New_York_Times_exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New York Times exercise\n",
    "\n",
    "The objective is to study the click behavior of people over time and establish useful segments for this purpose.\n",
    "\n",
    "## Import libraries and dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:25.686965Z",
     "start_time": "2019-03-01T06:47:23.956038Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "mpl.rcParams['agg.path.chunksize'] = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data of a single day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:40.213882Z",
     "start_time": "2019-03-01T06:47:25.689963Z"
    }
   },
   "outputs": [],
   "source": [
    "url_1 = 'http://stat.columbia.edu/~rachel/datasets/nyt1.csv'\n",
    "data1 = pd.read_csv(url_1, skiprows=1, header=None)\n",
    "col_names = ['Age', 'Gender', 'Impressions', 'Clicks', 'Signed_In']\n",
    "data1.columns = col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:40.239869Z",
     "start_time": "2019-03-01T06:47:40.215880Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
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       "      <td>73</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping the data to the day it belongs to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:40.322815Z",
     "start_time": "2019-03-01T06:47:40.242865Z"
    },
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>49</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
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       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Age  Gender  Impressions  Clicks  Signed_In      date\n",
       "0   36       0            3       0          1  2012/5/1\n",
       "1   73       1            3       0          1  2012/5/1\n",
       "2   30       0            3       0          1  2012/5/1\n",
       "3   49       1            3       0          1  2012/5/1\n",
       "4   47       1           11       0          1  2012/5/1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['date'] = '2012/5/1'\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning data\n",
    "Erasing all observations where people clicked with no impressions.\n",
    "\n",
    "### Removing people with 0 impressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:40.432748Z",
     "start_time": "2019-03-01T06:47:40.325812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3066"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data1['Impressions']==0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means there were 3066 observations of people that were not showed impressions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:40.531687Z",
     "start_time": "2019-03-01T06:47:40.435746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3066\n",
       "Name: Clicks, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_imps = data1[data1['Impressions']==0]\n",
    "no_imps.Clicks.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates that people that weren't shown impressions neither clicked. Therefore, these observations can be removed from our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:40.716570Z",
     "start_time": "2019-03-01T06:47:40.534683Z"
    }
   },
   "outputs": [],
   "source": [
    "data1 = data1[data1['Impressions']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:40.854485Z",
     "start_time": "2019-03-01T06:47:40.719568Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     80477\n",
       "4     80303\n",
       "6     66808\n",
       "3     64121\n",
       "7     48066\n",
       "2     38433\n",
       "8     30081\n",
       "9     16965\n",
       "1     15483\n",
       "10     8357\n",
       "11     3771\n",
       "12     1549\n",
       "13      647\n",
       "14      211\n",
       "15       64\n",
       "16       25\n",
       "17       11\n",
       "18        2\n",
       "20        1\n",
       "Name: Impressions, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.Impressions.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms that there are just observations with one or more impressions now.\n",
    "\n",
    "### Gender in strings\n",
    "\n",
    "The gender is classified with `1` equals male and `0` equals female. Let's change these numeric values to strings that are more descriptive.\n",
    "\n",
    "After that, let's change the type of `Gender` column from `object` to `category`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:41.045370Z",
     "start_time": "2019-03-01T06:47:40.857484Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    288229\n",
       "1    167146\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:41.477101Z",
     "start_time": "2019-03-01T06:47:41.048368Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    288229\n",
       "male      167146\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Gender'] = data1.Gender.apply(lambda x: 'male' if x else 'female')\n",
    "data1.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the count of males and females shows that there are almost **twice** females. This is a mistake because when people do not sign in, their gender is registered as `0`, and then this value was changed to `female` without truly knowing the gender.\n",
    "\n",
    "So let's replace 'female' with 'unknown' if `Signed_In` is `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:41.611016Z",
     "start_time": "2019-03-01T06:47:41.481097Z"
    }
   },
   "outputs": [],
   "source": [
    "data1['Gender'] = data1.Gender.astype(str)\n",
    "data1.loc[data1.Signed_In == 0, 'Gender'] = 'unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:41.755927Z",
     "start_time": "2019-03-01T06:47:41.617013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male       167146\n",
       "female     152052\n",
       "unknown    136177\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last result shows that the difference between males and females exposed to the impressions is not as huge as shown before. Now we can change back the data type to `category` once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:42.037752Z",
     "start_time": "2019-03-01T06:47:41.758925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 455375 entries, 0 to 458440\n",
      "Data columns (total 6 columns):\n",
      "Age            455375 non-null int64\n",
      "Gender         455375 non-null category\n",
      "Impressions    455375 non-null int64\n",
      "Clicks         455375 non-null int64\n",
      "Signed_In      455375 non-null int64\n",
      "date           455375 non-null object\n",
      "dtypes: category(1), int64(4), object(1)\n",
      "memory usage: 21.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data1['Gender'] = data1.Gender.astype('category')\n",
    "data1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorizing people by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:42.103712Z",
     "start_time": "2019-03-01T06:47:42.041750Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    455375.000000\n",
       "mean         29.484010\n",
       "std          23.606697\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%          31.000000\n",
       "75%          48.000000\n",
       "max         108.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.Age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning ages\n",
    "\n",
    "Let's create an age group **'unknown'** for the 0 years old. This entries come from people not logged-in. Therefore, the age is unknown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:42.376544Z",
     "start_time": "2019-03-01T06:47:42.106710Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 455375 entries, 0 to 458440\n",
      "Data columns (total 7 columns):\n",
      "Age            455375 non-null int64\n",
      "Gender         455375 non-null category\n",
      "Impressions    455375 non-null int64\n",
      "Clicks         455375 non-null int64\n",
      "Signed_In      455375 non-null int64\n",
      "date           455375 non-null object\n",
      "age_group      455375 non-null category\n",
      "dtypes: category(2), int64(4), object(1)\n",
      "memory usage: 21.7+ MB\n"
     ]
    }
   ],
   "source": [
    "bins = [0, 1, 18, 25, 35, 45, 55, 65, np.Inf]\n",
    "labels = ['unknown', '<18', '18-24', '25-34', '35-44', '45-54', '55-64', '65+']\n",
    "data1['age_group'] = pd.cut(data1.Age, bins=bins, labels=labels, right=False)\n",
    "data1['age_group'] = data1.age_group.astype('category')\n",
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:42.400527Z",
     "start_time": "2019-03-01T06:47:42.381542Z"
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    "scrolled": false
   },
   "outputs": [
    {
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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",
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       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>2012/5/1</td>\n",
       "      <td>35-44</td>\n",
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       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "      <td>male</td>\n",
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       "      <td>2012/5/1</td>\n",
       "      <td>65+</td>\n",
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       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>25-34</td>\n",
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       "    <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>2012/5/1</td>\n",
       "      <td>45-54</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",
