project_2_data_preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "alike-morgan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "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": 18,
   "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": 19,
   "id": "solid-crisis",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Standard</td>\n",
       "      <td>51665</td>\n",
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       "      <td>0</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    reservation_id group_id  room_id  room_group_id  date_from    date_to  \\\n",
       "0            14160               135            135 2017-09-01 2018-03-30   \n",
       "1            16075               118            118 2018-02-10 2018-02-12   \n",
       "2            16076               270            270 2018-02-28 2018-03-02   \n",
       "3            16635               294            294 2018-02-14 2018-02-15   \n",
       "4            16964               183            183 2018-02-03 2018-02-09   \n",
       "5            17173                64             64 2018-01-29 2018-02-02   \n",
       "6            17308               111            111 2018-03-28 2018-03-31   \n",
       "7           120165               162            162 2018-11-16 2018-11-17   \n",
       "8           120183                45             45 2018-08-16 2018-08-18   \n",
       "9           120184                64             64 2018-08-17 2018-08-18   \n",
       "10          120185               126            126 2018-08-16 2018-08-18   \n",
       "11          120186                65             65 2018-08-16 2018-08-18   \n",
       "12          120194               267            267 2018-08-10 2018-08-11   \n",
       "13          120209               370            362 2018-08-03 2018-08-05   \n",
       "14          120232               321            321 2018-07-22 2018-07-30   \n",
       "\n",
       "   booking_date        booking_time  n_people  n_children_1  n_children_2  \\\n",
       "0    2017-07-04 2017-07-04 10:52:00         1             0             0   \n",
       "1    2017-08-17 2017-08-17 15:01:00         5             0             0   \n",
       "2    2017-08-17 2017-08-17 15:08:00         4             0             0   \n",
       "3    2017-08-29 2017-08-29 13:58:00         2             0             0   \n",
       "4    2017-09-04 2017-09-04 15:52:00         4             0             0   \n",
       "5    2017-09-07 2017-09-07 13:21:00         2             0             0   \n",
       "6    2017-09-11 2017-09-11 10:31:00         5             0             0   \n",
       "7    2018-02-19 2018-02-19 17:44:00         5             0             0   \n",
       "8    2018-02-19 2018-02-19 17:44:00         1             0             0   \n",
       "9    2018-02-19 2018-02-19 17:44:00         1             0             0   \n",
       "10   2018-02-19 2018-02-19 17:44:00         1             0             0   \n",
       "11   2018-02-19 2018-02-19 17:44:00         1             0             0   \n",
       "12   2018-02-19 2018-02-19 17:44:00         3             0             0   \n",
       "13   2018-02-19 2018-02-19 17:44:00         4             0             0   \n",
       "14   2018-02-19 2018-02-19 17:44:00         3             0             0   \n",
       "\n",
       "    n_children_3  discount  accommodation_price  meal_price  service_price  \\\n",
       "0              0       NaN                 0.00         0.0            0.0   \n",
       "1              0       NaN               992.29         0.0            0.0   \n",
       "2              0       0.0               693.40         0.0            0.0   \n",
       "3              0       NaN               366.80         0.0            0.0   \n",
       "4              0       NaN              1064.60         0.0            0.0   \n",
       "5              0       NaN               713.00         0.0            0.0   \n",
       "6              0       0.0               800.00         0.0            0.0   \n",
       "7              0       0.0               402.00         0.0            0.0   \n",
       "8              0       0.0               660.00         0.0            0.0   \n",
       "9              0       0.0               320.00         0.0            0.0   \n",
       "10             0       0.0               720.00         0.0            0.0   \n",
       "11             0       0.0               480.00         0.0            0.0   \n",
       "12             0       0.0               595.98         0.0            0.0   \n",
       "13             0       0.0               915.00         0.0            0.0   \n",
       "14             0       0.0              3579.40         0.0            0.0   \n",
       "\n",
       "       paid rate_plan  client_id  \\\n",
       "0      0.00  Standard      51665   \n",
       "1      1.00  Standard      54117   \n",
       "2    693.40  Standard      54118   \n",
       "3      1.00  Standard      54790   \n",
       "4      1.00  Standard      55177   \n",
       "5      1.00  Standard      55412   \n",
       "6    800.00  Standard      55560   \n",
