  8.71  7.29  3.83  7.00  4.08  4.79  3.42  5.75  6.08  8.25 10.34
73  7 26  9.00  6.25  6.50  4.04  6.83  4.21  6.29  4.21  6.54  7.25  6.21 13.75
73  7 27  5.21  4.67  7.17  3.79  6.04  3.58  4.71  3.58  6.13  7.54  3.58  8.33
73  7 28  5.63  4.21  9.29  3.21  5.41  2.50  2.37  2.83  3.46  2.17  6.21  4.38
73  7 29  4.46  4.88  8.54  2.42  6.29  2.21  2.25  1.96  2.75  2.00  5.37  4.25
73  7 30  8.12  4.71  4.38  2.29  5.58  1.87  2.08  2.21  4.46  3.75  7.92  9.96
73  7 31  5.58  5.46  4.38  3.46  3.17  2.79  3.08  6.50  3.67  5.00 16.46 12.50
73  8  1  7.46 10.17  8.38  3.58  6.00  6.58  5.09  7.29  5.09  5.33 11.79 13.62
73  8  2  4.63  4.79  8.71  4.38  6.71  5.09  5.41  4.33  6.87  5.37  9.33 10.92
73  8  3 12.17  8.08  8.75  6.13 11.46  7.96  7.79  7.50  8.50  9.00 13.33 16.17
73  8  4 16.38 14.88 13.75  7.29 14.12 10.00 11.83  9.04 10.92  9.71 15.34 16.04
73  8  5  7.33  5.71  8.87  5.41  7.38  5.33  8.38  4.67  6.87  8.17 13.54 14.79
73  8  6 17.67 13.62 16.83  9.17 15.21  8.50 11.79  8.33 10.29 10.13 15.29 15.63
73  8  7 15.92 15.37 11.75  7.87 14.67  9.75 12.21 10.46 12.83 10.83 15.67 17.50
73  8  8 12.75 11.83 10.37  5.33  9.33  6.92  7.54  6.50  7.62  6.58 13.54 14.00
73  8  9 18.75 16.42 17.00 10.75 15.63 13.67 15.63 15.29 15.16 15.96 23.33 28.79
73  8 10  6.71  2.88  8.58  3.08  3.54  1.83  3.83  0.71  2.75  1.58  5.46  8.87
73  8 11  5.58  2.46 14.25  2.96  2.29  0.71  2.83  0.92  1.25  1.29  4.79  5.33
73  8 12  4.67  4.71  5.63  3.04  3.46  3.67  3.21  2.37  4.58  5.46  4.38  9.67
73  8 13  5.58  3.21  4.71  2.42  5.71  4.04  3.42  2.29  4.00  2.83  6.34  7.58
73  8 14  7.38  4.04  8.17  1.33  3.92  2.17  2.42  2.13  4.63  3.50  2.42  7.17
73  8 15  6.54  1.54  6.54  1.67  3.29  1.83  2.54  1.92  4.38  4.25  3.58  3.88
73  8 16  5.00  5.04  6.25  2.62  5.37  3.75  4.21  5.00  5.09  4.08 10.92 10.29
73  8 17 11.08  7.67  7.58  4.92  5.88  3.00  5.83  3.96  6.50  5.37  8.67 16.21
73  8 18  5.54  9.42  5.91  2.71  4.58  1.63  3.17  1.54  1.83  1.54  5.50  8.38
73  8 19  7.46  8.71  9.59  3.63  8.83  5.29  3.96  5.21  5.96  2.50  9.71 12.96
73  8 20  2.42  2.00  6.87  2.62  5.88  3.58  7.17  3.13  5.21  5.88  9.71 13.88
73  8 21  6.75  6.04  8.46  3.04  6.75  4.29  7.71  7.00  7.50  6.08  8.58 14.09
73  8 22  7.17  8.38  9.29  5.21  7.25  5.09  5.91  4.12  4.96  5.13  9.21 13.00
73  8 23  2.54  2.25  5.13  1.75  4.29  0.87  2.25  1.04  1.50  1.21  5.33  4.46
73  8 24  3.88  6.13  3.54  1.33  4.67  1.08  1.38  1.38  1.96  2.17  5.71  5.13
73  8 25  3.29  4.79  4.54  1.21  6.17  1.58  0.71  3.29  1.75  1.58  6.79  8.12
73  8 26  5.46  7.29  6.79  4.04  5.21  3.21  3.21  2.00  3.54  4.25  9.83  5.46
73  8 27 16.33  9.67  9.79  5.88 10.37  5.50  2.92  4.04  6.08  5.41 10.75 13.42
73  8 28  9.21  7.79  7.83  2.58  5.04  2.46  3.67  5.63  3.17  3.75 14.79  8.92
73  8 29 14.17 12.12 10.08  6.04 10.63  6.21  5.75  6.75  7.38  6.29 13.79 15.79
73  8 30 12.50 11.21  9.25  6.42 11.04  7.21 10.41  7.54  8.87  8.33 14.21 17.58
73  8 31 15.29 15.71 15.71  7.87 19.00 11.87 15.41 15.00 14.29 14.54 24.25 24.54
73  9  1 12.96 11.00 11.63  7.38 13.46  9.17 11.96  9.83 12.54 10.67 18.91 23.87
73  9  2 12.08 13.67 11.25  4.92 10.17  5.79  5.04  3.58  5.58  5.13 10.54 17.08
73  9  3 14.12 15.21 14.71  6.17  8.12  6.21  3.71  4.92  5.41  3.04  5.50  6.50
73  9  4  6.34 12.38  8.54  5.58  9.38  7.21  6.13 10.46  7.71  9.54 12.29  7.21
73  9  5  4.54  8.87  5.96  1.96  4.92  3.37  2.83  4.88  3.37  2.25 14.09  9.50
73  9  6  8.96 10.88 10.58  4.42  8.29  6.71  5.63 10.79  7.21  8.04 19.58 18.12
73  9  7  3.88  9.92  7.25  1.79  5.17  3.13  4.92  6.25  4.29  6.00 16.38 13.70
73  9  8  8.50  3.83  6.87  1.04  5.37  1.83  1.83  1.25  2.83  1.79  9.50  5.96
73  9  9  5.25  0.83 11.83  1.92  3.17  1.42  2.04  2.46  2.88  3.00  9.42  6.83
73  9 10  9.46  1.75 15.50  3.25  6.08  5.66  6.79  4.29  6.63  3.50  8.08  9.92
73  9 11 10.92 11.08  6.50  2.79  8.79  2.54  4.88  4.50  3.96  3.21  6.08  8.92
73  9 12 11.71 11.50 10.41  4.12  9.33  4.96  4.04  7.17  5.88  4.42  8.50  6.92
73  9 13 14.92 10.34 15.96  6.21  8.67  6.71 10.17  9.67  7.75  4.71  9.67 11.42
73  9 14 17.12 13.04 14.58  5.75  9.87  7.83  5.00 10.17  8.92  7.04 10.79 16.79
73  9 15 16.04 14.12 11.21  6.83 14.71  9.79  6.79 12.75 10.04  7.54 14.83 17.12
73  9 16 11.50  7.46 13.59  5.79  6.25  6.25  6.04  7.04  7.67  6.21 10.83 12.50
73  9 17  9.83 10.00  9.46  2.54  9.33  6.00  8.42  7.41  7.17  6.00 14.96 12.50
73  9 18 17.04 11.54 17.79  4.42  9.25  5.00  6.25  4.08  4.46  2.96  8.29 10.34
73  9 19  6.04  3.58  7.75  3.71  3.00  2.75  6.29  3.67  4.79  4.04  7.21 10.58
73  9 20  5.13  6.79  9.42  3.75  5.71  2.54  2.96  3.92  3.63  2.96  6.17  8.54
73  9 21 11.12 10.75 10.88  5.54  8.42  6.67  9.17  7.04  8.25  6.00 12.50 11.87
73  9 22 16.83 15.67 14.75  8.50 11.58  8.21 11.71  8.29 10.13  8.29 15.54 19.38
73  9 23  8.79  7.17 10.92  4.75  6.38  2.54  6.25  2.54  4.67  4.58  7.79 10.88
73  9 24 12.33 13.25 10.00  3.79  9.25  5.17  4.12  7.29  5.13  4.33 12.67 16.96
73  9 25  8.87 10.37  9.00  2.79  7.00  2.67  5.66  5.79  4.04  4.42 13.62 12.67
73  9 26 10.83  9.13 11.08  5.79  8.92  5.71  8.12  5.66  6.87  6.79 13.21 16.42
73  9 27 16.62 18.91 14.54  9.21 18.34 11.04 14.75 13.96 13.00 13.67 19.08 24.92
73  9 28 16.21 23.21 13.21  9.79 17.71  9.08 16.42 11.00 11.21 11.63 17.79 22.08
73  9 29 22.88 20.04 16.71 12.79 16.33  9.79 13.50 13.46 12.62 12.17 19.55 29.29
73  9 30 18.16 12.62 17.16  8.54 10.04  6.17 10.17  7.33  8.71  9.42 11.12 20.21
73 10  1 10.08  8.38  8.21  3.42  6.08  2.46  6.21  4.46  4.33  5.46  8.08 13.33
73 10  2  8.33  5.96 12.83  5.04  5.46  2.17  3.58  3.50  3.75  4.29  3.88  6.04
73 10  3 10.92  6.13 19.87  6.38  8.00  6.21  7.17  5.83  6.50  4.08  8.63  4.88
73 10  4 11.42  7.67 12.42  4.29  7.33  4.79  3.88  6.96  5.63  5.13  7.62 13.88
73 10  5  8.75  3.46 11.42  3.71  6.17  4.67  6.38  5.41  7.17  5.33  5.09 10.79
73 10  6 13.67  4.67  9.33  4.29  7.54  3.04  4.25  3.29  2.96  3.75  4.54  9.13
73 10  7  5.75  5.46  6.17  2.25  5.37  2.29  3.00  3.96  3.71  3.83  9.50 10.79
73 10  8 12.33 12.17 11.67  5.37  9.00  5.79  7.17  6.42  6.34  6.38 11.42 15.54
73 10  9 10.67 10.50 12.04  4.50  9.71  4.38  5.09  3.83  6.13  5.29  8.25 14.12
73 10 10 10.34 11.08 15.79  5.17 10.46  7.79  6.96  9.00  7.33  7.75 13.04 17.75
73 10 11 16.25 18.96 18.58  5.79 11.38  8.25  9.92 12.08  9.00  6.63 12.17 15.04
73 10 12 21.59 22.25 22.63  9.00 19.38 11.75 12.62 14.67 10.50  8.17 16.21 18.21
73 10 13 17.04 12.62 17.62  6.83 11.38  5.88  9.42 12.83  9.38  9.67 14.33 20.75
73 10 14 13.17 10.34  8.50  2.42  7.62  4.50  2.58 10.04  5.17  6.13 12.00 15.83
73 10 15 10.63 10.88 14.00  4.25  8.75  6.04 10.25 10.83  8.33  9.71 11.96 16.21
73 10 16 18.84 15.54 24.67  8.29 10.17  8.00 10.21  8.04  8.08  9.04 14.12 20.75
73 10 17 10.92  4.92 11.17  4.79  7.33  3.37  7.96  5.63  8.17  8.75 11.92 22.83
73 10 18 10.54  6.75  9.42  4.42  9.96  7.33 10.46  8.92  8.04  8.50 15.34 17.46
73 10 19 13.62 10.46 10.04  7.17 13.54  8.12 13.37 10.71 11.75 10.75 13.96 21.62
73 10 20 17.41 13.83 11.34  8.00 14.54  9.21 12.17 10.75 12.50 12.50 15.25 22.58
73 10 21  9.04  7.00  7.17  2.46  7.33  4.08  6.54  4.67  5.54  7.17 10.29 16.25
73 10 22 12.67  7.38  9.62  3.83  8.42  5.46  7.12  6.87  7.83  7.67 11.46 17.46
73 10 23  4.92  5.71  4.46  0.67  4.88  3.42  3.88  6.08  3.25  6.63 12.33 14.50
73 10 24 10.46 11.79  8.17  2.46  7.41  4.75  4.50  8.63  4.54  8.92 17.54 20.38
73 10 25  5.63  8.75  5.83  2.21  6.13  3.13  1.79  5.29  3.42  5.75 13.33 12.38
73 10 26 11.75 10.08  8.04  3.46  9.46  4.83  3.58  6.92  4.96  7.83 13.75 16.17
73 10 27  8.46  9.59  9.71  2.62  6.46  3.63  3.79  6.34  3.50  5.37 11.00 13.92
73 10 28  6.83  9.83 11.08  2.96  6.87  2.83  3.21  2.71  3.79  5.04  5.04 11.83
73 10 29  7.21  5.83  4.17  0.50  3.63  0.46  1.58  1.46  0.42  2.92  4.88 12.83
73 10 30 13.75 14.50  8.12  2.75 10.75  4.71  2.62  6.21  4.25  6.58  7.08  9.25
73 10 31 14.50 13.13 13.50  5.75 11.46  7.54  8.87  9.00  7.62  8.58  9.50 11.12
73 11  1 10.79 12.75  9.59  3.58  7.87  5.50  5.17  6.34  5.09  4.33  4.75 10.63
73 11  2 12.83 12.58  8.92  5.21 11.08  6.96  4.54  7.54  6.42  8.50  7.79 12.83
73 11  3 11.92 12.62 10.25  4.54  9.92  7.04  5.54  6.75  5.41  6.75  5.71 11.00
73 11  4 12.83 10.58 12.21  6.92  8.54  3.96  6.50  6.58  4.92  5.66 13.21 17.88
73 11  5 14.42 14.83 11.67  8.17 11.17  7.08 10.41  7.33  9.54  9.17 14.25 27.92
73 11  6  6.83  6.67  6.46  3.46  5.58  3.75  6.08  4.79  6.50  5.71  9.50 17.00
73 11  7 12.33 13.92 11.83  5.96 13.42  9.46 10.46 10.96 12.17 11.58 18.38 22.79
73 11  8 17.12 15.75 17.33  6.13 17.71 13.62 16.66 19.04 17.29 20.04 24.25 26.75
73 11  9 22.54 18.54 20.04 11.87 14.83 10.58 13.67  9.00 14.21  5.96  4.63  9.92
73 11 10 16.88 12.83 11.42  6.67 11.71  5.88  8.83  7.62 10.00  7.33 13.79 17.50
73 11 11  9.83  9.62 10.04  4.79  8.08  6.67  9.00  8.21  8.79  9.67 14.42 24.54
73 11 12 19.79 19.21 16.21 13.29 22.50 16.21 20.75 17.46 20.04 20.79 24.00 33.71
73 11 13 13.21 13.62  9.79  9.87 17.04 12.42 12.87 12.17 13.46 13.75 18.88 27.29
73 11 14 12.67 16.08  9.33  7.41 13.17  6.71  7.38  7.75  8.58  7.79 11.50 19.25
73 11 15  7.71 11.25  6.63  4.29  8.17  4.42  7.87  8.17  8.83  9.83 11.83 21.17
73 11 16  9.46  8.12  9.04  3.96  6.63  4.04  6.21  4.92  5.58  7.29  9.59 19.70
73 11 17 13.96 10.21  8.17  3.17 11.12  6.87  2.75  7.54  6.79  6.83 14.17 16.62
73 11 18 13.59  9.83 15.04  7.12 10.75  7.50 10.04 11.25 12.33 13.75 16.71 24.37
73 11 19  8.50  5.91 10.13  3.17  6.58  1.71  4.33  4.29  4.58  7.62  5.83 18.38
73 11 20  6.46  6.08  6.92  0.46  7.33  2.46  0.63  2.42  1.92  2.46  3.08  8.87
73 11 21  6.67  8.33  7.54  2.42  8.08  3.25  0.00  3.42  4.92  2.79  8.25 13.50
73 11 22  6.50  5.71  8.21  2.00  5.63  2.79  3.29  2.75  5.17  4.50  5.88 16.04
73 11 23  5.41  4.92  6.25  3.25  7.75  4.46  8.38  6.63  8.58  8.58 11.63 18.41
73 11 24 10.75 12.87 10.04  8.33 15.21  8.38 13.04 12.00 12.96 14.79 15.16 26.83
73 11 25  9.71  8.21 10.92  3.92  7.08  2.21  4.67  4.33  5.13  6.34 10.17 21.21
73 11 26  7.21  1.71 12.92  3.29  5.33  0.83  7.62  2.96  5.71  8.12  8.46 23.58
73 11 27  8.96  8.71  9.00  3.21  8.46  3.00  4.46  3.79  5.17  4.79  9.08 11.42
73 11 28 16.46 17.04 10.37  8.17 16.88  8.33  7.50 10.25  9.38  7.25 15.63 14.33
73 11 29  9.17  8.08 10.25  3.63  9.33  2.83  2.33  4.21  3.96  2.33  5.37  7.50
73 11 30 11.71  9.46 11.87  3.29  6.96  5.50  4.79  8.12  7.92  5.04  9.08 12.29
73 12  1 16.29 12.29 17.29  3.96 11.92  9.54  9.13  9.00  5.58  7.41 11.08 15.71
73 12  2  4.83  5.96  6.54  1.17  4.67  3.29  7.46  3.54  5.79  4.46  9.59 15.04
73 12  3 14.21 10.29  9.08  7.12 10.88  5.96 14.21 10.46 11.04  9.62 13.92 22.04
73 12  4 13.70  6.17 14.50  7.58  9.62  7.17 14.09  9.21  9.54 12.62 11.71 19.83
73 12  5  8.79  8.42  8.96  3.37  6.75  5.41  9.62  9.00  9.25  9.46 13.42 19.46
73 12  6 13.29 11.92  7.79  4.12  9.04  4.79 10.34  6.08  8.12  9.04  8.79 19.38
73 12  7 19.75 22.54 12.75  9.25 20.00 10.54 11.92 11.29 10.29  7.21 16.58 16.96
73 12  8 17.67 12.79 17.62  7.83 10.88  7.38 12.33  5.88  7.46  8.67 10.21 18.54
73 12  9 12.75 12.96  9.17  1.63 11.12  8.58  8.96  9.92 10.37 10.00 17.83 23.45
73 12 10 23.91 16.17 18.75 10.46 13.13 12.92 15.92 12.38 14.17 16.71 17.71 22.71
73 12 11 10.00 11.25  8.04  4.38  8.17  5.25 10.88  6.17  8.79  8.12 13.00 20.12
73 12 12 16.38 17.62 12.67 11.67 18.00 11.58 18.88 17.50 15.25 16.17 23.00 29.71
73 12 13 27.29 20.83 11.71 15.41 23.29 13.70 19.25 18.21 16.42 20.75 17.92 30.96
73 12 14 19.12 11.71 15.16 10.58 13.79  8.67 14.29  8.17 12.04 14.83 12.33 26.38
73 12 15 11.04 12.46 11.67  3.92 11.21  6.96 14.33  9.25 10.96 10.96 17.12 21.87
73 12 16 17.83 13.67 11.96  6.71 13.33  8.71 16.79 10.63 13.08 13.83 18.54 31.49
73 12 17 12.75 11.17  8.67  4.79 10.04  6.75 11.96  7.79 10.13  9.21 13.96 25.12
73 12 18 13.59 14.58 12.08  3.17  9.75  6.13  7.41  6.38  7.50  6.04  8.63 15.87
73 12 19 17.29 14.50 23.29  9.50 11.71 10.88 18.25  9.83 13.96 14.25  7.87 23.67
73 12 20 13.29 18.88  6.92  2.67 10.00  1.13  2.92  7.75  4.46  3.83 19.33 11.46
73 12 21 14.42 14.46  8.42  3.58  8.79  2.21  4.88  2.42  5.04  3.75  7.25  7.41
73 12 22  6.71  4.71  6.87  1.25  4.42  1.79  3.21  4.79  5.46  6.21  7.21  8.25
73 12 23 11.83  8.17  8.21  3.08  5.58  0.54  3.04  4.17  4.04  4.96  6.17 17.12
73 12 24  9.62  8.42  9.59  4.00  6.67  3.17  3.92  3.75  4.54  4.46  5.58  7.75
73 12 25  3.00  4.83  7.71  1.42  8.08  4.92 11.17  7.96  9.67  9.38 11.71 19.25
73 12 26  6.96  8.25  6.54  4.04  9.21  6.29 11.96  7.38  9.33  8.79 12.92 18.91
73 12 27 12.46 10.46 12.96  6.04 11.21  8.92 10.96 11.63 11.54 12.12 15.71 22.79
73 12 28 15.59 14.17 11.67  5.83 10.13  8.17  9.13  7.62  8.33  5.91 14.50 17.71
73 12 29 21.12 18.58 20.12 12.08 16.00 14.00 16.33 15.96 16.13 18.34 24.54 29.04
73 12 30  7.62  6.42  6.04  2.13  6.50  3.25  8.71  3.67  5.63  7.54 10.58 17.79
73 12 31 10.67 10.04  6.87  1.46  6.96  5.75  3.83  6.21  4.75  6.13 12.79 15.79
74  1  1 23.21 16.54 16.08  9.75 15.83 11.46  9.54 13.54 13.83 16.66 17.21 25.29
74  1  2 26.63 22.34 25.62 15.37 21.59 16.83 15.34 17.83 19.83 19.79 22.29 26.87
74  1  3 20.46 10.92 21.62 10.75 11.92 13.04 15.00 11.67 17.79 17.58 11.42 26.79
74  1  4 26.08 17.71 24.58 15.71 15.92 14.21 14.04 13.96 17.88 19.58 20.67 24.08
74  1  5 24.46 13.50 20.25 13.33 13.13 11.79 12.62 10.29 16.46 17.92 17.46 24.67
74  1  6 21.09 16.66 15.21  7.54 13.70  8.71  9.04  9.71 10.92 11.96 19.17 19.00
74  1  7 20.04 16.50 14.75  9.21 13.46  8.46 11.46  7.75 11.87 11.08 19.08 15.34
74  1  8 19.75 17.37 16.29 11.75 16.62 10.13 13.33 11.87 14.33 14.67 23.38 24.92
74  1  9  6.50 10.63  6.83  1.21 10.21  5.00  7.29  6.04  7.71  7.29 13.88 17.75
74  1 10 28.75 26.63 24.58 16.17 23.00 16.79 16.92 19.67 19.50 21.54 31.08 31.75
74  1 11 28.62 26.30 22.21 15.87 23.13 16.04 20.25 19.92 19.00 19.83 30.21 31.20
74  1 12 27.58 22.75 21.21 15.54 22.42 17.67 20.83 18.54 20.08 21.09 26.04 32.30
74  1 13 18.91 16.42 15.16  8.21 14.42  9.71 13.92  9.96 12.08 13.33 16.42 23.21
74  1 14 25.70 24.33 21.37 11.79 16.88 10.00 13.67 10.79 12.33 12.58 15.71 20.62
74  1 15 15.83 18.00 14.42  9.33 16.21 11.54 17.16 14.62 14.88 14.25 22.42 25.29
74  1 16 31.75 22.67 23.21 12.96 19.17 13.13 16.96 12.71 13.70 14.29 18.08 28.79
74  1 17 20.96 20.79 17.37  9.96 21.96 14.09 19.00 14.96 18.16 14.37 26.16 27.21
74  1 18 18.16 16.46 13.42  9.46 20.67 14.04 20.62 15.75 18.16 17.50 25.21 31.20
74  1 19 18.16 15.87 13.33  7.12 11.04  8.46 15.46 15.09 14.21 15.79 24.17 26.50
74  1 20 18.79 16.21 15.63  9.33 13.59  8.42  7.25 11.04 12.71 12.00 21.84 25.96
74  1 21 11.17 10.54 11.04  4.42  8.29  5.13  5.75  5.79  7.17  5.33  4.67 10.00
74  1 22 13.46 13.08 13.88  6.79 10.08  8.08  8.71 10.08 13.00 12.25 16.62 19.58
74  1 23 15.83 16.71 14.04  8.75 14.96 10.08 15.54 12.17 13.62 15.00 23.38 26.87
74  1 24 12.79 10.96  9.79  5.79 10.08  7.12 12.04  9.17 10.13 10.21 16.46 20.54
74  1 25 24.75 19.08 17.71 10.96 13.88 11.08 13.83 12.46 13.67 14.25 18.75 22.08
74  1 26 22.25 17.83 18.91 12.08 16.42 11.79 15.46 12.38 16.04 16.58 18.71 21.34
74  1 27 23.04 21.96 19.04 12.83 19.29 14.33 15.96 17.25 14.96 16.21 19.41 25.84
74  1 28 19.92 17.50 18.58 12.58 18.91 12.58 20.41 15.54 19.04 23.75 25.96 38.79
74  1 29 27.58 19.70 21.29 13.21 16.88 11.92 15.46 13.67 15.63 16.66 18.79 26.20
74  1 30 14.92 13.33 16.54  7.92 10.54  6.79 10.00  9.87 11.17 11.00 18.46 19.04
74  1 31 23.58 17.04 19.00 11.34 17.41 11.83 14.62 12.29 15.59 13.54 14.71 22.75
74  2  1 21.59 16.54 21.09 13.04 13.96 12.33 15.92 12.46 16.04 16.50  9.46 22.67
74  2  2 18.58 20.58 14.54 10.63 18.84 10.71 15.09 13.04 13.29 10.46 19.21 16.25
74  2  3 13.88 13.37 11.08  5.54 12.17  8.12 10.29  9.54 10.50  9.79 14.37 19.29
74  2  4  9.25  5.46  8.00  4.12  7.79  4.04  9.50  4.50  8.58  6.67 11.50 17.96
74  2  5 11.63 12.00  7.71  2.88  8.50  5.71  8.25  6.63  8.46  7.75 12.83 16.96
74  2  6 24.08 21.75 13.13  9.59 14.96  8.00 12.21  8.87 10.34  9.33 16.96 19.79
74  2  7 11.38  9.96  8.63  5.04  7.67  4.04  6.83  5.17  6.67  6.04  8.46 14.09
74  2  8 19.55 20.25 17.92  8.46 15.12 11.46 13.62 13.88 14.96 11.50 19.70 23.79
74  2  9 21.67 20.30 16.38  9.08 15.12  9.67 14.17 10.96 13.33 12.17 18.50 20.75
74  2 10 28.84 27.04 23.71 17.29 21.59 16.71 19.79 20.62 19.50 22.58 27.63 29.08
74  2 11 14.67 11.63 13.21  6.17 11.71  6.04 11.38 10.17  9.04  9.87 17.33 20.67
74  2 12 10.25  8.67  8.12  2.67  9.33  5.25  8.92  7.12  8.54  8.17  9.25 16.25
74  2 13 12.00 10.83  8.71  5.37  8.75  5.88  7.71  7.29  6.38  7.38  7.00  8.38
74  2 14 22.21 19.46 18.71  8.33 15.37 11.21 11.96 11.58 14.09 13.04 14.46 20.58
74  2 15 16.58 10.71 17.04  8.04 11.12  8.08 11.83  8.04 11.34 12.00  5.13 15.87
74  2 16 23.38 21.62 14.12 13.08 16.92 11.29 15.92 16.54 13.13 16.33 19.95 22.95
74  2 17 10.00  7.96  9.33  4.29  4.75  2.71  7.08  4.21  4.88  5.58  5.58 11.25
74  2 18 10.17 14.71  5.17  2.29  8.54  4.46  3.58  3.79  5.25  4.08  9.54 11.12
74  2 19 11.04  9.79  8.17  4.63  6.38  1.25  3.37  3.42  5.63  2.96  5.54  8.71
74  2 20  6.38  4.17  8.12  2.00  6.58  4.63 10.88  9.21  8.38  7.12 10.75 18.12
74  2 21  9.83 11.29 10.17  5.29 10.75  8.12 11.96 11.12 11.92 11.12 15.92 21.42
74  2 22 15.16 11.63 11.79  8.12 13.00  7.21 12.71 11.08 10.13  9.83 14.29 22.92
74  2 23  4.17  0.63  7.41  2.67  5.41  1.00  7.00  3.71  4.17  3.58  5.79 10.67
74  2 24  1.54  7.25  2.37  1.63  4.00  1.13  2.13  6.17  3.29  5.50 11.08 13.96
74  2 25  1.42  1.42  2.50  0.58  1.50  0.50  4.63  3.29  2.71  3.33  5.37 11.29
74  2 26  6.42  5.96  6.71  0.63  3.75  0.58  2.29  1.46  1.54  2.13  2.13  3.17
74  2 27  7.50  8.67  6.92  1.58  7.79  3.33  3.79  5.13  3.58  5.37  8.42 11.34
74  2 28 16.96 15.00 14.62  7.87 15.16  9.00 12.12 11.21 12.29 13.08 14.67 23.67
74  3  1 12.67 11.50 10.04  7.08  9.75  6.46 12.04  7.67  8.17  7.08  8.50 15.34
74  3  2 10.04  8.75  6.58  1.71  7.38  1.46  7.83  3.50  3.96  3.33  5.83 10.13
74  3  3 13.46  8.79 14.21  5.91  6.50  4.17  9.83  4.79  6.42  9.13  6.96 15.67
74  3  4 10.29  3.17 12.21  5.58  5.29  1.63  9.33  5.04  7.04  5.66  2.37 16.21
74  3  5 10.17  9.75  6.63  2.79  7.21  3.79  3.83  6.83  5.04  4.21  7.79 10.34
74  3  6 18.96 19.83 18.63  8.50 13.00 11.34 12.54  9.54 12.79 11.58 12.92 17.54
74  3  7 15.75 19.79 13.08  9.75 15.87 11.63  9.17 15.34 14.09 13.62 20.33 21.79
74  3  8 14.96 21.17 13.75  6.42 16.25  9.83  9.13 13.62 11.21  9.87 15.75 19.00
74  3  9 10.41 10.13 16.54  4.96  8.63  5.71 11.46  9.83  8.83  6.75 10.58 19.21
74  3 10 11.58  6.87 22.50  7.17  7.92  5.88 12.79  8.38  9.04  8.83  8.04 23.04
74  3 11  9.17  6.58 14.79  5.25  7.87  6.38 11.38 11.58  7.79  8.08 10.79 24.87
74  3 12 10.46  8.92  9.75  3.54  5.41  3.00  5.25  9.17  5.63  6.83  9.33 20.30
74  3 13  5.25  2.29  6.08  2.50  4.33  0.33  4.04  1.75  2.96  3.25  2.54  7.12
74  3 14 10.79 12.21 10.67  5.83 10.79  7.29  7.38 10.13  8.29  8.87 11.38 16.33
74  3 15 15.34 16.88 13.83 10.58 16.71 10.75 14.04 13.96 12.46  9.50 16.62 13.25
74  3 16 16.08 17.08 15.00 11.54 17.96 12.62 18.96 17.79 14.96 14.75 22.67 19.67
74  3 17 19.41 17.33 17.21 10.25 16.17  9.83 15.75 16.00 14.29 13.46 21.87 23.96
74  3 18 17.16 17.29 14.71  6.87 12.96  8.58 11.29 13.79 12.04 11.63 19.75 21.25
74  3 19 15.79 16.17 16.71  7.71 10.92  7.50  8.12 10.46  9.21  8.54 14.25 14.58
74  3 20 11.29  9.92  5.63  5.17  8.21  3.54  4.92  6.54  5.25  4.88  6.25  8.71
74  3 21 12.12 15.00  9.96  6.71 11.08  7.25  4.38 11.58  9.54  8.42 17.12 16.71
74  3 22 12.87 13.08  8.96  6.50 12.21  8.96  6.71 12.50 10.96  8.79 15.00 17.75
74  3 23 14.12  6.25 14.67  4.96  8.75  9.79 13.70 11.67 14.92 10.34  9.54 18.00
74  3 24 10.21  5.41 14.04  5.96  8.38  7.92 15.00 12.75 11.50  8.87 10.96 18.21
74  3 25  7.04  5.46  9.50  2.50  5.66  1.71  4.83  7.58  6.04  4.12  6.34 12.92
74  3 26  2.79  2.08 11.25  4.17  2.75  0.25  2.88  1.71  3.00  1.87  2.50  7.46
74  3 27  3.96  2.67 13.67  4.21  4.54  0.25  2.42  0.96  1.17  2.17  5.21  2.04
74  3 28  4.83  2.04 16.83  5.17  5.41  1.50  4.33  1.33  5.00  4.50  6.83  6.87
74  3 29  3.37  5.75  6.54  1.87  3.50  1.29  2.33  4.79  2.37  4.71  9.62  6.83
74  3 30  4.33 12.87  3.50  1.63 10.37  2.83  2.21  8.38  4.00  4.54 13.42  8.46
74  3 31 11.17 17.46  5.13  5.25 15.09  9.21  6.25 11.25 10.54  8.25 17.21 15.83
74  4  1 15.83 14.79 12.12  7.29 14.37  8.04  8.38 12.71  9.00  8.25 11.08 20.71
74  4  2 18.21 11.38 15.09 10.13 15.29  9.13  9.17 11.83 10.04  7.17 10.29 14.83
74  4  3 11.29  8.00 18.79  5.17  9.25  5.37  5.29  5.37  5.75  3.83  6.04  7.92
74  4  4  8.42 11.29  9.67  4.50  5.58  6.34  4.75 10.00  8.42  6.42  6.71  9.04
74  4  5 12.04  5.09 10.08  4.67  8.54  7.83  4.25 11.58  7.71  7.00 10.34 11.17
74  4  6 13.13  2.71 13.25  4.12  8.29  5.13  6.87  9.96  8.00  7.00  8.63 13.75
74  4  7 15.09  5.54 15.04  4.33  6.83  5.50  5.17  9.38  8.00  7.08  8.00  9.92
74  4  8 13.59  4.33 15.75  4.12  6.75  7.08  5.09  7.83  8.50  6.34  8.08  7.62
74  4  9 11.12  4.08 11.71  2.58  4.46  3.67  3.92  7.25  7.12  5.04  7.62  9.04
74  4 10  5.13  7.54  7.21  2.54  9.54  2.00  3.37 10.08  4.04  4.38  9.79  7.21
74  4 11  7.41 14.17 10.75  4.04 10.54  4.79  8.63 12.46  8.67  8.96 16.50 19.70
74  4 12  9.79  7.75 10.46  4.96 11.42  8.12 11.92 15.41 12.29  8.54 13.08 22.63
74  4 13 11.34  9.96 20.83  5.09 10.79  4.25  8.00  6.42  7.04  5.83  7.96  9.08
74  4 14  9.38  5.50 18.46  4.21  8.29  5.00  6.75  7.58  7.17  3.50  8.17  4.33
74  4 15  6.75  4.00  7.38  1.87  4.71  0.58  1.83  5.09  3.79  2.08  5.66  4.17
74  4 16  4.92  8.46  6.58  2.17  4.79  1.17  2.96  5.75  4.29  2.50  5.66  4.54
74  4 17  4.38  4.17 10.75  2.04  4.46  1.38  2.75  3.71  3.37  2.21  3.96  5.66
74  4 18  7.21  4.00 15.41  3.71  5.21  3.29  2.25  3.00  3.21  1.67  6.63  5.33
74  4 19 10.75  8.58  6.96  2.62  8.83  2.75  2.96  5.91  4.12  3.08  7.41 10.58
74  4 20  7.00  7.62  4.79  0.92  7.21  1.00  1.33  2.92  2.62  2.25  5.79 10.21
74  4 21  3.67  1.63  4.08  1.67  3.92  0.63  3.29  3.75  1.42  1.38  3.21  4.96
74  4 22  3.63  4.96  7.62  4.50  6.50  2.79  2.21  5.75  4.12  4.46  3.88  8.46
74  4 23  5.04  5.29 11.79  3.08  3.83  2.17  3.33  5.88  3.50  4.25  8.00  5.21
74  4 24  5.41  2.50 11.00  1.67  3.58  0.63  3.29  3.96  2.58  1.79  8.25  5.46
74  4 25  4.75  4.08 14.54  3.67  5.37  1.79  4.79  5.25  2.58  3.21  3.75  8.38
74  4 26  8.12  4.88 14.25  4.54  5.71  3.67  7.62  6.29  5.75  5.75  3.88 11.58
74  4 27 10.67  4.92 17.96  4.63  7.33  5.29  9.50  7.41  6.46  6.25  9.83  9.71
74  4 28 11.87  6.04 20.46  7.41  7.96  4.29 11.75  9.00  7.41  7.46  9.25 15.12
74  4 29 10.08  7.41  8.12  3.37  5.17  1.08  4.33  5.25  4.50  5.21  6.71  7.12
74  4 30 18.41 16.92 12.08  6.21 15.25  6.63  7.41 11.50 10.29  7.83 15.34 13.50
74  5  1 17.21 11.17 12.21 10.21 14.29  8.75 13.70 12.17 10.75 13.13 12.12 17.08
74  5  2 17.04 14.09 10.83  4.71  9.75  4.29  8.71  5.66  4.67  5.00  6.50 12.04
74  5  3 12.42 12.17 16.25  7.67 12.12  9.04 14.54 13.00 10.71 10.96 12.12 18.75
74  5  4  8.87  3.37 17.58  5.41  7.46  7.41 12.92 10.88  8.63  9.33 11.87 13.21
74  5  5  5.83  6.25 15.96  5.71  5.71  4.38 10.29  5.63  5.54  6.83  6.04  9.54
74  5  6  4.83  3.96  7.67  2.96  3.33  1.17  5.91  2.71  3.79  4.88  4.71 11.58
74  5  7  8.58 12.17  6.58  4.00  8.21  5.04  6.58  8.46  5.58  6.38 15.96 11.71
74  5  8 21.75 15.00 18.63 10.13 15.29  9.96 13.00 14.17 13.75 15.67 16.17 22.42
74  5  9 18.25 14.79 18.66 10.00 12.12 10.21 18.58 13.46 14.00 15.12 12.08 21.79
74  5 10 14.21  9.00 17.25  7.92  9.29  6.38 13.54 10.46 11.12 13.37 10.75 22.71
74  5 11 15.41 11.54 12.67  3.71  7.46  3.92  5.58  8.87  4.63  5.00 12.38 10.92
74  5 12 24.13 17.79 19.55 13.00 18.21 13.50 17.50 17.04 15.54 15.25 16.38 21.92
74  5 13 21.71 17.92 17.16 10.17 16.46 10.63 12.92 16.21 13.17 15.79 18.50 21.50
74  5 14 14.21 18.50 11.17  7.04 14.04  8.33  9.13 12.42 10.08 10.41 17.83 14.09
74  5 15 17.62 22.95 12.75 10.13 20.83 12.75 10.79 17.33 15.54 12.46 20.58 23.79
74  5 16 10.04 10.96 10.04  4.83  7.83  4.12  7.54  7.17  8.00  9.54  9.59 16.33
74  5 17 15.54 16.42 13.29  8.38 13.96  8.21  8.92 13.00 11.08 11.67 18.71 17.50
74  5 18  9.87  7.25  8.33  2.79  6.34  3.04  2.67  7.00  5.09  3.58 10.96  7.25
74  5 19  9.96 15.50  8.71  5.33  9.79  4.92  5.91 10.08  5.91  6.04 18.75  8.33
74  5 20 14.09 11.92 12.75  5.83 12.17  6.87  9.96 14.50 10.79 11.87 19.79 19.08
74  5 21 13.29 11.92  9.75  4.75  9.46  5.46  8.58 11.50  8.71  7.71 16.38 16.42
74  5 22 18.08 15.67 10.37  9.50 17.75 10.83 14.62 16.38 12.83 12.12 19.00 21.04
74  5 23 27.08 18.63 15.79 13.46 18.08 12.83 14.92 18.21 13.50 15.71 19.87 22.92
74  5 24 14.75 15.83 26.20 10.17 11.75  7.54 11.38 13.70  8.83 10.46 19.50 20.08
74  5 25  6.21  6.96 10.37  3.54  7.71  2.29  1.58  8.63  5.04  6.96 10.17 12.33
74  5 26  6.67  6.54  3.21  3.42  6.08  1.92  2.37  5.37  4.25  5.09  6.54 12.87
74  5 27  8.75  6.21  6.08  4.25  8.25  3.17  6.17  8.54  5.63  7.58 10.34 16.17
74  5 28 15.25 10.67  9.46  7.08 14.04  6.92 12.58 11.25 10.58 12.87 13.59 17.88
74  5 29  6.13  3.04  7.25  2.21  4.33  0.87  6.46  2.83  2.88  3.29  4.54  9.71
74  5 30  8.33  8.71  6.67  4.12  7.87  3.63  6.71  8.63  7.83  7.58  7.71 13.96
74  5 31  8.71  7.96  9.83  3.92  7.12  2.54  6.04  9.87  5.29  6.71 13.46 11.29
74  6  1 16.54 15.92 15.46  9.87 13.67  9.13  9.33 13.75 12.08 12.17 22.34 19.70
74  6  2 13.00  9.54 11.83  6.54 13.88  7.38 12.04 12.46 11.38 10.71 18.54 22.00
74  6  3  8.54  6.50  6.42  4.50  9.83  4.75  9.21 10.29  7.21  8.83 13.88 20.17
74  6  4  6.46  8.46  6.34  4.12  8.04  2.92  5.63  8.54  6.08  5.33 14.79 12.83
74  6  5 17.04 14.62 11.46  7.58 15.12  9.04 11.75 13.75 10.79  8.96 18.46 18.88
74  6  6 17.50 17.46 13.42 10.17 19.58 12.25 17.50 17.29 13.62 13.29 18.96 23.54
74  6  7  8.12  5.29 10.41  4.38  8.04  4.83 10.21  9.79  8.17  7.71 11.54 17.00
74  6  8 14.96 10.00  8.17  5.88 13.70  5.66  7.21  9.38  7.41  9.71 12.00 12.33
74  6  9 18.96 11.63 10.63  8.00 16.00  8.63 10.34 11.46 10.21 12.46 12.87 19.50
74  6 10 14.92  9.79 10.63  8.38 13.96  8.12 11.38 11.58  9.92 10.37 13.00 18.05
74  6 11 15.00  5.37  9.29  7.00 11.87  7.96 14.12 12.50  9.62 11.63 12.71 19.67
74  6 12  4.79  2.88  4.46  1.67  4.25  0.87  3.42  2.88  2.83  1.29  7.41 10.17
74  6 13  6.50  7.04  7.25  4.88  6.87  4.96  5.33  7.41  7.17  6.25  9.38 13.83
74  6 14  4.46  4.50  4.79  3.25  5.91  2.58  4.25  7.62  2.58  3.92  9.83  8.54
74  6 15  8.08  8.04 13.04  4.33  6.58  2.67  3.29  5.88  4.21  4.00  5.83  9.38
74  6 16  8.50  5.54  6.25  2.13  4.96  2.37  2.37  5.54  3.46  3.04 10.54  4.54
74  6 17 14.67 10.21  8.21  5.21 12.12  5.37  6.17  9.21  6.13  6.87 10.25  8.87
74  6 18 12.00  9.92 11.04  4.42  9.92  5.29  7.46  7.92  7.75  7.21 12.92 10.04
74  6 19 10.92 12.62 11.75  4.17  7.71  5.04  5.75 11.17  6.79  5.33 15.63  9.62
74  6 20 12.29 12.96  7.87  5.66 11.46  7.87  7.38 10.67  8.54  7.46 13.00 11.83
74  6 21 17.12 20.17  7.08  5.63 11.08  7.17  6.08 12.00  9.21  6.92 11.21 13.83
74  6 22 20.12 19.41 14.04  7.08 13.75  9.79 11.54 16.25 11.54  9.42 13.59 17.29
74  6 23 10.17  2.29 10.08  4.50  7.50  8.00  8.50 11.58  9.67  7.67 10.37 13.08
74  6 24  7.62  4.25 13.67  3.21  6.00  4.46  6.87  6.75  6.83  5.17  7.92  8.63
74  6 25 12.46  6.50 18.00  3.63  6.38  8.42  8.42  8.38  9.00  5.66 11.38  8.54
74  6 26  6.58  1.79 18.50  4.58  8.38  7.25 11.38 10.25 10.21  7.46  9.79 12.00
74  6 27  8.38  3.88 19.00  4.96  8.08  5.63 10.29  9.25  8.04  7.12  9.71  7.87
74  6 28  4.17  3.17  6.38  2.79  3.37  1.38  4.17  3.63  4.29  5.63  5.37  6.79
74  6 29  6.00  4.42  7.00  2.62  6.00  3.50  3.17  5.75  3.75  4.21  7.50  6.71
74  6 30 12.79  8.71 10.63  4.46  7.46  4.88  5.25  9.59  4.63  5.91 11.34 11.46
74  7  1 13.29  6.92 10.37  7.17 10.13  8.29 12.96 11.08  9.79 12.12 11.79 20.50
74  7  2 17.58 12.83 13.17  5.09  9.46  6.13  7.58  8.79  7.41  6.17  8.58  7.87
74  7  3 15.09 13.21 13.62  6.42 13.62  8.21 11.12  9.75  9.25  8.04 11.92 14.58
74  7  4 14.50 11.08 12.83  6.13 11.34  6.46  8.21  8.67  8.00  2.79 11.04  8.54
74  7  5 12.87  9.04 10.96  7.92 13.59  8.79 12.42 12.08 10.37  9.83 13.92 18.21
74  7  6  6.17  6.08  8.46  2.04  6.25  2.46  7.41  4.58  5.17  5.83  7.50 12.50
74  7  7  7.62 11.54  9.50  4.29  8.29  5.88  7.00 10.63  6.96  7.12 18.16  9.38
74  7  8 13.88 13.42 12.87  7.58 13.59  8.67  7.96 12.92 10.34 10.58 18.08 15.75
74  7  9 11.87 10.04 10.37  5.41 12.58  6.71 10.41  9.54  8.87  8.38 16.46 16.46
74  7 10 16.71 13.75 12.92  7.08 14.88  9.04 11.54 14.37 12.00 12.04 18.25 19.25
74  7 11 11.25  9.67 10.37  7.00 13.29  7.87 10.67 12.00  9.96 10.46 16.08 18.29
74  7 12  9.13  7.00  8.33  4.88  9.83  6.08  8.50  8.54  5.96  8.67 10.83 15.63
74  7 13 13.79  8.63  6.79  4.42  8.92  5.63  5.91  8.29  4.71  7.71 11.58 14.62
74  7 14 15.25 12.08 10.83  5.29 10.08  6.13  7.62  9.54  7.71  6.75 14.29 10.88
74  7 15 18.58 15.34 15.63  8.54 16.25  9.75 12.50 12.75 10.37  9.50 17.08 14.00
74  7 16 21.00 14.71 14.62 10.37 14.37 10.58 11.17 14.58 10.54 13.13 20.50 25.37
74  7 17 14.50  6.25  9.59  6.25  8.75  4.88  7.12  6.34  6.25  7.08  9.62 13.50
74  7 18  5.83  4.54  7.29  1.42  5.04  3.04  7.50  4.38  3.83  3.92 10.37 14.58
74  7 19  6.96  4.29  7.41  5.17 11.75  7.04 13.17  9.21  9.17  8.63 14.96 19.75
74  7 20 11.79  6.75  7.25  6.21 10.46  6.83 11.71 10.88  8.33  9.21 12.79 19.62
74  7 21  7.25  9.04  9.29  5.29 12.87  8.25 16.29 12.12 11.87 11.25 17.92 23.21
74  7 22 15.54 10.41 14.33  9.13 14.88 10.41 17.29 12.12 12.17 11.38 18.46 23.58
74  7 23  9.29  9.08  9.17  5.29  8.83  5.58 10.08  7.87  8.00  5.63 12.17 17.58
74  7 24 16.46 10.58  8.92  6.42 11.79  5.91  8.58  8.21  7.17  7.67 10.54 14.46
74  7 25  9.92  8.92  9.67  4.33  9.46  6.17  7.87  9.33  6.75  5.79 11.92 13.83
74  7 26  9.46  9.33  9.04  3.88  8.46  5.37  6.79 10.79  7.12  6.46 14.58 12.92
74  7 27 11.00 10.46 12.92  5.71 13.25  7.46 10.63  8.83  9.71  8.00 12.17 15.00
74  7 28 12.83  9.46 10.29  6.42 12.08  7.62 11.29  9.04 10.34  7.87 12.08 15.59
74  7 29 12.58 12.62 14.00  6.34 13.21  7.08  8.50  4.04  7.21  4.00  7.29 15.00
74  7 30 12.87 13.46 13.42  5.21 12.17  6.50  6.00  5.13  8.00  2.04  7.38  3.75
74  7 31 13.54  7.38 13.04  4.58  8.42  2.88  3.13  3.71  4.12  2.33  5.83  8.33
74  8  1  6.29  6.04  7.25  3.00  7.33  4.63  5.96  8.46  7.21  5.21 11.46 16.96
74  8  2  5.17  5.83  7.17  3.04  7.08  4.50  6.83  8.46  6.29  6.46 10.37 14.54
74  8  3  3.75  4.58 11.00  1.92  3.13  0.63  2.50  3.42  1.63  3.33  9.08 13.59
74  8  4  5.54  4.96 14.83  3.37  4.17  1.79  3.79  1.79  3.67  1.75  4.29  6.92
74  8  5 10.41 15.83  9.67  6.21 11.46  8.04  4.17 10.41  9.00  7.75 13.83 12.67
74  8  6 13.25 16.38 11.50  8.38 13.42  8.92  5.29 12.00 11.42 11.12 14.37 17.92
74  8  7 10.67 11.96 11.87  6.67 10.63  8.29  7.12 10.58 11.38  7.12 12.12 18.21
74  8  8  9.50  8.58  9.13  3.54  6.92  3.88  5.71  6.46  5.66  5.66 10.29 14.04
74  8  9 14.09 11.63 10.92  4.42 10.04  4.96  7.62  7.96  7.87  5.91 10.58 10.37
74  8 10 16.92 12.46 11.46  6.79 12.12  7.25 11.38 10.54  9.38  9.17 13.04 16.46
74  8 11  7.38  9.46  9.75  3.88  7.75  5.66  7.00  8.29  7.92  7.79 12.42 16.83
74  8 12  9.96  9.96 11.25  5.29 11.54  6.34 10.63  9.62 10.92  9.42 15.75 19.29
74  8 13  7.41  4.71  8.79  1.75  4.46  1.96  4.79  3.83  4.21  3.37  7.21  8.50
74  8 14 18.71 17.21 17.00  7.29 13.37 10.13 10.25 14.37 12.50 11.21 15.71 18.41
74  8 15 19.55 16.46 16.79  9.87 13.59 11.29 14.25 19.41 13.42 12.54 23.25 22.34
74  8 16  7.71  7.87 11.58  4.46  9.59  7.04 10.96 10.04  7.75  9.13 10.75 17.08
74  8 17  5.09  5.33  7.67  2.25  6.13  2.13  5.88  3.25  4.46  3.67  5.29 10.96
74  8 18  7.41  4.08  8.96  3.96  5.13  2.04  5.46  6.00  4.38  5.29  6.42  9.59
74  8 19  5.71  7.41  6.08  2.50  4.83  3.54  4.54  8.25  4.12  4.25 15.75 11.17
74  8 20 10.34 13.79 10.79  5.37 10.04  7.83  8.87 12.50 10.67 10.37 19.41 19.33
74  8 21  8.29  7.00 10.21  3.71  8.38  5.66  7.83  6.13  7.33  5.96  9.25 13.50
74  8 22  7.96 11.58  7.04  3.17  6.87  3.79  4.92  6.13  5.96  4.83 10.88  8.87
74  8 23 13.79 12.46 14.09  6.71 10.37  8.21  9.59 11.75 11.21 11.67 16.88 16.42
74  8 24 15.16 16.21 13.88  6.13  9.79  8.46 10.04 14.54 10.79 11.29 20.25 19.92
74  8 25 14.54 12.92 12.58  5.29 13.70  8.08 10.54 11.08 11.96  9.71 16.96 19.83
74  8 26 15.67 11.71  8.42  4.83  9.92  5.50  6.67  6.34  5.79  6.00 11.17 12.33
74  8 27 12.00 12.17  8.63  3.92  9.75  6.17  8.50  9.62  7.46  7.21 16.96 12.96
74  8 28 17.67 14.04 15.04  7.75 12.12  8.87  7.71 13.59 11.12 11.92 19.41 20.33
74  8 29  7.46  6.71  9.67  2.04  4.58  2.54  7.46  4.38  5.63  4.38  7.83  9.62
74  8 30  8.67  7.21 13.37  4.04  5.96  4.71  7.46  6.08  7.21  6.04  7.96 10.67
74  8 31  5.37  7.54  5.79  2.25  4.75  3.54  3.33  5.63  5.83  4.21  4.04 12.08
74  9  1 13.50 11.75  7.38  3.00  8.17  3.88  4.42  6.42  4.92  3.63 11.79 10.67
74  9  2 22.75 17.88 19.95 11.04 14.58 10.54 12.92  9.83 11.12  9.00  6.83 15.37
74  9  3 16.04 15.34 12.50  6.83 12.96  6.58 10.54  6.71  8.42  8.33  6.13 14.67
74  9  4 16.21 15.37 14.58  7.17 16.17  9.59 12.79  9.38 12.25  6.63 12.46 15.50
74  9  5 16.50 11.92 13.37  4.96 11.87  6.42  8.04  8.38  8.63  6.83  8.58 11.12
74  9  6 20.17 16.33 14.71  7.54 13.04  7.92  8.29  8.96  8.96  5.88 10.29 10.50
74  9  7 29.54 20.54 23.21 13.70 21.71 13.83 16.21 12.62 14.54 11.29 15.04 26.42
74  9  8 16.58 11.38 15.71  5.63 13.54  8.33 10.88  8.79 10.67  7.87 10.71 16.83
74  9  9 10.21  9.08  8.92  3.42  7.83  5.04  6.92  5.91  7.29  4.38  9.29 11.87
74  9 10 11.08 13.67 12.67  5.00  9.59  7.96  8.00  9.71  9.59  8.42 16.83 19.25
74  9 11 16.71 13.00 12.67  5.54 12.46  9.33  9.13 10.34  9.17  6.71  9.17 11.71
74  9 12 13.13  9.42 14.83  7.08  9.13  7.54  9.42  9.92 10.54  9.21  9.50 19.46
74  9 13 10.00  9.75  9.13  5.75 10.83  7.54 10.37  9.75  9.25  7.92 13.50 18.05
74  9 14 14.50 14.29 12.38  6.34 10.92  7.33  5.46  9.00  8.04  7.04 10.79 14.83
74  9 15 10.92  7.00  6.67  3.58  6.13  3.00  2.79  5.37  3.04  4.38  7.08 10.25
74  9 16  8.83  9.29  8.71  3.54  5.58  4.63  6.13  9.08  5.75  5.79 11.00 13.96
74  9 17 12.87 10.63 11.34  5.46  7.87  4.50  7.67  6.38  6.13  6.96 10.46 19.33
74  9 18  5.21  3.96  6.21  1.50  3.08  1.67  4.58  5.09  2.62  4.75  9.54 14.67
74  9 19 10.00  8.25  7.75  4.79 10.58  6.21  9.50 10.96  9.67  8.58 14.71 23.16
74  9 20 12.79 10.29 12.96  5.41 10.75  7.04  9.67 12.00  9.87  9.38 18.54 19.83
74  9 21 14.71 18.88 11.12  7.71 14.75  7.67 13.37 13.04 11.42 10.88 23.04 27.12
74  9 22 17.92 17.71 13.08  7.83 15.29  8.87 12.21 12.42 11.50  9.92 17.46 20.62
74  9 23 27.00 25.08 19.70 10.54 16.62  9.59 11.17 12.92 11.00 10.04 18.46 26.63
74  9 24 15.96 16.04 11.58  7.54 11.29  7.58 10.25  9.17  8.83  8.33 14.50 18.38
74  9 25 24.04 20.21 17.29 12.00 18.58 11.12 13.00 13.75 12.92  9.87 19.92 19.25
74  9 26  8.50  7.21  7.33  2.37  4.29  2.88  8.04  2.88  5.17  3.88  6.42 13.21
74  9 27 14.12  9.13 12.42  4.46  6.79  3.17  6.46  6.46  4.42  2.96 10.37  8.83
74  9 28 15.92 16.71 10.92  5.83 11.21  6.46  8.38  9.79  7.17  6.46 17.08 10.92
74  9 29 14.12 10.96  8.50  3.96  8.50  4.00  7.83  7.71  5.79  7.33 12.62 11.12
74  9 30  5.09  3.33  7.08  1.58  1.79  1.50  7.46  4.54  3.63  4.83  7.50 13.59
74 10  1  8.54  4.08  8.33  3.96  5.50  2.00  6.79  4.88  4.63  4.08  7.33 16.54
74 10  2 19.95 14.21 15.96  9.08 12.83  8.08 13.92 11.38 10.75 11.21 17.16 25.75
74 10  3 13.88 10.41 19.41  7.21  5.91  4.67  9.92  8.92  7.87  9.59 13.67 25.21
74 10  4 16.46 13.17 15.87  7.75 10.00  7.33 10.17  9.75  8.08 11.00 15.71 23.67
74 10  5 10.54  8.42 11.42  4.88  6.29  4.79  9.62  6.38  4.88  6.96 11.87 18.00
74 10  6 19.41 18.79 11.67  6.96 17.83  8.50 13.29 13.54 10.71  8.58 22.63 19.25
74 10  7 25.08 18.12 17.96 10.25 13.88  8.87 12.67 12.92  9.33  9.87 20.00 25.46
74 10  8 13.33  9.21 13.50  5.54  8.38  5.21  9.33  7.62  5.63  6.04 13.37 17.12
74 10  9 10.88  4.75  9.83  3.00  4.42  1.96  8.38  6.38  5.13  4.12  9.79 15.37
74 10 10 13.08  9.92 13.17  5.50  8.83  4.04 10.00  7.62  6.46  7.41 12.50 20.83
74 10 11 15.16  9.17 13.96  5.04  7.54  6.08  9.75  6.50  6.87  9.25 11.29 18.38
74 10 12  9.67  3.33 10.54  2.67  5.75  2.17  8.33  5.46  4.21  3.42  9.08 10.50
74 10 13  8.12  5.09  4.79  2.33  6.63  2.04  8.17  3.96  5.54  3.42  8.46 11.71
74 10 14  4.17  7.38  2.71  0.46  6.04  1.96  4.00  5.29  3.75  3.42 10.25  9.71
74 10 15  8.12  2.71  3.79  2.00  2.67  0.79  3.37  1.29  1.08  1.04  3.37  5.37
74 10 16  9.38  8.33  3.88  2.08  6.79  2.29  5.41  5.83  3.58  2.54 10.50 10.83
74 10 17 14.79  9.67 10.71  4.21  8.96  5.04  7.87  9.21  6.75  6.79 13.67 16.00
74 10 18 19.41 16.33 14.58  8.08 16.33  7.96 14.37  9.46 11.21  8.71 14.92 19.21
74 10 19 13.21 13.08  7.08  5.83 11.00  5.29 11.71  8.21  7.92  5.29 15.96 19.46
74 10 20 23.79 17.75 13.92 11.75 17.62 10.25 15.59 14.09 11.67  8.50 20.50 26.34
74 10 21 16.29 11.75 14.62  9.38  8.96  6.46 11.87  8.21  7.87  6.87 14.92 21.59
74 10 22 16.92 12.79 14.88  6.96  9.79  6.34 10.96  8.12  7.83  8.50 15.75 23.58
74 10 23 10.08  8.08 13.33  4.88  5.71  3.50  8.87  5.41  6.04  7.29  8.75 16.29
74 10 24  8.04  8.33  6.17  3.21  8.50  4.25 10.79  6.42  8.08  6.08 12.50 20.58
74 10 25 13.96  8.71  7.87  7.17 11.08  6.71 13.67 11.71 10.71  7.12 13.79 18.08
74 10 26 16.04 11.34  9.33  8.29 14.83  8.58 15.59 12.08 12.21 10.29 15.50 23.21
74 10 27 26.50 17.33 15.34 15.75 19.92 14.12 22.29 18.66 15.87 20.88 22.46 36.63
74 10 28 19.50 18.75 17.21 12.96 14.00  9.46 15.92 13.59 12.87 12.79 18.96 31.91
74 10 29 16.38 12.71 19.12  8.92 10.67  7.33 13.13  8.67  9.54  9.25 12.33 24.25
74 10 30 12.83  5.46 12.29  5.58  7.92  5.88 11.42  7.58  7.67  6.96  9.04 15.34
74 10 31  9.25  6.79  7.67  3.88  6.54  4.21  8.54  6.29  6.29  4.67  8.38  8.96
74 11  1 10.63 11.29  7.54  2.92  6.83  3.42  6.92  8.58  6.42  5.54 11.21 12.12
74 11  2 14.17 12.87 13.21  5.29 11.67  7.12  9.21 10.63 10.25 11.00 15.46 17.37
74 11  3 19.95 15.04 14.09 11.12 16.17  9.38 12.71 10.41 11.21  8.38 12.54 12.75
74 11  4  6.63  3.92 10.96  2.46  2.67  1.04  5.96  3.67  2.58  2.17  7.21  8.29
74 11  5  9.59 13.37  8.96  3.71  9.92  5.41  6.79  9.54  7.83  6.25 15.96 18.54
74 11  6  8.46  4.63 12.17  2.83  5.58  1.13  5.41  2.33  5.17  1.63  1.96  6.34
74 11  7 10.41 11.46  7.83  3.42  8.83  5.33  5.33 10.46  7.50  6.00 14.37 17.75
74 11  8 13.59 12.58 12.21  6.42 10.41  6.67 11.21  9.83 10.58  8.83 12.96 17.25
74 11  9 18.29 17.29 16.62 10.00 18.34 11.92 20.04 17.67 16.79 15.75 25.29 34.96
74 11 10 22.71 21.87 20.17 12.33 20.38 11.75 19.25 18.16 15.46 15.63 24.96 27.54
74 11 11 14.58 19.21 10.58  8.00 14.50  9.62 20.21 13.00 14.00 13.29 20.08 31.54
74 11 12 13.54 13.54  9.67  4.21 11.08  5.41 11.75  7.83  8.79  6.83 10.79 19.21
74 11 13 27.58 22.25 21.92 13.92 17.37 12.12 12.79 12.33 13.88 10.17 11.08 16.50
74 11 14 22.63 18.66 24.37 13.92 20.04 13.25 19.08  9.87 18.25 14.29  8.79 17.29
74 11 15  7.71  4.21  8.38  3.21  7.08  4.38 10.63  7.33  9.04  8.58 11.67 21.75
74 11 16  4.33  4.25  5.91  1.58  6.21  4.00  9.62  7.33  8.54  7.58 13.88 19.87
74 11 17  4.25  2.33  5.63  0.33  3.88  1.46  5.88  1.63  3.88  2.00  2.96 10.04
74 11 18  7.04  2.17  8.33  0.75  1.87  0.13  8.25  0.37  2.75  3.04  1.25 10.79
74 11 19 11.92 14.37  7.29  2.17  9.17  2.75  4.71  5.75  3.42  3.42  5.54  9.54
74 11 20 15.41 13.37 20.41  6.29 10.88  4.88 14.29 11.63 10.00  6.58  8.50 12.75
74 11 21  8.79  7.92 19.55  5.21  6.87  3.63  9.25  8.12  7.29  3.00  4.38 14.17
74 11 22  6.67  3.79  8.08  2.67  5.91  1.50  4.83  3.13  4.29  2.21  3.25 12.92
74 11 23 14.67  9.62  9.79  6.29  8.54  4.17  9.71  3.79  6.17  3.33  2.75  6.17
74 11 24 18.79 19.83 14.33 10.58 20.88 11.96 18.00 18.84 16.25 16.66 23.63 28.84
74 11 25 12.67 13.17 11.08  7.29 14.29  7.96 17.79 10.54 13.25 13.25 16.66 26.63
74 11 26  4.00  8.58  4.38  1.63  8.46  2.83  6.96  4.54  4.54  4.04  7.62 11.25
74 11 27 22.71 23.00 13.42 13.54 24.04 13.42 22.50 20.04 17.83 18.63 25.12 31.88
74 11 28 20.04 16.33 13.50 13.21 19.41 10.96 20.67 13.33 14.67 15.25 18.63 30.71
74 11 29  9.08 10.29  8.96  2.29  7.41  4.29  9.96  7.08  7.46  4.96  9.38 14.04
74 11 30 12.92 12.96 10.37  5.00 14.37  7.08 13.29 12.12 11.12  8.21 14.04 18.79
74 12  1 17.16 19.12 16.17  7.87 16.66 11.12 14.62 15.75 14.92 13.96 23.13 23.79
74 12  2 11.38 10.37 12.83  5.04  9.46  5.04  9.87  6.87  9.25  8.83 11.25 19.46
74 12  3 17.50 18.25 15.16  7.92 15.16 11.83 12.04 15.96 12.92 13.54 21.87 21.17
74 12  4 12.96 16.83 10.04  7.50 17.37  8.58 17.41 14.29 13.75 13.08 21.62 26.83
74 12  5 12.04 11.29  8.54  5.54 12.12  6.25 15.75 11.58 12.33  9.29 14.00 23.83
74 12  6 13.92 13.54 10.29  6.04 16.38  7.87 17.04 10.79 14.12 10.21 16.08 23.33
74 12  7 14.88 13.79 10.96 10.13 22.00 10.37 21.67 16.08 17.62 12.25 19.58 24.79
74 12  8 16.83 17.75 14.88  9.29 15.59 10.46 14.92 13.83 14.71 13.96 20.04 25.08
74 12  9 14.29 18.58  9.21  8.75 16.29  8.08 17.46 14.92 13.59 12.71 21.25 26.00
74 12 10 18.25 22.54 14.25 11.29 21.04 10.41 20.21 15.50 15.67 12.21 21.21 29.38
74 12 11 20.12 16.38 15.00  9.87 14.58  7.62 13.75 10.63 10.50 10.00 14.29 21.92
74 12 12 29.50 19.87 15.50 16.08 22.83 13.79 17.58 19.70 16.88 15.75 17.92 22.92
74 12 13 14.42 14.50 11.63  8.75 15.79  8.96 13.13 14.12 13.08 10.50 18.29 19.67
74 12 14 12.00 13.88 10.92  7.87 13.13  8.04 14.88 13.96 11.71  9.62 16.29 21.84
74 12 15  9.71 11.12  8.12  4.25  9.38  5.17 10.58  8.83  9.50  8.71 12.79 21.00
74 12 16 21.21 22.25 15.09 12.75 23.13 13.33 20.79 21.09 17.92 16.75 25.12 29.95
74 12 17 25.12 24.96 13.96 15.12 24.58 15.96 25.46 22.37 18.58 19.08 26.75 38.25
74 12 18 12.04 12.46  8.46  6.54 15.00  6.13 16.66  9.29 12.71  9.46 11.79 20.17
74 12 19 23.25 21.37 19.70  9.87 15.25 11.00 18.63 20.83 16.66 15.46 27.33 27.88
74 12 20 26.54 23.00 22.75 13.88 16.04 12.17 17.46 17.50 16.88 15.25 21.50 23.50
74 12 21 25.00 20.67 22.21 11.67 15.37 11.38 16.25 17.37 16.00 15.16 24.41 26.00
74 12 22 22.58 19.55 17.88 10.13 15.50  9.67 15.21 16.58 14.00 14.17 19.55 22.42
74 12 23 27.54 23.54 24.00 18.88 24.87 17.54 17.75 22.42 25.00 23.58 25.04 33.04
74 12 24 10.41 12.75  8.79  3.79 11.87  5.29 11.12  7.21 10.34  8.04 13.33 20.79
74 12 25 27.29 25.29 22.67 13.21 20.17 12.58 17.83 16.92 16.46 13.67 18.16 20.67
74 12 26 25.41 22.21 19.55 12.87 20.50 12.71 19.50 14.50 16.75 12.87 15.16 17.79
74 12 27 22.50 22.00 17.46 10.58 22.17 12.71 20.50 17.41 17.29 13.88 19.79 27.33
74 12 28 25.84 22.83 20.88 14.96 25.92 16.62 25.33 20.04 22.63 16.88 15.75 25.96
74 12 29 14.83 11.58 12.71  9.29 13.70  8.00 14.96 11.42 13.75  9.04 12.92 22.50
74 12 30 13.29 13.42 13.17  3.63  9.75  6.34 11.08 12.17 10.83  9.00 18.16 22.13
74 12 31 16.04 16.29 15.21  8.42 13.67  9.75 15.25 16.13 15.04 13.46 18.54 18.46
75  1  1 14.04 13.54 11.29  5.46 12.58  5.58  8.12  8.96  9.29  5.17  7.71 11.63
75  1  2  9.17 11.46  9.13  2.54  8.71  4.58  8.58 13.75 10.67 10.54 17.79 20.96
75  1  3  9.54  5.63  7.29  3.50  7.71  3.33  9.96  7.12  8.46  7.38 11.25 21.84
75  1  4  5.37  6.00  9.38  3.50  8.96  5.83 16.38 10.17 12.75 11.46 16.38 24.87
75  1  5 11.92 12.25 12.17  5.75 14.92 10.50 21.59 13.13 16.38 14.58 22.13 30.88
75  1  6 19.83 17.08 17.08 10.21 19.29 10.08 19.12 12.87 15.59 12.87 18.58 23.33
75  1  7 11.58 10.79  8.75  6.00 12.62  6.38 10.88 10.29  9.46  5.09 11.46 13.29
75  1  8 10.71 11.00  8.75  5.83 11.46  7.17 10.63 11.17 10.71  6.96 16.21 16.33
75  1  9 14.71 14.79 12.25  6.29 12.29  8.50 14.25 15.71 13.50 12.29 19.87 20.62
75  1 10 21.96 17.46 17.75 10.21 13.25  8.54 11.83 12.46 13.37 12.12 22.92 17.46
75  1 11 22.13 18.12 19.00 11.12 15.67 10.04 17.41 14.42 16.42 13.37 24.92 21.84
75  1 12 18.79 21.09 17.46 10.96 17.29 10.71 15.79 14.96 16.17 15.09 27.33 27.42
75  1 13 33.12 25.58 25.88 19.00 21.79 17.50 19.62 20.91 21.00 21.54 28.71 28.71
75  1 14 19.12 12.54 19.92 10.13 11.92  7.96 13.96 12.29 12.50 13.83 20.33 26.54
75  1 15 18.05 11.58 16.50 10.58 12.96  7.83 10.83  9.08 12.67 10.17 12.96 17.79
75  1 16  8.29  8.00  8.67  1.75  9.42  2.92  5.88  6.92  7.62  4.29 10.13 14.33
75  1 17  8.00 10.71  6.54  3.75 10.08  4.04  9.62  7.17  9.71  6.08 11.46  9.17
75  1 18 14.37 10.37 11.29  6.13  8.17  3.29  7.25  6.54  7.08  4.58 12.58  9.54
75  1 19 22.37 22.08 17.67  9.21 16.33 11.00 12.12 16.62 13.88 12.92 23.63 22.46
75  1 20 20.50 22.29 12.62  9.33 19.29 11.50 16.79 16.33 16.00 15.79 29.88 29.71
75  1 21 19.55 18.29 13.25  8.58 14.21  8.33 14.42 12.12 13.88 10.50 17.50 20.79
75  1 22 26.50 29.50 21.29 14.67 26.16 15.50 20.91 21.34 20.58 19.55 32.30 32.33
75  1 23 20.62 23.63 14.62 12.87 22.29 14.75 19.50 20.12 18.96 16.13 31.49 34.54
75  1 24 16.25 14.37 12.50  6.75 11.38  7.29 10.92 10.00 11.46  9.54 16.58 22.08
75  1 25 19.41 16.42 15.29  8.83 13.59  8.21 13.17 10.71 12.38 10.71 17.12 23.13
75  1 26 22.21 14.88 17.88  7.96 11.83  8.12 10.25 11.54 11.29 10.13 18.50 19.95
75  1 27 18.66 19.46 11.21  6.46 12.50  5.29  6.67  7.62  7.41  5.66 13.59 17.37
75  1 28 16.29 13.67 14.04  8.92 14.12  8.67 14.75 12.21 13.37 11.46 17.25 26.16
75  1 29 17.62 18.38 10.54  4.50 10.46  7.00  7.87 10.92  9.67  6.34 16.04 17.29
75  1 30 21.09 20.58 17.96 10.71 16.88 11.96 14.92 19.12 17.04 16.04 26.92 28.58
75  1 31 18.34 18.66 14.25  9.04 13.83  9.17 14.58 14.09 13.75 10.83 23.75 22.17
75  2  1 12.50 17.46 12.12  5.83 11.12  6.29  9.62 13.92 10.67  9.75 22.95 18.16
75  2  2 11.96 21.46  8.87  6.34 12.46  6.08  4.42  9.75 10.29  7.79 18.91 11.08
75  2  3 10.75 15.37  8.12  5.96 10.88  5.41  6.08  8.42  9.79  6.21 14.46 12.92
75  2  4 12.04  9.33  9.54  4.00  7.83  3.67  4.12  7.33  4.21  4.67  7.41 10.92
75  2  5 12.38  7.12 11.38  3.25  7.87  3.50  8.12  6.34  8.29  5.29  7.67  9.42
75  2  6 19.00 17.79 15.92  5.54 11.71  6.46  8.46 11.63  9.42  8.08 15.96 17.33
75  2  7 15.87 14.00 11.42  6.42 10.58  6.63  8.58 13.00 11.75  7.29 16.46 17.00
75  2  8 15.41 14.17 10.54  4.08 11.29  5.83  7.54  9.42  9.62  5.71 12.38 14.92
75  2  9 12.79 12.29  8.67  2.79  9.46  2.37  5.21  7.71  8.00  3.33  8.87  9.04
75  2 10 14.25 17.54 10.04  5.33 11.63  7.00  7.00  8.92  8.46  3.50 10.67 11.29
75  2 11 14.00 11.63 13.46  4.88 10.41  7.79  5.66  9.29  7.54  3.88 10.96  7.87
75  2 12  7.46  6.04  9.00  2.62  4.50  2.67  4.67  3.04  3.79  0.92  6.08  6.54
75  2 13 11.63 15.59  7.33  5.04 12.25  7.46  9.79  8.33 10.54  5.17 14.58  8.87
75  2 14 14.21  8.58  9.92  1.96  7.67  2.21  4.21  3.92  5.91  1.38  3.75  7.29
75  2 15  9.17  7.17  9.21  3.33  9.42  5.54  6.96  7.04  8.38  6.50  9.92 14.88
75  2 16 14.42 14.00 16.96 10.50 11.96 11.42 12.38 11.83 13.92 13.59 19.25 23.16
75  2 17 13.42 13.54 10.92  7.33 13.25  9.54 14.00 12.67 14.00 10.37 20.08 23.25
75  2 18  3.71  5.29  4.33  0.67  3.13  0.63  4.00  3.04  2.46  1.50  8.00 11.34
75  2 19 17.46 17.62 16.17  7.92 16.33 11.71 10.63 12.42 12.79 11.63 17.88 23.42
75  2 20  9.67  7.50 16.08  4.58  5.17  1.67  6.00  1.63  4.33  1.13  6.25  6.58
75  2 21  8.67  3.08 10.92  4.46  5.00  5.50  5.21  3.67  7.62  5.96 16.25 16.50
75  2 22 16.46 14.42 14.37  8.17 12.92  9.96  7.92 12.75 12.79 12.92 21.00 22.25
75  2 23 10.67  9.42 11.34  5.63  8.04  5.13  6.96  5.37  7.21  3.96  5.79 11.50
75  2 24 11.38 15.29  9.13  5.91 10.71  8.21  6.34 10.71 10.54  7.29 12.54 14.96
75  2 25 16.88 21.29 14.17  6.42 12.71 10.67  9.21 11.67 11.00  7.38 14.96 19.87
75  2 26 18.08 19.17 15.83  6.42 16.38 10.50  9.25 10.46 10.46  7.96 15.50 18.00
75  2 27 15.46 17.04 11.54  3.96 15.16  7.29  7.87  8.79  8.79  4.71 11.42 10.41
75  2 28 11.21 16.88 12.71  5.75 13.83  7.83  7.92  9.96  9.33  5.37 12.29  9.54
75  3  1 12.79 11.46 14.62  5.09 10.00  6.83 10.13  7.54  8.25  3.75  8.04 11.42
75  3  2 11.67 11.42 13.46  5.71 10.17  7.21 10.00  7.96  9.71  5.79 10.37 12.92
75  3  3  4.33  5.04  7.87  2.29  3.96  2.92  7.38  4.88  6.67  3.88  9.75 12.12
75  3  4  4.63  5.46  5.66  0.50  3.42  0.00  1.63  1.54  0.54  0.25  5.50  5.41
75  3  5  7.87  7.21  5.83  1.83  3.50  2.17  4.88  3.92  3.96  5.00  8.92  9.33
75  3  6 14.62 14.71 14.00  7.46 14.50  9.83 14.46 11.71 13.54 13.17 19.70 22.37
75  3  7 13.79 14.54 10.63  7.33 14.62  8.33 14.00  9.79 12.71 12.04 18.96 20.83
75  3  8  9.50  6.54  7.21  3.17  6.79  2.58  5.33  5.37  5.91  5.83 11.87 14.54
75  3  9 17.04 13.08 12.21  6.13 10.54  6.25 10.08  8.08  9.38  9.04 15.12 16.33
75  3 10 15.29 13.83 19.00  7.04 10.83  8.33 12.96 11.08 10.46 11.92 16.92 21.75
75  3 11 18.84 13.92 28.33 11.25 14.29 12.92 19.29 11.83 16.00 15.34 15.59 19.00
75  3 12 10.63  9.04 15.21  4.79  7.50  5.91 10.75  7.12  9.75  8.25  6.63 17.16
75  3 13  9.00  5.00 16.00  5.04  6.29  2.79  7.62  4.33  6.83  6.25  5.17  8.63
75  3 14 12.38  6.42 15.54  5.54  6.63  2.88  6.46  4.38  6.87  7.00  4.54  9.17
75  3 15 10.37  6.00 15.09  4.79  4.21  1.87  7.83  5.71  5.50  7.04  9.13 18.63
75  3 16 11.38  9.46 12.17  3.04  4.21  3.21  5.58  5.13  5.83  4.92  4.12 10.67
75  3 17 12.42  8.63 13.37  3.37  5.37  4.17  7.79  6.63  6.38  5.54  7.46 11.67
75  3 18 12.83 11.12 13.21  4.54  7.96  4.21 10.50  7.00  6.29  7.04  7.92 12.08
75  3 19 11.58  8.92 18.34  5.41  8.58  4.46  9.62  5.91  6.58  6.50  8.25 14.33
75  3 20 10.71  6.63 20.54  4.50  6.13  2.46  8.71  6.96  8.87  6.04  7.71 15.67
75  3 21 16.33 14.46 14.50  7.83 11.54 10.13 11.12  9.83 12.29 11.96 14.88 22.34
75  3 22 16.54 13.00  9.59  7.62 11.46  6.63 10.92  9.04 10.13  8.87 13.04 14.21
75  3 23 15.63  9.54 10.58  6.25 11.87  7.21 10.63  9.13  9.83 10.96 12.21 19.75
75  3 24 17.16  7.67 12.08  8.00  9.71  6.75 11.67  8.12 10.21 12.42  9.04 22.95
75  3 25  8.50  6.29  8.75  3.75  7.08  5.66 10.88  7.71 10.50  8.79 11.04 10.58
75  3 26  9.21  8.63 13.29  3.08  6.29  5.63  7.33  7.54  8.96  7.00 14.42 16.71
75  3 27 15.63 11.87 17.79  8.42  9.25  5.21 10.17  8.38 10.96 10.58 13.67 19.70
75  3 28 15.87 12.67 12.17  6.00 10.21  7.62  9.42  9.29 10.75 10.00 16.17 20.62
75  3 29 15.37 12.54 10.79  7.08  9.46  6.46  9.62  8.25  9.33  9.83 18.05 20.12
75  3 30 16.08 16.96 15.12  7.17  9.75  6.54  8.58  8.42  9.50  9.71 15.87 14.62
75  3 31 12.79 10.83  8.29  6.58  8.87  6.29  9.46  7.62  8.96  9.59 12.92 15.59
75  4  1 16.50 11.54 10.50  7.71  9.25  8.08 11.71  8.92 11.04 12.17 13.79 18.16
75  4  2 17.50 15.12 16.75  8.87 12.87  8.25 10.75 12.17 11.34 10.88 17.41 19.29
75  4  3 15.04 11.54 17.58  8.04  9.29  7.08 11.79  9.08 12.04 14.83 15.16 25.62
75  4  4 12.62 10.63 18.34  6.58  6.46  3.46  9.79  8.04  9.17  8.42 11.54 16.75
75  4  5  9.92  7.33 13.54  4.79  5.29  2.00  7.00  4.92  7.12  4.88 10.41 11.92
75  4  6 12.50  7.75 13.83  6.92  6.34  3.71  8.33  6.29  8.29  5.88  8.04 13.21
75  4  7 21.84 15.83 15.00 11.58 16.00 10.92 17.71 14.04 15.71 20.54 20.91 33.04
75  4  8 20.21 17.12 17.00 11.79 12.12 11.58 15.92 12.75 13.50 15.46 20.33 31.25
75  4  9 15.63  9.08 13.13  7.54  8.71  8.04 10.54  6.38  9.46 10.41 13.50 18.63
75  4 10 16.54 11.00  9.87  7.46 12.62  9.62 11.79 12.29 12.54 10.83 15.00 19.25
75  4 11 14.88  7.87  8.08  6.21  9.46  7.41 12.83  8.96 11.63 10.34 12.50 18.41
75  4 12  6.96  3.33  6.79  4.92  7.58  5.91 10.29  6.79  9.42  7.67  9.59 12.42
75  4 13 12.33 14.29 10.37  5.66  8.33  6.63  9.08 11.50 10.13  9.75 16.42 14.00
75  4 14 18.46 15.59 15.96  7.87 12.83  9.38 10.00  7.87 11.87  7.62 11.71 10.58
75  4 15 13.08  9.13  7.75  3.54  6.96  2.62  4.46  3.96  5.88  4.08  8.38  6.71
75  4 16  8.25 11.87  8.17  3.58  7.58  5.54  5.54  8.12  9.54  6.50 14.92 11.17
75  4 17 10.17 11.50 10.92  4.71  8.42  6.63  8.38  9.00  8.92  8.38 15.92 12.00
75  4 18  7.41 11.08  5.46  3.92 10.54  6.92  6.79  8.75  9.50  6.67 19.87 13.17
75  4 19 13.62 13.21  8.92  5.41 13.04  8.63  9.79 10.08 10.63  8.71 17.54 18.50
75  4 20 19.62 16.04 17.71  7.87 10.08  9.00 11.38  9.54 12.42  9.33 11.34  9.62
75  4 21 10.46  7.62 13.50  4.17  5.54  4.00  4.67  7.71  8.79  7.50  9.62  7.25
75  4 22  4.58  5.63  3.29  2.42  7.41  2.29  4.04  4.71  5.88  6.83  6.54  9.17
75  4 23  8.08  5.71 10.79  4.21  5.75  3.00  2.54  3.50  6.71  5.54  4.17  9.96
75  4 24  4.58  1.96 12.96  2.21  4.17  1.87  1.21  1.46  4.17  1.92  6.13  5.25
75  4 25  4.25  1.42  6.38  1.92  3.63  0.17  1.87  3.54  2.17  2.13  4.79  2.29
75  4 26  8.75  4.12  6.29  3.00  5.96  3.17  3.08  4.54  6.04  4.75  7.92 12.12
75  4 27  7.62  9.62 12.71  4.75  6.92  3.67  4.83  3.75  5.75  5.00  8.75 13.42
75  4 28 10.67 11.00  9.21  4.88 12.50  6.79  7.21  9.04 11.42  8.79 17.88 18.29
75  4 29 14.17 16.50 10.34  8.17 16.42  9.83 12.96 11.96 12.83 13.25 19.33 23.25
75  4 30 18.50 15.75 14.92  8.38 16.50  9.87 10.41 11.34 13.33 12.67 17.88 19.33
75  5  1 22.54 16.04 17.16  7.75 13.92  8.75 13.88 11.04 13.42 11.46 15.25 21.67
75  5  2 14.46 10.96 11.38  6.92 14.88  2.50  4.38  2.42  4.54  5.66  6.67 15.41
75  5  3  6.25  6.29  4.92  1.71  3.54  0.29  2.54  2.42  1.04  2.04  6.63  6.38
75  5  4  1.63  3.96  5.37  0.46  4.08  0.63  0.92  1.42  3.42  1.67  3.83  3.79
75  5  5  6.13  4.21 15.71  3.88  5.09  3.96  5.63  3.58  5.46  3.37  7.83  7.33
75  5  6  8.79  8.63 21.59  6.54  8.12  5.54  6.96  5.04  7.54  5.09 13.75  6.29
75  5  7 11.83  9.33 27.67  8.21  7.58  6.54 12.04  6.04  9.29  6.75  9.83 10.54
75  5  8 17.92 13.13 24.00  8.67 15.50  7.04 11.08 13.54 12.17 10.79 15.41 10.88
75  5  9 16.66 15.41 13.67  7.00 14.09  8.08  7.41 12.96 11.25 13.17 13.92 19.62
75  5 10 15.96 11.17  9.46  6.13 10.92  8.42  9.54  9.83 10.67 12.25 12.67 20.46
75  5 11 11.42  9.04  9.17  3.58  7.00  3.33  5.33  5.17  5.83  5.88 10.37 11.58
75  5 12 13.54 10.04 11.87  5.21 11.63  7.75  7.33  8.25 11.00  7.00 11.58 13.54
75  5 13  6.79  6.13  5.91  1.67  5.09  1.63  2.13  2.92  3.92  3.83  8.00  8.96
75  5 14  6.87  4.67  9.54  2.71  8.08  4.50  6.21  7.62  6.17  7.46 14.50 14.79
75  5 15 10.04  9.25 22.17  5.33  8.63  5.63  7.87  5.41  6.21  3.71 14.17 10.71
75  5 16  6.71  7.08 14.92  3.54  5.00  0.79  3.13  4.50  2.29  2.29  6.46  5.58
75  5 17  6.29  2.83 11.58  1.87  3.75  0.83  2.21  1.92  2.04  2.04  4.33  6.08
75  5 18  6.21  4.71  3.46  1.87  3.46  1.08  3.92  3.75  1.92  2.62  8.83  5.83
75  5 19  4.38  6.46  3.04  1.50  3.13  1.33  4.04  5.41  3.50  4.21 15.54 13.54
75  5 20  9.67  2.50 10.00  2.50  4.29  4.04  2.54  2.96  3.58  4.04  9.08 14.25
75  5 21  6.79  2.75 13.42  3.92  5.00  4.17  3.71  5.41  6.42  5.37 15.75 18.71
75  5 22  6.38  4.96 16.66  5.54  7.71  4.42  6.21  7.79  8.50  9.96 14.67 18.88
75  5 23  8.21  9.59 10.79  4.50  6.42  4.71  5.88  7.17  8.12  9.29 10.88 15.92
75  5 24  9.00  4.04 13.33  4.08  6.38  2.88  5.71  4.83  6.38  4.58 11.58 10.00
75  5 25 11.75  5.75 16.50  4.79  6.54  5.88  7.96  6.63  8.21  7.12 10.50 12.21
75  5 26 10.29  5.96 19.67  6.38  7.96  7.71  9.62  9.29 10.08  7.54 12.87  7.29
75  5 27 12.83  8.21 23.58  7.38  9.50  8.75  9.92  9.59  9.25  7.67 11.42  8.25
75  5 28 12.25  6.67 24.54  4.83  9.92  7.83  6.87  9.59  9.67  5.79 11.58  7.12
75  5 29  6.92  5.17 16.21  3.33  4.29  3.21  3.92  5.83  4.88  2.21 12.67  4.33
75  5 30  9.42  4.42 12.75  2.96  6.46  4.08  5.33  7.79  7.17  3.33 19.08 13.88
75  5 31  9.33  7.17  7.92  3.21  5.71  3.42  3.00  7.21  5.09  5.79  9.38 13.54
75  6  1 10.96  8.79  9.50  4.75  9.62  5.58  8.38  9.75  9.29  8.75 11.75 18.34
75  6  2 23.21 16.58 18.79 13.13 15.92 12.08 17.21 15.46 14.58 20.00 20.25 32.79
75  6  3  9.62  4.92 10.37  3.88  5.58  3.33  5.91  4.79  6.46  6.58  6.29 11.21
75  6  4 11.83 12.71 10.54  4.96  8.87  4.83  3.75  6.96  8.04  5.79 14.12 12.83
75  6  5 12.33 16.17 11.46  7.50 12.87  9.92  9.13 12.71 13.25 10.50 17.58 14.17
75  6  6 11.12 18.38 12.12  9.25 16.54 10.67  7.46 11.67 14.54 13.04 18.75 22.79
75  6  7  6.63  9.50  5.04  3.04  4.96  3.83  2.88  8.17  6.04  5.79 14.54 10.75
75  6  8  7.67  8.96  2.92  3.54  8.54  4.92  3.92  7.00  6.96  6.50  6.79  9.04
75  6  9  3.96  3.67  7.00  1.79  6.08  2.46  4.58  4.00  5.75  4.67  6.29 10.04
75  6 10  7.00  7.54  3.67  1.25  6.42  1.00  1.58  4.33  2.75  0.54  5.75  5.91
75  6 11  7.96  1.96  2.88  1.63  4.00  1.25  1.58  3.54  2.00  2.00 10.67  6.54
75  6 12 10.17  6.58  5.88  2.88  5.46  1.75  3.75  4.96  4.58  4.54  6.42 10.13
75  6 13 10.08  3.96  6.87  4.17  9.29  5.46  7.58  7.21  7.12  6.92 11.08 14.62
75  6 14 13.33  9.29  8.92  6.71 11.92  7.92  9.00 10.83  9.46  9.92 13.08 17.88
75  6 15 10.54 10.58  8.67  4.71  9.08  6.04  5.63  8.00  7.00  5.71 11.46 13.79
75  6 16  9.79  7.92  7.38  1.92  6.54  2.13  3.33  4.25  4.96  3.13  7.50  9.21
75  6 17 10.75  8.79 11.12  3.75  7.33  3.96  4.42  8.21  7.38  5.58 11.71 10.92
75  6 18 15.00 15.21 14.75  8.33 12.75  9.79 10.46 14.79 12.58 12.58 21.50 20.46
75  6 19 15.37 12.96 16.13  9.21 12.71  8.96 11.71 14.09 12.21 11.00 20.33 19.38
75  6 20  5.41  1.79  6.38  2.00  4.88  1.25  4.42  4.33  5.37  3.63  8.25 11.08
75  6 21  5.79  3.96  9.33  1.92  4.17  2.29  3.54  3.67  6.50  4.12  5.00 15.63
75  6 22  5.75  3.21  6.13  2.25  4.71  1.63  2.50  6.38  2.67  3.63  7.00  8.63
75  6 23 11.38  6.75  5.71  4.83  6.87  4.04  6.04  6.13  6.21  5.04  7.96 11.54
75  6 24  8.25  9.46 12.58  4.96  6.58  3.79  4.08  5.75  7.41  6.13  6.17  8.79
75  6 25  6.17  6.25  9.62  2.37  5.71  1.04  1.79  4.50  3.42  3.37  5.63  9.96
75  6 26  9.50  9.71 17.00  5.88  9.42  5.91  5.46  9.46  8.87  8.12 10.41 14.96
75  6 27 10.54  9.54 14.79  4.29  7.21  3.67  6.87  9.00  8.83  3.92 13.33 10.96
75  6 28 10.54  5.54 13.62  3.71  5.75  5.58  6.04  7.00  9.00  4.33  8.58  8.04
75  6 29  8.17  4.71 11.54  2.50  4.25  0.25  1.83  4.63  3.37  1.17  5.21  7.25
75  6 30  9.13  3.21  5.21  2.21  5.54  1.50  1.63  3.83  3.25  4.46  4.67 10.67
75  7  1  6.25  3.25  5.50  1.92  3.46  1.33  2.92  2.75  2.37  2.21  6.13 11.46
75  7  2  4.96  4.54  3.50  2.29  4.46  3.54  4.79  4.25  4.50  2.08  5.91  7.00
75  7  3 10.83  5.17  6.21  3.58  5.41  2.83  3.96  4.67  6.42  3.37  7.67  5.63
75  7  4 10.08  8.00  6.83  3.42  7.21  2.46  4.12  7.29  4.38  2.00 12.54  6.17
75  7  5  8.08  4.50  4.33  3.08  4.54  2.62  4.71  6.21  8.50  4.42 13.04  7.75
75  7  6  7.33  4.63  9.96  2.00  5.79  1.58  4.63  3.83  5.58  3.00  6.87  7.87
75  7  7 14.33  6.83 16.00  4.63  7.92  6.54  8.50  9.29 10.08  6.13  8.79  9.54
75  7  8 11.71  6.96 12.83  6.17  8.63  8.92  8.67 12.00 11.25  6.71 10.71 13.21
75  7  9 10.34  6.92  9.67  5.04 10.41  6.87  8.29  9.62  8.83  6.13  9.29 16.58
75  7 10 10.08  7.25 12.08  5.46  7.12  3.42  6.67  2.96  7.87  3.33  7.17  7.96
75  7 11  7.83  7.58  8.79  4.33  7.75  5.00  9.13  6.67  7.96  4.58  6.67  8.83
75  7 12 12.79 12.71 11.25  5.50 10.46  5.75  5.96  7.79  9.13  5.04  7.67 10.96
75  7 13 13.37  8.92 12.87  7.12  9.92  7.00  6.58  5.17 10.08  6.34  9.13  9.71
75  7 14 15.41 10.75 15.87  7.62  9.62  6.54 10.71  5.41 11.17  8.00  9.92  9.54
75  7 15 15.04 11.58 13.54  6.96 13.75  9.29 14.37  9.62 12.21 10.17 12.38 16.08
75  7 16  1.71  3.63  7.29  3.17  4.46  2.71  7.58  5.37  5.63  4.50  8.25  4.67
75  7 17  8.67  6.50  2.92  2.13  6.67  2.92  2.79  4.17  2.71  2.04  5.33  7.00
75  7 18  7.00  9.54  7.96  3.96  7.71  4.63  5.21  5.71  6.67  4.29 13.37  5.37
75  7 19 11.79 12.58 12.00  5.04  8.42  6.67  8.54 11.21 10.63  8.79 16.21 12.46
75  7 20 10.13 10.71  9.71  5.13 10.75  5.58  9.79  7.75  9.71  6.08 15.25 15.75
75  7 21 12.58 13.17 13.08  5.71 15.59  9.08 13.79 12.75 13.25 11.71 20.83 21.34
75  7 22 18.88 16.29 20.04  9.71 20.12 11.71 17.29 11.08 15.63 12.25 16.88 15.16
75  7 23 15.96 14.88 13.42  8.54 15.67  9.13 14.62 13.42 13.13 10.83 16.75 15.87
75  7 24 16.75 12.87 12.04  7.96 15.75  8.67 13.75 11.79 11.04 10.21 15.09 22.46
75  7 25  7.87  7.58  8.96  2.54  9.59  4.75  9.59  6.67  9.46  7.33 13.88 13.59
75  7 26  4.17  5.41  9.13  3.00  6.87  4.79  9.46  7.08  8.63  5.71 15.12 15.63
75  7 27  5.33  2.83  5.21  1.83  3.17  0.92  4.08  3.00  2.92  1.46 11.54 11.08
75  7 28  6.34  6.04  6.04  1.96  4.88  3.33  5.83 10.17  7.38  7.92 17.62 15.46
75  7 29  9.00  6.25 12.04  3.50  5.75  4.83  6.04  8.75  8.71  6.92 12.62 13.96
75  7 30  7.25  9.25 11.96  2.62  6.58  3.54  3.75  5.83  6.96  5.17  7.38 10.21
75  7 31  9.21 10.41 21.42  5.21  6.87  3.46  6.04  4.25  6.54  2.50  3.88  6.87
75  8  1  8.12  5.29 16.38  2.96  4.29  0.83  2.71  2.58  3.37  1.63  3.71 13.92
75  8  2  7.41  1.08 11.12  2.29  2.54  0.46  3.33  1.58  4.75  2.37  4.29  7.29
75  8  3  5.83  2.46  5.54  2.62  2.00  1.25  4.38  0.46  4.08  1.96  3.71  5.21
75  8  4  9.50  5.13  5.25  5.17  8.17  4.71  3.83  6.63  7.92  6.75  7.29 11.71
75  8  5 12.46 15.12 10.96  9.33 14.96  8.63  7.75  9.50 11.63  8.92  8.29 15.87
75  8  6 14.71 15.75 11.46 10.63 14.62  9.25  9.46 12.04 13.70 12.29 20.12 17.79
75  8  7  8.92 10.58  6.46  5.09 10.34  6.42  4.83  9.46  9.71  8.54 20.08 14.21
75  8  8  8.42  3.29  8.08  3.79  4.67  1.54  4.83  2.88  3.50  3.29  7.87  6.00
75  8  9 17.83  8.46 11.79  7.92  7.54  4.75  5.29  8.92  9.54  6.67  9.21 14.50
75  8 10  8.08  3.54  5.63  2.75  2.67  1.04  0.92  2.54  4.75  0.46  6.54  6.50
75  8 11  6.13  5.41  5.79  3.92  5.58  2.29  2.75  1.38  5.41  2.96  4.79  6.54
75  8 12 11.92  7.58  9.59  5.41  9.04  4.63  5.88  4.96  7.83  1.92  6.75  4.00
75  8 13  6.04  4.38  5.13  3.00  4.96  2.92  2.62  4.75  7.46  4.54  6.42  9.38
75  8 14  6.67  7.38  7.33  3.96  6.71  3.83  3.25  6.38  6.54  2.42 12.08  6.79
75  8 15  6.87  7.87  6.42  3.46  8.96  4.96  5.75  6.00  8.79  6.25 10.75  8.46
75  8 16  8.46  4.92 10.13  4.92  6.17  5.17  9.54  7.58  8.00  8.12  8.50 12.38
75  8 17  3.67  5.29  4.67  1.79  3.37  0.92  2.79  2.13  0.87  2.25  8.38  8.21
75  8 18  4.92  6.96  5.46  2.71  3.58  2.79  5.33  4.92  6.54  5.13  9.00 11.08
75  8 19 14.25 13.50 13.92  6.17 11.92  6.92  8.38 11.96 11.21  8.92 15.25 14.25
75  8 20 14.67 10.54 12.92  6.29 14.17  9.08 11.50 10.58 12.08 10.96 16.04 19.75
75  8 21 13.42  9.87  8.25  4.33 10.00  5.71  8.42  7.50  8.50  7.21 12.29 18.75
75  8 22 12.12  8.25 10.00  7.00  9.13  5.66  9.42  7.08  8.46  7.87  9.13 16.38
75  8 23  9.50  6.92  8.63  4.88  8.58  5.09  6.29  7.96  8.08  7.21 11.63 13.37
75  8 24  5.46  4.17  6.46  2.17  5.58  3.33  6.34  6.00  7.29  3.54 10.00 12.38
75  8 25  5.37  5.83  6.34  1.96  3.83  2.42  5.00  6.92  6.38  4.88 12.71 13.70
75  8 26  3.13  5.66  7.21  1.46  1.46  1.13  2.46  6.96  5.37  4.33 12.58 11.12
75  8 27  4.21  3.75  3.46  1.50  2.08  1.58  3.00  3.04  2.96  1.29  7.00  4.75
75  8 28  4.58  2.75  3.33  1.50  2.75  0.75  3.17  2.75  2.75  2.25  7.62  7.83
75  8 29 12.87  9.42 14.50  5.75  7.04  4.46  8.29  8.46  7.83  7.58 11.50 17.41
75  8 30 17.50  9.71 16.62  9.25  8.12  6.79 14.00  6.58 10.17 10.63  8.04 16.00
75  8 31  8.08  3.46  9.13  3.96  2.46  1.75  6.42  2.88  4.79  1.46  8.67  9.79
75  9  1  5.96  2.71  5.29  1.67  2.96  0.58  5.50  3.25  2.25  1.46 12.42 11.04
75  9  2  7.33  7.58  8.87  3.58  6.17  3.83  7.46  8.63  7.21  4.67 14.25 14.42
75  9  3  9.08  5.54  7.71  3.88  4.96  2.33  6.54  2.62  6.71  5.50  8.54 12.38
75  9  4  9.17  7.83  9.17  5.04  8.29  5.29  9.71  6.79  8.46  6.63 15.37 17.12
75  9  5  7.08  6.34  7.50  3.88  6.50  3.13  7.04  4.92  6.83  3.83 10.50 13.54
75  9  6  7.21  7.00  6.46  1.63  6.25  3.17  5.09  5.17  6.21  3.88  7.54  7.79
75  9  7  6.08  4.00  5.66  1.96  2.08  1.38  4.96  4.96  3.92  1.92  9.83  8.21
75  9  8 11.50  9.75 11.38  5.75 10.25  6.75 10.00  9.71 10.13  9.42 17.62 18.79
75  9  9 16.62 13.17 13.00  5.54 13.62  7.87 11.50  9.92 12.08  9.29 15.34 18.75
75  9 10 10.37  9.59  9.42  3.37  9.79  5.79  9.33  6.96  8.75  6.42 12.21 14.83
75  9 11 15.83 16.92 12.46  5.63 14.62  8.12  8.79  6.75  9.29  3.33 12.29 12.12
75  9 12 16.88 10.67 12.54  8.33 12.92  8.50 12.87  8.63 10.58 10.21 13.21 18.75
75  9 13 10.25  8.83 13.75  3.54  5.17  3.92  9.25  4.96  5.71  5.00  8.92 16.46
75  9 14 20.50 12.83 16.46  8.21 10.41  8.17 12.92  9.38 10.13 11.08 18.12 25.46
75  9 15 10.37  7.21 10.25  3.25  5.33  2.92  7.75  5.91  6.50  6.46 12.62 16.33
75  9 16  5.63  3.88  7.83  1.38  3.96  4.21  4.92  5.00  7.46  3.92  9.83 13.42
75  9 17  9.04  5.71 14.50  4.21  4.63  3.79  6.29  6.04  7.25  5.04 10.71 14.17
75  9 18  9.38  5.83  8.96  4.42  4.75  4.12  6.63  5.04  5.96  5.00  8.58 13.08
75  9 19 14.37 17.08 11.83  5.71 13.00  9.29  8.25 13.25 11.54 11.17 18.91 18.75
75  9 20 12.46 11.96 11.04  5.50 13.13  8.33 11.42  9.62 11.29 10.96 19.58 25.58
75  9 21  8.75 10.21  7.17  3.79  7.00  5.50  9.29  8.08  8.50  7.79 18.00 18.63
75  9 22 22.21 20.91 19.67 12.04 16.21 14.96 16.25 19.29 17.54 18.12 28.79 30.54
75  9 23 12.33 10.54 11.87  6.83 12.42  7.92 12.25 10.00 10.67 10.46 19.33 26.96
75  9 24 23.54 20.08 19.04  9.38 15.37 12.96 15.67 18.25 15.37 14.62 25.50 25.41
75  9 25 13.50 12.12 11.50  6.58 12.67  8.04 12.42 10.67 11.87 10.21 13.59 17.75
75  9 26 11.50  6.71  8.75  3.88  5.25  3.58  8.12  4.17  7.04  4.50  7.25 14.83
75  9 27 19.75 15.04 18.41  9.25 13.92 10.58 14.12 11.21 12.21 13.13 12.62 19.12
75  9 28 20.33 16.92 17.33  8.67 13.59  8.50  9.96 10.13 10.71  9.46 13.21 15.79
75  9 29 19.79 12.75 17.83 11.71 12.46 10.29 14.42 10.63 14.46 14.46 14.88 22.37
75  9 30 13.17 11.25 14.88  4.79  8.92  6.75  8.67  6.34  8.29  6.83  7.71 13.46
75 10  1 13.79 11.04 14.67  6.29 10.29  7.04 11.25  6.46 10.50  6.17  9.17 16.71
75 10  2 19.00 17.00 16.04  8.38 12.75  9.42  9.75  9.71 11.04  8.42 11.50 16.58
75 10  3 13.29 13.25 11.17  5.79 14.71  9.04 13.88 11.38 12.38 11.63 22.79 24.67
75 10  4 16.25 15.46 15.46  8.12 20.67 12.46 18.46 14.67 16.04 14.00 26.71 29.08
75 10  5 14.25 14.83 15.21  5.91 12.25  8.50 13.33 10.34 11.58 10.29 18.05 25.00
75 10  6  3.25  3.21  4.38  1.71  1.83  1.17  5.09  2.13  3.88  3.54  8.71 12.46
75 10  7  7.21  7.12  4.08  1.25  3.63  1.46  2.08  3.21  3.63  1.25  5.21  5.54
75 10  8  4.21  5.75  6.58  0.33  1.75  0.17  1.08  1.13  0.29  0.21  5.17  6.96
75 10  9  4.92  3.21  3.75  1.00  3.21  0.79  4.58  2.67  3.25  2.46  8.58  8.25
75 10 10 12.08 12.12 14.17  5.50  9.17  5.29  8.38  6.38  7.96  4.79 11.21 12.54
75 10 11  8.42  3.79 13.42  3.92  4.42  1.87  6.00  2.88  5.09  1.67  6.08  8.38
75 10 12  5.37  7.12  4.92  0.96  4.96  2.13  1.46  3.50  4.17  2.42 10.58  8.63
75 10 13 17.62 15.79 13.13  3.37 13.04  9.25  7.96 11.21  7.04  7.83 16.13 13.17
75 10 14  9.83  9.29 11.54  3.00 10.25  6.08  7.08  5.54  8.58  9.08 11.00 16.00
75 10 15 16.21 12.17 12.58  5.00 10.37  6.13  6.96  6.46  7.75  5.13  7.17 11.96
75 10 16 12.12 10.08  7.62  3.50  8.50  4.63  5.09  5.66  6.67  5.71 10.58  9.21
75 10 17  4.21  5.13  5.91  0.42  3.54  0.29  6.04  1.54  2.25  1.25  4.46  5.58
75 10 18 11.21 16.75  6.42  2.21 10.08  4.67  4.29  7.00  6.08  4.71 11.21 12.04
75 10 19 17.79 22.46 16.50  9.71 19.38 12.83 14.00 14.33 12.83 12.50 22.79 24.41
75 10 20 22.00 24.41 18.21  9.67 23.13 13.70 16.04 15.87 15.34 15.37 24.04 27.84
75 10 21 23.83 25.84 18.88  9.59 22.79 14.37 16.66 16.71 14.75 12.50 20.67 24.04
75 10 22 27.42 22.54 24.41 14.42 24.41 19.55 21.42 19.29 20.30 19.79 23.50 32.46
75 10 23 13.75 16.42 15.92  8.29 12.75  8.25 11.75 10.54 13.62 12.17 20.88 19.75
75 10 24 17.50 17.25 15.29  9.00 13.46  8.79  8.04 12.46 13.00 12.46 24.58 22.08
75 10 25 17.75 21.04 14.42 12.62 18.84 12.71  9.96 16.04 17.08 15.34 26.71 27.25
75 10 26 14.67 18.16 12.42  9.50 15.00 12.17  3.46 14.33 16.21 12.17 19.17 23.63
75 10 27 12.21 18.79 10.25  7.25 14.29  8.42  2.29  9.87 12.83  8.04 13.92 17.71
75 10 28 10.75 15.54 11.54  8.29 12.25  9.46  5.50  7.41 11.54  7.00 13.88 13.79
75 10 29 15.63 18.08 13.83  7.92 14.00 10.63  9.46  9.92 12.79  8.29 11.46 15.09
75 10 30 19.92 17.96 17.21  9.50 14.29  9.79 11.42 12.75 13.37 12.92 19.04 20.58
75 10 31 15.25 10.34 15.16  7.41 10.79  7.92 12.46  9.83 11.92 11.34 12.00 19.12
75 11  1 14.12 11.92  9.71  4.08  8.54  3.67  9.92  4.79  6.58  6.17 11.21 12.71
75 11  2 15.54 15.29 13.75  6.46 14.62  9.13  9.71 12.21 11.08 10.29 22.88 21.62
75 11  3  9.75 12.46  8.25  4.79 12.33  5.88 11.67  8.58  9.54  8.75 20.08 22.46
75 11  4 17.92 13.79 13.92  5.33 12.83  8.46 12.50 11.29 12.42 12.29 19.04 21.21
75 11  5 10.00 10.13  8.46  2.08  9.08  2.54  6.38  3.63  7.08  5.25 10.71 14.21
75 11  6  8.75  4.67  6.87  1.25  5.29  1.96  8.58  2.54  4.88  4.38  6.83 16.29
75 11  7  3.33  2.46  8.38  0.37  3.08  0.08  5.50  0.63  0.37  0.75  5.88  8.25
75 11  8  9.08  7.08 14.71  1.67  7.00  3.25  8.79  4.71  4.67  3.58  7.29  8.67
75 11  9 12.42  8.92 16.13  4.25  6.21  3.71 11.67  7.04  5.54  5.79  7.96 11.92
75 11 10  6.46  7.21  8.00  3.29  4.50  1.96  6.13  4.42  6.87  6.50  4.29 16.42
75 11 11  7.33  4.96  8.00  0.67  2.58  0.29  5.66  2.75  6.29  3.50  4.21 10.25
75 11 12 17.04 14.12 16.04  5.79 11.96  8.58  9.71  8.67  7.58  5.79  7.92 12.04
75 11 13 11.38  9.00 11.71  3.67 11.67  5.63  6.96  6.83  5.54  4.83  6.46 12.67
75 11 14  9.75  9.67  9.25  2.54  7.71  3.88  4.96  4.17  5.58  3.25  5.58  9.75
75 11 15 13.67 14.00 11.75  5.17 11.34  6.54  9.87  8.54  9.87  7.75 11.54 14.00
75 11 16 27.63 20.25 23.38 15.12 18.21 11.04 18.21 15.00 13.96 15.67 22.25 36.08
75 11 17 22.75 19.12 23.16 13.00 13.00  7.75 14.00 10.41 11.08 11.46 18.96 29.63
75 11 18  9.21  7.33  9.96  2.17  8.00  3.08 11.54  5.91  6.75  5.66 10.67 16.25
75 11 19 12.87 13.00  9.83  8.50 16.96 10.46 18.54 14.50 14.42 14.21 16.92 25.08
75 11 20 11.04  5.13  7.79  3.37  5.71  2.46  7.08  2.67  4.21  3.88  3.67  9.79
75 11 21  2.67  2.17  1.50  0.58  2.71  0.37  0.63  2.04  0.42  1.21  5.79  8.12
75 11 22 18.21 17.29 10.37  8.71 13.42  9.25  9.38 11.96 11.63 11.12 18.75 19.00
75 11 23 14.29  7.50 12.21  5.83  8.33  5.66  9.21  5.75 10.29  9.38 12.25 14.25
75 11 24  9.17 10.88  8.71  3.33 10.13  5.13 10.92  8.17  8.63  8.54 14.92 17.83
75 11 25 13.13 15.37  9.83  5.96 12.42  6.17 11.87  8.50 10.41  8.21 15.29 17.16
75 11 26 14.83 11.38  7.92  4.42 10.79  6.00 12.00  7.21  9.54  8.21 15.79 17.08
75 11 27 19.00 18.41 16.46  9.71 19.46 10.50 18.66 12.29 14.92 14.58 20.17 28.42
75 11 28  7.50  6.25  6.34  2.50  6.96  3.25 10.83  5.41  8.54  6.92  8.67 21.37
75 11 29  6.38  4.63  4.83  0.17  3.63  0.92  8.38  1.87  5.00  5.21  7.67 20.04
75 11 30 10.96 10.34  6.71  0.71  8.00  4.25 10.63  4.04  6.34  5.21  8.79 16.25
75 12  1 20.30 20.04 18.16  8.54 18.91  9.71 13.96 11.00 14.17 10.17 17.71 21.04
75 12  2 26.96 22.08 20.67 14.33 19.55 12.92 16.62 16.88 15.83 17.83 26.38 36.08
75 12  3 14.09 10.04 13.54  5.83  7.62  4.04 10.46  6.92  8.00  6.92 12.38 23.00
75 12  4  7.38  5.37  7.50  2.79  7.92  3.42 11.29  6.21 10.41  6.50  9.75 18.00
75 12  5  8.75  4.42  5.96  2.54  5.33  1.67 10.46  5.09  7.17  6.75  7.21 17.21
75 12  6  7.79  3.67  7.92  2.58  2.92  0.96  6.29  2.29  5.58  4.67  5.58 12.33
75 12  7  8.33  3.58  6.79  3.25  4.83  2.50  8.87  4.38  6.83  5.96  9.79 20.17
75 12  8  6.29  2.29 10.04  2.04  2.21  0.13  6.04  0.67  2.08  3.25  4.58 12.58
75 12  9  5.63  3.96  7.25  1.17  1.25  0.13  7.29  0.21  2.29  1.75  5.13 13.67
75 12 10  4.33  5.50  6.92  0.58  1.13  0.00  5.71  0.08  1.83  0.92  4.58  8.79
75 12 11  2.21  3.00  3.08  0.58  4.79  2.37  5.33  5.25  5.63  4.42 13.21 17.46
75 12 12 20.00 16.75 18.08  9.67 10.00  6.38 11.63  7.62  9.83  8.54 19.00 24.83
75 12 13 13.62  7.00 18.50  4.08  3.71  1.50  9.50  1.96  6.63  4.67  6.79 12.87
75 12 14  8.29  1.54  9.08  1.04  2.37  1.13  8.42  2.08  6.71  4.21  6.50 15.67
75 12 15  8.58  2.62  9.92  2.04  1.21  0.21  7.92  1.71  3.37  2.17  4.08 13.00
75 12 16  9.08  3.00 10.50  3.54  2.62  2.00  9.42  3.83  7.12  5.88 10.17 18.54
75 12 17 16.17 13.33 24.41  8.50  8.12  5.96 12.04  5.79  9.00  6.71 11.04 16.62
75 12 18 10.67  2.50  9.87  1.79  3.04  0.67 10.00  2.13  4.75  3.42  6.92 14.83
75 12 19  6.71  0.54  6.92  1.21  0.54  0.25  6.67  1.08  2.58  2.08  4.46 12.21
75 12 20  3.37  2.50  3.92  0.33  1.67  3.04  9.17  2.25  6.79  4.79  8.54 14.75
75 12 21  9.87  9.96  8.92  3.08  7.87  5.71 12.33  6.79 10.58  9.21 16.88 20.04
75 12 22 10.58 11.87  7.96  4.79  8.04  6.87 10.88 10.00 10.83 11.46 20.38 21.25
75 12 23 16.17 14.04 14.29  7.17  8.71  6.87 12.50  9.33 12.08 10.50 13.70 20.58
75 12 24 12.67 12.62  7.92  3.29  8.42  4.33 12.21  8.58 10.58  8.12 15.46 21.75
75 12 25 18.71 12.33 12.58  9.33 12.71  8.75 13.96 11.00 12.87 13.59 15.79 23.00
75 12 26  8.38  5.29  6.96  4.29  7.04  4.00 11.38  5.91 10.17  7.25 14.21 20.00
75 12 27 12.92 12.92 11.92  5.79  7.38  6.83 13.17 13.04 11.96 12.42 20.08 21.96
75 12 28 16.29 15.04 14.37  8.00  9.87  8.33 13.04 11.67 13.21 12.71 14.71 19.17
75 12 29 12.42 13.62 12.04  5.25  8.46  6.42  9.46  8.75  9.42  8.71 14.58 17.50
75 12 30 19.17 15.54 18.34  9.04 13.17 11.25 17.79 14.71 16.25 15.71 21.92 30.91
75 12 31 15.59 12.33 13.42  2.37  4.08  1.17  7.08  4.25  5.91  6.34 11.38 19.55
76  1  1 18.34 17.67 14.83  8.00 16.62 10.13 13.17  9.04 13.13  5.75 11.38 14.96
76  1  2 29.20 25.29 20.25 15.46 23.58 14.88 18.96 17.25 17.62 18.29 24.71 27.54
76  1  3 11.25  9.59  7.62  5.46  9.46  4.00 13.29  6.38 10.13  9.00 12.12 23.29
76  1  4 12.67 12.79 12.92  6.46 12.92  8.71 13.62 10.41 13.13  6.96 14.04 17.12
76  1  5 12.79 11.67 12.58  4.96  9.71  6.08 15.63 10.13 13.04 10.63 15.16 19.04
76  1  6 11.17 12.08 13.37  3.33  9.92  7.50 14.83 13.33 13.13 10.96 18.71 19.33
76  1  7 17.79 15.75 15.71  5.88 10.29  9.67 14.58 14.83 13.67 15.59 23.63 25.70
76  1  8 15.59 16.96 15.41  7.87 11.08  9.50 14.37  5.50 11.34  5.25  4.21 11.42
76  1  9 15.21 14.62 11.75  3.92  7.08  4.50  7.17  2.88  7.83  1.46  5.33  8.00
76  1 10 18.00 20.75 14.29 12.96 23.29 15.04 21.29 18.63 19.12 16.38 23.83 28.42
76  1 11 14.12 11.67 11.25  9.54 16.62 10.92 19.12 18.46 16.38 14.62 19.00 26.71
76  1 12 12.58 12.12 11.67  6.92 14.25  8.29 16.42 10.88 14.33 10.88 17.41 23.50
76  1 13 13.79 14.46 10.63  4.83  8.46  5.63 11.46  9.83 11.50  9.08 16.46 18.29
76  1 14  9.13  8.87  8.67  4.79  9.13  5.21 11.92  8.96 10.96 10.46 16.75 22.04
76  1 15  2.92  7.41  4.67  2.04  4.92  2.88  8.38  5.75  8.29  7.46 11.21 16.54
76  1 16  1.63  4.33  4.29  0.21  1.17  0.96  8.25  1.17  5.17  4.46  6.38 14.50
76  1 17  5.13  7.38  3.67  0.63  5.71  2.25  3.42  4.63  4.38  4.33 10.54 14.04
76  1 18 16.50 15.09 11.46  7.54 12.83  8.12 13.83 12.29 12.58 12.71 18.88 25.00
76  1 19 19.79 21.00 16.88 11.96 20.83 12.96 20.75 18.66 19.41 16.25 25.75 30.63
76  1 20 24.25 23.50 17.58 15.34 26.25 16.75 25.96 21.29 22.50 21.79 29.33 40.12
76  1 21 18.54 19.17 12.50 11.17 21.46 14.71 20.79 19.33 18.63 18.58 23.54 32.55
76  1 22 20.58 19.70 15.67 10.71 20.12 14.29 21.67 18.54 20.17 18.71 22.83 29.29
76  1 23 22.54 18.41 13.88 10.83 16.46  9.00 14.12 12.58 12.92 10.00 20.21 23.54
76  1 24 19.92 17.25 19.08  8.96 10.00  8.08 13.29  8.50 12.38 12.46 22.13 32.91
76  1 25 16.96 10.34 17.46  5.83  7.71  5.13 10.21  3.00  9.50  7.75 14.00 23.58
76  1 26 11.96  7.62 11.00  6.54  7.25  5.09 11.46  7.21 10.00  9.04 12.87 20.88
76  1 27 13.13 12.04  9.13  4.04  9.96  6.04  9.25  7.08  8.08  6.87 11.21 13.79
76  1 28 20.30 11.34 20.79 12.21 12.29 11.08 16.71  7.41 16.50 18.08  6.54 21.34
76  1 29 26.00 18.71 27.12 15.87 21.75 20.21 23.13 21.87 22.21 20.12 21.87 29.33
76  1 30 21.17 14.37 26.08 12.33 17.46 16.83 23.50 19.12 18.63 16.29 17.88 34.25
76  1 31 11.46  9.50 17.67  4.88  6.96  4.67 18.21  8.92 11.63  7.96  8.54 22.13
76  2  1  9.04  6.13 12.04  3.04  3.83  1.58 11.58  5.54  5.66  6.17  8.00 13.79
76  2  2 10.96 10.96 14.33  3.88  6.46  4.08 11.21  8.58  7.33  7.46 10.54 17.33
76  2  3 13.37 10.96 15.37  4.88  9.83  5.09 10.58  8.63  8.79  5.91  7.04 15.34
76  2  4 11.42 10.54 17.41  6.04  8.75  6.58 13.62  9.67 11.21  9.46 10.46 22.50
76  2  5 11.46 11.87 14.00  4.33  7.21  3.08 12.79  7.87 11.12 10.75  9.04 18.79
76  2  6  8.42 12.46  4.33  2.04  6.46  1.96  4.58  5.46  7.12  4.79  9.29 11.96
76  2  7 16.75 16.08 12.04  9.17 14.04  9.50  9.50 10.88 13.62  9.87 17.21 18.08
76  2  8 10.50 12.71  9.75  4.42  7.79  4.88 11.17  7.58  9.21  7.33 15.12 13.88
76  2  9 16.46 19.08 16.83  8.71 15.79 11.29 16.17 16.83 16.58 15.71 27.58 28.71
76  2 10 14.46 19.75 11.25  7.54 13.92  7.41 15.16 11.96 13.04 11.21 23.16 22.58
76  2 11 16.88 18.46 10.67  8.50 16.79  7.17 17.12 12.67 13.29 10.83 21.87 23.87
76  2 12 18.63 19.95 15.21 11.87 19.00 11.63 17.41 15.34 15.12 12.21 17.79 22.21
76  2 13 12.12  7.25 14.04  5.63  5.09  2.37  8.79  3.21  4.38  3.54  5.83  8.38
76  2 14  6.96  7.08  6.96  2.75  3.54  3.92  4.25  7.41  7.96  6.04 13.50 16.96
76  2 15  8.00 12.46 13.33  5.00  6.38  4.08  9.42  4.96  6.67  6.63  6.00 11.12
76  2 16 11.67  9.83 10.88  3.33  6.75  2.75  8.50  5.54  6.42  5.41  8.54 11.42
76  2 17  9.04 11.34  7.79  2.75  9.13  5.75  4.38  8.25  8.21  6.42 14.71 14.88
76  2 18  5.29 11.25  5.13  1.17  6.17  2.21  2.92  5.58  4.88  2.37 11.34 12.54
76  2 19 16.04 19.33 12.00  4.92 12.71  7.38 10.08 10.13 10.34  5.58 13.92 16.25
76  2 20 17.25 21.84 14.42  9.59 15.25 10.41 12.25 13.54 13.29 10.00 17.54 22.13
76  2 21 12.62 13.79 12.83  7.25  9.59  5.09 10.17  7.00 10.63  8.87 11.04 13.96
76  2 22 18.16 18.84 15.46 10.83 13.88 11.71 12.08 14.67 15.92 16.92 22.71 25.25
76  2 23 14.46  7.79 14.37  7.12  6.54  3.79 11.29  6.29  8.17  6.50 10.34 14.62
76  2 24  5.66  8.38  8.75  4.88  7.58  5.21 13.29 14.62 11.38 10.63 21.04 25.58
76  2 25 10.08 11.46 14.33  5.04  8.58  6.21 14.17 13.33 13.70 13.79 19.79 22.67
76  2 26  4.42  7.29  5.88  2.00  3.88  1.83  8.46  7.00  7.12  6.08 14.25 15.37
76  2 27  7.25 10.71  6.42  4.58  7.04  3.17  5.75  6.58  7.71  6.79 11.50 11.29
76  2 28  8.54 10.54  9.46  4.92  7.50  5.29  6.71  8.04 10.58  9.96 14.75 16.83
76  2 29 10.41 10.79 11.08  5.96  8.58  6.21 11.71 10.58 10.34 10.17 17.16 22.29
76  3  1  6.13  4.79  3.42  3.21  7.50  2.50  6.50  3.67  5.54  4.04  7.75 11.42
76  3  2  7.96  7.75  7.50  4.00  8.92  5.37  6.96  5.25  7.79  5.79  8.87 11.46
76  3  3  7.62 12.46  9.62  5.09  8.83  4.54 10.04  8.33 10.21  8.42 15.54 13.29
76  3  4 11.92 14.92 10.34  6.75 12.08  6.96  7.87 12.12 11.67 11.75 17.62 19.12
76  3  5 15.50 19.38  9.83  9.38 18.00 13.83 11.54 15.12 16.25 15.83 17.75 23.96
76  3  6 21.00 18.54 21.17 10.25 20.21 15.00 20.79 12.75 16.79 14.33 16.29 23.91
76  3  7 20.25 21.17 17.37  8.33 19.08 10.83 14.33 13.04 12.17 12.71 15.09 20.96
76  3  8 10.88 13.92  5.91  3.29 11.46  3.25  7.54  7.00  8.08  6.75 11.67  9.29
76  3  9 20.00 16.38 10.75  6.83 14.92  9.08  7.58  9.42 12.17  8.38 16.50 20.08
76  3 10 23.96 18.71 18.46 12.04 18.75 11.75 12.71 13.67 16.46 15.92 17.83 22.92
76  3 11 14.83  9.13 13.00  8.17  9.96  6.79 11.34  9.46 11.00 11.87 11.58 16.04
76  3 12 25.33 24.04 19.67 11.12 20.21 13.96 15.92 17.92 15.37 13.13 16.71 21.71
76  3 13 19.04 15.09 14.29  8.25 11.25  6.29 12.00  7.83  8.38  7.33 11.42 11.17
76  3 14 12.87 14.83 12.21  6.50 13.13  9.00 15.37 11.92 13.21 14.04 16.50 24.25
76  3 15  3.71  8.50  3.17  0.67  4.00  1.33  9.59  6.04  7.87  7.29 10.21 19.00
76  3 16  8.21  9.54  9.33  3.83  9.21  3.88 10.79  7.41 10.54  8.25 11.63 17.16
76  3 17  7.50  5.29  9.25  2.62  3.00  0.83  8.04  2.21  4.54  6.08  5.63  8.17
76  3 18 16.88 16.62 13.46  5.58 13.62  6.38 11.12 11.42 10.67  7.54 12.08 11.79
76  3 19 13.46 10.96 18.29  8.50 11.79 10.88 14.58 11.42 13.92 13.21 13.04 26.12
76  3 20 19.29 18.00 22.67 14.92 16.92 14.09 16.88 14.12 19.83 19.67 20.04 29.17
76  3 21  9.54 10.21  7.58  3.67  8.33  3.88  6.83  6.21  7.75  8.54  9.38 11.21
76  3 22 12.33  5.37 11.46  5.13  3.83  1.79  5.71  2.50  6.92  4.71  9.83 10.00
76  3 23  8.83 11.83  7.83  4.83 10.17  6.34  5.96  7.12 11.25  8.54 12.54 20.17
76  3 24 11.83 12.96 13.13  6.46 11.92  6.46 12.96 10.79 12.87 12.00 15.41 18.79
76  3 25 18.46 17.75 18.88  8.96 19.83 10.34 15.29 11.54 15.96 10.96 17.58 16.96
76  3 26 13.92 17.21 13.04  9.00 17.29 10.00 16.58 14.04 15.59 14.21 23.00 24.41
76  3 27 14.25 14.62 14.54  9.13 20.46 12.17 20.88 17.75 20.83 18.41 23.21 28.58
76  3 28 17.21 16.13 18.16  9.17 17.04 13.04 18.71 17.50 19.67 19.75 24.41 31.20
76  3 29 16.17 14.54 13.83  9.92 17.16 11.12 17.00 14.00 16.04 15.67 20.30 26.25
76  3 30 14.29 12.83 12.21  9.17 16.71  8.50 15.63 13.54 15.12 13.79 17.41 20.46
76  3 31 14.96 14.21 13.13  9.92 16.71 10.41 17.00 12.67 16.21 16.96 20.54 25.00
76  4  1  7.58  9.21  7.33  3.96  7.96  3.37  6.87  4.00  7.75  7.58 10.37 13.67
76  4  2 17.88 12.17 13.08  7.79 10.67  5.41 10.79  7.87  9.17  9.67 16.17 18.63
76  4  3 14.96 12.17 12.83  6.67 13.13  8.08 12.87 10.96 13.50 14.33 17.79 25.50
76  4  4 10.67  9.50  8.50  5.46 10.63  5.66 12.21  8.92 12.08 10.58 14.79 18.08
76  4  5 13.79  8.21  9.42  8.00 13.04  8.46 16.38 14.29 14.62 13.79 16.33 23.91
76  4  6 20.96 10.37  8.54  7.75 14.37  7.79 14.71 13.42 13.21 14.75 14.33 19.92
76  4  7 13.21 12.25 15.09  6.79 12.04  5.58 10.63  8.04  9.29 10.58 11.08 13.79
76  4  8  7.54  3.54  7.87  2.62  4.50  2.04  7.21  4.25  5.79  5.58  8.54 13.96
76  4  9  5.96  2.17  5.13  2.08  3.79  1.33  6.00  3.33  5.79  5.17  9.79 13.67
76  4 10 11.50 11.67 11.17  5.33  8.54  7.46 10.34 15.87 13.08 12.87 27.00 23.33
76  4 11 16.88 14.50 16.71  8.21 10.13  5.88 11.34  7.50  9.79  7.96 13.75 14.88
76  4 12 12.75  9.46 15.46  6.04  5.88  2.54  8.29  4.17  8.04  6.75 12.79 10.41
76  4 13 20.91 16.46 14.09 10.04 15.87 12.17 15.00 15.83 15.50 15.25 22.54 25.04
76  4 14 27.84 21.37 20.38 13.79 17.79 12.92 17.67 15.50 15.79 17.58 20.46 26.87
76  4 15 14.71 10.13 15.96  6.29  8.38  4.83  8.79  4.96  8.63  8.08  9.50 11.96
76  4 16  6.50  2.67  4.79  1.50  5.50  0.58  6.46  2.54  5.91  4.17 11.17 15.87
76  4 17  4.92  3.96  5.13  1.54  5.46  3.33  9.42  4.96  8.50  6.83 12.79 16.17
76  4 18  4.33  0.54  5.75  0.63  2.50  0.21  5.63  1.46  2.13  1.42  7.96  7.17
76  4 19 11.29  8.42 11.54  1.46  4.46  1.38  5.88  5.29  7.58  5.71  6.34  9.25
76  4 20 13.88 10.67 10.63  3.29  6.13  4.21  8.00  6.75  9.21  7.92  9.13 15.50
76  4 21 17.71 11.00 13.37  5.83  9.25  9.59  8.83 11.21 11.04  8.42 11.96 12.42
76  4 22 16.04 12.25 16.42  6.83 10.41 10.17 14.79 10.21 11.42  8.38 11.50 15.29
76  4 23 10.29  7.25 14.37  4.12  6.25  3.67  9.96  6.21  6.38  4.63  9.83  7.50
76  4 24  5.88  3.33 14.96  4.38  4.42  3.33  9.87  3.63  6.25  3.67  8.75  5.71
76  4 25  8.96  3.67 17.71  4.71  4.79  5.00 11.25  5.75  8.38  7.54 11.54  5.96
76  4 26 11.38  5.21 17.25  5.13  5.91  6.00  8.25  5.54  8.33  6.25 11.75  8.21
76  4 27 12.21  6.34 14.79  5.88  7.04  5.21  9.59  5.04  7.33  4.75  7.29  9.21
76  4 28  9.92  7.29 11.34  2.71  5.75  3.08  7.29  4.21  5.96  3.79  6.87  6.63
76  4 29  6.04  3.79  5.75  1.87  3.46  0.29  3.54  1.42  1.25  3.17  7.58 11.25
76  4 30  9.29  5.17  5.04  1.00  3.08  0.58  5.54  3.25  4.46  3.17 13.33  8.83
76  5  1 16.66 13.46 13.04  7.17 13.13  8.46 11.96 11.17 10.71 10.54 18.29 19.29
76  5  2 14.79 12.08 13.54  6.87 12.71  8.33 12.38  9.42 11.75  7.96 17.12 18.71
76  5  3 17.83 13.75 11.75 10.08 16.46  9.96 14.96 10.96 13.88 13.25 17.92 15.87
76  5  4  7.41  2.25  8.54  2.79  3.29  1.83  7.08  3.63  4.46  4.38  9.33  8.12
76  5  5  8.96  7.41  6.54  2.46  4.12  2.75  5.54  4.00  6.08  3.88 13.29  9.54
76  5  6  5.66  3.50 12.46  1.54  2.25  1.92  5.66  3.50  7.92  6.75 10.17 11.42
76  5  7  8.29  9.54  6.17  2.67  5.71  4.29  5.33  6.54  6.79  6.00 13.83 11.21
76  5  8  9.46  7.92  7.67  2.75  5.54  3.37  7.25  9.04  6.58  7.21 19.75 16.54
76  5  9  9.96  8.25  6.79  3.96  5.46  3.88  4.33  6.29  6.50  5.63 11.17 13.17
76  5 10 12.33 11.34 10.21  6.38 10.71  6.29 11.25  8.00  9.46  8.58 16.17 17.79
76  5 11 16.04 13.75 12.83  8.17 14.92  9.62 14.17 11.58 14.09 11.42 18.91 11.96
76  5 12 25.17 15.63 15.67 12.67 19.41 13.04 16.29 13.37 14.96 14.04 19.41 23.71
76  5 13 11.58  9.29 11.04  5.33  6.34  6.17  8.96  9.21 10.46  9.21 15.63 14.17
76  5 14 21.00 16.54 17.83 11.00 13.92 11.12 13.37 11.29 15.63 16.29 23.33 21.12
76  5 15 18.41 14.37 18.84  8.96 14.04  8.75 13.25  8.08 13.75 12.38 13.92 15.41
76  5 16 23.63 21.34 20.79 11.83 15.96 11.96 16.50 15.79 16.75 18.46 23.21 24.13
76  5 17 19.17 11.50 20.08 10.79  9.25  4.83 13.88  8.50 10.79  8.71 12.21 14.88
76  5 18  7.62  6.21  8.46  3.25  4.29  2.29  7.04  4.00  6.00  4.25  7.04  7.38
76  5 19 12.21 11.83  7.67  3.63  7.58  3.08  7.08  2.62  5.09  3.54  6.13  4.50
76  5 20 14.17  8.63  8.25  6.79 10.71  6.25  8.38  6.17  8.42  7.04 12.54  9.08
76  5 21 15.37 15.16 12.17  5.88 10.37  7.62  9.83  9.13 11.21 11.21 18.41 17.46
76  5 22 13.62 14.21 13.00  7.58 10.17  8.96 10.54  9.08 13.21  9.25 18.71 16.58
76  5 23 14.17 16.38 11.79  8.50 14.04 10.50  9.50 11.83 15.54 11.00 19.00 20.88
76  5 24 15.50 15.21 12.83  7.71 15.46  9.42 11.08 11.63 15.34 12.12 21.54 25.04
76  5 25 13.46 10.71  7.87  6.34 11.54  7.41 10.00  9.21 10.46  9.83 17.12 16.54
76  5 26  7.38  7.08  5.88  1.92  2.17  0.83  7.12  2.37  5.00  3.88  9.67 10.00
76  5 27  6.79 11.54  8.00  2.50  5.83  2.42  3.29  2.58  4.75  2.67 13.33  5.29
76  5 28  9.13  8.67  5.13  2.62  9.33  3.50  4.63  5.00  8.04  6.04 10.21 11.04
76  5 29 11.83 10.00 10.25  2.79  7.00  3.83  3.33  6.42  6.29  4.58 12.29  6.00
76  5 30 15.92 13.42 14.09  6.63 13.00  6.54  8.29 10.13 11.79  7.54 17.54 12.71
76  5 31  9.96 11.42 10.41  3.29  9.75  5.09  6.29  6.75  9.25  5.88 11.50  9.42
76  6  1 13.04  8.83 10.79  7.21 10.83  6.96 12.92  8.25  9.96 10.92 11.34 14.75
76  6  2  5.37  3.25  6.54  2.04  2.13  0.04  4.29  1.50  3.83  2.50  6.34  6.79
76  6  3  5.58  5.04  6.13  1.13  1.54  0.71  3.04  1.42  2.88  1.71  7.00  5.09
76  6  4  8.04 10.21 10.29  3.42  4.12  4.00  5.75  8.00  6.79  5.63 14.88 11.83
76  6  5  8.71 10.37 13.08  3.21  4.17  4.88  6.50 10.50  9.33  8.50 16.58 16.04
76  6  6  6.96 10.88 11.46  4.04  4.96  5.21  7.87  9.79  8.83  8.21 17.79 14.37
76  6  7 11.29 13.37  6.83  3.83 10.21  6.67  5.54  5.13  8.38  5.75 11.34  9.71
76  6  8 12.83 13.54 12.83  7.08  9.29  9.33  7.75  9.13 10.54  9.13 13.54 12.54
76  6  9 14.17 13.92 14.21  7.71 13.62  9.59 10.83 10.25 12.42 10.17 17.88 16.79
76  6 10 12.33 14.21 12.87  7.33 12.33  9.75 10.83 10.46 11.42  9.62 19.08 18.12
76  6 11 19.04 16.79 16.38  9.38 11.79 11.50 10.63 13.25 14.04 12.50 20.67 19.67
76  6 12 11.25  9.75 13.96  5.00 10.21  7.33 13.13 11.75 11.17 10.13 19.62 21.34
76  6 13 12.21 11.54 13.08  6.46  9.13  8.12 10.79 12.46 10.37 11.04 20.33 20.83
76  6 14  7.75  7.17 10.71  4.00  6.04  6.00 11.92  5.91 10.13  6.58 16.21 18.46
76  6 15  7.79  7.46 10.21  1.25  3.04  2.83  9.79  7.58  7.71  5.29 13.75 15.00
76  6 16  8.75  7.12  8.87  3.83  4.04  4.42  3.37  4.38  5.91  3.21  9.13  6.54
76  6 17 12.79  7.75  8.25  4.04  5.50  4.46  7.00  5.71  7.75  5.37 13.37  8.75
76  6 18 14.09  9.62 11.79  6.21 10.75  8.63 11.67  9.21 10.37 10.50 15.34 19.79
76  6 19  2.92  2.50  5.29  3.67  5.21  5.09 11.08  6.17  9.33  6.63 14.46 20.33
76  6 20  7.83  6.92  8.42  4.08  4.75  4.17  9.96  4.58  7.75  6.50  9.04 14.54
76  6 21 10.79 11.96 11.04  5.33  8.21  8.17  7.12  7.00 10.37  8.00 12.75 12.92
76  6 22 13.88 14.21 11.00  8.67 12.83  9.83  8.96  7.33 12.83 10.83 10.04 12.96
76  6 23 10.41 12.33 11.04  6.46  9.67  8.29 10.04  9.38 11.04 10.17 18.75 13.67
76  6 24 10.54 13.25 11.46  7.12 10.71  9.46 10.37 10.96 12.58  8.79 20.46 15.34
76  6 25  6.00 11.04  5.46  3.46  8.46  6.17  7.00  5.33  7.12  5.21  7.79  5.04
76  6 26  7.29  6.04  9.17  2.62  6.17  5.09  7.41  7.46  8.00  6.67 15.16 13.67
76  6 27  4.46  3.83  4.92  1.96  3.21  2.67  3.42  7.08  5.37  4.88 16.50 20.25
76  6 28  4.33  1.92  5.46  2.08  2.21  1.54  5.58  2.50  3.21  3.04  8.63  6.04
76  6 29  5.29  1.00  6.38  1.75  1.63  0.54  5.58  2.25  4.21  2.92  9.50  6.54
76  6 30  8.96  1.38 11.04  3.21  4.25  3.21  6.04  3.67  6.87  4.12  8.25  9.29
76  7  1  8.50  1.75  6.58  2.13  2.75  2.21  5.37  2.04  5.88  4.50  4.96 10.63
76  7  2  9.50  4.21  5.71  2.67  3.00  1.92  4.67  3.50  5.29  3.37  5.96  4.96
76  7  3  5.66  6.00  5.71  2.37  2.21  1.54  4.88  3.00  5.41  2.96  7.50  7.17
76  7  4  4.96  5.96  4.75  3.79  5.04  3.21  4.08  2.46  6.79  4.08  7.21  8.42
76  7  5  8.12  7.21  5.21  3.17  6.17  2.67  1.54  4.17  4.83  1.67  7.87  5.09
76  7  6  5.91  3.79  8.71  3.00  1.58  1.29  4.12  2.96  5.21  2.37  8.08  6.63
76  7  7  3.04  2.00  5.58  1.67  1.29  0.29  5.50  0.92  5.58  2.88  4.12  4.17
76  7  8  9.04  8.75  6.54  4.67  7.50  5.09  4.29  3.42  6.21  4.67  5.37  7.83
76  7  9 14.58 14.12 13.08  6.29 14.21  7.96 10.34 10.46 11.42  8.58 17.33 11.79
76  7 10 18.08 16.17 12.46  9.67 15.67 11.67  9.13  9.59 13.67 11.75 21.21 16.08
76  7 11 11.92  9.29  9.21  6.83 11.00  8.46  9.33  5.54 10.41  9.08 10.37 16.66
76  7 12  8.17  9.25  7.46  6.34  8.29  5.83  5.75  6.75  8.71  6.50 10.83 11.63
76  7 13 15.75 15.12 12.54  7.08 15.71  9.46  9.04  7.00 10.83  8.58  9.29 11.08
76  7 14 11.46 11.17 10.88  6.42 10.41  7.87 10.34  7.58  9.96  8.87 16.58 13.70
76  7 15 12.62  9.67 13.50  6.67 11.21  8.17 10.08  6.79 11.58  8.67 11.71 14.67
76  7 16  7.17  7.79  7.46  2.37  9.87  4.58  6.54  6.83  7.50  5.13 14.58 15.00
76  7 17  8.33  9.29  8.25  2.92  7.46  4.21  5.79  5.58  7.87  4.63 12.33 10.75
76  7 18 14.92 13.13 13.88  8.25 13.13  9.42 10.25 10.54 12.21 13.50 16.38 14.96
76  7 19  9.87  7.41  8.92  5.04  9.67  5.83  9.96  5.91  8.50  8.46 10.50 15.79
76  7 20 11.71  8.33  8.54  5.75  9.04  6.08  8.83  5.21  8.83  8.63 10.17 16.75
76  7 21  9.13  6.29  7.96  3.92  6.67  4.12  7.92  3.67  7.29  6.42  8.42 11.83
76  7 22  6.42  4.21  5.75  2.21  3.37  2.54  5.63  2.75  5.29  3.83  8.17 11.67
76  7 23  8.46  6.46  7.25  4.42  5.96  3.79  8.96  3.79  7.04  6.13  8.08 12.75
76  7 24 10.08  8.67  7.96  4.17  6.42  4.25  6.58  3.42  7.33  6.17  8.92 11.71
76  7 25  9.25  9.54  7.00  3.92  4.75  4.50  5.58  3.67  7.17  6.46 10.25 10.71
76  7 26  6.21  5.41  7.41  4.00  3.71  2.08  5.09  3.08  6.50  6.13  6.67  9.54
76  7 27  8.87  6.17  7.38  4.04  4.33  2.75  4.92  3.33  7.12  6.58  8.50 12.54
76  7 28  7.58  6.38  8.25  4.38  7.62  4.42  5.50  6.08  7.08  6.71 10.79 13.13
76  7 29 10.34  6.04  5.96  4.88  7.50  4.38  9.25  6.63  8.08  7.71 11.17 17.00
76  7 30 13.04  9.54  8.12  6.25 10.96  6.83  9.87  6.63  9.42  8.25 13.70 19.55
76  7 31 11.63  8.29  8.29  4.29  8.38  5.33  7.71  5.91  7.17  7.96 11.17 15.09
76  8  1 13.00  8.38  8.63  5.83 12.92  8.25 13.00  9.42 10.58 11.34 14.21 20.25
76  8  2  8.83  7.21  7.83  4.92  9.75  6.96 10.58  7.29  9.29  9.59 11.71 19.95
76  8  3  5.54  5.00  5.79  4.79  5.13  3.58  5.91  2.71  6.79  6.29  7.38 10.88
76  8  4  7.33  3.04  5.58  3.29  3.54  3.63  6.75  3.88  5.00  5.25  8.79 13.59
76  8  5  4.71  2.13  5.83  2.54  4.38  2.75  4.92  3.37  4.92  3.04  9.96 13.50
76  8  6  6.04  3.88  5.41  2.21  3.92  2.92  3.46  2.00  4.38  2.29  8.42 10.58
76  8  7  6.75  3.79  6.17  2.33  3.50  2.54  3.08  1.00  4.54  4.25  4.63  7.54
76  8  8  4.33  1.17  8.83  2.25  3.25  1.50  1.67  1.17  1.54  1.17  4.54  4.12
76  8  9  5.04  2.71  7.04  2.04  4.08  1.38  1.67  0.63  2.42  0.96  3.04  3.67
76  8 10  5.50  2.71  4.46  1.42  2.58  1.75  2.67  2.62  3.42  3.58  8.96  4.67
76  8 11  6.25  6.96  6.21  3.04  5.83  3.96  4.71  5.37  4.38  4.83 14.00 12.00
76  8 12  6.63  6.25  5.83  3.04  4.29  3.42  1.46  2.62  4.21  3.17  5.91  7.25
76  8 13  8.25  3.29  9.08  1.92  3.50  1.50  2.42  0.87  3.96  1.71  4.54  5.33
76  8 14  7.29  2.67 14.50  1.96  3.13  2.54  3.79  2.42  7.33  4.58  6.38  8.71
76  8 15  4.21  1.46  8.54  1.13  0.92  1.75  3.42  1.46  3.46  4.21  1.87  7.12
76  8 16  5.46  3.75  5.96  1.38  2.75  2.42  1.87  0.67  2.79  1.25  3.08  6.67
76  8 17  7.17  6.21  6.21  2.00  4.92  3.17  2.00  3.00  4.96  1.96  4.63  6.58
76  8 18  4.67  4.96  9.62  1.33  4.12  4.04  4.38  3.08  7.62  4.25  4.50 11.21
76  8 19  7.17  4.50  9.71  2.00  3.21  2.58  3.00  4.12  8.38  5.96  7.12  7.67
76  8 20  9.83 10.41 11.08  2.00  2.83  2.79  4.04  3.54  6.83  4.58  5.88  8.46
76  8 21 11.42 12.12  9.92  2.96  4.54  5.33  6.87  4.58  7.12  5.63  7.29  9.00
76  8 22 15.34 10.92  9.59  4.75  6.83  8.04  7.08  6.92 10.21  7.92  8.33 13.08
76  8 23  9.75  6.58  6.79  3.37  2.50  5.09  5.79  4.92  9.59  5.75  5.25 15.25
76  8 24  5.96  4.42  7.04  4.29  4.88  5.54  6.04  3.33  7.21  5.46  4.42  8.71
76  8 25  4.38  2.54  4.12  0.92  2.13  1.96  4.12  0.79  1.83  0.87  3.50  3.92
76  8 26  4.17  2.08 14.46  3.58  3.63  2.83  3.88  2.08  3.46  1.00  7.83  4.63
76  8 27  7.33  4.96 17.41  5.33  5.37  4.88  8.87  2.83  6.58  3.42  9.25  4.50
76  8 28  9.46  6.83 20.75  5.09  6.17  5.13 13.59  4.83  9.08  7.12  9.17 12.25
76  8 29 10.17  8.21 15.04  5.41  6.75  5.17  9.67  5.13  7.92  6.13  7.96  8.87
76  8 30 15.46 10.92 13.88  6.58  8.25  6.92  9.21  4.46  8.83  7.58  9.21 11.38
76  8 31  9.42  6.29  7.71  3.71  5.96  4.96  8.63  3.13  5.71  4.25  7.71 11.25
76  9  1 11.87 11.00  7.38  6.87  7.75  8.33 10.34  6.46 10.17  9.29 12.75 19.55
76  9  2 14.75 13.46 16.17  7.79  7.79  7.67 11.63  6.50 10.13 10.54 12.92 21.84
76  9  3  9.83  4.79  8.12  4.75  4.29  5.79 10.04  3.71  8.71  5.63  8.83 15.16
76  9  4  8.92  4.04  5.88  3.79  3.00  5.37  8.08  3.21  7.25  6.04  7.54 14.00
76  9  5  4.54  4.29  5.17  1.75  1.00  2.33  3.79  1.83  4.12  4.08  5.46 10.17
76  9  6  3.42  3.00  2.96  1.63  0.87  0.96  4.46  0.87  2.25  2.25  5.96 12.75
76  9  7  4.75  4.75  3.96  1.29  1.58  2.13  4.38  0.54  0.58  1.13  4.67  8.96
76  9  8 10.13  9.33  9.46  7.04  9.62  8.75 13.17 10.34 11.04  9.83 18.25 23.00
76  9  9 24.54 20.00 20.50 15.09 16.08 13.17 21.25 12.96 16.21 17.37 21.54 35.13
76  9 10 15.12 13.67 10.29  5.17 11.83  8.04 10.54  7.25  9.96  7.38 16.54 22.34
76  9 11 23.33 22.37 22.67 13.29 17.83 15.04 20.54 14.83 15.37 18.88 25.12 32.08
76  9 12 17.33 13.17 22.21  8.96 11.25  8.54 12.33  6.04 10.46 10.58 11.46 18.08
76  9 13  8.71  8.25  5.71  2.13  9.71  5.21  6.71  6.25  8.00  5.13 12.75 18.12
76  9 14 20.83 17.29 20.62  9.67 14.37 11.21 12.17 11.04 12.33 12.29 19.25 29.63
76  9 15 12.08  7.50 14.12  6.04  8.08  7.00  6.58  3.63  7.92  7.96 10.34 18.12
76  9 16  5.66  5.54  5.33  1.63  4.29  1.29  3.08  0.42  2.50  1.79  4.75  8.17
76  9 17 12.21 10.46  8.71  4.17 11.71  9.04  4.42  5.54  8.04  6.46  9.42 12.42
76  9 18  7.21  6.92  8.33  4.38  7.21  7.38  5.66  3.92  8.92  5.54  7.67 15.83
76  9 19 12.83 14.21 13.37  7.00 11.25 10.00  9.75  7.58 12.71  8.25 12.79 18.88
76  9 20 11.79 14.58 10.17  6.58 14.21  8.75  7.25  8.04 12.25 10.71 15.92 15.87
76  9 21  6.25  6.38  6.67  1.83  4.17  3.79  3.21  2.17  4.63  3.46  8.71  6.92
76  9 22  5.21  6.92  2.88  1.13  4.12  2.37  1.63  1.13  3.00  1.13  4.33  5.54
76  9 23 17.04 16.00 13.88  6.17 15.04  9.62  6.34 10.96 10.71  6.46 11.67 18.08
76  9 24 13.59 14.04 14.71  7.71 12.12 11.46 15.92 14.79 14.54 11.25 17.50 28.96
76  9 25 10.92 12.58  5.88  3.67  8.42  6.42 10.13  7.17 10.34  9.92 15.34 33.17
76  9 26 11.63 11.00  9.42  3.83  7.41  6.38  8.17  5.66  9.62  5.88  7.33 19.08
76  9 27 11.25  9.50 11.17  4.71  9.38  6.83  7.04  9.08  9.59  4.67 10.88 16.71
76  9 28 13.08 12.38 11.04  4.83 10.96  8.29  7.71  7.71 10.50  5.00  9.38 14.75
76  9 29 13.50  9.54  7.83  3.37 10.75  5.88  6.96  9.00  8.92  5.63 11.83  8.79
76  9 30  8.79  8.50  9.38  3.13  6.75  4.83  5.25  3.83  7.83  3.37  6.71  6.92
76 10  1 10.96  6.71 10.41  4.63  7.58  5.04  5.04  5.54  6.50  3.92  6.79  5.00
76 10  2 12.38  7.58  7.50  3.92  6.34  4.04  2.67  3.50  4.79  3.17  7.38 10.13
76 10  3 12.38 10.63 12.08  6.34 11.21  8.67  8.96  8.75 10.75  9.00 14.37 15.83
76 10  4 10.21  9.33  5.17  2.96  9.59  6.13  5.41  5.41  6.38  2.50 11.87 11.17
76 10  5 22.04 20.71 17.46 10.21 18.25 13.62 11.54 16.29 13.62 12.50 21.54 21.46
76 10  6 21.62 20.67 19.95 12.96 23.09 17.12 19.21 21.50 19.87 21.92 34.83 36.51
76 10  7 16.33 13.08 12.12  5.75 11.96  8.58  7.71  6.42  7.46  5.88 12.00 16.58
76 10  8  8.29  6.34  6.29  2.79  5.91  5.29  3.04  4.46  6.04  5.33  8.00 11.54
76 10  9  5.66  6.21  7.54  1.38  6.58  5.75  1.63  7.92  5.37  6.08 14.67 15.12
76 10 10 11.12 13.04 10.29  6.04 12.21 10.29  7.41 11.54 10.79 11.42 19.58 19.25
76 10 11 17.16 14.71 10.67  5.50 11.96  7.92  5.29  9.79  9.38  8.38 13.17 13.50
76 10 12  8.63  7.41  9.00  6.96  8.58  7.25  8.33  7.00  9.08  9.25 11.96 21.12
76 10 13  7.92  7.29  5.88  2.50  7.79  4.63  2.25  4.50  5.21  3.13  8.79 11.46
76 10 14 28.08 16.00 26.83 13.88 15.59 12.08 13.83  9.71 12.00 12.42 12.12 16.54
76 10 15 15.59 10.41 12.83 10.25 10.96 10.67 14.83  8.42 11.58 13.62 12.79 27.08
76 10 16 12.92 12.54 10.37  3.63 10.54  7.58  4.88  5.29  5.58  4.88  7.21 11.42
76 10 17 13.96  9.67 14.79  6.79  9.54 10.08 13.50  9.29 11.04 12.50  9.83 22.50
76 10 18  8.63  4.00  7.71  2.04  5.46  4.08  1.54  2.46  4.38  3.63  4.58 18.46
76 10 19  9.00 12.08  4.58  0.42  9.71  5.29  2.42  5.71  5.50  4.83  9.87 12.75
76 10 20 16.38 13.62  9.50  3.00 11.75  6.50  6.96  6.50  5.13  6.42 14.50  8.87
76 10 21 14.46 13.67 13.59  5.37 14.17  8.29  9.17  9.79 10.71 11.17 19.50 17.88
76 10 22  9.62  5.09  8.79  2.83  7.92  5.50  6.04  3.75  6.79  6.67  8.79 16.08
76 10 23 11.87  7.33 10.58  5.17  7.67  5.91  6.21  5.66  6.71  6.83  9.42 13.42
76 10 24  6.08  3.04  5.50  1.21  3.92  4.50  1.92  0.63  3.29  4.12  8.25 14.62
76 10 25  4.58  1.79  7.50  1.42  0.87  0.83  0.92  0.17  1.46  3.13  7.12  6.17
76 10 26 12.79 12.58  7.25  2.17 10.04  5.21  2.58  6.04  5.37  3.88  9.59  7.50
76 10 27  7.87  6.34  7.08  2.46  7.38  5.96  6.54  9.13  6.83  6.38 13.08 17.58
76 10 28  9.71  3.83 10.08  2.75  2.88  1.83  2.58  2.42  2.67  4.46  5.37  8.71
76 10 29 12.79  5.83 14.67  5.66  4.79  4.96  6.71  4.83  6.13  7.54  9.17 11.87
76 10 30 10.71  3.25 13.79  4.50  4.79  2.33  4.46  2.92  4.29  5.83  3.50  9.25
76 10 31 15.50 11.96 10.04  5.96 12.17  8.17  4.21  9.59  9.42 10.13 17.33 18.05
76 11  1 13.96 15.67 10.29  6.46 12.79  9.08 10.00  9.67 10.21 11.63 23.09 21.96
76 11  2 12.71 15.04  8.83  4.79 11.34  8.83 11.79  7.75  9.71  9.59 18.88 18.71
76 11  3  9.21  8.12  6.17  2.96  7.46  7.50  7.54  6.00  8.17  7.92 15.16 20.00
76 11  4 10.46 12.29  9.33  4.75  9.92  9.08  5.96  7.87  9.04  8.63 15.41 18.63
76 11  5 17.37 15.50 11.38  5.96 13.25  9.17  9.62  8.71 11.54 12.92 18.58 19.25
76 11  6 15.37 12.29 10.29  4.46 12.21 10.46  5.17  9.33  9.75 10.50 12.46 16.21
76 11  7 10.83 10.41  7.79  2.54  9.83  8.29  8.08  6.67  6.71  7.33 11.29 15.67
76 11  8  9.79  9.13  6.17  1.71  8.58  8.04  5.33  7.33  5.71  7.87 10.34 17.88
76 11  9  7.00  4.29  4.33  0.75  4.88  4.08  5.75  2.17  1.87  3.75 11.25 13.21
76 11 10  6.13  5.79  4.88  1.33  4.79  6.04  8.38  1.63  3.58  4.21  7.71 13.50
76 11 11  3.50  2.83  3.04  0.13  2.88  3.08  4.96  1.08  0.58  0.92  7.25  8.71
76 11 12  6.29  1.79  9.38  1.75  0.25  0.83  8.83  0.00  0.92  1.42  1.92  6.00
76 11 13  7.75  9.50  4.88  0.21  5.50  5.66  6.58  3.29  2.67  4.29  7.54  9.75
76 11 14 15.21  9.17 13.33  6.21  8.83 10.17 11.38  5.91 10.00  8.83  8.42 18.63
76 11 15 10.88 14.09  9.87  4.17  8.12 10.13  8.29  8.75  8.04  9.13 20.41 19.50
76 11 16  9.67  6.42 10.71  3.79  1.58  3.96  8.63  1.50  3.17  3.88  5.54  9.96
76 11 17 10.96 13.92 11.38  4.25  9.54  8.67  7.41  9.79  7.79  9.92 16.00 17.54
76 11 18  9.79  6.87 10.50  3.79  2.71  5.66  7.50  4.50  6.17  4.04  7.38  8.25
76 11 19  6.21  3.92  5.50  0.54  1.92  0.96  2.17  0.87  0.17  1.21  2.96  4.38
76 11 20  5.66  2.71  6.50  1.00  0.17  0.67  3.71  0.08  0.79  1.92  3.71  9.29
76 11 21  7.50  1.42  9.59  1.79  1.29  2.75  8.46  1.54  2.25  5.54  6.38 14.33
76 11 22  7.62  3.92  9.38  2.50  3.46  3.46  7.83  2.37  2.88  5.75  6.46 12.42
76 11 23  9.87  3.96  6.87  1.42  3.08  2.92  8.54  1.75  3.21  4.29  6.08 12.00
76 11 24  7.75  2.96  6.25  1.79  4.29  6.83 11.21  5.13  6.00  7.21 13.96 17.75
76 11 25 17.46 15.25 12.17  5.83  9.17 11.00 13.04 12.42 11.71 13.83 22.54 22.17
76 11 26 18.00 16.38 15.92  8.71 13.00 12.38 16.75 12.04 13.00 14.21 16.71 17.04
76 11 27 20.46 19.38 16.00 10.58 16.08 14.09 15.50 13.67 13.92 15.71 25.75 24.87
76 11 28 16.38 19.29 15.16  8.71 18.16 13.33 21.84 12.83 13.92 15.00 28.79 29.75
76 11 29 13.46 10.83 11.21  4.25 10.37  8.38 13.21  5.37  9.29  9.54 16.21 20.41
76 11 30 11.63 10.17  8.96  3.75  8.33  5.29  8.29  3.71  4.54  4.38  8.87 12.79
76 12  1 13.46 16.42  9.21  4.54 10.75  8.67 10.88  4.83  8.79  5.91  8.83 13.67
76 12  2 13.08 13.46  9.54  3.33  7.41  6.13 10.00  3.75  4.88  5.04 10.58 11.29
76 12  3 12.12 12.62  7.50  1.96  6.54  4.17  5.75  1.08  3.08  2.75  6.58  8.17
76 12  4 10.29  9.62  9.21  1.50  3.50  2.67 11.00  0.63  3.63  4.67  4.04  8.87
76 12  5 16.88 13.37 14.42  5.33 11.50  9.04 11.83  6.83  8.83  7.67 12.75 15.29
76 12  6 21.12 22.71 15.96  8.54 19.08 12.71 16.75 11.17 13.17 13.79 18.08 18.88
76 12  7 16.00 18.05 13.37  6.96 14.09 11.79 15.46  5.04 11.38  9.50  9.54 14.92
76 12  8 12.21  8.75  8.12  3.83  7.08  5.88 10.75  4.12  7.08  6.75 11.50 18.91
76 12  9  4.54  3.00  3.71  0.17  4.71  3.29  7.25  0.75  1.33  4.08  3.92 16.66
76 12 10  4.46  2.17  4.54  0.58  1.87  2.50  9.00  0.37  2.04  4.17  4.79 11.92
76 12 11  4.58  8.54  3.17  0.00  4.83  5.33  2.71  2.62  1.50  2.42  8.79 11.08
76 12 12 12.92 11.96  4.58  0.33  6.04  4.17  1.21  2.88  0.92  1.75  6.21  8.92
76 12 13 20.21 16.83 10.50  4.00 11.87  8.75  1.21  4.79  5.79  5.04  8.00 11.17
76 12 14 15.92 14.29 12.71  4.08 11.29  8.54  5.33  6.58  7.67  7.83  7.50 12.33
76 12 15 14.17 10.71 14.58  4.04  8.25  7.17  5.66  3.75  6.42  5.63  3.46 14.12
76 12 16  7.62  8.08 10.92  2.33  8.96  6.87  6.63  6.42  6.29  5.41  7.75 14.62
76 12 17  8.29  4.67  9.38  0.67  4.12  2.92  5.63  2.83  3.67  3.75  8.38 15.96
76 12 18 14.00  8.58 14.67  4.21  6.71  4.58  9.92  4.88  6.83  7.58  7.00 14.29
76 12 19  9.79  3.54  8.54  3.37  6.13  2.58  3.17  2.92  3.42  6.42  2.88 17.21
76 12 20  7.54  1.58  6.50  1.96  2.46  0.96  6.21  0.33  2.67  2.37  1.08  4.58
76 12 21  8.87  9.59  8.75  2.37 10.58  4.58  6.50  7.04  4.25  2.92  9.46  9.00
76 12 22 12.25  9.04 12.00  4.88  9.92  8.63  9.71  8.79  8.21 10.08 10.67 21.42
76 12 23 12.38 11.38 12.92  3.58  9.75  8.38  6.63  8.63  7.50  6.67  8.29 17.96
76 12 24 13.83 12.42 11.96  3.29  7.33  8.04  8.25  8.04  8.00  7.29  7.96 15.09
76 12 25  9.59  6.13 14.83  4.38  5.75  4.96  5.96  3.92  5.00  4.88  4.67 12.00
76 12 26  8.21  6.34  9.08  1.38  0.75  0.79  6.50  1.21  2.08  3.21  0.67 11.92
76 12 27  9.04  6.21  7.87  2.83  4.79  6.96 11.67  4.42  7.38  8.58 14.50 23.42
76 12 28  9.62  5.96 13.59  3.71  3.83  4.29  7.58  4.71  5.71  5.54  8.46 16.58
76 12 29 23.83 16.38 17.16  7.25 13.08 12.38 12.33 11.71 13.83 16.04 13.37 23.33
76 12 30 15.34 11.46 16.04  5.83  8.67  8.29  9.67  2.67  8.71  8.46  6.17 15.50
76 12 31  8.67  8.83  9.38  3.67  5.37  4.58  7.92  1.79  4.46  4.38  6.38 15.67
77  1  1 20.04 11.92 20.25  9.13  9.29  8.04 10.75  5.88  9.00  9.00 14.88 25.70
77  1  2  9.75  1.54 12.54  1.83  3.79  2.75  9.46  1.21  2.33  4.58  8.25 12.38
77  1  3 13.21 16.66  9.25  1.71  9.46  8.17  7.87  8.96  7.75  7.21 22.25 21.59
77  1  4 20.12 15.92 18.91  6.75 10.25 11.83 15.41 14.09 12.83 14.71 20.50 27.16
77  1  5 12.50  9.50 12.04  4.67  9.25  6.87 11.50  8.00  8.33  9.75 13.33 18.08
77  1  6  5.09  0.96  6.87  0.37  1.79  2.00  8.04  1.54  4.12  5.58 10.08 17.88
77  1  7  5.29  3.00  6.67  1.67  4.25  3.54  8.04  1.54  6.58  6.34  8.29 16.46
77  1  8  8.54  6.46  8.25  4.42  8.04  6.08 11.38  6.50  9.67  8.08 13.00 17.16
77  1  9 19.92 16.00  9.96  9.79 17.12 10.96 12.71 11.42 12.08 11.08 20.12 20.96
77  1 10 18.16 13.59 16.46  9.38 12.17  8.67 10.75 10.21  8.54 10.96 22.08 31.75
77  1 11 12.12 10.37 13.17  4.25  5.13  3.17  7.62  4.75  3.54  3.58 12.75 15.09
77  1 12  7.79  1.75  7.54  1.21  5.54  1.42  4.96  2.88  1.87  1.67  7.17 11.04
77  1 13 26.92 19.12 24.96 14.09 18.46 13.59 17.04 12.62 12.58 14.17 13.79 22.95
77  1 14 25.84 15.79 24.54 15.00 16.66 13.50 22.08  9.67 14.58 21.04 18.38 34.29
77  1 15 17.75 11.79 14.42 10.41 11.04  9.79 11.75  6.34  8.63  9.92 12.04 17.96
77  1 16  8.04  5.50  6.46  1.29  3.63  1.67  7.17  1.75  2.79  4.00  3.54 14.00
77  1 17 19.46 18.05 15.12  6.00 12.87 10.96  9.21 10.04  8.21  8.58 12.12 16.08
77  1 18 11.42  8.21 15.37  7.08  9.92 11.58 14.83  9.00 10.83 14.09 11.25 24.25
77  1 19 14.58 17.62 10.25  5.58 10.63  9.83  7.17 10.54  8.67 10.50 16.17 17.79
77  1 20 22.37 15.09 22.50 13.88 15.83 16.62 17.12 14.37 16.54 18.50 15.46 26.12
77  1 21 15.29 10.88 15.34  9.42 12.54 13.04 14.79 10.71 14.67 18.75 11.83 27.04
77  1 22  3.92  6.83  5.96  0.58  4.00  4.88  4.63  5.50  4.79  7.38 10.13 17.88
77  1 23  4.67  8.75  4.08  0.92  4.58  5.54  2.04  4.29  3.50  4.42 10.75 12.71
77  1 24 15.50 14.75 14.46  7.71 12.58 11.87 10.34 11.00 11.63 13.04 15.34 19.00
77  1 25 16.71 10.96 13.59  6.96 11.00  9.29 10.88  6.67 10.96 12.33  6.83 15.79
77  1 26  8.50 11.83  7.71  2.50  6.38  5.66  4.96  2.96  4.75  4.75  6.13  7.50
77  1 27  8.75  6.50 11.83  7.41 11.25 11.38 12.17 12.71 12.17 10.71 13.92 22.95
77  1 28  9.29 11.17 15.96  6.00  9.00  8.29  8.58  8.08  8.87  9.17  8.63 14.09
77  1 29  6.21  6.08  8.29  2.83  2.92  3.25  2.54  2.29  2.00  3.37  6.04 11.58
77  1 30 17.62 12.04 13.04  5.25 10.08 11.04 12.79  6.75 12.00  9.00  7.50 15.71
77  1 31 10.17  3.08  9.92  4.33  4.50  2.88  4.75  1.29  1.87  2.92  3.54 10.79
77  2  1 11.83  9.71 11.00  4.25  8.58  8.71  6.17  5.66  8.29  7.58 11.71 16.50
77  2  2 16.38 13.33 15.54  8.79 11.12 10.75  9.21  9.25 11.21 11.34 12.46 17.75
77  2  3 14.21 14.04 13.88  7.46 11.67 11.58 18.29  9.50 12.67 12.92 16.08 20.04
77  2  4 13.88 15.09 12.17  4.00 11.00 10.88 10.50  7.46  9.33  9.17 11.50 15.75
77  2  5 14.58 14.75 12.67  5.96 10.21  8.71 10.08  7.00  7.83  9.08 13.96 17.83
77  2  6 19.38 17.79 15.96  6.04 10.75  9.71  7.58  5.79  7.25  5.71  8.25 13.13
77  2  7 18.75 18.96 15.04 10.00 15.63 12.50 16.66  9.96 12.87 10.58 13.79 14.42
77  2  8  5.46  8.96  7.62  3.17  6.63  7.83  7.96  3.04  5.88  3.71  4.17  8.79
77  2  9  9.67 10.50 10.25  4.00  7.00  9.25  9.96  4.75  9.13  6.34  6.21 15.04
77  2 10 11.75  8.46 10.08  6.34 11.96  9.92 12.25  8.87 10.21  3.58 12.12 27.84
77  2 11  3.13  2.62  7.08  2.13  3.50  6.34  9.13  3.08  7.25  4.63  7.33 26.25
77  2 12  6.04  6.83  6.29  1.63  4.17  4.83  5.25  2.92  5.54  6.54  7.04 22.21
77  2 13  8.12  5.58  9.83  2.17  7.71  6.38  5.46  4.96  6.96  7.75  8.46 12.04
77  2 14 11.34  9.59  7.46  2.08  7.25  2.92  8.21  1.96  4.29  4.08  7.54  6.83
77  2 15 13.70 12.21 11.63  4.75 10.13 10.96 11.50  8.08  9.54  8.54 11.83 12.29
77  2 16  5.58  8.54  6.25  1.13  6.67  6.87  5.91  4.50  6.00  5.79 11.08 12.83
77  2 17 17.71 15.12 15.59  7.62 14.29 11.75 11.04 10.04  9.50  8.83 11.71 15.87
77  2 18 11.46  9.67 10.17  5.37  8.75  8.04 15.09  8.21 11.29 12.12 14.21 17.33
77  2 19 14.42 11.92 10.79  7.12 10.63 10.00 13.04  9.71 11.25  9.83 11.79 12.92
77  2 20 11.63 10.21 11.25  4.83  8.25  8.83  9.08  7.00  8.63  7.92  9.33 21.59
77  2 21  9.83  9.87  8.46  2.08  8.33  5.00  3.83  8.96  6.08 10.21 13.50 23.09
77  2 22 10.17 15.09 10.96  4.58 11.46  9.87 10.96 11.83  9.75 11.34 16.83 19.92
77  2 23 12.54 15.21 20.62 10.25 14.37 14.37 17.92 13.50 14.75 15.37 17.12 24.37
77  2 24  8.67  8.87 10.88  5.63 11.46 10.29 17.71 10.92 12.04 13.29 14.37 27.00
77  2 25  5.50  3.08 13.00  2.13  3.17  6.58 10.46  6.50  7.41  8.67  6.21 14.71
77  2 26 14.62 14.33 10.04  3.42 11.34  7.58  8.71  8.75  6.46  6.04  9.42 11.63
77  2 27 25.41 24.79 22.58 11.50 20.41 17.79 17.92 16.62 14.67 12.00 15.79 23.21
77  2 28 19.67 18.05 19.38 10.46 17.33 15.63 16.71 14.21 14.88 10.96 16.38 20.54
77  3  1  8.63 14.83 10.29  3.75  6.63  8.79  5.00  8.12  7.87  6.42 13.54 13.67
77  3  2 12.62 15.34 15.59  7.33 13.04 12.87 13.25 12.21 13.29 13.04 22.46 22.17
77  3  3 11.12 12.50 12.92  7.29 11.21 10.25 16.71 11.96 14.79 14.29 18.25 23.29
77  3  4 12.67 11.08 11.54  6.92 11.75 10.79 18.66 13.79 13.92 13.50 17.92 24.92
77  3  5  8.29  8.08 10.67  2.92  5.91  7.04 11.04  6.83  7.08  8.58 10.67 17.33
77  3  6 11.87 15.09 11.29  8.25 13.83 12.54  9.42  6.42 12.54  8.96  9.67 12.50
77  3  7 16.58 15.96 14.92  7.21 13.13 12.50 14.00 11.92 12.12  9.79 15.96 18.79
77  3  8 26.20 24.00 18.66 13.33 20.50 18.38 17.67 18.54 18.05 19.79 29.29 27.37
77  3  9 21.17 15.96 15.16  9.83 17.46 13.62 16.88 13.75 14.42 15.50 16.38 21.71
77  3 10 33.84 26.92 24.41 20.08 23.75 20.58 20.21 18.71 21.92 22.75 23.09 27.58
77  3 11 14.33 12.42 13.13  7.41 14.92 12.12 14.29 11.12 13.42 13.50 14.62 22.46
77  3 12  9.79  9.29 10.54  4.12  8.92  7.75  9.67  6.00  8.21  5.63  8.08 13.25
77  3 13 12.92 11.92 15.29  5.88  9.79  8.63 13.33  8.79 10.46  9.92 11.12 16.13
77  3 14 17.46 17.79 14.71  7.75 16.38 11.38 16.33 12.29 13.29 12.87 21.04 20.62
77  3 15 28.33 19.55 22.00 16.79 18.88 15.67 19.79 16.42 18.46 18.21 19.87 26.16
77  3 16 24.75 18.84 17.25 11.63 15.21 13.21 15.34 14.79 14.88 16.29 22.92 19.75
77  3 17 27.50 18.75 20.21 14.46 17.00 14.88 18.34 17.50 15.96 17.54 20.88 23.54
77  3 18 16.79 14.62 14.04  7.33 17.79 13.08 15.41 12.54 12.79 12.17 13.88 15.83
77  3 19 21.29 15.75 12.12  8.83 14.67 11.38 11.08 12.58  9.54  9.50 14.46 13.62
77  3 20 16.79 11.83 25.04  8.50 11.83 10.41 17.41  9.21  9.79 10.83 15.75 16.88
77  3 21 14.54 11.08 28.25  9.75 10.46  8.58 18.21  7.38 10.79  7.71  9.79 14.58
77  3 22 10.54  8.29 17.71  5.71  6.92  8.08 13.50  7.00  7.29  7.00  8.54 14.46
77  3 23  9.33  3.96  8.67  2.88  6.54  5.46 12.08  5.13  8.17  7.33  7.92 18.29
77  3 24 11.58 11.83  4.96  2.79  9.42  6.00  9.71  5.25  6.00  5.66  5.41 10.29
77  3 25 19.12 19.50 14.62  7.58 17.67 14.25 12.08 12.92 10.58  8.75 12.79 14.04
77  3 26 14.75 13.67 13.50  7.25 15.21 12.87 12.54 12.25 10.29  6.96 12.04 17.58
77  3 27 14.50 15.09 30.84 10.41 12.87 12.08 19.33 13.42 12.17 12.87 19.79 24.13
77  3 28 12.17  8.38 21.34  5.91  6.38  5.37 10.13  5.04  6.34  9.33  6.58 14.67
77  3 29  8.58  5.50  5.54  2.04  3.58  4.71  7.79  4.54  4.58  5.54 13.67 12.62
77  3 30 25.04 19.50 20.38 13.13 18.79 16.75 14.12 15.92 16.71 19.17 28.42 29.79
77  3 31 26.16 22.17 24.08 13.79 21.67 18.41 19.46 19.87 19.46 19.70 26.96 25.12
77  4  1 21.67 16.00 17.33 13.59 20.83 15.96 25.62 17.62 19.41 20.67 24.37 30.09
77  4  2 12.17  6.79 10.13  6.17  9.21  8.67 12.33  9.08  9.62 11.29 13.54 22.79
77  4  3 19.70 12.46 20.04 10.08 13.29 12.25 14.92 11.83 11.67 15.34 20.00 30.04
77  4  4 12.96  9.21  9.21  5.00  6.17  5.96  9.38  5.41  7.04  7.46 12.25 14.92
77  4  5  8.63  7.25  7.25  4.33  7.21  7.17 11.17  5.96  7.83  9.46 10.79 16.46
77  4  6 14.37 13.21 15.04  8.46 11.17 11.38 15.67 10.79 11.34 17.83 18.25 27.54
77  4  7 18.12 14.71 17.75  7.58 12.25 11.00 13.17 10.00  9.96 15.12 16.33 22.46
77  4  8 15.75  9.79 10.21  5.91 10.13  8.58  8.63  8.42  8.08  8.33 15.54 13.33
77  4  9 11.50  9.96  9.79  4.21  7.58  7.62  6.13  4.33  5.63  6.92  6.75  9.62
77  4 10 17.33 12.83  9.96  9.13 11.67 10.21 13.83  8.33  9.83 10.00 12.46 15.75
77  4 11 11.96 10.96 11.04  6.04 11.34 10.08 13.04 10.17 10.67  9.17 14.71 15.00
77  4 12 17.21 15.71 13.62 10.50 18.34 15.54 21.62 16.38 16.62 15.41 18.25 22.21
77  4 13 19.29 13.42 12.54 10.25 15.29 12.87 18.54 10.83 12.21 16.50 16.46 22.50
77  4 14 14.79  8.54 10.46  6.83 10.88  8.96 14.12  6.25  9.29 11.34 10.58 17.83
77  4 15  6.87  6.21  5.88  1.96  2.42  3.50  8.00  2.79  2.67  4.54  8.00  7.41
77  4 16 11.08  9.75  9.75  3.54  7.21  7.04  8.42  4.92  4.83  5.50 10.37 11.17
77  4 17  7.08  6.04 14.12  4.04  5.25  5.83  8.58  3.67  5.13  6.96 12.96 12.87
77  4 18  9.79  9.62  9.17  3.67  8.92  9.13  8.21  6.87  8.00  7.17  7.33  9.79
77  4 19  8.12  6.67 10.34  2.75  3.92  5.71  7.75  5.21  5.29  6.79  9.17 15.87
77  4 20 13.13 13.67 14.54  7.21  9.67 10.25 14.29 12.04 12.12 11.04 18.34 15.37
77  4 21 21.59 19.75 19.70 12.33 15.37 13.17 18.46 16.62 15.34 15.59 22.79 18.63
77  4 22 20.30 17.58 20.04 12.46 21.25 15.59 26.42 16.33 18.12 18.71 23.75 22.63
77  4 23 16.96 18.12 14.50 10.63 16.88 12.38 20.25 11.46 12.83 12.00 17.50 20.12
77  4 24 14.29 13.67 12.54  7.21 10.54 10.04 13.21  9.17 10.29  8.79 14.33 13.08
77  4 25 17.96 14.96 16.79  9.04 14.21 12.92 14.50 15.87 13.21 16.04 25.21 26.63
77  4 26 13.70 15.34  9.42  7.83 15.92 10.41 14.37 11.21 11.79 10.75 19.50 21.96
77  4 27 22.63 17.37 19.87 11.17 15.50 12.38 17.54  7.79 12.25 11.75 16.66 18.54
77  4 28 16.88 14.54 11.58  7.54 14.37 10.67 14.09 11.58 10.08 10.54 17.62 14.88
77  4 29 17.33 13.70 11.04  7.58 14.37 10.17 13.79  8.33 10.83 12.79 16.88 21.46
77  4 30 15.50 10.96  7.04  5.63 11.04  8.67 10.58  7.96  8.29  8.79 17.12 17.29
77  5  1  6.42  7.12  8.67  3.58  4.58  4.00  6.75  6.13  3.33  4.50 19.21 12.38
77  5  2  6.63  5.29 10.75  3.42  4.63  3.46  7.41  5.46  4.71  4.12 13.59 10.71
77  5  3 12.79  7.38 11.58  6.21  7.29  7.21 11.04  6.75  8.29  9.96 10.37 15.83
77  5  4  8.83  4.25  6.21  2.46  4.79  6.92  8.96  5.41  6.25  9.25 11.38 15.12
77  5  5  8.75  4.75  7.50  1.79  3.29  5.71  7.96  3.08  3.17  3.50  8.25 12.08
77  5  6  7.58  6.71  8.54  3.67  6.87  7.50  9.87  5.79  6.42  6.63 11.63 15.59
77  5  7 11.71 10.13 11.38  8.87 12.67 11.63 17.08 13.00 13.00 15.16 16.38 25.75
77  5  8  8.79  8.38  8.54  5.79  7.12  7.17  8.87  5.37  6.42  5.75  7.71  9.33
77  5  9 13.54  9.75  9.21  4.83  9.13  6.83  6.58  4.96  5.09  3.71  8.21  6.87
77  5 10  5.41  8.17  7.62  3.67  4.42  6.21  7.67  4.04  4.58  5.04 12.33 12.87
77  5 11 17.41 14.71 16.66  7.71 14.09 11.58 15.71 12.62 12.71 13.37 18.08 15.87
77  5 12 17.92 14.33  9.17  6.38 12.04  9.46 10.75  7.67  8.42  9.62 14.96 18.21
77  5 13  7.96  5.00 10.58  5.09  5.25  5.33  7.96  4.46  4.63  7.50  8.21 11.92
77  5 14  8.42  9.83  6.29  3.83  6.13  4.83  6.67  3.75  4.46  3.13  7.75  7.62
77  5 15 14.21 12.67 17.04  5.75  8.75  9.17  8.71  7.79  6.34  6.29 11.04 10.25
77  5 16 12.17 11.79 23.16  7.62  7.79  5.46  9.62  8.96  5.91  7.29 12.29 14.79
77  5 17  6.87  4.63 16.33  3.37  2.96  4.79  5.09  3.37  4.71  3.58  4.63  9.71
77  5 18  3.88  4.42 12.21  1.25  2.33  1.50  4.71  2.42  4.12  5.09  9.46  9.92
77  5 19  4.54  5.41  5.96  1.83  2.21  2.96  4.50  5.25  3.67  2.04 13.25  8.83
77  5 20  6.38  6.04 15.63  4.17  2.79  6.25 10.83  3.13  7.46  4.38  5.71 12.92
77  5 21 12.71  7.41 22.46  7.58  7.04  8.71 11.96  4.92  7.87  5.91  7.67  9.00
77  5 22 11.38  9.25 18.63  6.17  5.17  9.21  8.08  5.04  7.12  4.04  8.08  4.08
77  5 23 12.29  6.63 16.46  4.29  6.08  8.08  9.04  2.92  6.58  3.67 10.41  4.54
77  5 24 11.92  7.00 20.17  4.50  7.33  8.83 11.58  7.17  9.62  7.33 11.17 12.33
77  5 25 11.04  6.42 21.25  5.66  6.83  9.71 11.50  4.96  8.79  6.50  8.50 11.96
77  5 26 11.00  1.25 13.00  4.04  4.71  8.04 10.54  8.00 10.63  7.92  9.92 15.50
77  5 27  6.87  6.75  6.17  4.33  8.04  6.83  9.04  6.38  9.42  7.50  8.42 16.08
77  5 28  4.21  5.04  5.88  1.63  4.33  3.75  7.29  3.54  3.79  1.29  6.67  5.04
77  5 29  5.09  5.37 13.00  3.25  4.00  4.75 10.17  5.29  6.92  4.67  8.42  9.92
77  5 30  7.96  2.88 13.50  3.79  4.12  7.38 12.50  4.79  7.50  4.38  7.21 15.04
77  5 31  8.00  3.63 12.58  3.21  5.37  4.12  7.58  2.37  5.75  4.29  5.71  9.96
77  6  1  7.08  5.25  9.71  2.83  2.21  3.50  5.29  1.42  2.00  0.92  5.21  5.63
77  6  2  4.75  5.75 14.67  3.42  3.17  3.46  6.87  2.17  3.42  2.75  4.71  2.58
77  6  3  9.62  5.13  6.17  5.71  8.17  5.50  8.50  4.79  4.00  5.71  7.83 12.21
77  6  4 16.71  7.21 10.58  6.21 11.34  9.50 12.83  9.62 10.50  9.92 13.54 14.54
77  6  5 17.79 11.75 11.17  7.75 15.92 10.41 13.04  9.17 10.54 11.46 14.09 17.08
77  6  6 20.62 18.71 14.71  9.83 18.46 13.04 14.67 13.29 12.87 12.71 21.34 21.84
77  6  7 17.62 11.17 11.21  6.79 13.29  8.17 11.67  9.04  7.87  9.54 16.13 18.79
77  6  8  9.00  6.87  7.08  3.17  6.87  4.71  6.71  5.58  5.63  5.63 11.08 13.25
77  6  9  7.41  8.50 12.83  3.00  6.67  3.75  7.58  4.29  5.88  3.79 12.33  9.79
77  6 10 14.12 12.75 27.25  8.29 10.88  8.17 16.00 10.08  9.54 10.04 19.62 20.79
77  6 11 13.75 10.58 21.00  8.08  9.46 10.17 14.79  8.63  9.83 10.96 18.34 19.12
77  6 12  7.00  6.79  8.58  4.63  6.34  7.38  7.67  4.33  6.63  6.17  8.17  9.33
77  6 13 12.87  8.08  9.25  4.58  7.00  6.63  6.21  5.91  5.58  5.58  9.46  7.00
77  6 14 11.92 11.17 18.66  6.87  9.87  8.79 11.08  8.75  9.33  9.29 13.62 16.62
77  6 15  7.21  9.21 20.50  4.79  6.08  6.87 11.00  5.54  7.46  7.54 14.00  9.83
77  6 16  8.50  2.58 18.00  2.21  6.34  9.46 10.00  5.83  9.87  7.46 12.29  9.62
77  6 17 11.46  4.29 15.59  2.88  8.38  9.25  9.42  7.58  9.21  5.25 13.04  5.71
77  6 18  6.21  7.00 13.79  3.25  7.38  6.00  5.54  6.00  4.92  4.29 13.50 12.25
77  6 19  7.08  7.29  8.75  3.00  7.21  5.58  4.00  6.87  6.50  5.83 10.50 12.00
77  6 20  4.83  4.54 12.54  1.96  4.88  2.83  4.00  1.00  3.37  1.46  9.29  4.88
77  6 21  4.83  4.38 10.75  2.04  5.63  2.25  2.96  2.21  1.67  2.42  5.79  4.79
77  6 22  6.00  3.25  4.21  1.71  3.79  3.58  2.79  1.17  2.92  2.96  2.96  3.58
77  6 23  7.33  7.71  6.50  3.50  6.08  6.29  5.00  3.71  4.42  5.37  8.33  8.33
77  6 24 13.50 11.71 10.79  4.12 10.41  8.17  6.04  4.50  5.54  4.12  9.38  6.25
77  6 25 16.25  7.04  9.38  6.50 10.17  9.04  8.79  6.00  7.62  8.63  9.13 12.42
77  6 26  9.79  6.92  9.38  4.38  9.38  7.79  9.75  5.04  9.25  7.12 12.12 11.58
77  6 27 11.08  7.54 12.12  4.21  7.92  6.71 12.25  4.71  8.79  5.83  6.00  7.08
77  6 28 14.58  8.71  8.42  5.75  9.33  8.12  7.08  5.66  6.63  7.00  9.71 10.79
77  6 29 13.08 11.25  7.25  5.25 13.25  8.92  7.50  7.00  7.46  6.25 12.46 12.54
77  6 30 19.21 17.46 16.00  9.21 17.83 12.25 14.96  8.04 12.71 11.50 15.67 15.04
77  7  1 15.41 16.29 17.08  6.25 11.83 11.83 12.29 10.58 10.41  7.21 17.37  7.83
77  7  2 15.59 15.87 15.34  8.46 13.62 11.79 13.08 11.96 11.63 11.79 24.50 17.33
77  7  3  6.25 12.62  9.00  4.83 10.54  8.96  7.33  8.29  8.54 10.21 19.79 12.50
77  7  4  6.71 12.17  4.08  4.71 10.04  7.83  4.17  6.46  8.58  9.42 15.54 10.79
77  7  5  5.79  1.13  7.33  2.37  1.58  2.62  4.21  2.08  6.17  4.21 10.00  5.09
77  7  6  4.46  3.08  9.71  2.08  3.13  4.42  6.21  0.21  5.79  3.33  6.87  7.17
77  7  7  8.87  9.21  6.50  1.92  8.12  4.21  4.29  5.25  2.83  3.37  8.21  7.87
77  7  8 18.08 12.25 13.88  7.04 10.37  7.58  8.42  7.92  8.12  9.00 12.71 12.46
77  7  9  8.96  8.79 20.33  6.42  8.25  6.96  8.87  6.87  6.42  4.17 14.62  6.25
77  7 10  8.58  5.00 16.33  4.04  7.29  7.21  8.42  4.33  9.29  6.00 10.46  4.50
77  7 11  7.25  4.17 15.71  3.46  2.83  5.66  6.79  2.79  5.71  4.92  8.42  5.58
77  7 12  6.58  2.75 12.50  3.13  6.08  7.67  9.04  5.09  7.17  3.75 14.58  6.17
77  7 13  4.63  3.08 14.33  4.58  5.00  5.79  6.79  3.92  4.67  3.08 12.00  5.04
77  7 14  4.79  2.46  8.46  2.75  4.42  2.67  6.17  0.37  3.79  2.50  4.50  6.34
77  7 15  9.38  5.21  6.38  3.50  5.58  6.08  7.17  5.13  5.71  5.37 11.00 10.37
77  7 16 11.00  6.87  7.54  3.46  6.58  4.29  9.08  5.66  5.50  5.09 12.46 13.50
77  7 17 18.63 13.54 14.17  8.79 15.54 11.29 14.17 12.08 12.38 11.34 18.25 19.04
77  7 18 15.46 10.75 11.12  9.00 15.67 12.04 18.16 12.42 11.83 14.00 18.12 18.75
77  7 19 14.04 12.71 11.00  7.92 15.59 12.04 17.50 11.63 12.46 10.67 17.92 18.00
77  7 20 16.25  9.33 10.63  7.96 11.71  9.25 15.29  7.62 10.21 11.83 11.63 18.21
77  7 21  5.50  8.42  7.92  2.96  8.46  6.46  7.83  2.71  7.38  5.88 10.37 10.00
77  7 22  8.04 10.37  9.67  4.25  9.96  8.29 13.21  9.08 10.04  9.59 16.58 13.08
77  7 23 18.79 14.96 15.92  8.87 13.21 12.04 15.50 12.83 12.75 15.25 18.08 19.04
77  7 24 13.33 13.62 11.50  9.38 15.12 11.58 17.21 11.83 11.63 12.71 16.92 23.21
77  7 25 20.33 16.25 13.67 11.79 15.59 12.29 15.41  9.83 11.04 13.33 15.34 23.29
77  7 26 12.79  7.92  8.21  5.71 11.42  7.33 11.58  6.46  7.79  8.08 10.63 15.63
77  7 27 13.17  7.87  6.46  5.25  9.38  6.71  9.42  4.83  7.12  6.08  9.83 11.00
77  7 28 10.63  7.00 11.42  4.63  6.96  6.25  9.21  5.75  6.96  7.58  9.00 12.96
77  7 29  8.75  3.96  6.87  3.92  4.42  4.79  7.38  2.04  4.54  4.08  9.96 11.71
77  7 30 10.88  7.04  5.17  4.25  7.54  6.17  7.41  4.29  6.87  7.71  8.12 12.79
77  7 31  8.42  3.88  7.08  4.33  3.63  3.50  7.21  2.00  4.08  4.67  6.87 12.29
77  8  1  4.33  2.96  4.42  2.33  0.96  1.08  4.96  1.87  2.33  2.04 10.50  9.83
77  8  2  5.46  5.00  7.96  2.88  6.17  4.50  8.87  3.00  4.71  4.17  9.62 10.29
77  8  3 11.29  6.58  9.54  4.67  7.00  5.83  9.62  4.33  7.12  4.50 11.42 10.41
77  8  4 16.79 14.12 15.16  7.17 13.59 10.79 14.88  9.59 13.00 11.71 15.41 15.79
77  8  5  8.75  8.87 11.42  4.92  5.04  5.46  9.00  4.42  5.79  6.83  9.46 13.00
77  8  6  6.71  2.71 13.37  2.88  1.71  2.79  6.34  1.75  3.54  2.75  4.58  7.29
77  8  7  4.54  3.67  6.75  2.29  1.54  2.37  7.04  1.04  3.96  3.50  4.25  7.38
77  8  8  3.92  3.08  4.54  1.42  1.92  2.17  4.46  1.38  3.63  2.79  3.75  9.87
77  8  9  5.79  2.50  5.46  1.92  1.42  1.96  3.71  1.13  2.33  1.79  5.91  5.04
77  8 10  5.04 10.25  5.41  3.79  6.63  5.37  5.83  3.75  5.75  5.88 11.96  9.83
77  8 11  6.87 11.87  5.79  5.25  8.67  7.54  6.50  4.58  7.21  6.25 11.79 11.08
77  8 12  6.29 13.25  4.75  3.54  9.29  7.17  7.21  3.67  5.54  7.00  6.04 12.25
77  8 13 12.00 16.33  4.88  3.88 10.63  7.54  6.54  6.29  7.08  6.75 12.96 16.96
77  8 14  7.58  6.67  6.96  5.46  7.50  8.63  7.87  5.33  7.17  5.83  9.75 11.75
77  8 15  3.17  2.92  7.08  2.75  1.13  3.71  7.67  1.00  5.63  5.33  5.63 12.54
77  8 16 11.71  7.79 18.00  6.21  7.71  9.25 15.37  7.54 10.75  8.38 10.21 16.00
77  8 17 13.67 10.58 27.88 10.00 10.75 10.63 19.17  8.12 11.34 10.46 13.13 11.92
77  8 18 10.08  4.83 18.00  4.04  5.41  4.25  9.17  3.75  4.96  3.46  7.62  6.29
77  8 19  6.50  3.42 11.75  3.42  4.46  4.17  7.12  1.33  4.29  3.29  5.04  3.96
77  8 20 10.88  5.58 12.75  5.41  6.58  4.88  7.46  2.83  5.83  3.96  6.58  3.08
77  8 21 11.83  5.37  8.63  4.58  4.12  2.79  7.96  2.83  4.38  1.50  6.50  8.50
77  8 22  6.79  6.21 15.21  3.63  3.50  4.29  9.21  1.54  4.67  3.92  4.71 11.21
77  8 23 11.83 12.42 10.04  6.63 12.67  8.67  8.54  6.83  7.96  7.21 10.63 10.50
77  8 24 13.62  9.25 17.00  6.54  8.83 11.00 16.29  6.04 10.83 12.58  8.83 23.42
77  8 25 11.04  9.59  9.96  3.96 11.42  7.62  9.67  3.33  7.79  4.58  8.21  6.79
77  8 26  9.92 12.33  9.21  5.29 13.00  7.25 10.67  9.21  7.25  9.21 18.84 24.08
77  8 27 19.38 11.08 17.83  8.00  9.13  8.38 10.54  5.66  7.17  7.08 10.00 14.29
77  8 28 10.88 11.25 10.29  2.71  6.79  7.79  9.08 12.00  9.13  8.75 22.29 20.67
77  8 29 12.17  8.21 11.34  5.17  9.67  8.00 11.58 10.00  9.25  9.50 17.67 20.88
77  8 30  9.79  3.58  8.92  3.83  3.21  4.17  7.46  1.87  2.96  1.58  9.54  5.96
77  8 31  7.62  7.29 10.75  3.50  4.79  4.75  7.46  4.83  4.58  3.96 11.75 10.58
77  9  1 17.37 16.33 16.83  8.58 14.46 11.83 15.09 13.92 13.29 13.88 23.29 25.17
77  9  2 12.75 11.87 11.17  6.54 12.92 10.25 14.37 11.04 10.41 12.50 18.96 24.25
77  9  3 11.12 11.75  9.38  4.63 10.00  8.46 12.12  9.17  7.67  8.04 19.38 19.12
77  9  4 19.25 15.83 17.00  7.50 15.04 12.54 16.04 12.71 13.04 15.04 21.71 26.12
77  9  5  9.00 10.50  8.17  4.71  9.71  7.46 11.63  7.29  9.25  8.58 16.25 20.58
77  9  6 16.66 12.42 14.12  7.92 12.12  9.21 14.46  8.00 10.29  9.75 14.25 17.00
77  9  7  6.46  3.17  4.75  1.08  3.37  4.21  8.17  2.75  4.88  4.33 11.50 17.33
77  9  8 10.92  7.46  8.38  4.83  9.21  7.21 10.58  5.13  8.33  6.87 12.00 14.54
77  9  9 11.38 11.34 10.71  5.33  8.42  8.50 11.75  8.83  8.21  7.33 15.87 17.29
77  9 10 15.75 15.54 18.50  8.83 13.88 11.04 20.46 11.08 12.46  9.79 17.21 15.41
77  9 11 10.29  8.75 12.25  6.21 10.29 10.83 14.04 10.67 12.42 12.08 17.12 22.92
77  9 12  8.79  6.96  8.92  4.17  3.04  4.38  7.92  2.75  4.25  5.50  7.00 14.09
77  9 13  3.88  4.42  4.29  1.54  2.79  2.83  4.58  2.88  3.92  3.92  9.21 11.87
77  9 14  3.21  3.54  4.92  1.63  2.29  3.04  8.58  1.63  4.75  5.21  9.29 17.04
77  9 15  4.58  2.25  8.71  1.38  2.46  1.42  2.42  0.42  1.79  0.83  4.83  8.58
77  9 16 12.04  5.21 11.25  3.63  7.71  8.12 13.46  7.41  9.33  6.96 10.71 16.17
77  9 17 12.71 12.71 14.00  4.46  9.38  7.50 10.58  6.04  6.96  5.71 11.00 14.04
77  9 18 10.34  6.54 13.96  3.21  5.46  5.83  8.38  3.00  5.00  4.04  8.58  6.54
77  9 19  9.92  4.63 11.83  3.33  3.67  4.50  8.50  2.42  4.58  3.42  6.92  7.29
77  9 20  9.38  7.33 10.37  2.96  5.37  4.79  7.50  2.83  4.96  4.21  7.29  8.87
77  9 21  6.46  2.71  8.21  2.42  4.29  2.83  5.13  0.29  1.67  0.29  4.33  2.08
77  9 22  8.92 11.42  7.00  1.87  7.54  3.29  5.00  3.67  4.83  4.08  8.63  9.71
77  9 23 24.79 23.09 15.71 10.00 20.58 14.46 15.21 12.46 11.71 11.75 16.79 18.25
77  9 24 17.00 13.13 18.38 10.41 14.37 16.08 20.12 12.58 14.75 17.79 17.54 27.04
77  9 25 13.83 12.46 12.46  4.46 11.12  8.42 11.08 10.29  8.92  9.00 15.16 16.88
77  9 26 12.50 12.04 12.08  6.04  9.71  8.87  8.67 10.21  9.54 10.34 14.92 15.59
77  9 27 14.50 15.54 11.67  7.58 10.37  7.54  9.83  9.46  9.00  7.08 12.08 13.21
77  9 28 19.83 18.05 15.46 12.54 21.34 15.34 21.09 18.05 16.42 16.46 23.75 29.63
77  9 29 20.33 17.04 17.25 10.96 21.71 13.37 20.30 14.67 16.46 15.04 23.79 28.84
77  9 30 20.21 18.25 17.75 10.41 19.70 13.13 21.12 16.04 15.75 15.37 22.17 25.46
77 10  1 16.75 15.34 12.25  9.42 16.38 11.38 18.50 13.92 14.09 14.46 22.34 29.67
77 10  2  7.41  5.46  7.62  2.92  6.75  5.33 10.79  6.17  7.83  7.00  9.75 16.29
77 10  3 14.62 13.54 14.29  5.09 10.13  8.21 10.17  9.75 10.08  6.87 14.62 15.16
77 10  4 13.33 14.33  9.62  5.63 12.79  8.63 11.21  9.59  9.92  7.71 14.79 16.29
77 10  5  7.33  6.34  6.54  1.42  6.63  4.58  7.75  5.83  7.00  6.87 11.54 15.71
77 10  6 17.25 18.08 21.00  8.75 11.75  8.17 14.96 12.00 10.63 10.08 19.79 21.67
77 10  7  9.75  8.96  9.08  4.58  9.75  7.33 10.88  9.87  9.13  9.04 12.17 17.37
77 10  8  8.00  4.25  8.08  2.83  5.37  6.54 10.41 10.08  9.96 10.04 16.38 18.25
77 10  9 16.33 15.41 11.79  8.21 16.58 10.71 15.25 13.33 11.79  9.75 17.58 13.46
77 10 10 12.21 11.54  7.62  3.71  8.00  6.21  9.75  6.71  6.75  6.34  9.04 13.21
77 10 11 25.92 20.46 20.96 13.46 18.05 13.67 16.29 17.37 16.58 17.46 24.25 24.04
77 10 12 16.83 15.83 14.67  8.25 16.50 12.87 16.71 17.92 14.83 17.12 26.58 28.46
77 10 13 13.88 14.83 11.79  5.66 12.67  8.67 10.58 13.00 10.50  7.41 16.42 15.83
77 10 14 13.21 14.04 13.08  7.46 13.62 10.37 12.54 12.12 12.79  7.92 15.21 20.25
77 10 15 17.00 18.08 15.67  8.38 15.46 14.58 18.21 14.71 14.50  8.38 16.29 22.79
77 10 16 13.08 18.41 11.29  4.75 14.71 10.50 11.29 13.17 11.67  8.96 16.21 20.38
77 10 17 12.50 14.79 12.54  5.79 12.46 13.29 13.33  9.08 10.21  6.63 12.54 16.17
77 10 18 13.50 13.70 13.70  6.96 12.17 11.58 12.17 11.17 13.46  9.75 12.29 20.79
77 10 19 15.34 11.21 13.79  7.87  9.21  9.87 12.04 11.96 12.67 11.46 15.29 17.12
77 10 20  8.33 10.13 10.04  3.21  6.25  6.58  8.92  8.00  7.67  7.54 10.96 13.17
77 10 21 14.62 13.37 13.75  8.29 10.88  9.21 11.83  9.92 12.83  9.79 10.63 12.58
77 10 22 18.25 11.50 15.16  6.13  9.25  7.33 10.58  9.71  9.75 10.37 14.54 18.63
77 10 23 22.29 20.08 16.00  7.87 14.00 12.50 16.50 14.46 13.70 12.71 21.25 21.59
77 10 24 15.79 11.21 12.67  7.25  9.67  9.46 14.88 11.92 11.63 10.63 19.41 22.58
77 10 25 10.67 12.71  8.83  4.08  8.12  8.79  9.79 12.83  8.46  8.92 21.87 18.38
77 10 26 20.04 18.05 15.25  8.83 13.79 12.42 13.00 14.67 13.96 14.09 28.75 25.84
77 10 27 11.79 13.96  8.54  6.04 12.17  8.50 13.25 12.29 11.25 11.17 24.67 24.96
77 10 28 14.25 12.83 11.12  4.79 12.83  9.33 14.92 14.54 13.37 10.37 22.67 23.42
77 10 29 23.91 18.41 16.42 10.96 14.46 12.08 14.37 16.21 14.50 16.50 25.66 25.41
77 10 30 27.71 20.08 25.12 15.00 13.54 12.92 18.05 17.41 14.54 16.79 26.67 27.12
77 10 31 13.25 15.83  9.13  5.79 11.21  8.21 11.75  8.42  9.87  8.42 16.71 18.54
77 11  1 16.71 11.54 12.17  4.17  8.54  7.17 11.12  6.46  8.25  6.21 11.04 15.63
77 11  2 11.54 10.92  7.38  3.92  9.00  6.67  8.46  7.87  7.50  6.42 15.16 15.50
77 11  3 15.59 18.91 12.33  8.75 15.46  9.96 18.29 12.79 12.42 10.92 20.96 27.50
77 11  4 14.62 14.17 10.54  4.75 10.13  8.38 12.62  9.25  9.13  8.75 14.83 19.79
77 11  5 20.33 17.75 16.46  9.00 13.50 10.63 15.29 13.17 12.42 12.96 16.96 20.38
77 11  6 20.38 19.67 15.63  9.17 14.50 12.12 17.46 17.04 13.88 15.25 24.37 28.12
77 11  7 21.17 17.16 19.33 10.21 14.88 12.21 19.33  9.04 12.83 11.21 16.71 19.38
77 11  8 16.17 16.83 15.29  6.13 14.50 11.58 16.88 13.79 13.13 12.67 24.71 26.87
77 11  9 16.66 18.21 15.34  7.62 11.58 10.79 12.12 12.00 10.58  8.75 15.79 15.25
77 11 10 23.91 23.79 20.04 13.46 15.34 13.96 17.79 22.50 15.71 19.12 33.34 26.38
77 11 11 30.84 30.34 26.42 18.05 25.80 17.71 24.13 21.29 19.21 20.71 25.84 29.58
77 11 12 21.12 22.71 14.37 10.92 21.59 12.58 19.29 16.38 14.33 12.83 23.42 30.13
77 11 13 15.25 13.96 11.54  8.75 14.29 10.96 16.21 13.25 13.54 13.21 18.75 27.75
77 11 14 27.92 25.25 19.33 18.71 28.46 19.50 28.16 24.00 20.79 22.58 30.91 38.66
77 11 15 23.21 17.83 13.54 12.50 16.13 11.83 18.79 14.54 13.54 14.21 22.21 32.42
77 11 16 18.79 18.63 12.62  8.25 15.16 10.54 13.54 12.25 10.67  6.63 21.50 17.41
77 11 17 22.29 15.63 16.42  8.25 10.92  8.83 13.59 10.04 10.17 10.29 17.37 27.25
77 11 18 11.34  4.38 11.75  4.17  6.42  4.83 11.38  6.83  7.38  6.96 12.62 20.83
77 11 19 17.21 18.88 14.54 10.46 17.00 12.00 19.92 14.09 14.67 13.17 22.25 29.29
77 11 20 20.46 18.66 16.71  8.08 10.41  7.71 13.33  9.83 10.41  8.71 17.16 26.08
77 11 21 20.38 16.25 18.46 10.00 10.88  9.92 14.17 11.58 12.08 11.63 20.75 31.42
77 11 22 16.29 10.63 12.21  6.08  8.42  8.00 13.46  9.17  9.83 10.63 14.92 24.21
77 11 23 18.00 19.87 15.00 11.42 19.38 13.75 23.83 19.70 17.37 16.58 26.38 34.83
77 11 24 18.46 13.00 14.00  9.87  9.50  9.54 17.08 10.63 10.34 14.79 14.25 31.88
77 11 25  5.88  4.46  8.46  1.00  1.79  1.96  7.54  2.08  2.13  1.79  2.13  8.12
77 11 26  9.83 11.63  5.33  0.25  3.92  2.46  4.04  5.46  1.87  3.54  6.92 10.37
77 11 27  9.75 10.58  8.71  0.83  7.12  4.83  4.00  6.79  3.04  3.92 10.00 10.46
77 11 28  8.67 10.83  9.25  1.58  2.17  1.71  5.71  3.17  2.13  4.04  4.04  7.71
77 11 29  9.00  6.75  5.21  0.71  2.08  0.96  1.87  1.38  0.00  0.04  6.13  8.46
77 11 30  6.63  4.12  8.46  0.46  3.79  1.75  2.13  2.21  0.21  1.29  5.04  8.33
77 12  1 13.37 10.92 12.42  2.37  5.79  6.13  8.96  7.38  6.29  5.71  8.54 12.42
77 12  2 21.79 15.12 20.96 10.25 15.96 15.00 20.33 14.54 14.83 14.17 15.34 24.30
77 12  3 26.20 24.87 24.17 11.25 22.71 20.21 24.08 18.96 17.88 18.38 19.55 29.95
77 12  4 25.17 23.91 22.04 11.54 22.00 21.34 22.46 21.84 16.21 18.16 22.54 28.12
77 12  5 16.33 17.04 19.25  9.21 14.29 14.00 20.21 15.34 13.83 15.04 16.38 25.84
77 12  6 10.63  4.38 15.09  5.75  7.96  8.75 18.34 13.46 12.29 11.04 12.62 31.96
77 12  7 14.71 11.54 16.58  6.83 11.92 12.58 15.75 17.12 13.70 14.00 16.04 33.45
77 12  8  6.63  4.54 10.83  3.75  4.58  4.75  9.00  6.42  5.33  4.79  8.12 15.83
77 12  9 15.63 15.63 15.67  6.79 12.42 11.04 14.17 10.54 11.38 10.71 12.96 19.58
77 12 10 15.29 14.62 11.71  5.21 10.13  8.21  9.75 11.50 10.21 10.83 18.00 20.00
77 12 11 20.79 13.96 17.92  7.50 10.25  8.75 12.33  7.92  8.92  7.46  9.50 11.21
77 12 12 11.71 14.09 11.25  5.71 12.62  8.38 12.33  7.71  9.38  7.29 11.67 12.42
77 12 13 11.38 13.04 12.38  3.58  8.63  8.75 13.42 13.17 10.37 10.13 16.66 15.04
77 12 14 10.96 11.42 11.79  2.96  7.00  7.92 13.79 13.96  9.38 11.38 19.67 21.75
77 12 15  7.75 12.08  6.75  2.21  6.54  4.50  7.58  7.75  6.25  5.54  9.04 12.58
77 12 16  8.25  9.17  6.79  3.33  7.08  5.46  6.54  3.75  7.21  5.09  7.46 12.67
77 12 17  5.41  4.83  7.38  1.21  4.00  1.71  2.67  1.25  1.33  0.67  2.29  9.87
77 12 18  4.92  2.21  5.09  1.13  2.37  2.33  2.71  1.71  3.46  2.50  2.37  7.54
77 12 19  7.50  7.71  6.25  3.37  9.17  6.71  4.79  4.12  6.71  4.04  5.83 11.29
77 12 20 13.00 16.33 14.29  7.25 13.79 10.21 11.67  9.83 11.38  6.42 11.67 11.46
77 12 21 17.62 17.88 18.71  8.67 14.17 14.54 14.17 13.88 11.87 10.50 13.83 24.33
77 12 22 19.70 14.37 17.00  9.83 11.42 10.25 10.71  9.42 10.83 11.29 13.83 17.62
77 12 23 19.58 15.16 17.79  9.87 11.58 10.41 12.96  8.17  8.92  7.29 11.42 12.79
77 12 24 17.79 14.25 14.75 11.67 17.67 12.62 21.75 13.54 15.25 12.96 16.88 23.16
77 12 25 10.29  8.54  9.33  3.17  8.21  6.46 10.96  8.67  9.62  7.50 13.08 15.34
77 12 26 11.96 14.58  9.75  4.08 10.54  7.75 11.96  9.87 10.92  7.79 15.12 14.42
77 12 27 22.08 18.16 15.16 11.04 13.75 10.67 16.08 12.54 11.12 10.54 19.46 26.96
77 12 28 19.70 12.75 15.63  9.75 10.00  5.83 12.58  9.59 10.00  8.83 14.54 26.83
77 12 29 14.33 10.41  9.92  7.12 14.21  9.42 15.71 11.75 12.33 10.75 15.50 23.71
77 12 30 21.75 13.96 12.12 10.88 17.29 11.21 16.83 14.58 13.00 12.00 16.33 26.30
77 12 31 15.09  7.62  8.79  7.08 10.63  7.58 15.59 11.54 12.25  9.08 14.12 19.55
78  1  1  8.33  7.12  7.71  3.54  8.50  7.50 14.71 10.00 11.83 10.00 15.09 20.46
78  1  2 14.62 11.83 10.50  7.41 14.21  9.62 17.08 13.46 13.50 11.67 22.63 27.92
78  1  3 20.67 17.29 14.54 12.12 18.91 13.54 21.96 16.62 15.09 15.50 20.33 28.04
78  1  4 12.96 10.67  7.62  6.42 13.88  8.50  8.38 11.83  5.91  3.75 14.29 15.29
78  1  5 12.12 10.46 11.29  3.37  8.79  7.87 13.88 15.04 12.54 13.17 20.41 23.50
78  1  6 16.50 13.83 14.21  6.42  9.87  9.54 11.29 12.87 11.12 13.21 20.41 21.62
78  1  7  6.00  3.04  7.08  2.04  4.71  3.75  8.08  6.87  5.46  6.17 12.08 15.63
78  1  8  9.21 11.58  6.63  2.04  8.38  7.83  8.12 12.04  7.25 10.46 19.75 20.75
78  1  9 14.88 17.46 12.12  8.17 13.75  9.08 17.33 11.00 11.71  9.83 18.75 22.63
78  1 10 16.38 21.54 14.42  9.42 16.83  9.50 16.62 10.29 12.17 10.29 19.46 19.79
78  1 11 25.96 23.00 23.50 13.37 17.16 13.75 19.41 13.42 14.33 15.12 23.63 34.70
78  1 12 13.00  8.67 18.66  6.29  6.38  5.29 11.58  4.54  5.91  6.13  9.87 19.79
78  1 13  7.38  3.04  8.83  1.46  3.88  1.83  9.87  4.63  3.96  3.25  6.04 15.50
78  1 14  4.67  1.08  8.04  2.79  2.33  0.71  4.17  1.21  1.67  2.37  3.96 10.63
78  1 15  2.00  3.88  6.67  0.87  4.12  2.29  1.58  6.13  3.54  4.50  9.96 14.42
78  1 16 14.79 11.92 12.83  5.37  8.50  5.04  7.87  6.38  7.04  5.46  8.75 13.25
78  1 17 12.83  5.83  7.87  2.75  6.54  3.46  8.33  5.09  4.96  4.88  9.33 12.71
78  1 18 12.92 10.63 12.42  4.79 11.87  7.67  7.92  8.50  8.04  8.12 13.62 17.00
78  1 19 14.46 15.09  9.92  5.96 13.42  5.29 10.29  8.54  8.12  5.04 15.79 15.46
78  1 20 15.00 11.75 10.21  6.46 11.83  7.17 13.79 11.79 10.92 10.08 14.88 19.58
78  1 21 21.54 19.17 15.54  9.42 16.96 11.50 11.08 15.09 12.62 14.42 20.54 20.88
78  1 22 15.12 14.62 10.79  7.71 13.88  8.38 15.71 10.08 11.92 10.25 13.29 20.38
78  1 23 15.75 12.12 14.37  9.29 14.42 10.21 13.50 11.92 13.96 11.50 12.42 18.41
78  1 24 11.21  5.37  8.17  6.83  7.41  6.58 13.92  8.54  9.75  8.83 12.29 25.75
78  1 25  8.25 10.25  7.58  2.75 10.29  4.67  8.33  6.46  6.25  5.04 10.79 12.58
78  1 26 13.08 11.29 10.04  6.67 13.75  7.00 16.50 10.88 12.04 10.96 17.88 26.42
78  1 27 18.54  9.59 15.67  6.42 10.34  7.04 11.63  9.38  9.46  7.58 11.42 24.87
78  1 28 35.38 29.88 18.00 15.96 26.92 15.67 15.87 26.34 15.04 17.75 34.42 35.83
78  1 29 29.38 18.54 28.08 17.12 17.50 13.75 25.54 15.67 18.08 20.50 19.12 38.20
78  1 30  9.62  8.71  9.59  2.71  7.58  3.54  6.08  6.08  5.33  4.46 10.41 12.83
78  1 31 10.50  8.79  9.54  4.42 10.58  5.46  8.00  5.71  6.50  6.38  6.54 17.37
78  2  1 27.25 24.21 18.16 17.46 27.54 18.05 20.96 25.04 20.04 17.50 27.71 21.12
78  2  2 13.29  9.67 12.54  9.25 11.54  8.12 14.54 10.25 10.75 12.38 10.13 22.34
78  2  3  7.50 11.25  8.54  2.96 10.92  6.87  8.12  7.41  8.46  4.04 11.75 17.58
78  2  4  9.21 11.08  7.67  4.67 12.42  7.25  8.75 11.63  9.87  6.92 16.54 16.42
78  2  5 14.04 14.50  9.25  5.75 14.29  7.00 13.46  9.04 10.08  6.04 15.87 15.63
78  2  6  8.58  8.46  8.21  2.62 10.00  4.88  9.59  8.33  7.83  4.33  9.96 12.04
78  2  7  3.42  0.79  6.08  1.29  3.46  0.58  5.50  1.83  2.67  1.46  2.17  7.21
78  2  8 10.29  6.83 13.17  3.04  9.17  2.79  9.38  8.00  7.38  4.46  4.71 11.29
78  2  9 13.33 10.25 20.30  7.00 10.41  5.96 15.71  9.13  9.33  6.54  5.37 11.96
78  2 10  9.21  6.83 12.96  3.92  6.29  2.37  6.79  7.00  3.92  2.83  9.71 10.75
78  2 11 19.70 11.67 12.38  5.88 11.38  8.63  5.09 12.71  9.08  7.17 10.63 17.88
78  2 12 13.04  5.96 13.62  5.96  5.29  1.46  9.42  4.38  6.83  7.00  2.62 11.17
78  2 13 13.79  4.04  9.67  6.83  6.00  5.17  9.08  7.08  6.79  7.87  8.71 22.34
78  2 14  6.75  5.25  9.17  2.58  3.79  0.50  5.75  3.25  2.79  3.33  3.29 10.37
78  2 15 21.79 23.09 16.25  6.87 16.58 10.79 11.87 14.46 10.79  8.58 11.96 19.41
78  2 16 19.55 23.29 15.46  5.54 12.29  9.42 14.46 15.50 11.63  9.25 14.37 20.58
78  2 17 14.67 17.75 13.50  4.67 10.21  8.29 13.33 13.59 10.04  7.79 12.12 17.79
78  2 18 27.96 25.46 26.46 14.37 23.83 18.05 23.42 23.21 17.92 15.59 16.83 29.46
78  2 19 26.34 13.50 28.96 14.71 19.38 18.54 27.71 23.67 22.08 16.38 15.75 31.96
78  2 20 12.67  5.91 19.67  9.92  9.17 10.54 16.75 13.42 14.17 11.96  9.13 26.75
78  2 21 13.92 13.67 11.92  4.88 10.54  7.83 10.08  9.33  8.46  5.91  6.63 14.62
78  2 22 21.17 16.58 19.25 12.67 15.00 12.62 15.37 13.17 14.04 12.29  8.92 21.37
78  2 23 15.34 15.00 15.83 10.08 12.25 10.88 13.00 12.17 14.42 10.17  9.62 19.79
78  2 24 18.71 17.08 16.71 12.17 13.88 12.71 14.92 15.50 13.92 13.42 19.04 20.41
78  2 25  5.54  9.21 10.63  4.54  6.79  5.21  9.87  6.83  8.83  6.08  7.83 16.75
78  2 26  2.67  6.29  3.50  1.04  3.17  0.42  3.54  2.79  1.17  0.75  2.92  6.83
78  2 27 16.29 13.88 13.21  5.50 10.25  6.79  6.29 10.25  7.92  6.38  6.83 12.83
78  2 28 10.00  8.79 13.42  5.04  7.67  8.33 10.54 10.96 10.25 10.21  8.79 17.71
78  3  1 15.04  6.21 16.04  7.87  6.42  6.67 12.29  8.00 10.58  9.33  5.41 17.00
78  3  2  5.66  6.96 10.92  4.04  3.88  3.13  6.38  4.79  6.58  4.92  4.17 13.70
78  3  3  3.96  7.25  5.17  1.71  4.08  1.58  2.29  3.25  1.63  2.17  2.79  7.00
78  3  4  5.00  5.75  8.33  1.87  1.58  0.29  4.67  2.08  2.88  2.21  2.75  9.71
78  3  5 12.62 17.37  9.83  5.96 10.92  8.29  6.08 10.71 10.08  7.62 10.71 13.21
78  3  6 15.00 16.21 13.04  9.71 13.29 10.67  8.58 12.83 12.79 10.34 18.00 21.87
78  3  7 18.00 17.50 17.12  8.96 12.58 12.75 14.17 13.75 13.42 13.17 21.84 22.00
78  3  8  8.87  7.29  9.42  5.13  7.79  6.29 10.67  7.71  8.75  8.17  6.50 14.46
78  3  9  8.54 14.17 11.29  4.17  7.87  7.38  2.79 10.04  8.46  5.13 16.13 15.67
78  3 10  9.21 14.00 11.75  6.71  9.59  8.83  4.63  9.67  8.42  5.66 12.17 11.38
78  3 11 11.67 12.21 12.50  5.75 10.13  7.41  3.63  9.04  9.08  6.29 13.04 15.29
78  3 12 16.08 12.87 13.04  7.33 12.25 10.00 13.13 11.54 11.63 10.17 11.96 16.29
78  3 13 21.04 19.00 17.75  9.71 12.67 10.71 11.79 13.00 12.92 11.17 14.37 17.83
78  3 14 23.63 24.30 18.54 15.79 23.71 18.50 21.87 21.34 19.79 16.13 21.29 15.83
78  3 15 15.63 16.17 12.00  9.62 16.33 10.04 16.08 10.50 12.83  8.83 16.33 19.25
78  3 16 13.13 14.29  8.08  5.09  8.92  6.29  9.13  8.04  7.29  7.33 11.79 18.96
78  3 17  9.54  7.00 10.41  3.29  5.21  3.83  6.50  6.54  5.17  4.63  5.13 11.08
78  3 18  8.04  9.29 10.46  4.54  6.71  6.63  7.83  8.25  8.12  6.00 11.75 15.34
78  3 19 18.41 18.08 14.54 11.54 17.67 14.37 17.00 18.29 15.63 14.00 23.75 27.16
78  3 20 19.25 17.41 13.29 11.04 18.54 11.29 13.13 13.59 12.38  7.83 18.21 19.04
78  3 21 17.54 15.92 12.54  9.17 14.75 10.00 14.29 12.83 11.92  9.50 17.71 22.17
78  3 22 20.67 20.46 17.92 11.54 20.30 11.71 18.00 12.87 14.42 12.21 16.46 19.29
78  3 23 19.70 21.46 14.71 12.58 21.54 13.04 20.12 18.58 17.33 13.70 23.29 28.29
78  3 24 16.88 14.83 11.75  8.42 16.46  9.92 15.46 13.62 14.46 12.21 15.63 21.25
78  3 25 18.21 19.95 14.37 11.12 18.88 12.29 17.25 13.83 14.83 11.71 19.58 24.71
78  3 26 15.54 17.62 14.46  8.50 14.21 10.41 15.92 14.29 13.59 10.83 17.37 22.92
78  3 27 22.88 20.30 19.67 13.67 20.79 15.75 20.17 19.21 18.46 15.16 22.42 27.84
78  3 28 25.54 22.75 21.00 14.96 19.17 15.29 18.08 17.67 17.83 17.83 19.83 24.83
78  3 29 17.16 16.66 17.46 10.96 18.46 13.04 20.08 14.21 17.25 15.00 19.00 24.83
78  3 30 14.75 13.75 12.96  8.08 14.83  9.75 11.46 11.34 11.58  9.00 12.17 16.46
78  3 31  9.04  5.63 13.00  6.34  6.75  8.00 12.54 10.21 11.25 11.12  9.71 22.29
78  4  1  3.42  7.58  2.71  1.38  3.46  2.08  2.67  4.75  4.83  1.67  7.33 13.67
78  4  2  8.21  6.04  3.58  2.29  4.25  2.17  4.71  5.75  5.96  4.71  8.71 14.88
78  4  3  5.41  2.58 15.25  2.96  5.33  7.54  6.71  8.04  9.59  5.17  9.38 15.87
78  4  4  8.42  6.67 24.04  5.71  8.33  9.29  9.83  7.17  9.54  5.71 10.96  8.38
78  4  5 10.21  6.25 26.54  5.96  8.79  8.12  9.50  6.83  9.33  5.37  8.58 10.13
78  4  6  8.58  6.04 18.38  4.38  6.25  6.42  7.58  5.33  7.04  2.96  6.17  9.46
78  4  7  6.04  5.46 13.88  3.67  5.46  4.29  6.50  4.21  4.42  2.33  6.42  5.58
78  4  8  4.75  2.92 10.41  2.00  2.58  1.46  2.04  1.67  1.42  1.75  5.13  6.54
78  4  9 11.54 11.04  7.79  6.21  9.62  6.08  8.33  8.25  9.33  9.17 15.71 20.91
78  4 10 17.12 14.17 16.04  8.96 10.96  7.96 11.79 10.46  9.83 10.71 16.79 22.71
78  4 11 14.21 13.04 10.46  7.04 11.46  7.79  9.50  8.58  9.38  7.00 14.33 14.25
78  4 12 14.12 13.04  9.25  8.33 10.79  8.25  8.58  7.75  8.29  8.25 11.21 12.87
78  4 13 23.13 17.75 17.00 13.29 16.88 12.54 14.25 15.46 13.21 12.92 17.25 24.58
78  4 14 10.04  4.42  9.71  5.46  5.71  4.96  9.46  5.54  6.83  8.71  5.58 12.75
78  4 15  6.54 10.21  5.46  3.29  6.96  3.71  4.08  3.13  2.83  1.63  9.46  4.96
78  4 16 15.71 16.83  9.50  6.46 13.29  7.92  5.83 10.96  8.04  5.96 10.29  8.42
78  4 17 20.88 15.46 18.91 11.92 17.92 16.29 15.16 14.58 16.92 11.96 17.29 15.92
78  4 18  8.12  8.12  8.12  2.29  9.92  4.04  8.12  4.08  5.37  6.25  6.08 14.92
78  4 19 15.75 18.88 12.00  7.41 14.29 10.67 11.12 11.87 12.62 11.29 14.17 17.79
78  4 20 24.41 27.71 19.87 13.92 26.38 14.79 11.25 13.79 13.25 11.00 12.79 18.84
78  4 21 13.79 13.04  9.87  7.71 15.00  7.75  7.38  5.63  7.17  5.83  8.21  4.33
78  4 22 13.67 19.38  8.38  6.38 14.83  9.92  9.50 10.21 10.67  9.54 13.79 14.33
78  4 23 17.33 20.12 10.08  7.38 14.42  8.75  8.42 11.54 11.08 10.08 15.21 14.42
78  4 24 15.12 11.92 11.63  7.83 12.54 11.42  9.50 15.04 12.67 11.21 15.46 18.21
78  4 25 13.25 11.04 14.83  4.58 10.46  9.21 13.00 11.96 11.25  9.00 18.05 17.67
78  4 26  8.38 12.58 14.17  4.46  8.12  4.75  8.54  9.04  6.87  3.71 13.42 10.83
78  4 27  5.66  4.63  6.34  3.00  4.50  2.92  6.50  4.00  4.79  4.79  7.58 16.42
78  4 28  6.29  7.62  3.58  2.21  6.71  2.25  3.33  4.50  3.17  2.25  6.17 12.17
78  4 29  9.96  9.38 16.17  3.67  8.29  3.88  7.25  6.25  6.71  5.25  9.62 15.83
78  4 30 14.09 13.75 21.54  7.46 13.04 11.83 17.16 12.83 13.70 13.79 14.58 26.92
78  5  1 10.54 12.21  9.08  5.29 11.00 10.08 11.17 13.75 11.87 11.79 12.87 27.16
78  5  2 13.83 11.87  7.92  4.08 10.41  7.25  9.62 11.83  9.33 11.04 13.13 23.21
78  5  3 10.37  9.46  9.08  4.04  9.46  6.04  3.96  6.04  7.50  5.75 10.08 13.96
78  5  4  4.67  9.71  5.41  2.46  7.67  4.83  5.96  7.58  5.33  7.67 11.38 10.67
78  5  5 14.67 10.00  9.96  7.17  9.42  5.37  8.79  7.17  8.17  8.87 10.63 10.41
78  5  6 10.46  5.75  8.33  3.83  4.46  3.29  5.41  2.92  3.96  3.79  3.29  5.54
78  5  7  7.33  8.54  5.58  3.42  6.50  4.21  3.67  5.13  4.96  4.63  6.58 14.92
78  5  8  9.00  9.50  7.54  3.96  8.67  7.08  6.34  8.12  7.83  6.83  8.54 16.50
78  5  9  4.96  3.29 13.62  3.29  5.63  4.21  4.79  4.38  6.25  4.42  5.17  5.58
78  5 10  5.09  4.29  4.79  1.87  3.71  3.17  5.04  5.75  4.58  3.37 12.50  5.96
78  5 11 11.50  7.33  8.67  6.67 10.88  6.92 10.88  9.54  9.87  9.17 14.46 15.12
78  5 12 24.46 15.21 14.29 11.67 17.54 10.54 13.79 13.83 12.38 12.17 17.58 21.37
78  5 13 16.21 11.38  9.96  8.25 13.13  9.17 10.08 10.08  9.38 11.08 12.87 15.04
78  5 14 14.21 10.25  6.34  6.58 13.08  6.29  4.58  8.96  6.50  6.17 10.75  4.83
78  5 15  6.34  4.63  4.83  1.04  4.96  1.17  2.37  2.92  2.42  2.08  7.58  3.88
78  5 16  4.29  2.33  4.54  1.92  2.71  1.21  3.33  2.33  1.38  2.92  5.21  4.25
78  5 17  4.75  4.12  6.79  2.29  6.08  2.58  3.88  5.71  4.75  5.54  5.75  7.33
78  5 18  4.88  2.83  8.67  1.63  3.25  2.79  3.17  2.71  2.96  1.42  5.41  5.25
78  5 19  4.25  1.63  5.88  1.75  3.58  2.50  3.04  5.25  5.54  4.21  5.91  6.21
78  5 20  2.83  2.08  4.29  1.54  2.83  1.13  2.29  3.17  2.79  3.46  4.12 12.29
78  5 21  2.42  3.54  7.46  1.46  2.75  0.54  3.04  1.67  3.42  4.50  4.33 11.58
78  5 22  7.54  6.54  6.17  2.79  2.42  2.46  2.92  2.96  3.04  2.88  8.00  5.46
78  5 23 11.87 15.50  7.92  5.17  8.96  4.58  8.50  7.00  7.67  8.04 14.62 14.67
78  5 24 16.13  9.96  9.00  8.08  9.17  7.12  8.29  7.54  8.21 10.17 10.34 12.71
78  5 25  3.92  3.37  5.04  1.08  5.88  3.04  3.92  6.71  3.46  4.46 11.83 11.50
78  5 26  4.50  3.25  3.50  1.21  5.13  1.58  0.83  3.17  3.17  2.29  9.29  8.71
78  5 27  3.46  2.37  4.04  1.46  1.75  0.04  1.67  1.29  1.00  0.96  6.29  5.63
78  5 28  2.62  6.67  3.96  1.96  3.25  3.29  2.13  4.04  2.75  3.63  8.83  4.04
78  5 29  3.25  7.33  3.50  3.08  5.04  3.17  4.88  4.46  4.96  6.08 11.87  6.42
78  5 30  6.87  6.71  3.54  2.54  7.25  4.12  4.88  5.00  4.63  4.83  6.58  8.04
78  5 31  9.17 12.38  4.25  4.38  9.62  8.17  4.83  9.04  7.96  9.50 11.38 15.21
78  6  1 10.37 11.42  6.46  6.04 11.25  7.50  6.46  5.96  7.79  5.46  5.50 10.41
78  6  2  7.75 10.83  7.33  4.12 10.34  6.08  2.79  4.88  6.50  4.96  9.17  6.42
78  6  3 10.71  8.83  6.63  3.79  9.59  5.83  4.54  6.25  6.79  6.29  6.79 11.46
78  6  4 16.83 13.54  9.96  7.62 12.96 10.00  5.63  9.54  9.79 10.00  9.71 16.33
78  6  5 21.54 12.12 13.83 10.71 16.13 11.54 11.75 12.29 13.54 13.96 12.38 17.12
78  6  6 10.04  9.92  9.21  5.17 13.25  9.96  9.29  9.75 10.58  8.58 14.25 16.08
78  6  7 10.37  9.87  8.96  5.58 11.79  8.12  7.12  9.38  7.46  7.00 11.54 16.25
78  6  8  7.50  7.83  5.75  4.00  7.04  4.46  6.34  6.13  6.04  8.54 11.21 19.46
78  6  9 13.46  9.59  9.67  6.29 10.08  8.17  9.50  8.38  9.42 10.83 13.13 21.17
78  6 10 12.54  9.17 10.04  6.17  9.96  8.46  8.71  7.41  9.67 10.13 10.29 17.54
78  6 11  8.71  5.58  8.96  3.88  6.21  5.50  8.04  4.25  7.50  8.46  8.63 16.83
78  6 12  7.00  6.63 13.54  6.00  7.41  7.25  6.58  7.67  7.79  8.12 10.58 15.25
78  6 13  7.00  5.04  8.00  3.04  6.71  4.46  5.04  3.79  4.46  2.50  8.46 11.12
78  6 14  5.17  8.21  7.21  4.42  9.04  6.96  4.75  6.92  7.75  5.33 12.21 14.79
78  6 15 11.54 10.13  7.46  5.33 10.21  6.79  4.08  7.83  6.71  6.67  9.54 13.83
78  6 16 15.16 15.41 25.70 11.25 11.04 10.75 16.50 11.79 11.63 11.29 16.00 19.17
78  6 17 12.83 11.34 28.58 10.37  8.00  7.87 12.42  8.92  7.12  7.17 15.25  8.29
78  6 18  7.17  4.63  8.46  4.79  7.54  4.21  1.71  5.33  3.92  2.50  7.75  8.25
78  6 19 13.50  7.58  6.63  4.21  8.29  4.25  7.41  4.75  6.54  5.17 10.88 15.16
78  6 20  9.96  7.96  7.75  3.46  7.50  5.09  4.54  8.46  5.88  5.88 13.00 15.75
78  6 21 10.04  8.21 10.71  5.33 10.37  5.46  3.04  4.96  5.09  3.63  7.71 10.25
78  6 22  8.46  9.25  6.92  2.37  6.96  2.96  6.21  5.00  5.50  3.54  9.79 11.92
78  6 23 17.58 15.25 11.17  9.29 13.29 11.50 10.34 11.21 10.13 12.21 19.29 26.96
78  6 24 17.58 12.38 10.13 10.41 16.33 11.00 12.67 11.83 12.25 10.83 17.00 18.71
78  6 25 11.83 11.08  9.08  6.08  9.87  7.71  9.33  8.92  9.67  8.33 13.54 18.00
78  6 26 13.92  8.17 11.21  7.38 11.83  8.46  9.42  7.00  9.04  8.63 10.67 14.54
78  6 27 10.25  7.71 10.04  5.54 11.12  8.46 10.79  9.83  8.96  7.62 14.09 17.00
78  6 28  9.08  8.50  9.54  6.54 12.50  9.08 14.00  7.79 11.34  7.33 10.79 15.92
78  6 29 13.88 10.34  9.25  6.50 15.25  8.96  9.67  9.13  8.71  7.96 13.42 13.96
78  6 30  9.83  7.71  9.42  5.91 10.00  7.87  9.17  8.71  9.04  7.58 12.46 14.42
78  7  1 12.46 10.63 11.17  6.75 12.92  9.04 12.42  9.62 12.08  8.04 14.04 16.17
78  7  2 16.04 14.42 12.75 10.46 15.79 11.17 11.92 10.34 10.79  5.33 10.41 15.00
78  7  3 25.37 17.04 14.09 11.79 19.12 13.92 13.70 14.75 13.67 11.58 20.12 21.59
78  7  4 25.84 17.41 17.71 14.83 16.25 14.83 13.70 15.50 14.71 17.58 19.62 28.25
78  7  5 25.04 18.08 13.88 12.08 13.70 12.67 12.38 12.83 12.79 17.83 17.79 24.08
78  7  6 18.21 10.71 13.08  9.83 10.54 10.17 11.79 11.25 11.54 13.92 12.54 19.00
78  7  7 11.87  6.92  9.59  5.46 11.83  8.08 11.92  9.33 10.41  8.42 11.75 16.75
78  7  8 11.50 10.71  9.29  6.63 13.50  9.38 11.67  8.58  9.71  6.92 11.38 11.42
78  7  9  8.54  7.54  6.25  3.92  8.96  6.29  4.42  6.29  6.08  1.33 10.08  6.42
78  7 10  4.12  4.29  4.58  1.96  3.88  3.29  3.88  2.83  3.17  2.79  4.71  5.83
78  7 11 11.21  4.00  3.92  2.46  7.08  2.83  2.83  5.79  6.58  4.25  5.21  6.71
78  7 12  6.67  2.17  3.63  0.75  3.58  1.13  1.63  1.33  1.50  1.50  4.17  2.13
78  7 13  3.13  3.96  5.17  1.33  2.67  0.96  0.83  2.75  0.54  1.50 10.13  7.75
78  7 14  6.25  2.62  8.00  3.13  4.96  3.46  3.17  4.58  4.12  4.17 11.63  9.75
78  7 15  5.09  3.46  6.21  3.08  6.04  4.21  2.79  4.92  4.54  5.50  8.29 12.25
78  7 16  4.71  2.21  6.00  3.04  4.12  2.54  2.33  2.58  3.33  2.79  6.25  8.63
78  7 17  6.38  6.25  7.17  3.29  4.21  3.58  1.83  4.71  3.04  3.88  5.63  9.67
78  7 18 15.00  8.00  7.62  5.29 11.38  6.34  7.17  8.00  8.83  8.33 11.21 12.21
78  7 19 17.12  9.25  9.33  8.42 13.88 10.13 11.87 11.42 10.88 10.37 13.59 18.16
78  7 20 13.83  6.63  6.83  7.71 10.13  7.75  7.58  5.66  7.54  7.04  8.58 13.08
78  7 21  9.71 10.54  8.67  3.96  7.50  6.17  4.67  7.83  6.87  5.33 15.59  8.50
78  7 22 19.58 17.88 17.08 10.29 15.37 13.88 13.83 17.12 14.09 14.04 24.17 21.96
78  7 23 13.96 11.50 12.71  7.08 12.83 10.13 10.08 10.37 11.42  9.83 18.16 19.92
78  7 24 12.67 10.08 10.54  5.71 12.92  9.08  9.33  8.58 10.41  8.12 15.46 19.29
78  7 25 12.25 12.33 15.29  7.58 10.17  8.50  8.42  8.79  9.87  9.08 16.92 17.04
78  7 26 13.13 13.70 12.21  5.46  9.46  7.79  7.04  9.33  8.42  5.46 14.29  9.96
78  7 27 14.96 14.62 15.63  9.25 13.29 12.46 10.50 12.87 13.67 13.17 20.12 20.38
78  7 28 10.83 13.59  9.79  6.54 11.08  9.92  9.92 11.96 10.67  9.67 19.55 17.08
78  7 29  8.00  7.87  6.58  5.75  7.38  7.12  4.08  5.79  6.13  6.17 11.63 10.63
78  7 30 13.37 10.79  7.62  6.13  5.13  6.08  2.92  6.50  5.96  5.37  8.38  7.71
78  7 31 20.58 13.08 14.83 11.04 12.04  9.54 12.17 10.04 10.88 10.41 10.71 19.08
78  8  1 19.33 15.09 20.17  8.83 12.62 10.41  9.33 12.33  9.50  9.92 15.75 18.00
78  8  2 11.83  8.71 10.71  5.41  7.62  4.54  4.50  5.13  4.79  1.50 10.25  5.17
78  8  3 10.46  5.58  6.34  4.75  8.87  6.29  9.62  6.17  7.54  5.58  8.38 10.67
78  8  4  5.25  5.58  6.46  2.13  3.29  2.25  5.54  0.83  4.42  2.37  5.46 12.71
78  8  5 10.50  9.92  7.50  1.87  7.92  2.79  1.38  1.29  2.71  0.79  5.66  3.92
78  8  6  9.38  8.46  8.08  4.17  8.71  5.17  6.92  2.08  6.58  4.17  7.38 10.29
78  8  7 13.04  9.87  8.29  4.46  7.17  5.54  5.96  5.41  6.71  5.54  9.83 14.54
78  8  8 13.54  7.46  6.21  6.58  9.79  8.63  7.75  6.79  7.71  6.63 10.25 15.59
78  8  9  7.38  3.79  5.58  3.37  4.50  4.50  3.17  1.87  5.17  3.42  5.46  7.12
78  8 10  5.63 13.13  4.54  3.21  7.96  5.63  3.08  5.09  5.79  3.25 10.63  5.91
78  8 11 13.50 13.96 11.00  8.12 11.63  9.21  6.42  8.63 12.75 10.29 14.17 15.79
78  8 12  5.88  6.87  7.25  3.79  7.62  6.29  5.54  4.04  5.29  3.83  7.96 10.75
78  8 13 11.83 12.12 10.58  4.00  8.04  7.12  6.25  9.92  9.21  6.67 16.54 13.79
78  8 14 14.09 11.54 12.79  5.17  7.79  6.25  7.50  7.21  8.21  5.83 11.67  9.50
78  8 15 14.37 12.08 11.96  5.83 11.54  8.79  8.54  6.63  9.29  5.91  9.29 10.17
78  8 16 10.88  6.25  8.12  6.13 10.58  8.87 10.83  8.25  9.92  9.21 12.75 16.17
78  8 17  8.00 12.92  7.71  3.79  9.17  6.96  4.79  7.58  8.46  5.46 17.58 13.46
78  8 18 17.16 18.25 13.92 12.00 16.75 12.58  9.92 13.33 15.87 15.09 21.79 27.25
78  8 19  5.41  6.04 10.41  3.00  5.04  5.29  3.88  5.58  7.50  6.71 14.96  9.25
78  8 20  7.79 11.58  8.63  3.96  8.50  7.50  5.41 11.04  9.46  7.38 16.96 15.04
78  8 21 14.92 14.42 15.00  7.96 13.00 10.79 12.38 11.42 13.46  9.96  9.29  7.41
78  8 22 10.04  9.08 10.00  4.67  8.92  5.54  4.33  3.54  7.21  4.71  6.92 12.96
78  8 23  3.04  2.13  6.42  1.42  2.58  1.21  1.25  1.38  5.00  3.50  6.67 11.42
78  8 24  3.88  2.37  4.79  1.67  2.50  1.67  1.33  2.46  4.21  3.17  8.67 16.04
78  8 25  3.04  2.96  5.71  1.71  2.42  2.67  1.46  3.58  5.25  4.00  8.58  9.75
78  8 26  4.33  2.71 11.83  2.21  1.63  4.08  0.75  0.54  3.13  2.00  8.04  7.46
78  8 27  4.21  1.75  6.21  2.21  1.42  2.58  1.54  1.58  2.88  3.79  8.75  9.59
78  8 28  3.50  1.92  6.25  2.25  1.25  2.00  2.42  1.33  3.63  3.54  5.00 11.75
78  8 29 12.00  6.17  9.59  4.25  5.25  5.50  6.92  3.17  7.75  5.54  9.29 14.62
78  8 30 13.25  7.79 10.54  6.00  6.71  7.17  7.58  4.50  7.25  5.71  8.96 10.71
78  8 31 11.54  5.54  7.41  4.67  7.62  6.17  8.87  5.25  7.83  6.17 11.58 16.88
78  9  1  8.42  6.13  9.87  5.25  3.21  5.71  7.25  3.50  7.33  6.50  7.62 15.96
78  9  2  4.75  1.63  6.83  1.87  1.96  4.58  6.13  1.83  5.21  2.13  8.83 15.21
78  9  3  4.17  5.04  3.88  0.83  1.25  4.50  4.12  1.42  4.42  3.63  5.46  6.92
78  9  4  7.50 11.67  3.42  2.54  6.79  6.67  5.09  3.92  6.54  3.88  9.13 10.88
78  9  5  5.83  7.33  4.96  1.50  3.67  6.58  3.25  3.71  6.04  3.13  7.17  8.96
78  9  6 10.08 11.96 10.37  4.63  9.67  9.25  7.38  8.08  8.87  7.38 10.25 15.09
78  9  7  9.79  9.29  7.83  5.25 10.21 11.12  9.21  9.46  9.79  7.08 15.41 15.34
78  9  8 15.54 14.62 12.75  5.79 12.08 11.08  8.21 11.21 11.34  9.50 21.84 19.92
78  9  9 15.21 16.21 17.04  8.58 15.63 15.37 15.29 14.33 15.29 13.33 22.58 25.96
78  9 10 16.46 18.12 21.50 11.63 17.00 16.42 15.67 15.50 16.25 13.62 15.54 21.12
78  9 11 19.38 11.75 14.09 12.17 18.05 15.75 18.50 16.00 15.21 15.75 21.42 26.42
78  9 12  8.67 10.13 11.00  4.71  7.71 10.08  9.08 10.92  9.04  7.41 16.17 17.58
78  9 13 13.08 11.87 13.59  6.46 11.21 12.00 12.62 10.92 12.67  9.83 15.50 17.79
78  9 14 11.38 11.54 10.83  7.00 16.38 12.04 13.67 13.79 12.87 11.75 20.25 26.67
78  9 15 11.50 11.87 11.34  7.46 13.59 13.21 14.96 13.21 13.33 11.17 19.25 25.17
78  9 16 15.21 13.92 14.04  7.71 12.79 13.17 14.04 16.92 13.37 13.25 25.33 30.54
78  9 17  5.25  3.46  6.38  2.54  6.04  6.83  8.25  5.37  6.87  7.54 11.04 19.92
78  9 18  4.58  1.96  5.54  1.96  4.00  5.71  5.71  3.63  4.96  5.29 11.04 17.96
78  9 19  2.67  1.21  4.38  0.79  4.42  4.00  1.21  1.21  3.04  0.71  4.71  5.13
78  9 20  0.79  6.79  5.88  0.75  1.79  0.96  3.04  0.83  4.46  1.58  6.21 10.88
78  9 21  4.46 11.38  9.59  2.67  5.17  4.83  5.50  8.87  6.13  6.34 18.38 15.09
78  9 22  6.75 13.67 13.54  3.42  4.54  5.41  9.75 11.71  9.46  9.87 18.75 20.00
78  9 23 10.04 13.08 12.62  4.21  8.96  8.17 11.25 13.17 11.17 11.67 19.92 18.29
78  9 24 11.63 10.13 13.59  4.08 10.50  7.12 12.87  7.83  8.87  8.58 15.12 19.92
78  9 25  9.79 11.00  8.25  5.13 11.67  7.54 12.75  9.79 10.46 11.00 20.25 26.54
78  9 26 13.70 14.83 10.41  7.71 14.29  9.62 16.58 12.29 12.29 11.42 18.79 22.88
78  9 27 14.04 13.21  9.04  7.58 14.42  9.33 13.79  9.83 11.67 10.21 16.54 19.92
78  9 28 17.04 19.21 16.66 10.83 21.25 12.54 19.41 14.75 14.67 12.71 22.17 20.30
78  9 29 22.95 18.21 13.29 13.33 25.33 15.63 22.21 19.70 17.00 18.12 26.83 33.50
78  9 30 26.75 15.63 16.54 13.37 17.58 13.13 16.92 13.79 13.46 13.79 18.91 31.88
78 10  1  9.50  6.83 10.50  3.88  6.13  4.58  4.21  6.50  6.38  6.54 10.63 14.09
78 10  2  9.96  8.29  8.50  3.08  7.54  5.25  6.83  5.46  6.87  6.75 10.25 14.04
78 10  3  9.92  7.67  6.54  3.63  9.79  6.67  9.25  7.50  9.04  7.46 14.50 21.42
78 10  4  8.21  7.67  8.75  3.13  9.25  7.04 11.29  8.00  9.75  8.12 16.25 21.29
78 10  5 12.25 13.54 14.54  6.92 11.71 11.25 15.34 13.70 13.96 13.42 21.29 28.16
78 10  6 12.96 17.83 12.79  7.96 13.37  9.46  8.58 10.34 12.58 10.83 20.21 19.50
78 10  7 15.09 21.00 14.33  7.92 15.79 10.83  9.04 10.46 13.33 10.34 18.41 20.71
78 10  8 21.67 17.12 16.25 13.08 14.67 11.67  7.62 12.50 14.21 14.21 14.58 19.21
78 10  9 13.54 11.17 12.71  7.08  8.75  8.25  7.62  9.59  8.63 10.58 18.25 19.87
78 10 10 12.58  9.75 11.29  3.63 10.29  7.62  6.34 10.46  9.46  8.50 18.75 16.21
78 10 11 15.41 10.25 12.83 10.96 11.29 12.17 11.29 11.17 14.62 15.37 14.25 20.04
78 10 12  6.00  1.50  8.04  0.79  2.83  3.04  2.13  1.38  3.42  3.54  5.09 10.67
78 10 13  4.29  3.08  9.92  2.54  3.54  4.08  3.00  1.63  3.67  3.13  5.83  4.58
78 10 14  4.63  0.21  3.54  0.42  2.67  2.46  1.25  3.71  3.04  1.92 11.29 11.08
78 10 15 10.83  8.33  6.42  3.75  8.38  6.04  7.41  6.50  7.79  7.96 11.92 16.79
78 10 16 15.34 14.42 10.04  8.58 15.37  9.75 15.83 12.62 12.29 12.50 19.55 28.21
78 10 17 18.91 15.63 13.29 10.25 13.29 10.08 12.62 10.17  9.08 12.58 15.67 23.87
78 10 18  7.83  5.88  5.17  3.83  6.21  4.46  4.58  4.75  5.21  5.17  7.87 14.75
78 10 19  4.46  4.17  4.83  2.83  6.54  5.21  5.54  6.54  7.41  8.46 13.13 17.92
78 10 20  8.25  4.33  6.75  5.63  8.96  7.38  9.21  7.12  7.25  4.67  9.62  8.58
78 10 21  7.21  5.37  6.25  2.79  7.25  5.91  9.67  6.92  7.71  6.75 13.29 15.79
78 10 22  6.63  4.92  7.17  3.29  6.25  5.25  8.29  3.88  5.41  6.21  9.83 15.96
78 10 23  5.37 10.21  9.13  4.12  8.96  7.79 11.00  9.46 10.41  9.79 17.16 22.37
78 10 24 11.54 13.46 12.21  5.46 13.96 10.54 17.67 11.87 14.42 11.87 17.29 19.33
78 10 25  9.54  4.67  8.75  3.67  8.83  6.92 16.17  7.75 11.00 10.34 13.46 19.12
78 10 26  7.04  1.21  5.66  1.63  4.08  1.92  8.58  2.33  4.08  4.88  7.54 14.09
78 10 27  2.04  5.37  5.09  0.33  1.67  3.00  1.83  5.66  2.08  5.17 13.08 12.62
78 10 28  7.33  8.87  5.00  2.13  6.50  5.13  2.42  4.17  3.58  5.83 14.50 13.67
78 10 29  9.67 10.13 10.88  5.09  8.25  6.04  3.42  5.33  7.67  8.96 18.38 17.21
78 10 30 10.25 11.63 10.21  3.67  8.08  7.50  8.92  8.96  9.21 10.96 16.50 19.83
78 10 31  8.58  4.29 10.79  4.29  4.08  2.71  4.63  1.04  3.67  2.75  8.71 10.67
78 11  1 13.59 16.75 11.25  7.08 11.04  8.33  8.17 11.29 10.75 11.25 23.13 25.00
78 11  2  9.33 10.88  9.92  5.37 10.04  7.87 10.00 10.04 10.63 10.08 17.58 18.34
78 11  3 15.46 14.75 13.08  6.79 10.92  9.08 13.21 13.59 12.12 13.00 20.41 18.29
78 11  4 18.75 19.04 16.58  9.96 10.96 11.75 14.58 13.92 11.67 13.50 20.46 19.92
78 11  5 15.46 16.92 13.13  9.62 14.12 10.96  8.92  8.63 13.25 11.42 22.37 19.00
78 11  6 14.92 18.79 14.75 10.29 15.79  9.59 12.21 10.34 12.79 12.46 19.95 24.41
78 11  7 25.41 22.58 20.88 17.21 20.30 16.29 19.29 15.12 18.46 16.25 18.91 27.96
78 11  8 10.88  8.12  9.46  4.96  9.17  6.75  8.87  7.62  8.38  9.62 17.33 19.50
78 11  9 12.79 13.67  7.29  3.17 10.83  8.42  6.21 13.50  9.21 13.37 24.17 24.33
78 11 10  7.38 12.12  7.79  2.46  7.00  6.67  5.91  5.88  7.17  9.13 15.16 16.79
78 11 11 12.21 12.50  9.71  5.29  8.63  7.67  4.12  7.92  7.92  9.21 17.00 15.63
78 11 12 20.41 18.88 14.17 10.41 15.50 10.08 14.96 13.50 14.25 15.59 22.63 29.04
78 11 13 18.79 17.96 14.62  7.21 12.29 10.29 16.50 12.50 13.42 14.54 23.79 24.33
78 11 14 30.21 27.63 25.46 16.96 19.21 14.04 17.54 16.29 13.96 13.62 20.58 27.42
78 11 15 23.67 23.45 20.08 15.96 20.83 16.00 20.12 18.12 18.29 18.08 21.09 25.37
78 11 16 17.58 18.12 14.54  9.21 17.88 11.21 17.67 12.29 14.12 12.25 23.58 26.67
78 11 17 23.21 21.37 19.04 11.21 13.21 13.88 17.58 14.00 14.96 14.54 21.25 24.92
78 11 18 16.79 12.79 18.38  7.41  8.25  7.67 12.08 10.00 11.04 12.75 16.00 17.88
78 11 19 13.88 11.67  8.79  4.96  7.87  5.37 11.08  7.21  7.29  6.92 16.21 20.75
78 11 20 16.46 17.71 13.88  7.33 13.62  9.75 16.08 10.63 12.46 11.96 21.09 23.50
78 11 21 17.75 16.83 16.71  7.29 13.92  9.25 14.67 11.08 13.29 12.21 20.46 22.13
78 11 22 17.58 16.38 16.17  7.00 13.37 11.50 16.71 12.17 15.04 14.29 19.08 26.34
78 11 23 23.16 13.96 18.25  9.83  8.87  7.33 12.25  5.21  9.29  5.58  7.54 13.13
78 11 24 14.83  9.92 10.34  4.21  7.41  3.75  7.17  4.12  5.66  5.71 12.87 13.59
78 11 25 16.17 14.67 11.54  7.04  8.96  6.96 14.62  6.71 10.79  9.29 16.83 26.67
78 11 26  9.54  8.33  7.92  1.83  6.92  3.29  8.08  3.08  3.46  3.37  8.58 11.08
78 11 27  9.08  6.50  8.21  1.67  6.71  1.00  2.33  2.04  1.29  3.29  7.04 13.42
78 11 28  9.46  7.25  8.38  0.96  6.50  3.75  1.96  4.75  4.71  4.12  9.00 11.83
78 11 29 14.46 10.00 10.17  3.33  9.59  8.83  4.38  5.75 10.41  9.13 13.13 22.95
78 11 30 15.34  4.54 14.75  3.50  4.54  4.96  7.50  2.42  4.96  3.75  4.92 11.50
78 12  1 21.29 16.29 24.04 12.79 18.21 19.29 21.54 17.21 16.71 17.83 17.75 25.70
78 12  2 13.70 12.71 14.29  5.13  9.21  8.04 12.33  6.34  9.21 11.21  9.59 19.95
78 12  3 21.21 21.34 17.75 11.58 16.75 14.46 17.46 15.29 15.79 17.50 21.42 25.75
78 12  4  9.92 13.50  7.21  1.71 11.00  7.50  8.38  7.46 10.79 10.21 17.88 17.96
78 12  5 22.75 20.17 18.58  8.50 15.96 14.29 13.92 12.92 12.96 12.29 17.08 19.83
78 12  6 29.33 23.87 25.37 16.04 24.46 19.50 24.54 18.58 21.00 20.58 21.67 34.46
78 12  7 26.63 24.79 24.79 18.16 23.13 19.58 19.92 19.04 19.75 21.50 23.04 34.59
78 12  8 12.92 12.54 11.25  3.37  6.50  5.96 10.34  6.17  6.63  6.75  9.54 17.33
78 12  9 18.71 16.92 15.50  6.04 10.37  9.59 10.75  9.13  9.75 11.08 14.33 15.34
78 12 10 24.92 22.54 16.54 14.62 15.59 13.00 13.21 14.12 16.21 16.17 26.08 21.92
78 12 11 20.25 19.17 17.83 11.63 17.79 13.37 14.83 13.88 15.54 16.29 18.34 22.83
78 12 12 23.13 18.63 18.05  8.29 14.33 11.04 10.54 10.13 11.42 10.50 11.25 13.50
78 12 13 18.84 24.04 14.37  8.33 18.12 12.17 13.00 13.75 14.17 15.09 21.50 21.37
78 12 14 17.21 19.75 12.71  5.83 13.79  7.33  8.83  5.71  7.96  3.37  5.21  6.92
78 12 15 13.13  8.92 16.54  6.92  6.00  4.00 12.67  5.88  7.67  6.08  5.50 17.16
78 12 16 14.88  9.13 17.37  5.21  6.71  2.46  9.13  4.96  6.13  5.96 10.92 18.08
78 12 17  9.87  3.21  8.04  2.21  3.04  0.54  2.46  1.46  1.29  2.67  5.00  9.08
78 12 18  9.83 10.88  8.50  1.00  9.08  6.00  2.42  8.25  4.42  5.88 19.79 19.79
78 12 19 13.88 11.42 10.13  2.33  8.12  6.75  4.75  5.88  6.21  8.17  8.33 18.25
78 12 20  9.92  3.63 12.38  3.08  3.50  0.42  4.54  2.50  2.13  4.71  3.21 10.29
78 12 21 12.96  3.83 13.79  4.79  7.12  6.54 11.67  9.25  8.67  9.00 11.25 20.30
78 12 22  6.21  7.38 13.08  2.54  7.58  5.33  2.46  8.38  5.09  5.04  9.92 11.00
78 12 23 16.62 13.29 22.21  9.50 14.29 13.08 16.50 17.16 12.71 12.00 18.50 21.50
78 12 24  8.67  5.63 12.12  4.79  5.09  5.91 12.25  9.25 10.83 11.71 11.92 31.71
78 12 25  7.21  6.58  7.83  2.67  4.79  4.58  8.71  0.75  5.21  5.25  1.21 13.96
78 12 26 13.83 11.87 10.34  2.37  6.96  4.29  1.96  3.79  3.04  3.08  4.79 11.96
78 12 27 17.58 16.96 17.62  8.08 13.21 11.67 14.46 15.59 14.04 14.00 17.21 40.08
78 12 28 13.21  5.46 13.46  5.00  8.12  9.42 14.33 16.25 15.25 18.05 21.79 41.46
78 12 29 14.00 10.29 14.42  8.71  9.71 10.54 19.17 12.46 14.50 16.42 18.88 29.58
78 12 30 18.50 14.04 21.29  9.13 12.75  9.71 18.08 12.87 12.46 12.12 14.67 28.79
78 12 31 20.33 17.41 27.29  9.59 12.08 10.13 19.25 11.63 11.58 11.38 12.08 22.08


