Hotel_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a9ff217-28db-416a-838a-652532a6724d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5ce37c7-89c4-45e6-abda-12eed979bd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#==> 1. Data Import and Data Exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c21eba1-0aa4-4c27-83a0-21e60a7cc3e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings = pd.read_csv(\"datasets/fact_bookings.csv\")\n",
    "df_bookings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd313e2d-2b19-43d0-a6dd-49e4350d03f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b686620c-f2d7-4b44-8785-7425c06051e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['direct online', 'others', 'logtrip', 'tripster', 'makeyourtrip',\n",
       "       'journey', 'direct offline'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfa025e0-185e-46a0-b571-ac8a56137e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "others            55066\n",
       "makeyourtrip      26898\n",
       "logtrip           14756\n",
       "direct online     13379\n",
       "tripster           9630\n",
       "journey            8106\n",
       "direct offline     6755\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "ee9f4194-0d8d-41ce-b24f-c0510cda997a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "others            55061\n",
       "makeyourtrip      26896\n",
       "logtrip           14753\n",
       "direct online     13374\n",
       "tripster           9630\n",
       "journey            8106\n",
       "direct offline     6753\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.value_counts().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb4e3e35-1aa1-43d2-bb29-69d6084d58b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings.booking_platform.value_counts().plot(kind=\"bar\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac0a170a-2d3c-477e-9db0-80cb6b116499",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings.booking_platform.value_counts().plot(kind=\"barh\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "50bf1968-298e-4138-a41e-a17ad80d4110",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "372c732f-c0ec-4016-938b-e584fab06c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.int64(6500), np.int64(28560000))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.min(),df_bookings.revenue_generated.max(),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c585621-da10-4924-b144-fb7c23bf47c4",
   "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": 25,
   "id": "598ccece-a18f-4735-8216-e5ee91ececfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4f2bb3a-def8-4e48-9a4f-7d6deb6ba224",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e4634326-3951-4899-8363-fa2272a2e6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Mumbai       8\n",
       "Hyderabad    6\n",
       "Bangalore    6\n",
       "Delhi        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d8c84ae4-9a3f-4816-9c8a-ca5d55b115be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "3        17558      1-May-22           RT1                   30      19.0\n",
       "4        16558      1-May-22           RT1                   18      19.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "54bec069-2dca-41f6-a7f9-e904b2b003c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_category\n",
       "RT1    2300\n",
       "RT2    2300\n",
       "RT3    2300\n",
       "RT4    2300\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.room_category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b436ec0b-42bd-402f-8596-7eaf226ffed1",
   "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])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-1. Find out unique property ids in aggregate bookings dataset\n",
    "df_agg_bookings.property_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50e746a4-a860-48b0-8b8b-f259f1d32f7f",
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-2. Find out total bookings per property_id\n",
    "df_agg_bookings.groupby('property_id')['successful_bookings'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3f032830-9925-43fa-abe1-7383038038d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "      <td>17558</td>\n",
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       "      <td>16563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>100</td>\n",
       "      <td>41.0</td>\n",
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       "    <tr>\n",
       "      <th>4136</th>\n",
       "      <td>19558</td>\n",
       "      <td>11-Jun-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>50</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>19560</td>\n",
       "      <td>2-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>123</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>19559</td>\n",
       "      <td>25-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>35</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>18563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>20</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "3           17558      1-May-22           RT1                   30      19.0\n",
       "12          16563      1-May-22           RT1                  100      41.0\n",
       "4136        19558     11-Jun-22           RT2                   50      39.0\n",
       "6209        19560      2-Jul-22           RT1                  123      26.0\n",
       "8522        19559     25-Jul-22           RT1                   35      24.0\n",
       "9194        18563     31-Jul-22           RT4                   20      18.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-3. Find out days on which bookings are greater than capacity\n",
    "df_agg_bookings[df_agg_bookings.successful_bookings>df_agg_bookings.capacity]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "82d8ee9d-2ce5-46d4-ba9c-1b1b26d436b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(50.0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-4. Find out properties that have highest capacity\n",
    "df_agg_bookings.capacity.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6ba59e19-2b6a-4aa7-a410-744b91884c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>229</th>\n",
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       "      <td>3-May-22</td>\n",
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       "      <th>328</th>\n",
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       "      <td>27</td>\n",
       "      <td>50.0</td>\n",
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       "      <th>428</th>\n",
       "      <td>17558</td>\n",
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       "      <td>29</td>\n",
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       "      <td>22</td>\n",
       "      <td>50.0</td>\n",
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       "      <th>8828</th>\n",
       "      <td>17558</td>\n",
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       "      <td>50.0</td>\n",
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       "      <th>8928</th>\n",
       "      <td>17558</td>\n",
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       "    <tr>\n",
       "      <th>9128</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>30</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity\n",
       "27          17558      1-May-22           RT2                   38      50.0\n",
       "128         17558      2-May-22           RT2                   27      50.0\n",
       "229         17558      3-May-22           RT2                   26      50.0\n",
       "328         17558      4-May-22           RT2                   27      50.0\n",
       "428         17558      5-May-22           RT2                   29      50.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "8728        17558     27-Jul-22           RT2                   22      50.0\n",
       "8828        17558     28-Jul-22           RT2                   21      50.0\n",
       "8928        17558     29-Jul-22           RT2                   23      50.0\n",
       "9028        17558     30-Jul-22           RT2                   32      50.0\n",
       "9128        17558     31-Jul-22           RT2                   30      50.0\n",
       "\n",
       "[92 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings[df_agg_bookings.capacity == df_agg_bookings.capacity.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1767426f-eed6-42f8-a7db-ba3da0b3efad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#==> 2. Data Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "998c9742-14d6-4c31-ada2-dcb172449b8d",
   "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>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134578.000000</td>\n",
       "      <td>134578.000000</td>\n",
       "      <td>56679.000000</td>\n",
       "      <td>1.345780e+05</td>\n",
       "      <td>134578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.143315</td>\n",
       "      <td>2.036744</td>\n",
       "      <td>3.619048</td>\n",
       "      <td>1.537804e+04</td>\n",
       "      <td>12696.011822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.053454</td>\n",
       "      <td>1.031710</td>\n",
       "      <td>1.234970</td>\n",
       "      <td>9.304015e+04</td>\n",
       "      <td>6927.841641</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>1.000000</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  134578.000000  134578.000000   56679.000000       1.345780e+05   \n",
       "mean    18061.143315       2.036744       3.619048       1.537804e+04   \n",
       "std      1093.053454       1.031710       1.234970       9.304015e+04   \n",
       "min     16558.000000       1.000000       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     134578.000000  \n",
       "mean       12696.011822  \n",
       "std         6927.841641  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "de88cc07-7ca2-4e6f-bbef-56c5e418c5b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>May012216558RT11</td>\n",
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       "      <td>10010</td>\n",
       "      <td>10010</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>17924</th>\n",
