05. Funnel Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import  warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>450007</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>756838</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>568983</td>\n",
       "      <td>2015-04-09</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190794</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>537909</td>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id       date   device     sex\n",
       "0   450007 2015-02-28  Desktop  Female\n",
       "1   756838 2015-01-13  Desktop    Male\n",
       "2   568983 2015-04-09  Desktop    Male\n",
       "3   190794 2015-02-18  Desktop  Female\n",
       "4   537909 2015-01-15  Desktop    Male"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = pd.read_csv('./data/Funnel/user_table.csv', parse_dates=['date'])\n",
    "user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>313593</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>468315</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>264005</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>290784</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>639104</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id       page\n",
       "0   313593  home_page\n",
       "1   468315  home_page\n",
       "2   264005  home_page\n",
       "3   290784  home_page\n",
       "4   639104  home_page"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home = pd.read_csv('./data/Funnel/home_page_table.csv')\n",
    "home.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15866</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>347058</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>577020</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>780347</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>383739</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         page\n",
       "0    15866  search_page\n",
       "1   347058  search_page\n",
       "2   577020  search_page\n",
       "3   780347  search_page\n",
       "4   383739  search_page"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = pd.read_csv('./data/Funnel/search_page_table.csv')\n",
    "search.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>253019</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>310478</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>304081</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>901286</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>195052</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id          page\n",
       "0   253019  payment_page\n",
       "1   310478  payment_page\n",
       "2   304081  payment_page\n",
       "3   901286  payment_page\n",
       "4   195052  payment_page"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment = pd.read_csv('./data/Funnel/payment_page_table.csv')\n",
    "payment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>123100</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>704999</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>407188</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>538348</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>841681</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                       page\n",
       "0   123100  payment_confirmation_page\n",
       "1   704999  payment_confirmation_page\n",
       "2   407188  payment_confirmation_page\n",
       "3   538348  payment_confirmation_page\n",
       "4   841681  payment_confirmation_page"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirm = pd.read_csv('./data/Funnel/payment_confirmation_table.csv')\n",
    "confirm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>sex</th>\n",
       "      <th>page_home</th>\n",
       "      <th>page_search</th>\n",
       "      <th>page_payment</th>\n",
       "      <th>page_confirmation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>450007</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>756838</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>568983</td>\n",
       "      <td>2015-04-09</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190794</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>537909</td>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id       date   device     sex  page_home  page_search page_payment  \\\n",
       "0   450007 2015-02-28  Desktop  Female  home_page          NaN          NaN   \n",
       "1   756838 2015-01-13  Desktop    Male  home_page          NaN          NaN   \n",
       "2   568983 2015-04-09  Desktop    Male  home_page  search_page          NaN   \n",
       "3   190794 2015-02-18  Desktop  Female  home_page  search_page          NaN   \n",
       "4   537909 2015-01-15  Desktop    Male  home_page          NaN          NaN   \n",
       "\n",
       "  page_confirmation  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(left=user, right=home, how='left', on='user_id')\n",
    "data = pd.merge(left=data, right=search, how='left', on='user_id', suffixes=('_home', '_search'))\n",
    "data = pd.merge(left=data, right=payment, how='left', on='user_id')\n",
    "data = pd.merge(left=data, right=confirm, how='left', on='user_id', suffixes=('_payment', '_confirmation'))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 90400 entries, 0 to 90399\n",
      "Data columns (total 8 columns):\n",
      "user_id              90400 non-null int64\n",
      "date                 90400 non-null datetime64[ns]\n",
      "device               90400 non-null object\n",
      "sex                  90400 non-null object\n",
      "page_home            90400 non-null object\n",
      "page_search          45200 non-null object\n",
      "page_payment         6030 non-null object\n",