================================================
FILE: 06_Stats/Wind_Stats/wind.desc
================================================
wind   daily average wind speeds for 1961-1978 at 12 synoptic meteorological 
       stations in the Republic of Ireland (Haslett and raftery 1989).

These data were analyzed in detail in the following article:
   Haslett, J. and Raftery, A. E. (1989). Space-time Modelling with
   Long-memory Dependence: Assessing Ireland's Wind Power Resource
   (with Discussion). Applied Statistics 38, 1-50.

Each line corresponds to one day of data in the following format:
year, month, day, average wind speed at each of the stations in the order given
in Fig.4 of Haslett and Raftery : 
 RPT, VAL, ROS, KIL, SHA, BIR, DUB, CLA, MUL, CLO, BEL, MAL

Fortan format : ( i2, 2i3, 12f6.2) 

The data are in knots, not in m/s.

Permission granted for unlimited distribution.

Please report all anomalies to fraley@stat.washington.edu

Be aware that the dataset is 532494 bytes long (thats over half a
Megabyte).  Please be sure you want the data before you request it.


================================================
FILE: 07_Visualization/Chipotle/Exercise_with_Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Chipotle's Data\n",
    "\n",
    "Check out [Chipotle's Visualization Exercises Video Tutorial](https://youtu.be/BLD2mAB3kaw) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# set this so the \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
    "    \n",
    "chipo = pd.read_csv(url, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the Series of the names\n",
    "x = chipo.item_name\n",
    "\n",
    "# use the Counter class from collections to create a dictionary with keys(text) and frequency\n",
    "letter_counts = Counter(x)\n",
    "\n",
    "# convert the dictionary to a DataFrame\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "\n",
    "# sort the values from the top to the least value and slice the first 5 items\n",
    "df = df[0].sort_values(ascending = True)[45:50]\n",
    "\n",
    "# create the plot\n",
    "df.plot(kind='bar')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Number of Times Ordered')\n",
    "plt.title('Most ordered Chipotle\\'s Items')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the number of items orderered per order price\n",
    "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 36.7178857951459)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a list of prices\n",
    "chipo.item_price = [float(value[1:-1]) for value in chipo.item_price] # strip the dollar sign and trailing space\n",
    "\n",
    "# then groupby the orders and sum\n",
    "orders = chipo.groupby('order_id').sum()\n",
    "\n",
    "# creates the scatterplot\n",
    "# plt.scatter(orders.quantity, orders.item_price, s = 50, c = 'green')\n",
    "plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = 'green')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Order Price')\n",
    "plt.ylabel('Items ordered')\n",
    "plt.title('Number of items ordered per order price')\n",
    "plt.ylim(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create a question and a graph to answer your own question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: 07_Visualization/Chipotle/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Chipotle's Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# set this so the graphs open internally\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the number of items orderered per order price\n",
    "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. BONUS: Create a question and a graph to answer your own question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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================================================
FILE: 07_Visualization/Chipotle/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Chipotle's Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# set this so the \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the number of items orderered per order price\n",
    "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 36.7178857951459)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create a question and a graph to answer your own question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 07_Visualization/Online_Retail/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online Retails Purchase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Online_Retail/Online_Retail.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called online_rt\n",
    "Note: if you receive a utf-8 decode error, set `encoding = 'latin1'` in `pd.read_csv()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a histogram with the 10 countries that have the most 'Quantity' ordered except UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Exclude negative Quantity entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries (except UK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Investigate why the previous results look so uninformative.\n",
    "\n",
    "This section might seem a bit tedious to go through. But I've thought of it as some kind of a simulation of problems one might encounter when dealing with data and other people. Besides there is a prize at the end (i.e. Section 8).\n",
    "\n",
    "(But feel free to jump right ahead into Section 8 if you want; it doesn't require that you finish this section.)\n",
    "\n",
    "#### Step 7.1 Look at the first line of code in Step 6. And try to figure out if it leads to any kind of problem.\n",
    "##### Step 7.1.1 Display the first few rows of that DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.2 Think about what that piece of code does and display the dtype of `UnitPrice`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.3 Pull data from `online_rt`for `CustomerID`s 12346.0 and 12347.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.2 Reinterpreting the initial problem.\n",
    "\n",
    "To reiterate the question that we were dealing with:  \n",
    "\"Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries\"\n",
    "\n",
    "The question is open to a set of different interpretations.\n",
    "We need to disambiguate.\n",
    "\n",
    "We could do a single plot by looking at all the data from the top 3 countries.\n",
    "Or we could do one plot per country. To keep things consistent with the rest of the exercise,\n",
    "let's stick to the latter oprion. So that's settled.\n",
    "\n",
    "But \"top 3 countries\" with respect to what? Two answers suggest themselves:\n",
    "Total sales volume (i.e. total quantity sold) or total sales (i.e. revenue).\n",
    "This exercise goes for sales volume, so let's stick to that.\n",
    "\n",
    "##### Step 7.2.1 Find out the top 3 countries in terms of sales volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.2.2 \n",
    "\n",
    "Now that we have the top 3 countries, we can focus on the rest of the problem:  \n",
    "\"Quantity per UnitPrice by CustomerID\".  \n",
    "We need to unpack that.\n",
    "\n",
    "\"by CustomerID\" part is easy. That means we're going to be plotting one dot per CustomerID's on our plot. In other words, we're going to be grouping by CustomerID.\n",
    "\n",
    "\"Quantity per UnitPrice\" is trickier. Here's what we know:  \n",
    "*One axis will represent a Quantity assigned to a given customer. This is easy; we can just plot the total  Quantity for each customer.  \n",
    "*The other axis will represent a UnitPrice assigned to a given customer. Remember a single customer can have any number of orders with different prices, so summing up prices isn't quite helpful. Besides it's not quite clear what we mean when we say \"unit price per customer\"; it sounds like price of the customer! A reasonable alternative is that we assign each customer the average amount each has paid per item. So let's settle that question in that manner.\n",
    "\n",
    "#### Step 7.3 Modify, select and plot data\n",
    "##### Step 7.3.1 Add a column to online_rt called `Revenue` calculate the revenue (Quantity * UnitPrice) from each sale.\n",
    "We will use this later to figure out an average price per customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.2 Group by `CustomerID` and `Country` and find out the average price (`AvgPrice`) each customer spends per unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.3 Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.4 What to do now?\n",
    "We aren't much better-off than what we started with. The data are still extremely scattered around and don't seem quite informative.\n",
    "\n",
    "But we shouldn't despair!\n",
    "There are two things to realize:\n",
    "1) The data seem to be skewed towaards the axes (e.g. we don't have any values where Quantity = 50000 and AvgPrice = 5). So that might suggest a trend.\n",
    "2) We have more data! We've only been looking at the data from 3 different countries and they are plotted on different graphs.\n",
    "\n",
    "So: we should plot the data regardless of `Country` and hopefully see a less scattered graph.\n",
    "\n",
    "##### Step 7.4.1 Plot the data for each `CustomerID` on a single graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.4.2 Zoom in so we can see that curve more clearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Plot a line chart showing revenue (y) per UnitPrice (x).\n",
    "\n",
    "Did Step 7 give us any insights about the data? Sure! As average price increases, the quantity ordered decreses.  But that's hardly surprising. It would be surprising if that wasn't the case!\n",
    "\n",
    "Nevertheless the rate of drop in quantity is so drastic, it makes me wonder how our revenue changes with respect to item price. It would not be that surprising if it didn't change that much. But it would be interesting to know whether most of our revenue comes from expensive or inexpensive items, and how that relation looks like.\n",
    "\n",
    "That is what we are going to do now.\n",
    "\n",
    "#### 8.1 Group `UnitPrice` by intervals of 1 for prices [0,50), and sum `Quantity` and `Revenue`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.3 Plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.4 Make it look nicer.\n",
    "x-axis needs values.  \n",
    "y-axis isn't that easy to read; show in terms of millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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================================================
FILE: 07_Visualization/Online_Retail/Exercises_with_solutions_code.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online Retails Purchase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# set the graphs to show in the jupyter notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# set seaborn graphs to a better style\n",
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Online_Retail/Online_Retail.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called online_rt\n",
    "Note: if you receive a utf-8 decode error, set `encoding = 'latin1'` in `pd.read_csv()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Online_Retail/Online_Retail.csv'\n",
    "\n",
    "online_rt = pd.read_csv(path, encoding = 'latin1')\n",
    "\n",
    "online_rt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a histogram with the 10 countries that have the most 'Quantity' ordered except UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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aoWhvt4h4U0TsXinqAuYAjwBrV8qHkWoqPZU/DqwWEQNz+dosrNk8mtcjIlYAXgk82RTKOGDDpp/t+3NuZmbWd6W7WncB4yLiVfk+zBjgMuA2ICLidTmh7ANMljQNmB0R2+bt983lc4ApwN65fD9gcn59ZX5PXj4lr7+ApJmSHqr+kBKdmZkVUDT5SPozcBJwMzAVuFPSRZJmA/sDl+bye1nYmWA0cFpE3AusApyRyw8FxkTEVFKtZWwuPxbYJiLuzusc1u7zMjOzJVPkno+k11Rejyd1k25e5xpg827K7yL1hmsunwbs2E35U8AH+xWwmZm1lUc4MDOz4px8zMysOCefl6F5c1/oiH2YmS2tos/5WGsMWGEwD3xt98Wv2IuNvnRpi6IxM1tyrvmYmVlxTj5mZlack4+ZmRXn5GNmZsU5+ZiZWXFOPmZmVpyTj5mZFefkY2ZmxTn5mJlZcU4+ZmZWnJOPmZkV5+RjZmbFOfmYmVlxbR/VOiJWBW4BRkl6KCLGAEcA84HbgYMlvRARxwEfB/6dNz1b0lkRMRKYAKwK3AgcImluRKwPTALWAgSMlvRMRAwFLgA2Ap4A9pL0WLvP08zM+q6tNZ+IeCtwEzAivx8BfBb4f8Bm+fiH5dW3BD4saWT+OSuXTwIOlzQC6AIOyuXjgfGSNiYlsWNz+YnAFEmbAGcDp7fxFM3MbCm0u+ZzECm5/Ci//y9wqKRZABHxF2D9vGxL4IsRsQGphvMZ4H+AIZJuzeucBxwfEROAHYDdKuU3AMcA78/LAC4CzoqIQZLmNILKtaOhTbEO7+/JmplZ37Q1+Ug6ECAiGu+nAdNy2ZrA4cD+EbEKcAepVvR3UjI5FrgcmFHZ5QxSklgDmCVpblM5wDqNbXLz3CxgTWB6ZT9HAce17kzNzGxJ9Cn5RMSngPMbNZb+ioh1gcnAOZKuz8Xvqyw/BZgIXEm6N9TQBcwjNddVy8nljXWquirLGsaRElzVcGBKX8/BzMyWXl9rPpsBf4uIy4HvSbp9aQ8YERsDvwHOkHRKLlsf2FnSxLxaFzAHeARYu7L5MFIN5nFgtYgYKOnFvE6jZvNoXu+RiFgBeCXwZDUGSTOBmU1xLe0pmZnZEupThwNJBwGvJ93YHx8Rf4iIj0fESktysIh4JXAVMLaReLLngW9FxIYR0UW6T3RZbqabHRHb5vX2BSbn+zdTgL1z+X6kmhSk2tJ++fXepM4HC+73mJlZ/frc203S08BPgQuBV5MShCLiA0twvANJnQiOjog7888Jkp4ADgZ+Reo23QU0ktNo4LSIuBdYBTgjlx8KjImIqcD2wNhcfiywTUTcnddp9KYzM7MO0dd7Pu8ExgA7kxLQbpL+HBGvJdVAftXb9pJek1+eln+6W+dS4NJuyu8Ctu6mfBqwYzflTwEf7C0eMzOrV1/v+ZxFeq5mjKT/NAol3R8RZ7clMjMzW2b1tdntDElnVBNPRBwDIMldls3MbIn0WvOJiEOAlYFPN3UuGAQcAnyzjbFZh3vhxTkMHjio9n2Y2cvP4prd5gBvIiWgN1XK5wJHtysoe3kYPHAQe138yX7t4yd7f7dF0ZjZy0mvyUfSOcA5EbGbpJ8XisnMzJZxi2t2+5ykbwHvjIidmpdLOqJtkZmZ2TJrcc1ujQ4G/+pmWfPwNmZmZn2yuGa37+eXj0tapHG+0dvNzMxsSS1Jb7chlUXu7WZmZkvNvd3sZW3eCy8wYPDg2vdhZkvGvd3sZW3A4MHcvOvu/drHtr94yahOZtZmfR1e53cRcRywOpX5ctzbzczMlkZfk88k4DnSbKPu5WZmZv3S1+QzXNImbY3EzMyWG30dWHRaRLyirZGYmdlyo681nxnAnRFxPWnWUcD3fMzMbOn0Nfk8lH/MzMz6rU/JR9LxzWV9aYaLiFWBW4BRkh6KiJ2BU4EhwMWSxub1RgITgFWBG4FDJM2NiPVJnR3WIk2vPVrSMxExFLgA2Ah4AthL0mMRMRg4B9iSVEPbR9K9fTlHMzMrp0/3fCJi14i4KyLuj4gHImIa8NhitnkrcBMwIr8fAkwEdgU2AbaKiF3y6pOAwyWNIHXlPiiXjwfGS9oYuB04NpefCEzJnSDOBk7P5UcAz+byo4Dz+nJ+ZmZWVl87HHwb+DrwMHAo8Gvge4vZ5iDgMGB6fr81cJ+kByXNJSWcPSNiA2CIpFvzeufl8kHADsAl1fL8+v2kmg/ARcAuef0F5ZJuBNbMtadFRMTQiHhN9QcY3pcPwszM+q+vyedZSRcDtwKzgU8Co3rbQNKBkqZUitYhdVxomEH6wu+pfA1gVk5U1fJF9pWXzwLW7GVfzY4CHmz6mdLNemZm1gZ9TT6zI2JF4O/ASEnzWPKHTQc0bdMFzFuCcnJ5Y52qxe2r2Thgw6af7ft4HmZm1k997e32S+AK4GOkoXa2p/s5fnrzCLB25f0wUpNcT+WPA6tFxEBJL+Z1Gk14j+b1HomIFYBXAk9W9nV/074WIWkmMLNaFhFLeDpmZra0+lTzkfR14OOSHiV1GLgR2GMJj3UbEBHxuogYCOwDTJY0jVSz2javt28un0NqCts7l+8HTM6vr8zvycun5PUXlEfEdsBsSQ8vYZxmZtZmfe3t9hZgjfxvFykpLNENekmzgf2BS4GpwL0s7EwwGjgtIu4FVgHOyOWHAmMiYiqpWWxsLj8W2CYi7s7rHJbLzwRWzOVnkBKZmZl1mL42u1XHnB9Mas76I6kHW68kvaby+hpg827Wuau7feVa0Y7dlD8FfLCb8tmkpkEzM+tgfX3IdMPq+4jYkVRbMTMzW2J97e22CEnXA1u0NhQzM1te9Knmk+/1NHSRhq8Z0paIzMxsmbck93waz8/MJ3WzPqQtEZmZ2TJvsc1uEbEKaYy1PwN/JQ0A+i5g/YjYqb3hmZnZsqjXmk9ErA78jtQ1+upcvBNpkM+ngXe0NTozM1smLa7Z7XhggqSTK2VnRcQlwBxJs9oXmpmZLasWl3x2BEZWC3JtaBNgUJtiMjOzZdzi7vnMy+OqVT0N7E5lOm0zM7Ml0ZcOB6tW3+cx1HqdSM7MzKw3i0s+FwI/yNMpABARK5EmkpvUzsDMzGzZtbh7Pt8mzQz6QETcnsu2Aq7Ly8wMmDvnRVYYNLD2fZi9XPSafPL9ng9HxJbAdrn4REl/aHtkZi8jKwwayAlHX96vfXz5lF4nBzZbpvR1YNHbSc/2mJmZ9dtSDSxqZmbWH04+ZmZWnJOPmZkV19dRrVsmIg4EDq8UbQj8CHgFqVPDs7n8eEmXRcTOwKmkKRwuljQ272ckaZDTVYEbgUMkzY2I9UndwNcCBIyW9Ez7z8zMzPqqeM1H0gRJIyWNJM2G+jjwFdIcQTs0luXEMwSYCOxKGtJnq4jYJe9qEnC4pBGkOYYOyuXjgfGSNiZ1kji21LmZmVnfFK/5NPku8EXgOWB9YGJErAtcRhrUdGvgPkkPAkTEJGDPiJgKDJF0a97PecDxETEB2AHYrVJ+A3BM9aARMRQY2hTL8JaemZmZ9ai2ez65OW2IpJ8Cw4BrgY8D2wDbA58A1gFmVDabQUoSPZWvAcySNLepvNlRwINNP1NacmJmZrZYddZ8Dibdy0HSA8CHGgsi4kxgP+ASFs6gCql5bR4pafalnFzebBypVlQ1HCcgM7Miakk+ETEYeDuwf37/JmCEpEvzKl3AHOARYO3KpsOA6b2UPw6sFhED8+gMa+fyRUiaCcxsiqnf52VmZn1TV7PbZsDfJDV6tnUB4yLiVRExCBhDuu9zGxAR8bqIGAjsA0yWNA2YHRHb5u33zeVzSLWXvXP5fsDkMqdkZmZ9VVfy2YhUewFA0p+Bk4CbSVN23ynpIkmzSbWjS3P5vaSmOEg95U6LiHuBVYAzcvmhwJjcKWF7YGzbz8bMzJZILc1ukn4C/KSpbDypm3TzutcAm3dTfhepN1xz+TTSDKxmZtahPMKBmZkV5+RjZmbFOfmYmVlxTj5mZlack4+ZmRXn5GNmZsU5+ZiZWXFOPmZmVpyTj5mZFefkY2ZmxTn5mJlZcU4+ZmZWnJOPmZkV5+RjZmbFOfmYmVlxTj5mZlack4+ZmRVXy0ymEXEdsBYwJxcdDLyWNOX1IGCcpLPyujsDpwJDgIsljc3lI4EJwKrAjcAhkuZGxPrApLx/AaMlPVPq3MzMbPGK13wiogsYAWwuaaSkkcAjwNeA7YCRwJiIeENEDAEmArsCmwBbRcQueVeTgMMljQC6gINy+XhgvKSNgduBYwudmpmZ9VEdNZ/I/14VEa8GzgaeBq6V9BRARFwC7AHcANwn6cFcPgnYMyKmAkMk3Zr3dR5wfERMAHYAdquU3wAcs0gAEUOBoU1xDW/VCZqZWe/qSD6vAq4BPkVqYrseuBiYUVlnBrA1sE435cN7KV8DmCVpblN5s6OA4/p5HmZmtpSKJx9JvwN+13gfEeeQ7umcWFmtC5hHahac349ycnmzcaRaUdVwYEofT8PMzPqhePKJiO2AFSVdk4u6gIeAtSurDQOmk+4FLUn548BqETFQ0ot5nenNMUiaCcxsimvpT8rMzJZIHV2thwInR8RKEfFK4GPAR4F3RsSaEbEysDvwa+A2ICLidRExENgHmCxpGjA7IrbN+9w3l88h1V72zuX7AZOLnZmZmfVJ8eQj6XLgCuAO4I/AREk3A18CrgPuBC6U9HtJs4H9gUuBqcC9wCV5V6OB0yLiXmAV4Ixcfiipt9xUYHtS920zM+sgtTznI+lYmrpAS7oQuLCbda8BNu+m/C5Sp4Tm8mnAjq2K1czMWs8jHJiZWXFOPmZmVpyTj5mZFefkY2ZmxTn5mJlZcU4+ZmZWnJOPmZkV5+RjZmbFOfmYmVlxTj5my4i5c+YsfqUC+zDri1qG1zGz1lth0CC+84UD+rWPw086t0XRmPXONR8zMyvOycfMzIpz8jEzs+KcfMyspebN7W7m+nLb28uDOxyYWUsNWGEA9337pqXe/vWf2a6F0Vincs3HzMyKq6XmExHHAXvlt1dI+lxEnAtsBzyby4+XdFlE7AycCgwBLpY0Nu9jJDABWBW4EThE0tyIWB+YBKwFCBgt6ZlS52ZmZotXvOaTk8m7gTcDI4EtIuJDwJbADpJG5p/LImIIMBHYFdgE2Coidsm7mgQcLmkE0AUclMvHA+MlbQzcTtN03Wa27Js7d25H7MN6VkfNZwZwtKQXACLiHmD9/DMxItYFLgOOB7YG7pP0YF53ErBnREwFhki6Ne/zPOD4iJgA7ADsVim/ATimGkBEDAWGNsU1vIXnaGY1WmGFFTjllFP6tY+jjz66RdFYd4onH0l3N15HxOtJzW/bAzsChwL/AS4HPgE8Q0pWDTNISWKdHsrXAGZJmttU3uwo4Lj+n42ZWc/mvTiHAQMH1bZ9J6utt1tEvBG4AvisJAEfqiw7E9gPuASYX9msC5hHai7sSzm5vNk4Uq2oajgwZUnPw8ysJwMGDuKPV312qbff4t0ntzCazlJXh4NtgUuBoyT9OCLeBIyQdGlepQuYAzwCrF3ZdBgwvZfyx4HVImKgpBfzOtObjy9pJjCzKaZWnJqZWUeZ8+I8Bg3s3+39VuyjWfHkExHrAT8H9pZ0bS7uAsZFxLWkprYxwPnAbWmTeB3wILAPMFHStIiYHRHbSroZ2BeYLGlOREwB9gYuJNWeJpc8PzOzTjJo4AAOuvJP/drH2e97S4uiWaiOms9ngJWAUyu1je8BJwE3A4OASyVdBBAR+5NqSSsBV5Ka4gBGA2dHxKrAn4AzcvmhwPkRMRZ4GPhIm8/HzMyWUB0dDo4Ejuxh8fhu1r8G2Lyb8rtIveGay6eROi+YmVmH8ggHZmZWnJOPmZkV5+RjZmbFOfmYmVlxTj5mZlack4+ZmRXn5GNmZsU5+ZiZWXFOPmZmVpyTj5mZFefkY2ZmxTn5mJlZcU4+ZmZWnJOPmZkV5+RjZmbFOfmYmVlxTj5mZlZcHdNot11E7AOMJU3JPU7SWTWHZGZmFctczSci1gW+BmwHjATGRMQb6o3KzMyqlsWaz87AtZKeAoiIS4A9gBMaK0TEUGBo03YbADz22GOLPcCc557qV4CPPPJIv7YH+OczL/Q8We7UAAAXf0lEQVRr+8EtiAHghX8/36/tW/FZ/OuF/n0WrYgB4JkO+L2Y9ezs2mMA+OfT/1rqbYe0KIann366X9u36rN44snnao9h9lNP9Gv73uKofGcOXJJ9ds2fP78fIXWeiPgC8ApJY/P7A4GtJY2prPMV4Lh6IjQzWyZtL+mmvq68LNZ8BgDVjNoFzGtaZxxwXlPZYGAj4D7gxaU89nBgCrA90JpLlpdvHJ0QQ6fE0QkxdEocnRBDp8TRCTG0Io6BwNrAH5Zko2Ux+TxC+hAbhgHTqytImgnM7Gbbv/XnwBGxIAZJD/VnXy/3ODohhk6JoxNi6JQ4OiGGTomjE2JoYRz3L+kGy2Ly+S3wlYhYE3gW2B0Y0/smZmZW0jLX203So8CXgOuAO4ELJf2+3qjMzKxqWaz5IOlC4MK64zAzs+4tczWfms0Ejqf7+0nLWxydEEOnxNEJMXRKHJ0QQ6fE0Qkx1BbHMtfV2szMOp9rPmZmVpyTj5mZFefkY2ZmxS2Tvd3MOkFEvBlYhTTKxkBgQ0kT643KrDM4+bRBRKwqaVbdcdQhIraStETDbLQpjsHAZ4EADgeOAr4hqX+jkPb9+GcDOwKrA/eQRli/GSiefCJiVWA1UhIEQNLDhY795d6WSzqht+UtjmUoMJr0f1L9LIrFkON4Yzcx3FgyhhzHm4BXVctKxuHk0wIRMYo0pM9XSeMbrRkRn5F0XqHjnyPpE/n1xySdX1l2k6TtSsSRfSsi1gB+CPxI0uKHCW+Ps4AngLcAc4HXkb74P1ro+DsDI4AzgTOAlYFTCx17gYj4IvB54MlK8XzSOIYlNL5gtyaNIfZT0v/Hh4CHCsXQ8FPgP8BfWXT8x2Ii4izgA8ADlRjmAzsVjuMiYAvg0Upx0TicfFrjOOBA4MPA74HDgBt46eCl7fLmyusjgfMr719RKAYAJL0jIjYA9gWuioiHSZ/DLyTNKRjKFpLeEhG7SHouIj4G/KXg8adLmhMR9wCbSfpxRKxW8PgNnwBeK6l/Y+ovJUnHA0TEzcDbJD2X348jjUJS0jBJ7yp8zGbvBkJS/+Yi6b+RwCaSlnYQ5X5zh4MWkXQX8H7gl5KeIc2iWkpXD6+hhis8SdNINZ8LgU2BI4C/RsSHCoYxPze9Nc5/Dcp+Fo/m6T1uAQ6OiA8DKxY8fsPDQP8mGmqNNVn08x9Eanoq6Y6I2KzwMZs9wEv/RutwG6k1oDau+bTGPyPiTGBL4KMRcQrpj76U+T28Li4iPgHsRxpi/XxgO0mPRMQ6wB3AZYVCGUcaZHZYvsr+X+ArhY4Nqcbxfkl/iIifAR8BPlnw+A33ATdFxHXAgpnmSt/nAM4Gbo+IK0kXvaOA0wvHsCkpAf2T9Fl0AfMllWqChHQhMDUibmHR/4+PF4wB4Brg7oiYTmoGLf5ZOPm0xkdIbdinS3o2Ih6g7GR1gyNiPdIfdeN14+pqcME4AN4OHCfp+mqhpOkRcWipICT9KCL+CLyD1NPs/ZLa3uwWEcPyfa5XAbdExPrAL/JPHR5lYbt+bVfckk6OiGtJnTDmA3vl1oKSSta8e/Lr/FO3L5Lu70yrKwAPr9MPEbFfb8sl/bBQHA/RS41H0oYl4mjohN48uSfPlyR9OCI2Ab4PHCRJbT7u5ZJGRcSDpP+T6hd+6avsRkyvAF5LutE+RNKzNcSwAvAeXvp70fa/kYgYJenynv5eS/2dVuJZnXQvttoF/9rCMUwBdpBUWwJwzad/3pH/fS2p/fRKUhX2vcDdpPsebSfpNT0ti4glmle9vyLiO8AHqbk3D6mZ5ysAku6JiK8C5wBt7fknaVT+t2jC70lE7AT8gPQl9zbSvbd9JF1VOJQLgQ1I3c6rvxcl/ka2Ai5n4d9rVakYAIiIrwCfJt3z+hewLnA78NZSMWR/A26NiKuBBY8flGyOdfLpB0kHAOT29M0k/Su/fxXw81JxRMQQ0n2WJyVdUinfBTiZ1NZdynvojN48r5C0oHlD0tUR8a12HzQien2Op4a2/ZNICXeypMciYgfgIqB08tmM1Luqjg4wx+V/D2helv92StofWI90v+tEYGOgWHN0xcMsvC9dS3Osk09rrMOiPYqeJd1wL+V80lXlahGxFnAJcC7pS+ebBeOAzunN83hEHAJMyu8/DPyzwHFvKHCMJTEgJx0AJE2tTJtc0j2kKe1n1HFwgIj4AOkLvzrqxBBgrYJhTJc0KyL+Cmwu6WcRcVLB4wMLu8DXycmnNa4Ars69mrqAvYCLCx5/K+D1pPb0K0hP9v8GeF0Nz3d0Sm+eA4DxpJrfHFJSOLDdB216wPclbfvtPn43HskPQc/PT/gfRtmemA0rA8pfutXfi5LNsacBBwFHA18DdqPwc3DAfyJiX+CPwKdyb7OVC8dARMzjpfeJp0tar1QMTj4tIOn/ImJ3Fvbk+bakXxYMYaakuaSr/fWAQyX9rODxqzqiN08ePmZUXcfvoLb9g0lNPOuRaqXXAGMKxwDw9RqO2WympOsiYltgNUnHRMTUwjF8AvhI7o05itQRZmzhGJC04BnPiBhESsRvKxmDk0/r3As8Tm5yiogdCvbwql7B/LPGxIOk8zvhij8i3kNqYmnuXVWqt9n+dEDbvqTHSY8C1ErSDd0NtErZZsrnI2IEqQlwx9z1u+ijCPmRgx9FxP8CNwJfk/TvkjF0E9Mc4KcR8aWSx3XyaYHKeE33V4pL9vCqPuczsOk5n2KDSEJHXfGfCfwf9Y3jVWvbfqWrd7dKd/nukIFWx5IuBPYljXd3MKkHZDER8VHg28BNpAT83Yg4SNKVheOodjvvAt5Iap4uxsmnNeoer2kV0hVkI+FUa1wlB5GEDrniB/4l6fIajttQd9v+jqTfhy+TmtvOIz0GMJp67j3VPtCqpBuAG3LNfGdSZ4zStY6xpHEHHwXI4yD+ivSYRknVbufzSReKe5cMwMmnNWrt4dXbcz416IjePMCUiDiVdP+peoO7VFNotW3/AxRu28/j6xERmzV19jglj/xQWu0DrUbE5qRneoaT/l7viYj9JN3f+5Yt9TSVHn+SpkVEkWk+qrrrdl6ak09r1NrDKz+70aPCowvUfcXfsHX+tzrid8mm0K81/sAlHV3omN3pioidGk/Q52e/5tYQR2Og1d+Spt2A8gOtTiSNenE5QB7o9jzSdCil/AG4MiLOJf0/7AXMaDSDtXu0hW5G4FiEx3Z7+am7h1dvffZLjy5Q6xV/g6TunmYvadOIWCWPcF6nA4Hz88CukMby2reGODphoNWualOspMtiMZPdtcEQUs3nvfn9c/nnHZQZbeGg/O+ObT7OYnlstxapc7ymiDhU0vj8elNJf60sO13SkSXi6CQRsQ3wBRbtXbVBqSbKiLiN9OyVgAX3Ags/11KN59WkseWKTq+QB1btUeHOMN8kNXudTap1fBjYltT5oGgsTXENKX2/uJtx7uaTfk/vrX5/tJNrPi3QAT28DiQ9UAnpyuktlWUlmxSIiKNIN7kXac+XVHSMOVITy8mkDhBnkKZU+FPB43+u4LF6lG9oTwBeA2yfuxd/XNJDhUK4gYXNO437oo0BV0t3htkr/9toDm/E04ix7bH0MMrCyqT5jkraldQk3RgGbBRp9PNVIuJCSae1OwAnn9bYn3p7ePU2mVzpjhBHASPruoqs+K+kcyPiNcC/SWPflZzJdA9Jn6oWRMT5lB9+5/ukJPxN0vBCF5EuUHq9T9gqjQFWGyNLlzhmd/IDnTtLuj/f6/kE6WLkqyo7w24njLIAaaijt0iaCQsuoH9FetD0jznOtvJMpq0xXdIs0jMlm0u6gpSM6tDcjlq6XfUeyoyhtjizc1OogG2Upgtue+0rIibk2sXHIuLays8UFu38UMoajRGsJc2XdDawag1xlB5jcIGI+Axpfq0VI81kOol0xb860PbBZpvMlHQdcCt5lAXKj/gOqab1dOX988DqeaSUIt8Zrvm0Rt09vDrpxt3pwF8i4lYqvapqGNvtFNL4ev8L/D4iRpOaQtvtRFIT1+ks2hFkLikxl/Z8RAwn/45ExHbAf2uI4/484vdtLHoPrMR0BvsCb5P0XER8gzTV/YSI6AKmkprMS6l9lIXsUuDaiPgJqRKyO/DzfC+oyOCvTj6tUXcPrzdGmj0VYN3K6y7Kjq4N8A3SlWVtMyRmzwPvljQ/IrYkPeDY9pkz872Uh4DNI2JtSTMiYnvSE/0l7zk1/B9pLpvXRsSdpKv9PWuI40nS7+M2lbJSc+nMl/Rcfv0O8v3R/LtR4PCL+BLpAuWjwDHUMMoCgKQv5KbIdwEvAt+SdGXuqLNPiRjc220ZkG8q96jxwGGhWG6TVHoone7iuFvSG2s8/ndJV7SnkEYYvwpYUdJHC8exJjCTlHwHknozFX+osRLPq0qPKhARt5NGNFiF9ED4BvmiYAPgcklvKhDDuSza8WI+6Uv/AWC8pP+0O4YcR8c8E+iaTz9E98OSL1Cqh1fJ5NIHN0XEKcBkFp0hseg02tTbzAPpIdctSfcazpH0lYj4Q6FjV91ASj5XkL5oa0k8eXSBi4GV89X1jcBekkrUBr8B3En6vpuQE89epJG2S81rc303ZV2kSfZ+QpqEsYSOeSbQyacfGsOSR8TmktrepPMy8Zamf6HgL3VErJvHzaqzmQdSLWMAqUvrIRGxMjX0apL0htzjbxfghHy/4TpJpcfbOxP4EHBhHtn5k8D3WDgSRdtIuiSPPrKGpD/n4meAAyVd3+7j5xjO72lZRNxdIoYcR90PXy/g5NMaPwY2qTuIDvFjSd+v8fi/InUhPSAijpZ0Sk1x/JB04/ZmSbdFmjem+OcSEQOANUiJbwDpWbTSz5QArCzpnlg4o+rVEfHtUgeXNB2YXnlfeiDPl4iIYaSu1k8vbt02HHuR57+ACyn7/JeTT4tMzcN0NDfxlG5q6gSfooYv2Yrqc02jSfdcipN0akSMkzQvF+0g6V81hPJv0vAtZwFja6yhP5Wb3hq97kaz6NTzy6PXkWp+dQx3VOvzX+Dk0yqrk3rRNA9TXstQKjX7R+4+2pyITyh0/Oo9uNpGGo+I60hTV1fL6hheZ0/S7+F7gXfn542ul3R14Tg+CZxP6pk5E7iPdHGw3JJ0E2lenzqsIemqiPimpPnA2RFxWMkAnHxaoJPaUTvArZXXtX35Z3V25fxK5fUg0r2f4jNW5gdMr4qIoaR7Ll8EjgBeWTiUpyVtFxGvAAbmaTe2Bf5WOA5Lan/+y12tW6AT2k87Sf6CeS1pxIchkp4teOz/ksaogjTGXuN1F+l5j6IzeFbV0Q09P1S5E2msvV+Ter3dIKnoF02kOWs+L+nUStmfJL2ll82sTfKzbxNIf6f3k5//knRbqRhc82mN2ttPO0VE7AT8gNTb623AXyNin8YQLwWMKHScXjWN5twFbAq8unAMo0j3VfYF3kB6GHo74LqScWQPANvmL70DcvKru2a8XIqIjUkXZVuTBsDdiXRR8ufetms1J5/WqL39tIOcRPqCmyzpsfxQ20WkhyzbroOeeaoOIDqPNNp5sd5deTyzvYGPkSZtmwQcSRpp4WTSALAlPSdp94j4KvC7PLhnHZPaLdci4gjgM6TP/nogSM9f7Ui6aCzW+cEDi7ZG7e2nHWSApMcabyRNrTOYukjaMI/oPIJ0n+U5CiYf0pfI2/Pnvw95PDNSb8RSDzRWdQFIOpb0cOcNpJGVrayDSY+FbEOaYuI9ks4gdUzZvGQgTj6t8WnS+Fmvz+NnXUi6ylwePZKbe+ZHxNCI+BJQ9/QKxUXEhhFxEvAPUhPsdaR7gqU0j2f2a0jjmRWMoWpi44WkS0jPtyyXFyY1myPpWUmPA/crz7SbR30vWhN1s1sLSLo9IraiQ8bPqtnBpBGd1yPdyLwWGFNrRAXl5qSDgS2Ay0g1kLMLdjVvmJt7uK1CmsrhqhzfBtTT3LXIvThJd0bEYz2tbG0zr/L6xdqiwMmnJSJiI9IXzhrk5oX8TEfpaQRqlYdMeUzSRyLi96Qn6UdSz2RZdbmUNFbX2yT9HRaMAVhaJ4xnRkRMIM0QumVEVAd6HUTTbLdWxOvzc3jNr7tID70W4+TTGpcCvwWm0Flz6xQTEV8A3snCGVxXJN3E/ADwBVJPq+XBZsABpAFWHyJ1tij+d9YJ45llnTa/0fJuVN0BNPg5nxaIiDsljaw7jjpFxD3AVo025Ii4Q9KbI2IFQJJeW2+EZeXzHkWaYn0X0sXJWZ0wplhJETEs93pcv7vlqn+6dauJaz6tcUtu6/9FZSyv5c2LjcSTnQggaW5EFB84sW55OuKfk2aHXBPYj9QNfblKPqQHGUexaNfzhvmkJjlbDrnm0w+V+XwaD8stMllUqfl8OkEetfmtkp5uKl+NNIS/n2RfjkXEWrmHlRngmk+/NObz6U5ErFgylg5wAfDDiPiYpFkAEbEKqYvtpFojs05wfR5QtDGpnee/Ws75OZ8WiIjfNb0fANxeUzh1+QbwBDA9In4fEbeR5rP5Z3U8L1s+SXoD6WHXp0iT2t0TEeNrDstq5JpPP+Ruijvm19V7PXOBX9YRU13yQ2pjIuJ4Fs5Oebukf9QYlnWIDprUzjqE7/m0QEScLml5HdHAbLEi4j8snNTuV252MyefFomIfUgjB38d2EPSD2sOyaxjRMS7SaMnb0d6yr6uSe2sQzj5tECeM2U4aUiVbUhdbP8k6ehaAzPrME2T2g2TVHpSO+sQ7nDQGu8hjeE1W9J/gHeRHiw0M9IFWh5y6TbSkEuHke4B2XLKyac1Gp0NGtXIFVl0AD+z5VbTpHafJ82e2Wh+s+WUk09r/IQ0IdPqEXEUcCNpWgWz5Vqe1O440pQjjUntfk6atvnkGkOzmrmrdT9Uxqu6CJhJGtlge9KDlb+qKy6zDrIvaYTv5/K90V9KmhARXXg+n+Wak0//3MCiw+s0vA84gzS3j9nyrHlSu/GQJrWLiPqisto5+fRDniZ5gTyczCmkDggH1RKUWWfptEntrEP4nk+LRMQ7gca8KW/y8wtmwMJJ7W5l0UntrgG+VWtkVis/59NPEfEK4FRybcdJx2xREbEOlUntIuJ9wHOFJ7WzDuPk0w+5tnM2cDVwdNN8NmZm1gMnn37Ig4nOAaaz6PTZjfl8PFGWmVk33OGgfzZc/CpmZtbMNR8zMyvOvd3MzKw4Jx8zMyvO93zMWiQiBgJHkqaLXgEYTBpm6cuS/tviY20FfELSId0s2xL4vKQ9WnlMs1Zyzcesdb4LvA14p6SRwFZAABPacKw3kuaQeglJtzvxWKdzhwOzFoiI1wB3A2tLmlUpHwZsC/yWNIX0SFK3/MnAFyXNjYj5wJqS/pW3mQ+sCWwKfA14IL8eBBwMPAzcDKwG/Aw4HzgdeJY0jM1ngVMkbRoRg4FvAm8njTV4B3CEpFkR8UngEOAFYDZwsCQP9mlFuOZj1hpbAHdXEw+ApMckXUoaaPZJ4E3AlsDmwGf6sN+3khLJm4Fzga9L+gfwZWCKpAPyepsCH5G0GVBt4vs8aQy1LSRtTnom7Ru5iXAc8F5JWwE/IM2xY1aEk49Za8yj97+nXYDvSJqf7/98j77NdjtN0p359Z9I8+B05x+SpnVTPgrYFbgjIu4EdgPeIOlF4KfALRHxHdKUIOf0IR6zlnDyMWuN24BNIuKV1cKIWDciriA1eVXbuAeQmtEauvL6g5v2+3zldXfTdzT0NLTTQOBISSPzfaitgT0AJH0U+ADwd1IN6aIe9mHWck4+Zi0gaTpwATAxIlYFyP+OJzW3/Ro4PCK6ImJFYAxpTECAJ0hNcZB6yvXFXBZNXj35TT7u4IgYQBqL8KSIWCMi/gE8KWkcMJbUQcKsCCcfs9Y5lDQ75y25ieu2/P5A4AhgLeAv+UekzgTkZWdFxJ+ATYAZfTjWrcBGEfGzxaz3VeAhUkeDqaSa09G5c8OJwDUR8UfS1Aeeg8qKcW83MzMrzjUfMzMrzsnHzMyKc/IxM7PinHzMzKw4Jx8zMyvOycfMzIpz8jEzs+L+P3ZH7+WnQvySAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group by the Country\n",
    "countries = online_rt.groupby('Country').sum()\n",
    "\n",
    "# sort the value and get the first 10 after UK\n",
    "countries = countries.sort_values(by = 'Quantity',ascending = False)[1:11]\n",
    "\n",
    "# create the plot\n",
    "countries['Quantity'].plot(kind='bar')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('Quantity')\n",
    "plt.title('10 Countries with most orders')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Exclude negative Quantity entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online_rt = online_rt[online_rt.Quantity > 0]\n",
    "online_rt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries (except UK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28c5e47e400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# groupby CustomerID\n",
    "customers = online_rt.groupby(['CustomerID','Country']).sum()\n",
    "\n",
    "# there is an outlier with negative price\n",
    "customers = customers[customers.UnitPrice > 0]\n",
    "\n",
    "# get the value of the index and put in the column Country\n",
    "customers['Country'] = customers.index.get_level_values(1)\n",
    "\n",
    "# top three countries\n",
    "top_countries =  ['Netherlands', 'EIRE', 'Germany']\n",
    "\n",
    "# filter the dataframe to just select ones in the top_countries\n",
    "customers = customers[customers['Country'].isin(top_countries)]\n",
    "\n",
    "#################\n",
    "# Graph Section #\n",
    "#################\n",
    "\n",
    "# creates the FaceGrid\n",
    "g = sns.FacetGrid(customers, col=\"Country\")\n",
    "\n",
    "# map over a make a scatterplot\n",
    "g.map(plt.scatter, \"Quantity\", \"UnitPrice\", alpha=1)\n",
    "\n",
    "# adds legend\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Investigate why the previous results look so uninformative.\n",
    "\n",
    "This section might seem a bit tedious to go through. But I've thought of it as some kind of a simulation of problems one might encounter when dealing with data and other people. Besides there is a prize at the end (i.e. Section 8).\n",
    "\n",
    "(But feel free to jump right ahead into Section 8 if you want; it doesn't require that you finish this section.)\n",
    "\n",
    "#### Step 7.1 Look at the first line of code in Step 6. And try to figure out if it leads to any kind of problem.\n",
    "##### Step 7.1.1 Display the first few rows of that DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>74215</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <th>Iceland</th>\n",
       "      <td>2458</td>\n",
       "      <td>481.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <th>Finland</th>\n",
       "      <td>2341</td>\n",
       "      <td>178.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <th>Italy</th>\n",
       "      <td>631</td>\n",
       "      <td>605.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <th>Norway</th>\n",
       "      <td>197</td>\n",
       "      <td>65.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Quantity  UnitPrice\n",
       "CustomerID Country                            \n",
       "12346.0    United Kingdom     74215       1.04\n",
       "12347.0    Iceland             2458     481.21\n",
       "12348.0    Finland             2341     178.71\n",
       "12349.0    Italy                631     605.10\n",
       "12350.0    Norway               197      65.30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This takes our initial dataframe groups it primarily by 'CustomerID' and secondarily by 'Country'.\n",
    "#It sums all the (non-indexical) columns that have numerical values under each group.\n",
    "customers = online_rt.groupby(['CustomerID','Country']).sum().head()\n",
    "\n",
    "#Here's what it looks like:\n",
    "customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.2 Think about what that piece of code does and display the dtype of `UnitPrice`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.UnitPrice.dtype\n",
    "#So it's 'float64'\n",
    "#But why did we sum 'UnitPrice', to begin with?\n",
    "#If 'UnitPrice' wasn't something that we were interested in then it would be OK\n",
    "#since we wouldn't care whether UnitPrice was being summed or not.\n",
    "#But we want our graphs to reflect 'UnitPrice'!\n",
    "#Note that summing up 'UnitPrice' can be highly misleading.\n",
    "#It doesn't tell us much as to what the customer is doing.\n",
    "#Suppose, a customer places one order of 1000 items that are worth $1 each.\n",
    "#Another customer places a thousand orders of 1 item worth $1.\n",
    "#There isn't much of a difference between what the former and the latter customers did.\n",
    "#After all, they've spent the same amount of money.\n",
    "#so we should be careful when we're summing columns. Sometimes we intend to sum just one column\n",
    "#('Quantity' in this case) and another column like UnitPrice gets ito the mix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.3 Pull data from `online_rt`for `CustomerID`s 12346.0 and 12347.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
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     "outputs_hidden": false
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428966</th>\n",
       "      <td>573511</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>6</td>\n",
       "      <td>10/31/11 12:25</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286637</th>\n",
       "      <td>562032</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>8/2/11 8:48</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72267</th>\n",
       "      <td>542237</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>1/26/11 14:30</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148300</th>\n",
       "      <td>549222</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>4/7/11 10:43</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428967</th>\n",
       "      <td>573511</td>\n",
       "      <td>23173</td>\n",
       "      <td>REGENCY TEAPOT ROSES</td>\n",
       "      <td>2</td>\n",
       "      <td>10/31/11 12:25</td>\n",
       "      <td>9.95</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode               Description  Quantity  \\\n",
       "428966    573511     22423  REGENCY CAKESTAND 3 TIER         6   \n",
       "286637    562032     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "72267     542237     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "148300    549222     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "428967    573511     23173     REGENCY TEAPOT ROSES          2   \n",
       "\n",
       "           InvoiceDate  UnitPrice  CustomerID  Country  \n",
       "428966  10/31/11 12:25      12.75     12347.0  Iceland  \n",
       "286637     8/2/11 8:48      12.75     12347.0  Iceland  \n",
       "72267    1/26/11 14:30      12.75     12347.0  Iceland  \n",
       "148300    4/7/11 10:43      12.75     12347.0  Iceland  \n",
       "428967  10/31/11 12:25       9.95     12347.0  Iceland  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61619</th>\n",
       "      <td>541431</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>74215</td>\n",
       "      <td>1/18/11 10:01</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo StockCode                     Description  Quantity  \\\n",
       "61619    541431     23166  MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "\n",
       "         InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "61619  1/18/11 10:01       1.04     12346.0  United Kingdom  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(online_rt[online_rt.CustomerID == 12347.0].\n",
    "        sort_values(by='UnitPrice', ascending = False).head())\n",
    "display(online_rt[online_rt.CustomerID == 12346.0].\n",
    "        sort_values(by='UnitPrice', ascending = False).head())\n",
    "#The result is exactly what we'd suspected. Customer 12346.0 placed\n",
    "#one giant order, whereas 12347.0 placed a lot of smaller orders.\n",
    "#So we've identified one potential reason why our plots looked so weird at section 6.\n",
    "#At this stage we need to go back to the initial problem we've specified at section 6.\n",
    "#And make it more precise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.2 Reinterpreting the initial problem.\n",
    "\n",
    "To reiterate the question that we were dealing with:  \n",
    "\"Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries\"\n",
    "\n",
    "The question is open to a set of different interpretations.\n",
    "We need to disambiguate.\n",
    "\n",
    "We could do a single plot by looking at all the data from the top 3 countries.\n",
    "Or we could do one plot per country. To keep things consistent with the rest of the exercise,\n",
    "let's stick to the latter oprion. So that's settled.\n",
    "\n",
    "But \"top 3 countries\" with respect to what? Two answers suggest themselves:\n",
    "Total sales volume (i.e. total quantity sold) or total sales (i.e. revenue).\n",
    "This exercise goes for sales volume, so let's stick to that.\n",
    "\n",
    "##### Step 7.2.1 Find out the top 3 countries in terms of sales volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Netherlands', 'EIRE', 'Germany'], dtype='object', name='Country')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_volume = online_rt.groupby('Country').Quantity.sum().sort_values(ascending=False)\n",
    "\n",
    "top3 = sales_volume.index[1:4] #We are excluding UK\n",
    "top3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.2.2 \n",
    "\n",
    "Now that we have the top 3 countries, we can focus on the rest of the problem:  \n",
    "\"Quantity per UnitPrice by CustomerID\".  \n",
    "We need to unpack that.\n",
    "\n",
    "\"by CustomerID\" part is easy. That means we're going to be plotting one dot per CustomerID's on our plot. In other words, we're going to be grouping by CustomerID.\n",
    "\n",
    "\"Quantity per UnitPrice\" is trickier. Here's what we know:  \n",
    "*One axis will represent a Quantity assigned to a given customer. This is easy; we can just plot the total  Quantity for each customer.  \n",
    "*The other axis will represent a UnitPrice assigned to a given customer. Remember a single customer can have any number of orders with different prices, so summing up prices isn't quite helpful. Besides it's not quite clear what we mean when we say \"unit price per customer\"; it sounds like price of the customer! A reasonable alternative is that we assign each customer the average amount each has paid per item. So let's settle that question in that manner.\n",
    "\n",
    "#### Step 7.3 Modify, select and plot data\n",
    "##### Step 7.3.1 Add a column to online_rt called `Revenue` calculate the revenue (Quantity * UnitPrice) from each sale.\n",
    "We will use this later to figure out an average price per customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  Revenue  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom    15.30  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom    22.00  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online_rt['Revenue'] = online_rt.Quantity * online_rt.UnitPrice\n",
    "online_rt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.2 Group by `CustomerID` and `Country` and find out the average price (`AvgPrice`) each customer spends per unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>AvgPrice</th>\n",
       "      <th>Country</th>\n",
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       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12426.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>258</td>\n",
       "      <td>582.73</td>\n",
       "      <td>2.258643</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12427.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>533</td>\n",
       "      <td>825.80</td>\n",
       "      <td>1.549343</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12468.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>366</td>\n",
       "      <td>729.54</td>\n",
       "      <td>1.993279</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12471.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>8212</td>\n",
       "      <td>19824.05</td>\n",
       "      <td>2.414034</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12472.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>4148</td>\n",
       "      <td>6572.11</td>\n",
       "      <td>1.584405</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Quantity   Revenue  AvgPrice  Country\n",
       "CustomerID Country                                       \n",
       "12426.0    Germany       258    582.73  2.258643  Germany\n",
       "12427.0    Germany       533    825.80  1.549343  Germany\n",
       "12468.0    Germany       366    729.54  1.993279  Germany\n",
       "12471.0    Germany      8212  19824.05  2.414034  Germany\n",
       "12472.0    Germany      4148   6572.11  1.584405  Germany"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = online_rt[online_rt.Country.isin(top3)].groupby(['CustomerID','Country'])\n",
    "\n",
    "plottable = grouped['Quantity','Revenue'].agg('sum')\n",
    "plottable['AvgPrice'] = plottable.Revenue / plottable.Quantity\n",
    "\n",
    "# get the value of the index and put in the column Country\n",
    "plottable['Country'] = plottable.index.get_level_values(1)\n",
    "plottable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.3 Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################\n",
    "# Graph Section v 2#\n",
    "####################\n",
    "\n",
    "# creates the FaceGrid\n",
    "g = sns.FacetGrid(plottable, col=\"Country\")\n",
    "\n",
    "# map over a make a scatterplot\n",
    "g.map(plt.scatter, \"Quantity\", \"AvgPrice\", alpha=1)\n",
    "\n",
    "# adds legend\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.4 What to do now?\n",
    "We aren't much better-off than what we started with. The data are still extremely scattered around and don't seem quite informative.\n",
    "\n",
    "But we shouldn't despair!\n",
    "There are two things to realize:\n",
    "1) The data seem to be skewed towaards the axes (e.g. we don't have any values where Quantity = 50000 and AvgPrice = 5). So that might suggest a trend.\n",
    "2) We have more data! We've only been looking at the data from 3 different countries and they are plotted on different graphs.\n",
    "\n",
    "So: we should plot the data regardless of `Country` and hopefully see a less scattered graph.\n",
    "\n",
    "##### Step 7.4.1 Plot the data for each `CustomerID` on a single graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = online_rt.groupby(['CustomerID'])\n",
    "plottable = grouped['Quantity','Revenue'].agg('sum')\n",
    "plottable['AvgPrice'] = plottable.Revenue / plottable.Quantity\n",
    "\n",
    "# map over a make a scatterplot\n",
    "plt.scatter(plottable.Quantity, plottable.AvgPrice)\n",
    "plt.plot()\n",
    "\n",
    "\n",
    "#Turns out the graph is still extremely skewed towards the axes like an exponential decay function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.4.2 Zoom in so we can see that curve more clearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = online_rt.groupby(['CustomerID','Country'])\n",
    "plottable = grouped.agg({'Quantity': 'sum',\n",
    "                         'Revenue': 'sum'})\n",
    "plottable['AvgPrice'] = plottable.Revenue / plottable.Quantity\n",
    "\n",
    "# map over a make a scatterplot\n",
    "plt.scatter(plottable.Quantity, plottable.AvgPrice)\n",
    "\n",
    "#Zooming in. (I'm starting the axes from a negative value so that\n",
    "#the dots can be plotted in the graph completely.)\n",
    "plt.xlim(-40,2000) \n",
    "plt.ylim(-1,80)\n",
    "\n",
    "plt.plot()\n",
    "\n",
    "\n",
    "#And there is still that pattern, this time in close-up!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Plot a line chart showing revenue (y) per UnitPrice (x).\n",
    "\n",
    "Did Step 7 give us any insights about the data? Sure! As average price increases, the quantity ordered decreses.  But that's hardly surprising. It would be surprising if that wasn't the case!\n",
    "\n",
    "Nevertheless the rate of drop in quantity is so drastic, it makes me wonder how our revenue changes with respect to item price. It would not be that surprising if it didn't change that much. But it would be interesting to know whether most of our revenue comes from expensive or inexpensive items, and how that relation looks like.\n",
    "\n",
    "That is what we are going to do now.\n",
    "\n",
    "#### 8.1 Group `UnitPrice` by intervals of 1 for prices [0,50), and sum `Quantity` and `Revenue`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UnitPrice\n",
       "(0, 1]    1.107775e+06\n",
       "(1, 2]    2.691765e+06\n",
       "(2, 3]    2.024143e+06\n",
       "(3, 4]    8.651018e+05\n",
       "(4, 5]    1.219377e+06\n",
       "Name: Revenue, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#These are the values for the graph.\n",
    "#They are used both in selecting data from\n",
    "#the DataFrame and plotting the data so I've assigned\n",
    "#them to variables to increase consistency and make things easier\n",
    "#when playing with the variables.\n",
    "price_start = 0 \n",
    "price_end = 50\n",
    "price_interval = 1\n",
    "\n",
    "#Creating the buckets to collect the data accordingly\n",
    "buckets = np.arange(price_start,price_end,price_interval)\n",
    "\n",
    "#Select the data and sum\n",
    "revenue_per_price = online_rt.groupby(pd.cut(online_rt.UnitPrice, buckets)).Revenue.sum()\n",
    "revenue_per_price.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.3 Plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenue_per_price.plot()\n",
    "plt.xlabel('Unit Price (in intervals of '+str(price_interval)+')')\n",
    "plt.ylabel('Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.4 Make it look nicer.\n",
    "x-axis needs values.  \n",
    "y-axis isn't that easy to read; show in terms of millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenue_per_price.plot()\n",
    "\n",
    "#Place labels\n",
    "plt.xlabel('Unit Price (in buckets of '+str(price_interval)+')') \n",
    "plt.ylabel('Revenue')\n",
    "\n",
    "#Even though the data is bucketed in intervals of 1,\n",
    "#I'll plot ticks a little bit further apart from each other to avoid cluttering.\n",
    "plt.xticks(np.arange(price_start,price_end,3),\n",
    "           np.arange(price_start,price_end,3))\n",
    "plt.yticks([0, 500000, 1000000, 1500000, 2000000, 2500000],\n",
    "           ['0', '$0.5M', '$1M', '$1.5M', '$2M', '$2.5M'])\n",
    "plt.show()\n",
    "\n",
    "#Looks like a major chunk of our revenue comes from items worth $0-$3!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: 07_Visualization/Online_Retail/Online_Retail.csv
================================================
[File too large to display: 41.9 MB]

