chapter-02-eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing Data Science: chapter 2 - EDA\n",
    "\n",
    "Python code for the exercise on **E**ploratory **D**ata **A**nalysis, 2nd chapter of [Doing Data Science](http://shop.oreilly.com/product/0636920028529.do)\n",
    "\n",
    "Get input data files from https://github.com/oreillymedia/doing_data_science or http://stat.columbia.edu/~rachel/nytN.csv where `N=[1, .., 31]`.\n",
    "\n",
    "**Author**: Damien Garaud\n",
    "\n",
    "**Project on Github**: https://github.com/garaud/doing_pydata_science\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "* [Retrieve Some Data](#Retrieve-Some-Data)\n",
    "* [Extract Data with pandas](#Extract-Data-with-pandas)\n",
    "* [Clean up some Data](#Clean-up-some-Data)\n",
    "* [Categorize by Age](#Categorize-by-Age)\n",
    "* [Click-through-rate aka CTR by age categories](#Click-through-rate-aka-CTR-by-age-categories)\n",
    "* [New Category based on the Click Behavior](#New-Category-based-on-the-Click-Behavior)\n",
    "* [CTR Timeseries](#CTR-Timeseries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve Some Data\n",
    "\n",
    "Quoting:\n",
    "\n",
    "> Each file represents one (simulated) day's worth of ads shown and clicks recorded on the NY Times home page in May 2012\n",
    "\n",
    "You have a script `down_nyt.py` script which can download all `*.csv` file into the `nyt-data` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "URL = 'http://stat.columbia.edu/~rachel/datasets/nyt1.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#resp = requests.get(URL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save it to a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#with open('nyt-data/nyt1.csv', 'bw') as fid:\n",
    "#    fid.write(resp.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nyt-data/nyt1.csv  nyt-data/nyt4.csv  nyt-data/nyt7.csv\r\n",
      "nyt-data/nyt2.csv  nyt-data/nyt5.csv  nyt-data/nyt8.csv\r\n",
      "nyt-data/nyt3.csv  nyt-data/nyt6.csv  nyt-data/nyt9.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls nyt-data/*.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Age\",\"Gender\",\"Impressions\",\"Clicks\",\"Signed_In\"\r\n",
      "36,0,3,0,1\r\n",
      "73,1,3,0,1\r\n",
      "30,0,3,0,1\r\n",
      "49,1,3,0,1\r\n",
      "47,1,11,0,1\r\n",
      "47,0,11,1,1\r\n",
      "0,0,7,1,0\r\n",
      "46,0,5,0,1\r\n",
      "16,0,3,0,1\r\n"
     ]
    }
   ],
   "source": [
    "!head nyt-data/nyt1.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Data with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas version 0.20.3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context(\"talk\")\n",
    "print(\"pandas version {}\".format(pd.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a matplotlib version >= 1.4, you can use `plt.style.use(\"ggplot\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plt.style.use(\"ggplot\")\n",
    "# pd.options.display.mpl_style = \"default\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read the file or the 'content'\n",
    "#df = pd.read_csv(StringIO(resp.content))\n",
    "df = pd.read_csv(\"nyt-data/nyt1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>36</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Age  Gender  Impressions  Clicks  Signed_In\n",
       "0   36       0            3       0          1\n",
       "1   73       1            3       0          1\n",
       "2   30       0            3       0          1\n",
       "3   49       1            3       0          1\n",
       "4   47       1           11       0          1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up some Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The book tells you that `female=0` and `male=1`. I turn these integer values into strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    female\n",
       "1      male\n",
       "2    female\n",
       "3      male\n",
       "4      male\n",
       "Name: Gender, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].apply(lambda x: 'male' if x else 'female').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I overwrite these values into the existing `Gender` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Gender'] = df['Gender'].apply(lambda x: 'male' if x else 'female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
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       "      <td>male</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
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       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>male</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Impressions  Clicks  Signed_In\n",
       "0   36  female            3       0          1\n",
       "1   73    male            3       0          1\n",
       "2   30  female            3       0          1\n",
       "3   49    male            3       0          1\n",
       "4   47    male           11       0          1\n",
       "5   47  female           11       1          1\n",
       "6    0  female            7       1          0\n",
       "7   46  female            5       0          1\n",
       "8   16  female            3       0          1\n",
       "9   52  female            4       0          1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorize by Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Want to categorize by age `<18, 18-24, 25-34, 35-44, 45-54, 55-64, +65`.\n",
    "\n",
    "Create bins and labels and use `pd.cut` with `right=False` to not include rightmost edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_range = [0, 19, 25, 35, 45, 55, 65, np.inf]\n",
    "age_labels = ['18--', '18-24', '25-34', '35-44', '45-54', '55-64', '65++']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         35-44\n",
       "1          65++\n",
       "2         25-34\n",
       "3         45-54\n",
       "4         45-54\n",
       "5         45-54\n",
       "6          18--\n",
       "7         45-54\n",
       "8          18--\n",
       "9         45-54\n",
       "10         18--\n",
       "11        18-24\n",
       "12         18--\n",
       "13        55-64\n",
       "14        25-34\n",
       "15         18--\n",
       "16        35-44\n",
       "17        25-34\n",
       "18        35-44\n",
       "19         18--\n",
       "20        55-64\n",
       "21        55-64\n",
       "22        45-54\n",
       "23        25-34\n",
       "24         18--\n",
       "25        18-24\n",
       "26        18-24\n",
       "27        45-54\n",
       "28        45-54\n",
       "29        18-24\n",
       "          ...  \n",
       "458411    55-64\n",
       "458412     65++\n",
       "458413     18--\n",
       "458414    18-24\n",
       "458415    35-44\n",
       "458416    25-34\n",
       "458417    35-44\n",
       "458418    55-64\n",
       "458419    45-54\n",
       "458420    45-54\n",
       "458421    45-54\n",
       "458422    45-54\n",
       "458423    18-24\n",
       "458424    18-24\n",
       "458425    35-44\n",
       "458426    45-54\n",
       "458427    35-44\n",
       "458428    35-44\n",
       "458429    45-54\n",
       "458430     18--\n",
       "458431    18-24\n",
       "458432    25-34\n",
       "458433    18-24\n",
       "458434    55-64\n",
       "458435    45-54\n",
       "458436     18--\n",
       "458437     18--\n",
       "458438     65++\n",
       "458439     18--\n",
       "458440     18--\n",
       "Name: Age, Length: 458441, dtype: category\n",
       "Categories (7, object): [18-- < 18-24 < 25-34 < 35-44 < 45-54 < 55-64 < 65++]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df['Age'], bins=age_range, right=False, labels=age_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put this into a new columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Age_group'] = pd.cut(df['Age'], bins=age_range, right=False, labels=age_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>65++</td>\n",
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       "      <td>30</td>\n",
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       "      <td>25-34</td>\n",
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       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>45-54</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Impressions  Clicks  Signed_In Age_group\n",
       "0   36  female            3       0          1     35-44\n",
       "1   73    male            3       0          1      65++\n",
       "2   30  female            3       0          1     25-34\n",
