Hotel domain analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72227019",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">AtliQ Hotels Data Analysis Project<h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9d2e3a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ce70a2",
   "metadata": {},
   "source": [
    "***\n",
    "**Data Import and Data Exploration**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14a05c4",
   "metadata": {},
   "source": [
    "**Datasets**\n",
    "\n",
    "We have 5 csv files\n",
    "\n",
    "    dim_date.csv  \n",
    "    dim_hotels.csv\n",
    "    dim_rooms.csv\n",
    "    fact_aggregated_bookings\n",
    "    fact_bookings.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2398dc95",
   "metadata": {},
   "source": [
    "**Read bookings data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b1145dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings = pd.read_csv('datasets/fact_bookings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21671917",
   "metadata": {},
   "source": [
    "**Explore bookings data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98b460b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT11        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT13        16558     28-04-22      1/5/2022      4/5/2022   \n",
       "3  May012216558RT14        16558     28-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0       -3.0           RT1    direct online            1.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        2.0           RT1          logtrip            5.0    Checked Out   \n",
       "3       -2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "0              10010             10010  \n",
       "1               9100              3640  \n",
       "2            9100000              9100  \n",
       "3               9100              3640  \n",
       "4              10920             10920  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec669257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "561bc6ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RT1', 'RT2', 'RT3', 'RT4'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.room_category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "249f9f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['direct online', 'others', 'logtrip', 'tripster', 'makeyourtrip',\n",
       "       'journey', 'direct offline'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b998a70d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "others            55066\n",
       "makeyourtrip      26898\n",
       "logtrip           14756\n",
       "direct online     13379\n",
       "tripster           9630\n",
       "journey            8106\n",
       "direct offline     6755\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f98d7016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='booking_platform'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bookings.booking_platform.value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb205f36",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134590.000000</td>\n",
       "      <td>134587.000000</td>\n",
       "      <td>56683.000000</td>\n",
       "      <td>1.345900e+05</td>\n",
       "      <td>134590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.113493</td>\n",
       "      <td>2.036170</td>\n",
       "      <td>3.619004</td>\n",
       "      <td>1.537805e+04</td>\n",
       "      <td>12696.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.055847</td>\n",
       "      <td>1.034885</td>\n",
       "      <td>1.235009</td>\n",
       "      <td>9.303604e+04</td>\n",
       "      <td>6928.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134590.000000  134587.000000   56683.000000       1.345900e+05   \n",
       "mean    18061.113493       2.036170       3.619004       1.537805e+04   \n",
       "std      1093.055847       1.034885       1.235009       9.303604e+04   \n",
       "min     16558.000000     -17.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134590.000000  \n",
       "mean       12696.123256  \n",
       "std         6928.108124  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa56317",
   "metadata": {},
   "source": [
    "**Read rest of the files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "acec3d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date = pd.read_csv('datasets/dim_date.csv')\n",
    "df_hotels = pd.read_csv('datasets/dim_hotels.csv')\n",
    "df_rooms = pd.read_csv('datasets/dim_rooms.csv')\n",
    "df_agg_bookings = pd.read_csv('datasets/fact_aggregated_bookings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58ad4ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "120678c2",
   "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>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b58d35c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "Luxury      16\n",
       "Business     9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a1676d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_hotels.city.value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f88bece",
   "metadata": {},
   "source": [
    "***\n",
    "**Exercise: Explore aggregate bookings**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f143c06a",
   "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>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "3        17558      1-May-22           RT1                   30      19.0\n",
       "4        16558      1-May-22           RT1                   18      19.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5585a977",
   "metadata": {},
   "source": [
    "**Exercise-1. Find out unique property ids in aggregate bookings dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5bb56d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16559, 19562, 19563, 17558, 16558, 17560, 19558, 19560, 17561,\n",
       "       16560, 16561, 16562, 16563, 17559, 17562, 17563, 18558, 18559,\n",
       "       18561, 18562, 18563, 19559, 19561, 17564, 18560], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.property_id.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7f36cb",
   "metadata": {},
   "source": [
    "**Exercise-2. Find out total bookings per property_id**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff1dc471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id\n",
       "16558    3153\n",
       "16559    7338\n",
       "16560    4693\n",
       "16561    4418\n",
       "16562    4820\n",
       "16563    7211\n",
       "17558    5053\n",
       "17559    6142\n",
       "17560    6013\n",
       "17561    5183\n",
       "17562    3424\n",
       "17563    6337\n",
       "17564    3982\n",
       "18558    4475\n",
       "18559    5256\n",
       "18560    6638\n",
       "18561    6458\n",
       "18562    7333\n",
       "18563    4737\n",
       "19558    4400\n",