================================================
FILE: 07_Visualization/Online_Retail/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online Retails Purchase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Online_Retail/Online_Retail.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called online_rt\n",
    "Note: if you receive a utf-8 decode error, set `encoding = 'latin1'` in `pd.read_csv()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a histogram with the 10 countries that have the most 'Quantity' ordered except UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Exclude negative Quantity entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries (except UK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28c5e47e400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Investigate why the previous results look so uninformative.\n",
    "\n",
    "This section might seem a bit tedious to go through. But I've thought of it as some kind of a simulation of problems one might encounter when dealing with data and other people. Besides there is a prize at the end (i.e. Section 8).\n",
    "\n",
    "(But feel free to jump right ahead into Section 8 if you want; it doesn't require that you finish this section.)\n",
    "\n",
    "#### Step 7.1 Look at the first line of code in Step 6. And try to figure out if it leads to any kind of problem.\n",
    "##### Step 7.1.1 Display the first few rows of that DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>74215</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <th>Iceland</th>\n",
       "      <td>2458</td>\n",
       "      <td>481.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <th>Finland</th>\n",
       "      <td>2341</td>\n",
       "      <td>178.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <th>Italy</th>\n",
       "      <td>631</td>\n",
       "      <td>605.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <th>Norway</th>\n",
       "      <td>197</td>\n",
       "      <td>65.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Quantity  UnitPrice\n",
       "CustomerID Country                            \n",
       "12346.0    United Kingdom     74215       1.04\n",
       "12347.0    Iceland             2458     481.21\n",
       "12348.0    Finland             2341     178.71\n",
       "12349.0    Italy                631     605.10\n",
       "12350.0    Norway               197      65.30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.2 Think about what that piece of code does and display the dtype of `UnitPrice`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.1.3 Pull data from `online_rt`for `CustomerID`s 12346.0 and 12347.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428966</th>\n",
       "      <td>573511</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>6</td>\n",
       "      <td>10/31/11 12:25</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286637</th>\n",
       "      <td>562032</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>8/2/11 8:48</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72267</th>\n",
       "      <td>542237</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>1/26/11 14:30</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148300</th>\n",
       "      <td>549222</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>3</td>\n",
       "      <td>4/7/11 10:43</td>\n",
       "      <td>12.75</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428967</th>\n",
       "      <td>573511</td>\n",
       "      <td>23173</td>\n",
       "      <td>REGENCY TEAPOT ROSES</td>\n",
       "      <td>2</td>\n",
       "      <td>10/31/11 12:25</td>\n",
       "      <td>9.95</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>Iceland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode               Description  Quantity  \\\n",
       "428966    573511     22423  REGENCY CAKESTAND 3 TIER         6   \n",
       "286637    562032     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "72267     542237     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "148300    549222     22423  REGENCY CAKESTAND 3 TIER         3   \n",
       "428967    573511     23173     REGENCY TEAPOT ROSES          2   \n",
       "\n",
       "           InvoiceDate  UnitPrice  CustomerID  Country  \n",
       "428966  10/31/11 12:25      12.75     12347.0  Iceland  \n",
       "286637     8/2/11 8:48      12.75     12347.0  Iceland  \n",
       "72267    1/26/11 14:30      12.75     12347.0  Iceland  \n",
       "148300    4/7/11 10:43      12.75     12347.0  Iceland  \n",
       "428967  10/31/11 12:25       9.95     12347.0  Iceland  "
      ]
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       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
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       "      <th>61619</th>\n",
       "      <td>541431</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>74215</td>\n",
       "      <td>1/18/11 10:01</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "      InvoiceNo StockCode                     Description  Quantity  \\\n",
       "61619    541431     23166  MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "\n",
       "         InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "61619  1/18/11 10:01       1.04     12346.0  United Kingdom  "
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.2 Reinterpreting the initial problem.\n",
    "\n",
    "To reiterate the question that we were dealing with:  \n",
    "\"Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries\"\n",
    "\n",
    "The question is open to a set of different interpretations.\n",
    "We need to disambiguate.\n",
    "\n",
    "We could do a single plot by looking at all the data from the top 3 countries.\n",
    "Or we could do one plot per country. To keep things consistent with the rest of the exercise,\n",
    "let's stick to the latter oprion. So that's settled.\n",
    "\n",
    "But \"top 3 countries\" with respect to what? Two answers suggest themselves:\n",
    "Total sales volume (i.e. total quantity sold) or total sales (i.e. revenue).\n",
    "This exercise goes for sales volume, so let's stick to that.\n",
    "\n",
    "##### Step 7.2.1 Find out the top 3 countries in terms of sales volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Netherlands', 'EIRE', 'Germany'], dtype='object', name='Country')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.2.2 \n",
    "\n",
    "Now that we have the top 3 countries, we can focus on the rest of the problem:  \n",
    "\"Quantity per UnitPrice by CustomerID\".  \n",
    "We need to unpack that.\n",
    "\n",
    "\"by CustomerID\" part is easy. That means we're going to be plotting one dot per CustomerID's on our plot. In other words, we're going to be grouping by CustomerID.\n",
    "\n",
    "\"Quantity per UnitPrice\" is trickier. Here's what we know:  \n",
    "*One axis will represent a Quantity assigned to a given customer. This is easy; we can just plot the total  Quantity for each customer.  \n",
    "*The other axis will represent a UnitPrice assigned to a given customer. Remember a single customer can have any number of orders with different prices, so summing up prices isn't quite helpful. Besides it's not quite clear what we mean when we say \"unit price per customer\"; it sounds like price of the customer! A reasonable alternative is that we assign each customer the average amount each has paid per item. So let's settle that question in that manner.\n",
    "\n",
    "#### Step 7.3 Modify, select and plot data\n",
    "##### Step 7.3.1 Add a column to online_rt called `Revenue` calculate the revenue (Quantity * UnitPrice) from each sale.\n",
    "We will use this later to figure out an average price per customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/10 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "    InvoiceDate  UnitPrice  CustomerID         Country  Revenue  \n",
       "0  12/1/10 8:26       2.55     17850.0  United Kingdom    15.30  \n",
       "1  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  \n",
       "2  12/1/10 8:26       2.75     17850.0  United Kingdom    22.00  \n",
       "3  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  \n",
       "4  12/1/10 8:26       3.39     17850.0  United Kingdom    20.34  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.2 Group by `CustomerID` and `Country` and find out the average price (`AvgPrice`) each customer spends per unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>AvgPrice</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12426.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>258</td>\n",
       "      <td>582.73</td>\n",
       "      <td>2.258643</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12427.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>533</td>\n",
       "      <td>825.80</td>\n",
       "      <td>1.549343</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12468.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>366</td>\n",
       "      <td>729.54</td>\n",
       "      <td>1.993279</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12471.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>8212</td>\n",
       "      <td>19824.05</td>\n",
       "      <td>2.414034</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12472.0</th>\n",
       "      <th>Germany</th>\n",
       "      <td>4148</td>\n",
       "      <td>6572.11</td>\n",
       "      <td>1.584405</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                    Quantity   Revenue  AvgPrice  Country\n",
       "CustomerID Country                                       \n",
       "12426.0    Germany       258    582.73  2.258643  Germany\n",
       "12427.0    Germany       533    825.80  1.549343  Germany\n",
       "12468.0    Germany       366    729.54  1.993279  Germany\n",
       "12471.0    Germany      8212  19824.05  2.414034  Germany\n",
       "12472.0    Germany      4148   6572.11  1.584405  Germany"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.3.3 Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.8x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7.4 What to do now?\n",
    "We aren't much better-off than what we started with. The data are still extremely scattered around and don't seem quite informative.\n",
    "\n",
    "But we shouldn't despair!\n",
    "There are two things to realize:\n",
    "1) The data seem to be skewed towaards the axes (e.g. we don't have any values where Quantity = 50000 and AvgPrice = 5). So that might suggest a trend.\n",
    "2) We have more data! We've only been looking at the data from 3 different countries and they are plotted on different graphs.\n",
    "\n",
    "So: we should plot the data regardless of `Country` and hopefully see a less scattered graph.\n",
    "\n",
    "##### Step 7.4.1 Plot the data for each `CustomerID` on a single graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Step 7.4.2 Zoom in so we can see that curve more clearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Plot a line chart showing revenue (y) per UnitPrice (x).\n",
    "\n",
    "Did Step 7 give us any insights about the data? Sure! As average price increases, the quantity ordered decreses.  But that's hardly surprising. It would be surprising if that wasn't the case!\n",
    "\n",
    "Nevertheless the rate of drop in quantity is so drastic, it makes me wonder how our revenue changes with respect to item price. It would not be that surprising if it didn't change that much. But it would be interesting to know whether most of our revenue comes from expensive or inexpensive items, and how that relation looks like.\n",
    "\n",
    "That is what we are going to do now.\n",
    "\n",
    "#### 8.1 Group `UnitPrice` by intervals of 1 for prices [0,50), and sum `Quantity` and `Revenue`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UnitPrice\n",
       "(0, 1]    1.107775e+06\n",
       "(1, 2]    2.691765e+06\n",
       "(2, 3]    2.024143e+06\n",
       "(3, 4]    8.651018e+05\n",
       "(4, 5]    1.219377e+06\n",
       "Name: Revenue, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.3 Plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.4 Make it look nicer.\n",
    "x-axis needs values.  \n",
    "y-axis isn't that easy to read; show in terms of millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 07_Visualization/Scores/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create the data.\n",
    "\n",
    "***Exercise based on [Chris Albon](http://chrisalbon.com/) work, the credits belong to him.***\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame that should look like the one below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>female</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name  age  female  preTestScore  postTestScore\n",
       "0      Jason    Miller   42       0             4             25\n",
       "1      Molly  Jacobson   52       1            24             94\n",
       "2       Tina       Ali   36       1            31             57\n",
       "3       Jake    Milner   24       0             2             62\n",
       "4        Amy     Cooze   73       1             3             70"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age\n",
    "#### Hint: Don't forget to place the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a Scatterplot of preTestScore and postTestScore.\n",
    "### This time the size should be 4.5 times the postTestScore and the color determined by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 07_Visualization/Scores/Exercises_with_solutions_code.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create the data.\n",
    "\n",
    "***Exercise based on [Chris Albon](http://chrisalbon.com/) work, the credits belong to him.***\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame it should look like below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>female</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name  age  female  preTestScore  postTestScore\n",
       "0      Jason    Miller   42       0             4             25\n",
       "1      Molly  Jacobson   52       1            24             94\n",
       "2       Tina       Ali   36       1            31             57\n",
       "3       Jake    Milner   24       0             2             62\n",
       "4        Amy     Cooze   73       1             3             70"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], \n",
    "            'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], \n",
    "            'female': [0, 1, 1, 0, 1],\n",
    "            'age': [42, 52, 36, 24, 73], \n",
    "            'preTestScore': [4, 24, 31, 2, 3],\n",
    "            'postTestScore': [25, 94, 57, 62, 70]}\n",
    "\n",
    "df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'female', 'preTestScore', 'postTestScore'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age\n",
    "#### Hint: Don't forget to place the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114e89d10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mZpbLScLMzHI5SZiZWS4nCTMzy+UkYWZmuZwkzMwsl5OEmZnlcpIwM7NcThJmZpbLScLM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fcd090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.preTestScore, df.postTestScore, s=df.age)\n",
    "\n",
    "#set labels and titles\n",
    "plt.title(\"preTestScore x postTestScore\")\n",
    "plt.xlabel('preTestScore')\n",
    "plt.ylabel('preTestScore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a Scatterplot of preTestScore and postTestScore.\n",
    "### This time the size should be 4.5 times the postTestScore and the color determined by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11608c250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5YnKSMLOeKCJYs2YNlZWV9O3bt8f1Kl3wN9OZmVnnSWLgwJJcYCmajtokbtsq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\nyUnCzMwyOUmYmVmm/w+eYh5l4kpEgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11573c590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.preTestScore, df.postTestScore, s= df.postTestScore * 4.5, c = df.female)\n",
    "\n",
    "#set labels and titles\n",
    "plt.title(\"preTestScore x postTestScore\")\n",
    "plt.xlabel('preTestScore')\n",
    "plt.ylabel('preTestScore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 07_Visualization/Scores/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create the data.\n",
    "\n",
    "***Exercise based on [Chris Albon](http://chrisalbon.com/) work, the credits belong to him.***\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create the DataFrame it should look like below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>female</th>\n",
       "      <th>preTestScore</th>\n",
       "      <th>postTestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jason</td>\n",
       "      <td>Miller</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Molly</td>\n",
       "      <td>Jacobson</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tina</td>\n",
       "      <td>Ali</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jake</td>\n",
       "      <td>Milner</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amy</td>\n",
       "      <td>Cooze</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name  age  female  preTestScore  postTestScore\n",
       "0      Jason    Miller   42       0             4             25\n",
       "1      Molly  Jacobson   52       1            24             94\n",
       "2       Tina       Ali   36       1            31             57\n",
       "3       Jake    Milner   24       0             2             62\n",
       "4        Amy     Cooze   73       1             3             70"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age\n",
    "#### Hint: Don't forget to place the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114e89d10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sW7aS7JFTpd5Hv34HcddddxURlplZt3GS6IKmpibWrFkBrKowdwlbbLFFT4dk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fcd090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create a Scatterplot of preTestScore and postTestScore.\n",
    "### This time the size should be 4.5 times the postTestScore and the color determined by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11608c250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5YnKSMLOeKCJYs2YNlZWV9O3bt8f1Kl3wN9OZmVnnSWLgwJJcYCmajtokbtsq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\nyUnCzMwyOUmYmVmm/w+eYh5l4kpEgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11573c590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 07_Visualization/Tips/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html)  \n",
    "The dataset being used is tips from Seaborn.\n",
    "\n",
    "### Step 1. Import the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the Unnamed 0 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Plot the total_bill column histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatter plot presenting the relationship between total_bill and tip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Create one image with the relationship of total_bill, tip and size.\n",
    "#### Hint: It is just one function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Present the relationship between days and total_bill value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker\n",
    "### They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it using a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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================================================
FILE: 07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tips\n",
    "\n",
    "Check out [Tips Visualization Exercises Video Tutorial](https://youtu.be/oiuKFigW4YM) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html)  \n",
    "The dataset being used is tips from Seaborn.\n",
    "\n",
    "### Step 1. Import the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# print the graphs in the notebook\n",
    "% matplotlib inline\n",
    "\n",
    "# set seaborn style to white\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  total_bill   tip     sex smoker  day    time  size\n",
       "0           0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1           1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2           2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3           3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4           4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv'\n",
    "tips = pd.read_csv(url)\n",
    "\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the Unnamed 0 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del tips['Unnamed: 0']\n",
    "\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Plot the total_bill column histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3cWvGeLl+vXB7DgXGc889R0ZGBmPGyBRBb2Mfv0gIc3El7isgQIdOH9xv24yJ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\ngSGEEMIh/w9kO9eQWjBZ9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178171f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create histogram\n",
    "ttbill = sns.distplot(tips.total_bill);\n",
    "\n",
    "# set lables and titles\n",
    "ttbill.set(xlabel = 'Value', ylabel = 'Frequency', title = \"Total Bill\")\n",
    "\n",
    "# take out the right and upper borders\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatter plot presenting the relationship between total_bill and tip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1197d84d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9O3r37h3v5lAYvHtd1+T15FwIUQRc18HFL/4uqh7TDVf3YY+JtKv0vAWL1u1C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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11904aa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x =\"total_bill\", y =\"tip\", data = tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Create one image with the relationship of total_bill, tip and size.\n",
    "#### Hint: It is just one function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x11844c090>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WBr0Oc616XLYPwu4cQl3D8FblJoMOG67Px9ry+YjXabG2PAd25xAS9Tr09A3i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\niIhE+//yIo6VcM5ynAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1192acb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Present the relationship between days and total_bill value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uVZxFQ7rtdM1dVVV0lJTiE3fuawfk//0ZOku7h3W2FxRQ8MLLjPvDo/1Zpugn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\niYiIPAiDm4iIyIMwuImIiDzI/wPvE39ImLp8oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179414f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x = \"day\", y = \"total_bill\", data = tips, jitter = True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zsmz04ouuV21LPZWtNdhsNhRFcdpWWN7Ao6/toKHJCMDgMc3kqLYCUNdSz192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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179e40310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x = \"tip\", y = \"day\", hue = \"sex\", data = tips, jitter = True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9pfLycg0ZMkSbNm3S5MmTVVZWJknauHGjVq5cqa+++kr33HOPZsyYoTvuuEPz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\nmfnjav8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17906de90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"day\", y = \"total_bill\", hue = \"time\", data = tips);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11989e910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# better seaborn style\n",
    "sns.set(style = \"ticks\")\n",
    "\n",
    "# creates FacetGrid\n",
    "g = sns.FacetGrid(tips, col = \"time\")\n",
    "g.map(plt.hist, \"tip\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker\n",
    "### They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U8Otf/7pJxxFCiOYS7fNtW7J77rmHl156iX/961/0798fpRS9e/dG0zRSUlKCscrn85GT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\nQgghIkACrhBCCBEBEnCFEEKICPj/W9lawU2VX7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178d4b650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(tips, col = \"sex\", hue = \"smoker\")\n",
    "g.map(plt.scatter, \"total_bill\", \"tip\", alpha =.7)\n",
    "\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it using a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
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================================================
FILE: 07_Visualization/Tips/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html)  \n",
    "The dataset being used is tips from Seaborn.\n",
    "\n",
    "### Step 1. Import the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# print the graphs in the notebook\n",
    "% matplotlib inline\n",
    "\n",
    "# set seaborn style to white\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Tips/tips.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  total_bill   tip     sex smoker  day    time  size\n",
       "0           0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1           1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2           2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3           3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4           4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the Unnamed 0 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Plot the total_bill column histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3cWvGeLl+vXB7DgXGc889R0ZGBmPGyBRBb2Mfv0gIc3El7isgQIdOH9xv24yJ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\ngSGEEMIh/w9kO9eQWjBZ9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178171f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatter plot presenting the relationship between total_bill and tip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1197d84d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RaVBMg6lPnz5YvXq1e/vQoUO46qqrAADXXXcddu/eHcvDE1GCMlmbsXLjPjy+agdWbtwH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11904aa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Create one image with the relationship of total_bill, tip and size.\n",
    "#### Hint: It is just one function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x11844c090>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WBr0Oc616XLYPwu4cQl3D8FblJoMOG67Px9ry+YjXabG2PAd25xAS9Tr09A3i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\niIhE+//yIo6VcM5ynAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1192acb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Present the relationship between days and total_bill value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uVZxFQ7rtdM1dVVV0lJTiE3fuawfk//0ZOku7h3W2FxRQ8MLLjPvDo/1Zpugn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\niYiIPAiDm4iIyIMwuImIiDzI/wPvE39ImLp8oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179414f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zsmz04ouuV21LPZWtNdhsNhRFcdpWWN7Ao6/toKHJCMDgMc3kqLYCUNdSz192\n/ZN+QbGEegdfdD1O+/W6v9PslQ9eUEwRJnV/oB8Wq433vz7B1NHRhAV6Udtcx78PrSazOpdBof35\n0YhF+Hh4d1s9zlRR08Tz7+4lq7AWX72aX/lVMDIprN19X9n5NpnVuQCUGSox2yzcN3ZJj9RLCCG6\ng4R+N8uoyuHt/ascy+8e+oTYgGiGhid36vj6lgZH4J92oPQYy85xTKOxidSiQ3hodIzpMxydWuuy\nz6fHv+LjY19gsVn5tn4PT075OQF6f8f2z7ZkOQLf/jmy0ZyRdRablZNV2d0W+qcMjTTp8znz0kMT\nno+5pB8AVhvszk4nPELh68wtpFVkALC1sZpWs5GHr/1Jl89ps9n4OnMLqcWHiPAJY9HguQR7BTrt\n88//HSOrsBaAhmYLr3xwgH/9ejYatXNPWKOxyRH4px0qTetUPcwWK2u3ZXM0u5rE6AB+OD0RT538\nVxRC9Dz5S9PNtpw84rLu2+OHOh363jovAj39OdVS51gX4xfZ4f61zXX8av1z1LU2AODv6cerc3/n\n1Mxc2VjNqqNrHcv5dcX8++DHPDThHgAsFivpeTVO5Vob/YEix7KCQkJQbKc+Q2d4emhQzl6pMjv+\nqfYwsjLzbZRs1yb21OJD/OiTXzA4bADLx95BgKef03abzcb/Tm5kZ8F+QryDWDx0AdF+kXyZsYl3\nD60GIK0ig4zqHP485ymnVo/cknqnsk41tFJnaCXYX++0Xq/1JNgrkOqmU451Mf5Rnfrs7/wvjbXb\ncwDYd6KcoooGHr1zbKeOdVdHy9P575E11Lc0MCV+PDcPnufSWiWEOD8ZyNfNrAZ/l3UVxTr+tHIf\n//4i7byD1NQqNfeNW4Kfhw8AffwiuHPkog73/zbnO0fgg70p/h/73nfaZ1fBfpfj9pccxWazB+qq\njRkUVhictvdRJzMzYRIalQZfDx/uGX0bUb7h56z7mZpNLTSbWjrcbrGacU190PjWoQ0pRTNgN4qq\n/T51q81Ks7mFfSVHeHvfBy7b12dt5b3Dn5FzqoDUokM8t+VVzFYLuwsPOO1XWFdCcX2Z07qRA0Kd\nlvtG+LoEPoBKUfGzcXcS6Gn/eUf7RXLXyJs7/Lxn2nqwyGl559FSTGZrp451Rw2tBv64/Q2ya/Kp\nbKphddqXfJvz3aWulhBXJLnT72ZTk4azfvUhNJE5oNgwl8VxtFgNFAP2O7tXfzmN+tYGPjv+NaWG\nCsZEDWdkDPFdAAAgAElEQVRmv4mOO5eRkUP4+w3PU9tST4h3kMs5vsnewebcnfh6+KBR1C7bs2uc\nuwc2ZG932afVYqTB2Iifhw+paWUu239x22gSowNZNvpWVIqq03dVNpuN/xz8mPXZ2wCYlTCJu0bd\n4nK8Vq0FG07Br6itaAfuardcvcaTZrPrRcTR8nTqWxrw8/R1rNtbfNhpn+rmU+TU5BPqHURGdU5b\nHVQaAvTOrQR3zR+MzQb708vx19v45Z3jOvysQ8KTef2G56hraSDIK6DD/c4W7KenztDWlRLgo0Oj\nlrvWjpysyqHVYnRad7Q8nZn9Jl6iGglx5bokof/mm2+ya9cuzGYzKpWKFStWMHjw4Hb3/eijj/jh\nD3+IWu0abpej5LgglqXcyEffnsRqhWC9jmLa7qLzyxrIKqrlX+n/cITzwdI0jBYj85JmOPbTqDXt\nBv6uwv28ecadfHuj+geE9HP822azUdVU47KPXuPZ1poQ5kNOSVt3gpenhuhQe4iqVV373g+WHmNd\n5mbH8tdZWxgakczYPsOd9ms1mTBXRKMJLzq7CHu9TTpMxf3Q9slGrTPzm2m/IFjvzzNb/kJRfalj\nv2ZzC/f97wl+NGIRc/pPASDKN5yj5emOfdQqNeE+Idw65AYyq3OpaKxGo9KwZPgP8NE5Dwj09NDw\n00X2uu7fv5+oUJ9zfl61St2lwAe4e8FgnnsnleZWMzqNip/cNLRTF1U19S146tR4ebqO2biaxQVE\noyiKo2UKID4w5hLW6OrQYm4lqzqXKL8IgvRd+x0WV65eD/3s7Gw2bdrEqlX2wW7p6ek89thjrFmz\npt39//73v3PTTTddMaEPMH9iAvMnJgDwygcHKK5sC31FASMGl7vxHQV7nUL/tC25uzhUmka0fxTz\nB0xnb7HzmIFWs5Fp8RPYkZ+KyWomLiCaO4bddMb5FIaEJXEopxBrqxeK2ozNBuGxbWF359yB5JXW\nUVhuQKW24J2Qw3vH6tGptAR7BTIj4dpOP4pWUFfiuq62mLF9hlNa1cjG1Hw0ahVTR0djKhiItSEI\nxbcGTVAZiratT9/a6I+lIhaa/fnt/Sn0C+oLwIMpd/O31P+QX9t2sWC2mll5+BMmxY7DS6fnh4Pn\nklmdS86pAnRqLUuG/wB/Tz/8Pf3469xnyKstJMQryKl1oD1mi4131x3nYEYl8ZF+LJ07kEDfi38k\nb3j/UP79m9lkFdUSG+GHv4/HOfc3NLXy0OvrKC9VoaisTL82iIdumnLR9bhQhXUlmK2WXgveEO8g\nfjL6Nt4/soYmUzPXRI9kbv9pvXLuq1VWdR5/2PYaBmMjakXFXaNuZXbi5EtdLdELej30fXx8KCsr\nY/Xq1UyaNInk5GQ+/vhj9u7dy2uvvYbNZqOpqYmXXnqJvXv3UlVVxcMPP8xrr73W21XtFrfMHMDB\nkxWc+r4v/8bJ/YgLD0ar1mKymBz7nTmK3Gg28ub+/7I9P7Xt7qZwP5nVufQPjnM5x4LkWSwbdSsN\nxkaX0egAM+OncCj/P2iCKhzr8k/VU1JfRpRfBBHB3vzh52O4b/UfsKgbMagtbMjKcOy7q/AAv5/5\nq0593mHhA/mAz7Fhr7eCwvCIQVTUNPGLV7bQ2GIP9q925TF0hI1M20kUXStWoweYFBStCWuzN6YC\n+8BHS0MAkZ59HeXHBUbzpzlP8tiG58k5VdD2nVlM1LU24KXTE+DpxwuzH6fMUImfhw9e2rY+eZVK\n1ekBid8cqmP3SXu3TFZhLSVVjbzws+5pUvby1DIsMfT8OwKvrN1Meal9+I3NquLb7ae4fkwpSdEd\nD/DsCVarlZd3vkVq8SEABob254nJD/TKuWf2m8TU+AmYLSaZC6EbfHB0DQZjI2B/Muf9w58xNS4F\nXS/MByIurV4fyBceHs4bb7zBgQMHWLx4MXPnzmXz5s1kZWXx5z//mXfffZdZs2bx9ddfs2jRIkJD\nQ3nllVd6u5rdpk+oD289OYunf5LC3341jcXXJVDZWMNtQ29Erdi//kBPf24dcoPjmLUnN7Itb49T\ncybYm87TD/jQsm82LUcmYmsI4ebB8+jjF4FOo2s38AFyy06h8mx2WmdTmfnv0c8dy+lV2ZjNVsyl\n8ZgrYrBZ2n41MqpzyKkpoDMSgvry4Pi7iPbtgw/BJFin0HzKh80HCh2BD1Db0EKpZyqKzn4xpNK1\nEuPfh9DS+bQenYStxd4S4e2pwcfL9Q/RhL6jnZbjA2KI9HV+nj7CJ9Qp8Lsqvcj5O0vLqaa+0blv\n2WK1kF6ZRZmh8oLPcz75pXVnrVHYl5PXY+fryIHSo47ABzhRmcm2vD29dn6NSn1JAt9qs/LRsf/x\nsy+e4slv/sjxiozzH3SZq2ly/p1qNrfQ1M6YGXH16fU7/YKCAry9vfnDH/4AQFpaGvfccw+PPvoo\nzz77LN7e3pSXlzNqlH32N5vN5hJ+VxoPrZrRyeFsyd3FE2s/wGgxEeodzK+n/j9Uiop+QbH2gW3f\nO1mV0245arTsOVINVjW0+KDOS+GGO+ec9/wxIQGQ57o+t6aAsoYKInzDMNb50Zo2AWz2sLdURaEb\nuIfTXc2e398BWK1W3jv8KZvzduHn4cOS4T9w6a8fGTaCv++pptbQSiXw1IGdzJsQ73xylYVGi/Pj\ncQZrDY/fMpGn39pNTX2Lo7/bQ+vatXND0iy0Ki17iw8T5RvODwfPPe/30FVBvhpqGy2O5QBfD7w9\n2/7LVDXV8LvN/0f594E/P2kmd474YbfXY0j/AMqLzviDrDYxeUj/bj/P+VQ31bqua64hCK9er0tv\n+iZ7O6vT1gH2x19f2P46f7/hebx0F35BealNihvn9Bjv0PBkl0dfxdWp10P/5MmTfPjhh7zxxhto\ntVpiY2Px8/Pj+eefZ/PmzXh5efHYY4859lepVFd86IO97/2dAx9h/L5Jv7Kxmi8zNvGrife57Dsw\nNJHDZcdd1vsbhmKwtgVgY7OZonIDiTHnHoSTUXey3fWVTTX8v3VPs2z0rRw+6O0IfACrIRBboz+K\nTx3X9h1DlF8EABuzt/NFxrf28xubeGXn27x+w3NOfzAOnKyg1tD2aKLVaqPFaCIyxJvSKnuTYkJE\nML7B/cioznbsNypqKPFR/rz95CwyiqrQe5uJD2m/CVtRFK4fMI3rB0yjuq6Zj77KpKymiWuHRTFz\nXN92jwF7a8meokNE+IQyJ3EK+g7uHK1WKwEDMtBHZ2A1aVGXJ/PT6xagPmOSnrUnNjoCH+CLk98w\nq98klxaHi/XA3JmU1X7CiZNGtDort8zuR0xQSLeeozPG9BnGe0c+o9Vs/9mqFRUp0aOpzim/qHIN\nTUZeW32YA+nl9A3346eLhpPQx/XR10vlSFm603KLuZWM6hxGRLY/+PhKsHDgdfjovDhYmkaMfxQ3\nJZ//5qE37Mjfy5bcXZgbjYTXRRHt37tdWO6g10N/1qxZ5OTksGjRIry9vbFaraxYsYJ9+/Zx++23\n4+XlRUhICBUV9v7nMWPG8JOf/IR33323t6varepbG1weOStrqKDcUEm4j3Pf7g1JM1lzYj0t5rbg\n9PfwJcV7Ap8cz3Ks89FriQ4/9+jygtpivs7c0uF2GzY+OPI5Q9V3uGy7echcBsaGOk0sdKIy036c\nVbE/kmg1k1Wdy5gz7vbbG5gWGujNXx8Zxt7j5WjUCmMGRvC31Gwyqtv2CdbbuycOlR/l9YPv0mhs\nIso3nEcn/bTDILXZbPzmzV0UlNnnKth3ohyL1cqclDiXfbfnpfLqnnccy4fLjvPbab9ot9xNud9x\nouUEqEDlYUGJPUJigvN8CTXNrne+Nc213R76GpWaF5bc0q1lXohgr0B+N+0XfHHyW0xWM9f1n0pc\nYDTVXFzo/3NtGt8dtg8APVlwihf+s5d/PD7jspl8Jy4w2qlbQ6WoOj0R0+VKURRmJ05hduKlGxB6\nttSiQ/x1978cy09vfpnX5v8eT825B7qKrrkkj+zde++93HvvvU7rZsxwHbkO8MILL/RGlbpdVmEt\nVpuNAX3tQRbqHUxCYF+nwWelhkp+/uVv6Ovfh0cn3e+Y4lar1nLP6Nv4W+p/7K0cNoVk3bUsnJJI\ndV0L3x0pITzIi/t+MOy807eePp+lLhhbixeozSgWD1Sh+Y7Jb5rNrcydGMvuY6U0t9qbs8cOCufW\na1KcyjI0mzCW96H5qCc0+4BiRReTSUJgLBuytvHZia+xWq3MS5rBtcOi+O6I/Q95dJgPcyfE4anT\nMGlEH8De8rHrrMly1mfsYOHAOfxj73s0GpsAKGko57ENz7Nw0HXcmDzbJQjySusdgX/a1gPF7Yb+\ntzk7nJbTKjIobahoN6TP7mKx2qxkVuc5TUM8MXasUxiEegeTdMbjkr3hSNkJdhTsJUgfwNz+0877\nRMLFSgiK5cHxd3drmWk51U7LpdWN1NS3tDsp0qUwP2kmeaeK2Ft8GC+dniXDFnY4fkZcuLMnz6pv\nNXC8IoNRUUMvUY2uTjI5TzczW6w8+889HDhpb6kYnBDM75aPx0OrZsWk+/no2BcU1pWQe6oQs9U+\nsK2grpgPjq7lwZS7HOVMjruGghwNq3enYjX4s6UVWsoP8eRd1/DIHaPbPXd7BoX2R2XR0Zw5Eqxt\nP26NRYU2yh5s1/YdQ3LfEN54dAa7j5UR7O/J2EERTuXUGVr5xf9tpfKUEfg+WGxqjAXJZBSX8/aB\ntpnx3jv8KU/NeZBFM6bQ1GJicHywU7M4gNUKVosKRd02E11jo40GYyP1rc6zAzabW/jvkTXoNZ6O\nZ/ELaov5Nuc7LBYFtacFS0tbQAQHtN9k76Vz7ntWKSr0HdxFJIX0Y2vebseygkLfs5oaU2JG8fCE\nn7AtP5UgvT83Js9G08V5DS7GgZKjvLj9DceTEnuLDvGn655CpVxZE20mxQZSWt3oWA4N1BPQDY9G\ndhdPjQe/nHgvTaZmdGpdr/6M3Ul77/Xozhd8Cbsr66/DFWDX0VJH4IP9LmbbAfsz5UH6AO4bu4Sf\nXfMjR+CfVnzGhDOOsvbXYamOwtZqH8m+J62MOkPX3jUf5hPCrKgbnAIfwLs1msmx1/CjEYu4f+xS\nAIL99cy7Np6UIZGoVc531Jv3F1J5ynk0+2lfpWa7rDtemUlidADDEkNdAh9Apaix1bdNPmSzgcYQ\nQaDen4TA9vvkD5QeBaC4vownvnmRrzI3syFnE77D96LS2EfVhwTouW1WUrvH/2Dgdeg1bWEyd8B0\np5cOnWl6wgSiPNpaAGzYeP+I61wSKTGjWDHxPu4ZfVuv/4HalLvTEfgAhfWlLi8BuhLcvWAwo5LC\nUBSICfdlxdIxLr9/lwMvrV4CvwfNS5pBfIB97gcFhQXJs674bpTLkdzpd7OaetfHXs5eF+kTRpRv\nOCUNbX2hY6KGuRzne9ajalqNut2R7OczsG8Ya1RZ9lH/39P41fFAiusgwo6YLR0PphzdP4oTZ2VN\n/+D49nf+nloN2sBaTo+NVxTwCrO/vOaXE+/l3wc+IvWs6XT7+NpbH7bn73EMiARotTWxbGkY/byG\nkBQb6PJGvNMSg+N4bf6zHC1PJ8In9JzP66sUFU1W55/bgZJjNBqb8NZdHqPVfXWu4znOnmHwShDo\n68nvlo/HarWhugzDXvQOPw8fXpj9OPm1xeRm5DBtuEwW1BPkTr+bjR8SiYeuLVy1GhUThjlfrSqK\nwmOTf0ZKzChiA6JZNHguPxh0vUtZd8xJRndGyN82OwlPj65fp/l4adH1O4Ti0QSKBXVIEaHxp85/\n4BmmjY5G3c788P1jAlgwbgR3DFuIt1aPp8aDHw6ay+jz9MNZrBZQnFs7NB72S4AQryB+OfE+lo1a\njMf3ze9JIf1YOOg6ALy0rqEbEeDP4ITgDgP/NF8PHyb0HdOpCXq81a5v1vNQXz6TlyxInoX/GU9N\nTE+4lj5+Eec44vImgS8URSEuMBo/zZV38XqlkDv9bhYW5MULP53I2u3ZWK0wf2I8MeGug6sifEJ5\neMK53wk/NDGEfz45i2M5VcRG+LVbTmfEBcbgEXwKdeA2x7qxMfO6VEawv56Hbh3Jyx8c4PQTlEMS\ngnn++xnqbhw4mwXJswA6NeraQ6NjUuw1bMlre8HOzATn2e7m9J/ClLhrMJiaCPFq6wqYHj+BTTnf\nOVpKkkP69chgn6nBY/m04huaTS2oFRVLhv8Ajfry+S8T6RvGq/Oe4Wh5OkH6APp146uPhRBXp8vn\nL9hVJDEmgIdv7/xgu3MJ8PVg4vA+F1WGn4cPP0/5Mf8+8DG1LfWM7zuaG5Nnd7mcqaNjiI30Y09a\nGRHB3kwc7tqC0RXLx95Bv6BYcmsLGRqexLV9Xd8p76n1dJmFzcfDmz/NeZLDZcfRqXUMCU/qkcFr\n0foI3rjhD2RW5xLjF9XlF+v0Bk+Nh8vkSEII0REJfTcxPmY0KdGj2Lt/H+PGuIZrZ8VH+RMf1T0T\np2hUasdo/K7SqrVOcwP0FC+tnuERg3r8PEII0RukT9+NKIrimO9fCCGE+5EEEEIIIdyEhL4QQgjh\nJiT0hRBCCDchoS+EEEK4CQl9IYQQwk1I6AshhBBuQkJfCCGEcBMS+kIIIYSbkNAXQggh3ISEvhBC\nCOEmJPSFEEIINyGhL4QQQrgJCX0hhBDCTUjoCyGEEG5CQl8IIYRwExL6QgghhJuQ0BdCCCHchIS+\nEEII4SYk9IUQQgg3IaEvhBBCuIlOhf7bb79NZWVlT9dFCCGEED2oU6Hf0tLCkiVLWL58OV999RUm\nk6mn6yWEEEKIbtap0H/ggQdYv349y5cvZ8+ePdx4440888wznDhxoqfrJ4QQQohu0uk+/ebmZoqK\niigsLESlUuHn58fvf/97XnrppZ6snxBCCCG6iaYzOz3yyCPs3r2bKVOmcP/99zNmzBgAjEYjEydO\n5JFHHunRSgohhBDi4nUq9MePH8+zzz6Ll5eX03qdTseXX37ZIxUTQgghRPfqVOhPmzaNjz76iMbG\nRmw2G1arlaKiIv74xz8SGhra03UUQgghRDfo9EC+EydOsHbtWpqbm9m0aRMqlTziL4QQQlxJOpXc\np06d4sUXX2T69OnMnj2blStXkpmZ2dN1E0IIIUQ36lTo+/v7AxAfH096ejq+vr7yrL4QQghxhelU\nn35KSgoPPvggjz76KHfffTdpaWno9fqerpsQQgghutE5Q3/NmjWA/Q4/JiaGvXv3snjxYhRFoU+f\nPr1SQSGEEEJ0j3OG/p49ewAoLCwkPz+fyZMno1ar2bFjB4mJib1SQSGEEEJ0j3OG/vPPPw/A0qVL\n+fzzzwkKCgKgrq6On/3sZz1fOyGEEEJ0m04N5KuoqCAgIMCxrNfr5a17QgghxBWmUwP5pk6dyl13\n3cXs2bOxWq18/fXXXH/99T1dNyGEEEJ0o06F/uOPP8769etJTU1FURTuvvtuZsyY0dN1E0IIIUQ3\n6lToA8yZM4c5c+b0ZF2EEEII0YNkLl0hhBDCTUjoCyGEEG5CQl8IIYRwExL6QgghhJuQ0BdCCCHc\nhIS+EEII4SYk9IUQQgg3IaEvhBBCuAkJfSGEEMJNSOgLIYQQbkJCXwghhHATEvpCCCGEm5DQF0II\nIdyEhL4QQgjhJiT0hRBCCDchoS+EEEK4CQl9N1PeWkVBbfGlroYQQohLQHOpK+CumlvNfPldLsUV\nBlKGRHDNkEjHtiZjM2vS15NfW0wf33D0Wk9i/KMYFz0ClXJh12lGi4lfrXqH/EIjqn0HGDPcn0cn\n3Ytape6uj9Trck8VUmaoYGhYMj4e3t1SZn2rgTdS3+VQaRrB2gD844JJDI7rlrKFEOJSk9C/RJ57\nZw+HM6sA+GZvAQ/eMoJZ18QC8MqutzlcdhyAg6XHHMfMTJjI8rF3XND5/rJmCzkHwgGwAKmGEvYm\nHCYlZtRFfApXNpuNb7J3cLD0GNH+kdyYPBtvnVe3ngPgvcOfsjZ9IwB6rSe/mfoQ/YJiL7rcdw+u\nZn/JUQAqjDW8suttXp33zAVfbAkhxOVE/pJdAuU1TY7AP23DnnwADK2NjsA/26bcnRiMjR2Wm5ZT\nzeufHObDjSdpaDI6bTt4rMlp2VIdSVndqQup/jl9nr6Bt/b/l30lR1hzYj0vffdmt5+jtqWeL05+\n61huNrXwyfGvuqXsjOocp+XKxmpqm+sdy6VVjazdns2+E+XYbLZuOef5mK0W3t73Abf995fcu+Yp\ndhbs65XzXi6qmmr44MjnvHtwNUV1pZe6OkJc0eRO/xLw1KlRqxQs1rbQ8PHSAeCh0eGt1dNoanY5\nTgFUHVynHTxZwdNv7eJ0kTsOl/CXh6eiUikAeHloaMDcdoDKwsioQeesZ0lDOf4evu3eqe9PL+dI\nZhX9ov2ZOLyP4zzb81Od9jtWcZJTzXUE6v3Pea6uaDa1YLVZndY1Gps62LtronzDKTNUOpZ9PXwI\n0PsBcDizkqff2o3ZYj/3zLF9+X+LR3bLec/lyxNbWPtlI7b6SRgUMy/lbyZ5eSJB+oAeP/elZjA2\n8vjGF6lrsV94bczZwYuzHyfKN/wS10yIK5Pc6V8C/j4e/HB6f8ey3kPN4lkDANCqtSwdsajdvvZZ\niZPx0unbLXP9nnzOuIYgr7Se9PwaAJpMzTSGHAClLSg1fbLYXrir3bJqW+p5bMPzPLTuaZavfYx1\nGZuctq/dns3Tb+3m0y1Z/Om9/bz1+VHHtqCzwt1D44Fe6+lYbjVZ+PCbkzz3zh7Wbst2uvDprEjf\nMAaF9ndaNyPh2i6X055mc6vTstFixGyxXyx9ujnLEfgA3+4roKrW9eKsu238rhxbfah9wabBWNCf\nfTlZPX7ey8HeosOOwAdoNbeyPS/1HEcIIc5F7vQvkaXXD2Ti8CiKKw0M7x+K7/d3+gDTEyYwKmoI\nxfVl6FQajldm0TcgihERgzssz9tT2+G6nJoCLL4leA6vxlIfhMqrAZWXgUOlJpYM/4HLcZ8d/5qc\nUwUAmCwmVh76hPExox136x9/m+60//rd+dw1fzA6rZrFQ28ku6YAg7ERlaLijmE34anxcOz71w8P\nsu2g/emB3cfKqKxtZtmCIZ392hwenfRT1mdtpayhgnHRIxgVNbTLZbTnVHOd03Kr2UiTuQWdRucU\n+AA2Gxd00dJV9eU+wJnnUait0EPHvw5XjTMvGM+1TgjROT0W+i+++CLHjh2jqqqKlpYWoqOjycrK\nYsKECbz00ks9ddorSnyUP/FR7Td7B3j6oVNp2VdyhCi/cIaGD0RRFKd96gytVJxqIiHKn4VT+7H7\nWCn1jfa+/KmjoomNtDdLR3qHY7OqUHStaELsfaKWukBiY/u0e+7ShnKnZYvNSrmhikC9P9+kHaa2\nwYS9s8FOpeBo3u8XFMvrNzxHZnUufXwjCPJqa4I2ma3sOFziVPaW/UUXFPp6rSc3DZzT5ePOx1Oj\nc1rWqjT4efgAsGBSAseyqxwtKuOHRhIe1P2DFM8W5h/AqVrn8ReRgVd/0z7AmKhh9A+OJ7M6F4AI\nn1CmxY+/xLUS4srVY6H/6KOPAvDZZ5+Rm5vLww8/TGpqKh9++GFPnfKqUt10iic2vkh1tQ1LXQh9\nw3fy8q3LUavtzf7rduby1ppjmC1WwoK8eGb5eN56Yib70ysI8vNkcEKwo6zvDlRjyhmMNv44qCxY\n6kKw5A3jjiWzANhdeID1WVvRqbXcNHAOY/oM59AZgwkD9f4kfj8y/tOt6YDzndaQZG806raeIk+N\nB0PDk10+k1ql4Oeto7ahrQk90M/DZb8zWaw2PtiQzs4jJYQFevHj+YOJ+/5ipidUNzmHq8lqpra5\nniCvAK4ZEsmfHpxMaloZkSHeTB4Z3WP1ONMtMwbw7L/2OJZ9vLSMHdTWp91iNGMyW51ai64WGrWG\nZ6Y/wuGyE5isJkZGDkGndm3VEkJ0Tq837+fm5rJ8+XKqq6uZNm0aDzzwAEuXLuWZZ54hPj6eVatW\nUVVVxcKFC7nvvvsIDAxkypQpLFu2rLer2utKqgzkFtczKCGIDbnbqCzWY8oeDijkFMHzlu08tXQq\njc0m/rk2zdHcXFHTxMqvTvDYnWOZNML17r3VbEYTk4GitgCg9q9C5XeKIK8Ajldk8vLOtxz7HqvI\n4C/XP82PRiziu4J9BHsFcuuQG9Co7b8qGsU1WMYP69ygKpVKYdmCIfxl1QHMFhseOjV3zT93G/Vn\nW7L4cGMGAIXlBvJK63n7yVlOFxndyXLWAEEA1RktLAP6BjKgb2CPnLsj4wZH8OidY/h2byF+3jpu\nntEfT5395/Hp5iz+uyEdo8lCypBIHrljNB7a7p97wWBspKiujLjAaKfumt6gVqkZFdX11iAhhKte\nD32TycTrr7+O2Wx2hH5HqqurWbNmjePu9mr25Y4c/rHmKDYbaDUqxk02YS6N58xm9L1H6jA0m6gz\ntGI0WZyOr6jpePS6V2g1SpkZc1ksNqMn6qBStDGZAOwrPuy0r8li4lDZceYlzWBe0gyXsu6aNYbf\n5uzFZrX/TPwCzcwa0f5TAI3NJlpNFoL82loGpo6KZnhiCLkl9QzoG+B4aqEj+044dzVU17WQW1JH\n/5ieCd5ov0inx/a0Kg2el0Ef8sThfZg43PmCrrC8gXe+SHMs7zpayrrvclk4NbFbz7278ACv7fk3\nRosJb62eFZPuZ+BZAymFEFeGXg/9/v37o9Fo0Gg07Yb5mc8+R0dHu0Xgm8xWVn51gtMf3WS2UnjC\nH0VpPmv4lv0CICLYm5hwHwrLDY5tE4dHdVh+laGO1vSx2Brt/cDmslhUA+yT/kT4hrrsH+kb1mFZ\nIxNjeO0RPz7ZeYRQP28WTRrm6M8/0/tfp7N6UyZmi5UR/UOJi/TFZLExc1xfEqMDCPTrXJBGh3qT\nllPtWNZp1UQGd8/se+2ZkziZzOpcbN9/89f2HdPrd7adVVDW4LIur7S+nT0vnNVm5Z0DH2G0mABo\nNFDiJ8oAABfBSURBVDXz7sFPeH72Y916HiFE7+j1R/bOHowG4OHhQWWl/dno48ePn3Pfq5HZYqW5\n1ey0rrUVRsXHOa0L9POkoKyee57bSGG5AS9PDfExelKmG6jzP0BWdV675Q8JHOkIfDsVmsokAKbG\nT2BUpL3pVFEUZvebzOCwAeesb98If37xg0ksmTkKTw/X68bsolpWbTzp6H44lFnJmm05fPldLite\n3U5uifMIebPV4lIGgKHZxOGstkmMFAV+cuPg87YOXIydhfsdgQ+wv/SY45G9y83ghGB0ZzXlj0rq\n+ILtQpitFmpbnS8kqppquvUcQojec1k8srd06VKefvppoqKiCA9v6x92l9DXe2iYOKKP41E2gFnX\n9GXrgSKn/apqm/m/Dw46ng1vajFSHbqNMkM9hzNhQ9Y27hn8E06mK+h1GuZNjCcs0IvqWudnzwGs\nLfZR5zq1lscm/4wKQxVatbZbJtEprDB0uM1ktrJpXyHLFvizq2A/f938Ka0NenyCm/ntnOVOU+lu\n3V9IWXVbt4XNRo/15Z9WUu/cndDQaqDeaHBMhGO12kjLqcZotjC8f2iP1+c0o8VEWsVJ/Dx8Hd9R\ngK8H/7+9Ow+OqszXOP50p9NJyAqBAAkQFknYZEsMGRWCDoyRQYQBnMg6js61QEdGucpgiYylBYOj\nRem9OILWlBLQXAtlZFzQiygC4gSCAdl3CCFsISEbSa/3j1wbmwSyENIdzvfzV87pc07/0jR5+n37\nfd/z/MNDtHLtPpVdsulXQ+KVNrhpBxdaAwJ1W+wAZefnevbd3iW5SZ8DQPO54aE/btw4z88pKSlK\nSUnxbG/atEmSlJaWprS0tBrnZmVl3ejy/MafMgapZ+fWOpJfrIEJ7XR3chdt2+MdQIEWkwoKLy/D\na44okt1yuRXmdLv0928+UdWR6jnrX+fk6c0//1IlNu+WtSTZ3Xav7Ziwtp6fTxeW6+2Pd+n46RIN\nTozRQ6P71tqiP1Z0Up8cWCeHy6l7bhnm+Z53wC1tZQ0MqDHu4CdhIdWjr19ZvV62E9Xvh6JjLv2l\n/H1lPnK527iqlvOrbLVf82BekdZ8e0Qul1uj7+yu3t3a1HpcXZLi+uuT/es82z1ax3sC3+l0ad7S\nLfrxcHXvQ+f24Xr5j0M9v8+NUlhRpHlfveJpYd8Zn6InUh+SJA3o2U4Detb8iqYpPTZkumL3ttfh\nC8fVNyZBYxJH3tDnA3Dj+EVLH1KgJUBj03p47buyFWkJCFCfblGedfvdzprjHVyOy/uKSquUveeM\nOsUFqnpxl8s9J62ia34Q+MnCd7bqyP93wX/23TFJ0ozxA7yOKbp0UfPXv6pLjkpJ0r9P/qC/jvyz\n4qM6qXVEsP7yh1Rlfblf5ZV2VdmcOvn/rf+O0aFK/0VXnS45L9vJ7pcv6Dbr4jHvgWppgztp1fqD\nKq2o/oASFRakO2uZnXDmQoXmvrHZ84Hgux8L9Prs4ercPvyqv+PVPHjrGFnMAcot2K1QZ7Aev/P3\nnsey95zxBL5UPZBuXfaJGv9uTe3TA+u9utQ3Hc/W6IS71b0JbjBUHyGBwZrUf2yzPBeAG4vQ92OF\nJZVe25eqHHrovr5a/fVhHcgrUr/uXVTeoVw/nK5eBjfIFKLKM129zglvFShHUJUsnffLcbKn5A6Q\nKbRYQV1qX8a1qKTSE/g/2b7/bI3jtubv8AS+JDldTn13IkfxUdXdy7f2aKtbZ1T3Hrjdbu08dF6V\nVQ4NSoyRNTBA7nK75L6ia9zl/XaMjgzR4ieHa132CZlN0sgh8YoMqzmo7vtdBV49AA6nS5t3nlLG\nyMRaf8drCQwI1KT+YzWp/1jl5OQoutXlWQJX3sToavuaWklVzQF7JVVX/woFAK6G0PdjSYkx+uT8\nUc92144R6hEXpf+ckuTZ53YP1K6z+3WxslQ9Inpq/uFtKqiq/gpgYEI7DUqI0cbjRxXY8Zgs7fLl\ndgTKHFyhwKDap7xFhFrVJiJIF0oujwOI71BzMZzabvZytRvAmEymGl3QUaFhatX+vCpOXx54Ftut\n5jr27du00uT0mgv9/Fx0ZM2ZANH1nB3QEEP6dtA7rayeoLdazBrexN+h12Z411RtPJ7tmdnSLjS6\nzsGWAFAbQt+PTR/dR25V39EuvkNErcvVmkwmr9Xvljx9l344cE4hVov69YiWyWRSaufBWrZtpewW\nu0yW6q7yX3ar/QY1AQFmzcoYrNeytutCSZW6xUbokftrPu/gjv2UHNtf207tlCQlRHdXWrfUBv1+\nC36frkX//JfOnXcqvnOQnh/7QIPO/0lqv45K6hWjnH3VPRL9ekQ3+YA2qfpGSa/OGqZPNx+Vze7U\nPanxjfoKoaH6te+leWmztOHY94oMDteonncrkFXpADSCyd1cNwW/wXJycpSUlFT3gQZ15MIJ/WP7\n/+j0xbNK7zVc4/uMuubsCKfTpZJyW53z6Y8VnZTD5VCPNvGNnm3hdrubZKbGkfyLcrncuqVz06xL\nz3uqfnid6ofXqf54reqnMa8TLX2D6N6mi14a8XT1m6Rv3W+SgABzvRbQ6dr6+lvUTTU1s3vc9U83\nBICbWbMvzgMAAHyD0AcAwCAIfQAADILQBwDAIAh9AAAMgtAHAMAgCH0AAAyC0AcAwCAIfQAADILQ\nBwDAIAh9AAAMgtAHAMAgCH0AAAyC0AcAwCAIfQAADILQBwDAIAh9AAAMgtAHAMAgCH0AAAyC0AcA\nwCAIfQAADILQBwDAIAh9AAAMgtAHAMAgCH0AAAyC0AcAwCAIfQAADILQBwDAIAh9AAAMgtAHAMAg\nCH0AAAyC0AcAwCAIfQAADILQBwDAIAh9AAAMgtAHAMAgCH0AAAyC0AcAwCAIfQAADILQBwDAIAh9\nAAAMgtAHAMAgCH0AAAyC0AcAwCAIfQAADILQBwDAIAh9AAAMgtAHAMAgCH0AAAyC0AcAwCAIfQAA\nDILQBwDAIAh9AAAMgtAHAMAgCH0AAAyC0AcAwCAIfQAADILQB2pxvvyCnG6nr8sAgCZl8XUBaHny\nzpRqU26+IsKCdFdSJ7UKDvR1SU3mdNk5/W3j35VXUqAQc7ACYkOUFHurr8sCgCZBS/8GcLrc2r7v\nrLbtPSOH0+XrcprUobxiPfHK13rvy/1686Odeua/N8rlcjf6esWVJfrxzD5V2C81YZWNtzz3Q+WV\nFEiSLrkq9WZ2phxOh4+rAoCmQUu/idnsTs19Y5MOnCiWJHXtGKFFj995Xa1hm9Mua4B/tKZXrN0r\nx89C/nhBqXIPntXgxPYNvta3x/6tv2/NlNPlVEhgsObcOUN9YhKastwGy79Y4LV9sapUJbYytQmJ\n8lFFANB0aOk3sc07T3kCX5KOFZRow/aTjbpW0aWLeuHrxZqy6gnN+nS+9p071FRlNtrpwvIa+44X\nlDb4Ok6XU+/mrpLTVf29+SV7pVbuWN2omo6euqjnl36n/1i4Tu9+ukfO6+hdGRTbz2u7a1QnAh/A\nTcPnLf38/HyNGTNGffv2ldvtlslkUmpqqmbOnOk5Zvbs2Vq0aJEsFp+XW6eyCnvNfZdq7quPd3NX\naffZA5KkgrKzem3LP7Rk9Esym5v2s5rL7dLag99oW/5OxYa314S+oxQVElnrsb26tlH+Oe/g79cj\nusHPaXc5VFblfZ0Lly42/DoOl/7y1ve6UFIpSVq1/qBCgix6YETjegwm9R8rs0z64fRuhblC9Mc7\nH27UdQDAH/lFivbs2VPLly+/6uOvvvpqM1Zzfe4YEKv3vtjnCfqQoAANHRjXqGsdPH/Ea7vwUpGK\nK0vUplXTtjzX7Ptfvbfzn5KkXWf36/CF41r4qz/XeuyU9N7KPXBOhRerQ3bYoDj17Ny6wc8ZbAnS\nbXEDlJ2f69k3tGtKrceeLixX9u7TimnTSrf16aAAs8nz2PGCEk/g/2T7/rONDn1rQKCmDZqgaZqg\nnJwcxYQ2/AMNAPgrvwh9t9t7IFh2drZeeeUVWa1WTZw4Ua+99prWrl0rq9Xqowrrr01EsF790zB9\n/t0xuVxupf+iqzpEhzbqWlcOjzObTAq3Nu5a17LlRI7X9uGi4zpTdk7tw9rVOLZtVIiWzR2hnYfO\nKzLM2qjA/8njQ6Zrzf5YHSnK060xibq35101jtl9pFDzln4nu6O6y37owDg9MzXZ83iHtqEKsgao\nynZ5el18h/BG1wQANzO/CP1Dhw5p2rRpnu79iRMnymaz6YMPPpAkvf766z6usGFi24bp4TH96j6w\nDgEm7258l9utUnu52lga3tI/VlCilWv36sSpQv26/LDGDOvheaxdaLSOFud5toMCrIoIunpwWgMD\nlNy74QP3rhQcGKwH+t13zWNWf3PIE/iStDE3X1Pv7a2Obas//ISFBOqPEwdq6eqdKq2wq2/3aE26\np9d119YcHC6nlueu0sbj2WoTEqVpA8drQIc+vi4LwE3ML0L/yu797OxsdevWzYcV+YekuP767MB6\nz3a3qM6NGlRmszs1b+l3Ki6tkiS99fEuBVktuic1XpKU0X+MDhcdV2FFkSxmi6YNnKCQwOCm+SWu\nk7OW6YBOl/dAvbTBnXR7/1hVVNoVGRbUXKVdt88OfKW1B7+RJJXbKvTK5mV6874FCrW28m1hAG5a\nfhH6V3bvS/IarFbb40Ywqf9YmU1m5RbsVufIWE0ZMK5B5+87d1iZuauUX+BUcWlfr8f+vbvAE/qd\nIjrqv379oo4V5SkmNFoRwf7TPT5maHdt33/WsxZAcu/26hRTs75Ai9lngV9UWqmQIIuCrQ3777Tn\nitkYVY4qHSk6oVvbt4yeCgAtj1+Evslkuq7Hb1bWgEBNGzhe0waOb/C5lY4qLdr0hsptFXKbgiT1\n1s9naMa1C/M63mIO0C3RXa+v4AZyuVzaeDzbE3TJcf1rHDMoMUaL/5SmLT8WqH2bVkobXPugyL3n\nDupf+7+S2+3SqIS7myU4yy/Z9dflW5V74JxCggL0u9F9Ner2+vdQ3dKmq7af+tGzHWi2KD6ycYM+\nAaA+fB76cXFxysrK8tqXkpKilJTLI7m/+uqr5i7LL1U5bDpSdFxx4R3qbI0fKzqpcluFJMlkrZKl\n8wE58xPldpmU2KW1JtzdszlKvqalW7P05ZY8ucsj9K/INXpoxFnd12tEjeO6x0Wqe1ztUwil6qVz\nX/rmddld1Svn5Rbs1qJfPasuUTc2QD/8+qByD5yTJF2qcmrp6h81pG8HRUeG1Ov8MYkjdKr0jLac\n2KbI4Aj9btBEv+plAXDz8Xnoo34OFh7VX79dolJbuSxmix5Nnqy0bqlXPb5TRAdZAwJlc1ZPHQzs\neExj7khQp/KOGjl8SHOVfVU2h01ffFkhZ3F1i9x5IVZZXxyqNfTrsi1/hyfwJcnpdik7P/eGh/6J\n096LErlcbp08U1bv0LdarHoi9SE9njK9yddeAIDa8JemhVixY7VKbdWL2ThcDr2bu+qaa8KHBYVq\nZsp0RQZHyCSTkmP767cD09Um3D8+59ntbjmL23rtKz3ZuBkB7WqZS9+u1Y2fX5/UK8ZrOywkUInx\nDZ/CSOADaC7+kQCo04WKIq/tMlu5Kp1VCgu4+j/h7V2SlNppkOwuh4Is/rXGQfVKud5jNUyN/Ax6\nW+wApXYarO9PbpckDerYT3d0Sa7jrOuX/ouuKq2w65vteYqOCNHUUb0VHMR/KQD+i79QLcTtXZK1\neu9az/aADn0UVo+Fesxms4LM/hX4khTeyqqoMKuKy2yefbd0atxKg2azWU/d8QcVlJ6Vy+1SXESH\npirzmkwmkx4YkdDo1f8AoLkR+i3Eb/vdp/CgUO04vUfxUZ00rne6r0u6LiaTSc8/kqq/rdimgvMV\n6tk5SnOm3nZd1+wYHlP3QQBgYIR+C2E2mzU6cYRGJzZ8oJu/6tm5tZbNHSmb3SlrYICvywGAmx4j\niOBzBD4ANA9CHwAAgyD0AQAwCEIfAACDIPQBADAIQh8AAIMg9AEAMAhCHwAAgyD0AQAwCEIfAACD\nIPQBADAIQh8AAIMg9AEAMAhCHwAAgyD0AQAwCEIfAACDIPQBADAIQh8AAIMg9AEAMAhCHwAAgyD0\nAQAwCEIfAACDIPQBADAIQh8AAIMg9AEAMAhCHwAAgyD0AQAwCEIfAACDIPQBADAIk9vtdvu6iKaQ\nk5Pj6xIAAGhWSUlJDTr+pgl9AABwbXTvAwBgEIQ+AAAGQegDAGAQhD4AAAZB6AMAYBAtOvTdbrfm\nz5+vjIwMTZs2TXl5eb4uyW85HA4988wzmjx5sh544AGtX7/e1yX5tcLCQg0fPlxHjx71dSl+bdmy\nZcrIyND48eP14Ycf+rocv+RwODR79mxlZGRoypQpvKdqsWPHDk2dOlWSdOLECU2aNElTpkzRCy+8\n4OPK/M/PX6u9e/dq8uTJmjZtmh555BFduHChzvNbdOivW7dONptNWVlZmj17thYuXOjrkvzWmjVr\n1Lp1a61cuVJvvfWWXnzxRV+X5LccDofmz5+v4OBgX5fi17Kzs/XDDz8oKytLmZmZKigo8HVJfmnD\nhg1yuVzKysrSzJkztXjxYl+X5FfefvttPffcc7Lb7ZKkhQsX6qmnntKKFSvkcrm0bt06H1foP658\nrRYsWKDnn39ey5cv18iRI7Vs2bI6r9GiQz8nJ0dDhw6VJA0YMEC7du3ycUX+695779WsWbMkSS6X\nSxaLxccV+a9FixbpwQcfVExMjK9L8WubNm1SQkKCZs6cqRkzZuiuu+7ydUl+qWvXrnI6nXK73Sot\nLVVgYKCvS/Ir8fHxWrJkiWd79+7dSk5OliQNGzZMW7Zs8VVpfufK12rx4sVKTEyUVN1YCQoKqvMa\nLfovf1lZmcLDwz3bFotFLpdLZnOL/ixzQ4SEhEiqfs1mzZqlJ5980scV+aePPvpI0dHRuuOOO/Tm\nm2/6uhy/VlRUpFOnTmnp0qXKy8vTjBkztHbtWl+X5XdCQ0N18uRJpaenq7i4WEuXLvV1SX5l5MiR\nys/P92z/fL240NBQlZaW+qIsv3Tla9W2bVtJ0vbt2/Xee+9pxYoVdV6jRadjWFiYysvLPdsE/rUV\nFBRo+vTpGjdunEaNGuXrcvzSRx99pM2bN2vq1Knat2+f5syZo8LCQl+X5ZeioqI0dOhQWSwWdevW\nTUFBQfX6TtFo3nnnHQ0dOlRffPGF1qxZozlz5shms/m6LL/187/h5eXlioiI8GE1/u+zzz7TCy+8\noGXLlql169Z1Ht+iE3Lw4MHasGGDJCk3N1cJCQk+rsh/nT9/Xg8//LCefvppjRs3ztfl+K0VK1Yo\nMzNTmZmZ6tWrlxYtWqTo6Ghfl+WXkpKStHHjRknSmTNnVFlZWa8/OkYTGRmpsLAwSVJ4eLgcDodc\nLpePq/Jfffr00datWyVJ3377bYPXljeSjz/+WCtXrlRmZqbi4uLqdU6L7t4fOXKkNm/erIyMDEli\nIN81LF26VCUlJXrjjTe0ZMkSmUwmvf3227Jarb4uzW+ZTCZfl+DXhg8frm3btmnChAmemTS8ZjVN\nnz5dzz77rCZPnuwZyc8g0aubM2eO5s2bJ7vdrh49eig9Pd3XJfkll8ulBQsWKDY2Vo899phMJpNS\nUlL0+OOPX/M8brgDAIBBtOjufQAAUH+EPgAABkHoAwBgEIQ+AAAGQegDAGAQhD4AAAZB6AOol7Ky\nMj322GM6d+6cHn30UV+XA6ARCH0A9VJcXKx9+/apXbt2rB8PtFAszgOgXmbMmKFNmzYpLS1Ne/bs\n0fr16zV37lyZTCYdOHBAZWVlmjFjhu6//35flwrgKmjpA6iX5557TjExMXr22We9lts9c+aMPvjg\nA7377rt6+eWXuUER4McIfQANcmXn4Pjx42U2m9W+fXslJSUpJyfHR5UBqAuhD6BBrrypTkBAgOdn\np9PptQ3AvxD6AOrFYrHI6XTK7XZ7tfY///xzSVJ+fr527typ5ORkX5UIoA4t+ta6AJpPdHS0Onbs\nqLlz58psvtxeqKys1G9+8xvZ7Xa99NJLioyM9GGVAK6F0AdQLxaLRe+//36N/enp6Ro7dqwPKgLQ\nUHTvAwBgEMzTBwDAIGjpAwBgEIQ+AAAGQegDAGAQhD4AAAZB6AMAYBCEPgAABvF/hREqXXY0n/AA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179e40310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9pfLycg0ZMkSbNm3S5MmTVVZWJknauHGjVq5cqa+++kr33HOPZsyYoTvuuEPz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\nmfnjav8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17906de90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11989e910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker\n",
    "### They must be side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U8Otf/7pJxxFCiOYS7fNtW7J77rmHl156iX/961/0798fpRS9e/dG0zRSUlKCscrn85GT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\nQgghIkACrhBCCBEBEnCFEEKICPj/W9lawU2VX7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x178d4b650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it using a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 07_Visualization/Tips/tips.csv
================================================
,total_bill,tip,sex,smoker,day,time,size
0,16.99,1.01,Female,No,Sun,Dinner,2
1,10.34,1.66,Male,No,Sun,Dinner,3
2,21.01,3.5,Male,No,Sun,Dinner,3
3,23.68,3.31,Male,No,Sun,Dinner,2
4,24.59,3.61,Female,No,Sun,Dinner,4
5,25.29,4.71,Male,No,Sun,Dinner,4
6,8.77,2.0,Male,No,Sun,Dinner,2
7,26.88,3.12,Male,No,Sun,Dinner,4
8,15.04,1.96,Male,No,Sun,Dinner,2
9,14.78,3.23,Male,No,Sun,Dinner,2
10,10.27,1.71,Male,No,Sun,Dinner,2
11,35.26,5.0,Female,No,Sun,Dinner,4
12,15.42,1.57,Male,No,Sun,Dinner,2
13,18.43,3.0,Male,No,Sun,Dinner,4
14,14.83,3.02,Female,No,Sun,Dinner,2
15,21.58,3.92,Male,No,Sun,Dinner,2
16,10.33,1.67,Female,No,Sun,Dinner,3
17,16.29,3.71,Male,No,Sun,Dinner,3
18,16.97,3.5,Female,No,Sun,Dinner,3
19,20.65,3.35,Male,No,Sat,Dinner,3
20,17.92,4.08,Male,No,Sat,Dinner,2
21,20.29,2.75,Female,No,Sat,Dinner,2
22,15.77,2.23,Female,No,Sat,Dinner,2
23,39.42,7.58,Male,No,Sat,Dinner,4
24,19.82,3.18,Male,No,Sat,Dinner,2
25,17.81,2.34,Male,No,Sat,Dinner,4
26,13.37,2.0,Male,No,Sat,Dinner,2
27,12.69,2.0,Male,No,Sat,Dinner,2
28,21.7,4.3,Male,No,Sat,Dinner,2
29,19.65,3.0,Female,No,Sat,Dinner,2
30,9.55,1.45,Male,No,Sat,Dinner,2
31,18.35,2.5,Male,No,Sat,Dinner,4
32,15.06,3.0,Female,No,Sat,Dinner,2
33,20.69,2.45,Female,No,Sat,Dinner,4
34,17.78,3.27,Male,No,Sat,Dinner,2
35,24.06,3.6,Male,No,Sat,Dinner,3
36,16.31,2.0,Male,No,Sat,Dinner,3
37,16.93,3.07,Female,No,Sat,Dinner,3
38,18.69,2.31,Male,No,Sat,Dinner,3
39,31.27,5.0,Male,No,Sat,Dinner,3
40,16.04,2.24,Male,No,Sat,Dinner,3
41,17.46,2.54,Male,No,Sun,Dinner,2
42,13.94,3.06,Male,No,Sun,Dinner,2
43,9.68,1.32,Male,No,Sun,Dinner,2
44,30.4,5.6,Male,No,Sun,Dinner,4
45,18.29,3.0,Male,No,Sun,Dinner,2
46,22.23,5.0,Male,No,Sun,Dinner,2
47,32.4,6.0,Male,No,Sun,Dinner,4
48,28.55,2.05,Male,No,Sun,Dinner,3
49,18.04,3.0,Male,No,Sun,Dinner,2
50,12.54,2.5,Male,No,Sun,Dinner,2
51,10.29,2.6,Female,No,Sun,Dinner,2
52,34.81,5.2,Female,No,Sun,Dinner,4
53,9.94,1.56,Male,No,Sun,Dinner,2
54,25.56,4.34,Male,No,Sun,Dinner,4
55,19.49,3.51,Male,No,Sun,Dinner,2
56,38.01,3.0,Male,Yes,Sat,Dinner,4
57,26.41,1.5,Female,No,Sat,Dinner,2
58,11.24,1.76,Male,Yes,Sat,Dinner,2
59,48.27,6.73,Male,No,Sat,Dinner,4
60,20.29,3.21,Male,Yes,Sat,Dinner,2
61,13.81,2.0,Male,Yes,Sat,Dinner,2
62,11.02,1.98,Male,Yes,Sat,Dinner,2
63,18.29,3.76,Male,Yes,Sat,Dinner,4
64,17.59,2.64,Male,No,Sat,Dinner,3
65,20.08,3.15,Male,No,Sat,Dinner,3
66,16.45,2.47,Female,No,Sat,Dinner,2
67,3.07,1.0,Female,Yes,Sat,Dinner,1
68,20.23,2.01,Male,No,Sat,Dinner,2
69,15.01,2.09,Male,Yes,Sat,Dinner,2
70,12.02,1.97,Male,No,Sat,Dinner,2
71,17.07,3.0,Female,No,Sat,Dinner,3
72,26.86,3.14,Female,Yes,Sat,Dinner,2
73,25.28,5.0,Female,Yes,Sat,Dinner,2
74,14.73,2.2,Female,No,Sat,Dinner,2
75,10.51,1.25,Male,No,Sat,Dinner,2
76,17.92,3.08,Male,Yes,Sat,Dinner,2
77,27.2,4.0,Male,No,Thur,Lunch,4
78,22.76,3.0,Male,No,Thur,Lunch,2
79,17.29,2.71,Male,No,Thur,Lunch,2
80,19.44,3.0,Male,Yes,Thur,Lunch,2
81,16.66,3.4,Male,No,Thur,Lunch,2
82,10.07,1.83,Female,No,Thur,Lunch,1
83,32.68,5.0,Male,Yes,Thur,Lunch,2
84,15.98,2.03,Male,No,Thur,Lunch,2
85,34.83,5.17,Female,No,Thur,Lunch,4
86,13.03,2.0,Male,No,Thur,Lunch,2
87,18.28,4.0,Male,No,Thur,Lunch,2
88,24.71,5.85,Male,No,Thur,Lunch,2
89,21.16,3.0,Male,No,Thur,Lunch,2
90,28.97,3.0,Male,Yes,Fri,Dinner,2
91,22.49,3.5,Male,No,Fri,Dinner,2
92,5.75,1.0,Female,Yes,Fri,Dinner,2
93,16.32,4.3,Female,Yes,Fri,Dinner,2
94,22.75,3.25,Female,No,Fri,Dinner,2
95,40.17,4.73,Male,Yes,Fri,Dinner,4
96,27.28,4.0,Male,Yes,Fri,Dinner,2
97,12.03,1.5,Male,Yes,Fri,Dinner,2
98,21.01,3.0,Male,Yes,Fri,Dinner,2
99,12.46,1.5,Male,No,Fri,Dinner,2
100,11.35,2.5,Female,Yes,Fri,Dinner,2
101,15.38,3.0,Female,Yes,Fri,Dinner,2
102,44.3,2.5,Female,Yes,Sat,Dinner,3
103,22.42,3.48,Female,Yes,Sat,Dinner,2
104,20.92,4.08,Female,No,Sat,Dinner,2
105,15.36,1.64,Male,Yes,Sat,Dinner,2
106,20.49,4.06,Male,Yes,Sat,Dinner,2
107,25.21,4.29,Male,Yes,Sat,Dinner,2
108,18.24,3.76,Male,No,Sat,Dinner,2
109,14.31,4.0,Female,Yes,Sat,Dinner,2
110,14.0,3.0,Male,No,Sat,Dinner,2
111,7.25,1.0,Female,No,Sat,Dinner,1
112,38.07,4.0,Male,No,Sun,Dinner,3
113,23.95,2.55,Male,No,Sun,Dinner,2
114,25.71,4.0,Female,No,Sun,Dinner,3
115,17.31,3.5,Female,No,Sun,Dinner,2
116,29.93,5.07,Male,No,Sun,Dinner,4
117,10.65,1.5,Female,No,Thur,Lunch,2
118,12.43,1.8,Female,No,Thur,Lunch,2
119,24.08,2.92,Female,No,Thur,Lunch,4
120,11.69,2.31,Male,No,Thur,Lunch,2
121,13.42,1.68,Female,No,Thur,Lunch,2
122,14.26,2.5,Male,No,Thur,Lunch,2
123,15.95,2.0,Male,No,Thur,Lunch,2
124,12.48,2.52,Female,No,Thur,Lunch,2
125,29.8,4.2,Female,No,Thur,Lunch,6
126,8.52,1.48,Male,No,Thur,Lunch,2
127,14.52,2.0,Female,No,Thur,Lunch,2
128,11.38,2.0,Female,No,Thur,Lunch,2
129,22.82,2.18,Male,No,Thur,Lunch,3
130,19.08,1.5,Male,No,Thur,Lunch,2
131,20.27,2.83,Female,No,Thur,Lunch,2
132,11.17,1.5,Female,No,Thur,Lunch,2
133,12.26,2.0,Female,No,Thur,Lunch,2
134,18.26,3.25,Female,No,Thur,Lunch,2
135,8.51,1.25,Female,No,Thur,Lunch,2
136,10.33,2.0,Female,No,Thur,Lunch,2
137,14.15,2.0,Female,No,Thur,Lunch,2
138,16.0,2.0,Male,Yes,Thur,Lunch,2
139,13.16,2.75,Female,No,Thur,Lunch,2
140,17.47,3.5,Female,No,Thur,Lunch,2
141,34.3,6.7,Male,No,Thur,Lunch,6
142,41.19,5.0,Male,No,Thur,Lunch,5
143,27.05,5.0,Female,No,Thur,Lunch,6
144,16.43,2.3,Female,No,Thur,Lunch,2
145,8.35,1.5,Female,No,Thur,Lunch,2
146,18.64,1.36,Female,No,Thur,Lunch,3
147,11.87,1.63,Female,No,Thur,Lunch,2
148,9.78,1.73,Male,No,Thur,Lunch,2
149,7.51,2.0,Male,No,Thur,Lunch,2
150,14.07,2.5,Male,No,Sun,Dinner,2
151,13.13,2.0,Male,No,Sun,Dinner,2
152,17.26,2.74,Male,No,Sun,Dinner,3
153,24.55,2.0,Male,No,Sun,Dinner,4
154,19.77,2.0,Male,No,Sun,Dinner,4
155,29.85,5.14,Female,No,Sun,Dinner,5
156,48.17,5.0,Male,No,Sun,Dinner,6
157,25.0,3.75,Female,No,Sun,Dinner,4
158,13.39,2.61,Female,No,Sun,Dinner,2
159,16.49,2.0,Male,No,Sun,Dinner,4
160,21.5,3.5,Male,No,Sun,Dinner,4
161,12.66,2.5,Male,No,Sun,Dinner,2
162,16.21,2.0,Female,No,Sun,Dinner,3
163,13.81,2.0,Male,No,Sun,Dinner,2
164,17.51,3.0,Female,Yes,Sun,Dinner,2
165,24.52,3.48,Male,No,Sun,Dinner,3
166,20.76,2.24,Male,No,Sun,Dinner,2
167,31.71,4.5,Male,No,Sun,Dinner,4
168,10.59,1.61,Female,Yes,Sat,Dinner,2
169,10.63,2.0,Female,Yes,Sat,Dinner,2
170,50.81,10.0,Male,Yes,Sat,Dinner,3
171,15.81,3.16,Male,Yes,Sat,Dinner,2
172,7.25,5.15,Male,Yes,Sun,Dinner,2
173,31.85,3.18,Male,Yes,Sun,Dinner,2
174,16.82,4.0,Male,Yes,Sun,Dinner,2
175,32.9,3.11,Male,Yes,Sun,Dinner,2
176,17.89,2.0,Male,Yes,Sun,Dinner,2
177,14.48,2.0,Male,Yes,Sun,Dinner,2
178,9.6,4.0,Female,Yes,Sun,Dinner,2
179,34.63,3.55,Male,Yes,Sun,Dinner,2
180,34.65,3.68,Male,Yes,Sun,Dinner,4
181,23.33,5.65,Male,Yes,Sun,Dinner,2
182,45.35,3.5,Male,Yes,Sun,Dinner,3
183,23.17,6.5,Male,Yes,Sun,Dinner,4
184,40.55,3.0,Male,Yes,Sun,Dinner,2
185,20.69,5.0,Male,No,Sun,Dinner,5
186,20.9,3.5,Female,Yes,Sun,Dinner,3
187,30.46,2.0,Male,Yes,Sun,Dinner,5
188,18.15,3.5,Female,Yes,Sun,Dinner,3
189,23.1,4.0,Male,Yes,Sun,Dinner,3
190,15.69,1.5,Male,Yes,Sun,Dinner,2
191,19.81,4.19,Female,Yes,Thur,Lunch,2
192,28.44,2.56,Male,Yes,Thur,Lunch,2
193,15.48,2.02,Male,Yes,Thur,Lunch,2
194,16.58,4.0,Male,Yes,Thur,Lunch,2
195,7.56,1.44,Male,No,Thur,Lunch,2
196,10.34,2.0,Male,Yes,Thur,Lunch,2
197,43.11,5.0,Female,Yes,Thur,Lunch,4
198,13.0,2.0,Female,Yes,Thur,Lunch,2
199,13.51,2.0,Male,Yes,Thur,Lunch,2
200,18.71,4.0,Male,Yes,Thur,Lunch,3
201,12.74,2.01,Female,Yes,Thur,Lunch,2
202,13.0,2.0,Female,Yes,Thur,Lunch,2
203,16.4,2.5,Female,Yes,Thur,Lunch,2
204,20.53,4.0,Male,Yes,Thur,Lunch,4
205,16.47,3.23,Female,Yes,Thur,Lunch,3
206,26.59,3.41,Male,Yes,Sat,Dinner,3
207,38.73,3.0,Male,Yes,Sat,Dinner,4
208,24.27,2.03,Male,Yes,Sat,Dinner,2
209,12.76,2.23,Female,Yes,Sat,Dinner,2
210,30.06,2.0,Male,Yes,Sat,Dinner,3
211,25.89,5.16,Male,Yes,Sat,Dinner,4
212,48.33,9.0,Male,No,Sat,Dinner,4
213,13.27,2.5,Female,Yes,Sat,Dinner,2
214,28.17,6.5,Female,Yes,Sat,Dinner,3
215,12.9,1.1,Female,Yes,Sat,Dinner,2
216,28.15,3.0,Male,Yes,Sat,Dinner,5
217,11.59,1.5,Male,Yes,Sat,Dinner,2
218,7.74,1.44,Male,Yes,Sat,Dinner,2
219,30.14,3.09,Female,Yes,Sat,Dinner,4
220,12.16,2.2,Male,Yes,Fri,Lunch,2
221,13.42,3.48,Female,Yes,Fri,Lunch,2
222,8.58,1.92,Male,Yes,Fri,Lunch,1
223,15.98,3.0,Female,No,Fri,Lunch,3
224,13.42,1.58,Male,Yes,Fri,Lunch,2
225,16.27,2.5,Female,Yes,Fri,Lunch,2
226,10.09,2.0,Female,Yes,Fri,Lunch,2
227,20.45,3.0,Male,No,Sat,Dinner,4
228,13.28,2.72,Male,No,Sat,Dinner,2
229,22.12,2.88,Female,Yes,Sat,Dinner,2
230,24.01,2.0,Male,Yes,Sat,Dinner,4
231,15.69,3.0,Male,Yes,Sat,Dinner,3
232,11.61,3.39,Male,No,Sat,Dinner,2
233,10.77,1.47,Male,No,Sat,Dinner,2
234,15.53,3.0,Male,Yes,Sat,Dinner,2
235,10.07,1.25,Male,No,Sat,Dinner,2
236,12.6,1.0,Male,Yes,Sat,Dinner,2
237,32.83,1.17,Male,Yes,Sat,Dinner,2
238,35.83,4.67,Female,No,Sat,Dinner,3
239,29.03,5.92,Male,No,Sat,Dinner,3
240,27.18,2.0,Female,Yes,Sat,Dinner,2
241,22.67,2.0,Male,Yes,Sat,Dinner,2
242,17.82,1.75,Male,No,Sat,Dinner,2
243,18.78,3.0,Female,No,Thur,Dinner,2


================================================
FILE: 07_Visualization/Titanic_Disaster/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Titanic Disaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic).  \n",
    "To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Titanic_Desaster/train.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable titanic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Set PassengerId as the index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a pie chart presenting the male/female proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. How many people survived?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Create a histogram with the Fare payed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 07_Visualization/Titanic_Disaster/Exercises_code_with_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Titanic Disaster\n",
    "\n",
    "Check out [Titanic Visualization Exercises Video Tutorial](https://youtu.be/CBT0buoF_Ns) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic).  \n",
    "To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Titanic_Desaster/train.csv) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable titanic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv'\n",
    "\n",
    "titanic = pd.read_csv(url)\n",
    "\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Set PassengerId as the index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.set_index('PassengerId').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a pie chart presenting the male/female proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gliu3hKKi+vzww0ssWPAIixaNZfPmVixbdvmvHlNSUp2SkioAFBdXBYowpuSX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\nWlJBiYhIWvo/rI6mZkORh2MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e78690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sum the instances of males and females\n",
    "males = (titanic['Sex'] == 'male').sum()\n",
    "females = (titanic['Sex'] == 'female').sum()\n",
    "\n",
    "# put them into a list called proportions\n",
    "proportions = [males, females]\n",
    "\n",
    "# Create a pie chart\n",
    "plt.pie(\n",
    "    # using proportions\n",
    "    proportions,\n",
    "    \n",
    "    # with the labels being officer names\n",
    "    labels = ['Males', 'Females'],\n",
    "    \n",
    "    # with no shadows\n",
    "    shadow = False,\n",
    "    \n",
    "    # with colors\n",
    "    colors = ['blue','red'],\n",
    "    \n",
    "    # with one slide exploded out\n",
    "    explode = (0.15 , 0),\n",
    "    \n",
    "    # with the start angle at 90%\n",
    "    startangle = 90,\n",
    "    \n",
    "    # with the percent listed as a fraction\n",
    "    autopct = '%1.1f%%'\n",
    "    )\n",
    "\n",
    "# View the plot drop above\n",
    "plt.axis('equal')\n",
    "\n",
    "# Set labels\n",
    "plt.title(\"Sex Proportion\")\n",
    "\n",
    "# View the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 85)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eSICq7WIHmU8Bf6qUigHHgIe11p5S6svAk/jNaZ/WWucucrrERbQa5qysZnJ/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\n+rWhg8zCobPbiHQMEEsMYTb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a678c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creates the plot using\n",
    "lm = sns.lmplot(x = 'Age', y = 'Fare', data = titanic, hue = 'Sex', fit_reg=False)\n",
    "\n",
    "# set title\n",
    "lm.set(title = 'Fare x Age')\n",
    "\n",
    "# get the axes object and tweak it\n",
    "axes = lm.axes\n",
    "axes[0,0].set_ylim(-5,)\n",
    "axes[0,0].set_xlim(-5,85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. How many people survived?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Survived.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Create a histogram with the Fare payed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1177b9e50>"
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     "metadata": {},
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   ],
   "source": [
    "# sort the values from the top to the least value and slice the first 5 items\n",
    "df = titanic.Fare.sort_values(ascending = False)\n",
    "df\n",
    "\n",
    "# create bins interval using numpy\n",
    "binsVal = np.arange(0,600,10)\n",
    "binsVal\n",
    "\n",
    "# create the plot\n",
    "plt.hist(df, bins = binsVal)\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Fare Payed Histrogram')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "collapsed": true
   },
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================================================
FILE: 07_Visualization/Titanic_Disaster/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Titanic Disaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic).  \n",
    "To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/07_Visualization/Titanic_Desaster/train.csv) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable titanic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Set PassengerId as the index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a pie chart presenting the male/female proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gliu3hKKi+vzww0ssWPAIixaNZfPmVixbdvmvHlNSUp2SkioAFBdXBYowpuSX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\nWlJBiYhIWvo/rI6mZkORh2MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e78690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 85)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eSICq7WIHmU8Bf6qUigHHgIe11p5S6svAk/jNaZ/WWucucrrERbQa5qysZnJ/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\n+rWhg8zCobPbiHQMEEsMYTb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a678c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. How many people survived?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Create a histogram with the Fare payed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEZCAYAAACEkhK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c10e290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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================================================
FILE: 07_Visualization/Titanic_Disaster/train.csv
================================================
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
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187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
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200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
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231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
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233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
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237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
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239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
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252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
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255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
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276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
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311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
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335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
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339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
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343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
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348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
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368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
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373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
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376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
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385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
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393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
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397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
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404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
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409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
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421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
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424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
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426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
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428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
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438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
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446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
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452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
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492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
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529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
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544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
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550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
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564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
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677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
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726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
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792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
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794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
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800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
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804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
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808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
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811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
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871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q


