hotel_data_analytics-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "b75fb588-02fc-4c60-a9b4-5dbf9245ea6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mp\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "eb0323d9-564a-48c1-aaad-6d9cd6876387",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "75f7bdf1-8b7e-4bcd-8536-7f924a5c1eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings = pd.read_csv(\"fact_bookings.csv\")\n",
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "2def8ea9-2c50-4ad9-b644-2ceeda298646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134590, 12)"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Exploration \n",
    "df_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "e6f5ef7d-3acc-445b-9927-1ff4563458ee",
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "9e51b9b1-ea00-44d0-87c1-8c529e297c6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134590 entries, 0 to 134589\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   booking_id         134590 non-null  object \n",
      " 1   property_id        134590 non-null  int64  \n",
      " 2   booking_date       134590 non-null  object \n",
      " 3   check_in_date      134590 non-null  object \n",
      " 4   checkout_date      134590 non-null  object \n",
      " 5   no_guests          134587 non-null  float64\n",
      " 6   room_category      134590 non-null  object \n",
      " 7   booking_platform   134590 non-null  object \n",
      " 8   ratings_given      56683 non-null   float64\n",
      " 9   booking_status     134590 non-null  object \n",
      " 10  revenue_generated  134590 non-null  int64  \n",
      " 11  revenue_realized   134590 non-null  int64  \n",
      "dtypes: float64(2), int64(3), object(7)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_bookings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "29835add-1a4b-4627-9f71-f044cfb29225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134590.000000</td>\n",
       "      <td>134587.000000</td>\n",
       "      <td>56683.000000</td>\n",
       "      <td>1.345900e+05</td>\n",
       "      <td>134590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.113493</td>\n",
       "      <td>2.036170</td>\n",
       "      <td>3.619004</td>\n",
       "      <td>1.537805e+04</td>\n",
       "      <td>12696.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.055847</td>\n",
       "      <td>1.034885</td>\n",
       "      <td>1.235009</td>\n",
       "      <td>9.303604e+04</td>\n",
       "      <td>6928.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134590.000000  134587.000000   56683.000000       1.345900e+05   \n",
       "mean    18061.113493       2.036170       3.619004       1.537805e+04   \n",
       "std      1093.055847       1.034885       1.235009       9.303604e+04   \n",
       "min     16558.000000     -17.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134590.000000  \n",
       "mean       12696.123256  \n",
       "std         6928.108124  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "4653cd7d-503b-452f-b3d4-5252483107bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16558, 16559, 16560, 16561, 16562, 16563, 17558, 17559, 17560,\n",
       "       17561, 17562, 17563, 18558, 18559, 18560, 18561, 18562, 18563,\n",
       "       19558, 19559, 19560, 19561, 19562, 19563, 17564], dtype=int64)"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.property_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "17a78b54-36b4-411b-b2ff-22f3b4b1de91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RT1', 'RT2', 'RT3', 'RT4'], dtype=object)"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.room_category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "3d1201c9-e45f-4158-9f79-9331352d4539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.dtypes[\"room_category\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "48991089-961d-41f3-bf5c-eb74a4b660cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['direct online', 'others', 'logtrip', 'tripster', 'makeyourtrip',\n",
       "       'journey', 'direct offline'], dtype=object)"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.booking_platform.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "c8b3c0bb-8bae-4ebf-a014-60cd4d5af144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_category\n",
       "RT2    49505\n",
       "RT1    38446\n",
       "RT3    30566\n",
       "RT4    16073\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.room_category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "b0b64c60-5ffd-4444-bc16-a07b259263b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='room_category'>"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cuFBTpkzRoUOH5HA4NGXKFBUUFGjnzp32PkaMGKEjR46osLBQkpSSkqK+fftq3rx5kqT6+nolJiZqwoQJmjp16nl7rampUU1Njf3e5/MpMTFR1dXVcrlcX+U0XBIuZwcX5ju4MN/AlePz+RQTE/Olf78v+cbjuro6LVu2TMePH5fb7VZpaalOnz6t1NRUu6ZLly5q3769SkpKJEklJSXq3r27HXAkyePxyOfz2VeDSkpK/LbRUNOwjdraWpWWlvrVhIaGKjU11a45n5kzZyomJsZ+JSYmXurhAwCAa1yTQ86OHTsUHR0tp9OpsWPH6t1331VSUpK8Xq8cDodatmzpVx8bGyuv1ytJ8nq9fgGnYbxh7EI1Pp9PJ0+e1OHDh1VXV9doTcM2zmfatGmqrq62X/v372/q4QMAgOtEk5+uuu2221RWVqbq6mq98847ysjI0MaNG69Eb5ed0+mU0+kMdBsAAOAqaHLIcTgc6ty5syQpOTlZW7du1dy5czV8+HDV1tbqyJEjfldzqqqqFBcXJ0mKi4s75ymohqevzq754hNZVVVVcrlcioqKUlhYmMLCwhqtadgGAADAV/4ywPr6etXU1Cg5OVkREREqLi62x/bu3auKigq53W5Jktvt1o4dO/yegioqKpLL5VJSUpJdc/Y2GmoatuFwOJScnOxXU19fr+LiYrsGAACgSVdypk2bpgceeEDt27fX0aNHtXTpUm3YsEHr1q1TTEyMRo8erezsbLVq1Uoul0sTJkyQ2+1W//79JUmDBw9WUlKSHnvsMeXl5cnr9Wr69OnKzMy0P0YaO3as5s2bp8mTJ+uJJ57Q+vXrtWLFChUU/PtJhezsbGVkZKhPnz7q16+f5syZo+PHj2vUqFGX8dQAAIDrWZNCzsGDB/X444/rwIEDiomJUY8ePbRu3Trdf//9kqTZs2crNDRU6enpqqmpkcfj0YIFC+z1w8LCtHr1ao0bN05ut1vNmzdXRkaGcnNz7ZpOnTqpoKBAWVlZmjt3rhISErRo0SJ5PB67Zvjw4Tp06JBmzJghr9erXr16qbCw8JybkQEAQPD6yt+Tcz272OfsrxS+RyO4MN/BhfkGrpwr/j05AAAA1zJCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACM1KSQM3PmTPXt21ctWrRQu3btNGTIEO3du9ev5tSpU8rMzFTr1q0VHR2t9PR0VVVV+dVUVFQoLS1NzZo1U7t27TRp0iSdOXPGr2bDhg3q3bu3nE6nOnfurPz8/HP6mT9/vjp27KjIyEilpKRoy5YtTTkcAABgsCaFnI0bNyozM1MffvihioqKdPr0aQ0ePFjHjx+3a7KysvTee+9p5cqV2rhxoyorKzV06FB7vK6uTmlpaaqtrdXmzZu1ZMkS5efna8aMGXZNeXm50tLSdO+996qsrEwTJ07UmDFjtG7dOrtm+fLlys7OVk5OjrZt26aePXvK4/Ho4MGDX+V8AAAAQ4RYlmVd6sqHDh1Su3bttHHjRt19992qrq5W27ZttXTpUg0bNkyStGfPHnXt2lUlJSXq37+/1q5dqwcffFCVlZWKjY2VJC1cuFBTpkzRoUOH5HA4NGXKFBUUFGjnzp32vkaMGKEjR46osLBQkpSSkqK+fftq3rx5kqT6+nolJiZqwoQJmjp16kX17/P5FBMTo+rqarlcrks9DZes49SCq77Pa8Fns9IC3UJAMN/BhfkGrpyL/fv9le7Jqa6uliS1atVKklRaWqrTp08rNTXVrunSpYvat2+vkpISSVJJSYm6d+9uBxxJ8ng88vl82rVrl11z9jYaahq2UVtbq9LSUr+a0NBQpaam2jWNqampkc/n83sBAAAzXXLIqa+v18SJEzVgwADdfvvtkiSv1yuHw6GWLVv61cbGxsrr9do1ZwechvGGsQvV+Hw+nTx5UocPH1ZdXV2jNQ3baMzMmTMVExNjvxITE5t+4AAA4LpwySEnMzNTO3fu1LJlyy5nP1fUtGnTVF1dbb/2798f6JYAAMAVEn4pK40fP16rV6/Wpk2blJCQYC+Pi4tTbW2tjhw54nc1p6qqSnFxcXbNF5+Canj66uyaLz6RVVVVJZfLpaioKIWFhSksLKzRmoZtNMbpdMrpdDb9gAEAwHWnSVdyLMvS+PHj9e6772r9+vXq1KmT33hycrIiIiJUXFxsL9u7d68qKirkdrslSW63Wzt27PB7CqqoqEgul0tJSUl2zdnbaKhp2IbD4VBycrJfTX19vYqLi+0aAAAQ3Jp0JSczM1NLly7Vb37zG7Vo0cK+/yUmJkZRUVGKiYnR6NGjlZ2drVatWsnlcmnChAlyu93q37+/JGnw4MFKSkrSY489pry8PHm9Xk2fPl2ZmZn2VZaxY8dq3rx5mjx5sp544gmtX79eK1asUEHBv59WyM7OVkZGhvr06aN+/fppzpw5On78uEaNGnW5zg0AALiONSnkvP7665Kke+65x2/54sWL9b3vfU+SNHv2bIWGhio9PV01NTXyeDxasGCBXRsWFqbVq1dr3Lhxcrvdat68uTIyMpSbm2vXdOrUSQUFBcrKytLcuXOVkJCgRYsWyePx2DXDhw/XoUOHNGPGDHm9XvXq1UuFhYXn3IwMAACC01f6npzrHd+TExjB+j0azHdwYb6BK+eqfE8OAADAtYqQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACOFB7oBAACudx2nFgS6hYD4bFZaoFu4IK7kAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIzU5JCzadMmPfTQQ4qPj1dISIhWrVrlN25ZlmbMmKEbb7xRUVFRSk1N1aeffupX8/nnn2vkyJFyuVxq2bKlRo8erWPHjvnV/OlPf9LXv/51RUZGKjExUXl5eef0snLlSnXp0kWRkZHq3r271qxZ09TDAQAAhmpyyDl+/Lh69uyp+fPnNzqel5en1157TQsXLtRHH32k5s2by+Px6NSpU3bNyJEjtWvXLhUVFWn16tXatGmTnnrqKXvc5/Np8ODB6tChg0pLS/Xyyy/r+eef189+9jO7ZvPmzXrkkUc0evRo/fGPf9SQIUM0ZMgQ7dy5s6mHBAAADBTe1BUeeOABPfDAA42OWZalOXPmaPr06fr2t78tSfrFL36h2NhYrVq1SiNGjNDu3btVWFiorVu3qk+fPpKkn/zkJ/rmN7+p//mf/1F8fLzeeust1dbW6o033pDD4VC3bt1UVlamV1991Q5Dc+fO1Te+8Q1NmjRJkvSjH/1IRUVFmjdvnhYuXHhJJwMAAJjjst6TU15eLq/Xq9TUVHtZTEyMUlJSVFJSIkkqKSlRy5Yt7YAjSampqQoNDdVHH31k19x9991yOBx2jcfj0d69e/XPf/7Trjl7Pw01DftpTE1NjXw+n98LAACY6bKGHK/XK0mKjY31Wx4bG2uPeb1etWvXzm88PDxcrVq18qtpbBtn7+N8NQ3jjZk5c6ZiYmLsV2JiYlMPEQAAXCeC6umqadOmqbq62n7t378/0C0BAIAr5LKGnLi4OElSVVWV3/Kqqip7LC4uTgcPHvQbP3PmjD7//HO/msa2cfY+zlfTMN4Yp9Mpl8vl9wIAAGa6rCGnU6dOiouLU3Fxsb3M5/Ppo48+ktvtliS53W4dOXJEpaWlds369etVX1+vlJQUu2bTpk06ffq0XVNUVKTbbrtNN9xwg11z9n