       "      <td>May122218559RT44</td>\n",
       "      <td>18559</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>20900</td>\n",
       "      <td>20900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18020</th>\n",
       "      <td>May122218561RT22</td>\n",
       "      <td>18561</td>\n",
       "      <td>8/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>14-05-22</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9000</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18119</th>\n",
       "      <td>May122218562RT311</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>16800</td>\n",
       "      <td>16800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18121</th>\n",
       "      <td>May122218562RT313</td>\n",
       "      <td>18562</td>\n",
       "      <td>10/5/2022</td>\n",
       "      <td>12/5/2022</td>\n",
       "      <td>17-05-22</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>14400</td>\n",
       "      <td>5760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56715</th>\n",
       "      <td>Jun082218562RT12</td>\n",
       "      <td>18562</td>\n",
       "      <td>5/6/2022</td>\n",
       "      <td>8/6/2022</td>\n",
       "      <td>13-06-22</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>6500</td>\n",
       "      <td>6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119765</th>\n",
       "      <td>Jul202219560RT220</td>\n",
       "      <td>19560</td>\n",
       "      <td>19-07-22</td>\n",
       "      <td>20-07-22</td>\n",
       "      <td>22-07-22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>13500</td>\n",
       "      <td>13500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134586</th>\n",
       "      <td>Jul312217564RT47</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT11        16558     27-04-22      1/5/2022   \n",
       "3        May012216558RT14        16558     28-04-22      1/5/2022   \n",
       "17924    May122218559RT44        18559    12/5/2022     12/5/2022   \n",
       "18020    May122218561RT22        18561     8/5/2022     12/5/2022   \n",
       "18119   May122218562RT311        18562     5/5/2022     12/5/2022   \n",
       "18121   May122218562RT313        18562    10/5/2022     12/5/2022   \n",
       "56715    Jun082218562RT12        18562     5/6/2022      8/6/2022   \n",
       "119765  Jul202219560RT220        19560     19-07-22      20-07-22   \n",
       "134586   Jul312217564RT47        17564     30-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "0           2/5/2022       -3.0           RT1    direct online            1.0   \n",
       "3           2/5/2022       -2.0           RT1           others            NaN   \n",
       "17924       14-05-22      -10.0           RT4    direct online            NaN   \n",
       "18020       14-05-22      -12.0           RT2     makeyourtrip            NaN   \n",
       "18119       17-05-22       -6.0           RT3   direct offline            5.0   \n",
       "18121       17-05-22       -4.0           RT3    direct online            NaN   \n",
       "56715       13-06-22      -17.0           RT1           others            NaN   \n",
       "119765      22-07-22       -1.0           RT2           others            NaN   \n",
       "134586      1/8/2022       -4.0           RT4          logtrip            2.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "0         Checked Out              10010             10010  \n",
       "3           Cancelled               9100              3640  \n",
       "17924         No Show              20900             20900  \n",
       "18020       Cancelled               9000              3600  \n",
       "18119     Checked Out              16800             16800  \n",
       "18121       Cancelled              14400              5760  \n",
       "56715     Checked Out               6500              6500  \n",
       "119765    Checked Out              13500             13500  \n",
       "134586    Checked Out              38760             38760  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.no_guests<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4b63df22-8333-4ab4-96d7-6414e871d8f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "57dd5d73-6f23-4c81-9fa1-4bc45c2cdbdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134578, 12)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings = df_bookings[df_bookings.no_guests > 0]\n",
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c70f04c2-8b0b-4722-adfc-3983bd4722f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.int64(6500), np.int64(28560000))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.min(), df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "55edbf5a-ce89-4b42-b386-754ec9c5074e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(15378.036937686695), np.float64(93040.1549314641))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg, std = df_bookings.revenue_generated.mean(), df_bookings.revenue_generated.std()\n",
    "avg, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f8a1b4e5-9ebd-42fc-9c8a-124d6d3f90f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(294498.50173207896)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higher_limit = avg + 3*std\n",
    "higher_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bc443202-d88a-4e68-8df9-fc4695b85f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-263742.4278567056)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lower_limit = avg - 3*std\n",
    "Lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "985d9d7e-88aa-476a-a3a1-6c5401ed89e0",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [booking_id, property_id, booking_date, check_in_date, checkout_date, no_guests, room_category, booking_platform, ratings_given, booking_status, revenue_generated, revenue_realized]\n",
       "Index: []"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are in safe zone\n",
    "df_bookings[df_bookings.revenue_generated<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c22244e8-be2b-4001-897f-60ca32575ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <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>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>111</th>\n",
       "      <td>May012216559RT32</td>\n",
       "      <td>16559</td>\n",
       "      <td>29-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT3</td>\n",
       "      <td>direct online</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>28560000</td>\n",
       "      <td>28560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>May012216562RT22</td>\n",
       "      <td>16562</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>12600000</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>May012217559RT118</td>\n",
       "      <td>17559</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>2000000</td>\n",
       "      <td>4420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129176</th>\n",
       "      <td>Jul282216562RT26</td>\n",
       "      <td>16562</td>\n",
       "      <td>21-07-22</td>\n",
       "      <td>28-07-22</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>direct online</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10000000</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "111      May012216559RT32        16559     29-04-22      1/5/2022   \n",
       "315      May012216562RT22        16562     28-04-22      1/5/2022   \n",
       "562     May012217559RT118        17559     26-04-22      1/5/2022   \n",
       "129176   Jul282216562RT26        16562     21-07-22      28-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "2           4/5/2022        2.0           RT1          logtrip            5.0   \n",
       "111         2/5/2022        6.0           RT3    direct online            NaN   \n",
       "315         4/5/2022        2.0           RT2   direct offline            3.0   \n",
       "562         2/5/2022        2.0           RT1           others            NaN   \n",
       "129176      29-07-22        2.0           RT2    direct online            3.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "2         Checked Out            9100000              9100  \n",
       "111       Checked Out           28560000             28560  \n",
       "315       Checked Out           12600000             12600  \n",
       "562         Cancelled            2000000              4420  \n",
       "129176    Checked Out           10000000             12600  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated > higher_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7e241334-ea33-4ef1-b119-dcfb8f4a06db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>9100</td>\n",
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       "    <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",
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       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
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       "      <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",
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       "      <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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings = df_bookings[df_bookings.revenue_generated < higher_limit]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e78a6fe4-b4f1-43cc-84ff-95932baed7be",
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2ea3de48-e60e-4963-b633-a10e84fe9ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(33479.358661845814)"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "id": "38454ea7-7d98-4182-bf39-03579230c78b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "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",
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       "...                   ...          ...          ...           ...   \n",
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       "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_realized>higher_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0cd7339c-6064-4bc9-ac39-a12e93b64b5b",
   "metadata": {},
   "outputs": [
    {