================================================
FILE: 08_Creating_Series_and_DataFrames/Pokemon/Exercises-with-solutions-and-code.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pokemon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create the data.\n",
    "\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create a data dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data = {\"name\": ['Bulbasaur', 'Charmander','Squirtle','Caterpie'],\n",
    "            \"evolution\": ['Ivysaur','Charmeleon','Wartortle','Metapod'],\n",
    "            \"type\": ['grass', 'fire', 'water', 'bug'],\n",
    "            \"hp\": [45, 39, 44, 45],\n",
    "            \"pokedex\": ['yes', 'no','yes','no']                        \n",
    "            }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called pokemon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>evolution</th>\n",
       "      <th>hp</th>\n",
       "      <th>name</th>\n",
       "      <th>pokedex</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>45</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>yes</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>39</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>no</td>\n",
       "      <td>fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wartortle</td>\n",
       "      <td>44</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>yes</td>\n",
       "      <td>water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Metapod</td>\n",
       "      <td>45</td>\n",
       "      <td>Caterpie</td>\n",
       "      <td>no</td>\n",
       "      <td>bug</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    evolution  hp        name pokedex   type\n",
       "0     Ivysaur  45   Bulbasaur     yes  grass\n",
       "1  Charmeleon  39  Charmander      no   fire\n",
       "2   Wartortle  44    Squirtle     yes  water\n",
       "3     Metapod  45    Caterpie      no    bug"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon = pd.DataFrame(raw_data)\n",
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Ops...it seems the DataFrame columns are in alphabetical order. Place  the order of the columns as name, type, hp, evolution, pokedex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>hp</th>\n",
       "      <th>evolution</th>\n",
       "      <th>pokedex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>grass</td>\n",
       "      <td>45</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>fire</td>\n",
       "      <td>39</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Squirtle</td>\n",
       "      <td>water</td>\n",
       "      <td>44</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caterpie</td>\n",
       "      <td>bug</td>\n",
       "      <td>45</td>\n",
       "      <td>Metapod</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name   type  hp   evolution pokedex\n",
       "0   Bulbasaur  grass  45     Ivysaur     yes\n",
       "1  Charmander   fire  39  Charmeleon      no\n",
       "2    Squirtle  water  44   Wartortle     yes\n",
       "3    Caterpie    bug  45     Metapod      no"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon = pokemon[['name', 'type', 'hp', 'evolution','pokedex']]\n",
    "pokemon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Add another column called place, and insert what you have in mind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>hp</th>\n",
       "      <th>evolution</th>\n",
       "      <th>pokedex</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>grass</td>\n",
       "      <td>45</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>yes</td>\n",
       "      <td>park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>fire</td>\n",
       "      <td>39</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>no</td>\n",
       "      <td>street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Squirtle</td>\n",
       "      <td>water</td>\n",
       "      <td>44</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>yes</td>\n",
       "      <td>lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caterpie</td>\n",
       "      <td>bug</td>\n",
       "      <td>45</td>\n",
       "      <td>Metapod</td>\n",
       "      <td>no</td>\n",
       "      <td>forest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name   type  hp   evolution pokedex   place\n",
       "0   Bulbasaur  grass  45     Ivysaur     yes    park\n",
       "1  Charmander   fire  39  Charmeleon      no  street\n",
       "2    Squirtle  water  44   Wartortle     yes    lake\n",
       "3    Caterpie    bug  45     Metapod      no  forest"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon['place'] = ['park','street','lake','forest']\n",
    "pokemon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Present the type of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         object\n",
       "type         object\n",
       "hp            int64\n",
       "evolution    object\n",
       "pokedex      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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    "version": 2
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   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 08_Creating_Series_and_DataFrames/Pokemon/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pokemon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create the data.\n",
    "\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create a data dictionary that looks like the DataFrame below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called pokemon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>evolution</th>\n",
       "      <th>hp</th>\n",
       "      <th>name</th>\n",
       "      <th>pokedex</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>45</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>yes</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>39</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>no</td>\n",
       "      <td>fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wartortle</td>\n",
       "      <td>44</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>yes</td>\n",
       "      <td>water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Metapod</td>\n",
       "      <td>45</td>\n",
       "      <td>Caterpie</td>\n",
       "      <td>no</td>\n",
       "      <td>bug</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    evolution  hp        name pokedex   type\n",
       "0     Ivysaur  45   Bulbasaur     yes  grass\n",
       "1  Charmeleon  39  Charmander      no   fire\n",
       "2   Wartortle  44    Squirtle     yes  water\n",
       "3     Metapod  45    Caterpie      no    bug"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Ops...it seems the DataFrame columns are in alphabetical order. Place  the order of the columns as name, type, hp, evolution, pokedex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Add another column called place, and insert what you have in mind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Present the type of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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   "codemirror_mode": {
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    "version": 2
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   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 08_Creating_Series_and_DataFrames/Pokemon/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pokemon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create the data.\n",
    "\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create a data dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called pokemon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>evolution</th>\n",
       "      <th>hp</th>\n",
       "      <th>name</th>\n",
       "      <th>pokedex</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>45</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>yes</td>\n",
       "      <td>grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>39</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>no</td>\n",
       "      <td>fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wartortle</td>\n",
       "      <td>44</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>yes</td>\n",
       "      <td>water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Metapod</td>\n",
       "      <td>45</td>\n",
       "      <td>Caterpie</td>\n",
       "      <td>no</td>\n",
       "      <td>bug</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    evolution  hp        name pokedex   type\n",
       "0     Ivysaur  45   Bulbasaur     yes  grass\n",
       "1  Charmeleon  39  Charmander      no   fire\n",
       "2   Wartortle  44    Squirtle     yes  water\n",
       "3     Metapod  45    Caterpie      no    bug"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Ops...it seems the DataFrame columns are in alphabetical order. Place  the order of the columns as name, type, hp, evolution, pokedex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>hp</th>\n",
       "      <th>evolution</th>\n",
       "      <th>pokedex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>grass</td>\n",
       "      <td>45</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>fire</td>\n",
       "      <td>39</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Squirtle</td>\n",
       "      <td>water</td>\n",
       "      <td>44</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caterpie</td>\n",
       "      <td>bug</td>\n",
       "      <td>45</td>\n",
       "      <td>Metapod</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name   type  hp   evolution pokedex\n",
       "0   Bulbasaur  grass  45     Ivysaur     yes\n",
       "1  Charmander   fire  39  Charmeleon      no\n",
       "2    Squirtle  water  44   Wartortle     yes\n",
       "3    Caterpie    bug  45     Metapod      no"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Add another column called place, and insert what you have in mind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>hp</th>\n",
       "      <th>evolution</th>\n",
       "      <th>pokedex</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>grass</td>\n",
       "      <td>45</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>yes</td>\n",
       "      <td>park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>fire</td>\n",
       "      <td>39</td>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>no</td>\n",
       "      <td>street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Squirtle</td>\n",
       "      <td>water</td>\n",
       "      <td>44</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>yes</td>\n",
       "      <td>lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Caterpie</td>\n",
       "      <td>bug</td>\n",
       "      <td>45</td>\n",
       "      <td>Metapod</td>\n",
       "      <td>no</td>\n",
       "      <td>forest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name   type  hp   evolution pokedex   place\n",
       "0   Bulbasaur  grass  45     Ivysaur     yes    park\n",
       "1  Charmander   fire  39  Charmeleon      no  street\n",
       "2    Squirtle  water  44   Wartortle     yes    lake\n",
       "3    Caterpie    bug  45     Metapod      no  forest"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Present the type of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name         object\n",
       "type         object\n",
       "hp            int64\n",
       "evolution    object\n",
       "pokedex      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apple Stock\n",
    "\n",
    "Check out [Apple Stock Exercises Video Tutorial](https://youtu.be/wpXkR_IZcug) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "We are going to use Apple's stock price.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable apple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close    Volume  Adj Close\n",
       "0  2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "1  2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2  2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "3  2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "4  2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv'\n",
    "apple = pd.read_csv(url)\n",
    "\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.  Check out the type of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Volume         int64\n",
       "Adj Close    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Transform the Date column as a datetime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2014-07-08\n",
       "1   2014-07-07\n",
       "2   2014-07-03\n",
       "3   2014-07-02\n",
       "4   2014-07-01\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.Date = pd.to_datetime(apple.Date)\n",
    "\n",
    "apple['Date'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Set the date as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-08</th>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-07</th>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-03</th>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-02</th>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume  Adj Close\n",
       "Date                                                       \n",
       "2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple = apple.set_index('Date')\n",
    "\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Is there any duplicate dates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NO! All are unique\n",
    "apple.index.is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  Ops...it seems the index is from the most recent date. Make the first entry the oldest date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>28.75</td>\n",
       "      <td>28.87</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.75</td>\n",
       "      <td>117258400</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>27.38</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.25</td>\n",
       "      <td>27.25</td>\n",
       "      <td>43971200</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>25.37</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.25</td>\n",
       "      <td>26432000</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>25.87</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.87</td>\n",
       "      <td>25.87</td>\n",
       "      <td>21610400</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>26.63</td>\n",
       "      <td>26.75</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.63</td>\n",
       "      <td>18362400</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Adj Close\n",
       "Date                                                        \n",
       "1980-12-12  28.75  28.87  28.75  28.75  117258400       0.45\n",
       "1980-12-15  27.38  27.38  27.25  27.25   43971200       0.42\n",
       "1980-12-16  25.37  25.37  25.25  25.25   26432000       0.39\n",
       "1980-12-17  25.87  26.00  25.87  25.87   21610400       0.40\n",
       "1980-12-18  26.63  26.75  26.63  26.63   18362400       0.41"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.sort_index(ascending = True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Get the last business day of each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-31</th>\n",
       "      <td>30.481538</td>\n",
       "      <td>30.567692</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>25862523</td>\n",
       "      <td>0.473077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>31.754762</td>\n",
       "      <td>31.826667</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>7249866</td>\n",
       "      <td>0.493810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-27</th>\n",
       "      <td>26.480000</td>\n",
       "      <td>26.572105</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>4231831</td>\n",
       "      <td>0.411053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-31</th>\n",
       "      <td>24.937727</td>\n",
       "      <td>25.016818</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>7962690</td>\n",
       "      <td>0.387727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-30</th>\n",
       "      <td>27.286667</td>\n",
       "      <td>27.368095</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>6392000</td>\n",
       "      <td>0.423333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close    Volume  Adj Close\n",
       "Date                                                                       \n",
       "1980-12-31  30.481538  30.567692  30.443077  30.443077  25862523   0.473077\n",
       "1981-01-30  31.754762  31.826667  31.654762  31.654762   7249866   0.493810\n",
       "1981-02-27  26.480000  26.572105  26.407895  26.407895   4231831   0.411053\n",
       "1981-03-31  24.937727  25.016818  24.836364  24.836364   7962690   0.387727\n",
       "1981-04-30  27.286667  27.368095  27.227143  27.227143   6392000   0.423333"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_month = apple.resample('BM').mean()\n",
    "\n",
    "apple_month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  What is the difference in days between the first day and the oldest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12261"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(apple.index.max() - apple.index.min()).days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  How many months in the data we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "404"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_months = apple.resample('BM').mean()\n",
    "\n",
    "len(apple_months.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rtVoYY0ZL+iTZBWbNmvXpfl1dnerq6nIoBwAAACgu/fpJX/+6dPXV7nVHh/9e//6uNaOn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\nt0fmfdky6N49Mq5durTuM6++Gr70pXj+5z9HYSx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+HuiaXncvdrpL7aE4K8bl8iBustaV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\nQlYWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116367690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# makes the plot and assign it to a variable\n",
    "appl_open = apple['Adj Close'].plot(title = \"Apple Stock\")\n",
    "\n",
    "# changes the size of the graph\n",
    "fig = appl_open.get_figure()\n",
    "fig.set_size_inches(13.5, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: 09_Time_Series/Apple_Stock/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apple Stock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "We are going to use Apple's stock price.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable apple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.  Check out the type of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Transform the Date column as a datetime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Set the date as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Is there any duplicate dates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  Ops...it seems the index is from the most recent date. Make the first entry the oldest date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Get the last business day of each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  What is the difference in days between the first day and the oldest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  How many months in the data we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 09_Time_Series/Apple_Stock/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apple Stock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "We are going to use Apple's stock price.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable apple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close    Volume  Adj Close\n",
       "0  2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "1  2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2  2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "3  2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "4  2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.  Check out the type of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Volume         int64\n",
       "Adj Close    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Transform the Date column as a datetime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2014-07-08\n",
       "1   2014-07-07\n",
       "2   2014-07-03\n",
       "3   2014-07-02\n",
       "4   2014-07-01\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Set the date as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-08</th>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-07</th>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-03</th>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-02</th>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume  Adj Close\n",
       "Date                                                       \n",
       "2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Is there any duplicate dates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NO! All are unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  Ops...it seems the index is from the most recent date. Make the first entry the oldest date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>28.75</td>\n",
       "      <td>28.87</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.75</td>\n",
       "      <td>117258400</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>27.38</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.25</td>\n",
       "      <td>27.25</td>\n",
       "      <td>43971200</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>25.37</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.25</td>\n",
       "      <td>26432000</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>25.87</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.87</td>\n",
       "      <td>25.87</td>\n",
       "      <td>21610400</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>26.63</td>\n",
       "      <td>26.75</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.63</td>\n",
       "      <td>18362400</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Adj Close\n",
       "Date                                                        \n",
       "1980-12-12  28.75  28.87  28.75  28.75  117258400       0.45\n",
       "1980-12-15  27.38  27.38  27.25  27.25   43971200       0.42\n",
       "1980-12-16  25.37  25.37  25.25  25.25   26432000       0.39\n",
       "1980-12-17  25.87  26.00  25.87  25.87   21610400       0.40\n",
       "1980-12-18  26.63  26.75  26.63  26.63   18362400       0.41"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Get the last business day of each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-31</th>\n",
       "      <td>30.481538</td>\n",
       "      <td>30.567692</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>25862523</td>\n",
       "      <td>0.473077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>31.754762</td>\n",
       "      <td>31.826667</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>7249866</td>\n",
       "      <td>0.493810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-27</th>\n",
       "      <td>26.480000</td>\n",
       "      <td>26.572105</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>4231831</td>\n",
       "      <td>0.411053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-31</th>\n",
       "      <td>24.937727</td>\n",
       "      <td>25.016818</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>7962690</td>\n",
       "      <td>0.387727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-30</th>\n",
       "      <td>27.286667</td>\n",
       "      <td>27.368095</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>6392000</td>\n",
       "      <td>0.423333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close    Volume  Adj Close\n",
       "Date                                                                       \n",
       "1980-12-31  30.481538  30.567692  30.443077  30.443077  25862523   0.473077\n",
       "1981-01-30  31.754762  31.826667  31.654762  31.654762   7249866   0.493810\n",
       "1981-02-27  26.480000  26.572105  26.407895  26.407895   4231831   0.411053\n",
       "1981-03-31  24.937727  25.016818  24.836364  24.836364   7962690   0.387727\n",
       "1981-04-30  27.286667  27.368095  27.227143  27.227143   6392000   0.423333"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  What is the difference in days between the first day and the oldest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12261"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  How many months in the data we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "404"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8MQ7RXZ7a2lzgsLZwq1/Hd4UiWAAAAAAlxBtbEd3lqa3N74pUKPEtFtXVsduC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\nQlYWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116367690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 09_Time_Series/Apple_Stock/appl_1980_2014.csv
================================================
Date,Open,High,Low,Close,Volume,Adj Close
2014-07-08,96.27,96.80,93.92,95.35,65130000,95.35
2014-07-07,94.14,95.99,94.10,95.97,56305400,95.97
2014-07-03,93.67,94.10,93.20,94.03,22891800,94.03
2014-07-02,93.87,94.06,93.09,93.48,28420900,93.48
2014-07-01,93.52,94.07,93.13,93.52,38170200,93.52
2014-06-30,92.10,93.73,92.09,92.93,49482300,92.93
2014-06-27,90.82,92.00,90.77,91.98,64006800,91.98
2014-06-26,90.37,91.05,89.80,90.90,32595800,90.90
2014-06-25,90.21,90.70,89.65,90.36,36852200,90.36
2014-06-24,90.75,91.74,90.19,90.28,38988300,90.28
2014-06-23,91.32,91.62,90.60,90.83,43618200,90.83
2014-06-20,91.85,92.55,90.90,90.91,100813200,90.91
2014-06-19,92.29,92.30,91.34,91.86,35486400,91.86
2014-06-18,92.27,92.29,91.35,92.18,33493800,92.18
2014-06-17,92.31,92.70,91.80,92.08,29689800,92.08
2014-06-16,91.51,92.75,91.45,92.20,35561000,92.20
2014-06-13,92.20,92.44,90.88,91.28,54525000,91.28
2014-06-12,94.04,94.12,91.90,92.29,54749000,92.29
2014-06-11,94.13,94.76,93.47,93.86,45681000,93.86
2014-06-10,94.73,95.05,93.57,94.25,62777000,94.25
2014-06-09,92.70,93.88,91.75,93.70,75415000,93.70
2014-06-06,649.90,651.26,644.47,645.57,87484600,92.22
2014-06-05,646.20,649.37,642.61,647.35,75951400,92.48
2014-06-04,637.44,647.89,636.11,644.82,83870500,92.12
2014-06-03,628.46,638.74,628.25,637.54,73177300,91.08
2014-06-02,633.96,634.83,622.50,628.65,92337700,89.81
2014-05-30,637.98,644.17,628.90,633.00,141005200,90.43
2014-05-29,627.85,636.87,627.77,635.38,94118500,90.77
2014-05-28,626.02,629.83,623.78,624.01,78870400,89.14
2014-05-27,615.88,625.86,615.63,625.63,87216500,89.38
2014-05-23,607.25,614.73,606.47,614.13,58052400,87.73
2014-05-22,606.60,609.85,604.10,607.27,50190000,86.75
2014-05-21,603.83,606.70,602.06,606.31,49214900,86.62
2014-05-20,604.51,606.40,600.73,604.71,58709000,86.39
2014-05-19,597.85,607.33,597.33,604.59,79438800,86.37
2014-05-16,588.63,597.53,585.40,597.51,69064100,85.36
2014-05-15,594.70,596.60,588.04,588.82,57711500,84.12
2014-05-14,592.43,597.40,591.74,593.87,41601000,84.84
2014-05-13,592.00,594.54,590.70,593.76,39934300,84.82
2014-05-12,587.49,593.66,587.40,592.83,53302200,84.69
2014-05-09,584.54,586.25,580.33,585.54,72899400,83.65
2014-05-08,588.25,594.41,586.40,587.99,57574300,84.00
2014-05-07,595.25,597.29,587.73,592.33,70716100,84.15
2014-05-06,601.80,604.41,594.41,594.41,93641100,84.44
2014-05-05,590.14,601.00,590.00,600.96,71766800,85.37
2014-05-02,592.34,594.20,589.71,592.58,47878600,84.18
2014-05-01,592.00,594.80,586.36,591.48,61012000,84.03
2014-04-30,592.64,599.43,589.80,590.09,114160200,83.83
2014-04-29,593.74,595.98,589.51,592.33,84344400,84.15
2014-04-28,572.80,595.75,572.55,594.09,167371400,84.40
2014-04-25,564.53,571.99,563.96,571.94,97568800,81.25
2014-04-24,568.21,570.00,560.73,567.77,189977900,80.66
2014-04-23,529.06,531.13,524.45,524.75,98735000,74.55
2014-04-22,528.31,531.83,526.50,531.70,50640800,75.54
2014-04-21,525.34,532.14,523.96,531.17,45637200,75.46
2014-04-17,520.00,527.76,519.20,524.94,71083600,74.57
2014-04-16,518.05,521.09,514.14,519.01,53691400,73.73
2014-04-15,520.27,521.64,511.33,517.96,66622500,73.58
2014-04-14,521.90,522.16,517.21,521.68,51418500,74.11
2014-04-11,519.00,522.83,517.14,519.61,67929400,73.82
2014-04-10,530.68,532.24,523.17,523.48,59913000,74.37
2014-04-09,522.64,530.49,522.02,530.32,51542400,75.34
2014-04-08,525.19,526.12,518.70,523.44,60972100,74.36
2014-04-07,528.02,530.90,521.89,523.47,72462600,74.37
2014-04-04,539.81,540.00,530.58,531.82,68812800,75.55
2014-04-03,541.39,542.50,537.64,538.79,40586000,76.54
2014-04-02,542.38,543.48,540.26,542.55,45105200,77.08
2014-04-01,537.76,541.87,536.77,541.65,50190000,76.95
2014-03-31,539.23,540.81,535.93,536.74,42167300,76.25
2014-03-28,538.32,538.94,534.25,536.86,50141000,76.27
2014-03-27,540.02,541.50,535.12,537.46,55507900,76.35
2014-03-26,546.52,549.00,538.86,539.78,74942000,76.68
2014-03-25,541.50,545.75,539.59,544.99,70573300,77.42
2014-03-24,538.42,540.50,535.06,539.19,88925200,76.60
2014-03-21,531.93,533.75,526.33,532.87,93511600,75.70
2014-03-20,529.89,532.67,527.35,528.70,52099600,75.11
2014-03-19,532.26,536.24,529.00,531.26,56189000,75.47
2014-03-18,525.90,531.97,525.20,531.40,52411800,75.49
2014-03-17,527.70,529.97,525.85,526.74,49886200,74.83
2014-03-14,528.79,530.89,523.00,524.69,59299800,74.54
2014-03-13,537.44,539.66,529.16,530.65,64435700,75.39
2014-03-12,534.51,537.35,532.00,536.61,49831600,76.23
2014-03-11,535.45,538.74,532.59,536.09,69806100,76.16
2014-03-10,528.36,533.33,528.34,530.92,44646000,75.42
2014-03-07,531.09,531.98,526.05,530.44,55182400,75.36
2014-03-06,532.79,534.44,528.10,530.75,46372200,75.40
2014-03-05,530.92,534.75,529.13,532.36,50015700,75.63
2014-03-04,531.00,532.64,527.77,531.24,64785000,75.47
2014-03-03,523.42,530.65,522.81,527.76,59695300,74.98
2014-02-28,529.08,532.75,522.12,526.24,92992200,74.76
2014-02-27,517.14,528.78,516.05,527.67,75470500,74.96
2014-02-26,523.61,525.00,515.60,517.35,69054300,73.50
2014-02-25,529.38,529.57,521.00,522.06,57988000,74.17
2014-02-24,523.15,529.92,522.42,527.55,72227400,74.95
2014-02-21,532.79,534.57,524.60,525.25,69696200,74.62
2014-02-20,532.99,537.00,529.00,531.15,76464500,75.46
2014-02-19,544.75,546.89,534.35,537.37,78442000,76.34
2014-02-18,546.00,551.19,545.61,545.99,65062900,77.57
2014-02-14,542.47,545.98,541.21,543.99,68231100,77.28
2014-02-13,534.66,544.85,534.20,544.43,76849500,77.34
2014-02-12,536.95,539.56,533.24,535.92,77025200,76.13
2014-02-11,530.61,537.75,529.50,535.96,70564200,76.14
2014-02-10,518.66,531.99,518.00,528.99,86389800,75.15
2014-02-07,521.38,522.93,517.38,519.68,92570100,73.83
2014-02-06,510.06,513.50,507.81,512.51,64441300,72.81
2014-02-05,506.56,515.28,506.25,512.59,82086200,72.39
2014-02-04,505.85,509.46,502.76,508.79,94170300,71.85
2014-02-03,502.61,507.73,499.30,501.53,100366000,70.83
2014-01-31,495.18,501.53,493.55,500.60,116199300,70.69
2014-01-30,502.54,506.50,496.70,499.78,169625400,70.58
2014-01-29,503.95,507.37,498.62,500.75,125702500,70.72
2014-01-28,508.76,515.00,502.07,506.50,266380800,71.53
2014-01-27,550.07,554.80,545.75,550.50,138719700,77.74
2014-01-24,554.00,555.62,544.75,546.07,107338700,77.12
2014-01-23,549.94,556.50,544.81,556.18,100809800,78.54
2014-01-22,550.91,557.29,547.81,551.51,94996300,77.88
2014-01-21,540.99,550.07,540.42,549.07,82131700,77.54
2014-01-17,551.48,552.07,539.90,540.67,106684900,76.35
2014-01-16,554.90,556.85,551.68,554.25,57319500,78.27
2014-01-15,553.52,560.20,551.66,557.36,97909700,78.71
2014-01-14,538.22,546.73,537.66,546.39,83140400,77.16
2014-01-13,529.91,542.50,529.88,535.73,94623200,75.65
2014-01-10,539.83,540.80,531.11,532.94,76244000,75.26
2014-01-09,546.80,546.86,535.35,536.52,69787200,75.77
2014-01-08,538.81,545.56,538.69,543.46,64632400,76.75
2014-01-07,544.32,545.96,537.92,540.04,79302300,76.26
2014-01-06,537.45,546.80,533.60,543.93,103152700,76.81
2014-01-03,552.86,553.70,540.43,540.98,98116900,76.40
2014-01-02,555.68,557.03,552.02,553.13,58671200,78.11
2013-12-31,554.17,561.28,554.00,561.02,55771100,79.23
2013-12-30,557.46,560.09,552.32,554.52,63407400,78.31
2013-12-27,563.82,564.41,559.50,560.09,56471100,79.10
2013-12-26,568.10,569.50,563.38,563.90,51002000,79.63
2013-12-24,569.89,571.88,566.03,567.67,41888700,80.17
2013-12-23,568.00,570.72,562.76,570.09,125326600,80.51
2013-12-20,545.43,551.61,544.82,549.02,109103400,77.53
2013-12-19,549.50,550.00,543.73,544.46,80077200,76.89
2013-12-18,549.70,551.45,538.80,550.77,141465800,77.78
2013-12-17,555.81,559.44,553.38,554.99,57475600,78.37
2013-12-16,555.02,562.64,555.01,557.50,70648200,78.73
2013-12-13,562.85,562.88,553.67,554.43,83205500,78.30
2013-12-12,562.14,565.34,560.03,560.54,65572500,79.16
2013-12-11,567.00,570.97,559.69,561.36,89929700,79.27
2013-12-10,563.58,567.88,561.20,565.55,69567400,79.87
2013-12-09,560.90,569.58,560.90,566.43,80123400,79.99
2013-12-06,565.79,566.75,559.57,560.02,86088100,79.09
2013-12-05,572.65,575.14,566.41,567.90,111895000,80.20
2013-12-04,565.50,569.19,560.82,565.00,94452400,79.79
2013-12-03,558.30,566.38,557.68,566.32,112742000,79.97
2013-12-02,558.00,564.33,550.82,551.23,118136200,77.84
2013-11-29,549.48,558.33,547.81,556.07,79531900,78.53
2013-11-27,536.31,546.00,533.40,545.96,90862100,77.10
2013-11-26,524.12,536.14,524.00,533.40,100345700,75.33
2013-11-25,521.02,525.87,521.00,523.74,57327900,73.96
2013-11-22,519.52,522.16,518.53,519.80,55931400,73.41
2013-11-21,517.60,521.21,513.67,521.14,65506700,73.59
2013-11-20,519.23,520.42,514.33,515.00,48479200,72.73
2013-11-19,519.03,523.38,517.97,519.55,52234700,73.37
2013-11-18,524.99,527.19,518.20,518.63,61236000,73.24
2013-11-15,526.58,529.09,524.49,524.99,79480100,74.14
2013-11-14,522.81,529.28,521.87,528.16,70604800,74.59
2013-11-13,518.00,522.25,516.96,520.63,49305200,73.52
2013-11-12,517.67,523.92,517.00,520.01,51069200,73.43
2013-11-11,519.99,521.67,514.41,519.05,56863100,73.30
2013-11-08,514.58,521.13,512.59,520.56,69829200,73.51
2013-11-07,519.58,523.19,512.38,512.49,65655100,72.37
2013-11-06,524.15,524.86,518.20,520.92,55843900,73.56
2013-11-05,524.58,528.89,523.00,525.45,66303300,73.77
2013-11-04,521.10,526.82,518.81,526.75,61156900,73.96
2013-11-01,524.02,524.80,515.84,520.03,68722500,73.01
2013-10-31,525.00,527.49,521.27,522.70,68924100,73.39
2013-10-30,519.61,527.52,517.02,524.90,88540900,73.70
2013-10-29,536.27,539.25,514.54,516.68,158951800,72.54
2013-10-28,529.04,531.00,523.21,529.88,137610200,74.39
2013-10-25,531.32,533.23,525.11,525.96,84448000,73.84
2013-10-24,525.00,532.47,522.45,531.91,96191200,74.68
2013-10-23,519.00,525.67,519.00,524.96,78430800,73.70
2013-10-22,526.41,528.45,508.03,519.87,133515900,72.99
2013-10-21,511.77,524.30,511.52,521.36,99526700,73.20
2013-10-18,505.99,509.26,505.71,508.89,72635500,71.45
2013-10-17,499.98,504.78,499.68,504.50,63398300,70.83
2013-10-16,500.79,502.53,499.23,501.11,62775300,70.36
2013-10-15,497.51,502.00,495.52,498.68,80018400,70.01
2013-10-14,489.83,497.58,489.35,496.04,65474500,69.64
2013-10-11,486.99,493.84,485.16,492.81,66934700,69.19
2013-10-10,491.32,492.38,487.04,489.64,69650700,68.74
2013-10-09,484.64,487.79,478.28,486.59,75431300,68.32
2013-10-08,489.94,490.64,480.54,480.94,72729300,67.52
2013-10-07,486.56,492.65,485.35,487.75,78073100,68.48
2013-10-04,483.86,484.60,478.60,483.03,64717100,67.82
2013-10-03,490.51,492.35,480.74,483.41,80688300,67.87
2013-10-02,485.63,491.80,483.75,489.56,72296000,68.73
2013-10-01,478.45,489.14,478.38,487.96,88470900,68.51
2013-09-30,477.25,481.66,474.41,476.75,65039100,66.94
2013-09-27,483.78,484.67,480.72,482.75,57010100,67.78
2013-09-26,486.00,488.56,483.90,486.22,59305400,68.26
2013-09-25,489.20,489.64,481.43,481.53,79239300,67.61
2013-09-24,494.88,495.47,487.82,489.10,91086100,68.67
2013-09-23,496.10,496.91,482.60,490.64,190526700,68.89
2013-09-20,478.00,478.55,466.00,467.41,174825700,65.62
2013-09-19,470.70,475.83,469.25,472.30,101135300,66.31
2013-09-18,463.18,466.35,460.66,464.68,114215500,65.24
2013-09-17,447.96,459.71,447.50,455.32,99845200,63.93
2013-09-16,461.00,461.61,447.22,450.12,135926700,63.20
2013-09-13,469.34,471.83,464.70,464.90,74708900,65.27
2013-09-12,468.50,475.40,466.01,472.69,101012800,66.37
2013-09-11,467.01,473.69,464.81,467.71,224674100,65.67
2013-09-10,506.20,507.45,489.50,494.64,185798900,69.45
2013-09-09,505.00,507.92,503.48,506.17,85171800,71.07
2013-09-06,498.44,499.38,489.95,498.22,89881400,69.95
2013-09-05,500.25,500.68,493.64,495.27,59091900,69.54
2013-09-04,499.56,502.24,496.28,498.69,86258200,70.02
2013-09-03,493.10,500.60,487.35,488.58,82982200,68.60
2013-08-30,492.00,492.95,486.50,487.22,68074300,68.41
2013-08-29,491.65,496.50,491.13,491.70,59914400,69.03
2013-08-28,486.00,495.80,486.00,490.90,76902000,68.92
2013-08-27,498.00,502.51,486.30,488.59,106047200,68.60
2013-08-26,500.75,510.20,500.50,502.97,82741400,70.62
2013-08-23,503.27,503.35,499.35,501.02,55682900,70.34
2013-08-22,504.98,505.59,498.20,502.96,61051900,70.61
2013-08-21,503.59,507.15,501.20,502.36,83969900,70.53
2013-08-20,509.71,510.57,500.82,501.07,89672100,70.35
2013-08-19,504.34,513.74,504.00,507.74,127629600,71.29
2013-08-16,500.15,502.94,498.86,502.33,90576500,70.53
2013-08-15,496.42,502.40,489.08,497.91,122573500,69.91
2013-08-14,497.88,504.25,493.40,498.50,189093100,69.99
2013-08-13,470.94,494.66,468.05,489.57,220485300,68.73
2013-08-12,456.86,468.65,456.63,467.36,91108500,65.62
2013-08-09,458.64,460.46,453.65,454.45,66716300,63.80
2013-08-08,463.86,464.10,457.95,461.01,63944300,64.73
2013-08-07,463.80,467.00,461.77,464.98,74714500,64.85
2013-08-06,468.02,471.89,462.17,465.25,83714400,64.89
2013-08-05,464.69,470.67,462.15,469.45,79713900,65.48
2013-08-02,458.01,462.85,456.66,462.54,68695900,64.51
2013-08-01,455.75,456.80,453.26,456.68,51562700,63.70
2013-07-31,454.99,457.34,449.43,452.53,80739400,63.12
2013-07-30,449.96,457.15,449.23,453.32,77355600,63.23
2013-07-29,440.80,449.99,440.20,447.79,62014400,62.46
2013-07-26,435.30,441.04,434.34,440.99,50038100,61.51
2013-07-25,440.70,441.40,435.81,438.50,57373400,61.16
2013-07-24,438.93,444.59,435.26,440.51,147984200,61.44
2013-07-23,426.00,426.96,418.71,418.99,92348900,58.44
2013-07-22,429.46,429.75,425.47,426.31,51949100,59.46
2013-07-19,433.10,433.98,424.35,424.95,67180400,59.27
2013-07-18,433.38,434.87,430.61,431.76,54719700,60.22
2013-07-17,429.70,432.22,428.22,430.31,49747600,60.02
2013-07-16,426.52,430.71,424.17,430.20,54134500,60.00
2013-07-15,425.01,431.46,424.80,427.44,60479300,59.62
2013-07-12,427.65,429.79,423.41,426.51,69890800,59.49
2013-07-11,422.95,428.25,421.17,427.29,81573100,59.60
2013-07-10,419.60,424.80,418.25,420.73,70351400,58.68
2013-07-09,413.60,423.50,410.38,422.35,88146100,58.91
2013-07-08,420.11,421.00,410.65,415.05,74534600,57.89
2013-07-05,420.39,423.29,415.35,417.42,68506200,58.22
2013-07-03,420.86,422.98,417.45,420.80,60232200,58.69
2013-07-02,409.96,421.63,409.47,418.49,117466300,58.37
2013-07-01,402.69,412.27,401.22,409.22,97763400,57.08
2013-06-28,391.36,400.27,388.87,396.53,144629100,55.31
2013-06-27,399.25,401.39,393.54,393.78,84311500,54.92
2013-06-26,403.90,404.79,395.66,398.07,91931000,55.52
2013-06-25,405.70,407.79,398.83,402.63,78540700,56.16
2013-06-24,407.40,408.66,398.05,402.54,120186500,56.15
2013-06-21,418.49,420.00,408.10,413.50,120279600,57.67
2013-06-20,419.30,425.98,415.17,416.84,89327700,58.14
2013-06-19,431.40,431.66,423.00,423.00,77735000,59.00
2013-06-18,431.56,434.90,430.21,431.77,48756400,60.22
2013-06-17,431.44,435.70,430.36,432.00,64853600,60.25
2013-06-14,435.40,436.29,428.50,430.05,67966500,59.98
2013-06-13,432.50,437.14,428.75,435.96,71458100,60.81
2013-06-12,439.50,441.25,431.50,432.19,66306800,60.28
2013-06-11,435.74,442.76,433.32,437.60,71528100,61.04
2013-06-10,444.73,449.08,436.80,438.89,112538300,61.22
2013-06-07,436.50,443.24,432.77,441.81,101133900,61.62
2013-06-06,445.47,447.00,434.05,438.46,104233500,61.16
2013-06-05,445.65,450.72,443.71,445.11,72647400,62.08
2013-06-04,453.22,454.43,447.39,449.31,73182200,62.67
2013-06-03,450.73,452.36,442.48,450.72,93088100,62.87
2013-05-31,452.50,457.10,449.50,449.73,96075700,62.73
2013-05-30,445.65,454.50,444.51,451.58,88379900,62.99
2013-05-29,440.00,447.50,439.40,444.95,82644100,62.06
2013-05-28,449.90,451.11,440.85,441.44,96536300,61.57
2013-05-24,440.85,445.66,440.36,445.15,69041700,62.09
2013-05-23,435.95,446.16,435.79,442.14,88255300,61.67
2013-05-22,444.05,448.35,438.22,441.35,110759600,61.56
2013-05-21,438.15,445.48,434.20,439.66,114005500,61.32
2013-05-20,431.91,445.80,430.10,442.93,112894600,61.78
2013-05-17,439.05,440.09,431.01,433.26,106976100,60.43
2013-05-16,423.24,437.85,418.90,434.58,150801000,60.61
2013-05-15,439.16,441.00,422.36,428.85,185403400,59.82
2013-05-14,453.85,455.20,442.15,443.86,111779500,61.91
2013-05-13,451.51,457.90,451.50,454.74,79237200,63.43
2013-05-10,457.97,459.71,450.48,452.97,83713000,63.18
2013-05-09,459.81,463.00,455.58,456.77,99621900,63.71
2013-05-08,459.04,465.37,455.81,463.84,118149500,64.27
2013-05-07,464.97,465.75,453.70,458.66,120938300,63.55
2013-05-06,455.71,462.20,454.31,460.71,124160400,63.84
2013-05-03,451.31,453.23,449.15,449.98,90325200,62.35
2013-05-02,441.78,448.59,440.63,445.52,105457100,61.73
2013-05-01,444.46,444.93,434.39,439.29,126727300,60.87
2013-04-30,435.10,445.25,432.07,442.78,172884600,61.35
2013-04-29,420.45,433.62,420.00,430.12,160081600,59.60
2013-04-26,409.81,418.77,408.25,417.20,191024400,57.81
2013-04-25,411.23,413.94,407.00,408.38,96209400,56.59
2013-04-24,393.54,415.25,392.50,405.46,242412800,56.18
2013-04-23,403.99,408.38,398.81,406.13,166059600,56.27
2013-04-22,392.64,402.20,391.27,398.67,107480100,55.24
2013-04-19,387.97,399.60,385.10,390.53,152318600,54.11
2013-04-18,404.99,405.79,389.74,392.05,166574800,54.32
2013-04-17,420.27,420.60,398.11,402.80,236264000,55.81
2013-04-16,421.57,426.61,420.57,426.24,76442800,59.06
2013-04-15,427.00,427.89,419.55,419.85,79380000,58.17
2013-04-12,434.15,434.15,429.09,429.80,59653300,59.55
2013-04-11,433.72,437.99,431.20,434.33,82091100,60.18
2013-04-10,428.10,437.06,426.01,435.69,93982000,60.37
2013-04-09,426.36,428.50,422.75,426.98,76653500,59.16
2013-04-08,424.85,427.50,422.49,426.21,75207300,59.06
2013-04-05,424.50,424.95,419.68,423.20,95923800,58.64
2013-04-04,433.76,435.00,425.25,427.72,89611900,59.27
2013-04-03,431.37,437.28,430.31,431.99,90804000,59.86
2013-04-02,427.60,438.14,426.40,429.79,132379800,59.55
2013-04-01,441.90,443.70,427.74,428.91,97433000,59.43
2013-03-28,449.82,451.82,441.62,442.66,110709900,61.34
2013-03-27,456.46,456.80,450.73,452.08,82809300,62.64
2013-03-26,465.44,465.84,460.53,461.14,73573500,63.90
2013-03-25,464.69,469.95,461.78,463.58,125283900,64.23
2013-03-22,454.58,462.10,453.11,461.91,98776300,64.00
2013-03-21,450.22,457.98,450.10,452.73,95813900,62.73
2013-03-20,457.42,457.63,449.59,452.08,77165200,62.64
2013-03-19,459.50,460.97,448.50,454.49,131693800,62.97
2013-03-18,441.45,457.46,441.20,455.72,151549300,63.14
2013-03-15,437.93,444.23,437.25,443.66,160990200,61.47
2013-03-14,432.83,434.64,430.45,432.50,75968900,59.93
2013-03-13,428.45,434.50,425.36,428.35,101387300,59.35
2013-03-12,435.60,438.88,427.57,428.43,116477900,59.36
2013-03-11,429.75,439.01,425.14,437.87,118559000,60.67
2013-03-08,429.80,435.43,428.61,431.72,97870500,59.82
2013-03-07,424.50,432.01,421.06,430.58,117118400,59.66
2013-03-06,434.51,435.25,424.43,425.66,115062500,58.98
2013-03-05,421.48,435.19,420.75,431.14,159608400,59.74
2013-03-04,427.80,428.20,419.00,420.05,145688900,58.20
2013-03-01,438.00,438.18,429.98,430.47,138112100,59.65
2013-02-28,444.05,447.87,441.40,441.40,80628800,61.16
2013-02-27,448.43,452.44,440.65,444.57,146837600,61.60
2013-02-26,443.82,451.54,437.66,448.97,125374900,62.21
2013-02-25,453.85,455.12,442.57,442.80,93144800,61.35
2013-02-22,449.25,451.60,446.60,450.81,82663700,62.46
2013-02-21,446.00,449.17,442.82,446.06,111795600,61.81
2013-02-20,457.69,457.69,448.80,448.85,119075600,62.19
2013-02-19,461.10,462.73,453.85,459.99,108945900,63.74
2013-02-15,468.85,470.16,459.92,460.16,97936300,63.76
2013-02-14,464.52,471.64,464.02,466.59,88818800,64.65
2013-02-13,467.21,473.64,463.22,467.01,118801900,64.71
2013-02-12,479.51,482.38,467.74,467.90,152263300,64.83
2013-02-11,476.50,484.94,473.25,479.93,129372600,66.50
2013-02-08,474.00,478.81,468.25,474.98,158289600,65.81
2013-02-07,463.25,470.00,454.12,468.22,176145200,64.88
2013-02-06,456.47,466.50,452.58,457.35,148426600,63.00
2013-02-05,444.05,459.74,442.22,457.84,143336900,63.07
2013-02-04,453.91,455.94,442.00,442.32,119279300,60.93
2013-02-01,459.11,459.48,448.35,453.62,134871100,62.49
2013-01-31,456.98,459.28,454.98,455.49,79833600,62.75
2013-01-30,457.00,462.60,454.50,456.83,104288800,62.93
2013-01-29,458.50,460.20,452.12,458.27,142789500,63.13
2013-01-28,437.83,453.21,435.86,449.83,196379400,61.97
2013-01-25,451.69,456.23,435.00,439.88,302006600,60.60
2013-01-24,460.00,465.73,450.25,450.50,365213100,62.06
2013-01-23,508.81,514.99,504.77,514.01,215377400,70.81
2013-01-22,504.56,507.88,496.63,504.77,115386600,69.54
2013-01-18,498.52,502.22,496.40,500.00,118230700,68.88
2013-01-17,510.31,510.75,502.03,502.68,113419600,69.25
2013-01-16,494.64,509.44,492.50,506.09,172701200,69.72
2013-01-15,498.30,498.99,483.38,485.92,219193100,66.94
2013-01-14,502.68,507.50,498.51,501.75,183551900,69.12
2013-01-11,521.00,525.32,519.02,520.30,87626700,71.68
2013-01-10,528.55,528.72,515.52,523.51,150286500,72.12
2013-01-09,522.50,525.01,515.99,517.10,101901100,71.23
2013-01-08,529.21,531.89,521.25,525.31,114676800,72.37
2013-01-07,522.00,529.30,515.20,523.90,121039100,72.17
2013-01-04,536.97,538.63,525.83,527.00,148583400,72.60
2013-01-03,547.88,549.67,541.00,542.10,88241300,74.68
2013-01-02,553.82,555.00,541.63,549.03,140129500,75.63
2012-12-31,510.53,535.40,509.00,532.17,164873100,73.31
2012-12-28,510.29,514.48,508.12,509.59,88569600,70.20
2012-12-27,513.54,516.25,504.66,515.06,113780100,70.95
2012-12-26,519.00,519.46,511.12,513.00,75609100,70.67
2012-12-24,520.35,524.25,518.71,520.17,43938300,71.66
2012-12-21,512.47,519.67,510.24,519.33,149067100,71.54
2012-12-20,530.00,530.20,518.88,521.73,120422400,71.87
2012-12-19,531.47,533.70,525.50,526.31,112342300,72.50
2012-12-18,525.00,534.90,520.25,533.90,156421300,73.55
2012-12-17,508.93,520.00,501.23,518.83,189401800,71.47
2012-12-14,514.75,518.13,505.58,509.79,252394800,70.23
2012-12-13,531.15,537.64,525.80,529.69,156314900,72.97
2012-12-12,547.77,548.00,536.27,539.00,121786000,74.25
2012-12-11,539.77,549.56,537.37,541.39,148086400,74.58
2012-12-10,525.00,538.51,521.58,529.82,157621100,72.99
2012-12-07,553.40,555.20,530.00,533.25,196760200,73.46
2012-12-06,528.94,553.31,518.63,547.24,294303100,75.39
2012-12-05,568.91,569.25,538.77,538.79,261159500,74.22
2012-12-04,581.80,581.80,572.13,575.85,139267100,79.33
2012-12-03,593.65,594.59,585.50,586.19,91070000,80.75
2012-11-30,586.79,588.40,582.68,585.28,97829900,80.63
2012-11-29,590.22,594.25,585.25,589.36,128674700,81.19
2012-11-28,577.27,585.80,572.26,582.94,130216100,80.30
2012-11-27,589.55,590.42,580.10,584.78,133332500,80.56
2012-11-26,575.90,590.00,573.71,589.53,157644900,81.21
2012-11-23,567.17,572.00,562.60,571.50,68206600,78.73
2012-11-21,564.25,567.37,556.60,561.70,93250500,77.38
2012-11-20,571.91,571.95,554.58,560.91,160688500,77.27
2012-11-19,540.71,567.50,539.88,565.73,205829400,77.93
2012-11-16,525.20,530.00,505.75,527.68,316723400,72.69
2012-11-15,537.53,539.50,522.62,525.62,197477700,72.41
2012-11-14,545.50,547.45,536.18,536.88,119292600,73.96
2012-11-13,538.91,550.48,536.36,542.90,133237300,74.79
2012-11-12,554.15,554.50,538.65,542.83,128950500,74.78
2012-11-09,540.42,554.88,533.72,547.06,232478400,75.36
2012-11-08,560.63,562.23,535.29,537.75,264036500,74.08
2012-11-07,573.84,574.54,555.75,558.00,198412200,76.87
2012-11-06,590.23,590.74,580.09,582.85,93729300,79.93
2012-11-05,583.52,587.77,577.60,584.62,132283900,80.17
2012-11-02,595.89,596.95,574.75,576.80,149843400,79.10
2012-11-01,598.22,603.00,594.17,596.54,90324500,81.80
2012-10-31,594.88,601.96,587.70,595.32,127500800,81.64
2012-10-26,609.43,614.00,591.00,604.00,254608200,82.83
2012-10-25,620.00,622.00,605.55,609.54,164081400,83.59
2012-10-24,621.44,626.55,610.64,616.83,139631800,84.59
2012-10-23,631.00,633.90,611.70,613.36,176786400,84.11
2012-10-22,612.42,635.38,610.76,634.03,136682700,86.95
2012-10-19,631.05,631.77,609.62,609.84,186021500,83.63
2012-10-18,639.59,642.06,630.00,632.64,119156100,86.75
2012-10-17,648.87,652.79,644.00,644.61,97259400,88.40
2012-10-16,635.37,650.30,631.00,649.79,137442900,89.11
2012-10-15,632.35,635.13,623.85,634.76,108125500,87.05
2012-10-12,629.56,635.38,625.30,629.71,115003700,86.35
2012-10-11,646.50,647.20,628.10,628.10,136520300,86.13
2012-10-10,639.74,644.98,637.00,640.91,127589000,87.89
2012-10-09,638.65,640.49,623.55,635.85,209649300,87.20
2012-10-08,646.88,647.56,636.11,638.17,159498500,87.51
2012-10-05,665.20,666.00,651.28,652.59,148501500,89.49
2012-10-04,671.25,674.25,665.55,666.80,92681400,91.44
2012-10-03,664.86,671.86,662.63,671.45,106070300,92.08
2012-10-02,661.81,666.35,650.65,661.31,156998100,90.69
2012-10-01,671.16,676.75,656.50,659.39,135898700,90.42
2012-09-28,678.75,681.11,666.75,667.10,133777700,91.48
2012-09-27,664.29,682.17,660.35,681.32,148522500,93.43
2012-09-26,668.74,672.69,661.20,665.18,144125800,91.22
2012-09-25,688.26,692.78,673.00,673.54,129697400,92.36
2012-09-24,686.86,695.12,683.00,690.79,159941600,94.73
2012-09-21,702.41,705.07,699.36,700.09,142897300,96.00
2012-09-20,699.16,700.06,693.62,698.70,84142100,95.81
2012-09-19,700.26,703.99,699.57,702.10,81718700,96.28
2012-09-18,699.88,702.33,696.42,701.91,93375800,96.25
2012-09-17,699.35,699.80,694.61,699.78,99507800,95.96
2012-09-14,689.96,696.98,687.89,691.28,150118500,94.80
2012-09-13,677.37,685.50,674.77,682.98,149590000,93.66
2012-09-12,666.85,669.90,656.00,669.79,178058300,91.85
2012-09-11,665.11,670.10,656.50,660.59,125995800,90.59
2012-09-10,680.45,683.29,662.10,662.74,121999500,90.88
2012-09-07,678.05,682.48,675.77,680.44,82416600,93.31
2012-09-06,673.17,678.29,670.80,676.27,97799100,92.74
2012-09-05,675.57,676.35,669.60,670.23,84093800,91.91
2012-09-04,665.76,675.14,664.50,674.97,91973000,92.56
2012-08-31,667.25,668.60,657.25,665.24,84580300,91.23
2012-08-30,670.64,671.55,662.85,663.87,75674900,91.04
2012-08-29,675.25,677.67,672.60,673.47,50701700,92.35
2012-08-28,674.98,676.10,670.67,674.80,66854200,92.54
2012-08-27,679.99,680.87,673.54,675.68,106752100,92.66
2012-08-24,659.51,669.48,655.55,663.22,109335100,90.95
2012-08-23,666.11,669.90,661.15,662.63,105032200,90.87
2012-08-22,654.42,669.00,648.11,668.87,141330700,91.72
2012-08-21,670.82,674.88,650.33,656.06,203179900,89.97
2012-08-20,650.01,665.15,649.90,665.15,153346200,91.21
2012-08-17,640.00,648.19,638.81,648.11,110690300,88.88
2012-08-16,631.21,636.76,630.50,636.34,63633500,87.26
2012-08-15,631.30,634.00,627.75,630.83,64335600,86.51
2012-08-14,631.87,638.61,630.21,631.69,85042300,86.62
2012-08-13,623.39,630.00,623.25,630.00,69708100,86.39
2012-08-10,618.71,621.76,618.70,621.70,48734700,85.25
2012-08-09,617.85,621.73,617.80,620.73,55410600,85.12
2012-08-08,619.39,623.88,617.10,619.86,61176500,84.64
2012-08-07,622.77,625.00,618.04,620.91,72611700,84.78
2012-08-06,617.29,624.87,615.26,622.55,75525800,85.01
2012-08-03,613.63,617.98,611.56,615.70,86230200,84.07
2012-08-02,602.84,610.69,600.25,607.79,83039600,82.99
2012-08-01,615.91,616.40,603.00,606.81,96125400,82.86
2012-07-31,603.23,611.70,602.72,610.76,115581900,83.40
2012-07-30,590.92,599.44,587.82,595.03,94785600,81.25
2012-07-27,575.01,585.83,571.59,585.16,100984100,79.90
2012-07-26,579.76,580.40,570.36,574.88,101658200,78.50
2012-07-25,574.46,580.80,570.00,574.97,219328200,78.51
2012-07-24,607.38,609.68,598.51,600.92,141283100,82.05
2012-07-23,594.40,605.90,587.71,603.83,121993900,82.45
2012-07-20,613.03,614.44,603.70,604.30,99367800,82.51
2012-07-19,611.28,615.35,606.00,614.32,109215400,83.88
2012-07-18,606.59,608.34,603.56,606.26,63175000,82.78
2012-07-17,610.79,611.50,603.15,606.94,73406200,82.87
2012-07-16,605.12,611.62,605.02,606.91,75315100,82.87
2012-07-13,602.95,607.19,600.00,604.97,77856800,82.61
2012-07-12,600.24,603.47,592.68,598.90,107010400,81.78
2012-07-11,606.12,607.66,597.22,604.43,117330500,82.53
2012-07-10,617.97,619.87,605.31,608.21,127989400,83.05
2012-07-09,605.30,613.90,604.11,613.89,94851400,83.82
2012-07-06,607.09,608.44,601.58,605.88,104732600,82.73
2012-07-05,600.56,614.34,599.65,609.94,121095800,83.28
2012-07-03,594.88,600.00,594.00,599.41,60428200,81.85
2012-07-02,584.73,593.47,583.60,592.52,100023000,80.91
2012-06-29,578.00,584.00,574.25,584.00,105375200,79.74
2012-06-28,571.67,574.00,565.61,569.05,70709100,77.70
2012-06-27,575.00,576.74,571.92,574.50,50749300,78.45
2012-06-26,571.33,574.49,567.33,572.03,69134100,78.11
2012-06-25,577.30,579.80,570.37,570.77,76095600,77.94
2012-06-22,579.04,582.19,575.42,582.10,71117900,79.48
2012-06-21,585.44,588.22,577.44,577.67,81587800,78.88
2012-06-20,588.21,589.25,580.80,585.74,89735800,79.98
2012-06-19,583.40,590.00,583.10,587.41,90351100,80.21
2012-06-18,570.96,587.89,570.37,585.78,110103000,79.99
2012-06-15,571.00,574.62,569.55,574.13,83813800,78.39
2012-06-14,571.24,573.50,567.26,571.53,86393300,78.04
2012-06-13,574.52,578.48,570.38,572.16,73395000,78.13
2012-06-12,574.46,576.62,566.70,576.16,108845100,78.67
2012-06-11,587.72,588.50,570.63,571.17,147816200,77.99
2012-06-08,571.60,580.58,569.00,580.32,86879100,79.24
2012-06-07,577.29,577.32,570.50,571.72,94941700,78.07
2012-06-06,567.77,573.85,565.50,571.46,100363900,78.03
2012-06-05,561.27,566.47,558.33,562.83,97053600,76.85
2012-06-04,561.50,567.50,548.50,564.29,139248900,77.05
2012-06-01,569.16,572.65,560.52,560.99,130246900,76.60
2012-05-31,580.74,581.50,571.46,577.73,122918600,78.89
2012-05-30,569.20,579.99,566.56,579.17,132357400,79.08
2012-05-29,570.90,574.00,565.31,572.27,95127200,78.14
2012-05-25,564.59,565.85,558.47,562.29,82126800,76.78
2012-05-24,575.87,576.50,561.23,565.32,124057500,77.19
2012-05-23,557.50,572.80,553.23,570.56,146224400,77.91
2012-05-22,569.55,573.88,552.58,556.97,173717600,76.05
2012-05-21,534.50,561.54,534.05,561.28,157776500,76.64
2012-05-18,533.96,543.41,522.18,530.38,183073100,72.42
2012-05-17,545.31,547.50,530.12,530.12,179305000,72.39
2012-05-16,554.05,556.89,541.04,546.08,140224000,74.56
2012-05-15,561.45,563.22,551.75,553.17,119084000,75.53
2012-05-14,562.57,567.51,557.60,558.22,88156600,76.22
2012-05-11,565.00,574.47,564.35,566.71,99886500,77.38
2012-05-10,574.58,575.88,568.44,570.52,83300000,77.90
2012-05-09,563.70,573.98,560.85,569.18,120176000,77.72
2012-05-08,569.58,571.50,558.73,568.18,124313000,77.58
2012-05-07,561.50,572.77,561.23,569.48,115029600,77.76
2012-05-04,577.08,578.36,565.17,565.25,132498100,77.18
2012-05-03,590.50,591.40,580.30,581.82,97637400,79.44
2012-05-02,580.24,587.40,578.86,585.98,106847300,80.01
2012-05-01,584.90,596.76,581.23,582.13,152749800,79.49
2012-04-30,597.80,598.40,583.00,583.98,126536200,79.74
2012-04-27,605.07,606.18,600.50,603.00,101680600,82.34
2012-04-26,614.27,614.69,602.13,607.70,134017100,82.98
2012-04-25,615.64,618.00,606.00,610.00,226444400,83.29
2012-04-24,562.61,567.69,555.00,560.28,269037300,76.50
2012-04-23,570.61,576.67,556.62,571.70,241632300,78.06
2012-04-20,591.38,594.62,570.42,572.98,257746300,78.24
2012-04-19,600.22,604.73,584.52,587.44,208679800,80.21
2012-04-18,613.72,620.25,602.71,608.34,238632800,83.07
2012-04-17,578.94,610.00,571.91,609.70,256382000,83.25
2012-04-16,610.06,610.28,578.25,580.13,262696700,79.21
2012-04-13,624.11,624.70,603.51,605.23,214911200,82.64
2012-04-12,625.00,631.33,620.50,622.77,153584200,85.04
2012-04-11,636.20,636.87,623.34,626.20,174153700,85.50
2012-04-10,639.93,644.00,626.00,628.44,222431300,85.81
2012-04-09,626.13,639.84,625.30,636.23,149384200,86.87
2012-04-05,626.98,634.66,623.40,633.68,160324500,86.53
2012-04-04,624.35,625.86,617.00,624.31,143245200,85.25
2012-04-03,627.30,632.21,622.51,629.32,208639900,85.93
2012-04-02,601.83,618.77,600.38,618.63,149587900,84.47
2012-03-30,608.77,610.56,597.94,599.55,182759500,81.87
2012-03-29,612.78,616.56,607.23,609.86,152059600,83.27
2012-03-28,618.38,621.45,610.31,617.62,163865100,84.33
2012-03-27,606.18,616.28,606.06,614.48,151782400,83.90
2012-03-26,599.79,607.15,595.26,606.98,148935500,82.88
2012-03-23,600.49,601.80,594.40,596.05,107622200,81.39
2012-03-22,597.78,604.50,595.53,599.34,155967700,81.84
2012-03-21,602.74,609.65,601.41,602.50,161010500,82.27
2012-03-20,599.51,606.90,591.48,605.96,204165500,82.74
2012-03-19,598.37,601.77,589.05,601.10,225309000,82.08
2012-03-16,584.72,589.20,578.00,585.57,206371900,79.96
2012-03-15,599.61,600.01,578.55,585.56,289929500,79.96
2012-03-14,578.05,594.72,575.40,589.58,354711000,80.50
2012-03-13,557.54,568.18,555.75,568.10,172713800,77.57
2012-03-12,548.98,552.00,547.00,552.00,101820600,75.37
2012-03-09,544.21,547.74,543.11,545.17,104729800,74.44
2012-03-08,534.69,542.99,532.12,541.99,129114300,74.01
2012-03-07,536.80,537.78,523.30,530.69,199630200,72.46
2012-03-06,523.66,533.69,516.22,530.26,202559700,72.40
2012-03-05,545.42,547.48,526.00,533.16,202281100,72.80
2012-03-02,544.24,546.80,542.52,545.18,107928100,74.44
2012-03-01,548.17,548.21,538.77,544.47,170817500,74.34
2012-02-29,541.56,547.61,535.70,542.44,238002800,74.07
2012-02-28,527.96,535.41,525.85,535.41,150096800,73.11
2012-02-27,521.31,528.50,516.28,525.76,136895500,71.79
2012-02-24,519.67,522.90,518.64,522.41,103768000,71.33
2012-02-23,515.08,517.83,509.50,516.39,142006900,70.51
2012-02-22,513.08,515.49,509.07,513.04,120825600,70.05
2012-02-21,506.88,514.85,504.12,514.85,151398800,70.30
2012-02-17,503.11,507.77,500.30,502.12,133951300,68.56
2012-02-16,491.50,504.89,486.63,502.21,236138000,68.57
2012-02-15,514.26,526.29,496.89,497.67,376530000,67.95
2012-02-14,504.66,509.56,502.00,509.46,115099600,69.56
2012-02-13,499.53,503.83,497.09,502.60,129304000,68.63
2012-02-10,490.96,497.62,488.55,493.42,157825500,67.37
2012-02-09,480.76,496.75,480.56,493.17,221053700,67.34
2012-02-08,470.50,476.79,469.70,476.68,101972500,65.09
2012-02-07,465.25,469.75,464.58,468.83,79055900,64.02
2012-02-06,458.38,464.98,458.20,463.97,62353200,63.35
2012-02-03,457.30,460.00,455.56,459.68,71649900,62.77
2012-02-02,455.90,457.17,453.98,455.12,46699100,62.14
2012-02-01,458.41,458.99,455.55,456.19,67511500,62.29
2012-01-31,455.59,458.24,453.07,456.48,97920900,62.33
2012-01-30,445.71,453.90,445.39,453.01,94835300,61.86
2012-01-27,444.34,448.48,443.77,447.28,74927300,61.07
2012-01-26,448.36,448.79,443.14,444.63,80996300,60.71
2012-01-25,454.44,454.45,443.73,446.66,239578500,60.99
2012-01-24,425.10,425.10,419.55,420.41,136909500,57.40
2012-01-23,422.67,428.45,422.30,427.41,76515600,58.36
2012-01-20,427.49,427.50,419.75,420.30,103493600,57.39
2012-01-19,430.15,431.37,426.51,427.75,65434600,58.41
2012-01-18,426.96,429.47,426.30,429.11,69197800,58.59
2012-01-17,424.20,425.99,422.96,424.70,60724300,57.99
2012-01-13,419.70,420.45,418.66,419.81,56505400,57.32
2012-01-12,422.28,422.90,418.75,421.39,53146800,57.54
2012-01-11,422.68,422.85,419.31,422.55,53771200,57.70
2012-01-10,425.91,426.00,421.50,423.24,64549100,57.79
2012-01-09,425.50,427.75,421.35,421.73,98506100,57.59
2012-01-06,419.77,422.75,419.22,422.40,79573200,57.68
2012-01-05,414.95,418.55,412.67,418.03,67817400,57.08
2012-01-04,410.00,414.68,409.28,413.44,65005500,56.45
2012-01-03,409.40,412.50,409.00,411.23,75555200,56.15
2011-12-30,403.51,406.28,403.49,405.00,44915500,55.30
2011-12-29,403.40,405.65,400.51,405.12,53994500,55.32
2011-12-28,406.89,408.25,401.34,402.64,57165500,54.98
2011-12-27,403.10,409.09,403.02,406.53,66269000,55.51
2011-12-23,399.69,403.59,399.49,403.33,67349800,55.07
2011-12-22,397.00,399.13,396.10,398.55,50589700,54.42
2011-12-21,396.69,397.30,392.01,396.45,65737000,54.13
2011-12-20,387.76,396.10,387.26,395.95,84303800,54.07
2011-12-19,382.47,384.85,380.48,382.21,58882600,52.19
2011-12-16,380.36,384.15,379.57,381.02,105369600,52.03
2011-12-15,383.33,383.74,378.31,378.94,64050000,51.74
2011-12-14,386.70,387.38,377.68,380.19,101721900,51.91
2011-12-13,393.00,395.40,387.10,388.81,84732200,53.09
2011-12-12,391.68,393.90,389.45,391.84,75266800,53.50
2011-12-09,392.85,394.04,391.03,393.62,74248300,53.75
2011-12-08,391.45,395.50,390.23,390.66,94089100,53.34
2011-12-07,389.93,390.94,386.76,389.09,76186600,53.13
2011-12-06,392.51,394.63,389.38,390.95,70899500,53.38
2011-12-05,393.49,396.41,390.39,393.01,89302500,53.66
2011-12-02,389.83,393.63,388.58,389.70,94763900,53.21
2011-12-01,382.54,389.00,380.75,387.93,96795300,52.97
2011-11-30,381.29,382.28,378.30,382.20,101484600,52.19
2011-11-29,375.84,378.83,370.20,373.20,93963800,50.96
2011-11-28,372.35,376.72,370.33,376.12,86603300,51.36
2011-11-25,368.42,371.15,363.32,363.57,63690200,49.64
2011-11-23,374.51,375.84,366.88,366.99,107067800,50.11
2011-11-22,371.02,377.93,370.94,376.51,102255300,51.41
2011-11-21,370.40,371.68,365.91,369.01,111995100,50.39
2011-11-18,378.92,379.99,374.88,374.94,92984500,51.20
2011-11-17,383.98,384.58,375.50,377.41,119975100,51.53
2011-11-16,389.25,391.14,384.32,384.77,87302600,52.54
2011-11-15,380.80,389.50,379.45,388.83,107702700,53.09
2011-11-14,383.52,385.25,378.20,379.26,108226300,51.79
2011-11-11,386.61,388.70,380.26,384.62,163446500,52.52
2011-11-10,397.03,397.21,382.15,385.22,186188100,52.60
2011-11-09,396.97,400.89,394.23,395.28,139671000,53.97
2011-11-08,402.21,408.00,401.56,406.23,100110500,55.47
2011-11-07,399.91,400.00,396.13,399.73,67568900,54.58
2011-11-04,402.03,403.44,399.16,400.24,75557300,54.65
2011-11-03,399.07,403.40,395.36,403.07,110346600,55.04
2011-11-02,400.09,400.44,395.11,397.41,81837700,54.26
2011-11-01,397.41,399.50,393.22,396.51,132947500,54.14
2011-10-31,402.42,409.33,401.05,404.78,96375300,55.27
2011-10-28,403.00,406.35,402.51,404.95,80710700,55.29
2011-10-27,407.56,409.00,401.89,404.69,123666200,55.26
2011-10-26,401.76,402.55,393.15,400.60,114076200,54.70
2011-10-25,405.03,406.55,397.38,397.77,107606800,54.31
2011-10-24,396.18,406.50,395.40,405.77,125534500,55.41
2011-10-21,398.10,399.14,390.75,392.87,155311100,53.64
2011-10-20,400.00,400.35,394.21,395.31,137317600,53.98
2011-10-19,401.35,408.42,397.80,398.62,276014900,54.43
2011-10-18,421.76,424.81,415.99,422.24,220400600,57.65
2011-10-17,421.74,426.70,415.94,419.99,171511200,57.35
2011-10-14,416.83,422.00,415.27,422.00,143341800,57.62
2011-10-13,404.98,408.43,402.85,408.43,106546300,55.77
2011-10-12,407.34,409.25,400.14,402.19,155571500,54.92
2011-10-11,392.57,403.18,391.50,400.29,151421900,54.66
2011-10-10,379.09,388.81,378.21,388.81,110628700,53.09
2011-10-07,375.78,377.74,368.49,369.80,133864500,50.49
2011-10-06,373.33,384.78,371.80,377.37,203145600,51.53
2011-10-05,367.86,379.82,360.30,378.25,196617400,51.65
2011-10-04,374.57,381.80,354.24,372.50,308419300,50.86
2011-10-03,380.37,382.64,373.17,374.60,167274800,51.15
2011-09-30,387.12,388.89,381.18,381.32,136910200,52.07
2011-09-29,401.92,402.21,386.21,390.57,162771700,53.33
2011-09-28,400.19,403.74,396.51,397.01,107409400,54.21
2011-09-27,408.73,409.25,398.06,399.26,158124400,54.52
2011-09-26,399.86,403.98,391.30,403.17,203219100,55.05
2011-09-23,400.28,406.74,399.85,404.30,136569300,55.21
2011-09-22,401.03,409.82,396.70,401.82,242120200,54.87
2011-09-21,419.64,421.59,412.00,412.14,151494000,56.28
2011-09-20,415.25,422.86,411.19,413.45,193938500,56.45
2011-09-19,397.00,413.23,395.20,411.63,205965200,56.21
2011-09-16,395.54,400.50,395.03,400.50,174628300,54.69
2011-09-15,391.43,393.66,389.90,392.96,104454700,53.66
2011-09-14,387.02,392.21,385.76,389.30,133681100,53.16
2011-09-13,382.14,386.21,380.25,384.62,110140100,52.52
2011-09-12,373.00,380.88,371.90,379.94,116958100,51.88
2011-09-09,383.93,386.00,375.02,377.48,141203300,51.54
2011-09-08,382.40,388.61,382.31,384.14,104039600,52.45
2011-09-07,385.56,385.60,382.00,383.93,87644200,52.42
2011-09-06,367.37,380.33,366.48,379.74,127424500,51.85
2011-09-02,374.74,378.00,371.83,374.05,109734800,51.07
2011-09-01,385.82,387.34,380.72,381.03,85931300,52.03
2011-08-31,390.57,392.08,381.86,384.83,130646600,52.55
2011-08-30,388.25,391.84,386.21,389.99,104480600,53.25
2011-08-29,388.18,391.50,388.00,389.97,101317300,53.25
2011-08-26,371.17,383.80,370.80,383.58,160369300,52.38
2011-08-25,365.08,375.45,365.00,373.72,217836500,51.03
2011-08-24,373.47,378.96,370.60,376.18,156566900,51.37
2011-08-23,360.30,373.64,357.00,373.60,164208800,51.01
2011-08-22,364.51,364.88,355.09,356.44,133828800,48.67
2011-08-19,362.17,367.00,356.00,356.03,193972100,48.61
2011-08-18,370.84,372.65,361.37,366.05,212858800,49.98
2011-08-17,382.31,384.52,378.00,380.44,110515300,51.95
2011-08-16,381.52,383.37,376.06,380.48,124687500,51.95
2011-08-15,379.63,384.97,378.09,383.41,115136000,52.35
2011-08-12,378.07,379.64,374.23,376.99,132244000,51.48
2011-08-11,370.52,375.45,364.72,373.70,185492300,51.03
2011-08-10,371.15,374.65,362.50,363.69,219664200,49.66
2011-08-09,361.30,374.61,355.00,374.01,270645900,51.07
2011-08-08,361.69,367.77,353.02,353.21,285958400,48.23
2011-08-05,380.44,383.50,362.57,373.62,301147700,51.02
2011-08-04,389.41,391.32,377.35,377.37,217851900,51.53
2011-08-03,390.98,393.55,382.24,392.57,183127000,53.60
2011-08-02,397.65,397.90,388.35,388.91,159884900,53.10
2011-08-01,397.78,399.50,392.37,396.75,153209000,54.17
2011-07-29,387.64,395.15,384.00,390.48,158146100,53.32
2011-07-28,391.62,396.99,388.13,391.82,148508500,53.50
2011-07-27,400.59,402.64,392.15,392.59,164831100,53.61
2011-07-26,400.00,404.50,399.68,403.41,119145600,55.08
2011-07-25,390.35,400.00,389.62,398.50,147451500,54.41
2011-07-22,388.32,395.05,387.75,393.30,129182200,53.70
2011-07-21,386.95,390.06,383.90,387.29,131633600,52.88
2011-07-20,396.12,396.27,386.00,386.90,235335100,52.83
2011-07-19,378.00,378.65,373.32,376.85,204786400,51.46
2011-07-18,365.43,374.65,365.28,373.80,143163300,51.04
2011-07-15,361.17,365.00,359.17,364.92,121116800,49.83
2011-07-14,361.01,361.61,356.34,357.77,107633400,48.85
2011-07-13,358.33,360.00,356.38,358.02,97909700,48.89
2011-07-12,353.53,357.68,348.62,353.75,112902300,48.30
2011-07-11,356.34,359.77,352.82,354.00,110668600,48.34
2011-07-08,353.34,360.00,352.20,359.71,122408300,49.12
2011-07-07,354.67,358.00,354.00,357.20,99915900,48.77
2011-07-06,348.95,354.10,346.71,351.76,111156500,48.03
2011-07-05,343.00,349.83,342.50,349.43,88763500,47.71
2011-07-01,335.95,343.50,334.20,343.26,108828300,46.87
2011-06-30,334.70,336.13,332.84,335.67,80738700,45.83
2011-06-29,336.04,336.37,331.88,334.04,88136300,45.61
2011-06-28,333.65,336.70,333.44,335.26,73574900,45.78
2011-06-27,327.59,333.90,327.25,332.04,84953400,45.34
2011-06-24,331.37,333.15,325.09,326.35,109951800,44.56
2011-06-23,318.94,331.69,318.12,331.23,139939800,45.23
2011-06-22,325.16,328.90,322.38,322.61,97645800,44.05
2011-06-21,316.68,325.80,315.20,325.30,123345600,44.42
2011-06-20,317.36,317.70,310.50,315.32,160161400,43.06
2011-06-17,328.99,329.25,319.36,320.26,153755000,43.73
2011-06-16,326.90,328.68,318.33,325.16,127647800,44.40
2011-06-15,329.75,330.30,324.88,326.75,99799000,44.62
2011-06-14,330.00,333.25,329.31,332.44,83642300,45.39
2011-06-13,327.20,328.31,325.07,326.60,82368300,44.60
2011-06-10,330.55,331.66,325.51,325.90,108488800,44.50
2011-06-09,333.25,333.67,330.75,331.49,68772200,45.26
2011-06-08,331.78,334.80,330.65,332.24,83430900,45.37
2011-06-07,338.17,338.22,331.90,332.04,132446300,45.34
2011-06-06,345.70,347.05,337.81,338.04,115485300,46.16
2011-06-03,343.18,345.33,342.01,343.44,78312500,46.90
2011-06-02,346.50,347.98,344.30,346.10,84695800,47.26
2011-06-01,348.87,352.13,344.65,345.51,138670700,47.18
2011-05-31,341.10,347.83,341.00,347.83,104438600,47.49
2011-05-27,334.80,337.63,334.31,337.41,50899800,46.07
2011-05-26,335.97,336.89,334.43,335.00,55640200,45.74
2011-05-25,333.43,338.56,332.85,336.78,73556000,45.99
2011-05-24,335.50,335.90,331.34,332.19,80481800,45.36
2011-05-23,329.97,335.98,329.42,334.40,95900000,45.66
2011-05-20,339.56,340.95,335.02,335.22,84492100,45.77
2011-05-19,342.08,342.41,338.67,340.53,65292500,46.50
2011-05-18,336.47,341.05,336.00,339.87,83694100,46.41
2011-05-17,332.00,336.14,330.73,336.14,113083600,45.90
2011-05-16,339.20,341.22,332.60,333.30,112443800,45.51
2011-05-13,345.66,346.25,340.35,340.50,81529000,46.49
2011-05-12,346.12,347.12,342.27,346.57,80500000,47.32
2011-05-11,349.02,350.00,345.24,347.23,84000000,47.41
2011-05-10,348.89,349.69,346.66,349.45,70522900,47.72
2011-05-09,347.86,349.20,346.53,347.60,51186800,47.46
2011-05-06,349.69,350.00,346.21,346.66,70033600,47.33
2011-05-05,348.40,350.95,346.05,346.75,83992300,47.35
2011-05-04,348.26,351.83,346.88,349.57,97312600,47.73
2011-05-03,347.99,349.89,345.62,348.20,78337000,47.55
2011-05-02,349.74,350.47,345.50,346.28,110678400,47.28
2011-04-29,346.78,353.95,346.67,350.13,251586300,47.81
2011-04-28,346.19,349.75,345.52,346.75,90239800,47.35
2011-04-27,352.24,352.35,347.10,350.15,89053300,47.81
2011-04-26,353.62,354.99,349.35,350.42,84700000,47.85
2011-04-25,350.34,353.75,350.30,353.01,66636500,48.20
2011-04-21,355.00,355.13,348.52,350.70,188452600,47.89
2011-04-20,343.51,345.75,341.50,342.41,175166600,46.75
2011-04-19,333.10,337.98,331.71,337.86,104844600,46.13
2011-04-18,326.10,332.23,320.16,331.85,152474700,45.31
2011-04-15,333.30,333.64,326.80,327.46,113401400,44.71
2011-04-14,334.80,336.00,332.06,332.42,75450200,45.39
2011-04-13,335.02,336.14,332.52,336.13,86555000,45.90
2011-04-12,330.49,333.73,330.20,332.40,106409800,45.39
2011-04-11,334.06,335.67,330.02,330.80,99736700,45.17
2011-04-08,339.92,340.15,333.95,335.06,94383800,45.75
2011-04-07,338.10,340.43,336.03,338.08,93361800,46.16
2011-04-06,341.22,343.90,337.14,338.04,100634800,46.16
2011-04-05,336.99,342.25,336.00,338.89,120682800,46.27
2011-04-04,344.31,344.60,338.40,341.19,115021200,46.59
2011-04-01,351.11,351.59,343.30,344.56,104665400,47.05
2011-03-31,346.36,349.80,346.06,348.51,68504800,47.59
2011-03-30,350.64,350.88,347.44,348.63,82351500,47.60
2011-03-29,347.66,350.96,346.06,350.96,88225200,47.92
2011-03-28,353.15,354.32,350.44,350.44,77338800,47.85
2011-03-25,348.07,352.06,347.02,351.54,112227500,48.00
2011-03-24,341.85,346.00,338.86,344.97,101178000,47.10
2011-03-23,339.28,340.22,335.95,339.19,93249100,46.31
2011-03-22,342.56,342.62,339.14,341.20,81480700,46.59
2011-03-21,335.99,339.74,335.26,339.30,102350500,46.33
2011-03-18,337.13,338.20,330.00,330.67,188303500,45.15
2011-03-17,336.83,339.61,330.66,334.64,164855600,45.69
2011-03-16,342.00,343.00,326.26,330.01,290502800,45.06
2011-03-15,342.10,347.84,340.10,345.43,180270300,47.17
2011-03-14,353.18,356.48,351.31,353.56,108989300,48.28
2011-03-11,345.33,352.32,345.00,351.99,117770100,48.06
2011-03-10,349.12,349.77,344.90,346.67,126884800,47.34
2011-03-09,354.69,354.76,350.60,352.47,113326500,48.13
2011-03-08,354.91,357.40,352.25,355.76,89079200,48.58
2011-03-07,361.40,361.67,351.31,355.36,136530800,48.52
2011-03-04,360.07,360.29,357.75,360.00,113316700,49.16
2011-03-03,357.19,359.79,355.92,359.56,125197100,49.10
2011-03-02,349.96,354.35,348.40,352.12,150647700,48.08
2011-03-01,355.47,355.72,347.68,349.31,114034200,47.70
2011-02-28,351.24,355.05,351.12,353.21,100768500,48.23
2011-02-25,345.26,348.43,344.80,348.16,95004700,47.54
2011-02-24,344.02,345.15,338.37,342.88,124975200,46.82
2011-02-23,338.77,344.64,338.61,342.62,167963600,46.78
2011-02-22,342.15,345.40,337.72,338.61,218138900,46.24
2011-02-18,358.71,359.50,349.52,350.56,204014300,47.87
2011-02-17,357.25,360.27,356.52,358.30,132645800,48.92
2011-02-16,360.80,364.90,360.50,363.13,120289400,49.58
2011-02-15,359.19,359.97,357.55,359.90,71043700,49.14
2011-02-14,356.79,359.48,356.71,359.18,77604100,49.04
2011-02-11,354.75,357.80,353.54,356.85,91893200,48.73
2011-02-10,357.39,360.00,348.00,354.54,232137500,48.41
2011-02-09,355.19,359.00,354.87,358.16,120686300,48.91
2011-02-08,353.68,355.52,352.15,355.20,95260200,48.50
2011-02-07,347.89,353.25,347.64,351.88,121255400,48.05
2011-02-04,343.64,346.70,343.51,346.50,80460100,47.31
2011-02-03,343.80,344.24,338.55,343.44,98449400,46.90
2011-02-02,344.45,345.25,343.55,344.32,64738800,47.02
2011-02-01,341.30,345.65,340.98,345.03,106658300,47.11
2011-01-31,335.80,340.04,334.30,339.32,94311700,46.33
2011-01-28,344.17,344.40,333.53,336.10,148014300,45.89
2011-01-27,343.78,344.69,342.83,343.21,71256500,46.86
2011-01-26,342.96,345.60,341.50,343.85,126718900,46.95
2011-01-25,336.33,341.44,334.57,341.40,136717000,46.62
2011-01-24,326.87,337.45,326.72,337.45,143670800,46.08
2011-01-21,333.77,334.88,326.63,326.72,188600300,44.61
2011-01-20,336.43,338.30,330.12,332.68,191197300,45.43
2011-01-19,348.35,348.60,336.88,338.84,283903200,46.27
2011-01-18,329.52,344.76,326.00,340.65,470249500,46.51
2011-01-14,345.89,348.48,344.44,348.48,77210000,47.58
2011-01-13,345.16,346.64,343.85,345.68,74195100,47.20
2011-01-12,343.25,344.43,342.00,344.42,75647600,47.03
2011-01-11,344.88,344.96,339.47,341.64,111027000,46.65
2011-01-10,338.83,343.23,337.17,342.45,112140000,46.76
2011-01-07,333.99,336.35,331.90,336.12,77982800,45.90
2011-01-06,334.72,335.25,332.90,333.73,75107200,45.57
2011-01-05,329.55,334.34,329.50,334.00,63879900,45.61
2011-01-04,332.44,332.50,328.15,331.29,77270200,45.24
2011-01-03,325.64,330.26,324.84,329.57,111284600,45.00
2010-12-31,322.95,323.48,321.31,322.56,48377000,44.04
2010-12-30,325.48,325.51,323.05,323.66,39373600,44.19
2010-12-29,326.22,326.45,325.10,325.29,40784800,44.42
2010-12-28,325.91,326.66,325.06,325.47,43981000,44.44
2010-12-27,322.85,325.44,321.52,324.68,62454000,44.33
2010-12-23,325.00,325.15,323.17,323.60,55789300,44.19
2010-12-22,324.36,325.72,323.55,325.16,66480400,44.40
2010-12-21,323.00,324.39,322.05,324.20,64088500,44.27
2010-12-20,321.60,323.25,318.23,322.21,96402600,44.00
2010-12-17,321.63,321.79,320.23,320.61,96732300,43.78
2010-12-16,321.09,322.61,320.10,321.25,80507700,43.87
2010-12-15,320.00,323.00,319.19,320.36,104328000,43.74
2010-12-14,321.73,322.54,319.00,320.29,87752000,43.73
2010-12-13,324.37,325.06,321.00,321.67,109953900,43.92
2010-12-10,319.65,321.05,318.60,320.56,65627800,43.77
2010-12-09,322.13,322.50,319.02,319.76,73537800,43.66
2010-12-08,319.63,321.02,317.11,321.01,80483900,43.83
2010-12-07,323.80,323.99,318.12,318.21,97863500,43.45
2010-12-06,318.64,322.33,318.42,320.15,112120400,43.71
2010-12-03,317.01,318.65,316.34,317.44,85523200,43.34
2010-12-02,317.53,319.00,314.89,318.15,115709300,43.44
2010-12-01,315.27,317.75,315.00,316.40,115437700,43.20
2010-11-30,313.54,314.36,310.87,311.15,125464500,42.49
2010-11-29,315.50,317.48,311.38,316.87,111446300,43.27
2010-11-26,313.74,317.70,312.94,315.00,59396400,43.01
2010-11-24,312.00,315.40,311.75,314.80,103431300,42.98
2010-11-23,310.45,311.75,306.56,308.73,129861900,42.16
2010-11-22,306.68,313.36,305.87,313.36,98268800,42.79
2010-11-19,307.97,308.40,305.24,306.73,96210800,41.88
2010-11-18,305.20,309.67,304.69,308.43,123622800,42.11
2010-11-17,301.20,303.99,297.76,300.50,119862400,41.03
2010-11-16,305.72,307.60,299.32,301.59,164412500,41.18
2010-11-15,308.46,310.54,306.27,307.04,100901500,41.92
2010-11-12,316.00,316.50,303.63,308.03,198961700,42.06
2010-11-11,315.00,318.40,314.25,316.65,90321000,43.24
2010-11-10,316.64,318.77,313.55,318.03,96056800,43.43
2010-11-09,321.05,321.30,314.50,316.08,95886000,43.16
2010-11-08,317.20,319.77,316.76,318.62,70439600,43.51
2010-11-05,317.99,319.57,316.75,317.13,90313300,43.30
2010-11-04,315.45,320.18,315.03,318.27,160622000,43.46
2010-11-03,311.37,312.88,308.53,312.80,127087100,42.71
2010-11-02,307.00,310.19,307.00,309.36,108482500,42.24
2010-11-01,302.22,305.60,302.20,304.18,105972300,41.53
2010-10-29,304.23,305.88,300.87,300.98,107627800,41.10
2010-10-28,307.95,308.00,300.90,305.24,137762800,41.68
2010-10-27,307.65,309.90,305.60,307.83,99750700,42.03
2010-10-26,306.87,309.74,305.65,308.05,98232400,42.06
2010-10-25,309.09,311.60,308.44,308.84,98115500,42.17
2010-10-22,309.07,310.04,306.30,307.47,93194500,41.98
2010-10-21,312.36,314.74,306.80,309.52,137865000,42.26
2010-10-20,309.00,314.25,306.87,310.53,180406100,42.40
2010-10-19,303.40,313.77,300.02,309.49,308196000,42.26
2010-10-18,318.47,319.00,314.29,318.00,273252700,43.42
2010-10-15,307.44,315.00,304.91,314.74,230548500,42.98
2010-10-14,301.69,302.47,300.40,302.31,108824100,41.28
2010-10-13,300.20,301.96,299.80,300.14,157523100,40.98
2010-10-12,295.41,299.50,292.49,298.54,139636000,40.76
2010-10-11,294.74,297.24,294.60,295.36,106938300,40.33
2010-10-08,291.71,294.50,290.00,294.07,164600800,40.15
2010-10-07,290.34,290.48,286.91,289.22,102099900,39.49
2010-10-06,289.59,291.99,285.26,289.19,167717200,39.49
2010-10-05,282.00,289.45,281.82,288.94,125491800,39.45
2010-10-04,281.60,282.90,277.77,278.64,108825500,38.05
2010-10-01,286.15,286.58,281.35,282.52,112035700,38.58
2010-09-30,289.00,290.00,281.25,283.75,168347900,38.74
2010-09-29,287.23,289.81,286.00,287.37,117411000,39.24
2010-09-28,291.77,291.77,275.00,286.86,258760600,39.17
2010-09-27,293.98,294.73,291.01,291.16,120708700,39.76
2010-09-24,292.10,293.53,290.55,292.32,162372000,39.91
2010-09-23,286.33,292.76,286.00,288.92,196529200,39.45
2010-09-22,282.71,287.98,282.41,287.75,146322400,39.29
2010-09-21,283.86,287.35,282.79,283.77,167018600,38.75
2010-09-20,276.08,283.78,275.85,283.23,164669400,38.67
2010-09-17,277.69,277.96,273.68,275.37,158619300,37.60
2010-09-16,270.24,276.67,269.50,276.57,163025800,37.76
2010-09-15,268.17,270.38,267.84,270.22,107342200,36.90
2010-09-14,266.21,269.17,265.52,268.06,102037600,36.60
2010-09-13,265.82,268.28,265.76,267.04,97195000,36.46
2010-09-10,263.19,264.50,261.40,263.41,96885600,35.97
2010-09-09,265.04,266.52,262.92,263.07,109643800,35.92
2010-09-08,259.78,264.39,259.10,262.92,131637800,35.90
2010-09-07,256.64,259.53,256.25,257.81,85639400,35.20
2010-09-03,255.09,258.78,254.50,258.77,130197200,35.33
2010-09-02,251.26,252.17,248.57,252.17,103856900,34.43
2010-09-01,247.47,251.46,246.28,250.33,174259400,34.18
2010-08-31,241.85,244.56,240.35,243.10,105196700,33.19
2010-08-30,240.76,245.75,240.68,242.50,95822300,33.11
2010-08-27,241.75,242.61,235.56,241.62,137097800,32.99
2010-08-26,245.45,245.75,240.28,240.28,116626300,32.81
2010-08-25,238.04,243.99,237.20,242.89,149216900,33.17
2010-08-24,242.67,243.00,238.65,239.93,150641400,32.76
2010-08-23,251.79,252.00,245.25,245.80,103510400,33.56
2010-08-20,249.39,253.92,249.00,249.64,96057500,34.09
2010-08-19,252.84,253.48,248.68,249.88,106676500,34.12
2010-08-18,252.36,254.67,251.58,253.07,84924000,34.56
2010-08-17,250.08,254.63,249.20,251.97,105660100,34.41
2010-08-16,247.58,250.01,246.62,247.64,79607500,33.81
2010-08-13,251.65,251.88,249.09,249.10,88717300,34.01
2010-08-12,246.69,253.10,246.12,251.79,133730100,34.38
2010-08-11,255.40,255.69,249.81,250.19,155013600,34.16
2010-08-10,259.85,260.45,257.55,259.41,112980000,35.42
2010-08-09,261.48,262.15,259.57,261.75,75782000,35.74
2010-08-06,259.78,261.49,257.63,260.09,111224400,35.51
2010-08-05,261.73,263.18,260.55,261.70,72274300,35.73
2010-08-04,262.84,264.28,260.31,262.98,105093800,35.91
2010-08-03,261.01,263.26,259.42,261.93,104413400,35.77
2010-08-02,260.44,262.59,259.62,261.85,107013900,35.75
2010-07-30,255.89,259.70,254.90,257.25,112052500,35.13
2010-07-29,260.71,262.65,256.10,258.11,160951700,35.24
2010-07-28,263.67,265.99,260.25,260.96,129996300,35.63
2010-07-27,260.87,264.80,260.30,264.08,146192900,36.06
2010-07-26,260.00,260.10,257.71,259.28,105137900,35.40
2010-07-23,257.09,260.38,256.28,259.94,133347200,35.49
2010-07-22,257.68,260.00,255.31,259.02,161329700,35.37
2010-07-21,265.09,265.15,254.00,254.24,296417800,34.72
2010-07-20,242.90,252.90,240.01,251.89,268737700,34.39
2010-07-19,249.88,249.88,239.60,245.58,256119500,33.53
2010-07-16,253.18,254.97,248.41,249.90,259964600,34.12
2010-07-15,248.23,256.97,247.30,251.45,206216500,34.33
2010-07-14,249.38,255.80,249.00,252.73,203011900,34.51
2010-07-13,256.32,256.40,246.43,251.80,297731000,34.38
2010-07-12,258.53,261.85,254.86,257.29,140719600,35.13
2010-07-09,256.89,259.90,255.16,259.62,108330600,35.45
2010-07-08,262.48,262.90,254.89,258.09,184536100,35.24
2010-07-07,250.49,258.77,249.75,258.67,163639000,35.32
2010-07-06,251.00,252.80,246.16,248.63,153808900,33.95
2010-07-02,250.49,250.93,243.20,246.94,173460700,33.72
2010-07-01,254.30,254.80,243.22,248.48,255724000,33.93
2010-06-30,256.71,257.97,250.01,251.53,184863000,34.35
2010-06-29,264.12,264.39,254.30,256.17,283336200,34.98
2010-06-28,266.93,269.75,264.52,268.30,146237000,36.64
2010-06-25,270.06,270.27,265.81,266.70,137485600,36.42
2010-06-24,271.00,273.20,268.10,269.00,178569300,36.73
2010-06-23,274.58,274.66,267.90,270.97,192114300,37.00
2010-06-22,272.16,275.97,271.50,273.85,179315500,37.39
2010-06-21,277.69,279.01,268.73,270.17,194122600,36.89
2010-06-18,272.25,275.00,271.42,274.07,196155400,37.42
2010-06-17,270.60,272.90,269.50,271.87,218213800,37.12
2010-06-16,261.10,267.75,260.63,267.25,195919500,36.49
2010-06-15,255.64,259.85,255.50,259.69,146268500,35.46
2010-06-14,255.96,259.15,254.01,254.28,150740100,34.72
2010-06-11,248.23,253.86,247.37,253.51,136439800,34.62
2010-06-10,244.84,250.98,242.20,250.51,194089000,34.21
2010-06-09,251.47,251.90,242.49,243.20,213657500,33.21
2010-06-08,253.24,253.80,245.65,249.33,250192600,34.04
2010-06-07,258.29,259.15,250.55,250.94,221735500,34.26
2010-06-04,258.21,261.90,254.63,255.96,189576100,34.95
2010-06-03,265.18,265.55,260.41,263.12,162526700,35.93
2010-06-02,264.54,264.80,260.33,263.95,172137000,36.04
2010-06-01,259.69,265.94,258.96,260.83,219118200,35.62
2010-05-28,259.39,259.40,253.35,256.88,203903700,35.08
2010-05-27,250.60,253.89,249.11,253.35,166570600,34.59
2010-05-26,250.08,252.13,243.75,244.11,212663500,33.33
2010-05-25,239.35,246.76,237.16,245.22,262001600,33.48
2010-05-24,247.28,250.90,246.26,246.76,188559700,33.69
2010-05-21,232.82,244.50,231.35,242.32,305972800,33.09
2010-05-20,241.88,243.85,236.21,237.76,320728800,32.46
2010-05-19,249.50,252.92,244.85,248.34,256431700,33.91
2010-05-18,256.98,258.55,250.26,252.36,195669600,34.46
2010-05-17,254.70,256.18,247.71,254.22,190708700,34.71
2010-05-14,255.16,256.48,249.50,253.82,189840700,34.66
2010-05-13,263.22,265.00,256.40,258.36,149928100,35.28
2010-05-12,259.24,263.13,258.70,262.09,163594900,35.79
2010-05-11,251.84,259.89,250.50,256.52,212226700,35.03
2010-05-10,250.25,254.65,248.53,253.99,246076600,34.68
2010-05-07,243.71,246.57,225.21,235.86,419004600,32.21
2010-05-06,253.83,258.25,199.25,246.25,321465200,33.62
2010-05-05,253.03,258.14,248.73,255.99,220775800,34.95
2010-05-04,262.89,263.29,256.75,258.68,180954900,35.32
2010-05-03,263.84,267.88,262.88,266.35,113585500,36.37
2010-04-30,269.31,270.57,261.00,261.09,135615900,35.65
2010-04-29,263.02,270.00,262.01,268.64,139710200,36.68
2010-04-28,263.25,264.00,256.41,261.60,189600600,35.72
2010-04-27,267.27,267.84,260.52,262.04,177335900,35.78
2010-04-26,271.88,272.46,268.19,269.50,119767200,36.80
2010-04-23,267.99,272.18,267.00,270.83,199238900,36.98
2010-04-22,258.24,266.75,256.20,266.47,198356200,36.39
2010-04-21,258.80,260.25,255.73,259.22,245597800,35.40
2010-04-20,248.54,249.25,242.96,244.59,184581600,33.40
2010-04-19,247.03,247.89,241.77,247.07,141731100,33.74
2010-04-16,248.57,251.14,244.55,247.40,187636400,33.78
2010-04-15,245.78,249.03,245.51,248.92,94196200,33.99
2010-04-14,245.28,245.81,244.07,245.69,101019100,33.55
2010-04-13,241.86,242.80,241.11,242.43,76552700,33.10
2010-04-12,242.20,243.07,241.81,242.29,83256600,33.08
2010-04-09,241.43,241.89,240.46,241.79,83545700,33.02
2010-04-08,240.44,241.54,238.04,239.95,143247300,32.76
2010-04-07,239.55,241.92,238.66,240.60,157125500,32.85
2010-04-06,238.20,240.24,237.00,239.54,111754300,32.71
2010-04-05,234.98,238.51,234.77,238.49,171126900,32.56
2010-04-01,237.41,238.73,232.75,235.97,150786300,32.22
2010-03-31,235.49,236.61,234.46,235.00,107664900,32.09
2010-03-30,236.60,237.48,234.25,235.85,131827500,32.20
2010-03-29,233.00,233.87,231.62,232.39,135186100,31.73
2010-03-26,228.95,231.95,228.55,230.90,160218800,31.53
2010-03-25,230.92,230.97,226.25,226.65,135571100,30.95
2010-03-24,227.64,230.20,227.51,229.37,149445100,31.32
2010-03-23,225.64,228.78,224.10,228.36,150607800,31.18
2010-03-22,220.47,226.00,220.15,224.75,114104900,30.69
2010-03-19,224.79,225.24,221.23,222.25,139861400,30.35
2010-03-18,224.10,225.00,222.61,224.65,85527400,30.67
2010-03-17,224.90,226.45,223.27,224.12,112739200,30.60
2010-03-16,224.18,224.98,222.51,224.45,111727000,30.65
2010-03-15,225.38,225.50,220.25,223.84,123375700,30.56
2010-03-12,227.37,227.73,225.75,226.60,104080900,30.94
2010-03-11,223.91,225.50,223.32,225.50,101425100,30.79
2010-03-10,223.83,225.48,223.20,224.84,149054500,30.70
2010-03-09,218.31,225.00,217.89,223.02,230064800,30.45
2010-03-08,220.01,220.09,218.25,219.08,107472400,29.91
2010-03-05,214.94,219.70,214.63,218.95,224905100,29.90
2010-03-04,209.28,210.92,208.63,210.71,91510300,28.77
2010-03-03,208.94,209.87,207.94,209.33,93013200,28.58
2010-03-02,209.93,210.83,207.74,208.85,141636600,28.52
2010-03-01,205.75,209.50,205.45,208.99,137523400,28.54
2010-02-26,202.38,205.17,202.00,204.62,126865200,27.94
2010-02-25,197.38,202.86,196.89,202.00,166281500,27.58
2010-02-24,198.23,201.44,197.84,200.66,115141600,27.40
2010-02-23,200.00,201.33,195.71,197.06,143773700,26.91
2010-02-22,202.34,202.50,199.19,200.42,97640900,27.37
2010-02-19,201.86,203.20,201.11,201.67,103867400,27.54
2010-02-18,201.63,203.89,200.92,202.93,105706300,27.71
2010-02-17,204.19,204.31,200.86,202.55,109099200,27.66
2010-02-16,201.94,203.69,201.52,203.40,135934400,27.77
2010-02-12,198.11,201.64,195.50,200.38,163867200,27.36
2010-02-11,194.88,199.75,194.06,198.67,137586400,27.13
2010-02-10,195.89,196.60,194.26,195.12,92590400,26.64
2010-02-09,196.42,197.50,194.75,196.19,158221700,26.79
2010-02-08,195.69,197.88,194.00,194.12,119567700,26.51
2010-02-05,192.63,196.00,190.85,195.46,212576700,26.69
2010-02-04,196.73,198.37,191.57,192.05,189413000,26.22
2010-02-03,195.17,200.20,194.42,199.23,153832000,27.20
2010-02-02,195.91,196.32,193.38,195.86,174585600,26.74
2010-02-01,192.37,196.00,191.30,194.73,187469100,26.59
2010-01-29,201.08,202.20,190.25,192.06,311488100,26.22
2010-01-28,204.93,205.50,198.70,199.29,293375600,27.21
2010-01-27,206.85,210.58,199.53,207.88,430642100,28.39
2010-01-26,205.95,213.71,202.58,205.94,466777500,28.12
2010-01-25,202.51,204.70,200.19,203.07,266424900,27.73
2010-01-22,206.78,207.50,197.16,197.75,220441900,27.00
2010-01-21,212.08,213.31,207.21,208.07,152038600,28.41
2010-01-20,214.91,215.55,209.50,211.73,153038200,28.91
2010-01-19,208.33,215.19,207.24,215.04,182501900,29.36
2010-01-15,210.93,211.60,205.87,205.93,148516900,28.12
2010-01-14,210.11,210.46,209.02,209.43,108223500,28.60
2010-01-13,207.87,210.93,204.10,210.65,151473000,28.76
2010-01-12,209.19,209.77,206.42,207.72,148614900,28.36
2010-01-11,212.80,213.00,208.45,210.11,115557400,28.69
2010-01-08,210.30,212.00,209.06,211.98,111902700,28.94
2010-01-07,211.75,212.00,209.05,210.58,119282800,28.75
2010-01-06,214.38,215.23,210.75,210.97,138040000,28.81
2010-01-05,214.60,215.59,213.25,214.38,150476200,29.27
2010-01-04,213.43,214.50,212.38,214.01,123432400,29.22
2009-12-31,213.13,213.35,210.56,210.73,88102700,28.77
2009-12-30,208.83,212.00,208.31,211.64,103021100,28.90
2009-12-29,212.63,212.72,208.73,209.10,111301400,28.55
2009-12-28,211.72,213.95,209.61,211.61,161141400,28.89
2009-12-24,203.55,209.35,203.35,209.04,125222300,28.54
2009-12-23,201.20,202.38,200.81,202.10,86381400,27.60
2009-12-22,199.44,200.85,198.66,200.36,87378900,27.36
2009-12-21,196.05,199.75,195.67,198.23,152976600,27.07
2009-12-18,193.17,195.50,192.60,195.43,152192600,26.69
2009-12-17,194.26,195.00,191.00,191.86,97209700,26.20
2009-12-16,195.10,196.50,194.55,195.03,88246200,26.63
2009-12-15,195.83,197.51,193.27,194.17,104864900,26.51
2009-12-14,195.37,197.43,192.56,196.98,123947600,26.90
2009-12-11,197.78,198.00,193.43,194.67,107443700,26.58
2009-12-10,199.50,199.70,196.12,196.43,122417400,26.82
2009-12-09,191.28,198.16,190.31,197.80,171195500,27.01
2009-12-08,189.36,192.35,188.70,189.87,172599700,25.93
2009-12-07,193.32,193.77,188.68,188.95,178689700,25.80
2009-12-04,199.70,199.88,190.28,193.32,206721200,26.40
2009-12-03,197.42,198.98,196.27,196.48,112179900,26.83
2009-12-02,198.96,201.42,195.75,196.23,178815000,26.79
2009-12-01,202.24,202.77,196.83,196.97,116440800,26.90
2009-11-30,201.11,201.68,198.77,199.91,106214500,27.30
2009-11-27,199.22,202.96,198.37,200.59,73814300,27.39
2009-11-25,205.40,205.65,203.76,204.19,71613500,27.88
2009-11-24,205.33,205.88,202.90,204.44,79609600,27.92
2009-11-23,203.00,206.00,202.95,205.88,118724200,28.11
2009-11-20,199.15,200.39,197.76,199.92,101666600,27.30
2009-11-19,204.61,204.61,199.80,200.51,135581600,27.38
2009-11-18,206.54,207.00,204.00,205.96,93580200,28.12
2009-11-17,206.08,207.44,205.00,207.00,99128400,28.26
2009-11-16,205.48,208.00,205.01,206.63,121301600,28.21
2009-11-13,202.87,204.83,202.07,204.45,85810200,27.92
2009-11-12,203.14,204.87,201.43,201.99,90932800,27.58
2009-11-11,204.56,205.00,201.83,203.25,110967500,27.75
2009-11-10,201.02,204.98,201.01,202.98,100298800,27.72
2009-11-09,196.94,201.90,196.26,201.46,132213900,27.51
2009-11-06,192.51,195.19,192.40,194.34,73774400,26.54
2009-11-05,192.40,195.00,191.82,194.03,96200300,26.49
2009-11-04,190.73,193.85,190.23,190.81,121882600,26.05
2009-11-03,187.85,189.52,185.92,188.75,130635400,25.77
2009-11-02,189.80,192.88,185.57,189.31,169745800,25.85
2009-10-30,196.06,196.80,188.17,188.50,179381300,25.74
2009-10-29,195.00,196.81,192.14,196.35,142567600,26.81
2009-10-28,197.71,198.02,191.10,192.40,204596700,26.27
2009-10-27,201.66,202.81,196.45,197.37,189137900,26.95
2009-10-26,203.67,206.75,200.10,202.48,121084600,27.65
2009-10-23,205.70,205.80,203.23,203.94,105196700,27.85
2009-10-22,204.70,207.85,202.51,205.20,197848000,28.02
2009-10-21,199.52,208.71,199.23,204.92,298431700,27.98
2009-10-20,200.60,201.75,197.85,198.76,285259800,27.14
2009-10-19,187.85,190.00,185.55,189.86,235557700,25.92
2009-10-16,189.35,190.36,187.84,188.05,107856700,25.68
2009-10-15,189.63,190.92,189.53,190.56,93389100,26.02
2009-10-14,192.25,192.32,190.23,191.29,93877700,26.12
2009-10-13,190.63,191.17,189.70,190.02,87005100,25.95
2009-10-12,191.02,191.51,189.64,190.81,72006200,26.05
2009-10-09,188.97,190.70,188.62,190.47,73318000,26.01
2009-10-08,190.66,191.45,188.89,189.27,109552800,25.84
2009-10-07,189.76,190.55,189.03,190.25,116417000,25.98
2009-10-06,187.74,190.01,187.30,190.01,151271400,25.94
2009-10-05,186.20,186.86,184.27,186.02,105783300,25.40
2009-10-02,181.41,185.94,181.35,184.90,138327000,25.25
2009-10-01,185.35,186.22,180.70,180.86,131177900,24.70
2009-09-30,186.13,186.45,182.61,185.35,134896300,25.31
2009-09-29,186.73,187.40,184.31,185.38,86346400,25.31
2009-09-28,183.87,186.68,183.33,186.15,84361200,25.42
2009-09-25,182.01,185.50,181.44,182.37,111309800,24.90
2009-09-24,187.20,187.70,182.77,183.82,137720100,25.10
2009-09-23,185.40,188.90,185.03,185.50,148390900,25.33
2009-09-22,185.19,185.38,182.85,184.48,89188400,25.19
2009-09-21,184.29,185.16,181.62,184.02,109428900,25.13
2009-09-18,185.83,186.55,184.76,185.02,150395700,25.26
2009-09-17,181.98,186.79,181.97,184.55,202643000,25.20
2009-09-16,177.99,182.75,177.88,181.87,188505800,24.83
2009-09-15,174.04,175.65,173.59,175.16,106617700,23.92
2009-09-14,170.83,173.90,170.25,173.72,80502800,23.72
2009-09-11,172.91,173.18,170.87,172.16,87240300,23.51
2009-09-10,172.06,173.25,170.81,172.56,122783500,23.56
2009-09-09,172.78,174.47,169.70,171.14,202771800,23.37
2009-09-08,172.98,173.14,172.00,172.93,78761900,23.61
2009-09-04,167.28,170.70,167.09,170.31,93657200,23.26
2009-09-03,166.44,167.10,165.00,166.55,73488800,22.74
2009-09-02,164.62,167.61,164.11,165.18,91062300,22.55
2009-09-01,167.99,170.00,164.94,165.30,117257000,22.57
2009-08-31,168.16,168.85,166.50,168.21,77834400,22.97
2009-08-28,172.27,172.49,168.53,170.05,113425200,23.22
2009-08-27,168.75,169.57,164.83,169.45,112295400,23.14
2009-08-26,168.92,169.55,166.76,167.41,75999700,22.86
2009-08-25,169.46,170.94,169.13,169.40,81088700,23.13
2009-08-24,170.12,170.71,168.27,169.06,101732400,23.08
2009-08-21,167.65,169.37,166.80,169.22,104018600,23.11
2009-08-20,164.98,166.72,164.61,166.33,85507800,22.71
2009-08-19,162.75,165.30,162.45,164.60,103317900,22.48
2009-08-18,161.63,164.24,161.41,164.00,107788100,22.39
2009-08-17,163.55,163.59,159.42,159.59,131095300,21.79
2009-08-14,167.94,168.23,165.53,166.78,76454000,22.77
2009-08-13,166.65,168.67,166.50,168.42,109995200,23.00
2009-08-12,162.55,166.71,162.46,165.31,111267800,22.57
2009-08-11,163.69,164.38,161.88,162.83,88835600,22.23
2009-08-10,165.66,166.60,163.66,164.72,75073600,22.49
2009-08-07,165.49,166.60,164.80,165.51,96838700,22.60
2009-08-06,165.58,166.51,163.09,163.91,85404200,22.38
2009-08-05,165.75,167.39,164.21,165.11,105795900,22.54
2009-08-04,164.93,165.57,164.21,165.55,98952700,22.61
2009-08-03,165.21,166.64,164.87,166.43,98560000,22.73
2009-07-31,162.99,165.00,162.91,163.39,105634200,22.31
2009-07-30,161.70,164.72,161.50,162.79,117401200,22.23
2009-07-29,158.90,160.45,158.25,160.03,95539500,21.85
2009-07-28,158.88,160.10,157.60,160.00,90888700,21.85
2009-07-27,160.17,160.88,157.26,160.10,108327800,21.86
2009-07-24,156.95,160.00,156.50,159.99,109590600,21.85
2009-07-23,156.63,158.44,155.56,157.82,131740700,21.55
2009-07-22,157.79,158.73,156.11,156.74,218526000,21.40
2009-07-21,153.29,153.43,149.75,151.51,218695400,20.69
2009-07-20,153.27,155.04,150.89,152.91,183881600,20.88
2009-07-17,149.08,152.02,148.63,151.75,150538500,20.72
2009-07-16,145.76,148.02,145.57,147.52,98392700,20.14
2009-07-15,145.04,147.00,144.32,146.88,121396800,20.06
2009-07-14,142.03,143.18,141.16,142.27,86811900,19.43
2009-07-13,139.54,142.34,137.53,142.34,120875300,19.44
2009-07-10,136.34,138.97,136.32,138.52,111318900,18.91
2009-07-09,137.76,137.99,135.93,136.36,85756300,18.62
2009-07-08,135.92,138.04,134.42,137.22,143982300,18.74
2009-07-07,138.48,139.68,135.18,135.40,115399200,18.49
2009-07-06,138.70,138.99,136.25,138.61,124672100,18.93
2009-07-02,141.25,142.83,139.79,140.02,92619800,19.12
2009-07-01,143.50,144.66,142.52,142.83,103544700,19.50
2009-06-30,142.58,143.80,141.80,142.43,108556000,19.45
2009-06-29,143.46,143.95,141.54,141.97,141904000,19.39
2009-06-26,139.79,143.56,139.74,142.44,109846100,19.45
2009-06-25,135.75,140.20,135.21,139.86,147361900,19.10
2009-06-24,135.42,137.50,134.86,136.22,121381400,18.60
2009-06-23,136.40,136.95,132.88,134.01,176633100,18.30
2009-06-22,140.67,141.56,136.33,137.37,158728500,18.76
2009-06-19,138.07,139.50,136.90,139.48,180464200,19.05
2009-06-18,136.11,138.00,135.59,135.88,106920100,18.55
2009-06-17,136.67,137.45,134.53,135.58,142853200,18.51
2009-06-16,136.66,138.47,136.10,136.35,128701300,18.62
2009-06-15,136.01,136.93,134.89,136.09,134937600,18.58
2009-06-12,138.81,139.10,136.04,136.97,140771400,18.70
2009-06-11,139.55,141.56,138.55,139.95,131205900,19.11
2009-06-10,142.28,142.35,138.30,140.25,172155900,19.15
2009-06-09,143.81,144.56,140.55,142.72,169241100,19.49
2009-06-08,143.82,144.23,139.43,143.85,232913100,19.64
2009-06-05,145.31,146.40,143.21,144.67,158179000,19.75
2009-06-04,140.13,144.18,140.04,143.74,137658500,19.63
2009-06-03,140.00,141.11,139.07,140.95,141299900,19.25
2009-06-02,138.99,141.34,138.35,139.49,114055900,19.05
2009-06-01,136.47,139.99,136.00,139.35,113124900,19.03
2009-05-29,135.39,135.90,133.85,135.81,114133600,18.54
2009-05-28,133.45,135.39,132.03,135.07,121888200,18.44
2009-05-27,131.78,134.98,130.91,133.05,161605500,18.17
2009-05-26,124.76,130.83,124.55,130.78,159231800,17.86
2009-05-22,124.05,124.18,121.75,122.50,74499600,16.73
2009-05-21,125.15,126.78,122.89,124.18,101986500,16.96
2009-05-20,127.63,129.21,125.30,125.87,97146000,17.19
2009-05-19,126.82,129.31,125.74,127.45,93105600,17.40
2009-05-18,123.73,126.70,121.57,126.65,114710400,17.29
2009-05-15,122.32,124.62,121.61,122.42,91891800,16.72
2009-05-14,119.78,123.53,119.70,122.95,111956600,16.79
2009-05-13,123.21,124.02,119.38,119.49,148992900,16.32
2009-05-12,129.56,129.71,123.25,124.42,152370400,16.99
2009-05-11,127.37,130.96,127.12,129.57,101164700,17.69
2009-05-08,129.04,131.23,126.26,129.19,116991000,17.64
2009-05-07,132.33,132.39,127.90,129.06,132944000,17.62
2009-05-06,133.33,133.50,130.22,132.50,118384700,18.09
2009-05-05,131.75,132.86,131.12,132.71,99563800,18.12
2009-05-04,128.24,132.25,127.68,132.07,152339600,18.03
2009-05-01,125.80,127.95,125.80,127.24,99379000,17.37
2009-04-30,126.22,127.00,124.92,125.83,124622400,17.18
2009-04-29,124.85,126.85,123.83,125.14,114527700,17.09
2009-04-28,123.35,126.21,123.26,123.90,113964200,16.92
2009-04-27,122.90,125.00,122.66,124.73,120172500,17.03
2009-04-24,124.64,125.14,122.97,123.90,135191000,16.92
2009-04-23,126.62,127.20,123.51,125.40,236289200,17.12
2009-04-22,122.63,125.35,121.20,121.51,234691800,16.59
2009-04-21,118.89,122.14,118.60,121.76,117671400,16.63
2009-04-20,121.73,122.99,119.16,120.50,116616500,16.45
2009-04-17,121.18,124.25,120.25,123.42,124373900,16.85
2009-04-16,119.19,123.15,118.79,121.45,148361500,16.58
2009-04-15,117.20,118.25,115.76,117.64,103220600,16.06
2009-04-14,119.57,120.17,117.25,118.31,113655500,16.15
2009-04-13,120.01,120.98,119.00,120.22,97309100,16.42
2009-04-09,118.42,120.00,117.96,119.57,132689200,16.33
2009-04-08,115.43,116.79,114.58,116.32,113907500,15.88
2009-04-07,116.53,116.67,114.19,115.00,134145200,15.70
2009-04-06,114.94,118.75,113.28,118.45,164516100,16.17
2009-04-03,114.19,116.13,113.52,115.99,159060300,15.84
2009-04-02,110.14,114.75,109.78,112.71,203091700,15.39
2009-04-01,104.09,109.00,103.89,108.69,147343000,14.84
2009-03-31,105.45,107.45,105.00,105.12,142520000,14.35
2009-03-30,104.51,105.01,102.61,104.49,125699000,14.27
2009-03-27,108.23,108.53,106.40,106.85,123218200,14.59
2009-03-26,107.83,109.98,107.58,109.87,154063000,15.00
2009-03-25,107.58,108.36,103.86,106.49,161654500,14.54
2009-03-24,106.36,109.44,105.39,106.50,160153000,14.54
2009-03-23,102.71,108.16,101.75,107.66,166599300,14.70
2009-03-20,102.09,103.11,100.57,101.59,173896800,13.87
2009-03-19,101.85,103.20,100.25,101.62,125045200,13.88
2009-03-18,99.91,103.48,99.72,101.52,199009300,13.86
2009-03-17,95.24,99.69,95.07,99.66,196661500,13.61
2009-03-16,96.53,97.39,94.18,95.42,199311000,13.03
2009-03-13,96.30,97.20,95.01,95.93,150292100,13.10
2009-03-12,92.90,96.58,92.00,96.35,192114300,13.16
2009-03-11,89.81,94.07,89.58,92.68,211593200,12.66
2009-03-10,84.87,89.17,84.36,88.63,211064700,12.10
2009-03-09,84.18,87.60,82.57,83.11,174574400,11.35
2009-03-06,88.34,88.40,82.33,85.30,252786800,11.65
2009-03-05,90.46,91.87,88.45,88.84,176724800,12.13
2009-03-04,90.18,92.77,89.45,91.17,185350900,12.45
2009-03-03,88.93,90.74,87.88,88.37,181085100,12.07
2009-03-02,88.12,91.20,87.67,87.94,192732400,12.01
2009-02-27,87.93,91.30,87.67,89.31,176664600,12.19
2009-02-26,92.00,92.92,88.96,89.19,157467100,12.18
2009-02-25,89.86,92.92,89.25,91.16,208263300,12.45
2009-02-24,87.45,90.89,87.00,90.25,201776400,12.32
2009-02-23,91.65,92.00,86.51,86.95,196745500,11.87
2009-02-20,89.40,92.40,89.00,91.20,187579000,12.45
2009-02-19,93.37,94.25,90.11,90.64,230701100,12.38
2009-02-18,95.05,95.85,92.72,94.37,171194800,12.89
2009-02-17,96.87,97.04,94.28,94.53,169559600,12.91
2009-02-13,98.99,99.94,98.12,99.16,152244400,13.54
2009-02-12,95.83,99.75,95.83,99.27,204297100,13.55
2009-02-11,96.37,98.31,95.77,96.82,168743400,13.22
2009-02-10,101.33,102.51,97.06,97.83,212265200,13.36
2009-02-09,100.00,103.00,99.50,102.51,178752700,14.00
2009-02-06,97.02,100.00,97.00,99.72,171802400,13.62
2009-02-05,92.77,97.25,92.62,96.46,187311600,13.17
2009-02-04,93.22,96.25,93.10,93.55,202105400,12.77
2009-02-03,91.92,93.38,90.28,92.98,149827300,12.70
2009-02-02,89.10,92.00,88.90,91.51,139561800,12.50
2009-01-30,92.60,93.62,90.01,90.13,162869700,12.31
2009-01-29,93.09,94.34,92.60,93.00,148182300,12.70
2009-01-28,92.12,95.00,91.50,94.20,215351500,12.86
2009-01-27,90.19,91.55,89.74,90.73,154509600,12.39
2009-01-26,88.86,90.97,88.30,89.64,173059600,12.24
2009-01-23,86.82,89.87,86.50,88.36,190942500,12.07
2009-01-22,88.04,90.00,85.82,88.36,352382100,12.07
2009-01-21,79.39,82.88,79.31,82.83,272317500,11.31
2009-01-20,81.93,82.00,78.20,78.20,229978700,10.68
2009-01-16,84.30,84.38,80.40,82.33,261906400,11.24
2009-01-15,80.57,84.12,80.05,83.38,457908500,11.39
2009-01-14,86.24,87.25,84.72,85.33,255416000,11.65
2009-01-13,88.24,89.74,86.35,87.71,199599400,11.98
2009-01-12,90.46,90.99,87.55,88.66,154429100,12.11
2009-01-09,93.21,93.38,90.14,90.58,136711400,12.37
2009-01-08,90.43,93.15,90.04,92.70,168375200,12.66
2009-01-07,91.81,92.50,90.26,91.01,188262200,12.43
2009-01-06,95.95,97.17,92.39,93.02,322327600,12.70
2009-01-05,93.17,96.18,92.71,94.58,295402100,12.91
2009-01-02,85.88,91.04,85.16,90.75,186503800,12.39
2008-12-31,85.97,87.74,85.34,85.35,151885300,11.65
2008-12-30,87.42,88.05,84.72,86.29,241900400,11.78
2008-12-29,86.52,87.62,85.07,86.61,171500000,11.83
2008-12-26,86.64,87.42,85.24,85.81,77081200,11.72
2008-12-24,86.14,86.25,84.55,85.04,67833500,11.61
2008-12-23,86.87,87.87,85.90,86.38,158757900,11.79
2008-12-22,90.02,90.03,84.69,85.74,211185100,11.71
2008-12-19,89.94,90.94,88.80,90.00,200480000,12.29
2008-12-18,89.31,90.83,88.44,89.43,214354000,12.21
2008-12-17,91.03,91.10,88.02,89.16,323465100,12.17
2008-12-16,93.98,96.48,92.75,95.43,273376600,13.03
2008-12-15,95.99,96.21,93.00,94.75,222939500,12.94
2008-12-12,92.80,99.00,92.53,98.27,260293600,13.42
2008-12-11,97.35,101.24,94.83,95.00,260154300,12.97
2008-12-10,97.87,99.49,96.50,98.21,234511900,13.41
2008-12-09,98.04,103.60,97.21,100.06,300874000,13.66
2008-12-08,97.28,100.80,95.80,99.72,296285500,13.62
2008-12-05,90.35,94.49,88.86,94.00,260948800,12.84
2008-12-04,94.43,95.21,89.06,91.41,272842500,12.48
2008-12-03,89.40,96.23,88.80,95.90,334670000,13.09
2008-12-02,90.03,92.65,86.50,92.47,287180600,12.63
2008-12-01,91.30,92.27,88.92,88.93,230941900,12.14
2008-11-28,94.70,94.76,91.86,92.67,74443600,12.65
2008-11-26,89.92,95.25,89.85,95.00,224959000,12.97
2008-11-25,94.63,94.71,88.16,90.80,308823200,12.40
2008-11-24,85.21,94.79,84.84,92.95,360564400,12.69
2008-11-21,81.93,84.12,79.14,82.58,392317800,11.28
2008-11-20,85.24,86.45,80.00,80.49,429203600,10.99
2008-11-19,89.44,91.58,86.21,86.29,292975200,11.78
2008-11-18,89.64,90.99,86.86,89.91,302423800,12.28
2008-11-17,88.48,90.55,87.26,88.14,290631600,12.04
2008-11-14,93.76,93.99,90.00,90.24,351316700,12.32
2008-11-13,89.87,96.44,86.02,96.44,463521800,13.17
2008-11-12,92.43,93.24,90.01,90.12,294744100,12.31
2008-11-11,94.81,97.17,92.26,94.77,306134500,12.94
2008-11-10,100.17,100.40,94.50,95.88,280955500,13.09
2008-11-07,99.24,99.85,95.72,98.24,273813400,13.41
2008-11-06,101.05,102.78,98.00,99.10,329768600,13.53
2008-11-05,108.91,109.72,102.99,103.30,314113800,14.11
2008-11-04,109.99,111.79,106.67,110.99,349670300,15.16
2008-11-03,105.93,109.10,104.86,106.96,264484500,14.60
2008-10-31,107.40,110.78,105.14,107.59,414939000,14.69
2008-10-30,108.23,112.19,107.61,111.04,409522400,15.16
2008-10-29,100.86,109.54,99.94,104.55,487744600,14.28
2008-10-28,95.43,100.50,92.37,99.91,408533300,13.64
2008-10-27,95.07,97.63,91.86,92.09,302192800,12.57
2008-10-24,90.33,97.90,90.11,96.38,397514600,13.16
2008-10-23,96.51,99.25,91.90,98.23,418857600,13.41
2008-10-22,97.37,101.25,92.93,96.87,562202200,13.23
2008-10-21,96.95,97.90,91.16,91.49,548415000,12.49
2008-10-20,99.78,100.03,93.64,98.44,387292500,13.44
2008-10-17,99.60,102.04,85.89,97.40,440556900,13.30
2008-10-16,99.77,103.43,91.74,101.89,495130300,13.91
2008-10-15,103.84,107.00,97.89,97.95,396043900,13.37
2008-10-14,116.26,116.40,103.14,104.08,495248600,14.21
2008-10-13,104.55,110.53,101.02,110.26,384769000,15.06
2008-10-10,85.70,100.00,85.00,96.80,554824900,13.22
2008-10-09,93.35,95.80,86.60,88.74,404345900,12.12
2008-10-08,85.91,96.33,85.68,89.79,551935300,12.26
2008-10-07,100.48,101.50,88.95,89.16,469693000,12.17
2008-10-06,91.96,98.78,87.54,98.14,526854300,13.40
2008-10-03,104.00,106.50,94.65,97.07,573599600,13.25
2008-10-02,108.01,108.79,100.00,100.10,402341100,13.67
2008-10-01,111.92,112.36,107.39,109.12,324121000,14.90
2008-09-30,108.25,115.00,106.30,113.66,406670600,15.52
2008-09-29,119.62,119.68,100.59,105.26,655514300,14.37
2008-09-26,124.91,129.80,123.00,128.24,281612800,17.51
2008-09-25,129.80,134.79,128.52,131.93,251511400,18.01
2008-09-24,127.27,130.95,125.15,128.71,261753800,17.57
2008-09-23,131.85,135.80,126.66,126.84,320091100,17.32
2008-09-22,139.94,140.25,130.66,131.05,214178300,17.89
2008-09-19,142.60,144.20,136.31,140.91,357718900,19.24
2008-09-18,130.57,135.43,120.68,134.09,419063400,18.31
2008-09-17,138.49,138.51,127.83,127.83,300113800,17.45
2008-09-16,133.86,142.50,132.15,139.88,299959100,19.10
2008-09-15,142.03,147.69,140.36,140.36,230158600,19.17
2008-09-12,150.91,150.91,146.50,148.94,198256800,20.34
2008-09-11,148.18,152.99,146.00,152.65,242783800,20.84
2008-09-10,152.32,154.99,148.80,151.61,243285700,20.70
2008-09-09,156.86,159.96,149.79,151.68,311256400,20.71
2008-09-08,164.57,164.89,151.46,157.92,261494800,21.56
2008-09-05,158.59,162.40,157.65,160.18,196721000,21.87
2008-09-04,165.86,167.91,160.81,161.22,185846500,22.01
2008-09-03,166.84,168.68,164.00,166.96,183708700,22.80
2008-09-02,172.40,173.50,165.00,166.19,195190800,22.69
2008-08-29,172.96,173.50,169.04,169.53,149822400,23.15
2008-08-28,175.28,176.25,172.75,173.74,107846200,23.72
2008-08-27,173.31,175.76,172.19,174.67,119445200,23.85
2008-08-26,172.76,174.88,172.61,173.64,111387500,23.71
2008-08-25,176.15,176.23,171.66,172.55,121106300,23.56
2008-08-22,175.82,177.50,175.57,176.79,109902800,24.14
2008-08-21,174.47,175.45,171.89,174.29,134936200,23.80
2008-08-20,174.77,176.94,173.61,175.84,126737800,24.01
2008-08-19,174.54,177.07,171.81,173.53,154051100,23.69
2008-08-18,175.57,177.81,173.82,175.39,138003600,23.95
2008-08-15,179.04,179.75,175.05,175.74,177062900,24.00
2008-08-14,178.33,180.45,177.84,179.32,177825200,24.49
2008-08-13,177.98,180.00,175.90,179.30,210586600,24.48
2008-08-12,173.52,179.29,173.51,176.73,209069700,24.13
2008-08-11,170.07,176.50,169.67,173.56,222826100,23.70
2008-08-08,163.86,169.65,163.75,169.55,178499300,23.15
2008-08-07,162.71,166.15,161.50,163.57,168093100,22.33
2008-08-06,159.97,167.40,158.00,164.19,197852200,22.42
2008-08-05,155.42,160.80,154.82,160.64,172092900,21.93
2008-08-04,156.60,157.90,152.91,153.23,148131900,20.92
2008-08-01,159.90,159.99,155.75,156.66,136159800,21.39
2008-07-31,157.54,162.20,156.98,158.95,159374600,21.70
2008-07-30,157.78,160.49,156.08,159.88,181295800,21.83
2008-07-29,155.41,159.45,153.65,157.08,171017700,21.45
2008-07-28,162.34,162.47,154.02,154.40,195178200,21.08
2008-07-25,160.40,163.00,158.65,162.12,158409300,22.14
2008-07-24,164.32,165.26,158.45,159.03,209904800,21.71
2008-07-23,164.99,168.37,161.56,166.26,265442100,22.70
2008-07-22,149.00,162.76,146.53,162.02,469898100,22.12
2008-07-21,166.90,167.50,161.12,166.29,340117400,22.71
2008-07-18,168.52,169.65,165.00,165.15,217103600,22.55
2008-07-17,174.10,174.98,171.39,171.81,189381500,23.46
2008-07-16,170.20,172.93,168.60,172.81,186947600,23.60
2008-07-15,172.48,173.74,166.39,169.64,260010800,23.16
2008-07-14,179.24,179.30,173.08,173.88,221513600,23.74
2008-07-11,175.47,177.11,171.00,172.58,232502900,23.56
2008-07-10,174.92,177.34,171.37,176.63,210172200,24.12
2008-07-09,180.20,180.91,174.14,174.25,223944000,23.79
2008-07-08,175.40,179.70,172.74,179.55,222087600,24.52
2008-07-07,173.16,177.13,171.90,175.16,205097900,23.92
2008-07-03,169.59,172.17,165.75,170.12,130840500,23.23
2008-07-02,175.20,177.45,168.18,168.18,209379800,22.96
2008-07-01,164.23,174.72,164.00,174.68,277820200,23.85
2008-06-30,170.19,172.00,166.62,167.44,171049200,22.86
2008-06-27,166.51,170.57,164.15,170.09,260562400,23.22
2008-06-26,174.07,174.84,168.01,168.26,217402500,22.98
2008-06-25,174.61,178.83,173.88,177.39,161112700,24.22
2008-06-24,172.37,175.78,171.63,173.25,155486800,23.66
2008-06-23,174.74,175.88,171.56,173.16,161445200,23.64
2008-06-20,179.35,181.00,175.00,175.27,222091800,23.93
2008-06-19,178.55,182.34,176.80,180.90,197987300,24.70
2008-06-18,181.12,182.20,177.35,178.75,202867000,24.41
2008-06-17,178.10,181.99,177.41,181.43,224914200,24.77
2008-06-16,171.30,177.90,169.07,176.84,262932600,24.15
2008-06-13,171.64,174.16,165.31,172.37,336489300,23.54
2008-06-12,181.49,182.60,171.20,173.26,327083400,23.66
2008-06-11,184.34,186.00,179.59,180.81,240387700,24.69
2008-06-10,180.51,186.78,179.02,185.64,285235300,25.35
2008-06-09,184.79,184.94,175.75,181.61,472098200,24.80
2008-06-06,188.00,189.95,185.55,185.64,241605700,25.35
2008-06-05,186.34,189.84,185.70,189.43,188861400,25.87
2008-06-04,184.02,187.09,183.23,185.19,181745900,25.29
2008-06-03,186.86,188.20,182.34,185.37,187630100,25.31
2008-06-02,188.60,189.65,184.53,186.10,169960000,25.41
2008-05-30,187.45,189.54,187.38,188.75,152546100,25.77
2008-05-29,186.76,188.20,185.50,186.69,161796600,25.49
2008-05-28,187.41,187.95,183.72,187.01,185994900,25.54
2008-05-27,182.75,186.43,181.84,186.43,197476300,25.46
2008-05-23,180.77,181.99,177.80,181.17,226729300,24.74
2008-05-22,179.26,181.33,172.00,177.05,301683900,24.18
2008-05-21,185.67,187.95,176.25,178.19,289414300,24.33
2008-05-20,181.82,186.16,180.12,185.90,242462500,25.38
2008-05-19,187.86,188.69,181.30,183.60,236455100,25.07
2008-05-16,190.11,190.30,187.00,187.62,191442300,25.62
2008-05-15,186.81,189.90,184.20,189.73,218302000,25.91
2008-05-14,191.23,192.24,185.57,186.26,229205900,25.43
2008-05-13,188.61,191.45,187.86,189.96,205809100,25.94
2008-05-12,185.21,188.87,182.85,188.16,204640800,25.69
2008-05-09,183.16,184.25,181.37,183.45,168268100,25.05
2008-05-08,183.77,186.50,183.07,185.06,224771400,25.27
2008-05-07,186.05,188.20,180.54,182.59,289283400,24.93
2008-05-06,184.66,187.12,182.18,186.66,229717600,25.49
2008-05-05,181.92,185.31,181.05,184.73,213639300,25.22
2008-05-02,180.19,181.92,178.55,180.94,251520500,24.71
2008-05-01,174.96,180.00,174.86,180.00,225894200,24.58
2008-04-30,176.19,180.00,172.92,173.95,284881100,23.75
2008-04-29,171.11,175.66,170.25,175.05,230869100,23.90
2008-04-28,169.75,173.75,169.13,172.24,196803600,23.52
2008-04-25,170.70,171.10,166.42,169.73,248118500,23.18
2008-04-24,165.34,169.98,159.19,168.94,424016600,23.07
2008-04-23,164.05,164.84,161.08,162.89,376047700,22.24
2008-04-22,167.40,168.00,158.09,160.20,359893100,21.87
2008-04-21,162.21,168.50,161.76,168.16,259788200,22.96
2008-04-18,159.12,162.26,158.38,161.04,256691400,21.99
2008-04-17,154.17,156.00,153.35,154.49,176066800,21.09
2008-04-16,151.72,154.10,150.62,153.70,198943500,20.99
2008-04-15,149.40,149.72,145.72,148.38,174509300,20.26
2008-04-14,146.77,149.25,144.54,147.78,211271900,20.18
2008-04-11,152.72,153.30,146.40,147.14,302519000,20.09
2008-04-10,151.13,155.42,150.60,154.55,238940800,21.10
2008-04-09,153.31,153.89,150.46,151.44,218349600,20.68
2008-04-08,153.55,156.45,152.32,152.84,253573600,20.87
2008-04-07,156.13,159.69,155.11,155.89,289581600,21.29
2008-04-04,152.19,154.71,150.75,153.08,213604300,20.90
2008-04-03,147.06,153.63,147.00,151.61,262892000,20.70
2008-04-02,148.78,151.20,145.85,147.49,261242100,20.14
2008-04-01,146.30,149.66,143.61,149.53,258141800,20.42
2008-03-31,143.27,145.71,142.52,143.50,192016300,19.59
2008-03-28,141.80,144.65,141.60,143.01,178652600,19.53
2008-03-27,144.95,145.31,139.99,140.25,249957400,19.15
2008-03-26,140.87,145.74,140.64,145.06,295521100,19.81
2008-03-25,139.96,143.10,137.33,140.98,263097800,19.25
2008-03-24,134.01,140.85,133.64,139.53,266730100,19.05
2008-03-20,131.12,133.29,129.18,133.27,227196900,18.20
2008-03-19,133.12,134.29,129.67,129.67,252634200,17.71
2008-03-18,129.18,133.00,128.67,132.82,301280000,18.14
2008-03-17,122.55,128.59,122.55,126.73,268149700,17.30
2008-03-14,129.88,130.30,124.20,126.61,289160200,17.29
2008-03-13,124.10,129.50,123.00,127.94,315525700,17.47
2008-03-12,127.04,128.68,125.17,126.03,264907300,17.21
2008-03-11,124.10,127.48,122.00,127.35,290985800,17.39
2008-03-10,121.98,123.46,119.37,119.69,249897200,16.34
2008-03-07,120.41,122.98,119.05,122.25,307615700,16.69
2008-03-06,124.61,127.50,120.81,120.93,368424700,16.51
2008-03-05,123.58,125.14,122.25,124.49,305459000,17.00
2008-03-04,121.99,124.88,120.40,124.62,446345900,17.02
2008-03-03,124.44,125.98,118.00,121.73,398260800,16.62
2008-02-29,129.29,130.21,124.80,125.02,313870200,17.07
2008-02-28,127.20,132.20,125.77,129.91,404563600,17.74
2008-02-27,118.23,123.05,118.09,122.96,368784500,16.79
2008-02-26,117.64,121.09,115.44,119.15,376222000,16.27
2008-02-25,118.59,120.17,116.66,119.74,314193600,16.35
2008-02-22,122.48,122.51,115.87,119.46,382469500,16.31
2008-02-21,126.05,126.47,120.86,121.54,234528700,16.60
2008-02-20,122.20,124.60,121.68,123.82,241859800,16.91
2008-02-19,125.99,126.75,121.44,122.18,251261500,16.68
2008-02-15,126.27,127.08,124.06,124.63,225325100,17.02
2008-02-14,129.40,130.80,127.01,127.46,238524300,17.40
2008-02-13,126.68,129.78,125.63,129.40,242133500,17.67
2008-02-12,130.70,131.00,123.62,124.86,306495000,17.05
2008-02-11,128.01,129.98,127.20,129.45,300358100,17.68
2008-02-08,122.08,125.70,121.60,125.48,338993200,17.13
2008-02-07,119.97,124.78,117.27,121.24,520832900,16.55
2008-02-06,130.83,131.92,121.77,122.00,393318100,16.66
2008-02-05,130.43,134.00,128.90,129.36,285260500,17.66
2008-02-04,134.21,135.90,131.42,131.65,224808500,17.98
2008-02-01,136.24,136.59,132.18,133.75,252686000,18.26
2008-01-31,129.45,136.65,129.40,135.36,336418600,18.48
2008-01-30,131.37,135.45,130.00,132.18,310762900,18.05
2008-01-29,131.15,132.79,129.05,131.54,274995700,17.96
2008-01-28,128.16,133.20,126.45,130.01,368711000,17.75
2008-01-25,138.99,139.09,129.61,130.01,388684800,17.75
2008-01-24,139.99,140.70,132.01,135.60,501466700,18.52
2008-01-23,136.19,140.00,126.14,139.07,843242400,18.99
2008-01-22,148.06,159.98,146.00,155.64,608688500,21.25
2008-01-18,161.71,165.75,159.61,161.36,431085900,22.03
2008-01-17,161.51,165.36,158.42,160.89,439464900,21.97
2008-01-16,165.23,169.01,156.70,159.64,553461300,21.80
2008-01-15,177.72,179.22,164.66,169.04,585819500,23.08
2008-01-14,177.52,179.42,175.17,178.78,275112600,24.41
2008-01-11,176.00,177.85,170.00,172.69,308071400,23.58
2008-01-10,177.58,181.00,175.41,178.02,370743800,24.31
2008-01-09,171.30,179.50,168.30,179.40,453470500,24.50
2008-01-08,180.14,182.46,170.80,171.25,380954000,23.38
2008-01-07,181.25,183.60,170.23,177.64,518048300,24.26
2008-01-04,191.45,193.00,178.89,180.05,363958000,24.58
2008-01-03,195.41,197.39,192.69,194.93,210516600,26.62
2008-01-02,199.27,200.26,192.55,194.84,269794700,26.60
2007-12-31,199.50,200.50,197.75,198.08,134833300,27.05
2007-12-28,200.59,201.56,196.88,199.83,174911800,27.29
2007-12-27,198.95,202.96,197.80,198.57,198881900,27.11
2007-12-26,199.01,200.96,196.82,198.95,175933100,27.17
2007-12-24,195.03,199.33,194.79,198.80,120050700,27.15
2007-12-21,190.12,193.91,189.89,193.91,248490200,26.48
2007-12-20,185.43,187.83,183.33,187.21,193514300,25.56
2007-12-19,182.98,184.64,180.90,183.12,206869600,25.00
2007-12-18,186.52,187.33,178.60,182.98,305650800,24.99
2007-12-17,190.72,192.65,182.98,184.40,256173400,25.18
2007-12-14,190.37,193.20,189.54,190.39,168578200,26.00
2007-12-13,190.19,192.12,187.82,191.83,216154400,26.19
2007-12-12,193.44,194.48,185.76,190.86,306415200,26.06
2007-12-11,194.75,196.83,187.39,188.54,277731300,25.74
2007-12-10,193.59,195.66,192.69,194.21,180594400,26.52
2007-12-07,190.54,194.99,188.04,194.30,266516600,26.53
2007-12-06,186.19,190.10,186.12,189.95,224952700,25.94
2007-12-05,182.89,186.00,182.41,185.50,223100500,25.33
2007-12-04,177.15,180.90,176.99,179.81,193449900,24.55
2007-12-03,181.86,184.14,177.70,178.86,240367400,24.42
2007-11-30,187.34,187.70,179.70,182.22,296950500,24.88
2007-11-29,179.43,185.17,179.15,184.29,262731700,25.16
2007-11-28,176.82,180.60,175.35,180.22,287728000,24.61
2007-11-27,175.22,175.79,170.01,174.81,329257600,23.87
2007-11-26,173.59,177.27,172.35,172.54,326438700,23.56
2007-11-23,172.00,172.05,169.75,171.54,116439400,23.42
2007-11-21,165.84,172.35,164.67,168.46,304452400,23.00
2007-11-20,165.67,171.79,163.53,168.85,385910700,23.06
2007-11-19,166.10,168.20,162.10,163.95,288607200,22.39
2007-11-16,165.30,167.02,159.33,166.39,345873500,22.72
2007-11-15,166.39,169.59,160.30,164.30,371852600,22.43
2007-11-14,177.16,177.57,163.74,166.11,362292000,22.68
2007-11-13,160.85,170.98,153.76,169.96,434861700,23.21
2007-11-12,165.28,167.70,150.63,153.76,442266300,21.00
2007-11-09,171.15,175.12,165.21,165.37,381595200,22.58
2007-11-08,186.67,186.90,167.77,175.47,472594500,23.96
2007-11-07,190.61,192.68,186.13,186.30,248581900,25.44
2007-11-06,187.05,192.00,185.27,191.79,238681800,26.19
2007-11-05,185.29,188.96,184.24,186.18,201044200,25.42
2007-11-02,189.21,189.44,183.49,187.87,250528600,25.65
2007-11-01,188.60,190.10,180.00,187.44,201259100,25.59
2007-10-31,187.63,190.12,184.95,189.95,208327700,25.94
2007-10-30,186.18,189.37,184.73,187.00,234853500,25.53
2007-10-29,185.45,186.59,184.70,185.09,135138500,25.27
2007-10-26,185.29,185.37,182.88,184.70,176719200,25.22
2007-10-25,184.87,185.90,181.66,182.78,243400500,24.96
2007-10-24,185.81,187.21,179.24,185.93,322120400,25.39
2007-10-23,188.56,188.60,182.76,186.16,448791000,25.42
2007-10-22,170.35,174.90,169.96,174.36,412374900,23.81
2007-10-19,174.24,174.63,170.00,170.42,322945000,23.27
2007-10-18,171.50,174.19,171.05,173.50,205919000,23.69
2007-10-17,172.69,173.04,169.18,172.75,281903300,23.59
2007-10-16,165.54,170.18,165.15,169.58,266957600,23.16
2007-10-15,167.98,169.57,163.50,166.98,269482500,22.80
2007-10-12,163.01,167.28,161.80,167.25,247044000,22.84
2007-10-11,169.49,171.88,153.21,162.23,410998000,22.15
2007-10-10,167.55,167.88,165.60,166.79,166897500,22.77
2007-10-09,170.20,171.11,166.68,167.86,276071600,22.92
2007-10-08,163.49,167.91,162.97,167.91,208982200,22.93
2007-10-05,158.37,161.58,157.70,161.45,235867800,22.05
2007-10-04,158.00,158.08,153.50,156.24,164239600,21.33
2007-10-03,157.78,159.18,157.01,157.92,173129600,21.56
2007-10-02,156.55,158.59,155.89,158.45,198017400,21.64
2007-10-01,154.63,157.41,152.93,156.34,209267100,21.35
2007-09-28,153.44,154.60,152.75,153.47,153775300,20.96
2007-09-27,153.77,154.52,152.32,154.50,164549700,21.10
2007-09-26,154.47,155.00,151.25,152.77,243817000,20.86
2007-09-25,146.84,153.22,146.82,153.18,298137700,20.92
2007-09-24,146.73,149.85,146.65,148.28,263040400,20.25
2007-09-21,141.14,144.65,140.31,144.15,284720100,19.68
2007-09-20,140.15,141.79,139.32,140.31,172960200,19.16
2007-09-19,143.02,143.16,139.40,140.77,256720100,19.22
2007-09-18,139.06,142.85,137.83,140.92,266022400,19.24
2007-09-17,138.99,140.59,137.60,138.41,198342900,18.90
2007-09-14,136.57,138.98,136.20,138.81,151830000,18.95
2007-09-13,138.83,139.00,136.65,137.20,164040800,18.73
2007-09-12,135.99,139.40,135.75,136.85,255692500,18.69
2007-09-11,137.90,138.30,133.75,135.49,242971400,18.50
2007-09-10,136.99,138.04,133.95,136.71,371959700,18.67
2007-09-07,132.01,132.30,130.00,131.77,357644000,17.99
2007-09-06,135.56,137.57,132.71,135.01,475315400,18.43
2007-09-05,144.97,145.84,136.10,136.76,582055600,18.67
2007-09-04,139.94,145.73,139.84,144.16,329210700,19.68
2007-08-31,139.49,139.65,137.41,138.48,219221800,18.91
2007-08-30,132.67,138.25,132.30,136.25,358895600,18.60
2007-08-29,129.88,134.18,129.54,134.08,291715200,18.31
2007-08-28,130.99,132.41,126.63,126.82,294841400,17.32
2007-08-27,133.39,134.66,132.10,132.25,176859900,18.06
2007-08-24,130.53,135.37,129.81,135.30,227958500,18.47
2007-08-23,133.09,133.34,129.76,131.07,216709500,17.90
2007-08-22,131.22,132.75,130.33,132.51,265441400,18.09
2007-08-21,122.21,128.96,121.00,127.57,325761800,17.42
2007-08-20,123.96,124.50,120.50,122.22,200829300,16.69
2007-08-17,122.01,123.50,119.82,122.06,298765600,16.67
2007-08-16,117.01,118.50,111.62,117.05,466672500,15.98
2007-08-15,122.74,124.86,119.65,119.90,248213000,16.37
2007-08-14,128.29,128.30,123.71,124.03,184751700,16.94
2007-08-13,128.32,129.35,126.50,127.79,188227900,17.45
2007-08-10,123.12,127.75,120.30,125.00,352687300,17.07
2007-08-09,131.11,133.00,125.09,126.39,281348900,17.26
2007-08-08,136.76,136.86,132.00,134.01,202024200,18.30
2007-08-07,134.94,137.24,132.63,135.03,237484100,18.44
2007-08-06,132.90,135.27,128.30,135.25,231292600,18.47
2007-08-03,135.26,135.95,131.50,131.85,169796900,18.00
2007-08-02,136.65,136.96,134.15,136.49,213161200,18.64
2007-08-01,133.64,135.38,127.77,135.00,437539200,18.43
2007-07-31,142.97,143.48,131.52,131.76,440598200,17.99
2007-07-30,144.33,145.45,139.57,141.43,276747100,19.31
2007-07-27,146.19,148.92,143.78,143.85,290274600,19.64
2007-07-26,145.91,148.50,136.96,146.00,546657300,19.94
2007-07-25,137.35,138.36,135.00,137.26,374045700,18.74
2007-07-24,138.88,141.00,134.15,134.89,448823200,18.42
2007-07-23,143.31,145.22,140.93,143.70,259122500,19.62
2007-07-20,141.65,144.18,140.00,143.75,291943400,19.63
2007-07-19,140.30,140.81,139.65,140.00,183222900,19.12
2007-07-18,138.19,138.44,136.04,138.12,189214200,18.86
2007-07-17,138.30,139.60,137.50,138.91,177489900,18.97
2007-07-16,138.39,139.98,137.50,138.10,234028200,18.86
2007-07-13,135.03,137.85,134.52,137.73,226901500,18.81
2007-07-12,133.85,134.24,132.39,134.07,176152200,18.31
2007-07-11,132.07,133.70,131.31,132.39,205443000,18.08
2007-07-10,128.88,134.50,128.81,132.35,313751900,18.07
2007-07-09,132.38,132.90,129.18,130.33,248955000,17.80
2007-07-06,133.13,133.34,130.40,132.30,218673700,18.06
2007-07-05,128.80,132.97,128.69,132.75,363262900,18.13
2007-07-03,122.00,127.40,121.50,127.17,290620400,17.36
2007-07-02,121.05,122.09,119.30,121.26,248715600,16.56
2007-06-29,121.97,124.00,121.09,122.04,284460400,16.66
2007-06-28,122.36,122.49,120.00,120.56,209535900,16.46
2007-06-27,120.61,122.04,119.26,121.89,243674200,16.64
2007-06-26,123.98,124.00,118.72,119.65,336251300,16.34
2007-06-25,124.19,125.09,121.06,122.34,241350900,16.70
2007-06-22,123.85,124.45,122.38,123.00,157969000,16.80
2007-06-21,121.70,124.29,120.72,123.90,216761300,16.92
2007-06-20,123.87,124.66,121.50,121.55,224378000,16.60
2007-06-19,124.69,125.01,122.91,123.66,235756500,16.89
2007-06-18,123.28,125.18,122.54,125.09,227651200,17.08
2007-06-15,120.62,120.67,119.86,120.50,202804700,16.45
2007-06-14,117.20,119.45,116.42,118.75,243316500,16.21
2007-06-13,121.15,121.19,115.40,117.50,430338300,16.04
2007-06-12,119.35,121.71,118.31,120.38,356641600,16.44
2007-06-11,126.00,126.15,119.54,120.19,468564600,16.41
2007-06-08,125.82,125.83,122.29,124.49,310420600,17.00
2007-06-07,124.99,127.61,123.19,124.07,478769900,16.94
2007-06-06,122.30,124.05,121.95,123.64,278060300,16.88
2007-06-05,121.41,122.69,120.50,122.67,230196400,16.75
2007-06-04,118.63,121.73,117.90,121.33,221668300,16.57
2007-06-01,121.10,121.19,118.29,118.40,221315500,16.17
2007-05-31,120.07,122.17,119.54,121.19,324266600,16.55
2007-05-30,114.30,118.88,113.53,118.77,369611200,16.22
2007-05-29,114.45,114.86,112.69,114.35,161423500,15.61
2007-05-25,112.00,113.78,111.50,113.62,158239900,15.51
2007-05-24,112.81,114.46,110.37,110.69,221840500,15.11
2007-05-23,114.02,115.00,112.59,112.89,227843700,15.41
2007-05-22,112.49,113.75,112.01,113.54,143102400,15.50
2007-05-21,110.31,112.45,110.05,111.98,159973100,15.29
2007-05-18,110.23,110.64,109.77,110.02,155336300,15.02
2007-05-17,107.15,109.87,107.15,109.44,183822800,14.94
2007-05-16,108.53,108.83,103.42,107.34,281691900,14.66
2007-05-15,109.57,110.20,106.48,107.52,238628600,14.68
2007-05-14,109.62,110.00,108.25,109.36,162986600,14.93
2007-05-11,107.74,109.13,106.78,108.74,163424100,14.85
2007-05-10,106.63,108.84,105.92,107.34,299314400,14.66
2007-05-09,104.91,106.96,104.89,106.88,179439400,14.59
2007-05-08,103.47,105.15,103.42,105.06,195999300,14.35
2007-05-07,101.08,104.35,101.01,103.92,215389300,14.19
2007-05-04,100.80,101.60,100.50,100.81,95496800,13.77
2007-05-03,100.73,101.45,100.01,100.40,144019400,13.71
2007-05-02,99.65,100.54,99.47,100.39,126286300,13.71
2007-05-01,99.59,100.35,98.55,99.47,133130900,13.58
2007-04-30,100.09,101.00,99.67,99.80,154127400,13.63
2007-04-27,98.18,99.95,97.69,99.92,174850900,13.64
2007-04-26,101.58,102.50,98.30,98.84,434444500,13.50
2007-04-25,94.23,95.40,93.80,95.35,296786000,13.02
2007-04-24,93.96,96.39,91.30,93.24,263813200,12.73
2007-04-23,91.59,93.80,91.42,93.51,195072500,12.77
2007-04-20,90.89,91.18,90.55,90.97,130694900,12.42
2007-04-19,90.19,91.25,89.83,90.27,106478400,12.33
2007-04-18,90.16,90.85,89.60,90.40,116011000,12.34
2007-04-17,92.00,92.30,89.70,90.35,187980100,12.34
2007-04-16,90.57,91.50,90.25,91.43,152258400,12.48
2007-04-13,90.90,91.40,90.06,90.24,179985400,12.32
2007-04-12,92.04,92.31,90.72,92.19,164168900,12.59
2007-04-11,93.90,93.95,92.33,92.59,137254600,12.64
2007-04-10,93.67,94.26,93.41,94.25,88116700,12.87
2007-04-09,95.21,95.30,93.04,93.65,103335400,12.79
2007-04-05,94.12,94.68,93.52,94.68,88879000,12.93
2007-04-04,94.94,95.14,94.13,94.27,119196000,12.87
2007-04-03,94.14,95.23,93.76,94.50,145983600,12.90
2007-04-02,94.14,94.25,93.02,93.65,125498100,12.79
2007-03-30,94.28,94.68,92.75,92.91,150139500,12.69
2007-03-29,94.19,94.19,92.23,93.75,181430900,12.80
2007-03-28,94.88,95.40,93.15,93.24,235584300,12.73
2007-03-27,95.71,96.83,95.00,95.46,233013200,13.03
2007-03-26,93.99,95.90,93.30,95.85,216246800,13.09
2007-03-23,93.35,94.07,93.30,93.52,112721000,12.77
2007-03-22,93.73,94.36,93.00,93.96,140373100,12.83
2007-03-21,91.99,94.00,91.65,93.87,171724000,12.82
2007-03-20,91.35,91.84,91.06,91.48,122229100,12.49
2007-03-19,90.24,91.55,89.59,91.13,178240300,12.44
2007-03-16,89.54,89.99,89.32,89.59,142926000,12.23
2007-03-15,89.96,90.36,89.31,89.57,139874700,12.23
2007-03-14,88.60,90.00,87.92,90.00,199146500,12.29
2007-03-13,89.41,90.60,88.40,88.40,216972700,12.07
2007-03-12,88.07,89.99,87.99,89.87,182352100,12.27
2007-03-09,88.80,88.85,87.40,87.97,112959000,12.01
2007-03-08,88.59,88.72,87.46,88.00,127752800,12.02
2007-03-07,88.05,88.97,87.45,87.72,156571100,11.98
2007-03-06,87.80,88.31,87.40,88.19,180796700,12.04
2007-03-05,85.89,88.65,85.76,86.32,209724900,11.79
2007-03-02,86.77,87.54,85.21,85.41,215000100,11.66
2007-03-01,84.03,88.31,83.75,87.06,353882200,11.89
2007-02-28,83.00,85.60,83.00,84.61,229868800,11.55
2007-02-27,86.30,87.08,83.41,83.93,286453300,11.46
2007-02-26,89.84,90.00,87.61,88.51,153962200,12.09
2007-02-23,89.16,90.34,88.85,89.07,129473400,12.16
2007-02-22,90.80,90.81,88.53,89.51,209556200,12.22
2007-02-21,85.98,89.49,85.96,89.20,288828400,12.18
2007-02-20,84.65,86.16,84.16,85.90,154425600,11.73
2007-02-16,85.25,85.41,84.66,84.83,99967000,11.58
2007-02-15,85.44,85.62,84.78,85.21,90915300,11.64
2007-02-14,84.63,85.64,84.57,85.30,126995400,11.65
2007-02-13,85.16,85.29,84.30,84.70,145246500,11.57
2007-02-12,84.43,85.18,83.63,84.88,181017900,11.59
2007-02-09,85.88,86.20,83.21,83.27,215135200,11.37
2007-02-08,85.43,86.51,85.41,86.18,169757700,11.77
2007-02-07,84.48,86.38,83.55,86.15,266706300,11.76
2007-02-06,84.45,84.47,82.86,84.15,216098400,11.49
2007-02-05,84.30,85.23,83.94,83.94,144713100,11.46
2007-02-02,84.12,85.25,83.70,84.75,155382500,11.57
2007-02-01,86.23,86.27,84.74,84.74,166085500,11.57
2007-01-31,84.86,86.00,84.35,85.73,214017300,11.71
2007-01-30,86.43,86.49,85.25,85.55,144492600,11.68
2007-01-29,86.30,86.65,85.53,85.94,225416100,11.73
2007-01-26,87.11,87.37,84.99,85.38,246718500,11.66
2007-01-25,87.11,88.50,86.03,86.25,226493400,11.78
2007-01-24,86.68,87.15,86.08,86.70,231953400,11.84
2007-01-23,85.73,87.51,85.51,85.70,301856100,11.70
2007-01-22,89.14,89.16,85.65,86.79,363506500,11.85
2007-01-19,88.63,89.65,88.12,88.50,341118400,12.08
2007-01-18,92.10,92.11,89.05,89.07,591151400,12.16
2007-01-17,97.56,97.60,94.82,94.95,411565000,12.96
2007-01-16,95.68,97.25,95.45,97.10,311019100,13.26
2007-01-12,94.59,95.06,93.23,94.62,328172600,12.92
2007-01-11,95.94,96.78,95.10,95.80,360063200,13.08
2007-01-10,94.75,97.80,93.45,97.00,738220000,13.24
2007-01-09,86.45,92.98,85.15,92.57,837324600,12.64
2007-01-08,85.96,86.53,85.28,85.47,199276700,11.67
2007-01-05,85.77,86.20,84.40,85.05,208685400,11.61
2007-01-04,84.05,85.95,83.82,85.66,211815100,11.70
2007-01-03,86.29,86.58,81.90,83.80,309579900,11.44
2006-12-29,83.95,85.40,83.36,84.84,269107300,11.58
2006-12-28,80.22,81.25,79.65,80.87,279969200,11.04
2006-12-27,78.15,82.00,76.77,81.52,483938700,11.13
2006-12-26,82.15,82.57,80.89,81.51,122672200,11.13
2006-12-22,83.46,84.04,81.60,82.20,153325900,11.22
2006-12-21,84.70,85.48,82.20,82.90,225899800,11.32
2006-12-20,86.47,86.67,84.74,84.76,141922900,11.57
2006-12-19,84.73,86.68,83.62,86.31,227851400,11.79
2006-12-18,87.63,88.00,84.59,85.47,180394200,11.67
2006-12-15,89.02,89.22,87.33,87.72,184984800,11.98
2006-12-14,89.05,90.00,88.26,88.55,208082700,12.09
2006-12-13,87.95,89.07,87.15,89.05,214263000,12.16
2006-12-12,88.61,88.84,85.53,86.14,256655000,11.76
2006-12-11,88.90,89.30,88.05,88.75,124945100,12.12
2006-12-08,87.23,89.39,87.00,88.26,196069300,12.05
2006-12-07,90.03,90.50,86.90,87.04,251206900,11.88
2006-12-06,90.64,91.39,89.67,89.83,159546100,12.27
2006-12-05,91.65,92.33,90.87,91.27,165709600,12.46
2006-12-04,91.88,92.05,90.50,91.12,177384200,12.44
2006-12-01,91.80,92.33,90.10,91.32,198769900,12.47
2006-11-30,92.21,92.68,91.06,91.66,217621600,12.52
2006-11-29,93.00,93.15,90.25,91.80,289270800,12.53
2006-11-28,90.36,91.97,89.91,91.81,259043400,12.54
2006-11-27,92.51,93.16,89.50,89.54,268709000,12.23
2006-11-24,89.53,93.08,89.50,91.63,129669400,12.51
2006-11-22,88.99,90.75,87.85,90.31,167985300,12.33
2006-11-21,87.42,88.60,87.11,88.60,155666700,12.10
2006-11-20,85.40,87.00,85.20,86.47,142698500,11.81
2006-11-17,85.14,85.94,85.00,85.85,116606000,11.72
2006-11-16,84.87,86.30,84.62,85.61,173485200,11.69
2006-11-15,85.05,85.90,84.00,84.05,163830800,11.48
2006-11-14,84.80,85.00,83.90,85.00,147238700,11.61
2006-11-13,83.22,84.45,82.64,84.35,112668500,11.52
2006-11-10,83.55,83.60,82.50,83.12,93466100,11.35
2006-11-09,82.90,84.69,82.12,83.34,230763400,11.38
2006-11-08,80.02,82.69,79.89,82.45,172729200,11.26
2006-11-07,80.45,81.00,80.13,80.51,131483100,10.99
2006-11-06,78.95,80.06,78.43,79.71,108644200,10.88
2006-11-03,79.36,79.53,77.79,78.29,107972200,10.69
2006-11-02,78.92,79.32,78.50,78.98,116370800,10.78
2006-11-01,81.10,81.38,78.36,79.16,152798100,10.81
2006-10-31,81.45,81.68,80.23,81.08,125368600,11.07
2006-10-30,79.99,80.90,79.50,80.42,124979400,10.98
2006-10-27,81.75,82.45,80.01,80.41,148741600,10.98
2006-10-26,81.90,82.60,81.13,82.19,108189200,11.22
2006-10-25,81.35,82.00,81.01,81.68,121303700,11.15
2006-10-24,81.21,81.68,80.20,81.05,115803100,11.07
2006-10-23,79.99,81.90,79.75,81.46,208126800,11.12
2006-10-20,78.97,79.99,78.67,79.95,159853400,10.92
2006-10-19,79.26,79.95,78.16,78.99,378244300,10.79
2006-10-18,74.75,75.37,73.91,74.53,283476900,10.18
2006-10-17,75.04,75.27,74.04,74.29,120231300,10.14
2006-10-16,75.19,75.88,74.79,75.40,127173200,10.30
2006-10-13,75.63,76.88,74.74,75.02,171049200,10.24
2006-10-12,73.61,75.39,73.60,75.26,148213800,10.28
2006-10-11,73.42,73.98,72.60,73.23,142963800,10.00
2006-10-10,74.54,74.58,73.08,73.81,132897100,10.08
2006-10-09,73.80,75.08,73.53,74.63,109555600,10.19
2006-10-06,74.42,75.04,73.81,74.22,116739700,10.13
2006-10-05,74.53,76.16,74.13,74.83,170970800,10.22
2006-10-04,74.10,75.46,73.16,75.38,207270700,10.29
2006-10-03,74.45,74.95,73.19,74.08,197677200,10.12
2006-10-02,75.10,75.87,74.30,74.86,178159800,10.22
2006-09-29,77.11,77.52,76.68,76.98,101453100,10.51
2006-09-28,77.02,77.48,75.95,77.01,180902400,10.52
2006-09-27,77.17,77.47,75.82,76.41,202593300,10.43
2006-09-26,76.18,77.78,76.10,77.61,275737000,10.60
2006-09-25,73.81,75.86,73.72,75.75,214748100,10.34
2006-09-22,74.30,74.34,72.58,73.00,166278000,9.97
2006-09-21,75.25,76.06,74.02,74.65,198531200,10.19
2006-09-20,74.38,75.68,74.22,75.26,205697800,10.28
2006-09-19,74.10,74.36,72.80,73.77,177512300,10.07
2006-09-18,73.80,74.86,73.30,73.89,176319500,10.09
2006-09-15,74.60,74.98,73.29,74.10,245463400,10.12
2006-09-14,73.72,74.67,73.46,74.17,200432400,10.13
2006-09-13,72.85,74.32,72.30,74.20,286534500,10.13
2006-09-12,72.81,73.45,71.45,72.63,421171800,9.92
2006-09-11,72.43,73.73,71.42,72.50,237281100,9.90
2006-09-08,73.37,73.57,71.91,72.52,223980400,9.90
2006-09-07,70.60,73.48,70.25,72.80,316989400,9.94
2006-09-06,71.08,71.69,69.70,70.03,243525800,9.56
2006-09-05,68.97,71.50,68.55,71.48,253114400,9.76
2006-09-01,68.48,68.65,67.82,68.38,102123700,9.34
2006-08-31,67.28,68.30,66.66,67.85,143674300,9.26
2006-08-30,67.34,67.82,66.68,66.96,170035600,9.14
2006-08-29,66.99,67.26,65.12,66.48,236833100,9.08
2006-08-28,68.50,68.61,66.68,66.98,184540300,9.15
2006-08-25,67.34,69.05,67.31,68.75,135989700,9.39
2006-08-24,67.89,68.19,66.27,67.81,163797900,9.26
2006-08-23,68.00,68.65,66.94,67.31,134064700,9.19
2006-08-22,66.68,68.32,66.50,67.62,144242000,9.23
2006-08-21,67.30,67.31,66.15,66.56,131556600,9.09
2006-08-18,67.71,68.40,67.26,67.91,134088500,9.27
2006-08-17,68.00,68.66,67.18,67.59,145287100,9.23
2006-08-16,67.10,68.07,66.33,67.98,195321000,9.28
2006-08-15,65.34,66.50,64.80,66.45,215338200,9.07
2006-08-14,64.05,65.22,63.60,63.94,179405100,8.73
2006-08-11,63.23,64.13,62.58,63.65,194382300,8.69
2006-08-10,63.25,64.81,62.70,64.07,174440000,8.75
2006-08-09,65.43,65.60,63.40,63.59,238959700,8.68
2006-08-08,67.09,67.11,64.51,64.78,249466000,8.85
2006-08-07,67.72,69.60,66.31,67.21,311378200,9.18
2006-08-04,67.05,68.61,64.96,68.30,463216600,9.33
2006-08-03,67.91,70.00,67.81,69.59,210261100,9.50
2006-08-02,67.65,68.68,67.51,68.16,137692100,9.31
2006-08-01,67.22,67.93,65.94,67.18,177941400,9.17
2006-07-31,66.83,68.63,66.28,67.96,223210400,9.28
2006-07-28,63.94,65.68,63.50,65.59,172876900,8.96
2006-07-27,64.50,65.02,62.86,63.40,183761200,8.66
2006-07-26,62.00,64.64,61.68,63.87,224606900,8.72
2006-07-25,61.78,62.09,60.78,61.93,147267400,8.46
2006-07-24,61.26,62.10,60.43,61.42,180714100,8.39
2006-07-21,59.82,61.15,59.64,60.72,222973100,8.29
2006-07-20,60.96,61.59,59.72,60.50,493036600,8.26
2006-07-19,52.96,55.08,52.36,54.10,347685800,7.39
2006-07-18,53.16,53.85,51.85,52.90,250112100,7.22
2006-07-17,51.73,53.11,51.65,52.37,256135600,7.15
2006-07-14,52.50,52.89,50.16,50.67,248259200,6.92
2006-07-13,52.03,54.12,51.41,52.25,312476500,7.13
2006-07-12,55.17,55.24,52.92,52.96,231832300,7.23
2006-07-11,55.11,55.99,54.53,55.65,206255700,7.60
2006-07-10,55.70,56.49,54.50,55.00,132336400,7.51
2006-07-07,55.48,56.55,54.67,55.40,199840200,7.56
2006-07-06,57.09,57.40,55.61,55.77,158302200,7.62
2006-07-05,57.15,57.60,56.56,57.00,129560200,7.78
2006-07-03,57.52,58.18,57.34,57.95,48692700,7.91
2006-06-30,57.59,57.75,56.50,57.27,184923900,7.82
2006-06-29,56.76,59.09,56.39,58.97,218349600,8.05
2006-06-28,57.29,57.30,55.41,56.02,212676100,7.65
2006-06-27,59.09,59.22,57.40,57.43,137652900,7.84
2006-06-26,59.17,59.20,58.37,58.99,116634000,8.05
2006-06-23,59.72,60.17,58.73,58.83,165050900,8.03
2006-06-22,58.20,59.75,58.07,59.58,241408300,8.14
2006-06-21,57.74,58.71,57.30,57.86,215824000,7.90
2006-06-20,57.61,58.35,57.29,57.47,168243600,7.85
2006-06-19,57.83,58.18,57.00,57.20,176143800,7.81
2006-06-16,58.96,59.19,57.52,57.56,209525400,7.86
2006-06-15,57.30,59.74,56.75,59.38,297595900,8.11
2006-06-14,58.28,58.78,56.69,57.61,219534000,7.87
2006-06-13,57.61,59.10,57.36,58.33,270160800,7.96
2006-06-12,59.40,59.73,56.96,57.00,179446400,7.78
2006-06-09,61.18,61.56,59.10,59.24,193959500,8.09
2006-06-08,58.44,60.93,57.15,60.76,349370700,8.30
2006-06-07,60.10,60.40,58.35,58.56,187626600,8.00
2006-06-06,60.22,60.63,58.91,59.72,181509300,8.15
2006-06-05,61.15,61.15,59.97,60.00,151446400,8.19
2006-06-02,62.99,63.10,60.88,61.66,171446800,8.42
2006-06-01,59.85,62.28,59.52,62.17,235627000,8.49
2006-05-31,61.76,61.79,58.69,59.77,320244400,8.16
2006-05-30,63.29,63.30,61.22,61.22,140850500,8.36
2006-05-26,64.31,64.56,63.14,63.55,108237500,8.68
2006-05-25,64.26,64.45,63.29,64.33,115843000,8.78
2006-05-24,62.99,63.65,61.56,63.34,229007800,8.65
2006-05-23,64.86,65.19,63.00,63.15,173603500,8.62
2006-05-22,63.87,63.99,62.77,63.38,179743900,8.65
2006-05-19,63.26,64.88,62.82,64.51,246466500,8.81
2006-05-18,65.68,66.26,63.12,63.18,164610600,8.63
2006-05-17,64.71,65.70,64.07,65.26,188548500,8.91
2006-05-16,68.10,68.25,64.75,64.98,234185000,8.87
2006-05-15,67.37,68.38,67.12,67.79,132294400,9.26
2006-05-12,67.85,68.69,66.86,67.70,160443500,9.24
2006-05-11,70.79,70.84,67.55,68.15,203172200,9.31
2006-05-10,71.29,71.33,69.61,70.60,114972200,9.64
2006-05-09,71.82,72.56,70.62,71.03,132916700,9.70
2006-05-08,72.99,73.80,71.72,71.89,148712900,9.82
2006-05-05,71.86,72.25,71.15,71.89,140977900,9.82
2006-05-04,71.22,72.89,70.46,71.13,215105100,9.71
2006-05-03,71.83,71.95,70.18,71.14,171747800,9.71
2006-05-02,70.15,71.98,70.11,71.62,192915800,9.78
2006-05-01,70.77,71.54,69.16,69.60,187595100,9.50
2006-04-28,69.38,71.30,69.20,70.39,190009400,9.61
2006-04-27,67.73,69.86,67.35,69.36,211486800,9.47
2006-04-26,66.65,68.28,66.40,68.15,177721600,9.31
2006-04-25,65.96,66.59,65.56,66.17,132265700,9.04
2006-04-24,66.85,66.92,65.50,65.75,176757000,8.98
2006-04-21,68.19,68.64,66.47,67.04,197246700,9.15
2006-04-20,69.51,70.00,66.20,67.63,416745700,9.23
2006-04-19,66.82,67.00,65.47,65.65,271508300,8.96
2006-04-18,65.04,66.47,64.79,66.22,198711100,9.04
2006-04-17,66.51,66.84,64.35,64.81,180484500,8.85
2006-04-13,66.34,67.44,65.81,66.47,183669500,9.08
2006-04-12,68.01,68.17,66.30,66.71,184973600,9.11
2006-04-11,68.99,69.30,67.07,67.99,234829000,9.28
2006-04-10,70.29,70.93,68.45,68.67,225878800,9.38
2006-04-07,70.93,71.21,68.47,69.79,386309700,9.53
2006-04-06,68.30,72.05,68.20,71.24,665942200,9.73
2006-04-05,64.71,67.21,64.15,67.21,558352200,9.18
2006-04-04,62.10,62.22,61.05,61.17,232981000,8.35
2006-04-03,63.67,64.12,62.61,62.65,203947800,8.55
2006-03-31,63.25,63.61,62.24,62.72,203839300,8.56
2006-03-30,62.82,63.30,61.53,62.75,347662700,8.57
2006-03-29,59.13,62.52,57.67,62.33,586708500,8.51
2006-03-28,59.63,60.14,58.25,58.71,342580700,8.02
2006-03-27,60.35,61.38,59.40,59.51,277018000,8.13
2006-03-24,60.25,60.94,59.03,59.96,267995000,8.19
2006-03-23,61.82,61.90,59.61,60.16,356956600,8.21
2006-03-22,62.16,63.25,61.27,61.67,336473900,8.42
2006-03-21,61.81,64.34,61.39,61.81,336341600,8.44
2006-03-20,65.22,65.46,63.87,63.99,151360300,8.74
2006-03-17,64.75,65.54,64.11,64.66,203010500,8.83
2006-03-16,66.85,66.90,64.30,64.31,187409600,8.78
2006-03-15,67.71,68.04,65.52,66.23,222999000,9.04
2006-03-14,65.77,67.32,65.50,67.32,160505100,9.19
2006-03-13,65.05,66.28,64.79,65.68,215296900,8.97
2006-03-10,64.05,64.49,62.45,63.19,260785700,8.63
2006-03-09,65.98,66.47,63.81,63.93,199826200,8.73
2006-03-08,66.29,67.20,65.35,65.66,163312800,8.97
2006-03-07,65.76,66.90,65.08,66.31,218219400,9.05
2006-03-06,67.69,67.72,64.94,65.48,228166400,8.94
2006-03-03,69.40,69.91,67.53,67.72,184417100,9.25
2006-03-02,68.99,69.99,68.67,69.61,156318400,9.50
2006-03-01,68.84,69.49,68.02,69.10,190954400,9.44
2006-02-28,71.58,72.40,68.10,68.49,316745100,9.35
2006-02-27,71.99,72.12,70.65,70.99,197810200,9.69
2006-02-24,72.14,72.89,71.20,71.46,133686000,9.76
2006-02-23,71.79,73.00,71.43,71.75,214229400,9.80
2006-02-22,69.00,71.67,68.00,71.32,244559700,9.74
2006-02-21,70.59,70.80,68.68,69.08,194901700,9.43
2006-02-17,70.30,70.89,69.61,70.29,143999800,9.60
2006-02-16,69.91,71.01,69.48,70.57,237043800,9.64
2006-02-15,67.16,69.62,66.75,69.22,289942800,9.45
2006-02-14,65.10,68.10,65.00,67.64,290234700,9.24
2006-02-13,66.63,66.75,64.64,64.71,220874500,8.84
2006-02-10,65.18,67.67,62.90,67.31,440119400,9.19
2006-02-09,69.10,69.23,64.53,64.95,287441000,8.87
2006-02-08,68.49,69.08,66.00,68.81,238278600,9.40
2006-02-07,68.27,69.48,66.68,67.60,347207700,9.23
2006-02-06,72.02,72.51,66.74,67.30,412941900,9.19
2006-02-03,72.24,72.79,71.04,71.85,173030900,9.81
2006-02-02,75.10,75.36,72.05,72.10,176830500,9.84
2006-02-01,74.95,76.46,74.64,75.42,130296600,10.30
2006-01-31,75.50,76.34,73.75,75.51,228385500,10.31
2006-01-30,71.17,76.60,70.87,75.00,349600300,10.24
2006-01-27,72.95,73.60,71.10,72.03,238466200,9.84
2006-01-26,74.53,75.43,71.93,72.33,295346800,9.88
2006-01-25,77.39,77.50,73.25,74.20,318946600,10.13
2006-01-24,78.76,79.42,75.77,76.04,285563600,10.38
2006-01-23,76.10,79.56,76.00,77.67,264932500,10.61
2006-01-20,79.28,80.04,75.83,76.09,283689700,10.39
2006-01-19,81.25,81.66,78.74,79.04,423962000,10.79
2006-01-18,83.08,84.05,81.85,82.49,300159300,11.26
2006-01-17,85.70,86.38,83.87,84.71,208905900,11.57
2006-01-13,84.99,86.01,84.60,85.59,194076400,11.69
2006-01-12,84.97,86.40,83.62,84.29,320202400,11.51
2006-01-11,83.84,84.80,82.59,83.90,373448600,11.46
2006-01-10,76.25,81.89,75.83,80.86,569967300,11.04
2006-01-09,76.73,77.20,75.74,76.05,168760200,10.38
2006-01-06,75.25,76.70,74.55,76.30,176114400,10.42
2006-01-05,74.83,74.90,73.75,74.38,112355600,10.16
2006-01-04,75.13,75.98,74.50,74.97,154900900,10.24
2006-01-03,72.38,74.75,72.25,74.75,201808600,10.21
2005-12-30,70.91,72.43,70.34,71.89,156065700,9.82
2005-12-29,73.78,73.82,71.42,71.45,122506300,9.76
2005-12-28,74.47,74.76,73.32,73.57,99528800,10.05
2005-12-27,74.00,75.18,73.95,74.23,147647500,10.14
2005-12-23,74.17,74.26,73.30,73.35,57464400,10.02
2005-12-22,73.91,74.49,73.60,74.02,92652700,10.11
2005-12-21,72.60,73.61,72.54,73.50,118934200,10.04
2005-12-20,71.63,72.38,71.12,72.11,119777000,9.85
2005-12-19,71.11,72.60,71.04,71.38,132323800,9.75
2005-12-16,72.14,72.30,71.06,71.11,167792800,9.71
2005-12-15,72.68,72.86,71.35,72.18,140290500,9.86
2005-12-14,72.53,73.30,70.27,72.01,362679100,9.83
2005-12-13,74.85,75.46,74.21,74.98,123454100,10.24
2005-12-12,74.87,75.35,74.56,74.91,131248600,10.23
2005-12-09,74.21,74.59,73.35,74.33,138850600,10.15
2005-12-08,73.20,74.17,72.60,74.08,197619800,10.12
2005-12-07,74.23,74.46,73.12,73.95,169866200,10.10
2005-12-06,73.93,74.83,73.35,74.05,214257400,10.11
2005-12-05,71.95,72.53,71.49,71.82,145917800,9.81
2005-12-02,72.27,72.74,70.70,72.63,223940500,9.92
2005-12-01,68.95,71.73,68.81,71.60,203223300,9.78
2005-11-30,68.43,68.85,67.52,67.82,148918700,9.26
2005-11-29,69.99,70.30,67.35,68.10,222858300,9.30
2005-11-28,70.72,71.07,69.07,69.66,254629900,9.51
2005-11-25,67.66,69.54,67.50,69.34,98753200,9.47
2005-11-23,66.88,67.98,66.69,67.11,121463300,9.16
2005-11-22,64.84,66.76,64.52,66.52,135070600,9.08
2005-11-21,64.82,65.19,63.72,64.96,127927800,8.87
2005-11-18,65.31,65.43,64.37,64.56,131240200,8.82
2005-11-17,65.59,65.88,64.25,64.52,169051400,8.81
2005-11-16,63.15,65.06,63.09,64.95,196128800,8.87
2005-11-15,61.60,63.08,61.46,62.28,134210300,8.50
2005-11-14,61.54,61.98,60.91,61.45,92483300,8.39
2005-11-11,61.54,62.11,61.34,61.54,106362200,8.40
2005-11-10,60.64,61.20,59.01,61.18,166336100,8.35
2005-11-09,60.00,61.21,60.00,60.11,138232500,8.21
2005-11-08,59.95,60.38,59.10,59.90,118441400,8.18
2005-11-07,60.85,61.67,60.14,60.23,159707800,8.22
2005-11-04,60.35,61.24,59.62,61.15,219508800,8.35
2005-11-03,60.26,62.32,60.07,61.85,221095700,8.45
2005-11-02,57.72,60.00,57.60,59.95,214265100,8.19
2005-11-01,57.24,58.14,56.87,57.50,187421500,7.85
2005-10-31,55.20,57.98,54.75,57.59,235211200,7.86
2005-10-28,56.04,56.43,54.17,54.47,192446800,7.44
2005-10-27,56.99,57.01,55.41,55.41,102885300,7.57
2005-10-26,56.28,57.56,55.92,57.03,157898300,7.79
2005-10-25,56.40,56.85,55.69,56.10,116281900,7.66
2005-10-24,55.25,56.79,55.09,56.79,152438300,7.75
2005-10-21,56.84,56.98,55.36,55.66,199181500,7.60
2005-10-20,54.47,56.50,54.35,56.14,339440500,7.67
2005-10-19,52.07,54.96,51.21,54.94,252170800,7.50
2005-10-18,53.25,53.95,52.20,52.21,152397000,7.13
2005-10-17,53.98,54.23,52.68,53.44,154208600,7.30
2005-10-14,54.03,54.35,52.79,54.00,258888000,7.37
2005-10-13,49.44,53.95,49.27,53.74,466393900,7.34
2005-10-12,48.65,50.30,47.87,49.25,674371600,6.72
2005-10-11,51.23,51.87,50.40,51.59,306471200,7.04
2005-10-10,51.76,51.91,50.28,50.37,126876400,6.88
2005-10-07,51.72,51.93,50.55,51.30,169470700,7.00
2005-10-06,53.20,53.49,50.87,51.70,189384300,7.06
2005-10-05,54.33,54.36,52.75,52.78,152692400,7.21
2005-10-04,54.95,55.35,53.64,53.75,134864800,7.34
2005-10-03,54.16,54.54,53.68,54.44,126888300,7.43
2005-09-30,52.33,53.65,51.88,53.61,132908300,7.32
2005-09-29,51.23,52.59,50.81,52.34,159211500,7.15
2005-09-28,53.07,53.11,50.59,51.08,281386000,6.97
2005-09-27,53.92,54.24,53.43,53.44,85425900,7.30
2005-09-26,54.03,54.56,53.32,53.84,136640700,7.35
2005-09-23,52.10,53.50,51.84,53.20,139614300,7.26
2005-09-22,51.88,52.47,51.32,51.90,115931900,7.09
2005-09-21,52.96,53.05,51.86,52.11,108686900,7.12
2005-09-20,52.99,53.81,52.92,53.19,204957200,7.26
2005-09-19,51.05,52.89,51.05,52.64,195932800,7.19
2005-09-16,50.23,51.21,49.95,51.21,147751100,6.99
2005-09-15,50.00,50.18,49.33,49.87,103789000,6.81
2005-09-14,51.06,51.19,49.46,49.61,118606600,6.77
2005-09-13,51.02,51.29,50.32,50.82,123221000,6.94
2005-09-12,51.10,51.63,50.58,51.40,113199100,7.02
2005-09-09,50.07,51.35,49.79,51.31,153910400,7.01
2005-09-08,49.35,50.12,49.14,49.78,175660100,6.80
2005-09-07,49.05,49.40,47.92,48.68,240768500,6.65
2005-09-06,46.70,48.88,46.55,48.80,204654800,6.66
2005-09-02,46.30,46.80,46.12,46.22,55594700,6.31
2005-09-01,47.00,47.17,46.09,46.26,89091800,6.32
2005-08-31,46.86,47.03,46.27,46.89,100739100,6.40
2005-08-30,45.99,46.79,45.92,46.57,129690400,6.36
2005-08-29,45.27,46.03,45.26,45.84,64073800,6.26
2005-08-26,46.12,46.34,45.36,45.74,65264500,6.25
2005-08-25,46.12,46.49,45.81,46.06,69063400,6.29
2005-08-24,45.60,47.12,45.59,45.77,143017700,6.25
2005-08-23,45.85,46.10,45.32,45.74,73901100,6.25
2005-08-22,46.15,46.75,45.26,45.87,96933200,6.26
2005-08-19,46.28,46.70,45.77,45.83,94142300,6.26
2005-08-18,46.91,47.00,45.75,46.30,110639900,6.32
2005-08-17,46.40,47.44,46.37,47.15,124931100,6.44
2005-08-16,47.39,47.50,46.21,46.25,134405600,6.32
2005-08-15,46.48,48.33,46.45,47.68,271681900,6.51
2005-08-12,43.46,46.22,43.36,46.10,229009200,6.29
2005-08-11,43.39,44.12,43.25,44.00,67995900,6.01
2005-08-10,44.00,44.39,43.31,43.38,90236300,5.92
2005-08-09,42.93,43.89,42.91,43.82,95209800,5.98
2005-08-08,43.00,43.25,42.61,42.65,44095800,5.82
2005-08-05,42.49,43.36,42.02,42.99,60482800,5.87
2005-08-04,42.89,43.00,42.29,42.71,67326000,5.83
2005-08-03,43.19,43.31,42.77,43.22,64580600,5.90
2005-08-02,42.89,43.50,42.61,43.19,74218900,5.90
2005-08-01,42.57,43.08,42.08,42.75,78562400,5.84
2005-07-29,43.56,44.38,42.26,42.65,140520100,5.82
2005-07-28,43.85,44.00,43.30,43.80,62827800,5.98
2005-07-27,43.83,44.07,42.67,43.99,70937300,6.01
2005-07-26,44.01,44.11,43.36,43.63,67148200,5.96
2005-07-25,43.99,44.28,43.73,43.81,73656800,5.98
2005-07-22,43.44,44.00,43.39,44.00,75276600,6.01
2005-07-21,43.70,44.04,42.90,43.29,101066000,5.91
2005-07-20,42.86,43.80,42.65,43.63,113348900,5.96
2005-07-19,41.52,43.23,41.07,43.19,167765500,5.90
2005-07-18,41.41,42.10,41.37,41.49,146574400,5.67
2005-07-15,40.97,41.57,40.46,41.55,171920700,5.67
2005-07-14,40.79,42.01,40.23,40.75,524015100,5.56
2005-07-13,38.29,38.50,37.90,38.35,171208800,5.24
2005-07-12,38.23,38.40,37.91,38.24,96759600,5.22
2005-07-11,38.37,38.65,37.78,38.10,97197100,5.20
2005-07-08,37.87,38.28,37.47,38.25,72683800,5.22
2005-07-07,36.81,37.76,36.80,37.63,95930800,5.14
2005-07-06,37.71,38.16,37.20,37.39,98656600,5.11
2005-07-05,36.55,38.15,36.50,37.98,113567300,5.19
2005-07-01,36.83,36.97,36.29,36.50,62500200,4.98
2005-06-30,36.61,37.16,36.31,36.81,104597500,5.03
2005-06-29,37.23,37.29,36.12,36.37,112089600,4.97
2005-06-28,37.49,37.59,37.17,37.31,87574900,5.09
2005-06-27,36.84,38.10,36.68,37.10,150042900,5.07
2005-06-24,39.09,39.12,37.68,37.76,102677400,5.16
2005-06-23,38.83,39.78,38.65,38.89,168563500,5.31
2005-06-22,38.26,38.60,38.14,38.55,106231300,5.26
2005-06-21,37.72,38.19,37.38,37.86,92631700,5.17
2005-06-20,37.85,38.09,37.45,37.61,80929100,5.14
2005-06-17,38.47,38.54,37.83,38.31,149031400,5.23
2005-06-16,37.19,38.08,36.82,37.98,136918600,5.19
2005-06-15,36.87,37.30,36.30,37.13,140835800,5.07
2005-06-14,35.92,36.15,35.75,36.00,86961700,4.92
2005-06-13,35.89,36.61,35.82,35.90,108943100,4.90
2005-06-10,37.40,37.40,35.52,35.81,169733200,4.89
2005-06-09,37.00,37.94,36.82,37.65,97563900,5.14
2005-06-08,36.63,37.25,36.57,36.92,101001600,5.04
2005-06-07,37.60,37.73,36.45,36.54,186316200,4.99
2005-06-06,38.33,38.63,37.56,37.92,202991600,5.18
2005-06-03,38.16,38.58,37.77,38.24,239217300,5.22
2005-06-02,40.05,40.32,39.60,40.04,93493400,5.47
2005-06-01,39.89,40.76,39.86,40.30,113453200,5.50
2005-05-31,40.66,40.74,39.58,39.76,101051300,5.43
2005-05-27,40.64,40.79,40.01,40.56,79002000,5.54
2005-05-26,39.94,40.94,39.94,40.74,131380200,5.56
2005-05-25,39.50,39.95,39.32,39.78,99001700,5.43
2005-05-24,39.45,39.99,39.03,39.70,148365000,5.42
2005-05-23,37.85,39.90,37.85,39.76,260643600,5.43
2005-05-20,37.25,37.65,37.19,37.55,113162700,5.13
2005-05-19,35.78,37.68,35.78,37.55,198290400,5.13
2005-05-18,35.45,37.56,34.99,35.84,159180700,4.89
2005-05-17,35.14,35.46,34.54,35.36,147086100,4.83
2005-05-16,34.56,35.70,34.53,35.55,118573700,4.85
2005-05-13,34.20,35.23,34.07,34.77,175678300,4.75
2005-05-12,35.42,35.59,34.00,34.13,242560500,4.66
2005-05-11,35.20,35.67,33.11,35.61,510495300,4.86
2005-05-10,36.75,37.25,36.33,36.42,110065900,4.97
2005-05-09,37.28,37.45,36.75,36.97,88923800,5.05
2005-05-06,36.89,37.33,36.79,37.24,81561900,5.08
2005-05-05,37.25,37.27,36.47,36.68,96841500,5.01
2005-05-04,36.11,37.20,36.10,37.15,112044100,5.07
2005-05-03,36.40,36.74,36.03,36.21,124184900,4.94
2005-05-02,36.21,36.65,36.02,36.43,116480000,4.97
2005-04-29,36.15,36.23,35.22,36.06,167907600,4.92
2005-04-28,36.29,36.34,35.24,35.54,143776500,4.85
2005-04-27,35.89,36.36,35.51,35.95,153472200,4.91
2005-04-26,36.78,37.51,36.12,36.19,202626900,4.94
2005-04-25,36.49,37.02,36.11,36.98,186615100,5.05
2005-04-22,36.84,37.00,34.90,35.50,209782300,4.85
2005-04-21,36.40,37.21,35.90,37.18,189898100,5.08
2005-04-20,37.66,37.74,35.44,35.51,236282900,4.85
2005-04-19,36.60,37.44,35.87,37.09,270410700,5.06
2005-04-18,35.00,36.30,34.00,35.62,331794400,4.86
2005-04-15,36.62,37.25,35.28,35.35,432021800,4.83
2005-04-14,38.81,39.56,36.84,37.26,688298100,5.09
2005-04-13,42.95,42.99,40.39,41.04,342986700,5.60
2005-04-12,42.49,43.19,42.01,42.66,245265300,5.83
2005-04-11,44.15,44.25,41.91,41.92,205415700,5.72
2005-04-08,43.70,44.45,43.54,43.74,162487500,5.97
2005-04-07,42.33,43.75,42.25,43.56,126746900,5.95
2005-04-06,42.40,42.81,42.15,42.33,103706400,5.78
2005-04-05,41.22,42.24,41.09,41.89,139059900,5.72
2005-04-04,40.99,41.31,40.16,41.09,145003600,5.61
2005-04-01,42.09,42.18,40.57,40.89,160321000,5.58
2005-03-31,42.45,42.52,41.59,41.67,159033700,5.69
2005-03-30,42.07,42.80,41.82,42.80,98739900,5.84
2005-03-29,42.56,42.83,41.50,41.75,115339000,5.70
2005-03-28,42.75,42.96,42.47,42.53,68852700,5.81
2005-03-24,42.91,43.00,42.50,42.50,88176200,5.80
2005-03-23,42.45,43.40,42.02,42.55,152455800,5.81
2005-03-22,43.71,43.96,42.68,42.83,137853800,5.85
2005-03-21,43.29,43.97,42.86,43.70,135282000,5.97
2005-03-18,43.33,43.44,42.50,42.96,235037600,5.87
2005-03-17,41.53,42.88,41.32,42.25,200480000,5.77
2005-03-16,41.21,42.31,40.78,41.18,174453300,5.62
2005-03-15,40.64,41.14,40.25,40.96,127152200,5.59
2005-03-14,40.52,40.79,39.52,40.32,151346300,5.51
2005-03-11,40.21,40.59,39.80,40.27,158207700,5.50
2005-03-10,39.53,40.26,39.10,39.83,194277300,5.44
2005-03-09,39.64,40.28,38.83,39.35,330616300,5.37
2005-03-08,41.90,42.16,40.10,40.53,255362800,5.53
2005-03-07,42.80,43.25,42.35,42.75,112658000,5.84
2005-03-04,42.76,43.01,41.85,42.81,189154700,5.85
2005-03-03,44.37,44.41,41.22,41.79,352913400,5.71
2005-03-02,44.25,44.89,44.08,44.12,114540300,6.02
2005-03-01,44.99,45.11,44.16,44.50,117047000,6.08
2005-02-28,44.68,45.14,43.96,44.86,162902600,6.13
2005-02-25,89.62,89.91,88.19,88.99,228877600,6.08
2005-02-24,88.48,89.31,87.73,88.93,379757000,6.07
2005-02-23,86.72,88.45,85.55,88.23,336295400,6.02
2005-02-22,86.30,88.30,85.29,85.29,304823400,5.82
2005-02-18,87.74,87.86,86.25,86.81,290813600,5.93
2005-02-17,90.65,90.88,87.45,87.81,379618400,6.00
2005-02-16,88.15,90.20,87.35,90.13,409810800,6.15
2005-02-15,86.66,89.08,86.00,88.41,578054400,6.04
2005-02-14,82.73,84.79,82.05,84.63,317865800,5.78
2005-02-11,79.86,81.76,78.94,81.21,300263600,5.54
2005-02-10,78.72,79.28,76.66,78.36,273254800,5.35
2005-02-09,81.04,81.99,78.10,78.74,297864000,5.38
2005-02-08,79.07,81.38,78.79,80.90,222504800,5.52
2005-02-07,78.93,79.35,77.50,78.94,131114200,5.39
2005-02-04,77.87,78.93,77.53,78.84,140889000,5.38
2005-02-03,79.10,79.43,77.33,77.81,182912800,5.31
2005-02-02,77.95,79.91,77.69,79.63,255015600,5.44
2005-02-01,77.05,77.77,76.58,77.53,169598800,5.29
2005-01-31,74.58,77.89,74.51,76.90,420274400,5.25
2005-01-28,72.62,73.98,72.44,73.98,200403000,5.05
2005-01-27,72.16,72.92,71.55,72.64,124056800,4.96
2005-01-26,72.66,72.75,71.22,72.25,184874200,4.93
2005-01-25,71.37,72.84,70.94,72.05,242307800,4.92
2005-01-24,70.98,71.78,70.55,70.76,210407400,4.83
2005-01-21,71.31,71.60,70.00,70.49,227833200,4.81
2005-01-20,69.65,71.27,69.47,70.46,228730600,4.81
2005-01-19,70.49,71.46,69.75,69.88,187973800,4.77
2005-01-18,69.85,70.70,67.75,70.65,251615000,4.82
2005-01-14,70.25,71.72,69.19,70.20,442685600,4.79
2005-01-13,73.71,74.42,69.73,69.80,791179200,4.77
2005-01-12,65.45,65.90,63.30,65.46,479925600,4.47
2005-01-11,68.25,69.15,64.14,64.56,652906800,4.41
2005-01-10,69.83,70.70,67.88,68.96,431327400,4.71
2005-01-07,65.00,69.63,64.75,69.25,556862600,4.73
2005-01-06,64.67,64.91,63.33,64.55,176388800,4.41
2005-01-05,64.46,65.25,64.05,64.50,170108400,4.40
2005-01-04,63.79,65.47,62.97,63.94,274202600,4.37
2005-01-03,64.78,65.11,62.60,63.29,172998000,4.32
2004-12-31,64.89,65.00,64.03,64.40,69647200,4.40
2004-12-30,64.81,65.03,64.22,64.80,86335200,4.42
2004-12-29,63.81,64.98,63.57,64.44,112390600,4.40
2004-12-28,63.30,64.25,62.05,64.18,152938800,4.38
2004-12-27,64.80,65.15,62.88,63.16,139872600,4.31
2004-12-23,63.75,64.25,63.60,64.01,61482400,4.37
2004-12-22,63.66,64.36,63.40,63.75,141457400,4.35
2004-12-21,63.56,63.77,61.60,63.69,266103600,4.35
2004-12-20,65.47,66.00,61.76,62.72,292031600,4.28
2004-12-17,66.84,67.04,64.90,64.99,195874000,4.44
2004-12-16,66.15,67.50,66.05,66.60,281528800,4.55
2004-12-15,65.24,65.46,64.66,65.26,99590400,4.46
2004-12-14,65.40,65.88,65.02,65.29,103930400,4.46
2004-12-13,65.62,65.90,64.60,64.91,98760200,4.43
2004-12-10,65.03,66.05,64.70,65.15,193943400,4.45
2004-12-09,62.81,64.40,62.07,63.99,185375400,4.37
2004-12-08,63.08,64.43,62.05,63.28,172975600,4.32
2004-12-07,65.93,66.73,62.56,62.89,264224800,4.29
2004-12-06,64.25,66.24,62.95,65.78,311980200,4.49
2004-12-03,64.53,65.00,61.75,62.68,309712200,4.28
2004-12-02,66.13,66.90,64.66,65.21,246860600,4.45
2004-12-01,67.79,67.95,66.27,67.79,200138400,4.63
2004-11-30,68.79,68.79,67.05,67.05,257129600,4.58
2004-11-29,68.95,69.57,67.41,68.44,428229200,4.67
2004-11-26,65.35,65.76,64.34,64.55,137536000,4.41
2004-11-24,61.69,65.20,61.55,64.05,347697000,4.37
2004-11-23,62.30,62.45,61.05,61.27,227862600,4.18
2004-11-22,61.80,64.00,57.90,61.35,642052600,4.19
2004-11-19,55.49,56.91,54.50,55.17,191319800,3.77
2004-11-18,54.30,55.45,54.29,55.39,114787400,3.78
2004-11-17,55.19,55.45,54.22,54.90,99437800,3.75
2004-11-16,55.16,55.20,54.48,54.94,73775800,3.75
2004-11-15,55.20,55.46,54.34,55.24,94011400,3.77
2004-11-12,55.01,55.69,54.84,55.50,98925400,3.79
2004-11-11,54.95,55.43,54.23,55.30,101824800,3.78
2004-11-10,53.95,55.39,53.91,54.75,127169000,3.74
2004-11-09,54.23,54.55,53.38,54.05,118941200,3.69
2004-11-08,54.27,55.45,53.86,54.38,131730200,3.71
2004-11-05,54.86,55.00,52.04,54.72,301261800,3.74
2004-11-04,55.03,55.55,54.37,54.45,232156400,3.72
2004-11-03,54.37,56.11,53.99,55.31,301043400,3.78
2004-11-02,52.40,54.08,52.40,53.50,182497000,3.65
2004-11-01,52.50,53.26,52.04,52.45,150512600,3.58
2004-10-29,51.84,53.20,51.80,52.40,202554800,3.58
2004-10-28,49.98,52.22,49.50,52.19,216066200,3.56
2004-10-27,48.51,50.62,48.17,50.30,298373600,3.43
2004-10-26,47.45,48.05,46.97,47.97,148590400,3.28
2004-10-25,47.20,47.84,47.07,47.55,98161000,3.25
2004-10-22,47.54,47.67,47.02,47.41,120766800,3.24
2004-10-21,47.48,48.13,47.36,47.94,181126400,3.27
2004-10-20,47.18,47.60,46.65,47.47,151277000,3.24
2004-10-19,48.10,48.35,47.31,47.42,200498200,3.24
2004-10-18,44.70,47.75,44.70,47.75,300188000,3.26
2004-10-15,44.88,45.61,44.19,45.50,257782000,3.11
2004-10-14,43.19,45.75,42.55,44.98,692106800,3.07
2004-10-13,38.87,39.76,38.74,39.75,290752000,2.71
2004-10-12,38.50,38.58,37.65,38.29,115047800,2.61
2004-10-11,38.80,39.06,38.20,38.59,80967600,2.63
2004-10-08,39.56,39.77,38.84,39.06,89807200,2.67
2004-10-07,40.54,40.93,39.46,39.62,106537200,2.70
2004-10-06,39.50,40.76,39.47,40.64,111575800,2.77
2004-10-05,38.56,39.67,38.40,39.37,101540600,2.69
2004-10-04,39.18,39.18,38.75,38.79,143521000,2.65
2004-10-01,39.12,39.19,38.58,38.67,116351200,2.64
2004-09-30,39.00,39.27,38.45,38.75,106253000,2.65
2004-09-29,37.93,38.86,37.82,38.68,68377400,2.64
2004-09-28,37.46,38.29,37.45,38.04,88296600,2.60
2004-09-27,36.95,37.98,36.83,37.53,99379000,2.56
2004-09-24,37.45,38.00,37.15,37.29,92372000,2.55
2004-09-23,37.04,37.50,36.93,37.27,99351000,2.54
2004-09-22,38.10,38.14,36.81,36.92,100422000,2.52
2004-09-21,37.75,38.87,37.46,38.01,96663000,2.60
2004-09-20,36.88,37.98,36.87,37.71,61250000,2.57
2004-09-17,36.55,37.38,36.40,37.14,125577200,2.54
2004-09-16,35.20,36.76,35.08,36.35,125479200,2.48
2004-09-15,35.36,35.48,34.80,35.20,58167200,2.40
2004-09-14,35.24,35.55,34.78,35.49,63705600,2.42
2004-09-13,35.88,36.07,35.32,35.59,70494200,2.43
2004-09-10,35.66,36.23,35.46,35.87,82003600,2.45
2004-09-09,36.10,36.30,35.28,35.70,115334800,2.44
2004-09-08,35.70,36.57,35.68,36.35,85881600,2.48
2004-09-07,35.40,36.19,35.23,35.76,75489400,2.44
2004-09-03,35.01,35.92,35.01,35.23,73367000,2.41
2004-09-02,35.50,35.81,34.83,35.66,101581200,2.43
2004-09-01,34.30,35.99,34.19,35.86,128931600,2.45
2004-08-31,34.07,34.95,34.00,34.49,94140200,2.35
2004-08-30,34.00,34.72,33.96,34.12,54535600,2.33
2004-08-27,34.68,34.76,34.00,34.35,97203400,2.35
2004-08-26,33.04,35.18,32.74,34.66,238964600,2.37
2004-08-25,31.87,33.15,31.73,33.05,126404600,2.26
2004-08-24,31.26,31.95,31.19,31.95,93534000,2.18
2004-08-23,30.86,31.27,30.60,31.08,63665000,2.12
2004-08-20,30.71,30.99,30.49,30.80,79195200,2.10
2004-08-19,31.51,31.86,30.36,30.71,97230000,2.10
2004-08-18,30.51,31.85,30.49,31.74,91163800,2.17
2004-08-17,30.58,31.13,30.35,30.87,80754800,2.11
2004-08-16,31.00,31.72,30.64,30.78,108918600,2.10
2004-08-13,30.60,31.28,30.40,30.84,82012000,2.11
2004-08-12,30.45,30.85,30.28,30.37,56550200,2.07
2004-08-11,31.10,31.13,30.26,31.01,80598000,2.12
2004-08-10,30.39,31.54,30.35,31.52,87759000,2.15
2004-08-09,29.85,30.45,29.81,30.30,72711800,2.07
2004-08-06,30.90,31.10,29.70,29.78,123072600,2.03
2004-08-05,31.81,32.30,31.25,31.39,61125400,2.14
2004-08-04,31.19,32.12,31.17,31.79,69122200,2.17
2004-08-03,31.45,31.72,31.15,31.29,52907400,2.14
2004-08-02,31.18,32.20,31.13,31.58,91273000,2.16
2004-07-30,32.65,33.00,32.00,32.34,60755800,2.21
2004-07-29,32.47,32.82,32.13,32.64,55539400,2.23
2004-07-28,32.31,32.41,31.16,32.27,71262800,2.20
2004-07-27,31.80,32.75,31.57,32.43,106251600,2.21
2004-07-26,30.85,31.45,30.78,31.26,98483000,2.13
2004-07-23,31.53,31.75,30.48,30.70,68392800,2.10
2004-07-22,31.25,31.73,31.06,31.68,83529600,2.16
2004-07-21,32.42,32.71,31.34,31.62,75314400,2.16
2004-07-20,31.95,32.20,31.55,32.20,80936800,2.20
2004-07-19,32.01,32.22,31.66,31.97,133292600,2.18
2004-07-16,32.80,32.92,32.12,32.20,122095400,2.20
2004-07-15,32.66,33.63,32.11,32.93,441931000,2.25
2004-07-14,28.86,29.97,28.74,29.58,208950000,2.02
2004-07-13,29.25,29.60,29.02,29.22,79044000,1.99
2004-07-12,30.02,30.04,28.93,29.14,127905400,1.99
2004-07-09,30.27,30.50,30.03,30.03,52215800,2.05
2004-07-08,30.13,30.68,29.95,30.14,58345000,2.06
2004-07-07,30.85,31.36,30.13,30.39,99498000,2.07
2004-07-06,31.27,31.42,30.80,30.95,87245200,2.11
2004-07-02,30.48,31.18,29.73,31.08,227670800,2.12
2004-07-01,32.10,32.48,31.90,32.30,85485400,2.21
2004-06-30,32.56,32.97,31.89,32.54,93261000,2.22
2004-06-29,32.07,32.99,31.41,32.50,147638400,2.22
2004-06-28,34.18,34.19,32.21,32.49,130274200,2.22
2004-06-25,33.07,33.70,33.00,33.70,80857000,2.30
2004-06-24,33.51,33.70,32.98,33.18,63128800,2.27
2004-06-23,33.00,33.83,32.89,33.70,97717200,2.30
2004-06-22,32.30,33.09,32.29,33.00,90127800,2.25
2004-06-21,33.12,33.50,32.12,32.33,97553400,2.21
2004-06-18,32.66,33.41,32.43,32.91,101563000,2.25
2004-06-17,32.56,33.13,32.21,32.81,137830000,2.24
2004-06-16,30.66,33.32,30.53,32.74,227410400,2.24
2004-06-15,30.54,31.14,30.26,30.69,111158600,2.10
2004-06-14,30.65,30.68,29.50,30.12,60996600,2.06
2004-06-10,30.20,30.97,30.20,30.74,64394400,2.10
2004-06-09,30.09,30.71,30.00,30.20,87301200,2.06
2004-06-08,29.99,30.44,29.83,30.35,103905200,2.07
2004-06-07,29.04,29.98,28.81,29.81,73969000,2.04
2004-06-04,28.56,29.25,28.51,28.78,99778000,1.96
2004-06-03,28.72,28.99,28.29,28.40,62732600,1.94
2004-06-02,28.03,29.17,27.80,28.92,79678200,1.97
2004-06-01,27.79,28.20,27.61,28.06,45533600,1.92
2004-05-28,28.08,28.27,27.80,28.06,36429400,1.92
2004-05-27,28.46,28.60,27.82,28.17,58993200,1.92
2004-05-26,28.33,28.78,28.00,28.51,80542000,1.95
2004-05-25,27.50,28.51,27.29,28.41,79994600,1.94
2004-05-24,27.29,27.90,27.11,27.34,58900800,1.87
2004-05-21,26.90,27.20,26.73,27.11,44973600,1.85
2004-05-20,26.63,27.00,26.47,26.71,49074200,1.82
2004-05-19,27.40,27.50,26.42,26.47,93898000,1.81
2004-05-18,26.97,27.29,26.80,27.06,51515800,1.85
2004-05-17,26.70,27.06,26.36,26.64,75111400,1.82
2004-05-14,27.25,27.32,26.45,27.06,64450400,1.85
2004-05-13,27.10,27.72,26.90,27.19,57463000,1.86
2004-05-12,26.79,27.34,26.24,27.30,61355000,1.86
2004-05-11,26.40,27.19,26.40,27.14,76293000,1.85
2004-05-10,26.27,26.60,25.94,26.28,62494600,1.79
2004-05-07,26.55,27.57,26.55,26.67,104759200,1.82
2004-05-06,26.40,26.75,25.90,26.58,65889600,1.81
2004-05-05,26.20,26.75,25.96,26.65,59526600,1.82
2004-05-04,25.97,26.55,25.50,26.14,69995800,1.78
2004-05-03,26.00,26.33,25.74,26.07,74408600,1.78
2004-04-30,26.71,26.96,25.49,25.78,116625600,1.76
2004-04-29,26.45,27.00,25.98,26.77,115197600,1.83
2004-04-28,26.82,27.01,26.34,26.45,57792000,1.81
2004-04-27,27.24,27.44,26.69,26.94,70966000,1.84
2004-04-26,27.58,27.64,27.00,27.13,57782200,1.85
2004-04-23,27.70,28.00,27.05,27.70,78957200,1.89
2004-04-22,27.56,28.18,27.11,27.78,86146200,1.90
2004-04-21,27.60,28.12,27.37,27.73,81468800,1.89
2004-04-20,28.21,28.41,27.56,27.73,88629800,1.89
2004-04-19,28.12,28.75,27.83,28.35,178088400,1.94
2004-04-16,29.15,29.31,28.50,29.18,100732800,1.99
2004-04-15,28.82,29.58,28.16,29.30,440361600,2.00
2004-04-14,26.74,27.07,26.31,26.64,159933200,1.82
2004-04-13,27.98,28.03,26.84,26.93,109099200,1.84
2004-04-12,27.50,28.10,27.49,28.04,57635200,1.91
2004-04-08,27.88,28.00,27.20,27.53,60229400,1.88
2004-04-07,27.61,27.70,26.92,27.31,63779800,1.86
2004-04-06,27.71,28.15,27.43,27.83,64498000,1.90
2004-04-05,27.48,28.37,27.44,28.32,96418000,1.93
2004-04-02,27.75,27.93,27.23,27.50,68619600,1.88
2004-04-01,26.89,27.27,26.62,27.11,79583000,1.85
2004-03-31,27.92,27.98,26.95,27.04,97693400,1.85
2004-03-30,27.74,27.95,27.34,27.92,89919200,1.91
2004-03-29,27.37,27.99,27.20,27.91,87682000,1.91
2004-03-26,27.00,27.36,26.91,27.04,104973400,1.85
2004-03-25,26.14,26.91,25.89,26.87,141611400,1.83
2004-03-24,25.27,25.75,25.27,25.50,107053800,1.74
2004-03-23,25.88,26.00,25.22,25.29,96378800,1.73
2004-03-22,25.37,26.17,25.25,25.86,104757800,1.77
2004-03-19,25.56,26.94,25.54,25.86,102144000,1.77
2004-03-18,25.94,26.06,25.59,25.67,80270400,1.75
2004-03-17,25.96,26.38,25.78,26.19,102858000,1.79
2004-03-16,26.55,26.61,25.39,25.82,151358200,1.76
2004-03-15,27.03,27.35,26.26,26.45,120429400,1.81
2004-03-12,27.32,27.78,27.17,27.56,82306000,1.88
2004-03-11,27.27,28.04,27.09,27.15,148962800,1.85
2004-03-10,27.04,28.14,26.94,27.68,251741000,1.89
2004-03-09,25.90,27.23,25.75,27.10,154590800,1.85
2004-03-08,26.62,26.79,25.80,26.00,130718000,1.78
2004-03-05,24.95,27.49,24.90,26.74,385149800,1.83
2004-03-04,23.93,25.22,23.91,25.16,165055800,1.72
2004-03-03,23.60,24.19,23.60,23.92,56282800,1.63
2004-03-02,24.00,24.10,23.77,23.81,64171800,1.63
2004-03-01,24.10,24.30,23.87,24.02,80420200,1.64
2004-02-27,22.96,24.02,22.95,23.92,117209400,1.63
2004-02-26,22.88,23.18,22.80,23.04,49602000,1.57
2004-02-25,22.28,22.90,22.21,22.81,69069000,1.56
2004-02-24,22.14,22.74,22.00,22.36,64764000,1.53
2004-02-23,22.34,22.46,21.89,22.19,48519800,1.51
2004-02-20,22.50,22.51,22.21,22.40,69400800,1.53
2004-02-19,23.33,23.64,22.41,22.47,80770200,1.53
2004-02-18,23.18,23.44,23.05,23.26,35408800,1.59
2004-02-17,23.10,23.49,23.10,23.16,42739200,1.58
2004-02-13,23.85,24.10,22.83,23.00,78995000,1.57
2004-02-12,23.61,23.99,23.60,23.73,45997000,1.62
2004-02-11,23.09,23.87,23.05,23.80,87136000,1.62
2004-02-10,22.62,23.12,22.44,22.98,63835800,1.57
2004-02-09,22.62,22.86,22.50,22.67,47065200,1.55
2004-02-06,22.45,22.89,22.40,22.71,48335000,1.55
2004-02-05,21.82,22.91,21.81,22.42,88211200,1.53
2004-02-04,22.00,22.09,21.70,21.79,76388200,1.49
2004-02-03,22.30,22.40,22.00,22.26,45203200,1.52
2004-02-02,22.46,22.81,22.08,22.32,71857800,1.52
2004-01-30,22.65,22.87,22.42,22.56,46324600,1.54
2004-01-29,22.63,22.80,22.19,22.68,53174800,1.55
2004-01-28,22.84,23.38,22.41,22.52,68850600,1.54
2004-01-27,23.04,23.25,22.80,23.07,76767600,1.58
2004-01-26,22.46,23.06,22.43,23.01,67817400,1.57
2004-01-23,22.42,22.74,22.25,22.56,56792400,1.54
2004-01-22,22.56,22.83,22.18,22.18,51251200,1.51
2004-01-21,22.70,22.97,22.43,22.61,56665000,1.54
2004-01-20,22.67,22.80,22.25,22.73,78986600,1.55
2004-01-16,22.89,23.04,22.61,22.72,93205000,1.55
2004-01-15,22.91,23.40,22.50,22.85,254552200,1.56
2004-01-14,24.40,24.54,23.78,24.20,155010800,1.65
2004-01-13,24.70,24.84,23.86,24.12,169754200,1.65
2004-01-12,23.25,24.00,23.10,23.73,121886800,1.62
2004-01-09,23.23,24.13,22.79,23.00,106864800,1.57
2004-01-08,22.84,23.73,22.65,23.36,115075800,1.59
2004-01-07,22.10,22.83,21.93,22.59,146718600,1.54
2004-01-06,22.25,22.42,21.71,22.09,127337000,1.51
2004-01-05,21.42,22.39,21.42,22.17,98754600,1.51
2004-01-02,21.55,21.75,21.18,21.28,36160600,1.45
2003-12-31,21.35,21.53,21.18,21.37,43612800,1.46
2003-12-30,21.18,21.50,21.15,21.28,51213400,1.45
2003-12-29,20.91,21.16,20.86,21.15,58364600,1.44
2003-12-26,20.35,20.91,20.34,20.78,25923800,1.42
2003-12-24,19.72,20.59,19.65,20.41,44368800,1.39
2003-12-23,19.92,19.95,19.60,19.81,77124600,1.35
2003-12-22,19.65,19.89,19.25,19.85,94266200,1.36
2003-12-19,20.19,20.42,19.62,19.70,113390200,1.34
2003-12-18,19.90,20.18,19.90,20.04,82728800,1.37
2003-12-17,20.08,20.13,19.79,19.88,68565000,1.36
2003-12-16,20.19,20.49,20.01,20.12,93489200,1.37
2003-12-15,21.49,21.49,20.07,20.17,97227200,1.38
2003-12-12,21.32,21.32,20.70,20.89,48168400,1.43
2003-12-11,20.25,21.34,20.21,21.21,45784200,1.45
2003-12-10,20.45,20.61,19.96,20.38,67834200,1.39
2003-12-09,21.17,21.25,20.40,20.45,33786200,1.40
2003-12-08,20.78,21.08,20.41,21.05,37059400,1.44
2003-12-05,20.90,21.15,20.73,20.85,46544400,1.42
2003-12-04,20.94,21.17,20.77,21.15,44485000,1.44
2003-12-03,21.54,21.84,20.96,21.03,47824000,1.44
2003-12-02,21.60,21.90,21.41,21.54,51324000,1.47
2003-12-01,21.04,21.85,21.00,21.71,90384000,1.48
2003-11-28,20.78,21.07,20.52,20.91,19024600,1.43
2003-11-26,20.89,21.15,20.25,20.72,61282200,1.41
2003-11-25,21.23,21.25,20.61,20.68,67163600,1.41
2003-11-24,20.50,21.27,20.45,21.15,95456200,1.44
2003-11-21,20.34,20.58,19.85,20.28,60459000,1.38
2003-11-20,20.10,21.08,20.10,20.38,59897600,1.39
2003-11-19,20.56,20.65,20.26,20.42,86146200,1.39
2003-11-18,21.21,21.34,20.35,20.41,66798200,1.39
2003-11-17,21.35,21.37,20.95,21.13,57064000,1.44
2003-11-14,22.48,22.61,21.28,21.46,59262000,1.47
2003-11-13,22.07,22.56,21.92,22.42,53193000,1.53
2003-11-12,21.48,22.72,21.48,22.33,75000800,1.52
2003-11-11,21.90,22.02,21.48,21.54,53768400,1.47
2003-11-10,22.45,22.65,21.84,21.90,58569000,1.50
2003-11-07,23.19,23.24,22.45,22.50,52536400,1.54
2003-11-06,22.91,23.15,22.65,23.12,99268400,1.58
2003-11-05,22.82,23.13,22.47,23.03,80617600,1.57
2003-11-04,23.07,23.10,22.59,22.91,62308400,1.56
2003-11-03,22.83,23.30,22.78,23.15,75710600,1.58
2003-10-31,23.30,23.35,22.78,22.89,54538400,1.56
2003-10-30,23.99,24.00,22.87,23.09,65139200,1.58
2003-10-29,23.51,23.90,23.34,23.69,66770200,1.62
2003-10-28,22.56,23.77,22.40,23.72,62928600,1.62
2003-10-27,22.75,22.89,22.49,22.60,40503400,1.54
2003-10-24,22.56,22.85,22.23,22.60,54964000,1.54
2003-10-23,22.73,23.15,22.59,22.99,41302800,1.57
2003-10-22,22.94,23.20,22.68,22.76,40399800,1.55
2003-10-21,23.31,23.40,22.75,23.18,44115400,1.58
2003-10-20,22.60,23.34,22.38,23.22,69783000,1.59
2003-10-17,23.38,23.49,22.43,22.75,89952800,1.55
2003-10-16,23.80,23.84,22.41,23.25,243920600,1.59
2003-10-15,24.85,25.01,24.58,24.82,152525800,1.69
2003-10-14,24.32,24.74,24.19,24.55,68854800,1.68
2003-10-13,23.73,24.41,23.72,24.35,69966400,1.66
2003-10-10,23.50,23.81,23.37,23.68,43709400,1.62
2003-10-09,23.30,23.67,22.79,23.45,86937200,1.60
2003-10-08,23.25,23.54,22.73,23.06,107167200,1.57
2003-10-07,22.05,23.41,21.91,23.22,104543600,1.59
2003-10-06,21.67,22.33,21.58,22.29,67082400,1.52
2003-10-03,20.99,21.86,20.88,21.69,74900000,1.48
2003-10-02,20.80,20.80,20.28,20.57,51014600,1.40
2003-10-01,20.71,21.10,20.19,20.79,59028200,1.42
2003-09-30,21.09,21.22,20.44,20.72,71356600,1.41
2003-09-29,21.49,21.67,20.65,21.30,91425600,1.45
2003-09-26,20.30,21.70,20.15,20.69,86812600,1.41
2003-09-25,21.34,21.37,20.25,20.43,143595200,1.39
2003-09-24,22.21,22.31,21.08,21.32,75321400,1.46
2003-09-23,22.02,22.46,21.88,22.43,33112800,1.53
2003-09-22,22.18,22.50,21.92,22.08,44955400,1.51
2003-09-19,22.88,23.05,22.43,22.58,51489200,1.54
2003-09-18,22.10,22.99,21.95,22.88,63226800,1.56
2003-09-17,22.37,22.38,21.85,22.12,72349200,1.51
2003-09-16,22.21,22.69,22.20,22.36,67251800,1.53
2003-09-15,22.81,22.90,22.12,22.21,56711200,1.52
2003-09-12,22.51,23.14,22.31,23.10,44997400,1.58
2003-09-11,22.25,22.79,22.10,22.56,53421200,1.54
2003-09-10,22.25,22.61,22.11,22.18,56222600,1.51
2003-09-09,22.53,22.67,22.12,22.37,45092600,1.53
2003-09-08,22.48,22.79,22.47,22.74,41811000,1.55
2003-09-05,22.73,23.15,22.41,22.50,60033400,1.54
2003-09-04,23.16,23.25,22.77,22.83,49945000,1.56
2003-09-03,22.80,23.32,22.76,22.95,67207000,1.57
2003-09-02,22.66,22.90,22.40,22.85,60533200,1.56
2003-08-29,22.20,22.85,22.05,22.61,65788800,1.54
2003-08-28,21.33,22.22,21.33,22.19,79906400,1.51
2003-08-27,20.91,21.48,20.66,21.48,56425600,1.47
2003-08-26,20.75,21.07,20.35,21.05,41239800,1.44
2003-08-25,20.78,20.91,20.49,20.86,34445600,1.42
2003-08-22,21.81,22.00,20.64,20.88,62566000,1.43
2003-08-21,21.03,21.71,20.95,21.68,63831600,1.48
2003-08-20,20.18,21.27,20.14,21.01,68303200,1.43
2003-08-19,20.37,20.45,20.00,20.32,33422200,1.39
2003-08-18,19.86,20.41,19.72,20.34,48193600,1.39
2003-08-15,20.02,20.07,19.66,19.71,31466400,1.35
2003-08-14,20.21,20.33,19.94,19.97,48195000,1.36
2003-08-13,19.86,20.34,19.58,20.18,71024800,1.38
2003-08-12,19.76,19.80,19.46,19.70,41109600,1.34
2003-08-11,19.82,19.93,19.51,19.66,34307000,1.34
2003-08-08,20.11,20.13,19.60,19.64,34414800,1.34
2003-08-07,19.73,20.09,19.42,19.93,43594600,1.36
2003-08-06,20.06,20.17,19.50,19.63,61366200,1.34
2003-08-05,21.35,21.40,20.10,20.38,62360200,1.39
2003-08-04,20.53,21.50,20.28,21.21,57528800,1.45
2003-08-01,21.00,21.27,20.64,20.73,37401000,1.42
2003-07-31,20.74,21.35,20.57,21.08,75366200,1.44
2003-07-30,20.77,20.90,20.17,20.28,43398600,1.38
2003-07-29,20.99,21.08,20.52,20.72,49280000,1.41
2003-07-28,21.50,21.50,20.86,20.99,42589400,1.43
2003-07-25,20.41,21.57,20.40,21.54,54171600,1.47
2003-07-24,21.04,21.50,20.38,20.51,57309000,1.40
2003-07-23,20.95,20.96,20.46,20.79,35758800,1.42
2003-07-22,20.87,20.96,20.50,20.80,49606200,1.42
2003-07-21,20.69,20.80,20.30,20.61,45952200,1.41
2003-07-18,20.90,21.18,20.40,20.86,74709600,1.42
2003-07-17,20.19,20.95,20.13,20.90,187803000,1.43
2003-07-16,19.97,20.00,19.38,19.87,62732600,1.36
2003-07-15,20.02,20.24,19.43,19.61,51661400,1.34
2003-07-14,20.01,20.40,19.87,19.90,47101600,1.36
2003-07-11,19.66,20.00,19.53,19.85,34214600,1.36
2003-07-10,19.88,19.94,19.37,19.58,42733600,1.34
2003-07-09,20.21,20.45,19.89,19.89,53411400,1.36
2003-07-08,19.52,20.50,19.49,20.40,64184400,1.39
2003-07-07,19.27,20.18,19.13,19.87,71568000,1.36
2003-07-03,19.00,19.55,18.98,19.13,34442800,1.31
2003-07-02,19.03,19.40,19.02,19.27,81324600,1.32
2003-07-01,18.87,19.18,18.51,19.09,45248000,1.30
2003-06-30,18.68,19.21,18.59,19.06,55538000,1.30
2003-06-27,19.30,19.31,18.48,18.73,91448000,1.28
2003-06-26,18.70,19.32,18.70,19.29,40426400,1.32
2003-06-25,18.86,19.40,18.71,19.09,82453000,1.30
2003-06-24,19.47,19.67,18.72,18.78,128595600,1.28
2003-06-23,19.30,19.69,18.75,19.06,76840400,1.30
2003-06-20,19.35,19.58,18.90,19.20,89136600,1.31
2003-06-19,19.36,19.61,18.77,19.14,95382000,1.31
2003-06-18,18.45,19.48,18.31,19.12,113745800,1.31
2003-06-17,18.41,18.50,17.99,18.19,44366000,1.24
2003-06-16,17.60,18.27,17.45,18.27,59631600,1.25
2003-06-13,17.75,17.95,17.13,17.42,47811400,1.19
2003-06-12,17.55,17.88,17.45,17.77,63147000,1.21
2003-06-11,17.15,17.51,16.81,17.45,56278600,1.19
2003-06-10,16.89,17.29,16.75,17.18,44161600,1.17
2003-06-09,16.94,17.04,16.63,16.79,64988000,1.15
2003-06-06,17.74,18.04,17.14,17.15,60347000,1.17
2003-06-05,17.45,17.74,17.33,17.64,51374400,1.20
2003-06-04,17.30,17.79,17.14,17.60,67800600,1.20
2003-06-03,17.44,17.67,17.02,17.31,90214600,1.18
2003-06-02,18.10,18.29,17.27,17.45,104647200,1.19
2003-05-30,18.12,18.18,17.53,17.95,95687200,1.23
2003-05-29,18.29,18.50,17.90,18.10,83441400,1.24
2003-05-28,18.50,18.66,18.15,18.28,84919800,1.25
2003-05-27,17.96,18.90,17.91,18.88,72532600,1.29
2003-05-23,18.21,18.46,17.96,18.32,51679600,1.25
2003-05-22,17.89,18.40,17.74,18.24,44615200,1.25
2003-05-21,17.79,18.09,17.67,17.85,76252400,1.22
2003-05-20,18.10,18.16,17.60,17.79,104055000,1.21
2003-05-19,18.53,18.65,18.06,18.10,111472200,1.24
2003-05-16,18.59,19.01,18.28,18.80,85407000,1.28
2003-05-15,18.60,18.85,18.47,18.73,71248800,1.28
2003-05-14,18.83,18.84,18.43,18.55,88872000,1.27
2003-05-13,18.43,18.97,17.95,18.67,111699000,1.27
2003-05-12,18.15,18.74,18.13,18.56,104843200,1.27
2003-05-09,18.33,18.40,17.88,18.30,147096600,1.25
2003-05-08,17.70,18.07,17.29,18.00,171934000,1.23
2003-05-07,17.33,18.24,17.11,17.65,263594800,1.21
2003-05-06,16.12,17.90,16.10,17.50,378623000,1.19
2003-05-05,14.77,16.88,14.75,16.09,388927000,1.10
2003-05-02,14.46,14.59,14.34,14.45,80295600,0.99
2003-05-01,14.25,14.39,14.00,14.36,85689800,0.98
2003-04-30,13.93,14.35,13.85,14.22,114543800,0.97
2003-04-29,13.98,14.16,13.58,14.06,114559200,0.96
2003-04-28,13.48,13.96,13.43,13.86,159199600,0.95
2003-04-25,13.46,13.58,13.23,13.35,51329600,0.91
2003-04-24,13.52,13.61,13.00,13.44,81277000,0.92
2003-04-23,13.53,13.63,13.36,13.58,52420200,0.93
2003-04-22,13.18,13.62,13.09,13.51,75142200,0.92
2003-04-21,13.13,13.19,12.98,13.14,38080000,0.90
2003-04-17,13.20,13.25,12.72,13.12,154064400,0.90
2003-04-16,12.99,13.67,12.92,13.24,254044000,0.90
2003-04-15,13.59,13.60,13.30,13.39,75992000,0.91
2003-04-14,13.71,13.75,13.50,13.58,125739600,0.93
2003-04-11,14.05,14.44,12.93,13.20,348177200,0.90
2003-04-10,14.20,14.39,14.20,14.37,26775000,0.98
2003-04-09,14.52,14.62,14.14,14.19,36681400,0.97
2003-04-08,14.51,14.65,14.36,14.45,32233600,0.99
2003-04-07,14.85,14.95,14.41,14.49,49215600,0.99
2003-04-04,14.52,14.67,14.39,14.41,36505000,0.98
2003-04-03,14.56,14.70,14.35,14.46,36428000,0.99
2003-04-02,14.36,14.69,14.27,14.60,42842800,1.00
2003-04-01,14.20,14.31,14.07,14.16,38585400,0.97
2003-03-31,14.33,14.53,14.04,14.14,64164800,0.97
2003-03-28,14.40,14.62,14.37,14.57,36325800,0.99
2003-03-27,14.32,14.70,14.32,14.49,30598400,0.99
2003-03-26,14.55,14.56,14.30,14.41,44585800,0.98
2003-03-25,14.41,14.83,14.37,14.55,41924400,0.99
2003-03-24,14.67,14.80,14.35,14.37,40275200,0.98
2003-03-21,15.09,15.15,14.82,15.00,74487000,1.02
2003-03-20,14.93,14.99,14.60,14.91,40794600,1.02
2003-03-19,15.07,15.15,14.79,14.95,35329000,1.02
2003-03-18,15.00,15.09,14.82,15.00,57495200,1.02
2003-03-17,14.89,15.07,14.71,15.01,99978200,1.02
2003-03-14,14.68,15.01,14.64,14.78,38274600,1.01
2003-03-13,14.47,14.80,14.17,14.72,83861400,1.00
2003-03-12,14.17,14.39,14.06,14.22,55640200,0.97
2003-03-11,14.36,14.49,14.12,14.23,40297600,0.97
2003-03-10,14.51,14.67,14.30,14.37,33643400,0.98
2003-03-07,14.47,14.71,14.31,14.53,50246000,0.99
2003-03-06,14.58,14.60,14.40,14.56,24964800,0.99
2003-03-05,14.61,14.80,14.52,14.62,31670800,1.00
2003-03-04,14.74,14.81,14.44,14.56,31603600,0.99
2003-03-03,15.01,15.16,14.55,14.65,50940400,1.00
2003-02-28,14.86,15.09,14.77,15.01,48774600,1.02
2003-02-27,14.57,15.00,14.51,14.86,38585400,1.01
2003-02-26,14.99,15.02,14.48,14.50,54273800,0.99
2003-02-25,14.68,15.08,14.58,15.02,47160400,1.03
2003-02-24,14.86,15.03,13.80,14.74,45063200,1.01
2003-02-21,14.82,15.06,14.65,15.00,39361000,1.02
2003-02-20,14.85,14.96,14.71,14.77,56088200,1.01
2003-02-19,15.07,15.15,14.68,14.85,60092200,1.01
2003-02-18,14.75,15.30,14.72,15.27,72724400,1.04
2003-02-14,14.61,14.72,14.35,14.67,60824400,1.00
2003-02-13,14.41,14.64,14.24,14.54,52123400,0.99
2003-02-12,14.27,14.60,14.27,14.39,57171800,0.98
2003-02-11,14.50,14.63,14.20,14.35,41195000,0.98
2003-02-10,14.26,14.57,14.06,14.35,41972000,0.98
2003-02-07,14.55,14.60,14.07,14.15,67425400,0.97
2003-02-06,14.36,14.59,14.22,14.43,44787400,0.99
2003-02-05,14.71,14.93,14.44,14.45,55403600,0.99
2003-02-04,14.45,14.65,14.31,14.60,79353400,1.00
2003-02-03,14.41,14.91,14.35,14.66,66196200,1.00
2003-01-31,14.19,14.55,14.05,14.36,85306200,0.98
2003-01-30,14.98,15.07,14.29,14.32,101764600,0.98
2003-01-29,14.55,15.10,14.30,14.93,93261000,1.02
2003-01-28,14.24,14.69,14.16,14.58,71563800,1.00
2003-01-27,13.68,14.50,13.65,14.13,97851600,0.96
2003-01-24,14.24,14.24,13.56,13.80,76367200,0.94
2003-01-23,14.05,14.36,13.95,14.17,57064000,0.97
2003-01-22,13.98,14.15,13.80,13.88,53785200,0.95
2003-01-21,14.21,14.41,14.00,14.02,63364000,0.96
2003-01-17,14.56,14.56,14.08,14.10,66690400,0.96
2003-01-16,14.21,14.76,14.21,14.62,139767600,1.00
2003-01-15,14.59,14.70,14.26,14.43,92782200,0.99
2003-01-14,14.69,14.82,14.49,14.61,46715200,1.00
2003-01-13,14.90,14.90,14.36,14.63,44735600,1.00
2003-01-10,14.58,14.82,14.49,14.72,43775200,1.00
2003-01-09,14.62,14.92,14.50,14.68,53813200,1.00
2003-01-08,14.58,14.71,14.44,14.55,57411200,0.99
2003-01-07,14.79,15.00,14.47,14.85,85586200,1.01
2003-01-06,15.03,15.38,14.88,14.90,97633200,1.02
2003-01-03,14.80,14.93,14.59,14.90,36863400,1.02
2003-01-02,14.36,14.92,14.35,14.80,45357200,1.01
2002-12-31,14.00,14.36,13.95,14.33,50181600,0.98
2002-12-30,14.08,14.15,13.84,14.07,38760400,0.96
2002-12-27,14.31,14.38,14.01,14.06,20008800,0.96
2002-12-26,14.42,14.81,14.28,14.40,21355600,0.98
2002-12-24,14.44,14.47,14.30,14.36,9835000,0.98
2002-12-23,14.16,14.55,14.12,14.49,31456600,0.99
2002-12-20,14.29,14.56,13.78,14.14,79524200,0.97
2002-12-19,14.53,14.92,14.10,14.20,86879800,0.97
2002-12-18,14.80,14.86,14.50,14.57,37675400,0.99
2002-12-17,14.85,15.19,14.66,15.08,55665400,1.03
2002-12-16,14.81,15.10,14.61,14.85,62906200,1.01
2002-12-13,15.14,15.15,14.65,14.79,41195000,1.01
2002-12-12,15.51,15.55,15.01,15.19,37335200,1.04
2002-12-11,15.30,15.49,15.08,15.49,63375200,1.06
2002-12-10,14.75,15.45,14.73,15.28,77152600,1.04
2002-12-09,14.94,14.95,14.67,14.75,59021200,1.01
2002-12-06,14.65,15.19,14.52,14.95,61339600,1.02
2002-12-05,15.03,15.08,14.53,14.63,60849600,1.00
2002-12-04,15.18,15.19,14.50,14.97,81439400,1.02
2002-12-03,15.20,15.34,15.10,15.16,56967400,1.04
2002-12-02,15.90,16.10,15.01,15.18,99685600,1.04
2002-11-29,15.79,15.88,15.41,15.50,35858200,1.06
2002-11-27,15.60,15.86,15.45,15.72,71699600,1.07
2002-11-26,15.85,15.90,15.27,15.41,60065600,1.05
2002-11-25,16.03,16.14,15.71,15.97,49856800,1.09
2002-11-22,16.09,16.30,15.90,16.01,56964600,1.09
2002-11-21,15.90,16.44,15.75,16.35,104620600,1.12
2002-11-20,15.30,15.70,15.25,15.53,52185000,1.06
2002-11-19,15.55,15.75,15.01,15.27,52738000,1.04
2002-11-18,16.19,16.20,15.52,15.65,41144600,1.07
2002-11-15,16.23,16.24,15.76,15.95,40248600,1.09
2002-11-14,15.90,16.41,15.78,16.30,35428400,1.11
2002-11-13,15.50,16.07,15.28,15.59,57934800,1.06
2002-11-12,15.32,16.04,15.28,15.64,55948200,1.07
2002-11-11,15.74,15.89,15.12,15.16,38243800,1.04
2002-11-08,16.01,16.20,15.52,15.84,47516000,1.08
2002-11-07,16.94,17.10,15.81,16.00,84044800,1.09
2002-11-06,17.08,17.32,16.70,17.22,54097400,1.18
2002-11-05,16.75,16.96,16.35,16.90,52673600,1.15
2002-11-04,16.50,17.38,16.35,16.89,94204600,1.15
2002-11-01,15.94,16.50,15.89,16.36,47457200,1.12
2002-10-31,15.99,16.44,15.92,16.07,73959200,1.10
2002-10-30,15.49,16.37,15.48,15.98,67669000,1.09
2002-10-29,15.57,15.88,14.96,15.44,64794800,1.05
2002-10-28,15.55,15.95,15.25,15.61,87325000,1.07
2002-10-25,14.69,15.45,14.59,15.42,69767600,1.05
2002-10-24,15.02,15.21,14.55,14.69,43687000,1.00
2002-10-23,14.63,14.98,14.50,14.88,52259200,1.02
2002-10-22,14.47,14.88,14.26,14.70,54537000,1.00
2002-10-21,14.26,14.63,14.00,14.56,59630200,0.99
2002-10-18,14.00,14.35,13.93,14.34,72074800,0.98
2002-10-17,14.21,14.38,13.98,14.11,117324200,0.96
2002-10-16,14.86,15.13,13.90,14.56,76906200,0.99
2002-10-15,15.22,15.25,14.78,15.16,101379600,1.04
2002-10-14,14.55,14.98,14.44,14.77,48601000,1.01
2002-10-11,14.25,14.78,14.10,14.51,73669400,0.99
2002-10-10,13.63,14.22,13.58,14.11,80393600,0.96
2002-10-09,13.54,13.85,13.41,13.59,89171600,0.93
2002-10-08,13.90,13.96,13.36,13.68,113411200,0.93
2002-10-07,13.97,14.21,13.76,13.77,61174400,0.94
2002-10-04,14.36,14.40,13.99,14.03,47706400,0.96
2002-10-03,14.18,14.60,14.06,14.30,54474000,0.98
2002-10-02,14.33,14.63,14.10,14.17,57337000,0.97
2002-10-01,14.59,14.60,14.00,14.51,85605800,0.99
2002-09-30,14.40,14.57,14.14,14.50,59424400,0.99
2002-09-27,14.49,14.85,14.48,14.72,51538200,1.00
2002-09-26,15.10,15.19,14.55,14.70,52161200,1.00
2002-09-25,14.69,15.17,14.65,14.93,63670600,1.02
2002-09-24,14.40,14.82,14.40,14.64,62665400,1.00
2002-09-23,14.76,14.96,14.45,14.85,65927400,1.01
2002-09-20,14.62,14.94,14.52,14.87,88197200,1.02
2002-09-19,14.75,14.80,14.48,14.58,51486400,1.00
2002-09-18,14.69,15.09,14.52,15.02,82160400,1.03
2002-09-17,14.57,15.03,14.57,14.80,106999200,1.01
2002-09-16,14.14,14.61,14.12,14.50,71660400,0.99
2002-09-13,14.13,14.34,14.05,14.17,70737800,0.97
2002-09-12,14.20,14.51,14.12,14.14,67457600,0.97
2002-09-11,14.34,14.60,14.15,14.29,50603000,0.98
2002-09-10,14.41,14.49,14.12,14.33,62367200,0.98
2002-09-09,14.28,14.53,14.15,14.37,39561200,0.98
2002-09-06,14.51,14.65,14.23,14.38,45397800,0.98
2002-09-05,14.22,14.36,14.05,14.18,56544600,0.97
2002-09-04,14.20,14.78,14.17,14.48,105165200,0.99
2002-09-03,14.49,14.55,14.05,14.05,69234200,0.96
2002-08-30,14.73,15.14,14.58,14.75,48379800,1.01
2002-08-29,14.65,15.08,14.51,14.70,41042400,1.00
2002-08-28,14.80,15.12,14.65,14.70,61993400,1.00
2002-08-27,15.71,15.74,14.71,14.85,65557800,1.01
2002-08-26,15.95,15.95,15.16,15.53,47492200,1.06
2002-08-23,15.90,15.93,15.45,15.73,40811400,1.07
2002-08-22,16.20,16.25,15.66,15.97,64577800,1.09
2002-08-21,16.01,16.24,15.45,16.12,50607200,1.10
2002-08-20,15.97,16.09,15.53,15.91,46656400,1.09
2002-08-19,15.78,16.25,15.72,15.98,54139400,1.09
2002-08-16,15.45,16.10,15.28,15.81,61306000,1.08
2002-08-15,15.25,15.75,15.01,15.61,80519600,1.07
2002-08-14,14.67,15.35,14.54,15.17,99771000,1.04
2002-08-13,14.90,15.21,14.55,14.59,67467400,1.00
2002-08-12,14.90,15.02,14.69,14.99,44941400,1.02
2002-08-09,15.25,15.25,14.75,15.00,51429000,1.02
2002-08-08,14.77,15.38,14.77,15.30,56837200,1.04
2002-08-07,15.09,15.36,14.35,15.03,83368600,1.03
2002-08-06,14.21,15.23,14.08,14.74,68013400,1.01
2002-08-05,14.51,14.70,13.97,13.99,51006200,0.96
2002-08-02,14.74,15.00,14.25,14.45,44765000,0.99
2002-08-01,15.11,15.42,14.73,14.80,57239000,1.01
2002-07-31,15.40,15.42,14.90,15.26,77674800,1.04
2002-07-30,14.85,15.51,14.56,15.43,88709600,1.05
2002-07-29,14.48,15.10,14.37,15.02,68740000,1.03
2002-07-26,14.46,14.53,13.80,14.34,51926000,0.98
2002-07-25,14.93,14.95,14.01,14.36,119838600,0.98
2002-07-24,14.33,15.22,14.25,15.20,101648400,1.04
2002-07-23,14.90,15.13,14.44,14.47,99972600,0.99
2002-07-22,14.75,15.19,14.61,14.92,107724400,1.02
2002-07-19,14.70,15.17,14.53,14.96,96301800,1.02
2002-07-18,15.50,15.56,14.75,14.99,139865600,1.02
2002-07-17,16.13,16.20,15.19,15.63,303871400,1.07
2002-07-16,18.15,18.57,17.61,17.86,111692000,1.22
2002-07-15,17.43,18.60,16.81,18.23,73998400,1.24
2002-07-12,18.55,18.79,17.26,17.51,110873000,1.20
2002-07-11,17.26,18.35,16.97,18.30,93419200,1.25
2002-07-10,17.71,18.17,17.25,17.32,51720200,1.18
2002-07-09,18.09,18.29,17.46,17.53,56687400,1.20
2002-07-08,18.52,18.61,17.68,18.01,52801000,1.23
2002-07-05,17.71,18.75,17.71,18.74,40412400,1.28
2002-07-03,16.81,17.68,16.75,17.55,49757400,1.20
2002-07-02,17.03,17.16,16.83,16.94,76297200,1.16
2002-07-01,17.71,17.88,17.05,17.06,55672400,1.16
2002-06-28,17.10,17.82,17.00,17.72,67464600,1.21
2002-06-27,16.79,17.27,16.42,17.06,62914600,1.16
2002-06-26,16.80,17.29,15.98,16.55,139738200,1.13
2002-06-25,17.40,17.68,16.86,17.14,75300400,1.17
2002-06-24,16.77,17.73,16.70,17.27,107983400,1.18
2002-06-21,16.97,17.49,16.79,16.85,111294400,1.15
2002-06-20,17.17,17.60,16.85,17.11,99159200,1.17
2002-06-19,17.37,17.60,16.88,17.12,427366800,1.17
2002-06-18,20.42,20.59,19.98,20.15,88340000,1.38
2002-06-17,20.24,20.63,19.85,20.54,81152400,1.40
2002-06-14,19.24,20.36,18.11,20.10,106225000,1.37
2002-06-13,20.02,20.05,19.38,19.54,88020800,1.33
2002-06-12,20.41,20.75,19.94,20.09,132179600,1.37
2002-06-11,21.64,21.70,20.41,20.46,87374000,1.40
2002-06-10,21.48,21.84,21.34,21.48,69393800,1.47
2002-06-07,21.76,21.94,20.93,21.40,153094200,1.46
2002-06-06,22.96,23.23,22.04,22.16,64999200,1.51
2002-06-05,22.83,22.83,22.35,22.72,69270600,1.55
2002-06-04,22.88,23.04,22.18,22.78,86955400,1.56
2002-06-03,23.39,23.45,22.58,22.91,58777600,1.56
2002-05-31,24.09,24.25,23.28,23.30,91373800,1.59
2002-05-30,23.77,24.38,23.51,24.20,49093800,1.65
2002-05-29,23.92,24.44,23.45,23.98,55448400,1.64
2002-05-28,23.69,24.20,23.43,23.98,37429000,1.64
2002-05-24,24.99,24.99,23.96,24.15,41543600,1.65
2002-05-23,24.45,25.24,24.07,25.18,92349600,1.72
2002-05-22,23.37,24.37,23.32,24.32,72718800,1.66
2002-05-21,24.83,25.00,23.40,23.46,70247800,1.60
2002-05-20,24.57,24.93,24.53,24.74,67478600,1.69
2002-05-17,25.49,25.78,24.61,25.01,59123400,1.71
2002-05-16,25.06,25.45,24.75,25.21,56763000,1.72
2002-05-15,25.37,25.98,24.84,25.28,83956600,1.73
2002-05-14,24.45,25.68,24.22,25.61,131626600,1.75
2002-05-13,23.52,24.09,22.94,23.94,66402000,1.63
2002-05-10,24.29,24.29,22.98,23.32,58849000,1.59
2002-05-09,24.25,24.35,23.80,24.19,56154000,1.65
2002-05-08,23.20,24.52,23.04,24.37,109170600,1.66
2002-05-07,22.94,22.95,22.14,22.47,60687200,1.53
2002-05-06,23.35,23.50,22.46,22.65,62416200,1.55
2002-05-03,23.57,24.02,23.43,23.51,57695400,1.61
2002-05-02,23.81,24.34,23.60,23.69,59836000,1.62
2002-05-01,24.29,24.29,23.36,23.98,53676000,1.64
2002-04-30,23.89,24.38,23.75,24.27,70240800,1.66
2002-04-29,23.16,24.06,23.09,23.96,68072200,1.64
2002-04-26,24.28,24.37,23.00,23.01,76245400,1.57
2002-04-25,23.56,24.34,23.55,24.12,48550600,1.65
2002-04-24,24.30,24.50,23.68,23.77,35112000,1.62
2002-04-23,24.54,24.78,24.09,24.25,58367400,1.66
2002-04-22,24.84,24.93,24.23,24.53,67356800,1.67
2002-04-19,25.49,25.49,24.93,24.98,93851800,1.71
2002-04-18,25.50,25.52,24.88,25.41,100427600,1.73
2002-04-17,25.93,26.17,25.38,26.11,99062600,1.78
2002-04-16,25.15,25.99,25.12,25.74,153644400,1.76
2002-04-15,25.06,25.15,24.80,25.00,74842600,1.71
2002-04-12,25.01,25.17,24.57,25.06,80060400,1.71
2002-04-11,25.03,25.20,24.75,24.86,101813600,1.70
2002-04-10,24.21,24.95,24.01,24.66,56245000,1.68
2002-04-09,24.59,25.00,24.01,24.10,47882800,1.65
2002-04-08,24.16,24.68,23.78,24.56,65378600,1.68
2002-04-05,24.95,25.19,24.10,24.74,69587000,1.69
2002-04-04,23.67,25.05,23.67,24.90,84624400,1.70
2002-04-03,24.05,24.49,23.60,23.75,53632600,1.62
2002-04-02,24.00,24.30,23.87,24.07,50948800,1.64
2002-04-01,23.38,24.70,23.28,24.46,49761600,1.67
2002-03-28,23.70,23.88,23.46,23.67,27113800,1.62
2002-03-27,23.35,23.72,23.26,23.47,31925600,1.60
2002-03-26,23.20,23.64,23.00,23.46,64460200,1.60
2002-03-25,24.07,24.09,23.24,23.35,65707600,1.59
2002-03-22,24.22,24.56,23.87,24.09,50548400,1.64
2002-03-21,23.86,24.30,23.26,24.27,154088200,1.66
2002-03-20,24.66,25.14,24.50,24.92,73579800,1.70
2002-03-19,24.69,25.30,24.30,24.85,60586400,1.70
2002-03-18,24.95,25.05,24.32,24.74,76139000,1.69
2002-03-15,24.46,24.96,24.25,24.95,60225200,1.70
2002-03-14,24.30,24.60,23.87,24.43,54324200,1.67
2002-03-13,24.37,24.85,24.15,24.49,50191400,1.67
2002-03-12,24.51,24.74,24.10,24.72,63513800,1.69
2002-03-11,24.60,25.14,24.10,25.06,65696400,1.71
2002-03-08,24.74,25.09,24.30,24.66,67443600,1.68
2002-03-07,24.06,24.53,23.61,24.38,64562400,1.66
2002-03-06,23.48,24.34,22.93,24.07,56551600,1.64
2002-03-05,24.15,24.43,23.40,23.53,68675600,1.61
2002-03-04,23.26,24.58,22.76,24.29,87064600,1.66
2002-03-01,21.93,23.50,21.82,23.45,87248000,1.60
2002-02-28,22.15,22.59,21.35,21.70,114234400,1.48
2002-02-27,23.94,24.25,20.94,21.96,257539800,1.50
2002-02-26,23.91,24.37,23.25,23.67,65032800,1.62
2002-02-25,22.85,24.72,22.36,23.81,106712200,1.63
2002-02-22,21.66,22.95,21.50,22.74,101619000,1.55
2002-02-21,22.92,23.00,21.45,21.50,111687800,1.47
2002-02-20,22.77,23.20,22.35,23.13,71360800,1.58
2002-02-19,23.76,23.87,22.48,22.62,97564600,1.54
2002-02-15,24.53,24.98,23.85,23.90,65046800,1.63
2002-02-14,25.05,25.23,24.38,24.60,65042600,1.68
2002-02-13,24.73,25.24,24.65,25.01,78218000,1.71
2002-02-12,24.66,25.04,24.45,24.71,56070000,1.69
2002-02-11,23.93,25.00,23.74,24.98,99650600,1.71
2002-02-08,24.40,24.64,23.37,24.03,88832800,1.64
2002-02-07,24.65,25.29,24.08,24.30,86958200,1.66
2002-02-06,25.60,25.98,24.15,24.67,149394000,1.68
2002-02-05,25.09,25.98,25.08,25.45,114221800,1.74
2002-02-04,24.32,25.52,24.20,25.35,130593400,1.73
2002-02-01,24.34,24.96,24.34,24.41,99576400,1.67
2002-01-31,24.16,24.73,24.11,24.72,117111400,1.69
2002-01-30,23.07,24.14,22.94,24.09,117894000,1.64
2002-01-29,23.22,23.54,22.85,23.07,60081000,1.58
2002-01-28,23.40,23.55,22.72,23.27,46611600,1.59
2002-01-25,22.89,23.42,22.66,23.25,46478600,1.59
2002-01-24,22.91,23.51,22.90,23.21,86000600,1.58
2002-01-23,21.80,23.04,21.59,23.02,110819800,1.57
2002-01-22,22.27,22.37,21.82,21.82,81828600,1.49
2002-01-18,22.00,22.60,21.96,22.17,84702800,1.51
2002-01-17,21.96,22.74,21.87,22.48,165144000,1.53
2002-01-16,21.41,21.41,20.50,20.78,141723400,1.42
2002-01-15,21.32,21.76,21.21,21.70,72580200,1.48
2002-01-14,21.01,21.40,20.90,21.15,103999000,1.44
2002-01-11,21.39,21.84,20.60,21.05,87200400,1.44
2002-01-10,21.22,21.46,20.25,21.23,113184400,1.45
2002-01-09,22.80,22.93,21.28,21.65,81958800,1.48
2002-01-08,22.75,23.05,22.46,22.61,112509600,1.54
2002-01-07,23.72,24.00,22.75,22.90,111146000,1.56
2002-01-04,23.34,23.95,22.99,23.69,102494000,1.62
2002-01-03,23.00,23.75,22.77,23.58,153001800,1.61
2002-01-02,22.05,23.30,21.96,23.30,132374200,1.59
2001-12-31,22.51,22.66,21.83,21.90,34445600,1.50
2001-12-28,21.97,23.00,21.96,22.43,74781000,1.53
2001-12-27,21.58,22.25,21.58,22.07,47877200,1.51
2001-12-26,21.35,22.30,21.14,21.49,36600200,1.47
2001-12-24,20.90,21.45,20.90,21.36,12657400,1.46
2001-12-21,21.01,21.54,20.80,21.00,64083600,1.43
2001-12-20,21.40,21.47,20.62,20.67,55216000,1.41
2001-12-19,20.58,21.68,20.47,21.62,72489200,1.48
2001-12-18,20.89,21.33,20.22,21.01,58809800,1.43
2001-12-17,20.40,21.00,20.19,20.62,43428000,1.41
2001-12-14,20.73,20.83,20.09,20.39,47471200,1.39
2001-12-13,21.49,21.55,20.50,21.00,49460600,1.43
2001-12-12,21.87,21.92,21.25,21.49,48115200,1.47
2001-12-11,22.67,22.85,21.65,21.78,51368800,1.49
2001-12-10,22.29,22.99,22.23,22.54,42502600,1.54
2001-12-07,22.46,22.71,22.00,22.54,50878800,1.54
2001-12-06,23.48,23.50,22.14,22.78,84733600,1.56
2001-12-05,22.36,24.03,22.17,23.76,142144800,1.62
2001-12-04,21.05,22.56,20.72,22.40,95104800,1.53
2001-12-03,21.06,21.28,20.60,21.05,45291400,1.44
2001-11-30,20.47,21.44,20.25,21.30,75978000,1.45
2001-11-29,20.60,20.70,20.19,20.42,50691200,1.39
2001-11-28,20.85,21.21,20.41,20.53,62652800,1.40
2001-11-27,21.20,21.52,20.50,21.00,67138400,1.43
2001-11-26,19.94,21.55,19.88,21.37,115172400,1.46
2001-11-23,19.71,19.95,19.57,19.84,15001000,1.35
2001-11-21,19.61,19.80,19.26,19.68,50395800,1.34
2001-11-20,19.82,20.20,19.50,19.53,69146000,1.33
2001-11-19,19.00,20.05,18.96,20.00,83147400,1.37
2001-11-16,19.27,19.29,18.40,18.97,57666000,1.30
2001-11-15,19.45,19.90,19.23,19.45,53257400,1.33
2001-11-14,19.59,19.90,19.15,19.61,55287400,1.34
2001-11-13,19.08,19.39,18.71,19.37,56168000,1.32
2001-11-12,18.66,19.17,17.96,18.75,50374800,1.28
2001-11-09,18.60,19.25,18.55,18.71,33573400,1.28
2001-11-08,19.63,19.89,18.57,18.71,85535800,1.28
2001-11-07,19.46,20.13,19.33,19.59,95747400,1.34
2001-11-06,18.96,19.62,18.53,19.57,79004800,1.34
2001-11-05,18.84,19.25,18.61,19.07,58948400,1.30
2001-11-02,18.52,18.86,18.16,18.57,49301000,1.27
2001-11-01,17.65,18.78,17.25,18.59,78248800,1.27
2001-10-31,17.73,18.40,17.44,17.56,68437600,1.20
2001-10-30,17.38,18.00,17.06,17.60,69190800,1.20
2001-10-29,18.57,18.67,17.60,17.63,59795400,1.20
2001-10-26,18.86,19.25,18.62,18.67,69741000,1.27
2001-10-25,18.44,19.25,18.16,19.19,63737800,1.31
2001-10-24,18.06,19.09,17.75,18.95,93606800,1.29
2001-10-23,19.12,19.42,17.87,18.14,171245200,1.24
2001-10-22,18.21,19.07,18.09,19.02,97984600,1.30
2001-10-19,17.94,18.40,17.88,18.30,41697600,1.25
2001-10-18,17.29,18.23,17.29,18.00,153143200,1.23
2001-10-17,18.34,18.41,16.96,16.99,71384600,1.16
2001-10-16,18.09,18.20,17.77,18.01,50737400,1.23
2001-10-15,17.95,18.38,17.95,17.99,79688000,1.23
2001-10-12,17.31,18.08,16.86,18.01,71953000,1.23
2001-10-11,16.92,17.74,16.85,17.74,83540800,1.21
2001-10-10,16.10,16.85,15.95,16.82,76939800,1.15
2001-10-09,16.05,16.20,15.63,16.00,43506400,1.09
2001-10-08,15.57,16.35,15.50,16.20,51996000,1.11
2001-10-05,15.40,16.15,14.99,16.14,85671600,1.10
2001-10-04,15.35,16.25,14.99,15.88,100280600,1.08
2001-10-03,14.95,15.36,14.83,14.98,170760800,1.02
2001-10-02,15.43,15.83,14.88,15.05,58970800,1.03
2001-10-01,15.49,15.99,15.23,15.54,52052000,1.06
2001-09-28,15.71,15.91,15.39,15.51,91277200,1.06
2001-09-27,15.25,15.75,15.20,15.51,80560200,1.06
2001-09-26,15.81,15.89,14.93,15.15,123449200,1.03
2001-09-25,16.14,16.22,15.35,15.54,93601200,1.06
2001-09-24,16.11,16.84,15.95,16.45,73634400,1.12
2001-09-21,14.80,16.25,14.68,15.73,142629200,1.07
2001-09-20,16.29,16.95,15.50,15.68,102793600,1.07
2001-09-19,16.50,17.10,15.60,17.02,93329600,1.16
2001-09-18,16.90,17.72,16.17,16.28,81775400,1.11
2001-09-17,16.00,17.07,15.73,16.99,114501800,1.16
2001-09-10,17.00,17.50,16.92,17.37,77211400,1.19
2001-09-07,17.50,18.10,17.20,17.28,60457600,1.18
2001-09-06,18.40,18.93,17.65,17.72,70592200,1.21
2001-09-05,18.24,18.95,18.12,18.55,90014400,1.27
2001-09-04,18.50,19.08,18.18,18.25,87053400,1.25
2001-08-31,17.73,18.60,17.65,18.55,54226200,1.27
2001-08-30,17.74,18.18,17.28,17.83,92173200,1.22
2001-08-29,18.44,18.83,17.83,17.83,59992800,1.22
2001-08-28,18.90,19.14,18.40,18.40,42933800,1.26
2001-08-27,18.60,19.30,18.16,18.92,43911000,1.29
2001-08-24,18.00,18.62,17.65,18.57,72583000,1.27
2001-08-23,18.20,18.34,17.58,17.81,54269600,1.22
2001-08-22,17.94,18.25,17.61,18.21,43493800,1.24
2001-08-21,18.14,18.14,17.70,17.92,46425400,1.22
2001-08-20,18.14,18.23,17.81,18.12,63075600,1.24
2001-08-17,18.00,18.45,17.99,18.07,52106600,1.23
2001-08-16,18.27,18.75,17.97,18.65,72023000,1.27
2001-08-15,18.76,18.94,18.20,18.44,72319800,1.26
2001-08-14,19.20,19.36,18.67,18.73,57237600,1.28
2001-08-13,19.10,19.33,18.76,19.09,36999200,1.30
2001-08-10,19.04,19.32,18.59,19.02,46740400,1.30
2001-08-09,18.96,19.15,18.72,19.05,50166200,1.30
2001-08-08,19.26,19.70,18.54,18.90,69042400,1.29
2001-08-07,19.33,19.67,18.98,19.25,42137200,1.31
2001-08-06,19.04,19.66,19.00,19.13,24913000,1.31
2001-08-03,19.89,19.90,19.00,19.50,46513600,1.33
2001-08-02,19.65,19.87,19.26,19.82,63022400,1.35
2001-08-01,19.01,19.78,18.95,19.06,76034000,1.30
2001-07-31,19.27,19.42,18.51,18.79,58756600,1.28
2001-07-30,19.12,19.36,18.51,18.93,60839800,1.29
2001-07-27,18.75,19.25,18.50,18.96,83533800,1.29
2001-07-26,18.48,18.80,17.85,18.59,92285200,1.27
2001-07-25,19.12,19.30,17.97,18.47,110969600,1.26
2001-07-24,19.39,19.92,18.73,19.09,87094000,1.30
2001-07-23,20.09,20.50,19.51,19.54,60340000,1.33
2001-07-20,19.70,20.06,19.49,19.98,111146000,1.36
2001-07-19,21.23,21.42,19.75,19.96,215285000,1.36
2001-07-18,21.78,22.78,20.42,20.79,284253200,1.42
2001-07-17,23.98,25.22,23.01,25.10,161957600,1.71
2001-07-16,24.88,25.10,23.91,23.96,69666800,1.64
2001-07-13,24.13,25.01,23.84,24.85,113685600,1.70
2001-07-12,23.30,24.81,23.30,24.36,153700400,1.66
2001-07-11,21.03,22.55,21.00,22.54,117626600,1.54
2001-07-10,22.95,23.07,20.84,21.14,98817600,1.44
2001-07-09,22.09,23.00,21.68,22.70,84366800,1.55
2001-07-06,22.76,22.96,21.72,22.03,75730200,1.50
2001-07-05,23.60,23.77,23.01,23.19,38073000,1.58
2001-07-03,23.51,24.18,23.50,23.84,28135800,1.63
2001-07-02,23.64,24.23,23.14,23.90,57512000,1.63
2001-06-29,23.66,25.10,23.20,23.25,128847600,1.59
2001-06-28,23.05,23.91,22.94,23.54,87102400,1.61
2001-06-27,23.83,24.00,22.50,23.34,93532600,1.59
2001-06-26,23.34,23.77,23.01,23.75,68195400,1.62
2001-06-25,22.50,24.00,22.45,23.99,109887400,1.64
2001-06-22,22.48,23.00,21.76,22.26,71506400,1.52
2001-06-21,21.55,23.00,21.10,22.49,85332800,1.54
2001-06-20,20.00,21.85,19.98,21.67,107905000,1.48
2001-06-19,20.85,21.40,20.01,20.19,80271800,1.38
2001-06-18,20.41,20.85,20.00,20.33,86478000,1.39
2001-06-15,20.10,20.75,19.35,20.44,113656200,1.40
2001-06-14,20.04,20.45,19.77,19.88,74337200,1.36
2001-06-13,21.42,21.73,20.06,20.47,127871800,1.40
2001-06-12,19.77,20.69,19.76,20.31,75948600,1.39
2001-06-11,21.05,21.07,19.95,20.04,73500000,1.37
2001-06-08,21.65,21.65,20.71,21.32,85656200,1.46
2001-06-07,20.71,21.70,20.45,21.66,81295200,1.48
2001-06-06,20.93,20.93,20.33,20.73,55794200,1.42
2001-06-05,20.80,21.10,20.35,20.94,117948600,1.43
2001-06-04,21.08,21.11,20.46,20.66,70480200,1.41
2001-06-01,20.13,21.09,19.98,20.89,114018800,1.43
2001-05-31,19.80,20.24,19.49,19.95,110723200,1.36
2001-05-30,20.76,20.76,19.30,19.78,194269600,1.35
2001-05-29,22.32,22.50,20.81,21.47,128997400,1.47
2001-05-25,23.20,23.29,22.50,22.76,39685800,1.55
2001-05-24,23.29,23.30,22.62,23.20,67939200,1.58
2001-05-23,23.75,23.75,22.86,23.23,70260400,1.59
2001-05-22,24.00,24.13,23.40,23.50,103229000,1.60
2001-05-21,23.63,23.91,23.05,23.56,115249400,1.61
2001-05-18,23.36,23.64,23.12,23.53,39762800,1.61
2001-05-17,24.23,24.33,23.25,23.55,83029800,1.61
2001-05-16,23.26,24.50,22.85,24.10,80582600,1.65
2001-05-15,23.37,25.50,23.04,23.18,59256400,1.58
2001-05-14,22.89,23.68,22.75,23.29,77305200,1.59
2001-05-11,23.01,23.49,22.76,22.85,50761200,1.56
2001-05-10,24.21,24.50,22.95,23.00,72244200,1.57
2001-05-09,24.14,24.55,23.67,23.98,81222400,1.64
2001-05-08,25.35,25.45,23.95,24.57,78859200,1.68
2001-05-07,25.62,25.76,24.84,24.96,69137600,1.70
2001-05-04,24.24,25.85,23.96,25.75,70263200,1.76
2001-05-03,25.97,26.25,24.73,24.96,75385800,1.70
2001-05-02,26.34,26.70,25.76,26.59,92131200,1.82
2001-05-01,25.41,26.50,25.20,25.93,106813000,1.77
2001-04-30,26.70,27.12,24.87,25.49,123694200,1.74
2001-04-27,25.20,26.29,24.75,26.20,113253000,1.79
2001-04-26,25.17,26.10,24.68,24.69,199924200,1.69
2001-04-25,24.21,24.86,23.57,24.72,82695200,1.69
2001-04-24,24.33,24.75,23.51,24.03,94284400,1.64
2001-04-23,24.34,25.00,24.00,24.25,135381400,1.66
2001-04-20,24.93,25.63,24.60,25.04,173350800,1.71
2001-04-19,25.55,25.75,23.60,25.72,468417600,1.76
2001-04-18,21.57,24.08,21.08,22.79,275210600,1.56
2001-04-17,21.20,21.21,19.60,20.40,171299800,1.39
2001-04-16,22.09,22.40,20.86,21.44,71306200,1.46
2001-04-12,21.42,23.02,21.15,22.42,74733400,1.53
2001-04-11,22.98,23.00,21.28,21.80,83524000,1.49
2001-04-10,20.90,22.70,20.78,22.04,114343600,1.50
2001-04-09,20.69,21.34,20.06,20.54,66645600,1.40
2001-04-06,20.80,21.04,19.90,20.59,81222400,1.41
2001-04-05,20.60,22.50,20.00,20.87,111690600,1.42
2001-04-04,19.76,20.25,18.75,19.50,171371200,1.33
2001-04-03,21.36,21.40,20.13,20.24,92171800,1.38
2001-04-02,22.09,22.66,21.40,21.59,85227800,1.47
2001-03-30,22.55,22.72,21.34,22.07,100087400,1.51
2001-03-29,21.77,23.45,21.50,22.53,153266400,1.54
2001-03-28,22.08,22.50,21.50,22.17,146165600,1.51
2001-03-27,21.94,23.05,21.90,22.87,135955400,1.56
2001-03-26,23.13,23.75,21.13,21.78,183612800,1.49
2001-03-23,22.06,23.56,22.00,23.00,236222000,1.57
2001-03-22,20.38,21.75,20.19,21.62,180825400,1.48
2001-03-21,19.78,20.87,19.38,20.12,92843800,1.37
2001-03-20,20.72,20.94,19.69,19.69,124801600,1.34
2001-03-19,19.75,20.62,19.50,20.56,89002200,1.40
2001-03-16,19.00,20.31,18.87,19.63,117579000,1.34
2001-03-15,20.87,21.38,19.69,19.69,132329400,1.34
2001-03-14,18.50,20.50,18.44,20.44,119443800,1.40
2001-03-13,18.87,19.56,18.19,19.56,110832400,1.34
2001-03-12,19.69,19.87,18.12,18.63,97755000,1.27
2001-03-09,20.62,20.69,20.00,20.25,74783800,1.38
2001-03-08,20.69,21.13,20.44,20.81,51214800,1.42
2001-03-07,21.31,21.62,20.75,21.25,104885200,1.45
2001-03-06,20.72,22.06,20.69,21.50,182950600,1.47
2001-03-05,19.38,20.50,19.25,20.38,81043200,1.39
2001-03-02,18.31,20.44,18.25,19.25,101550400,1.31
2001-03-01,17.81,18.75,17.19,18.75,82615400,1.28
2001-02-28,19.38,19.44,18.12,18.25,127058400,1.25
2001-02-27,19.28,19.44,18.69,19.38,87129000,1.32
2001-02-26,19.06,19.69,18.56,19.50,51609600,1.33
2001-02-23,18.63,18.87,18.25,18.81,73466400,1.28
2001-02-22,19.06,19.38,18.00,18.81,107990400,1.28
2001-02-21,18.25,19.94,18.25,18.87,97564600,1.29
2001-02-20,19.19,19.44,18.19,18.31,78723400,1.25
2001-02-16,19.00,19.50,18.75,19.00,65977800,1.30
2001-02-15,19.69,20.56,19.69,20.06,77854000,1.37
2001-02-14,19.19,19.63,18.50,19.50,77280000,1.33
2001-02-13,19.94,20.44,19.00,19.12,59267600,1.31
2001-02-12,19.06,20.00,18.81,19.69,68530000,1.34
2001-02-09,20.50,20.81,18.69,19.12,147520800,1.31
2001-02-08,20.56,21.06,20.19,20.75,151032000,1.42
2001-02-07,20.66,20.87,19.81,20.75,98471800,1.42
2001-02-06,20.16,21.39,20.00,21.13,115677800,1.44
2001-02-05,20.50,20.56,19.75,20.19,71528800,1.38
2001-02-02,21.13,21.94,20.50,20.62,106835400,1.41
2001-02-01,20.69,21.50,20.50,21.13,92423800,1.44
2001-01-31,21.50,22.50,21.44,21.62,182676200,1.48
2001-01-30,21.56,22.00,20.87,21.75,173105800,1.48
2001-01-29,19.56,21.75,19.56,21.69,213882200,1.48
2001-01-26,19.50,19.81,19.06,19.56,120705200,1.34
2001-01-25,20.56,20.56,19.75,19.94,122427200,1.36
2001-01-24,20.62,20.69,19.56,20.50,179272800,1.40
2001-01-23,19.31,20.94,19.06,20.50,219882600,1.40
2001-01-22,19.06,19.63,18.44,19.25,129831800,1.31
2001-01-19,19.44,19.56,18.69,19.50,194166000,1.33
2001-01-18,17.81,18.75,17.63,18.69,306752600,1.28
2001-01-17,17.56,17.56,16.50,16.81,210218400,1.15
2001-01-16,17.44,18.25,17.00,17.12,76529600,1.17
2001-01-12,17.88,18.00,17.06,17.19,105844200,1.17
2001-01-11,16.25,18.50,16.25,18.00,200933600,1.23
2001-01-10,16.69,17.00,16.06,16.56,145195400,1.13
2001-01-09,16.81,17.64,16.56,17.19,147232400,1.17
2001-01-08,16.94,16.98,15.94,16.56,93424800,1.13
2001-01-05,16.94,17.37,16.06,16.37,103089000,1.12
2001-01-04,18.14,18.50,16.81,17.06,184849000,1.16
2001-01-03,14.50,16.69,14.44,16.37,204268400,1.12
2001-01-02,14.88,15.25,14.56,14.88,113078000,1.02
2000-12-29,14.69,15.00,14.50,14.88,157584000,1.02
2000-12-28,14.38,14.94,14.31,14.81,76294400,1.01
2000-12-27,14.34,14.81,14.19,14.81,81366600,1.01
2000-12-26,14.88,15.00,14.25,14.69,54203800,1.00
2000-12-22,14.13,15.00,14.13,15.00,79513000,1.02
2000-12-21,14.25,15.00,13.87,14.06,91711200,0.96
2000-12-20,13.78,14.62,13.62,14.38,141332800,0.98
2000-12-19,14.38,15.25,14.00,14.00,93501800,0.96
2000-12-18,14.56,14.62,13.94,14.25,81452000,0.97
2000-12-15,14.56,14.69,14.00,14.06,128486400,0.96
2000-12-14,15.03,15.25,14.44,14.44,65829400,0.99
2000-12-13,15.56,15.56,14.88,15.00,86221800,1.02
2000-12-12,15.25,16.00,15.00,15.37,96565000,1.05
2000-12-11,15.19,15.37,14.88,15.19,83127800,1.04
2000-12-08,14.81,15.31,14.44,15.06,108906000,1.03
2000-12-07,14.44,14.88,14.00,14.31,102229400,0.98
2000-12-06,14.62,15.00,14.00,14.31,343616000,0.98
2000-12-05,16.94,17.44,16.37,17.00,153494600,1.16
2000-12-04,17.19,17.19,16.44,16.69,92880200,1.14
2000-12-01,17.00,17.50,16.81,17.06,96426400,1.16
2000-11-30,16.69,17.00,16.13,16.50,202399400,1.13
2000-11-29,18.09,18.31,17.25,17.56,123037600,1.20
2000-11-28,18.69,19.00,17.94,18.03,67281200,1.23
2000-11-27,19.87,19.94,18.50,18.69,64698200,1.28
2000-11-24,18.86,19.50,18.81,19.31,40233200,1.32
2000-11-22,18.81,19.12,18.38,18.50,70133000,1.26
2000-11-21,19.19,19.50,18.75,18.81,75488000,1.28
2000-11-20,18.59,19.50,18.25,18.94,102016600,1.29
2000-11-17,19.19,19.25,18.25,18.50,111545000,1.26
2000-11-16,19.50,19.81,18.87,19.00,59843000,1.30
2000-11-15,20.03,20.19,19.25,19.87,70589400,1.36
2000-11-14,19.94,20.50,19.56,20.25,102250400,1.38
2000-11-13,18.75,20.00,18.25,19.38,107954000,1.32
2000-11-10,19.36,19.87,19.06,19.06,105562800,1.30
2000-11-09,19.87,20.50,19.06,20.19,119208600,1.38
2000-11-08,21.38,21.44,19.81,20.06,105522200,1.37
2000-11-07,21.50,21.81,20.81,21.31,75490800,1.46
2000-11-06,22.44,22.62,20.87,21.44,98369600,1.46
2000-11-03,23.00,23.00,21.94,22.25,128955400,1.52
2000-11-02,21.13,22.44,21.06,22.31,147673400,1.52
2000-11-01,19.44,20.87,19.44,20.50,143841600,1.40
2000-10-31,19.75,20.25,19.25,19.56,221470200,1.34
2000-10-30,19.12,19.94,18.75,19.31,159797400,1.32
2000-10-27,18.87,19.19,17.88,18.56,186125800,1.27
2000-10-26,18.81,18.87,17.50,18.50,180462800,1.26
2000-10-25,19.06,19.19,18.44,18.50,165992400,1.26
2000-10-24,20.69,20.87,18.81,18.87,201112800,1.29
2000-10-23,20.27,20.56,19.44,20.38,137823000,1.39
2000-10-20,19.06,20.38,18.94,19.50,197815800,1.33
2000-10-19,19.16,19.81,18.31,18.94,376681200,1.29
2000-10-18,19.44,21.06,18.75,20.12,208566400,1.37
2000-10-17,21.69,21.94,19.69,20.12,150430000,1.37
2000-10-16,22.31,23.25,21.38,21.50,205044000,1.47
2000-10-13,20.25,22.13,20.00,22.06,311938200,1.51
2000-10-12,20.31,20.81,19.50,20.00,297766000,1.37
2000-10-11,20.12,21.00,19.12,19.63,299605600,1.34
2000-10-10,21.62,22.44,20.50,20.87,172775400,1.43
2000-10-09,22.62,22.88,21.13,21.75,149391200,1.48
2000-10-06,22.69,22.94,21.00,22.19,153164200,1.51
2000-10-05,23.50,24.50,22.00,22.06,218251600,1.51
2000-10-04,22.37,23.75,21.88,23.62,366506000,1.61
2000-10-03,24.94,25.00,22.19,22.31,509530000,1.52
2000-10-02,26.69,26.75,23.50,24.25,606197200,1.66
2000-09-29,28.19,29.00,25.38,25.75,1855410200,1.76
2000-09-28,49.31,53.81,48.12,53.50,244896400,3.65
2000-09-27,51.75,52.75,48.25,48.94,100564800,3.34
2000-09-26,53.31,54.75,51.37,51.44,72734200,3.51
2000-09-25,52.75,55.50,52.06,53.50,108887800,3.65
2000-09-22,50.31,52.44,50.00,52.19,181675200,3.56
2000-09-21,58.50,59.63,55.25,56.69,127622600,3.87
2000-09-20,59.41,61.44,58.56,61.05,56847000,4.17
2000-09-19,59.75,60.50,58.56,59.94,67877600,4.09
2000-09-18,55.25,60.75,55.06,60.66,106134000,4.14
2000-09-15,57.75,58.19,54.25,55.23,98628600,3.77
2000-09-14,58.56,59.63,56.81,56.86,106638000,3.88
2000-09-13,56.75,59.50,56.75,58.00,76496000,3.96
2000-09-12,57.34,60.06,57.00,57.75,46999400,3.94
2000-09-11,58.69,60.38,58.13,58.44,46845400,3.99
2000-09-08,61.63,61.63,58.50,58.88,48879600,4.02
2000-09-07,59.12,62.56,58.25,62.00,54366200,4.23
2000-09-06,61.38,62.38,57.75,58.44,88851000,3.99
2000-09-05,62.66,64.13,62.25,62.44,74660600,4.26
2000-09-01,61.31,63.62,61.12,63.44,64218000,4.33
2000-08-31,58.97,61.50,58.94,60.94,104899200,4.16
2000-08-30,59.00,60.00,58.70,59.50,71348200,4.06
2000-08-29,57.88,59.44,57.69,59.19,66757600,4.04
2000-08-28,57.25,59.00,57.06,58.06,89751200,3.96
2000-08-25,56.50,57.50,56.38,56.81,83615000,3.88
2000-08-24,54.67,56.62,53.38,56.11,77691600,3.83
2000-08-23,51.47,54.75,51.06,54.31,59215800,3.71
2000-08-22,50.62,52.81,50.37,51.69,69200600,3.53
2000-08-21,50.25,51.56,49.62,50.50,33616800,3.45
2000-08-18,51.37,51.81,49.88,50.00,47544000,3.41
2000-08-17,48.38,52.44,48.31,51.44,67725000,3.51
2000-08-16,46.87,49.00,46.81,48.50,35918400,3.31
2000-08-15,47.25,47.94,46.50,46.69,28550200,3.19
2000-08-14,47.59,47.69,46.31,47.06,39165000,3.21
2000-08-11,46.84,48.00,45.56,47.69,59514000,3.26
2000-08-10,48.00,48.44,47.38,47.56,62928600,3.25
2000-08-09,48.12,48.44,47.25,47.50,94910200,3.24
2000-08-08,47.94,48.00,46.31,46.75,44168600,3.19
2000-08-07,47.87,49.06,47.19,47.94,46837000,3.27
2000-08-04,49.47,51.25,46.31,47.38,65780400,3.23
2000-08-03,45.56,48.06,44.25,48.00,84974400,3.28
2000-08-02,49.00,49.94,47.19,47.25,40588800,3.23
2000-08-01,50.31,51.16,49.25,49.31,34321000,3.37
2000-07-31,49.16,51.62,48.75,50.81,38824800,3.47
2000-07-28,52.28,52.50,46.87,48.31,59473400,3.30
2000-07-27,50.00,53.25,49.88,52.00,73746400,3.55
2000-07-26,49.84,51.25,49.25,50.06,52617600,3.42
2000-07-25,50.31,50.62,49.06,50.06,52901800,3.42
2000-07-24,52.56,52.88,47.50,48.69,103042800,3.32
2000-07-21,54.36,55.62,52.94,53.56,49058800,3.66
2000-07-20,55.00,57.06,54.12,55.12,116393200,3.76
2000-07-19,55.19,56.81,51.75,52.69,114468200,3.60
2000-07-18,58.50,58.88,56.88,57.25,79601200,3.91
2000-07-17,58.25,58.81,57.13,58.31,65000600,3.98
2000-07-14,57.13,59.00,56.88,57.69,47569200,3.94
2000-07-13,58.50,60.63,54.75,56.50,111414800,3.86
2000-07-12,58.13,58.94,56.38,58.88,56358400,4.02
2000-07-11,57.00,59.25,55.44,56.94,89474000,3.89
2000-07-10,54.09,58.25,53.75,57.13,99449000,3.90
2000-07-07,52.59,54.81,52.12,54.44,65900800,3.72
2000-07-06,52.50,52.94,49.62,51.81,77386400,3.54
2000-07-05,53.25,55.19,50.75,51.62,66304000,3.52
2000-07-03,52.12,54.31,52.12,53.31,17707200,3.64
2000-06-30,52.81,54.94,51.69,52.37,80774400,3.58
2000-06-29,53.06,53.94,51.06,51.25,50915200,3.50
2000-06-28,53.31,55.38,51.50,54.44,71607200,3.72
2000-06-27,53.78,55.50,51.62,51.75,50867600,3.53
2000-06-26,52.50,54.75,52.12,54.12,46338600,3.70
2000-06-23,53.78,54.63,50.81,51.69,51241400,3.53
2000-06-22,55.75,57.62,53.56,53.75,116928000,3.67
2000-06-21,50.50,56.94,50.31,55.62,122500000,3.80
2000-06-20,98.50,103.94,98.37,101.25,125347600,3.46
2000-06-19,90.56,97.88,89.81,96.62,98501200,3.30
2000-06-16,93.50,93.75,89.06,91.19,75891200,3.11
2000-06-15,91.25,93.37,89.00,92.38,62143200,3.15
2000-06-14,94.69,96.25,90.12,90.44,69361600,3.09
2000-06-13,91.19,94.69,88.19,94.50,87864000,3.23
2000-06-12,96.37,96.44,90.88,91.19,72584400,3.11
2000-06-09,96.75,97.94,94.38,95.75,63089600,3.27
2000-06-08,97.63,98.50,93.12,94.81,59631600,3.24
2000-06-07,93.62,97.00,91.62,96.56,84254800,3.30
2000-06-06,91.97,96.75,90.31,92.87,131370400,3.17
2000-06-05,93.31,95.25,89.69,91.31,80917200,3.12
2000-06-02,93.75,99.75,89.00,92.56,198212000,3.16
2000-06-01,81.75,89.56,80.38,89.13,225960000,3.04
2000-05-31,86.88,91.25,83.81,84.00,108376800,2.87
2000-05-30,87.62,88.12,81.75,87.56,178264800,2.99
2000-05-26,88.00,89.87,85.25,86.37,45287200,2.95
2000-05-25,88.50,92.66,86.00,87.27,101687600,2.98
2000-05-24,86.19,89.75,83.00,87.69,169615600,2.99
2000-05-23,90.50,93.37,85.63,85.81,129396400,2.93
2000-05-22,93.75,93.75,86.00,89.94,188876800,3.07
2000-05-19,99.25,99.25,93.37,94.00,185166800,3.21
2000-05-18,103.00,104.94,100.62,100.75,93444400,3.44
2000-05-17,103.62,103.69,100.37,101.38,99523200,3.46
2000-05-16,104.52,109.06,102.75,105.69,110112800,3.61
2000-05-15,108.06,108.06,100.12,101.00,169733200,3.45
2000-05-12,106.00,110.50,104.77,107.62,76728400,3.67
2000-05-11,101.38,104.25,99.00,102.81,124936000,3.51
2000-05-10,104.06,105.00,98.75,99.31,133772800,3.39
2000-05-09,110.31,111.25,104.88,105.44,81785200,3.60
2000-05-08,112.09,113.69,110.00,110.13,46225200,3.76
2000-05-05,110.81,114.75,110.72,113.13,71019200,3.86
2000-05-04,115.13,115.25,110.56,110.69,99878800,3.78
2000-05-03,118.94,121.25,111.63,115.06,122449600,3.93
2000-05-02,123.25,126.25,117.50,117.87,59108000,4.02
2000-05-01,124.87,125.12,121.88,124.31,56548800,4.24
2000-04-28,127.13,127.50,121.31,124.06,62395200,4.24
2000-04-27,117.19,127.00,116.58,126.75,81650800,4.33
2000-04-26,126.62,128.00,120.00,121.31,91728000,4.14
2000-04-25,122.13,128.75,122.06,128.31,97910400,4.38
2000-04-24,115.00,120.50,114.75,120.50,110905200,4.11
2000-04-20,123.69,124.75,117.06,118.88,180530000,4.06
2000-04-19,126.19,130.25,119.75,121.12,130037600,4.13
2000-04-18,123.50,126.88,119.37,126.88,97731200,4.33
2000-04-17,109.50,123.94,109.06,123.88,102390400,4.23
2000-04-14,109.31,118.00,109.00,111.88,166905200,3.82
2000-04-13,111.50,120.00,108.50,113.81,132456800,3.89
2000-04-12,119.00,119.00,104.88,109.25,235284000,3.73
2000-04-11,123.50,124.87,118.06,119.44,135455600,4.08
2000-04-10,131.69,132.75,124.75,125.00,53065600,4.27
2000-04-07,127.25,131.87,125.50,131.75,60608800,4.50
2000-04-06,130.63,134.50,123.25,125.19,64906800,4.27
2000-04-05,126.47,132.88,124.00,130.38,114416400,4.45
2000-04-04,132.63,133.00,116.75,127.31,165082400,4.35
2000-04-03,135.50,139.50,129.44,133.31,82140800,4.55
2000-03-31,127.44,137.25,126.00,135.81,101158400,4.64
2000-03-30,133.56,137.69,125.44,125.75,103600000,4.29
2000-03-29,139.38,139.44,133.83,135.94,59959200,4.64
2000-03-28,137.25,142.00,137.12,139.12,50741600,4.75
2000-03-27,137.63,144.75,136.87,139.56,69795600,4.76
2000-03-24,142.44,143.94,135.50,138.69,111728400,4.73
2000-03-23,142.00,150.38,140.00,141.31,140641200,4.82
2000-03-22,132.78,144.38,131.56,144.19,141999200,4.92
2000-03-21,122.56,136.75,121.62,134.94,131082000,4.61
2000-03-20,123.50,126.25,122.38,123.00,51122400,4.20
2000-03-17,120.13,125.00,119.62,125.00,76260800,4.27
2000-03-16,117.31,122.00,114.50,121.56,94525200,4.15
2000-03-15,115.62,120.25,114.12,116.25,110902400,3.97
2000-03-14,121.22,124.25,114.00,114.25,107144800,3.90
2000-03-13,122.13,126.50,119.50,121.31,75989200,4.14
2000-03-10,121.69,127.94,121.00,125.75,62151600,4.29
2000-03-09,120.87,125.00,118.25,122.25,69179600,4.17
2000-03-08,122.87,123.94,118.56,122.00,67807600,4.16
2000-03-07,126.44,127.44,121.12,122.87,68252800,4.19
2000-03-06,126.00,129.13,125.00,125.69,52640000,4.29
2000-03-03,124.87,128.23,120.00,128.00,80841600,4.37
2000-03-02,127.00,127.94,120.69,122.00,77814800,4.16
2000-03-01,118.56,132.06,118.50,130.31,269250800,4.45
2000-02-29,113.56,117.25,112.56,114.62,92240400,3.91
2000-02-28,110.13,115.00,108.38,113.25,82082000,3.87
2000-02-25,114.81,117.00,110.13,110.37,62286000,3.77
2000-02-24,117.31,119.12,111.75,115.20,94108000,3.93
2000-02-23,113.23,119.00,111.00,116.25,118274800,3.97
2000-02-22,110.13,116.94,106.69,113.81,105574000,3.89
2000-02-18,114.62,115.38,110.87,111.25,58360400,3.80
2000-02-17,115.19,115.50,113.13,114.88,72374400,3.92
2000-02-16,117.75,118.12,112.12,114.12,94561600,3.90
2000-02-15,115.25,119.94,115.19,119.00,121436000,4.06
2000-02-14,109.31,115.87,108.62,115.81,91884800,3.95
2000-02-11,113.63,114.12,108.25,108.75,53062800,3.71
2000-02-10,112.88,113.87,110.00,113.50,75745600,3.87
2000-02-09,114.12,117.13,112.44,112.62,74841200,3.84
2000-02-08,114.00,116.12,111.25,114.88,102160800,3.92
2000-02-07,108.00,114.25,105.94,114.06,110266800,3.89
2000-02-04,103.94,110.00,103.62,108.00,106330000,3.69
2000-02-03,100.31,104.25,100.25,103.31,118798400,3.53
2000-02-02,100.75,102.12,97.00,98.81,116048800,3.37
2000-02-01,104.00,105.00,100.00,100.25,79508800,3.42
2000-01-31,101.00,103.87,94.50,103.75,175420000,3.54
2000-01-28,108.19,110.87,100.62,101.62,105837200,3.47
2000-01-27,108.81,113.00,107.00,110.00,85036000,3.75
2000-01-26,110.00,114.19,109.75,110.19,91789600,3.76
2000-01-25,105.00,113.13,102.38,112.25,124286400,3.83
2000-01-24,108.44,112.75,105.12,106.25,110219200,3.63
2000-01-21,114.25,114.25,110.19,111.31,123981200,3.80
2000-01-20,115.50,121.50,113.50,113.50,457783200,3.87
2000-01-19,105.62,108.75,103.37,106.56,149410800,3.64
2000-01-18,101.00,106.00,100.44,103.94,114794400,3.55
2000-01-14,100.00,102.25,99.38,100.44,97594000,3.43
2000-01-13,94.48,98.75,92.50,96.75,258171200,3.30
2000-01-12,95.00,95.50,86.50,87.19,244017200,2.98
2000-01-11,95.94,99.38,90.50,92.75,110387200,3.17
2000-01-10,102.00,102.25,94.75,97.75,126266000,3.34
2000-01-07,96.50,101.00,95.50,99.50,115183600,3.40
2000-01-06,106.13,107.00,95.00,95.00,191993200,3.24
2000-01-05,103.75,110.56,103.00,104.00,194580400,3.55
2000-01-04,108.25,110.62,101.19,102.50,128094400,3.50
2000-01-03,104.88,112.50,101.69,111.94,133949200,3.82
1999-12-31,100.94,102.88,99.50,102.81,40952800,3.51
1999-12-30,102.19,104.12,99.63,100.31,51786000,3.42
1999-12-29,96.81,102.19,95.50,100.69,71125600,3.44
1999-12-28,99.13,99.63,95.00,98.19,61894000,3.35
1999-12-27,104.38,104.44,99.25,99.31,42098000,3.39
1999-12-23,101.81,104.25,101.06,103.50,57383200,3.53
1999-12-22,102.88,104.56,98.75,99.94,81768400,3.41
1999-12-21,98.19,103.06,97.94,102.50,76899200,3.50
1999-12-20,99.56,99.63,96.62,98.00,70996800,3.35
1999-12-17,100.88,102.00,98.50,100.00,123751600,3.41
1999-12-16,98.00,98.37,94.00,98.31,115956400,3.36
1999-12-15,93.25,97.25,91.06,97.00,155744400,3.31
1999-12-14,98.37,99.75,94.75,94.87,108967600,3.24
1999-12-13,102.39,102.50,98.94,99.00,132490400,3.38
1999-12-10,105.31,109.25,99.00,103.00,159440400,3.52
1999-12-09,111.00,111.00,100.88,105.25,213799600,3.59
1999-12-08,116.25,117.87,109.50,110.06,103087600,3.76
1999-12-07,116.56,118.00,114.00,117.81,111255200,4.02
1999-12-06,114.56,117.31,111.44,116.00,116695600,3.96
1999-12-03,112.19,115.56,111.88,115.00,161980000,3.93
1999-12-02,103.13,110.62,101.75,110.19,141839600,3.76
1999-12-01,101.00,104.50,100.06,103.06,154641200,3.52
1999-11-30,98.12,103.75,97.38,97.88,210795200,3.34
1999-11-29,94.25,99.75,93.25,94.56,116040400,3.23
1999-11-26,94.75,95.50,94.13,95.06,33017600,3.25
1999-11-24,93.00,95.00,91.69,94.69,53776800,3.23
1999-11-23,91.75,95.25,88.50,92.81,135828000,3.17
1999-11-22,91.75,91.75,89.25,90.63,50590400,3.09
1999-11-19,89.50,92.87,88.06,92.44,78128400,3.16
1999-11-18,91.06,91.12,88.44,89.62,91196000,3.06
1999-11-17,90.69,94.75,90.00,90.25,91142800,3.08
1999-11-16,90.00,91.75,88.50,91.19,58464000,3.11
1999-11-15,89.62,92.87,88.50,89.44,64976800,3.05
1999-11-12,91.94,92.00,87.38,90.63,69764800,3.09
1999-11-11,91.59,92.63,89.87,92.25,67468800,3.15
1999-11-10,88.25,93.25,88.12,91.44,144474400,3.12
1999-11-09,94.38,94.50,88.00,89.62,202294400,3.06
1999-11-08,87.75,97.73,86.75,96.37,237731200,3.29
1999-11-05,84.62,88.38,84.00,88.31,104202000,3.01
1999-11-04,82.06,85.38,80.62,83.63,94771600,2.85
1999-11-03,81.63,83.25,81.00,81.50,82115600,2.78
1999-11-02,78.00,81.69,77.31,80.25,99808800,2.74
1999-11-01,80.00,80.69,77.37,77.62,69644400,2.65
1999-10-29,78.81,81.06,78.81,80.13,130762800,2.74
1999-10-28,77.06,79.00,76.06,77.88,126022400,2.66
1999-10-27,74.38,76.63,73.44,76.38,110768000,2.61
1999-10-26,74.94,75.50,73.31,75.06,90358800,2.56
1999-10-25,74.25,76.12,73.75,74.50,81648000,2.54
1999-10-22,77.12,77.25,73.38,73.94,104876800,2.52
1999-10-21,72.56,77.06,72.37,76.12,198363200,2.60
1999-10-20,70.00,75.25,70.00,75.13,270351200,2.56
1999-10-19,71.63,75.00,68.44,68.50,255645600,2.34
1999-10-18,73.87,74.25,71.13,73.25,194101600,2.50
1999-10-15,71.13,75.81,70.19,74.56,293294400,2.55
1999-10-14,69.25,73.31,69.00,73.19,474700800,2.50
1999-10-13,66.62,69.50,63.75,64.03,159182800,2.19
1999-10-12,67.88,69.63,67.00,67.69,140938000,2.31
1999-10-11,66.00,68.25,66.00,66.69,65780400,2.28
1999-10-08,66.19,66.31,63.50,65.56,95701200,2.24
1999-10-07,68.44,68.62,64.87,66.38,151471600,2.27
1999-10-06,69.38,69.63,67.00,67.19,201068000,2.29
1999-10-05,65.62,68.13,64.75,67.94,203551600,2.32
1999-10-04,62.38,64.87,62.38,64.56,114839200,2.20
1999-10-01,62.12,62.44,59.50,61.72,153697600,2.11
1999-09-30,59.56,64.19,59.25,63.31,227021200,2.16
1999-09-29,60.25,61.25,58.00,59.06,164320800,2.02
1999-09-28,61.50,62.00,57.44,59.62,353740800,2.04
1999-09-27,66.38,66.75,61.19,61.31,237048000,2.09
1999-09-24,63.37,67.02,63.00,64.94,294968800,2.22
1999-09-23,71.13,71.25,63.00,63.31,285938800,2.16
1999-09-22,69.75,71.63,69.02,70.31,280792400,2.40
1999-09-21,73.19,73.25,69.00,69.25,839389600,2.36
1999-09-20,77.00,80.13,76.88,79.06,114167200,2.70
1999-09-17,77.31,77.75,76.25,76.94,69319600,2.63
1999-09-16,76.06,78.06,73.87,76.81,110471200,2.62
1999-09-15,78.87,79.12,75.25,75.37,89894000,2.57
1999-09-14,74.72,78.50,74.69,77.81,97073200,2.66
1999-09-13,77.06,77.06,74.81,75.00,63000000,2.56
1999-09-10,76.00,77.69,74.69,77.44,114690800,2.64
1999-09-09,75.50,75.94,73.87,75.56,133520800,2.58
1999-09-08,76.19,77.69,74.50,74.50,190551200,2.54
1999-09-07,73.75,77.94,73.50,76.38,246198400,2.61
1999-09-03,71.94,75.25,70.50,73.50,408816800,2.51
1999-09-02,67.63,71.44,66.87,70.56,223787200,2.41
1999-09-01,67.00,68.81,66.00,68.62,197156400,2.34
1999-08-31,62.59,65.88,62.06,65.25,158636800,2.23
1999-08-30,65.00,65.00,62.00,62.06,84148400,2.12
1999-08-27,62.75,65.00,62.69,64.75,111708800,2.21
1999-08-26,61.13,63.12,61.13,62.12,101122000,2.12
1999-08-25,60.69,61.50,60.12,61.37,73791200,2.10
1999-08-24,60.38,60.75,59.94,60.38,125566000,2.06
1999-08-23,59.38,61.37,59.31,60.75,88891600,2.07
1999-08-20,59.25,59.38,58.19,59.19,81986800,2.02
1999-08-19,59.81,60.50,58.56,58.75,137505200,2.01
1999-08-18,60.06,62.00,59.62,60.12,117143600,2.05
1999-08-17,60.31,60.38,58.94,60.31,80234000,2.06
1999-08-16,59.81,60.69,59.50,60.50,69232800,2.07
1999-08-13,60.63,62.00,59.87,60.06,74608800,2.05
1999-08-12,59.06,61.37,58.62,60.00,166527200,2.05
1999-08-11,56.00,59.75,55.94,59.69,212584400,2.04
1999-08-10,54.00,56.00,53.63,55.38,104056400,1.89
1999-08-09,54.34,55.19,54.25,54.44,58321200,1.86
1999-08-06,54.06,55.31,53.50,54.13,108889200,1.85
1999-08-05,53.50,54.87,52.13,54.75,80634400,1.87
1999-08-04,55.19,55.88,53.25,53.81,92856400,1.84
1999-08-03,56.75,57.44,53.63,55.25,92094800,1.89
1999-08-02,55.63,58.00,55.50,55.75,90610800,1.90
1999-07-30,54.50,56.12,54.50,55.69,95785200,1.90
1999-07-29,53.38,55.25,53.12,53.88,68868800,1.84
1999-07-28,53.88,55.38,53.00,54.37,82227600,1.86
1999-07-27,52.62,53.94,52.50,53.69,98977200,1.83
1999-07-26,52.87,53.00,50.87,50.94,87796800,1.74
1999-07-23,52.81,53.75,52.69,53.31,57262800,1.82
1999-07-22,53.63,53.88,51.12,52.38,101682000,1.79
1999-07-21,54.06,55.44,52.87,54.06,179541600,1.85
1999-07-20,54.56,55.50,52.75,52.87,110518800,1.80
1999-07-19,53.94,55.81,52.31,54.44,140324800,1.86
1999-07-16,53.63,54.50,53.00,53.06,102874800,1.81
1999-07-15,55.88,55.94,51.31,53.25,422951200,1.82
1999-07-14,54.50,56.62,54.50,55.94,156139200,1.91
1999-07-13,53.50,54.19,52.87,53.69,70814800,1.83
1999-07-12,55.50,55.63,54.19,54.50,75978000,1.86
1999-07-09,54.50,55.63,53.00,55.63,152174400,1.90
1999-07-08,51.12,55.06,50.87,54.50,406260400,1.86
1999-07-07,47.37,50.75,47.00,49.88,274789200,1.70
1999-07-06,45.94,47.62,45.81,47.37,113453200,1.62
1999-07-02,45.53,46.88,45.19,46.31,30920400,1.58
1999-07-01,46.31,46.56,45.25,45.31,37304400,1.55
1999-06-30,45.69,46.94,44.94,46.31,85817200,1.58
1999-06-29,42.72,45.56,42.62,45.38,95096400,1.55
1999-06-28,42.44,42.94,42.37,42.56,69423200,1.45
1999-06-25,42.50,42.69,42.06,42.19,73533600,1.44
1999-06-24,43.63,43.63,42.25,42.31,108340400,1.44
1999-06-23,45.06,45.09,43.56,43.69,132874000,1.49
1999-06-22,46.31,46.94,45.38,45.38,37769200,1.55
1999-06-21,47.00,47.25,46.00,46.50,33787600,1.59
1999-06-18,45.38,47.25,45.19,47.13,52015600,1.61
1999-06-17,47.62,48.00,45.75,46.38,56100800,1.58
1999-06-16,46.38,48.06,46.38,47.94,56254800,1.64
1999-06-15,45.19,46.75,45.13,46.06,32597600,1.57
1999-06-14,46.50,46.63,45.13,45.44,39270000,1.55
1999-06-11,48.12,48.50,46.25,46.44,46261600,1.59
1999-06-10,47.87,48.25,47.31,48.12,79262400,1.64
1999-06-09,47.44,48.50,47.44,48.44,88446400,1.65
1999-06-08,48.75,48.81,47.56,47.69,78414000,1.63
1999-06-07,48.12,49.00,47.50,48.94,104571600,1.67
1999-06-04,47.62,48.19,47.25,48.12,92170400,1.64
1999-06-03,46.88,48.00,46.81,47.44,122127600,1.62
1999-06-02,44.50,47.94,44.00,46.56,130264400,1.59
1999-06-01,45.00,45.31,44.37,44.81,115256400,1.53
1999-05-28,43.31,44.31,43.13,44.06,50282400,1.50
1999-05-27,43.19,43.75,42.69,43.50,84190400,1.48
1999-05-26,41.75,44.37,41.25,44.06,109387600,1.50
1999-05-25,41.56,42.44,40.94,41.50,91627200,1.42
1999-05-24,43.63,44.31,41.88,41.94,65231600,1.43
1999-05-21,43.00,44.31,42.56,43.94,115796800,1.50
1999-05-20,45.44,45.75,42.50,42.50,104428800,1.45
1999-05-19,45.50,45.75,43.50,45.19,74569600,1.54
1999-05-18,44.81,46.00,44.37,45.25,104594000,1.54
1999-05-17,43.75,44.69,43.00,44.37,52690400,1.51
1999-05-14,45.13,45.81,44.37,44.37,56658000,1.51
1999-05-13,46.44,46.81,45.50,46.19,73880800,1.58
1999-05-12,44.88,46.50,44.12,46.50,98781200,1.59
1999-05-11,44.88,46.19,43.56,44.75,114648800,1.53
1999-05-10,46.75,46.94,44.62,45.25,98249200,1.54
1999-05-07,44.62,45.87,42.75,45.87,108679200,1.57
1999-05-06,46.56,46.88,44.00,44.50,108287200,1.52
1999-05-05,46.31,47.00,44.62,47.00,144824400,1.60
1999-05-04,48.25,48.63,46.19,46.50,202809600,1.59
1999-05-03,46.06,50.00,45.75,49.56,367609200,1.69
1999-04-30,44.00,47.13,44.00,46.00,368082400,1.57
1999-04-29,43.25,44.37,41.78,43.00,197327200,1.47
1999-04-28,44.62,45.69,43.63,44.06,238747600,1.50
1999-04-27,43.00,45.81,43.00,45.75,526512000,1.56
1999-04-26,39.50,41.25,39.25,40.94,231982800,1.40
1999-04-23,36.25,39.44,36.25,39.19,261710400,1.34
1999-04-22,35.06,36.63,35.06,36.38,185043600,1.24
1999-04-21,34.00,34.38,33.50,34.38,87850000,1.17
1999-04-20,33.87,34.75,33.50,34.06,130964400,1.16
1999-04-19,35.69,36.00,33.50,33.87,230454000,1.16
1999-04-16,35.88,36.06,35.25,35.44,125554800,1.21
1999-04-15,35.37,36.19,34.31,35.75,433619200,1.22
1999-04-14,35.25,37.06,35.00,35.53,170256800,1.21
1999-04-13,36.31,36.81,34.50,34.63,103096000,1.18
1999-04-12,35.00,36.87,34.88,36.25,98954800,1.24
1999-04-09,36.25,37.25,35.94,36.75,67135600,1.25
1999-04-08,36.87,37.06,36.00,36.87,74102000,1.26
1999-04-07,38.06,38.25,36.38,37.12,102953200,1.27
1999-04-06,36.81,38.31,36.81,38.00,157147200,1.30
1999-04-05,36.00,37.88,36.00,37.06,115234000,1.27
1999-04-01,36.06,36.69,35.75,36.06,65514400,1.23
1999-03-31,36.38,37.12,35.88,35.94,105588000,1.23
1999-03-30,35.00,36.38,35.00,35.88,138630800,1.22
1999-03-29,33.50,35.44,33.44,35.37,142217600,1.21
1999-03-26,33.75,33.81,33.00,33.25,63459200,1.14
1999-03-25,34.38,34.88,33.37,33.81,99990800,1.15
1999-03-24,33.25,33.75,32.50,33.69,100038400,1.15
1999-03-23,34.44,34.44,32.75,33.00,103888400,1.13
1999-03-22,34.00,35.19,32.94,35.06,148402800,1.20
1999-03-19,35.94,36.00,32.88,33.50,134125600,1.14
1999-03-18,34.38,35.62,34.25,35.50,56770000,1.21
1999-03-17,35.94,36.06,33.94,34.06,91579600,1.16
1999-03-16,35.00,35.56,34.94,35.50,99957200,1.21
1999-03-15,33.31,35.00,33.25,34.06,88040400,1.16
1999-03-12,32.31,33.50,32.31,33.19,67849600,1.13
1999-03-11,32.25,33.87,32.00,32.19,118414800,1.10
1999-03-10,34.19,34.19,32.44,32.56,136570000,1.11
1999-03-09,34.31,34.38,33.50,34.12,79923200,1.16
