Insights generation.ipynb

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   "execution_count": 41,
   "id": "c81e79bc-d174-4515-b4ce-d59ca94f7f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "619065d7-2660-4d44-abe1-45c6f46777f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bookings=pd.read_csv('D:\\\\21.CODEBASICS\\\\BOOTCAMP\\\\Python\\\\PyCharm-Learn Python\\\\1-CODEBASICS\\\\PYTHON-HOTEL DATA ANALYTICS PROJECT\\\\datasets\\\\fact_bookings.csv')\n",
    "df_hotels=pd.read_csv('D:\\\\21.CODEBASICS\\\\BOOTCAMP\\\\Python\\\\PyCharm-Learn Python\\\\1-CODEBASICS\\\\PYTHON-HOTEL DATA ANALYTICS PROJECT\\\\datasets\\\\dim_hotels.csv')\n",
    "df_rooms=pd.read_csv('D:\\\\21.CODEBASICS\\\\BOOTCAMP\\\\Python\\\\PyCharm-Learn Python\\\\1-CODEBASICS\\\\PYTHON-HOTEL DATA ANALYTICS PROJECT\\\\datasets\\\\dim_rooms.csv')\n",
    "df_aggregated_bookings=pd.read_csv('D:\\\\21.CODEBASICS\\\\BOOTCAMP\\\\Python\\\\PyCharm-Learn Python\\\\1-CODEBASICS\\\\PYTHON-HOTEL DATA ANALYTICS PROJECT\\\\datasets\\\\fact_aggregated_bookings.csv')\n",
    "df_date=pd.read_csv('D:\\\\21.CODEBASICS\\\\BOOTCAMP\\\\Python\\\\PyCharm-Learn Python\\\\1-CODEBASICS\\\\PYTHON-HOTEL DATA ANALYTICS PROJECT\\\\datasets\\\\dim_date.csv')\n",
    "#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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2662db52-0dfc-48ad-a7e1-93620cb587f6",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The business questions asked in the scenarios are called : ad-hoc questions:\n",
    "\n",
    "#QUESTION-1:WHAT IS THE AVERAGE OCCUPANCY RATE PER ROOM TYPE?\n",
    "\n",
    "#STEP-1--\n",
    "df_aggregated_bookings.groupby('room_category')['occ_pct'].mean().round(2)"
   ]
  },
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   "execution_count": 13,
   "id": "82e16fef-9317-441f-b483-8a4d79dda643",
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      "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": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "#STEP-2--\n",
    "#But RT1,RT2.. We don't know what kind of rooms are these,so, we can merge these rooms with rooms DataFrame to show the corresponding room class.\n",
    "#There is no common column between the two DataFrames,in such cases left_on='room_category',right_on='room_id' type of join still work.\n",
    "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": 11,
   "id": "a8b3aa5c-e2a8-4648-bbe3-0b201825284f",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-3--\n",
    "#Get average occupancy based on room class now:\n",
    "df.groupby('room_class')['occ_pct'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3d1ef21-38f5-4849-b26a-862a2ca19cb1",
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       "<p>9200 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "0           16559      1-May-22           RT1                   25      30.0   \n",
       "1           19562      1-May-22           RT1                   28      30.0   \n",
       "2           19563      1-May-22           RT1                   23      30.0   \n",
       "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]"
      ]
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "#STEP-4--\n",
    "#There are two same column,room_id and room_category,one of which we can drop.\n",
    "df.drop('room_id',axis=1,inplace=True)\n",
    "df"
   ]
  },
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   "id": "a6c91a04-1844-4f6a-9b9d-dca4f3035e2c",
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       "      <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>RT4</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 × 11 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_id    room_class  property_name  category       city  \n",
       "0       83.33     RT1      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "1       66.67     RT1      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "2       56.67     RT1      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "3       70.00     RT1      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "4       53.33     RT1      Standard  Atliq Exotica    Luxury     Mumbai  \n",
       "...       ...     ...           ...            ...       ...        ...  \n",
       "9195    40.00     RT4  Presidential     Atliq City  Business  Hyderabad  \n",
       "9196    60.00     RT4  Presidential     Atliq City  Business  Hyderabad  \n",
       "9197    53.33     RT4  Presidential     Atliq City  Business  Hyderabad  \n",
       "9198    60.00     RT4  Presidential     Atliq City  Business  Hyderabad  \n",
       "9199    80.00     RT4  Presidential     Atliq City  Business  Hyderabad  \n",
       "\n",
       "[9200 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#QUESTION-2: WHAT IS THE AVERAGE OCCUPANCY RATE PER CITY?\n",
    "#STEP-1--\n",
    "#city information is in the df_hotels table,we will again have to merge and name the new table,df.\n",
    "df=pd.merge(df,df_hotels,on='property_id')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b248c0cc-a921-49bf-89c3-135301cd88bc",
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-2--\n",
    "#Groupby city to get the average:\n",
    "df.groupby(\"city\")[\"occ_pct\"].mean().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16702bc0-52c5-4185-87d0-eb548474bda3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>W 19</td>\n",
