project_1_data_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "alike-morgan",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import Markdown, display, HTML\n",
    "from collections import defaultdict\n",
    "\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "\n",
    "# Fix the dying kernel problem (only a problem in some installations - you can remove it, if it works without it)\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "friendly-herald",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from data_preprocessing.dataset_specification import DatasetSpecification\n",
    "from data_preprocessing.data_preprocessing_toolkit import DataPreprocessingToolkit\n",
    "from data_preprocessing.people_identifier import PeopleIdentifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prepared-signal",
   "metadata": {},
   "source": [
    "# Load original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "solid-crisis",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    reservation_id group_id  room_id  room_group_id  date_from    date_to  \\\n0            14160               135            135 2017-09-01 2018-03-30   \n1            16075               118            118 2018-02-10 2018-02-12   \n2            16076               270            270 2018-02-28 2018-03-02   \n3            16635               294            294 2018-02-14 2018-02-15   \n4            16964               183            183 2018-02-03 2018-02-09   \n5            17173                64             64 2018-01-29 2018-02-02   \n6            17308               111            111 2018-03-28 2018-03-31   \n7           120165               162            162 2018-11-16 2018-11-17   \n8           120183                45             45 2018-08-16 2018-08-18   \n9           120184                64             64 2018-08-17 2018-08-18   \n10          120185               126            126 2018-08-16 2018-08-18   \n11          120186                65             65 2018-08-16 2018-08-18   \n12          120194               267            267 2018-08-10 2018-08-11   \n13          120209               370            362 2018-08-03 2018-08-05   \n14          120232               321            321 2018-07-22 2018-07-30   \n15          120256               158            158 2018-07-15 2018-08-14   \n16          120264               213            213 2018-07-13 2018-07-15   \n17          120279               131            131 2018-07-07 2018-07-08   \n18          120285                20             20 2018-07-04 2018-07-07   \n19          120292               213            213 2018-07-03 2018-07-09   \n20          120314                36             36 2018-06-23 2018-07-01   \n21          120368               270            270 2018-05-28 2018-06-02   \n22          120371               227            227 2018-05-26 2018-06-09   \n23          120372               162            162 2018-05-26 2018-05-30   \n24          120373               131            131 2018-05-30 2018-06-02   \n25          120375               247            247 2018-05-26 2018-08-31   \n26          120377               126            126 2018-05-25 2018-05-26   \n27          120378                51             51 2018-05-22 2018-05-30   \n28          120379               325            325 2018-05-22 2018-05-24   \n29          120399               158            158 2018-04-30 2018-07-14   \n\n   booking_date        booking_time  n_people  n_children_1  n_children_2  \\\n0    2017-07-04 2017-07-04 10:52:00         1             0             0   \n1    2017-08-17 2017-08-17 15:01:00         5             0             0   \n2    2017-08-17 2017-08-17 15:08:00         4             0             0   \n3    2017-08-29 2017-08-29 13:58:00         2             0             0   \n4    2017-09-04 2017-09-04 15:52:00         4             0             0   \n5    2017-09-07 2017-09-07 13:21:00         2             0             0   \n6    2017-09-11 2017-09-11 10:31:00         5             0             0   \n7    2018-02-19 2018-02-19 17:44:00         5             0             0   \n8    2018-02-19 2018-02-19 17:44:00         1             0             0   \n9    2018-02-19 2018-02-19 17:44:00         1             0             0   \n10   2018-02-19 2018-02-19 17:44:00         1             0             0   \n11   2018-02-19 2018-02-19 17:44:00         1             0             0   \n12   2018-02-19 2018-02-19 17:44:00         3             0             0   \n13   2018-02-19 2018-02-19 17:44:00         4             0             0   \n14   2018-02-19 2018-02-19 17:44:00         3             0             0   \n15   2018-02-19 2018-02-19 17:44:00         1             0             0   \n16   2018-02-19 2018-02-19 17:44:00         4             0             0   \n17   2018-02-19 2018-02-19 17:44:00         6             0             0   \n18   2018-02-19 2018-02-19 17:44:00         3             0             0   \n19   2018-02-19 2018-02-19 17:44:00         4             0             0   \n20   2018-02-19 2018-02-19 17:44:00         3             0             0   \n21   2018-02-19 2018-02-19 17:44:00         4             0             0   \n22   2018-02-19 2018-02-19 17:44:00         6             0             0   \n23   2018-02-19 2018-02-19 17:44:00         7             0             0   \n24   2018-02-19 2018-02-19 17:44:00         1             0             0   \n25   2018-02-19 2018-02-19 17:44:00         1             0             0   \n26   2018-02-19 2018-02-19 17:44:00         1             0             0   \n27   2018-02-19 2018-02-19 17:44:00         1             0             0   \n28   2018-02-19 2018-02-19 17:44:00         1             0             0   \n29   2018-02-19 2018-02-19 17:44:00         1             0             0   \n\n    n_children_3  discount  accommodation_price  meal_price  service_price  \\\n0              0       NaN                 0.00         0.0            0.0   \n1              0       NaN               992.29         0.0            0.0   \n2              0       0.0               693.40         0.0            0.0   \n3              0       NaN               366.80         0.0            0.0   \n4              0       NaN              1064.60         0.0            0.0   \n5              0       NaN               713.00         0.0            0.0   \n6              0       0.0               800.00         0.0            0.0   \n7              0       0.0               402.00         0.0            0.0   \n8              0       0.0               660.00         0.0            0.0   \n9              0       0.0               320.00         0.0            0.0   \n10             0       0.0               720.00         0.0            0.0   \n11             0       0.0               480.00         0.0            0.0   \n12             0       0.0               595.98         0.0            0.0   \n13             0       0.0               915.00         0.0            0.0   \n14             0       0.0              3579.40         0.0            0.0   \n15             0       0.0                 0.00         NaN            NaN   \n16             0       0.0                 0.00         NaN            NaN   \n17             0       0.0               640.00         0.0            0.0   \n18             0       0.0               793.00         0.0            0.0   \n19             0       0.0                 0.00         NaN            NaN   \n20             0       0.0              2970.00         0.0            0.0   \n21             0       0.0              1799.20         0.0            0.0   \n22             0       0.0              5891.20         0.0            0.0   \n23             0       0.0              2172.40         0.0            0.0   \n24             0       0.0                 0.00         0.0            0.0   \n25             0       0.0                 0.00         NaN            NaN   \n26             0       0.0               480.00         0.0            0.0   \n27             0       0.0                 0.00         0.0            0.0   \n28             0       0.0                 0.00         NaN            NaN   \n29             0       0.0                 0.00         NaN            NaN   \n\n       paid rate_plan  client_id  \\\n0      0.00  Standard      51665   \n1      1.00  Standard      54117   \n2    693.40  Standard      54118   \n3      1.00  Standard      54790   \n4      1.00  Standard      55177   \n5      1.00  Standard      55412   \n6    800.00  Standard      55560   \n7    402.00  Standard      63419   \n8    660.00  Standard      61777   \n9    320.00  Standard      61778   \n10   720.00  Standard      61777   \n11   480.00  Standard      61777   \n12   595.98  Standard      61378   \n13   915.00  Standard      61105   \n14  3579.40  Standard      61807   \n15     0.00  Standard       1031   \n16     1.00  Standard      61391   \n17   640.00  Standard      60668   \n18   793.00  Standard      61508   \n19     1.00  Standard      61536   \n20  2970.00  Standard      59251   \n21  1799.20  Standard      61237   \n22  5891.20  Standard      61428   \n23  2172.40  Standard      61522   \n24     0.00  Standard       1031   \n25     0.00  Standard      54132   \n26   480.00  Standard      61739   \n27     0.00  Standard       1031   \n28     0.00  Standard       1031   \n29     0.00  Standard      61851   \n\n                                          client_name  \\\n0   86bd787ca115281ad9642c5fd6e79e6f2d87841c2fd9c6...   \n1   ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...   \n2   4db36724fc28085e053a3003dce55368ee207cce37d355...   \n3   e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...   \n4   5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...   \n5   4aebfe125cf6c059588792b9fb871afe282a8806299dfe...   \n6   1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...   \n7   d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...   \n8   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                         \n29                                                      \n\n                                                phone  is_company  \\\n0                                                               0   \n1   318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...           0   \n2   cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...           0   \n3   1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...           0   \n4   3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...           0   \n5   ea16c664798581a9d93a3128d772b8b89e05743edbbfae...           0   \n6   516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...           0   \n7   6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...           0   \n8                                                               0   \n9                                                               0   \n10                                                              0   \n11                                                              0   \n12 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                                        0   \n27                                                              0   \n28                                                              0   \n29                                                              0   \n\n    reservation_status  \n0                    1  \n1                    1  \n2                    2  \n3                    1  \n4                    1  \n5                    1  \n6                    4  \n7                    4  \n8                    4  \n9                    4  \n10                   4  \n11                   4  \n12                   4  \n13                   4  \n14                   3  \n15                   0  \n16                   0  \n17                   4  \n18                   4  \n19                   0  \n20                   3  \n21                   3  \n22                   3  \n23                   4  \n24                   2  \n25                   0  \n26                   4  \n27                   2  \n28                   0  \n29                   0  ",
