Hotel Performance Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1bfc1b32",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">AtliQ Hotels Data Analysis Project<h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "89483d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "723ba31e",
   "metadata": {},
   "source": [
    "***\n",
    "### ==> 1. Data Import and Data Exploration\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df93c23",
   "metadata": {},
   "source": [
    "### Datasets\n",
    "We have 5 csv file \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": "011e2bf2",
   "metadata": {},
   "source": [
    "**Read bookings data in a dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf8b91a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings = pd.read_csv('datasets/fact_bookings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31bac374",
   "metadata": {},
   "source": [
    "**Explore bookings data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a8c2a0c",
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8fa34e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b94cd62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RT1', 'RT2', 'RT3', 'RT4'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.room_category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fec634c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['direct online', 'others', 'logtrip', 'tripster', 'makeyourtrip',\n",
       "       'journey', 'direct offline'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc978b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "others            55066\n",
       "makeyourtrip      26898\n",
       "logtrip           14756\n",
       "direct online     13379\n",
       "tripster           9630\n",
       "journey            8106\n",
       "direct offline     6755\n",
       "Name: booking_platform, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f74b893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "6255174c",
   "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>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0db3ce",
   "metadata": {},
   "source": [
    "**Read rest of the files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6b6a853",
   "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": 12,
   "id": "a1b924f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2413ba6a",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85d717c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Luxury      16\n",
       "Business     9\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d5d9094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai       8\n",
       "Hyderabad    6\n",
       "Bangalore    6\n",
       "Delhi        5\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2bfb6838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "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": "code",
   "execution_count": 21,
   "id": "48fc524d",
   "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",
       "  </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"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0a0b0b27",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.property_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0d08b996",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby(\"property_id\")[\"successful_bookings\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1ba01640",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "      <td>25-Jul-22</td>\n",
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       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>18563</td>\n",
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       "      <td>RT4</td>\n",
       "      <td>20</td>\n",
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      ],
      "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.successful_bookings>df_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "732583ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.capacity.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3aec1a3",
   "metadata": {},
   "source": [
    "***\n",
    "### ==> 2. Data Cleaning\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc5fae61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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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       "      <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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e864a4f1",
   "metadata": {},
   "source": [
    "**(1) Clean invalid guests**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a8d4c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>May122218559RT44</td>\n",
       "      <td>18559</td>\n",
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       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-10.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>20900</td>\n",
       "      <td>20900</td>\n",
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       "    <tr>\n",
       "      <th>18020</th>\n",
       "      <td>May122218561RT22</td>\n",
       "      <td>18561</td>\n",
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       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-12.0</td>\n",
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       "      <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",
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       "      <td>5760</td>\n",
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       "    <tr>\n",
       "      <th>56715</th>\n",
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       "      <td>18562</td>\n",
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       "      <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",
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       "      <td>13500</td>\n",
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       "    <tr>\n",
       "      <th>134586</th>\n",
       "      <td>Jul312217564RT47</td>\n",
       "      <td>17564</td>\n",
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       "      <td>31-07-22</td>\n",
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       "      <td>RT4</td>\n",
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      ],
      "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.no_guests<=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f085f599",
   "metadata": {},
   "source": [
    "As you can see above, number of guests having less than zero value represents data error. We can ignore these records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "de2e951d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>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>...</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>134584</th>\n",
       "      <td>Jul312217564RT45</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>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134587</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134588</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134589</th>\n",
       "      <td>Jul312217564RT410</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>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134578 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "1           2/5/2022        2.0           RT1           others            NaN   \n",
       "2           4/5/2022        2.0           RT1          logtrip            5.0   \n",
       "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "5           3/5/2022        2.0           RT1           others            4.0   \n",
       "6           6/5/2022        2.0           RT1           others            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134584      1/8/2022        2.0           RT4           others            2.0   \n",
       "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134587      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134588      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134589      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "1           Cancelled               9100              3640  \n",
       "2         Checked Out            9100000              9100  \n",
       "4         Checked Out              10920             10920  \n",
       "5         Checked Out               9100              9100  \n",
       "6           Cancelled               9100              3640  \n",
       "...               ...                ...               ...  \n",
       "134584    Checked Out              32300             32300  \n",
       "134585    Checked Out              32300             32300  \n",
       "134587      Cancelled              32300             12920  \n",
       "134588    Checked Out              32300             32300  \n",
       "134589      Cancelled              32300             12920  \n",
       "\n",
       "[134578 rows x 12 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings = df_bookings[df_bookings.no_guests>0]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8f25684b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134578, 12)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638b98cc",
   "metadata": {},
   "source": [
    "**(2) Outlier removal in revenue generated**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ad2a5f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6500, 28560000)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.min(),df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "33f415ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15378.036937686695, 13500.0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.mean(), df_bookings.revenue_generated.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "10607c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg, std = df_bookings.revenue_generated.mean(), df_bookings.revenue_generated.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "33a429f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15378.036937686695, 93040.1549314641)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6891e0dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294498.50173207896"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_limit = avg + 3*std\n",