       "19559    4729\n",
       "19560    6079\n",
       "19561    5736\n",
       "19562    5812\n",
       "19563    5413\n",
       "Name: successful_bookings, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby(\"property_id\")[\"successful_bookings\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae77d32",
   "metadata": {},
   "source": [
    "**Exercise-3. Find out days on which bookings are greater than capacity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e71ce6f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>17558</td>\n",
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       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>100</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4136</th>\n",
       "      <td>19558</td>\n",
       "      <td>11-Jun-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>19560</td>\n",
       "      <td>2-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>123</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>19559</td>\n",
       "      <td>25-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>35</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>18563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>20</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "12          16563      1-May-22           RT1                  100      41.0\n",
       "4136        19558     11-Jun-22           RT2                   50      39.0\n",
       "6209        19560      2-Jul-22           RT1                  123      26.0\n",
       "8522        19559     25-Jul-22           RT1                   35      24.0\n",
       "9194        18563     31-Jul-22           RT4                   20      18.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.successful_bookings>df_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6384f78e",
   "metadata": {},
   "source": [
    "**Exercise-4. Find out properties that have highest capacity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "95eb57d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.capacity.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "79b37c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
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       "      <td>38</td>\n",
       "      <td>50.0</td>\n",
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       "      <th>128</th>\n",
       "      <td>17558</td>\n",
       "      <td>2-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>17558</td>\n",
       "      <td>3-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>26</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>17558</td>\n",
       "      <td>4-May-22</td>\n",
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       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>17558</td>\n",
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       "      <td>50.0</td>\n",
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       "      <th>8728</th>\n",
       "      <td>17558</td>\n",
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       "      <td>22</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>8828</th>\n",
       "      <td>17558</td>\n",
       "      <td>28-Jul-22</td>\n",
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       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>8928</th>\n",
       "      <td>17558</td>\n",
       "      <td>29-Jul-22</td>\n",
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       "      <td>23</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>17558</td>\n",
       "      <td>30-Jul-22</td>\n",
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       "      <td>32</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>30</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "27          17558      1-May-22           RT2                   38      50.0\n",
       "128         17558      2-May-22           RT2                   27      50.0\n",
       "229         17558      3-May-22           RT2                   26      50.0\n",
       "328         17558      4-May-22           RT2                   27      50.0\n",
       "428         17558      5-May-22           RT2                   29      50.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "8728        17558     27-Jul-22           RT2                   22      50.0\n",
       "8828        17558     28-Jul-22           RT2                   21      50.0\n",
       "8928        17558     29-Jul-22           RT2                   23      50.0\n",
       "9028        17558     30-Jul-22           RT2                   32      50.0\n",
       "9128        17558     31-Jul-22           RT2                   30      50.0\n",
       "\n",
       "[92 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.capacity==df_agg_bookings.capacity.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164ae638",
   "metadata": {},
   "source": [
    "***\n",
    "**Data Cleaning**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a54b884",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134590.000000</td>\n",
       "      <td>134587.000000</td>\n",
       "      <td>56683.000000</td>\n",
       "      <td>1.345900e+05</td>\n",
       "      <td>134590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.113493</td>\n",
       "      <td>2.036170</td>\n",
       "      <td>3.619004</td>\n",
       "      <td>1.537805e+04</td>\n",
       "      <td>12696.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.055847</td>\n",
       "      <td>1.034885</td>\n",
       "      <td>1.235009</td>\n",
       "      <td>9.303604e+04</td>\n",
       "      <td>6928.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134590.000000  134587.000000   56683.000000       1.345900e+05   \n",
       "mean    18061.113493       2.036170       3.619004       1.537805e+04   \n",
       "std      1093.055847       1.034885       1.235009       9.303604e+04   \n",
       "min     16558.000000     -17.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134590.000000  \n",
       "mean       12696.123256  \n",
       "std         6928.108124  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccab1747",
   "metadata": {},
   "source": [
    "**Clean invalid guests**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd38529d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>May012216558RT11</td>\n",
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       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
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       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17924</th>\n",
       "      <td>May122218559RT44</td>\n",
       "      <td>18559</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>20900</td>\n",
       "      <td>20900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18020</th>\n",
       "      <td>May122218561RT22</td>\n",
       "      <td>18561</td>\n",