       "3   49    male            3       0          1     45-54\n",
       "4   47    male           11       0          1     45-54\n",
       "5   47  female           11       1          1     45-54\n",
       "6    0  female            7       1          0      18--\n",
       "7   46  female            5       0          1     45-54\n",
       "8   16  female            3       0          1      18--\n",
       "9   52  female            4       0          1     45-54"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Click-through-rate aka CTR by age categories\n",
    "\n",
    "Impressions and click-rate distributions according to this age category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's group data by this `Age_group` just for the impressions and clicks columns and count the number of each.\n",
    "\n",
    "Simple use the `groupby` function to the column `Age_group`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18--</th>\n",
       "      <td>781723</td>\n",
       "      <td>22003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>176584</td>\n",
       "      <td>1709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>290511</td>\n",
       "      <td>2937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>355824</td>\n",
       "      <td>3662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-54</th>\n",
       "      <td>322109</td>\n",
       "      <td>3232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-64</th>\n",
       "      <td>224688</td>\n",
       "      <td>4556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65++</th>\n",
       "      <td>144120</td>\n",
       "      <td>4350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Impressions  Clicks\n",
       "Age_group                     \n",
       "18--            781723   22003\n",
       "18-24           176584    1709\n",
       "25-34           290511    2937\n",
       "35-44           355824    3662\n",
       "45-54           322109    3232\n",
       "55-64           224688    4556\n",
       "65++            144120    4350"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Age_group')[['Impressions', 'Clicks']].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are ! I count the number of values for each category for the two columns. Compute the CTR (click-through-rate, i.e. `clicks/impression`) and let's plot some stuff."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some stats about age population categorizes by the age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\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>len</th>\n",
       "      <th>amin</th>\n",
       "      <th>mean</th>\n",
       "      <th>amax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18--</th>\n",
       "      <td>156358</td>\n",
       "      <td>0</td>\n",
       "      <td>1.974168</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>35270</td>\n",
       "      <td>19</td>\n",
       "      <td>21.269039</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>58174</td>\n",
       "      <td>25</td>\n",
       "      <td>29.503352</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>70860</td>\n",
       "      <td>35</td>\n",
       "      <td>39.494680</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-54</th>\n",
       "      <td>64288</td>\n",
       "      <td>45</td>\n",
       "      <td>49.492580</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-64</th>\n",
       "      <td>44738</td>\n",
       "      <td>55</td>\n",
       "      <td>59.498189</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65++</th>\n",
       "      <td>28753</td>\n",
       "      <td>65</td>\n",
       "      <td>72.988697</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              len  amin       mean  amax\n",
       "Age_group                               \n",
       "18--       156358     0   1.974168    18\n",
       "18-24       35270    19  21.269039    24\n",
       "25-34       58174    25  29.503352    34\n",
       "35-44       70860    35  39.494680    44\n",
       "45-54       64288    45  49.492580    54\n",
       "55-64       44738    55  59.498189    64\n",
       "65++        28753    65  72.988697   108"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Age_group')['Age'].agg([len,  np.min, np.mean, np.max])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to store *Impressions* and *Clicks* numbers for each age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grouped = df.groupby(\"Age_group\")[['Impressions', 'Clicks']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_age_group = grouped.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18--</th>\n",
       "      <td>781723</td>\n",
       "      <td>22003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>176584</td>\n",
       "      <td>1709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>290511</td>\n",
       "      <td>2937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>355824</td>\n",
       "      <td>3662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-54</th>\n",
       "      <td>322109</td>\n",
       "      <td>3232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Impressions  Clicks\n",
       "Age_group                     \n",
       "18--            781723   22003\n",
       "18-24           176584    1709\n",
       "25-34           290511    2937\n",
       "35-44           355824    3662\n",
       "45-54           322109    3232"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also call different aggregation functions such as `max`, `mean` or `sum`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_stat = grouped.agg([len, np.mean, np.max, np.sum])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\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>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Impressions</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Clicks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "      <th>amax</th>\n",
       "      <th>sum</th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "      <th>amax</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_group</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>18--</th>\n",
       "      <td>156358</td>\n",
       "      <td>4.999571</td>\n",
       "      <td>18</td>\n",
       "      <td>781723</td>\n",
       "      <td>156358</td>\n",
       "      <td>0.140722</td>\n",
       "      <td>4</td>\n",
       "      <td>22003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-24</th>\n",
       "      <td>35270</td>\n",
       "      <td>5.006635</td>\n",
       "      <td>17</td>\n",
       "      <td>176584</td>\n",
       "      <td>35270</td>\n",
       "      <td>0.048455</td>\n",
       "      <td>2</td>\n",
       "      <td>1709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34</th>\n",
       "      <td>58174</td>\n",
       "      <td>4.993829</td>\n",
       "      <td>17</td>\n",
       "      <td>290511</td>\n",
       "      <td>58174</td>\n",
       "      <td>0.050486</td>\n",
       "      <td>2</td>\n",
       "      <td>2937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-44</th>\n",
       "      <td>70860</td>\n",
       "      <td>5.021507</td>\n",
       "      <td>20</td>\n",
       "      <td>355824</td>\n",
       "      <td>70860</td>\n",
       "      <td>0.051679</td>\n",
       "      <td>3</td>\n",
       "      <td>3662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-54</th>\n",
       "      <td>64288</td>\n",
       "      <td>5.010406</td>\n",
       "      <td>17</td>\n",
       "      <td>322109</td>\n",
       "      <td>64288</td>\n",
       "      <td>0.050274</td>\n",
       "      <td>2</td>\n",
       "      <td>3232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-64</th>\n",
       "      <td>44738</td>\n",
       "      <td>5.022308</td>\n",
       "      <td>16</td>\n",
       "      <td>224688</td>\n",
       "      <td>44738</td>\n",
       "      <td>0.101837</td>\n",
       "      <td>3</td>\n",
       "      <td>4556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65++</th>\n",
       "      <td>28753</td>\n",
       "      <td>5.012347</td>\n",
       "      <td>16</td>\n",
       "      <td>144120</td>\n",
       "      <td>28753</td>\n",
       "      <td>0.151289</td>\n",
       "      <td>3</td>\n",
       "      <td>4350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Impressions                         Clicks                      \n",
       "                  len      mean amax     sum     len      mean amax    sum\n",
