panda.ipynb

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      "source": [
        "PANDAS\n"
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        "id": "He_BcfHrsw_v"
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      "source": [
        "import pandas as pd"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"https://raw.githubusercontent.com/Premalatha-success/Yhills_July12_Analytics/main/hotel_bookings.csv\")"
      ],
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        "id": "yrjFoRjh0JDP"
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        "df.shape"
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              "(119390, 32)"
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        "df.head(10)"
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            "text/plain": [
              "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
              "0  Resort Hotel            0        342               2015               July   \n",
              "1  Resort Hotel            0        737               2015               July   \n",
              "2  Resort Hotel            0          7               2015               July   \n",
              "3  Resort Hotel            0         13               2015               July   \n",
              "4  Resort Hotel            0         14               2015               July   \n",
              "5  Resort Hotel            0         14               2015               July   \n",
              "6  Resort Hotel            0          0               2015               July   \n",
              "7  Resort Hotel            0          9               2015               July   \n",
              "8  Resort Hotel            1         85               2015               July   \n",
              "9  Resort Hotel            1         75               2015               July   \n",
              "\n",
              "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
              "0                        27                          1   \n",
              "1                        27                          1   \n",
              "2                        27                          1   \n",
              "3                        27                          1   \n",
              "4                        27                          1   \n",
              "5                        27                          1   \n",
              "6                        27                          1   \n",
              "7                        27                          1   \n",
              "8                        27                          1   \n",
              "9                        27                          1   \n",
              "\n",
              "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  deposit_type  \\\n",
              "0                        0                     0       2  ...    No Deposit   \n",
              "1                        0                     0       2  ...    No Deposit   \n",
              "2                        0                     1       1  ...    No Deposit   \n",
              "3                        0                     1       1  ...    No Deposit   \n",
              "4                        0                     2       2  ...    No Deposit   \n",
              "5                        0                     2       2  ...    No Deposit   \n",
              "6                        0                     2       2  ...    No Deposit   \n",
              "7                        0                     2       2  ...    No Deposit   \n",
              "8                        0                     3       2  ...    No Deposit   \n",
              "9                        0                     3       2  ...    No Deposit   \n",
              "\n",
              "   agent company days_in_waiting_list customer_type    adr  \\\n",
              "0    NaN     NaN                    0     Transient    0.0   \n",
              "1    NaN     NaN                    0     Transient    0.0   \n",
              "2    NaN     NaN                    0     Transient   75.0   \n",
              "3  304.0     NaN                    0     Transient   75.0   \n",
              "4  240.0     NaN                    0     Transient   98.0   \n",
              "5  240.0     NaN                    0     Transient   98.0   \n",
              "6    NaN     NaN                    0     Transient  107.0   \n",
              "7  303.0     NaN                    0     Transient  103.0   \n",
              "8  240.0     NaN                    0     Transient   82.0   \n",
              "9   15.0     NaN                    0     Transient  105.5   \n",
              "\n",
              "   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n",
              "0                            0                          0           Check-Out   \n",
              "1                            0                          0           Check-Out   \n",
              "2                            0                          0           Check-Out   \n",
              "3                            0                          0           Check-Out   \n",
              "4                            0                          1           Check-Out   \n",
              "5                            0                          1           Check-Out   \n",
              "6                            0                          0           Check-Out   \n",
              "7                            0                          1           Check-Out   \n",
              "8                            0                          1            Canceled   \n",
              "9                            0                          0            Canceled   \n",
              "\n",
              "  reservation_status_date  \n",
              "0              07-01-2015  \n",
              "1              07-01-2015  \n",
              "2              07-02-2015  \n",
              "3              07-02-2015  \n",
              "4              07-03-2015  \n",
              "5              07-03-2015  \n",
              "6              07-03-2015  \n",