       "7    402.00  Standard      63419   \n",
       "8    660.00  Standard      61777   \n",
       "9    320.00  Standard      61778   \n",
       "10   720.00  Standard      61777   \n",
       "11   480.00  Standard      61777   \n",
       "12   595.98  Standard      61378   \n",
       "13   915.00  Standard      61105   \n",
       "14  3579.40  Standard      61807   \n",
       "\n",
       "                                          client_name  \\\n",
       "0   86bd787ca115281ad9642c5fd6e79e6f2d87841c2fd9c6...   \n",
       "1   ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...   \n",
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       "14  cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...   \n",
       "\n",
       "                                                email  \\\n",
       "0                                                       \n",
       "1                                                       \n",
       "2   f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...   \n",
       "3   f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...   \n",
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       "7   5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...   \n",
       "8                                                       \n",
       "9                                                       \n",
       "10                                                      \n",
       "11                                                      \n",
       "12  a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...   \n",
       "13  37827cbb5aec4ed83d13193d766324efb93251cd44b28b...   \n",
       "14                                                      \n",
       "\n",
       "                                                phone  is_company  \\\n",
       "0                                                               0   \n",
       "1   318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...           0   \n",
       "2   cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...           0   \n",
       "3   1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...           0   \n",
       "4   3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...           0   \n",
       "5   ea16c664798581a9d93a3128d772b8b89e05743edbbfae...           0   \n",
       "6   516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...           0   \n",
       "7   6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...           0   \n",
       "8                                                               0   \n",
       "9                                                               0   \n",
       "10                                                              0   \n",
       "11                                                              0   \n",
       "12  446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...           0   \n",
       "13  27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...           0   \n",
       "14  5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...           0   \n",
       "\n",
       "    reservation_status  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    2  \n",
       "3                    1  \n",
       "4                    1  \n",
       "5                    1  \n",
       "6                    4  \n",
       "7                    4  \n",
       "8                    4  \n",
       "9                    4  \n",
       "10                   4  \n",
       "11                   4  \n",
       "12                   4  \n",
       "13                   4  \n",
       "14                   3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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(15))"
   ]
  },
  {
   "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": 8,
   "id": "swedish-iceland",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <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>True</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>"
      ],
      "text/plain": [
       "    n_people  n_children_1  n_children_2  n_children_3  accommodation_price  \\\n",
       "0          5             0             0             0               992.29   \n",
       "1          4             0             0             0               693.40   \n",
       "2          2             0             0             0               366.80   \n",
       "3          4             0             0             0              1064.60   \n",
       "4          2             0             0             0               713.00   \n",
       "5          5             0             0             0               800.00   \n",
       "6          5             0             0             0               402.00   \n",
       "7          1             0             0             0               660.00   \n",
       "8          1             0             0             0               320.00   \n",
       "9          1             0             0             0               720.00   \n",
       "10         1             0             0             0               480.00   \n",
       "11         3             0             0             0               595.98   \n",