       "Age_group                                                                 \n",
       "18--           156358  4.999571   18  781723  156358  0.140722    4  22003\n",
       "18-24           35270  5.006635   17  176584   35270  0.048455    2   1709\n",
       "25-34           58174  4.993829   17  290511   58174  0.050486    2   2937\n",
       "35-44           70860  5.021507   20  355824   70860  0.051679    3   3662\n",
       "45-54           64288  5.010406   17  322109   64288  0.050274    2   3232\n",
       "55-64           44738  5.022308   16  224688   44738  0.101837    3   4556\n",
       "65++            28753  5.012347   16  144120   28753  0.151289    3   4350"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_stat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the click rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_age_group['CTR'] = df_age_group['Clicks'] / df_age_group['Impressions']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f487f951400>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f487ea685c0>], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Am9PyR0naLS+VtJWk9wNnkAwwKDergUvovrUrx58vx5+f7hw7lFH8uQwugMYh\n0DOBqUA/YC5wekSsknQCcF1EDCpY/2DgSmA8sAj4SkQ8UFA+kSTR7E3Sv/OdiLgio9MxM7MS5ZZ4\nzMysd/KUOWZmliknHjMzy5QTj5mZZcqJx8zMMuXEY2ZmmXLiMTOzTDnxmJlZppx4zMwsU048ZmaW\nKSeeHEjaJ+8YejNJj0hq9Z4h5crx56s7xy/p2dbX6np53Raht7uXtt0rqOxIWgTsHRHL846lOZIu\naKZod+AcSesi4tIsY2oLx5+v7h5/M3bIOwDwXG25SP9gu0XikXR9M0UnAnOA9RFxeoYhlUzSZpLb\nbLxRVLQPMA/YGBEfyTywEjn+fHX3+Jsi6Z+l3pW5K7nGkw/lHUAbnAo8BixsoqxP+ihX/w/4T+Bb\nEfHLhoWSVgKfjIjXc4usNI4/X909fiR9vmhRX0knkn4HRcQt2UflGk8uJG0XEa/mHUcpJB0CfBf4\nIXBlRGxOl68EJpf7fz5J2wM/At4EzoyIf3SX2MHx560HxP/bokX7kvyQBIi8amweXJCPr+UdQKki\n4l5gD2An4ElJ78s5pDaJiKURcRBJv9qTkk7LO6a2cPz56gHxf7jwAdQUvM+tmdA1nhx0pz6eQpI+\nDlwL/AI4E9i1O/zqa5D+ev0hcCCwbXeKHRrjvwE4CMefue7+9wPl08fjxJMDSdURMTjvONpD0mCS\nO8EeBOwZEStyDsnMSiTpjYgYnnccbmrLx+/zDqC9IqI6Ik6PiB27S9KRVHgL9Z0kHSppRJ4xdYSk\nSyV1uxozgKQdJY3PO472SGM/U9IZkspiWHI7HJZ3AOAaj5VAUj/gX4GXI+LNorJ9IuIP+UTWMkm7\nAr8B3g18G/gT8GOgL7ABODAiyuKCuqZI+lAzRXcDU4FVEfHH7CJqG0mXRcTX0tcjgTuBvdPip4Aj\nyvw6sF8B/xUR8yUdRBL/i2nxROATEfFobgF2Y048GZD0UWAycG9EvCDpZJJfHvOBmRFRn2uALZD0\nXuABYAzJyJ7/jIgbCsrLtr9K0r3AXGAzcAXwFeB7QGX6PCYijsovwpal15EEzQ+/j4iozDCkNin8\n20ivB9sGOI3knL5PkjhPzTHEFklaDYyOiHpJTwDfi4ifpmXHAf8REXvlGmQ35cTTxSR9EZhFciHa\nvwAXk1wb8CvgKOD2iDgvvwhbJuke4BngUpJO1RuAb0fElWl52fZXpcNex5I0KW8AhkfEurRsJPCX\niHh3jiFWH3WpAAALJklEQVS2SNJsYGvgtIhYXLC8WwznLfzbkLQQ+EhELEnfbwP8MSLKtslK0jqS\nxLNR0irgXQ0/EiUJWBMRQ3MNso3KZXCB+3i63tkkTTofAo4huSbm8Ig4Fzgc+HSewZVgD+CSiFgf\nEb8G9gO+IumsnOMqRVVE1EXEJmBdQ9IBiIhVQFkmzAYR8RmSUYQPSvpy3vG0Q+Gv2sENSQcgIpYB\n5d7P9iTJDB0AfwMKazd78c4ZDbqDsrh43TMXdL0xEfFE+voxgIh4oeFZUu6/PlpRlT5qASJigaSP\nAb+VtIktv1zKzQpJW0fEPynqVE1/ca9rerPyERF3SHoUuDpt3jmV8v7MC20l6YH0dX9J20bEa9BY\n41yfX2glOR94WNKHSWr990q6Iy07GpiWW2TtVxZ/O048XW+lpD0i4imSjtWQtFNEvChpIrA65/ha\n8wLwIeChhgUR8Xw6o8GDwIC8AivB1cBQ4J9NDID4BMlFgWUvIlYDx0s6imSwxLCcQyrVjILXf2DL\nGuZ+JH2HZSsinpa0F3AR8FlgK5JWimeBUyLitjzjK4Wki4oW9S9cFhHfzDgkwH08XU7SucB0kj/W\nISQTax4H3EHSx3NHRDQ3C27u0nmdBkXE95so24dk1E+3miixO5M0HHgfSf9Ibd7xWHmTdGPRouOB\nn6WvIyJOyTgkwIknE5KOACaQXPG/guQX1O4kvwJnlfOotp5G0rSIuDzvONrL8eerB8RfFoMLnHhy\nJmn7iFiadxxtIenaiOgOgwveoZyHf5fC8eerB8RfFonHo9pylF6Y+UrecbTD5/IOoAPKYlRPBzj+\nfDn+TuDEk7+y+ENoo+4Yc4Mlra9S1hx/vrp7/GUxu7yb2rpYOuS4JZXlfPV5UyT9JiIOzTsOM+ue\nnHi6mKS1JBeRLmqiuB/JNDrdKvF0V5K2IrnwT8ATEVHu15FswfHny/F3Hl/H0/X+DKyPiEeKC9I+\nnrJvtuquc81JujUiTkhfjyOZt+3dJBfRrZR0UES8nF+ELXP8+XL8Xcd9PF3vGuCfzZTVAidnGEub\npXPN3Ql8Eng0nSrn6ySDIk4BLssxvNYcXvD6MpKZI4aSXID5AMn8c+XM8efL8XcRN7VZiyS9CJwU\nEU9I2o9kBoPJac1nZ5Ja0Lhcg2xG0SSVS4A9Gu4hlE5V9NeI2CbPGFvi+PPl+LuOazzWmhbnmiOZ\nPblcFf6q6hMFN65L528r9+sxHH++HH8XceKx1qyUtEf6unGuOYBuMNfcQEkLJC0AhqfxAt1mklDH\nny/H30U8uMBa8wOSmagb5pq7FLgznaX3KODneQbXiuJ5qOoKXk8Gbs0wlvZw/Ply/F3EfTzWKs81\nVz4kDSMZmfR/EbE573jaQ9KZwC3dbThydydpCMlM80EyyWx1brE48VhHdMe55roLSduRJPtJJHeu\nfQ64GxhIcl3YQRFRtlMuSWru7q5/AQ4Clpf7XVSLSepD8r3Z2oXhuZP0PeCHEfGspA+Q3FKjT1q8\nATg4Iv6aR2zu47F2K/e55iQNl/QrSW9IekTS5KLycm+jvxJ4ArgZ+G+SX6vjgW2BvwK53EulDV4D\nXk2fG16/SjIgZX76umylNbOG1wMl3QrUANWS/lfSoPyiK8mngefT17OA70bE1ukkof9DcjfkXLjG\nY+2WJp4NEVGWP2AkXU/yJf094EDgCyS3HX80LW8cblqOJP0DGAdUAtUkIwwbhsNuCzweEdvlF2HL\nJP2R5Iv6XGBtw2KSpHMo8I/C22GXm8KZqCV9G/g34CskTVVXkDRXfTXHEFskqRoYHhF1klYCYyOi\nLi2rBFbmNVN1WX5hWPmQtKm5B8mXYTn/cjkUODEifhMR/0nS2Xq7krtKQnnHDrBVRGyMiDeBdUXD\nYV+j/O9Eug9wP8nND98fEUsiYjGwGXi1nJNOqnBWkSOAqRHxx4h4HJgKHJNLVKX7G29fRLoU2Kmg\nbBLwVuYRpTyqzVqzgVbmmss2nDYZDKxpeBMRcyRVAHdL+nh+YZVstaTBaSfwFwsLJI0C3swnrNJE\n0pxypaS7gB9JOh74d8o/4TcojHNkw/VrABHxkqTROcTUFhcCv5Q0Bfg1cJ+kH6dlJwPX5RWYE4+1\npjvPNfcq8F6ScwAgIn6Zts3fT5I4y9nPgLFAdUQUD1v/NEmTVdmLiAWS9if5ATOf5AdBd9A/ba4F\nkKRREbEyfTOUHGsMpYiIByUdTXIJxAdJWrguBBYDV0XEFXnF5j4ea5GkzwD/jIi5TZRVkDRl3Zx9\nZK2TdDlQFxHfaKLsyySdrd2yuVmSoLFW0W1ImgDsC8yOiI15x9MSSTcWLboqIp5Jyz4DnBkRH84+\nsraTNAAYTvIjJvdBNU48ZmaWqW75a8/MzLovJx4zM8uUE4+ZmWXKicesBJLGSaqV9HJDx76ZtY8T\nj1lpTiOZN60K+EjOsZREUt+8YzBrihOPWSskVZFccHcT8FOSJFRYPkHSQ5I2Slok6XOS/k/S9IJ1\nBku6RtLfJdVIelLSgW2IYYqkp9JjPC/pY+nrqWn5OEkh6XhJD0haD1yQlu0j6Q/p+qsk/TCdqbhh\n3zdJuq/oeFMlbSx4Pz09pxMlvZLu635J25f+SZolnHjMWncYUA/8FrgFOErSSGi8lukOkotR9wU+\nBZxOMkcc6ToimVV6J5JpViYDs4HfSNq1tYNLGgjcQzLtyR7AmSQXBfZpYvWZwI3Ae4AbJY0luVj2\nJeADwGdJamw/bMsHkNqOJOl+EtgfGAHc1o79WC/nmQvMWnc68JP0/jcLJD0DfB74Dsnko+8Fxqfz\nkCHpJGBhwfYHkFw5PrrgHijfkfRRkiTy/1o5/glAf+CkiKhJj3EuSSIsdm3hLAeSZgCrgFPTCSKf\nk/RFkulTJkTEwib20Zyt0hheSff9+XR/+0bEY23Yj/VyrvGYtUDJPXE+TlLTaXALbze37Uwyy/Li\nhsL0i3l5wfpTSGpEDc1sNZJqgI8BE2ndzsBzDUkn9QRNz3n2p6L3u5DMYl1498lHC8ra4h+F9/+J\niOdJ5sJr636sl3ONx6xlp5L8QPtb0WC2Skn7lbiPCmA1sHcTZRs6EFtTo+vaM3Ho5ib21VQzXqkx\nmLXINR6zZqT3LDkF+C/g/UWPe0hqPS8AYyTtULDdjsC7CnY1HxgJVEbEy0WPZSWE8gLwnqIbj+1Z\n4mk8D+ydDpBosH9BGSS3My++W+juTexrjKRxDW8k7QwMTeMzK5kTj1nzDgG2Aa6LiL8VPkia2z4J\nzCO5JfUtknZXcovhG4H1vN0U9hBJf8yvJB0uaUdJe0j6qqRS7ulyK7CRZLDAeyXtS3JHyc20fouB\na0iS3g2Sdkn7lb4P/LKgf+dB4L2SvpiO0DsDOLaJfW0AbpL0AUl7kNwZdV5E/L6EczBr5MRj1rzT\ngD80Uyu5m+SL/wTgaJJRb4+TjPK6gaRpbSM0ziB9WLrN94AXgTtJbpTW6s3Q0hvBHUZyN9L5wPXA\nxWlxizM8R8TfSfqoJgFPA/8LPEzShNiwzoPAN9LHn4H9gBlN7O5VkqR6O/B7kruKfrK1+M2KeXZq\ns04maQywDPhURNzeRcfYg2QgwQci4umuOEbR8aYDn42If+3qY1nP58EFZh0k6QigFvg/kr6Sy4C/\nA/e1tF0bj/E54DWSm3j9C/DfwDNZJB2zzuamNrOOG0CSCJ4naYb6J7B/RKwvZWNJPygcZl30aLi1\n+EjgxyTJ7UaSZrODO/1MzDLgpjaznEkaDQxppnhDiSPfzLoNJx4zM8uUm9rMzCxTTjxmZpYpJx4z\nM8uUE4+ZmWXKicfMzDL1/wENzg3pevemVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487eaf2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_age_group[['Impressions', 'CTR']].plot(kind='bar', subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Category based on the Click Behavior\n",
    "\n",
    "You can split the population into two new categories:\n",
    "\n",
    "  - impressions and no click\n",
    "  - impressions and one or more click(s)\n",
    "  \n",
    "You can assume that there isn't click when impression is equal to zero. For that, you can just drop all rows where `Impressions` is 0. Group by 'Clicks' and see what you get."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows according to `Impresssions` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask_imp = df['Impressions'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(458441,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_imp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[mask_imp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455375, 6)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drop 3066 values\n"
     ]
    }
   ],
   "source": [
    "print(\"Drop {} values\".format(mask_imp.shape[0] - df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by **Clicks**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "click_group = df.groupby('Clicks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of different Clicks values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "click_group.groups.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, you can see, sometimes, there is one more than one click."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can count the number of elements in each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clicks\n",
       "0    415537\n",
       "1     37372\n",
       "2      2330\n",
       "3       127\n",
       "4         9\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "click_group[\"Gender\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I counted the number of elements for the column \"Gender\". It's quite arbitrary because you have the same values for each column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a column `has_click` and set `True` if there is more than one click, `False` otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dag/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df.loc[:, 'has_click'] = df['Clicks'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "      <th>Age_group</th>\n",