    "higher_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d9af20ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-263742.4278567056"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit = avg - 3*std\n",
    "lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "47494fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
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       "      <th>booking_platform</th>\n",
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       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "43edda00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>revenue_realized</th>\n",
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       "  </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",
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       "      <td>logtrip</td>\n",
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       "      <td>9100000</td>\n",
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       "      <td>May012216559RT32</td>\n",
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       "      <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",
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       "      <td>May012216562RT22</td>\n",
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       "      <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",
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       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
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       "      <td>12600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
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       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
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       "      <td>Cancelled</td>\n",
       "      <td>2000000</td>\n",
       "      <td>4420</td>\n",
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       "    <tr>\n",
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       "      <td>Jul282216562RT26</td>\n",
       "      <td>16562</td>\n",
       "      <td>21-07-22</td>\n",
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       "      <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",
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       "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated>higher_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7df920b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </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",
       "    <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>134584</th>\n",
       "      <td>Jul312217564RT45</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>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134587</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134588</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134589</th>\n",
       "      <td>Jul312217564RT410</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>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134573 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "7        May012216558RT18        16558     26-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "1           2/5/2022        2.0           RT1           others            NaN   \n",
       "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "5           3/5/2022        2.0           RT1           others            4.0   \n",
       "6           6/5/2022        2.0           RT1           others            NaN   \n",
       "7           3/5/2022        2.0           RT1          logtrip            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134584      1/8/2022        2.0           RT4           others            2.0   \n",
       "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134587      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134588      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134589      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "1           Cancelled               9100              3640  \n",
       "4         Checked Out              10920             10920  \n",
       "5         Checked Out               9100              9100  \n",
       "6           Cancelled               9100              3640  \n",
       "7             No Show               9100              9100  \n",
       "...               ...                ...               ...  \n",
       "134584    Checked Out              32300             32300  \n",
       "134585    Checked Out              32300             32300  \n",
       "134587      Cancelled              32300             12920  \n",
       "134588    Checked Out              32300             32300  \n",
       "134589      Cancelled              32300             12920  \n",
       "\n",
       "[134573 rows x 12 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings = df_bookings[df_bookings.revenue_generated<=higher_limit]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "744ff366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134573, 12)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "42f72c0d",
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0ea510f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33479.358661845814"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "id": "0f0a9c5e",
   "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",
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       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>May012216559RT41</td>\n",
       "      <td>16559</td>\n",
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       "      <td>1/5/2022</td>\n",
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       "      <th>139</th>\n",
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       "      <td>45220</td>\n",
       "      <td>45220</td>\n",
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       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>May012216559RT47</td>\n",
       "      <td>16559</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>35530</td>\n",
       "      <td>35530</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>May012216559RT413</td>\n",
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       "      <td>41990</td>\n",
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       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>May012216560RT45</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>39900</td>\n",
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       "    <tr>\n",
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       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
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       "      <th>134474</th>\n",
       "      <td>Jul312219562RT412</td>\n",
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       "      <td>6/8/2022</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RT4</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>37050</td>\n",
       "      <td>37050</td>\n",
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       "    <tr>\n",
       "      <th>134581</th>\n",
       "      <td>Jul312217564RT42</td>\n",
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       "      <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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_realized>higher_limit]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989c1d34",
   "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": 51,
   "id": "a1a7c22f",
   "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>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  room_id    room_class\n",
       "0     RT1      Standard\n",
       "1     RT2         Elite\n",
       "2     RT3       Premium\n",
       "3     RT4  Presidential"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b00f90c7",
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.room_category==\"RT4\"].revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a2189ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50583"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean + 3*standard deviation\n",
    "23439+3*9048"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b748e2",
   "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": 54,
   "id": "79dedebd",
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "20ca1a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134573, 12)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae0c675",
   "metadata": {},
   "source": [
    "Total values in our dataframe is 134573. Out of that 77897 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": "code",
   "execution_count": 57,
   "id": "fff50f55",
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "29c11ac3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
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       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22       NaN\n",
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   ]
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  {
   "cell_type": "code",
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   "id": "a6082fb6",
   "metadata": {},
   "outputs": [
    {
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      ]