       "      <td>2012/5/1</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Impressions  Clicks  Signed_In      date age_group\n",
       "0   36  female            3       0          1  2012/5/1     35-44\n",
       "1   73    male            3       0          1  2012/5/1       65+\n",
       "2   30  female            3       0          1  2012/5/1     25-34\n",
       "3   49    male            3       0          1  2012/5/1     45-54\n",
       "4   47    male           11       0          1  2012/5/1     45-54"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:42.527448Z",
     "start_time": "2019-03-01T06:47:42.402526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "      <th>date</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age   Gender  Impressions  Clicks  Signed_In      date age_group\n",
       "6     0  unknown            7       1          0  2012/5/1   unknown\n",
       "10    0  unknown            8       1          0  2012/5/1   unknown\n",
       "12    0  unknown            4       0          0  2012/5/1   unknown\n",
       "15    0  unknown            6       0          0  2012/5/1   unknown\n",
       "19    0  unknown            5       0          0  2012/5/1   unknown"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1.Age == 0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender vs age group\n",
    "The next will plot the number of females and males in each age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:43.155059Z",
     "start_time": "2019-03-01T06:47:42.530446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='age_group', data=data1, hue='Gender')\n",
    "plt.title('People in each age group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping by age and gender\n",
    "\n",
    "This is necessary to plot the rest of the distributions in different groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:43.271991Z",
     "start_time": "2019-03-01T06:47:43.158057Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>685483.0</td>\n",
       "      <td>19480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;18</td>\n",
       "      <td>female</td>\n",
       "      <td>21800.0</td>\n",
       "      <td>683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt;18</td>\n",
       "      <td>male</td>\n",
       "      <td>47439.0</td>\n",
       "      <td>1382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18-24</td>\n",
       "      <td>female</td>\n",
       "      <td>95057.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18-24</td>\n",
       "      <td>male</td>\n",
       "      <td>108528.0</td>\n",
       "      <td>1165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25-34</td>\n",
       "      <td>female</td>\n",
       "      <td>136292.0</td>\n",
       "      <td>1388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25-34</td>\n",
       "      <td>male</td>\n",
       "      <td>154219.0</td>\n",
       "      <td>1549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>35-44</td>\n",
       "      <td>female</td>\n",
       "      <td>166324.0</td>\n",
       "      <td>1707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>35-44</td>\n",
       "      <td>male</td>\n",
       "      <td>189500.0</td>\n",
       "      <td>1955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45-54</td>\n",
       "      <td>female</td>\n",
       "      <td>151650.0</td>\n",
       "      <td>1542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45-54</td>\n",
       "      <td>male</td>\n",
       "      <td>170459.0</td>\n",
       "      <td>1690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55-64</td>\n",
       "      <td>female</td>\n",
       "      <td>104220.0</td>\n",
       "      <td>2105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>55-64</td>\n",
       "      <td>male</td>\n",
       "      <td>120468.0</td>\n",
       "      <td>2451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>65+</td>\n",
       "      <td>female</td>\n",
       "      <td>91956.0</td>\n",
       "      <td>2765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>65+</td>\n",
       "      <td>male</td>\n",
       "      <td>52164.0</td>\n",
       "      <td>1585.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_group   Gender  Impressions   Clicks\n",
       "2    unknown  unknown     685483.0  19480.0\n",
       "3        <18   female      21800.0    683.0\n",
       "4        <18     male      47439.0   1382.0\n",
       "6      18-24   female      95057.0   1002.0\n",
       "7      18-24     male     108528.0   1165.0\n",
       "9      25-34   female     136292.0   1388.0\n",
       "10     25-34     male     154219.0   1549.0\n",
       "12     35-44   female     166324.0   1707.0\n",
       "13     35-44     male     189500.0   1955.0\n",
       "15     45-54   female     151650.0   1542.0\n",
       "16     45-54     male     170459.0   1690.0\n",
       "18     55-64   female     104220.0   2105.0\n",
       "19     55-64     male     120468.0   2451.0\n",
       "21       65+   female      91956.0   2765.0\n",
       "22       65+     male      52164.0   1585.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = data1.groupby(['age_group', 'Gender'], as_index=False)['Impressions', 'Clicks'].sum()\n",
    "grouped = grouped.dropna()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:43.471864Z",
     "start_time": "2019-03-01T06:47:43.275985Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>CTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>685483.0</td>\n",
       "      <td>19480.0</td>\n",
       "      <td>2.841792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;18</td>\n",
       "      <td>female</td>\n",
       "      <td>21800.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>3.133028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt;18</td>\n",
       "      <td>male</td>\n",
       "      <td>47439.0</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>2.913215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18-24</td>\n",
       "      <td>female</td>\n",
       "      <td>95057.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>1.054104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18-24</td>\n",
       "      <td>male</td>\n",
       "      <td>108528.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>1.073456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25-34</td>\n",
       "      <td>female</td>\n",
       "      <td>136292.0</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>1.018402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25-34</td>\n",
       "      <td>male</td>\n",
       "      <td>154219.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>1.004416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>35-44</td>\n",
       "      <td>female</td>\n",
       "      <td>166324.0</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>1.026310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>35-44</td>\n",
       "      <td>male</td>\n",
       "      <td>189500.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>1.031662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>45-54</td>\n",
       "      <td>female</td>\n",
       "      <td>151650.0</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>1.016815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45-54</td>\n",
       "      <td>male</td>\n",
       "      <td>170459.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>0.991441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55-64</td>\n",
       "      <td>female</td>\n",
       "      <td>104220.0</td>\n",
       "      <td>2105.0</td>\n",
       "      <td>2.019766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>55-64</td>\n",
       "      <td>male</td>\n",
       "      <td>120468.0</td>\n",
       "      <td>2451.0</td>\n",
       "      <td>2.034565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>65+</td>\n",
       "      <td>female</td>\n",
       "      <td>91956.0</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>3.006873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>65+</td>\n",
       "      <td>male</td>\n",
       "      <td>52164.0</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>3.038494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_group   Gender  Impressions   Clicks       CTR\n",
       "2    unknown  unknown     685483.0  19480.0  2.841792\n",
       "3        <18   female      21800.0    683.0  3.133028\n",
       "4        <18     male      47439.0   1382.0  2.913215\n",
       "6      18-24   female      95057.0   1002.0  1.054104\n",
       "7      18-24     male     108528.0   1165.0  1.073456\n",
       "9      25-34   female     136292.0   1388.0  1.018402\n",
       "10     25-34     male     154219.0   1549.0  1.004416\n",
       "12     35-44   female     166324.0   1707.0  1.026310\n",
       "13     35-44     male     189500.0   1955.0  1.031662\n",
       "15     45-54   female     151650.0   1542.0  1.016815\n",
       "16     45-54     male     170459.0   1690.0  0.991441\n",
       "18     55-64   female     104220.0   2105.0  2.019766\n",
       "19     55-64     male     120468.0   2451.0  2.034565\n",
       "21       65+   female      91956.0   2765.0  3.006873\n",
       "22       65+     male      52164.0   1585.0  3.038494"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped['CTR'] = (grouped.Clicks / grouped.Impressions) * 100\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impressions vs age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:43.943571Z",