      "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>reservation_id</th>\n      <th>group_id</th>\n      <th>room_id</th>\n      <th>room_group_id</th>\n      <th>date_from</th>\n      <th>date_to</th>\n      <th>booking_date</th>\n      <th>booking_time</th>\n      <th>n_people</th>\n      <th>n_children_1</th>\n      <th>n_children_2</th>\n      <th>n_children_3</th>\n      <th>discount</th>\n      <th>accommodation_price</th>\n      <th>meal_price</th>\n      <th>service_price</th>\n      <th>paid</th>\n      <th>rate_plan</th>\n      <th>client_id</th>\n      <th>client_name</th>\n      <th>email</th>\n      <th>phone</th>\n      <th>is_company</th>\n      <th>reservation_status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>14160</td>\n      <td></td>\n      <td>135</td>\n      <td>135</td>\n      <td>2017-09-01</td>\n      <td>2018-03-30</td>\n      <td>2017-07-04</td>\n      <td>2017-07-04 10:52:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>Standard</td>\n      <td>51665</td>\n      <td>86bd787ca115281ad9642c5fd6e79e6f2d87841c2fd9c6...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>16075</td>\n      <td></td>\n      <td>118</td>\n      <td>118</td>\n      <td>2018-02-10</td>\n      <td>2018-02-12</td>\n      <td>2017-08-17</td>\n      <td>2017-08-17 15:01:00</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>992.29</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>Standard</td>\n      <td>54117</td>\n      <td>ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...</td>\n      <td></td>\n      <td>318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>16076</td>\n      <td></td>\n      <td>270</td>\n      <td>270</td>\n      <td>2018-02-28</td>\n      <td>2018-03-02</td>\n      <td>2017-08-17</td>\n      <td>2017-08-17 15:08:00</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>693.40</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>693.40</td>\n      <td>Standard</td>\n      <td>54118</td>\n      <td>4db36724fc28085e053a3003dce55368ee207cce37d355...</td>\n      <td>f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...</td>\n      <td>cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>16635</td>\n      <td></td>\n      <td>294</td>\n      <td>294</td>\n      <td>2018-02-14</td>\n      <td>2018-02-15</td>\n      <td>2017-08-29</td>\n      <td>2017-08-29 13:58:00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>366.80</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>Standard</td>\n      <td>54790</td>\n      <td>e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...</td>\n      <td>f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...</td>\n      <td>1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>16964</td>\n      <td></td>\n      <td>183</td>\n      <td>183</td>\n      <td>2018-02-03</td>\n      <td>2018-02-09</td>\n      <td>2017-09-04</td>\n      <td>2017-09-04 15:52:00</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>1064.60</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>Standard</td>\n      <td>55177</td>\n      <td>5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...</td>\n      <td>6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...</td>\n      <td>3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>17173</td>\n      <td></td>\n      <td>64</td>\n      <td>64</td>\n      <td>2018-01-29</td>\n      <td>2018-02-02</td>\n      <td>2017-09-07</td>\n      <td>2017-09-07 13:21:00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>713.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>Standard</td>\n      <td>55412</td>\n      <td>4aebfe125cf6c059588792b9fb871afe282a8806299dfe...</td>\n      <td>0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...</td>\n      <td>ea16c664798581a9d93a3128d772b8b89e05743edbbfae...</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>17308</td>\n      <td></td>\n      <td>111</td>\n      <td>111</td>\n      <td>2018-03-28</td>\n      <td>2018-03-31</td>\n      <td>2017-09-11</td>\n      <td>2017-09-11 10:31:00</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>800.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>800.00</td>\n      <td>Standard</td>\n      <td>55560</td>\n      <td>1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...</td>\n      <td>6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...</td>\n      <td>516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>120165</td>\n      <td></td>\n      <td>162</td>\n      <td>162</td>\n      <td>2018-11-16</td>\n      <td>2018-11-17</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>402.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>402.00</td>\n      <td>Standard</td>\n      <td>63419</td>\n      <td>d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...</td>\n      <td>5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...</td>\n      <td>6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>120183</td>\n      <td></td>\n      <td>45</td>\n      <td>45</td>\n      <td>2018-08-16</td>\n      <td>2018-08-18</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>660.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>660.00</td>\n      <td>Standard</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>120184</td>\n      <td></td>\n      <td>64</td>\n      <td>64</td>\n      <td>2018-08-17</td>\n      <td>2018-08-18</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>320.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>320.00</td>\n      <td>Standard</td>\n      <td>61778</td>\n      <td>364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>120185</td>\n      <td></td>\n      <td>126</td>\n      <td>126</td>\n      <td>2018-08-16</td>\n      <td>2018-08-18</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>720.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>720.00</td>\n      <td>Standard</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>120186</td>\n      <td></td>\n      <td>65</td>\n      <td>65</td>\n      <td>2018-08-16</td>\n      <td>2018-08-18</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>480.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>480.00</td>\n      <td>Standard</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>120194</td>\n      <td></td>\n      <td>267</td>\n      <td>267</td>\n      <td>2018-08-10</td>\n      <td>2018-08-11</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>595.98</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>595.98</td>\n      <td>Standard</td>\n      <td>61378</td>\n      <td>f929533e542ff1302069567400b5ba584dc27dcbd63aae...</td>\n      <td>a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...</td>\n      <td>446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>120209</td>\n      <td></td>\n      <td>370</td>\n      <td>362</td>\n      <td>2018-08-03</td>\n      <td>2018-08-05</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>915.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>915.00</td>\n      <td>Standard</td>\n      <td>61105</td>\n      <td>8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...</td>\n      <td>37827cbb5aec4ed83d13193d766324efb93251cd44b28b...</td>\n      <td>27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>120232</td>\n      <td></td>\n      <td>321</td>\n      <td>321</td>\n      <td>2018-07-22</td>\n      <td>2018-07-30</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>3579.40</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3579.40</td>\n      <td>Standard</td>\n      <td>61807</td>\n      <td>cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...</td>\n      <td></td>\n      <td>5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>120256</td>\n      <td></td>\n      <td>158</td>\n      <td>158</td>\n      <td>2018-07-15</td>\n      <td>2018-08-14</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>Standard</td>\n      <td>1031</td>\n      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>120264</td>\n      <td></td>\n      <td>213</td>\n      <td>213</td>\n      <td>2018-07-13</td>\n      <td>2018-07-15</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.00</td>\n      <td>Standard</td>\n      <td>61391</td>\n      <td>4d9a25ef49b785020f71d87e7202cd209bdc4197b2649c...</td>\n      <td>25dec48fdcf1d2e7dbe97aff20d52cae0b2660f5eae1d8...</td>\n      <td>97edf16b92140283616026230d86f5ded26d8e09863a76...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>120279</td>\n      <td></td>\n      <td>131</td>\n      <td>131</td>\n      <td>2018-07-07</td>\n      <td>2018-07-08</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>640.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>640.00</td>\n      <td>Standard</td>\n      <td>60668</td>\n      <td>37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...</td>\n      <td>a9ba8f12a4509f4c598578fa743236beda2b791c938954...</td>\n      <td>7ec6cc25066b8b7008b78d25027a2159d2116522611223...</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>120285</td>\n      <td></td>\n      <td>20</td>\n      <td>20</td>\n      <td>2018-07-04</td>\n      <td>2018-07-07</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>793.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>793.00</td>\n      <td>Standard</td>\n      <td>61508</td>\n      <td>1f277c7e23db63f3d3b99a1c91959d2d87cf65dd150c16...</td>\n      <td>4d70747ed821eca5c6fb0ec95f2a8e005369e53440a99c...</td>\n      <td>1fda0af8c40fa82143ccbeac216fe4e1b6f82b5b57bd00...</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>120292</td>\n      <td></td>\n      <td>213</td>\n      <td>213</td>\n      <td>2018-07-03</td>\n      <td>2018-07-09</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.00</td>\n      <td>Standard</td>\n      <td>61536</td>\n      <td>d2125403e9a982b702f1e2ae3d961f6a2b1113cac9b4ab...</td>\n      <td>b068c4964a0082cea54c0c6b5b31907f8b5009cc5c29a9...