       "      <td>8/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9000</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18119</th>\n",
       "      <td>May122218562RT311</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>16800</td>\n",
       "      <td>16800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18121</th>\n",
       "      <td>May122218562RT313</td>\n",
       "      <td>18562</td>\n",
       "      <td>10/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>14400</td>\n",
       "      <td>5760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56715</th>\n",
       "      <td>Jun082218562RT12</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/6/2022</td>\n",
       "      <td>8/6/2022</td>\n",
       "      <td>13-06-22</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>6500</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119765</th>\n",
       "      <td>Jul202219560RT220</td>\n",
       "      <td>19560</td>\n",
       "      <td>19-07-22</td>\n",
       "      <td>20-07-22</td>\n",
       "      <td>22-07-22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>13500</td>\n",
       "      <td>13500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134586</th>\n",
       "      <td>Jul312217564RT47</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT11        16558     27-04-22      1/5/2022   \n",
       "3        May012216558RT14        16558     28-04-22      1/5/2022   \n",
       "17924    May122218559RT44        18559    12/5/2022     12/5/2022   \n",
       "18020    May122218561RT22        18561     8/5/2022     12/5/2022   \n",
       "18119   May122218562RT311        18562     5/5/2022     12/5/2022   \n",
       "18121   May122218562RT313        18562    10/5/2022     12/5/2022   \n",
       "56715    Jun082218562RT12        18562     5/6/2022      8/6/2022   \n",
       "119765  Jul202219560RT220        19560     19-07-22      20-07-22   \n",
       "134586   Jul312217564RT47        17564     30-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "0           2/5/2022       -3.0           RT1    direct online            1.0   \n",
       "3           2/5/2022       -2.0           RT1           others            NaN   \n",
       "17924       14-05-22      -10.0           RT4    direct online            NaN   \n",
       "18020       14-05-22      -12.0           RT2     makeyourtrip            NaN   \n",
       "18119       17-05-22       -6.0           RT3   direct offline            5.0   \n",
       "18121       17-05-22       -4.0           RT3    direct online            NaN   \n",
       "56715       13-06-22      -17.0           RT1           others            NaN   \n",
       "119765      22-07-22       -1.0           RT2           others            NaN   \n",
       "134586      1/8/2022       -4.0           RT4          logtrip            2.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "0         Checked Out              10010             10010  \n",
       "3           Cancelled               9100              3640  \n",
       "17924         No Show              20900             20900  \n",
       "18020       Cancelled               9000              3600  \n",
       "18119     Checked Out              16800             16800  \n",
       "18121       Cancelled              14400              5760  \n",
       "56715     Checked Out               6500              6500  \n",
       "119765    Checked Out              13500             13500  \n",
       "134586    Checked Out              38760             38760  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.no_guests<=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dda4bc6",
   "metadata": {},
   "source": [
    " We can ignore these records as you can see above that the number of guests having less than zero value represents data error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec37e362",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings = df_bookings[df_bookings.no_guests>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2745a215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134578, 12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6b034f",
   "metadata": {},
   "source": [
    "**Outlier removal in revenue generated**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8fe72ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6500, 28560000)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.min(), df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28d6901d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15378.036937686695, 13500.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.mean(), df_bookings.revenue_generated.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0ba0424",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg, std = df_bookings.revenue_generated.mean(), df_bookings.revenue_generated.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dbb0d367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294498.50173207896"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_limit = avg + 3*std\n",
    "higher_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d4dbae33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-263742.4278567056"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit = avg - 3*std\n",
    "lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d5883cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [booking_id, property_id, booking_date, check_in_date, checkout_date, no_guests, room_category, booking_platform, ratings_given, booking_status, revenue_generated, revenue_realized]\n",
       "Index: []"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6232ee08",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
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       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
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       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>May012216559RT32</td>\n",
       "      <td>16559</td>\n",
       "      <td>29-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>28560000</td>\n",
       "      <td>28560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>May012216562RT22</td>\n",
       "      <td>16562</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>12600000</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>May012217559RT118</td>\n",
       "      <td>17559</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2000000</td>\n",
       "      <td>4420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129176</th>\n",
       "      <td>Jul282216562RT26</td>\n",
       "      <td>16562</td>\n",
       "      <td>21-07-22</td>\n",
       "      <td>28-07-22</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>direct online</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10000000</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "111      May012216559RT32        16559     29-04-22      1/5/2022   \n",