     "start_time": "2019-03-01T06:47:43.474862Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='age_group', y='Impressions', data=grouped, hue='Gender')\n",
    "plt.title('Impressions vs age group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impressions vs gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:44.529210Z",
     "start_time": "2019-03-01T06:47:43.946570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Gender', y='Impressions', kind=\"bar\", data=grouped, hue='age_group')\n",
    "plt.title('Impressions vs gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CTR vs age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:45.064877Z",
     "start_time": "2019-03-01T06:47:44.532207Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='age_group', y='CTR', kind=\"bar\", data=grouped, hue='Gender')\n",
    "plt.title('CTR vs age group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CTR vs gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:45.676497Z",
     "start_time": "2019-03-01T06:47:45.068875Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sTTN7tGL5UDM7ukjSeIKZ3W1m/2dmW9fY93NmdoeZdRRJGn9lZreb2VNmtltRxszsJDN71MweMbPdi/VnmNkXivdXm9n5xft9iySTa5rZRDM7x8weM7M/mtn76jmnltwSTCmtBlxEnsl3HjDG3U+tKmPAqcAIcqbLke5+fyvaJyKyiBkcEZuZ2QjybBef6dpQZDz+ATAiIl4u0pcMJefFWhcYR5638EvkRJAbktOh3GNmE4AJ5ISU44BhvDu/4VbAZcX7dYA9I2I/M3Pgy8Bve230wpxxH8wBDnH3+1NKywD3pZRucvfHK8rsRD6JdYDNgTOLnyKl8tAti9dVbqMmt0OkB1cVP+/j3TxcANuSc13tEBGvVay/JiLmAY8XySMhB6BLI2Iu8LyZ/ZX8peRbgIPNbD3yxLwrmNlQYEvgu8BKwNNFMshabehWSwKWuz9LzsOCu7+eUppIjryVAWsX4CJ3D+DOlNLyKaWhxb5Sp3p+WeoXpcgiYQ7zP/ZZsuL9W8XPucwfB54CPgh8GLi3RnnI0ztV/pxPREwzsxWAHcm9rRXJmY5nRcTrZrZSVX1zgbpuCbb8GVZKaU3y78y7qjYNY/7MlFOLdSIi0nfPA+83s5XMbAm6mXG9yjPkW30XmdlHeyk7AdjdzAaZ2crAp4C7i213kHNjTSD3uA4tfi6Ulg5rTyktDVwJHOzur1VtrhWt3zMUNKU0ChgF4O50dHQ0rH3Tei8C0NBjNl71x7rg2nWe9VyHReUaQOPP9dU2HHNR14xh6L2JiLfN7Fhy5+Bp4Ik693vSzL4GXG5mn++h6NXk23wPkX9X/yginiu23UK+rTjJzJ4h97LKE7BSSkPIwepid7+qRpGpzJ9KeVVqpFJ29zHAmGIxZsyY0eim9qodx6xffc9P6tGfz7M/t62R1wAaf65D2nDMRUlnZ2e7m/COiPgV8Ksets+geH4UEWOBscX7B4D1imIjq/ZZuvgZwA+LV3W95wHnFe/fJmdW7to2mXezKhMRP6/3fFo1StDIjZ/o7r/optg4YHRK6TLyYItX9fxKRES6tKqH9UlgL+CRlFLXyJAjgNUB3P0scqrmEcAk8rD2fVrUNhERKYFWjRK8lW5GlFSUCeA7rWiPiIiUj2a6EBGRUlDAEhGRUlDAEhGRUlB6ERGRJrvn7M83NL3Ipvtft9Df6zKz0eQv934IWLkY4o6ZLUee1291coz4eURcsLDHawT1sEREFhFmtriZdX0n6jbypLfPVBX7DvB4RGwIbAOcbGaN/XLhAlLAEhEZ4Mzs383sZOBJ8jyBRMQDxZd4qwWwjOVp2pcGZpLnJWw73RIUERmAip5UAvYlf63oAmCDiHi9l11PI0/kMB1YBti9mKm97RSwREQGpmeBh4FvRURd8wgWPgs8CGxHfr51k5ndUpVupC10S1BEZGDajTyX9NVmdqSZrVHnfvsAV0U2iTxx7rrNamRfKGCJiAxAEfHHiNidnGjxVeBaM/uTma3Zy67/ALYHKJI1foScJ6vtdEtQRKTJGjEMfUFFxEvAqcCpZrYZOWEiZvZd4EfAKsDDZnZ9RHwL+Ckw1sweIT/7OqxryHu7KWCJiCwiIuLuivc1U49ExHRgh1a2q166JSgiIqWggCUiIqWggCUiIqWggCUiIqWggCUiIqWggCUiIqWwUMPaU0qLA/u5++kNao+IyIBz9m8+29D0IvvvdWOv3+sys/OBnYEXImL9Yt1w4CxgSfKEtt+uHOpese/FwCbA28DdwP4R8XbF9k2BO8nzDF6x8GdUn7oCVkppe2A4MMndr00pDQa+DRxGnslXAUtEpH8ZS57I9qKKdScCx0TEH8xsRLG8TY19Lwa+Xry/BPgWcCaAmQ0CTgBubEqre9BrwEopHQb8F/AY8NGU0hnkE3wLGOXuv29qC0VEpM8iYkKNaZgCWLZ4vxx5RvZa+17f9d7M7gZWrdh8EHAlsGmj2lqvenpY+wOfdvf7UkpbkJN+HerupzS3aSIi0mAHAzea2c/JYxg+0VNhMxsC7AV8r1geBuxKnsm95QGrnkEXHe5+H4C730nuWf2yqa0SEZFmOBD4fkSsBnwfOK+X8mcAEyLilmL5l+S5Bec2sY3dqvcZlpEnQTRgdrHunWDn7v0iuZeIiPToGxS9JeBy4FwAM7sR+ABwbzEBLmZ2FLAy+S5bl02Ay3IyYjqAEWY2JyKuaUXj6wlYSzN/emSrWDbyPdFBDW6XiIg03nTg08DN5Nt6fwOIiM9WFjKzb5ETOW5fmW04ItaqKDMWGN+qYAX1Bay1ei8iIiLdqWcYeqOZ2aXkAXIdZjYVOArYj5xmZDD5btmobnY/C3gGuKPoTV0VEcc2vdG9qCdgneHun2t6S0REpGEiYs9uNm1cx769xoaIGNnXNi2segZdbN30VoiIiPRCUzOJiEgp1HNLcMmU0kU9FXD3vRvUHhERkZrqCVgB/L3ZDREREelJPQHrLXc/puktERER6UE9z7BaPhxTRESkWj09rDtTSie4+2HVG1JKxwPXFFM2iYhIDSOuOaSh6UWu/+LJ9aQXWY08U/sqwDxgTEScamZHk7+P9WJR9IjKyW4r9v8psEux7wvAyIiYXrG95SlG6ulhvQ1M6GbbzcBPGtYaERFplDnAIRHx78AWwHfMbL1i2ykRMbx4vSdYFU6KiA0iYjgwHjiya0O7UozUE7A2BG7oZtufqONLaCIi0loR8WxE3F+8fx2YCAzrw/6vVSwuRR6A16UrxcgLDWhq3eoJWMsCi3ezbQiwTOOaIyIijVbkxdoIuKtYNdrMHjaz881shR72O87MpgBfo+hhVaQYOaupja6hnmdYTwA7ANfW2LZDsb1HKaV3UjW7+/o1tm9T1P90seoqd2/7vFUiImVnZkuTe0MHR8RrZnYm8FNyj+mnwMnAN2vtGxE/AX5iZocDo8nzEb6TYqSYZ7Bl6glYpwBnp5QGkQdYzCtSi3wROB34QR11jOW9qZqr3eLuO9dRl4iI1KFIwHglcHFEXAUQEc9XbD+H/HwKM7uA3AubHhEjqqq6BPg9OWC1LcVIr7cE3f0S4ETgQmB2Smk6eZbfscCJ7n5pHXVMAGYuXFNFRKReliPKecDEiPhFxfqhFcV2BR4FiIh9ikEYI4py61SU+wLF3bSIWCsi1oyINYErgG/3p3xYuPsvUkrnAlsCKwEvAXe4+2s979knW6aUHiLnaznU3R9rYN0iIm1TzzD0JvgkOb39I2b2YLHuCGBPMxtOviU4mfkTNFY63sw+Qh7W/gxwQHOb27u6AhZAEZyaNYTxfmANd5+VUhoBXAOsU6tgSmkURQ4Xd6ejo6NhjZhWZ7lGHrPxGvc3RLvOs57rsKhcA2j8ub7ahmNK60XErdSe+KG7YezV+3+5jjIj+9ishVJ3wGqmyp6au1+fUjojpdTh7jNqlB0DjCkWY8aM9xRpunYcs37dDejsu/58nv25bY28BtD4cx3ShmMuSjo7O9vdhAGrX6QXSSmtklKy4v1m5Ha91N5WiYhIf9KSHlZK6Z1UzSmlrlTNQwDc/SxgN+DAlNIc4J/AHu7e0KlMRESk3FoSsNy9u1TNXdtPIw97FxERqalf3BIUERHpjQKWiIiUQr8YJSgiMpDtfMXFDX0mP363r9WTXmRJcqaNJci/66+IiKPMbCzwad79hsPIiHiwdi1gZr8G9omIpavW7wZcDmwaEfcu0In0kQKWiMjA9BawXUTMKqZoutXM/lBs+2E9OazMbBNg+RrrlwG+y7uT6baEbgmKiAxAkc0qFocUr7p7ekXOq5OAH9XY/FPylH2zF7adfaGAJSIyQJnZoGJapheAmyKiq0d0XJFe5BQzW6Kb3UcD4yLi2ao6NwJWi4jxzWt5bQpYIiIDVETMLTIGrwpsZmbrA4cD6wKbAisCh1XvZ2adwFeAX1etX4ycweOQJje9JgUsEZEBLiJeAW4GdiwyEUdEvAVcAGwGYGY3mtmDZnYuOc3I2sAkM5sM/JuZTSIn7F0fuLlYvwUwrnjW1XQadCEiMgCZ2crA2xHxipm9D/gMcIKZDY2IZ4v0I1/k3fQin62qYpWKumZFxNrFYkfF+puBQzVKUERkgKhnGHoTDAUuLAZPLAZ4RIw3s78UwcyAB+kHaUPqpYAlIjIARcTD5Ft71eu3W4C6lu5m/TZ9b9mC0zMsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQ1rFxFpst2uvL+h6UWu+PLH6/5eV/E9rHuBaRGxc73pRXorZ2abAncCu9cz83sjKGCJiAxs3wMmAstWrKsrvUh35YogeAJwY2OaWB/dEhQRGaDMbFXgc8C5Da76IOBK8izwLaOAJSIycP2SnM9qXtX6etKL1CxnZsOAXYGzmtPk7ilgiYgMQGa2M/BCRNxXtanX9CK9lPslcFhEzG14o3uhZ1giIgPTJ4EvmNkIYElgWTP7bUR8vdj+lpldABwKOb0I8AHg3oj4VkXixvnKAZsAl+XJ3ukARpjZnIi4ptknpIAlIjIARcTh5F4SZrYNOQ3I1+tNL9JDubUqyowFxrciWIEClohI0/VlGHoLXFxnepF6y7WMApaIyAAXETeTMw7XnV6knnIRMXJh2tVXGnQhIiKloIAlIiKloIAlIiKloIAlIiKloIAlIiKloIAlIiKloGHtIiJN9offzWhoepGddu+o63tdZjYZeB2YC8yJiE3M7GhgP+DFotgREXF9N/sfBIwG5gC/j4gfVWxbHXgcODoifr6Ap9InClgiIgPbthExo2rdKb0FGTPbFtgF2CAi3jKz91fXAfyhge3slQKWlNJ1f96nrnKf3/6CJrdEZMA6EDg+It4CiIh3UomY2ReBp4A3WtkgBawFUM8vS/2iFJF+IIA/mlkAZ0fEmGL9aDPbm5yJ+JCIeLnGvh8Gtjaz44DZ5LkI7zGzpcgzt/8H706I2xIadCEiMnB9MiI+DuwEfMfMPgWcCXwIGA48C5zczb6DgRWALYAfAl5MhHsM+ZbirGY3vlaDmi6ldD6wM/CCu69fY7sBpwIjgDeBke5+fyvaJgIw6vb/ravcmE9s2+SWiDROREwvfr5gZlcDm0XEhK7tZnYOML54fwGwETA9IkYAU4GrIiKAu81sHjmdyObAbmZ2IrA8MM/MZkfEac0+n1bdEhwLnAZc1M32nYB1itfm5L8ANm9Jy9pMvyhFpBmKW3eLRcTrxfsdgGO70oYUxXbl3bQh1c86rgG2A242sw8DiwMzImLrimMcDcxqRbCCFgUsd5+QUlqzhyK7ABe5ewB3ppSWTykNdfdne9inX/vW3d31sue3GB+vq9x373iurnL7snpd5USkdeodht5gHwCuLhItDgYuiYgbzOw3Zjac/HxrMrB/N/ufD5xvZo8C/wK+UfS22qa/DLoYBkypWJ5arHtPwEopjQJGAbg7HR0dDWvEtIbVNDA08rPti0Zeh0b/4VDfZ/JaXXXVq9HX4dU2HFNaLyKeAjassX6vOvf/F/D1XsocvUCNW0D9JWDV+uujZiR39zFA10iXmDGj+usF0ij6bN/rq9c92muZRvdyG30dhrThmIuSzs7OdjdhwOovowSnAqtVLK8KTG9TW0REpB/qLz2sccDolNJl5MEWr5b5+ZWIiDReq4a1XwpsA3SklKYCR1HcmXD3s4DryUPaJ5GHtdc3jYGIiCwyWjVKcM9etgfwnVa0RUREyqm/PMMSERHpUX95hiUiMmA9d/ITDf3+0iqHrFtvepHlgXOB9ckjr78JfJY604v0N+phiYgMXKcCN0TEuuTvZE0s1p8SEcOL13uClZkdbWYjW9jOuqiHJSIyAJnZssCngJHwzheB/1XMfFFK6mGJiAxMHyTf9rvAzB4ws3OLOQUhpxd52MzON7MV2tjGPlHAEhEZmAYDHwfOjIiNyMkWf0w36UXM7GNm9qCZPQgcQJ4o98HitVJbzqCKbgmKiAxMU4GpEXFXsXwF8OOIeL6rQGV6kYh4hBzEumZhnxwRY1vZ4N6ohyUiMgBFxHPAFDP7SLFqe+BxMxtaUeyd9CJloB6WiEiT1TsMvQkOAi42s8WBp8izCP2qzvQi/Y4ClojIABURDwIDcfL9AAALDElEQVSbVK3uNb1Iq9OG1Eu3BEVEpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQUsEREpBQGt+pAKaUdgVOBQcC57n581faRwEnAtGLVae5+bqvaJyIi/VtLAlZKaRBwOvAfwFTgnpTSOHd/vKro79x9dCvaJCIi5dKqW4KbAZPc/Sl3/xdwGbBLi44tIiIDQKtuCQ4DplQsTwU2r1HuyymlTwH/B3zf3afUKCMiIougVgUsq7EuqpavAy5197dSSgcAFwLbVe+UUhoFjAJwdzo6OhrWyGm9F1mkNPKz7Qtdh/k1+jq82oZjijRCqwLWVGC1iuVVgemVBdz9pYrFc4ATalXk7mOAMcVizJgxo4HNlEr6bPuHRl+HIW045qKks7Oz3U0YsFr1DOseYJ2U0loppcWBPYBxlQVSSkMrFr8ATGxR20REpARa0sNy9zkppdHAjeRh7ee7+2MppWOBe919HPDdlNIXgDnATGBkK9omIiLl0LLvYbn79cD1VeuOrHh/OHB4q9ojIiLlopkuRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFBSwRESkFAa36kAppR2BU4FBwLnufnzV9iWAi4CNgZeA3d19cqvaJyIi/VtLelgppUHA6cBOwHrAniml9aqK7Qu87O5rA6cAJ7SibSIiUg6tuiW4GTDJ3Z9y938BlwG7VJXZBbiweH8FsH1KyVrUPhER6edaFbCGAVMqlqcW62qWcfc5wKvASi1pnYiI9HuteoZVq6cUC1CGlNIoYBSAu9PZ2bnwrSt07n9dXeU2raPM/gvXlCbraHcDelTPdajnGoCuQ00Hfr09x110BLV/n8lCalUPayqwWsXyqsD07sqklAYDywEzqyty9zHuvom7b0L+R1HqV0rpvna3QS9dh/7yGkDXQZqgVT2se4B1UkprAdOAPYCvVpUZB3wDuAPYDfiLu7+nhyUiIoumlvSwimdSo4EbgYl5lT+WUjo2pfSFoth5wEoppUnAD4Aft6JtIiJSDi37Hpa7Xw9cX7XuyIr3s4GvtKo9/ciYdjdAAF2H/kLXQbplEbrrJiIi/Z+mZhIRkVJQwFpIKaXvppQmppQublL9R6eUDm1G3VJbSmmblNL4drdjIEkpjUwpndbudki5tewZ1gD2bWAnd3+63Q0RERnIFLAWQkrpLOCDwLiU0mXAh4CPkT/Xo9392pTSSOCL5El/1wdOBhYH9gLeAka4+8yU0n7kL0QvDkwC9nL3N6uO9yHynIwrA28C+7n7E00/0RJKKa0J3ADcCmwBPARcABwDvB/4WlH0l8D7gH8C+7j7k1X1LAX8mqrr2oJT6NeKz3e8u69fLB8KLA1sA9wFbAssD+zr7rdU7fs54D+BzwM/B14DNgFWAX7k7lcU07KdSJ5/NICfufvvUkpnADe4+7iU0tXk+Ue/mVLaF1gLOBf4A/m6f4L8NZpd3P2fTfswpGV0S3AhuPsB5C9AbwssRf7u2KbF8knFLzvIgeqr5DkVjwPedPeNyN8527soc5W7b+ruG5KH/u9b45BjgIPcfWPgUOCM5pzZgLE2OUPABsC65GuwFfmzOwJ4AvhUcS2OBP67Rh0/ofvrKrUNdvfNgIOBoyo3pJR2JX9lZYS7zyhWDyVfl52BriwOXwKGAxsCnyF/7kOBCcDWRZlh5Mm0KfbvCozrAKe7+0eBV4AvN/TspG3Uw2qcHYAvVDxvWhJYvXj/v+7+OvB6SulVoGvuoUfIv0wB1k8p/Yz8V+nS5O+svSOltDT5L8bLU0pdq5doxokMIE+7+yMAKaXHgD+7e6SUHgHWJM+mcmFKaR3yX/FDatTR3XWd2OzGl9hVxc/7yJ9zl23JPakd3P21ivXXuPs84PGU0geKdVsBl7r7XOD5lNJfyTNy3QIcXGR7eBxYoQhkWwLfJc8/+rS7P9hNG6TEFLAax4Av17iltDn51l+XeRXL83j3GowFvujuDxW3Ebepqn8x4BV3H97YZg9ovX3uPyX/MbFrcYvr5hp11Lyuwhzmv0OzZMX7rs95LvP/jnmKfAv9w8C9NcrDu9Ma1ZzeyN2npZRWAHYk97ZWBBIwy91fTymtVFXfXPItXxkAdEuwcW4EDupKiZJS2qiP+y8DPJtSGsK7z1feUfxF+nRK6StF/ZZS2nAh27yoW478jANgZDdlFva6DlTPA+9PKa1UJF/duY59niHf6rsopfTRXspOAHZPKQ1KKa0MfAq4u9h2B/l24wRyj+tQ3r0dKAOYAlbj/JR8S+nhlNKjxXJf/Bf5YfVN5GcrtXwN2Del9BDwGO/NKSZ9cyLwPyml28iDYmpZ2Os6ILn728Cx5H+z4+n+32z1fk+S/x1fXgwi6s7VwMPkwTJ/IQ/GeK7Ydgv5Odkk4H5yL0sBaxGgmS5ERKQU1MMSEZFSUMASEZFSUMASEZFSUMASEZFSUMASEZFSUMAS6UExy/it7W6HiGimCymplNIewPfJ8zS+ATwNXAic6e76robIAKQelpROSukQ8qS2J5Fn+P4AcADwSfJs9/1CSqm7LyOLyAJQD0tKJaW0HHmGhb3d/cqKTQ9QTGlVTBV0HHmOuSXIsyZ8393/mVLaBvgtcApwGHmuuSPc/YJi35XIaUi2Ic/eUD0J8brkdCMbAy8C/+XuXmwbS05TsgbwafJMJH9q5PmLLMrUw5Ky2ZIchHrKSXUCeYLV4eQUI8PI6UO6rEKeR3AYOY3L6cWEqpDzjc0mp7z4ZvEC3smNdRNwCTmn1p7AGVXz4n2VHCyXIedkEpEGUQ9LyqYDmOHuc7pWpJRuJ+dFWoI8i/d+wAbuPrPY/t/kIHN4scvbwLFFHdenlGYBH0kp3UPOnfQxd38DeDSldCF54lXIE7xO7uqNAfenlK4EdiPP7QhwrbvfVryf3eBzF1mkKWBJ2bwEdKSUBncFLXf/BEBKaSr5eda/AfdV5A0z5p/c9qXKgEfO3rw0OZPzYGBKxbZnKt6vAWyeUnqlYt1g4DcVy5X7ikgDKWBJ2dxBzne0C3Blje0zyM+RPuru02ps78mL5DxPq/Hu7OOrV2yfAvzV3f+jhzo0QlGkSRSwpFTc/ZWU0jHkZ0cG3EDuIW0ALEVOzngOcEpKabS7v5BSGgas7+43dltxrntuSukq4OiU0jfJmWq/AUwuiowHjk8p7QVcVqwbTk4eqAzEIk2mQRdSOu5+IvAD4EfAC+RkgmeTR/3dXvycBNyZUnqNPFLvI3VWP5p8e/A5chborudVuPvrwA7AHsD0oswJ5GdnItJkyoclIiKloB6WiIiUggKWiIiUggKWiIiUggKWiIiUggKWiIiUggKWiIiUggKWiIiUggKWiIiUggKWiIiUwv8DPvlVOwdTPc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Gender', y='CTR', kind=\"bar\", data=grouped, hue='age_group')\n",