</td>\n      <td>18796a14e703d952770ff333e440744e5645f36395ab23...</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>120314</td>\n      <td></td>\n      <td>36</td>\n      <td>36</td>\n      <td>2018-06-23</td>\n      <td>2018-07-01</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>2970.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2970.00</td>\n      <td>Standard</td>\n      <td>59251</td>\n      <td>1fc1fc3c2f76244d172dce793478abe25bfa262bf84d88...</td>\n      <td>03f8d9987cae994de741857336b1d5bfa6e13567691336...</td>\n      <td>fbf6f729bdbc5c399fda4952800159bad22b606de316dc...</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>120368</td>\n      <td></td>\n      <td>270</td>\n      <td>270</td>\n      <td>2018-05-28</td>\n      <td>2018-06-02</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1799.20</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1799.20</td>\n      <td>Standard</td>\n      <td>61237</td>\n      <td>90c2ec1072904a2ba8b88c66c17930e8b85f8d0f10b4e2...</td>\n      <td>02f2e085320d0930db4c9d722d315e7e77235dac96303f...</td>\n      <td>7e2f2677699ae3b5b58365cf109eac9fc4803d83b900d4...</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>120371</td>\n      <td></td>\n      <td>227</td>\n      <td>227</td>\n      <td>2018-05-26</td>\n      <td>2018-06-09</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>5891.20</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5891.20</td>\n      <td>Standard</td>\n      <td>61428</td>\n      <td>d96743d064f148fd88ea765513663700c2873765710c90...</td>\n      <td>4bc6914285bb73a34da5fc07ea6ed16eb3e08b0e6a5230...</td>\n      <td>00d2c14014162af4c5c454800a93dfdcb4ea9ee96d109c...</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>120372</td>\n      <td></td>\n      <td>162</td>\n      <td>162</td>\n      <td>2018-05-26</td>\n      <td>2018-05-30</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>2172.40</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2172.40</td>\n      <td>Standard</td>\n      <td>61522</td>\n      <td>2f31fadcb70beff6db9b09d6f62463beaa005569998791...</td>\n      <td>22893f17c5a4cd6e0913a85ecdbc597ff2ff891e347570...</td>\n      <td>b29bb8fee8266f9f23b7200a0979430bdd101cc2dc2e58...</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>120373</td>\n      <td></td>\n      <td>131</td>\n      <td>131</td>\n      <td>2018-05-30</td>\n      <td>2018-06-02</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>Standard</td>\n      <td>1031</td>\n      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>120375</td>\n      <td></td>\n      <td>247</td>\n      <td>247</td>\n      <td>2018-05-26</td>\n      <td>2018-08-31</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>Standard</td>\n      <td>54132</td>\n      <td>4255913c4a150c7f6171ba2807857992e91b34f1b2862e...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>120377</td>\n      <td></td>\n      <td>126</td>\n      <td>126</td>\n      <td>2018-05-25</td>\n      <td>2018-05-26</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>480.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>480.00</td>\n      <td>Standard</td>\n      <td>61739</td>\n      <td>b486b37b72db13811533a126c556cc8867eb2bbc432b8d...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>120378</td>\n      <td></td>\n      <td>51</td>\n      <td>51</td>\n      <td>2018-05-22</td>\n      <td>2018-05-30</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>Standard</td>\n      <td>1031</td>\n      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>120379</td>\n      <td></td>\n      <td>325</td>\n      <td>325</td>\n      <td>2018-05-22</td>\n      <td>2018-05-24</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>Standard</td>\n      <td>1031</td>\n      <td>d2c7d67c462db4ac6637597e7843b0c080cd41baa5dae0...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>120399</td>\n      <td></td>\n      <td>158</td>\n      <td>158</td>\n      <td>2018-04-30</td>\n      <td>2018-07-14</td>\n      <td>2018-02-19</td>\n      <td>2018-02-19 17:44:00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.00</td>\n      <td>Standard</td>\n      <td>61851</td>\n      <td>fa1b8b5a01558c675885bdd7b844e74c7b061079f9623b...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data_path = os.path.join(\"data\", \"hotel_data\")\n",
    "\n",
    "original_data = pd.read_csv(os.path.join(data_path, \"hotel_data_original.csv\"), index_col=0)\n",
    "\n",
    "original_data = original_data.replace({\"\\\\N\": \"\"})\n",
    "original_data = original_data.fillna(\"\")\n",
    "\n",
    "numeric_columns = [\"n_people\", \"n_children_1\", \"n_children_2\", \"n_children_3\",\n",
    "                   \"discount\", \"accommodation_price\", \"meal_price\", \"service_price\",\n",
    "                   \"paid\"]\n",
    "\n",
    "for column in numeric_columns:\n",
    "    original_data.loc[:, column] = pd.to_numeric(original_data.loc[:, column], errors=\"coerce\")\n",
    "\n",
    "original_data = original_data.astype(\n",
    "        {\n",
    "            \"date_from\": np.datetime64,\n",
    "            \"date_to\": np.datetime64,\n",
    "            \"booking_time\": np.datetime64,\n",
    "            \"booking_date\": np.datetime64,\n",
    "            \"n_people\": np.int64,\n",
    "            \"n_children_1\": np.int64,\n",
    "            \"n_children_2\": np.int64,\n",
    "            \"n_children_3\": np.int64,\n",
    "            \"discount\": np.float64,\n",
    "            \"accommodation_price\": np.float64,\n",
    "            \"meal_price\": np.float64,\n",
    "            \"service_price\": np.float64,\n",
    "            \"paid\": np.float64,\n",
    "        }\n",
    "    )\n",
    "\n",
    "display(original_data.head(30))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endangered-lingerie",
   "metadata": {},
   "source": [
    "# Preprocess the data\n",
    "\n",
    "- Identify users by client_id, name hash, phone hash, email hash.\n",
    "- Fix date_to - originally it points to the last full day of stay, not the departure date.\n",
    "- Add length of stay.\n",
    "- Add book to arrival.\n",
    "- Add number of rooms (important for group reservations).\n",
    "- Add indicator for stays encompasing a weekend.\n",
    "- Add night price.\n",
    "- Fix book to arrival to be not smaller than 0.\n",
    "- Filter out companies as recommendations for such clients should work differently.\n",
    "- Aggregate group reservations into single interactions.\n",
    "\n",
    "<span style=\"color:red\"><font size=\"4\">**Task:**</font></span><br> \n",
    "In the file data_preprocessing/data_preprocessing_toolkit write code for the following methods which work on the DataFrame with hotel data:\n",
    "  - add_length_of_stay - Adds length_of_stay column which is the difference between date_from and date_to (in days), i.e. the number of nights the customer stayed at the hotel.\n",
    "  - add_book_to_arrival - Adds book_to_arrival column which is the difference between date_from and booking_date (in days).\n",
    "  - add_weekend_stay - Adds weekend_stay column with 'True'/'False' strings indicating if the interval date_from to date_to contains any weekend days (defined as Friday and Saturday).\n",
    "  - add_night_price - Adds night_price column with the average price per one night per room - calculated as accomodation_price divided by length_of_stay and by n_rooms (there can be many rooms in group reservations - 'n_rooms' column).\n",
    "  - sum_npeople - Sums n_people, n_children_1, n_children_2, n_children_3 and sets the result to the n_people column.\n",
    "  - filter_out_company_clients - Filters out company clients is_company=0.\n",
    "  - filter_out_long_stays - Leaves only stays with length_of_stay less or equal to 21.\n",
    "  - filter_out_low_prices - Leaves only stays with accommodation price bigger than 50. Smaller prices are considered not reliable and likely a mistake of the hotel staff.\n",
    "  - aggregate_group_reservations - Aggregates every group reservation into one reservation with aggregated data (for self.sum_columns a sum is taken, for self.mean_columns a mean, for self.mode_columns a mode, for self.first_columns the first value). This one is the most challenging - see instructions in the py file (remember that with the %load_ext autoreload %autoreload 2 options in the first cell of this notebook you don't have to restart the notebook - the changes in the py file will be immediately used in the notebook whenever a method from the py file is invoked).\n",
    "  \n",
    "You have to pass all assertions in the below cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "swedish-iceland",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    n_people  n_children_1  n_children_2  n_children_3  accommodation_price  \\\n0          5             0             0             0               992.29   \n1          4             0             0             0               693.40   \n2          2             0             0             0               366.80   \n3          4             0             0             0              1064.60   \n4          2             0             0             0               713.00   \n5          5             0             0             0               800.00   \n6          5             0             0             0               402.00   \n7          1             0             0             0               660.00   \n8          1             0             0             0               320.00   \n9          1             0             0             0               720.00   \n10         1             0             0             0               480.00   \n11         3             0             0             0               595.98   \n12         4             0             0             0               915.00   \n13         3             0             0             0              3579.40   \n14         6             0             0             0               640.00   \n\n    meal_price  service_price     paid  n_rooms  discount  room_id  \\\n0          0.0            0.0     1.00        1       NaN      118   \n1          0.0            0.0   693.40        1       0.0      270   \n2          0.0            0.0     1.00        1       NaN      294   \n3          0.0            0.0     1.00        1       NaN      183   \n4          0.0            0.0     1.00        1       NaN       64   \n5          0.0            0.0   800.00        1       0.0      111   \n6          0.0            0.0   402.00        1       0.0      162   \n7          0.0            0.0   660.00        1       0.0       45   \n8          0.0            0.0   320.00        1       0.0       64   \n9          0.0            0.0   720.00        1       0.0      126   \n10         0.0            0.0   480.00        1       0.0       65   \n11         0.0            0.0   595.98        1       0.0      267   \n12         