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       "    </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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b0a73eb2-1505-41bc-b995-6e04e4f00b6c",
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.room_category == \"RT4\"].revenue_realized.describe()"
   ]
  },
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   "execution_count": 64,
   "id": "617c4b50-f7a2-44e4-9036-62d104230628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50585.105672000005"
      ]
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     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "23439.308444 + 3*9048.599076"
   ]
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   "cell_type": "code",
   "execution_count": 65,
   "id": "40438384-7fa7-4a32-bde9-b268b1b0fc3f",
   "metadata": {},
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    {
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       "        booking_id  property_id  booking_date  check_in_date  checkout_date  \\\n",
       "1            False        False         False          False          False   \n",
       "4            False        False         False          False          False   \n",
       "5            False        False         False          False          False   \n",
       "6            False        False         False          False          False   \n",
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       "\n",
       "        no_guests  room_category  booking_platform  ratings_given  \\\n",
       "1           False          False             False           True   \n",
       "4           False          False             False          False   \n",
       "5           False          False             False          False   \n",
       "6           False          False             False           True   \n",
       "7           False          False             False           True   \n",
       "...           ...            ...               ...            ...   \n",
       "134584      False          False             False          False   \n",
       "134585      False          False             False          False   \n",
       "134587      False          False             False           True   \n",
       "134588      False          False             False          False   \n",
       "134589      False          False             False           True   \n",
       "\n",
       "        booking_status  revenue_generated  revenue_realized  \n",
       "1                False              False             False  \n",
       "4                False              False             False  \n",
       "5                False              False             False  \n",
       "6                False              False             False  \n",
       "7                False              False             False  \n",
       "...                ...                ...               ...  \n",
       "134584           False              False             False  \n",
       "134585           False              False             False  \n",
       "134587           False              False             False  \n",
       "134588           False              False             False  \n",
       "134589           False              False             False  \n",
       "\n",
       "[134573 rows x 12 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
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    }
   ],
   "source": [
    "df_bookings.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d66d19dc-3433-4127-a6c1-a3a4d3e80d78",
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "33358db6-83ff-402b-98d2-e10ef65242d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id            0\n",
       "check_in_date          0\n",
       "room_category          0\n",
       "successful_bookings    0\n",
       "capacity               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-1. In aggregate bookings find columns that have null values. \n",
    "#Fill these null values with whatever you think is the appropriate subtitute (possible ways is to use mean or median)\n",
    "df_agg_bookings.isnull().sum()\n"
   ]
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   "execution_count": 86,
   "id": "b61981f4-657d-4bc6-b332-ddabe06c4dd4",
   "metadata": {},
   "outputs": [
    {
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   "execution_count": 69,
   "id": "15b926df-f362-4c72-af8d-c7cd5a8bebf1",
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    {
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     "metadata": {},
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   "execution_count": 90,
   "id": "d2480083-eebe-4a9e-91af-40121b8fb341",
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   "execution_count": 91,
   "id": "8bb45515-d038-47d0-a76c-4b81c3aeee07",
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       "    property_id check_in_date room_category  successful_bookings   capacity\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": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-2. In aggregate bookings find out records that have successful_bookings value greater than capacity. Filter those records\n",
    "df_agg_bookings[df_agg_bookings.successful_bookings > df_agg_bookings.capacity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3b1157e9-799a-4009-900f-e8d5dcb1b194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9200, 5)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cfdcd093-8012-400d-bb9b-1052f257e30e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9194, 5)"
      ]
     },
     "execution_count": 95,
     "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": "code",
   "execution_count": null,
   "id": "6b4b604c-d63d-4552-a718-eceff1527a3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#==> 3. Data Transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f90112fa-d991-4834-ab70-0f9b6cfccba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
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       "      <td>30.0</td>\n",
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       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "4        16558      1-May-22           RT1                   18      19.0\n",
       "5        17560      1-May-22           RT1                   28      40.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "41944a93-6212-48b9-843e-e34bd5a4f148",
   "metadata": {},
   "outputs": [
    {
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       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "</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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# occupency % = successful_bookings/capacity\n",
    "df_agg_bookings[\"occ_pct\"] = df_agg_bookings[\"successful_bookings\"]/df_agg_bookings[\"capacity\"]\n",
    "df_agg_bookings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ce86748d-d6a7-4c72-910d-d9b3f0c82047",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1-May-22</td>\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   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  \n",
       "4    94.74  \n",
       "5    70.00  "
      ]
     },
     "execution_count": 103,
     "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(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c41706-c732-4a8c-905b-d96cb45799d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating column \n",
    "#df['pe_ratio'] = df.apply(lambda x: x['price']/x['eps'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "373cd298-dd66-4541-9427-0fb2e904c36e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalization\n",
    "# def convert(x):\n",
    "#    ifx[\"currency\"] == 'USD':\n",
    "#        return x['price']*80\n",
    "#     return x['price']\n",
    "# df['price_inr'] = df.apply(convert, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d465642-baea-42f5-98b1-a6057225e6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge 2 table for better understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3058a5e0-8499-4d4c-8c2e-2f33f203e37e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#aggregation\n",
    "# df.groupby(\"industry\")[\"revenue\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f59ba4b7-eaba-490c-b934-285cb9ee7cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#==> 4 insight generation or Ab Hoc Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f2d341-5704-4e89-8aeb-c7a6b74cbce8",
   "metadata": {},
   "source": [
    "**1. What is the average occupancy rate in each of the room categories?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b59455d4-bc86-408f-85cd-0314ab41bf83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_category\n",
       "RT1    57.89\n",
       "RT2    58.01\n",
       "RT3    58.03\n",
       "RT4    59.28\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby(\"room_category\")[\"occ_pct\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "6ef1fd8d-f3bf-46e8-a79d-25c4114b37b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>room_id</th>\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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    "df_rooms"
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   "cell_type": "code",
   "execution_count": 112,
   "id": "e7e330cc-f69c-4ba6-8243-d4ca0ce6d108",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9189</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>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9190</th>\n",
       "      <td>16559</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>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9191</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9192</th>\n",
       "      <td>19563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9193</th>\n",
       "      <td>17561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9194 rows × 8 columns</p>\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",
       "4           17560      1-May-22           RT1                   28      40.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "9189        16563     31-Jul-22           RT4                   13      18.0   \n",
