Data Analysis in Hospitality Domain.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Data Cleaning\n",
    "\n",
    "**The first part of any Data Analysis process is Data Cleaning,in this project,I have applied Data Cleaning in three steps:**\n",
    "\n",
    "1.Number of guests had negative values,which is an error,fixed it.\n",
    "\n",
    "2.Removed outliers from Revenue Generated & Revenue Realized column.\n",
    "\n",
    "3.Handled NaN values on ratings_given column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba854dc8-19a7-4f3e-9b42-be8e6d2d07e6",
   "metadata": {},
   "source": [
    "***Methods used for Data preprocessing,inspection and Cleaning***:\n",
    "1. .describe()\n",
    "2. .min()\n",
    "3. .max()\n",
    "4. .mean()\n",
    "5. .std()\n",
    "6. .isnull()\n",
    "7. 68-95-99.7 rule to exclude outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "900e9eab-46d8-4638-837e-447023b14d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uploading the necessarylibraries:\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "aca29e9f-4d27-47aa-bcb0-0b06257fa712",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uploading the FataFrames:\n",
    "df_bookings = pd.read_csv('D:\\\\fact_bookings.csv')\n",
    "df_hotels = pd.read_csv('D:\\\\dim_hotels.csv')\n",
    "df_rooms = pd.read_csv('D:\\\\dim_rooms.csv')\n",
    "df_date = pd.read_csv('D:\\\\dim_date.csv')\n",
    "df_aggregated_bookings = pd.read_csv('D:\\\\fact_aggregated_bookings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e72e2d2c-2254-42a8-8d9c-c4f575cdf1d1",
   "metadata": {},
   "source": [
    "**I.Cleaning the Bookings table**:\n",
    "\n",
    "1.Describe the table to inspect data.\n",
    "\n",
    "2.Checking and Cleaning rows where number of guests are negative.\n",
    "\n",
    "3.Saving the Cleaned data in the same Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2903e243-26a1-4883-b360-e7903e5c2ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134590.000000</td>\n",
       "      <td>134587.000000</td>\n",
       "      <td>56683.000000</td>\n",
       "      <td>1.345900e+05</td>\n",
       "      <td>134590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18061.113493</td>\n",
       "      <td>2.036170</td>\n",
       "      <td>3.619004</td>\n",
       "      <td>1.537805e+04</td>\n",
       "      <td>12696.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1093.055847</td>\n",
       "      <td>1.034885</td>\n",
       "      <td>1.235009</td>\n",
       "      <td>9.303604e+04</td>\n",
       "      <td>6928.108124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16558.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.500000e+03</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17558.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.900000e+03</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17564.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.350000e+04</td>\n",
       "      <td>11700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18563.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.800000e+04</td>\n",
       "      <td>15300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19563.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.856000e+07</td>\n",
       "      <td>45220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         property_id      no_guests  ratings_given  revenue_generated  \\\n",
       "count  134590.000000  134587.000000   56683.000000       1.345900e+05   \n",
       "mean    18061.113493       2.036170       3.619004       1.537805e+04   \n",
       "std      1093.055847       1.034885       1.235009       9.303604e+04   \n",
       "min     16558.000000     -17.000000       1.000000       6.500000e+03   \n",
       "25%     17558.000000       1.000000       3.000000       9.900000e+03   \n",
       "50%     17564.000000       2.000000       4.000000       1.350000e+04   \n",
       "75%     18563.000000       2.000000       5.000000       1.800000e+04   \n",
       "max     19563.000000       6.000000       5.000000       2.856000e+07   \n",
       "\n",
       "       revenue_realized  \n",
       "count     134590.000000  \n",
       "mean       12696.123256  \n",
       "std         6928.108124  \n",
       "min         2600.000000  \n",
       "25%         7600.000000  \n",
       "50%        11700.000000  \n",
       "75%        15300.000000  \n",
       "max        45220.000000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "465554e6-61ac-477a-9220-c188680e0b5e",
   "metadata": {},
   "outputs": [
    {
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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>134584</th>\n",
       "      <td>Jul312217564RT45</td>\n",
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       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>32300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
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       "      <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",
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       "      <td>Jul312217564RT410</td>\n",
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       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\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",
       "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",
       "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  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings=df_bookings[df_bookings.no_guests>=0]\n",
    "df_bookings.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d9f287a-42c1-459f-858f-ce83df10827f",
   "metadata": {},
   "source": [
    "**II.a.Removing outliers from revenue_generated column using 68-95-99.7 rule**:\n",
    "\n",
    "1.Check maximum and minimum revenue generated from bookings DataFrame.\n",
    "\n",
    "2.Calculate upper limit and lower limit for the column by calculating mean and standard deviation.\n",
    "\n",
    "3.Check outliers for all existing revenue entries.\n",
    "\n",
    "4.Clean the outliers and saved it to the same DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "13fa7eb3-3499-46cb-a093-3a707cf6b475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28560000\n",
      "6500\n",
      "15378.036937686695 93040.1549314641\n",
      "294498.50173207896\n",
      "-263742.4278567056\n"
     ]
    }
   ],
   "source": [
    "max_revenue=df_bookings.revenue_generated.max()\n",
    "min_revenue=df_bookings.revenue_generated.min()\n",
    "print(max_revenue)\n",
    "print(min_revenue)\n",
    "\n",
    "avg,std_dev=df_bookings.revenue_generated.mean(),df_bookings.revenue_generated.std()\n",
    "print(avg,std_dev)\n",
    "\n",
    "higher_limit=avg+3*std_dev\n",
    "print(higher_limit)\n",
    "\n",
    "lower_limit=avg-3*std_dev\n",
    "print(lower_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e60e5e35-3ea6-4bd6-8e73-c1154733de39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               booking_id  property_id booking_date check_in_date  \\\n",
      "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
      "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
      "5        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
      "6        May012216558RT17        16558     28-04-22      1/5/2022   \n",
      "7        May012216558RT18        16558     26-04-22      1/5/2022   \n",
      "...                   ...          ...          ...           ...   \n",
      "134584   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
      "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
      "134587   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
      "134588   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
      "134589  Jul312217564RT410        17564     31-07-22      31-07-22   \n",
      "\n",
      "       checkout_date  no_guests room_category booking_platform  ratings_given  \\\n",
      "1           2/5/2022        2.0           RT1           others            NaN   \n",
      "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
      "5           3/5/2022        2.0           RT1           others            4.0   \n",
      "6           6/5/2022        2.0           RT1           others            NaN   \n",
      "7           3/5/2022        2.0           RT1          logtrip            NaN   \n",
      "...              ...        ...           ...              ...            ...   \n",
      "134584      1/8/2022        2.0           RT4           others            2.0   \n",
      "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
      "134587      2/8/2022        1.0           RT4         tripster            NaN   \n",
      "134588      1/8/2022        2.0           RT4          logtrip            2.0   \n",
      "134589      1/8/2022        2.0           RT4     makeyourtrip            NaN   \n",
      "\n",
      "       booking_status  revenue_generated  revenue_realized  \n",
      "1           Cancelled               9100              3640  \n",
      "4         Checked Out              10920             10920  \n",
      "5         Checked Out               9100              9100  \n",
      "6           Cancelled               9100              3640  \n",
      "7             No Show               9100              9100  \n",