       "      <th>has_click</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35-44</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65++</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25-34</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45-54</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>male</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45-54</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Impressions  Clicks  Signed_In Age_group  has_click\n",
       "0   36  female            3       0          1     35-44      False\n",
       "1   73    male            3       0          1      65++      False\n",
       "2   30  female            3       0          1     25-34      False\n",
       "3   49    male            3       0          1     45-54      False\n",
       "4   47    male           11       0          1     45-54      False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can continue to play with `groupby` with one label or multiple labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe you want to know the \"click\" behavior according to the gender AND the Age category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "click_gender_age_grp = df.groupby(['has_click', 'Gender', 'Age_group'])[[\"Impressions\", \"Clicks\"]].sum().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">Impressions</th>\n",
       "      <th colspan=\"7\" halign=\"left\">Clicks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Age_group</th>\n",
       "      <th>18--</th>\n",
       "      <th>18-24</th>\n",
       "      <th>25-34</th>\n",
       "      <th>35-44</th>\n",
       "      <th>45-54</th>\n",
       "      <th>55-64</th>\n",
       "      <th>65++</th>\n",
       "      <th>18--</th>\n",
       "      <th>18-24</th>\n",
       "      <th>25-34</th>\n",
       "      <th>35-44</th>\n",
       "      <th>45-54</th>\n",
       "      <th>55-64</th>\n",
       "      <th>65++</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_click</th>\n",
       "      <th>Gender</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 rowspan=\"2\" valign=\"top\">False</th>\n",
       "      <th>female</th>\n",
       "      <td>609712</td>\n",
       "      <td>77711</td>\n",
       "      <td>128230</td>\n",
       "      <td>156280</td>\n",
       "      <td>142708</td>\n",
       "      <td>92237</td>\n",
       "      <td>76350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>52701</td>\n",
       "      <td>88788</td>\n",
       "      <td>145248</td>\n",
       "      <td>177955</td>\n",
       "      <td>160547</td>\n",
       "      <td>106503</td>\n",
       "      <td>43261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">True</th>\n",
       "      <th>female</th>\n",
       "      <td>110254</td>\n",
       "      <td>4663</td>\n",
       "      <td>8062</td>\n",
       "      <td>10044</td>\n",
       "      <td>8942</td>\n",
       "      <td>11983</td>\n",
       "      <td>15606</td>\n",
       "      <td>20368</td>\n",
       "      <td>797</td>\n",
       "      <td>1388</td>\n",
       "      <td>1707</td>\n",
       "      <td>1542</td>\n",
       "      <td>2105</td>\n",
       "      <td>2765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>9056</td>\n",
       "      <td>5422</td>\n",
       "      <td>8971</td>\n",
       "      <td>11545</td>\n",
       "      <td>9912</td>\n",
       "      <td>13965</td>\n",
       "      <td>8903</td>\n",
       "      <td>1635</td>\n",
       "      <td>912</td>\n",
       "      <td>1549</td>\n",
       "      <td>1955</td>\n",
       "      <td>1690</td>\n",
       "      <td>2451</td>\n",
       "      <td>1585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Impressions                                                \\\n",
       "Age_group               18--  18-24   25-34   35-44   45-54   55-64   65++   \n",
       "has_click Gender                                                             \n",
       "False     female      609712  77711  128230  156280  142708   92237  76350   \n",
       "          male         52701  88788  145248  177955  160547  106503  43261   \n",
       "True      female      110254   4663    8062   10044    8942   11983  15606   \n",
       "          male          9056   5422    8971   11545    9912   13965   8903   \n",
       "\n",
       "                 Clicks                                      \n",
       "Age_group          18-- 18-24 25-34 35-44 45-54 55-64  65++  \n",
       "has_click Gender                                             \n",
       "False     female      0     0     0     0     0     0     0  \n",
       "          male        0     0     0     0     0     0     0  \n",
       "True      female  20368   797  1388  1707  1542  2105  2765  \n",
       "          male     1635   912  1549  1955  1690  2451  1585  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "click_gender_age_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, there no Clicks for the row `has_click=False`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep only `has_click=True` rows and compute the CTR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctr_gender_age = click_gender_age_grp.loc[True][\"Clicks\"] / click_gender_age_grp.loc[True][\"Impressions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Age_group</th>\n",
       "      <th>18--</th>\n",
       "      <th>18-24</th>\n",
       "      <th>25-34</th>\n",
       "      <th>35-44</th>\n",
       "      <th>45-54</th>\n",
       "      <th>55-64</th>\n",
       "      <th>65++</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</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>female</th>\n",
       "      <td>0.184737</td>\n",
       "      <td>0.170920</td>\n",
       "      <td>0.172166</td>\n",
       "      <td>0.169952</td>\n",
       "      <td>0.172445</td>\n",
       "      <td>0.175666</td>\n",
       "      <td>0.177175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.180543</td>\n",
       "      <td>0.168204</td>\n",
       "      <td>0.172667</td>\n",
       "      <td>0.169337</td>\n",
       "      <td>0.170500</td>\n",
       "      <td>0.175510</td>\n",
       "      <td>0.178030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Age_group      18--     18-24     25-34     35-44     45-54     55-64  \\\n",
       "Gender                                                                  \n",
       "female     0.184737  0.170920  0.172166  0.169952  0.172445  0.175666   \n",
       "male       0.180543  0.168204  0.172667  0.169337  0.170500  0.175510   \n",
       "\n",
       "Age_group      65++  \n",
       "Gender               \n",
       "female     0.177175  \n",
       "male       0.178030  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctr_gender_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CTR values for each age category between male and female are quite similar, higher for the youngest one and lower for the age group `35-44`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max CTR value 0.1847370616939068\n"
     ]
    }
   ],
   "source": [
    "ctr_gender_age_max = np.max(ctr_gender_age.values)\n",
    "print('Max CTR value {}'.format(ctr_gender_age_max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relative discrepancy with the max value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Age_group</th>\n",
       "      <th>18--</th>\n",
       "      <th>18-24</th>\n",
       "      <th>25-34</th>\n",
       "      <th>35-44</th>\n",
       "      <th>45-54</th>\n",
       "      <th>55-64</th>\n",
       "      <th>65++</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</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>female</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.479308</td>\n",
       "      <td>6.804994</td>\n",
       "      <td>8.003186</td>\n",
       "      <td>6.654008</td>\n",
       "      <td>4.910512</td>\n",
       "      <td>4.093178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>2.270132</td>\n",
       "      <td>8.949718</td>\n",
       "      <td>6.533382</td>\n",
       "      <td>8.336003</td>\n",
       "      <td>7.706444</td>\n",
       "      <td>4.994589</td>\n",
       "      <td>3.630665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Age_group      18--     18-24     25-34     35-44     45-54     55-64  \\\n",
       "Gender                                                                  \n",