       "9190        16559     31-Jul-22           RT4                   13      18.0   \n",
       "9191        17558     31-Jul-22           RT4                    3       6.0   \n",
       "9192        19563     31-Jul-22           RT4                    3       6.0   \n",
       "9193        17561     31-Jul-22           RT4                    3       4.0   \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  \n",
       "4       70.00     RT1      Standard  \n",
       "...       ...     ...           ...  \n",
       "9189    72.22     RT4  Presidential  \n",
       "9190    72.22     RT4  Presidential  \n",
       "9191    50.00     RT4  Presidential  \n",
       "9192    50.00     RT4  Presidential  \n",
       "9193    75.00     RT4  Presidential  \n",
       "\n",
       "[9194 rows x 8 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_agg_bookings,df_rooms, left_on =\"room_category\", right_on =\"room_id\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3c7dd819-7a97-4ba2-9ac9-ebe3e05d1f53",
   "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": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"room_class\")[\"occ_pct\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f4db1e49-af7e-4423-afa0-45cdd16f4fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "      <td>Standard</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
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       "      <td>93.33</td>\n",
       "      <td>Standard</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "      <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": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#axis = 1, drop the column not row\n",
    "# inplace = true means modify that data frame\n",
    "df.drop(\"room_id\", axis=1, inplace = True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed3236b-19da-46ff-8bc9-e33a271c8291",
   "metadata": {},
   "source": [
    "**2.Print Average occupancy rate per city?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c614b166-f312-42ce-b8a9-f3a7ec7c0c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "855e0575-d56a-46d4-921f-d158f455a149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_x</th>\n",
       "      <th>category_x</th>\n",
       "      <th>city_x</th>\n",
       "      <th>property_name_y</th>\n",
       "      <th>category_y</th>\n",
       "      <th>city_y</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",
       "      <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",
       "      <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",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name_x category_x city_x  \\\n",
       "0               9100              3640    Atliq Grands     Luxury  Delhi   \n",
       "1              10920             10920    Atliq Grands     Luxury  Delhi   \n",
       "2               9100              9100    Atliq Grands     Luxury  Delhi   \n",
       "3               9100              3640    Atliq Grands     Luxury  Delhi   \n",
       "4               9100              9100    Atliq Grands     Luxury  Delhi   \n",
       "\n",
       "  property_name_y category_y city_y  \n",
       "0    Atliq Grands     Luxury  Delhi  \n",
       "1    Atliq Grands     Luxury  Delhi  \n",
       "2    Atliq Grands     Luxury  Delhi  \n",
       "3    Atliq Grands     Luxury  Delhi  \n",
       "4    Atliq Grands     Luxury  Delhi  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_hotels, on=\"property_id\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "be5bd583-5aa5-40d8-8e7f-b5e3b837c990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    56.332376\n",
       "Delhi        61.507341\n",
       "Hyderabad    58.120652\n",
       "Mumbai       57.908668\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"city\")[\"occ_pct\"].mean().plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "c682406d-f43d-4b99-a12d-69f11292c95b",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Column not found: occ_pct'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[215], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcity_x\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mocc_pct\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mmean()\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\groupby\\generic.py:1951\u001b[0m, in \u001b[0;36mDataFrameGroupBy.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(key) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1945\u001b[0m     \u001b[38;5;66;03m# if len == 1, then it becomes a SeriesGroupBy and this is actually\u001b[39;00m\n\u001b[0;32m   1946\u001b[0m     \u001b[38;5;66;03m# valid syntax, so don't raise\u001b[39;00m\n\u001b[0;32m   1947\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1948\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot subset columns with a tuple with more than one element. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1949\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse a list instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1950\u001b[0m     )\n\u001b[1;32m-> 1951\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\base.py:244\u001b[0m, in \u001b[0;36mSelectionMixin.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    242\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    243\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj:\n\u001b[1;32m--> 244\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn not found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    245\u001b[0m     ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj[key]\u001b[38;5;241m.\u001b[39mndim\n\u001b[0;32m    246\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gotitem(key, ndim\u001b[38;5;241m=\u001b[39mndim)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Column not found: occ_pct'"
     ]
    }
   ],
   "source": [
    "df.groupby(\"city_x\")[\"occ_pct\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b96f899-d91f-4800-b285-a56f92d63c04",
   "metadata": {},
   "source": [
    "**3. what was the occupancy better? weekday or weekend?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "c6d084c2-a378-4ee4-84fb-eb0d0d7f4953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>27-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 4 columns</p>\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\n",
       "3   04-May-22  May 22    W 19  weekeday\n",
       "4   05-May-22  May 22    W 19  weekeday\n",
       "..        ...     ...     ...       ...\n",
       "87  27-Jul-22  Jul 22    W 31  weekeday\n",
       "88  28-Jul-22  Jul 22    W 31  weekeday\n",
       "89  29-Jul-22  Jul 22    W 31  weekeday\n",
       "90  30-Jul-22  Jul 22    W 31   weekend\n",
       "91  31-Jul-22  Jul 22    W 32   weekend\n",
       "\n",
       "[92 rows x 4 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "814f5660-64d0-4cb6-9505-c167ff08c01b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name_x</th>\n",
       "      <th>category_x</th>\n",
       "      <th>city_x</th>\n",
       "      <th>property_name_y</th>\n",
       "      <th>category_y</th>\n",
       "      <th>city_y</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>19563</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>15</td>\n",
       "      <td>29.0</td>\n",
       "      <td>51.72</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</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>18560</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>19</td>\n",
       "      <td>30.0</td>\n",
       "      <td>63.33</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</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>19562</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</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>3</th>\n",
       "      <td>19563</td>\n",
       "      <td>10-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 Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</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>4</th>\n",
       "      <td>17558</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>11</td>\n",
       "      <td>19.0</td>\n",
       "      <td>57.89</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Atliq Grands</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>...</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",
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       "      <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>6492</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>16559</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 Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>19563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>17561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6497 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0           19563     10-May-22           RT3                   15      29.0   \n",
       "1           18560     10-May-22           RT1                   19      30.0   \n",
       "2           19562     10-May-22           RT1                   18      30.0   \n",
       "3           19563     10-May-22           RT1                   16      30.0   \n",
       "4           17558     10-May-22           RT1                   11      19.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "6492        16563     31-Jul-22           RT4                   13      18.0   \n",
       "6493        16559     31-Jul-22           RT4                   13      18.0   \n",
       "6494        17558     31-Jul-22           RT4                    3       6.0   \n",
       "6495        19563     31-Jul-22           RT4                    3       6.0   \n",
       "6496        17561     31-Jul-22           RT4                    3       4.0   \n",
       "\n",
       "      occ_pct    room_class property_name_x category_x     city_x  \\\n",
       "0       51.72       Premium    Atliq Palace   Business  Bangalore   \n",
       "1       63.33      Standard      Atliq City   Business  Hyderabad   \n",
       "2       60.00      Standard       Atliq Bay     Luxury  Bangalore   \n",
       "3       53.33      Standard    Atliq Palace   Business  Bangalore   \n",
       "4       57.89      Standard    Atliq Grands     Luxury     Mumbai   \n",