              "7              07-03-2015  \n",
              "8              05-06-2015  \n",
              "9               4/22/2015  \n",
              "\n",
              "[10 rows x 32 columns]"
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              "      <th></th>\n",
              "      <th>hotel</th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
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              "      <th>days_in_waiting_list</th>\n",
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              "      <th>required_car_parking_spaces</th>\n",
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              "      <th>0</th>\n",
              "      <td>Resort Hotel</td>\n",
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              "      <td>Transient</td>\n",
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              "      <td>Check-Out</td>\n",
              "      <td>07-01-2015</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>737</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>No Deposit</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Transient</td>\n",
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              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>07-01-2015</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>07-02-2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>304.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>75.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>07-02-2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>07-03-2015</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>07-03-2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
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              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Transient</td>\n",
              "      <td>107.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>07-03-2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>303.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>103.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>07-03-2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>05-06-2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>75</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>105.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>4/22/2015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 32 columns</p>\n",
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        "df.tail(10)"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "             hotel  is_canceled  lead_time  arrival_date_year  \\\n",
              "119380  City Hotel            0         44               2017   \n",
              "119381  City Hotel            0        188               2017   \n",
              "119382  City Hotel            0        135               2017   \n",
              "119383  City Hotel            0        164               2017   \n",
              "119384  City Hotel            0         21               2017   \n",
              "119385  City Hotel            0         23               2017   \n",
              "119386  City Hotel            0        102               2017   \n",
              "119387  City Hotel            0         34               2017   \n",
              "119388  City Hotel            0        109               2017   \n",
              "119389  City Hotel            0        205               2017   \n",
              "\n",
              "       arrival_date_month  arrival_date_week_number  \\\n",
              "119380             August                        35   \n",
              "119381             August                        35   \n",
              "119382             August                        35   \n",
              "119383             August                        35   \n",
              "119384             August                        35   \n",
              "119385             August                        35   \n",
              "119386             August                        35   \n",
              "119387             August                        35   \n",
              "119388             August                        35   \n",
              "119389             August                        35   \n",
              "\n",
              "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
              "119380                         31                        1   \n",
              "119381                         31                        2   \n",
              "119382                         30                        2   \n",
              "119383                         31                        2   \n",
              "119384                         30                        2   \n",
              "119385                         30                        2   \n",
              "119386                         31                        2   \n",
              "119387                         31                        2   \n",
              "119388                         31                        2   \n",
              "119389                         29                        2   \n",
              "\n",
              "        stays_in_week_nights  adults  ...  deposit_type  agent company  \\\n",
              "119380                     3       2  ...    No Deposit    9.0     NaN   \n",
              "119381                     3       2  ...    No Deposit   14.0     NaN   \n",
              "119382                     4       3  ...    No Deposit    7.0     NaN   \n",
              "119383                     4       2  ...    No Deposit   42.0     NaN   \n",
              "119384                     5       2  ...    No Deposit  394.0     NaN   \n",
              "119385                     5       2  ...    No Deposit  394.0     NaN   \n",
              "119386                     5       3  ...    No Deposit    9.0     NaN   \n",
              "119387                     5       2  ...    No Deposit    9.0     NaN   \n",
              "119388                     5       2  ...    No Deposit   89.0     NaN   \n",
              "119389                     7       2  ...    No Deposit    9.0     NaN   \n",
              "\n",