       "12         4             0             0             0               915.00   \n",
       "13         3             0             0             0              3579.40   \n",
       "14         6             0             0             0               640.00   \n",
       "\n",
       "    meal_price  service_price     paid  n_rooms  discount  room_id  \\\n",
       "0          0.0            0.0     1.00        1       NaN      118   \n",
       "1          0.0            0.0   693.40        1       0.0      270   \n",
       "2          0.0            0.0     1.00        1       NaN      294   \n",
       "3          0.0            0.0     1.00        1       NaN      183   \n",
       "4          0.0            0.0     1.00        1       NaN       64   \n",
       "5          0.0            0.0   800.00        1       0.0      111   \n",
       "6          0.0            0.0   402.00        1       0.0      162   \n",
       "7          0.0            0.0   660.00        1       0.0       45   \n",
       "8          0.0            0.0   320.00        1       0.0       64   \n",
       "9          0.0            0.0   720.00        1       0.0      126   \n",
       "10         0.0            0.0   480.00        1       0.0       65   \n",
       "11         0.0            0.0   595.98        1       0.0      267   \n",
       "12         0.0            0.0   915.00        1       0.0      370   \n",
       "13         0.0            0.0  3579.40        1       0.0      321   \n",
       "14         0.0            0.0   640.00        1       0.0      131   \n",
       "\n",
       "    room_group_id  date_from    date_to booking_date rate_plan  \\\n",
       "0             118 2018-02-10 2018-02-13   2017-08-17  Standard   \n",
       "1             270 2018-02-28 2018-03-03   2017-08-17  Standard   \n",
       "2             294 2018-02-14 2018-02-16   2017-08-29  Standard   \n",
       "3             183 2018-02-03 2018-02-10   2017-09-04  Standard   \n",
       "4              64 2018-01-29 2018-02-03   2017-09-07  Standard   \n",
       "5             111 2018-03-28 2018-04-01   2017-09-11  Standard   \n",
       "6             162 2018-11-16 2018-11-18   2018-02-19  Standard   \n",
       "7              45 2018-08-16 2018-08-19   2018-02-19  Standard   \n",
       "8              64 2018-08-17 2018-08-19   2018-02-19  Standard   \n",
       "9             126 2018-08-16 2018-08-19   2018-02-19  Standard   \n",
       "10             65 2018-08-16 2018-08-19   2018-02-19  Standard   \n",
       "11            267 2018-08-10 2018-08-12   2018-02-19  Standard   \n",
       "12            362 2018-08-03 2018-08-06   2018-02-19  Standard   \n",
       "13            321 2018-07-22 2018-07-31   2018-02-19  Standard   \n",
       "14            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  \\\n",
       "0                3              177         True        1      54117   \n",
       "1                3              195         True        2      54118   \n",
       "2                2              169        False        3      54790   \n",
       "3                7              152         True        4      55177   \n",
       "4                5              144         True        5      55412   \n",
       "5                4              198         True        6      55560   \n",
       "6                2              270         True        7      63419   \n",
       "7                3              178         True        8      61777   \n",
       "8                2              179         True        9      61778   \n",
       "9                3              178         True        8      61777   \n",
       "10               3              178         True        8      61777   \n",
       "11               2              172         True       10      61378   \n",
       "12               3              165         True       11      61105   \n",
       "13               9              153         True       12      61807   \n",
       "14               2              138         True       15      60668   \n",
       "\n",
       "                                          client_name  \\\n",
       "0   ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...   \n",
       "1   4db36724fc28085e053a3003dce55368ee207cce37d355...   \n",
       "2   e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...   \n",
       "3   5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...   \n",
       "4   4aebfe125cf6c059588792b9fb871afe282a8806299dfe...   \n",
       "5   1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...   \n",
       "6   d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...   \n",
       "7   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "8   364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...   \n",
       "9   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "10  f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "11  f929533e542ff1302069567400b5ba584dc27dcbd63aae...   \n",
       "12  8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...   \n",
       "13  cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...   \n",