       "...       ...           ...             ...        ...        ...   \n",
       "6492    72.22  Presidential    Atliq Palace   Business      Delhi   \n",
       "6493    72.22  Presidential   Atliq Exotica     Luxury     Mumbai   \n",
       "6494    50.00  Presidential    Atliq Grands     Luxury     Mumbai   \n",
       "6495    50.00  Presidential    Atliq Palace   Business  Bangalore   \n",
       "6496    75.00  Presidential       Atliq Blu     Luxury     Mumbai   \n",
       "\n",
       "     property_name_y category_y     city_y       date  mmm yy week no  \\\n",
       "0       Atliq Palace   Business  Bangalore  10-May-22  May 22    W 20   \n",
       "1         Atliq City   Business  Hyderabad  10-May-22  May 22    W 20   \n",
       "2          Atliq Bay     Luxury  Bangalore  10-May-22  May 22    W 20   \n",
       "3       Atliq Palace   Business  Bangalore  10-May-22  May 22    W 20   \n",
       "4       Atliq Grands     Luxury     Mumbai  10-May-22  May 22    W 20   \n",
       "...              ...        ...        ...        ...     ...     ...   \n",
       "6492    Atliq Palace   Business      Delhi  31-Jul-22  Jul 22    W 32   \n",
       "6493   Atliq Exotica     Luxury     Mumbai  31-Jul-22  Jul 22    W 32   \n",
       "6494    Atliq Grands     Luxury     Mumbai  31-Jul-22  Jul 22    W 32   \n",
       "6495    Atliq Palace   Business  Bangalore  31-Jul-22  Jul 22    W 32   \n",
       "6496       Atliq Blu     Luxury     Mumbai  31-Jul-22  Jul 22    W 32   \n",
       "\n",
       "      day_type  \n",
       "0     weekeday  \n",
       "1     weekeday  \n",
       "2     weekeday  \n",
       "3     weekeday  \n",
       "4     weekeday  \n",
       "...        ...  \n",
       "6492   weekend  \n",
       "6493   weekend  \n",
       "6494   weekend  \n",
       "6495   weekend  \n",
       "6496   weekend  \n",
       "\n",
       "[6497 rows x 17 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, df_date, left_on = \"check_in_date\", right_on = \"date\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b1af0ebe-d9f5-4e0d-aa71-0a5e76252637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_type\n",
       "weekeday    50.88\n",
       "weekend     72.34\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"day_type\")[\"occ_pct\"].mean().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5316cb75-a70b-40cc-9ec1-f77da5ceb627",
   "metadata": {},
   "source": [
    "**4. in june, what is the occupancy for different cities?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2821262c-66da-46d4-a055-e19e7ad2fe05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mmm yy\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "29574ecb-37d0-43c8-ad82-e657e25c933b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name_x</th>\n",
       "      <th>category_x</th>\n",
       "      <th>city_x</th>\n",
       "      <th>property_name_y</th>\n",
       "      <th>category_y</th>\n",
       "      <th>city_y</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>30.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>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>19562</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>19</td>\n",
       "      <td>30.0</td>\n",
       "      <td>63.33</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</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>19563</td>\n",
       "      <td>10-Jun-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 Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</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>17558</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>47.37</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Atliq Grands</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>2204</th>\n",
       "      <td>16558</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>11</td>\n",
       "      <td>19.0</td>\n",
       "      <td>57.89</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>10-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 24</td>\n",
       "      <td>weekeday</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <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>4294</th>\n",
       "      <td>17562</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4295</th>\n",
       "      <td>19563</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4296</th>\n",
       "      <td>16560</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42.86</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4297</th>\n",
       "      <td>19558</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42.86</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4298</th>\n",
       "      <td>17561</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>30-Jun-22</td>\n",
       "      <td>Jun 22</td>\n",
       "      <td>W 27</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2099 rows × 17 columns</p>\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        19562     10-Jun-22           RT1                   19      30.0   \n",
       "2202        19563     10-Jun-22           RT1                   17      30.0   \n",
       "2203        17558     10-Jun-22           RT1                    9      19.0   \n",
       "2204        16558     10-Jun-22           RT1                   11      19.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "4294        17562     30-Jun-22           RT4                    3       6.0   \n",
       "4295        19563     30-Jun-22           RT4                    3       6.0   \n",
       "4296        16560     30-Jun-22           RT4                    3       7.0   \n",
       "4297        19558     30-Jun-22           RT4                    3       7.0   \n",
       "4298        17561     30-Jun-22           RT4                    3       4.0   \n",
       "\n",
       "      occ_pct    room_class property_name_x category_x     city_x  \\\n",
       "2200    66.67      Standard   Atliq Exotica     Luxury     Mumbai   \n",
       "2201    63.33      Standard       Atliq Bay     Luxury  Bangalore   \n",
       "2202    56.67      Standard    Atliq Palace   Business  Bangalore   \n",
       "2203    47.37      Standard    Atliq Grands     Luxury     Mumbai   \n",
       "2204    57.89      Standard    Atliq Grands     Luxury      Delhi   \n",
       "...       ...           ...             ...        ...        ...   \n",
       "4294    50.00  Presidential       Atliq Bay     Luxury     Mumbai   \n",
       "4295    50.00  Presidential    Atliq Palace   Business  Bangalore   \n",
       "4296    42.86  Presidential      Atliq City   Business      Delhi   \n",
       "4297    42.86  Presidential    Atliq Grands     Luxury  Bangalore   \n",
       "4298    75.00  Presidential       Atliq Blu     Luxury     Mumbai   \n",
       "\n",
       "     property_name_y category_y     city_y       date  mmm yy week no  \\\n",
       "2200   Atliq Exotica     Luxury     Mumbai  10-Jun-22  Jun 22    W 24   \n",
       "2201       Atliq Bay     Luxury  Bangalore  10-Jun-22  Jun 22    W 24   \n",
       "2202    Atliq Palace   Business  Bangalore  10-Jun-22  Jun 22    W 24   \n",
       "2203    Atliq Grands     Luxury     Mumbai  10-Jun-22  Jun 22    W 24   \n",
       "2204    Atliq Grands     Luxury      Delhi  10-Jun-22  Jun 22    W 24   \n",
       "...              ...        ...        ...        ...     ...     ...   \n",
       "4294       Atliq Bay     Luxury     Mumbai  30-Jun-22  Jun 22    W 27   \n",
       "4295    Atliq Palace   Business  Bangalore  30-Jun-22  Jun 22    W 27   \n",
       "4296      Atliq City   Business      Delhi  30-Jun-22  Jun 22    W 27   \n",
       "4297    Atliq Grands     Luxury  Bangalore  30-Jun-22  Jun 22    W 27   \n",
       "4298       Atliq Blu     Luxury     Mumbai  30-Jun-22  Jun 22    W 27   \n",
       "\n",
       "      day_type  \n",
       "2200  weekeday  \n",
       "2201  weekeday  \n",
       "2202  weekeday  \n",
       "2203  weekeday  \n",
       "2204  weekeday  \n",
       "...        ...  \n",
       "4294  weekeday  \n",
       "4295  weekeday  \n",
       "4296  weekeday  \n",
       "4297  weekeday  \n",
       "4298  weekeday  \n",
       "\n",
       "[2099 rows x 17 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jun22 = df[df[\"mmm yy\"]==\"Jun 22\"]\n",
    "df_jun22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "82abebeb-cadf-43cc-bbec-14bd0281b065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city_x\n",
       "Bangalore    56.44\n",
       "Mumbai       58.38\n",
       "Hyderabad    58.46\n",
       "Delhi        62.47\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jun22.groupby(\"city_x\")[\"occ_pct\"].mean().round(2).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "e4041c62-6591-449c-99bc-ec3d758d9b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city_x\n",
       "Delhi        62.47\n",
       "Hyderabad    58.46\n",
       "Mumbai       58.38\n",
       "Bangalore    56.44\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jun22.groupby(\"city_x\")[\"occ_pct\"].mean().round(2).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b4134cc1-a929-40ae-a740-83ad46d2c725",
   "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>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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
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       "      <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",
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       "    <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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19558</td>\n",
       "      <td>Atliq Grands</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>30</td>\n",
       "      <td>40</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19560</td>\n",
       "      <td>Atliq City</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>20</td>\n",
       "      <td>26</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17561</td>\n",
       "      <td>Atliq Blu</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>18</td>\n",
       "      <td>26</td>\n",
       "      <td>69.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17564</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</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>10</td>\n",