              "       days_in_waiting_list customer_type     adr  \\\n",
              "119380                    0     Transient  140.75   \n",
              "119381                    0     Transient   99.00   \n",
              "119382                    0     Transient  209.00   \n",
              "119383                    0     Transient   87.60   \n",
              "119384                    0     Transient   96.14   \n",
              "119385                    0     Transient   96.14   \n",
              "119386                    0     Transient  225.43   \n",
              "119387                    0     Transient  157.71   \n",
              "119388                    0     Transient  104.40   \n",
              "119389                    0     Transient  151.20   \n",
              "\n",
              "        required_car_parking_spaces  total_of_special_requests  \\\n",
              "119380                            0                          1   \n",
              "119381                            0                          0   \n",
              "119382                            0                          0   \n",
              "119383                            0                          0   \n",
              "119384                            0                          2   \n",
              "119385                            0                          0   \n",
              "119386                            0                          2   \n",
              "119387                            0                          4   \n",
              "119388                            0                          0   \n",
              "119389                            0                          2   \n",
              "\n",
              "        reservation_status reservation_status_date  \n",
              "119380           Check-Out              09-04-2017  \n",
              "119381           Check-Out              09-05-2017  \n",
              "119382           Check-Out              09-05-2017  \n",
              "119383           Check-Out              09-06-2017  \n",
              "119384           Check-Out              09-06-2017  \n",
              "119385           Check-Out              09-06-2017  \n",
              "119386           Check-Out              09-07-2017  \n",
              "119387           Check-Out              09-07-2017  \n",
              "119388           Check-Out              09-07-2017  \n",
              "119389           Check-Out              09-07-2017  \n",
              "\n",
              "[10 rows x 32 columns]"
            ],
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              "      <td>4</td>\n",
              "      <td>3</td>\n",
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              "      <td>No Deposit</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>42.0</td>\n",
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              "      <th>119384</th>\n",
              "      <td>City Hotel</td>\n",
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              "      <td>5</td>\n",
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              "      <td>394.0</td>\n",
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              "      <td>City Hotel</td>\n",
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              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Transient</td>\n",
              "      <td>157.71</td>\n",
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              "      <td>Check-Out</td>\n",
              "      <td>09-07-2017</td>\n",
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              "      <td>City Hotel</td>\n",
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              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
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              "      <td>City Hotel</td>\n",
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              "      <td>29</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
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              "      <td>9.0</td>\n",
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              "      <td>Transient</td>\n",
              "      <td>151.20</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>09-07-2017</td>\n",
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              "  </tbody>\n",
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              "<p>10 rows × 32 columns</p>\n",
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          "metadata": {},
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    {
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      "source": [
        "df.columns"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "BIV2FMGo1xv1",
        "outputId": "a6e67310-fd9e-4970-d35c-1ce77e083f1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',\n",
              "       'arrival_date_month', 'arrival_date_week_number',\n",
              "       'arrival_date_day_of_month', 'stays_in_weekend_nights',\n",
              "       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',\n",
              "       'country', 'market_segment', 'distribution_channel',\n",
              "       'is_repeated_guest', 'previous_cancellations',\n",
              "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
              "       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',\n",
              "       'company', 'days_in_waiting_list', 'customer_type', 'adr',\n",
              "       'required_car_parking_spaces', 'total_of_special_requests',\n",
              "       'reservation_status', 'reservation_status_date'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Puum1q0R11ec",
        "outputId": "fb499e4f-4fa6-40cf-cba8-6a40224d12ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hotel                              object\n",
              "is_canceled                         int64\n",
              "lead_time                           int64\n",
              "arrival_date_year                   int64\n",
              "arrival_date_month                 object\n",
              "arrival_date_week_number            int64\n",