    "plt.title('CTR vs gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clicks vs age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:46.326096Z",
     "start_time": "2019-03-01T06:47:45.680495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='age_group', y='Clicks', kind=\"bar\", data=grouped, hue='Gender')\n",
    "plt.title('Clicks vs age group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clicks vs gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:46.932719Z",
     "start_time": "2019-03-01T06:47:46.330094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SEZFKUoASEZFKUoASEZFKUoASEREsq1RMqFRjREQ6nZkdbWZ/NbNnzexBM9u9lA8zs9PMbI6ZPWpmE8wsmdnyZfvrzeynZjbbzB43s5PMbFgvn9XbMW8ys2+Y2e+B54E3mVmXmU0ys7lmNs3MDqo73rlmdlLd++3MbGbd++lmdkw5r3lmdo6Zvaa/31W7M+qKiCxr/gq8B3gC+CTwczN7MzAO2AUYC/wT+EW3/c4DngTeDKxEzqE3A/jxEj7roF6OCbB3qfMwOQHs9cADQBfwVmCKmT2SUrqhyfP7DLBT+bxfAl8pjz5TD0pEpIVSSr9IKc1KKb2cUroE+AuwFeDAGSmlmSmlecDJtX3MbG1yEDk8pfTPlNJTwOnkvHhL0uMx65ybUnogpbQAWAd4N3BUSumFlNJU4CxyEGvW91NKM1JKc4FvAHv2Yd/FqAclItJCZrYP8EVg/VK0MjCC3GOZUVe1/vUbyUmQZ5u9kt1juW51GlnSMRuVdQFzU0rP1pU9BmzRy+f0dLzHyjH7RQFKRKRFzOyNwE+A9wO3ppQWmtlU8tDabGB0XfV1617PAF4ERpSeTrOWdMya+qSAs4A1zGyVuiC1HvB4ef1P4HV19ddpcLz6z1ivHLNfNMQnItI6K5EDwt8BzGx/YNOyLYAvmNkoM1uNnCkcgJTSbODXwGlmtqqZLWdmG5rZ+3r5vB6P2UhKaQbwB+D/mdlrzOztwIHABaXKVGBXM1vDzNYBDm9wmM+Z2WgzWwM4Friklzb2SAFKRKRFUkoPAqcBt5InPLwN+H3Z/BNyELoPuAeYDCwAFpbt+wArAA8C84BLgZG9fGRvx2xkT/Lw4yzgCuC4lNKUsu1nwL3A9HLcRsHnwrLtkfI4qUGdpijlu1K+yzJKKd8HxmClfDezXYAfpZTeWOVjdjv+dOCzKaXrB+J46kGJiFSAmb3WzHY1s+XNbBRwHLkHU6ljtpIClIhINRjwdfLw3T3AQ8DXet3J7Edm9lyDx4/6e8yq0Cw+EZEKSCk9D2zZj/0OAQ5ZQpU+H7O/UkrrD+Tx1IMSEZFKUoASEZFKUoASEZFKUoASEZFK0iQJEZFB8Pczfz6gN5m+4dC9BvR+KzPbD9gipTRhII87kNSDEhGRSmpJD8rdzwY+DDwVEZuWsm8BHwH+Tc6Psn9EPO3u65Pn6j9cdr8tIg4p+2wOnAu8lrxkxxciIrn7GuQlN9YnL8HhETGvFecmIlIFZrY+cHVKadPy/kjySunbAX8EtgdWAw5MKd3Sbd8PkXM2fQQ4FXiGvIL5OsCXU0qXWl5G/RRy2o8EnJRSusTMfghcm1KaZGZXAPNSSgeY2YHABuR0HdcAvwPeRV54dlxK6V+9nVOrelDnAjt3K5sCbBoRbwf+DzimbttfI2JsedTP7z8TGA+MKY/aMY8GboiIMcAN5b2IiGTLp5S2Ii/uelz9hpLR92hg15TSnFI8kpwX6sMsyiH1MXLiw3cAHwC+ZWYjgZvJCRgBRgEbl9fvBmqBcAzwg5TSJsDTwMebaXRLAlRE3AzM7Vb264ioLRt/G4svCf8q7j4SWDUibo2IBJwP7FY2jyNnm6Q879bgECIiy6rLy/NdLMpDBblXdRTwoZLQsObKklDxQWDtUvZu4KKU0sKU0pPAb8k3Ad8CvMfMNiYvZPtkCVz/SV4ZHeDRkvywURt6VJVrUAeQu4A1G7j7Pe7+W3evj8wz6+rMLGUAa0fEbIDyvNZgN1hEpGIWsPj/6a+pe/1ieV7I4pd2HgFWAd7S7Vgv1r22bs+LSSk9DqxOHtG6mRywHHiuLqdU/fG6t6FHbZ/F5+7/Q/5ia/lGZgPrRcQ/yjWnK919Exp/OX2eJePu48nDhEQEI0aM6F/DRYa4+U3U0e/HkPIksJaZrQk8Rx6eu7aXfR4DjgSuMLNPppQeWELdm4GDzew8YA3gvcCXyrZbycOHOwBrklOBXNrfE6lpa4By933JX+L7y7AdEfEiJdpGxF3u/ldydJ/J4sOAo1mUqfFJdx8ZEbPLUOBTPX1mREwEJpa3ac6cOT1VFelow5uoo9+P3nV1Nc5oPtDTwnuTUnrJzE4gT4h4FPhzk/s9bGafAX5hZh9ZQtUryMN295I7B19OKT1Rtt0C7JhSmmZmj5ED2C2ND9O8tgUod9+ZPPb5voh4vq78DcDciFjo7m8iX1x7JCLmuvuz7r4N+QewD/C9stskYF/yxbx9gataeCoiIpWQUvou8N0lbJ9Duf6TUjqXPIGNlNI9LJrcsF+3fVYuz4ncY/oS3aSUfgr8tLx+iZw5uLZtOouyBpNSOrXZ82nVNPOLyFMdR7j7TPIskmOAFYEp7g6LppO/FzjB3WtZHw+JiNoEi0NZNM38GhZdtzoZCHc/EPgb8MkWnJaIiAwiZdRVRl1ZRimj7sAYrIy6Up1ZfCIiIotRgBIRkUpSgBIRkUpSgBIRkUpq+426IiKd6I4ff2RAZ6BtefAvl3oyhplNIN9QuyHwhtrae2b2euDnwHrkuHBqSumcpf28paUelIhIBzOzFcysdl/S78kLvT7WrdrngAdTSu8g3xJ0mpmt0LpWNqYAJSLSgczsP8zsNHLqordAviG33DjbXQJWKSk1ViYv7r2gQb2W0hCfiEiHKD0lBw4kr196DvD2ukVbe/J98oo8s8iLx34qpfTyYLa1GQpQIiKdYzZwH/DZlFJTa/EVOwFTyYu9bghMMbNbUkrPDEIbm6YhPhGRzvEJcsbaK8zsa2b2xib32x+4PGXTyIvNvnWwGtksBSgRkQ6RUvp1SulT5OSC84GrzOz6kg5+Sf4GvB/AzNYGNiLnimorrcWntfhkGaW1+AZG1dfiM7OtgNkppRlmdhjwZWAdclqiySmlz5pZF3kh7pHka1cnp5R+3q421+galIhIB0sp3V73umE6jpTSLGDHVrarGf0e4nP37d39vQPZGBERkZqmA5S7/9bdty2vjwIuBi5y92MHq3EiIrLs6ksPalPgtvL6IPLdxtsAhwxwm0RERPp0DWo5ILn7hoBFxEMA7r76oLRMRESWaX0JUL8j3208ErgCoASrOYPQLhERWcb1ZYhvP+Bp8l3Kx5WytwJnDHCbRERE+tSDWjEiFpsQERG/cvfNBrhNIiJD3o9/ttOA3mR68N7X9Xq/lZmdDXwYeCqltGkpGwv8CHgNeQHY/6qfel637wXAFsBLwO3AwSmll+q2b0meh/CplNKlS39GvetLD+rX7r5GfYG7bwVMHtgmiYhIP50L7Nyt7BTg6ymlscDXyvtGLiCPir0NeC3w2doGMxsGfBO4boDbu0R9CVATyUFqZQB3fxd59dsDB6NhIiLSNymlm8mpMhYrBlYtr19PXrG80b6Ty1p8idyDGl23+fPAZeTVJ1qm6QAVEd8FrgImu/su5IkSe0WEelAiItV1OPAtM5sBnAocs6TKZjYc2Bu4trwfBexOHiZsqT6tJBERJwJ3AJcAn4yI6welVSIiMlAOBf47pbQu8N/AT3up/0Pg5pTSLeX9d4CjUkoLB7GNDS1xkoS7zyB3D+stVx4/d3cAImK9QWmdiIgsrX2BL5TXvwDOAjCz64C1gTtTSp8tZccBbwAOrtt/C+DinGyXEcCuZrYgpXTlYDe8t1l8ew12A0REZFDNAt4H3EROSPgXgJTSTvWVzOyz5MSF76/PpptS2qCuzrnA1a0ITqB0G0q3IcsspdsYGFVKt2FmF5GXoRsBPEm+Z/Vh8v2qywMvkKeZ39Vg3wXAY0AtPfzlKaUTutU5lxygWjLNvOn7oNz9cuD0iLilruw9wBci4hOD0TgREWleSmnPHjZt3sS+vcaDlNJ+fW3T0ujLjbrvAz7ZrexWoKmunru/cgNZRGxaytYgT7hYH5gOeETMc3cjR/xdgeeB/SLi7rLPvsBXymFPiojzSvnm5HsAXku+N+sLEbFMdw9FRIayvsziewFYqVvZyuS7jptxLq++gexo4IaIGAPcUN4D7AKMKY/xwJnwSkA7Dtga2Ao4rm6x2jNL3dp+3T9LRESGkL4EqOuAH7v7qgDl+fuUufK9iYhGN5CNA84rr88DdqsrPz8iUkTcBqzm7iPJF/CmRMTciJgHTAF2LttWjYhbS6/p/LpjiYjIENSXAHUE+W7kue7+FDnYvJ58E1h/rR0RswHK81qlfBQwo67ezFK2pPKZDcpFRGSIavoaVOmxfKj0VkYDMyLiiUFqV6NZMakf5a/i7uPJQ4FEBCNGjOhvG0WGtPlN1NHvh7RTbzfqWm2igbvXeltPlscrZRHxcuMj9OpJdx8ZEbNL4Kut8zQTWLeu3mjyXP6Z5CmU9eU3lfLRDeq/SkRMJK8rCJDmzFE6K1k2DW+ijn4/etfV1dXuJnSs3npQ81m0yOACXt0rsVI2rJ+fP4l8l/PJ5fmquvIJ7n4xeULE/BLErgP+t25ixI7AMREx192fdfdtgD8C+wDf62ebRESW2q5XHjGgs4gn73baEu+3MrN1ydff1wFeBiamlM4ws+OBg4C/l6rHppRetYaqmZ1Ivv7/MrmzsF9KaVbd9pan2+gtQG1S93qDHms1wd1fuYHM3WeSZ+OdDIS7Hwj8jUXT2CeTp5hPI08z3x+gBKLaeoAAJ0REbeLFoSyaZn5NeYiILCsWAEeklO42s1WAu8xsStl2ekrp1F72/1ZK6asAZnYYOTXHIeV9W9JtaCUJrSQhyyitJDEwelpJotU9qO7M7CryTOttgeeaCFD1+x4DrJdSOrS8P5x8S9GWVGUlCXf/GT1MNqgXEfsMWItERGSpmNn6wGbkSx7bAhPMbB/gTnIva14P+32DfIlkPrB9Kaul29iBHKBaprdp5tOAvzbxEBGRCjCzlcnJBQ9PKT1DXsRgQ2AsMBs4rad9U0r/U9JyXABMKMXVTLcREV93922Bj0bEUd23u/s3yYkLRUSkzUqywcuAC1JKlwOklJ6s2/4T4Ory+hxyL2tWSmnXboe6EPgVea5AZdNtABxLTmDVyG+A/wE+MmAtEhGRPrMcQX4KPJRS+nZd+ciU0uzydnfgTwAppf277T8mpfSX8vajwJ9Lvbal22gmQI2l5+WMrgfOHrjmiIh0hr5OahgA25JTtd9vZlNL2bHAnmY2ljyfYDqLJyOsd7KZbUSeZv4YZQZfOzUToFYFVgD+1WDbcGCVAW2RiIj0WUrpdzReVedV9zz1sP/Hm6izXx+btVSaWYvvz+QbYhvZsWwXEREZUM30oE4nr2I+DLgyIl4uSxztBvwA+OJgNlBERJZNvfagIuJC4BRyOowX3H0WOTfUucApEXHRoLZQRESWSU2l24iIb5PTV3wEOLI8j46I0wexbSIisgzrS7qNZ2jxOkwiIrLs6kvCQhERkZZpugclIiLN+/ClFwzoYrFXf+IzvaXbeA1wM7Ai+f/2S1NKx5Wba9/HohyV+6WUpjY+CpjZ94D9U0ordyv/BPALYMuU0p39PpE+UIASEekMLwI7pJSeK0se/c7MammHvtTMCuRmtgWwWoPyVYDDyIvPtoyG+EREOkDKnitvh5dH0724kvPpW8CXG2w+kTyb+4WlbWdfKECJiHQIMxtWljl6CpiSUqr1eL5hZveZ2elmtmIPu08AJtWt21c75mbAuimlqwev5Y0pQImIdIiU0sKU0lhgNLCVmW0KHAO8lZzLaQ3gVZkpzKyLnNH8e93KlyMv1nDEIDe9IQUoEZEOk1J6GrgJ2DmlNLsM/70InANsBWBm15nZVDM7i5x2483ANDObDrzOzKaR11rdFLiplG8DTCrXqgadJkmIiHQAM3sD8FJK6Wkzey3wAeCbtXQbJR3HbixKt7FTt0OsU3es51JKby5vR9SV3wQcqVl8IiJDWG/TwgfBSOC8MtlhOSBSSleb2Y0leBkwlQqk0WiWApSISAdIKd1HHqrrXr5DP461cg/l2/W9Zf2na1AiIlJJClAiIlJJClAiIlJJClAiIlJJClAiIlJJClAiIlJJmmYuIjIIPnHZ3QOabuPSj7+zqfuqyn1QdwKPp5Q+3Gy6jd7qmdmWwG3Ap5pZGX0gtDVAuftGwCV1RW8CvkZe7v0g4O+l/NiImFz2OQY4EFgIHBYR15XynYEzgGHAWRGnw+6JAAAS10lEQVRxcktOQkSkWr4APASsWlfWVLqNnuqVoPdNWpxVva0BKiIeBsYCuPsw4HHgCmB/4PSIOLW+vrtvDOwBbAJ0Ade7+1vK5h8AHwRmAne4+6SIeLAlJyIiUgFmNhr4EPAN4IsDeOjPA5eRF5xtmSpdg3o/8NeIeGwJdcYBF0fEixHxKDCNvPDhVsC0iHgkIv4NXFzqiogsS75Dzuf0crfyZtJtNKxnZqOA3YEfDU6Te1ala1B7ABfVvZ/g7vuQx1KPiIh5wCjyGGjNzFIGMKNb+daNPsTdxwPjASKCESNGNKom0vHm915Fvx9DiJl9GHgqpXSXmW1Xt+kY4AlgBWAiOd3GCQ0O0VO97wBHpZQW5vVmW6cSAcrdVwA+Sv6CAM4kZ3BM5fk04ADyYofdJRr3BBteoIyIieQvHyDNmTOn/w0XGcKGN1FHvx+96+rqancTarYFPmpmuwKvAVY1s5+nlPYq2180s3OAIyGn2wDWBu5MKX22LlHhYvWALYCLS3AaAexqZgtSSlcO9glVIkABuwB3R8STALVnAHf/CVDL5DgTWLduv9HArPK6p3IRkY6XUjqG8kd+6UEdmVLaq9l0G0uot0FdnXOBq1sRnKA6AWpP6ob33H1kRNSi+e6ULwqYBFzo7t8mT5IYA9xO7lmNcfcNyBMt9gA+3aK2i4i8SrPTwlvggibTbTRbr2XaHqDc/XXk2XcH1xWf4u5jycN002vbIuIBdw/gQWAB8LmIWFiOM4E8BXIYcHZEPNCykxARqZCU0k3kjLpNp9topl5Kab+laVdfWUoDei/ZUJNmzdJIoCybhl91Y691XhrX51RCy5yurq6q9JQ6TpWmmYuIiLxCAUpERCpJAUpERCpJAUpERCpJAUpERCqp7dPMRUQ60TWXzBnQKdK7fGpEr7MFzWw68Cw528OClNIWZnY83bJDpJQm97D/54EJ5Nt4fpVS+nLdtvXIt/gcn1I6tdH+A00BSkSks2yfUuq+RtXpvQUVM9uevMj221NKL5rZWt2PAVwzgO3slQKUiIgAHAqcnFJ6ESCl9FRtg5ntBjwC/LOVDdI1KBGRzpGAX5vZXWY2vq58QkmjcbaZrd7Dvm8B3mNmfzSz35YMupjZSuSVzb8+uE1/NQUoEZHOsW1K6Z3kBbg/Z2bvJWeH2JCcHHY2OTtEI8sDqwPbAF8Coiwc+3XyEOFzg934Rg0SEZEOkFKaVZ6fMrMrgK1SSjfXtpvZK9khSkqNzYBZKaVdydkiLk95/bvbzexlcnqNrYFPmNkpwGrAy2b2Qkrp+4N9PgpQIiIdoAzFLZdSera83hE4oZZGo1R7JTtESmn/boe4EtgBuMnM3kJOXDgnpfSeus84HniuFcEJFKBERAZFM9PCB9jawBUlseDywIUppWvN7Gdm9qrsEA2cDZxtZn8C/g3sm9q8mrgClIhIB0gpPQK8o0H53k3u/29gr17qHN+vxvWTJkmIiEglKUCJiEglKUCJiEglKUCJiEglKUCJiEglKUCJiEglaZq5iMggeOK0Pw/oPUTrHPHWZtJtrAacBWxKvu/pAGAnmky3UTXqQYmIdI4zgGtTSm8l3xP1UCk/PaU0tjxeFZzM7Hgz26+F7WyKelAiIh3AzFYF3gvsB6/cePvvsrLEkKQelIhIZ3gTeRjvHDO7x8zOKmvyQXPpNipHAUpEpDMsD7wTODOltBk5ueDR9JBuw8zeZmZTzWwqcAh5Ydmp5bFmW86gGw3xiYh0hpnAzJTSH8v7S4GjU0pP1irUp9tIKd1PDlq1Vcqnp5TObWWDe6MelIhIB0gpPQHMMLONStH7gQfNbGRdtVfSbQwF6kGJiAyCZqaFD4LPAxeY2QrAI8D+wHebTLdROZUIUO4+HXgWWAgsiIgt3H0N4BJgffKX6hExz92NPJVyV+B5YL+IuLscZ1/gK+WwJ0XEea08DxGRdkopTQW26Fbca7qNVqfRaFaVhvi2j4ixEVH7co8GboiIMcAN5T3ALsCY8hhPvgBICWjHkdMTbwUc5+5DZraKiIgsrkoBqrtxQK0HdB6wW135+RGRIuI2YDV3H0m+W3pKRMyNiHnAFGDnVjdaREQGRiWG+Mhjo7929wT8OCImAmtHxGyAiJjt7muVuqOAGXX7zixlPZUvxt3Hk3teRAQjRowY6HMRGRLmN1FHvx/STlUJUNtGxKwShKa4+5+XULfRhce0hPLFlOA3sbZ9zpw5fW6sSCcY3kQd/X70rqurq91N6FiVGOKLiFnl+SngCvI1pCfL0B3l+alSfSawbt3uo4FZSygXEZEhqO0Byt1XcvdVaq+BHcnz9CcB+5Zq+wJXldeTgH3c3dx9G2B+GQq8DtjR3VcvkyN2LGUiIjIEtT1AAWsDv3P3e4HbgV9FxLXAycAH3f0vwAfLe4DJ5Pn904CfAP8FEBFzgROBO8rjhFImIiJDkKU0oClLhpo0a5ZGAWXZNPyqG3ut89K4HVrQkqGtq6tr6C4XXnFV6EGJiIi8igKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhUkgKUiIhU0vLt/HB3Xxc4H1gHeBmYGBFnuPvxwEHA30vVYyNictnnGOBAYCFwWERcV8p3Bs4AhgFnRcTJrTyXTrXcRc/0WuflPVdtQUtEZFnT1gAFLACOiIi73X0V4C53n1K2nR4Rp9ZXdveNgT2ATYAu4Hp3f0vZ/APgg8BM4A53nxQRD7bkLEREZMC1NUBFxGxgdnn9rLs/BIxawi7jgIsj4kXgUXefBmxVtk2LiEcA3P3iUlcBSkRkiGp3D+oV7r4+sBnwR2BbYIK77wPcSe5lzSMHr9vqdpvJooA2o1v51oPdZhERGTyVCFDuvjJwGXB4RDzj7mcCJwKpPJ8GHABYg90TjSd7pB4+azwwHiAiGDFixNKfQAebS+/XoPQdDk3zm6ijn620U9sDlLsPJwenCyLicoCIeLJu+0+Aq8vbmcC6dbuPBmaV1z2VLyYiJgITy9s0Z86cpT2FjtbMNE99h72795YVeq3zjvf8uwUtWWR4E3X0s+1dV1dXu5vQsdo9i8+AnwIPRcS368pHlutTALsDfyqvJwEXuvu3yZMkxgC3k3tWY9x9A+Bx8kSKT7fmLEREZDC0uwe1LbA3cL+7Ty1lxwJ7uvtY8jDddOBggIh4wN2DPPlhAfC5iFgI4O4TgOvI08zPjogHWnkiIiIysCylhpdqlhVp1qyGI4FS6D6ogVHJIb6rbuy1zkvjdmhBS4a2rq6uRtfGZQBoJQkREamkdg/xSRs181f9Zi1oh4hII+pBiYhIJakHJbIE4//wm17rTHzX9i1oiciyRz0oERGpJAUoERGpJAUoERGpJF2DGoJ0XaRaDrv1iV7rHMh6LWiJSGdRgBKpCN0ULbI4BagOpb/qe/fZ20/rtc5yvLMFLRGRRnQNSkREKkk9qApp5i96WLb/qn/8lwc3VW/UR348yC0RkcGmHpSIiFSSApSIiFSSApSIiFSSrkFJR/rlDfv3XmmVTQe/ISLSb+pBiYhIJakH1SL6i15EpG/UgxIRkUpSgBIRkUpSgBIRkUpSgBIRkUrSJIkB0NTyO68b/HaIiHQS9aBERKSSFKBERKSSFKBERKSSFKBERKSSFKBERKSSOmoWn7vvDJwBDAPOioiT29wkERHpp47pQbn7MOAHwC7AxsCe7r5xe1slIiL91TEBCtgKmBYRj0TEv4GLgXFtbpOIiPRTJwWoUcCMuvczS5mIiAxBnXQNyhqUpe4F7j4eGA8QEXR1dS31B3cd/Mte62zZxHGaWI+iDUa0uwGLaea7hqH6fbf4uz50r9Z+XudKNP7/R5ZSJ/WgZgLr1r0fDczqXikiJkbEFhGxBfkf1ZB7uPtd7W7DsvLQd63vusmHDIJO6kHdAYxx9w2Ax4E9gE+3t0kiItJfHdODiogFwATgOuChXBQPtLdVIiLSX53UgyIiJgOT292OFpjY7gYsQ/Rdt46+a1mMpfSqeQQiIiJt1zFDfCIi0lkUoNrA3Q9z94fc/YJBOv7x7n7kYBx7Webu27n71e1ux1Dj7vu5+/fb3Q4ZejrqGtQQ8l/ALhHxaLsbIiJSVQpQLebuPwLeBExy94uBDYG3kX8Wx0fEVe6+H7AbedHbTYHTgBWAvYEXgV0jYq67H0S+6XgFYBqwd0Q83+3zNiSvUfgG4HngoIj486CfaEW5+/rAtcDvgG2Ae4FzgK8DawGfKVW/A7wW+Bewf0Q83O04KwHfo9vPrgWn0HblO7w6IjYt748EVga2A/4IbA+sBhwYEbd02/dDwFeAjwCnAs8AWwDrAF+OiEvd3YBTyOtqJuCkiLjE3X8IXBsRk9z9CmBeRBzg7gcCGwBnAdeQf7bvIt9uMi4i/jVoX4YMKg3xtVhEHEK+gXh7YCXgxojYsrz/VvmPD3Jg+jR5jcFvAM9HxGbArcA+pc7lEbFlRLyDPLX+wAYfORH4fERsDhwJ/HBwzmxIeTN51fu3A28lf8/vJn8/xwJ/Bt5bvu+vAf/b4Bj/Q88/u2XZ8hGxFXA4cFz9BnffHTia/AfWnFI8kvzdfxioZR/4GDAWeAfwAfJ3OxK4GXhPqTOKvCg0Zf9aIBwD/CAiNgGeBj4+oGcnLaUeVHvtCHy07nrRa4D1yuvfRMSzwLPuPh+orfFzP/k/VoBN3f0k8l+rK5PvAXuFu69M/kvyF+5eK15xME5kiHk0Iu4HcPcHgBsiIrn7/cD6wOuB89x9DPkv+OENjtHTz+6hwW58xV1enu8if5c125N7SjtGxDN15VdGxMvAg+6+dil7N3BRRCwEnnT335JXr7oFOLxkKXgQWL0Erv8EDgPWJP9sp/bQBhliFKDay4CPNxg+2po8lFfzct37l1n0czsX2C0i7i3Dgtt1O/5ywNMRMXZgmz3k9fbdnkj+A2H3Mpx1U4NjNPzZLSMWsPjoy2vqXte+y4Us/v/LI+Sh7bcAdzaoD4uWDGq4dFBEPO7uqwM7k3tTawAOPBcRz7r7mt2Ot5A8TCtDlIb42us64PNlzB1336yP+68CzHb34Sy6dvKK8pfqo+7+yXJ8c/d3LGWblwWvJ1+/ANivhzpL+7Mbyp4E1nL3Nd19RfLwXG8eIw/dne/um/RS92bgU+4+zN3fALwXuL1su5U8fHgzuUd1JIuG96TDKEC114nk4aP73P1P5X1ffJV8UXoK+bpJI58BDnT3e4EHUI6sZpwC/D93/z15okojS/uzG7Ii4iXgBPK/vavp+d9e9/0eJv97/EWZvNOTK4D7yBNYbiRPnniibLuFfJ1rGnA3uRelANWhtJKEiIhUknpQIiJSSQpQIiJSSQpQIiJSSQpQIiJSSQpQIiJSSQpQIv1QVuj+XbvbIdLJtJKEdBR33wP4b/Jahv8EHgXOA86MCN1TITKEqAclHcPdjyAvAvst8urYawOHANuSV3yvBHfv6eZfEamjHpR0BHd/PXl1g30i4rK6TfdQloEqy/J8g7x+24rkFQv+OyL+5e7bAT8HTgeOIq/jdmxEnFP2XZOclmM78soJ3RfmfSs5/cbmwN+Br0ZElG3nktN2vBF4H3k1j+sH8vxFOpF6UNIp/pMcdJaUk+mb5MVKx5JTbowip9OoWYe8Dt8ocuqSH5TFSSHn1HqBnB7igPIAXskNNQW4kJxTak/gh93WnPs0OTiuQs5XJCK9UA9KOsUIYE5ELKgVuPsfyDmDViSvgH0Q8PaImFu2/y85qBxTdnkJOKEcY7K7Pwds5O53kPMKvS0i/gn8yd3PIy9iCnmx1Om13hZwt7tfBnyCvP4hwFUR8fvy+oUBPneRjqQAJZ3iH8AId1++FqQi4l0A7j6TfD3qdcBddbmxjMUXg/1HfYAjZyBemZyNeHlgRt22x+pevxHY2t2fritbHvhZ3fv6fUWkCQpQ0iluJecCGgdc1mD7HPJ1oE0i4vEG25fk7+QcSOuyaOXu9eq2zwB+GxEfXMIxNINQpI8UoKQjRMTT7v518rUfA64l94DeDqxETkb4E+B0d58QEU+5+yhg04i4rscD52MvdPfLgePd/QByltZ9gemlytXAye6+N3BxKRtLTqS3rGfYFek3TZKQjhERpwBfBL4MPEVOrPdj8qy8P5TnacBt7v4MeSbdRk0efgJ5uO8Jcibj2vUmIuJZcgr4PYBZpc43yde+RKSflA9KREQqST0oERGpJAUoERGpJAUoERGpJAUoERGpJAUoERGpJAUoERGpJAUoERGpJAUoERGpJAUoERGppP8PDqJy8oN5StgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Gender', y='Clicks', kind=\"bar\", data=grouped, hue='age_group')\n",
    "plt.title('Clicks vs gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorizing by log-in\n",
    "The information whether people logged-in or didn't is contained in the `Signed_In` column. There, `0` means not logged in and `1` means logged in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:46.967697Z",
     "start_time": "2019-03-01T06:47:46.937716Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    319198\n",
       "0    136177\n",
       "Name: Signed_In, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.Signed_In.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot count of logged-in and not logged-in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:47.526352Z",
     "start_time": "2019-03-01T06:47:46.976692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAFuCAYAAAB3ByjqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHf1JREFUeJzt3X20XVV57/HvNLyU1opgCuYQWmhNextsBbG8FHqvRYvB2w5oq49YK6Giaa1UrV6rcr3Fio5ifUHaImNEwARrwef6UnMtNjJUtLagKK2tQG2jYIlJwUhAlCpNWPePNY/sHE5Ozj6cs3dm8v2Mscfea64515rrZI/fnplr7bVL13VIktr0qHF3QJI0d4a4JDXMEJekhhniktQwQ1ySGmaIS1LDDHHtVkopry+lbBh3PyaVUo4opXSllJN3Ue/2UsrrRtUvaZIhrpEppRxQSrmglPJvpZT/LKV8s5RyYynlpQPV3gqcMK4+PgI/B1w07k7Ml1LKyfXD64hx90Uz22fcHdBe5VLgF4GXAV8EHgMcA/zoZIWu674NfHssvXsEuq77xrj7oL2TI3GN0hnAW7qu+6uu627ruu6LXdet6bruDZMVpptOKaW8vJSysZRyfyllfSnl+XWUuLSuP7uUsq2UclIp5aZa78ZSyrFTtvOEUsoHSin3lFK2llI+Vkr5mSl1opSyoZTy3VLK3wM/O5sDmzqdUpffUEq5uJRydynlzlLKW0spi3axnUNKKe+u9b9bSvlyKeUFA+tPKKV8uv5PZmsp5S9LKYfs4u+3w6h6V3+vWu9va/Pbatvr6rqj6r/BPaWU75RSbi2lPH82fyMtDENco7QZWFFKOXi2DUopv0Y/xfIW4EnAVcCbp6n6KOCP6Uf5Twa2AllK2adu51DgM8BdwC/QT9l8GbiulPIjtc4xwNXA/637eitw8dBH+ZDfoz/m44GXAi8HzprhWA8APlX3/Txged3G/XX944GPARuB44BfAZ4IfGAOfZvp73UHcHqtdxywBPi1unwV8E3g54GfAV5R22pcuq7z4WMkD+Ak4GvAduCfgNX0YVEG6rwe2DCw/HfAe6Zs50KgA5bW5bPr8pMH6pxQy35qYLs3TNlOAb4CvLwu/wXw91PqnFu3c/Iuju124HVTltdNqfM3wFUzbOMc4LuTxzXN+gvoA3y/gbIn1f799+n+frXs5FrniCH+Xju0Gah3L3D2uN9LPh56OBLXyHRd93fAT9CPhNcCh9KPIteVUspOmi0HbphSdv10m6efZ5/09fp8aH3+OeDYUsq3Jx/AfcARwLKBff3dlO1+ZnChlPLRKduYyT9OWf76QH+mcyxwS9d1G3ey/ij6D6IHJgu6rvsifbAetYu+TLWrv9fOvBW4rJRyXZ26efKQ+9U888SmRqrrum3A39fH20opvwm8B/jv9FMJ0zabxaYf7Lpu+zRtHjXw/HH6kfVU99bnMot9vRA4YBb9AXhgynLHrqcwd7X/na2fLH+Q/jgG7TtN/V39vabfSdddUEp5L7ACOAU4r5TyJ13XeXnlmDgS17jdWp8P2cn6W4ATp5TN5RLEz9OPVr/edd2GKY/JK0tupp/yGbTDctd1O7SfQz9m8gXgqMkTttO4GTixlLLfZEEp5UnAgXUd9HP+h0w5gTqX0fLkB9DDTsR2XffVruve2XXds4A/BF48h+1rnhjiGplSyqdKKb9TSnlKKeXHSilPA94J3AN8cifN3gacWUr5vXp1yVk8dHJwmJvh/zl9IP1VKeUX6pd4Ti6lvKmU8vO1zkX0IfmmUspPllJ+FXjl0Ac6d1fRnzNYV0p5einlyFLK00opzxk4hscAa0opTyz9F5DeA3ym67rJq0k+CfwgcEEp5SdKKc8GXjKHvnyNflT/zHrFzIGllEeXUi4ppZxS+3YM/Yj8lrkfsh4pQ1yj9FH6qy6uob8y5N3AvwEndV23ZboGXdd9EPgD4DXAP9f2f1RXf3e2O+667k76Ef0W4IN1/+8Ffoz+ChK6rvsC8BvAmXVfrwF+f5gDfCS6rrsf+B/Al+ivkrkVuIQ6fVOP4VRgKXAj8JFa99cHtvFl4EX1GL4EvAA4bw59uRN4Lf3fYDPwYWAbcBBwee3beuBO+r+ZxqR0nb/so7aUUv4QeFnXdY8bd1+kcfPEpnZrpZR96ac0rgG+Q/+Nz1fRj1ClvZ4jce3W6pdPPkJ/+d0PA7cBV9J/83PbOPsm7Q4McUlqmCc2JalhhrgkNcwTm9PrNm3aNO4+SNqLTUxM7OxWFDtwJC5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0zxCWpYYa4JDXMEJekhhniktQw72Io7WbOXnv9uLugebRm5YkLun1H4pLUMENckhpmiEtSwwxxSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0byV0MI+IHgE8D+9d9vj8zz4+II4GrgYOBm4DnZ+YDEbE/cCVwLPBN4DmZeXvd1muBc4DtwEszc30tXwFcDCwCLsvMC2v5tPsYxXFL0kIb1Uj8e8Apmfkk4GhgRUScALwZuCgzlwFb6cOZ+rw1M58AXFTrERHLgTOBo4AVwDsjYlFELAIuAU4DlgPPrXWZYR+S1LyRhHhmdpn57bq4b310wCnA+2v5WuCM+vr0ukxd/7SIKLX86sz8XmbeBmwAjquPDZn51TrKvho4vbbZ2T4kqXkj+1GIOlr+AvAE+lHzV4B7MnNbrbIROKy+Pgy4AyAzt0XEvcDjavkNA5sdbHPHlPLja5ud7WNq/1YBq+o+Wbx48dwOVJIGLHSWjCzEM3M7cHREPBb4EPDT01Tr6nPZybqdlU/3P4qZ6k/Xv9XA6sk6W7Zsma6aJA1lrlkyMTExq3ojvzolM+8BrgNOAB4bEZMfJEuBTfX1RuBwgLr+QODuwfIpbXZWvmWGfUhS80YS4hHxI3UETkQcADwduBX4JPCsWm0l8OH6el1dpq7/RGZ2tfzMiNi/XnWyDPgccCOwLCKOjIj96E9+rqttdrYPSWreqEbiS4BPRsQ/0QfutZn5EeDVwCsiYgP9/PXltf7lwONq+SuA1wBk5s1AArcAfwO8JDO31znvc4H19B8OWesywz4kqXml66adIt7bdZs2Oeui8fDX7vcsc/21+4mJienO6T2M39iUpIYZ4pLUMENckhpmiEtSwwxxSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0zxCWpYYa4JDXMEJekhhniktQwQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1zBCXpIYZ4pLUMENckhpmiEtSwwxxSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsP2GcVOIuJw4Erg8cCDwOrMvDgiXg+8CPhGrXpeZl5T27wWOAfYDrw0M9fX8hXAxcAi4LLMvLCWHwlcDRwM3AQ8PzMfiIj9676PBb4JPCczb1/wg5akERjVSHwb8MrM/GngBOAlEbG8rrsoM4+uj8kAXw6cCRwFrADeGRGLImIRcAlwGrAceO7Adt5ct7UM2Er/AUB93pqZTwAuqvUkaY8wkhDPzM2ZeVN9fR9wK3DYDE1OB67OzO9l5m3ABuC4+tiQmV/NzAfoR96nR0QBTgHeX9uvBc4Y2Nba+vr9wNNqfUlq3kimUwZFxBHAMcBngZOAcyPiLODz9KP1rfQBf8NAs408FPp3TCk/HngccE9mbpum/mGTbTJzW0TcW+tvmdKvVcCqWo/Fixc/4mOVpIXOkpGGeEQ8GvgA8PLM/FZEXApcAHT1+W3AC4DpRsod0//PoZuhPrtY932ZuRpYPbl+y5YtU6tI0tDmmiUTExOzqjeyq1MiYl/6AH9vZn4QIDPvzMztmfkg8C766RLoR9KHDzRfCmyaoXwL8NiI2GdK+Q7bqusPBO6e36OTpPEYSYjXOejLgVsz8+0D5UsGqv0q8KX6eh1wZkTsX686WQZ8DrgRWBYRR0bEfvQnP9dlZgd8EnhWbb8S+PDAtlbW188CPlHrS1LzRjWdchLwfOCfI+Ifa9l59FeXHE0/vXE78NsAmXlzRCRwC/2VLS/JzO0AEXEusJ7+EsMrMvPmur1XA1dHxBuBf6D/0KA+vyciNtCPwM9cyAOVpFEqXeegdBrdpk2bdl1LWgBnr71+3F3QPFqz8sQ5tZuYmJjVVXR+Y1OSGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