       "      <td>16</td>\n",
       "      <td>62.50</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",
       "3        19558   Atliq Grands    Luxury  Bangalore           RT1   Standard   \n",
       "4        19560     Atliq City  Business  Bangalore           RT1   Standard   \n",
       "5        17561      Atliq Blu    Luxury     Mumbai           RT1   Standard   \n",
       "6        17564  Atliq Seasons  Business     Mumbai           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",
       "3     01-Aug-22  Aug-22    W 32  weekeday                   30        40   \n",
       "4     01-Aug-22  Aug-22    W 32  weekeday                   20        26   \n",
       "5     01-Aug-22  Aug-22    W 32  weekeday                   18        26   \n",
       "6     01-Aug-22  Aug-22    W 32  weekeday                   10        16   \n",
       "\n",
       "     occ%  \n",
       "0  100.00  \n",
       "1   70.00  \n",
       "2   76.67  \n",
       "3   75.00  \n",
       "4   76.92  \n",
       "5   69.23  \n",
       "6   62.50  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august = pd.read_csv(\"datasets/new_data_august.csv\")\n",
    "df_august"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "58141231-080e-4526-9e10-bd8f16c365a1",
   "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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "2e580f65-a96e-43e1-8cfc-494b7791c78b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_id', 'check_in_date', 'room_category', 'successful_bookings',\n",
       "       'capacity', 'occ_pct', 'room_class', 'property_name_x', 'category_x',\n",
       "       'city_x', 'property_name_y', 'category_y', 'city_y', 'date', 'mmm yy',\n",
       "       'week no', 'day_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d338bc4c-fab3-413f-9a25-08c5f6f9a6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 13)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "1067cf0b-5878-4c4b-bc6c-2e7240e55579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6497, 17)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "e7b93a7e-d758-4c69-92cc-eac43c350d11",
   "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_x</th>\n",
       "      <th>category_x</th>\n",
       "      <th>city_x</th>\n",
       "      <th>...</th>\n",
       "      <th>category_y</th>\n",
       "      <th>city_y</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>...</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>19563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>...</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>17561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>...</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "6494        17558     31-Jul-22           RT4                    3       6.0   \n",
       "6495        19563     31-Jul-22           RT4                    3       6.0   \n",
       "6496        17561     31-Jul-22           RT4                    3       4.0   \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_x category_x     city_x  ...  \\\n",
       "6494     50.0  Presidential    Atliq Grands     Luxury     Mumbai  ...   \n",
       "6495     50.0  Presidential    Atliq Palace   Business  Bangalore  ...   \n",
       "6496     75.0  Presidential       Atliq Blu     Luxury     Mumbai  ...   \n",
       "6497      NaN      Standard             NaN        NaN        NaN  ...   \n",
       "6498      NaN      Standard             NaN        NaN        NaN  ...   \n",
       "6499      NaN      Standard             NaN        NaN        NaN  ...   \n",
       "6500      NaN      Standard             NaN        NaN        NaN  ...   \n",
       "6501      NaN      Standard             NaN        NaN        NaN  ...   \n",
       "6502      NaN      Standard             NaN        NaN        NaN  ...   \n",
       "6503      NaN      Standard             NaN        NaN        NaN  ...   \n",
       "\n",
       "     category_y     city_y       date  mmm yy week no  day_type  \\\n",
       "6494     Luxury     Mumbai  31-Jul-22  Jul 22    W 32   weekend   \n",
       "6495   Business  Bangalore  31-Jul-22  Jul 22    W 32   weekend   \n",
       "6496     Luxury     Mumbai  31-Jul-22  Jul 22    W 32   weekend   \n",
       "6497        NaN        NaN        NaN  Aug-22    W 32  weekeday   \n",
       "6498        NaN        NaN        NaN  Aug-22    W 32  weekeday   \n",
       "6499        NaN        NaN        NaN  Aug-22    W 32  weekeday   \n",
       "6500        NaN        NaN        NaN  Aug-22    W 32  weekeday   \n",
       "6501        NaN        NaN        NaN  Aug-22    W 32  weekeday   \n",
       "6502        NaN        NaN        NaN  Aug-22    W 32  weekeday   \n",
       "6503        NaN        NaN        NaN  Aug-22    W 32  weekeday   \n",
       "\n",
       "      property_name  category       city    occ%  \n",
       "6494            NaN       NaN        NaN     NaN  \n",
       "6495            NaN       NaN        NaN     NaN  \n",
       "6496            NaN       NaN        NaN     NaN  \n",
       "6497  Atliq Exotica    Luxury     Mumbai  100.00  \n",
       "6498      Atliq Bay    Luxury  Bangalore   70.00  \n",
       "6499   Atliq Palace  Business  Bangalore   76.67  \n",
       "6500   Atliq Grands    Luxury  Bangalore   75.00  \n",
       "6501     Atliq City  Business  Bangalore   76.92  \n",
       "6502      Atliq Blu    Luxury     Mumbai   69.23  \n",
       "6503  Atliq Seasons  Business     Mumbai   62.50  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 150,
     "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": "markdown",
   "id": "4e25d83f-adda-4376-9779-2f65e194375e",
   "metadata": {},
   "source": [
    "**6.print revenue realized per city?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1f58ab1e-a5d7-4ae0-928f-b4e1fe32e26a",
   "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>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",
       "  </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",
       "\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",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "1               9100              3640  \n",
       "4              10920             10920  \n",
       "5               9100              9100  \n",
       "6               9100              3640  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "2c13f972-9cba-4bba-9287-6e4d36090df3",
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "a71badfa-d03b-4617-801b-bf081a52e313",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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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",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
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       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT19</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>tripster</td>\n",
       "      <td>NaN</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>6</th>\n",
       "      <td>May012216558RT110</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>1.0</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>7</th>\n",
       "      <td>May012216558RT111</td>\n",
       "      <td>16558</td>\n",
       "      <td>29-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>7/5/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>5.0</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>8</th>\n",
       "      <td>May012216558RT112</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</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",
       "3   May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "4   May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "5   May012216558RT19        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "6  May012216558RT110        16558     28-04-22      1/5/2022      2/5/2022   \n",
       "7  May012216558RT111        16558     29-04-22      1/5/2022      7/5/2022   \n",
       "8  May012216558RT112        16558     26-04-22      1/5/2022      2/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "4        2.0           RT1          logtrip            NaN        No Show   \n",
       "5        2.0           RT1         tripster            NaN    Checked Out   \n",
       "6        1.0           RT1           others            4.0    Checked Out   \n",
       "7        1.0           RT1     makeyourtrip            5.0    Checked Out   \n",
       "8        2.0           RT1          logtrip            5.0    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  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "4               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "5               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "6               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "7               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "8               9100              9100  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all = pd.merge(df_bookings, df_hotels, on = \"property_id\")\n",
    "df_booking_all.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3db30f83-4201-4e64-b1c7-ad7683c73403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Delhi        294404488\n",
       "Hyderabad    325179310\n",
       "Bangalore    420383550\n",
       "Mumbai       668569251\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all.groupby(\"city\")[\"revenue_realized\"].sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaf7487-cab5-445f-bec6-7a9de06fa8a7",
   "metadata": {},
   "source": [
    "**7.print month by month revenue?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "e72f45b4-12ac-44d4-9a54-8a034f9f1da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
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       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
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       "      <td>16558</td>\n",
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       "      <td>Delhi</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",
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       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
