Funnel Analysis.ipynb

{
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   "source": [
    "# Goal\n",
    "The goal is to perform funnel analysis for an e-commerce website.\n",
    "\n",
    "Typically, websites have a clear path to conversion: for instance, you land on the home page, then you search, select a product, and buy it. At each of these steps, some users will drop off and leave the site. The sequence of pages that lead to conversion is called 'funnel'.\n",
    "\n",
    "Data Science can have a tremendous impact on funnel optimization. Funnel analysis allows to understand where/when our users abandon the website. It gives crucial insights on user behavior and on ways to improve the user experience. Also, it often allows to discover bugs."
   ]
  },
  {
   "attachments": {},
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   "source": [
    "# Description\n",
    "\n",
    "You are looking at data from an e-commerce website. The site is very simple and has just 4 pages:\n",
    "- The first page is the home page. When you come to the site for the first time, you can only land on the home page as a first page.\n",
    "\n",
    "- From the home page, the user can perform a search and land on the search page. \n",
    "\n",
    "- From the search page, if the user clicks on a product, she will get to the payment page,where she is asked to provide payment information in order to buy that product.\n",
    "\n",
    "- If she does decide to buy, she ends up on the confirmation page\n",
    "\n",
    "The company CEO isn't very happy with the volume of sales and, especially, of sales coming from new users. Therefore, she asked you to investigate whether there is something wrong in the conversion funnel or, in general, if you could suggest how conversion rate can be improved.\n",
    "\n",
    "Specifically, she is interested in :\n",
    "- A full picture of funnel conversion rate for both desktop and mobile\n",
    "\n",
    "- Some insights on what the product team should focus on in order to improve conversion rate as well as anything you might discover that could help improve conversion rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_page_table = pd.read_csv('/Users/yangzongkun/Desktop/sd/DS Take Home 4-5/5.Funnel_Analysis/home_page_table.csv')\n",
    "search_page_table = pd.read_csv('/Users/yangzongkun/Desktop/sd/DS Take Home 4-5/5.Funnel_Analysis/search_page_table.csv')\n",
    "payment_page_table = pd.read_csv('/Users/yangzongkun/Desktop/sd/DS Take Home 4-5/5.Funnel_Analysis/payment_page_table.csv')\n",
    "payment_confirmation_table = pd.read_csv('/Users/yangzongkun/Desktop/sd/DS Take Home 4-5/5.Funnel_Analysis/payment_confirmation_table.csv')\n",
    "user_table = pd.read_csv('/Users/yangzongkun/Desktop/sd/DS Take Home 4-5/5.Funnel_Analysis/user_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
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       "  <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",
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      "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"
      ]
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   ],
   "source": [
    "user_table.head()"
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   "cell_type": "code",
   "execution_count": 186,
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   "outputs": [
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
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       "    <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",
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      "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"
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   "source": [
    "home_page_table.head()"
   ]
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   "cell_type": "code",
   "execution_count": 187,
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15866</td>\n",
       "      <td>search_page</td>\n",
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       "    <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",
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       "      <th>4</th>\n",
       "      <td>383739</td>\n",
       "      <td>search_page</td>\n",
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      "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": 187,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "search_page_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
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   "outputs": [
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       "  <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",
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      "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": 188,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "payment_page_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
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       "      <th>page</th>\n",
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       "      <th>0</th>\n",
       "      <td>123100</td>\n",
       "      <td>payment_confirmation_page</td>\n",
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       "    <tr>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>841681</td>\n",
       "      <td>payment_confirmation_page</td>\n",
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      "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": 189,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "payment_confirmation_table.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.merge(user_table,home_page_table,left_on='user_id',right_on='user_id',how='left')\n",
    "data=pd.merge(data,search_page_table,left_on='user_id',right_on='user_id',how='left',suffixes=('_home', '_search'))\n",
    "data=pd.merge(data,payment_page_table,left_on='user_id',right_on='user_id',how='left')\n",
    "data=pd.merge(data,payment_confirmation_table,left_on='user_id',right_on='user_id',how='left',suffixes=('_payment', '_confirmation'))\n",
    "data.head()\n",
    "\n",
    "#The suffixes parameter is used to specify a suffix to be added to overlapping column names. \n",
    "# In this case, if there are any columns with the same name in both dataframes, the columns in the data dataframe will have the suffix '_home' added\n",
    "# and the columns in the search_page_table dataframe will have the suffix '_search' added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   user_id            90400 non-null  int64 \n",
      " 1   date               90400 non-null  object\n",
      " 2   device             90400 non-null  object\n",
      " 3   sex                90400 non-null  object\n",
      " 4   page_home          90400 non-null  object\n",
      " 5   page_search        45200 non-null  object\n",
      " 6   page_payment       6030 non-null   object\n",
      " 7   page_confirmation  452 non-null    object\n",
      "dtypes: int64(1), object(7)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Visit Probability for Each Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Visit Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>page_home</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>page_search</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>page_payment</th>\n",
       "      <td>0.066704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>page_confirmation</th>\n",
       "      <td>0.005000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Visit Probability\n",
       "page_home                   1.000000\n",
       "page_search                 0.500000\n",
       "page_payment                0.066704\n",
       "page_confirmation           0.005000"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Visit_Probability=1-pd.DataFrame(data.isnull().sum()).rename(columns={0: 'Visit Probability'}) /len(data)\n",
    "Visit_Probability=Visit_Probability.loc['page_home':,['Visit Probability']]\n",
    "Visit_Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]),\n",
       " [Text(0, 0, 'page_home'),\n",
       "  Text(1, 0, 'page_search'),\n",
       "  Text(2, 0, 'page_payment'),\n",
       "  Text(3, 0, 'page_confirmation')])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Visit_Probability.plot(kind='bar')\n",