              "arrival_date_day_of_month           int64\n",
              "stays_in_weekend_nights             int64\n",
              "stays_in_week_nights                int64\n",
              "adults                              int64\n",
              "children                          float64\n",
              "babies                              int64\n",
              "meal                               object\n",
              "country                            object\n",
              "market_segment                     object\n",
              "distribution_channel               object\n",
              "is_repeated_guest                   int64\n",
              "previous_cancellations              int64\n",
              "previous_bookings_not_canceled      int64\n",
              "reserved_room_type                 object\n",
              "assigned_room_type                 object\n",
              "booking_changes                     int64\n",
              "deposit_type                       object\n",
              "agent                             float64\n",
              "company                           float64\n",
              "days_in_waiting_list                int64\n",
              "customer_type                      object\n",
              "adr                               float64\n",
              "required_car_parking_spaces         int64\n",
              "total_of_special_requests           int64\n",
              "reservation_status                 object\n",
              "reservation_status_date            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZLFPPJ0165S",
        "outputId": "5af406a1-66d3-435a-cf61-e16964a646f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 119390 entries, 0 to 119389\n",
            "Data columns (total 32 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           119390 non-null  object \n",
            " 1   is_canceled                     119390 non-null  int64  \n",
            " 2   lead_time                       119390 non-null  int64  \n",
            " 3   arrival_date_year               119390 non-null  int64  \n",
            " 4   arrival_date_month              119390 non-null  object \n",
            " 5   arrival_date_week_number        119390 non-null  int64  \n",
            " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
            " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
            " 8   stays_in_week_nights            119390 non-null  int64  \n",
            " 9   adults                          119390 non-null  int64  \n",
            " 10  children                        119386 non-null  float64\n",
            " 11  babies                          119390 non-null  int64  \n",
            " 12  meal                            119390 non-null  object \n",
            " 13  country                         118902 non-null  object \n",
            " 14  market_segment                  119390 non-null  object \n",
            " 15  distribution_channel            119390 non-null  object \n",
            " 16  is_repeated_guest               119390 non-null  int64  \n",
            " 17  previous_cancellations          119390 non-null  int64  \n",
            " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
            " 19  reserved_room_type              119390 non-null  object \n",
            " 20  assigned_room_type              119390 non-null  object \n",
            " 21  booking_changes                 119390 non-null  int64  \n",
            " 22  deposit_type                    119390 non-null  object \n",
            " 23  agent                           103050 non-null  float64\n",
            " 24  company                         6797 non-null    float64\n",
            " 25  days_in_waiting_list            119390 non-null  int64  \n",
            " 26  customer_type                   119390 non-null  object \n",
            " 27  adr                             119390 non-null  float64\n",
            " 28  required_car_parking_spaces     119390 non-null  int64  \n",
            " 29  total_of_special_requests       119390 non-null  int64  \n",
            " 30  reservation_status              119390 non-null  object \n",
            " 31  reservation_status_date         119390 non-null  object \n",
            "dtypes: float64(4), int64(16), object(12)\n",
            "memory usage: 29.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqtrfaLE2F_V",
        "outputId": "dcd02282-d22a-407f-a9e1-a9cc525eedb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hotel                                  0\n",
              "is_canceled                            0\n",
              "lead_time                              0\n",
              "arrival_date_year                      0\n",
              "arrival_date_month                     0\n",
              "arrival_date_week_number               0\n",
              "arrival_date_day_of_month              0\n",
              "stays_in_weekend_nights                0\n",
              "stays_in_week_nights                   0\n",
              "adults                                 0\n",
              "children                               4\n",
              "babies                                 0\n",
              "meal                                   0\n",
              "country                              488\n",
              "market_segment                         0\n",
              "distribution_channel                   0\n",
              "is_repeated_guest                      0\n",
              "previous_cancellations                 0\n",
              "previous_bookings_not_canceled         0\n",
              "reserved_room_type                     0\n",
              "assigned_room_type                     0\n",
              "booking_changes                        0\n",
              "deposit_type                           0\n",
              "agent                              16340\n",
              "company                           112593\n",
              "days_in_waiting_list                   0\n",
              "customer_type                          0\n",
              "adr                                    0\n",