4aahr2AwAAgluTQ86xY8dUVlamsrIySf+62bisrEwVFRUKCQnRxIkT9eKLL+q3v/2tduzYoccff1zx8fEaMmSIJKlr1676xje+oSeffFJbtmzRBx98oPHjx2vEiBGKj4+XJH33u9+Vw+HQ6NGjtWvXLi1fvlxz585Vdna23cezzz6rwsJCvfLKK9qzZ4+ef/55ffzxxxo/fvxXPysAAOC61+RHyD/++GPde++99vuG4JGRkaH8/HxNnjxZx48f11NPPaUjR47orrvuUmFhoSIjI+113nrrLY0fP1733XefQkNDlZ6ertdee80ej4mJ0f/+7/8qMzNTycnJatOmjWbMmOH3XTp33nmnli5dqunTp+u5557TLbfcolWrVun222+/pBMBAADM0uSQc88998iyrPOOh4SEKDc3V7m5ueetadWqlZYuXXrB/fTo0UPvv//+BWu+853v6Dvf+c6FGwYAAEEpqJ6uAgAAwYOQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjXfchZ/78+erYsaMiIyOVkpKiLVu2BLolAABwDbiuQ87y5cuVnZ2tnJwcbdu2TT179pTH49HBgwcD3RoAAAiw6zrkvPrqq3ryySc1atQoJSUlaeHChWrWrJneeOONQLcGAAACLDzQDVyq2tpalZaWatq0afay0NBQpaamqqSkpNF1ampqVFNTY7+vrq6WJPl8vivb7HnU15wIyH4DLVDnO9CY7+DCfAcX5jsw+7Us64J1123IOXz4sOrq6hQbG+u3PDY2Vnv27Gl0nZkzZ+qFF144Z3liYuIV6RGNi5kT6A5wNTHfwYX5Di6Bnu+jR48qJibmvOPXbci5FNOmTVN2drb9vr6+Xp9//rlat26tkJCQAHZ2dfl8PiUmJmr//v1yuVyBbgdXGPMdXJjv4BKs821Zlo4ePar4+PgL1l23IadNmzYKCwtTVVWV3/KqqirFxcU1uo7T6ZTT6fRb1rJlyyvV4jXP5XIF1X8UwY75Di7Md3AJxvm+0BWcBtftjccOh0PJyckqLi62l9XX16u4uFhutzuAnQEAgGvBdXslR5Kys7OVkZGhPn36qF+/fpozZ46OHz+uUaNGBbo1AAAQYNd1yBk+fLgOHTqkGTNmyOv1qlevXiosLDznZmT4czqdysnJOeejO5iJ+Q4uzHdwYb4vLMT6suevAAAArkPX7T05AAAAF0LIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAADXsfLycp05cybQbVyTCDlB4MCBA3rzzTe1Zs0a1dbW+o0dP35cubm5AeoMV9u+ffs0aNCgQLeBy6ioqEg5OTlav369JGnTpk164IEHNGjQIC1evDjA3eFquO222/Tpp58Guo1rEl8GaLitW7dq8ODBqq+v1+nTp3XTTTdp1apV6tatm6R//aBpfHy86urqAtwprobt27erd+/ezLch3nzzTY0aNUo9evTQJ598op/85CfKysrSsGHDVF9frzfffFNvvfWWhg0bFuhWcRkMHTq00eW/+c1vNGjQILVo0UKS9Otf//pqtnVNu65/1gFf7rnnntPDDz+sRYsW6fjx45oyZYoGDhyooqIi3XHHHYFuD5fZa6+9dsHxv//971epE1wNr7zyil555RU988wzKi4u1kMPPaSXXnpJWVlZkqSkpCTNmTOHkGOIVatW6e6771anTp3OGYuOjr6oX+UONlzJMVyrVq304Ycf6tZbb7WXzZo1S3l5eVq3bp3at2/PlRyDhIaG6sYbb5TD4Wh0vLa2Vl6vl/k2RHR0tHbs2GH/0XM4HPr444/Vo0cPSdKePXt011136fDhw4FsE5fJsmXLNGnSJOXm5vr9EHVERIS2b9+upKSkAHZ3beJKThA4deqU3/upU6cqPDxcgwcP1htvvBGgrnAldOjQQT/+8Y/1H//xH42Ol5WVKTk5+Sp3hSslIiLC7z47p9Op6Ohov/cnT54MRGu4AkaMGKH+/fvr0Ucf1erVq7Vo0SLdcMMNgW7rmsaNx4a7/fbbtXnz5nOW//CHP9S0adP0yCOPBKArXCnJyckqLS0973hISIi4eGuOzp07a8+ePfb7v//9734fZezbt08JCQmBaA1XSMeOHbVp0ybdfvvt6tmzp9atW6eQkJBAt3XN4kqO4R5//HFt3LhRY8eOPWds8uTJsixLCxcuDEBnuBJyc3N14sSJ844nJSWpvLz8KnaEK+m5557z+z95l8vlN/7xxx+f96oerl+hoaF64YUXdP/99+vxxx/n4+cL4J4cw1VUVCghIUGhoVy0CwYVFRVKTEzk/+yCBPMdXBqb72PHjmnfvn3q2rXree/FC2aEHMOFhYXpwIEDateuXaBbwVXAfAcX5ju4MN9Nx//eG44MG1yY7+DCfAcX5rvpCDlBgEvZwYX5Di7Md3BhvpuGj6sMFxoaqqeeekrNmjW7YN2rr756lTrClcR8BxfmO7gw303H01VBYMeOHRe8IY3/MzAL8x1cmO/gwnw3DVdyDBcaGiqv18uNakGC+Q4uzHdwYb6bjntyDHcxqZ5vRDUH8x1cmO/gwnw3HSHHcBe6UFdTU6NXXnml0R97w/WJ+Q4uzHdwYb6bjpBjuNdff12zZ89Wnz59dOedd2rVqlWSpMWLF6tTp06aM2eO/YvFuP4x38GF+Q4uzPclsGC0yZMnWzExMVZ6erp14403WuHh4daTTz5pde/e3Xr77betM2fOBLpFXEbMd3BhvoML8910PF1luHfeeUe/+MUv9K1vfUs7d+5Ujx49dObMGW3fvp278A3EfAcX5ju4MN9Nx9NVhnM4HCovL9dNN90kSYqKitKWLVvUvXv3AHeGK4H5Di7Md3BhvpuOe3IMV1dX5/edCuHh4YqOjg5gR7iSmO/gwnwHF+a76fi4ynCWZel73/uenE6nJOnUqVMaO3asmjdv7lf361//OhDt4TJjvoML8x1cmO+mI+QYLiMjw+/9o48+GqBOcDUw38GF+Q4uzHfTcU8OAAAwEvfkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHQFB6/vnn1atXr0C3AeAKIuQAuKDa2tpAtxAUTp8+HegWAOMQcgD4ueeeezR+/HhNnDhRbdq0kcfj0caNG9WvXz85nU7deOONmjp1qs6cOWOvU1NTo2eeeUbt2rVTZGSk7rrrLm3dutUe37Bhg0JCQrRu3TrdcccdioqK0qBBg3Tw4EGtXbtWXbt2lcvl0ne/+12dOHHiovqsr69XXl6eOnfuLKfTqfbt2+ull16yx6dMmaJbb71VzZo109e+9jX913/9lx0k8vPz9cILL9g/bBgSEqL8/HxJ0pEjRzRmzBi1bdtWLpdLgwYN0vbt2/32/eKLL6pdu3Zq0aKFxowZo6lTp/pdFaqvr1dubq4SEhLkdDrVq1cvFRYW2uOfffaZQkJCtHz5cg0cOFCRkZH62c9+JpfLpXfeecdvX6tWrVLz5s119OjRizovAM4SuB9AB3AtGjhwoBUdHW1NmjTJ2rNnj7VhwwarWbNm1tNPP23t3r3bevfdd602bdpYOTk59jrPPPOMFR8fb61Zs8batWuXlZGRYd1www3WP/7xD8uyLOv3v/+9Jcnq37+/9Yc//MHatm2b1blzZ2vgwIHW4MGDrW3btlmbNm2yWrdubc2aNeui+pw8ebJ1ww03WPn5+daf//xn6/3337d+/vOf2+M/+tGPrA8++MAqLy+3fvvb31qxsbHWj3/8Y8uyLOvEiRPWD37wA6tbt27WgQMHrAMHDlgnTpywLMuyUlNTrYceesjaunWr9cknn1g/+MEPrNatW9vH8uabb1qRkZHWG2+8Ye3du9d64YUXLJfLZfXs2dPe96uvvmq5XC7r7bfftvbs2WNNnjzZioiIsD755BPLsiyrvLzckmR17NjR+tWvfmX95S9/sSorK60nn3zS+uY3v+l3nN/61resxx9/vGmTCMCyLMsi5ADwM3DgQOuOO+6w3z/33HPWbbfdZtXX19vL5s+fb0VHR1t1dXXWsWPHrIiICOutt96yx2tra634+HgrLy/Psqx/h5zf/e53ds3MmTMtSda+ffvsZd///vctj8fzpT36fD7L6XT6hZov8/LLL1vJycn2+5ycHL9gYlmW9f7771sul8s6deqU3/Kbb77Z+ulPf2pZlmWlpKRYmZmZfuMDBgzw21Z8fLz10ksv+dX07dvXevrppy3L+nfImTNnjl/NRx99ZIWFhVmVlZWWZVlWVVWVFR4ebm3YsOGijxPAv/FxFYBzJCcn2//evXu33G63QkJC7GUDBgzQsWPH9Le//U379u3T6dOnNWDAAHs8IiJC/fr10+7du/2226NHD/vfsbGx9kdJZy87ePDgl/a3e/du1dTU6L777jtvzfLlyzVgwADFxcUpOjpa06dPV0VFxQW3u337dh07dkytW7dWdHS0/SovL9e+ffskSXv37lW/fv381jv7vc/nU2Vlpd/5kP51zr54Pvr06XPOdrp166YlS5ZIkt5880116NBBd9999wX7BtA4fqATwDm++KvGl0tERIT975CQEL/3Dcvq6+u/dDtRUVEXHC8pKdHIkSP1wgsvyOPxKCYmRsuWLdMrr7xywfWOHTumG2+8URs2bDhnrGXLll/aV1M1dp7HjBmj+fPna+rUqVq8eLFGjRrlFzABXDyu5AC4oK5du6qkpETWWb/l+8EHH6hFixZKSEjQzTffLIfDoQ8++MAeP336tLZu3aqkpKQr0tMtt9yiqKgoFRcXNzq+efNmdejQQf/5n/+pPn366JZbbtFf//pXvxqHw6G6ujq/Zb1795bX61V4eLg6d+7s92rTpo0k6bbbbvO7qVqS33uXy6X4+Hi/8yH965xdzPl49NFH9de//lWvvfaa/u///u+cX54GcPG4kgPggp5++mnNmTNHEyZM0Pjx47V3717l5OQoOztboaGhat68ucaNG6dJkyapVatWat++vfLy8nTixAmNHj36ivQUGRmpKVOmaPLkyXI4HBowYIAOHTqkXbt2afTo0brllltUUVGhZcuWqW/fviooKNC7777rt42OHTuqvLxcZWVlSkhIUIsWLZSamiq3260hQ4YoLy9Pt956qyorK1VQUKCHH35Yffr00YQJE/Tkk0+qT58+uvPOO7V8+XL96U9/8vvYbdKkScrJydHNN9+sXr16afHixSorK9Nbb731pcd2ww03aOjQoZo0aZIGDx6shISEy37+gKAR6JuCAFxbBg4caD377LN+yzZs2GD17dvXcjgcVlxcnDVlyhTr9OnT9vjJkyetCRMmWG3atLGcTqc1YMAAa8uWLfZ4w43H//znP+1lixcvtmJiYvz209jNwOdTV1dnvfjii1aHDh2siIgIq3379tZ///d/2+OTJk2yWrdubUVHR1vDhw+3Zs+e7be/U6dOWenp6VbLli0tSdbixYsty/rXTc0TJkyw4uPjrYiICCsxMdEaOXKkVVFRYa+bm5trtWnTxoqOjraeeOIJ65lnnrH69+/v19vzzz9v3XTTTVZERITVs2dPa+3atfZ4w43Hf/zjHxs9tuLiYkuStWLFios6FwAaF2JZZ12DBgA02f3336+4uDj98pe/vCzb++Uvf6msrCxVVlbK4XBclm0CwYiPqwCgCU6cOKGFCxfK4/EoLCxMb7/9tn73u9+pqKjosmz7wIEDmjVrlr7//e8TcICviBuPAVxzKioq/B7h/uLryx4Fv5JCQkK0Zs0a3X333UpOTtZ7772nX/3qV0pNTf3K287Ly1OXLl0UFxenadOmXYZugeDGx1UArjlnzpzRZ599dt7xjh07KjycC9EALoyQAwAAjMTHVQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAI/0/vzOiO13ERtwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bookings.room_category.value_counts().plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "e6dc8217-4192-4325-824d-3f17f261b52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13500.0"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "9ee9b7e7-26ad-43f3-bed7-aec245143586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0  01-May-22  May 22    W 19   weekend\n",
       "1  02-May-22  May 22    W 19  weekeday\n",
       "2  03-May-22  May 22    W 19  weekeday\n",
       "3  04-May-22  May 22    W 19  weekeday\n",
       "4  05-May-22  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date = pd.read_csv(\"dim_date.csv\")\n",
    "df_hotels = pd.read_csv(\"dim_hotels.csv\")\n",
    "df_rooms = pd.read_csv(\"dim_rooms.csv\")\n",
    "df_agg_bookings = pd.read_csv(\"fact_aggregated_bookings.csv\")\n",
    "df_date.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "43a109dc-a939-45e9-ba05-f478a9de2efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92, 4)"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "60091ead-217a-4567-8e18-ce21628c043a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "4191098b-0357-4396-b40a-6d7941a45ce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16561</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category    city\n",
       "0        16558   Atliq Grands    Luxury   Delhi\n",
       "1        16559  Atliq Exotica    Luxury  Mumbai\n",
       "2        16560     Atliq City  Business   Delhi\n",
       "3        16561      Atliq Blu    Luxury   Delhi\n",
       "4        16562      Atliq Bay    Luxury   Delhi"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "792190f0-ccfb-4667-a2dd-a5326893b364",
   "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": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.property_name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "e5f55d92-3afb-4b2d-8519-a4dc37f3a64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Luxury', 'Business'], dtype=object)"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "912bafd0-419f-4350-8394-3f40228efbdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Delhi', 'Mumbai', 'Hyderabad', 'Bangalore'], dtype=object)"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.city.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "a001e55b-7872-402f-9e70-ec3e9583cad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.property_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "5c1e4818-94fc-48a9-a0e2-0173daba28e1",