       "315      May012216562RT22        16562     28-04-22      1/5/2022   \n",
       "562     May012217559RT118        17559     26-04-22      1/5/2022   \n",
       "129176   Jul282216562RT26        16562     21-07-22      28-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "2           4/5/2022        2.0           RT1          logtrip            5.0   \n",
       "111         2/5/2022        6.0           RT3    direct online            NaN   \n",
       "315         4/5/2022        2.0           RT2   direct offline            3.0   \n",
       "562         2/5/2022        2.0           RT1           others            NaN   \n",
       "129176      29-07-22        2.0           RT2    direct online            3.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "2         Checked Out            9100000              9100  \n",
       "111       Checked Out           28560000             28560  \n",
       "315       Checked Out           12600000             12600  \n",
       "562         Cancelled            2000000              4420  \n",
       "129176    Checked Out           10000000             12600  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated>higher_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fbde1d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134573, 12)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings = df_bookings[df_bookings.revenue_generated<=higher_limit]\n",
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3df8cf63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    134573.000000\n",
       "mean      12695.983585\n",
       "std        6927.791692\n",
       "min        2600.000000\n",
       "25%        7600.000000\n",
       "50%       11700.000000\n",
       "75%       15300.000000\n",
       "max       45220.000000\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c9253a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33479.358661845814"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_limit = df_bookings.revenue_realized.mean() + 3*df_bookings.revenue_realized.std()\n",
    "higher_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c88165cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>137</th>\n",
       "      <td>May012216559RT41</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>May012216559RT43</td>\n",
       "      <td>16559</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>45220</td>\n",
       "      <td>45220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>May012216559RT47</td>\n",
       "      <td>16559</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>35530</td>\n",
       "      <td>35530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>May012216559RT413</td>\n",
       "      <td>16559</td>\n",
       "      <td>24-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>7/5/2022</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>41990</td>\n",
       "      <td>41990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>May012216560RT45</td>\n",
       "      <td>16560</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>34580</td>\n",
       "      <td>34580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134328</th>\n",
       "      <td>Jul312219560RT49</td>\n",
       "      <td>19560</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134331</th>\n",
       "      <td>Jul312219560RT412</td>\n",
       "      <td>19560</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134467</th>\n",
       "      <td>Jul312219562RT45</td>\n",
       "      <td>19562</td>\n",
       "      <td>28-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134474</th>\n",
       "      <td>Jul312219562RT412</td>\n",
       "      <td>19562</td>\n",
       "      <td>25-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>6/8/2022</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>37050</td>\n",
       "      <td>37050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134581</th>\n",
       "      <td>Jul312217564RT42</td>\n",
       "      <td>17564</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1299 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "137      May012216559RT41        16559     27-04-22      1/5/2022   \n",
       "139      May012216559RT43        16559     1/5/2022      1/5/2022   \n",
       "143      May012216559RT47        16559     28-04-22      1/5/2022   \n",
       "149     May012216559RT413        16559     24-04-22      1/5/2022   \n",
       "222      May012216560RT45        16560     30-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134328   Jul312219560RT49        19560     31-07-22      31-07-22   \n",
       "134331  Jul312219560RT412        19560     31-07-22      31-07-22   \n",
       "134467   Jul312219562RT45        19562     28-07-22      31-07-22   \n",
       "134474  Jul312219562RT412        19562     25-07-22      31-07-22   \n",
       "134581   Jul312217564RT42        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "137         7/5/2022        4.0           RT4           others            NaN   \n",
       "139         2/5/2022        6.0           RT4         tripster            3.0   \n",
       "143         3/5/2022        3.0           RT4           others            5.0   \n",
       "149         7/5/2022        5.0           RT4          logtrip            NaN   \n",
       "222         3/5/2022        5.0           RT4           others            3.0   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134328      2/8/2022        6.0           RT4    direct online            5.0   \n",
       "134331      1/8/2022        6.0           RT4           others            2.0   \n",
       "134467      1/8/2022        6.0           RT4     makeyourtrip            4.0   \n",
       "134474      6/8/2022        5.0           RT4   direct offline            5.0   \n",
       "134581      1/8/2022        4.0           RT4     makeyourtrip            4.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "137       Checked Out              38760             38760  \n",
       "139       Checked Out              45220             45220  \n",
       "143       Checked Out              35530             35530  \n",
       "149       Checked Out              41990             41990  \n",
       "222       Checked Out              34580             34580  \n",
       "...               ...                ...               ...  \n",
       "134328    Checked Out              39900             39900  \n",
       "134331    Checked Out              39900             39900  \n",
       "134467    Checked Out              39900             39900  \n",
       "134474    Checked Out              37050             37050  \n",
       "134581    Checked Out              38760             38760  \n",
       "\n",
       "[1299 rows x 12 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_realized>higher_limit]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6a9900",