              "required_car_parking_spaces            0\n",
              "total_of_special_requests              0\n",
              "reservation_status                     0\n",
              "reservation_status_date                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
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          "height": 364
        },
        "id": "HilkMWKRjcP7",
        "outputId": "36c22148-c182-426b-f98a-a2e343c3a795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         is_canceled      lead_time  arrival_date_year  \\\n",
              "count  119390.000000  119390.000000      119390.000000   \n",
              "mean        0.370416     104.011416        2016.156554   \n",
              "std         0.482918     106.863097           0.707476   \n",
              "min         0.000000       0.000000        2015.000000   \n",
              "25%         0.000000      18.000000        2016.000000   \n",
              "50%         0.000000      69.000000        2016.000000   \n",
              "75%         1.000000     160.000000        2017.000000   \n",
              "max         1.000000     737.000000        2017.000000   \n",
              "\n",
              "       arrival_date_week_number  arrival_date_day_of_month  \\\n",
              "count             119390.000000              119390.000000   \n",
              "mean                  27.165173                  15.798241   \n",
              "std                   13.605138                   8.780829   \n",
              "min                    1.000000                   1.000000   \n",
              "25%                   16.000000                   8.000000   \n",
              "50%                   28.000000                  16.000000   \n",
              "75%                   38.000000                  23.000000   \n",
              "max                   53.000000                  31.000000   \n",
              "\n",
              "       stays_in_weekend_nights  stays_in_week_nights         adults  \\\n",
              "count            119390.000000         119390.000000  119390.000000   \n",
              "mean                  0.927599              2.500302       1.856403   \n",
              "std                   0.998613              1.908286       0.579261   \n",
              "min                   0.000000              0.000000       0.000000   \n",
              "25%                   0.000000              1.000000       2.000000   \n",
              "50%                   1.000000              2.000000       2.000000   \n",
              "75%                   2.000000              3.000000       2.000000   \n",
              "max                  19.000000             50.000000      55.000000   \n",
              "\n",
              "            children         babies  is_repeated_guest  \\\n",
              "count  119386.000000  119390.000000      119390.000000   \n",
              "mean        0.103890       0.007949           0.031912   \n",
              "std         0.398561       0.097436           0.175767   \n",
              "min         0.000000       0.000000           0.000000   \n",
              "25%         0.000000       0.000000           0.000000   \n",
              "50%         0.000000       0.000000           0.000000   \n",
              "75%         0.000000       0.000000           0.000000   \n",
              "max        10.000000      10.000000           1.000000   \n",
              "\n",
              "       previous_cancellations  previous_bookings_not_canceled  \\\n",
              "count           119390.000000                   119390.000000   \n",
              "mean                 0.087118                        0.137097   \n",
              "std                  0.844336                        1.497437   \n",
              "min                  0.000000                        0.000000   \n",
              "25%                  0.000000                        0.000000   \n",
              "50%                  0.000000                        0.000000   \n",
              "75%                  0.000000                        0.000000   \n",
              "max                 26.000000                       72.000000   \n",
              "\n",
              "       booking_changes          agent      company  days_in_waiting_list  \\\n",
              "count    119390.000000  103050.000000  6797.000000         119390.000000   \n",
              "mean          0.221124      86.693382   189.266735              2.321149   \n",
              "std           0.652306     110.774548   131.655015             17.594721   \n",
              "min           0.000000       1.000000     6.000000              0.000000   \n",
              "25%           0.000000       9.000000    62.000000              0.000000   \n",
              "50%           0.000000      14.000000   179.000000              0.000000   \n",
              "75%           0.000000     229.000000   270.000000              0.000000   \n",
              "max          21.000000     535.000000   543.000000            391.000000   \n",
              "\n",
              "                 adr  required_car_parking_spaces  total_of_special_requests  \n",
              "count  119390.000000                119390.000000              119390.000000  \n",
              "mean      101.831122                     0.062518                   0.571363  \n",
              "std        50.535790                     0.245291                   0.792798  \n",
              "min        -6.380000                     0.000000                   0.000000  \n",
              "25%        69.290000                     0.000000                   0.000000  \n",
              "50%        94.575000                     0.000000                   0.000000  \n",
              "75%       126.000000                     0.000000                   1.000000  \n",
              "max      5400.000000                     8.000000                   5.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2730e9d1-e4ad-46c1-958c-d93abba740bd\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_week_nights</th>\n",
              "      <th>adults</th>\n",
              "      <th>children</th>\n",