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       "    <tr>\n",
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       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
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       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
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       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
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       "      <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      1/5/2022      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "\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": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all.head(4)"
   ]
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   "execution_count": 174,
   "id": "743b9f17-3369-414e-b3fb-0abcc193c429",
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    {
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       "        date  mmm yy week no  day_type\n",
       "0 2022-05-01  May 22    W 19   weekend\n",
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   "id": "e1538bdc-4ff7-49e9-9f1c-00a9b5fceebe",
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       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13795 rows × 23 columns</p>\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",
       "3      May052216558RT14        16558     3/5/2022    2022-05-05      6/5/2022   \n",
       "4      May052216558RT15        16558     30-04-22    2022-05-05     10/5/2022   \n",
       "...                 ...          ...          ...           ...           ...   \n",
       "13790  Jul072217564RT44        17564     4/7/2022    2022-07-07      8/7/2022   \n",
       "13791  Jul072217564RT45        17564     5/7/2022    2022-07-07      8/7/2022   \n",
       "13792  Jul072217564RT46        17564     5/7/2022    2022-07-07      9/7/2022   \n",
       "13793  Jul072217564RT47        17564     5/7/2022    2022-07-07      8/7/2022   \n",
       "13794  Jul072217564RT48        17564     5/7/2022    2022-07-07      8/7/2022   \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",
       "3            2.0           RT1         tripster            3.0    Checked Out   \n",
       "4            4.0           RT1           others            4.0    Checked Out   \n",
       "...          ...           ...              ...            ...            ...   \n",
       "13790        1.0           RT4     makeyourtrip            NaN    Checked Out   \n",
       "13791        1.0           RT4           others            2.0    Checked Out   \n",
       "13792        1.0           RT4     makeyourtrip            NaN    Checked Out   \n",
       "13793        2.0           RT4           others            NaN      Cancelled   \n",
       "13794        1.0           RT4          journey            1.0    Checked Out   \n",
       "\n",
       "       ...  category    city     date_x mmm yy_x week no_x day_type_x  \\\n",
       "0      ...    Luxury   Delhi 2022-05-05   May 22      W 19   weekeday   \n",
       "1      ...    Luxury   Delhi 2022-05-05   May 22      W 19   weekeday   \n",
       "2      ...    Luxury   Delhi 2022-05-05   May 22      W 19   weekeday   \n",
       "3      ...    Luxury   Delhi 2022-05-05   May 22      W 19   weekeday   \n",
       "4      ...    Luxury   Delhi 2022-05-05   May 22      W 19   weekeday   \n",
       "...    ...       ...     ...        ...      ...       ...        ...   \n",
       "13790  ...  Business  Mumbai 2022-07-07   Jul 22      W 28   weekeday   \n",
       "13791  ...  Business  Mumbai 2022-07-07   Jul 22      W 28   weekeday   \n",
       "13792  ...  Business  Mumbai 2022-07-07   Jul 22      W 28   weekeday   \n",
       "13793  ...  Business  Mumbai 2022-07-07   Jul 22      W 28   weekeday   \n",
       "13794  ...  Business  Mumbai 2022-07-07   Jul 22      W 28   weekeday   \n",
       "\n",
       "          date_y mmm yy_y week no_y day_type_y  \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  \n",
       "3     2022-05-05   May 22      W 19   weekeday  \n",
       "4     2022-05-05   May 22      W 19   weekeday  \n",
       "...          ...      ...       ...        ...  \n",
       "13790 2022-07-07   Jul 22      W 28   weekeday  \n",
       "13791 2022-07-07   Jul 22      W 28   weekeday  \n",
       "13792 2022-07-07   Jul 22      W 28   weekeday  \n",
       "13793 2022-07-07   Jul 22      W 28   weekeday  \n",
       "13794 2022-07-07   Jul 22      W 28   weekeday  \n",
       "\n",
       "[13795 rows x 23 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df_booking_all, df_date, left_on=\"check_in_date\", right_on=\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "085f7090-6852-4dac-9a38-1ad41a9ce702",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13795 entries, 0 to 13794\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   booking_id         13795 non-null  object        \n",
      " 1   property_id        13795 non-null  int64         \n",
      " 2   booking_date       13795 non-null  object        \n",
      " 3   check_in_date      13795 non-null  datetime64[ns]\n",
      " 4   checkout_date      13795 non-null  object        \n",
      " 5   no_guests          13795 non-null  float64       \n",
      " 6   room_category      13795 non-null  object        \n",
      " 7   booking_platform   13795 non-null  object        \n",
      " 8   ratings_given      5673 non-null   float64       \n",
      " 9   booking_status     13795 non-null  object        \n",
      " 10  revenue_generated  13795 non-null  int64         \n",
      " 11  revenue_realized   13795 non-null  int64         \n",
      " 12  property_name      13795 non-null  object        \n",
      " 13  category           13795 non-null  object        \n",
      " 14  city               13795 non-null  object        \n",
      " 15  date               13795 non-null  datetime64[ns]\n",
      " 16  mmm yy             13795 non-null  object        \n",
      " 17  week no            13795 non-null  object        \n",
      " 18  day_type           13795 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(12)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#bcoz check_in_date is a object \n",
    "df_booking_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "6921df7a-7276-41b2-8862-648d28debc9c",
   "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     datetime64[ns]\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: datetime64[ns](1), object(3)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "##bcoz date is a object \n",
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "ba84ecd1-740f-4d51-a098-edad904a3803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2022-05-01\n",
       "1    2022-05-02\n",
       "2    2022-05-03\n",
       "3    2022-05-04\n",
       "4    2022-05-05\n",
       "        ...    \n",
       "87   2022-07-27\n",
       "88   2022-07-28\n",
       "89   2022-07-29\n",
       "90   2022-07-30\n",
       "91   2022-07-31\n",
       "Name: date, Length: 92, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df_date[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "7eb929fb-db41-4290-abcf-97af13ec8d25",
   "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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-05-04</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\n",
       "3 2022-05-04  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date[\"date\"]= pd.to_datetime(df_date[\"date\"])\n",
    "df_date.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "de41a530-64b1-4a17-a57a-ef48550ac7ed",
   "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     datetime64[ns]\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: datetime64[ns](1), object(3)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# date  ====>    datetime64[ns]\n",
    "\n",
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "0b03f7ce-9a54-41c0-a197-e6e518565960",
   "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",
       "      <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",
       "      <td>7/5/2022</td>\n",
       "      <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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May052216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>tripster</td>\n",
       "      <td>3.0</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",
       "      <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>4</th>\n",
       "      <td>May052216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>10/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</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",
       "      <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>...</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",
       "      <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>13790</th>\n",
       "      <td>Jul072217564RT44</td>\n",
       "      <td>17564</td>\n",
       "      <td>4/7/2022</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>8/7/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 28</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13791</th>\n",
       "      <td>Jul072217564RT45</td>\n",
       "      <td>17564</td>\n",
       "      <td>5/7/2022</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>8/7/2022</td>\n",
       "      <td>1.0</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",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 28</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13792</th>\n",
       "      <td>Jul072217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>5/7/2022</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>9/7/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 28</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13793</th>\n",
       "      <td>Jul072217564RT47</td>\n",
       "      <td>17564</td>\n",
       "      <td>5/7/2022</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>8/7/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 28</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13794</th>\n",