       "14  37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...   \n",
       "\n",
       "                                                email  \\\n",
       "0                                                       \n",
       "1   f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...   \n",
       "2   f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...   \n",
       "3   6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...   \n",
       "4   0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...   \n",
       "5   6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...   \n",
       "6   5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...   \n",
       "7                                                       \n",
       "8                                                       \n",
       "9                                                       \n",
       "10                                                      \n",
       "11  a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...   \n",
       "12  37827cbb5aec4ed83d13193d766324efb93251cd44b28b...   \n",
       "13                                                      \n",
       "14  a9ba8f12a4509f4c598578fa743236beda2b791c938954...   \n",
       "\n",
       "                                                phone  is_company  night_price  \n",
       "0   318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...           0       330.76  \n",
       "1   cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...           0       231.13  \n",
       "2   1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...           0       183.40  \n",
       "3   3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...           0       152.09  \n",
       "4   ea16c664798581a9d93a3128d772b8b89e05743edbbfae...           0       142.60  \n",
       "5   516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...           0       200.00  \n",
       "6   6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...           0       201.00  \n",
       "7                                                               0       220.00  \n",
       "8                                                               0       160.00  \n",
       "9                                                               0       240.00  \n",
       "10                                                              0       160.00  \n",
       "11  446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...           0       297.99  \n",
       "12  27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...           0       305.00  \n",
       "13  5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...           0       397.71  \n",
       "14  7ec6cc25066b8b7008b78d25027a2159d2116522611223...           0       320.00  "
      ]
     },
     "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 - done\n",
    "preprocessed_data = dp_toolkit.filter_out_low_prices(preprocessed_data)  # Code this method - done\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 - done\n",
    "\n",
    "preprocessed_data = dp_toolkit.filter_out_long_stays(preprocessed_data)  # Code this method - done\n",
    "\n",
    "preprocessed_data = dp_toolkit.add_book_to_arrival(preprocessed_data)  # Code this method - done\n",
    "preprocessed_data = dp_toolkit.add_weekend_stay(preprocessed_data)  # Code this method - done\n",
    "preprocessed_data = dp_toolkit.add_nrooms(preprocessed_data)\n",
    "preprocessed_data = dp_toolkit.sum_npeople(preprocessed_data)  # Code this method - done\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 - done\n",
    "\n",
    "preprocessed_data = dp_toolkit.add_night_price(preprocessed_data)  # Code this method - done\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",
    "assert preprocessed_data.iloc[15261]['room_group_id'] == 483\n",
    "assert preprocessed_data.iloc[15266]['room_group_id'] == 460\n",
    "assert preprocessed_data.iloc[15267]['room_group_id'] == 463\n",
    "\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": 9,
   "id": "interracial-rendering",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "      <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>True</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] True</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>"
      ],
      "text/plain": [
       "    n_people  n_children_1  n_children_2  n_children_3  accommodation_price  \\\n",
       "0          5             0             0             0               992.29   \n",
       "1          4             0             0             0               693.40   \n",
       "2          2             0             0             0               366.80   \n",
       "3          4             0             0             0              1064.60   \n",
       "4          2             0             0             0               713.00   \n",
       "5          5             0             0             0               800.00   \n",
       "6          5             0             0             0               402.00   \n",
       "7          1             0             0             0               660.00   \n",