              "      <th>babies</th>\n",
              "      <th>is_repeated_guest</th>\n",
              "      <th>previous_cancellations</th>\n",
              "      <th>previous_bookings_not_canceled</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>agent</th>\n",
              "      <th>company</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>adr</th>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <th>total_of_special_requests</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119386.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>103050.000000</td>\n",
              "      <td>6797.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.370416</td>\n",
              "      <td>104.011416</td>\n",
              "      <td>2016.156554</td>\n",
              "      <td>27.165173</td>\n",
              "      <td>15.798241</td>\n",
              "      <td>0.927599</td>\n",
              "      <td>2.500302</td>\n",
              "      <td>1.856403</td>\n",
              "      <td>0.103890</td>\n",
              "      <td>0.007949</td>\n",
              "      <td>0.031912</td>\n",
              "      <td>0.087118</td>\n",
              "      <td>0.137097</td>\n",
              "      <td>0.221124</td>\n",
              "      <td>86.693382</td>\n",
              "      <td>189.266735</td>\n",
              "      <td>2.321149</td>\n",
              "      <td>101.831122</td>\n",
              "      <td>0.062518</td>\n",
              "      <td>0.571363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.482918</td>\n",
              "      <td>106.863097</td>\n",
              "      <td>0.707476</td>\n",
              "      <td>13.605138</td>\n",
              "      <td>8.780829</td>\n",
              "      <td>0.998613</td>\n",
              "      <td>1.908286</td>\n",
              "      <td>0.579261</td>\n",
              "      <td>0.398561</td>\n",
              "      <td>0.097436</td>\n",
              "      <td>0.175767</td>\n",
              "      <td>0.844336</td>\n",
              "      <td>1.497437</td>\n",
              "      <td>0.652306</td>\n",
              "      <td>110.774548</td>\n",
              "      <td>131.655015</td>\n",
              "      <td>17.594721</td>\n",
              "      <td>50.535790</td>\n",
              "      <td>0.245291</td>\n",
              "      <td>0.792798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.380000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>69.290000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.575000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>38.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>270.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>31.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>535.000000</td>\n",
              "      <td>543.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>5400.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2730e9d1-e4ad-46c1-958c-d93abba740bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2730e9d1-e4ad-46c1-958c-d93abba740bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "Ytyk1nkTjnDH",
        "outputId": "89b50469-0734-4886-d063-a7a6d90844c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             hotel    is_canceled      lead_time  arrival_date_year  \\\n",
              "count       119390  119390.000000  119390.000000      119390.000000   \n",
              "unique           2            NaN            NaN                NaN   \n",
              "top     City Hotel            NaN            NaN                NaN   \n",
              "freq         79330            NaN            NaN                NaN   \n",
              "mean           NaN       0.370416     104.011416        2016.156554   \n",
              "std            NaN       0.482918     106.863097           0.707476   \n",
              "min            NaN       0.000000       0.000000        2015.000000   \n",
              "25%            NaN       0.000000      18.000000        2016.000000   \n",
              "50%            NaN       0.000000      69.000000        2016.000000   \n",
              "75%            NaN       1.000000     160.000000        2017.000000   \n",
              "max            NaN       1.000000     737.000000        2017.000000   \n",
              "\n",
              "       arrival_date_month  arrival_date_week_number  \\\n",
              "count              119390             119390.000000   \n",
              "unique                 12                       NaN   \n",
              "top                August                       NaN   \n",
              "freq                13877                       NaN   \n",
              "mean                  NaN                 27.165173   \n",
              "std                   NaN                 13.605138   \n",
              "min                   NaN                  1.000000   \n",
              "25%                   NaN                 16.000000   \n",
              "50%                   NaN                 28.000000   \n",
              "75%                   NaN                 38.000000   \n",
              "max                   NaN                 53.000000   \n",
              "\n",
              "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
              "count               119390.000000            119390.000000   \n",
              "unique                        NaN                      NaN   \n",
              "top                           NaN                      NaN   \n",
              "freq                          NaN                      NaN   \n",
              "mean                    15.798241                 0.927599   \n",
              "std                      8.780829                 0.998613   \n",
              "min                      1.000000                 0.000000   \n",
              "25%                      8.000000                 0.000000   \n",
              "50%                     16.000000                 1.000000   \n",
              "75%                     23.000000                 2.000000   \n",
              "max                     31.000000                19.000000   \n",
              "\n",