1999-03-08,33.25,34.69,33.19,34.38,137667600,1.17
1999-03-05,34.31,34.31,32.38,33.19,117009200,1.13
1999-03-04,34.50,34.50,32.38,33.44,91817600,1.14
1999-03-03,34.75,35.12,33.50,34.19,73337600,1.17
1999-03-02,34.12,35.31,33.75,34.63,170763600,1.18
1999-03-01,34.81,34.81,33.62,33.75,121956800,1.15
1999-02-26,36.50,37.00,34.50,34.81,166812800,1.19
1999-02-25,37.31,37.69,36.50,36.94,66150000,1.26
1999-02-24,38.81,39.00,37.37,37.44,53188800,1.28
1999-02-23,38.56,39.56,37.94,38.44,80544800,1.31
1999-02-22,37.37,38.87,37.25,38.44,74667600,1.31
1999-02-19,36.25,37.69,36.19,37.19,90423200,1.27
1999-02-18,37.56,37.88,35.56,36.00,125042400,1.23
1999-02-17,38.13,38.69,36.94,37.00,74015200,1.26
1999-02-16,38.87,38.87,37.88,38.31,75056800,1.31
1999-02-12,39.12,39.12,37.00,37.69,107226000,1.29
1999-02-11,38.75,39.75,38.56,39.63,141299200,1.35
1999-02-10,36.87,38.69,36.00,38.31,140907200,1.31
1999-02-09,37.94,39.06,37.06,37.19,175288400,1.27
1999-02-08,36.69,37.94,36.25,37.75,117056800,1.29
1999-02-05,38.25,38.38,35.50,36.31,194300400,1.24
1999-02-04,40.19,40.25,37.75,37.88,115945200,1.29
1999-02-03,39.00,40.56,38.75,40.19,84686000,1.37
1999-02-02,40.37,40.75,39.00,39.19,76790000,1.34
1999-02-01,41.69,41.94,40.31,40.94,69728400,1.40
1999-01-29,41.19,41.56,40.00,41.19,60678800,1.41
1999-01-28,40.87,41.25,40.31,40.87,84070000,1.40
1999-01-27,41.00,41.38,39.94,40.13,91238000,1.37
1999-01-26,39.94,40.87,39.63,40.50,140011200,1.38
1999-01-25,39.25,39.56,38.81,39.38,96334000,1.34
1999-01-22,37.69,39.50,37.06,38.75,86441600,1.32
1999-01-21,40.44,40.56,37.50,38.81,150122000,1.32
1999-01-20,41.06,42.00,40.50,40.56,194530000,1.38
1999-01-19,41.94,42.31,40.37,40.87,133722400,1.40
1999-01-15,41.81,42.12,40.00,41.31,251501600,1.41
1999-01-14,45.50,46.00,41.06,41.38,430964800,1.41
1999-01-13,42.88,47.31,42.25,46.50,261954000,1.59
1999-01-12,46.31,46.63,44.12,46.12,205184000,1.57
1999-01-11,45.75,46.06,44.88,45.87,140243600,1.57
1999-01-08,46.56,46.88,44.00,45.00,169708000,1.54
1999-01-07,42.25,45.06,42.12,45.00,357254800,1.54
1999-01-06,44.12,44.12,41.00,41.75,337142400,1.43
1999-01-05,41.94,43.94,41.50,43.31,352528400,1.48
1999-01-04,42.12,42.25,40.00,41.25,238221200,1.41
1998-12-31,40.50,41.38,39.50,40.94,67922400,1.40
1998-12-30,40.13,41.12,40.00,40.06,59340400,1.37
1998-12-29,41.12,41.50,40.25,40.81,96838000,1.39
1998-12-28,39.00,41.12,39.00,40.87,181328000,1.40
1998-12-24,39.88,40.00,39.19,39.25,49996800,1.34
1998-12-23,38.62,40.50,38.38,39.81,308758800,1.36
1998-12-22,36.38,38.13,36.00,38.00,287700000,1.30
1998-12-21,35.37,35.62,34.25,35.06,89362000,1.20
1998-12-18,33.37,35.37,33.25,35.19,197873200,1.20
1998-12-17,32.94,33.75,32.75,33.44,82653200,1.14
1998-12-16,33.75,34.19,32.63,32.81,93587200,1.12
1998-12-15,32.75,33.62,32.75,33.56,66178000,1.15
1998-12-14,32.88,33.31,32.25,32.50,125361600,1.11
1998-12-11,32.25,34.00,32.00,33.75,172499600,1.15
1998-12-10,32.69,32.94,31.87,32.00,97812400,1.09
1998-12-09,32.69,32.88,31.62,32.00,148229200,1.09
1998-12-08,33.94,33.94,32.00,32.06,170027200,1.09
1998-12-07,33.37,33.75,32.75,33.75,141649200,1.15
1998-12-04,34.31,34.44,32.00,32.75,180342400,1.12
1998-12-03,36.31,36.50,33.62,33.69,156511600,1.15
1998-12-02,34.12,36.87,33.50,36.00,240620800,1.23
1998-12-01,32.00,34.81,31.62,34.12,216434400,1.16
1998-11-30,34.56,34.81,31.75,31.94,140372400,1.09
1998-11-27,35.06,35.12,34.75,35.06,38276000,1.20
1998-11-25,35.88,36.06,34.94,35.12,75950000,1.20
1998-11-24,36.13,36.75,35.75,35.94,79937200,1.23
1998-11-23,35.56,36.81,35.19,36.25,144488400,1.24
1998-11-20,36.44,36.75,34.75,35.31,99806000,1.21
1998-11-19,35.50,37.19,35.44,35.75,86632000,1.22
1998-11-18,35.19,36.00,34.88,35.44,82415200,1.21
1998-11-17,35.75,35.81,34.75,34.81,52682000,1.19
1998-11-16,35.94,36.75,35.44,36.00,96132400,1.23
1998-11-13,34.94,36.06,34.69,35.69,197954400,1.22
1998-11-12,33.13,34.44,32.88,34.00,148775200,1.16
1998-11-11,35.75,35.81,32.75,33.56,237126400,1.15
1998-11-10,36.19,36.25,35.00,35.12,220995600,1.20
1998-11-09,37.69,38.13,35.50,36.63,165197200,1.25
1998-11-06,37.88,38.25,37.25,38.06,199334800,1.30
1998-11-05,38.38,39.38,38.06,38.19,151779600,1.30
1998-11-04,38.56,39.12,38.13,38.69,156970800,1.32
1998-11-03,37.37,38.25,37.31,37.81,92612800,1.29
1998-11-02,37.50,37.75,37.25,37.62,63442400,1.28
1998-10-30,36.81,37.50,36.25,37.12,79410800,1.27
1998-10-29,36.44,37.44,35.81,36.44,86144800,1.24
1998-10-28,35.25,37.00,35.12,36.81,90927200,1.26
1998-10-27,38.00,38.94,35.06,35.25,134548400,1.20
1998-10-26,36.06,37.75,35.50,37.44,118960800,1.28
1998-10-23,36.75,36.87,35.12,35.50,88995200,1.21
1998-10-22,36.87,37.62,36.25,36.75,79343600,1.25
1998-10-21,36.75,37.44,35.75,37.12,107654400,1.27
1998-10-20,37.94,38.19,36.00,36.06,95522000,1.23
1998-10-19,36.69,38.06,35.88,37.50,118944000,1.28
1998-10-16,37.12,38.06,36.50,36.69,153890800,1.25
1998-10-15,36.25,37.25,35.50,36.63,210168000,1.25
1998-10-14,39.75,41.31,36.81,37.37,570004400,1.28
1998-10-13,38.06,39.19,36.00,38.75,235407200,1.32
1998-10-12,37.50,38.44,36.56,37.44,155724800,1.28
1998-10-09,31.75,35.25,30.75,35.12,167059200,1.20
1998-10-08,31.00,31.19,28.50,30.81,172303600,1.05
1998-10-07,32.38,33.31,31.87,31.94,118339200,1.09
1998-10-06,33.69,34.31,32.50,32.56,99965600,1.11
1998-10-05,34.00,34.56,31.50,32.19,137970000,1.10
1998-10-02,35.50,36.25,34.12,35.06,118893600,1.20
1998-10-01,36.75,38.00,35.37,35.69,92554000,1.22
1998-09-30,38.75,39.25,38.00,38.13,41795600,1.30
1998-09-29,39.06,40.00,38.13,39.50,76283200,1.35
1998-09-28,39.75,40.19,38.00,39.06,101354400,1.33
1998-09-25,38.19,39.19,37.62,38.75,57072400,1.32
1998-09-24,37.88,39.56,37.75,38.50,120710800,1.31
1998-09-23,37.25,38.38,36.56,38.31,71979600,1.31
1998-09-22,37.12,37.62,36.38,37.00,64484000,1.26
1998-09-21,35.69,36.94,35.31,36.94,73967600,1.26
1998-09-18,36.06,36.75,35.56,36.75,76269200,1.25
1998-09-17,36.06,37.12,35.88,36.00,67323200,1.23
1998-09-16,38.62,38.75,37.00,37.31,64719200,1.27
1998-09-15,36.75,38.56,36.50,38.19,108413200,1.30
1998-09-14,38.25,38.81,37.12,37.19,61768000,1.27
1998-09-11,38.50,39.63,36.87,37.62,88071200,1.28
1998-09-10,36.25,38.25,35.75,38.13,131720400,1.30
1998-09-09,38.06,38.13,37.00,37.37,88673200,1.28
1998-09-08,38.00,38.25,36.75,38.25,100699200,1.31
1998-09-04,35.50,36.44,33.75,35.12,94318000,1.20
1998-09-03,35.00,35.12,34.00,34.63,102438000,1.18
1998-09-02,35.50,37.37,35.25,35.56,210750400,1.21
1998-09-01,31.38,35.37,30.62,34.12,217268800,1.16
1998-08-31,34.75,34.88,31.00,31.19,217056000,1.06
1998-08-28,37.12,38.50,34.12,34.19,233063600,1.17
1998-08-27,39.25,39.25,35.62,37.50,278560800,1.28
1998-08-26,39.88,41.12,39.50,40.37,101620400,1.38
1998-08-25,42.37,42.37,40.31,40.81,123891600,1.39
1998-08-24,43.44,43.50,40.13,41.19,152544000,1.41
1998-08-21,40.00,43.56,39.00,43.00,203344400,1.47
1998-08-20,41.00,41.12,40.25,40.62,97980400,1.39
1998-08-19,43.50,43.75,41.00,41.00,121497600,1.40
1998-08-18,42.44,43.38,42.25,42.56,151488400,1.45
1998-08-17,41.00,42.81,39.88,41.94,232719200,1.43
1998-08-14,40.69,40.75,39.50,40.50,112694400,1.38
1998-08-13,39.94,40.75,39.38,39.44,97694800,1.35
1998-08-12,39.75,40.94,39.48,40.06,172443600,1.37
1998-08-11,37.75,41.00,37.37,39.00,439868800,1.33
1998-08-10,36.31,38.06,36.25,37.94,122150000,1.30
1998-08-07,37.19,37.37,36.00,36.50,74505200,1.25
1998-08-06,35.06,36.87,34.88,36.87,109653600,1.26
1998-08-05,33.75,36.00,33.50,36.00,113520400,1.23
1998-08-04,35.50,36.00,34.00,34.19,73480400,1.17
1998-08-03,34.25,35.56,33.25,35.12,75440400,1.20
1998-07-31,36.63,36.75,34.50,34.63,45777200,1.18
1998-07-30,35.81,36.75,35.50,36.50,90574400,1.25
1998-07-29,33.75,35.88,33.69,35.12,111930000,1.20
1998-07-28,34.06,34.63,33.00,33.62,56344400,1.15
1998-07-27,34.25,34.88,33.25,34.44,53558400,1.18
1998-07-24,35.37,35.50,33.81,34.69,67821600,1.18
1998-07-23,34.81,35.62,34.75,34.94,63282800,1.19
1998-07-22,34.94,35.62,34.25,35.00,70182000,1.19
1998-07-21,36.13,37.00,35.56,35.62,82376000,1.22
1998-07-20,36.56,36.63,35.50,36.25,95972800,1.24
1998-07-17,37.25,37.25,36.19,36.87,157388000,1.26
1998-07-16,37.88,38.13,35.75,37.50,640337600,1.28
1998-07-15,33.69,34.69,33.50,34.44,148741600,1.18
1998-07-14,33.94,34.00,33.13,33.44,137132800,1.14
1998-07-13,31.94,34.12,31.87,33.94,178847200,1.16
1998-07-10,32.19,32.63,31.75,32.06,75630800,1.09
1998-07-09,32.94,33.62,31.44,31.69,141652000,1.08
1998-07-08,30.75,32.94,30.69,32.56,233203600,1.11
1998-07-07,30.37,30.88,30.00,30.50,60368000,1.04
1998-07-06,29.50,30.37,29.13,30.37,67737600,1.04
1998-07-02,29.69,30.06,29.00,29.00,74527600,0.99
1998-07-01,28.88,30.00,28.50,29.94,78528800,1.02
1998-06-30,28.62,28.81,28.12,28.69,32765600,0.98
1998-06-29,28.25,28.81,28.06,28.69,41546400,0.98
1998-06-26,28.50,28.62,27.75,28.19,27778800,0.96
1998-06-25,28.56,28.81,28.31,28.56,47952800,0.98
1998-06-24,27.75,28.62,27.31,28.25,68448800,0.96
1998-06-23,27.44,28.12,27.25,27.81,57764000,0.95
1998-06-22,27.00,27.56,26.75,27.38,33642000,0.93
1998-06-19,27.38,27.44,26.75,27.06,34389600,0.92
1998-06-18,27.75,28.06,27.19,27.31,29999200,0.93
1998-06-17,28.00,28.56,27.94,28.12,46793600,0.96
1998-06-16,27.69,28.12,27.31,28.00,32421200,0.96
1998-06-15,27.25,28.25,27.25,27.50,34165600,0.94
1998-06-12,27.63,28.25,27.38,28.12,55963600,0.96
1998-06-11,28.19,28.62,27.81,27.81,45029600,0.95
1998-06-10,28.00,29.00,27.63,28.06,57307600,0.96
1998-06-09,27.38,28.50,27.38,28.25,68936000,0.96
1998-06-08,27.00,27.69,26.81,27.25,31656800,0.93
1998-06-05,26.87,27.25,26.37,26.87,30830800,0.92
1998-06-04,26.62,26.87,25.81,26.81,39034800,0.92
1998-06-03,27.12,27.25,26.19,26.31,36285200,0.90
1998-06-02,26.44,27.31,26.00,26.87,44825200,0.92
1998-06-01,26.50,27.63,25.63,26.25,79923200,0.90
1998-05-29,27.50,27.56,26.44,26.62,54180000,0.91
1998-05-28,26.75,27.88,26.75,27.44,74622800,0.94
1998-05-27,25.69,26.81,25.63,26.75,92548400,0.91
1998-05-26,28.06,28.25,26.62,26.69,77943600,0.91
1998-05-22,28.75,28.75,27.31,27.88,66648400,0.95
1998-05-21,29.56,29.69,28.62,28.88,32748800,0.99
1998-05-20,29.63,29.87,28.75,29.56,47544000,1.01
1998-05-19,28.94,29.44,28.81,29.38,54566400,1.00
1998-05-18,29.38,29.56,28.37,28.50,58097200,0.97
1998-05-15,30.06,30.37,29.25,29.56,68146400,1.01
1998-05-14,30.37,30.44,29.75,30.06,40670000,1.03
1998-05-13,30.06,30.81,29.63,30.44,78604400,1.04
1998-05-12,30.56,30.75,29.94,30.12,64453200,1.03
1998-05-11,30.88,31.62,30.75,30.94,166255600,1.06
1998-05-08,30.06,30.50,29.94,30.44,67704000,1.04
1998-05-07,30.56,30.62,29.87,30.19,138224800,1.03
1998-05-06,29.87,30.44,29.25,30.31,224252000,1.03
1998-05-05,29.25,29.87,29.13,29.69,104820800,1.01
1998-05-04,28.88,29.50,28.88,29.06,142786000,0.99
1998-05-01,27.50,28.25,26.87,28.00,46018000,0.96
1998-04-30,27.38,27.63,27.06,27.38,44987600,0.93
1998-04-29,26.94,27.44,26.75,27.00,47384400,0.92
1998-04-28,27.88,28.00,26.25,26.94,59292800,0.92
1998-04-27,26.75,27.75,26.75,27.75,102449200,0.95
1998-04-24,27.75,28.25,27.50,27.94,53886000,0.95
1998-04-23,27.44,29.00,27.19,27.69,118823600,0.95
1998-04-22,28.75,29.00,27.50,27.50,71237600,0.94
1998-04-21,29.06,29.13,28.50,29.00,87007200,0.99
1998-04-20,27.63,29.50,27.56,29.00,129444000,0.99
1998-04-17,28.56,28.62,27.69,27.94,148041600,0.95
1998-04-16,29.25,29.63,28.19,28.62,459488400,0.98
1998-04-15,27.19,27.50,26.62,27.44,139378400,0.94
1998-04-14,26.37,27.25,26.37,26.94,81961600,0.92
1998-04-13,25.63,26.69,25.00,26.44,72074800,0.90
1998-04-09,25.06,25.88,25.00,25.63,42576800,0.87
1998-04-08,25.25,25.38,24.69,25.00,56299600,0.85
1998-04-07,25.81,26.00,24.87,25.50,73175200,0.87
1998-04-06,27.00,27.00,26.19,26.25,86898000,0.90
1998-04-03,27.12,27.25,26.81,27.06,50766800,0.92
1998-04-02,27.31,27.44,26.94,27.31,48577200,0.93
1998-04-01,27.44,27.81,27.06,27.50,46720800,0.94
1998-03-31,27.44,27.81,27.25,27.50,66724000,0.94
1998-03-30,26.75,27.50,26.75,27.44,62675200,0.94
1998-03-27,26.62,27.31,26.37,26.94,63898800,0.92
1998-03-26,26.75,27.00,26.44,26.56,50741600,0.91
1998-03-25,27.63,27.75,26.37,27.16,96843600,0.93
1998-03-24,26.37,28.00,26.25,28.00,168982800,0.96
1998-03-23,25.94,26.25,24.62,26.13,103684000,0.89
1998-03-20,26.69,26.87,26.00,26.37,53869200,0.90
1998-03-19,26.87,26.94,26.56,26.75,40014800,0.91
1998-03-18,26.00,26.94,26.00,26.94,69249600,0.92
1998-03-17,26.50,26.69,25.88,26.34,102564000,0.90
1998-03-16,27.12,27.25,26.19,26.69,100590000,0.91
1998-03-13,27.25,27.25,26.25,27.12,141540000,0.93
1998-03-12,26.13,27.00,25.56,27.00,186090800,0.92
1998-03-11,25.12,26.19,24.56,26.13,303584400,0.89
1998-03-10,23.00,24.50,22.94,24.06,178225600,0.82
1998-03-09,23.75,24.31,22.50,22.75,143732400,0.78
1998-03-06,23.88,24.50,23.37,24.44,166616800,0.83
1998-03-05,23.25,24.25,23.12,24.06,168781200,0.82
1998-03-04,22.87,24.75,22.87,24.44,204456000,0.83
1998-03-03,21.88,23.19,21.62,23.12,83518400,0.79
1998-03-02,23.56,23.56,22.25,22.75,100111200,0.78
1998-02-27,23.31,23.88,22.56,23.62,129900400,0.81
1998-02-26,22.31,23.56,21.88,23.50,148783600,0.80
1998-02-25,21.31,22.75,20.94,22.31,178166800,0.76
1998-02-24,21.31,21.37,20.75,21.31,114147600,0.73
1998-02-23,20.12,21.62,20.00,21.25,119372400,0.73
1998-02-20,20.50,20.56,19.81,20.00,81354000,0.68
1998-02-19,20.88,20.94,20.00,20.44,99915200,0.70
1998-02-18,19.56,20.75,19.56,20.56,123648000,0.70
1998-02-17,19.50,19.75,19.50,19.62,45687600,0.67
1998-02-13,19.19,19.87,19.00,19.50,51998800,0.67
1998-02-12,19.13,19.44,19.06,19.37,50937600,0.66
1998-02-11,19.50,19.50,18.88,19.00,52917200,0.65
1998-02-10,19.13,19.56,19.06,19.44,105504000,0.66
1998-02-09,18.38,19.50,18.38,19.19,123667600,0.65
1998-02-06,18.38,18.69,18.25,18.50,50584800,0.63
1998-02-05,18.25,18.50,18.00,18.31,59567200,0.63
1998-02-04,18.06,18.50,18.00,18.25,42548800,0.62
1998-02-03,17.69,18.63,17.69,18.31,100654400,0.63
1998-02-02,18.50,18.50,17.38,17.69,159185600,0.60
1998-01-30,18.31,18.88,18.25,18.31,40611200,0.63
1998-01-29,18.94,19.13,18.50,18.50,52970400,0.63
1998-01-28,19.19,19.37,18.63,19.19,37780400,0.65
1998-01-27,19.19,19.69,19.00,19.13,28058800,0.65
1998-01-26,19.44,19.56,18.81,19.44,36610000,0.66
1998-01-23,19.37,19.69,19.25,19.50,58290400,0.67
1998-01-22,18.69,19.75,18.63,19.25,82432000,0.66
1998-01-21,18.75,19.06,18.56,18.91,47552400,0.65
1998-01-20,19.06,19.31,18.63,19.06,60390400,0.65
1998-01-16,19.44,19.44,18.69,18.81,61588800,0.64
1998-01-15,19.19,19.75,18.63,19.19,139818000,0.65
1998-01-14,19.87,19.94,19.25,19.75,147316400,0.67
1998-01-13,18.63,19.62,18.50,19.50,159213600,0.67
1998-01-12,17.44,18.63,17.13,18.25,129099600,0.62
1998-01-09,18.12,19.37,17.50,18.19,221636800,0.62
1998-01-08,17.44,18.63,16.94,18.19,193505200,0.62
1998-01-07,18.81,19.00,17.31,17.50,260405600,0.60
1998-01-06,15.94,20.00,14.75,18.94,453118400,0.65
1998-01-05,16.50,16.56,15.19,15.87,162968400,0.54
1998-01-02,13.63,16.25,13.50,16.25,179527600,0.55
1997-12-31,13.12,13.63,12.94,13.12,101589600,0.45
1997-12-30,13.00,13.44,12.75,13.19,85626800,0.45
1997-12-29,13.31,13.44,12.87,13.12,69549200,0.45
1997-12-26,13.06,13.38,13.00,13.31,26969600,0.45
1997-12-24,13.00,13.25,13.00,13.12,24458000,0.45
1997-12-23,13.12,13.31,12.94,12.94,114707600,0.44
1997-12-22,13.88,14.00,13.19,13.31,39869200,0.45
1997-12-19,13.56,13.88,13.25,13.69,47653200,0.47
1997-12-18,14.00,14.00,13.75,13.81,50512000,0.47
1997-12-17,14.31,14.56,13.94,13.94,66323600,0.48
1997-12-16,14.00,14.37,14.00,14.31,46407200,0.49
1997-12-15,14.12,14.25,13.75,13.94,41473600,0.48
1997-12-12,14.75,14.88,14.00,14.12,40140800,0.48
1997-12-11,14.44,14.56,13.88,14.56,64234800,0.50
1997-12-10,15.06,15.06,14.50,14.75,48720000,0.50
1997-12-09,15.50,15.69,15.00,15.25,60762800,0.52
1997-12-08,15.56,15.75,15.38,15.56,33395600,0.53
1997-12-05,15.56,16.00,15.56,15.81,55367200,0.54
1997-12-04,16.00,16.00,15.63,15.63,49910000,0.53
1997-12-03,16.06,16.12,15.69,15.75,85764000,0.54
1997-12-02,17.38,17.50,15.87,15.87,99204000,0.54
1997-12-01,17.69,17.94,17.25,17.75,21809200,0.61
1997-11-28,17.62,17.87,17.44,17.75,10329200,0.61
1997-11-26,17.38,17.69,17.25,17.50,15103200,0.60
1997-11-25,17.69,17.87,16.88,17.38,51357600,0.59
1997-11-24,17.56,18.00,17.50,17.62,39337200,0.60
1997-11-21,18.63,18.69,18.00,18.19,24444000,0.62
1997-11-20,18.19,18.63,18.12,18.50,32043200,0.63
1997-11-19,17.87,18.31,17.87,18.25,19896800,0.62
1997-11-18,18.50,18.50,18.06,18.06,36660400,0.62
1997-11-17,18.88,18.94,18.33,18.50,51256800,0.63
1997-11-14,18.25,18.50,18.00,18.44,33759600,0.63
1997-11-13,18.00,18.06,17.50,18.00,64380400,0.61
1997-11-12,18.06,18.50,17.56,17.62,52015600,0.60
1997-11-11,19.00,19.00,18.12,18.38,83120800,0.63
1997-11-10,21.00,21.50,18.50,18.69,349560400,0.64
1997-11-07,18.88,20.00,18.75,19.75,198903600,0.67
1997-11-06,18.88,19.50,18.88,19.00,154271600,0.65
1997-11-05,18.25,18.63,18.06,18.38,96779200,0.63
1997-11-04,17.75,18.12,17.50,17.94,42148400,0.61
1997-11-03,17.56,17.75,17.06,17.38,31502800,0.59
1997-10-31,17.38,17.38,16.62,17.03,66771600,0.58
1997-10-30,17.06,17.56,16.50,16.50,47238800,0.56
1997-10-29,18.44,18.50,17.25,17.50,44396800,0.60
1997-10-28,16.00,18.50,15.87,18.12,85828400,0.62
1997-10-27,16.75,18.12,16.75,16.75,82339600,0.57
1997-10-24,18.12,18.38,16.50,16.56,97059200,0.57
1997-10-23,18.00,18.19,17.75,17.75,46695600,0.61
1997-10-22,19.06,19.25,18.50,18.56,37794400,0.63
1997-10-21,18.88,19.31,18.69,19.06,118818000,0.65
1997-10-20,20.12,20.19,18.63,18.69,102958800,0.64
1997-10-17,21.12,21.12,19.87,20.12,109667600,0.69
1997-10-16,21.12,22.06,20.88,21.50,184797200,0.73
1997-10-15,22.13,24.75,22.13,23.81,202717200,0.81
1997-10-14,22.69,22.75,22.19,22.69,41454000,0.77
1997-10-13,22.75,22.87,22.19,22.69,39656400,0.77
1997-10-10,21.50,22.75,21.50,22.69,67600400,0.77
1997-10-09,21.25,22.50,21.19,21.75,46832800,0.74
1997-10-08,21.75,21.81,21.31,21.50,27210400,0.73
1997-10-07,21.88,22.00,21.81,21.81,27322400,0.74
1997-10-06,22.19,22.25,21.69,21.94,23324000,0.75
1997-10-03,22.00,22.25,21.69,22.13,40558000,0.76
1997-10-02,21.44,22.00,21.37,21.94,33852000,0.75
1997-10-01,21.69,21.75,21.37,21.53,32617200,0.73
1997-09-30,22.00,22.31,21.69,21.69,35142800,0.74
1997-09-29,21.69,22.25,21.56,22.06,41809600,0.75
1997-09-26,21.50,21.94,21.12,21.31,52080000,0.73
1997-09-25,21.31,21.75,21.00,21.12,55846000,0.72
1997-09-24,21.69,21.75,21.37,21.50,55608000,0.73
1997-09-23,22.25,22.25,21.69,21.75,50134000,0.74
1997-09-22,22.13,23.06,22.00,22.81,50092000,0.78
1997-09-19,22.19,22.19,21.75,21.94,23732800,0.75
1997-09-18,21.50,22.50,21.50,22.31,42291200,0.76
1997-09-17,22.00,22.00,21.69,21.81,21691600,0.74
1997-09-16,22.06,22.14,21.75,21.94,33555200,0.75
1997-09-15,21.88,22.13,21.50,21.50,24228400,0.73
1997-09-12,22.19,22.25,21.44,22.06,28420000,0.75
1997-09-11,22.87,23.00,22.06,22.38,52469200,0.76
1997-09-10,21.75,23.12,21.69,22.94,68516000,0.78
1997-09-09,21.31,21.88,21.25,21.81,39757200,0.74
1997-09-08,22.25,22.25,21.44,21.50,43789200,0.73
1997-09-05,22.63,22.87,22.00,22.19,34176800,0.76
1997-09-04,22.56,22.87,22.25,22.50,30634800,0.77
1997-09-03,22.38,23.25,22.31,22.50,71033200,0.77
1997-09-02,22.00,22.56,21.94,22.38,46510800,0.76
1997-08-29,21.81,22.00,21.50,21.75,27417600,0.74
1997-08-28,22.13,22.50,22.00,22.00,23917600,0.75
1997-08-27,22.38,22.75,21.88,22.69,47658800,0.77
1997-08-26,22.63,23.00,22.13,22.25,56551600,0.76
1997-08-25,23.62,23.69,22.94,23.06,34658400,0.79
1997-08-22,23.44,24.00,23.37,23.62,56907200,0.81
1997-08-21,24.50,24.69,23.88,24.00,64820000,0.82
1997-08-20,24.44,25.12,24.19,24.62,81076800,0.84
1997-08-19,23.69,24.50,23.31,24.44,72290400,0.83
1997-08-18,23.31,23.75,22.75,23.62,54460000,0.81
1997-08-15,23.12,23.44,22.81,23.25,65240000,0.79
1997-08-14,23.62,24.25,22.69,23.00,108612000,0.79
1997-08-13,22.25,23.88,20.44,23.62,300356000,0.81
1997-08-12,24.06,24.25,21.88,22.06,262099600,0.75
1997-08-11,26.31,26.44,23.50,24.56,387749600,0.84
1997-08-08,27.81,28.37,26.13,26.81,453541200,0.92
1997-08-07,28.75,29.56,28.37,29.19,938859600,1.00
1997-08-06,25.25,27.75,25.00,26.31,1047620000,0.90
1997-08-05,19.94,20.00,19.48,19.75,61782000,0.67
1997-08-04,19.19,19.81,19.19,19.75,152829600,0.67
1997-08-01,17.62,19.19,17.56,19.19,120478400,0.65
1997-07-31,17.38,17.75,17.25,17.50,65954000,0.60
1997-07-30,16.94,17.69,16.75,17.38,93576000,0.59
1997-07-29,16.44,16.62,16.37,16.50,17810800,0.56
1997-07-28,16.44,16.50,16.25,16.44,27627600,0.56
1997-07-25,15.87,16.56,15.75,16.25,54490800,0.55
1997-07-24,16.12,16.12,15.63,15.81,33373200,0.54
1997-07-23,16.75,16.88,16.00,16.12,35322000,0.55
1997-07-22,16.37,16.69,16.31,16.56,57834000,0.57
1997-07-21,17.56,17.69,16.00,16.16,88729200,0.55
1997-07-18,17.87,17.94,17.06,17.34,79391200,0.59
1997-07-17,17.00,18.12,16.44,17.50,186566800,0.60
1997-07-16,15.81,16.50,15.63,16.44,111563200,0.56
1997-07-15,15.75,16.00,15.63,15.94,104588400,0.54
1997-07-14,15.25,15.63,14.88,15.63,102751600,0.53
1997-07-11,13.38,15.50,13.31,15.19,183736000,0.52
1997-07-10,12.87,13.38,12.75,13.25,123127200,0.45
1997-07-09,13.81,13.88,13.63,13.69,35504000,0.47
1997-07-08,13.88,14.00,13.69,13.75,23923200,0.47
1997-07-07,13.94,14.25,13.75,13.81,47868800,0.47
1997-07-03,13.12,13.88,13.00,13.69,46695600,0.47
1997-07-02,13.25,13.38,13.00,13.06,62490400,0.45
1997-07-01,13.94,14.00,13.12,13.19,112669200,0.45
1997-06-30,14.75,14.75,14.00,14.25,42795200,0.49
1997-06-27,14.69,14.81,14.62,14.69,39488400,0.50
1997-06-26,15.13,15.13,14.62,14.69,95496800,0.50
1997-06-25,15.31,15.38,15.00,15.13,49658000,0.52
1997-06-24,15.44,15.56,15.25,15.31,27787200,0.52
1997-06-23,15.50,15.63,15.38,15.38,24886400,0.52
1997-06-20,15.69,15.75,15.50,15.56,27546400,0.53
1997-06-19,16.00,16.00,15.69,15.75,30256800,0.54
1997-06-18,16.12,16.25,15.75,15.94,27412000,0.54
1997-06-17,15.56,16.50,15.50,16.34,35562800,0.56
1997-06-16,15.87,15.87,15.38,15.50,33502000,0.53
1997-06-13,16.06,16.12,15.75,15.81,33017600,0.54
1997-06-12,16.37,16.37,16.00,16.06,19672800,0.55
1997-06-11,16.31,16.44,16.25,16.31,26350800,0.56
1997-06-10,16.75,16.75,16.06,16.25,34762000,0.55
1997-06-09,16.69,16.94,16.62,16.62,18701200,0.57
1997-06-06,16.62,16.75,16.50,16.75,13218800,0.57
1997-06-05,16.62,17.13,16.56,16.69,16153200,0.57
1997-06-04,16.62,16.75,16.50,16.62,20101200,0.57
1997-06-03,16.75,16.94,16.62,16.69,16310000,0.57
1997-06-02,17.00,17.00,16.75,16.94,10396400,0.58
1997-05-30,16.50,17.00,16.37,16.62,44332400,0.57
1997-05-29,17.13,17.13,16.62,16.62,27795600,0.57
1997-05-28,17.38,17.50,17.00,17.00,21884800,0.58
1997-05-27,16.75,17.38,16.75,17.25,20521200,0.59
1997-05-23,16.62,17.00,16.62,16.88,16758000,0.58
1997-05-22,16.75,16.88,16.50,16.62,19191200,0.57
1997-05-21,17.13,17.13,16.50,16.88,30562000,0.58
1997-05-20,17.00,17.44,16.75,17.25,21207200,0.59
1997-05-19,17.50,17.62,17.00,17.00,13064800,0.58
1997-05-16,17.50,17.62,17.25,17.25,23324000,0.59
1997-05-15,17.75,18.00,17.50,17.75,24752000,0.61
1997-05-14,17.87,18.00,17.50,17.69,33910800,0.60
1997-05-13,17.50,17.87,17.00,17.56,49254800,0.60
1997-05-12,17.25,17.62,17.00,17.56,41244000,0.60
1997-05-09,17.00,17.50,17.00,17.06,47093200,0.58
1997-05-08,16.62,17.13,16.50,17.00,20734000,0.58
1997-05-07,16.88,17.00,16.37,16.50,28554400,0.56
1997-05-06,17.00,17.13,16.75,16.88,20787200,0.58
1997-05-05,17.00,17.13,16.75,17.00,24623200,0.58
1997-05-02,17.00,17.13,16.75,17.00,25496800,0.58
1997-05-01,16.88,17.13,16.75,17.00,18085200,0.58
1997-04-30,17.00,17.25,16.75,17.00,64408400,0.58
1997-04-29,18.00,18.00,17.50,17.69,12938800,0.60
1997-04-28,17.75,17.87,17.50,17.62,11692800,0.60
1997-04-25,17.62,17.87,17.38,17.50,21845600,0.60
1997-04-24,18.50,18.50,17.75,17.87,18734800,0.61
1997-04-23,18.38,18.50,18.12,18.12,13622000,0.62
1997-04-22,18.12,18.50,17.87,18.50,23662800,0.63
1997-04-21,18.63,18.63,18.00,18.00,22288000,0.61
1997-04-18,19.13,19.13,18.38,18.38,35361200,0.63
1997-04-17,18.25,19.13,18.12,19.00,54866000,0.65
1997-04-16,18.63,19.00,18.38,18.56,21554400,0.63
1997-04-15,19.13,19.25,18.12,18.44,34011600,0.63
1997-04-14,18.38,18.88,18.00,18.75,28089600,0.64
1997-04-11,18.88,18.88,18.12,18.25,19891200,0.62
1997-04-10,19.00,19.13,18.50,18.88,29246000,0.64
1997-04-09,19.25,19.25,18.88,19.00,61247200,0.65
1997-04-08,19.62,19.62,18.63,19.13,48456800,0.65
1997-04-07,19.75,19.87,19.25,19.50,63814800,0.67
1997-04-04,19.13,19.62,19.00,19.25,118812400,0.66
1997-04-03,18.50,19.13,18.25,18.88,137214000,0.64
1997-04-02,17.87,18.06,17.62,18.00,55608000,0.61
1997-04-01,17.62,17.81,17.38,17.50,55064800,0.60
1997-03-31,18.63,19.37,17.25,18.25,242561200,0.62
1997-03-27,17.50,19.25,17.25,18.63,284726400,0.64
1997-03-26,16.37,16.88,16.25,16.75,26709200,0.57
1997-03-25,16.62,16.62,16.08,16.50,28140000,0.56
1997-03-24,16.50,16.62,16.25,16.50,17805200,0.56
1997-03-21,17.50,17.50,16.37,16.62,34115200,0.57
1997-03-20,16.00,17.50,15.87,17.25,79259600,0.59
1997-03-19,16.37,16.37,15.87,16.12,52057600,0.55
1997-03-18,16.37,16.50,16.12,16.25,31768800,0.55
1997-03-17,16.25,16.50,16.00,16.50,48188000,0.56
1997-03-14,16.37,16.75,16.25,16.56,57604400,0.57
1997-03-13,16.37,16.37,16.12,16.37,26272400,0.56
1997-03-12,16.25,16.75,16.12,16.25,17749200,0.55
1997-03-11,16.62,16.62,16.00,16.37,24626000,0.56
1997-03-10,16.62,16.75,16.44,16.62,24796800,0.57
1997-03-07,16.75,16.75,16.37,16.50,17654000,0.56
1997-03-06,17.00,17.00,16.50,16.62,29072400,0.57
1997-03-05,16.62,17.00,16.50,17.00,24040800,0.58
1997-03-04,16.25,16.50,16.00,16.50,25799200,0.56
1997-03-03,16.50,16.50,16.00,16.12,32614400,0.55
1997-02-28,16.88,16.88,16.25,16.25,30469600,0.55
1997-02-27,17.00,17.13,16.75,17.00,25748800,0.58
1997-02-26,17.00,17.13,16.75,17.13,25793600,0.58
1997-02-25,17.00,17.38,16.88,16.88,34521200,0.58
1997-02-24,16.25,16.88,16.25,16.62,29397200,0.57
1997-02-21,16.88,17.00,16.00,16.37,52771600,0.56
1997-02-20,17.62,17.62,17.00,17.00,31236800,0.58
1997-02-19,17.87,17.87,17.13,17.62,60323200,0.60
1997-02-18,16.62,17.87,16.25,17.87,92069600,0.61
1997-02-14,16.25,16.37,16.00,16.31,59312400,0.56
1997-02-13,15.75,16.12,15.50,16.12,48958000,0.55
1997-02-12,15.75,15.87,15.50,15.75,44066400,0.54
1997-02-11,15.87,16.00,15.50,15.69,35019600,0.54
1997-02-10,16.12,16.12,15.63,15.63,46351200,0.53
1997-02-07,16.50,16.50,15.75,15.81,58816800,0.54
1997-02-06,15.25,16.12,15.25,16.00,99876000,0.55
1997-02-05,15.25,15.63,15.25,15.25,98621600,0.52
1997-02-04,16.25,16.37,15.13,15.38,178161200,0.52
1997-02-03,16.88,17.00,16.25,16.31,92027600,0.56
1997-01-31,16.62,16.62,16.50,16.62,49907200,0.57
1997-01-30,16.75,16.75,16.50,16.75,34983200,0.57
1997-01-29,16.62,16.75,16.50,16.62,37926000,0.57
1997-01-28,17.00,17.00,16.50,16.62,52640000,0.57
1997-01-27,17.13,17.25,16.62,16.62,53510800,0.57
1997-01-24,17.25,17.25,16.88,16.88,47070800,0.58
1997-01-23,17.25,17.38,17.13,17.25,43086400,0.59
1997-01-22,17.38,17.50,17.00,17.19,51405200,0.59
1997-01-21,17.00,17.25,16.88,17.25,71206800,0.59
1997-01-20,16.88,17.13,16.75,16.94,72906400,0.58
1997-01-17,16.75,17.13,16.62,16.75,81286800,0.57
1997-01-16,17.13,17.13,16.62,16.75,167826400,0.57
1997-01-15,18.00,18.00,17.13,17.25,108273200,0.59
1997-01-14,18.38,18.38,17.75,17.87,63943600,0.61
1997-01-13,18.50,18.50,18.12,18.12,76437200,0.62
1997-01-10,17.62,18.25,17.62,18.25,88429600,0.62
1997-01-09,17.75,17.87,17.50,17.75,111664000,0.61
1997-01-08,18.25,18.38,17.38,17.62,275032800,0.60
1997-01-07,18.12,18.25,17.50,17.50,244232800,0.60
1997-01-06,17.62,18.34,17.25,17.87,470708000,0.61
1997-01-03,21.12,22.25,21.00,21.75,29929200,0.74
1997-01-02,21.12,21.25,20.75,21.00,35778400,0.72
1996-12-31,21.37,21.50,20.75,20.88,95936400,0.71
1996-12-30,23.12,23.25,21.75,21.75,65450000,0.74
1996-12-27,22.87,23.75,22.87,23.12,34249600,0.79
1996-12-26,23.25,23.25,22.87,23.00,21221200,0.79
1996-12-24,23.25,23.37,22.87,23.12,14403200,0.79
1996-12-23,24.00,24.25,23.25,23.25,83076000,0.79
1996-12-20,22.50,23.62,21.37,23.50,136609200,0.80
1996-12-19,23.00,23.25,22.25,22.25,34221600,0.76
1996-12-18,22.75,23.12,22.63,23.12,51268000,0.79
1996-12-17,22.38,22.50,22.25,22.50,39312000,0.77
1996-12-16,23.50,23.50,22.50,22.63,37310000,0.77
1996-12-13,23.75,23.88,23.25,23.25,22274000,0.79
1996-12-12,24.13,24.25,23.88,23.88,21750400,0.82
1996-12-11,23.75,24.25,23.75,24.00,40840800,0.82
1996-12-10,24.87,25.00,24.25,24.50,46071200,0.84
1996-12-09,25.25,25.38,24.81,25.00,39662000,0.85
1996-12-06,24.38,25.38,24.00,25.12,57346800,0.86
1996-12-05,25.00,25.25,25.00,25.00,35534800,0.85
1996-12-04,25.12,25.38,24.87,25.00,47706400,0.85
1996-12-03,25.25,25.50,25.00,25.12,68882800,0.86
1996-12-02,24.13,25.12,23.88,25.12,43744400,0.86
1996-11-29,24.50,24.62,24.00,24.13,10572800,0.82
1996-11-27,24.13,24.62,24.13,24.50,22260000,0.84
1996-11-26,24.87,25.00,24.00,24.25,28246400,0.83
1996-11-25,25.38,25.50,25.00,25.00,19737200,0.85
1996-11-22,24.50,25.25,24.50,25.25,25995200,0.86
1996-11-21,24.87,25.00,24.38,24.50,17651200,0.84
1996-11-20,24.87,25.38,24.87,25.00,25774000,0.85
1996-11-19,24.87,25.12,24.62,24.87,31108000,0.85
1996-11-18,25.00,25.12,24.50,24.75,38208800,0.84
1996-11-15,25.88,26.00,25.00,25.00,32678800,0.85
1996-11-14,25.50,25.75,25.38,25.63,12132400,0.87
1996-11-13,25.38,25.88,25.00,25.56,20902000,0.87
1996-11-12,26.13,26.25,25.12,25.25,35739200,0.86
1996-11-11,26.37,26.37,25.88,26.00,23133600,0.89
1996-11-08,25.88,26.25,25.75,26.25,47177200,0.90
1996-11-07,25.38,26.00,25.25,25.88,38768800,0.88
1996-11-06,25.63,25.75,24.87,25.50,45077200,0.87
1996-11-05,24.50,25.88,24.50,25.50,94528000,0.87
1996-11-04,24.38,24.50,23.75,24.38,22817200,0.83
1996-11-01,23.37,24.25,23.12,24.25,52833200,0.83
1996-10-31,23.25,23.37,22.25,23.00,48554800,0.79
1996-10-30,23.50,24.00,22.87,22.87,64262800,0.78
1996-10-29,24.62,24.75,23.12,23.25,49907200,0.79
1996-10-28,25.12,25.12,24.50,24.50,29999200,0.84
1996-10-25,24.87,25.00,24.50,24.50,19390000,0.84
1996-10-24,25.00,25.00,24.50,24.75,21092400,0.84
1996-10-23,24.75,25.25,24.38,24.75,40014800,0.84
1996-10-22,25.63,25.63,24.25,24.87,53429600,0.85
1996-10-21,26.50,26.62,25.50,25.63,46902800,0.87
1996-10-18,26.50,26.62,26.00,26.56,95664800,0.91
1996-10-17,27.50,27.75,26.37,26.37,256656400,0.90
1996-10-16,25.25,26.13,24.62,25.75,83686400,0.88
1996-10-15,25.75,25.88,25.00,25.25,90764800,0.86
1996-10-14,24.50,25.38,24.25,25.25,67421200,0.86
1996-10-11,24.38,24.62,24.00,24.25,30172800,0.83
1996-10-10,23.88,24.50,23.75,24.19,69174000,0.83
1996-10-09,23.37,23.62,22.87,23.00,21302400,0.79
1996-10-08,23.50,24.25,23.25,23.25,47608400,0.79
1996-10-07,23.00,23.37,22.87,23.12,23928800,0.79
1996-10-04,22.87,23.12,22.13,22.81,33364800,0.78
1996-10-03,23.62,23.75,22.38,22.38,56929600,0.76
1996-10-02,23.62,24.62,23.12,23.62,69204800,0.81
1996-10-01,22.00,24.75,22.00,24.62,134811600,0.84
1996-09-30,22.13,22.38,22.13,22.19,21361200,0.76
1996-09-27,22.25,22.38,22.13,22.31,20392400,0.76
1996-09-26,22.38,22.50,22.25,22.38,25821600,0.76
1996-09-25,22.50,22.63,22.00,22.38,27260800,0.76
1996-09-24,22.38,22.87,22.38,22.50,35946400,0.77
1996-09-23,22.87,22.87,22.38,22.38,11440800,0.76
1996-09-20,23.37,23.50,22.75,22.87,37287600,0.78
1996-09-19,23.62,23.62,23.37,23.37,29867600,0.80
1996-09-18,23.00,24.13,22.87,23.50,88340000,0.80
1996-09-17,22.87,23.12,22.50,23.00,52292800,0.79
1996-09-16,21.50,23.00,21.37,22.38,61163200,0.76
1996-09-13,20.38,21.25,20.38,21.00,41652800,0.72
1996-09-12,21.00,21.12,20.25,20.38,65228800,0.70
1996-09-11,21.50,21.75,21.00,21.12,36800400,0.72
1996-09-10,22.13,22.13,21.50,21.50,38928400,0.73
1996-09-09,22.63,22.75,21.88,22.00,37060800,0.75
1996-09-06,23.12,23.25,22.63,23.00,60208400,0.79
1996-09-05,23.50,23.75,22.87,22.87,69896400,0.78
1996-09-04,23.88,24.62,23.88,24.13,25362400,0.82
1996-09-03,24.13,24.38,23.88,24.13,17074400,0.82
1996-08-30,24.75,24.75,24.25,24.25,26432000,0.83
1996-08-29,24.87,24.87,24.38,24.50,26731600,0.84
1996-08-28,24.87,25.00,24.50,24.87,40899600,0.85
1996-08-27,24.13,25.00,24.00,24.86,72326800,0.85
1996-08-26,23.88,24.13,23.50,24.13,22419600,0.82
1996-08-23,23.00,24.00,23.00,23.88,50864800,0.82
1996-08-22,23.00,23.25,22.87,23.25,21921200,0.79
1996-08-21,23.50,23.62,22.87,23.00,28336000,0.79
1996-08-20,23.88,23.88,23.37,23.50,52939600,0.80
1996-08-19,22.38,23.62,22.38,23.62,56579600,0.81
1996-08-16,22.63,22.63,22.13,22.50,35439600,0.77
1996-08-15,22.63,22.75,22.25,22.25,26905200,0.76
1996-08-14,22.63,23.00,22.63,22.75,17964800,0.78
1996-08-13,22.87,23.12,22.38,22.50,25877600,0.77
1996-08-12,23.37,23.62,22.38,23.00,37836400,0.79
1996-08-09,22.25,23.37,22.13,23.12,57696800,0.79
1996-08-08,22.38,22.38,21.88,22.13,25379200,0.76
1996-08-07,21.75,22.63,21.62,22.38,62115200,0.76
1996-08-06,21.00,21.50,20.75,21.50,23396800,0.73
1996-08-05,21.62,21.88,20.88,21.00,25253200,0.72
1996-08-02,21.62,22.00,21.25,21.62,31987200,0.74
1996-08-01,22.00,22.00,21.12,21.25,27540800,0.73
1996-07-31,21.25,22.00,21.25,22.00,23195200,0.75
1996-07-30,22.63,22.75,21.25,21.37,47350800,0.73
1996-07-29,22.00,22.50,21.75,22.25,48924400,0.76
1996-07-26,21.50,22.00,21.12,22.00,30920400,0.75
1996-07-25,21.12,21.37,20.75,21.00,28607600,0.72
1996-07-24,20.00,21.00,19.87,20.81,66018400,0.71
1996-07-23,20.50,20.63,20.25,20.50,32530400,0.70
1996-07-22,20.88,20.88,20.00,20.25,38052000,0.69
1996-07-19,20.88,21.00,20.75,20.75,66494400,0.71
1996-07-18,21.50,21.75,20.36,20.88,224263200,0.71
1996-07-17,17.38,17.50,16.62,16.88,58399600,0.58
1996-07-16,17.38,17.38,16.00,16.88,72304400,0.58
1996-07-15,18.12,18.12,17.13,17.19,33306000,0.59
1996-07-12,18.38,18.38,17.25,18.06,67247600,0.62
1996-07-11,18.75,18.88,17.38,17.87,72788800,0.61
1996-07-10,19.13,19.50,18.75,18.75,42347200,0.64
1996-07-09,19.50,19.62,19.00,19.00,46956000,0.65
1996-07-08,19.62,19.87,19.00,19.13,47227600,0.65
1996-07-05,19.37,19.75,19.25,19.50,26538400,0.67
1996-07-03,20.38,20.38,19.37,19.37,72153200,0.66
1996-07-02,21.37,21.50,21.00,21.00,22251600,0.72
1996-07-01,21.12,21.50,21.00,21.50,32995200,0.73
1996-06-28,20.88,21.00,20.63,21.00,28921200,0.72
1996-06-27,20.00,21.00,19.75,20.63,57310400,0.70
1996-06-26,20.63,20.75,19.62,19.87,101082800,0.68
1996-06-25,22.13,22.25,20.38,20.63,61740000,0.70
1996-06-24,22.63,22.63,22.13,22.25,30690800,0.76
1996-06-21,22.87,22.87,22.38,22.63,40462800,0.77
1996-06-20,23.37,23.37,22.50,22.75,36772400,0.78
1996-06-19,23.12,23.37,22.63,23.12,33616800,0.79
1996-06-18,23.62,23.75,22.63,22.75,55806800,0.78
1996-06-17,24.13,24.13,23.62,23.62,28232400,0.81
1996-06-14,24.75,24.75,23.88,23.94,36240400,0.82
1996-06-13,24.38,24.92,24.00,24.62,47854800,0.84
1996-06-12,24.50,24.50,24.00,24.25,37979200,0.83
1996-06-11,24.25,24.25,24.00,24.00,38264800,0.82
1996-06-10,24.38,24.50,24.00,24.13,26591600,0.82
1996-06-07,24.00,24.38,23.50,24.38,66942400,0.83
1996-06-06,25.00,25.25,24.13,24.25,90524000,0.83
1996-06-05,25.38,25.50,24.25,25.12,127526000,0.86
1996-06-04,24.00,24.38,23.88,24.19,190559600,0.83
1996-06-03,25.88,26.00,24.75,24.75,31365600,0.84
1996-05-31,25.63,26.62,25.50,26.13,40661600,0.89
1996-05-30,24.87,25.75,24.75,25.50,25866400,0.87
1996-05-29,26.25,26.25,24.75,24.87,54880000,0.85
1996-05-28,26.75,27.25,26.37,26.37,25463200,0.90
1996-05-24,26.25,26.87,26.13,26.75,28310800,0.91
1996-05-23,26.13,26.62,25.75,26.25,31012800,0.90
1996-05-22,27.38,27.38,25.75,26.06,50470000,0.89
1996-05-21,28.00,28.12,27.12,27.12,28596400,0.93
1996-05-20,27.88,28.12,27.63,27.94,21128800,0.95
1996-05-17,28.37,28.37,27.50,27.63,30825200,0.94
1996-05-16,28.25,28.62,27.88,28.37,32519200,0.97
1996-05-15,27.88,28.88,27.75,28.50,73091200,0.97
1996-05-14,27.75,28.00,27.50,27.50,49406000,0.94
1996-05-13,27.12,27.63,26.62,27.06,46754400,0.92
1996-05-10,26.25,27.38,26.00,27.25,27647200,0.93
1996-05-09,26.37,26.50,25.75,26.13,24519600,0.89
1996-05-08,27.25,27.25,25.63,26.75,46698400,0.91
1996-05-07,26.37,27.38,26.25,26.87,88384800,0.92
1996-05-06,24.87,25.88,24.75,25.63,72371600,0.87
1996-05-03,24.13,24.13,23.50,23.88,27115200,0.82
1996-05-02,24.50,24.50,23.50,23.75,47076400,0.81
1996-05-01,24.38,24.75,24.13,24.38,28176400,0.83
1996-04-30,24.87,24.87,24.13,24.38,34165600,0.83
1996-04-29,25.00,25.00,24.50,24.75,30262400,0.84
1996-04-26,25.00,25.12,24.62,24.75,47216400,0.84
1996-04-25,24.38,24.87,24.13,24.87,43601600,0.85
1996-04-24,24.62,24.75,24.19,24.25,32085200,0.83
1996-04-23,25.12,25.25,24.62,24.75,42487200,0.84
1996-04-22,25.25,25.50,24.87,25.12,27778800,0.86
1996-04-19,24.62,25.12,24.62,25.06,25449200,0.86
1996-04-18,25.38,25.39,24.25,24.75,54311600,0.84
1996-04-17,25.88,26.00,25.12,25.25,21352800,0.86
1996-04-16,25.88,26.00,25.63,25.88,25354000,0.88
1996-04-15,25.50,25.75,25.00,25.75,38519600,0.88
1996-04-12,25.88,25.88,25.38,25.50,20358800,0.87
1996-04-11,26.13,26.25,25.50,25.75,24567200,0.88
1996-04-10,26.13,26.50,25.88,26.00,43691200,0.89
1996-04-09,24.87,26.50,24.38,26.00,58769200,0.89
1996-04-08,23.88,24.50,23.75,24.38,42207200,0.83
1996-04-04,24.62,24.62,24.00,24.13,21512400,0.82
1996-04-03,25.12,25.12,24.33,24.56,18060000,0.84
1996-04-02,25.63,25.63,24.87,25.00,25359600,0.85
1996-04-01,25.12,25.88,24.52,25.50,39659200,0.87
1996-03-29,24.25,24.75,23.75,24.56,41630400,0.84
1996-03-28,24.75,25.63,24.13,24.19,73973200,0.83
1996-03-27,23.25,25.25,23.00,25.25,107324000,0.86
1996-03-26,24.00,24.50,23.62,23.88,40199600,0.82
1996-03-25,25.50,25.75,24.00,24.00,41092800,0.82
1996-03-22,25.25,25.38,24.87,25.38,26891200,0.87
1996-03-21,25.50,25.50,25.00,25.12,27496000,0.86
1996-03-20,25.75,25.75,25.12,25.25,28996800,0.86
1996-03-19,26.37,26.50,25.63,25.75,31091200,0.88
1996-03-18,25.94,26.13,25.75,26.13,27283200,0.89
1996-03-15,26.00,26.00,25.50,25.88,25345600,0.88
1996-03-14,25.88,25.88,25.50,25.63,23340800,0.87
1996-03-13,25.88,26.13,25.63,25.75,24920000,0.88
1996-03-12,26.00,26.37,25.63,25.81,24038000,0.88
1996-03-11,26.25,26.37,25.75,25.88,31752000,0.88
1996-03-08,25.75,26.25,25.00,26.00,37251200,0.89
1996-03-07,26.25,26.37,25.38,25.81,65016000,0.88
1996-03-06,26.75,26.87,26.13,26.19,24763200,0.89
1996-03-05,26.50,26.75,26.25,26.62,29610000,0.91
1996-03-04,27.25,27.38,26.25,26.25,46888800,0.90
1996-03-01,27.63,27.63,26.62,26.87,57783600,0.92
1996-02-29,27.50,27.75,27.25,27.50,28221200,0.94
1996-02-28,28.88,28.88,27.63,27.75,46978400,0.95
1996-02-27,29.87,29.87,28.50,28.62,37290400,0.98
1996-02-26,30.00,30.12,29.50,29.50,29570800,1.01
1996-02-23,29.87,30.25,29.63,29.87,43321600,1.02
1996-02-22,30.00,30.12,29.63,29.87,46046000,1.02
1996-02-21,29.38,29.75,29.13,29.63,55459600,1.01
1996-02-20,28.00,29.50,28.00,29.00,94228400,0.99
1996-02-16,28.12,28.37,27.50,27.50,39110400,0.94
1996-02-15,27.63,28.12,27.38,28.00,30520000,0.96
1996-02-14,28.25,28.25,27.44,27.63,40796000,0.94
1996-02-13,28.00,28.88,27.88,28.12,57125600,0.96
1996-02-12,28.12,28.50,28.00,28.37,48568800,0.97
1996-02-09,27.88,28.50,27.63,27.75,51422000,0.95
1996-02-08,27.50,28.12,27.50,27.88,65791600,0.95
1996-02-07,29.75,29.75,27.75,28.25,90081600,0.96
1996-02-06,29.25,30.00,29.25,29.63,56554400,1.01
1996-02-05,29.69,29.75,29.00,29.25,79682400,1.00
1996-02-02,28.88,29.63,28.75,29.25,138994800,1.00
1996-02-01,27.50,28.37,27.50,28.37,83260800,0.97
1996-01-31,27.75,28.00,27.38,27.63,82014800,0.94
1996-01-30,27.00,28.12,26.86,27.31,155710800,0.93
1996-01-29,29.00,29.75,28.75,29.13,83148800,0.99
1996-01-26,30.37,31.25,28.62,30.62,183937600,1.05
1996-01-25,31.75,32.00,30.12,30.25,111300000,1.03
1996-01-24,32.12,32.25,31.75,32.25,163973600,1.10
1996-01-23,33.75,34.00,31.00,31.62,247072000,1.08
1996-01-22,29.75,31.00,29.25,30.50,124936000,1.04
1996-01-19,31.00,31.75,29.38,29.87,207306400,1.02
1996-01-18,32.88,33.37,30.37,31.94,174596800,1.09
1996-01-17,34.38,34.38,33.75,34.00,59102400,1.16
1996-01-16,34.38,34.75,33.62,34.56,88228000,1.18
1996-01-15,33.75,34.50,33.37,34.12,90770400,1.16
1996-01-12,34.75,34.75,33.25,33.87,100464000,1.16
1996-01-11,32.63,35.00,32.38,35.00,189184800,1.19
1996-01-10,32.50,34.75,32.25,34.25,91358400,1.17
1996-01-09,34.63,34.63,32.75,32.75,62804000,1.12
1996-01-08,34.50,35.50,34.00,34.63,30335200,1.18
1996-01-05,31.62,34.25,31.38,34.25,111482000,1.17
1996-01-04,32.38,32.38,31.38,31.56,75045600,1.08
1996-01-03,32.00,32.88,31.87,32.12,107458400,1.10
1996-01-02,32.25,32.25,31.75,32.12,34823600,1.10
1995-12-29,32.00,32.38,31.62,31.87,76034000,1.09
1995-12-28,32.12,32.75,31.87,32.00,62498800,1.09
1995-12-27,32.12,33.37,31.87,32.38,67141200,1.11
1995-12-26,32.50,32.50,31.75,32.06,34876800,1.09
1995-12-22,32.63,32.88,32.12,32.25,58665600,1.10
1995-12-21,32.75,32.75,31.62,32.50,83218800,1.11
1995-12-20,33.50,33.62,32.50,32.63,91434000,1.11
1995-12-19,32.75,33.25,32.25,32.75,107716000,1.12
1995-12-18,35.12,35.25,31.87,32.25,166633600,1.10
1995-12-15,35.50,36.63,34.38,35.25,181720000,1.20
1995-12-14,38.87,39.38,38.00,38.25,83375600,1.31
1995-12-13,38.25,39.00,36.75,38.38,171225600,1.31
1995-12-12,38.62,38.62,38.00,38.00,44388400,1.30
1995-12-11,39.50,39.63,38.38,38.62,27913200,1.32
1995-12-08,38.75,39.38,37.88,39.38,35338800,1.34
1995-12-07,38.75,38.75,37.88,38.56,35481600,1.32
1995-12-06,39.75,39.88,38.38,38.75,50276800,1.32
1995-12-05,38.50,39.88,38.25,39.50,90899200,1.35
1995-12-04,40.13,40.13,39.00,39.50,120170400,1.35
1995-12-01,38.00,38.25,37.12,37.62,51052400,1.28
1995-11-30,38.87,39.00,38.00,38.13,43713600,1.30
1995-11-29,40.13,40.13,39.00,39.25,26317200,1.34
1995-11-28,39.38,40.13,39.25,40.00,44072000,1.37
1995-11-27,40.62,40.62,39.38,39.38,28968800,1.34
1995-11-24,38.87,40.37,38.75,40.19,27487600,1.37
1995-11-22,38.62,39.25,38.50,38.62,24701600,1.32
1995-11-21,38.75,38.75,37.88,38.62,47902400,1.32
1995-11-20,40.25,40.25,38.50,38.62,37114000,1.31
1995-11-17,40.00,40.37,39.75,40.13,32132800,1.37
1995-11-16,40.87,41.50,39.50,39.94,56557200,1.36
1995-11-15,42.00,42.00,40.13,41.00,62034000,1.40
1995-11-14,41.00,42.50,41.00,41.50,101819200,1.41
1995-11-13,40.25,41.25,40.00,40.87,79343600,1.39
1995-11-10,39.38,40.25,38.87,39.75,55778800,1.35
1995-11-09,39.75,40.00,38.87,39.38,65027200,1.34
1995-11-08,39.75,41.00,38.75,38.87,89706400,1.32
1995-11-07,37.75,40.50,37.50,39.63,184097200,1.35
1995-11-06,36.50,38.75,36.38,38.13,77943600,1.30
1995-11-03,36.75,36.87,35.88,36.50,44858800,1.24
1995-11-02,36.87,36.87,36.25,36.63,38189200,1.25
1995-11-01,36.63,37.12,35.50,36.63,48308400,1.25
1995-10-31,35.25,36.63,35.12,36.31,72304400,1.24
1995-10-30,34.88,35.25,34.63,35.25,43909600,1.20
1995-10-27,34.88,34.88,34.12,34.75,38553200,1.18
1995-10-26,34.88,35.00,34.50,34.88,31466400,1.19
1995-10-25,35.25,35.37,34.75,34.75,33325600,1.18
1995-10-24,35.50,35.50,34.88,35.12,53373600,1.20
1995-10-23,35.12,35.12,34.75,35.12,49450800,1.20
1995-10-20,35.25,35.25,34.63,35.12,96583200,1.20
1995-10-19,35.88,36.13,34.75,34.75,236224800,1.18
1995-10-18,37.00,39.56,36.75,37.37,128100000,1.27
1995-10-17,36.50,36.87,35.88,36.63,44654400,1.25
1995-10-16,36.25,37.00,35.88,36.13,45516800,1.23
1995-10-13,35.75,36.87,35.50,36.00,58797200,1.23
1995-10-12,35.00,35.37,34.75,35.31,40513200,1.20
1995-10-11,35.25,35.62,34.12,34.88,83218800,1.19
1995-10-10,34.38,35.00,33.62,34.69,100066400,1.18
1995-10-09,35.37,35.75,34.38,34.81,93142000,1.18
1995-10-06,36.75,37.00,35.62,35.69,77260400,1.21
1995-10-05,36.25,36.63,35.88,36.50,61017600,1.24
1995-10-04,36.63,37.00,36.00,36.38,66693200,1.24
1995-10-03,38.13,38.50,37.12,37.62,72455600,1.28
1995-10-02,37.75,38.50,37.50,37.62,98000000,1.28
1995-09-29,38.00,38.25,36.87,37.25,70854000,1.27
1995-09-28,36.50,37.88,36.50,37.75,82796000,1.28
1995-09-27,37.50,37.50,34.75,36.25,112809200,1.23
1995-09-26,37.75,37.88,37.12,37.37,62725600,1.27
1995-09-25,38.25,38.27,37.37,37.52,78803200,1.28
1995-09-22,36.87,37.25,36.38,37.06,99660400,1.26
1995-09-21,36.50,37.50,36.38,37.00,86833600,1.26
1995-09-20,37.25,37.37,36.50,36.63,80452400,1.25
1995-09-19,36.75,37.12,36.13,36.75,122505600,1.25
1995-09-18,36.38,36.81,35.88,36.69,155372000,1.25
1995-09-15,37.37,39.88,35.50,35.88,302990800,1.22
1995-09-14,41.38,41.63,39.75,40.00,137639600,1.36
1995-09-13,42.88,43.38,42.00,42.37,80687600,1.44
1995-09-12,44.50,44.88,42.62,42.94,81564000,1.46
1995-09-11,44.88,45.50,44.25,44.25,43122800,1.51
1995-09-08,44.75,44.88,44.50,44.75,43694000,1.52
1995-09-07,44.00,45.31,43.75,44.75,65581600,1.52
1995-09-06,43.87,44.17,43.50,43.75,50190000,1.49
1995-09-05,43.50,43.50,42.75,43.50,44993200,1.48
1995-09-01,43.00,43.50,42.88,42.94,24595200,1.46
1995-08-31,43.38,43.50,43.00,43.00,21966000,1.46
1995-08-30,43.25,43.75,43.13,43.38,38368400,1.48
1995-08-29,43.00,43.25,42.50,43.13,79265200,1.47
1995-08-28,44.88,45.00,43.00,43.00,60760000,1.46
1995-08-25,45.87,45.87,44.62,44.75,33586000,1.52
1995-08-24,45.62,46.25,45.50,45.75,71982400,1.56
1995-08-23,44.88,45.87,44.62,45.50,63450800,1.55
1995-08-22,44.37,45.13,44.12,44.75,54261200,1.52
1995-08-21,44.88,45.38,44.12,44.12,67944800,1.50
1995-08-18,44.88,45.13,43.75,44.88,60289600,1.53
1995-08-17,44.62,45.50,44.12,44.62,61723200,1.52
1995-08-16,44.00,44.50,43.63,44.50,73158400,1.51
1995-08-15,43.87,44.12,43.13,44.06,79466800,1.50
1995-08-14,43.00,43.75,42.88,43.38,41851600,1.47
1995-08-11,42.88,43.13,41.88,43.06,51732800,1.46
1995-08-10,43.13,43.25,42.62,42.75,41006000,1.45
1995-08-09,42.62,43.75,42.50,43.13,92254400,1.46
1995-08-08,43.63,43.75,42.37,42.50,58648800,1.44
1995-08-07,44.12,44.62,43.13,43.38,48440000,1.47
1995-08-04,45.00,45.13,43.75,44.25,48078800,1.50
1995-08-03,44.12,45.62,43.87,45.00,53482800,1.53
1995-08-02,43.87,45.00,43.75,44.37,68782000,1.51
1995-08-01,44.88,44.88,43.50,43.50,52729600,1.48
1995-07-31,45.50,45.62,44.75,45.00,39631200,1.53
1995-07-28,46.75,47.25,45.00,45.50,65234400,1.54
1995-07-27,45.50,47.50,45.50,46.81,81295200,1.59
1995-07-26,46.25,46.25,45.38,45.38,42862400,1.54
1995-07-25,46.00,46.38,45.62,45.75,65881200,1.55
1995-07-24,44.00,45.50,43.75,45.38,53656400,1.54
1995-07-21,43.00,44.88,43.00,43.75,189470400,1.48
1995-07-20,46.00,47.37,45.00,47.06,82818400,1.60
1995-07-19,47.00,48.00,45.00,45.50,130258800,1.54
1995-07-18,49.00,49.56,47.75,48.12,63658000,1.63
1995-07-17,48.88,49.75,48.63,49.00,56540400,1.66
1995-07-14,47.37,49.00,47.00,48.75,69482000,1.65
1995-07-13,47.37,48.75,47.13,47.62,88082400,1.62
1995-07-12,47.25,48.00,46.12,47.00,70952000,1.60
1995-07-11,47.75,48.63,47.06,47.13,53673200,1.60
1995-07-10,48.63,49.88,48.12,48.63,74482800,1.65
1995-07-07,46.88,49.25,46.75,48.63,96779200,1.65
1995-07-06,46.50,47.00,45.75,47.00,46023600,1.60
1995-07-05,46.88,47.87,46.50,46.50,44265200,1.58
1995-07-03,46.50,47.13,46.25,46.94,9847600,1.59
1995-06-30,47.25,47.87,46.12,46.44,41372800,1.58
1995-06-29,46.38,48.12,46.00,47.25,58139200,1.60
1995-06-28,46.00,47.50,45.38,46.63,66589600,1.58
1995-06-27,47.37,48.25,46.38,46.38,54275200,1.57
1995-06-26,48.25,48.50,47.62,48.12,38194800,1.63
1995-06-23,48.75,49.00,47.75,48.75,57990800,1.65
1995-06-22,49.00,49.62,48.63,49.12,118479200,1.67
1995-06-21,47.62,50.13,46.75,49.37,156503200,1.68
1995-06-20,46.00,47.75,46.00,47.37,184632000,1.61
1995-06-19,43.87,45.25,43.50,44.37,117384400,1.51
1995-06-16,43.87,44.00,43.50,43.87,22302000,1.49
1995-06-15,43.63,43.75,43.38,43.63,23189600,1.48
1995-06-14,43.87,43.87,43.38,43.63,29512000,1.48
1995-06-13,44.50,44.62,43.87,44.00,31486000,1.49
1995-06-12,44.00,44.50,43.87,44.17,53029200,1.50
1995-06-09,43.63,43.75,43.13,43.50,46656400,1.48
1995-06-08,43.38,43.38,42.12,42.94,34034000,1.46
1995-06-07,44.12,44.12,43.13,43.13,31130400,1.46
1995-06-06,43.63,44.37,43.50,44.00,78817200,1.49
1995-06-05,42.37,43.50,42.12,43.50,63663600,1.48
1995-06-02,41.88,42.37,41.50,42.12,26423600,1.43
1995-06-01,41.88,42.50,41.75,42.19,46681600,1.43
1995-05-31,42.12,42.12,41.00,41.56,39883200,1.41
1995-05-30,42.62,42.88,41.50,42.00,49095200,1.43
1995-05-26,43.00,43.13,42.25,42.69,28638400,1.45
1995-05-25,43.25,44.00,43.00,43.38,45715600,1.47
1995-05-24,43.75,44.25,42.88,43.50,66166800,1.47
1995-05-23,44.12,44.37,43.50,43.87,69165600,1.48
1995-05-22,42.50,44.12,42.25,44.12,92971200,1.49
1995-05-19,42.88,43.75,42.62,42.75,80648400,1.45
1995-05-18,44.12,44.12,43.25,43.38,92892800,1.47
1995-05-17,43.75,44.37,43.50,44.00,65786000,1.49
1995-05-16,43.13,44.37,42.50,43.75,83129200,1.48
1995-05-15,43.13,43.75,42.50,43.63,98338800,1.48
1995-05-12,40.87,43.69,40.50,43.63,161988400,1.48
1995-05-11,41.63,41.63,40.37,41.00,130905600,1.39
1995-05-10,41.50,41.88,40.75,41.44,68768000,1.40
1995-05-09,40.62,41.38,40.00,41.25,80732400,1.40
1995-05-08,39.88,41.00,39.75,40.50,96742800,1.37
1995-05-05,38.75,39.12,38.13,38.87,52001600,1.32
1995-05-04,38.25,39.88,38.00,38.50,75910800,1.30
1995-05-03,38.25,38.62,38.00,38.13,42196000,1.29
1995-05-02,38.25,38.38,37.50,38.13,30002000,1.29
1995-05-01,38.25,38.75,38.00,38.25,44489200,1.29
1995-04-28,38.00,38.38,37.50,38.25,48829200,1.29
1995-04-27,38.50,38.50,37.75,37.88,34966400,1.28
1995-04-26,37.62,38.75,37.37,38.25,57610000,1.29
1995-04-25,39.12,39.38,37.25,37.75,68409600,1.28
1995-04-24,39.00,39.63,38.50,39.00,68059600,1.32
1995-04-21,37.25,39.50,37.12,39.12,166656000,1.32
1995-04-20,37.12,38.50,36.63,37.62,82376000,1.27
1995-04-19,37.50,37.50,35.62,36.38,69857200,1.23
1995-04-18,38.50,38.62,37.50,37.50,57783600,1.27
1995-04-17,38.13,39.38,37.88,38.38,52203200,1.30
1995-04-13,39.25,39.25,37.88,38.25,43590400,1.29
1995-04-12,38.25,39.63,37.37,39.00,118678000,1.32
1995-04-11,36.75,37.88,36.63,37.75,53628400,1.28
1995-04-10,36.87,37.00,36.13,36.63,29450400,1.24
1995-04-07,37.00,37.12,36.25,36.75,73931200,1.24
1995-04-06,37.25,38.00,35.53,36.75,180706400,1.24
1995-04-05,34.12,34.75,33.75,34.75,66214400,1.18
1995-04-04,35.75,35.88,33.62,33.87,107049600,1.15
1995-04-03,35.50,35.75,35.12,35.50,38575600,1.20
1995-03-31,35.12,35.62,34.75,35.25,45810800,1.19
1995-03-30,34.63,35.50,34.50,35.37,68353600,1.20
1995-03-29,34.00,34.88,33.87,34.38,124219200,1.16
1995-03-28,36.25,36.34,34.12,34.38,172449200,1.16
1995-03-27,37.62,37.62,36.63,37.19,35700000,1.26
1995-03-24,37.37,37.88,37.25,37.75,32029200,1.28
1995-03-23,37.88,38.00,36.98,37.12,42523600,1.26
1995-03-22,36.25,39.50,36.25,38.06,119786800,1.29
1995-03-21,35.50,36.75,35.25,36.25,76342000,1.23
1995-03-20,35.12,35.62,35.00,35.25,47471200,1.19
1995-03-17,35.50,35.50,34.88,35.12,53911200,1.19
1995-03-16,35.25,36.00,35.00,35.25,79184000,1.19
1995-03-15,35.50,36.25,34.88,35.00,182742000,1.18
1995-03-14,38.25,38.25,34.50,35.00,181966400,1.18
1995-03-13,39.63,39.63,38.00,38.13,81438000,1.29
1995-03-10,39.63,40.37,39.38,39.50,34353200,1.34
1995-03-09,39.88,40.37,39.38,39.75,49170800,1.35
1995-03-08,38.75,40.13,37.75,39.56,91218400,1.34
1995-03-07,39.88,39.88,38.25,38.31,37696400,1.30
1995-03-06,39.75,40.00,39.50,39.75,33180000,1.35
1995-03-03,39.75,40.69,39.50,40.25,36442000,1.36
1995-03-02,40.13,40.75,39.75,40.00,67186000,1.35
1995-03-01,39.75,40.13,39.42,40.00,56112000,1.35
1995-02-28,38.50,39.88,38.00,39.50,55742400,1.34
1995-02-27,38.25,39.00,38.11,38.25,67202800,1.29
1995-02-24,40.13,40.37,38.50,39.00,142203600,1.32
1995-02-23,41.12,41.88,40.00,40.19,78677200,1.36
1995-02-22,40.62,41.00,40.13,40.81,73354400,1.38
1995-02-21,42.62,42.75,40.87,41.00,75395600,1.39
1995-02-17,42.88,43.00,42.50,42.50,30447200,1.44
1995-02-16,43.13,43.25,42.62,43.19,54695200,1.46
1995-02-15,43.25,43.50,42.50,42.56,46118800,1.44
1995-02-14,43.75,44.12,42.62,42.94,41403600,1.45
1995-02-13,43.50,44.50,43.25,43.75,70842800,1.48
1995-02-10,43.63,44.19,43.38,43.75,87740800,1.48
1995-02-09,42.12,43.87,42.12,43.63,118848800,1.47
1995-02-08,41.00,42.37,40.87,42.31,100716000,1.43
1995-02-07,40.37,41.00,40.00,40.81,50400000,1.38
1995-02-06,40.75,40.75,39.50,40.50,60757200,1.37
1995-02-03,42.00,42.12,40.37,40.50,79802800,1.37
1995-02-02,40.13,41.88,40.13,41.63,50895600,1.40
1995-02-01,40.75,40.75,39.88,40.13,39592000,1.35
1995-01-31,40.50,40.87,40.00,40.37,53194400,1.36
1995-01-30,40.13,40.50,39.88,40.13,57646400,1.35
1995-01-27,39.88,40.37,39.00,39.88,74642400,1.35
1995-01-26,40.87,41.50,39.25,39.50,61597200,1.33
1995-01-25,39.50,42.00,39.50,40.98,129267600,1.38
1995-01-24,42.25,42.37,41.38,41.63,54524400,1.40
1995-01-23,41.88,42.62,41.00,42.25,99635200,1.43
1995-01-20,47.00,47.00,42.50,42.62,250090400,1.44
1995-01-19,45.50,46.00,45.00,45.87,78573600,1.55
1995-01-18,45.00,45.62,44.75,45.62,31914400,1.54
1995-01-17,44.50,45.50,44.12,45.00,82527200,1.52
1995-01-16,44.88,45.25,44.25,44.50,47244400,1.50
1995-01-13,46.12,46.12,44.37,44.88,87844400,1.51
1995-01-12,46.12,46.38,44.75,45.38,137944800,1.53
1995-01-11,43.75,48.06,42.69,46.75,218456000,1.58
1995-01-10,41.25,44.00,41.25,43.69,153697600,1.47
1995-01-09,41.63,41.88,41.00,41.20,68521600,1.39
1995-01-06,41.63,43.13,41.12,42.00,269155600,1.42
1995-01-05,39.25,39.38,38.75,38.87,18410000,1.31
1995-01-04,38.62,39.63,38.62,39.38,39670400,1.33
1995-01-03,38.87,38.87,37.88,38.38,25967200,1.30
1994-12-30,39.38,39.88,38.75,39.00,18272800,1.32
1994-12-29,39.25,39.88,39.12,39.50,30335200,1.33
1994-12-28,39.12,39.25,38.25,39.12,22290800,1.32
1994-12-27,39.25,39.75,38.87,39.12,20479200,1.32
1994-12-23,38.50,39.38,38.50,38.87,23472400,1.31
1994-12-22,38.50,38.87,38.25,38.62,33269600,1.30
1994-12-21,37.88,38.50,37.50,38.38,39359600,1.30
1994-12-20,39.12,39.25,38.38,38.50,43786400,1.30
1994-12-19,37.25,39.38,37.25,39.12,83204800,1.32
1994-12-16,37.25,37.75,36.75,37.25,44945600,1.26
1994-12-15,38.00,38.38,36.87,37.12,56898800,1.25
1994-12-14,36.50,38.13,36.50,37.88,77856800,1.28
1994-12-13,36.63,36.94,36.25,36.38,29800400,1.23
1994-12-12,36.38,36.75,35.50,36.50,56019600,1.23
1994-12-09,35.88,36.38,34.75,36.25,65181200,1.22
1994-12-08,36.87,37.00,35.75,35.88,42464800,1.21
1994-12-07,37.50,37.81,36.06,36.63,34325200,1.24
1994-12-06,37.00,38.38,36.87,37.56,59522400,1.27
1994-12-05,36.50,37.37,36.13,37.19,45068800,1.26
1994-12-02,36.50,36.75,35.62,36.56,43064000,1.23
1994-12-01,37.00,37.62,36.00,36.19,77330400,1.22
1994-11-30,38.38,39.38,37.00,37.25,78008000,1.26
1994-11-29,38.00,38.50,37.75,38.25,36033200,1.29
1994-11-28,37.62,38.25,37.31,37.81,34669600,1.28
1994-11-25,36.87,37.75,36.75,37.75,21056000,1.27
1994-11-23,37.00,37.88,36.38,36.87,81953200,1.24
1994-11-22,37.75,39.12,37.25,37.37,56084000,1.26
1994-11-21,40.00,40.25,38.00,38.13,50649200,1.29
1994-11-18,40.00,40.50,39.63,40.00,36758400,1.35
1994-11-17,40.87,41.00,39.88,40.00,37609600,1.35
1994-11-16,40.75,41.56,40.62,40.94,46849600,1.38
1994-11-15,42.50,43.00,41.25,41.38,41904800,1.39
1994-11-14,41.25,42.75,41.25,42.50,34907600,1.43
1994-11-11,41.25,41.50,41.00,41.12,15568000,1.38
1994-11-10,41.75,41.88,41.00,41.31,38245200,1.39
1994-11-09,42.75,43.00,41.00,41.63,101584000,1.40
1994-11-08,40.62,42.62,40.25,42.25,87242400,1.42
1994-11-07,40.37,41.25,40.13,40.75,28260400,1.37
1994-11-04,41.50,41.63,40.00,40.37,48011600,1.36
1994-11-03,41.75,42.00,41.00,41.50,27630400,1.40
1994-11-02,43.13,43.25,41.38,41.38,54686800,1.39
1994-11-01,42.88,43.48,42.37,43.13,54524400,1.45
1994-10-31,42.00,43.38,41.50,43.19,88975600,1.45
1994-10-28,42.37,42.88,41.75,42.12,68331200,1.42
1994-10-27,43.25,43.75,42.50,42.75,39852400,1.44
1994-10-26,42.62,43.27,42.62,43.25,49193200,1.46
1994-10-25,41.63,42.62,41.50,42.62,75370400,1.43
1994-10-24,42.75,43.13,41.88,42.25,51125200,1.42
1994-10-21,40.75,42.75,40.75,42.62,80676400,1.43
1994-10-20,41.25,41.81,40.50,41.00,54535600,1.38
1994-10-19,41.00,42.12,41.00,41.25,87771600,1.39
1994-10-18,40.62,41.63,40.50,41.25,117171600,1.39
1994-10-17,40.87,41.50,38.87,39.75,75997600,1.34
1994-10-14,41.50,42.00,40.87,41.12,44013200,1.38
1994-10-13,42.62,42.88,40.62,41.12,131325600,1.38
1994-10-12,39.63,42.62,39.12,42.12,149329600,1.42
1994-10-11,41.38,41.88,39.38,39.63,210576800,1.33
1994-10-10,37.12,39.63,37.00,38.87,130852400,1.31
1994-10-07,36.13,37.06,35.50,37.00,91098000,1.25
1994-10-06,37.37,37.48,36.00,36.25,131728800,1.22
1994-10-05,33.62,38.13,33.37,37.88,177450000,1.27
1994-10-04,33.25,34.00,33.00,33.75,40597200,1.14
1994-10-03,33.62,33.75,32.50,33.13,32398800,1.11
1994-09-30,34.12,34.50,33.62,33.69,17925600,1.13
1994-09-29,33.75,34.38,33.37,34.12,27344800,1.15
1994-09-28,34.00,34.38,33.62,33.87,20316800,1.14
1994-09-27,33.75,34.12,33.37,33.87,27272000,1.14
1994-09-26,33.87,34.50,33.62,33.94,35425600,1.14
1994-09-23,33.87,34.50,33.87,33.94,33219200,1.14
1994-09-22,34.25,34.25,33.62,33.87,36559600,1.14
1994-09-21,34.50,34.63,33.75,34.12,58710400,1.15
1994-09-20,35.12,35.37,34.38,34.56,49313600,1.16
1994-09-19,36.38,36.75,35.50,35.50,43587600,1.19
1994-09-16,35.88,37.25,35.50,36.38,91036400,1.22
1994-09-15,35.12,36.13,35.12,36.00,64738800,1.21
1994-09-14,35.62,35.75,35.00,35.12,24771600,1.18
1994-09-13,35.75,36.25,35.62,35.81,26056800,1.21
1994-09-12,35.62,35.75,35.37,35.75,22635200,1.20
1994-09-09,35.75,36.00,35.37,35.75,39309200,1.20
1994-09-08,36.00,36.25,35.62,36.13,39709600,1.22
1994-09-07,35.62,36.63,35.37,36.13,50974000,1.22
1994-09-06,35.25,35.62,35.00,35.56,22856400,1.20
1994-09-02,35.25,35.50,35.00,35.37,25326000,1.19
1994-09-01,35.37,35.75,34.63,35.00,51072000,1.18
1994-08-31,36.00,37.37,35.75,36.19,87959200,1.22
1994-08-30,35.25,36.38,35.12,36.25,45519600,1.22
1994-08-29,35.75,36.13,35.25,35.37,38026800,1.19
1994-08-26,35.25,36.13,35.25,35.75,51049600,1.20
1994-08-25,34.25,36.38,34.25,35.06,74698400,1.18
1994-08-24,34.75,35.00,34.38,34.88,42896000,1.17
1994-08-23,34.88,35.88,34.75,35.00,53611600,1.18
1994-08-22,34.75,35.00,34.63,34.88,38105200,1.17
1994-08-19,34.75,35.00,34.25,34.88,32636800,1.17
1994-08-18,34.75,35.25,34.50,34.63,51564800,1.17
1994-08-17,34.88,35.37,34.63,35.00,71545600,1.18
1994-08-16,34.38,34.75,34.00,34.75,38934000,1.17
1994-08-15,34.75,35.00,34.25,34.63,30018800,1.17
1994-08-12,34.38,35.12,33.87,34.75,44912000,1.17
1994-08-11,34.25,35.12,33.87,34.31,74522000,1.15
1994-08-10,33.62,34.88,33.25,34.63,63392000,1.16
1994-08-09,33.50,33.87,33.13,33.62,19650400,1.13
1994-08-08,33.13,34.00,33.00,33.75,35319200,1.13
1994-08-05,32.88,33.37,32.88,33.25,21753200,1.11
1994-08-04,33.13,33.75,33.13,33.25,46188800,1.11
1994-08-03,32.75,33.25,32.12,33.13,56711200,1.11
1994-08-02,33.50,33.62,32.38,32.56,67390400,1.09
1994-08-01,33.62,33.75,32.75,33.37,57318800,1.12
1994-07-29,31.87,34.00,31.87,33.69,138941600,1.13
1994-07-28,31.00,32.12,30.88,31.87,61328400,1.07
1994-07-27,31.25,31.38,30.62,31.06,33446000,1.04
1994-07-26,31.75,32.00,31.13,31.38,47202400,1.05
1994-07-25,31.13,31.87,30.75,31.69,105663600,1.06
1994-07-22,31.62,31.97,30.00,31.00,196644000,1.04
1994-07-21,26.62,28.50,26.50,28.00,72368800,0.94
1994-07-20,27.38,27.63,26.37,26.62,54342400,0.89
1994-07-19,28.62,28.75,27.38,27.69,29092000,0.93
1994-07-18,28.12,29.00,28.00,28.37,19107200,0.95
1994-07-15,28.23,28.62,27.50,28.25,23741200,0.95
1994-07-14,29.63,29.75,28.25,28.62,45166800,0.96
1994-07-13,28.50,30.25,28.50,29.69,112565600,1.00
1994-07-12,27.00,28.44,26.37,28.37,60578000,0.95
1994-07-11,27.12,27.38,26.62,27.00,26605600,0.91
1994-07-08,26.50,27.63,26.50,27.06,52057600,0.91
1994-07-07,25.88,27.00,25.50,26.81,42537600,0.90
1994-07-06,26.25,26.50,26.00,26.13,24346000,0.88
1994-07-05,25.63,26.75,25.63,26.50,21462000,0.89
1994-07-01,26.37,26.50,25.38,25.75,44819600,0.86
1994-06-30,26.25,26.87,26.25,26.50,25432400,0.89
1994-06-29,26.75,27.12,25.88,26.13,33891200,0.88
1994-06-28,26.25,27.12,25.63,26.75,43556800,0.90
1994-06-27,25.25,26.25,24.62,26.25,63988400,0.88
1994-06-24,25.12,26.13,24.75,25.61,73214400,0.86
1994-06-23,26.25,26.25,24.87,25.12,50974000,0.84
1994-06-22,26.25,26.75,26.00,26.25,28464800,0.88
1994-06-21,26.87,27.25,25.75,26.00,60818800,0.87
1994-06-20,26.25,27.25,26.00,27.12,49974400,0.91
1994-06-17,26.00,26.75,25.88,26.50,56123200,0.89
1994-06-16,27.75,27.75,26.13,26.37,54555200,0.88
1994-06-15,27.00,28.00,26.87,27.81,39869200,0.93
1994-06-14,27.25,27.38,26.62,27.06,38589600,0.91
1994-06-13,26.37,27.19,26.37,27.00,23226000,0.91
1994-06-10,27.12,27.38,26.37,26.50,35683200,0.89
1994-06-09,25.63,27.00,25.50,27.00,73382400,0.91
1994-06-08,27.50,27.63,26.00,26.13,68541200,0.88
1994-06-07,27.25,27.75,27.25,27.50,35061600,0.92
1994-06-06,27.50,27.75,27.00,27.38,31508400,0.92
1994-06-03,27.12,28.00,26.75,27.63,88421200,0.93
1994-06-02,28.37,28.50,27.12,27.38,96230400,0.92
1994-06-01,28.50,28.62,27.88,28.25,96440400,0.95
1994-05-31,29.50,29.50,28.50,29.25,64349600,0.98
1994-05-27,30.25,30.75,29.50,29.94,27171200,1.00
1994-05-26,31.50,31.50,30.25,30.50,18258800,1.02
1994-05-25,30.25,31.75,30.00,31.25,34028400,1.04
1994-05-24,31.00,31.25,30.25,30.75,31612000,1.03
1994-05-23,31.00,31.25,30.00,30.50,29988000,1.02
1994-05-20,31.75,32.25,31.00,31.06,24536400,1.04
1994-05-19,30.75,32.50,30.50,32.12,68395600,1.07
1994-05-18,29.75,30.75,29.25,30.62,30965200,1.02
1994-05-17,29.75,29.75,28.75,29.38,45026800,0.98
1994-05-16,30.00,30.50,29.50,29.50,33846400,0.99
1994-05-13,29.75,30.50,29.25,30.00,23153200,1.00
1994-05-12,30.50,30.75,29.50,29.69,26776400,0.99
1994-05-11,31.00,31.50,29.75,30.25,36380400,1.01
1994-05-10,31.75,32.00,31.00,31.00,36710800,1.04
1994-05-09,32.25,32.50,30.75,31.25,35117600,1.04
1994-05-06,32.25,32.75,31.25,32.31,46944800,1.08
1994-05-05,33.25,33.75,32.25,32.88,72083200,1.10
1994-05-04,31.00,33.25,30.50,33.00,91039200,1.10
1994-05-03,31.00,31.25,29.50,30.25,33224800,1.01
1994-05-02,30.00,31.25,30.00,31.00,30805600,1.04
1994-04-29,30.00,30.50,29.75,30.00,23696400,1.00
1994-04-28,31.00,31.25,29.75,30.25,25118800,1.01
1994-04-26,31.50,31.50,31.00,31.25,41056400,1.04
1994-04-25,29.75,31.00,29.50,31.00,89810000,1.04
1994-04-22,31.25,32.00,28.50,29.75,174456800,0.99
1994-04-21,28.50,30.50,27.00,29.63,102634000,0.99
1994-04-20,29.25,30.00,28.00,28.25,70462000,0.94
1994-04-19,29.75,30.00,28.50,29.00,41563200,0.97
1994-04-18,30.50,30.50,29.25,29.63,57573600,0.99
1994-04-15,31.25,31.50,30.00,30.25,47087600,1.01
1994-04-14,30.50,31.75,30.00,31.50,55498800,1.05
1994-04-13,32.25,32.50,31.25,31.75,58284800,1.06
1994-04-12,33.37,33.37,31.75,32.00,34207600,1.07
1994-04-11,33.50,33.50,32.50,33.50,26706400,1.12
1994-04-08,33.75,34.00,33.25,33.50,44212000,1.12
1994-04-07,33.50,33.75,32.75,33.37,19342400,1.11
1994-04-06,34.00,34.00,32.75,33.50,32272800,1.12
1994-04-05,33.75,34.25,33.50,33.50,24474800,1.12
1994-04-04,32.25,33.25,31.75,33.25,42075600,1.11
1994-03-31,32.50,33.50,31.50,33.25,52264800,1.11
1994-03-30,32.50,33.25,31.75,32.50,42456400,1.09
1994-03-29,33.25,33.75,32.25,32.75,53379200,1.09
1994-03-28,33.00,34.00,32.75,33.25,70644000,1.11
1994-03-25,34.75,34.75,32.75,32.75,85909600,1.09
1994-03-24,35.12,35.25,34.00,34.63,47023200,1.16
1994-03-23,35.25,35.50,34.25,35.12,54171600,1.17
1994-03-22,35.25,35.50,34.50,35.00,60706800,1.17
1994-03-21,36.38,36.50,35.25,35.50,61628000,1.19
1994-03-18,36.75,36.75,35.75,36.38,55918800,1.21
1994-03-17,36.75,37.00,36.25,36.50,39057200,1.22
1994-03-16,37.50,37.75,36.50,36.75,36792000,1.23
1994-03-15,38.25,38.25,37.25,37.62,51136400,1.26
1994-03-14,38.50,38.50,37.75,38.13,110426400,1.27
1994-03-11,37.00,37.75,36.75,37.25,40460000,1.24
1994-03-10,37.25,37.62,36.75,37.25,35940800,1.24
1994-03-09,36.63,37.50,36.00,37.50,62134800,1.25
1994-03-08,38.00,38.00,36.75,37.00,46513600,1.24
1994-03-07,37.00,38.13,36.75,37.88,77599200,1.27
1994-03-04,36.00,37.50,35.75,36.75,56711200,1.23
1994-03-03,35.75,36.25,35.50,35.75,47118400,1.19
1994-03-02,35.25,36.25,34.75,35.62,73536400,1.19
1994-03-01,36.75,36.75,35.75,36.25,52967600,1.21
1994-02-28,36.25,37.00,36.00,36.50,30956800,1.22
1994-02-25,37.00,37.25,35.50,36.00,59206000,1.20
1994-02-24,37.00,37.25,36.25,36.63,49464800,1.22
1994-02-23,37.25,38.25,37.00,37.25,65133600,1.24
1994-02-22,36.25,37.50,35.75,37.25,53642400,1.24
1994-02-18,36.50,37.00,36.25,36.25,37268000,1.21
1994-02-17,37.25,37.88,36.25,37.00,36288000,1.24
1994-02-16,37.50,37.50,36.75,36.75,30506000,1.23
1994-02-15,36.75,37.50,36.25,37.12,32443600,1.24
1994-02-14,37.00,38.00,36.75,37.00,61387200,1.24
1994-02-11,36.25,37.50,36.25,37.00,41062000,1.24
1994-02-10,36.25,37.50,36.00,36.50,75507600,1.22
1994-02-09,35.75,36.50,35.25,36.25,46746000,1.21
1994-02-08,36.00,36.50,35.25,35.75,71346800,1.19
1994-02-07,33.50,37.12,33.50,36.50,181361600,1.22
1994-02-04,33.50,35.00,33.25,33.50,88502400,1.11
1994-02-03,33.00,33.62,32.50,33.50,34498800,1.11
1994-02-02,33.25,33.25,32.50,33.00,36612800,1.10
1994-02-01,33.00,33.50,32.25,33.25,39180400,1.11
1994-01-31,33.50,33.75,32.75,32.75,59595200,1.09
1994-01-28,34.25,34.75,33.75,34.00,34109600,1.13
1994-01-27,33.50,34.25,33.00,34.12,33062400,1.14
1994-01-26,33.75,34.00,33.25,33.50,41451200,1.11
1994-01-25,34.75,35.00,33.25,33.87,110583200,1.13
1994-01-24,33.25,35.25,33.25,35.00,173037200,1.16
1994-01-21,33.25,33.50,32.25,33.37,245033600,1.11
1994-01-20,29.50,30.75,29.50,29.87,67020800,0.99
1994-01-19,29.25,29.75,28.75,29.25,70397600,0.97
1994-01-18,30.25,30.25,29.00,29.38,90700400,0.98
1994-01-17,31.00,31.50,30.00,30.37,36428000,1.01
1994-01-14,30.75,31.75,30.50,31.00,53628400,1.03
1994-01-13,30.00,30.75,29.75,30.62,132899200,1.02
1994-01-12,32.25,32.25,30.50,30.50,109779600,1.02
1994-01-11,33.50,33.75,31.75,31.87,88849600,1.06
1994-01-10,33.00,33.87,32.75,33.62,50397200,1.12
1994-01-07,32.00,33.25,31.25,33.13,74698400,1.10
1994-01-06,33.75,34.00,32.50,32.75,91627200,1.09
1994-01-05,31.75,33.87,31.75,33.75,153034000,1.12
1994-01-04,30.25,31.50,30.00,31.50,71293600,1.05
1994-01-03,29.50,30.00,29.00,29.87,45382400,0.99
1993-12-31,29.75,30.25,29.25,29.25,40241600,0.97
1993-12-30,28.50,30.25,28.50,29.75,78638000,0.99
1993-12-29,29.25,29.25,28.50,28.50,26838000,0.95
1993-12-28,28.75,29.50,28.50,29.13,39874800,0.97
1993-12-27,27.75,28.75,27.25,28.50,39984000,0.95
1993-12-23,27.25,27.25,26.50,27.25,56739200,0.91
1993-12-22,27.25,28.50,27.00,28.00,45343200,0.93
1993-12-21,28.50,28.75,27.25,27.50,62781600,0.92
1993-12-20,29.25,29.75,28.25,28.50,47258400,0.95
1993-12-17,29.50,29.75,29.13,29.50,36288000,0.98
1993-12-16,29.50,29.75,29.00,29.38,31592400,0.98
1993-12-15,29.00,29.75,29.00,29.75,30970800,0.99
1993-12-14,29.25,29.75,29.00,29.13,73416000,0.97
1993-12-13,28.25,29.50,27.75,29.50,61082000,0.98
1993-12-10,30.25,30.50,27.75,28.25,124314400,0.94
1993-12-09,31.75,32.00,29.75,30.00,45690400,1.00
1993-12-08,32.00,32.25,31.50,31.87,9898000,1.06
1993-12-07,32.00,32.25,31.50,32.25,15962800,1.07
1993-12-06,31.50,32.50,31.25,32.25,39244800,1.07
1993-12-03,31.75,32.00,31.00,31.50,30116800,1.05
1993-12-02,31.75,32.00,31.00,31.75,25163600,1.06
1993-12-01,32.00,32.25,31.25,31.50,27804000,1.05
1993-11-30,31.75,32.63,31.50,31.50,28165200,1.05
1993-11-29,32.25,32.50,31.50,31.75,24178000,1.06
1993-11-26,32.75,33.00,32.25,32.63,10861200,1.09
1993-11-24,32.75,33.50,32.63,33.00,22610000,1.10
1993-11-23,32.50,33.00,31.25,33.00,46541600,1.10
1993-11-22,32.75,33.00,32.25,32.50,37651600,1.08
1993-11-19,33.00,33.50,32.50,33.00,30741200,1.10
1993-11-18,33.50,33.75,33.00,33.50,28602000,1.11
1993-11-17,34.00,35.00,32.75,33.50,75656000,1.11
1993-11-16,32.00,34.25,31.75,34.00,75770800,1.13
1993-11-15,31.50,32.75,31.50,32.00,39275600,1.06
1993-11-12,31.50,32.00,30.50,31.75,35915600,1.05
1993-11-11,30.75,32.00,30.50,31.38,35607600,1.04
1993-11-10,30.25,30.75,30.00,30.75,19244400,1.02
1993-11-09,31.00,31.25,29.75,30.12,42812000,1.00
1993-11-08,32.00,32.12,30.50,30.75,41748000,1.02
1993-11-05,31.87,32.25,30.75,31.87,94508400,1.06
1993-11-04,31.50,32.25,30.75,32.25,46342800,1.07
1993-11-03,33.00,33.00,31.00,31.62,44240000,1.05
1993-11-02,31.25,33.00,31.00,32.75,56061600,1.09
1993-11-01,30.75,31.50,30.25,31.50,26493600,1.04
1993-10-29,31.00,31.75,30.50,30.75,34216000,1.02
1993-10-28,31.75,32.25,31.00,31.00,61115600,1.03
1993-10-27,30.00,32.25,29.75,31.75,114766400,1.05
1993-10-26,29.75,30.00,29.00,29.75,55619200,0.99
1993-10-25,30.25,30.50,29.63,30.00,54782000,0.99
1993-10-22,30.50,31.50,29.75,30.25,99019200,1.00
1993-10-21,27.50,31.25,27.25,30.25,156777600,1.00
1993-10-20,28.00,28.25,27.25,27.75,34602400,0.92
1993-10-19,28.25,28.50,27.25,27.75,53393200,0.92
1993-10-18,28.00,28.75,27.75,28.37,83249600,0.94
1993-10-15,27.75,28.50,26.75,28.25,238812000,0.94
1993-10-14,24.00,24.50,23.50,23.75,40171600,0.79
1993-10-13,24.25,24.25,23.50,24.00,44251200,0.80
1993-10-12,24.00,25.00,23.75,24.00,76585600,0.80
1993-10-11,22.75,24.00,22.75,23.75,40286400,0.79
1993-10-08,23.25,23.25,22.25,22.63,34851600,0.75
1993-10-07,23.50,23.75,22.75,23.00,33726000,0.76
1993-10-06,23.75,24.00,23.37,23.62,43820000,0.78
1993-10-05,23.00,24.00,23.00,23.50,44077600,0.78
1993-10-04,22.63,23.00,22.00,22.75,48210400,0.75
1993-10-01,22.75,23.00,22.50,22.75,83997200,0.75
1993-09-30,24.00,24.00,23.00,23.37,68726000,0.78
1993-09-29,24.25,24.87,23.75,23.88,59186400,0.79
1993-09-28,24.75,25.00,24.25,24.75,23637600,0.82
1993-09-27,25.00,25.25,24.25,24.75,28294000,0.82
1993-09-24,25.00,25.25,24.50,25.00,19143600,0.83
1993-09-23,25.50,25.50,24.50,24.75,32737600,0.82
1993-09-22,24.25,25.50,24.25,25.50,27622000,0.85
1993-09-21,24.75,25.25,23.88,24.50,36624000,0.81
1993-09-20,25.25,25.50,24.75,24.87,27759200,0.82
1993-09-17,24.38,25.50,24.25,25.25,43008000,0.84
1993-09-16,24.25,25.00,24.25,24.75,21490000,0.82
1993-09-15,24.50,25.00,23.50,24.50,64430800,0.81
1993-09-14,24.25,25.00,24.00,24.25,69160000,0.80
1993-09-13,26.25,26.50,24.75,25.25,63946400,0.84
1993-09-10,26.25,26.25,25.38,26.25,33622400,0.87
1993-09-09,26.75,27.00,26.00,26.00,37382800,0.86
1993-09-08,26.25,27.00,26.00,26.75,56658000,0.89
1993-09-07,26.00,27.00,25.75,26.25,35884800,0.87
1993-09-03,26.00,26.00,25.25,25.75,40734400,0.85
1993-09-02,26.00,26.25,25.25,25.75,70565600,0.85
1993-09-01,26.50,26.75,25.75,26.13,56392000,0.87
1993-08-31,26.50,26.75,26.00,26.50,31967600,0.88
1993-08-30,26.50,26.50,25.88,26.00,68434800,0.86
1993-08-27,27.00,27.00,26.25,26.50,46642400,0.88
1993-08-26,27.25,27.25,26.50,26.87,44035600,0.89
1993-08-25,28.00,28.25,26.75,27.25,36442000,0.90
1993-08-24,28.25,28.75,27.75,28.00,25314800,0.93
1993-08-23,28.00,28.75,27.50,28.37,22794800,0.94
1993-08-20,27.75,28.00,27.00,28.00,24984400,0.93
1993-08-19,28.75,28.75,27.50,27.50,38032400,0.91
1993-08-18,29.00,29.75,28.25,28.50,47180000,0.95
1993-08-17,27.75,28.50,27.25,28.37,27045200,0.94
1993-08-16,27.50,28.00,27.25,27.50,25611600,0.91
1993-08-13,26.50,27.75,26.25,27.38,34703200,0.90
1993-08-12,27.50,27.75,26.00,26.50,84543200,0.87
1993-08-11,28.50,28.50,27.00,27.50,41742400,0.91
1993-08-10,29.50,29.75,28.25,28.50,38194800,0.94
1993-08-09,29.25,30.25,29.00,29.75,40353600,0.98
1993-08-06,29.25,30.25,29.25,29.25,31480400,0.97
1993-08-05,30.75,30.75,29.00,29.50,52343200,0.97
1993-08-04,29.25,30.50,29.00,30.25,60748800,1.00
1993-08-03,29.00,29.25,28.75,29.00,44119600,0.96
1993-08-02,28.25,29.25,28.00,28.50,54076400,0.94
1993-07-30,27.50,28.25,27.00,27.75,53611600,0.92
1993-07-29,27.00,27.50,26.75,27.25,30343600,0.90
1993-07-28,26.25,27.00,26.25,26.87,22948800,0.89
1993-07-27,26.75,27.50,26.25,26.50,49652400,0.87
1993-07-26,26.75,27.50,26.00,26.87,38206000,0.89
1993-07-23,27.00,27.50,26.00,26.25,58444400,0.87
1993-07-22,26.00,27.00,25.75,26.50,52794000,0.87
1993-07-21,26.00,26.75,25.50,26.25,113976800,0.87
1993-07-20,26.25,27.75,25.75,26.87,132977600,0.89
1993-07-19,28.00,28.75,25.50,25.63,201558000,0.85
1993-07-16,28.50,29.63,26.50,27.50,530149200,0.91
1993-07-15,37.25,37.75,35.25,35.75,84509600,1.18
1993-07-14,36.75,37.50,35.75,37.25,61574800,1.23
1993-07-13,38.75,38.75,37.00,37.25,39527600,1.23
1993-07-12,36.75,38.13,36.25,38.00,43470000,1.25
1993-07-09,37.00,37.25,36.50,36.75,39219600,1.21
1993-07-08,36.50,37.50,36.25,36.50,34742400,1.21
1993-07-07,37.50,37.88,36.25,36.50,56758800,1.21
1993-07-06,38.25,39.00,37.50,37.75,38813600,1.25
1993-07-02,38.25,38.75,37.75,38.50,47908000,1.27
1993-07-01,39.00,39.75,38.00,38.00,54541200,1.25
1993-06-30,38.75,39.75,38.50,39.50,50064000,1.30
1993-06-29,40.25,40.25,38.50,39.00,73567200,1.29
1993-06-28,40.50,40.50,38.75,40.13,88404400,1.32
1993-06-25,40.37,40.75,39.50,40.00,64290800,1.32
1993-06-24,40.50,41.75,40.00,41.75,55708800,1.38
1993-06-23,41.75,41.75,40.00,40.50,45180800,1.34
1993-06-22,40.87,42.00,39.75,41.38,84095200,1.37
1993-06-21,40.50,40.50,39.50,39.63,68395600,1.31
1993-06-18,41.63,42.12,39.75,41.00,77823200,1.35
1993-06-17,42.50,42.50,40.50,41.25,102359600,1.36
1993-06-16,42.25,43.25,41.50,42.25,88270000,1.39
1993-06-15,45.25,45.25,41.88,42.00,112081200,1.39
1993-06-14,44.00,44.75,43.50,44.62,62372800,1.47
1993-06-11,45.00,45.25,43.38,43.75,60580800,1.44
1993-06-10,43.50,44.75,42.75,44.50,138426400,1.47
1993-06-09,45.00,45.62,44.00,44.25,294604800,1.46
1993-06-08,48.75,50.00,48.00,49.50,155274000,1.63
1993-06-07,54.50,54.75,50.38,50.75,120576400,1.68
1993-06-04,55.75,56.25,54.50,54.87,53421200,1.81
1993-06-03,57.00,57.25,56.00,56.37,39214000,1.86
1993-06-02,56.75,58.25,56.00,57.00,50120000,1.88
1993-06-01,56.50,57.75,56.50,57.00,33768000,1.88
1993-05-28,57.00,57.50,56.25,56.62,45987200,1.87
1993-05-27,57.75,58.50,57.25,57.50,49322000,1.89
1993-05-26,56.00,57.75,55.38,57.75,30391200,1.90
1993-05-25,56.75,57.50,55.75,56.37,45180800,1.86
1993-05-24,56.75,58.75,56.75,57.63,37578800,1.90
1993-05-21,58.75,59.13,56.75,57.50,37049600,1.89
1993-05-20,57.25,59.00,57.25,58.75,72632000,1.94
1993-05-19,54.75,57.50,54.50,57.25,43192800,1.89
1993-05-18,55.50,56.25,55.00,55.50,40868800,1.83
1993-05-17,55.50,56.00,55.00,55.75,17410400,1.84
1993-05-14,55.25,56.00,55.00,55.50,29352400,1.83
1993-05-13,53.50,55.75,53.50,55.50,90431600,1.83
1993-05-12,54.25,54.75,53.00,53.25,26306000,1.75
1993-05-11,55.00,55.25,54.00,54.50,39594800,1.80
1993-05-10,55.00,55.88,55.00,55.00,34482000,1.81
1993-05-07,53.50,54.75,53.50,54.75,20473600,1.80
1993-05-06,54.50,54.75,53.50,53.75,17614800,1.77
1993-05-05,53.00,55.50,53.00,54.50,63266000,1.80
1993-05-04,52.25,54.25,52.00,53.38,42705600,1.76
1993-05-03,51.25,52.00,51.00,51.88,16296000,1.71
1993-04-30,50.75,52.50,50.75,51.25,33084800,1.69
1993-04-29,51.50,51.75,50.13,50.75,20610800,1.67
1993-04-28,49.75,52.00,49.75,51.37,40810000,1.69
1993-04-27,48.75,50.25,48.75,50.25,32418400,1.66
1993-04-26,49.25,49.75,48.50,49.00,25701200,1.61
1993-04-23,49.75,50.25,48.75,49.25,33535600,1.62
1993-04-22,49.25,50.50,49.00,50.00,39418400,1.65
1993-04-21,50.25,50.75,49.25,49.62,51318400,1.64
1993-04-20,48.75,50.25,48.25,50.00,60012400,1.65
1993-04-19,48.50,49.50,48.25,48.50,56966000,1.60
1993-04-16,48.25,48.75,47.37,48.12,171698800,1.59
1993-04-15,48.25,48.25,46.75,47.25,54675600,1.56
1993-04-14,48.25,48.75,47.62,48.75,42515200,1.61
1993-04-13,50.50,51.25,48.25,48.50,41120800,1.60
1993-04-12,49.50,51.00,49.50,50.00,23262400,1.65
1993-04-08,50.00,50.50,49.00,49.75,40857600,1.64
1993-04-07,49.00,50.75,48.50,50.50,40712000,1.66
1993-04-06,50.00,50.25,48.75,48.75,42092400,1.61
1993-04-05,50.00,50.50,49.50,50.00,37293200,1.65
1993-04-02,50.50,51.25,49.50,50.13,63448000,1.65
1993-04-01,51.25,52.00,51.00,51.75,27050800,1.71
1993-03-31,52.50,52.75,51.25,51.50,55759200,1.70
1993-03-30,51.12,52.25,50.25,52.25,66012800,1.72
1993-03-29,52.25,52.50,50.75,51.00,65427600,1.68
1993-03-26,54.75,54.75,52.50,53.25,37940000,1.75
1993-03-25,53.75,54.75,53.50,54.75,42761600,1.80
1993-03-24,52.75,54.25,52.50,53.75,35767200,1.77
1993-03-23,53.25,54.00,52.62,52.75,25634000,1.74
1993-03-22,53.50,53.88,52.75,53.25,41300000,1.75
1993-03-19,55.00,55.25,53.50,53.75,38525200,1.77
1993-03-18,55.00,55.63,54.50,54.50,26546800,1.80
1993-03-17,56.50,57.00,55.00,55.12,44055200,1.82
1993-03-16,57.25,57.75,56.50,56.50,25320400,1.86
1993-03-15,56.00,57.25,55.38,57.00,34008800,1.88
1993-03-12,56.75,56.75,55.50,56.25,31673600,1.85
1993-03-11,57.00,57.25,56.25,56.88,36153600,1.87
1993-03-10,56.75,57.25,56.00,56.75,33124000,1.87
1993-03-09,56.50,57.50,56.50,56.75,38707200,1.87
1993-03-08,55.00,56.75,55.00,56.50,44251200,1.86
1993-03-05,54.75,55.75,54.75,55.00,27904800,1.81
1993-03-04,54.50,55.25,53.50,55.00,47084800,1.81
1993-03-03,54.00,55.00,53.25,54.62,50674400,1.80
1993-03-02,53.00,54.50,53.00,54.25,36923600,1.79
1993-03-01,53.00,53.50,52.75,53.25,29825600,1.75
1993-02-26,54.25,54.25,52.25,53.00,73721200,1.75
1993-02-25,53.25,54.75,53.25,54.75,41806800,1.80
1993-02-24,52.13,53.88,52.13,53.63,71640800,1.77
1993-02-23,55.00,55.25,54.00,54.25,48518400,1.79
1993-02-22,55.00,56.00,54.75,55.12,24690400,1.82
1993-02-19,55.25,55.50,54.75,55.00,44450000,1.81
1993-02-18,55.00,55.25,53.50,55.00,70030800,1.81
1993-02-17,53.25,54.00,52.00,53.88,62395200,1.78
1993-02-16,53.50,53.50,51.50,53.00,101934000,1.75
1993-02-12,55.00,55.50,53.75,53.88,68849200,1.78
1993-02-11,55.75,56.25,55.00,55.12,42067200,1.81
1993-02-10,57.00,57.25,55.00,55.75,67071200,1.83
1993-02-09,57.00,57.38,56.50,56.88,59665200,1.87
1993-02-08,57.00,57.50,55.50,56.50,70268800,1.86
1993-02-05,59.25,59.50,56.25,57.25,91904400,1.88
1993-02-04,60.00,60.25,59.00,59.50,52038000,1.96
1993-02-03,61.00,61.00,58.50,60.00,66046400,1.97
1993-02-02,60.75,61.50,60.25,60.25,45584000,1.98
1993-02-01,59.25,61.25,59.25,61.25,60138400,2.01
1993-01-29,60.25,61.25,59.00,59.50,66525200,1.96
1993-01-28,60.00,60.25,59.25,59.87,46009600,1.97
1993-01-27,61.00,61.75,58.75,60.25,56655200,1.98
1993-01-26,60.50,62.00,60.50,60.75,71405600,2.00
1993-01-25,59.25,60.50,59.25,60.00,50568000,1.97
1993-01-22,60.25,60.25,59.00,59.50,36736000,1.96
1993-01-21,59.75,60.25,58.75,60.00,46104800,1.97
1993-01-20,59.75,60.25,59.50,60.00,39684400,1.97
1993-01-19,59.75,60.50,59.25,59.75,68510400,1.96
1993-01-18,59.50,60.00,58.00,59.50,83409200,1.96
1993-01-15,61.00,62.25,60.00,60.25,225657600,1.98
1993-01-14,64.00,65.25,63.75,65.00,91952000,2.14
1993-01-13,61.50,64.00,61.25,63.50,49910000,2.09
1993-01-12,62.75,63.75,61.50,61.50,86539600,2.02
1993-01-11,62.00,64.38,61.75,64.13,68432000,2.11
1993-01-08,60.75,63.00,59.75,62.25,80234000,2.05
1993-01-07,61.75,62.50,60.63,61.00,68034400,2.01
1993-01-06,60.75,62.00,60.50,61.75,70350000,2.03
1993-01-05,58.00,59.25,57.25,59.25,46564000,1.95
1993-01-04,59.50,60.00,57.75,58.25,32284000,1.91
1992-12-31,58.75,60.00,58.75,59.75,23058000,1.96
1992-12-30,59.75,59.75,58.75,58.75,25146800,1.93
1992-12-29,59.50,60.75,59.50,59.62,29069600,1.96
1992-12-28,59.25,59.75,59.25,59.50,17612000,1.96
1992-12-24,60.00,60.00,59.00,59.00,11491200,1.94
1992-12-23,60.25,60.50,59.25,59.75,28084000,1.96
1992-12-22,59.75,61.25,59.75,60.63,70042000,1.99
1992-12-21,58.25,60.00,58.00,59.62,64016400,1.96
1992-12-18,57.50,59.25,57.25,58.25,58864400,1.91
1992-12-17,55.25,57.50,55.25,56.88,58466800,1.87
1992-12-16,56.25,57.00,54.50,55.00,56481600,1.81
1992-12-15,56.75,57.00,55.50,56.37,45634400,1.85
1992-12-14,57.50,57.75,56.75,57.25,27627600,1.88
1992-12-11,57.25,58.25,57.25,57.50,30046800,1.89
1992-12-10,57.25,57.63,56.50,57.25,35047600,1.88
1992-12-09,57.75,58.00,57.25,57.63,39852400,1.89
1992-12-08,57.75,58.75,57.75,58.12,49159600,1.91
1992-12-07,56.75,57.75,56.75,57.75,36055600,1.90
1992-12-04,57.25,57.50,56.50,56.88,23945600,1.87
1992-12-03,56.50,57.63,56.12,57.50,46897200,1.89
1992-12-02,58.25,58.50,57.00,57.25,24444000,1.88
1992-12-01,57.25,59.00,56.75,58.25,32536000,1.91
1992-11-30,56.25,57.50,55.63,57.50,40126800,1.89
1992-11-27,56.50,57.25,56.25,56.50,11799200,1.85
1992-11-25,57.00,57.25,56.00,56.50,29335600,1.85
1992-11-24,57.00,57.50,56.50,57.50,39205600,1.89
1992-11-23,56.50,57.00,56.25,56.75,38180800,1.86
1992-11-20,58.50,58.75,57.00,57.50,38872400,1.89
1992-11-19,57.75,59.50,57.75,58.25,60135600,1.91
1992-11-18,56.00,58.25,55.50,57.75,76202000,1.89
1992-11-17,57.25,57.50,54.87,55.25,42201600,1.81
1992-11-16,56.25,57.75,56.00,57.38,16886800,1.88
1992-11-13,57.00,57.25,56.00,56.25,21187600,1.85
1992-11-12,57.00,57.50,56.37,56.88,26899600,1.87
1992-11-11,56.50,58.25,56.25,56.75,35106400,1.86
1992-11-10,55.00,56.50,54.75,56.25,30556400,1.85
1992-11-09,56.00,56.00,54.75,55.25,28232400,1.81
1992-11-06,54.75,56.50,54.75,55.75,65993200,1.83
1992-11-05,52.50,55.00,52.50,55.00,74513600,1.80
1992-11-04,52.00,52.75,52.00,52.50,35490000,1.72
1992-11-03,52.50,52.50,51.50,52.00,28187600,1.71
1992-11-02,52.50,52.75,51.75,52.25,42523600,1.71
1992-10-30,53.50,53.50,52.00,52.50,32457600,1.72
1992-10-29,52.25,54.00,51.50,53.25,53474400,1.75
1992-10-28,51.25,52.75,50.75,52.25,49148400,1.71
1992-10-27,51.50,52.50,51.00,51.50,52990000,1.69
1992-10-26,48.75,51.50,48.50,51.50,62672400,1.69
1992-10-23,49.25,49.50,48.25,48.75,22856400,1.60
1992-10-22,48.50,49.25,48.25,48.75,21117600,1.60
1992-10-21,49.25,49.50,48.00,48.50,28562800,1.59
1992-10-20,49.00,50.00,48.50,49.12,71811600,1.61
1992-10-19,49.00,49.25,48.50,49.00,49011200,1.61
1992-10-16,46.75,49.50,46.50,49.00,112837200,1.61
1992-10-15,45.75,46.00,45.25,45.50,18855200,1.49
1992-10-14,45.25,46.25,45.00,46.00,23931600,1.51
1992-10-13,44.75,46.00,44.00,45.38,36794800,1.49
1992-10-12,43.25,44.25,43.25,44.00,17908800,1.44
1992-10-09,43.50,44.00,43.00,43.38,14686000,1.42
1992-10-08,44.00,44.25,43.00,43.50,31743600,1.43
1992-10-07,45.00,45.25,43.50,43.75,28327600,1.44
1992-10-06,43.75,45.00,42.75,44.75,28361200,1.47
1992-10-05,43.25,43.75,41.50,43.50,66239600,1.43
1992-10-02,44.50,44.75,43.00,43.75,28386400,1.44
1992-10-01,44.75,45.13,44.25,44.25,30682400,1.45
1992-09-30,45.00,45.50,44.50,45.13,25012400,1.48
1992-09-29,44.50,45.50,44.00,44.88,39317600,1.47
1992-09-28,45.00,45.00,43.75,44.75,37380000,1.47
1992-09-25,46.25,46.50,45.25,45.50,34367200,1.49
1992-09-24,47.25,47.75,46.25,46.25,31413200,1.52
1992-09-23,46.00,47.50,45.50,47.50,30993200,1.56
1992-09-22,46.75,46.75,45.25,45.75,27885200,1.50
1992-09-21,46.75,47.75,46.25,46.50,22419600,1.53
1992-09-18,45.75,46.88,45.25,46.50,28901600,1.53
1992-09-17,47.25,47.25,45.38,46.00,43108800,1.51
1992-09-16,47.75,48.25,46.50,47.00,44679600,1.54
1992-09-15,49.25,49.25,47.75,48.25,54630800,1.58
1992-09-14,49.00,50.00,48.50,49.50,53670400,1.62
1992-09-11,49.00,49.25,47.50,47.62,44970800,1.56
1992-09-10,48.00,49.50,47.50,49.25,57044400,1.62
1992-09-09,48.00,49.25,47.75,49.00,39300800,1.61
1992-09-08,46.75,48.00,46.50,47.75,17500000,1.57
1992-09-04,48.25,48.25,46.75,47.25,15808800,1.55
1992-09-03,49.00,49.25,47.75,47.75,52964800,1.57
1992-09-02,46.50,48.75,46.50,48.50,47474000,1.59
1992-09-01,46.25,46.50,45.75,46.50,15072400,1.53
1992-08-31,45.00,46.25,44.75,46.00,30279200,1.51
1992-08-28,44.25,45.25,44.00,45.00,15310400,1.48
1992-08-27,44.75,45.13,44.25,44.50,20686400,1.46
1992-08-26,44.25,44.50,43.25,44.25,30265200,1.45
1992-08-25,43.25,44.50,43.25,44.37,33090400,1.46
1992-08-24,44.25,44.75,43.25,43.25,38043600,1.42
1992-08-21,44.75,45.25,44.00,44.62,27367200,1.46
1992-08-20,44.75,45.00,44.25,44.75,27227200,1.47
1992-08-19,44.62,45.25,44.50,44.50,42635600,1.46
1992-08-18,44.50,45.25,44.50,44.75,28078400,1.47
1992-08-17,44.25,44.75,43.75,44.75,32177600,1.47
1992-08-14,45.00,45.25,44.50,44.75,34025600,1.46
1992-08-13,44.50,45.50,44.25,44.75,42747600,1.46
1992-08-12,43.75,44.25,43.25,44.12,30346400,1.44
1992-08-11,44.50,44.50,43.00,43.50,30326800,1.42
1992-08-10,43.25,44.50,43.00,44.12,22862000,1.44
1992-08-07,42.00,43.75,41.50,43.38,54790400,1.42
1992-08-06,44.25,44.50,42.75,44.00,64492400,1.44
1992-08-05,45.50,45.50,44.50,44.75,34815200,1.46
1992-08-04,45.00,45.75,44.75,45.50,29929200,1.49
1992-08-03,46.75,47.25,45.50,45.75,17136000,1.50
1992-07-31,47.25,47.50,46.75,46.75,22677200,1.53
1992-07-30,47.25,47.50,46.75,47.25,34473600,1.55
1992-07-29,46.63,47.75,46.50,47.25,62692000,1.55
1992-07-28,45.50,46.50,45.25,46.50,33560800,1.52
1992-07-27,45.75,46.50,45.25,45.25,599200,1.48
1992-07-24,44.50,46.25,44.00,45.87,33742800,1.50
1992-07-23,44.50,44.75,43.75,44.75,42879200,1.46
1992-07-22,45.25,45.50,44.00,44.25,40493600,1.45
1992-07-21,45.50,46.25,45.00,45.75,32986800,1.50
1992-07-20,44.75,45.25,44.00,44.75,48031200,1.46
1992-07-17,45.00,46.00,44.62,45.00,105910000,1.47
1992-07-16,47.75,49.00,47.25,48.75,34949600,1.59
1992-07-15,47.50,49.00,47.25,48.00,43615600,1.57
1992-07-14,47.00,48.00,47.00,47.50,31497200,1.55
1992-07-13,45.75,47.13,45.25,47.00,31390800,1.54
1992-07-10,46.00,46.25,44.88,45.75,35949200,1.50
1992-07-09,46.00,46.50,45.75,45.87,41448400,1.50
1992-07-08,44.00,45.75,44.00,45.75,48988800,1.50
1992-07-07,46.25,46.25,43.50,44.25,51772000,1.45
1992-07-06,46.50,46.75,45.50,46.25,30500400,1.51
1992-07-02,49.00,49.00,45.75,46.25,64162000,1.51
1992-07-01,48.00,49.50,47.75,49.00,35882000,1.60
1992-06-30,46.75,48.25,46.50,48.00,48336400,1.57
1992-06-29,45.75,47.13,45.25,46.75,47107200,1.53
1992-06-26,45.75,46.00,44.50,45.25,27591200,1.48
1992-06-25,46.50,46.50,45.25,45.62,40152000,1.49
1992-06-24,45.50,46.00,45.25,46.00,52766000,1.51
1992-06-23,45.00,45.50,44.50,45.25,77887600,1.48
1992-06-22,44.00,44.75,42.75,44.25,97484800,1.45
1992-06-19,46.00,46.00,43.75,44.75,106859200,1.46
1992-06-18,47.50,49.00,44.75,45.25,108430000,1.48
1992-06-17,49.00,49.25,47.00,47.50,76062000,1.55
1992-06-16,51.75,52.00,48.75,49.25,91338800,1.61
1992-06-15,54.00,54.00,52.50,52.62,47297600,1.72
1992-06-12,54.50,55.00,54.25,54.62,24127600,1.79
1992-06-11,53.75,54.25,53.50,53.88,35128800,1.76
1992-06-10,54.00,54.75,53.50,53.75,31651200,1.76
1992-06-09,54.25,54.25,53.50,54.00,25320400,1.77
1992-06-08,55.00,55.00,54.00,54.25,26084800,1.77
1992-06-05,54.75,55.25,54.25,54.87,28182000,1.80
1992-06-04,54.25,54.75,53.50,54.50,45038000,1.78
1992-06-03,56.50,56.50,54.00,54.13,75143600,1.77
1992-06-02,57.50,57.50,56.25,56.50,38920000,1.85
1992-06-01,57.25,59.50,56.00,57.50,62011600,1.88
1992-05-29,59.75,60.63,59.50,59.75,44562000,1.95
1992-05-28,60.00,60.25,59.00,59.50,31810800,1.94
1992-05-27,59.25,60.25,59.00,60.25,38522400,1.97
1992-05-26,59.50,59.75,58.75,59.25,23903600,1.93
1992-05-22,59.00,59.75,59.00,59.50,11617200,1.94
1992-05-21,60.25,60.25,58.75,59.13,34423200,1.93
1992-05-20,59.75,60.25,59.25,60.00,43302000,1.96
1992-05-19,60.75,60.75,59.00,59.38,32919600,1.94
1992-05-18,61.50,61.50,60.00,60.38,32272800,1.97
1992-05-15,61.00,61.25,60.50,60.63,30326800,1.98
1992-05-14,62.75,63.00,60.25,61.37,39230800,2.00
1992-05-13,62.50,63.25,62.25,62.75,24368400,2.05
1992-05-12,62.25,63.00,61.75,62.25,19261200,2.03
1992-05-11,62.00,62.75,61.50,62.25,22724800,2.03
1992-05-08,61.50,62.88,61.00,62.00,49674800,2.02
1992-05-07,61.50,62.25,60.50,60.75,43089200,1.98
1992-05-06,60.75,62.12,60.50,61.75,44497600,2.02
1992-05-05,60.50,60.63,59.50,60.50,45021200,1.98
1992-05-04,59.50,61.25,59.25,60.50,30808400,1.98
1992-05-01,60.00,60.75,58.25,59.25,33594400,1.93
1992-04-30,57.25,60.25,56.50,60.12,65066400,1.96
1992-04-29,54.25,57.00,54.25,57.00,49725200,1.86
1992-04-28,55.25,55.75,53.00,54.25,43531600,1.77
1992-04-27,56.00,56.25,55.00,55.75,35067200,1.82
1992-04-24,57.00,58.25,56.00,56.50,24570000,1.84
1992-04-23,57.50,58.25,56.00,57.00,45704400,1.86
1992-04-22,56.25,58.00,56.25,57.63,42882000,1.88
1992-04-21,57.00,57.25,56.00,56.25,45091200,1.84
1992-04-20,59.00,59.00,56.00,56.75,51511600,1.85
1992-04-16,60.25,60.75,58.50,59.00,64671600,1.93
1992-04-15,58.00,60.88,57.50,60.50,54339600,1.98
1992-04-14,57.75,59.25,57.25,58.75,36100400,1.92
1992-04-13,55.50,56.75,55.25,56.50,30707600,1.84
1992-04-10,57.25,57.50,55.00,55.50,68516000,1.81
1992-04-09,56.00,58.25,55.25,57.25,48034000,1.87
1992-04-08,57.00,57.00,54.75,55.88,91756000,1.82
1992-04-07,61.00,61.25,57.25,57.25,57554000,1.87
1992-04-06,59.00,61.00,59.00,60.75,25496800,1.98
1992-04-03,58.75,59.25,58.50,59.00,29114400,1.93
1992-04-02,59.00,59.50,58.37,58.75,33493600,1.92
1992-04-01,57.25,59.25,57.25,59.00,39914000,1.93
1992-03-31,58.25,59.75,58.00,58.25,53158000,1.90
1992-03-30,61.25,61.25,57.75,58.12,84758800,1.90
1992-03-27,63.88,64.00,60.50,61.00,66133200,1.99
1992-03-26,64.75,65.25,63.75,64.00,30755200,2.09
1992-03-25,65.00,65.00,64.25,64.50,30388400,2.11
1992-03-24,63.50,65.00,63.25,65.00,52354400,2.12
1992-03-23,63.00,63.75,63.00,63.00,12518800,2.06
1992-03-20,63.00,63.25,63.00,63.25,13540800,2.07
1992-03-19,63.75,63.75,62.75,63.00,29629600,2.06
1992-03-18,63.25,64.00,63.00,63.75,20258000,2.08
1992-03-17,63.50,63.75,62.75,62.88,21274400,2.05
1992-03-16,62.75,63.50,61.75,63.37,14072800,2.07
1992-03-13,63.25,63.75,62.00,63.12,19796000,2.06
1992-03-12,63.25,63.75,61.50,62.75,38225600,2.05
1992-03-11,63.75,64.25,63.00,63.25,32914000,2.07
1992-03-10,64.00,64.75,63.75,63.75,30674000,2.08
1992-03-09,63.75,64.25,63.50,63.75,27235600,2.08
1992-03-06,63.50,64.00,63.00,64.00,33572000,2.09
1992-03-05,64.50,65.50,63.00,63.50,59180800,2.07
1992-03-04,66.25,66.75,64.75,65.00,28842800,2.12
1992-03-03,67.75,68.00,66.25,66.38,24819200,2.17
1992-03-02,67.75,68.50,67.25,67.25,22313200,2.20
1992-02-28,68.50,69.00,67.00,67.50,22598800,2.20
1992-02-27,70.00,70.00,68.00,68.50,30542400,2.24
1992-02-26,68.25,70.00,68.25,69.88,57271200,2.28
1992-02-25,66.25,68.50,65.25,68.50,56803600,2.24
1992-02-24,66.25,66.50,65.75,66.13,42851200,2.16
1992-02-21,64.75,65.50,64.50,65.00,37895200,2.12
1992-02-20,62.50,64.75,62.25,64.63,32715200,2.11
1992-02-19,62.75,63.00,61.75,62.00,23917600,2.02
1992-02-18,64.25,64.50,62.75,62.75,17088400,2.05
1992-02-14,63.75,64.25,63.25,64.13,18146800,2.09
1992-02-13,65.25,65.25,63.75,64.25,19003600,2.09
1992-02-12,63.75,65.50,63.00,65.25,34490400,2.13
1992-02-11,63.00,63.75,62.25,62.88,30503200,2.05
1992-02-10,64.00,64.25,63.00,63.12,21610400,2.06
1992-02-07,64.25,64.75,62.75,64.00,36884400,2.09
1992-02-06,65.75,66.00,64.00,64.13,23284800,2.09
1992-02-05,66.25,66.75,65.12,66.13,40376000,2.16
1992-02-04,65.75,66.25,65.00,65.75,48232800,2.14
1992-02-03,64.75,66.25,64.50,65.75,39533200,2.14
1992-01-31,64.00,65.25,63.50,64.75,36139600,2.11
1992-01-30,63.50,63.75,62.75,63.75,21778400,2.08
1992-01-29,64.75,65.75,63.25,63.25,36139600,2.06
1992-01-28,64.75,65.37,63.00,65.25,43430800,2.13
1992-01-27,64.75,65.25,64.25,64.50,20862800,2.10
1992-01-24,64.50,65.75,64.00,64.63,44402400,2.11
1992-01-23,64.25,64.75,63.00,64.50,34588400,2.10
1992-01-22,61.50,63.75,61.25,63.50,45920000,2.07
1992-01-21,64.25,64.25,61.00,61.13,48521200,1.99
1992-01-20,64.50,65.25,64.00,64.00,52416000,2.09
1992-01-17,67.75,69.00,64.75,64.75,212088800,2.11
1992-01-16,63.75,64.25,62.50,62.75,73382400,2.05
1992-01-15,64.50,65.00,63.00,63.50,81435200,2.07
1992-01-14,62.25,64.75,62.25,64.50,68451600,2.10
1992-01-13,62.25,62.75,61.50,62.00,26964000,2.02
1992-01-10,61.50,62.50,61.00,62.25,49056000,2.03
1992-01-09,60.50,62.25,60.25,62.25,52127600,2.03
1992-01-08,58.50,61.25,58.50,60.50,58186800,1.97
1992-01-07,57.50,59.50,57.50,59.13,35366800,1.93
1992-01-06,58.75,59.00,57.75,58.00,28560000,1.89
1992-01-03,60.00,60.25,58.25,59.00,47563600,1.92
1992-01-02,55.75,59.75,55.50,59.50,58408000,1.94
1991-12-31,57.38,58.00,56.00,56.37,33507600,1.84
1991-12-30,55.00,57.25,55.00,56.75,45911600,1.85
1991-12-27,54.75,55.75,54.50,55.00,41935600,1.79
1991-12-26,52.75,55.00,52.25,54.87,33625200,1.79
1991-12-24,52.00,53.75,51.75,52.25,47140800,1.70
1991-12-23,50.50,51.75,50.00,51.50,25790800,1.68
1991-12-20,51.25,51.50,50.25,50.25,32046000,1.64
1991-12-19,51.25,51.75,50.75,50.75,28831600,1.65
1991-12-18,50.25,52.00,50.00,51.75,46650800,1.69
1991-12-17,50.50,51.00,50.25,50.50,24460800,1.65
1991-12-16,50.38,50.75,50.00,50.50,19297600,1.65
1991-12-13,49.75,50.75,49.75,50.38,23780400,1.64
1991-12-12,49.37,49.75,49.00,49.37,22937600,1.61
1991-12-11,49.25,49.75,48.50,49.00,21140000,1.60
1991-12-10,49.00,49.50,48.50,49.12,30654400,1.60
1991-12-09,49.00,50.00,48.75,49.12,24458000,1.60
1991-12-06,49.50,49.75,48.50,48.75,49246400,1.59
1991-12-05,50.50,51.00,49.25,50.00,24799600,1.63
1991-12-04,50.75,50.75,50.00,50.50,20137600,1.65
1991-12-03,52.00,52.00,50.25,50.50,25715200,1.65
1991-12-02,50.75,52.00,50.00,51.75,29724800,1.69
1991-11-29,50.50,51.50,50.50,50.75,8523200,1.65
1991-11-27,51.25,51.50,50.50,51.00,15808800,1.66
1991-11-26,51.50,52.00,50.00,51.50,34818000,1.68
1991-11-25,51.00,52.25,51.00,51.25,19608400,1.67
1991-11-22,51.00,51.75,50.25,51.25,24460800,1.67
1991-11-21,50.50,51.75,50.50,51.00,26703600,1.66
1991-11-20,51.25,52.00,50.25,50.50,42025200,1.65
1991-11-19,51.75,51.75,49.75,51.25,71372000,1.67
1991-11-18,50.00,52.50,50.00,52.13,59684800,1.70
1991-11-15,54.50,54.75,49.75,50.00,64237600,1.63
1991-11-14,54.25,55.25,54.00,54.75,47000800,1.78
1991-11-13,54.00,54.50,53.50,54.13,46480000,1.76
1991-11-12,54.25,54.75,53.75,54.50,41672400,1.77
1991-11-11,53.50,54.50,53.25,53.75,41235600,1.75
1991-11-08,51.25,53.75,51.00,53.25,93956800,1.73
1991-11-07,48.50,50.50,48.25,49.75,74183200,1.62
1991-11-06,49.00,49.25,47.50,48.00,59197600,1.56
1991-11-05,49.75,50.50,48.75,48.75,53900000,1.58
1991-11-04,50.75,50.75,48.50,49.75,48823600,1.62
1991-11-01,51.25,52.00,50.50,51.00,50316000,1.66
1991-10-31,50.75,51.75,50.00,51.50,57951600,1.67
1991-10-30,52.00,52.75,49.50,49.75,37060800,1.62
1991-10-29,51.50,52.00,50.75,51.75,25309200,1.68
1991-10-28,51.50,51.75,50.75,51.50,19465600,1.67
1991-10-25,51.75,52.25,50.75,51.25,26742800,1.67
1991-10-24,53.00,53.25,51.50,52.13,44475200,1.69
1991-10-23,55.00,55.25,52.75,53.12,42207200,1.73
1991-10-22,55.50,56.25,54.50,54.50,52052000,1.77
1991-10-21,55.25,55.88,54.25,54.75,29173200,1.78
1991-10-18,55.12,55.50,54.50,55.00,111739600,1.79
1991-10-17,53.00,53.25,51.50,52.38,37903600,1.70
1991-10-16,52.50,54.00,52.25,53.50,50218000,1.74
1991-10-15,50.50,52.50,50.00,52.50,72052400,1.71
1991-10-14,49.00,50.25,48.75,49.88,27969200,1.62
1991-10-11,48.12,48.88,46.50,48.50,30013200,1.58
1991-10-10,48.75,49.00,46.75,47.75,39303600,1.55
1991-10-09,48.25,48.75,47.75,48.00,33185600,1.56
1991-10-08,48.12,48.50,46.50,48.25,43064000,1.57
1991-10-07,48.00,48.75,47.50,48.12,16175600,1.56
1991-10-04,48.00,48.75,47.50,48.25,19843600,1.57
1991-10-03,50.00,50.00,47.50,47.75,45250800,1.55
1991-10-02,51.75,51.75,49.50,49.75,4496800,1.62
1991-10-01,49.25,51.25,49.00,50.75,32844000,1.65
1991-09-30,49.25,49.75,49.00,49.50,15800400,1.61
1991-09-27,50.00,50.75,48.75,49.00,15702400,1.59
1991-09-26,50.25,50.25,49.00,50.00,17805200,1.63
1991-09-25,50.25,50.50,49.25,50.50,13616400,1.64
1991-09-24,49.50,50.38,48.25,50.25,26524400,1.63
1991-09-23,50.00,50.75,49.25,49.50,21915600,1.61
1991-09-20,49.75,51.00,49.50,50.63,47037200,1.65
1991-09-19,50.25,50.50,49.50,49.75,44584400,1.62
1991-09-18,48.75,50.50,48.50,50.13,30338000,1.63
1991-09-17,47.00,49.00,46.75,49.00,33852000,1.59
1991-09-16,49.25,49.25,46.50,47.25,51444400,1.54
1991-09-13,50.00,50.25,48.50,48.63,41683600,1.58
1991-09-12,51.25,51.25,49.75,50.63,29803200,1.65
1991-09-11,50.75,51.00,49.50,50.50,44500400,1.64
1991-09-10,52.75,53.38,49.75,50.13,45710000,1.63
1991-09-09,51.75,53.50,51.50,53.25,31620400,1.73
1991-09-06,51.00,51.75,50.50,51.50,19818400,1.67
1991-09-05,51.50,51.75,50.75,51.00,19471200,1.66
1991-09-04,52.75,52.75,51.37,51.50,29946000,1.67
1991-09-03,52.75,53.25,52.00,52.50,17094000,1.71
1991-08-30,53.00,53.25,52.25,53.00,16534000,1.72
1991-08-29,53.25,53.88,52.50,53.00,28338800,1.72
1991-08-28,54.00,54.25,53.12,53.25,26896800,1.73
1991-08-27,53.00,54.00,52.75,54.00,25088000,1.76
1991-08-26,53.00,53.50,52.50,53.00,25398800,1.72
1991-08-23,54.00,55.50,52.75,53.00,60104800,1.72
1991-08-22,54.00,54.75,53.75,54.25,41412000,1.76
1991-08-21,52.50,54.13,52.00,53.75,55843200,1.75
1991-08-20,51.50,51.75,50.50,51.00,49856800,1.66
1991-08-19,49.50,51.62,48.50,50.50,80620400,1.64
1991-08-16,52.75,54.25,52.25,53.25,39701200,1.73
1991-08-15,55.00,55.00,53.00,53.25,36386000,1.73
1991-08-14,54.75,55.00,53.88,54.87,50178800,1.78
1991-08-13,52.00,54.00,52.00,53.50,71646400,1.74
1991-08-12,50.75,52.25,50.50,51.75,35632800,1.68
1991-08-09,50.50,51.00,49.75,50.75,38600800,1.65
1991-08-08,50.75,51.75,50.00,50.50,47362000,1.64
1991-08-07,49.50,51.00,49.37,50.38,52903200,1.63
1991-08-06,48.75,50.25,47.75,49.50,55106800,1.61
1991-08-05,49.75,49.75,48.25,48.50,25191600,1.57
1991-08-02,49.75,50.25,49.00,50.00,68252800,1.62
1991-08-01,46.00,49.25,45.75,49.12,112106400,1.59
1991-07-31,46.50,46.88,45.00,46.25,25701200,1.50
1991-07-30,45.50,46.75,45.50,46.50,22965600,1.51
1991-07-29,45.25,45.50,44.50,45.50,13325200,1.48
1991-07-26,45.75,45.75,44.75,44.88,18558400,1.46
1991-07-25,45.25,45.75,45.00,45.25,16450000,1.47
1991-07-24,45.25,45.75,44.50,45.00,32863600,1.46
1991-07-23,46.25,46.50,44.50,45.00,33264000,1.46
1991-07-22,45.75,46.25,45.50,46.00,27168400,1.49
1991-07-19,45.25,46.25,45.00,46.00,32104800,1.49
1991-07-18,44.00,45.13,43.00,44.88,99579200,1.46
1991-07-17,43.50,44.50,42.25,42.50,52234000,1.38
1991-07-16,45.50,45.75,43.50,43.75,55748000,1.42
1991-07-15,46.75,46.75,45.50,45.50,34496000,1.48
1991-07-12,47.25,47.25,46.25,46.75,33188400,1.52
1991-07-11,47.00,47.25,46.00,46.75,36478400,1.52
1991-07-10,47.50,48.25,46.75,47.25,39144000,1.53
1991-07-09,47.25,48.25,46.50,46.88,56610400,1.52
1991-07-08,45.25,47.25,45.00,46.75,76770400,1.52
1991-07-05,43.00,46.00,42.75,45.62,82888400,1.48
1991-07-03,42.25,43.50,41.75,43.13,77593600,1.40
1991-07-02,42.25,42.75,41.75,42.25,30035600,1.37
1991-07-01,42.25,43.00,41.75,42.50,48706000,1.38
1991-06-28,42.25,42.50,40.25,41.50,56660800,1.35
1991-06-27,42.50,42.75,41.75,42.50,37800000,1.38
1991-06-26,42.75,43.50,42.25,43.00,62610800,1.39
1991-06-25,42.00,43.00,41.75,42.37,56980000,1.37
1991-06-24,41.75,42.25,41.25,41.75,51996000,1.35
1991-06-21,42.00,42.50,41.75,42.00,51503200,1.36
1991-06-20,41.25,42.00,40.75,42.00,36010800,1.36
1991-06-19,41.75,42.25,41.25,41.75,44735600,1.35
1991-06-18,42.25,43.25,41.50,42.12,61171600,1.37
1991-06-17,41.00,42.25,41.00,42.00,41650000,1.36
1991-06-14,42.75,42.75,40.75,41.12,56322000,1.33
1991-06-13,42.50,43.00,41.75,42.12,52841600,1.37
1991-06-12,44.00,44.75,41.25,42.37,108908800,1.37
1991-06-11,45.00,45.50,44.25,44.62,47140800,1.45
1991-06-10,46.00,47.13,45.75,46.00,41860000,1.49
1991-06-07,46.25,47.00,45.62,46.12,38186400,1.50
1991-06-06,48.25,48.25,46.50,46.63,42126000,1.51
1991-06-05,49.25,49.25,47.75,48.00,33322800,1.56
1991-06-04,49.50,49.50,48.50,49.12,46071200,1.59
1991-06-03,47.00,49.50,46.75,49.25,55017200,1.60
1991-05-31,47.50,47.75,46.25,47.00,54465600,1.52
1991-05-30,47.00,47.75,46.50,47.62,39586400,1.54
1991-05-29,46.25,47.75,45.87,47.00,96000800,1.52
1991-05-28,46.00,46.25,45.25,46.00,42859600,1.49
1991-05-24,45.50,46.00,45.00,45.87,24281600,1.49
1991-05-23,46.50,46.75,44.75,45.13,52164000,1.46
1991-05-22,45.75,46.50,45.50,46.25,56817600,1.50
1991-05-21,45.25,46.50,44.75,45.25,87449600,1.47
1991-05-20,47.25,47.50,44.00,44.25,65542400,1.44
1991-05-17,48.75,48.75,46.50,47.00,117765200,1.52
1991-05-16,51.00,51.25,48.50,49.00,95533200,1.59
1991-05-15,51.50,52.00,49.00,50.50,129586800,1.63
1991-05-14,52.75,53.75,52.50,53.50,54236000,1.73
1991-05-13,52.25,53.50,51.50,52.75,61236000,1.71
1991-05-10,51.50,53.25,50.75,51.25,60432400,1.66
1991-05-09,50.00,51.50,49.75,50.75,59553200,1.64
1991-05-08,50.75,50.75,49.25,49.75,44195200,1.61
1991-05-07,51.00,51.25,50.50,50.63,67620000,1.64
1991-05-06,48.50,50.50,48.25,50.25,53082400,1.63
1991-05-03,49.00,49.50,48.25,49.00,60928000,1.59
1991-05-02,47.75,49.75,47.50,49.00,202781600,1.59
1991-05-01,48.00,49.00,47.00,47.25,467093200,1.53
1991-04-30,57.75,58.25,54.50,55.00,177861600,1.78
1991-04-29,58.50,60.25,58.25,58.25,51676800,1.88
1991-04-26,58.50,59.00,57.75,58.62,31264800,1.90
1991-04-25,59.75,59.75,58.50,58.50,78845200,1.89
1991-04-24,61.75,62.00,60.50,60.75,26362000,1.97
1991-04-23,62.25,63.00,60.25,61.50,59323600,1.99
1991-04-22,59.50,62.00,58.75,61.50,64254400,1.99
1991-04-19,61.00,61.50,59.50,59.62,71825600,1.93
1991-04-18,62.75,63.00,60.75,61.00,61840800,1.97
1991-04-17,65.00,65.00,62.00,63.25,80600800,2.05
1991-04-16,63.25,64.50,62.50,64.25,155195600,2.08
1991-04-15,61.75,64.50,60.00,62.25,425096000,2.01
1991-04-12,71.50,73.25,69.75,71.75,91929600,2.32
1991-04-11,67.75,71.38,67.50,71.00,88897200,2.30
1991-04-10,68.50,69.25,66.75,66.87,54101600,2.16
1991-04-09,69.75,70.00,68.25,68.75,29862000,2.22
1991-04-08,69.25,70.00,68.75,70.00,18118800,2.26
1991-04-05,71.75,71.75,68.75,69.38,38852800,2.24
1991-04-04,70.00,72.00,69.50,71.50,42109200,2.31
1991-04-03,72.50,72.75,70.00,70.00,60032000,2.26
1991-04-02,69.00,72.75,68.50,72.75,73231200,2.35
1991-04-01,68.00,69.50,67.50,68.50,29481200,2.22
1991-03-28,69.25,70.00,67.75,68.00,19675600,2.20
1991-03-27,70.00,70.25,68.50,69.25,47555200,2.24
1991-03-26,64.75,70.25,64.75,70.00,83406400,2.26
1991-03-25,63.50,65.00,63.25,64.50,33964000,2.09
1991-03-22,64.00,64.75,62.25,63.25,84532000,2.05
1991-03-21,68.25,68.75,63.75,64.75,74200000,2.10
1991-03-20,69.25,69.50,66.87,67.75,90426000,2.19
1991-03-19,66.50,70.25,65.75,69.50,105548800,2.25
1991-03-18,65.75,68.25,65.75,67.75,53502400,2.19
1991-03-15,65.75,66.50,65.25,66.25,51209200,2.14
1991-03-14,66.75,67.50,64.50,65.25,56767200,2.11
1991-03-13,62.75,66.50,62.75,66.25,43638000,2.14
1991-03-12,63.00,63.75,62.50,62.88,58419200,2.03
1991-03-11,64.50,64.75,62.25,63.50,43842400,2.05
1991-03-08,67.75,68.25,65.00,65.00,80550400,2.10
1991-03-07,63.50,67.50,63.25,67.25,80438400,2.18
1991-03-06,64.00,65.62,62.88,63.00,130989600,2.04
1991-03-05,59.00,63.25,59.00,63.12,110362000,2.04
1991-03-04,58.00,58.75,57.00,58.37,22089200,1.89
1991-03-01,57.00,59.00,57.00,57.75,31533600,1.87
1991-02-28,58.25,58.50,56.25,57.25,56840000,1.85
1991-02-27,58.25,58.50,57.50,58.25,43593200,1.88
1991-02-26,57.50,58.75,56.50,58.25,62504400,1.88
1991-02-25,60.25,60.50,57.50,58.00,89818400,1.88
1991-02-22,59.00,61.75,58.50,59.75,58142000,1.93
1991-02-21,61.25,62.25,58.75,59.00,47717600,1.91
1991-02-20,59.50,61.75,59.25,61.00,53410000,1.97
1991-02-19,57.50,60.25,57.38,60.00,56562800,1.94
1991-02-15,57.25,58.50,57.25,57.63,91403200,1.86
1991-02-14,60.00,60.00,56.75,57.13,94418800,1.84
1991-02-13,60.00,60.25,58.00,60.00,63887600,1.94
1991-02-12,61.00,61.25,59.38,60.00,56187600,1.94
1991-02-11,60.00,61.50,59.75,61.37,80757600,1.98
1991-02-08,57.50,60.25,57.50,59.87,78388800,1.93
1991-02-07,57.00,58.75,55.75,57.75,130043200,1.86
1991-02-06,57.75,58.25,56.50,56.88,55641600,1.84
1991-02-05,55.25,58.00,54.75,57.75,89028800,1.86
1991-02-04,55.75,56.00,55.00,55.25,66962000,1.78
1991-02-01,55.50,57.87,55.50,55.75,111137600,1.80
1991-01-31,55.50,56.00,54.75,55.50,60648000,1.79
1991-01-30,53.25,55.75,53.25,55.50,84193200,1.79
1991-01-29,54.25,54.50,52.25,53.75,53888800,1.74
1991-01-28,53.25,55.25,53.25,54.50,68370400,1.76
1991-01-25,52.00,53.63,52.00,53.50,55952400,1.73
1991-01-24,51.50,52.75,51.50,52.13,58483600,1.68
1991-01-23,51.25,52.25,51.00,51.75,61065200,1.67
1991-01-22,51.00,52.50,50.50,51.25,106932000,1.65
1991-01-21,49.75,51.50,49.75,50.75,81076800,1.64
1991-01-18,48.75,50.75,48.50,50.25,235810400,1.62
1991-01-17,52.50,52.75,49.00,51.25,147918400,1.65
1991-01-16,47.00,50.00,46.75,49.75,97658400,1.61
1991-01-15,46.50,46.75,46.00,46.75,48014400,1.51
1991-01-14,46.00,46.75,46.00,46.25,52710000,1.49
1991-01-11,47.00,47.25,46.00,47.00,76913200,1.52
1991-01-10,45.75,47.25,45.75,47.13,108830400,1.52
1991-01-09,44.25,46.00,43.75,45.25,116816000,1.46
1991-01-08,43.75,43.87,42.50,43.25,54672800,1.40
1991-01-07,43.00,45.25,43.00,43.25,77700000,1.40
1991-01-04,43.00,44.25,43.00,43.25,35380800,1.40
1991-01-03,43.50,44.25,43.00,43.00,37545200,1.39
1991-01-02,42.75,44.00,42.00,43.50,38746400,1.40
1990-12-31,43.00,43.25,42.75,43.00,11068400,1.39
1990-12-28,43.25,43.50,42.75,43.00,15982400,1.39
1990-12-27,43.25,44.00,43.25,43.50,24413200,1.40
1990-12-26,44.00,44.25,43.00,43.75,25768400,1.41
1990-12-24,44.75,45.00,44.00,44.00,14680400,1.42
1990-12-21,44.25,45.25,43.50,45.00,86534000,1.45
1990-12-20,41.25,44.50,41.25,44.00,100268000,1.42
1990-12-19,42.50,42.50,41.12,41.88,35165200,1.35
1990-12-18,41.00,42.50,40.75,42.25,55246800,1.36
1990-12-17,39.00,40.50,39.00,40.13,32776800,1.30
1990-12-14,40.25,40.50,39.50,39.88,21767200,1.29
1990-12-13,39.50,41.00,39.50,40.75,40182800,1.32
1990-12-12,39.75,40.00,39.00,39.63,60589200,1.28
1990-12-11,41.25,41.50,40.00,40.00,86970800,1.29
1990-12-10,42.25,42.50,41.50,41.75,62647200,1.35
1990-12-07,41.00,42.75,41.00,42.50,82415200,1.37
1990-12-06,41.25,41.75,40.50,41.25,133061600,1.33
1990-12-05,38.50,40.25,37.88,40.13,54597200,1.30
1990-12-04,37.50,38.75,37.50,38.50,38038000,1.24
1990-12-03,37.25,38.25,37.00,38.13,41350400,1.23
1990-11-30,36.25,37.25,36.25,36.75,30377200,1.19
1990-11-29,37.00,37.00,36.25,36.75,31676400,1.19
1990-11-28,37.75,38.50,36.75,36.75,43727600,1.19
1990-11-27,37.00,38.25,36.75,37.50,41146000,1.21
1990-11-26,36.00,37.00,36.00,36.75,20364400,1.19
1990-11-23,36.25,37.00,36.00,36.38,13300000,1.17
1990-11-21,35.25,36.25,34.75,36.13,30802800,1.17
1990-11-20,36.50,36.75,35.25,35.50,38407600,1.15
1990-11-19,35.50,36.38,35.25,36.38,55977600,1.17
1990-11-16,35.75,36.00,34.75,35.12,45752000,1.13
1990-11-15,36.75,37.00,35.50,36.00,40443200,1.16
1990-11-14,35.75,37.25,35.75,37.00,47686800,1.19
1990-11-13,36.25,36.50,35.75,36.00,35487200,1.16
1990-11-12,35.50,36.75,35.25,36.25,36262800,1.17
1990-11-09,35.00,35.75,34.50,35.50,49557200,1.14
1990-11-08,33.00,35.00,33.00,34.50,49812000,1.11
1990-11-07,33.50,33.75,32.63,33.25,50744400,1.07
1990-11-06,33.50,34.50,33.25,33.50,46191600,1.08
1990-11-05,32.25,33.50,32.00,33.25,46118800,1.07
1990-11-02,30.50,32.38,30.50,31.75,37153200,1.02
1990-11-01,30.50,31.00,29.75,30.50,22663200,0.98
1990-10-31,30.50,31.87,30.25,30.75,37189600,0.99
1990-10-30,29.75,30.75,28.88,30.37,24511200,0.98
1990-10-29,30.25,30.50,29.75,29.87,30870000,0.96
1990-10-26,29.75,31.25,29.75,30.00,33549600,0.97
1990-10-25,30.25,31.25,29.63,30.00,38365600,0.97
1990-10-24,30.75,31.00,30.00,30.50,35456400,0.98
1990-10-23,31.00,31.50,30.25,31.00,41762000,1.00
1990-10-22,31.50,31.50,30.50,31.13,63184800,1.00
1990-10-19,31.25,31.75,30.25,31.38,233433200,1.01
1990-10-18,26.50,28.75,26.50,28.50,78750000,0.92
1990-10-17,25.25,26.50,25.00,26.50,77266000,0.85
1990-10-16,27.50,27.50,24.25,25.00,76308400,0.80
1990-10-15,28.50,28.75,26.62,27.75,50254400,0.89
1990-10-12,28.25,28.50,27.00,28.25,57162000,0.91
1990-10-11,26.75,27.88,25.50,27.75,51494800,0.89
1990-10-10,27.25,28.00,26.00,26.50,36976800,0.85
1990-10-09,28.50,29.00,27.75,28.00,30144800,0.90
1990-10-08,28.75,29.25,28.25,29.13,15383200,0.94
1990-10-05,27.00,28.75,27.00,28.00,24872400,0.90
1990-10-04,26.75,28.00,26.25,28.00,53373600,0.90
1990-10-03,29.75,29.75,26.75,27.00,67060000,0.87
1990-10-02,31.00,32.00,29.50,29.63,67746000,0.95
1990-10-01,29.50,31.00,29.25,30.50,38914400,0.98
1990-09-28,28.50,29.00,27.25,29.00,44010400,0.93
1990-09-27,30.00,30.50,28.00,28.25,35585200,0.91
1990-09-26,30.00,30.50,29.75,29.75,23534000,0.96
1990-09-25,30.50,30.75,29.25,30.00,39488400,0.97
1990-09-24,31.50,31.50,29.75,30.25,34624800,0.97
1990-09-21,32.00,32.50,31.00,31.50,38466400,1.01
1990-09-20,32.25,32.25,31.25,31.62,25233600,1.02
1990-09-19,33.25,33.75,32.00,32.50,45614800,1.05
1990-09-18,33.75,33.75,33.00,33.37,31152800,1.07
1990-09-17,34.00,35.25,33.50,33.75,19418000,1.09
1990-09-14,33.50,34.25,33.25,34.00,28478800,1.09
1990-09-13,34.50,34.75,33.00,33.75,24315200,1.09
1990-09-12,34.50,34.50,33.50,34.00,25102000,1.09
1990-09-11,36.00,36.13,33.75,34.00,44567600,1.09
1990-09-10,37.00,37.00,35.75,35.75,18995200,1.15
1990-09-07,35.50,36.75,35.12,36.38,14543200,1.17
1990-09-06,35.50,36.00,35.25,35.75,21907200,1.15
1990-09-05,37.25,37.25,35.75,36.00,16013200,1.16
1990-09-04,36.50,37.50,36.50,37.00,20686400,1.19
1990-08-31,36.00,37.25,36.00,37.00,24864000,1.19
1990-08-30,37.25,37.50,36.00,36.25,30648800,1.17
1990-08-29,38.00,38.13,36.75,37.25,37732800,1.20
1990-08-28,37.50,38.38,37.25,38.13,20048000,1.23
1990-08-27,36.75,38.00,36.25,37.75,29366400,1.21
1990-08-24,35.25,36.00,34.75,35.50,18354000,1.14
1990-08-23,34.25,35.00,33.50,34.50,35924000,1.11
1990-08-22,37.00,37.00,34.88,35.12,30679600,1.13
1990-08-21,35.75,36.75,35.25,36.25,40261200,1.17
1990-08-20,36.50,37.50,36.25,36.75,18765600,1.18
1990-08-17,38.50,38.50,35.75,36.50,61527200,1.17
1990-08-16,39.00,39.63,38.50,38.50,30973600,1.24
1990-08-15,40.00,40.25,39.25,39.25,23013200,1.26
1990-08-14,40.00,40.00,39.25,39.75,24542000,1.28
1990-08-13,38.00,40.00,37.88,39.88,39029200,1.28
1990-08-10,38.75,39.25,38.25,38.75,25676000,1.24
1990-08-09,40.25,40.50,39.25,39.50,24096800,1.27
1990-08-08,39.50,40.75,39.50,40.13,25634000,1.29
1990-08-07,40.25,40.62,38.75,39.50,49632800,1.27
1990-08-06,39.00,40.50,38.50,39.50,44914800,1.27
1990-08-03,43.50,43.75,39.75,41.25,67242000,1.32
1990-08-02,41.25,43.75,41.25,43.50,55781600,1.40
1990-08-01,42.00,42.75,41.50,42.37,23377200,1.36
1990-07-31,42.50,42.75,41.50,42.00,24001600,1.35
1990-07-30,40.75,42.50,40.75,42.37,21364000,1.36
1990-07-27,41.25,41.75,40.50,41.38,15579200,1.33
1990-07-26,42.25,42.50,41.00,41.38,20084400,1.33
1990-07-25,42.00,43.25,41.75,42.25,26230400,1.36
1990-07-24,42.00,42.25,41.00,42.12,48479200,1.35
1990-07-23,41.00,41.75,40.00,41.50,67547200,1.33
1990-07-20,42.00,42.50,40.75,41.00,47961200,1.32
1990-07-19,40.75,42.50,40.00,41.75,146496000,1.34
1990-07-18,44.50,45.00,43.00,44.62,72091600,1.43
1990-07-17,45.75,46.00,44.00,44.25,34213200,1.42
1990-07-16,46.75,47.13,45.25,45.62,44926000,1.46
1990-07-13,47.50,47.75,46.75,46.75,57744400,1.50
1990-07-12,46.75,47.50,46.50,47.37,45617600,1.52
1990-07-11,46.75,47.00,45.75,47.00,61538400,1.51
1990-07-10,47.00,47.50,46.75,47.00,90356000,1.51
1990-07-09,45.00,47.00,44.75,46.63,78864800,1.50
1990-07-06,43.50,45.00,43.25,44.75,52264800,1.44
1990-07-05,43.75,44.25,43.25,43.50,26866000,1.40
1990-07-03,43.87,44.50,43.75,44.00,24875200,1.41
1990-07-02,44.50,44.50,43.75,44.00,33852000,1.41
1990-06-29,43.00,44.88,42.75,44.75,81298000,1.44
1990-06-28,42.75,43.25,41.75,43.00,62484800,1.38
1990-06-27,40.75,42.00,40.25,41.50,24306800,1.33
1990-06-26,41.75,42.00,40.37,40.62,31813600,1.30
1990-06-25,41.50,41.75,40.25,41.25,30500400,1.32
1990-06-22,42.00,42.62,41.25,41.50,70994000,1.33
1990-06-21,40.00,42.00,40.00,41.88,52150000,1.34
1990-06-20,39.88,40.25,39.75,40.00,38684800,1.28
1990-06-19,39.00,39.75,38.38,39.63,39306400,1.27
1990-06-18,39.25,39.50,39.00,39.25,27848800,1.26
1990-06-15,39.75,40.00,39.12,39.50,36036000,1.27
1990-06-14,40.00,40.25,39.25,39.75,35081200,1.28
1990-06-13,40.37,40.75,39.75,39.75,34736800,1.28
1990-06-12,39.12,40.50,38.75,40.50,41258000,1.30
1990-06-11,37.75,39.00,37.75,39.00,39474400,1.25
1990-06-08,38.50,38.50,37.50,38.25,83470800,1.23
1990-06-07,39.50,39.75,38.50,39.00,46608800,1.25
1990-06-06,39.00,39.50,38.75,39.50,52936800,1.27
1990-06-05,41.00,41.00,39.00,39.50,74858000,1.27
1990-06-04,40.75,41.00,39.75,40.75,44856000,1.31
1990-06-01,41.38,42.00,40.75,40.75,39309200,1.31
1990-05-31,41.50,41.50,41.00,41.25,25771200,1.32
1990-05-30,41.63,41.75,41.25,41.38,69204800,1.33
1990-05-29,40.00,41.25,39.25,41.00,60802000,1.32
1990-05-25,39.50,40.75,39.00,40.00,80830400,1.28
1990-05-24,42.25,42.25,41.50,42.00,37032800,1.35
1990-05-23,41.25,42.50,41.25,42.00,51878400,1.35
1990-05-22,40.13,41.50,40.00,41.38,75272400,1.33
1990-05-21,39.50,40.00,38.75,39.50,65620800,1.27
1990-05-18,41.25,41.50,39.50,39.75,64615600,1.27
1990-05-17,41.75,42.25,41.00,41.50,38396400,1.33
1990-05-16,41.75,41.75,41.00,41.63,21826000,1.33
1990-05-15,41.38,42.00,41.00,41.75,37346400,1.34
1990-05-14,42.75,42.75,41.25,41.75,56596400,1.34
1990-05-11,41.38,42.75,40.75,42.62,53810400,1.36
1990-05-10,41.75,41.75,40.50,41.38,44760800,1.32
1990-05-09,41.63,42.00,41.25,41.88,24309600,1.34
1990-05-08,41.00,42.00,41.00,41.75,28114800,1.34
1990-05-07,39.75,41.75,39.75,41.50,33997600,1.33
1990-05-04,40.00,40.75,39.25,40.00,42383600,1.28
1990-05-03,39.75,40.25,39.75,40.00,41577200,1.28
1990-05-02,39.75,40.00,39.25,39.75,33857600,1.27
1990-05-01,39.75,40.00,39.38,39.63,40902400,1.27
1990-04-30,39.25,39.75,39.00,39.38,34098400,1.26
1990-04-27,39.00,39.50,38.75,39.12,29103200,1.25
1990-04-26,39.00,39.50,38.13,38.87,35540400,1.24
1990-04-25,38.75,39.00,38.25,38.75,33143600,1.24
1990-04-24,40.00,40.50,38.50,38.75,75933200,1.24
1990-04-23,40.25,40.50,39.50,39.75,32088000,1.27
1990-04-20,40.87,41.50,39.75,40.25,80880800,1.29
1990-04-19,41.75,43.13,40.00,40.25,120369200,1.29
1990-04-18,43.25,43.75,42.50,43.25,48361600,1.38
1990-04-17,43.25,43.50,42.75,43.25,32776800,1.38
1990-04-16,43.50,44.25,43.25,43.75,56722400,1.40
1990-04-12,43.00,44.00,42.50,43.25,52950800,1.38
1990-04-11,41.50,43.00,41.50,42.50,53289600,1.36
1990-04-10,41.25,42.00,41.00,41.25,32830000,1.32
1990-04-09,39.75,41.50,39.50,41.12,26370400,1.32
1990-04-06,40.25,41.25,39.75,39.88,29559600,1.28
1990-04-05,41.00,41.25,40.00,40.25,27048000,1.29
1990-04-04,41.50,42.00,40.75,41.25,37433200,1.32
1990-04-03,40.50,41.75,40.50,41.75,34927200,1.34
1990-04-02,40.00,40.62,39.50,40.25,37192400,1.29
1990-03-30,40.00,41.00,40.00,40.25,55837600,1.29
1990-03-29,41.00,41.50,40.75,41.12,24222800,1.32
1990-03-28,42.00,42.12,41.00,41.25,25734800,1.32
1990-03-27,42.00,42.25,41.25,42.00,21151200,1.34
1990-03-26,42.50,43.38,42.00,42.25,32015200,1.35
1990-03-23,41.25,43.00,41.00,42.25,56996800,1.35
1990-03-22,41.75,42.25,40.75,40.75,57915200,1.30
1990-03-21,41.25,42.25,41.25,41.63,38183600,1.33
1990-03-20,42.25,43.00,40.75,41.38,97829200,1.32
1990-03-19,40.50,42.50,40.00,42.37,107948400,1.36
1990-03-16,40.00,40.75,39.12,40.25,161190400,1.29
1990-03-15,36.50,38.00,36.50,36.75,30058000,1.18
1990-03-14,36.75,37.25,36.50,37.00,25446400,1.18
1990-03-13,36.50,37.25,36.25,36.87,37144800,1.18
1990-03-12,37.25,37.50,36.25,36.63,40989200,1.17
1990-03-09,36.75,37.50,36.25,36.87,57618400,1.18
1990-03-08,35.75,37.00,35.00,36.75,55960800,1.18
1990-03-07,35.00,36.00,35.00,35.37,51055200,1.13
1990-03-06,35.00,35.25,34.50,35.25,39004000,1.13
1990-03-05,33.50,34.75,33.50,34.50,45617600,1.10
1990-03-02,33.50,34.75,33.25,33.75,26224800,1.08
1990-03-01,33.50,34.75,33.25,34.25,50974000,1.10
1990-02-28,33.50,34.00,33.25,34.00,27333600,1.09
1990-02-27,34.00,34.25,33.50,33.50,18488400,1.07
1990-02-26,33.00,34.25,33.00,34.00,19902400,1.09
1990-02-23,32.75,33.50,32.75,33.25,37489200,1.06
1990-02-22,34.00,34.50,33.00,33.00,48795600,1.06
1990-02-21,32.75,34.25,32.50,34.00,43976800,1.09
1990-02-20,33.50,33.75,33.00,33.50,30811200,1.07
1990-02-16,34.25,34.50,33.75,33.75,31802400,1.08
1990-02-15,33.75,34.25,33.50,34.25,24491600,1.09
1990-02-14,34.50,34.75,33.75,34.25,24015600,1.09
1990-02-13,34.00,35.00,33.75,34.50,25541600,1.10
1990-02-12,34.25,34.50,33.75,34.00,18729200,1.08
1990-02-09,33.50,34.50,33.25,34.25,42019600,1.09
1990-02-08,33.25,33.50,32.25,33.00,46659200,1.05
1990-02-07,33.00,34.00,32.50,33.25,78111600,1.06
1990-02-06,34.75,35.00,34.00,34.75,18480000,1.11
1990-02-05,34.25,35.25,34.00,35.00,25438000,1.12
1990-02-02,33.25,34.75,33.25,34.25,29618400,1.09
1990-02-01,34.50,34.63,33.50,33.62,29268400,1.07
1990-01-31,34.50,34.75,33.00,34.00,35985600,1.08
1990-01-30,33.25,34.50,33.00,34.00,29111600,1.08
1990-01-29,33.00,33.50,32.12,33.25,29982400,1.06
1990-01-26,34.00,34.00,32.25,32.75,45312400,1.04
1990-01-25,34.25,34.75,34.00,34.12,27885200,1.09
1990-01-24,32.50,34.25,32.25,34.00,42448000,1.08
1990-01-23,33.75,34.25,33.00,33.75,35218400,1.08
1990-01-22,34.00,34.50,33.25,33.25,36402800,1.06
1990-01-19,33.75,34.50,33.50,34.25,66284400,1.09
1990-01-18,33.00,33.50,32.25,32.38,68322800,1.03
1990-01-17,34.75,34.75,33.00,33.25,49324800,1.06
1990-01-16,33.50,35.00,32.75,34.88,53561200,1.11
1990-01-15,34.50,35.75,34.25,34.25,40434800,1.09
1990-01-12,34.25,34.75,33.75,34.50,42974400,1.10
1990-01-11,36.25,36.25,34.50,34.50,52763200,1.10
1990-01-10,37.62,37.62,35.75,36.00,49929600,1.15
1990-01-09,38.00,38.00,37.00,37.62,21534800,1.20
1990-01-08,37.50,38.00,37.00,38.00,25393200,1.21
1990-01-05,37.75,38.25,37.00,37.75,30828000,1.20
1990-01-04,38.25,38.75,37.25,37.62,55378400,1.20
1990-01-03,38.00,38.00,37.50,37.50,51998800,1.20
1990-01-02,35.25,37.50,35.00,37.25,45799600,1.19
1989-12-29,34.75,35.75,34.38,35.25,38102400,1.12
1989-12-28,35.00,35.25,34.25,34.63,37814000,1.10
1989-12-27,35.50,35.75,35.00,35.12,64251600,1.12
1989-12-26,36.75,36.75,35.25,35.50,33821200,1.13
1989-12-22,36.25,37.25,36.00,36.50,46146800,1.16
1989-12-21,35.75,36.25,35.50,36.25,76202000,1.16
1989-12-20,35.75,36.25,35.25,35.75,44497600,1.14
1989-12-19,34.50,35.50,34.50,35.00,62798400,1.12
1989-12-18,33.75,35.00,33.75,34.75,76801200,1.11
1989-12-15,34.75,35.00,32.50,33.75,129542000,1.08
1989-12-14,35.75,36.13,34.50,34.88,76188000,1.11
1989-12-13,36.00,36.50,35.50,36.00,97440000,1.15
1989-12-12,39.25,39.50,35.00,36.00,256354000,1.15
1989-12-11,41.00,41.50,38.38,39.25,162503600,1.25
1989-12-08,42.50,43.00,41.25,41.75,63145600,1.33
1989-12-07,42.25,43.25,42.00,42.75,44604000,1.36
1989-12-06,45.00,45.25,41.00,42.75,83745200,1.36
1989-12-05,45.25,45.75,44.50,45.00,30441600,1.44
1989-12-04,43.75,45.50,43.75,45.25,24340400,1.44
1989-12-01,44.50,45.00,43.63,44.00,36556800,1.40
1989-11-30,43.75,44.50,43.50,44.25,15862000,1.41
1989-11-29,43.50,44.25,42.50,44.00,38236800,1.40
1989-11-28,43.75,44.25,42.75,44.12,33843600,1.41
1989-11-27,44.75,45.25,43.75,44.00,26286400,1.40
1989-11-24,44.75,45.00,44.75,44.75,6963600,1.43
1989-11-22,45.50,45.75,44.50,44.75,24486000,1.43
1989-11-21,45.25,46.50,45.25,45.25,35061600,1.44
1989-11-20,45.00,45.50,44.50,45.25,27017200,1.44
1989-11-17,44.50,45.25,44.50,44.75,22139600,1.43
1989-11-16,44.50,44.75,43.75,44.75,24141600,1.42
1989-11-15,45.00,45.25,44.00,44.25,24446800,1.41
1989-11-14,46.50,46.75,44.50,44.75,21095200,1.42
1989-11-13,46.50,47.25,46.50,46.50,17004400,1.48
1989-11-10,45.75,47.00,45.75,46.75,16214800,1.49
1989-11-09,45.00,46.00,44.50,46.00,22047200,1.46
1989-11-08,44.25,45.25,44.25,45.00,35658000,1.43
1989-11-07,43.25,44.50,43.25,44.00,37830800,1.40
1989-11-06,43.50,44.00,43.00,43.25,30772000,1.38
1989-11-03,44.00,44.50,43.25,43.25,43663200,1.38
1989-11-02,45.00,45.00,43.00,44.00,113167600,1.40
1989-11-01,46.25,46.75,45.75,46.12,15296400,1.47
1989-10-31,45.75,46.50,45.50,46.50,22999200,1.48
1989-10-30,45.50,46.00,45.00,45.75,21744800,1.46
1989-10-27,45.25,45.75,44.50,45.25,32354000,1.44
1989-10-26,45.50,46.50,45.00,45.25,42316400,1.44
1989-10-25,47.75,47.75,46.25,46.50,29786400,1.48
1989-10-24,46.25,48.50,45.25,47.62,54110000,1.52
1989-10-23,48.00,48.25,46.25,46.75,30489200,1.49
1989-10-20,47.75,49.25,47.50,48.00,65377200,1.53
1989-10-19,48.25,49.50,48.25,48.75,27974800,1.55
1989-10-18,46.50,48.25,46.00,48.25,36008000,1.53
1989-10-17,46.00,48.75,45.00,47.25,62510000,1.50
1989-10-16,44.75,46.75,42.50,46.75,106229200,1.49
1989-10-13,48.75,49.50,45.00,45.75,50279600,1.46
1989-10-12,49.00,49.25,48.50,48.75,20661200,1.55
1989-10-11,48.75,49.25,48.00,48.88,39239200,1.55
1989-10-10,49.75,50.38,48.50,49.50,71780800,1.57
1989-10-09,48.00,49.75,47.50,49.50,48888000,1.57
1989-10-06,46.25,48.25,46.00,48.12,90426000,1.53
1989-10-05,44.50,46.50,44.25,45.50,61320000,1.45
1989-10-04,43.75,44.62,43.50,44.25,39793600,1.41
1989-10-03,44.25,44.50,43.13,43.63,42624400,1.39
1989-10-02,44.50,44.75,43.75,44.37,34350400,1.41
1989-09-29,45.25,45.50,44.50,44.50,17452400,1.42
1989-09-28,45.00,45.75,45.00,45.50,19854800,1.45
1989-09-27,44.25,45.13,44.00,44.75,22531600,1.42
1989-09-26,45.00,45.50,44.75,45.25,19331200,1.44
1989-09-25,44.75,45.75,44.75,45.25,34039600,1.44
1989-09-22,44.75,45.25,44.25,44.88,18124400,1.43
1989-09-21,45.00,46.00,44.25,44.75,50240400,1.42
1989-09-20,44.00,45.00,43.75,44.62,29537200,1.42
1989-09-19,44.25,44.50,43.00,43.25,20199200,1.38
1989-09-18,44.50,45.00,44.00,44.00,15789200,1.40
1989-09-15,45.00,45.25,44.25,45.00,31217200,1.43
1989-09-14,45.00,45.25,44.50,44.75,32821600,1.42
1989-09-13,46.25,46.63,45.00,45.00,32172000,1.43
1989-09-12,45.50,46.75,45.00,46.00,25897200,1.46
1989-09-11,44.75,46.00,44.50,45.75,24648400,1.46
1989-09-08,44.75,45.25,44.50,45.00,13958000,1.43
1989-09-07,44.75,45.50,44.75,44.75,28473200,1.42
1989-09-06,44.75,44.88,44.00,44.75,21688800,1.42
1989-09-05,44.50,45.38,44.50,44.75,28705600,1.42
1989-09-01,44.50,44.75,44.25,44.62,18530400,1.42
1989-08-31,44.50,45.00,44.25,44.50,14072800,1.42
1989-08-30,44.00,44.75,44.00,44.50,29024800,1.42
1989-08-29,44.75,45.00,43.75,44.12,44226000,1.40
1989-08-28,44.50,45.00,44.00,44.75,20414800,1.42
1989-08-25,44.00,45.00,44.00,44.75,40348000,1.42
1989-08-24,43.75,44.50,43.50,44.12,40731600,1.40
1989-08-23,43.00,44.25,42.50,43.75,43411200,1.39
1989-08-22,42.00,43.00,42.00,42.88,27958000,1.36
1989-08-21,42.25,43.25,42.00,42.25,34456800,1.34
1989-08-18,41.75,42.50,41.50,42.25,21016800,1.34
1989-08-17,40.25,41.25,40.00,41.00,38329200,1.30
1989-08-16,41.50,41.75,40.00,40.37,30133600,1.28
1989-08-15,40.75,41.50,40.75,41.38,40933200,1.31
1989-08-14,41.50,42.00,40.50,40.75,25706800,1.29
1989-08-11,44.00,44.00,41.25,41.88,57520400,1.33
1989-08-10,44.00,44.00,42.75,43.25,38091200,1.37
1989-08-09,44.00,45.75,43.87,44.00,48790000,1.40
1989-08-08,43.50,44.75,43.50,44.12,51548000,1.40
1989-08-07,43.00,44.00,42.62,43.75,42053200,1.39
1989-08-04,41.25,42.75,41.12,42.75,45838800,1.36
1989-08-03,40.50,41.50,40.50,41.25,43234800,1.31
1989-08-02,39.75,40.50,39.50,40.50,25351200,1.29
1989-08-01,39.75,40.25,39.25,39.88,34885200,1.27
1989-07-31,39.25,40.00,39.00,39.75,27966400,1.26
1989-07-28,39.25,39.75,39.00,39.38,29834000,1.25
1989-07-27,38.25,39.50,38.00,39.25,43268400,1.25
1989-07-26,38.25,38.50,37.75,38.25,58436000,1.21
1989-07-25,39.25,39.75,38.00,38.75,52460800,1.23
1989-07-24,39.75,39.75,39.25,39.25,28996800,1.25
1989-07-21,39.75,40.00,39.00,40.00,34871200,1.27
1989-07-20,40.75,41.25,39.75,40.00,59018400,1.27
1989-07-19,39.50,40.75,39.00,40.50,59743600,1.29
1989-07-18,40.75,40.75,38.75,39.25,119327600,1.25
1989-07-17,40.75,41.25,39.75,40.75,32723600,1.29
1989-07-14,40.75,41.00,39.75,40.75,64330000,1.29
1989-07-13,40.00,41.00,39.50,40.62,56358400,1.29
1989-07-12,39.75,40.25,39.50,40.00,31032400,1.27
1989-07-11,40.75,41.00,39.75,39.75,60981200,1.26
1989-07-10,41.00,41.25,40.00,40.50,50923600,1.29
1989-07-07,41.25,42.00,40.50,41.25,26527200,1.31
1989-07-06,40.75,41.75,40.25,41.25,43481200,1.31
1989-07-05,40.50,40.75,40.00,40.50,29789200,1.29
1989-07-03,41.75,41.75,40.75,40.75,12087600,1.29
1989-06-30,40.50,41.75,39.50,41.25,41185200,1.31
1989-06-29,41.00,41.25,40.00,40.62,58380000,1.29
1989-06-28,42.25,42.25,41.00,41.75,64257200,1.33
1989-06-27,43.75,44.25,42.50,42.62,26446000,1.35
1989-06-26,44.00,44.00,43.25,43.50,45959200,1.38
1989-06-23,43.25,44.25,43.25,43.87,30973600,1.39
1989-06-22,42.50,43.75,42.00,43.25,34300000,1.37
1989-06-21,43.00,43.50,42.25,42.50,32466000,1.35
1989-06-20,44.00,44.00,42.25,43.00,33633600,1.36
1989-06-19,44.50,44.75,43.50,44.00,45780000,1.40
1989-06-16,44.75,45.50,43.50,44.50,135500400,1.41
1989-06-15,49.50,49.75,47.50,47.50,40350800,1.51
1989-06-14,49.00,50.25,48.25,49.62,62826400,1.57
1989-06-13,47.50,48.75,47.00,48.50,57744400,1.54
1989-06-12,46.75,47.75,46.25,47.50,20216000,1.51
1989-06-09,47.25,47.75,46.50,47.00,23604000,1.49
1989-06-08,48.50,49.00,47.25,47.62,44503200,1.51
1989-06-07,46.75,48.50,46.75,48.25,43918000,1.53
1989-06-06,46.75,47.00,46.25,46.75,36251600,1.48
1989-06-05,48.75,49.00,46.50,47.00,31029600,1.49
1989-06-02,48.50,49.50,48.50,49.00,31119200,1.56
1989-06-01,47.75,49.25,47.50,48.75,44875600,1.55
1989-05-31,47.50,48.12,47.00,47.75,28803600,1.52
1989-05-30,48.25,49.00,47.37,47.50,27980400,1.51
1989-05-26,48.25,49.00,48.00,48.50,28128800,1.54
1989-05-25,47.25,49.00,47.25,48.25,58091600,1.53
1989-05-24,45.25,47.75,45.25,47.75,74401600,1.52
1989-05-23,46.00,46.00,45.25,45.50,33616800,1.44
1989-05-22,45.75,46.25,45.25,46.00,47600000,1.46
1989-05-19,44.75,46.25,44.75,45.75,82692400,1.45
1989-05-18,45.25,45.50,44.75,44.75,52813600,1.42
1989-05-17,45.25,45.50,45.00,45.25,62115200,1.43
1989-05-16,46.00,46.25,45.00,45.38,57167600,1.44
1989-05-15,44.75,46.25,44.75,46.00,79475200,1.46
1989-05-12,44.50,45.00,44.00,45.00,116785200,1.43
1989-05-11,43.25,44.25,43.00,43.87,75236000,1.39
1989-05-10,43.00,43.50,42.50,43.25,58609600,1.37
1989-05-09,42.00,43.00,42.00,42.50,86693600,1.35
1989-05-08,41.50,42.25,41.50,42.25,51480800,1.34
1989-05-05,42.50,42.75,41.50,41.50,115189200,1.31
1989-05-04,40.25,41.25,40.00,41.00,47227600,1.30
1989-05-03,39.75,40.75,39.75,40.25,55134800,1.27
1989-05-02,39.00,40.25,39.00,39.88,53936400,1.26
1989-05-01,38.50,39.25,38.50,39.00,20165600,1.24
1989-04-28,39.25,39.50,38.50,39.00,25964400,1.24
1989-04-27,39.50,40.00,39.00,39.38,34846000,1.25
1989-04-26,40.00,40.25,39.12,39.75,46533200,1.26
1989-04-25,40.00,40.50,39.75,40.00,29044400,1.27
1989-04-24,40.00,40.25,39.50,40.13,27697600,1.27
1989-04-21,40.50,40.87,39.75,40.13,28792400,1.27
1989-04-20,40.75,41.50,40.25,40.75,44954000,1.29
1989-04-19,40.00,41.63,39.75,40.87,106470000,1.29
1989-04-18,39.50,40.50,39.25,40.13,140246400,1.27
1989-04-17,38.50,39.25,38.00,39.25,35036400,1.24
1989-04-14,39.00,39.25,38.25,38.75,30839200,1.23
1989-04-13,38.75,39.50,38.25,38.50,45318000,1.22
1989-04-12,38.25,39.25,37.88,38.50,96978000,1.22
1989-04-11,37.50,38.00,37.00,37.75,36635200,1.20
1989-04-10,37.25,38.00,36.75,37.00,33843600,1.17
1989-04-07,36.00,37.50,36.00,37.37,88746000,1.18
1989-04-06,34.75,36.13,34.50,36.00,39093600,1.14
1989-04-05,34.50,35.25,34.25,35.00,30063600,1.11
1989-04-04,34.50,34.88,33.87,34.50,28932400,1.09
1989-04-03,35.50,36.25,34.75,35.00,41571600,1.11
1989-03-31,35.00,35.75,34.75,35.62,46337200,1.13
1989-03-30,34.25,35.00,34.00,34.75,26311600,1.10
1989-03-29,34.00,34.50,34.00,34.25,18600400,1.08
1989-03-28,34.00,34.50,34.00,34.00,35313600,1.08
1989-03-27,34.25,34.50,33.50,33.75,37914800,1.07
1989-03-23,34.00,34.50,33.75,34.38,29727600,1.09
1989-03-22,34.25,34.75,33.75,33.87,36212400,1.07
1989-03-21,35.50,35.50,34.75,34.88,32048800,1.10
1989-03-20,35.00,35.25,34.50,34.88,45362800,1.10
1989-03-17,34.50,35.75,34.00,34.88,59281600,1.10
1989-03-16,35.00,35.50,34.50,35.25,48059200,1.12
1989-03-15,35.25,35.50,34.75,35.00,22514800,1.11
1989-03-14,35.00,35.50,34.88,35.25,40485200,1.12
1989-03-13,35.00,35.50,34.75,35.00,32776800,1.11
1989-03-10,34.50,35.00,34.25,35.00,25678800,1.11
1989-03-09,35.25,35.75,34.50,34.50,33359200,1.09
1989-03-08,35.62,36.25,35.25,35.25,54073600,1.12
1989-03-07,35.50,36.00,35.00,35.75,65172800,1.13
1989-03-06,35.00,35.88,34.50,35.50,42128800,1.12
1989-03-03,35.25,35.25,34.00,34.75,96944400,1.10
1989-03-02,35.75,36.25,34.75,35.00,94082800,1.11
1989-03-01,36.25,36.50,35.50,36.00,42532000,1.14
1989-02-28,36.50,36.75,36.00,36.25,44004800,1.15
1989-02-27,36.00,36.50,35.75,36.50,28980000,1.16
1989-02-24,37.00,37.00,36.00,36.00,38032400,1.14
1989-02-23,36.50,37.00,36.25,36.75,23842000,1.16
1989-02-22,37.25,37.50,36.50,36.75,59581200,1.16
1989-02-21,36.87,37.75,36.75,37.50,47639200,1.19
1989-02-17,36.25,37.00,36.25,36.75,29212400,1.16
1989-02-16,36.25,37.25,36.00,36.38,63924000,1.15
1989-02-15,35.75,36.25,35.50,36.25,82656000,1.14
1989-02-14,36.87,37.00,35.25,35.75,222894000,1.13
1989-02-13,36.75,37.25,36.75,37.00,58797200,1.17
1989-02-10,38.25,38.25,37.00,37.25,87085600,1.18
1989-02-09,38.25,39.00,38.00,38.25,40202400,1.21
1989-02-08,39.00,39.50,38.00,38.25,39253200,1.21
1989-02-07,38.25,39.25,38.25,39.00,41288800,1.23
1989-02-06,39.50,39.50,38.25,38.50,29184400,1.22
1989-02-03,40.00,40.25,39.00,39.25,44727200,1.24
1989-02-02,39.50,40.25,39.25,39.75,118372800,1.26
1989-02-01,37.75,39.63,37.37,39.25,121889600,1.24
1989-01-31,37.25,37.75,36.75,37.75,115088400,1.19
1989-01-30,37.62,38.00,37.25,37.37,146624800,1.18
1989-01-27,38.25,39.25,36.25,37.62,531792800,1.19
1989-01-26,40.75,42.12,40.62,41.75,71316000,1.32
1989-01-25,41.75,42.00,41.00,41.50,27734000,1.31
1989-01-24,41.00,41.75,40.75,41.63,55823600,1.31
1989-01-23,40.75,41.25,40.75,41.00,45133200,1.29
1989-01-20,40.50,41.50,40.25,41.00,43433600,1.29
1989-01-19,40.50,41.00,40.00,40.50,63996800,1.28
1989-01-18,40.75,41.12,39.50,39.75,121982000,1.26
1989-01-17,43.25,43.50,40.00,40.37,189151200,1.28
1989-01-16,43.25,44.00,43.00,43.75,42148400,1.38
1989-01-13,42.75,43.50,42.37,43.25,48476400,1.37
1989-01-12,42.25,43.00,42.00,42.75,37578800,1.35
1989-01-11,42.25,42.50,41.25,42.12,39032000,1.33
1989-01-10,42.50,42.88,41.50,42.62,25830000,1.35
1989-01-09,43.00,43.13,42.25,43.00,19826800,1.36
1989-01-06,42.25,43.50,42.25,42.62,49666400,1.35
1989-01-05,42.00,43.25,41.25,42.25,76832000,1.33
1989-01-04,40.75,42.12,40.50,42.00,59987200,1.33
1989-01-03,40.25,40.50,40.00,40.37,25004000,1.28
1988-12-30,40.50,41.25,40.25,40.25,20423200,1.27
1988-12-29,40.25,40.75,40.25,40.50,29453200,1.28
1988-12-28,40.50,40.75,39.75,40.25,12885600,1.27
1988-12-27,41.00,41.50,40.50,40.50,14996800,1.28
1988-12-23,41.00,41.38,41.00,41.12,10239600,1.30
1988-12-22,41.75,42.00,40.75,41.00,26507600,1.29
1988-12-21,41.00,42.00,41.00,41.75,60491200,1.32
1988-12-20,41.00,41.50,40.62,41.00,68546800,1.29
1988-12-19,40.25,41.00,40.00,40.75,58581600,1.29
1988-12-16,39.50,40.50,39.25,40.13,45872400,1.27
1988-12-15,40.00,40.50,39.25,39.50,28142800,1.25
1988-12-14,38.50,40.00,38.50,39.75,48325200,1.26
1988-12-13,38.50,38.75,38.25,38.75,30637600,1.22
1988-12-12,39.25,39.50,38.50,38.50,29470000,1.22
1988-12-09,39.25,39.50,38.75,39.12,11239200,1.24
1988-12-08,39.25,39.25,38.75,39.12,14865200,1.24
1988-12-07,39.00,39.50,38.75,39.38,24533600,1.24
1988-12-06,39.25,39.75,39.00,39.50,26233200,1.25
1988-12-05,39.50,40.00,38.75,39.50,38603600,1.25
1988-12-02,38.25,39.88,38.00,39.25,83428800,1.24
1988-12-01,37.75,39.00,37.50,38.75,53040400,1.22
1988-11-30,36.75,38.00,36.75,37.62,41960800,1.19
1988-11-29,36.50,36.75,36.00,36.75,23167200,1.16
1988-11-28,36.50,36.75,36.00,36.50,34840400,1.15
1988-11-25,36.25,36.75,36.00,36.50,12073600,1.15
1988-11-23,35.75,37.00,35.50,36.87,46998000,1.16
1988-11-22,36.50,36.87,36.00,36.13,37046800,1.14
1988-11-21,37.50,37.75,36.25,36.63,55476400,1.16
1988-11-18,38.50,38.50,38.00,38.00,14397600,1.20
1988-11-17,38.00,38.50,38.00,38.25,19885600,1.20
1988-11-16,39.00,39.25,37.75,38.00,36960000,1.20
1988-11-15,39.00,39.25,38.75,39.00,20000400,1.23
1988-11-14,38.75,39.00,38.25,38.87,21308000,1.22
1988-11-11,39.00,39.63,38.50,38.50,27171200,1.21
1988-11-10,39.50,39.75,39.00,39.50,24978800,1.24
1988-11-09,38.25,39.38,38.00,39.25,50430800,1.24
1988-11-08,37.50,38.75,37.37,38.50,38631600,1.21
1988-11-07,37.25,37.75,37.00,37.50,42520800,1.18
1988-11-04,36.75,38.00,36.75,37.75,38449600,1.19
1988-11-03,37.25,37.50,36.75,37.12,60614400,1.17
1988-11-02,38.25,38.25,36.75,37.25,52130400,1.17
1988-11-01,38.50,38.75,37.75,38.00,35924000,1.20
1988-10-31,38.75,38.75,37.50,38.62,60726400,1.22
1988-10-28,39.00,39.50,38.50,38.50,21120400,1.21
1988-10-27,38.75,39.25,38.25,39.00,35921200,1.23
1988-10-26,40.00,40.00,38.50,39.25,47180000,1.24
1988-10-25,40.25,40.25,39.75,39.88,21296800,1.26
1988-10-24,41.25,41.25,39.63,40.00,33790400,1.26
1988-10-21,41.25,41.75,40.75,41.00,30900800,1.29
1988-10-20,40.00,41.63,40.00,41.50,43366400,1.31
1988-10-19,39.75,40.75,39.50,40.00,69330800,1.26
1988-10-18,39.00,39.50,38.25,39.38,35649600,1.24
1988-10-17,38.50,39.00,38.25,38.50,23422000,1.21
1988-10-14,39.50,39.50,38.13,38.75,39312000,1.22
1988-10-13,38.50,39.75,38.50,39.00,41115200,1.23
1988-10-12,38.50,39.00,38.00,38.75,33236000,1.22
1988-10-11,38.25,39.50,38.25,39.00,48638800,1.23
1988-10-10,39.50,39.75,37.50,38.50,83160000,1.21
1988-10-07,39.00,39.75,38.38,39.75,114396800,1.25
1988-10-06,40.50,40.87,39.25,39.75,41941200,1.25
1988-10-05,41.25,41.75,40.50,40.87,30800000,1.29
1988-10-04,42.25,42.75,41.12,41.50,12913600,1.31
1988-10-03,43.00,43.25,42.00,42.50,22694000,1.34
1988-09-30,44.00,44.00,43.25,43.25,23223200,1.36
1988-09-29,43.75,44.25,43.50,44.00,26518800,1.39
1988-09-28,43.50,44.12,43.25,43.50,21173600,1.37
1988-09-27,42.50,43.50,42.50,43.38,40745600,1.37
1988-09-26,43.75,44.00,42.50,42.75,21758800,1.35
1988-09-23,43.50,44.25,43.50,43.75,25370800,1.38
1988-09-22,43.00,44.00,42.75,44.00,36416800,1.39
1988-09-21,41.75,43.00,41.50,42.75,22836800,1.35
1988-09-20,41.75,42.25,41.38,41.50,25670400,1.31
1988-09-19,42.00,42.25,41.25,41.75,23032800,1.32
1988-09-16,41.50,42.75,41.38,42.25,30940000,1.33
1988-09-15,42.00,42.75,41.50,41.63,41440000,1.31
1988-09-14,41.75,42.37,41.50,42.00,59642800,1.32
1988-09-13,40.25,41.25,40.00,41.00,29920800,1.29
1988-09-12,41.00,41.75,40.13,41.00,37007600,1.29
1988-09-09,38.75,41.00,37.75,40.50,58668400,1.28
1988-09-08,38.25,39.50,37.75,38.75,51814000,1.22
1988-09-07,39.00,39.50,37.75,38.25,44777600,1.20
1988-09-06,40.00,40.00,38.75,38.87,35862400,1.22
1988-09-02,39.50,40.00,39.00,39.75,46575200,1.25
1988-09-01,39.75,39.75,38.50,38.87,61684000,1.22
1988-08-31,41.00,41.12,39.50,39.88,59421600,1.26
1988-08-30,40.75,41.00,40.00,40.87,12642000,1.29
1988-08-29,40.75,41.00,40.50,40.87,14308000,1.29
1988-08-26,40.00,40.75,40.00,40.25,10038000,1.27
1988-08-25,40.25,40.50,39.25,40.13,31920000,1.26
1988-08-24,39.75,40.75,39.50,40.75,31368400,1.28
1988-08-23,39.75,40.25,39.25,39.50,40894000,1.24
1988-08-22,40.25,40.75,39.50,39.75,42548800,1.25
1988-08-19,42.50,42.75,40.50,40.75,56840000,1.28
1988-08-18,42.00,43.00,41.75,42.50,18516400,1.34
1988-08-17,42.50,42.75,41.75,42.00,29736000,1.32
1988-08-16,41.00,43.25,40.75,42.50,30688000,1.34
1988-08-15,42.25,42.25,40.50,41.25,41669600,1.30
1988-08-12,43.00,43.00,42.25,42.50,19370400,1.34
1988-08-11,42.25,43.25,42.00,43.25,26513200,1.36
1988-08-10,43.75,43.75,41.75,41.88,36951600,1.32
1988-08-09,44.00,44.25,43.00,43.50,42506800,1.37
1988-08-08,44.50,44.75,44.00,44.00,7484400,1.38
1988-08-05,44.50,45.00,44.25,44.25,13165600,1.39
1988-08-04,44.75,45.25,44.50,44.62,17228400,1.40
1988-08-03,44.75,44.75,44.00,44.75,27711600,1.41
1988-08-02,45.00,45.50,44.50,44.62,30321200,1.40
1988-08-01,44.50,45.75,44.25,45.00,21484400,1.41
1988-07-29,43.25,44.50,43.00,44.37,39737600,1.40
1988-07-28,42.50,43.00,42.25,42.62,23170000,1.34
1988-07-27,42.75,43.25,42.50,42.75,29131200,1.34
1988-07-26,42.75,43.25,42.25,42.75,25382000,1.34
1988-07-25,42.75,43.25,42.25,42.75,26474000,1.34
1988-07-22,43.00,43.25,42.50,42.50,25961600,1.34
1988-07-21,43.75,44.00,42.75,43.00,37256800,1.35
1988-07-20,44.75,45.00,44.00,44.25,30021600,1.39
1988-07-19,45.00,45.50,43.87,44.75,30576000,1.41
1988-07-18,45.38,46.00,45.25,45.50,28375200,1.43
1988-07-15,45.00,45.50,44.75,45.00,20756400,1.41
1988-07-14,44.75,45.25,44.50,45.00,15702400,1.41
1988-07-13,44.75,45.00,44.25,44.75,28792400,1.41
1988-07-12,45.00,45.25,44.50,44.75,25225200,1.41
1988-07-11,45.50,45.50,44.88,45.13,18407200,1.42
1988-07-08,45.50,46.00,45.00,45.25,26348000,1.42
1988-07-07,46.50,46.50,45.25,45.87,26401200,1.44
1988-07-06,47.13,47.50,46.12,46.50,39138400,1.46
1988-07-05,46.50,47.25,46.12,47.25,26112800,1.49
1988-07-01,46.50,46.88,46.25,46.50,23634800,1.46
1988-06-30,46.25,46.75,46.00,46.25,28672000,1.45
1988-06-29,46.00,46.75,45.75,46.38,35862400,1.46
1988-06-28,44.75,46.25,44.50,46.25,40642000,1.45
1988-06-27,44.50,45.38,44.50,44.50,20904800,1.40
1988-06-24,45.00,45.50,44.50,45.00,18678800,1.41
1988-06-23,45.75,45.75,45.00,45.00,17847200,1.41
1988-06-22,45.50,45.87,45.00,45.62,48890800,1.43
1988-06-21,44.00,45.00,43.87,44.88,30898000,1.41
1988-06-20,44.37,44.75,44.00,44.12,19650400,1.39
1988-06-17,44.75,44.75,44.25,44.75,23847600,1.41
1988-06-16,45.00,45.25,44.25,44.50,26843600,1.40
1988-06-15,45.25,45.75,45.00,45.75,30520000,1.44
1988-06-14,45.25,46.00,45.00,45.25,73105200,1.42
1988-06-13,45.00,45.25,44.25,45.00,37240000,1.41
1988-06-10,43.50,44.75,43.00,44.50,44240000,1.40
1988-06-09,45.00,45.25,43.25,43.50,67480000,1.37
1988-06-08,44.25,45.50,44.00,45.00,64680000,1.41
1988-06-07,43.75,45.25,43.50,44.00,77840000,1.38
1988-06-06,42.75,44.00,42.75,44.00,41160000,1.38
1988-06-03,41.75,43.25,41.75,43.00,43960000,1.35
1988-06-02,42.00,42.50,41.50,41.75,33320000,1.31
1988-06-01,41.50,42.50,41.25,42.50,57400000,1.34
1988-05-31,40.00,41.50,39.75,41.50,30800000,1.30
1988-05-27,39.25,40.00,39.00,39.75,20988800,1.25
1988-05-26,38.50,39.50,38.50,39.38,21445200,1.24
1988-05-25,39.00,39.75,38.50,38.50,33880000,1.21
1988-05-24,38.00,39.00,37.75,38.87,35560000,1.22
1988-05-23,38.50,38.87,37.37,38.00,45920000,1.19
1988-05-20,39.25,39.50,38.75,38.75,20434400,1.22
1988-05-19,39.50,39.75,38.50,39.00,62440000,1.23
1988-05-18,40.50,40.75,39.50,39.75,43680000,1.25
1988-05-17,41.50,42.00,40.25,40.50,48440000,1.27
1988-05-16,40.50,41.38,40.00,41.25,18690000,1.30
1988-05-13,40.25,40.50,40.00,40.50,17850000,1.27
1988-05-12,39.50,40.25,39.50,39.75,20745200,1.25
1988-05-11,40.25,40.75,39.50,39.50,43680000,1.24
1988-05-10,40.50,41.00,40.25,40.87,23976400,1.28
1988-05-09,41.25,41.25,40.50,40.75,19093200,1.28
1988-05-06,41.63,41.75,41.25,41.25,26759600,1.29
1988-05-05,42.00,42.25,41.50,41.75,17614800,1.31
1988-05-04,41.88,43.13,41.75,42.00,56000000,1.32
1988-05-03,41.00,42.25,40.75,41.75,31080000,1.31
1988-05-02,40.75,41.25,40.50,41.00,20549200,1.29
1988-04-29,41.25,41.50,40.50,41.00,22498000,1.29
1988-04-28,41.75,42.00,41.25,41.38,24791200,1.30
1988-04-27,41.75,42.00,41.50,41.75,31640000,1.31
1988-04-26,41.00,41.75,40.75,41.50,43960000,1.30
1988-04-25,40.25,41.00,40.00,40.87,37520000,1.28
1988-04-22,39.75,40.25,39.50,40.13,26910800,1.26
1988-04-21,40.37,40.50,39.00,39.50,44520000,1.24
1988-04-20,40.25,40.50,39.25,39.75,53760000,1.25
1988-04-19,40.13,41.50,40.13,40.25,53082400,1.26
1988-04-18,39.75,40.75,39.25,40.00,42560000,1.26
1988-04-15,39.75,40.00,38.50,39.50,58240000,1.24
1988-04-14,40.50,41.50,39.00,39.50,47040000,1.24
1988-04-13,41.75,42.00,41.00,41.25,35840000,1.29
1988-04-12,41.75,42.25,41.25,41.75,43400000,1.31
1988-04-11,41.75,42.00,41.00,41.50,37240000,1.30
1988-04-08,40.75,41.75,39.75,41.00,50680000,1.29
1988-04-07,41.75,42.37,40.75,40.75,40880000,1.28
1988-04-06,39.50,41.75,39.00,41.75,47600000,1.31
1988-04-05,39.25,39.50,38.50,39.25,36960000,1.23
1988-04-04,39.75,40.50,38.50,38.75,45360000,1.22
1988-03-31,39.75,40.50,39.25,40.00,54320000,1.26
1988-03-30,40.75,41.25,38.75,39.50,92960000,1.24
1988-03-29,41.50,42.00,40.62,41.00,53480000,1.29
1988-03-28,40.00,41.75,39.50,41.50,43120000,1.30
1988-03-25,40.75,41.25,40.00,40.13,32760000,1.26
1988-03-24,41.75,42.50,40.00,40.87,80080000,1.28
1988-03-23,44.00,44.00,41.88,42.50,52360000,1.33
1988-03-22,44.00,44.50,43.25,44.00,29794800,1.38
1988-03-21,44.37,44.62,43.00,43.87,56840000,1.38
1988-03-18,45.00,45.50,44.25,44.75,68040000,1.40
1988-03-17,46.25,46.50,44.75,45.00,65240000,1.41
1988-03-16,44.88,46.38,44.50,46.12,29680000,1.45
1988-03-15,46.00,46.25,44.75,45.00,45360000,1.41
1988-03-14,45.75,46.50,45.50,46.25,24530800,1.45
1988-03-11,45.50,45.75,44.50,45.75,39480000,1.44
1988-03-10,47.00,47.25,45.25,45.25,44240000,1.42
1988-03-09,46.25,47.25,46.25,46.75,33600000,1.47
1988-03-08,46.75,47.00,46.00,46.25,36120000,1.45
1988-03-07,46.75,47.75,46.50,46.88,51800000,1.47
1988-03-04,46.00,47.00,45.50,46.88,52360000,1.47
1988-03-03,44.50,47.00,44.50,46.50,118440000,1.46
1988-03-02,43.75,45.00,43.50,44.75,73080000,1.40
1988-03-01,43.25,43.50,42.50,43.25,42840000,1.36
1988-02-29,41.75,43.25,41.50,43.00,28000000,1.35
1988-02-26,42.00,42.25,41.25,41.75,20585600,1.31
1988-02-25,42.00,43.00,41.75,41.75,44800000,1.31
1988-02-24,42.75,43.00,42.00,42.25,36400000,1.33
1988-02-23,43.25,43.75,42.25,42.75,55160000,1.34
1988-02-22,41.50,43.63,41.50,43.25,50120000,1.36
1988-02-19,41.75,42.00,41.50,41.75,22691200,1.31
1988-02-18,41.63,42.75,41.50,41.75,35840000,1.31
1988-02-17,41.25,42.50,41.25,41.88,64120000,1.31
1988-02-16,41.00,41.25,40.00,41.25,38640000,1.29
1988-02-12,40.62,41.50,40.50,41.00,34440000,1.29
1988-02-11,41.00,41.25,40.25,40.62,36960000,1.27
1988-02-10,39.75,41.50,39.75,41.00,57120000,1.28
1988-02-09,39.00,39.88,38.75,39.75,29120000,1.24
1988-02-08,38.50,39.25,37.75,38.75,50960000,1.21
1988-02-05,40.00,40.37,38.50,38.62,33040000,1.21
1988-02-04,39.50,40.13,39.00,39.75,49840000,1.24
1988-02-03,41.00,41.25,39.25,39.50,56560000,1.24
1988-02-02,41.50,41.88,40.50,41.25,47880000,1.29
1988-02-01,41.75,42.50,41.38,41.75,49840000,1.31
1988-01-29,41.50,41.75,40.25,41.50,66360000,1.30
1988-01-28,40.00,41.50,39.75,41.25,58240000,1.29
1988-01-27,40.25,40.50,38.75,39.75,64680000,1.24
1988-01-26,40.75,41.00,39.25,39.75,35840000,1.24
1988-01-25,39.50,41.50,39.50,40.87,50120000,1.28
1988-01-22,40.50,40.75,38.25,39.25,111440000,1.23
1988-01-21,40.50,40.75,39.38,40.13,123480000,1.26
1988-01-20,43.00,43.00,38.25,39.75,170240000,1.24
1988-01-19,42.25,43.25,41.38,42.75,68600000,1.34
1988-01-18,43.00,43.00,42.00,42.75,31360000,1.34
1988-01-15,43.50,45.00,42.50,42.88,85960000,1.34
1988-01-14,42.75,42.88,42.00,42.25,33040000,1.32
1988-01-13,42.00,43.25,41.12,42.25,52920000,1.32
1988-01-12,43.00,43.50,39.75,42.00,100240000,1.32
1988-01-11,40.00,42.75,39.75,42.50,101080000,1.33
1988-01-08,44.50,45.25,39.50,40.00,121520000,1.25
1988-01-07,43.50,44.75,42.50,44.50,53200000,1.39
1988-01-06,45.00,45.00,43.75,43.75,67200000,1.37
1988-01-05,46.00,46.25,44.25,44.62,77280000,1.40
1988-01-04,42.75,44.75,42.25,44.75,82600000,1.40
1987-12-31,42.50,43.00,41.88,42.00,29400000,1.32
1987-12-30,42.50,43.75,42.50,43.38,38920000,1.36
1987-12-29,40.50,42.25,40.25,42.12,29680000,1.32
1987-12-28,42.25,42.50,39.50,40.25,57400000,1.26
1987-12-24,42.00,43.00,41.75,42.62,17486000,1.33
1987-12-23,41.75,42.75,41.25,42.25,42840000,1.32
1987-12-22,41.75,41.75,40.50,41.50,32200000,1.30
1987-12-21,40.50,41.75,40.25,41.75,47040000,1.31
1987-12-18,39.50,41.25,39.25,40.50,75600000,1.27
1987-12-17,40.50,40.75,39.25,39.25,81480000,1.23
1987-12-16,37.75,39.75,37.25,39.25,82600000,1.23
1987-12-15,37.75,38.25,37.00,37.50,74760000,1.17
1987-12-14,34.50,37.50,34.25,37.25,85400000,1.17
1987-12-11,34.75,34.75,33.50,34.00,30520000,1.06
1987-12-10,33.75,36.00,33.25,34.75,69160000,1.09
1987-12-09,34.50,36.25,33.87,35.00,44800000,1.10
1987-12-08,33.50,34.88,33.25,34.50,63560000,1.08
1987-12-07,31.00,33.25,31.00,33.00,50960000,1.03
1987-12-04,30.25,31.25,29.75,30.75,61040000,0.96
1987-12-03,33.00,33.37,29.75,30.50,79800000,0.96
1987-12-02,33.25,33.50,32.50,32.50,35560000,1.02
1987-12-01,33.50,34.00,32.75,33.25,45360000,1.04
1987-11-30,33.75,34.50,30.50,33.00,104160000,1.03
1987-11-27,36.25,36.50,34.75,35.00,17670800,1.10
1987-11-25,37.00,37.00,36.00,36.50,23100000,1.14
1987-11-24,36.75,37.75,36.13,37.00,49280000,1.16
1987-11-23,35.50,36.25,34.75,36.25,24348800,1.14
1987-11-20,34.00,36.00,33.25,35.50,62720000,1.11
1987-11-19,36.50,36.50,34.00,34.50,45640000,1.08
1987-11-18,35.75,36.50,34.50,36.25,66360000,1.14
1987-11-17,36.75,37.00,35.00,35.00,67200000,1.10
1987-11-16,37.75,38.50,36.50,36.75,46200000,1.15
1987-11-13,39.25,39.50,37.00,37.25,38640000,1.16
1987-11-12,38.50,40.00,38.38,38.75,61600000,1.21
1987-11-11,37.25,38.25,36.75,37.25,46480000,1.16
1987-11-10,36.50,37.50,36.00,36.25,57960000,1.13
1987-11-09,37.00,37.50,36.25,37.25,52640000,1.16
1987-11-06,38.25,39.50,37.00,37.75,46760000,1.18
1987-11-05,36.25,38.75,36.25,38.00,63840000,1.19
1987-11-04,35.50,37.25,34.75,36.00,58520000,1.12
1987-11-03,38.00,38.50,34.25,36.25,78400000,1.13
1987-11-02,38.75,39.50,37.50,38.75,47040000,1.21
1987-10-30,40.00,43.00,38.50,38.62,105280000,1.21
1987-10-29,34.25,40.00,32.25,39.50,82880000,1.23
1987-10-28,30.75,33.75,29.25,33.50,104720000,1.05
1987-10-27,29.50,32.25,29.00,30.25,113960000,0.95
1987-10-26,34.50,35.00,27.63,28.00,78400000,0.87
1987-10-23,35.75,36.50,34.25,35.50,49560000,1.11
1987-10-22,39.25,40.50,36.00,36.75,96320000,1.15
1987-10-21,38.50,42.00,38.00,40.50,133560000,1.27
1987-10-20,38.50,42.00,32.63,34.50,142240000,1.08
1987-10-19,48.25,48.25,35.50,36.50,119000000,1.14
1987-10-16,52.25,53.00,47.50,48.25,105000000,1.51
1987-10-15,53.25,54.50,51.75,52.00,87080000,1.62
1987-10-14,53.75,54.00,52.00,53.25,64680000,1.66
1987-10-13,54.50,54.75,53.25,54.50,40600000,1.70
1987-10-12,54.25,54.37,51.75,53.25,49840000,1.66
1987-10-09,54.25,55.50,54.00,54.13,36400000,1.69
1987-10-08,55.50,56.00,53.25,54.25,41160000,1.70
1987-10-07,55.50,55.75,54.25,55.50,56000000,1.73
1987-10-06,59.50,59.50,55.50,55.75,50400000,1.74
1987-10-05,58.50,59.75,57.75,59.25,33600000,1.85
1987-10-02,58.25,58.75,57.50,58.50,24124800,1.83
1987-10-01,56.75,58.75,56.50,58.25,29120000,1.82
1987-09-30,54.25,57.00,54.25,56.50,30520000,1.77
1987-09-29,56.00,56.00,54.25,54.50,42840000,1.70
1987-09-28,57.50,58.75,55.50,55.75,50960000,1.74
1987-09-25,56.75,58.00,56.50,57.50,26630800,1.80
1987-09-24,55.25,57.87,55.25,56.50,45640000,1.77
1987-09-23,54.13,56.00,53.75,55.25,63644000,1.73
1987-09-22,50.50,54.25,50.25,54.13,38360000,1.69
1987-09-21,51.75,52.75,50.25,50.25,32200000,1.57
1987-09-18,52.00,52.25,51.37,51.75,17799600,1.62
1987-09-17,52.00,52.25,51.00,52.00,16699200,1.62
1987-09-16,51.75,52.62,51.25,51.75,42000000,1.62
1987-09-15,53.00,53.00,51.50,51.75,26152000,1.62
1987-09-14,54.75,55.25,52.75,53.00,20476400,1.66
1987-09-11,54.00,55.50,52.75,54.50,31080000,1.70
1987-09-10,53.25,54.50,53.12,53.75,35000000,1.68
1987-09-09,50.25,53.00,49.50,52.75,39480000,1.65
1987-09-08,50.25,50.50,48.50,49.88,43960000,1.56
1987-09-04,51.25,51.75,50.00,50.50,27109600,1.58
1987-09-03,52.50,52.75,50.25,51.25,46200000,1.60
1987-09-02,52.00,53.25,50.75,52.00,57400000,1.62
1987-09-01,54.75,55.25,52.50,52.50,34720000,1.64
1987-08-31,52.25,54.25,51.75,54.00,37520000,1.69
1987-08-28,52.00,52.50,51.50,52.00,23954000,1.62
1987-08-27,52.25,52.75,51.50,52.00,31080000,1.62
1987-08-26,53.00,53.50,52.00,52.00,49000000,1.62
1987-08-25,52.75,53.25,52.00,52.00,34160000,1.62
1987-08-24,53.00,53.50,52.25,52.25,30240000,1.63
1987-08-21,51.75,53.75,51.50,53.00,35000000,1.66
1987-08-20,50.25,52.50,49.75,51.75,43960000,1.62
1987-08-19,49.50,50.00,49.00,50.00,16718800,1.56
1987-08-18,49.25,49.50,48.25,48.75,59360000,1.52
1987-08-17,49.50,50.00,48.75,49.50,36400000,1.55
1987-08-14,48.50,50.00,48.00,49.00,26213600,1.53
1987-08-13,48.75,50.25,48.50,49.00,49000000,1.53
1987-08-12,49.50,49.75,48.25,48.75,40320000,1.52
1987-08-11,49.50,50.25,48.75,49.50,67760000,1.55
1987-08-10,48.25,48.25,45.75,48.25,19499200,1.51
1987-08-07,46.25,47.25,46.00,46.50,38080000,1.45
1987-08-06,43.25,46.75,42.75,46.25,63000000,1.44
1987-08-05,42.25,43.50,42.00,43.25,32480000,1.35
1987-08-04,40.50,42.25,40.00,42.25,30240000,1.32
1987-08-03,41.00,41.50,40.25,40.25,15839600,1.26
1987-07-31,41.25,42.00,41.25,41.25,18261600,1.29
1987-07-30,41.00,41.50,40.75,41.50,26073600,1.30
1987-07-29,42.00,42.00,40.50,41.00,24707200,1.28
1987-07-28,42.50,42.75,41.75,41.88,18572400,1.31
1987-07-27,42.50,43.00,42.00,42.25,14159600,1.32
1987-07-24,41.50,42.75,41.50,42.50,29400000,1.33
1987-07-23,43.00,43.50,40.50,41.75,18684400,1.30
1987-07-22,41.50,42.75,41.25,42.50,15232000,1.33
1987-07-21,42.00,42.50,41.25,41.38,27748000,1.29
1987-07-20,43.00,43.25,41.50,41.75,31080000,1.30
1987-07-17,44.25,44.75,42.75,43.25,23049600,1.35
1987-07-16,44.00,44.00,43.25,44.00,23646000,1.37
1987-07-15,43.00,44.75,42.25,44.00,67760000,1.37
1987-07-14,41.00,43.00,41.00,43.00,64400000,1.34
1987-07-13,39.00,40.75,38.75,40.50,63840000,1.26
1987-07-10,38.00,39.25,37.75,38.00,39200000,1.19
1987-07-09,37.25,38.75,37.25,37.75,59920000,1.18
1987-07-08,39.25,39.25,36.50,37.25,85400000,1.16
1987-07-07,40.50,41.00,38.75,39.25,50960000,1.22
1987-07-06,40.75,41.75,40.50,40.75,21372400,1.27
1987-07-02,40.00,41.00,39.75,40.62,20389600,1.27
1987-07-01,40.75,40.75,39.75,40.00,23707600,1.25
1987-06-30,40.50,41.00,39.75,40.50,36120000,1.26
1987-06-29,40.50,40.75,40.00,40.75,25326000,1.27
1987-06-26,40.75,41.50,40.00,40.50,31920000,1.26
1987-06-25,42.00,42.50,40.50,40.50,30240000,1.26
1987-06-24,41.50,43.25,40.50,42.00,29680000,1.31
1987-06-23,42.00,42.12,40.75,41.25,20213200,1.29
1987-06-22,41.25,42.25,40.87,42.00,42280000,1.31
1987-06-19,41.50,41.75,40.37,41.00,31360000,1.28
1987-06-18,40.25,41.75,39.50,41.50,57400000,1.30
1987-06-17,41.50,42.50,40.00,40.50,74480000,1.26
1987-06-16,41.50,41.75,38.00,41.50,85680000,1.30
1987-06-15,79.00,79.50,77.50,78.50,64960000,1.22
1987-06-12,79.00,79.75,78.75,79.00,25440800,1.23
1987-06-11,78.50,80.00,78.00,79.00,31343200,1.23
1987-06-10,78.75,80.25,78.00,78.50,36556800,1.22
1987-06-09,77.50,79.50,77.50,78.50,31763200,1.22
1987-06-08,77.75,78.00,76.75,77.75,50461600,1.21
1987-06-05,78.75,78.75,77.75,77.75,32732000,1.21
1987-06-04,78.00,78.75,77.00,78.50,38399200,1.22
1987-06-03,77.25,79.50,77.25,77.75,42828800,1.21
1987-06-02,77.50,78.00,77.00,77.25,34372800,1.21
1987-06-01,79.50,79.50,77.50,77.75,20826400,1.21
1987-05-29,80.25,80.50,79.00,79.00,23150400,1.23
1987-05-28,79.50,80.25,78.50,80.00,37805600,1.25
1987-05-27,78.00,80.25,77.50,79.50,45175200,1.24
1987-05-26,74.50,78.00,74.00,78.00,38063200,1.22
1987-05-22,75.00,75.50,73.75,74.12,24276000,1.16
1987-05-21,74.75,75.75,74.50,74.50,43450400,1.16
1987-05-20,73.00,75.00,72.50,74.50,72240000,1.16
1987-05-19,75.75,75.75,72.63,73.25,59920000,1.14
1987-05-18,78.25,78.50,75.50,75.75,60480000,1.18
1987-05-15,79.25,79.25,78.00,78.25,36489600,1.22
1987-05-14,78.25,79.50,78.25,79.25,37122400,1.24
1987-05-13,75.75,78.63,75.50,78.50,77840000,1.22
1987-05-12,76.00,76.50,75.00,75.50,64960000,1.18
1987-05-11,77.00,79.50,76.75,77.00,49319200,1.20
1987-05-08,80.50,81.00,79.00,79.00,46183200,1.23
1987-05-07,79.75,81.00,79.75,80.25,45197600,1.25
1987-05-06,80.50,82.25,79.25,80.00,71680000,1.25
1987-05-05,80.00,80.75,78.00,80.25,57680000,1.25
1987-05-04,79.50,80.25,79.00,79.75,35526400,1.24
1987-05-01,79.50,80.00,78.75,80.00,33180000,1.25
1987-04-30,78.00,80.00,77.75,79.25,63280000,1.23
1987-04-29,77.25,79.75,77.00,77.75,72800000,1.21
1987-04-28,75.75,77.87,75.50,77.00,81200000,1.20
1987-04-27,74.25,75.25,73.25,75.00,95760000,1.17
1987-04-24,75.75,76.50,74.50,74.75,63840000,1.16
1987-04-23,74.25,77.25,74.25,76.00,76160000,1.18
1987-04-22,76.62,77.00,74.00,74.25,100800000,1.16
1987-04-21,70.25,75.00,69.50,74.75,108080000,1.16
1987-04-20,71.50,72.75,70.75,71.13,37290400,1.11
1987-04-16,71.25,73.25,71.00,71.50,86800000,1.11
1987-04-15,69.50,71.00,68.75,71.00,87360000,1.11
1987-04-14,66.75,69.75,66.50,68.00,101920000,1.06
1987-04-13,70.00,70.25,67.50,67.50,35554400,1.05
1987-04-10,71.25,71.50,69.75,70.25,54460000,1.09
1987-04-09,68.75,71.50,67.75,71.00,59360000,1.11
1987-04-08,67.75,70.25,67.50,69.00,57680000,1.07
1987-04-07,69.75,70.25,67.75,67.75,64960000,1.06
1987-04-06,71.50,72.75,69.25,70.00,72240000,1.09
1987-04-03,71.50,71.87,70.25,71.75,134960000,1.12
1987-04-02,68.25,71.75,67.00,71.75,194320000,1.12
1987-04-01,63.00,67.00,62.38,66.75,54465600,1.04
1987-03-31,62.25,64.75,62.25,64.50,68320000,1.00
1987-03-30,63.50,64.25,62.25,62.50,64960000,0.97
1987-03-27,67.25,67.50,64.75,65.00,33476800,1.01
1987-03-26,66.75,67.75,66.50,67.25,35756000,1.05
1987-03-25,66.50,67.00,65.25,66.75,68320000,1.04
1987-03-24,67.75,68.50,66.25,66.25,67200000,1.03
1987-03-23,68.00,68.25,66.25,67.50,61600000,1.05
1987-03-20,68.25,69.75,68.25,68.25,86800000,1.06
1987-03-19,65.75,68.50,65.50,68.37,51682400,1.07
1987-03-18,67.25,67.50,64.75,66.00,75600000,1.03
1987-03-17,65.50,68.00,65.00,67.00,61040000,1.04
1987-03-16,63.50,65.25,62.50,65.25,61600000,1.02
1987-03-13,65.25,66.00,63.50,63.50,49403200,0.99
1987-03-12,66.00,66.25,63.62,65.25,75600000,1.02
1987-03-11,67.25,68.00,66.25,66.25,54616800,1.03
1987-03-10,64.50,66.88,64.50,66.75,61040000,1.04
1987-03-09,66.50,66.75,64.50,64.63,63840000,1.01
1987-03-06,67.25,68.37,66.75,67.25,44094400,1.05
1987-03-05,67.50,69.00,67.25,68.50,84560000,1.07
1987-03-04,65.75,68.25,65.37,67.63,112000000,1.05
1987-03-03,67.50,68.13,64.75,65.00,109200000,1.01
1987-03-02,70.25,70.50,67.00,67.50,99120000,1.05
1987-02-27,69.13,71.00,67.75,70.00,101360000,1.09
1987-02-26,69.50,71.37,68.00,69.13,124880000,1.08
1987-02-25,65.50,69.50,64.63,69.13,113680000,1.08
1987-02-24,63.25,66.00,63.12,65.50,89040000,1.02
1987-02-23,60.87,64.25,59.62,63.12,87920000,0.98
1987-02-20,62.38,62.50,60.63,61.25,47661600,0.95
1987-02-19,63.50,63.50,61.75,62.38,78400000,0.97
1987-02-18,66.62,67.37,63.38,63.50,117600000,0.99
1987-02-17,62.13,66.50,61.87,66.38,102480000,1.03
1987-02-13,58.63,62.50,58.00,62.13,127680000,0.97
1987-02-12,57.00,59.88,57.00,58.63,177520000,0.91
1987-02-11,53.00,56.75,52.75,56.50,85680000,0.88
1987-02-10,52.50,52.75,51.63,52.75,41697600,0.82
1987-02-09,52.88,53.37,52.25,52.62,39250400,0.82
1987-02-06,54.00,54.00,52.88,54.00,73360000,0.84
1987-02-05,55.00,55.13,53.12,53.87,85120000,0.84
1987-02-04,55.50,55.50,54.37,55.00,54460000,0.86
1987-02-03,56.00,56.12,54.75,55.50,44654400,0.86
1987-02-02,55.50,56.00,54.25,55.88,61600000,0.87
1987-01-30,54.00,55.88,52.62,55.50,102480000,0.86
1987-01-29,55.88,57.25,53.37,54.13,139440000,0.84
1987-01-28,53.00,55.75,52.12,55.38,103600000,0.86
1987-01-27,50.00,53.12,49.87,52.75,94640000,0.82
1987-01-26,50.00,50.50,49.50,49.75,87920000,0.78
1987-01-23,52.50,53.00,50.25,50.25,114800000,0.78
1987-01-22,48.88,52.62,48.50,52.50,118160000,0.82
1987-01-21,50.87,51.13,49.00,49.00,133280000,0.76
1987-01-20,55.00,55.75,51.50,51.63,193760000,0.80
1987-01-19,48.75,53.12,47.87,53.12,90720000,0.83
1987-01-16,50.00,50.00,47.75,48.75,101920000,0.76
1987-01-15,48.25,51.37,48.00,49.87,136640000,0.78
1987-01-14,44.63,48.25,44.50,48.13,126000000,0.75
1987-01-13,45.12,45.38,44.63,44.63,52931200,0.70
1987-01-12,45.50,45.75,44.75,45.50,58240000,0.71
1987-01-09,44.75,45.75,44.37,45.38,59920000,0.71
1987-01-08,44.75,45.12,44.50,44.75,72800000,0.70
1987-01-07,43.87,44.88,43.63,44.75,108640000,0.70
1987-01-06,43.13,44.00,42.62,43.75,81200000,0.68
1987-01-05,41.25,43.25,41.00,43.00,59920000,0.67
1987-01-02,40.37,41.13,40.13,40.87,30217600,0.64
1986-12-31,41.00,41.38,40.37,40.50,33140800,0.63
1986-12-30,40.50,41.50,40.37,41.00,37038400,0.64
1986-12-29,41.00,41.13,40.25,40.50,29411200,0.63
1986-12-26,41.88,41.88,41.00,41.00,22467200,0.64
1986-12-24,42.00,42.12,41.62,41.88,23940000,0.65
1986-12-23,42.25,42.38,41.88,42.12,61040000,0.66
1986-12-22,42.00,42.50,41.75,42.12,41092800,0.66
1986-12-19,41.38,42.50,41.38,42.12,49772800,0.66
1986-12-18,41.13,41.88,40.75,41.38,43764000,0.64
1986-12-17,42.38,42.50,40.87,41.25,37777600,0.64
1986-12-16,41.62,42.50,41.62,42.50,37984800,0.66
1986-12-15,41.00,41.75,40.37,41.75,52264800,0.65
1986-12-12,42.87,43.00,41.25,41.25,45029600,0.64
1986-12-11,43.63,43.87,42.62,42.87,56560000,0.67
1986-12-10,42.38,43.75,42.00,43.50,61040000,0.68
1986-12-09,42.38,42.62,41.13,42.38,75600000,0.66
1986-12-08,43.63,43.87,42.38,42.50,86800000,0.66
1986-12-05,42.62,43.75,42.50,43.75,65520000,0.68
1986-12-04,42.62,42.75,42.00,42.50,67200000,0.66
1986-12-03,41.62,43.00,41.50,42.75,84000000,0.67
1986-12-02,40.50,41.75,40.00,41.50,92400000,0.65
1986-12-01,40.00,40.13,39.12,40.13,86800000,0.63
1986-11-28,40.50,40.63,39.63,40.00,55137600,0.62
1986-11-26,40.13,41.25,40.00,40.50,126560000,0.63
1986-11-25,38.00,40.37,38.00,40.25,212240000,0.63
1986-11-24,36.25,38.12,36.00,38.00,94080000,0.59
1986-11-21,35.25,36.25,35.12,36.00,71680000,0.56
1986-11-20,34.88,35.38,34.88,35.25,73920000,0.55
1986-11-19,35.12,35.25,34.50,35.00,75600000,0.55
1986-11-18,36.37,36.75,35.12,35.38,42515200,0.55
1986-11-17,35.25,37.00,35.00,36.37,35420000,0.57
1986-11-14,35.50,35.50,34.88,35.25,33779200,0.55
1986-11-13,36.50,36.50,35.50,35.50,34378400,0.55
1986-11-12,35.75,36.63,35.62,36.63,32748800,0.57
1986-11-11,35.50,35.75,35.25,35.50,12544000,0.55
1986-11-10,35.87,35.87,35.12,35.38,26471200,0.55
1986-11-07,36.00,36.13,34.88,35.75,35789600,0.56
1986-11-06,36.63,36.87,35.75,36.13,82880000,0.56
1986-11-05,35.75,37.13,35.50,37.00,156240000,0.58
1986-11-04,34.88,35.87,33.87,35.75,61600000,0.56
1986-11-03,34.75,35.12,34.62,35.00,37956800,0.55
1986-10-31,34.25,34.88,34.25,34.62,30324000,0.54
1986-10-30,33.50,34.75,33.37,34.25,73360000,0.53
1986-10-29,33.50,33.50,33.13,33.37,21358400,0.52
1986-10-28,34.00,34.13,33.00,33.37,35560000,0.52
1986-10-27,33.50,34.00,33.25,34.00,37800000,0.53
1986-10-24,33.13,33.25,32.75,33.00,18832800,0.51
1986-10-23,32.50,33.13,32.50,33.13,30783200,0.52
1986-10-22,32.75,32.87,32.25,32.50,23620800,0.51
1986-10-21,33.00,33.00,32.63,32.75,28431200,0.51
1986-10-20,33.50,33.63,32.87,32.87,37245600,0.51
1986-10-17,33.75,34.00,33.37,33.63,37968000,0.52
1986-10-16,33.37,33.87,33.25,33.63,33941600,0.52
1986-10-15,33.50,33.50,32.75,33.37,51352000,0.52
1986-10-14,34.62,35.25,33.75,34.00,49834400,0.53
1986-10-13,33.13,34.62,33.00,34.62,24920000,0.54
1986-10-10,32.87,33.37,32.37,33.25,14632800,0.52
1986-10-09,32.75,33.25,32.63,33.00,19488000,0.51
1986-10-08,32.87,33.00,32.25,32.75,27893600,0.51
1986-10-07,34.00,34.13,32.87,33.00,31998400,0.51
1986-10-06,33.75,34.25,33.63,34.13,23626400,0.53
1986-10-03,34.38,34.75,33.37,33.75,34686400,0.53
1986-10-02,33.75,34.38,33.50,34.13,23704800,0.53
1986-10-01,33.37,34.50,33.37,34.13,34647200,0.53
1986-09-30,32.87,33.87,32.63,33.50,45197600,0.52
1986-09-29,33.63,33.87,31.62,32.50,52236800,0.51
1986-09-26,34.13,34.38,33.87,34.25,17505600,0.53
1986-09-25,35.12,35.25,33.63,34.50,46950400,0.54
1986-09-24,36.13,36.37,34.00,35.12,44217600,0.55
1986-09-23,35.25,36.25,35.12,36.13,84560000,0.56
1986-09-22,33.50,35.38,33.50,35.25,59920000,0.55
1986-09-19,33.75,33.87,33.25,33.63,31903200,0.52
1986-09-18,34.25,34.50,33.75,34.00,24757600,0.53
1986-09-17,34.88,35.00,34.25,34.25,29215200,0.53
1986-09-16,33.13,35.12,32.50,34.88,61600000,0.54
1986-09-15,32.25,33.13,32.00,33.13,55680800,0.52
1986-09-12,32.50,32.75,31.75,31.75,57120000,0.49
1986-09-11,34.62,34.75,32.50,32.63,33588800,0.51
1986-09-10,35.62,35.87,34.75,35.00,18916800,0.55
1986-09-09,34.62,36.00,34.62,35.75,37693600,0.56
1986-09-08,35.00,35.00,33.63,34.75,31550400,0.54
1986-09-05,35.62,35.87,35.00,35.12,24623200,0.55
1986-09-04,35.00,35.50,34.75,35.50,49700000,0.55
1986-09-03,34.75,34.88,34.13,34.75,29372000,0.54
1986-09-02,37.13,37.13,34.75,34.75,58240000,0.54
1986-08-29,37.63,38.00,36.87,37.00,33807200,0.58
1986-08-28,37.00,38.00,36.87,37.75,54924800,0.59
1986-08-27,36.63,37.00,36.25,37.00,36758400,0.58
1986-08-26,36.37,36.87,36.37,36.63,32810400,0.57
1986-08-25,36.50,36.87,36.37,36.37,31600800,0.57
1986-08-22,35.87,36.63,35.87,36.25,28929600,0.56
1986-08-21,36.13,36.37,35.75,35.75,48664000,0.56
1986-08-20,35.25,36.50,35.25,36.25,42828800,0.56
1986-08-19,35.12,35.50,34.62,35.38,34445600,0.55
1986-08-18,35.75,35.87,35.00,35.38,36836800,0.55
1986-08-15,36.13,36.50,35.62,35.75,34294400,0.56
1986-08-14,36.00,37.00,36.00,36.00,57680000,0.56
1986-08-13,34.25,36.25,34.25,36.00,113680000,0.56
1986-08-12,33.37,34.38,33.37,34.25,61040000,0.53
1986-08-11,31.88,33.50,31.75,33.50,45858400,0.52
1986-08-08,31.88,32.37,31.62,31.62,27535200,0.49
1986-08-07,31.12,32.63,31.12,31.75,43349600,0.49
1986-08-06,32.12,32.12,31.00,31.12,46300800,0.48
1986-08-05,31.62,32.37,31.50,32.12,29472800,0.50
1986-08-04,31.38,31.50,30.63,31.50,32541600,0.49
1986-08-01,31.12,31.75,31.12,31.38,37520000,0.49
1986-07-31,30.50,31.50,30.50,31.25,70560000,0.49
1986-07-30,31.25,31.50,30.00,30.50,63840000,0.48
1986-07-29,32.25,32.25,30.75,31.25,148960000,0.49
1986-07-28,33.87,34.00,32.25,32.37,61600000,0.50
1986-07-25,33.13,34.00,33.00,34.00,54364800,0.53
1986-07-24,34.25,34.38,33.00,33.13,36142400,0.52
1986-07-23,34.62,34.62,34.13,34.13,44872800,0.53
1986-07-22,33.50,34.62,33.25,34.62,59920000,0.54
1986-07-21,33.00,33.75,32.75,33.50,57120000,0.52
1986-07-18,32.25,32.50,31.25,31.75,77280000,0.49
1986-07-17,33.50,33.75,32.12,32.25,62720000,0.50
1986-07-16,35.50,35.62,32.75,33.50,134960000,0.52
1986-07-15,35.00,35.00,34.25,34.88,74480000,0.54
1986-07-14,37.13,37.37,36.25,36.25,59360000,0.56
1986-07-11,35.38,37.75,35.25,37.13,56000000,0.58
1986-07-10,34.75,35.38,34.62,35.38,52141600,0.55
1986-07-09,34.25,34.75,34.00,34.62,91280000,0.54
1986-07-08,35.25,35.25,34.13,34.25,68420800,0.53
1986-07-07,37.63,37.75,35.38,35.62,45455200,0.56
1986-07-03,36.13,37.75,35.62,37.63,45292800,0.59
1986-07-02,35.38,36.25,35.38,36.13,36209600,0.56
1986-07-01,35.87,36.13,34.75,35.38,21929600,0.55
1986-06-30,35.87,36.25,35.75,35.87,17690400,0.56
1986-06-27,36.25,36.75,35.50,35.87,12549600,0.56
1986-06-26,35.87,36.37,35.50,36.25,29232000,0.56
1986-06-25,35.00,36.00,35.00,35.87,32995200,0.56
1986-06-24,34.75,35.12,34.38,34.88,35498400,0.54
1986-06-23,36.00,36.25,34.62,34.75,29080800,0.54
1986-06-20,35.00,36.13,35.00,36.00,40325600,0.56
1986-06-19,34.25,35.75,33.87,35.00,86161600,0.55
1986-06-18,34.25,34.75,32.50,34.25,107413600,0.53
1986-06-17,35.87,36.00,34.00,34.25,55512800,0.53
1986-06-16,36.37,36.87,35.62,35.87,43400000,0.56
1986-06-13,36.00,36.37,35.25,36.37,35750400,0.57
1986-06-12,36.13,36.37,36.00,36.00,32272800,0.56
1986-06-11,36.00,36.25,35.50,36.13,46715200,0.56
1986-06-10,36.00,36.00,35.12,36.00,61723200,0.56
1986-06-09,37.75,37.88,35.87,36.00,61756800,0.56
1986-06-06,38.88,38.88,37.50,37.75,44340800,0.59
1986-06-05,38.75,39.12,38.50,38.88,36971200,0.61
1986-06-04,37.88,38.88,37.75,38.75,75163200,0.60
1986-06-03,37.13,38.12,37.13,37.88,81474400,0.59
1986-06-02,37.00,37.37,36.75,37.13,49812000,0.58
1986-05-30,37.00,37.25,36.50,37.00,31858400,0.58
1986-05-29,37.25,37.25,36.50,37.00,25356800,0.58
1986-05-28,36.87,37.50,36.75,37.25,51783200,0.58
1986-05-27,37.00,37.00,36.37,36.87,21162400,0.57
1986-05-23,36.75,37.13,36.37,37.00,34960800,0.58
1986-05-22,37.00,37.50,35.75,36.75,55126400,0.57
1986-05-21,35.38,37.25,35.00,37.00,86682400,0.58
1986-05-20,35.62,35.62,34.25,35.38,61448800,0.55
1986-05-19,36.00,36.50,35.50,35.62,52376800,0.56
1986-05-16,36.00,36.25,35.12,36.00,79811200,0.56
1986-05-15,36.87,37.00,35.62,36.00,55636000,0.56
1986-05-14,36.00,37.37,36.00,36.87,120747200,0.57
1986-05-13,36.37,36.50,35.25,36.00,117941600,0.56
1986-05-12,33.37,36.63,33.25,36.37,100105600,0.57
1986-05-09,33.00,33.63,32.75,33.37,55624800,0.52
1986-05-08,31.50,33.13,31.50,33.00,58340800,0.51
1986-05-07,32.63,32.87,31.25,31.50,49700000,0.49
1986-05-06,32.25,33.25,32.25,32.63,54633600,0.51
1986-05-05,30.50,32.50,30.50,32.12,37335200,0.50
1986-05-02,30.25,31.00,30.13,30.50,23396800,0.48
1986-05-01,30.25,30.25,29.75,30.25,64484000,0.47
1986-04-30,31.25,31.62,30.25,30.25,34445600,0.47
1986-04-29,32.00,32.25,26.87,31.25,33174400,0.49
1986-04-28,32.25,32.75,31.75,32.00,36383200,0.50
1986-04-25,31.38,32.63,31.38,32.25,65268000,0.50
1986-04-24,29.63,31.50,29.50,31.38,114592800,0.49
1986-04-23,29.87,30.37,29.37,29.63,65368800,0.46
1986-04-22,30.37,31.25,29.63,29.87,81967200,0.47
1986-04-21,29.87,30.75,29.87,30.37,68387200,0.47
1986-04-18,29.00,29.87,28.75,29.75,61919200,0.46
1986-04-17,28.25,29.13,28.00,29.00,67524800,0.45
1986-04-16,27.38,28.50,27.38,28.25,52707200,0.44
1986-04-15,26.87,27.50,26.87,27.38,32849600,0.43
1986-04-14,27.00,27.25,26.75,26.87,21240800,0.42
1986-04-11,27.25,27.50,27.00,27.00,18916800,0.42
1986-04-10,27.13,27.38,26.87,27.25,27496000,0.42
1986-04-09,27.62,27.75,26.87,27.13,33829600,0.42
1986-04-08,27.25,27.75,27.25,27.62,48305600,0.43
1986-04-07,26.75,27.50,26.25,27.25,30032800,0.42
1986-04-04,27.00,27.00,26.63,26.75,31488800,0.42
1986-04-03,27.25,27.62,26.87,27.00,52768800,0.42
1986-04-02,27.25,27.38,26.25,27.25,81323200,0.42
1986-04-01,28.25,28.25,27.00,27.25,55680800,0.42
1986-03-31,28.25,28.50,28.00,28.25,46950400,0.44
1986-03-27,28.25,29.00,28.25,28.25,54751200,0.44
1986-03-26,27.88,28.75,27.88,28.25,55535200,0.44
1986-03-25,26.75,27.88,26.75,27.88,70268800,0.43
1986-03-24,27.62,27.62,26.37,26.75,73578400,0.42
1986-03-21,28.25,28.75,27.50,27.62,65094400,0.43
1986-03-20,28.00,29.63,28.00,28.25,226032800,0.44
1986-03-19,26.87,27.25,26.37,26.50,47471200,0.41
1986-03-18,26.00,27.25,25.87,26.87,62339200,0.42
1986-03-17,26.00,26.00,25.37,26.00,29680000,0.41
1986-03-14,24.75,26.25,24.75,26.13,96213600,0.41
1986-03-13,24.75,25.00,24.38,24.75,28991200,0.39
1986-03-12,24.88,25.12,24.75,24.75,21420000,0.39
1986-03-11,24.62,24.88,24.50,24.88,25765600,0.39
1986-03-10,24.75,24.88,24.62,24.62,18872000,0.38
1986-03-07,25.37,25.37,24.75,24.75,24046400,0.39
1986-03-06,25.25,25.75,25.12,25.37,25334400,0.40
1986-03-05,24.62,25.50,24.25,25.25,44256800,0.39
1986-03-04,24.62,25.00,24.50,24.62,22276800,0.38
1986-03-03,25.00,25.12,24.50,24.62,27204800,0.38
1986-02-28,25.63,25.87,24.88,25.00,31281600,0.39
1986-02-27,26.00,26.13,25.50,25.63,27031200,0.40
1986-02-26,26.37,26.75,26.00,26.00,41182400,0.41
1986-02-25,25.75,26.37,25.12,26.37,56184800,0.41
1986-02-24,25.25,25.75,25.00,25.75,61779200,0.40
1986-02-21,25.12,25.75,25.12,25.25,47269600,0.39
1986-02-20,25.00,25.37,24.88,25.12,34479200,0.39
1986-02-19,23.88,25.50,23.88,25.00,89919200,0.39
1986-02-18,23.75,24.00,23.25,23.88,37027200,0.37
1986-02-14,23.88,24.12,23.75,23.75,34378400,0.37
1986-02-13,24.00,24.00,23.75,23.88,27344800,0.37
1986-02-12,23.88,24.00,23.75,24.00,33264000,0.37
1986-02-11,23.88,24.00,23.50,23.88,38365600,0.37
1986-02-10,24.00,24.50,23.75,23.88,27960800,0.37
1986-02-07,24.12,24.12,23.50,24.00,32351200,0.37
1986-02-06,23.75,24.25,23.63,24.12,33555200,0.38
1986-02-05,23.75,23.88,23.50,23.75,49291200,0.37
1986-02-04,23.88,24.38,23.75,23.75,65044000,0.37
1986-02-03,23.13,24.00,22.87,23.88,87505600,0.37
1986-01-31,23.00,23.25,22.87,23.13,36926400,0.36
1986-01-30,23.50,23.50,22.87,23.00,59220000,0.36
1986-01-29,22.25,24.38,22.00,23.63,147392000,0.37
1986-01-28,22.13,22.37,22.00,22.25,55574400,0.35
1986-01-27,22.63,22.75,22.00,22.13,97395200,0.34
1986-01-24,23.00,23.37,22.63,22.63,27994400,0.35
1986-01-23,23.37,23.50,22.75,23.00,39104800,0.36
1986-01-22,24.00,24.12,22.37,23.37,35750400,0.36
1986-01-21,23.88,24.12,23.75,24.00,37990400,0.37
1986-01-20,24.00,24.00,23.37,23.88,31852800,0.37
1986-01-17,24.50,24.75,23.88,24.00,86346400,0.37
1986-01-16,23.88,24.75,23.88,24.50,133694400,0.38
1986-01-15,23.25,24.00,23.13,23.88,105868000,0.37
1986-01-14,23.00,23.75,22.50,23.25,68174400,0.36
1986-01-13,22.75,23.13,22.50,23.00,53855200,0.36
1986-01-10,22.63,23.13,22.63,22.75,38309600,0.35
1986-01-09,22.87,23.00,21.87,22.63,111809600,0.35
1986-01-08,23.00,23.50,22.75,22.87,151900000,0.36
1986-01-07,22.25,23.00,22.13,23.00,117633600,0.36
1986-01-06,22.37,22.37,21.87,22.25,46261600,0.35
1986-01-03,22.25,22.37,22.13,22.37,60541600,0.35
1986-01-02,22.00,22.25,21.75,22.25,29355200,0.35
1985-12-31,22.25,22.37,22.00,22.00,21812000,0.34
1985-12-30,22.37,22.63,22.13,22.25,26919200,0.35
1985-12-27,21.75,22.63,21.75,22.37,30721600,0.35
1985-12-26,21.75,22.00,21.62,21.75,11463200,0.34
1985-12-24,21.87,22.00,21.62,21.75,16150400,0.34
1985-12-23,22.37,22.50,21.62,21.87,35806400,0.34
1985-12-20,22.50,22.75,22.25,22.37,51508800,0.35
1985-12-19,22.25,22.75,22.13,22.50,67530400,0.35
1985-12-18,21.38,22.87,21.38,22.25,139949600,0.35
1985-12-17,20.88,21.00,20.38,20.62,27266400,0.32
1985-12-16,20.00,21.25,20.00,20.88,72228800,0.33
1985-12-13,20.00,20.25,19.75,20.00,62787200,0.31
1985-12-12,19.87,20.25,19.87,20.00,31315200,0.31
1985-12-11,19.50,20.13,19.50,19.75,59404800,0.31
1985-12-10,19.37,19.63,19.25,19.50,50226400,0.30
1985-12-09,19.75,20.00,19.25,19.37,34966400,0.30
1985-12-06,20.13,20.13,19.63,19.75,16363200,0.31
1985-12-05,20.50,20.75,20.00,20.13,31287200,0.31
1985-12-04,20.13,20.62,20.13,20.50,41277600,0.32
1985-12-03,20.25,20.38,20.00,20.13,38768800,0.31
1985-12-02,20.13,20.25,20.00,20.25,25048800,0.32
1985-11-29,20.00,20.13,19.87,20.13,24757600,0.31
1985-11-27,19.37,20.13,19.25,20.00,47930400,0.31
1985-11-26,19.13,19.50,19.00,19.37,41115200,0.30
1985-11-25,19.00,19.25,19.00,19.13,24298400,0.30
1985-11-22,19.00,19.25,18.87,19.00,32188800,0.30
1985-11-21,19.00,19.25,19.00,19.00,25737600,0.30
1985-11-20,19.25,19.37,19.00,19.00,24768800,0.30
1985-11-19,19.87,20.00,19.25,19.25,23581600,0.30
1985-11-18,19.87,20.00,19.87,19.87,16139200,0.31
1985-11-15,20.00,20.25,19.87,19.87,20395200,0.31
1985-11-14,20.00,20.13,20.00,20.00,34876800,0.31
1985-11-13,19.87,19.87,19.37,19.37,25390400,0.30
1985-11-12,20.00,20.25,19.87,19.87,43411200,0.31
1985-11-11,20.50,20.75,20.00,20.00,44693600,0.31
1985-11-08,20.50,20.75,20.50,20.50,73528000,0.32
1985-11-07,19.63,19.87,19.63,19.63,79284800,0.31
1985-11-06,19.25,19.37,19.25,19.25,50114400,0.30
1985-11-05,18.75,19.13,18.63,18.63,26885600,0.29
1985-11-04,18.75,19.13,18.75,18.75,38931200,0.29
1985-11-01,18.63,19.00,18.63,18.63,23139200,0.29
1985-10-31,19.00,19.25,18.63,18.63,38768800,0.29
1985-10-30,19.00,19.00,19.00,19.00,56644000,0.30
1985-10-29,18.00,18.00,17.88,17.88,32720800,0.28
1985-10-28,18.00,18.12,18.00,18.00,14868000,0.28
1985-10-25,18.37,18.37,18.00,18.00,15820000,0.28
1985-10-24,18.37,18.87,18.37,18.37,68157600,0.29
1985-10-23,18.00,18.50,18.00,18.00,37094400,0.28
1985-10-22,18.00,18.25,18.00,18.00,106136800,0.28
1985-10-21,17.75,17.75,17.25,17.25,29719200,0.27
1985-10-18,18.25,18.37,17.75,17.75,57607200,0.28
1985-10-17,18.25,19.13,18.25,18.25,87046400,0.28
1985-10-16,18.00,18.12,18.00,18.00,72111200,0.28
1985-10-15,17.00,17.12,17.00,17.00,73472000,0.26
1985-10-14,16.63,16.63,16.63,16.63,38796800,0.26
1985-10-11,16.00,16.25,16.00,16.00,29573600,0.25
1985-10-10,15.87,16.00,15.87,15.87,65436000,0.25
1985-10-09,15.13,15.25,15.00,15.00,20703200,0.23
1985-10-08,15.13,15.13,15.13,15.13,21744800,0.24
1985-10-07,15.00,15.25,15.00,15.00,22982400,0.23
1985-10-04,15.50,15.50,15.00,15.00,17382400,0.23
1985-10-03,15.63,15.63,15.50,15.50,12230400,0.24
1985-10-02,15.75,15.87,15.63,15.63,5376000,0.24
1985-10-01,15.75,15.87,15.75,15.75,22086400,0.25
1985-09-30,15.87,16.00,15.75,15.75,9161600,0.25
1985-09-27,15.88,16.00,15.88,15.88,250400,0.25
1985-09-26,15.87,16.00,15.87,15.87,13372800,0.25
1985-09-25,16.50,16.50,15.87,15.87,26124000,0.25
1985-09-24,16.88,17.25,16.50,16.50,22024800,0.26
1985-09-23,16.88,17.12,16.88,16.88,29646400,0.26
1985-09-20,17.00,17.12,16.75,16.75,33807200,0.26
1985-09-19,17.00,17.00,17.00,17.00,46580800,0.26
1985-09-18,16.25,16.25,16.25,16.25,30021600,0.25
1985-09-17,15.25,15.25,15.25,15.25,45936800,0.24
1985-09-16,15.75,15.75,15.25,15.25,9245600,0.24
1985-09-13,16.13,16.13,15.75,15.75,17634400,0.25
1985-09-12,16.13,16.13,16.13,16.13,27792800,0.25
1985-09-11,15.50,15.63,15.50,15.50,21772800,0.24
1985-09-10,15.37,15.63,15.37,15.37,30441600,0.24
1985-09-09,15.25,15.37,15.25,15.25,33079200,0.24
1985-09-06,15.00,15.00,15.00,15.00,23200800,0.23
1985-09-05,14.87,15.00,14.87,14.87,8204000,0.23
1985-09-04,14.87,15.13,14.87,14.87,11888800,0.23
1985-09-03,15.00,15.00,14.75,14.75,9363200,0.23
1985-08-30,15.00,15.00,15.00,15.00,10718400,0.23
1985-08-29,15.25,15.25,14.87,14.87,14028000,0.23
1985-08-28,15.25,15.37,15.25,15.25,10236800,0.24
1985-08-27,15.25,15.25,15.25,15.25,10729600,0.24
1985-08-26,15.13,15.13,15.13,15.13,8915200,0.24
1985-08-23,14.87,15.00,14.75,14.75,11004000,0.23
1985-08-22,15.25,15.25,14.87,14.87,30828000,0.23
1985-08-21,15.25,15.25,15.25,15.25,19252800,0.24
1985-08-20,15.25,15.25,15.25,15.25,16738400,0.24
1985-08-19,15.00,15.25,15.00,15.00,11967200,0.23
1985-08-16,14.62,14.87,14.62,14.62,20938400,0.23
1985-08-15,14.62,14.75,14.50,14.50,26297600,0.23
1985-08-14,15.25,15.25,14.62,14.62,72475200,0.23
1985-08-13,15.25,15.50,15.25,15.25,10595200,0.24
1985-08-12,15.25,15.25,15.00,15.00,13748000,0.23
1985-08-09,15.25,15.25,15.25,15.25,15237600,0.24
1985-08-08,15.13,15.25,15.13,15.13,36943200,0.24
1985-08-07,15.25,16.00,14.87,14.87,37934400,0.23
1985-08-06,15.37,15.75,15.25,15.25,15769600,0.24
1985-08-05,15.75,15.87,15.37,15.37,23083200,0.24
1985-08-02,15.87,15.87,15.75,15.75,24354400,0.25
1985-08-01,15.87,16.13,15.87,15.87,12891200,0.25
1985-07-31,16.25,16.37,15.87,15.87,20126400,0.25
1985-07-30,16.25,16.37,16.25,16.25,22366400,0.25
1985-07-29,16.63,16.63,16.00,16.00,19437600,0.25
1985-07-26,16.63,16.75,16.63,16.63,32631200,0.26
1985-07-25,16.63,16.75,16.63,16.63,78769600,0.26
1985-07-24,16.50,16.75,16.25,16.25,42179200,0.25
1985-07-23,16.88,17.12,16.50,16.50,42173600,0.26
1985-07-22,17.38,17.38,16.88,16.88,48076000,0.26
1985-07-19,17.38,17.38,17.38,17.38,28728000,0.27
1985-07-18,17.62,17.62,17.25,17.25,44766400,0.27
1985-07-17,17.62,17.88,17.62,17.62,29545600,0.27
1985-07-16,17.75,17.88,17.50,17.50,35840000,0.27
1985-07-15,17.88,18.25,17.75,17.75,19420800,0.28
1985-07-12,18.00,18.00,17.88,17.88,11760000,0.28
1985-07-11,18.00,18.12,18.00,18.00,16223200,0.28
1985-07-10,18.00,18.00,18.00,18.00,26510400,0.28
1985-07-09,17.62,17.75,17.62,17.62,36976800,0.27
1985-07-08,17.62,17.75,17.62,17.62,23055200,0.27
1985-07-05,17.62,17.75,17.62,17.62,9144800,0.27
1985-07-03,17.50,17.50,17.50,17.50,17124800,0.27
1985-07-02,18.12,18.25,17.25,17.25,19432000,0.27
1985-07-01,18.12,18.25,18.12,18.12,25860800,0.28
1985-06-28,18.37,18.50,18.00,18.00,33936000,0.28
1985-06-27,18.37,18.50,18.37,18.37,48115200,0.29
1985-06-26,18.12,18.12,18.12,18.12,33051200,0.28
1985-06-25,17.50,17.88,17.50,17.50,73477600,0.27
1985-06-24,17.25,17.50,17.25,17.25,51441600,0.27
1985-06-21,16.13,16.50,16.13,16.13,41535200,0.25
1985-06-20,15.75,15.75,15.75,15.75,47700800,0.25
1985-06-19,15.63,15.87,15.63,15.63,42996800,0.24
1985-06-18,15.25,15.50,15.25,15.25,66304000,0.24
1985-06-17,14.87,15.00,14.87,14.87,59085600,0.23
1985-06-14,14.87,15.75,14.75,14.75,141416800,0.23
1985-06-13,15.75,15.87,14.87,14.87,94880800,0.23
1985-06-12,16.13,16.25,15.75,15.75,61997600,0.25
1985-06-11,16.13,16.50,16.13,16.13,75180000,0.25
1985-06-10,16.37,16.50,16.13,16.13,79032800,0.25
1985-06-07,17.00,17.00,16.37,16.37,118809600,0.26
1985-06-06,17.00,17.00,17.00,17.00,67799200,0.26
1985-06-05,17.25,17.75,16.88,16.88,71601600,0.26
1985-06-04,17.25,17.38,17.25,17.25,100480800,0.27
1985-06-03,17.00,17.00,16.00,16.00,144004000,0.25
1985-05-31,17.62,18.00,17.38,17.38,92355200,0.27
1985-05-30,17.62,17.88,17.62,17.62,78730400,0.27
1985-05-29,17.12,17.25,17.12,17.12,61639200,0.27
1985-05-28,17.88,17.88,16.88,16.88,127741600,0.26
1985-05-24,19.75,19.75,18.12,18.12,147369600,0.28
1985-05-23,20.50,20.50,19.75,19.75,59791200,0.31
1985-05-22,20.75,20.88,20.62,20.62,30139200,0.32
1985-05-21,21.25,21.25,20.75,20.75,38136000,0.32
1985-05-20,21.75,22.25,21.38,21.38,49296800,0.33
1985-05-17,21.38,22.13,21.25,21.75,52964800,0.34
1985-05-16,21.38,22.00,21.38,21.38,57635200,0.33
1985-05-15,20.00,20.38,20.00,20.00,32608800,0.31
1985-05-14,20.00,20.13,19.75,19.75,30436000,0.31
1985-05-13,20.25,20.38,20.00,20.00,21806400,0.31
1985-05-10,20.00,20.50,20.00,20.25,34020000,0.32
1985-05-09,20.00,20.13,20.00,20.00,31768800,0.31
1985-05-08,19.87,19.87,19.87,19.87,36097600,0.31
1985-05-07,20.00,20.00,20.00,20.00,26902400,0.31
1985-05-06,20.00,20.25,19.75,19.75,14033600,0.31
1985-05-03,19.25,20.13,19.25,20.00,39530400,0.31
1985-05-02,20.62,20.62,19.25,19.25,82443200,0.30
1985-05-01,21.25,21.38,20.88,20.88,14336000,0.33
1985-04-30,21.25,21.38,21.25,21.25,23682400,0.33
1985-04-29,21.87,22.00,21.12,21.12,15551200,0.33
1985-04-26,22.00,22.63,21.87,21.87,29926400,0.34
1985-04-25,22.00,22.13,22.00,22.00,21907200,0.34
1985-04-24,22.13,22.50,22.00,22.00,19734400,0.34
1985-04-23,22.13,22.25,22.13,22.13,29573600,0.34
1985-04-22,22.50,22.50,21.62,21.62,25648000,0.34
1985-04-19,22.87,22.87,22.37,22.50,24007200,0.35
1985-04-18,22.87,23.00,22.87,22.87,50607200,0.36
1985-04-17,22.63,22.87,22.63,22.63,30811200,0.35
1985-04-16,21.62,21.75,21.62,21.62,16912000,0.34
1985-04-15,21.38,21.62,21.38,21.38,14957600,0.33
1985-04-12,21.38,21.38,20.75,20.88,18132800,0.33
1985-04-11,21.38,22.00,21.38,21.38,36668800,0.33
1985-04-10,21.00,21.25,21.00,21.00,56728000,0.33
1985-04-09,19.63,19.75,19.63,19.63,65973600,0.31
1985-04-08,20.88,21.00,19.63,19.63,49683200,0.31
1985-04-04,21.00,21.12,20.62,20.88,40465600,0.33
1985-04-03,21.00,21.12,21.00,21.00,60664800,0.33
1985-04-02,21.62,21.75,21.00,21.00,56856800,0.33
1985-04-01,22.13,22.63,21.62,21.62,28515200,0.34
1985-03-29,21.87,22.25,21.87,22.13,21795200,0.34
1985-03-28,21.87,22.25,21.87,21.87,32401600,0.34
1985-03-27,22.50,22.75,21.87,21.87,27837600,0.34
1985-03-26,22.50,22.50,22.50,22.50,30357600,0.35
1985-03-25,22.25,22.25,21.62,21.62,27490400,0.34
1985-03-22,22.63,23.00,22.25,22.25,20092800,0.35
1985-03-21,22.63,23.00,22.63,22.63,40616800,0.35
1985-03-20,22.25,22.63,22.25,22.25,101242400,0.35
1985-03-19,22.87,23.13,22.00,22.00,42862400,0.34
1985-03-18,22.87,23.13,22.87,22.87,31192000,0.36
1985-03-15,21.75,23.13,21.62,22.63,45354400,0.35
1985-03-14,21.75,21.87,21.75,21.75,60401600,0.34
1985-03-13,23.00,23.00,21.75,21.75,62781600,0.34
1985-03-12,23.00,23.25,23.00,23.00,54857600,0.36
1985-03-11,22.25,22.37,22.25,22.25,71500800,0.35
1985-03-08,22.13,22.13,20.75,21.50,118389600,0.33
1985-03-07,24.62,24.75,22.13,22.13,183495200,0.34
1985-03-06,25.87,25.87,24.62,24.62,48400800,0.38
1985-03-05,25.87,25.87,25.87,25.87,32692800,0.40
1985-03-04,25.25,26.00,25.25,25.25,38276000,0.39
1985-03-01,24.75,24.88,24.00,24.88,61857600,0.39
1985-02-28,25.12,25.12,24.75,24.75,79766400,0.39
1985-02-27,26.75,26.75,25.12,25.12,100895200,0.39
1985-02-26,27.25,27.38,26.75,26.75,47241600,0.42
1985-02-25,27.62,27.75,27.25,27.25,24634400,0.42
1985-02-22,26.87,27.88,26.87,27.62,56632800,0.43
1985-02-21,26.87,27.00,26.87,26.87,77056000,0.42
1985-02-20,27.62,27.75,26.37,26.37,54992000,0.41
1985-02-19,27.88,27.88,27.62,27.62,37458400,0.43
1985-02-15,27.62,28.12,27.38,28.00,43405600,0.44
1985-02-14,28.38,28.62,27.62,27.62,106708000,0.43
1985-02-13,29.75,29.75,28.38,28.38,131756800,0.44
1985-02-12,30.50,30.63,29.75,29.75,56627200,0.46
1985-02-11,30.50,30.75,30.50,30.50,86738400,0.48
1985-02-08,29.87,30.00,29.50,29.87,33006400,0.47
1985-02-07,30.00,30.37,29.87,29.87,61370400,0.47
1985-02-06,30.00,30.00,30.00,30.00,48608000,0.47
1985-02-05,29.50,30.00,29.50,29.50,47510400,0.46
1985-02-04,29.25,29.37,29.25,29.25,54504800,0.46
1985-02-01,29.00,29.13,28.38,28.62,34434400,0.45
1985-01-31,29.87,30.00,29.00,29.00,69059200,0.45
1985-01-30,29.87,30.50,29.87,29.87,123110400,0.47
1985-01-29,30.25,30.50,29.87,29.87,55932800,0.47
1985-01-28,30.25,30.63,30.25,30.25,103045600,0.47
1985-01-25,29.00,29.63,28.38,29.63,79615200,0.46
1985-01-24,29.63,29.63,29.00,29.00,99265600,0.45
1985-01-23,30.13,30.25,29.63,29.63,107626400,0.46
1985-01-22,30.13,30.25,30.13,30.13,106209600,0.47
1985-01-21,29.25,29.50,29.25,29.25,81356800,0.46
1985-01-18,28.12,29.25,28.00,28.62,88166400,0.45
1985-01-17,30.25,30.75,28.12,28.12,136880800,0.44
1985-01-16,30.25,30.75,30.25,30.25,47471200,0.47
1985-01-15,30.63,31.12,30.00,30.00,66242400,0.47
1985-01-14,30.63,30.88,30.63,30.63,67608800,0.48
1985-01-11,30.00,30.25,29.50,29.75,51262400,0.46
1985-01-10,30.00,30.13,30.00,30.00,69266400,0.47
1985-01-09,28.75,29.13,28.75,28.75,41680800,0.45
1985-01-08,28.25,28.50,28.00,28.00,35280000,0.44
1985-01-07,28.38,28.50,28.25,28.25,42728000,0.44
1985-01-04,28.38,28.50,28.00,28.38,34316800,0.44
1985-01-03,28.38,29.13,28.38,28.38,41652800,0.44
1985-01-02,29.13,29.13,27.88,27.88,43825600,0.43
1984-12-31,29.13,29.25,29.13,29.13,51940000,0.45
1984-12-28,27.75,28.87,27.62,28.75,41333600,0.45
1984-12-27,27.75,27.88,27.75,27.75,24690400,0.43
1984-12-26,27.62,27.88,27.62,27.62,16794400,0.43
1984-12-24,27.50,27.62,27.50,27.50,16884000,0.43
1984-12-21,27.38,27.50,26.75,27.00,30973600,0.42
1984-12-20,27.50,28.00,27.38,27.38,34960800,0.43
1984-12-19,28.62,28.75,27.50,27.50,79374400,0.43
1984-12-18,28.62,28.75,28.62,28.62,85142400,0.45
1984-12-17,27.00,27.25,27.00,27.00,31309600,0.42
1984-12-14,25.75,26.63,25.75,26.37,24035200,0.41
1984-12-13,25.75,26.25,25.75,25.75,16710400,0.40
1984-12-12,26.37,26.37,25.50,25.50,27518400,0.40
1984-12-11,26.75,27.13,26.37,26.37,30945600,0.41
1984-12-10,27.25,27.25,26.75,26.75,27871200,0.42
1984-12-07,27.38,28.38,27.13,27.25,123631200,0.42
1984-12-06,27.38,27.50,27.38,27.38,79318400,0.43
1984-12-05,26.13,26.13,26.13,26.13,65727200,0.41
1984-12-04,24.88,25.37,24.88,24.88,30094400,0.39
1984-12-03,24.75,24.88,24.38,24.38,24500000,0.38
1984-11-30,25.37,25.63,24.62,24.75,27176800,0.39
1984-11-29,25.87,25.87,25.37,25.37,43719200,0.40
1984-11-28,25.87,26.50,25.87,25.87,102631200,0.40
1984-11-27,24.62,24.88,24.62,24.62,31852800,0.38
1984-11-26,24.00,24.00,24.00,24.00,25160800,0.37
1984-11-23,23.37,24.12,23.37,23.75,34272000,0.37
1984-11-21,23.13,23.25,23.13,23.13,44682400,0.36
1984-11-20,22.63,22.75,22.63,22.63,65811200,0.35
1984-11-19,23.25,23.37,21.87,21.87,58245600,0.34
1984-11-16,23.75,24.12,23.13,23.25,41440000,0.36
1984-11-15,23.75,24.00,23.75,23.75,26650400,0.37
1984-11-14,23.75,24.00,23.75,23.75,26084800,0.37
1984-11-13,24.12,24.62,23.50,23.50,31668000,0.37
1984-11-12,24.12,24.25,24.12,24.12,28313600,0.38
1984-11-09,24.75,24.88,23.00,23.25,73533600,0.36
1984-11-08,25.75,25.75,24.75,24.75,22030400,0.39
1984-11-07,26.25,26.37,25.75,25.75,57887200,0.40
1984-11-06,26.25,26.37,26.25,26.25,56330400,0.41
1984-11-05,24.88,25.37,24.75,24.75,26342400,0.39
1984-11-02,25.00,25.12,24.75,24.88,6921600,0.39
1984-11-01,25.00,25.25,25.00,25.00,11760000,0.39
1984-10-31,25.00,25.25,24.88,24.88,15058400,0.39
1984-10-30,25.00,25.25,25.00,25.00,18648000,0.39
1984-10-29,24.75,24.88,24.75,24.75,12661600,0.39
1984-10-26,25.25,25.25,24.50,24.62,28711200,0.38
1984-10-25,26.25,26.25,25.25,25.25,39541600,0.39
1984-10-24,26.25,26.50,26.25,26.25,41753600,0.41
1984-10-23,26.00,26.25,26.00,26.00,46608800,0.41
1984-10-22,25.63,26.00,25.37,25.37,28688800,0.40
1984-10-19,25.63,27.38,25.50,25.63,81530400,0.40
1984-10-18,25.63,25.75,25.63,25.63,61790400,0.40
1984-10-17,24.88,25.00,24.88,24.88,39160800,0.39
1984-10-16,24.00,24.12,23.88,23.88,29506400,0.37
1984-10-15,24.00,24.25,24.00,24.00,60816000,0.37
1984-10-12,23.75,23.88,22.50,22.75,66449600,0.35
1984-10-11,23.88,24.50,23.75,23.75,45690400,0.37
1984-10-10,24.62,24.62,23.88,23.88,91212800,0.37
1984-10-09,24.88,25.00,24.62,24.62,31315200,0.38
1984-10-08,24.88,25.00,24.88,24.88,11743200,0.39
1984-10-05,25.37,25.37,24.75,24.88,24393600,0.39
1984-10-04,25.37,25.63,25.37,25.37,31371200,0.40
1984-10-03,25.12,25.50,25.12,25.12,30105600,0.39
1984-10-02,24.75,25.63,24.75,24.75,29562400,0.39
1984-10-01,25.00,25.00,24.50,24.50,24444000,0.38
1984-09-28,25.75,25.75,24.62,25.12,58352000,0.39
1984-09-27,25.75,25.87,25.75,25.75,26482400,0.40
1984-09-26,26.13,27.25,25.75,25.75,27742400,0.40
1984-09-25,26.50,26.50,26.13,26.13,41697600,0.41
1984-09-24,26.87,27.00,26.63,26.63,19751200,0.41
1984-09-21,27.13,27.88,26.50,26.87,24959200,0.42
1984-09-20,27.13,27.38,27.13,27.13,16542400,0.42
1984-09-19,27.62,27.88,27.00,27.00,26572000,0.42
1984-09-18,28.62,28.87,27.62,27.62,24326400,0.43
1984-09-17,28.62,29.00,28.62,28.62,48188000,0.45
1984-09-14,27.62,28.50,27.62,27.88,61717600,0.43
1984-09-13,27.50,27.62,27.50,27.50,51833600,0.43
1984-09-12,26.87,27.00,26.13,26.13,33280800,0.41
1984-09-11,26.63,27.38,26.63,26.87,38096800,0.42
1984-09-10,26.50,26.63,25.87,26.37,16156000,0.41
1984-09-07,26.50,26.87,26.25,26.50,20815200,0.41
1984-09-06,26.25,26.87,26.25,26.50,32743200,0.41
1984-09-05,26.25,26.63,26.00,26.25,25939200,0.41
1984-09-04,26.50,26.75,26.00,26.25,29960000,0.41
1984-08-31,27.00,27.13,26.13,26.50,34462400,0.41
1984-08-30,27.50,27.88,27.00,27.00,12740000,0.42
1984-08-29,28.25,28.38,27.25,27.50,18530400,0.43
1984-08-28,27.88,28.25,27.62,28.25,14789600,0.44
1984-08-27,28.12,28.12,27.38,27.88,21918400,0.43
1984-08-24,28.12,28.50,27.88,28.12,17724000,0.44
1984-08-23,28.00,28.62,28.00,28.12,20854400,0.44
1984-08-22,28.50,29.25,27.75,28.00,55104000,0.44
1984-08-21,27.38,28.75,27.38,28.50,44884000,0.44
1984-08-20,27.50,27.62,26.63,27.38,34613600,0.43
1984-08-17,28.12,28.25,27.13,27.50,38483200,0.43
1984-08-16,27.88,28.38,27.50,28.12,36204000,0.44
1984-08-15,28.75,28.75,27.62,27.88,44721600,0.43
1984-08-14,30.00,30.25,28.50,28.87,43517600,0.45
1984-08-13,28.50,30.25,28.12,30.00,60362400,0.47
1984-08-10,29.75,30.88,28.38,28.50,99344000,0.44
1984-08-09,28.50,30.00,27.88,29.75,64405600,0.46
1984-08-08,29.63,30.25,28.25,28.50,73600800,0.44
1984-08-07,29.25,30.00,27.88,29.63,83120800,0.46
1984-08-06,27.38,30.50,27.25,29.25,156699200,0.46
1984-08-03,24.12,27.50,24.00,27.38,154515200,0.43
1984-08-02,25.00,25.37,24.12,24.12,75919200,0.38
1984-08-01,25.50,25.75,24.25,25.00,71433600,0.39
1984-07-31,25.50,25.87,24.88,25.50,49907200,0.40
1984-07-30,27.13,27.25,25.25,25.50,31259200,0.40
1984-07-27,27.25,27.50,27.00,27.13,18485600,0.42
1984-07-26,26.75,27.62,26.50,27.25,35834400,0.42
1984-07-25,26.75,27.38,26.75,26.75,50114400,0.42
1984-07-24,25.12,27.00,25.00,26.63,44811200,0.41
1984-07-23,25.37,25.37,24.50,25.12,23508800,0.39
1984-07-20,25.37,25.75,25.25,25.37,8293600,0.40
1984-07-19,25.37,25.75,25.12,25.37,19476800,0.40
1984-07-18,25.75,25.87,25.25,25.37,26006400,0.40
1984-07-17,25.75,26.00,25.37,25.75,21212800,0.40
1984-07-16,26.37,26.37,25.00,25.75,50747200,0.40
1984-07-13,26.63,27.13,26.00,26.37,33986400,0.41
1984-07-12,26.50,27.13,26.37,26.63,42173600,0.41
1984-07-11,26.87,27.25,26.13,26.50,30273600,0.41
1984-07-10,26.25,27.13,26.13,26.87,43075200,0.42
1984-07-09,25.12,26.37,24.75,26.25,47667200,0.41
1984-07-06,24.75,25.50,24.25,25.12,23912000,0.39
1984-07-05,25.25,25.50,24.38,24.75,23296000,0.39
1984-07-03,25.63,25.75,24.88,25.25,44766400,0.39
1984-07-02,26.50,26.63,25.12,25.63,39916800,0.40
1984-06-29,26.37,27.75,26.37,26.50,35498400,0.41
1984-06-28,25.25,26.75,25.25,26.37,29579200,0.41
1984-06-27,26.00,26.25,24.25,25.25,94320800,0.39
1984-06-26,27.25,27.38,26.00,26.00,37161600,0.41
1984-06-25,28.62,28.87,27.00,27.25,41871200,0.42
1984-06-22,29.00,29.50,28.62,28.62,21151200,0.45
1984-06-21,30.25,30.63,29.00,29.00,35476000,0.45
1984-06-20,29.37,30.25,28.75,30.25,29881600,0.47
1984-06-19,29.63,30.37,29.37,29.37,40236000,0.46
1984-06-18,29.00,29.75,28.38,29.63,28649600,0.46
1984-06-15,28.87,29.37,28.87,29.00,22444800,0.45
1984-06-14,29.75,29.75,28.75,28.87,25239200,0.45
1984-06-13,29.25,29.87,29.25,29.75,28929600,0.46
1984-06-12,28.62,29.50,28.50,29.13,29282400,0.45
1984-06-11,28.62,28.87,28.25,28.62,21061600,0.45
1984-06-08,28.75,28.87,28.00,28.62,27244000,0.45
1984-06-07,29.00,29.13,28.12,28.75,25636800,0.45
1984-06-06,27.88,29.13,27.75,29.00,40364800,0.45
1984-06-05,29.13,29.13,27.75,27.88,82107200,0.43
1984-06-04,30.37,30.75,29.37,29.63,37072000,0.46
1984-06-01,29.37,30.37,29.25,30.37,60575200,0.47
1984-05-31,29.00,29.75,28.75,29.37,41753600,0.46
1984-05-30,29.37,29.50,28.00,29.00,79609600,0.45
1984-05-29,29.50,29.75,28.87,29.37,39065600,0.46
1984-05-25,29.37,29.87,29.13,29.50,30027200,0.46
1984-05-24,30.25,30.25,28.87,29.37,48328000,0.46
1984-05-23,30.88,31.12,30.25,30.25,42240800,0.47
1984-05-22,31.88,31.88,30.00,30.88,75314400,0.48
1984-05-21,29.75,32.25,29.63,31.88,108763200,0.50
1984-05-18,29.13,29.87,28.75,29.75,48367200,0.46
1984-05-17,30.50,30.50,28.75,29.13,70487200,0.45
1984-05-16,31.88,32.12,30.37,30.50,54930400,0.48
1984-05-15,31.62,32.12,31.50,31.88,25676000,0.50
1984-05-14,32.12,32.12,31.25,31.62,22321600,0.49
1984-05-11,33.13,33.25,31.00,32.25,49431200,0.50
1984-05-10,33.13,33.63,32.25,33.13,59656800,0.52
1984-05-09,32.87,34.38,32.50,33.13,101253600,0.52
1984-05-08,31.25,33.13,31.25,32.87,63750400,0.51
1984-05-07,30.13,31.38,29.87,31.12,40017600,0.48
1984-05-04,31.62,31.62,30.00,30.13,65111200,0.47
1984-05-03,33.00,33.00,31.00,31.62,81855200,0.49
1984-05-02,33.25,33.50,32.37,33.00,79329600,0.51
1984-05-01,31.75,33.25,31.75,33.25,101628800,0.52
1984-04-30,30.13,31.38,29.87,31.38,73287200,0.49
1984-04-27,29.75,30.75,29.25,30.13,92999200,0.47
1984-04-26,27.75,29.87,27.75,29.75,79626400,0.46
1984-04-25,27.88,28.12,27.38,27.62,48720000,0.43
1984-04-24,28.38,28.87,27.75,27.88,70392000,0.43
1984-04-23,28.25,29.13,28.00,28.38,73466400,0.44
1984-04-19,28.00,28.38,27.75,28.25,30850400,0.44
1984-04-18,27.50,28.12,27.38,28.00,49918400,0.44
1984-04-17,26.75,27.88,26.75,27.50,83238400,0.43
1984-04-16,25.75,26.37,25.12,26.25,17029600,0.41
1984-04-13,25.75,26.37,25.50,25.75,25849600,0.40
1984-04-12,24.50,26.00,24.12,25.75,19600000,0.40
1984-04-11,24.75,25.37,24.25,24.50,17651200,0.38
1984-04-10,24.00,24.75,24.00,24.75,14274400,0.39
1984-04-09,23.50,24.25,23.50,23.50,13563200,0.37
1984-04-06,24.12,24.38,23.00,23.50,21397600,0.37
1984-04-05,24.50,24.88,24.12,24.12,20703200,0.38
1984-04-04,25.00,25.12,24.50,24.50,26919200,0.38
1984-04-03,24.88,25.12,24.62,25.00,11026400,0.39
1984-04-02,24.75,25.25,24.50,24.88,13664000,0.39
1984-03-30,25.37,25.50,24.50,24.75,11435200,0.39
1984-03-29,25.50,25.75,25.25,25.37,9794400,0.40
1984-03-28,25.12,25.63,25.12,25.50,18872000,0.40
1984-03-27,25.75,25.87,24.88,25.00,24824800,0.39
1984-03-26,25.50,26.13,25.25,25.75,14240800,0.40
1984-03-23,25.50,25.75,25.00,25.50,15282400,0.40
1984-03-22,26.00,26.00,25.12,25.50,12796000,0.40
1984-03-21,26.00,26.63,25.87,26.00,11916800,0.41
1984-03-20,26.25,26.75,25.12,26.00,25132800,0.41
1984-03-19,26.50,26.50,25.87,26.25,20647200,0.41
1984-03-16,26.75,27.75,26.37,26.63,31175200,0.41
1984-03-15,26.63,27.00,26.37,26.75,13820800,0.42
1984-03-14,27.00,27.13,26.50,26.63,14901600,0.41
1984-03-13,27.38,27.75,26.75,27.00,38220000,0.42
1984-03-12,26.50,27.50,26.50,27.38,31259200,0.43
1984-03-09,26.87,26.87,26.25,26.37,16514400,0.41
1984-03-08,26.50,27.13,26.50,26.87,32446400,0.42
1984-03-07,25.75,26.63,25.12,26.50,24141600,0.41
1984-03-06,26.75,27.25,25.63,25.75,24746400,0.40
1984-03-05,27.25,27.38,26.37,26.75,18401600,0.42
1984-03-02,27.00,28.00,26.87,27.25,47812800,0.42
1984-03-01,26.25,27.13,25.63,27.00,33090400,0.42
1984-02-29,25.50,26.87,25.25,26.25,33510400,0.41
1984-02-28,27.00,27.13,25.12,25.50,42481600,0.40
1984-02-27,27.13,27.50,26.37,27.00,30391200,0.42
1984-02-24,26.87,27.50,26.87,27.13,19454400,0.42
1984-02-23,27.38,27.38,26.00,26.87,38763200,0.42
1984-02-22,26.25,27.62,26.25,27.38,55843200,0.43
1984-02-21,25.00,26.25,24.88,26.13,30072000,0.41
1984-02-17,25.37,26.00,25.00,25.00,33661600,0.39
1984-02-16,25.12,25.50,24.50,25.37,26308800,0.40
1984-02-15,25.63,26.75,24.88,25.12,50209600,0.39
1984-02-14,24.25,25.75,24.25,25.63,52264800,0.40
1984-02-13,24.38,24.62,23.88,24.25,26432000,0.38
1984-02-10,23.63,25.00,23.63,24.38,35991200,0.38
1984-02-09,23.25,24.12,22.63,23.63,58699200,0.37
1984-02-08,24.12,24.50,23.25,23.25,37055200,0.36
1984-02-07,23.25,24.25,22.37,24.12,54432000,0.38
1984-02-06,24.50,24.50,23.13,23.25,41389600,0.36
1984-02-03,24.88,25.50,24.50,24.50,36372000,0.38
1984-02-02,24.62,25.00,24.12,24.88,33728800,0.39
1984-02-01,24.75,25.50,24.50,24.62,40779200,0.38
1984-01-31,24.75,25.25,23.13,24.75,86273600,0.39
1984-01-30,26.13,26.63,24.12,24.75,69367200,0.39
1984-01-27,27.62,27.75,25.63,26.13,48524000,0.41
1984-01-26,27.00,28.00,27.00,27.62,42123200,0.43
1984-01-25,27.25,28.87,26.87,27.00,65968000,0.42
1984-01-24,28.87,29.00,26.50,27.25,80057600,0.42
1984-01-23,28.62,29.13,28.38,28.87,69591200,0.45
1984-01-20,29.00,29.13,28.25,28.62,35336000,0.45
1984-01-19,28.75,29.50,28.50,29.00,37430400,0.45
1984-01-18,28.62,29.25,28.12,28.75,55126400,0.45
1984-01-17,27.88,28.75,27.88,28.62,37268000,0.45
1984-01-16,27.25,28.25,27.13,27.88,34395200,0.43
1984-01-13,27.88,28.25,26.75,27.25,30436000,0.42
1984-01-12,28.00,28.38,27.62,27.88,27585600,0.43
1984-01-11,27.62,28.50,27.50,28.00,43988000,0.44
1984-01-10,26.25,27.62,26.25,27.62,43047200,0.43
1984-01-09,27.75,27.75,25.37,26.25,53933600,0.41
1984-01-06,28.25,28.62,27.25,27.75,42123200,0.43
1984-01-05,27.88,29.00,27.62,28.25,76428800,0.44
1984-01-04,25.75,28.00,25.75,27.88,73152800,0.43
1984-01-03,24.38,26.13,24.38,25.63,37548000,0.40
1983-12-30,24.38,25.00,24.25,24.38,22965600,0.38
1983-12-29,25.00,25.25,24.38,24.38,25687200,0.38
1983-12-28,24.75,25.25,24.50,25.00,32138400,0.39
1983-12-27,24.62,25.00,24.62,24.75,24108000,0.39
1983-12-23,24.75,24.88,24.25,24.62,12140800,0.38
1983-12-22,24.25,24.75,24.12,24.75,32636800,0.39
1983-12-21,23.37,24.25,23.25,24.25,42946400,0.38
1983-12-20,24.00,24.00,23.00,23.37,44436000,0.36
1983-12-19,24.75,25.00,23.88,24.00,43400000,0.37
1983-12-16,24.38,25.00,24.25,24.75,46216800,0.39
1983-12-15,23.37,24.75,23.37,24.38,79150400,0.38
1983-12-14,22.50,23.63,21.62,23.37,50472800,0.36
1983-12-13,21.50,22.75,21.38,22.50,49386400,0.35
1983-12-12,21.62,21.75,21.00,21.50,16284800,0.33
1983-12-09,21.50,22.13,21.25,21.62,20692000,0.34
1983-12-08,21.00,22.13,21.00,21.50,34406400,0.33
1983-12-07,20.50,21.50,20.25,21.00,22288000,0.33
1983-12-06,20.38,20.62,20.25,20.50,12997600,0.32
1983-12-05,19.87,20.50,19.75,20.38,11289600,0.32
1983-12-02,20.25,20.25,19.75,19.87,21341600,0.31
1983-12-01,20.38,20.88,20.00,20.25,19168800,0.32
1983-11-30,20.75,21.00,20.38,20.38,16083200,0.32
1983-11-29,21.00,21.50,20.50,20.75,23822400,0.32
1983-11-28,20.50,21.12,20.38,21.00,18099200,0.33
1983-11-25,20.38,20.62,20.38,20.50,9324000,0.32
1983-11-23,21.50,21.50,20.00,20.38,28588000,0.32
1983-11-22,21.50,21.75,21.25,21.50,26297600,0.33
1983-11-21,20.62,21.62,20.62,21.50,26252800,0.33
1983-11-18,20.50,20.88,20.25,20.62,19975200,0.32
1983-11-17,20.00,20.75,20.00,20.50,22596000,0.32
1983-11-16,19.75,20.50,19.63,20.00,25569600,0.31
1983-11-15,19.75,19.87,19.00,19.75,29657600,0.31
1983-11-14,20.00,20.25,19.63,19.75,27070400,0.31
1983-11-11,19.63,20.38,19.50,20.00,29008000,0.31
1983-11-10,19.25,20.13,19.25,19.63,55518400,0.31
1983-11-09,17.88,19.25,17.50,19.25,88368000,0.30
1983-11-08,19.50,19.50,17.25,17.88,305379200,0.28
1983-11-07,21.12,21.62,20.75,21.00,38029600,0.33
1983-11-04,21.87,22.00,21.00,21.12,36685600,0.33
1983-11-03,23.50,23.63,21.00,21.87,71500800,0.34
1983-11-02,23.00,24.12,23.00,23.50,50618400,0.37
1983-11-01,22.63,24.00,21.62,23.00,82096000,0.36
1983-10-31,21.12,23.00,21.12,22.63,43293600,0.35
1983-10-28,21.12,21.38,20.38,20.88,20300000,0.33
1983-10-27,20.13,21.62,20.13,21.12,24460800,0.33
1983-10-26,21.25,21.50,20.00,20.13,32228000,0.31
1983-10-25,21.12,21.87,21.00,21.25,42112000,0.33
1983-10-24,19.87,21.12,17.88,21.12,64848000,0.33
1983-10-21,20.38,20.88,19.63,19.87,39250400,0.31
1983-10-20,21.50,22.13,19.87,20.38,32922400,0.32
1983-10-19,19.37,22.25,19.13,21.50,71848000,0.33
1983-10-18,20.75,20.75,18.87,19.37,95743200,0.30
1983-10-17,22.75,22.75,20.88,21.00,54779200,0.33
1983-10-14,23.00,23.75,22.50,22.75,69815200,0.35
1983-10-13,21.75,24.00,21.75,23.00,105128800,0.36
1983-10-12,19.37,21.25,19.25,21.12,118154400,0.33
1983-10-11,19.75,19.87,19.13,19.37,63190400,0.30
1983-10-10,20.13,20.13,18.37,19.75,129281600,0.31
1983-10-07,22.25,23.75,20.13,20.38,61583200,0.32
1983-10-06,22.50,22.87,21.75,22.25,58234400,0.35
1983-10-05,22.87,23.25,22.13,22.50,47667200,0.35
1983-10-04,23.13,23.63,22.75,22.87,42403200,0.36
1983-10-03,23.13,23.50,22.63,23.13,38225600,0.36
1983-09-30,22.75,23.63,22.50,23.13,29467200,0.36
1983-09-29,22.87,23.75,22.63,22.75,70694400,0.35
1983-09-28,23.50,23.50,22.13,22.87,93374400,0.36
1983-09-27,24.88,25.00,23.00,23.50,104277600,0.37
1983-09-26,25.87,25.87,24.38,24.88,192192000,0.39
1983-09-22,31.50,32.63,31.12,32.50,36030400,0.51
1983-09-21,32.12,32.63,31.38,31.50,26588800,0.49
1983-09-20,32.00,33.50,32.00,32.12,56604800,0.50
1983-09-19,29.37,32.25,29.25,32.00,50495200,0.50
1983-09-16,30.13,30.13,29.13,29.37,56436800,0.46
1983-09-15,31.62,31.75,29.75,30.13,39709600,0.47
1983-09-14,32.00,32.75,31.00,31.62,45382400,0.49
1983-09-13,30.63,32.50,30.37,32.00,51044000,0.50
1983-09-12,30.63,32.50,30.25,30.63,66578400,0.48
1983-09-09,31.75,31.88,30.50,30.63,53172000,0.48
1983-09-08,34.62,35.00,31.25,31.75,76764800,0.49
1983-09-07,39.25,39.25,33.87,34.62,96213600,0.54
1983-09-06,38.75,39.75,38.75,39.37,45421600,0.61
1983-09-02,36.37,38.00,36.25,38.00,32334400,0.59
1983-09-01,37.25,38.50,35.50,36.37,54532800,0.57
1983-08-31,33.13,37.25,33.13,37.25,50058400,0.58
1983-08-30,31.25,33.50,31.25,32.87,58486400,0.51
1983-08-29,30.88,31.62,30.00,31.25,34574400,0.49
1983-08-26,30.50,31.00,30.25,30.88,23296000,0.48
1983-08-25,30.25,30.75,30.00,30.50,47443200,0.48
1983-08-24,31.75,31.75,30.13,30.25,28324800,0.47
1983-08-23,33.63,33.63,31.62,31.88,23396800,0.50
1983-08-22,33.75,34.13,33.25,33.63,21341600,0.52
1983-08-19,33.50,34.00,33.25,33.75,14649600,0.53
1983-08-18,33.13,33.87,33.00,33.50,20434400,0.52
1983-08-17,33.87,34.25,32.75,33.13,23609600,0.52
1983-08-16,34.38,34.75,33.50,33.87,22842400,0.53
1983-08-15,33.50,34.38,33.37,34.38,38068800,0.54
1983-08-12,33.75,34.50,33.13,33.50,18659200,0.52
1983-08-11,34.25,34.75,33.25,33.75,22545600,0.53
1983-08-10,34.38,34.62,33.50,34.25,40493600,0.53
1983-08-09,34.00,34.88,33.75,34.38,37592800,0.54
1983-08-08,33.87,34.75,33.13,34.00,19202400,0.53
1983-08-05,33.25,34.50,33.00,33.87,32855200,0.53
1983-08-04,34.88,35.25,31.75,33.25,73029600,0.52
1983-08-03,34.38,35.62,33.87,34.88,30956800,0.54
1983-08-02,34.50,35.00,34.25,34.38,25412800,0.54
1983-08-01,34.88,36.37,34.25,34.50,58111200,0.54
1983-07-29,34.00,35.25,33.87,34.88,55081600,0.54
1983-07-28,36.25,36.75,34.00,34.00,67620000,0.53
1983-07-27,39.12,40.37,35.75,36.25,75079200,0.56
1983-07-26,43.13,43.37,37.50,39.12,67244800,0.61
1983-07-25,43.75,43.75,42.50,43.13,19107200,0.67
1983-07-22,43.37,43.87,43.25,43.75,29108800,0.68
1983-07-21,41.25,44.37,41.00,43.37,79346400,0.68
1983-07-20,43.75,44.13,40.75,41.25,76221600,0.64
1983-07-19,44.25,46.37,43.37,43.75,42784000,0.68
1983-07-18,44.37,44.50,43.25,44.25,20406400,0.69
1983-07-15,46.00,46.00,44.13,44.37,16990400,0.69
1983-07-14,46.12,46.87,45.75,46.00,18726400,0.72
1983-07-13,46.37,46.50,45.38,46.12,32250400,0.72
1983-07-12,47.50,48.00,46.12,46.37,18799200,0.72
1983-07-11,46.25,48.25,46.25,47.50,28229600,0.74
1983-07-08,46.75,46.75,46.00,46.25,17544800,0.72
1983-07-07,47.37,47.50,46.50,46.75,22360800,0.73
1983-07-06,47.25,47.50,46.37,47.37,23979200,0.74
1983-07-05,49.25,49.38,47.13,47.25,20512800,0.74
1983-07-01,48.88,49.75,48.62,49.25,43064000,0.77
1983-06-30,49.12,50.00,48.62,48.88,27641600,0.76
1983-06-29,46.87,49.62,45.75,49.12,73595200,0.77
1983-06-28,50.37,50.63,46.50,46.87,87292800,0.73
1983-06-27,53.25,53.25,50.37,50.37,30760800,0.78
1983-06-24,53.63,54.37,53.12,53.25,11911200,0.83
1983-06-23,55.13,55.13,53.50,53.63,33499200,0.84
1983-06-22,53.87,55.62,53.87,55.38,35240800,0.86
1983-06-21,53.37,54.00,52.38,53.75,31365600,0.84
1983-06-20,56.12,56.50,52.88,53.37,34893600,0.83
1983-06-17,57.25,57.50,56.12,56.12,14011200,0.87
1983-06-16,54.63,57.25,54.63,57.25,30721600,0.89
1983-06-15,55.88,55.88,53.25,54.37,48339200,0.85
1983-06-14,57.25,57.75,55.75,56.00,42632800,0.87
1983-06-13,59.25,59.38,55.00,57.25,44816800,0.89
1983-06-10,59.50,59.88,59.12,59.25,9357600,0.92
1983-06-09,59.88,60.50,58.37,59.50,13697600,0.93
1983-06-08,60.63,60.87,59.38,59.88,21011200,0.93
1983-06-07,62.75,63.25,60.63,60.63,24544800,0.94
1983-06-06,61.37,62.75,61.37,62.75,26023200,0.98
1983-06-03,58.50,61.63,58.50,61.37,16133600,0.96
1983-06-02,58.13,58.50,57.75,58.50,19857600,0.91
1983-06-01,57.75,58.25,57.25,58.13,24522400,0.91
1983-05-31,59.25,59.25,56.62,57.75,11384800,0.90
1983-05-27,59.38,60.00,59.12,59.38,14156800,0.93
1983-05-26,60.00,60.37,58.88,59.38,26392800,0.93
1983-05-25,60.50,61.00,59.12,60.00,38432800,0.93
1983-05-24,57.50,60.50,57.50,60.50,26924800,0.94
1983-05-23,56.87,57.50,55.75,57.50,30436000,0.90
1983-05-20,54.13,57.00,53.37,56.87,36523200,0.89
1983-05-19,52.50,54.25,52.50,54.13,17572800,0.84
1983-05-18,51.88,53.00,51.88,52.50,39250400,0.82
1983-05-17,51.75,52.00,51.13,51.88,38589600,0.81
1983-05-16,53.12,53.12,51.50,51.75,17298400,0.81
1983-05-13,52.88,53.63,52.88,53.12,12241600,0.83
1983-05-12,53.37,53.37,52.38,52.88,24606400,0.82
1983-05-11,54.75,55.00,53.00,53.37,13815200,0.83
1983-05-10,54.37,55.38,54.13,54.75,12975200,0.85
1983-05-09,55.13,55.25,53.87,54.37,17292800,0.85
1983-05-06,54.87,55.75,53.75,55.13,25037600,0.86
1983-05-05,51.50,55.00,51.50,54.87,35123200,0.85
1983-05-04,48.50,51.50,48.50,51.50,32278400,0.80
1983-05-03,49.00,49.12,47.63,48.50,26499200,0.76
1983-05-02,50.50,50.75,48.38,49.00,24270400,0.76
1983-04-29,50.00,50.75,49.38,50.50,77078400,0.79
1983-04-28,49.50,50.25,48.88,50.00,19852000,0.78
1983-04-27,50.00,51.13,49.00,49.50,21509600,0.77
1983-04-26,48.62,50.63,48.50,50.00,24858400,0.78
1983-04-25,51.00,51.37,48.38,48.62,31427200,0.76
1983-04-22,52.00,52.50,50.75,51.00,31796800,0.79
1983-04-21,51.25,52.75,51.25,52.00,57512000,0.81
1983-04-20,46.50,51.00,46.50,50.63,72083200,0.79
1983-04-19,47.00,47.37,46.25,46.50,58469600,0.72
1983-04-18,46.00,47.87,46.00,47.00,38892000,0.73
1983-04-15,45.00,46.12,45.00,45.75,28750400,0.71
1983-04-14,44.00,45.12,43.63,45.00,34092800,0.70
1983-04-13,42.50,44.13,42.50,44.00,47443200,0.69
1983-04-12,41.62,42.62,41.62,42.50,43512000,0.66
1983-04-11,39.37,41.88,38.75,41.62,57618400,0.65
1983-04-08,39.63,39.87,38.62,39.37,37564800,0.61
1983-04-07,40.00,40.25,39.37,39.63,36377600,0.62
1983-04-06,40.37,40.50,39.50,40.00,53496800,0.62
1983-04-05,41.13,42.00,40.37,40.37,30525600,0.63
1983-04-04,42.25,42.25,40.13,41.13,31847200,0.64
1983-03-31,44.25,44.50,42.25,42.25,21285600,0.66
1983-03-30,43.75,44.37,43.75,44.25,21952000,0.69
1983-03-29,42.62,44.13,42.62,43.75,25933600,0.68
1983-03-28,43.00,43.00,41.75,42.50,18642400,0.66
1983-03-25,43.13,43.87,43.00,43.13,14515200,0.67
1983-03-24,42.38,43.63,42.25,43.13,25614400,0.67
1983-03-23,44.50,44.63,42.25,42.38,35190400,0.66
1983-03-22,44.00,45.12,44.00,44.50,25250400,0.69
1983-03-21,43.00,44.13,42.75,44.00,26006400,0.69
1983-03-18,42.38,43.50,42.25,43.00,21532000,0.67
1983-03-17,42.00,42.38,41.88,42.38,11037600,0.66
1983-03-16,42.00,43.50,41.75,42.00,27742400,0.65
1983-03-15,41.38,42.00,40.13,42.00,18765600,0.65
1983-03-14,42.25,42.25,40.37,41.38,42968800,0.64
1983-03-11,43.00,43.75,41.38,42.38,21940800,0.66
1983-03-10,43.63,44.13,42.62,43.00,28151200,0.67
1983-03-09,42.38,43.63,41.62,43.63,49834400,0.68
1983-03-08,43.50,43.50,41.75,42.38,55160000,0.66
1983-03-07,44.63,44.75,42.50,43.75,38169600,0.68
1983-03-04,45.25,45.38,43.25,44.63,37951200,0.70
1983-03-03,46.75,47.25,45.12,45.25,32883200,0.70
1983-03-02,46.37,47.00,46.25,46.75,26488000,0.73
1983-03-01,45.62,46.63,45.50,46.37,35067200,0.72
1983-02-28,46.75,46.87,45.50,45.62,33073600,0.71
1983-02-25,48.13,48.62,46.50,46.75,28672000,0.73
1983-02-24,47.25,48.38,47.25,48.13,28873600,0.75
1983-02-23,46.50,47.13,46.12,46.87,27008800,0.73
1983-02-22,45.62,47.75,45.62,46.50,49196000,0.72
1983-02-18,44.00,45.88,43.50,45.38,28722400,0.71
1983-02-17,44.50,44.50,42.62,44.00,34042400,0.69
1983-02-16,45.38,45.38,44.25,44.50,29142400,0.69
1983-02-15,46.25,46.63,44.88,45.38,28795200,0.71
1983-02-14,46.50,46.50,45.12,46.25,31544800,0.72
1983-02-11,45.38,47.25,45.38,46.50,50887200,0.72
1983-02-10,42.25,45.25,42.25,45.00,59180800,0.70
1983-02-09,41.88,42.50,40.75,42.25,45203200,0.66
1983-02-08,42.25,42.87,41.38,41.88,42028000,0.65
1983-02-07,44.00,44.63,41.50,42.25,35728000,0.66
1983-02-04,44.63,45.38,43.87,44.00,53586400,0.69
1983-02-03,42.87,44.75,42.50,44.63,63134400,0.70
1983-02-02,41.75,43.75,41.13,42.87,66763200,0.67
1983-02-01,40.87,41.75,40.25,41.75,52740800,0.65
1983-01-31,41.00,41.62,40.13,40.87,47000800,0.64
1983-01-28,40.75,42.00,40.50,41.00,99433600,0.64
1983-01-27,38.12,41.00,38.00,40.75,26079200,0.63
1983-01-26,37.00,38.50,37.00,38.12,50803200,0.59
1983-01-25,35.25,37.50,35.00,36.63,41759200,0.57
1983-01-24,37.37,37.37,34.62,35.25,78853600,0.55
1983-01-21,37.37,39.00,37.00,37.37,100648800,0.58
1983-01-20,33.63,37.37,33.63,37.37,176960000,0.58
1983-01-19,33.37,34.00,33.25,33.63,42414400,0.52
1983-01-18,34.13,34.88,32.50,33.37,54947200,0.52
1983-01-17,33.00,34.62,32.75,34.13,58716000,0.53
1983-01-14,30.88,33.00,30.88,33.00,46160800,0.51
1983-01-13,30.75,31.00,30.25,30.75,20568800,0.48
1983-01-12,29.50,31.50,29.50,30.75,44245600,0.48
1983-01-11,28.75,29.50,28.75,29.13,347200,0.45
1983-01-10,27.50,29.00,27.25,28.75,68835200,0.45
1983-01-07,29.13,29.50,27.50,27.50,43013600,0.43
1983-01-06,30.25,30.37,29.00,29.13,24449600,0.45
1983-01-05,30.13,30.50,29.63,30.25,35386400,0.47
1983-01-04,28.50,30.25,28.00,30.13,55927200,0.47
1983-01-03,29.87,30.25,28.25,28.50,28207200,0.44
1982-12-31,30.00,30.37,29.87,29.87,12415200,0.47
1982-12-30,31.38,31.75,29.63,30.00,39216800,0.47
1982-12-29,32.50,32.63,31.00,31.38,20176800,0.49
1982-12-28,32.75,33.75,32.12,32.50,28341600,0.51
1982-12-27,32.00,32.87,31.75,32.75,15467200,0.51
1982-12-23,31.12,32.00,30.88,32.00,21744800,0.50
1982-12-22,30.37,31.12,30.37,31.12,25306400,0.48
1982-12-21,30.00,30.25,29.50,30.25,19986400,0.47
1982-12-20,30.13,30.25,29.75,30.00,17444000,0.47
1982-12-17,28.75,30.37,28.62,30.13,20182400,0.47
1982-12-16,28.25,29.25,28.00,28.75,35291200,0.45
1982-12-15,28.38,28.50,27.62,28.25,32698400,0.44
1982-12-14,28.62,30.37,28.00,28.38,67513600,0.44
1982-12-13,29.00,29.00,28.62,28.62,23844800,0.45
1982-12-10,31.00,31.00,28.87,29.25,41871200,0.46
1982-12-09,32.63,32.63,31.00,31.50,48664000,0.49
1982-12-08,33.87,34.88,33.00,33.13,28078400,0.52
1982-12-07,33.50,34.62,32.75,33.87,41820800,0.53
1982-12-06,31.75,33.75,31.50,33.50,36646400,0.52
1982-12-03,32.12,32.12,31.38,31.75,11894400,0.49
1982-12-02,32.50,33.00,32.00,32.50,41182400,0.51
1982-12-01,31.88,33.75,31.88,32.50,51710400,0.51
1982-11-30,28.87,32.00,28.75,31.88,39799200,0.50
1982-11-29,29.00,29.37,28.00,28.87,12488000,0.45
1982-11-26,29.50,29.87,28.38,29.00,25496800,0.45
1982-11-24,28.87,30.50,28.75,29.50,18435200,0.46
1982-11-23,28.50,29.75,28.50,28.87,22125600,0.45
1982-11-22,30.88,30.88,28.12,28.12,25312000,0.44
1982-11-19,31.38,31.62,30.75,30.88,24326400,0.48
1982-11-18,31.38,31.88,31.25,31.38,38169600,0.49
1982-11-17,30.00,31.50,30.00,31.38,36036000,0.49
1982-11-16,31.62,31.75,29.87,30.00,45505600,0.47
1982-11-15,32.37,32.75,31.25,31.62,31147200,0.49
1982-11-12,33.00,34.00,32.37,32.37,32776800,0.50
1982-11-11,31.00,33.00,30.50,33.00,30788800,0.51
1982-11-10,30.00,31.50,30.00,31.00,50696800,0.48
1982-11-09,28.87,30.13,28.75,29.87,44945600,0.47
1982-11-08,30.13,30.37,28.75,28.87,29797600,0.45
1982-11-05,30.75,30.75,29.63,30.13,35375200,0.47
1982-11-04,30.75,31.88,30.50,31.00,82269600,0.48
1982-11-03,28.62,30.75,28.62,30.75,58783200,0.48
1982-11-02,27.00,29.50,27.00,28.62,77711200,0.45
1982-11-01,25.37,27.00,25.12,26.75,26090400,0.42
1982-10-29,25.12,25.37,24.75,25.37,29528800,0.40
1982-10-28,25.12,25.37,24.75,25.12,54420800,0.39
1982-10-27,24.50,25.25,24.50,25.12,47790400,0.39
1982-10-26,24.38,24.62,23.25,24.50,41938400,0.38
1982-10-25,25.87,26.00,24.25,24.38,46233600,0.38
1982-10-22,26.00,26.75,25.87,25.87,40420800,0.40
1982-10-21,25.37,26.75,25.00,26.00,56879200,0.41
1982-10-20,24.00,25.63,23.88,25.37,60524800,0.40
1982-10-19,23.50,24.25,23.50,24.00,30710400,0.37
1982-10-18,23.00,23.63,23.00,23.50,23587200,0.37
1982-10-15,23.50,23.50,22.63,23.00,36153600,0.36
1982-10-14,23.50,23.88,23.13,23.63,44665600,0.37
1982-10-13,23.25,24.25,23.00,23.50,49711200,0.37
1982-10-12,24.00,24.38,23.00,23.25,64736000,0.36
1982-10-11,23.50,24.75,23.50,24.00,78433600,0.37
1982-10-08,21.87,23.63,21.75,23.50,68885600,0.37
1982-10-07,20.38,22.00,20.38,21.87,77918400,0.34
1982-10-06,18.87,20.25,18.87,20.25,43383200,0.32
1982-10-05,18.75,19.25,18.75,18.87,20059200,0.29
1982-10-04,18.50,18.87,18.00,18.75,17332000,0.29
1982-10-01,18.50,18.75,18.50,18.50,11564000,0.29
1982-09-30,18.37,18.37,18.25,18.25,18670400,0.28
1982-09-29,18.37,18.50,18.37,18.37,16391200,0.29
1982-09-28,18.37,18.63,18.37,18.37,21380800,0.29
1982-09-27,18.12,18.37,18.12,18.12,9536800,0.28
1982-09-24,18.25,18.25,18.12,18.12,44548000,0.28
1982-09-23,18.75,18.87,18.75,18.75,34955200,0.29
1982-09-22,18.75,18.87,18.75,18.75,25844000,0.29
1982-09-21,18.25,18.37,18.25,18.25,9167200,0.28
1982-09-20,17.88,18.00,17.88,17.88,9783200,0.28
1982-09-17,17.88,17.88,17.75,17.75,13512800,0.28
1982-09-16,18.37,18.37,18.12,18.12,20092800,0.28
1982-09-15,18.87,18.87,18.75,18.75,17936800,0.29
1982-09-14,18.87,19.00,18.87,18.87,25373600,0.29
1982-09-13,18.25,18.37,18.25,18.25,14722400,0.28
1982-09-10,18.12,18.25,18.12,18.12,14016800,0.28
1982-09-09,17.75,17.75,17.62,17.62,15898400,0.27
1982-09-08,18.00,18.12,18.00,18.00,18082400,0.28
1982-09-07,17.50,17.50,17.38,17.38,20344800,0.27
1982-09-03,18.37,18.50,18.37,18.37,26135200,0.29
1982-09-02,18.25,18.37,18.25,18.25,18855200,0.28
1982-09-01,17.62,17.62,17.50,17.50,20641600,0.27
1982-08-31,18.00,18.12,18.00,18.00,35140000,0.28
1982-08-30,17.12,17.25,17.12,17.12,20109600,0.27
1982-08-27,17.00,17.00,16.88,16.88,24662400,0.26
1982-08-26,17.75,17.88,17.75,17.75,52645600,0.28
1982-08-25,17.25,17.38,17.25,17.25,89269600,0.27
1982-08-24,16.13,16.25,16.13,16.13,38942400,0.25
1982-08-23,15.37,15.50,15.37,15.37,17421600,0.24
1982-08-20,14.75,14.87,14.75,14.75,13714400,0.23
1982-08-19,14.38,14.50,14.38,14.38,11905600,0.22
1982-08-18,14.25,14.38,14.25,14.25,31264800,0.22
1982-08-17,14.25,14.50,14.25,14.25,11933600,0.22
1982-08-16,13.38,13.50,13.38,13.38,9604000,0.21
1982-08-13,13.13,13.25,13.13,13.13,6490400,0.20
1982-08-12,13.25,13.25,13.13,13.13,7655200,0.20
1982-08-11,13.25,13.38,13.25,13.25,17472000,0.21
1982-08-10,13.13,13.25,13.13,13.13,28061600,0.20
1982-08-09,12.37,12.50,12.37,12.37,14028000,0.19
1982-08-06,12.37,12.37,12.25,12.25,24208800,0.19
1982-08-05,12.50,12.50,12.37,12.37,17438400,0.19
1982-08-04,13.00,13.00,12.87,12.87,20966400,0.20
1982-08-03,13.13,13.13,13.00,13.00,22467200,0.20
1982-08-02,13.88,14.00,13.88,13.88,23598400,0.22
1982-07-30,13.50,13.62,13.50,13.50,9654400,0.21
1982-07-29,13.38,13.50,13.38,13.38,15467200,0.21
1982-07-28,13.00,13.00,12.87,12.87,13378400,0.20
1982-07-27,13.50,13.62,13.50,13.50,8080800,0.21
1982-07-26,13.62,13.62,13.50,13.50,14212800,0.21
1982-07-23,14.25,14.25,14.12,14.12,4575200,0.22
1982-07-22,14.38,14.50,14.38,14.38,8803200,0.22
1982-07-21,14.25,14.38,14.25,14.25,17925600,0.22
1982-07-20,14.25,14.38,14.25,14.25,12426400,0.22
1982-07-19,13.38,13.50,13.38,13.38,20944000,0.21
1982-07-16,13.25,13.38,13.25,13.25,19252800,0.21
1982-07-15,12.75,12.87,12.75,12.75,16447200,0.20
1982-07-14,12.50,12.75,12.50,12.50,17780000,0.19
1982-07-13,12.37,12.50,12.37,12.37,28593600,0.19
1982-07-12,11.63,11.75,11.63,11.63,15848000,0.18
1982-07-09,11.37,11.50,11.37,11.37,32104800,0.18
1982-07-08,11.12,11.12,11.00,11.00,41081600,0.17
1982-07-07,11.50,11.63,11.50,11.50,7593600,0.18
1982-07-06,11.63,11.63,11.50,11.50,21924000,0.18
1982-07-02,12.13,12.13,12.00,12.00,14526400,0.19
1982-07-01,12.75,12.75,12.63,12.63,13932800,0.20
1982-06-30,12.75,12.87,12.75,12.75,16906400,0.20
1982-06-29,12.87,12.87,12.75,12.75,8954400,0.20
1982-06-28,13.25,13.25,13.13,13.13,6288800,0.20
1982-06-25,13.38,13.38,13.25,13.25,6669600,0.21
1982-06-24,13.75,13.88,13.75,13.75,11037600,0.21
1982-06-23,13.75,14.00,13.75,13.75,13188000,0.21
1982-06-22,13.38,13.62,13.38,13.38,4390400,0.21
1982-06-21,12.87,13.00,12.87,12.87,7134400,0.20
1982-06-18,13.13,13.13,12.87,12.87,4967200,0.20
1982-06-17,13.25,13.25,13.13,13.13,7291200,0.20
1982-06-16,13.38,13.50,13.38,13.38,10432800,0.21
1982-06-15,13.38,13.50,13.38,13.38,8803200,0.21
1982-06-14,13.38,13.50,13.38,13.38,7498400,0.21
1982-06-11,13.38,13.50,13.38,13.38,13658400,0.21
1982-06-10,12.87,13.00,12.87,12.87,8601600,0.20
1982-06-09,12.87,12.87,12.75,12.75,8461600,0.20
1982-06-08,13.13,13.13,13.00,13.00,7851200,0.20
1982-06-07,13.13,13.25,13.13,13.13,9290400,0.20
1982-06-04,13.25,13.25,13.13,13.13,9419200,0.20
1982-06-03,13.75,13.75,13.50,13.50,9940000,0.21
1982-06-02,14.00,14.12,14.00,14.00,8226400,0.22
1982-06-01,13.88,13.88,13.75,13.75,11900000,0.21
1982-05-28,14.00,14.12,14.00,14.00,4799200,0.22
1982-05-27,14.12,14.12,14.00,14.00,7812000,0.22
1982-05-26,14.38,14.38,14.25,14.25,10819200,0.22
1982-05-25,14.38,14.50,14.38,14.38,12891200,0.22
1982-05-24,14.38,14.50,14.38,14.38,7996800,0.22
1982-05-21,14.25,14.38,14.25,14.25,9710400,0.22
1982-05-20,14.12,14.25,14.12,14.12,6904800,0.22
1982-05-19,14.12,14.12,14.00,14.00,18821600,0.22
1982-05-18,14.25,14.25,14.12,14.12,30508800,0.22
1982-05-17,14.50,14.50,14.38,14.38,19051200,0.22
1982-05-14,14.87,14.87,14.62,14.62,23934400,0.23
1982-05-13,15.25,15.37,15.25,15.25,13613600,0.24
1982-05-12,15.25,15.25,15.13,15.13,17752000,0.24
1982-05-11,15.63,15.63,15.50,15.50,25754400,0.24
1982-05-10,16.13,16.13,16.00,16.00,7901600,0.25
1982-05-07,16.25,16.37,16.25,16.25,21179200,0.25
1982-05-06,16.00,16.13,16.00,16.00,18866400,0.25
1982-05-05,15.75,15.75,15.50,15.50,13484800,0.24
1982-05-04,15.75,15.87,15.75,15.75,18496800,0.25
1982-05-03,15.25,15.37,15.25,15.25,20675200,0.24
1982-04-30,14.75,14.87,14.75,14.75,69350400,0.23
1982-04-29,14.62,14.75,14.62,14.62,20557600,0.23
1982-04-28,14.75,14.75,14.62,14.62,24808000,0.23
1982-04-27,15.37,15.37,15.25,15.25,17567200,0.24
1982-04-26,15.75,15.87,15.75,15.75,14481600,0.25
1982-04-23,15.37,15.50,15.37,15.37,12073600,0.24
1982-04-22,15.50,15.50,15.37,15.37,13148800,0.24
1982-04-21,15.75,15.75,15.63,15.63,18256000,0.24
1982-04-20,15.87,15.87,15.75,15.75,20137600,0.25
1982-04-19,16.75,16.75,16.50,16.50,10320800,0.26
1982-04-16,16.88,17.00,16.88,16.88,26012000,0.26
1982-04-15,16.37,16.50,16.37,16.37,41070400,0.26
1982-04-14,16.13,16.25,16.13,16.13,28397600,0.25
1982-04-13,16.13,16.13,16.00,16.00,21324800,0.25
1982-04-12,17.50,17.62,17.38,17.38,11076800,0.27
1982-04-08,17.50,17.62,17.50,17.50,5997600,0.27
1982-04-07,17.50,17.50,17.38,17.38,7274400,0.27
1982-04-06,17.75,17.75,17.62,17.62,17897600,0.27
1982-04-05,17.75,17.88,17.75,17.75,21660800,0.28
1982-04-02,17.75,17.88,17.75,17.75,21201600,0.28
1982-04-01,17.75,17.88,17.75,17.75,14784000,0.28
1982-03-31,16.88,17.00,16.88,16.88,12538400,0.26
1982-03-30,16.88,17.00,16.88,16.88,19488000,0.26
1982-03-29,16.63,16.75,16.63,16.63,16900800,0.26
1982-03-26,16.37,16.37,16.25,16.25,12695200,0.25
1982-03-25,16.63,16.63,16.50,16.50,21028000,0.26
1982-03-24,16.75,16.75,16.63,16.63,12902400,0.26
1982-03-23,17.75,17.75,17.62,17.62,13988800,0.27
1982-03-22,17.88,18.00,17.88,17.88,17298400,0.28
1982-03-19,16.63,16.75,16.63,16.63,16452800,0.26
1982-03-18,15.25,15.37,15.25,15.25,14084000,0.24
1982-03-17,14.25,14.25,14.12,14.12,12622400,0.22
1982-03-16,15.00,15.00,14.87,14.87,11788000,0.23
1982-03-15,15.25,15.25,15.13,15.13,12840800,0.24
1982-03-12,15.37,15.37,15.25,15.25,11636800,0.24
1982-03-11,16.25,16.50,16.25,16.25,5644800,0.25
1982-03-10,16.37,16.37,16.25,16.25,21733600,0.25
1982-03-09,16.50,16.63,16.50,16.50,13126400,0.26
1982-03-08,16.50,16.50,16.37,16.37,8786400,0.26
1982-03-05,16.75,16.75,16.63,16.63,11328800,0.26
1982-03-04,18.12,18.12,18.00,18.00,9592800,0.28
1982-03-03,18.37,18.50,18.37,18.37,5913600,0.29
1982-03-02,18.37,18.50,18.37,18.37,8702400,0.29
1982-03-01,18.37,18.50,18.37,18.37,8825600,0.29
1982-02-26,18.25,18.37,18.25,18.25,4356800,0.28
1982-02-25,18.37,18.37,18.25,18.25,7700000,0.28
1982-02-24,18.37,18.50,18.37,18.37,9486400,0.29
1982-02-23,18.37,18.37,18.25,18.25,8635200,0.28
1982-02-22,18.63,18.63,18.50,18.50,6658400,0.29
1982-02-19,18.87,18.87,18.75,18.75,3399200,0.29
1982-02-18,18.87,19.00,18.87,18.87,7095200,0.29
1982-02-17,18.63,18.75,18.63,18.63,6395200,0.29
1982-02-16,18.50,18.50,18.37,18.37,8579200,0.29
1982-02-12,18.75,18.87,18.75,18.75,4911200,0.29
1982-02-11,18.63,18.63,18.50,18.50,6132000,0.29
1982-02-10,18.75,18.87,18.75,18.75,9699200,0.29
1982-02-09,18.50,18.63,18.50,18.50,14476000,0.29
1982-02-08,18.63,18.63,18.50,18.50,7924000,0.29
1982-02-05,19.75,19.87,19.75,19.75,10074400,0.31
1982-02-04,19.87,19.87,19.75,19.75,5510400,0.31
1982-02-03,20.25,20.38,20.25,20.25,7868000,0.32
1982-02-02,20.25,20.50,20.25,20.25,13568800,0.32
1982-02-01,20.38,20.38,20.13,20.13,9632000,0.31
1982-01-29,20.38,20.50,20.38,20.38,13288800,0.32
1982-01-28,20.13,20.25,20.13,20.13,9900800,0.31
1982-01-27,19.50,19.75,19.50,19.50,7840000,0.30
1982-01-26,19.63,19.63,19.37,19.37,5303200,0.30
1982-01-25,20.25,20.25,20.13,20.13,11177600,0.31
1982-01-22,20.75,20.88,20.75,20.75,6064800,0.32
1982-01-21,20.62,20.75,20.62,20.62,8332800,0.32
1982-01-20,20.25,20.38,20.25,20.25,6456800,0.32
1982-01-19,20.13,20.13,19.87,19.87,13876800,0.31
1982-01-18,20.38,20.62,20.38,20.38,7000000,0.32
1982-01-15,20.00,20.25,20.00,20.00,11676000,0.31
1982-01-14,18.75,18.87,18.75,18.75,6428800,0.29
1982-01-13,18.00,18.00,17.88,17.88,10438400,0.28
1982-01-12,18.12,18.12,18.00,18.00,14980000,0.28
1982-01-11,18.75,18.75,18.63,18.63,8332800,0.29
1982-01-08,19.87,20.00,19.87,19.87,14151200,0.31
1982-01-07,19.25,19.25,19.00,19.00,17511200,0.30
1982-01-06,20.75,20.75,20.62,20.62,16520000,0.32
1982-01-05,21.12,21.12,20.88,20.88,8960000,0.33
1982-01-04,22.13,22.13,22.00,22.00,17813600,0.34
1981-12-31,22.13,22.25,22.13,22.13,13664000,0.34
1981-12-30,22.13,22.25,22.13,22.13,8047200,0.34
1981-12-29,21.25,21.50,21.25,21.25,6059200,0.33
1981-12-28,21.12,21.12,20.88,20.88,9144800,0.33
1981-12-24,21.87,22.00,21.87,21.87,7229600,0.34
1981-12-23,21.87,21.87,21.75,21.75,7224000,0.34
1981-12-22,22.25,22.37,22.25,22.25,13456800,0.35
1981-12-21,22.00,22.00,21.87,21.87,14100800,0.34
1981-12-18,22.87,23.00,22.87,22.87,17931200,0.36
1981-12-17,21.12,21.25,21.12,21.12,12863200,0.33
1981-12-16,19.50,19.63,19.50,19.50,16363200,0.30
1981-12-15,18.63,18.75,18.63,18.63,7828800,0.29
1981-12-14,18.37,18.37,18.12,18.12,6311200,0.28
1981-12-11,18.87,19.00,18.75,18.75,19023200,0.29
1981-12-10,18.87,19.00,18.87,18.87,9352000,0.29
1981-12-09,18.87,19.00,18.87,18.87,8568000,0.29
1981-12-08,19.00,19.00,18.75,18.75,12656000,0.29
1981-12-07,19.13,19.25,19.13,19.13,14823200,0.30
1981-12-04,19.00,19.13,19.00,19.00,34288800,0.30
1981-12-03,18.63,18.63,18.50,18.50,5107200,0.29
1981-12-02,18.75,18.87,18.75,18.75,9391200,0.29
1981-12-01,18.63,18.75,18.63,18.63,5846400,0.29
1981-11-30,18.75,18.75,18.63,18.63,5992000,0.29
1981-11-27,18.87,19.00,18.87,18.87,9312800,0.29
1981-11-25,18.37,18.50,18.37,18.37,13137600,0.29
1981-11-24,18.12,18.12,18.00,18.00,5538400,0.28
1981-11-23,18.37,18.37,18.12,18.12,5740000,0.28
1981-11-20,19.00,19.13,19.00,19.00,9525600,0.30
1981-11-19,18.87,19.00,18.87,18.87,10001600,0.29
1981-11-18,18.87,19.00,18.87,18.87,7285600,0.29
1981-11-17,18.25,18.37,18.25,18.25,8853600,0.28
1981-11-16,18.00,18.00,17.88,17.88,5639200,0.28
1981-11-13,18.25,18.25,18.12,18.12,5252800,0.28
1981-11-12,19.50,19.63,19.50,19.50,9979200,0.30
1981-11-11,18.87,19.00,18.87,18.87,6860000,0.29
1981-11-10,18.37,18.50,18.37,18.37,4188800,0.29
1981-11-09,18.25,18.37,18.25,18.25,5096000,0.28
1981-11-06,18.00,18.12,18.00,18.00,6148800,0.28
1981-11-05,18.00,18.00,17.88,17.88,5840800,0.28
1981-11-04,19.37,19.37,19.25,19.25,5952800,0.30
1981-11-03,19.87,19.87,19.75,19.75,7095200,0.31
1981-11-02,20.00,20.13,20.00,20.00,9228800,0.31
1981-10-30,20.00,20.13,20.00,20.00,13182400,0.31
1981-10-29,19.87,19.87,19.75,19.75,7621600,0.31
1981-10-28,20.00,20.13,20.00,20.00,11043200,0.31
1981-10-27,19.37,19.63,19.37,19.37,21397600,0.30
1981-10-26,19.00,19.13,19.00,19.00,6820800,0.30
1981-10-23,19.13,19.13,19.00,19.00,6977600,0.30
1981-10-22,19.63,19.63,19.50,19.50,8069600,0.30
1981-10-21,19.63,19.75,19.63,19.63,19224800,0.31
1981-10-20,19.63,19.75,19.63,19.63,8932000,0.31
1981-10-19,18.63,18.75,18.63,18.63,5146400,0.29
1981-10-16,18.37,18.37,18.25,18.25,9116800,0.28
1981-10-15,18.50,18.63,18.50,18.50,7358400,0.29
1981-10-14,18.25,18.25,18.12,18.12,7744800,0.28
1981-10-13,19.25,19.50,19.25,19.25,11048800,0.30
1981-10-12,19.25,19.37,19.25,19.25,6837600,0.30
1981-10-09,18.63,18.87,18.63,18.63,13630400,0.29
1981-10-08,18.50,18.63,18.50,18.50,7772800,0.29
1981-10-07,17.88,18.12,17.88,17.88,9710400,0.28
1981-10-06,17.00,17.00,16.88,16.88,7089600,0.26
1981-10-05,17.00,17.25,17.00,17.00,10774400,0.26
1981-10-02,16.50,16.63,16.50,16.50,11261600,0.26
1981-10-01,15.25,15.37,15.25,15.25,15282400,0.24
1981-09-30,15.25,15.37,15.25,15.25,12499200,0.24
1981-09-29,15.13,15.25,15.13,15.13,23671200,0.24
1981-09-28,14.38,14.50,14.38,14.38,22932000,0.22
1981-09-25,14.50,14.50,14.25,14.25,8652000,0.22
1981-09-24,16.50,16.50,16.37,16.37,4575200,0.26
1981-09-23,16.75,16.75,16.50,16.50,7050400,0.26
1981-09-22,17.00,17.00,16.88,16.88,11855200,0.26
1981-09-21,17.88,18.00,17.88,17.88,12258400,0.28
1981-09-18,17.75,17.88,17.75,17.75,6580000,0.28
1981-09-17,17.75,17.75,17.62,17.62,4844000,0.27
1981-09-16,18.25,18.25,18.12,18.12,4838400,0.28
1981-09-15,18.63,18.63,18.50,18.50,4877600,0.29
1981-09-14,19.13,19.13,19.00,19.00,6921600,0.30
1981-09-11,19.75,19.75,19.63,19.63,4384800,0.31
1981-09-10,19.87,20.00,19.87,19.87,8702400,0.31
1981-09-09,19.75,19.87,19.75,19.75,7632800,0.31
1981-09-08,19.87,19.87,19.75,19.75,6361600,0.31
1981-09-04,20.50,20.50,20.38,20.38,3813600,0.32
1981-09-03,20.88,20.88,20.62,20.62,9368800,0.32
1981-09-02,21.75,21.87,21.75,21.75,4844000,0.34
1981-09-01,21.38,21.50,21.38,21.38,9256800,0.33
1981-08-31,20.13,20.25,20.13,20.13,10236800,0.31
1981-08-28,20.13,20.25,20.13,20.13,9508800,0.31
1981-08-27,19.13,19.25,19.13,19.13,6479200,0.30
1981-08-26,19.13,19.13,19.00,19.00,8400000,0.30
1981-08-25,19.37,19.50,19.37,19.37,10175200,0.30
1981-08-24,19.00,19.00,18.87,18.87,5768000,0.29
1981-08-21,20.38,20.38,20.13,20.13,10477600,0.31
1981-08-20,21.62,21.75,21.62,21.62,4278400,0.34
1981-08-19,21.62,21.62,21.38,21.38,5168800,0.33
1981-08-18,21.87,21.87,21.62,21.62,4250400,0.34
1981-08-17,22.37,22.37,22.13,22.13,4726400,0.34
1981-08-14,23.13,23.13,22.87,22.87,6048000,0.36
1981-08-13,23.37,23.37,23.25,23.25,6871200,0.36
1981-08-12,24.12,24.12,24.00,24.00,6568800,0.37
1981-08-11,24.75,24.75,24.50,24.50,17864000,0.38
1981-08-07,25.25,25.37,25.25,25.25,2301600,0.39
1981-08-06,25.37,25.37,25.25,25.25,2632000,0.39
1981-08-05,25.87,26.00,25.87,25.87,4373600,0.40
1981-08-04,25.12,25.25,25.12,25.12,7918400,0.39
1981-08-03,25.00,25.00,24.75,24.75,3108000,0.39
1981-07-31,25.00,25.12,25.00,25.00,2738400,0.39
1981-07-30,24.62,24.88,24.62,24.62,2475200,0.38
1981-07-29,23.88,23.88,23.75,23.75,3875200,0.37
1981-07-28,24.25,24.25,24.12,24.12,5712000,0.38
1981-07-27,25.00,25.12,25.00,25.00,4334400,0.39
1981-07-24,24.00,24.12,24.00,24.00,7212800,0.37
1981-07-23,23.25,23.37,23.25,23.25,8612800,0.36
1981-07-22,22.87,22.87,22.63,22.63,5667200,0.35
1981-07-21,24.12,24.12,24.00,24.00,7985600,0.37
1981-07-20,24.25,24.25,24.12,24.12,5913600,0.38
1981-07-17,25.87,26.00,25.87,25.87,4956000,0.40
1981-07-16,25.00,25.25,25.00,25.00,3808000,0.39
1981-07-15,24.38,24.50,24.38,24.38,2738400,0.38
1981-07-14,23.75,24.00,23.75,23.75,4944800,0.37
1981-07-13,22.75,22.87,22.75,22.75,11435200,0.35
1981-07-10,22.37,22.37,22.25,22.25,13792800,0.35
1981-07-09,24.25,24.25,24.12,24.12,8220800,0.38
1981-07-08,26.13,26.25,26.13,26.13,4155200,0.41
1981-07-07,25.12,25.37,25.12,25.12,3959200,0.39
1981-07-06,25.12,25.12,24.88,24.88,4132800,0.39
1981-07-02,25.75,25.87,25.75,25.75,7571200,0.40
1981-07-01,25.87,25.87,25.75,25.75,42616000,0.40
1981-06-30,26.13,26.13,26.00,26.00,8976800,0.41
1981-06-29,28.38,28.38,28.12,28.12,2648800,0.44
1981-06-26,29.37,29.37,29.13,29.13,5947200,0.45
1981-06-25,29.50,29.63,29.50,29.50,6064800,0.46
1981-06-24,29.13,29.13,28.87,28.87,5756800,0.45
1981-06-23,29.63,29.87,29.63,29.63,3757600,0.46
1981-06-22,29.25,29.25,29.13,29.13,2710400,0.45
1981-06-19,30.37,30.37,30.25,30.25,6876800,0.47
1981-06-18,31.25,31.38,31.12,31.12,5762400,0.48
1981-06-17,31.38,31.38,31.25,31.25,6893600,0.49
1981-06-16,31.88,31.88,31.75,31.75,9312800,0.49
1981-06-15,32.50,32.50,32.37,32.37,35940800,0.50
1981-06-12,32.63,32.63,32.50,32.50,6451200,0.51
1981-06-11,32.87,33.00,32.87,32.87,9744000,0.51
1981-06-10,31.50,31.88,31.50,31.50,6305600,0.49
1981-06-09,31.12,31.25,31.12,31.12,29898400,0.48
1981-06-08,30.63,30.63,30.50,30.50,23374400,0.48
1981-06-05,31.75,31.75,31.62,31.62,14420000,0.49
1981-06-04,32.12,32.25,32.12,32.12,14016800,0.50
1981-06-03,31.50,31.75,31.50,31.50,9861600,0.49
1981-06-02,31.62,31.62,31.50,31.50,10108000,0.49
1981-06-01,33.13,33.25,33.13,33.13,12812800,0.52
1981-05-29,33.13,33.25,33.13,33.13,14845600,0.52
1981-05-28,33.00,33.13,33.00,33.00,18496800,0.51
1981-05-27,33.00,33.13,33.00,33.00,37374400,0.51
1981-05-26,31.38,31.38,31.25,31.25,21336000,0.49
1981-05-22,31.38,31.62,31.38,31.38,7856800,0.49
1981-05-21,30.00,30.13,30.00,30.00,8052800,0.47
1981-05-20,28.38,28.62,28.38,28.38,3320800,0.44
1981-05-19,27.62,27.62,27.50,27.50,6356000,0.43
1981-05-18,28.00,28.25,28.00,28.00,1041600,0.44
1981-05-15,27.50,27.88,27.50,27.50,1226400,0.43
1981-05-14,27.13,27.13,26.87,26.87,1232000,0.42
1981-05-13,27.38,27.62,27.25,27.25,1226400,0.42
1981-05-12,27.38,27.75,27.38,27.38,1064000,0.43
1981-05-11,27.50,27.50,27.38,27.38,2984800,0.43
1981-05-08,28.00,28.12,28.00,28.00,1976800,0.44
1981-05-07,27.75,27.88,27.75,27.75,2340800,0.43
1981-05-06,27.50,27.50,27.38,27.38,4737600,0.43
1981-05-05,28.25,28.25,28.12,28.12,4384800,0.44
1981-05-04,28.38,28.38,28.25,28.25,3612000,0.44
1981-05-01,28.38,28.62,28.38,28.38,4138400,0.44
1981-04-30,28.38,28.62,28.38,28.38,3152800,0.44
1981-04-29,28.00,28.00,27.88,27.88,3410400,0.43
1981-04-28,28.38,28.38,28.25,28.25,8047200,0.44
1981-04-27,28.87,28.87,28.75,28.75,9632000,0.45
1981-04-24,29.25,29.25,29.00,29.00,8764000,0.45
1981-04-23,29.25,29.37,29.25,29.25,14504000,0.46
1981-04-22,28.50,28.62,28.50,28.50,4748800,0.44
1981-04-21,27.50,27.62,27.50,27.50,7134400,0.43
1981-04-20,25.75,25.87,25.75,25.75,8836800,0.40
1981-04-16,25.12,25.12,25.00,25.00,5969600,0.39
1981-04-15,26.63,26.63,26.50,26.50,8512000,0.41
1981-04-14,27.88,28.00,27.88,27.88,1663200,0.43
1981-04-13,27.88,28.00,27.88,27.88,4015200,0.43
1981-04-10,27.88,28.00,27.88,27.88,8366400,0.43
1981-04-09,27.50,27.62,27.50,27.50,3124800,0.43
1981-04-08,27.00,27.25,27.00,27.00,5488000,0.42
1981-04-07,25.87,25.87,25.75,25.75,2671200,0.40
1981-04-06,26.13,26.13,26.00,26.00,5700800,0.41
1981-04-03,26.50,26.63,26.50,26.50,4121600,0.41
1981-04-02,26.37,26.50,26.37,26.37,7851200,0.41
1981-04-01,24.38,24.38,24.25,24.25,8517600,0.38
1981-03-31,24.75,24.75,24.50,24.50,3998400,0.38
1981-03-30,24.75,25.00,24.75,24.75,2475200,0.39
1981-03-27,24.88,24.88,24.75,24.75,3063200,0.39
1981-03-26,25.75,25.75,25.63,25.63,3068800,0.40
1981-03-25,26.37,26.37,26.13,26.13,1764000,0.41
1981-03-24,26.75,26.75,26.63,26.63,7039200,0.41
1981-03-23,26.75,27.00,26.75,26.75,5504800,0.42
1981-03-20,25.75,26.00,25.75,25.75,3651200,0.40
1981-03-19,25.63,25.63,25.50,25.50,9452800,0.40
1981-03-18,25.75,26.00,25.75,25.75,9234400,0.40
1981-03-17,24.25,24.50,24.25,24.25,10936800,0.38
1981-03-16,23.13,23.37,23.13,23.13,9307200,0.36
1981-03-13,22.37,22.37,22.25,22.25,57825600,0.35
1981-03-12,22.50,22.63,22.50,22.50,14812000,0.35
1981-03-11,21.87,21.87,21.62,21.62,7464800,0.34
1981-03-10,22.63,22.63,22.50,22.50,7095200,0.35
1981-03-09,23.75,23.75,23.63,23.63,3830400,0.37
1981-03-06,25.87,25.87,25.63,25.63,2900800,0.40
1981-03-05,26.00,26.00,25.87,25.87,1344000,0.40
1981-03-04,26.13,26.13,26.00,26.00,3427200,0.41
1981-03-03,26.37,26.37,26.25,26.25,4043200,0.41
1981-03-02,26.63,26.75,26.63,26.63,2940000,0.41
1981-02-27,26.50,26.75,26.50,26.50,3690400,0.41
1981-02-26,25.63,25.75,25.63,25.63,2710400,0.40
1981-02-25,25.25,25.37,25.25,25.25,4872000,0.39
1981-02-24,24.00,24.00,23.75,23.75,4244800,0.37
1981-02-23,24.62,24.75,24.62,24.62,3528000,0.38
1981-02-20,24.38,24.38,24.25,24.25,6092800,0.38
1981-02-19,25.75,25.75,25.63,25.63,5577600,0.40
1981-02-18,27.25,27.50,27.25,27.25,4810400,0.42
1981-02-17,26.13,26.25,26.13,26.13,3068800,0.41
1981-02-13,25.75,25.75,25.50,25.50,2788800,0.40
1981-02-12,26.25,26.25,26.13,26.13,3640000,0.41
1981-02-11,26.50,26.50,26.37,26.37,3460800,0.41
1981-02-10,27.25,27.38,27.25,27.25,4586400,0.42
1981-02-09,27.50,27.50,27.25,27.25,4188800,0.42
1981-02-06,28.75,28.87,28.75,28.75,3466400,0.45
1981-02-05,28.62,28.87,28.62,28.62,1982400,0.45
1981-02-04,28.62,28.75,28.62,28.62,6966400,0.45
1981-02-03,27.62,27.75,27.62,27.62,4788000,0.43
1981-02-02,26.75,26.75,26.63,26.63,5941600,0.41
1981-01-30,28.50,28.50,28.25,28.25,11547200,0.44
1981-01-29,30.00,30.00,29.87,29.87,10976000,0.47
1981-01-28,31.12,31.12,31.00,31.00,7039200,0.48
1981-01-27,32.25,32.25,32.00,32.00,5924800,0.50
1981-01-26,32.37,32.37,32.25,32.25,6160000,0.50
1981-01-23,32.87,33.00,32.75,32.75,2805600,0.51
1981-01-22,32.87,33.13,32.87,32.87,8887200,0.51
1981-01-21,32.50,32.75,32.50,32.50,3976000,0.51
1981-01-20,32.00,32.00,31.88,31.88,7520800,0.50
1981-01-19,32.87,33.00,32.87,32.87,10393600,0.51
1981-01-16,31.12,31.12,31.00,31.00,3348800,0.48
1981-01-15,31.25,31.50,31.25,31.25,3516800,0.49
1981-01-14,30.63,30.75,30.63,30.63,3572800,0.48
1981-01-13,30.63,30.63,30.50,30.50,5762400,0.48
1981-01-12,31.88,31.88,31.62,31.62,5924800,0.49
1981-01-09,31.88,32.00,31.88,31.88,5376000,0.50
1981-01-08,30.37,30.37,30.25,30.25,9956800,0.47
1981-01-07,31.00,31.00,30.88,30.88,13921600,0.48
1981-01-06,32.37,32.37,32.25,32.25,11289600,0.50
1981-01-05,33.87,33.87,33.75,33.75,8932000,0.53
1981-01-02,34.50,34.75,34.50,34.50,5415200,0.54
1980-12-31,34.25,34.25,34.13,34.13,8937600,0.53
1980-12-30,35.25,35.25,35.12,35.12,17220000,0.55
1980-12-29,36.00,36.13,36.00,36.00,23290400,0.56
1980-12-26,35.50,35.62,35.50,35.50,13893600,0.55
1980-12-24,32.50,32.63,32.50,32.50,12000800,0.51
1980-12-23,30.88,31.00,30.88,30.88,11737600,0.48
1980-12-22,29.63,29.75,29.63,29.63,9340800,0.46
1980-12-19,28.25,28.38,28.25,28.25,12157600,0.44
1980-12-18,26.63,26.75,26.63,26.63,18362400,0.41
1980-12-17,25.87,26.00,25.87,25.87,21610400,0.40
1980-12-16,25.37,25.37,25.25,25.25,26432000,0.39
1980-12-15,27.38,27.38,27.25,27.25,43971200,0.42
1980-12-12,28.75,28.87,28.75,28.75,117258400,0.45