       "8          1             0             0             0               320.00   \n",
       "9          1             0             0             0               720.00   \n",
       "10         1             0             0             0               480.00   \n",
       "11         3             0             0             0               595.98   \n",
       "12         4             0             0             0               915.00   \n",
       "13         3             0             0             0              3579.40   \n",
       "14         6             0             0             0               640.00   \n",
       "\n",
       "    meal_price  service_price     paid  n_rooms  discount  room_id  \\\n",
       "0          0.0            0.0     1.00        1       NaN      118   \n",
       "1          0.0            0.0   693.40        1       0.0      270   \n",
       "2          0.0            0.0     1.00        1       NaN      294   \n",
       "3          0.0            0.0     1.00        1       NaN      183   \n",
       "4          0.0            0.0     1.00        1       NaN       64   \n",
       "5          0.0            0.0   800.00        1       0.0      111   \n",
       "6          0.0            0.0   402.00        1       0.0      162   \n",
       "7          0.0            0.0   660.00        1       0.0       45   \n",
       "8          0.0            0.0   320.00        1       0.0       64   \n",
       "9          0.0            0.0   720.00        1       0.0      126   \n",
       "10         0.0            0.0   480.00        1       0.0       65   \n",
       "11         0.0            0.0   595.98        1       0.0      267   \n",
       "12         0.0            0.0   915.00        1       0.0      370   \n",
       "13         0.0            0.0  3579.40        1       0.0      321   \n",
       "14         0.0            0.0   640.00        1       0.0      131   \n",
       "\n",
       "    room_group_id   date_from    date_to booking_date rate_plan  \\\n",
       "0             118  2018-02-10 2018-02-13   2017-08-17  Standard   \n",
       "1             270  2018-02-28 2018-03-03   2017-08-17  Standard   \n",
       "2             294  2018-02-14 2018-02-16   2017-08-29  Standard   \n",
       "3             183  2018-02-03 2018-02-10   2017-09-04  Standard   \n",
       "4              64  2018-01-29 2018-02-03   2017-09-07  Standard   \n",
       "5             111  2018-03-28 2018-04-01   2017-09-11  Standard   \n",
       "6             162  2018-11-16 2018-11-18   2018-02-19  Standard   \n",
       "7              45  2018-08-16 2018-08-19   2018-02-19  Standard   \n",
       "8              64  2018-08-17 2018-08-19   2018-02-19  Standard   \n",
       "9             126  2018-08-16 2018-08-19   2018-02-19  Standard   \n",
       "10             65  2018-08-16 2018-08-19   2018-02-19  Standard   \n",
       "11            267  2018-08-10 2018-08-12   2018-02-19  Standard   \n",
       "12            362  2018-08-03 2018-08-06   2018-02-19  Standard   \n",
       "13            321  2018-07-22 2018-07-31   2018-02-19  Standard   \n",
       "14            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  \\\n",
       "0                3              177         True        1      54117   \n",
       "1                3              195         True        2      54118   \n",
       "2                2              169        False        3      54790   \n",
       "3                7              152         True        4      55177   \n",
       "4                5              144         True        5      55412   \n",
       "5                4              198         True        6      55560   \n",
       "6                2              270         True        7      63419   \n",
       "7                3              178         True        8      61777   \n",
       "8                2              179         True        9      61778   \n",
       "9                3              178         True        8      61777   \n",
       "10               3              178         True        8      61777   \n",
       "11               2              172         True       10      61378   \n",
       "12               3              165         True       11      61105   \n",
       "13               9              153         True       12      61807   \n",
       "14               2              138         True       15      60668   \n",
       "\n",
       "                                          client_name  \\\n",
       "0   ca83ddae9b7d15212b5391c815a689b8acfd8ef31d0d80...   \n",
       "1   4db36724fc28085e053a3003dce55368ee207cce37d355...   \n",
       "2   e41ecdb28a96d0b3e294aea6e854d8dc39a1d61bb3dfe4...   \n",
       "3   5380adccf08ea3000791aad3ccc478e3b6a8de440910aa...   \n",
       "4   4aebfe125cf6c059588792b9fb871afe282a8806299dfe...   \n",
       "5   1f4b60816f6efcb45dfa67da7a6adab42d4a05b90a9278...   \n",
       "6   d47bcb623e5031df97cd9faf472e28d9fe40f1386bbd92...   \n",
       "7   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "8   364e80d6c0608116ff8808b339ff25dc2e3f8f2211ba38...   \n",