       "female     0.000000  7.479308  6.804994  8.003186  6.654008  4.910512   \n",
       "male       2.270132  8.949718  6.533382  8.336003  7.706444  4.994589   \n",
       "\n",
       "Age_group      65++  \n",
       "Gender               \n",
       "female     4.093178  \n",
       "male       3.630665  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ctr_gender_age_max - ctr_gender_age) / ctr_gender_age_max * 100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CTR Timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load every files, i.e. one file by day for a month, and compute the CTR for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PATH = 'nyt-data'\n",
    "BASENAME = \"nyt\"\n",
    "EXT = \"csv\"\n",
    "DAY_NUMBER = 31\n",
    "AGE_RANGE = [0, 19, 25, 35, 45, 55, 65, np.inf]\n",
    "AGE_LABELS = ['18--', '18-24', '25-34', '35-44', '45-54', '55-64', '65++']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gender_filter(df):\n",
    "    colname = \"Gender\"\n",
    "    df[colname] = df[colname].apply(lambda x: 'male' if x else 'female')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_age_group(df):\n",
    "    df['Age_group'] = pd.cut(df['Age'], bins=AGE_RANGE,\n",
    "                             right=False, labels=AGE_LABELS)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def drop_no_impression_rows(df):\n",
    "    mask_imp = df['Impressions'] > 0\n",
    "    return df[mask_imp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_has_click(df):\n",
    "    df.loc[:, 'has_click'] = df['Clicks'] > 0\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean(df):\n",
    "    df = gender_filter(df)\n",
    "    df = add_age_group(df)\n",
    "    df = drop_no_impression_rows(df)\n",
    "    df = add_has_click(df)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_gender_age_ctr(df):\n",
    "    grp_col = ['has_click', 'Gender', 'Age_group']\n",
    "    gender_age_grp = df.groupby(grp_col)[[\"Impressions\", \"Clicks\"]].sum().unstack()\n",
    "    return (gender_age_grp.loc[True][\"Clicks\"] /\n",
    "            gender_age_grp.loc[True][\"Impressions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def main():\n",
    "    \"\"\"Loop on each file and return a dict of date: dataframe where the dataframe is\n",
    "    the CTR by gender and by age group.\n",
    "    \"\"\"\n",
    "    result = OrderedDict()\n",
    "    for idx in range(1, DAY_NUMBER + 1):\n",
    "        date_name = \"may {}\".format(idx)\n",
    "        fname = os.path.join(PATH, BASENAME + str(idx) + \".\" + EXT)\n",
    "        df = pd.read_csv(fname)\n",
    "        df = clean(df)\n",
    "        result[date_name] = get_gender_age_ctr(df)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ctr_age_by_date(d):\n",
    "    return pd.DataFrame([x.sum() for x in d.values()], index=d.keys())\n",
    "\n",
    "def ctr_gender_by_date(d):\n",
    "    return pd.DataFrame([x.sum(axis=1) for x in d.values()], index=d.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dag/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py:337: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/dag/miniconda3/lib/python3.5/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "result = main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(result, open(\"result.pkl\", \"bw\"), -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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8auVUNThVz1pHGYMAgo8LuhG17eoOlyE1USS/vNrBCz+lMfx/tvP0dwcuufOt\nDkmSqKmpwelUHSfWjBKqVx5HkRV6RcazJjsJyVLErAFhlJ7KY9u2zcTFxTdrH4KPC8bbutc3L3Bu\nOoFzW36TZXsUWUFKKeXaX4p5DFd8EKlBQR4QgPHeBPSDgy4ZQxBA98ILL7zQ2YNoL6xWO5ey3JKq\nG1jI/O9T6qvwRsT48fZNfUgMb1vRZL0o0NXPndHdA7h9YDh9Qj3R60TyK2zYJYW8Chubj5ewZO8J\nMkqqcDHoCPV2bXJ+jygKuLkZO2XOnZtPQJUTsacvum4d4xkURAGl0FpbtKKgS2hcxqG4ys5TKw+z\n/MApFCDAw0i1Q+JEhY1ii52RsX4t/p47c87bG6esUGyx4+FyaXlPmzvnH/+STXpJNYnh3swaENbs\n/XkY9cQGePBTaiHFVXasDomrolonGdKW/GPtUbLLrIR4ufD36+PRnydFw8Oox82g45esMo4VVTEg\n3JtQ76alqbTmPP/wl2xWHFQ7QSwY0417hkay+nAhZpuT/flmJiUEo2vHtBIppRQl14LgY0R/VcfK\nBAld3JGzK8HiQM6zoOvjj6Bv3PhQFIU1qeq96GC+GVmBQ/lm9p8wc02sPy7nWa+z+OmnVdx//1zW\nrl2Noih8ufzffHXkRyb2upbjVw8mKesEhpRVHN20lM2bf2bUqDHce+8fEMXmHYdg0CHG+yIXWqHc\njnKiCqXCjhjjfd50JEVR1IruH7LUvEC7jCLAKsHOs1gpC3Xnqm4B56xXd553Fpox2EkUWWr4y+oj\nfPFrHnZJDbE8M74780ZGY2rnG6BOFIj0defabgHMGhBG90ATdqfMiQobDkkhvbian1ILWX7gJAWV\nNXi7GQj0MF7QaOksw0Qpr0Hapnom9CNDEXw6MNztlJGPV6BUOtANDEQ4KwF6V1Yp8747yPHiagBu\n6BXMopv6ICsK+/PNpBVaMOpE+oe3zIC9Uo3BvXnl/Om/h/i/7VkUVtYwpKsP+jZKLm8tzZlzq0Pi\nxXVHccoKdw6OoGeXluWMRfi6YXNIHMg3c/BkJQnBJrr6tY+OZnP4NaeM93dkA/D02O7EBV/Y85UQ\n7MmOzFKKq+wcOmlmWp+QJhliLT3PNx0r5vWfjwMwpXcwDw6Pws/DSJSfO+vTiii02CmuquGaWP92\nk6Jy7jwFZjtidx90sR3zoFqHIAgIoR7IB0ugRkKxSY2OIa/cynOrUvl30glsThk3g8iobv5kllST\nX2FjW0YREnLtAAAgAElEQVQJI2L88LyEHsy6d4/jnnse4O5b7mK2eA13xt7InQOmo7+lJ89szcAe\n0J05t9/Bu3+dz6233sHQoVc12xCsQ9CJiHG+KGY7SpENpciGfKoasZv3Odd8Ob8K55ps5D2FUK16\nLcVu3himRLPDTWF7XgUppyq5JsafAFNDw08zBtuRS/Em2ZHewKag14nEBngwMSGIGf1CCfF2xWxz\nUmipweqQSTmlJn2vPVJEpc1JkKcLXq7nhoI6yzCRDpaiZFeCqw79mPAO1fwTTAakpEJQQAjxQPRT\nPR1OSea97Zm8uuE4VoeMu0HHXyb24P6rumLQiQyO9CGzpJrMkmp+zSkn2v/C2nPn40ozBsutDt7Y\neJyFmzIor231l1ZoYXtGKYMjffE+TwiyI2nOnG86Vsy6tCJ0Avx1Yg/cDC0XXR4Y4c2e7DIKLXZ2\nZZUxMSGo3R8aL4SsKDz9QyolVXZ6dvFk/ujYi167REEgLsjEioOnKLc5MeoFBoRfPCm/Jed5RkkV\nf/5vCg5ZoVcXT16d3LP+gSLa3x1FUdibV0FaYRW+7kZ6tdBQvxCKQ0bamAcK6AYFIQZ2fL6n4K4H\nQUDJsaAUWBHDPeormp2SzL+T8njmx1Sya/UE1XtRb25ODCUq2IvNRwoprXawLq2IgRHerUpbamsU\nmxPHMjWnD72IYUYsn2cUsyenHDeDyCs39sS1Fb+5MxFEAbGbN9RpRpbbkbMrEWO9EYw65BIbzvW5\nSFvzwawqBwjhHhhuiFLDwe56eod4sT6tkAqbk7RCC5N7d2kQedOMwXbkUrtJdqY3sCm4GnT06uLJ\n1D5dmNQzCG83A0UWOxU2JxU2J7/lVvBNcj67ssqwSzJhXq71P7bOMkycm0+AxYEY74uue/vqC56N\nYBCRs8xgcYBRhy7WmxMVVv68PIX1aWrRQHyQiXdv6cugyNNjEwSBETF+/JpTTqHFzrb0EgZH+hLc\nTNmfK8UYPDNEtT9fzW3qEejBdQnBHDpppqTawaqUAiJ83TpdgLk5c/7+9iyyy6wMjfLlpn6tqzgX\nRYHBkb6sSinAXOPk8KlKJvUM7jSZljWphSzbr3rkX7whnlDvphk6gSYXyq0OUk5VciDfzIS4oIsa\n+c09zyttTh5ZdpDiKjt+7gbev6XvOfsYEOFNWoGFnDIru7LLGBDhTUgbqysoJyzIh1RVCP2YcIRO\n6sAihHioec1VTuQTVej6+HG4qIr536ewJrUQSVbwczfwt4lxPDQ8Ck9XA6IoMKRbIDE+rmw5VoLZ\n5mTN4UK6B3pcEl5pxSnjWJ6BcsoKAuinRlMV5MpfVh3BISncPjCMUY2EYluDIAiqkLebDjmzEqqc\nSMcrUAqtSBtyUUrVHFQhwBX9xEj0I0IQzygs1IsCUX7urE4tpMhiJ8BkbBAt0IzBduRSukmuPlzA\n/OWnvYHDo/1Y1EnewKbg5WpgYIQPM/uHMjzaDxe9ml9oc8oUWmrYkVnK13tPcPhUJaIoEO7jiqfJ\nlerqGmocMpU1TsqsDoosNeRX2Mguq+Z4UTVHCiwcOGlmb24Fu7PL2JFRyqbjxaxPK2L14QJ+OFTA\ntvRS3I06wnxcLzg3itmuPokB+hEhCL4d/9SqVDlQci0oVU42eij8eXkKJyrUyubbB4bx4g0J+DXS\nDUavE7km1p+NR4sotznZml7CmO4BjXpdz8eVYAzmlFl55sdUvv5NDVG56kUeHRnNXybGcXW0H/3C\nvNiVVYbZ5mTD0WLMNgeDI33aNc/rQjR1zittTl7ZcAxZUQsr4oJaXzzg6aonwteNDWlFnKqsQQEG\nR3bsAxCAzSGxYOVhquwSo2L9uXPIuRXEF6JfmBc/phRgqZHILrVyfcL5ZbOgeee5JKsey0MnK9GL\nAm/f1LtRr7sgCAyP8WPzsWLKrA5+ySxlfFxgmz6US4dKUfKqEPxc0A8NbrPtNhdBFBBC3JEPlYBN\n4rfMMh7cnUFJlerBmtanC29O60VCF8/676FuzgNddVwV5cv2jBIqbE7WpxXh42ZoF09qU1FkBeeq\nbJTa3vD6iRHo4nxZvCeXXdnluOpFXp6c0CpP/IUQQzwQAlyR0yvAKqHUCVN7GtCPDUc/NhzRr/F7\nV7iPGzll1aQXV7PvRAWTe3Wpb9PY2cagoFzBHa1LSy2XhEbSxqNFPPVDKgAmFx2Pj47lhp7Bl6QR\neCGcktr+bk1qAZuPl1DjPF1p5qoXMepFqu0SzjYSKY3wcWVG/1Cm9O7S6EXa+Vsh0uZ8cNFhfKjX\nOfkbHYFcWI3jy6MA3IWF48h4u+p54fo4RsT4X3T9zJJq7vlPMpYaia6+bvzr9v5NNgh1OgE/P9Ml\nc543B7tT5otfc/lsdw722rGPiPHjybHdzvHQFFlqeO7HVJJPqF7DXl08eWVyQpt7cppCU+d85aFT\n/HPtUQw6gXUPXdWmRsZrG46xbP9JBODdGX0Y0rVj+2N/vjuH97ZnoRNgyV2DiGqBp2jdkUKeW3UE\ngJdvTLigGHdzzvP3t2fyr92q5uHT47px80U8slml1dz1lao/mBBs4qNb+7VZaNH+n2Mo+VWI/fwx\njItok222hswfMwhNMyOj8BDVWHyNPDuhe6Oh+rPnvKCyhj8vP8SxItWZMXtQOPOuie5wz7SiKDg3\nnkDep0ZedMO7oB/WhUqbkymf7MZSI3HHwPAOaX0o51pwrFR7ceuGBaPrF3DeAp0zKa6yM+Nfv1Jl\nl7guIYh/TlKrnOvmvLPQPIPtjFIrbVBS7aBnF08+urXfJesNvBiiKBDp68aYHoHcOiCUKD93rA6J\nk2YbDlmhxilzITvQoBPwdNHj62Yg0NOFUG9Xuvq6ERvgQUKwiT4hXiSGeyMKAgWVNZhtTnZllbE0\n+QQFlTV08XJtoE/m3JIPlQ7EeB90PTr2hlhHelUN0qES3BSBQmT0ESbendGXhCY+Ofu6q0/ZPx0p\noqw2fDYxPqhdE+s7m+S8CuYvT2HD0WIkBQJNRp6/Lo4Hr+6KZyOGsIdRz/U9g3FIauFNkcXOqsMF\nxAa4E+nbsSGrps75u1szyauwcU2sPzf2atsq0sGRvmzLKKGk2sHu7HKuTwiq9y60N2XVdp75MRWH\npDCjfyg39GqZxyvG352DJyvJK7ex/4SZaX26YDzPjbSpc77xWDFvbEwHYGqfLvzh6q4Xvc76uBmI\nDfBg3ZEiiqrsFFbWMKpb6wtKFIeEtPGEmi84OBgxoPME/our7Pxz7VHeOnqKUejxQ2Skmyt3zO1L\nxHkM+bPn3OSiZ2J8EGkFFvIqbBzIN5NZUs2IGL8OLe6S9hSqxRmA2C8A/cgQBEHgy19z2ZlVhkut\nV7Ajfg+CtxFdYqCqFxhmanK+urtRh7tRzy+ZpRwvrqJ/mBdhPm6aZ7A9uRQ8Jruzynj0u4MAtYZg\nx1aUdQTFlhqS8irw8nRFtjtw1elwM+pwN+hwM4i4G3W4GXTNajd0pKCSb/fls/ZIUQMP5MAIb2Ym\nhjEy2Av548MA6KdFd3ilnqIoLD9wkrc2Z/CY08hUjBR56gi5r3eLQph1niRQq46fn9jjojeky80z\nWGF18M7WTFYcUqU+BNSG7g+NiGqy52xbegkv/JSG2aYWmNw5JIIHh0edV9KkrWnKnJdW25n0wS4k\nBV66Ib5dOodklVYz9997sTpkhnX15e2be3eIl+b1n4/z7b58PIw6lt87GN9WiEfnlVu59fMk7JLC\nrYmhPDGm8XZhTZnz9OIq7v46GatDpk+IJx