       "      <td>weekeday</td>\n",
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       "      <th>4</th>\n",
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       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>28-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>29-Jul-22</td>\n",
       "      <td>Jul 22</td>\n",
       "      <td>W 31</td>\n",
       "      <td>weekeday</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>30-Jul-22</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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#QUESTION-3:WHEN WAS THE OCCUPANCY BETTER? WEEKDAY OR WEEKEND?\n",
    "#Date information is in df_date.\n",
    "#Join df_date with df,as usual.\n",
    "#First check the dim_date DataFrame:\n",
    "df_date\n",
    "#We can join df check in date with date of df_date DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d6472b71-17f4-4eaf-a077-b49b14cef0c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>room_category</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
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       "      <th>property_name</th>\n",
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       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
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       "      <td>25</td>\n",
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       "      <td>10-May-22</td>\n",
       "      <td>RT3</td>\n",
       "      <td>20</td>\n",
       "      <td>32.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>RT3</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16559</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>72.22</td>\n",
       "      <td>RT4</td>\n",
       "      <td>Presidential</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19562</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>18</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>10-May-22</td>\n",
       "      <td>May 22</td>\n",
       "      <td>W 20</td>\n",
       "      <td>weekeday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    </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>RT4</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",
       "    </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>RT1</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",
       "    </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>RT2</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",
       "    </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>RT3</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",
       "    </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>RT4</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6500 rows × 15 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_id    room_class  property_name  category       city  \\\n",
       "0       60.00     RT1      Standard  Atliq Exotica    Luxury     Mumbai   \n",
       "1       60.98     RT2         Elite  Atliq Exotica    Luxury     Mumbai   \n",
       "2       62.50     RT3       Premium  Atliq Exotica    Luxury     Mumbai   \n",
       "3       72.22     RT4  Presidential  Atliq Exotica    Luxury     Mumbai   \n",
       "4       60.00     RT1      Standard      Atliq Bay    Luxury  Bangalore   \n",
       "...       ...     ...           ...            ...       ...        ...   \n",
       "6495    58.82     RT4  Presidential  Atliq Seasons  Business     Mumbai   \n",
       "6496    73.33     RT1      Standard     Atliq City  Business  Hyderabad   \n",
       "6497    85.00     RT2         Elite     Atliq City  Business  Hyderabad   \n",
       "6498    70.83     RT3       Premium     Atliq City  Business  Hyderabad   \n",
       "6499    80.00     RT4  Presidential     Atliq City  Business  Hyderabad   \n",
       "\n",
       "           date  mmm yy week no  day_type  \n",
       "0     10-May-22  May 22    W 20  weekeday  \n",
       "1     10-May-22  May 22    W 20  weekeday  \n",
       "2     10-May-22  May 22    W 20  weekeday  \n",
       "3     10-May-22  May 22    W 20  weekeday  \n",
       "4     10-May-22  May 22    W 20  weekeday  \n",
       "...         ...     ...     ...       ...  \n",
       "6495  31-Jul-22  Jul 22    W 32   weekend  \n",
       "6496  31-Jul-22  Jul 22    W 32   weekend  \n",
       "6497  31-Jul-22  Jul 22    W 32   weekend  \n",
       "6498  31-Jul-22  Jul 22    W 32   weekend  \n",
       "6499  31-Jul-22  Jul 22    W 32   weekend  \n",
       "\n",
       "[6500 rows x 15 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-1--\n",
    "df=pd.merge(df,df_date,left_on='check_in_date',right_on='date')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2954408a-b3a1-4ba2-aa21-1c6869b4e3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_type\n",
       "weekeday    50.90\n",
       "weekend     72.39\n",
       "Name: occ_pct, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-2--\n",
    "df.groupby('day_type')['occ_pct'].mean().round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "71378101-9f3a-48bb-83a0-1d660c502fde",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_10580\\2430803866.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(yticklabels,color='white')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Occupancy Percentage')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#STEP-3--Visualization:\n",