       "9   f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "10  f02a11d6b6f5bd1bf1a003a655d1c28df9362c7f2f74be...   \n",
       "11  f929533e542ff1302069567400b5ba584dc27dcbd63aae...   \n",
       "12  8373dfd3bf2b44222dce774ee032a32b74a495f6ddd355...   \n",
       "13  cdd642820a12bb4a8f2407ed1d02b67435991afef52d9c...   \n",
       "14  37a1b20c8796e967f521788e5f6dc03fe769784dcc4e9b...   \n",
       "\n",
       "                                                email  \\\n",
       "0                                                       \n",
       "1   f9c0564c66d6a830c4964a30ac261038dd7cf762b0641c...   \n",
       "2   f6a8c77530865b7e437eb746c3564c4cbdc522c10d35f6...   \n",
       "3   6d08a7230580a09f1fde268bb7c1a5d74a55bdcc9183f8...   \n",
       "4   0d6aafda88cc3d5844da8c60ca9d1b6682f1ce1a4dfe12...   \n",
       "5   6163ca5013b2bc940219a59d0e30ec401ecd01bb498e03...   \n",
       "6   5213ac7a6db98631330ac74a241ffdf840e1857481a0b5...   \n",
       "7                                                       \n",
       "8                                                       \n",
       "9                                                       \n",
       "10                                                      \n",
       "11  a90206b6164e13331d087034d9d9a963a9a4bcf8b2969f...   \n",
       "12  37827cbb5aec4ed83d13193d766324efb93251cd44b28b...   \n",
       "13                                                      \n",
       "14  a9ba8f12a4509f4c598578fa743236beda2b791c938954...   \n",
       "\n",
       "                                                phone  is_company  \\\n",
       "0   318faec979ecaf8adaee0c8e5d7531a67f309b7247d30b...           0   \n",
       "1   cb550ba6d303bf230379073bcbdd55c37229eab3f173dc...           0   \n",
       "2   1c56315c10c9d8153ca7820648900befbd9109fb6cfb81...           0   \n",
       "3   3aff5ce689580e51de899de8ec75e8a8eaa470e4e99df4...           0   \n",
       "4   ea16c664798581a9d93a3128d772b8b89e05743edbbfae...           0   \n",
       "5   516b31d7892e1b5f4b6078ea0fc4c63a06bb9ceceb885d...           0   \n",
       "6   6416a3bc7ea31b09ae63628a143b160d7976978cdbd298...           0   \n",
       "7                                                               0   \n",
       "8                                                               0   \n",
       "9                                                               0   \n",
       "10                                                              0   \n",
       "11  446e3878681ff4dac3067bf75b8ec3b7e5be2cf8a02c75...           0   \n",
       "12  27673c0003a9f52f8e73269e2e5799dd8d4c2b0bc0dc07...           0   \n",
       "13  5aeffc70468d19eba135a79c74024c206abe83c29fc6f0...           0   \n",
       "14  7ec6cc25066b8b7008b78d25027a2159d2116522611223...           0   \n",
       "\n",
       "    night_price            term length_of_stay_bucket room_segment  \\\n",
       "0        330.76  WinterVacation                 [2-3]    [260-360]   \n",
       "1        231.13  WinterVacation                 [2-3]    [160-260]   \n",
       "2        183.40  WinterVacation                 [2-3]    [160-260]   \n",
       "3        152.09  WinterVacation                 [4-7]    [160-260]   \n",
       "4        142.60  WinterVacation                 [4-7]      [0-160]   \n",
       "5        200.00          Easter                 [4-7]    [260-360]   \n",
       "6        201.00       OffSeason                 [2-3]    [260-360]   \n",
       "7        220.00      HighSeason                 [2-3]    [160-260]   \n",
       "8        160.00      HighSeason                 [2-3]      [0-160]   \n",
       "9        240.00      HighSeason                 [2-3]    [160-260]   \n",
       "10       160.00      HighSeason                 [2-3]    [160-260]   \n",
       "11       297.99      HighSeason                 [2-3]    [160-260]   \n",
       "12       305.00      HighSeason                 [2-3]    [160-260]   \n",
       "13       397.71      HighSeason               [8-inf]    [160-260]   \n",
       "14       320.00       LowSeason                 [2-3]    [160-260]   \n",
       "\n",
       "   n_people_bucket                                               item  item_id  \n",
       "0          [5-inf]  WinterVacation [2-3] Standard [260-360] [5-inf...        0  \n",
       "1            [3-4]  WinterVacation [2-3] Standard [160-260] [3-4] ...        1  \n",
       "2            [2-2]  WinterVacation [2-3] Standard [160-260] [2-2] ...        2  \n",
       "3            [3-4]  WinterVacation [4-7] Standard [160-260] [3-4] ...        3  \n",
       "4            [2-2]   WinterVacation [4-7] Standard [0-160] [2-2] True        4  \n",
       "5          [5-inf]       Easter [4-7] Standard [260-360] [5-inf] True        5  \n",
       "6          [5-inf]    OffSeason [2-3] Standard [260-360] [5-inf] True        6  \n",
       "7            [1-1]     HighSeason [2-3] Standard [160-260] [1-1] True        7  \n",
       "8            [1-1]       HighSeason [2-3] Standard [0-160] [1-1] True        8  \n",
       "9            [1-1]     HighSeason [2-3] Standard [160-260] [1-1] True        7  \n",
       "10           [1-1]     HighSeason [2-3] Standard [160-260] [1-1] True        7  \n",