================================================
FILE: 09_Time_Series/Getting_Financial_Data/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Financial Data - Pandas Datareader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will get data from a website.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Get an API key for one of the APIs that are supported by Pandas Datareader, preferably for AlphaVantage.\n",
    "\n",
    "If you do not have an API key for any of the supported APIs, it is easiest to get one for [AlphaVantage](https://www.alphavantage.co/support/#api-key). (Note that the API key is shown directly after the signup. You do *not* receive it via e-mail.)\n",
    "\n",
    "(For a full list of the APIs that are supported by Pandas Datareader, [see here](https://pydata.github.io/pandas-datareader/readers/index.html). As the APIs are provided by third parties, this list may change.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Use Pandas Datarader to read the daily time series for the Apple stock (ticker symbol AAPL) between 01/01/2015 and today, assign it to df_apple and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Add a new column \"stock\" to the dataframe and add the ticker symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Repeat the two previous steps for a few other stocks, always creating a new dataframe: Tesla, IBM and Microsoft. (Ticker symbols TSLA, IBM and MSFT.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Combine the four separate dataFrames into one combined dataFrame df that holds the information for all four stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Shift the stock column into the index (making it a multi-level index consisting of the ticker symbol and the date)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Create a dataFrame called vol, with the volume values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Aggregate the data of volume to weekly.\n",
    "Hint: Be careful to not sum data from the same week of 2015 and other years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Find all the volume traded in the year of 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
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}