      "...               ...                ...               ...  \n",
      "134584    Checked Out              32300             32300  \n",
      "134585    Checked Out              32300             32300  \n",
      "134587      Cancelled              32300             12920  \n",
      "134588    Checked Out              32300             32300  \n",
      "134589      Cancelled              32300             12920  \n",
      "\n",
      "[134573 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_bookings[df_bookings.revenue_generated>higher_limit]\n",
    "\n",
    "df_bookings[df_bookings.revenue_generated<higher_limit]\n",
    "\n",
    "df_bookings=df_bookings[df_bookings.revenue_generated<higher_limit]\n",
    "print(df_bookings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc303582-bd1d-4097-8589-1031c7f4d496",
   "metadata": {},
   "source": [
    "**III.b.Removing outliers from revenue_realized column using 68-95-99.7 rule**:\n",
    "\n",
    "1.Check maximum and minimum revenue realized from bookings DataFrame.\n",
    "\n",
    "2.Calculate upper limit and lower limit for the column by calculating mean and standard deviation.\n",
    "\n",
    "3.Inspect the DataFrame to realise the possibility of having valid revenue entries even when they exceed the general 68-95-99.7.\n",
    "\n",
    "4.Presidential room types are always high end rooms in terms of price.\n",
    "\n",
    "5.Calculate another 68-95-99.7 rule on PresidentiaL room type with high price and removed outliers(if any) separately.\n",
    "\n",
    "6.Save it to the same DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fe236252-b70a-47e2-9f02-0025582febd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    134573.000000\n",
       "mean      12695.983585\n",
       "std        6927.791692\n",
       "min        2600.000000\n",
       "25%        7600.000000\n",
       "50%       11700.000000\n",
       "75%       15300.000000\n",
       "max       45220.000000\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1483a762-8a31-4325-8ae3-072d8bd74380",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33479.358661845814\n"
     ]
    }
   ],
   "source": [
    "higher_limit=df_bookings.revenue_realized.mean()+3*df_bookings.revenue_realized.std()\n",
    "print(higher_limit)  #it is less than  45220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "81476a79-f1c8-44d8-9621-151cbd4edd15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               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]\n"
     ]
    },
    {
     "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_bookings[df_bookings.revenue_realized>higher_limit])\n",
    "\n",
    "df_rooms \n",
    "\n",
    "df_bookings[df_bookings.room_category==\"RT4\"].revenue_realized.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "67b9829e-56ef-4eb8-ab80-60600b3a341c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50585"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23439.308444+3*9048.599076 \n",
    "round(50585.105672000005)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2ba550-f31e-4b52-8a3e-480eb6ab7407",
   "metadata": {},
   "source": [
    "**III.Checking NaN Values:**\n",
    "\n",
    "1.Check the NaN values for all the columns in bookings DataFrame and Sum the NaN values.\n",
    "\n",
    "2.Inspect the sum for all the columns.\n",
    "\n",
    "3.ratings_given column Can have null values since not not all customers give rating after checking out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3bf87389-e2c9-41d8-9445-50db14997df9",
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9be4c96-3f0a-47eb-b762-0366edecd4d6",
   "metadata": {},
   "source": [
    "# Data Transformation\n",
    "**After the necessary cleaning was done,now,we need to transform the data based on our need,data transformations may include one to a number of steps,like,adding a new column to converting column values,encoding categorical values,data aggregration,merging the DataFrames,data normalization,feature  Engineering etc.**\n",
    "\n",
    "**in this project,Data Transformation is applied by adding a calculated column:**\n",
    "\n",
    "Here I have Checked the aggregated_bookings DataFrame and added a new column occupancy percentage(occ_pct)\n",
    "\n",
    "                                occ_pct=successful_bookings/capacity\n",
    "\n",
    "Checking the DataFrame-->Adding the column-->Converting the decimal values to percentages by using the Lambda function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad6abed-87e2-4d9a-85e4-a1905033056e",
   "metadata": {},
   "source": [
    "***Methods used for Data Transformation***:\n",
    "1. .head()\n",
    "2. .apply()\n",
    "3.  lambda function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1211ac07-9631-4e2f-b30c-7d216f4a7bc9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id check_in_date room_category  successful_bookings  capacity\n",
       "0        16559      1-May-22           RT1                   25      30.0\n",
       "1        19562      1-May-22           RT1                   28      30.0\n",
       "2        19563      1-May-22           RT1                   23      30.0\n",
       "3        17558      1-May-22           RT1                   30      19.0\n",
       "4        16558      1-May-22           RT1                   18      19.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggregated_bookings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "26c838ba-0638-4174-a591-522f3a8206a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      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",
      "       occ_pct  \n",
      "0     0.833333  \n",
      "1     0.933333  \n",
      "2     0.766667  \n",
      "3     1.578947  \n",
      "4     0.947368  \n",
      "...        ...  \n",
      "9195  0.722222  \n",
      "9196  0.722222  \n",
      "9197  0.500000  \n",
      "9198  0.500000  \n",
      "9199  0.750000  \n",
      "\n",
      "[9200 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "df_aggregated_bookings['occ_pct']=df_aggregated_bookings['successful_bookings']/df_aggregated_bookings['capacity']\n",
    "print(df_aggregated_bookings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "60fefa40-a4f0-47fd-83bd-6963d5e1495c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>72.22</td>\n",
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       "    <tr>\n",
       "      <th>9197</th>\n",
       "      <td>17558</td>\n",
       "      <td>31-Jul-22</td>\n",
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       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9198</th>\n",
       "      <td>19563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
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       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9200 rows × 6 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",
       "      occ_pct  \n",
       "0       83.33  \n",
       "1       93.33  \n",
       "2       76.67  \n",
       "3      157.89  \n",
       "4       94.74  \n",
       "...       ...  \n",
       "9195    72.22  \n",
       "9196    72.22  \n",
       "9197    50.00  \n",
       "9198    50.00  \n",
       "9199    75.00  \n",
       "\n",
       "[9200 rows x 6 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggregated_bookings['occ_pct'] = df_aggregated_bookings['occ_pct'].apply(lambda x: round(x * 100, 2))\n",
    "df_aggregated_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ecc4b566-6b37-4f0c-86cb-af3f4993cc0e",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9198</th>\n",
       "      <td>19563</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9199</th>\n",
       "      <td>17561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9200 rows × 6 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",
       "      occ_pct  \n",
       "0       83.33  \n",
       "1       93.33  \n",
       "2       76.67  \n",
       "3      157.89  \n",
       "4       94.74  \n",
       "...       ...  \n",
       "9195    72.22  \n",
       "9196    72.22  \n",
       "9197    50.00  \n",
       "9198    50.00  \n",
       "9199    75.00  \n",
       "\n",
       "[9200 rows x 6 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Another method of adding that column is:\n",
    "df_aggregated_bookings['occ_pct'] = df_aggregated_bookings.apply(lambda row: row['successful_bookings']/row['capacity'], axis=1)\n",
    "df_aggregated_bookings['occ_pct'] = df_aggregated_bookings['occ_pct'].apply(lambda x: round(x*100, 2))\n",
    "df_aggregated_bookings\n",
    "#By running the cell again and again the pct values doesn't change here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ce9171-e6ec-4632-ac1e-94f6f3c1a35a",
   "metadata": {},
   "source": [
    "# Insight Generation\n",
    "\n",
    "**The business questions asked in the scenarios are called ad-hoc questions,in this project we will be finding solutions of total 11 ad-hoc questions.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a1da87-48f8-4ac5-b5b8-58d7b28eadd9",
   "metadata": {},
   "source": [
    "***Methods used for Insight generation***:\n",
    "\n",
    "**For calculation and insight generation:**\n",
    "1. .groupby()\n",
    "\n",
    "2. .mean()\n",
    "\n",
    "3. .round()\n",
    "\n",
    "4. .merge() and joins\n",
    "\n",