       "11           [3-4]     HighSeason [2-3] Standard [160-260] [3-4] True        9  \n",
       "12           [3-4]     HighSeason [2-3] Standard [160-260] [3-4] True        9  \n",
       "13           [3-4]   HighSeason [8-inf] Standard [160-260] [3-4] True       10  \n",
       "14         [5-inf]    LowSeason [2-3] Standard [160-260] [5-inf] True       11  "
      ]
     },
     "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",
    "\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": 10,
   "id": "acknowledged-crime",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users: 13798\n",
      "\n",
      "Number of items: 763\n",
      "\n",
      "Number of interactions: 15268\n",
      "\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>item_id</th>\n",
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       "      <th>23</th>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         n_users\n",
       "item_id         \n",
       "51           679\n",
       "55           581\n",
       "22           538\n",
       "32           409\n",
       "199          226\n",
       "78           224\n",
       "9            212\n",
       "61           204\n",
       "57           196\n",
       "23           195"
      ]
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       "      <th>5207</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         n_items\n",
       "user_id         \n",
       "706           84\n",
       "1736          29\n",
       "50            23\n",
       "96            22\n",
       "1             22\n",
       "11401         13\n",
       "3336          13\n",
       "1287          13\n",
       "1281          13\n",
       "5207          12"
      ]
     },
     "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": 11,
   "id": "victorian-bottom",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>5</th>\n",
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       "    <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>HighSeason</td>\n",
       "      <td>[2-3]</td>\n",
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       "      <td>[0-160]</td>\n",
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       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
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       "      <td>12</td>\n",
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       "      <td>HighSeason</td>\n",
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       "      <td>[160-260]</td>\n",
       "      <td>[3-4]</td>\n",
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       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  item_id            term length_of_stay_bucket rate_plan  \\\n",
       "0         1        0  WinterVacation                 [2-3]  Standard   \n",
       "1         2        1  WinterVacation                 [2-3]  Standard   \n",
       "2         3        2  WinterVacation                 [2-3]  Standard   \n",
       "3         4        3  WinterVacation                 [4-7]  Standard   \n",
       "4         5        4  WinterVacation                 [4-7]  Standard   \n",
       "5         6        5          Easter                 [4-7]  Standard   \n",
       "6         7        6       OffSeason                 [2-3]  Standard   \n",
       "7         8        7      HighSeason                 [2-3]  Standard   \n",
       "8         9        8      HighSeason                 [2-3]  Standard   \n",
       "9         8        7      HighSeason                 [2-3]  Standard   \n",
       "10        8        7      HighSeason                 [2-3]  Standard   \n",
       "11       10        9      HighSeason                 [2-3]  Standard   \n",
       "12       11        9      HighSeason                 [2-3]  Standard   \n",
       "13       12       10      HighSeason               [8-inf]  Standard   \n",
       "14       15       11       LowSeason                 [2-3]  Standard   \n",
       "\n",
       "   room_segment n_people_bucket weekend_stay  \n",
       "0     [260-360]         [5-inf]         True  \n",
       "1     [160-260]           [3-4]         True  \n",
       "2     [160-260]           [2-2]        False  \n",
       "3     [160-260]           [3-4]         True  \n",
       "4       [0-160]           [2-2]         True  \n",
       "5     [260-360]         [5-inf]         True  \n",
       "6     [260-360]         [5-inf]         True  \n",
       "7     [160-260]           [1-1]         True  \n",
       "8       [0-160]           [1-1]         True  \n",
       "9     [160-260]           [1-1]         True  \n",
       "10    [160-260]           [1-1]         True  \n",
       "11    [160-260]           [3-4]         True  \n",
       "12    [160-260]           [3-4]         True  \n",
       "13    [160-260]           [3-4]         True  \n",
       "14    [160-260]         [5-inf]         True  "
      ]
     },
     "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(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "spread-colombia",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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