   "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>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  room_id    room_class\n",
       "0     RT1      Standard\n",
       "1     RT2         Elite\n",
       "2     RT3       Premium\n",
       "3     RT4  Presidential"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "6b27400a-e0e6-42a1-a337-5a29e7f1cde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "3        17558      1-May-22           RT1                   30      19.0\n",
       "4        16558      1-May-22           RT1                   18      19.0"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "3a26116a-5c27-4fb9-a1af-18ce133b0190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Delhi        5\n",
       "Hyderabad    6\n",
       "Bangalore    6\n",
       "Mumbai       8\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.city.value_counts().sort_values() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "b2a076e1-8dca-49e5-b1cd-641489b17a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9200, 5)"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "e21e1423-4ba1-4a80-a3b7-9ac02cba3c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "ef1e781a-f8a5-4334-91d9-71e60e821dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
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       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
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       "    <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": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.no_guests <=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "7898b159-ce96-48d1-83d3-2777fe66f0bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
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       "               booking_id  property_id booking_date check_in_date  \\\n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
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       "\n",
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       "4         Checked Out              10920             10920  \n",
       "5         Checked Out               9100              9100  \n",
       "6           Cancelled               9100              3640  \n",
       "...               ...                ...               ...  \n",
       "134584    Checked Out              32300             32300  \n",
       "134585    Checked Out              32300             32300  \n",
       "134587      Cancelled              32300             12920  \n",
       "134588    Checked Out              32300             32300  \n",
       "134589      Cancelled              32300             12920  \n",
       "\n",
       "[134578 rows x 12 columns]"
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     },
     "execution_count": 440,
     "metadata": {},
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   "source": [
    "df_bookings = df_bookings[df_bookings.no_guests > 0]\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "2c18798f-b570-4534-9305-a22664d2c437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6500, 28560000)"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_generated.min(), df_bookings.revenue_generated.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "1a5cab86-9b39-4d73-84b4-8cf291282ff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg , std = df_bookings.revenue_generated.mean(), df_bookings.revenue_generated.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "d6e04f67-464b-41d6-bdd8-bfaff46163f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15378.036937686695, 93040.1549314641)"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "57189abb-05ff-41c4-a4e1-e73e93f8e0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "high_limit = avg + 3*std\n",
    "high_limit\n"
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  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "045f5e9b-80bd-42b4-b6f2-fb5a1ff06702",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "low_limit = avg - 3*std\n",
    "low_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "21a63ee7-79ff-4887-8e5d-6b21cf3a502f",
   "metadata": {},
   "outputs": [
    {
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       "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",
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       "      <td>2.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>direct offline</td>\n",
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       "      <td>Checked Out</td>\n",
       "      <td>12600000</td>\n",
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       "    </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",
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       "      <td>NaN</td>\n",
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       "      <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",
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       "      <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": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_generated > high_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "43133105-91d3-43fe-8bb0-a9158db250e5",
   "metadata": {},
   "outputs": [
    {
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       "               booking_id  property_id booking_date check_in_date  \\\n",
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       "...                   ...          ...          ...           ...   \n",
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       "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
       "\n",
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       "\n",
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       "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",
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       "134589      Cancelled              32300             12920  \n",
       "\n",
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     "execution_count": 448,
     "metadata": {},
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   "source": [
    "df_bookings = df_bookings[df_bookings.revenue_generated < high_limit]\n",
    "df_bookings"
   ]
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  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "c89bfde8-0563-4e32-91cd-f331ac787492",
   "metadata": {},
   "outputs": [
    {
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    "df_bookings.revenue_generated.describe()"
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  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "ef4d9309-e491-4499-8571-f2cda00b8902",
   "metadata": {},
   "outputs": [
    {
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       "Name: revenue_realized, dtype: float64"
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     "execution_count": 450,
     "metadata": {},
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    "df_bookings.revenue_realized.describe()"
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  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "e260de40-e1d5-43a6-975e-b2be74b58215",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "avg_rr = df_bookings.revenue_realized.mean()\n",
    "avg_rr"
   ]
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  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "81e3b435-9ff8-413a-81f6-1ae4563757d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6927.791692242814"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "std_rr = df_bookings.revenue_realized.std()\n",
    "std_rr"
   ]
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  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "7ac8dabd-d58c-481a-a7a2-91e90c5bf899",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_rr = avg_rr + 3 * std_rr"
   ]
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   "execution_count": 454,
   "id": "9cf89a3c-b394-4c05-8534-a16434972ab8",
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   "outputs": [
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       "      <td>Checked Out</td>\n",
       "      <td>34580</td>\n",
       "      <td>34580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134328</th>\n",
       "      <td>Jul312219560RT49</td>\n",
       "      <td>19560</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134331</th>\n",
       "      <td>Jul312219560RT412</td>\n",