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     "execution_count": 59,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "26db0ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg_bookings.capacity.fillna(df_agg_bookings.capacity.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5e1c1629",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22      25.0\n",
       "15        17563      1-May-22           RT1                   21      25.0"
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  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4e58a020",
   "metadata": {},
   "outputs": [
    {
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       "      <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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.successful_bookings>df_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8b6daa7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9200, 5)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0c0be764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9194, 5)"
      ]
     },
     "execution_count": 64,
     "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": "80836b8a",
   "metadata": {},
   "source": [
    "***\n",
    "### ==> 3. Data Transformation\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1a0eebc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <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",
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       "      <th>4</th>\n",
       "      <td>16558</td>\n",
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       "      <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",
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      ],
      "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ad8fe1",
   "metadata": {},
   "source": [
    "**Create occupancy percentage column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6e203286",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg_bookings['occ_pct'] = df_agg_bookings.apply(lambda row: row['successful_bookings']/row['capacity'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5e29013e",
   "metadata": {},
   "outputs": [
    {
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       "      <td>30.0</td>\n",
       "      <td>0.833333</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>0.933333</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>0.766667</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",
       "      <td>0.947368</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",
       "      <td>0.700000</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   \n",
       "\n",
       "    occ_pct  \n",
       "0  0.833333  \n",
       "1  0.933333  \n",
       "2  0.766667  \n",
       "4  0.947368  \n",
       "5  0.700000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0b46a8",
   "metadata": {},
   "source": [
    "Convert it to a percentage value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ef99de36",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>property_id</th>\n",
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       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    </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": 68,
     "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": 69,
   "id": "281b79d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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",
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       "  <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",
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       "      <th>4</th>\n",
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       "      <td>16558</td>\n",
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       "      <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",
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       "      <td>3640</td>\n",
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       "    <tr>\n",
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       "      <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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9c5bfe7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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": "9690a677",
   "metadata": {},
   "source": [
    "***\n",
    "### ==> 4. Insights Generation\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76b0eb5",
   "metadata": {},
   "source": [
    "**Average occupancy rate in each of the room categories**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8e62075a",
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby(\"room_category\")[\"occ_pct\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b02ed3e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Presidential</td>\n",
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      ],
      "text/plain": [
       "  room_id    room_class\n",
       "0     RT1      Standard\n",
       "1     RT2         Elite\n",
       "2     RT3       Premium\n",
       "3     RT4  Presidential"
      ]
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     "execution_count": 72,
     "metadata": {},
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   ],
   "source": [
    "df_rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d74275ca",
   "metadata": {},
   "outputs": [
    {
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      ],
      "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": 73,
     "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": 74,
   "id": "9958ee3d",
   "metadata": {},
   "outputs": [
    {
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       "      <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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"room_id\",axis=1, inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b4688bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_class\n",
       "Elite           58.01\n",
       "Premium         58.03\n",
       "Presidential    59.28\n",
       "Standard        57.89\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"room_class\")[\"occ_pct\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2701ff5",
   "metadata": {},
   "source": [
    "**Print average occupancy rate per city**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7a8b90f6",
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3257f2a4",
   "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",
       "    </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",
       "      <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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_hotels, on=\"property_id\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "23c17874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    56.33\n",
       "Delhi        61.51\n",
       "Hyderabad    58.12\n",
       "Mumbai       57.91\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"city\")[\"occ_pct\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "daedad68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city'>"
      ]
     },
     "execution_count": 85,
     "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.groupby(\"city\")[\"occ_pct\"].mean().round(2).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90861db",
   "metadata": {},
   "source": [
    "**When was the occupancy better? Weekday or Weekend?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "42f2b5f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "    </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",
       "      <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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16559</td>\n",
       "      <td>4-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16559</td>\n",
       "      <td>5-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>16</td>\n",
       "      <td>30.0</td>\n",
       "      <td>53.33</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",
       "3        16559      4-May-22           RT1                   21      30.0   \n",
       "4        16559      5-May-22           RT1                   16      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  \n",
       "3    70.00   Standard  Atliq Exotica   Luxury  Mumbai  \n",
       "4    53.33   Standard  Atliq Exotica   Luxury  Mumbai  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b7bfccb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>0</th>\n",
       "      <td>01-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
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      ],
      "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c736b6fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
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       "      <th>mmm yy</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
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       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <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",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <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": 88,
     "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": 89,
   "id": "e45d5e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_type\n",
       "weekeday    50.88\n",
       "weekend     72.34\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day_type\")[\"occ_pct\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3bc85c9",
   "metadata": {},
   "source": [
    "**In the month of June, what is the occupancy for different cities?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "81c6e411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mmm yy\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d7b3483d",
   "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",
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       "      <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",