   "metadata": {},
   "source": [
    "One observation we can have in above dataframe is that all rooms are RT4 which means presidential suit. Now since RT4 is a luxurious room it is likely their rent will be higher. To make a fair analysis, we need to do data analysis only on RT4 room types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4ae3037e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16071.000000\n",
       "mean     23439.308444\n",
       "std       9048.599076\n",
       "min       7600.000000\n",
       "25%      19000.000000\n",
       "50%      26600.000000\n",
       "75%      32300.000000\n",
       "max      45220.000000\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.room_category==\"RT4\"].revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "94480648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50583"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean + 3*standard deviation\n",
    "23439+3*9048"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0dc1c9",
   "metadata": {},
   "source": [
    "Here higher limit comes to be 50583 and in our dataframe above we can see that max value for revenue realized is 45220. Hence we can conclude that there is no outlier and we don't need to do any data cleaning on this particular column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2afa2688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
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       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [booking_id, property_id, booking_date, check_in_date, checkout_date, no_guests, room_category, booking_platform, ratings_given, booking_status, revenue_generated, revenue_realized]\n",
       "Index: []"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.booking_id==\"May012216558RT213\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0c306f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_id               0\n",
       "property_id              0\n",
       "booking_date             0\n",
       "check_in_date            0\n",
       "checkout_date            0\n",
       "no_guests                0\n",
       "room_category            0\n",
       "booking_platform         0\n",
       "ratings_given        77897\n",
       "booking_status           0\n",
       "revenue_generated        0\n",
       "revenue_realized         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f93a77d",
   "metadata": {},
   "source": [
    "Total values in our dataframe is 134576. Out of that 77899 rows has null rating. Since there are many rows with null rating, we should not filter these values. Also we should not replace this rating with a median or mean rating etc "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ea9226f",
   "metadata": {},
   "source": [
    "**Exercise-1. In aggregate bookings find columns that have null values. Fill these null values with whatever you think is the appropriate subtitute (possible ways is to use mean or median)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0e6c68af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id            0\n",
       "check_in_date          0\n",
       "room_category          0\n",
       "successful_bookings    0\n",
       "capacity               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1cda1d54",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22       NaN\n",
       "14        17562      1-May-22           RT1                   12       NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.capacity.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4bf2ff40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_agg_bookings.capacity.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5f56b34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg_bookings.capacity.fillna(df_agg_bookings.capacity.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "41f98d1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22      25.0\n",
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   "source": [
    "98"
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   "cell_type": "code",
   "execution_count": 45,
   "id": "8906ab77",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th>4136</th>\n",
       "      <td>19558</td>\n",
       "      <td>11-Jun-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>19560</td>\n",
       "      <td>2-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>123</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>19559</td>\n",
       "      <td>25-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>35</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>18563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>20</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "12          16563      1-May-22           RT1                  100      41.0\n",
       "4136        19558     11-Jun-22           RT2                   50      39.0\n",
       "6209        19560      2-Jul-22           RT1                  123      26.0\n",
       "8522        19559     25-Jul-22           RT1                   35      24.0\n",
       "9194        18563     31-Jul-22           RT4                   20      18.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.successful_bookings>df_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "09f04479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9200, 5)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7aef0a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9194, 5)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings = df_agg_bookings[df_agg_bookings.successful_bookings<=df_agg_bookings.capacity]\n",
    "df_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "561c4b36",
   "metadata": {},
   "source": [
    "***\n",
    "**Data Transformation**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6763a2a1",
   "metadata": {},
   "source": [
    "**Create occupancy percentage column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b11f34a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "4        16558      1-May-22           RT1                   18      19.0\n",
       "5        17560      1-May-22           RT1                   28      40.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "38c76ca0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_agg_bookings['occ_pct'] = df_agg_bookings.apply(lambda row: row['successful_bookings']/row['capacity'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05338be8",
   "metadata": {},
   "source": [
    "Convert it to a percentage value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1dca6a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings['occ_pct'] = df_agg_bookings['occ_pct'].apply(lambda x: round(x*100, 2))\n",
    "df_agg_bookings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7bc9049e",