0.0            0.0   915.00        1       0.0      370   \n13         0.0            0.0  3579.40        1       0.0      321   \n14         0.0            0.0   640.00        1       0.0      131   \n\n    room_group_id  date_from    date_to booking_date rate_plan  \\\n0             118 2018-02-10 2018-02-13   2017-08-17  Standard   \n1             270 2018-02-28 2018-03-03   2017-08-17  Standard   \n2             294 2018-02-14 2018-02-16   2017-08-29  Standard   \n3             183 2018-02-03 2018-02-10   2017-09-04  Standard   \n4              64 2018-01-29 2018-02-03   2017-09-07  Standard   \n5             111 2018-03-28 2018-04-01   2017-09-11  Standard   \n6             162 2018-11-16 2018-11-18   2018-02-19  Standard   \n7              45 2018-08-16 2018-08-19   2018-02-19  Standard   \n8              64 2018-08-17 2018-08-19   2018-02-19  Standard   \n9             126 2018-08-16 2018-08-19   2018-02-19  Standard   \n10             65 2018-08-16 2018-08-19   2018-02-19  Standard   \n11            267 2018-08-10 2018-08-12   2018-02-19  Standard   \n12            362 2018-08-03 2018-08-06   2018-02-19  Standard   \n13            321 2018-07-22 2018-07-31   2018-02-19  Standard   \n14            131 2018-07-07 2018-07-09   2018-02-19  Standard   \n\n    length_of_stay  book_to_arrival weekend_stay  user_id  client_id  \\\n0                3              177         True        1      54117   \n1                3              195         True        2      54118   \n2                2              169        False        3      54790   \n3                7              152         True        4      55177   \n4                5              144         True        5      55412   \n5                4              198         True        6      55560   \n6                2              270         True        7      63419   \n7                3              178         True        8      61777   \n8                2              179         True        9      61778   \n9                3              178         True        8      61777   \n10               3              178         True        8      61777   \n11               2              172         True       10      61378   \n12               3              165         True       11      61105   \n13               9              153        False       12      61807   \n14               2              138         True       15      60668   \n\n                                          client_name  \\\n0   ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...   \n1   4db36724fc28085e053a3003dce55368ee207cce37d355...   \n2   e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...   \n3   5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...   \n4   4aebfe125cf6c059588792b9fb871afe282a8806299dfe...   \n5   1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...   \n6   d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...   \n7   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n8   364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...   \n9   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n10  f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n11  f929533e542ff1302069567400b5ba584dc27dcbd63aae...   \n12  8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...   \n13  cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...   \n14  37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...   \n\n                                                email  \\\n0                                                       \n1   f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...   \n2   f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...   \n3   6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...   \n4   0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...   \n5   6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...   \n6   5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...   \n7                                                       \n8                                                       \n9                                                       \n10                                                      \n11  a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...   \n12  37827cbb5aec4ed83d13193d766324efb93251cd44b28b...   \n13                                                      \n14  a9ba8f12a4509f4c598578fa743236beda2b791c938954...   \n\n                                                phone  is_company  night_price  \n0   318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...           0       330.76  \n1   cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...           0       231.13  \n2   1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...           0       183.40  \n3   3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...           0       152.09  \n4   ea16c664798581a9d93a3128d772b8b89e05743edbbfae...           0       142.60  \n5   516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...           0       200.00  \n6   6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...           0       201.00  \n7                                                               0       220.00  \n8                                                               0       160.00  \n9                                                               0       240.00  \n10                                                              0       160.00  \n11  446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...           0       297.99  \n12  27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...           0       305.00  \n13  5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...           0       397.71  \n14  7ec6cc25066b8b7008b78d25027a2159d2116522611223...           0       320.00  ",
      "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>n_people</th>\n      <th>n_children_1</th>\n      <th>n_children_2</th>\n      <th>n_children_3</th>\n      <th>accommodation_price</th>\n      <th>meal_price</th>\n      <th>service_price</th>\n      <th>paid</th>\n      <th>n_rooms</th>\n      <th>discount</th>\n      <th>room_id</th>\n      <th>room_group_id</th>\n      <th>date_from</th>\n      <th>date_to</th>\n      <th>booking_date</th>\n      <th>rate_plan</th>\n      <th>length_of_stay</th>\n      <th>book_to_arrival</th>\n      <th>weekend_stay</th>\n      <th>user_id</th>\n      <th>client_id</th>\n      <th>client_name</th>\n      <th>email</th>\n      <th>phone</th>\n      <th>is_company</th>\n      <th>night_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>992.29</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>118</td>\n      <td>118</td>\n      <td>2018-02-10</td>\n      <td>2018-02-13</td>\n      <td>2017-08-17</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>177</td>\n      <td>True</td>\n      <td>1</td>\n      <td>54117</td>\n      <td>ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...</td>\n      <td></td>\n      <td>318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...</td>\n      <td>0</td>\n      <td>330.76</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>693.40</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>693.40</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>270</td>\n      <td>270</td>\n      <td>2018-02-28</td>\n      <td>2018-03-03</td>\n      <td>2017-08-17</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>195</td>\n      <td>True</td>\n      <td>2</td>\n      <td>54118</td>\n      <td>4db36724fc28085e053a3003dce55368ee207cce37d355...</td>\n      <td>f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...</td>\n      <td>cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...</td>\n      <td>0</td>\n      <td>231.13</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>366.80</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>294</td>\n      <td>294</td>\n      <td>2018-02-14</td>\n      <td>2018-02-16</td>\n      <td>2017-08-29</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>169</td>\n      <td>False</td>\n      <td>3</td>\n      <td>54790</td>\n      <td>e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...</td>\n      <td>f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...</td>\n      <td>1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...</td>\n      <td>0</td>\n      <td>183.40</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1064.60</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>183</td>\n      <td>183</td>\n      <td>2018-02-03</td>\n      <td>2018-02-10</td>\n      <td>2017-09-04</td>\n      <td>Standard</td>\n      <td>7</td>\n      <td>152</td>\n      <td>True</td>\n      <td>4</td>\n      <td>55177</td>\n      <td>5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...</td>\n      <td>6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...</td>\n      <td>3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...</td>\n      <td>0</td>\n      <td>152.09</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>713.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>64</td>\n      <td>64</td>\n      <td>2018-01-29</td>\n      <td>2018-02-03</td>\n      <td>2017-09-07</td>\n      <td>Standard</td>\n      <td>5</td>\n      <td>144</td>\n      <td>True</td>\n      <td>5</td>\n      <td>55412</td>\n      <td>4aebfe125cf6c059588792b9fb871afe282a8806299dfe...</td>\n      <td>0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...</td>\n      <td>ea16c664798581a9d93a3128d772b8b89e05743edbbfae...</td>\n      <td>0</td>\n      <td>142.60</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>800.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>800.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>111</td>\n      <td>111</td>\n      <td>2018-03-28</td>\n      <td>2018-04-01</td>\n      <td>2017-09-11</td>\n      <td>Standard</td>\n      <td>4</td>\n      <td>198</td>\n      <td>True</td>\n      <td>6</td>\n      <td>55560</td>\n      <td>1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...</td>\n      <td>6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...</td>\n      <td>516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...</td>\n      <td>0</td>\n      <td>200.00</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>402.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>402.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>162</td>\n      <td>162</td>\n      <td>2018-11-16</td>\n      <td>2018-11-18</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>270</td>\n      <td>True</td>\n      <td>7</td>\n      <td>63419</td>\n      <td>d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...</td>\n      <td>5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...</td>\n      <td>6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...</td>\n      <td>0</td>\n      <td>201.00</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>660.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>660.