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       "  </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": 98,
     "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": 99,
   "id": "4353fe44",
   "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": 99,
     "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": 100,
   "id": "a0710092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='city'>"
      ]
     },
     "execution_count": 100,
     "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": "47ad5bc4",
   "metadata": {},
   "source": [
    "**We got new data for the month of august. Append that to existing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7f5bd60c",
   "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",
       "      <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": 101,
     "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": 103,
   "id": "1cf108e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 13)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0c725fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497, 14)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ce7afa95",
   "metadata": {},
   "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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "39e74c74",
   "metadata": {},
   "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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "07b6dbf9",
   "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",
       "      <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": 108,
     "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": 109,
   "id": "5290e609",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6504, 15)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50efd78",
   "metadata": {},
   "source": [
    "**Print revenue realized per city**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1522e8c8",
   "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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "047f910f",
   "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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8ec2f176",
   "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",
       "      <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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all = pd.merge(df_bookings, df_hotels, on=\"property_id\")\n",
    "df_bookings_all.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "97e984b2",
   "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": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"city\")[\"revenue_realized\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "24495bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4888/0axZM0ydOrXu/rMjwSvVokULqNVqbN++HXFxcQBqpzqPHDmCnj17AgAOHTqEkpISvPbaa4iNjQWAuqnUi0lOTsamTfUvIL1p0yYkJiZCpVJd9HlpaWk4ePAgEhISGvR1/BPfCeQcOTuBz+8Fari6vauwgFOdjU2SgBk/HcHxohq8dncHaLwcO8lmMpmQn58PACgrK8MHH3yA6upqDBo0CJWVlcjOzsayZcvQpUsXrF69ut7+sivh7++PESNG4Omnn0ZISAiaNGmCF198EUqlEgpF7RXr4+LioNFo8P7772Ps2LE4cOAApk+ffsntPvnkk+jSpQumT5+Oe++9F5s3b8YHH3yAWbMufb3IKVOm4Prrr8eECRMwevRo+Pr64uDBg/j555/xwQcfXPHXxalOcrxDa4CFA1l6LoYjPnGW7z6DB+ZsQUm1yaHbXbt2LaKiohAVFYXrrrsO27dvx1dffYWbbroJt99+OyZNmoQJEyagY8eO+PPPP/H88883+DXefvttdOvWDQMHDsStt96KHj16IDk5ue5AkvDwcCxYsABfffUV2rRpg9deew0zZsy45DbT0tLw5ZdfYtmyZWjXrh1eeOEFvPTSS5c8EhQAOnTogPXr1+PIkSO48cYbkZqaihdeeAHR0dEN+pp4VCc51rY5wA+TucC0C9oYOwbDj/YSHUPWYkO8MW9EF7SK8L/8g11UTU0NYmJi8NZbb+GRRx4RHeeqcMRHjvPby8Cap1h6LsrMqU7hTpcaMPTjzcjIrRAd5Yrt3r0bn3/+ObKysrBr1y4MGzYMAHDHHXcITnb1WHzkGL++9Nc5euSqTNLFDxqgxlOmt2DYp1tx4Iz7lN/ZE8lvvfVW1NTU4I8//kBYWJjoWFeNU5107X6dDvxx6Tl9Eu+7mCfxRFYn0THoL4HeaiwZfR3axQSKjiI7HPHRtfntZZaemzDx4BaXUmGoHfk1xonuVB+Lj67eb69wetONmOyc6nQ1FQYLhn+6FXtPl4uOIissPro66/731xJk5C7MEkd8rqjSaMVD87bhYC4vz9VYWHzUcOteBda/LjoFNZCBxeeyKgwWPDRvK44XVYuOIgssPmqYP94C1r92+ceRy+FUp2srrjZj+KdbcabcIDqKx2Px0ZXb+0XtaQvkljjic325FUYMm7MFhVVG0VE8GouPrsyJDcDKhl3skVyL0ca3uzs4WaLHQ3O3oVx/bVc2oIvjO4Eur/AQ8MVwwMY3ojszcqrTbRzKr8IjC3fAbOUqSM7A4qNLqy4EltwDGN1nlQm6ML2dU53uZOepMry4MkN0DI/E4qOLM9cAS4cCFRe+ijK5FwOnOt3O59uysXQr33+OxncCXZjdBnz9CJC7W3QSchADpzrd0rSVGdh5qlR0DI/C4qML+2EKcOQH0SnIgfQsPrdkttkxbvEuFFbySE9HYfHR+XYtArbPEZ2CHExvZfG5q8IqE8Yu3smDXRyExUf15R8A1kwWnYKcQG/n292d7coux4srD4iO4RH4TqBzTFXAlw8BVq4c4YlqbBzxubvPt53Gkq2nRMdweyw+Omflv4HSLNEpyElqrHy7e4L/rjzIg12uEd8JVGvrJ0DGt6JTkJNIChWsEt/unsBss2Ps4l0o4MEuV43vBALO7AJ+mio6BTmTSiM6ATlQEQ92uSYsPrkzlANfjeByZJ5OpRadgBxsd3Y5pq86KDqGW2Lxyd2KcUA5V4bwdBJHfB5p8dZT2JxVIjqG22HxydnOBcDhNaJTUCOQlCw+TyRJwDPL98FgtomO4lZYfHJVcQb46XnRKaiR2JWc6vRUp0r0mPHTYdEx3AqLT65WTQJMlaJTUCPhVKdnm7/pBHZll4mO4TZ4nRI52rsMOPqj6BSNZsMpK97804yduTbkVUv49l5vDE46NwJS/PfCfwC8casWT/fQXvC+V/8wYfkhCw4V2+HtpUD3WBVev1WL1mHnThL/z49GLNhjhq9Ggddu0WFYh3Ov+VWGBYv2WfD9/T4O+iovzcYRn0ezS8CUr/dh1eM3QOvFhQouhyM+uakuBNY+IzpFo6oxS0iJUOLDAboL3p/3pF+9j3m366AAcHebi5fF+lNWjO+iwZZHfPHzgz6w2IE+i/WoMUsAgO8PW7B0vwU/PeiLN27VYfT3BhTraw89rzBKmPqb6aJ5nMGuYPF5uqOF1Xj/12OiY7gFjvjkZvV/AIO8pkT6t1Kjf6uzv/jPX44t0q/+33/fHbbi5uYqtAi++N+Fa4f71vt8wR06NJlRjZ15NvRs5oXMYjtuilehc3Ttx8QfjThRJiHMB5j8sxHjOqsRF9h4f3dyH588zF6fhf7tI9E2OlB0FJfGEZ+cZHwLZH4vOoVLK6i2Y/VRKx5Jbdg+sQpT7X9DvBUAgJQIFXbk2lBmkLAz1waDRUJCiBIbs63YlW/D49c17j43TnXKg9UuYfLX+2C18cT2S2HxyYW+FFjztOgULm/hXgv8NcBdyVc+GWKXJExca0SPWBXaNandv9I3wQvDO6jRZU41Rn5nwMLB3vDVAONWGzE73Rsf7bCg9QfV6DGvBhmFzj8U3capTtnIyK3ExxuOi47h0lh8cvHTc0BNkegULm/ebguGtVdD56W44ueMX23EgUIblg3xrnf7tJt0OPa4P/aP88OdyWq8+ocZtzb3gloFvLzBhI2jfDA6VY2HVjj/ahgsPnl599ejOFZYJTqGy2LxyUHOTmDPUtEpXN4fp6w4XGLH6LQrn4acsMaAVUetWDfCF00DLv52OlRsw+L9FkzvrcXvJ63o2UyFcF8lhrZVY1eeHVUmyRFfwkVZWXyyYrbaMfnrfbDbnftz5a5YfJ5OkoC1UwDwDXA5c3db0ClKiZTIyx8OLkkSJqwx4NtDVvz2kA+aX+JAGEmSMGaVEW/30cJPo4DNDlj+2gVz9r82J//vYfHJz67scizZxuUIL4TF5+n2fQHkbBedQqhqs4Q9+Tbsya/dl3aizI49+TZkV5w7AKDSJOGrg5aLjvZuWVSDD7adW8h7/BojFu+zYOld3vDXKpBfbUd+tR0Gy/kN9ukuC8J9FBjUurZ8esR54bcTVmzJseKdzSa0CVciSHflU6tXw8oDuGXpvV+PcjmzC+C7wYPpLXrUbJ2FcNFBBNuRa8PNC/V1n//nJxMAE0akqLFgcO1+uWUHLJAk4P52Fx4ZZZXa687DA4CPdlgAADf9bbsAMP8OHUZ2PFeeBdV2vPKHCX8+cu70h64xKjzZTYv0pQY08VVg4eD6+wadwaLgW12OiqpMWPDnSYy7qaXoKC5FIUkS58A81Ed7PsL8jPkY5dMSIzN/h7dZf/knkUfKiH0A6UcHio5BAgT5qLFh8s0I0HG6+yxOdXqoYkMxFmQsgMFqwKzKAxiUkITvk3tDgnOn1Mg1WTi5I1vlegvm8PSGelh8HuqjPR9Bbz03wiswFOP/jMdwf0pP7IzrJDAZicDik7d5G0+guNokOobLYPF5oBMVJ7D86PIL3pdReQIjVUWYlNYPp0PjGzcYCWMGp7nkrMZsw4fruI7nWSw+D/T+7vdhlayXfMwvZQdxR5AKb6amo9Kb6/p5OjO4Yr/cLdmajdxy5y+W4A44/+Fhjlccxy+nfrmix1rsFiwq34/v4+IwVh2FoQd/g5f90oVJ7sksuf5bvWLzl9Af2QxLaQ4UXhpoY5IR3Gsk1KFNz3usJEko/GoajCd2IvzOqfBJ7HbJbVuKT6Ns/XwYsw8Akg3q0DiE3/ksvAKaAABKf52DmgO/QqHWIajXCPi1vbnuuTWHNqLmwK9oMuRFx37BjcxstePdX47i9SEdREcRzvXfDdQg8/bPg9TAk9XLzBV41VyBZW264EmDAr2y/nRSOhLFHaY6jacPwD8tHZrIVoBkQ/n6RSj48nlEP/IRlJr6l3Cq2vEdrvQ4LUtZHvKXTIZfh9sQdMMwKDQ+sBRnQ/HXxXn1x7aiJnM9mgydDmtZLkp+eBfezdOg8gmE3VSD8g2LEHHfy47+coX4ZlcOxvRqgRbhfqKjCMWpTg+SX5OP1SdWX/XzT9ScwQR7Dh5NvQ2HI5MdmIxEM9ldf6ozYuhL8Gt/KzThzaBp0gKh6ZNgqyyCuaD+vilzwXFUbvsWYf0nXtF2yzcsgnfLzgi++WFoIlpCHRwFn1bXQeUbBACwlJyGLrY9tFGt4NumFxQaH1grCgAAZevmwz91QN3I0N1Z7RLe/vmI6BjCsfg8yKKDi2B1wFTllvLDGOpjwLS0dBT7ecYbXu5Mbji5YzfVAACUunOjE7vFiOLv30RIn3FQ+QVfdhuSZIfh+A54BUej4Ivncfr9Ychb9B/oj2yue4wmvDnM+cdgM1bDlH8MktUEr+BoGHMyYC7Ign+nQY7/4gRavT8PB3MrRccQisXnISpMFfjmyDcO255dsuObsv1IjwrBJykDYPJqvKuFk+MZJdcf8f2dJNlR9uscaGPaQBMeX3d72a+fQhuTDJ9W11/Rduw1FZDMBlRu/RreLTohYuh0+CR2Q9G3/4Mxez8AwLtFJ/i2vQn5CyehZPU7CEufBKVai9IfZyGk73hU7V6DM3PGIH/x0zAXnXLGl9uoJAmY8dNh0TGEcr8/A+mClh5aWu+8PUfRW/V4v/IAvk5si8ftAUg/9DsUXPDa7Zjs7vVWL/3pI5iLTiFy2Bt1t+mPboUxey+iRr53xduRpNpl5rwTrkdAl8EAAE1EC5jOZKJqzw/QxbUHAATdMAxBNwyre175xqXQxXeEQqlCxeYvEP3whzAc24aS1W8jauS7DvgKxfrtUCGOFlShVYS/6ChCcMTnAQxWAz7P/Nypr5FnKMKzpiwMT+mFPbGpTn0tcjyTG434Sn/+CIas7Yi4/3/wCgiru914ai+sZfk4PfNenHrjdpx643YAQNGKV5G/9JkLbkvlEwAoVVCHxda7XR0aC1vlha9PaSk5jZqD6xB043AYs/dD17QdVD6B8Em6EeaCLNhNnrH035Kt8r1yg3v9GUgXtPzocpSZyhrltfZVHseDXkCftP6YdDIDTUvl++ZxJwY3OLhFkiSU/TIb+iObEXH/q1AHRda7P/D6e+CX0qfebXnzJiC492h4J3S94DYVKjW0ka1gLT1T73ZL6RmoLnDAiiRJKPnxQwT3Hg2lxhuQ7JDO7jc/+1/Jft7z3NHyXTmY0i8J3hrX/9lwNI743JzVbsXCjIWN/ro/lWXgjmAN3k5NR7UuoNFfnxrGHaY6S3/+CNUZvyNs0NNQanxgqy6DrboMdkvtUlsqv2BowuPrfQCAV0B4vZI8M2cs9EfOnZITcN1dqMn8A1V71sJSlovKnd/DcGwb/NMGnJeheu+PUHkHwCfhOgCANiYZxlP7YDpzCJXbv4M6NK7ewTburNJoxfd7c0XHEML13w10SWtPrkVeTZ6Q1zbbzZhfvh/fxcfjMa9IDMn4FSqJ1/5yRXo3GPFV714DACj4/Nl6t4cOmAi/9rde8XaspTn1piN9ErsjtO9jqNjyFcp+/QReITEIv/P/oGvatt7zbDVlqNj8JSKHv1l3mza6NQK63onCr/8LpU8gwtInXc2X5rKWbD2FoV1iL/9AD8PLErm5UWtHYUfBDtExAAAt/ZriSb2EG7M2X/7B1Kgm+L+LVUVyvzIjXciqf9+AdjHyWraQU51u7HTVaews2Ck6Rp2s6hw8Zj+Dsal9cCyiteg49DdGNxjxkRiLt7j/KRoNxeJzY98d+67By5M1hk3lhzDE14yX0tJR4sdRhitwh4NbSIyVe3NRZbSIjtGoWHxuyi7ZsTJrpegYF2WTbPiqbD8GRofh0w79YVZpRUeSNb2Nb3W6ML3Zhm93n7n8Az0I3w1uakveFmEHtTREtaUG71Zl4PakDvih9U2i48iW3sYRH13cUpmd08fic1Mrjq0QHaFBzugLMNl8HMNTbsa+pimi48hODYuPLuFQfhV2nCwVHaPRsPjcUKW5Er9l/yY6xlXZW5mF4epyTE4bgNzgONFxZINTnXQ5clrJhe8GN/TD8R9gsplEx7hqEiT8UHYAt4dqMTM1HTVaea4X2Jg44qPLWb0/D2U1ZtExGgWLzw252zTnxZhsJswt34/05i3wVdvbYFPwl7MzSAolTHa+1enSzFY7Vu93/eMGHIHvBjdzrOwYDpQcEB3DoUpMZXhJfxj3tOuGP5tfJzqO51G6/tXXyTX8mlkgOkKjYPG5mZ9O/SQ6gtMcrc7GGORhXGofHG/SSnQcz+GlEZ2A3MSfWSUwmD1/2UEWn5tZn7NedASn21h+CHf72fByWjrKfENFx3F7kpLFR1fGZLVj47Fi0TGcjsXnRooNxcgsyRQdo1FYJSu+KNuP9JgIzO/QDxYVf3lfLYlTndQAcpjuZPG5kQ05G1xyiTJnqrJU4+2qg7g9qSN+at1TdBy3JPGPBmqA3w4VwtOvXcDicyPrT3v+NOfF5Ojz8aT5JEZ07I0DMe1Fx3Erdo74qAEKq0zYf6ZCdAynYvG5CbPNjC15W0THEG5XxTE8oKnEM2kDkB/UVHQct8Dio4b6NbNQdASnYvG5ie3526G36i//QBmQIGF12QEMCvPGex3Todd6xhWxncXOg1uogX495Nn7+Vh8bkIOR3M2lNFmwpyK/UhvnoBv2twKu4I/zhfCER81VEZuJQoqjaJjOA1/U7iJDTkbREdwWcWmUkwzHMHQ9j2wtXkX0XFcjk3B4qOGkSTPnu5k8bmBY2XHcKZaXtfLuhqHq05hNAowIbUvToS3FB3HZbD46Gp48mkNLD43sCl3k+gIbmV9eSbu8pfwatpAVPgEi44jnI1TnXQVNmUVw2jxzFVcWHxuYE/hHtER3I5VsmJp2T4MiI3GwvbyPgHeyhEfXQWjxY4/szxzFRcWnxvYV7RPdAS3VWmuwozqgxiclIpfWt0oOo4QVoWX6AjkpnadKhcdwSlYfC4uvyYfhQbP3cncWLL1eZhkPYVRHW/Bwei2ouM0Kis44qOrcyDXM09kZ/G5OI72HGtHxVHcp63G1LQBKAiMFh2nUXCqk67WgTOVoiM4BYvPxe0v3i86gseRIGFl2QEMCvfDhx3Todf4