   "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>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "5  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "6  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "7  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "5        2.0           RT1           others            4.0    Checked Out   \n",
       "6        2.0           RT1           others            NaN      Cancelled   \n",
       "7        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "1               9100              3640  \n",
       "4              10920             10920  \n",
       "5               9100              9100  \n",
       "6               9100              3640  \n",
       "7               9100              9100  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "33580acc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 9194 entries, 0 to 9199\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   property_id          9194 non-null   int64  \n",
      " 1   check_in_date        9194 non-null   object \n",
      " 2   room_category        9194 non-null   object \n",
      " 3   successful_bookings  9194 non-null   int64  \n",
      " 4   capacity             9194 non-null   float64\n",
      " 5   occ_pct              9194 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 502.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_agg_bookings.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296cb8e2",
   "metadata": {},
   "source": [
    "***\n",
    "**4. Insights Generation**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5075bc",
   "metadata": {},
   "source": [
    "**1. What is an average occupancy rate in each of the room categories?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "79edd377",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
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       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2c5f1568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_category\n",
       "RT1    57.889643\n",
       "RT2    58.009756\n",
       "RT3    58.028213\n",
       "RT4    59.277925\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby(\"room_category\")[\"occ_pct\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e474a952",
   "metadata": {},
   "source": [
    "Print room categories such as Standard, Premium, Elite etc along with average occupancy percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6e7fc086",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94.74</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "3        16558      1-May-22           RT1                   18      19.0   \n",
       "\n",
       "   occ_pct room_id room_class  \n",
       "0    83.33     RT1   Standard  \n",
       "1    93.33     RT1   Standard  \n",
       "2    76.67     RT1   Standard  \n",
       "3    94.74     RT1   Standard  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_agg_bookings, df_rooms, left_on=\"room_category\", right_on=\"room_id\")\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "eba64feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94.74</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "3        16558      1-May-22           RT1                   18      19.0   \n",
       "\n",
       "   occ_pct room_class  \n",
       "0    83.33   Standard  \n",
       "1    93.33   Standard  \n",
       "2    76.67   Standard  \n",
       "3    94.74   Standard  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"room_id\",axis=1, inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4beb9856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_class\n",
       "Elite           58.009756\n",
       "Premium         58.028213\n",
       "Presidential    59.277925\n",
       "Standard        57.889643\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"room_class\")[\"occ_pct\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dc71a97f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57.88964285714285"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.room_class==\"Standard\"].occ_pct.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ae5668",
   "metadata": {},
   "source": [
    "**2. Print average occupancy rate per city**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d4144616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9566a7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
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       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>2-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>30.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16559</td>\n",
       "      <td>3-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>17</td>\n",
       "      <td>30.0</td>\n",
       "      <td>56.67</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        16559      2-May-22           RT1                   20      30.0   \n",
       "2        16559      3-May-22           RT1                   17      30.0   \n",
       "\n",
       "   occ_pct room_class  property_name category    city  \n",
       "0    83.33   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "1    66.67   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "2    56.67   Standard  Atliq Exotica   Luxury  Mumbai  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_hotels, on=\"property_id\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ba361b4b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    56.332376\n",
       "Delhi        61.507341\n",
       "Hyderabad    58.120652\n",
       "Mumbai       57.909181\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"city\")[\"occ_pct\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd541b5",
   "metadata": {},
   "source": [
    "**3. When was the occupancy better? Weekday or Weekend?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2a54cedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>date</th>\n",
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       "      <td>01-May-22</td>\n",
       "      <td>May 22</td>\n",
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       "      <td>weekend</td>\n",
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       "    <tr>\n",
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       "      <td>02-May-22</td>\n",
       "      <td>May 22</td>\n",
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       "      <td>weekeday</td>\n",
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       "      <td>03-May-22</td>\n",
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       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0  01-May-22  May 22    W 19   weekend\n",
       "1  02-May-22  May 22    W 19  weekeday\n",
       "2  03-May-22  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1efc775d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
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       "      <td>60.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