/M7Hde47IxPtmZzYe/qJXR80fHclsLZIDORM4y4/hO\n1VM0PtRbLeToYGRFYcXBU7yzNbO+kcHUAC+eKAFBAd3QYPQjGu/Ecr45d0oyr/58vF6up0+IFwun\n9WzVedBUpEMlONeqXl+xuzf6G6MQRAFLjZOpn+zBbHNy24Aw5o+ObfextBZJVrj762RSCyxE+rrx\nn7kDcXPRdapnsEm/FkmSeO211xg2bBiJiYnMmzeP0tLSRpdNSkpi+vTpDBkyhIEDBzJ9+nTWrVvX\nYBmn08miRYsYPXo0/fv3Z9y4cWzZsqX+8zlz5tC7d28SExPrX5s2bWrWga1LOdWs5duLL5PUk7dP\niCf9w7w6eTRtj1LtxCetgnFHq7jOx8SobgEMivShVxdPov3d6eLliperodl9J+ODPfnrxDh+fGAo\n80ZGE+Kl5gP+llvBUysP89Hi/er+DSJi147NXzHbHDz9QyqvbDhOjVMmpfahP7BSQrS1TLR7Su8u\n3DNUDSWtSingX7tz2mq4nY5aQV/ALZ8l1RuC3QM9+Oz2/iwY261ZIdSRsf58OXtAfeL14j25PPzt\nAYo7QEC4qfxc6/F01YuMjL14qkBLiPJz56mx3QHYlV3GpztzmiyG21KySqv57wG1aOTOIRGtNgDC\nfdy4p7Zjybf78jl8qrJF2zHbHCxYkYLVIePvYeS1KT2bZQgC3DMskmu7qd/V25vTScopb9FY6qjr\nRywEuHaKIZhVUs2DSw/w8vpj9Y0MnhnXjWfn9kNf20VD2lOAXFDdrO3qdSLPje/OwyOiADh40sw9\n/9lHdmnzttNc5DwLznXqvVQI80B/fdd6T9y3+/Ix25wYdQJzBoe36zjaCp0o8Mz47oiCmjdd186x\nM2nSL+ajjz5i48aNfPvtt2zduhWAJ598stFlo6Ojeffdd9m9ezdJSUk8++yzLFiwgPT09Pplnn/+\neXbs2MEnn3xCcnIyX331FbGxDa35hx9+mOTk5PrX6NGjm3Vgzy4/iLWTu2mkFVjYna1eVGYPjrgs\nQ8ONoSiK+uT7Qxb2D1OQNucjHaug5OsjKG085z5uBuYOiWD5vUN4c2ovhtQmzA+0q3O5wVnD39cf\nJaWFN5LmciDfzOwv97LxmJqzMirWn/lz+oFOHY+c2TSl/8b4w/AoJtTmTn2wI5ufUgtbP+BOJqfM\nyiPLDvL8mjTKrA5c9SJ/HBXDF7MH0CukZQ9Hod6ufHxrP25NVPPBkvMquOPLvfyaU9aWQ28x64+o\n39uobv7tlsQOqge5Lkz70c5sXlp/DLuz/TpGvLctE0lWCDIZW+05q2POoAi6+rohK/DqhmNIzcw3\nlmQ1DSe33IZeFHhtckKL5E9EQeCF6+OI9ndHUuDpHw6TX1sI1hLqjMGO7DoCai7uxzuzuf3L30jO\nqwBgbI8Avr1rEDf1C0UUBHTDQxB8jKCAc20uSjOjCoIgcPfQSF6cFI9BJ5BXbuPe/+xjX+3+2hrF\nIauGoAKCnwuGadH1nTuq7E6+SsoDYHrfkEtK+uZiJAR7ckt/9Rr22e4ccmrlfTqLJhmDS5cu5b77\n7iMiIgJPT08WLFjAtm3byMvLO2dZf39/wsLCEAQBRVEQBAFZlsnOVl3wGRkZLFu2jJdffpnYWFWb\nKjg4mPDwtrXoiy12lu3Lb9NtNpc6r2Ckrxuj2slD0JEoZjvOX05h/+Qwju8y1DZwsgJ6EUQB2WzH\nsbN9PLI6UWBUN3/eu6Uvy2b2p39tW+2NioNVhwu566tk7v46mdWHC9rlhigrCp/tzuGBJfs4aa7B\nqBNYMKYbb0ztibeXC2KketG/WGu6CyEKAn+7Lo6+oaqR9I+1afUX9MsNhyTzr1053LY4iV9rvSwj\nYvxYevcgZg8Kb3VY16gXeWJMN16+MQEPo47SagePLjvIJzuzkTsx86Wgsqa+0GV8XNuHh8/mqbHd\nGBGjdiRZcfAUDy5tHy/p3rxyNh8vAeCRkdFtVmRh1Is8PU71cKYWWJp9zf5gRxa/ZKoPAQvGdmtV\nv2EPo543p/bC5KKjwuZkwYoUbI7mP9wqNZLahg4QOtAY3H+igtlf7uWjX7JxSKrR/ubUXrw6uWeD\n3vOCQUQ/UfXIKkVWpF8LWrS/iQlBvDujD16ueipsTh5ZdoB1R9r+AVbaeQqlrEaVkJnUFeGMnu7L\n9p2kwubEoBOYM7jzi3Say4PDowjwMGKXFF5df6xTx3JR/3VlZSX5+fn07t27/r3IyEhMJhNpaWnn\nNeIGDRqE1WrF6XQyePBgRowYAcDu3bsxmUxs3bqVu+66C51Ox6hRo1iwYAEm0+kfzuLFi/n8888J\nDAxkypQp3HPPPRgMzROd/XxPLjP6h+LWCfpO+RU2NqQVATBncDjGS6gHYXNQJBnpeAXOAyWqttIZ\niCHu6Pv6o4v3xbmnAMfOApy/FqHv418vwNweRJTZcaCGiIeP6Eru/nyySq0cOlnJoZNpvL0lg5v6\nhXBz/9BztPtkRcEhKdglGackY5cUHJJc+3fd/9V/1Ze67PL9J+u9vFF+brw6pSc9zpALkWO9kTMr\nkbPMiICga5mx467T8T839ebOf+8lr9zGghUpLJ6dSGQjid6iKDT491IhOa+CF9ceJbNEvSEGeBh5\nalw3xvQIaHPv+HU9g0joYuLJFYc5VlTFh79kc+CkmRdviG+XPKaLzfnGY+pv3tNFz4hYP3QtPA+a\nikmn539v7s3727P4dGcOB0+amfPvZN6c3qv+oaK1yIrCoi1q1WR8kIkberetvuHQaF9u6BXMqpQC\n3t+Rxfj4QALP+N2eb843pBXx+R71gfumfiHcktg6LUeA6AB3XpmcwGPLDnG0qIqX1h/jpRvjm3Xe\nSieroPZ5xBDl2eJrQXPYcryYJ5anIClqLu6sgWE8PCLqvO0cdV09URIDcCYXI+0qwBDngxhwWiuy\nqdeWwV19+fyOROYtO8iJChvPrTpCgaWGO4e0TSRMPlWtCvsD+iFBGEJVmaD169fy7bffcDA1DaNk\nZ+rfvybU5/Q956uvvmD58mWUlZXh5+fPrFm3c/PNM8+7n9Wrf2T58u/Izs5EFEUSEnrxyCOP0a1b\n93OW3blzB/PnP8bkyVN59tm/ter4vN0NLBjbjadWHmZXVudGNi5qDFosqrv7TEMNwMvLq/6zxkhK\nSsJut7N161YyMjLQ6VSDrKysDIvFwsGDB1mzZg3V1dXMmzePV199lRdffBGA+fPnExMTg8lk4uDB\ngzzxxBNYLBYef/zxJh+YQSdQVu1g1dFiHrim4xNKF23PRlIgwGTkth5dsP2Yg2ucL6ahjSfsXmo4\nCqqo+rWA6uQC5Cpn/fuiux73xCA8BnfB0OW0fpd8vYmCw2VIFXaUrafwvbtXu4XFizLUxGz3BD8e\nnBjPHybEseN4CZ//ksXPRwoorXbwyc4cPtudi6+7EbtTwlFr4LVW9uaWgeH8fWov3I0NfzrOAXpO\nbcgDu4yHWcI1tuX6b35+8MW9Q5n+f79QYXXwp+UpLH94OL6N6BUC+Ph0rhBzHRVWB6+sTmVJbf6L\nIMCcYV15YmJcs/QTm4ufn4kfHvPnbysOsTQpj52ZZdzxRTLv3ZHIwK7t08f3fHO+4ZjqPbu+Txe6\nBHVcjvBfp/ZhYEwAT3y7n+IqOw/8Zz8vTuvNzDbwlqzcn1+fhvG3Kb0I8G/7HN2/T+/DtnRV3HjR\njmzeu33AOcucOedppyp5YU0aAAO7+vLKLf1w0bfNQ/9kPxO5Fgev/5TGT6mFDIz25/5rmi5VUr6z\nkBrAEOqBf2j760AmZZXy9MpUJAViAjxYOLMfiZEXV1eQp3WnILMSqbwGef0J/B/qd05FbFOuLX5+\nJlbMG8F9i5PYl1vOoi2ZFNsk/jGlV6sqjRVJpvDLo6CAPsCN4Bu7IdR6pENDg4gceh179fHo933L\nnybG4+ejGrM///wzn376EZ9//jn9+/cnOTmZu+++m5494xg+fHij+xIEifnz/0RiYiJ6vZ733nuP\n+fPnsWHDBlxdTxuZlZWVLFr0FgMGDMDFxdAmBR8zr/Jg9ZEithwtavW2WsNFjUEPD/VkONvwM5vN\n5xiIZ2M0Ghk3bhz3338/Xl5ezJo1q357f/zjHzGZTJhMJu6//37++te/1huDiYmJ9dvo378/jz32\nGAsXLmyWMThzUARf7c7h/c3p3NAjoEO9g+VWB0t+VQsA5kUHUv7RQbDL2A6XYPPSIQZ3voJ7Yyh2\nCelIueoFzK9q8JkY5al6Abt5I+tFKlGg9PQ5IYoC3pNiKP3PEWqOllG0Jx9997av8FWqHNRkqKFT\nKdqT0tox9PR35fXJ8eRfE8W3yfl8f0ANH7Q2ZGbUCRh1Ij7uBh4cHsWkXsHYLDYayyYSAlxRim2U\n7yvA6Nu6pHEfHbw5rScPfXOArJJq7v5s9zkVkqIo4OPjQXl5FXIbaTu2lG3pJby49ihFFjugFoj8\nZWIP+oR64ayuobS6/Qs8nh4TS89AD15df4xTZhu3friLx0ZFc8eg8DZ7MLnQnOeUWTlQG9a/Nsa3\n/tzsKIaFefLZHf15fHkKeeU2nvzuAEkZxTw+JrbZBVx11DhlXlmlVu2PiPEj3s/1osclV9iRT1gQ\nPI3omhgmFYF510Tz0rpjrDpwkut65DC8Nvx99pybbQ7u/WIv1XaJQJORl2+Io8psperCu2gWt/YJ\nJjmzlPVpRbyyJpUwk55hUU17sLAeVYsrlVD3dj8HjhdVce/X+6hxyoR4ufD+LX0INBmavF/9+HCk\nb9Ox51ZSuD4Tw2A1tUEUBXxMBsx5x5p0bdEDH4w38damQnZllbF9TwFP5Kfx5NjuTZJ5kT1DQdfw\nYdex6xSOk+q3qhsfTlnl6Zy6mB592bzJiuJRiQi4yVL9MR85cozY2O5ERnajtNRC167diY3txt69\n+0lI6Nfo/q+/fioANpsM2Jk5czYffPABycmHiIuLr1/upZf+yaRJk8nKyqSmxtFm3+8/ru/Bmq6d\nqzRy0TuWl5cXoaGhpKSkkJCQAEBubi4Wi4W4uLgm7USSpPqcwbptnH1xvrACvdjsSrmHR3fjm19z\nKat28M3eEx2aT7B07wlsDpnJOiNjUiycKb5X83MehpndLpliEkVRUE5WIx0sQU4rB8cZ+XaeBnS9\n/ND19qvvZykDnCfh2K1vAOJ2E3KuBfvGPIg0NUmEszlIR8vVEIxehK6e50hNBJtceHRkNPcNi+SX\nzFKqHRIGUcSgFzGIqmGnrzXwDDoBg06sfdX+XxQw1i6rE4VzvqcLSbiI0V5IxTak4xVI17Q+ZNU/\n1Ju/TuzB82vS2Jdn5vnVR/jnpHNDVrKsdJq0jNnm4K3NGaxKUfOOXPQiD1zVldsHhqHXiR0+rht6\nBhMXaOLpHw6TXWblrU0Z7M2t4G8T4/B0bbuqzsbmfO1hNZzl524gMcynU76TGD8PPr89kedWpbI7\nu5ylyfkcK6ri1ckJDTQ6m8p/kvI4aa5BFFRj7exjUhQFpdyOkmdBzrOohROVjvrP9WPC0CWeX1D6\nTKb07sLKg6c4eLKSV9cfY8mdAxvkJsqygt0h8/TKVPLKbRh0Aq9N7omfm7Fd5vqvE3uQWVLN8eIq\nnl6ZyuI7Egn3uXDbPcXmRCmoNVrCTe16Dpw023jk2wNU1jjxcTPwzs198HNv5lxEeiL28kNOKcWx\nLR8h2kvt5CTZ4d1heJU3T9XgdYC6CH8RsKRp60meEZTesaXeIJRLbDh2qPnnYj9/CPVocFzLkvMp\nrXagr71Gn/nZmDET+OGHFSQnJ9O7d18OHtxPTk4OQ4Zc1eS52bNnN66uroSGhtevs3v3To4eTeOJ\nJ57htddeRFEufD9oDu56HTMT26Yoq6U06U49c+ZMPv7443oj8I033mDEiBGN5guuXbuWtLQ0nE4n\nNTU1LF26lF27dtXnDA4aNIgePXqwaNEiqqurKSkp4ZNPPmH8+PGA6nHctGkTVVVVKIrC4cOHeeed\nd5g0aVKzDizMx41pfVWx1y9+zaO6gyqLbQ6JpXvzuRMjT0muCLKC4OuCfoz6RSt5VWrhRSejKArS\noRIci9Nw/OeY2kPTIYMoIPbwxnBTDMb7eqIfHlJvCF4MQRAwjgtXk1Yq7Ei/tkMy8VHV8yLGeNZX\nlDWGq0HHmB6B3NirCxMTghjTPYCRsf4MjfJlYIQPfUK9iA/2JDbAg0hfN0K8XAnwMOLtZsDNoEOv\nE5ttsIsxalhQKatRE57bgEk9g3ngqq4ArD1SxEe1WmiXAlvTS7j189/qDcG+oV78e84A5g6J6FAh\n2rPpFujB4tmJ9ZXZm4+XcNfXyZRXOy6yZutYW5s8P7ZHYIfpHjaGt5uB/72pD7MHqdfn5LwK5v47\nmdSC5lXcl1sdfFbbzWNanxBi/D1UJYESG9K+Yhw/qmoCjn+l4lyXi3y4rIEhCODceALnzlNNepgX\nBYGnx3VHJ8CJChuf7TlXbuP/tmfV51Y9OaYbfdooL7Ix3Aw63pjaEy9XPWabkwUrDmO9SEGJnFfr\nnxRADG+/4pHyagfzlh2kyGLHzSDyvzf1bnHPYP21oeChB6eCY137SxRdDEVR1OphSQGTAf3Ihg/W\nNofEF7+qxavDuvpy9mXax8eXa68dy2OPPcjo0Vfx2GMPcu+9DxAT07iO5dnk5GTz6qsv8uijf8Ld\nXY1kVlVZePPNV3n66b+g13e8VFBH0KSjeuCBBzCbzcyYMQO73c7w4cN54403AFi5ciXPP/88ycnJ\nABQVFbFw4UKKioowGAxER0ezcOHC+li9KIp88MEHvPDCCwwfPhyTycTEiROZP38+oGoQvv/++zzx\nxBPIskxQUBCTJ0/mgQceaPbB3TMsku8PnKLc6mDZvnzmDml/7+CaQ6e436pjMuoTjhDmgWFqNLjq\nEDLMKFmVOLfkI8Z4X9CYaW/ko+X1Ap6gluyLffzR9fRrlS6WGOiGrn8AUnIx0p4CdL38ELzaJpFf\nsTpRctQbmti943uyXgwh1ANcdWCTkDLN6H2b5hG5GPddFUluuZU1qYV8siuHcB+3Fnd+aAsqrA7e\n2pzO6lpPmIte5OERUdyaGNZpPYPPxsOo58Ub4kkM92bhpnRyyqw8uTKFd2f0bbYGXVM4XlxFRm3B\nzIQLtFbrKPSiwB9HxRAXZOLFdUcpqKzh/iX7eW5Cd65PaNq5869dOVhqnPTWG3jY04RjZSZyngWs\njQjMwkcAACAASURBVBhERhExzAMhzIQYYULwdcHxYxZKjgXpl1Ngc6K7NuyiD1g9gkzMGhDOV7/l\n8cWeXK6PDyI2SL0hrztSWK/HdnO/EKb1bf/863AfN16+IYHH/nuQ48VV/OOnNF6+MeG8x6Hkqtcn\nIdgdwaV9UpOq7RJ/XH6I7DIrelHgjSm9WtUrWHDVox8bjnNlluqsOFACAwLh0d8ozznabLmfOvZk\nl/PWpuPUnOE9i/Jz45oYf0bG+jWocJZNp8PE8r5ilNo0Jf348HPmccXBU5RU2dEJcF1CEHvP2u/i\nxZ+yfv1aPvvsa6KiosnMzODpp+fj4uLCjTdO4/HHH+PAAdVemTDhehYseLZ+3czMDP7850e47bbZ\nTJs2o/79d999m7Fjx9OjRzxXKld8B5KX1h7lu/0n8XEzsOK+Ie3apsZpdbL3owP0c6o3G7GHD/rr\nI+tDpXKJDccXR0AG3VXB6K/unGISxerE/tkRsDoRQtzRXxuGEOLeqtD1mYr1zioH9n+p2xd7eGOY\nHN0m45YOlqhPjHpBVfXvhCrxi+FYlY18pAyhqyfGGW1XuGR3yjz63UGS8yrQi4LakzbKt8M7kGw5\nXsIrG45RUqXmBvYP8+KvE+OI9HVDccjIh0sRQjwQgy4cTutI1qQW8LfVarFBU7u7nI/zdWao64kb\nZDLywwNDO7xn64U4UlDJghWHOVWpeqtvHxjGvGtiGvVeKrKCUmil9GgZB349SV/+n73zjpOivP/4\n+5mZ3Wt7vXJ3HNfo0hREBUWRYosKYo0hpmjUxBjjzxhNjCUaQ0xiYozRaGKJUVQUSxQVVLCCgjTp\nR73e++3e7sw8vz9mb+Hgjmt7Deb9et3rBbszs8/Mzj7zfb7l81WJpI1zCVUt4y/dhZLuQiSFHVF8\nIHUT/Z39mLv83vwxsWhzMzps29XkNbj06a8oa/By0tBo/nnFBMq8knmPfYbHZzIxLYrHLh3f7TzI\n7vD82gL+usoqXPvx9EyumZrR5nbe57Yjyz2oU5LQgpAqcjg+w+TnS7ewer/lHQ1mhxvf//ZZqUIO\nhdAfjCYhM67Hc8v+qiZe31zC+9vLKPPnE7cwKT2ac0YlMnNEIjFhVoGZrPPifWY7+EyUUbE4zh/W\nap9m3WTev76kvMHLBWOT+VZSPbfc8mNWrVoT2OYXv/gZmZnZ3HjjTwOv/e1vD1NUVMiDD/6x3bHu\n2LGdW2+9iWuu+QELFlzR6r0FC75FQ0MDqv+ec7utVIC4uHiWLHmrG1fmSOzexL2I2+0lJz6cJRuL\naPIauEI0JvZAh+poyHovNS/sJMNt/XBqx8QQed4wxCETlgjXoNlAFjchS5osL1wvrR6Phr4iH1nc\nBA4F56U5KIlhPc5hbNXLUlUQoSrm7jpkZbP10IjpuRio8VkxssaLkh2NOrZ3qkR7jCGth1+9D/Wk\nxFbff09QFcEZOfGszKuk2u1jVV4lZw5PIDU+ok96E9e4fTywfBePfboPt88gxC8e/ctZw4kJcyCb\nDXxL92BuqMTcWoWSEYmI7L8+m4cyPNGFKSXrC2rZVd5IiKZ2ex5oq0+ulJLfrdhFnUfn4nFDODVr\nYN2bCa4Qzh2dxJaSeorrmtlcXM/mojqmZce1yskzNlbgW7Ibc0MFIUVNZKAQ0mIIhmkoWZGoExJQ\nz0hFm5GKOjoOJTUC4XK0OX8IRaAMj0E2+JBlbmS5B1nuRsmNPqpB6FAV0qJDWb6jnOK6ZqJCNRa9\nv5PKRi9JLiePXjqeyC50rQkG44ZEcqDaze6KJtYeqGHMkEgyYlsveGSTjrHK0knUTk0Oypx3KKaU\n3LNsB6t2WxXrt56Vw0XjgudQUNJdGN9UgtfErPTgOikZj8fXo7klJszBKZmxXHFiGpMzYtAUQWGt\nB69hUlLXzKd7qnhxXSFbS+oRSNJXlyOqmiFMxTEv+4jo2dJNxSzfUY6CyT1zc2msLmP58ne56qqF\nGIaBqqqUlpbwwQfvM3XqqURHR7Nv317+9a/HOfXUaUyceGSVOsCmTRu49dab+NGPfsz8+Zce8f7Z\nZ8/h4osvCfwVFxeTlZXNffc9GAgl9xS7N3Ev0rKqWbRiF0s2FhMdqvHmtVOD7h00y934XtsDDT4M\nJG/EKVz5vbarlqRHP8RrFoPjW5lBHUuHY91bZ40VUM9KC7Qm6imHe0yklPhe2IUsabJU4xeO6pHe\nlvToeP+xBUyJdl4G6uiB9cBtQbp1vP/4xpJDuDATNcjh7PxqN997YT21Hp206FD+88OpxKjBS2Ru\ni492VfD7Fbuo8ufcTfJ7A4f6H4ayScf32u6DifMAoSqOK4f3qt5kVzCl5Ff/28aKnRUIYNGFYzhr\neEKXj9OWZ3BLST3X/NcKOz139SRGJ/dte8TOohsmf1m1h5fWWwZLanQof7xojGUslzThe2FnQB+v\nHJMNGKSOiWfiyamIuJBuLxillBgfF2GstaQzxFAXjouyjroQllJy6+tb+GTPwbanDlXw5OUTut25\npqd4fAY/eHEDO8sbcYWoPPvtE1sZhMbOGvS39oECzh+PC2rkQkrJn1fuYfHXhQB8b+pQbpwenIjL\noRjbqtHfsfKSYy7OwTsiKuhzi1c3+WJfFe9uK+eTPZWB3vNzcXAX1vXcMyWe4dPSWuUee/1ewbIG\nLxN929n2v38ecexXXnmTxMQknnzyH3zwwfvU1tYQGRnFWWfN4oYbbmo33++mm37Ehg1ft5KRAfjj\nHx9hwoRJR2z/wAP3oKoqv/zlXd2+DofT357B48IYLK1vZt6/vsRnyKO6+LuDub8e35t7LekYJHfj\n5soFo5k6rH2dJ2NTJfpyK/fFcVlun7Uskl7DcsHX+xBDwnFcMbzDkE1naeshaZY04fvvTuv9M1PR\nTup+OMPYUoX+7gFQ/SHifvCodhbv4l3IwkaUE+JwzA3evdbCxsJabnhlEz7/dU6PCeWUYbGckhnH\n5IxoIpzB8ZrUNPl46MM83veLp4dqCj85PYtLJ6UGwqCy3otvyW5klRWCVE9JxthQAR4Dopw4rxyO\ncPWexmBX8PgMrntpI9tKGwjVFJ66YiIjk7v222vrPn945W5eWFfI0JhQXv3+lAGjFNAeb35Twu9X\n7MJnSEI1hXvnjGD6mipkpQdinPzG4eXD8jqGJ0bwn6tPDEoeqJQS48syjE+t3sYiOQzH/Jyj5icX\n13m47Om1ePzGwt3njuSCMf2XK9sypoXPr6fG7SMu3MHNM7I5d3QSQgh8KwowN1YgUiNwXnmkWHFP\neGbNAf7+6T4ALhqXwq9mD++V+0xKif7GXszdVhcdx+x0lPFdXzR1loZmnVV5lXy6pZRb8nWiUfgM\nH7fjJibMwawRCZwzOolxqVG8vqmYB1fkoQh46ZrJZHazYGag0t/G4DEfJpYSXCEalY0+tpbUs7Os\ngUsmDsEZhPCdsaUK/X/7QJfUq3CzbKQpKYybTs866g9VJIZZbcsadWRZE8q4+D55gOgri5AHGkAV\nOObnoEQE7yHdVvhMuBzIeq8VIipqtCRqurlaNj4rRlY3o2RFoZ4wsFv7ySYdeaAB2eBDnZwY9O82\nJSqU7PgI1uyvplk3qfPobC1t4P0d5fxnbQFfHaihotFLqEMhPsLZrc//cGc5P1v6Dd/4xYZPTI/m\nkUvGcVpWXOB4sroZ78t5UOMFxWoVpZ2YiJIWgbm9GtwG5oEGlFGxQZcY6g6aqnB6dhzLd5RT69H5\ndE8lc0Yldsl4Pvw+N6Xkgfd30ug1WDAxlSmdEPvtb0YmuZg6LJbP91ZR59HJ3lXPBL9Td+NJsfx9\nuyXpce+5I8mIDc4DVwhh5RZGaIG5z9xdi5IT3e7CLjJEIzpM49M9VVx3RjZXn5TW6+kQHREZojE2\nJZLlO8ppaDZYmVfJ1wW1jE2JIvKrMnAbqGPiUDKC5x1+c3MJf/xoNwBn5sZzz7mjeq1QSwiBMiwS\nWWDNX+aeOlBFr1VGOzWFEUkuZuxxE1LZjE8VPJEoONDoxaObbC1t4M1vSnl7Sylr9tfg9pnMGZXI\n/AnBz8fsb/o7THxcGINgyU28sqGIJp9JhFNlYnr3cwellBirSzE+KgQJvigHCz217ENyy4xschOP\n/sMRQiDiQzG3VEGTjnA5UFJ6d5VjFjRgfGCV46unpaCOCG74si1jEEBJjcDYbOWhSLeOmtv1z5XN\nBvqKApCgTk0eUMUJbRKiYm6stJKgs6N7xTOWFR/OwqlDmTs+lWiHQrNuUtHgxZRQXNfMVwdqWLqp\nhFc3FLOjrAG3zyQuwtlhikR1k5f73t3JP7/Yj9tnEuZQ+PlZOdx2di7RYQfPwyx343s5Dxp00ATa\nhdmBkLiIclr3984a66Ff0oQyKiZoXuieEO7UmDw0hne2llLr0fm6oJZzRyd1Wgrn8Pt8fWFtIOz6\ny1m5vdICrzdIigxh7qhEavfVc71bRUHwcZTg4Yoa6jw6p2TGct1pmUH/XCUlHBEXYnme3DrGrhqU\nrChEWNsG+ZiUSL53ylDOm5TeJ7mxnSE1OpS5oxMpqPFwoNpNcV0zqzaWcLnb+m1pp6YELV9wVV4l\nv1m2HYmVnvHQRWN7pRr+UIRDwTE6FrXCg1HdbDkQdBOR4eoVp4Wxq8aqOAdCzk7n3Dk5nDcmibhw\nJ5WNXmrcPhqaDdw+EwE8cMHoQfM76wq2MdiLHDp5uEI0qhp9bOmhd1AaEn15PuY6f/5LWgR/i4c1\nlY0MiQrhjtkjOlVJKKKcyGoPssKDWdSIOj6+17wnUjfRX9sDHgORGIp2TscVfV2lPWNQOBSEpmDu\nq0eWe1Ayu15YYO6qtQwLBbQ5QweEl+mohGkYW6qg2US4NJShvZNDpqmCnCHRnJAUwUUnpHDZpFRG\nJUcS4VSpbvLR6DXw6Ca7K5pYmVfJf9cWsDKvgqJaDwJIdIW08jCs2FHOLUu3sNWvRzd5qOUNPCUz\nrtVDwCxqxLdktxUKdio45uegDmt9jkp8KCJMw9xbB7Veq/BnePSACKEmuJxkx4ezYkc55Y1eDlS7\nO903+fD7/Nkv89lW2kBuQgTXnjqsw/0HEmGKwqmbalDcBgcw+FlzPdUeHUXAHy4cS1w77Q97ipIQ\nhjIk3Cq08hiYO6qtgqN2Fk1OTWlzbulPokIdzB2VyIhEF5uK6hjXDGfhwIfkq5xwMuJ7XlSwvqCW\n/3tjC7opGZ4Ywd8uGd9ur+FgozgU4qem0XSg1tJNLWpENuqW4R7E37D06PiW7gGfiUiPQJtpdQuK\nCnUwKT2aBROHMCM3gQinSpPXYMGEVGYNAOmm3sA2BnuRwyeP4YkRLPF7B8OdKpO66B2UzQa+N/Yi\nW6QSRsZQc1Yqd3+YhynhR6dldqk5vDIkHGOj5TVDN1Gyeicx2vi8BDOvFgQ4Ls5GCZLu36G0ZwyC\npbll5tVCk44sc3c5LG58XoKsakYMi0TrxfyVYCGEsLoylDSBz0TtpTEffs1DHSo5CRHMyE3gqpPS\nOHtEYqBjQnmjF8OUVDX52FhUxztby3hhXQGbiuqocft4fm0BT35xAI9uEu5QuXVmDrfOzCX6sJ7C\n5r56qwDJZ1oVf5fmoqS2/eBTUsLBlMjCRmSFB3wSJXNgFFdkxofj1BS+PFDD3somTAlTMjr2Wh96\nzX2G5Lfv7cSjm1xxYlqX55P+xvisGJln5YZtmRzH8pI6pIRvnZDMvF7W8BMxIShDXQcNwu3VVmVy\n9JFz09Hmlv5ECEFmfDgXjxvCkL0NJDaZbMLg5p3F7CxrYHxqFK5uGm955Y385NVNuH0mqVEhPH7Z\n+D71himKINwVgjcjArOmGVnhQZa6kTXNVmg/SM4E/cNCZEEjaP7UpcM8xEIIEiKcTM2MZcHEVE4a\nOvD0ZYOFbQz2IodPHhEhGtVNB72D8ycM6bTLPZAoX2QJy6pTktBmpfP0V/msL6wjOlTj3vNGdUn7\nysqVkcj8BmRpk+U5CQ9uSNEsa0JfZrUUUicn9lq+3VGNwUPD4o06ItLR6f7M0mtYxTYStJOTB2xf\n5yMQYG6rhkbd8vr2giZiR9c8LtzJ+NQozhuTzFUnpXFiejSx4Q4avQbVbh+6KcmvcfPFvmr2+gWT\np2TE8Mgl45g6LPbIVnw7a9Df3BfoDOC4LBcl8eghezHUhazzIcutvFFCVZQhwZFi6CkTUqMormtm\nZ3kj6wtqGRobyvAOUjwOveZr9lWxdJMV3rpz9nCiQgdGoUxnMEua0N/zzwsnJZJzZgbTsuMYFhvO\nD08d1icdZESUEyUryloo+g1CkRB2RAV6T41BKSWypAljUyXm/npEanjQJJ/AksKJW1cJHoP1kQqf\neJvZX+1m6aZiVEUwNiWySzl+RbUern95EzVundgwB49fPoEh0X1bld9yzT3NPsiJsvKgS92WUVjm\nlwfqgToEgHmg3kq1AtTpqag5g2sxFWxsY7AXaWvyyE046B2M6KR30MqP2m0lygvQzk5Hm5pMo9fg\nrne24zMkV09O59RONjI/FJEcjrGt2tIfrG5GGXPkQ7i7SFOiL90LjTrEOHFckNXjH3B7dDRhi2gn\nssqDrPSHxcfFd6oDi7mr1hJCFViCtf3YtaUriEgHxrpyMCUiPrRXjNiuPCQ1VSE9JoxTM+O4dGIq\nF41LISc+ghBNobLRS5hD5ZYzs7n1rJw2jRrjm0prUSFBxIbgvDwXJbbjvCghBEpWFLK0yfKW7qtH\nxIegJPR/3qcQgmlZcXydX0NJvaV5NnloDClR7T94D73mT31xgJ3ljYxNieyT7kbBIpA20qRbrTIv\nyESogkRXCONTo/q0laCIcKDmRmPsqbMKjnbWIKKdrRYZ3TEGZbOBubsW46sy9OUFmOsrkAWNyMJG\nq6gpN3gdoGS9F8PfR3f0xTmkDY1iY2EdDV6DLw/U8OGuCrLjw0nthEFX3eTlhlc2UVzXTLhD5dEF\n48hN7PvF0+HXXMmKAsPv5a9uxixsQBke0+2UHekzrQhDs4FIDrPm9gGQQtKf9LcxODierEEkKTIk\nEAL579oCGpr1o25v7q/Ht3gXNPhAU9AuykKdaIX9lm4qptFrCfBeNql71U3CoaDNsPaVBxoCJf3B\nwFhbhiyzygQds4f2uyGlzUgFTQGPge6fPDvC3GX1cRYZke0mmQ9EhKagZFheJnNP8L7TYJEcGcKF\n41L43QWjef/GU3n/hlOYPyG1zQlZX1dmtS6UIBJDcVye26UWg0IVaN/KRPiLpPRlBzAPdK1Pbm/h\nUBX+cOFY0qJD8RmS297YSlGtp8P9vLrJR7sqAJgzanDlMBmrSy0ZGUCb2//zgogJwXnFcERCKEjr\n/tC/Lu/SMVp6JutfleF9OQ/vY5vR/7ff6pfs9s/xkf4uF8VN+BbvQtZ5j3LEzmPmN1j/0ATKkAgu\nGJvCku9NZv74IQhgb2UT17+8iXuWbaeqqf3PbPTq3PzaNxzwt5n7w0VjGNODNnPBRAiBdnoq6unW\ns1MWNOJ7JQ/pPvrzsz2Mz4qh1q9CMCf4Oew2Xee4MwYBvnvyUJyqoNaj87K/ErAtjG3V+F7bbeX0\nhWs4Ls8NuLJ9hsmLfgHQC8YmE9eDfA5leDTCrzWoryxE+nW1eoJZ5cH4wjK4lPHxQZU66C4i0ol6\niqUTZm6qwCxzH3V76TMw91hGgzpi8IUQlBwrB9TcXx+U77S3UIRo0wiUUqJ/Voyx0vqNiNQIHJfl\nIrohSSQcKo55WYgYJxgS3xt7McuP/v33FTHhDh6edwKuEJVqt4+fv/5Nh4vEz/dW0eg1EMCsEYPH\nGDRLmjC+LAWs8LCS1n+6Zoci/GkHYoi1YDA+KkT/rJijyeBK3cTcV4f+YQHef23D98x2jI+LkPkN\nYAKKlaagnpGK45pROK8dg3ZuBiggq5rxvrgLs7Jjw78jzAOWMShSIwKesugwB3fMHs6/rpzICL9n\n7+2tZSz491pe3ViEedh5+QyTX7yxlW2lDQgsWZ+jadX2F9rJyWiz0gGQpW58L+UhG3xdOoZZ3Ijh\nN/bVKYNAHeI44bg0BhNdh3gH17XtHZQ+w+qDa4KIC8F51fBW8i/vbiujvMGLAL59UnqPxiOEQDsr\nDQRQ67XCiz1ASqviGd3K7eqNHpndRT0pEREbYnkAPiw46mRv7q0H3QQBSu4gNAZbCoJ8ppUkPYiQ\nUmJ8VIix2jIcRGYkjgXZiNDue2dFuAPHJTkQroHXChMFyzvTU7Liw3nwgtGoAnZXNPHrt7djmO3f\nm+9uKwOs/qpJkcFtO9ZbSN20hNv9oX51Wv/0Rm8PEabhuDQH4a9MN1aXWgUGh8wRst6LsakS3+t7\n8P79G3yv7sFYX2F5mcBqmzcmFu2CTJw3jsN5WS7alCSrul0I1DFxaBdngyagwYdv8S7M4p79Nls8\ng201DxiXGsWzV5/IrWflEOFUqW/W+f2KPL7/wga2+6v2W9rMfXnAioL838ycoPUb7g3UCQlo5w0D\nAbLSY4ns1zR3al9pmNZzVVrP1RbngE3/c9zlDLYwIjGCJRuLafIahDkUTkxvXaVk7qu3QgyAc+Go\nVmExU0ruXraDarePmcMTuGRiz40tEeGwknRLmpDFTahjYrvdZcPcVIm5wepfqZ03rMMk/2DQ2bwe\noQiIDrFEiet9iNiQdsdnfFGCrPQghrrQJg4e70sLIkQNVFGLUDXo1eK9VWUpTYn+3gHMzVYrsJa2\niULreRGMCNWsKtLt1VbRwL46S5R6AOSCpseEERXq4PO9VeTXuGn0Gkf0GVYUgVQV7nz9G3RT8t2T\nhw6YUF5HGJ+XWNW7gOPiLJQg984NBkJVUEbEWHImlR5rPqzyYJa6aVp+AOPjYsw9dcjqZvAb6yIp\nDHVcPNoZqWhnpaEOj0FJCG03n02JDUHJiDxYuLKtBiUlrFvagLK2GeMLa8GkTRvSZvqEIgQnDIni\n/LHJlDd42V3ZRHmDlzc2l1Dr9vHJnir+t8U6xvdPyeCak4PftairdDS3KIlhiKQw635yGxg7ayzZ\nsA4KII01pVYOOOC4aGDeg/2FnTPYTyQc4h18YV3hEd5Bc6+V5yWGhB+hf/X53ir2+KsvvzOlZ17B\nQ9FOS4FQFXwm+ifF3TqGrPOif2yF9ZSRMQOyQkvNjkLJtgwjfVUR0mscsY30mYFcu2ALZPclLaFi\nY0/dUb2gAwWpm+hv7QsshJQT4tDOHxbU6kslJRzHhZmBcJ3v9b1I38AIo182KZXL/Iu7F78u5LVN\nR/4OV2wrw+MzUQXM7EZ/4/5goIaH20JoCtr5w1DGWcoHxvYa6j/KD+Q/41BQcqPRZg/F+aOxOL8z\nEm3aEJQhEZ0uQlBSI3BcngsuB+gmvqV7rEK+LhLIF3QogZzY9kh0hfDABaN59JJxZMSGYUp4aX0R\nr2607rF541O4/rTBo1Wp5kTjuCQbHAo06vheysMsaWp3e7PCHYg0qJMSenQPPvbYI1x99WXMmTOD\niy46h0WL7qeurjbw/jvvvMXpp09h9uzTA393333nUY+5ZMlirrhiPueccybnnXc2P//5T8jL29Xm\ntkuXLmH69Mk888xT3T6HgcbgycjvBb47JZ2lm4qp8+gs/rqQH/pFY6WUAWNQyTzSm/PcV1Ynj0np\n0ZwQxKbpIkxDOy0F/cNCzG3VmBMT2tVwawspJfoHBVaOY6iKNjMtaGMLNtpZaXj310OjjrG69IhQ\ntrm/3tKyY3CGiFtQsqKsCbDWa2klxvetRERXkF6/jqY/B0o