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>45</td>\n      <td>45</td>\n      <td>2018-08-16</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>178</td>\n      <td>True</td>\n      <td>8</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>220.00</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>320.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>320.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>64</td>\n      <td>64</td>\n      <td>2018-08-17</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>179</td>\n      <td>True</td>\n      <td>9</td>\n      <td>61778</td>\n      <td>364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>160.00</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>720.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>720.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>126</td>\n      <td>126</td>\n      <td>2018-08-16</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>178</td>\n      <td>True</td>\n      <td>8</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>240.00</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>480.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>480.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>65</td>\n      <td>65</td>\n      <td>2018-08-16</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>178</td>\n      <td>True</td>\n      <td>8</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>160.00</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>595.98</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>595.98</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>267</td>\n      <td>267</td>\n      <td>2018-08-10</td>\n      <td>2018-08-12</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>172</td>\n      <td>True</td>\n      <td>10</td>\n      <td>61378</td>\n      <td>f929533e542ff1302069567400b5ba584dc27dcbd63aae...</td>\n      <td>a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...</td>\n      <td>446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...</td>\n      <td>0</td>\n      <td>297.99</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>915.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>915.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>370</td>\n      <td>362</td>\n      <td>2018-08-03</td>\n      <td>2018-08-06</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>165</td>\n      <td>True</td>\n      <td>11</td>\n      <td>61105</td>\n      <td>8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...</td>\n      <td>37827cbb5aec4ed83d13193d766324efb93251cd44b28b...</td>\n      <td>27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...</td>\n      <td>0</td>\n      <td>305.00</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3579.40</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3579.40</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>321</td>\n      <td>321</td>\n      <td>2018-07-22</td>\n      <td>2018-07-31</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>9</td>\n      <td>153</td>\n      <td>False</td>\n      <td>12</td>\n      <td>61807</td>\n      <td>cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...</td>\n      <td></td>\n      <td>5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...</td>\n      <td>0</td>\n      <td>397.71</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>640.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>640.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>131</td>\n      <td>131</td>\n      <td>2018-07-07</td>\n      <td>2018-07-09</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>138</td>\n      <td>True</td>\n      <td>15</td>\n      <td>60668</td>\n      <td>37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...</td>\n      <td>a9ba8f12a4509f4c598578fa743236beda2b791c938954...</td>\n      <td>7ec6cc25066b8b7008b78d25027a2159d2116522611223...</td>\n      <td>0</td>\n      <td>320.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessed_data = original_data.copy()\n",
    "\n",
    "dataset_specification = DatasetSpecification()\n",
    "dp_toolkit = DataPreprocessingToolkit()\n",
    "\n",
    "id_column_names = dataset_specification.get_id_columns()\n",
    "\n",
    "people_identifier = PeopleIdentifier()\n",
    "preprocessed_data = people_identifier.add_pid(preprocessed_data, id_column_names, \"user_id\")\n",
    "\n",
    "preprocessed_data = dp_toolkit.filter_out_company_clients(preprocessed_data)  # Code this method\n",
    "preprocessed_data = dp_toolkit.filter_out_low_prices(preprocessed_data)  # Code this method\n",
    "\n",
    "preprocessed_data = dp_toolkit.fix_date_to(preprocessed_data)\n",
    "preprocessed_data = dp_toolkit.add_length_of_stay(preprocessed_data)  # Code this method\n",
    "\n",
    "preprocessed_data = dp_toolkit.filter_out_long_stays(preprocessed_data)  # Code this method\n",
    "\n",
    "preprocessed_data = dp_toolkit.add_book_to_arrival(preprocessed_data)  # Code this method\n",
    "preprocessed_data = dp_toolkit.add_weekend_stay(preprocessed_data)  # Code this method\n",
    "preprocessed_data = dp_toolkit.add_nrooms(preprocessed_data)\n",
    "preprocessed_data = dp_toolkit.sum_npeople(preprocessed_data)  # Code this method\n",
    "preprocessed_data = dp_toolkit.clip_book_to_arrival(preprocessed_data)\n",
    "\n",
    "preprocessed_data = dp_toolkit.aggregate_group_reservations(preprocessed_data)  # Code this method\n",
    "\n",
    "preprocessed_data = dp_toolkit.add_night_price(preprocessed_data)  # Code this method\n",
    "\n",
    "preprocessed_data = preprocessed_data.reset_index(drop=True)\n",
    "\n",
    "assert len(preprocessed_data.loc[preprocessed_data[\"is_company\"] == 1]) == 0\n",
    "assert len(preprocessed_data.loc[preprocessed_data[\"length_of_stay\"] > 21]) == 0\n",
    "assert len(preprocessed_data.loc[preprocessed_data[\"accommodation_price\"] <= 50]) == 0\n",
    "\n",
    "assert preprocessed_data.iloc[1]['length_of_stay'] == 3\n",
    "assert preprocessed_data.iloc[2]['length_of_stay'] == 2\n",
    "assert preprocessed_data.iloc[3]['length_of_stay'] == 7\n",
    "\n",
    "assert preprocessed_data.iloc[2150]['book_to_arrival'] == 11\n",
    "assert preprocessed_data.iloc[2151]['book_to_arrival'] == 28\n",
    "assert preprocessed_data.iloc[2152]['book_to_arrival'] == 12\n",
    "\n",
    "assert preprocessed_data.iloc[2150]['weekend_stay'] == 'False'\n",
    "assert preprocessed_data.iloc[2151]['weekend_stay'] == 'True'\n",
    "assert preprocessed_data.iloc[2152]['weekend_stay'] == 'False'\n",
    "\n",
    "assert preprocessed_data.iloc[3650]['n_people'] == 2\n",
    "assert preprocessed_data.iloc[3651]['n_people'] == 4\n",
    "assert preprocessed_data.iloc[3652]['n_people'] == 1\n",
    "\n",
    "assert preprocessed_data.iloc[0]['night_price'] == 330.76\n",
    "assert preprocessed_data.iloc[1]['night_price'] == 231.13\n",
    "assert preprocessed_data.iloc[2]['night_price'] == 183.40\n",
    "\n",
    "# Assertions for group reservations\n",
    "\n",
    "assert preprocessed_data.iloc[15258]['rate_plan'] == 'Nonref'\n",
    "assert preprocessed_data.iloc[15259]['rate_plan'] == 'Standard'\n",
    "assert preprocessed_data.iloc[15260]['rate_plan'] == 'Standard'\n",
    "assert preprocessed_data.iloc[15258]['accommodation_price'] == 1397.06\n",
    "assert preprocessed_data.iloc[15261]['accommodation_price'] == 2953.10\n",
    "assert preprocessed_data.iloc[15264]['accommodation_price'] == 1738.80\n",
    "assert preprocessed_data.iloc[15258]['n_people'] == 6\n",
    "assert preprocessed_data.iloc[15259]['n_people'] == 7\n",
    "assert preprocessed_data.iloc[15260]['n_people'] == 11\n",
    "assert preprocessed_data.iloc[15265]['night_price'] == 206.67\n",
    "assert preprocessed_data.iloc[15266]['night_price'] == 138.92\n",
    "assert preprocessed_data.iloc[15267]['night_price'] == 119.07\n",
    "\n",
    "# assert preprocessed_data.iloc[15261]['room_group_id'] == 481\n",
    "# assert preprocessed_data.iloc[15266]['room_group_id'] == 78\n",
    "# assert preprocessed_data.iloc[15267]['room_group_id'] == 463\n",
    "display(preprocessed_data.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coupled-river",
   "metadata": {},
   "source": [
    "# Bucket important features to reduce the offer space size\n",
    "\n",
    "Without this step every pair (user_id, item_id) would have at most a single interaction. The base item space has around $2^{25} \\sim 3.3 \\text{mln}$ elements. Therefore, values for selected features are aggregated into buckets:\n",
    "\n",
    "```python\n",
    "column_values_dict = {\n",
    "    'term': ['WinterVacation', 'Easter', 'OffSeason', 'HighSeason', 'LowSeason', 'MayLongWeekend', 'NewYear', 'Christmas'],\n",
    "    'length_of_stay_bucket': ['[0-1]', '[2-3]', '[4-7]', '[8-inf]'],\n",
    "    'rate_plan': ['Standard', 'Nonref'],\n",
    "    'room_segment': ['[0-160]', '[160-260]', '[260-360]', '[360-500]', '[500-900]'],\n",
    "    'n_people_bucket': ['[1-1]', '[2-2]', '[3-4]', '[5-inf]'],\n",
    "    'weekend_stay': ['True', 'False']\n",
    "}\n",
    "```\n",
    "\n",
    "Explanation:\n",
    "  - term - the term of the arrival date,\n",
    "  - length_of_stay_bucket - aggregated length of stay,\n",
    "  - rate_plan - rate plan which distinguishes if a given booking was refundable or nonrefundable (in reality rate plans are much more complex, they define prices for all rooms for every date, they include features like free breakfast, wine in the room etc.),\n",
    "  - room_segment - for every room its average price is calculated, then every room assigned to an appropriate price range, which is a proxy for room quality,\n",
    "  - n_people_bucket - aggregated number of people in a reservation,\n",
    "  - weekend_stay - indicates if the stay encompassed a weekend.\n",
    "\n",
    "The buckets are chosen based on expert knowledge of people working in the hotel industry for many years. Alternatively, clustering techniques could be used, but on a relatively small dataset expert methods are typically better.\n",
    "\n",
    "The above aggregations reduce the number of possible items to $8 * 4 * 2 * 5 * 4 * 2 = 2560$.\n",
    "\n",
    "### The recommenders will be trained and evaluated on such aggregated data. To get a proper offer for a user one would have to decode those buckets into specific values, but this is a much easier task and can be achieved based on simple rules.\n",
    "\n",
    "<span style=\"color:red\"><font size=\"4\">**Task:**</font></span><br> \n",
    "In the file data_preprocessing/data_preprocessing_toolkit write code for the map_night_price_to_room_segment_buckets method. You must calculate the average of night prices for every **room_group_id** and map those prices to buckets (you can apply the map_value_to_bucket method which is available in the data_preprocessing_toolkit, the buckets are available under self.room_segment_buckets). The new column should be named 'room_segment'. You have to pass all assertions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "interracial-rendering",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[0-160]\n"