    "sns.set(style=\"whitegrid\")  # Set Seaborn style\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax=df.groupby('day_type')['occ_pct'].mean().round(2).plot(kind='bar',color='orange')\n",
    "\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",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()) + '%', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
    "    \n",
    "plt.title('AVERAGE OCCUPANCY PERCENTAGE BY DAY TYPE')\n",
    "plt.xlabel('Day Type')\n",
    "plt.ylabel('Average Occupancy Percentage')\n",
    "#Weekend occupancy is more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fb7ea70f-1516-45d0-a1a5-2568271e4110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['May 22', 'Jun 22', 'Jul 22'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#QUESTION-4:SHOW THE MONTHLY OCCUPANCY RATES FOR DIFFERENT MONTHS.\n",
    "#First,check the number of months we have in out data,\n",
    "df_date['mmm yy'].unique()\n",
    "#we have data for may,2022 to july,2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3906aeb8-2381-46ab-a3ef-75e433aca8af",
   "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": [
    "#Step-1--\n",
    "\n",
    "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": 106,
   "id": "fa1f5f13-6e3a-4f71-803b-29c4744f00c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_10580\\1837008853.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 0x22577fd7700>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#STEP-2--\n",
    "#Create the visual:\n",
    "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=(10, 6), 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')\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": "code",
   "execution_count": null,
   "id": "ade5e4e7-5363-45a9-b624-74d9b5a5a646",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion: \n",
    "#1.As we can see In all three months,Delhi shows highest level of room occupancy.\n",
    "#2.Hyderabad and Mumbai hotels doesn't show much difference in monthly occupancy rate.\n",
    "#3.bangalore has least occupancy rate among all the cities throughout all three months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "a2b40a7b-5e59-402e-8044-afabe45ccdc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>property_id</th>\n",
       "      <th>property_name</th>\n",
       "      <th>category</th>\n",
       "      <th>city</th>\n",
       "      <th>room_category</th>\n",
       "      <th>room_class</th>\n",
       "      <th>check_in_date</th>\n",
       "      <th>mmm yy</th>\n",
       "      <th>week no</th>\n",
       "      <th>day_type</th>\n",
       "      <th>successful_bookings</th>\n",
       "      <th>capacity</th>\n",
       "      <th>occ%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16559</td>\n",
       "      <td>Atliq Exotica</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19562</td>\n",
       "      <td>Atliq Bay</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19563</td>\n",
       "      <td>Atliq Palace</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>76.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19558</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19560</td>\n",
       "      <td>Atliq City</td>\n",
       "      <td>Business</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>76.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17561</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>69.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17564</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>RT1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>Aug-22</td>\n",
       "      <td>W 32</td>\n",
       "      <td>weekeday</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>62.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  property_name  category       city room_category room_class  \\\n",
       "0        16559  Atliq Exotica    Luxury     Mumbai           RT1   Standard   \n",
       "1        19562      Atliq Bay    Luxury  Bangalore           RT1   Standard   \n",
       "2        19563   Atliq Palace  Business  Bangalore           RT1   Standard   \n",
       "3        19558   Atliq Grands    Luxury  Bangalore           RT1   Standard   \n",
       "4        19560     Atliq City  Business  Bangalore           RT1   Standard   \n",
       "5        17561      Atliq Blu    Luxury     Mumbai           RT1   Standard   \n",
       "6        17564  Atliq Seasons  Business     Mumbai           RT1   Standard   \n",
       "\n",
       "  check_in_date  mmm yy week no  day_type  successful_bookings  capacity  \\\n",
       "0     01-Aug-22  Aug-22    W 32  weekeday                   30        30   \n",
       "1     01-Aug-22  Aug-22    W 32  weekeday                   21        30   \n",
       "2     01-Aug-22  Aug-22    W 32  weekeday                   23        30   \n",
       "3     01-Aug-22  Aug-22    W 32  weekeday                   30        40   \n",
       "4     01-Aug-22  Aug-22    W 32  weekeday                   20        26   \n",
       "5     01-Aug-22  Aug-22    W 32  weekeday                   18        26   \n",
       "6     01-Aug-22  Aug-22    W 32  weekeday                   10        16   \n",