io7kVBZwqpOuTnG1CfkVnnc+H4vPxXHE5zwGmxGzK/ZjUMtErGhzKyQoREdyChYfXYsDHrhuJ4vPhVntVhwsOSg6hscrNJbgecMR3NvhRmyP97wT4C2c6qRr4In7+Vh8LuxI2REYbZ43zeCqMqtO4mFFAR5P7YdTYS1Ex3EYs8QRH109jvioUXGaU4x15QcxOBB4PTUdFd5BouNcMzOnOukaeOIBLiw+F8YDW8Sx2q1YXL4f6XFNsbh9X1jcePUTjvjoWuRXGlFcbRIdw6FYfC4sszRTdATZqzBX4vXqTNzVphN+a3WD6DhXxQyV6Ajk5jztwrQsPhclSRJyqnJEx6C/nKzJxRPWbIzueCsORbURHadBOOKja5XB4qPGUKgvhMFqEB2D/mFrxRHc663H82npKAqIFB3nihhZfHSNPG0/H4vPRWVXZYuOQBdhl+xYUbYf6RGB+ChlAIxqb9GRLokjPrpWnOqkRnG66rToCHQZBqsBsyoPYGCrZHyf3NtlT4A32rmPj67NmXIDKvQW0TEchsXnorIrOeJzFwWGYvyf8RjuT+mJnXGdRMc5j4kjPnKAnHK96AgOw+JzUZzqdD8ZlScwUlWESWn9cDo0XnScOkaJb3O6doVVnnNKA98RLopTne7rl7KDuCNIhTdT01HpHSg6Dgw2jvjo2hVVsvjIyTjV6d4sdgsWle/HwLg4LG3XB1aluPIxStzHR9eusMpzlk9k8bmgYkMx9FbPmU+XszJzBV6tOYS72nTB+pbdhWQw8OAWcgBOdZJTcbTneU7UnMEEew4eTb0NhyOTG/W1OdVJjlDIqU5ypkJ9oegI5CRbyg9jqI8B09LSUezXpFFe02Dn25yuHac6yakqzZ61SgLVZ5fs+KZsP9KjQvBJygCYvHROfb0aG6c66dpxqpOcisUnD3qrHu9XHsCgxLZYlXSz006A17P4yAFYfORUlSYWn5zkGYrwrCkLw1N6YU9sqsO3b7DxbU7Xzmy1e8zqLXxHuCCO+ORpX+VxPOhVgifT+iMnJM5h2+WIjxzFU/bzsfhcEItP3n4qy8AdwRq8nZqOal3ANW+vhiM+chBPme7kO8IFVZmrREcgwcx2M+aX70d6fDy+aNcHNsXVj9o44iNH4YiPnIYjPjqr1FSOl2sO4e5212Fjy24Nfr6k0jghFcmVp5zLx+JzQTy4hf4pqzoH4+xnMDa1D45FtL7yJ6rUzgtFslNltIqO4BAsPhdUZeFUJ13YpvJDGOJrxktp6SjxC7/s4yUlR3zkOFa7JDqCQ7D4XIwkSag2V4uOQS7MJtnwVdl+DIwOw6cd+sOs0l70sZzqJEey2e2iIzgEi8/FWOwW2CSb6BjkBqotNXi3KgO3J3XA2tY3XfAxkpJTneQ4HPGRUygUzlm9gzzXGX0BnjYfx/CUm7GvaUq9++wc8ZEDWW0sPnIC1TUctk7ytrcyC8PV5ZicNgB5wbEAADtHfORAHPGRUygV/F9CV0+ChB/KDmBQqA7vdkxHhTZIdCTyIJ6yj48X6nJBSoUSdskzfsBIDJPNhEXlB5F2LA1rDs0RHYc8hDbsJgApl3uYy2PxuSAllLCDxUdXL8YWgDd/joDX7s2io5AH8U5pJzqCQ7D4XJBSoQRcdCq95nANitcUw3DKAGu5FXH/jkNAp3PrSVorrMj/Mh/VGdWw6W3wTfRF1PAoaCMvfsg9AFRsq0DB8gJYii3QRGoQeU8k/FP86+4v/qEYRWuKAADhA8IR1j+s7j59lh65i3LR8oWWUKh4cNANxlg88YUBUm6m6CjkYRRKzzgGgTuUXJAr7+ezm+zQxekQ/WD0efdJkoRT752CuciMuMfjkPDfBKjD1Dj55knYTRcfweqP6nF69mkE9wxGy5daIiA1ANnvZcOYU7suoPG0EQXfFiB2XCxix8WiYHkBjKdr75NsEnIX5iJ6RDRLD8DoknZ4YnY+pNx80VHIAym8WHzkJK5cfP4d/BFxd0S9Ud5Z5gIzDFkGRI+Ihk8LH2ijtIh+KBp2sx3lW8ovus3in4vh394f4QPCoYvWIeLuCOia6VDySwkAwJRngq6pDn5t/ODXxg+6WB1MebVrBhb/UAzf1r7waeHjlK/XXSgk4I0jaegzZy8kg0F0HPJUHPGRs7hy8V2KZKmdn1Woz428FEoFFGoF9Ef0F32e4ZgBvm18693m194PhqzaX+DaplqYC8wwl5hhLjbDlG+CtqkWpkITyv4oQ5O7mjjhq3EfwXZvLNjQBvHfbAMkF50jJ4+gULnn76Z/4j4+F+SuxaeN0kIdqkbBVwWIGRkDhVaBkh9LYC21wlpx8cVtrRVWeAXW/1H0CvCCpaL2as9nR4En3zwJAIgcEgldtA4n3jiByKGRqD5QjcIVhVCoFIgaFgXf1r7/fAmP1cEcgee+9QKO7xMdhWRA6esZ7y0WnwvSXmLtRVem8FIg7t9xODP3DDLHZwJK1E5PdvC75oN1QnqHIKR3SN3nZRvLoNQp4ZPggyPPHEHLF1vCUmbB6Y9OI/HNRCjV7vnHQ0PcU9kaQxefhlTBq3lQ41AFBYuO4BAu8dvh5MmTUCgU2LNnj9Nfa8GCBQgKCnLKtuPj4zFz5sxr3k6gNvDawwjiHe+NhOkJSJ6VjKSZSYh/Kh62ahs04RdfOssr0Ou8EaG10gp14IVXHbFWWVH4XSGih0dDf1wPbaQW2kgt/JL9INkkmPPNDv2aXNELp1Jxz+xDLD1qVKpgGRbfyJEjMXjw4PNu//3336FQKFBeXu6gWPIWoDn/wBF3o/JRwSvAC6Z8EwwnDPBP87/oY70TvFFzsKbebdUZ1fBu6X3Bx+ctzUNYnzCoQ9SAvfbIzrMkmwTJQ5ZVuhAfuxpztqeg3dLtgI2LmVPj8pTi85ipTrPZDI3GMxbkDXLhZaZsRhvMBedGVOZiMwynDFD5qaAJ1aBiWwVU/rX/NuYYkbckDwFpAfBvd674cj7JgVewFyLviQQAhN0WhuOvHUfxD8XwT/FH+dZyGE8YETMy5rzXrz5QDXOBGU0fbQoA8G7uDVOeCVX7qmAptUChVEAb5Z5TxZfT0hqCV9YEQpmxU3QUkimVk2bLGptDpzpramoQEBCAr7/+ut7tK1asgK+vL6qqai+wum3bNqSmpkKn06Fz587YvXv3eds6cOAA+vfvDz8/P0RERODBBx9EcXFx3f033XQTJkyYgIkTJyIsLAx9+/YFALz99tto3749fH19ERsbi8ceewzV1edf327FihVo1aoVdDod+vbti9OnT9fdl5WVhTvuuAMRERHw8/NDly5d8Msvv9R7fmFhIQYNGgRvb280b94cS5Ysufpv3D8E6YIcti1HM5wwIOvFLGS9mAUAyP88H1kvZqFweSGA2gNVcubk4OizR5G3JA9B3YPQdFzTetswl5hhLT83tenTygexY2JRur4Ux144hsodlYh7PA66prp6z7Ob7chd/Nc5e8raI0fVIWpEDY/CmU/PoOj7IjQd3RRKjUvM4DtUX30LvLbADmXGUdFRSMa8goNER3AIh474fH19cd9992H+/PkYMmRI3e1nP/f390d1dTUGDhyI2267DYsXL8aJEyfwxBNP1NtOeXk5evfujdGjR+Odd96BwWDAlClTMHToUPz22291j1u4cCHGjRuHTZs21d2mVCrx3nvvoXnz5jh+/Dgee+wxTJ48GbNmzap7jF6vxyuvvIJFixZBo9Hgsccew3333Ve3nerqagwYMACvvPIKtFotFi1ahEGDBuHw4cOIi4sDUDvtm5ubi3Xr1kGtVuPxxx9HYWGhQ76PrryPzy/ZD+0WXHzZotDbQhF6W+glt9Hi2Rbn3RbYNRCBXS/9dSs1SiS+lnje7SG9QhDSK+QCz/AMTxSk4IbF+yGZPX/fJbk22U51rlq1Cn5+fvVus/1tX8Po0aPRvXt35OXlISoqCoWFhVizZk3diGnp0qWw2+2YO3cudDod2rZti5ycHIwbN65uGx988AFSU1Pxv//9r+62efPmITY2FkeOHEFiYu0vv1atWuGNN96ol2XixIl1/46Pj8fLL7+MsWPH1is+i8WCDz74ANdddx2A2gJNTk7Gtm3b0LVrV6SkpCAl5dxCrNOnT8e3336LlStXYsKECThy5Ah++OEHbNu2DV26dAEAzJ07F8nJyQ39dl5QqO7SxUHy4CUp8faBFESu2u6qK9iRjCh0Oih9PGOhiAbPCd18883Ys2dPvY9PP/207v6uXbuibdu2WLhwIQBg8eLFaNasGXr27AkAyMzMRIcOHaDTnZvG6tatW73X2Lt3L9atWwc/P7+6j6SkJAC105BnderU6bx8v/zyC2655RbExMTA398fDz74IEpKSqDXnzuB2svLq66wACApKQlBQUHIzKxd27C6uhpPPfUUkpOTERQUBD8/P2RmZiI7O7vua/Dy8qr3+me34Qjh3uEO2Q65r0ibHxb+nIDIVdtFRyEC4Dn794CrGPH5+voiISGh3m05OTn1Ph89ejQ+/PBDPPPMM5g/fz5GjRrVoCuLV1dXY9CgQXj99dfPuy8qKqpelr87efIkBg4ciHHjxuGVV15BSEgINm7ciEceeQRmsxk+V/jXylNPPYWff/4ZM2bMQEJCAry9vTFkyBCYG2mqKdyHxSdn3YxN8Z+vzJByDoqOQlTHk4rPKUcBDB8+HKdOncJ7772HgwcPYsSIEXX3JScnY9++fTAajXW3bdmypd7z09LSkJGRgfj4eCQkJNT7+GfZ/d3OnTtht9vx1ltv4frrr0diYiJyc3PPe5zVasWOHTvqPj98+DDKy8vrpio3bdqEkSNH4s4770T79u0RGRmJkydP1j0+KSkJVqsVO3fuPG8bjhDmHXb5B5FHGlnWFv/5pAhSzvk/t0QiqTzkwBbAScUXHByMu+66C08//TT69OmDpk3PHdX3wAMPQKFQ4NFHH8XBgwexZs0azJgxo97zx48fj9LSUtx///3Yvn07srKy8OOPP2LUqFH19if+U0JCAiwWC95//30cP34cn332GWbPnn3e49RqNf79739j69at2LlzJ0aOHInrr78eXbt2BVC773D58uXYs2cP9u7diwceeAD2v115uHXr1ujXrx/GjBlTt43Ro0fD2/vC5501FKc65UchAa8eS8OAj/dBqqm5/BOIGplXuOf8XnLacd9npxcffvjherf7+fnh+++/x/79+5GamoqpU6eeN6UZHR2NTZs2wWazoU+fPmjfvj0mTpyIoKAgKJUXj5ySkoK3334br7/+Otq1a4clS5bg1VdfPe9xPj4+mDJlCh544AH06NEDfn5++OKLL+ruf/vttxEcHIzu3btj0KBB6Nu3L9LS0uptY/78+YiOjkavXr1w11134V//+heaNHHMYsl+Gj/4qy9+wjd5lkC7DvM3tkPLr7jINLkubfPmoiM4jEKSnPNO++yzzzBp0iTk5uZ6zInljWnY6mHYV8yFhz1dO0sEXvhODRw9KToK0SXFzJyJgH59RcdwCIeP+PR6PbKysvDaa69hzJgxLL2r1DzQc/66ogu7qyoRL86tYemRW9C08JzfSQ4vvjfeeANJSUmIjIzEs88+6+jNy0bLoJaiI5AT/d/pVNw3+wiksnLRUYguT6WCJj5edAqHcdpUJ12b9afXY8JvE0THIAfTSV54d1dbBP/E9TbJfajj4pDw04+iYziMxyxS7WlaBJ6/rBe5t3hrEF5bGwLlfpYeuRdN83jRERyKxeeiYvxjoFVpYbKZREchB7jFEI+xn1dAKjgiOgpRg2mbe9Yf4p63jL2HUCqUiA+IFx2DHGBCYQeMnZ0DqaBIdBSiq+JJB7YALD6XxulO96aCAjMPpqHn3F2Q/rZSEZG70bbwrN9FnOp0YS2CPOuHTU6a2H3x9rpYaLZtEx2F6JppPOjkdYDF59I44nNPXU0xePprK6TsA6KjEF0zVXAwvEI961JpLD4XlhSSJDoCNdCD5W1w+2dZkKq53iZ5Bu/UVNERHI7F58LiAuIQ5h2GYkOx6Ch0BV7OSkPi1zsg/W1BcyJ359Mp7fIPcjM8uMXFpTXxvB86TxMo6TDvz/ZI/HIbwNIjD+Od5nm/g1h8Li4twvN+6DxJkiUMc5aHw2/9btFRiBxOodPBu21b0TEcjlOdLq5TRCfREegibq9qhQeX5kMqzRcdhcgpvNu3h8IDLzTA4nNxicGJ8Ff7o8pSJToK/c2UM6notHQ3JKtVdBQip/H2wP17AKc6XZ5SoUTHJh1Fx6C/aCUVPtrdEZ0WbQdYeuThfDp55owTi88NcD+fa4izBWHBD/EIXbtDdBQi51MqPfJUBoBTnW6B+/nEu8nQDOOXVUHKPyw6ClGj0LZuDZWfn+gYTsHicwPtQtvxSg0CjS1uj1sWZ0IycL1Nkg8fDzyN4SxOdboBtUqN9mHtRceQHRUUeCszDb3n7Gbpkez4dOkiOoLTsPjcRI+YHqIjyEqY3RcL1iUhdgUXmSb5UWg08L3hBtExnIbF5yZua3ab6Aiy0ckchY++8Id2y37RUYiE8O3WDSo/X9ExnIbF5yaaBTRDQlCC6Bge7/6KZDzzSTmkkzmioxAJ43/braIjOBWLz43cEneL6Age7b8n0nDnxwchVXGxAJIxlQp+vXuLTuFULD43cmszz/4rTBQ/SYO5W9ojedk2wGYTHYdIKJ/UVHiFhIiO4VQsPjeSFJKEGL8Y0TE8SitrKOauiIL/Oi4yTQR4/jQnwOJzO5zudJz06gS8Ot8CxaEs0VGIXIb/rSw+cjGc7nSMp/I6YsTsLNiLS0VHIXIZujZtoI7x/FklrtziZjqGd+RV2a+BRlLhnX0dEL5mu+goRC5HDtOcAEd8bkehUKB3rGcfceUsMbYALPixBUuP6CLkMM0JsPjcUt/4vqIjuJ0bjXGYuVgDr92ZoqMQuSRtqwRoW7USHaNRsPjcUJfILojzjxMdw22MLmmHx2fnQcrlldKJLibonqGiIzQaFp8bUigUGJI4RHQMl6eQgDeOpKHPnL2QDAbRcYhclkKnQ+DgO0THaDQsPjc1OGEw1Eq16BguK9Tug4Xr2yD+m22AJImOQ+TSAvr3hyogQHSMRsPic1PBumCe2nARHcwR+OirIOg27xMdhcgtBN93r+gIjYrF58buSbxHdASXM7QyCc9/WgUczxYdhcgtaJOS4J2SIjpGo2LxubEukV3QPLC56Bgu44VTqRgyOxNSRaXoKERuI/he+RzUchaLz81x1Af42NWYsz0F7ZZu5yLTRA2g9PFBwKDbRcdodCw+N3d7y9uhVWlFxxCmpTUE876PQeAvO0VHIXI7AenpHn3B2Yth8bm5QG0g+jTrIzqGEP1qWuK1BXYoDx4THYXILQXJ7KCWs1h8HuDeJPn98E7MT8Ejs09AKuKapURXQ9euHbzbthUdQwgWnwdICU9B54jOomM0Ci9Jiff3p6L7/J2QzGbRcYjcVuijj4qOIAyLz0OMSxknOoLTRdn8sfDnBESs4iLTRNdCm5gI/z63iY4hDIvPQ3SN6oq0JmmiYzhNd2Ms3lvqDfXOg6KjELm9sMfGQaFQiI4hDIvPg4xJGSM6glOMKm2LSZ8UQsrJFR2FyO1pWyXAv6+8r/DC4vMg3aO7IyXcc1ZgUEjAa0fT0P+TfZBqakTHIfIIYePkPdoDWHweZ2zKWNERHCLQrsP8jW3R4msuMk3kKJqWLeHfr5/oGMKx+DzMDTE3oH1Ye9Exrkk7SwTmfBMGn417RUch8ihh48ZBoeSvfX4HPJA7j/rurmqNF+fWAMdOio5C5FE0LVogYEB/0TFcAovPA/Vs2hNtQtuIjtFgU0+n4t7ZhyGVlYuOQuRxwsaN5WjvL/wueKjxHceLjnDFdJIXPt6ZgpTF2wGrVXQcIo+jiY9HwIABomO4DBafh+rZtCd6RPcQHeOy4q1BWLAqDsE/cZFpImdp8vRTUKhUomO4DBafB5vSdQq8lF6iY1zUrfrmeHORAsoDR0RHIfJYvj1vhP8tt4iO4VJYfB6seWBzPJj8oOgYFzShsAPGzM6GVFAkOgqRx1JoNIicOlV0DJfD4vNwY1LGINw7XHSMOiooMDMjFT3n7oJkMomOQ+TRQkaNgqZZM9ExXA6Lz8P5qn0xqdMk0TEAAE3svlj4S2tEr+Qi00TO5hUVhbCxnrmM4bVi8cnAoJaDkNokVWiGrqYYfPi5HzTbDwjNQSQXEVOmQOntLTqGS2LxycSzXZ+FUiHmf/eD5W3w9CfFkLLPCHl9Irnx7d4NAf3kvRD1pbjuIX/kUMmhyRjSagi+PPJlo77uK1lpaPX1Dkh2e6O+LjXMsrIyLCsvxxmrBQCQoNFgXGgYevr5AQCyzWa8WVSIXQYDzJKEG3x9MbVJBMK8Lv4rZIdej3mlJcgwmlBks+K96Bjc6u9f7zHzSkswr7QUAPBISAhGhYTW3bfXYMD0gnwsaxYPL5kvqtwgajUinntOdAqXxhGfjPw79d8I1AY2ymsFSjrM39Qerb7cBrD0XF6E2guTwsPxVbN4fNUsHtf5+GLCmRwcNZmgt9vxaM5pKADMj43Fkrg4WCQJ48/kwH6JBcT1djtaa3V4PiLigvcfNhrxQXExZkRF482oaLxXXIwjJiMAwCpJ+G9BPl6MiGTpNVDIgw9C26KF6BgujcUnI0G6IEzpMsXpr9PGEo45y8Phu2G301+LHONmP3/08vNDvEaDeI0GE8PD4aNUYp/BgN0GA85YLPhfZBQStTokanV4NTIKB4xGbNHrL7rNnn5+eCI8/LxR3lnHzWYkarW43tcX3Xx9kajV4rjZDACYV1qKzt4+aM99VA3iFRGB8PGPiY7h8lh8MjOo5SD0adbHadu/vaoV/jvXABw54bTXIOeySRLWVFbCIElI8faGWbJDAUDzt5GXVqGAEsAuw8WL73IStVqcNJuRa7HgjMWCU2YzWmm0yDab8W1FOZ4ID7v2L0Zmol6eDqWvr+gYLo/7+GTohW4vYE/hHhQaCh263WfOpCJt6W5IXG/TLR0xGXH/qVMwSxJ8lEq8Fx2DBK0WISoVvJVKvFVchIlh4ZAAvF1UCBuAIqvtql+vpVaLieHhGH36NABgYng4Wmq1ePh0Np4Mb4KNNTX4sLgYXgoF/q9JBDr7+DjmC/VQQffeC78bbxQdwy0oJIlX+ZSjTWc2Yewvjrl8kVZSYebu9gj9cYdDtkdimCUJeRYLqu12/FhViW8qKrAwNg4JWi021dTgpYJ85FgsUAIYEBCALJMJ7XXeeDEy8rLbbnP40AUPbvmnFRUV+LW6Ci9GRCL9xHF80SweBVYLJufm4ecWLaDh1QUuSB0XhxYrvoWSfxxcEY74ZKpHTA/c1/o+LDu87Jq2E2cLwutrQ6Hax9JzdxqFAs00GgBAW50OB4xGfFZWhv9GRqKHry9+bNESZVYrVAoFAlQq3HjsKPpr1A57/TKrFbNKirEoNg77jIa6/Y3xGg2skHDSYkaiVuew1/MYSiWiX3uVpdcA/PNJxp7s/CTiA+Kv+vk3G+Lx1iIlVPsOOy4UuQwJgEWqf0RusJcXAlQqbKmpQanNht5/ne7gCK8VFeKh4GBEqtWwS4Dlb5NRNkmCjXNTFxT6yCPwSUsTHcOtsPhkTOelw2s3vgYvRcMH/uOKOuCxj3Mg5Tt2PyGJ8XZRIXbo9ThjMeOIyYi3iwqxTa/HwIDa01+WV5Rjr8GAbLMZKysqMCn3DB4KDkZzjbZuG6NOZ2NJWVnd5zV2OzKNRmQaa09ROGOxINNoRK7Fct7r/1lTg5NmMx4ICgYAtNPpcMJsxobqanxZXg6lQoHmf41G6Rxdhw4If/zfomO4HU51ylzbsLb4V8q/MGvPrCt6vAoKvJmZiqYrtoF/gHuOUpsNz+Tloshmg79SiUStFnOaxqL7X0cInjSb8U5RESpsNsSo1RgTGoYRwcH1tnHabEaZ7dyBTRlGA0b+deAKALxeVPtH0uCAAPwvKrrudqPdjpcLCvBWdDSUfx05GqlWY2qTCEzNz4NGocCrkVHQcf9ePUpfX8S8NQMKteOmm+WCB7cQbHYbHvrhIewr3nfJx4XZffHO73HQbt3fSMmI6GKi33wDgYMGiY7hlvgnFEGlVOH1nq8jQBNw0cd0Mkfhoy/8WXpELiDwjttZeteAIz6qs/HMRoz/dTzs/zig4YGKZNz52UlIVVWCkhHRWdrWrRG/dAlPVL8GHPFRnRtibsD4juPr3fbSiTQMnp3B0iNyAarQUMTO+pCld4044qN6JEnCxHUTsS17I97d2hr+67jeJpErUKjViFu4gKcuOACLj85TY6lB9pP/AX7aIDoKEf0l6pVXEHT3XaJjeAROddJ5fNW+aPnUVKiCgkRHISIAISNHsvQciMVHF6SJi0PMe+8CPEeISCjfXj3RZPLTomN4FBYfXZRv166IevEF0TGIZEuT0BIxb70FBU/edyh+N+mSgoYMQcioUaJjEMmOKigIsbNmQeXA9VCpFouPLqvJ5KcRcDtPliVqNGo1Yt59F5q4ONFJPBKLjy5LoVAg+tVX4X/bbaKjEHk+lQoxb74J3+u6ik7isVh8dEUUKhVi3poBX17hmch5/rq2XkC/vqKTeDQWH10xhUaDpu+/B5/OnUVHIfI8CgWiXvov1+BsBCw+ahClToems2dD17696ChEHiXi+ecQNGSI6BiywOKjBlP5+SJuzifQJiaKjkLkEZpMmYKQBx4QHUM2WHx0VVRBQYibNxea+HjRUYjcWvjEiQgdNVJ0DFlh8dFV8woLQ9z8eVBHR1/+wUR0ntBxYxE2dozoGLLD4qNroo6KQtyihdA0ayY6CpFbCXn4YTR54gnRMWSJV2cgh7CWluL0mLEw7ucV2okuJ2z8eIT/e4LoGLLF4iOHsev1yHn8CdRs3Cg6CpFr8vJC1LQXefSmYCw+cijJYkHu1KmoXPm96ChELkXh44OmM9+BX8+eoqPIHouPHE6SJBS+OQOl8+aJjkLkElShoYidPRve7duJjkJg8ZETlcxfgMI33gD4I0YypomPR+ycT6CJjRUdhf7C4iOnqvh+FXL/7/8Ai0V0FKJG552SgqazP4JXcLDoKPQ3LD5yuupNm3DmiYmwV1eLjkLUaPx690bM229BqdOJjkL/wOKjRmE+eRI5jz8B05EjoqMQOV3w8OGIePYZKFQq0VHoAlh81GjsBgPyp01DxXcrRUchcgqlry+ipr+EgAEDREehS2DxUaMrW/YFCv73P0hms+goRA6jbd0aMTPfgbZ5c9FR6DJYfCSEYf8BnHniCVhyc0VHIbpmgXffhcjnn+f+PDfB4iNhbOXlODN5Mmo2/CE6CtFVUXh7I/KFFxB052DRUagBWHwklCRJKP7oIxR/8CFgt4uOQ3TFNC1aIGbmO9DxupRuh8VHLqF60ybkPvMMbEXFoqMQXVZAejqiXvovlL6+oqPQVWDxkcuwlZej4NVXedQnuSylvz+aTH4awffcIzoKXQMWH7mc6g0bkPfiNFjz8kRHIarjd8stiHzhBagjmoiOQteIxUcuyVZdjcI33kT5V19xrU8SShUWhsjnpiKgXz/RUchBWHzk0mq2bEHec8/DkpMjOgrJUOBddyFiymSoAgNFRyEHYvGRy7Pr9Sh8ZybKlizhkZ/UKNRNmyLqpf/Ct3t30VHICVh85Db0u3Yhb+pzMJ84IToKeSqVCiEPPojwJx6H0ttbdBpyEhYfuRXJYkHZsi9QPGsWbGVlouOQB/FOSUHEc1Ph3b696CjkZCw+cku2qiqUfDIHpYsWQTKZRMchN6aJj0f4pEkI6NtHdBRqJCw+cmuWvDwUzXwXFStX8uhPahBVeBjCx49H0JAhUHh5iY5DjYjFRx7BePAgCt58E/rNW0RHIRen9PVFyCMPI3TkSCh9fETHIQFYfORRqjdsQOGbM2A6elR0FHIxCrUaQffdh7BxY+EVEiI6DgnE4iOPI9lsqFj5PUrnzYXp6DHRcUg0hQIB/fsjfNJEaGJjRachF8DiI48lSRJqNmxAydx50G/bJjoONTKFtzeC7hyMkIcegiY+XnQcciEsPpIFw4EMlM6bi8offwJsNtFxyIm8mjRB8LBhCL53KFRBQaLjkAti8ZGsmHPOoHThQpR/8w0kvV50HHIgbZtkhI4YgYABA6BQq0XHIRfG4iNZslVUoOzzZShdspjXAHRnCgX8broJISNHwve6rqLTkJtg8ZGsSWYzqtavR+XKlaj+fT0ki0V0JLoCqqAgBAwciOBhD0DbvLnoOORmWHxEf7GVl6NizRpUfrcShr17Rcehf1Kr4derJ4IGD4Zfr16czqSrxuIjugDzyZMo/+47VK78HpYzZ0THkS+FAt4dOyIgPR0B6QPgFRwsOhF5ABYf0SVIkgTDjh2oWLkSlWt/hL2qSnQkWdC1a4eA/v0R0L8f1NHRouOQh2HxEV0hyWKBfudOVK/7HdXr18N88qToSB5DodPBp1Mn+PboAf9bb4EmLk50JPJgLD6iq2Q+dQrV69ej+o+N0O/YAclgEB3JfSgU0CUnw7dHd/h27w7vTp2g1GhEpyKZYPEROYBkNkO/Zw9q/vwTNZs3w3gggyfK/4NXVBR8u3eDb/fu8O3WjetlkjAsPiInsNfUwJiZCWNGBgwZGTBmHKy9crzdLjpao1D4+ECXmAhtchJ0ycnw6dwZ2hYtRMciAsDiI2o0dr0exkOHYDyQAWNGBowHM2A6fsLtR4aq0FDokpKga5MMbVISdMltoIlvBoVSKToa0QWx+IgEshsMMB0+DPPpHFhyc2HJy4UlNxfWvDxYzuTC7grLqikUUIWGQh0ZCa/ICKgjo6COioQ2IQHapGSoI5qITkjUICw+IhdmKy+HJS+vthTP5MKSlwdbRQXsBj0kvQF2Q+2HZDTAXu9zY/0r0isUUOh0UGq1UOh0UGg1UGp1527TaqH09oZXRATUkRHwioiEOioSXpGRUDdpAgUPPLmkadOmYcWKFdizZ881bUehUODbb7/F4MGDHZKLLsxLdAAiujhVUBBUQUHQJSc36HmSJEEyGCDZbLXF5sHFNXLkSCxcuBBjxozB7Nmz6903fvx4zJo1CyNGjMCCBQvEBGyAvLw8BPMkfafjJDyRB1IoFFD6+EDl7+/RpXdWbGwsli1bBsPfTikxGo1YunQp4tzonMDIyEhotVrRMTwei4+I3F5aWhpiY2OxfPnyutuWL1+OuLg4pKam1t0WHx+PmTNn1ntux44dMW3atLrPFQoFPv74YwwcOBA+Pj5ITk7G5s2bcezYMdx0003w9fVF9+7dkZWVdV6Ojz/+GLGxsfDx8cHQoUNRUVFRd9/27dtx2223ISwsDIGBgejVqxd27dpV7/kKhQIrVqy4tm8GXRaLj4g8wsMPP4z58+fXfT5v3jyMGjXqqrY1ffp0PPTQQ9izZw+SkpLwwAMPYMyYMXj22WexY8cOSJKECRMm1HvOsWPH8OWXX+L777/H2rVrsXv3bjz22GN191dVVWHEiBHYuHEjtmzZglatWmHAgAGo4jJ4jY7FR0QeYfjw4di4cSNOnTqFU6dOYdOmTRg+fPhVbWvUqFEYOnQoEhMTMWXKFJw8eRLDhg1D3759kZycjCeeeAK///57vecYjUYsWrQIHTt2RM+ePfH+++9j2bJlyM/PBwD07t0bw4cPR1JSEpKTk/HJJ59Ar9dj/fr11/qlUwPx4BYi8gjh4eFIT0/HggULIEkS0tPTERYWdlXb6tChQ92/IyIiAADt27evd5vRaERlZSUCAgIAAHFxcYiJial7TLdu3WC323H48GFERkaioKAAzz33HH7//XcUFhbCZrNBr9cjOzv7qjLS1WPxEZHHePjhh+umID/88MPz7lcqlfjnGVyWC1x8WP23a/0pFIqL3mZvwEo8I0aMQElJCd599100a9YMWq0W3bp1g9lsvuJtkGOw+IjIY/Tr1w9msxkKhQJ9+/Y97/7w8HDk5eXVfV5ZWYkTJ0445LWzs7ORm5uL6L8uo7RlyxYolUq0bt0aALBp0ybMmjULAwYMAACcPn0axcXFDnltahgWHxF5DJVKhczMzLp//1Pv3r2xYMECDBo0CEFBQXjhhRcu+LirodPpMGLECMyYMQOVlZV4/PHHMXToUERGRgIAWrVqhc8++wydO3dGZWUlnn76aXh7ezvktalheHALEXmUgICAuv1u//Tss8+iV69eGDhwINLT0zF48GC0bNnSIa+bkJCAu+66CwMGDECfPn3QoUMHzJo1q+7+uXPnoqysDGlpaXjwwQfx+OOPo0kTLvcmApcsIyIiWeGIj4iIZIXFR0REssLiIyIiWWHxERGRrLD4iIhIVlh8REQkKyw+IiKSFRYfERHJCouPiIhkhcVHRESywuIjIiJZYfEREZGssPiIiEhWWHxERCQrLD4iIpIVFh8REckKi4+IiGSFxUdERLLC4iMiIllh8RERkayw+IiISFZYfEREJCssPiIikhUWHxERyQqLj4iIZIXFR0REssLiIyIiWWHxERGRrLD4iIhIVlh8REQkKyw+IiKSFRYfERHJCouPiIhkhcVHRESywuIjIiJZYfEREZGs/D/zewCV5UJQagAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"city\")[\"revenue_realized\"].sum().plot(kind=\"pie\", autopct='%1.1f%%')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ca3225",
   "metadata": {},
   "source": [
    "**Print month by month revenue**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "904e8521",
   "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>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>0</th>\n",
       "      <td>01-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <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": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a2b99387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date[\"mmm yy\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "af0ef823",
   "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",
       "      <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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "221fd916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   date      92 non-null     object\n",
      " 1   mmm yy    92 non-null     object\n",
      " 2   week no   92 non-null     object\n",
      " 3   day_type  92 non-null     object\n",
      "dtypes: object(4)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "2341fc8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <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>0</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-05-02</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-05-03</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0 2022-05-01  May 22    W 19   weekend\n",
       "1 2022-05-02  May 22    W 19  weekeday\n",
       "2 2022-05-03  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date[\"date\"] = pd.to_datetime(df_date[\"date\"])\n",
    "df_date.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d0ddb18e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 134573 entries, 0 to 134572\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   booking_id         134573 non-null  object \n",
      " 1   property_id        134573 non-null  int64  \n",
      " 2   booking_date       134573 non-null  object \n",
      " 3   check_in_date      134573 non-null  object \n",
      " 4   checkout_date      134573 non-null  object \n",
      " 5   no_guests          134573 non-null  float64\n",
      " 6   room_category      134573 non-null  object \n",
      " 7   booking_platform   134573 non-null  object \n",
      " 8   ratings_given      56676 non-null   float64\n",
      " 9   booking_status     134573 non-null  object \n",
      " 10  revenue_generated  134573 non-null  int64  \n",
      " 11  revenue_realized   134573 non-null  int64  \n",
      " 12  property_name      134573 non-null  object \n",
      " 13  category           134573 non-null  object \n",
      " 14  city               134573 non-null  object \n",
      "dtypes: float64(2), int64(3), object(10)\n",
      "memory usage: 16.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_bookings_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "79e0691d",
   "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",
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       "      <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>2022-01-05</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>2022-01-05</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>2022-01-05</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>2022-01-05</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",
       "  </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    2022-01-05      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22    2022-01-05      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022    2022-01-05      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22    2022-01-05      6/5/2022   \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",
       "\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  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all[\"check_in_date\"] = pd.to_datetime(df_bookings_all[\"check_in_date\"])\n",
    "df_bookings_all.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "3a1893b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <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>0</th>\n",
       "      <td>May052216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>15-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>tripster</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May052216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>7/5/2022</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",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May052216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May052216558RT11        16558     15-04-22    2022-05-05      7/5/2022   \n",
       "1  May052216558RT12        16558     30-04-22    2022-05-05      7/5/2022   \n",
       "2  May052216558RT13        16558     1/5/2022    2022-05-05      6/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        3.0           RT1         tripster            5.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        3.0           RT1   direct offline            5.0    Checked Out   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \\\n",