1zBCXpIYZ4pLUMENckhpmiEtSwwxxSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0zxCWpYYa4JDXMEJekhhniktQwQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1zBCXpIYZ4pLUMENckhpmiEtSwwxxSWqYIS5JDdtnFDuJiMOBK4HHAw8CqzPz4og4GHgfcARwOxCZuTUiCnAx8EzgfuDszLypbmsl8Lq66Tdm5tpafiywBjgAuAZ4WWZ2O9vHAh+yJI3EqEbi24BXZuZPAycAL4mI5cBrgI9n5jLg43UZ4DRgWX2sAi4FqIF8PnA8cBxwfkQcVNtcWutOtltRy3e2D0lq3khCPDM3T46kM/M+4FbgMOB0YG2tthY4o74+HbgyM7vMvAF4bEQsAZ4BXJuZd9fR9LXAirruMZl5fWZ29KP+wW1Ntw9Jat5IplMGRcQRwDHAZ4FDM3Mz9EEfEYfUaocBdww021jLZirfOE05M+xjar9W0Y/kyUwWL14810OUpO9b6CwZaYhHxKOBDwAvz8xvRcTOqpZpyro5lM9aZq4GVk+23bJlyzDNJWlac82SiYmJWdUb2dUpEbEvfYC/NzM/WIvvrFMh1Oe7avlG4PCB5kuBTbsoXzpN+Uz7kKTmjSTE69UmlwO3ZubbB1atA1bW1yuBDw+UnxURJSJOAO6tUyLrgVMj4qB6QvNUYH1dd19EnFD3ddaUbU23D0lq3qimU04Cng/8c0T8Yy07D7gQyIg4B/h34Nl13TX0lxduoL/E8LcAMvPuiLgAuLHWe0Nm3l1fv5iHLjH8aH0wwz4kqXml64aaOt5bdJs2bdp1LWkBnL32+nF3QfNozcoT59RuYmJiunN9D+M3NiWpYYa4JDXMEJekhhniktQwQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGzDvGI+F87KX/F/HVHkjSMYUbif7iT8tftpFyStMB2eQOsiDilvlwUEb/Ijvfu/nHgvoXomCRp12ZzF8PL6/MPAFcMlHfAfwC/N9+dkiTNzi5DPDOPBIiIKzPzrIXvkiRptmZ9P/HBAI+IR01Z9+B8dkqSNDuzDvGIeDJwCfCz9FMr0M+Pd8Ci+e+aJGlXhvlln7XA/wNeQP9rO5KkMRsmxH8M+N+Z6U8BSdJuYpjrxD9E/8PEkqTdxDAj8R8APhQRn6G/tPD7vGpFksZjmBC/pT4kSbuJYS4x/KOF7IgkaXjDXGJ4ys7WZeYn5qc7kqRhDDOdcvmU5R8B9gM20t9DRZI0YsNMpxw5uBwRi+jvYOgNsCRpTOb8oxCZuR14E/AH89cdSdIwHukv+/wS4H1TJGlMhjmxeQf9fVIm/SD9teO/O9+dkiTNzjAnNn9zyvJ3gH/NzG/NY38kSUMY5sTmp+D7t6E9FLjTW9BK0ngNM53yw/S3on0OsC/wXxFxNfDSzLx3gfonSZrBMCc2/wz4IeBngAPq8w8Cf7oA/ZIkzcIwc+IrgB/PzMl7if9rRPwW8JX575YkaTaGGYl/l/5bmoMWA9+bv+5IkoYxzEj8MuDaiHg78DX6H4n4feBdC9ExSdKuDRPibwK+DjwPmAA2AX+SmVPvqfIwEXEF8MvAXZn5xFr2euBFwDdqtfMy85q67rXAOcB2+hOn62v5CuBi+t/0vCwzL6zlRwJXAwcDNwHPz8wHImJ/4ErgWOCbwHMy8/YhjlmSdmvDTKdcDHw5M5+emcsz8+nArRHxjlm0XUM/pz7VRZl5dH1MBvhy4EzgqNrmnRGxqN6r5RLgNGA58NxaF+DNdVvLgK30HwDU562Z+QTgolpPkvYYw4T4c4HPTyn7AvAbu2qYmZ8G7p7lfk4Hrs7M72XmbcAG4Lj62JCZX83MB+hH3qdHRAFOAd5f268FzhjY1tr6+v3A02p9SdojDBPiHf00xqBFQ25jqnMj4p8i4oqIOKiWHQbcMVBnYy3bWfnjgHsyc9uU8h22VdffW+tL0h5hmDnxvwUuiIg/yMwH6zc3X1/L5+JS4AL6D4cLgLcBLwCmGyl3TP9h0c1Qn12s20FErAJWAWQmixcvnqnvkjQrC50lw4T4y4CPAJsj4mvAjwKbgV+Zy44z887J1xHxrrpt6EfShw9UXUp/EpWdlG8BHhsR+9TR9mD9yW1tjIh9gAPZybROZq4GVtfFbsuWLXM5LEnawVyzZGJiYlb1Zj0VkpkbgSfTzzO/hX7e+dhaPrSIWDKw+KvAl+rrdcCZEbF/vepkGfA54EZgWUQcGRH70Z/8XJeZHfBJ4Fm1/UrgwwPbWllfPwv4RK0vSXuEYUbi1Bte3VAfsxYRVwFPBRZHxEbgfOCpEXE0/fTG7cBv133cHBEJ3AJsA15Sf4CCiDgXWE8/F39FZt5cd/Fq4OqIeCPwDzz0U3KXA++JiA30I/Azh+m3JO3uStc5MJ1Gt2nTpl3XkhbA2WuvH3cXNI/WrDxxTu0mJiZmdSXdI/1lH0nSGBniktQwQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1bKhvbGpmm1/1wnF3QfNsyVsuG3cXpBk5EpekhhniktQwQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1zBCXpIYZ4pLUMENckhpmiEtSwwxxSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0zxCWpYYa4JDXMEJekhhniktQwQ1ySGrbPKHYSEVcAvwzclZlPrGUHA+8DjgBuByIzt0ZEAS4GngncD5ydmTfVNiuB19XNvjEz19byY4E1wAHANcDLMrPb2T4W+HAlaWRGNRJfA6yYUvYa4OOZuQz4eF0GOA1YVh+rgEvh+6F/PnA8cBxwfkQcVNtcWutOtluxi31I0h5hJCGemZ8G7p5SfDqwtr5eC5wxUH5lZnaZeQPw2IhYAjwDuDYz766j6WuBFXXdYzLz+szsgCunbGu6fUjSHmGcc+KHZuZmgPp8SC0/DLhjoN7GWjZT+cZpymfahyTtEUYyJz6kMk1ZN4fyoUTEKvopGTKTxYsXD7sJNg/dQru7ubwPpEEL/R4aZ4jfGRFLMnNznRK5q5ZvBA4fqLcU2FTLnzql/LpavnSa+jPt42EyczWwui52W7ZsmdNBac/i+0CP1FzfQxMTE7OqN87plHXAyvp6JfDhgfKzIqJExAnAvXUqZD1wakQcVE9ongqsr+vui4gT6pUtZ03Z1nT7kKQ9wqguMbyKfhS9OCI20l9lciGQEXEO8O/As2v1a+gvL9xAf4nhbwFk5t0RcQFwY633hsycPFn6Yh66xPCj9cEM+5CkPULpuqGnj/cG3aZNm3Zda4rNr3rhAnRF47TkLZeNfJ9nr71+5PvUwlmz8sQ5tZuYmJjufN/D+I1NSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0zxCWpYYa4JDXMEJekhhniktQwQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1zBCXpIYZ4pLUMENckhpmiEtSwwxxSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0zxCWpYYa4JDVsn3F3ICJuB+4DtgPbMvMpEXEw8D7gCOB2IDJza0QU4GLgmcD9wNmZeVPdzkrgdXWzb8zMtbX8WGANcABwDfCyzOxGcnCStMB2l5H4L2bm0Zn5lLr8GuDjmbkM+HhdBjgNWFYfq4BLAWronw8cDxwHnB8RB9U2l9a6k+1WLPzhSNJo7C4hPtXpwNr6ei1wxkD5lZnZZeYNwGMjYgnwDODazLw7M7cC1wIr6rrHZOb1dfR95cC2JKl5u0OId8DHIuILEbGqlh2amZsB6vMhtfww4I6Bthtr2UzlG6cpl6Q9wtjnxIGTMnNTRBwCXBsR/zJD3TJNWTeH8oepHyCrADKTxYsXz9zraWweuoV2d3N5H0iDFvo9NPYQz8xN9fmuiPgQ/Zz2nRGxJDM31ymRu2r1jcDhA82XAptq+VOnlF9Xy5dOU3+6fqwGVtfFbsuWLY/gqLSn8H2gR2qu76GJiYlZ1RvrdEpE/FBE/PDka+BU4EvAOmBlrbYS+HB9vQ44KyJKRJwA3FunW9YDp0bEQfWE5qnA+rruvog4oV7ZctbAtiSpeeOeEz8U+ExEfBH4HPDXmfk3wIXAL0XEvwG/VJehv0Twq8AG4F3A7wJk5t3ABcCN9fGGWgbwYuCy2uYrwEdHcFySNBKl67xkehrdpk3TzrrMaPOrXrgAXdE4LXnLZSPf59lrrx/5PrVw1qw8cU7tJiYmpjun9zDjHolLkh4BQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1zBCXpIYZ4pLUMENckhpmiEtSwwxxSWqYIS5JDTPEJalhhrgkNcwQl6SGGeKS1DBDXJIaZohLUsMMcUlqmCEuSQ0zxCWpYYa4JDXMEJekhhniktQwQ1ySGmaIS1LDDHFJapghLkkNM8QlqWGGuCQ1zBCXpIYZ4pLUMENckhpmiEtSwwxxSWrYPuPuwChExArgYmARcFlmXjjmLknSvNjjR+IRsQi4BDgNWA48NyKWj7dXkjQ/9vgQB44DNmTmVzPzAeBq4PQx90mS5sXeEOKHAXcMLG+sZZLUvL1hTrxMU9ZNLYiIVcAqgMxkYmJi6B1NvPeaodtIU33stb8+7i5o99AxfX7tYG8I8Y3A4QPLS4FNUytl5mpg9ag61bKI+HxmPmXc/VDbfB/Nj70hxG8ElkXEkcDXgTOB3xhvlyRpfuzxc+KZuQ04F1gP3NoX5c3j7ZUkzY+9YSROZl4DOGE9f5x20nzwfTQPStc97ByfJKkRe/x0iiTtyfaK6RTNH29hoEciIq4Afhm4KzOfOO7+7AkciWvWvIWB5sEaYMW4O7EnMcQ1DG9hoEckMz8N3D3ufuxJDHENw1sYSLsZQ1zDmNUtDCSNjiGuYczqFgaSRserUzQMb2Eg7Wb8so+GEhHPBN5Bf4nhFZn5pjF3SQ2JiKuApwKLgTuB8zPz8rF2qnGGuCQ1zDlxSWqYIS5JDTPEJalhhrgkNcwQl6SGeZ249noR8TxgZWaeOuL9HgHcBuxbf4FKGpohrr1GRJwM/AlwFLCd/uf6Xp6Z7wXeO86+zSQirgP+IjMvG3dftPsxxLVXiIjHAB8BXgwksB/wC8D3xtkv6ZEyxLW3+EmAzLyqLv8n8DGAiDgbeGFmnlyXTwX+DHg8/Qj9KOA9mXnZZF3gBuAc4B7gdzPzo7XtgcDbgWcCDwLvpv9W4vZ6P/Y3A2cD3wLeNuxBRMRTgb8ALgJeTf8/ivMy893Dbkt7Bk9sam/xr8D2iFgbEadFxEHTVYqIxcD7gdcCjwO+DPz8lGrH1/LF9NMzl0fE5B0e1wLbgCcAxwCn0oc+wIvof9XmGOApwLPmeCyPBw6kvw3wOcAlOzse7fkMce0VMvNbwMn0t859F/CNiFgXEYdOqfpM4ObM/GA92finwH9MqfO1zHxXZm6nD+0lwKF1W6fRz7N/JzPvoh8xn1nbBfCOzLwjM+8G/niOh/NfwBsy878y8xrg28BPzXFbapzTKdprZOat9FMZRMR/o5+WeAewfqDaBAM/fJGZXURsnLKp/xhYf39EADwaOBjYF9hcy6AfKE1ub4dtA1+b46F8c8rVLPfX/WsvZIhrr5SZ/xIRa4DfZscQ30x/n3QA6jTJUmbnDvoTpYt3csngZna8H/uPDtNnaTqGuPYKdeT9P4H3ZebGiDgceC79CcpBfw38eUScQX81y+/Qz0HvUmZujoiPAW+LiP9DP81xJLA0Mz9Ff1XMSyPiI8B3gNfMw6FpL+ecuPYW99GfkPxsRHyHPry/BLxysFJmbgGeTX/C8pvAcuDzzP5SxLPoL1+8BdhKf5J0SV33LvpR/xeBm4APzv1wpJ73E5dmEBGPov9Zuudl5ifH3R9pKqdTpCki4hnAZ+mvJX8V/Q9ET512kXYLhrj0cCcCf8lD0yJnZOZ/LuQOI+LbO1l1Wmb+7ULuW21zOkWSGuaJTUlqmCEuSQ0zxCWpYYa4JDXMEJekhhniktSw/w9PNDw0mML49AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Signed_In', kind='count', data=data1)\n",
    "plt.title('Signed-in counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last plot shows that almost twice people logged in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot gender counts and not logged in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:48.236911Z",
     "start_time": "2019-03-01T06:47:47.529350Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Signed_In', kind='count', data=data1, hue='Gender')\n",
    "plt.title('Gender counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing not logged-in data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's subset the group of people that did not log in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:48.258900Z",
     "start_time": "2019-03-01T06:47:48.240910Z"
    }
   },
   "outputs": [],
   "source": [
    "unsigned = data1[data1.Signed_In == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:48.509741Z",
     "start_time": "2019-03-01T06:47:48.261896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>136177.0</td>\n",
       "      <td>136177.000000</td>\n",
       "      <td>136177.000000</td>\n",
       "      <td>136177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.033765</td>\n",
       "      <td>0.143049</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.209780</td>\n",
       "      <td>0.386644</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Age    Impressions         Clicks  Signed_In\n",
       "count  136177.0  136177.000000  136177.000000   136177.0\n",
       "mean        0.0       5.033765       0.143049        0.0\n",
       "std         0.0       2.209780       0.386644        0.0\n",
       "min         0.0       1.000000       0.000000        0.0\n",
       "25%         0.0       3.000000       0.000000        0.0\n",
       "50%         0.0       5.000000       0.000000        0.0\n",
       "75%         0.0       6.000000       0.000000        0.0\n",
       "max         0.0      18.000000       4.000000        0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsigned.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:48.563709Z",
     "start_time": "2019-03-01T06:47:48.512741Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 136177 entries, 6 to 458440\n",
      "Data columns (total 7 columns):\n",
      "Age            136177 non-null int64\n",
      "Gender         136177 non-null category\n",
      "Impressions    136177 non-null int64\n",
      "Clicks         136177 non-null int64\n",
      "Signed_In      136177 non-null int64\n",
      "date           136177 non-null object\n",
      "age_group      136177 non-null category\n",
      "dtypes: category(2), int64(4), object(1)\n",
      "memory usage: 6.5+ MB\n"
     ]
    }
   ],
   "source": [
    "unsigned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:48.670643Z",
     "start_time": "2019-03-01T06:47:48.565707Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "      <th>date</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012/5/1</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age   Gender  Impressions  Clicks  Signed_In      date age_group\n",
       "6     0  unknown            7       1          0  2012/5/1   unknown\n",
       "10    0  unknown            8       1          0  2012/5/1   unknown\n",
       "12    0  unknown            4       0          0  2012/5/1   unknown\n",