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       "      <td>10-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>25</td>\n",
       "      <td>41.0</td>\n",
       "      <td>60.98</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>20</td>\n",
       "      <td>32.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559     10-May-22           RT1                   18      30.0   \n",
       "1        16559     10-May-22           RT2                   25      41.0   \n",
       "2        16559     10-May-22           RT3                   20      32.0   \n",
       "\n",
       "   occ_pct room_class  property_name category    city       date  mmm yy  \\\n",
       "0    60.00   Standard  Atliq Exotica   Luxury  Mumbai  10-May-22  May 22   \n",
       "1    60.98      Elite  Atliq Exotica   Luxury  Mumbai  10-May-22  May 22   \n",
       "2    62.50    Premium  Atliq Exotica   Luxury  Mumbai  10-May-22  May 22   \n",
       "\n",
       "  week no  day_type  \n",
       "0    W 20  weekeday  \n",
       "1    W 20  weekeday  \n",
       "2    W 20  weekeday  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_date, left_on=\"check_in_date\", right_on=\"date\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "74b9dddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_type\n",
       "weekeday    50.88\n",
       "weekend     72.34\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day_type\")[\"occ_pct\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908fd7b2",
   "metadata": {},
   "source": [
    "**4: In the month of June, what is the occupancy for different cities**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "58fe91f6-c464-46eb-bce0-6a96f22ed804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mmm yy\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "01cf3d06",
   "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>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>30.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>26</td>\n",
       "      <td>41.0</td>\n",
       "      <td>63.41</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2202</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>20</td>\n",
       "      <td>32.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>61.11</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "2200        16559     10-Jun-22           RT1                   20      30.0   \n",
       "2201        16559     10-Jun-22           RT2                   26      41.0   \n",
       "2202        16559     10-Jun-22           RT3                   20      32.0   \n",
       "2203        16559     10-Jun-22           RT4                   11      18.0   \n",
       "\n",
       "      occ_pct    room_class  property_name category    city       date  \\\n",
       "2200    66.67      Standard  Atliq Exotica   Luxury  Mumbai  10-Jun-22   \n",
       "2201    63.41         Elite  Atliq Exotica   Luxury  Mumbai  10-Jun-22   \n",
       "2202    62.50       Premium  Atliq Exotica   Luxury  Mumbai  10-Jun-22   \n",
       "2203    61.11  Presidential  Atliq Exotica   Luxury  Mumbai  10-Jun-22   \n",
       "\n",
       "      mmm yy week no  day_type  \n",
       "2200  Jun 22    W 24  weekeday  \n",
       "2201  Jun 22    W 24  weekeday  \n",
       "2202  Jun 22    W 24  weekeday  \n",
       "2203  Jun 22    W 24  weekeday  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_june_22 = df[df[\"mmm yy\"]==\"Jun 22\"]\n",
    "df_june_22.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "57ae00a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Delhi        62.47\n",
       "Hyderabad    58.46\n",
       "Mumbai       58.38\n",
       "Bangalore    56.44\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_june_22.groupby('city')['occ_pct'].mean().round(2).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b113d091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_june_22.groupby('city')['occ_pct'].mean().round(2).sort_values(ascending=False).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822ea61a",
   "metadata": {},
   "source": [
    "**5: We got new data for the month of august. Append that to existing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "30d867c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>room_category</th>\n",
       "      <th>room_class</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category       city room_category room_class  \\\n",
       "0        16559  Atliq Exotica    Luxury     Mumbai           RT1   Standard   \n",
       "1        19562      Atliq Bay    Luxury  Bangalore           RT1   Standard   \n",
       "2        19563   Atliq Palace  Business  Bangalore           RT1   Standard   \n",
       "\n",
       "  check_in_date  mmm yy week no  day_type  successful_bookings  capacity  \\\n",
       "0     01-Aug-22  Aug-22    W 32  weekeday                   30        30   \n",
       "1     01-Aug-22  Aug-22    W 32  weekeday                   21        30   \n",
       "2     01-Aug-22  Aug-22    W 32  weekeday                   23        30   \n",
       "\n",
       "     occ%  \n",
       "0  100.00  \n",
       "1   70.00  \n",
       "2   76.67  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august = pd.read_csv(\"datasets/new_data_august.csv\")\n",
    "df_august.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "927c5677",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_id', 'property_name', 'category', 'city', 'room_category',\n",
       "       'room_class', 'check_in_date', 'mmm yy', 'week no', 'day_type',\n",
       "       'successful_bookings', 'capacity', 'occ%'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "95366815",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_id', 'check_in_date', 'room_category', 'successful_bookings',\n",
       "       'capacity', 'occ_pct', 'room_class', 'property_name', 'category',\n",
       "       'city', 'date', 'mmm yy', 'week no', 'day_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d406e514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 13)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "53392f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497, 14)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f047c610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>16563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>32</td>\n",
       "      <td>38.0</td>\n",
       "      <td>84.21</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>16563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>14</td>\n",
       "      <td>20.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>16563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6497</th>\n",
       "      <td>16559</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6498</th>\n",
       "      <td>19562</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>19563</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6500</th>\n",