       "0              10010             10010  Atliq Grands   Luxury  Delhi   \n",
       "1               9100              3640  Atliq Grands   Luxury  Delhi   \n",
       "2              10010             10010  Atliq Grands   Luxury  Delhi   \n",
       "\n",
       "        date  mmm yy week no  day_type  \n",
       "0 2022-05-05  May 22    W 19  weekeday  \n",
       "1 2022-05-05  May 22    W 19  weekeday  \n",
       "2 2022-05-05  May 22    W 19  weekeday  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all = pd.merge(df_bookings_all, df_date, left_on=\"check_in_date\", right_on=\"date\")\n",
    "df_bookings_all.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "52460379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mmm yy\n",
       "Jul 22    389940912\n",
       "Jun 22    377191229\n",
       "May 22    408375641\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"mmm yy\")[\"revenue_realized\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742f6aca",
   "metadata": {},
   "source": [
    "**Print revenue realized per hotel type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f43241e4",
   "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": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.property_name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "7bb51795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_name\n",
       "Atliq Bay        179416721\n",
       "Atliq Blu        179203544\n",
       "Atliq City       196555383\n",
       "Atliq Exotica    219076161\n",
       "Atliq Grands     145860641\n",
       "Atliq Palace     209474575\n",
       "Atliq Seasons     45920757\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"property_name\")[\"revenue_realized\"].sum().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "aec26013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='property_name'>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGDUHYWRiIgI+fv7q3jx4mratKliYmKcrrto0SLZbDaHR/Hixe+4YAAAULjkOoxERkZq5MiRGj9+vPbv36+goCB16NBBFy9edLqNl5eXEhMT7Y+zZ8/+paIBAEDhkeswMn36dD3zzDMaNGiQ6tSpozlz5qhkyZJasGCB021sNpsqV65sf1SqVOkvFQ0AAAqPXIWR9PR07du3T+3atfv9Ddzc1K5dO+3cudPpdtevX1e1atXk5+en7t2769tvv73l56SlpSklJcXhAQAACqdchZFLly4pIyMjy5mNSpUqKSkpKdttAgMDtWDBAq1Zs0YffvihMjMz1bx5c33//fdOP2fy5MkqU6aM/eHn55ebMgEAQAFyz0fThIaGqn///goODlbLli316aefqmLFinr//fedbvPKK6/o6tWr9kdCQsK9LhMAABhSJDcre3t7y93dXRcuXHBYfuHCBVWuXDlH71G0aFGFhIToxIkTTtfx8PCQh4dHbkoDAAAFVK7OjBQrVkyNGjVSVFSUfVlmZqaioqIUGhqao/fIyMjQoUOHVKVKldxVCgAACqVcnRmRpJEjR2rAgAFq3LixmjRpopkzZyo1NVWDBg2SJPXv319Vq1bV5MmTJUkTJkxQs2bNVL16dSUnJ+vtt9/W2bNnNWTIkLv7lQAAgAIp12Gkd+/e+vHHHzVu3DglJSUpODhYX375pb2p9dy5c3Jz+/2Ey//+9z8988wzSkpKUrly5dSoUSPt2LFDderUuXtfBQAAKLByHUYkadiwYRo2bFi2r23ZssXh+YwZMzRjxow7+RgAAOAC7iiMAACAu8//X+tMl2B35q0uefZZ3CgPAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYNQdhZGIiAj5+/urePHiatq0qWJiYm65/scff6xatWqpePHiql+/vtavX39HxQIAgMIn12EkMjJSI0eO1Pjx47V//34FBQWpQ4cOunjxYrbr79ixQ3369NHTTz+tAwcOKCwsTGFhYTp8+PBfLh4AABR8uQ4j06dP1zPPPKNBgwapTp06mjNnjkqWLKkFCxZku/4777yjjh07avTo0apdu7YmTpyohg0bavbs2X+5eAAAUPDlKoykp6dr3759ateu3e9v4Oamdu3aaefOndlus3PnTof1JalDhw5O1wcAAK6lSG5WvnTpkjIyMlSpUiWH5ZUqVdLRo0ez3SYpKSnb9ZOSkpx+TlpamtLS0uzPr169KklKSUnJTblZZKb99Je2v9v+6tdzN7FvnGPfOJef9k1+2i8S++ZW2DfOFbZ9c/M9LMu65Xq5CiN5ZfLkyXr99dezLPfz8zNQzb1TZqbpCvIv9o1z7JvssV+cY984x75x7m7um2vXrqlMmTJOX89VGPH29pa7u7suXLjgsPzChQuqXLlytttUrlw5V+tL0iuvvKKRI0fan2dmZurKlSuqUKGCbDZbbkq+61JSUuTn56eEhAR5eXkZrSW/Yd84x75xjn3jHPsme+wX5/LbvrEsS9euXZOvr+8t18tVGClWrJgaNWqkqKgohYWFSboRFKKiojRs2LBstwkNDVVUVJReeOEF+7LNmzcrNDTU6ed4eHjIw8PDYVnZsmVzU+o95+XllS/+R+dH7Bvn2DfOsW+cY99kj/3iXH7aN7c6I3JTri/TjBw5UgMGDFDjxo3VpEkTzZw5U6mpqRo0aJAkqX///qpataomT54sSRoxYoRatmypadOmqUuXLlqxYoX27t2ruXPn5vajAQBAIZTrMNK7d2/9+OOPGjdunJKSkhQcHKwvv/zS3qR67tw5ubn9PkinefPmWrZsmV599VX93//9n2rUqKHVq1erXr16d++rAAAABdYdNbAOGzbM6WWZLVu2ZFnWq1cv9erV604+Kt/x8PDQ+PHjs1xGAvvmVtg3zrFvnGPfZI/94lxB3Tc263bjbQAAAO4hbpQHAACMIowAAACjCCMAAMAowggAoMD45ZdfTJeAe4AwchsDBgxQdHS06TLypdatW6tNmzZOH65s/PjxOnv2rOky8rWffvpJR48e1cGDBx0ermzx4sVat26d/fmYMWNUtmxZNW/e3KW/nzIzMzVx4kRVrVpVpUuX1qlTpyRJY8eO1fz58w1XZ9bPP/+sn376/X42Z8+e1cyZM7Vp0yaDVeUeYeQ2rl69qnbt2qlGjRp68803df78edMl5RvBwcEKCgqyP+rUqaP09HTt379f9evXN12eUWvWrNGDDz6otm3batmyZQ43fnR1P/74ox577DF5enqqbt26CgkJcXi4sjfffFMlSpSQdOOO5xEREQoPD5e3t7defPFFw9WZM2nSJC1atEjh4eEqVqyYfXm9evU0b948g5WZ1717dy1ZskSSlJycrKZNm2ratGnq3r273nvvPcPV5YKF27p48aI1bdo0q0GDBlaRIkWsjh07Wh9//LGVnp5uurR8afz48daoUaNMl2Hc/v37reeff97y9va2ypYtaz377LNWTEyM6bKM69u3r9WiRQtrz549VqlSpaxNmzZZS5cutQIDA60vvvjCdHlGlShRwjp79qxlWZY1ZswYq1+/fpZlWdbhw4ctb29vk6UZ9eCDD1pfffWVZVmWVbp0aevkyZOWZVnWkSNHrLJly5oszbgKFSpYhw8ftizLsj744AOrQYMGVkZGhrVy5UqrVq1ahqvLOcJILu3bt88aNmyYVbx4ccvb29t64YUXrPj4eNNl5SvHjx+3ypUrZ7qMfCM9Pd1atWqV9dhjj1lFixa16tevb82cOdNKTk42XZoRlStXtnbv3m1ZlmV5enpax44dsyzLstasWWO1aNHCZGnGVaxY0dq/f79lWZYVHBxsLVmyxLIsyzpx4oRVqlQpk6UZVbx4cevMmTOWZTmGkW+//dal94tlOQbYXr16Wa+99pplWZZ17tw5q0SJEiZLyxUu0+RCYmKiNm/erM2bN8vd3V2dO3fWoUOHVKdOHc2YMcN0efnGzp07Vbx4cdNl5BuWZenXX39Venq6LMtSuXLlNHv2bPn5+SkyMtJ0eXkuNTVVPj4+kqRy5crpxx9/lCTVr19f+/fvN1macY8++qiGDBmiIUOGKD4+Xp07d5Ykffvtt/L39zdbnEF16tTRN998k2X5J5984vKX9qpXr67Vq1crISFBGzduVPv27SVJFy9ezDc3ysuJO5oO3pX8+uuv+vzzz7Vw4UJt2rRJDRo00AsvvKC+ffva/0d/9tlnGjx4sMtd0+3Ro4fDc8uylJiYqL1792rs2LGGqso/9u3bp4ULF2r58uXy8PBQ//79FRERoerVq0uS3n33XQ0fPly9e/c2XGneCgwM1LFjx+Tv76+goCC9//778vf315w5c1SlShXT5RkVERGhV199VQkJCVq1apUqVKgg6cb3Up8+fQxXZ864ceM0YMAAnT9/XpmZmfr000917NgxLVmyRF988YXp8owaN26c+vbtqxdffFFt27ZVaGioJGnTpk0FKqgxHfxteHt7KzMzU3369NEzzzyj4ODgLOskJycrJCREp0+fzvsCDbp5p+ab3NzcVLFiRbVp08aezl1V/fr1dfToUbVv317PPPOMunbtKnd3d4d1Ll26JB8fH2VmZhqq0owPP/xQv/32mwYOHKh9+/apY8eOunLliooVK6ZFixa5XDhDznzzzTeaMGGC4uLidP36dTVs2FDjxo1z+d81kpSUlKTExEQFBQXZb1QbExMjLy8v1apVy3B1OUMYuY2lS5eqV69eXHZArkycOFGDBw9W1apVTZeS790c4nv//ffL29vbdDl5LjfDmRs0aHAPKwHM4TLNbfTr1890CSiAbvaG/NnPP/+st99+W+PGjTNQVf5UsmRJNWzY0HQZxgQHB8tms8myLNlstluum5GRkUdV5S979uxRZmammjZt6rB89+7dcnd3V+PGjQ1VZl5qaqreeustRUVF6eLFi1nOtN6ckyW/48xIDuzdu1crV67UuXPnlJ6e7vDap59+aqgqM8qVK3fbX5g3Xbly5R5Xk3+5u7srMTHR3qh50+XLl+Xj4+OyBxVJ6tmzp5o0aaKXX37ZYXl4eLj27Nmjjz/+2FBlZvxxMrMDBw7opZde0ujRo+3X/nfu3Klp06YpPDxcYWFhhqo0q0mTJhozZoz+9re/OSz/9NNPNWXKFO3evdtQZeb16dNHW7duVb9+/VSlSpUsv59HjBhhqLLc4czIbaxYsUL9+/dXhw4dtGnTJrVv317x8fG6cOGCHn/8cdPl5bmZM2eaLqFAcPZXblxcnMqXL2+govwjOjpar732WpblnTp10rRp0/K+IMOqVatm/3evXr00a9Ys+yga6calGT8/P40dO9Zlw8h3332X7dmzkJAQfffddwYqyj82bNigdevWqUWLFqZL+UsII7fx5ptvasaMGRo6dKg8PT31zjvvKCAgQP/4xz9csvN/wIABpkvI126eObLZbKpZs6ZDIMnIyND169f17LPPGqzQvOvXrzvMonlT0aJFlZKSYqCi/OPQoUMKCAjIsjwgIMClD7oeHh66cOGCHnjgAYfliYmJKlLEtQ9j5cqVKxR/4HCZ5jZKlSplH+NfoUIFbdmyRfXr19eRI0fUpk0bJSYmmi4xX7AsS//973/1888/q3nz5tn2S7iCxYsXy7IsDR48WDNnzlSZMmXsrxUrVkz+/v720++uqkmTJnrsscey9M289tprWrt2rfbt22eoMvMaNmxon+L8ZmBLT0/XkCFDdPjwYZedh6VPnz5KTEzUmjVr7D9TycnJCgsLk4+Pj1auXGm4QnM+/PBDrVmzRosXL1bJkiVNl3PHXDtS5kC5cuV07do1SVLVqlV1+PBh1a9fX8nJyQ43J3IlycnJGjFihPbv369mzZpp2rRp6ty5s3bs2CFJ8vHxsc/J4mpunjkKCAhQ8+bNVbRoUcMV5T9jx45Vjx49dPLkSfsNFaOiorR8+XKX6xf5szlz5qhr166677777D8/Bw8elM1m09q1aw1XZ87UqVP1yCOPqFq1ava5M2JjY1WpUiUtXbrUcHVmTZs2TSdPnlSlSpXk7++f5XdOQQmwnBm5jb59+6px48YaOXKkJk6cqHfffVfdu3fX5s2b1bBhQ5drYJWkIUOGKDo6WgMGDNDatWvl5uYmy7I0c+ZMubm5acyYMSpdurTL/fJMSUmxT4R3u8sNBWlmxHth3bp1evPNNxUbG6sSJUqoQYMGGj9+vFq2bGm6NONSU1P10Ucf6ejRo5Kk2rVrq2/fvipVqpThysy6uV/i4uLs3zN9+vRx+cD/+uuv3/L18ePH51Elfw1h5DauXLmiX375Rb6+vsrMzFR4eLh27NihGjVq6NVXX3XJyxFVq1bVsmXL1LJlS50/f15+fn76+uuv1apVK0k3Jtvp1q2bkpKSzBaax/44gsbNzS3bBtabja2uPJoGAP6MMIJcK1KkiBISEuwNvCVLltShQ4f04IMPSroxG2DVqlVd7oC7detWtWjRQkWKFNHWrVtvuS5nAODM8ePH9d///jfbOSNcfX6a7777LtspFrp162aoovxj3759OnLkiCSpbt26BWoqeIkwkis0ad7g5uampKQk+xwanp6eiouLs3e6X7hwQb6+vi4XRuBc+fLlFR8fL29v79vOVePK89N88MEHeu655+Tt7a3KlSs77CebzVZgrv/fbadOndLjjz+uQ4cO2SeIk2TfP678u+bixYt68skntWXLFpUtW1bSjb6+1q1ba8WKFapYsaLZAnOIBlYnaNK8tXnz5ql06dKSpN9++02LFi2yT+V9s+HXFR0/flzjxo3T+++/n6Uv5OrVq3ruuec0adKkLEMUC7sZM2bI09PT/u+cTpznaiZNmqQ33ngjy4Rwrm7EiBEKCAhQVFSUAgICFBMTo8uXL2vUqFGaOnWq6fKMev7553Xt2jV9++23ql27tqQbZ5AGDBig4cOHa/ny5YYrzBnOjDhBk6Zz/v7+OTqYuNqNAyXp73//u8qWLavw8PBsX3/55ZeVkpKi9957L48rQ0Hg5eWl2NhYlwurt+Pt7a2vv/5aDRo0UJkyZRQTE6PAwEB9/fXXGjVqlA4cOGC6RGPKlCmjr776Sg899JDD8piYGLVv317JyclmCsslzow4sWHDBnuT5sCBA+1NmjfvjTBlyhSXvU555swZ0yXkW1u3btWHH37o9PUnnnhCffv2zcOK8h+myneuV69e2rRpk8tPjPdnGRkZ9jNr3t7e+uGHHxQYGKhq1arp2LFjhqszKzMzM9sRRUWLFi1QdwQnjDhx4cIF1axZU9KN0SPFixeXn5+f/fX7779fP/74o6nykE+dO3cuy0H2j7y9vZWQkJCHFeU/zk7GpqWlZTszqyupXr26xo4dq127dql+/fpZDjLDhw83VJlZ9erVU1xcnAICAtS0aVOFh4erWLFimjt3rsufRWrTpo1GjBih5cuXy9fXV5J0/vx5vfjii2rbtq3h6nKOMOJEZmam3N3d7c/d3d2zNJMBf1amTBmdPHnS4X4jf3TixAmXnWNk1qxZkm787Pyx50i68ZdvdHS0atWqZaq8fGHu3LkqXbq0tm7dmmVEls1mc9kw8uqrryo1NVWSNGHCBD322GN6+OGHVaFCBUVGRhquzqzZs2erW7du8vf3t//BnJCQoHr16t3yLG1+Q8+IE25ubpo0aZL9F+bLL7+s0aNHOzRpjhs3zqVPKSOrJ554Qr/++qs+++yzbF/v3r27ihUr5pIzjd6858rZs2d13333OYT9m1PlT5gwIctt4oHsXLlyJVd3ES/MLMvSV1995TBRXrt27QxXlTuEESdo0sSdOHDggEJDQ/XYY49pzJgxCgwMlCQdPXpU4eHhWrdunXbs2JHtHUhdRevWrfXpp5+67NB44F5LTk62D/MtKAgjwF32xRdfaPDgwbp8+bLD8goVKmjevHku2/icnT/PFwHp+++/1+eff57t5F7Tp083VFXe69GjR47XdcXbctw0ZcoU+fv7q3fv3pJunJ1dtWqVKleurPXr1ysoKMhwhTlDzwjuWG5u9+5KfRKPPfaYzp49qy+//FInTpyQZVmqWbOm2rdvX6Dvqnk3LVmyRG+//baOHz8uSapZs6ZGjx6tfv36Ga7MrKioKHXr1k0PPPCAjh49qnr16unMmTOyLMvlzqb98Y7XcG7OnDn66KOPJEmbN2/W5s2btWHDBq1cuVKjR4/Wpk2bDFeYM5wZwR1zdv+VP+JeLPiz6dOna+zYsRo2bJhatGghSdq2bZsiIiI0adIkvfjii4YrNKdJkybq1KmTXn/9dfvMxj4+PnrqqafUsWNHPffcc6ZLRD5TokQJxcfHy8/PTyNGjNAvv/yi999/X/Hx8WratKn+97//mS4xRzgzgju2cOFC/etf/9LAgQMVGhoqSdq5c6cWL16syZMny9/f32yByJfeffddvffee+rfv799Wbdu3VS3bl299tprLh1Gjhw5Yp8xs0iRIvr5559VunRpTZgwQd27dyeMIIty5copISFBfn5++vLLLzVp0iRJN/4QLEh/BBJGcMeWLFmi6dOnq0+fPvZl3bp1U/369TV37lxt2bLFXHHItxITE9W8efMsy5s3b67ExEQDFeUfpUqVsveJVKlSRSdPnlTdunUlSZcuXTJZmnGffPKJVq5cmW0vjaves0e60VvTt29f1ahRQ5cvX1anTp0k3Wimr169uuHqcs7NdAEouHbu3KnGjRtnWd64cWPFxMQYqAgFQfXq1bVy5cosyyMjI1WjRg0DFeUfzZo107Zt