       "15    0  unknown            6       0          0  2012/5/1   unknown\n",
       "19    0  unknown            5       0          0  2012/5/1   unknown"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsigned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:48.809557Z",
     "start_time": "2019-03-01T06:47:48.675642Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     23935\n",
       "4     23871\n",
       "6     19994\n",
       "3     19347\n",
       "7     14428\n",
       "2     11591\n",
       "8      8912\n",
       "9      5084\n",
       "1      4676\n",
       "10     2494\n",
       "11     1109\n",
       "12      448\n",
       "13      193\n",
       "14       61\n",
       "15       19\n",
       "16        7\n",
       "17        6\n",
       "18        2\n",
       "Name: Impressions, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsigned.Impressions.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:48.931482Z",
     "start_time": "2019-03-01T06:47:48.812557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    118401\n",
       "1     16191\n",
       "2      1475\n",
       "3       101\n",
       "4         9\n",
       "Name: Clicks, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsigned.Clicks.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:51.177737Z",
     "start_time": "2019-03-01T06:47:48.935481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Clicks', y='Impressions', kind='violin', data=unsigned, inner=\"quartile\", palette=\"ch:.25\")\n",
    "plt.title('Clicks vs Impressions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:51.203720Z",
     "start_time": "2019-03-01T06:47:51.180735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Impressions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>580205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>94477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clicks  Impressions\n",
       "0       0       580205\n",
       "1       1        94477\n",
       "2       2         9966\n",
       "3       3          764\n",
       "4       4           71"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsigned_grouped = unsigned.groupby('Clicks', as_index=False)['Impressions'].sum()\n",
    "unsigned_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:51.263684Z",
     "start_time": "2019-03-01T06:47:51.206720Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>CTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>580205</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>94477</td>\n",
       "      <td>0.001058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9966</td>\n",
       "      <td>0.020068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>764</td>\n",
       "      <td>0.392670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>5.633803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clicks  Impressions       CTR\n",
       "0       0       580205  0.000000\n",
       "1       1        94477  0.001058\n",
       "2       2         9966  0.020068\n",
       "3       3          764  0.392670\n",
       "4       4           71  5.633803"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unsigned_grouped['CTR'] = (unsigned_grouped.Clicks / unsigned_grouped.Impressions) * 100\n",
    "unsigned_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:51.654487Z",
     "start_time": "2019-03-01T06:47:51.265683Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot('Clicks', 'CTR', data=unsigned_grouped)\n",
    "plt.title('CTR vs clicks in non-registered people')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Clicks vs No-clicks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:51.665482Z",
     "start_time": "2019-03-01T06:47:51.658485Z"
    }
   },
   "outputs": [],
   "source": [
    "data1['Clicked'] = data1.Clicks > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:52.672856Z",
     "start_time": "2019-03-01T06:47:51.668480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot('Clicked', data=data1, hue='age_group', kind='count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:53.456369Z",
     "start_time": "2019-03-01T06:47:52.675854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 441.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot('Clicked', data=data1, hue='Gender', kind='count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging all days into a single data frame\n",
    "\n",
    "Here, the purpose is to get all the data from 1 May 2012 to 31 May 2012.\n",
    "The order should be as fallows:\n",
    "1. Get one `.csv` file.\n",
    "2. Add a column with the `date` it was recorded.\n",
    "3. Repeat these steps in a loop for the 31 `.csv` files.\n",
    "4. Merge the 31 `.csv` files into a single `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:53.464366Z",
     "start_time": "2019-03-01T06:47:53.459369Z"
    }
   },
   "outputs": [],
   "source": [
    "url_name = 'http://stat.columbia.edu/~rachel/datasets/nyt{}.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:47:53.638284Z",
     "start_time": "2019-03-01T06:47:53.467364Z"
    }
   },
   "outputs": [],
   "source": [
    "def monthly_reader(url, start, end):\n",
    "    '''Reads csv files from a given url for every number from start to end\n",
    "    and returns a data frame'''\n",
    "    dfs_list = []\n",
    "    for i in range(start, end+1):\n",
    "        dfi = pd.read_csv(url.format(i))\n",
    "        dfi['date'] = '2012/5/{}'.format(i)\n",
    "        dfs_list.append(dfi)\n",
    "    return pd.concat(dfs_list, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:52:50.902349Z",
     "start_time": "2019-03-01T06:47:53.641281Z"
    }
   },
   "outputs": [],
   "source": [
    "df = monthly_reader(url_name, 1, 31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:52:50.923337Z",
     "start_time": "2019-03-01T06:52:50.905348Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Signed_In</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2012/5/1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Impressions  Clicks  Signed_In      date\n",
       "0   36       0            3       0          1  2012/5/1\n",
       "1   73       1            3       0          1  2012/5/1\n",
       "2   30       0            3       0          1  2012/5/1\n",
       "3   49       1            3       0          1  2012/5/1\n",
       "4   47       1           11       0          1  2012/5/1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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    "df.head()"
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    "ExecuteTime": {
     "end_time": "2019-03-01T06:52:52.184848Z",
     "start_time": "2019-03-01T06:52:50.926336Z"
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    {
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       "         Age  Gender  Impressions  Clicks  Signed_In       date\n",
       "850963    28       0            3       0          1   2012/5/2\n",
       "2435810    0       0            5       0          0   2012/5/6\n",
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       "7101384   42       1            8       0          1  2012/5/15\n",
       "1969202    0       0            5       0          0   2012/5/5"
      ]
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     "execution_count": 47,
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    "df.sample(5)"
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     "start_time": "2019-03-01T06:52:52.188847Z"
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       "          Age  Gender  Impressions  Clicks  Signed_In       date\n",
       "14905860   41       1            4       0          1  2012/5/31\n",
       "14905861    0       0            5       0          0  2012/5/31\n",
       "14905862   22       1            3       0          1  2012/5/31\n",
       "14905863   59       0            5       1          1  2012/5/31\n",
       "14905864   29       1            4       0          1  2012/5/31"
      ]
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    "df.tail()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
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     "start_time": "2019-03-01T06:52:52.204835Z"
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14905865, 6)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
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    "df.shape"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the data frame and the last index confirm the merge was done correctly and also the indexing.\n",
    "### Cleaning the merged data frame\n",
    "Now let's clean the complete data frame, that is:\n",
    "1. Remove observations with `0` impressions.\n",
    "2. Assign `'unknown'` gender for observations with unlogged people.\n",
    "3. Create `'unknown'` age group for observations with unlogged people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:06.712075Z",
     "start_time": "2019-03-01T06:52:52.338412Z"
    }
   },
   "outputs": [],
   "source": [
    "# Select only observations with impressions greater than 0.\n",
    "df = df[df['Impressions'] > 0]\n",
    "\n",
    "# Assign 'unknown' for people that did not sign in, \n",
    "#        'male' for 1 and 'famele' for 0.\n",
    "df.loc[df.Gender == 1, 'Gender'] = 'male'\n",
    "df.loc[df.Gender == 0, 'Gender'] = 'female'\n",
    "df.loc[df.Signed_In == 0, 'Gender'] = 'unknown'\n",
    "\n",
    "# Create groups according to ages.\n",
    "bins = [0, 1, 18, 25, 35, 45, 55, 65, np.Inf]\n",