       "      <td>19560</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134467</th>\n",
       "      <td>Jul312219562RT45</td>\n",
       "      <td>19562</td>\n",
       "      <td>28-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>39900</td>\n",
       "      <td>39900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134474</th>\n",
       "      <td>Jul312219562RT412</td>\n",
       "      <td>19562</td>\n",
       "      <td>25-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>6/8/2022</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>37050</td>\n",
       "      <td>37050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134581</th>\n",
       "      <td>Jul312217564RT42</td>\n",
       "      <td>17564</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1299 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "137      May012216559RT41        16559     27-04-22      1/5/2022   \n",
       "139      May012216559RT43        16559     1/5/2022      1/5/2022   \n",
       "143      May012216559RT47        16559     28-04-22      1/5/2022   \n",
       "149     May012216559RT413        16559     24-04-22      1/5/2022   \n",
       "222      May012216560RT45        16560     30-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134328   Jul312219560RT49        19560     31-07-22      31-07-22   \n",
       "134331  Jul312219560RT412        19560     31-07-22      31-07-22   \n",
       "134467   Jul312219562RT45        19562     28-07-22      31-07-22   \n",
       "134474  Jul312219562RT412        19562     25-07-22      31-07-22   \n",
       "134581   Jul312217564RT42        17564     31-07-22      31-07-22   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "137         7/5/2022        4.0           RT4           others            NaN   \n",
       "139         2/5/2022        6.0           RT4         tripster            3.0   \n",
       "143         3/5/2022        3.0           RT4           others            5.0   \n",
       "149         7/5/2022        5.0           RT4          logtrip            NaN   \n",
       "222         3/5/2022        5.0           RT4           others            3.0   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134328      2/8/2022        6.0           RT4    direct online            5.0   \n",
       "134331      1/8/2022        6.0           RT4           others            2.0   \n",
       "134467      1/8/2022        6.0           RT4     makeyourtrip            4.0   \n",
       "134474      6/8/2022        5.0           RT4   direct offline            5.0   \n",
       "134581      1/8/2022        4.0           RT4     makeyourtrip            4.0   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized  \n",
       "137       Checked Out              38760             38760  \n",
       "139       Checked Out              45220             45220  \n",
       "143       Checked Out              35530             35530  \n",
       "149       Checked Out              41990             41990  \n",
       "222       Checked Out              34580             34580  \n",
       "...               ...                ...               ...  \n",
       "134328    Checked Out              39900             39900  \n",
       "134331    Checked Out              39900             39900  \n",
       "134467    Checked Out              39900             39900  \n",
       "134474    Checked Out              37050             37050  \n",
       "134581    Checked Out              38760             38760  \n",
       "\n",
       "[1299 rows x 12 columns]"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.revenue_realized > high_rr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "59544461-ef2c-4f4f-a5e6-15fed1485ef6",
   "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": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[df_bookings.room_category == \"RT4\"].revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "c508338b-abf1-4242-b135-304f5933cb57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50585.105672000005"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " 23439.308444 + 3 * 9048.599076"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "a3e7bc59-4ed0-4f22-bf17-0b912d8936f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# no outliers in revenue_realized "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "67852c7d-9f8e-4970-a72e-993ea4b169bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handling NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "3b418f78-5baf-4275-a7bc-ea58579d1d31",
   "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": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "2537ab80-b1fc-4cf2-b04b-ca370cad9908",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "d4de15f8-ece3-414c-9440-12507d9b502e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise-1. In aggregate bookings find columns that have null values. Fill these null values with whatever you think is the appropriate subtitute\n",
    "#(possible ways is to use mean or median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "68559c2e-d6f7-4e7f-8976-a8decc37bb59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_id            0\n",
       "check_in_date          0\n",
       "room_category          0\n",
       "successful_bookings    0\n",
       "capacity               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "7a906948-6511-464b-8f9c-0169fd16d154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9198.000000\n",
       "mean       25.280496\n",
       "std        11.442080\n",
       "min         3.000000\n",
       "25%        18.000000\n",
       "50%        25.000000\n",
       "75%        34.000000\n",
       "max        50.000000\n",
       "Name: capacity, dtype: float64"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.capacity.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "1dee8f9d-6adb-4718-82d1-80b5d85547c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>check_in_date</th>\n",
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      "text/plain": [
       "    property_id check_in_date room_category  successful_bookings  capacity\n",
       "8         17561      1-May-22           RT1                   22       NaN\n",
       "14        17562      1-May-22           RT1                   12       NaN"
      ]
     },
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     "metadata": {},
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   ],
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    "df_agg_bookings[df_agg_bookings.capacity.isnull()]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 465,
   "id": "98a2755e-11dc-418f-8e1f-76559b8834e5",
   "metadata": {},
   "outputs": [
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       "      <td>19563</td>\n",
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       "      <td>17561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9200 rows × 5 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           17558      1-May-22           RT1                   30      19.0\n",
       "4           16558      1-May-22           RT1                   18      19.0\n",
       "...           ...           ...           ...                  ...       ...\n",
       "9195        16563     31-Jul-22           RT4                   13      18.0\n",
       "9196        16559     31-Jul-22           RT4                   13      18.0\n",
       "9197        17558     31-Jul-22           RT4                    3       6.0\n",
       "9198        19563     31-Jul-22           RT4                    3       6.0\n",
       "9199        17561     31-Jul-22           RT4                    3       4.0\n",
       "\n",
       "[9200 rows x 5 columns]"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings = df_agg_bookings.fillna({\n",
    "    'capacity' : df_agg_bookings.capacity.mean()\n",
    "})\n",
    "df_agg_bookings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "2f6dfd6f-7d4c-4c8d-9370-122ee96e6827",
   "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": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.isnull().sum() #hence NA value from capacity column is removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "59c61826-9c8e-4658-84bf-062a3b3dc400",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exercise-2. In aggregate bookings find out records that have successful_bookings value greater than capacity. \n",