    "5.  .tail() & .head()\n",
    "\n",
    "6.  .drop()\n",
    "\n",
    "7.  .unique()\n",
    "\n",
    "8.  sort_values()\n",
    "\n",
    "9.  .read_csv\n",
    "\n",
    "10. .columns()\n",
    "\n",
    "11. .concat()\n",
    "\n",
    "12. .info()\n",
    "\n",
    "13. .to_datetime()\n",
    "\n",
    "14. .sum()\n",
    "\n",
    "15. Unit conversion(to millions)\n",
    "    \n",
    "**For visualization:**\n",
    "\n",
    "1. matplotlib.pyplot as plt()\n",
    "\n",
    "2. seaborn as sns()\n",
    "\n",
    "3. General matplotlib methods(e.g: .xlabel/.ylabel/.figure/.set_style etc).\n",
    "\n",
    "4. .annotate()\n",
    "\n",
    "5. for loops\n",
    "\n",
    "6. .format()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37e7b2f-80b6-4838-999c-deaf9620b798",
   "metadata": {},
   "source": [
    "**Question 1:**\n",
    "\n",
    "**WHAT IS THE AVERAGE OCCUPANCY RATE PER ROOM TYPE?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4628bf0-76a8-4fdf-9941-978086e00cac",
   "metadata": {},
   "source": [
    "*Step-1  By grouping with 'room_category',average occupancy percetage/rate was calculated.*\n",
    "\n",
    "*Step-2: After that the DataFrame,df_aggregated_bookings was merged with df_rooms to get the corrosponding room_class,named it df.*\n",
    "\n",
    "*Step-3: The merged DataFrame was grouped by room_class and on occ_pct the average was calculated and rounded up.*\n",
    "\n",
    "*Step-4:  Found that there are two columns with same value and different names,dropped one and finalized the desired result.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8b1ab078-1e1a-410a-9856-c4d0a1d0e1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_category\n",
       "RT1    58.22\n",
       "RT2    58.04\n",
       "RT3    58.03\n",
       "RT4    59.30\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aggregated_bookings.groupby('room_category')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "db42bc70-eff7-49b6-ae51-5651d6622c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9196</th>\n",
       "      <td>16559</td>\n",
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       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>RT4</td>\n",
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       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\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",
       "      occ_pct room_id    room_class  \n",
       "9196    72.22     RT4  Presidential  \n",
       "9197    50.00     RT4  Presidential  \n",
       "9198    50.00     RT4  Presidential  \n",
       "9199    75.00     RT4  Presidential  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.merge(df_aggregated_bookings,df_rooms,left_on='room_category',right_on='room_id')\n",
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5e7329a0-72aa-4eb6-a880-d28abd022b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_class\n",
       "Elite           58.04\n",
       "Premium         58.03\n",
       "Presidential    59.30\n",
       "Standard        58.22\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('room_class')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "17a01cd6-8e9f-46ee-9eaa-1a56a605e517",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
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       "    <tr>\n",
       "      <th>9197</th>\n",
       "      <td>17558</td>\n",
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       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9198</th>\n",
       "      <td>19563</td>\n",
       "      <td>31-Jul-22</td>\n",
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       "      <td>3</td>\n",
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       "      <td>50.00</td>\n",
       "      <td>Presidential</td>\n",
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       "      <td>75.00</td>\n",
       "      <td>Presidential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9200 rows × 7 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",
       "      occ_pct    room_class  \n",
       "0       83.33      Standard  \n",
       "1       93.33      Standard  \n",
       "2       76.67      Standard  \n",
       "3      157.89      Standard  \n",
       "4       94.74      Standard  \n",
       "...       ...           ...  \n",
       "9195    72.22  Presidential  \n",
       "9196    72.22  Presidential  \n",
       "9197    50.00  Presidential  \n",
       "9198    50.00  Presidential  \n",
       "9199    75.00  Presidential  \n",
       "\n",
       "[9200 rows x 7 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('room_id',axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "243af437-6b2b-4bb1-8945-2bccda59c72c",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "**Presidential rooms has highest average occupancy rate,all other types of rooms have almost same average occupancy rate.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd66a4c3-a467-427d-b1b0-c4c6b3bb71a6",
   "metadata": {},
   "source": [
    "**Question 2:**\n",
    "\n",
    "**WHAT IS THE AVERAGE OCCUPANCY RATE PER CITY?**\n",
    "\n",
    "\n",
    "*Step-1: City information is in df_hotels,so, merged df with df_hotels on the 'property_id' column to get corresponding cities.*\n",
    "\n",
    "*Step-2: After that grouped by the 'city' column and calculated the average of occupancy percentage. and rounded up.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bbe34e42-678a-4a83-a306-115288c51287",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16559</td>\n",
       "      <td>4-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16559</td>\n",
       "      <td>5-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>16</td>\n",
       "      <td>30.0</td>\n",
       "      <td>53.33</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9195</th>\n",
       "      <td>18560</td>\n",
       "      <td>27-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9196</th>\n",
       "      <td>18560</td>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9197</th>\n",
       "      <td>18560</td>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>53.33</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9198</th>\n",
       "      <td>18560</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9199</th>\n",
       "      <td>18560</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>12</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9200 rows × 10 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           16559      2-May-22           RT1                   20      30.0   \n",
       "2           16559      3-May-22           RT1                   17      30.0   \n",
       "3           16559      4-May-22           RT1                   21      30.0   \n",
       "4           16559      5-May-22           RT1                   16      30.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "9195        18560     27-Jul-22           RT4                    6      15.0   \n",
       "9196        18560     28-Jul-22           RT4                    9      15.0   \n",
       "9197        18560     29-Jul-22           RT4                    8      15.0   \n",
       "9198        18560     30-Jul-22           RT4                    9      15.0   \n",
       "9199        18560     31-Jul-22           RT4                   12      15.0   \n",
       "\n",
       "      occ_pct    room_class  property_name  category       city  \n",
       "0       83.33      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "1       66.67      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "2       56.67      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "3       70.00      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "4       53.33      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "...       ...           ...            ...       ...        ...  \n",
       "9195    40.00  Presidential     Atliq City  Business  Hyderabad  \n",
       "9196    60.00  Presidential     Atliq City  Business  Hyderabad  \n",
       "9197    53.33  Presidential     Atliq City  Business  Hyderabad  \n",
       "9198    60.00  Presidential     Atliq City  Business  Hyderabad  \n",
       "9199    80.00  Presidential     Atliq City  Business  Hyderabad  \n",
       "\n",
       "[9200 rows x 10 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.merge(df,df_hotels,on='property_id')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ca95c8ea-67f8-4413-912f-73c16d44c6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    57.0\n",
       "Delhi        62.0\n",
       "Hyderabad    58.0\n",
       "Mumbai       58.0\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"city\")[\"occ_pct\"].mean().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376fb24c-99c1-47a5-95f3-e72cb6218c86",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "**Delhi has the highest average occupancy rate followed by all other cities with almost same average occupancy rate.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e9fa91-ff2b-4e69-b3b2-10950d0f4c97",
   "metadata": {},
   "source": [
    "**Question 3:**\n",
    "\n",
    "**WHEN WAS THE OCCUPANCY BETTER? WEEKDAY OR WEEKEND?**\n",
    "\n",
    "\n",
    "*Step-1: Date information is in df_date,so, merged df with df_date on the 'check_in_date' column to get corresponding Date information.*\n",