================================================
FILE: 09_Time_Series/Getting_Financial_Data/Exercises_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Financial Data - Pandas Datareader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will get data from a website.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# package to extract data from various Internet sources into a DataFrame\n",
    "# make sure you have it installed\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "# package for dates\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 1, 0, 0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_dt = dt.datetime(2015, 1, 1, 0, 0)\n",
    "start_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Get an API key for one of the APIs that are supported by Pandas Datareader, preferably for AlphaVantage.\n",
    "\n",
    "If you do not have an API key for any of the supported APIs, it is easiest to get one for [AlphaVantage](https://www.alphavantage.co/support/#api-key). (Note that the API key is shown directly after the signup. You do *not* receive it via e-mail.)\n",
    "\n",
    "(For a full list of the APIs that are supported by Pandas Datareader, [see here](https://pydata.github.io/pandas-datareader/readers/index.html). As the APIs are provided by third parties, this list may change.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Use Pandas Datarader to read the daily time series for the Apple stock (ticker symbol AAPL) between 01/01/2015 and today, assign it to df_apple and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.390</td>\n",
       "      <td>111.440</td>\n",
       "      <td>107.3500</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.290</td>\n",
       "      <td>108.650</td>\n",
       "      <td>105.4100</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.540</td>\n",
       "      <td>107.430</td>\n",
       "      <td>104.6300</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.200</td>\n",
       "      <td>108.200</td>\n",
       "      <td>106.6950</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.230</td>\n",
       "      <td>112.150</td>\n",
       "      <td>108.7000</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>150.370</td>\n",
       "      <td>151.880</td>\n",
       "      <td>149.4300</td>\n",
       "      <td>150.00</td>\n",
       "      <td>59222803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <td>149.940</td>\n",
       "      <td>151.488</td>\n",
       "      <td>149.3400</td>\n",
       "      <td>151.00</td>\n",
       "      <td>59256210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17</th>\n",
       "      <td>150.995</td>\n",
       "      <td>155.000</td>\n",
       "      <td>150.9900</td>\n",
       "      <td>153.49</td>\n",
       "      <td>88807000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-18</th>\n",
       "      <td>153.710</td>\n",
       "      <td>158.670</td>\n",
       "      <td>153.0500</td>\n",
       "      <td>157.87</td>\n",
       "      <td>137827673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-19</th>\n",
       "      <td>157.650</td>\n",
       "      <td>161.020</td>\n",
       "      <td>156.5328</td>\n",
       "      <td>160.55</td>\n",
       "      <td>116744360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1735 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               open     high       low   close     volume\n",
       "2015-01-02  111.390  111.440  107.3500  109.33   53204626\n",
       "2015-01-05  108.290  108.650  105.4100  106.25   64285491\n",
       "2015-01-06  106.540  107.430  104.6300  106.26   65797116\n",
       "2015-01-07  107.200  108.200  106.6950  107.75   40105934\n",
       "2015-01-08  109.230  112.150  108.7000  111.89   59364547\n",
       "...             ...      ...       ...     ...        ...\n",
       "2021-11-15  150.370  151.880  149.4300  150.00   59222803\n",
       "2021-11-16  149.940  151.488  149.3400  151.00   59256210\n",
       "2021-11-17  150.995  155.000  150.9900  153.49   88807000\n",
       "2021-11-18  153.710  158.670  153.0500  157.87  137827673\n",
       "2021-11-19  157.650  161.020  156.5328  160.55  116744360\n",
       "\n",
       "[1735 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apple = web.DataReader(\"AAPL\", \"av-daily\", start=start_dt, api_key=\"AZOBAQ2SK8AC1MUD\")\n",
    "df_apple\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Add a new column \"stock\" to the dataframe and add the ticker symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.390</td>\n",
       "      <td>111.440</td>\n",
       "      <td>107.3500</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.290</td>\n",
       "      <td>108.650</td>\n",
       "      <td>105.4100</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.540</td>\n",
       "      <td>107.430</td>\n",
       "      <td>104.6300</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.200</td>\n",
       "      <td>108.200</td>\n",
       "      <td>106.6950</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.230</td>\n",
       "      <td>112.150</td>\n",
       "      <td>108.7000</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>150.370</td>\n",
       "      <td>151.880</td>\n",
       "      <td>149.4300</td>\n",
       "      <td>150.00</td>\n",
       "      <td>59222803</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <td>149.940</td>\n",
       "      <td>151.488</td>\n",
       "      <td>149.3400</td>\n",
       "      <td>151.00</td>\n",
       "      <td>59256210</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17</th>\n",
       "      <td>150.995</td>\n",
       "      <td>155.000</td>\n",
       "      <td>150.9900</td>\n",
       "      <td>153.49</td>\n",
       "      <td>88807000</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-18</th>\n",
       "      <td>153.710</td>\n",
       "      <td>158.670</td>\n",
       "      <td>153.0500</td>\n",
       "      <td>157.87</td>\n",
       "      <td>137827673</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-19</th>\n",
       "      <td>157.650</td>\n",
       "      <td>161.020</td>\n",
       "      <td>156.5328</td>\n",
       "      <td>160.55</td>\n",
       "      <td>116744360</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1735 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               open     high       low   close     volume stock\n",
       "2015-01-02  111.390  111.440  107.3500  109.33   53204626  AAPL\n",
       "2015-01-05  108.290  108.650  105.4100  106.25   64285491  AAPL\n",
       "2015-01-06  106.540  107.430  104.6300  106.26   65797116  AAPL\n",
       "2015-01-07  107.200  108.200  106.6950  107.75   40105934  AAPL\n",
       "2015-01-08  109.230  112.150  108.7000  111.89   59364547  AAPL\n",
       "...             ...      ...       ...     ...        ...   ...\n",
       "2021-11-15  150.370  151.880  149.4300  150.00   59222803  AAPL\n",
       "2021-11-16  149.940  151.488  149.3400  151.00   59256210  AAPL\n",
       "2021-11-17  150.995  155.000  150.9900  153.49   88807000  AAPL\n",
       "2021-11-18  153.710  158.670  153.0500  157.87  137827673  AAPL\n",
       "2021-11-19  157.650  161.020  156.5328  160.55  116744360  AAPL\n",
       "\n",
       "[1735 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apple['stock'] = 'AAPL'\n",
    "df_apple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Repeat the two previous steps for a few other stocks, always creating a new dataframe: Tesla, IBM and Microsoft. (Ticker symbols TSLA, IBM and MSFT.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tesla = web.DataReader(\"TSLA\", \"av-daily\", start=start_dt, api_key=\"AZOBAQ2SK8AC1MUD\")\n",
    "df_tesla['stock'] = \"TSLA\"\n",
    "\n",
    "df_ibm = web.DataReader(\"IBM\", \"av-daily\", start=start_dt, api_key=\"AZOBAQ2SK8AC1MUD\")\n",
    "df_ibm['stock'] = \"IBM\"\n",
    "\n",
    "df_microsoft = web.DataReader(\"MSFT\", \"av-daily\", start=start_dt, api_key=\"AZOBAQ2SK8AC1MUD\")\n",
    "df_microsoft['stock'] = \"MSFT\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Combine the four separate dataFrames into one combined dataFrame df that holds the information for all four stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.39</td>\n",
       "      <td>111.44</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.29</td>\n",
       "      <td>108.65</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.54</td>\n",
       "      <td>107.43</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.20</td>\n",
       "      <td>108.20</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.23</td>\n",
       "      <td>112.15</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>337.54</td>\n",
       "      <td>337.88</td>\n",
       "      <td>334.034</td>\n",
       "      <td>336.07</td>\n",
       "      <td>16723009</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <td>335.68</td>\n",
       "      <td>340.67</td>\n",
       "      <td>335.510</td>\n",
       "      <td>339.51</td>\n",
       "      <td>20886832</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17</th>\n",
       "      <td>338.94</td>\n",
       "      <td>342.19</td>\n",
       "      <td>338.000</td>\n",
       "      <td>339.12</td>\n",
       "      <td>19053380</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-18</th>\n",
       "      <td>338.18</td>\n",
       "      <td>342.45</td>\n",
       "      <td>337.120</td>\n",
       "      <td>341.27</td>\n",
       "      <td>22463533</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-19</th>\n",
       "      <td>342.64</td>\n",
       "      <td>345.10</td>\n",
       "      <td>342.200</td>\n",
       "      <td>343.11</td>\n",
       "      <td>21095274</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6940 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              open    high      low   close    volume stock\n",
       "2015-01-02  111.39  111.44  107.350  109.33  53204626  AAPL\n",
       "2015-01-05  108.29  108.65  105.410  106.25  64285491  AAPL\n",
       "2015-01-06  106.54  107.43  104.630  106.26  65797116  AAPL\n",
       "2015-01-07  107.20  108.20  106.695  107.75  40105934  AAPL\n",
       "2015-01-08  109.23  112.15  108.700  111.89  59364547  AAPL\n",
       "...            ...     ...      ...     ...       ...   ...\n",
       "2021-11-15  337.54  337.88  334.034  336.07  16723009  MSFT\n",
       "2021-11-16  335.68  340.67  335.510  339.51  20886832  MSFT\n",
       "2021-11-17  338.94  342.19  338.000  339.12  19053380  MSFT\n",
       "2021-11-18  338.18  342.45  337.120  341.27  22463533  MSFT\n",
       "2021-11-19  342.64  345.10  342.200  343.11  21095274  MSFT\n",
       "\n",
       "[6940 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.concat([df_apple, df_tesla, df_ibm, df_microsoft])\n",
    "df_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Shift the stock column into the index (making it a multi-level index consisting of the ticker symbol and the date)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>111.39</td>\n",
       "      <td>111.44</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>108.29</td>\n",
       "      <td>108.65</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>106.54</td>\n",
       "      <td>107.43</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>107.20</td>\n",
       "      <td>108.20</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>109.23</td>\n",
       "      <td>112.15</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>337.54</td>\n",
       "      <td>337.88</td>\n",
       "      <td>334.034</td>\n",
       "      <td>336.07</td>\n",
       "      <td>16723009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>335.68</td>\n",
       "      <td>340.67</td>\n",
       "      <td>335.510</td>\n",
       "      <td>339.51</td>\n",
       "      <td>20886832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>338.94</td>\n",
       "      <td>342.19</td>\n",
       "      <td>338.000</td>\n",
       "      <td>339.12</td>\n",
       "      <td>19053380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-18</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>338.18</td>\n",
       "      <td>342.45</td>\n",
       "      <td>337.120</td>\n",
       "      <td>341.27</td>\n",
       "      <td>22463533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-19</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>342.64</td>\n",
       "      <td>345.10</td>\n",
       "      <td>342.200</td>\n",
       "      <td>343.11</td>\n",
       "      <td>21095274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6940 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    open    high      low   close    volume\n",
       "           stock                                           \n",
       "2015-01-02 AAPL   111.39  111.44  107.350  109.33  53204626\n",
       "2015-01-05 AAPL   108.29  108.65  105.410  106.25  64285491\n",
       "2015-01-06 AAPL   106.54  107.43  104.630  106.26  65797116\n",
       "2015-01-07 AAPL   107.20  108.20  106.695  107.75  40105934\n",
       "2015-01-08 AAPL   109.23  112.15  108.700  111.89  59364547\n",
       "...                  ...     ...      ...     ...       ...\n",
       "2021-11-15 MSFT   337.54  337.88  334.034  336.07  16723009\n",
       "2021-11-16 MSFT   335.68  340.67  335.510  339.51  20886832\n",
       "2021-11-17 MSFT   338.94  342.19  338.000  339.12  19053380\n",
       "2021-11-18 MSFT   338.18  342.45  337.120  341.27  22463533\n",
       "2021-11-19 MSFT   342.64  345.10  342.200  343.11  21095274\n",
       "\n",
       "[6940 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = df_all.set_index('stock', append=True)\n",
    "df_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Create a dataFrame called vol, with the volume values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>16723009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>20886832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>19053380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-18</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>22463533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-19</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>21095274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6940 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    volume\n",
       "           stock          \n",
       "2015-01-02 AAPL   53204626\n",
       "2015-01-05 AAPL   64285491\n",
       "2015-01-06 AAPL   65797116\n",
       "2015-01-07 AAPL   40105934\n",
       "2015-01-08 AAPL   59364547\n",
       "...                    ...\n",
       "2021-11-15 MSFT   16723009\n",
       "2021-11-16 MSFT   20886832\n",
       "2021-11-17 MSFT   19053380\n",
       "2021-11-18 MSFT   22463533\n",
       "2021-11-19 MSFT   21095274\n",
       "\n",
       "[6940 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol = df_all.filter(['volume'])\n",
    "vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Aggregate the data of volume to weekly.\n",
    "Hint: Be careful to not sum data from the same week of 2015 and other years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2015</th>\n",
       "      <th>1</th>\n",
       "      <td>53204626</td>\n",
       "      <td>5525341</td>\n",
       "      <td>27913852</td>\n",
       "      <td>4764443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>282868187</td>\n",
       "      <td>24440360</td>\n",
       "      <td>158596624</td>\n",
       "      <td>22622034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304226647</td>\n",
       "      <td>23272056</td>\n",
       "      <td>157088136</td>\n",
       "      <td>30799137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198737041</td>\n",
       "      <td>31230797</td>\n",
       "      <td>137352632</td>\n",
       "      <td>16215501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>465842684</td>\n",
       "      <td>32927307</td>\n",
       "      <td>437786778</td>\n",
       "      <td>15720217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2021</th>\n",
       "      <th>42</th>\n",
       "      <td>340691290</td>\n",
       "      <td>59731582</td>\n",
       "      <td>91315937</td>\n",
       "      <td>109982713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>392739936</td>\n",
       "      <td>34288134</td>\n",
       "      <td>159314433</td>\n",
       "      <td>220925116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>322688958</td>\n",
       "      <td>29791780</td>\n",
       "      <td>120621826</td>\n",
       "      <td>178974628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>281026556</td>\n",
       "      <td>29109912</td>\n",
       "      <td>106622220</td>\n",
       "      <td>182318404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>461858046</td>\n",
       "      <td>24205817</td>\n",
       "      <td>100222028</td>\n",
       "      <td>134998459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "stock           AAPL       IBM       MSFT       TSLA\n",
       "year week                                           \n",
       "2015 1      53204626   5525341   27913852    4764443\n",
       "     2     282868187  24440360  158596624   22622034\n",
       "     3     304226647  23272056  157088136   30799137\n",
       "     4     198737041  31230797  137352632   16215501\n",
       "     5     465842684  32927307  437786778   15720217\n",
       "...              ...       ...        ...        ...\n",
       "2021 42    340691290  59731582   91315937  109982713\n",
       "     43    392739936  34288134  159314433  220925116\n",
       "     44    322688958  29791780  120621826  178974628\n",
       "     45    281026556  29109912  106622220  182318404\n",
       "     46    461858046  24205817  100222028  134998459\n",
       "\n",
       "[360 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol_week = vol.rename_axis(index=['dt', 'stock'])\n",
    "vol_week.reset_index(inplace=True)\n",
    "vol_week.dt = pd.to_datetime(vol_week.dt)\n",
    "vol_week['year'] = vol_week['dt'].map(lambda x: x.year)\n",
    "vol_week['week'] = vol_week['dt'].map(lambda x: x.week)\n",
    "vol_week.pop('dt')\n",
    "\n",
    "vol_week.set_index(['year', 'week', 'stock'], inplace=True)\n",
    "vol_week = vol_week.groupby([pd.Grouper(level='stock'), pd.Grouper(level='year'), pd.Grouper(level='week')]).sum()\n",
    "\n",
    "vol_week = vol_week.reset_index()\n",
    "vol_week = vol_week.pivot(index=['year', 'week'], columns='stock')\n",
    "vol_week.columns.droplevel(0)\n",
    "vol_week.columns = vol_week.columns.droplevel(0)\n",
    "vol_week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Find all the volume traded in the year of 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>stock</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>13064316775</td>\n",
       "      <td>1105545521</td>\n",
       "      <td>9057582311</td>\n",
       "      <td>1086708380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stock         AAPL         IBM        MSFT        TSLA\n",
       "year                                                  \n",
       "2015   13064316775  1105545521  9057582311  1086708380"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol_year = vol_week.groupby('year').sum()\n",
    "vol_year.loc[2015:2015]"
   ]
  }
 ],
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================================================
FILE: 09_Time_Series/Getting_Financial_Data/Exercises_with_solutions_and_code.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Financial Data - Pandas Datareader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will get data from a website.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# package to extract data from various Internet sources into a DataFrame\n",
    "# make sure you have it installed\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "# package for dates\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 1, 0, 0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = dt.datetime(2015, 1, 1)\n",
    "end = dt.datetime.today()\n",
    "\n",
    "start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Get an API key for one of the APIs that are supported by Pandas Datareader, preferably for AlphaVantage.\n",
    "\n",
    "If you do not have an API key for any of the supported APIs, it is easiest to get one for [AlphaVantage](https://www.alphavantage.co/support/#api-key). (Note that the API key is shown directly after the signup. You do *not* receive it via e-mail.)\n",
    "\n",
    "(For a full list of the APIs that are supported by Pandas Datareader, [see here](https://pydata.github.io/pandas-datareader/readers/index.html). As the APIs are provided by third parties, this list may change.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Use Pandas Datarader to read the daily time series for the Apple stock (ticker symbol AAPL) between 01/01/2015 and today, assign it to df_apple and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.3900</td>\n",
       "      <td>111.4400</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.2900</td>\n",
       "      <td>108.6500</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.5400</td>\n",
       "      <td>107.4300</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.2000</td>\n",
       "      <td>108.2000</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.2300</td>\n",
       "      <td>112.1500</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>514.7900</td>\n",
       "      <td>515.1400</td>\n",
       "      <td>495.745</td>\n",
       "      <td>503.43</td>\n",
       "      <td>86484442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>498.7900</td>\n",
       "      <td>500.7172</td>\n",
       "      <td>492.210</td>\n",
       "      <td>499.30</td>\n",
       "      <td>52873947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>504.7165</td>\n",
       "      <td>507.9700</td>\n",
       "      <td>500.330</td>\n",
       "      <td>506.09</td>\n",
       "      <td>40755567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>508.5700</td>\n",
       "      <td>509.9400</td>\n",
       "      <td>495.330</td>\n",
       "      <td>500.04</td>\n",
       "      <td>38888096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>504.0500</td>\n",
       "      <td>505.7700</td>\n",
       "      <td>498.310</td>\n",
       "      <td>499.23</td>\n",
       "      <td>46907479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1425 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high      low   close    volume\n",
       "2015-01-02  111.3900  111.4400  107.350  109.33  53204626\n",
       "2015-01-05  108.2900  108.6500  105.410  106.25  64285491\n",
       "2015-01-06  106.5400  107.4300  104.630  106.26  65797116\n",
       "2015-01-07  107.2000  108.2000  106.695  107.75  40105934\n",
       "2015-01-08  109.2300  112.1500  108.700  111.89  59364547\n",
       "...              ...       ...      ...     ...       ...\n",
       "2020-08-24  514.7900  515.1400  495.745  503.43  86484442\n",
       "2020-08-25  498.7900  500.7172  492.210  499.30  52873947\n",
       "2020-08-26  504.7165  507.9700  500.330  506.09  40755567\n",
       "2020-08-27  508.5700  509.9400  495.330  500.04  38888096\n",
       "2020-08-28  504.0500  505.7700  498.310  499.23  46907479\n",
       "\n",
       "[1425 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apple = web.DataReader(name=\"AAPL\",\n",
    "                          data_source=\"av-daily\",\n",
    "                          start=start,\n",
    "                          end=end,\n",
    "                          api_key=\"your_alpha_vantage_api_key_goes_here\")\n",
    "\n",
    "df_apple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Add a new column \"stock\" to the dataframe and add the ticker symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.3900</td>\n",
       "      <td>111.4400</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.2900</td>\n",
       "      <td>108.6500</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.5400</td>\n",
       "      <td>107.4300</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.2000</td>\n",
       "      <td>108.2000</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.2300</td>\n",
       "      <td>112.1500</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>514.7900</td>\n",
       "      <td>515.1400</td>\n",
       "      <td>495.745</td>\n",
       "      <td>503.43</td>\n",
       "      <td>86484442</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>498.7900</td>\n",
       "      <td>500.7172</td>\n",
       "      <td>492.210</td>\n",
       "      <td>499.30</td>\n",
       "      <td>52873947</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>504.7165</td>\n",
       "      <td>507.9700</td>\n",
       "      <td>500.330</td>\n",
       "      <td>506.09</td>\n",
       "      <td>40755567</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>508.5700</td>\n",
       "      <td>509.9400</td>\n",
       "      <td>495.330</td>\n",
       "      <td>500.04</td>\n",
       "      <td>38888096</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>504.0500</td>\n",
       "      <td>505.7700</td>\n",
       "      <td>498.310</td>\n",
       "      <td>499.23</td>\n",
       "      <td>46907479</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1425 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high      low   close    volume stock\n",
       "2015-01-02  111.3900  111.4400  107.350  109.33  53204626  AAPL\n",
       "2015-01-05  108.2900  108.6500  105.410  106.25  64285491  AAPL\n",
       "2015-01-06  106.5400  107.4300  104.630  106.26  65797116  AAPL\n",
       "2015-01-07  107.2000  108.2000  106.695  107.75  40105934  AAPL\n",
       "2015-01-08  109.2300  112.1500  108.700  111.89  59364547  AAPL\n",
       "...              ...       ...      ...     ...       ...   ...\n",
       "2020-08-24  514.7900  515.1400  495.745  503.43  86484442  AAPL\n",
       "2020-08-25  498.7900  500.7172  492.210  499.30  52873947  AAPL\n",
       "2020-08-26  504.7165  507.9700  500.330  506.09  40755567  AAPL\n",
       "2020-08-27  508.5700  509.9400  495.330  500.04  38888096  AAPL\n",
       "2020-08-28  504.0500  505.7700  498.310  499.23  46907479  AAPL\n",
       "\n",
       "[1425 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apple[\"stock\"] = \"AAPL\"\n",
    "df_apple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Repeat the two previous steps for a few other stocks, always creating a new dataframe: Tesla, IBM and Microsoft. (Ticker symbols TSLA, IBM and MSFT.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tesla\n",
    "df_tesla = web.DataReader(name=\"TSLA\",\n",
    "                          data_source=\"av-daily\",\n",
    "                          start=start,\n",
    "                          end=end,\n",
    "                          api_key=\"your_alpha_vantage_api_key_goes_here\")\n",
    "\n",
    "df_tesla[\"stock\"] = \"TSLA\"\n",
    "\n",
    "# IBM\n",
    "df_ibm = web.DataReader(name=\"IBM\",\n",
    "                        data_source=\"av-daily\",\n",
    "                        start=start,\n",
    "                        end=end,\n",
    "                        api_key=\"your_alpha_vantage_api_key_goes_here\")\n",
    "\n",
    "df_ibm[\"stock\"] = \"IBM\"\n",
    "\n",
    "# Microsoft\n",
    "df_microsoft = web.DataReader(name=\"MSFT\",\n",
    "                              data_source=\"av-daily\",\n",
    "                              start=start,\n",
    "                              end=end,\n",
    "                              api_key=\"your_alpha_vantage_api_key_goes_here\")\n",
    "\n",
    "df_microsoft[\"stock\"] = \"MSFT\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Combine the four separate dataFrames into one combined dataFrame df that holds the information for all four stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.39</td>\n",
       "      <td>111.440</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.29</td>\n",
       "      <td>108.650</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.54</td>\n",
       "      <td>107.430</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.20</td>\n",
       "      <td>108.200</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.23</td>\n",
       "      <td>112.150</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>214.79</td>\n",
       "      <td>215.520</td>\n",
       "      <td>212.430</td>\n",
       "      <td>213.69</td>\n",
       "      <td>25460147</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>213.10</td>\n",
       "      <td>216.610</td>\n",
       "      <td>213.100</td>\n",
       "      <td>216.47</td>\n",
       "      <td>23043696</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>217.88</td>\n",
       "      <td>222.090</td>\n",
       "      <td>217.360</td>\n",
       "      <td>221.15</td>\n",
       "      <td>39600828</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>222.89</td>\n",
       "      <td>231.150</td>\n",
       "      <td>219.400</td>\n",
       "      <td>226.58</td>\n",
       "      <td>57602195</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>228.18</td>\n",
       "      <td>230.644</td>\n",
       "      <td>226.580</td>\n",
       "      <td>228.91</td>\n",
       "      <td>26292896</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5700 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              open     high      low   close    volume stock\n",
       "2015-01-02  111.39  111.440  107.350  109.33  53204626  AAPL\n",
       "2015-01-05  108.29  108.650  105.410  106.25  64285491  AAPL\n",
       "2015-01-06  106.54  107.430  104.630  106.26  65797116  AAPL\n",
       "2015-01-07  107.20  108.200  106.695  107.75  40105934  AAPL\n",
       "2015-01-08  109.23  112.150  108.700  111.89  59364547  AAPL\n",
       "...            ...      ...      ...     ...       ...   ...\n",
       "2020-08-24  214.79  215.520  212.430  213.69  25460147  MSFT\n",
       "2020-08-25  213.10  216.610  213.100  216.47  23043696  MSFT\n",
       "2020-08-26  217.88  222.090  217.360  221.15  39600828  MSFT\n",
       "2020-08-27  222.89  231.150  219.400  226.58  57602195  MSFT\n",
       "2020-08-28  228.18  230.644  226.580  228.91  26292896  MSFT\n",
       "\n",
       "[5700 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [df_apple, df_tesla, df_ibm, df_microsoft]\n",
    "\n",
    "df = pd.concat(frames)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Shift the stock column into the index (making it a multi-level index consisting of the ticker symbol and the date)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>111.39</td>\n",
       "      <td>111.440</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>108.29</td>\n",
       "      <td>108.650</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>106.54</td>\n",
       "      <td>107.430</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>107.20</td>\n",
       "      <td>108.200</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>109.23</td>\n",
       "      <td>112.150</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>214.79</td>\n",
       "      <td>215.520</td>\n",
       "      <td>212.430</td>\n",
       "      <td>213.69</td>\n",
       "      <td>25460147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>213.10</td>\n",
       "      <td>216.610</td>\n",
       "      <td>213.100</td>\n",
       "      <td>216.47</td>\n",
       "      <td>23043696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>217.88</td>\n",
       "      <td>222.090</td>\n",
       "      <td>217.360</td>\n",
       "      <td>221.15</td>\n",
       "      <td>39600828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>222.89</td>\n",
       "      <td>231.150</td>\n",
       "      <td>219.400</td>\n",
       "      <td>226.58</td>\n",
       "      <td>57602195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>228.18</td>\n",
       "      <td>230.644</td>\n",
       "      <td>226.580</td>\n",
       "      <td>228.91</td>\n",
       "      <td>26292896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5700 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    open     high      low   close    volume\n",
       "           stock                                            \n",
       "2015-01-02 AAPL   111.39  111.440  107.350  109.33  53204626\n",
       "2015-01-05 AAPL   108.29  108.650  105.410  106.25  64285491\n",
       "2015-01-06 AAPL   106.54  107.430  104.630  106.26  65797116\n",
       "2015-01-07 AAPL   107.20  108.200  106.695  107.75  40105934\n",
       "2015-01-08 AAPL   109.23  112.150  108.700  111.89  59364547\n",
       "...                  ...      ...      ...     ...       ...\n",
       "2020-08-24 MSFT   214.79  215.520  212.430  213.69  25460147\n",
       "2020-08-25 MSFT   213.10  216.610  213.100  216.47  23043696\n",
       "2020-08-26 MSFT   217.88  222.090  217.360  221.15  39600828\n",
       "2020-08-27 MSFT   222.89  231.150  219.400  226.58  57602195\n",
       "2020-08-28 MSFT   228.18  230.644  226.580  228.91  26292896\n",
       "\n",
       "[5700 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(keys=\"stock\", append=True, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Create a dataFrame called vol, with the volume values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>25460147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>23043696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>39600828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>57602195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>26292896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5700 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    volume\n",
       "           stock          \n",
       "2015-01-02 AAPL   53204626\n",
       "2015-01-05 AAPL   64285491\n",
       "2015-01-06 AAPL   65797116\n",
       "2015-01-07 AAPL   40105934\n",
       "2015-01-08 AAPL   59364547\n",
       "...                    ...\n",
       "2020-08-24 MSFT   25460147\n",
       "2020-08-25 MSFT   23043696\n",
       "2020-08-26 MSFT   39600828\n",
       "2020-08-27 MSFT   57602195\n",
       "2020-08-28 MSFT   26292896\n",
       "\n",
       "[5700 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol = df['volume']\n",
    "vol = pd.DataFrame(vol)\n",
    "vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Aggregate the data of volume to weekly.\n",
    "Hint: Be careful to not sum data from the same week of 2015 and other years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2015</th>\n",
       "      <th>1</th>\n",
       "      <td>53204626</td>\n",
       "      <td>5525341</td>\n",
       "      <td>27913852</td>\n",
       "      <td>4764443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>282868187</td>\n",
       "      <td>24440360</td>\n",
       "      <td>158596624</td>\n",
       "      <td>22622034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304226647</td>\n",
       "      <td>23272056</td>\n",
       "      <td>157088136</td>\n",
       "      <td>30799137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198737041</td>\n",
       "      <td>31230797</td>\n",
       "      <td>137352632</td>\n",
       "      <td>16215501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>465842684</td>\n",
       "      <td>32927307</td>\n",
       "      <td>437786778</td>\n",
       "      <td>15720217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020</th>\n",
       "      <th>31</th>\n",
       "      <td>211898609</td>\n",
       "      <td>20010578</td>\n",
       "      <td>149372422</td>\n",
       "      <td>61152261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>250852555</td>\n",
       "      <td>17701697</td>\n",
       "      <td>217598950</td>\n",
       "      <td>37091084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>235474473</td>\n",
       "      <td>18634659</td>\n",
       "      <td>141752091</td>\n",
       "      <td>71049854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>208464758</td>\n",
       "      <td>15908978</td>\n",
       "      <td>132258021</td>\n",
       "      <td>90804281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>265909531</td>\n",
       "      <td>16959794</td>\n",
       "      <td>171999762</td>\n",
       "      <td>88736115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>296 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "stock           AAPL       IBM       MSFT      TSLA\n",
       "year week                                          \n",
       "2015 1      53204626   5525341   27913852   4764443\n",
       "     2     282868187  24440360  158596624  22622034\n",
       "     3     304226647  23272056  157088136  30799137\n",
       "     4     198737041  31230797  137352632  16215501\n",
       "     5     465842684  32927307  437786778  15720217\n",
       "...              ...       ...        ...       ...\n",
       "2020 31    211898609  20010578  149372422  61152261\n",
       "     32    250852555  17701697  217598950  37091084\n",
       "     33    235474473  18634659  141752091  71049854\n",
       "     34    208464758  15908978  132258021  90804281\n",
       "     35    265909531  16959794  171999762  88736115\n",
       "\n",
       "[296 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = vol.index.get_level_values(0)\n",
    "date = pd.DatetimeIndex(date) # ensure that it's a datetimeindex, instead of a regular index\n",
    "\n",
    "vol['week'] = date.isocalendar().week.values\n",
    "# .values is necessary to obtain only the week *values*\n",
    "# otherwise pandas interprets it as a part of an index; this would be a problem as the same week appears multiple times\n",
    "# (same week number in different years, same week for different stocks)\n",
    "\n",
    "vol['year'] = date.year\n",
    "\n",
    "pd.pivot_table(vol, values='volume', index=['year', 'week'],\n",
    "               columns=['stock'], aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Find all the volume traded in the year of 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>stock</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>13064316775</td>\n",
       "      <td>1105545521</td>\n",
       "      <td>9057582311</td>\n",
       "      <td>1086708380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stock         AAPL         IBM        MSFT        TSLA\n",
       "year                                                  \n",
       "2015   13064316775  1105545521  9057582311  1086708380"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol_2015 = vol[vol['year'] == 2015]\n",
    "\n",
    "pd.pivot_table(vol_2015, values='volume', index=['year'],\n",
    "               columns=['stock'], aggfunc=np.sum)"
   ]
  }
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================================================
FILE: 09_Time_Series/Getting_Financial_Data/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Financial Data - Pandas Datareader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will get data from a website.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# package to extract data from various Internet sources into a DataFrame\n",
    "# make sure you have it installed\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "# package for dates\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 1, 0, 0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Get an API key for one of the APIs that are supported by Pandas Datareader, preferably for AlphaVantage.\n",
    "\n",
    "If you do not have an API key for any of the supported APIs, it is easiest to get one for [AlphaVantage](https://www.alphavantage.co/support/#api-key). (Note that the API key is shown directly after the signup. You do *not* receive it via e-mail.)\n",
    "\n",
    "(For a full list of the APIs that are supported by Pandas Datareader, [see here](https://pydata.github.io/pandas-datareader/readers/index.html). As the APIs are provided by third parties, this list may change.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Use Pandas Datarader to read the daily time series for the Apple stock (ticker symbol AAPL) between 01/01/2015 and today, assign it to df_apple and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.3900</td>\n",
       "      <td>111.4400</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.2900</td>\n",
       "      <td>108.6500</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.5400</td>\n",
       "      <td>107.4300</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.2000</td>\n",
       "      <td>108.2000</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.2300</td>\n",
       "      <td>112.1500</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>514.7900</td>\n",
       "      <td>515.1400</td>\n",
       "      <td>495.745</td>\n",
       "      <td>503.43</td>\n",
       "      <td>86484442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>498.7900</td>\n",
       "      <td>500.7172</td>\n",
       "      <td>492.210</td>\n",
       "      <td>499.30</td>\n",
       "      <td>52873947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>504.7165</td>\n",
       "      <td>507.9700</td>\n",
       "      <td>500.330</td>\n",
       "      <td>506.09</td>\n",
       "      <td>40755567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>508.5700</td>\n",
       "      <td>509.9400</td>\n",
       "      <td>495.330</td>\n",
       "      <td>500.04</td>\n",
       "      <td>38888096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>504.0500</td>\n",
       "      <td>505.7700</td>\n",
       "      <td>498.310</td>\n",
       "      <td>499.23</td>\n",
       "      <td>46907479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1425 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high      low   close    volume\n",
       "2015-01-02  111.3900  111.4400  107.350  109.33  53204626\n",
       "2015-01-05  108.2900  108.6500  105.410  106.25  64285491\n",
       "2015-01-06  106.5400  107.4300  104.630  106.26  65797116\n",
       "2015-01-07  107.2000  108.2000  106.695  107.75  40105934\n",
       "2015-01-08  109.2300  112.1500  108.700  111.89  59364547\n",
       "...              ...       ...      ...     ...       ...\n",
       "2020-08-24  514.7900  515.1400  495.745  503.43  86484442\n",
       "2020-08-25  498.7900  500.7172  492.210  499.30  52873947\n",
       "2020-08-26  504.7165  507.9700  500.330  506.09  40755567\n",
       "2020-08-27  508.5700  509.9400  495.330  500.04  38888096\n",
       "2020-08-28  504.0500  505.7700  498.310  499.23  46907479\n",
       "\n",
       "[1425 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Add a new column \"stock\" to the dataframe and add the ticker symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.3900</td>\n",
       "      <td>111.4400</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.2900</td>\n",
       "      <td>108.6500</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.5400</td>\n",
       "      <td>107.4300</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.2000</td>\n",
       "      <td>108.2000</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.2300</td>\n",
       "      <td>112.1500</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>514.7900</td>\n",
       "      <td>515.1400</td>\n",
       "      <td>495.745</td>\n",
       "      <td>503.43</td>\n",
       "      <td>86484442</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>498.7900</td>\n",
       "      <td>500.7172</td>\n",
       "      <td>492.210</td>\n",
       "      <td>499.30</td>\n",
       "      <td>52873947</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>504.7165</td>\n",
       "      <td>507.9700</td>\n",
       "      <td>500.330</td>\n",
       "      <td>506.09</td>\n",
       "      <td>40755567</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>508.5700</td>\n",
       "      <td>509.9400</td>\n",
       "      <td>495.330</td>\n",
       "      <td>500.04</td>\n",
       "      <td>38888096</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>504.0500</td>\n",
       "      <td>505.7700</td>\n",
       "      <td>498.310</td>\n",
       "      <td>499.23</td>\n",
       "      <td>46907479</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1425 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high      low   close    volume stock\n",
       "2015-01-02  111.3900  111.4400  107.350  109.33  53204626  AAPL\n",
       "2015-01-05  108.2900  108.6500  105.410  106.25  64285491  AAPL\n",
       "2015-01-06  106.5400  107.4300  104.630  106.26  65797116  AAPL\n",
       "2015-01-07  107.2000  108.2000  106.695  107.75  40105934  AAPL\n",
       "2015-01-08  109.2300  112.1500  108.700  111.89  59364547  AAPL\n",
       "...              ...       ...      ...     ...       ...   ...\n",
       "2020-08-24  514.7900  515.1400  495.745  503.43  86484442  AAPL\n",
       "2020-08-25  498.7900  500.7172  492.210  499.30  52873947  AAPL\n",
       "2020-08-26  504.7165  507.9700  500.330  506.09  40755567  AAPL\n",
       "2020-08-27  508.5700  509.9400  495.330  500.04  38888096  AAPL\n",
       "2020-08-28  504.0500  505.7700  498.310  499.23  46907479  AAPL\n",
       "\n",
       "[1425 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Repeat the two previous steps for a few other stocks, always creating a new dataframe: Tesla, IBM and Microsoft. (Ticker symbols TSLA, IBM and MSFT.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Combine the four separate dataFrames into one combined dataFrame df that holds the information for all four stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>111.39</td>\n",
       "      <td>111.440</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>108.29</td>\n",
       "      <td>108.650</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>106.54</td>\n",
       "      <td>107.430</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>107.20</td>\n",
       "      <td>108.200</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>109.23</td>\n",
       "      <td>112.150</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>214.79</td>\n",
       "      <td>215.520</td>\n",
       "      <td>212.430</td>\n",
       "      <td>213.69</td>\n",
       "      <td>25460147</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <td>213.10</td>\n",
       "      <td>216.610</td>\n",
       "      <td>213.100</td>\n",
       "      <td>216.47</td>\n",
       "      <td>23043696</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <td>217.88</td>\n",
       "      <td>222.090</td>\n",
       "      <td>217.360</td>\n",
       "      <td>221.15</td>\n",
       "      <td>39600828</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <td>222.89</td>\n",
       "      <td>231.150</td>\n",
       "      <td>219.400</td>\n",
       "      <td>226.58</td>\n",
       "      <td>57602195</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <td>228.18</td>\n",
       "      <td>230.644</td>\n",
       "      <td>226.580</td>\n",
       "      <td>228.91</td>\n",
       "      <td>26292896</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5700 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              open     high      low   close    volume stock\n",
       "2015-01-02  111.39  111.440  107.350  109.33  53204626  AAPL\n",
       "2015-01-05  108.29  108.650  105.410  106.25  64285491  AAPL\n",
       "2015-01-06  106.54  107.430  104.630  106.26  65797116  AAPL\n",
       "2015-01-07  107.20  108.200  106.695  107.75  40105934  AAPL\n",
       "2015-01-08  109.23  112.150  108.700  111.89  59364547  AAPL\n",
       "...            ...      ...      ...     ...       ...   ...\n",
       "2020-08-24  214.79  215.520  212.430  213.69  25460147  MSFT\n",
       "2020-08-25  213.10  216.610  213.100  216.47  23043696  MSFT\n",
       "2020-08-26  217.88  222.090  217.360  221.15  39600828  MSFT\n",
       "2020-08-27  222.89  231.150  219.400  226.58  57602195  MSFT\n",
       "2020-08-28  228.18  230.644  226.580  228.91  26292896  MSFT\n",
       "\n",
       "[5700 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Shift the stock column into the index (making it a multi-level index consisting of the ticker symbol and the date)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>111.39</td>\n",
       "      <td>111.440</td>\n",
       "      <td>107.350</td>\n",
       "      <td>109.33</td>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>108.29</td>\n",
       "      <td>108.650</td>\n",
       "      <td>105.410</td>\n",
       "      <td>106.25</td>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>106.54</td>\n",
       "      <td>107.430</td>\n",
       "      <td>104.630</td>\n",
       "      <td>106.26</td>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>107.20</td>\n",
       "      <td>108.200</td>\n",
       "      <td>106.695</td>\n",
       "      <td>107.75</td>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>109.23</td>\n",
       "      <td>112.150</td>\n",
       "      <td>108.700</td>\n",
       "      <td>111.89</td>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>214.79</td>\n",
       "      <td>215.520</td>\n",
       "      <td>212.430</td>\n",
       "      <td>213.69</td>\n",
       "      <td>25460147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>213.10</td>\n",
       "      <td>216.610</td>\n",
       "      <td>213.100</td>\n",
       "      <td>216.47</td>\n",
       "      <td>23043696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>217.88</td>\n",
       "      <td>222.090</td>\n",
       "      <td>217.360</td>\n",
       "      <td>221.15</td>\n",
       "      <td>39600828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>222.89</td>\n",
       "      <td>231.150</td>\n",
       "      <td>219.400</td>\n",
       "      <td>226.58</td>\n",
       "      <td>57602195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>228.18</td>\n",
       "      <td>230.644</td>\n",
       "      <td>226.580</td>\n",
       "      <td>228.91</td>\n",
       "      <td>26292896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5700 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    open     high      low   close    volume\n",
       "           stock                                            \n",
       "2015-01-02 AAPL   111.39  111.440  107.350  109.33  53204626\n",
       "2015-01-05 AAPL   108.29  108.650  105.410  106.25  64285491\n",
       "2015-01-06 AAPL   106.54  107.430  104.630  106.26  65797116\n",
       "2015-01-07 AAPL   107.20  108.200  106.695  107.75  40105934\n",
       "2015-01-08 AAPL   109.23  112.150  108.700  111.89  59364547\n",
       "...                  ...      ...      ...     ...       ...\n",
       "2020-08-24 MSFT   214.79  215.520  212.430  213.69  25460147\n",
       "2020-08-25 MSFT   213.10  216.610  213.100  216.47  23043696\n",
       "2020-08-26 MSFT   217.88  222.090  217.360  221.15  39600828\n",
       "2020-08-27 MSFT   222.89  231.150  219.400  226.58  57602195\n",
       "2020-08-28 MSFT   228.18  230.644  226.580  228.91  26292896\n",
       "\n",
       "[5700 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Create a dataFrame called vol, with the volume values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>53204626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>64285491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>65797116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>40105934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>59364547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>25460147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-25</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>23043696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-26</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>39600828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-27</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>57602195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-28</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>26292896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5700 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    volume\n",
       "           stock          \n",
       "2015-01-02 AAPL   53204626\n",
       "2015-01-05 AAPL   64285491\n",
       "2015-01-06 AAPL   65797116\n",
       "2015-01-07 AAPL   40105934\n",
       "2015-01-08 AAPL   59364547\n",
       "...                    ...\n",
       "2020-08-24 MSFT   25460147\n",
       "2020-08-25 MSFT   23043696\n",
       "2020-08-26 MSFT   39600828\n",
       "2020-08-27 MSFT   57602195\n",
       "2020-08-28 MSFT   26292896\n",
       "\n",
       "[5700 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Aggregate the data of volume to weekly.\n",
    "Hint: Be careful to not sum data from the same week of 2015 and other years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stock</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2015</th>\n",
       "      <th>1</th>\n",
       "      <td>53204626</td>\n",
       "      <td>5525341</td>\n",
       "      <td>27913852</td>\n",
       "      <td>4764443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>282868187</td>\n",
       "      <td>24440360</td>\n",
       "      <td>158596624</td>\n",
       "      <td>22622034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304226647</td>\n",
       "      <td>23272056</td>\n",
       "      <td>157088136</td>\n",
       "      <td>30799137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198737041</td>\n",
       "      <td>31230797</td>\n",
       "      <td>137352632</td>\n",
       "      <td>16215501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>465842684</td>\n",
       "      <td>32927307</td>\n",
       "      <td>437786778</td>\n",
       "      <td>15720217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020</th>\n",
       "      <th>31</th>\n",
       "      <td>211898609</td>\n",
       "      <td>20010578</td>\n",
       "      <td>149372422</td>\n",
       "      <td>61152261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>250852555</td>\n",
       "      <td>17701697</td>\n",
       "      <td>217598950</td>\n",
       "      <td>37091084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>235474473</td>\n",
       "      <td>18634659</td>\n",
       "      <td>141752091</td>\n",
       "      <td>71049854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>208464758</td>\n",
       "      <td>15908978</td>\n",
       "      <td>132258021</td>\n",
       "      <td>90804281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>265909531</td>\n",
       "      <td>16959794</td>\n",
       "      <td>171999762</td>\n",
       "      <td>88736115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>296 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "stock           AAPL       IBM       MSFT      TSLA\n",
       "year week                                          \n",
       "2015 1      53204626   5525341   27913852   4764443\n",
       "     2     282868187  24440360  158596624  22622034\n",
       "     3     304226647  23272056  157088136  30799137\n",
       "     4     198737041  31230797  137352632  16215501\n",
       "     5     465842684  32927307  437786778  15720217\n",
       "...              ...       ...        ...       ...\n",
       "2020 31    211898609  20010578  149372422  61152261\n",
       "     32    250852555  17701697  217598950  37091084\n",
       "     33    235474473  18634659  141752091  71049854\n",
       "     34    208464758  15908978  132258021  90804281\n",
       "     35    265909531  16959794  171999762  88736115\n",
       "\n",
       "[296 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Find all the volume traded in the year of 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>stock</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>13064316775</td>\n",
       "      <td>1105545521</td>\n",
       "      <td>9057582311</td>\n",
       "      <td>1086708380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stock         AAPL         IBM        MSFT        TSLA\n",
       "year                                                  \n",
       "2015   13064316775  1105545521  9057582311  1086708380"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
 ],
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================================================
FILE: 09_Time_Series/Investor_Flow_of_Funds_US/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investor - Flow of Funds - US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: https://github.com/rgrp for sharing the dataset.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.  What is the frequency of the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Set the column Date as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the type of the index?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Set the index to a DatetimeIndex type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  Change the frequency to monthly, sum the values and assign it to monthly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. You will notice that it filled the dataFrame with months that don't have any data with NaN. Let's drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Good, now we have the monthly data. Now change the frequency to year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: 09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investor - Flow of Funds - US\n",
    "\n",
    "Check out [Investor Flow of Funds Exercises Video Tutorial](https://youtu.be/QG6WbOgC9QE) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: https://github.com/rgrp for sharing the dataset.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-12-05</td>\n",
       "      <td>-7426</td>\n",
       "      <td>-6060</td>\n",
       "      <td>-1367</td>\n",
       "      <td>-74</td>\n",
       "      <td>5317</td>\n",
       "      <td>4210</td>\n",
       "      <td>1107</td>\n",
       "      <td>-2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-12-12</td>\n",
       "      <td>-8783</td>\n",
       "      <td>-7520</td>\n",
       "      <td>-1263</td>\n",
       "      <td>123</td>\n",
       "      <td>1818</td>\n",
       "      <td>1598</td>\n",
       "      <td>219</td>\n",
       "      <td>-6842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-12-19</td>\n",
       "      <td>-5496</td>\n",
       "      <td>-5470</td>\n",
       "      <td>-26</td>\n",
       "      <td>-73</td>\n",
       "      <td>103</td>\n",
       "      <td>3472</td>\n",
       "      <td>-3369</td>\n",
       "      <td>-5466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-26</td>\n",
       "      <td>-4451</td>\n",
       "      <td>-4076</td>\n",
       "      <td>-375</td>\n",
       "      <td>550</td>\n",
       "      <td>2610</td>\n",
       "      <td>3333</td>\n",
       "      <td>-722</td>\n",
       "      <td>-1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>-11156</td>\n",
       "      <td>-9622</td>\n",
       "      <td>-1533</td>\n",
       "      <td>-158</td>\n",
       "      <td>2383</td>\n",
       "      <td>2103</td>\n",
       "      <td>280</td>\n",
       "      <td>-8931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Total Equity  Domestic Equity  World Equity  Hybrid  \\\n",
       "0  2012-12-05         -7426            -6060         -1367     -74   \n",
       "1  2012-12-12         -8783            -7520         -1263     123   \n",
       "2  2012-12-19         -5496            -5470           -26     -73   \n",
       "3  2012-12-26         -4451            -4076          -375     550   \n",
       "4  2013-01-02        -11156            -9622         -1533    -158   \n",
       "\n",
       "   Total Bond  Taxable Bond  Municipal Bond  Total  \n",
       "0        5317          4210            1107  -2183  \n",
       "1        1818          1598             219  -6842  \n",
       "2         103          3472           -3369  -5466  \n",
       "3        2610          3333            -722  -1291  \n",
       "4        2383          2103             280  -8931  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.  What is the frequency of the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weekly data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Set the column Date as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-05</th>\n",
       "      <td>-7426</td>\n",
       "      <td>-6060</td>\n",
       "      <td>-1367</td>\n",
       "      <td>-74</td>\n",
       "      <td>5317</td>\n",
       "      <td>4210</td>\n",
       "      <td>1107</td>\n",
       "      <td>-2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-12</th>\n",
       "      <td>-8783</td>\n",
       "      <td>-7520</td>\n",
       "      <td>-1263</td>\n",
       "      <td>123</td>\n",
       "      <td>1818</td>\n",
       "      <td>1598</td>\n",
       "      <td>219</td>\n",
       "      <td>-6842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19</th>\n",
       "      <td>-5496</td>\n",
       "      <td>-5470</td>\n",
       "      <td>-26</td>\n",
       "      <td>-73</td>\n",
       "      <td>103</td>\n",
       "      <td>3472</td>\n",
       "      <td>-3369</td>\n",
       "      <td>-5466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-26</th>\n",
       "      <td>-4451</td>\n",
       "      <td>-4076</td>\n",
       "      <td>-375</td>\n",
       "      <td>550</td>\n",
       "      <td>2610</td>\n",
       "      <td>3333</td>\n",
       "      <td>-722</td>\n",
       "      <td>-1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-11156</td>\n",
       "      <td>-9622</td>\n",
       "      <td>-1533</td>\n",
       "      <td>-158</td>\n",
       "      <td>2383</td>\n",
       "      <td>2103</td>\n",
       "      <td>280</td>\n",
       "      <td>-8931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity  Hybrid  Total Bond  \\\n",
       "Date                                                                          \n",
       "2012-12-05         -7426            -6060         -1367     -74        5317   \n",
       "2012-12-12         -8783            -7520         -1263     123        1818   \n",
       "2012-12-19         -5496            -5470           -26     -73         103   \n",
       "2012-12-26         -4451            -4076          -375     550        2610   \n",
       "2013-01-02        -11156            -9622         -1533    -158        2383   \n",
       "\n",
       "            Taxable Bond  Municipal Bond  Total  \n",
       "Date                                             \n",
       "2012-12-05          4210            1107  -2183  \n",
       "2012-12-12          1598             219  -6842  \n",
       "2012-12-19          3472           -3369  -5466  \n",
       "2012-12-26          3333            -722  -1291  \n",
       "2013-01-02          2103             280  -8931  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the type of the index?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'2012-12-05', u'2012-12-12', u'2012-12-19', u'2012-12-26',\n",
       "       u'2013-01-02', u'2013-01-09', u'2014-04-02', u'2014-04-09',\n",
       "       u'2014-04-16', u'2014-04-23', u'2014-04-30', u'2014-05-07',\n",
       "       u'2014-05-14', u'2014-05-21', u'2014-05-28', u'2014-06-04',\n",
       "       u'2014-06-11', u'2014-06-18', u'2014-06-25', u'2014-07-02',\n",
       "       u'2014-07-09', u'2014-07-30', u'2014-08-06', u'2014-08-13',\n",
       "       u'2014-08-20', u'2014-08-27', u'2014-09-03', u'2014-09-10',\n",
       "       u'2014-11-05', u'2014-11-12', u'2014-11-19', u'2014-11-25',\n",
       "       u'2015-01-07', u'2015-01-14', u'2015-01-21', u'2015-01-28',\n",
       "       u'2015-02-04', u'2015-02-11', u'2015-03-04', u'2015-03-11',\n",
       "       u'2015-03-18', u'2015-03-25', u'2015-04-01', u'2015-04-08'],\n",
       "      dtype='object', name=u'Date')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index\n",
    "# it is a 'object' type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Set the index to a DatetimeIndex type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tseries.index.DatetimeIndex"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df.index)\n",
    "type(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  Change the frequency to monthly, sum the values and assign it to monthly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>-26156.0</td>\n",
       "      <td>-23126.0</td>\n",
       "      <td>-3031.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>9848.0</td>\n",
       "      <td>12613.0</td>\n",
       "      <td>-2765.0</td>\n",
       "      <td>-15782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>3661.0</td>\n",
       "      <td>-1627.0</td>\n",
       "      <td>5288.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>12149.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>18540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>10842.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>9794.0</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>8493.0</td>\n",
       "      <td>7193.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>24267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>-2203.0</td>\n",
       "      <td>-8720.0</td>\n",
       "      <td>6518.0</td>\n",
       "      <td>3172.0</td>\n",
       "      <td>13767.0</td>\n",
       "      <td>10192.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>14736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>2319.0</td>\n",
       "      <td>-6546.0</td>\n",
       "      <td>8865.0</td>\n",
       "      <td>4588.0</td>\n",
       "      <td>9715.0</td>\n",
       "      <td>7551.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>16621.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>-7051.0</td>\n",
       "      <td>-11128.0</td>\n",
       "      <td>4078.0</td>\n",
       "      <td>2666.0</td>\n",
       "      <td>7506.0</td>\n",
       "      <td>7026.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>3122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>-5508.0</td>\n",
       "      <td>7452.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>-1013.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>5723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>-2767.0</td>\n",
       "      <td>-6596.0</td>\n",
       "      <td>3829.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>3984.0</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>2816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-30</th>\n",
       "      <td>-2753.0</td>\n",
       "      <td>-7239.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>14528.0</td>\n",
       "      <td>11566.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>12502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>3471.0</td>\n",
       "      <td>-1164.0</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>7368.0</td>\n",
       "      <td>2762.0</td>\n",
       "      <td>4606.0</td>\n",
       "      <td>12569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>5508.0</td>\n",
       "      <td>3509.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>9099.0</td>\n",
       "      <td>7443.0</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>16359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>5691.0</td>\n",
       "      <td>-8176.0</td>\n",
       "      <td>13867.0</td>\n",
       "      <td>2829.0</td>\n",
       "      <td>9138.0</td>\n",
       "      <td>7267.0</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>17657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>379.0</td>\n",
       "      <td>-4628.0</td>\n",
       "      <td>5007.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>1772.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity  Hybrid  Total Bond  \\\n",
       "Date                                                                          \n",
       "2012-12-31      -26156.0         -23126.0       -3031.0   526.0      9848.0   \n",
       "2013-01-31        3661.0          -1627.0        5288.0  2730.0     12149.0   \n",
       "2013-02-28           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-03-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-04-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-05-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-06-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-07-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-08-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-09-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-10-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-11-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-12-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-01-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-02-28           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-03-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-04-30       10842.0           1048.0        9794.0  4931.0      8493.0   \n",
       "2014-05-31       -2203.0          -8720.0        6518.0  3172.0     13767.0   \n",
       "2014-06-30        2319.0          -6546.0        8865.0  4588.0      9715.0   \n",
       "2014-07-31       -7051.0         -11128.0        4078.0  2666.0      7506.0   \n",
       "2014-08-31        1943.0          -5508.0        7452.0  1885.0      1897.0   \n",
       "2014-09-30       -2767.0          -6596.0        3829.0  1599.0      3984.0   \n",
       "2014-10-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-11-30       -2753.0          -7239.0        4485.0   729.0     14528.0   \n",
       "2014-12-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2015-01-31        3471.0          -1164.0        4635.0  1729.0      7368.0   \n",
       "2015-02-28        5508.0           3509.0        1999.0  1752.0      9099.0   \n",
       "2015-03-31        5691.0          -8176.0       13867.0  2829.0      9138.0   \n",
       "2015-04-30         379.0          -4628.0        5007.0   970.0       423.0   \n",
       "\n",
       "            Taxable Bond  Municipal Bond    Total  \n",
       "Date                                               \n",
       "2012-12-31       12613.0         -2765.0 -15782.0  \n",
       "2013-01-31        9414.0          2735.0  18540.0  \n",
       "2013-02-28           NaN             NaN      NaN  \n",
       "2013-03-31           NaN             NaN      NaN  \n",
       "2013-04-30           NaN             NaN      NaN  \n",
       "2013-05-31           NaN             NaN      NaN  \n",
       "2013-06-30           NaN             NaN      NaN  \n",
       "2013-07-31           NaN             NaN      NaN  \n",
       "2013-08-31           NaN             NaN      NaN  \n",
       "2013-09-30           NaN             NaN      NaN  \n",
       "2013-10-31           NaN             NaN      NaN  \n",
       "2013-11-30           NaN             NaN      NaN  \n",
       "2013-12-31           NaN             NaN      NaN  \n",
       "2014-01-31           NaN             NaN      NaN  \n",
       "2014-02-28           NaN             NaN      NaN  \n",
       "2014-03-31           NaN             NaN      NaN  \n",
       "2014-04-30        7193.0          1300.0  24267.0  \n",
       "2014-05-31       10192.0          3576.0  14736.0  \n",
       "2014-06-30        7551.0          2163.0  16621.0  \n",
       "2014-07-31        7026.0           481.0   3122.0  \n",
       "2014-08-31       -1013.0          2910.0   5723.0  \n",
       "2014-09-30        2479.0          1504.0   2816.0  \n",
       "2014-10-31           NaN             NaN      NaN  \n",
       "2014-11-30       11566.0          2962.0  12502.0  \n",
       "2014-12-31           NaN             NaN      NaN  \n",
       "2015-01-31        2762.0          4606.0  12569.0  \n",
       "2015-02-28        7443.0          1656.0  16359.0  \n",
       "2015-03-31        7267.0          1870.0  17657.0  \n",
       "2015-04-30         514.0           -91.0   1772.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly = df.resample('M').sum()\n",
    "monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. You will notice that it filled the dataFrame with months that don't have any data with NaN. Let's drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>-26156.0</td>\n",
       "      <td>-23126.0</td>\n",
       "      <td>-3031.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>9848.0</td>\n",
       "      <td>12613.0</td>\n",
       "      <td>-2765.0</td>\n",
       "      <td>-15782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>3661.0</td>\n",
       "      <td>-1627.0</td>\n",
       "      <td>5288.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>12149.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>18540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>10842.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>9794.0</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>8493.0</td>\n",
       "      <td>7193.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>24267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>-2203.0</td>\n",
       "      <td>-8720.0</td>\n",
       "      <td>6518.0</td>\n",
       "      <td>3172.0</td>\n",
       "      <td>13767.0</td>\n",
       "      <td>10192.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>14736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>2319.0</td>\n",
       "      <td>-6546.0</td>\n",
       "      <td>8865.0</td>\n",
       "      <td>4588.0</td>\n",
       "      <td>9715.0</td>\n",
       "      <td>7551.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>16621.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>-7051.0</td>\n",
       "      <td>-11128.0</td>\n",
       "      <td>4078.0</td>\n",
       "      <td>2666.0</td>\n",
       "      <td>7506.0</td>\n",
       "      <td>7026.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>3122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>-5508.0</td>\n",
       "      <td>7452.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>-1013.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>5723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>-2767.0</td>\n",
       "      <td>-6596.0</td>\n",
       "      <td>3829.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>3984.0</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>2816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-30</th>\n",
       "      <td>-2753.0</td>\n",
       "      <td>-7239.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>14528.0</td>\n",
       "      <td>11566.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>12502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>3471.0</td>\n",
       "      <td>-1164.0</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>7368.0</td>\n",
       "      <td>2762.0</td>\n",
       "      <td>4606.0</td>\n",
       "      <td>12569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>5508.0</td>\n",
       "      <td>3509.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>9099.0</td>\n",
       "      <td>7443.0</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>16359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>5691.0</td>\n",
       "      <td>-8176.0</td>\n",
       "      <td>13867.0</td>\n",
       "      <td>2829.0</td>\n",
       "      <td>9138.0</td>\n",
       "      <td>7267.0</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>17657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>379.0</td>\n",
       "      <td>-4628.0</td>\n",
       "      <td>5007.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>1772.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity  Hybrid  Total Bond  \\\n",
       "Date                                                                          \n",
       "2012-12-31      -26156.0         -23126.0       -3031.0   526.0      9848.0   \n",
       "2013-01-31        3661.0          -1627.0        5288.0  2730.0     12149.0   \n",
       "2014-04-30       10842.0           1048.0        9794.0  4931.0      8493.0   \n",
       "2014-05-31       -2203.0          -8720.0        6518.0  3172.0     13767.0   \n",
       "2014-06-30        2319.0          -6546.0        8865.0  4588.0      9715.0   \n",
       "2014-07-31       -7051.0         -11128.0        4078.0  2666.0      7506.0   \n",
       "2014-08-31        1943.0          -5508.0        7452.0  1885.0      1897.0   \n",
       "2014-09-30       -2767.0          -6596.0        3829.0  1599.0      3984.0   \n",
       "2014-11-30       -2753.0          -7239.0        4485.0   729.0     14528.0   \n",
       "2015-01-31        3471.0          -1164.0        4635.0  1729.0      7368.0   \n",
       "2015-02-28        5508.0           3509.0        1999.0  1752.0      9099.0   \n",
       "2015-03-31        5691.0          -8176.0       13867.0  2829.0      9138.0   \n",
       "2015-04-30         379.0          -4628.0        5007.0   970.0       423.0   \n",
       "\n",
       "            Taxable Bond  Municipal Bond    Total  \n",
       "Date                                               \n",
       "2012-12-31       12613.0         -2765.0 -15782.0  \n",
       "2013-01-31        9414.0          2735.0  18540.0  \n",
       "2014-04-30        7193.0          1300.0  24267.0  \n",
       "2014-05-31       10192.0          3576.0  14736.0  \n",
       "2014-06-30        7551.0          2163.0  16621.0  \n",
       "2014-07-31        7026.0           481.0   3122.0  \n",
       "2014-08-31       -1013.0          2910.0   5723.0  \n",
       "2014-09-30        2479.0          1504.0   2816.0  \n",
       "2014-11-30       11566.0          2962.0  12502.0  \n",
       "2015-01-31        2762.0          4606.0  12569.0  \n",
       "2015-02-28        7443.0          1656.0  16359.0  \n",
       "2015-03-31        7267.0          1870.0  17657.0  \n",
       "2015-04-30         514.0           -91.0   1772.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly = monthly.dropna()\n",
    "monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Good, now we have the monthly data. Now change the frequency to year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>-26156.0</td>\n",
       "      <td>-23126.0</td>\n",
       "      <td>-3031.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>9848.0</td>\n",
       "      <td>12613.0</td>\n",
       "      <td>-2765.0</td>\n",
       "      <td>-15782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>3661.0</td>\n",
       "      <td>-1627.0</td>\n",
       "      <td>5288.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>12149.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>18540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>330.0</td>\n",
       "      <td>-44689.0</td>\n",
       "      <td>45021.0</td>\n",
       "      <td>19570.0</td>\n",
       "      <td>59890.0</td>\n",
       "      <td>44994.0</td>\n",
       "      <td>14896.0</td>\n",
       "      <td>79787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>15049.0</td>\n",
       "      <td>-10459.0</td>\n",
       "      <td>25508.0</td>\n",
       "      <td>7280.0</td>\n",
       "      <td>26028.0</td>\n",
       "      <td>17986.0</td>\n",
       "      <td>8041.0</td>\n",
       "      <td>48357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity   Hybrid  Total Bond  \\\n",
       "Date                                                                           \n",
       "2012-01-01      -26156.0         -23126.0       -3031.0    526.0      9848.0   \n",
       "2013-01-01        3661.0          -1627.0        5288.0   2730.0     12149.0   \n",
       "2014-01-01         330.0         -44689.0       45021.0  19570.0     59890.0   \n",
       "2015-01-01       15049.0         -10459.0       25508.0   7280.0     26028.0   \n",
       "\n",
       "            Taxable Bond  Municipal Bond    Total  \n",
       "Date                                               \n",
       "2012-01-01       12613.0         -2765.0 -15782.0  \n",
       "2013-01-01        9414.0          2735.0  18540.0  \n",
       "2014-01-01       44994.0         14896.0  79787.0  \n",
       "2015-01-01       17986.0          8041.0  48357.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = monthly.resample('AS-JAN').sum()\n",
    "year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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================================================
FILE: 09_Time_Series/Investor_Flow_of_Funds_US/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investor - Flow of Funds - US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "Special thanks to: https://github.com/rgrp for sharing the dataset.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
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       "      <td>2012-12-05</td>\n",
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       "      <td>-74</td>\n",
       "      <td>5317</td>\n",
       "      <td>4210</td>\n",
       "      <td>1107</td>\n",
       "      <td>-2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-12-12</td>\n",
       "      <td>-8783</td>\n",
       "      <td>-7520</td>\n",
       "      <td>-1263</td>\n",
       "      <td>123</td>\n",
       "      <td>1818</td>\n",
       "      <td>1598</td>\n",
       "      <td>219</td>\n",
       "      <td>-6842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-12-19</td>\n",
       "      <td>-5496</td>\n",
       "      <td>-5470</td>\n",
       "      <td>-26</td>\n",
       "      <td>-73</td>\n",
       "      <td>103</td>\n",
       "      <td>3472</td>\n",
       "      <td>-3369</td>\n",
       "      <td>-5466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-26</td>\n",
       "      <td>-4451</td>\n",
       "      <td>-4076</td>\n",
       "      <td>-375</td>\n",
       "      <td>550</td>\n",
       "      <td>2610</td>\n",
       "      <td>3333</td>\n",
       "      <td>-722</td>\n",
       "      <td>-1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>-11156</td>\n",
       "      <td>-9622</td>\n",
       "      <td>-1533</td>\n",
       "      <td>-158</td>\n",
       "      <td>2383</td>\n",
       "      <td>2103</td>\n",
       "      <td>280</td>\n",
       "      <td>-8931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Total Equity  Domestic Equity  World Equity  Hybrid  \\\n",
       "0  2012-12-05         -7426            -6060         -1367     -74   \n",
       "1  2012-12-12         -8783            -7520         -1263     123   \n",
       "2  2012-12-19         -5496            -5470           -26     -73   \n",
       "3  2012-12-26         -4451            -4076          -375     550   \n",
       "4  2013-01-02        -11156            -9622         -1533    -158   \n",
       "\n",
       "   Total Bond  Taxable Bond  Municipal Bond  Total  \n",
       "0        5317          4210            1107  -2183  \n",
       "1        1818          1598             219  -6842  \n",
       "2         103          3472           -3369  -5466  \n",
       "3        2610          3333            -722  -1291  \n",
       "4        2383          2103             280  -8931  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.  What is the frequency of the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# weekly data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Set the column Date as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-05</th>\n",
       "      <td>-7426</td>\n",
       "      <td>-6060</td>\n",
       "      <td>-1367</td>\n",
       "      <td>-74</td>\n",
       "      <td>5317</td>\n",
       "      <td>4210</td>\n",
       "      <td>1107</td>\n",
       "      <td>-2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-12</th>\n",
       "      <td>-8783</td>\n",
       "      <td>-7520</td>\n",
       "      <td>-1263</td>\n",
       "      <td>123</td>\n",
       "      <td>1818</td>\n",
       "      <td>1598</td>\n",
       "      <td>219</td>\n",
       "      <td>-6842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19</th>\n",
       "      <td>-5496</td>\n",
       "      <td>-5470</td>\n",
       "      <td>-26</td>\n",
       "      <td>-73</td>\n",
       "      <td>103</td>\n",
       "      <td>3472</td>\n",
       "      <td>-3369</td>\n",
       "      <td>-5466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-26</th>\n",
       "      <td>-4451</td>\n",
       "      <td>-4076</td>\n",
       "      <td>-375</td>\n",
       "      <td>550</td>\n",
       "      <td>2610</td>\n",
       "      <td>3333</td>\n",
       "      <td>-722</td>\n",
       "      <td>-1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-11156</td>\n",
       "      <td>-9622</td>\n",
       "      <td>-1533</td>\n",
       "      <td>-158</td>\n",
       "      <td>2383</td>\n",
       "      <td>2103</td>\n",
       "      <td>280</td>\n",
       "      <td>-8931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity  Hybrid  Total Bond  \\\n",
       "Date                                                                          \n",
       "2012-12-05         -7426            -6060         -1367     -74        5317   \n",
       "2012-12-12         -8783            -7520         -1263     123        1818   \n",
       "2012-12-19         -5496            -5470           -26     -73         103   \n",
       "2012-12-26         -4451            -4076          -375     550        2610   \n",
       "2013-01-02        -11156            -9622         -1533    -158        2383   \n",
       "\n",
       "            Taxable Bond  Municipal Bond  Total  \n",
       "Date                                             \n",
       "2012-12-05          4210            1107  -2183  \n",
       "2012-12-12          1598             219  -6842  \n",
       "2012-12-19          3472           -3369  -5466  \n",
       "2012-12-26          3333            -722  -1291  \n",
       "2013-01-02          2103             280  -8931  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. What is the type of the index?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'2012-12-05', u'2012-12-12', u'2012-12-19', u'2012-12-26',\n",
       "       u'2013-01-02', u'2013-01-09', u'2014-04-02', u'2014-04-09',\n",
       "       u'2014-04-16', u'2014-04-23', u'2014-04-30', u'2014-05-07',\n",
       "       u'2014-05-14', u'2014-05-21', u'2014-05-28', u'2014-06-04',\n",
       "       u'2014-06-11', u'2014-06-18', u'2014-06-25', u'2014-07-02',\n",
       "       u'2014-07-09', u'2014-07-30', u'2014-08-06', u'2014-08-13',\n",
       "       u'2014-08-20', u'2014-08-27', u'2014-09-03', u'2014-09-10',\n",
       "       u'2014-11-05', u'2014-11-12', u'2014-11-19', u'2014-11-25',\n",
       "       u'2015-01-07', u'2015-01-14', u'2015-01-21', u'2015-01-28',\n",
       "       u'2015-02-04', u'2015-02-11', u'2015-03-04', u'2015-03-11',\n",
       "       u'2015-03-18', u'2015-03-25', u'2015-04-01', u'2015-04-08'],\n",
       "      dtype='object', name=u'Date')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it is a 'object' type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Set the index to a DatetimeIndex type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tseries.index.DatetimeIndex"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  Change the frequency to monthly, sum the values and assign it to monthly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>-26156.0</td>\n",
       "      <td>-23126.0</td>\n",
       "      <td>-3031.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>9848.0</td>\n",
       "      <td>12613.0</td>\n",
       "      <td>-2765.0</td>\n",
       "      <td>-15782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>3661.0</td>\n",
       "      <td>-1627.0</td>\n",
       "      <td>5288.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>12149.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>18540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>10842.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>9794.0</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>8493.0</td>\n",
       "      <td>7193.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>24267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>-2203.0</td>\n",
       "      <td>-8720.0</td>\n",
       "      <td>6518.0</td>\n",
       "      <td>3172.0</td>\n",
       "      <td>13767.0</td>\n",
       "      <td>10192.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>14736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>2319.0</td>\n",
       "      <td>-6546.0</td>\n",
       "      <td>8865.0</td>\n",
       "      <td>4588.0</td>\n",
       "      <td>9715.0</td>\n",
       "      <td>7551.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>16621.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>-7051.0</td>\n",
       "      <td>-11128.0</td>\n",
       "      <td>4078.0</td>\n",
       "      <td>2666.0</td>\n",
       "      <td>7506.0</td>\n",
       "      <td>7026.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>3122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>-5508.0</td>\n",
       "      <td>7452.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>-1013.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>5723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>-2767.0</td>\n",
       "      <td>-6596.0</td>\n",
       "      <td>3829.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>3984.0</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>2816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-30</th>\n",
       "      <td>-2753.0</td>\n",
       "      <td>-7239.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>14528.0</td>\n",
       "      <td>11566.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>12502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>3471.0</td>\n",
       "      <td>-1164.0</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>7368.0</td>\n",
       "      <td>2762.0</td>\n",
       "      <td>4606.0</td>\n",
       "      <td>12569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>5508.0</td>\n",
       "      <td>3509.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>9099.0</td>\n",
       "      <td>7443.0</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>16359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>5691.0</td>\n",
       "      <td>-8176.0</td>\n",
       "      <td>13867.0</td>\n",
       "      <td>2829.0</td>\n",
       "      <td>9138.0</td>\n",
       "      <td>7267.0</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>17657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>379.0</td>\n",
       "      <td>-4628.0</td>\n",
       "      <td>5007.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>1772.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity  Hybrid  Total Bond  \\\n",
       "Date                                                                          \n",
       "2012-12-31      -26156.0         -23126.0       -3031.0   526.0      9848.0   \n",
       "2013-01-31        3661.0          -1627.0        5288.0  2730.0     12149.0   \n",
       "2013-02-28           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-03-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-04-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-05-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-06-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-07-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-08-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-09-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-10-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-11-30           NaN              NaN           NaN     NaN         NaN   \n",
       "2013-12-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-01-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-02-28           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-03-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-04-30       10842.0           1048.0        9794.0  4931.0      8493.0   \n",
       "2014-05-31       -2203.0          -8720.0        6518.0  3172.0     13767.0   \n",
       "2014-06-30        2319.0          -6546.0        8865.0  4588.0      9715.0   \n",
       "2014-07-31       -7051.0         -11128.0        4078.0  2666.0      7506.0   \n",
       "2014-08-31        1943.0          -5508.0        7452.0  1885.0      1897.0   \n",
       "2014-09-30       -2767.0          -6596.0        3829.0  1599.0      3984.0   \n",
       "2014-10-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2014-11-30       -2753.0          -7239.0        4485.0   729.0     14528.0   \n",
       "2014-12-31           NaN              NaN           NaN     NaN         NaN   \n",
       "2015-01-31        3471.0          -1164.0        4635.0  1729.0      7368.0   \n",
       "2015-02-28        5508.0           3509.0        1999.0  1752.0      9099.0   \n",
       "2015-03-31        5691.0          -8176.0       13867.0  2829.0      9138.0   \n",
       "2015-04-30         379.0          -4628.0        5007.0   970.0       423.0   \n",
       "\n",
       "            Taxable Bond  Municipal Bond    Total  \n",
       "Date                                               \n",
       "2012-12-31       12613.0         -2765.0 -15782.0  \n",
       "2013-01-31        9414.0          2735.0  18540.0  \n",
       "2013-02-28           NaN             NaN      NaN  \n",
       "2013-03-31           NaN             NaN      NaN  \n",
       "2013-04-30           NaN             NaN      NaN  \n",
       "2013-05-31           NaN             NaN      NaN  \n",
       "2013-06-30           NaN             NaN      NaN  \n",
       "2013-07-31           NaN             NaN      NaN  \n",
       "2013-08-31           NaN             NaN      NaN  \n",
       "2013-09-30           NaN             NaN      NaN  \n",
       "2013-10-31           NaN             NaN      NaN  \n",
       "2013-11-30           NaN             NaN      NaN  \n",
       "2013-12-31           NaN             NaN      NaN  \n",
       "2014-01-31           NaN             NaN      NaN  \n",
       "2014-02-28           NaN             NaN      NaN  \n",
       "2014-03-31           NaN             NaN      NaN  \n",
       "2014-04-30        7193.0          1300.0  24267.0  \n",
       "2014-05-31       10192.0          3576.0  14736.0  \n",
       "2014-06-30        7551.0          2163.0  16621.0  \n",
       "2014-07-31        7026.0           481.0   3122.0  \n",
       "2014-08-31       -1013.0          2910.0   5723.0  \n",
       "2014-09-30        2479.0          1504.0   2816.0  \n",
       "2014-10-31           NaN             NaN      NaN  \n",
       "2014-11-30       11566.0          2962.0  12502.0  \n",
       "2014-12-31           NaN             NaN      NaN  \n",
       "2015-01-31        2762.0          4606.0  12569.0  \n",
       "2015-02-28        7443.0          1656.0  16359.0  \n",
       "2015-03-31        7267.0          1870.0  17657.0  \n",
       "2015-04-30         514.0           -91.0   1772.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. You will notice that it filled the dataFrame with months that don't have any data with NaN. Let's drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>-26156.0</td>\n",
       "      <td>-23126.0</td>\n",
       "      <td>-3031.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>9848.0</td>\n",
       "      <td>12613.0</td>\n",
       "      <td>-2765.0</td>\n",
       "      <td>-15782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>3661.0</td>\n",
       "      <td>-1627.0</td>\n",
       "      <td>5288.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>12149.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>18540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>10842.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>9794.0</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>8493.0</td>\n",
       "      <td>7193.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>24267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>-2203.0</td>\n",
       "      <td>-8720.0</td>\n",
       "      <td>6518.0</td>\n",
       "      <td>3172.0</td>\n",
       "      <td>13767.0</td>\n",
       "      <td>10192.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>14736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>2319.0</td>\n",
       "      <td>-6546.0</td>\n",
       "      <td>8865.0</td>\n",
       "      <td>4588.0</td>\n",
       "      <td>9715.0</td>\n",
       "      <td>7551.0</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>16621.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>-7051.0</td>\n",
       "      <td>-11128.0</td>\n",
       "      <td>4078.0</td>\n",
       "      <td>2666.0</td>\n",
       "      <td>7506.0</td>\n",
       "      <td>7026.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>3122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>-5508.0</td>\n",
       "      <td>7452.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>-1013.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>5723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>-2767.0</td>\n",
       "      <td>-6596.0</td>\n",
       "      <td>3829.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>3984.0</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>2816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-30</th>\n",
       "      <td>-2753.0</td>\n",
       "      <td>-7239.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>14528.0</td>\n",
       "      <td>11566.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>12502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>3471.0</td>\n",
       "      <td>-1164.0</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>7368.0</td>\n",
       "      <td>2762.0</td>\n",
       "      <td>4606.0</td>\n",
       "      <td>12569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>5508.0</td>\n",
       "      <td>3509.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>9099.0</td>\n",
       "      <td>7443.0</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>16359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>5691.0</td>\n",
       "      <td>-8176.0</td>\n",
       "      <td>13867.0</td>\n",
       "      <td>2829.0</td>\n",
       "      <td>9138.0</td>\n",
       "      <td>7267.0</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>17657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>379.0</td>\n",
       "      <td>-4628.0</td>\n",
       "      <td>5007.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>1772.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity  Hybrid  Total Bond  \\\n",
       "Date                                                                          \n",
       "2012-12-31      -26156.0         -23126.0       -3031.0   526.0      9848.0   \n",
       "2013-01-31        3661.0          -1627.0        5288.0  2730.0     12149.0   \n",
       "2014-04-30       10842.0           1048.0        9794.0  4931.0      8493.0   \n",
       "2014-05-31       -2203.0          -8720.0        6518.0  3172.0     13767.0   \n",
       "2014-06-30        2319.0          -6546.0        8865.0  4588.0      9715.0   \n",
       "2014-07-31       -7051.0         -11128.0        4078.0  2666.0      7506.0   \n",
       "2014-08-31        1943.0          -5508.0        7452.0  1885.0      1897.0   \n",
       "2014-09-30       -2767.0          -6596.0        3829.0  1599.0      3984.0   \n",
       "2014-11-30       -2753.0          -7239.0        4485.0   729.0     14528.0   \n",
       "2015-01-31        3471.0          -1164.0        4635.0  1729.0      7368.0   \n",
       "2015-02-28        5508.0           3509.0        1999.0  1752.0      9099.0   \n",
       "2015-03-31        5691.0          -8176.0       13867.0  2829.0      9138.0   \n",
       "2015-04-30         379.0          -4628.0        5007.0   970.0       423.0   \n",
       "\n",
       "            Taxable Bond  Municipal Bond    Total  \n",
       "Date                                               \n",
       "2012-12-31       12613.0         -2765.0 -15782.0  \n",
       "2013-01-31        9414.0          2735.0  18540.0  \n",
       "2014-04-30        7193.0          1300.0  24267.0  \n",
       "2014-05-31       10192.0          3576.0  14736.0  \n",
       "2014-06-30        7551.0          2163.0  16621.0  \n",
       "2014-07-31        7026.0           481.0   3122.0  \n",
       "2014-08-31       -1013.0          2910.0   5723.0  \n",
       "2014-09-30        2479.0          1504.0   2816.0  \n",
       "2014-11-30       11566.0          2962.0  12502.0  \n",
       "2015-01-31        2762.0          4606.0  12569.0  \n",
       "2015-02-28        7443.0          1656.0  16359.0  \n",
       "2015-03-31        7267.0          1870.0  17657.0  \n",
       "2015-04-30         514.0           -91.0   1772.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Good, now we have the monthly data. Now change the frequency to year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Equity</th>\n",
       "      <th>Domestic Equity</th>\n",
       "      <th>World Equity</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Total Bond</th>\n",
       "      <th>Taxable Bond</th>\n",
       "      <th>Municipal Bond</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>-26156.0</td>\n",
       "      <td>-23126.0</td>\n",
       "      <td>-3031.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>9848.0</td>\n",
       "      <td>12613.0</td>\n",
       "      <td>-2765.0</td>\n",
       "      <td>-15782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>3661.0</td>\n",
       "      <td>-1627.0</td>\n",
       "      <td>5288.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>12149.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>18540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>330.0</td>\n",
       "      <td>-44689.0</td>\n",
       "      <td>45021.0</td>\n",
       "      <td>19570.0</td>\n",
       "      <td>59890.0</td>\n",
       "      <td>44994.0</td>\n",
       "      <td>14896.0</td>\n",
       "      <td>79787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>15049.0</td>\n",
       "      <td>-10459.0</td>\n",
       "      <td>25508.0</td>\n",
       "      <td>7280.0</td>\n",
       "      <td>26028.0</td>\n",
       "      <td>17986.0</td>\n",
       "      <td>8041.0</td>\n",
       "      <td>48357.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Equity  Domestic Equity  World Equity   Hybrid  Total Bond  \\\n",
       "Date                                                                           \n",
       "2012-01-01      -26156.0         -23126.0       -3031.0    526.0      9848.0   \n",
       "2013-01-01        3661.0          -1627.0        5288.0   2730.0     12149.0   \n",
       "2014-01-01         330.0         -44689.0       45021.0  19570.0     59890.0   \n",
       "2015-01-01       15049.0         -10459.0       25508.0   7280.0     26028.0   \n",
       "\n",
       "            Taxable Bond  Municipal Bond    Total  \n",
       "Date                                               \n",
       "2012-01-01       12613.0         -2765.0 -15782.0  \n",
       "2013-01-01        9414.0          2735.0  18540.0  \n",
       "2014-01-01       44994.0         14896.0  79787.0  \n",
       "2015-01-01       17986.0          8041.0  48357.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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================================================
FILE: 10_Deleting/Iris/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise may seem a little bit strange, but keep doing it.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create columns for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 1. sepal_length (in cm)\n",
    "# 2. sepal_width (in cm)\n",
    "# 3. petal_length (in cm)\n",
    "# 4. petal_width (in cm)\n",
    "# 5. class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Is there any missing value in the dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Good, now lets substitute the NaN values to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Now let's delete the column class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9.  Set the first 3 rows as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Delete the rows that have NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Reset the index so it begins with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "version": 2
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  }
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================================================
FILE: 10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris\n",
    "\n",
    "Check out [Iris Exercises Video Tutorial](https://youtu.be/yAtzFLCWSZo) to watch a data scientist go through the exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise may seem a little bit strange, but keep doing it.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5.1</th>\n",
       "      <th>3.5</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.2</th>\n",
       "      <th>Iris-setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   5.1  3.5  1.4  0.2  Iris-setosa\n",
       "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
       "4  5.4  3.9  1.7  0.4  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "iris = pd.read_csv(url)\n",
    "\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create columns for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "1           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "2           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "3           5.0          3.6           1.4          0.2  Iris-setosa\n",
       "4           5.4          3.9           1.7          0.4  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. sepal_length (in cm)\n",
    "# 2. sepal_width (in cm)\n",
    "# 3. petal_length (in cm)\n",
    "# 4. petal_width (in cm)\n",
    "# 5. class\n",
    "\n",
    "iris.columns = ['sepal_length','sepal_width', 'petal_length', 'petal_width', 'class']\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Is there any missing value in the dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "class           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(iris).sum()\n",
    "# nice no missing value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0            4.9          3.0           1.4          0.2  Iris-setosa\n",
       "1            4.7          3.2           1.3          0.2  Iris-setosa\n",
       "2            4.6          3.1           1.5          0.2  Iris-setosa\n",
       "3            5.0          3.6           1.4          0.2  Iris-setosa\n",
       "4            5.4          3.9           1.7          0.4  Iris-setosa\n",
       "5            4.6          3.4           1.4          0.3  Iris-setosa\n",
       "6            5.0          3.4           1.5          0.2  Iris-setosa\n",
       "7            4.4          2.9           1.4          0.2  Iris-setosa\n",
       "8            4.9          3.1           1.5          0.1  Iris-setosa\n",
       "9            5.4          3.7           1.5          0.2  Iris-setosa\n",
       "10           4.8          3.4           NaN          0.2  Iris-setosa\n",
       "11           4.8          3.0           NaN          0.1  Iris-setosa\n",
       "12           4.3          3.0           NaN          0.1  Iris-setosa\n",
       "13           5.8          4.0           NaN          0.2  Iris-setosa\n",
       "14           5.7          4.4           NaN          0.4  Iris-setosa\n",
       "15           5.4          3.9           NaN          0.4  Iris-setosa\n",
       "16           5.1          3.5           NaN          0.3  Iris-setosa\n",
       "17           5.7          3.8           NaN          0.3  Iris-setosa\n",
       "18           5.1          3.8           NaN          0.3  Iris-setosa\n",
       "19           5.4          3.4           NaN          0.2  Iris-setosa"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.iloc[10:30,2:3] = np.nan\n",
    "iris.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Good, now lets substitute the NaN values to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width           class\n",
       "0             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "1             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "2             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "3             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "4             5.4          3.9           1.7          0.4     Iris-setosa\n",
       "5             4.6          3.4           1.4          0.3     Iris-setosa\n",
       "6             5.0          3.4           1.5          0.2     Iris-setosa\n",
       "7             4.4          2.9           1.4          0.2     Iris-setosa\n",
       "8             4.9          3.1           1.5          0.1     Iris-setosa\n",
       "9             5.4          3.7           1.5          0.2     Iris-setosa\n",
       "10            4.8          3.4           1.0          0.2     Iris-setosa\n",
       "11            4.8          3.0           1.0          0.1     Iris-setosa\n",
       "12            4.3          3.0           1.0          0.1     Iris-setosa\n",
       "13            5.8          4.0           1.0          0.2     Iris-setosa\n",
       "14            5.7          4.4           1.0          0.4     Iris-setosa\n",
       "15            5.4          3.9           1.0          0.4     Iris-setosa\n",
       "16            5.1          3.5           1.0          0.3     Iris-setosa\n",
       "17            5.7          3.8           1.0          0.3     Iris-setosa\n",
       "18            5.1          3.8           1.0          0.3     Iris-setosa\n",
       "19            5.4          3.4           1.0          0.2     Iris-setosa\n",
       "20            5.1          3.7           1.0          0.4     Iris-setosa\n",
       "21            4.6          3.6           1.0          0.2     Iris-setosa\n",
       "22            5.1          3.3           1.0          0.5     Iris-setosa\n",
       "23            4.8          3.4           1.0          0.2     Iris-setosa\n",
       "24            5.0          3.0           1.0          0.2     Iris-setosa\n",
       "25            5.0          3.4           1.0          0.4     Iris-setosa\n",
       "26            5.2          3.5           1.0          0.2     Iris-setosa\n",
       "27            5.2          3.4           1.0          0.2     Iris-setosa\n",
       "28            4.7          3.2           1.0          0.2     Iris-setosa\n",
       "29            4.8          3.1           1.0          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "119           6.9          3.2           5.7          2.3  Iris-virginica\n",
       "120           5.6          2.8           4.9          2.0  Iris-virginica\n",
       "121           7.7          2.8           6.7          2.0  Iris-virginica\n",
       "122           6.3          2.7           4.9          1.8  Iris-virginica\n",
       "123           6.7          3.3           5.7          2.1  Iris-virginica\n",
       "124           7.2          3.2           6.0          1.8  Iris-virginica\n",
       "125           6.2          2.8           4.8          1.8  Iris-virginica\n",
       "126           6.1          3.0           4.9          1.8  Iris-virginica\n",
       "127           6.4          2.8           5.6          2.1  Iris-virginica\n",
       "128           7.2          3.0           5.8          1.6  Iris-virginica\n",
       "129           7.4          2.8           6.1          1.9  Iris-virginica\n",
       "130           7.9          3.8           6.4          2.0  Iris-virginica\n",
       "131           6.4          2.8           5.6          2.2  Iris-virginica\n",
       "132           6.3          2.8           5.1          1.5  Iris-virginica\n",
       "133           6.1          2.6           5.6          1.4  Iris-virginica\n",
       "134           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "135           6.3          3.4           5.6          2.4  Iris-virginica\n",
       "136           6.4          3.1           5.5          1.8  Iris-virginica\n",
       "137           6.0          3.0           4.8          1.8  Iris-virginica\n",
       "138           6.9          3.1           5.4          2.1  Iris-virginica\n",
       "139           6.7          3.1           5.6          2.4  Iris-virginica\n",
       "140           6.9          3.1           5.1          2.3  Iris-virginica\n",
       "141           5.8          2.7           5.1          1.9  Iris-virginica\n",
       "142           6.8          3.2           5.9          2.3  Iris-virginica\n",
       "143           6.7          3.3           5.7          2.5  Iris-virginica\n",
       "144           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "145           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "146           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "147           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "148           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.petal_length.fillna(1, inplace = True)\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Now let's delete the column class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           4.9          3.0           1.4          0.2\n",
       "1           4.7          3.2           1.3          0.2\n",
       "2           4.6          3.1           1.5          0.2\n",
       "3           5.0          3.6           1.4          0.2\n",
       "4           5.4          3.9           1.7          0.4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del iris['class']\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9.  Set the first 3 rows as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           NaN          NaN           NaN          NaN\n",
       "1           NaN          NaN           NaN          NaN\n",
       "2           NaN          NaN           NaN          NaN\n",
       "3           5.0          3.4           1.5          0.2\n",
       "4           4.4          2.9           1.4          0.2"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.iloc[0:3 ,:] = np.nan\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Delete the rows that have NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "3           5.0          3.4           1.5          0.2\n",
       "4           4.4          2.9           1.4          0.2\n",
       "5           4.9          3.1           1.5          0.1\n",
       "6           5.4          3.7           1.5          0.2\n",
       "7           4.8          3.4           1.0          0.2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = iris.dropna(how='any')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Reset the index so it begins with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.0          3.4           1.5          0.2\n",
       "1           4.4          2.9           1.4          0.2\n",
       "2           4.9          3.1           1.5          0.1\n",
       "3           5.4          3.7           1.5          0.2\n",
       "4           4.8          3.4           1.0          0.2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = iris.reset_index(drop = True)\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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================================================
FILE: 10_Deleting/Iris/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise may seem a little bit strange, but keep doing it.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5.1</th>\n",
       "      <th>3.5</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.2</th>\n",
       "      <th>Iris-setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   5.1  3.5  1.4  0.2  Iris-setosa\n",
       "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
       "4  5.4  3.9  1.7  0.4  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create columns for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "1           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "2           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "3           5.0          3.6           1.4          0.2  Iris-setosa\n",
       "4           5.4          3.9           1.7          0.4  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. sepal_length (in cm)\n",
    "# 2. sepal_width (in cm)\n",
    "# 3. petal_length (in cm)\n",
    "# 4. petal_width (in cm)\n",
    "# 5. class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Is there any missing value in the dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "class           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0            4.9          3.0           1.4          0.2  Iris-setosa\n",
       "1            4.7          3.2           1.3          0.2  Iris-setosa\n",
       "2            4.6          3.1           1.5          0.2  Iris-setosa\n",
       "3            5.0          3.6           1.4          0.2  Iris-setosa\n",
       "4            5.4          3.9           1.7          0.4  Iris-setosa\n",
       "5            4.6          3.4           1.4          0.3  Iris-setosa\n",
       "6            5.0          3.4           1.5          0.2  Iris-setosa\n",
       "7            4.4          2.9           1.4          0.2  Iris-setosa\n",
       "8            4.9          3.1           1.5          0.1  Iris-setosa\n",
       "9            5.4          3.7           1.5          0.2  Iris-setosa\n",
       "10           4.8          3.4           NaN          0.2  Iris-setosa\n",
       "11           4.8          3.0           NaN          0.1  Iris-setosa\n",
       "12           4.3          3.0           NaN          0.1  Iris-setosa\n",
       "13           5.8          4.0           NaN          0.2  Iris-setosa\n",
       "14           5.7          4.4           NaN          0.4  Iris-setosa\n",
       "15           5.4          3.9           NaN          0.4  Iris-setosa\n",
       "16           5.1          3.5           NaN          0.3  Iris-setosa\n",
       "17           5.7          3.8           NaN          0.3  Iris-setosa\n",
       "18           5.1          3.8           NaN          0.3  Iris-setosa\n",
       "19           5.4          3.4           NaN          0.2  Iris-setosa"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Good, now lets substitute the NaN values to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.4</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.4</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>10</th>\n",
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       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width           class\n",
       "0             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "1             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "2             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "3             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "4             5.4          3.9           1.7          0.4     Iris-setosa\n",
       "5             4.6          3.4           1.4          0.3     Iris-setosa\n",
       "6             5.0          3.4           1.5          0.2     Iris-setosa\n",
       "7             4.4          2.9           1.4          0.2     Iris-setosa\n",
       "8             4.9          3.1           1.5          0.1     Iris-setosa\n",
       "9             5.4          3.7           1.5          0.2     Iris-setosa\n",
       "10            4.8          3.4           1.0          0.2     Iris-setosa\n",
       "11            4.8          3.0           1.0          0.1     Iris-setosa\n",
       "12            4.3          3.0           1.0          0.1     Iris-setosa\n",
       "13            5.8          4.0           1.0          0.2     Iris-setosa\n",
       "14            5.7          4.4           1.0          0.4     Iris-setosa\n",
       "15            5.4          3.9           1.0          0.4     Iris-setosa\n",
       "16            5.1          3.5           1.0          0.3     Iris-setosa\n",
       "17            5.7          3.8           1.0          0.3     Iris-setosa\n",
       "18            5.1          3.8           1.0          0.3     Iris-setosa\n",
       "19            5.4          3.4           1.0          0.2     Iris-setosa\n",
       "20            5.1          3.7           1.0          0.4     Iris-setosa\n",
       "21            4.6          3.6           1.0          0.2     Iris-setosa\n",
       "22            5.1          3.3           1.0          0.5     Iris-setosa\n",
       "23            4.8          3.4           1.0          0.2     Iris-setosa\n",
       "24            5.0          3.0           1.0          0.2     Iris-setosa\n",
       "25            5.0          3.4           1.0          0.4     Iris-setosa\n",
       "26            5.2          3.5           1.0          0.2     Iris-setosa\n",
       "27            5.2          3.4           1.0          0.2     Iris-setosa\n",
       "28            4.7          3.2           1.0          0.2     Iris-setosa\n",
       "29            4.8          3.1           1.0          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "119           6.9          3.2           5.7          2.3  Iris-virginica\n",
       "120           5.6          2.8           4.9          2.0  Iris-virginica\n",
       "121           7.7          2.8           6.7          2.0  Iris-virginica\n",
       "122           6.3          2.7           4.9          1.8  Iris-virginica\n",
       "123           6.7          3.3           5.7          2.1  Iris-virginica\n",
       "124           7.2          3.2           6.0          1.8  Iris-virginica\n",
       "125           6.2          2.8           4.8          1.8  Iris-virginica\n",
       "126           6.1          3.0           4.9          1.8  Iris-virginica\n",
       "127           6.4          2.8           5.6          2.1  Iris-virginica\n",
       "128           7.2          3.0           5.8          1.6  Iris-virginica\n",
       "129           7.4          2.8           6.1          1.9  Iris-virginica\n",
       "130           7.9          3.8           6.4          2.0  Iris-virginica\n",
       "131           6.4          2.8           5.6          2.2  Iris-virginica\n",
       "132           6.3          2.8           5.1          1.5  Iris-virginica\n",
       "133           6.1          2.6           5.6          1.4  Iris-virginica\n",
       "134           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "135           6.3          3.4           5.6          2.4  Iris-virginica\n",
       "136           6.4          3.1           5.5          1.8  Iris-virginica\n",
       "137           6.0          3.0           4.8          1.8  Iris-virginica\n",
       "138           6.9          3.1           5.4          2.1  Iris-virginica\n",
       "139           6.7          3.1           5.6          2.4  Iris-virginica\n",
       "140           6.9          3.1           5.1          2.3  Iris-virginica\n",
       "141           5.8          2.7           5.1          1.9  Iris-virginica\n",
       "142           6.8          3.2           5.9          2.3  Iris-virginica\n",
       "143           6.7          3.3           5.7          2.5  Iris-virginica\n",
       "144           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "145           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "146           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "147           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "148           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Now let's delete the column class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           4.9          3.0           1.4          0.2\n",
       "1           4.7          3.2           1.3          0.2\n",
       "2           4.6          3.1           1.5          0.2\n",
       "3           5.0          3.6           1.4          0.2\n",
       "4           5.4          3.9           1.7          0.4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9.  Set the first 3 rows as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           NaN          NaN           NaN          NaN\n",
       "1           NaN          NaN           NaN          NaN\n",
       "2           NaN          NaN           NaN          NaN\n",
       "3           5.0          3.4           1.5          0.2\n",
       "4           4.4          2.9           1.4          0.2"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Delete the rows that have NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "3           5.0          3.4           1.5          0.2\n",
       "4           4.4          2.9           1.4          0.2\n",
       "5           4.9          3.1           1.5          0.1\n",
       "6           5.4          3.7           1.5          0.2\n",
       "7           4.8          3.4           1.0          0.2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Reset the index so it begins with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.0          3.4           1.5          0.2\n",
       "1           4.4          2.9           1.4          0.2\n",
       "2           4.9          3.1           1.5          0.1\n",
       "3           5.4          3.7           1.5          0.2\n",
       "4           4.8          3.4           1.0          0.2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
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================================================
FILE: 10_Deleting/Wine/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is a adaptation from the UCI Wine dataset.\n",
    "The only pupose is to practice deleting data with pandas.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the first, fourth, seventh, nineth, eleventh, thirteenth and fourteenth columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Assign the columns as below:\n",
    "\n",
    "The attributes are (donated by Riccardo Leardi, riclea '@' anchem.unige.it):  \n",
    "1) alcohol  \n",
    "2) malic_acid  \n",
    "3) alcalinity_of_ash  \n",
    "4) magnesium  \n",
    "5) flavanoids  \n",
    "6) proanthocyanins  \n",
    "7) hue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Set the values of the first 3 rows from alcohol as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Now set the value of the rows 3 and 4 of magnesium as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Fill the value of NaN with the number 10 in alcohol and 100 in magnesium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Count the number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create an array of 10 random numbers up until 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  Use random numbers you generated as an index and assign NaN value to each of cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12.  How many missing values do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Delete the rows that contain missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Print only the non-null values in alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15.  Reset the index, so it starts with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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================================================
FILE: 10_Deleting/Wine/Exercises_code_and_solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is a adaptation from the UCI Wine dataset.\n",
    "The only pupose is to practice deleting data with pandas.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <th>14.23</th>\n",
       "      <th>1.71</th>\n",
       "      <th>2.43</th>\n",
       "      <th>15.6</th>\n",
       "      <th>127</th>\n",
       "      <th>2.8</th>\n",
       "      <th>3.06</th>\n",
       "      <th>.28</th>\n",
       "      <th>2.29</th>\n",
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       "      <th>1065</th>\n",
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       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
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       "      <td>2.67</td>\n",
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       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  14.23  1.71  2.43  15.6  127   2.8  3.06   .28  2.29  5.64  1.04  3.92  \\\n",
       "0  1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
       "1  1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
       "2  1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
       "3  1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
       "4  1  14.20  1.76  2.45  15.2  112  3.27  3.39  0.34  1.97  6.75  1.05  2.85   \n",
       "\n",
       "   1065  \n",
       "0  1050  \n",
       "1  1185  \n",
       "2  1480  \n",
       "3   735  \n",
       "4  1450  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'\n",
    "wine = pd.read_csv(url)\n",
    "\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the first, fourth, seventh, nineth, eleventh, thirteenth and fourteenth columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>14.23</th>\n",
       "      <th>1.71</th>\n",
       "      <th>15.6</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>100</td>\n",
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       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
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       "      <td>3.39</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   14.23  1.71  15.6  127  3.06  2.29  5.64\n",
       "0  13.20  1.78  11.2  100  2.76  1.28  4.38\n",
       "1  13.16  2.36  18.6  101  3.24  2.81  5.68\n",
       "2  14.37  1.95  16.8  113  3.49  2.18  7.80\n",
       "3  13.24  2.59  21.0  118  2.69  1.82  4.32\n",
       "4  14.20  1.76  15.2  112  3.39  1.97  6.75"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = wine.drop(wine.columns[[0,3,6,8,11,12,13]], axis = 1)\n",
    "\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Assign the columns as below:\n",
    "\n",
    "The attributes are (donated by Riccardo Leardi, riclea '@' anchem.unige.it):  \n",
    "1) alcohol  \n",
    "2) malic_acid  \n",
    "3) alcalinity_of_ash  \n",
    "4) magnesium  \n",
    "5) flavanoids  \n",
    "6) proanthocyanins  \n",
    "7) hue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0    13.20        1.78               11.2        100        2.76   \n",
       "1    13.16        2.36               18.6        101        3.24   \n",
       "2    14.37        1.95               16.8        113        3.49   \n",
       "3    13.24        2.59               21.0        118        2.69   \n",
       "4    14.20        1.76               15.2        112        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.columns = ['alcohol', 'malic_acid', 'alcalinity_of_ash', 'magnesium', 'flavanoids', 'proanthocyanins', 'hue']\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Set the values of the first 3 rows from alcohol as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      NaN        1.78               11.2        100        2.76   \n",
       "1      NaN        2.36               18.6        101        3.24   \n",
       "2      NaN        1.95               16.8        113        3.49   \n",
       "3    13.24        2.59               21.0        118        2.69   \n",
       "4    14.20        1.76               15.2        112        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.iloc[0:3, 0] = np.nan\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Now set the value of the rows 3 and 4 of magnesium as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      NaN        1.78               11.2      100.0        2.76   \n",
       "1      NaN        2.36               18.6      101.0        3.24   \n",
       "2      NaN        1.95               16.8        NaN        3.49   \n",
       "3    13.24        2.59               21.0        NaN        2.69   \n",
       "4    14.20        1.76               15.2      112.0        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.iloc[2:4, 3] = np.nan\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Fill the value of NaN with the number 10 in alcohol and 100 in magnesium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0    10.00        1.78               11.2      100.0        2.76   \n",
       "1    10.00        2.36               18.6      101.0        3.24   \n",
       "2    10.00        1.95               16.8      100.0        3.49   \n",
       "3    13.24        2.59               21.0      100.0        2.69   \n",
       "4    14.20        1.76               15.2      112.0        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.alcohol.fillna(10, inplace = True)\n",
    "\n",
    "wine.magnesium.fillna(100, inplace = True)\n",
    "\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Count the number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol              0\n",
       "malic_acid           0\n",
       "alcalinity_of_ash    0\n",
       "magnesium            0\n",
       "flavanoids           0\n",
       "proanthocyanins      0\n",
       "hue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create an array of 10 random numbers up until 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 0, 5, 0, 9, 4, 0, 7, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random = np.random.randint(10, size = 10)\n",
    "random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  Use random numbers you generated as an index and assign NaN value to each of cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.87</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.52</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.64</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      NaN        1.78               11.2      100.0        2.76   \n",
       "1    10.00        2.36               18.6      101.0        3.24   \n",
       "2      NaN        1.95               16.8      100.0        3.49   \n",
       "3      NaN        2.59               21.0      100.0        2.69   \n",
       "4      NaN        1.76               15.2      112.0        3.39   \n",
       "5      NaN        1.87               14.6       96.0        2.52   \n",
       "6    14.06        2.15               17.6      121.0        2.51   \n",
       "7      NaN        1.64               14.0       97.0        2.98   \n",
       "8    13.86        1.35               16.0       98.0        3.15   \n",
       "9      NaN        2.16               18.0      105.0        3.32   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  \n",
       "5             1.98  5.25  \n",
       "6             1.25  5.05  \n",
       "7             1.98  5.20  \n",
       "8             1.85  7.22  \n",
       "9             2.38  5.75  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.alcohol[random] = np.nan\n",
    "wine.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12.  How many missing values do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol              7\n",
       "malic_acid           0\n",
       "alcalinity_of_ash    0\n",
       "magnesium            0\n",
       "flavanoids           0\n",
       "proanthocyanins      0\n",
       "hue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Delete the rows that contain missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "1     10.00        2.36               18.6      101.0        3.24   \n",
       "6     14.06        2.15               17.6      121.0        2.51   \n",
       "8     13.86        1.35               16.0       98.0        3.15   \n",
       "10    14.12        1.48               16.8       95.0        2.43   \n",
       "11    13.75        1.73               16.0       89.0        2.76   \n",
       "\n",
       "    proanthocyanins   hue  \n",
       "1              2.81  5.68  \n",
       "6              1.25  5.05  \n",
       "8              1.85  7.22  \n",
       "10             1.57  5.00  \n",
       "11             1.81  5.60  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = wine.dropna(axis = 0, how = \"any\")\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Print only the non-null values in alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      True\n",
       "6      True\n",
       "8      True\n",
       "10     True\n",
       "11     True\n",
       "12     True\n",
       "13     True\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "20     True\n",
       "21     True\n",
       "22     True\n",
       "23     True\n",
       "24     True\n",
       "25     True\n",
       "26     True\n",
       "27     True\n",
       "28     True\n",
       "29     True\n",
       "30     True\n",
       "31     True\n",
       "32     True\n",
       "33     True\n",
       "34     True\n",
       "35     True\n",
       "36     True\n",
       "       ... \n",
       "147    True\n",
       "148    True\n",
       "149    True\n",
       "150    True\n",
       "151    True\n",
       "152    True\n",
       "153    True\n",
       "154    True\n",
       "155    True\n",
       "156    True\n",
       "157    True\n",
       "158    True\n",
       "159    True\n",
       "160    True\n",
       "161    True\n",
       "162    True\n",
       "163    True\n",
       "164    True\n",
       "165    True\n",
       "166    True\n",
       "167    True\n",
       "168    True\n",
       "169    True\n",
       "170    True\n",
       "171    True\n",
       "172    True\n",
       "173    True\n",
       "174    True\n",
       "175    True\n",
       "176    True\n",
       "Name: alcohol, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = wine.alcohol.notnull()\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      10.00\n",
       "6      14.06\n",
       "8      13.86\n",
       "10     14.12\n",
       "11     13.75\n",
       "12     14.75\n",
       "13     14.38\n",
       "14     13.63\n",
       "15     14.30\n",
       "16     13.83\n",
       "17     14.19\n",
       "18     13.64\n",
       "19     14.06\n",
       "20     12.93\n",
       "21     13.71\n",
       "22     12.85\n",
       "23     13.50\n",
       "24     13.05\n",
       "25     13.39\n",
       "26     13.30\n",
       "27     13.87\n",
       "28     14.02\n",
       "29     13.73\n",
       "30     13.58\n",
       "31     13.68\n",
       "32     13.76\n",
       "33     13.51\n",
       "34     13.48\n",
       "35     13.28\n",
       "36     13.05\n",
       "       ...  \n",
       "147    13.32\n",
       "148    13.08\n",
       "149    13.50\n",
       "150    12.79\n",
       "151    13.11\n",
       "152    13.23\n",
       "153    12.58\n",
       "154    13.17\n",
       "155    13.84\n",
       "156    12.45\n",
       "157    14.34\n",
       "158    13.48\n",
       "159    12.36\n",
       "160    13.69\n",
       "161    12.85\n",
       "162    12.96\n",
       "163    13.78\n",
       "164    13.73\n",
       "165    13.45\n",
       "166    12.82\n",
       "167    13.58\n",
       "168    13.40\n",
       "169    12.20\n",
       "170    12.77\n",
       "171    14.16\n",
       "172    13.71\n",
       "173    13.40\n",
       "174    13.27\n",
       "175    13.17\n",
       "176    14.13\n",
       "Name: alcohol, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.alcohol[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15.  Reset the index, so it starts with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0    10.00        2.36               18.6      101.0        3.24   \n",
       "1    14.06        2.15               17.6      121.0        2.51   \n",
       "2    13.86        1.35               16.0       98.0        3.15   \n",
       "3    14.12        1.48               16.8       95.0        2.43   \n",
       "4    13.75        1.73               16.0       89.0        2.76   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             2.81  5.68  \n",
       "1             1.25  5.05  \n",
       "2             1.85  7.22  \n",
       "3             1.57  5.00  \n",
       "4             1.81  5.60  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = wine.reset_index(drop = True)\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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================================================
FILE: 10_Deleting/Wine/Solutions.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is a adaptation from the UCI Wine dataset.\n",
    "The only pupose is to practice deleting data with pandas.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>14.23</th>\n",
       "      <th>1.71</th>\n",
       "      <th>2.43</th>\n",
       "      <th>15.6</th>\n",
       "      <th>127</th>\n",
       "      <th>2.8</th>\n",
       "      <th>3.06</th>\n",
       "      <th>.28</th>\n",
       "      <th>2.29</th>\n",
       "      <th>5.64</th>\n",
       "      <th>1.04</th>\n",
       "      <th>3.92</th>\n",
       "      <th>1065</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  14.23  1.71  2.43  15.6  127   2.8  3.06   .28  2.29  5.64  1.04  3.92  \\\n",
       "0  1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
       "1  1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
       "2  1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
       "3  1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
       "4  1  14.20  1.76  2.45  15.2  112  3.27  3.39  0.34  1.97  6.75  1.05  2.85   \n",
       "\n",
       "   1065  \n",
       "0  1050  \n",
       "1  1185  \n",
       "2  1480  \n",
       "3   735  \n",
       "4  1450  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the first, fourth, seventh, nineth, eleventh, thirteenth and fourteenth columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>14.23</th>\n",
       "      <th>1.71</th>\n",
       "      <th>15.6</th>\n",
       "      <th>127</th>\n",
       "      <th>3.06</th>\n",
       "      <th>2.29</th>\n",
       "      <th>5.64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   14.23  1.71  15.6  127  3.06  2.29  5.64\n",
       "0  13.20  1.78  11.2  100  2.76  1.28  4.38\n",
       "1  13.16  2.36  18.6  101  3.24  2.81  5.68\n",
       "2  14.37  1.95  16.8  113  3.49  2.18  7.80\n",
       "3  13.24  2.59  21.0  118  2.69  1.82  4.32\n",
       "4  14.20  1.76  15.2  112  3.39  1.97  6.75"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Assign the columns as below:\n",
    "\n",
    "The attributes are (donated by Riccardo Leardi, riclea '@' anchem.unige.it):  \n",
    "1) alcohol  \n",
    "2) malic_acid  \n",
    "3) alcalinity_of_ash  \n",
    "4) magnesium  \n",
    "5) flavanoids  \n",
    "6) proanthocyanins  \n",
    "7) hue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0    13.20        1.78               11.2        100        2.76   \n",
       "1    13.16        2.36               18.6        101        3.24   \n",
       "2    14.37        1.95               16.8        113        3.49   \n",
       "3    13.24        2.59               21.0        118        2.69   \n",
       "4    14.20        1.76               15.2        112        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Set the values of the first 3 rows from alcohol as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      NaN        1.78               11.2        100        2.76   \n",
       "1      NaN        2.36               18.6        101        3.24   \n",
       "2      NaN        1.95               16.8        113        3.49   \n",
       "3    13.24        2.59               21.0        118        2.69   \n",
       "4    14.20        1.76               15.2        112        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Now set the value of the rows 3 and 4 of magnesium as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      NaN        1.78               11.2      100.0        2.76   \n",
       "1      NaN        2.36               18.6      101.0        3.24   \n",
       "2      NaN        1.95               16.8        NaN        3.49   \n",
       "3    13.24        2.59               21.0        NaN        2.69   \n",
       "4    14.20        1.76               15.2      112.0        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Fill the value of NaN with the number 10 in alcohol and 100 in magnesium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0    10.00        1.78               11.2      100.0        2.76   \n",
       "1    10.00        2.36               18.6      101.0        3.24   \n",
       "2    10.00        1.95               16.8      100.0        3.49   \n",
       "3    13.24        2.59               21.0      100.0        2.69   \n",
       "4    14.20        1.76               15.2      112.0        3.39   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Count the number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol              0\n",
       "malic_acid           0\n",
       "alcalinity_of_ash    0\n",
       "magnesium            0\n",
       "flavanoids           0\n",
       "proanthocyanins      0\n",
       "hue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create an array of 10 random numbers up until 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 0, 5, 0, 9, 4, 0, 7, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  Use random numbers you generated as an index and assign NaN value to each of cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.76</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.87</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.52</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.64</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0      NaN        1.78               11.2      100.0        2.76   \n",
       "1    10.00        2.36               18.6      101.0        3.24   \n",
       "2      NaN        1.95               16.8      100.0        3.49   \n",
       "3      NaN        2.59               21.0      100.0        2.69   \n",
       "4      NaN        1.76               15.2      112.0        3.39   \n",
       "5      NaN        1.87               14.6       96.0        2.52   \n",
       "6    14.06        2.15               17.6      121.0        2.51   \n",
       "7      NaN        1.64               14.0       97.0        2.98   \n",
       "8    13.86        1.35               16.0       98.0        3.15   \n",
       "9      NaN        2.16               18.0      105.0        3.32   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             1.28  4.38  \n",
       "1             2.81  5.68  \n",
       "2             2.18  7.80  \n",
       "3             1.82  4.32  \n",
       "4             1.97  6.75  \n",
       "5             1.98  5.25  \n",
       "6             1.25  5.05  \n",
       "7             1.98  5.20  \n",
       "8             1.85  7.22  \n",
       "9             2.38  5.75  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12.  How many missing values do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol              7\n",
       "malic_acid           0\n",
       "alcalinity_of_ash    0\n",
       "magnesium            0\n",
       "flavanoids           0\n",
       "proanthocyanins      0\n",
       "hue                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Delete the rows that contain missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "1     10.00        2.36               18.6      101.0        3.24   \n",
       "6     14.06        2.15               17.6      121.0        2.51   \n",
       "8     13.86        1.35               16.0       98.0        3.15   \n",
       "10    14.12        1.48               16.8       95.0        2.43   \n",
       "11    13.75        1.73               16.0       89.0        2.76   \n",
       "\n",
       "    proanthocyanins   hue  \n",
       "1              2.81  5.68  \n",
       "6              1.25  5.05  \n",
       "8              1.85  7.22  \n",
       "10             1.57  5.00  \n",
       "11             1.81  5.60  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Print only the non-null values in alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      True\n",
       "6      True\n",
       "8      True\n",
       "10     True\n",
       "11     True\n",
       "12     True\n",
       "13     True\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "20     True\n",
       "21     True\n",
       "22     True\n",
       "23     True\n",
       "24     True\n",
       "25     True\n",
       "26     True\n",
       "27     True\n",
       "28     True\n",
       "29     True\n",
       "30     True\n",
       "31     True\n",
       "32     True\n",
       "33     True\n",
       "34     True\n",
       "35     True\n",
       "36     True\n",
       "       ... \n",
       "147    True\n",
       "148    True\n",
       "149    True\n",
       "150    True\n",
       "151    True\n",
       "152    True\n",
       "153    True\n",
       "154    True\n",
       "155    True\n",
       "156    True\n",
       "157    True\n",
       "158    True\n",
       "159    True\n",
       "160    True\n",
       "161    True\n",
       "162    True\n",
       "163    True\n",
       "164    True\n",
       "165    True\n",
       "166    True\n",
       "167    True\n",
       "168    True\n",
       "169    True\n",
       "170    True\n",
       "171    True\n",
       "172    True\n",
       "173    True\n",
       "174    True\n",
       "175    True\n",
       "176    True\n",
       "Name: alcohol, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      10.00\n",
       "6      14.06\n",
       "8      13.86\n",
       "10     14.12\n",
       "11     13.75\n",
       "12     14.75\n",
       "13     14.38\n",
       "14     13.63\n",
       "15     14.30\n",
       "16     13.83\n",
       "17     14.19\n",
       "18     13.64\n",
       "19     14.06\n",
       "20     12.93\n",
       "21     13.71\n",
       "22     12.85\n",
       "23     13.50\n",
       "24     13.05\n",
       "25     13.39\n",
       "26     13.30\n",
       "27     13.87\n",
       "28     14.02\n",
       "29     13.73\n",
       "30     13.58\n",
       "31     13.68\n",
       "32     13.76\n",
       "33     13.51\n",
       "34     13.48\n",
       "35     13.28\n",
       "36     13.05\n",
       "       ...  \n",
       "147    13.32\n",
       "148    13.08\n",
       "149    13.50\n",
       "150    12.79\n",
       "151    13.11\n",
       "152    13.23\n",
       "153    12.58\n",
       "154    13.17\n",
       "155    13.84\n",
       "156    12.45\n",
       "157    14.34\n",
       "158    13.48\n",
       "159    12.36\n",
       "160    13.69\n",
       "161    12.85\n",
       "162    12.96\n",
       "163    13.78\n",
       "164    13.73\n",
       "165    13.45\n",
       "166    12.82\n",
       "167    13.58\n",
       "168    13.40\n",
       "169    12.20\n",
       "170    12.77\n",
       "171    14.16\n",
       "172    13.71\n",
       "173    13.40\n",
       "174    13.27\n",
       "175    13.17\n",
       "176    14.13\n",
       "Name: alcohol, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15.  Reset the index, so it starts with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>hue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid  alcalinity_of_ash  magnesium  flavanoids  \\\n",
       "0    10.00        2.36               18.6      101.0        3.24   \n",
       "1    14.06        2.15               17.6      121.0        2.51   \n",
       "2    13.86        1.35               16.0       98.0        3.15   \n",
       "3    14.12        1.48               16.8       95.0        2.43   \n",
       "4    13.75        1.73               16.0       89.0        2.76   \n",
       "\n",
       "   proanthocyanins   hue  \n",
       "0             2.81  5.68  \n",
       "1             1.25  5.05  \n",
       "2             1.85  7.22  \n",
       "3             1.57  5.00  \n",
       "4             1.81  5.60  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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================================================
FILE: 11_Indexing/Exercises.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ex - "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data \n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Contributor Covenant Code of Conduct