     ]
    },
    {
     "data": {
      "text/plain": "    n_people  n_children_1  n_children_2  n_children_3  accommodation_price  \\\n0          5             0             0             0               992.29   \n1          4             0             0             0               693.40   \n2          2             0             0             0               366.80   \n3          4             0             0             0              1064.60   \n4          2             0             0             0               713.00   \n5          5             0             0             0               800.00   \n6          5             0             0             0               402.00   \n7          1             0             0             0               660.00   \n8          1             0             0             0               320.00   \n9          1             0             0             0               720.00   \n10         1             0             0             0               480.00   \n11         3             0             0             0               595.98   \n12         4             0             0             0               915.00   \n13         3             0             0             0              3579.40   \n14         6             0             0             0               640.00   \n\n    meal_price  service_price     paid  n_rooms  discount  room_id  \\\n0          0.0            0.0     1.00        1       NaN      118   \n1          0.0            0.0   693.40        1       0.0      270   \n2          0.0            0.0     1.00        1       NaN      294   \n3          0.0            0.0     1.00        1       NaN      183   \n4          0.0            0.0     1.00        1       NaN       64   \n5          0.0            0.0   800.00        1       0.0      111   \n6          0.0            0.0   402.00        1       0.0      162   \n7          0.0            0.0   660.00        1       0.0       45   \n8          0.0            0.0   320.00        1       0.0       64   \n9          0.0            0.0   720.00        1       0.0      126   \n10         0.0            0.0   480.00        1       0.0       65   \n11         0.0            0.0   595.98        1       0.0      267   \n12         0.0            0.0   915.00        1       0.0      370   \n13         0.0            0.0  3579.40        1       0.0      321   \n14         0.0            0.0   640.00        1       0.0      131   \n\n    room_group_id   date_from    date_to booking_date rate_plan  \\\n0             118  2018-02-10 2018-02-13   2017-08-17  Standard   \n1             270  2018-02-28 2018-03-03   2017-08-17  Standard   \n2             294  2018-02-14 2018-02-16   2017-08-29  Standard   \n3             183  2018-02-03 2018-02-10   2017-09-04  Standard   \n4              64  2018-01-29 2018-02-03   2017-09-07  Standard   \n5             111  2018-03-28 2018-04-01   2017-09-11  Standard   \n6             162  2018-11-16 2018-11-18   2018-02-19  Standard   \n7              45  2018-08-16 2018-08-19   2018-02-19  Standard   \n8              64  2018-08-17 2018-08-19   2018-02-19  Standard   \n9             126  2018-08-16 2018-08-19   2018-02-19  Standard   \n10             65  2018-08-16 2018-08-19   2018-02-19  Standard   \n11            267  2018-08-10 2018-08-12   2018-02-19  Standard   \n12            362  2018-08-03 2018-08-06   2018-02-19  Standard   \n13            321  2018-07-22 2018-07-31   2018-02-19  Standard   \n14            131  2018-07-07 2018-07-09   2018-02-19  Standard   \n\n    length_of_stay  book_to_arrival weekend_stay  user_id  client_id  \\\n0                3              177         True        1      54117   \n1                3              195         True        2      54118   \n2                2              169        False        3      54790   \n3                7              152         True        4      55177   \n4                5              144         True        5      55412   \n5                4              198         True        6      55560   \n6                2              270         True        7      63419   \n7                3              178         True        8      61777   \n8                2              179         True        9      61778   \n9                3              178         True        8      61777   \n10               3              178         True        8      61777   \n11               2              172         True       10      61378   \n12               3              165         True       11      61105   \n13               9              153        False       12      61807   \n14               2              138         True       15      60668   \n\n                                          client_name  \\\n0   ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...   \n1   4db36724fc28085e053a3003dce55368ee207cce37d355...   \n2   e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...   \n3   5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...   \n4   4aebfe125cf6c059588792b9fb871afe282a8806299dfe...   \n5   1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...   \n6   d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...   \n7   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n8   364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...   \n9   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n10  f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n11  f929533e542ff1302069567400b5ba584dc27dcbd63aae...   \n12  8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...   \n13  cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...   \n14  37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...   \n\n                                                email  \\\n0                                                       \n1   f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...   \n2   f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...   \n3   6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...   \n4   0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...   \n5   6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...   \n6   5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...   \n7                                                       \n8                                                       \n9                                                       \n10                                                      \n11  a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...   \n12  37827cbb5aec4ed83d13193d766324efb93251cd44b28b...   \n13                                                      \n14  a9ba8f12a4509f4c598578fa743236beda2b791c938954...   \n\n                                                phone  is_company  \\\n0   318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...           0   \n1   cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...           0   \n2   1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...           0   \n3   3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...           0   \n4   ea16c664798581a9d93a3128d772b8b89e05743edbbfae...           0   \n5   516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...           0   \n6   6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...           0   \n7                                                               0   \n8                                                               0   \n9                                                               0   \n10                                                              0   \n11  446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...           0   \n12  27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...           0   \n13  5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...           0   \n14  7ec6cc25066b8b7008b78d25027a2159d2116522611223...           0   \n\n    night_price            term length_of_stay_bucket room_segment  \\\n0        330.76  WinterVacation                 [2-3]    [260-360]   \n1        231.13  WinterVacation                 [2-3]    [160-260]   \n2        183.40  WinterVacation                 [2-3]    [160-260]   \n3        152.09  WinterVacation                 [4-7]    [160-260]   \n4        142.60  WinterVacation                 [4-7]      [0-160]   \n5        200.00          Easter                 [4-7]    [260-360]   \n6        201.00       OffSeason                 [2-3]    [260-360]   \n7        220.00      HighSeason                 [2-3]    [160-260]   \n8        160.00      HighSeason                 [2-3]      [0-160]   \n9        240.00      HighSeason                 [2-3]    [160-260]   \n10       160.00      HighSeason                 [2-3]    [160-260]   \n11       297.99      HighSeason                 [2-3]    [160-260]   \n12       305.00      HighSeason                 [2-3]    [160-260]   \n13       397.71      HighSeason               [8-inf]    [160-260]   \n14       320.00       LowSeason                 [2-3]    [160-260]   \n\n   n_people_bucket                                               item  item_id  \n0          [5-inf]  WinterVacation [2-3] Standard [260-360] [5-inf...        0  \n1            [3-4]  WinterVacation [2-3] Standard [160-260] [3-4] ...        1  \n2            [2-2]  WinterVacation [2-3] Standard [160-260] [2-2] ...        2  \n3            [3-4]  WinterVacation [4-7] Standard [160-260] [3-4] ...        3  \n4            [2-2]   WinterVacation [4-7] Standard [0-160] [2-2] True        4  \n5          [5-inf]       Easter [4-7] Standard [260-360] [5-inf] True        5  \n6          [5-inf]    OffSeason [2-3] Standard [260-360] [5-inf] True        6  \n7            [1-1]     HighSeason [2-3] Standard [160-260] [1-1] True        7  \n8            [1-1]       HighSeason [2-3] Standard [0-160] [1-1] True        8  \n9            [1-1]     HighSeason [2-3] Standard [160-260] [1-1] True        7  \n10           [1-1]     HighSeason [2-3] Standard [160-260] [1-1] True        7  \n11           [3-4]     HighSeason [2-3] Standard [160-260] [3-4] True        9  \n12           [3-4]     HighSeason [2-3] Standard [160-260] [3-4] True        9  \n13           [3-4]  HighSeason [8-inf] Standard [160-260] [3-4] False       10  \n14         [5-inf]    LowSeason [2-3] Standard [160-260] [5-inf] True       11  ",