    "#Filter those records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "3c2f2708-368d-4d76-b7e2-f7d5d5a2c28c",
   "metadata": {},
   "outputs": [
    {
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       "      property_id check_in_date room_category  successful_bookings  capacity\n",
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       "1           19562      1-May-22           RT1                   28      30.0\n",
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       "...           ...           ...           ...                  ...       ...\n",
       "9195        16563     31-Jul-22           RT4                   13      18.0\n",
       "9196        16559     31-Jul-22           RT4                   13      18.0\n",
       "9197        17558     31-Jul-22           RT4                    3       6.0\n",
       "9198        19563     31-Jul-22           RT4                    3       6.0\n",
       "9199        17561     31-Jul-22           RT4                    3       4.0\n",
       "\n",
       "[9200 rows x 5 columns]"
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     "execution_count": 468,
     "metadata": {},
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    "df_agg_bookings"
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   "execution_count": 469,
   "id": "34320bea-157f-42b1-94d8-0a1d9f9e3f09",
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       "      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",
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       "4           16558      1-May-22           RT1                   18      19.0\n",
       "5           17560      1-May-22           RT1                   28      40.0\n",
       "...           ...           ...           ...                  ...       ...\n",
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       "9196        16559     31-Jul-22           RT4                   13      18.0\n",
       "9197        17558     31-Jul-22           RT4                    3       6.0\n",
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       "9199        17561     31-Jul-22           RT4                    3       4.0\n",
       "\n",
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     "execution_count": 469,
     "metadata": {},
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    "df_agg_bookings = df_agg_bookings[df_agg_bookings.successful_bookings <= df_agg_bookings.capacity]\n",
    "df_agg_bookings"
   ]
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  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "786598d1-29c6-49b1-9504-b43a5f39d907",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA TRANSFORMATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "743f929c-3828-474e-8f60-c1b8be903416",
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    {
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       "   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",
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       "4        16558      1-May-22           RT1                   18      19.0\n",
       "5        17560      1-May-22           RT1                   28      40.0"
      ]
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     "execution_count": 471,
     "metadata": {},
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    "df_agg_bookings.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "dfc06c96-b9a4-4e0f-9b69-9ef5833ae61d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hai\\AppData\\Local\\Temp\\ipykernel_2868\\2056784228.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_agg_bookings[\"occ_pct\"] = df_agg_bookings[\"successful_bookings\"] / df_agg_bookings[\"capacity\"]\n"
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       "      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",
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       "...           ...           ...           ...                  ...       ...   \n",
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       "\n",
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    "df_agg_bookings[\"occ_pct\"] = df_agg_bookings[\"successful_bookings\"] / df_agg_bookings[\"capacity\"]\n",
    "df_agg_bookings"
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   "id": "33722b25-32ef-4fec-ab45-0ad18fff747c",
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      "C:\\Users\\hai\\AppData\\Local\\Temp\\ipykernel_2868\\4124902895.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_agg_bookings[\"occ_pct\"] = df_agg_bookings[\"occ_pct\"].apply(lambda x: round(x*100,2))\n"
     ]
    },
    {
     "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>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "4        16558      1-May-22           RT1                   18      19.0   \n",
       "5        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  \n",
       "4    94.74  \n",
       "5    70.00  "
      ]
     },
     "execution_count": 473,
     "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()\n",
    "                                                            \n",
    "                                                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "e1648f1c-713a-43cb-80d1-f19b6932d45f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#INSGIHTS GENERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "e4496782-94e6-4ecb-83c2-5d8496b633e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. What is an average occupancy rate in each of the room categories?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "b2bd9558-f178-4b5b-8bc9-806945f0b9b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>25</td>\n",
       "      <td>30.0</td>\n",
       "      <td>83.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>30.0</td>\n",
       "      <td>93.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16558</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17560</td>\n",
       "      <td>1-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>28</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        16559      1-May-22           RT1                   25      30.0   \n",
       "1        19562      1-May-22           RT1                   28      30.0   \n",
       "2        19563      1-May-22           RT1                   23      30.0   \n",
       "4        16558      1-May-22           RT1                   18      19.0   \n",
       "5        17560      1-May-22           RT1                   28      40.0   \n",
       "\n",
       "   occ_pct  \n",
       "0    83.33  \n",
       "1    93.33  \n",
       "2    76.67  \n",
       "4    94.74  \n",
       "5    70.00  "
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "57578e5a-cc2d-4b83-8b9d-831d8e4b58e1",
   "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": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg_bookings.groupby(\"room_category\").occ_pct.mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "54bc5139-2cc0-4250-8751-1fad063d176c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>room_id</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  room_id    room_class\n",
       "0     RT1      Standard\n",
       "1     RT2         Elite\n",
       "2     RT3       Premium\n",
       "3     RT4  Presidential"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "2610d13f-34a2-4c93-b679-0bfc8ca14943",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.89</td>\n",
       "      <td>Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.01</td>\n",
       "      <td>Elite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.03</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.28</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   occ_pct    room_class\n",
       "0    57.89      Standard\n",
       "1    58.01         Elite\n",
       "2    58.03       Premium\n",
       "3    59.28  Presidential"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_avg_rc = pd.merge(df_agg_bookings.groupby(\"room_category\").occ_pct.mean().round(2), df_rooms, left_on = \"room_category\", right_on = \"room_id\")\n",
    "df_avg_rc = df_avg_rc.drop(\"room_id\", axis = \"columns\")\n",
    "df_avg_rc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "1557f6d1-3489-4c3f-b912-b1aa02335762",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. Print average occupancy rate per city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "1c4313bf-570b-49a2-964b-492ace5e679b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hotels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "ddc2ca52-8046-4cd8-8446-978a5a1bdf1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    56.33\n",