       "\n",
       "     occ%  \n",
       "0  100.00  \n",
       "1   70.00  \n",
       "2   76.67  \n",
       "3   75.00  \n",
       "4   76.92  \n",
       "5   69.23  \n",
       "6   62.50  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#QUESTION:5--Your business manager has given you a new DataFrame for August month,combine it with the existing DataFrame to update the DataFrame.\n",
    "#Concatinate the new DataFrame with the existing df_month dataframe.\n",
    "#First check the column counts and columns are same or not since that is the first criteria of concatinaion:\n",
    "df_august=pd.read_csv('D:\\\\21.CODEBASICS\\\\BOOTCAMP\\\\Python\\\\PyCharm-Learn Python\\\\1-CODEBASICS\\\\PYTHON-HOTEL DATA ANALYTICS PROJECT\\\\datasets\\\\new_data_august.csv')\n",
    "df_august\n",
    "#check the columns:\n",
    "print(df_august.columns)\n",
    "print(df.columns)\n",
    "#same columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "0f616993-a7b0-48dd-b260-868773dc5666",
   "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_id</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>RT4</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>RT1</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>RT2</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>RT3</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>RT4</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>RT1</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>RT2</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>RT3</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>RT4</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>RT1</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>RT2</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>RT3</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>RT4</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>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>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>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>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>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>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>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_id    room_class  property_name  category       city  \\\n",
       "6487    66.67     RT4  Presidential  Atliq Exotica    Luxury  Bangalore   \n",
       "6488    58.33     RT1      Standard      Atliq Blu    Luxury  Bangalore   \n",
       "6489    53.33     RT2         Elite      Atliq Blu    Luxury  Bangalore   \n",
       "6490    62.07     RT3       Premium      Atliq Blu    Luxury  Bangalore   \n",
       "6491    57.14     RT4  Presidential      Atliq Blu    Luxury  Bangalore   \n",
       "6492    50.00     RT1      Standard  Atliq Seasons  Business     Mumbai   \n",
       "6493    47.50     RT2         Elite  Atliq Seasons  Business     Mumbai   \n",
       "6494    45.83     RT3       Premium  Atliq Seasons  Business     Mumbai   \n",
       "6495    58.82     RT4  Presidential  Atliq Seasons  Business     Mumbai   \n",
       "6496    73.33     RT1      Standard     Atliq City  Business  Hyderabad   \n",
       "6497    85.00     RT2         Elite     Atliq City  Business  Hyderabad   \n",
       "6498    70.83     RT3       Premium     Atliq City  Business  Hyderabad   \n",
       "6499    80.00     RT4  Presidential     Atliq City  Business  Hyderabad   \n",
       "6500      NaN     NaN      Standard  Atliq Exotica    Luxury     Mumbai   \n",
       "6501      NaN     NaN      Standard      Atliq Bay    Luxury  Bangalore   \n",
       "6502      NaN     NaN      Standard   Atliq Palace  Business  Bangalore   \n",
       "6503      NaN     NaN      Standard   Atliq Grands    Luxury  Bangalore   \n",
       "6504      NaN     NaN      Standard     Atliq City  Business  Bangalore   \n",
       "6505      NaN     NaN      Standard      Atliq Blu    Luxury     Mumbai   \n",
       "6506      NaN     NaN      Standard  Atliq Seasons  Business     Mumbai   \n",
       "\n",
       "           date  mmm yy week no  day_type    occ%  \n",
       "6487  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6488  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6489  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6490  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6491  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6492  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6493  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6494  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6495  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6496  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6497  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6498  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6499  31-Jul-22  Jul 22    W 32   weekend     NaN  \n",
       "6500        NaN  Aug-22    W 32  weekeday  100.00  \n",
       "6501        NaN  Aug-22    W 32  weekeday   70.00  \n",
       "6502        NaN  Aug-22    W 32  weekeday   76.67  \n",
       "6503        NaN  Aug-22    W 32  weekeday   75.00  \n",
       "6504        NaN  Aug-22    W 32  weekeday   76.92  \n",
       "6505        NaN  Aug-22    W 32  weekeday   69.23  \n",