9KRF1RttFJT1FyYxCm5OB/u4BZFEj\n+jv7rSKTAZBIfstZORyocbN6XzV/+CCP9OhQTj4kf+vNDdZia8qw2EHRCWGgh4fbQigCbXY6RriG\nsaYUNT4UkRmJyIxCpEcERXheSQjDeeVwSzKsuhn9nf1It452YuejEC3GoEiL6LRSw9TMWF5YeBL/\n+Sqfp9ccwGtIzsyN5/aze6ffcHfxGT4KGwsCPbjbJA7khWH43suHZgNeK8MxJx1xuHyUlPhW7Eeq\nbojScJ4YB40FgbeTwpJxKJ3PRVZVld/85j6ys3Opr6/n/vvv5ne/u5ff//7PgW1SU9N46aXXO33M\nU0+dzqxZ5xATE4PP52PJkpe47babee21t1t9LyUlxSxe/Dw5ObmdPvZg4Lg2BhNcIcwfP4QXvy7k\nhXWFXHFiGq4QDVnVDHVWUmyLB6uFb4rrWF9grUAWBtEr2IIyIQGxqRJZ4UH/qBDHVZ2fIMztNQFv\nmnZWWtA1C4OJiAlBnZyEsaYUY105yglxrfTFzJ3+KuK0iF5p59ZXiJRwCNPArWPurUMZoMagdOtW\nDp9/Za9OS0GdmtyrDyd1bByywYfxaTFmXi36hwVoZ6f3+wNRUwQPXjCa77+wgb1VTfzyrW38+6qJ\nZMaFU+f2smqnlS84d5BUEQ+06uHOIoRAmz4E52kpxCVFUl3deHTDpDufEeXEccVwfEute9/4qBDc\nOuppKR3eh1LKQPFIW/mCRyNEU/jhqcM4b0wy20vrOT0nvtf6DXcHn+njW68voLChsHM7HKqstNP/\ndzhR/j+Az1u/lRI2hGdnLO60QfijH/048O/Y2FguueQy7r33V50bazukpbV+nquqQnl5GU1NjURE\nHPx+H3zwt1x33Y0sXbqkR5830Bgcs0IvsnBKOiGaQn2z5R2EgyFiwjREcut8tv/4vYLZ8eGcltV1\nXcGOEIq/mASQJU2WUHMnkE06+ofW2JSsSJTRA68S7XDUqUmW3IMpWyWKS93E3O3v8jKIQ8RgfZ9K\nlpVTNhAlZgBkg8+qCvQbgtrMNLRTOn4YBgP15CQUv1STubESY01pr39mZ3CFaPx53liiQzXqm3Xu\nWLqFxvcPwGNbmW84cKiCM3MHfoh4MIWH20M4lF69F0V4G4UrKwqQRykgAizdWX8lbXfVGlKjQ5k5\nIrFLzQpsjmTdui/JyRne6rWyslIuvHAu8+efz91330FRUceG7caNGzjnnDOZOfM0/va3h7nqfqdj\nLAAAIABJREFUqu+0MgRff/1VQkNDOPvsOUE/h/7muPYMguUdvGTCEF5YZ3kHL5+URkhLiDgrstUk\nlF/tDmiLXT05vVM9iLuDkhGJMjwac1ct+ifFlrhnB8Uk+keFVq9Yh4I2a2i/e1c6g3CoaDPS0P+3\nD7m/HjOvFnV4jKVB57VCxOrwwRsibkHJjsbcWo0sbEB69B5V5AYbKSW+13ZbniMB2jkZqGOCv8hp\nj5ZKer3Rh7mrFuOzEoTL0WudcrpCekwYD100lr++/A131ihoNdbC7AZCUNJc3W411lcMxvBwfyGc\nlvSRvuwA5o4azE2V6G4d7bxh7YakA/mCTgVxjMmjOBQHb138FtuLdnfNG9vow/devlXgA2jTUjD2\n1lmdu8JUnJfkQBvPsq6GiQ9l5coPePPN13n00X8GXpswYRLPPruY9PShVFdX8fjjj3LLLT/mmWde\nJCys/e9qwoSJvPvuSurr61m27H8kJR2s6i4pKeG55/7NE0883a1xDnQG9mzWR3xnylBe3VhMfbPO\nkrUFXOWXATm8+vO/6wqQQKLLyTmje7f0X5uRindPHTT5c+pmtF+xbOyutaozAe2M1C4JAvc3yghL\nY1HmN6CvLELJjDoYIh4SjogcPOfSHkpmpOWDN61cSHXkwPHaysJGZLk/hPitTNThfe+JFYpAO2+Y\nlbtV2GhJT4Q7ULN7p1d3Z5GGZNz+Jv5BBAqgI6lHEovCd2sF0pQDIsexPQZreLi/EKpVuKKHaZgb\nKjB31eJ7bQ+Oi7LaXIy3CKcr6a4BfR90F4fqIC0ivWvGYATIS4ZaC8xSN6w0rBeJQDsnEzUuuPPL\nhx+u4KGHfseiRX9m5MhRgdcPDfnGxydw++2/Zu7cGWzZspnJk0/m6qsvo7TUKtz5zne+x8KF3291\n3MjISBYsuJzzzptJZmY2mZlZLFr0WxYu/D6JiQNX9qcn2LMDkBDh5JIJ1qp557oSS7JAgDLsoOu/\nqsnLW99YIs5XnpjW6259ER2COsW66YyvyzGr2hZHlc0G+gorPCzSIlAm9L9HpSsIIaxCFwHUeS3p\ngTy/Z3aQh4hbECEqIm1gdiMx/FXDIjG0XwzBFoSm4Lg4yyqwkVgVzT3Uf+sJZqUH3ws7MVaXokio\nCVW4gUZ+g1XR6qr1Yawt67fxdcSxEB7uD1rmI3VaCgAyvwHfy3nIxtbCylLKg8UjXcwXPNaxwu65\niPSDRSRKbjRKkKM8b7/9ZsAQPPHEyR2PS4hAKtLzz7/M8uWfsHz5J0cYgi2YponP56Ow0Hq+fvXV\nGv75z8c4//yzOf/8s9m8eSPPP/8MN974w+CdVD9iG4N+vjNlKCGawiTduiRiSESr9mcvry/Ca0gi\nnGpAkqa3UU9OsqQPTImxqu1OKfonRVbeiirQ5gyO8PDhKAlhqJOsZHxjTalVlcbglpQ5nJZCJHNv\nXce5SH2E1M2AF1YZ1f/eShGq4Zif3VruY1dNn0rySCnR15Xh+8+OgJSJOimBhGvHkpwby3oMtiRa\n4Szji5J2F2n9iR0e7hlCCLRTUg522ihzHyGsLKuaocmSI+tq8cjxgAhRcczPQRkXj8hwoc0KbmHY\nK68s5u9//yt//vPfGD9+4hHvf/75p5SVlSKlpK6ulj//eRHR0TGMHTuu3WO+/vqrgX1qamp4+OE/\n4HSGMGbMCQC89trbPPPMCzz9tPU3atQY5s+/lPvvXxS08+pP7DCxn4QIJ5eMT+GUry3PjW9oBC0B\nSrfPYIlfTuKSCUP6LFdIOFS0M1LR39mPuacOY28d6iGhazO/AXOjJS6tnprSqhp3sKGeloKxvTow\nwYqU8EEV7u4IJTvKMujdBrKkCdEFyaDewtxbd9DwHgDGIPirOy/JtvqBuw30N/dZBs3kRJTRcb0a\n6pR1XnzvHrDamQG4HDjOyQhECB781hi+Ka7jtKxEav62HtngQ1+eb7VRG0CLMDs8HBzUCQkQpqG/\nsx9qvHgX77IMnKSwg/dIqHrM5QsGC+FQcMwZ2vGG3eCvf/0jqqry059e3+r15cs/AWD9+nUsWnQ/\njY0NREREMG7cBB5++O+Eh7evBblt2xaefvpJGhsbCA+PYPToMfzlL48RG2vNjUlJrbulOBwOwsMj\niIsbXNG49hByMCjhdpOqqoYu5TtU59cR/vIeAN4a52LBHEtH6KWvC/njR7vRFMGb155MoqvvVNOl\nlPgW5yGLGhGxITi+OxKhKkifie+57cgaLyIxDMe3R3Ra56o3UFVBXJyry9f8UIxvKtHfy7eOd0Yq\n2pRjJzdDSonv39uQNV7Uqclo03vurenpNfe9uRdzVy1iqAvnZQNLM8ssbUL/uCigeQhAmIY6KQF1\nYkIrr31PkVJibqmyirD8hUvKmFhLnumwYp+Wa162pgDv0r0AaLPSLcNhAGCWNOF7YSdIKzysnTlw\ntUY7SzDmlp5gHqjH98Ze695wKjjmZWOsL8fcWYuSG43joqw+H1Nv09/X/Hik5Zr3F/aS8RAii62w\nUCUmj+0opd6jo5uS/66zcgbOHZ3Up4YgHJJTB8jqZqsPJ/5WbTVeqwJ07tB+NQSDhTI2DmVkjJW/\nNnZgeKqChRDiYKh4T20HW/c+0qMf7PAyAGWIlORwnJfm4rh6BMqoGCun1K1jfF6C959b8H1Q0Ol+\nqEdDNvnQ39hrLUJaxNq/lYnj3GFHrfrWhscEclr1j4uQ9f3fY9kOD/cOSkYkjstyLb1Qr4lvyW6r\nbzp2iNjm2MEOEx+Cuc/6gX8lDOq9Bi+sKyAzLpziOuuhc3UviEx3BiU5HGVcHObmKowvShBxoYHk\ndXVKEkry0dsgDRaEEDguyOzvYfQaSnY0xtcVyHIPZpWnX8P65s5aMCSoIuiJ3cFESQ5HOT8TOb0Z\n4+sKjM2V4DMxN1Tg3ViBMjwadXISyuEdDzqBsasGfXkBuP25X9lRVt5tROckLrSZaXgP1IPHKuLS\nLs7q13CxHR7uPZTkcBz+biXUea3fDiAybGPQ5tjAni38yGYDWehvw+XPy3vx60L+veYAANOz48iO\n7788L236EAhRwGuiv77n4Or/lJR+G5NN1xBDXVZxBGBuquzXsRh+KSIlO2pA6R62h4gOQTsrDed1\nY1CnD4EIDaRl1Ppe2IX3pV0Yu2s7VWwimw187+5Hf3OfZQg6FLQ5Qy1jrpOGIICIcAQE4s09dZjb\na7p7ej3Grh7ufZTYEJxXDkck+BdxEdqAbi9pY9MVbGPQj3mgHkxAwGlnZhCqKTR6DfZUWl0ZFk7p\nnUTYziLCHain+g0///NOm2Ov/gcTQhGo4yxBZ2NLFdLfe7mvkXXeQAL8YOhUcygiVEObmozzh2Os\n+z/OStuQBY3or+/F98x2jM2VSL3ta2seqMf77HbMLX5JnbQInN8diTouvltePWV0LCLTKjDRPypA\n+gug+hI7PNx3CJcDx+W5qKck4zh/2IAqHLKx6Qm2JeGnJX9KpEYQFxvGpRMPijyfMCSSiWn9K4AL\noE5MDDz8lAnxKOn26n+woY6Lt/LfPEZA1qWvafEKEqIeIaw+WBCagjouHsc1o9DmZQU0zWRVM/r7\n+Xif3Iq+phTpsYwz6TPRPyrE98puqLekmNQzUq1K4Oju5wELIXDMHgoOxap+XtnJXq5BxA4P9y0i\nVEObNgRlaPda0NnYDEQGfnyoD5BSBvIFWx6OV09J59WNxTT5DL47ZWDo9wlV4FiQg5nfgDKAuljY\ndB4R6UTJicbMq8XYWIE6tu9av7VgbvOHiEfGtNtqa7AghEDNjkbNjrZCpV+VYe6qsTr3fFqMsaYU\nZUwcMr/e0oYDRGIY2nkZKAnBkQQRUU6004egf1iIua0aY1QManbf5GGae+vs8LCNjU2PsY1BsNpx\ntTQc9xuDceFOnrxiAiX1zZyRM3B0hESks097x9oEH3VCPGZeLbK4CbOsCSWp7wqAzHI3ssLyIg3E\nKuKeoKSEo3wrE1nTjL6uHPObKqvYZKNVgY8A9eRk1FOTEUHuIKRMTEBsr0EWNaIvL0C5xtVhP/Ge\nYmypQn/fDg/b2Nj0nMHtFggS5l5/izCXA5F4MCF4RJJrQBmCNscGYlgkxFiC2sbGvi0kafEKEulA\npPW/8HVvIGJCcJydbhWbTEuxEv3jQnBcMRxt+pCgG4Lgl4CaMxRUAQ0+9E+Kg/4ZLUgp0b8stfIE\nTaxzW5Bjh4dtbGy6jT17cNAYVDIjB0Q42ObYRgiBOt4SKTa3VSP9XUB6Gyklht8YVEfHHvP3ugjT\n0E5JwfmjsTiuGYXSy11flPhQ1FOsLgXmxgrMgoYO9ug60pQYHxVi+I1NkRqB44rhx1S3Hhsbm76n\nU8agYRgsWrSIU045hUmTJnHTTTdRVVXV5rZr165l3rx5nHzyyZx00knMmzeP999/v9U2uq7zyCOP\ncNZZZzFx4kRmzZrFqlWrAu+73W7uuOMOpkyZwuTJk7nzzjvxeHqnB6j06MiiRoBBm0xvM/hQT4iz\nvEg+E3Nr27+lYCMLGg6mQxxjIeKjIYToM8NXnZIciC7o7+cHtWJc6ib62/sDwvNKbrTlEQxiNxYb\nm8HCE0/8nUsvvZA5c2ZwwQWz+fWvf0FJSQkA77zzFqefPoXZs08P/N19952dPvZvfnMH06dPZuPG\nDW2+v3TpEqZPn8wzzzwVlHMZCHRqFvnnP//Jhx9+yCuvvEJMTAx33nknv/jFL3jqqSMvRFZWFo8+\n+iipqVY17tq1a/nhD39ITk4OOTk5ANx9993k5eXx1FNPkZ2dTVlZGT6fL3CMBx54gL1797Js2TKE\nEPz4xz/mwQcf5N577w3GObfC3N9gSbUoBHqQ2tj0NiJMQxkZg7m1GmNTpZVz1ssGi7HVL6eSGBq0\n4gmb1ghVoM3JwPfCTqtj0OoStNNTO96xA6RHx/fGXmSBf+E6IR5tZjpCOba9uzY27XHOOefz7W9/\nF5fLhcfj4cknH+Oee+7k8cf/DUBqahovvfR6l4+7atWH1Na23yWqpKSYxYufJydnYLXw7CmdMgZf\nfvllbrzxRoYOtbT2brvtNmbPnk1BQQHp6a27csTHH8yxM00TIQSmabJ//35ycnLYs2cPS5Ys4Z13\n3gkYh8nJBxtAezwe3nzzTZ544gkSEqxQ2s0338z111/PnXfeSUhIcNvBtYSIRWpEryd829gcijoh\nAXNrNbLCgyxsRPSiVJDUTavKFlBG2wVIvYmSEo56UhLG2jKMr8pQRsT0qEuQrPfie23PwcKfaSmo\nU5OP+TC/Tf8ivV70ggJMs/d7EytJyQhH5wXfAYYNywz8W0qJEAoHDuzv0Thqa2t49NG/8pe//J3L\nL7+4zW0efPC3XHfdjSxduqRHnzXQ6NAYrK+vp6ioiBNOOCHwWkZGBi6Xix07dhxhDLYwefJk3G43\nuq4zZcoUpk+fDsCaNWtwuVx8/PHHXHPNNaiqyowZM7jttttwuVzs3buX5uZmxo4dGzjWmDFj8Hg8\n7N27l1GjRnX65JQOVs2WpIxlDGo50ajHQH/f/qLlWnd0zW0OoqRHoCeGWu3pNlXi6KJnuivXXM+r\nh2YrZOkYG4ti3+vdorPXXDl9CJ68Wquy+f18Qr8zslv9w81KD96X85D1PhDgnDMUbUJCt8Y+WLHn\nlr5HGDq7z70YX2Hf6GYqQ1JJWLykywbhe+8t46GHHqSxsRFVVbn55p+jqgJFgbKyUi68cC6apjF+\n/ARuuOEnpKamHfV4f/nLQ1x66eVkZFiOL1WllV2wdOmrhIWFMmfOXF5//VUURQTNbujv+7tDY7Ch\nwUqCdrlaey2ioqIC77XF2rVr8Xq9fPzxx+zZswdVtbxu1dXVNDQ0sHnzZpYtW0ZTUxM33XQTv//9\n77n//vtpbLTCIJGRBx+MLf8+2ue1RUzM0RPGvYUNuBstUdq4SSk44o7N6sq+pKNrbtOahmnp1Lye\nh7GzhminE9XV9UKAzlzzijyrrWJITjQJw2zPYE/pzDX3XDqCiic3I8vcOLbUEHVm17oYNe+vo+KF\nXUi3jnAoxF01irDRx6+6gT239B3S66UvdQ5URRAXG4Fwdm3+u/LKS7nyykspLy9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jAAAg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\n7Gb4sL0Rb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fzxDy3vsR9oAgLtQWZmjm/0aHyjR9OkqKPgzEnyz5yk7HwqWo3GUHh16pev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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487e6a1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"darkgrid\", {'xtick.major.size': 8.0}):\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "    ctr_age_by_date(result).plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E1GAwymwkytz6n16jwUBanNpeRjKDIrQkGBRCCCFCLJC2MqoMX69ByQyKUJNg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\nQog09v8Bd1rL+2XSF4cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487e457550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"darkgrid\", {'xtick.major.size': 8.0}):\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "    ctr_gender_by_date(result).plot(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align: center;\"><a href=\"#Doing-Data-Science:-chapter-2---EDA\">Back to Top</a></h3>"
   ]
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}