2yRJnTt31qhRo/TGG29o8ODBatasmeHqzJk1a5YGDRqkSpUq6cCBA2rSpIkqVKigU6dO2Q++rmrGjBkaNmyY6tSpo82bN9uno0hMTNQ///lPw9XlHD0jt0GTpnOBgYHq3r17lvuwjBkzRmvWrHH5aZqRvVWrVql3795q166dvWdk+/btioqK0sqVK/X4448brtCcU6dO6fr162rQoIFSU1M1atQo7dixQzVq1ND06dOdTqZX2NWqVUvjx49Xnz59HO4SPm7cOF25ckWzZ882XSL+IsLIbdCk6dz69evVs2dPVa9e3T5RVUxMjI4fP65Vq1apc+fOhis0hxB7a/v27dOMGTN05MgRSTcmaRo1apRCQkIMV2ZORkaGtm/frgYNGhS4OSLutZIlS+rIkSOqVq2afHx8tHnzZgUFBen48eNq1qxZlmH0rui7777L9hJWQZlKgJ6R26BJ07nOnTsrPj5e7733nn3mv65du+rZZ591uI+PKypbtiwh9hYaNWqU7VTVP/30k8sOf3Z3d1f79u115MgRwsifVK5cWVeuXFG1atV0//33a9euXQoKCtLp06ed3uvIVZw6dUqPP/64Dh06JJvNlmXunoLy+4Uwchs0ad6an5+f3nzzTdNl5DuEWOfatm2rJUuWqGrVqg7LY2Ji9P/+3/9TfHy8ocrMq1evnk6dOmWfOh83tGnTRp9//rlCQkI0aNAgvfjii/rkk0+0d+/eXE2OVhiNGDFCAQEBioqKUkBAgGJiYnT58mWNGjVKU6dONV1ezlm4pRIlSljx8fFZlh87dswqUaKEgYrMiouLszIyMuz/vtXDlbVp08ZatmxZluUfffSR1bJly7wvKB/p3LmzVb58eWvFihWWZVlWRkaGNX78eKto0aLWiBEjzBZn2IYNG6zg4GBr7dq11g8//GBdvXrV4eGqMjIyrF9//dX+fPny5dbzzz9vzZo1y0pLSzNYmXkVKlSw/7718vKyjh49almWZUVFRVnBwcEmS8sVekZugyZNR25ubkpKSpKPj4+9nya7byFXvfxwU8mSJRUXF5dldEh8fLyCg4P1008/Gaosf4iIiNCYMWPUvXt3nTlzRmfPntXChQvVvn1706UZ5eb2+wDHP17ms1z4kt6uXbu0du1apaenq23bturYsaPpkvKVcuXKaf/+/QoICNCDDz6oefPmqXXr1jp58qTq169fYH7XcJnmNmbMmKGePXtqw4YN2TZpuprTp0+rYsWK9n8je35+fvrggw+yhNh58+a5fD+NJA0dOlTff/+9pkyZoiJFimjLli3Zzj3iav773/+aLiFf+eSTT9S7d2+VKFFCRYsW1fTp0zVlyhS99NJLpkvLN+rVq6e4uDgFBASoadOmCg8PV7FixTR37lw98MADpsvLMc6M5EBCQoJDk2bt2rVp0sQtMdLIuf/9738aMmSIoqKi9Pbbb2vr1q1avXq1wsPDC9S8CLj3GjVqpIceekgRERFyd3fX5MmT9fbbb+vKlSumS8s3Nm7cqNTUVPXo0UMnTpzQY489pvj4eFWoUEGRkZFq06aN6RJzhDCCXPn8889zvG5BGVJ2rxBis1e1alUFBARo6dKl9kbNyMhI/fOf/1SzZs20bt06wxWakZKSYh/mvX79ev3222/219zd3dWlSxdTpRlTunRpxcbG2mcSTU9PV6lSpXT+/Hn5+PgYri7/unLlisqVK1eg7oZNGMnGwYMHVa9ePbm5uengwYO3XLdBgwZ5VFX+8Mdr2rfiqte3cXsTJ07Uv//97yzfS99//70GDRqkzZs3G6rMnC+++EJjx47VgQMHJEmenp5KTU21v26z2RQZGam//e1vpko04o89ajf9cdIz/O7EiRM6efKkHnnkEZUoUcLeZ1RQEEayQZMm7gQhFneqW7duCgsL0+DBgyVlPeCGh4dry5YtWr9+vcky85ybm5smTZpkn+Jckl5++WWNHj1a3t7e9mXDhw83UV6+cPnyZT3xxBP673//K5vNpuPHj+uBBx7Q4MGDVa5cOU2bNs10iTlCGMnG2bNndf/998tms+ns2bO3XNdVp2eWbszB0rt3b3l4eDgsT09P14oVKxzuyuoKCLG3Fh4erueff14lSpSQdGMK+MaNG9u/f65du6aXX35Z//nPf0yWaURAQIC+/PJLBQYGSsoaRg4dOqS2bdvq4sWLJsvMc/7+/rf9695ms+nUqVN5VFH+079/f128eFHz5s1T7dq17d83Gzdu1MiRI/Xtt9+aLjFHCCO4Y+7u7kpMTMxy7fby5cvy8fFxuQMuIfbW/vz94uXlpdjYWPsB98KFC/L19XW57xtJKl68uI4ePWqfDG/v3r0KCgpS0aJFJd0YuVarVi2lpaUZrBL5UeXKlbVx40YFBQU5hNhTp06pQYMGun79uukSc4ShvdmgSTNnnF2T/P7771WmTBkDFZn1x4DhimHjdv78dw9/B/2ufPnyOnHihD2M/Plu2MePH1f58uUNVIb8LjU1NdtbKFy5ciXLWev8jDCSjbCwsByt56qn20NCQmSz2WSz2dS2bVsVKfL7t1FGRoZOnz7tkhMTEWJxpx555BHNmjVL7dq1y/b1WbNm6ZFHHsnjqlAQPPzww1qyZIkmTpwo6cZxKTMzU+Hh4WrdurXh6nKOMJKNzMxM0yXkazfDWmxsrDp06ODQXFasWDH5+/urZ8+ehqozhxCLO/Xyyy8rNDRUvXr10pgxY1SzZk1J0rFjxzRlyhR99dVX2rFjh+EqkR+Fh4erbdu22rt3r9LT0zVmzBh9++23unLlirZv3266vBwjjNwGTZpZjR8/XtKN5rLevXurePHiWdY5fPiw6tWrl9elGUWIvb158+bZw+tvv/2mRYsW2UdFXLt2zWRpRoWEhCgyMlJDhgzRp59+6vBauXLltGLFCjVs2NBQdcjP6tWrp/j4eM2ePVuenp66fv26evTooaFDh6pKlSqmy8sxGlhvgybNnLt27ZqWL1+uefPmad++fS69bwixWeVkZITk2rcZ+Omnn7Rx40YdP35cklSjRg21b99epUqVMlwZcG8RRm7Dzc1NFy5csN+P5aa4uDi1bt2aaYklRUdHa/78+Vq1apV8fX3Vo0cP9ezZUw899JDp0owhxAJ3T0pKSo7XvTmLbWF36dIlpaamOjTLf/vtt5o6dapSU1MVFhamvn37Gqwwd7hM4wRNmreWlJSkRYsWaf78+UpJSdETTzyhtLQ0rV69WnXq1DFdnnGMNALunrJly972rJqr3dn4+eefl6+vr31Ss4sXL+rhhx+Wr6+vHnzwQQ0cOFAZGRnq16+f4UpzhjDiBE2aznXt2lXR0dHq0qWLZs6cqY4dO8rd3V1z5swxXZpxhFjg7lu4cKH+9a9/aeDAgQoNDZUk7dy5U4sXL9bkyZPtQ6Jdya5du7Ro0SL78yVLlqh8+fKKjY1VkSJFNHXqVEVERBBGCjqaNJ3bsGGDhg8frueee041atQwXU6+QogF7r4lS5Zo+vTp6tOnj31Zt27dVL9+fc2dO1dbtmwxV5whSUlJDiHs66+/Vo8ePex/AHXr1k2TJ082VF3uEUZuY8CAAQ7PadKUtm3bpvnz56tRo0aqXbu2+vXrpyeffNJ0WfkCIRa4+3bu3JntmdfGjRtryJAhBioyz8vLS8nJyfaekZiYGD399NP21202W4GasTdnt2CFoqOjNWDAAFWpUkVTp05VmzZttGvXLtNlGdGsWTN98MEHSkxM1D/+8Q+tWLFCvr6+yszM1ObNm116iOZNAwYMcAgi165d09y5c9WkSRMFBQUZrAz5WUpKSo4frsTPz08ffPBBluXz5s2Tn5+fgYrMa9asmWbNmqXMzEx98sknunbtmtq0aWN/PT4+vkDtG0bT3EJ2TZpz5sxRXFwcTZp/cuzYMc2fP19Lly5VcnKyHn300VzNSFpYMdIoK0ZGOHfzBou34mqNmpK0fv169ezZU9WrV1fTpk0l3TgTcPz4ca1atUqdO3c2XGHeO3jwoNq2bauUlBT99ttv+r//+z/7LKyS1K9fP5UqVarA9PIRRpz4Y5PmU089ZW/SLFq0KGHkFjIyMrR27VotWLDAZcMIIfbWOOA6t3jx4hw3arZs2dJQlWYkJCTovffe09GjRyVJtWvX1rPPPlug/vq/2y5duqTt27ercuXK9pB207p161SnTh0FBAQYqi53CCNOFClSJNsmTcIIboUQe3sccJ1r27athgwZ4tCoKUnLli1z2UZNuAbCiBO7du3S/PnzFRkZ6dCkWaVKFQ4qcIoQe3sccJ0rWbKk4uLisoxSi4+PV3BwsH766SdDleW9gwcPql69enJzc9PBgwdvuW6DBg3yqCrcKzSwOkGTJu7Etm3bdO3aNTVq1EhNmzbV7NmzdenSJdNl5Ss7d+5U48aNsyxv3LixYmJiDFSUf9Co+bvg4GD7z05wcLBCQkIUHByc5RESEmK4UtwNnBnJBZo0kVOpqamKjIzUggULFBMTo4yMDE2fPl2DBw+Wp6en6fKMCgwMVPfu3RUeHu6wfMyYMVqzZo2OHTtmqDLzaNT83dmzZ3X//ffLZrPp7Nmzt1z3j1Oio2AijNwBmjSRG4RYRxxwb41GTbgiwgiQRwixv+OAi9vJzc9It27d7mEl+VthGSpPGAEAg2jUzJ6bW85aGl1xCPgfFZah8oQRAPccB1zn3NzclJSUJB8fH/uBJbtfy/n9YAIzCstQecIIgHuOA65zNGre3pIlS9S7d295eHg4LE9PT9eKFSvUv39/Q5WZV1iGyhNGANxzHHDxV7i7uysxMVE+Pj4Oyy9fviwfHx+XC7B/VFjmpuGuvQDuuT8GDMKGIxo1b+9mz8Offf/99ypTpoyBivKPm3PT/HmofEGbm4YwAuCe44DrXFhYWI7Wc8VLWCEhIbLZbLLZbGrbtq2KFPn9kJWRkaHTp0+rY8eOBis0b8aMGerZs6c2bNiQ7VD5goLLNADuOUZG4E68/vrr9v+OGjVKpUuXtr9WrFgx+fv7q2fPnipWrJipEvOFwjBUnjACAPkEjZrZW7x4sXr37q3ixYtnee3w4cOqV6+egapwNxFGAOQpDrjO0aiZM9euXdPy5cs1b9487du3z+X2S2EcKk8YAZCnOOA65+bmpgsXLqhixYoOy+Pi4tS6dWtduXLFUGX5Q3R0tObPn69Vq1bJ19dXPXr0UM+ePfXQQw+ZLi1PFcah8jSwAshTjIzIikZN55KSkrRo0SLNnz9fKSkpeuKJJ5SWlqbVq1erTp06pssz4vTp0/bAevr0acPV3B2EEQB5ggOuczdH1MTGxqpDhw5OGzVdTdeuXRUdHa0uXbpo5syZ6tixo9zd3TVnzhzTpRlVGIfKE0YA5AkOuM6NHz9ekuTv70+j5h9s2LBBw4cP13PPPZdlUi9XVhiHytMzAiBPMTIi51y9UXPXrl2aP3++IiMjVbt2bfXr109PPvmkqlSpori4OJe9TFMYh8oTRgAY5eoH3OzQqOkoNTVVkZGRWrBggWJiYpSRkaHp06dr8ODB8vT0NF0e7oKcxSsAuMuio6M1YMAAValSRVOnTlWbNm20a9cu02UZk5SUpLfeeks1atRQr1695OXlZW/UfOutt1w2iEhSqVKlNHjwYG3btk2HDh3SqFGj9NZbb8nHx6fAXIa4V5YsWaK0tLQsy9PT07VkyRIDFd0ZzowAyDPZjYyYM2eOS59ylxwbNZ966il7o2bRokVdft84k5GRobVr12rBggW56qEobArLUHnOjADIE127dlVgYKAOHjyomTNn6ocfftC7775ruqx8YcOGDXr66af1+uuvq0uXLnJ3dzddUr7n7u6usLAwlw4iUuEZKs9oGgB5gpERzm3btk3z589Xo0aNHBo1AWcK21B5wgiAPMEB17lmzZqpWbNmmjlzpr1Rc+TIkcrMzNTmzZvl5+dHoyYcFLah8vSMAMhTjIzImWPHjmn+/PlaunSpkpOT9eijj7r8JQlkVViGyhNGABjDAff2aNREbhTUofKEEQDGccAF/pqCPjcNYQQAgAKoMA2VZ2gvAAAFTGEbKs9oGgAACpjCNlSeMyMAABQw27Zt07Vr19SoUSM1bdpUs2fP1qVLl0yXdcfoGQEAoIAqLEPlCSMAABQCBXmoPGEEAIBCpCAOlSeMAAAAo2hgBQAARhFGAACAUYQRAABgFGEEAAAYRRgBkCdatWqlF154wXQZAPIhwgjgAtLT013yswEUDIQRoABq1aqVhg0bpmHDhqlMmTLy9vbW2LFjdXOkvr+/vyZOnKj+/fvLy8tLf//73yVJq1atUt26deXh4SF/f39NmzbN4X1vbtenTx+VKlVKVatWVUREhMM6ycnJGjJkiCpWrCgvLy+1adNGcXFx9tdfe+01BQcHa968eQoICFDx4sU1cOBAbd26Ve+8845sNptsNptOnz6t6tWra+rUqQ7vHxsbK5vNphMnTtx2P9hsNs2bN0+PP/64SpYsqRo1ajjMq5CRkaGnn35aAQEBKlGihAIDA/XOO+84vMfAgQMVFhamN998U5UqVVLZsmU1YcIE/fbbbxo9erTKly+v++67TwsXLnTYLiEhQU888YTKli2r8uXLq3v37jpz5sxtawaQDQtAgdOyZUurdOnS1ogRI6yjR49aH374oVWyZElr7ty5lmVZVrVq1SwvLy9r6tSp1okTJ6wTJ05Ye/futdzc3KwJEyZYx44dsxYuXGiVKFHCWrhwof19q1WrZnl6elqTJ0+2jh07Zs2aNctyd3e3Nm3aZF+nXbt2VteuXa09e/ZY8fHx1qhRo6wKFSpYly9ftizLssaPH2+VKlXK6tixo7V//34rLi7OSk5OtkJDQ61nnnnGSkxMtBITE63ffvvNeuONN6w6deo4fG3Dhw+3HnnkkRztB0nWfffdZy1btsw6fvy4NXz4cKt06dL2WtLT061x48ZZe/bssU6dOmXfT5GRkfb3GDBggOXp6WkNHTrUOnr0qDV//nxLktWhQwfrjTfesOLj462JEydaRYsWtRISEuzvW7t2bWvw4MHWwYMHre+++87q27evFRgYaKWlpeX+fyjg4ggjQAHUsmVLq3bt2lZmZqZ92csvv2zVrl3bsqwboSIsLMxhm759+1qPPvqow7LRo0c7hIFq1apZHTt2dFind+/eVqdOnSzLsqxvvvnG8vLysn755ReHdR588EHr/ffftyzrRhgpWrSodfHixSw1jxgxwmHZ+fPnLXd3d2v37t2WZd04yHt7e1uLFi3K0X6QZL366qv259evX7ckWRs2bHC6zdChQ62ePXvanw8YMMCqVq2alZGRYV8WGBhoPfzww/bnv/32m1WqVClr+fLllmVZ1tKlS63AwECH/Z+WlmaVKFHC2rhxY45qB/A7LtMABVSzZs1ks9nsz0NDQ3X8+HFlZGRIkho3buyw/pEjR9SiRQuHZS1atHDY5ub7/FFoaKiOHDkiSYqLi9P169dVoUIFlS5d2v44ffq0Tp48ad+mWrVqqlix4m2/Bl9fX3Xp0kULFiyQJK1du1ZpaWnq1atXTnaBJKlBgwb2f5cqVUpeXl66ePGifVlERIQaNWqkihUrqnTp0po7d67OnTvn8B5169aVm9vvvw4rVaqk+vXr25+7u7urQoUK9veNi4vTiRMn5Onpad8H5cuX1y+//OKwHwDkTBHTBQC4N0qVKnXX3/P69euqUqWKtmzZkuW1smXL3tFnDxkyRP369dOMGTO0cOFC9e7dWyVLlszx9kWLFnV4brPZlJmZKUlasWKFXnrpJU2bNk2hoaHy9PTU22+/rd27d9/2PW71vtevX1ejRo300UcfZaknJyEMgCPCCFBA/fmAumvXLtWoUUPu7u7Zrl+7dm1t377dYdn27dtVs2ZNh2127dqV5X1r164tSWrYsKGSkpJUpEgR+fv756reYsWKOZyBualz584qVaqU3nvvPX355ZeKjo7O1fveyvbt29W8eXP985//tC+7G2cuGjZsqMjISPn4+MjLy+svvx/g6rhMAxRQ586d08iRI3Xs2DEtX75c7777rkaMGOF0/VGjRikqKkoTJ05UfHy8Fi9erNmzZ+ull15yWG/79u0KDw9XfHy8IiIi9PHHH9vft127dgoNDVVYWJg2bdqkM2fOaMeOHfr3v/+tvXv33rJef39/7d69W2fOnNGlS5fsZxnc3d01cOBAvfLKK6pRo0aWy0R/RY0aNbR3715t3LhR8fHxGjt2rPbs2fOX3/epp56St7e3unfvrm+++UanT5/Wli1bNHz4cH3//fd3oXLAtRBGgAKqf//++vnnn9WkSRMNHTpUI0aMsA/hzU7Dhg21cuVKrVixQvXq1dO4ceM0YcIEDRw40GG9UaNGae/evQoJCdGkSZM0ffp0dejQQdKNSxXr16/XI488okGDBqlmzZp68skndfbsWVWqVOmW9b700ktyd3dXnTp1VLFiRYe+jaefflrp6ekaNGjQne+QbPzjH/9Qjx491Lt3bzVt2lSXL192OEtyp0qWLKno6Gjdf//96tGjh2rXrq2nn35av/zyC2dKgDtgs6z/f2ICAAVGq1atFBwcrJkzZ97V9/X399cLL7yQ5zOlfvPNN2rbtq0SEhJuG2oAFD70jAAwJi0tTT/++KNee+019erViyACuCgu0wAwZvny5apWrZqSk5MVHh7u8NpHH33kMHz4j4+6desaqhjAvcBlGgD50rVr13ThwoVsXytatKiqVauWxxUBuFcIIwAAwCgu0wAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACM+v8AyUsEmh6BBo8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"property_name\")[\"revenue_realized\"].sum().round(2).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcfcdfaf",
   "metadata": {},
   "source": [
    "**Print average rating per city**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5e825005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    3.40\n",
       "Delhi        3.78\n",
       "Hyderabad    3.66\n",
       "Mumbai       3.64\n",
       "Name: ratings_given, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all.groupby(\"city\")[\"ratings_given\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81028ea9",
   "metadata": {},
   "source": [
    "**Print a pie chart of revenue realized per booking platform**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e69fe307",
   "metadata": {},
   "outputs": [
    {
     "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\", autopct='%1.1f%%')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "314b20d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}