       "6506        NaN  Aug-22    W 32  weekeday   62.50  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Concatinate the New august DataFrame with df DataFrame.\n",
    "updated_df=pd.concat([df,df_august],ignore_index=True,axis=0)\n",
    "updated_df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "91dee65f-277e-401b-8863-cf17229f62b1",
   "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_id</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>1279</th>\n",
       "      <td>18561</td>\n",
       "      <td>31-Jul-22</td>\n",
       "      <td>RT2</td>\n",
       "      <td>30</td>\n",
       "      <td>40.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>RT2</td>\n",
       "      <td>Elite</td>\n",
       "      <td>Atliq Blu</td>\n",
       "      <td>Luxury</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>1280</th>\n",
       "      <td>16559</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>16559</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>19558</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>19558</td>\n",
       "      <td>01-Aug-22</td>\n",
       "      <td>RT1</td>\n",
       "      <td>30</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      property_id check_in_date room_category  successful_bookings  capacity  \\\n",
       "1279        18561     31-Jul-22           RT2                   30      40.0   \n",
       "1280        16559     01-Aug-22           RT1                   30      30.0   \n",
       "1281        16559     01-Aug-22           RT1                   30      30.0   \n",
       "1282        19558     01-Aug-22           RT1                   30      40.0   \n",
       "1283        19558     01-Aug-22           RT1                   30      40.0   \n",
       "\n",
       "      occ_pct room_id room_class  property_name category       city  \\\n",
       "1279     75.0     RT2      Elite      Atliq Blu   Luxury  Hyderabad   \n",
       "1280      NaN     NaN   Standard  Atliq Exotica   Luxury     Mumbai   \n",
       "1281      NaN     NaN   Standard  Atliq Exotica   Luxury     Mumbai   \n",
       "1282      NaN     NaN   Standard   Atliq Grands   Luxury  Bangalore   \n",
       "1283      NaN     NaN   Standard   Atliq Grands   Luxury  Bangalore   \n",
       "\n",
       "           date  mmm yy week no  day_type   occ%  \n",
       "1279  31-Jul-22  Jul 22    W 32   weekend    NaN  \n",
       "1280        NaN  Aug-22    W 32  weekeday  100.0  \n",
       "1281        NaN  Aug-22    W 32  weekeday  100.0  \n",
       "1282        NaN  Aug-22    W 32  weekeday   75.0  \n",
       "1283        NaN  Aug-22    W 32  weekeday   75.0  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#QUESTION:6--Show the trend in successful_bookings in august in each city.\n",
    "#STEP-1--#Find average bookings for August and merge the data with updated_df.\n",
    "bookings_august=pd.merge(updated_df,updated_df[updated_df['mmm yy']=='Aug-22']['successful_bookings'])\n",
    "bookings_august.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "4f58d620-d883-4c80-8cca-457be136919f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    18.5\n",
       "Delhi        16.7\n",
       "Hyderabad    17.7\n",
       "Mumbai       17.8\n",
       "Name: successful_bookings, dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-2--Find the citywise booking trend:\n",
    "trend_data = bookings_august.groupby('city')['successful_bookings'].mean().round(1)\n",
    "trend_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "087e7895-e929-4c71-b6c3-814213f80ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Successful Bookings')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#STEP-3-- Create the trendline:\n",
    "\n",
    "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=(6, 4))\n",
    "sns.lineplot(x=trend_data.index, y=trend_data.values,linestyle='dashed',color='orange')\n",
    "plt.ylim(15, 20)\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": "code",
   "execution_count": 359,
   "id": "7c600f36-fade-464b-8142-7c8d87dc5169",
   "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>May012216558RT11</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>9100</td>\n",
       "      <td>3640</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May012216558RT15</td>\n",
       "      <td>16558</td>\n",
       "      <td>27-04-22</td>\n",
       "      <td>1/5/2022</td>\n",
       "      <td>2/5/2022</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10920</td>\n",
       "      <td>10920</td>\n",
       "      <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>134585</th>\n",
       "      <td>Jul312217564RT46</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>3/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134586</th>\n",
       "      <td>Jul312217564RT47</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>38760</td>\n",
       "      <td>38760</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134587</th>\n",
       "      <td>Jul312217564RT48</td>\n",
       "      <td>17564</td>\n",
       "      <td>30-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>2/8/2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>tripster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134588</th>\n",
       "      <td>Jul312217564RT49</td>\n",
       "      <td>17564</td>\n",