       "Delhi        61.51\n",
       "Hyderabad    58.12\n",
       "Mumbai       57.91\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_avg_occ_city = pd.merge(df_hotels, df_agg_bookings, on = \"property_id\")\n",
    "df_avg_occ = df_avg_occ_city.groupby(\"city\").occ_pct.mean().round(2)\n",
    "df_avg_occ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "d949609b-368e-4057-bd31-51c97784fd54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# **3. When was the occupancy better? Weekday or Weekend?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "3a9afd2a-216e-4f4c-9446-f5d96495eefa",
   "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",
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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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0  01-May-22  May 22    W 19   weekend\n",
       "1  02-May-22  May 22    W 19  weekeday\n",
       "2  03-May-22  May 22    W 19  weekeday\n",
       "3  04-May-22  May 22    W 19  weekeday\n",
       "4  05-May-22  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "9e4013a3-29e3-432e-bf1c-4b4e76609458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   date      92 non-null     object\n",
      " 1   mmm yy    92 non-null     object\n",
      " 2   week no   92 non-null     object\n",
      " 3   day_type  92 non-null     object\n",
      "dtypes: object(4)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_date.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "4fbdf82f-355a-4344-adeb-7ce498ef2e4d",
   "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>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>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>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>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>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>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0        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",
       "   occ_pct       date  mmm yy week no  day_type  \n",
       "0    51.72  10-May-22  May 22    W 20  weekeday  \n",
       "1    63.33  10-May-22  May 22    W 20  weekeday  \n",
       "2    60.00  10-May-22  May 22    W 20  weekeday  \n",
       "3    53.33  10-May-22  May 22    W 20  weekeday  \n",
       "4    57.89  10-May-22  May 22    W 20  weekeday  "
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_occ_wkd = pd.merge(df_agg_bookings, df_date, left_on = \"check_in_date\", right_on = \"date\")\n",
    "df_occ_wkd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "74b60cff-d788-489c-81a9-f81d2773a900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='day_type'>"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_occ_wk = df_occ_wkd.groupby(\"day_type\").occ_pct.mean().round(2).plot(kind = \"bar\")\n",
    "df_occ_wk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "ccff3792-cbef-465d-898a-7470dda09fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In the month of June, what is the occupancy for different cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "824baf6c-620e-40d4-ac02-7a8f8eaf2979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0  01-May-22  May 22    W 19   weekend\n",
       "1  02-May-22  May 22    W 19  weekeday\n",
       "2  03-May-22  May 22    W 19  weekeday\n",
       "3  04-May-22  May 22    W 19  weekeday\n",
       "4  05-May-22  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "f7845539-eb8d-4ae6-8e3e-4cd1c22853e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    56.44\n",
       "Mumbai       58.38\n",
       "Hyderabad    58.46\n",
       "Delhi        62.47\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jun_occ_cit = pd.merge(df_agg_bookings, df_hotels, on = \"property_id\")\n",
    "df_jun_occ_cit = pd.merge(df_jun_occ_cit, df_date, left_on = \"check_in_date\" , right_on = \"date\")\n",
    "df_jun_occ_cit = df_jun_occ_cit[df_jun_occ_cit[\"mmm yy\"] == \"Jun 22\"]\n",
    "df_jun_occ_cit = df_jun_occ_cit.groupby(\"city\").occ_pct.mean().round(2).sort_values()\n",
    "df_jun_occ_cit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "f245c029-b2b5-4112-82cb-a687e510071e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5: We got new data for the month of august. Append that to existing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "af4592c6-e228-4986-b346-0f0c48986ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 13)"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_august=pd.read_csv(\"new_data_august.csv\")\n",
    "df_august.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "c5e9ea47-7263-4d4e-998e-7b6531194926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>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",
       "    </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>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>25</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.56</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19563</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19563</td>\n",
       "      <td>11-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>11-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>16559</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>61.11</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekend</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>16559</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.67</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>16559</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>29</td>\n",
       "      <td>41.0</td>\n",
       "      <td>70.73</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>16559</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>22</td>\n",
       "      <td>32.0</td>\n",
       "      <td>68.75</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</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>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6497 rows × 13 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           19563     10-May-22           RT1                   16      30.0   \n",
       "2           19563     10-May-22           RT2                   25      45.0   \n",
       "3           19563     10-May-22           RT4                    3       6.0   \n",
       "4           19563     11-May-22           RT1                   15      30.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "6492        16559     30-Jul-22           RT4                   11      18.0   \n",
       "6493        16559     31-Jul-22           RT1                   23      30.0   \n",
       "6494        16559     31-Jul-22           RT2                   29      41.0   \n",
       "6495        16559     31-Jul-22           RT3                   22      32.0   \n",
       "6496        16559     31-Jul-22           RT4                   13      18.0   \n",
       "\n",
       "      occ_pct       date  mmm yy week no  day_type  property_name  category  \\\n",
       "0       51.72  10-May-22  May 22    W 20  weekeday   Atliq Palace  Business   \n",
       "1       53.33  10-May-22  May 22    W 20  weekeday   Atliq Palace  Business   \n",
       "2       55.56  10-May-22  May 22    W 20  weekeday   Atliq Palace  Business   \n",
       "3       50.00  10-May-22  May 22    W 20  weekeday   Atliq Palace  Business   \n",
       "4       50.00  11-May-22  May 22    W 20  weekeday   Atliq Palace  Business   \n",
       "...       ...        ...     ...     ...       ...            ...       ...   \n",
       "6492    61.11  30-Jul-22  Jul 22    W 31   weekend  Atliq Exotica    Luxury   \n",
       "6493    76.67  31-Jul-22  Jul 22    W 32   weekend  Atliq Exotica    Luxury   \n",
       "6494    70.73  31-Jul-22  Jul 22    W 32   weekend  Atliq Exotica    Luxury   \n",
       "6495    68.75  31-Jul-22  Jul 22    W 32   weekend  Atliq Exotica    Luxury   \n",
       "6496    72.22  31-Jul-22  Jul 22    W 32   weekend  Atliq Exotica    Luxury   \n",
       "\n",
       "           city  \n",
       "0     Bangalore  \n",
       "1     Bangalore  \n",
       "2     Bangalore  \n",
       "3     Bangalore  \n",
       "4     Bangalore  \n",
       "...         ...  \n",
       "6492     Mumbai  \n",
       "6493     Mumbai  \n",