      "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>n_people</th>\n      <th>n_children_1</th>\n      <th>n_children_2</th>\n      <th>n_children_3</th>\n      <th>accommodation_price</th>\n      <th>meal_price</th>\n      <th>service_price</th>\n      <th>paid</th>\n      <th>n_rooms</th>\n      <th>discount</th>\n      <th>room_id</th>\n      <th>room_group_id</th>\n      <th>date_from</th>\n      <th>date_to</th>\n      <th>booking_date</th>\n      <th>rate_plan</th>\n      <th>length_of_stay</th>\n      <th>book_to_arrival</th>\n      <th>weekend_stay</th>\n      <th>user_id</th>\n      <th>client_id</th>\n      <th>client_name</th>\n      <th>email</th>\n      <th>phone</th>\n      <th>is_company</th>\n      <th>night_price</th>\n      <th>term</th>\n      <th>length_of_stay_bucket</th>\n      <th>room_segment</th>\n      <th>n_people_bucket</th>\n      <th>item</th>\n      <th>item_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>992.29</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>118</td>\n      <td>118</td>\n      <td>2018-02-10</td>\n      <td>2018-02-13</td>\n      <td>2017-08-17</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>177</td>\n      <td>True</td>\n      <td>1</td>\n      <td>54117</td>\n      <td>ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...</td>\n      <td></td>\n      <td>318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...</td>\n      <td>0</td>\n      <td>330.76</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>WinterVacation [2-3] Standard [260-360] [5-inf...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>693.40</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>693.40</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>270</td>\n      <td>270</td>\n      <td>2018-02-28</td>\n      <td>2018-03-03</td>\n      <td>2017-08-17</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>195</td>\n      <td>True</td>\n      <td>2</td>\n      <td>54118</td>\n      <td>4db36724fc28085e053a3003dce55368ee207cce37d355...</td>\n      <td>f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...</td>\n      <td>cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...</td>\n      <td>0</td>\n      <td>231.13</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>WinterVacation [2-3] Standard [160-260] [3-4] ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>366.80</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>294</td>\n      <td>294</td>\n      <td>2018-02-14</td>\n      <td>2018-02-16</td>\n      <td>2017-08-29</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>169</td>\n      <td>False</td>\n      <td>3</td>\n      <td>54790</td>\n      <td>e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...</td>\n      <td>f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...</td>\n      <td>1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...</td>\n      <td>0</td>\n      <td>183.40</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>WinterVacation [2-3] Standard [160-260] [2-2] ...</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1064.60</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>183</td>\n      <td>183</td>\n      <td>2018-02-03</td>\n      <td>2018-02-10</td>\n      <td>2017-09-04</td>\n      <td>Standard</td>\n      <td>7</td>\n      <td>152</td>\n      <td>True</td>\n      <td>4</td>\n      <td>55177</td>\n      <td>5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...</td>\n      <td>6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...</td>\n      <td>3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...</td>\n      <td>0</td>\n      <td>152.09</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>WinterVacation [4-7] Standard [160-260] [3-4] ...</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>713.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.00</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>64</td>\n      <td>64</td>\n      <td>2018-01-29</td>\n      <td>2018-02-03</td>\n      <td>2017-09-07</td>\n      <td>Standard</td>\n      <td>5</td>\n      <td>144</td>\n      <td>True</td>\n      <td>5</td>\n      <td>55412</td>\n      <td>4aebfe125cf6c059588792b9fb871afe282a8806299dfe...</td>\n      <td>0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...</td>\n      <td>ea16c664798581a9d93a3128d772b8b89e05743edbbfae...</td>\n      <td>0</td>\n      <td>142.60</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>[0-160]</td>\n      <td>[2-2]</td>\n      <td>WinterVacation [4-7] Standard [0-160] [2-2] True</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>800.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>800.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>111</td>\n      <td>111</td>\n      <td>2018-03-28</td>\n      <td>2018-04-01</td>\n      <td>2017-09-11</td>\n      <td>Standard</td>\n      <td>4</td>\n      <td>198</td>\n      <td>True</td>\n      <td>6</td>\n      <td>55560</td>\n      <td>1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...</td>\n      <td>6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...</td>\n      <td>516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...</td>\n      <td>0</td>\n      <td>200.00</td>\n      <td>Easter</td>\n      <td>[4-7]</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>Easter [4-7] Standard [260-360] [5-inf] True</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>402.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>402.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>162</td>\n      <td>162</td>\n      <td>2018-11-16</td>\n      <td>2018-11-18</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>270</td>\n      <td>True</td>\n      <td>7</td>\n      <td>63419</td>\n      <td>d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...</td>\n      <td>5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...</td>\n      <td>6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...</td>\n      <td>0</td>\n      <td>201.00</td>\n      <td>OffSeason</td>\n      <td>[2-3]</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>OffSeason [2-3] Standard [260-360] [5-inf] True</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>660.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>660.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>45</td>\n      <td>45</td>\n      <td>2018-08-16</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>178</td>\n      <td>True</td>\n      <td>8</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>220.00</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>HighSeason [2-3] Standard [160-260] [1-1] True</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>320.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>320.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>64</td>\n      <td>64</td>\n      <td>2018-08-17</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>179</td>\n      <td>True</td>\n      <td>9</td>\n      <td>61778</td>\n      <td>364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>160.00</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>[0-160]</td>\n      <td>[1-1]</td>\n      <td>HighSeason [2-3] Standard [0-160] [1-1] True</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>720.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>720.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>126</td>\n      <td>126</td>\n      <td>2018-08-16</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>178</td>\n      <td>True</td>\n      <td>8</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>240.00</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>HighSeason [2-3] Standard [160-260] [1-1] True</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>480.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>480.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>65</td>\n      <td>65</td>\n      <td>2018-08-16</td>\n      <td>2018-08-19</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>178</td>\n      <td>True</td>\n      <td>8</td>\n      <td>61777</td>\n      <td>f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...</td>\n      <td></td>\n      <td></td>\n      <td>0</td>\n      <td>160.00</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>HighSeason [2-3] Standard [160-260] [1-1] True</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>595.98</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>595.98</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>267</td>\n      <td>267</td>\n      <td>2018-08-10</td>\n      <td>2018-08-12</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>172</td>\n      <td>True</td>\n      <td>10</td>\n      <td>61378</td>\n      <td>f929533e542ff1302069567400b5ba584dc27dcbd63aae...</td>\n      <td>a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...</td>\n      <td>446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...</td>\n      <td>0</td>\n      <td>297.99</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>HighSeason [2-3] Standard [160-260] [3-4] True</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>915.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>915.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>370</td>\n      <td>362</td>\n      <td>2018-08-03</td>\n      <td>2018-08-06</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>3</td>\n      <td>165</td>\n      <td>True</td>\n      <td>11</td>\n      <td>61105</td>\n      <td>8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...</td>\n      <td>37827cbb5aec4ed83d13193d766324efb93251cd44b28b...</td>\n      <td>27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...</td>\n      <td>0</td>\n      <td>305.00</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>HighSeason [2-3] Standard [160-260] [3-4] True</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3579.40</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3579.40</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>321</td>\n      <td>321</td>\n      <td>2018-07-22</td>\n      <td>2018-07-31</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>9</td>\n      <td>153</td>\n      <td>False</td>\n      <td>12</td>\n      <td>61807</td>\n      <td>cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...</td>\n      <td></td>\n      <td>5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...</td>\n      <td>0</td>\n      <td>397.71</td>\n      <td>HighSeason</td>\n      <td>[8-inf]</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>HighSeason [8-inf] Standard [160-260] [3-4] False</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>6</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>640.00</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>640.00</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>131</td>\n      <td>131</td>\n      <td>2018-07-07</td>\n      <td>2018-07-09</td>\n      <td>2018-02-19</td>\n      <td>Standard</td>\n      <td>2</td>\n      <td>138</td>\n      <td>True</td>\n      <td>15</td>\n      <td>60668</td>\n      <td>37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...</td>\n      <td>a9ba8f12a4509f4c598578fa743236beda2b791c938954...</td>\n      <td>7ec6cc25066b8b7008b78d25027a2159d2116522611223...</td>\n      <td>0</td>\n      <td>320.00</td>\n      <td>LowSeason</td>\n      <td>[2-3]</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>LowSeason [2-3] Standard [160-260] [5-inf] True</td>\n      <td>11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessed_data = dp_toolkit.map_dates_to_terms(preprocessed_data)\n",
    "preprocessed_data = dp_toolkit.map_lengths_of_stay_to_nights_buckets(preprocessed_data)\n",