    "labels = ['unknown', '<18', '18-24', '25-34', '35-44', '45-54', '55-64', '65+']\n",
    "df['age_group'] = pd.cut(df.Age, bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Setting dtypes for each column to improve memory usage and speed.\n",
    "df = df.astype({'age_group':'category',\n",
    "               'Gender':'category',\n",
    "                'Signed_In':'bool',\n",
    "               'date':'datetime64[ns]'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:06.726066Z",
     "start_time": "2019-03-01T06:53:06.714073Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14805865 entries, 0 to 14905864\n",
      "Data columns (total 7 columns):\n",
      "Age            int64\n",
      "Gender         category\n",
      "Impressions    int64\n",
      "Clicks         int64\n",
      "Signed_In      bool\n",
      "date           datetime64[ns]\n",
      "age_group      category\n",
      "dtypes: bool(1), category(2), datetime64[ns](1), int64(3)\n",
      "memory usage: 607.2 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:11.239272Z",
     "start_time": "2019-03-01T06:53:06.731064Z"
    }
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   "outputs": [
    {
     "data": {
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       "      <th>std</th>\n",
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       "      <th>25%</th>\n",
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       "      <th>50%</th>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <th>75%</th>\n",
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       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.150000e+02</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
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      ],
      "text/plain": [
       "                Age   Impressions        Clicks\n",
       "count  1.480586e+07  1.480586e+07  1.480586e+07\n",
       "mean   2.624290e+01  5.033280e+00  9.839128e-02\n",
       "std    2.412952e+01  2.205137e+00  3.199722e-01\n",
       "min    0.000000e+00  1.000000e+00  0.000000e+00\n",
       "25%    0.000000e+00  3.000000e+00  0.000000e+00\n",
       "50%    2.600000e+01  5.000000e+00  0.000000e+00\n",
       "75%    4.600000e+01  6.000000e+00  0.000000e+00\n",
       "max    1.150000e+02  2.100000e+01  6.000000e+00"
      ]
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     "execution_count": 52,
     "metadata": {},
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   "source": [
    "df.describe()"
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  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:11.254260Z",
     "start_time": "2019-03-01T06:53:11.242268Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index          118446920\n",
       "Age            118446920\n",
       "Gender          14805969\n",
       "Impressions    118446920\n",
       "Clicks         118446920\n",
       "Signed_In       14805865\n",
       "date           118446920\n",
       "age_group       14806249\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.memory_usage()"
   ]
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   "cell_type": "code",
   "execution_count": 54,
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    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:11.391177Z",
     "start_time": "2019-03-01T06:53:11.257259Z"
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       "   Age  Gender  Impressions  Clicks  Signed_In       date age_group\n",
       "0   36  female            3       0       True 2012-05-01     35-44\n",
       "1   73    male            3       0       True 2012-05-01       65+\n",
       "2   30  female            3       0       True 2012-05-01     25-34\n",
       "3   49    male            3       0       True 2012-05-01     45-54\n",
       "4   47    male           11       0       True 2012-05-01     45-54"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df.head()"
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       "      <td>2012-05-05</td>\n",
       "      <td>65+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age   Gender  Impressions  Clicks  Signed_In       date age_group\n",
       "11168131    0  unknown            4       0      False 2012-05-24   unknown\n",
       "5975253     0  unknown            5       0      False 2012-05-13   unknown\n",
       "65562      58     male            5       0       True 2012-05-01     55-64\n",
       "12535465    0  unknown            7       0      False 2012-05-27   unknown\n",
       "1870458    84   female            1       0       True 2012-05-05       65+"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 56,
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    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:12.904240Z",
     "start_time": "2019-03-01T06:53:12.881255Z"
    },
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "          Age   Gender  Impressions  Clicks  Signed_In       date age_group\n",
       "14905860   41     male            4       0       True 2012-05-31     35-44\n",
       "14905861    0  unknown            5       0      False 2012-05-31   unknown\n",
       "14905862   22     male            3       0       True 2012-05-31     18-24\n",
       "14905863   59   female            5       1       True 2012-05-31     55-64\n",
       "14905864   29     male            4       0       True 2012-05-31     25-34"
      ]
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     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.tail()"
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  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
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     "end_time": "2019-03-01T06:53:15.848744Z",
     "start_time": "2019-03-01T06:53:12.907238Z"
    }
   },
   "outputs": [],
   "source": [
    "df_grouped = df.groupby(['date', 'age_group', 'Gender', 'Signed_In'], as_index=False)['Impressions', 'Clicks'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:15.860740Z",
     "start_time": "2019-03-01T06:53:15.850743Z"
    }
   },
   "outputs": [],
   "source": [
    "df_grouped.dropna(inplace=True)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
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     "end_time": "2019-03-01T06:53:16.061873Z",
     "start_time": "2019-03-01T06:53:15.863736Z"
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   },
   "outputs": [],
   "source": [
    "df_grouped['CTR'] = (df_grouped.Clicks / df_grouped.Impressions) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2019-03-01T06:53:16.064862Z"
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       "         date age_group   Gender  Signed_In  Impressions   Clicks       CTR\n",
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       "15 2012-05-01     18-24     male       True     108528.0   1165.0  1.073456"
      ]
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     "start_time": "2019-03-01T06:53:16.247496Z"
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       "           date age_group  Gender  Signed_In  Impressions  Clicks       CTR\n",
       "1117 2012-05-24     18-24  female       True      73019.0   824.0  1.128473\n",
       "715  2012-05-15       65+  female       True      70208.0  2016.0  2.871468\n",
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       "459  2012-05-10     35-44    male       True     182847.0  1777.0  0.971851\n",
       "1465 2012-05-31     35-44  female       True     162336.0  1647.0  1.014562\n",
       "7    2012-05-01       <18  female       True      21800.0   683.0  3.133028\n",
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       "1429 2012-05-30     55-64  female       True      84995.0  1681.0  1.977763\n",
       "793  2012-05-17     35-44  female       True     124930.0  1224.0  0.979749"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_grouped.sample(10)"
   ]
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    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:16.664461Z",
     "start_time": "2019-03-01T06:53:16.466360Z"
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       "           date age_group  Gender  Signed_In  Impressions  Clicks       CTR\n",
       "1473 2012-05-31     45-54    male       True     157481.0  1578.0  1.002026\n",
       "1477 2012-05-31     55-64  female       True     103821.0  2127.0  2.048718\n",
       "1479 2012-05-31     55-64    male       True     111429.0  2273.0  2.039864\n",
       "1483 2012-05-31       65+  female       True      88676.0  2676.0  3.017727\n",
       "1485 2012-05-31       65+    male       True      49105.0  1485.0  3.024132"
      ]
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     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_grouped.tail()"
   ]
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   "cell_type": "code",
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    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:23.211093Z",
     "start_time": "2019-03-01T06:53:16.669459Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='date', y='CTR', data=df_grouped, hue='age_group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-01T06:53:25.207022Z",
     "start_time": "2019-03-01T06:53:23.215090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='date', y='CTR', data=df_grouped, hue='Gender');"
   ]
  }
 ],
 "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": "3.6.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "239px",
    "width": "367px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}