       "6494     Mumbai  \n",
       "6495     Mumbai  \n",
       "6496     Mumbai  \n",
       "\n",
       "[6497 rows x 13 columns]"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.merge(df_agg_bookings, df_date, left_on = \"check_in_date\", right_on = \"date\")\n",
    "df= pd.merge(df, df_hotels, on = \"property_id\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "ba80d12e-e8d4-4ff9-8f1e-2c06acd9929c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>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>room_class</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>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>Standard</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>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>Standard</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>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>Standard</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>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>Standard</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>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>Standard</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>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>Standard</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>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>Standard</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "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 date  mmm yy week no  day_type  property_name  category  \\\n",
       "6497      NaN  NaN  Aug-22    W 32  weekeday  Atliq Exotica    Luxury   \n",
       "6498      NaN  NaN  Aug-22    W 32  weekeday      Atliq Bay    Luxury   \n",
       "6499      NaN  NaN  Aug-22    W 32  weekeday   Atliq Palace  Business   \n",
       "6500      NaN  NaN  Aug-22    W 32  weekeday   Atliq Grands    Luxury   \n",
       "6501      NaN  NaN  Aug-22    W 32  weekeday     Atliq City  Business   \n",
       "6502      NaN  NaN  Aug-22    W 32  weekeday      Atliq Blu    Luxury   \n",
       "6503      NaN  NaN  Aug-22    W 32  weekeday  Atliq Seasons  Business   \n",
       "\n",
       "           city room_class    occ%  \n",
       "6497     Mumbai   Standard  100.00  \n",
       "6498  Bangalore   Standard   70.00  \n",
       "6499  Bangalore   Standard   76.67  \n",
       "6500  Bangalore   Standard   75.00  \n",
       "6501  Bangalore   Standard   76.92  \n",
       "6502     Mumbai   Standard   69.23  \n",
       "6503     Mumbai   Standard   62.50  "
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = pd.concat([df,df_august], ignore_index = True, axis = 0)\n",
    "df_new.tail(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "184443f1-6c4f-4dea-87c1-b6229463be5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#6. Print revenue realized per city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "4887c7df-cb54-49dd-b5ec-b59431b8f866",
   "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>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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "5  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "6  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "7  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "5        2.0           RT1           others            4.0    Checked Out   \n",
       "6        2.0           RT1           others            NaN      Cancelled   \n",
       "7        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "1               9100              3640  \n",
       "4              10920             10920  \n",
       "5               9100              9100  \n",
       "6               9100              3640  \n",
       "7               9100              9100  "
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "ddee381c-ff0f-4ec7-acd8-67e5af8e1552",
   "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": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rr_cit = pd.merge(df_bookings, df_hotels, on = \"property_id\")\n",
    "df_rr_cit = df_rr_cit.groupby(\"city\").revenue_realized.sum().sort_values()\n",
    "df_rr_cit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "b3ec5eed-c236-4553-b013-d63f7e1d4e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. Print month by month revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "c774a169-6c90-4783-92fc-871706aa1182",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>checkout_date</th>\n",
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       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
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       "    <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "1  May012216558RT12        16558     30-04-22      1/5/2022      2/5/2022   \n",
       "4  May012216558RT15        16558     27-04-22      1/5/2022      2/5/2022   \n",
       "5  May012216558RT16        16558     1/5/2022      1/5/2022      3/5/2022   \n",
       "6  May012216558RT17        16558     28-04-22      1/5/2022      6/5/2022   \n",
       "7  May012216558RT18        16558     26-04-22      1/5/2022      3/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "4        4.0           RT1    direct online            5.0    Checked Out   \n",
       "5        2.0           RT1           others            4.0    Checked Out   \n",
       "6        2.0           RT1           others            NaN      Cancelled   \n",
       "7        2.0           RT1          logtrip            NaN        No Show   \n",
       "\n",
       "   revenue_generated  revenue_realized  \n",
       "1               9100              3640  \n",
       "4              10920             10920  \n",
       "5               9100              9100  \n",
       "6               9100              3640  \n",
       "7               9100              9100  "
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "22718024-40f9-4a94-91fa-9ed9bb4c1b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hai\\AppData\\Local\\Temp\\ipykernel_2868\\993311048.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_bookings[\"check_in_date\"] = pd.to_datetime(df_bookings[\"check_in_date\"], format='mixed', dayfirst = True)\n"
     ]
    },
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       "      <td>RT1</td>\n",
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       "      <td>Checked Out</td>\n",
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       "      <td>3640</td>\n",
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       "      <td>16558</td>\n",
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       "      <td>RT1</td>\n",
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       "      <td>9100</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\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",
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       "      <td>Jul312217564RT45</td>\n",
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       "      <td>30-07-22</td>\n",
       "      <td>2022-07-31</td>\n",
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       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>2022-07-31</td>\n",
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       "      <td>Checked Out</td>\n",
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       "      <td>2022-07-31</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134588</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>2022-07-31</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>2022-07-31</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    2022-05-01   \n",
       "4        May012216558RT15        16558     27-04-22    2022-05-01   \n",
       "5        May012216558RT16        16558     1/5/2022    2022-05-01   \n",
       "6        May012216558RT17        16558     28-04-22    2022-05-01   \n",
       "7        May012216558RT18        16558     26-04-22    2022-05-01   \n",
       "...                   ...          ...          ...           ...   \n",
       "134584   Jul312217564RT45        17564     30-07-22    2022-07-31   \n",
       "134585   Jul312217564RT46        17564     29-07-22    2022-07-31   \n",
       "134587   Jul312217564RT48        17564     30-07-22    2022-07-31   \n",
       "134588   Jul312217564RT49        17564     29-07-22    2022-07-31   \n",