       "      <td>19558</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6501</th>\n",
       "      <td>19560</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6502</th>\n",
       "      <td>17561</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>69.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6503</th>\n",
       "      <td>17564</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "6494        16563     31-Jul-22           RT2                   32      38.0   \n",
       "6495        16563     31-Jul-22           RT3                   14      20.0   \n",
       "6496        16563     31-Jul-22           RT4                   13      18.0   \n",
       "6497        16559     01-Aug-22           RT1                   30      30.0   \n",
       "6498        19562     01-Aug-22           RT1                   21      30.0   \n",
       "6499        19563     01-Aug-22           RT1                   23      30.0   \n",
       "6500        19558     01-Aug-22           RT1                   30      40.0   \n",
       "6501        19560     01-Aug-22           RT1                   20      26.0   \n",
       "6502        17561     01-Aug-22           RT1                   18      26.0   \n",
       "6503        17564     01-Aug-22           RT1                   10      16.0   \n",
       "\n",
       "      occ_pct    room_class  property_name  category       city       date  \\\n",
       "6494    84.21         Elite   Atliq Palace  Business      Delhi  31-Jul-22   \n",
       "6495    70.00       Premium   Atliq Palace  Business      Delhi  31-Jul-22   \n",
       "6496    72.22  Presidential   Atliq Palace  Business      Delhi  31-Jul-22   \n",
       "6497      NaN      Standard  Atliq Exotica    Luxury     Mumbai        NaN   \n",
       "6498      NaN      Standard      Atliq Bay    Luxury  Bangalore        NaN   \n",
       "6499      NaN      Standard   Atliq Palace  Business  Bangalore        NaN   \n",
       "6500      NaN      Standard   Atliq Grands    Luxury  Bangalore        NaN   \n",
       "6501      NaN      Standard     Atliq City  Business  Bangalore        NaN   \n",
       "6502      NaN      Standard      Atliq Blu    Luxury     Mumbai        NaN   \n",
       "6503      NaN      Standard  Atliq Seasons  Business     Mumbai        NaN   \n",
       "\n",
       "      mmm yy week no  day_type    occ%  \n",
       "6494  Jul 22    W 32   weekend     NaN  \n",
       "6495  Jul 22    W 32   weekend     NaN  \n",
       "6496  Jul 22    W 32   weekend     NaN  \n",
       "6497  Aug-22    W 32  weekeday  100.00  \n",
       "6498  Aug-22    W 32  weekeday   70.00  \n",
       "6499  Aug-22    W 32  weekeday   76.67  \n",
       "6500  Aug-22    W 32  weekeday   75.00  \n",
       "6501  Aug-22    W 32  weekeday   76.92  \n",
       "6502  Aug-22    W 32  weekeday   69.23  \n",
       "6503  Aug-22    W 32  weekeday   62.50  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_df = pd.concat([df, df_august], ignore_index = True, axis = 0)\n",
    "latest_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "94c111ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6504, 15)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61aa3997",
   "metadata": {},
   "source": [
    "**6. Print revenue realized per city**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "caa98149",
   "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>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "5  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "6  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "7  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "5        2.0           RT1           others            4.0    Checked Out   \n",
       "6        2.0           RT1           others            NaN      Cancelled   \n",
       "7        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "1               9100              3640  \n",
       "4              10920             10920  \n",
       "5               9100              9100  \n",
       "6               9100              3640  \n",
       "7               9100              9100  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "79131c3a",
   "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>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16561</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi\n",
       "3        16561      Atliq Blu    Luxury   Delhi\n",
       "4        16562      Atliq Bay    Luxury   Delhi"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5fc72559",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all = pd.merge(df_bookings, df_hotels, on=\"property_id\")\n",
    "df_bookings_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "54e418e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    420383550\n",
       "Delhi        294404488\n",
       "Hyderabad    325179310\n",
       "Mumbai       668569251\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"city\")[\"revenue_realized\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02360e8c",
   "metadata": {},
   "source": [
    "**Exercise-1. Print revenue realized per hotel type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "074fab45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Atliq Grands', 'Atliq Exotica', 'Atliq City', 'Atliq Blu',\n",
       "       'Atliq Bay', 'Atliq Palace', 'Atliq Seasons'], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.property_name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f778ac07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_name\n",
       "Atliq Seasons     66086735\n",
       "Atliq Grands     211462134\n",
       "Atliq Bay        259996918\n",
       "Atliq Blu        260851922\n",
       "Atliq City       285798439\n",
       "Atliq Palace     304081863\n",
       "Atliq Exotica    320258588\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"property_name\")[\"revenue_realized\"].sum().round(2).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d86978",
   "metadata": {},
   "source": [
    "**Exercise-2 Print average rating per city**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4d4398ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    3.41\n",
       "Delhi        3.78\n",
       "Hyderabad    3.66\n",
       "Mumbai       3.65\n",
       "Name: ratings_given, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"city\")[\"ratings_given\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9e8576",
   "metadata": {},
   "source": [
    "**Exercise-3 Print a pie chart of revenue realized per booking platform**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "91ba08d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='revenue_realized'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"booking_platform\")[\"revenue_realized\"].sum().plot(kind=\"pie\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}