    "preprocessed_data = dp_toolkit.map_night_prices_to_room_segment_buckets(preprocessed_data)  # Code this method\n",
    "preprocessed_data = dp_toolkit.map_npeople_to_npeople_buckets(preprocessed_data)\n",
    "print(preprocessed_data.iloc[4]['room_segment'])\n",
    "assert preprocessed_data.iloc[4]['room_segment'] == '[0-160]'\n",
    "assert preprocessed_data.iloc[1]['room_segment'] == '[160-260]'\n",
    "assert preprocessed_data.iloc[0]['room_segment'] == '[260-360]'\n",
    "assert preprocessed_data.iloc[2820]['room_segment'] == '[360-500]'\n",
    "\n",
    "preprocessed_data = dp_toolkit.map_item_to_item_id(preprocessed_data)\n",
    "\n",
    "preprocessed_data.to_csv(os.path.join(data_path, \"hotel_data_preprocessed.csv\"))\n",
    "\n",
    "display(preprocessed_data.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offshore-biography",
   "metadata": {},
   "source": [
    "# Base statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acknowledged-crime",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users: 13798\n",
      "\n",
      "Number of items: 789\n",
      "\n",
      "Number of interactions: 15268\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "         n_users\nitem_id         \n50           679\n54           581\n22           538\n32           409\n205          226\n78           224\n9            212\n56           196\n60           191\n144          189",
      "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>n_users</th>\n    </tr>\n    <tr>\n      <th>item_id</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>50</th>\n      <td>679</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>581</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>538</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>409</td>\n    </tr>\n    <tr>\n      <th>205</th>\n      <td>226</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>224</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>212</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>196</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>191</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>189</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "         n_items\nuser_id         \n706           84\n1736          29\n50            23\n96            22\n1             22\n11401         13\n3336          13\n1287          13\n1281          13\n5207          12",
      "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>n_items</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>706</th>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>11401</th>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>3336</th>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>1281</th>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>5207</th>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of users: {}\".format(len(preprocessed_data['user_id'].unique())))\n",
    "print()\n",
    "print(\"Number of items: {}\".format(len(preprocessed_data['item_id'].unique())))\n",
    "print()\n",
    "print(\"Number of interactions: {}\".format(len(preprocessed_data)))\n",
    "print()\n",
    "\n",
    "n_user = preprocessed_data.loc[:, ['user_id', 'item_id']].groupby('item_id').count().sort_values(by='user_id', ascending=False)\n",
    "n_user = n_user.rename(columns={'user_id': 'n_users'})\n",
    "display(n_user.head(10))\n",
    "\n",
    "n_item = preprocessed_data.loc[:, ['user_id', 'item_id']].groupby('user_id').count().sort_values(by='item_id', ascending=False)\n",
    "n_item = n_item.rename(columns={'item_id': 'n_items'})\n",
    "display(n_item.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blessed-knitting",
   "metadata": {},
   "source": [
    "# Prepare the dataset for recommenders\n",
    "\n",
    "One could consider many features describing each interaction but from the business perspective term, length_of_stay_bucket, rate_plan, room_segment, n_people_bucket, weekend_stay are the most important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "victorian-bottom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id  item_id            term length_of_stay_bucket rate_plan  \\\n0         1        0  WinterVacation                 [2-3]  Standard   \n1         2        1  WinterVacation                 [2-3]  Standard   \n2         3        2  WinterVacation                 [2-3]  Standard   \n3         4        3  WinterVacation                 [4-7]  Standard   \n4         5        4  WinterVacation                 [4-7]  Standard   \n5         6        5          Easter                 [4-7]  Standard   \n6         7        6       OffSeason                 [2-3]  Standard   \n7         8        7      HighSeason                 [2-3]  Standard   \n8         9        8      HighSeason                 [2-3]  Standard   \n9         8        7      HighSeason                 [2-3]  Standard   \n10        8        7      HighSeason                 [2-3]  Standard   \n11       10        9      HighSeason                 [2-3]  Standard   \n12       11        9      HighSeason                 [2-3]  Standard   \n13       12       10      HighSeason               [8-inf]  Standard   \n14       15       11       LowSeason                 [2-3]  Standard   \n15       16       12       LowSeason                 [4-7]  Standard   \n16       18       13       LowSeason               [8-inf]  Standard   \n17       19       14       OffSeason                 [4-7]  Standard   \n18       20       15       OffSeason               [8-inf]  Standard   \n19       21       16       OffSeason                 [4-7]  Standard   \n\n   room_segment n_people_bucket weekend_stay  \n0     [260-360]         [5-inf]         True  \n1     [160-260]           [3-4]         True  \n2     [160-260]           [2-2]        False  \n3     [160-260]           [3-4]         True  \n4       [0-160]           [2-2]         True  \n5     [260-360]         [5-inf]         True  \n6     [260-360]         [5-inf]         True  \n7     [160-260]           [1-1]         True  \n8       [0-160]           [1-1]         True  \n9     [160-260]           [1-1]         True  \n10    [160-260]           [1-1]         True  \n11    [160-260]           [3-4]         True  \n12    [160-260]           [3-4]         True  \n13    [160-260]           [3-4]        False  \n14    [160-260]         [5-inf]         True  \n15    [160-260]           [3-4]         True  \n16    [160-260]           [3-4]         True  \n17    [160-260]           [3-4]         True  \n18    [260-360]         [5-inf]         True  \n19    [260-360]         [5-inf]         True  ",
      "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>user_id</th>\n      <th>item_id</th>\n      <th>term</th>\n      <th>length_of_stay_bucket</th>\n      <th>rate_plan</th>\n      <th>room_segment</th>\n      <th>n_people_bucket</th>\n      <th>weekend_stay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2</td>\n      <td>WinterVacation</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[2-2]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>3</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>4</td>\n      <td>WinterVacation</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[0-160]</td>\n      <td>[2-2]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>5</td>\n      <td>Easter</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>6</td>\n      <td>OffSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>7</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>8</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[0-160]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>8</td>\n      <td>7</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>8</td>\n      <td>7</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[1-1]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>10</td>\n      <td>9</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>11</td>\n      <td>9</td>\n      <td>HighSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>12</td>\n      <td>10</td>\n      <td>HighSeason</td>\n      <td>[8-inf]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>11</td>\n      <td>LowSeason</td>\n      <td>[2-3]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>12</td>\n      <td>LowSeason</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>18</td>\n      <td>13</td>\n      <td>LowSeason</td>\n      <td>[8-inf]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>19</td>\n      <td>14</td>\n      <td>OffSeason</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[160-260]</td>\n      <td>[3-4]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>20</td>\n      <td>15</td>\n      <td>OffSeason</td>\n      <td>[8-inf]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>21</td>\n      <td>16</td>\n      <td>OffSeason</td>\n      <td>[4-7]</td>\n      <td>Standard</td>\n      <td>[260-360]</td>\n      <td>[5-inf]</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "item_features = ['term', 'length_of_stay_bucket', 'rate_plan', 'room_segment', 'n_people_bucket', 'weekend_stay']\n",
    "\n",
    "interactions_df = preprocessed_data.loc[:, ['user_id', 'item_id'] + item_features]\n",
    "\n",
    "column_values_dict = {\n",
    "    'term': ['WinterVacation', 'Easter', 'OffSeason', 'HighSeason', 'LowSeason', 'MayLongWeekend', 'NewYear', 'Christmas'],\n",
    "    'length_of_stay_bucket': ['[0-1]', '[2-3]', '[4-7]', '[8-inf]'],\n",
    "    'rate_plan': ['Standard', 'Nonref'],\n",
    "    'room_segment': ['[0-160]', '[160-260]', '[260-360]', '[360-500]', '[500-900]'],\n",
    "    'n_people_bucket': ['[1-1]', '[2-2]', '[3-4]', '[5-inf]'],\n",
    "    'weekend_stay': ['True', 'False']\n",
    "}\n",
    "\n",
    "interactions_df['term'] = pd.Categorical(\n",
    "    interactions_df['term'], categories=column_values_dict['term'])\n",
    "interactions_df['length_of_stay_bucket'] = pd.Categorical(\n",
    "    interactions_df['length_of_stay_bucket'], categories=column_values_dict['length_of_stay_bucket'])\n",
    "interactions_df['rate_plan'] = pd.Categorical(\n",
    "    interactions_df['rate_plan'], categories=column_values_dict['rate_plan'])\n",
    "interactions_df['room_segment'] = pd.Categorical(\n",
    "    interactions_df['room_segment'], categories=column_values_dict['room_segment'])\n",
    "interactions_df['n_people_bucket'] = pd.Categorical(\n",
    "    interactions_df['n_people_bucket'], categories=column_values_dict['n_people_bucket'])\n",
    "interactions_df['weekend_stay'] = pd.Categorical(\n",
    "    interactions_df['weekend_stay'], categories=column_values_dict['weekend_stay'])\n",
    "\n",
    "interactions_df.to_csv(os.path.join(data_path, \"hotel_data_interactions_df.csv\"))\n",
    "\n",
    "display(interactions_df.head(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "spread-colombia",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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