       "      <td>Jul072217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>5/7/2022</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>8/7/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>journey</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 28</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13795 rows × 19 columns</p>\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",
       "3      May052216558RT14        16558     3/5/2022    2022-05-05      6/5/2022   \n",
       "4      May052216558RT15        16558     30-04-22    2022-05-05     10/5/2022   \n",
       "...                 ...          ...          ...           ...           ...   \n",
       "13790  Jul072217564RT44        17564     4/7/2022    2022-07-07      8/7/2022   \n",
       "13791  Jul072217564RT45        17564     5/7/2022    2022-07-07      8/7/2022   \n",
       "13792  Jul072217564RT46        17564     5/7/2022    2022-07-07      9/7/2022   \n",
       "13793  Jul072217564RT47        17564     5/7/2022    2022-07-07      8/7/2022   \n",
       "13794  Jul072217564RT48        17564     5/7/2022    2022-07-07      8/7/2022   \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",
       "3            2.0           RT1         tripster            3.0    Checked Out   \n",
       "4            4.0           RT1           others            4.0    Checked Out   \n",
       "...          ...           ...              ...            ...            ...   \n",
       "13790        1.0           RT4     makeyourtrip            NaN    Checked Out   \n",
       "13791        1.0           RT4           others            2.0    Checked Out   \n",
       "13792        1.0           RT4     makeyourtrip            NaN    Checked Out   \n",
       "13793        2.0           RT4           others            NaN      Cancelled   \n",
       "13794        1.0           RT4          journey            1.0    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",
       "3                   9100              9100   Atliq Grands    Luxury   Delhi   \n",
       "4                  10920             10920   Atliq Grands    Luxury   Delhi   \n",
       "...                  ...               ...            ...       ...     ...   \n",
       "13790              32300             32300  Atliq Seasons  Business  Mumbai   \n",
       "13791              32300             32300  Atliq Seasons  Business  Mumbai   \n",
       "13792              32300             32300  Atliq Seasons  Business  Mumbai   \n",
       "13793              32300             12920  Atliq Seasons  Business  Mumbai   \n",
       "13794              32300             32300  Atliq Seasons  Business  Mumbai   \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  \n",
       "3     2022-05-05  May 22    W 19  weekeday  \n",
       "4     2022-05-05  May 22    W 19  weekeday  \n",
       "...          ...     ...     ...       ...  \n",
       "13790 2022-07-07  Jul 22    W 28  weekeday  \n",
       "13791 2022-07-07  Jul 22    W 28  weekeday  \n",
       "13792 2022-07-07  Jul 22    W 28  weekeday  \n",
       "13793 2022-07-07  Jul 22    W 28  weekeday  \n",
       "13794 2022-07-07  Jul 22    W 28  weekeday  \n",
       "\n",
       "[13795 rows x 19 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all['check_in_date'] = pd.to_datetime(df_booking_all['check_in_date'], errors='coerce')\n",
    "df_booking_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "9fb34165-9d40-42ae-a2c7-8d0b40bcee54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13795 entries, 0 to 13794\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   booking_id         13795 non-null  object        \n",
      " 1   property_id        13795 non-null  int64         \n",
      " 2   booking_date       13795 non-null  object        \n",
      " 3   check_in_date      13795 non-null  datetime64[ns]\n",
      " 4   checkout_date      13795 non-null  object        \n",
      " 5   no_guests          13795 non-null  float64       \n",
      " 6   room_category      13795 non-null  object        \n",
      " 7   booking_platform   13795 non-null  object        \n",
      " 8   ratings_given      5673 non-null   float64       \n",
      " 9   booking_status     13795 non-null  object        \n",
      " 10  revenue_generated  13795 non-null  int64         \n",
      " 11  revenue_realized   13795 non-null  int64         \n",
      " 12  property_name      13795 non-null  object        \n",
      " 13  category           13795 non-null  object        \n",
      " 14  city               13795 non-null  object        \n",
      " 15  date               13795 non-null  datetime64[ns]\n",
      " 16  mmm yy             13795 non-null  object        \n",
      " 17  week no            13795 non-null  object        \n",
      " 18  day_type           13795 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(12)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_booking_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "86095357-936b-4f3a-b599-cb6500fbdf67",
   "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>...</th>\n",
       "      <th>week no_x</th>\n",
       "      <th>day_type_x</th>\n",
       "      <th>date_y</th>\n",
       "      <th>mmm yy_y</th>\n",
       "      <th>week no_y</th>\n",
       "      <th>day_type_y</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",
       "      <td>7/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>tripster</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>...</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</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>...</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</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>...</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</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>3</th>\n",
       "      <td>May052216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>tripster</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>...</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</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",
       "<p>4 rows × 27 columns</p>\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",
       "3  May052216558RT14        16558     3/5/2022    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",
       "3        2.0           RT1         tripster            3.0    Checked Out   \n",
       "\n",
       "   ...  week no_x  day_type_x     date_y mmm yy_y week no_y day_type_y  \\\n",
       "0  ...       W 19    weekeday 2022-05-05   May 22      W 19   weekeday   \n",
       "1  ...       W 19    weekeday 2022-05-05   May 22      W 19   weekeday   \n",
       "2  ...       W 19    weekeday 2022-05-05   May 22      W 19   weekeday   \n",
       "3  ...       W 19    weekeday 2022-05-05   May 22      W 19   weekeday   \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  \n",
       "3 2022-05-05  May 22    W 19  weekeday  \n",
       "\n",
       "[4 rows x 27 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all = pd.merge(df_booking_all, df_date, left_on=\"check_in_date\", right_on=\"date\")\n",
    "df_booking_all.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "fe28cb17-5053-4703-987f-a0830329892d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['booking_id', 'property_id', 'booking_date', 'check_in_date',\n",
       "       'checkout_date', 'no_guests', 'room_category', 'booking_platform',\n",
       "       'ratings_given', 'booking_status', 'revenue_generated',\n",
       "       'revenue_realized', 'property_name', 'category', 'city', 'date_x',\n",
       "       'mmm yy_x', 'week no_x', 'day_type_x', 'date_y', 'mmm yy_y',\n",
       "       'week no_y', 'day_type_y', 'date', 'mmm yy', 'week no', 'day_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "fd1402eb-ad12-4a3e-bbbf-805fb7b5ca58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mmm yy_x\n",
       "Jul 22    60278496\n",
       "Jun 22    52903014\n",
       "May 22    60961428\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all.groupby(\"mmm yy_x\")[\"revenue_realized\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "af287614-ab05-4b32-98a3-4daf86e2f38d",
   "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": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-1. Print revenue realized per hotel type\n",
    "\n",
    "df_booking_all.property_name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "cd74f411-cd68-4103-82f7-12b729dc5324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_name\n",
       "Atliq Seasons     6672245\n",
       "Atliq Grands     21644446\n",
       "Atliq Blu        26459751\n",
       "Atliq Bay        26936115\n",
       "Atliq City       29047727\n",
       "Atliq Palace     30945855\n",
       "Atliq Exotica    32436799\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_booking_all.groupby(\"property_name\")[\"revenue_realized\"].sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "072c7fa0-4526-44dc-aa09-b6c9d4460741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    3.41\n",
       "Mumbai       3.63\n",
       "Hyderabad    3.65\n",
       "Delhi        3.79\n",
       "Name: ratings_given, dtype: float64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-2 Print average rating per city\n",
    "\n",
    "df_booking_all.groupby(\"city\")[\"ratings_given\"].mean().sort_values().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "bdb011f4-daf7-48b6-b575-be7dc2771ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "direct offline     8986465\n",
       "journey           10757858\n",
       "tripster          11959078\n",
       "direct online     17488976\n",
       "logtrip           18605339\n",
       "makeyourtrip      34034257\n",
       "others            72310965\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Exercise-3 Print a pie chart of revenue realized per booking platform\n",
    "df_booking_all.groupby(\"booking_platform\")[\"revenue_realized\"].sum().sort_values().round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c5e3d09-9faf-4918-8d6e-c325b1a5d358",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_booking_all.groupby(\"booking_platform\")[\"revenue_realized\"].sum().plot(kind= \"pie\")\n"
   ]
  }
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