    "\n",
    "*Step-2: After that grouped by the 'day_type' column and calculated the average of occupancy percentage. and rounded up.*\n",
    "\n",
    "*Step-3: Created the column chart.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5417f34c-fdbe-494b-86b2-2010f76a50ab",
   "metadata": {},
   "outputs": [
    {
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       "      <td>01-May-22</td>\n",
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       "      <td>weekeday</td>\n",
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       "      <th>88</th>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>30-Jul-22</td>\n",
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       "      <td>weekend</td>\n",
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       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  mmm yy week no  day_type\n",
       "0   01-May-22  May 22    W 19   weekend\n",
       "1   02-May-22  May 22    W 19  weekeday\n",
       "2   03-May-22  May 22    W 19  weekeday\n",
       "3   04-May-22  May 22    W 19  weekeday\n",
       "4   05-May-22  May 22    W 19  weekeday\n",
       "..        ...     ...     ...       ...\n",
       "87  27-Jul-22  Jul 22    W 31  weekeday\n",
       "88  28-Jul-22  Jul 22    W 31  weekeday\n",
       "89  29-Jul-22  Jul 22    W 31  weekeday\n",
       "90  30-Jul-22  Jul 22    W 31   weekend\n",
       "91  31-Jul-22  Jul 22    W 32   weekend\n",
       "\n",
       "[92 rows x 4 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date #Check the df_date table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2b49c471-8c07-4d41-bb2c-c28ab2eb3d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19562</td>\n",
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       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
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       "      <td>Bangalore</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
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       "      <td>RT4</td>\n",
       "      <td>10</td>\n",
       "      <td>17.0</td>\n",
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       "      <td>18560</td>\n",
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       "      <td>RT1</td>\n",
       "      <td>22</td>\n",
       "      <td>30.0</td>\n",
       "      <td>73.33</td>\n",
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       "      <td>RT2</td>\n",
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       "      <td>Atliq City</td>\n",
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       "      <td>Jul 22</td>\n",
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       "      <td>31-Jul-22</td>\n",
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       "      <td>17</td>\n",
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       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>18560</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>12</td>\n",
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       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6500 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0           16559     10-May-22           RT1                   18      30.0   \n",
       "1           16559     10-May-22           RT2                   25      41.0   \n",
       "2           16559     10-May-22           RT3                   20      32.0   \n",
       "3           16559     10-May-22           RT4                   13      18.0   \n",
       "4           19562     10-May-22           RT1                   18      30.0   \n",
       "...           ...           ...           ...                  ...       ...   \n",
       "6495        17564     31-Jul-22           RT4                   10      17.0   \n",
       "6496        18560     31-Jul-22           RT1                   22      30.0   \n",
       "6497        18560     31-Jul-22           RT2                   34      40.0   \n",
       "6498        18560     31-Jul-22           RT3                   17      24.0   \n",
       "6499        18560     31-Jul-22           RT4                   12      15.0   \n",
       "\n",
       "      occ_pct    room_class  property_name  category       city       date  \\\n",
       "0       60.00      Standard  Atliq Exotica    Luxury     Mumbai  10-May-22   \n",
       "1       60.98         Elite  Atliq Exotica    Luxury     Mumbai  10-May-22   \n",
       "2       62.50       Premium  Atliq Exotica    Luxury     Mumbai  10-May-22   \n",
       "3       72.22  Presidential  Atliq Exotica    Luxury     Mumbai  10-May-22   \n",
       "4       60.00      Standard      Atliq Bay    Luxury  Bangalore  10-May-22   \n",
       "...       ...           ...            ...       ...        ...        ...   \n",
       "6495    58.82  Presidential  Atliq Seasons  Business     Mumbai  31-Jul-22   \n",
       "6496    73.33      Standard     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "6497    85.00         Elite     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "6498    70.83       Premium     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "6499    80.00  Presidential     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "\n",
       "      mmm yy week no  day_type  \n",
       "0     May 22    W 20  weekeday  \n",
       "1     May 22    W 20  weekeday  \n",
       "2     May 22    W 20  weekeday  \n",
       "3     May 22    W 20  weekeday  \n",
       "4     May 22    W 20  weekeday  \n",
       "...      ...     ...       ...  \n",
       "6495  Jul 22    W 32   weekend  \n",
       "6496  Jul 22    W 32   weekend  \n",
       "6497  Jul 22    W 32   weekend  \n",
       "6498  Jul 22    W 32   weekend  \n",
       "6499  Jul 22    W 32   weekend  \n",
       "\n",
       "[6500 rows x 14 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.merge(df,df_date,left_on='check_in_date',right_on='date')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b5e18224-eda2-404c-a48e-9718c925ffa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_type\n",
       "weekeday    50.90\n",
       "weekend     72.39\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('day_type')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "14936a2f-715a-49ab-bb47-74ed12b8be0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_12224\\704016296.py:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(yticklabels, color='white')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set Seaborn style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Assuming df has columns 'day_type' and 'occ_pct'\n",
    "plt.figure(figsize=(4, 4))\n",
    "\n",
    "# Group by 'day_type' and calculate the mean of 'occ_pct'\n",
    "avg_occ_pct = df.groupby('day_type')['occ_pct'].mean().round(2)\n",
    "\n",
    "# Plotting\n",
    "ax = avg_occ_pct.plot(kind='bar', color='orange')\n",
    "\n",
    "yticks = ax.get_yticks()\n",
    "yticklabels = ['{}%'.format(int(x)) for x in yticks]\n",
    "ax.set_yticklabels(yticklabels, color='white')\n",
    "\n",
    "# Annotate each bar with its height\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height()}%', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center', xytext=(0, 10), textcoords='offset points', fontsize=8)\n",
    "\n",
    "plt.title('AVERAGE OCCUPANCY PERCENTAGE BY DAY TYPE')\n",
    "plt.xlabel('Day Type')\n",
    "plt.ylabel('Average Occupancy Percentage')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c76507-3217-436d-a579-32f49a50090c",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "**The weekend days have much higher occupancy rate(>70%) than that of weekdays(50.9%) probably because of holidays.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2f06bd-525f-44a2-9096-386d1c8ade09",
   "metadata": {},
   "source": [
    "**Question 4:**\n",
    "\n",
    "**SHOW THE MONTHLY OCCUPANCY RATES FOR DIFFERENT MONTHS.?**\n",
    "\n",
    "\n",
    "*Step-1: First we checked the months we have in our DataFrame.*\n",
    "\n",
    "*Step-2: After that grouped by the 'mmm yy' and 'city' columns and calculated the average of occupancy percentage. and rounded up.*\n",
    "\n",
    "*Step-3: Stored the value in a new DataFrame,named it 'df_citywise_occupancy_per_month'.*\n",
    "\n",
    "*Step-4: Created the clustered column chart.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "996d427f-21f5-4dd9-9ba5-cb1d29aa227d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date['mmm yy'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "56af4cc8-39a9-465c-b607-6f0a6ee5c3a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mmm yy  city     \n",
      "Jun 22  Delhi        62.47\n",
      "May 22  Delhi        59.65\n",
      "Jul 22  Delhi        59.18\n",
      "Jun 22  Hyderabad    58.46\n",
      "        Mumbai       58.38\n",
      "May 22  Hyderabad    57.06\n",
      "        Mumbai       56.80\n",
      "Jun 22  Bangalore    56.58\n",
      "Jul 22  Hyderabad    55.36\n",
      "May 22  Bangalore    55.28\n",
      "Jul 22  Mumbai       55.24\n",
      "        Bangalore    54.07\n",
      "Name: occ_pct, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_citywise_occupancy_per_month = df.groupby(['mmm yy', 'city'])['occ_pct'].mean().round(2).sort_values(ascending=False)\n",