## Our Pledge

In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to making participation in our project and
our community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, sex characteristics, gender identity and expression,
level of experience, education, socio-economic status, nationality, personal
appearance, race, religion, or sexual identity and orientation.

## Our Standards

Examples of behavior that contributes to creating a positive environment
include:

* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members

Examples of unacceptable behavior by participants include:

* The use of sexualized language or imagery and unwelcome sexual attention or
 advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic
 address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
 professional setting

## Our Responsibilities

Project maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.

Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
permanently any contributor for other behaviors that they deem inappropriate,
threatening, offensive, or harmful.

## Scope

This Code of Conduct applies both within project spaces and in public spaces
when an individual is representing the project or its community. Examples of
representing a project or community include using an official project e-mail
address, posting via an official social media account, or acting as an appointed
representative at an online or offline event. Representation of a project may be
further defined and clarified by project maintainers.

## Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at gui.psamora@gmail.com. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
Further details of specific enforcement policies may be posted separately.

Project maintainers who do not follow or enforce the Code of Conduct in good
faith may face temporary or permanent repercussions as determined by other
members of the project's leadership.

## Attribution

This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html

[homepage]: https://www.contributor-covenant.org

For answers to common questions about this code of conduct, see
https://www.contributor-covenant.org/faq


================================================
FILE: LICENSE
================================================
BSD 3-Clause License

Copyright (c) 2018, Guilherme Samora
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

================================================
FILE: README.md
================================================
# Pandas Exercises

Fed up with a ton of tutorials but no easy way to find exercises I decided to create a repo just with exercises to practice pandas.
Don't get me wrong, tutorials are great resources, but to learn is to do. So unless you practice you won't learn.

There will be three different types of files:  
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1. Exercise instructions  
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2. Solutions without code  
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3. Solutions with code and comments

My suggestion is that you learn a topic in a tutorial, video or documentation and then do the first exercises.
Learn one more topic and do more exercises. If you are stuck, don't go directly to the solution with code files. Check the solutions only and try to get the correct answer.

Suggestions and collaborations are more than welcome.🙂 Please open an issue or make a PR indicating the exercise and your problem/solution.

# Lessons

|				                                  |				                                   |                   |
|:-----------------------------------------------:|:----------------------------------------------:|:-----------------:|
|[Getting and knowing](#getting-and-knowing)      | [Merge](#merge)                                |[Time Series](#time-series)|
|[Filtering and Sorting](#filtering-and-sorting)  | [Stats](#stats)                                |[Deleting](#deleting)       |
|[Grouping](#grouping)							  | [Visualization](#visualization)                |Indexing           |
|[Apply](#apply)							      | [Creating Series and DataFrames](#creating-series-and-dataframes) 		            |Exporting|

### [Getting and knowing](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data)  
- [ ] [Chipotle](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data/Chipotle)
- [ ] [Occupation](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data/Occupation)
- [ ] [World Food Facts](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data/World%20Food%20Facts)


### [Filtering and Sorting](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting)
- [ ] [Chipotle](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting/Chipotle)
- [ ] [Euro12](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting/Euro12)  
- [ ] [Fictional Army](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting/Fictional%20Army)

### [Grouping](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping)
- [ ] [Alcohol Consumption](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping/Alcohol_Consumption)  
- [ ] [Occupation](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping/Occupation)  
- [ ] [Regiment](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping/Regiment)

### [Apply](https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply)
- [ ] [Students Alcohol Consumption](https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply/Students_Alcohol_Consumption)  
- [ ] [US_Crime_Rates](https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply/US_Crime_Rates)     

### [Merge](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge)
- [ ] [Auto_MPG](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge/Auto_MPG)  
- [ ] [Fictitious Names](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge/Fictitous%20Names)  
- [ ] [House Market](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge/Housing%20Market)  

### [Stats](https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats)
- [ ] [US_Baby_Names](https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats/US_Baby_Names)  
- [ ] [Wind_Stats](https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats/Wind_Stats)

### [Visualization](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization)
- [ ] [Chipotle](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Chipotle)  
- [ ] [Titanic Disaster](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Titanic_Desaster)  
- [ ] [Scores](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Scores)  
- [ ] [Online Retail](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Online_Retail)  
- [ ] [Tips](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Tips)  

### [Creating Series and DataFrames](https://github.com/guipsamora/pandas_exercises/tree/master/08_Creating_Series_and_DataFrames)  
- [ ] [Pokemon](https://github.com/guipsamora/pandas_exercises/tree/master/08_Creating_Series_and_DataFrames/Pokemon)  

### [Time Series](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series)  
- [ ] [Apple_Stock](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series/Apple_Stock)  
- [ ] [Getting_Financial_Data](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series/Getting_Financial_Data)  
- [ ] [Investor_Flow_of_Funds_US](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series/Getting_Financial_Data)  

### [Deleting](https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting)  
- [ ] [Iris](https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting/Iris)  
- [ ] [Wine](https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting/Wine)  

# Video Solutions

Video tutorials of data scientists working through the above exercises:

[Data Talks - Pandas Learning By Doing](https://www.youtube.com/watch?v=pu3IpU937xs&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv)


================================================
FILE: Template/Exercises.ipynb
================================================
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   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data \n",
    "\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
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    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called "
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   "metadata": {
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   "outputs": [],
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    "### Step 7. "
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   "cell_type": "code",
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    "### Step 8. "
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
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    "### Step 9. "
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
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   "cell_type": "markdown",
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    "### Step 10. "
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  {
   "cell_type": "code",
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   "outputs": [],
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    "### Step 11. "
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    "### Step 13. "
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
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    "### Step 14. "
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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    "### Step 15. "
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================================================
FILE: Template/Solutions.ipynb
================================================
{
 "cells": [
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   "metadata": {},
   "source": [
    "# Ex - "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will create a data \n",
    "\n",
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    "\n",
    "### Step 1. Import the necessary libraries"
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  {
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   "metadata": {},
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   "metadata": {
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   "metadata": {
    "collapsed": true
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    "### Step 7. "
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    "### Step 8. "
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   "metadata": {
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    "### Step 10. "
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   "metadata": {
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 16. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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================================================
FILE: requirements.txt
================================================
numpy==1.22.0
matplotlib==2.0.2
seaborn==0.8.1
pandas==0.23.4