      "page_confirmation    452 non-null object\n",
      "dtypes: datetime64[ns](1), int64(1), object(6)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visit Probability for Each Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>conversion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>page_home</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>page_search</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>page_payment</td>\n",
       "      <td>0.066704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>page_confirmation</td>\n",
       "      <td>0.005000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               index  conversion\n",
       "4          page_home    1.000000\n",
       "5        page_search    0.500000\n",
       "6       page_payment    0.066704\n",
       "7  page_confirmation    0.005000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_ratio = 1 - pd.DataFrame(data.isnull().sum()).rename(columns={0: 'conversion'}) / len(data)\n",
    "conv_ratio = conv_ratio.reset_index()[4:]\n",
    "conv_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.barplot(x='index', y='conversion', data=conv_ratio, ax=ax)\n",
    "ax.set_xlabel('Page', fontsize=12)\n",
    "ax.set_ylabel('Ratio of Visitors', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversion Rate for Each Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conversion_rate(df):\n",
    "    \"\"\" function to calculate conversion rates for home, search and payment page\"\"\"\n",
    "    rates = []\n",
    "    tmp = df[~df['page_home'].isnull()]\n",
    "    rates.append(1 - tmp['page_search'].isnull().sum() / len(tmp))\n",
    "    \n",
    "    tmp = df[~df['page_search'].isnull()]\n",
    "    rates.append(1 - tmp['page_payment'].isnull().sum() / len(tmp))\n",
    "    \n",
    "    tmp = df[~df['page_payment'].isnull()]\n",
    "    rates.append(1 - tmp['page_confirmation'].isnull().sum() / len(tmp))\n",
    "    \n",
    "    return rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Page</th>\n",
       "      <th>Conversion Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>home</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>search</td>\n",
       "      <td>0.133407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>payment</td>\n",
       "      <td>0.074959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Page  Conversion Rate\n",
       "0     home         0.500000\n",
       "1   search         0.133407\n",
       "2  payment         0.074959"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates = conversion_rate(data)\n",
    "names = ['home', 'search', 'payment']\n",
    "maps = {'Page': ['home', 'search', 'payment'], 'Conversion Rate': rates}\n",
    "overall_conv = pd.DataFrame(maps, columns=['Page', 'Conversion Rate'])\n",
    "\n",
    "overall_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.barplot(x='Page', y='Conversion Rate', data=overall_conv, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Desktop' 'Mobile']\n"
     ]
    }
   ],
   "source": [
    "print(data['device'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Page    |  Desktop   |   Mobile  \n",
      "----------------------------------------\n",
      "home       |   0.500000 |   0.500000\n",
      "search     |   0.100000 |   0.200000\n",
      "payment    |   0.049834 |   0.100000\n"
     ]
    }
   ],
   "source": [
    "desk_rates = conversion_rate(data[data['device'] == 'Desktop'])\n",
    "mobile_rates = conversion_rate(data[data['device'] == 'Mobile'])\n",
    "names = ['home', 'search', 'payment']\n",
    "print('{0:^10s} | {1:^10s} | {2:^10s}'.format('Page', 'Desktop', 'Mobile'))\n",
    "print('-' * 40)\n",
    "for name, desk_rate, mobile_rate in zip(names, desk_rates, mobile_rates):\n",
    "    print('{0:10s} | {1:10.6f} | {2:10.6f}'.format(name, desk_rate, mobile_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "maps = {'Page': ['home', 'search', 'payment', 'home', 'search', 'payment'], \n",
    "        'Device': ['Desktop', 'Desktop', 'Desktop', 'Mobile', 'Mobile', 'Mobile'], \n",
    "        'Conversion Rate': desk_rates + mobile_rates}\n",
    "device_conv = pd.DataFrame(maps, columns=['Page', 'Device', 'Conversion Rate'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.barplot(x='Page', y='Conversion Rate', data=device_conv, ax=ax, hue='Device')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Female' 'Male']\n"
     ]
    }
   ],
   "source": [
    "print(data['sex'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Page    |    Male    |   Female  \n",
      "----------------------------------------\n",
      "home       |   0.496944 |   0.503073\n",
      "search     |   0.130083 |   0.136708\n",
      "payment    |   0.072014 |   0.077742\n"
     ]
    }
   ],
   "source": [
    "male_rates = conversion_rate(data[data['sex'] == 'Male'])\n",
    "female_rates = conversion_rate(data[data['sex'] == 'Female'])\n",
    "names = ['home', 'search', 'payment']\n",
    "print('{0:^10s} | {1:^10s} | {2:^10s}'.format('Page', 'Male', 'Female'))\n",
    "print('-' * 40)\n",
    "for name, male_rate, female_rate in zip(names, male_rates, female_rates):\n",
    "    print('{0:10s} | {1:10.6f} | {2:10.6f}'.format(name, male_rate, female_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "maps = {'Page': ['home', 'search', 'payment', 'home', 'search', 'payment'], \n",
    "        'Sex': ['Male', 'Male', 'Male', 'Female', 'Female', 'Female'], \n",
    "        'Conversion Rate': male_rates + female_rates}\n",
    "sex_conv = pd.DataFrame(maps, columns=['Page', 'Sex', 'Conversion Rate'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "sns.barplot(x='Page', y='Conversion Rate', data=sex_conv, ax=ax, hue='Sex')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}