    "print(df_citywise_occupancy_per_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1ffa1aca-bf86-4930-b24b-7765aacb62bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_12224\\19982133.py:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(yticklabels,color='white')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20c62a64f10>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "df_citywise_occupancy_per_month = df.groupby(['mmm yy', 'city'])['occ_pct'].mean().round(1).unstack()\n",
    "\n",
    "ax = df_citywise_occupancy_per_month.plot(kind='bar', stacked=False, figsize=(8, 4), width=0.8)\n",
    "\n",
    "# Add '%' to y-axis tick labels\n",
    "yticks = ax.get_yticks()\n",
    "yticklabels = []\n",
    "\n",
    "for x in yticks:\n",
    "    yticklabels.append('{}%'.format(int(x)))\n",
    "\n",
    "ax.set_yticklabels(yticklabels,color='white')\n",
    "\n",
    "# Add data labels to each column with rounded values\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.annotate('{:.1f}%'.format(height), (p.get_x() + p.get_width() / 2., height),\n",
    "                ha='center', va='center', xytext=(0, 10), textcoords='offset points',fontsize=9)\n",
    "\n",
    "# Add title and labels\n",
    "plt.title('Citywise Occupancy Percentage Per Month')\n",
    "plt.xlabel('Month and Year')\n",
    "plt.ylabel('Occupancy Percentage')\n",
    "\n",
    "# Add legend\n",
    "ax.legend(title='City', loc='upper left', bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4413d0cd-5333-4144-bb67-a9eb2e902179",
   "metadata": {},
   "source": [
    "**Conclusion**:\n",
    "\n",
    "**1.As we can see In all three months,Delhi shows highest level of room occupancy.**\n",
    "\n",
    "**2.Hyderabad and Mumbai hotels doesn't show much difference in monthly occupancy rate.**\n",
    "\n",
    "**3.bangalore has least occupancy rate among all the cities throughout all three months.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fb50247-1d11-4aa1-84cd-407a9bf21996",
   "metadata": {},
   "source": [
    "**Question 5:**\n",
    "\n",
    "**YOUR BUSINESS MANAGER HAS GIVEN YOU A NEW DATAFRAME FOR AUGUST MONTH, COMBINE IT WITH THE EXISTING DATAFRAME TO UPDATE THE DATAFRAME.**\n",
    "\n",
    "\n",
    "*Step-1: After uploading the new August DataFrame,first we checked the column of the new August DataFrame since concatenation requires the same similar column counts and names.*\n",
    "\n",
    "*Step-2: After that attempted the concatenation of august data with df DataFrame.*\n",
    "\n",
    "*Step-3: Stored the new values in a new DataFrame and named it 'updated_df.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "756f14dc-5d3b-4224-b430-b9f907598fb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['property_id', 'property_name', 'category', 'city', 'room_category',\n",
      "       'room_class', 'check_in_date', 'mmm yy', 'week no', 'day_type',\n",
      "       'successful_bookings', 'capacity', 'occ%'],\n",
      "      dtype='object')\n",
      "Index(['property_id', 'check_in_date', 'room_category', 'successful_bookings',\n",
      "       'capacity', 'occ_pct', 'room_class', 'property_name', 'category',\n",
      "       'city', 'date', 'mmm yy', 'week no', 'day_type'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df_august=pd.read_csv('D:\\\\new_data_august.csv')\n",
    "df_august\n",
    "\n",
    "print(df_august.columns)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "07dc6844-4ed1-4b2b-9a6d-49f7091f71b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6487</th>\n",
       "      <td>19559</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6488</th>\n",
       "      <td>19561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>21</td>\n",
       "      <td>36.0</td>\n",
       "      <td>58.33</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6489</th>\n",
       "      <td>19561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>24</td>\n",
       "      <td>45.0</td>\n",
       "      <td>53.33</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6490</th>\n",
       "      <td>19561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>18</td>\n",
       "      <td>29.0</td>\n",
       "      <td>62.07</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6491</th>\n",
       "      <td>19561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>57.14</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>17564</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>17564</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>19</td>\n",
       "      <td>40.0</td>\n",
       "      <td>47.50</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>17564</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>11</td>\n",
       "      <td>24.0</td>\n",
       "      <td>45.83</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>17564</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>10</td>\n",
       "      <td>17.0</td>\n",
       "      <td>58.82</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>18560</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>22</td>\n",
       "      <td>30.0</td>\n",
       "      <td>73.33</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6497</th>\n",
       "      <td>18560</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>34</td>\n",
       "      <td>40.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6498</th>\n",
       "      <td>18560</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>17</td>\n",
       "      <td>24.0</td>\n",
       "      <td>70.83</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>18560</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>12</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekend</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6500</th>\n",
       "      <td>16559</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6501</th>\n",
       "      <td>19562</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6502</th>\n",
       "      <td>19563</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6503</th>\n",
       "      <td>19558</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6504</th>\n",
       "      <td>19560</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6505</th>\n",
       "      <td>17561</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>69.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6506</th>\n",
       "      <td>17564</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "6487        19559     31-Jul-22           RT4                    2       3.0   \n",
       "6488        19561     31-Jul-22           RT1                   21      36.0   \n",
       "6489        19561     31-Jul-22           RT2                   24      45.0   \n",
       "6490        19561     31-Jul-22           RT3                   18      29.0   \n",
       "6491        19561     31-Jul-22           RT4                    4       7.0   \n",
       "6492        17564     31-Jul-22           RT1                    8      16.0   \n",
       "6493        17564     31-Jul-22           RT2                   19      40.0   \n",
       "6494        17564     31-Jul-22           RT3                   11      24.0   \n",
       "6495        17564     31-Jul-22           RT4                   10      17.0   \n",
       "6496        18560     31-Jul-22           RT1                   22      30.0   \n",
       "6497        18560     31-Jul-22           RT2                   34      40.0   \n",
       "6498        18560     31-Jul-22           RT3                   17      24.0   \n",
       "6499        18560     31-Jul-22           RT4                   12      15.0   \n",
       "6500        16559     01-Aug-22           RT1                   30      30.0   \n",
       "6501        19562     01-Aug-22           RT1                   21      30.0   \n",
       "6502        19563     01-Aug-22           RT1                   23      30.0   \n",
       "6503        19558     01-Aug-22           RT1                   30      40.0   \n",
       "6504        19560     01-Aug-22           RT1                   20      26.0   \n",
       "6505        17561     01-Aug-22           RT1                   18      26.0   \n",
       "6506        17564     01-Aug-22           RT1                   10      16.0   \n",
       "\n",
       "      occ_pct    room_class  property_name  category       city       date  \\\n",
       "6487    66.67  Presidential  Atliq Exotica    Luxury  Bangalore  31-Jul-22   \n",
       "6488    58.33      Standard      Atliq Blu    Luxury  Bangalore  31-Jul-22   \n",
       "6489    53.33         Elite      Atliq Blu    Luxury  Bangalore  31-Jul-22   \n",
       "6490    62.07       Premium      Atliq Blu    Luxury  Bangalore  31-Jul-22   \n",
       "6491    57.14  Presidential      Atliq Blu    Luxury  Bangalore  31-Jul-22   \n",
       "6492    50.00      Standard  Atliq Seasons  Business     Mumbai  31-Jul-22   \n",
       "6493    47.50         Elite  Atliq Seasons  Business     Mumbai  31-Jul-22   \n",
       "6494    45.83       Premium  Atliq Seasons  Business     Mumbai  31-Jul-22   \n",
       "6495    58.82  Presidential  Atliq Seasons  Business     Mumbai  31-Jul-22   \n",