       "      <td>29-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>32300</td>\n",
       "      <td>32300</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134589</th>\n",
       "      <td>Jul312217564RT410</td>\n",
       "      <td>17564</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>31-07-22</td>\n",
       "      <td>1/8/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT4</td>\n",
       "      <td>makeyourtrip</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>32300</td>\n",
       "      <td>12920</td>\n",
       "      <td>Atliq Seasons</td>\n",
       "      <td>Business</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134590 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               booking_id  property_id booking_date check_in_date  \\\n",
       "0        May012216558RT11        16558     27-04-22      1/5/2022   \n",
       "1        May012216558RT12        16558     30-04-22      1/5/2022   \n",
       "2        May012216558RT13        16558     28-04-22      1/5/2022   \n",
       "3        May012216558RT14        16558     28-04-22      1/5/2022   \n",
       "4        May012216558RT15        16558     27-04-22      1/5/2022   \n",
       "...                   ...          ...          ...           ...   \n",
       "134585   Jul312217564RT46        17564     29-07-22      31-07-22   \n",
       "134586   Jul312217564RT47        17564     30-07-22      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",
       "0           2/5/2022       -3.0           RT1    direct online            1.0   \n",
       "1           2/5/2022        2.0           RT1           others            NaN   \n",
       "2           4/5/2022        2.0           RT1          logtrip            5.0   \n",
       "3           2/5/2022       -2.0           RT1           others            NaN   \n",
       "4           2/5/2022        4.0           RT1    direct online            5.0   \n",
       "...              ...        ...           ...              ...            ...   \n",
       "134585      3/8/2022        1.0           RT4     makeyourtrip            2.0   \n",
       "134586      1/8/2022       -4.0           RT4          logtrip            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  property_name  \\\n",
       "0         Checked Out              10010             10010   Atliq Grands   \n",
       "1           Cancelled               9100              3640   Atliq Grands   \n",
       "2         Checked Out            9100000              9100   Atliq Grands   \n",
       "3           Cancelled               9100              3640   Atliq Grands   \n",
       "4         Checked Out              10920             10920   Atliq Grands   \n",
       "...               ...                ...               ...            ...   \n",
       "134585    Checked Out              32300             32300  Atliq Seasons   \n",
       "134586    Checked Out              38760             38760  Atliq Seasons   \n",
       "134587      Cancelled              32300             12920  Atliq Seasons   \n",
       "134588    Checked Out              32300             32300  Atliq Seasons   \n",
       "134589      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",
       "134585  Business  Mumbai  \n",
       "134586  Business  Mumbai  \n",
       "134587  Business  Mumbai  \n",
       "134588  Business  Mumbai  \n",
       "134589  Business  Mumbai  \n",
       "\n",
       "[134590 rows x 15 columns]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#QUESTION:7--\n",
    "#Calculate the revenue realized per city.\n",
    "#Revenue realized is in df_bookings DataFrame and city information is in df_hotels,so,we can join them:\n",
    "#STEP-1--\n",
    "#Joining the tables/DataFrames:\n",
    "df_bookings_all=pd.merge(df_bookings,df_hotels,on='property_id')\n",
    "df_bookings_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "62904bd1-c295-4311-94db-b6bc02401930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Bangalore    420.40\n",
       "Delhi        294.50\n",
       "Hyderabad    325.23\n",
       "Mumbai       668.64\n",
       "Name: revenue_realized, dtype: float64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-2--\n",
    "#Group by city and sum the revenue realized.\n",
    "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": 210,
   "id": "4c2fc5dd-70da-4c95-b755-e5d06f9e4e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'City')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#STEP-3--\n",
    "#Create the visualization:\n",
    "revenue_realized_in_mln=((df_bookings_all.groupby('city')['revenue_realized'].sum())/1e6).round(2).plot(kind='barh')\n",
    "plt.title('Total Revenue Realized by City (in Millions)')\n",
    "plt.xlabel('Revenue realized(in millions)')\n",
    "plt.ylabel('City')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "67df9ba3-5dc0-4c93-8dcb-a113a75647c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion:\n",
    "#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",
    "#Mumbai has seen the highest amount of revenue realized,means the most number of cancelled bookings were done in Mumbai."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "f00c5b49-e055-4332-864c-95038c962c6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134590 entries, 0 to 134589\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   booking_id         134590 non-null  object \n",