    "\n",
    "plt.ylabel('Ratio of Visitors')\n",
    "plt.xlabel('Page')\n",
    "plt.xticks(rotation=0)#to make the X axis labels horizontal in a bar chart"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversion Rate for Each Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>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": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to calculate conversion rates for home, search and payment page\n",
    "\n",
    "def conversion_rate(df):\n",
    "    rates = []\n",
    "    \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": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5, 0.13340707964601772, 0.07495854063018237]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates = conversion_rate(data)\n",
    "rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Page': ['home', 'search', 'payment'],\n",
       " 'Conversion Rate': [0.5, 0.13340707964601772, 0.07495854063018237]}"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['home', 'search', 'payment']\n",
    "maps = {'Page': ['home', 'search', 'payment'], 'Conversion Rate': rates}\n",
    "maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_conv = pd.DataFrame(maps, columns=['Page', 'Conversion Rate'])\n",
    "\n",
    "overall_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]),\n",
       " [Text(0, 0, 'home'), Text(1, 0, 'search'), Text(2, 0, 'payment')])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall_conv.plot(x='Page',y='Conversion Rate',kind='bar')\n",
    "\n",
    "plt.ylabel('Conversion Rate')\n",
    "plt.xlabel('Page')\n",
    "plt.xticks(rotation=0)#to make the X axis labels horizontal in a bar chart"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversion rate for both desktop and mobile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>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": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The conversion_rate function is used to calculate the conversion rate for each page for two different device types, desktop and mobile.\n",
    "desk_rates = conversion_rate(data[data['device'] == 'Desktop'])\n",
    "mobile_rates = conversion_rate(data[data['device'] == 'Mobile'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5, 0.09999999999999998, 0.04983388704318936]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desk_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5, 0.19999999999999996, 0.09999999999999998]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobile_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Page    |  Desktop   |   Mobile  \n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print('{0:^10s} | {1:^10s} | {2:^10s}'.format('Page', 'Desktop', 'Mobile'))\n",
    "print('-' * 40)\n",
    "# The table header is defined using the format method of the print function \n",
    "# with the string '{0:^10s} | {1:^10s} | {2:^10s}' as the first argument. \n",
    "# This string specifies that three columns should be printed\n",
    "# each with a width of 10 characters and with the contents centered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "home       |   0.500000 |   0.500000\n",
      "search     |   0.100000 |   0.200000\n",
      "payment    |   0.049834 |   0.100000\n"
     ]
    }
   ],
   "source": [
    "names = ['home', 'search', 'payment']\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))\n",
    "\n",
    "# The table body is generated using a for loop that iterates over the zip of the names, desk_rates, and mobile_rates lists.\n",
    "# In each iteration, a single row of the table is printed using the print function and the format method\n",
    "# The first column is a string (s), while the second and third columns are floating-point numbers (f) with 6 decimal places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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": [
    "#combine it \n",
    "print('{0:^10s} | {1:^10s} | {2:^10s}'.format('Page', 'Desktop', 'Mobile'))\n",
    "print('-' * 40)\n",
    "\n",
    "names = ['home', 'search', 'payment']\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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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>Device</th>\n",
       "      <th>Conversion Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>home</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>search</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>payment</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0.049834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>home</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>search</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>payment</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Page   Device  Conversion Rate\n",
       "0     home  Desktop         0.500000\n",
       "1   search  Desktop         0.100000\n",
       "2  payment  Desktop         0.049834\n",
       "3     home   Mobile         0.500000\n",
       "4   search   Mobile         0.200000\n",
       "5  payment   Mobile         0.100000"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\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",
    "# a dictionary called maps is created that contains three keys: 'Page', 'Device', and 'Conversion Rate'. \n",
    "# The values for the 'Page' key are a list of page names ['home', 'search', 'payment', 'home', 'search', 'payment']. \n",
    "# The values for the 'Device' key are a list of device types ['Desktop', 'Desktop', 'Desktop', 'Mobile', 'Mobile', 'Mobile']. \n",
    "# The values for the 'Conversion Rate' key are a concatenation of the desk_rates and mobile_rates lists.\n",
    "\n",
    "device_conv = pd.DataFrame(maps, columns=['Page', 'Device', 'Conversion Rate'])\n",
    "# The columns argument is used to specify the order of the columns in the resulting DataFrame.\n",
    "device_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversion rate by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>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": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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",
    "\n",
    "print('{0:^10s} | {1:^10s} | {2:^10s}'.format('Page', 'Male', 'Female'))\n",
    "print('-' * 40)\n",
    "\n",
    "names = ['home', 'search', 'payment']\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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>Sex</th>\n",
       "      <th>Conversion Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>home</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.496944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>search</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.130083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>payment</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.072014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>home</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.503073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>search</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.136708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>payment</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.077742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Page     Sex  Conversion Rate\n",
       "0     home    Male         0.496944\n",
       "1   search    Male         0.130083\n",
       "2  payment    Male         0.072014\n",
       "3     home  Female         0.503073\n",
       "4   search  Female         0.136708\n",
       "5  payment  Female         0.077742"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maps = {'Page': ['home', 'search', 'payment', 'home', 'search', 'payment'], \n",
    "        'Sex': ['Male', 'Male', 'Male', 'Female', 'Female', 'Female'], \n",
    "        'Conversion Rate': male_rates + female_rates}\n",
    "\n",
    "sex_conv = pd.DataFrame(maps, columns=['Page', 'Sex', 'Conversion Rate'])\n",
    "# The columns argument is used to specify the order of the columns in the resulting DataFrame.\n",
    "sex_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "untitled",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a476a4d55e9d2eba0bf3220d935cd405961ec1ca78e4143d34c6c517149b6ade"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}