       "6496    73.33      Standard     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "6497    85.00         Elite     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "6498    70.83       Premium     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "6499    80.00  Presidential     Atliq City  Business  Hyderabad  31-Jul-22   \n",
       "6500      NaN      Standard  Atliq Exotica    Luxury     Mumbai        NaN   \n",
       "6501      NaN      Standard      Atliq Bay    Luxury  Bangalore        NaN   \n",
       "6502      NaN      Standard   Atliq Palace  Business  Bangalore        NaN   \n",
       "6503      NaN      Standard   Atliq Grands    Luxury  Bangalore        NaN   \n",
       "6504      NaN      Standard     Atliq City  Business  Bangalore        NaN   \n",
       "6505      NaN      Standard      Atliq Blu    Luxury     Mumbai        NaN   \n",
       "6506      NaN      Standard  Atliq Seasons  Business     Mumbai        NaN   \n",
       "\n",
       "      mmm yy week no  day_type    occ%  \n",
       "6487  Jul 22    W 32   weekend     NaN  \n",
       "6488  Jul 22    W 32   weekend     NaN  \n",
       "6489  Jul 22    W 32   weekend     NaN  \n",
       "6490  Jul 22    W 32   weekend     NaN  \n",
       "6491  Jul 22    W 32   weekend     NaN  \n",
       "6492  Jul 22    W 32   weekend     NaN  \n",
       "6493  Jul 22    W 32   weekend     NaN  \n",
       "6494  Jul 22    W 32   weekend     NaN  \n",
       "6495  Jul 22    W 32   weekend     NaN  \n",
       "6496  Jul 22    W 32   weekend     NaN  \n",
       "6497  Jul 22    W 32   weekend     NaN  \n",
       "6498  Jul 22    W 32   weekend     NaN  \n",
       "6499  Jul 22    W 32   weekend     NaN  \n",
       "6500  Aug-22    W 32  weekeday  100.00  \n",
       "6501  Aug-22    W 32  weekeday   70.00  \n",
       "6502  Aug-22    W 32  weekeday   76.67  \n",
       "6503  Aug-22    W 32  weekeday   75.00  \n",
       "6504  Aug-22    W 32  weekeday   76.92  \n",
       "6505  Aug-22    W 32  weekeday   69.23  \n",
       "6506  Aug-22    W 32  weekeday   62.50  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_df=pd.concat([df,df_august],ignore_index=True,axis=0)\n",
    "updated_df.tail(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7468f7c8-209d-4f8b-899d-10cf609c71d4",
   "metadata": {},
   "source": [
    "**Conclusion**:\n",
    "\n",
    "**We have only week 32 Data of August,the data can be incomplete.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a87a88-ce86-43cd-9eac-d40497664e0e",
   "metadata": {},
   "source": [
    "**Question 6:**\n",
    "\n",
    "**SHOW THE TREND IN SUCCESSFUL BOOKINGS IN AUGUST IN EACH CITY.**\n",
    "\n",
    "*Step-1: extracted the August month information from df DataFrame and saved it to a new DataFrame,august_bookings.*\n",
    "\n",
    "*Step-2: Grouped by 'city' on the august_bookings DataFrame and calculated the average bookings.*\n",
    "\n",
    "*Step-3: Visualized the trend.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "833631fd-af35-4561-9336-673f8a9ecd88",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ_pct</th>\n",
       "      <th>room_class</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6500</th>\n",
       "      <td>16559</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6501</th>\n",
       "      <td>19562</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6502</th>\n",
       "      <td>19563</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>23</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6503</th>\n",
       "      <td>19558</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6504</th>\n",
       "      <td>19560</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>20</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6505</th>\n",
       "      <td>17561</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>69.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6506</th>\n",
       "      <td>17564</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "6500        16559     01-Aug-22           RT1                   30      30.0   \n",
       "6501        19562     01-Aug-22           RT1                   21      30.0   \n",
       "6502        19563     01-Aug-22           RT1                   23      30.0   \n",
       "6503        19558     01-Aug-22           RT1                   30      40.0   \n",
       "6504        19560     01-Aug-22           RT1                   20      26.0   \n",
       "6505        17561     01-Aug-22           RT1                   18      26.0   \n",
       "6506        17564     01-Aug-22           RT1                   10      16.0   \n",
       "\n",
       "      occ_pct room_class  property_name  category       city date  mmm yy  \\\n",
       "6500      NaN   Standard  Atliq Exotica    Luxury     Mumbai  NaN  Aug-22   \n",
       "6501      NaN   Standard      Atliq Bay    Luxury  Bangalore  NaN  Aug-22   \n",
       "6502      NaN   Standard   Atliq Palace  Business  Bangalore  NaN  Aug-22   \n",
       "6503      NaN   Standard   Atliq Grands    Luxury  Bangalore  NaN  Aug-22   \n",
       "6504      NaN   Standard     Atliq City  Business  Bangalore  NaN  Aug-22   \n",
       "6505      NaN   Standard      Atliq Blu    Luxury     Mumbai  NaN  Aug-22   \n",
       "6506      NaN   Standard  Atliq Seasons  Business     Mumbai  NaN  Aug-22   \n",
       "\n",
       "     week no  day_type    occ%  \n",
       "6500    W 32  weekeday  100.00  \n",
       "6501    W 32  weekeday   70.00  \n",
       "6502    W 32  weekeday   76.67  \n",
       "6503    W 32  weekeday   75.00  \n",
       "6504    W 32  weekeday   76.92  \n",
       "6505    W 32  weekeday   69.23  \n",
       "6506    W 32  weekeday   62.50  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "august_bookings=updated_df[updated_df['mmm yy']=='Aug-22']\n",
    "august_bookings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "19186713-4d13-45db-93e0-92e4e983530d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    23.5\n",
       "Mumbai       19.3\n",
       "Name: successful_bookings, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend_data = august_bookings.groupby('city')['successful_bookings'].mean().round(1)\n",
    "trend_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "513b49f0-588b-469a-89cc-b6483d4b56cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Successful Bookings')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a line plot\n",
    "sns.set(style=\"darkgrid\", rc={\"axes.facecolor\": \"#333333\", \"grid.color\": \"#555555\"})\n",
    "\n",
    "plt.figure(figsize=(4, 4))\n",
    "sns.lineplot(x=trend_data.index, y=trend_data.values,linestyle='dashed',color='orange')\n",
    "plt.ylim(15, 30)\n",
    "# Add title and labels\n",
    "plt.title('Mean Successful Bookings in August 2022 by City')\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Mean Successful Bookings')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbb3b89-2c8c-42bf-9ea6-81bbc54ce934",
   "metadata": {},
   "source": [
    "**Conclusion**:\n",
    "\n",
    "**1.We have august data for two cities only, Mumbai and Bangalore.**\n",
    "\n",
    "**2.The number of average successful bookings has seen a sharp drop from Bangalore to Mumbai (23.5 to 19.3).**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b57a69a-bae9-4730-b2c8-bc5dc3fb1c87",
   "metadata": {},
   "source": [
    "**Question 7:**\n",
    "\n",
    "**CALCULATE THE REVENUE REALIZED PER CITY.**\n",
    "\n",
    "*Step-1: merged the Revenue realized(df_bookings DataFrame) and city(df_hotels DataFrame) on 'property_id',stored it in a new df,df_bookings_all.*\n",
    "\n",
    "*Step-2: Grouped by 'city' and calculated sum of the Revenue realized,converted it to millions and rounded the value.*\n",
    "\n",
    "*Step-3: Visualized the result using bar chart.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c8c3327e-502b-488f-be0d-fb06cf524c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT18</td>\n",
       "      <td>16558</td>\n",
       "      <td>26-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Show</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134568</th>\n",
       "      <td>Jul312217564RT45</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>others</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134569</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</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>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</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>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</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>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134573 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "1        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "2        May012216558RT16        16558     1/5/2022      1/5/2022   \n",
       "3        May012216558RT17        16558     28-04-22      1/5/2022   \n",
       "4        May012216558RT18        16558     26-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134568   Jul312217564RT45        17564     30-07-22      31-07-22   \n",
       "134569   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134570   Jul312217564RT48        17564     30-07-22      31-07-22   \n",
       "134571   Jul312217564RT49        17564     29-07-22      31-07-22   \n",
       "134572  Jul312217564RT410        17564     31-07-22      31-07-22   \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  property_name  \\\n",