       "134589  Jul312217564RT410        17564     31-07-22    2022-07-31   \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": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings[\"check_in_date\"] = pd.to_datetime(df_bookings[\"check_in_date\"], format='mixed', dayfirst = True)\n",
    "df_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "25763a79-ca15-4ed2-9b98-93a269b39008",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 134573 entries, 1 to 134589\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   booking_id         134573 non-null  object        \n",
      " 1   property_id        134573 non-null  int64         \n",
      " 2   booking_date       134573 non-null  object        \n",
      " 3   check_in_date      134573 non-null  datetime64[ns]\n",
      " 4   checkout_date      134573 non-null  object        \n",
      " 5   no_guests          134573 non-null  float64       \n",
      " 6   room_category      134573 non-null  object        \n",
      " 7   booking_platform   134573 non-null  object        \n",
      " 8   ratings_given      56676 non-null   float64       \n",
      " 9   booking_status     134573 non-null  object        \n",
      " 10  revenue_generated  134573 non-null  int64         \n",
      " 11  revenue_realized   134573 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(3), object(6)\n",
      "memory usage: 17.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_bookings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "bf75b746-c15b-4a36-a167-41825c7c961c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "        date  mmm yy week no  day_type\n",
       "0 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\n",
       "4 2022-05-05  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 529,
     "metadata": {},
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    }
   ],
   "source": [
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "a82bec4a-b87d-4b9d-97ca-6c7e085a367b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "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\n",
       "4 2022-05-05  May 22    W 19  weekeday"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date[\"date\"] = pd.to_datetime(df_date[\"date\"])\n",
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "6785d29e-6f2e-44e9-87d7-7dec585d1d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4.0</td>\n",
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       "      <td>10920</td>\n",
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       "      <td>W 19</td>\n",
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       "      <td>2022-05-01</td>\n",
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       "      <td>2022-05-01</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>3640</td>\n",
       "      <td>2022-05-01</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2022-05-01</td>\n",
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       "      <td>logtrip</td>\n",
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       "      <td>9100</td>\n",
       "      <td>2022-05-01</td>\n",
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       "    <tr>\n",
       "      <th>134568</th>\n",
       "      <td>Jul312217564RT45</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
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       "      <td>32300</td>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>Jul 22</td>\n",
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       "      <td>weekend</td>\n",
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       "    <tr>\n",
       "      <th>134569</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134570</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134571</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>2022-07-31</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",
       "      <td>2022-07-31</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134572</th>\n",
       "      <td>Jul312217564RT410</td>\n",
       "      <td>17564</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2022-07-31</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",
       "      <td>2022-07-31</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134573 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT12        16558     30-04-22    2022-05-01   \n",
       "1        May012216558RT15        16558     27-04-22    2022-05-01   \n",
       "2        May012216558RT16        16558     1/5/2022    2022-05-01   \n",
       "3        May012216558RT17        16558     28-04-22    2022-05-01   \n",
       "4        May012216558RT18        16558     26-04-22    2022-05-01   \n",
       "...                   ...          ...          ...           ...   \n",
       "134568   Jul312217564RT45        17564     30-07-22    2022-07-31   \n",
       "134569   Jul312217564RT46        17564     29-07-22    2022-07-31   \n",
       "134570   Jul312217564RT48        17564     30-07-22    2022-07-31   \n",
       "134571   Jul312217564RT49        17564     29-07-22    2022-07-31   \n",
       "134572  Jul312217564RT410        17564     31-07-22    2022-07-31   \n",
       "\n",
       "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
       "0           2/5/2022        2.0           RT1           others            NaN   \n",
       "1           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "2           3/5/2022        2.0           RT1           others            4.0   \n",
       "3           6/5/2022        2.0           RT1           others            NaN   \n",
       "4           3/5/2022        2.0           RT1          logtrip            NaN   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134568      1/8/2022        2.0           RT4           others            2.0   \n",
       "134569      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134570      2/8/2022        1.0           RT4         tripster            NaN   \n",
       "134571      1/8/2022        2.0           RT4          logtrip            2.0   \n",
       "134572      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
       "\n",
       "       booking_status  revenue_generated  revenue_realized       date  mmm yy  \\\n",
       "0           Cancelled               9100              3640 2022-05-01  May 22   \n",
       "1         Checked Out              10920             10920 2022-05-01  May 22   \n",
       "2         Checked Out               9100              9100 2022-05-01  May 22   \n",
       "3           Cancelled               9100              3640 2022-05-01  May 22   \n",
       "4             No Show               9100              9100 2022-05-01  May 22   \n",
       "...               ...                ...               ...        ...     ...   \n",
       "134568    Checked Out              32300             32300 2022-07-31  Jul 22   \n",
       "134569    Checked Out              32300             32300 2022-07-31  Jul 22   \n",
       "134570      Cancelled              32300             12920 2022-07-31  Jul 22   \n",
       "134571    Checked Out              32300             32300 2022-07-31  Jul 22   \n",
       "134572      Cancelled              32300             12920 2022-07-31  Jul 22   \n",
       "\n",
       "       week no day_type  \n",
       "0         W 19  weekend  \n",
       "1         W 19  weekend  \n",
       "2         W 19  weekend  \n",
       "3         W 19  weekend  \n",
       "4         W 19  weekend  \n",
       "...        ...      ...  \n",
       "134568    W 32  weekend  \n",
       "134569    W 32  weekend  \n",
       "134570    W 32  weekend  \n",
       "134571    W 32  weekend  \n",
       "134572    W 32  weekend  \n",
       "\n",
       "[134573 rows x 16 columns]"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rr_month = pd.merge(df_bookings, df_date, left_on = \"check_in_date\", right_on = \"date\")\n",
    "df_rr_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "571b54c4-a658-4efb-8380-ef86bfe10f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mmm yy\n",
       "Jun 22    553925855\n",
       "Jul 22    572843348\n",
       "May 22    581767396\n",
       "Name: revenue_realized, dtype: int64"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rr_month = df_rr_month.groupby(\"mmm yy\")[\"revenue_realized\"].sum().sort_values()\n",
    "df_rr_month                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de20c039-a484-4414-9c0c-a3b9dac3f89c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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