      " 1   property_id        134590 non-null  int64  \n",
      " 2   booking_date       134590 non-null  object \n",
      " 3   check_in_date      134590 non-null  object \n",
      " 4   checkout_date      134590 non-null  object \n",
      " 5   no_guests          134587 non-null  float64\n",
      " 6   room_category      134590 non-null  object \n",
      " 7   booking_platform   134590 non-null  object \n",
      " 8   ratings_given      56683 non-null   float64\n",
      " 9   booking_status     134590 non-null  object \n",
      " 10  revenue_generated  134590 non-null  int64  \n",
      " 11  revenue_realized   134590 non-null  int64  \n",
      " 12  property_name      134590 non-null  object \n",
      " 13  category           134590 non-null  object \n",
      " 14  city               134590 non-null  object \n",
      "dtypes: float64(2), int64(3), object(10)\n",
      "memory usage: 15.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#QUESTION:8--\n",
    "#Calculate the month by month revenue.\n",
    "#The revenue generated on the check in date,so,the check in date of updated_df will be joined with the date column of df_date.\n",
    "#But,\n",
    "df_bookings_all.info()\n",
    "\n",
    "#By doing careful observation we see thecheck in date column is object datatype,we need to convert them to proper datetype.\n",
    "#..Otherwise we can't join it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "e9696e22-184b-4a0d-8524-c3268507067f",
   "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>May012216558RT11</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>-3.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>direct online</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>10010</td>\n",
       "      <td>10010</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May012216558RT12</td>\n",
       "      <td>16558</td>\n",
       "      <td>30-04-22</td>\n",
       "      <td>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>2</th>\n",
       "      <td>May012216558RT13</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>4/5/2022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RT1</td>\n",
       "      <td>logtrip</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Checked Out</td>\n",
       "      <td>9100000</td>\n",
       "      <td>9100</td>\n",
       "      <td>Atliq Grands</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May012216558RT14</td>\n",
       "      <td>16558</td>\n",
       "      <td>28-04-22</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id  property_id booking_date check_in_date checkout_date  \\\n",
       "0  May012216558RT11        16558     27-04-22    2022-01-05      2/5/2022   \n",
       "1  May012216558RT12        16558     30-04-22    2022-01-05      2/5/2022   \n",
       "2  May012216558RT13        16558     28-04-22    2022-01-05      4/5/2022   \n",
       "3  May012216558RT14        16558     28-04-22    2022-01-05      2/5/2022   \n",
       "\n",
       "   no_guests room_category booking_platform  ratings_given booking_status  \\\n",
       "0       -3.0           RT1    direct online            1.0    Checked Out   \n",
       "1        2.0           RT1           others            NaN      Cancelled   \n",
       "2        2.0           RT1          logtrip            5.0    Checked Out   \n",
       "3       -2.0           RT1           others            NaN      Cancelled   \n",
       "\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            9100000              9100  Atliq Grands   Luxury  Delhi  \n",
       "3               9100              3640  Atliq Grands   Luxury  Delhi  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-1--Converting the \"check_in_date\" column to datetime.\n",
    "df_bookings_all[\"check_in_date\"] = pd.to_datetime(df_bookings_all[\"check_in_date\"], errors='coerce')\n",
    "df_bookings_all.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "a66441e4-d3ed-473c-bdd2-e10dc5904b93",
   "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": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-2--Merging with date column.\n",
    "#Please note: Running this cell repeatedly will cause data addition of date table each time.\n",
    "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": 382,
   "id": "be6d3d42-16e5-45f6-83dd-d2b573139c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mmm yy\n",
       "Jul 22    71526945\n",
       "Jun 22    62310255\n",
       "May 22    71647470\n",
       "Name: revenue_generated, dtype: int64"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#STEP-3-- Calculating the Revenue generated:\n",
    "revenue_generated=df_bookings_all.groupby('mmm yy')['revenue_generated'].sum()\n",
    "revenue_generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "f2f0e33e-9bc3-4060-84dd-ef24976972e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Revenue generated (in Million)')"
      ]
     },
     "execution_count": 396,
     "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": [
    "#STEP-4--Creating the visual:\n",
    "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)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}