       "0           Cancelled               9100              3640   Atliq Grands   \n",
       "1         Checked Out              10920             10920   Atliq Grands   \n",
       "2         Checked Out               9100              9100   Atliq Grands   \n",
       "3           Cancelled               9100              3640   Atliq Grands   \n",
       "4             No Show               9100              9100   Atliq Grands   \n",
       "...               ...                ...               ...            ...   \n",
       "134568    Checked Out              32300             32300  Atliq Seasons   \n",
       "134569    Checked Out              32300             32300  Atliq Seasons   \n",
       "134570      Cancelled              32300             12920  Atliq Seasons   \n",
       "134571    Checked Out              32300             32300  Atliq Seasons   \n",
       "134572      Cancelled              32300             12920  Atliq Seasons   \n",
       "\n",
       "        category    city  \n",
       "0         Luxury   Delhi  \n",
       "1         Luxury   Delhi  \n",
       "2         Luxury   Delhi  \n",
       "3         Luxury   Delhi  \n",
       "4         Luxury   Delhi  \n",
       "...          ...     ...  \n",
       "134568  Business  Mumbai  \n",
       "134569  Business  Mumbai  \n",
       "134570  Business  Mumbai  \n",
       "134571  Business  Mumbai  \n",
       "134572  Business  Mumbai  \n",
       "\n",
       "[134573 rows x 15 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all=pd.merge(df_bookings,df_hotels,on='property_id')\n",
    "df_bookings_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4adcd37b-d6b8-4916-997d-534cd8f5dcae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    420.38\n",
       "Delhi        294.40\n",
       "Hyderabad    325.18\n",
       "Mumbai       668.57\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_realized_in_mln=((df_bookings_all.groupby('city')['revenue_realized'].sum())/1e6).round(2)\n",
    "revenue_realized_in_mln"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "238c41bc-6407-4cd7-b7b6-b0f34a4f5b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'City')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenue_realized_in_mln=((df_bookings_all.groupby('city')['revenue_realized'].sum())/1e6).round(2)\n",
    "ax = revenue_realized_in_mln.plot(kind='barh', color='skyblue', edgecolor='black', figsize=(4, 4))\n",
    "plt.title('Total Revenue Generated per Month-Year (in Million)', fontsize=10)\n",
    "\n",
    "plt.title('Total Revenue Realized by City (in Millions)')\n",
    "plt.xlabel('Revenue realized(in millions)')\n",
    "plt.ylabel('City')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536edfd3-b3ad-452c-88f6-7e18550b877c",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "  \n",
    "**1.Delhi has seen highest level of occupancy rate as well as least revenue realized,means,the number of cancelled bookings in Delhi hotels are least.**\n",
    "\n",
    "**2.Mumbai has seen the highest amount of revenue realized,means the most number of cancelled bookings were done in Mumbai.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd7062d-650e-4d20-8b31-82792446e62a",
   "metadata": {},
   "source": [
    "**Question 8:**\n",
    "\n",
    "**CALCULATE THE MONTH BY MONTH REVENUE.**\n",
    "\n",
    "*Step-1: Checked the Datatype of df_bookings_all and df_date DataFrame,both 'check_in_date' and 'date' are 'object' datatype.*\n",
    "\n",
    "*Step-2: converted the object datatype to Datetime datatype since we need to merge the two tables using the two datetime columns.*\n",
    "\n",
    "*Step-3: Merged the two DataFrames and updated the df_bookings_all DataFrame.*\n",
    "\n",
    "*Step-4: Grouped by 'mmm yy' column and calculated the revenue generated for each month,converted the value to millions and rounded it,stored the result in a new revenue_generated DataFrame.*\n",
    "\n",
    "*Step-5: Created the column chart visual.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bb0469c8-d7c7-424c-902d-b62e7acbcc2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13795 entries, 0 to 13794\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   booking_id         13795 non-null  object        \n",
      " 1   property_id        13795 non-null  int64         \n",
      " 2   booking_date       13795 non-null  object        \n",
      " 3   check_in_date      13795 non-null  datetime64[ns]\n",
      " 4   checkout_date      13795 non-null  object        \n",
      " 5   no_guests          13795 non-null  float64       \n",
      " 6   room_category      13795 non-null  object        \n",
      " 7   booking_platform   13795 non-null  object        \n",
      " 8   ratings_given      5673 non-null   float64       \n",
      " 9   booking_status     13795 non-null  object        \n",
      " 10  revenue_generated  13795 non-null  int64         \n",
      " 11  revenue_realized   13795 non-null  int64         \n",
      " 12  property_name      13795 non-null  object        \n",
      " 13  category           13795 non-null  object        \n",
      " 14  city               13795 non-null  object        \n",
      " 15  date               13795 non-null  datetime64[ns]\n",
      " 16  mmm yy             13795 non-null  object        \n",
      " 17  week no            13795 non-null  object        \n",
      " 18  day_type           13795 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(12)\n",
      "memory usage: 2.0+ MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92 entries, 0 to 91\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   date      92 non-null     datetime64[ns]\n",
      " 1   mmm yy    92 non-null     object        \n",
      " 2   week no   92 non-null     object        \n",
      " 3   day_type  92 non-null     object        \n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_bookings_all.info()\n",
    "df_date.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "281dc556-93d7-457f-a1c5-5f7d55d6e163",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_12224\\3356156343.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  df_date['date']=pd.to_datetime(df_date[\"date\"])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT16</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>3/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT17</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT12        16558     30-04-22    2022-01-05      2/5/2022   \n",
       "1  May012216558RT15        16558     27-04-22    2022-01-05      2/5/2022   \n",
       "2  May012216558RT16        16558     1/5/2022    2022-01-05      3/5/2022   \n",
       "3  May012216558RT17        16558     28-04-22    2022-01-05      6/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        2.0           RT1           others            NaN      Cancelled   \n",
       "1        4.0           RT1    direct online            5.0    Checked Out   \n",
       "2        2.0           RT1           others            4.0    Checked Out   \n",
       "3        2.0           RT1           others            NaN      Cancelled   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \n",
       "0               9100              3640  Atliq Grands   Luxury  Delhi  \n",
       "1              10920             10920  Atliq Grands   Luxury  Delhi  \n",
       "2               9100              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all[\"check_in_date\"] = pd.to_datetime(df_bookings_all[\"check_in_date\"], errors='coerce')\n",
    "df_date['date']=pd.to_datetime(df_date[\"date\"])\n",
    "df_bookings_all.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "43033051-bb9d-4f60-b6d5-1ce282c4e66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>property_id</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>checkout_date</th>\n",
       "      <th>no_guests</th>\n",
       "      <th>room_category</th>\n",
       "      <th>booking_platform</th>\n",
       "      <th>ratings_given</th>\n",
       "      <th>booking_status</th>\n",
       "      <th>revenue_generated</th>\n",
       "      <th>revenue_realized</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May052216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>15-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>7/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>tripster</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May052216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>7/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May052216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>6/5/2022</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct offline</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2022-05-05</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May052216558RT11        16558     15-04-22    2022-05-05      7/5/2022   \n",
       "1  May052216558RT12        16558     30-04-22    2022-05-05      7/5/2022   \n",
       "2  May052216558RT13        16558     1/5/2022    2022-05-05      6/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0        3.0           RT1         tripster            5.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        3.0           RT1   direct offline            5.0    Checked Out   \n",