              "        stays_in_week_nights         adults  ...  deposit_type          agent  \\\n",
              "count          119390.000000  119390.000000  ...        119390  103050.000000   \n",
              "unique                   NaN            NaN  ...             3            NaN   \n",
              "top                      NaN            NaN  ...    No Deposit            NaN   \n",
              "freq                     NaN            NaN  ...        104641            NaN   \n",
              "mean                2.500302       1.856403  ...           NaN      86.693382   \n",
              "std                 1.908286       0.579261  ...           NaN     110.774548   \n",
              "min                 0.000000       0.000000  ...           NaN       1.000000   \n",
              "25%                 1.000000       2.000000  ...           NaN       9.000000   \n",
              "50%                 2.000000       2.000000  ...           NaN      14.000000   \n",
              "75%                 3.000000       2.000000  ...           NaN     229.000000   \n",
              "max                50.000000      55.000000  ...           NaN     535.000000   \n",
              "\n",
              "            company days_in_waiting_list customer_type            adr  \\\n",
              "count   6797.000000        119390.000000        119390  119390.000000   \n",
              "unique          NaN                  NaN             4            NaN   \n",
              "top             NaN                  NaN     Transient            NaN   \n",
              "freq            NaN                  NaN         89613            NaN   \n",
              "mean     189.266735             2.321149           NaN     101.831122   \n",
              "std      131.655015            17.594721           NaN      50.535790   \n",
              "min        6.000000             0.000000           NaN      -6.380000   \n",
              "25%       62.000000             0.000000           NaN      69.290000   \n",
              "50%      179.000000             0.000000           NaN      94.575000   \n",
              "75%      270.000000             0.000000           NaN     126.000000   \n",
              "max      543.000000           391.000000           NaN    5400.000000   \n",
              "\n",
              "        required_car_parking_spaces  total_of_special_requests  \\\n",
              "count                 119390.000000              119390.000000   \n",
              "unique                          NaN                        NaN   \n",
              "top                             NaN                        NaN   \n",
              "freq                            NaN                        NaN   \n",
              "mean                       0.062518                   0.571363   \n",
              "std                        0.245291                   0.792798   \n",
              "min                        0.000000                   0.000000   \n",
              "25%                        0.000000                   0.000000   \n",
              "50%                        0.000000                   0.000000   \n",
              "75%                        0.000000                   1.000000   \n",
              "max                        8.000000                   5.000000   \n",
              "\n",
              "        reservation_status reservation_status_date  \n",
              "count               119390                  119390  \n",
              "unique                   3                     926  \n",
              "top              Check-Out              10/21/2015  \n",
              "freq                 75166                    1461  \n",
              "mean                   NaN                     NaN  \n",
              "std                    NaN                     NaN  \n",
              "min                    NaN                     NaN  \n",
              "25%                    NaN                     NaN  \n",
              "50%                    NaN                     NaN  \n",
              "75%                    NaN                     NaN  \n",
              "max                    NaN                     NaN  \n",
              "\n",
              "[11 rows x 32 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hotel</th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_week_nights</th>\n",
              "      <th>adults</th>\n",
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              "      <th>adr</th>\n",
              "      <th>required_car_parking_spaces</th>\n",
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>119390</td>\n",
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              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>3</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>City Hotel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>August</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Transient</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>10/21/2015</td>\n",
              "    </tr>\n",
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              "      <th>freq</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>104641</td>\n",
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              "      <td>89613</td>\n",
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              "      <td>2.500302</td>\n",
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              "      <td>16.000000</td>\n",
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              "      <td>2.000000</td>\n",
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              "      <td>179.000000</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>23.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "      <td>270.000000</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>5.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>11 rows × 32 columns</p>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-340e0a06-6d2e-4f84-8de6-1a2c16cf8125 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-340e0a06-6d2e-4f84-8de6-1a2c16cf8125');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D9PmhoCVkOOS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}