       "\n",
       "   revenue_generated  revenue_realized property_name category   city  \\\n",
       "0              10010             10010  Atliq Grands   Luxury  Delhi   \n",
       "1               9100              3640  Atliq Grands   Luxury  Delhi   \n",
       "2              10010             10010  Atliq Grands   Luxury  Delhi   \n",
       "\n",
       "        date  mmm yy week no  day_type  \n",
       "0 2022-05-05  May 22    W 19  weekeday  \n",
       "1 2022-05-05  May 22    W 19  weekeday  \n",
       "2 2022-05-05  May 22    W 19  weekeday  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bookings_all = pd.merge(df_bookings_all, df_date, left_on=\"check_in_date\", right_on=\"date\")\n",
    "df_bookings_all.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "89f8c960-e879-4ce1-bf81-1f790ef8f320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mmm yy\n",
       "Jul 22    71.5\n",
       "Jun 22    62.3\n",
       "May 22    71.6\n",
       "Name: revenue_generated, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_generated=((df_bookings_all.groupby('mmm yy')['revenue_generated'].sum())/1e6).round(1)\n",
    "revenue_generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "887da3cf-9216-460f-a736-c113c109cf8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Revenue generated (in Million)')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenue_generated_in_mln=(df_bookings_all.groupby('mmm yy')['revenue_generated'].sum()/1e6).round(1)\n",
    "revenue_generated_in_mln=revenue_generated_in_mln.sort_index(ascending=False)\n",
    "ax = revenue_generated_in_mln.plot(kind='bar', color='skyblue', edgecolor='black', figsize=(4, 4))\n",
    "plt.title('Total Revenue Generated per Month-Year (in Million)', fontsize=10)\n",
    "\n",
    "# Adding labels\n",
    "plt.xlabel('Month-Year', fontsize=8)\n",
    "plt.ylabel('Revenue generated (in Million)', fontsize=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8db78f8-a4d7-4191-b96a-d9a95fce8f56",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "**The aggregate revenue across all the cities exhibits a near between May and July;however,the revenue for June(62.3 millions),at 71.6 million and 71.5 million respectively,significantly lags behind the figures for the other two months.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b9eb5b-d0bc-4873-836b-a77ea7382884",
   "metadata": {},
   "source": [
    "**Question 9:**\n",
    "\n",
    "**CALCULATE THE REVENUE REALIZED PER HOTEL TYPE.**\n",
    "\n",
    "*Step-1: Merged the df_bookings and df_hotels DataFrames on 'property_id' and stored the result in a new DataFrame 'revenue_realized_hotel_type'.*\n",
    "\n",
    "*Step-2: Grouped the 'revenue_realized_hotel_type' DataFrame with 'property_name' column and on top of it calculated the 'revenue_realized',converted it to millions and rouded it.*\n",
    "\n",
    "*Step-3: Created the column chart visual.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "04fb5f4d-920d-427a-91bd-bad9435fc248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_name\n",
       "Atliq Bay        260.0\n",
       "Atliq Blu        260.9\n",
       "Atliq City       285.8\n",
       "Atliq Exotica    320.3\n",
       "Atliq Grands     211.5\n",
       "Atliq Palace     304.1\n",
       "Atliq Seasons     66.1\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_realized_hotel_type = pd.merge(df_bookings, df_hotels, on='property_id')\n",
    "revenue_realized_hotel_type = (revenue_realized_hotel_type.groupby('property_name')['revenue_realized'].sum()/1e6).round(1)\n",
    "revenue_realized_hotel_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "cfdc321f-07c5-4194-8066-3c9c2c4e4f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6]),\n",
       " [Text(0, 0, 'Atliq Bay'),\n",
       "  Text(1, 0, 'Atliq Blu'),\n",
       "  Text(2, 0, 'Atliq City'),\n",
       "  Text(3, 0, 'Atliq Exotica'),\n",
       "  Text(4, 0, 'Atliq Grands'),\n",
       "  Text(5, 0, 'Atliq Palace'),\n",
       "  Text(6, 0, 'Atliq Seasons')])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = revenue_realized_hotel_type.plot(kind='bar', color='skyblue', edgecolor='black', figsize=(4,3))\n",
    "\n",
    "# Adding a title\n",
    "plt.title('Total Revenue Realized by Hotel Type (in Million)', fontsize=10)\n",
    "\n",
    "# Adding labels\n",
    "plt.xlabel('Hotel Type', fontsize=8)\n",
    "plt.ylabel('Revenue (in Million)', fontsize=8)\n",
    "\n",
    "# Rotating x-axis labels for better readability\n",
    "plt.xticks(rotation=45, ha='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87142fac-f0ff-4013-8e0e-a3d5495eeabb",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "**1.The Exotica type Hotels of AtliQ Industries has seen highest amount of revenue generated from cancellation followed by AtliQ Palace and AtliQ city.**\n",
    "\n",
    "**2.The expensive hotels has seen more cancellation amount than normal and premium hotels.**\n",
    "\n",
    "**3.AtliQ Seasons has demonstrated exceptional resilience in the face of cancellations, boasting the lowest cancellation rate at 66.1 million, significantly surpassing other hotel types. This notable achievement can be attributed to its competitive # strategy and strategically advantageous locations, contributing to a more appealing value proposition for customer.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbc8bc30-ce5f-490e-b025-482cbfd9a3f0",
   "metadata": {},
   "source": [
    "**Question 10:**\n",
    "\n",
    "**PER CITY, WHAT IS THE AVERAGE RATING GIVEN?**\n",
    "\n",
    "*Step-1: Merged the df_bookings and df_hotels DataFrames on 'property_id' and stored the result in a new DataFrame 'average_rating_by_customer'.*\n",
    "\n",
    "*Step-2: Grouped the 'average_rating_by_customer' DataFrame with 'city' column and on top of it calculated the average of 'ratings_given',and rouded it.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9fc20744-3642-4adf-af00-b59f65bcd9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    3.41\n",
       "Delhi        3.78\n",
       "Hyderabad    3.66\n",
       "Mumbai       3.65\n",
       "Name: ratings_given, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_rating_by_customer=pd.merge(df_bookings,df_hotels,on='property_id')\n",
    "average_rating_by_customer=average_rating_by_customer.groupby('city')['ratings_given'].mean().round(2)\n",
    "average_rating_by_customer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17869168-dd6b-4f26-8810-d3d1d01ced9d",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "**1.The average ratings are almost same for all the cities.**\n",
    "\n",
    "**2.None of the ratings are greater than or equal to 4.**\n",
    "\n",
    "**3.Uniform average ratings across all cities suggest a need for comprehensive service quality review, with potential steps including staff training, amenity refinement, and tailored marketing to enhance overall customer satisfaction.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c9ffdb-eb71-4cfa-81f5-066dc1ca3440",
   "metadata": {},
   "source": [
    "**Question 11:**\n",
    "\n",
    "**CALCULATE THE REVENUE REALIZED PER BOOKING PLATFORM.**\n",
    "\n",
    "*Step-1: Merged the df_bookings and df_hotels DataFrames on 'property_id' and stored the result in a new DataFrame 'revenue_realized_booking_platform'.*\n",
    "\n",
    "*Step-2: Grouped the 'revenue_realized_booking_platform' DataFrame with 'booking_platform' colmnn and on top of it calculated the 'revenue_realized',converted it to millions and rouded it.*\n",
    "\n",
    "*Step-3: Created the pie chart visual.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d9e874e8-b638-4acd-96da-09dcf68d17a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_platform\n",
       "direct offline     86.4\n",
       "direct online     168.9\n",
       "journey           102.5\n",
       "logtrip           187.5\n",
       "makeyourtrip      340.8\n",
       "others            699.3\n",
       "tripster          123.1\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_realized_booking_platform = pd.merge(df_bookings, df_hotels, on='property_id')\n",
    "revenue_realized_booking_platform = (revenue_realized_booking_platform.groupby('booking_platform')['revenue_realized'].sum()/1e6).round(1)\n",
    "revenue_realized_booking_platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "90b3cc97-1f55-4a74-835d-9827183610ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = revenue_realized_booking_platform.plot(kind='pie', figsize=(4, 3), autopct='%1.1f%%', startangle=90,textprops={'fontsize': 7, 'fontweight': 'bold'})\n",
    "\n",
    "plt.title('Total Revenue Realized per Booking Platform', fontsize=10)\n",
    "\n",
    "ax.set_ylabel('') #To hide the y-axis label\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87778ffa-7806-4c55-88d1-a024a4c46526",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "**1.Majority of the bookings(40.9%) are from 'others' type of transaction,unknown.**\n",
    "\n",
    "**2.A strategic business step is suggested which would involve conducting a thorough analysis to categorize and understand the nature of these transactions, enabling targeted initiatives to capture valuable market segments.**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
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}